Linear Regression Explained: Theory + Case Study with Python | House Price Prediction

Master Linear Regression from scratch in this comprehensive video that combines theory with practical application using Python! 🎓📊

What you'll learn: ✔️ Linear Regression Theory: Best fit line, OLS method, R², Adjusted R², RMSE, and assumptions explained in detail.
✔️ Case Study: Predicting house prices with Python using Scikit-learn and Statsmodels.
✔️ Feature engineering techniques that enhance model performance.
✔️ Testing assumptions of Linear Regression.
✔️ Insights and actionable business recommendations.

This video is perfect for data science beginners and professionals who want to learn how to apply Linear Regression to real-world problems.

👉 Chapters: 00:00 - Introduction
01:00 - Linear Regression Theory
13:06 - Best Fit Line
18:29 - TSS, RSS, SSE
20:30 - R2 and Adjusted R2
24:11 - RMSE, MSE, MAE
27:53 - Multicollinearity
30:09 - Assumptions of Linear Regression
36:32 - House Price Prediction Case Study
1:07:50 - Feature Engineering and Results
1:15:10 - Testing Assumptions
1:17:50 - Insights and Business Recommendations

📹 Learn, Apply, and Master Linear Regression with this all-in-one tutorial!

👍 Like, Comment, and Subscribe for more data science tutorials!

#LinearRegression #Python #MachineLearning #DataScience #HousePricePrediction #FeatureEngineering #yogisdatalab

[sbanhawy]

Wonderful video, Yogi. Thanks for sharing.
1- Can we've a video about Time Series Analysis?
2- When you did feature engineering by combining pairs of features, does that mean we may need to remove the original solo features we used for the new combined feature - to avoid multicolinearity and duplicates?
3- last but not least, can you share the Python document for the coding you used? I want to use your codes 😊

[pronobgogoi]

I am glad I stumbled upon your channel. Appreciate your videos. Very enlightening.