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R

(R Language) Determining poisonous mushrooms in data with decision tree models

(R Language) Determining poisonous mushrooms in data with decision tree models
You will document in a report the results of each step of the mining process, analyze and interpret the results. Suggest the characteristics to use when determining if a mushroom is safe to eat. Make recommendations for additional analysis and variables to examine to build other classifications such as use of the mushrooms that are not poisonous.
The report should include the following:
Code walk through: in this section provide a step by step explanation of how the code is interacting with and/or transforming the data. Provide examples from the output to support your explanations.
Analysis: Based on the output, analyze the data and the relationships revealed about the variables of interest. Explains the insights provided by the output. Use visualizations to support your analysis.
Interpretation and Recommendations: Interpret the results of your analysis and explain what the results mean for the data owner. Provide recommendations for actions to be taken based on your interpretation. Support those with the data. Explain why and what explicit variables you suggest incorporating. For example, median income by city and state from the census.gov website might be useful for examining home ownership.
Both the r-code file and the word file are required.No of pages 7

Categories
R

(R Language) Determining poisonous mushrooms in data with decision tree models

(R Language) Determining poisonous mushrooms in data with decision tree models
Details You will document in a report the results of each step of the mining process, analyze and interpret the results. Suggest the characteristics to use when determining if a mushroom is safe to eat. Make recommendations for additional analysis and variables to examine to build other classifications such as use of the mushrooms that are not poisonous.
The report should include the following:
Code walk through: in this section provide a step by step explanation of how the code is interacting with and/or transforming the data. Provide examples from the output to support your explanations.
Analysis: Based on the output, analyze the data and the relationships revealed about the variables of interest. Explains the insights provided by the output. Use visualizations to support your analysis.
Interpretation and Recommendations: Interpret the results of your analysis and explain what the results mean for the data owner. Provide recommendations for actions to be taken based on your interpretation. Support those with the data. Explain why and what explicit variables you suggest incorporating. For example, median income by city and state from the census.gov website might be useful for examining home ownership.
Both the r-code file and the word file are required.
Apa style formatting. No of pages 8 (1800 words) References page required and intext citations are must

Categories
R

Is it more expensive or less expensive to live in FL or NY?

Review the attached file. Suzie has an issue. She can either move to NY or FL and needs to review some data that her agent gave her. The agent reviewed house prices and crime ratings for houses that Suzie would be interested in based on her selection criteria. She wants to live in an area with lower crime but wants to know a few things:
Is it more expensive or less expensive to live in FL or NY?
Is the crime rate higher in FL or NY (Note a low score in crime means lower crime)?
Is the crime rate higher in lower or higher house price areas?
Using the R tool, show the data in the tool to answer each of the questions. Also, show the data visualization to go along with the summary.
If you were Suzie, where would you move based on the questions above?
After you gave Suzie the answer above (to #4), she gave you some additional information that you need to consider:She has $100,000 to put down for the house.
If she moves to NY she will have a job earning $120,000 per year.
If she moves to FL she will have a job earning $75,000 per year.
She wants to know the following:On average what location will she be able to pay off her house first based on average housing prices and income she will receive?
Where should she move and why? Please show graphics and thoroughly explain your answer here based on the new information provided above.
Note: The screenshots should be copied and pasted and must be legible. Only upload the word document. Be sure to answer all of the questions above and number the answers. Be sure to also explain the rational for each answer and also ensure that there are visuals for each question above. Use at least two peer reviewed sources to support your work.

Categories
R

visualizing the data of engineering graduates in different categories like specialization or GPA or different bachelor’s degree.

I am writing a research paper and presentation so my part is doing conclusion part and concluding each graphs and r files I have attached the file here. I am unable to attach r file here but I can copy those codes and paste it
https://dev.to/bpb_online/6-phases-of-data-analyti… Go to this link for relating my assigned work. you have to do Communication result for me looking at those graphs
course name is analyzing and visualization
brief overview
this research paper is about visualizing the data of engineering graduates in different categories like specialization or GPA or different bachelor’s degree. The paper investigates and provides the analysis graph and regression model prediction of the salaries and the driven factors. There are numerous variables that can impact the salary offers like GPA of the student or what bachelor’s they are graduating. Today is an era of data and information. Every organization needs to store the data of each functionality, it can be transactional, sales data, inventory data, cost information, product information and many more. This data can be used for multiple purposes to do a decision making which can benefitted the organization as well as helps to understand the market and users. The data volume is also increasing with time, since every other company, organization, school, medical institutes, and all other industries. To analyze these high volumed data, the industries are using various tools and technologies like R or Python programming to do all sorts of data ETL and visualization.