This project focuses on applying advanced simulation methods for derivatives pricing. It includes Monte-Carlo, Variance Reduction Techniques, Distribution Sampling Methods, Euler Schemes, and Milstein Schemes.

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

Fast Online Triplet mining in Pytorch

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.

BI Master - Automated methods to detect and classify human diseases from medical images. Convolutional Neural Network, Data Augmentation, Transfer Learning, Tensorflow, Keras, Xception, ImageNet, StratifiedKFold.

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.

Loan Approval Prediction

CSCI316 Group assignment 1

Data sampling library

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.

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

Perform Data Sampling with Python

Credit card fraud detection using various sampling methods and machine learning algorithms.

Loan approval system using svm

Stratified Sampling and Quadratic Regression to investigate the Impact of Severity Types and Nitrogen Level on Gudgrass Growth

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

Predicting house prices in an area

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