Question No$.2$: Classification

You are tasked to build and assess the performance of the following classification models $($no normalization is required for this question$)$:

Decision Tree with maximum depth of $3($max$\_$depth $=3)$

Decision Tree with maximum depth of $5($max$\_$depth$=5)$

Naive Bayes

K$-$Nearest Neighbours with $5$ neighbours $(n_{?}$neighbors $=5)$

K$-$Nearest Neighbours with $10$ neighbours $(n_{?}$neighbors $=10)$

Support Vector Machines $($SVM$)$ with polynomial kernel.

Support Vector Machines $($SVM$)$ with radial basis function kernel.

You need to build the above modes for each of the following two cases. For each case$-$model combination, report the model accuracy. Split the pre$-$processed data into training and testing sets with a ratio of $75$:$25$ and set the random state for data splitting to $255.$ Report your answers in the table below.

Case $1$: Predict "Cancer" using "Genetic Risk", "Alcohol use", and "Weight Loss".

Case $2$: Predict "Cancer" using "Age", "Genetic Risk", "Alcohol use", "Obesity", "Weight Loss", and "Marker".

$\backslash$table$[[$Model$,$Model Accuracy$],[1),$Case $1],[$Case $2,]]$

Decision Tree with maximum depth of $3$

Decision Tree with maximum depth of $5$

Naive Bayes

K$-$Nearest Neighbors with $5$ neighbors

K$-$Nearest Neighbors with $10$ neighbors

Support Vector Machines with polynomial kernel

Support Vector Machines with radial basis function kernel

$$ DONT USE CHATGPT CODING