Obesity Level Analysis

Project Description

This project was created as part of studies at Gdańsk University of Technology in the Data Science program. Its goal is to analyze obesity-related data and develop a classification model that can determine obesity levels based on lifestyle features.

Analysis Objective

Obesity is a global health issue that has serious consequences for both physical and mental health. In this project:

• An exploratory data analysis (EDA) was conducted,
• Four classification models were built: Random Forest, LightGBM, Gradient Boosting, and Logistic Regression,
• The models were tested and their effectiveness was evaluated.

Repository Contents

• Analiza_poziomów_otyłości.ipynb - Jupyter Notebook containing the code for analysis and model training.
• data/ - Folder containing the dataset used in the analysis.
• pyproject.toml - Configuration file containing project dependencies.
• README.md - This file describing the project.

Technologies Used

This project utilizes:

• Python (NumPy, Pandas, Matplotlib, Seaborn, Scikit-learn, LightGBM),
• Jupyter Notebook for conducting the analysis,
• Classification algorithms: Random Forest, LightGBM, Gradient Boosting, Logistic Regression.

How to Run the Project?

1. Clone the repository:

   git clone https://github.com/toster3d/Obesity-Levels.git
   
   cd your-repository

2. Install dependencies:

   pip install .

3. Run Jupyter Notebook:

   jupyter notebook

4. Open Analiza_poziomów_otyłości.ipynb and execute the cells.

Results and Conclusions

The classification model enables effective prediction of obesity levels based on lifestyle data. Detailed results and conclusions from the analysis can be found in the Jupyter Notebook.

Author

Project created by Jagoda Spychała as part of studies at Gdańsk University of Technology.