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+# $$Multi-Class \space Prediction \space of \space Obesity \space Risk$$
+<div id="header" align="center">
+  <img src="https://media.giphy.com/media/v1.Y2lkPTc5MGI3NjExZ3RlY3NqZ2tqbjh1ZnQ4dWx3d3BldmluYXMzamlvYzUxbXNxcWltZSZlcD12MV9pbnRlcm5hbF9naWZfYnlfaWQmY3Q9Zw/wdFph4zhLiBPi/giphy.gif" width="600"/>
+</div>
+<!-- ![Project Banner](insert_banner_image_url_here) -->
+
+## 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)