Prediction of gene/protein localization Data Set Description: This dataset was used in the 2001 kdd cup data mining competition. (http://www.cs.wisc.edu/~dpage/kddcup2001/). There were in fact two...

Prediction of gene/protein localization Data Set Description: This dataset was used in the 2001 kdd cup data mining competition. (http://www.cs.wisc.edu/~dpage/kddcup2001/). There were in fact two tasks in the competition with this dataset, the prediction of the "Function" attribute, and prediction of the "Localization" attribute. Here we focus on the prediction of “Localization” (this is somewhat easier as genes can have many functions, but only one localization, at least in this dataset). The dataset provides a variety of details about the several genes of one particular type of organism. The main dataset (Genes_relation.data and Genes_related.test) contains row data of the following form: Gene ID, Essential, Class, Complex, Phenotype, Motif, Chromosome Number, Function, Localization. The description of data attributes was given in file Genes_relation.names. The first attribute is a discrete variable corresponding to the gene (there are 1243 gene values). Also the remaining 8 attributes consist of discrete variables, most of them related to the proteins coded by the gene, e.g. the "Function" attribute describes some crucial functions the respective protein is involved in, and the "Localization" is simply the part of the cell where the protein is localized. In addition to the above files, there are also data files (Interactions_relations.data and Interactions_relation.test) which contain information about interactions between pairs of genes. Data File Size: ▪ Gene_relation files: 6275 examples (4346 training, 1929 test), 8 categorical attributes. ▪ Interaction_relation files: 1806 records, 2 attributes (one categorical; one numerical) Task: Perform exploratory data analysis to get a good feel for the data and prepare the data for data mining. The task in this dataset is to make predictions on the attribute "Localization". Detailed knowledge of the biology should not be necessary for this assignment. One word of caution: your classifier for localization should not use “function”, since both fields will be withheld from the test genes when they are provided. Challenge: This dataset is a great challenge. One issue is that there is a high proportion of missing variables in the Genes_relation data. The other issue is how to use the interaction data effectively. Keys: The keys are provided in the file keys.txt. Use this file to evaluate the accuracy of your solution. References: • Talk overview slides about this problem and also the winner presentation in the KDD 2001 competition can be found on-line. • See also Answers to Questions of General Interest from Question Period 1 and Answers to Questions of General Interest from Question Period 2 Deliverables: ▪ (30 points) All workable program files: in this assignment, you are required to design and implement a classification algorithm to predict gene localization. You may choose any classification algorithm we covered in class and implement it in any programming language of your choice. However, you must implement the underlying classification algorithm from scratch. In other words, you are NOT allowed to use existing machine learning related libraries in your program. ▪ (2 points) A result file in the format of , in each row. ▪ (8 points) A report that includes o how to run your program; o the methods you used to handle missing data and/or interaction data; o the method you used to build the classifier; o the accuracy of your solution. The overall accuracy generated by your solution is expected to be higher than 40%. • Include all the files into a single .zip file and submit your file