Modern technology has made almost everything available at the touch of a button. But in our haste to stay up with these technological developments, our health frequently takes a back seat. The prevalence of numerous chronic diseases has increased as a result of this. Lung cancer is an example of a serious disease. Machine learning and similar approaches have been the subject of much discussion among researchers over their potential for accurate lung cancer prediction. We study machine learning algorithms with a particular focus on understanding how they handle different types of restrictions, such as changes in data quantity and test-train ratio. This research investigates the use of six different machine learning methods for predicting the occurrence of lung cancer: decision tree classifier (DTC), support vector machine classifier (SVMC), Naive Bayes classifier (NBC), logistic regression classifier (LRC), and random forest classifier (RFC).