Best Bike Sharing Demand Prediction Using Python Machine Learning

Bike Sharing Demand Prediction

Bike Sharing Demand Prediction is one of the most innovative approaches to sustainable urban transportation. Unlike traditional rentals, these systems are fully automated, allowing users to pick up a bicycle at one station and return it to another. This flexibility not only reduces traffic congestion but also promotes healthier lifestyles in busy cities. With over 500 bike-sharing programs worldwide and hundreds of thousands of bicycles in circulation, predicting demand has become a key factor in keeping the system efficient and reliable.The Bike Sharing Demand Prediction Project uses Python and Machine Learning to analyze real-world datasets and forecast the number of bikes rented on both daily and hourly bases. By applying advanced algorithms, it helps operators optimize supply, minimize downtime, and plan services more effectively. Beyond operations, the data also supports research and urban planning by providing insights into mobility patterns and city transport trends.

Project Overview

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Available Features

• Daily Prediction Model: Predicts overall bike demand on a daily basis using historical data.
• Hourly Prediction Model: Forecasts demand for each hour of the day, allowing fine-grained insights.
• Pre-Trained Models:includes the hour_model_deployed and day_model_best deployed versions for immediate usage.
• Data Preprocessing: manages CSV datasets containing seasonality, temperature, humidity, and meteorological information.
• Exploratory Data Analysis: Provides visual and statistical analysis of demand trends.
• Deployment Ready: Includes files such as main.py, Procfile, and setup.sh to make the system deployable.
• Requirements File: requirements.txt lists all dependencies for quick environment setup.

Dataset

1. day.csv –includes compiled daily data on weather, demand numbers, and other relevant factors.
2. hour.csv – Provides hourly-level data that captures variations within each day.
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Installation and Setup

To run this project smoothly, follow the steps below.

Step 1: Extract the Project

Unzip the file Bike-Sharing-Demand-Prediction-main.zip to your desired folder.

Step 2: Open in VS Code

• Launch Visual Studio Code.
• Open the extracted folder using File > Open Folder.

Step 3: Create Virtual Environment

Open the terminal in VS Code and run:

python -m venv venv

Activate the environment:

• On Windows: venv\Scripts\activate
• On Mac/Linux: source venv/bin/activate

Step 4: Install Dependencies

Use the requirements.txt file to install all required libraries:

pip install -r requirements.txt

Step 5: Run the Project

Execute the main file:

python main.py

Modeling Techniques

The project applies machine learning regression models trained on features such as:

• Weather conditions
• Temperature and humidity
• Seasonal variations
• Working days vs holidays

Evaluation Metrics

The project evaluates performance using metrics such as:

• R² Score – Measures accuracy of predictions.
• Mean Squared Error (MSE) – Captures average error in predictions.
• Root Mean Squared Error (RMSE) – Reflects overall model reliability.

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