bkg6

shap

A game theoretic approach to explain the output of any machine learning model.

data

Data and code behind the articles and graphics at FiveThirtyEight

ipython

Official repository for IPython itself. Other repos in the IPython organization contain things like the website, documentation builds, etc.

Machine-Learning-Tutorials

machine learning and deep learning tutorials, articles and other resources

bootswatch

Themes for Bootstrap

amazon-dsstne

Deep Scalable Sparse Tensor Network Engine (DSSTNE) is an Amazon developed library for building Deep Learning (DL) machine learning (ML) models

ipython-notebooks

A collection of IPython notebooks covering various topics.

spark-py-notebooks

Apache Spark & Python (pySpark) tutorials for Big Data Analysis and Machine Learning as IPython / Jupyter notebooks

DAT8

General Assembly's 2015 Data Science course in Washington, DC

awesome-algorithmic-trading

A curated list of awesome algorithmic trading frameworks, libraries, software and resources

DIY-Data-Science

Machine Learning Tool Guides and Theory Notes

stock_market_prediction

Team Buffalox8 predicts directional movement of stock prices.

Spark-practice

Apache Spark (PySpark) Practice on Real Data

stats-110

R-Shinyapp-Tutorial

gnidart

Trading-Algorithms

This repository contains the customized trading algorithms that I have created using the Quantopian IDE.

PyDataSeattle

For the pandas tutorial at PyData Seattle: https://www.youtube.com/watch?v=otCriSKVV_8

awesome-stock-trading

Curated list of resources for traders, such as tools, websites, and books related to trading stocks.

PySparkCookbook

A repository for a PySpark Cookbook by Tomasz Drabas and Denny Lee

kaggle-allstate

Allstate Purchase Prediction Challenge on Kaggle

ggvis-maps

Examples of various kinds of maps in ggvis (with & without shiny)

ggShinyApp

Shiny app to build custom ggplot themes

plotly-shiny

Templates for embedding interactive plotly graphs in Shiny apps

Competitions

Competition repository

data-science-curriculum

My data science curriculum

SIR-shiny

A shiny app for teaching dynamic programming - A Susceptible Infected Recovered (SIR) Model

ML_StockPrediction_BattleFin

Machine Learning Kaggle BattleFin Stock Prediction competition

QuantInsti_InterIIT2023

This project examines the effectiveness of candlestick patterns and price action trading strategies in the context of the Indian equities market. Through developing and testing three distinct hypotheses, we provide a nuanced and insightful examination of these techniques and contribute to the ongoing discourse on financial predictions.

data.gov.in

Exploring the datasets