In this lab, we are predicting the natural log of the sum of all transactions per user.
This is a great chance to practice all of our skills to date in order to create a regression model.
Start by importing the data and analyzing it briefly. Then, start fitting a model and performing successive iterations to tune and refine your model.
All data is stored in a csv file, 'train.csv' in the Data folder.
This data frame contains the following columns:
per capita crime rate by town.
proportion of residential land zoned for lots over 25,000 sq.ft.
proportion of non-retail business acres per town.
Charles River dummy variable (= 1 if tract bounds river; 0 otherwise).
nitrogen oxides concentration (parts per 10 million).
average number of rooms per dwelling.
proportion of owner-occupied units built prior to 1940.
weighted mean of distances to five Boston employment centres.
index of accessibility to radial highways.
full-value property-tax rate per $10,000.
pupil-teacher ratio by town.
1000(Bk - 0.63)^2 where Bk is the proportion of blacks by town.
lower status of the population (percent).
median value of owner-occupied homes in $10000s.
Source Harrison, D. and Rubinfeld, D.L. (1978) Hedonic prices and the demand for clean air. J. Environ. Economics and Management 5, 81–102.
Belsley D.A., Kuh, E. and Welsch, R.E. (1980) Regression Diagnostics. Identifying Influential Data and Sources of Collinearity. New York: Wiley.
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