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COMP8046 Information Analytics Assignment Sample MTU Ireland

COMP8046 Information Analytics module is designed to give students an introduction to the area of data analytics and its associated techniques. The module will explore the different approaches that can be taken when analyzing data, including supervised and unsupervised learning, and will cover a range of topics such as data cleaning, feature engineering, model selection, and evaluation. Students will have the opportunity to apply these techniques to real-world data sets to gain insights into the data.

This module is suitable for students with a background in computing or a related discipline who are interested in learning more about data analytics. It will also be of interest to those who wish to apply data analytics techniques to their research projects.

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In this section, we are describing some assigned activities. These are:

Assignment Activity 1: Describe the concepts, principles, methods, and techniques of machine learning and its role in knowledge discovery.

Machine learning is a branch of artificial intelligence that deals with the construction and study of algorithms that can learn from data. These algorithms are used to find patterns in data and make predictions about future data. Machine learning is used in a variety of applications, such as recommender systems, spam filtering, and computer vision.

The main concepts in machine learning are:

  • Supervised learning: This is where the data contains labels that indicate what the desired output should be. The algorithm learns from this data to produce a model that can then be used to make predictions on new data.
  • Unsupervised learning: This is where the data does not contain any labels indicating what the desired output should be. The algorithm must learn from the data itself to find patterns and make predictions.
  • Reinforcement learning: This is where the algorithm learns by trial and error, receiving rewards for correct predictions and punishments for incorrect predictions. overtime, the algorithm learns to maximize its rewards and minimize its punishments.

Machine learning can be used for a variety of tasks, such as:

  • Classification: This is where the data is divided into groups based on some criteria. For example, a classifier could be used to group images by whether they contain a dog or not.
  • Regression: This is where a model is learned that can predict a continuous value, such as a price or temperature.
  • Clustering: This is where data is grouped based on similarity. For example, a clustering algorithm could group images that contain similar objects.
  • Dimensionality reduction: This is where the number of features in the data is reduced while still retaining as much information as possible. This can be useful for visualization or for making machine learning algorithms run faster.

There are a variety of methods that can be used for machine learning, such as:

  • Linear regression: This is where a line of best fit is found for data that has a linear relationship.
  • Logistic regression: This is where a logistic function is used to model data that has a binary outcome (i.e. it can only have two possible values, such as true or false).
  • Support vector machines: This is where a hyperplane is found that maximizes the margin between different groups of data.
  • Decision trees: This is where a model is learned that can be used to make predictions by following a series of if-then-else rules.
  • Neural networks: This is where a network of artificial neurons is used to learn from data.

Machine learning is a powerful tool for knowledge discovery, as it can be used to automatically find patterns in data. It is also a rapidly growing field, with new algorithms and techniques being developed all the time.

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Assignment Activity 2: Utilise data pre-processing and manipulation techniques on data from a specific application domain.

Data pre-processing is a crucial step in data analysis, as it can help to remove noise and outliers, standardize features, and make the data more amenable to machine learning algorithms. There are a variety of different techniques that can be used for data pre-processing, such as:

  • Data cleaning: This is where missing or incorrect data is corrected.
  • Data normalization: This is where the data is transformed so that it has a mean of 0 and a standard deviation of 1.
  • Data standardization: This is where the data is transformed so that all features have a mean of 0 and a standard deviation of 1.
  • Data discretization: This is where continuous data is converted into discrete data.
  • Data reduction: This is where the number of features in the data is reduced.

All of these techniques can be used to improve the performance of machine learning algorithms, and it is often advisable to try a few different methods to see which gives the best results.

Assignment Activity 3: Select and apply appropriate machine learning algorithms to a range of datasets.

There are a variety of different machine learning algorithms, and the best algorithm for a particular task will depend on the nature of the data and the desired output. Some of the most popular machine learning algorithms include:

  • Linear regression: This is used for predicting continuous values, such as prices or temperatures.
  • Logistic regression: This is used for predicting binary values, such as true or false.
  • Support vector machines: This is used for finding hyperplanes that maximally separate different groups of data.
  • Decision trees: This is used for making predictions by following a series of if-then-else rules.
  • Neural networks: This is used for learning from data using a network of artificial neurons.

These are just a few of the many different machine learning algorithms that are available, and there is often more than one algorithm that can be used for a particular task. It is often advisable to try a few different algorithms to see which gives the best results.

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Assignment Activity 4: Analyse and interpret patterns and knowledge discovered from the application of machine learning algorithms to problems from a specific application domain.

Once a machine learning algorithm has been applied to a dataset, it is important to analyze the results to understand what has been learned. This can be done using a variety of different techniques, such as:

  • Data visualization: This is where the data is visualized to see if there are any patterns or trends.
  • Statistical analysis: This is where statistical methods are used to analyze the data.
  • Machine learning models: This is where a machine learning model is used to make predictions on new data.

It is often helpful to use multiple techniques to get a better understanding of the data.

Assignment Activity 5: Evaluate the accuracy of machine learning algorithms.

It is important to evaluate the accuracy of machine learning algorithms, as this will determine how well they can be used for decision-making. There are a variety of different measures that can be used for assessing accuracy, such as:

  • Classification accuracy: This is the percentage of correctly classified instances.
  • Mean absolute error: This is the average difference between the predicted and actual values.
  • Root mean squared error: This is the square root of the average difference between the predicted and actual values.

It is often advisable to use multiple measures to get a better understanding of the accuracy of a machine learning algorithm.

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