A multi-label classification model for classifying comments from Wikipedia talk page edits into different types of toxicity(insult, threat, identity hate, etc).
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Updated
Oct 20, 2019 - R
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.
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
A 4th semester exploratory project that utilizes collaborative filtering and content-based techniques to recommend movies based on user preferences and viewing history. The system analyzes user data and movie attributes to deliver personalized movie suggestions.
Machine Learning Model to predict student graduation grade
Repository riset untuk Minggu 1 Bulan Oktober
Search engine that queries for information to find the best results based on custom analysers and indexing techniques.
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.
Semantic search using elasticsearch and custom search methods with text and vector. Evaluate the search results with ground truth by the metrics recall, mrr.
Very useful image related utilities
Evaluation for object detection models
Sentiment Analysis of Reviews using Python
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…
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