Pinned Repositories
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's Repositories
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