Statistical Predictabilty in Big Data Analytics with Data Partitioning - Saritha, K; Abraham, Sajimon (International Journal of Computer Sciences and Engineering, 2018)
The huge volumes of data which cannot be manipulated easily by commonly available tools are termed as Big Data. Big Data analytics gives competitive opportunities in designing business plans for Business Analytics. The results are used for taking intelligent business decisions; hence it must be accurate and well-timed. For analytical purpose we use Multiple Linear Regression (MLR) model in the statistical method, a type of Supervised Machine Learning Algorithm. Performance of the particular MLR model with one quantitative dependent attribute and four independent attributes are evaluated using splitting up of the whole data set with Cross-Validation technique. This technique is used to validate the accuracy of model developed from training data with test data to control the problem like over fitting. Here we use Hold-Out Cross Validation method with serial and random partitioning. The data set from UCI machine learning repository are evaluated through simulation methods to check the performance. The model generated in training data are validated with test data, the evaluation shows that the result obtained is a generalized one. The proposed MLR model can be used in the new data set for an accurate result. Here we obtained that the accuracy, measuring with random partitioning is a better method.