The growth of data in the healthcare industry grows exponentially and the annual growth rate is about 40%, managing this amount of data is challenging task. Big Data architecture and frameworks affords the platform for data storage and processing of massive volume of data in healthcare industry. The paper aims to provide Big Data technologies and Machine Learning algorithms to predict Parkinson’s Disease (PD). The dataset from PPMI are used in the current study and observe the progression of the Parkinson’s Disease. The Movement Disorder Society-Unified Parkinson’s Disease (MDS-UPDRS) features are used for the prediction model. The current study focuses on machine learning algorithms from python libraries such as pandas, ski-kit learn, numpy and matplotlib. The important features obtained are tremor, bradykinesia, facial expression is observed as important features for classification. It is observed that logistic regression and multi class classifier performed with accuracy of 99.04% than the other algorithms such as Naïve Bayes, k-Nearest Neighbor, SVM and Neural Network.

Digital media as a platform is one of highest interactive yet sensitive arenas of information technology. With the rise of Online Social Networks, the idea of authentic identity is often seen on the brink of attack with the impact of cases of Social Engineering and other forms of Cyber Threats. The research here mainly aims at developing a Profile Verification Model with existing datasets from platforms such as Instagram. This includes understanding the behavioural aspects of interactions with masked identity elements such as bots and fake accounts Using the concepts of Supervised Machine Learning, its objective is to use existing algorithms such as Random Forest Classifier, K-Nearest Neighbour Classifier, and Linear SVM, using data pre-processing techniques to transform valid input training and testing formats. Finally, drawing comparative results using mathematical operations for accuracy and testing and depict the same through data visualization tools.