--- slug: bikes-glm title: Generalized Linear Models for Bikes Prediction type: Academic Project description: Predicting the number of bikes rented in a bike-sharing system using Generalized Linear Models and various statistical techniques. publishedAt: 2025-01-24 readingTime: 1 status: Completed tags: - R - Statistics - GLM - Mathematics icon: i-ph-bicycle-duotone --- 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](https://go.arthurdanjou.fr/glm-bikes-code) ## 📄 Detailed Report