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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. A project applying Generalized Linear Models to predict bike rentals based on environmental and temporal features. 2025-01-24 1 Completed
R
Statistics
GLM
Mathematics
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

::BackgroundTitle{title="Detailed Report"} ::