Pyspark for Machine learning

Introduction:

Spark is the most important component of Hadoop used in data science and big data. It is an open-source engine that offers to process real-time streaming, graph processing, memory processing with fast speed, and with the standard user interface. Industries require a powerful engine that provides fast access to data, fast processing the data, and data management, therefore they are acquiring spark for data science for developing the sophisticated models of data science that give the opportunities to avoid risks. This is as important as Python and SQL for data science and it is mainly designed for data scientists. It as a way to easy to learn if you already are known to python and SQL, and other programming languages.

What is pyspark for machine learning?

Apache-spark has a library named MLlib that is used to perform machine learning tasks by using a spark framework and now we have a python API for apache-spark that is known as Pyspark. Pyspark used in machine learning that contains many algorithms and ML utilities.

Spark MLlib means a spark machine learning library. Pyspark is very easy to use for a large amount of data in machine learning and it works on distributed systems. It is used for data analysis and for building various Machine learning models like regression model including both linear and logistic, classification models including XGboost, ADAboost with a decision tree, and random forest. That all possible with Pyspark MLlib.

Features of Pyspark:

1. Resilient Distributed Datasets: Spark provides a resilient distributed dataset(RDD) that runs the main function and executes that various parallel operations on clusters. RDD is the collection of the elements partitioned into the nodes of the cluster that could be operated in parallel and it automatically removes the failed nodes. These RDDs are basically are the datasets that provide two types of data operations such as Transformation and Actions. Transformations refer to the operations that work on the input data to apply the transform method on them.
2. Data frame: As we all know that data frames are made up of structured and unstructured data that is organized with named columns or features. This supports various types of data to access and process that are in the form of JSON, text, csv, Html, and RDD. The data frames are immutable and distributed in nature.
3. Machine learning: If you ever work with sklearn to build an ML model you have to use two main machine learning algorithms that are transformers and estimators. Transformers used as same in the data frame with inputs data and estimators used to take the inputs and produce the trained output using the fit() function.

Let’s see some implementation with Pyspark for Machine learning:

1. Loading the data:

2. Prepare and visualize the data:

ref: data bricks

3.Building the machine learning model:

4. To make predictions:

Pyspark allows data scientists an API that given them better working experience with python on data science. Data scientists and data analysts use pyspark for preforming the distributed transformations on large data sets for getting the best possible outcomes of the ML model.

Data science and Machine learning completed with the knowledge of Apache-spark and this the best time to invest your career in data science and Machine learning. There are many data science courses are available and if you want to build your career join Learnbay.

References:

https://towardsdatascience.com/first-time-machine-learning-model-with-pyspark-3684cf406f54

https://databricks.com/spark/getting-started-with-apache-spark/machine-learning