Design machine learning processes to build predictive models

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Reference no: EM133829819

Artificial Intelligence and Machine Learning in IT

Assessment - The Machine Learning Demonstration

Assessment - Simulation and Evaluation

Task
Your second assessment requires you to complete a demonstration and presentation in class (week 9) within a group setting (3 - 4 students) by using Python libraries in Machine Learning. You will need to understand different types of machine learning algorithms and some commonly used Python libraries prior to building the predictive models using the supplied dataset.

Assessment Description
Python library is a collection of modules that are linked together. It has code bundles that can be used repeatedly in different programs. It makes programming easier and simpler due to the re-use attribute of a Python library. Get Instant Solutions for Your Assignments!

In the previous workshops, you have learned and seen some commonly used Python libraries such as Pandas, Matplotlib, NumPy, SciKit-Learn (SK-Learn) etc. You will be given a dataset, and you need to choose two applications that you learnt to develop predictive models and explain the process in the context of machine learning algorithms as a presentation format. The assessment must be completed in class within a time limit.

Learning outcome 1: Design machine learning processes to build predictive models

Learning outcome 2: Create supervised and unsupervised machine learning algorithms

Assessment Instructions

Part A: Simulation of Machine Learning

Your facilitator would have assigned you to a group of 3 to 4 students prior to this in-class assessment. As a group, you will:

Discuss and work together on Python libraries (in PyCharm) to analyse the given dataset which will enable you to assess the characteristics of data and discover findings that lead to implications.
Use the provided dataset to create a predictive model. Consider the following:

The appropriate machine learning algorithms.
The output from running the model.
Take note of your discussion and simulation for the group presentation.

Part B: Evaluation and Presentation

In the same group, you are required to create a group oral presentation to evaluate and showcase your work from Part A considering the following:
Use a presentation tool such as PowerPoint.
Show the applications used to assess the dataset and create the model by selected Python libraries.
The process of choosing the appropriate machine learning algorithms.
The output of the predictive model.
Other methods you could use for prediction and contrast with the one you have used.
Recommendations that you will provide based on the findings and implications.

Reference no: EM133829819

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