/*
Program to implement the multivariate linear regression model for sales prediction.
Developed by: Syed Abdul Wasih H
RegisterNumber:  212221240057
*/
import pandas as pd
import matplotlib.pyplot as plt
df = pd.read_csv('Advertising_data.csv')
df.head()
df.describe()
df.isnull().sum()
x = df[['TV','Radio','Newspaper']]
y = df["Sales"]
from sklearn.model_selection import train_test_split
x_train, x_test,y_train,y_test = train_test_split(x,y,test_size=0.2, random_state=101)
from sklearn.linear_model import LinearRegression
l = LinearRegression()
l.fit(x_train,y_train)
y_pred = l.predict(x_test)
print("Regression Slope: ",l.coef_[0])
print("Regression Intercept:",l.intercept_)
from sklearn import metrics
MSE = metrics.mean_squared_error(y_test,y_pred)
print("MSE is {}".format(MSE))
r2 = metrics.r2_score(y_test,y_pred)
print("R Squared Error is {} ".format(r2))
l.predict([[150.3,240.5,234.5]])