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Arthur DANJOU
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- Implemented a new research project on Dropout strategies in deep learning, including detailed objectives, methodology, and usage instructions. - Created a project for predicting bike rentals using Generalized Linear Models, outlining objectives, methodology, and key findings. - Developed a machine learning project for loan prediction, detailing objectives, methodology, and a report on model performance. - Added a project focused on breast cancer detection using various classification models, including objectives, methodology, and resources. - Updated package.json with author information and upgraded dependencies.
1.9 KiB
1.9 KiB
slug, title, type, description, publishedAt, readingTime, status, tags, icon
| slug | title | type | description | publishedAt | readingTime | status | tags | icon | ||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| glm-bikes | 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 |
|
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