This project aims to analyze and predict performance trends in Grand Tours (Tour de France, Giro d’Italia, La Vuelta) using data from ProCyclingStats and Strava. We will scrape and integrate race and rider statistics, perform exploratory data analysis (EDA), classify stages based on terrain and difficulty, and build predictive models for race performance. In addition to predicting race times, we will conduct statistical analyses on factors such as time gaps between riders, stage properties (like climbs, distance), and historical performance trends. Key research questions include identifying trends in rider speeds, distances, and climbing efforts, understanding factors influencing stage outcomes, and developing machine learning models to estimate race times. Expected outcomes include insights into performance trends, predictive models, and interactive visualizations. Challenges include data gaps, consistency issues.