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House-Price-Prediction-Model

★ 42 Open GitHub ↗

A machine learning model that predicts house prices based on features like size, location, and amenities. Helps buyers and sellers estimate property values accurately.

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# 🏠 California House Price Prediction App

An **AI-powered interactive web application** built with **Streamlit** that predicts median house prices in California based on housing features.  
The model is trained using the **California Housing Dataset** with **Linear Regression** and **feature scaling**.

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## 🚀 Demo  
🔗 [Live App on Streamlit](https://housepriceprediction07.streamlit.app/)

## 🚀 Video Demo  
[https://github.com/user-attachments/assets/1f5be119-6838-428f-b336-6883eb5aecdf](https://github.com/user-attachments/assets/1f5be119-6838-428f-b336-6883eb5aecdf)

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## 📌 Features  
- Predicts **California house prices** instantly.  
- Simple **sidebar input form** for housing features.  
- Powered by a **Linear Regression model** trained on real-world data.  
- Uses a **Scaler** for accurate normalization of inputs.  
- Clean and user-friendly **Streamlit interface**.  
- Includes an **About Me** sidebar with portfolio links.

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## 🔍 Usage  
1. Open the app in your browser.  
2. Enter the required housing features:  
   - Median Income (`MedInc`)  
   - House Age (`HouseAge`)  
   - Average Rooms (`AveRooms`)  
   - Average Bedrooms (`AveBedrms`)  
   - Population  
   - Average Occupancy (`AveOccup`)  
   - Latitude  
3. Click **Predict House Price**.  
4. The app will display the **predicted median house value** 💲.

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## 📊 Dataset  
- **Dataset Name**: California Housing Dataset  
- **Source**: [Scikit-learn Built-in Dataset](https://scikit-learn.org/stable/datasets/real_world.html#california-housing-dataset)  
- **Attributes**:  
  - `MedInc` – Median Income  
  - `HouseAge` – House Age  
  - `AveRooms` – Average Rooms  
  - `AveBedrms` – Average Bedrooms  
  - `Population` – Population in the area  
  - `AveOccup` – Average Occupancy  
  - `Latitude` – Latitude location  
  - Target: Median House Value

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## ⚙️ Tech Stack  
- **Python 3.9+**  
- **Streamlit** (Web App UI)  
- **Scikit-learn** (Linear Regression Model, Scaler)  
- **Pandas & NumPy** (Data Processing)  
- **Pickle** (Model Saving & Loading)

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## 📸 Screenshots  
### 🏠 Home Page  
<img width="1911" height="832" alt="image" src="https://github.com/user-attachments/assets/2062a894-059d-40cc-8dc6-652862f7b6a5" />

### ✅ Prediction Result (+Pos) 
<img width="1908" height="835" alt="image" src="https://github.com/user-attachments/assets/fe7f0eb3-5625-4641-a4ed-465e9af92fd7" />

### ✅ Prediction Result (-Neg)
<img width="1902" height="815" alt="image" src="https://github.com/user-attachments/assets/c872b011-6101-48b5-a9b9-db24fa540dd2" />

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## 👨‍💻 Author  
**Mirza Yasir Abdullah Baig**

- 💼 [LinkedIn](https://www.linkedin.com/in/mirza-yasir-abdullah-baig/)  
- 🌐 [Kaggle](https://www.kaggle.com/code/mirzayasirabdullah07)  
- 💻 [GitHub](https://github.com/mirzayasirabdullahbaig07)

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## ❤️ Acknowledgements  
- [California Housing Dataset](https://scikit-learn.org/stable/datasets/real_world.html#california-housing-dataset)  
- [Streamlit Documentation](https://docs.streamlit.io/)  
- [Scikit-learn](https://scikit-learn.org/stable/)

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## ⚠️ Disclaimer  
This project is for **educational purposes only**.  
It is not intended for commercial use but as a demonstration of **machine learning in real estate prediction**.

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House-Price-Prediction-Model

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