House Prediction Machine Learning Model

This repository contains a machine learning model for predicting house prices based on various features. The model is trained on a dataset of historical house sales data and uses regression techniques to make predictions.

Dataset

The dataset used for training and testing the model is included in the repository (data.csv). It contains various features such as the number of bedrooms, bathrooms, square footage, and location, along with the corresponding sale prices.

Installation

To run the machine learning model, you will need Python 3.x along with the following libraries:

• NumPy
• Pandas
• Scikit-learn

You can install these dependencies using pip:

• pip install pandas scikit-learn

Model Evaluation

The model's performance is evaluated using various metrics such as, Mean Squared Error (MSE), and R-squared value. These metrics are displayed after running the House_Price_Prediction.py script.

Future Improvements

• Feature Engineering: Explore additional features or transformations to improve model performance.
• Model Tuning: Experiment with different algorithms and hyperparameters to optimize the model.
• Deployment: Deploy the model as a web service or integrate it into a mobile application for real-time predictions.

Acknowledgments

• The dataset used in this project is sourced from [source] and is for educational purposes only.
• This project is inspired by [similar project] and [another similar project].

Feel free to contribute to this project by opening issues or pull requests. Your feedback and contributions are highly appreciated!