[{"data":1,"prerenderedAt":150},["ShallowReactive",2],{"project-glm-bikes":3},{"id":4,"title":5,"description":6,"extension":7,"favorite":8,"icon":9,"meta":10,"publishedAt":137,"readingTime":138,"shortDescription":139,"slug":140,"status":141,"stem":142,"tags":143,"type":148,"__hash__":149},"projects\u002Fprojects\u002Fglm-bikes.md","Generalized Linear Models for Bikes Prediction","Predicting the number of bikes rented in a bike-sharing system using Generalized Linear Models and various statistical techniques.","md",false,"i-ph-bicycle-duotone",{"body":11},{"type":12,"value":13,"toc":128},"minimark",[14,23,28,44,48,51,83,87,90,104,108,118,122],[15,16,17,18,22],"p",{},"This project was completed as part of the ",[19,20,21],"strong",{},"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.",[24,25,27],"h2",{"id":26},"project-objectives","Project Objectives",[29,30,31,35,38,41],"ul",{},[32,33,34],"li",{},"Determine the best predictive model for bicycle rental counts",[32,36,37],{},"Analyze the impact of key features (temperature, humidity, wind speed, seasonality, etc.)",[32,39,40],{},"Apply and evaluate different generalized linear modeling techniques",[32,42,43],{},"Validate model assumptions and performance metrics",[24,45,47],{"id":46},"methodology","Methodology",[15,49,50],{},"The study uses a rigorous statistical workflow, including:",[29,52,53,59,65,71,77],{},[32,54,55,58],{},[19,56,57],{},"Exploratory Data Analysis (EDA)"," - Understanding feature distributions and relationships",[32,60,61,64],{},[19,62,63],{},"Model Comparison"," - Testing multiple GLM families (Poisson, Negative Binomial, Gaussian)",[32,66,67,70],{},[19,68,69],{},"Feature Selection"," - Identifying the most influential variables",[32,72,73,76],{},[19,74,75],{},"Model Diagnostics"," - Validating assumptions and checking residuals",[32,78,79,82],{},[19,80,81],{},"Cross-validation"," - Ensuring robust performance estimates",[24,84,86],{"id":85},"key-findings","Key Findings",[15,88,89],{},"The analysis identified critical factors influencing bike-sharing demand:",[29,91,92,95,98,101],{},[32,93,94],{},"Seasonal patterns and weather conditions",[32,96,97],{},"Temperature and humidity effects",[32,99,100],{},"Holiday and working day distinctions",[32,102,103],{},"Time-based trends and cyclical patterns",[24,105,107],{"id":106},"resources","Resources",[15,109,110,111],{},"You can find the code here: ",[112,113,117],"a",{"href":114,"rel":115},"https:\u002F\u002Fgo.arthurdanjou.fr\u002Fglm-bikes-code",[116],"nofollow","GLM Bikes Code",[24,119,121],{"id":120},"detailed-report","Detailed Report",[123,124],"iframe",{"src":125,"width":126,"height":127},"\u002Fprojects\u002Fbikes-glm.pdf","100%","1000px",{"title":129,"searchDepth":130,"depth":130,"links":131},"",2,[132,133,134,135,136],{"id":26,"depth":130,"text":27},{"id":46,"depth":130,"text":47},{"id":85,"depth":130,"text":86},{"id":106,"depth":130,"text":107},{"id":120,"depth":130,"text":121},"2025-01-24",1,"A project applying Generalized Linear Models to predict bike rentals based on environmental and temporal features.","glm-bikes","Completed","projects\u002Fglm-bikes",[144,145,146,147],"R","Statistics","GLM","Mathematics","Academic Project","hOkOrkH3oe-Xr2fNxKS6J0Kft7fdPAGHO7lisc5tL70",1777982163871]