This document summarizes a project that uses machine learning to predict whether classroom project requests on the crowdfunding site DonorsChoose.org will be funded based on the teacher's essay. It introduces the problem of teachers spending their own money on classroom supplies, describes using logistic regression on essays vectorized with tf-idf to perform binary classification of funded vs. not funded projects, and discusses future directions including using natural language processing techniques and support vector machines.

1. FundAClassroom.today
Inspire your supporters on DonorsChoose.org
Bobby Kim, Fellow at Insight Data Science

2. What is the problem?
• In 2013-2014, US teachers on average $513 out-of-pocket
for their classrooms.1
• DonorsChoose.org, an online crowdfunded charity is
helping reduce the burden.
• Can we predict whether a project will be funded
or not based simply on a teacher’s essay?
1. http://www.forbes.com/sites/nicoleleinbachreyhle/2014/08/19/teachers-spend-own-money-school-supplies/

3. FundAClassroom.Today

4. Supervised learning – binary classification
• Data: CSV dataset from DonorsChoose.org
• ~200,000 essays going back to 2012 – 75/25 Funded/Not Funded
• Vectorize DonorsChoose essays using tf-idf, build vocabulary of
4000 words
• Model: L2 Logistic Regression
• Validation:
• 5-fold cross validation for model tuning using training set (90%)
• ROC AUC using test set (10%)

9. About me
• PhD in Computational Biophysics, Rice University
• Built models for protein folding simulations using experimental
protein structures and sequence data

10. Future Directions
• Feature Engineering using NLP
• TextBlob – sentiment analysis, lemmatization, parts of
speech tagging, misspelling
• TextSTAT – reading level, subjectivity
• Supervised learning
• SVMs

11. Essay Format
• Paragraph 1 – Open with the challenge facing your
students.
• Paragraph 2 – Tell us more about your students.
• Paragraph 3 – Inspire your potential donors with an
overview of the resources you’re requesting
• Paragraph 4 – Close by sharing why your project is so
important

14. Data Story