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FYS-STK4155/doc/Programs/ProjectsData/bankdata.ipynb
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mhjensen 67ade4e63c added codes
2020-05-29 11:39:02 +02:00

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{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "JwgkLVvobjT7"
},
"source": [
">Project 2"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "gCE4vXI0QxS3"
},
"source": [
"# Uploading data "
]
},
{
"cell_type": "code",
"execution_count": 571,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 33
},
"colab_type": "code",
"id": "G4AopMBGba-v",
"outputId": "febd4701-f5d4-4a70-ddfe-6c57900b6572"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
]
}
],
"source": [
"from google.colab import drive\n",
"drive.mount('/content/drive')"
]
},
{
"cell_type": "code",
"execution_count": 572,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 305
},
"colab_type": "code",
"id": "PVH3n_QDb2L3",
"outputId": "47fc0dc7-252f-468a-cb94-ee4aecb27bcf"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" bank-additional.csv\n",
" bank-additional-full.csv\n",
" bank-additional-names.txt\n",
" bank.csv\n",
"'Copia de Marketing Campaign_Retail Bank_Telemarketing.ipynb'\n",
" COPYbank.csv\n",
"'Copy of WORKING-WELL-Copia de Marketing Campaign_Retail Bank_Telemarketing.ipynb'\n",
" \u001b[0m\u001b[01;34mDataFiles\u001b[0m/\n",
" Distrubution-age-duration.png\n",
"'Ising2DFM_reSample_L40_T=All_labels.pkl'\n",
"'Ising2DFM_reSample_L40_T=All.pkl'\n",
" IsingData.zip\n",
" NB12_CIX-DNN_ising_TFlow.ipynb\n",
" P2-anl1.ipynb\n",
" P2.ipynb\n",
" \u001b[01;34mResults\u001b[0m/\n",
"'Untitled spreadsheet.gsheet'\n",
"'WORKING-WELL-Copia de Marketing Campaign_Retail Bank_Telemarketing.ipynb'\n"
]
}
],
"source": [
"ls"
]
},
{
"cell_type": "code",
"execution_count": 573,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 49
},
"colab_type": "code",
"id": "BVg_qaVmb5qU",
"outputId": "433cd93f-3e82-476c-d550-5383acfe13ac"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[Errno 2] No such file or directory: 'drive/My Drive/Colab Notebooks/MLP2/'\n",
"/content/drive/My Drive/Colab Notebooks/MLP2\n"
]
}
],
"source": [
"cd drive/'My Drive'/'Colab Notebooks'/MLP2/"
]
},
{
"cell_type": "code",
"execution_count": 574,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 305
},
"colab_type": "code",
"id": "g8iBQivFb6YY",
"outputId": "06d50170-5f25-4bf1-a5b7-b369bc0cad4b"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" bank-additional.csv\n",
" bank-additional-full.csv\n",
" bank-additional-names.txt\n",
" bank.csv\n",
"'Copia de Marketing Campaign_Retail Bank_Telemarketing.ipynb'\n",
" COPYbank.csv\n",
"'Copy of WORKING-WELL-Copia de Marketing Campaign_Retail Bank_Telemarketing.ipynb'\n",
" \u001b[0m\u001b[01;34mDataFiles\u001b[0m/\n",
" Distrubution-age-duration.png\n",
"'Ising2DFM_reSample_L40_T=All_labels.pkl'\n",
"'Ising2DFM_reSample_L40_T=All.pkl'\n",
" IsingData.zip\n",
" NB12_CIX-DNN_ising_TFlow.ipynb\n",
" P2-anl1.ipynb\n",
" P2.ipynb\n",
" \u001b[01;34mResults\u001b[0m/\n",
"'Untitled spreadsheet.gsheet'\n",
"'WORKING-WELL-Copia de Marketing Campaign_Retail Bank_Telemarketing.ipynb'\n"
]
}
],
"source": [
"ls"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "orWoQ9snQ6RI"
},
"source": []
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "4DS-XsEvQ6qx"
},
"source": [
"# Data Frame "
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 476
},
"colab_type": "code",
"id": "daE5RqBjdkAS",
"outputId": "9c9e107a-5892-4385-9f54-d0660d1f880b"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"--1--data\n",
"--1.2-read file\n",
"--1.2.2--readed\n",
"(41188, 21)\n"
]
},
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>age</th>\n",
" <th>job</th>\n",
" <th>marital</th>\n",
" <th>education</th>\n",
" <th>default</th>\n",
" <th>housing</th>\n",
" <th>loan</th>\n",
" <th>contact</th>\n",
" <th>month</th>\n",
" <th>day_of_week</th>\n",
" <th>...</th>\n",
" <th>campaign</th>\n",
" <th>pdays</th>\n",
" <th>previous</th>\n",
" <th>poutcome</th>\n",
" <th>emp.var.rate</th>\n",
" <th>cons.price.idx</th>\n",
" <th>cons.conf.idx</th>\n",
" <th>euribor3m</th>\n",
" <th>nr.employed</th>\n",
" <th>y</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>56</td>\n",
" <td>housemaid</td>\n",
" <td>married</td>\n",
" <td>basic.4y</td>\n",
" <td>no</td>\n",
" <td>no</td>\n",
" <td>no</td>\n",
" <td>telephone</td>\n",
" <td>may</td>\n",
" <td>mon</td>\n",
" <td>...</td>\n",
" <td>1</td>\n",
" <td>999</td>\n",
" <td>0</td>\n",
" <td>nonexistent</td>\n",
" <td>1.1</td>\n",
" <td>93.994</td>\n",
" <td>-36.4</td>\n",
" <td>4.857</td>\n",
" <td>5191.0</td>\n",
" <td>no</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>57</td>\n",
" <td>services</td>\n",
" <td>married</td>\n",
" <td>high.school</td>\n",
" <td>unknown</td>\n",
" <td>no</td>\n",
" <td>no</td>\n",
" <td>telephone</td>\n",
" <td>may</td>\n",
" <td>mon</td>\n",
" <td>...</td>\n",
" <td>1</td>\n",
" <td>999</td>\n",
" <td>0</td>\n",
" <td>nonexistent</td>\n",
" <td>1.1</td>\n",
" <td>93.994</td>\n",
" <td>-36.4</td>\n",
" <td>4.857</td>\n",
" <td>5191.0</td>\n",
" <td>no</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>37</td>\n",
" <td>services</td>\n",
" <td>married</td>\n",
" <td>high.school</td>\n",
" <td>no</td>\n",
" <td>yes</td>\n",
" <td>no</td>\n",
" <td>telephone</td>\n",
" <td>may</td>\n",
" <td>mon</td>\n",
" <td>...</td>\n",
" <td>1</td>\n",
" <td>999</td>\n",
" <td>0</td>\n",
" <td>nonexistent</td>\n",
" <td>1.1</td>\n",
" <td>93.994</td>\n",
" <td>-36.4</td>\n",
" <td>4.857</td>\n",
" <td>5191.0</td>\n",
" <td>no</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>40</td>\n",
" <td>admin.</td>\n",
" <td>married</td>\n",
" <td>basic.6y</td>\n",
" <td>no</td>\n",
" <td>no</td>\n",
" <td>no</td>\n",
" <td>telephone</td>\n",
" <td>may</td>\n",
" <td>mon</td>\n",
" <td>...</td>\n",
" <td>1</td>\n",
" <td>999</td>\n",
" <td>0</td>\n",
" <td>nonexistent</td>\n",
" <td>1.1</td>\n",
" <td>93.994</td>\n",
" <td>-36.4</td>\n",
" <td>4.857</td>\n",
" <td>5191.0</td>\n",
" <td>no</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>56</td>\n",
" <td>services</td>\n",
" <td>married</td>\n",
" <td>high.school</td>\n",
" <td>no</td>\n",
" <td>no</td>\n",
" <td>yes</td>\n",
" <td>telephone</td>\n",
" <td>may</td>\n",
" <td>mon</td>\n",
" <td>...</td>\n",
" <td>1</td>\n",
" <td>999</td>\n",
" <td>0</td>\n",
" <td>nonexistent</td>\n",
" <td>1.1</td>\n",
" <td>93.994</td>\n",
" <td>-36.4</td>\n",
" <td>4.857</td>\n",
" <td>5191.0</td>\n",
" <td>no</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>65</th>\n",
" <td>37</td>\n",
" <td>admin.</td>\n",
" <td>married</td>\n",
" <td>university.degree</td>\n",
" <td>no</td>\n",
" <td>no</td>\n",
" <td>no</td>\n",
" <td>telephone</td>\n",
" <td>may</td>\n",
" <td>mon</td>\n",
" <td>...</td>\n",
" <td>1</td>\n",
" <td>999</td>\n",
" <td>0</td>\n",
" <td>nonexistent</td>\n",
" <td>1.1</td>\n",
" <td>93.994</td>\n",
" <td>-36.4</td>\n",
" <td>4.857</td>\n",
" <td>5191.0</td>\n",
" <td>no</td>\n",
" </tr>\n",
" <tr>\n",
" <th>66</th>\n",
" <td>44</td>\n",
" <td>blue-collar</td>\n",
" <td>single</td>\n",
" <td>basic.9y</td>\n",
" <td>no</td>\n",
" <td>yes</td>\n",
" <td>no</td>\n",
" <td>telephone</td>\n",
" <td>may</td>\n",
" <td>mon</td>\n",
" <td>...</td>\n",
" <td>1</td>\n",
" <td>999</td>\n",
" <td>0</td>\n",
" <td>nonexistent</td>\n",
" <td>1.1</td>\n",
" <td>93.994</td>\n",
" <td>-36.4</td>\n",
" <td>4.857</td>\n",
" <td>5191.0</td>\n",
" <td>no</td>\n",
" </tr>\n",
" <tr>\n",
" <th>67</th>\n",
" <td>33</td>\n",
" <td>admin.</td>\n",
" <td>married</td>\n",
" <td>unknown</td>\n",
" <td>no</td>\n",
" <td>yes</td>\n",
" <td>no</td>\n",
" <td>telephone</td>\n",
" <td>may</td>\n",
" <td>mon</td>\n",
" <td>...</td>\n",
" <td>1</td>\n",
" <td>999</td>\n",
" <td>0</td>\n",
" <td>nonexistent</td>\n",
" <td>1.1</td>\n",
" <td>93.994</td>\n",
" <td>-36.4</td>\n",
" <td>4.857</td>\n",
" <td>5191.0</td>\n",
" <td>no</td>\n",
" </tr>\n",
" <tr>\n",
" <th>68</th>\n",
" <td>56</td>\n",
" <td>admin.</td>\n",
" <td>married</td>\n",
" <td>basic.9y</td>\n",
" <td>no</td>\n",
" <td>yes</td>\n",
" <td>no</td>\n",
" <td>telephone</td>\n",
" <td>may</td>\n",
" <td>mon</td>\n",
" <td>...</td>\n",
" <td>2</td>\n",
" <td>999</td>\n",
" <td>0</td>\n",
" <td>nonexistent</td>\n",
" <td>1.1</td>\n",
" <td>93.994</td>\n",
" <td>-36.4</td>\n",
" <td>4.857</td>\n",
" <td>5191.0</td>\n",
" <td>no</td>\n",
" </tr>\n",
" <tr>\n",
" <th>69</th>\n",
" <td>44</td>\n",
" <td>blue-collar</td>\n",
" <td>single</td>\n",
" <td>basic.4y</td>\n",
" <td>unknown</td>\n",
" <td>yes</td>\n",
" <td>yes</td>\n",
" <td>telephone</td>\n",
" <td>may</td>\n",
" <td>mon</td>\n",
" <td>...</td>\n",
" <td>1</td>\n",
" <td>999</td>\n",
" <td>0</td>\n",
" <td>nonexistent</td>\n",
" <td>1.1</td>\n",
" <td>93.994</td>\n",
" <td>-36.4</td>\n",
" <td>4.857</td>\n",
" <td>5191.0</td>\n",
" <td>no</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>70 rows × 21 columns</p>\n",
"</div>"
],
"text/plain": [
" age job marital education default housing loan \\\n",
"0 56 housemaid married basic.4y no no no \n",
"1 57 services married high.school unknown no no \n",
"2 37 services married high.school no yes no \n",
"3 40 admin. married basic.6y no no no \n",
"4 56 services married high.school no no yes \n",
".. ... ... ... ... ... ... ... \n",
"65 37 admin. married university.degree no no no \n",
"66 44 blue-collar single basic.9y no yes no \n",
"67 33 admin. married unknown no yes no \n",
"68 56 admin. married basic.9y no yes no \n",
"69 44 blue-collar single basic.4y unknown yes yes \n",
"\n",
" contact month day_of_week ... campaign pdays previous poutcome \\\n",
"0 telephone may mon ... 1 999 0 nonexistent \n",
"1 telephone may mon ... 1 999 0 nonexistent \n",
"2 telephone may mon ... 1 999 0 nonexistent \n",
"3 telephone may mon ... 1 999 0 nonexistent \n",
"4 telephone may mon ... 1 999 0 nonexistent \n",
".. ... ... ... ... ... ... ... ... \n",
"65 telephone may mon ... 1 999 0 nonexistent \n",
"66 telephone may mon ... 1 999 0 nonexistent \n",
"67 telephone may mon ... 1 999 0 nonexistent \n",
"68 telephone may mon ... 2 999 0 nonexistent \n",
"69 telephone may mon ... 1 999 0 nonexistent \n",
"\n",
" emp.var.rate cons.price.idx cons.conf.idx euribor3m nr.employed y \n",
"0 1.1 93.994 -36.4 4.857 5191.0 no \n",
"1 1.1 93.994 -36.4 4.857 5191.0 no \n",
"2 1.1 93.994 -36.4 4.857 5191.0 no \n",
"3 1.1 93.994 -36.4 4.857 5191.0 no \n",
"4 1.1 93.994 -36.4 4.857 5191.0 no \n",
".. ... ... ... ... ... .. \n",
"65 1.1 93.994 -36.4 4.857 5191.0 no \n",
"66 1.1 93.994 -36.4 4.857 5191.0 no \n",
"67 1.1 93.994 -36.4 4.857 5191.0 no \n",
"68 1.1 93.994 -36.4 4.857 5191.0 no \n",
"69 1.1 93.994 -36.4 4.857 5191.0 no \n",
"\n",
"[70 rows x 21 columns]"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"from pandas import read_csv\n",
"import matplotlib.pyplot as plt\n",
"import warnings\n",
"warnings.filterwarnings('ignore')\n",
"import os\n",
"\n",
"# Where to save the figures and data files\n",
"PROJECT_ROOT_DIR = \"Results\"\n",
"FIGURE_ID = \"Results/FigureFiles\"\n",
"DATA_ID = \"DataFiles/\"\n",
"\n",
"if not os.path.exists(PROJECT_ROOT_DIR):\n",
" os.mkdir(PROJECT_ROOT_DIR)\n",
"\n",
"if not os.path.exists(FIGURE_ID):\n",
" os.makedirs(FIGURE_ID)\n",
"\n",
"if not os.path.exists(DATA_ID):\n",
" os.makedirs(DATA_ID)\n",
"\n",
"def image_path(fig_id):\n",
" return os.path.join(FIGURE_ID, fig_id)\n",
"\n",
"def data_path(dat_id):\n",
" return os.path.join(DATA_ID, dat_id)\n",
"\n",
"def save_fig(fig_id):\n",
" plt.savefig(image_path(fig_id) + \".png\", format='png')\n",
"\n",
"print(\"--1--data\")\n",
"linkname = 'bank-additional-full.csv'\n",
"print(\"--1.2-read file\")\n",
"dataset1 = pd.read_csv(linkname, sep = ';')\n",
"print(\"--1.2.2--readed\")\n",
"\n",
"# View the first 5 rows in the dataset\n",
"print(dataset1.shape)\n",
"display(dataset1.head(70))\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "3uxT4yhmRFOo"
},
"source": [
"# Raw plots"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "iLTi_cPrRUJR"
},
"source": [
"## Visual1\n"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 715
},
"colab_type": "code",
"id": "vs6Yn9L07Zea",
"outputId": "d72d31ad-5efe-490c-a54b-6b31b4b0c75d"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"*\n"
]
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 864x720 with 4 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"(41188, 21)\n"
]
}
],
"source": [
"data1 = dataset1[dataset1['y'] == 'yes']\n",
"data2 = dataset1[dataset1['y'] == 'no']\n",
"print(\"*\")\n",
"\n",
"\n",
"fig, ax = plt.subplots(2, 2, figsize=(12,10))\n",
"b1 = ax[0, 0].bar(data1['day_of_week'].unique(),height = data1['day_of_week'].value_counts(),color='#FF0006')\n",
"b2 = ax[0, 0].bar(data2['day_of_week'].unique(),height = data2['day_of_week'].value_counts(),bottom = data1['day_of_week'].value_counts(),color = '#00B9FF') \n",
"ax[0, 0].title.set_text('Day of week')\n",
"#ax[0, 0].legend((b1[0], b2[0]), ('Yes', 'No'))\n",
"ax[0, 1].bar(data1['month'].unique(),height = data1['month'].value_counts(),color='#FF0006')\n",
"ax[0, 1].bar(data2['month'].unique(),height = data2['month'].value_counts(),bottom = data1['month'].value_counts(),color = '#00B9FF') \n",
"ax[0, 1].title.set_text('Month')\n",
"ax[1, 0].bar(data1['job'].unique(),height = data1['job'].value_counts(),color='#FF0006')\n",
"ax[1, 0].bar(data1['job'].unique(),height = data2['job'].value_counts()[data1['job'].value_counts().index],bottom = data1['job'].value_counts(),color = '#00B9FF') \n",
"ax[1, 0].title.set_text('Type of Job')\n",
"ax[1, 0].tick_params(axis='x',rotation=90)\n",
"ax[1, 1].bar(data1['education'].unique(),height = data1['education'].value_counts(),color='#FF0006') #row=0, col=1\n",
"ax[1, 1].bar(data1['education'].unique(),height = data2['education'].value_counts()[data1['education'].value_counts().index],bottom = data1['education'].value_counts(),color = '#00B9FF') \n",
"ax[1, 1].title.set_text('Education')\n",
"ax[1, 1].tick_params(axis='x',rotation=90)\n",
"#ax[0, 1].xticks(rotation=90)\n",
"plt.figlegend((b1[0], b2[0]), ('Yes', 'No'),loc=\"right\",title = \"Term deposit\")\n",
"\n",
"save_fig('DataVisual-dow_mth_job_edu')\n",
"\n",
"plt.show()\n",
"\n",
"print(dataset1.shape)"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "LXbSGuenR1j5"
},
"source": [
"## Visual 2 "
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 622
},
"colab_type": "code",
"id": "yqyrXweToF6C",
"outputId": "e46d79a3-e698-49b4-f62c-a8766bac7d59"
},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 1080x720 with 6 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"(41188, 21)\n"
]
}
],
"source": [
"fig, ax = plt.subplots(2, 3, figsize=(15,10))\n",
"\n",
"b1 = ax[0, 0].bar(data1['marital'].unique(),height = data1['marital'].value_counts(),color='#FF0006')\n",
"b2 = ax[0, 0].bar(data1['marital'].unique(),height = data2['marital'].value_counts()[data1['marital'].value_counts().index],bottom = data1['marital'].value_counts(),color = '#00B9FF') \n",
"ax[0, 0].title.set_text('Marital Status')\n",
"#ax[0, 0].legend((b1[0], b2[0]), ('Yes', 'No'))\n",
"ax[0, 1].bar(data1['housing'].unique(),height = data1['housing'].value_counts(),color='#FF0006')\n",
"ax[0, 1].bar(data1['housing'].unique(),height = data2['housing'].value_counts()[data1['housing'].value_counts().index],bottom = data1['housing'].value_counts(),color = '#00B9FF') \n",
"ax[0, 1].title.set_text('(Has) housing (loan)')\n",
"ax[0, 2].bar(data1['loan'].unique(),height = data1['loan'].value_counts(),color='#FF0006')\n",
"ax[0, 2].bar(data1['loan'].unique(),height = data2['loan'].value_counts()[data1['loan'].value_counts().index],bottom = data1['loan'].value_counts(),color = '#00B9FF') \n",
"ax[0, 2].title.set_text('(Has personal) loan')\n",
"ax[1, 0].bar(data1['contact'].unique(),height = data1['contact'].value_counts(),color='#FF0006')\n",
"ax[1, 0].bar(data1['contact'].unique(),height = data2['contact'].value_counts()[data1['contact'].value_counts().index],bottom = data1['contact'].value_counts(),color = '#00B9FF') \n",
"ax[1, 0].title.set_text('Contact')\n",
"ax[1, 1].bar(data1['default'].unique(),height = data1['default'].value_counts(),color='#FF0006')\n",
"ax[1, 1].bar(data1['default'].unique(),height = data2['default'].value_counts()[data1['default'].value_counts().index],bottom = data1['default'].value_counts(),color = '#00B9FF') \n",
"ax[1, 1].title.set_text('(Has credit in) Default')\n",
"ax[1, 2].bar(data1['poutcome'].unique(),height = data1['poutcome'].value_counts(),color='#FF0006')\n",
"ax[1, 2].bar(data1['poutcome'].unique(),height = data2['poutcome'].value_counts()[data1['poutcome'].value_counts().index],bottom = data1['poutcome'].value_counts(),color = '#00B9FF') \n",
"ax[1, 2].title.set_text('Outcome of the previous marketing campaign')\n",
"plt.figlegend((b1[0], b2[0]), ('Yes', 'No'),loc=\"right\",title = \"Term deposit\")\n",
"\n",
"save_fig('DataVisual-marit_hous_loan_tcont_def_prevmc')\n",
"\n",
"plt.show()\n",
"\n",
"print(dataset1.shape)\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "y2m9rgffSCJW"
},
"source": [
"## Visual with %"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "snk4lYarSP5k"
},
"source": [
"### Week"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 406
},
"colab_type": "code",
"id": "v1-oZM4ToMN0",
"outputId": "ebcef2c0-cc9d-4469-fa3a-d94c7c5db506"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"+a\n",
"++a\n"
]
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 360x360 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"(41188, 21)\n"
]
}
],
"source": [
"count_day_of_week_response_pct = pd.crosstab(dataset1['y'],dataset1['day_of_week']).apply(lambda x: x/x.sum() * 100)\n",
"count_day_of_week_response_pct = count_day_of_week_response_pct.transpose()\n",
"print(\"+a\")\n",
"plot_day_of_the_week = count_day_of_week_response_pct['yes'].sort_values(ascending = True).plot(kind ='bar',\n",
" figsize = (5,5)) \n",
"plt.title('Subscription Rate by Day of the week')\n",
"plt.xlabel('Day of the week')\n",
"plt.ylabel('Subscription Rate')\n",
"# Label each bar\n",
"for rec, label in zip(plot_day_of_the_week.patches,\n",
" count_day_of_week_response_pct['yes'].sort_values(ascending = True).round(1).astype(str)):\n",
" plot_day_of_the_week.text(rec.get_y() + rec.get_x() + 0.3,#rec.get_width(), #x,y,s ORDER of entries\n",
" rec.get_height() + rec.get_y(),#rec.get_y()+ rec.get_height(), \n",
" label+'%', \n",
" ha = 'center', \n",
" va='bottom')\n",
"print(\"++a\")\n",
"\n",
"\n",
"save_fig('DataVisual-Sr_week')\n",
"\n",
"plt.show()\n",
"\n",
"print(dataset1.shape)"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "sUMiKON2SVN8"
},
"source": [
"### Month"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 406
},
"colab_type": "code",
"id": "sfdfeaYkpKJh",
"outputId": "d047b106-008e-43d7-ed0f-4ae5bd9e5445"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"+b\n",
"++b\n"
]
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 360x360 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"(41188, 21)\n"
]
}
],
"source": [
"count_month_response_pct = pd.crosstab(dataset1['y'],dataset1['month']).apply(lambda x: x/x.sum() * 100)\n",
"count_month_response_pct = count_month_response_pct.transpose()\n",
"print(\"+b\")\n",
"plot_month = count_month_response_pct['yes'].sort_values(ascending = True).plot(kind ='bar',\n",
" figsize = (5,5))\n",
"plt.title('Subscription Rate by Month')\n",
"plt.xlabel('Month')\n",
"plt.ylabel('Subscription Rate')\n",
"# Label each bar\n",
"for rec, label in zip(plot_month.patches,\n",
" count_month_response_pct['yes'].sort_values(ascending = True).round(1).astype(str)):\n",
" plot_month.text(rec.get_y() + rec.get_x() + 0.3,\n",
" rec.get_height() + rec.get_y(),\n",
" label+'%', fontsize=8, \n",
" ha = 'center', \n",
" va='bottom')\n",
"print(\"++b\")\n",
"\n",
"save_fig('DataVisual-Sr_month')\n",
"\n",
"plt.show()\n",
"\n",
"print(dataset1.shape)"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "BvHkmsBgSfBq"
},
"source": [
"### Job"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 449
},
"colab_type": "code",
"id": "QXiYeNf5prO9",
"outputId": "97da5fcd-299e-486c-e9ab-39eac2452f0c"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"+c\n",
"++c\n"
]
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 360x360 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"(41188, 21)\n"
]
}
],
"source": [
"count_job_response_pct = pd.crosstab(dataset1['y'],dataset1['job']).apply(lambda x: x/x.sum() * 100)\n",
"count_job_response_pct = count_job_response_pct.transpose()\n",
"print(\"+c\")\n",
"\n",
"plot_job = count_job_response_pct['yes'].sort_values(ascending = True).plot(kind ='bar',\n",
" figsize = (5,5))\n",
"plt.title('Subscription Rate by Job')\n",
"plt.ylabel('Subscription Rate')\n",
"plt.xlabel('Job Category')\n",
"\n",
"# Label each bar\n",
"for rec, label in zip(plot_job.patches,\n",
" count_job_response_pct['yes'].sort_values(ascending = True).round(1).astype(str)):\n",
" plot_job.text(rec.get_y() + rec.get_x() + 0.3,\n",
" rec.get_height() + rec.get_y(), \n",
" label+'%', fontsize=8,\n",
" ha = 'center', \n",
" va='bottom')\n",
"\n",
"print(\"++c\")\n",
"save_fig('DataVisual-Sr_job')\n",
"\n",
"plt.show()\n",
"\n",
"print(dataset1.shape)"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "x9jtNDBySlLC"
},
"source": [
"### Edu"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 473
},
"colab_type": "code",
"id": "gs-k_daexqNF",
"outputId": "0027341f-3565-4be1-fcd9-722386f1a2bd"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"+d\n",
"++d\n"
]
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 360x360 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"(41188, 21)\n"
]
}
],
"source": [
"count_education_response_pct = pd.crosstab(dataset1['y'],dataset1['education']).apply(lambda x: x/x.sum() * 100)\n",
"count_education_response_pct = count_education_response_pct.transpose()\n",
"print(\"+d\")\n",
"plot_education = count_education_response_pct['yes'].sort_values(ascending = True).plot(kind ='bar',\n",
" figsize = (5,5))\n",
"plt.title('Subscription Rate by Education')\n",
"plt.ylabel('Subscription Rate')\n",
"plt.xlabel('Education Category')\n",
"\n",
"# Label each bar\n",
"for rec, label in zip(plot_education.patches,\n",
" count_education_response_pct['yes'].sort_values(ascending = True).round(1).astype(str)):\n",
" plot_education.text(rec.get_y() + rec.get_x() + 0.3,\n",
" rec.get_height() + rec.get_y(),\n",
" label+'%',\n",
" ha = 'center', \n",
" va='bottom')\n",
"\n",
"print(\"++d\")\n",
"\n",
"save_fig('DataVisual-Sr_edu')\n",
"\n",
"plt.show()\n",
"\n",
"print(dataset1.shape)"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "H0o1ZmCgSqt_"
},
"source": [
"### Mari"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 427
},
"colab_type": "code",
"id": "d5rfOopGnAzL",
"outputId": "0180ef15-b5e7-4f0d-fde6-58ef83cac664"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"+\n",
"++\n"
]
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 360x360 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"(41188, 21)\n"
]
}
],
"source": [
"count_marital_response_pct = pd.crosstab(dataset1['y'],dataset1['marital']).apply(lambda x: x/x.sum() * 100)\n",
"count_marital_response_pct = count_marital_response_pct.transpose()\n",
"print(\"+\")\n",
"\n",
"plot_marital = count_marital_response_pct['yes'].sort_values(ascending = True).plot(kind ='bar',\n",
" figsize = (5,5))\n",
" \n",
"plt.title('Subscription Rate by Marital Status')\n",
"plt.ylabel('Subscription Rate')\n",
"plt.xlabel('Marital Status')\n",
"\n",
"# Label each bar\n",
"for rec, label in zip(plot_marital.patches,\n",
" count_marital_response_pct['yes'].sort_values(ascending = True).round(1).astype(str)):\n",
" plot_marital.text(rec.get_y() + rec.get_x() + 0.3,\n",
" rec.get_height() + rec.get_y(),\n",
" label+'%',\n",
" ha = 'center', \n",
" va='bottom')\n",
"\n",
"print(\"++\")\n",
"\n",
"save_fig('DataVisual-Sr_marital')\n",
"\n",
"plt.show()\n",
"\n",
"print(dataset1.shape)"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "E7ODW8QMSuVm"
},
"source": [
"### Housing"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 427
},
"colab_type": "code",
"id": "5oJhMZvfm7uu",
"outputId": "a9f2ec38-ab8c-4627-c0c3-7721728aeeb2"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"+1\n",
"++1\n"
]
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 360x360 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"(41188, 21)\n"
]
}
],
"source": [
"count_housing_response_pct = pd.crosstab(dataset1['y'],dataset1['housing']).apply(lambda x: x/x.sum() * 100)\n",
"count_housing_response_pct = count_housing_response_pct.transpose()\n",
"print(\"+1\")\n",
"\n",
"plot_housing = count_housing_response_pct['yes'].sort_values(ascending = True).plot(kind ='bar',\n",
" figsize = (5,5))\n",
" \n",
"plt.title('Subscription Rate by Housing loan')\n",
"plt.ylabel('Subscription Rate')\n",
"plt.xlabel('Housing loan')\n",
"\n",
"# Label each bar\n",
"for rec, label in zip(plot_housing.patches,\n",
" count_housing_response_pct['yes'].sort_values(ascending = True).round(1).astype(str)):\n",
" plot_housing.text(rec.get_y() + rec.get_x() + 0.3,\n",
" rec.get_height() + rec.get_y(),\n",
" label+'%',\n",
" ha = 'center', \n",
" va='bottom')\n",
"\n",
"print(\"++1\")\n",
"save_fig('DataVisual-Sr_housing')\n",
"\n",
"plt.show()\n",
"\n",
"print(dataset1.shape)\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "xnk76nQeS1co"
},
"source": [
"### Loan"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 427
},
"colab_type": "code",
"id": "xNXuppNWh1dQ",
"outputId": "f35d6232-5cb4-4091-b56b-2a663361226f"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"+2\n",
"++2\n"
]
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 360x360 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"(41188, 21)\n"
]
}
],
"source": [
"count_loan_response_pct = pd.crosstab(dataset1['y'],dataset1['loan']).apply(lambda x: x/x.sum() * 100)\n",
"count_loan_response_pct = count_loan_response_pct.transpose()\n",
"print(\"+2\")\n",
"\n",
"plot_loan = count_loan_response_pct['yes'].sort_values(ascending = True).plot(kind ='bar',\n",
" figsize = (5,5))\n",
" \n",
"plt.title('Subscription Rate by Loan(personal)')\n",
"plt.ylabel('Subscription Rate')\n",
"plt.xlabel('Loan Category')\n",
"\n",
"# Label each bar\n",
"for rec, label in zip(plot_loan.patches,\n",
" count_loan_response_pct['yes'].sort_values(ascending = True).round(1).astype(str)):\n",
" plot_loan.text(rec.get_y() + rec.get_x() + 0.3,\n",
" rec.get_height() + rec.get_y(),\n",
" label+'%',\n",
" ha = 'center', \n",
" va='bottom')\n",
"\n",
"print(\"++2\")\n",
"save_fig('DataVisual-Sr_loan')\n",
"\n",
"plt.show()\n",
"\n",
"print(dataset1.shape)"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "m5wVOoMbS5Xf"
},
"source": [
"### Contact"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 432
},
"colab_type": "code",
"id": "hzGwVIOmnADD",
"outputId": "aba32fdb-7255-4826-b2f9-c6de7793948b"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"+3\n",
"++3\n"
]
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 360x360 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"(41188, 21)\n"
]
}
],
"source": [
"count_contact_response_pct = pd.crosstab(dataset1['y'],dataset1['contact']).apply(lambda x: x/x.sum() * 100)\n",
"count_contact_response_pct = count_contact_response_pct.transpose()\n",
"print(\"+3\")\n",
"\n",
"plot_contact = count_contact_response_pct['yes'].sort_values(ascending = True).plot(kind ='bar',\n",
" figsize = (5,5))\n",
" \n",
"plt.title('Subscription Rate by Contact')\n",
"plt.ylabel('Subscription Rate')\n",
"plt.xlabel('Contact Category')\n",
"\n",
"# Label each bar\n",
"for rec, label in zip(plot_contact.patches,\n",
" count_contact_response_pct['yes'].sort_values(ascending = True).round(1).astype(str)):\n",
" plot_contact.text(rec.get_y() + rec.get_x() + 0.3,\n",
" rec.get_height() + rec.get_y(),\n",
" label+'%',\n",
" ha = 'center', \n",
" va='bottom')\n",
"\n",
"print(\"++3\")\n",
"save_fig('DataVisual-Sr_contact')\n",
"\n",
"plt.show()\n",
"\n",
"print(dataset1.shape)"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "WY5MNxOnTCwq"
},
"source": [
"### Defa"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 427
},
"colab_type": "code",
"id": "xFXGIxXdm8JN",
"outputId": "44283710-5545-4cf5-910a-03eb3c98d357"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"+4\n",
"++4\n"
]
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 360x360 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"(41188, 21)\n"
]
}
],
"source": [
"count_default_response_pct = pd.crosstab(dataset1['y'],dataset1['default']).apply(lambda x: x/x.sum() * 100)\n",
"count_default_response_pct = count_default_response_pct.transpose()\n",
"print(\"+4\")\n",
"plot_default = count_default_response_pct['yes'].sort_values(ascending = True).plot(kind ='bar',\n",
" figsize = (5,5))\n",
"plt.title('Subscription Rate by Default')\n",
"plt.ylabel('Subscription Rate')\n",
"plt.xlabel('Default Category')\n",
"# Label each bar\n",
"for rec, label in zip(plot_default.patches,\n",
" count_default_response_pct['yes'].sort_values(ascending = True).round(1).astype(str)):\n",
" plot_default.text(rec.get_y() + rec.get_x() + 0.3,\n",
" rec.get_height() + rec.get_y(),\n",
" label+'%',\n",
" ha = 'center', \n",
" va='bottom')\n",
"print(\"++4\")\n",
"save_fig('DataVisual-Sr_default')\n",
"\n",
"plt.show()\n",
"\n",
"print(dataset1.shape)"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "BQZEaD4ETFyE"
},
"source": [
"### Poutc"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 438
},
"colab_type": "code",
"id": "JjkNU72_nkla",
"outputId": "11621891-925e-44f1-e63d-bab938ee1c0a"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"+5\n",
"++5\n"
]
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 360x360 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"(41188, 21)\n"
]
}
],
"source": [
"count_poutcome_response_pct = pd.crosstab(dataset1['y'],dataset1['poutcome']).apply(lambda x: x/x.sum() * 100)\n",
"count_poutcome_response_pct = count_poutcome_response_pct.transpose()\n",
"print(\"+5\")\n",
"plot_poutcome = count_poutcome_response_pct['yes'].sort_values(ascending = True).plot(kind ='bar',\n",
" figsize = (5,5))\n",
"plt.title('Subscription Rate by Previous outcome')\n",
"plt.ylabel('Subscription Rate')\n",
"plt.xlabel('Previous outcome Category')\n",
"\n",
"# Label each bar\n",
"for rec, label in zip(plot_poutcome.patches,\n",
" count_poutcome_response_pct['yes'].sort_values(ascending = True).round(1).astype(str)):\n",
" plot_poutcome.text(rec.get_y() + rec.get_x() + 0.3,\n",
" rec.get_height() + rec.get_y(),\n",
" label+'%',\n",
" ha = 'center', \n",
" va='bottom')\n",
"\n",
"print(\"++5\")\n",
"save_fig('DataVisual-Sr_poutcome')\n",
"\n",
"plt.show()\n",
"\n",
"print(dataset1.shape)"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "8w8OOsSiHZce"
},
"source": [
"# CLEANING"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 444
},
"colab_type": "code",
"id": "2DbSiaWOL_o2",
"outputId": "910da5ae-2559-4907-fb15-7d2d30a9f8b6"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"--2Clean\n",
"(40119, 21)\n"
]
},
{
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" <th></th>\n",
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" <td>no</td>\n",
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" <td>no</td>\n",
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],
"text/plain": [
" age job marital education default housing loan \\\n",
"0 56 housemaid married basic.4y no no no \n",
"1 57 services married high.school unknown no no \n",
"2 37 services married high.school no yes no \n",
"3 40 admin. married basic.6y no no no \n",
"4 56 services married high.school no no yes \n",
".. ... ... ... ... ... ... ... \n",
"67 33 admin. married unknown no yes no \n",
"68 56 admin. married basic.9y no yes no \n",
"69 44 blue-collar single basic.4y unknown yes yes \n",
"70 41 management married basic.6y no no no \n",
"71 44 management divorced university.degree no yes no \n",
"\n",
" contact month day_of_week ... campaign pdays previous poutcome \\\n",
"0 telephone may mon ... 1 999 0 nonexistent \n",
"1 telephone may mon ... 1 999 0 nonexistent \n",
"2 telephone may mon ... 1 999 0 nonexistent \n",
"3 telephone may mon ... 1 999 0 nonexistent \n",
"4 telephone may mon ... 1 999 0 nonexistent \n",
".. ... ... ... ... ... ... ... ... \n",
"67 telephone may mon ... 1 999 0 nonexistent \n",
"68 telephone may mon ... 2 999 0 nonexistent \n",
"69 telephone may mon ... 1 999 0 nonexistent \n",
"70 telephone may mon ... 1 999 0 nonexistent \n",
"71 telephone may mon ... 1 999 0 nonexistent \n",
"\n",
" emp.var.rate cons.price.idx cons.conf.idx euribor3m nr.employed y \n",
"0 1.1 93.994 -36.4 4.857 5191.0 no \n",
"1 1.1 93.994 -36.4 4.857 5191.0 no \n",
"2 1.1 93.994 -36.4 4.857 5191.0 no \n",
"3 1.1 93.994 -36.4 4.857 5191.0 no \n",
"4 1.1 93.994 -36.4 4.857 5191.0 no \n",
".. ... ... ... ... ... .. \n",
"67 1.1 93.994 -36.4 4.857 5191.0 no \n",
"68 1.1 93.994 -36.4 4.857 5191.0 no \n",
"69 1.1 93.994 -36.4 4.857 5191.0 no \n",
"70 1.1 93.994 -36.4 4.857 5191.0 no \n",
"71 1.1 93.994 -36.4 4.857 5191.0 no \n",
"\n",
"[70 rows x 21 columns]"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"#################\n",
"print(\"--2Clean\")\n",
"# Step 1: Delete the rows which column 'poutcome' contains 'other'\n",
"condition0 = dataset1.poutcome == 'other'\n",
"#print(\"--2.1\")\n",
"#print(\"--2.1.2 dataset2\")\n",
"datasetcondition0 = dataset1.drop(dataset1[condition0].index, axis = 0, inplace = False)\n",
"#Does not affect the data _?\n",
"\n",
"#Delete the rows with 'unknown' in marital housing and loan\n",
"condition0 = datasetcondition0.marital == 'unknown'\n",
"dataset02 = datasetcondition0.drop(datasetcondition0[condition0].index, axis = 0, inplace = False)\n",
"\n",
"condition00 = dataset02.housing == 'unknown'\n",
"dataset5 = dataset02.drop(dataset02[condition00].index, axis = 0, inplace = False)\n",
"\n",
"#condition000 = dataset002.loan == 'unknown'\n",
"#dataset0002 = dataset002.drop(dataset002[condition000].index, axis = 0, inplace = False)\n",
"\n",
"# We found out an \"unknown\" in edu\n",
"#condition0000 = dataset0002.education == 'unknown'\n",
"#dataset5 = dataset0002.drop(dataset0002[condition0000].index, axis = 0, inplace = False)\n",
"\n",
"#erase calls with duration 0\n",
"#condition000 = dataset002.loan == 'unknown'\n",
"#dataset2 = dataset002.drop(dataset002[condition000].index, axis = 0, inplace = False)\n",
"\n",
"#print(\"--2.1\")\n",
"#print(\"--2.1.2 dataset2\")\n",
"#display(dataset2.head())\n",
"# Step 2: Replace 'unknown' in default housing and loan with 'other'\n",
"#print(\"--2.1.3 to replace\")\n",
"\n",
"dataset5['loan'] = dataset5['loan'].replace(['unknown'],'no')\n",
"#print(\"--2.1.4 replaced\")\n",
"\n",
"print(dataset5.shape)\n",
"display(dataset5.head(70))\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 0,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "t8npaVVZL_4l"
},
"outputs": [],
"source": [
"# Step 2: Replace 'unknown' in default housing and loan with 'other'\n",
"#print(\"--2.1.3 to replace\")\n",
"\n",
"#dataset2[['job','education',]] = dataset2[['job','education']].replace(['unknown'],'other')\n",
"#print(\"--2.1.4 replaced\")"
]
},
{
"cell_type": "code",
"execution_count": 0,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "ny42jye_8SQR"
},
"outputs": [],
"source": [
"############################ 2.2 Drop outliers in the column 'balance\n",
"#from scipy.stats import zscore\n",
"#dataset2[['balance']].mean()\n",
"#dataset2[['balance']].mean()\n",
"#dataset2['balance_outliers'] = dataset2['balance']\n",
"#dataset2['balance_outliers']= zscore(dataset2['balance_outliers'])\n",
"#condition1 = (dataset2['balance_outliers']>3) | (dataset2['balance_outliers']<-3 )\n",
"#dataset3 = dataset2.drop(dataset2[condition1].index, axis = 0, inplace = False)\n",
"#dataset4 = dataset3.drop('balance_outliers', axis=1)"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "fWShMJgKTbHH"
},
"source": [
"## Transform to response_binary"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 444
},
"colab_type": "code",
"id": "3yf42YDl_Ww6",
"outputId": "01be241e-8b0c-44c8-c300-e950c9bfa5de"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"-----2.3.1\n",
"(40119, 22)\n"
]
},
{
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" <td>5191.0</td>\n",
" <td>no</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>56</td>\n",
" <td>services</td>\n",
" <td>married</td>\n",
" <td>high.school</td>\n",
" <td>no</td>\n",
" <td>no</td>\n",
" <td>yes</td>\n",
" <td>telephone</td>\n",
" <td>may</td>\n",
" <td>mon</td>\n",
" <td>...</td>\n",
" <td>999</td>\n",
" <td>0</td>\n",
" <td>nonexistent</td>\n",
" <td>1.1</td>\n",
" <td>93.994</td>\n",
" <td>-36.4</td>\n",
" <td>4.857</td>\n",
" <td>5191.0</td>\n",
" <td>no</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>148</th>\n",
" <td>40</td>\n",
" <td>admin.</td>\n",
" <td>married</td>\n",
" <td>university.degree</td>\n",
" <td>unknown</td>\n",
" <td>yes</td>\n",
" <td>no</td>\n",
" <td>telephone</td>\n",
" <td>may</td>\n",
" <td>mon</td>\n",
" <td>...</td>\n",
" <td>999</td>\n",
" <td>0</td>\n",
" <td>nonexistent</td>\n",
" <td>1.1</td>\n",
" <td>93.994</td>\n",
" <td>-36.4</td>\n",
" <td>4.857</td>\n",
" <td>5191.0</td>\n",
" <td>no</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>149</th>\n",
" <td>51</td>\n",
" <td>blue-collar</td>\n",
" <td>married</td>\n",
" <td>basic.4y</td>\n",
" <td>no</td>\n",
" <td>yes</td>\n",
" <td>no</td>\n",
" <td>telephone</td>\n",
" <td>may</td>\n",
" <td>mon</td>\n",
" <td>...</td>\n",
" <td>999</td>\n",
" <td>0</td>\n",
" <td>nonexistent</td>\n",
" <td>1.1</td>\n",
" <td>93.994</td>\n",
" <td>-36.4</td>\n",
" <td>4.857</td>\n",
" <td>5191.0</td>\n",
" <td>no</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>150</th>\n",
" <td>60</td>\n",
" <td>blue-collar</td>\n",
" <td>married</td>\n",
" <td>basic.9y</td>\n",
" <td>unknown</td>\n",
" <td>no</td>\n",
" <td>no</td>\n",
" <td>telephone</td>\n",
" <td>may</td>\n",
" <td>mon</td>\n",
" <td>...</td>\n",
" <td>999</td>\n",
" <td>0</td>\n",
" <td>nonexistent</td>\n",
" <td>1.1</td>\n",
" <td>93.994</td>\n",
" <td>-36.4</td>\n",
" <td>4.857</td>\n",
" <td>5191.0</td>\n",
" <td>no</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>151</th>\n",
" <td>56</td>\n",
" <td>entrepreneur</td>\n",
" <td>married</td>\n",
" <td>unknown</td>\n",
" <td>unknown</td>\n",
" <td>yes</td>\n",
" <td>no</td>\n",
" <td>telephone</td>\n",
" <td>may</td>\n",
" <td>mon</td>\n",
" <td>...</td>\n",
" <td>999</td>\n",
" <td>0</td>\n",
" <td>nonexistent</td>\n",
" <td>1.1</td>\n",
" <td>93.994</td>\n",
" <td>-36.4</td>\n",
" <td>4.857</td>\n",
" <td>5191.0</td>\n",
" <td>no</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>152</th>\n",
" <td>39</td>\n",
" <td>services</td>\n",
" <td>divorced</td>\n",
" <td>high.school</td>\n",
" <td>unknown</td>\n",
" <td>yes</td>\n",
" <td>no</td>\n",
" <td>telephone</td>\n",
" <td>may</td>\n",
" <td>mon</td>\n",
" <td>...</td>\n",
" <td>999</td>\n",
" <td>0</td>\n",
" <td>nonexistent</td>\n",
" <td>1.1</td>\n",
" <td>93.994</td>\n",
" <td>-36.4</td>\n",
" <td>4.857</td>\n",
" <td>5191.0</td>\n",
" <td>no</td>\n",
" <td>0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>150 rows × 22 columns</p>\n",
"</div>"
],
"text/plain": [
" age job marital education default housing loan \\\n",
"0 56 housemaid married basic.4y no no no \n",
"1 57 services married high.school unknown no no \n",
"2 37 services married high.school no yes no \n",
"3 40 admin. married basic.6y no no no \n",
"4 56 services married high.school no no yes \n",
".. ... ... ... ... ... ... ... \n",
"148 40 admin. married university.degree unknown yes no \n",
"149 51 blue-collar married basic.4y no yes no \n",
"150 60 blue-collar married basic.9y unknown no no \n",
"151 56 entrepreneur married unknown unknown yes no \n",
"152 39 services divorced high.school unknown yes no \n",
"\n",
" contact month day_of_week ... pdays previous poutcome \\\n",
"0 telephone may mon ... 999 0 nonexistent \n",
"1 telephone may mon ... 999 0 nonexistent \n",
"2 telephone may mon ... 999 0 nonexistent \n",
"3 telephone may mon ... 999 0 nonexistent \n",
"4 telephone may mon ... 999 0 nonexistent \n",
".. ... ... ... ... ... ... ... \n",
"148 telephone may mon ... 999 0 nonexistent \n",
"149 telephone may mon ... 999 0 nonexistent \n",
"150 telephone may mon ... 999 0 nonexistent \n",
"151 telephone may mon ... 999 0 nonexistent \n",
"152 telephone may mon ... 999 0 nonexistent \n",
"\n",
" emp.var.rate cons.price.idx cons.conf.idx euribor3m nr.employed \\\n",
"0 1.1 93.994 -36.4 4.857 5191.0 \n",
"1 1.1 93.994 -36.4 4.857 5191.0 \n",
"2 1.1 93.994 -36.4 4.857 5191.0 \n",
"3 1.1 93.994 -36.4 4.857 5191.0 \n",
"4 1.1 93.994 -36.4 4.857 5191.0 \n",
".. ... ... ... ... ... \n",
"148 1.1 93.994 -36.4 4.857 5191.0 \n",
"149 1.1 93.994 -36.4 4.857 5191.0 \n",
"150 1.1 93.994 -36.4 4.857 5191.0 \n",
"151 1.1 93.994 -36.4 4.857 5191.0 \n",
"152 1.1 93.994 -36.4 4.857 5191.0 \n",
"\n",
" response response_binary \n",
"0 no 0 \n",
"1 no 0 \n",
"2 no 0 \n",
"3 no 0 \n",
"4 no 0 \n",
".. ... ... \n",
"148 no 0 \n",
"149 no 0 \n",
"150 no 0 \n",
"151 no 0 \n",
"152 no 0 \n",
"\n",
"[150 rows x 22 columns]"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"############################## 2.3 Creating and transforming data\n",
"# Step 1: Change column name: 'y' to 'response'\n",
"#dataset4.rename(index=str, columns={'y': 'response'}, inplace = True)\n",
"#dataset2.rename(index=str, columns={'y': 'response'}, inplace = True)\n",
"dataset5.rename(index=str, columns={'y': 'response'}, inplace = True)\n",
"\n",
"def convert(dataset5, new_column, old_column):\n",
" dataset5[new_column] = dataset5[old_column].apply(lambda x: 0 if x == 'no' else 1)\n",
" return dataset5[new_column].value_counts()\n",
"#To appreciate the change when displaying the table 22 col\n",
"convert(dataset5, \"response_binary\", \"response\")\n",
"#convert(dataset5, \"response\", \"response\")\n",
"print(\"-----2.3.1\")\n",
"\n",
"print(dataset5.shape)\n",
"display(dataset5.head(150))\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "RnOt6AYZ_YH3"
},
"source": [
"## Transf sto S"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 444
},
"colab_type": "code",
"id": "RoZjWcR5Iapd",
"outputId": "8097234c-baca-4828-aced-b76d7c2e3398"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"-----2.3.3\n",
"(40119, 22)\n"
]
},
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>age</th>\n",
" <th>job</th>\n",
" <th>marital</th>\n",
" <th>education</th>\n",
" <th>default</th>\n",
" <th>housing</th>\n",
" <th>loan</th>\n",
" <th>contact</th>\n",
" <th>month</th>\n",
" <th>day_of_week</th>\n",
" <th>...</th>\n",
" <th>pdays</th>\n",
" <th>previous</th>\n",
" <th>poutcome</th>\n",
" <th>emp.var.rate</th>\n",
" <th>cons.price.idx</th>\n",
" <th>cons.conf.idx</th>\n",
" <th>euribor3m</th>\n",
" <th>nr.employed</th>\n",
" <th>response</th>\n",
" <th>response_binary</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>56</td>\n",
" <td>housemaid</td>\n",
" <td>married</td>\n",
" <td>basic.4y</td>\n",
" <td>no</td>\n",
" <td>no</td>\n",
" <td>no</td>\n",
" <td>telephone</td>\n",
" <td>may</td>\n",
" <td>mon</td>\n",
" <td>...</td>\n",
" <td>999</td>\n",
" <td>0</td>\n",
" <td>nonexistent</td>\n",
" <td>1.1</td>\n",
" <td>93.994</td>\n",
" <td>-36.4</td>\n",
" <td>4.857</td>\n",
" <td>5191.0</td>\n",
" <td>no</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>57</td>\n",
" <td>services</td>\n",
" <td>married</td>\n",
" <td>high.school</td>\n",
" <td>unknown</td>\n",
" <td>no</td>\n",
" <td>no</td>\n",
" <td>telephone</td>\n",
" <td>may</td>\n",
" <td>mon</td>\n",
" <td>...</td>\n",
" <td>999</td>\n",
" <td>0</td>\n",
" <td>nonexistent</td>\n",
" <td>1.1</td>\n",
" <td>93.994</td>\n",
" <td>-36.4</td>\n",
" <td>4.857</td>\n",
" <td>5191.0</td>\n",
" <td>no</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>37</td>\n",
" <td>services</td>\n",
" <td>married</td>\n",
" <td>high.school</td>\n",
" <td>no</td>\n",
" <td>yes</td>\n",
" <td>no</td>\n",
" <td>telephone</td>\n",
" <td>may</td>\n",
" <td>mon</td>\n",
" <td>...</td>\n",
" <td>999</td>\n",
" <td>0</td>\n",
" <td>nonexistent</td>\n",
" <td>1.1</td>\n",
" <td>93.994</td>\n",
" <td>-36.4</td>\n",
" <td>4.857</td>\n",
" <td>5191.0</td>\n",
" <td>no</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>40</td>\n",
" <td>admin.</td>\n",
" <td>married</td>\n",
" <td>basic.6y</td>\n",
" <td>no</td>\n",
" <td>no</td>\n",
" <td>no</td>\n",
" <td>telephone</td>\n",
" <td>may</td>\n",
" <td>mon</td>\n",
" <td>...</td>\n",
" <td>999</td>\n",
" <td>0</td>\n",
" <td>nonexistent</td>\n",
" <td>1.1</td>\n",
" <td>93.994</td>\n",
" <td>-36.4</td>\n",
" <td>4.857</td>\n",
" <td>5191.0</td>\n",
" <td>no</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>56</td>\n",
" <td>services</td>\n",
" <td>married</td>\n",
" <td>high.school</td>\n",
" <td>no</td>\n",
" <td>no</td>\n",
" <td>yes</td>\n",
" <td>telephone</td>\n",
" <td>may</td>\n",
" <td>mon</td>\n",
" <td>...</td>\n",
" <td>999</td>\n",
" <td>0</td>\n",
" <td>nonexistent</td>\n",
" <td>1.1</td>\n",
" <td>93.994</td>\n",
" <td>-36.4</td>\n",
" <td>4.857</td>\n",
" <td>5191.0</td>\n",
" <td>no</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>148</th>\n",
" <td>40</td>\n",
" <td>admin.</td>\n",
" <td>married</td>\n",
" <td>university.degree</td>\n",
" <td>unknown</td>\n",
" <td>yes</td>\n",
" <td>no</td>\n",
" <td>telephone</td>\n",
" <td>may</td>\n",
" <td>mon</td>\n",
" <td>...</td>\n",
" <td>999</td>\n",
" <td>0</td>\n",
" <td>nonexistent</td>\n",
" <td>1.1</td>\n",
" <td>93.994</td>\n",
" <td>-36.4</td>\n",
" <td>4.857</td>\n",
" <td>5191.0</td>\n",
" <td>no</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>149</th>\n",
" <td>51</td>\n",
" <td>blue-collar</td>\n",
" <td>married</td>\n",
" <td>basic.4y</td>\n",
" <td>no</td>\n",
" <td>yes</td>\n",
" <td>no</td>\n",
" <td>telephone</td>\n",
" <td>may</td>\n",
" <td>mon</td>\n",
" <td>...</td>\n",
" <td>999</td>\n",
" <td>0</td>\n",
" <td>nonexistent</td>\n",
" <td>1.1</td>\n",
" <td>93.994</td>\n",
" <td>-36.4</td>\n",
" <td>4.857</td>\n",
" <td>5191.0</td>\n",
" <td>no</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>150</th>\n",
" <td>60</td>\n",
" <td>blue-collar</td>\n",
" <td>married</td>\n",
" <td>basic.9y</td>\n",
" <td>unknown</td>\n",
" <td>no</td>\n",
" <td>no</td>\n",
" <td>telephone</td>\n",
" <td>may</td>\n",
" <td>mon</td>\n",
" <td>...</td>\n",
" <td>999</td>\n",
" <td>0</td>\n",
" <td>nonexistent</td>\n",
" <td>1.1</td>\n",
" <td>93.994</td>\n",
" <td>-36.4</td>\n",
" <td>4.857</td>\n",
" <td>5191.0</td>\n",
" <td>no</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>151</th>\n",
" <td>56</td>\n",
" <td>entrepreneur</td>\n",
" <td>married</td>\n",
" <td>unknown</td>\n",
" <td>unknown</td>\n",
" <td>yes</td>\n",
" <td>no</td>\n",
" <td>telephone</td>\n",
" <td>may</td>\n",
" <td>mon</td>\n",
" <td>...</td>\n",
" <td>999</td>\n",
" <td>0</td>\n",
" <td>nonexistent</td>\n",
" <td>1.1</td>\n",
" <td>93.994</td>\n",
" <td>-36.4</td>\n",
" <td>4.857</td>\n",
" <td>5191.0</td>\n",
" <td>no</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>152</th>\n",
" <td>39</td>\n",
" <td>services</td>\n",
" <td>divorced</td>\n",
" <td>high.school</td>\n",
" <td>unknown</td>\n",
" <td>yes</td>\n",
" <td>no</td>\n",
" <td>telephone</td>\n",
" <td>may</td>\n",
" <td>mon</td>\n",
" <td>...</td>\n",
" <td>999</td>\n",
" <td>0</td>\n",
" <td>nonexistent</td>\n",
" <td>1.1</td>\n",
" <td>93.994</td>\n",
" <td>-36.4</td>\n",
" <td>4.857</td>\n",
" <td>5191.0</td>\n",
" <td>no</td>\n",
" <td>0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>150 rows × 22 columns</p>\n",
"</div>"
],
"text/plain": [
" age job marital education default housing loan \\\n",
"0 56 housemaid married basic.4y no no no \n",
"1 57 services married high.school unknown no no \n",
"2 37 services married high.school no yes no \n",
"3 40 admin. married basic.6y no no no \n",
"4 56 services married high.school no no yes \n",
".. ... ... ... ... ... ... ... \n",
"148 40 admin. married university.degree unknown yes no \n",
"149 51 blue-collar married basic.4y no yes no \n",
"150 60 blue-collar married basic.9y unknown no no \n",
"151 56 entrepreneur married unknown unknown yes no \n",
"152 39 services divorced high.school unknown yes no \n",
"\n",
" contact month day_of_week ... pdays previous poutcome \\\n",
"0 telephone may mon ... 999 0 nonexistent \n",
"1 telephone may mon ... 999 0 nonexistent \n",
"2 telephone may mon ... 999 0 nonexistent \n",
"3 telephone may mon ... 999 0 nonexistent \n",
"4 telephone may mon ... 999 0 nonexistent \n",
".. ... ... ... ... ... ... ... \n",
"148 telephone may mon ... 999 0 nonexistent \n",
"149 telephone may mon ... 999 0 nonexistent \n",
"150 telephone may mon ... 999 0 nonexistent \n",
"151 telephone may mon ... 999 0 nonexistent \n",
"152 telephone may mon ... 999 0 nonexistent \n",
"\n",
" emp.var.rate cons.price.idx cons.conf.idx euribor3m nr.employed \\\n",
"0 1.1 93.994 -36.4 4.857 5191.0 \n",
"1 1.1 93.994 -36.4 4.857 5191.0 \n",
"2 1.1 93.994 -36.4 4.857 5191.0 \n",
"3 1.1 93.994 -36.4 4.857 5191.0 \n",
"4 1.1 93.994 -36.4 4.857 5191.0 \n",
".. ... ... ... ... ... \n",
"148 1.1 93.994 -36.4 4.857 5191.0 \n",
"149 1.1 93.994 -36.4 4.857 5191.0 \n",
"150 1.1 93.994 -36.4 4.857 5191.0 \n",
"151 1.1 93.994 -36.4 4.857 5191.0 \n",
"152 1.1 93.994 -36.4 4.857 5191.0 \n",
"\n",
" response response_binary \n",
"0 no 0 \n",
"1 no 0 \n",
"2 no 0 \n",
"3 no 0 \n",
"4 no 0 \n",
".. ... ... \n",
"148 no 0 \n",
"149 no 0 \n",
"150 no 0 \n",
"151 no 0 \n",
"152 no 0 \n",
"\n",
"[150 rows x 22 columns]"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Step 2: Drop column \"response_binary\" which is useless\n",
"#datasetNEW = dataset5.drop('response_binary', axis=1)\n",
"#dataset4 = dataset3.drop('balance_outliers', axis=1)\n",
"#print(\"-----2.3.2\")\n",
"\n",
"# Step 2: Drop column \"contact\" which is useless\n",
"#dataset5 = dataset4.drop('contact', axis=1)\n",
"####dataset5 = dataset2.drop('contact', axis=1)\n",
"#print(\"-----2.3.2\")\n",
"\n",
"# Step 3: Change the unit of 'duration' from seconds to minutes\n",
"dataset5['duration'] = dataset5['duration'].apply(lambda n:n/60).round(2)\n",
"print(\"-----2.3.3\")\n",
"\n",
"print(dataset5.shape)\n",
"display(dataset5.head(150))"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "iqMtSbRi_uQS"
},
"source": [
"!!If run more without previous codes, then all responses yes ==1, as it finds only numbers!!\n",
"Pay attention to \n",
"* 0-0\n",
"* 2-0\n",
"* 3-0\n",
"* 4-0\n",
"* 6-0\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "EbNSX_RkTmYf"
},
"source": [
"## Month and Days education \"#\""
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 260
},
"colab_type": "code",
"id": "CVqFa2SA0BmM",
"outputId": "cd5aa1bd-7430-4323-83cd-dd45b0405561"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"-----2.3.4\n",
"-----2.3.5\n",
"(40119, 22)\n"
]
},
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
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" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>age</th>\n",
" <th>job</th>\n",
" <th>marital</th>\n",
" <th>education</th>\n",
" <th>default</th>\n",
" <th>housing</th>\n",
" <th>loan</th>\n",
" <th>contact</th>\n",
" <th>month</th>\n",
" <th>day_of_week</th>\n",
" <th>...</th>\n",
" <th>pdays</th>\n",
" <th>previous</th>\n",
" <th>poutcome</th>\n",
" <th>emp.var.rate</th>\n",
" <th>cons.price.idx</th>\n",
" <th>cons.conf.idx</th>\n",
" <th>euribor3m</th>\n",
" <th>nr.employed</th>\n",
" <th>response</th>\n",
" <th>response_binary</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>56</td>\n",
" <td>housemaid</td>\n",
" <td>married</td>\n",
" <td>basic.4y</td>\n",
" <td>no</td>\n",
" <td>no</td>\n",
" <td>no</td>\n",
" <td>telephone</td>\n",
" <td>5</td>\n",
" <td>1</td>\n",
" <td>...</td>\n",
" <td>999</td>\n",
" <td>0</td>\n",
" <td>nonexistent</td>\n",
" <td>1.1</td>\n",
" <td>93.994</td>\n",
" <td>-36.4</td>\n",
" <td>4.857</td>\n",
" <td>5191.0</td>\n",
" <td>no</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>57</td>\n",
" <td>services</td>\n",
" <td>married</td>\n",
" <td>high.school</td>\n",
" <td>unknown</td>\n",
" <td>no</td>\n",
" <td>no</td>\n",
" <td>telephone</td>\n",
" <td>5</td>\n",
" <td>1</td>\n",
" <td>...</td>\n",
" <td>999</td>\n",
" <td>0</td>\n",
" <td>nonexistent</td>\n",
" <td>1.1</td>\n",
" <td>93.994</td>\n",
" <td>-36.4</td>\n",
" <td>4.857</td>\n",
" <td>5191.0</td>\n",
" <td>no</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>37</td>\n",
" <td>services</td>\n",
" <td>married</td>\n",
" <td>high.school</td>\n",
" <td>no</td>\n",
" <td>yes</td>\n",
" <td>no</td>\n",
" <td>telephone</td>\n",
" <td>5</td>\n",
" <td>1</td>\n",
" <td>...</td>\n",
" <td>999</td>\n",
" <td>0</td>\n",
" <td>nonexistent</td>\n",
" <td>1.1</td>\n",
" <td>93.994</td>\n",
" <td>-36.4</td>\n",
" <td>4.857</td>\n",
" <td>5191.0</td>\n",
" <td>no</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>40</td>\n",
" <td>admin.</td>\n",
" <td>married</td>\n",
" <td>basic.6y</td>\n",
" <td>no</td>\n",
" <td>no</td>\n",
" <td>no</td>\n",
" <td>telephone</td>\n",
" <td>5</td>\n",
" <td>1</td>\n",
" <td>...</td>\n",
" <td>999</td>\n",
" <td>0</td>\n",
" <td>nonexistent</td>\n",
" <td>1.1</td>\n",
" <td>93.994</td>\n",
" <td>-36.4</td>\n",
" <td>4.857</td>\n",
" <td>5191.0</td>\n",
" <td>no</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>56</td>\n",
" <td>services</td>\n",
" <td>married</td>\n",
" <td>high.school</td>\n",
" <td>no</td>\n",
" <td>no</td>\n",
" <td>yes</td>\n",
" <td>telephone</td>\n",
" <td>5</td>\n",
" <td>1</td>\n",
" <td>...</td>\n",
" <td>999</td>\n",
" <td>0</td>\n",
" <td>nonexistent</td>\n",
" <td>1.1</td>\n",
" <td>93.994</td>\n",
" <td>-36.4</td>\n",
" <td>4.857</td>\n",
" <td>5191.0</td>\n",
" <td>no</td>\n",
" <td>0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows × 22 columns</p>\n",
"</div>"
],
"text/plain": [
" age job marital education default housing loan contact \\\n",
"0 56 housemaid married basic.4y no no no telephone \n",
"1 57 services married high.school unknown no no telephone \n",
"2 37 services married high.school no yes no telephone \n",
"3 40 admin. married basic.6y no no no telephone \n",
"4 56 services married high.school no no yes telephone \n",
"\n",
" month day_of_week ... pdays previous poutcome emp.var.rate \\\n",
"0 5 1 ... 999 0 nonexistent 1.1 \n",
"1 5 1 ... 999 0 nonexistent 1.1 \n",
"2 5 1 ... 999 0 nonexistent 1.1 \n",
"3 5 1 ... 999 0 nonexistent 1.1 \n",
"4 5 1 ... 999 0 nonexistent 1.1 \n",
"\n",
" cons.price.idx cons.conf.idx euribor3m nr.employed response \\\n",
"0 93.994 -36.4 4.857 5191.0 no \n",
"1 93.994 -36.4 4.857 5191.0 no \n",
"2 93.994 -36.4 4.857 5191.0 no \n",
"3 93.994 -36.4 4.857 5191.0 no \n",
"4 93.994 -36.4 4.857 5191.0 no \n",
"\n",
" response_binary \n",
"0 0 \n",
"1 0 \n",
"2 0 \n",
"3 0 \n",
"4 0 \n",
"\n",
"[5 rows x 22 columns]"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"\n",
"# Step 4: Change 'month' from words to numbers for easier analysis\n",
"lst = [dataset5]\n",
"for column in lst:\n",
" column.loc[column[\"month\"] == \"jan\", \"month\"] = 1\n",
" column.loc[column[\"month\"] == \"feb\", \"month\"] = 2\n",
" column.loc[column[\"month\"] == \"mar\", \"month\"] = 3\n",
" column.loc[column[\"month\"] == \"apr\", \"month\"] = 4\n",
" column.loc[column[\"month\"] == \"may\", \"month\"] = 5\n",
" column.loc[column[\"month\"] == \"jun\", \"month\"] = 6\n",
" column.loc[column[\"month\"] == \"jul\", \"month\"] = 7\n",
" column.loc[column[\"month\"] == \"aug\", \"month\"] = 8\n",
" column.loc[column[\"month\"] == \"sep\", \"month\"] = 9\n",
" column.loc[column[\"month\"] == \"oct\", \"month\"] = 10\n",
" column.loc[column[\"month\"] == \"nov\", \"month\"] = 11\n",
" column.loc[column[\"month\"] == \"dec\", \"month\"] = 12\n",
"print(\"-----2.3.4\")\n",
"\n",
"# Step 4.1: Change 'day' from words to numbers for easier analysis\n",
"lst = [dataset5]\n",
"for column in lst:\n",
" column.loc[column[\"day_of_week\"] == \"mon\", \"day_of_week\"] = 1\n",
" column.loc[column[\"day_of_week\"] == \"tue\", \"day_of_week\"] = 2\n",
" column.loc[column[\"day_of_week\"] == \"wed\", \"day_of_week\"] = 3\n",
" column.loc[column[\"day_of_week\"] == \"thu\", \"day_of_week\"] = 4\n",
" column.loc[column[\"day_of_week\"] == \"fri\", \"day_of_week\"] = 5\n",
"print(\"-----2.3.5\")\n",
"print(dataset5.shape)\n",
"display(dataset5.head())"
]
},
{
"cell_type": "code",
"execution_count": 594,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 650
},
"colab_type": "code",
"id": "NCnkidzORjgh",
"outputId": "f0925a03-c659-44db-8e51-76552caeaf68"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"-----2.3.5\n",
"(40119, 22)\n"
]
},
{
"data": {
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" <td>-36.4</td>\n",
" <td>4.857</td>\n",
" <td>5191.0</td>\n",
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" <td>0</td>\n",
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" <td>37</td>\n",
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" <td>40</td>\n",
" <td>admin.</td>\n",
" <td>married</td>\n",
" <td>1</td>\n",
" <td>no</td>\n",
" <td>no</td>\n",
" <td>no</td>\n",
" <td>telephone</td>\n",
" <td>5</td>\n",
" <td>1</td>\n",
" <td>2.52</td>\n",
" <td>1</td>\n",
" <td>999</td>\n",
" <td>0</td>\n",
" <td>nonexistent</td>\n",
" <td>1.1</td>\n",
" <td>93.994</td>\n",
" <td>-36.4</td>\n",
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" <td>56</td>\n",
" <td>services</td>\n",
" <td>married</td>\n",
" <td>1</td>\n",
" <td>no</td>\n",
" <td>no</td>\n",
" <td>yes</td>\n",
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" <td>1</td>\n",
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" <td>1</td>\n",
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" <td>no</td>\n",
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" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" age job marital ... nr.employed response response_binary\n",
"0 56 housemaid married ... 5191.0 no 0\n",
"1 57 services married ... 5191.0 no 0\n",
"2 37 services married ... 5191.0 no 0\n",
"3 40 admin. married ... 5191.0 no 0\n",
"4 56 services married ... 5191.0 no 0\n",
"\n",
"[5 rows x 22 columns]"
]
},
"metadata": {
"tags": []
},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"-----2.3.x\n",
"(40119, 22)\n"
]
},
{
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" <td>2</td>\n",
" <td>1</td>\n",
" <td>no</td>\n",
" <td>no</td>\n",
" <td>no</td>\n",
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" <td>5</td>\n",
" <td>1</td>\n",
" <td>2.52</td>\n",
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" <td>999</td>\n",
" <td>0</td>\n",
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" <th>4</th>\n",
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" <td>services</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>no</td>\n",
" <td>no</td>\n",
" <td>yes</td>\n",
" <td>telephone</td>\n",
" <td>5</td>\n",
" <td>1</td>\n",
" <td>5.12</td>\n",
" <td>1</td>\n",
" <td>999</td>\n",
" <td>0</td>\n",
" <td>nonexistent</td>\n",
" <td>1.1</td>\n",
" <td>93.994</td>\n",
" <td>-36.4</td>\n",
" <td>4.857</td>\n",
" <td>5191.0</td>\n",
" <td>no</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>98</th>\n",
" <td>37</td>\n",
" <td>technician</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>no</td>\n",
" <td>no</td>\n",
" <td>no</td>\n",
" <td>telephone</td>\n",
" <td>5</td>\n",
" <td>1</td>\n",
" <td>3.28</td>\n",
" <td>1</td>\n",
" <td>999</td>\n",
" <td>0</td>\n",
" <td>nonexistent</td>\n",
" <td>1.1</td>\n",
" <td>93.994</td>\n",
" <td>-36.4</td>\n",
" <td>4.857</td>\n",
" <td>5191.0</td>\n",
" <td>no</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>99</th>\n",
" <td>44</td>\n",
" <td>blue-collar</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>no</td>\n",
" <td>no</td>\n",
" <td>no</td>\n",
" <td>telephone</td>\n",
" <td>5</td>\n",
" <td>1</td>\n",
" <td>4.28</td>\n",
" <td>1</td>\n",
" <td>999</td>\n",
" <td>0</td>\n",
" <td>nonexistent</td>\n",
" <td>1.1</td>\n",
" <td>93.994</td>\n",
" <td>-36.4</td>\n",
" <td>4.857</td>\n",
" <td>5191.0</td>\n",
" <td>no</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>100</th>\n",
" <td>54</td>\n",
" <td>services</td>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" <td>no</td>\n",
" <td>yes</td>\n",
" <td>no</td>\n",
" <td>telephone</td>\n",
" <td>5</td>\n",
" <td>1</td>\n",
" <td>3.82</td>\n",
" <td>1</td>\n",
" <td>999</td>\n",
" <td>0</td>\n",
" <td>nonexistent</td>\n",
" <td>1.1</td>\n",
" <td>93.994</td>\n",
" <td>-36.4</td>\n",
" <td>4.857</td>\n",
" <td>5191.0</td>\n",
" <td>no</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>101</th>\n",
" <td>49</td>\n",
" <td>blue-collar</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>no</td>\n",
" <td>no</td>\n",
" <td>no</td>\n",
" <td>telephone</td>\n",
" <td>5</td>\n",
" <td>1</td>\n",
" <td>0.92</td>\n",
" <td>3</td>\n",
" <td>999</td>\n",
" <td>0</td>\n",
" <td>nonexistent</td>\n",
" <td>1.1</td>\n",
" <td>93.994</td>\n",
" <td>-36.4</td>\n",
" <td>4.857</td>\n",
" <td>5191.0</td>\n",
" <td>no</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>102</th>\n",
" <td>54</td>\n",
" <td>services</td>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" <td>no</td>\n",
" <td>no</td>\n",
" <td>no</td>\n",
" <td>telephone</td>\n",
" <td>5</td>\n",
" <td>1</td>\n",
" <td>6.67</td>\n",
" <td>1</td>\n",
" <td>999</td>\n",
" <td>0</td>\n",
" <td>nonexistent</td>\n",
" <td>1.1</td>\n",
" <td>93.994</td>\n",
" <td>-36.4</td>\n",
" <td>4.857</td>\n",
" <td>5191.0</td>\n",
" <td>no</td>\n",
" <td>0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>100 rows × 22 columns</p>\n",
"</div>"
],
"text/plain": [
" age job marital ... nr.employed response response_binary\n",
"0 56 housemaid 2 ... 5191.0 no 0\n",
"1 57 services 2 ... 5191.0 no 0\n",
"2 37 services 2 ... 5191.0 no 0\n",
"3 40 admin. 2 ... 5191.0 no 0\n",
"4 56 services 2 ... 5191.0 no 0\n",
".. ... ... ... ... ... ... ...\n",
"98 37 technician 0 ... 5191.0 no 0\n",
"99 44 blue-collar 2 ... 5191.0 no 0\n",
"100 54 services 2 ... 5191.0 no 0\n",
"101 49 blue-collar 2 ... 5191.0 no 0\n",
"102 54 services 2 ... 5191.0 no 0\n",
"\n",
"[100 rows x 22 columns]"
]
},
"metadata": {
"tags": []
},
"output_type": "display_data"
}
],
"source": [
"# Step 4.2: Change from words to numbers for easier analysis\n",
"#'basic.4y','basic.6y','basic.9y','high.school','illiterate','professional.course','university.degree','unknown'\n",
"lst = [dataset5]\n",
"for column in lst:\n",
" column.loc[column[\"education\"] == \"illiterate\", \"education\"] = 0\n",
" column.loc[column[\"education\"] == \"unknown\", \"education\"] = 0\n",
" column.loc[column[\"education\"] == \"basic.4y\", \"education\"] = 1\n",
" column.loc[column[\"education\"] == \"basic.6y\", \"education\"] = 1\n",
" column.loc[column[\"education\"] == \"basic.9y\", \"education\"] = 1\n",
" column.loc[column[\"education\"] == \"basic.9y\", \"education\"] = 1\n",
" column.loc[column[\"education\"] == \"high.school\", \"education\"] = 1\n",
" column.loc[column[\"education\"] == \"professional.course\", \"education\"] = 2\n",
" column.loc[column[\"education\"] == \"university.degree\", \"education\"] = 2\n",
"\n",
"\n",
"print(\"-----2.3.5\")\n",
"print(dataset5.shape)\n",
"display(dataset5.head())\n",
"\n",
"\n",
"lst = [dataset5]\n",
"for column in lst:\n",
" column.loc[column[\"marital\"] == \"single\", \"marital\"] = 0\n",
" column.loc[column[\"marital\"] == \"divorced\", \"marital\"] = 1\n",
" column.loc[column[\"marital\"] == \"married\", \"marital\"] = 2\n",
"\n",
"print(\"-----2.3.x\")\n",
"print(dataset5.shape)\n",
"display(dataset5.head(100))"
]
},
{
"cell_type": "code",
"execution_count": 0,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "j5Y6DD0E0D9e"
},
"outputs": [],
"source": [
"#'''Convert Duration Call into 5 category'''\n",
"#def duration(dataset5):\n",
"# dataset5.loc[dataset5[\"day_of_week\"] == \"mon\", 'day_of_week'] = 1\n",
"# data.loc[(data['duration'] > 102) & (data['duration'] <= 180) , 'duration'] = 2\n",
"# data.loc[(data['duration'] > 180) & (data['duration'] <= 319) , 'duration'] = 3\n",
"# data.loc[(data['duration'] > 319) & (data['duration'] <= 645), 'duration'] = 4\n",
"# data.loc[data['duration'] > 645, 'duration'] = 5\n",
"# return data\n",
"#duration(data);"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "by3EXu28TzeT"
},
"source": [
"## Filter \"s<5\""
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 33
},
"colab_type": "code",
"id": "7o3Q0fUBIzQQ",
"outputId": "bf994253-0c50-437c-cd94-5a04dc02c850"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(40066, 22)\n"
]
}
],
"source": [
"####################### 2.4 Filtering\n",
"# Step 1: Drop rows that 'duration' < 5s\n",
"condition2 = (dataset5['duration']<5/60)\n",
"#dataset6 = dataset5.drop(dataset5[condition2].index, axis = 0, inplace = False)\n",
"dataset7 = dataset5.drop(dataset5[condition2].index, axis = 0, inplace = False)\n",
"# Step 2: Drop customer values with 'other' education\n",
"#condition3 = (dataset6['education'] == 'other')\n",
"#dataset7 = dataset6.drop(dataset6[condition3].index, axis = 0, inplace = False)\n",
"\n",
"plt.show()\n",
"\n",
"print(dataset7.shape)"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "kf2iLjIuUBqR"
},
"source": [
"## Distributions"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "w4e5SecsUJdr"
},
"source": [
"### (histo) Age "
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 201
},
"colab_type": "code",
"id": "vgpgbvxhpyii",
"outputId": "7452728e-782e-477e-8791-e26b5788671f"
},
"outputs": [
{
"data": {
"image/png": 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D9wCHpqtESM+HpMlquSnTjnkiYhvwPGXnz6T3982azHrJCdzMAJA0ArgROCsiXuhu0gplPd2UqVc3bPLNmsx6zwnczJC0B1nyviYibkrFm0v3dUjPW1J5LTdl2jGPpOHAAcAz9V8Ss6HDCdxsiEvHoi8HVkfEhblRS4DZaXg2O2+wVMtNmfJ1nQzc4b8MNuuffl0HbmaDwruB04CHJN2fys4BFgCLJc0BngA+CjXflOly4GpJa8n2vNsbvVBmg50TuNkQFxF3UfkYNWR/FVxpnj7dlCkiXib9ADCz+nAXupmZWQE5gZuZmRWQE7iZmVkBOYGbmZkVkE9iG6La5t3a7zrWLzihDpGYmVktvAduZmZWQE7gZmZmBeQEbmZmVkD9SuCS1kt6SNL9kjpS2UhJyyQ9mp4Pyk0/X9JaSWskHZ8rn5LqWSvpYv/NoJmZWffqsQf+noiYHBFT0+t5wPKImAQsT6+RdDjZ7ROPAGYCl0oalua5DJhLdk/lSWm8mZmZVdGILvRZwKI0vAj4UK78uoh4JSLWAWuBaelfjvaPiLvTnxtclZvHzMzMKuhvAg/gF5LulTQ3lR2a/pWI9HxIKh8HbMjN25nKxqXh8vLdSJorqUNSR1dXVz9DNzMzK67+Xgf+7ojYKOkQYJmkR7qZttJx7eimfPfCiIXAQoCpU6f6rwjNzGzI6tceeERsTM9bgJ8A04DNqVuc9LwlTd4JTMjNPh7YmMrHVyg3MzOzKmpO4JL2lbRfaRh4P7ASWALMTpPNBm5Ow0uAdkl7SZpIdrLaitTNvlXS9HT2+Sdz85iZmVkF/elCPxT4Sbriazjwo4j4uaTfAYslzQGeIP0HcESskrQYeBjYBpwREdtTXacDVwL7ALelh5mZmVVRcwKPiMeAoyqUPw3MqDLP+cD5Fco7gCNrjcXMzGyo8Z3YzMzMCsgJ3MzMrICcwM3MzArICdzMzKyAnMDNzMwKyAnczMysgJzAzczMCsgJ3MzMrICcwM0MSVdI2iJpZa5spKRlkh5Nzwflxs2XtFbSGknH58qnSHoojbs43R6ZdAvl61P5PZLaBnL5zAYjJ3Azg+xWxjPLyuYByyNiErA8vUbS4UA7cESa51JJw9I8lwFzyf7rYFKuzjnAsxHxZuAi4IKGLYnZEOEEbmZExJ3AM2XFs4BFaXgR8KFc+XUR8UpErAPWAtPSvw/uHxF3R0QAV5XNU6rrBmBGae/czGrjBG5m1Rya/i2Q9HxIKh8HbMhN15nKxqXh8vJd5omIbcDzwMHlbyhprqQOSR1dXV11XBSzwccJ3Mz6qtKec3RT3t08uxZELIyIqRExdfTo0f0I0WzwcwI3s2o2p25x0vOWVN4JTMhNNx7YmMrHVyjfZR5Jw4ED2L3L3sz6wAnczKpZAsxOw7OBm3Pl7enM8olkJ6utSN3sWyVNT8e3P1k2T6muk4E70nFyM6tRzf8HbmaDh6RrgWOBUZI6ga8AC4DFkuYATwAfBYiIVZIWAw8D24AzImJ7qup0sjPa9wFuSw+Ay4GrJa0l2/NuH4DFMhvUak7gkiaQnWX6BuA1YGFEfEfSucA/AaUzUM6JiKVpnvlkl5NsBz4bEben8insbPRLgTP969xs4ETEKVVGzagy/fnA+RXKO4AjK5S/TPoBYGb10Z898G3A2RFxn6T9gHslLUvjLoqIb+YnLrt2dCzwS0lvSb/cS9eO/pYsgc9k5y93MzMzK1PzMfCI2BQR96XhrcBqdl4yUkkt146amZlZBXU5iS3dFvFo4J5U9BlJD6bbM5Zuv1jLtaPl7+NrRM3MzKhDApc0ArgROCsiXiDrDj8MmAxsAr5VmrTC7D1dO7proa8RNTMzA/qZwCXtQZa8r4mImwAiYnNEbI+I14DvAdPS5LVcO2pmZmYV1JzA03WelwOrI+LCXPmY3GQfBkr/blTLtaNmZmZWQX/OQn83cBrwkKT7U9k5wCmSJpN1g68H/hlqvnbUzMzMKqg5gUfEXVQ+fr20m3n6dO2omZmZVeZbqZqZmRWQE7iZmVkBOYGbmZkV0KD+M5O2ebc2OwQzM7OGGNQJ3BqrXj+Q1i84oS71mJkNJe5CNzMzKyAncDMzswJyF7qZDXkLfv9UXeqZd/SoutRj1hveAzczMysgJ3AzM7MCcgI3MzMrICdwMzOzAnICNzMzKyAncDMzswJyAjczMysgJ3AzM7MCcgI3MzMroJa5E5ukmcB3gGHA9yNiQZNDsgFSjz9F8R+iFMNgb+f1uKOb7+ZmvdUSCVzSMOAS4H1AJ/A7SUsi4uHmRmZF4X9Ga31u52b11RIJHJgGrI2IxwAkXQfMAtywbUD5h0BDuZ33gu/Lbr3VKgl8HLAh97oTeGf5RJLmAnPTyxclranx/UYB9WklA8PxNk5DYtUF9a5xh/J439iwd6q/Htt5DW18yH/Xqpnfv9m9XhunlngrtvNWSeCqUBa7FUQsBBb2+82kjoiY2t96BorjbZwixQrFi7dMj+28r228SOvDsTZGkWKF+sbbKmehdwITcq/HAxubFIuZNYbbuVkdtUoC/x0wSdJESXsC7cCSJsdkZvXldm5WRy3RhR4R2yR9Brid7PKSKyJiVQPfst/d8APM8TZOkWKF4sW7Q4PaeZHWh2NtjCLFCnWMVxG7HWo2MzOzFtcqXehmZmbWB07gZmZmBTToE7ikCZJ+JWm1pFWSzkzlIyUtk/Roej6o2bGWSBom6feSbkmvWznWAyXdIOmRtI6PadV4JX0ufQdWSrpW0t6tFKukKyRtkbQyV1Y1PknzJa2VtEbS8c2JujkkzUzLvVbSvGbHU65o2x1vcxqj0ducQZ/AgW3A2RHxdmA6cIakw4F5wPKImAQsT69bxZnA6tzrVo71O8DPI+JtwFFkcbdcvJLGAZ8FpkbEkWQnUbXTWrFeCcwsK6sYX/oOtwNHpHkuTbcqHfRyt2T9AHA4cEpaH62kaNsdb3PqbEC2ORExpB7AzWT3Yl4DjEllY4A1zY4txTI+fajvBW5JZa0a6/7AOtLJkLnylouXnXcBG0l29cUtwPtbLVagDVjZ07oku9HW/Nx0twPHNHs9D9A6Oga4Pfd6l3XRio9W3u54m9OwWBu+zRkKe+A7SGoDjgbuAQ6NiE0A6fmQ5kW2i28DXwBey5W1aqxvArqAH6Tut+9L2pcWjDci/gh8E3gC2AQ8HxG/oAVjLVMtvkq3JR03wLE1S6GWvQDbHW9zGmAgtjlDJoFLGgHcCJwVES80O55KJP09sCUi7m12LL00HPgr4LKIOBp4iRbouqokHWeaBUwExgL7SvpEc6Pql17dfniQKsyyt/p2x9ucxhmIbc6QSOCS9iBrRNdExE2peLOkMWn8GGBLs+LLeTdwkqT1wHXAeyX9kNaMFbI9n86IuCe9voGscbVivMcB6yKiKyJeBW4C3kVrxppXLb6hfFvSQix7QbY73uY0TsO3OYM+gUsScDmwOiIuzI1aAsxOw7PJjlE1VUTMj4jxEdFGdrLDHRHxCVowVoCIeBLYIOmtqWgG2V9DtmK8TwDTJb0+fSdmkJ380oqx5lWLbwnQLmkvSROBScCKJsTXDC1/S9aibHe8zWmoxm9zmn2gfwBOJPhrsu61B4H70+ODwMFkJ248mp5HNjvWsriPZecJJS0bKzAZ6Ejr96fAQa0aL/BV4BFgJXA1sFcrxQpcS3as7FWyPY053cUHfBH4A9lJMR9o9vod4HX1QeB/0/J/sdnxVIivcNsdb3MaEmtDtzm+laqZmVkBDfoudDMzs8HICdzMzKyAnMDNzMwKyAnczMysgJzAzczMCsgJ3MzMrICcwM3MzAro/wGA51jLfWJggwAAAABJRU5ErkJggg==\n",
"text/plain": [
"<Figure size 504x180 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"(40066, 22)\n"
]
}
],
"source": [
"###################### 3.Explo\n",
"### 3.1 Visu\n",
"dist_age_duration = plt.figure(figsize = (7,2.5))\n",
"\n",
"ra1 = dist_age_duration.add_subplot(1,2,1) \n",
"#ra2 = dist_age_balance.add_subplot(1,2,2)\n",
"ra3 = dist_age_duration.add_subplot(1,2,2)\n",
"\n",
"ra1.hist(dataset7['age'])\n",
"ra1.set_title('The Distribution of Age')\n",
"\n",
"#ra2.hist(dataset7['balance'], color = 'skyblue')\n",
"#ra2.set_title('The Distribution of Balance')\n",
"ra3.hist(dataset7['duration'], color = 'skyblue')\n",
"ra3.set_title('The Distribution of Duration')\n",
"\n",
"plt.tight_layout()\n",
"\n",
"plt.savefig('Distrubution-age-duration')\n",
"\n",
"plt.show()\n",
"\n",
"print(dataset7.shape)"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "rXR6QSO-URuw"
},
"source": [
"### age & durati"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 266
},
"colab_type": "code",
"id": "HRb4o5uUubqM",
"outputId": "249d30ae-9a73-4db7-835b-bab1b5f57347"
},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 504x180 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"(40066, 22)\n"
]
}
],
"source": [
"############################ 3.2 Visualize the relationship between 'age' and 'duration'¶\n",
"\n",
"scatter_age_duration = dataset7.plot.scatter('age','duration',figsize = (7,2.5))\n",
"\n",
"plt.title('The Relationship between Age and Duration ')\n",
"\n",
"plt.show()\n",
"\n",
"\n",
"print(dataset7.shape)"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 252
},
"colab_type": "code",
"id": "PtvFCJHOraJn",
"outputId": "5d5d0026-944f-44fc-8b96-ef39fdd6acac"
},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 269.25x216 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"(40066, 22)\n"
]
}
],
"source": [
"############### \n",
"import seaborn as sns\n",
"dur_cam = sns.lmplot(x='age', y='duration',data = dataset7,\n",
" hue = 'response',\n",
" fit_reg = False,\n",
" scatter_kws={'alpha':0.3}, height =3)\n",
"\n",
"plt.axis([0,100,0,90])#x(age) y(s)\n",
"plt.ylabel('Duration of Calls (min)')\n",
"plt.xlabel('Age')\n",
"plt.title('The Relationship between the Age and Duration of calls')\n",
"\n",
"# Annotation\n",
"#plt.axhline(y=5, linewidth=2, color=\"k\", linestyle='--')\n",
"#plt.annotate('Higher subscription rate when calls <5',xytext = (35,13),\n",
"# arrowprops=dict(color = 'k', width=1),xy=(30,6))\n",
"plt.show()\n",
"\n",
"print(dataset7.shape)"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "u-0DenwtUc3s"
},
"source": [
"### RELA DURA & CAMP"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 324
},
"colab_type": "code",
"id": "DGDhQHFXu7vp",
"outputId": "5c7c067f-4ca5-434a-98a4-7c0fe297224d"
},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 341.25x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"(40066, 22)\n"
]
}
],
"source": [
"############### 3.4 Visualize the relationship between 'duration' & 'campaign': with response result¶\n",
"import seaborn as sns\n",
"dur_cam = sns.lmplot(x='duration', y='campaign',data = dataset7,\n",
" hue = 'response',\n",
" fit_reg = False,\n",
" scatter_kws={'alpha':0.6}, height =4)\n",
"\n",
"plt.axis([0,90,0,90])\n",
"plt.ylabel('Number of Calls')#campaign\n",
"plt.xlabel('Duration of Calls (Minutes)')\n",
"plt.title('The Relationship between the Duration and Number of Calls (with Response Result)')\n",
"\n",
"# Annotation\n",
"plt.axhline(y=5, linewidth=2, color=\"k\", linestyle='--')\n",
"plt.annotate('Higher subscription rate when calls <5',xytext = (35,13),\n",
" arrowprops=dict(color = 'k', width=1),xy=(30,6))\n",
"\n",
"\n",
"save_fig('DataVisual-Durat_campaign')\n",
"\n",
"plt.show()\n",
"\n",
"print(dataset7.shape)\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "ppQ9AQwiVDeP"
},
"source": [
"### Scatter Matrix"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 451
},
"colab_type": "code",
"id": "Vjd6q-_RvJYm",
"outputId": "ff994fe8-aef2-4858-e28b-efc0cb90d49a"
},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 576x432 with 9 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"(40066, 22)\n"
]
}
],
"source": [
"#Scattered Matrix\n",
"from pandas.plotting import scatter_matrix\n",
"matrix = scatter_matrix(dataset7[['age','duration','education','campaign']],figsize=(8,6))\n",
"\n",
"plt.suptitle('The Scatter Matrix of Age, Duration, education and Campaign')\n",
"plt.show()\n",
"\n",
"print(dataset7.shape)"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "ov75wEfGWPtL"
},
"source": [
"# ML"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 228
},
"colab_type": "code",
"id": "s5GcWKUJU_2j",
"outputId": "4383d9ce-1156-45dc-8139-5c3eed8aa7d2"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(40066, 22)\n"
]
},
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>age</th>\n",
" <th>job</th>\n",
" <th>marital</th>\n",
" <th>education</th>\n",
" <th>default</th>\n",
" <th>housing</th>\n",
" <th>loan</th>\n",
" <th>contact</th>\n",
" <th>month</th>\n",
" <th>day_of_week</th>\n",
" <th>...</th>\n",
" <th>pdays</th>\n",
" <th>previous</th>\n",
" <th>poutcome</th>\n",
" <th>emp.var.rate</th>\n",
" <th>cons.price.idx</th>\n",
" <th>cons.conf.idx</th>\n",
" <th>euribor3m</th>\n",
" <th>nr.employed</th>\n",
" <th>response</th>\n",
" <th>response_binary</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>56</td>\n",
" <td>housemaid</td>\n",
" <td>married</td>\n",
" <td>basic.4y</td>\n",
" <td>no</td>\n",
" <td>no</td>\n",
" <td>no</td>\n",
" <td>telephone</td>\n",
" <td>5</td>\n",
" <td>1</td>\n",
" <td>...</td>\n",
" <td>999</td>\n",
" <td>0</td>\n",
" <td>nonexistent</td>\n",
" <td>1.1</td>\n",
" <td>93.994</td>\n",
" <td>-36.4</td>\n",
" <td>4.857</td>\n",
" <td>5191.0</td>\n",
" <td>no</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>57</td>\n",
" <td>services</td>\n",
" <td>married</td>\n",
" <td>high.school</td>\n",
" <td>unknown</td>\n",
" <td>no</td>\n",
" <td>no</td>\n",
" <td>telephone</td>\n",
" <td>5</td>\n",
" <td>1</td>\n",
" <td>...</td>\n",
" <td>999</td>\n",
" <td>0</td>\n",
" <td>nonexistent</td>\n",
" <td>1.1</td>\n",
" <td>93.994</td>\n",
" <td>-36.4</td>\n",
" <td>4.857</td>\n",
" <td>5191.0</td>\n",
" <td>no</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>37</td>\n",
" <td>services</td>\n",
" <td>married</td>\n",
" <td>high.school</td>\n",
" <td>no</td>\n",
" <td>yes</td>\n",
" <td>no</td>\n",
" <td>telephone</td>\n",
" <td>5</td>\n",
" <td>1</td>\n",
" <td>...</td>\n",
" <td>999</td>\n",
" <td>0</td>\n",
" <td>nonexistent</td>\n",
" <td>1.1</td>\n",
" <td>93.994</td>\n",
" <td>-36.4</td>\n",
" <td>4.857</td>\n",
" <td>5191.0</td>\n",
" <td>no</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>40</td>\n",
" <td>admin.</td>\n",
" <td>married</td>\n",
" <td>basic.6y</td>\n",
" <td>no</td>\n",
" <td>no</td>\n",
" <td>no</td>\n",
" <td>telephone</td>\n",
" <td>5</td>\n",
" <td>1</td>\n",
" <td>...</td>\n",
" <td>999</td>\n",
" <td>0</td>\n",
" <td>nonexistent</td>\n",
" <td>1.1</td>\n",
" <td>93.994</td>\n",
" <td>-36.4</td>\n",
" <td>4.857</td>\n",
" <td>5191.0</td>\n",
" <td>no</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>56</td>\n",
" <td>services</td>\n",
" <td>married</td>\n",
" <td>high.school</td>\n",
" <td>no</td>\n",
" <td>no</td>\n",
" <td>yes</td>\n",
" <td>telephone</td>\n",
" <td>5</td>\n",
" <td>1</td>\n",
" <td>...</td>\n",
" <td>999</td>\n",
" <td>0</td>\n",
" <td>nonexistent</td>\n",
" <td>1.1</td>\n",
" <td>93.994</td>\n",
" <td>-36.4</td>\n",
" <td>4.857</td>\n",
" <td>5191.0</td>\n",
" <td>no</td>\n",
" <td>0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows × 22 columns</p>\n",
"</div>"
],
"text/plain": [
" age job marital education default housing loan contact \\\n",
"0 56 housemaid married basic.4y no no no telephone \n",
"1 57 services married high.school unknown no no telephone \n",
"2 37 services married high.school no yes no telephone \n",
"3 40 admin. married basic.6y no no no telephone \n",
"4 56 services married high.school no no yes telephone \n",
"\n",
" month day_of_week ... pdays previous poutcome emp.var.rate \\\n",
"0 5 1 ... 999 0 nonexistent 1.1 \n",
"1 5 1 ... 999 0 nonexistent 1.1 \n",
"2 5 1 ... 999 0 nonexistent 1.1 \n",
"3 5 1 ... 999 0 nonexistent 1.1 \n",
"4 5 1 ... 999 0 nonexistent 1.1 \n",
"\n",
" cons.price.idx cons.conf.idx euribor3m nr.employed response \\\n",
"0 93.994 -36.4 4.857 5191.0 no \n",
"1 93.994 -36.4 4.857 5191.0 no \n",
"2 93.994 -36.4 4.857 5191.0 no \n",
"3 93.994 -36.4 4.857 5191.0 no \n",
"4 93.994 -36.4 4.857 5191.0 no \n",
"\n",
" response_binary \n",
"0 0 \n",
"1 0 \n",
"2 0 \n",
"3 0 \n",
"4 0 \n",
"\n",
"[5 rows x 22 columns]"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"\n",
"print(dataset7.shape)\n",
"\n",
"display(dataset7.head())"
]
},
{
"cell_type": "code",
"execution_count": 0,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "poRjFbg7YzHk"
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "AZyMdgrXBrhG"
},
"source": [
"##Goal\n",
" \n",
"The main objective of this project is to identify the most responsive customers before the marketing campaign so that the bank will be able to efficiently reach out to them, saving time and marketing resources. To achieve this objective, classification algorithms will be employed. By analyzing customer statistics, a classification model will be built to classify all clients into two groups: \"yes\" to term deposits and \"no\" to term deposits."
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 170
},
"colab_type": "code",
"id": "f9wW9ZAJWIwC",
"outputId": "62c1fbea-a55d-4214-ca18-912b49b6a232"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(40066, 22)\n"
]
},
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>age</th>\n",
" <th>job</th>\n",
" <th>marital</th>\n",
" <th>education</th>\n",
" <th>default</th>\n",
" <th>housing</th>\n",
" <th>loan</th>\n",
" <th>contact</th>\n",
" <th>month</th>\n",
" <th>day_of_week</th>\n",
" <th>...</th>\n",
" <th>pdays</th>\n",
" <th>previous</th>\n",
" <th>poutcome</th>\n",
" <th>emp.var.rate</th>\n",
" <th>cons.price.idx</th>\n",
" <th>cons.conf.idx</th>\n",
" <th>euribor3m</th>\n",
" <th>nr.employed</th>\n",
" <th>response</th>\n",
" <th>response_binary</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>56</td>\n",
" <td>housemaid</td>\n",
" <td>married</td>\n",
" <td>basic.4y</td>\n",
" <td>no</td>\n",
" <td>no</td>\n",
" <td>no</td>\n",
" <td>telephone</td>\n",
" <td>5</td>\n",
" <td>1</td>\n",
" <td>...</td>\n",
" <td>999</td>\n",
" <td>0</td>\n",
" <td>nonexistent</td>\n",
" <td>1.1</td>\n",
" <td>93.994</td>\n",
" <td>-36.4</td>\n",
" <td>4.857</td>\n",
" <td>5191.0</td>\n",
" <td>no</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>57</td>\n",
" <td>services</td>\n",
" <td>married</td>\n",
" <td>high.school</td>\n",
" <td>unknown</td>\n",
" <td>no</td>\n",
" <td>no</td>\n",
" <td>telephone</td>\n",
" <td>5</td>\n",
" <td>1</td>\n",
" <td>...</td>\n",
" <td>999</td>\n",
" <td>0</td>\n",
" <td>nonexistent</td>\n",
" <td>1.1</td>\n",
" <td>93.994</td>\n",
" <td>-36.4</td>\n",
" <td>4.857</td>\n",
" <td>5191.0</td>\n",
" <td>no</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>37</td>\n",
" <td>services</td>\n",
" <td>married</td>\n",
" <td>high.school</td>\n",
" <td>no</td>\n",
" <td>yes</td>\n",
" <td>no</td>\n",
" <td>telephone</td>\n",
" <td>5</td>\n",
" <td>1</td>\n",
" <td>...</td>\n",
" <td>999</td>\n",
" <td>0</td>\n",
" <td>nonexistent</td>\n",
" <td>1.1</td>\n",
" <td>93.994</td>\n",
" <td>-36.4</td>\n",
" <td>4.857</td>\n",
" <td>5191.0</td>\n",
" <td>no</td>\n",
" <td>0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>3 rows × 22 columns</p>\n",
"</div>"
],
"text/plain": [
" age job marital education default housing loan contact \\\n",
"0 56 housemaid married basic.4y no no no telephone \n",
"1 57 services married high.school unknown no no telephone \n",
"2 37 services married high.school no yes no telephone \n",
"\n",
" month day_of_week ... pdays previous poutcome emp.var.rate \\\n",
"0 5 1 ... 999 0 nonexistent 1.1 \n",
"1 5 1 ... 999 0 nonexistent 1.1 \n",
"2 5 1 ... 999 0 nonexistent 1.1 \n",
"\n",
" cons.price.idx cons.conf.idx euribor3m nr.employed response \\\n",
"0 93.994 -36.4 4.857 5191.0 no \n",
"1 93.994 -36.4 4.857 5191.0 no \n",
"2 93.994 -36.4 4.857 5191.0 no \n",
"\n",
" response_binary \n",
"0 0 \n",
"1 0 \n",
"2 0 \n",
"\n",
"[3 rows x 22 columns]"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"print(dataset7.shape)\n",
"display(dataset7.head(3))\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "0jxPWk0_Wu4V"
},
"source": [
"## Housing, defa, loan to \"#\""
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 372
},
"colab_type": "code",
"id": "i5F9XO32Lx4Z",
"outputId": "80625bdf-a8a1-4371-a017-9842a9b253df"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(40066, 22)\n"
]
},
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
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" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>age</th>\n",
" <th>job</th>\n",
" <th>marital</th>\n",
" <th>education</th>\n",
" <th>default</th>\n",
" <th>housing</th>\n",
" <th>loan</th>\n",
" <th>contact</th>\n",
" <th>month</th>\n",
" <th>day_of_week</th>\n",
" <th>...</th>\n",
" <th>pdays</th>\n",
" <th>previous</th>\n",
" <th>poutcome</th>\n",
" <th>emp.var.rate</th>\n",
" <th>cons.price.idx</th>\n",
" <th>cons.conf.idx</th>\n",
" <th>euribor3m</th>\n",
" <th>nr.employed</th>\n",
" <th>response</th>\n",
" <th>response_binary</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>56</td>\n",
" <td>housemaid</td>\n",
" <td>married</td>\n",
" <td>basic.4y</td>\n",
" <td>1.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>telephone</td>\n",
" <td>5</td>\n",
" <td>1</td>\n",
" <td>...</td>\n",
" <td>999</td>\n",
" <td>0</td>\n",
" <td>nonexistent</td>\n",
" <td>1.1</td>\n",
" <td>93.994</td>\n",
" <td>-36.4</td>\n",
" <td>4.857</td>\n",
" <td>5191.0</td>\n",
" <td>no</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>57</td>\n",
" <td>services</td>\n",
" <td>married</td>\n",
" <td>high.school</td>\n",
" <td>0.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>telephone</td>\n",
" <td>5</td>\n",
" <td>1</td>\n",
" <td>...</td>\n",
" <td>999</td>\n",
" <td>0</td>\n",
" <td>nonexistent</td>\n",
" <td>1.1</td>\n",
" <td>93.994</td>\n",
" <td>-36.4</td>\n",
" <td>4.857</td>\n",
" <td>5191.0</td>\n",
" <td>no</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>37</td>\n",
" <td>services</td>\n",
" <td>married</td>\n",
" <td>high.school</td>\n",
" <td>1.0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>telephone</td>\n",
" <td>5</td>\n",
" <td>1</td>\n",
" <td>...</td>\n",
" <td>999</td>\n",
" <td>0</td>\n",
" <td>nonexistent</td>\n",
" <td>1.1</td>\n",
" <td>93.994</td>\n",
" <td>-36.4</td>\n",
" <td>4.857</td>\n",
" <td>5191.0</td>\n",
" <td>no</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>40</td>\n",
" <td>admin.</td>\n",
" <td>married</td>\n",
" <td>basic.6y</td>\n",
" <td>1.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>telephone</td>\n",
" <td>5</td>\n",
" <td>1</td>\n",
" <td>...</td>\n",
" <td>999</td>\n",
" <td>0</td>\n",
" <td>nonexistent</td>\n",
" <td>1.1</td>\n",
" <td>93.994</td>\n",
" <td>-36.4</td>\n",
" <td>4.857</td>\n",
" <td>5191.0</td>\n",
" <td>no</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>56</td>\n",
" <td>services</td>\n",
" <td>married</td>\n",
" <td>high.school</td>\n",
" <td>1.0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>telephone</td>\n",
" <td>5</td>\n",
" <td>1</td>\n",
" <td>...</td>\n",
" <td>999</td>\n",
" <td>0</td>\n",
" <td>nonexistent</td>\n",
" <td>1.1</td>\n",
" <td>93.994</td>\n",
" <td>-36.4</td>\n",
" <td>4.857</td>\n",
" <td>5191.0</td>\n",
" <td>no</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>45</td>\n",
" <td>services</td>\n",
" <td>married</td>\n",
" <td>basic.9y</td>\n",
" <td>0.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>telephone</td>\n",
" <td>5</td>\n",
" <td>1</td>\n",
" <td>...</td>\n",
" <td>999</td>\n",
" <td>0</td>\n",
" <td>nonexistent</td>\n",
" <td>1.1</td>\n",
" <td>93.994</td>\n",
" <td>-36.4</td>\n",
" <td>4.857</td>\n",
" <td>5191.0</td>\n",
" <td>no</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>59</td>\n",
" <td>admin.</td>\n",
" <td>married</td>\n",
" <td>professional.course</td>\n",
" <td>1.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>telephone</td>\n",
" <td>5</td>\n",
" <td>1</td>\n",
" <td>...</td>\n",
" <td>999</td>\n",
" <td>0</td>\n",
" <td>nonexistent</td>\n",
" <td>1.1</td>\n",
" <td>93.994</td>\n",
" <td>-36.4</td>\n",
" <td>4.857</td>\n",
" <td>5191.0</td>\n",
" <td>no</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>41</td>\n",
" <td>blue-collar</td>\n",
" <td>married</td>\n",
" <td>unknown</td>\n",
" <td>0.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>telephone</td>\n",
" <td>5</td>\n",
" <td>1</td>\n",
" <td>...</td>\n",
" <td>999</td>\n",
" <td>0</td>\n",
" <td>nonexistent</td>\n",
" <td>1.1</td>\n",
" <td>93.994</td>\n",
" <td>-36.4</td>\n",
" <td>4.857</td>\n",
" <td>5191.0</td>\n",
" <td>no</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>24</td>\n",
" <td>technician</td>\n",
" <td>single</td>\n",
" <td>professional.course</td>\n",
" <td>1.0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>telephone</td>\n",
" <td>5</td>\n",
" <td>1</td>\n",
" <td>...</td>\n",
" <td>999</td>\n",
" <td>0</td>\n",
" <td>nonexistent</td>\n",
" <td>1.1</td>\n",
" <td>93.994</td>\n",
" <td>-36.4</td>\n",
" <td>4.857</td>\n",
" <td>5191.0</td>\n",
" <td>no</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>25</td>\n",
" <td>services</td>\n",
" <td>single</td>\n",
" <td>high.school</td>\n",
" <td>1.0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>telephone</td>\n",
" <td>5</td>\n",
" <td>1</td>\n",
" <td>...</td>\n",
" <td>999</td>\n",
" <td>0</td>\n",
" <td>nonexistent</td>\n",
" <td>1.1</td>\n",
" <td>93.994</td>\n",
" <td>-36.4</td>\n",
" <td>4.857</td>\n",
" <td>5191.0</td>\n",
" <td>no</td>\n",
" <td>0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>10 rows × 22 columns</p>\n",
"</div>"
],
"text/plain": [
" age job marital education default housing loan \\\n",
"0 56 housemaid married basic.4y 1.0 0 0 \n",
"1 57 services married high.school 0.0 0 0 \n",
"2 37 services married high.school 1.0 1 0 \n",
"3 40 admin. married basic.6y 1.0 0 0 \n",
"4 56 services married high.school 1.0 0 1 \n",
"5 45 services married basic.9y 0.0 0 0 \n",
"6 59 admin. married professional.course 1.0 0 0 \n",
"7 41 blue-collar married unknown 0.0 0 0 \n",
"8 24 technician single professional.course 1.0 1 0 \n",
"9 25 services single high.school 1.0 1 0 \n",
"\n",
" contact month day_of_week ... pdays previous poutcome \\\n",
"0 telephone 5 1 ... 999 0 nonexistent \n",
"1 telephone 5 1 ... 999 0 nonexistent \n",
"2 telephone 5 1 ... 999 0 nonexistent \n",
"3 telephone 5 1 ... 999 0 nonexistent \n",
"4 telephone 5 1 ... 999 0 nonexistent \n",
"5 telephone 5 1 ... 999 0 nonexistent \n",
"6 telephone 5 1 ... 999 0 nonexistent \n",
"7 telephone 5 1 ... 999 0 nonexistent \n",
"8 telephone 5 1 ... 999 0 nonexistent \n",
"9 telephone 5 1 ... 999 0 nonexistent \n",
"\n",
" emp.var.rate cons.price.idx cons.conf.idx euribor3m nr.employed \\\n",
"0 1.1 93.994 -36.4 4.857 5191.0 \n",
"1 1.1 93.994 -36.4 4.857 5191.0 \n",
"2 1.1 93.994 -36.4 4.857 5191.0 \n",
"3 1.1 93.994 -36.4 4.857 5191.0 \n",
"4 1.1 93.994 -36.4 4.857 5191.0 \n",
"5 1.1 93.994 -36.4 4.857 5191.0 \n",
"6 1.1 93.994 -36.4 4.857 5191.0 \n",
"7 1.1 93.994 -36.4 4.857 5191.0 \n",
"8 1.1 93.994 -36.4 4.857 5191.0 \n",
"9 1.1 93.994 -36.4 4.857 5191.0 \n",
"\n",
" response response_binary \n",
"0 no 0 \n",
"1 no 0 \n",
"2 no 0 \n",
"3 no 0 \n",
"4 no 0 \n",
"5 no 0 \n",
"6 no 0 \n",
"7 no 0 \n",
"8 no 0 \n",
"9 no 0 \n",
"\n",
"[10 rows x 22 columns]"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"#dataset7 = pd.get_dummies(dataset7, columns = ['job'])\n",
"#dataset7 = pd.get_dummies(dataset7, columns = ['education'])\n",
"dataset7['housing'] = dataset7['housing'].map({'yes': 1, 'no': 0})\n",
"dataset7['default'] = dataset7['default'].map({'no': 1, 'unknown': 0})\n",
"dataset7['loan'] = dataset7['loan'].map({'yes': 1, 'no': 0})####################################################\n",
"\n",
"#dataset7_response = pd.DataFrame(dataset['response_binary'])\n",
"#dataset7 = pd.merge(dataset7, dataset_response, left_index = True, right_index = True)\n",
"print(dataset7.shape)\n",
"display(dataset7.head(10))\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "eP_sY-MJW9Zg"
},
"source": [
"## Contact to \"#\""
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 372
},
"colab_type": "code",
"id": "2_E-U0bEUHxd",
"outputId": "a39a69e3-b629-4548-9555-9130d304787a"
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>age</th>\n",
" <th>job</th>\n",
" <th>marital</th>\n",
" <th>education</th>\n",
" <th>default</th>\n",
" <th>housing</th>\n",
" <th>loan</th>\n",
" <th>contact</th>\n",
" <th>month</th>\n",
" <th>day_of_week</th>\n",
" <th>...</th>\n",
" <th>pdays</th>\n",
" <th>previous</th>\n",
" <th>poutcome</th>\n",
" <th>emp.var.rate</th>\n",
" <th>cons.price.idx</th>\n",
" <th>cons.conf.idx</th>\n",
" <th>euribor3m</th>\n",
" <th>nr.employed</th>\n",
" <th>response</th>\n",
" <th>response_binary</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>56</td>\n",
" <td>housemaid</td>\n",
" <td>married</td>\n",
" <td>basic.4y</td>\n",
" <td>1.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>5</td>\n",
" <td>1</td>\n",
" <td>...</td>\n",
" <td>999</td>\n",
" <td>0</td>\n",
" <td>nonexistent</td>\n",
" <td>1.1</td>\n",
" <td>93.994</td>\n",
" <td>-36.4</td>\n",
" <td>4.857</td>\n",
" <td>5191.0</td>\n",
" <td>no</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>57</td>\n",
" <td>services</td>\n",
" <td>married</td>\n",
" <td>high.school</td>\n",
" <td>0.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>5</td>\n",
" <td>1</td>\n",
" <td>...</td>\n",
" <td>999</td>\n",
" <td>0</td>\n",
" <td>nonexistent</td>\n",
" <td>1.1</td>\n",
" <td>93.994</td>\n",
" <td>-36.4</td>\n",
" <td>4.857</td>\n",
" <td>5191.0</td>\n",
" <td>no</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>37</td>\n",
" <td>services</td>\n",
" <td>married</td>\n",
" <td>high.school</td>\n",
" <td>1.0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>5</td>\n",
" <td>1</td>\n",
" <td>...</td>\n",
" <td>999</td>\n",
" <td>0</td>\n",
" <td>nonexistent</td>\n",
" <td>1.1</td>\n",
" <td>93.994</td>\n",
" <td>-36.4</td>\n",
" <td>4.857</td>\n",
" <td>5191.0</td>\n",
" <td>no</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>40</td>\n",
" <td>admin.</td>\n",
" <td>married</td>\n",
" <td>basic.6y</td>\n",
" <td>1.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>5</td>\n",
" <td>1</td>\n",
" <td>...</td>\n",
" <td>999</td>\n",
" <td>0</td>\n",
" <td>nonexistent</td>\n",
" <td>1.1</td>\n",
" <td>93.994</td>\n",
" <td>-36.4</td>\n",
" <td>4.857</td>\n",
" <td>5191.0</td>\n",
" <td>no</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>56</td>\n",
" <td>services</td>\n",
" <td>married</td>\n",
" <td>high.school</td>\n",
" <td>1.0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>5</td>\n",
" <td>1</td>\n",
" <td>...</td>\n",
" <td>999</td>\n",
" <td>0</td>\n",
" <td>nonexistent</td>\n",
" <td>1.1</td>\n",
" <td>93.994</td>\n",
" <td>-36.4</td>\n",
" <td>4.857</td>\n",
" <td>5191.0</td>\n",
" <td>no</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>45</td>\n",
" <td>services</td>\n",
" <td>married</td>\n",
" <td>basic.9y</td>\n",
" <td>0.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>5</td>\n",
" <td>1</td>\n",
" <td>...</td>\n",
" <td>999</td>\n",
" <td>0</td>\n",
" <td>nonexistent</td>\n",
" <td>1.1</td>\n",
" <td>93.994</td>\n",
" <td>-36.4</td>\n",
" <td>4.857</td>\n",
" <td>5191.0</td>\n",
" <td>no</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>59</td>\n",
" <td>admin.</td>\n",
" <td>married</td>\n",
" <td>professional.course</td>\n",
" <td>1.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>5</td>\n",
" <td>1</td>\n",
" <td>...</td>\n",
" <td>999</td>\n",
" <td>0</td>\n",
" <td>nonexistent</td>\n",
" <td>1.1</td>\n",
" <td>93.994</td>\n",
" <td>-36.4</td>\n",
" <td>4.857</td>\n",
" <td>5191.0</td>\n",
" <td>no</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>41</td>\n",
" <td>blue-collar</td>\n",
" <td>married</td>\n",
" <td>unknown</td>\n",
" <td>0.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>5</td>\n",
" <td>1</td>\n",
" <td>...</td>\n",
" <td>999</td>\n",
" <td>0</td>\n",
" <td>nonexistent</td>\n",
" <td>1.1</td>\n",
" <td>93.994</td>\n",
" <td>-36.4</td>\n",
" <td>4.857</td>\n",
" <td>5191.0</td>\n",
" <td>no</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>24</td>\n",
" <td>technician</td>\n",
" <td>single</td>\n",
" <td>professional.course</td>\n",
" <td>1.0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>5</td>\n",
" <td>1</td>\n",
" <td>...</td>\n",
" <td>999</td>\n",
" <td>0</td>\n",
" <td>nonexistent</td>\n",
" <td>1.1</td>\n",
" <td>93.994</td>\n",
" <td>-36.4</td>\n",
" <td>4.857</td>\n",
" <td>5191.0</td>\n",
" <td>no</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>25</td>\n",
" <td>services</td>\n",
" <td>single</td>\n",
" <td>high.school</td>\n",
" <td>1.0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>5</td>\n",
" <td>1</td>\n",
" <td>...</td>\n",
" <td>999</td>\n",
" <td>0</td>\n",
" <td>nonexistent</td>\n",
" <td>1.1</td>\n",
" <td>93.994</td>\n",
" <td>-36.4</td>\n",
" <td>4.857</td>\n",
" <td>5191.0</td>\n",
" <td>no</td>\n",
" <td>0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>10 rows × 22 columns</p>\n",
"</div>"
],
"text/plain": [
" age job marital education default housing loan \\\n",
"0 56 housemaid married basic.4y 1.0 0 0 \n",
"1 57 services married high.school 0.0 0 0 \n",
"2 37 services married high.school 1.0 1 0 \n",
"3 40 admin. married basic.6y 1.0 0 0 \n",
"4 56 services married high.school 1.0 0 1 \n",
"5 45 services married basic.9y 0.0 0 0 \n",
"6 59 admin. married professional.course 1.0 0 0 \n",
"7 41 blue-collar married unknown 0.0 0 0 \n",
"8 24 technician single professional.course 1.0 1 0 \n",
"9 25 services single high.school 1.0 1 0 \n",
"\n",
" contact month day_of_week ... pdays previous poutcome emp.var.rate \\\n",
"0 1 5 1 ... 999 0 nonexistent 1.1 \n",
"1 1 5 1 ... 999 0 nonexistent 1.1 \n",
"2 1 5 1 ... 999 0 nonexistent 1.1 \n",
"3 1 5 1 ... 999 0 nonexistent 1.1 \n",
"4 1 5 1 ... 999 0 nonexistent 1.1 \n",
"5 1 5 1 ... 999 0 nonexistent 1.1 \n",
"6 1 5 1 ... 999 0 nonexistent 1.1 \n",
"7 1 5 1 ... 999 0 nonexistent 1.1 \n",
"8 1 5 1 ... 999 0 nonexistent 1.1 \n",
"9 1 5 1 ... 999 0 nonexistent 1.1 \n",
"\n",
" cons.price.idx cons.conf.idx euribor3m nr.employed response \\\n",
"0 93.994 -36.4 4.857 5191.0 no \n",
"1 93.994 -36.4 4.857 5191.0 no \n",
"2 93.994 -36.4 4.857 5191.0 no \n",
"3 93.994 -36.4 4.857 5191.0 no \n",
"4 93.994 -36.4 4.857 5191.0 no \n",
"5 93.994 -36.4 4.857 5191.0 no \n",
"6 93.994 -36.4 4.857 5191.0 no \n",
"7 93.994 -36.4 4.857 5191.0 no \n",
"8 93.994 -36.4 4.857 5191.0 no \n",
"9 93.994 -36.4 4.857 5191.0 no \n",
"\n",
" response_binary \n",
"0 0 \n",
"1 0 \n",
"2 0 \n",
"3 0 \n",
"4 0 \n",
"5 0 \n",
"6 0 \n",
"7 0 \n",
"8 0 \n",
"9 0 \n",
"\n",
"[10 rows x 22 columns]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"(40066, 22)\n"
]
}
],
"source": [
"#To Create a colum for each var\n",
"#dataset7 = pd.get_dummies(dataset7, columns = ['job'])\n",
"#dataset7 = pd.get_dummies(dataset7, columns = ['education'])\n",
"\n",
"dataset7['contact'] = dataset7['contact'].map({'telephone': 1, 'cellular': 0})\n",
"#dataset7['default'] = dataset7['default'].map({'no': 1, 'unknown': 0})\n",
"#dataset7['loan'] = dataset7['loan'].map({'yes': 1, 'no': 0})\n",
"\n",
"#dataset7_response = pd.DataFrame(dataset['response_binary'])\n",
"#dataset7 = pd.merge(dataset7, dataset_response, left_index = True, right_index = True)\n",
"display(dataset7.head(10))\n",
"\n",
"print(dataset7.shape)"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "GmloKiFYVOuc"
},
"source": [
"### CORRELATION Matrix"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 760
},
"colab_type": "code",
"id": "M8L0ciuyvp6z",
"outputId": "8cc1759d-83a0-48b8-f4cb-ec6b26f635cd"
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>age</th>\n",
" <th>job</th>\n",
" <th>marital</th>\n",
" <th>education</th>\n",
" <th>default</th>\n",
" <th>housing</th>\n",
" <th>loan</th>\n",
" <th>contact</th>\n",
" <th>month</th>\n",
" <th>day_of_week</th>\n",
" <th>...</th>\n",
" <th>pdays</th>\n",
" <th>previous</th>\n",
" <th>poutcome</th>\n",
" <th>emp.var.rate</th>\n",
" <th>cons.price.idx</th>\n",
" <th>cons.conf.idx</th>\n",
" <th>euribor3m</th>\n",
" <th>nr.employed</th>\n",
" <th>response</th>\n",
" <th>response_binary</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>56</td>\n",
" <td>housemaid</td>\n",
" <td>married</td>\n",
" <td>basic.4y</td>\n",
" <td>1.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>5</td>\n",
" <td>1</td>\n",
" <td>...</td>\n",
" <td>999</td>\n",
" <td>0</td>\n",
" <td>nonexistent</td>\n",
" <td>1.1</td>\n",
" <td>93.994</td>\n",
" <td>-36.4</td>\n",
" <td>4.857</td>\n",
" <td>5191.0</td>\n",
" <td>no</td>\n",
" <td>0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>1 rows × 22 columns</p>\n",
"</div>"
],
"text/plain": [
" age job marital education default housing loan contact month \\\n",
"0 56 housemaid married basic.4y 1.0 0 0 1 5 \n",
"\n",
" day_of_week ... pdays previous poutcome emp.var.rate cons.price.idx \\\n",
"0 1 ... 999 0 nonexistent 1.1 93.994 \n",
"\n",
" cons.conf.idx euribor3m nr.employed response response_binary \n",
"0 -36.4 4.857 5191.0 no 0 \n",
"\n",
"[1 rows x 22 columns]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 1080x720 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"(40066, 22)\n"
]
}
],
"source": [
"display(dataset7.head(1))\n",
"corr_data = dataset7[['age','job','marital','education','default','housing','loan','month','day_of_week','duration','campaign','pdays','previous','poutcome','emp.var.rate','cons.price.idx','cons.conf.idx','euribor3m','nr.employed','response_binary']]\n",
"corr = corr_data.corr()\n",
"#housing\tloan\tcontact\tmonth\tday_of_week\tduration\tcampaign\tpdays\tprevious\t\n",
"#poutcome\temp.var.rate\tcons.price.idx\tcons.conf.idx\teuribor3m\tnr.employed\t\n",
"cor_plot = sns.heatmap(corr,annot=True,cmap='BuPu',linewidths=0.2,annot_kws={'size':10})\n",
"fig=plt.gcf()\n",
"fig.set_size_inches(15,10)\n",
"plt.xticks(fontsize=10,rotation=-60)\n",
"plt.yticks(fontsize=10)\n",
"plt.title('Correlation Matrix')\n",
"\n",
"save_fig('Datav-CORREMat')\n",
"\n",
"plt.show()\n",
"\n",
"print(dataset7.shape)\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 0,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "_6wDVcMGuMgM"
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "oqXMkwqecMdP"
},
"source": [
"# Modelling\n",
"\n",
"\"https://www.kaggle.com/suhasshastry/bank-marketing-analysis-for-term-deposit/notebook\""
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "N2y0x9QqcWy6"
},
"source": [
"Given data set is highly imbalanced, i.e. number of data belonging to 'no' category is way higher than 'yes' category."
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "nk8ndAFeeCcM"
},
"source": [
"### X & y"
]
},
{
"cell_type": "code",
"execution_count": 0,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "mAENzpJlcn4j"
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "goRC2R-RMWC_"
},
"source": [
"print(\"____sataset7 shape\")\n",
"print(dataset7.shape)\n",
"predictors = dataset7.iloc[:,0:20]#21 response #22 response binary\n",
"#predictors = predictors.drop(['pdays'],axis=1)\n",
"Y = dataset7.iloc[:,21] #reponse binary\n",
"X = pd.get_dummies(predictors)\n",
"print(\"____predictors shape\")\n",
"print(predictors.shape)\n",
"print(\"____y shape\")\n",
"print(y.shape)\n",
"print(\"____table predictors\")\n",
"display(predictors.head(300))\n",
"print(\"____y show\")\n",
"display(y.head(3))"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "Ipjznv-k-pAf"
},
"source": [
"## orig data"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 236
},
"colab_type": "code",
"id": "79R7QFJL-oAd",
"outputId": "1a40a4c0-8722-4bed-e21a-c9f1e0cc7678"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"--1--data\n",
"--1.2-read file\n",
"--1.2.2--readed\n",
"(41188, 21)\n"
]
},
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
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" <th>default</th>\n",
" <th>housing</th>\n",
" <th>loan</th>\n",
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" <th>month</th>\n",
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" <td>56</td>\n",
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" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>1 rows × 21 columns</p>\n",
"</div>"
],
"text/plain": [
" age job marital education default housing loan contact month \\\n",
"0 56 housemaid married basic.4y no no no telephone may \n",
"\n",
" day_of_week ... campaign pdays previous poutcome emp.var.rate \\\n",
"0 mon ... 1 999 0 nonexistent 1.1 \n",
"\n",
" cons.price.idx cons.conf.idx euribor3m nr.employed y \n",
"0 93.994 -36.4 4.857 5191.0 no \n",
"\n",
"[1 rows x 21 columns]"
]
},
"metadata": {},
"output_type": "display_data"
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"(38437, 21)\n"
]
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" age job marital education default housing loan contact month \\\n",
"0 56 housemaid married basic.4y no no no telephone may \n",
"\n",
" day_of_week ... campaign pdays previous poutcome emp.var.rate \\\n",
"0 mon ... 1 999 0 nonexistent 1.1 \n",
"\n",
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"\n",
"[1 rows x 21 columns]"
]
},
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],
"source": [
"#linkname = '../input/bank_cleaned.csv'\n",
"#dataset = read_csv(linkname)\n",
"#dataset = dataset.drop(['Unnamed: 0'], axis=1)\n",
"\n",
"print(\"--1--data\")\n",
"linkname = 'bank-additional-full.csv'\n",
"print(\"--1.2-read file\")\n",
"datasetO = pd.read_csv(linkname, sep = ';')\n",
"print(\"--1.2.2--readed\")\n",
"print(datasetO.shape)\n",
"display(datasetO.head(1))\n",
"\n",
"\n",
"################\n",
"# Step 1: Delete the rows which column 'poutcome' contains 'other'\n",
"conditionO = datasetO.poutcome == 'other'\n",
"datasetconditionO0 = datasetO.drop(datasetO[conditionO].index, axis = 0, inplace = False)\n",
"\n",
"conditionO0 = datasetconditionO0.marital == 'unknown'\n",
"datasetO02 = datasetconditionO0.drop(datasetconditionO0[conditionO0].index, axis = 0, inplace = False)\n",
"\n",
"conditionO00 = datasetO02.housing == 'unknown'\n",
"datasetO002 = datasetO02.drop(datasetO02[conditionO00].index, axis = 0, inplace = False)\n",
"\n",
"conditionO000 = datasetO002.loan == 'unknown'\n",
"datasetO0002 = datasetO002.drop(datasetO002[conditionO000].index, axis = 0, inplace = False)\n",
"\n",
"# We found out an \"unknown\" in edu\n",
"conditionO0000 = datasetO0002.education == 'unknown'\n",
"datasetO5 = datasetO0002.drop(datasetO0002[conditionO0000].index, axis = 0, inplace = False)\n",
"\n",
"#erase calls with duration 0\n",
"#condition000 = dataset002.loan == 'unknown'\n",
"#dataset2 = dataset002.drop(dataset002[condition000].index, axis = 0, inplace = False)\n",
"\n",
"print(datasetO5.shape)\n",
"display(datasetO5.head(1))\n",
"\n",
"#datasetO5.rename(index=str, columns={'y': 'response'}, inplace = True)\n",
"\n",
"#def convert(datasetO5, new_column, old_column):\n",
"# datasetO5[new_column] = datasetO5[old_column].apply(lambda x: 0 if x == 'no' else 1)\n",
"# return datasetO5[new_column].value_counts()\n",
"#To appreciate the change when displaying the table 22 col\n",
"#convert(datasetO5, \"response_binary\", \"response\")\n",
"#convert(dataset5, \"response\", \"response\")\n",
"#print(\"-----2.3.1\")\n",
"\n",
"#print(datasetO5.shape)\n",
"#display(datasetO5.head(1))\n",
"\n",
"\n"
]
},
{
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"id": "MpbPK0DrRmTj",
"outputId": "24336abd-b3b5-4efe-97b2-2bdfab3e3958"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"Int64Index: 38437 entries, 0 to 41187\n",
"Data columns (total 21 columns):\n",
"age 38437 non-null int64\n",
"job 38437 non-null object\n",
"marital 38437 non-null object\n",
"education 38437 non-null object\n",
"default 38437 non-null object\n",
"housing 38437 non-null object\n",
"loan 38437 non-null object\n",
"contact 38437 non-null object\n",
"month 38437 non-null object\n",
"day_of_week 38437 non-null object\n",
"duration 38437 non-null int64\n",
"campaign 38437 non-null int64\n",
"pdays 38437 non-null int64\n",
"previous 38437 non-null int64\n",
"poutcome 38437 non-null object\n",
"emp.var.rate 38437 non-null float64\n",
"cons.price.idx 38437 non-null float64\n",
"cons.conf.idx 38437 non-null float64\n",
"euribor3m 38437 non-null float64\n",
"nr.employed 38437 non-null float64\n",
"y 38437 non-null object\n",
"dtypes: float64(5), int64(5), object(11)\n",
"memory usage: 6.5+ MB\n"
]
}
],
"source": [
"datasetO5.info()"
]
},
{
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},
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" age job marital education default housing loan contact \\\n",
"0 56 housemaid married basic.4y no no no telephone \n",
"1 57 services married high.school unknown no no telephone \n",
"2 37 services married high.school no yes no telephone \n",
"3 40 admin. married basic.6y no no no telephone \n",
"4 56 services married high.school no no yes telephone \n",
"\n",
" month day_of_week ... campaign pdays previous poutcome emp.var.rate \\\n",
"0 may mon ... 1 999 0 nonexistent 1.1 \n",
"1 may mon ... 1 999 0 nonexistent 1.1 \n",
"2 may mon ... 1 999 0 nonexistent 1.1 \n",
"3 may mon ... 1 999 0 nonexistent 1.1 \n",
"4 may mon ... 1 999 0 nonexistent 1.1 \n",
"\n",
" cons.price.idx cons.conf.idx euribor3m nr.employed y \n",
"0 93.994 -36.4 4.857 5191.0 no \n",
"1 93.994 -36.4 4.857 5191.0 no \n",
"2 93.994 -36.4 4.857 5191.0 no \n",
"3 93.994 -36.4 4.857 5191.0 no \n",
"4 93.994 -36.4 4.857 5191.0 no \n",
"\n",
"[5 rows x 21 columns]"
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"text/plain": [
" age job marital education default housing loan contact \\\n",
"0 56 housemaid married basic.4y no no no telephone \n",
"1 57 services married high.school unknown no no telephone \n",
"2 37 services married high.school no yes no telephone \n",
"3 40 admin. married basic.6y no no no telephone \n",
"4 56 services married high.school no no yes telephone \n",
"\n",
" month day_of_week duration campaign previous poutcome emp.var.rate \\\n",
"0 may mon 261 1 0 nonexistent 1.1 \n",
"1 may mon 149 1 0 nonexistent 1.1 \n",
"2 may mon 226 1 0 nonexistent 1.1 \n",
"3 may mon 151 1 0 nonexistent 1.1 \n",
"4 may mon 307 1 0 nonexistent 1.1 \n",
"\n",
" cons.price.idx cons.conf.idx euribor3m nr.employed \n",
"0 93.994 -36.4 4.857 5191.0 \n",
"1 93.994 -36.4 4.857 5191.0 \n",
"2 93.994 -36.4 4.857 5191.0 \n",
"3 93.994 -36.4 4.857 5191.0 \n",
"4 93.994 -36.4 4.857 5191.0 "
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"0 no\n",
"1 no\n",
"2 no\n",
"3 no\n",
"4 no\n",
"Name: y, dtype: object"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>age</th>\n",
" <th>duration</th>\n",
" <th>campaign</th>\n",
" <th>previous</th>\n",
" <th>emp.var.rate</th>\n",
" <th>cons.price.idx</th>\n",
" <th>cons.conf.idx</th>\n",
" <th>euribor3m</th>\n",
" <th>nr.employed</th>\n",
" <th>job_admin.</th>\n",
" <th>...</th>\n",
" <th>month_oct</th>\n",
" <th>month_sep</th>\n",
" <th>day_of_week_fri</th>\n",
" <th>day_of_week_mon</th>\n",
" <th>day_of_week_thu</th>\n",
" <th>day_of_week_tue</th>\n",
" <th>day_of_week_wed</th>\n",
" <th>poutcome_failure</th>\n",
" <th>poutcome_nonexistent</th>\n",
" <th>poutcome_success</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>56</td>\n",
" <td>261</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
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" <td>93.994</td>\n",
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" <td>93.994</td>\n",
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" <td>1</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>37</td>\n",
" <td>226</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>1.1</td>\n",
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" <tr>\n",
" <th>3</th>\n",
" <td>40</td>\n",
" <td>151</td>\n",
" <td>1</td>\n",
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" <td>1.1</td>\n",
" <td>93.994</td>\n",
" <td>-36.4</td>\n",
" <td>4.857</td>\n",
" <td>5191.0</td>\n",
" <td>1</td>\n",
" <td>...</td>\n",
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" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>56</td>\n",
" <td>307</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>1.1</td>\n",
" <td>93.994</td>\n",
" <td>-36.4</td>\n",
" <td>4.857</td>\n",
" <td>5191.0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
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"<p>5 rows × 58 columns</p>\n",
"</div>"
],
"text/plain": [
" age duration campaign previous emp.var.rate cons.price.idx \\\n",
"0 56 261 1 0 1.1 93.994 \n",
"1 57 149 1 0 1.1 93.994 \n",
"2 37 226 1 0 1.1 93.994 \n",
"3 40 151 1 0 1.1 93.994 \n",
"4 56 307 1 0 1.1 93.994 \n",
"\n",
" cons.conf.idx euribor3m nr.employed job_admin. ... month_oct \\\n",
"0 -36.4 4.857 5191.0 0 ... 0 \n",
"1 -36.4 4.857 5191.0 0 ... 0 \n",
"2 -36.4 4.857 5191.0 0 ... 0 \n",
"3 -36.4 4.857 5191.0 1 ... 0 \n",
"4 -36.4 4.857 5191.0 0 ... 0 \n",
"\n",
" month_sep day_of_week_fri day_of_week_mon day_of_week_thu \\\n",
"0 0 0 1 0 \n",
"1 0 0 1 0 \n",
"2 0 0 1 0 \n",
"3 0 0 1 0 \n",
"4 0 0 1 0 \n",
"\n",
" day_of_week_tue day_of_week_wed poutcome_failure poutcome_nonexistent \\\n",
"0 0 0 0 1 \n",
"1 0 0 0 1 \n",
"2 0 0 0 1 \n",
"3 0 0 0 1 \n",
"4 0 0 0 1 \n",
"\n",
" poutcome_success \n",
"0 0 \n",
"1 0 \n",
"2 0 \n",
"3 0 \n",
"4 0 \n",
"\n",
"[5 rows x 58 columns]"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"predictors = datasetO5.iloc[:,0:20] \n",
"predictors = predictors.drop(['pdays'],axis=1)\n",
"\n",
"y = datasetO5.iloc[:,20]\n",
"X = pd.get_dummies(predictors)\n",
"print(datasetO5.shape)\n",
"display(datasetO5.head())\n",
"\n",
"print(predictors.shape)\n",
"display(predictors.head())\n",
"\n",
"display(y.head())\n",
"display(X.head())"
]
},
{
"cell_type": "code",
"execution_count": 610,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 403
},
"colab_type": "code",
"id": "dK-7gnRUuyV4",
"outputId": "350de1d8-76ac-43a1-8d7b-f8f99d8baa7b"
},
"outputs": [
{
"data": {
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0NLm5uclkMmnr1q168cUXtXr1ajk4OCg4OPiG5ygsLFRKSop8fX3l5ORkicuotuwmrrR2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\n24L/pgAAAABJRU5ErkJggg==\n",
"text/plain": [
"<Figure size 720x432 with 1 Axes>"
]
},
"metadata": {
"tags": []
},
"output_type": "display_data"
}
],
"source": [
"from pylab import rcParams\n",
"import matplotlib.ticker as mtick # For specifying the axes tick format \n",
"\n",
"df = datasetO5 \n",
"\n",
"rcParams['figure.figsize']=10,6\n",
"\n",
"ax = (df['loan'].value_counts()*100.0 /len(df)).plot(kind='bar', stacked = True, rot = 0)\n",
"ax.yaxis.set_major_formatter(mtick.PercentFormatter())\n",
"ax.set_ylabel('% Customers with loan')\n",
"ax.set_xlabel('Personal loan')\n",
"ax.set_ylabel('% Customers')\n",
"ax.set_title('Term deposit distribution')\n",
"\n",
"totals = [] # creating a list to collect the plt.patches data\n",
"\n",
"# finding the values and append to list\n",
"for i in ax.patches:\n",
" totals.append(i.get_width())\n",
"\n",
"total = sum(totals) # setting individual bar lables using above list\n",
"\n",
"for i in ax.patches:\n",
" # getting_width pulls left or right; get_y pushes up or down\n",
" ax.text(i.get_x()+.15, i.get_height()-3.5, \\\n",
" str(round((i.get_height()/total), 1))+'%', color='white', weight = 'bold')\n",
" \n"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 65
},
"colab_type": "code",
"id": "OHv1Tt4uk0og",
"outputId": "f0a3142d-cae4-4f77-be57-5c0c41efb89a"
},
"outputs": [
{
"data": {
"text/plain": [
"no 34160\n",
"yes 4277\n",
"Name: y, dtype: int64"
]
},
"execution_count": 32,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pd.Series(y).value_counts()"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "UBsaMuQkWste"
},
"outputs": [],
"source": [
"#from sklearn.model_selection import train_test_split\n",
"#from sklearn.model_selection import cross_val_score\n",
"#from sklearn.model_selection import KFold\n",
"from sklearn.metrics import accuracy_score\n",
"#print(\"libraries\")"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "gT9wItR0WsSW"
},
"source": [
"## Dta split"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "brklhUYdWr4_"
},
"outputs": [],
"source": [
"# 20% of the data will be used for testing\n",
"#test_size= 0.20\n",
"#seed = 7\n",
"#X_train, X_test, Y_train, Y_test= train_test_split(X, y, test_size=test_size, random_state=seed)\n",
"#print(\"____data splited\")"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "NUUEfwi4WrhD"
},
"source": [
"## Compare dif clas algor"
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "kWG-0yjpmTHa"
},
"outputs": [],
"source": [
"# Time for Classification Models\n",
"import time\n",
"\n",
"from sklearn.decomposition import PCA\n",
" \n",
"from sklearn.linear_model import LogisticRegression\n",
"from sklearn.model_selection import train_test_split\n",
"from sklearn.svm import SVC\n",
"from sklearn.neighbors import KNeighborsClassifier\n",
"from sklearn import tree\n",
"from sklearn.neural_network import MLPClassifier\n",
"from sklearn.neighbors import KNeighborsClassifier\n",
"from sklearn.ensemble import GradientBoostingClassifier\n",
"from sklearn.gaussian_process.kernels import RBF\n",
"from sklearn.ensemble import RandomForestClassifier\n",
"from sklearn.naive_bayes import GaussianNB\n",
"\n",
"dict_classifiers = {\n",
" \"Logistic Regression\": LogisticRegression(solver='lbfgs', max_iter=5000),\n",
" \"Nearest Neighbors\": KNeighborsClassifier(),\n",
" \"Linear SVM\": SVC(gamma = 'auto'),\n",
" \"Gradient Boosting Classifier\": GradientBoostingClassifier(),\n",
" \"Decision Tree\": tree.DecisionTreeClassifier(),\n",
" \"Random Forest\": RandomForestClassifier(n_estimators=18),\n",
" \"Neural Net\": MLPClassifier(alpha=1),\n",
" \"Naive Bayes\": GaussianNB()\n",
"}"
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "9uTxtOL9eIf-"
},
"outputs": [],
"source": [
"X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3)"
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "OKYNe_H4pa8q"
},
"outputs": [],
"source": [
"no_classifiers = len(dict_classifiers.keys())\n",
"\n",
"def batch_classify(X_train, Y_train, verbose = True):\n",
" df_results = pd.DataFrame(data=np.zeros(shape=(no_classifiers,3)), columns = ['classifier', 'train_score', 'training_time'])\n",
" count = 0\n",
" for key, classifier in dict_classifiers.items():\n",
" t_start = time.process_time()\n",
" classifier.fit(X_train, Y_train)\n",
" t_end = time.process_time()\n",
" t_diff = t_end - t_start\n",
" train_score = classifier.score(X_train, Y_train)\n",
" df_results.loc[count,'classifier'] = key\n",
" df_results.loc[count,'train_score'] = train_score\n",
" df_results.loc[count,'training_time'] = t_diff\n",
" if verbose:\n",
" print(\"trained {c} in {f:.2f} s\".format(c=key, f=t_diff))\n",
" count+=1\n",
" return df_results"
]
},
{
"cell_type": "code",
"execution_count": 617,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 289
},
"colab_type": "code",
"id": "7XyaPCvPpgKV",
"outputId": "768be8c2-a305-4af8-ec42-6a7fe1b8c5aa"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"trained Logistic Regression in 5.60 s\n",
"trained Nearest Neighbors in 0.31 s\n",
"trained Linear SVM in 159.79 s\n",
"trained Gradient Boosting Classifier in 5.82 s\n",
"trained Decision Tree in 0.27 s\n",
"trained Random Forest in 0.59 s\n",
"trained Neural Net in 6.35 s\n",
"trained Naive Bayes in 0.07 s\n",
" classifier train_score training_time\n",
"4 Decision Tree 1.000000 0.266150\n",
"5 Random Forest 0.997510 0.588621\n",
"2 Linear SVM 0.958038 159.793320\n",
"1 Nearest Neighbors 0.929678 0.308423\n",
"3 Gradient Boosting Classifier 0.924438 5.815561\n",
"0 Logistic Regression 0.912990 5.595267\n",
"6 Neural Net 0.865230 6.349941\n",
"7 Naive Bayes 0.860472 0.070996\n"
]
}
],
"source": [
"df_results = batch_classify(X_train, y_train)\n",
"print(df_results.sort_values(by='train_score', ascending=False))"
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "vZ-CPEp0ztna"
},
"outputs": [],
"source": [
"# Use Cross-validation.\n",
"from sklearn.model_selection import cross_val_score\n",
"\n",
"# Logistic Regression\n",
"log_reg = LogisticRegression(solver='lbfgs', max_iter=5000)\n",
"log_scores = cross_val_score(log_reg, X_train, y_train, cv=3)\n",
"log_reg_mean = log_scores.mean()\n",
"\n",
"# SVC\n",
"svc_clf = SVC(gamma='auto')\n",
"svc_scores = cross_val_score(svc_clf, X_train, y_train, cv=3)\n",
"svc_mean = svc_scores.mean()\n",
"\n",
"# KNearestNeighbors\n",
"knn_clf = KNeighborsClassifier()\n",
"knn_scores = cross_val_score(knn_clf, X_train, y_train, cv=3)\n",
"knn_mean = knn_scores.mean()\n",
"\n",
"# Decision Tree\n",
"tree_clf = tree.DecisionTreeClassifier()\n",
"tree_scores = cross_val_score(tree_clf, X_train, y_train, cv=3)\n",
"tree_mean = tree_scores.mean()\n",
"\n",
"# Gradient Boosting Classifier\n",
"grad_clf = GradientBoostingClassifier()\n",
"grad_scores = cross_val_score(grad_clf, X_train, y_train, cv=3)\n",
"grad_mean = grad_scores.mean()\n",
"\n",
"# Random Forest Classifier\n",
"rand_clf = RandomForestClassifier(n_estimators=18)\n",
"rand_scores = cross_val_score(rand_clf, X_train, y_train, cv=3)\n",
"rand_mean = rand_scores.mean()\n",
"\n",
"# NeuralNet Classifier\n",
"neural_clf = MLPClassifier(alpha=1)\n",
"neural_scores = cross_val_score(neural_clf, X_train, y_train, cv=3)\n",
"neural_mean = neural_scores.mean()\n",
"\n",
"# Naives Bayes\n",
"nav_clf = GaussianNB()\n",
"nav_scores = cross_val_score(nav_clf, X_train, y_train, cv=3)\n",
"nav_mean = neural_scores.mean()\n",
"\n",
"# Create a Dataframe with the results.\n",
"d = {'Classifiers': ['Logistic Reg.', 'SVC', 'KNN', 'Dec Tree', 'Grad B CLF', 'Rand FC', 'Neural Classifier', 'Naives Bayes'], \n",
" 'Crossval Mean Scores': [log_reg_mean, svc_mean, knn_mean, tree_mean, grad_mean, rand_mean, neural_mean, nav_mean]}\n",
"\n",
"result_df = pd.DataFrame(data=d)"
]
},
{
"cell_type": "code",
"execution_count": 59,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 278
},
"colab_type": "code",
"id": "Jcd6Keb9z_iY",
"outputId": "997d5afb-746c-4f6c-e57d-e5055f7594b3"
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Classifiers</th>\n",
" <th>Crossval Mean Scores</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>Grad B CLF</td>\n",
" <td>0.916372</td>\n",
" </tr>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>Logistic Reg.</td>\n",
" <td>0.909980</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>Rand FC</td>\n",
" <td>0.908381</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>KNN</td>\n",
" <td>0.900911</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>SVC</td>\n",
" <td>0.892771</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>Dec Tree</td>\n",
" <td>0.888571</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>Neural Classifier</td>\n",
" <td>0.870729</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>Naives Bayes</td>\n",
" <td>0.870729</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Classifiers Crossval Mean Scores\n",
"4 Grad B CLF 0.916372\n",
"0 Logistic Reg. 0.909980\n",
"5 Rand FC 0.908381\n",
"2 KNN 0.900911\n",
"1 SVC 0.892771\n",
"3 Dec Tree 0.888571\n",
"6 Neural Classifier 0.870729\n",
"7 Naives Bayes 0.870729"
]
},
"execution_count": 59,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"result_df = result_df.sort_values(by=['Crossval Mean Scores'], ascending=False)\n",
"result_df"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "Xxz7SSvEmeAY"
},
"source": []
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 33
},
"colab_type": "code",
"id": "kLFWNEwvFq35",
"outputId": "733c85af-33df-479f-9052-99c647ffa533"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Gradient Boost Classifier accuracy is 0.92\n"
]
}
],
"source": [
"# Use Cross-validation.\n",
"from sklearn.model_selection import cross_val_score\n",
"\n",
"# Gradient Boosting Classifier\n",
"grad_clf = GradientBoostingClassifier()\n",
"grad_scores = cross_val_score(grad_clf, X_train, y_train, cv=3)\n",
"grad_mean = grad_scores.mean()\n",
"\n",
"\n",
"from sklearn.model_selection import cross_val_predict\n",
"\n",
"y_train_pred = cross_val_predict(grad_clf, X_train, y_train, cv=5)\n",
"\n",
"from sklearn.metrics import accuracy_score\n",
"\n",
"grad_clf.fit(X_train, y_train)\n",
"print (\"Gradient Boost Classifier accuracy is %2.2f\" % accuracy_score(y_train, y_train_pred))\n",
"predicted_probas =grad_clf.predict_proba(X_test)"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "2v4tAwncxqhe"
},
"source": []
},
{
"cell_type": "code",
"execution_count": 621,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 438
},
"colab_type": "code",
"id": "miHAphAW-LXd",
"outputId": "5468d34d-8258-4214-adef-3e9447b2058f"
},
"outputs": [
{
"data": {
"image/png": 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dRqvC+8XBMCMjg7/85S/86U9/omfPnrz00kvMmjWLtLQ0+vTpw0033URBwf9e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\n",
"text/plain": [
"<Figure size 720x432 with 2 Axes>"
]
},
"metadata": {
"tags": []
},
"output_type": "display_data"
}
],
"source": [
"from yellowbrick.classifier import ClassificationReport\n",
"\n",
"# Specify the target classes\n",
"classes = [\"yes\", \"no\"]\n",
"\n",
"visualizer = ClassificationReport(grad_clf, classes=classes, support=True, force_model=True)\n",
"\n",
"visualizer.fit(X_train, y_train) # Fit the visualizer and the model\n",
"visualizer.score(X_test, y_test) # Evaluate the model on the test data\n",
"visualizer.poof() # Draw/show/poof the data"
]
},
{
"cell_type": "code",
"execution_count": 54,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 161
},
"colab_type": "code",
"id": "72P5yqQn-kJz",
"outputId": "a896bb0b-7ece-41e8-d48a-894f1cab2800"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" precision recall f1-score support\n",
"\n",
" no 0.94 0.97 0.96 10284\n",
" yes 0.66 0.52 0.58 1248\n",
"\n",
" accuracy 0.92 11532\n",
" macro avg 0.80 0.75 0.77 11532\n",
"weighted avg 0.91 0.92 0.92 11532\n",
"\n"
]
}
],
"source": [
"from sklearn.metrics import classification_report\n",
"y_pred = grad_clf.predict(X_test)\n",
"print(classification_report(y_test,y_pred,digits=2))"
]
},
{
"cell_type": "code",
"execution_count": 55,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 33
},
"colab_type": "code",
"id": "NrtgPzMi-plF",
"outputId": "4ddc7dfc-ecb2-46fc-9eda-6f4b014bdc73"
},
"outputs": [
{
"data": {
"text/plain": [
"0.9192681234824835"
]
},
"execution_count": 55,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"accuracy_score(y_test,y_pred)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "1pBfZ6Kn_l8t"
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 56,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 409
},
"colab_type": "code",
"id": "rUNcQ_0GIIQ1",
"outputId": "eaff6341-f6aa-414b-d72d-c23d071a7644"
},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 720x432 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"from sklearn.metrics import confusion_matrix\n",
"\n",
"conf_matrix = confusion_matrix(y_train, y_train_pred)\n",
"f, ax = plt.subplots(figsize=(10, 6))\n",
"sns.heatmap(conf_matrix, annot=True, fmt=\"d\", linewidths=.5, ax=ax)\n",
"plt.title(\"Confusion Matrix\", fontsize=12)\n",
"plt.subplots_adjust(left=0.15, right=0.99, bottom=0.15, top=0.99)\n",
"ax.set_yticks(np.arange(conf_matrix.shape[0]) + 0.5, minor=False)\n",
"ax.set_xticklabels(\"\")\n",
"ax.set_yticklabels(['Refused T. Deposits', 'Accepted T. Deposits'], fontsize=12, rotation=360)\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 57,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "hX7UcbZ66l6O"
},
"outputs": [],
"source": [
"import sklearn.metrics\n",
"import pandas as pd\n",
"\n",
"def calc_cumulative_gains(df: pd.DataFrame, actual_col: str, predicted_col:str, probability_col:str):\n",
" df.sort_values(by=probability_col, ascending=False, inplace=True)\n",
"\n",
" subset = df[df[predicted_col] == True]\n",
"\n",
" rows = []\n",
" for group in np.array_split(subset, 10):\n",
" score = sklearn.metrics.accuracy_score(group[actual_col].tolist(),\n",
" group[predicted_col].tolist(),\n",
" normalize=False)\n",
"\n",
" rows.append({'NumCases': len(group), 'NumCorrectPredictions': score})\n",
"\n",
" lift = pd.DataFrame(rows)\n",
" print(\"done\")\n"
]
},
{
"cell_type": "code",
"execution_count": 58,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 381
},
"colab_type": "code",
"id": "ehlp7ekijIlQ",
"outputId": "f2cd3dfc-2d81-4312-ca3b-b374e6afa4f3"
},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"from scikitplot.metrics import plot_lift_curve\n",
"import scikitplot as skplt\n",
"\n",
"skplt.metrics.plot_cumulative_gain(y_test, predicted_probas)\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 0,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "i1WM3TKl-9pE"
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 553,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 33
},
"colab_type": "code",
"id": "qHBszo5du7pr",
"outputId": "616b7948-8154-4baa-a474-15ff8d978883"
},
"outputs": [
{
"data": {
"text/plain": [
"(26905,)"
]
},
"execution_count": 553,
"metadata": {
"tags": []
},
"output_type": "execute_result"
}
],
"source": [
"y_scores = cross_val_predict(grad_clf, X_train, y_train, cv=5, method=\"decision_function\")\n",
"# hack to work around issue #9589 introduced in Scikit-Learn 0.19.0\n",
"if y_scores.ndim == 2:\n",
" y_scores = y_scores[:, 1]\n",
"y_scores.shape"
]
},
{
"cell_type": "code",
"execution_count": 0,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "GvXetYKg_zf_"
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 0,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "iREtlDG__z_P"
},
"outputs": [],
"source": [
"import sklearn.metrics\n",
"import pandas as pd\n",
"\n",
"def calc_cumulative_gains(df: pd.DataFrame, actual_col: str, predicted_col:str, probability_col:str):\n",
" df.sort_values(by=probability_col, ascending=False, inplace=True)\n",
"\n",
" subset = df[df[predicted_col] == True]\n",
"\n",
" rows = []\n",
" for group in np.array_split(subset, 10):\n",
" score = sklearn.metrics.accuracy_score(group[actual_col].tolist(),\n",
" group[predicted_col].tolist(),\n",
" normalize=False)\n",
"\n",
" rows.append({'NumCases': len(group), 'NumCorrectPredictions': score})\n",
"\n",
" lift = pd.DataFrame(rows)\n",
" print(\"done\")\n"
]
},
{
"cell_type": "code",
"execution_count": 558,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 409
},
"colab_type": "code",
"id": "hWfgspD__z_a",
"outputId": "5a83b3d5-91b7-438a-d78a-76e30ac2b6cf"
},
"outputs": [
{
"data": {
"image/png": 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QrY4flZzsHumIWFpaGl9++SW5ubm88MILxv2Qi5PZGzZPT082bNhQ5H1/NGsAQUFBLFmy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\nKaWUCnGasCmllFJKhbi/AKfk586erz8iAAAAAElFTkSuQmCC\n",
"text/plain": [
"<Figure size 720x432 with 1 Axes>"
]
},
"metadata": {
"tags": []
},
"output_type": "display_data"
}
],
"source": [
"from scikitplot.metrics import plot_lift_curve\n",
"import scikitplot as skplt\n",
"\n",
"skplt.metrics.plot_cumulative_gain(y_test, predicted_probas)\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 559,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 425
},
"colab_type": "code",
"id": "VB_K_TZM_zNn",
"outputId": "823c4150-741b-4cde-d330-8388e9151148"
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7f4dad9ea048>"
]
},
"execution_count": 559,
"metadata": {
"tags": []
},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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T8pmyQ6i3mGTqhoiIiNyFQcsNYkN6udSyi/fK0AkRERG5E4OWm9w99BWn5V2nV/OsFhER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\n",
"text/plain": [
"<Figure size 720x432 with 1 Axes>"
]
},
"metadata": {
"tags": []
},
"output_type": "display_data"
}
],
"source": [
"skplt.metrics.plot_lift_curve(y_test, predicted_probas, title='Lift Curve', ax=None, figsize=None, title_fontsize='large', text_fontsize='medium') "
]
},
{
"cell_type": "code",
"execution_count": 624,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 1000
},
"colab_type": "code",
"id": "o-oN6ejKmdwe",
"outputId": "a34be8a7-218c-471f-94f0-b455f732216f"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Log Reg -\n",
"Accuracy: 0.91\n",
" precision recall f1-score support\n",
"\n",
" 0 0.93 0.97 0.95 10266\n",
" 1 0.65 0.40 0.50 1266\n",
"\n",
" accuracy 0.91 11532\n",
" macro avg 0.79 0.69 0.72 11532\n",
"weighted avg 0.90 0.91 0.90 11532\n",
"\n",
"SVC -\n",
" precision recall f1-score support\n",
"\n",
" 0 0.90 1.00 0.94 10295\n",
" 1 0.62 0.06 0.12 1237\n",
"\n",
" accuracy 0.90 11532\n",
" macro avg 0.76 0.53 0.53 11532\n",
"weighted avg 0.87 0.90 0.86 11532\n",
"\n",
"KNN -\n",
" precision recall f1-score support\n",
"\n",
" 0 0.94 0.96 0.95 10275\n",
" 1 0.56 0.47 0.51 1257\n",
"\n",
" accuracy 0.90 11532\n",
" macro avg 0.75 0.71 0.73 11532\n",
"weighted avg 0.90 0.90 0.90 11532\n",
"\n",
"DT -\n",
" precision recall f1-score support\n",
"\n",
" 0 0.94 0.94 0.94 10214\n",
" 1 0.52 0.53 0.52 1318\n",
"\n",
" accuracy 0.89 11532\n",
" macro avg 0.73 0.73 0.73 11532\n",
"weighted avg 0.89 0.89 0.89 11532\n",
"\n",
"GBC -\n",
" precision recall f1-score support\n",
"\n",
" 0 0.94 0.97 0.96 10272\n",
" 1 0.66 0.52 0.59 1260\n",
"\n",
" accuracy 0.92 11532\n",
" macro avg 0.80 0.75 0.77 11532\n",
"weighted avg 0.91 0.92 0.91 11532\n",
"\n",
"RFC -\n",
" precision recall f1-score support\n",
"\n",
" 0 0.93 0.97 0.95 10219\n",
" 1 0.66 0.45 0.53 1313\n",
"\n",
" accuracy 0.91 11532\n",
" macro avg 0.80 0.71 0.74 11532\n",
"weighted avg 0.90 0.91 0.90 11532\n",
"\n",
"NN -\n",
" precision recall f1-score support\n",
"\n",
" 0 0.90 0.99 0.94 10255\n",
" 1 0.67 0.12 0.20 1277\n",
"\n",
" accuracy 0.90 11532\n",
" macro avg 0.78 0.56 0.57 11532\n",
"weighted avg 0.87 0.90 0.86 11532\n",
"\n",
"GNB -\n",
" precision recall f1-score support\n",
"\n",
" 0 0.94 0.89 0.92 10293\n",
" 1 0.38 0.55 0.45 1239\n",
"\n",
" accuracy 0.86 11532\n",
" macro avg 0.66 0.72 0.68 11532\n",
"weighted avg 0.88 0.86 0.87 11532\n",
"\n"
]
}
],
"source": [
"#term_deposits.info()\n",
"#display(term_deposits.head(1))\n",
"#df = df.rename(columns={'deposit':'y', 'day': 'day_of_week'})\n",
"\n",
"#predictors = term_deposits.iloc[:,1:16] \n",
"#predictors = predictors.drop(['pdays'],axis=1)\n",
"\n",
"#y = term_deposits.iloc[:,0]\n",
"#X = pd.get_dummies(predictors)\n",
"#print(term_deposits.shape)\n",
"#display(term_deposits.head())\n",
"#print(predictors.shape)\n",
"#display(predictors.head())\n",
"#display(y.head())\n",
"#display(X.head())\n",
"\n",
"import numpy as np\n",
"import pandas as pd\n",
"import matplotlib.pyplot as plt\n",
"import sklearn.linear_model as lm\n",
"from sklearn.tree import DecisionTreeClassifier\n",
"from sklearn.ensemble import RandomForestClassifier\n",
"from sklearn.model_selection import train_test_split, KFold\n",
"from sklearn.preprocessing import StandardScaler, label_binarize\n",
"from sklearn.metrics import accuracy_score,confusion_matrix,roc_curve, auc, f1_score, precision_score, recall_score\n",
"from sklearn.svm import SVC\n",
"from imblearn.over_sampling import RandomOverSampler, SMOTE\n",
"from imblearn.under_sampling import RandomUnderSampler\n",
"\n",
"from sklearn.metrics import classification_report\n",
"\n",
"# Logistic Regression\n",
"#log_reg = LogisticRegression(solver='lbfgs', max_iter=5000)\n",
"#log_scores = cross_val_score(log_reg, X_train, y_train, cv=3)\n",
"#log_reg_mean = log_scores.mean()\n",
"X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3)\n",
"model = lm.LogisticRegression(random_state=0, solver='lbfgs',multi_class='auto',max_iter=1000).fit(X_train,y_train)\n",
"y_pred = model.predict_proba(X_test)\n",
"y_pred = y_pred[:,1]\n",
"y_test = label_binarize(y_test,classes=['no','yes'])\n",
"fpr_imb, tpr_imb, _ = roc_curve(y_test, y_pred)\n",
"roc_auc_imb = auc(fpr_imb, tpr_imb)\n",
"y_pred = model.predict(X_test)\n",
"y_pred = label_binarize(y_pred,classes=['no','yes'])\n",
"print(\"Log Reg -\")\n",
"#print(\"Precision: \",round(precision_score(y_test,y_pred),2),\"Recall: \",round(recall_score(y_test,y_pred),2))\n",
"print(\"Accuracy: \",round(accuracy_score(y_test, y_pred),2))\n",
"print(classification_report(y_test,y_pred,digits=2))\n",
"\n",
"# clf=svm.SVC(probability=True)\n",
"#svc_clf = SVC(gamma='auto')\n",
"#svc_scores = cross_val_score(svc_clf, X_train, y_train, cv=3)\n",
"#svc_mean = svc_scores.mean()\n",
"X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3)\n",
"model = SVC(probability=True).fit(X_train,y_train)\n",
"y_pred = model.predict_proba(X_test)\n",
"y_pred = y_pred[:,1]\n",
"y_test = label_binarize(y_test,classes=['no','yes'])\n",
"fpr_svc, tpr_svc, _ = roc_curve(y_test, y_pred)\n",
"roc_auc_svc = auc(fpr_svc, tpr_svc)\n",
"y_pred = model.predict(X_test)\n",
"y_pred = label_binarize(y_pred,classes=['no','yes'])\n",
"print(\"SVC -\")\n",
"#print(\"Precision: \",round(precision_score(y_test,y_pred),2),\"Recall: \",round(recall_score(y_test,y_pred),2))\n",
"#print(\"Accuracy: \",round(accuracy_score(y_test, y_pred),2))\n",
"print(classification_report(y_test,y_pred,digits=2))\n",
"\n",
"# KNearestNeighbors\n",
"#knn_clf = KNeighborsClassifier()\n",
"#knn_scores = cross_val_score(knn_clf, X_train, y_train, cv=3)\n",
"#knn_mean = knn_scores.mean()\n",
"X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3)\n",
"model = KNeighborsClassifier().fit(X_train,y_train)\n",
"y_pred = model.predict_proba(X_test)\n",
"y_pred = y_pred[:,1]\n",
"y_test = label_binarize(y_test,classes=['no','yes'])\n",
"fpr_knn, tpr_knn, _ = roc_curve(y_test, y_pred)\n",
"roc_auc_knn = auc(fpr_knn, tpr_knn)\n",
"y_pred = model.predict(X_test)\n",
"y_pred = label_binarize(y_pred,classes=['no','yes'])\n",
"print(\"KNN -\")\n",
"#print(\"Precision: \",round(precision_score(y_test,y_pred),2),\"Recall: \",round(recall_score(y_test,y_pred),2))\n",
"#print(\"Accuracy: \",round(accuracy_score(y_test, y_pred),2))\n",
"print(classification_report(y_test,y_pred,digits=2))\n",
"\n",
"# Decision Tree\n",
"#tree_clf = tree.DecisionTreeClassifier()\n",
"#tree_scores = cross_val_score(tree_clf, X_train, y_train, cv=3)\n",
"#tree_mean = tree_scores.mean()\n",
"X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3)\n",
"model = tree.DecisionTreeClassifier().fit(X_train,y_train)\n",
"y_pred = model.predict_proba(X_test)\n",
"y_pred = y_pred[:,1]\n",
"y_test = label_binarize(y_test,classes=['no','yes'])\n",
"fpr_DT, tpr_DT, _ = roc_curve(y_test, y_pred)\n",
"roc_auc_DT = auc(fpr_DT, tpr_DT)\n",
"y_pred = model.predict(X_test)\n",
"y_pred = label_binarize(y_pred,classes=['no','yes'])\n",
"print(\"DT -\")\n",
"#print(\"Precision: \",round(precision_score(y_test,y_pred),2),\"Recall: \",round(recall_score(y_test,y_pred),2))\n",
"#print(\"Accuracy: \",round(accuracy_score(y_test, y_pred),2))\n",
"print(classification_report(y_test,y_pred,digits=2))\n",
"\n",
"# Gradient Boosting Classifier\n",
"#grad_clf = GradientBoostingClassifier()\n",
"#grad_scores = cross_val_score(grad_clf, X_train, y_train, cv=3)\n",
"#grad_mean = grad_scores.mean()\n",
"X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3)\n",
"model = GradientBoostingClassifier().fit(X_train,y_train)\n",
"y_pred = model.predict_proba(X_test)\n",
"y_pred = y_pred[:,1]\n",
"y_test = label_binarize(y_test,classes=['no','yes'])\n",
"fpr_GBC, tpr_GBC, _ = roc_curve(y_test, y_pred)\n",
"roc_auc_GBC = auc(fpr_GBC, tpr_GBC)\n",
"y_pred = model.predict(X_test)\n",
"y_pred = label_binarize(y_pred,classes=['no','yes'])\n",
"print(\"GBC -\")\n",
"#print(\"Precision: \",round(precision_score(y_test,y_pred),2),\"Recall: \",round(recall_score(y_test,y_pred),2))\n",
"#print(\"Accuracy: \",round(accuracy_score(y_test, y_pred),2))\n",
"print(classification_report(y_test,y_pred,digits=2))\n",
"\n",
"# Random Forest Classifier\n",
"#rand_clf = RandomForestClassifier(n_estimators=18)\n",
"#rand_scores = cross_val_score(rand_clf, X_train, y_train, cv=3)\n",
"#rand_mean = rand_scores.mean()\n",
"X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3)\n",
"model = RandomForestClassifier().fit(X_train,y_train)\n",
"y_pred = model.predict_proba(X_test)\n",
"y_pred = y_pred[:,1]\n",
"y_test = label_binarize(y_test,classes=['no','yes'])\n",
"fpr_RFC, tpr_RFC, _ = roc_curve(y_test, y_pred)\n",
"roc_auc_RFC = auc(fpr_RFC, tpr_RFC)\n",
"y_pred = model.predict(X_test)\n",
"y_pred = label_binarize(y_pred,classes=['no','yes'])\n",
"print(\"RFC -\")\n",
"#print(\"Precision: \",round(precision_score(y_test,y_pred),2),\"Recall: \",round(recall_score(y_test,y_pred),2))\n",
"#print(\"Accuracy: \",round(accuracy_score(y_test, y_pred),2))\n",
"print(classification_report(y_test,y_pred,digits=2))\n",
"\n",
"# NeuralNet Classifier\n",
"#neural_clf = MLPClassifier(alpha=1)\n",
"#neural_scores = cross_val_score(neural_clf, X_train, y_train, cv=3)\n",
"#neural_mean = neural_scores.mean()\n",
"X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3)\n",
"model = MLPClassifier().fit(X_train,y_train)\n",
"y_pred = model.predict_proba(X_test)\n",
"y_pred = y_pred[:,1]\n",
"y_test = label_binarize(y_test,classes=['no','yes'])\n",
"fpr_NN, tpr_NN, _ = roc_curve(y_test, y_pred)\n",
"roc_auc_NN = auc(fpr_NN, tpr_NN)\n",
"y_pred = model.predict(X_test)\n",
"y_pred = label_binarize(y_pred,classes=['no','yes'])\n",
"print(\"NN -\")\n",
"#print(\"Precision: \",round(precision_score(y_test,y_pred),2),\"Recall: \",round(recall_score(y_test,y_pred),2))\n",
"#print(\"Accuracy: \",round(accuracy_score(y_test, y_pred),2))\n",
"print(classification_report(y_test,y_pred,digits=2))\n",
"\n",
"# Naives Bayes\n",
"#nav_clf = GaussianNB()\n",
"#nav_scores = cross_val_score(nav_clf, X_train, y_train, cv=3)\n",
"#nav_mean = neural_scores.mean()\n",
"X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3)\n",
"model = GaussianNB().fit(X_train,y_train)\n",
"y_pred = model.predict_proba(X_test)\n",
"y_pred = y_pred[:,1]\n",
"y_test = label_binarize(y_test,classes=['no','yes'])\n",
"fpr_GNB, tpr_GNB, _ = roc_curve(y_test, y_pred)\n",
"roc_auc_GNB = auc(fpr_GNB, tpr_GNB)\n",
"y_pred = model.predict(X_test)\n",
"y_pred = label_binarize(y_pred,classes=['no','yes'])\n",
"print(\"GNB -\")\n",
"#print(\"Precision: \",round(precision_score(y_test,y_pred),2),\"Recall: \",round(recall_score(y_test,y_pred),2))\n",
"#print(\"Accuracy: \",round(accuracy_score(y_test, y_pred),2))\n",
"print(classification_report(y_test,y_pred,digits=2))\n"
]
},
{
"cell_type": "code",
"execution_count": 442,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 419
},
"colab_type": "code",
"id": "hrIDyBa4vUpu",
"outputId": "562e1c0e-016d-41ce-ece9-b61ce1beeb8f"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"++4\n"
]
},
{
"data": {
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K5OTk0CgxgZqBEQQ9WnMjwtskEwh48FVn4diXDQLY0hOCY1acLAJPOKLKCAIEKAPBBYIY\nOkdBQBQ1qFWNMCbK9XG9VcfwlJUT8AfQ2IyIavlZ4qv0IEg21CobiAJSoBxfoBhBOD03QBRqtgU0\nusYIonyvfZ4cAoEqAgGJHKeXOKuGtcdd3L3gGABzr+9B7yYxBAIe3li9hbfW53NPtxju7xkLwIdb\nC3llVQ5/7pTMM0M6ALD95HHGzdtOtEnN6rtbAVDk9tFr9l4iDGo2PzySKq+brOKjjPnwEH1Sw5gx\nMhmvv5qNR77kva1RBCSJ8W1ykAT5b3lHthFJ8tIprgi1Ss4mdrjESlUghnHtuhOX7aRs8WKOd2uK\nlBhJRnwTdn38PS/NOEpmVRyJiUa+WdQXtTacI0fygvrlYrjkecxmzpxJeHg4N998c8j+ESNG8M47\n7xAbG8uECROYPn06jRs3bnCM6urq4Bf76YLHClcWW7dupWPHjr/1NH4V/AGJcq8PURCwaGvffZwe\nL5KEvM8fwF/poVoAv0aNXi0ilVZQfrSAwrIKSp2V2CQwSxK+Kg9bThZRUV5FmtmARe1A0h+mzFVJ\nflo8iR1SaRsdR+l6I3v/8S2uYUdI7bGTMJ0Gf6UHx4EcAgEJQZLwOzUcfkguaB0x6ijJ/7f1rNeR\ntmAFx50lrJ/zGS3nrcKQWlj/WivUFM5vSsnyJKqO2miz6CtEbW0JJpXZR9nqeDSimjYf3IK2IIJK\ni4a1nd/F0qGAeLMVtcOM9mgSByqycQeqcWeGU7EnEgC1vQq1rbrB+Ql+kdGuIYg17g0/2NfhEirp\nHN0Ca4Ua+iZy+PAh8rJzceaGA2r5S0IUMAkqdKIKQRSoEgWqRQFEEcET4ORN3XjB76baH2BJjB3D\n8SIa9W3NE0Vl/O9oLjFFLpqcKsYvCjgDEn5RYFSTOG5ulYyYYOX9bcf4eNdJ/CoBvyjiFwWKLQbK\nDVrGt0vm32O64HW6OeCqotOcFfK1BCR0Xh9SjVgICALHnxlLlNWAo8pDr7eWcriknD93aSIrkzq8\nNrIjVr0Wj89PQXl9wblr924y2rQh2qxHW/MFW+qupsLjC9mncHlypTw7HZUeCiuqiLMYMOk0/GvT\nIV5ctht/QGLzQ8OJscj1c+1PfcLQtEY8MziDppEWTpZWMPzdFTzevxW3d20GwMI9WXy5+yQjWyVy\nbYb8orE3r4zNJ4toEWWmTZwGd7WLfFcpX+7Jo1W0jyhTBW6PE1eVC722JRmJvUm2mylwnuDbXbOJ\nNCdwdbtJAASkAPPWPYWEhEalw6C1YtRaMGqtGLXWmm35R1PkxByXitYo15w98dBfKXh3NvGTn0b7\nl8d46qmVvPfeNiQJYmJMvPLKQP70p7Z4vZ6frFsumcVsz549TJs2jezsbNRqNUuXLqV///4kJCQw\naNAgnn/+eR555BEAhg8fflZRpqDwW+ALBKjw+kPEVZXPz4bcUoxqFZ2jrFQXuzh5rJCVu44T6faQ\noVJRVeCgLKeUQ8cK0Dnc2D0+/FVe/FVeCl69kbKmMdydkcLBR+ZxaPYyzM+PY//QDAYmRRH2zXY2\n3VlbEkfQ+DG2LMV9IAypWo0e8Ny9C81E2dIUXfNDIeRlNUW642YKc/Zi71uC3redqhrDlbFZ7XV5\nS+QHhCgI+IotuDbEyy/N8js/IKAXNVhEA7y/myh/FYmaCDQ7GmEqjgqO4/C7a1pDVJxAuKsJosGA\nOP6viHo1gkpEFATECj9RqeHoYqxouyUCYABanboHYZVAbFwcQoUXGtvQpkoEfH6EDBHhepEvKpzk\n+APYm0Vwc5sUBLWIqFHxj73HCdS89Y/KLcYXkNCoVGBM580xnYmKkJcN2k1fxDGdjfKm9S14i+7o\nz8CWjQB4dvEOXly+u/ZgeW3NyepR7eiSKtcKrfjPD5RWeym16Mms6XuaAQNbkzCsPQCtEyPod8Zx\nAFe1j/uuaoHGrEdj1tO40sPHE3vVawcQbtASGylfh12rZu+TYxpsVxetWkVCmKne/nyjpt7+cKOO\ncKPykqtwfo6XlLPsYC7J4SYGt4gHYPr3e3nzh0we6ZvOA71bkut00/zlL9GrVbwxpgu9GkfTNt7O\nnd2a8fTiHQTq2H+uzUhi7uYjFFVUseSugTSJtHDgiWsA8PiqKHPnc1WKhtGtewLgqiphw+EFuD1O\ndAEnB7MrOZhdO78kM7jc8s9poixxJNtlIWXWh5MSmUGYsXZVThRExnR8BIPGgkZ99r+DI7fcSMn/\nPqP5wm/RDhgMgH389QSs4XzkyuDFZjNxOKpRq0UefLArzzzTB6v15/9dXTJh1rp1a+bNm3fW4507\nd+bTTz+9VKdXUDgrpy1XZo0alShQXOnh33tOcF1sOJaKaqoLnHjbJfOfA9nEmXR0+nYnxZuPkDh5\nNJv9PlKsRlQvf8nBWd8BYAO8QF3bk7HmX0edfSavj4BWjQCoTTrUZj1qScKucmLIm4w3fSEd1oEU\nkNvXaA8ct4+nsqgRkk5NzNgvg+MFdiSjVmnRokKXE4bNbaCXNZ3A/wJELM5A5ZPwSwGqAz5UgoCI\niOhX0To8WV6GOSTCrgZeiCQJKrzwcmNMQHLWSZIS7wppkihJEGmEljVBPONrDiRYwFzrbpDrdDN3\n8xHynJWsPpJPnFV+a6ZLMjq1ioW315Y5euTTDWSVVbDs4KmQc7UqFrit81XB7af+vRyvP1B/3sCL\nw9sHP1d4fJRX14qypHATETVixKqrXV6Isxpo36g2GCkgSRRVVPNQn5b0SKkVo29f1403r+1S75wm\nrRpdHavTNW2SuKZmOfFchBm0jG+Xct52Cgq/BJIkcbK0gmS7GUmSeHrxDj788Qi3dG4S/LvJdrjp\n+o9vibca2PzQCAC2Z5dw9+cbGd06MSjMnFVesh1uXNVyrd0Io449j43izs82MGttJn2axNApMYKU\ncBM3dkzGrPFS5DqF2+Pkjs4St3eKJhBwsGL/BzSOzKBpjGwRzCk7xKrM/5IU0Yr+LScCoBLU5JQd\nCl6HKKgw1LFu1Vq4LBh1p/fZgu2NWit9026sdz+shsiQ7eLPPqHs20Ukz3gLdU2dXl3jVESzGc+p\n2meSpXtPxIzOvJk2C4ejmqFDmzJjxhDS0kLH+zlcEc7/Cgp18fgDHCotJwC0ibQG9x9zVODy+Eix\nGbFq5S/eokoP2S43Vnc1ljwnrsN5rNpwAN/xIpKqvPhLyqkscBBd6GJNnS/7XkffxKpVY9KoKVi1\nj5zFO4i9oQcZ7ZJJshiQYsPQRVrQR1vxhZmwxoZhjg1DH2VBH2VDF21FH2UF2wEqfLMQVAKi5lUq\nXWvJ2e7GOEJN54xYGj/YkcrvNlIy8iSuPvK5hTMCeNqaktAXJEDjCIp33oyk8mI91ButKza04RA/\nxtHNoE3oA8J85g2MM0O47E8RCMhvsqJYuzwWCEhU+fz8mFVMQJI4cKCML7LyaRxh5p3r5LyEs9cd\nYNIXGxHXNRwlnfXsWGKtBnKdlTyzeEdw/568WmuUQRO6hLb+eAGZBc56Y93apWnI9sN9WuIP1L6B\nl1V5GNEyAVEUSLHXWoa2P3I1p1/UDRoValXDkVH39GzBPT1bNHisLmbdxfuKKChcDhwvKafJiwu4\nq3szbu6YStekSEa2SuCVFXtwVNb6ZfoDErnOStR1ngfJ4SZu79o05OXlkb7p3NmtKVqVl0pPOQat\nmWS7mc//1I69OT+w/9RnbDvqwu1xUuUtR/ZmbBibIRKQhZlZH06EOQGrPiJ4XK81M7DVrUERplMb\nEM58SF4kkiRRtX8fhvRWwX2F78/BtXYNYVePJmKc/KYZ+9BjxD/xDKJOx9GjpURFGbFYdJhMWt5+\newSSBCNGNPvFs0UowkzhiuCUqxIJSLQYyK2oYlNeKYWVnhBh9mN+GdlrM+lW6aPTn3qjsRg46XKz\n7aa3MK7cG2ynq/kprjO+AKhtRgzRVnRRVuyiyD1tG+P1ZJPzmJPEe2Mxpa5gcOJfUKmt8OQYIsYv\nwn9kO+W21Qg+fXAxUAIit0zDuCsDV8x+Sjp8L5vU6rj/SFofklq25hhymhM7L4Gwtb3QnopBnW2A\noY2hwA2jmiFsah/0K4rghnr3JhCQ8AYC7M93UFBeRaW3khmr9zEyPYFH+skPnk0nCnl80baQfmuP\nFQCyJenY09cG98c89xkl7vpO9FG5Ou7v1ZJWsWGMaZPEpC82hyxRNESsxcATA+SgjwqPj7bx9qDV\nTDzjWfbOdd2p8Mj3pGmkhaRwE5oGxNRLIzqc85ynUYSUwu+NzHwHKw/lYdFrmNipNnjgo23HKHN7\nuL5DCvYaq/BT327HoFHx1MA2hBu0PDGgNS+v2EOFx0fPxtGkRdv4YdIQ0mPDguPEWw2cfOZafIEq\nSivycHucWDRO7uteSaVnP/kOLTG2xtgMWk6VbObHY1+TFtedbk1GA+DxV3Ewb/MZsxbQa8xnWLhO\nW7ws2IwxwZaR5gRG1viBnUYURBLCz//idKFIksSeDq2oOnSQjMxj6BJkF4vouycRNvIaLN17BNuq\nbTYqKjy8PGUl06ev54EHujJt2iAARoxo/ovN6UwUYaZw2ZHvriavoopkq5EwnYaDpeUsOJxLqs1I\ngimO8DwHusU7SMjMYe30b+j5yQMIgkCK1Ujg9W85lplD8y5NCWsfiV3rIywpgkC0iibPbq9dQjTp\nUBl1qHRqRJ2GiMSHMdtlf59TB8aRnd8S8iHgKwkqubIyCH+5EarlGhAFKh5diC9CXqyU1FUh74TS\nwgOwJ4AhTCKhSegyoFitRa/rADMHQ6Fs9VIBZ3oHVXn9rD1WQPX+bM6kTVw4SeEmqrx+TJM/avA+\nhhm0DGwRR9t4O6WVnqAQO5Mz3/UE5KgqSYI2cWFo/R70RhNatUhqhGx/izTpOPjENSEWqpBrrBGS\n8TYjU+ssL56Lq1LPHpmtoPB7YldOKauP5PHXXi2D+xbsPkl2mZvRrRNJDJf/rkb9ayVL9mdT1KoN\nVr2WZ5bsYEtWMYGAFCLMXlq+m/35Dvo2jQkKs5WHcslzVbH8YC7L7h7E9e1T+GuvFsRYZEcLQXJg\n0ezjQI4Tt8dFpceJu+YnIPlpCLPOToxNdn+QrVdGxDrWK6shgm5NRoc4zxs0Zjna9DcgUF1Nwbuz\nqdi6hdT35yEIAoIgoG/REn9FBZ7jtcLMfs21IX0lSeLTT/fy2GPLOHVKtuQXFbmRJOmS51NVhJnC\nZYUvEOCDvScBGN44Bm2RC9XGQyQt24Vrx3HmH8rHW1YRFBMngY75DgyxYXSK0qG7uRFi08UU+TtS\nuKWcxJbLGDP7TiSu4/C22qU/CfABvgBQCQGpNmo4EHDJgqwO5j0d0RwxoZp/CspkK13sG9cjaWUL\nj+ba/miq44PtxXE6GKdGTZ2lRKMGBiRDkhWPz8/MHzJ5+/MN2PS1PllvjulMj8bR7Msro81ri856\nn965rht3dGuGKECUWUdheW3k4sDmcbg9PmIseprVWBS7JEWy6r7B9cZJjwkjwhTqrFowZXzIdkNR\nYWqVSJPIi8vNo6DwR+BkaQUev5+mNX97b68/yLKDOdzVrTlD0uRnRPvXvwZgQrsUomuiFWetzeT7\nw/mkx9qCwqyi2odfgvXHCxma1ojPb+nDR9uOcfCMJf/r2iaS7bDh9eVxtPA47monT/RPpcqvp01c\nOPtz1rArawUZiQOIsfQFoMydz9bjSxq8Bo1KX8fCZcFQ47sVa631SU2JbEPjqIyQfjq1kbS4hssw\n/hoEqqupOnQQY2s5NY6g0ZD7+qv4CguIe3Qyxlay9b7xO++jslrPKrB27Mjj/vsX88MP8ndRx45x\nvPnmMHr0SPxVrkMRZgq/OuVeHzqViKYmG/LnB7MpdHsY1zwe1d5TDFm6m20r93DgQC676/gk6ZFX\nBI2JFiJ7x2BrmYa1SSp+9RFO7H2EStdadDW+5KetVwUnHqNxxhYCAQvxzT4565wM5q7wyX44WEoj\nzWMgPgQ/nIJ9xQhVWkRvnWWxlna4pjnmPuNALULrSFCJSJJEYQPpCqp9ATadLGJIi3gseg2VXh/m\nyR83OA9njTNtsygrf+nenHc2HMRu1NI1OSqkXWJNlJ1WrWLxnQNpGWNDrzn7W6ndqKNXasxZjyso\nKJwdSZLYkV0KQPuEWl+rI0UuvtqbxaSr0tCoRA4VOkl7ZSFvX9eNcIOOCJOOHdklfLk7K+g4D3Bd\n22R+zCoi11UZFGajWyeSHl5lmKYAACAASURBVBMWEkH7t2Ht2LRrH10TdRwv2oW72knzcBcJJidL\nd28OWriSTdUkm2D78do5925xA6lRaQDszVbjC3ip8pYHj4ebYmnVqFeo83zNZ43q/PlCL7cqPN78\nfHa1aYag09H+WC6CWk6P0+jp51GZzegSawNy1DbbWcfZt6+Qjh3nEAhIREUZeemlAdx6aztUZ/FR\nvRQowkzhV2VHgYOlJwoY1SSWFuFmqnJLcZeU41KJ+AMSJ/+7loMzl8ipIQBdnJa48dWEj/gBUadG\n1An4/acQRTNxTT/EYGmCIKhRFdf6SajUkag00UQmPIvFLodhi6IOa8S40Ml4/PDNUdieD7Nrox1D\n5U3NQzI9Ah7tDL0TwXb2cOi45+ef9djGB4bROSmSKq+fcIOW0koPoiDw8cRewSXCJjWpHjQqkSnD\n2jFrbJfzPgDrflEoKCicnY0nCtmX5+C2rnJAyZwNB/lmXzaRJh3/ur7Wt+jWj9fx7y1H8b8uRwZK\nEnSa8Q2CAL7pE4Ptxn6wit25ZYxunUhqhCXoc/nWD5lc01q2rtzVvRmDW8TTvlF4sN9HN/ek0is7\nx58oyiE2rElwWXNX1iq+3LaddkkD6Nk4A31JFnmOQ2w6uvCs1yUKatm6pasVV1Z97XOheUxnmsV0\nQqOqfXZZDZF0bjziJ9/L3xJvfj75s9/EX+Em+bUZAGhiYtDEN0I0GPHk5db6jt1+17mGAghZnkxP\nj2LUqBakpNh47rm+hIXpL92FnAVFmCn8okiSRLnXH8z9teZUEbuLnMRKWjoCcWb5l/yrI3n0+s8q\nSnf8h0539yWyXzre3BeIGC/QOqMcs/5BIjulI0RsIe/Ynfh9RXIG8hrXh0CgnMKTTxHX9H0M5i7Y\n4x4mKukVtPrmtRE7R8pgf578+WApbMyB3YVymoeNOWe/iMldaz3Tq/0wqX1ICoi6FJZX8e3+bEa3\nTsSm1xBlbli0FZZXBx3qw406Xr66Ax0T7HRIiGiwPVBviVFBQaFhXly2i2UHc1l135DgvuYvfUmO\n003RlAnoNSoqvT56vrmEOKuBcW2TapLyBlCrBL47EPo8WHesEKNWxfydJxjXNhmAdvHh9V6SeqXG\nEJAkdueWkRphoWmkhexnRxKQnFRVH+WAy4XgdxJjcHIkz8nuk7I/V5W3Aup4pQ5r85eg71a1r4Iy\ndz6uqlp3inBTDEkRrRpwnpd/tGrDOV/gzpWr60rAV1yMNz8vJIoyd/o0BL2ehBdeRGWU/eZarf0R\nlbleHPo5WbnyGA89tJS5c0fToUMcAP/73/iQSPVfG0WYKfyivLPrOBJwT1v5IRMjCGzbfITqvcdZ\n8/Iymv11KPdf1QK9SuSAvyX2GwDWUXq8ZgAR9G1siPoC9Ekfo9WPJP+4FoOlB9aICZjC5AevRpeC\nINT++hotvWBfMew6AAdKYNb2s0/ycFnotlqEP7WC9tEwIa1eZvXT5Djc3Pu/TSzae4rEMCNZZbUZ\nDe/o1pQHe6eT98L4BvueyZ3dmp2/kYKCQgin82/tzCmlZ0oUTwyUfYlmrTtAvquKnTkltI2XLUWx\nFj1Hil0UVVSREGZCr1Yxpk0SC3afxFTz4jipVxqt48LwdA/NizdnfDecVV76NZX9UgVBYt39fXFX\nOwkE/EFn9ru7VnNNWiltE+VnhkoUyS7dyO5Tq855HQIC+jriSiXWukqkxXUnNao9ljoWr1hbKrG2\n1IaG+t3j/GE1B0YMwtS5C+kr1gKydSxh6isY22Qg1Cl5dDGi7MSJMh59dBnz5+8DYPr09Xz00ViA\n31SUgSLMFH4G+4qdrMkuJtFsYESq/ACLqfBwatkufpi9nPItRynbdZLomvxgpwBTShjqJp+hb/Qk\n8anfknN0OKIqHKOtL0h+EFSYrH1Ra5PQ6GRx17TjyXNPZH8x9G7YZwuAnjVZ2E844epU6BYv5/FK\nsECS9ez9kPN1efwBHujdkjkbDvH9YdkCV1eUAezNdZAUZmxoCAUFhQsk1+nmRGkFsRYDKXYzJ0rK\nGfT2cgY0j+Uv3ZvTNNKCXq0i1+FmW3atRenJAW3Yk1dGgq3WP+vVUR0RBYFGtpooREFg3k090at7\nB61LkiTRPcWKu9pFTumhoM+WXnAiqZysOSAvN1Z6XMFIxbGdHsNSk2eryHWKrJL9pERmEGmR61Ra\n9RGEm+KCjvNnlvgxaq3otSZEoWGf0LqC7I+Ge+8eCma/hS41lbiHHwfA1L4jotGIymxB8vkQ1LJs\niXvw0Z92DreXV19dx7Rp66iq8mE0anjqqV48/PBvF7RwJoowU/jJNA0zs+hoPta8PPYv2ELWwg34\nDu8hIt9IFmDplE/cbYXoYmxIYXoimzUiEHkXxdlgi7wJS9RAmtkLUKkv4kH0aSYcc8DrP8rbKgH8\nZ+TSamSG4anQoxFc3eSCh67y+llxKJd5W47y+c4TmLRqKjw++jSJYVzbZJ4f2pYuyZH8d+tR7uvZ\ngoQwE1qVSOzpbPYKCgoUlVdxsNBJhElHi2jZydofCLDpRBGiKNCtTiDLntxSFu09xeQBrREEgc92\nnODhhVu4v1caM67pTKRJR5v4MOZsOITXH+C9CT14cmBrOidFEGWq9f2Z1Cut3jw6NrKGLOEdzPuR\nMncerRr1xqST57Xu0HwOF5y9XmxddGojBq0Fn98b3JcW352UyDZEWmqj9ZrFdqZZbOcLvFt/bKqO\nHAZA30T2+fOVFFP4wXvom6cFhZnKbKb98TxE/c/39dq48RQTJszn5Ek5zdENN7Tm1VcHkZBw7hf0\nXxtFmClcFPuKXSw+ls/DHZtQfvJTui+ciUqdh9TvFEk9AxR/3QTvvluI7t4aY4/FVKnWBPvWXSxw\nFP2XqMTnzy3Kqnyw9DjM3g5b8xtuU1eUvd4XbkqHnxg98/KK3UxdVlsz8XSy09VH8om1yA+F4S0b\nMbyBOogKCr93TpfXirHog8Wm39t4iElfbOa2Lk3557iuACw/lMtN/1nL+HbJfDyxNwCVXj+93lqK\nSavG+XJtkuTbPlnP1lMlTOyUSkKYiRiLnq5JkSTXpIsw6TS8MKQt70/ogc0g+3lKko8eyXoqPU6O\nFZ6qsXKF5uFye5z4/B5u7jEFdc0y4ZGCreQ7j5NoTw8KM73WjEala6B4tVzex1DHp0st1k9YHGNN\nuTQ3+w9A/tuzOPnoA0TechuNZ8k1gs3detDouSnYBg8NaftLiDKAxEQrxcVu2raNYebMYfTqlfyL\njPtLowgzhYtiZ668fLB3Rw/U1T8SfXXo8Yirj5D69Hi0+qZUOCxUumQRk5OTQ3x8PCCh0TfFFlk/\ngz0Am3PhpY2wNQ+qGk5yCMgO+R1j5Qz5IDvrn8cvoKi8iv9sPcp/tx1j26kSBAFsei3zbrqKTgl2\n7ujajKnLdqNXqxjcIo67ujenc2IEdqPuN/c5UFC4lBwpcpHjdJMaYaGRzYgkSege/y8AntfkHH85\nDrm8VocEe1CYBSQJrz+AX6p97Yow6uieHEXzqForhCgIdE+OqleGq2tyFGpR5MesYhLCTFzfvjHX\nt2/MyeK97MtZR/OYzrSOk6MZ1x78nJPFe/H466ekaQiVqKHaW4FaJ0dsN4vpTKI9HbO+NjqyQ/IQ\nOqUMu9jbpXCROFZ8R8G772C/9joixl8PgKXnVajCwhCNtcvPokZD/GNP/GLnLSmp5O23t/B//9cT\nlUqkUSMra9feRps20b9q+ouLRRFmCudEkiQWHc2jkUmP4T9r0cz4FmnO7YR3fgXX0QEA2CLvRlTr\nMNn6YwobEnTKN9n6YbLJicVO5G8lMqFjwyf5MRf+vgWWn2j4uFkDI5vCn1tDRpTsrP8TiHnu8zOu\nDcoqPUz492oW3zmQHilReF67CZV4+f7BKihcDBXVXrblVxA4WUTnJLmG6v58B+M+WMXETqlMHnDa\neT6TN9Zk8vqojjzYJx0gpB4pQKxVLq8Vb631pby9a1P+3LlJsNIDwKAW8Qyqk7MrEPAjSRUsuLUV\nbo+TzNwNslWr2snI5i4Gpjjxe/YCtT5DPx77BldVCY3Cm2MzyEufASmAx1+FKKjOWry6ri+XRqUL\niVQ8XSi7LuLPrLmoUB/J76diy2a0ySloY+Uox+ojRyj7eiGCShUUZobWGbQ/nhf0Gfsl8fsDvPvu\nNp5+eiXFxZXY7QbuvrsTAO3axZ6n92+PIswUzomz2o29cAzesgjUMdlEjvKRtn8oMYO6YW4yF1vU\nxPMPci4eXwVz99Tf/2hn6JUAXeN+0tLkhuOF3Dt/E8dLyyl9UX4Q9E6NZs3RAgY0i6VTYgSjWyfS\nJi4cjUqsU5NRsYwp/HpIkoQk1UaBSZLEP9bsp2+T2GB+un15ZXy24wRpMVauby9biFcczOXZJTvo\nnhLF9FHyF87GE4X0fHMJXZMiWf+AbAU6XlrB3StOkL6njN2PjwKg1F1NZoGTDzYfCQqzVLuFqxpH\nE1dHdFW/elPIXBs1UF5LFATKqnKp9JSTYK+tZ7jh8AIKXVk1RaxDU0OcDX/Ah0qUv5JSIjPw+qtQ\nCbXLh50bD6dL6tXo1MbLLrmpQi3HH7iXog/+ReIr04md9CAAYVePApUqZIlSEAS4BKJszZoT3H//\nYnbulN1f+vVLoWfPXydj/y+FIswU6lHl87PjxGLsRXcQkIqIBDnlfjOIbQYqzSRE1f6fLsqOlMHE\nb+BQaej+q5vA7W1kp/2LXDr0+QO8vf4gJ0or+GL3CY6XVASP7csrIz02jGV3D0IQUCxiCr8pp5NZ\n/nPtAf66YDP39mzBzGu7APD+5sM8+tVW/q9/q6AwyyxwMmXZLq5pkxgUZil2MxtPFFFUUR0UZg1h\n1KhoH22kdWJtvrxWsWE80jedYXV8JSf1Sgs60EuSRLXPHfTVqqx2Bv243B4nGpWW3i2uD/b9Zuds\nApKPm7r/LZgx3lFZSEnF6dxgAgaNpTb/lq5+MWuD1hoSpdgxJdTHCMCgVUqAXW4Uzn2Poo/mkfjK\ndMwd5YAHa+++uFZ/j6ir9QvTxje6oESvP4esLAePP76cTz6RX/STkmz8/e+DufballeckFeEmUIQ\nT9VhSvNms8p1P5qvVhE2oCjkeKPm8lKg1pCGqLq4JH4AlHtgxP/kfGNnsvvPEHvxYzqrPJi1GlYe\nzuPlFXvIc1WGHJ85pgstomVfF/Vl7FOg8PvndLmer+/oz9C0eAanycs8/1x3ICjMBjST9204Xhjs\n1zLGxnODM4IRjgApdhOP9k0PCjWAbslRwUz1p2kcYeGdgSl07NgRSZLw+KsQqODVkbXLeluOL8ZV\nWVwrxOqkhmgIvabWJ0gQBOLCmgASPr8nKMw6NR4OkoThNy5irfDL4a+owLV6Jdb+g4LO+O5dOynf\nsA7H4m+Cwsw+bgL2667/1cXQ118f5JNP9qDXq5k8uSePPdYTo7F+wMaVgCLMFAhIEgt2fkKrqolo\ntR2Jnl5E3heV7JoxksRxXekw7TZ0tuifNrg/AJ9mErMzG97fEHpsRCr8vR/YLzzdxNLMHD7efgyr\nTsOsdQcAOPLUGAa3iKdH4yi+2HWSKcPaAfBg75YYtcqvuMKvz7IDOby0fDdev8S7E7rTMsbGvnw5\nRP+ZxTvomGCnaaSVaVd3YNJVtakeUuzmeuKqZYyNZ4e0DdmnEkWm1RFXXl91SETi6QhFvcYCyJam\nap+bTzZNQaPSc1P354N9TxbvxVkZ+hKmVelDohKDxaxrPtdlUKtb611/pDnhwm+WwhXBgeEDqNi6\nheYLv8U2YDAAUbfdgaVPP2z9BwbbCb/SioQkSRw5UkrTprJl+a67OnL0aCmTJnUhOTnsPL0vb5Rv\nrT8gHn+A3IoqVAJoip+kNO8tThe68Hi24jpkRheXTo9//YXY/q0v/gReP3x6AObthW3yOn/IY7ql\nHb4cc1GCLKu0gpSpXzR4rLiimhS7mc9v6XPxc1VQuACkmhqIgiDw361H2ZNbRqRJxyP9akvEjP1g\nFY5KD8vvGYxZp2F060QeW7SN8prC9O0b2Xl2cAY9G0cHC1c/Wqd/Q9TNMh+QAuzPWXfW1BANYTfF\n0YirADkPl05tQqcxhvhztU8aDEghzvTqCyhirfD7RJIkTj33JI7F35L23SrU4XIUq3XAYBBFOWqq\nBmObthjbtD3bUJeMvXsLeOCBJWzceIoDBybRqJEVlUrktdcG/+pzuRQowuwPSHZ5JZ8dzKFf2Dqs\nxW+FHDvyeE90mh4M2vgIhpiLeOuo9kPCbIg0QFFlvcOSAMKN6TAkBYZdWGmRp7/dTlFFNW9f1w29\nRkXzKCsHC50APNo3nUY2Ix0SIuiYePZ6kwoKP5dv9p1i1L++x/HS9WhVIsdLytmbX8bBAmeIMFt2\nIJdKr5/9+Q66p0QRY9HTJSmStJolyKRwE8/VWL78AR+VnvIQ65ZRayU5Un4RcrgL+WbXPzFqrVzT\n4SFALuOz/cR3+AJezkQlquvn4dJasegjKDpRLfcXBG7o9ky9vo2jMn7ZG6ZwReHJy6V8w3rsY+Ry\nRIIgULF5E5X79+JcuRz72OsAaPTMCyQ8+7ffcqqUlVXx/POreOutzfj9EuHhevbtK6RRo8srQezP\nRRFmfyA8/gBqUUAjikQatJQEEok2j6OqfD5Hn+xJ2eo4ksZ1o9sH96I2XOAb84ESuOqj2u26osyq\nhX5J8Fpfth3dS8eOZ0mXUcORIhd788o4XlLOQwu3BPfPvLYLUWY9zwzOoFtyJKkRihOwwqXlcJET\nrUpFUriJ9TX+Xh9sPsJ9V7Vg8oDWzNlwiBHpoct1r43qSHFFNU0izPgCXtRkYdc7OZwf6jxfGYxU\nDCUhvEVQmOk0Rjy+yhCHeEEQaJ3Qp366CJ0FrersRayLTlxYZnuFPx4Br5fd7dMJuFyYMo+hS5Cj\nF+Ofeg5BEDB1rS1T9Fs60Pv9Ad5/fztPPrmSoiI3oihwzz2dmDKlHxERv79SeIow+4OwraCMZScK\nmdTGTtWhzgwzDqX4swEc+LGIyiMDcGfaSX98FG1fnHDhPgJlVaGiDKCFHb64BsJ0oD23w2+l10eu\nszIotNq9vgi3p77T8elUFjd2aFzvmILCz6XS62PdsUIGNItFEARGvreSb/dnM2N0J+7v3ZKRrRJY\ntCeLdvEmSipyqfQ4Gd/WQoRZjmoscp1iw+EFpNkj6dNdTpxc7fOwcv+/z3pOARGD1hxi5Yow1+b+\n0qmNXN/1aXTq0C+ddkkDzxxKQeGC8DkcnHz0ASoPZJK+egOCICBqNIQNuxq/00HA5Qq2tfa6vNxC\n7rvvW955R37B6N07mTffHErbtpd/PrKfiiLM/iBoRRG15CRrR0sAnJ457H21gIA7iui+6XT/51hi\nahJLXjCd5tV+frYH3NoazOe3tLk9Pu74dAOf7jhO69gwdj42EpBzKVV6/RwpdvGnTqmMa5tczyqh\noPBzcHt8LD+YS5hBS+8mMQCYJ3/M6FYx7Mk5yrA0LR3jK8jMF3l2yRZSbSup9Dh5pKeLw7nrOZwr\nj9M8tgs9ml4LgCCIFFdk45d8wfNoVXqS7OnoNeYGUkRY0WtM50xuKggCes1PiHxWUED2E6vct5fq\n48cIHyE/X1UWC44Vy/AV5FOVuR9DS/l5n/qvf1/26ST+8peOLFlymGnTBjJ+fKvLfr4/F0WY/Y4p\n9/o4WlZBm0grMdoiRgS6Bo9VHYsnPCOddi9eT0zfixRka7Jg7MLa7V4J8NcO5+0WCEg8+OWPwWhK\ngD15ZcHPpwWagsLPRZIkvH45UvF0hCFA2itfkO2opmmEmgNPytatvw1pzrNLD1LoLCNMnU2KFSZ1\nVWPT+yiqNSKgUxuDwirMUBulbDNEMaLtfSHRioIg0D/9T7/OxSooUJsfD6D62FH2dm2HKjycsGO5\nCGo1gijS+O330CUmo09rGex3uYmcqiofM2ZsYNeuAj7+WPZ7a98+jsOH70f9E6u+XGkowux3zIac\nErYXODhaVkxacfPgfrN9DClN/0XGOCOi5iJ+BdZlwzUL6u//fNR5u2aXe+g5+SO8/tqaeqNaJfDG\nmC4Xfn4FBcDr94RGJVafdqB3haSL8AW8SBLM3d4BtTqMlfcOZumdrbj/i2X4pNp6ibd0aU6ieT0x\nFgtGbXsMuropIuQkqGcrYg2gVmmIslxZmcUVfj9UHsgk64lHEXR6mn08HwB9ahPM3Xuib9IUv9OJ\n2i6nlAgbfPnWBZUkiUWLDvLww0s5ckROPv7II93p1Ele4v+jiDJQhNnvjlVZRUQbdaRHWEi0GDjq\ncGPxb4PydDDvIyLueaKSn774gVecgOsXhe57byiMbHLWLP1Hi128t/EQ9/ZsgV2vZvrIjjzw5Y8A\nnHpubEj5FwWF09GGpwVQgfMkJ4v3EmlJICVSLh2UU3aY7/a8d44x4ONdcQxrBrEWDaJoo8ILO07I\naVtSIpJ4Z3w/rIbaSN6EsHD+1OOhS3VZCgq/GAGvl/IN68Dvx9pPrlWsDgvH8d0SRIOBQHU1ok4H\nQMtlq3/LqV4UmZlFPPjgEpYuPQJAenoUb7wxNCjK/mgowux3xN5iJ5vySmliM5FqchFRMYfRliLC\nrS/zzfDPMGUYsNw1ApIvYtCP9sEDK0P3Pd8Dbs8AfcO/Pl5/AP3j/w1urzqcz8SmRq69qhW3dmmC\nSXdlZmNW+GkEAn4qva6gdauhPFyVHhfVPjc9m46lWaycQbzUncue7NU0i+kUFGYGjQVRkFNDGHUW\nvj9qoaBCy309oogwhRGQjPxl4Wb2FMTy5pguTGjfmELPfsbU5BIzaC2kRv36eZcUFH4qUqB2lcGx\ndDGHr78WU9dupNcIM01MDE0/+QJz565BUXYl8cQTy5k+fQM+XwCbTcff/taPe+7phEbzx60WoQiz\nK5x9xS7WZhdzV0YKEaoihlrmoit5leMl8nG1Jo7w2Pvo+/lL5K3cQ6MzihCfk7Kq+qLsi2tkn7Jz\ncMenoRn+U+xm0iN0xNsUC9nvlZLyHApdWUSY44msWdY7UbyXDYcXUOUtv6AxBEHE468KbkdbkumQ\nPCSYRd5V5eWauTt4YuA99G8ai1atYt7ONWw/VYJKpePF4S0IM2gZm5GHxx9gZCt5Hg/0btng+RQU\nLmccK5dx6vlnkJqnQecPALD27Y+hTVssPXuF+JSFX31+d5LLlUBAwu8PcNddHZg6tT9RUabzd/qd\nowizKxhfIMCio3kY1CoOHJqMVDydM9+XAoEK1JpY7B3DsXe8sMSuQH1L2YoJkBF13m7/WL2P/2w9\nGtz2vnYzoiiwdauSS+lKQZICVHndQUtWie8YO0+W1bFwyRavns3G0Shc9l08UbyXnVkraJvYPyjM\n1KKGKm85AgL6mtQQdbPLh25b0WuMCHUiFfcXiIz9oIhnh8RzTw+Yv+sEm04WMeLdlYxuncgXt/bl\nti5NOdzERazFQKxFzuX1mVIBQuEKw+dw4FyxDF1KCqYOclF6Qa3BvW0LOJ3BdiqzmdYbruxn6aZN\np3A4qhk8uAkATz/dmwkTWtOhQ9xvPLPLB0WYXcGIgsAt6YksPJJHeMQo0PopyZ2BWpuEVf84m8Ye\npPXT16LqHH7+weryWWaoKLsz44JEGUCXpMjg56xnxyKexf9M4bfDVVWCq7IYmzEKk06u7nC8aBd7\nsn8IOtFLUiCkT/bJ+uO4qx3Bz1GWJJrFdArm9gKIsaYwvvOT6LWmkESpp8lzVuKq9pJglqMZ524+\nzB2fbmD/5NE0j7IiCAIF5VX8b+cJ7unRglu7NKVljI3nl+zkk4m9ABjUIp5BP/uOKCj8ukiSBIEA\ngkr+uyiY80+yX3iGyFtuo3GNMDN370mz+Qs5bLmy6z6eJjfXxRNPrODDD3eSkGAlM/M+TCYtFotO\nEWVnoAizK5Aqn5+12cW0i7YRoS5ioH8gUWH7kMI6EJ38GmW7T7Jy0MtU5ZVxcNZSkq/vgai6gIgW\nZzWM/AL2Fdfu+2AYjGhyzm4nSsrpM2sp2x+5mrbx4WROHk2zqN9XiYzLnbMVsT4dqdiz6VhsRllc\n78payaH8LXRvMoYWcV2D/YtcWcHxtGpD0KLldnlIjGtcz8Jl0Nbm2UqwtyDB3iJkTmqVNlhzcWtW\nMXvyyrihfQpatYpDhU7SXlnIP8d1JcqsJ8ygZdVh2UH/y90nebx/azonRjC+XTLPDKotGdQtOYol\nf1GSrCpcuRT8aw65r08j/olniJr4ZwDCho3Aufw7TB07B9uJGg1hQ0cgXOGrDR6Pnzfe2Mjf/raG\n8nIPWq2KiROVMmDnQhFmVyAnnJVsLXAQVzaBosofAHCVfoU1YhxeVyWrx7xOVV4ZMf1b0ft/D59f\nlEkSTNsMr/8Yuv/LMf/P3l0HRln/ARx/X+1u3V2MscE2YnT36BSVEBBMLCwwf1hggIgidoEoGCCl\nIqUgKbVRIwZsY2Ow7rx8fn8c3DEllXGL7+sfnud7z3N8Dra7z33j84WugZe/54IzBWWEv2EuoeH1\n0jIOPzOMGL/68Q2vNjAYdVToSrBXOaNSmgeqz+QdIS0/8UKZCHPiZTBdfhPri8q0hZbEzMMxAH/X\ncNQq65y/QI+mDGrxEA5qZ+xVLigV1gUa8fHxtG109e20LrUm8Syrj6QzLCaYUS1DSMkvpcP83wAY\nFhOEh1KBu70dng5qHvlpDyNigsEepnSOoEuYN/0izd+elQo530/scd1/ryDUNtr0NIo3rMN1wCDU\nIRdWXRmN6NLTKN2+1ZKYOTRvSbMNW2wXaA357bdTPPXUBk6eNH/ZHz68KfPm9adJEw8bR1a7icSs\njvg9PZe2Pq64a+zQKOWEq0+gqNhuedxkNA8rxT+5mPLUHNxjQ+n1y7MoNNeoxJ9dDh2+hQpr1XJG\nN4UFfeEaCZ3RZLIkZQDTekWLpOw6mTexLv1bD5f5vGPj4dgpNQBsObGEc4Un6Rs9iWAP8yT24ooc\nUnMPVXs+hVxVrfaWIDHUNQAAIABJREFUg53zhX0UzefujtahgqiALkQFdKl2/8X7/o2tydm89fsR\nPrmjI2Gezhw+X8g3+1MIcXdkVMsQQt0deW1gK15Zfwj1hZ8pLycN79/WngHNAvBwMCecXcJ86BLm\nc7W/ShBqNZNej0yhsGxrl/HqDAqWfU/w7Hfwe+xJANxvuwOHNm0tc8nqK53OyNSp60hJKaRpU0/m\nzx/IwIFNbB1WnSASszoiPruI+Owi+gZ70c7PHa12MhdnATXtqEUmU5C+Yg8pX29FoVHR5dvHrp2U\nvbsf3tpdvW3vRAhzvWY8BqMJ9SUlMca3DePtYdffq1JfmUxGkMks2+1kFCSRU5pmHVq8UC5Ca/jn\nJtYXNQ/sgZ3SvA+ck9oDJ7U7pkvmfAV7ROGkcbcMKTrYuaBSqGukgndqfilnS3W0MppQKuT8dCiN\nh5bv5q42YSwY1QGjycSczYlsOpnJ9tQcwjydGRYTRLCbI60CzHMbFXI5T/eMZka/6sMX48Tep0I9\nkv78NPK+/ZrINetwamcunO0+YhRSVRX2l1TaV3l7o/K+vjm7dU1pqRZJAhcXNXZ2Cj74YBAnTuTx\n2GMdsLvG3smClUjM6ogWXi6cKiyjtY8bJmOZpYfMw386MpmC8vQ89j5kLrwZ+/Z4XK+2x6QkwaCf\nID7b2tbeD5aPAMfrqzG25XQWcpkMkyQR5OrA4nFd//VrqwtMkokqfXm1+luVulJaBfe1JETrj3xO\nVnEqw2KnWjakTi84xsmsPf94PvMm1s6W3i0H9aVzt5wt13VuMvIf93o4BeDhdHMLL2oNRvp+vIkj\nWYVkvXYn9hd2hOiyYD05ZVWcbxuLr7M9zXxcKKzUsTXZ/LNzMenacOI8IW7mZe6xgR7EBlYfqnCw\nE281Qv1RfjCB4vW/4ffkdOQac++2pNVhLC6mbPcuS2LmMeI2PEbcZstQbwmTSWLJksM899zv3Hln\nNAsWmHcYGDw4gsGDI2wcXd0j3i1rKUmSeHv/adr4uBLj6cLgMF8qvc5QVZqOvXNnPANnkH/udbyC\nX6E8PY/f+8xCV1CG/4BWRD7S/+pPvupU9aTs5P3grrlmTNmllQS8+hP7nxpCl0beZL56Bz8cOMOj\n3ZrWuv3W/o2ckjSKK3KqTZqvtCRhZUiY/nFPVEBX1Ep7AMvKw0vrdgV7NLOUhri0TIT6GptY17Sc\n0kr8X/2JO1uF8sPdPcgtq6J1kAd/peWyLz3fssF3Iw9H7DCiuPD/29jTmSXjuxEXaR0ajYv0xzhv\nok1ehyDcCqbKSuT29pbz1IcfoPLIIRzbd8C1r/n91u+Jp/F9/Ck0YTdQlqge2LfvHI8/vp7duzMA\nOHAgC4PB1KC2ULrZRGJWS5XpjbirVSTkFONip8LF8CcZJ4bi4jUeB5fuuPnci2fAs1RmlPN7n1mU\np+bg0T6crt9NvXqSdCAbpmy0np97GK6ji9loMhHwqnkftnbvrWX9g33p1zSAx7o3+68vtUZIkoTO\nWGVJrPQGLaFezS2Pbzq6iKKKbIbFPoZGZV5dePjsFjIKT1zxOdVKR0vv1sVE61I9m92FSmFXrTRE\nsEeUZW7YraIzGDmdV4pKISfC2wVJknh5/UFO5ZbSMdSLp3pGk11WRUt/d5YfSuMHIMjNkX6R/rho\nVHRuZB1m+euJwcTHx+PlZE7cHeyUYghSaDAkk4mTtw2hbNcOWp1MR+luHp73mnA3VUlJqHz9LNeq\nGzWs34vs7DJefPEPFi06iCSBn58Tc+bEMWFCS1Em6T8SiVkt5Wyn5P4WoZTpDJw/4EAGEgAleUvx\nD/8alTqEjJ/3s+/RhVSeL8SjfTh91r+AndtVqiabJOi/3Hr+6+3XTMokSeKrPad5e/NRS9u9HZrQ\nr6lt9zArqcynXFtUbQL93yfRGy/svQjmYqchnjGWpLVcW0S5togKbYklMfN3a4Ja5VB98vwlw4sK\n+dV/XS72nNW0lPxSfJw0OF3Y2uqe73dSrjOwcGwXnNQqMooraDH3F8I8nDj9v9sorNThoFKSkl+K\nUZJ4qie08HdnWEwQrw20bk80vHkww5uLzbiFhkkyGCjZ8jvlBw8Q8MwLAMjkciStFpNWS/mB/bj2\nMVfN83v0CVuGanOZmaU0a/YRJSVaVCo5Tz3ViRkzeuDsXPe2hKqNRGJWC1UZjCjlMuRSBfknusGF\npAygcWySJbkoSEil8nwh3t2a0nPNM1dPygD2ZlqP3+0NHa9d1G/h3tNMWW5dIBDl68oXYzrf0Ou5\nXgajnkp9CXqjDo8LqwglycT2k8uo1JfRP+Y+y2v/88QSCsozr/Z0qBTqasOHJsmIQmb+ke/ZdBwK\nuQontXUVaUxgtxp5Xf9WQYWWb/enEObhZEmY+n68kT+Ts/ntgb4MaBZAld7IkcwiDpwr4LM7OwGg\nksuJ8nUl8MIWWB4Oaqb3jqGJtwsBLtbkceag2Fv/ogShFjGWlqJwNs/plIxGTk8Yg6m8HM9xE1AH\nmX/nQhd8gsrbx9JbJoC/vzMDBoRTXq7nvfcGEBnpaeuQ6hWRmNUypToDHx9KxV2top93Erqq05bH\nmnaswlhlnecU8/wIHEO8CJvU89q1yh7aCCtOWs8nxlwzFr3RRJ8mfgyJDmTtsXPMGhTLY92aXvO+\nvzOaDOhMFeSUpF928+qLxzpDJQAuGi9GtZsOmPdPzChMQmeoRGuoQKMyJ59eTsGXJF7Ve7Yc1C44\nqFwsdb8ux93R74qP2VKV3kh2aSWhHk48+0s8W05n4aaxsyRmPcN9+TM5m9N5JQwgAI1Kwf2dIjia\nVYTjhQn2we6OJD5bfe88lULOna1uZPd6Qai/dFmZnBwxGGNJMS2PJSOTyZCr1fg88BAylR0ypfWj\n0T7yxt/z6pvk5AKmTdvIc891pXNn83vRN9/chkYjUoiaIP5Va5kqg5EBTovZXxqFr9cY8ou6Y9Dn\nEhK9iVOfbObEe2uJ2/oKDgEeKDR2hN/b+9pP+t2x6knZa9deQenz0jLahXiy9v4+PNenOd/e1Q1X\n++rlN0ySEb1RZxnC0xu0JJ7bitFkoF3YYMt1y/fNpkpfRtLhq/+dcpkCeztnHNXVy3V0jbgDldxa\nRR6gS8Soa76GuqC0So/BZMLdQc19P+zi633J3NkqlKUTuvHF6M4opy/hng7WJHJ67xim9YrGUW1d\nPftQl0hbhC4IdYKxpITCtT9jLC7G96FHAVD5+KLPzcFUUY7uXIaldyz49Tm2DLXWKSvT8eab25k3\n7y90OiPFxVq2bJkEIJKyGiT+ZWuJ1OJyqowmmrk7kF88myFBs7GT8giOWgeASW8gdcl2ylJyyFx3\niPD7riMhkyTotwwO5Vrbjt4LPg5XvgfznoX5FVo2nDhPuc5AM289qbnbLJtXm2txlVCpL8PPNYyB\nLR403yiTcejsZuQyBW0bDbIMOzrauWLQG3B18rrs5tUXJ9SrldU3sb4o1PPavXt1jdFk4vlfD/Du\n1mMsvqsrE9o2vrC6Fb5LSAVAJpOhmzsehdz6byLKTgjC1UmShLGwEKWHuWSLPieb1Acmo3B3x+f+\nKciUSmRyOc3WbkLdOBy5WsyL+jtJkvj++0SeeWYT58+XAjBxYktmzxbbod0K4l2+ljhZWM7B3GLK\nmIAzUJzxPH6B0yyPy1VKev78DNl/HCX0euZ4SRK0WATZFda2tbdfNSmTJIlHV+zls7+svWsV2vOs\nP/IFJslw2XuMJmu7SmFH69D+aFSOSJiQYV5YMDT2MRISEmgb23AL0BpNJt7YdITXNh5mx9SBtA70\nYHzbMN7deowfDpxhQtvGtAnyZN7wdnx2ZydLMnZpUiYIwtWV7t5F8oQxOLRsReTKXwHQNInAa8Ik\n7Fu0QtLrLcOU9lHRtgy11kpOLmDSpNXs3GneO7dtW38++GCQZQhTqHkiMaslegd7kZbzJ86m6hvW\nnl29j6DhbZHJ5Wi8XK4vKQP49mj1pCxtCjhcvXhs4zdWkV5orUi/9ZEubD7+LSbJQLBHNAFuEdXL\nRaickcurr+psFdznH89bH2qc/VvbU7LxdFDj46SxbOz+XUIqnRt509jTiaQXRhDuaS0o+/fhYkEQ\nLk93LoOCVT+h8vHDc/RYADRh4eizMqlydEQyGpEpzO9PYZ9+ZctQ6xQXFzWJiTl4ezswe3YckyfH\nivIXt5j4Om5DkiSxOT2XjNJKJN0ZepjusjwW2aGUo2+tZvvt77L34Rt8U5EkmPan9TzrkWsmZQCz\nh7SxHO95oh/5xb9QpS/D37UJvZuNJyqgM6FezfF2DsFR7faPpEyo7pejZ+n10Ub2nc1HLpfRKsCd\nmQNb8UJfcz01F40dTbxcGnTiKgjXy6TVYigstJyXH0jg7PPTyf70A0ubyteXFgeO0eLgcUtSJlyd\nXm9k4cID6HRGALy9Hfn553GcPDmVe+9tLZIyGxCJmQ1llWvZl11EclE52ooE1A7m8gWhMTtJ/mIn\nh19aBjIZvr1vcI7Vl5fMsl88+JqbkV80skUwrQM90M29i+KyDRRWZOFi70WvqLtEEnadEjMLeWPT\nYSRJok8T86T9e3/YhZOdkmg/N/7XryUBrlef4ycIQnV5SxZzIMSH83PftLS59OqD59jx+D78eLVr\nNRGR4svOdfrjjxRat/6M++77mQ8/3Gtp79EjFDe3a+8GI9QMMZRpSzIIdbYnqbCMnsF3UFa4Fne/\nqRRsVbHv0YUAtP/oXhqN7XJ9z2cwwaTfYOMZa9vga28P8uLaBL7Zn0LGK3ew/+khpOYe5mzBceyU\n9sRFT0atFInE9Zr03U4Oni/k7nbhBLs7su2xAXQK9RJzxQThOlUcOUTBimW49I7Dpad5kZNdSCim\n8nJ0aWmW6xROTjT+crGtwqzTzpwpYtq0jaxceRyAxo3dadbMy8ZRCReJxMyG/B01jAwuJjN5Arqq\nlfiHf0nF2UL+mvw8SBItXrmdiCk3sApmysbqSdnBSVe9/HReCVGzf8YkSTjYKfhkVxIPd2lKI68W\nlGsH4ekUiIu9+GW9moSMfF5Zf4gPR3UgxN2RLmE+qJUKEs4VEOzuSNcwH1uHKAi1miE/H5lajcLJ\nvANH0frfyHxnDvrcXEti5tS5K62SzmAXGGTLUOu8igo9c+bs4O23d1FVZcDBQcWMGd156qnOovxF\nLSL+J2ws/Vh3TMYSUg5GENGmnB3jFqArLCdgUCzNZ9xAra5jefCztRjttTYmP5VbQrPZayznFToj\no1s1AsyT9ZsH9bzRl9IgtX/vN8C8cMLwzgQ+GNXBxhEJQt1x9n/PkvXBfBp99DneEycD4D50BIbc\nXNxH3Ga5Tq5SiaTsJli16jgzZ24D4K67WjBnThxBQS7XuEu41cT4io28n5DMTwcWYjKWAOAZ+AJH\n3/qF/N2ncAjyoNPXDyO7keGve9ZZjzfdedWkTJKkaknZPR3CyXp1APtSFlNaVXDDr6WhmbflKJFv\nrgag6I2xPNwlkj8f7S/mtQjCVRT/voEzjz1E1elTljZ1aBgyhQLd2XRLm31UNCFvv4tz1+62CLPe\nyc+3rs4fN64F993Xmu3b72Hp0lEiKaulRGJmIx5qFc21D1rONYaHOf72zwB0/uZRNF438AsTsxBS\nis3HMzpDrO9VL6/QGVhwW3sAXujbnC/HdCExYyNZxSkcSNt4Yy+kASis0PL13mTAXKn/h4NnSM4v\nZVdqDs4aFR/e3pHuja/+by4IDU1V8mkkk3ULubzvlpD79ZcUrV9rafMcN4HW6TkEvviyLUKs1/Lz\nK3jkkbWEhs4nNdW8mlUul/Hll8Pp1i3ExtEJVyOGMm1ksNt35JoLKtOoRTz7Jv+IsUpP6Liu+Pa8\nzsKH+zJh8IrqbVPbXP7aC/ak5dI+2IveTfxYOqEbY1uHAeZtj+ztnGgTOvBGX0q95/vKcuwUcnqE\n+9DY05kHOkXw0Y4kyybhgiBUlzR8ICWbfydqy06c2ncEwGvC3dg3bYZr/0GW6y5uIC7cPAaDic8/\nj2fGjM0UFlahUMjYujWNsDCxCXtdIXrMbMTBxbqlUvFeJWdX7kXhoKb17HHX9wQG0z+TsnMPwxVq\nzhzLKiLw1Z/osmA9T6zeR4S3C2NbhyFJEgB2Sg2dwkdipxRLpAF+OJBKpd68q8GI5sFIEpzMNQ87\nP9g5kkPPDCPUw8mWIQqCzUmSRO7ihZyeOBZTVZWlXRMegcLNDV26dYjStU8/Ap77n9gUvAb9+ecZ\n2rT5jEcf/Y3Cwir69g3j0KGHmDw51tahCTdA9JjdYpIk8U78aQY1isLNYxy+IfPY0Na8cW7MCyNw\nCPK89pPkVMDA5dbzlSOh++UnxpZU6Rj37XbWnzhvaVt28AwfjOrAyay95Jam0yl8JAq5+FG46MPt\nJ3hi9T6ifV35+b7eLJ8kFkIIAoBkNFJ54jgOMeYiyTKZjOxPP6LyyCFKJ9+La9/+AAS+MouQue9Z\ntj8Sat7s2Tt44YU/AGjUyI158/pz223NxNzXOkj0mN1ixwvKMJkk1qZm4x2+GJXGhzbzJuLfvyVR\nTw+59hNUGcxzys5eGAdt6nHFpAwg8q3V1ZKyNwbHkvbS7WQWJfNX8mpOZe/nfOHJK97fUMz5I5H5\nW49hMkk80DkCgGPZxTQSvWKCAIBJp+NQZChHu7StVoHf/8lphL7/MQ4trL0ySjc3kZTdYsOHN8XF\nRc3Mmb04duwRRo2KEklZHSV+c26xJo65jDBFY5R5IDceAEUgAQNjCRh4HV3NVQYI/tR63tEfvr1y\nMleu1ZNbpgUgxs+VnVMH4axRUVKZx5YTS5AkEzGBPQj2bLib+ZpMElUGI0sTUgDILq3i9cGxFLw+\nRuxbKTRYxtJSsj/7CO3pU5Z9JuV2dmgiIpE7OKBNP4PS3TxnyXPMXVd7KqEGSJLEqlUn+PXXk3z1\n1XBkMhnR0d5kZDyFs7Pa1uEJ/5HoMbuFJMlEysFIABRSAfqCG/jnL9dXT8qGN4Ffb79qWQxHtQrt\n2+OZ0jmSQ9OH4axRoTVU8PuxxegMlQR7RNG2UcOc7H+uuALFtG+Jz8jHXqVgfJvGeDio6RPhh0Iu\nF0mZ0KAYy8upSDxiOZfZ2ZE59y3ylixGm3HW0h6xbDUtDifh2Kq1LcIUgMTEHOLivuX225exaNFB\n1q+31q8USVn9UKOJ2ZtvvsmYMWMYO3Yshw8frvbY0qVLGTNmDOPGjeONN96oyTBqBZOxnKQ91g97\nR8VjrAmZzp4HP7/2zXojNPrMen5XFHx15YRKkiQ6vLeWs4XlKBVyPr6jIzKZDJPJyJ8nvqOkMhd3\nBz96RI5FLmuYufmwLzcDEPfpJkqq9DzXtzl/PjqAfk0DbByZINxaVadOciDEh1N3jrAsBpKr1QS9\n+gbhS5ah9LDOe1W4uIjhMRspLKzk8cfXERv7KZs3p+LhYc/HHw+mX79wW4cm3GQ1NpS5d+9e0tLS\n+PHHH0lOTubFF1/kxx9/BKCsrIyvvvqKjRs3olQquffeezl48CCxsfV35ci50/dVO69KGAosQuFw\nHd9wAj6xHk+Mhnf7XPXyb+NTiM8ooNHrK1lzX2+GRpvnoO1N/ZXMotNoVE70jZ6EStnwvl1V6Y2k\nFZaRMG0on/11kvxyregdExoM7dl0sj9egEylInjmWwCow5ugdPdA5eeHsbAQpYcHAL4PP2bLUIVL\nfP31QaZP30h+fiVyuYxHHmnHzJm98fQUJXvqoxpLzP766y/i4sz7PIaHh1NcXExZWRlOTk6oVCpU\nKhUVFRU4ODhQWVmJq6trTYVSKyhV5jc7Aw5EdyhC3klO4JDWyBTX6LGat896HOVxzaRs5eF07vl+\nl+X8YlJ2/PxfnMj8C7lMQZ+oiThpGl5Nmx0pOfT8aAP3dAjn8zs7M6VzpK1DEoQapcvKRDqTCm3b\nAiBptWR/MB+FmxtBL89CplQik8tpcTgJhYP4kK+tzpwpIj+/kp49Q1mwYBAtW4qC1vVZjY1j5eXl\n4e5u/fD38PAgNzcXALVazaOPPkpcXBy9e/emVatWhIWF1VQoNldQpcM37CMat68isn0R8gtbLTkE\neWLvf5UESZJg9h7r+barT7L9em8ydy7eajnf/YS5kOO5wpPsTfkFMBeS9XEJ/ZevpG6L9HYm2M2B\nRXuTOZpdZOtwBKFGFf6yhkNNgpE+ft/SpmkSQdBrbxDxw0q4ZEhSJGW1S0ZGCX/+ecZy/uyzXVmx\nYjRbtkwSSVkDcMtWZV6cuwDmoczPPvuM9evX4+TkxKRJkzhx4gTNmjW76nMkJibWdJg3XbmpGJ3p\nPY7J5tJaXoE9JioTzmLfOhjZFYrBXtR24F+W42MftaQyPv6K11YZTNy37ITlfF7PYOS5aezMPkKy\ndjMSJryVzSg6ayL+7JWfpybFXyX+mmIwSXx9NI+4EBcauap5INqdVt4B6M6nEH/+2vcLZrb4vxOu\nn3T8GNLKZciiYpCNutPcprEHe3vQ2LN//37r3LBe5pEMDh60UbTClWi1RpYuTWHhwtM4OChYubI3\nTk4qjh8/TGgoJCQk2DpE4RaoscTMx8eHvLw8y3lOTg7e3t4AJCcnExwcjMeFuQzt2rUjMTHxmolZ\n8+bNUavr1ryopMO9kCp2ECStI7KdjrwdSfz+8DK8uzal37ZXr3zj9oxqp9Gje1z175EkiZSIKB5f\ntY93R7Qj3MsZg1HHmgPvY0JPiGcMvZuNR2ajyf7x8fG0vTCccittT8nmQFEOK1MymDu8Lf+7/dbH\nUNfZ6v9OuDxJkqg8dhSFkxPq0EYAFGae4/TGdTgUFRD9xmzLtaaMPA4kJor/v1pOkiR+/jmJp5/e\nSEqKuUbckCGRNG3anHPnksT/Xx2k1Wr/dWdSjX1Kd+3alQ0bNgBw9OhRfHx8cHIyF+sMDAwkOTmZ\nqgtbeCQmJtKoUaOaCsVmJElCqtgBgMb9LuRyOckLtwDg0yPqyjfmVcKo1dbz3MtPwq3SG7nn+50o\npn3L2aIKQtwdWXNfb8K9zPvPKRV2tAjqiZdzMN0jx9gsKbOFQ+cLOJJZSLcwH2IDPfB3sWdETLCt\nwxKE/+z87Nc52jGW7M8+srS59OpD0BtzCPv4y2rXyuvYF9mG6PjxXAYOXMrIkT+SklJITIw3f/xx\nNz/9NBo/P1HguiGqsR6zNm3aEBMTw9ixY5HJZLzyyiusXLkSZ2dn+vXrx3333cfdd9+NQqGgdevW\ntGvXrqZCsQmDycRn+zfS98J5QOhMytPzSFu2G4DG9/S68s0vbrMe/3r7ZS9Jzisl8i1r8hb2+kr0\ncyfw95XskX4daOLbrkGVxfjl6FlGLvyTab2ieWtIa+YMbYOLRiWW+Qt1TuHPq8ld9AU+Dz6M26Ch\nADh37Y7S2weFvXVemMLJCf8nptkqTOFfkiSJsWNXcPhwNm5uGmbO7MXDD7dHqWw479fCP9XoHLPp\n06dXO790qHLs2LGMHTu2Jv96mzqQXUQv0yjLuZ2mEQmvfopJqyd0TGecw68wgTOtBFadMh938DdX\n9/+bkipdtaRMKZeRMmMU8gtz1k5l78fLKQh3Rz+ABpOUGU0m9EaJtkGeDI4KZN6fx3h7WFtRDkOo\nE0x6PWV/7cQhpgVKT3PtsMqk4xRv2oAqINCamHXrQWxyBjJ5w/i9rm9MJonKSj2OjnbIZDLeeacf\nK1YcZ9as3nh7O9o6PKEWEL/ZNaSR9A0KdAA4ug2h6OhZUr/dhkypoOWsMVe+ccRK6/Hiwf94+ExB\nGe7/+9Fyvua+3mjnTiDQ1fztOas4hV2nVvDb4U+o1JXenBdTB2xKOo/dM0vZmpyNn7M9d7QKJf3l\ny/c2CkJtlDJ5PEmD4yj87RdLm8ftown7bCFBL8+ytMnkcpGU1VG7dp2lQ4cveOKJ9Za2fv3C+fTT\noSIpEyzEb3cNqDQY8fB9GLWDuWBuUORPHJ6xDMkk0eTBvlfuLcupgHNl5uMhjcHLvtrDaQVluNnb\nkTdrNKHujrzcv6WlTtlFXk7BhHq1IDqgG/Z2zjf9tdU2xZXm5NftQq/Y0C83YzCZmNQ+3JKsCkJt\nk/XhfI726ETlySRLm0vP3miaRiFTqSxtmsbheI2/G5WvKJFQl50/X8rEiavo2nUh8fGZbNyYTGmp\n1tZhCbWU2MT8Jss79xa/Z5gwON3OoPB1eDp6U3gojYyf96Owt6P5jNsuf6MkQezX1vMP4qo9rDMY\nafzGKg5MG0qktwspM0ZxOUqFip5NxwHSZR+vT2ZuOMRrGw+jmzue9iFeDIsJYs7QNtgpFbYOTRAs\nDMXFlGz5A/fhIy09XeUJ8VQk7Kd4w2/YRzYFwPv+Kfg8+LAtQxVuMq3WwPz5u5k1axvl5XrUagXT\np3fhhRe64egoplgIlycSs5vIoM8l7+xLxAIpZSdx1iwC4NicNQCE398He1+3y9/8zj7Qm8zHsT7g\nbP2lrdQbaPqW+Tlaz/sV/dwJ1W41mgwcPruF5kE9USnsLkxyr98T3QsrtLy28TD+Lva8vfkoL8S1\nYPW9vW0dliD8w7GendCePkXUlp04te8IgO8jU/EcPRbnHtafWTE8Wb+Ul+to3fozTp0qAGDkyGbM\nm9efxo0b3q4rwo0RidlNdDreOlG/VdS72CnklJzKJH35buQqBVHThl755rf3Wo9/sfaGGU0mnJ7/\n3nIe4GJvmeQP5lU9u06vJDkngYLy8/SNnnRzXkwtpjMYcVarqJhzF3d/t5P7O0XYOiRBQDIaSX/m\nKUq3byV6+x7kGg0Abv0HUeHrh6TXW651atfBVmEKt4ijox1dugSjVMp5//2BYrNx4bqJxOwmqSjZ\nbjl28ZpIgIt5VVXi66uQTBKNJ/fAMdjz8jffvdZ6fOI+0Fj/W+yeWWo59nRQk/C35C7x3DaScxJQ\nyFXEhlQf/qyPTCaJlnN/4Y0hrekfGcCPd1+98K4g1BRtehrlBxLwGGGeniBTKCj9ayeVx49SunMb\nrn37AxA8Z54MwWfsAAAgAElEQVQo1dIAlJRoef31bQwa1ITevc1bDL7//kAcHFSoVGJ6hXD9RN/5\nTVBWuI70Y9YhCYX/JwAUHz/HmaU7kKsUxPzvCnPLDuXAulTruWf1Cf9PXihE29zPjZxZo/F20lge\nS88/SvwZ8+qeHpFj8HQKvBkvp1b7Zn8KpVoDoxdvQ3GNLa0EoaYYS0s50rIpyXePxVBYaGkPfnMO\nUVt24tLb+iVJJGX1m8kksXjxQZo2/ZC5c3fx5JMbLFsQurpqRFIm3DDRY3YTqDSN8A37mOzUR4iX\nvY1nbjEDHTU4hngS++ZYdMUVODXyvvzND2+yHmc+8o+Hh0QH0sLfnckdqneDF5SdZ1vSj4BEm9AB\nhHo1v4mvqPYxmSR+P5XJpPaN2ZOeyx8ns3CwEz++Qs3TZ2eR9sxTGLKzaLbBvHOHwtkZ1wGDkClV\nGIuLULqb5w259q7/vdaC1b5955g6dR179pwDoHPnIBYsGCSSceE/EZ9sN4HaPgqlXRByl7voopMR\n5GTu9VI6aoh+dviVb9QZ4dSFb9t3NoUL1Z6jZ68hKbeE+zs14bM7O//jtgpdKX8cX4zBpKOxdywt\ngnrd7JdU66ieWYJGqeDUiyP5cFQHFGKitFADJJOJioMJ6HOycRs4BACFmzvFG37DVF6OLvM8dv4B\nAET8uMqWoQo2lJ9fwTPPbGLRIvNG8P7+TsyZE8f48S2rzQEWhH9DfLr9RyZjOeXF2ziSb8LV3okw\nV0dUCjkmo+naN8cstB7P7QWY979Myi0BoLhS/49bDCY9m49/Q7m2GG/nELpE3F7vv50VVGhpHehB\nlcHI9J/jRVIm3FQXh50AKg4mcKxHJ9Kemmppl6vVhH+9lBaHkyxJmdCwyeUyfvnlJCqVnOee60pS\n0mNMnNhKJGXCTSE+4f6jtKM9OHu8D8aUluRVmgsGlp3JZU3YVI7N/fnKN/p/DEUXCgz2CgZHc1FJ\nx+e/s1zywajqK7ckSWLXqRXklZ7FUe1Gn6iJKOUq6quTuSX0+GA9jnZK/npiEB/d3pFv7upq67CE\neqL8YAInBseROuVeS5tDbBsc2rTDtd9ATJWVlna3QUPRNBar6hqy339PoarKAIC7uz1LltzG0aOP\nMHt2HM7OYrN44eYRidl/IEkmtBWHAChVdmJhYjoAaT/uovJcAUVHzl7+xqErwHChR81eCctHAND6\nnV8tl0zpHFltoj/A4YwtpOQeRCm3o2/0pHpf2X/oF5vZeSaXTSczUSnkPNQlEqVC/MgKN85UWUnx\npvWU7thmaVM4OVO67U+KN6xDMpl/H2VyOTHbdtNowccoHMTOEQKcPl3A8OHf06/ft8ybt8vSPmBA\nEyIirrDSXhD+AzHH7D8oLbBuJF7iOpsHQ8zbI0U/OxzPto1xuFx5DEmCPZnW8/SHLIcnLwxhhnk4\n8fEdHavddibvCAfSNgIyejQdi4fjPzc3rw8kSSKnrApfZ3v2PDkIr5eWoTUYbR2WUAdJJpOlaGvB\nyuWkTrkX134DcO5mLrGiaRJBk+9X4Ny1uyjuKvxDWZmON97Yxrvv7kanM+LsbIerq+baNwrCfyQS\ns/8gL+MVAJR2IQyPCLO0y2Qy/OJaXP6mOZcUkk28B4C96Xm0C/Jk71ODScopYWTz4H/cJkOGQq6i\ndUgcIZ7RN+9F1DLK6UtoE+TB1O7NmNCmMSeeH0GEt4utwxLqkMKfV3PujdfwGHUHAc/9DwDXuAE4\nxLbBqXP1oXD3YSNsEaJQi0mSxNKlR3juud85f74UgMmTY3nrrb74+TnZODqhIRCJ2X+gqzwOQKqx\nJ2EmCUmrpyIjH5eIq/RmbbtkeNPXkdGLt5KQUcATPZoxpXMkMX6X37Ip1Ks5I50CcFLX7+08Xh8U\ny4x1B0nIKODuduEiKROuSp+XR/Gm9Ti0aIVD8wtfhmQyKo8eocTT05KYqXx9idmx9yrPJAhmGzcm\nM3GiecVt+/YBfPDBIDp2DLJxVEJDIvrv/yWjoQRkjgCckj+KQi4j9Ztt/Bo1jUMzfrzyjfuyzH++\n2oWTuSWsOJxOakEZL68/9I/Ntw1GHfll5yznzhqPerkC02SS+GRnEhU6A+PahHHi+RHMH9ne1mEJ\ntZAkSUhG69B21ntvk/rAZPK++8bS5tInjshf1hO5au3lnkIQ/uHipH6A/v3DGT06hkWLRrB79/0i\nKRNuOZGY/QsmSWJTRiWR7QsIiNrNI23bYTKaOP7uWpAk3FqE/POm7HLw/tByqr0zkqjZayznebNG\nV7tckkzsOLWctYc+IS0vscZeS22gemYJj63cy9AvNyOXyUQvmXBZmfPf4VBECEVrf7G0uQ0Zjkvf\nfjjGtrG0KRwdce0dh1wtVsoJV6fXG5k/fzfBwe9x4kQeYJ6K8uOPdzB5cqwofyHYhEjM/oVNaTkc\nyi0hv8qAi2s7ADI3HKLsdBaOjbwJvv1vGxTrjdB8kfVcBg5zrMUpl4zv9o/aXBISaqUjCrkCV4cr\n7BpQR2kNRgJeXc6jK/YAcOrFkcRF+nM8u5ggV7ESToCqUyfJ+nA++uxsS5uk16PPyqR0p3VlpXOX\nbjRdsw7P0eNsEaZQh23alEyrVp/y1FMbyMur4Icf6vcXYKHuEHPM/oUYDxWhWVEcOfUcvVu8gkxu\nR/IXmwFo8mBf5H8bkmTGDuvx1DacmBIDb5trnI1qGcK4NmH8nVymoHOTkbQI6oWT5vLzzuqqXWdy\nifJx5dNdJ3l7aBsaezqz7oG+4ttpA2bSapHZ2VmG6tOfn0bxhnUoXN3wnjgZAK8Jk3AbOBj75i1t\nGKlQ16WkFDJt2kZWrz4BQHi4O/PnD2TIkAgbRyYIZiIx+xeUeS8BEFA5B2SzqDhXwLm1CciUChpP\n7vnPGxYesR6/3IVmQMmbY/n+wBnu71T9zaCgPBNHtStqpbnnqD4lZVkllew7m8ewmGC2nMrC20mD\no9pcIFckZQ1X6sP3U7BiGdE79mEf2RQAj9tHo3B1q1bU1c4/QFTeF/6T778/wj33rEGrNeLoqOKl\nl3rw5JOdUKvFR6FQe4ifxhuUlJ+LlPMZAA4uPZHJ5CQv+hPJaCL49o7Y+16SSGmNEPSJ5bT8h6G8\nvGY/APNGtPtHUlauLWJT4kJUSjUDmj+Ao9q15l/QLWKSJAJf+4mHukQS6e3CzEGxtg5JuMUkSaJs\n9y5KtvxBwPMzLLXDTFotpooKyvfutiRmXndNxOuuibYMV6iHOnQIBGDixJbMnh1HQED9LtIt1E1i\njtkNSjw9z3IcGLEMk9FE8ldbAIh4sG/1i1tY98JMdJPj8tt25m87zrHsYvafza92qd6o5Y9ji6nU\nl+Jo54q9qu7XyzGZJD7cfoKMonIqDCYe69aUT3edJLOk8to3C/WCsaKi2nnK/ZM4/+ZMyuP3WdoC\n//cKrZLO4DVh0q0OT6jnDh7M4okn1ln2PQ0P9yA5+XG++eY2kZQJtZboMbsBJVo9MdI7AChUvihU\nnpxfd5CK9DycGvvg2yfGevGGVCg074VpDHKmVR9rDqxSyGh6ycpDSTKxPelHCsozcdF40avZeOTy\nv81Tq4N+PHiGJ1bvo4m3M94qBbe3DGJkixB6NfGzdWhCDTNVVZE0tB8VRxNpnZqJXKNBJpPhPfk+\nDLm5KN2s9fg04U1sGKlQH+XlVfDSS5v5/PMETCaJjh2DuOsuc527wECx6luo3URidgNUunjLcUCT\nxQCc/tI86T/8vt7Vt3WZYK2hFHe/H6TkALBhShxxkdUL0CakbSS94Bh2Cg19oyehVtXtlYmJmYU0\n93endaAHAL8dO8ekUAU9wn1tHJlQE0xaLcWbNlB1+iT+T04HQK7RYCyvQKqqouLoEZzamuvSBTzz\ngi1DFeo5g8HEp5/u5+WXt1BYWIVCIePJJzsyeLCY2C/UHSIxu05VBiNau7ZEtC8kP2Mmjq5xVGYV\nce7XBGQKOY0nXTLpf81p6/HH/di221px/O9JWXJOAkcy/kSGnF5R4+t8aYyl8Snc/d1Oqt4eT6iH\nI/ufGkKrAHcOHEiwdWjCTWQoLkbpap4Daaqq4vT4OwHwnnQfSndzb1j4oiXYBQahcBZDRkLN27r1\nDI89to7ERPOX4Li4xrz//kCio+v2e6rQ8IjE7Dr9cDQBX42eRt4tiAqdC0DqN9uQDEaChrfD3v+S\nrZKmbLAe39mU+C7ezN92nHdHtKv2nNklZ9h5agUAHcOHE+BW97/Vnc4rxU4hZ8w321h5Ty9aB3nY\nOiThJqo6fYqTdwxHrrGn+W5zsq10dcXn/odQ+fiAyWS51r5ZlK3CFBqg3bszSEzMISzMjXffHcCI\nEU3r5U4pQv0nErPr1NH4AGWFFRgZDR6vA1B2JheZUkH4/b2tF645DUbzRFPm9QIgNtCDr8dV3zy5\ntKqALce/xSQZifLvQjP/TrfiZdSYKr0RkyTxyoBWzNx4mP9daRN3oc4wFBRQ+PMqpDNnoG1bAOyC\nQ9BnnkdmZ4ehqAilm3kVcui8920YqdAQVVToSUzMsay0fPLJTmg0SqZMaYdGIz7ahLpLrMq8TjJd\nIs6kEOBiHYrs8PF93J71Kf4DWlkvfGErAJUKUBw6gGLat8zbcrTac+kN5hWYVfpyAtwiaN94yC15\nDTVl+aE0HJ//jqNZRQAY502kbbCnjaMSbpRkNGIoKLCcV6Umc+axKUjfLrKsapOr1URv3U3r1ExL\nUiYIt5IkSSxffpSoqI8YOHAJ+fnmlb9qtZInnugkkjKhzhOJ2XUorsi1HLt43lHtMTt3J2ul/3I9\n5JpLQUx6srHlmvgM64edSTKxNel7iiqycbX3pmezu5DL6vYKzKScYro39qHT++tYf+LctW8Qap3i\nTes5GBZA2rTHLW2OrdviMXocsgmT4JKNw+2bRSFTig8/4dY7ciSbPn2+YfTon0hPLyYkxJWcnHJb\nhyUIN5V4d70Ovx7fQNsLx0o788rCvL2n8WjbGLniktx2nrk2k1YOK3Ktdcq+HW8dxiytzCen5Axq\npQN9oyejVtrXePw1yWgy8b+4FgyNDiLYzRFPR7FxdG1XlZpCwYpl2Ec2w334SADUYeEYCvKpSj6N\nJEnIZDJkcjnhC78lPj5eJGKCTRUUVPLyy1v45JP9mEwSnp72vPFGH+6/vw0KhehfEOoX8W57DVqj\nCbkhHQC5yjyMWZqczcbOL+Ec4cfQ4+9aJ5iW6gB4YKg7YJ4EnTNzdLUNyl0dvBkS+yhV+nJc7Ov2\ncN+jK/bw6a6TlL41jthAMcm/tjKWlyPp9Zahx7Kd2zn36gxc+w+0JGaaJhG0OHgcdXgTMWFaqHVG\nj17OH3+kolDImDq1A6++2gsPj7r9pVYQrkR81bgGpUxGG/v1ADg4mfvNKjLycWrsg0e78OofYl8n\nUiWHpW7WlWkXe5D0Bq2lzdXeG1+XRjUffA3SG02EujsC8OCyv2wcjXAlWR8t4ECIDzmffWRpc+03\nEK+Jk/G+b0q1azVNIkRSJtQaBoP1ffSVV3rSu3cjDhyYwoIFg0RSJtRrosfsGhRyGW4ubamU6dA4\ndQTAt2c0w07Ox1BuTbb4KQkAjQmqxsXRbst+Nj4UB0BJZT6/Hf6EVsF9iArocstfw83Uaf5vTGjb\nmIFRAYxsEcK2lBw+uaNuryitL8r27aFg5XI8Rt2JU3vzz6o6tBGSTofu7FnLdSpfX8I++dJWYQrC\nVZ09W8yzz/6OQiFjyZJRAHTvHsrmzWLLLqFhEInZVeiNJrIqtAQ3eo+K0p1oHK11yGQyGSonjfXi\n57aSrZGR6iSnUzt/DrUbZnnoXGESVfoyzhUm0cy/EzJZ3eyorNAZsFcpeGL1PubTjqndo/j1/j62\nDqvB0mVlonR1Q25v7j0oXLOS7A/mg0xmScxc4/oTm3zOXGNMEGqxqioD77yzi7fe2kFFhR57eyXn\nz5eKPS2FBkckZlfxVWIaaA+hUMfwQCvzBP7zGw7hFhOEQ9Al88NKtFCio9ttTnj4ONPvtwO8Pri1\n5eGogC44qF3xdw2vs0nZxW2WnuoZTVGlnse6NbN1SA1a6qMPkrd4IU2++8kyT8z9tjtAJsPjNuvK\nYblGg1yjudLTCILNSZLE6tUnmDZtI6mp5pI7d94Zzdy5/URSJjRIIjG7imHhfpQmRmDUeqOr2omC\nIHZN+BB9UTlDT7yH88W9H0et5qyDjBRnBSmVFfS9UPNJZ6jCTmn+UAz1jLnSX1PrxZ/Np8P838id\nNZrhzYMZ3jzY1iE1KIU/r6bwl9UEvz4bla95A3h1ozDk9vboMqxDlE5t21v2pBSEusBgMDF06Hds\n2JAMQPPmPixYMJDevcNsHJkg2E7d7L65RXzszPXHFKZclHYBpP3wF7qCMtxiG1mTMoCThWzxs+a4\nL/VvSUruIVYnvEt+Wd2v6/XEKnMZkDbzfrVxJPWfJElUnjherS138Vfkf7+Eoo3rLW0+Dz5C6/Qc\nfB+ZeqtDFISbRqmUExbmhru7hg8/HMSBA1NEUiY0eKLH7DLSSypYdyaHHkzh4ho1mUxN0ofmD8am\njw2wXlyshUoD5Uo7ANoGeWCvUnIqax8VuhKyilPxdAq8xa/gv5MkiafW7OeeDuHseHwgO1JycBYV\ntWvcse4dqTiYQIsDx9BERALgfe+DOPfohUv3npbrLm4gLgh1idFoYuHCAzRq5Ea/fuEAvPlmX2bN\n6oOXl4ONoxOE2kF80l7GujM5FGn1YNwCgL1TJ3J3JlF44AxqL2dCx3S2Xjx3LwCzW5jLYnRv7IvW\nUEFWSQoy5DTxaXPL478ZPtxxgg+2nyDEzZFgN0e6NRaTx28myWAg56vPKdu1g8aLliC7UOtOE9kU\n3bkMtGmplsTMfciwqz2VINQJO3em8/jj60lIyCQiwoPExEews1Pg7i5KXwjCpcRQ5mXc3zyUvkGu\nyC4UiQ2M/ImURX8C0OSBvig0dtaLPzsEQIaj+Z/Sz9mejIIkJMmEr2sj1Kq6+S1wVMtQBkUF8swv\n8bjb2137BuGqTHo9FYlHrA0KBVkL3qVgxTLK4/dZmkPf/YDY5Axc4wZc5lkEoe45d66ECRNW0q3b\nIhISMgkKcmHWrN6oVOLjRxAuR/SYXYZCLiPK4SgXp1XLlT5kbjwMQMjoS2p2XVIA8ctGjVhEOQ90\njuBg2nLztXVwwn+5Vo/WaCLQ1YGHu0SyZHw3UXT0PzIUF3M4ujGSXk/r9BzkGg0ymYyA5/6HTC5H\n0yTScq3YGFyoL7RaA++9t5vXX99GebketVrBM8904fnnu+HoKL7sCcKViMTsb1KKy3FSKfFwaIFf\n4y/RVR6j5Ng5Ks8XovFzw61FiPXi746hlcNWXyX3TO3OPYDRZCCj0FxsNsQj2jYv4l9aeyyD4V9t\noYW/G8/2ac5dbcQk3BtlyM8n+9MP0efk0Oh9c7V9pasr6tAwTDoduoyzaJpEAOB99z22DFUQapRO\nZ2TBgj2Ul+u57bZmzJvXn7Awd1uHJQi1nkjM/ub3tFyKdXpaeLkwsNFkAI6/txYA/34trL1HZTqY\n9ieTu9uzvJGKrLIqvJw0ZBYlYzDqcHf0x0lTt96E2gZ58uv9fRj65Waa+bjYOpw6wVBcjC7jLA4x\nzc0NSiXn334TgKBXX0fpbv4ZaLZhCwpnUZNJqN+SkvIIDHTByckOZ2c1n38+DI1GSVxcY1uHJgh1\nhhjk/5vGro6YJIhyqUKSDABkbjAPY/r3b2W9MOxzADYEqpBkMt7echSA9IJjQN3qLTuRXczyQ2no\njSa6N/ZBP3cCbYLq9gbrt0JZ/D4OhvqSct/dljalqyvBr88m4seVyB2s8wtFUibUZ8XFVUyfvpHm\nzT/hrbe2W9qHDo0USZkg3CDRY/Y3caHexIV6k3a0BxnZzrg6zyTnz6PI5DL84i70iqSVACABxXbm\nHrTbW4YgSSbOXkzMPOtOYjb5+53sO5vPUz2jeGd4u2vf0ABVnTpJ9scfoPTxIfCFlwBwaN4SuZMT\nSjc3TFVVlgr7flOfsmWognDLmEwSixcf5Pnn/yAnpxyZDEpKtNe+URCEKxKJ2d/ojCZUchmVpbsA\nKFl3Bya9kaDh7dD4XKgd9fjvABSorZPiWwd6kFd2jkpdKY5qNzwcA2557P/WZ6M70WbeWsbENrJ1\nKLWGNj0NSau1lKwwlBST88Un2AWHEPD8DGQyGXK1mtjTGWLLI6FB2rMng6lT17Fv33kAunQJ5oMP\nBtGmjb+NIxOEuk0MZV7iWH4J2zLy0FYctbRps+TIVQqaTOlrbkjIhl3mN6L2d5pX0CnkMuyUCtLz\nzfcFe0TV+pWMn+xMQjHtW3an5RLm4YT27fG0D/GydVi1Qt6SxRyODufcmzMtbY6t2xL48kzCv/m+\n2rUiKRMaooMHs+jU6Sv27TtPQIAzS5eOYseOe0RSJgg3gegxu8SBnGJkMhkpmb9ZMtY2b08ievpI\n1F4X5ggNMJfCyFXLSJOby2V4OZqLy1qHMWt/mYyVh9MJ93Sm64L15M0ajVLRMHP0ku1byfn8E1z7\nD8R74mQAnDp1Qe7sXG2OmEwuJ+DZF20UpSDYnskkIZebv3C2auXLiBFNiY725sUXu+PkJMpfCMLN\nIhKzS4S5OnIwtxhZyVsAaBzN860sQ5iHc60Xz+nBhvZeDPniD449NwJJkmgR1JtzhSfxc6mdZSZe\n33SYs0XlfHx7RzY93I8vdp9iaHQg7g5qW4d2S0gmExWHDqDy8cUuMAgAXXoahat+wlReZknMNE0i\nzPXGVCobRisItce6daeYNm0jP/xwBy1b+iKTyVi1akytHxkQhLrourpJCgsLOXLEXLXcZDJd4+q6\nq0uAB4+0CkOOeTWmPPdOTHqD9YK+PwLwZRMV5YPC6NLIm/zXx+Bmb4dMJiPcpzU9mo5BLlfYIvyr\nKq7U8cr6Q6w7fh67Z5ZSWqXngU4R+LvUzZ0J/o2zL0znWPeO5C1ZbGlz7T+IkLnzCZm3oNq1IikT\nBDh1Kp+hQ79j8ODvOH48j/nzd1seE0mZINSMayZmv/76K2PGjOGFF14AYNasWSxfvrzGA7Ml39B3\nAdgxYB9bR84zN+7JtDw+pYsD0XPW8MXuUzipa/8H+F9ncimq1FE55y48HOw4/MwwnDW1P+7/Iu+H\npZwYHEfJ9q2WNufuvVAFBiG7ZF6Yytsb34cfQxMmlvQLwkWlpVqef/53YmI+Zu3aUzg72zFvXn8+\n/XSorUMThHrvmonZokWLWLNmDe4XCmU+99xzLFu2rMYDu9XOl1WxJ7OQrPIqnDxGoiyeiEvTIPwH\ntDRf8Jz5A35ZqDmh0RpMjLqwC0Clroydp1ZwrvCkTWK/mpT8Urp9sJ6TuSWU6QwcnD6MGL/6te2P\nqaqK4k3rMZaVWdoqEw9Tuu1Pitb9amlzGzSEVidS8X9imi3CFIQ6Ydu2NJo2/ZA5c3ai15u4555Y\nTp6cytNPd8bOrvaNBghCfXPNOWbOzs7Y29tbzjUaDap6OMyzJ7OAk0XldPcuxDe0PU0GLKLJADAZ\nLwzdHs0zX9fFC6gAINjdEYCMguOcyt5Hha6YQPfIyz29zXyfkEqgqwMDP/8D7dvjbR1OjTh5+zBK\nt26hyXc/4T58JABed92NQ6s2uMb1t1wnU4gPFUG4lrAwN4qKqujYMZAFCwbRoUOgrUMShAblmomZ\nu7s7q1atQqvVcvToUX777Tc8PDxuRWy3VAtvFzSyfDyyupBW2o7Q5juRyRTI/7ZacbM7UAbP9bGu\nvPR1DSM2JA53B79bHPXVSZLEi3EtGNkiBJ3BVC9WXp57cyaFa1bR9Od1qHzN/94uvftiLCqCS+a8\n2EfHYB9d+1fHCoKt5eSU88kn+5gxowcKhZzgYFd2776f5s19LKswBUG4da75Sf3aa69x5MgRysvL\nmTFjBlqtljfeeONWxHZLNXFzorOzeWJrVfl+ylLyrA9uSbccHi4z95a1CrAmpy72XsSGxBHq1fzW\nBHsdKvUGlNOXUK4zEOPnRuugupdM63NzKVi1olpbefw+Ko8eoWjjekub/7TniNm5D/dhI251iIJQ\nZ+n1Rt577y8iIj7g1Ve3smjRQctjLVv6iqRMEGzkmj1m27dv5+WXX67W9v333zNu3LgaC+pWO19W\nxenCInzOPQxA1Rkffun6JP22vYp316bw5GYAMu2tb1RDo2t39/5L68xvslNX7mXRuK42jubGSSYT\nie1bYsjLxeHAMUsFfv/pz+P32JM4delmuVasDhOEG7NxYzJPPrme48fNX0AHD46gR49QG0clCAJc\nJTE7duwYR48eZeHChVRWVlraDQYDH330Ub1KzNamZhGk2G85T58bjdJRjUf7cJAkOG+eVC492ZYv\nu3mx7vg5HC+sxjx8dgsgo4lvWxzsas9G1W8Pbcv8bceRbB3IdTBWVJD+zJOUJ8QTs3MfMrkcmVyO\n26Ah6M6fw1hRbrnWuXPdSzIFobZISSnk6ac3sGZNEgARER68994AhgypXXNjBaEhu2Jiplaryc/P\np7S0lPj4eEu7TCbj2Wefva4nf/PNNzl06BAymYwXX3yRli1bWh7LzMzk6aefRq/XEx0dzcyZM6/y\nTDXLJEFpaaL5RBdM2UFvAgZFobBTwi+nLdcFTGnLeLWcSe3CL9xn5Oi57WgN/2fvvsNrPP8Hjr/P\nzN6TDIkRK2ZKixixxWiV1u5AaauoEavU6heltEar3/pptahStKq1Sm0RpGYEMSKJDNnJyTjJOXl+\nf5w65JtEqAzhfl2X68qzP885Ts4n930/nzsbT4f6T01iFpeRTTVrc+LnvIaj5dM3ZVBuxDVyroQb\nux7lZmZkHDxAXtRtss6GYunXAgCvr9aI1jBBKEN//HGNHTuuYmmpZtasdowf/yImJqLOuCA8TUr8\nRNaqVYtatWrx0ksv0bRp00Lb9u7dW+qJT506xe3bt9m8eTM3btxgxowZbN682bh90aJFDB8+nC5d\nujB37pmG/oMAACAASURBVFxiY2OpXr1yJv4e3diLm5G25N+1Ii/OUBbEpdM/48X23AJAL4P2aw9w\nbFx343F3M26j1WVjZeqAjZlzhcddnN3hd+i99i+COjRkYa/mlR0OYHgI4Z68+DguNmuA3NzcUF3f\n1BSZTIbXiq9Qubhi5ns/eRdJmSA8GUmSiIzU4OdnWH7vvRbExmYybtyLVKv2dPwhKQhCYaX+qeTs\n7MzixYtJTU0FIC8vj5CQELp16/bQ44KDg+ncuTNgSPLS09PRaDRYWlpSUFBAaGgoy5YZCrnOnj37\nSe/jiXlU60+6MovruwxJp4NfTdAXwBZDk//5t+sSfDuehfsvMr1zIwCik+/Pjfm0JBEqhRw7MzWL\nD4ZVemKmjbxF1NSJSDodzJgDgNq1GtYdOqJ0dkGfnobc1PBkpU3nh/9/EgTh8Zw9G8fYsbs5ezaW\n1q2bU62aFUqlnIULO1d2aIIgPESpidmUKVNo164dBw8eZOjQoRw4cIDFixeXeuKkpCQaNrxfrsDe\n3p7ExEQsLS1JSUnBwsKChQsXEhYWxgsvvMCkSaUX/bx06VKp+zyuWEnFDcmEtnINMp0nUV8bEoVI\nfQrZi/fi9c9+b5ikgA4+3nOOrnZ5SJJEhPYsADmJCkJTQou/QAWZfiyG0Y2c8LIxoZ6Nmg8DPAp1\nQZc3SaeD8DDIzUHW4iXDOo0Gac8uAGTjJ9+PZ65hLtK0mDsQc6fCYhT+vYr8vyQ8mdRULV99dZVf\nf41CksDeXs3OncH4+TlUdmjCvyQ+f8+XUhMzhULBqFGjOHr0KEOGDKF///5MnDiR1q1bP9aFHuzO\nkiSJhIQE3njjDdzc3Bg1ahSHDh2iQ4cODz2Hr68vJiZlO+H2xbAoFHk6FNWc8Uq140riLsw9HWkZ\n0AZeXwVAnhzCs/IA8Pd2xs/Pj9SseC6dzcJUZYF/iy7IZZVXI+x6UgYHfrzMiz6eBDZuxNF7/Rbl\nTJIkY0th+sH9XPtgFGa+jfF9d4xxn9Qft2LRrDkX4+Lxq6C4hLIVGhoq3rsqID9fz+rVZ5g9+yhp\nabkolXLGjWtJnz42tG//UmWHJ/xL4vNXNWm12n/dmFRqNqHVaomPj0cmkxEdHY1SqeTOndJbOZyd\nnUlKul8L7O7duzg5OQGGorXVq1fH09MThUJBq1atiIiI+Fc38KRecLGlmlkuppHeJEeuQGmXi20j\nT4i7P71P99H3S2Nse7sDAFEphm5MD/v6lZqUAdzNzKWlpwML9l/CTFX+A3kzjx/lcid/oiZ/aFxn\n1bot5o2bYt2+A5Jeb1xv17M36upPd2kRQXgWvPPOTsaP30NaWi5du9biwoV3Wbq0G5aWz95MLYLw\nLCv1W3zkyJEEBwczYsQIXn75ZRQKBb16lT6RbZs2bVi5ciUDBw4kLCwMZ2dnLC0tDRdVKvHw8CAy\nMhIvLy/CwsLo2bPnk9/Nv+DraI3yumHAeb7dKvRZr2LXxBP6bDfuczjLkKR52Vtgb25osYtKvpeY\nNajgiItq7e3M4THdUJRDQUi9RkPGwQOoXFywbGn4q1tmakpWyEl0DyTechMTGp44U9JpBEEoBw+2\nWo8Z04Ljx6NZurQrvXv7PDXjXgVBeDylJmb3BvCD4UnLrKwsbGxsSj1x8+bNadiwIQMHDkQmkzF7\n9my2b9+OlZUVXbp0YcaMGUybNg1JkvDx8aFjx45PdidlIPemB1KeAnu/mvB1CADaxo40rmbJhbhU\nzk4yJKRZ2nSSNTEo5Cqq29auzJDRaPNJy8nD3daizM4p6fXGeSUTv19L9NRJ2L820JiYWTTzo/ZP\n27FuH1Bm1xQE4dFlZ+fz6afHiIhI4ccf+wHQooUbV66MQfEMTL0mCM+zEhOzgoICtmzZQkREBM2a\nNaNXr14olUrUajVz5859pCcpJ0+eXGi5Xr16xp9r1KjBpk2bniD0J3cqPpW7mgzupVZXP2iO3ESF\na6t6gCExM/m+J3+7WaLVFWCqMiQr0SnhAFS3rYNSoa6EyO+zmfETrWo48U6rOrzZotYTnSvpx/XE\nLl6Ay3tjcRn9PgC23QJJ2f4zli1eNO4nk8ux69Xnia4lCMLjkySJn3++zOTJ+4iOzgBg+nR/GjVy\nARBJmSA8A0r8FM+fP59Tp05Ro0YNfvrpJ9avX09wcDB9+vTB1PTpK1r6b6Rr88lJ32lc1qerce3Y\nENWCU8Z1089FkJ6bb0zKAKKSwwDwdKj8bsyXajgSfDsRa9PHG0eSFxdL4rq15F5/YGxfQQHa6xFk\nHj5oXGVauw4NDhzD5f2xZRWyIAj/woULCQQEfM+AAVuJjs6gWTNXjh5925iUCYLwbCixxSw8PJyf\nfvoJgP79+xMQEICbmxuff/45vr5Pz2TdTyLAw5Ezd7cYl9v/FoTSwhReM8yNGW4jZ/HBMF7ycuJl\nXw8A8nS5xKffRIYMD/v6lRL3g46P6wEUfuq1OPcG5N/rooz9zzwS1/0fbrPmUn3qRwDYBvam3r5D\nxi5LQRAqnyRJTJiwl5UrT1FQIOHgYMaCBZ0YMaKZaCEThGdQiZ9qlep+C4y5uTne3t78/PPPz0xS\nBqCUy6lT9yvsq0/Bteb/4dazOS5Naxq3rwoytIi9+t0h4zqtLhs3Ox+q2dbBVFV247oeR06+DpOg\nDaw6eoX0HEMZj4cN9I2ZO4tz3tXJPHbEuM62z8vYBvbCrGEj4zqlvT1Wrf2RKcUULYLwtJDJZMjl\nMmQyGDeuJRERYxk1yk8kZYLwjCrxG/h/v+jVajUKhaKEvauey8kZRGXm0K1GQ2TWC+5vSM41/vj1\ntWgAXm3saVxnZWpPpwZvltpCVZ5+uRiNrkBi/K+neatl4XFl2ZcukrbnD1xGj0FhZZhyRcrPQ5eS\njCb4uHHAvm3XHth27VHhsQuCULrDhyPJy9PTpYvh8z17dnuGD2+Gr+/TMfWbIAjlp8TE7O7du2zd\nutW4nJiYWGi5f//+5RtZOTsdn4ZH/mouaW5jHvIOcfvOU3N4ANUdDOM1dA3sAUP337SORVsJK/pR\ndEmSuJaYQWRKFoObe9PGy4mD1xMwl/TA/dbN2x++j+ZkMGY+9bDr8woAzqPH4DjsbUx96lZozIIg\nPJ6oqHSCgv5ky5YwPD1tCA8fg7m5ChsbU2xsno2xvYIgPFyJiVmzZs0KTQPRtGnTQstVPTFTK+RU\nz1yCpG5EStRPRG3NwLVLY1h7G4AZHvdbxJq72wOQlp1AtjYDFxtvFPKK6e7LyddhplKy6WwkwzYe\nw9/bmRaeDnjaWdBm2RT+3r+XJmHXUbkYppJyGDAEU596qN3djecw8fAs6fSCIDwFcnLyWbLkBIsW\nHSMnR4eZmZKRI5shL4fahIIgPN1KzC4WLlxYkXFUuFfc47mdBrK8i3j3/wrH2jqc/OvCdMPDAMf+\nKdXm62prbB27EhfMlbiTNPHoRLMaXco9xqk7Q/kj/A7nJvQgMO0GYJh+KSEzF3tzE6T8PCStFs3p\nU8byFc7vvFvucQmCUDYkSWL79nAmTdrH7dvpAAwY0JDFi7vg6Vl6vUhBEJ49z+0o7/gbI40/OzZq\nheO9MfByGRRILOnYmG/zMxnQzMu4n5WpA7bmLrjb16O86TUaDl6Px0QhJ+j3swwbP5jD6Rn4nT6P\nmYvhF7bHws/wXr0WlbMYdyIIVZFWqzcmZY0bu7BiRXfat/eq7LAEQahEz2VidjIuBcu8VJSAlf2r\n9zfcSocCCQlo1cWHNtaFJ0xv6NaWhm5tyzU2XUoK51/uScGdaI5euYX59J/46Y12mA0fhaTXITcz\nM+5rJsaMCUKVk5qag0Ihx9raBFNTJatWBRIVlc6oUX4oleJJS0F43j2XvwWupWah1BsmYldmDSZ0\n0npidpyBdRcBGN3KjBNJ6Wi0+eUahz4ri+StW4j/crlxXdCRG7Rs+T6xkgrp9i3OT+5FHSdr3Od8\ngsf8RWK8mCBUUXp9Ad98E4qPzyrmzDlkXN+rlw/vv99CJGWCIACPkJhduXKFV199le7duwPw5Zdf\ncv78+XIPrDy1dlWTajIQgPTTaq5+sYvoX0/DV+e4aCtnbR017b/ci0arMx4TkXCGjJzkJ7quJEno\n0tKMy/r0NG6+NZg7c2dRkGso01HDzlAbbeOstZjW8cG3mt0TXVMQhMp3/HgULVqsYfTo30lKyubi\nxbvo9QWVHZYgCE+hUhOzefPmsWDBApycnAAIDAys8g8G1LZ3oZHXEGychpMZngiAtbPhycs/3O+X\nnnC1NnQbZuamcDxiKzvPrUBfoCt6wkeQdf4sFxrU4vqAvsZ16upuOL41guofzUbKN7TOvezrwW8j\nAtj0Vod/dR1BEJ4ed+5kMGTIdvz9v+Ps2Xg8PKzZvLk/+/YNFQViBUEoVqljzJRKZaHJx729vVFW\n4crwBZLEzeRb1LRrj6VdDy6dnQuA7c9RgBkhjoYiul3rVjceE5V8GQA3u7qPVCYjPyGelF+2obCy\nwnHIGwCYeNUkL/YOkl5PgVaL3MQwfs171X+RJAnl5A283rQGK/u2pGcD94edXhCEKiA6Op369b8k\nKysfExMFU6e2YepUf8zNH29eW0EQni+l/smmVCqJjo42low4fPhwpVa9f1LHY1NIuv4KqVlR6PNy\nSAm9BYBDuiHh+tPD8EuztZeT8ZjSJi0vyM9Hl5JiXM4Ou0jU5PHEr/zCuE5pY4NvyDmaXLllTMru\nydXpecHDgS3nbmP1mJORC4LwdPLwsKFr11q8+mp9wsPHMHdugEjKBEEoVanNP1OnTuX999/n1q1b\n+Pn54ebmxuLFiysitnIRnZmDLzFcv/ImdUy/QZ+txcLTEVONih9qqsj5p55jj/puAOTmZ3E3IxK5\nTIG7XdEyGSnbfiZy7GjsXxuE1/IvAbBq0w771wdh06kLkiQZk1qzeoUnPZ+56ywBtV3p5FON91rX\nxdnKFBPlszPtlSA8T8LDE5k4cR9z53agZUvD749Nm/phYlJ1exgEQah4pf7GUKlU7Ny5k5SUFNRq\nNZaWlhURV7l5rbYTN85koC4IJSU0HgB7R0fQQJT9/ZejuZthzFlMyhUkJKrZ1EStLDolitrLC31G\nBtrIm8Z1chMTan27/qFx/Hk1loUHLrH5XCT9GtdgUa/mZXF7giBUsPT0XObOPczKlafQ6QqQJIk9\ne4YCiKRMEITHVupvjffeew8rKyv69OlDr169KiKmclWQd8P4c+oZwyTlDlGGAf0zr+l5Z0t/bM3U\nxqlQolIM48s87A3dmPkJ8cQtX4pVm3bY9eyNRTM/Gl+KwMTL+5FjiM/IoZWXE9veak+/dYeZ1KH4\nLlJBEJ5eBQUS69adY/r0A9y9m4VMBqNH+zF/fkBlhyYIQhVWamK2d+9eLl26xO7duxk4cCDe3t68\n/PLLBAYGVkR8ZSpHpyc19RAAcoUtKWcN82Laa00M84CP98PF6n4BV50+n9jUawB4Ohi6ITWnQkhY\n8Tk5Fy9g17M3Mrn8sZIyxaT12JqpiZvTn1caeaJfOqxsbk4QhApz5UoSw4b9wpkzsQD4+3uyYkV3\nmjWrVsmRCYJQ1T3S89q+vr4EBQWxceNGqlevzpQpU8o7rnJxPjGdK9G/A2Bi3pS0C1EA2Cks0Mrh\nuyYW6Avu1xaKS7+OriAfB0s3LExsATD1qUv16bNwGDT0sa+fkq1lbNt6pOXk8eul6DK4I0EQKoOt\nrSlXrybh5mbFjz++ypEjb4mkTBCEMlFqi9ndu3fZt28fe/bsISUlhcDAQP7444+KiK3MeViZEaNW\nQx7ItXXR56Rgbm6OiVzF5GamfP7baUb+dpqcTwejViruP41pf7+r0axuPdw+mv2vrm9vbsKrjTxJ\nyMzh9aZeZXFLgiBUAK1Wx3ffnWPEiGaoVApcXS3ZtWsITZu6YmmpruzwBEF4hpSamPXr14/AwECm\nTp1Ko0aNStv9qeZmaUac00coU+6Qd6sOEIJdvimYwucNDSUs3GzMUSsVFEgFRKdcAcDToeETX3vm\nrrMMaOZFay8n2tVyeeLzCYJQMf744xoffriX69dTyMvTM27ci4Ch+1IQBKGslZiY3b17F2dnZ374\n4QdjQdno6Pvdbx4eHuUfXTl4waMF6abjifrNcE92CguuWt/v0d0wxB+ApMxocvM1WJrYY2tuSKTy\nExLIOHoYyxYtManh9cjXTMvJY+GBSyw8cInTEwJp7u5QdjckCEK5uHo1iQkT9rJ793UA6tVzpGFD\np1KOEgRBeDIlJmaffvopS5cuZcSIEchkskJFZWUyGQcOHKiQAMuKJElEJV/CzNQNJ8chNJolo46V\nF8wPZrvD/dphbWs6A5CWnYBcpsDTob6xDlnGkUPcfHsINt0D8dn62yNfW18g8fOb7Rm0/ohIygTh\nKZeRoWX+/MMsXx5Cfn4B1tYmzJ3bgTFjWqBSiTqDgiCUrxITs6VLlwKwZs0aatWqVWjb2bNnyzeq\ncpBXUMC1Gx9jShIW9eZiYdMR073RIFcR5+cIZNPZp5oxCfNxbYmXY+NCc2Mq7e2w6R6IdUCnx7q2\ng4UJgfXduDvv9bK8JUEQysH27eF89lkwMhmMGNGMBQs64exsUdlhCYLwnCgxMcvIyCAtLY0ZM2bw\n2WefGdfn5+czbdo09u7dWyEBlpV8vYSdIgFL3Sny0nMwt5aQnUkAINhWBnlQ06Fw8dz/LShr06kr\nNp26PtZ1M3Pz+fNaHF18qmFjJgYJC8LT6O7dLGPyNWxYY06ciGbUKD9eeKF6KUcKgiCUrRITs7Nn\nz/L9998THh7Om2++aVwvl8vx9/evkODKkqVaiaOpjFwN/D1hE1nn9tFB74a1wpyN7wTwZ24WtqaG\nxClLm46Z2hK57Mm7LX48e4v3t4bgbGlK3NzXnvh8giCUnYQEDdOnH2DLljAuXx6Dp6cNCoWcb77p\nXdmhCYLwnCoxMWvfvj3t27dn06ZNDBo0qCJjKheSJJGrCQEgLzkHbVIGFipDF61JTTt6YWfc99CV\njWTkJNHFdziOlu4A6NLS0KWmYOLlbezufBSB9Qxz5r3VolYpewqCUFHy8vSsXBnCvHlHyMjQolLJ\nOX48Ck/Pqv3kuSAIVV+Jidm2bdvo168fCQkJLF++vMj28ePHl2tgZe1cQgL3avp32fct2WMPodge\nTUabakTFpeJua4GtmRpdQT5aXTa6gjxszJyNx6f9voNb747AYdBQaq5ZV+r1kjS5mKoUeNhZiOr+\ngvAU2bPnOh9+uIerV5MB6NmzDp9/3o06dcSDOYIgVL4SEzO53FBC4l6pjKouODaWjoAkt0GptsH6\nrqHCv7d3Dmmf/c5bLWqxdmBrlHIVfZtPIjsvHZXi/piwgpwclE7OmNUvfV5Lnb4Al9k/Y6ZSsHFo\nW172rZqlRQThWTNnziHmzj0MQJ069nzxRXcCA+tUclSCIAj3lZh19e3bF4APPvgAjUaDpaUlSUlJ\nREZG0rx58woLsKw0d8iHOJDL/hnQf+wOnzVUkyY3lAHxsr8/8F8mkxmnYLrH+Z13cRo5GkmnozSa\nPB2Dm3vz49+3aC+KyQrCU6Nfv/osXx7CjBn+jB//Emq1KH8hCMLTpdS5MufPn8/u3btJS0tj4MCB\nbNiwgTlz5lRAaGWrhXsD0Cwldo0TJ5vMAWCq3/0Jy2d09kWnzydLm1biOWQyGXKVqtRr2ZqpWT/E\nH/3SYdiKJzEFoVJIksTGjRd4++0dxjqMjRq5EBMzgaCgNiIpEwThqVRqYnb58mVee+01du/eTd++\nffniiy+4fft2RcRWphQKK7RHqhG3tj6qiEwAzPMNv6zXD/FHIZcTk3qFn08v4njE1kLHFuTnFyqw\n+zBTd4ZyNialbIMXBOGxhIbG4u//HUOH/sK6dec4eDDSuM3CQvyxJAjC06vUxOxeQnLo0CE6duwI\nQF5eXvlGVcbScvM5FZfC7V9PA+CqsiFHAdkqw9OVHWu7AhCdfBkAa7PC064kff8tZ2u4ELds8UOv\nk5Ov4/fLMbzw+R/8din6ofsKglD2EhOzGDVqJy1arOHEiWhcXCz47ruX6dDBq7JDEwRBeCSlJmbe\n3t4EBgaSlZVF/fr1+fXXX7GxsamI2MrMzfQsVNd9cQ/6Fvt26biqbNEoZbxUwxEAV2szCgr0RKf+\nM2m5feEB/jlXwtGnpCAzMS1y7geZKhVM7eSLl70FfcSAf0GoUKtXn6ZOnZWsWfM3CoWcyZNbce3a\nWN56qyly+aOXuBEEQahMpT5y+cknn3Dt2jXjtEy1a9dm8eKHtxw9bexN1eSq7kIdcDZ3QyGT4/Si\nG8fH9TDuk5ARSZ4uBxszJ2zMC7eYeS75HNcPJyE3NfvfUxcik8no09CDAU29yuM2BEF4iDt3MklP\n19KtWy2++KI79eo5VnZIgiAIj63UxCw3N5e//vqL5cuXI5PJaNq0KbVr166I2MqMqyKGyH9+rnHG\nELvkak6+To9aaRgAHJUcBoCHQ9FyGDKZDBP3h7eAjdt+Ci97S15p5EFNB6syi10QhOLdvJnK7dtp\nBAR4AzB9uj+tWrkTGFjnsYpAC4IgPE1K7cqcNWsWGo2GgQMH8vrrr5OUlMTMmTMrIrYykxJ23Piz\nvYdhPNm8Fyz48e9IolKzkCSJqBTD+LL/7cZ8VP8NvkbQzlBORSU9ecCCIJQoKyuPmTP/okGDLxk8\neDuZmVrAMKi/Z08fkZQJglClldpilpSUxLJly4zLAQEBDBtWdSrZ5xcUcPvyeezqQ0G2FbLbhicy\nl169Tdalm2wc6k/XOiZkadMwVVniZFW4ZSxx3VpStv+M88h3sevzSrHXkCSJ7we3YUPoLfo3rlHu\n9yQIzyNJkti8OYygoD+JickAoHPnmmi1eqxEI7UgCM+IUhOznJwccnJyMDMzjK/Kzs5Gq9WWe2Bl\n5UhMMqaK/QDIs3wAkICsfD0ADVxsiUoJBQytZTJZ4UbEjKOHyfhrP3Z9+hZ7/uQsLYmaXAY282Zg\nM+9yugtBeL6dOxfPuHG7OXo0CgA/v2qsWNGD1q3FQzaCIDxbSk3MBgwYQI8ePfD19QUgLCysSs2T\nWc/ekqjEAvJszbBRGKZeGdL2/iB+Hydr9l0ydGMWN77MY/5C7Pr0xaK5X5FtEYkZ1Fu0g0HNvOjk\nU423W1atsXeCUBUUFEgMHryN8PAknJzMWbCgE2+/3RSFotSRGIIgCFVOqYlZ//79adOmDWFhYchk\nMmbNmoWLS9WZZshJL3F4ShMsm9Sg+5h3gXMc9DDB0G4GOn0mKVmxKOVqqtnWKnK8urob9i8X31rm\naGHCjM6+LNh/iXda+ZTjXQjC80WnKyA3V4elpRq5XMbSpV3Zt+8Gs2d3wNb24WVrBEEQqrKHJmaH\nDx/m5s2b+Pn50blz54qKqczk6vREnrwGgFrth/rPVCTgrtKQlG0c6k/MP7XL3Ox8UMpLn27pntDo\nZDxszenfpAYT2zfAztykzOMXhOfRoUORjBu3m9atPfj6614A9OhRhx49xGTjgiA8+0rsC1i5ciWr\nV6/m7t27zJw5k99++60i4yoT+6MS2avPxWbZi9QNagYHbhNrJmOChR0A3eu5kayJAcDVpuj4sJTt\nW4mZM5Psi+cLrY/LyKblF7s4HZ1Mk+r2IikThDJw+3Yar7/+MwEB33Px4l32779JdnZ+ZYclCIJQ\noUpsMTt27BgbN25EqVSSmZnJ2LFj6dOnT0XG9sSs1UoUjhpq2U+mQF4DmIBbjsSrvX1JuBaDrZma\nJh6dcLOri4OlW5HjU7ZtIXXHdkxr18G8URPj+g1nbgHQZ+1B9EurzhOqgvA0ysnJZ/Hi4yxadJzc\nXB3m5iqmT/dn0qRWmJk9eiu2IAjCs6DExEytVqNUGjZbWVmh1+srLKiy0s7dkRes1URdBlk6RFnI\nmNPElG9beNG6hRcAlqZ2WJraFXu80/CRmNapg2WbtoXWB3VsSN/GHkQkZpb3LQjCMy09PZemTf9L\nZGQaAAMH+rJ4cWc8PKrWtG+CIAhlpcTE7H+LNFbVoo3Rf24BN9DJEpjY0ozb7mYcvpFA+1qlP8Bg\n06krNp26FlqXqMnF0cKE2o7W1Ha0Lq+wBeG5YGNjSuvWHtjYmLBiRQ/atRN1AAVBeL6VmJjduHGD\nKVOmlLj8tM+XmZWv42Z6NmmnjuPUF9Rh3uyrrkQr03Hoejzta7kQm3adW4nn8HRoiId9/VLP+VdE\nHF2+3o+rlRlXp7+MpYnoZhGEx5GSksPs2Qd57bWGxiRs9eqeWFioRPkLQRAEHpKYTZ48udByq1at\nyj2YshSfpWXXjTjattABoI50R6swtPr1qG8YTxaffoOIhDOYqa2KJGYZh/5Cn5mJVWt/lA4OxvV9\nG3ny66UokZQJwmPQ6wtYs+ZvZs78i+TkHIKDYzh9+h1kMhnW1uLhGUEQhHtKTMz69i2+dldVoVbI\nqWZlhmmdepATzlUrFWQZtvm52wPg7dgEM5UlTlaeRY6PX/UF6Xt2UfO7jTi8NoDsPB3+3s40d3fg\n+0GtK/JWBKFKO3LkNuPG7eb8+QQAAgK8WL68e5UdHiEIglCeSi0wW1V5WJnxRgNPYq+bkZEDW2Re\nANR1skYhN3SZ2Fm4YmfhWuzxVm3aImm1WLZoCYDL7C2ET30ZNxtz8YUiCI8gIUHDhx/u5aefLgHg\n6WnD0qVd6devvvgMCYIglOCZHtSRev428muT8Oq5hNhwQ7mLF2s4PtKx1SYEUXfnXky8vAmNTiY7\nT8+4X05TIEnlGbIgPDOUSjn79t3A1FTJnDntCQ8fQ//+DURSJgiC8BCP1GKWmppKTEwMjRo1oqCg\nALn86c/nErJyCV+xm9h1h3nBvCaBMRYUdHVjQnvDfJipWfFEJl3E1aZmsVMxPUghl7Gktx/rz9w0\ntrYJglCYJEns2hVB5841MTFR4uBgzqZN/ahb14EaNWwrOzxBEIQqodQs4/fff2fAgAFMnz4dgPnz\ns5UCCAAAIABJREFU5/Pzzz+Xe2BPat3laOIS/8auSxQW1hKjI3XsHNmRxtUNNcvi029yPvoANxPP\nFjk2J/wyeXdikP5pHWvqZs+bLWpxcEzXIvsKggDh4Yl067aBXr028cUXJ43ru3atJZIyQRCEx1Bq\nYvbdd9+xY8cO7OwMCc3UqVPZsmVLuQf2JCRJoq6dJfb21/CeE0LWt1/C+80K7ZOSFQeAnUW1IsdH\nBX3I+bpepO/5w7jOwcIEWzN1+QYuCFVMWlouEybsoXHjr/nzz5vY2Zni4GBe2WEJgiBUWaUmZlZW\nVpiZmRmXTU1NUame7lIRMpmMXu4O2DS4CUBuSHMGmaZz4FqccZ/UrHgA7MyLDv5X2NqhsLXFvEkz\nVp+4imLSet7ZHFwxwQtCFVBQILF27d/4+Kzkiy9C0OsLePddP65dG8vIkc0rOzxBEIQqq9TEzM7O\njl9++QWtVktYWBhLlizB3t6+ImL71+I0uURduYP1i4bk6665nC1xiXT9734ACqQC0rIN2+yLaTGr\nvWEzzaITUVWrTmJmLq5WZnx76nrF3YAgPOV+//0aI0fuJDExm7ZtPfn779GsXt0LR0fRWiYIgvAk\nSk3M5s6dy8WLF8nKymLmzJlotVo++eSTRzr5ggULGDBgAAMHDuTChQvF7rN06VKGDSvbicCP3kkm\n9uot5CYFAESfNZS8GNe2HgCZucnoCvIxV9tgoir+i0QmkyGTyfi4WxNOTQgkd/GQMo1REKqa7Ox8\n48+9e/vw+usN2bSpH4cPv0XTpsWXnREEQRAeT6lPZVpbW/Pxxx8/9olPnTrF7du32bx5Mzdu3GDG\njBls3ry50D7Xr1/n9OnTZd41mqvXE3tnF47/TLt3LtUbXMFUqQDud2PaF1PDTK/RILewQCaTcSMp\nE0cLE9xsRCuA8PzKy9OzaNExliw5QUjISGrXtkcmk7F5c//KDk0QBOGZU2pi1r59+2LrDh06dOih\nxwUHB9O5c2cAatWqRXp6OhqNBktLS+M+ixYtYsKECaxateoxw364QG8XruoiyIs3Q51ih8bCkPjl\n6vQApD5k4P+1vj3R3rxBwpc/EvjnbTrVcWVOtya09nYu0xgF4WknSRK//36N998/TExMNgDbtl1m\n6lT/So5MEATh2VVqYvbjjz8af87Pzyc4OBitVlvqiZOSkmjYsKFx2d7ensTERGNitn37dlq2bImb\nm9sjB3vp0qVH3jf+W0cY6IRHmjfrPA23qcpOJTQ0lEjtFQDSEnIITQ41HiNJEtL1CEi8S6wmizca\nOPDD5XimN7EhNCX6ka8tFC80NLT0nYSnQmSkhmXLwjhxIhEAb29LJk1qyEsvmYn3sQoS71nVJt6/\n50upidn/Jk5eXl6MGDGCt95667EuJD1QMT8tLY3t27fz3XffkZCQ8Mjn8PX1xcTk4RMex2flokrN\n4mpENinb69M8pSMMMGwb0NYPPw8Hbp3eD1rwa9QaW3OXwnHevEPe7UhaeHnTJ0vLarUSU5Xi0W9U\nKFZoaCh+fn6VHYbwCNatO8c77xxBpyvAxsaEkSNrsXDhq6jE56BKEp+9qk28f1WTVqt9rMakB5Wa\nmAUHFy4TER8fT1RUVKkndnZ2Jikpybh89+5dnJycADh58iQpKSkMGTKEvLw8oqKiWLBgATNmzHjc\n+IvYFhGL/ck/UTrkYK5tiCzfhG0p5mzt5IyfhwN5ulw02lTkMiXWZkWnZ5LJZBzRmuCdlEENO0tU\nClHpX3i+tGrljkIh4+23m/PJJx2Jjr4ikjJBEIQKUmpi9tVXXxl/lslkWFpaMnfu3FJP3KZNG1au\nXMnAgQMJCwvD2dnZ2I3ZvXt3unfvDkBMTAzTp08vk6QMwN/NgRTVeqr9dhL9vpcwXelJn2Ze9Bxk\nKDB7v36ZC3JZ8V823b85AMDq/i8yqpVPmcQlCE+rkJAYNmy4wIoVPZDJZNSt60hk5Ie4uho+r9Gi\nF18QBKHClJqYTZs2rdBYsUfVvHlzGjZsyMCBA5HJZMyePZvt27djZWVFly5d/lWwj6KOrSW3GoQB\nYHfRjcs2cm7naOjxT8tX6j/1y+yKeSLzWv8+SDIZH7w8nq/OxdK93qOPfxOEqiY+XsO0afv5/vvz\nALRrV4PXXjN81u8lZYIgCELFKjUx+/TTT/nhhx/+1cknT55caLlevXpF9nF3d2f9+vX/6vzFMVcp\nUMgyAbBJccHjZSs+cJBR8246dZ1t8HFtSXXb2kWOK9BqyTjwJ5JOx7K165nzqgl25g8fzyYIVVFe\nnp4VK0KYN+8wmZl5qNUKJk1qRY8edSo7NEEQhOdeqYlZ9erVGTZsGE2aNClUb2z8+PHlGti/IUkS\n8Zo047JpuDc0hVXht+nephZ1nW2Qy+TFjy1Tq/nj691kJtzFNEtPo2oiKROePXv3XmfcuD1cu5YM\nQJ8+dVm6tCu1az/ds3kIgiA8L0pNzNzd3XF3d6+IWJ7YHU0uhw/swO+fXso/XO7P8dmtbvVSj195\nMYHUnDysL0TRqJpdeYUpCJXmzJlYrl1Lpm5dB774ojvduxdtPRaeLzExMYwbN47t27f/63P85z//\n4Y033sDDw6PINo1Gw7lz5/D39+ebb76hRYsWNGvW7KHn2759O8uXL8fT0xOA7Oxs+vfvz6BBg/51\njI/jyJEjxMTEMHjw4H91fExMDL1798bX1xeAvLw8fHx8mDNnDgqFgpycHBYuXMiFCxdQKpU4Ojoy\ne/ZsqlUz1NaMjIxkwYIFpKSkkJmZib+/P1OnTkWtVpfZPT4uvV7Pu+++y6xZs4zvS2XIzMxk0qRJ\nZGZmYm5uztKlS7G1tS20z4oVKzh69CgKhYLJkyfzwgsvcOvWrULF8ufPn4+XlxcnTpxg2bJlKBQK\n2rVrx5gxY/j555/57bffjPteunSJ3bt3M2PGDP773/+W/3zhUgl27NhR0qYKl5ubK505c0bKzc19\n6H7XUzXSvo3dpPBghRQerJBG9P4/ST7xB0k+8QdJkiQpIydJ2nl2lXT61q4ix+r1BVJqtlaST/xB\nSs/Rlst9PM/OnDlT2SE8lzIycqWTJ6ONyzk5+dJXX52StFrdI59DvHdVW2nvX3R0tNS3b99yu/7J\nkyelRYsWPdYx27ZtK3SMVquVevToIUVHRz/kqKdHca/p1KlTpV9++UWSJEmaNWuWtHLlSuO2M2fO\nSIGBgVJeXp6k0+mkXr16SSEhIZIkSdLp06elefPmScuWLau4GyjG+vXrpa+//rpSY5AkSVq5cqW0\nZs0aSZIk6aeffpIWL15caHtYWJg0YMAASa/XS2lpadKAAQMkSZKk//znP9KpU6ckSZKk7du3SzNn\nzpQkSZJ69OghxcbGSnq9Xho0aJAUERFR6HwhISHSnDlzJEmSpO+++076v//7v0eK81HzluKU2GK2\ndetW+vTpU75ZYRnztjFHUacLOdmhqE57o/indNrof56sTNHEkaSJxkRlVuTY6MnjkTk6kTZ9DFam\nlfdXiSCUBUmS2LjxIlOm/Elenp6IiLHY2ZlhaqrkvfdaVHZ4QhVw9epV5s2bh1wux8LCgkWLFmFh\nYUFQUBCxsbE0a9aM3bt3c+TIEYYNG8asWbPQ6XTMnTsXtVqNWq3m888/Z968eWg0Gry8vDh79izd\nunXD39+fadOmcefOHUxMTFi8eDEuLi4lxqJWq/Hx8SE6Oppq1aoxa9YsoqOj0el0jBs3jlatWnHi\nxAkWLFiAo6Mj3t7e2Nvb07JlS7799luys7OZOnUqsbGxfPvttyiVSnx9fZk2bRqxsbEEBQUhl8vR\n6/UsWbKEkJAQIiIimDp1Kt9//z27du0CoFOnTowaNYpp06bh7OxMWFgYsbGxfPbZZ6U+JNe4cWNu\n376NRqPh6NGj/Pnnn8Ztfn5+NG7cmAMHDmBubk7NmjVp2dIwx7NMJjPG96D8/Pwir+Hx48eNcWdl\nZdG7d2/++usvunbtSrt27XBwcODXX39l7969APzyyy9cuXKF4cOH89FHH5Gfn49CoeCTTz6hevXC\nvUzr1683Tqv422+/sWHDBuRyOXXq1GH+/Pls376dI0eOcPfuXT7//HP279/Pzp07kcvldO7cmeHD\nhxMfH09QUBAAOp2OTz/9tFDr26FDh1i7dm2h677++uv07t3buBwcHMyCBQsACAgI4N133y20f2Rk\nJA0bNkQul2NjY4OVlRUxMTGFKj/ExcXh4uJCdHQ0NjY2xpbK9u3bExwcTO3a93sSvvzySz777DNj\nLC+//DIjRox46Hv9pErtyqxK5DIZXi0mAZOg/yr29jPcXmtvQ/206rZ16N5oFDJZ4f/g+qwsDu7Y\ni2dmIh0mTP7f0wpClXLmTCzjxu0mODgGgJYt3UhOzsHOrugfJMJTZtBO2H+7TE9Zu4Ut7Hr8AqX/\n+c9/mDJlCk2aNGHt2rX88MMP+Pr6otVq2bJlCwcPHuT7778vdMz27dsZNGgQr7zyCsHBwSQmJjJi\nxAgiIiIYMGAAZ8+eBeDXX3/F0dGRpUuX8scff3DgwIGHdhsmJSVx4cIFZs2axc6dO3FycjJ29b35\n5pvs3LmTzz77jMWLF1O3bl2GDBlCmzZtALh27Rp79+4lPz+fWbNmsXnzZtRqNePHjyc0NJQLFy7Q\nunVrxowZQ1hYGImJicbrRkdH88svv7B161YAXnvtNWOpp7y8PNauXcumTZv49ddfH5qY5efnc+DA\nAQYNGkR0dDQ1a9ZEqSz89Vu/fn1u3bqFmZkZ9evXL7TN1NS0yDmLew2L2w8MSVC7du1o164dJ0+e\nJCIigjp16nDgwAGGDx/O8uXLGT58OK1bt+bw4cN89dVXfPLJJ8bjY2NjUavVxi7DnJwc/u///g9r\na2uGDBnC1atXAUPC89NPPxETE8OePXvYtGkTAIMGDaJ79+4kJSUxZswYXnrpJbZu3cqPP/7ItGnT\njNfp0KEDHTp0KPF1BMP/BXt7w5hYBwcH7t69W2i7j48Pq1evJicnh6ysLMLDw0lOTsbd3Z3w8HCm\nTJmCmZkZ69at48qVK8ZzgWGGougH6gNduHCBatWqGWuwmpub4+DgQGRkJF5eXg+N80mUmJidPXu2\n2BdIkiRkMlmpc2VWhjuaHBzN1JgYpsTEJ11PtIUcWzNDC5hKaYKrTc0ix8nkcqa+NpOkfFh7MZa3\nWtaqyLAFoUzcvZvFjBkH+Pbbs0gSuLhY8OmnnRk2rAlyedH5bgXhYW7cuEGTJk0AePHFF1m1ahVm\nZmY0b94cMLQu/G9y0alTJ+bMmUNkZCSBgYHUqlWL8+fPFzl3WFgYrVq1AqBnz57FXn/Xrl1cunQJ\nrVZLUlISM2fOxMHBgbNnzxIaGsrff/8NGCqs5+XlcefOHRo0aABAu3bt0OsNXwR169ZFrVYTHh5O\nbGyssbUjMzOT2NhY2rRpwwcffEBmZibdunWjWbNm3Lx5E4Dw8HCaNGlivM/mzZtz5YphSr8XXngB\nAFdXVy5cuFAk/lu3bjFs2DDA0Po4cuRIOnfuzJUrV4yxPUiSJBQKBTKZrNjtj/IaPmycYOPGjQHo\n2rUrBw8exNPTk4iICJo1a8ZHH33ErVu3WL16NXq9vlCyAoYC8a6u90tM2djY8P777wOG/ydpaYaH\n7ho1aoRMJuPixYvcvn2bN954A4CsrCzu3LmDu7s7n3zyCStXriQjI+NfleJ6kPTAjEL31K5dmwED\nBvD222/j7u5OvXr1jPvVr1+fnTt3snHjRhYuXEjfvn0fev6tW7cW2cfFxYW4uLjKScwaNGjAsmXL\nyu3C5WHntVgc//qKWnbeNFXq2BeiIz1sKDZmD++alJuZ4e3qQFJ0Mi08HSooWkEoW/36beHYsShU\nKjkffvgSM2e2w9paPF1cpWzqXfo+j+l6aChPOqFPfn4+crncmDyAoYvtf7Vq1YqtW7dy8OBBpk2b\nxpQpU4o9n0KhoKCg4KHXDAwMZOrUqeTk5PDqq68aky6VSsW7775Lr169Sjz2wdjuDZhXqVT4+voW\n6SoD2LFjB8ePH2fZsmX069ev0Hke/PK/9zrcu4d7iksQvL29jaWgxo0bh7e3N2B4oO7WrVvk5eUV\nGsx/5coVOnfujFqtZuPGjYXOlZeXR2RkJD4+9wueF/caPnjfOp2u0LZ7A9Y7d+7Mhx9+SJ06dWjb\nti0ymQyVSsXy5ctxdnYuch//e+68vDzmzZvHjh07cHJyYvTo0UWuoVKp6NChA/PmzSt0junTp+Pv\n78+gQYPYs2dPkQaeR+nKdHZ2JjExESsrKxISEoqNeejQoQwdOhSAAQMG4ObmxqFDh2jTpg0qlYru\n3buzceNGRo8eXWiGov89X0hICDNnzizxNSkvJc43pFarcXNzK/Hf0ygvJgVf/+WYNfwQvWU2ZOYZ\nk7J8nZa/Lv/AheiDxR578sNALgT1pqGrbbHbBeFplJ9//y/refM60KNHbS5efI/Fi7uIpEx4InXq\n1DF2PZ4+fRpfX188PT2N8/8dO3asSMvOhg0bSEtLo0+fPrz55puEh4cjl8uLJAmNGjXi5MmTABw8\neJCvv/66xDjMzMwYM2aMcVxRkyZNOHDAMDtLcnKysQHBycmJGzduoNfrOX78eJHzeHt7c+PGDZKT\nDaViVqxYQUJCAn/88QcRERF07tyZ8ePHF5rfsH79+pw7dw6dTodOp+P8+fNFuhkfRVBQEJ999hk5\nOTlYWloSEBDAqlWrjNv//vtvLl++TIcOHWjTpg137tzhr7/+AqCgoIAlS5YYx7k97DW0tLQ0du2V\nNPG5i4sLMpmM33//nW7duhlf0/379wOGMVw7d+4sdIyzszPx8Ybi7FlZWSgUCpycnIiLi+PSpUvk\n5+cX2r9hw4aEhISQk5ODJEl88skn5ObmkpqaiqenJ5IkceDAgSLHdejQgfXr1xf692BSBoZZhfbs\n2QPAvn37aNu2baHtKSkpvPPOO0iSREREBAUFBTg5ObF582YOHz4MwPnz5/H29sbd3R2NRkNMTAw6\nnY6DBw8au8ATEhKwsLAo8iRsQkJCodbD8lBii9m9Zs+qpJ+Jgsx/fpbSrfj7LR+a/7Ocmh1PVMpl\nNNpUGnsEFDou4auVmNSuTf2AzhUaryD8WzdvpjJx4l5sbEz5/vtXAAgI8CYgwLuSIxOqoge73cCQ\nSMycOZO5c+cik8mwsbFh4cKFqFQqtm3bxqBBg2jZsmWRMgWenp6MHz8eKysr1Go1CxcuJCUlhc8+\n+6zQl1lgYCAnTpxg6NChKJVKPv3004fG16tXLzZs2MCxY8fo0aMHJ0+eZODAgej1ej744AMAPvzw\nQ8aOHYu7uzs1a9YsMljezMyMGTNm8M4776BWq2nQoAHOzs54eXkxe/ZszM3NUSgUzJw509j96u7u\nzoABAxg6dCiSJPHaa6/9q4YJDw8PunXrxurVq5k4cSIzZsxg6dKl9OnTB7Vajb29PcuXLze2wq1d\nu5aPP/6YVatWkZeXR9euXY33+bDX0MLCgtWrVzNs2DDat29fbKsmQMeOHfnhhx9YsmQJAB988AEz\nZszgjz/+QCaTsXDhwkL7V69eHa1WS3p6OnZ2drRp04Z+/fpRr149Ro4cycKFC3nzzTcL7f/GG28w\nZMgQFAoFnTt3xtTUlAEDBjB//nzc3NyMD4wcO3YMf3//R34thw0bRlBQEIMHD8ba2tp4Dw+WbKlf\nvz79+vVDLpcbx8pNnz6djz76iHXr1hmTRYA5c+YwadIk42t6r2UzMTGxSJduTk4OSUlJxn3Ki0wq\nrg32KXNvlnZfX19MTEpuBbiyegs0G4yUo+LO+GV062KJh605kbP6cSXuJCdv/Eot5+a09XndeIwu\nNZU27y2maeJNFv36LXZWFhVxS8+d0NBQ/PyetENF0GjyWLjwKEuXBqPV6rGyUnP9+jicncvv/614\n76q2snz/0tLSCAkJoVu3biQkJPDmm28aWy8q27Fjx/Dy8sLd3Z2PP/6YFi1aFGltqYqels/fDz/8\nQG5uLqNGjarsUCrN999/T15eHu+8806p+z5q3lKcZ+apzOx8HZmaI1gBMrN80lWGvxQc/plW6f7k\n5YWbICMT0znj1oAzbg1YWonF+wThYSRJ4qefLhEU9Cd37hjahd94owmLFnUq16RMEB5kYWHB7t27\nWbt2LQUFBUyfPr2yQzKSJIkPPvgACwsLHBwcjN10QtkYPHgw7733Hj169Ci2kPCzLj4+nkOHDvHf\n//633K/1zCRmm67eoVrGDawAstT8p7HhseEa9obJmFOz4gCwt6xW6DjPmh789X5Xvg25jqVJOVfz\nFYR/IT9fT5cu6zl82FBGwc+vGitX9qBVq+fvl6NQuVQqFV988UVlh1Gstm3bFhlvJJQdpVLJmjVr\nKjuMSuPq6sp3331XIdd6ZhIzL2tzVDZ3ADA50ojz9oa+ejOVAkkqIDX7XotZ4cRMrVTQvpYL7WuV\nXNxQECqTSqWgTh17Ll9OZNGizrz1VlNR/kIQBOEZVeJTmVVNJ08ndOccyLlhjTrNxrh+fo+maLRp\n5Ou1mKmsMFNbGrfl6fSk//Un+Q8UFBSEypafr2fFihAOHLhpXPfpp124dm0sw4c3E0mZIAjCM+yZ\naTEr0OmJ3+RK/CZXYnzbwT8PWNZ0sOJ2chgAdhb3x5eFRifT6au9uMTd4oMLs/ngzDFk8mcmTxWq\nqAMHbjJ+/B7CwhKpW9eBixffQ6VSYG8vqvYLgiA8D56ZxCwjyTAgWi1TokbG8Ja1ic3IBu6PL7Oz\nuN+NKQH9vO3Ynp2Nr08NkZQJlSoyMo1Jk/axfXs4ADVr2vHpp51RKsX/S0EQhOfJM5OYfXs8HJ8X\n45HfdqJrY1de6NUcjdZQvM448P+BxOwFDwdWvd2FNQoFcvnISolZEHJy8lm06BiLF58gN1eHubmK\nmTPbMmFCK0xNn5mPp/CU27hxIzt27ECtVpObm8vEiRNxdXVl/PjxhYqNSpJEx44d2bp1K2ZmZixc\nuJBLly5hYmKCjY0Nc+bMMU4IfU/Hjh1xdXU1Vqo3NTVlwYIFxknL161bZ7w2wOTJk2nRogUA2dnZ\nj3SNirZs2TLq1atHYGBgpcaxYMECzp8/j0wmY8aMGUXqj+7fv5/Vq1ejVqvp2bOnsRr+4sWLCQ0N\nRafTMXr0aLp27UpcXBxTpkxBr9fj5OTEkiVLUKvVNGzY0DgNFxjer6CgIN56660qWe+0SpCqgNzc\nXOnMmTNSbm5usdsLCgqkld/9JZ1a7SKFByuk1KB3Cm3fenqx9N3RqVKyJrYiwhWKcebMmcoO4amU\nnp4rubgskWCONHjwNik6Or2yQypCvHdVW2nvX3R0tNSnTx8pLy9PkiRJunXrljRkyBBJkiSpb9++\n0vXr1437nj59Who+fLgkSZI0c+ZMafXq1cZtu3btkgYMGFDk/AEBAZJGozEub9u2TZo+fbokSZL0\n+++/SyNHjpRycnIkSZKk+Ph4qWfPntKNGzce6xoVKTw83PgaVISS3r+QkBBp1KhRkiRJ0vXr16XX\nX3+90Ha9Xi+1a9dOSk5OlvR6vTR8+HApLi5OCg4OlkaOHClJkiSlpKRI7du3lyRJkqZNmybt2rVL\nkiRJWrp0qbRx40ZJkiSpZcuWRa6dkJAg9evXTyooKCiTe3wWlZa3PMwz8Se5TCajr5MN8fGGecO+\n8m5IzbO36FHPDXO1RGZuCnKZAhszwwzxyVlaTkcn0dgMqtd4OqeXEp5dFy8mUKuWPebmKqytTViz\npjd2dmb4+3tWdmjCc0ij0aDVasnPz0elUuHl5cWGDRsAQ8X9Xbt2MXbsWAB2795Nr1690Gg0HDt2\nzDiND0CPHj2M09k8TJMmTdi2bRtgKNi5YMECTE0N5Y1cXFwYOXIkGzZsYOLEiY98jTVr1rB3717k\ncjkTJ07E3d2dcePGGSf1fvXVV1mxYgWrVq1CpVKRlpZGTEwMX375JdWrV+fOnTuMHTuWn3/+mVmz\nZhEdHY1Op2PcuHHGicLvWb9+PYMGDQIM81vOnTsXpVKJXC5n+fLlaDQagoKCMDc3Z+jQoVhZWbFs\n2TKUSiXVqlVj/vz5yOVypk6dSkJCAtnZ2YwdO5aAgPsz0iQkJDB58mTAMNm6lZUVjRo1KjT3aHBw\nMJ07GwZT16pVi/T0dDQaDZaW/5SISk3F2traWL3+pZde4sSJE7z88svGli5ra2tycnLQ6/WEhIQw\nd+5cAAICAvj2228ZPHhwse/hvRkTgoODad26danvufB4nonEDMCumReZUSkAbEjUcHXDMW5+1JeC\nAg1KuRIrUwcUcsPtHrwez4AfjtAq6gL/jT9Cw6MhlRm68JxITs7m448P8vXXocyc2Za5cw2/iHv3\nrlvJkQlPE8Wk9SVuW93/RUa1Mkxk/U3wNd7bWvLvLv3SYSVue1C9evVo3LgxnTp1on379rRr146u\nXbuiVCrp2bMnI0aMYOzYsRQUFHD48GEmTJhAdHQ03t7ehSbyBsMXfWn27NljnJD8zp071KpVq0g8\nO3bseORrREZGsnfvXrZs2UJ0dDTffPMN7733XonXt7GxYf78+Xz55ZccPHiQIUOGcODAAbp27crO\nnTtxcnJiwYIFpKSk8OabbxaZN/LkyZMEBQUBhrk6Z82aRYMGDVi+fDk7d+4kICCA8PBwDh48iJ2d\nHa+88grr1q3D1taWxYsXs2fPHtq0aYO/vz99+/YlOjqa8ePHF0rMXFxcjBOgl1T5PykpiYYNGxqX\n7e3tSUxMNCZm9vb2ZGVlERkZiZubGyEhIfw/e+cdFsW1/vHP7rL0IiAIiMYeBOwFFbso0auJxoYK\ndmNBuZYYVIISG5aosUTMtVwVezQajAm22DugRowYFTSASu9tYZnfH3uZuAEV8zMiZj7P4/MwM6e8\nM2fdfec957zf1q1bo1AoMDQ0BGD//v107NgRhUJBXl6eOJ1saWlJ0v+yFahUKmbMmEF8fDy9N52C\nAAAgAElEQVTu7u6MGjUKgFatWnHlyhXJMfsbeGccMwMbY/hd83dyviZdRo0qRsjlxgxr+wX5hbli\nWX2lgjYWujS7/hAdS8uKMFfiH4RaXcx//hPO55+fIjU1D4VCRn5+0csrSki8IZYtW8aDBw84d+4c\nmzZtYvfu3Wzfvp1q1aphbm7O3bt3ycjIwNHREWNjY2QyWSkB8xcxbtw4FAoFsbGxtGjRQozMPA+5\nXF7uPn799VeaNGmCXC7nvffeY9GiRcTFxT23fEm0qEePHixZskR0zAICAti6dSvh4eFEREQAGlkd\nlUqlJWSdlZUlaoRaWlry5Zdfkp+fT2JioigBVaNGDczNzUlOTubRo0dixDE3Nxdzc3NMTU25desW\ne/fuRS6Xk56e/tL7fBnCn9QVZTIZS5YsYc6cOZiYmGBvb691/cSJE+zfv58tW7a8sK3PPvuMDz/8\nEJlMhqenJy1btqRRo0bY2Ng8Vyhd4v/HO+GYnY1LIerxSTr+7zilwAwDpULM9ySTybXyl/V2tKe3\n42CEWQMoSkurAIsl/imcOfMQH59QfvklAYCuXWuzevUHODtbV7BlEm8r5Y10fdK2gRg9+/8gCAIq\nlYq6detSt25dvLy86NmzJ48fP6Z69er06dOH0NBQMjMzRcfD3t6e6OjoUk7LrVu3aNSoUak+Nm7c\niJGRETt27ODhw4diVMfe3p6oqCgaNmwolr1z5w716tUrdx8lmwqe5c/i3UVFf7wIKZUahZf69euT\nmJjIkydPyMrKonbt2iiVSiZMmEDv3r2f+7yebXvRokWMGzeOjh07snnzZnJzc7X6UCqVWFtbi9Gv\nEg4ePEhGRga7du0iPT2dAQMGaF0vz1SmtbU1ycnJ4nFiYiJWVlZa7bRu3Zpdu3YBsGLFClGA/dy5\nc2zYsIFNmzZhYmICgKGhIfn5+ejr65OQkIC1teY7qmTaFjTTob/99luZYyzx+ngn9uIXFRdjfPsW\nAIU5mnxPjW3NgdJvEc8iUyhQVq369xso8Y/kypU4Onfexi+/JPDee2bs3z+QEye8JKdM4q1i//79\n+Pv7i9+VWVlZFBcXY/m/2QR3d3cuXrxIWFgYnTp1AsDY2Jhu3bppyTMdPXqUpUuXvvA718PDg6tX\nrxIVFQXAiBEjWLp0KXl5eYDGudiyZQuenp7l7sPJyYmIiAiKiopITk7G29sbY2NjUlJSEASBpKQk\nYmNjy7Snc+fOrFq1iq5duwKa9W8nT54ENNOUK1euLFXH2NiYjIwMQCPqXrNmTVQqFWfOnKGwsFCr\nrJmZZvbm/v37gGZ9WlRUFGlpadjb2yOXyzl+/DgqlUqrXslUZnBwMP7+/gQHB2s5ZQCurq4cPXoU\ngNu3b2NtbS06vCWMHTuWlJQUcnNzOXXqFG3btiUrK4tly5bxzTffiJE/gHbt2ontHTt2jA4dOhAd\nHc2MGTMQBIGioiIiIiKoX78+oHEebWy0taclXg/vRMSsa00rLv4WT5G9kqRIzYemiqEugiCw79pi\njHSr4N5oLEqFHnmFReSq1FgavZrau4REeSguFsRIbevW1fnww/dp0cKWmTPbYWAgabFKvH18/PHH\nREdHM3DgQAwNDSkqKuLzzz8XF+SbmZlhaWlJlSpVtCJXc+bMYfny5fTp0wdTU1NsbGxYt25dqWjV\ns+jo6PDZZ58REBDA7t276dWrF7m5uXh4eKCnp4dMJmPmzJmiSHZ5+rC3t+ejjz7C09MTQRCYNm0a\nZmZmtGvXjv79++Pg4KAVkXuW7t274+HhQUhICKDZXHD58mU8PDxQq9VMnjy5VB0XFxfCwsLo1q0b\nnp6eeHt7U6NGDby8vJg/f36pFBqLFi1i9uzZYvRs8ODBGBsbM3HiRG7cuEH//v3F+yqrv+fRvHlz\nnJyc8PDwQCaTMW/ePAC+++47TExM6N69O4MGDWL06NHIZDI++eQTLCws2Lt3L2lpaUydOlVsa+nS\npUyZMgVfX1/27t2LnZ0dffv2RalUYmNjw4ABA5DL5XTt2lWcCr527Rp9+/Ytt70S5UcmvOj15i2h\noKCAyMhInJ2d0dMr26GKmRdC6vLT7GtZky9bWbLyo5aMdbHl22tL0Fca4+HyOQCHb8fSd8tp3B/f\nYn21XGp9te5N3so/luctYH1XEASBgwej8PU9waFDg3FyshbPv+iHqjLwro/du440fq+XO3fusHLl\nyjcm6P02jl9ycjLjx49n//79lf777e+iPH7L86j0U5nFgkBGQSG1rOxpYVSHZhZWjG5dj+b2Fhjp\nVWGIy1x6OI0Wyydk5WMoh6pJcRQ+fVyBlku8K0RGJuLmFkz//vu4fz+VdeuuitekLy0JiXeLhg0b\n4uDgQGhoaEWbUmEEBgYyd+5c6fvtb6LST2XGZOSy/95jbJxS8dJV4aFrgsfgP/LO6CkN0VMaisdj\n29RnZItapP3aBCNFWS1KSJSPtLQ85s07zfr111CrBSwsDFi4sAvjxr1db7cSEhKvlxkzZlS0CRXK\nihUrKtqEd5pK75jJZGCpq0MLeX9+O5BDjYjVGL2kjo5SiVUTSUpC4q9z/PgDhgw5QEpKHnK5jEmT\nWjJ/fhcsLQ1fXllCQkJCQuI5VHrHrI6ZEVamRsTn5QDglaND37AHDG9Zl2ORm9GRK2lbrx8GuiYV\nbKnEu0T9+pbk5BTSqdN7rFnTk8aNq1W0SRISEhIS7wCVfo0ZQNKjueLfPyYqWXz8FkVqFY/T7xOb\nFoWujiaFxrG7j6kbsJfJ4/1J2rq5osyVqITExWUyd+4pios1e2Vq1apCePgnnDo1QnLKJCQkJCRe\nG5XaMcstVHM2Lpk84VvxXGGxkupmhqTnJgACZgZWohTT9bhUHmapSI55ROapkxVktURlIj+/iMWL\nz/H+++tYsOAs27bdEK85OlpJi18lJCQkJF4rlXoqMzmvgEtP0uiaXAWTGhkc/vZfAPR2sic15wkA\nFka2YvkZnR1xr6qkKFzAqrYkXi7xfARBICTkLtOnHyM6WqMO8fHHDencuVbFGiYh8Zr57rvvuHfv\nHr6+vuK5uLg4unfvzsGDB3FwcBDLgSbvWdeuXRk1ahReXl5i+XXr1rFkyRKttrt27YqNjY2YnV9f\nX5/FixdTrZomyrx161a+//57MT/ap59+SqtWrQCNfFFgYCCRkZHo6elhZmZGQEAAtra2VCQrV67E\nwcGhVL6yN83ixYu5efMmMpmMOXPmiPnFSti5cychISHI5XKcnZ3x8/MjKCiIixcvAlBcXExycjJH\njx7l4sWLrFy5EoVCQceOHfH29hbbyc/Pp3fv3kyaNIkePXowceJE1q9fLyoGSLx+KrVjZqanpJ2d\nBYZZmrQXZwoagB641KxKWo4m07K50R+ZiXUUcpo2eR+aSKLREs/nzp0kpk49yrFjDwBNZGz16g9w\nc6tTwZZJSLw56tWrx4oVK8rM12Vpacm+ffvo169fqWzzf6ZEjgk0zt3q1atZvHgxR44c4cKFC+ze\nvVuUARozZgxr1qyhTp06BAYGUr16dRYsWADATz/9xLRp09izZ8/rv9lyEhUVxe3bt5k+fXqF2QBw\n9epVHj16xN69e3nw4AFz5sxh79694vXs7Gw2b97MsWPH0NHRYfTo0dy4cYOJEyeKAu8HDx4kJSUF\ngIULF7J582aqVauGp6cn7u7u1KtXD4CgoCBRwcDY2Jjhw4ezatUq5s6di8TfQ6V3zFyrVeHiV++h\nsMgjysAaiqFVzaqcuK2JmJkbVezblUTlIzT0PseOPcDMTI/587swcWJLlEopt4rEu8+KFSswMDDg\nww8/xMnJiby8PC5dukTbtm21yunr69O3b182b97Mv//973K336RJEw4cOADAtm3bWLx4sagwUK1a\nNcaOHcuOHTuYPn0658+f58SJE2Ldnj174urqWqrNjRs3cvToUeRyOdOnT8fe3h4fHx+tCN+aNWtY\nt24dSqWS9PR04uLi+Prrr7GzsyM+Pp4pU6bw7bff4u/vT2xsLEVFRfj4+JS67+DgYFE7Mioqii++\n+AIdHR3kcjmrV68mOzubmTNnYmhoiKenJyYmJqxcuRIdHR1sbW1ZsGABcrkcX19fEhISyM3NZcqU\nKXTp0kXs41mdTNBIZLVr105LkunSpUu4ubkBULduXTIyMsjOzhadZKVSiVKpJDc3F0NDQ/Ly8kTn\nCjTaoSVC9bGxsZiZmYmRyE6dOnHp0iXq1avHgwcPuH//Pp07dxbrurm58eWXX5KTkyM63BKvl0rt\nmAFkRT3m95XNMK6XR0xvTXRMRy4jrWQq01DzYbv6ezLLDl+hU+oDRn3ggnErlwqzWeLtorhYICoq\nGUdHjQDw5MmtSU3Nw8fHBSsr6YtH4s2y9fysVypvaVSdPs2mlKo/sv2S51Upk59++oknT57w5Zdf\nEhcXB8C0adPw9fWlTZs2pcoPHjyYAQMGMHTo0HL3ERoaiqOjIwDx8fHUrVtX67qDgwPff/89sbGx\n1K5dG4VC+4XI1NRU6/jhw4ccPXqUffv2ERsby3/+8x8xIlQWZmZmLFiwgK+//ppTp04xbNgwTp48\nSY8ePTh8+DBWVlYsXryY1NRURowYweHDh7XqX758mZkzZwIaLU1/f38cHR1ZvXo1hw8fpkuXLty5\nc4dTp05hbm5O37592bp1K1WqVGHZsmWEhobi6upK+/bt6devH7Gxsfz73//WcsxKdDJLKCvzf3Jy\nMk5OTuKxhYUFSUlJomOmp6eHt7c3bm5u6Onp8a9//YvatWuL5Y8dO0b79u3R19cnKSkJCwsLrbZK\ntEWXLl2Kv78/hw4dEq/LZDKcnZ25ceNGmY6yxP+fSu2YpecX8iAiBgC9WDvW9GuFka6SXFUGKnU+\nejqGYpqMizGJHIxORfj1Jv+KuyE5ZhIAXLoUi49PKPfupfDbb1OwtjZCqVSwYEHXijZNQuKNce/e\nPY4dO8aPP/6odb5WrVo4OjqWOg8a3cvx48ezdu1aPvnkk+e2PW7cOBQKBbGxsbRo0YIvvvjihbbI\n5XJkMhlqtfqldv/66680adIEuVzOe++9x6JFi0SnsixK1mH16NGDJUuWiI5ZQEAAW7duJTw8nIiI\nCEAjqaNSqbT0QbOyskThb0tLS7788kvy8/NJTEykT58+ANSoUQNzc3OSk5N59OgRU6ZonObc3FzM\nzc0xNTXl1q1b7N27F7lcTnp6+kvv82X8WVkxOzubb775htDQUIyNjRkxYgRRUVHiesEDBw68dBwO\nHTpE06ZNRd3SZ6lWrRpPnjz5f9stUTaV2jGLSsvi+q0oarg8pWqRHPf2mg9dbOodQLPwv2TXXN9G\nNdGPf0RVoRpVOrasMJsl3g6ePMli1qyTbN9+EwA7OxMePEjF2lqKkElULK8a6Xod9ePj46lfvz6h\noaF89NFHWte8vb0ZM2YMw4YNQ0dH+yejZ8+ebNu2jYcPHz637ZI1Zjt27ODhw4diVMfe3p6oqCgt\ngfE7d+5Qr1497O3tiY6OLuUY3bp1i0aNGonHJZsKnuXPO6WLiorEv5VKJQD169cnMTGRJ0+ekJWV\nRe3atVEqlUyYMIHevXs/916ebXvRokWMGzeOjh07snnzZnJzc7X6KBEtfzb6BZq1XRkZGezatYv0\n9HQGDBigdb08U5nW1tYkJyeLx4mJiVhZWYnHDx48oEaNGmIkrGXLlkRGRuLg4EBubi5Pnz7F3t6+\nzLYSEhKwtrbm9OnTxMbGcvr0aZ4+fYquri42Nja0a9fuuc9H4vVQqdNlmOrqYFtwj3orz2E447h4\nPi3nKaC98L+WhTEThvZkwMrFWPT9+I3bKvF2UFBQxLJlF2jQYB3bt99EV1fBnDntuXt3Mm3bln4z\nlJD4J9C5c2cWL17M+vXrtX6kAapWrYqbm9tzF91PmzaNlStXvrQPDw8Prl69SlRUFAAjRoxg6dKl\n5OXlARrnYsuWLXh6emJsbEy3bt346quvxPpHjx5l6dKlWtEhJycnIiIiKCoqIjk5GW9vb4yNjUlJ\nSUEQBJKSksRpubLuedWqVXTtqomON2nShJMnNWmUUlJSyrwnY2NjMjIyAEhPT6dmzZqoVCrOnDlD\nYWGhVtmSNV3372s2ogUHBxMVFUVaWhr29vbI5XKOHz+OSqXSqlcylVnyz9/fX8spA3B1deXo0aMA\n3L59G2tra61NGNWrV+fBgwfk5+cDEBkZSa1atQDN2rg6df7YyGRvb092djZxcXEUFRVx6tQpXF1d\n+eqrrzhw4AD79u1j4MCBTJo0SXTKEhISsLH54/dV4vVSqSNmjpampCdqdmQKdpl4H7hCr4bVMZJL\nC/8lymbEiEPs3XsbgI8+ep8VK3pQt67FS2pJSLz7WFhY4OPjQ0BAALNmaa9zGz16NLt37y6znouL\nC1WrVn1p+zo6Onz22WcEBASwe/duevXqRW5uLh4eHujp6SGTyZg5c6Y4dTZnzhyWL19Onz59MDU1\nxcbGhnXr1mlFrezt7fnoo4/w9PREEASmTZuGmZkZ7dq1o3///jg4OGhF5J6le/fueHh4EBISAmii\nf5cvX8bDwwO1Ws3kyZPLvNewsDC6deuGp6cn3t7e1KhRAy8vL+bPn18qhcaiRYuYPXu2GD0bPHgw\nxsbGTJw4kRs3btC/f3/xvsrq73k0b94cJycnPDw8kMlkzJs3D9DsejUxMaF79+6MGTOG4cOHo1Ao\naNasGS1bamaK/rymDCAgIEDU/+zVq5fWerQ/IwgCkZGRzJ8/v9z2SrwaMuHPk9NvIQUFBURGRuLs\n7Iyenp7WtfOfdKTq6Is8vteY7jemMbdHYxpXPUFGXhJ9mk7B0rg6dxMz+O78LVzIomPXtuj8b42A\nxJujrAWsbwpBEMQv80uXYhkzJoSvvvqAHj3qvqSmBFTs2En8/5HG7/Vx584dVq5cWWYKkb+Lt238\nTpw4wfnz5wkICKhoU95qXuS3vIxKPZWZUVCIjZNGNDrdVBNGNlDK0dMxRKnQw8zQGoCf7z3l8wsx\nrNpyiLi5cyrMXok3S2ZmAZ99dhxPz4PiubZtaxAZOUlyyiQkJF6Zhg0b4uDgQGhoaEWbUiFkZ2ez\nbds2pk2bVtGmvNNU6qnM4F9jadhGh+oChGdqMvnLkNOryUQEoRiZTON3OttWYVRVAacHORi3ca9I\nkyXeAMXFAsHBN5k16yRPn2Yjk8HcuR15/33NdItcLskoSUhI/DVKpvz+iRgbG5fazCDx+qnUjpmR\nrg7kK0AG0Zl2ALjW1uxMKXHKADrUqUaH2cOB4RVhpsQb5Nq1eKZM+YkrV+IBaNPGnrVre4pOmYSE\nhISExNtMpXbM/vUkk6SF+sg/sSG9QLMjpV5VXYqFYuSySj1LK/GKCILA+PE/sHGjJgeRjY0xy5a5\nMWxYYylCJiEhISFRaajUjtnj/17g13Ny6rSsyWUdTTbpiIf7SM6KpbvzGKqZ1uJpZh6R92NpamdO\nVRurl7QoUVmRyWQYGOigVMqZPr0tfn4dMDF5tQWXEhISEhISFU2ldcxU6mKq2NvSwMAOq5/a8MMP\nH5OcU0CB6gBFxYUY65kDcOROHJ/su0KPe5fZ1NaG6rP9K9hyidfF0aP3USjkorh4QEBnJk9uTf36\nlhVsmYSEhISExF+j0jpm1xMzOD2gIS7GmTSJVVOzltX/UiJMpaAwF10dA0CzDq2JLJdGKQ/Rqy1l\nLH4XePAglenTjxEScpfatavw66/e6OvrYG5ugLm5QUWbJyFRqYiLi6NPnz44OzsjCAIKhYIJEybQ\ntm1bDh06xIEDBygoKODevXs4OzsDGg1FOzs7sQ0nJyeaN28OaDLtl2hOPpv09O9k4sSJBAUF/eX6\nXl5eouB3SXqdefPmUa9ePQAOHz7Mf//7X5RKJYWFhYwfPx53d81GsqKiIr766ivOnz+PgYEBSqUS\nPz8/3n///ddyb3+VPXv2kJWVxbhx4yrUjk2bNhEaGopMJmPy5Ml06tRJvKZWqxk5cqR4nJiYSL9+\n/RgxYgSzZs0iJSWFgoICJk2apKUneu7cOcaOHcvdu3cB+PHHH9myZQtyuZy2bdsybdo0pk+fzsiR\nI0UZrkqFUAnIz88XwsLChPz8fPHcsYcJwtoLd4U7lxTCnUsKQa3Of0ELgqAuKBDUeXl/t6kSzyEs\nLOz/3UZWVoEwe/YJQVd3gQABgrHxYmHZsvNCQUHRa7BQ4nm8jrGTqDheNn6xsbFCv379xONHjx4J\nPXv2FO7cufPcMn+mdevWWsdr1qwR1q1b9xctfvN4enoKd+/eFY8vX74sDB8+XBAEQYiIiBA+/vhj\nIS0tTRAEQcjKyhI8PDyEixcvCoIgCEFBQYK/v79QXFwsCIIghIeHC25ubkJhYeFrse2v/P9LTk4W\n+vXrJ6jV6tdiw1/l999/F/r16ycUFBQIKSkpgru7u1BU9Pzv6zFjxgiPHz8Wjhw5IvznP/8RBEEQ\n4uLihB49eohl8vPzBU9PT8HV1VUQBEHIzc0VunTpImRlZQnFxcXCgAEDhHv37gkJCQlC//79xXF5\n05Tlt5SXShsx62Cgh/364/C/ZMkjdl+hVY2q+HR0LLO8/Bm9NYnKhSAI7Np1i88+O8Hjx1kAjBjR\nhMDAbtjamlSwdRIS7xY1a9ZkwoQJ7Nq16y9nd2/cuDFHjhwB4NixY2zZsgUdHR2cnZ2ZNWsWWVlZ\n+Pj4kJ+fT6dOndi3bx8///wzPXr0oGPHjlhaWvLxxx/j5+dHYWEhCoWChQsXYmdnx8KFC4mMjESt\nVjNkyBA+/vhjXFxcuHLlCnfv3mX+/PnI5XKMjIxYsmQJd+/eZefOnchkMqKjo3F3d39plv0mTZrw\n6NEjALZv346Pj48oXm5sbMz06dPZtGkTbdu2Zc+ePYSEhIhJrJs3b86BAwdK6YoeOnSI4OBg5HI5\no0aNolevXqLdAD4+PgwbNoyrV68SGxtLXFwc5ubmtG3blhYtWpCfn0+vXr04fvw4a9asISwsDLVa\njaenZyl9z7179/Lhhx8il8t5+vQpM2fOBDTRvaVLl1KzZk169OiBo6Mjrq6uNGvWjPnz5yOTycTn\nZmpqSmBgIL/88gsFBQUMGTKEgQMHin38OdoFYGtry7Jly8TjK1eu0KFDB3R1dbGwsKB69ercv3+/\nzGjixYsXqVWrFra2ttja/qHa8+TJE6pVqyYeb9iwgaFDh7J8+XIADAwMCAkJEaOzVapUIT09nXr1\n6lGrVi0uXbpU6fQ9K+3WxZRrD4i69oN4vCviEckZxzgQtoy4NE14M6egkIy8gooyUeI1kZdXxKxZ\nJ3n8OIuWLe24dGkMW7f2lZwyiXeSa8Y6XDPW/lH/beBHXDPWIf3Hw+K5xC0buWasw8PJE8RzqieP\nuWasw416/z/dV2dnZ1Hj8VURBIFjx47h6OhITk4OQUFBbN++nR07dvDkyRPCw8M5dOgQdevWZffu\n3ZiY/PH/uKioiI4dOzJx4kRWr17N6NGj2bZtGyNGjGD9+vWkp6dz+vRp9uzZw65du7QEykEjgfTZ\nZ58RHBxMq1at2L59OwC//PILS5YsYc+ePeXKwxUaGoqjo+YlPzo6upSsU8OGDYmJiSErKws9PT1M\nTU21rv/5ODs7m/Xr17Nz5042b97M4cOHeRGFhYXs2rWL7t27ExGh2Wl+4cIFXF1duX79OvHx8ezc\nuZPt27cTFBQkamKWcPnyZVq1agVopge9vb0JDg6mf//+7Nq1C4DY2Fi8vb0ZOHAgCxYsYP78+Wzb\ntg1XV1d27txJQUEB1atXZ/fu3ezatYvVq1dr9aFQKLQ0PYODg7WcMoDk5GQt+ScLCwuSkpLKvOft\n27czfLh2SisPDw8+/fRT5szRJIaPiYkhKiqKnj17apUrccru3r1LfHw8TZo0AaBVq1ai41uZqJQR\ns2JBIPHeE3Sr5QKQmKFZ/G2im0NWfhZKuWY33ne3Yhm5+wID48L5un8bLAd5VJjNEq9GUlIO+vo6\nmJjoYWioZN26nqSm5jFiRFMp/YWExN9MTk4OCoWi3OWzs7Px8vICNKLdffr0wdPTk9u3b/P48WPG\njBkDQFZWFo8fP+bBgwe0bt0agG7durF582axrZI1QdevXycmJoagoCDUajUWFhZUqVKFWrVqMXHi\nRD744AP69u2rZceDBw/EH2UXFxfWrVuHi4sLjo6OGBi8eP3p7NmzMTQ0JDExEXt7ewIDAwHNju/i\n4mKtsoIgIJdr4hpqtfqlzyc6Opo6deqgr6+Pvr7+S9fDlTyDrl27sm7dOgBOnjxJr169iIiI4ObN\nm+LzLi4uJikpSdQYBY0zViIybmVlxcKFC1m7di2ZmZk4OTkBmkhT/fr1AY3j6u+v2RinUqlo1KgR\nenp6ZGRk4OHhgVKpJC0t7aX3+TKE5yhAJiQkkJubS82aNbXO79mzhzt37jBz5kxCQkIIDAzk888/\nL7ONhw8f8umnn7JixQqUSiUANjY2hIeH/7/tftNUSscsNb+Qy7djcWySDICusgAQsDDIA8DMUJMW\nIyk7H6WgxuLxQ2TKDhVlrsQrUFioZv36a8ybd5pPPmnBsmXdAfjoI4cKtkxC4s3QKruo1LkG335f\n6pz16HFYj9Ze2K1ra1dm/VclMjLyueLfZfFsRvilS5dSrVo1dHR0UCqVODs7azleAGFhYaJj86wo\nOSD+qCqVSlavXo21tbXW9U2bNnH79m1++OEHvv/+e7Zs2VKmTYWFhWIff55WLIvAwEAaNGjAqVOn\n2Ldvn9hvnTp1iIyMFB0d0Ghm1qtXDxMTE4qKikhOTtYScr99+zaOjo7ivcnl8lLOXVn2/vkZmJqa\nYm5uTnR0NNevX2f+/Pncv3+fAQMGMH78+Be2V9L3mjVraN++PUOGDCE0NJTTp09r9QEaJ2379u1a\nY3H16lUuX75McHAwSqWSZs2aabVfnqlMa2trYmJixOOEhIRS4wlw5swZ2rRpIx5HRuURq08AACAA\nSURBVEZiaWmJra0tDRs2RK1W8/jxY6Kjo/n0008BjfPp6enJjh07ePr0Kd7e3ixbtuyVPrdvK5Vy\nKlNdXIxhVj4KM8005a+ZllTRL0IhL0JPxwh9pREA0zs7kr5wMPNWfY5pl24VabJEOThxIpqmTb9h\n6tSjZGQUEBWVTHFx2W9YEhISfw+///47W7duLfWjW14mTZrEzp07SUxMpHbt2jx48ICUlBRA4yQk\nJCRQs2ZNIiMjATh79myZ7TRp0oQTJ04AcOnSJQ4fPkxcXBzbt2/HyckJX19f0tPTterUr1+f69ev\nA3Dt2jVxF+mr0KVLF1QqlejADB8+nLVr15KamgpoooOrVq0Sn8+wYcMIDAwUp1XDw8OZNWsWKpVK\nbLNOnTrExMSQk5NDQUEBo0aNEnd/5uXlkZeXx507d8q0p1WrVmzYsIGmTZuio6ND48aNOXXqFMXF\nxRQUFLBgwYJSdaytrXn69CkAaWlp1KxZE0EQOHnypJYDWIKDg4M4DkeOHOHSpUukpaVhY2ODUqnk\n5MmTqNVqrXsqz1RmmzZtOH36NCqVioSEBBITE8Wdrs9y69YtHBz+ePkOCwsTHe7k5GRyc3OxtbXl\nxIkT7Nu3T3Scd+zYAYCfnx8BAQFiNLCEhIQELYe6slApI2bVjPSpoxZI/akWVo2zOPS4DbYmGiet\nJFpWgr6hAfotW1aEmRLlJCYmjRkzjnHwYBQAdeuas2qVO717Nyj1Ni0hIfH6iYmJwcvLC5VKhVqt\nZu7cuVrpMF4FExMTxo4dy9KlS1mxYgVz5sxh3Lhx6Orq4ujoiLW1Nf369WPSpEl4eXnRrl07MbL1\nLJMnT2bOnDkcOXIEmUxGYGAg1tbWXL9+nR9//BGlUkn//v216nz++ed88cUXyGQyzMzMCAwM5Pbt\n2698D7Nnz8bb25u2bdvStGlTpk2bxtixY8V0GcOHD6fl/35Xxo4dy4YNG+jXrx9mZmaYmJgQFBSE\nnt4fCa4NDQ3x8fFh1KhRAIwcORKZTMaQIUMYNGgQdevWLeVUlNCyZUt8fHz4+uuvAc3mAhcXFwYP\nHowgCAwdOrRUHRcXF8LCwnB0dGTw4MEsWLCA6tWr4+Xlhb+/P+fPn9cq7+fnh7+/Pxs3bkRPT48V\nK1agUCjYuHEjnp6euLm50blzZwICAli8eHG5n6OdnR2DBg3C09MTmUxGQEAAcrmcs2fPEhcXJ9qe\nlJSEpeUf+Sc9PDzw8/Nj6NCh5OfnM3fu3DI/I6D57IaFhbFmzRrx3MiRI+nWrRvXrl0rNd1dGZAJ\nz5v0fYsoKCggMjISZ2dn8cN+ossXJJ6NosnEFDoY96NFjQKGNXlK/WqtcK3f/yUtSrxpwsPDadGi\nRanz0dFpODp+TUGBGiMjJZ9/3pFp09qgp1cp3xneSZ43dhKVg7dx/OLj44mOjqZDhw5cv36dtWvX\nPndK8p/OXxm/pKQkxo8fz4EDB/6xL7fJycmMHz+e/fv3V8gzKMtvKS+V9tcv74kmhG22z43/ftuN\n6CJNGLaKoWb+ev/NR8zZ9TP9Mx/gN/JfGLu0rTBbJZ5PnTrmuLvXw9RUjyVLulG9uunLK0lISFRq\nTExM2Lp1qxgF8vPzq2CL3i2srKwYOHAgmzdvZuzYsRVtToUQGBjI3LlzK6Vj+rc6ZosXL+bmzZvI\nZDLmzJmjlYH38uXLrFy5ErlcTu3atVm0aNFzQ5V/5seYBDLiU7AdFkWVW+/xYYv3OBYDTzLAzEAz\nlRkem8KDIh2S70RRlNrmJS1KvClu3HjKtGlHWbGiB82ba3LV7N8/EKWy/DvAJCQkKjempqalNgRI\nvF6GDBlS0SZUKCtWrKhoE/4yf9vi/6tXr/Lo0SP27t3LokWLWLRokdb1uXPnsmbNGvbs2UNOTg7n\nzp0rd9s6eYUYmKdRfdIt4oK+BD05GXmJwB9rzAI+aMK5PvWZ4PEBxq2laFlFk56uYuLEH2jR4j+c\nPv2QL744I16TnDIJCQkJCQkNf1vE7NKlS7i5uQFQt25dMjIyyM7OFhPBfffdd+LfFhYWr5Qjpa2u\nDucdNTtk1JkmDNtzjh61M5HLdDD6n3i5no6Cdp3bQGcpWlaRFBUVs2FDGH5+p8jMLEShkDF1qgvz\n5nWuaNMkJCQkJCTeOv62iFlycjLm5ubi8Z8z/pY4ZYmJiVy4cEFL2PRl5CdkYNJCEyEryDPkykNN\nhmozg6rIZZUyA8g7yS+/JNCs2TdMmfITmZmFuLnV4ZdfJrJq1QdUqaJf0eZJSEhISEi8dbyxxf9l\nbf5MSUlhwoQJzJs3T8uJex4leW8SLz/ARKZp7/o9VzFVRnGBDuHh4YQl5BBy7jadipLp2qU5smqV\nL4/Ju0BSUj4PHqRgZ2fA9OlOdOpUjby83wkP/72iTZN4RSpj9myJP5DGr3Ijjd8/i7/NMbO2tiY5\nOVk8TkxMxMrqjxxj2dnZjBs3jqlTp9K+fftytens7EwBcs7Fq2mRbwM85JLaEpVMTrbKnBYNGtHI\nvgWHQ28SqjLG8sZZxvdsg9lbtlX8XSUvr5DNm68zYUJLdHQ0kcsTJ2rSrJkNv/76y1u3ZV+ifLyN\n6RYkyk95xu/Ro0cEBgaKiWDt7OyYN28eFhYWfPfdd6xevVpMUqqrq8uyZcvETPebN2/mhx9+QF9f\nH0EQmDZtGi4uLlrtz5o1i9u3b1OlShUEQaCwsJCZM2eKucAuXLjA2rVrEQSBgoICBg0apJWfqzx9\nvGnOnj3L6dOnmTt37t/az8vGLyQkhG3btiGXyxk0aJCW0DhoZKpKdifWqlWLgIAALSWE6dOno6ur\ny5IlSwDNsw4JCUFHR4d58+bRuHFjiouLWblyJfv37+fy5csA7Nixg6Kior+ciPhdpyRdxl9C+JsI\nDw8XRo4cKQiCIERGRgoeHh5a1/38/IRDhw6Vq638/HwhLCxMyM/PFxJy8oUlV38Trl+yEe5cUggf\nrJgiyKdvF/bdeCiWj0pIF1Z8tU34fvIMoTA19fXdlESZFBcXC99+e1t4771VAgQIX399tVSZsLCw\nCrBM4nUgjV3l5mXjV1RUJPTp00e4du2aeO6bb74Rpk+fLgiCIBw4cEBYsmSJeG3t2rXChg0bBEEQ\nhJCQEGH8+PFCQUGBIAiCEB0dLXTs2FFIT0/X6sPX11f4+eefxeNHjx4JPXr0EARBEOLi4gR3d3ch\nLi5OEARBKCgoECZPnizs27fvlfp4kxQUFAi9e/cWsrOz//a+XjR+OTk5Qo8ePYTMzEwhLy9P+Ne/\n/iWkpaVplZkwYYJw+vRpQRAEYd26dUJISIh47fz580L//v0FX19fQRAE4bfffhP69esnFBYWCpGR\nkcLq1asFQRCEoKAgYceOHULr1q3FusXFxcLAgQOFp0+fvrZ7fZd41m95Vf62iFnz5s1xcnLCw8MD\nmUzGvHnz+O677zAxMaF9+/YcOnSIR48esX//fgB69+7N4MGDX9qulYEuM1rUJWVPH7IMQkgp0OS9\n6u1YXSzzvrUZ7/97+POakHiN3LqVwL//HcqpUw8BaNKkGo0bV6tYoyQkJMrNhQsXqF+/vhi9Ak02\ne+E5ucdTUlJEofDg4GAWL16Mrq4uALVr1+bw4cOYmr44H2HNmjXJzs5GrVaze/duvLy8qF5d8x2u\nq6vL7NmzGT9+PAMHDix3HxcuXGDlypUoFAp69erFyJEj6dq1K4cPH8bIyIilS5eKot1nz54lMTGR\n9957DxcXFzE7vLu7O3v37uXIkSMcPnwYuVyOm5sbo0eP1urrp59+ok2bNhgZGZGdnc2MGTPIzc0l\nPz8ff39/GjduTI8ePejYsSOWlpZ8/PHH+Pn5UVhYiEKhYOHChdjZ2bFlyxaOHj1KcXExnTp1YvLk\nyVr9+Pj48Pvvv2NiYgJo9C2fTcR78+ZNGjVqJF5v3rw5ERERdO3aVSzz6NEjMVVVhw4d2LVrF336\n9EGlUhEUFMTEiRM5fvw4AKdOnaJnz57o6Ojg5OQkqhF4enpibGyslV1fJpMxcOBAdu3axbRp0144\n3hKvxt+6xqxEbLSEZ7Ww/mqITyaTcX/NT+gfaIJejDMRg6pgZaRCT0da9P8mSU3NY968UwQFhaFW\nC1hYGLBoUVfGjWuOQiGNhYTEX2XptXsA+LaqL57b/9tjHmTk0L++LfWqaDZO3UjM4OijRJpYmfJB\nLc3LUJaqiPU3YzBWKvBuWqdc/UVHR/P+++9rnftzTskff/yRyMhI0tLSMDIy4rPPPgM0Gfzr1q2r\nVfZlThlodCytrKxQKBRER0fTrZu2lrGdnR1paWkUFxeXqw9BEPjiiy/Ys2cPZmZmTJo0CQ8Pj+f2\n/+TJE/bs2UN4eDjbt2+nb9++REVFUb16dbKysggNDWX37t2AJh/YBx98oCVRdfnyZbp06QJosuwP\nHDgQNzc3Ll26xMaNG1m7di1FRUV07NiRjh07MmfOHEaPHk27du04c+YM69evZ+HChQDs2rULuVxO\nt27dGDlypLgxDjTaoi+aykxOTsbCwkI8/vMmO4AGDRpw5swZ+vbty7lz58QlRt988w1DhgzR6i8+\nPh6FQsGYMWMoKipi9uzZODg4aJV5lpYtW3LgwIHnPmeJv0aly/xfXFjEDd9dFBeqcbduw6JaZlRr\ndolD4Sv5uOWn3HycSuhP5+hgY0ybru2QGxhUtMnvJPv3/8q6ddeQy2V4e7di/vwuWFhIz1pCorIh\nl8tFAW6AiRMnkp2dzdOnTwkJCQGgV69e+Pr6AnDo0CHmzp3L8uXLEQRBFON+GStXrmTLli2kpaVh\naGgoJgCVyWSo1eoy68hksnL1kZqaip6enuikfPPNNy+0pVGjRshkMpo3b46fnx8qlYqTJ0/i7u7O\nrVu3ePToEcOHa2ZdcnJyiI+P13LMEhMTRXHsqlWrsn79ejZv3oxKpcLQ0FAsVxKpun79OjExMQQF\nBaFWq0U79fX18fT0REdHh7S0NNLT05/rBJWHsqKcvr6+BAQE8N1339G6dWsEQeDhw4dERkYyZcoU\nrly5olVfrVazadMmwsPD8fPze6HjZWNjI4qlS7w+Kp1jFh6bjL6vBXZGF9E9Y8aoRm04rWuGsb7m\ng/7jr/F8/msmg/fup5G5HibtyrexQOLlPHmSha2tJmQ+ZkwzIiKeMGlSK2nqUkLiNfJspKyEAQ1K\nC4o3tTajqbWZ1jkTXZ0y67+I+vXrs337dvE4KCgIgK5du1JcXFyqvLu7O6tXrwagRo0a/Prrrzg7\nO4vXo6KiqFu3LkqlUqve9OnT6dKlC1FRUfj5+VG7dm0A6tSpQ2RkpNZUanx8PFZWVshksnL1IZfL\ny7T1WQoLC8W/n63n4uLCtWvXOHPmDBs2bCA8PJzOnTszf/78F7ZXwrZt26hWrRrLly/n1q1bLFu2\nrFQ/SqWS1atXY21trXWPW7du5eDBgxgZGdG7d+9Sbb9sKrOsTXZNmzbVasPW1lZ0VM+dO0diYiKn\nT5/m8ePHDBo0iOzsbFJTU9m4cSNVq1alTp06yGQyWrZsSXx8fLmegcTrpdLNORXoKSnsVQXTjjdI\n7X8S6wZODGo9GzenEQA0t7dgUHEiHZT5GDZu+pLWJMpDbGwGQ4YcoH79tcTHZwKgUMjZsKG35JRJ\nSFRy2rRpw9OnT/n555/Fc7dv3yYnJweForQqx82bN0WnasSIESxdupTc3FxAMy06depUMjMzn9uf\ng4MDTk5OWlOFO3fu5PffNWl0CgsLWbJkCSNGjCh3H+bm5qjVahISEhAEgfHjx5OZmYmxsTFJSUmo\n1Wpu3rxZpj3du3fn0KFDGBgYYGFhgZOTE1euXCEvLw9BEFi4cCH5+fladaytrUlISAAgLS2NmjVr\nAnDixAktB7CEJk2acOLECUCTfP3w4cOkpaVhYWGBkZERt2/fJj4+vlTdNWvW4O/vT3BwMMHBwaWE\n3ps0acKtW7fIzMwkJyeHiIgILQe3pI3Tp08DmsTuXbt2ZeTIkRw+fJh9+/Yxb948OnfuzLhx4+jY\nsSPnz58HNLs5bW1ty3xmJSQkJIiRQ4nXR6WLmDW1MsM67VeKsyHhYX2+ifiNbtm2dGug+QC5O1TH\nfdWMCrby3SA/v4gVKy6yePF5cnML0dfX4erVePr1k4TGJSTeFWQyGZs2bWL+/Pl8/fXXKJVKDA0N\nCQoKQl9fkwi6ZI1ZCQEBAYBmijMnJ4fBgwdjamqKnp4eX331FZaWli/sc+rUqQwYMEBcu/Xll18y\nc+ZMBEFApVLx4Ycfigvyy9vHvHnz8PHxAaBnz56Ympri6enJhAkTqF27NvXq1SvTljZt2vDpp5+K\nde3s7Bg+fDjDhg1DoVDg5uYmPocSXFxcCAsLo3v37nz00Uf4+voSGhrKsGHD+OGHH0pN/02ePJk5\nc+Zw5MgRZDIZgYGB2NnZYWRkhIeHBy1atMDDw4MvvviCrVu3vvDZPYu+vj4zZsxgzJgxyGQyvL29\nMTEx4c6dOxw/fhwfHx969+7NZ599xtq1a2nZsiWdO3d+bntNmzbl7Nmz4ka8klQgCxYs4LfffiM7\nOxsvLy+6du3KqFGjuHbtWoWnLXkXkQnP23rzFlGSD8TZ2Zn8B0kkJrmj1rvLjsheLIvqj3+PZvj3\naPzyhiTKhSAIfP/9XaZPP0pMTDoAAwc6snx5d957r8pfalPKhVV5kcauciON3+unoKCAAQMGsHfv\nXq01ZX8Hb/P4DR48mK+++uqlkbV/Is/6LXp6eq9Ut9JNZd7ffAq13l0Afkt9j1W9fsPeMISCojxi\n03I4dS6CjNSMCraycjNr1gn69dtLTEw6zs7W/PzzcPbtG/iXnTIJCQmJdwk9PT0+/fRTcQPDP5Gd\nO3fywQcfSE7Z30Clc8wePnws/n05oyFGump0FUXoKvTZ/8sj3A7dZozndHIjb1WglZWbIUMaUbWq\nIWvX9uT69fF06VK7ok2SkJCQeKvo1KkT/v7+FW1GhTFs2DBGjRpV0Wa8k1S6NWb5OQ/Fv42MNH6l\nmYFm946RjhyHnCSc02LRr9+ggiysXKjVxWzZcp2LF+P4738/AqBpUxt+/30qBgbKl9SWkJCQkJCQ\neJ1UOsfM4Pc8ngY7UF3PHtvaGvHyKoaaLcifuDrwiasDxarJyP+XJVri+Vy8GMuUKT8REfEEgNGj\nm9Khw3sAklMmISEhISFRAVQqx6wwI5f8uwUkxDSjflUXbBdotk+bGVpplZOcshfz+HEWvr4n2LHj\nFwDs7U358svutG9fs4Itk5CQkJCQ+GdTqRyzvATNon4jCzNMi6CLiSahoJmBNdkFhcjUaowM9V/U\nxD+elSsvMXfuKXJyCtHTUzBzZjtmzWqPkZHkzEpISEhISFQ0lcoxy0jORL9OBoadM1DeMsLMxJAc\nNBGz7dei+fd3lxkde5W1a/zQtS2dKVsCEhNzyMkppF8/B1as6EHt2uYVbZKEhEQFEhcXR58+fcTM\n+iqVigYNGhAQEIBCoaBr167Y2NhoJZsNDg6mqKiIr776ivPnz2NgYIBSqcTPz6+U7qaXlxe5ubkY\nGhqK0krz5s0T84odPnyY//73vyiVSgoLCxk/fjzu7u4A5e7jTbNnzx6ysrIYN25chdqxadMmQkND\nkclkTJ48mU6dOmldj4iIYMmSJSiVSlq0aMH06dPFa4IgMGTIEFxdXZkyZQpZWVnMmDGDrKwsUTKr\nSpUq7Nu3j/379yOXy3FwcGDevHksW7aMFi1a4Obm9qZv+R9BpXLMspOzMGvzBLsxt0g/VEiOYQvk\nMgUmehY8TnqATBCo8vR3lFbWL2/sH8Ldu8k8eZJN5861APDz60D37nXo1q18AscSEhLvPrVr1yY4\nOFg8njVrFocPHxaTvG7cuBEjIyOtOps2bSIzM5ODBw8ik8mIiIhg8uTJ/PTTT+joaP+0BAYG0qCB\nZkPWlStXWLBgAdu2beP69ets3bqVLVu2UKVKFbKzsxk3bhympqa0bdv2lfp4U6SkpIjOSkUSGxvL\njz/+yJ49e8jOzmbo0KG0b99ey4EOCAhg5cqV1KtXjzlz5hAREUHz5s0B+Pbbb7WUBrZt20br1q0Z\nO3Yse/fuZePGjUyePJkjR46wc+dOlEolw4cP5/r160ydOpWBAwfi6uqKgaRH/dqpVI6Zbr4KmZ5G\n7DbRRPPhk8lNkcsVLOzXhtndnMmJboasgv7Dvk1kZhYwf/4ZVq++QrVqRty9OxkjI11MTPQkp0xC\nQuKFNG7cmEePHr2wzJ49ewgJCRHFxZs3b86BAwde6jA1adJEbHv79u34+PhQpYomR6KxsTHTp09n\n06ZNtG3bttx9HDp0iODgYORyOaNGjaJXr164uLiIAt0+Pj4MGzaMq1evEhsbS1xcHObm5owcOZJW\nrVqRn59Pr169OH78OGvWrCEsLAy1Wo2np2cpDcu9e/fy4YcfIpfLefr0KTNnzgQ00b2lS5dSs2ZN\nevTogaOjI66urjRr1oz58+drMgcYGbFkyRJMTU0JDAzkl19+oaCggCFDhjBw4ECxD7VazciRIwHI\nysrCxMQEW1tbLR3OK1eu0KFDB3R1dbGwsKB69ercv39fK5qYlJQkRibbt2/PhQsXaN68OampqRw+\nfBgPDw9RhPzSpUssXrwYgC5dujBhwgRmzpzJtm3bAMjLyyM7OxsrKyv09PTo0qULP/zwg5bdEq+H\nyuXB5Kowck4B4IadxvRHaX/sHjQyNcaoaZMKMe1tobhYYPv2m8yadYKEhBxkMujZsx4qlZo/vfBK\nSEi8hexSDHml8ubNa9Pz2uJS9Yeqd/+l/gsLCzl58iRDhjzfjqysLPT09DA11ZZn+/NxWYSGhuLo\n6AhodC8bNmyodb1hw4bExMSUu4/s7GzWr19PSEgIKpUKX19fevXq9cL727VrF4cOHeLnn3+mVatW\nXLhwAVdXV65fv058fDw7d+5EpVLRr1+/UpJMly9fxtfXF9CIhnt7e9OmTRv279/Prl27mDVrFrGx\nsXz99dfUr1+fESNGMH/+fGrVqsXOnTvZuXMno0ePpnr16syePZv8/Hzc3Ny0HByFQiFGMJ+X+T85\nORkLCwvx2MLCgqSkJC3HzN7enmvXrtGyZUsuXrwoRtOWL1/OtGnTePjwYZntWVpakpiYKF77z3/+\nw/bt2xk+fDg1atQAoFWrVhw8eFByzP4GKpVjps4vpChV8x9E30hFLlCMWcUa9RZx9Wo8U6b8xNWr\n8QC0bWvP2rU9adFCWm8nISHxfGJiYvDy8gLg7t27jB07Vmv90Lhx48QfdXNzcxYtWoRarS53+7Nn\nz8bQ0JDExETs7e0JDAwENDqdxcXFWmUFQUAu1+SoLE8f0dHR1KlTB319ffT19QkKCnph+caNNfJ9\nXbt2ZfPmzfj6+nLy5El69epFREQEN2/eFJ9FcXExSUlJojMCGmesRLjbysqKhQsXsnbtWjIzM3Fy\ncgLAwMCA+vXrA/DLL7+IiWhVKhWNGjVCT0+PjIwMPDw8UCqVpKWlvfQ+X0ZZ6oqLFi1i0aJFKBQK\nHBwcyM7O5tq1aygUCpo3b67lmL2orU8++YThw4czbtw4WrRoQYsWLbCxsRGjbRKvl0rlmMWlZGHT\nUPMBjte3xBywNbPlv1fvs2zbEbxkSfgumYXC2LhiDa0A1Opihg37jvv3U7G1NWb58u4MHdpInAKQ\nkJCoHPzVSNf/p/6za8x8fHyoXVtb7aOsNWZFRUUkJydTtWpV8dzt27dxdHQs9b1Tssbs1KlT7Nu3\nD2trzTrgOnXqEBkZKTo6AHfu3KFevXqYmJiUqw+5XF7Kufszz66lUio1syympqZYW1sTHR3N9evX\nmT9/Pvfv32fAgAGMHz/+he2V9L1mzRrat2/PkCFDCA0N5fTp01p9gMZJ2759u9YzuXr1KpcvXyY4\nOBilUkmzZs202i/PVKa1tTUxMTHicUJCgvhcS2jQoIE4Fblnzx4yMzM5efIkkZGRDBo0iNTUVFQq\nFTVq1MDa2pqkpCRMTEzEttLT07l37x6tWrVCX1+fjh07EhER8dZqd74rVCpJpkLnmhSZaCJkKXl6\n3EsxoJ7Ve1yJ+p3f9C1I/vUO8n/QfJ1KpSY7WwWAQiFn1Sp3Zs1y5e7dyQwb1lhyyiQkJF6ZmTNn\n8uWXX5KXl/fCcsOGDSMwMJCioiJAM+U2a9YsVCrVc+t06dIFlUolOjDDhw9n7dq1pKamApppyVWr\nVolOSXn6qFOnDjExMeTk5FBQUMCoUaPE3Z95eXnk5eVx586dMu3p3r07GzZsoGnTpujo6NC4cWNO\nnTpFcXExBQUFLFiwoFQda2trMVKUlpZGzZo1EQSBkydPajmAJTg4OHD27FkAjhw5wqVLl0hLS8PG\nxgalUsnJkydRq9Va91QylRkcHIy/vz/BwcFaThlAmzZtOH36NCqVioSEBBITE8X1ZCXMnj2bqKgo\n1Go133//PZ07d2bWrFkcOnSIffv2MWnSJAYOHEjfvn1xdXUlNDQUgGPHjtGhQweKioqYNWsWOTk5\nANy6dUt02hMSErQcaonXR6WKmHXq3Yzz9z7FOGc630a9z/1Me+b3aUgru9oMrnoFK5eh/xhn5Kef\n7jF16lG6d6/DunWa9RS9ezegd29JikpCQuKvU6NGDdzd3QkKCtJKr/Bnxo4dy4YNG+jXrx9mZmaY\nmJgQFBSEnp7eC9ufPXs23t7etG3blqZNmzJt2jTGjh0rpssYPnw4LVu2LHcfhoaG+Pj4iLqNI0eO\nRCaTMWTIEAYNGkTdunXFKcY/4+bmxsKFC/n6668BzeYCFxcXBg8ejCAIDB06tFQdFxcXwsLCcHR0\nZPDgwSxYsIDq1avj5eWFv78/58+f1yrv5+eHv78/GzduRE9PjxUrVqBQKNi4cSOenp64ubnRuXNn\nAgICxMX35cHOzo5Bgwbh6emJTCYjICAAuVzO2bNniYuLY+jQoQwYMIDZs2cDzghA3wAAIABJREFU\n0Lt3b3FnbFl4eXkxc+ZMhg4diqmpKcuXL8fExARvb2+GDx+Ojo4O77//Pt26dQPg2rVruLi4lNte\nifIjE8qamH7LKCgoIDIyEmdnZ1IKBf6vvTsPq6r6Aj7+vcwyCsmQiEMOqWilZoL6QzHBudSQIQEV\nc8ohzVScwnnWUFIz0zSHhFex1Ayb0N5ywuFnipIGSUiIoKACMlw47x+83J83BofEy631eR6fp3v3\n3eesc3fiYp999rqx0ZtXUkOwNjIlc8mjLZTVd1eu3GTSpEN89dUVAFq2tOfMmZGYmtbsHLuyBayi\n5pOx028yfk9WRkYGo0aNYs+ePU9lIqAmjl9BQQGDBg1i165dmJub6zqcGun+vOVBv6z8lV7dykw7\n9Av83wSe3TqIwydvc6Rbmwd3+oe4e7eA0NDvcHVdx1dfXcHKyoQVK7w4e3ZUjU/KhBDin8Le3p5B\ngwaxadMmXYeiM+Hh4YwbN06SsmqiV/+iJ2xbh6HBeey8Pbj8mgnNrS9wIN6CbZ9E0d/FCv8JIf/I\nPcxu3bpH69br+fPPuwAMG/YSixa9ipPTv+8hByGE0LWqthL5NyjbLkRUD72aMTPtmYnLpP9i5HoS\n8xuG2JjZc+jUr+w2cODoN0fgvh2P/0ns7Grxn//Up0MHZ06ceIvNm1+XpEwIIYT4B9Kr6SW7l9RQ\nBL9ec2T7ucaEvOvMsPYG1E1LooO32z9m4f+NG7nMnPk9w4a1oWPH0v1zNm7sh4WFCQYG/4xrFEII\nIUR5epWY2dSyJ78IvrS15isXY1pk3Ka/Wzvathyp69CeiKKiYtaujWPOnMPcvl1AfHwGR48OB8DK\n6tEWDwohhBBC/+hVYlaUdweAP/NKNxv0fv6fs6P9t98m8s47MVy6lAlAz55NCA/voeOohBBCCPE0\n6dUasyL1aQDGuaXxjnsylsYGLF/2CWd++L8VlqPQB6mpdxgwIBJv7+1cupRJkyZ27N8fwMGDb/L8\n83UefAAhhPgbrl27RosWLUhISNC8Fx0dTXR0NFBauqisKkDZ50NDQ8sdp1u3brz55psEBQUxePBg\nhg8fTnp6uqZ9y5YtDBgwAD8/P/z8/IiLi9O05eXlMXv2bAYMGIC/vz+jRo0iLS2tOi73kaxatYqD\nBw/qOgwWLVqEn58f/v7+/PLLL+Xav/vuO9544w0CAgLYvn27VltZLc6y8UxLS2Po0KEEBgYydOhQ\nMjIyALh9+zbDhw9nwoQJmr7vvvtuhecT1UuvErPC9FoAFGNCYYEh/+fIOULTTfk4fIveri8zMTEk\nNvZ3LCyMWbLkVS5cGEPfvs309nqEEPqnSZMmrFy5ssK2Z555hqioKHJych54nI0bN7Jt2zZ27NhB\nnz59WL16NVC64/3PP//M559/TmRkJGvWrGHu3LkkJSUBpSWbnJ2d2bt3L7t27aJ///5MmjTpyV3g\nY0hISCA+Pr7KguhPw6VLl0hOTiYyMlJT+/J+JSUlzJ8/n40bN7Jjxw5iY2O1aliuX78eG5v/1ZQO\nDw/H19eX7du34+XlxaeffgpAWFhYuf3SQkNDmTdvnt5OfOgrvUrMiu+aUJRrRhEWGOeY097JigFF\naXi2qKfr0B6aoijs3XuJwsLS4rz29hZERQ3i8uXxTJvWWfYkE0I8da6urpibm3Ps2LFybWZmZvj7\n+z/yvl0vvvgiycnJAGzdupVp06ZhZmYGgKOjI2+99Rbbt28nJyeHn376iREjRmj69urVi48//rjc\nMTdu3IiPjw++vr4cP36ca9euMXDgQE37wIEDNTN6s2fPZvz48QwYMIA///wTgNTUVAYOHEhxcTEz\nZswgKCiIgICACq9727Ztmm0xEhISCAgIICgoiCFDhpCdnc21a9cICAhg+PDhxMbGcurUKd58802C\ng4OZNm0ahYWFqNVqJk+eTGBgIAMHDiQ2NlbrHOnp6QQFBWn9+WvppQsXLmgKyjdu3Jjbt29rJclZ\nWVlYW1tjZ2eHgYEBbm5uHD16FIDExER+++03unbtqvl8WFgYPXqULpOxtbUlOzsbgAULFpRLzBwc\nHGjYsGGF34+oPnqVBSSGdiKvVVeYZUFObi38errj19Nd12E9tLNn05gwIYaffvqDFSu8mDy5IwDe\n3o11HJkQoqZIOF75j2WnRuup7ViawGSnb+T672Mq/WxzN/UjnXfSpElMmzYNNze3cm1+fn74+PhU\nWKKoMjExMbRs2RIoTYgaN9b+Ode8eXO+/PJLUlJSaNSoEYZ/2e7I2tpa6/XVq1c5dOgQUVFRpKSk\n8PHHHzNmTOXXb2Njw/z581m7di2xsbEMHjyY77//Hm9vb/bv34+9vT2LFi3i1q1bDBkyhP3792v1\nP378OFOmTAHg5s2bzJ49m5YtW7J69Wr279+Pp6cnly5dIjY2FltbW/r378+WLVuoXbs2y5YtIyYm\nhk6dOtG5c2cGDBhASkoK77zzDp6enppzODo6at0mrkh2dja2traa13Z2dmRkZGBpaal5nZuby9Wr\nV3F2dubEiRO88sorACxdupTZs2fzxRdfaPqXbQpbXFzMzp07GTt2LIDmeH/Vvn17Tpw4QceOHauM\nUzw5epWYAdSqA/eA6WpnXYfy0DIz85g16wc+/vg0igL29uY4Oso+ZEKImqNhw4a0bNmywjVVRkZG\njBo1ioiICEaOrPwp+BEjRmBoaEhKSgrt2rVj7ty5VZ7TwMAAlUpFcXHxA+O7ePEiL774IgYGBjRo\n0ICFCxdy7dq1Sj//wgsvAODt7c2SJUs0idmcOXPYsmULp0+f5syZM0Bp+ZzCwkJMTEw0/e/evUvt\n2rWB0tu5K1asID8/nxs3btCvXz+gtK6ora0tmZmZJCcnM378eKB0zZytrS3W1tacP3+eyMhIDAwM\nNLNTf8dfbyuqVCqWLFnCjBkzsLKyol690jtIX3zxBS+99BIuLi7ljlFcXMzUqVNxc3PD3b3qyQ0n\nJydOnz79t+MWD0+vEjPz5lncbPQsqmIoNrXhyKEjtO/UHnPLmlkWQq0uYf36ON5//zDZ2fkYGRkw\nfvwrvP9+F2rXNtN1eEKIGuhhZ7pqO47QzJ49KWPHjmX48OEMHjwYo79UUenVqxdbt27l6tWrlfbf\nuHEjFhYWbN++natXr2pmYerVq0dCQgItWrTQfPbSpUs0adKEevXqkZSUVC4xOn/+PK1bt9a8NjQ0\npKSkROt8f12Lq1b/77szNjYGoGnTpty4cYO0tDTu3r1Lo0aNMDY2ZvTo0fTt27fSa7n/2AsXLmTE\niBF4eHiwadMm8vLytM5hbGyMg4NDudmvvXv3cvv2bXbu3El2djY+Pj5a7enp6bz33nta77Vu3Zqp\nU6dqXpclfmVu3LiBvb29Vp9XXnmFnTt3ArBy5UqcnZ359ttvSUlJ4fDhw1y/fh0TExOcnJzo2LEj\n06dPp0GDBowbN67S6xe6o1drzJ5bcIy23p9imW7E5uIMun3zBxOGTX1wRx354osEJkyIITs7Hy+v\n5zh3bjSrVvWQpEwIUSPVqVOH7t27s2vXrgrbJ02axKpVqx54HH9/f06ePKl50nPIkCEsXbqUe/fu\nAaXJxebNmwkMDMTS0pJXX32V8PBwTf9Dhw6xdOlSrdkhV1dXzpw5g1qtJjMzk7Fjx2JpacnNmzdR\nFIWMjAxSUlIqjKdr16588MEHdOvWDShd//b9998DpbcpK7omS0tLbt++DZTeTqxfvz6FhYUcOXKE\noqIirc+WLa7/7bffgNL1aQkJCWRlZVGvXj0MDAz49ttvKSws1OpXdivz/j/3J2VQOvN36NAhAOLj\n43FwcCh32/Gtt97i5s2b5OXlERsbi7u7O+Hh4ezZs4eoqCgGDRrE22+/TceOHdm3bx/GxsZaT19W\nJT09HScnp4f6rHgy9GrGDKBYMeJUiRn5FPBcbiZt69TSdUhacnIKsbQs/a1v4MAW+Pu3wt/fldde\ne16etBRC1HghISF8/vnnFbZ16NCBOnUevI2PkZERU6dOZc6cOXz++ef07t2bvLw8/P39MTU1RaVS\nMWXKFM1tthkzZrB8+XL69euHtbU1Tk5OfPjhh1o/M+vVq8frr79OYGAgiqIwadIkbGxs6NixI2+8\n8QbNmzfXmpG7n5eXF/7+/uzbtw8onf07fvw4/v7+FBcXVzhz1KFDB06dOsWrr75KYGAgY8eOxcXF\nhaCgIObNm1fuac2FCxcyffp0zeyZn58flpaWjBkzhv/+97+88cYbmut6lJmqZs2akZqair+/PyqV\nirCwMKB0SxMrKyu8vLzw9fUlJCQElUrFyJEjsbOzq/R4O3fupKCggKCgIKD0gYLZs2czdOhQ7ty5\no3kg4e2338bd3Z24uDj69+//0PGKv0+l6MFzsAUFBVy4cIFaRf1Jy3bm4z/fYGn/MTS0s0QpKUFl\noPuJv7y8IpYu/Yk1a05y5sxIGjWyfXCnf5HTp0+Xe+JH6AcZO/0m4/d4Ll26xKpVq9i4caNO49Dl\n+GVmZjJq1Ch2794tEwuPqCxvadWqFaamj1a5R/cZzSMyMFaTdteUBrYWADpPyhRFISoqnubNP2Te\nvB/Jzs5n375fdRqTEEKIv6dFixY0b96cmJgYXYeiM4sXL+b999+XpOwp07tbmQWKDa0vQ37ePWpZ\n6HbR/y+/pDNhwtccOVK6V0+bNk6sWdOLzp3r6zQuIYQQf9/kyZN1HYJOVbbpsKheejdjdlddl+SC\nYmrPjGT6mNk6i+Ojj07Rps0GjhxJ5plnarFhQ1/i4kZIUiaEEEKIx6Z3M2b/venMnRI1xQYGOBiV\nPLhDNenSpQEmJoaMHNmWOXO6Ymtbsx5CEEIIIYT+0avEbNPW5YSbF7GjQxP2v9Ec8vOe2rl//DGZ\nqKh4IiJ6oVKpaNHCnj/+mIi9vcVTi0EIIYQQ/2x6lZgtum7Iyp8yYZsT1LEFqv/Jx5SU20yZ8i2R\nkfEAeHk9x+uvNweQpEwIIYQQT5ReJWZplnd41rwE02erPyG6d6+IFSuOsnjxT9y7p6ZWLSNCQztL\nXUshxD9OcnIyixcv5ubNmwDUrVuXsLAw7OzsiI6OZvXq1XzzzTeax/5DQ0M1e3H169ePVq1aAVBY\nWMiUKVN4+eWXtY4fGhpKfHw8tWvXRlEUioqKtD73888/ExERgaIoFBQU4Ovrq1WXc9OmTRw4cAAz\nMzPNHmYdOnSo9u+lKj/++COHDx/m/fff12kc+/btY+vWrRgYGODr68ugQYO02uPi4li1ahVGRkaY\nm5uzbNkyrKysmDdvHr/++itqtbpcv8uXLzNw4EBiYmKoV68eUVFR7N69GwMDA5o3b05YWBjLli2j\nXbt2mgLr4glS9EB+fr5y6tQp5VR0S2XMxFlKqxHLlbUL1lTb+b78MkFp2DBcgTkKzFF8ff+Pkpyc\nXW3n+zc4deqUrkMQj0nGTr89aPzUarXSr18/JS4uTvPehg0blHfffVdRFEXZs2eP0rdvX2XDhg2a\n9mnTpikpKSlKSkqKMmDAAM37J0+eVEJCQsqdY9q0acoPP/ygeZ2cnKx4e3sriqIo165dU3r06KFc\nu3ZNURRFKSgoUMaNG6dERUUpiqIo+/btU0aNGqUUFBQoiqIoSUlJioeHh5KdrbufyQUFBUrfvn2V\nnJycaj9XVeOXm5ureHt7K3fu3FHu3bun9OnTR8nKytL6zIABA5TExERFURRl/fr1yoYNG5S4uDhl\n/vz5iqIoSk5OjuLm5qYUFxcriqIoJSUlSkhIiNKrVy8lJSVFycvLU4KDg5XCwkJFURQlKChIOX36\ntJKfn6/069dPycvLq47L1ntleUt+fv4j99WrGbNaz97hZIEpF60cycn8vdrOc/ZsGlevZtO6tQNr\n1vSia9eG1XYuIYTQpZ9//pmmTZtqzXK99dZbWuWQ3nzzTXbu3Imvr6+msHdFMjMzcXBweOA569ev\nT05ODsXFxXz++ecEBQXh7OwMgImJCdOnT2fUqFEMGjSIbdu2sWjRIk0dzUaNGrF//36sra3LXceq\nVaswNDSkd+/eDB06lG7durF//34sLCxYunQpTZs2BUpnu27cuEGDBg3o0KGDZmf7Hj16EBkZyVdf\nfcX+/fsxMDCge/fuhISEaJ3r66+/xs3NDQsLC3Jycpg8eTJ5eXnk5+cze/ZsXnjhBby9vfHw8OCZ\nZ55h4MCBzJw5k6KiIgwNDVmwYAF169Zl8+bNHDp0iJKSErp06VKuIsCECRP4448/sLKyAkprcm7e\nvFnTfu7cOVq3bq1pb9u2LWfOnNGUnoLSWptlxdNv377Nc889x8svv6wZ71u3bmFjY4PB/98TdM+e\nPbi7u3PkyBEAatWqxdatWwG4d+8eOTk52NvbY2pqiqenJwcOHCg3Syf+Hr1KzC5kNcIhw44DXpY0\nb9PviR03K+seCQmZuLuXlgeZOrUTzs7WDB36EkZGerejiBBCjxlOLi2EXbwySPPea5t+4KuLqXwR\n0pV+rqU/pz4+dpkxu0/wllsTNgxyB+DP23m4zNvDs9a1uBbmU+7YFUlKSuL555/Xes/gLxt3m5qa\nMmzYMD766CNCQ0O12n7//XeCgoIoKCggPT2dTZs2PfCccXFx2NvbY2hoSFJSEq+++qpWe926dcnK\nyqKkpITU1FQaN9ZeQvLXpExRFObOncuuXbuwsbHh7bffxt/fv9Lzp6WlsWvXLk6fPs1nn31G//79\nSUhIwNnZmbt37xITE6MpSxUQEEDPnj2pW7eupv/x48fx9PQEICMjg0GDBtG9e3eOHTvGxo0biYiI\nQK1W4+HhgYeHBzNmzCAkJISOHTty5MgR1q1bx4IFC4DSEkkGBga8+uqrDB06VKsO5po1a6rc+T8z\nM1Or/JKdnR0ZGRlan5kxYwaBgYFYW1tjY2OjtTfbhAkTOHPmDMuXLwcgKyuLL7/8kk8//VSTmJX5\n+OOP+eyzzwgODtaU0mrfvj179+6VxOwJ06vErKDYEKdCU3q96gmNK/+t7WEVF5ewadNZZs78AQMD\nFZcvj8PGxoxatYx56622TyBiIYSo2QwMDFCr1ZrXY8aMIScnh+vXr2tqSwL079+fQYMGkZqaqtW/\nUaNGbNtWmkwmJiYyceJE9u7di5GR9j8vq1atYvPmzWRlZWFubq7ZvFSlUlFcXFxhbCqVCkVRUBSl\nyt3nb926hampqSZJ2bBhQ5XX3Lp1a1QqFW3btmXmzJkUFhby/fff06NHD86fP09ycjLBwcEA5Obm\nkpqaqpWY3bhxQ1PYu06dOqxbt45NmzZRWFiIufn/Nj5/4YUXADh79iy///4769evp7i4WBOnmZkZ\ngYGBGBkZkZWVRXZ2drkC5Y9CqaDC4vz58/nwww9p164dS5cuZefOnZprW7NmDampqQwfPpzdu3ez\nYsUK3nnnnXJjBzBy5EiCg4MZMWIE7dq1o127djg5OXH9+vXHjldUTK8SM3WJETlGKngCs1g///wH\n48d/zdmzpf9TeXg0IDs7Hxsbs799bCGEeFz3z5SV2Te8W7n3Rro3Y6R7M6336tqYV9i/Kk2bNuWz\nzz7TvF6/fj0A3bp1o6Tkf3tFGhgYMH78eFavXl1uRq1M48aNMTU1JS0tTTOrUubdd9/F09OThIQE\nZs6cSaNGjQB47rnnuHDhgtat1NTUVOzt7VGpVLi4uHDx4kXNAwYACQkJNG7cGGNjY01s98dakaKi\nIs1/39+vQ4cOxMXFceTIET766CNOnz5N165dmTdvXpXHK7N161YcHR1Zvnw558+fZ9myZeXOY2xs\nzOrVq7Vu86amprJlyxb27t2LhYUFffv2LXfsB93KdHBwIDMzU/P6xo0bvPTSS1rH+PXXXzUzbh07\ndmT//v0kJiYCpePl7OyMi4sLSUlJHDt2jCtXrgDw22+/MW7cOLZs2cKVK1do3749ZmZmeHh4cObM\nGam/Wo306j5dzh0bcg2ucPDrx69dlpp6h8DAaDp3/pSzZ6/j4mJNZKQPhw8PoUGDvz8LJ4QQ+sTN\nzY3r16/zww8/aN6Lj48nNzcXQ0NDrc927dqV69ev8+uvFdcDzs7OJiMjA0dHx0rP17x5c1xdXbVu\nFe7YsYM//vgDKE2glixZwpAhQwAYMmQIS5cuJS+vdN/KpKQkJk6cyJ07dzTHtLW1pbi4mPT0dBRF\nYdSoUdy5cwdLS0syMjIoLi7m3LlzFcbj5eXFF198Qa1atbCzs8PV1ZUTJ05w7949FEVhwYIF5Ofn\na/VxcHAgPT0dKL39V79+acWX7777TisBLPPiiy/y3XffAXDs2DH2799PVlYWdnZ2WFhYEB8fT2pq\narm+a9asYfbs2Wzbto1t27ZpJWVlxz1//jx37twhNzeXM2fOlHsitk6dOvz2228AnD9/ngYNGpCU\nlMSqVauA0nVjv//+O/Xq1eOHH34gKiqKqKgoXF1d+fDDD1Gr1YSGhpKbm6s5RllSnZ6erpk5FE+O\nXs2YnUhzJaZ+E9wTE+n9mMcYODCKkydTMTU1ZNq0Tkyb1hlzc+MnGqcQQugLlUrFJ598wrx581i7\ndi3GxsaYm5uzfv16zMzK30F47733tNYUla0xAygoKGD27NmahfqVmThxIj4+Ppq1WytWrGDKlCko\nikJhYSGvvfaaZkF+7969yc3Nxc/PD2tra0xNTQkPD+eZZ57ROmZYWBgTJkwAoFevXlhbWxMYGMjo\n0aNp1KgRTZo0qTAWNzc33nvvPU3funXrEhwczODBgzE0NKR79+7lvocOHTpw6tQpvLy8eP3115k2\nbRoxMTEMHjyYAwcOsGfPHq3Pjxs3jhkzZvDVV1+hUqlYvHgxdevWxcLCAn9/f9q1a4e/vz9z585l\ny5YtVX539zMzM2Py5MkMHz4clUrF2LFjsbKy4tKlS3z77bdMmDCBuXPnMmvWLIyNjbGxsWHRokVY\nWVlx/Phx/P39KSwsZOTIkVpr1e5Xp04dxo4dS3BwMEZGRjz//POaNYFxcXE637bkn0ilVHRTuoYp\nKCjgwoUL/LR3B9ePGjEotBdtvT0fqm/pX/RiTE1Lc9Dvvkti3bo4Vq70plGj6t+gVpSqagGrqNlk\n7PSbjN+TV1BQgI+PD5GRkVpryqpDTR2/goICBg0axK5du6r9O9BHZXlLq1atNPv/PSy9upXpUGjI\nQoO2D52UXbqUQc+eO3j77a8073Xv/hzR0X6SlAkhhHgspqamvPfee5oHGP6NwsPDGTdunCRl1UCv\nbmUamhphMKjFAz93+3Y+c+ceISLiJGp1CXZ2tVi+/B52dlJoXAghxN/XpUsXunTpouswdGbatGm6\nDuEfS69mzM6pirlkn1Npe0mJwubNZ2nW7EM++OA4xcUljBzZloSEsZKUCSGEEKLG06sZs620xfjU\nceYM7FSuraBAjYfHFk6eLN1jp1MnF9as6UXbts8+7TCFEEIIIR6LXiVm7ia/4vFS+wrbTE2NaNnS\nntTUOyxf7oW/f6sqNyQUQgghhKhp9CoxC25wmm4DwwAoLCxm9erjvPxyXTw9S/dUWbnSGxMTQywt\nq35UWwghhBCiJqrWxGzRokWcO3cOlUrFjBkzNOUpAI4ePaopOOvh4cHYsWMf4oilO3scPHiFiRNj\nuHLlFq6u9pw7NxpDQwNZRyaEEEIIvVZtidnJkydJTk4mMjKSxMREZsyYQWRkpKZ9wYIFbNq0CUdH\nRwIDA+nRo0elGwCWOX+5CR/12cnBg6UlI55//hlWrvTG0FCvnmEQQgghhKhQtSVmx44do3v37kBp\nPa7bt2+Tk5ODpaUlKSkp2NjY8OyzpQvzu3TpwrFjxx6YmK371InU1CtYW5sSFtaFceNewcTEsMo+\nQgghhBD6otoSs8zMTFxdXTWv7ezsyMjI0NQuu7/8g52dHSkpKZUeq6w4QZ06tfDxcWXKlE7UqWOO\noqgpKFBX1yWIJ6ygoEDXIYjHJGOn32T89JuMn/4pLCwE/pe/PIqntvj/71R+KivsumlT6TYZ168n\ncf36EwlLPEUXLlzQdQjiMcnY6TcZP/0m46e/ioqKKqw5W5VqS8wcHBzIzMzUvL5x4wb29vYVtqWn\np+Pg4FDpsSwsLGjWrBnGxsayBYYQQgghajRFUSgqKsLCwuKR+1ZbYtapUyciIiLw9/cnPj4eBwcH\nLC0tAahXrx45OTlcu3YNJycnYmNjWbFiRaXHMjAwwMrKqrpCFUIIIYR4oh51pqyMSvk79xgfYMWK\nFZw6dQqVSkVYWBgXL17EysoKLy8v4uLiNMmYt7c3w4cPr64whBBCCCH0QrUmZkIIIYQQ4uHJBmBC\nCCGEEDWEJGZCCCGEEDVEjUzMFi1ahJ+fH/7+/vzyyy9abUePHsXHxwc/Pz/Wrl2rowhFZaoau+PH\nj+Pr64u/vz/Tp0+npKRER1GKylQ1fmVWrlxJUFDQU45MPEhVY5eWlkZAQAA+Pj68//77OopQVKWq\n8duxYwd+fn4EBASwcOFCHUUoqnL58mW6d+/O9u3by7U9ct6i1DAnTpxQRo4cqSiKovz222+Kr6+v\nVnuvXr2UP//8UykuLlYCAgKUK1eu6CJMUYEHjZ2Xl5eSlpamKIqijB8/Xjl8+PBTj1FU7kHjpyiK\ncuXKFcXPz08JDAx82uGJKjxo7CZMmKB88803iqIoypw5c5TU1NSnHqOoXFXjd/fuXcXT01MpKipS\nFEVRhg0bppw9e1YncYqK5ebmKoGBgcqsWbOUbdu2lWt/1Lylxs2YVVbKCdAq5WRgYKAp5SRqhqrG\nDiA6OhonJyegtNpDVlaWTuIUFXvQ+AEsWbKESZMm6SI8UYWqxq6kpITTp0/TrVs3AMLCwqhbt67O\nYhXlVTV+xsbGGBsbk5eXh1qt5t69e9jY2OgyXPEXJiYmbNy4scL9WB8nb6lxiVlmZia2traa12Wl\nnIAKSzmVtQndq2rsAM0+djdu3ODnn3+mS5cuTz1GUbkHjV90dDSvvPJriQeJAAAIeklEQVQKzs7O\nughPVKGqsbt16xYWFhYsXryYgIAAVq5cqaswRSWqGj9TU1PGjh1L9+7d8fT05MUXX6RRo0a6ClVU\nwMjIqNI9yx4nb6lxidlfKbKbh96qaOxu3rzJ6NGjCQsL0/pBJGqe+8cvOzub6Ohohg0bpsOIxMO6\nf+wURSE9PZ3g4GC2b9/OxYsXOXz4sO6CEw90//jl5OSwYcMGYmJi+P777zl37hwJCQk6jE5UtxqX\nmD3JUk7i6apq7KD0B8yIESOYOHEinTt31kWIogpVjd/x48e5desWgwcPZty4ccTHx7No0SJdhSr+\noqqxs7W1pW7dutSvXx9DQ0Pc3d25cuWKrkIVFahq/BITE3FxccHOzg4TExNefvllqZ2pRx4nb6lx\niVmnTp04dOgQQJWlnNRqNbGxsXTq1EmX4Yr7VDV2ULo+aciQIXh4eOgqRFGFqsavZ8+eHDx4kKio\nKD788ENcXV2ZMWOGLsMV96lq7IyMjHBxceHq1auadrkVVrNUNX7Ozs4kJiaSn58PlBY0b9iwoa5C\nFY/ocfKWGrnzv5Ry0l+VjV3nzp1p3749bdq00Xy2b9+++Pn56TBa8VdV/d0rc+3aNaZPn862bdt0\nGKn4q6rGLjk5mdDQUBRFoVmzZsyZMwcDgxr3e/m/WlXjt2vXLqKjozE0NKRNmzZMnTpV1+GK+1y4\ncIGlS5eSmpqKkZERjo6OdOvWjXr16j1W3lIjEzMhhBBCiH8j+ZVJCCGEEKKGkMRMCCGEEKKGkMRM\nCCGEEKKGkMRMCCGEEKKGkMRMCCGEEKKGMNJ1AEII/Xft2jV69uyptR0KwIwZM2jRokWFfSIiIlCr\n1X+r9uaJEyd4++23admyJQAFBQW0bNmSmTNnYmxs/EjH+vHHH4mPj2fMmDGcOXMGe3t7XFxcWLhw\nIa+//jqtWrV67DgjIiKIjo6mXr16AKjVapycnJg3bx5WVlaV9ktPTycpKQl3d/fHPrcQQr9IYiaE\neCLs7Ox0srdZs2bNNOdVFIVJkyYRGRlJYGDgIx3Hw8NDs/lxdHQ0vXv3xsXFhZkzZz6ROF977TWt\nJHT58uV89NFHTJkypdI+J06cIDExURIzIf5FJDETQlSrxMREwsLCMDQ0JCcnh4kTJ/Kf//xH065W\nq5k1axa///47KpWKFi1aEBYWRmFhIfPmzSM5OZnc3Fz69u1LSEhIledSqVS0a9eOpKQkAA4fPsza\ntWsxMzOjVq1azJ8/H0dHR1asWMHx48cxMTHB0dGRpUuXcuDAAY4ePUqPHj2IiYnhl19+Yfr06axb\nt44xY8awcuVKZs6cSdu2bQEYOnQow4YNo2nTpsydO5d79+6Rl5fHu+++S8eOHR/4vbRp04aoqCgA\nTp06xYoVKzAxMSE/P5+wsDCsra0JDw9HURRq167N4MGDH/n7EELoH0nMhBDVKjMzk3feeYf27dtz\n9uxZ5s+fr5WYXb58mXPnzvH1118DEBUVxd27d4mMjMTBwYEFCxZQXFyMr68vHTt2pHnz5pWeq6Cg\ngNjYWHx8fLh37x6zZs1i9+7dODk5sX37dsLDwwkNDWXHjh2cOnUKQ0NDDh48qFXLzsvLi88++4wx\nY8bg7u7OunXrAOjXrx+HDh2ibdu23Lx5k8TERDp37syYMWMICQnBzc2NjIwM/Pz8+OabbzAyqvzH\nq1qt5sCBA7z00ktAaZH4OXPm0Lx5cw4cOMCGDRtYs2YNAwYMQK1WM2zYMD755JNH/j6EEPpHEjMh\nxBNx69YtgoKCtN5bvXo19vb2LFu2jA8++ICioiKys7O1PtO4cWNsbW0ZMWIEnp6e9OrVCysrK06c\nOMH169eJi4sDoLCwkD/++KNcInL58mWt83p6etK7d28uXbrEM888g5OTEwCvvPIKu3btwsbGhv/8\n5z8EBgbi5eVF7969NZ+pSp8+fQgICGD69OnExMTQs2dPDA0NOXHiBLm5uaxduxYorU158+ZNHB0d\ntfrv27ePM2fOoCgKFy9eJDg4mJEjRwJQp04dli1bRkFBAXfv3sXGxqbc+R/2+xBC6DdJzIQQT0Rl\na8wmT55Mnz598PHx4fLly4wePVqr3dTUlJ07dxIfH6+Z7fr8888xMTFh7Nix9OzZs8rz3r/G7H4q\nlUrrtaIomvfWrFlDYmIiR44cITAwkIiIiAdeX9nDAL/88gtff/01oaGhAJiYmBAREYGdnV2V/e9f\nYzZ69GicnZ01s2pTp05l7ty5uLu7Exsby+bNm8v1f9jvQwih32S7DCFEtcrMzKRp06YAHDx4kMLC\nQq328+fPs3fvXlxdXRk3bhyurq5cvXqVdu3aaW5vlpSUsHjx4nKzbVVp2LAhN2/e5M8//wTg2LFj\nvPjii6SkpLBlyxYaN25MSEgIXl5eJCQkaPVVqVQUFRWVO2a/fv3YvXs3t2/f1jyleX+ct27dYuHC\nhQ+MLSwsjIiICK5fv671HRUXFxMTE6P5jlQqFWq1utx5Huf7EELoB0nMhBDVKiQkhKlTpzJ8+HDa\ntWuHjY0NS5Ys0bTXr1+fQ4cO4e/vT3BwMNbW1rRt25bBgwdjbm6On58fvr6+WFlZUbt27Yc+r5mZ\nGQsXLmTSpEkEBQVx7NgxJk6ciKOjIxcvXsTHx4chQ4aQmpqKt7e3Vt9OnToRFhbGN998o/W+t7c3\n+/fvp0+fPpr3Zs6cyXfffcebb77JyJEjcXNze2Bszz77LCNGjGD27NkAjBgxgiFDhjB69GgGDBhA\nWloaW7Zs4eWXXyY6Oprw8PC//X0IIfSDSlEURddBCCGEEEIImTETQgghhKgxJDETQgghhKghJDET\nQgghhKghJDETQgghhKghJDETQgghhKghJDETQgghhKghJDETQgghhKghJDETQgghhKgh/h8ySMBQ\n8Y55qQAAAABJRU5ErkJggg==\n",
"text/plain": [
"<Figure size 720x432 with 1 Axes>"
]
},
"metadata": {
"tags": []
},
"output_type": "display_data"
}
],
"source": [
"plt.figure()\n",
"lw = 2\n",
"plt.plot(fpr_imb, tpr_imb,\n",
" label='Logistic Regresion ROC curve (area = {0:0.4f})'\n",
" ''.format(roc_auc_imb),\n",
" color='deeppink', linestyle='-', linewidth=2)\n",
"\n",
"plt.plot(fpr_svc, tpr_svc,\n",
" label='SVC ROC curve (area = {0:0.4f})'\n",
" ''.format(roc_auc_svc),\n",
" color='b', linestyle='--', linewidth=2)\n",
"\n",
"plt.plot(fpr_knn, tpr_knn,\n",
" label='kNN ROC curve (area = {0:0.4f})'\n",
" ''.format(roc_auc_knn),\n",
" color='g', linestyle='-.', linewidth=2)\n",
"\n",
"plt.plot(fpr_DT, tpr_DT,\n",
" label='DT Regresion ROC curve (area = {0:0.4f})'\n",
" ''.format(roc_auc_DT),\n",
" color='r', linestyle=':', linewidth=2)\n",
"\n",
"plt.plot(fpr_GBC, tpr_GBC,\n",
" label='GBC ROC curve (area = {0:0.4f})'\n",
" ''.format(roc_auc_GBC),\n",
" color='c', linestyle=':', linewidth=2)\n",
"\n",
"plt.plot(fpr_RFC, tpr_RFC,\n",
" label='RFC ROC curve (area = {0:0.4f})'\n",
" ''.format(roc_auc_RFC),\n",
" color='m', linestyle='-.', linewidth=2)\n",
"\n",
"plt.plot(fpr_NN, tpr_NN,\n",
" label='NN ROC curve (area = {0:0.4f})'\n",
" ''.format(roc_auc_NN),\n",
" color='y', linestyle='--', linewidth=2)\n",
"\n",
"plt.plot(fpr_GNB, tpr_GNB,\n",
" label='GNB ROC curve (area = {0:0.4f})'\n",
" ''.format(roc_auc_GNB),\n",
" color='b', linestyle='dotted', linewidth=2)\n",
"\n",
"plt.plot([0, 1], [0, 1], color='navy', lw=lw, linestyle='--')\n",
"plt.xlim([0.0, 1.0])\n",
"plt.ylim([0.0, 1.00])\n",
"plt.xlabel('False Positive Rate')\n",
"plt.ylabel('True Positive Rate')\n",
"plt.title('ROC curve for all the models')\n",
"plt.legend(loc=\"lower right\")\n",
"\n",
"print(\"++4\")\n",
"save_fig('roc-ALL')\n",
"\n",
"plt.show()\n",
"\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 347,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 33
},
"colab_type": "code",
"id": "6uGhANnWFI4H",
"outputId": "eefc3f04-fd69-4088-9021-cc68323e4c26"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"More analysis\n"
]
}
],
"source": [
"print(\"More analysis\")"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "3u7fpQMJwzKI"
},
"source": []
},
{
"cell_type": "code",
"execution_count": 372,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 440
},
"colab_type": "code",
"id": "VXr2nwGDH_AP",
"outputId": "ebb3e2b3-ee65-4b3e-f92f-8a978f2be2f9"
},
"outputs": [
{
"ename": "TypeError",
"evalue": "ignored",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-372-a55c3919b490>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 7\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 8\u001b[0m \u001b[0mvisualizer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m# Fit the visualizer and the model\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 9\u001b[0;31m \u001b[0mvisualizer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mscore\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_test\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_test\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mscore\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"binary\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpos_label\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"neg\"\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m# Evaluate the model on the test data##############recall_average = recall_score(Y_test, y_predict, average=\"binary\", pos_label=\"neg\")\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 10\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 11\u001b[0m \u001b[0mvisualizer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpoof\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m# Draw/show/poof the data\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python3.6/dist-packages/yellowbrick/classifier/classification_report.py\u001b[0m in \u001b[0;36mscore\u001b[0;34m(self, X, y, **kwargs)\u001b[0m\n\u001b[1;32m 130\u001b[0m \u001b[0my_pred\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 131\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 132\u001b[0;31m \u001b[0mscores\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mprecision_recall_fscore_support\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_pred\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 133\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 134\u001b[0m \u001b[0;31m# Calculate the percentage for the support metric\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python3.6/dist-packages/sklearn/metrics/_classification.py\u001b[0m in \u001b[0;36mprecision_recall_fscore_support\u001b[0;34m(y_true, y_pred, beta, labels, pos_label, average, warn_for, sample_weight, zero_division)\u001b[0m\n\u001b[1;32m 1482\u001b[0m \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"beta should be >=0 in the F-beta score\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1483\u001b[0m labels = _check_set_wise_labels(y_true, y_pred, average, labels,\n\u001b[0;32m-> 1484\u001b[0;31m pos_label)\n\u001b[0m\u001b[1;32m 1485\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1486\u001b[0m \u001b[0;31m# Calculate tp_sum, pred_sum, true_sum ###\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python3.6/dist-packages/sklearn/metrics/_classification.py\u001b[0m in \u001b[0;36m_check_set_wise_labels\u001b[0;34m(y_true, y_pred, average, labels, pos_label)\u001b[0m\n\u001b[1;32m 1299\u001b[0m str(average_options))\n\u001b[1;32m 1300\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1301\u001b[0;31m \u001b[0my_type\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_true\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_pred\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_check_targets\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_true\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_pred\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1302\u001b[0m \u001b[0mpresent_labels\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0munique_labels\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_true\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_pred\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1303\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0maverage\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'binary'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python3.6/dist-packages/sklearn/metrics/_classification.py\u001b[0m in \u001b[0;36m_check_targets\u001b[0;34m(y_true, y_pred)\u001b[0m\n\u001b[1;32m 101\u001b[0m \u001b[0my_pred\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcolumn_or_1d\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_pred\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 102\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0my_type\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"binary\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 103\u001b[0;31m \u001b[0munique_values\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0munion1d\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_true\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_pred\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 104\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0munique_values\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 105\u001b[0m \u001b[0my_type\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"multiclass\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m<__array_function__ internals>\u001b[0m in \u001b[0;36munion1d\u001b[0;34m(*args, **kwargs)\u001b[0m\n",
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"\u001b[0;32m<__array_function__ internals>\u001b[0m in \u001b[0;36munique\u001b[0;34m(*args, **kwargs)\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python3.6/dist-packages/numpy/lib/arraysetops.py\u001b[0m in \u001b[0;36munique\u001b[0;34m(ar, return_index, return_inverse, return_counts, axis)\u001b[0m\n\u001b[1;32m 260\u001b[0m \u001b[0mar\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0masanyarray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mar\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 261\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0maxis\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 262\u001b[0;31m \u001b[0mret\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_unique1d\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mar\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreturn_index\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreturn_inverse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreturn_counts\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 263\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0m_unpack_tuple\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mret\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 264\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python3.6/dist-packages/numpy/lib/arraysetops.py\u001b[0m in \u001b[0;36m_unique1d\u001b[0;34m(ar, return_index, return_inverse, return_counts)\u001b[0m\n\u001b[1;32m 308\u001b[0m \u001b[0maux\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mar\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mperm\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 309\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 310\u001b[0;31m \u001b[0mar\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msort\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 311\u001b[0m \u001b[0maux\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mar\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 312\u001b[0m \u001b[0mmask\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mempty\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0maux\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbool_\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mTypeError\u001b[0m: '<' not supported between instances of 'str' and 'int'"
]
}
],
"source": [
"from yellowbrick.classifier import ClassificationReport\n",
"\n",
"# Specify the target classes\n",
"classes = [\"yes\", \"no\"]\n",
"\n",
"visualizer = ClassificationReport(grad_clf, classes=classes, support=True, force_model=True)\n",
"\n",
"visualizer.fit(X_train, y_train) # Fit the visualizer and the model\n",
"visualizer.score(X_test, y_test)#,score=\"binary\", pos_label=\"neg\") # Evaluate the model on the test data##############recall_average = recall_score(Y_test, y_predict, average=\"binary\", pos_label=\"neg\")\n",
"\n",
"visualizer.poof() # Draw/show/poof the data"
]
},
{
"cell_type": "code",
"execution_count": 388,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 360
},
"colab_type": "code",
"id": "T7Ntmwy-IT7j",
"outputId": "dd20e474-d89f-4628-e746-3060f1876e52"
},
"outputs": [
{
"ename": "ValueError",
"evalue": "ignored",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-388-62c38cd6a28a>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0msklearn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmetrics\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mprecision_score\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrecall_score\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0mclasses\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"yes\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"no\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Precision Score: '\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mprecision_score\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_train_pred\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 4\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Recall Score: '\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrecall_score\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_train_pred\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python3.6/dist-packages/sklearn/metrics/_classification.py\u001b[0m in \u001b[0;36mprecision_score\u001b[0;34m(y_true, y_pred, labels, pos_label, average, sample_weight, zero_division)\u001b[0m\n\u001b[1;32m 1670\u001b[0m \u001b[0mwarn_for\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'precision'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1671\u001b[0m \u001b[0msample_weight\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msample_weight\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1672\u001b[0;31m zero_division=zero_division)\n\u001b[0m\u001b[1;32m 1673\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mp\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1674\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python3.6/dist-packages/sklearn/metrics/_classification.py\u001b[0m in \u001b[0;36mprecision_recall_fscore_support\u001b[0;34m(y_true, y_pred, beta, labels, pos_label, average, warn_for, sample_weight, zero_division)\u001b[0m\n\u001b[1;32m 1482\u001b[0m \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"beta should be >=0 in the F-beta score\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1483\u001b[0m labels = _check_set_wise_labels(y_true, y_pred, average, labels,\n\u001b[0;32m-> 1484\u001b[0;31m pos_label)\n\u001b[0m\u001b[1;32m 1485\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1486\u001b[0m \u001b[0;31m# Calculate tp_sum, pred_sum, true_sum ###\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python3.6/dist-packages/sklearn/metrics/_classification.py\u001b[0m in \u001b[0;36m_check_set_wise_labels\u001b[0;34m(y_true, y_pred, average, labels, pos_label)\u001b[0m\n\u001b[1;32m 1306\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpresent_labels\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m>=\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1307\u001b[0m raise ValueError(\"pos_label=%r is not a valid label: \"\n\u001b[0;32m-> 1308\u001b[0;31m \"%r\" % (pos_label, present_labels))\n\u001b[0m\u001b[1;32m 1309\u001b[0m \u001b[0mlabels\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mpos_label\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1310\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mValueError\u001b[0m: pos_label=1 is not a valid label: array(['no', 'yes'], dtype='<U3')"
]
}
],
"source": [
"# Let's find the scores for precision and recall.\n",
"from sklearn.metrics import precision_score, recall_score\n",
"classes = ([\"yes\", \"no\"])\n",
"print('Precision Score: ', precision_score(y_train, y_train_pred,))\n",
"print('Recall Score: ', recall_score(y_train, y_train_pred))"
]
},
{
"cell_type": "code",
"execution_count": 357,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 328
},
"colab_type": "code",
"id": "JMYjmkW3IXaC",
"outputId": "0f96ccc7-bc5a-449c-a86d-76d64a069100"
},
"outputs": [
{
"ename": "ValueError",
"evalue": "ignored",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-357-72b49d053db2>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0msklearn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmetrics\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mf1_score\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mf1_score\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_train_pred\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;32m/usr/local/lib/python3.6/dist-packages/sklearn/metrics/_classification.py\u001b[0m in \u001b[0;36mf1_score\u001b[0;34m(y_true, y_pred, labels, pos_label, average, sample_weight, zero_division)\u001b[0m\n\u001b[1;32m 1097\u001b[0m \u001b[0mpos_label\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mpos_label\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maverage\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0maverage\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1098\u001b[0m \u001b[0msample_weight\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msample_weight\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1099\u001b[0;31m zero_division=zero_division)\n\u001b[0m\u001b[1;32m 1100\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1101\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python3.6/dist-packages/sklearn/metrics/_classification.py\u001b[0m in \u001b[0;36mfbeta_score\u001b[0;34m(y_true, y_pred, beta, labels, pos_label, average, sample_weight, zero_division)\u001b[0m\n\u001b[1;32m 1224\u001b[0m \u001b[0mwarn_for\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'f-score'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1225\u001b[0m \u001b[0msample_weight\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msample_weight\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1226\u001b[0;31m zero_division=zero_division)\n\u001b[0m\u001b[1;32m 1227\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1228\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python3.6/dist-packages/sklearn/metrics/_classification.py\u001b[0m in \u001b[0;36mprecision_recall_fscore_support\u001b[0;34m(y_true, y_pred, beta, labels, pos_label, average, warn_for, sample_weight, zero_division)\u001b[0m\n\u001b[1;32m 1482\u001b[0m \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"beta should be >=0 in the F-beta score\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1483\u001b[0m labels = _check_set_wise_labels(y_true, y_pred, average, labels,\n\u001b[0;32m-> 1484\u001b[0;31m pos_label)\n\u001b[0m\u001b[1;32m 1485\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1486\u001b[0m \u001b[0;31m# Calculate tp_sum, pred_sum, true_sum ###\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python3.6/dist-packages/sklearn/metrics/_classification.py\u001b[0m in \u001b[0;36m_check_set_wise_labels\u001b[0;34m(y_true, y_pred, average, labels, pos_label)\u001b[0m\n\u001b[1;32m 1306\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpresent_labels\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m>=\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1307\u001b[0m raise ValueError(\"pos_label=%r is not a valid label: \"\n\u001b[0;32m-> 1308\u001b[0;31m \"%r\" % (pos_label, present_labels))\n\u001b[0m\u001b[1;32m 1309\u001b[0m \u001b[0mlabels\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mpos_label\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1310\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mValueError\u001b[0m: pos_label=1 is not a valid label: array(['no', 'yes'], dtype='<U3')"
]
}
],
"source": [
"from sklearn.metrics import f1_score\n",
"f1_score(y_train, y_train_pred)\n"
]
},
{
"cell_type": "code",
"execution_count": 358,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 172
},
"colab_type": "code",
"id": "Wc3jmC-hIe85",
"outputId": "37ee9a67-82c5-4c53-a939-c83e0ccb0099"
},
"outputs": [
{
"ename": "NameError",
"evalue": "ignored",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-358-03389acfbbdb>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0my_scores\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgrad_clf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdecision_function\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0msome_instance\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0my_scores\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mNameError\u001b[0m: name 'some_instance' is not defined"
]
}
],
"source": [
"y_scores = grad_clf.decision_function([some_instance])\n",
"y_scores"
]
},
{
"cell_type": "code",
"execution_count": 359,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 33
},
"colab_type": "code",
"id": "fMLsBL4BIt4b",
"outputId": "fd3f7f66-367d-4636-8315-f91332272972"
},
"outputs": [
{
"data": {
"text/plain": [
"(26905,)"
]
},
"execution_count": 359,
"metadata": {
"tags": []
},
"output_type": "execute_result"
}
],
"source": [
"y_scores = cross_val_predict(grad_clf, X_train, y_train, cv=5, method=\"decision_function\")\n",
"# hack to work around issue #9589 introduced in Scikit-Learn 0.19.0\n",
"if y_scores.ndim == 2:\n",
" y_scores = y_scores[:, 1]\n",
"y_scores.shape"
]
},
{
"cell_type": "code",
"execution_count": 360,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 695
},
"colab_type": "code",
"id": "RoPPQnWUIzlG",
"outputId": "c6aed2b3-41ef-4f45-c064-1e78f676d881"
},
"outputs": [
{
"ename": "ValueError",
"evalue": "ignored",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-360-fa210cfdd50f>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0mvisualizer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mDiscriminationThreshold\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgrad_clf\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m \u001b[0mvisualizer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m# Fit the data to the visualizer\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 6\u001b[0m \u001b[0mvisualizer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpoof\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m# Draw/show/poof the data\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python3.6/dist-packages/yellowbrick/classifier/threshold.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, X, y, **kwargs)\u001b[0m\n\u001b[1;32m 224\u001b[0m trials = [\n\u001b[1;32m 225\u001b[0m \u001b[0mmetric\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 226\u001b[0;31m \u001b[0;32mfor\u001b[0m \u001b[0midx\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mn_trials\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 227\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mmetric\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_split_fit_score_trial\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0midx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 228\u001b[0m ]\n",
"\u001b[0;32m/usr/local/lib/python3.6/dist-packages/yellowbrick/classifier/threshold.py\u001b[0m in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m 225\u001b[0m \u001b[0mmetric\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 226\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0midx\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mn_trials\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 227\u001b[0;31m \u001b[0;32mfor\u001b[0m \u001b[0mmetric\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_split_fit_score_trial\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0midx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 228\u001b[0m ]\n\u001b[1;32m 229\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python3.6/dist-packages/yellowbrick/classifier/threshold.py\u001b[0m in \u001b[0;36m_split_fit_score_trial\u001b[0;34m(self, X, y, idx)\u001b[0m\n\u001b[1;32m 305\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 306\u001b[0m \u001b[0;31m# Compute the curve metrics and thresholds\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 307\u001b[0;31m \u001b[0mcurve_metrics\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mprecision_recall_curve\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_test\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_scores\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 308\u001b[0m \u001b[0mprecision\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrecall\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mthresholds\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcurve_metrics\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 309\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python3.6/dist-packages/sklearn/metrics/_ranking.py\u001b[0m in \u001b[0;36mprecision_recall_curve\u001b[0;34m(y_true, probas_pred, pos_label, sample_weight)\u001b[0m\n\u001b[1;32m 671\u001b[0m fps, tps, thresholds = _binary_clf_curve(y_true, probas_pred,\n\u001b[1;32m 672\u001b[0m \u001b[0mpos_label\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mpos_label\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 673\u001b[0;31m sample_weight=sample_weight)\n\u001b[0m\u001b[1;32m 674\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 675\u001b[0m \u001b[0mprecision\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtps\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mtps\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mfps\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python3.6/dist-packages/sklearn/metrics/_ranking.py\u001b[0m in \u001b[0;36m_binary_clf_curve\u001b[0;34m(y_true, y_score, pos_label, sample_weight)\u001b[0m\n\u001b[1;32m 562\u001b[0m \u001b[0;34m\"take value in {{0, 1}} or {{-1, 1}} or \"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 563\u001b[0m \"pass pos_label explicitly.\".format(\n\u001b[0;32m--> 564\u001b[0;31m classes_repr=classes_repr))\n\u001b[0m\u001b[1;32m 565\u001b[0m \u001b[0;32melif\u001b[0m \u001b[0mpos_label\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 566\u001b[0m \u001b[0mpos_label\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m1.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mValueError\u001b[0m: y_true takes value in {'no', 'yes'} and pos_label is not specified: either make y_true take value in {0, 1} or {-1, 1} or pass pos_label explicitly."
]
},
{
"data": {
"image/png": 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A7969OyMjI9m+fXt6e3vT29t7yvmHH344TzzxRLZt25adO3dm//79NRsWAGaKqoEeGBhI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\n",
"text/plain": [
"<Figure size 576x396 with 1 Axes>"
]
},
"metadata": {
"tags": []
},
"output_type": "display_data"
}
],
"source": [
"from yellowbrick.classifier import DiscriminationThreshold\n",
"\n",
"visualizer = DiscriminationThreshold(grad_clf)\n",
"\n",
"visualizer.fit(X_train, y_train) # Fit the data to the visualizer\n",
"visualizer.poof() # Draw/show/poof the data"
]
},
{
"cell_type": "code",
"execution_count": 361,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 750
},
"colab_type": "code",
"id": "M9okrB0GI4q2",
"outputId": "3b361cc9-76bc-402c-f9cd-f008db47a7b0"
},
"outputs": [
{
"ename": "TypeError",
"evalue": "ignored",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-361-19113347e5c0>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0mvisualizer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m# Fit the training data to the visualizer\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m \u001b[0mvisualizer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mscore\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_test\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_test\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;31m# Evaluate the model on the test data\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 7\u001b[0m plt.annotate('ROC Score of 92% \\n', xy=(0.25, 0.9), xytext=(0.4, 0.85),\n\u001b[1;32m 8\u001b[0m \u001b[0marrowprops\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mshrink\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0.05\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python3.6/dist-packages/yellowbrick/classifier/rocauc.py\u001b[0m in \u001b[0;36mscore\u001b[0;34m(self, X, y, **kwargs)\u001b[0m\n\u001b[1;32m 235\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 236\u001b[0m \u001b[0;31m# Set score to the base score if neither macro nor micro\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 237\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mscore_\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mestimator\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mscore\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 238\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 239\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mscore_\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python3.6/dist-packages/sklearn/base.py\u001b[0m in \u001b[0;36mscore\u001b[0;34m(self, X, y, sample_weight)\u001b[0m\n\u001b[1;32m 367\u001b[0m \"\"\"\n\u001b[1;32m 368\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m\u001b[0mmetrics\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0maccuracy_score\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 369\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0maccuracy_score\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msample_weight\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msample_weight\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 370\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 371\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
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"\u001b[0;31mTypeError\u001b[0m: '<' not supported between instances of 'str' and 'int'"
]
},
{
"data": {
"image/png": 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ht/bw1qEWeHrXSZ//c0um8In5ExN1iqW6kxDiDOjq6uJLX/oSmqZx/fXX43a7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\n",
"text/plain": [
"<Figure size 576x396 with 1 Axes>"
]
},
"metadata": {
"tags": []
},
"output_type": "display_data"
}
],
"source": [
"from yellowbrick.classifier import ROCAUC\n",
"\n",
"visualizer = ROCAUC(grad_clf, classes=[\"yes\", \"no\"])\n",
"\n",
"visualizer.fit(X_train, y_train) # Fit the training data to the visualizer\n",
"visualizer.score(X_test, y_test)# Evaluate the model on the test data\n",
"plt.annotate('ROC Score of 92% \\n', xy=(0.25, 0.9), xytext=(0.4, 0.85),\n",
" arrowprops=dict(shrink=0.05),\n",
" )\n",
"plt.annotate('Minimum ROC Score of 50% \\n (This is the minimum score to get)', xy=(0.5, 0.5), xytext=(0.6, 0.3),\n",
" arrowprops=dict(shrink=0.05),\n",
" )\n",
"visualizer.poof() # Draw/show/poof the data"
]
},
{
"cell_type": "code",
"execution_count": 362,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 172
},
"colab_type": "code",
"id": "E53Fu_LqI7lD",
"outputId": "454746d9-c8e7-4da6-881c-000daa5d0aee"
},
"outputs": [
{
"ename": "NameError",
"evalue": "ignored",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-362-b897be00ba33>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mcutoff_prob\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mthreshold\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mabs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrecalls\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0;36m0.6\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0margmin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0mround\u001b[0m\u001b[0;34m(\u001b[0m \u001b[0mfloat\u001b[0m\u001b[0;34m(\u001b[0m \u001b[0mcutoff_prob\u001b[0m \u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m2\u001b[0m \u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mNameError\u001b[0m: name 'threshold' is not defined"
]
}
],
"source": [
"cutoff_prob = threshold[(np.abs(recalls - 0.6)).argmin()]\n",
"round( float( cutoff_prob ), 2 )"
]
},
{
"cell_type": "code",
"execution_count": 363,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 376
},
"colab_type": "code",
"id": "Q5HFcd3hJFBv",
"outputId": "4bcbd0b7-ed05-48ce-efc1-e60a2572034d"
},
"outputs": [
{
"ename": "ValueError",
"evalue": "ignored",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-363-1224a810a5ce>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0msklearn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmetrics\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mprecision_recall_curve\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mprecisions\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrecalls\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mthreshold\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mprecision_recall_curve\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_scores\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mprecision_recall_curve\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mprecisions\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrecalls\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mthresholds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0mfig\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0max\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msubplots\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfigsize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m6\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python3.6/dist-packages/sklearn/metrics/_ranking.py\u001b[0m in \u001b[0;36mprecision_recall_curve\u001b[0;34m(y_true, probas_pred, pos_label, sample_weight)\u001b[0m\n\u001b[1;32m 671\u001b[0m fps, tps, thresholds = _binary_clf_curve(y_true, probas_pred,\n\u001b[1;32m 672\u001b[0m \u001b[0mpos_label\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mpos_label\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 673\u001b[0;31m sample_weight=sample_weight)\n\u001b[0m\u001b[1;32m 674\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 675\u001b[0m \u001b[0mprecision\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtps\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mtps\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mfps\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python3.6/dist-packages/sklearn/metrics/_ranking.py\u001b[0m in \u001b[0;36m_binary_clf_curve\u001b[0;34m(y_true, y_score, pos_label, sample_weight)\u001b[0m\n\u001b[1;32m 562\u001b[0m \u001b[0;34m\"take value in {{0, 1}} or {{-1, 1}} or \"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 563\u001b[0m \"pass pos_label explicitly.\".format(\n\u001b[0;32m--> 564\u001b[0;31m classes_repr=classes_repr))\n\u001b[0m\u001b[1;32m 565\u001b[0m \u001b[0;32melif\u001b[0m \u001b[0mpos_label\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 566\u001b[0m \u001b[0mpos_label\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m1.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mValueError\u001b[0m: y_true takes value in {'no', 'yes'} and pos_label is not specified: either make y_true take value in {0, 1} or {-1, 1} or pass pos_label explicitly."
]
}
],
"source": [
"# How can we decide which threshold to use? We want to return the scores instead of predictions with this code.\n",
"from sklearn.metrics import precision_recall_curve\n",
"precisions, recalls, threshold = precision_recall_curve(y_train, y_scores)\n",
"\n",
"def precision_recall_curve(precisions, recalls, thresholds):\n",
" fig, ax = plt.subplots(figsize=(10,6))\n",
" plt.plot(thresholds, precisions[:-1], \"r--\", label=\"Precisions\")\n",
" plt.plot(thresholds, recalls[:-1], \"#424242\", label=\"Recalls\")\n",
" plt.title(\"Precision and Recall \\n Tradeoff\", fontsize=12)\n",
" plt.ylabel(\"Level of Precision and Recall\", fontsize=12)\n",
" plt.xlabel(\"Thresholds\", fontsize=12)\n",
" plt.legend(loc=\"best\", fontsize=12)\n",
" plt.xlim([-2, 4.7])\n",
" plt.ylim([0, 1])\n",
" plt.axvline(x=0.13, linewidth=3, color=\"#0B3861\")\n",
" plt.annotate('Best Precision and \\n Recall Balance \\n is at 0.12 \\n threshold ', xy=(0.13, 0.83), xytext=(55, -40),\n",
" textcoords=\"offset points\",\n",
" arrowprops=dict(facecolor='black', shrink=0.05),\n",
" fontsize=12, \n",
" color='k')\n",
" \n",
"precision_recall_curve(precisions, recalls, threshold)\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 364,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 172
},
"colab_type": "code",
"id": "Oy6zTj4NJKGJ",
"outputId": "2ba4d868-d92f-4d7e-93e2-dc70e5fc8317"
},
"outputs": [
{
"ename": "NameError",
"evalue": "ignored",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-364-b897be00ba33>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mcutoff_prob\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mthreshold\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mabs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrecalls\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0;36m0.6\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0margmin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0mround\u001b[0m\u001b[0;34m(\u001b[0m \u001b[0mfloat\u001b[0m\u001b[0;34m(\u001b[0m \u001b[0mcutoff_prob\u001b[0m \u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m2\u001b[0m \u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mNameError\u001b[0m: name 'threshold' is not defined"
]
}
],
"source": [
"cutoff_prob = threshold[(np.abs(recalls - 0.6)).argmin()]\n",
"round( float( cutoff_prob ), 2 )"
]
},
{
"cell_type": "code",
"execution_count": 365,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 376
},
"colab_type": "code",
"id": "zTYaiqzCJNoG",
"outputId": "391eae7a-5144-47e1-8b10-8473f6933ea7"
},
"outputs": [
{
"data": {
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QlpYGgLu7O+7u7qSnp2Oz2cjIyKBMmZsbsmYzDAAahwYUbOAl2CWLfRrbTKvO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\nD6JgFxER8SAKdhEREQ/y/ztyJvW48s7sAAAAAElFTkSuQmCC\n",
"text/plain": [
"<Figure size 576x396 with 1 Axes>"
]
},
"metadata": {
"tags": []
},
"output_type": "display_data"
}
],
"source": [
"from yellowbrick.classifier import PrecisionRecallCurve\n",
"\n",
"# Create the visualizer, fit, score, and poof it\n",
"viz = PrecisionRecallCurve(GradientBoostingClassifier())\n",
"viz.fit(X_train, y_train)\n",
"viz.score(X_test, y_test)\n",
"viz.poof()"
]
},
{
"cell_type": "code",
"execution_count": 366,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 376
},
"colab_type": "code",
"id": "CHRJR8HbJQhO",
"outputId": "de6471b6-17a5-44d9-a363-8e3d7e6f5859"
},
"outputs": [
{
"data": {
"image/png": 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79/L+++8zevRoHnrooTK3FTVq1Ci+/vproqKibEuhVmWJVyi9bGthuZYtW0Zw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\n",
"text/plain": [
"<Figure size 576x396 with 1 Axes>"
]
},
"metadata": {
"tags": []
},
"output_type": "display_data"
}
],
"source": [
"from yellowbrick.model_selection import LearningCurve\n",
"\n",
"visualizer = LearningCurve(\n",
" grad_clf, scoring='f1_weighted', cv=5)\n",
"\n",
"visualizer.fit(X_train, y_train) # Fit the data to the visualizer\n",
"visualizer.poof() # Draw/show/poof the data"
]
},
{
"cell_type": "code",
"execution_count": 367,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 277
},
"colab_type": "code",
"id": "Eu3JajXmJTfL",
"outputId": "ad5d1e9c-ba52-42fe-9655-4b759d7a7242"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Collecting scikit-plot\n",
" Downloading https://files.pythonhosted.org/packages/7c/47/32520e259340c140a4ad27c1b97050dd3254fdc517b1d59974d47037510e/scikit_plot-0.3.7-py3-none-any.whl\n",
"Requirement already satisfied: scikit-learn>=0.18 in /usr/local/lib/python3.6/dist-packages (from scikit-plot) (0.22.1)\n",
"Requirement already satisfied: scipy>=0.9 in /usr/local/lib/python3.6/dist-packages (from scikit-plot) (1.4.1)\n",
"Requirement already satisfied: joblib>=0.10 in /usr/local/lib/python3.6/dist-packages (from scikit-plot) (0.14.1)\n",
"Requirement already satisfied: matplotlib>=1.4.0 in /usr/local/lib/python3.6/dist-packages (from scikit-plot) (3.1.3)\n",
"Requirement already satisfied: numpy>=1.11.0 in /usr/local/lib/python3.6/dist-packages (from scikit-learn>=0.18->scikit-plot) (1.17.5)\n",
"Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.6/dist-packages (from matplotlib>=1.4.0->scikit-plot) (2.4.6)\n",
"Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.6/dist-packages (from matplotlib>=1.4.0->scikit-plot) (0.10.0)\n",
"Requirement already satisfied: python-dateutil>=2.1 in /usr/local/lib/python3.6/dist-packages (from matplotlib>=1.4.0->scikit-plot) (2.6.1)\n",
"Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.6/dist-packages (from matplotlib>=1.4.0->scikit-plot) (1.1.0)\n",
"Requirement already satisfied: six in /usr/local/lib/python3.6/dist-packages (from cycler>=0.10->matplotlib>=1.4.0->scikit-plot) (1.12.0)\n",
"Requirement already satisfied: setuptools in /usr/local/lib/python3.6/dist-packages (from kiwisolver>=1.0.1->matplotlib>=1.4.0->scikit-plot) (45.1.0)\n",
"Installing collected packages: scikit-plot\n",
"Successfully installed scikit-plot-0.3.7\n"
]
}
],
"source": [
"pip install scikit-plot "
]
},
{
"cell_type": "code",
"execution_count": 0,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "OBw-xYGSJVpQ"
},
"outputs": [],
"source": [
"import sklearn.metrics\n",
"import pandas as pd\n",
"\n",
"def calc_cumulative_gains(df: pd.DataFrame, actual_col: str, predicted_col:str, probability_col:str):\n",
" df.sort_values(by=probability_col, ascending=False, inplace=True)\n",
"\n",
" subset = df[df[predicted_col] == True]\n",
"\n",
" rows = []\n",
" for group in np.array_split(subset, 10):\n",
" score = sklearn.metrics.accuracy_score(group[actual_col].tolist(),\n",
" group[predicted_col].tolist(),\n",
" normalize=False)\n",
"\n",
" rows.append({'NumCases': len(group), 'NumCorrectPredictions': score})\n",
"\n",
" lift = pd.DataFrame(rows)\n",
" print(\"done\")\n"
]
},
{
"cell_type": "code",
"execution_count": 370,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 382
},
"colab_type": "code",
"id": "urMfp1boJYN3",
"outputId": "cb55de4c-66c9-43ae-f483-a3e2d23d241c"
},
"outputs": [
{
"data": {
"image/png": 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hMVeMykVsilGqqKdK0QghTE1RFL47co0xv58wat1XdXVkRS9fGngUVzG6h637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\nQkgTQUWfEEIIaSL+Hz5V2ICpxq5cAAAAAElFTkSuQmCC\n",
"text/plain": [
"<Figure size 576x396 with 1 Axes>"
]
},
"metadata": {
"tags": []
},
"output_type": "display_data"
}
],
"source": [
"from scikitplot.metrics import plot_lift_curve\n",
"import scikitplot as skplt\n",
"\n",
"skplt.metrics.plot_cumulative_gain(y_test, predicted_probas)\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 371,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 398
},
"colab_type": "code",
"id": "B8zBgXznJbiC",
"outputId": "35d9d76f-a71c-4b60-c17e-5099dd87fb02"
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7f4d9ff8b048>"
]
},
"execution_count": 371,
"metadata": {
"tags": []
},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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grbDqErad/gzVylIrVUVE1DEWCerk5GQ8+OCDSEpKwhNPPIHMzExLdEsOyMXJ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\nRU5ErkJggg==\n",
"text/plain": [
"<Figure size 576x396 with 1 Axes>"
]
},
"metadata": {
"tags": []
},
"output_type": "display_data"
}
],
"source": [
"skplt.metrics.plot_lift_curve(y_test, predicted_probas, title='Lift Curve', ax=None, figsize=None, title_fontsize='large', text_fontsize='medium') "
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "fmey_pyDFIjt"
},
"source": []
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "mJPx06R-OJ4g"
},
"source": [
"#**Imbalance data**"
]
},
{
"cell_type": "code",
"execution_count": 235,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 65
},
"colab_type": "code",
"id": "vUSL2DOEDEQJ",
"outputId": "3904b55a-68bf-443e-9b16-ed71af8042f5"
},
"outputs": [
{
"data": {
"text/plain": [
"no 34160\n",
"yes 4277\n",
"Name: y, dtype: int64"
]
},
"execution_count": 235,
"metadata": {
"tags": []
},
"output_type": "execute_result"
}
],
"source": [
"y.value_counts()"
]
},
{
"cell_type": "code",
"execution_count": 237,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 65
},
"colab_type": "code",
"id": "AsB5LqhOOOhJ",
"outputId": "5c615750-f7bf-4027-f4ba-0b68edbe06e8"
},
"outputs": [
{
"data": {
"text/plain": [
"yes 4277\n",
"no 4277\n",
"dtype: int64"
]
},
"execution_count": 237,
"metadata": {
"tags": []
},
"output_type": "execute_result"
}
],
"source": [
"import numpy as np\n",
"import pandas as pd\n",
"import matplotlib.pyplot as plt\n",
"import sklearn.linear_model as lm\n",
"from sklearn.tree import DecisionTreeClassifier\n",
"from sklearn.ensemble import RandomForestClassifier\n",
"from sklearn.model_selection import train_test_split, KFold\n",
"from sklearn.preprocessing import StandardScaler, label_binarize\n",
"from sklearn.metrics import accuracy_score,confusion_matrix,roc_curve, auc, f1_score, precision_score, recall_score\n",
"from sklearn.svm import SVC\n",
"from imblearn.over_sampling import RandomOverSampler, SMOTE\n",
"from imblearn.under_sampling import RandomUnderSampler\n",
"\n",
"rus = RandomUnderSampler(random_state=0)\n",
"X_Usampled, y_Usampled = rus.fit_resample(X, y)\n",
"pd.Series(y_Usampled).value_counts()"
]
},
{
"cell_type": "code",
"execution_count": 238,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 65
},
"colab_type": "code",
"id": "fe9A5CksOOef",
"outputId": "2505313c-3d4c-453d-f3a4-b4df1b7df3e0"
},
"outputs": [
{
"data": {
"text/plain": [
"yes 34160\n",
"no 34160\n",
"dtype: int64"
]
},
"execution_count": 238,
"metadata": {
"tags": []
},
"output_type": "execute_result"
}
],
"source": [
"sm = SMOTE(random_state=0)\n",
"X_SMOTE, y_SMOTE = sm.fit_resample(X, y)\n",
"pd.Series(y_SMOTE).value_counts()"
]
},
{
"cell_type": "code",
"execution_count": 239,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 33
},
"colab_type": "code",
"id": "EV93T6WXOOW6",
"outputId": "c6abf5e1-2cb3-44a4-e2cb-06c5fde3f078"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Accuracy: 0.89\n"
]
}
],
"source": [
"X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3)\n",
"sc = StandardScaler()\n",
"sc.fit(X_train)\n",
"X_train_std = sc.transform(X_train)\n",
"X_test_std = sc.transform(X_test)\n",
"perp_model = lm.Perceptron().fit(X_train_std,y_train)\n",
"y_pred = perp_model.predict(X_test_std)\n",
"print(\"Accuracy: \",round(accuracy_score(y_test, y_pred),2))"
]
},
{
"cell_type": "code",
"execution_count": 241,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 97
},
"colab_type": "code",
"id": "Yt4VOq2MOsLA",
"outputId": "343e36ca-e1c7-496e-cc68-0ee03828da06"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Accuracy: 0.8\n",
"Confusion Matrix: \n",
" [[8133 2079]\n",
" [ 188 1132]]\n",
"Precision: 0.35 Recall: 0.86\n"
]
}
],
"source": [
"X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3)\n",
"sm = SMOTE(random_state=0)\n",
"X_SMOTE, y_SMOTE = sm.fit_resample(X_train, y_train)\n",
"sc = StandardScaler()\n",
"sc.fit(X_SMOTE)\n",
"X_train_std = sc.transform(X_SMOTE)\n",
"X_test_std = sc.transform(X_test)\n",
"perp_model = lm.Perceptron().fit(X_train_std,y_SMOTE)\n",
"y_pred = perp_model.predict(X_test_std)\n",
"print(\"Accuracy: \",round(accuracy_score(y_test, y_pred),2))\n",
"mat = confusion_matrix(y_test,y_pred)\n",
"print(\"Confusion Matrix: \\n\",mat)\n",
"y_test = label_binarize(y_test,classes=['no','yes'])\n",
"y_pred = label_binarize(y_pred,classes=['no','yes'])\n",
"print(\"Precision: \",round(precision_score(y_test,y_pred),2),\"Recall: \",round(recall_score(y_test,y_pred),2))"
]
},
{
"cell_type": "code",
"execution_count": 242,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 65
},
"colab_type": "code",
"id": "H0siKVloOOO_",
"outputId": "793eb0ac-35d1-46df-a77d-22ae2143518b"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[8133 2079]\n",
" [ 188 1132]]\n",
"Precision: 0.0 Recall: 0.0\n"
]
}
],
"source": [
"mat = confusion_matrix(y_test,y_pred)#,labels=['no','yes'])\n",
"print(mat)\n",
"y_test = label_binarize(y_test,classes=['no','yes'])\n",
"y_pred = label_binarize(y_pred,classes=['no','yes'])\n",
"print(\"Precision: \",round(precision_score(y_test,y_pred),2),\"Recall: \",round(recall_score(y_test,y_pred),2))"
]
},
{
"cell_type": "code",
"execution_count": 243,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 33
},
"colab_type": "code",
"id": "G5tWAs0fO7ns",
"outputId": "7d326b53-72c8-46fd-ce7c-dfd89862daed"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Precision: 0.61 Recall: 0.59\n"
]
}
],
"source": [
"X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3)\n",
"tree = DecisionTreeClassifier(criterion=\"entropy\", max_depth=7)\n",
"model = tree.fit(X_train,y_train)\n",
"y_pred = model.predict(X_test)\n",
"y_test = label_binarize(y_test,classes=['no','yes'])\n",
"y_pred = label_binarize(y_pred,classes=['no','yes'])\n",
"print(\"Precision: \",round(precision_score(y_test,y_pred),2),\"Recall: \",round(recall_score(y_test,y_pred),2))"
]
},
{
"cell_type": "code",
"execution_count": 244,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 33
},
"colab_type": "code",
"id": "6aBzpA-jO7kE",
"outputId": "c3738179-b628-435c-c7c4-488000d005e1"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Precision: 0.46 Recall: 0.81\n"
]
}
],
"source": [
"X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3)\n",
"tree = DecisionTreeClassifier(criterion=\"entropy\", max_depth=7)\n",
"X_SMOTE, y_SMOTE = sm.fit_resample(X_train, y_train)\n",
"model = tree.fit(X_SMOTE,y_SMOTE)\n",
"y_pred = model.predict(X_test)\n",
"y_test = label_binarize(y_test,classes=['no','yes'])\n",
"y_pred = label_binarize(y_pred,classes=['no','yes'])\n",
"print(\"Precision: \",round(precision_score(y_test,y_pred),2),\"Recall: \",round(recall_score(y_test,y_pred),2))"
]
},
{
"cell_type": "code",
"execution_count": 245,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 33
},
"colab_type": "code",
"id": "MDVtZlV_O7il",
"outputId": "f9157ecd-ced1-4b10-8d2f-e17e077af737"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Precision: 0.28 Recall: 0.71\n"
]
}
],
"source": [
"X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3)\n",
"forest = RandomForestClassifier(n_estimators= 1000,criterion=\"gini\", max_depth=5,min_samples_split = 0.4,min_samples_leaf=1, class_weight=\"balanced\")\n",
"model = forest.fit(X_train,y_train)\n",
"y_pred = model.predict(X_test)\n",
"pd.Series(y_pred).value_counts()\n",
"y_test = label_binarize(y_test,classes=['no','yes'])\n",
"y_pred = label_binarize(y_pred,classes=['no','yes'])\n",
"print(\"Precision: \",round(precision_score(y_test,y_pred),2),\"Recall: \",round(recall_score(y_test,y_pred),2))"
]
},
{
"cell_type": "code",
"execution_count": 246,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 33
},
"colab_type": "code",
"id": "ktFNOc9WO7go",
"outputId": "1a705e49-20bf-4f62-8d2c-24603f919017"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Precision: 0.32 Recall: 0.69\n"
]
}
],
"source": [
"X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3)\n",
"forest = RandomForestClassifier(n_estimators= 1000,criterion=\"gini\", max_depth=5,min_samples_split = 0.4,min_samples_leaf=1, class_weight=\"balanced\")\n",
"X_SMOTE, y_SMOTE = sm.fit_resample(X_train, y_train)\n",
"model = forest.fit(X_SMOTE,y_SMOTE)\n",
"y_pred = model.predict(X_test)\n",
"pd.Series(y_pred).value_counts()\n",
"y_test = label_binarize(y_test,classes=['no','yes'])\n",
"y_pred = label_binarize(y_pred,classes=['no','yes'])\n",
"print(\"Precision: \",round(precision_score(y_test,y_pred),2),\"Recall: \",round(recall_score(y_test,y_pred),2))"
]
},
{
"cell_type": "code",
"execution_count": 247,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 145
},
"colab_type": "code",
"id": "-lEHNtruO7fN",
"outputId": "6b48449c-674f-4983-fbf7-9c8f988713ae"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Imbalanced -\n",
"Precision: 0.67 Recall: 0.41\n",
"Random undersampled -\n",
"Precision: 0.43 Recall: 0.86\n",
"Random oversampled -\n",
"Precision: 0.44 Recall: 0.88\n",
"SMOTE -\n",
"Precision: 0.42 Recall: 0.87\n"
]
}
],
"source": [
"X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3)\n",
"model = lm.LogisticRegression(random_state=0, solver='lbfgs',multi_class='auto',max_iter=1000).fit(X_train,y_train)\n",
"y_pred = model.predict_proba(X_test)\n",
"y_pred = y_pred[:,1]\n",
"y_test = label_binarize(y_test,classes=['no','yes'])\n",
"fpr_imb, tpr_imb, _ = roc_curve(y_test, y_pred)\n",
"roc_auc_imb = auc(fpr_imb, tpr_imb)\n",
"y_pred = model.predict(X_test)\n",
"y_pred = label_binarize(y_pred,classes=['no','yes'])\n",
"print(\"Imbalanced -\")\n",
"print(\"Precision: \",round(precision_score(y_test,y_pred),2),\"Recall: \",round(recall_score(y_test,y_pred),2))\n",
"# Undersampled\n",
"X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3)\n",
"rus = RandomUnderSampler(random_state=0)\n",
"X_Usampled, y_Usampled = rus.fit_resample(X_train, y_train)\n",
"model = lm.LogisticRegression(random_state=0, solver='lbfgs',multi_class='auto',max_iter=5000).fit(X_Usampled,y_Usampled)\n",
"y_pred = model.predict_proba(X_test)\n",
"y_pred = y_pred[:,1]\n",
"y_test = label_binarize(y_test,classes=['no','yes'])\n",
"fpr_us, tpr_us, _ = roc_curve(y_test, y_pred)\n",
"roc_auc_us = auc(fpr_us, tpr_us)\n",
"y_pred = model.predict(X_test)\n",
"y_pred = label_binarize(y_pred,classes=['no','yes'])\n",
"print(\"Random undersampled -\")\n",
"print(\"Precision: \",round(precision_score(y_test,y_pred),2),\"Recall: \",round(recall_score(y_test,y_pred),2))\n",
"# Oversampled\n",
"X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3)\n",
"ros = RandomOverSampler(random_state=0)\n",
"X_Osampled, y_Osampled = ros.fit_resample(X_train, y_train)\n",
"model = lm.LogisticRegression(random_state=0, solver='lbfgs',multi_class='auto',max_iter=5000).fit(X_Osampled, y_Osampled)\n",
"y_pred = model.predict_proba(X_test)\n",
"y_pred = y_pred[:,1]\n",
"y_test = label_binarize(y_test,classes=['no','yes'])\n",
"fpr_os, tpr_os, _ = roc_curve(y_test, y_pred)\n",
"roc_auc_os = auc(fpr_os, tpr_os)\n",
"y_pred = model.predict(X_test)\n",
"y_pred = label_binarize(y_pred,classes=['no','yes'])\n",
"print(\"Random oversampled -\")\n",
"print(\"Precision: \",round(precision_score(y_test,y_pred),2),\"Recall: \",round(recall_score(y_test,y_pred),2))\n",
"# SMOTE\n",
"X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3)\n",
"sm = SMOTE(random_state=0)\n",
"X_SMOTE, y_SMOTE = sm.fit_resample(X_train, y_train)\n",
"model = lm.LogisticRegression(random_state=0, solver='lbfgs',multi_class='auto',max_iter=5000).fit(X_SMOTE,y_SMOTE)\n",
"y_pred = model.predict_proba(X_test)\n",
"y_pred = y_pred[:,1]\n",
"y_test = label_binarize(y_test,classes=['no','yes'])\n",
"fpr_smote, tpr_smote, _ = roc_curve(y_test, y_pred)\n",
"roc_auc_smote = auc(fpr_smote, tpr_smote)\n",
"y_pred = model.predict(X_test)\n",
"y_pred = label_binarize(y_pred,classes=['no','yes'])\n",
"print(\"SMOTE -\")\n",
"print(\"Precision: \",round(precision_score(y_test,y_pred),2),\"Recall: \",round(recall_score(y_test,y_pred),2))"
]
},
{
"cell_type": "code",
"execution_count": 51,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 296
},
"colab_type": "code",
"id": "scGySTYwO7dn",
"outputId": "cbd0aaac-f2a2-49d0-f287-3e78a2582f03"
},
"outputs": [
{
"data": {
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XvcdYLiEFwqIo6l6W5oPG0L49JERF822/XhyM9ISKVm+rno2FpFRqBvegfZ+2VKsGa3+c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\nYII=\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"tags": []
},
"output_type": "display_data"
}
],
"source": [
"plt.figure()\n",
"lw = 2\n",
"plt.plot(fpr_imb, tpr_imb,\n",
" label='Imbalanced data ROC curve (area = {0:0.4f})'\n",
" ''.format(roc_auc_imb),\n",
" color='deeppink', linestyle=':', linewidth=2)\n",
"\n",
"plt.plot(fpr_us, tpr_us,\n",
" label='Undersampled data ROC curve (area = {0:0.4f})'\n",
" ''.format(roc_auc_us),\n",
" color='blue', linestyle='--', linewidth=2)\n",
"\n",
"plt.plot(fpr_os, tpr_os,\n",
" label='Random Oversampled data ROC curve (area = {0:0.4f})'\n",
" ''.format(roc_auc_os),\n",
" color='darkred', linestyle='--', linewidth=2)\n",
"\n",
"plt.plot(fpr_smote, tpr_smote,\n",
" label='SMOTE data ROC curve (area = {0:0.4f})'\n",
" ''.format(roc_auc_smote),\n",
" color='darkgreen', linestyle='--', linewidth=2)\n",
"\n",
"plt.plot([0, 1], [0, 1], color='navy', lw=lw, linestyle='--')\n",
"plt.xlim([0.0, 1.0])\n",
"plt.ylim([0.0, 1.00])\n",
"plt.xlabel('False Positive Rate')\n",
"plt.ylabel('True Positive Rate')\n",
"plt.title('Receiver operating characteristic example')\n",
"plt.legend(loc=\"lower right\")\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 0,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "-9WiYDllO7af"
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 52,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 49
},
"colab_type": "code",
"id": "WhB-KK94O7Ud",
"outputId": "a7e3bbc9-241a-4cdb-fdef-85941f67949b"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Linear kernel- Precision: 0.89 Recall: 0.73\n",
"Guassian kernel- Precision: 0.83 Recall: 0.87\n"
]
}
],
"source": [
"sm = SMOTE(random_state=0)\n",
"X_SMOTE, y_SMOTE = sm.fit_resample(X, y)\n",
"X_train, X_test, y_train, y_test = train_test_split(X_SMOTE, y_SMOTE, test_size=0.3)\n",
"svm = SVC(kernel='linear')\n",
"model = svm.fit(X_train, y_train)\n",
"y_pred = model.predict(X_test)\n",
"y_test = label_binarize(y_test,classes=['no','yes'])\n",
"y_pred = label_binarize(y_pred,classes=['no','yes'])\n",
"print(\"Linear kernel- \",\"Precision: \",round(precision_score(y_test,y_pred),2),\"Recall: \",round(recall_score(y_test,y_pred),2))\n",
"fpr_linear, tpr_linear, _ = roc_curve(y_test, y_pred)\n",
"roc_auc_linear = auc(fpr_linear, tpr_linear)\n",
"sm = SMOTE(random_state=0)\n",
"X_SMOTE, y_SMOTE = sm.fit_resample(X, y)\n",
"X_train, X_test, y_train, y_test = train_test_split(X_SMOTE, y_SMOTE, test_size=0.3)\n",
"svm = SVC(kernel='rbf')\n",
"model = svm.fit(X_train, y_train)\n",
"y_pred = model.predict(X_test)\n",
"y_test = label_binarize(y_test,classes=['no','yes'])\n",
"y_pred = label_binarize(y_pred,classes=['no','yes'])\n",
"print(\"Guassian kernel- \",\"Precision: \",round(precision_score(y_test,y_pred),2),\"Recall: \",round(recall_score(y_test,y_pred),2))\n",
"fpr_rbf, tpr_rbf, _ = roc_curve(y_test, y_pred)\n",
"roc_auc_rbf = auc(fpr_rbf, tpr_rbf)"
]
}
],
"metadata": {
"colab": {
"collapsed_sections": [],
"name": "P2.ipynb",
"provenance": [],
"toc_visible": true
},
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.8"
}
},
"nbformat": 4,
"nbformat_minor": 1
}
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