Code for paper "Semantic Diversity-aware Prototype-based Learning for Unbiased Scene Graph Generation (ECCV 2024)"

This notebook is a study of the application of sklearn Logistic Regression model and analysis of metric quality with a focus on the impact of imbalanced data. The problem presented is the analysis of sales of newspapers of a local stand in order to classify the probability of the newspaper being Sold Out or Not, given a set of features.

Membership Inference Attacks on Imbalanced Federated Learning setups

Exploration and optimization of a ML pipeline, delving into various techniques for enhancing different stages of ML workflows, including data preprocessing, feature engineering, model selection, and hyperparameter tuning.

software vulnerability detection

Detection of dermoscopic structures for melanoma diagonsis

ECG Arrhythmia Detection with ResNet and Transfer Learning

Detección de cardiopatías en pacientes mediante el uso de datos clínicos utilizando técnicas de Machine Learning y Deep Learning.

Predicting the churn in the last month using the data (features) from the first three months and identify customers at high risk of churn and the main indicators of churn.

Predicting whether a client will subscribe for a term deposit after a bank marketing campaign

This was my first project ever on Python. It's also my first attempt at EDA for my Executive PGP Course, with IIIT-B and UpGrad.

A real world data analysis and sentiment analysis using NLP and supervised classification machine learning model #4

Dice loss for data-imbalanced NLP tasks

Customer Retention Analysis : Predict customer churn

compare the performance of cross entropy, focal loss, and dice loss in solving the problem of data imbalance

Classification of Body postures using different ML algorithms and comparing their performances.

Submission for HR Analytics Hackathon - AnalysticsVidya.

pytorch implementation of Shrinkage loss in our ECCV paper 2018: Deep regression tracking with shrinkage loss

Deep Regression Tracking with Shrinkage Loss (ECCV 2018).

The final project for the CE888: Data Science and Decision Making module (Spring Term) at the University of Essex