The growing field of educational data mining seeks to analyse educational data in order to develop models for improving education and the effectiveness of educational institutions. Educational data mining is utilised to develop novel approaches for extracting information from educational databases, enabling improved decision-making within the educational system. The main objective of this research paper is to investigate recent advancements in data mining techniques within the field of educational research, while also analysing the methodologies employed by previous researchers in this area. The predictive capabilities of various machine learning algorithms, namely Logistic Regression, Gaussian Naive Bayes, Support Vector Machine, Random Forest, K-Nearest Neighbour, and XGBoost Classifier, were evaluated and compared for their effectiveness in determining students' academic performance. The utilisation of Random Forest and XGBoost classifiers in analysing scholastic, behavioural, and additional student features has demonstrated superior accuracy compared to other algorithms. The training and testing of these classification models achieved an impressive accuracy rate of approximately (96.46% & 87.50%) and (95.05% & 84.38%), respectively. Employing this technique can provide educators with valuable insights into students' motivations and behaviours, ultimately leading to more effective instruction and reduced student failure rates. Students' achievements significantly influence the delivery of education.

Predicting College placements based on academic performance is critical to supporting educational institutions and students in making informed decisions about future career paths. The present research investigates the use of Machine Learning (ML) algorithms to predict college students' placements using academic performance data. The study makes use of a dataset that includes a variety of academic markers, such as grades, test scores, and extracurricular activities, obtained from a varied sample of college students. To create predictive models, the study analyses numerous ML algorithms, including Logistic Regression, Gaussian Naive Bayes, Random Forest, Support Vector Machine, and K-Nearest Neighbour. The predictive models are evaluated using performance criteria such as accuracy, precision, recall, and F1-score. The most effective machine learning method for forecasting students' placements based on academic achievement is identified through a comparative study. The findings show that Random Forest approaches have the potential to effectively forecast college student placements. The findings show that academic factors such as grades and test scores have a considerable impact on prediction accuracy. The findings of this study could be beneficial to educational institutions, students, and career counsellors.