IDE: Jupyter Notebook

Technology: Python

Problem Task : Let we have a data set(insurance.csv) having two attributes like age and buy_insurance and these two attribute containing training data. We will predict who will buy insurance by implementing Logistic Regression technique.

#Import required libraries
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from sklearn import linear_model
import seaborn as sns

#Create a scatter plot
plt.scatter(df.age,df.buy_insurance,marker='+',color='red')

Output:

#We split data set into training and testing data set
from sklearn.model_selection import train_test_split
X_train,X_test,y_train,y_test=train_test_split(df[['age']],df.buy_insurance,test_size=0.3)
X_test

Output:

#Crearte a LogisticRegression model
from sklearn.linear_model import LogisticRegression
model=LogisticRegression()
model.fit(X_train,y_train)

Output:

#predict which age value will buy insurance
model.predict(X_test)

Output: array([1, 1, 1, 0, 0], dtype=int64)

#Check the accuracy
model.score(X_test,y_test)

Output: 1.0