A multi-label classification model for classifying comments from Wikipedia talk page edits into different types of toxicity(insult, threat, identity hate, etc).

It is important that credit card companies are able to recognize fraudulent credit card transactions so that customers are not charged for items that they did not purchase but by others illegally. Some huge transactions can also done by suspicious figure, it need to catch em.

Scan food barcodes and see if they're recalled by FDA

In this study we evaluate the accuracy of our Aurora SDG classification model version 5, to match research papers to the Sustainable Development Goals (SDG's) of the United Nations. The aim of this investigation is to be transparent about the accuracy of the model, because this model might get used in reporting and strategy analysis by Universit…

This notebook describes how to compute and derive insights from various classification evaluation metrics.

Classification Techniques

Atom Text Editor Simple Line Mark&Recall

Developed a Convolutional Neural Network based on VGG16 architecture to diagnose COVID-19 and classify chest X-rays of patients suffering from COVID-19, Ground Glass Opacity and Viral Pneumonia. This repository contains the link to the dataset, python code for visualizing the obtained data and developing the model using Keras API.

Detect public transport modes using different ML models.

McDonald's Restaurant reviews analysis

This project aims to predict customer churn using machine learning techniques. By understanding the factors that contribute to churn, businesses can take proactive measures to retain customers and maximize their customer base. The project focuses on developing a predictive model using machine learning algorithms to forecast customer churn.

This repository contains my collections of labs' notebooks from Udacity's Intro to ML with TensorFlow.

Machine Learning Model to predict student graduation grade

Repository riset untuk Minggu 1 Bulan Oktober

Machine Learning Course Design

Windows Recall but for Linux and better (It is very bad).

A Comprehensive Guide to Titanic Machine Learning from Disaster

This project is about detecting fraudulent credit card transactions. The dataset tends to be highly imbalanced, with less than 0.2% of the observations labelled as fraudulent. To address this issue we have to take into account the bank's objective (maximizing precision or recall) and restrictions. The performance and efficiency of many classific…

my implementation calculate precision, recall, f1, accuracy, and GLEU score

Cardiovascular Risk Prediction - Classification