import pandas as pd
import numpy as np
import seaborn as sns
import plotly.graph_objects as go
from plotly.subplots import make_subplots
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
from sklearn.linear_model import LinearRegression
from sklearn.naive_bayes import GaussianNB
from sklearn.preprocessing import StandardScaler
from sklearn import metrics

Przygotowanie danych:

df = pd.read_csv('data.csv')
del df['id']
df = df[df['gender'].map(lambda x: str(x) != "Other")]
df = df.dropna()
df.info()
df

<class 'pandas.core.frame.DataFrame'>
Int64Index: 454 entries, 0 to 498
Data columns (total 11 columns):
 #   Column             Non-Null Count  Dtype  
---  ------             --------------  -----  
 0   gender             454 non-null    object 
 1   age                454 non-null    float64
 2   hypertension       454 non-null    int64  
 3   heart_disease      454 non-null    int64  
 4   ever_married       454 non-null    object 
 5   work_type          454 non-null    object 
 6   Residence_type     454 non-null    object 
 7   avg_glucose_level  454 non-null    float64
 8   bmi                454 non-null    float64
 9   smoking_status     454 non-null    object 
 10  stroke             454 non-null    int64  
dtypes: float64(3), int64(3), object(5)
memory usage: 42.6+ KB

	gender	age	hypertension	heart_disease	ever_married	work_type	Residence_type	avg_glucose_level	bmi	smoking_status	stroke
0	Male	67.0	0	1	Yes	Private	Urban	228.69	36.6	formerly smoked	1
2	Male	80.0	0	1	Yes	Private	Rural	105.92	32.5	never smoked	1
3	Female	49.0	0	0	Yes	Private	Urban	171.23	34.4	smokes	1
4	Female	79.0	1	0	Yes	Self-employed	Rural	174.12	24.0	never smoked	1
5	Male	81.0	0	0	Yes	Private	Urban	186.21	29.0	formerly smoked	1
...	...	...	...	...	...	...	...	...	...	...	...
494	Female	55.0	0	0	Yes	Private	Rural	111.19	39.7	formerly smoked	0
495	Female	71.0	0	0	Yes	Private	Urban	93.28	34.7	never smoked	0
496	Male	5.0	0	0	No	children	Rural	122.19	35.0	Unknown	0
497	Female	14.0	0	0	No	children	Rural	129.53	21.3	never smoked	0
498	Female	15.0	0	0	No	children	Rural	114.53	29.1	Unknown	0

454 rows × 11 columns

df = pd.get_dummies(df, columns=['gender'], drop_first=True)
df = pd.get_dummies(df, columns=['ever_married'], drop_first=True)
df = pd.get_dummies(df, columns=['work_type'], drop_first=True)
df = pd.get_dummies(df, columns=['Residence_type'], drop_first=True)
df = pd.get_dummies(df, columns=['smoking_status'], drop_first=True)

df

	age	hypertension	heart_disease	avg_glucose_level	bmi	stroke	gender_Male	ever_married_Yes	work_type_Never_worked	work_type_Private	work_type_Self-employed	work_type_children	Residence_type_Urban	smoking_status_formerly smoked	smoking_status_never smoked	smoking_status_smokes
0	67.0	0	1	228.69	36.6	1	1	1	0	1	0	0	1	1	0	0
2	80.0	0	1	105.92	32.5	1	1	1	0	1	0	0	0	0	1	0
3	49.0	0	0	171.23	34.4	1	0	1	0	1	0	0	1	0	0	1
4	79.0	1	0	174.12	24.0	1	0	1	0	0	1	0	0	0	1	0
5	81.0	0	0	186.21	29.0	1	1	1	0	1	0	0	1	1	0	0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
494	55.0	0	0	111.19	39.7	0	0	1	0	1	0	0	0	1	0	0
495	71.0	0	0	93.28	34.7	0	0	1	0	1	0	0	1	0	1	0
496	5.0	0	0	122.19	35.0	0	1	0	0	0	0	1	0	0	0	0
497	14.0	0	0	129.53	21.3	0	0	0	0	0	0	1	0	0	1	0
498	15.0	0	0	114.53	29.1	0	0	0	0	0	0	1	0	0	0	0

454 rows × 16 columns

df_train, df_test = train_test_split(df, test_size=0.2, stratify=df['stroke'])

df_train['stroke'].value_counts(dropna=False)

0    196
1    167
Name: stroke, dtype: int64

df_test['stroke'].value_counts(dropna=False)

0    49
1    42
Name: stroke, dtype: int64

Wizualizacja

sns.pairplot(df, x_vars=['avg_glucose_level','bmi'], y_vars='age', height=7, aspect=0.7, diag_kind = None)

<seaborn.axisgrid.PairGrid at 0x19196a188e0>

features = {'Smoker': df['smoking_status_smokes'].values,
     'Male': df['gender_Male'].values,
     'Urban': df['Residence_type_Urban']}
df_vis = pd.DataFrame(features)

fig = make_subplots(rows=1, cols=3, subplot_titles=('Smoker', 'Male', 'Urban'))
L= len(df_vis)

cnames = list(df_vis.columns)
for k, name in enumerate(cnames):
    n_true = df_vis[name].sum()
    fig.add_trace(go.Bar(x=['False', 'True'], y=[n_true, L-n_true], name=name ), 1,k+1)
fig.update_layout(barmode='relative',  bargap=0.05, width=700, height=400)

Regresja liniowa

X_cols = list(set(df_train.columns) - {'stroke'})
y_cols = 'stroke'

X_train_lin = df_train[X_cols]
y_train_lin = df_train[y_cols]

X_test_lin = df_test[X_cols]
y_test_lin = df_test[y_cols]

clf_lin = LinearRegression().fit(X_train_lin, y_train_lin)
test_pred_lin = clf_lin.predict(X_test_lin)

def regression_results(y_true, y_pred):

    # Regression metrics
    explained_variance=metrics.explained_variance_score(y_true, y_pred)
    mean_absolute_error=metrics.mean_absolute_error(y_true, y_pred) 
    mse=metrics.mean_squared_error(y_true, y_pred) 
    mean_squared_log_error=metrics.mean_squared_log_error(y_true, y_pred)
    median_absolute_error=metrics.median_absolute_error(y_true, y_pred)
    r2=metrics.r2_score(y_true, y_pred)

    print('explained_variance: ', round(explained_variance,4))    
    print('mean_squared_log_error: ', round(mean_squared_log_error,4))
    print('r2: ', round(r2,4))
    print('MAE: ', round(mean_absolute_error,4))
    print('MSE: ', round(mse,4))
    print('RMSE: ', round(np.sqrt(mse),4))

test_pred_lin = np.round(np.clip(test_pred_lin, 0, 1))
regression_results(y_test_lin, test_pred_lin)

explained_variance:  0.0292
mean_squared_log_error:  0.1162
r2:  0.0272
MAE:  0.2418
MSE:  0.2418
RMSE:  0.4917

print(metrics.classification_report(y_test_lin, test_pred_lin))

              precision    recall  f1-score   support

           0       0.79      0.76      0.77        49
           1       0.73      0.76      0.74        42

    accuracy                           0.76        91
   macro avg       0.76      0.76      0.76        91
weighted avg       0.76      0.76      0.76        91

test_pred_lin

array([1., 0., 1., 0., 1., 0., 0., 0., 0., 0., 0., 1., 1., 0., 0., 1., 1.,
       0., 0., 1., 0., 1., 1., 1., 1., 1., 1., 0., 0., 1., 1., 0., 1., 1.,
       0., 1., 0., 0., 0., 0., 1., 0., 0., 1., 0., 1., 1., 0., 0., 1., 1.,
       0., 0., 0., 0., 1., 0., 0., 0., 0., 0., 0., 1., 1., 1., 1., 1., 1.,
       1., 1., 1., 1., 1., 1., 0., 0., 1., 1., 0., 0., 1., 0., 0., 0., 1.,
       0., 1., 0., 0., 1., 0.])

y_test_lin.values

array([1, 1, 1, 0, 1, 0, 0, 0, 0, 1, 0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 1, 1,
       1, 1, 1, 1, 1, 1, 0, 1, 0, 0, 1, 1, 0, 1, 1, 0, 0, 0, 1, 0, 0, 1,
       0, 0, 1, 0, 0, 1, 0, 0, 0, 1, 1, 1, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0,
       1, 0, 1, 1, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 1, 1, 0,
       0, 1, 0], dtype=int64)

Regresja logistyczna

num_cols = ['age', 'avg_glucose_level', 'bmi']
cat_cols = list(set(df.columns) - {'stroke', 'age', 'avg_glucose_level', 'bmi'})

scaler = StandardScaler()
scaler.fit(df_train[num_cols])

X_num_train = scaler.transform(df_train[num_cols])
X_cat_train = df_train[cat_cols].to_numpy()
X_train = np.hstack((X_num_train, X_cat_train))
y_train = df_train['stroke']

X_num_test = scaler.transform(df_test[num_cols])
X_cat_test = df_test[cat_cols].to_numpy()
X_test = np.hstack((X_num_test, X_cat_test))
y_test = df_test['stroke']

clf_log = LogisticRegression()

clf_log.fit(X_train, y_train)

test_pred_log  = clf_log.predict(X_test)

regression_results(y_test, test_pred_log)

explained_variance:  -0.0165
mean_squared_log_error:  0.1214
r2:  -0.017
MAE:  0.2527
MSE:  0.2527
RMSE:  0.5027

print('\nClassification Report')
print(metrics.classification_report(y_test, test_pred_log))

Classification Report
              precision    recall  f1-score   support

           0       0.77      0.76      0.76        49
           1       0.72      0.74      0.73        42

    accuracy                           0.75        91
   macro avg       0.75      0.75      0.75        91
weighted avg       0.75      0.75      0.75        91

test_pred_log

array([1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 1, 0, 1,
       1, 1, 1, 1, 1, 0, 0, 1, 1, 0, 1, 1, 0, 1, 0, 0, 0, 0, 1, 0, 0, 1,
       0, 1, 1, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1,
       1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0,
       0, 1, 0], dtype=int64)

y_test.values

array([1, 1, 1, 0, 1, 0, 0, 0, 0, 1, 0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 1, 1,
       1, 1, 1, 1, 1, 1, 0, 1, 0, 0, 1, 1, 0, 1, 1, 0, 0, 0, 1, 0, 0, 1,
       0, 0, 1, 0, 0, 1, 0, 0, 0, 1, 1, 1, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0,
       1, 0, 1, 1, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 1, 1, 0,
       0, 1, 0], dtype=int64)

Naiwny Bayes

gnb = GaussianNB()

gnb = gnb.fit(X_train, y_train)

test_pred_bay  = gnb.predict(X_test)

regression_results(y_test, test_pred_bay)

explained_variance:  0.2187
mean_squared_log_error:  0.1003
r2:  0.1599
MAE:  0.2088
MSE:  0.2088
RMSE:  0.4569

print('\nClassification Report')
print(metrics.classification_report(y_test, test_pred_bay))

Classification Report
              precision    recall  f1-score   support

           0       0.89      0.69      0.78        49
           1       0.72      0.90      0.80        42

    accuracy                           0.79        91
   macro avg       0.81      0.80      0.79        91
weighted avg       0.81      0.79      0.79        91

test_pred_bay

array([1, 1, 1, 0, 1, 1, 0, 0, 0, 0, 0, 1, 1, 0, 1, 1, 1, 0, 0, 1, 1, 1,
       1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 0, 0, 0, 0, 1, 0, 0, 1,
       0, 1, 0, 0, 0, 0, 1, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1,
       1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 0, 0, 1, 0, 0, 0, 1, 1, 1, 1,
       0, 1, 0], dtype=int64)

y_test.values

array([1, 1, 1, 0, 1, 0, 0, 0, 0, 1, 0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 1, 1,
       1, 1, 1, 1, 1, 1, 0, 1, 0, 0, 1, 1, 0, 1, 1, 0, 0, 0, 1, 0, 0, 1,
       0, 0, 1, 0, 0, 1, 0, 0, 0, 1, 1, 1, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0,
       1, 0, 1, 1, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 1, 1, 0,
       0, 1, 0], dtype=int64)

3.5 MiB Raw Blame History Unescape Escape

Przygotowanie danych:

Wizualizacja

Regresja liniowa

Regresja logistyczna

Naiwny Bayes

3.5 MiB

Raw Blame History