A repo holding the implementation as well as some theoretical explanation of the important relevant concepts. It is going to be in development for a long long time. I'll keep adding things everytime I have something to add to it, and I have the time for it. One can use it to learn the basics of Machine Learning from kind of scratch.

This project focuses on evaluating different classification models for detecting and analyzing the risk of near-Earth objects (NEOs). The models are assessed using key metrics such as Confusion Matrix, Recall, AUC-ROC, and PR-AUC to understand their performance in distinguishing between the two classes (risky vs. non-risky NEOs).

machine learning techniques to predict company defaults by optimizing the trade-off between recall (minimizing false negatives) and precision (avoiding false positives). Logistic Regression and Random Forest models were trained, with emphasis on recall to ensure accurate identification of high-risk companies.

Search Engine based on Cranfield dataset

Support vector machine in medical disease detection. Both linear and non-linear data can be fitted in svm through its kernel specialization In medical we focus on precision or recall rather than accuracy.

End-to-end ML pipeline for stroke risk prediction with XGBoost, FastAPI API, and Streamlit dashboard — designed to handle severe class imbalance and maximize recall.

Code to detect credit card fraud detecton

A binary classifier to identify high-probability exoplanet candidates.

Classification machine learning models were trained and used to identify what features contributed to customer churn rate.

MNIST Handwritten Digits Classification

Information Retrieval with Lucene and CISI dataset. Index documents and search between them with IB, DFR, BM-25, TF-IDF, Boolean, Axiomatic, LM-Dirichlet similarity and calculate Recall, Precision, MAP (Mean Average Precision) and F-Measure

Sentiment analysis is part of the NLP techniques that consists in extracting emotions related to some raw texts.

Telecom Customer Churn Prediction Using Machine Learning!

Three classification models trained to predict failures of machines on the production line.

Consolidating tutorial codes for breast cancer tumor detection, covering ML fundamentals like classification, feature engineering, training, evaluation, and key performance metrics.

Summary of NLP work to automate construction management for non-compliance, punch list, and database creation.

machine learning

- Nesse trabalho vou explorar uma base vista em projetos passados, diabetes dataset. - Nela encontramos informações sobre algumas características de pacientes. Queremos estudar as características das pacientes e encontrar possíveis relações

Fraud Detection with Machine Learning

Proyecto de detección de objetos con YOLO, datos de Roboflow y registro automático en Excel.