1 ISYE 6501 HOMEWOR K 10 2023 LATEST UPATE GEORGIA INSTITUTE OF TECHNOLOGY 2 # clean env rm(list = ls()) df_breast <- read.csv( "C:/ISYE 6501 homework/breast -cancer-wisconsin.data.txt" , header=FALSE) head(df_breast, 2) Question 14.1 The breast cancer data set breast -cancer -wisconsin.data.txt from http://archive.ics.uci.edu/ml/machine - learning -databases/breast -cancer -wisconsin/ (description at http://archive.ics.uci.edu/ml/datasets/ Breast+Cancer+Wisconsin+%28Original%29 ) has missing values. 1. Use the mean/mode imputat ion method to impute values for the missing data. 2. Use regression to impute values for the missing data. 3. Use regression with perturbation to impute values for the missing data. Answer Steps: 1. load the data and run EDA 2. Find the missing data. Observation: column V7 contains 16 missing values. Based on lecture, need to calculate the percentage of missing value, which is 2.28% (less than 5%) for this dataset. So it is reasonable to impute the missing value. 3. Impute the missi ng value using mean, mode, regression and regression with perturbation.Please refer to the specific code for each process. One thing to mention, since V7 is the categorical data, in the real life, it is ok to impute by choosing mode method. While here mean method is also processed. Data description: 1. Sample code number: id number 2. Clump Thickness: 1 - 10 3. Uniformity of Cell Size: 1 - 10 4. Uniformity of Cell Shape: 1 - 10 5. Marginal Adhesion: 1 - 10 6. Single Epithelial Cell Size: 1 - 10 7. Bare Nuclei: 1 - 10 8. Bland Chromatin: 1 - 10 9. Normal Nucleoli: 1 - 10 10. Mitoses: 1 - 10 11. Class: (2 for benign, 4 for malignant) R code:
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