coreyjwade Goto Github PK

3-point_net_gain

Introducing 3NG, a comprehensive 3-point metric combining accuracy and volume

amazonbooks

pandas/Jupyter Notebook for ranking reviewers

bca_datasets

bis_enrollment

Data Analysis of BIS Enrollment with Regression, StatsModel, Bootstrapping and Confidence Intervals

body_temperature

EDA of Mean Body Temperature Challenging 98.6

bookreviews

BookReviews_DataSet contains first exploration of Helpful_Reviews

coreyjwade.github.io

data

Data and code behind the articles and graphics at FiveThirtyEight

fractions_class

This is a fractions class in progress.

future_user_adoption

Use ML to predict factors most likely to influence future user adoption including RFE and feature_importances_.

hands-on-gradient-boosting-with-xgboost-and-scikit-learn-

Hands-On Gradient Boosting with XGBoost and Scikit-learn Published by Packt

hbs

Early version of YourClock. Originally known as Happy Billionth Second.

hbsclone

heart_disease

helpful_reviews

Predict helpful reviews and star ratings with new helpful rating metric and Logistic Regression.

hospital_readmission

EDA for improving hospital readmission rates.

jupyternotebooks

A few notes from an image recognition project.

linear_regression

Linear regression using Boston Housing dataset

logistic_regression

Brief tutorial on logistic regression for determining male/female based on height/weight.

machine_learning

Notes taken from Data Camp Machine Learning courses

movies

Second Springboard Capstone Project: A Movie Recommender

my_projects

naive_bayes_sentiment_analysis

Natural Language Processing and ML Prediction using Naive Bayes and CountVectorizer.

neural_nets

nyc_cab_fare

Predict New York City cab fares with Neural Nets, Random Forests and LightGBM.

odsc

For Nov. 1 ODSC West Attendees

odsc_west_2023

For those attending Introducing Machine Learning with Scikit-learn on Oct. 30, 2023

pathstream_colab_datasets

Publicly available datasets.

pathstreamdata

probabilistic-programming-and-bayesian-methods-for-hackers

aka "Bayesian Methods for Hackers": An introduction to Bayesian methods + probabilistic programming with a computation/understanding-first, mathematics-second point of view. All in pure Python ;)