Logistic Regression Project - Solutions

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

This data set contains the following features:

'Daily Time Spent on Site': consumer time on site in minutes
'Age': cutomer 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 consumer was male
'Country': Country of consumer
'Timestamp': Time at which consumer clicked on Ad or closed window
'Clicked on Ad': 0 or 1 indicated clicking on Ad

Import Libraries

Import a few libraries you think you'll need (Or just import them as you go along!)



In [97]:

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

Get the Data

Read in the advertising.csv file and set it to a data frame called ad_data.



In [98]:

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

Check the head of ad_data



In [40]:

    
ad_data.head()









    Out[40]:






  
    
      
      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
    
    
      2
      69.47
      26
      59785.94
      236.50
      Organic bottom-line service-desk
      Davidton
      0
      San Marino
      2016-03-13 20:35:42
      0
    
    
      3
      74.15
      29
      54806.18
      245.89
      Triple-buffered reciprocal time-frame
      West Terrifurt
      1
      Italy
      2016-01-10 02:31:19
      0
    
    
      4
      68.37
      35
      73889.99
      225.58
      Robust logistical utilization
      South Manuel
      0
      Iceland
      2016-06-03 03:36:18
      0

Use info and describe() on ad_data



In [41]:

    
ad_data.info()









    



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



In [42]:

    
ad_data.describe()









    Out[42]:






  
    
      
      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

Exploratory Data Analysis

Let's use seaborn to explore the data!

Try recreating the plots shown below!

Create a histogram of the Age



In [48]:

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









    Out[48]:





<matplotlib.text.Text at 0x11a05b908>

Create a jointplot showing Area Income versus Age.



In [64]:

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









    Out[64]:





<seaborn.axisgrid.JointGrid at 0x120bbb390>

Create a jointplot showing the kde distributions of Daily Time spent on site vs. Age.



In [66]:

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

Create a jointplot of 'Daily Time Spent on Site' vs. 'Daily Internet Usage'



In [72]:

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









    Out[72]:





<seaborn.axisgrid.JointGrid at 0x121e8cb00>

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



In [84]:

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









    Out[84]:





<seaborn.axisgrid.PairGrid at 0x12a97fdd8>

Logistic Regression

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

You'll have the freedom here to choose columns that you want to train on!

Split the data into training set and testing set using train_test_split



In [85]:

    
from sklearn.model_selection import train_test_split



In [88]:

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



In [89]:

    
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.



In [91]:

    
from sklearn.linear_model import LogisticRegression



In [92]:

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









    Out[92]:





LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,
          intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,
          penalty='l2', random_state=None, solver='liblinear', tol=0.0001,
          verbose=0, warm_start=False)

Predictions and Evaluations

Now predict values for the testing data.



In [94]:

    
predictions = logmodel.predict(X_test)

Create a classification report for the model.



In [95]:

    
from sklearn.metrics import classification_report



In [96]:

    
print(classification_report(y_test,predictions))









    



             precision    recall  f1-score   support

          0       0.87      0.96      0.91       162
          1       0.96      0.86      0.91       168

avg / total       0.91      0.91      0.91       330

	Daily Time Spent on Site	Age	Area Income	Daily Internet Usage	Ad Topic Line	City	Male	Country	Timestamp
0	68.95	35	61833.90	256.09	Cloned 5thgeneration orchestration	Wrightburgh	0	Tunisia	2016-03-27 00:53:11
1	80.23	31	68441.85	193.77	Monitored national standardization	West Jodi	1	Nauru	2016-04-04 01:39:02
2	69.47	26	59785.94	236.50	Organic bottom-line service-desk	Davidton	0	San Marino	2016-03-13 20:35:42
3	74.15	29	54806.18	245.89	Triple-buffered reciprocal time-frame	West Terrifurt	1	Italy	2016-01-10 02:31:19
4	68.37	35	73889.99	225.58	Robust logistical utilization	South Manuel	0	Iceland	2016-06-03 03:36:18