This is a group-based project (2 – 4 students) using PYTHON programming language to analyse a specific problem in the following areas, such as Pharmacy, Library, Holiday Booking System, Medical Practice, Concert hall, Motor mechanic, Sales, Customers behaviour, Primary School, Role-playing game and manufacturing etc. The dataset should have at least 4000 rows and 10 columns (for example, type of variables may be categorical, continuous and discrete) after cleaning and there is not any maximum limit. Your group would need to formulate a set of objectives in the domain of chosen dataset and the ML project should address the achievement of these objectives. Fundamentally, the objectives should provide a clear outline about your project. For example, which features are the most important for predicting target variable (X)? You can start with a simple approach so you can achieve something quickly and then progress to more complicated approaches during this group project.
The group should consider the following guidelines during the development of Machine Learning (ML) project.
1. Justification for the selection of machine learning approaches for the chosen problem using any data mining framework, such as CRISP-DM, KDD or SEMMA for the implementation. CCT College Dublin Dr. Muhammad Iqbal
2. For ML techniques (Classification, Regression and Clustering), you should plan on trying multiple approaches (at least two), with proper parameterselection techniques and a comparison between the chosen modelling approaches.
3. You should train ML modelling techniques and subsequently, test the models. Perform a comparison of two or more ML modelling techniques. You may use a statistical approach to argue that one feature is more important than some other feature.
4. Depending on the complexity of the problem, you should use cross-validation approach to justify the authenticity of your ML modelling results. Your group will present the findings and defend the results in the report (MS Doc/ pdf or any other readable format). Your report should capture the following aspects that are relevant to your approach.
i. Brief description and motivation of the problem for Machine Learning. (250 words, 10 marks)
ii. What is/are the objectives of the problem(s) that are addressed in your project (Classification/ Regression/ Clustering Rules/ Information extraction etc..) (100 words, 10 marks)
iii. Characterization of the data set: source URLS; size; number of attributes; has/ does not have missing values; number of examples etc. Clean and remove the missing values from the dataset. Provide a clear strategy. (100 words, 10 marks)
iv. Train the ML models based on three different splits and discuss the variation in accuracy/ score obtained from the models in the training as well as testing. (400 words, 30 marks)
v. Interpret the results based on problem specification and objectives. The ML modelling results should neither overfitted nor underfitted. Justify with arguments. (500 – 750 words, 20 marks)
vi. Provide the explanation of code that will be used to solve the problem. Comments must be provided along with code. (200 words, 10 marks)
viii. Conclusions based on the predictions and classification. Harvard style citations and References must be provided in the report. (200 words, 10 marks)
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