Journal of Big Data Technology and Business Analytics <p><strong>JBDTBA</strong> is a peer reviewed journal of Computer Science domain published by MAT Journals Pvt. Ltd. It is a print and e-journal focused towards the rapid publication of research and review papers based on Big Data Technology and Business Analytics. It includes topics related to Capturing Data, Data Storage, Data Analysis, Search, Sharing, Transfer, Visualization, Querying, Updating, Information Privacy, and Data Source, Statistical Computing, R Programming Language , Natural Language Processing (NLP), MapReduce, Hadoop Distributed File System (HDFS), Database Management System (DBMS), Cloud Computing, Artificial Intelligence, Algorithm, Data Lake, Hadoop, Dashboards, Data Virtualization, Data Supply Chains, Data Mining, Python, Structured Data, Architectures for Massively Parallel Processing, Distributed File Systems and Databases; and Scalable Storage Systems. The contributions related to Social Media Analytics, Statistics and Econometrics in Business Analytics, Use of Novel Data Science Techniques in Business Analytics, Robotics and Autonomous Vehicles, Marketing Analytics, Methods of Decision Making, Supply Chain Analytics, Transportation Analytics, Ethical and Social Implications of Business Analytics and AI, Applications of AI and Machine Learning Methods in Business Analytics are also welcome.</p> en-US Tue, 21 May 2024 00:00:00 +0000 OJS 60 Machine Learning-Based Traffic Flow Prediction Model <p>This project aims to create a web application using Flask that forecasts traffic flow on roads by utilizing cutting-edge deep learning algorithms. The program uses a Multi-Layer Perceptron (MLP) regression model to estimate real-time traffic volume based on various input data, such as the date, time, temperature, and weather, taking holidays into consideration. The study thoroughly investigates data preparation procedures, including categorical variable encoding, feature extraction, and sorting. Moreover, it entails the MLP regression model's thorough implementation, which includes training, hyperparameter adjustment, and assessment. The careful execution of the traffic prediction model integration into the Flask framework allows for smooth application setup and interaction. This project aims to provide stakeholders and developers with a powerful tool for comprehending and controlling road traffic, with possible uses in urban planning and transportation management.</p> T. Bhaskar, Deokar Anushka, Asane Renuka, Chandar Gauri, Khond Deepti Copyright (c) 2024 Journal of Big Data Technology and Business Analytics Tue, 21 May 2024 00:00:00 +0000