📊 Project: Analysis & Data Crawling for Two Football Pages – Manchester United & Liverpool FC ⚽🔍
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Updated
Mar 11, 2026
📊 Project: Analysis & Data Crawling for Two Football Pages – Manchester United & Liverpool FC ⚽🔍
Project Context Clues aim to vectorize CyberOps recon and threat intel data for AI driven systems.
Assistente feita em Python utilizando Speech_recognition, e APIs do Google
In this Course we are explaining some basic python uses in Data Science and also we will practice top python libraries such as NumPy, pandas, matplotlib, sklearn and so more and last we learn some algorithms.
Projeto de demonstração para testar a API do HIDROWEB da Agência Nacional de Águas (ANA). https://www.snirh.gov.br/hidroweb
I have built a Customer Complaint Analyzer which Seamlessly process and categorize consumer complaints across text, voice, images, and videos. This analyzer Harness the power of AI to streamline your customer service and improve satisfaction.
StanCal :: ANA Standards Calculator
Natural language processing (NLP) and macroeconomic analysis platform evaluating sentiment, hedging, and tone across decades of Indian Ministry of Finance annual reports (1991–2025). Correlates linguistic shifts like jargon density with GDP, inflation, and major policy milestones like the 1991 LPG reforms.
ANA乗ろう。
Aplicação R/Shiny para consulta, triagem e análise de dados hidrológicos de estações da ANA.
Support & Resistance – TradingView Pine Script
ANA Starter — a base Agent-Native App template (비서·메모). Same look, logo, and runtime as the reference ANA dashboard; pre-wired with a 할일/스케줄 · 업무/공부 menu, seeded and ready to grow by talking. Use this template to start a new ANA.
Estudo de caso técnico sobre análise de dados ambientais da Lagoa dos Patos com Python, pandas e Matplotlib.
Consulting-grade GFCI market research report: Eaton vs. Siemens. Covers SWOT, Porter's Five Forces, product benchmarking, demand forecasting & regional analysis. Portfolio project for Business Analyst, Market Research & Strategy roles.
This project evaluates movie recommendation systems, comparing **Content-Based Filtering (CBF)**, **Collaborative Filtering (CF)**, and **Hybrid Systems**. The **hybrid approach** proves most effective, combining CBF and CF to deliver more personalized, accurate, and explainable recommendations, enhancing the user experience in movie discovery.
EDA project focused on data cleaning, visualization, and extracting actionable insights from raw datasets.
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