1. MUHAMMAD ZOHAIB KHAN - Department of Computer Science, Sindh Madressatul Islam University, Karachi, Sindh, Pakistan.
2. ABDULLAH AYUB KHAN - Department of Computer Science, Sindh Madressatul Islam University, Karachi, Sindh, Pakistan & Faculty of Computing Science and Information Technology, Benazir Bhutto Shaheed University Lyari, Karachi, Sindh, Pakistan.
3. ASIF ALI LAGHARI - Department of Computer Science, Sindh Madressatul Islam University, Karachi, Sindh, Pakistan.
4. ZAFFAR AHMED SHAIKH - Faculty of Computing Science and Information Technology, Benazir Bhutto Shaheed University Lyari,
Karachi, Sindh, Pakistan.
5. MUHAMMAD ADNAN KAIM KHANI - Department of Computer Science, Sindh Madressatul Islam University, Karachi, Sindh, Pakistan.
6. DMITRY MORKOVKIN - Financial University under the Government of the Russian Federation, Moscow, Russia.
7. OLGA GAVEL - Financial University under the Government of the Russian Federation, Moscow, Russia.
8. SERGEY SHKODINSKY - Financial Research Institute of the Ministry of Finance of the Russian Federation, Moscow; Russia & RUDN University, Moscow, Russia.
9. SVETLANA MAKAR - Financial University under the Government of the Russian Federation, Moscow, Russia & National Research Mordovia State University, Moscow, Russia.
10. DENIS TABUROV - Financial University under the Government of the Russian Federation, Moscow, Russia.
The rapid advancement in Information Technology (IT) and the development of Artificial Intelligence (AI) makes systems more efficient and effective in performing day-to-day tasks, such as identification, extraction, detection, and recognition-related problems. These pose a serious indication towards the concept of Machine learning (ML) and the proposed efficient techniques for distinct purposes, which are either performed artificially supervised, semi-supervised or unsupervised ML. However, the ML systems have the potential to self-learn and adapt themselves by explicitly programmed existence based on earlier experience. ML and data mining approaches are justified in this research. It is featured in the Principal Component Analysis (PCA) method of supervised and unsupervised learning. Both machine learning algorithm approaches use a variety of methodologies. For this purpose, we use the same datasets for classification, regression, and clustering procedures, as well as the PCA data mining technique, to evaluate and perform on the K-Means, K-nearest neighbor (KNN), and support vector machine (SVM) algorithms. Finally, we present simulations that show the estimated criteria, parameters, and efficiency with effective algorithms’ performance, and state-of-the-art results.
Machine Learning, Supervised Learning, Unsupervised Learning, Support vector machine (SVM), K-Means, Principal component analysis (PCA).