https://github.com/t4vexx/ia-classification-analysis

This GitHub repository hosts my AI evaluation work, featuring a Kaggle dataset analysis, experiments with three ML algorithms (including hyperparameter tuning), and a detailed exploration of wine quality data through outlier detection, correlation, and normalization techniques.
https://github.com/t4vexx/ia-classification-analysis

ai artificial-intelligence bert-embeddings bert-model confusion-matrix correlation-analysis embedding fake-news-detection logistic-regression python3 random-forest-classifier svm-classifier

Last synced: 6 months ago
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This GitHub repository hosts my AI evaluation work, featuring a Kaggle dataset analysis, experiments with three ML algorithms (including hyperparameter tuning), and a detailed exploration of wine quality data through outlier detection, correlation, and normalization techniques.

• Host: GitHub
• URL: https://github.com/t4vexx/ia-classification-analysis
• Owner: T4vexx
• Created: 2025-04-02T01:17:22.000Z (6 months ago)
• Default Branch: main
• Last Pushed: 2025-04-02T01:21:43.000Z (6 months ago)
• Last Synced: 2025-04-02T02:25:56.808Z (6 months ago)
• Topics: ai, artificial-intelligence, bert-embeddings, bert-model, confusion-matrix, correlation-analysis, embedding, fake-news-detection, logistic-regression, python3, random-forest-classifier, svm-classifier
• Language: Jupyter Notebook
• Homepage:
• Size: 5.59 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

          
# AI Evaluation Repository

This repository contains my work for the AI evaluation, featuring data exploration, machine learning experiments, and detailed analyses as specified in the evaluation guidelines.

## Repository Structure

- **Avaliação_IA_P1.pdf**  

  Contains the assignment instructions and evaluation criteria.

- **Prova1_IA_exercicios_1e2.ipynb**  

  Notebook for exercises 1 and 2, including:

  - Dataset selection from Kaggle.

  - Data preprocessing.

  - Application and tuning of three machine learning algorithms.

  - Evaluation using appropriate metrics and visualization techniques.

- **Prova1_IA_exercicio_3.ipynb**  

  Notebook for exercise 3, focusing on:

  - Outlier analysis using boxplots.

  - Correlation matrix generation for the Wine Quality dataset.

  - Data normalization and its impact on model performance.

## Requirements

- Python 3.x

- Jupyter Notebook

- Common libraries: NumPy, pandas, scikit-learn, matplotlib, seaborn, torch etc.

## How to Run

1. Clone this repository.

2. Install the required Python packages (preferably in a virtual environment).

3. Open the notebooks in Jupyter Notebook or JupyterLab.

4. Run the cells sequentially to reproduce the analyses and results.

## Overview

The project involves a comprehensive exploration of machine learning techniques applied to a chosen Kaggle dataset and an in-depth analysis of the Wine Quality dataset. The work demonstrates data preprocessing, model tuning, evaluation through multiple metrics, and visual analysis to compare algorithm performance and understand dataset characteristics.

Feel free to explore the notebooks and review the methodologies and findings presented in each exercise.