You are given the dataset stones.csv which contains data about the height, width, density, compactness and texture of some mineral stones. For each stone, in the first column is given the class it belongs to (A, B, C, D, E or F).

a. Split the dataset randomly into 60% train and 40% test and build a classification model based on decision trees. Generate the confusion matrix and classification report.

b. Split the dataset randomly into 60% train and 40% test and build a classification model based on KNN with K=5. Generate the confusion matrix and classification report.

c. Split the dataset randomly into 60% train and 40% test and build a classification model based on SVM with polynomial kernel of degree 3. Generate the confusion matrix and classification report.

d. Use any classification technique that you consider reasonable, to classify a new entry with height = 6.4, width = 4.15, density = 7.1, compactness = 8.8 and texture = 7.5t