--- slug: glm-bikes 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. shortDescription: A project applying Generalized Linear Models to predict bike rentals based on environmental and temporal features. 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 that predict bicycle rentals in a bike-sharing system using environmental and temporal features. ::BackgroundTitle{title="Project Objectives"} :: - Determine the best predictive model for bicycle rental counts - Analyze the impact of key features (temperature, humidity, wind speed, seasonality, etc.) - Apply and evaluate different generalized linear modeling techniques - Validate model assumptions and performance metrics ::BackgroundTitle{title="Methodology"} :: The study uses a rigorous statistical workflow, 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 ::BackgroundTitle{title="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 ::BackgroundTitle{title="Resources"} :: You can find the code here: [GLM Bikes Code](https://go.arthurdanjou.fr/glm-bikes-code) ::BackgroundTitle{title="Detailed Report"} ::