Practical 3: Linear Regression

Objective

Build, train, and evaluate linear regression models for predicting continuous values.

Duration

3-4 hours

Prerequisites

• Practicals 1-2 completed
• Understanding of supervised learning

What You’ll Learn

• ✅ Implement linear regression from scratch
• ✅ Use Scikit-learn for regression
• ✅ Evaluate model performance
• ✅ Interpret coefficients
• ✅ Visualize regression results

📋 Tasks

1. Build Simple Linear Regression

from sklearn.linear_model import LinearRegression
from sklearn.model_selection import train_test_split

X = data[['feature1', 'feature2']].values
y = data['target'].values

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

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

2. Evaluate Model

from sklearn.metrics import mean_squared_error, r2_score

y_pred = model.predict(X_test)
mse = mean_squared_error(y_test, y_pred)
rmse = np.sqrt(mse)
r2 = r2_score(y_test, y_pred)

print(f"MSE: {mse:.4f}")
print(f"RMSE: {rmse:.4f}")
print(f"R² Score: {r2:.4f}")

📊 Learning Outcomes

• Understand linear regression theory
• Implement regression model
• Split data properly
• Evaluate using MSE, RMSE, R²
• Visualize predictions vs actual
• Interpret model coefficients