Gym Congestion Analysis Project

Introduction

This project aims to analyze gym congestion based on survey responses and swipe data. We'll explore various factors such as gender distribution, preferred gym zones, and perceived congestion.

Setup

This project is implemented in a Jupyter Notebook using Python 3. Required libraries include pandas, numpy, matplotlib, seaborn, and datetime.

Dependencies

• pandas: Data manipulation
• numpy: Numerical operations
• matplotlib: Plotting graphs
• seaborn: Statistical data visualization

Data Import

We import data from the following sources:

• Survey responses from "Fitness & Rec Survey 2024!!! (Responses) - Form Responses.csv"
• Swipe data from two Excel files for fall and spring semesters.

Data Cleaning

• Remove student identifiers and irrelevant columns.
• Filter out rows where gender is not Male or Female.
• Exclude specific dates representing breaks.

Data Merging

We merge the swipe data from fall and spring semesters to predict total year congestion.

Analysis

Affiliation

We analyze the distribution of survey respondents by affiliation.

Average Male vs Female Weekly Swipe Frequency

We compare the average swipe frequency of males and females throughout the week using swipe data.

Male vs Female Average Daily Swipe Time

We compare the average daily swipe time of males and females throughout the week using swipe data.

Male vs Female Gym Zone Usage

We analyze gym zone usage based on survey responses, comparing male and female preferences.

Students Who Believe Gym is Congested

We visualize survey responses regarding students' perception of gym congestion.

Overlay Model: Time of Day vs Gym Congestion

We aim to chart congestion levels of different gym zones throughout the day, overlaying swipe data with survey responses.