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:

   • Register or log in to Kaggle and download the dataset files (train.csv and test.csv) from House Prices: Advanced Regression Techniques.
   • Place the downloaded files in the project directory.

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.

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