1. Neal Kurande, WinaGodwin Anyanwu Jr., Adam Chau
IRS-ML
Neal Kurande, WinaGodwin Anyanwu Jr., Adam Chau

2. Team Members WinaGodwin Anyanwu Jr. 3rd Year Computer Science Major
Experience: Java, C, Android, Python
Adam Chau
2nd Year Computer Science Major
Experience: Java, Python, JavaScript, SQL
Neal Kurande
3rd year Computer Engineer
Experience: C/C++, Python, Java, JavaScript, MATLAB

3. What is IRS?
The Intelligent Response System aims to create a user interface that leverages the the ITS database to provide feedback that TA’s can use to improve student performance in target subject areas.
This is accomplished by accessing the SQL database in python, pulling specific data, and then converting it to a json file that’s read by a GUI.

4. ITS Student Feedback Loop

5. Last Semester The IRS project was split into two teams
The IRS-ML team worked on pulling data from the database
Accessed question score, rating, and duration
The IRS-GUI team worked on creating a GUI that could visualize this data
This was completed by generating json files on the backend that the front end team would then convert using REACT.

6. Last Semester

7. Last Semester

8. Semester Goals To analyze data using machine learning techniques
To modularize the code to improve future developer experience
To make the system dynamic and update in realtime based on the SQL database

9. General Improvements Code Modularized Implementation Improvements
File structure changed
Code separated into methods
Implementation Improvements
Can choose data by semester
Can select data by pre and post-test (Chapters 1- 7 & Chapter 8 respectively)
Json names are generated based on the data parameters
Used the Github Wiki
Wiki now exists

10. K-Means Clustering
A common form of clustering that creates n-number of clusters from a dataset
K-Means is an iterative algorithm-creates n number of clusters, finds the centroid then remakes the clusters
K-Means needs data preprocessing
Need to first eliminate outliers from the dataset
Normalize all dimensions of the dataset or create appropriate weights for each dimension
Eliminate NaN data points

11. K-Means Pictures

12. Agglomerative Clustering
A form of hierarchical clustering that uses a bottom-up approach
Clusters are grouped together using the euclidean distance
Data for spring 2018 and summer 2018 used to make seven clusters for the graphs

13. Agglomerative Clustering

14. Challenges Downloading and installing Ubuntu Version Control
Insufficient Data
Accessing more data streams
Choosing relevant ML algorithms

15. Next Steps
Incorporating other data streams into the clustering algorithms
Using a different type of clustering or unsupervised learning
Integrating with the cloud to run clustering in real time
Displaying data via the IRS-GUI

16. DEMO!