This is my first Machine Learning project. It is a house price prediction model designed for properties located in Hyderabad. The model predicts house prices based on a few basic property features.
The current version of the model takes the following inputs:
- Property type
- Area (in square feet)
- Location of the house
- Status of the house
Based on these inputs, the model predicts the estimated price of the property.
The project uses the Random Forest Regression algorithm for price prediction. The entire project was developed using Python.
As this is my first ML project, the codebase is not yet fully optimized or well-structured. Additionally, the model currently considers only a limited set of features.
Some important factors that are not yet included are:
- Proximity to metro stations
- Accessibility and connectivity
- Nearby amenities
- Infrastructure and neighborhood quality
- Other location-specific factors
Because of these limitations, this project should be considered a preliminary version rather than a production-ready solution.
Planned improvements include:
- Adding more location-based features
- Improving data preprocessing
- Enhancing model performance and accuracy
- Refactoring and organizing the codebase
- Creating a more user-friendly interface
- Python
- Pandas
- NumPy
- Scikit-learn
- Random Forest Regressor
This project was created as a learning experience and represents my first step into Machine Learning. Any feedback or suggestions are welcome.
Thank you for checking out my project!