Feature Selection Correlation-Based pada Prediksi Nasabah Bank Telemarketing untuk Deposito
Pre-processing is an important step in classifying data. This is useful for preparing data so that the classification technique applied can generate quality and accurate patterns. One of the data pre-processing techniques that are often used to determine the most influential attributes on a dataset is feature selection. This study employs the correlation-based feature selection method to select relevant and influential attributes in predicting potential customers for deposit offers. Correlation Attribute Evaluation is used as a selector of the feature selection method and generates 11 attributes that have the highest ranking. The classification is carried out using the Multilayer Perceptron Neural Networks using these 11 attributes. The classification results of the selected attributes have the highest accuracy rate of 80.5% and the lowest accuracy rate of 79.1%.
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