![]() ![]() In addition, conventional techniques for data analysis cannot handle vast amounts of data in real-time. We also talk about recent developments in the research into Big Data machines and provide future research directions.ĭue to the quantity and nature of big data, traditional approaches are no longer able to cope. We used ”SUSY,” ”HIGGS,” ”BANK,” and ”HEPMASS” dataset from the UCI data repository. Moreover, We have evaluated the unsupervised learning methods like K-means and Gaussian Mixer Models on the data set SUSY and Hepmass to determine the robustness of PySpark, in comparison with the classification and regression models. ![]() In addition, we have tested general regression methods such as Linear Regressor (LR), Decision Tree Regressor (DTR), Random Forest Regressor (RFR), and Gradient Boosted Tree Regressor (GBTR) on SUSY and Higgs datasets. Logistic Classifier (LC), Decision Tree Classifier (DTc), Random Forest Classifier (RFC), and Gradient Boosted Tree Classifier (GBTC) are four machine learning algorithms that are compared across platforms. We have evaluated and compared several ML algorithms to analyze the platform’s qualities, compared Apache Spark ML-lib against Rapid Miner and Sklearn, which are two additional Big data and machine learning processing platforms. In this contribution, we consider Apache Spark ML-lib as a computationally independent machine learning library, which is open-source, distributed, scalable, and platform. Apache Spark Machine learning library (ML-lib) is a famous platform used for big data analysis, it includes several useful features for machine learning applications, involving regression, classification, and dimension reduction, as well as clustering and features extraction. As the amount of data created daily reaches quintillion bytes, A complex big data infrastructure becomes more and more relevant. Machine learning algorithms on huge and complicated data sets, computationally expensive on the other hand, processing requires hardware and logical resources, such as space, CPU, and memory. ![]() Artificial intelligence, specifically machine learning, has been applied in a variety of methods by the research group to transform several data sources into valuable facts and understanding, allowing for superior pattern identification skills. ![]()
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