Improving Efficiency of Machine Learning Model for Bank Customer Data Using Genetic Algorithm Approach

Abstract

Machine learning techniques are very useful in extracting useful patterns from customer datasets. Although Machine Learning techniques give good results in most of the cases, there is a need to improve the efficiency of ML models in different ways. Feature selection is one of the most important tasks in machine learning. A genetic algorithm is a heuristic method that simulates the selection process. Genetic algorithms come under the category of evolutionary algorithms, which are generally used for generating solutions to optimization problems using selection, crossover, mutation methods. In this paper, we proposed a genetic algorithm-based feature selection model to improve the efficiency of Machine Learning techniques for customer related datasets. We applied a genetic algorithm-based feature selection for two different customer information datasets from UCI repository and achieved good results. All the experiments are implemented in Python language which provides vast packages for machine learning tasks.