Project | Bike Sharing Demand Forecasting

As cities grow, so does the demand for alternative transportation methods like bike-sharing services. Predicting bike-sharing demand helps operators allocate resources effectively, ensuring bikes are available where and when needed. In this project, we will build a bike-sharing demand forecasting model and visualize key insights using various charts and an interactive map. The focus will be on predicting the number of bikes required based on time, weather, and location.

By the end of this post, you’ll understand the importance of bike-sharing demand prediction and how to implement a machine-learning solution to address it.

Project Workflow

The project will consist of:

1. Loading and Exploring Data
2. Preprocessing Data
3. Building and Evaluating the Model
4. Visualizing Insights
5. Creating an Interactive Dashboard

Let’s dive in!

Project Setup

Before we dive into the project, ensure you have the necessary libraries installed. You can install them using the following command:

pip install pandas numpy matplotlib seaborn scikit-learn plotly ipywidgets dash