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---
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"}
::
<iframe src="/projects/bikes-glm.pdf" width="100%" height="1000px">
</iframe>