Application

Various algorithms such as Linear Regression, Logistic Regression, Decision Trees, K Nearest Neighbour, Naive Bayes, Artificial Neural Networks, Support Vector Machines have been explored. Along with these, various Ensemble Methods such as Bagging, Boosting and Stacking have been put to use using Python and R. For performing Regression problems, Boston dataset has been used while for Classification problems, Titanic dataset has been used.

In the Unsupervised Learning setup, Clustering is done using K Means, DBScan and Hierarchical Clustering. For Dimensionality Reduction, methods such as Principal Component Analysis and Factor Analysis are explored while for Anomaly Detection, methods such as One Class SVM and Isolation Forest are used.

For Time Series Forecasting, Averaging techniques, Smoothening techniques, Time Series decomposition and ARIMA methods are used. Here the Air Passengers dataset (a built-in dataset found in R) has been used for exploring these techniques.