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Dataset You will work with the Colon.csv file, which contains gene data from each patient. The dataset includes various gene expression measurements ( features )

Dataset
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
Test these two clusters on the 5 stage classification" SVM, KNN, NB", use the group with less number of feartures.

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