agabka/projekt.ipynb at 7c37ef736260ef8feee5bfa8b798dda397dc4d0d

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import plotly.figure_factory as ff
import seaborn as sns
sns.set()

dane = pd.read_csv(r'C:\Users\HP\Desktop\podyplomówka\cancer_patient_data_sets.csv', index_col = 0)
dane.head()

	Patient Id	Age	Gender	Air Pollution	Alcohol use	Dust Allergy	OccuPational Hazards	Genetic Risk	chronic Lung Disease	Balanced Diet	...	Fatigue	Weight Loss	Shortness of Breath	Wheezing	Swallowing Difficulty	Clubbing of Finger Nails	Frequent Cold	Dry Cough	Snoring	Level
index
0	P1	33	1	2	4	5	4	3	2	2	...	3	4	2	2	3	1	2	3	4	Low
1	P10	17	1	3	1	5	3	4	2	2	...	1	3	7	8	6	2	1	7	2	Medium
2	P100	35	1	4	5	6	5	5	4	6	...	8	7	9	2	1	4	6	7	2	High
3	P1000	37	1	7	7	7	7	6	7	7	...	4	2	3	1	4	5	6	7	5	High
4	P101	46	1	6	8	7	7	7	6	7	...	3	2	4	1	4	2	4	2	3	High

5 rows × 25 columns

dane.info()

<class 'pandas.core.frame.DataFrame'>
Index: 1000 entries, 0 to 999
Data columns (total 25 columns):
 #   Column                    Non-Null Count  Dtype 
---  ------                    --------------  ----- 
 0   Patient Id                1000 non-null   object
 1   Age                       1000 non-null   int64 
 2   Gender                    1000 non-null   int64 
 3   Air Pollution             1000 non-null   int64 
 4   Alcohol use               1000 non-null   int64 
 5   Dust Allergy              1000 non-null   int64 
 6   OccuPational Hazards      1000 non-null   int64 
 7   Genetic Risk              1000 non-null   int64 
 8   chronic Lung Disease      1000 non-null   int64 
 9   Balanced Diet             1000 non-null   int64 
 10  Obesity                   1000 non-null   int64 
 11  Smoking                   1000 non-null   int64 
 12  Passive Smoker            1000 non-null   int64 
 13  Chest Pain                1000 non-null   int64 
 14  Coughing of Blood         1000 non-null   int64 
 15  Fatigue                   1000 non-null   int64 
 16  Weight Loss               1000 non-null   int64 
 17  Shortness of Breath       1000 non-null   int64 
 18  Wheezing                  1000 non-null   int64 
 19  Swallowing Difficulty     1000 non-null   int64 
 20  Clubbing of Finger Nails  1000 non-null   int64 
 21  Frequent Cold             1000 non-null   int64 
 22  Dry Cough                 1000 non-null   int64 
 23  Snoring                   1000 non-null   int64 
 24  Level                     1000 non-null   object
dtypes: int64(23), object(2)
memory usage: 203.1+ KB

dane0 = dane[dane['Level'] == 'High'][['Air Pollution', 'Smoking', 'Passive Smoker']]
dane0

	Air Pollution	Smoking	Passive Smoker
index
2	4	2	3
3	7	7	7
4	6	8	7
5	4	2	3
10	6	7	8
...	...	...	...
995	6	7	8
996	6	7	8
997	4	2	3
998	6	8	7
999	6	2	3

365 rows × 3 columns

dane.describe().T

	count	mean	std	min	25%	50%	75%	max
Age	1000.0	37.174	12.005493	14.0	27.75	36.0	45.0	73.0
Gender	1000.0	1.402	0.490547	1.0	1.00	1.0	2.0	2.0
Air Pollution	1000.0	3.840	2.030400	1.0	2.00	3.0	6.0	8.0
Alcohol use	1000.0	4.563	2.620477	1.0	2.00	5.0	7.0	8.0
Dust Allergy	1000.0	5.165	1.980833	1.0	4.00	6.0	7.0	8.0
OccuPational Hazards	1000.0	4.840	2.107805	1.0	3.00	5.0	7.0	8.0
Genetic Risk	1000.0	4.580	2.126999	1.0	2.00	5.0	7.0	7.0
chronic Lung Disease	1000.0	4.380	1.848518	1.0	3.00	4.0	6.0	7.0
Balanced Diet	1000.0	4.491	2.135528	1.0	2.00	4.0	7.0	7.0
Obesity	1000.0	4.465	2.124921	1.0	3.00	4.0	7.0	7.0
Smoking	1000.0	3.948	2.495902	1.0	2.00	3.0	7.0	8.0
Passive Smoker	1000.0	4.195	2.311778	1.0	2.00	4.0	7.0	8.0
Chest Pain	1000.0	4.438	2.280209	1.0	2.00	4.0	7.0	9.0
Coughing of Blood	1000.0	4.859	2.427965	1.0	3.00	4.0	7.0	9.0
Fatigue	1000.0	3.856	2.244616	1.0	2.00	3.0	5.0	9.0
Weight Loss	1000.0	3.855	2.206546	1.0	2.00	3.0	6.0	8.0
Shortness of Breath	1000.0	4.240	2.285087	1.0	2.00	4.0	6.0	9.0
Wheezing	1000.0	3.777	2.041921	1.0	2.00	4.0	5.0	8.0
Swallowing Difficulty	1000.0	3.746	2.270383	1.0	2.00	4.0	5.0	8.0
Clubbing of Finger Nails	1000.0	3.923	2.388048	1.0	2.00	4.0	5.0	9.0
Frequent Cold	1000.0	3.536	1.832502	1.0	2.00	3.0	5.0	7.0
Dry Cough	1000.0	3.853	2.039007	1.0	2.00	4.0	6.0	7.0
Snoring	1000.0	2.926	1.474686	1.0	2.00	3.0	4.0	7.0

dane[['Genetic Risk']].median()

Genetic Risk    5.0
dtype: float64

dane.columns

Index(['Patient Id', 'Age', 'Gender', 'Air Pollution', 'Alcohol use',
       'Dust Allergy', 'OccuPational Hazards', 'Genetic Risk',
       'chronic Lung Disease', 'Balanced Diet', 'Obesity', 'Smoking',
       'Passive Smoker', 'Chest Pain', 'Coughing of Blood', 'Fatigue',
       'Weight Loss', 'Shortness of Breath', 'Wheezing',
       'Swallowing Difficulty', 'Clubbing of Finger Nails', 'Frequent Cold',
       'Dry Cough', 'Snoring', 'Level'],
      dtype='object')

dane2 = dane.groupby('Gender').size()
dane2

Gender
1    598
2    402
dtype: int64


_ = dane['Gender'].value_counts().plot(kind = 'bar')
_ = plt.legend()

dane3 = [dane.groupby('Smoking').size()]
dane3

[Smoking
 1    181
 2    222
 3    172
 4     59
 5     10
 6     60
 7    207
 8     89
 dtype: int64]

_ = dane['Smoking'].value_counts().plot(kind = 'pie')

dane4 = [dane.groupby('Passive Smoker').size()]
dane4

[Passive Smoker
 1     60
 2    284
 3    140
 4    161
 5     30
 6     30
 7    187
 8    108
 dtype: int64]

_ = dane['Passive Smoker'].value_counts().plot(kind = 'pie')

dane.groupby(['Smoking','Gender']).size()

Smoking  Gender
1        1         102
         2          79
2        1         102
         2         120
3        1          79
         2          93
4        1          49
         2          10
5        1          10
6        1          28
         2          32
7        1         167
         2          40
8        1          61
         2          28
dtype: int64

dane6 = dane.groupby(['Smoking','Gender'])
_ = dane6[['Smoking', 'Gender']].value_counts().plot(kind = 'bar')
_ = plt.legend()

dane[['Smoking']].median()

Smoking    3.0
dtype: float64

x = dane[['Genetic Risk', 'Smoking','Alcohol use']]
x.sort_values('Genetic Risk')

	Genetic Risk	Smoking	Alcohol use
index
725	1	4	3
59	1	4	3
727	1	3	3
940	1	4	2
819	1	1	2
...	...	...	...
537	7	8	8
538	7	7	7
755	7	4	7
533	7	4	7
812	7	7	7

1000 rows × 3 columns

dane7 = dane['Air Pollution'].value_counts()
dane7.sort_values()

Air Pollution
8     19
5     20
7     30
4     90
1    141
3    173
2    201
6    326
Name: count, dtype: int64

_ = dane7 = dane['Air Pollution'].value_counts().plot(kind = 'bar')
_ = plt.legend()

import numpy as np
import sklearn

_ = dane8 = dane['Genetic Risk'].value_counts()
dane8.sort_values()

Genetic Risk
4     40
1     40
5    100
6    108
3    173
2    212
7    327
Name: count, dtype: int64

dane

	Patient Id	Age	Gender	Air Pollution	Alcohol use	Dust Allergy	OccuPational Hazards	Genetic Risk	chronic Lung Disease	Balanced Diet	...	Fatigue	Weight Loss	Shortness of Breath	Wheezing	Swallowing Difficulty	Clubbing of Finger Nails	Frequent Cold	Dry Cough	Snoring	Level
index
0	P1	33	1	2	4	5	4	3	2	2	...	3	4	2	2	3	1	2	3	4	Low
1	P10	17	1	3	1	5	3	4	2	2	...	1	3	7	8	6	2	1	7	2	Medium
2	P100	35	1	4	5	6	5	5	4	6	...	8	7	9	2	1	4	6	7	2	High
3	P1000	37	1	7	7	7	7	6	7	7	...	4	2	3	1	4	5	6	7	5	High
4	P101	46	1	6	8	7	7	7	6	7	...	3	2	4	1	4	2	4	2	3	High
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
995	P995	44	1	6	7	7	7	7	6	7	...	5	3	2	7	8	2	4	5	3	High
996	P996	37	2	6	8	7	7	7	6	7	...	9	6	5	7	2	4	3	1	4	High
997	P997	25	2	4	5	6	5	5	4	6	...	8	7	9	2	1	4	6	7	2	High
998	P998	18	2	6	8	7	7	7	6	7	...	3	2	4	1	4	2	4	2	3	High
999	P999	47	1	6	5	6	5	5	4	6	...	8	7	9	2	1	4	6	7	2	High

1000 rows × 25 columns

dane['Level'].value_counts()

Level
High      365
Medium    332
Low       303
Name: count, dtype: int64

data = dane.replace({'Level':{'High' : 3, 'Medium' : 2, 'Low' : 1}})
data.head()

	Patient Id	Age	Gender	Air Pollution	Alcohol use	Dust Allergy	OccuPational Hazards	Genetic Risk	chronic Lung Disease	Balanced Diet	...	Fatigue	Weight Loss	Shortness of Breath	Wheezing	Swallowing Difficulty	Clubbing of Finger Nails	Frequent Cold	Dry Cough	Snoring	Level
index
0	P1	33	1	2	4	5	4	3	2	2	...	3	4	2	2	3	1	2	3	4	1
1	P10	17	1	3	1	5	3	4	2	2	...	1	3	7	8	6	2	1	7	2	2
2	P100	35	1	4	5	6	5	5	4	6	...	8	7	9	2	1	4	6	7	2	3
3	P1000	37	1	7	7	7	7	6	7	7	...	4	2	3	1	4	5	6	7	5	3
4	P101	46	1	6	8	7	7	7	6	7	...	3	2	4	1	4	2	4	2	3	3

5 rows × 25 columns

import sklearn

np.random.seed(10)
np.set_printoptions(precision=6, suppress=True)

X = data.drop(['Level', 'Patient Id'], axis=1)
y = data['Level']


print("Y shape:", y.shape)
print("X shape:", X.shape)

Y shape: (1000,)
X shape: (1000, 23)

from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split (X, y)

print("X_train shape:", X_train.shape)
print("y_train shape:", y_train.shape)
print("X_test shape:", X_test.shape)
print("y_test shape:", y_test.shape)

X_train shape: (750, 23)
y_train shape: (750,)
X_test shape: (250, 23)
y_test shape: (250,)

from sklearn.linear_model import LogisticRegression
classifier = LogisticRegression()
classifier.fit(X_train, y_train)

c:\Users\HP\anaconda3\lib\site-packages\sklearn\linear_model\_logistic.py:460: ConvergenceWarning:

lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression

LogisticRegression()

In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.

LogisticRegression

LogisticRegression()

y_prob = classifier.predict_proba(X_test)
y_prob

array([[0.019763, 0.980237, 0.      ],
       [0.      , 0.      , 1.      ],
       [0.      , 0.000002, 0.999998],
       [0.999979, 0.000021, 0.      ],
       [0.      , 0.000038, 0.999962],
       [0.000022, 0.983401, 0.016577],
       [0.      , 0.023981, 0.976019],
       [0.025065, 0.943631, 0.031305],
       [0.      , 0.011278, 0.988722],
       [0.077079, 0.922921, 0.      ],
       [0.000003, 0.000326, 0.999672],
       [0.000473, 0.999527, 0.      ],
       [0.16753 , 0.83247 , 0.      ],
       [0.995731, 0.004269, 0.      ],
       [0.949387, 0.050613, 0.      ],
       [0.21037 , 0.78963 , 0.      ],
       [0.      , 0.      , 1.      ],
       [0.91181 , 0.045917, 0.042272],
       [0.      , 0.002178, 0.997822],
       [0.984437, 0.015558, 0.000005],
       [0.      , 0.002066, 0.997934],
       [0.99922 , 0.00078 , 0.      ],
       [0.      , 0.000298, 0.999702],
       [0.      , 0.004236, 0.995764],
       [0.      , 0.000004, 0.999996],
       [0.238042, 0.760375, 0.001583],
       [0.025065, 0.943631, 0.031305],
       [0.99808 , 0.001917, 0.000003],
       [0.997777, 0.002223, 0.      ],
       [0.238042, 0.760375, 0.001583],
       [0.913746, 0.086254, 0.      ],
       [0.002141, 0.997859, 0.      ],
       [0.      , 0.004236, 0.995764],
       [0.99922 , 0.00078 , 0.      ],
       [0.00192 , 0.99808 , 0.      ],
       [0.      , 0.012453, 0.987547],
       [0.00145 , 0.99855 , 0.      ],
       [0.      , 0.      , 1.      ],
       [0.00248 , 0.99752 , 0.      ],
       [0.998712, 0.000937, 0.000351],
       [0.238042, 0.760375, 0.001583],
       [0.001213, 0.97811 , 0.020677],
       [0.      , 0.      , 1.      ],
       [0.015839, 0.984161, 0.      ],
       [0.      , 0.023981, 0.976019],
       [0.000003, 0.000326, 0.999672],
       [0.930462, 0.069418, 0.000121],
       [0.      , 0.002178, 0.997822],
       [0.      , 0.000003, 0.999997],
       [0.001321, 0.998655, 0.000024],
       [0.001178, 0.998807, 0.000014],
       [0.00035 , 0.99965 , 0.      ],
       [0.05124 , 0.948155, 0.000606],
       [0.      , 0.      , 1.      ],
       [0.001321, 0.998655, 0.000024],
       [0.      , 0.      , 1.      ],
       [0.      , 0.004285, 0.995715],
       [0.000004, 0.02071 , 0.979285],
       [0.969283, 0.030717, 0.      ],
       [0.000063, 0.040843, 0.959093],
       [0.      , 0.002066, 0.997934],
       [0.942577, 0.018654, 0.038768],
       [0.      , 0.000002, 0.999998],
       [0.870813, 0.129142, 0.000044],
       [0.      , 0.      , 1.      ],
       [0.913746, 0.086254, 0.      ],
       [0.000637, 0.999363, 0.      ],
       [0.      , 0.000003, 0.999997],
       [0.      , 0.000298, 0.999702],
       [0.029989, 0.970011, 0.      ],
       [0.994782, 0.000846, 0.004371],
       [0.999889, 0.000075, 0.000035],
       [0.947557, 0.052443, 0.      ],
       [0.029989, 0.970011, 0.      ],
       [0.129826, 0.870174, 0.      ],
       [0.055579, 0.94417 , 0.000251],
       [0.002141, 0.997859, 0.      ],
       [0.001398, 0.9986  , 0.000002],
       [0.      , 0.000062, 0.999938],
       [0.99808 , 0.001917, 0.000003],
       [0.      , 0.000004, 0.999996],
       [0.969283, 0.030717, 0.      ],
       [0.      , 0.004236, 0.995764],
       [0.001178, 0.998807, 0.000014],
       [0.      , 0.      , 1.      ],
       [0.      , 0.014023, 0.985977],
       [0.      , 0.      , 1.      ],
       [0.033577, 0.966423, 0.      ],
       [0.99808 , 0.001917, 0.000003],
       [0.      , 0.000062, 0.999938],
       [0.099592, 0.900408, 0.      ],
       [0.000177, 0.999787, 0.000036],
       [0.997699, 0.002301, 0.      ],
       [0.930462, 0.069418, 0.000121],
       [0.00248 , 0.99752 , 0.      ],
       [0.00248 , 0.99752 , 0.      ],
       [0.238042, 0.760375, 0.001583],
       [0.101768, 0.898232, 0.      ],
       [0.029989, 0.970011, 0.      ],
       [0.98255 , 0.01745 , 0.      ],
       [0.      , 0.004285, 0.995715],
       [0.000332, 0.999668, 0.      ],
       [0.999328, 0.000672, 0.      ],
       [0.      , 0.023981, 0.976019],
       [0.997777, 0.002223, 0.      ],
       [0.949387, 0.050613, 0.      ],
       [0.930462, 0.069418, 0.000121],
       [0.00028 , 0.99972 , 0.      ],
       [0.      , 0.004236, 0.995764],
       [0.      , 0.      , 1.      ],
       [0.998712, 0.000937, 0.000351],
       [0.      , 0.000038, 0.999962],
       [0.000898, 0.99788 , 0.001222],
       [0.91181 , 0.045917, 0.042272],
       [0.984437, 0.015558, 0.000005],
       [0.000003, 0.000326, 0.999672],
       [0.099592, 0.900408, 0.      ],
       [0.033577, 0.966423, 0.      ],
       [0.969283, 0.030717, 0.      ],
       [0.999328, 0.000672, 0.      ],
       [0.      , 0.      , 1.      ],
       [0.      , 0.008068, 0.991932],
       [0.      , 0.000298, 0.999702],
       [0.003572, 0.996428, 0.      ],
       [0.997777, 0.002223, 0.      ],
       [0.      , 0.000002, 0.999998],
       [0.000063, 0.040843, 0.959093],
       [0.947557, 0.052443, 0.      ],
       [0.907855, 0.092061, 0.000084],
       [0.00145 , 0.99855 , 0.      ],
       [0.101768, 0.898232, 0.      ],
       [0.      , 0.009143, 0.990857],
       [0.861344, 0.134685, 0.00397 ],
       [0.      , 0.004236, 0.995764],
       [0.990975, 0.009025, 0.      ],
       [0.870813, 0.129142, 0.000044],
       [0.995587, 0.000049, 0.004364],
       [0.      , 0.000298, 0.999702],
       [0.942577, 0.018654, 0.038768],
       [0.      , 0.000005, 0.999995],
       [0.000898, 0.99788 , 0.001222],
       [0.99808 , 0.001917, 0.000003],
       [0.099592, 0.900408, 0.      ],
       [0.099592, 0.900408, 0.      ],
       [0.999979, 0.000021, 0.      ],
       [0.995587, 0.000049, 0.004364],
       [0.001398, 0.9986  , 0.000002],
       [0.000003, 0.000326, 0.999672],
       [0.999889, 0.000075, 0.000035],
       [0.861344, 0.134685, 0.00397 ],
       [0.000003, 0.000326, 0.999672],
       [0.969283, 0.030717, 0.      ],
       [0.238042, 0.760375, 0.001583],
       [0.000977, 0.999023, 0.      ],
       [0.002141, 0.997859, 0.      ],
       [0.000005, 0.000077, 0.999918],
       [0.997699, 0.002301, 0.      ],
       [0.      , 0.004236, 0.995764],
       [0.      , 0.      , 1.      ],
       [0.999425, 0.000575, 0.      ],
       [0.974593, 0.025407, 0.      ],
       [0.002453, 0.997528, 0.000019],
       [0.84426 , 0.15574 , 0.      ],
       [0.000898, 0.99788 , 0.001222],
       [0.949387, 0.050613, 0.      ],
       [0.      , 0.000032, 0.999968],
       [0.000332, 0.999668, 0.      ],
       [0.99808 , 0.001917, 0.000003],
       [0.05124 , 0.948155, 0.000606],
       [0.999889, 0.000075, 0.000035],
       [0.      , 0.      , 1.      ],
       [0.099592, 0.900408, 0.      ],
       [0.      , 0.000298, 0.999702],
       [0.99808 , 0.001917, 0.000003],
       [0.      , 0.000298, 0.999702],
       [0.598003, 0.391491, 0.010506],
       [0.002141, 0.997859, 0.      ],
       [0.001321, 0.998655, 0.000024],
       [0.129826, 0.870174, 0.      ],
       [0.033577, 0.966423, 0.      ],
       [0.101768, 0.898232, 0.      ],
       [0.999979, 0.000021, 0.      ],
       [0.870813, 0.129142, 0.000044],
       [0.002453, 0.997528, 0.000019],
       [0.129826, 0.870174, 0.      ],
       [0.      , 0.000032, 0.999968],
       [0.      , 0.004285, 0.995715],
       [0.      , 0.000004, 0.999996],
       [0.      , 0.000345, 0.999655],
       [0.033577, 0.966423, 0.      ],
       [0.055579, 0.94417 , 0.000251],
       [0.99922 , 0.00078 , 0.      ],
       [0.997699, 0.002301, 0.      ],
       [0.      , 0.014023, 0.985977],
       [0.      , 0.008068, 0.991932],
       [0.998712, 0.000937, 0.000351],
       [0.000637, 0.999363, 0.      ],
       [0.001321, 0.998655, 0.000024],
       [0.930462, 0.069418, 0.000121],
       [0.001398, 0.9986  , 0.000002],
       [0.002453, 0.997528, 0.000019],
       [0.05124 , 0.948155, 0.000606],
       [0.      , 0.      , 1.      ],
       [0.001111, 0.998889, 0.      ],
       [0.      , 0.000203, 0.999797],
       [0.000063, 0.040843, 0.959093],
       [0.930462, 0.069418, 0.000121],
       [0.019763, 0.980237, 0.      ],
       [0.033577, 0.966423, 0.      ],
       [0.659404, 0.319765, 0.020831],
       [0.      , 0.      , 1.      ],
       [0.999979, 0.000021, 0.      ],
       [0.      , 0.002178, 0.997822],
       [0.      , 0.000085, 0.999915],
       [0.974593, 0.025407, 0.      ],
       [0.98255 , 0.01745 , 0.      ],
       [0.000308, 0.999692, 0.      ],
       [0.      , 0.      , 1.      ],
       [0.974593, 0.025407, 0.      ],
       [0.947557, 0.052443, 0.      ],
       [0.913746, 0.086254, 0.      ],
       [0.015839, 0.984161, 0.      ],
       [0.974593, 0.025407, 0.      ],
       [0.      , 0.      , 1.      ],
       [0.129826, 0.870174, 0.      ],
       [0.000177, 0.999787, 0.000036],
       [0.238042, 0.760375, 0.001583],
       [0.99808 , 0.001917, 0.000003],
       [0.      , 0.      , 1.      ],
       [0.001213, 0.97811 , 0.020677],
       [0.      , 0.008068, 0.991932],
       [0.999979, 0.000021, 0.      ],
       [0.001398, 0.9986  , 0.000002],
       [0.015839, 0.984161, 0.      ],
       [0.      , 0.021595, 0.978405],
       [0.002453, 0.997528, 0.000019],
       [0.      , 0.000005, 0.999995],
       [0.907855, 0.092061, 0.000084],
       [0.974593, 0.025407, 0.      ],
       [0.      , 0.000032, 0.999968],
       [0.91181 , 0.045917, 0.042272],
       [0.000898, 0.99788 , 0.001222],
       [0.      , 0.002066, 0.997934],
       [0.055579, 0.94417 , 0.000251],
       [0.      , 0.014023, 0.985977],
       [0.000029, 0.006112, 0.993859],
       [0.      , 0.002178, 0.997822],
       [0.      , 0.023981, 0.976019],
       [0.999889, 0.000075, 0.000035],
       [0.000308, 0.999692, 0.      ]])

y_pred = classifier.predict(X_test)
y_pred

array([2, 3, 3, 1, 3, 2, 3, 2, 3, 2, 3, 2, 2, 1, 1, 2, 3, 1, 3, 1, 3, 1,
       3, 3, 3, 2, 2, 1, 1, 2, 1, 2, 3, 1, 2, 3, 2, 3, 2, 1, 2, 2, 3, 2,
       3, 3, 1, 3, 3, 2, 2, 2, 2, 3, 2, 3, 3, 3, 1, 3, 3, 1, 3, 1, 3, 1,
       2, 3, 3, 2, 1, 1, 1, 2, 2, 2, 2, 2, 3, 1, 3, 1, 3, 2, 3, 3, 3, 2,
       1, 3, 2, 2, 1, 1, 2, 2, 2, 2, 2, 1, 3, 2, 1, 3, 1, 1, 1, 2, 3, 3,
       1, 3, 2, 1, 1, 3, 2, 2, 1, 1, 3, 3, 3, 2, 1, 3, 3, 1, 1, 2, 2, 3,
       1, 3, 1, 1, 1, 3, 1, 3, 2, 1, 2, 2, 1, 1, 2, 3, 1, 1, 3, 1, 2, 2,
       2, 3, 1, 3, 3, 1, 1, 2, 1, 2, 1, 3, 2, 1, 2, 1, 3, 2, 3, 1, 3, 1,
       2, 2, 2, 2, 2, 1, 1, 2, 2, 3, 3, 3, 3, 2, 2, 1, 1, 3, 3, 1, 2, 2,
       1, 2, 2, 2, 3, 2, 3, 3, 1, 2, 2, 1, 3, 1, 3, 3, 1, 1, 2, 3, 1, 1,
       1, 2, 1, 3, 2, 2, 2, 1, 3, 2, 3, 1, 2, 2, 3, 2, 3, 1, 1, 3, 1, 2,
       3, 2, 3, 3, 3, 3, 1, 2], dtype=int64)

from sklearn.metrics import confusion_matrix
from sklearn.metrics import accuracy_score
from mlxtend.plotting import plot_confusion_matrix
import seaborn as sns
sns.set()

cm = confusion_matrix(y_test, y_pred)
plot_confusion_matrix(cm)

acc = accuracy_score(y_test, y_pred)
print('Accuracy',':', acc)

Accuracy : 0.992

def plot_confusion_matrix(cm):
    cm = cm[::-1]
    cm = pd.DataFrame(cm, columns=['pred_1', 'pred_2', 'pred_3'], index=['true_1', 'true_2', 'true_3'])
    fig = ff.create_annotated_heatmap(z = cm.values, x = list(cm.columns), y = list(cm.index), colorscale = 'ice', showscale = True, reversescale = True)
    fig.update_layout(width=400, height=400, title='Confusion Matrix - Accuracy: {:.4f}'.format(acc), font_size=14)
    fig.show()
plot_confusion_matrix(cm)

from sklearn.metrics import classification_report
print(classification_report(y_test, y_pred, target_names=['pred_1', 'pred_2', 'pred_3']))

              precision    recall  f1-score   support

      pred_1       0.97      1.00      0.99        76
      pred_2       1.00      0.98      0.99        89
      pred_3       1.00      1.00      1.00        85

    accuracy                           0.99       250
   macro avg       0.99      0.99      0.99       250
weighted avg       0.99      0.99      0.99       250

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