Studies to classify human activities can contribute to the development of new systems that will facilitate daily life by evaluating the interaction of individuals with their environment. In this study, a novel data set is presented to be used in classifying the activities that individuals perform during the day. First of all, various deep architectural models presented in the study were tested with publicly available datasets well-known in the literature. Afterwards, various classification experiments were carried out by using our novel dataset, which was created with the sensor data collected with the smartphone located onto the belly region of ten volunteer individuals consisting of five males and five females aged between 25 and 55 years. Data of each activity at two different positions were taken, and also, 15 seconds raw data including 4 dynamic and 3 static activities were acquired. With 20 Hz sampling frequency for each activity position, 20 readings are made per signal window in 1 second. Thanks to the software tool developed for the study, various human activities were succesfully classified in experiments by allowing different network parameters and layer selection for the deep learning architectures including recurrent neural network models and convolutional neural network model. The novel dataset contains raw data, as well as, it involves some alternative subsets created with the use of Butterworth filter. As a result of experiments, the classification performance at accuracy rate of 97% to 99% for various activities of individuals was obtained on various datasets. The suitability of using the novel data set in studies on classification and prediction of human activities has been proven.