multi_linear_regr.py

# Import necessary libraries
import numpy as np
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_squared_error, r2_score

# Sample dataset for demonstration
# Create a DataFrame with multiple independent variables and a dependent variable
data = {
'Feature1': [1, 2, 3, 4, 5],
'Feature2': [2, 4, 6, 8, 10],
'Feature3': [1, 3, 5, 7, 9],
'Target': [2.3, 4.1, 6.0, 7.8, 9.6]
}

# Convert the dataset to a pandas DataFrame
df = pd.DataFrame(data)

# Separate independent variables (features) and the dependent variable (target)
X = df[['Feature1', 'Feature2', 'Feature3']] # Features
y = df['Target'] # Target variable

# Split the dataset 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 a Multiple Linear Regression model
model = LinearRegression()

# Train the model on the training data
model.fit(X_train, y_train)

# Make predictions on the test data
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 the results
print("Model Coefficients:", model.coef_)
print("Model Intercept:", model.intercept_)
print("Mean Squared Error (MSE):", mse)
print("R-squared Score:", r2)

# Example prediction
sample_data = pd.DataFrame([[3, 6, 5]], columns=['Feature1', 'Feature2', 'Feature3'])
predicted_target = model.predict(sample_data)
print("Predicted Target Value:", predicted_target[0])