/student-performance

Develop model to predict student performance on math course in secondary education using data science process known as CRISP-DM (Cross Industry Process for Data Mining)

Primary LanguageJupyter NotebookMIT LicenseMIT

Student Performance

This notebook develops model to predict student performance on math course in secondary education using data science process known as CRISP-DM (Cross Industry Process for Data Mining).

The dataset for this notebook can be downloaded here

You can read the post I published on The StartUp here

This project aims to answer the top 3 questions that one curious about Machine Learning ofthen has:

  1. What is the overall Machine Learning workflow?
  2. How to apply Machine Learning model to a dataset?
  3. What is the best overall Machine Learning model that one can be applied to almost all dataset, and what are the tips to imporve overall model performance.

The entire model training will be performed using Jupyter Notebook.

File Description

Student Performance.ipynb -> Jupyter Notebook for step-by-step model training
student-mat.csv -> Student results on math course
student.txt -> Attributes for student-mat.csv (Math course)

Python Libraries Used

numpy, pandas, matplotlib, seaborn, sklearn

Reference:

Machine Learning Accelerator by EliteDataScience