Predicting the success rate of a start-up using LSTM with a swish activation function

The researchers emphasised more small-scale start-ups which required improvement in the economy decreased the failure rate by avoiding the valuable resources. The resources were required to be used without wasting them lead to the development of the economy and reduces the rate of unemployment. Therefore, to minimise resource wastage and to avoid the risk of failure the researchers considered certain factors that affected the failure and success of small-scale industries. The present research uses the Crunch Base dataset that predicts the success or failure of a start-up by using the Long Short Term Memory (LSTM). The LSTM unit Recurrent Neural Network (RNN) uses the Swish activation function in Feed Forward Neural (FFN) Network for the classification. The proposed LSTM obtained better accuracy of 71.64% when compared with existing methods such as RNN that attained 65.67% and Artificial Neural Network (ANN) of 69.7%.