Predicts House Prices 🏡 | Machine Learning Project with Python + Streamlit

🏡 Welcome to Code2faith — where we learn Machine Learning step by step with real projects! In this video, we’ll build an *AI-powered House Price Prediction App* using **Python, Scikit-learn, and Streamlit**. This is a *complete ML project* — from data preprocessing to model training, evaluation, and web deployment. --- 📘 What You’ll Learn: ✅ Understand the basics of Multiple Linear Regression ✅ Clean and preprocess real-world housing data ✅ Train a predictive model using Scikit-learn ✅ Evaluate model performance using R², MAE, RMSE ✅ Save the trained model as a `.pkl` file ✅ Build a Streamlit web app for real-time predictions --- 📂 Project Files: 🗂️ Dataset: King County House Sales Dataset (Kaggle) 🧠 Model: house_price_model.pkl 💻 UI: Streamlit app (app.py) --- 🧰 Tools & Libraries Used: Python 🐍 Pandas, NumPy for data handling Matplotlib, Seaborn for visualization Scikit-learn for ML model Streamlit for deployment Pickle for model saving --- 🚀 Run the Project: ```bash git clone https://github.com/vikassharma/House-... cd House-Price-Prediction-ML pip install -r requirements.txt streamlit run app/app.py