Generalized Linear Models for Bikes Prediction

Overview

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

Code Repository: GLM Bikes Code
Full Report: See embedded PDF below

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