This project provides a tuning process on the different sets of parameters in predicting "Daily and Sports Activities", human activities performed while wearing sensor units on the chest, arms, and legs. The dataset comprises motion sensor data of 19 different daily activities and sports each performed by 8 subjects in their own style for 5 minutes.
The combination of layers with high densities, dropouts, and optimizers in two layers neural network are considered. The optimal model in this project is the model with 1406 nodes in each layer and dropout of 0.3, with 93% of accuracy.
Source: https://archive.ics.uci.edu/ml/datasets/Daily+and+Sports+Activities with additional modification (size > 25 MB)