<p>You’re looking for an algorithm with identifies the explained variance of each feature.  The first to my mind was Principal Component Analysis.  But the more I thought about it, I personally am more comfortable with decision trees and their interpretability will often give a similar insight.</p>

CC BY-SA 4.0
2024-04-02T13:41:14.220Z
2024-04-02T13:41:14.220Z
foreverska
<p>I have a dataset with several qualitative and quantitative attributes, including age, location (longitude, latitude), city, parent occupation, family size, GPA etc. My task is to find the attributes/factors that contribute the highest towards the academic performance of the students. Which machine learning algorithm is the most suitable for this task?</p>
<p>A friend suggested using clustering algorithm that can segment the students into different performance groups, but I think that will not solve my problem.</p>

<machine-learning><prediction><algorithm-request><features>
Machine Learning Algorithm for identifying the factors contributing to academic performance of students
Dawood Ahmad
# Machine Learning Algorithm for identifying the factors contributing to academic performance of students

(Asked by: username_0 on 2024-04-02)

I have a dataset with several qualitative and quantitative attributes, including age, location (longitude, latitude), city, parent occupation, family size, GPA etc. My task is to find the attributes/factors that contribute the highest towards the academic performance of the students. Which machine learning algorithm is the most suitable for this task?

A friend suggested using clustering algorithm that can segment the students into different performance groups, but I think that will not solve my problem.

**Tags:** machine-learning, prediction, algorithm-request, features


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## 1 answers

### Answer by username_1 on 2024-04-02. Score: 1

You’re looking for an algorithm with identifies the explained variance of each feature. The first to my mind was Principal Component Analysis. But the more I thought about it, I personally am more comfortable with decision trees and their interpretability will often give a similar insight.


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