Reference no: EM132518037 , Length: 25 Pages

MATH2319 Machine Learning Assignment - RMIT University, Australia

Summary - In this course, you will be required to complete a major group course project. You will be working in groups of 1, 2, or 3 students for your project. The idea with the course project will be to apply what you have learned in class and more importantly, to have fun! The project shall entail the following tasks.

(1) Data pre-processing (dealing with missing values, dropping ID-like columns, data aggregation, etc.) as appropriate.

(2) Data exploration and visualisation (charts, graphs, interactions, etc) as appropriate.

(3) Predictive modelling as appropriate.

Purpose - The purpose of the course project is to apply machine learning techniques on a problem of your own choosing using Python and the Scikit-Learn module. The project will be hands-on and it will require you to select appropriate performance metrics for your problem and also properly compare performance of different methods. You will also gain experience on documenting your findings and your code in a Jupyter Notebook environment. In addition, you will get the opportunity to receive feedback on your performance and the report that you submit.

Programming Language Requirements -

The project must be done in Python 3. We will not mark any reports using any language other than Python.

Important Compliance Policy: You may not submit any previous work from RMIT or any other place for this project, even if they were done by yourself. RMIT policy is that a particular work can be submitted only once for credit.

Plotting and Algorithms Requirements -

A) Plotting Requirements

The minimum number of different plots you need to include in your report for the data exploration & visualisation part are as follows:

At least 3 plots of each one of the following: one-variable plots, two-variable plots, and three-variable plots (minimum 9 plots)

These plots must be meaningful and they need to make sense with respect to the goals and objectives of your project.

As a clarification, the plot requirements above are only for the data exploration & visualisation part. You will need to include some more plots for the predictive modelling part as appropriate. Specifically, we would like to see at least one plot showing the results of your fine-tuning process for each one of your algorithms.

In addition, for each one of plots in your project report, you will need to label the x- and y-axes as appropriate and add a meaningful title.

B) Algorithm Requirements

The minimum number of different ML algorithms you need to try on your problem are as follows: at least 4 algorithms.

Note - Total report for around 23-25 pages including code and plots.

Attachment:- Machine Learning Assignment Files.rar