lanceculnane Goto Github PK

abprint

This prints the z-score and probability of two proportions (A and B) being A-B = 0

abtest

This has lots of functions from "Data Science by scratch" needed to do an A/B Test

bayesian-approach-predicting-well-production

Predicting oil production from horizontal wells using Bayesian hierarchical linear-regression

bitpredict

Machine learning for high frequency bitcoin price prediction

brain_segmentation

brijmohan.github.io

chefnet

ckanext-datarefuge_theme

CKAN Theme for Data Refuge Repository

corn-futres-capstone

donaldtrumpva

Based on user input, this calculates how many minutes Trump would spend on the phone helping veterans, fulfilling his campaign promises.

donor_lifetimes_non_profit

The object of this work is to quantify the factors in influencing donor retention for a non-profit. I stratified the data and applied a patient survival model to predict the lifetimes and the hazards that will surface at different points of a donor's life. Then I calculated the Customer Lifetime Value (CLV) for each donor based on predicted lifetime, cost, and donation history. This allows the non-profit to make donor retention decisions based on data and analytics, not on instincts.

electricity-conservation

My Galvanize Capstone project- feature importance to help understand electricity conservation of school buildings

escher

A self-writing program, like M.C. Escher's "Drawing Hands"; completely unsupervised

finance_apis

A few freely-available finance APIs.

google-interview-university

A complete daily plan for studying to become a Google software engineer.

iremedy

lessons-learned-data-science-interviews

Lessons learned the hard way through over 30+ data science interviews

machine_learning_for_trading

Udacity - Machine Learning for Trading

mastermind3.py

A first rough draft- It is a text-based version of the awesome boardgame, Mastermind

monte-carlo-blackjack-sim

Monte Carlo type of simulation which outputs your % chance of winning given: 1) what the dealer is showing and 2) your first two cards and a strategy similar to the dealers. I used this to quickly determine my probability of winning each hand, so that I could create a strategy of when to split, hit, stay etc. But I found that the house always wins! The house wins because they win in the case where both player and dealer bust, and there is a high probability of that happening.

neuronica_openoffice

newspaper

News, full-text, and article metadata extraction in Python 3

pocketsphinx-scores

python_ocr_tutorial

This is a tutorial on getting OCR running on a simple web server, using python, flask, tesseract-ocr, and leptonica

r-miscs

random_scripts

Random Useful Scripts

scheduling-assistant

A personal assistant that helps you manage your schedule and understands natural language.

seaclass

An Interactive R Tool for Classification Problems

seq2seq

Sequence to Sequence Learning with Keras

spark-install

Installation guide for Apache Spark + Hadoop on Mac/Linux