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Direct marketing involves telephone calls, personalized emails, and messages, newsletters, which catch the eye of the customer, and in turn, attract them towards the company. In this paper data of a Portuguese bank is considered. The bank makes a call to its potential customers regarding its term deposit schemes and is interested in customers that will invest in term deposits. With the advent of machine learning algorithms in prediction, comparative study of various algorithms such as Support Vector Machine (SVM), Gaussian Naive Bayes, Random Forest, Light Gradient Boosting (GBM), and Extreme Gradient Boosting is performed for the said data set. Light GBM gives the best results among these in very less processing time.

Oropharyngeal carcinoma is a type of head and neck cancer caused by division of cells present in the throat, which can affect a single region or multiple regions of the throat. A commonly found disease can be fatal if not diagnosed at an early stage. Various medical methodologies have been developed with time; however, the prediction of survival is still uncertain. In this research, the medical treatment offered to patients along with detailed information about them such as their drinking habits, age, time of diagnosis, and type of cancer was taken into consideration. In this paper, comparative analysis of neural network, three bagging, and three boosting algorithms was performed for prediction of oropharyngeal cancer. The bagging algorithms considered are support vector classifier (SVC), decision tree, and random forest. Boosting algorithms are AdaBoost, XGBoost, and CatBoost. Based on accuracy, time of processing and various performance parameters CatBoost was determined as the best algorithm to predict the survival chances of a cancer patient.