私について
私はマリオ・アルバレス、UPV（バレンシア工科大学）のデータサイエンスの学生であり、プロサイクリングチームでの実務経験を持っています。データ分析とエリートスポーツのパフォーマンスを組み合わせ、すべての指標を結果を向上させる意思決定へと変えます。
もっと見るUPV
学歴
バレンシア工科大学 データサイエンス学士号。
エリート
経験
プロサイクリングチーム内での実務経験。
Data + Sport
アプローチ
スポーツパフォーマンスの向上に応用される分析。
実績
GitHubのパブリックリポジトリの自動セレクション。
This repository contains coursework, practical exercises, and study materials for a university data visualization course. It includes solved programming tasks using R and Python libraries like ggplot2 and Plotly, alongside practice dashboards built in Tableau.
GitHubで見るThis repository contains practical assignments for a university model evaluation course, covering topics such as cost-sensitive classification, fairness metrics on the COMPAS dataset, and model interpretability using partial dependence plots. The exercises and analyses are implemented using Python and R across Jupyter Notebooks and R Markdown reports.
GitHubで見るAn academic data analysis project in R that explores and characterizes a physicochemical crystal dataset. It covers data cleaning, preprocessing, and dimensionality reduction through Principal Component Analysis (PCA) using packages such as FactoMineR, factoextra, and ggplot2.
GitHubで見るA Streamlit web application that helps manage cycling teams by scraping race and rider data from ProCyclingStats and FirstCycling to analyze upcoming events and cyclist specialties. It is built with Python using BeautifulSoup, Selenium, Pandas, and the Gemini API to process data and extract UCI points regulations from PDFs.
GitHubで見るDeveloped as an academic project, this tool scrapes UCI rankings and race data from ProCyclingStats to calculate race difficulty scores tailored to the Cofidis cycling team's calendar. It is built with Python, using Selenium and BeautifulSoup for web scraping, Pandas for data processing, and Streamlit for the interactive interface.
GitHubで見る
