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Arthur DANJOU
ba91408b6d
- Created index.md for personal introduction and interests. - Added languages.json to specify language proficiencies. - Developed profile.md detailing academic background, skills, and career goals. - Introduced multiple project markdown files showcasing personal and academic projects, including ArtChat, ArtHome, and various data science initiatives. - Implemented skills.json to outline technical skills and competencies. - Compiled uses.md to document hardware and software tools utilized for development and personal projects.
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slug, title, type, description, publishedAt, readingTime, status, tags, emoji
| slug | title | type | description | publishedAt | readingTime | status | tags | emoji | |||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| bikes-glm | Generalized Linear Models for Bikes Prediction | Academic Project | Predicting the number of bikes rented in a bike-sharing system using Generalized Linear Models and various statistical techniques. | 2025-01-24 | 1 | Completed |
|
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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