### Assignment - Using Aggregation Functions for Data Analysis

**Assignment Detail:- ** Assignment - Using Aggregation Functions for Data Analysis
The provided zip file contains the data file -RedWine-txt- and the R code -AggWaFit718-R- to use with the following tasks, include these in your R working directory- You can use the R script -template-R- to organise your code-
Red wine quality Dataset -
The given dataset, "RedWine-txt", is used to model wine quality based on physicochemical tests- The dataset provides the 1,599 red wine samples from the north of Portugal- It is a modified version of the data used in the study -1-- This dataset includes 5 variables, denoted as X1, X2, X3, X4, X5, and Y, described as follows:
X1 - citric acid
X2 - chlorides
X3 - total sulfur dioxide
X4 - pH
X5 - alcohol
Y - quality -score between 0 and 10-
-1- P- Cortez, A- Cerdeira, F- Almeida, T- Matos and J- Reis- Modeling wine preferences by data mining from physicochemical properties- In Decision Support Systems, Elsevier, 47-4-:547-553, 2009-
Assignment Parts -
1- Understand the data
-i- Import the txt file -RedWine-txt- and save it to your R working directory-
-ii- Assign the data to a matrix, e-g- using the-data <- as-matrix-read-table-"RedWine-txt "--
-iii- The variable of interest is quality -Y-- To investigate Y, generate a subset of 440 data, e-g- using: my-data <- the-data-sample-1:1599,440-,c-1:6--
-The following tasks are based on the 440 sample data-
-iv- Using scatter plots and histograms to understand the relationship between each of the variables X1, X2, X3, X4, X5 and the variable of interest Y-
2- Transform the data
Choose any four from the five variables -X1, X2, ---, X5-- Make appropriate transformations to the chosen four variables and the variable of interest Y individually, so that the values can be aggregated in order to predict the variable of interest- Assign your transformed data along with your transformed variable of interest to an array-
3- Build models and investigate the importance of each variable
-i- Import AggWaFit718-R file to your working directory and load into the R workspace using, source-"AggWaFit718-R"-
-ii- Evaluating the following fitting functions on the transformed data:
-A weighted arithmetic mean -WAM-
-Weighted power means -WPM- with P=2
-An ordered weighted averaging function -OWA-
4- Use your model for prediction
Using your best fitting model based on Q3, predict the wine quality for the input: X1=1; X2= 0-075; X3=41; X4=3-53; X5=9-3-
5- Summarising your data analysis procedures in up to 20 slides for a 5-minutes presentation- The slides should include the following contents:
- What kinds of the data distribution you have identified in the raw data-
- Explain the transformations applied for the selected four variables and the variable of interest-
- Include two tables - one with the error measures and correlation coefficients, and one summarising the weights/parameters and any other useful information learned for your data-
- Explain the importance of each of the variables -the four variables that you have selected--
- Which fitting function is the best fitting model on your selected data-
- Give your prediction result and comment on whether you think it is reasonable-
- Discuss the best conditions -in terms of your chosen four variables- under which a higher quality wine will occur-
- Comment the implications and the limitations of the fitting model you used for prediction-
Attachment:- Aggregation Functions for Data Analysis Assignment File-rar

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