Classification$1.$ Conceptual QuestionsExplain the difference between supervised and unsupervised learning. Which category does classification belong to and why?Define the terms true positive, false negative, precision, and recall in the context of classification models$\mathrm{.}2.$ Coding$/$PracticalGiven a labeled dataset of your choice, train a decision tree classifier in Python using scikit$-$learn and evaluate its performance using a confusion matrix.Compare and contrast the performance of K$-$Nearest Neighbors and Support Vector Machines on a dataset of your choice.Regression$1.$ Conceptual QuestionsWhat is the key difference between regression and classification? Provide an example of each.Explain the significance of the coefficient of determination $($R$^2)$ in regression analysis$\mathrm{.}2.$ Coding$/$PracticalFit a multiple linear regression model to a dataset with at least three predictors using Python. Report the coefficients and assess the model's goodness$-$of$-$fit.Explain and implement Ridge or Lasso regression to handle multicollinearity in a dataset.Clustering$1.$ Conceptual QuestionsWhat is the main difference between clustering and classification?Compare the K$-$Means and DBSCAN clustering algorithms in terms of handling noise and cluster shapes$\mathrm{.}2.$ Coding$/$PracticalPerform K$-$Means clustering on the Iris dataset in Python. Plot the clusters and their centroids.Implement a hierarchical clustering algorithm on a dataset of your choice and generate a dendrogram to visualize the clustering process.