Exercise of Salary Data_Linear Regression .py

#!/usr/bin/env python
# coding: utf-8

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### importing of libraries -
import pandas as pd
import seaborn as sns ### for sytlist Visualisation
import matplotlib.pyplot as plt


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data =pd.read_csv("E:/Python docs/Salary_Data.csv")
print(data.head(5))


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print(data.columns)


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sns.scatterplot(x='YearsExperience',y='Salary',data=data)


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X =data['YearsExperience']
print(X)


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y =data['Salary']
print(y)


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from sklearn.model_selection import train_test_split


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X =data['YearsExperience'].values.reshape(-1,1)


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y =data['Salary'].values.reshape(-1,1)


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X_train,X_test,y_train,y_test = train_test_split(X,y,test_size=0.2)


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from sklearn import linear_model
from sklearn.linear_model import LinearRegression


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model = LinearRegression()


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model_train = model.fit(X_train,y_train)
print(model_train)


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pred = model_train.predict(X_test)
pred


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y_test


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Y_test


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from sklearn.metrics import r2_score


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r2_score(y_test,pred)


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