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.

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.

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.

MNIST Handwritten Digits Classification

Code to detect credit card fraud detecton

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

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).

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

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

This is my Hamoye Stage C tag-along project. The notebook focuses on applying Machine Learning Classification models and Measuring Classification Performance.

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

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

IncomePredict is a Python-based data science project that analyzes income levels using data visualization, encoding techniques, and machine learning models.

Telecom Customer Churn Prediction Using Machine Learning!

Model DecisionTree and evaluation model

- 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

To recognize fraudulent credit card transactions so that customers are not charged for items that they did not purchase

In this repository, We are going to be working on upGrad's lead score data set and to see how can we solve the problem using Exploratory data analysis techniques and using supervised machine learning models.

Predicting passenger survival on the Titanic using an ensemble machine learning approach, achieving a Kaggle score of 0.77990. This project leverages stacking with Random Forest, Gradient Boosting, and SVM, enhanced by feature engineering and hyperparameter tuning, to model survival patterns effectively.