1. Funding
Education
through
Donors
Choose
General Assembly 2016
Fernando Hidalgo

2. Problem
Description
Task: Predict Whether a Donor’s Choose
Project will get Funded
Experience: Donor’s Choose Data from Sept
2002 - Currently
Performance: Classification Accuracy, the
Number of correct prediction out of all
predictions made.

3. The Data

4. Labels
Completed:
592,757
&
Expired:
261,536
Class Skewness:
Use F1 Score as a way to use recall
and precision in check.
Baseline:
.69

5. Features Abbreviations Descriptions
total_price_excluding_optional_support Total Price of the Project
(integer)(dollars)
students_reached # of students that are project reaches
(integer)
school_type Types of School:
Charter, magnet, year_round, nlns, kipp,
Charter_ready_promise
(categorical)
date_posted Day that the project was posted
(categorical)
resource_type Type of Resources the project asks
(categorical)
grade_level The Grade Level of the Project
(categorical
poverty_level Poverty Level (categorial)
school_state From what state the project is posted
(categorical)
Eligible_double_your_impact_match
Whether it was eligible to be matched
(categorical
teacher_prefix The Prefix of the Teacher Posting
(categorical)
primary_focus_area The Project’s Primary Area of Focus
(categorical)
primary_focus_subject The Project’s Primary Subject of Focus
(categorical)
Original
Features

6. Feature
Engineering
New Features Description
price_per_student total_price/students_reach
ed
project_length Date_expiration -
date_posted
month_posted Extracted from
date_posted
day_posted Extracted from
date_posted

7. Visualizations

9. Rate of Projects Funded to Total Projects per Resource

10. Rate of Projects Funded to Total Projects per Month

11. Rate of Projects Funded to Total Projects per Grades

12. Rate of Projects Funded to Total Projects per Primary Focus Area

13. Rate of Projects Funded to Total Projects per Teacher Prefix

14. Rate of Projects Funded to Total Projects per Poverty Level

15. Relationship Between Project Length and Funding

16. Relationship Between Project Price and Funding

17. Relationship Between Price per Student and Funding

18. Predictive
Model

19. The 3 Models:
1. AdaBoost
2. Random
Forest
3. Logistic
Regression

20. GridSearch
Accuracy Scores
using F1 Score Metric
Model Accuracy Best Parameter
Random Forest 0.759 Criterion: Entropy
AdaBoost .7676 N_estimators: 60
Logistic Regression 0.811 Penalty: L2
Simplest Model with Best Score:
Logistic Regression

21. Checking Feature
Significance:
Using Random Forest Classifier
The top 5 Features Seem to Have Most of the
Predictive Power

22. Using Only the 5 Most Significant
Features
1. Total_price_excluding_optional_support
2. Eligible_double_your_impact_match
3. Resource_Type_Books
4. Resource_Type_Technology
5. price_per_student
New Score with
Logistic Regression:
.8171

23. Overview
● Model Improvement of .1271 over the baseline using
Logistic Regression with F1 Score.
● Most of Predictive Power Lies in 5 Features
● Ethical Implications:
○ The features with the most predictive power are not
ones that can be changed without fabrication

24. Model Improvements
Add Prescriptive Data:
Project Essays
Project Materials
Use Data Based on Location:
Census
Skewed Data:
Find Reasons
Methods