This repository contains files associated with a report submitted as assessed coursework for Machine Learning and Predictive Analytics - UFCFMJ-15-M at the University of the West of England, Bristol.
Higher Education Institutions (HEI) have a vested interest in ensuring that students are successful in their studies. The motivation is multi-faceted – by ensuring positive student experience and good student outcomes, HEIs reap the benefits of an enhanced reputation, improved metrics affecting league table placement and more stable student numbers. HEIs are subsequently able to attract and retain more students and further improve their educational services.
Learner Analytics is a field of research which can support students and HEIs by improving student experience, enriching learning and teaching, identifying at-risk students, and bettering retention and progression. This exploratory report investigates several predictive learning algorithms trained on student engagement data to predict multiclass and binary outcomes.
It is proof-of-concept in nature, exploring the viability and generalisability of learning algorithms on engagement behaviour during a course. It compares several models and briefly discusses the results in the context of the HEI.
The Open University Learning Analytics dataset is used to train, validate and test classification learner models.
This repository has not been prepared for general use. It is a repository of working files and notebooks which were used to produce the report. Notebooks contain code, commentary, thoughts and ideas.
They are not intended to be read as a narrative, although they are referenced in the report and supply additional information.
It is my intention to keep working on some of the findings and additional research questions.
As such, the code is presented as-is with no guarantees.
All code was written in Python 3.11.0.
The following packages are required:
- numpy
- pandas
- matplotlib
- seaborn
- sklearn
- additional packages may be required
data folder contains the data used in the report as well as generated files coursework folder contains the ipython notebook used to generate the report in Word format
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there will be minor variations between the Word Document and the ipython file
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the ipython file does not contain any code code folder contains various ipython notebooks used to explore the data and generate the report
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V1 folder contains the first version of my code
- notebooks are named in the order they were created / used
- some code needs to be updated to function properly
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V2 folder contains the second version of my code
- notebooks are named in the order they are meant to be read
- these notebooks are more complete and more in line with the report content
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V2/00_data_description contains a description of the data used in the report
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V2/00_introduction contains the introduction to the report
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V2/01_data.ipynb contains initial data exploration
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V2/02_EDA.ipynb contains exploratory data analysis
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V2/03_Features.ipynb contains feature engineering code and narrative
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V2/04_1_model_prep.ipynb contains code to prepare data for modelling
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V2/04_2_model_reduce.ipynb contains code to reduce the number of features and feature space (PCA)
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v2/04_3_model_fit.ipynb contains code to fit models
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V2/04_4_model_tune.ipynb contains code to tune models
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V2/04_5_model_test.ipynb contains code to prepare test scenario
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V2/04_6_model_test_model.ipynb contains code to test model on 50/100/150 test sets
Your report (in a form of a discussion paper) should cover the following points:
- Discuss a machine learning problem given your chosen application; identify the prob-lem, the requirements for a predictive model and its impact.
- Describe and analysis a dataset and its characteristics; size, representation and at-tributes,
- Discuss whether bivariate or multivariate analysis is most suitable for your predictive model.
- Choose/apply (a) learning algorithm(s) and identify its/their categories; supervised, un-supervised, semi-supervised.
- Analytically evaluate your choice of machine learning solution; its suitability, cost, and apply an error evaluation metric to justify your choice, e.g., classification accuracy of classification problems, MSE and/or R^2 (R squared) for regression models, etc.
- Choose a learning algorithm which you think is less suitable for your predictive model and justify your “rejection” reasons.
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