🏠 AI Powered House Price Prediction System
Transforming Real Estate Insights using Machine Learning
🌟 Project Overview
This project is a Machine Learning based House Price Prediction Web App that predicts the selling price of a house based on input features.
The model is trained using structured housing data and deployed using Streamlit for real-time interactive predictions.
This project demonstrates:
✔ Data preprocessing ✔ Regression modeling ✔ Model serialization ✔ Web app deployment ✔ End-to-end ML pipeline
🧠 How It Works
1️⃣ Model is trained using train.csv 2️⃣ Data is cleaned and preprocessed 3️⃣ Regression model is built using Scikit-Learn 4️⃣ Model is saved as house_model.pkl 5️⃣ Streamlit loads the model 6️⃣ User inputs house features 7️⃣ App predicts price instantly 💰
📂 Project Structure SCT_ML_1/ │ ├── app.py # Streamlit Web Application ├── model.py # Model training script ├── house_model.pkl # Saved trained model ├── train.csv # Training dataset ├── test.csv # Testing dataset ├── sample_submission.csv # Sample output format ├── requirements.txt # Required libraries └── README.md # Project documentation
🚀 Technologies Used
🐍 Python
📊 Pandas
🤖 Scikit-Learn
🌐 Streamlit
💾 Pickle
⚙️ Installation Guide
Step 1: Clone Repository git clone https://github.com/your-username/your-repo-name.git cd your-repo-name
Step 2: Install Dependencies pip install -r requirements.txt
If requirements.txt is empty, add this:
streamlit pandas scikit-learn numpy
Step 3: Run the Application python -m streamlit run app.py ##important
Then open:
🎯 Key Features
✨ Clean and Interactive UI ✨ Real-Time Price Prediction ✨ Pre-trained ML Model ✨ Simple and Easy to Use ✨ Beginner-Friendly ML Deployment
📈 Model Details
Type: Supervised Learning
Problem: Regression
Algorithm: Linear Regression / Random Forest (based on implementation)
Evaluation Metrics: MAE / RMSE
🔮 Future Enhancements
🚀 Deploy on Streamlit Cloud 📊 Add Visualizations 📈 Show Model Accuracy 💡 Add Advanced Feature Engineering