You will work with the Colon.csv file, which contains gene data from each patient. The dataset includes various

gene expression measurements $($features$)$ and a label indicating the stage information.

Preparing the Data: a$.$ Split your Colon.csv into Train and Test datasets. b$.$ Apply the PCA and KPCA models

$($RBF$,$ Polynomial, Linear, and combined kernels$)$ trained on the Train dataset to transform the Test dataset. c$.$

Ensure the dimensionality reduction is consistent with what was performed on the training data.

Covariance Matrix Analysis: a$.$ Calculate the covariance matrix of the dataset. b$.$ Identify the top $10$ features

with the highest covariance values.

Classification Experiment: For this part, you will implement the following classifiers using sklearn and

compare their performance:

KNN

Bayes

Naive Bayes

LDA

SVM

You will implement the Bayes classifier from scratch.

a$.$ Implement a Bayes classifier from scratch. b$.$ For each classifier $($KNN$,$ Bayes, Naive Bayes, LDA, and SVM$),$

test the classifiers on:

Whole data

Data reduced by PCA

Data reduced by KPCA with RBF$,$ Polynomial, and Linear kernels

Data reduced by top $10$ features c$.$ For each classifier and each dimensionality reduction technique, find

the best number of dimensions that yields the highest classification accuracy. d$.$ Evaluate the

classification performance using accuracy metrics $($e$.$g$.,$ accuracy, precision, recall$)$ and compare the

effectiveness of PCA features, KPCA features, and Data reduced by top $10$ features.

Clustering Experiment: In this section, you will perform clustering on the dataset points and features.

a$.$ Cluster the data points into $5$ clusters using the following methods:

Kmeans

Kernel Kmeans Use $"$ RBF$,$ polynomial, and Linear"

Expectation Maximization

b$.$ Compare the clustering results using appropriate evaluation metrics and visualizations.

Cluster the features into $2$ groups using the following methods:

Kmeans

Kernel Kmeans $"$ Use RBF kernel, Polynomial Kernel, and linear Kernel"

Expectation Maximization