Write a python programme that independently compiles a SQLite database (file) ideally via sqlalchemy and load the training data into a single five-column spreadsheet / table in the file from a csv file with five columns (Table 1). Then also load fifty ideal functions, which are also provided via a CSV-file, must be loaded into another sqlite table (table 2). After the training data and the ideal functions have been loaded into the database, the test data (B) must be loaded line-by-line from another CSV-file (Table 3). Thereafter,

(1.) Use three criteria e.g. least squares, etc. to select four ideal functions from Table 2. (2.) Use the test data to check whether the the four ideal functions 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). (3.) Load the results on a sqlite table 4. the training data, the test data, the chosen ideal functions as well as the corresponding / assigned datasets are visualized under an appropriately chosen representation of the deviation via bokeh.

Please create a Python-program which also fulfills the following criteria:

−Its design is sensibly object-oriented. −It includes at least one inheritance. −It includes standard- und user-defined exception handlings −it makes use of Pandas’ packages as well as data visualization via Bokeh, sqlalchemy,as well as others

−Write unit-tests for all useful elements. −Your code needs to be documented in its entirety and also include Documentation Strings, known as”docstrings“

Assume that your successfully created project is on the Version Control System Git and has a Branch called develop. On this Branch, all operations of the developer team are combined. Write the Git-commands necessary to clone the branch and? develop on your local PC. Imagine that you have added a new function. Write all necessary Git-commands to introduce this project to the team’s develop Branch. Please note: You need the commands for commit, push. Afterwards, you would make a Pull-request and your contribution would be added “merged” to the develop Branch after one or several of your team has reviewed your changes.

Test data

x	y
-8.9	79.15706
16.9	-1.2192687
-13.6	9.033831
12.6	0.7720728

Training set

x	y1	y2	y3	y4
-20.0	398.67856	-0.89438707	9.256861	-15999.565
-19.9	394.83707	-0.35809556	9.112177	-15761.245
-19.8	391.25342	-0.3636879	8.745257	-15525.128
-19.7	387.44333	-0.66927093	8.881724	-15290.525

Ideal functions

x	y1	y2	y3	y4	y5	y6	y7	y8	y9	y10
-20.0	-0.9129453	0.40808207	9.087055	5.408082	-9.087055	0.9129453	-0.8390715	-0.85091937	0.81616414	18.258905
-19.9	-0.8676441	0.4971858	9.132356	5.4971857	-9.132356	0.8676441	-0.8652126	0.16851768	0.9943716	17.266117
-19.8	-0.81367373	0.58132184	9.186326	5.5813217	-9.186326	0.81367373	-0.88919115	0.6123911	1.1626437	16.11074
-19.7	-0.75157344	0.65964943	9.248426	5.6596494	-9.248426	0.75157344	-0.91094714	-0.99466854	1.3192989	14.805996