Explain with a case study of logistic regression in Python.

In order to implement a logistic regression in Python we need to import following basic libraries

import pandas as pd

import numpy as np

import matplotlib.pyplot as plt

import seaborn as sns

%matplotlib inline

Now we will import the data and explore the data

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

We will visualize the data in order to explore the weightage of the data

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

Now we will split the data to create a model

from sklearn.model_selection import train_test_split

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

y=ad_data['Clicked on Ad']

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

Now we will create and fit the model

Now we will predict the test data and evaluate the model

from sklearn.linear_model import LogisticRegression

log model = LogisticRegression()

log model.fit(X_train,y_train)

predictions = log model.predict(X_test)

from sklearn.metrics import classification_report

print(classification_report(y_test,predictions))

This is how we implement logistic Regression in Python