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Alzheimer's disease is a mind-issue sickness. The sickness is dealt with and attempted to direct the illness with different procedures. The goal is to foster a technique to observe likely amyloid-based biomarkers for early AD identification utilizing the ML approach. The principal focus is on supervised learning algorithms. This algorithm trains the machines with predefined training data and foresees the results. Linear Regression is utilized as the proposed calculation. Additionally, it has shown an extraordinary execution over conventional ML in distinguishing perplexing constructions in complex high-layered data. Our model portrays a specificity and sensitivity of 79% and 95% respectively in comparison with the Support Vector Machine(SVM).