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This project is accomplished in both R language and Python. The major objective of the project is to train the machine to classify the data based on the data given. In order to do so, the method employed is feature selection. Under the category of feature selection, the project will encounter the methods of Recursive Feature Elimination (RFE), L…

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Applied-Machine-Learning-Feature-Selection-and-Classification

This project is accomplished in both R language and Python. The major objective of the project is to train the machine to classify the data based on the data given. In order to do so, the method employed is feature selection. Under the category of feature selection, the project will encounter the methods of Recursive Feature Elimination (RFE), L-0 norm feature selection and Fisher score for the feature selection. The methods of classification used are Random Forest, Multilayer Perceptron, Radial Basis Function Support Vector Machine (RBF SVM) and k-Nearest Neighbor.

Note:- Download Anaconda if your dataset is very large or to directly run this code.

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This project is accomplished in both R language and Python. The major objective of the project is to train the machine to classify the data based on the data given. In order to do so, the method employed is feature selection. Under the category of feature selection, the project will encounter the methods of Recursive Feature Elimination (RFE), L…

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