Student-Performance prediction

Purpose of the project:

Almost all performance prediction models foucus on predicting how well the student will perform at the end of the year/semester in terms of grades. Using the past students' grades, they predict the upcoming students grades. However, this model is a bit different. Its purpose is to predict whether a student will pass/fail the course during the semester based on his/her grades during two stages: 20% and 50% of the course work. This can help the student to decide whether to compelete or withdraw the course in order not to deduct his/her GPA.

##Project data:

In this project, we used the Student-Performance-and-Engagement dataset offered by Western-OC2 lab. We used the binary-Student-Performance dataset and Muliclass-Student-Performance datasets only.

The data consists of the following features:

Feature	Description	Type	Value/s
Student Id	Student identifier	Nominal	std000, …, std485
Quiz01	Quiz1 Mark	Numeric	0,…,100
Assign.01	Assign.01 Mark	Numeric	0,…,100
Midterm	Midterm Mark	Numeric	0,…,100
Assign.02	Assign.02 Mark	Numeric	0,…,100
Assign.03	Assign.03 Mark	Numeric	0,…,100
Final Exam	Final Exam Mark	Numeric	0,…,100
Final Grade	Total Final Mark	Numeric	0,…,100
Student Category	Final Grade	Nominal	G,W

Project steps:

During building this project, we followed a scientific paper which addressed the same issue. All out hyperparameters are taken from this paper. The paper can be found here.

• Data analysis steps:

  • There were not many steps during our data analysis step as the data was already clean and normalized. However, we performed 4 steps:

    • We drew the histograms of all our attributes to get an insight about their distributons.

    • We performed PCA (Principal components analysis) to reduce the features to only 2 features for data plotting and to decide which features contribute the most to the data or which features holds the greater variance in our data.

    • We noticed that our target feature (Final Grade) is not balanced, so we cannot use accuracy as our model performance metric. So, we decided to deal with issue in two ways:

      • We used oversampling using SMOTE and ADASYN in order to balance the categories and then use accuracy as performance metric.
      • Leave the categories as they are ans use recall, percision, F1, and ROC-AUC scores as performance metices as they can deal with skewe categories.

• Training steps:

  • We used five learning methods to train out model:

    • Support-Vector-Machine
    • Random Forest
    • K-nearest neighbors.
    • Multiple layer perceptron.
    • Naive Bayes.

  • All of the hyperprameters can be found in the paper as mentioned above.

Installation:

Just download the notebook, import it in Jupyter or Colab and run it!