The various models can be applied on the same dataset. It provides you a visualization tool to inspect the data. Then, WEKA would give you the statistical output of the model processing. You would select an algorithm of your choice, set the desired parameters and run it on the dataset. Note that under each category, WEKA provides the implementation of several algorithms. The Attributes Selection allows the automatic selection of features to create a reduced dataset. Next, depending on the kind of ML model that you are trying to develop you would select one of the options such as Classify, Cluster, or Associate. Then, you would save the preprocessed data in your local storage for applying ML algorithms. You use the data preprocessing tools provided in WEKA to cleanse the data. This data may contain several null values and irrelevant fields. If you observe the beginning of the flow of the image, you will understand that there are many stages in dealing with Big Data to make it suitable for machine learning −įirst, you will start with the raw data collected from the field. What WEKA offers is summarized in the following diagram −
Download weka jar file for import software#
WEKA - an open source software provides tools for data preprocessing, implementation of several Machine Learning algorithms, and visualization tools so that you can develop machine learning techniques and apply them to real-world data mining problems. In the upcoming chapters, you will learn about Weka, a software that accomplishes all the above with ease and lets you work with big data comfortably. While doing so, you would prefer visualization of the processed data and thus you also require visualization tools. You may like to test the different algorithms under the same class to build an efficient machine learning model. Even within the same type, for example classification, there are several algorithms available.
The type of algorithms that you apply is based largely on your domain knowledge. Once the data is ready, you would apply various Machine Learning algorithms such as classification, regression, clustering and so on to solve the problem at your end. In short, your big data needs lots of preprocessing before it can be used for Machine Learning. The irrelevant data columns or ‘features’ as termed in Machine Learning terminology, must be removed before the data is fed into a machine learning algorithm. To train the machine to analyze big data, you need to have several considerations on the data −īesides, not all the columns in the data table would be useful for the type of analytics that you are trying to achieve. The foundation of any Machine Learning application is data - not just a little data but a huge data which is termed as Big Data in the current terminology.
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