Insurance Prediction in Logistic Regression in Machine Learning
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Program 1
import pandas as pd import numpy as np from matplotlib import pyplot as plt df=pd.read_csv("D://scikit_data/insurancedata/insurance_data.csv") df.head(10) plt.scatter(df.age,df.bought_insurance,marker='*',color='blue') plt.show() from sklearn.model_selection import train_test_split x_train,x_test,y_train,y_test=train_test_split(df[['age']],df.bought_insurance,test_size=0.1) len(x_train) x_train len(x_test) x_test from sklearn.linear_model import LogisticRegression model=LogisticRegression() model.fit(x_train,y_train) model.predict(x_test) model.score(x_test,y_test) model.score(x_train,y_train) model.predict([[87]]) n=int(input("Enter age: ")) x=model.predict([[n]]) if(x==[1]): print("Purchase") else: print("Not Purchase") x_test model.predict_proba(x_test)
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