import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_squared_error, r2_score

data = pd.read_csv(r'C:\Users\HP\Desktop\OIB-SIP\salesprediction\dataset\Advertising.csv')

print("First 5 rows of the dataset:")

First 5 rows of the dataset:
   Unnamed: 0     TV  Radio  Newspaper  Sales
0           1  230.1   37.8       69.2   22.1
1           2   44.5   39.3       45.1   10.4
2           3   17.2   45.9       69.3    9.3
3           4  151.5   41.3       58.5   18.5
4           5  180.8   10.8       58.4   12.9

print(data.head())

print("\nChecking for missing values:")

Checking for missing values:
Unnamed: 0    0
TV            0
Radio         0
Newspaper     0
Sales         0
dtype: int64

print(data.isnull().sum())

sns.pairplot(data)
plt.show()

X = data[['TV', 'Radio', 'Newspaper']]   
y = data['Sales']  

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

model = LinearRegression()
model.fit(X_train, y_train)

y_pred = model.predict(X_test)

mse = mean_squared_error(y_test, y_pred)
r2 = r2_score(y_test, y_pred)

print(f"\nMean Squared Error: {mse:.2f}")
print(f"R-squared Score: {r2:.2f}")

Mean Squared Error: 3.17
R-squared Score: 0.90

plt.scatter(y_test, y_pred)
plt.xlabel("Actual Sales")
plt.ylabel("Predicted Sales")
plt.title("Actual vs Predicted Sales")
plt.grid(True)
plt.show()