Bangalore House Price Prediction

Project Overview

This project focuses on predicting house prices in Bangalore using a dataset containing 13,321 entries and 9 features. By leveraging various machine learning algorithms, the project aims to enhance predictive accuracy and reduce forecasting errors in real estate price estimations.

Key Features

• Dataset: The dataset comprises 13,321 entries with 9 features, providing the necessary information to build and evaluate predictive models.
• Predictive Models: Implemented multiple machine learning models including linear regression, decision tree regression, and random forest regression.
• Performance Improvements: Achieved a 25% increase in predictive accuracy and a 30% reduction in forecasting errors in quarterly financial projections.

Project Structure

• data/: Contains the dataset used for training and evaluation.
• models/: Includes the implementation of linear regression, decision tree regression, and random forest regression models.
• notebooks/: Jupyter notebooks detailing the data exploration, feature engineering, and model development processes.
• src/: Source code for data preprocessing, feature extraction, and model training.
• results/: Stores the results of model evaluation and performance metrics.

How to Run the Project

1. Clone the repository:

   git clone https://github.com/yourusername/bangalore-house-price-prediction.git
   cd bangalore-house-price-prediction

2. Install the required dependencies:

   pip install -r requirements.txt

3. Run the data preprocessing and feature engineering:

   python src/preprocess.py

4. Train the linear regression model:

   python src/train_linear_regression.py

5. Train the decision tree regression model:

   python src/train_decision_tree.py

6. Train the random forest regression model:

   python src/train_random_forest.py

7. Evaluate the models and compare performance:

   python src/evaluate_models.py

Results

The project demonstrated significant improvements in predicting house prices in Bangalore. By implementing advanced machine learning algorithms, the predictive accuracy was increased by 25%, and forecasting errors were reduced by 30%, contributing to more reliable financial projections.

Conclusion

This project successfully applied machine learning techniques to the real estate domain, specifically focusing on predicting house prices in Bangalore. The use of random forest regression was particularly effective, offering substantial improvements over simpler models.

Future Work

• Hyperparameter Tuning: Further tuning of model hyperparameters to optimize performance.
• Feature Engineering: Exploration of additional features to enhance model accuracy.
• Model Deployment: Deploying the predictive models as a web service for real-time house price estimation.

Contact

For any questions or collaboration opportunities, feel free to reach out:

• Name: Supratim Das
• Email: supratimdaswork@gmail.com