For: Regis University - MSDS -692 Data Science Practicum I

Project Name: Credit Transaction (India/Australia)

I will be researching credit card sales in India/ Australia. The sales transaction will be examined for Male and Females as to what the cards are being utilized for.

I will also look at to see which type of credit card has the most usage.

Getting Started -Data Science Practicum I (Master Certificate in Data Science)

##Heading EDA Criteria- I looked at a few data set and in trying to determine the exploratory data analysis and seeing which data set will allow me the opportunity to provide enough information for my hypothesis. After choosing my data set I looked at what possible tool sets and packages I can use in determining my criteria. I was able to determine that with this current data set I should be able to do some data manipulation, data visualization to do some analysis that can possibly answer my question. Therefore, I found two data sets that I will be utilizing for this project. The dataset are compiled with information of credit card usage, in terms of what the monies are spent on and which gender is utilized the usage of CC frequently.

1. Data Exploration
2. Data analysis
3. Data Massage

Process Flow: The purpose of the flow chart is to show step-by-step flow of the process.

Git Hub was installed! And it has the following information about the project has been placed on GitHub:

o Data set (India and Australia)

o R-Studio Codes

o Data project – part 1 (Weka)

o Data project - Part II (R-Studio)

o Data project - Part III (Tableau)

Prerequisites - Installation: Download the following software:

R-Studio for Clustering //R-Studio codes setwd("C:/Users/guest 1/Desktop")

data <- read.csv("OnlineRetail_v13_Australia.csv", header = TRUE) str(data)

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summary(data)

save.image("C:/Users/guest 1/Desktop/DSP Project Workspace/OnlineRetail_v13_Australia.rdata.RData") barplot(prop.table(table(data$Gender))) barplot(prop.table(table(data$Quantity))) barplot(prop.table(table(data$UnitPrice))) barplot(prop.table(table(data$Cost)))

barplot(prop.table(table(data$Cost)))

data <- read.csv("OnlineRetail_v13_Australia.csv", header = TRUE) str(data)

summary(data)

barplot(prop.table(table(data$StkCode)))

barplot(prop.table(table(data$CardType))) barplot(prop.table(table(data$IntChg))) barplot(prop.table(table(data$Loss))) barplot(prop.table(table(data$Gender))) save.image("C:/Users/guest 1/Desktop/DSP Project Workspace/OnlineRetail_v13_Australiav1.rdata.RData")

data <- read.csv("OnlineRetail_v15_India.csv", header = TRUE) str(data)

data <- read.csv("OnlineRetail_v15_India.csv", header = TRUE) str(data)

barplot(prop.table(table(data$CardType))) barplot(prop.table(table(data$IntChg))) barplot(prop.table(table(data$Loss))) barplot(prop.table(table(data$CustomerID))) save.image("C:/Users/guest 1/Desktop/DSP Project Workspace/OnlineRetail_v15_India.rdata.RData")

data <- read.csv("OnlineRetail_v15_India.csv", header = TRUE) str(data) © 2018 GitHub, Inc. Terms Privacy Security Status Help Contact GitHub API Training Shop Blog About

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##Machine Learning for K-means

KNN

Tableau

Weka

software installation download can be found at : https://www.rstudio.com/products/rstudio/download https://machinelearningmastery.com/download-install-weka-machinewww.tableau.com/Tableau/Download

Coding style - generated using RStudio, Tableau and Weka

Utilized the above application as noted.

Asmita M