INFS4203 Project Phase II

Track 1: Data-oriented project

In Phase II, you will be provided with the test data named Ecoli_test.csv. The first row describes features names. Except the first row, each row in the data file corresponds to one data point. There are 917 test data points in this file, and each column represents the same feature as the training data Ecoli.csv. Note that the test data only has 106 columns, without labels, i.e., without the final column Target (Column 107) in Ecoli.csv. Labels for the test data will not be released and will be used by the teaching team for marking only.
In this phase, you will need to implement the ideas in your proposal and classify the test data. In the marking phase, F1 of the test data will be used for making. When calculating F1, 1 is counted as positive label, and 0 as negative label. You need to submit

• A result report on
  • Test result: the prediction on test data (in integer type) and
  • Evaluation result: the evaluated accuracy and F1 on the training data using cross validation (in float type).
• A readme file with clear and thorough description of your coding environment (operation system, programming language and its version, additional packages installed etc.) and instructions on how to run the codes such that your reported test and evaluation results can be reproduced. If you need any additional justification or references for your implemented methods, please also include them in the readme file. The readme file can be in any text format, such as .docx, .pdf, or .txt.
• Your implemented codes which include all your training procedures and a main function in a main file (for example, main.py) to pre-processing and train on the training data, prediction on the test data, and generate the submitted result report sxxxxxxx.csv file. Codes for the pre-processing and training procedure are required to be submitted. But the hyperparameter tuning or model selection procedure are NOT required to be submitted. You are recommended to include the best hyperparameters, models, pre-processing methods etc. directly in your submitted codes.

Track 2: Competition-oriented project

In this phase, you need to submit:

• A result report of the Public Leader Board results, including a screenshot and an URL of the Public Leader Board.
• A readme file with clear and thorough description of your coding environment (operation system, hardware requirement, programming language and its version, additional packages installed etc.) and instructions on how to run the code such that your final submission to Kaggle can be reproduced
• Your implemented codesincluding training and test codes which have a main function to generate the final submission to Kaggle.

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