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MiniProject: Mining Accident Analysis In Machine Learning

Project Objective

Employers are required to report any serious work-related injuries and death to the authority. This information helps employers, workers and the authority to evaluate the safety of a workplace, understand industry hazards, and implement worker protections to reduce and eliminate hazards.

In this mini-project, assume you are engaged by a client to perform text mining on the accident reports to help find answers to the following questions:

1. What are the major types of accidents reflected in the reports?

  • No labels, supervised or non-supervised?

  • Clustering or Topic modelling?

  • All data or partial data?

2. Which type of accidents are more common?

  • Frequency of doc wrt topic

3. Can we find out the more risky occupations in such accidents?

  • Information Extraction, how to identify “occupations” words?

4. Which part of the body is injured most? (Optional)

  • Information Extraction, how to identify “body” words?

The dataset is in file “osha.txt“.

Data understanding and cleaning

Load the data file into R. – read.delim(), header=FALSE

e.g. textdata <- read.delim("osha.txt", header=FALSE, sep="\t", quote = "", stringsAsFactors = FALSE)

Explore your data:

- How many records do you have? How many variables?

- Examine the first few records in the datasets.

- What information does the dataset contain?

- Which fields are useful for your study?

- How long are the reports generally?

- How’s the data quality?

- What are the contents of the reports roughly? [ Create a word cloud for the dataset ]

  • Vectorsource, corpus, DTM

  • Term frequency summary

  • Wordcloud

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