Gym Visitor Bump-Time Prediction

This project aims to predict the number of visitors to a public gym using machine learning techniques. It leverages the power of machine learning and web development to provide insights into fitness studio visitor trends. It consists of several components:

NodeJS-Express Server

• Creates a TensorFlow model using Linear Regression.
• Normalizes inputs and outputs to get better results.
• Computes the model with the Adam optimizer and mean absolute error loss function
• Provides the trained model through a static folder.
• Supplies real measurement data for comparison.

React.JS Client

• Allows users to select dates using a calendar DatePicker.
• Based on the selected date it generates an input tensor, that includes features such as time floats, holiday indicator, and binary representations of days and months
• Then calculates the visitor count prediction using the model served by the NodeJS-Express server
• Displays a chart representing both measurement data and predictions based on the selected date.

Demo

A live demo of the project can be viewed here. To prevent misuse, the POST /model route is not available in this release.

Screenshots

Raw Data Heatmap created using Matplotlib

Compare Prediction and Measurement

API Reference

Get all measurements based on given date

  GET /measurements/?date=${date}

Parameter	Type	Description
date	string	Required

Create linear regression model based on training data, located in /server/data.json

  POST /model

Get created linear regression model

  GET /model.json

Authors

• @simonliebold