Analyse Current and Historical Data to Predict Future Trends Using Spark and MLlib

In today's data-driven world, businesses are constantly looking for ways to gain an edge on the competition. One way to do this is to use predictive analytics to identify future trends and make informed decisions.

Apache Spark is a powerful open-source distributed computing engine that can be used for a variety of big data applications, including predictive analytics. Spark's MLlib library provides a set of machine learning algorithms that can be used to build predictive models.

Practical Predictive Analytics: Analyse current and historical data to predict future trends using R, Spark, and more

by Ralph Winters

4.7 out of 5

Language	:	English
File size	:	21150 KB
Text-to-Speech	:	Enabled
Screen Reader	:	Supported
Enhanced typesetting	:	Enabled
Print length	:	963 pages
Hardcover	:	122 pages
Item Weight	:	8.5 ounces
Dimensions	:	6 x 0.47 x 9 inches

FREE

In this article, we will show you how to use Spark and MLlib to analyse current and historical data to predict future trends. We will use a real-world dataset to build a predictive model that can predict the future sales of a product.

Prerequisites

Before you begin, you will need to have the following:

• A Hadoop cluster
• Apache Spark installed on your Hadoop cluster
• The Spark MLlib library installed on your Spark cluster
• A dataset to analyse

Getting Started

Once you have the prerequisites installed, you can begin by loading your dataset into Spark. You can do this using the following code:

scala val data = spark.read.csv("hdfs:///path/to/your/dataset.csv")

Once you have loaded your dataset into Spark, you can begin to analyse it. You can use the following code to get a summary of your dataset:

scala data.describe().show()

This will give you a summary of the numerical columns in your dataset, including the mean, standard deviation, and minimum and maximum values.

You can also use the following code to plot the distribution of a particular column in your dataset:

scala data.groupBy("column_name").count().orderBy("count", "desc").show()

This will plot a bar chart showing the distribution of the values in the specified column.

Building a Predictive Model

Once you have analysed your dataset, you can begin to build a predictive model. You can use the following code to create a linear regression model:

scala val lr = new LinearRegression() val model = lr.fit(data)

Once you have created a model, you can evaluate it on a test set. You can use the following code to evaluate a model:

scala val test_data = spark.read.csv("hdfs:///path/to/your/test_dataset.csv") val predictions = model.transform(test_data) val mse = predictions.select(mean(pow($"label" - $"prediction", 2))).first().getDouble(0)

This will calculate the mean squared error (MSE) of the model on the test set.

Using the Model to Predict Future Trends

Once you have a model that you are satisfied with, you can use it to predict future trends. You can use the following code to predict the future sales of a product:

scala val new_data = spark.read.csv("hdfs:///path/to/your/new_dataset.csv") val predictions = model.transform(new_data)

This will create a new DataFrame containing the predicted sales for each row in the new dataset.

In this article, we have shown you how to use Spark and MLlib to analyse current and historical data to predict future trends. We used a real-world dataset to build a predictive model that can predict the future sales of a product. You can use the same techniques to build predictive models for your own data.

Practical Predictive Analytics: Analyse current and historical data to predict future trends using R, Spark, and more

by Ralph Winters

4.7 out of 5

Language	:	English
File size	:	21150 KB
Text-to-Speech	:	Enabled
Screen Reader	:	Supported
Enhanced typesetting	:	Enabled
Print length	:	963 pages
Hardcover	:	122 pages
Item Weight	:	8.5 ounces
Dimensions	:	6 x 0.47 x 9 inches

FREE