Seybold Report ISSN: 1533-9211
Shastri VishwaShree
Assistant Professor, Department of Electronics and Communication Engineering, Sridevi Women’s Engineering College, Hyderabad, India, shastrivishwashree@gmail.com
Karangulla Nitheesha
U.G Student, Department of Electronics and Communication Engineering, Sridevi Women’s Engineering College, Hyderabad, India, Nitheesha16@gmail.com
Kolla Harshini
U.G Student, Department of Electronics and Communication Engineering, Sridevi Women’s Engineering College, Hyderabad, India, harshinikolla2001@gmail.com
Vangeepuram janaki
U.G Student, Department of Electronics and Communication Engineering, Sridevi Women’s Engineering College, Hyderabad, India, vangeepuramjanaki@gmail.com
Vol 17, No 07 ( 2022 ) | DOI: 10.5281/zenodo.6877410 | Licensing: CC 4.0 | Pg no: 122-131 | Published on: 25-07-2022
Abstract
Big Data Analytics (BDA) is a sophisticated way of advanced analytic technique against very large, diverse big data. Itis outlined as data sets whose size or sort is on the far side of the power of ancient relative databases to capture, manage, and method the information with low latency this includes tools for knowledge mining, which shifts through data sets in search of patterns and relationships prognosticative analytics, which builds models to forecast client behavior and different future actions, scenarios, and trends machine learning, which faucets varied algorithms to research large data sets deep learning, which may be a lot of advanced effect of machine learning for effective visualization of data in this paper we will apply the criminal data tothe big data analytics to know about the trends visualization and prediction of the data we also use several deep learning techniques for the accurate analysis The predictive results show that the Prophet model and Keras stateful LSTM perform better than neural network models, it is useful for police departments and law enforcement organizations to better understand crime issues whichwill enable them to track activities, predict the likelihoodof incidents, effectively deploy resources and optimize the decision-making process.
Keywords:
Big data analytics, Lstm, Neural network