Add House Price Regression Python Solution

house_price_regression.py

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+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
+
+# Create synthetic dataset
+np.random.seed(42)
+n_samples = 100
+
+square_feet = np.random.randint(800, 4000, n_samples)
+bedrooms = np.random.randint(1, 6, n_samples)
+bathrooms = np.random.randint(1, 4, n_samples)
+
+# Assume a simple formula for price with some noise
+price = (square_feet * 200) + (bedrooms * 10000) + (bathrooms * 15000) + np.random.randint(-20000, 20000, n_samples)
+
+# Build DataFrame
+df = pd.DataFrame({
+    'square_feet': square_feet,
+    'bedrooms': bedrooms,
+    'bathrooms': bathrooms,
+    'price': price
+})
+
+# Features & Target
+X = df[['square_feet', 'bedrooms', 'bathrooms']]
+y = df['price']
+
+# Train-test split
+X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
+
+# Train model
+model = LinearRegression()
+model.fit(X_train, y_train)
+
+# Predictions
+y_pred = model.predict(X_test)
+
+# Evaluation
+print("Mean Squared Error:", mean_squared_error(y_test, y_pred))
+print("R² Score:", r2_score(y_test, y_pred))
+
+# Coefficients
+print("\nModel Coefficients:")
+print(f"Square Footage: {model.coef_[0]:.2f}")
+print(f"Bedrooms: {model.coef_[1]:.2f}")
+print(f"Bathrooms: {model.coef_[2]:.2f}")
+print(f"Intercept: {model.intercept_:.2f}")
+
+# Example prediction
+new_house = [[2500, 3, 2]]
+predicted_price = model.predict(new_house)
+print("\nPredicted price for new house:", int(predicted_price[0]))
+
+
+plt.scatter(y_test, y_pred, alpha=0.7, color='blue')
+plt.xlabel("Actual Prices")
+plt.ylabel("Predicted Prices")
+plt.title("Actual vs Predicted House Prices")
+plt.plot([y.min(), y.max()], [y.min(), y.max()], 'r--')
+plt.show()