Fitbit Raw Data Processor & Visualizer

Process and visualize your Fitbit fitness tracker data with ease. This repository contains a Jupyter notebook that helps you break down your raw Fitbit data into consolidated health and fitness metrics and provides visually appealing charts and graphs showcasing your personal health & fitness trends over time.

Table of Contents

• Fitbit Raw Data Processor & Visualizer
  • Table of Contents
  • Setup
  • Usage
  • Example Charts
  • Dependencies
  • Support

Setup

1. Download Your Fitbit Data:

   • Visit Fitbit's Data Export Page.
   • Request the time range you'd like to export.
   • When the export is ready click on Download and it will provide you with a .zip file.

2. Prepare the Data:

   • Unzip the downloaded file.
   • Place the unzipped folder inside the data directory of this repository.

3. Environment Setup:

   • Ensure you have Jupyter Notebook installed. If not, you can install it using pip: bashpip install jupyter
   • Install the required dependencies (see Dependencies section).

Usage

1. Clone this Repository:

   • Open your terminal or command prompt.
   • Navigate to the directory where you want to clone the repository.
   • Run the following commands:

git clone https://github.com/schbz/FitbitEDA.git
cd schbz/FitbitEDA

1. Open the Notebook:

   • in root directory of the project, launch Jupyter Notebook:

     bashjupyter notebook

2. Configure Your Data Input:

   • Open the provided notebook from the Jupyter interface.
   • Locate the cell with a line similar to: user_folder = '23_Dec'
   • Replace 23_dec with the name of the folder you placed in the data directory.

3. Run the Notebook:

   • Run all cells in the notebook.
   • Once processed, you'll find several consolidated .csv files inside the output directory.
   • Additionally, the notebook will display various charts and graphs detailing your health and fitness trends.

Example Charts

Here are some example charts you can expect from this notebook:

Dependencies

This notebook relies on several Python libraries for data processing and visualization:

• pandas
• matplotlib
• seaborn
• numpy
• scipy

You can install them using pip:

pip install pandas matplotlib seaborn numpy scipy

Support

For any issues or enhancements, please open a GitHub issue.