This Python program uses machine learning tools to analyze and graph data that represent the performance results for students. It has functionalities of reading a dataset, categorizing student performances and displaying statistics to each group where the users can be able to add more information about students. KMeans clustering is utilized in the program to group students according to their scores, which allows determining peers who are closest with almost equal results.
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Data Preprocessing: Rat works with pandas and scikit-learn to read student performance data, turning the categorical variables into numeric kinds of values.
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Clustering: Allows dividing students into groups based on their average scores using KMeans clustering, so that the users may choose intervals.
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User Interaction: Provides an interface easy to work with, which allows a user easily enter new information about the student and provides relevant data on other students who perform in close proximity.
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Data Visualization: With Matplotlib, it generates a scatter plot containing the clusters created by KMeans algorithm.
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Clone the repository.
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Take care that the necessary libraries are installed (pandas, scikit-learn and matplotlib).
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The data is only analyzed and interactable when you run the program, as instructed (python main.py).
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Best to use on the IDE Spyder from Anaconda Navigator due to its flexability to work and visualize data.
- Go ahead and open issues, propose changes or submit pull requests to enhance the functionality of this program as well as how it will be used.
The repository is licensed under the MIT License.