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spark-py-notebooks

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

spark-timeseries

A library for time series analysis on Apache Spark

sparklingpandas-ex

Examples of using SparklingPandas and Pandas with PySpark

spatialanalytics

Where 2.0 Workshop Code: Spatial Analysis of Tweets using Hadoop, Pig, Python & Mechanical Turk. Slides here: http://www.slideshare.net/kevinweil/spatial-analytics-where-20-2010

sql-server-samples

Official Microsoft GitHub Repository containing code samples for SQL Server

sql-tutorial

A Gentle Introduction to SQL Using SQLite

sr-captcha

Article describing how the technical means Silk Road 1's captcha was broken.

srepho.github.io

srlie

The SRL-based Open IE extractor. A principal component of Open IE 4.0.

staff_attrition_experiment

stan

Stan development repository (home page is linked below). The master branch contains the current release. The develop branch contains the latest stable development. See the Developer Process Wiki for details.

stanbol-uima-engine

Apache UIMA engines for Apache Stanbol

stancon_talks

Talks from StanCon

stanford-cme-106-probability-and-statistics

VIP cheatsheets for Stanford's CME 106 Probability and Statistics for Engineers

stanford-cs-221-artificial-intelligence

VIP cheatsheets for Stanford's CS 221 Artificial Intelligence

stanford-cs-229-machine-learning

VIP cheatsheets for Stanford's CS 229 Machine Learning

stanford-cs-230-deep-learning

VIP cheatsheets for Stanford's CS 230 Deep Learning

stanford-project-predicting-stock-prices-using-a-lstm-network

Stanford Project: Artificial Intelligence is changing virtually every aspect of our lives. Today’s algorithms accomplish tasks that until recently only expert humans could perform. As it relates to finance, this is an exciting time to adopt a disruptive technology that will transform how everyone invests for generations. Models that explain the returns of individual stocks generally use company and stock characteristics, e.g., the market prices of financial instruments and companies’ accounting data. These characteristics can also be used to predict expected stock returns out-of-sample. Most studies use simple linear models to form these predictions [1] or [2]. An increasing body of academic literature documents that more sophisticated tools from the Machine Learning (ML) and Deep Learning (DL) repertoire, which allow for nonlinear predictor interactions, can improve the stock return forecasts [3], [4] or [5]. The main goal of this project is to investigate whether modern DL techniques can be utilized to more efficiently predict the movements of the stock market. Specifically, we train a LSTM neural network with time series price-volume data and compare its out-of-sample return predictability with the performance of a simple logistic regression (our baseline model).

start-summit-2017-blockchain-machine-learning-workshop

Presentation Material and Sample Code from the DataReply Blockchain and Machine Learning Workshop at START Summit 2017 in Switzerland

statistics-for-data-scientists

Code and data associated with the book "Statistics for Data Scientists: 50 Essential Concepts"

statlearning-notebooks

Python notebooks for exercises covered in Stanford statlearning class (where exercises were in R).

stock-predictor

Listens for Stock news on Twitter, performs sentiment analysis by mining information from an online news source, performs supervised predictive modeling and suggests buy or sell decisions of the stock. Computes portfolio returns over time.

stock-selection-a-framework

This project demonstrates how to apply machine learning algorithms to distinguish "good" stocks from the "bad" stocks.

stock_price_prediction_with_rnns

Implemented Recurrent Neural Networks in Keras with candlestick stock price information to predict future price movement.

stock_sentiment_analysis

Analyses sentiments about company based on Finviz news

stockpredictor

Predict stock movement with Machine Learning and Deep Learning algorithms

stocks

Project to fetch and analyse stock data

stocks_candlestick_chart

A simple candlestick chart built using Python and Bokeh

store_sale

1000+ Store Sale Forecasting (Rossmann Kaggle Data Science Challenge, RMSE 0.11)

storm

Distributed and fault-tolerant realtime computation: stream processing, continuous computation, distributed RPC, and more