LinearRegression_Sklearn (1).py

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

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#Linear Reg. using sklearn
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt


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data=pd.read_csv(r'C:\Users\blankblack\Desktop\data_for_lr.csv')


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data.head(10)


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data.info()


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#handle null value
data=data.dropna()


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data.info()


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data.shape


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#splitting data
training_input=np.array(data.x[0:500]).reshape(500,1)
training_output=np.array(data.y[0:500]).reshape(500,1)
test_input=np.array(data.x[500:700]).reshape(199,1)
test_output=np.array(data.y[500:700]).reshape(199,1)


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#linear regression
#1.training model
from sklearn.linear_model import LinearRegression

linear_regressor=LinearRegression()
linear_regressor.fit(train_input,train_output)


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linear_regressor


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#predict test input
predicted_value=linear_regressor.predict(test_input.reshape(-1,1))


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predicted_value


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test_output


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from sklearn.metrics import mean_squared_error

error=mean_squared_error(test_output,predicted_value)


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error


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#visualizing error
#original hypo
plt.plot(test_input,test_output,'*',color='green')

#model hypo
plt.plot(test_input,predicted_value,'_',color='red')
plt.xlabel('input')
plt.ylabel('output')
plt.show()


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