mushroom.csv file here https://drive.google.com/file/d/1M4FMZTKYM1qkInhcDXEvj6z5TErxva3b/view?usp=sharing

Your task is the build a classification model that predicts the edibility of mushrooms (class variable in the dataset). You have been provide with a dataset as a mushrooms.csv file.

Here is a description of the attributes

Attribute description:

1. cap-shape: bell=b,conical=c,convex=x,flat=f, knobbed=k,sunken=s

2. cap-surface: fibrous=f,grooves=g,scaly=y,smooth=s

3. cap-color: brown=n,buff=b,cinnamon=c,gray=g,green=r, pink=p,purple=u,red=e,white=w,yellow=y

4. bruises?: bruises=t,no=f

5. odor: almond=a,anise=l,creosote=c,fishy=y,foul=f, musty=m,none=n,pungent=p,spicy=s

6. gill-attachment: attached=a,descending=d,free=f,notched=n

7. gill-spacing: close=c,crowded=w,distant=d

8. gill-size: broad=b,narrow=n

9. gill-color: black=k,brown=n,buff=b,chocolate=h,gray=g, green=r,orange=o,pink=p,purple=u,red=e, white=w,yellow=y

10. stalk-shape: enlarging=e,tapering=t

11. stalk-root: bulbous=b,club=c,cup=u,equal=e, rhizomorphs=z,rooted=r,missing=?

12. stalk-surface-above-ring: fibrous=f,scaly=y,silky=k,smooth=s

13. stalk-surface-below-ring: fibrous=f,scaly=y,silky=k,smooth=s

14. stalk-color-above-ring: brown=n,buff=b,cinnamon=c,gray=g,orange=o, pink=p,red=e,white=w,yellow=y

15. stalk-color-below-ring: brown=n,buff=b,cinnamon=c,gray=g,orange=o, pink=p,red=e,white=w,yellow=y

16. veil-type: partial=p,universal=u

17. veil-color: brown=n,orange=o,white=w,yellow=y

18. ring-number: none=n,one=o,two=t

19. ring-type: cobwebby=c,evanescent=e,flaring=f,large=l, none=n,pendant=p,sheathing=s,zone=z

20. spore-print-color: black=k,brown=n,buff=b,chocolate=h,green=r, orange=o,purple=u,white=w,yellow=y

21. population: abundant=a,clustered=c,numerous=n, scattered=s,several=v,solitary=y

22. habitat: grasses=g,leaves=l,meadows=m,paths=p, urban=u,waste=w,woods=d

23. class: p = poisonous, e=edible

Build a classification tree. Random_state =2020. Training partition 0.7. stratify = y, max_depth = 6, use Entropy

A.Print the confusion matrix. Also visualize the confusion matrix using plot_confusion_matrix from sklearn.metrics

B.What was the accuracy on the training partition?

C.What was the accuracy on the test partition?

D.Show the classification tree.

E.List the top three most important features in your decision tree for determining toxicity.

F.Classify the following mushroom.

class

?

cap-shape

x

cap-surface

s

cap-color

n

bruises

t

odor

y

gill-attachment

f

gill-spacing

c

gill-size

n

gill-color

k

stalk-shape

e

stalk-root

e

stalk-surface-above-ring

s

stalk-surface-below-ring

s

stalk-color-above-ring

w

stalk-color-below-ring

w

veil-type

p

veil-color

w

ring-number

o

ring-type

p

spore-print-color

r

population

s

habitat

u