https://github.com/ygalvao/uow_ai_final_project

This was my Final Project for the Artificial Intelligence Diploma program of The University of Winnipeg - Professional, Applied and Continuing Education (PACE).
https://github.com/ygalvao/uow_ai_final_project

data-analysis data-analytics dbscan elections k-means k-means-clustering machine-learning som som-clustering

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This was my Final Project for the Artificial Intelligence Diploma program of The University of Winnipeg - Professional, Applied and Continuing Education (PACE).

• Host: GitHub
• URL: https://github.com/ygalvao/uow_ai_final_project
• Owner: ygalvao
• License: mit
• Created: 2023-11-07T04:43:05.000Z (about 1 year ago)
• Default Branch: main
• Last Pushed: 2023-12-25T01:50:13.000Z (about 1 year ago)
• Last Synced: 2023-12-25T02:40:50.900Z (about 1 year ago)
• Topics: data-analysis, data-analytics, dbscan, elections, k-means, k-means-clustering, machine-learning, som, som-clustering
• Language: Jupyter Notebook
• Homepage: https://www.kaggle.com/code/yurigalvao/using-clustering-ml-models-to-analyze-elections
• Size: 9.35 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# Using Machine Learning to Analyze the 2nd Round of the 2022 Brazilian Presidential Election

## Overview

The Jupyter Notebook in this repository is my Final Project for the Artificial Intelligence Diploma program of The University of Winnipeg - Professional, Applied and Continuing Education (PACE). The idea of this project is to extract the available data generated by the Electronic Voting Machines (EVM) that were used in the 2nd Round of the 2022 Brazilian Presidential Election, clean it, analyze it, and then use clustering models to find data patterns - especially hidden or non-intuitive patterns - and anomalies.

## Prerequisites

- Jupyter Notebook or JupyterLab

- Python 3.7 or higher

- Necessary Python packages (listed in `requirements.txt`)

## Installation and Setup

1. Clone the repository or download the Jupyter Notebook file.

2. Install Jupyter Notebook or JupyterLab if you haven't already.

3. Set up a Python environment and install the required packages using:

   ```bash

   pip install -r requirements.txt

   ```

4. Open the Jupyter Notebook environment and navigate to the notebook file to begin your analysis.

## Running the Notebook

To run the notebook:

1. Open the Jupyter Notebook or JupyterLab.

2. Navigate to the location of the using-ml-to-analyze-the-2022-brazilian-elections.ipynb file.

3. Open the notebook and run the cells sequentially to perform the analysis.

#### PS.: in the Data Ingestion phase, item 2.2, there is a cell dedicated to run the "Bra Scraper 2022" which is essentially the web scraper bot that gets all the desired data of EVMs from the TSE website. If necessary, run it apart from the Notebook.

## License

This project is licensed under the MIT License - see the LICENSE file for details.