Assignment-2
DS-600 Data Mining
Exploratory data analysis in R programming
留学生数据分析代写 The dataset is one of the historical sales of supermarket company which has recorded in 3 different branches for 3 months data.
This project should be done by individually.
Due date: 3/16/2021 by 11:59 PM ET
Points: 100
Task: 留学生数据分析代写
Perform data cleaning and data preprocessing techniques in R programming, analyze, visualize, and conclude your analysis based on your research and results which you find after applying data preprocessing techniques and analysis.
Data cleaning process should be done before data preprocessing. For data cleaning process please review “measurement and data collection issues” from Lecture – 2 notes. 留学生数据分析代写
Data Preprocessing Techniques: (Apply all these techniques in your analysis)
- Aggregation
- Sampling
- Dimensionality reduction
- Feature subset selection
- Feature creation
- Discretization andbinarization
- Variabletransformation
What to submit in assignment? 留学生数据分析代写
Submit a pdf document maximum limit 5 pages on Blackboard.
What to cover in PDF?
- Data Cleaningresults
- Data Preprocessing results (Please include all steps results one byone)
- Visualizations (Include Graphs and Plots in this section and one line description below each plot) 留学生数据分析代写
- Conclusion
- R code (This is mandatory to use R programming for thisassignment)
Data Information—
Data name: Sales in Supermarket
Data location: Week – 2 Folder of Week-by-Week section on Blackboard
Data info: The growth of supermarkets in most populated cities are increasing and market competitions are also high. The dataset is one of the historical sales of supermarket company which has recorded in 3 different branches for 3 months data.
Attribute information– 留学生数据分析代写
Invoice id: Computer generated sales slip invoice identification number
Branch: Branch of supercenter (3 branches are available identified by A, B and C).
City: Location of supercenters
Customer type: Type of customers, recorded by Members for customers using member card and Normal for without member card.
Gender: Gender type of customer
Product line: General item categorization groups – Electronic accessories, Fashion accessories, Food and beverages, Health and beauty, Home and lifestyle, Sports and travel
Unit price: Price of each product in $
Quantity: Number of products purchased by customer Tax: 5% tax fee for customer buying
Total: Total price including tax
Date: Date of purchase (Record available from January 2019 to March 2019)
Time: Purchase time (10am to 9pm) 留学生数据分析代写
Payment: Payment used by customer for purchase (3 methods are available – Cash, Credit card and E-Wallet)
COGS: Cost of goods sold
Gross margin percentage: Gross margin percentage Gross income: Gross income
Rating: Customer stratification rating on their overall shopping experience (On a scale of 1 to 10)
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