https://github.com/chouaib-629/quantileregression
Quantile regression for delivery time and some scenarios.
https://github.com/chouaib-629/quantileregression
data-science data-visualization delivery-time jupyter-notebook linear-regression machine-learning matplotlib model-compression numpy pandas predictive-modeling python python3 quantile-regression regression-models scikit-learn scipy statistical-analysis statsmodels
Last synced: 26 days ago
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Quantile regression for delivery time and some scenarios.
- Host: GitHub
- URL: https://github.com/chouaib-629/quantileregression
- Owner: chouaib-629
- Created: 2024-12-20T20:09:55.000Z (4 months ago)
- Default Branch: main
- Last Pushed: 2024-12-20T20:11:09.000Z (4 months ago)
- Last Synced: 2025-04-07T15:23:42.565Z (26 days ago)
- Topics: data-science, data-visualization, delivery-time, jupyter-notebook, linear-regression, machine-learning, matplotlib, model-compression, numpy, pandas, predictive-modeling, python, python3, quantile-regression, regression-models, scikit-learn, scipy, statistical-analysis, statsmodels
- Language: Jupyter Notebook
- Homepage:
- Size: 589 KB
- Stars: 2
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Quantile Regression Analysis Project
This project implements quantile regression analysis to explore the relationship between predictors and a response variable, specifically focusing on delivery time based on the distance to the customer. It provides various statistical tests, visualizations, and comparisons between linear and quantile regression models. The goal is to understand how different quantiles of the response variable can be modeled and how they differ from traditional linear regression.
## Table of Contents
- [Features](#features)
- [Technologies Used](#technologies-used)
- [Getting Started](#getting-started)
- [Usage](#usage)
- [Contributing](#contributing)
- [Contact Information](#contact-information)
## Features
- Implementation of quantile regression using the `statsmodels` library.
- Visualization of quantile regression results with smoothed lines.
- Statistical tests including Wald Test, Score Test, and Lagrange Multiplier Test.
- Comparison of quantile coefficients across different quantiles.
- Support for weighted quantile regression and associated tests.
- Enhanced plotting functions to compare linear and quantile regression models.
- Calculation of quantile loss for model evaluation.
## Technologies Used
- Python
- NumPy
- Matplotlib
- Scikit-learn
- Statsmodels
- SciPy
- Jupiter Notebook
## Getting Started
To get started with this project, follow these steps:
1. **Clone the repository** to your local machine:
```bash
git clone https://github.com/chouaib-629/quantileRegression.git
```
2. Navigate to the project directory:
```bash
cd quantileRegression
```
3. Install the required dependencies using pip:
```bash
pip install numpy matplotlib scikit-learn statsmodels scipy
```
## Usage
This project includes four key files:
1. `functions.py`: This Python script contains all the functions necessary for performing quantile regression analysis, including visualization functions, statistical tests (Wald, Score, and Lagrange Multiplier tests), and loss functions. You can import this module in your Jupyter notebooks to access these functions.
2. `quantileRegression.ipynb`: This Jupyter Notebook is used for performing standard quantile regression analysis. It includes steps for loading the dataset, fitting quantile regression models, visualizing the results, and conducting statistical tests to evaluate the models.
3. `quantileRegressionWeighted.ipynb`: This notebook extends the analysis to weighted quantile regression. It allows you to explore how incorporating weights affects the regression results and includes visualizations and statistical tests specific to weighted models.
4. `quantileRegression_scenarios.ipynb`: This notebook is designed to analyze different scenarios using quantile regression. It includes comparisons of linear regression and quantile regression under various conditions, providing insights into how the models perform across different quantiles and datasets.
To run the notebooks, open them in Jupyter Notebook or Jupyter Lab, and execute the cells sequentially to see the results.
## Contributing
Contributions are welcome! To contribute:
1. Fork the repository.
2. Create a new branch:
```bash
git checkout -b feature/feature-name
```
3. Commit your changes:
```bash
git commit -m "Add feature description"
```
4. Push to the branch:
```bash
git push origin feature/feature-name
```
5. Open a pull request.
## Contact Information
For questions or support, please contact [Me](mailto:[email protected]).