House_Price.py

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

# Load the dataset
df = pd.read_csv('C:\\Users\\HP\\Desktop\\Linear Reagression\\USA_Housing.csv')

# Display the first few rows of the dataset
print(df.head())

# Features (exclude 'Price' and 'Address')
X = df[['Avg. Area Income', 'Avg. Area House Age', 'Avg. Area Number of Rooms', 'Avg. Area Number of Bedrooms', 'Area Population']]

# Target variable (Price)
y = df['Price']

# Split the data into training and testing sets (80% train, 20% test)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# Create and train the Linear Regression model
model = LinearRegression()
model.fit(X_train, y_train)

# Make predictions on the test set
y_pred = model.predict(X_test)

# Evaluate the model
mse = mean_squared_error(y_test, y_pred)
r2 = r2_score(y_test, y_pred)

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

# Plot the results: Actual vs Predicted Prices
plt.figure(figsize=(10, 6))
plt.scatter(y_test, y_pred, color='blue', alpha=0.5, label='Predicted Prices')
plt.plot([y_test.min(), y_test.max()], [y_test.min(), y_test.max()], 'r--', lw=2, label='Perfect Prediction')
plt.xlabel('Actual Prices')
plt.ylabel('Predicted Prices')
plt.title('Actual vs Predicted House Prices')
plt.legend()
plt.tight_layout()
plt.show()

# Display the coefficients of the model
coefficients = pd.DataFrame({'Feature': X.columns, 'Coefficient': model.coef_})
print("\nModel Coefficients:")
print(coefficients)

# Predict the price for a new house based on specific features
new_house_features = np.array([[70000, 6, 7, 4, 30000]])  # Example values for new house features
predicted_price = model.predict(new_house_features)
print(f"\nPredicted price for the new house: ${predicted_price[0]:.2f}")