https://github.com/sarathir-dev/house-price-prediction-model

A machine learning project that predicts house prices using ensemble models with preprocessing, feature engineering, and visualization, achieving 71% accuracy on real estate data for price estimation.
https://github.com/sarathir-dev/house-price-prediction-model

house-price-prediction machine-learning prediction-model random-forest real-estate regression sklearn stacking xgboost

Last synced: about 1 month ago
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A machine learning project that predicts house prices using ensemble models with preprocessing, feature engineering, and visualization, achieving 71% accuracy on real estate data for price estimation.

• Host: GitHub
• URL: https://github.com/sarathir-dev/house-price-prediction-model
• Owner: sarathir-dev
• Created: 2025-07-24T05:10:28.000Z (3 months ago)
• Default Branch: main
• Last Pushed: 2025-07-25T06:42:51.000Z (3 months ago)
• Last Synced: 2025-07-31T12:15:14.950Z (2 months ago)
• Topics: house-price-prediction, machine-learning, prediction-model, random-forest, real-estate, regression, sklearn, stacking, xgboost
• Language: Jupyter Notebook
• Homepage:
• Size: 4.08 MB
• Stars: 0
• Watchers: 0
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

          
# House Price Prediction using Machine Learning

A machine learning pipeline for predicting house prices based on features like area, bedrooms, location amenities, and furnishing status. Built with `pandas`, `scikit-learn`, `XGBoost`, and `matplotlib`, this project includes data preprocessing, feature engineering, model training, evaluation, and visualization.

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## Project Overview

This project aims to accurately estimate house prices using regression techniques. We performed:

- Data Cleaning & Feature Engineering  

- Stacking Ensemble (Random Forest + XGBoost)  

- Log transformation of target variable  

- Standardization and Imputation  

- Evaluation using RMSE, R² Score, and Relative Accuracy  

- Model Export for Deployment

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## Dataset

The dataset consists of residential house listings with the following key features:

- `price`, `area`, `bedrooms`, `bathrooms`, `stories`

- Amenities: `mainroad`, `guestroom`, `basement`, `hotwaterheating`, `airconditioning`, `parking`

- Preferences: `prefarea`, `furnishingstatus`

Sample (cleaned data):

| price     | area | bedrooms | bathrooms | mainroad | guestroom | ... |

|-----------|------|----------|-----------|----------|-----------|-----|

| 13300000  | 7420 | 4        | 2         | yes      | no        | ... |

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## Model Architecture

The pipeline includes:

- **Preprocessing**: Imputation + Scaling using `StandardScaler`

- **Feature Engineering**: Total rooms, area per room, etc.

- **Model**: Stacking Regressor (Random Forest + XGBoost + RidgeCV)

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## Results

| Metric                | Value             |

|-----------------------|------------------|

| RMSE                  | ₹1,447,704.47     |

| R² Score              | 0.5854            |

| Relative Accuracy     | 71.09%            |

>  **Note**: Further tuning or using deep learning methods may improve performance.

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## Visualizations

More visualizations available in the notebook:

- Feature Distributions

- Correlation Heatmap

- Residual Plot

## 📄 License

This project is licensed under the **MIT License**.  

Feel free to use, modify, and share it with attribution.

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