Writing a CSV File with Scala and Using it to Create a Machine Learning Model

Saanya Lasod 24 Nov, 2020 • 5 min read

This article was published as a part of the Data Science Blogathon.


Scala is difficult to learn, true, but it’s worth the hard work. Scala has much easier syntax and is also more expressive. Scala codes are much concise than Java’s and an engineer who can write short and expressive code while also making it a type-safe and high-performance application are be considered valuable.

In this project, that I made as a college project, we’ll see how to write in a .csv file using Scala, which we will then use to create a basic fruit detection Machine Learning model.


Data Set

The data set that we will use can be found here.

The data set contains 4 fruits – Apple, Mandarin, Orange, and Lemons. And we will classify them,
solely on the basis of the given height, width, mass, and color score.

CSV Scala - data

Although our dataset is already cleaned, if you wish to use a different dataset, make sure to clean and preprocess the data using python or any other way you want, to get the maximum out of your data, while training the model.

Writing in the CSV file

For writing the CSV file, we’ll use Scala’s BufferedWriter,

FileWriter and csvWriter.

 write csv file

We need to import all the above files before moving forward to deciding a path and giving column headings to our file.

import the above files

We take a few rows of our data to take as input for the training dataset and to use it in writing our CSV file.

1. val out = new BufferedWriter(new FileWriter("D:/Academic/Assignments/Scala/Fruits.csv")) //this line will locate the file in the said directory
2. val writer = new CSVWriter(out) //this creates a csvWriter object for our file
3. val FruitSchema=Array("fruit_label","fruit_name","fruit_subtype","mass","width","height","color_score") // these are the schemas/headings of our csv file

Then we create arrays of our dataset, according to our schema plan. 

arrays of  dataset

To write this data into our csv file, we need to add this code snippet,

list of records
1. var listOfRecords=List() // this creates a list which holds our data
2. writer.writeAll(listOfRecords) // this adds our data into csv file
3. out.close() //closing the file

Whew, we got that right,

CSV Scala


Creating a file using random data

We’ve created our CSV file using Scala. Although, there is one more way to do this, where we generate our data randomly, using ranges which we can then convert to lists.

Firstly, we import all the required libraries.

CSV Scala

Then, we’ll now create our lists and ranges, which will contain the data we need in our CSV file.

CSV Scala
1. val widthList = Range.BigDecimal(5.8,9.6,0.1).toList // BigDecimal(starting number, ending number, step count) is used to accept float in range and the toList function converts this range to a list
2. val random = new Random() // this function is used to generate the data randomly 

Now we will put all this data in our CSV

1. var listOfRecords = new ListBuffer[Array[String]]() // this buffer holds all our data
2. listOfRecords += csvFields // this adds our schemas/headings
3. for(i<- 1 until 50){ listOfRecords+=Array(i.toString,nameList(random.nextInt(nameList.length)),massList(random.nextInt(massList.length)).toString(), widthList(random.nextInt(widthList.length)).toString(),heightList(random.nextInt(heightList.length)).toString(),colorList(random.nextInt(colorList.length)).toString())}  //the loop that which adds data to buffer



I used the Vlookup function in excel to add the fruit label.

This code generates the data purely randomly, so we need to be very careful before using it.

Creating Machine Learning model

To build our model, we’ll use Jupyter IDE of python.

I added a few more rows of data in my first CSV file, to get more accurate results.

Let’s get started, by importing the required libraries.

CSV Scala - Import

Now, it’s always better to have all the CSV files and python files in the same folder, so that it’s easy for us to code and organize and for python to find the file. Now, we will read in our CSV file.

Import - CSV Scala

We can also visualize the data using the seaborn library of python, to understand the data better. I, for one, have skipped it for now.

 Lets split our data into training and test data,

CSV Scala - Splitting data

After splitting the data, let’s check what model we can use, I first tried using Decision Tree, as we have a comparatively lesser data

CSV Scala - Decision Tree

But, we can clearly see that this model is overfitting, so we reject this. Now, let’s check for K Nearest Neighbor,

CSV Scala - KNN Classifier

We can see that accuracy for both, training and test set is pretty good, so we can use this model, as it is neither overfitting nor underfitting.

Let’s fit our data in the KNN model and check for the best neighbor value.

CSV Scala - Model evaluation

After finding the perfect value, let’s see the prediction score of our model,

Model Evaluation

It’s not the best, but because we took a small dataset for this project, this is quite nice, now, we’ll finally plot the decision boundaries of our project.

Python code

And, we’re done!


We have now learned, how to create CSV files using Scala and the basics of Machine Learning!

Saanya Lasod 24 Nov 2020

Frequently Asked Questions

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Responses From Readers


Abhijeet kapse
Abhijeet kapse 09 Nov, 2020

Good work.

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