mhichar Goto Github PK

100-pandas-puzzles

100 data puzzles for pandas, ranging from short and simple to super tricky (60% complete)

amazon-kinesis-client-python

Amazon Kinesis Client Library for Python

course-v3

The 3rd edition of course.fast.ai

election-transparency

A Data4Democracy community working to make elections and elections data more transparent

employee-attrition-aif360

Walkthrough the data science life cycle with different tools, techniques, and algorithms. Use AIF360, pandas, and Jupyter notebooks to build and deploy a model on Watson Machine Learning.

epi-to-lc

Mappings of problems from the book Elements of Programming Interviews (EPI) to Leetcode

gdl_code

The official code repository for examples in the O'Reilly book 'Generative Deep Learning'

gym-bandit-environments

Multi-armed bandits environments for OpenAI Gym

indeed-resume-scraper

Python resume scraper for Indeed.com

ira_ads

An analysis of the political ads released by the "Russian Trolls" prior to the 2016 U.S. Presidential Election.

kernels

numpy-100

100 numpy exercises (100% complete)

pokedex-demo

Rasa Demo for a digital assistant for pokemon

public-python-notebooks

This is my public python notebook repository!

russian-ad-datasets

Datasets derived from the 2018 IRA ad release.

russian-ad-pdfs

Original PDFs from the 2018 IRA ad release.

scikit-survival

Survival analysis built on top of scikit-learn

scipy-2017-tutorial-pandas

SciPy 2017 Pandas Tutorial

snap

Stanford Network Analysis Platform (SNAP) is a general purpose network analysis and graph mining library.

startbootstrap-portfolio-item

A Bootstrap portfolio item page template created by Start Bootstrap

stockmarketgan

Stock Market Prediction Using Unsupervised Features

stockpredictionai

In this noteboook I will create a complete process for predicting stock price movements. Follow along and we will achieve some pretty good results. For that purpose we will use a Generative Adversarial Network (GAN) with LSTM, a type of Recurrent Neural Network, as generator, and a Convolutional Neural Network, CNN, as a discriminator. We use LSTM for the obvious reason that we are trying to predict time series data. Why we use GAN and specifically CNN as a discriminator? That is a good question: there are special sections on that later.

stream-model

wine-quality-score

Prediction of wine quality using only partial least squares regression (PlsRglm package)