In this project, we will try to create a model that will predict whether or not a particular internet user will click on an ad based off the features of that user. For that, we will be working with a fake advertising data set, indicating whether or not a particular internet user clicked on an Advertisement.

This data set contains the following features:

• ‘Daily Time Spent on Site’: consumer time on site in minutes
• ‘Age’: customer age in years
• ‘Area Income’: Avg. Income of geographical area of consumer
• ‘Daily Internet Usage’: Avg. minutes a day consumer is on the internet
• ‘Ad Topic Line’: Headline of the advertisement
• ‘City’: City of consumer
• ‘Male’: Whether or not the consumer was male
• ‘Country’: Country of consumer
• ‘Timestamp’: The time at which the consumer clicks on Ad or closes window
• ‘Clicked on Ad’: 0 or 1 indicated clicking on Ad

Import Required Packages

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
%matplotlib inline

Import Data

ad_data = pd.read_csv('advertising.csv')

Check the head of ad_data

ad_data.head()

** Use info and describe() on ad_data**

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

ad_data.describe()

Exploratory Data Analysis

We will use seaborn to explore the data!

** Create a histogram of the Age**

sns.set_style('whitegrid')
ad_data['Age'].hist(bins = 30)
plt.xlabel('Age')

Text(0.5, 0, 'Age')

Create a jointplot showing Area Income versus Age.

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

<seaborn.axisgrid.JointGrid at 0x1fe36fcc6d0>

** Create a jointplot of ‘Daily Time Spent on Site’ vs. ‘Daily Internet Usage’**

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

<seaborn.axisgrid.JointGrid at 0x1fe36a78c10>

** Finally, create a pairplot with the hue defined by the ‘Clicked on Ad’ column feature.**

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

<seaborn.axisgrid.PairGrid at 0x1fe377fba60>

Logistic Regression

Now it’s time to do a train test split, and train our model!

** Define input and output variables**

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

** Split the data into training set and testing set using train_test_split**

from sklearn.model_selection import train_test_split

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=42)

** Train and fit a logistic regression model on the training set.**

from sklearn.linear_model import LogisticRegression

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

LogisticRegression()

LogisticRegression()

Predictions and Evaluations

** Now predict values for the testing data.**

y_predict = clf.predict(X_test)

** Create a classification report for the model.**

from sklearn.metrics import classification_report

print(classification_report(y_test,y_predict))

              precision    recall  f1-score   support

           0       0.86      0.96      0.91       162
           1       0.96      0.85      0.90       168

    accuracy                           0.91       330
   macro avg       0.91      0.91      0.91       330
weighted avg       0.91      0.91      0.91       330

from sklearn.metrics import confusion_matrix

conf_matrix = confusion_matrix(y_test, y_predict)
print(conf_matrix)

plt.style.use('seaborn-poster')
plt.matshow(conf_matrix, cmap = 'coolwarm')     
plt.gca().xaxis.tick_bottom()                   
plt.title('Confusion Matrix')
plt.ylabel('Actual Class')
plt.xlabel('Predicted Class')
for (i, j), corr_value in np.ndenumerate(conf_matrix):
    plt.text(j, i, corr_value, ha = 'center', va = 'center', fontsize = 20)     
plt.show()

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