My Personal Machine Learning and Data Science Training Repository
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Updated
Mar 17, 2017 - Python
My Personal Machine Learning and Data Science Training Repository
Package provides python implementation of statistical inference engine
A little exploration of R's power for statistical inference
Statistical learning and inference algorithms implemented in Python 3
Descriptive and predictive analytics on prescription volume for three outpatient pharmacies.
Code and data for the KDD2020 paper "Learning Opinion Dynamics From Social Traces"
Reproduce analyses from "Efficient nonparametric statistical inference on population feature importance using Shapley values"
Hypothesis and statistical testing in Python
Implementation of "Statistical & ML Approaches to Depression in the U.S."
Hidden Markov Model (HMM) is a statistical Markov model in which the system being modelled is assumed to be a Markov process Z with unobservable "hidden" states. HMM assumes that there is another process X whose behaviour "depends" on Z. The goal is to learn about Z by observing X.
Code and data for the CIKM2021 paper "Learning Ideological Embeddings From Information Cascades"
Python code for kernel inference - optimal estimation of covariance functions
Python package for probabilistic delimiter detection.
Perform inference on algorithm-agnostic variable importance in Python
This repository contains code which accompanies the paper: Assessing Model Behaviour on Extreme Counterfactuals (https://www.researchgate.net/publication/360912277_Assessing_Model_Behaviour_on_Extreme_Counterfactuals)
Characterize exogenous particles in blood-sample image-sets using machine learning.
Built a Diabetes diagnosis prediction model and deployed it on GCP using Dockers and Kubernetes rendering done by Flask API.
Implementation of "Testing Directed Acyclic Graph via Structural, Supervised and Generative Adversarial Learning" (JASA, 2023+)
A Bayesian model of series convergence using Gaussian sums
Data Activist Project on Gender Pay Gap using boxplot data visualizations
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