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Prodigy-ML-01

A linear regression model to predict the prices of houses based on their square footage and the number of bedrooms and bathrooms. README file:

House Price Prediction with Linear Regression

Overview

This project implements a linear regression model to predict house prices based on square footage, number of bedrooms, and bathrooms. The model utilizes the "House Prices: Advanced Regression Techniques" dataset from Kaggle, which includes features like living area, bedrooms above ground, bathrooms, and sale prices.

Requirements

  • Python 3.x
  • Libraries:
    • pandas
    • numpy
    • scikit-learn

Installation

  1. Install required libraries:

    pip install -r requirements.txt

  2. Download the dataset from Kaggle:

Usage

  1. Run the Jupyter notebook ML_Task01.ipynb or execute the Python script ML_Task01.py.

    jupyter notebook ML_Task01.ipynb

    or

    python ML_Task01.py

  2. Follow the instructions in the notebook/script to:

    • Load and preprocess the dataset.
    • Train a linear regression model using features (GrLivArea, BedroomAbvGr, FullBath) to predict house prices (SalePrice).
    • Evaluate the model's performance using metrics like Mean Squared Error (MSE) and R-squared.
    • Make predictions for new data points.

File Descriptions

  • ML_Task01.ipynb: Jupyter notebook containing the implementation of the linear regression model.
  • ML_Task01.py: Python script for the same implementation as the notebook.
  • train.csv: Dataset used for training the model.
  • test.csv: Dataset used for testing the model.
  • README.md: This file, providing an overview of the project, installation instructions, usage guide, and file descriptions.

Credits

Crafted With Love by Sam Naveenkumar .V

  • Kaggle for providing the "House Prices: Advanced Regression Techniques" dataset.
  • Scikit-learn and Pandas libraries for their machine learning and data manipulation functionalities.

License

This project is licensed under the MIT License - see the LICENSE file for details.