Career Prediction Model for Computing College Students in Kenya

Abstract

Choosing an acceptable professional career route is one of the most essential decisions that students must make in our society today. The increasing number of alternative jobs and prospects in computing, makes this decision more challenging. The goal of this study was to identify computing career parameters, compare and contrast Random Forest and Naive Bayes (NB) supervised machine learning algorithms and then develop a prototype. This objectives were accomplished through CRISP-DM using the Kaggle repository data 4 ver1 dataset. Computing professional parameters for prediction include professional skills and abilities, CPGA, communication skills, analytical skills, team player, personal interest and professional experience. The algorithms for predicting careers have been thoroughly examined. Due to their excellent prediction accuracy, Random forest and Nave Bayes were identified for career prediction system. The model was developed using five, ten, fifteen and nineteen attributes. This study examined the percentage F1 score, recall, precision and accuracy of these two cutting-edge supervised learning approaches. Testing and training of both algorithms was done using the same datasets with varied number of attributes. The findings demonstrated that the Random Forest algorithm outperformed the Naïve Bayes algorithm that had an accuracy of 89.885% as well as higher recall, precision in addition F1 score. There was the gradual increase in all performance metrics using different number of attributes until it reaches the point of stagnation. A prototype based on the Random Forest method was developed. The prototype developed was evaluated for accuracy in career prediction for computing college students. This prototype can be used by computer graduates and Human Resource managers to make more accurate and consistent prediction on computing careers.