Skip to content
Developed a model that can predict the likelihood that a given student will pass, thus helping diagnose whether or not an intervention is necessary. Project 2 of the Udacity Machine Learning Nanodegree Program - details included.
Branch: master
Clone or download
Fetching latest commit…
Cannot retrieve the latest commit at this time.
Permalink
Type Name Latest commit message Commit time
Failed to load latest commit information.
ModelComplexities.png
README.md
student-data.csv
student_intervention.html
student_intervention.ipynb
svm.png
svm2.png

README.md

Student-Intervention-ML

This Project Has Been Confirmed As Successful By A Udacity Reviewer.

What I Did

Developed a model that can predict the likelihood that a given student will pass using classification. Thus helping diagnose whether or not an intervention is necessary. I tested three different classifiers on the data: Naive Bayes, Gradient Boosting, and Support Vector Machines. I chose the most optimal classifier through analysis of its results and then tuned it with a grid search and their F1 Scores to find the optimal parameters for prediction. The details of the project can be seen in the Python notebook provided in this repository.

What I Learned

From this unit of the Nanodegree program and project, I learned about the various classification algorithms that are used for Machine Learning. By analysis of multiple classifiers on the dataset, I was able to understand the strengths and weaknesses of various classifier algorithms. I reinforced my current understanding by performing model fitting, data preperation, and using the F1 score to optimize classifier parameters alongside grid search.

Things I learned from this project:

  • The strengths and weaknesses of various Machine Learning classifiers (e.g. Naive Bayes, Decision Trees, SVM, etc.)
  • General applications of multiple Machine Learning classifiers (Spam Detection, Student Intervention, etc.)
  • Evaluating performance of various ML classifiers to find the best model for the situation (training time, testing time, F1 scores, etc.)
  • Reinforced concepts learned such as model fitting, data preperation, splitting into training & testing sets, and model tuning.

alt text

You can’t perform that action at this time.