Data Mining & Statistical Modeling EPA Report Code and Excel

1. Describe the difference between classification and regression, supervised and unsupervised learning, and training and testing data.

The initial post must be between 250-300 words in length. Cite your sources in a clickable reference list at the end. Do not copy without providing proper attribution (quotation marks and in-line citations). Write in essay format not in bulleted, numbered or other list format. 

2. Describe an appropriate visualization technique for each of prediction, classification, time series forecasting, and unsupervised learning.

The initial post must be between 250-300 words in length. Cite your sources in a clickable reference list at the end. Do not copy without providing proper attribution (quotation marks and in-line citations). Write in essay format not in bulleted, numbered or other list format. 

3. Write a short paragraph on the following topics you learned from Chapter 7 of the book. 

Describe Euclidean distance in your own words. What happens to the Euclidean distance when there are large number of features (columns)?

The initial post must be between 250-300 words in length. Cite your sources in a clickable reference list at the end. Do not copy without providing proper attribution (quotation marks and in-line citations). Write in essay format not in bulleted, numbered or other list format. 

4. Describe the constraints for using Logistic Regression. Also provide a business use case for the algorithm.

The initial post must be between 250-300 words in length. Cite your sources in a clickable reference list at the end. Do not copy without providing proper attribution (quotation marks and in-line citations). Write in essay format not in bulleted, numbered or other list format. 

5. Describe various predictive ensemble methods and a business use of such methods.  

The initial post must be between 250-300 words in length. Cite your sources in a clickable reference list at the end. Do not copy without providing proper attribution (quotation marks and in-line citations). Write in essay format not in bulleted, numbered or other list format. 

6. Describe various types of collaborative filtering and a business use case.  

The initial post must be between 250-300 words in length. Cite your sources in a clickable reference list at the end. Do not copy without providing proper attribution (quotation marks and in-line citations). Write in essay format not in bulleted, numbered or other list format. 

7. Final Case Analysis
Attached Files:
•      Final case analysis (1).zip (2.968 KB)

Introduction:  
Environmental Protection Agency (or EPA for short) is responsible for regulating the amount of pollutant emission from all automobiles that run on American roads. You are asked to analyze the data released by EPA for more than a decade, specifically for three time periods: 2010 – 12, 2014-16, and 2018 – 20. There are several objectives to this case analysis, one of which is to test and learn about the possible changes in the amount of pollutions emitted by vehicles overtime. You are also asked to analyze similarities between vehicles over the three time periods and empirically determine if certain vehicles became more (or less) polluting over the period of study.
 

You will analyze various aspects of vehicle induced pollution using R programing. You are expected to submit findings in a report format. The report must be at least 20 pages long with written description and explanation of your findings to the questions asked below. Make sure to run all code using R Markdown and create a formal report with your remarks, comments or explanations embedded within the document.
 

You are given nine years of individual EPA data in csv format. The data files are not very large (each file is approx. 1 MB) . Each yearly file contains thousands of vehicles along with their vital information and pollution testing records. Each file contains 42 columns, the details of which are given in the Data Dictionary document. Please note that the original data had more columns, and some of them were removed for the consistency purposes. The deleted columns also exist in the data dictionary and you are asked to ignore them while referring to the dictionary.