Code and Datasets for the paper "Identifying Sepsis Subphenotypes via Time-Aware Multi-ModalAuto-Encoder", published on KDD 2020.

Performing Exploratory Data Analysis on LendingClub Dataset

Applied Data Science Project

LLM-based for highly remote sensing data imputation

Data imputation is used when there are missing values in a dataset. It helps fill in these gaps with estimated values, enabling analysis and modeling. Imputation is crucial for maintaining dataset integrity and ensuring accurate insights from incomplete data.

Missing data imputation using the exact conditional likelihood of Deep Latent Variable Models

Applied Data Science Project

A repo to explore how different data imputation methods affect machine bias

JOB-A-THON|MAY(2021)

Data Science stroke prediction project

Uses neural network to predict max bench press weight

Risk Analytics using Python

Using data science and analysis to gain insight on demographic distribution, identify clusters in which various platforms lie, and building predictive models to predict the probability of a user using a particular application.

6th place entry for the Genentech – 404 Challenge

Travail de préparation et d'exploration du dataset d'Open Food Facts.

Data Analytics and ML

Hormone Therapy Decision Support System for Breast Cancer

Three datasets, Drug consumption, labor negotiation, and Heart disease are oversampled and undersampled and 6 algorithsm(SVM, DT, K-Neighbors, RandomForest, MLP, GradientBoosting) are modeled and their accuracies are tested. Performed Friedman to find difference between performances

Instructional materials (course files) for the BBT4206 course (Business Intelligence II) using R. Topic: Data Imputation.