Cardio Good Fitness

Project – Cardio Good Fitness
Model Report
Table of Contents
1 Project Objective……………………………………………………………………………………………………………..3
2 Assumptions……………………………………………………………………………………………………………………3
3 Exploratory Data Analysis – Step by step approach ……………………………………………………………..3
3.1 Environment Set up and Data Import………………………………………………………………………….3
3.1.1 Install necessary Packages and Invoke Libraries…………………………………………………….3
3.1.2 Set up working Directory ……………………………………………………………………………………3
3.1.3 Import and Read the Dataset………………………………………………………………………………4
3.2 Variable Identification……………………………………………………………………………………………….4
3.2.1 Variable Identification – Inferences……………………………………………………………………..4
3.3 Univariate Analysis……………………………………………………………………………………………………4
3.4 Bi-Variate Analysis…………………………………………………………………………………………………….5
3.5 Missing Value Identification……………………………………………………………………………………….5
3.6 Outlier Identification…………………………………………………………………………………………………5
3.7 Variable Transformation / Feature Creation ………………………………………………………………..5
4 Conclusion………………………………………………………………………………………………………………………5
5 Appendix A – Source Code………………………………………………………………………………………………..5
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1 Project Objective
The objective of the report is to explore the cardio data set (“CardioGoodFitness”) in R and generate insights about the data set. This exploration report will consists of the following:
Importing the dataset in R
Understanding the structure of dataset
Graphical exploration
Descriptive statistics
Insights from the dataset
2 Assumptions

3 Exploratory Data Analysis – Step by step approach
A Typical Data exploration activity consists of the following steps:

  1. Environment Set up and Data Import
  2. Variable Identification
  3. Univariate Analysis
  4. Bi-Variate Analysis
  5. Missing Value Treatment (Not in scope for our project)
  6. Outlier Treatment (Not in scope for our project)
  7. Variable Transformation / Feature Creation
  8. Feature Exploration
    We shall follow these steps in exploring the provided dataset.
    Although Steps 5 and 6 are not in scope for this project, a brief about these steps (and other steps as well) is given, as these are important steps for Data Exploration journey.
    3.1 Environment Set up and Data Import
    3.1.1 Install necessary Packages and Invoke Libraries
    Use this section to install necessary packages and invoke associated libraries. Having all the packages at the same places increases code readability.
    3.1.2 Set up working Directory
    Setting a working directory on starting of the R session makes importing and exporting data files and code files easier. Basically, working directory is the location/ folder on the PC where you have the data, codes etc. related to the project.
    Please refer Appendix A for Source Code.
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    3.1.3 Import and Read the Dataset
    The given dataset is in .csv format. Hence, the command ‘read.csv’ is used for importing the
    file.
    Please refer Appendix A for Source Code.
    3.2 Variable Identification

    3.2.1 Variable Identification – Inferences

    3.3 Univariate Analysis




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    3.4 Bi-Variate Analysis


    3.5 Missing Value Identification

    3.6 Outlier Identification
    3.7 Variable Transformation / Feature Creation


    4 Conclusion

    5 Appendix A – Source Code

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Exploratory Data Analysis – CardioFitness

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Environment Set up and Data Import

Setup Working Directory setwd(“D:/M1 Project”) getwd()

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Read Input File

cgf_data=read.csv(“CardioGoodFitness.csv”)
attach(cgf_data)
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Find out Total Number of Rows and Columns

dim(cgf_data)

[1] 180 9

Find out Names of the Columns (Features)

names(cgf_data)

[1] “Product” “Age” “Gender” “Education”

[5] “MaritalStatus” “Usage” “Fitness” “Income”

[9] “Miles”

Find out Class of each Feature, along with internal structure

str(cgf_data)

‘data.frame’: 180 obs. of 9 variables:

$ Product : Factor w/ 3 levels “TM195″,”TM498”,..: 1 1 1 1 1 1 1

1 1 1 …

$ Age : int 18 19 19 19 20 20 21 21 21 21 …

$ Gender : Factor w/ 2 levels “Female”,”Male”: 2 2 1 2 2 1 1 2 2

1 …

$ Education : int 14 15 14 12 13 14 14 13 15 15 …

$ MaritalStatus: Factor w/ 2 levels “Partnered”,”Single”: 2 2 1 2 1 1

1 2 2 1 …

$ Usage : int 3 2 4 3 4 3 3 3 5 2 …

$ Fitness : int 4 3 3 3 2 3 3 3 4 3 …

$ Income : int 29562 31836 30699 32973 35247 32973 35247 32973

35247 37521 …

$ Miles : int 112 75 66 85 47 66 75 85 141 85 …

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T H E – E N D

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