Data-Mining-Mushrooms/notebooks/mushroom_analysis.ipynb

In [1]:

from sklearn.datasets import fetch_openml

mushroom = fetch_openml(data_id=43922, as_frame=True)

X = mushroom.data
y = mushroom.target

print("Feature data:", X.shape)
print("Target data:", y.shape)
display(X.head())
display(y.head())
print(y.value_counts())

Feature data: (8124, 22)
Target data: (8124,)

	cap_shape	cap_surface	cap_color	bruises	odor	gill_attachment	gill_spacing	gill_size	gill_color	stalk_shape	...	stalk_surface_below_ring	stalk_color_above_ring	stalk_color_below_ring	veil_type	veil_color	ring_number	ring_type	spore-print-color	population	habitat
0	x	s	n	t	p	f	c	n	k	e	...	s	w	w	p	w	o	p	k	s	u
1	x	s	y	t	a	f	c	b	k	e	...	s	w	w	p	w	o	p	n	n	g
2	b	s	w	t	l	f	c	b	n	e	...	s	w	w	p	w	o	p	n	n	m
3	x	y	w	t	p	f	c	n	n	e	...	s	w	w	p	w	o	p	k	s	u
4	x	s	g	f	n	f	w	b	k	t	...	s	w	w	p	w	o	e	n	a	g

5 rows × 22 columns

0    p
1    e
2    e
3    p
4    e
Name: class, dtype: category
Categories (2, str): ['e', 'p']

class
e    4208
p    3916
Name: count, dtype: int64

In [2]:

print("Missing cells:", X.isna().sum().sum())
print("Rows with missing cells:", X.isna().any(axis=1).sum())
print(X["stalk_root"].value_counts(dropna=False))

Missing cells: 0
Rows with missing cells: 0
stalk_root
b    3776
?    2480
e    1120
c     556
r     192
Name: count, dtype: int64

In [ ]:

a = "hello"
print(a)