How to make prediction in simple linear regression in python?

Description

To predict the dependent variable for the corresponding independent variable using python.

   Import necessary libraries.

   Load the sample data set.

   Assign the independent(X)and dependent(y) variables.

   Train our data set in 70:30 manner.

   Pass the X_train and Y_train  in to the linear model.

   Predict Y according to X.

#import the libraries

import pandas as pd

from sklearn.model_selection import train_test_split

from sklearn.linear_model import LinearRegression

#Sample data set

data={‘age’:[25,26,25,23,30,29,23,24,26,25],

‘rating’:[4,3.6,2.5,2.25,4.5,4.4,3.9,3.5,3.7,3.2],

‘bonus’:[1300,1400,1250,1100,1500,

1450,1150,1100,1250,1200],’salary’:[2500,

2600,2400,2200,3000,2900,2300,

2200,2450,2350],}

#create data frame

df=pd.DataFrame(data)

#Measuring descriptive statistics

print(df.describe())

#Take independent variable

X = df.iloc[:, :1].values

#Take dependent variable

y = df.iloc[:, 1].values

#Train our data

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.7, random_state=0)

#building linear model

regressor = LinearRegression()

#Fit the variable to the linear model

t=regressor.fit(X_train, y_train)

#print the linear model results

print(“Regression intercept is\n”,regressor.intercept_)

print(“Regression coefficient is\n”,regressor.coef_)

#make the predictions

y_pred = regressor.predict(X_test)

df1 = pd.DataFrame({‘Actual’: y_test, ‘Predicted’: y_pred})

#print the prediction as data frame

print(df1)

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