Name: Plamen Rabadzhiyski
Type: User
Company: Data Science AI
Bio: Lean Six Sigma Master Balck Belt turned data scientist who tries to inspire people to use data and technology.
Twitter: p_rabadzhiyski
Location: Sofia, Bulgaria
Blog: plamen.ai
Plamen Rabadzhiyski's Projects
AIND Term 2 -- Lesson on Convolutional Neural Networks
š Papers & tech blogs by companies sharing their work on data science & machine learning in production.
Source code for 'Business Case Analysis with R' by Robert D. Brown III
Some useful documents that help me in my daily work.
Further student resources for DrivenData's 'Machine Learning with the Experts: School Budgets' DataCamp course.
Repository for experimenting with data.
Repo for the Deep Learning Nanodegree Foundations program.
Analyzing message data for disaster response.
Contains files related to content and project of DSND Term 2
Example using Firebase authentication with Shiny
Free R-Tips is a FREE Newsletter provided by Business Science. It comes with bite-sized code tutorials every week.
A series of Jupyter notebooks that walk you through the fundamentals of Machine Learning and Deep Learning in Python using Scikit-Learn, Keras and TensorFlow 2.
Graph visualizer for JIRA tickets' dependencies
A Python package for Bayesian forecasting with object-oriented design and probabilistic models under the hood.
Tool in pypi to calculate the most popular project management metrics like Earned Value, Planned Value, Schedule Variance, Cost Variance. Schedule Performance Index, and Cost Performance Index.
Example Apps for Polished
Data Science Projects
Predicting customer churn with the help of Spark framework.
Pymodeltime offers a unified framework tailored to address a broad spectrum of requirements, including time series forecasting and various machine learning models.
Time series easier, faster, more fun. Pytimetk.
Config files for my GitHub profile.
Project part of the Udacity Data Science Nanodegree Program
An end-to-end tutorial creating an R Shiny app that uses the reticulate package with Python 3
Predicting customer churn with Spark.