How to store multiple columns of a csv dataset in a single variable in java so that the variable can be used as input feature for ml model

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    I have done this in python. Here is my python code:
    Here X is the input variable in which I stored all the 
    input columns of csv file and y is the target variable.

          dataset=pandas.read_csv("newone.csv")
          features = [0,1,4,5,6,7]
          X =dataset.iloc[:,features]
          y =dataset.iloc[:,2]
    How can I do this in java? 

Here is my java code in which I read the csv file but I am able to store only one column value of the csv in a variable.

    public static void main(String[] args) throws IOException {
            BufferedReader reader = Files.newBufferedReader(Paths.get("C:/Users/N/Desktop/newone.csv"));
            CSVParser csvParser = new CSVParser(reader,
                    CSVFormat.DEFAULT.withHeader("Enounter", "Relation", "Event", "Tag","Encounter_no", "Diagonosis", "User_Id", "Client_Id").withIgnoreHeaderCase().withTrim());
            for (CSVRecord csvRecord : csvParser) {
                encntr=csvRecord.get("Encounter");
      }
    }


    ----------

Consider using MyKong's code on how to read CSV files in Java. Something like the following snippet below (heavily borrowed from MyKong's better version):

br = new BufferedReader(new FileReader(csvFile));
while ((line = br.readLine()) != null) {
    String[] valuesFromLine = line.split(cvsSplitBy);
    String secondValue = valuesFromLine[1];
    // do something with second value
}

Classify structured data with feature columns, TensorFlow.js for ML using JavaScript We will use Keras to define the model, and feature columns as a bridge to map Build an input pipeline to batch and shuffle the rows using tf.data. The dataset we downloaded was a single CSV file. large CSV file (so large that it does not fit into memory), we would use tf.​data to  This tutorial demonstrates how to classify structured data (e.g. tabular data in a CSV). We will use Keras to define the model, and feature columns as a bridge to map from columns in a CSV to features used to train the model. This tutorial contains complete code to: Load a CSV file using Pandas. Build an input pipeline to batch and shuffle the

Load CSV data, This tutorial provides an example of how to load CSV data from a file into a from https://storage.googleapis.com/tf-datasets/titanic/train.csv 32768/30874 You can load this using pandas, and pass the NumPy arrays to TensorFlow. The only column you need to identify explicitly is the one with the value that the model is  Functions can be used after each other. It’s very important to understand that pandas’s logic is very linear (compared to SQL, for instance). So if you apply a function, you can always apply another one on it. In this case, the input of the latter function will always be the output of the previous function. E.g. combine these two selection

Download these jar https://mvnrepository.com/artifact/org.apache.poi/poi-ooxml/3.15 and https://mvnrepository.com/artifact/org.apache.poi/poi/3.15 and add them to your build path

import java.io.FileInputStream;
import java.io.IOException;

import org.apache.poi.ss.usermodel.Cell;
import org.apache.poi.ss.usermodel.Row;
import org.apache.poi.xssf.usermodel.XSSFSheet;
import org.apache.poi.xssf.usermodel.XSSFWorkbook;

public class ExcelReader {

    public static void main(String[] args) throws IOException 
    {

        //specify your file path
        FileInputStream file = new FileInputStream("D:\\test.xlsx");

        XSSFWorkbook workbook = new XSSFWorkbook(file);

        //to fetch sheet
         XSSFSheet sheet = workbook.getSheetAt(0);

         // for iterating through rows
        for(int c=0;c<=sheet.getLastRowNum();c++)
        {
            // for iterating through columns
            Row rows = sheet.getRow(c);
            for(int b=0;b<=rows.getLastCellNum();b++)
            {   
                Cell cells=rows.getCell(b);
                //to read cell value as string
                String comp=cells.getStringCellValue(); 

            }

        }

    }

}

Data Cleaning and Preparation for Machine Learning – Dataquest, We've downloaded our data set and named it lending_club_loans.csv , but now we need to skip row 1 so pandas can parse the data properly. loans_2007 that it had 56 columns, so we know that this preview DataFrame has 56 rows (​one This is because the model would be also learning from features that won't be  You need to read those variable names into Stata first. So, read in the variable names as a Stata variable, use levelsof to store those names in a local macro, and then start work on your main dataset(s) I would give sample code but you don't give example data to make that easier.

Turning Machine Learning Models into APIs, So? Won't it be possible to integrate your machine learning model into your friend's application? If a frontend developer needs to use your ML Model to create an ML For this tutorial, you will use the Titanic dataset which is one of the Create an API endpoint that takes input variables, transforms them  The following examples describe some typical ways that users apply Select Columns in Dataset in machine learning, and provides some tips for how to select the columns: I want to remove text columns from the dataset so I can apply a math operation to all numeric columns. Many operations require that only numeric columns be present in the dataset.

How to build your first Neural Network to predict house prices with , In this post, we will be exploring how to use a package called Keras to dataset. As you can see, it is all stored in an array now: Converting We want to extract out the first 10 columns, and so the '0:10' after the The X variables have 10 input features, while the Y variables only has one feature to predict. Hi all, Need your help in a proc export procedure. I have a dataset with several columns. I need to export that table into as many CSV files as distinct values in the first column (let's call it "C1"), and name that CSV file something like "output_" and then the value of the C1. Basically almost

How to Develop Multivariate Multi-Step Time Series Forecasting , Machine learning algorithms can be applied to time series offer benefits such as the ability to handle multiple input variables with noisy We can group data by the 'chunkID' variable (column index 1). store the forecast row The dataset has 39 target variables, and we develop one model per target  Grocery Store Data Set This is a small data set consisting of 20 transactions.

Comments
  • you may need to use POI library to read excel/CSV files. which will provide inbuilt functions to read cell at a particular location. add a screenshot of csv data to let us better understand how you are reading the cells in python
  • Can you suggest me some reference link for POI library regarding my problem.
  • I am able to read the csv I have updated my code. please take a look.
  • Instead of putting your fields into a single variable, consider using the HashMap datastructure, calling the put method to add them and the corresponding get method to retrieve them.
  • I don't want to unite the columns here I just want to divide the dataset in input data and ouput data(target label) which I did in my python code.
  • Is POI is compatible with jdk 8 because the latest version of POI is compatible with jdk 6 only.
  • poi.apache.org/howtobuild.html as per source, it would support jdk 6 and later versions too
  • POI 4.0 will support jdk 8 and it is not released yet.