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Fit a best ML model

Using the data file (trial1), create a working file, as follows, to simulate missing data.

? 10% data from each predictor should be independently and randomly knocked off and substituted with NA (this will ensure that an entire row does not consist of NAs)

? Name the working file as 1.csv.

Using the working file analyse the data and generate your best possible ML model. In your report explain your overall strategy and all steps, with reasons, taken for generating the best model. Justify with evidence why your final proposed model is the best model.

Using the working file [login to view URL] created in Part-1, do the following:

Discretize the response variable Y into a qualitative variable with 5 levels.

Likewise discretize any two of the predictor variables into qualitative variables with 3 and 5 levels respectively.

Name the modified data file as [login to view URL]

Using this working file analyse the data and generate your best possible ML model. In your report explain your overall strategy and all steps, with reasons, taken for generating the best model. Justify with evidence why your final proposed model is the best model.

Skills: Data Mining, Data Science, Python, Statistics

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adanikov

Hello! I’m looking forward to help you with your project. I’m experienced data scientist and Python Programmer, with 5 years of experience in development various IT and Data Science projects.

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fatihuludag

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