Task 3: Linear Regression – AI & ML Internship

📌 Objective

Implement and understand Simple Linear Regression and Multiple Linear Regression using Python.

🛠 Tools & Libraries

Scikit-learn

Pandas

Matplotlib

Seaborn for visualization

📂 Dataset

You can use any dataset relevant to the task. Example: House Price Prediction Dataset Download Dataset (Replace # with dataset link if available)

🚀 Steps Followed

1. Import and preprocess the dataset

2. Split dataset into train-test sets

3. Fit a Linear Regression model using sklearn.linear_model

4. Evaluate the model using:

MAE (Mean Absolute Error)

MSE (Mean Squared Error)

R² Score

5. Plot regression line and interpret coefficients

📊 Evaluation Metrics

MAE: Average absolute difference between predicted and actual values

MSE: Squared difference, penalizes large errors

R² Score: Measures how well the model explains the variance in data

📖 What I Learned

Regression modeling

Model evaluation metrics

Interpretation of coefficients

❓ Interview Questions

1. What assumptions does linear regression make?

2. How do you interpret the coefficients?

3. What is R² score and its significance?

4. When would you prefer MSE over MAE?

5. How do you detect multicollinearity?

6. Difference between simple and multiple regression?

7. Can linear regression be used for classification?

8. What happens if you violate regression assumptions?

AUTHOR NAME - RAKSHITH N