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centroids

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We'll use Python to build and evaluate several machine learning models to predict credit risk. Being able to predict credit risk with machine learning algorithms can help banks and financial institutions predict anomalies, reduce risk cases, monitor portfolios, and provide recommendations on what to do in cases of fraud.

  • Updated Mar 5, 2022
  • Jupyter Notebook

This project consists in the implementation of the K-Means and Mini-Batch K-Means clustering algorithms. This is not to be considered as the final and most efficient algorithm implementation as the objective here is to make a clear comparison between the sequential and parallel execution of the clustering steps.

  • Updated Jul 9, 2023
  • C++

This project focuses on developing a sentiment classification model using Multi-Layer Perceptrons (MLPs) with variations in text representation techniques and hyperparameter tuning, leveraging a balanced subset of the Kaggle Twitter Sentiment Analysis dataset. Additionally, a single instance of logistic regression was applied for comparison.

  • Updated Sep 17, 2024
  • Jupyter Notebook

This project aims to build a complete pattern recognition system to solve classification problems using the k-Nearest Neighbors (KNN) algorithm. To classify chest X-ray images into three categories: COVID-19 positive, pneumonia positive, and normal. To achieve this, we utilize the COVID-19 Chest X-ray dataset available on Kaggle.

  • Updated May 14, 2024
  • Jupyter Notebook

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