Barada01

100-Days-Of-ML-Code

100 Days of ML Coding

1804_python_healthcare

pdf, py, and jupyter notebook files for https://pythonhealthcare.org/

2015

Public material for CS109

2015lab1

2019-Datathon

Directory contains scripts for preprocessing data and creating predictive statistical models.

2020-CS109A

acm-chil-website

A possible framework for the ACM-CHIL 2020 conference website

algorithms

Collection of algorithms and data structures implemented in Python and C++

Alzheimers_Disease_Progression

Code for analysis of ADNI data

applied-machine-learning-intensive

Applied Machine Learning Intensive

Barada01/2020-CS109A

Barada01/Alzheimers_Disease_Progression

Code for analysis of ADNI data

Barada01/applied-machine-learning-intensive

Applied Machine Learning Intensive

Barada01/code_snippets

Barada01/Dash-by-Plotly

Interactive data analytics

Barada01/Data-Analysis

Data Science Using Python

Barada01/Disease-Prediction-from-Symptoms

Disease Prediction based on Symptoms.

Barada01/effective-pandas

Source code for my collection of articles on using pandas.

Barada01/exploratory-data-analysis

Examples for exploratory data analysis for clinical data

Barada01/GNN_for_EHR

Code for "Graph Neural Network on Electronic Health Records for Predicting Alzheimer’s Disease"

Barada01/google-research

Google Research

Barada01/handson-unsupervised-learning

Code for Hands-on Unsupervised Learning Using Python (O'Reilly Media)

Barada01/imgclsmob

Sandbox for training deep learning networks

Barada01/lifelines

Survival analysis in Python

Barada01/luminol

Anomaly Detection and Correlation library

Barada01/Machine-Learning-with-Python

Practice and tutorial-style notebooks covering wide variety of machine learning techniques

Barada01/msbase2020

Barada01/multiple-sclerosis-imc-analysis

Analysis for V. Ramaglia et al. "Multiplexed imaging of immune cells in staged multiple sclerosis lesions by mass cytometry", 2019

Barada01/Prediction-of-Clinical-Risk-Factors-of-Diabetes-Using-ML-Resolving-Class-Imbalance

Being the most common and rapidly growing disease, Diabetes affecting a huge number of people from all span of ages each year that reduces the lifespan. Having a high affecting rate, it increases the significance of initial diagnosis. Diabetes brings other complicated complications like cardiovascular disease, kidney failure, stroke, damaging the vital organs etc. Early diagnosis of diabetes reduces the likelihood of transiting it into a chronic and severe state. The identification and analysis of risk factors of different spinal attributes help to identify the prevalence of diabetes in medical diagnosis. The prevalence measure and identification of diabetes in the early stages reduce the chances of future complications. In this research, the collective NHANES dataset of 1999-2000 to 2015-2016 was used and the purposes of this research were to analyze and ascertain the potential risk factors correlated with diabetes by using Logistic Regression, ANOVA and also to identify the abnormalities by using multiple supervised machine learning algorithms. Class imbalance, outlier problems were handled and experimental results show that age, blood-related diabetes, cholesterol and BMI are the most significant risk factors that associated with diabetes. Along with this, the highest accuracy score .90 was achieved with the random forest classification method.

Barada01/pySankey

create sankey diagrams with matplotlib

Barada01/pysheeet

Python Cheat Sheet

Barada01/python

Python tutorials

Barada01/python-cheatsheet

Comprehensive Python Cheatsheet

Barada01/PythonDataScienceHandbook

Python Data Science Handbook: full text in Jupyter Notebooks

Barada01/pythonVSCode

This extension is now maintained in the Microsoft fork.

Barada01/rpack_pira

Analysis as an R package: Analysis of real-world data to study the Progression Independent of Relapse Activity within a Polish population of Relapsing-Remitting Multiple Sclerosis subjects.

Barada01/scikit-survival

Survival analysis built on top of scikit-learn

Barada01/TabularDataModel

Barada01/TimeGAN

Codebase for Time-series Generative Adversarial Networks (TimeGAN) - NeurIPS 2019

Barada01/tutorial