A linear regression model to predict the prices of houses based on their square footage and the number of bedrooms and bathrooms. README file:
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.
- Python 3.x
- Libraries:
- pandas
- numpy
- scikit-learn
-
Install required libraries:
pip install -r requirements.txt
-
Download the dataset from Kaggle:
- Register or log in to Kaggle and download the dataset files (
train.csv
andtest.csv
) from House Prices: Advanced Regression Techniques. - Place the downloaded files in the project directory.
- Register or log in to Kaggle and download the dataset files (
-
Run the Jupyter notebook
ML_Task01.ipynb
or execute the Python scriptML_Task01.py
.jupyter notebook ML_Task01.ipynb
or
python ML_Task01.py
-
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.
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.
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.
This project is licensed under the MIT License - see the LICENSE file for details.