This is the data test for the Junior Data Analyst position at Azavea. Please complete the questions below and submit your responses in the next week. The first question is conceptual and a written response is expected. The second question and its several parts are applied. A submission of the code used to solve the problems, the answers, along with any plots or graphics generated are expected as responses.
This first question is conceptual and written responses are expected. For each item below, indicate whether the appropriate method would be classification or regression, and whether we are most interested in inference or prediction. Please include a written sentence or two explaining why you made this choice. Also, indidate what n and p (predictors) are for each section.
(a) A dataset contains data for 350 manufacturing companies in Europe. The following variables are included in the data for each company: industry, number of employees, salary of the CEO, and total profit. We are interested in learning which variables impact the CEO's salary.
(b) A market research company is hired to help a startup analyze their new product. We want to know whether the product will be a success or failure. Similar products exist on the market so the market research company gathers data on 31 similar products. The company records the following data points about each previously launched product: price of the product, competition price, marketing budget, ten other variables, and whether or not it succeeded or failed.
(c) Every week data is collected for the world stock market in 2012. The data points collected include the % change in the dollar, the % change in the market in the United States, the % change in the market in China, and the % change in the market in France. We are interested in predicting the % change in the dollar in relation to the changes every week in the world stock markets.
For this second applied question you will develop several predictive models. These should be written in R or Python and the code should be submitted. The models will predict whether a car will get high or low gas mileage. The question will be based on the Cars_mileage data set that is a part of this repo.
(a) Create a binary variable that represents whether the car's mpg is above or below the dataset's median. Above the median should be represented as 1. Name this variable mpg_binary.
(b) Which of the other variables seem most likely to be useful in predicting whether a car's mpg is above or below its median? Describe your findings and submit visual representations of the relationship between mpg_binary and other variables.
(c) Split the data into a training set and a test set.
(d) Perform two of the following in order to predict mpg_binary:
LDA (Linear discriminant analysis)
QDA (Quadratic discriminant analysis)
Logistic regression
KNN (K-nearest neighbors)
Decision Tree
Random Forests
Gradient Boosting
LASSO (Least Absolute Shrinkage and Selection Operator)
Elastic Net Method
Ridge regression analysis
For each of the two you select: What is the test error(s) of the model obtained? Do you have any other observations?