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In this project, I explore different methods for detecting credit card fraud transactions; including using the Catboost algorithm with undersampling & oversampling methods, and using an almost new approach, by using deep learning and autoencoder.
It's Technocolabs Software Data Scientist Internship Project (1st Dec 2021 - 15th Jan 2022). In this project the team was instructed to analysis big data of Spotify users and to perform Statistical and Exploratory Data Analysis and Model Development for Predicting Listener Behavior.
Assess credit risk of applicants using supervised machine learning. Several different machine learning techniques such as SMOTE, SMOTEENN, RANDOM FOREST, EASY ENSEMBLE were applied, the models were assessed using accuracy score, precision and accuracy to choose the best technique that applies to this type of problem.
Predict if a transaction is a fraud transaction or not, also, dealing with unbalanced data and finding the pattern using correlation between the features.
Under-sampling based consensus clustering is applied on the three best clustering algorithms found after applying several Clustering Algorithms like K-means, K-modes, K-prototypes , K-means++ and fuzzy K-means on the majority class data of the IMBALANCED colon dataset to produce a BALANCED dataset.
The project was intended to detect fraudulent transactions from a highly imbalanced dataset.To solve the imbalance dataset problem random undersampling techniques were used.
Toxicity detection on imbalanced social media data. Focused on 2 main topics of toxicity detection: Class imbalance problem and detecting toxic comments.