https://github.com/alexisbalayre/multiple-linear-regression-project
Multiple Linear Regression Analysis Project
https://github.com/alexisbalayre/multiple-linear-regression-project
gradient-descent linear-regression multiple-linear-regression optimization
Last synced: 7 months ago
JSON representation
Multiple Linear Regression Analysis Project
- Host: GitHub
- URL: https://github.com/alexisbalayre/multiple-linear-regression-project
- Owner: AlexisBalayre
- Created: 2023-10-13T10:29:06.000Z (almost 2 years ago)
- Default Branch: main
- Last Pushed: 2024-01-12T10:29:00.000Z (over 1 year ago)
- Last Synced: 2025-01-23T09:35:37.908Z (9 months ago)
- Topics: gradient-descent, linear-regression, multiple-linear-regression, optimization
- Language: Jupyter Notebook
- Homepage:
- Size: 6.56 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# Multiple Linear Regression Analysis Project
## Overview 🌍
This project, led by Hyejoon Lee and Alexis Balayre, explores multiple linear regression techniques applied to different datasets. The focus is on predicting outcomes based on several independent variables, using custom regression models and comparing them with standard library implementations.
## Key Features 🌟
- **Custom Regression Models**: Implementation of multiple linear and polynomial regression models.
- **Comparative Analysis**: Comparison of custom models with standard libraries.
- **Dataset Exploration**: Use of diverse datasets like Advertising and Auto datasets.
- **Gradient Descent Optimisation**: Application of gradient descent for model optimisation.
## Getting Started 🚀
### Installation
1. **Clone the repository**:
- `git clone git@github.com:AlexisBalayre/multiple-linear-regression-project.git`
2. **Navigate to the project directory**:
- `cd multiple-linear-regression-project`
3. **Create a virtual environment**:
```bash
python3 -m venv venv
```
4. **Activate the virtual environment**:
- On Windows:
```bash
.\venv\Scripts\activate
```
- On Unix or MacOS:
```bash
source venv/bin/activate
```
5. **Install required libraries**:
```bash
pip install -r requirements.txt
```
### Usage
- Run Jupyter notebooks `assessment_advertising-dataset.ipynb` and `assessment_auto-dataset.ipynb` to execute the analysis on respective datasets.
- Explore the `library/` directory for custom model implementations.
## Project Structure 📂
- `data/`: Contains datasets used in the project.
- `library/`: Custom regression model implementations.
- `*.ipynb`: Jupyter notebooks for dataset assessments.
- `requirements.txt`: List of required Python packages.
## Dependencies 🛠️
- Python 3.x
- NumPy
- Pandas
- Matplotlib
- Scikit-learn
## Contributors 🤝
- Hyejoon Lee
- Alexis Balayre