University : Rochester Institute of Technology UniLearnO is not sponsored or endorsed by this college or university.
Subject Code : ISTE600
Country : United Arab Emirates
Assignment Task :

ISTE-600 Project Description
The purpose of this project is to follow the process of going from data to knowledge using a data set that applies to a real-world problem. For this project, you will form teams of 4 to 5 students. Your team’s objective is to locate a data set in order to help solve a specific problem. This means that when locating a data set, you should be thinking about an impactful problem that working with this data set would solve and how this data set will allow you to work towards solving the problem. You may use any data mining software packages or libraries you wish for performing data mining tasks and any programming language for cleaning and pre-processing the data.

 

Project Proposal
By this deliverable, you will have established the data set you would like to use and the problem that the data set will help solve.
The proposal should be on the order of 2 pages, where a page is defined as 12-point Times New Roman font with 1” margins and 1.5 spacing. Excluding any figures, your document should be about 600-650 words long. Include the following sections.
• Problem Addressed – give a 2-3 sentence description of the problem your team is working to solve. Be as specific as possible.
• Data Set – provide a description of the data set and your reasoning for how this data set applies to the problem. Keep in mind that you could source multiple data sets. Make sure that you provide a link to any data set you plan to use!
• Pre-processing Steps – describe what data pre-processing steps you think might be required. Is there something mentioned in the description of the data set or something you noticed when initially looking at the data that lead you to these steps?
• Data Mining Approach – is it a supervised or unsupervised approach that you’ll be taking? Give an explanation is to why you believe it would be supervised or unsupervised. You may find that you need a mix of both approaches. Perhaps you need to cluster the data first to determine classes and then perform classification, for example.

Project Report 
• Executive Summary – give a high-level description of the problem and what will be included in the remainder of the report.
• Problem Description
• Data Exploration
o Source(s) of the data – provide link(s) and any background on the data that might be of interest. For example, some data is “simulated” and did not come from a real-world source.

  •  Number of records
  •  Attribute description – begin with a high-level description of each attribute, including what the attribute is, its type (continuous, discrete). Using a table to do this is ideal. Next, for each attribute, provide summary statistics (min, max, mean, standard deviation, frequency, mode). There should be figures/plots for at least some of the attributes, especially ones that appear to be interesting.
  •  Missing values – did you find any missing values? If so, how do you plan to deal with these?
  •  Outliers – did you find any outliers? If so, how do you plan to deal with these?
  • Data Preprocessing – which preprocessing steps did you use? Below is some guidance on what to write about if you performed any of the following steps.
  • Discretization - show the discretization scheme you used and the new distribution for any attributes you discretized.
  • Sampling - describe the process you used and show the summary statistics of the attributes for the sampled data set.
  • Aggregation – describe the process used for aggregating data within an attribute and show the new distribution. Give reasoning for why you did this.
  • Dimensionality Reduction/Feature Selection – how/why did you decide to remove features from the data set. What was the result of having done this?

• Data Mining Techniques/Algorithms Used - Describe the techniques and algorithms used at a high level and why you decided to use them.
• Results - Perform an appropriate analysis of results. For example, discuss errors in classification models using confusion matrices. The purpose here is to compare the results obtained from various models or approaches that you tried.
• Conclusions and Lessons Learned – What are the major takeaways from this project in terms of how well you were able to solve the problem you stated. What did you learn from working on the project together as a team?

 

This ISTE 600: IT Computer Science Assignment has been solved by our IT Experts at UniLearnO. Our Assignment Writing Experts are efficient to provide a fresh solution to this question. We are serving more than 10000+ Students in Australia, UK & US by helping them to score HD in their academics. Our Experts are well trained to follow all marking rubrics & referencing style.

Be it a used or new solution, the quality of the work submitted by our assignment experts remains unhampered. You may continue to expect the same or even better quality with the used and new assignment solution files respectively. There’s one thing to be noticed that you could choose one between the two and acquire an HD either way. You could choose a new assignment solution file to get yourself an exclusive, plagiarism (with free Turnitin file), expert quality assignment or order an old solution file that was considered worthy of the highest distinction.

  • Uploaded By : Grace
  • Posted on : April 18th, 2019
  • Downloads : 104

Whatsapp Tap to ChatGet instant assistance