# $$Multi-Class \space Prediction \space of \space Obesity \space Risk$$

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## Project Description

This repository contains the code and materials for our Machine Learning project on "Multi-Class Prediction of Obesity Risk".

In this project, we focused on predicting the risk of obesity using a multi-class classification approach. Our work involved various stages including exploratory data analysis (EDA), feature engineering, and building predictive models using a diverse set of machine learning algorithms and choose the best model for our pipeline.

## Key Components

- **Exploratory Data Analysis (EDA):** We thoroughly examined the dataset to understand its underlying patterns and distributions.

- **Feature Engineering:** We engineered relevant features to enhance the predictive power of our models.

- **Modeling:** We implemented multiple machine learning models including Logistic Regression, Decision Tree, Random Forest, SVC, KNN Classifier, XGBoost, LGBM, Catboost, and Adaboost and choose the best one.

- **Pipeline:** We utilized a pipeline to streamline our machine learning workflow and ensure reproducibility.

## Repository Structure

- `Data/`: Contains the **dataset** used in the project, the **submission file** and the **Presentation** slides summarizing our project findings.

- `Preprocessing/`: unfinished Jupyter notebooks containing the code for EDA, feature engineering, and modeling and picture of submission on Kaggle competition.

- `Multi_Class Prediction of Obesity Risk`: Jupyter notebooks containing the code for EDA, feature engineering, and modeling.

- `README.md`: You are here! It provides an overview of the project and instructions for replicating our work.

## Getting Started

To replicate our project, follow these steps:

1. Clone this repository to your local machine.

2. Navigate to the `Multi_Class Prediction of Obesity Risk` Jupyter notebook.

3. Open the Jupyter notebooks and execute the code cells sequentially.

4. Refer to the presentation slides in the `Data/` directory for a summary of our findings.

## Additional Resources

- Kaggle: [[Kaggle Competition](https://www.kaggle.com/competitions/playground-series-s4e2)]

## Authors

This project was created by:

1. [Amina Mohamed](https://github.com/am231am)

2. [Ashraf Mahmoud](https://github.com/AshrafMah)

3. [Nagham Ehab](https://github.com/Naghamehab5)

4. [Shorouq Hossam](https://github.com/ShorouqHossamMohammed)