jeh0753 Goto Github PK

boomsetdataclean

Tool for cleaning event attendance data from Boomset

churn_case

Identified customers who are likely to churn using logistic regression and Random Forest classifiers. Evaluated results using Area Under the Curve (AUC) score.

coinyasa

dotfiles

drafttest

fancyimpute

Multivariate imputation and matrix completion algorithms implemented in Python

first_app

first app for ruby rails tutorial

fraud_detection

A Gradient Boosted Model that triages fraudulent events, and recommends whether or not to flag a given event for further investigation.

hello-world

jeh0753.github.io

kmeans

K Means algorithm coded in Scipy and Numpy, tested on Iris dataset.

knn

K Nearest Neighbors algorithms coded in numpy and scipy. Includes both a matrix imputer and a supervised learning KNN algorithm.

kruegerreplication

A Replication of Alan Krueger’s "What Makes a Terrorist?"

minesweeperneuralnet

Teaching a neural net to play mine sweeper

mnist

C-NN using Keras and Theano to categorize handwritten numbers, 99.3% accuracy

models

Models and examples built with TensorFlow

newgittest

night_sky

Unsupervised Learning case study on UFO sighting data.

nn_fun

Some simple examples showing off power of Neural Networks

scala_practice

Simple solutions to scala challenges

scikit-learn

scikit-learn: machine learning in Python

sparsemf

Open source matrix factorization recommender for sparse matrices

t-watch

Real Time Twitter Sentiment Analysis Product

timetofailure

Kaggle competition and Linear Algebra practice

tractor_case

Predicted tractor sales prices at auctions using multiple datasets. Applied linear regression, imputing missing data with k-Nearest Neighbors (kNN), and evaluated results using root mean squared logarithmic error (RMSLE score).