Basics : Logistic Regression

Objective: To figure out if a user clicked on an advertisement and predict whether or not they will click on an ad based off the features of that user.

Source: Udemy | Python for Data Science and Machine Learning Bootcamp
Data used in the below analysis: link

#importing libraries we'll need
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
#used to view plots within jupyter notebook
%matplotlib inline
sns.set_style('whitegrid')

#read data from csv file
ad_data = pd.read_csv('advertising.csv')

ad_data.head(2)#view data

	Daily Time Spent on Site	Age	Area Income	Daily Internet Usage	Ad Topic Line	City	Male	Country	Timestamp	Clicked on Ad
0	68.95	35	61833.90	256.09	Cloned 5thgeneration orchestration	Wrightburgh	0	Tunisia	2016-03-27 00:53:11	0
1	80.23	31	68441.85	193.77	Monitored national standardization	West Jodi	1	Nauru	2016-04-04 01:39:02	0

#view some info on the dataset
ad_data.describe()

	Daily Time Spent on Site	Age	Area Income	Daily Internet Usage	Male	Clicked on Ad
count	1000.000000	1000.000000	1000.000000	1000.000000	1000.000000	1000.00000
mean	65.000200	36.009000	55000.000080	180.000100	0.481000	0.50000
std	15.853615	8.785562	13414.634022	43.902339	0.499889	0.50025
min	32.600000	19.000000	13996.500000	104.780000	0.000000	0.00000
25%	51.360000	29.000000	47031.802500	138.830000	0.000000	0.00000
50%	68.215000	35.000000	57012.300000	183.130000	0.000000	0.50000
75%	78.547500	42.000000	65470.635000	218.792500	1.000000	1.00000
max	91.430000	61.000000	79484.800000	269.960000	1.000000	1.00000

ad_data.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 1000 entries, 0 to 999
Data columns (total 10 columns):
 #   Column                    Non-Null Count  Dtype  
---  ------                    --------------  -----  
 0   Daily Time Spent on Site  1000 non-null   float64
 1   Age                       1000 non-null   int64  
 2   Area Income               1000 non-null   float64
 3   Daily Internet Usage      1000 non-null   float64
 4   Ad Topic Line             1000 non-null   object
 5   City                      1000 non-null   object
 6   Male                      1000 non-null   int64  
 7   Country                   1000 non-null   object
 8   Timestamp                 1000 non-null   object
 9   Clicked on Ad             1000 non-null   int64  
dtypes: float64(3), int64(3), object(4)
memory usage: 78.2+ KB

#Plots to explore the dataset
sns.distplot(ad_data['Age'],bins=30,kde=False)

sns.jointplot(x='Age',y='Area Income',data=ad_data)

sns.jointplot(x='Age',y='Daily Time Spent on Site',data=ad_data,kind='kde')

sns.jointplot(x='Daily Time Spent on Site',y='Daily Internet Usage',data=ad_data)

sns.pairplot(data=ad_data,hue='Clicked on Ad',palette='bwr')

Now we start training the model to predict whether the user clicked on Ad

#import required libraries and split the data into train and test
from sklearn.model_selection import train_test_split

ad_data.columns

Index(['Daily Time Spent on Site', 'Age', 'Area Income',
       'Daily Internet Usage', 'Ad Topic Line', 'City', 'Male', 'Country',
       'Timestamp', 'Clicked on Ad'],
      dtype='object')

We can get rid of ‘Ad Topic Line’, ‘City’,’Country’,’Timestamp’ as these are not numeric column and we are not dealing with non-numeric data to train models yet

X = ad_data[['Daily Time Spent on Site', 'Age', 'Area Income',
       'Daily Internet Usage','Male',]]
y = ad_data['Clicked on Ad']

from sklearn.model_selection import train_test_split
X_train,X_test, y_train,y_test =train_test_split(X, y, test_size=0.3, random_state=101)

#import Logistic Regression model and fit data
from sklearn.linear_model import LogisticRegression

logmodel = LogisticRegression()
logmodel.fit(X_train,y_train)

LogisticRegression()

predictions = logmodel.predict(X_test)

#Get important metrics to evaluate model
from sklearn.metrics import classification_report
print(classification_report(y_test,predictions))

              precision    recall  f1-score   support

           0       0.91      0.95      0.93       157
           1       0.94      0.90      0.92       143

    accuracy                           0.93       300
   macro avg       0.93      0.93      0.93       300
weighted avg       0.93      0.93      0.93       300

Result: We were able to train a model with an accuracy and recall of 93% indicating its a well fitted model!