Generalized Linear Models for Bikes Prediction

This project was completed as part of the Generalized Linear Models course at Paris-Dauphine PSL University. The objective was to develop and compare statistical models to predict the number of bicycle rentals in a bike-sharing system based on various environmental and temporal characteristics.

📊 Project Objectives

• Determine the best predictive model for bicycle rental counts
• Analyze the impact of various features (temperature, humidity, wind speed, seasonality, etc.)
• Apply and evaluate different generalized linear modeling techniques
• Validate model assumptions and performance metrics

🔍 Methodology

The study employs rigorous statistical approaches including:

• Exploratory Data Analysis (EDA) - Understanding feature distributions and relationships
• Model Comparison - Testing multiple GLM families (Poisson, Negative Binomial, Gaussian)
• Feature Selection - Identifying the most influential variables
• Model Diagnostics - Validating assumptions and checking residuals
• Cross-validation - Ensuring robust performance estimates

📁 Key Findings

The analysis identified critical factors influencing bike-sharing demand:

• Seasonal patterns and weather conditions
• Temperature and humidity effects
• Holiday and working day distinctions
• Time-based trends and cyclical patterns

📚 Resources

You can find the code here: GLM Bikes Code

📄 Detailed Report