Published Paper Details:

In this paper �Performance Evaluation of Various Binary Classifiers for Big Data� describe each step along the way to create a scalable machine learning system suitable to process large quantities of data. The techniques described in the research paper will aid in creating value from a dataset in a scalable fashion while still being accessible to non-specialized computer scientists and computer enthusiasts. Common challenges in the task will be explored and discussed with varying depth. A few areas in machine learning will get particular focus and will be demonstrated with a supplied case-study using Adult Census Income data. Currently, every industry uses Big Data for essential information extraction. Adult Census Income Records (ACIR) store massive data and are continuously updated with information such as age, Income, Marital Status, No. of Dependents etc. There are various methods by which data is generated and collected, including databases, websites, mobile applications, wearable technologies, and sensors. The continuous row of data will improve service, research and, ultimately, easy to find income records of adults. Thus, it is important to implement advanced data analysis techniques to obtain more precise prediction results. Machine Learning (ML) has acquired an important place in Big Data. ML has the capability to run predictive analysis, detect patterns or red flags. Because predictive models have dependent and independent variables, ML algorithms perform mathematical calculations to find the best suitable mathematical equations to predict dependent variables using a given set of independent variables. These model performances depend on datasets and response, or dependent, variable types such as binary or multi-class, supervised or unsupervised. Machine learning techniques will contribution towards making Big Data symmetric applications among the most significant sources of new data in the future. In this context, network systems are endowed with the capacity to access varieties of experimental symmetric data across a plethora of network devices, study the data information, obtain knowledge, and make informed decisions based on the dataset at its disposal. The current research analyzed incremental, or streaming or online, algorithm performance with offline or batch learning (these terms are used interchangeably) using performance measures such as accuracy, model complexity, and time consumption. Batch learning algorithms are provided with the specific dataset, which always constrains the size of the dataset depending on memory consumption.