The data contains the selling prices of different houses based on different characteristics like area, no. of rooms,, no. of Bathrooms, etc.. This project aims to predict housing prices based on various features using the Linear Regression algorithm. It is implemented in Python and utilizes popular libraries such as NumPy, Pandas, and scikit-learn.

The dataset used for this project is the "Housing Prices" dataset, which contains information about different houses such as the number of bedrooms, total square footage, location, etc., along with their corresponding sale prices. The dataset is provided in the file Raw_Housing_Prices.csv.

To run this project, you need to have the following dependencies installed:

• Python 3.x
• NumPy
• Pandas
• scikit-learn

You can install these dependencies using pip: pip install numpy pandas scikit-learn

1. Clone the repository:

git clone https://github.com/DestructorAMAN/Housing_Price_Prediction_Linear_Regression.git

2. Navigate to the project directory:

cd housing-pricing-linear-regression

3. Run the main.py script:

python main.py This will load the dataset, preprocess the data, train the linear regression model, and provide predictions for the housing prices.

4. You can modify the parameters in the main.py script to experiment with different configurations, such as feature selection, hyperparameter tuning, etc.

After running the main.py script, the model's performance metrics, such as mean squared error (MSE), mean absolute error (MAE), and R-squared value, will be displayed in the console. Additionally, the predictions for housing prices will be saved in the file predictions.csv.

If you want to contribute to this project, you can follow these steps:

1. Fork the repository.
2. Create a new branch for your changes.
3. Make your modifications and additions.
4. Commit your changes and push them to your forked repository.
5. Submit a pull request, explaining your changes and the rationale behind them.

• The "Housing Prices" dataset used in this project is sourced from Internshala and can be accessed at kaggle.

If you have any questions or suggestions, feel free to reach out to the project maintainer:

• Aman Raj Sharma
• [email protected]

Feel free to update the above information with your own details and modify the structure according to your project's needs.