import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_squared_error, r2_score
 
# Create synthetic dataset
np.random.seed(0)
n_samples = 100
X = np.random.rand(n_samples, 3)  # 3 features
y = 3 * X[:, 0] + 2 * X[:, 1] + 5 * X[:, 2] + np.random.randn(n_samples)  # Linear equation with noise
data = pd.DataFrame({'Feature1': X[:, 0], 'Feature2': X[:, 1], 'Feature3': X[:, 2], 'Price': y})
 
# Split data into features (X) and target variable (y)
X = data[['Feature1', 'Feature2', 'Feature3']]
y = data['Price']
 
# Split data into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
 
# Create and train the multi-linear regression model
model = LinearRegression()
model.fit(X_train, y_train)
 
# Make predictions
y_pred = model.predict(X_test)
 
# Calculate Mean Squared Error (MSE)
mse = mean_squared_error(y_test, y_pred)
 
# Calculate R-squared (R2) score
r2 = r2_score(y_test, y_pred)
 
print(f"Mean Squared Error (MSE): {mse}")
print(f"R-squared (R2) Score: {r2}")
 
# Scatter plot of actual vs. predicted prices
plt.scatter(y_test, y_pred)
plt.xlabel("Actual Prices")
plt.ylabel("Predicted Prices")
plt.title("Actual Prices vs. Predicted Prices")
plt.show()
 
# Display model coefficients (feature importance)
coefficients = pd.DataFrame({'Feature': X.columns, 'Coefficient': model.coef_})
print(coefficients)
 

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