@Gayathri Devi, This prediction depends on the date you have. You may have labelled or unlabelled data based on which you have different algorithms. Assuming your data is labelled, then you have to find if you are trying to solve a regression problem or a classification problem. Based on that you can choose the algorithms. Since you have written that you want to find outliers , I'm assuming that it is a regression problem. Then you can use algorithms like Linear Regression, Support Vector Regression, Decision tree regression, Random forest regression etc. If your data is unlabelled, you have to use a unsupervised learning method. You will have algorithms like K-Means clustering, Hierarchical clustering etc. The main part of any solving machine learning problem is learning what your data is and choosing the right algorithm for your problem. So you may need to spend more time in analysing data and choosing the right algorithm. Here are few links for the concepts mentioned above. You can find these algorithms in spark. https://spark.apache.org/docs/latest/ml-guide.html https://machinelearningmastery.com/classification-versus-regression-in-machine-learning/ https://www.quora.com/What-is-the-main-difference-between-classification-problems-and-regression-problems-in-machine-learning https://machinelearningmastery.com/supervised-and-unsupervised-machine-learning-algorithms/ https://stackoverflow.com/questions/19170603/what-is-the-difference-between-labeled-and-unlabeled-data Happy machine learning 🙂 . -Aditya
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