COIT12209 : Different Facets of Data Science and its Applications - Different Numeric Variables - Engineering Assignment Help

Assignment Task :

Assessment 2 is an individual assessment. This assessment relates to unit learning outcomes 2, 3, 4, and 5 stated in the e- unit profile. In assessment 2, you are assigned tasks which assess your unit knowledge gained about different facets of Data Science and its applications. You are required to write a report discussing the tasks given below. This assignment forms 40% of the total assessment for the unit. Please note that ALL submitted assignment reports are passed through a computerized copy detection system and it is extremely easy for teaching staff to identify copied or otherwise plagiarised work. 

• Copying (plagiarism) can incur penalties ranging from deduction of marks to failing the unit or even exclusion from the University. 

• CQUniversity values academic honesty and integrity and demands ethical behaviour in all aspects of academic endeavours. The University investigates and deals with incidents of misconduct among its student community and by former students in a consistent manner, affording natural justice, and deciding outcomes and penalties that are appropriate, fair and just.

The tasks 

Winemaking or vinification is the invention of wine. This wine making process starts with the selection of fruits, its fermentation into alcohol and bottling of the finished liquid. The process history of wine-making stretches over millennia. There are mixtures of wines with different tastes and quality, such as red and white wines. The wine industry keen to know the insightful information of wine by analysing the physicochemical data. This information will help to understand the quality of wines. 

As a data scientist, you have been requested to prepare a report to demonstrate the outcomes of your analysis to the executives. Your target audience, the executives of the wine industry, have extensive business experience but limited ICT knowledge. They would like to see how the new findings of wine quality may benefit the wine industry. 

The main body of the report should include the following analyses. 

1. Write R code to load the two wine datasets into the defined local variable called “df1” and “df2”?  

2. Write R code to add a label column to both “df1” and “df2” data frame indicating a label “red” or “white” and then combine “df1” and “df2” into a single data frame called “wine”. Write down the summary of the data.  

3. Write R code to check the missing value and create a heat map of missing value. Write your explanation with a screenshot (Hints: Install Amelia package and use missing map function) and discuss the quality of the data.  

4. What is a correlation matrix? Show the correlation between different numeric variables and write your explanation. 

5. Create a histogram of residual sugar from the wine data. Show the gradient of red and white wines by using color and discuss your thoughts on the created plot.  

6. Create a histogram of citric.acid from the wine data. Show the gradient of red and white wines by color. It would be best if you put an explanation for the histogram in your report.  

7. Create a scatterplot of residual sugar versus citric.acid, color by red and white wines. It would be best if you put an explanation for the scatterplot in your report.  

8. Create a scatterplot of volatile.acidity versus residual.sugar, color by red and white wines. It would be best if you put an explanation for for the scatterplot in your report.  

9. Create a histogram of alcohol from the wine data, color by red and white wines and discuss your thoughts on the create plot.  

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