Python Small Student Program

its a python code project with K-Means Clustering with scikit- Project Details in UCI Machine Learning Repository ([login to view URL]) , there is a data set with name Yeast Data Set. [login to view URL] This data set includes 1484 examples with 8 features of protein signal measurements and analyzes, which are categorized into 10 cell sites. For more details see the relevant publications listed on the data set description page The following information can be found in the columns of the [login to view URL] file: 1) Sequence Name: Accession number for the SWISS-PROT database 2) mcg: McGeoch's method for signal sequence recognition. 3) gvh: von Heijne's method for signal sequence recognition. 4) alm: Score of the ALOM membrane spanning region prediction program. 5) mit: Score of discriminant analysis of the amino acid content of the N-terminal region (20 residues long) of mitochondrial and non-mitochondrial proteins. 6) erl: Presence of "HDEL" substring (thought to act as a signal for retention in the endoplasmic reticulum lumen). Binary attribute. 7) pox: Peroxisomal targeting signal in the C-terminus. 8) vac: Score of discriminant analysis of the amino acid content of vacuolar and extracellular proteins. 9) nuc: Score of discriminant analysis of nuclear localization signals of nuclear and non-nuclear proteins. 10) Class Distribution (CYT, NUC, MIT, ME3, ME2, ME1, EXC, VAC, POX , ERL) Download from the Data Folder the files (1) [login to view URL] (contains the data of the data set) and (2) [login to view URL] (contains the data for the specific data set) and write code in the python programming language which: 1. will read the [login to view URL] file, delete the first column and convert the values of the last column from category type to binary. To do this, 10 columns must be created (which are the classes) and if the classes follow the order [CYT, NUC, MIT, ME3, ME2, ME1, EXC, VAC, POX, ERL] there must be 1 in the column of the current class and 0 in the rest, ie the CYT class is expressed as follows: 1000000000, while the ME3 class is expressed as: 0001000000. 2. will calculate for the Class Distribution class variable: Numerical Average, Average (median), Low Average, High Average, Standard Sample Deviation and Sample Variation. 3. will plot in the form of a bar chart for the class Distribution variable the Numerical Mean and the Variation 4. will use the k-means algorithm to create the clustering model. This means that the target attribute must be completely omitted in order to perform unsupervised classification. In the final stage, the actual values of the target characteristic can be correlated with the optically formed clusters. 5. will implement a simple graphical user interface (you can use the Tkinter GUI: [login to view URL]) where the user can select the features to be considered to run grouping and will display the relevant information resulting from the above questions 2-4.