Data Science Project in Kotlin

• Mutli Linear Regression
• Simple Linear Regression

Libraries Used

• Kotlin-grass - kotlin library for parsing csv file to data class
• Kotlin-csv - kotlin library for reading csv file
• Koma - library for scientific computing
• Apache Commons Math - OLM Multi Linear Regression
• Kotlin-Statistics - Idiomatic math and statistical extensions for Kotlin

Flavors

• Idiomatic Categorization
• Annotation Categorization

Usage:a. Idiomatic Appraoch

1. create data class
2. parse csv file with data class
3. categorized using extension function
4. create category keys
5. create array of doublearray(matrix equation) for independent variables
6. create array of double for independent variables
7. feed arrays to OLSML

b. Class Annotation

1. create a data class
2. extend data class with ScientificData class
3. mark class property with annotation
4. parse csv file with data class
5. categorize and create keys by instantiating and initializing CategoryKey
6. retrieve category keys, and dependent, indepedent array of: doubles, array of doubles
7. feed arrays to OLSML

c. Annoations

1. @Category

   • identifies that the property is a category variable

2. @Dependent

   • mark the property as dependent variable
   • make sure that there is only one dependent variable annotated

Creating ScientificData class

data classCompany(
valrnd:Double?,
valadmin:Double?,
valmarketing:Double?,
@Category
valstate:String?,
@DependentVar
valprofit:Double?,
@Category
valtech:String?
): ScientificData()

Parsing data to ScientificData class from resource Folder

val data = dataClassFromCsv<Company>("/Company.csv").toList()

using idiomatic approach

//-- Multi linear regression without ScientificData class and annotationval category1 =CategoryKeys(data)
.addCategory(key ="state", cat = {it.state!!} )
// categorizing data setsval categorizedData = data.categorized(
category = {
categorizeByVariable { map ->
map["state"] = it.state!!
}
},
numeric = {
doubleArrayOf(it.rnd!!, it.admin!!, it.marketing!!)
}
)
//-- creating array of array of doublesval doubleEQ =DoubleEQ(category1.mappedKeys)
val xD = doubleEQ.createEQ(categorizedData) // independentval yD = data.mapNotNull { it.profit }.toDoubleArray() // dependent//-- solving multi linear regressionval olsml =OLSML(yD, xD)
val summary = olsml.summary()

using class annotation

//-- categorizing data setsval category2 =CategoryKeys(data).initCategoryData()
//-- creating array of array of doublesval doubleEQ2 =DoubleEQ(category2.getCategoryKeys())
//-- resulting array, array of doubles are arranged alphabetically according to data class property nameval xW = doubleEQ2.createEQ(category2.getCategorizedData())
val yW = category2.getDependentValues()
val olsml =OLSML(yW, xW)
val summary = olsml.summary()

removing columns from double array eg. backward elimination approach

val matProcessed = create(arrayDoubleArray)
val removedCol = matProcessed.removeColumns(1,0,3)
val arrayVal = removedCol.to2DArray()