UNI-PROG3-CW2-MLWP/Q2.ipynb

This is the Q2 Notebook!

It's tracked via GitHub! hence the need for this line for the init commit

Part A

Q: A. Using (Rating + Reviews + Content Rating + Size in Bytes + Installs_Num), using Logistic regression and KNN, find and discuss the best classification model to predict “Category” (use the training/validation/test partition without cross-validation). [8 marks]

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Part B

Q: Using (Rating + Reviews + Category + Size in Bytes + Installs_Num), using Logistic regression and KNN, find and discuss the best classification model to predict “Content Rating” (use the training/validation/test partition without cross-validation). [7 marks]

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Part C

Q: By considering Installs_Num as categorical feature and using (Rating + Reviews + Category + Size in Bytes + Content Rating), find and discuss the best classification model to predict “Installs_Num” (using Logistic regression and KNN) (use the training/test partition without cross-validation) [8 marks]

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