abebual

Breast-Cancer-Classification-with-PyTorch-and-Deep-Learning

In this project we will build a classifier CNN model to detect ICD breast cancer in histopathological images.

Clinical-Deterioration-Prediction-Model---KNN

Clinical Deterioration Prediction Model - KNN

Clinical-Deterioration-Prediction-Model--Ensemble-Algorithms-

Clinical Deterioration Prediction Model - Bayesian Linear Regression

Clinical-Deterioration-Prediction-Model--Inferential-Statistics

Clinical Deterioration Prediction Model: Inferential Statistics

Clinical-Deterioration-Prediction-Model--Logistic-Regression

Clinical-Deterioration-Prediction-Model--Logistic-Regression

Computer-Vision-Project-Monreader

Computer-Vision-Project-Monreader

Predicting-ICU-Patient-Clinical-Deterioration---Report

For this project, I used publicly available Electronic Health Records (EHRs) datasets. The MIT Media Lab for Computational Physiology has developed MIMIC-IIIv1.4 dataset based on 46,520 patients who stayed in critical care units of the Beth Israel Deaconess Medical Center of Boston between 2001 and 2012. MIMIC-IIIv1.4 dataset is freely available to researchers across the world. A formal request should be made directly to www.mimic.physionet.org, to gain access to the data. There is a required course on human research ‘Data or Specimens Only Research’ prior to data access request. I have secured one here -www.citiprogram.org/verify/?kb6607b78-5821-4de5-8cad-daf929f7fbbf-33486907. We built flexible and better performing model using the same 17 variables used in the SAPS II severity prediction model. The question ‘Can we improve the prediction performance of widely used severity scores using a more flexible model?’ is the central question of our project. I used the exact 17 variables used to develop the SAPS II severity prediction algorithm. These are 13 physiological variables, three underlying (chronic) disease variables and one admission variable. The physiological variables includes demographic (age), vital (Glasgow Comma Scale, systolic blood pressure, Oxygenation, Renal, White blood cells count, serum bicarbonate level, blood sodium level, blood potassium level, and blood bilirubin level). The three underlying disease variables includes Acquired Immunodeficiency Syndrome (AIDS), metastatic cancer, and hematologic malignancy. Finally, whether admission was scheduled surgical or unscheduled surgical was included in the model. The dataset has 26 relational tables including patient’s hospital admission, callout information when patient was ready for discharge, caregiver information, electronic charted events including vital signs and any additional information relevant to patient care, patient demographic data, list of services the patient was admitted or transferred under, ICU stay types, diagnoses types, laboratory measurments, microbiology tests and sensitivity, prescription data and billing information. Although I have full access to the MIMIC-IIIv1.4 datasets, I can not share any part of the data publicly. If you are interested to learn more about the data, there is a MIMIC III Demo dataset based on 100 patients https://mimic.physionet.org/gettingstarted/demo/. If you are interested to requesting access to the data - https://mimic.physionet.org/gettingstarted/access/. Linked repositories: Exploratory-Data-Analysis-Clinical-Deterioration, Data-Wrangling-MIMICIII-Database, Clinical-Deterioration-Prediction-Model--Inferential-Statistics, Clinical-Deterioration-Prediction-Model--Ensemble-Algorithms-, Clinical-Deterioration-Prediction-Model--Logistic-Regression, Clinical-Deterioration-Prediction-Model---KNN © 2020 GitHub, Inc.

Term-Deposit-Marketing-Prediction-5MSttBBp1mlzn2hA

The data comes from direct marketing efforts of a European banking institution. The marketing campaign involves making a phone call to a customer, often multiple times to ensure a product subscription, in this case a term deposit. Term deposits are usually short-term deposits with maturities ranging from one month to a few years. The customer must understand when buying a term deposit that they can withdraw their funds only after the term ends. All customer information that might reveal personal information is removed due to privacy concerns.

Text-Processing-Naive-Bayes-in-Python

In the mini-project, you'll learn the basics of text analysis using a subset of movie reviews from the rotten tomatoes database. You'll also use a fundamental technique in Bayesian inference, called Naive Bayes. This mini-project is based on Lab 10 of Harvard's CS109 class. Please free to go to the original lab for additional exercises and solutions.

ValueInvestor-Predicting-TimeSeries-Data

Value Investor

abebual/character-based-cnn

Implementation of character based convolutional neural network

abebual/uno-card-game_rl

Tackling the UNO Card Game with Reinforcement Learning

abebual/coding-interview-university

A complete computer science study plan to become a software engineer.

abebual/Relax-Inc-Future-User-Adoption-Prediction

A take-home challenge to predict `Relax Inc` service adopted users. Adopted user - a user who has logged into the product on three separate days in at least one seven-day period.

abebual/Breast-Cancer-Classification-with-PyTorch-and-Deep-Learning

In this project we will build a classifier CNN model to detect ICD breast cancer in histopathological images.

abebual/Ultimate-Technologies-Inc--Take-home-Challenge

Data Analysis Interview Challenge

abebual/Determining-Sample-Size

abebual/gans

GANs in slanted land

abebual/Time-spent-between-survey-questions-

Calculate time spent between two consecutive survey questions

abebual/Text-Processing-Naive-Bayes-in-Python

In the mini-project, you'll learn the basics of text analysis using a subset of movie reviews from the rotten tomatoes database. You'll also use a fundamental technique in Bayesian inference, called Naive Bayes. This mini-project is based on Lab 10 of Harvard's CS109 class. Please free to go to the original lab for additional exercises and solutions.

abebual/Clustering-Algorithms-in-Python

abebual/donkeycar

Open source hardware and software platform to build a small scale self driving car.

abebual/Logistic-Regression-and-Hyperparameter-Tuning

Logistics Regression and Hyper-parameter Tuning

abebual/Linear-Regression-Models-in-Python

Linear Regression Models and Prediction using linear regression

abebual/pyGAM

[HELP REQUESTED] Generalized Additive Models in Python

abebual/Inferential-statistics-Bootstrapping

In this mini-project, you'll use medical charge data to make inferences about the population using bootstrapping (ie. simulating repeated re-runs of an experiment.). Bootstrapping is about using computing power to essentially re-run the sample draw again and again and again to see what actually happens.

abebual/Bayesian-School-of-Inference

In a Bayesian Probabilistic programming context, we can build models for systesm and then let the data tell us how likely certain values for our model parameters are.

abebual/Data-Wrangling-MIMICIII-Database

Extract data from MIMIC III Datasets and Organize by Patient

abebual/high-frequency-checks

A Stata template for running high frequency checks of incoming research data at Innovations for Poverty Action

abebual/aws-lambda-developer-guide

The AWS Lambda Developer Guide

abebual/Inferential-Statistics-Frequentism

Having completed this project notebook, you will have hands-on experience: sampling and calculating probabilities from a normal distribution; the correct way to estimate the standard deviation of a population (the population parameter) from a sample; what a sampling distribution is and how the Central Limit Theorem applies; how to calculate critical values and confidence intervals; performing inference using such data to answer business questions, forming a hypthesis and framing the null and alternative hypothesis, and testing thsi using a t-test.

abebual/API-requests-and-data-wrangling

Calling API data and data wrangling project based on data captured from the Internet.

abebual/Data-Wrangling-Using-JSON-Data

Data Wrangling Project Using Json data

abebual/Data-Wrangling-Using-SQL-Database

abebual/docs

TensorFlow documentation

abebual/Functions-that-can-stores-a-matrix-and-its-inverse-of-a-matrix.

Repository for Programming Assignment 2 for R Programming on Coursera

abebual/mimic3-benchmarks

Python suite to construct benchmark machine learning datasets from the MIMIC-III clinical database.

abebual/onecyclelr

One cycle policy learning rate scheduler in PyTorch

abebual/3D-Printer-2018

FAQ's and information about 3D Printers for the 2018 Class

abebual/production-data-science

Production Data Science: a workflow for collaborative data science aimed at production