Python Code Solution
Budget: ₹600 – ₹1,500 INR
You get (A) 4 training datasets and (B) one test dataset, as well as (C) datasets for 50 ideal functions. All data
respectively consists of x-y-pairs of values.
Your task is to write a Python-program that uses training data to choose the four ideal functions which are the
best fit out of the fifty provided (C) *.
i) Afterwards, the program must use the test data provided (B) to determine for each and every x-ypair of values whether or not they can be assigned to the four chosen ideal functions**; if so, the
program also needs to execute the mapping and save it together with the deviation at hand
ii) All data must be visualized logically
iii) Where possible, create/ compile suitable unit-test
* The criterion for choosing the ideal functions for the training function is how they minimize the sum of all ydeviations squared (Least-Square)
** The criterion for mapping the individual test case to the four ideal functions is that the existing maximum
deviation of the calculated regression does not exceed the largest deviation between training dataset (A) and
the ideal function (C) chosen for it by more than factor sqrt(2)
In order to give proof of your skills in Python related to this course, you need to adhere to certain criteria when
solving the exercise; these criteria are subsequently described in Uploaded files (details)
Code should be explained in comments, theory should be added before stating the code, total page including code should be (17-24pages)
Plagiarism should be less than 15%, work should be own and not copied, Code should not be pushed to Github or online. Once accepted then i will provide datasets and more details, Since i have answer the output derived should match mine with the graphs and tables.
respectively consists of x-y-pairs of values.
Your task is to write a Python-program that uses training data to choose the four ideal functions which are the
best fit out of the fifty provided (C) *.
i) Afterwards, the program must use the test data provided (B) to determine for each and every x-ypair of values whether or not they can be assigned to the four chosen ideal functions**; if so, the
program also needs to execute the mapping and save it together with the deviation at hand
ii) All data must be visualized logically
iii) Where possible, create/ compile suitable unit-test
* The criterion for choosing the ideal functions for the training function is how they minimize the sum of all ydeviations squared (Least-Square)
** The criterion for mapping the individual test case to the four ideal functions is that the existing maximum
deviation of the calculated regression does not exceed the largest deviation between training dataset (A) and
the ideal function (C) chosen for it by more than factor sqrt(2)
In order to give proof of your skills in Python related to this course, you need to adhere to certain criteria when
solving the exercise; these criteria are subsequently described in Uploaded files (details)
Code should be explained in comments, theory should be added before stating the code, total page including code should be (17-24pages)
Plagiarism should be less than 15%, work should be own and not copied, Code should not be pushed to Github or online. Once accepted then i will provide datasets and more details, Since i have answer the output derived should match mine with the graphs and tables.