1.4 MiB
1.4 MiB
In [571]:
from google.colab import drive
drive.mount('/content/drive')Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount("/content/drive", force_remount=True).
In [572]:
lsbank-additional.csv bank-additional-full.csv bank-additional-names.txt bank.csv 'Copia de Marketing Campaign_Retail Bank_Telemarketing.ipynb' COPYbank.csv 'Copy of WORKING-WELL-Copia de Marketing Campaign_Retail Bank_Telemarketing.ipynb' [0m[01;34mDataFiles[0m/ Distrubution-age-duration.png 'Ising2DFM_reSample_L40_T=All_labels.pkl' 'Ising2DFM_reSample_L40_T=All.pkl' IsingData.zip NB12_CIX-DNN_ising_TFlow.ipynb P2-anl1.ipynb P2.ipynb [01;34mResults[0m/ 'Untitled spreadsheet.gsheet' 'WORKING-WELL-Copia de Marketing Campaign_Retail Bank_Telemarketing.ipynb'
In [573]:
cd drive/'My Drive'/'Colab Notebooks'/MLP2/[Errno 2] No such file or directory: 'drive/My Drive/Colab Notebooks/MLP2/' /content/drive/My Drive/Colab Notebooks/MLP2
In [574]:
lsbank-additional.csv bank-additional-full.csv bank-additional-names.txt bank.csv 'Copia de Marketing Campaign_Retail Bank_Telemarketing.ipynb' COPYbank.csv 'Copy of WORKING-WELL-Copia de Marketing Campaign_Retail Bank_Telemarketing.ipynb' [0m[01;34mDataFiles[0m/ Distrubution-age-duration.png 'Ising2DFM_reSample_L40_T=All_labels.pkl' 'Ising2DFM_reSample_L40_T=All.pkl' IsingData.zip NB12_CIX-DNN_ising_TFlow.ipynb P2-anl1.ipynb P2.ipynb [01;34mResults[0m/ 'Untitled spreadsheet.gsheet' 'WORKING-WELL-Copia de Marketing Campaign_Retail Bank_Telemarketing.ipynb'
In [1]:
import pandas as pd
import numpy as np
from pandas import read_csv
import matplotlib.pyplot as plt
import warnings
warnings.filterwarnings('ignore')
import os
# Where to save the figures and data files
PROJECT_ROOT_DIR = "Results"
FIGURE_ID = "Results/FigureFiles"
DATA_ID = "DataFiles/"
if not os.path.exists(PROJECT_ROOT_DIR):
os.mkdir(PROJECT_ROOT_DIR)
if not os.path.exists(FIGURE_ID):
os.makedirs(FIGURE_ID)
if not os.path.exists(DATA_ID):
os.makedirs(DATA_ID)
def image_path(fig_id):
return os.path.join(FIGURE_ID, fig_id)
def data_path(dat_id):
return os.path.join(DATA_ID, dat_id)
def save_fig(fig_id):
plt.savefig(image_path(fig_id) + ".png", format='png')
print("--1--data")
linkname = 'bank-additional-full.csv'
print("--1.2-read file")
dataset1 = pd.read_csv(linkname, sep = ';')
print("--1.2.2--readed")
# View the first 5 rows in the dataset
print(dataset1.shape)
display(dataset1.head(70))
--1--data --1.2-read file --1.2.2--readed (41188, 21)
| age | job | marital | education | default | housing | loan | contact | month | day_of_week | ... | campaign | pdays | previous | poutcome | emp.var.rate | cons.price.idx | cons.conf.idx | euribor3m | nr.employed | y | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 56 | housemaid | married | basic.4y | no | no | no | telephone | may | mon | ... | 1 | 999 | 0 | nonexistent | 1.1 | 93.994 | -36.4 | 4.857 | 5191.0 | no |
| 1 | 57 | services | married | high.school | unknown | no | no | telephone | may | mon | ... | 1 | 999 | 0 | nonexistent | 1.1 | 93.994 | -36.4 | 4.857 | 5191.0 | no |
| 2 | 37 | services | married | high.school | no | yes | no | telephone | may | mon | ... | 1 | 999 | 0 | nonexistent | 1.1 | 93.994 | -36.4 | 4.857 | 5191.0 | no |
| 3 | 40 | admin. | married | basic.6y | no | no | no | telephone | may | mon | ... | 1 | 999 | 0 | nonexistent | 1.1 | 93.994 | -36.4 | 4.857 | 5191.0 | no |
| 4 | 56 | services | married | high.school | no | no | yes | telephone | may | mon | ... | 1 | 999 | 0 | nonexistent | 1.1 | 93.994 | -36.4 | 4.857 | 5191.0 | no |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 65 | 37 | admin. | married | university.degree | no | no | no | telephone | may | mon | ... | 1 | 999 | 0 | nonexistent | 1.1 | 93.994 | -36.4 | 4.857 | 5191.0 | no |
| 66 | 44 | blue-collar | single | basic.9y | no | yes | no | telephone | may | mon | ... | 1 | 999 | 0 | nonexistent | 1.1 | 93.994 | -36.4 | 4.857 | 5191.0 | no |
| 67 | 33 | admin. | married | unknown | no | yes | no | telephone | may | mon | ... | 1 | 999 | 0 | nonexistent | 1.1 | 93.994 | -36.4 | 4.857 | 5191.0 | no |
| 68 | 56 | admin. | married | basic.9y | no | yes | no | telephone | may | mon | ... | 2 | 999 | 0 | nonexistent | 1.1 | 93.994 | -36.4 | 4.857 | 5191.0 | no |
| 69 | 44 | blue-collar | single | basic.4y | unknown | yes | yes | telephone | may | mon | ... | 1 | 999 | 0 | nonexistent | 1.1 | 93.994 | -36.4 | 4.857 | 5191.0 | no |
70 rows × 21 columns
In [2]:
data1 = dataset1[dataset1['y'] == 'yes']
data2 = dataset1[dataset1['y'] == 'no']
print("*")
fig, ax = plt.subplots(2, 2, figsize=(12,10))
b1 = ax[0, 0].bar(data1['day_of_week'].unique(),height = data1['day_of_week'].value_counts(),color='#FF0006')
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')
ax[0, 0].title.set_text('Day of week')
#ax[0, 0].legend((b1[0], b2[0]), ('Yes', 'No'))
ax[0, 1].bar(data1['month'].unique(),height = data1['month'].value_counts(),color='#FF0006')
ax[0, 1].bar(data2['month'].unique(),height = data2['month'].value_counts(),bottom = data1['month'].value_counts(),color = '#00B9FF')
ax[0, 1].title.set_text('Month')
ax[1, 0].bar(data1['job'].unique(),height = data1['job'].value_counts(),color='#FF0006')
ax[1, 0].bar(data1['job'].unique(),height = data2['job'].value_counts()[data1['job'].value_counts().index],bottom = data1['job'].value_counts(),color = '#00B9FF')
ax[1, 0].title.set_text('Type of Job')
ax[1, 0].tick_params(axis='x',rotation=90)
ax[1, 1].bar(data1['education'].unique(),height = data1['education'].value_counts(),color='#FF0006') #row=0, col=1
ax[1, 1].bar(data1['education'].unique(),height = data2['education'].value_counts()[data1['education'].value_counts().index],bottom = data1['education'].value_counts(),color = '#00B9FF')
ax[1, 1].title.set_text('Education')
ax[1, 1].tick_params(axis='x',rotation=90)
#ax[0, 1].xticks(rotation=90)
plt.figlegend((b1[0], b2[0]), ('Yes', 'No'),loc="right",title = "Term deposit")
save_fig('DataVisual-dow_mth_job_edu')
plt.show()
print(dataset1.shape)*
(41188, 21)
In [3]:
fig, ax = plt.subplots(2, 3, figsize=(15,10))
b1 = ax[0, 0].bar(data1['marital'].unique(),height = data1['marital'].value_counts(),color='#FF0006')
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')
ax[0, 0].title.set_text('Marital Status')
#ax[0, 0].legend((b1[0], b2[0]), ('Yes', 'No'))
ax[0, 1].bar(data1['housing'].unique(),height = data1['housing'].value_counts(),color='#FF0006')
ax[0, 1].bar(data1['housing'].unique(),height = data2['housing'].value_counts()[data1['housing'].value_counts().index],bottom = data1['housing'].value_counts(),color = '#00B9FF')
ax[0, 1].title.set_text('(Has) housing (loan)')
ax[0, 2].bar(data1['loan'].unique(),height = data1['loan'].value_counts(),color='#FF0006')
ax[0, 2].bar(data1['loan'].unique(),height = data2['loan'].value_counts()[data1['loan'].value_counts().index],bottom = data1['loan'].value_counts(),color = '#00B9FF')
ax[0, 2].title.set_text('(Has personal) loan')
ax[1, 0].bar(data1['contact'].unique(),height = data1['contact'].value_counts(),color='#FF0006')
ax[1, 0].bar(data1['contact'].unique(),height = data2['contact'].value_counts()[data1['contact'].value_counts().index],bottom = data1['contact'].value_counts(),color = '#00B9FF')
ax[1, 0].title.set_text('Contact')
ax[1, 1].bar(data1['default'].unique(),height = data1['default'].value_counts(),color='#FF0006')
ax[1, 1].bar(data1['default'].unique(),height = data2['default'].value_counts()[data1['default'].value_counts().index],bottom = data1['default'].value_counts(),color = '#00B9FF')
ax[1, 1].title.set_text('(Has credit in) Default')
ax[1, 2].bar(data1['poutcome'].unique(),height = data1['poutcome'].value_counts(),color='#FF0006')
ax[1, 2].bar(data1['poutcome'].unique(),height = data2['poutcome'].value_counts()[data1['poutcome'].value_counts().index],bottom = data1['poutcome'].value_counts(),color = '#00B9FF')
ax[1, 2].title.set_text('Outcome of the previous marketing campaign')
plt.figlegend((b1[0], b2[0]), ('Yes', 'No'),loc="right",title = "Term deposit")
save_fig('DataVisual-marit_hous_loan_tcont_def_prevmc')
plt.show()
print(dataset1.shape)
(41188, 21)
In [4]:
count_day_of_week_response_pct = pd.crosstab(dataset1['y'],dataset1['day_of_week']).apply(lambda x: x/x.sum() * 100)
count_day_of_week_response_pct = count_day_of_week_response_pct.transpose()
print("+a")
plot_day_of_the_week = count_day_of_week_response_pct['yes'].sort_values(ascending = True).plot(kind ='bar',
figsize = (5,5))
plt.title('Subscription Rate by Day of the week')
plt.xlabel('Day of the week')
plt.ylabel('Subscription Rate')
# Label each bar
for rec, label in zip(plot_day_of_the_week.patches,
count_day_of_week_response_pct['yes'].sort_values(ascending = True).round(1).astype(str)):
plot_day_of_the_week.text(rec.get_y() + rec.get_x() + 0.3,#rec.get_width(), #x,y,s ORDER of entries
rec.get_height() + rec.get_y(),#rec.get_y()+ rec.get_height(),
label+'%',
ha = 'center',
va='bottom')
print("++a")
save_fig('DataVisual-Sr_week')
plt.show()
print(dataset1.shape)+a ++a
(41188, 21)
In [5]:
count_month_response_pct = pd.crosstab(dataset1['y'],dataset1['month']).apply(lambda x: x/x.sum() * 100)
count_month_response_pct = count_month_response_pct.transpose()
print("+b")
plot_month = count_month_response_pct['yes'].sort_values(ascending = True).plot(kind ='bar',
figsize = (5,5))
plt.title('Subscription Rate by Month')
plt.xlabel('Month')
plt.ylabel('Subscription Rate')
# Label each bar
for rec, label in zip(plot_month.patches,
count_month_response_pct['yes'].sort_values(ascending = True).round(1).astype(str)):
plot_month.text(rec.get_y() + rec.get_x() + 0.3,
rec.get_height() + rec.get_y(),
label+'%', fontsize=8,
ha = 'center',
va='bottom')
print("++b")
save_fig('DataVisual-Sr_month')
plt.show()
print(dataset1.shape)+b ++b
(41188, 21)
In [6]:
count_job_response_pct = pd.crosstab(dataset1['y'],dataset1['job']).apply(lambda x: x/x.sum() * 100)
count_job_response_pct = count_job_response_pct.transpose()
print("+c")
plot_job = count_job_response_pct['yes'].sort_values(ascending = True).plot(kind ='bar',
figsize = (5,5))
plt.title('Subscription Rate by Job')
plt.ylabel('Subscription Rate')
plt.xlabel('Job Category')
# Label each bar
for rec, label in zip(plot_job.patches,
count_job_response_pct['yes'].sort_values(ascending = True).round(1).astype(str)):
plot_job.text(rec.get_y() + rec.get_x() + 0.3,
rec.get_height() + rec.get_y(),
label+'%', fontsize=8,
ha = 'center',
va='bottom')
print("++c")
save_fig('DataVisual-Sr_job')
plt.show()
print(dataset1.shape)+c ++c
(41188, 21)
In [7]:
count_education_response_pct = pd.crosstab(dataset1['y'],dataset1['education']).apply(lambda x: x/x.sum() * 100)
count_education_response_pct = count_education_response_pct.transpose()
print("+d")
plot_education = count_education_response_pct['yes'].sort_values(ascending = True).plot(kind ='bar',
figsize = (5,5))
plt.title('Subscription Rate by Education')
plt.ylabel('Subscription Rate')
plt.xlabel('Education Category')
# Label each bar
for rec, label in zip(plot_education.patches,
count_education_response_pct['yes'].sort_values(ascending = True).round(1).astype(str)):
plot_education.text(rec.get_y() + rec.get_x() + 0.3,
rec.get_height() + rec.get_y(),
label+'%',
ha = 'center',
va='bottom')
print("++d")
save_fig('DataVisual-Sr_edu')
plt.show()
print(dataset1.shape)+d ++d
(41188, 21)
In [8]:
count_marital_response_pct = pd.crosstab(dataset1['y'],dataset1['marital']).apply(lambda x: x/x.sum() * 100)
count_marital_response_pct = count_marital_response_pct.transpose()
print("+")
plot_marital = count_marital_response_pct['yes'].sort_values(ascending = True).plot(kind ='bar',
figsize = (5,5))
plt.title('Subscription Rate by Marital Status')
plt.ylabel('Subscription Rate')
plt.xlabel('Marital Status')
# Label each bar
for rec, label in zip(plot_marital.patches,
count_marital_response_pct['yes'].sort_values(ascending = True).round(1).astype(str)):
plot_marital.text(rec.get_y() + rec.get_x() + 0.3,
rec.get_height() + rec.get_y(),
label+'%',
ha = 'center',
va='bottom')
print("++")
save_fig('DataVisual-Sr_marital')
plt.show()
print(dataset1.shape)+ ++
(41188, 21)
In [9]:
count_housing_response_pct = pd.crosstab(dataset1['y'],dataset1['housing']).apply(lambda x: x/x.sum() * 100)
count_housing_response_pct = count_housing_response_pct.transpose()
print("+1")
plot_housing = count_housing_response_pct['yes'].sort_values(ascending = True).plot(kind ='bar',
figsize = (5,5))
plt.title('Subscription Rate by Housing loan')
plt.ylabel('Subscription Rate')
plt.xlabel('Housing loan')
# Label each bar
for rec, label in zip(plot_housing.patches,
count_housing_response_pct['yes'].sort_values(ascending = True).round(1).astype(str)):
plot_housing.text(rec.get_y() + rec.get_x() + 0.3,
rec.get_height() + rec.get_y(),
label+'%',
ha = 'center',
va='bottom')
print("++1")
save_fig('DataVisual-Sr_housing')
plt.show()
print(dataset1.shape)
+1 ++1
(41188, 21)
In [10]:
count_loan_response_pct = pd.crosstab(dataset1['y'],dataset1['loan']).apply(lambda x: x/x.sum() * 100)
count_loan_response_pct = count_loan_response_pct.transpose()
print("+2")
plot_loan = count_loan_response_pct['yes'].sort_values(ascending = True).plot(kind ='bar',
figsize = (5,5))
plt.title('Subscription Rate by Loan(personal)')
plt.ylabel('Subscription Rate')
plt.xlabel('Loan Category')
# Label each bar
for rec, label in zip(plot_loan.patches,
count_loan_response_pct['yes'].sort_values(ascending = True).round(1).astype(str)):
plot_loan.text(rec.get_y() + rec.get_x() + 0.3,
rec.get_height() + rec.get_y(),
label+'%',
ha = 'center',
va='bottom')
print("++2")
save_fig('DataVisual-Sr_loan')
plt.show()
print(dataset1.shape)+2 ++2
(41188, 21)
In [11]:
count_contact_response_pct = pd.crosstab(dataset1['y'],dataset1['contact']).apply(lambda x: x/x.sum() * 100)
count_contact_response_pct = count_contact_response_pct.transpose()
print("+3")
plot_contact = count_contact_response_pct['yes'].sort_values(ascending = True).plot(kind ='bar',
figsize = (5,5))
plt.title('Subscription Rate by Contact')
plt.ylabel('Subscription Rate')
plt.xlabel('Contact Category')
# Label each bar
for rec, label in zip(plot_contact.patches,
count_contact_response_pct['yes'].sort_values(ascending = True).round(1).astype(str)):
plot_contact.text(rec.get_y() + rec.get_x() + 0.3,
rec.get_height() + rec.get_y(),
label+'%',
ha = 'center',
va='bottom')
print("++3")
save_fig('DataVisual-Sr_contact')
plt.show()
print(dataset1.shape)+3 ++3
(41188, 21)
In [12]:
count_default_response_pct = pd.crosstab(dataset1['y'],dataset1['default']).apply(lambda x: x/x.sum() * 100)
count_default_response_pct = count_default_response_pct.transpose()
print("+4")
plot_default = count_default_response_pct['yes'].sort_values(ascending = True).plot(kind ='bar',
figsize = (5,5))
plt.title('Subscription Rate by Default')
plt.ylabel('Subscription Rate')
plt.xlabel('Default Category')
# Label each bar
for rec, label in zip(plot_default.patches,
count_default_response_pct['yes'].sort_values(ascending = True).round(1).astype(str)):
plot_default.text(rec.get_y() + rec.get_x() + 0.3,
rec.get_height() + rec.get_y(),
label+'%',
ha = 'center',
va='bottom')
print("++4")
save_fig('DataVisual-Sr_default')
plt.show()
print(dataset1.shape)+4 ++4
(41188, 21)
In [13]:
count_poutcome_response_pct = pd.crosstab(dataset1['y'],dataset1['poutcome']).apply(lambda x: x/x.sum() * 100)
count_poutcome_response_pct = count_poutcome_response_pct.transpose()
print("+5")
plot_poutcome = count_poutcome_response_pct['yes'].sort_values(ascending = True).plot(kind ='bar',
figsize = (5,5))
plt.title('Subscription Rate by Previous outcome')
plt.ylabel('Subscription Rate')
plt.xlabel('Previous outcome Category')
# Label each bar
for rec, label in zip(plot_poutcome.patches,
count_poutcome_response_pct['yes'].sort_values(ascending = True).round(1).astype(str)):
plot_poutcome.text(rec.get_y() + rec.get_x() + 0.3,
rec.get_height() + rec.get_y(),
label+'%',
ha = 'center',
va='bottom')
print("++5")
save_fig('DataVisual-Sr_poutcome')
plt.show()
print(dataset1.shape)+5 ++5
(41188, 21)
In [14]:
#################
print("--2Clean")
# Step 1: Delete the rows which column 'poutcome' contains 'other'
condition0 = dataset1.poutcome == 'other'
#print("--2.1")
#print("--2.1.2 dataset2")
datasetcondition0 = dataset1.drop(dataset1[condition0].index, axis = 0, inplace = False)
#Does not affect the data _?
#Delete the rows with 'unknown' in marital housing and loan
condition0 = datasetcondition0.marital == 'unknown'
dataset02 = datasetcondition0.drop(datasetcondition0[condition0].index, axis = 0, inplace = False)
condition00 = dataset02.housing == 'unknown'
dataset5 = dataset02.drop(dataset02[condition00].index, axis = 0, inplace = False)
#condition000 = dataset002.loan == 'unknown'
#dataset0002 = dataset002.drop(dataset002[condition000].index, axis = 0, inplace = False)
# We found out an "unknown" in edu
#condition0000 = dataset0002.education == 'unknown'
#dataset5 = dataset0002.drop(dataset0002[condition0000].index, axis = 0, inplace = False)
#erase calls with duration 0
#condition000 = dataset002.loan == 'unknown'
#dataset2 = dataset002.drop(dataset002[condition000].index, axis = 0, inplace = False)
#print("--2.1")
#print("--2.1.2 dataset2")
#display(dataset2.head())
# Step 2: Replace 'unknown' in default housing and loan with 'other'
#print("--2.1.3 to replace")
dataset5['loan'] = dataset5['loan'].replace(['unknown'],'no')
#print("--2.1.4 replaced")
print(dataset5.shape)
display(dataset5.head(70))
--2Clean (40119, 21)
| age | job | marital | education | default | housing | loan | contact | month | day_of_week | ... | campaign | pdays | previous | poutcome | emp.var.rate | cons.price.idx | cons.conf.idx | euribor3m | nr.employed | y | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 56 | housemaid | married | basic.4y | no | no | no | telephone | may | mon | ... | 1 | 999 | 0 | nonexistent | 1.1 | 93.994 | -36.4 | 4.857 | 5191.0 | no |
| 1 | 57 | services | married | high.school | unknown | no | no | telephone | may | mon | ... | 1 | 999 | 0 | nonexistent | 1.1 | 93.994 | -36.4 | 4.857 | 5191.0 | no |
| 2 | 37 | services | married | high.school | no | yes | no | telephone | may | mon | ... | 1 | 999 | 0 | nonexistent | 1.1 | 93.994 | -36.4 | 4.857 | 5191.0 | no |
| 3 | 40 | admin. | married | basic.6y | no | no | no | telephone | may | mon | ... | 1 | 999 | 0 | nonexistent | 1.1 | 93.994 | -36.4 | 4.857 | 5191.0 | no |
| 4 | 56 | services | married | high.school | no | no | yes | telephone | may | mon | ... | 1 | 999 | 0 | nonexistent | 1.1 | 93.994 | -36.4 | 4.857 | 5191.0 | no |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 67 | 33 | admin. | married | unknown | no | yes | no | telephone | may | mon | ... | 1 | 999 | 0 | nonexistent | 1.1 | 93.994 | -36.4 | 4.857 | 5191.0 | no |
| 68 | 56 | admin. | married | basic.9y | no | yes | no | telephone | may | mon | ... | 2 | 999 | 0 | nonexistent | 1.1 | 93.994 | -36.4 | 4.857 | 5191.0 | no |
| 69 | 44 | blue-collar | single | basic.4y | unknown | yes | yes | telephone | may | mon | ... | 1 | 999 | 0 | nonexistent | 1.1 | 93.994 | -36.4 | 4.857 | 5191.0 | no |
| 70 | 41 | management | married | basic.6y | no | no | no | telephone | may | mon | ... | 1 | 999 | 0 | nonexistent | 1.1 | 93.994 | -36.4 | 4.857 | 5191.0 | no |
| 71 | 44 | management | divorced | university.degree | no | yes | no | telephone | may | mon | ... | 1 | 999 | 0 | nonexistent | 1.1 | 93.994 | -36.4 | 4.857 | 5191.0 | no |
70 rows × 21 columns
In [0]:
# Step 2: Replace 'unknown' in default housing and loan with 'other'
#print("--2.1.3 to replace")
#dataset2[['job','education',]] = dataset2[['job','education']].replace(['unknown'],'other')
#print("--2.1.4 replaced")In [0]:
############################ 2.2 Drop outliers in the column 'balance
#from scipy.stats import zscore
#dataset2[['balance']].mean()
#dataset2[['balance']].mean()
#dataset2['balance_outliers'] = dataset2['balance']
#dataset2['balance_outliers']= zscore(dataset2['balance_outliers'])
#condition1 = (dataset2['balance_outliers']>3) | (dataset2['balance_outliers']<-3 )
#dataset3 = dataset2.drop(dataset2[condition1].index, axis = 0, inplace = False)
#dataset4 = dataset3.drop('balance_outliers', axis=1)In [15]:
############################## 2.3 Creating and transforming data
# Step 1: Change column name: 'y' to 'response'
#dataset4.rename(index=str, columns={'y': 'response'}, inplace = True)
#dataset2.rename(index=str, columns={'y': 'response'}, inplace = True)
dataset5.rename(index=str, columns={'y': 'response'}, inplace = True)
def convert(dataset5, new_column, old_column):
dataset5[new_column] = dataset5[old_column].apply(lambda x: 0 if x == 'no' else 1)
return dataset5[new_column].value_counts()
#To appreciate the change when displaying the table 22 col
convert(dataset5, "response_binary", "response")
#convert(dataset5, "response", "response")
print("-----2.3.1")
print(dataset5.shape)
display(dataset5.head(150))
-----2.3.1 (40119, 22)
| age | job | marital | education | default | housing | loan | contact | month | day_of_week | ... | pdays | previous | poutcome | emp.var.rate | cons.price.idx | cons.conf.idx | euribor3m | nr.employed | response | response_binary | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 56 | housemaid | married | basic.4y | no | no | no | telephone | may | mon | ... | 999 | 0 | nonexistent | 1.1 | 93.994 | -36.4 | 4.857 | 5191.0 | no | 0 |
| 1 | 57 | services | married | high.school | unknown | no | no | telephone | may | mon | ... | 999 | 0 | nonexistent | 1.1 | 93.994 | -36.4 | 4.857 | 5191.0 | no | 0 |
| 2 | 37 | services | married | high.school | no | yes | no | telephone | may | mon | ... | 999 | 0 | nonexistent | 1.1 | 93.994 | -36.4 | 4.857 | 5191.0 | no | 0 |
| 3 | 40 | admin. | married | basic.6y | no | no | no | telephone | may | mon | ... | 999 | 0 | nonexistent | 1.1 | 93.994 | -36.4 | 4.857 | 5191.0 | no | 0 |
| 4 | 56 | services | married | high.school | no | no | yes | telephone | may | mon | ... | 999 | 0 | nonexistent | 1.1 | 93.994 | -36.4 | 4.857 | 5191.0 | no | 0 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 148 | 40 | admin. | married | university.degree | unknown | yes | no | telephone | may | mon | ... | 999 | 0 | nonexistent | 1.1 | 93.994 | -36.4 | 4.857 | 5191.0 | no | 0 |
| 149 | 51 | blue-collar | married | basic.4y | no | yes | no | telephone | may | mon | ... | 999 | 0 | nonexistent | 1.1 | 93.994 | -36.4 | 4.857 | 5191.0 | no | 0 |
| 150 | 60 | blue-collar | married | basic.9y | unknown | no | no | telephone | may | mon | ... | 999 | 0 | nonexistent | 1.1 | 93.994 | -36.4 | 4.857 | 5191.0 | no | 0 |
| 151 | 56 | entrepreneur | married | unknown | unknown | yes | no | telephone | may | mon | ... | 999 | 0 | nonexistent | 1.1 | 93.994 | -36.4 | 4.857 | 5191.0 | no | 0 |
| 152 | 39 | services | divorced | high.school | unknown | yes | no | telephone | may | mon | ... | 999 | 0 | nonexistent | 1.1 | 93.994 | -36.4 | 4.857 | 5191.0 | no | 0 |
150 rows × 22 columns
In [16]:
# Step 2: Drop column "response_binary" which is useless
#datasetNEW = dataset5.drop('response_binary', axis=1)
#dataset4 = dataset3.drop('balance_outliers', axis=1)
#print("-----2.3.2")
# Step 2: Drop column "contact" which is useless
#dataset5 = dataset4.drop('contact', axis=1)
####dataset5 = dataset2.drop('contact', axis=1)
#print("-----2.3.2")
# Step 3: Change the unit of 'duration' from seconds to minutes
dataset5['duration'] = dataset5['duration'].apply(lambda n:n/60).round(2)
print("-----2.3.3")
print(dataset5.shape)
display(dataset5.head(150))-----2.3.3 (40119, 22)
| age | job | marital | education | default | housing | loan | contact | month | day_of_week | ... | pdays | previous | poutcome | emp.var.rate | cons.price.idx | cons.conf.idx | euribor3m | nr.employed | response | response_binary | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 56 | housemaid | married | basic.4y | no | no | no | telephone | may | mon | ... | 999 | 0 | nonexistent | 1.1 | 93.994 | -36.4 | 4.857 | 5191.0 | no | 0 |
| 1 | 57 | services | married | high.school | unknown | no | no | telephone | may | mon | ... | 999 | 0 | nonexistent | 1.1 | 93.994 | -36.4 | 4.857 | 5191.0 | no | 0 |
| 2 | 37 | services | married | high.school | no | yes | no | telephone | may | mon | ... | 999 | 0 | nonexistent | 1.1 | 93.994 | -36.4 | 4.857 | 5191.0 | no | 0 |
| 3 | 40 | admin. | married | basic.6y | no | no | no | telephone | may | mon | ... | 999 | 0 | nonexistent | 1.1 | 93.994 | -36.4 | 4.857 | 5191.0 | no | 0 |
| 4 | 56 | services | married | high.school | no | no | yes | telephone | may | mon | ... | 999 | 0 | nonexistent | 1.1 | 93.994 | -36.4 | 4.857 | 5191.0 | no | 0 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 148 | 40 | admin. | married | university.degree | unknown | yes | no | telephone | may | mon | ... | 999 | 0 | nonexistent | 1.1 | 93.994 | -36.4 | 4.857 | 5191.0 | no | 0 |
| 149 | 51 | blue-collar | married | basic.4y | no | yes | no | telephone | may | mon | ... | 999 | 0 | nonexistent | 1.1 | 93.994 | -36.4 | 4.857 | 5191.0 | no | 0 |
| 150 | 60 | blue-collar | married | basic.9y | unknown | no | no | telephone | may | mon | ... | 999 | 0 | nonexistent | 1.1 | 93.994 | -36.4 | 4.857 | 5191.0 | no | 0 |
| 151 | 56 | entrepreneur | married | unknown | unknown | yes | no | telephone | may | mon | ... | 999 | 0 | nonexistent | 1.1 | 93.994 | -36.4 | 4.857 | 5191.0 | no | 0 |
| 152 | 39 | services | divorced | high.school | unknown | yes | no | telephone | may | mon | ... | 999 | 0 | nonexistent | 1.1 | 93.994 | -36.4 | 4.857 | 5191.0 | no | 0 |
150 rows × 22 columns
In [17]:
# Step 4: Change 'month' from words to numbers for easier analysis
lst = [dataset5]
for column in lst:
column.loc[column["month"] == "jan", "month"] = 1
column.loc[column["month"] == "feb", "month"] = 2
column.loc[column["month"] == "mar", "month"] = 3
column.loc[column["month"] == "apr", "month"] = 4
column.loc[column["month"] == "may", "month"] = 5
column.loc[column["month"] == "jun", "month"] = 6
column.loc[column["month"] == "jul", "month"] = 7
column.loc[column["month"] == "aug", "month"] = 8
column.loc[column["month"] == "sep", "month"] = 9
column.loc[column["month"] == "oct", "month"] = 10
column.loc[column["month"] == "nov", "month"] = 11
column.loc[column["month"] == "dec", "month"] = 12
print("-----2.3.4")
# Step 4.1: Change 'day' from words to numbers for easier analysis
lst = [dataset5]
for column in lst:
column.loc[column["day_of_week"] == "mon", "day_of_week"] = 1
column.loc[column["day_of_week"] == "tue", "day_of_week"] = 2
column.loc[column["day_of_week"] == "wed", "day_of_week"] = 3
column.loc[column["day_of_week"] == "thu", "day_of_week"] = 4
column.loc[column["day_of_week"] == "fri", "day_of_week"] = 5
print("-----2.3.5")
print(dataset5.shape)
display(dataset5.head())-----2.3.4 -----2.3.5 (40119, 22)
| age | job | marital | education | default | housing | loan | contact | month | day_of_week | ... | pdays | previous | poutcome | emp.var.rate | cons.price.idx | cons.conf.idx | euribor3m | nr.employed | response | response_binary | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 56 | housemaid | married | basic.4y | no | no | no | telephone | 5 | 1 | ... | 999 | 0 | nonexistent | 1.1 | 93.994 | -36.4 | 4.857 | 5191.0 | no | 0 |
| 1 | 57 | services | married | high.school | unknown | no | no | telephone | 5 | 1 | ... | 999 | 0 | nonexistent | 1.1 | 93.994 | -36.4 | 4.857 | 5191.0 | no | 0 |
| 2 | 37 | services | married | high.school | no | yes | no | telephone | 5 | 1 | ... | 999 | 0 | nonexistent | 1.1 | 93.994 | -36.4 | 4.857 | 5191.0 | no | 0 |
| 3 | 40 | admin. | married | basic.6y | no | no | no | telephone | 5 | 1 | ... | 999 | 0 | nonexistent | 1.1 | 93.994 | -36.4 | 4.857 | 5191.0 | no | 0 |
| 4 | 56 | services | married | high.school | no | no | yes | telephone | 5 | 1 | ... | 999 | 0 | nonexistent | 1.1 | 93.994 | -36.4 | 4.857 | 5191.0 | no | 0 |
5 rows × 22 columns
In [594]:
# Step 4.2: Change from words to numbers for easier analysis
#'basic.4y','basic.6y','basic.9y','high.school','illiterate','professional.course','university.degree','unknown'
lst = [dataset5]
for column in lst:
column.loc[column["education"] == "illiterate", "education"] = 0
column.loc[column["education"] == "unknown", "education"] = 0
column.loc[column["education"] == "basic.4y", "education"] = 1
column.loc[column["education"] == "basic.6y", "education"] = 1
column.loc[column["education"] == "basic.9y", "education"] = 1
column.loc[column["education"] == "basic.9y", "education"] = 1
column.loc[column["education"] == "high.school", "education"] = 1
column.loc[column["education"] == "professional.course", "education"] = 2
column.loc[column["education"] == "university.degree", "education"] = 2
print("-----2.3.5")
print(dataset5.shape)
display(dataset5.head())
lst = [dataset5]
for column in lst:
column.loc[column["marital"] == "single", "marital"] = 0
column.loc[column["marital"] == "divorced", "marital"] = 1
column.loc[column["marital"] == "married", "marital"] = 2
print("-----2.3.x")
print(dataset5.shape)
display(dataset5.head(100))-----2.3.5 (40119, 22)
| age | job | marital | education | default | housing | loan | contact | month | day_of_week | duration | campaign | pdays | previous | poutcome | emp.var.rate | cons.price.idx | cons.conf.idx | euribor3m | nr.employed | response | response_binary | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 56 | housemaid | married | 1 | no | no | no | telephone | 5 | 1 | 4.35 | 1 | 999 | 0 | nonexistent | 1.1 | 93.994 | -36.4 | 4.857 | 5191.0 | no | 0 |
| 1 | 57 | services | married | 1 | unknown | no | no | telephone | 5 | 1 | 2.48 | 1 | 999 | 0 | nonexistent | 1.1 | 93.994 | -36.4 | 4.857 | 5191.0 | no | 0 |
| 2 | 37 | services | married | 1 | no | yes | no | telephone | 5 | 1 | 3.77 | 1 | 999 | 0 | nonexistent | 1.1 | 93.994 | -36.4 | 4.857 | 5191.0 | no | 0 |
| 3 | 40 | admin. | married | 1 | no | no | no | telephone | 5 | 1 | 2.52 | 1 | 999 | 0 | nonexistent | 1.1 | 93.994 | -36.4 | 4.857 | 5191.0 | no | 0 |
| 4 | 56 | services | married | 1 | no | no | yes | telephone | 5 | 1 | 5.12 | 1 | 999 | 0 | nonexistent | 1.1 | 93.994 | -36.4 | 4.857 | 5191.0 | no | 0 |
-----2.3.x (40119, 22)
| age | job | marital | education | default | housing | loan | contact | month | day_of_week | duration | campaign | pdays | previous | poutcome | emp.var.rate | cons.price.idx | cons.conf.idx | euribor3m | nr.employed | response | response_binary | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 56 | housemaid | 2 | 1 | no | no | no | telephone | 5 | 1 | 4.35 | 1 | 999 | 0 | nonexistent | 1.1 | 93.994 | -36.4 | 4.857 | 5191.0 | no | 0 |
| 1 | 57 | services | 2 | 1 | unknown | no | no | telephone | 5 | 1 | 2.48 | 1 | 999 | 0 | nonexistent | 1.1 | 93.994 | -36.4 | 4.857 | 5191.0 | no | 0 |
| 2 | 37 | services | 2 | 1 | no | yes | no | telephone | 5 | 1 | 3.77 | 1 | 999 | 0 | nonexistent | 1.1 | 93.994 | -36.4 | 4.857 | 5191.0 | no | 0 |
| 3 | 40 | admin. | 2 | 1 | no | no | no | telephone | 5 | 1 | 2.52 | 1 | 999 | 0 | nonexistent | 1.1 | 93.994 | -36.4 | 4.857 | 5191.0 | no | 0 |
| 4 | 56 | services | 2 | 1 | no | no | yes | telephone | 5 | 1 | 5.12 | 1 | 999 | 0 | nonexistent | 1.1 | 93.994 | -36.4 | 4.857 | 5191.0 | no | 0 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 98 | 37 | technician | 0 | 2 | no | no | no | telephone | 5 | 1 | 3.28 | 1 | 999 | 0 | nonexistent | 1.1 | 93.994 | -36.4 | 4.857 | 5191.0 | no | 0 |
| 99 | 44 | blue-collar | 2 | 1 | no | no | no | telephone | 5 | 1 | 4.28 | 1 | 999 | 0 | nonexistent | 1.1 | 93.994 | -36.4 | 4.857 | 5191.0 | no | 0 |
| 100 | 54 | services | 2 | 0 | no | yes | no | telephone | 5 | 1 | 3.82 | 1 | 999 | 0 | nonexistent | 1.1 | 93.994 | -36.4 | 4.857 | 5191.0 | no | 0 |
| 101 | 49 | blue-collar | 2 | 1 | no | no | no | telephone | 5 | 1 | 0.92 | 3 | 999 | 0 | nonexistent | 1.1 | 93.994 | -36.4 | 4.857 | 5191.0 | no | 0 |
| 102 | 54 | services | 2 | 0 | no | no | no | telephone | 5 | 1 | 6.67 | 1 | 999 | 0 | nonexistent | 1.1 | 93.994 | -36.4 | 4.857 | 5191.0 | no | 0 |
100 rows × 22 columns
In [0]:
#'''Convert Duration Call into 5 category'''
#def duration(dataset5):
# dataset5.loc[dataset5["day_of_week"] == "mon", 'day_of_week'] = 1
# data.loc[(data['duration'] > 102) & (data['duration'] <= 180) , 'duration'] = 2
# data.loc[(data['duration'] > 180) & (data['duration'] <= 319) , 'duration'] = 3
# data.loc[(data['duration'] > 319) & (data['duration'] <= 645), 'duration'] = 4
# data.loc[data['duration'] > 645, 'duration'] = 5
# return data
#duration(data);In [18]:
####################### 2.4 Filtering
# Step 1: Drop rows that 'duration' < 5s
condition2 = (dataset5['duration']<5/60)
#dataset6 = dataset5.drop(dataset5[condition2].index, axis = 0, inplace = False)
dataset7 = dataset5.drop(dataset5[condition2].index, axis = 0, inplace = False)
# Step 2: Drop customer values with 'other' education
#condition3 = (dataset6['education'] == 'other')
#dataset7 = dataset6.drop(dataset6[condition3].index, axis = 0, inplace = False)
plt.show()
print(dataset7.shape)(40066, 22)
In [19]:
###################### 3.Explo
### 3.1 Visu
dist_age_duration = plt.figure(figsize = (7,2.5))
ra1 = dist_age_duration.add_subplot(1,2,1)
#ra2 = dist_age_balance.add_subplot(1,2,2)
ra3 = dist_age_duration.add_subplot(1,2,2)
ra1.hist(dataset7['age'])
ra1.set_title('The Distribution of Age')
#ra2.hist(dataset7['balance'], color = 'skyblue')
#ra2.set_title('The Distribution of Balance')
ra3.hist(dataset7['duration'], color = 'skyblue')
ra3.set_title('The Distribution of Duration')
plt.tight_layout()
plt.savefig('Distrubution-age-duration')
plt.show()
print(dataset7.shape)
(40066, 22)
In [20]:
############################ 3.2 Visualize the relationship between 'age' and 'duration'¶
scatter_age_duration = dataset7.plot.scatter('age','duration',figsize = (7,2.5))
plt.title('The Relationship between Age and Duration ')
plt.show()
print(dataset7.shape)
(40066, 22)
In [22]:
###############
import seaborn as sns
dur_cam = sns.lmplot(x='age', y='duration',data = dataset7,
hue = 'response',
fit_reg = False,
scatter_kws={'alpha':0.3}, height =3)
plt.axis([0,100,0,90])#x(age) y(s)
plt.ylabel('Duration of Calls (min)')
plt.xlabel('Age')
plt.title('The Relationship between the Age and Duration of calls')
# Annotation
#plt.axhline(y=5, linewidth=2, color="k", linestyle='--')
#plt.annotate('Higher subscription rate when calls <5',xytext = (35,13),
# arrowprops=dict(color = 'k', width=1),xy=(30,6))
plt.show()
print(dataset7.shape)
(40066, 22)
In [23]:
############### 3.4 Visualize the relationship between 'duration' & 'campaign': with response result¶
import seaborn as sns
dur_cam = sns.lmplot(x='duration', y='campaign',data = dataset7,
hue = 'response',
fit_reg = False,
scatter_kws={'alpha':0.6}, height =4)
plt.axis([0,90,0,90])
plt.ylabel('Number of Calls')#campaign
plt.xlabel('Duration of Calls (Minutes)')
plt.title('The Relationship between the Duration and Number of Calls (with Response Result)')
# Annotation
plt.axhline(y=5, linewidth=2, color="k", linestyle='--')
plt.annotate('Higher subscription rate when calls <5',xytext = (35,13),
arrowprops=dict(color = 'k', width=1),xy=(30,6))
save_fig('DataVisual-Durat_campaign')
plt.show()
print(dataset7.shape)
(40066, 22)
In [24]:
#Scattered Matrix
from pandas.plotting import scatter_matrix
matrix = scatter_matrix(dataset7[['age','duration','education','campaign']],figsize=(8,6))
plt.suptitle('The Scatter Matrix of Age, Duration, education and Campaign')
plt.show()
print(dataset7.shape)
(40066, 22)
In [25]:
print(dataset7.shape)
display(dataset7.head())(40066, 22)
| age | job | marital | education | default | housing | loan | contact | month | day_of_week | ... | pdays | previous | poutcome | emp.var.rate | cons.price.idx | cons.conf.idx | euribor3m | nr.employed | response | response_binary | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 56 | housemaid | married | basic.4y | no | no | no | telephone | 5 | 1 | ... | 999 | 0 | nonexistent | 1.1 | 93.994 | -36.4 | 4.857 | 5191.0 | no | 0 |
| 1 | 57 | services | married | high.school | unknown | no | no | telephone | 5 | 1 | ... | 999 | 0 | nonexistent | 1.1 | 93.994 | -36.4 | 4.857 | 5191.0 | no | 0 |
| 2 | 37 | services | married | high.school | no | yes | no | telephone | 5 | 1 | ... | 999 | 0 | nonexistent | 1.1 | 93.994 | -36.4 | 4.857 | 5191.0 | no | 0 |
| 3 | 40 | admin. | married | basic.6y | no | no | no | telephone | 5 | 1 | ... | 999 | 0 | nonexistent | 1.1 | 93.994 | -36.4 | 4.857 | 5191.0 | no | 0 |
| 4 | 56 | services | married | high.school | no | no | yes | telephone | 5 | 1 | ... | 999 | 0 | nonexistent | 1.1 | 93.994 | -36.4 | 4.857 | 5191.0 | no | 0 |
5 rows × 22 columns
In [0]:
In [26]:
print(dataset7.shape)
display(dataset7.head(3))
(40066, 22)
| age | job | marital | education | default | housing | loan | contact | month | day_of_week | ... | pdays | previous | poutcome | emp.var.rate | cons.price.idx | cons.conf.idx | euribor3m | nr.employed | response | response_binary | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 56 | housemaid | married | basic.4y | no | no | no | telephone | 5 | 1 | ... | 999 | 0 | nonexistent | 1.1 | 93.994 | -36.4 | 4.857 | 5191.0 | no | 0 |
| 1 | 57 | services | married | high.school | unknown | no | no | telephone | 5 | 1 | ... | 999 | 0 | nonexistent | 1.1 | 93.994 | -36.4 | 4.857 | 5191.0 | no | 0 |
| 2 | 37 | services | married | high.school | no | yes | no | telephone | 5 | 1 | ... | 999 | 0 | nonexistent | 1.1 | 93.994 | -36.4 | 4.857 | 5191.0 | no | 0 |
3 rows × 22 columns
In [27]:
#dataset7 = pd.get_dummies(dataset7, columns = ['job'])
#dataset7 = pd.get_dummies(dataset7, columns = ['education'])
dataset7['housing'] = dataset7['housing'].map({'yes': 1, 'no': 0})
dataset7['default'] = dataset7['default'].map({'no': 1, 'unknown': 0})
dataset7['loan'] = dataset7['loan'].map({'yes': 1, 'no': 0})####################################################
#dataset7_response = pd.DataFrame(dataset['response_binary'])
#dataset7 = pd.merge(dataset7, dataset_response, left_index = True, right_index = True)
print(dataset7.shape)
display(dataset7.head(10))
(40066, 22)
| age | job | marital | education | default | housing | loan | contact | month | day_of_week | ... | pdays | previous | poutcome | emp.var.rate | cons.price.idx | cons.conf.idx | euribor3m | nr.employed | response | response_binary | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 56 | housemaid | married | basic.4y | 1.0 | 0 | 0 | telephone | 5 | 1 | ... | 999 | 0 | nonexistent | 1.1 | 93.994 | -36.4 | 4.857 | 5191.0 | no | 0 |
| 1 | 57 | services | married | high.school | 0.0 | 0 | 0 | telephone | 5 | 1 | ... | 999 | 0 | nonexistent | 1.1 | 93.994 | -36.4 | 4.857 | 5191.0 | no | 0 |
| 2 | 37 | services | married | high.school | 1.0 | 1 | 0 | telephone | 5 | 1 | ... | 999 | 0 | nonexistent | 1.1 | 93.994 | -36.4 | 4.857 | 5191.0 | no | 0 |
| 3 | 40 | admin. | married | basic.6y | 1.0 | 0 | 0 | telephone | 5 | 1 | ... | 999 | 0 | nonexistent | 1.1 | 93.994 | -36.4 | 4.857 | 5191.0 | no | 0 |
| 4 | 56 | services | married | high.school | 1.0 | 0 | 1 | telephone | 5 | 1 | ... | 999 | 0 | nonexistent | 1.1 | 93.994 | -36.4 | 4.857 | 5191.0 | no | 0 |
| 5 | 45 | services | married | basic.9y | 0.0 | 0 | 0 | telephone | 5 | 1 | ... | 999 | 0 | nonexistent | 1.1 | 93.994 | -36.4 | 4.857 | 5191.0 | no | 0 |
| 6 | 59 | admin. | married | professional.course | 1.0 | 0 | 0 | telephone | 5 | 1 | ... | 999 | 0 | nonexistent | 1.1 | 93.994 | -36.4 | 4.857 | 5191.0 | no | 0 |
| 7 | 41 | blue-collar | married | unknown | 0.0 | 0 | 0 | telephone | 5 | 1 | ... | 999 | 0 | nonexistent | 1.1 | 93.994 | -36.4 | 4.857 | 5191.0 | no | 0 |
| 8 | 24 | technician | single | professional.course | 1.0 | 1 | 0 | telephone | 5 | 1 | ... | 999 | 0 | nonexistent | 1.1 | 93.994 | -36.4 | 4.857 | 5191.0 | no | 0 |
| 9 | 25 | services | single | high.school | 1.0 | 1 | 0 | telephone | 5 | 1 | ... | 999 | 0 | nonexistent | 1.1 | 93.994 | -36.4 | 4.857 | 5191.0 | no | 0 |
10 rows × 22 columns
In [28]:
#To Create a colum for each var
#dataset7 = pd.get_dummies(dataset7, columns = ['job'])
#dataset7 = pd.get_dummies(dataset7, columns = ['education'])
dataset7['contact'] = dataset7['contact'].map({'telephone': 1, 'cellular': 0})
#dataset7['default'] = dataset7['default'].map({'no': 1, 'unknown': 0})
#dataset7['loan'] = dataset7['loan'].map({'yes': 1, 'no': 0})
#dataset7_response = pd.DataFrame(dataset['response_binary'])
#dataset7 = pd.merge(dataset7, dataset_response, left_index = True, right_index = True)
display(dataset7.head(10))
print(dataset7.shape)| age | job | marital | education | default | housing | loan | contact | month | day_of_week | ... | pdays | previous | poutcome | emp.var.rate | cons.price.idx | cons.conf.idx | euribor3m | nr.employed | response | response_binary | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 56 | housemaid | married | basic.4y | 1.0 | 0 | 0 | 1 | 5 | 1 | ... | 999 | 0 | nonexistent | 1.1 | 93.994 | -36.4 | 4.857 | 5191.0 | no | 0 |
| 1 | 57 | services | married | high.school | 0.0 | 0 | 0 | 1 | 5 | 1 | ... | 999 | 0 | nonexistent | 1.1 | 93.994 | -36.4 | 4.857 | 5191.0 | no | 0 |
| 2 | 37 | services | married | high.school | 1.0 | 1 | 0 | 1 | 5 | 1 | ... | 999 | 0 | nonexistent | 1.1 | 93.994 | -36.4 | 4.857 | 5191.0 | no | 0 |
| 3 | 40 | admin. | married | basic.6y | 1.0 | 0 | 0 | 1 | 5 | 1 | ... | 999 | 0 | nonexistent | 1.1 | 93.994 | -36.4 | 4.857 | 5191.0 | no | 0 |
| 4 | 56 | services | married | high.school | 1.0 | 0 | 1 | 1 | 5 | 1 | ... | 999 | 0 | nonexistent | 1.1 | 93.994 | -36.4 | 4.857 | 5191.0 | no | 0 |
| 5 | 45 | services | married | basic.9y | 0.0 | 0 | 0 | 1 | 5 | 1 | ... | 999 | 0 | nonexistent | 1.1 | 93.994 | -36.4 | 4.857 | 5191.0 | no | 0 |
| 6 | 59 | admin. | married | professional.course | 1.0 | 0 | 0 | 1 | 5 | 1 | ... | 999 | 0 | nonexistent | 1.1 | 93.994 | -36.4 | 4.857 | 5191.0 | no | 0 |
| 7 | 41 | blue-collar | married | unknown | 0.0 | 0 | 0 | 1 | 5 | 1 | ... | 999 | 0 | nonexistent | 1.1 | 93.994 | -36.4 | 4.857 | 5191.0 | no | 0 |
| 8 | 24 | technician | single | professional.course | 1.0 | 1 | 0 | 1 | 5 | 1 | ... | 999 | 0 | nonexistent | 1.1 | 93.994 | -36.4 | 4.857 | 5191.0 | no | 0 |
| 9 | 25 | services | single | high.school | 1.0 | 1 | 0 | 1 | 5 | 1 | ... | 999 | 0 | nonexistent | 1.1 | 93.994 | -36.4 | 4.857 | 5191.0 | no | 0 |
10 rows × 22 columns
(40066, 22)
In [29]:
display(dataset7.head(1))
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']]
corr = corr_data.corr()
#housing loan contact month day_of_week duration campaign pdays previous
#poutcome emp.var.rate cons.price.idx cons.conf.idx euribor3m nr.employed
cor_plot = sns.heatmap(corr,annot=True,cmap='BuPu',linewidths=0.2,annot_kws={'size':10})
fig=plt.gcf()
fig.set_size_inches(15,10)
plt.xticks(fontsize=10,rotation=-60)
plt.yticks(fontsize=10)
plt.title('Correlation Matrix')
save_fig('Datav-CORREMat')
plt.show()
print(dataset7.shape)
| age | job | marital | education | default | housing | loan | contact | month | day_of_week | ... | pdays | previous | poutcome | emp.var.rate | cons.price.idx | cons.conf.idx | euribor3m | nr.employed | response | response_binary | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 56 | housemaid | married | basic.4y | 1.0 | 0 | 0 | 1 | 5 | 1 | ... | 999 | 0 | nonexistent | 1.1 | 93.994 | -36.4 | 4.857 | 5191.0 | no | 0 |
1 rows × 22 columns
(40066, 22)
In [0]:
In [0]:
In [30]:
#linkname = '../input/bank_cleaned.csv'
#dataset = read_csv(linkname)
#dataset = dataset.drop(['Unnamed: 0'], axis=1)
print("--1--data")
linkname = 'bank-additional-full.csv'
print("--1.2-read file")
datasetO = pd.read_csv(linkname, sep = ';')
print("--1.2.2--readed")
print(datasetO.shape)
display(datasetO.head(1))
################
# Step 1: Delete the rows which column 'poutcome' contains 'other'
conditionO = datasetO.poutcome == 'other'
datasetconditionO0 = datasetO.drop(datasetO[conditionO].index, axis = 0, inplace = False)
conditionO0 = datasetconditionO0.marital == 'unknown'
datasetO02 = datasetconditionO0.drop(datasetconditionO0[conditionO0].index, axis = 0, inplace = False)
conditionO00 = datasetO02.housing == 'unknown'
datasetO002 = datasetO02.drop(datasetO02[conditionO00].index, axis = 0, inplace = False)
conditionO000 = datasetO002.loan == 'unknown'
datasetO0002 = datasetO002.drop(datasetO002[conditionO000].index, axis = 0, inplace = False)
# We found out an "unknown" in edu
conditionO0000 = datasetO0002.education == 'unknown'
datasetO5 = datasetO0002.drop(datasetO0002[conditionO0000].index, axis = 0, inplace = False)
#erase calls with duration 0
#condition000 = dataset002.loan == 'unknown'
#dataset2 = dataset002.drop(dataset002[condition000].index, axis = 0, inplace = False)
print(datasetO5.shape)
display(datasetO5.head(1))
#datasetO5.rename(index=str, columns={'y': 'response'}, inplace = True)
#def convert(datasetO5, new_column, old_column):
# datasetO5[new_column] = datasetO5[old_column].apply(lambda x: 0 if x == 'no' else 1)
# return datasetO5[new_column].value_counts()
#To appreciate the change when displaying the table 22 col
#convert(datasetO5, "response_binary", "response")
#convert(dataset5, "response", "response")
#print("-----2.3.1")
#print(datasetO5.shape)
#display(datasetO5.head(1))
--1--data --1.2-read file --1.2.2--readed (41188, 21)
| age | job | marital | education | default | housing | loan | contact | month | day_of_week | ... | campaign | pdays | previous | poutcome | emp.var.rate | cons.price.idx | cons.conf.idx | euribor3m | nr.employed | y | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 56 | housemaid | married | basic.4y | no | no | no | telephone | may | mon | ... | 1 | 999 | 0 | nonexistent | 1.1 | 93.994 | -36.4 | 4.857 | 5191.0 | no |
1 rows × 21 columns
(38437, 21)
| age | job | marital | education | default | housing | loan | contact | month | day_of_week | ... | campaign | pdays | previous | poutcome | emp.var.rate | cons.price.idx | cons.conf.idx | euribor3m | nr.employed | y | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 56 | housemaid | married | basic.4y | no | no | no | telephone | may | mon | ... | 1 | 999 | 0 | nonexistent | 1.1 | 93.994 | -36.4 | 4.857 | 5191.0 | no |
1 rows × 21 columns
In [608]:
datasetO5.info()<class 'pandas.core.frame.DataFrame'> Int64Index: 38437 entries, 0 to 41187 Data columns (total 21 columns): age 38437 non-null int64 job 38437 non-null object marital 38437 non-null object education 38437 non-null object default 38437 non-null object housing 38437 non-null object loan 38437 non-null object contact 38437 non-null object month 38437 non-null object day_of_week 38437 non-null object duration 38437 non-null int64 campaign 38437 non-null int64 pdays 38437 non-null int64 previous 38437 non-null int64 poutcome 38437 non-null object emp.var.rate 38437 non-null float64 cons.price.idx 38437 non-null float64 cons.conf.idx 38437 non-null float64 euribor3m 38437 non-null float64 nr.employed 38437 non-null float64 y 38437 non-null object dtypes: float64(5), int64(5), object(11) memory usage: 6.5+ MB
In [31]:
predictors = datasetO5.iloc[:,0:20]
predictors = predictors.drop(['pdays'],axis=1)
y = datasetO5.iloc[:,20]
X = pd.get_dummies(predictors)
print(datasetO5.shape)
display(datasetO5.head())
print(predictors.shape)
display(predictors.head())
display(y.head())
display(X.head())(38437, 21)
| age | job | marital | education | default | housing | loan | contact | month | day_of_week | ... | campaign | pdays | previous | poutcome | emp.var.rate | cons.price.idx | cons.conf.idx | euribor3m | nr.employed | y | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 56 | housemaid | married | basic.4y | no | no | no | telephone | may | mon | ... | 1 | 999 | 0 | nonexistent | 1.1 | 93.994 | -36.4 | 4.857 | 5191.0 | no |
| 1 | 57 | services | married | high.school | unknown | no | no | telephone | may | mon | ... | 1 | 999 | 0 | nonexistent | 1.1 | 93.994 | -36.4 | 4.857 | 5191.0 | no |
| 2 | 37 | services | married | high.school | no | yes | no | telephone | may | mon | ... | 1 | 999 | 0 | nonexistent | 1.1 | 93.994 | -36.4 | 4.857 | 5191.0 | no |
| 3 | 40 | admin. | married | basic.6y | no | no | no | telephone | may | mon | ... | 1 | 999 | 0 | nonexistent | 1.1 | 93.994 | -36.4 | 4.857 | 5191.0 | no |
| 4 | 56 | services | married | high.school | no | no | yes | telephone | may | mon | ... | 1 | 999 | 0 | nonexistent | 1.1 | 93.994 | -36.4 | 4.857 | 5191.0 | no |
5 rows × 21 columns
(38437, 19)
| age | job | marital | education | default | housing | loan | contact | month | day_of_week | duration | campaign | previous | poutcome | emp.var.rate | cons.price.idx | cons.conf.idx | euribor3m | nr.employed | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 56 | housemaid | married | basic.4y | no | no | no | telephone | may | mon | 261 | 1 | 0 | nonexistent | 1.1 | 93.994 | -36.4 | 4.857 | 5191.0 |
| 1 | 57 | services | married | high.school | unknown | no | no | telephone | may | mon | 149 | 1 | 0 | nonexistent | 1.1 | 93.994 | -36.4 | 4.857 | 5191.0 |
| 2 | 37 | services | married | high.school | no | yes | no | telephone | may | mon | 226 | 1 | 0 | nonexistent | 1.1 | 93.994 | -36.4 | 4.857 | 5191.0 |
| 3 | 40 | admin. | married | basic.6y | no | no | no | telephone | may | mon | 151 | 1 | 0 | nonexistent | 1.1 | 93.994 | -36.4 | 4.857 | 5191.0 |
| 4 | 56 | services | married | high.school | no | no | yes | telephone | may | mon | 307 | 1 | 0 | nonexistent | 1.1 | 93.994 | -36.4 | 4.857 | 5191.0 |
0 no 1 no 2 no 3 no 4 no Name: y, dtype: object
| age | duration | campaign | previous | emp.var.rate | cons.price.idx | cons.conf.idx | euribor3m | nr.employed | job_admin. | ... | month_oct | month_sep | day_of_week_fri | day_of_week_mon | day_of_week_thu | day_of_week_tue | day_of_week_wed | poutcome_failure | poutcome_nonexistent | poutcome_success | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 56 | 261 | 1 | 0 | 1.1 | 93.994 | -36.4 | 4.857 | 5191.0 | 0 | ... | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 |
| 1 | 57 | 149 | 1 | 0 | 1.1 | 93.994 | -36.4 | 4.857 | 5191.0 | 0 | ... | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 |
| 2 | 37 | 226 | 1 | 0 | 1.1 | 93.994 | -36.4 | 4.857 | 5191.0 | 0 | ... | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 |
| 3 | 40 | 151 | 1 | 0 | 1.1 | 93.994 | -36.4 | 4.857 | 5191.0 | 1 | ... | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 |
| 4 | 56 | 307 | 1 | 0 | 1.1 | 93.994 | -36.4 | 4.857 | 5191.0 | 0 | ... | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 |
5 rows × 58 columns
In [610]:
from pylab import rcParams
import matplotlib.ticker as mtick # For specifying the axes tick format
df = datasetO5
rcParams['figure.figsize']=10,6
ax = (df['loan'].value_counts()*100.0 /len(df)).plot(kind='bar', stacked = True, rot = 0)
ax.yaxis.set_major_formatter(mtick.PercentFormatter())
ax.set_ylabel('% Customers with loan')
ax.set_xlabel('Personal loan')
ax.set_ylabel('% Customers')
ax.set_title('Term deposit distribution')
totals = [] # creating a list to collect the plt.patches data
# finding the values and append to list
for i in ax.patches:
totals.append(i.get_width())
total = sum(totals) # setting individual bar lables using above list
for i in ax.patches:
# getting_width pulls left or right; get_y pushes up or down
ax.text(i.get_x()+.15, i.get_height()-3.5, \
str(round((i.get_height()/total), 1))+'%', color='white', weight = 'bold')
In [32]:
pd.Series(y).value_counts()Out [32]:
no 34160 yes 4277 Name: y, dtype: int64
In [33]:
#from sklearn.model_selection import train_test_split
#from sklearn.model_selection import cross_val_score
#from sklearn.model_selection import KFold
from sklearn.metrics import accuracy_score
#print("libraries")In [34]:
# 20% of the data will be used for testing
#test_size= 0.20
#seed = 7
#X_train, X_test, Y_train, Y_test= train_test_split(X, y, test_size=test_size, random_state=seed)
#print("____data splited")In [39]:
# Time for Classification Models
import time
from sklearn.decomposition import PCA
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import train_test_split
from sklearn.svm import SVC
from sklearn.neighbors import KNeighborsClassifier
from sklearn import tree
from sklearn.neural_network import MLPClassifier
from sklearn.neighbors import KNeighborsClassifier
from sklearn.ensemble import GradientBoostingClassifier
from sklearn.gaussian_process.kernels import RBF
from sklearn.ensemble import RandomForestClassifier
from sklearn.naive_bayes import GaussianNB
dict_classifiers = {
"Logistic Regression": LogisticRegression(solver='lbfgs', max_iter=5000),
"Nearest Neighbors": KNeighborsClassifier(),
"Linear SVM": SVC(gamma = 'auto'),
"Gradient Boosting Classifier": GradientBoostingClassifier(),
"Decision Tree": tree.DecisionTreeClassifier(),
"Random Forest": RandomForestClassifier(n_estimators=18),
"Neural Net": MLPClassifier(alpha=1),
"Naive Bayes": GaussianNB()
}In [40]:
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3)In [41]:
no_classifiers = len(dict_classifiers.keys())
def batch_classify(X_train, Y_train, verbose = True):
df_results = pd.DataFrame(data=np.zeros(shape=(no_classifiers,3)), columns = ['classifier', 'train_score', 'training_time'])
count = 0
for key, classifier in dict_classifiers.items():
t_start = time.process_time()
classifier.fit(X_train, Y_train)
t_end = time.process_time()
t_diff = t_end - t_start
train_score = classifier.score(X_train, Y_train)
df_results.loc[count,'classifier'] = key
df_results.loc[count,'train_score'] = train_score
df_results.loc[count,'training_time'] = t_diff
if verbose:
print("trained {c} in {f:.2f} s".format(c=key, f=t_diff))
count+=1
return df_resultsIn [617]:
df_results = batch_classify(X_train, y_train)
print(df_results.sort_values(by='train_score', ascending=False))trained Logistic Regression in 5.60 s
trained Nearest Neighbors in 0.31 s
trained Linear SVM in 159.79 s
trained Gradient Boosting Classifier in 5.82 s
trained Decision Tree in 0.27 s
trained Random Forest in 0.59 s
trained Neural Net in 6.35 s
trained Naive Bayes in 0.07 s
classifier train_score training_time
4 Decision Tree 1.000000 0.266150
5 Random Forest 0.997510 0.588621
2 Linear SVM 0.958038 159.793320
1 Nearest Neighbors 0.929678 0.308423
3 Gradient Boosting Classifier 0.924438 5.815561
0 Logistic Regression 0.912990 5.595267
6 Neural Net 0.865230 6.349941
7 Naive Bayes 0.860472 0.070996
In [42]:
# Use Cross-validation.
from sklearn.model_selection import cross_val_score
# Logistic Regression
log_reg = LogisticRegression(solver='lbfgs', max_iter=5000)
log_scores = cross_val_score(log_reg, X_train, y_train, cv=3)
log_reg_mean = log_scores.mean()
# SVC
svc_clf = SVC(gamma='auto')
svc_scores = cross_val_score(svc_clf, X_train, y_train, cv=3)
svc_mean = svc_scores.mean()
# KNearestNeighbors
knn_clf = KNeighborsClassifier()
knn_scores = cross_val_score(knn_clf, X_train, y_train, cv=3)
knn_mean = knn_scores.mean()
# Decision Tree
tree_clf = tree.DecisionTreeClassifier()
tree_scores = cross_val_score(tree_clf, X_train, y_train, cv=3)
tree_mean = tree_scores.mean()
# Gradient Boosting Classifier
grad_clf = GradientBoostingClassifier()
grad_scores = cross_val_score(grad_clf, X_train, y_train, cv=3)
grad_mean = grad_scores.mean()
# Random Forest Classifier
rand_clf = RandomForestClassifier(n_estimators=18)
rand_scores = cross_val_score(rand_clf, X_train, y_train, cv=3)
rand_mean = rand_scores.mean()
# NeuralNet Classifier
neural_clf = MLPClassifier(alpha=1)
neural_scores = cross_val_score(neural_clf, X_train, y_train, cv=3)
neural_mean = neural_scores.mean()
# Naives Bayes
nav_clf = GaussianNB()
nav_scores = cross_val_score(nav_clf, X_train, y_train, cv=3)
nav_mean = neural_scores.mean()
# Create a Dataframe with the results.
d = {'Classifiers': ['Logistic Reg.', 'SVC', 'KNN', 'Dec Tree', 'Grad B CLF', 'Rand FC', 'Neural Classifier', 'Naives Bayes'],
'Crossval Mean Scores': [log_reg_mean, svc_mean, knn_mean, tree_mean, grad_mean, rand_mean, neural_mean, nav_mean]}
result_df = pd.DataFrame(data=d)In [59]:
result_df = result_df.sort_values(by=['Crossval Mean Scores'], ascending=False)
result_dfOut [59]:
| Classifiers | Crossval Mean Scores | |
|---|---|---|
| 4 | Grad B CLF | 0.916372 |
| 0 | Logistic Reg. | 0.909980 |
| 5 | Rand FC | 0.908381 |
| 2 | KNN | 0.900911 |
| 1 | SVC | 0.892771 |
| 3 | Dec Tree | 0.888571 |
| 6 | Neural Classifier | 0.870729 |
| 7 | Naives Bayes | 0.870729 |
In [43]:
# Use Cross-validation.
from sklearn.model_selection import cross_val_score
# Gradient Boosting Classifier
grad_clf = GradientBoostingClassifier()
grad_scores = cross_val_score(grad_clf, X_train, y_train, cv=3)
grad_mean = grad_scores.mean()
from sklearn.model_selection import cross_val_predict
y_train_pred = cross_val_predict(grad_clf, X_train, y_train, cv=5)
from sklearn.metrics import accuracy_score
grad_clf.fit(X_train, y_train)
print ("Gradient Boost Classifier accuracy is %2.2f" % accuracy_score(y_train, y_train_pred))
predicted_probas =grad_clf.predict_proba(X_test)Gradient Boost Classifier accuracy is 0.92
In [621]:
from yellowbrick.classifier import ClassificationReport
# Specify the target classes
classes = ["yes", "no"]
visualizer = ClassificationReport(grad_clf, classes=classes, support=True, force_model=True)
visualizer.fit(X_train, y_train) # Fit the visualizer and the model
visualizer.score(X_test, y_test) # Evaluate the model on the test data
visualizer.poof() # Draw/show/poof the data
In [54]:
from sklearn.metrics import classification_report
y_pred = grad_clf.predict(X_test)
print(classification_report(y_test,y_pred,digits=2)) precision recall f1-score support
no 0.94 0.97 0.96 10284
yes 0.66 0.52 0.58 1248
accuracy 0.92 11532
macro avg 0.80 0.75 0.77 11532
weighted avg 0.91 0.92 0.92 11532
In [55]:
accuracy_score(y_test,y_pred)Out [55]:
0.9192681234824835
In [ ]:
Warning:
Output truncated. This notebook contains too many cells to display efficiently.