mcs_kfold stands for "monte carlo stratified k fold". This library attempts to achieve equal distribution of discrete/categorical variables in all folds. The greatest advantage of this method is that it can be applied to multi-dimensional targets.

Fast Online Triplet mining in Pytorch

使用比赛方提供的脱敏数据，进行客户信贷流失预测。

An optimal stratified sample design for Commodity Flow Survey (CFS) based on Simulated Annealing and Genetic Algorithm. A script in Procedural PostgreSQL is used to generate a frame with 100,000 records based on publicly available data.

Data consists of tweets scrapped using Twitter API. Objective is sentiment labelling using a lexicon approach, performing text pre-processing (such as language detection, tokenisation, normalisation, vectorisation), building pipelines for text classification models for sentiment analysis, followed by explainability of the final classifier

Data sampling library

The objective is to analyze flight delays in the United States. Data from airlines, airports, and runways will be collected and processed. Machine learning models will be built using logistic regression, decision trees, and XGB classifiers. Visualizations will be created in Tableau, and Excel dashboards and SQL queries will be used for analysis.

WiDS Datathon 2020 on patient health through data from MIT’s GOSSIS (Global Open Source Severity of Illness Score) initiative.

Data sampling library

Data sampling library

Perform Data Sampling with Python

A C library with Python bindings for efficient stratified random sampling from binary buffers or files.

Web scraper to get professor information, and a mass emailer that sends a website with a survey.

This repository contains Natural Language Processing Projects like Sarcasm Detection, Quora Insincere Questions Classification & Edgar Sentiment Analysis

Kaggle Challenge

Predicting house prices in an area

Employing advanced techniques, the project seamlessly integrates binary and multiclass classifiers for character classification. It offers a comprehensive analysis and adeptly addresses challenges in the realm of computer vision.This project was part of my uOttawa Master's in Computer Vision course (2023).

Performing common visual data analytic tasks using Python and D3.js.