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cs231n.github.io

Public facing notes page

datasci_course_materials

Public repository for course materials for the Spring 2013 session of Introduction to Data Science, an online coursera course.

dc-michelin-challenge

Guess the correct restaurants on DC's Michelin Guide by 10/12/16. Win $500 and an invite to the French Ambassador's residence.

decisiontrees_lab

In this lab, we will discover how to apply decision trees to regression and classification problems. In particular, we will use regression trees to predict the price of used cars and classification trees to predict the acceptability of those cars.

deep-learning-models

Keras code and weights files for popular deep learning models.

dsi-dc-1

Data Science Immersive - DC

ensembles_bagging_lab

Compare the performance of simple Decision Trees vs also using Bagging.

find_phone_task

predicting-west-nile-virus

predicting_uber_demand

project-3

project-4

Using different modeling techniques (logistic, kNN, cross-validation, grid search, feature scaling) and the stumbleupon kaggle data set, create models to predict whether a link is evergreen content.

project-5

Create models using Logistic Regression, kNN Classifiers, Decision Tree Classifiers, Bagging, and GridSearchCV to predict whether passengers on the Titanic survived. The data set is from the Kaggle Titanic competition and was obtained through SQL queries.

project-6

Obtained the data by scraping the IMDB Top 250 movies page, and every user review for the top 100 movies. We then using stemming and a TfidfVectorizer, and use the tokenized words to build models to predict the review score for each review. Model types include DecisionTreeClassifier, RandomForestClassifier, ExtraTreesClassifier, and AdaBoostClassifier.

project_1

support_vector_machines_lab

thinkster-django-angular-boilerplate

Boilerplate for the Thinkster.io Django and AngularJS tutorial.

travel_app

usajobs_json_parser

Takes in multiple JSON files obtained through the USA Jobs API, parses the data into a DataFrame and exports to .csv