Predict House Prices with Machine Learning (Python)

This Deep Dive walks through a complete, end-to-end machine learning pipeline for housing price prediction using Python. Starting from raw, messy real estate data, we build a supervised regression model with Random Forest, explaining every step in plain terms—no black boxes, no skipped logic. You’ll see how a computer actually learns to value a house: from data cleaning and feature encoding to model training and evaluation using industry-standard metrics. What you’ll learn: - Data loading and inspection with pandas & NumPy - Handling missing values and data integrity issues - One-hot encoding categorical features correctly - Train-test split and why it matters - Random Forest regression intuition - Model evaluation with MAE, MSE, MAPE, and R² Timestamps 00:00 Introduction & problem setup 01:14 Data science toolkit overview 01:42 Loading the California housing dataset 03:53 Handling missing values 05:06 One-hot encoding & dummy variable trap 06:42 Feature/target definition 06:59 Train-test split & reproducibility 08:34 Random Forest regression intuition 10:01 Model evaluation & metrics 12:54 Full ML lifecycle recap 14:06 What data is missing? 👉 Subscribe for more Deep Dives in Data Science, Machine Learning, and real-world Python projects. #machinelearningproject #datascienceproject #pythonprojects #randomforest #regression