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Initial commit: subindo projeto de classificação balanceada
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‎.github/workflows/ci.yml‎

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name: CI
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on:
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push:
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branches: ["**"]
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pull_request:
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branches: ["**"]
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jobs:
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tests:
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runs-on: ubuntu-latest
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steps:
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- name: Checkout repository
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uses: actions/checkout@v4
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- name: Set up Python
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uses: actions/setup-python@v5
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with:
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python-version: "3.12"
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- name: Install dependencies
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run: |
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python -m pip install --upgrade pip
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pip install -r requirements.txt
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- name: Run tests
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run: pytest -q

‎.gitignore‎

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# Python cache and compiled files
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__pycache__/
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*.py[cod]
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*$py.class
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*.pyd
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*.pyo
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# Python runtime files
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.python-version
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# Test, coverage and reports
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.coverage
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.coverage.*
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coverage.xml
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htmlcov/
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pytestdebug.log
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# Logs
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*.log
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# Virtual environments
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.venv/
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venv/
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env/
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ENV/
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# Distribution / packaging
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build/
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dist/
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*.egg-info/
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.eggs/
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pip-wheel-metadata/
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# Tool caches
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.pytest_cache/
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.mypy_cache/
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.ruff_cache/
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.ipynb_checkpoints/
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.tox/
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.nox/
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# IDE/editor
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.vscode/
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.idea/
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*.code-workspace
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# OS files
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.DS_Store
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Thumbs.db
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desktop.ini
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# Generated outputs
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graficos/
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# Local environment files
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.env
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.env.*
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# Type checker / language server
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.pyright/

‎LICENSE‎

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MIT License
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Copyright (c) 2026 [fullname]
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Permission is hereby granted, free of charge, to any person obtaining a copy
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of this software and associated documentation files (the "Software"), to deal
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in the Software without restriction, including without limitation the rights
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to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
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copies of the Software, and to permit persons to whom the Software is
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furnished to do so, subject to the following conditions:
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The above copyright notice and this permission notice shall be included in all
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copies or substantial portions of the Software.
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THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
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AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
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LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
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OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
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SOFTWARE.

‎README.md‎

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# Projeto de Deteccao de Anomalias em Cartoes de Credito
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Projeto desenvolvido por: Hélio Júnior
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Plataforma de Estudos: DIO - Digital Innovation One
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Este projeto aplica tecnicas de Machine Learning para deteccao de fraude em transacoes de cartao de credito usando a base publica hospedada em:
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https://storage.googleapis.com/download.tensorflow.org/data/creditcard.csv
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## Objetivo
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Comparar modelos e abordagens para classificacao de transacoes fraudulentas, incluindo:
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- Regressao Logistica
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- Random Forest
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- Pipeline com threshold customizado
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- XGBoost
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- Busca de hiperparametros com GridSearchCV
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- Explicabilidade com SHAP (opcional)
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## Estrutura do Projeto
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- main.py: atalho para iniciar a aplicacao
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- run.ps1: script de execucao com um comando
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- src/main.py: orquestracao principal do fluxo
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- src/data_processing.py: carga e preparo dos dados
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- src/logistic_analysis.py: treino, metricas e graficos da Regressao Logistica
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- src/balancing.py: undersampling e SMOTE
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- src/random_forest_analysis.py: treino e avaliacao de Random Forest
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- src/pipeline_analysis.py: pipeline de padronizacao + Regressao Logistica e threshold customizado
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- src/xgboost_analysis.py: treino, avaliacao e importancia de features do XGBoost
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- src/hyperparameter_tuning.py: ajuste de hiperparametros com GridSearchCV
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- src/shap_analysis.py: explicabilidade com SHAP
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- .gitignore: arquivos e pastas ignorados pelo Git
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- requirements.txt: dependencias do projeto
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- analise.ipynb: notebook para analises adicionais
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## Requisitos
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- Python 3.10+
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- Ambiente virtual recomendado
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## Instalacao
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No PowerShell, dentro da pasta do projeto:
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```powershell
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py -3.12 -m venv .venv
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.\.venv\Scripts\Activate.ps1
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python -m pip install --upgrade pip
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python -m pip install -r requirements.txt
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```
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## Como Executar
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```powershell
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python .\main.py
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```
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Ou com um comando dedicado:
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```powershell
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.\run.ps1
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```
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## Testes
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Executar testes locais:
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```powershell
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pytest -q
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```
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Os testes atuais validam importacao e integridade basica dos modulos.
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## CI
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Foi adicionado pipeline de CI em .github/workflows/ci.yml para:
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- instalar dependencias
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- executar pytest automaticamente em push e pull request
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## Saidas Geradas
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Durante a execucao, o projeto:
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- Exibe relatorios de classificacao no terminal para cada modelo
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- Exibe AUC e Average Precision para Regressao Logistica
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- Gera imagens em graficos/: roc_curve.png, precision_recall_curve.png, xgboost_feature_importance.png e shap_bar.png (se SHAP estiver instalado e funcional)
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## Observacoes
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- Se o pacote SHAP nao estiver instalado, a etapa de explicabilidade e ignorada automaticamente.
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- O backend grafico esta configurado para modo nao interativo (Agg), ideal para execucao em terminal.
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## Melhorias Futuras
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- Salvar metricas em arquivos CSV/JSON
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- Adicionar comparacao visual entre modelos
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- Inserir validacao cruzada para todos os modelos
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- Criar testes unitarios de comportamento para cada modulo
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## Licenca
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Projeto para fins educacionais.

‎analise.ipynb‎

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‎main.py‎

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#Projeto desenvolvido por: [Hélio Júnior]
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#Plataforma de Estudos: [DIO - Digital Innovation One]
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from src.main import main
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if __name__ == "__main__":
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main()

‎requirements.txt‎

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pandas
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matplotlib
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scikit-learn
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imbalanced-learn
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xgboost
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shap
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pytest

‎run.ps1‎

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$pythonExe = ".\.venv\Scripts\python.exe"
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if (-not (Test-Path $pythonExe)) {
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Write-Host "Ambiente .venv nao encontrado. Crie com: py -3.12 -m venv .venv"
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exit 1
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}
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& $pythonExe -m src.main

‎src/__init__.py‎

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#Projeto desenvolvido por: [Hélio Júnior]
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#Plataforma de Estudos: [DIO - Digital Innovation One]

‎src/balancing.py‎

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#Projeto desenvolvido por: [Hélio Júnior]
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#Plataforma de Estudos: [DIO - Digital Innovation One]
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import pandas as pd
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from imblearn.over_sampling import SMOTE
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def create_undersampled_dataframe(df, target_col: str = "Class", random_state: int = 42) -> pd.DataFrame:
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fraudes = df[df[target_col] == 1]
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normais = df[df[target_col] == 0].sample(n=len(fraudes), random_state=random_state)
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return pd.concat([fraudes, normais])
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def apply_smote(X, y):
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smote = SMOTE()
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return smote.fit_resample(X, y)

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