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

This project presents and discusses data-driven predictive models for predicting the defaulters among the credit card users.About Data Cleaning,Exploratory Data Analysis ,Handling Class Imbalance, Transforming Data , Fitting Different Model ,Cross Validation & Hyperparameter Tunning, Comparison of Model ,Combined ROC Curve, Feature Impotance.

Search engine that queries for information to find the best results based on custom analysers and indexing techniques.

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.

Machine Learning Model to predict student graduation grade

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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

Handy reference notes for common data sciences topics