https://github.com/bottomsnode/sct_ml_1

This repository contains a straightforward implementation of a linear regression model for predicting house prices. The model takes into account three key features: the square footage of the house, the number of bedrooms, and the number of bathrooms.
https://github.com/bottomsnode/sct_ml_1

matplotlib numpy pandas scikitlearn-machine-learning seaborn

Last synced: 1 day ago
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This repository contains a straightforward implementation of a linear regression model for predicting house prices. The model takes into account three key features: the square footage of the house, the number of bedrooms, and the number of bathrooms.

• Host: GitHub
• URL: https://github.com/bottomsnode/sct_ml_1
• Owner: BottomsNode
• License: mit
• Created: 2024-06-29T18:15:31.000Z (5 months ago)
• Default Branch: main
• Last Pushed: 2024-06-29T19:52:36.000Z (5 months ago)
• Last Synced: 2024-07-01T00:20:39.675Z (5 months ago)
• Topics: matplotlib, numpy, pandas, scikitlearn-machine-learning, seaborn
• Language: Jupyter Notebook
• Homepage:
• Size: 1.3 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# SCT_ML_1

![House Price Prediction](https://github.com/BottomsNode/SCT_ML_1/blob/main/Task%201%20%20ML.png)

# House Price Prediction with Linear Regression

This project implements a linear regression model to predict house prices based on square footage, number of bedrooms, and number of bathrooms.

## Dataset

The dataset used for this project contains information about various features of houses and their corresponding sale prices. It includes features like:

- GrLivArea: Above grade (ground) living area square feet

- BedroomAbvGr: Number of bedrooms above grade (does NOT include basement bedrooms)

- FullBath: Number of full bathrooms above grade

- SalePrice: Sale price of the house in dollars

## Files

- `train.csv`: Training dataset containing features and target variables.

- `test.csv`: Testing dataset used for making predictions.

- `sample_submission.csv`: Sample submission file with the format required for submission.

## Dependencies

- Python 3.x

- Libraries: pandas, numpy, scikit-learn, matplotlib, seaborn

## Usage

1. Clone the repository:

   ```bash

   git clone https://github.com/your/repository.git

   cd repository

   

2. Install dependencies:

   pip install -r requirements.txt

3. Run the script:

   python house_price_prediction.py

4. The script will train the linear regression model, make predictions on the test set, and display various visualizations to evaluate the model's performance.