sandeshregmi Goto Github PK

covid-ga-model

Code for fitting and simulating a stochastic, mechanistic model of COVID-19 transmission in Georgia.

creclinicaltrials

cstab

Selection of Number of Clusters via Resampled Normalized Cluster Stability

d4

Deep drug-drug interaction discovery and demystification (D4)

dat18nyc

General Assembly repo for Data Science 18

data_mine_journal

In 2003, I began my practice of journaling: writing down my thoughts, plans, fears, and introspections into a simple text file. Since then, I've written 631,597 words into 787 total journal entries, spanning 17 years. Journaling is an invaluable tool to better understand one's own mind: to analyze decision making patterns, identify trends, and find purpose in this confusing world. Over the years, I've conducted plenty of ad-hoc deep-dives into my journal. For example, when recovering from an injury, I used my journal to identify triggers that caused aggravation and plotted those over time to better understand how my recovery process was being shaped by my behavior. What if there was a way to do this type of analysis programmatically? Luckily, there is - it's called Natural Language Processing - and there are plenty of libraries that allow us to parse and analyze vast amounts of text. We will use TextBlob, a popular python NLP library built on top of NLKT and Pattern, to calculate the following: Descriptive statistics: words per year, entries per year, overall words. Most common words using word clouds and N-gram analysis (combinations of words) Parts of Speech tagging (Noun, verb, adjective).  Sentiment polarity analysis - how has the tone (positive/negative) changed over time?

data_science_delivered

Observations from Ian on successfully delivering data science products

datascientistdiary

Repository created for blog purposes.

deep-learning-from-scratch-pytorch

Deep Learning from Scratch with PyTorch

deep-learning-with-python-notebooks

Jupyter notebooks for the code samples of the book "Deep Learning with Python"

deepai

Detection of Accounting Anomalies using Deep Autoencoder Neural Networks - A lab we prepared for NVIDIA's GPU Technology Conference 2018 that will walk you through the detection of accounting anomalies using deep autoencoder neural networks. The majority of the lab content is based on Jupyter Notebook, Python and PyTorch.

denoise-transformer-autoencoder

depression-detection

Comparing Selective Masking Methods for Depression Detection in Social Media

diabetes_readmission

Predicting hospital readmission for patients with diabetes

disaster-tweets-classifier

Classifying tweets about or not about disasters

dissertation

PhD dissertation code for 3 studies

doctorconsultationfeesprediction

Predict Doctor's Consultation Fees - Analytics India Magazine

ds_portfolio

ecommerce-webapp

E-Commerce site project in Python Django focuses mainly on dealing with online shopping, and order management

eda_app

Python Web App to Automate EDA

employee-bg-checks

epi-mcmc

Scripts for epidemiological modeling of an epidemic

explainingtitanic

A Dash dashboard app that that displays model quality, permutation importances, SHAP values and interactions, and individual trees for sklearn RandomForestClassifiers.

fairness_tutorial

KDD 2020 tutorial on ML Fairness

fhir-sdoh-clinicalcare

Gravity project FHIR implementation guide covering social determinants of health, including data capture, recording SDOH issues and making referrals

financial_news_analysis

fine-tuning-bert

fraud-detection-handbook

Reproducible Machine Learning for Credit Card Fraud Detection - Practical Handbook

frauddetection

Data Anomaly and Fraud Detection with Python and R

gee

Pytorch implementation of GEE: A Gradient-based Explainable Variational Autoencoder for Network Anomaly Detection