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R (RStudio) | ggplot2 | k-means clustering | Analyzed the 2015-16 and 2016-17 academic result datasets of all my batchmates using cluster analysis to segment them into groups, detect outliers in their academic performance and analyze it for better refinement of habits to help improve their future academic results.

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Students-Academic-Result-Analysis

The project aimed to present and justify the capability of the data mining process with k-means clustering algorithm in the context of the higher education system.

R (RStudio) | ggplot2 | k-means clustering

Image 1: Data-flow diagram (Level 1)

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Image 2: Data (.CSV format) for the 2016 - 2017 academic year (student roll number and percentage)

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Image 3: Importing data into R

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Image 4: Clusters in scatter plot (2016 - 2017 academic year data)

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Image 5: Result from the console

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Image 6 and 7: Determining the clusters and optimal value of k using the elbow graph

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Image 8 and 9: Finding the cluster's centers for 2016 - 2017 data

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• This project was an effort in motivating to advance the traditional educational process with the help of a data mining technique. The presented model can act as a guideline for the higher education system to improve its decision-making processes with the help of generated insights.

• This improvement may bring a lot of advantages to the higher education system by maximizing educational system efficiency, increasing student's promotion rate, retention rate, transition rate, education improvement ratio, success, learning outcome, minimizing the cost of system processes, and decreasing student drop-out rate.

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R (RStudio) | ggplot2 | k-means clustering | Analyzed the 2015-16 and 2016-17 academic result datasets of all my batchmates using cluster analysis to segment them into groups, detect outliers in their academic performance and analyze it for better refinement of habits to help improve their future academic results.

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