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    Microsoft R PreProcessing

    Uploaded by

    Manisha Panda
    This document provides an overview of data pre-processing techniques for machine learning models. It discusses handling missing values, discretization, standardization, dummy variables, and splitting data into training and test sets. An exercise is provided to demonstrate these techniques on a dataset using R code, including reading in data, handling missing values, splitting numeric and categorical features, standardization, discretization, dummy encoding, and splitting the data into train and test sets.

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    © All Rights Reserved
    Download as DOCX, PDF, TXT or read online from Scribd

    Microsoft R PreProcessing

    Uploaded by

    Manisha Panda
    16 views1 page
    This document provides an overview of data pre-processing techniques for machine learning models. It discusses handling missing values, discretization, standardization, dummy variables, and splitting data into training and test sets. An exercise is provided to demonstrate these techniques on a dataset using R code, including reading in data, handling missing values, splitting numeric and categorical features, standardization, discretization, dummy encoding, and splitting the data into train and test sets.

    Original Description:

    R Pre processing document

    Original Title

    20171023 Microsoft R PreProcessing

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    This document provides an overview of data pre-processing techniques for machine learning models. It discusses handling missing values, discretization, standardization, dummy variables, and splitting data into training and test sets. An exercise is provided to demonstrate these techniques on a dataset using R code, including reading in data, handling missing values, splitting numeric and categorical features, standardization, discretization, dummy encoding, and splitting the data into train and test sets.

    Copyright:

    © All Rights Reserved
    Download as DOCX, PDF, TXT or read online from Scribd
    Download as docx, pdf, or txt
    16 views1 page

    Microsoft R PreProcessing

    Uploaded by

    Manisha Panda
    This document provides an overview of data pre-processing techniques for machine learning models. It discusses handling missing values, discretization, standardization, dummy variables, and splitting data into training and test sets. An exercise is provided to demonstrate these techniques on a dataset using R code, including reading in data, handling missing values, splitting numeric and categorical features, standardization, discretization, dummy encoding, and splitting the data into train and test sets.

    Copyright:

    © All Rights Reserved
    Download as DOCX, PDF, TXT or read online from Scribd
    Download as docx, pdf, or txt
    of 1

    20171023_Microsoft_PreProcessing

    Objective:

    In this session, you will learn data pre-processing steps, and data aggregation and manipulation
    techniques used before moving onto data modeling.

    Key takeaways:

     Handling missing values


     Discretization and standardization
     Dummy variables
     Splitting data into train and test

    Exercise: Please write R code to do the following tasks:

    1) Clear the environment and set working directory


    2) Read data from url https://archive.ics.uci.edu/ml/machine-learning-databases/adult/adult.data
    in R environment. Name this data frame “adult”.
    3) Change attribute names to “age”, “workclass”, “fnlwgt”, “education”, “education-num”, “marital-
    status”, “occupation”, “relationship”, “race”, “gender”, “capital-gain”,”capital-loss”, “hours-per-
    week”, “native-country”,”profits”
    4) Find out which rows contain " ?" in "workclass", "occupation" and “native-country” attributes
    and replace with NA. Check data summary to observe whether all " ?" has been replaced by NAs
    5) Using central Imputation, impute NA values in the data frame. Check whether all NA values have
    been imputed.
    6) Split the data frame into two data frames. One containing only numeric data vectors and other
    one containing only categorical vector.
    7) Perform standardization and discretization of attributes in numeric vector using “equalwidth”
    and “equalfreq”. Observe the tables to know difference between two methods.
    8) Create dummy variables for “race attribute” in categorical data
    9) Create a new data frame by adding standardized numeric data, categorical data (eliminate “race”
    from this) and dummy variables created for “race”
    10) Split the data into 60% train and 40% test set

    Inspire…Educate…Transform. Page 1

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