Evaluation of Neural Network Model for Better Classification of Data and Optimum Solution of Real-World Problems

Abstract

The implementation of Artificial Intelligence in providing the optimum solution to real-world problems is exponentially growing. The ability of Artificial Intelligence that finds its implementation in a broad spectrum of areas and fields has certainly increased its demand in the recent past. The concept of Neural Networks which originally has its roots in Artificial Intelligence is also gaining significant popularity and is gradually being implemented in many fields to yield benefits, most of the time beyond expectations. Neural Networks mimic the operations of a human brain to derive various relations between the data sets. Neural Networks find their use in a wide variety of fields that includes and is not limited to finance, algorithm trading, credit risk modeling, forecasting, marketing research, fraud detection, and risk detection and assessment. This research paper provides the evaluation of the Neural Network model and reflects it on the real-world data to provide optimum solutions for the problems with enhancement in the accuracy of results. This research discusses the appropriate use of hyperparameters like learning rate, batch size, optimizers, and activation functions like Sigmoid, ReLU, and Softmax to appropriately match a real-world problem. This research paper is a mere effort to evaluate the Neural Network model to better suit a real-world problem.