LOGISTIC REGRESSION
EXERCISE
02_Logistic_Regression_Prj


Logistic Regression Project

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. 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 [ ]:
import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
%matplotlib inline
from sklearn import model_selection
from sklearn import linear_model
from sklearn import metrics

Get the Data

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

In [ ]:
ad_data = pd.read_csv('/content/advertising.csv')

Check the head of ad_data

In [ ]:
ad_data.head()
Out[ ]:
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 [ ]:
ad_data.describe()
Out[ ]:
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
In [ ]:
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

Exploratory Data Analysis

Let's use seaborn to explore the data!

Try recreating the plots shown below!

Create a histogram of the Age

In [ ]:
sns.set_style = "whitegrid"
sns.histplot(x = 'Age', data = ad_data, bins =30)
Out[ ]:
<matplotlib.axes._subplots.AxesSubplot at 0x7f24db53bf90>
In [ ]:
ad_data['Age'].hist(bins = 30)
Out[ ]:
<matplotlib.axes._subplots.AxesSubplot at 0x7f24daf58d50>
In [ ]:
 
Out[ ]:
<matplotlib.text.Text at 0x11a05b908>

Create a jointplot showing Area Income versus Age.

In [ ]:
sns.jointplot(x = 'Age', y= 'Area Income', data = ad_data, hue = "Clicked on Ad")
Out[ ]:
<seaborn.axisgrid.JointGrid at 0x7f24dc2a2a50>

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

In [ ]:
sns.jointplot(kind = 'kde', x= "Area Income", y = "Age", data = ad_data, hue = "Clicked on Ad", cmap = "Blues")
/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:1163: UserWarning: cmap parameter ignored when using hue mapping.
  warnings.warn(msg, UserWarning)
Out[ ]:
<seaborn.axisgrid.JointGrid at 0x7f24db6df8d0>
In [ ]:
 

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

In [ ]:
sns.jointplot(x = "Daily Time Spent on Site", y = "Daily Internet Usage", hue = "Clicked on Ad", data = ad_data, cmap = "Green")
Out[ ]:
<seaborn.axisgrid.JointGrid at 0x7f24db9e57d0>
In [ ]:
 
Out[ ]:
<seaborn.axisgrid.JointGrid at 0x121e8cb00>

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

In [ ]:
sns.pairplot(hue = "Clicked on Ad", data = ad_data)
Out[ ]:
<seaborn.axisgrid.PairGrid at 0x7f24dc350e50>