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Arthur DANJOU
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- Created CLAUDE.md to provide development commands, architecture overview, and environment variables for the Nuxt 3 portfolio website. - Refactored project pages to remove unused color mappings and improve project filtering logic. - Updated content.config.ts to enforce stricter project type definitions and added short descriptions for projects. - Deleted outdated project files and added new projects related to hackathons and academic research. - Enhanced existing project descriptions with short summaries for better clarity.
53 lines
2.0 KiB
Markdown
53 lines
2.0 KiB
Markdown
---
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slug: glm-bikes
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title: Generalized Linear Models for Bikes Prediction
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type: Academic Project
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description: Predicting the number of bikes rented in a bike-sharing system using Generalized Linear Models and various statistical techniques.
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shortDescription: A project applying Generalized Linear Models to predict bike rentals based on environmental and temporal features.
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publishedAt: 2025-01-24
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readingTime: 1
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status: Completed
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tags:
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- R
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- Statistics
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- GLM
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- Mathematics
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icon: i-ph-bicycle-duotone
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---
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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.
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## 📊 Project Objectives
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- Determine the best predictive model for bicycle rental counts
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- Analyze the impact of various features (temperature, humidity, wind speed, seasonality, etc.)
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- Apply and evaluate different generalized linear modeling techniques
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- Validate model assumptions and performance metrics
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## 🔍 Methodology
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The study employs rigorous statistical approaches including:
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- **Exploratory Data Analysis (EDA)** - Understanding feature distributions and relationships
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- **Model Comparison** - Testing multiple GLM families (Poisson, Negative Binomial, Gaussian)
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- **Feature Selection** - Identifying the most influential variables
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- **Model Diagnostics** - Validating assumptions and checking residuals
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- **Cross-validation** - Ensuring robust performance estimates
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## 📁 Key Findings
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The analysis identified critical factors influencing bike-sharing demand:
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- Seasonal patterns and weather conditions
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- Temperature and humidity effects
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- Holiday and working day distinctions
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- Time-based trends and cyclical patterns
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## 📚 Resources
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You can find the code here: [GLM Bikes Code](https://go.arthurdanjou.fr/glm-bikes-code)
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## 📄 Detailed Report
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<iframe src="/projects/bikes-glm.pdf" width="100%" height="1000px">
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</iframe>
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