This repository contains a machine learning model to predict house prices in Bangalore, India. The model is trained on a dataset comprising various features such as area, number of bedrooms, location, and other relevant factors affecting house prices in the city. The project aims to provide accurate price estimates for residential properties in Bangalore to assist both buyers and sellers in making informed decisions.
The dataset used for training and evaluating the model can be found in the data directory. It contains labeled examples of residential properties with corresponding house prices. The dataset has been preprocessed to handle missing values, feature engineering, and scaling.
The machine learning model used for predicting house prices is implemented in Python using popular libraries such as scikit-learn and pandas. The model is trained on the processed dataset and optimized to achieve the best possible performance. The Jupyter Notebook Bangalore_House_Price_Prediction.ipynb in the notebooks directory demonstrates the model development process, feature analysis, and evaluation metrics.
To run the model locally, ensure you have the following dependencies installed:
- Python (>=3.6)
- scikit-learn
- pandas
- numpy
- matplotlib
- Jupyter Notebook
You can install the required packages using the following command:
pip install -r requirements.txtMade by Ravi M Damor