LINUX PROGRAMMING AND DATA MINING Lab Manual Pdf – LPDM Lab manual pdf file
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Lab manual experiment names
LINUX PROGRAMMING
1 a) Write a shell script that displays a list of all the files in the current directory to which the user has read, write and execute permissions.
b) * Illustrate to redirect the standard input (stdin) and the standard output (stdout) of a process, so that scanf () reads from the pipe and printf () writes into the pipe?
2 a) Write a shell script that receives any number of file names as arguments checks if every argument supplied is a file or a directory and reports accordingly. Whenever the argument is a file, the number of lines on it is also reported. b) *Illustrate by writing c program where process forks to a child, and create a child process by using forks and suddenly terminates itself?
3 Write a shell script that accepts a list of file names as its arguments, counts and reports the occurrence of each word that is present in the first argument file on other argument files.
4 Write a shell script to list all of the directory files in a directory.
5 Write a shell script to find factorial of a given integer.
6 Write an awk script to count the number of lines in a file that do not contain vowels.
7 Write an awk script to find the number of characters, words and lines in a file.
8 Write a c program that makes a copy of a file using standard I/O and system calls.
DATA MINING
1 List all the categorical (or nominal) attributes and the real-valued attributes separately.
2 What attributes do you think might be crucial in making the credit assessment? Come up with some simple rules in plain English using your selected attributes.
3 *What attributes do you think might be crucial in making the bank assessment?
4 One type of model that you can create is a Decision Tree -train a Decision Tree using the complete dataset as the training data. Report the model obtained after training.
5 Suppose you use your above model trained on the complete dataset, and classify credit good/bad for each of the examples in the dataset. What % of examples can you classify correctly? (This is also called testing on the training set) Why do you think you cannot get 100 % training accuracy?
6 *Find out the correctly classified instances, root mean squared error, kappa statistics, and mean absolute error for weather data set?
7 Is testing on the training set as you did above a good idea? Why or Why not?
8 One approach for solving the problem encountered in the previous question is using cross-validation? Describe what is cross -validation briefly. Train a Decision Tree again using cross – validation and report your results. Does your accuracy increase/decrease? Why?
