House Price Prediction using Machine Learning | Linear Regression | Mirza Yasir Abdullah Baig

Описание: Welcome to my California House Price Prediction Model — an AI-powered web application that predicts median house prices across California districts using the California Housing Dataset from Scikit-learn.

In this video, I’ll walk you through how I built, trained, evaluated, and deployed a Machine Learning Regression Model — from data preprocessing to real-time deployment using Streamlit.

🔍 What You’ll Learn:

✅ End-to-end ML pipeline — data cleaning, scaling, and model training
✅ Using Linear Regression to predict continuous values
✅ Model evaluation using R², MAE, and RMSE
✅ Streamlit web app deployment with real-time predictions
✅ Visualizations — correlation heatmaps, residual plots, and feature analysis
✅ Saving models & scalers as .pkl files for deployment

💻 Tech Stack:

Python | Scikit-learn | NumPy | Pandas | Matplotlib | Seaborn | Streamlit | Pickle

📈 Model Performance:

✔️ R² Score: ~0.73
✔️ Low MAE & RMSE
✔️ Reliable predictions for real-world housing data

🌐 Project Links: https://yasirinsights.com/

🔹 GitHub Repository: https://github.com/mirzayasirabdullah...
🔹 Kaggle Notebook: https://www.kaggle.com/mirzayasirabdu...
🔹 linkedIn:   / mirza-yasir-abdullah-baig  

📊 Project Highlights:

Focused on interpretable AI with Linear Regression
Captured real-world housing trends (e.g., income vs. house price)
Built a clean, interactive UI for both technical and non-technical users
Demonstrated full-cycle ML workflow: Strategy → Implementation → Analysis → Reporting

👨‍💻 By: Mirza Yasir Abdullah Baig
🎯 Aspiring Machine Learning Engineer | AI Enthusiast | Data Science Learner

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