Welcome to our Rental Bikes Interactive Machine Learning Dashboard project! As part of our master's coursework, we created a highly engaging and dynamic project that seamlessly blends machine learning and interactive visualization using Streamlit and PyCaret.
The objective of this project is to forecast bike rental demand using machine learning models. We streamlined the entire machine learning process, from preprocessing to model selection, tuning, and deployment, using PyCaret. The user-friendly Streamlit framework was employed to present the model's insights in a way that is both visually appealing and easy to understand.
At the heart of this project are the PyCaret and Streamlit libraries. PyCaret is an open-source, low-code machine learning library in Python that automates machine learning workflows. Streamlit, on the other hand, is a fast, easy way to create and share beautiful, custom web apps for machine learning and data science.
This combination enabled us to create an intuitive, interactive dashboard where users can tweak parameters and instantly see the impact on bike rental demand forecasts. Machine learning models were trained on historical bike rental data, capturing various influencing factors like weather conditions, day of the week, time of the year, etc.
You can access our Rental Bikes Interactive Machine Learning Dashboard via this link. Use the controls to adjust parameters and observe how different scenarios affect bike rental demand. This application is intended as a proof of concept and should not be used for actual business decision-making without further validation.
Please note: This project is for academic or personal use and its performance may vary based on numerous factors.
