Gym Safety ETL and Analysis

Project Overview

This project involves extracting, transforming, and analyzing a dataset of gym exercises. The analysis includes calculating safety scores for exercises, evaluating models, and generating visualizations. The process is automated through a series of Python scripts, with enhanced interactivity and logging for better usability and debugging.

Business Problem

Unsafe lifting practices in gyms pose risks to member safety, which can be mitigated by providing easily accessible, data-driven visualizations of proper exercise techniques categorized by difficulty and muscle group. The project aims to develop a recommendation system that enhances client satisfaction and reduces injury rates, directly contributing to higher retention and client loyalty.

Data Sets Used

• megaGymDataset.csv: Contains data on various exercises, including type, body part, equipment, difficulty level, rating, and description.
• dataset-metadata.json: Metadata for the datasets.

Techniques Employed

• Mean Imputation: Handling missing values by imputing the mean rating for each exercise level.
• Encoding Categorical Variables
• K-Nearest Neighbor to predict safety scores
  • GridSearchCV to find the optimal number of neighbors
  • MSE, MAE, R^2 metrics

Expected Outputs

• Analysis/evaluation/visualizations in clear, readable files
• Summary statistics of the exercise dataset.
• Visualization of exercises grouped into clusters
• Recommendations of exercises based on their difficulty.
• Logging to all files
• Non-technical visualiztion for the user
• Technical visulation to represent model performance

Setup Instructions

Setting Up Kaggle API Keys

To run this project, you may need access to datasets hosted on Kaggle. Follow the steps below to set up your Kaggle API keys:

1. Obtain Your Kaggle API Key:

   • Log in to your Kaggle account.
   • Go to your account settings by clicking on your profile picture in the top right corner and selecting "Account."
   • Scroll down to the "API" section and click "Create New API Token."
   • A file named kaggle.json will be downloaded, containing your Kaggle API credentials.

2. Place the API Key:

   • Move the kaggle.json file to a secure location:
     • Windows: C:\Users\<YourUsername>\.kaggle\kaggle.json
   • Ensure that the .kaggle directory is hidden and that the kaggle.json file is accessible only by you.

3. Using the API Key in This Project:

   • The Kaggle API is required to download datasets automatically when you run the scripts.
   • Ensure you have the Kaggle Python package installed:

     python -m pip install kaggle

   • Authenticate your Kaggle API in your scripts:

     import kaggle
     kaggle.api.authenticate()

   • The datasets will be automatically downloaded using the API when you run the project.

Cloning the Repository

Clone the repository to your local machine using the following command:
git clone https://github.com/username/inst414-final-project-luciano-iocco.git
Create a virtual environment and select the most recent version of Python. The current working Python version is 3.11.1. requirements.txt contains all of the dependencies needed to run this project. Install the required packages using python -m pip install -r requirements.txt
Run the main script to execute the ETL process and analysis (effectively run the entire program) python main.py

Logging

Logging is configured to write to "gym_project.log". The log includes detailed information about each step of the process, including any errors that occur along with their time, level, and a message.

Code Package Structure

∙ gym_project.log: Contains logging information recorded during runtime.
data/
∙ downloaded/ : Contains raw downloaded datasets.
∙ processed/ : Contains processed data files.
outputs/
∙ descriptive_analysis.csv: Output file for descriptive_analysis script.
∙ prescriptive_analysis.csv: Output file for prescriptive_analysis script.
analysis/
∙ descriptive_analysis.py: Performs descriptive statistical analysis.
∙ prescriptive_analysis.py: Evaluates models and provides recommendations. (WIP -- currently all done in descriptive_analysis.py)
etl/
∙ extract.py: Loads the raw dataset in a DataFrame
∙ transform.py: Processes the raw data, handles missing values and calculates safety scores for exercises.
vis/
∙ visualizations.py: Generates visualizations to help understand the data and results.
log/ ∙ logging information gets automatically output to this folder ∙ main.py outputs to gym-project.log