California Housing Dataset | Complete Linear Regression Walkthrough

This tutorial walks you through linear regression using real housing data from California. Learn data exploration, assumption checking, and model preparation with Python. This video, part of the *Machine Learning with Scikit-learn, PyTorch & Hugging Face Professional Certificate* on Coursera covers linear regression fundamentals with real California housing data from Sklearn. You'll discover: *Complete data exploration workflow with Pandas and NumPy *Data visualization techniques using Matplotlib and Seaborn *Correlation analysis to identify key price drivers (median income = 0.69 correlation!) *Linear regression assumptions: linearity, independence, multicollinearity *Variance Inflation Factor (VIF) for detecting redundant features *Feature relationship analysis through scatter plots and heatmaps *Professional data science methodology from EDA to model prep 📌 Enroll in the complete *Machine Learning with Scikit-learn, PyTorch & Hugging Face Professional Certificate →* https://bit.ly/4gAEE8t ✅ Subscribe for complete ML certificate tutorials → https://www.youtube.com/@coursera 💬 Comment: What other real estate factors would you include? School districts? Crime rates? 👇 00:00 Introduction – Linear regression for house price prediction 00:27 Library Setup – NumPy, Pandas, Matplotlib, Seaborn, Sklearn imports 00:46 Dataset Loading – California housing data exploration 01:22 Summary Statistics – Understanding data distribution and ranges 01:45 Data Visualization – Histograms showing feature distributions 02:33 Correlation Analysis – Heatmap reveals median income as key driver 03:32 ML Assumptions – Linear regression requirements explained 04:09 Linearity Check – Scatter plots for assumption validation 04:41 Multicollinearity Test – VIF analysis for feature redundancy 05:39 Next Steps – Model building preview for Part 2 #LinearRegression #HousePrices #PythonTutorial #MachineLearning #DataScience