Which algorithms are used in this bike-sharing demand prediction project?

The system implements and compares two algorithms: Decision Tree Regressor and Logistic Regression. Decision Tree achieved superior performance with R² of 0.7845 and 85.6% binary accuracy.

What is the accuracy of the bike-sharing demand prediction model?

The Decision Tree model achieves an R² score of 0.7845 with RMSE of 85.23 and binary classification accuracy of 85.6%, demonstrating strong predictive capability.

Who can benefit from this bike-sharing demand prediction system?

Bike-sharing operators, urban mobility planners, city transportation departments, and fleet managers can use this system for station rebalancing, operational optimization, and sustainable mobility planning.

What technologies are used to build this project?

The project is built using Python with Pandas, NumPy, Scikit-learn, and Matplotlib for machine learning. The web interface is developed using Flask with HTML, CSS, and JavaScript.

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Machine Learning

Urban Bike-Sharing Demand Prediction Using Machine Learning

The Urban Bike-Sharing Demand Prediction Using Machine Learning system is a comprehensive machine learning solution that predicts bike rental demand using the Capital Bikeshare dataset. This project is ideal for B.Tech, MCA, CSE, and engineering students as their final year project. It implements Decision Tree and Logistic Regression with feature engineering, achieving 0.7845 R² and 85.6% accuracy.

The system utilizes the Capital Bikeshare dataset containing 17,379 hourly observations with features including temporal variables, weather conditions, and user counts. It provides a user-friendly web interface for data upload, exploratory data analysis, model training, and real-time demand prediction, enabling bike-sharing operators to optimize station rebalancing and improve operational efficiency.

Python 3.8+ Machine Learning Decision Tree Logistic Regression Scikit-learn Pandas NumPy Matplotlib Seaborn Flask HTML/CSS/JS

Key Features:

Multi-Model Demand Prediction
Decision Tree (R²: 0.7845)
Logistic Regression (R²: 0.6542)
Feature Engineering (rush hour, seasonal features)
Regression + Binary Classification
Interactive Visualizations
Real-time Demand Predictions
Model Performance Comparison
Feature Importance Analysis
Flask Web Application

Algorithms Used

📊 Logistic Regression

Linear classifier with L2 regularization, C=1.0, solver='lbfgs', max_iter=1000

🎯 Accuracy: 82.3%

🌳 Decision Tree

Interpretable tree-based regressor with max_depth=15, min_samples_split=10

🎯 R²: 0.7845

🔧 Feature Engineering

rush_hour, weekend, is_night, temp_atemp_ratio, season_name, weather_name

📊 Top Feature: Hour

📊 Cross-Validation

5-fold stratified CV with leak-free evaluation

📊 CV Mean: 0.7869

Methodology & Workflow

1 Data Collection
17,379 hourly bike rental observations

2 Data Preprocessing
Cleaning, encoding, feature engineering, scaling

3 Exploratory Data Analysis
Statistical analysis and visualization of patterns

4 Model Training
Decision Tree and Logistic Regression with 5-fold CV

5 Model Evaluation
R², RMSE, MAE, Accuracy, F1-Score, ROC-AUC

6 Web Deployment
Flask web app with real-time demand prediction

Model Performance Comparison

Metric	Logistic Regression	Decision Tree	Best
R² Score	0.6542	0.7845	Decision Tree
RMSE	115.67	85.23	Decision Tree
MAE	74.38	52.41	Decision Tree
Binary Accuracy	82.3%	85.6%	Decision Tree
Binary F1-Score	0.831	0.862	Decision Tree
CV Mean R²	0.6582	0.7869	Decision Tree

Project Package Includes:

Complete Source Code Documentation (50+ pages) Video Tutorial Capital Bikeshare Dataset Flask Web App Model Files (Pickle) Visualizations 24/7 Expert Support

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