To create a model, one needs to find a suitable dataset. Initially, data cleaning has to be done. This includes filling missing values, removing outliers, etc. Then, one needs to divide the dataset into two parts: the Training and the Testing dataset. The ratio of training to testing is preferably 80:20. Algorithms like Decision tree, Support Vector Machine (SVM), Linear and Logistic Regression, K- Nearest Neighbours, etc., can be applied. After training, testing is done using the testing dataset. The model's prediction is compared to the actual values, and finally, the accuracy is computed.