Introduction to Logistic Regression

Learning Objectives

  1. Create Seaborn plots for Exploratory Data Analysis
  2. Train a Logistic Regression Model using Scikit-Learn

Introduction

This lab is in introduction to logistic regression using Python and Scikit-Learn. This lab serves as a foundation for more complex algorithms and machine learning models that you will encounter in the course. In this lab, we will use a synthetic 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.

Each learning objective will correspond to a #TODO in this student lab notebook -- try to complete this notebook first and then review the solution notebook.

Import Libraries


In [ ]:
!sudo chown -R jupyter:jupyter /home/jupyter/training-data-analyst

In [3]:
import os
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
%matplotlib inline

Load the Dataset

We will use a synthetic advertising dataset. 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 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

In [18]:
# TODO 1: Read in the advertising.csv file and set it to a data frame called ad_data.
# TODO: Your code goes here

Check the head of ad_data


In [19]:
ad_data.head()


Out[19]:
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 [20]:
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 [21]:
ad_data.describe()


Out[21]:
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

Let's check for any null values.


In [22]:
ad_data.isnull().sum()


Out[22]:
Daily Time Spent on Site    0
Age                         0
Area Income                 0
Daily Internet Usage        0
Ad Topic Line               0
City                        0
Male                        0
Country                     0
Timestamp                   0
Clicked on Ad               0
dtype: int64

Exploratory Data Analysis (EDA)

Let's use seaborn to explore the data! Try recreating the plots shown below!

TODO 1: Create a histogram of the Age


In [28]:
# TODO: Your code goes here


Out[28]:
Text(0.5, 0, 'Age')

TODO 1: Create a jointplot showing Area Income versus Age.


In [29]:
# TODO: Your code goes here


Out[29]:
<seaborn.axisgrid.JointGrid at 0x7f9391624d68>

TODO 2: Create a jointplot showing the kde distributions of Daily Time spent on site vs. Age.


In [30]:
# TODO: Your code goes here


TODO 1: Create a jointplot of 'Daily Time Spent on Site' vs. 'Daily Internet Usage'


In [31]:
# TODO: Your code goes here


Out[31]:
<seaborn.axisgrid.JointGrid at 0x7f939100da90>

Logistic Regression

Logistic regression is a supervised machine learning process. It is similar to linear regression, but rather than predict a continuous value, we try to estimate probabilities by using a logistic function. Note that even though it has regression in the name, it is for classification. While linear regression is acceptable for estimating values, logistic regression is best for predicting the class of an observation

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!


In [44]:
from sklearn.model_selection import train_test_split

Next, let's define the features and label. Briefly, feature is input; label is output. This applies to both classification and regression problems.


In [45]:
X = ad_data[['Daily Time Spent on Site', 'Age', 'Area Income','Daily Internet Usage', 'Male']]
y = ad_data['Clicked on Ad']

TODO 2: Split the data into training set and testing set using train_test_split


In [46]:
# TODO: Your code goes here

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


In [47]:
from sklearn.linear_model import LogisticRegression

In [48]:
logmodel = LogisticRegression()
logmodel.fit(X_train,y_train)


Out[48]:
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 [41]:
predictions = logmodel.predict(X_test)

Create a classification report for the model.


In [42]:
from sklearn.metrics import classification_report

In [49]:
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

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