Forecasting Students Academic Achievement Using Machine Learning Techniques

In today's competitive world, investigating students learning performance is the major component for accessing any educational systems, Predicting the students' performance in academics is a difficult task. In order to address this, researchers use the EDM (Educational Data Mining) technique to examine data from educational settings and enhance the educational system based on the collected data. This allows for a more accurate and comprehensive understanding of students' performance and helps them achieve better educational outcomes. Prediction of student performance in academics helps to analyze students community in better understandable and communicable with the wide range of socio demographic(age, gender, family, size, obesity, marital status of parents, occupation,),learning practices(school level), student related(stress and lifestyle) variables Numerous methods are employed to assess students' academic performance; however, the primary goal of this work is to build a prediction model through the application of machine learning techniques, such as logistic regression, K-Nearest Neighbor algorithm (KNN), and Support Vector Machine (SVM).Multiple approaches are used here to measure the performance of academics of students along with good prediction method based on accuracy. The predictive models offer valuable insights into the factors influencing academic success, allowing for the allocation of resources through social media and interventions more effectively. The prediction is a variable that helps to predict how many hours they spend on internet in some activities based on data collected. The result is a predictor variable that aids in forecasting academic achievement on the final exam of the semester (CGPA). The study identifies the lagged part of the studies and how it affect the education history in future. The results as the prediction have been increased which helps the institution and others in encouraging and help the students in different ways. The health-conscious lifestyle positively helps to improve the performance in academics. However, gender is not significant predictor to predict performance and classify their talents based on their skills helps to enhance their performance