Java Deep Learning Projects

Feature engineering and input dataset preparation

In this sub-section, we will see some basic feature engineering and dataset preparation that can be fed into the MLP classifier. So let's start by creating SparkSession, which is the gateway to access Spark:

SparkSession spark = SparkSession                     .builder()                     .master("local[*]")                     .config("spark.sql.warehouse.dir", "/tmp/spark")                     .appName("SurvivalPredictionMLP")                     .getOrCreate();

Then let's read the training set and see a glimpse of it:

Dataset<Row> df = spark.sqlContext()                .read()                .format("com.databricks.spark.csv")                .option("header", "true")                .option("inferSchema", "true")                .load("data/train.csv");df.show();

A snapshot of the dataset can be seen as follows:

A snapshot of the Titanic survival dataset