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

Customer Churn Prediction using Machine Learning (Imbalanced Classification) Customer Churn Prediction using Machine Learning (Imbalanced Classification)

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.

(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.

Supervised Learning project from TripleTen

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.

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.

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.

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

Developed an ensemble ML classification model to predict U.S. visa case outcomes (Certified vs Denied) using applicant and employer attributes. Performed EDA, sampling, and model tuning (Random Forest, Gradient Boosting, XGBoost) to improve decision efficiency and identify key policy drivers like education, experience, and wage trends.

End-to-end machine learning workflow on the Combined Cycle Power Plant dataset: data cleaning, EDA, outlier removal, feature engineering, class balancing, and model evaluation for regression and classification. Includes code, visualizations and best practices in a single Jupyter notebook.

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!

A dual-part finance and retail analytics project covering credit default prediction for companies using machine learning (Logistic Regression & Random Forest) and market risk analysis of a five-stock Indian equity portfolio using historical price and return data.