Data Science Asked by Justin Messi on September 25, 2021
I am currently working on a regression problem which requires me to predict the costs of a fixed asset. I have used several variables to do so and derived a predicted cost. However, my superior has wanted me to incorporate time as a variable in the regression model which I am at a loss on how to do so. My data set looks something like this which does not resemble a time series dataset.
Name Capacity OEM Country Date of valuation MONTH YEAR Cost
A1 220 JAPAN JAPAN 1/1/2012 1 2012 300,000,000
A2 220 JAPAN JAPAN 1/1/2012 1 2012 300000000
B1 400 CHINA CHINA 1/3/2013 3 2013 475000000
B2 400 CHINA CHINA 1/3/2013 3 2013 475000000
B3 400 CHINA CHINA 1/3/2013 3 2013 475000000
B4 400 CHINA CHINA 1/3/2013 3 2013 475000000
C1 750 INDIA USA 1/5/2016 5 2016 268000000
C2 750 INDIA USA 1/5/2016 5 2016 268000000
The variables that I have used are capacity, OEM and country. Any help on how to incorporate time to my regression problem is welcomed.
Theres an approach I'd take which consists of two steps. The second is optional buy highly recommended.
Hope this helps.
EDIT1: Also, not asked, but depending on the algorithm you're using, you may want to normalize the numerical features you have.
Answered by 89f3a1c on September 25, 2021
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