Reference no: EM132327790

Practical Data Science Assignment - Data Modelling and Presentation

Introduction - This assignment focuses on data modelling, a core step in the data science process. You will need to develop and implement appropriate steps, in IPython, to complete the corresponding tasks.

This assignment is intended to give you practical experience with the typical 5th and 6th steps of the data science process: data modelling, and presentation and automation.

Task 1: Data Retrieving

This assignment will focus on data modelling, and you can choose to focus on one approach: Classification or Clustering.

For this assignment, you need to select one suitable dataset, from the following options:

1. Find and then analyse your own data set, in a domain that is of interest to you. If you choose this option, you will need to:

• include a detailed description of the data in your report in Task 4, and describe each attribute of it, including the type, the range of possible values, whether it contains any missing values/errors
• submit a copy of the dataset, to allow the assessment of your modelling result.

2. Select one data set from the UCI Repository. Choose one dataset from either the Classification or Clustering task.

Being a careful data scientist, you know that it is vital to set the goal of the project, then thoroughly pre-process any available data (each attribute) before starting to analyse and model it. In your report in Task 4, You need to clearly state the goal of your project, and the design/steps of pre-processing your data.

Please ensure you understand the data you selected, including the meaning of each attribute. For datasets from the UCI repository, you can obtain this information from the corresponding Web page under the sections Data Set Information and Attribute Information.

Task 2: Data Exploration

Explore the selected data, carrying out the following tasks:

Explore each column (or at least 10 columns if there are more than 10 columns), using appropriate descriptive statistics and graphs (if appropriate), e.g. the distribution of a numerical attribute, the proportion of each value of a categorical attribute. For each explored column, please think carefully and report in your report in Task 4):

1) The way you used to explore a column (e.g. the graph); 2) what you can observe from the way you used to explore it.

(Please format each graph carefully, and use it in your final report. You need to include appropriate labels on the x-axis and y-axis, a title, and a legend. The fonts should be sized for good readability. Components of the graphs should be coloured appropriately, if applicable.)

Explore the relationship between all pairs of attributes (or at least 10 pairs of attributes, if there are more in the data), and show their relationship in an appropriate graph. You may choose which pairs of columns to focus on, but you need to generate a visualisation graph for each pair of attributes. Each of the attribute pair should address a plausible hypothesis for the data concerned. In your report, for each plot (pair of attributes), state the hypothesis that you are investigating. Then, briefly discuss any interesting relationships (or lack of relationships) that you can observe from your visualisation.

Task 3: Data Modelling

Model the data by treating it as either a Classification or Clustering Task, depending on which dataset you previously selected.

You must choose two models within the particular Task category (i.e. two Classification models, or two Clustering models), and carry out the following steps for each model:

Select the appropriate model (e.g. DecisionTree for classification) from sklearn.

If you choose to do a Classification Task,

Split the data into training set and the test set. Specifically, please split the data at the following ratio:

• 50% for training and 50% for testing;
• 60% for training and 40% for testing;
• 80% for training and 20% for testing;

 For each of the training/testing split, perform the following steps:

Train the model by selecting appropriate values for each parameter in the model.

• You need to show how do you choose this value, and justify why you choose it (for example, k in the KNearestNeighbor model).

Test the accuracy of the model on the test set, and report the performance of the model in the following terms:

• Confusion Matrix
• Classification Error Rate
• Precision
• Recall
• F1-Score

If you choose to do a Clustering Task,

Train the model by selecting appropriate values for each parameter in the model.

• Show how do you choose this value, and justify why you choose it (for example, k in the k-means model).

Determine the optimal number of clusters.

Evaluate the performance of the clustering model by:

• Checking the clustering results against the true observation labels
• Constructing a \confusion matrix" to analyse the meaning of each cluster by looking at the majority of observations in the cluster. (You can do this by using a pen and a piece of paper, as we did in Practical Exercise 3 in Tute/Lab 06 (week7); if you prefer, you can also explore how to do this step directly in IPython.)

After you have built two Classification models, or two Clustering models, on your data, the next step is to compare the models. You need to include the results of this comparison, including a recommendation of which model should be used, in your report (see next section).

Task 4: Report

Write your report and save it in a file called report.pdf, and it must be in PDF format, and must be at most 12 (in single column format) pages (including figures and references) with a font size between 10 and 12 points Penalties will apply if the report does not satisfy the requirement. Remember to clearly cite any sources (including books, research papers, course notes, etc.) that you referred to while designing aspects of your programs.

Your report must have the following structure:

A cover page, including

• Title
• Author (your name(s))
• Affiliations
• Contact details
• Date of report

Table of Content

An abstract/executive summary

Introduction

Methodology

Results

Discussion

Conclusion

References

Task 5: Presentation

You will be required to do a presentation for your assignment 2 in Week 12's Tute/Prac:

The presentation should

• the goal of the project.
• briefly describe your chosen data set.
• the data preparation steps.
• state the hypotheses/questions that you were investigating,
• then explain what the analysis and results were.
• the final conclusion and recommendation.

The presentations are a maximum of 3 minutes per group, and we suggest each group to have at most 3 slides, and print them out on a4 paper, to put on the document camera for presentation (to save time connecting computers between presentations).

If you have your teammates are in different Tute/Prac sessions, you can choose to attend one of the sessions and do the presentation together. But, if you prefer to do the presentation separately in each of your sessions, which is also acceptable.

Note - This is practical data science assignment. Need presentation of 3 slides and explanation of project in 1 page word file. In this practical data science assignment there are two options to choose modelling the data. Please choose CLASSIFICATION.

Presentation includes:- The presentation should briefly describe your chosen data set, state the hypotheses/questions that you were investigating, then explain what the analysis and results were.

Attachment:- Assignment File.rar