Minute News - Flask

A fast and flexible news aggregation platform built using Flask, MongoDB, and web scraping techniques.

Real-Time News Aggregation Platform

  • Efficient news delivery through Flask framework
  • Automated updates with NewsAPI integration
  • Flexible database management using MongoDB
  • Optimized web scraping for real-time data and summaries

This project utilizes Flask and MongoDB to create a scalable news website, delivering updated content quickly and efficiently through integration with NewsAPI and custom web scraping tools.

Minute News Flask Application

Core Features

  • Flask Framework: Enables quick development and deployment of the web app
  • MongoDB: Used as the primary NoSQL database to store news articles
  • NewsAPI: Integrates to fetch the latest news from multiple sources
  • Web Scraping: Automates data extraction and uses online summarization tools to condense articles

The project allows for fast database updates and smooth news delivery, combining APIs with powerful scraping techniques to provide real-time news summaries from reliable sources.

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Train Your Classifier

A straightforward approach to train a machine learning model for image classification using Python and TensorFlow.

Image Classification Model Training

  • Setup development environment with Jupyter Notebook
  • Prepare datasets organized by categories (e.g., cats, dogs, birds)
  • Train a TensorFlow model using deep learning techniques
  • Enhance performance through transfer learning

This project empowers you to build an image classification model, enabling automated recognition of images in various categories such as animals and objects.

Bird Classification Reptile Classification Monkey Classification
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Author: Miguel Angelo do Amaral Junior

COVID-19 Data Dashboard

An interactive application for real-time visualization and analysis of COVID-19 data, built using Streamlit, Pandas, and Plotly.

Real-Time COVID-19 Data Analysis

  • Country Selection: Analyze specific countries like Germany, Brazil, and the United States.
  • Interactive Data Visualization: Compare data with line, scatter, and bar charts.
  • Summary Statistics: View total cases, deaths, mortality rates, and daily averages.
  • Date Range Control: Customize analysis periods with a date selector.

This project uses Python 3.11 and Streamlit to provide a flexible tool for exploring COVID-19 trends across different countries and periods, with dynamic updates based on user inputs.

COVID-19 Data Dashboard Application

Core Features

  • Python 3.11 and Streamlit: Power the interactive user interface and data handling.
  • Pandas: Efficiently manages and processes COVID-19 data for analysis.
  • Plotly: Generates interactive charts for data visualization.
  • Requests: Retrieves real-time COVID-19 data from online sources.

The dashboard facilitates in-depth analysis of COVID-19 data, helping users understand the pandemic's impact over time and across different countries.

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Data Queue Keeper

Integrating messaging services with data storage using Docker Compose.

Overview of Data Queue Keeper

  • RabbitMQ: Manages message queues for efficient data handling
  • MongoDB: Provides persistent data storage
  • Python Application: Consumes messages and stores data

This project demonstrates a scalable system that integrates RabbitMQ and MongoDB for reliable data processing, showcasing modern practices like containerization with Docker Compose.

RabbitMQ Architecture
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Airflow Guide

Comprehensive guide for setting up Apache Airflow with Virtualenv and environment variables.

Setting Up Apache Airflow

  • Install dependencies: Using Virtualenv for isolation
  • Configure environment variables: Best practices for configuration
  • Understand key components: DAGs, Tasks, and Operators
  • Start Airflow locally: Run the database, web server, and scheduler

This guide provides a step-by-step approach to setting up Apache Airflow for local use, focusing on best practices for configuration and examples of DAGs.

Airflow Interface
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