COMP5411FA - Dataset - Missingness - Categorical - Data Assessment Answer

COMP5411FA - Dataset - Missingness - Categorical - Data Assessment Answer 

Assessment Task:

Write a program that will open a data file (“data.csv”) containing multiple rows and columns (no missing value). Assume both your program and the data file are in the same directory so that explicitly mentioning the path is not required while loading the file. Each column represents a feature and each row is an instance. The dataset should have at least 2 continuous features, 1 categorical feature and 100 instances. Your program then should randomly create missingness in each feature. For each feature implement the following:
a. Randomly remove 5% of values and mark the removed values as missing
b. Employ 3 nearest neighbor imputation methods (1-NN, k-NN and weighted k-NN) [hint: distance from complete cases]. Choose any k>=5. For continuous features, use 2 different distance measures and for categorical, use 1 distance measure. You cannot use any existing library to calculate distance or to implement imputation.
c. For each feature, calculate the accuracy of imputation. You will define your own accuracy measures for both continuous and categorical features [hint: how different the imputed values are from the original values].
d. For each feature, conduct above steps (a-c) on the original data.csv file.

Write a report which will have the following 3 sections:
a. Data: Describe features, instances and the source.
b. Methods: Explain the 3 imputation methods, 3 distance methods, 2 feature scaling methods and imputation accuracy measures used in your experiments.
c. Tools: Mention which language (R or Python), version and IDE you used for implementation. Mention if any library needs to be installed for running your code. Provide installation instructions.
d. Results: Present imputation accuracies for each feature for all 3 different missing percentages (5%, 10%, and 20%) and different imputation methods. Use tables and graphs for presenting the results. Also, present comparative analysis of imputation performance when original values are used for distance calculation vs. when scaled values are used. All of these should be reported for two different distance measures.

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