Official Code for "Confidence Matters: Enhancing Medical Image Classification Through Uncertainty-Driven Contrastive Self-distillation" accepted at MICCAI2024

(WIP): 'Aporia' in Greek means 'inconsistent'. A Python library that detects and fixes dataset issues using both rule-based methods and ML models. It evaluates dataset quality across multiple metrics, including missing values, duplicates, outliers, class imbalance, and label consistency. It also suggests fixes based on the metric scores.

Fundamentals of Machine Learning Assignment Repository

Predicting which people would be likely to convert from free users to premium subscribers in the next 6 month period, if they are targeted by our promotional campaign.

This project focuses on detecting fraudulent credit card transactions using Machine Learning and Data Analytics. It applies advanced techniques such as EDA (Exploratory Data Analysis), feature engineering, and imbalance handling (SMOTE, undersampling) to improve fraud detection accuracy.

A binary classification task performed with machine learning in Python. The dataset's target distribution is heavily imbalanced. The model performance was evaluated with F1 scores.

Predicting company bankruptcy using various machine learning models. The dataset is sourced from Kaggle: Company Bankruptcy Prediction.

Developing a machine learning model to predict customer churn as it is essential for proactively retaining valuable customers.

Supervised Learning project from TripleTen

This is a production-ready, end-to-end system developed to detect and classify racist tweets using advanced Natural Language Processing (NLP) techniques. Built on top of BERTweet (vinai/bertweet-base) and fine-tuned with a robust, k-fold cross-validation training pipeline, powered by streamlit UI!

Analysis of bank marketing campaigns using machine learning to predict term deposit subscriptions, optimizing campaign strategies through comparative evaluation of classification models.