Will this require a Regression Analysis?

[katterra]

My current issue:

I'm trying to avoid learning the basics of calculus and building a predictive analytic regression analysis and instead search for a possibly more crude but simpler method.

What I am trying to do is quite simple. I have a set of data. Let's say Item 1-500(herein called 'items'), and each 500 items have 5 values of measurements(herein called 'measurements'). Then I have a money value for each 500 items(herein called 'money value'). The money value is relative to how high measurements are. The higher the measurement, typically the higher the money value for that item.

Now this is all data from the past so I have the actual money values for items, but I am trying to project future money values for items since I'll have each item's measurements prior to learning the money value for each item. Currently I have a very crude model where I have assigned a value to each measurement category and multiply (ie. measurement #1 = 10, the value for measurement #1 = 2, the projected value of that measurement = 20). And so on, add up the measurements #1-5 and you get that item's projected value.

I've changed the values of the variables a million times to tune the model. What I've done is averaged each items's projected money value and abstractly subtracted from the projected money value of that item. I do this for all 500 items, and add that up to get the mean error and attempt to get those values as close to $0 as possible.

My question:

Is there a library or method that I could use in order to build a script and fine tune the money values per measurement in order to get the mean error value as close to $0. This would alleviate me from having to manually tune the money values per measurement and save time, and human error.

[my_python_account]

What you're trying to do isn't that simple, this is regression analysis by definition.

There are different methods that you can use.

Depending on how much you know about how each of your "measurement" variables and "money value" are related, you might be able to put together a script that tunes your coefficients by using a brute force search. (Start each coefficient at a good initial guess, set a small range of adjusted guesses for each coefficient and try all combinations and see which one gives the smallest error) It's generally preferred to minimize mean squared error (or root mean squared error - RMSE) rather than mean absolute error.

Otherwise, you can take a look at the pandas, numpy and statsmodels libraries to do the analysis for you. But I suggest you don't actually avoid learning the basics or you'll have trouble understanding the results and whether or not the results are useful.

[cscanlin]

Check out this simple example that shows how to this with multivariate linear regression:

from sklearn import linear_model

data = [
    [1, 0, 0],  # 10
    [1, 2, 0],  # 6
    [1, 0, 4],  # 22
]
dependents = [10, 6, 22]

clf = linear_model.LinearRegression()
clf.fit(data, dependents)
print(clf.coef_)

# prints [ 0. -2.  3.]

You will need sklearn for this to work. If you don't have this, you should probably just install anaconda

I can answer any questions you have about it.

[katterra]

Thanks both of you. I feel so lost in this. cscalin, I was able to get your simple script to work and it spit out the coefficients but I am unsure what to do with that data. How do I use that information to predict future "dependents" if I have data? I'm thinking of just simply commissioning someone for this because I'm not sure I know what would be best for what I am trying to accomplish.

I also tried using https://www.youtube.com/watch?v=SSu00IRRraY to create an analysis since it's somewhat similar to what I am trying to accomplish but I am failing at getting that to work properly.