Walking pattern analysis known as Gait analysis, is one of the key indicators among various clinical parameters, such as cardiovascular disease, in identifying symptoms of diseases. With sensors, the walking patterns of people can be retrieved to identify the differences between patients and healthy controls. Collection of health data is a crucial subject as it requires approval from different individuals/authorities. This paper presents an analysis of Gait datasets with nine Kinematic gait parameters by using classification models, a new data augmentation algorithm. Classification analysis of the Gait dataset has been performed using Neural Network which has 98.65% accuracy. We develop a new GDAA (Gait Data Augmentation Algorithm) for augmentation of Gait data and its time complexity is O(nf). The result of GDAA falls between the minimum range and maximum range of the original dataset. To evaluate optimum classification results, we performed data analysis of the augmented datasets of varying sizes. After a rigorous analysis of augmented data, we found the accuracy of the Neural Network was 97.1%.