Data Visualization

Prediction task is to determine whether a person makes over 50K a year Part 2

Prediction task is to determine whether a person makes over 50K a year Part 2

Split the Data It is common practice when building machine learning models to split the source data, using some of it to train the model and reserving some to test the trained model. In this project, you will use 70% of the data for training, and reserve 30% for testing. %scala val splits = IncomeFinalDF.randomSplit(Array(0.7, 0.3)) val train = splits(0) val test = splits(1) val train_rows = train.count() val test_rows = test.count() println("Training Rows: " + train_rows + " Testing Rows: " + test_rows) Prepare the Training Data To train the Classification model, you need a training data set that…
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Classifying gender based on personal preferences Part 1

Classifying gender based on personal preferences Part 1

Gender is a social construct. The way males and females are treated differently since birth moulds their behaviour and personal preferences into what society expects for their gender. This small dataset is designed to provide an idea about whether a person's gender can be predicted with an accuracy significantly above 50% based on their personal preferences. Attribute Information or Dataset Details: FavoriteColorFavoriteMusicGenreFavoriteBeverageFavoriteSoftDrinkGender Technology Used​ Apache SparkSpark SQL Apache Spark MLLibScalaDataFrame-based APIDatabricks Notebook Introduction Welcome to this project on Mobile Price Classification in Apache Spark Machine Learning using Databricks platform community edition server which allows you to execute your spark code, free…
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Classifying gender based on personal preferences Part 2

Classifying gender based on personal preferences Part 2

Collecting all String Columns into an Array %scala var StringfeatureCol = Array("FavoriteColor", "FavoriteMusicGenre", "FavoriteBeverage", "FavoriteSoftDrink", "Gender"); StringIndexer encodes a string column of labels to a column of label indices. Example of StringIndexer %scala import org.apache.spark.ml.feature.StringIndexer val df = spark.createDataFrame( Seq((0, "a"), (1, "b"), (2, "c"), (3, "a"), (4, "a"), (5, "c")) ).toDF("id", "category") df.show() val indexer = new StringIndexer() .setInputCol("category") .setOutputCol("categoryIndex") val indexed = indexer.fit(df).transform(df) indexed.show() Output: +---+--------+ | id|category| +---+--------+ | 0| a| | 1| b| | 2| c| | 3| a| | 4| a| | 5| c| +---+--------+ +---+--------+-------------+ | id|category|categoryIndex| +---+--------+-------------+ | 0| a| 0.0| | 1|…
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Mobile Price Classification

Mobile Price Classification

Machine Learning Project for Mobile Price Classification based on the available attributes Problem Statement or Business Problem Bob has started his own mobile company. He wants to give a tough fight to big companies like Apple, Samsung etc. He does not know how to estimate the price of mobiles his company creates. In this competitive mobile phone market, you cannot simply assume things. To solve this problem he collects sales data of mobile phones of various companies. Bob wants to find out some relation between features of a mobile phone(eg:- RAM, Internal Memory, etc) and its selling price. But he…
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Predicting the Cellular Localization Sites of Proteins in Yest

Predicting the Cellular Localization Sites of Proteins in Yest

Machine Learning Project Predicting the Cellular Localization Sites of Proteins in Yest based on the available attributes Data Set Information Sequence Name: Accession number for the SWISS-PROT databasemcg: McGeoch's method for signal sequence recognition.gvh: von Heijne's method for signal sequence recognition.alm: Score of the ALOM membrane spanning region prediction program.mit: Score of discriminant analysis of the amino acid content of the N-terminal region (20 residues long) of mitochondrial and non-mitochondrial proteins.erl: Presence of "HDEL" substring (thought to act as a signal for retention in the endoplasmic reticulum lumen). Binary attribute.pox: Peroxisomal targeting signal in the C-terminus.vac: Score of discriminant analysis…
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YouTube Spam Comment Prediction

YouTube Spam Comment Prediction

Machine Learning Project YouTube Spam Comment Prediction. The study of the classification YouTube comment as spam based on the available attributes Data Set Information COMMENT_ID: StringAUTHOR: StringDATE: StringCONTENT: StringCLASS: Double Technology Used Apache SparkSpark SQL Apache Spark MLLibScalaDataFrame-based APIDatabricks Notebook Challenges Process Comma-separated values file (ie file with .csv as Extensions) with user define a schema for data Convert String data to Numeric format so we can process the data in Apache Spark ML Library. Introduction Welcome to this project on creating prediction model to Identify spam comment in Apache Spark Machine Learning using Databricks platform community edition server which allows…
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Identify the Type of animal (7 Types) based on the available attributes

Identify the Type of animal (7 Types) based on the available attributes

Machine Learning Project Animal Classification. The study of the classification of types of animal, Identify the Type of animal (7 Types) based on the available attributes Data Set Information​ A simple database containing 17 Boolean-valued attributes. The "type" attribute appears to be the class attribute. Here is a breakdown of which animals are in which type:  Class# -- Set of animals:====== ====================================================1 -- (41) aardvark, antelope, bear, boar, buffalo, calf, cavy, cheetah, deer, dolphin, elephant, fruitbat, giraffe, girl, goat, gorilla, hamster, hare, leopard, lion, lynx, mink, mole, mongoose, opossum, oryx, platypus, polecat, pony, porpoise, puma, pussycat, raccoon, reindeer, seal, sealion,…
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Glass Identification

Glass Identification

Machine Learning Project for Glass Identification. Problem Statement or Business Problem​ From USA Forensic Science Service; 6 types of glass; defined in terms of their oxide content (i.e. Na, Fe, K, etc) The study of the classification of types of glass was motivated by the criminological investigation. At the scene of the crime, the glass left can be used as evidence...if it is correctly identified! Attribute Information or Dataset Details: Id number: 1 to 214RI: refractive indexNa: Sodium (unit measurement: weight percent in corresponding oxide, as are attributes 4-10)Mg: MagnesiumAl: AluminumSi: SiliconK: PotassiumCa: CalciumBa: BariumFe: IronType of glass: (class attribute)building_windows_float_processed…
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Predicting the age of abalone from physical measurements Part 1

Predicting the age of abalone from physical measurements Part 1

Machine Learning Project Predicting the age of abalone from physical measurements. Abalone is a common name for any of a group of small to very large sea snails, marine gastropod molluscs in the family Haliotidae. Other common names are ear shells, sea ears, and muttonfish or muttonshells in Australia, ormer in the UK, perlemoen in South Africa, and paua in New Zealand. Abalone are marine snails. Problem Statement or Business Problem​ Predict the age of abalone from physical measurements Predicting the age of abalone from physical measurements. The age of abalone is determined by cutting the shell through the cone,…
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Predicting the age of abalone from physical measurements Part 2

Predicting the age of abalone from physical measurements Part 2

Histogram for Sex and Age​ %sql select Sex, Age from AbaloneData; Plot Option Age Distribution​ %sql select count(Sex), Sex from AbaloneData group by Sex; Histogram for Lenght in mm in Abalone ​ %sql select Length_in_mm from AbaloneData; Histogram for Height in mm in Abalone %sql select Height_in_mm from AbaloneData; Histogram for rings in Abalone %sql select Rings from AbaloneData; Creating a Regression Model​ In this tutorial , you will implement a regression model that uses features of abalone to predict the age of abalone from physical measurements Import Spark SQL and Spark ML Libraries​ First, import the libraries you will…
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