coderjolly/health-analytics-using-ML-and-wearble-sensors

This project leverages machine learning to analyze health data from wearable sensors. It focuses on classifying obesity levels, age groups, and calorie consumption habits. With a dataset of physical, lifestyle & demographic features, we used techniques like Support Vector Machines, Random Forests and XGBoost.

Health Analytics using ML and Wearable Sensors

Directory Structure

├── data/                                           <- Contains data files.
│   ├── 1_ObesityDataSet_raw_and_data_synthetic.csv <- Raw and synthetic data for ObesityDataSet.
│   └── 2_Data_Clean.csv                            <- Cleaned data file.
├── notebooks/                                      <- Contains Jupyter notebooks.
│   ├── 1_Data_Cleaning_Part1.ipynb                 <- Notebook for Data Cleaning Part 1.
│   └── 2_Part1.ipynb                               <- Notebook for Part 1.
├── .gitignore                                      <- List of files and folders git should ignore
├── LICENSE                                         <- License file for the project.
├── README.md                                       <- Top-level README file providing an overview of the project.
└── requirements.txt                                <- File listing project dependencies.