Code for analyses in "Obesity and risk of female reproductive disorders: A Mendelian Randomisation Study"

Code to reproduce analysis and figures for 'Genetic mapping of etiologic brain cell types for obesity' (Timshel, eLife 2020)

OCS (BP): Examine global patterns of obesity across rural and urban regions

Conducted research and developed a system under Dr Booma Poolan Marikannan on provisional analysis for obesity issues using numerous data mining techniques by using a past medical dataset from the Kaggle.  Executed the project using tools such as PyCaret, Pandas, NumPy, Matplotlib, Seaborn, Scikit-Learn, and Pickle, and evaluated the classificat…

Estimation of Obesity Levels

Repository to preview, describe, and link to multiple health-related Tableau dashboards.

Analysis of Spatial and Temporal Data Course Final Project - Obesity Classification

Some Awsome Things

This repository contains the required code to reproduce the results reported on our paper entitled: Explaining the widening distribution of Body Mass Index: A decomposition analysis of trends for England, 2002/04-2012/14

CHAMP data wrangling codes: cleaning, reshaping and quantitive analysis of child measurement data

This repository demonstrates the usage of a Random Forest Model to to determine risk factors that lead to obesity.

Classification of Obesity Status in Indonesia Using XGBoost & ADASYN-N Method

Python & R scripts collection for AdipoAtlas project

Using D3, this repository takes the data from the US Census Bureau's 2014 ACS 1-year estimates and creates animated visualizations from it.

Scripts for assessing longitudinal quantitative traits in UKBIOBANK-linked primary care data

This repository contains the documentation for reproducibility of the study "Preoperative atelectasis in patients with obesity undergoing bariatric surgery: a cross-sectional study".

[In Production] Adaptation of Nathaniel Daw's Two-Step Sequential Learning Task. Designed for a study of reward prediction for food with college undergraduates.

Use of OLS method, Linear Regression, K-means, Agglomerative Hierarchical, DBSCAN, Decision Tree, Random Forest, Logistic Regression, Support Vector Classifier, K-nearest neighbors, and Naive Bayes algorithms in the case study to estimate obesity levels.