sandmhan / student_success

Prediction model for assessing student success.

student_success

Contributors

Andrew Wirz

Gianmarco Huaytan

Mounish Kandumalla

Vinh Tang

Austin Sanders

Overview

Prediction model for assessing student success using the K-Nearest-Neighbor algorithm. The model will take in a plethora of answers provided by the prediction form. These answers will be broken up and passed into each prediction model that correspond with those retrieved features. Once each model is ran, the weighted grade predictions are summed up to provide the final academic performance prediction. From this, we can determine if an individual is expected to have poor academic performance based on the external factors that are not explicitely associated such as household income, alcohol consumption, etc..

How to run model

Final To-Do:

1. Create Final model for data set
2. Create input form for testing full model
3. Write up report (Can do most before code is done)
4. Create presentation
5. Record presentation
6. ???
7. Profit 🤑