Reference no: EM133853573

Machine Learning Applications

Learning Objective 1: Explore programming functions to source, store and prepare data for machine learning applications.

Learning Objective 2: Evaluate the application of machine learning in the context of organisational data.

Learning Objective 3: Create advanced insights of strategic organisational value with the aid of machine learning.

Assessment - Exploratory Data Analysis Presentation

Task - In groups of 4 class members, you are to perform several data analysis and visualization tasks.

Assessment Description

Becoming a machine learning guru not only requires good grasp over the statistical concepts. It also requires the ability to explain the machine learning problem(s) in the context of the organisational goals. Performing exploratory data analysis is used to gain deeper understanding of the data. With which we can then formulate the machine learning problem and create insights to solve the problem. Book assignment help service now!

As a machine learning guru your team lead has asked you to give a presentation on the insights of the data.

Data

A list of data sets will be provided by your lecturer at the commencement of the test. Each data set comprises of the following:

Data file: A .csv file containing the data.
Information file: A .txt file containing a brief description about the data.

Tasks

Part A: Coding - Exploratory Data Analysis

As a group, you are required to download the list of data sets from MyKBS. Select any one data set you want to work with from the list of data sets. Once settled with the data set of your liking, address the following:

Analysis Problem: Create a potential use case for the data set selected. Think of this as what you want to understand from this data. And then create 4-5 different sub questions to build your understanding about the use case.

Loading Data: Load and summarise the data set using Python.

Feature Engineering: Perform necessary preliminary checks on the data keeping your use case in mind. For example: impute the missing values, create new feature columns, and more.

Exploratory Analysis: Create different visualisations for each of the sub questions to gain insights about the data.

Part B: Group Presentation

Build Power Point Presentation: Summarise all your findings and compose the findings how it addresses your use case in a power point presentation. Justify your answers with reference to the data analysis you have performed above. Tips: Explain your data, design of your new feature engineering steps, reflect on your visualisations in the slide deck.

Give Presentation: Each student contextualises the presentation to a non-technical audience in a business context. Each group will get 7 minutes to present their findings. Stick to the time limit otherwise you will be penalised for overtime. (90 mins for the whole class)