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https://github.com/sanjurajveer/housing-price-prediction
https://github.com/sanjurajveer/housing-price-prediction
Last synced: 22 days ago
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- Host: GitHub
- URL: https://github.com/sanjurajveer/housing-price-prediction
- Owner: sanjurajveer
- Created: 2025-01-11T16:53:58.000Z (about 1 month ago)
- Default Branch: main
- Last Pushed: 2025-01-11T17:00:46.000Z (about 1 month ago)
- Last Synced: 2025-01-11T17:47:08.155Z (about 1 month ago)
- Language: Jupyter Notebook
- Size: 0 Bytes
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
Here’s a simple **README.md** template for easy copy-pasting:
---
# 🏠 Housing Price Prediction Using XGBoost
This project predicts housing prices using the **XGBoost Regressor**, achieving excellent accuracy. It includes data preprocessing, feature engineering, model training, and evaluation.
## 📊 Model Performance
- **Mean Absolute Error (MAE):** 0.2241
- **Root Mean Squared Error (RMSE):** 0.3071
- **R² Score:** 0.8806
## 🚀 How to Run the Project
### Prerequisites
1. Install Python (>=3.8).
2. Install dependencies:
```bash
pip install -r requirements.txt
```
### Steps to Run
1. Clone the repository:
```bash
git clone https://github.com/your-username/housing-price-prediction.git
cd housing-price-prediction
```
2. Add the dataset to the `data/` folder (e.g., from [Kaggle](https://www.kaggle.com/c/house-prices-advanced-regression-techniques/data)).
3. Run the Jupyter notebook or training script:
```bash
jupyter notebook notebooks/housing_price_prediction.ipynb
```
OR
```bash
python src/train_model.py
```
4. Generate predictions:
```bash
python src/predict.py
```
## 🛠️ Features
- Data preprocessing (handling missing values, scaling, encoding).
- Feature engineering and skewness handling.
- XGBoost model with hyperparameter tuning.
- Evaluation metrics: MAE, RMSE, and R² Score.
## 🤖 Technologies Used
- Python
- XGBoost
- Pandas, NumPy, Scikit-learn
- Matplotlib, Seaborn
## 📈 Future Improvements
- Experiment with other algorithms (e.g., LightGBM, CatBoost).
- Further hyperparameter tuning for better accuracy.
- Deploy the model for real-time predictions.
---
Feel free to adjust this for your project!