Description
In this project you are required to build a model for a classification task using the
following files (training.xlsx and test.xlsx). You can use any machine learning
algorithm given that you can explain it carefully in your discussion.
In your work, you should go through the basic EDA steps and understand and
analyze the features, test the correlation between the features, perform any
necessary steps related to the features such as missing values, … etc.
In your submission, you should provide a report of the problem at hand, what
are the EDA steps that you performed, explain the machine learning algorithm
that you selected, present the technical details of the implementation, proper
analysis of the training and test error. In terms of performance measures, you
should show the accuracy, precision, recall, and f-score on the test set.
Additionally, state the details related to the bias and variance on this set.
The report should have the following sections:
1. Introduction: includes a general introduction of the work
2. EDA: any details, graphs, pre-processing steps of the data
3. Algorithm: explain the used algorithm
4. Results: state the results
This is a group work (groups of twos). You have to turn in a softcopy of your
Python code and a Word document containing the information required to as
specified above. The document should be on a paper-format. Please send your
submission as a response to this message only.
The link of the data folder:
https://www.dropbox.com/sh/acsrl64l4bjqsmu/AAAEzx6teVx3l2oSKFaT3esa?dl=0
