notebook.community

Edit and run

محاولة لإستكشاف افضل الطرق لتحسين اداء نموذج بيما





In [53]:



    


import numpy as np
import pandas as pd
import seaborn as sb
from sklearn.metrics import classification_report
from sklearn.metrics import confusion_matrix
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn import preprocessing

%matplotlib inline

df = pd.read_csv('diabetes.csv')
df.head(20) #لاستعراض ال20 السجلات الاولى من إطار البيانات



    


















    
Out[53]:










  
    

      
      Pregnancies
      Glucose
      BloodPressure
      SkinThickness
      Insulin
      BMI
      DiabetesPedigreeFunction
      Age
      Outcome
    

  
  
    

      0
      6
      148
      72
      35
      0
      33.6
      0.627
      50
      1
    

    

      1
      1
      85
      66
      29
      0
      26.6
      0.351
      31
      0
    

    

      2
      8
      183
      64
      0
      0
      23.3
      0.672
      32
      1
    

    

      3
      1
      89
      66
      23
      94
      28.1
      0.167
      21
      0
    

    

      4
      0
      137
      40
      35
      168
      43.1
      2.288
      33
      1
    

    

      5
      5
      116
      74
      0
      0
      25.6
      0.201
      30
      0
    

    

      6
      3
      78
      50
      32
      88
      31.0
      0.248
      26
      1
    

    

      7
      10
      115
      0
      0
      0
      35.3
      0.134
      29
      0
    

    

      8
      2
      197
      70
      45
      543
      30.5
      0.158
      53
      1
    

    

      9
      8
      125
      96
      0
      0
      0.0
      0.232
      54
      1
    

    

      10
      4
      110
      92
      0
      0
      37.6
      0.191
      30
      0
    

    

      11
      10
      168
      74
      0
      0
      38.0
      0.537
      34
      1
    

    

      12
      10
      139
      80
      0
      0
      27.1
      1.441
      57
      0
    

    

      13
      1
      189
      60
      23
      846
      30.1
      0.398
      59
      1
    

    

      14
      5
      166
      72
      19
      175
      25.8
      0.587
      51
      1
    

    

      15
      7
      100
      0
      0
      0
      30.0
      0.484
      32
      1
    

    

      16
      0
      118
      84
      47
      230
      45.8
      0.551
      31
      1
    

    

      17
      7
      107
      74
      0
      0
      29.6
      0.254
      31
      1
    

    

      18
      1
      103
      30
      38
      83
      43.3
      0.183
      33
      0
    

    

      19
      1
      115
      70
      30
      96
      34.6
      0.529
      32
      1

هذه الدالة تعطينا توصيف كامل للبيانات و تكشف لنا في ما إذا كانت هناك قيم مفقودة





In [3]:



    


df.info()



    


















    






<class 'pandas.core.frame.DataFrame'>
RangeIndex: 768 entries, 0 to 767
Data columns (total 9 columns):
Pregnancies                 768 non-null int64
Glucose                     768 non-null int64
BloodPressure               768 non-null int64
SkinThickness               768 non-null int64
Insulin                     768 non-null int64
BMI                         768 non-null float64
DiabetesPedigreeFunction    768 non-null float64
Age                         768 non-null int64
Outcome                     768 non-null int64
dtypes: float64(2), int64(7)
memory usage: 54.1 KB

سيبورن مكتبة جميلة للرسوميات سهلة في الكتابة لكن مفيدة جداً في المعلومات التي ممكن ان نقراءها عبر الهيستوقرام

فائدها ممكن ان تكون في

1- تلخيص توزيع البينات في رسوميات

2- فهم او الإطلاع على القيم الفريدة

3- تحمل الرسوميات معنى اعمق من الكلمات





In [4]:



    


sb.countplot(x='Outcome',data=df, palette='hls')



    


















    
Out[4]:








<matplotlib.axes._subplots.AxesSubplot at 0xb5372e8>










    
























In [5]:



    


sb.countplot(x='Pregnancies',data=df, palette='hls')



    


















    
Out[5]:








<matplotlib.axes._subplots.AxesSubplot at 0xb640198>










    
























In [6]:



    


sb.countplot(x='Glucose',data=df, palette='hls')



    


















    
Out[6]:








<matplotlib.axes._subplots.AxesSubplot at 0xc416cc0>










    
























In [7]:



    


sb.heatmap(df.corr())



    


















    
Out[7]:








<matplotlib.axes._subplots.AxesSubplot at 0xb872cf8>










    
























In [8]:



    


sb.pairplot(df, hue="Outcome")



    


















    
Out[8]:








<seaborn.axisgrid.PairGrid at 0xcac5080>










    
























In [12]:



    


from scipy.stats import kendalltau
sb.jointplot(df['Pregnancies'], df['Glucose'], kind="hex", stat_func=kendalltau, color="#4CB391")



    


















    
Out[12]:








<seaborn.axisgrid.JointGrid at 0x17c8dba8>










    
























In [31]:



    


import matplotlib.pyplot as plt
g = sb.FacetGrid(df, row="Pregnancies", col="Outcome", margin_titles=True)
bins = np.linspace(0, 50, 13)
g.map(plt.hist, "BMI", color="steelblue", bins=bins, lw=0)



    


















    
Out[31]:








<seaborn.axisgrid.FacetGrid at 0x657146d8>










    
























In [33]:



    


sb.pairplot(df, vars=["Pregnancies", "BMI"])



    


















    
Out[33]:








<seaborn.axisgrid.PairGrid at 0x2574b1d0>

تجربة استخدام تقييس و تدريج الخواص لتحسين اداء النموذج





In [48]:



    


columns = ['Pregnancies', 'Glucose', 'BloodPressure', 'SkinThickness', 'Insulin', 'BMI', 'DiabetesPedigreeFunction', 'Age']
labels = df['Outcome'].values
features = df[list(columns)].values

X_train, X_test, y_train, y_test = train_test_split(features, labels, test_size=0.30)

clf = RandomForestClassifier(n_estimators=1)
clf = clf.fit(X_train, y_train)

accuracy = clf.score(X_train, y_train)
print ' اداء النموذج في عينة التدريب بدقة ', accuracy*100

accuracy = clf.score(X_test, y_test)
print ' اداء النموذج في عينة الفحص بدقة ', accuracy*100

ypredict = clf.predict(X_train)
print '\n Training classification report\n', classification_report(y_train, ypredict)
print "\n Confusion matrix of training \n", confusion_matrix(y_train, ypredict)

ypredict = clf.predict(X_test)
print '\n Training classification report\n', classification_report(y_test, ypredict)
print "\n Confusion matrix of training \n", confusion_matrix(y_test, ypredict)



    


















    






 اداء النموذج في عينة التدريب بدقة  85.6610800745
 اداء النموذج في عينة الفحص بدقة  70.1298701299

 Training classification report
             precision    recall  f1-score   support

          0       0.87      0.90      0.89       339
          1       0.82      0.78      0.80       198

avg / total       0.86      0.86      0.86       537


 Confusion matrix of training 
[[306  33]
 [ 44 154]]

 Training classification report
             precision    recall  f1-score   support

          0       0.82      0.74      0.78       161
          1       0.51      0.61      0.55        70

avg / total       0.72      0.70      0.71       231


 Confusion matrix of training 
[[119  42]
 [ 27  43]]

تجربة تحسين اداء النموذج باستخدام طريقة

standard scaler





In [41]:



    


#scaling
scaler = StandardScaler()

# Fit only on training data
scaler.fit(X_train)
X_train = scaler.transform(X_train)
# apply same transformation to test data
X_test = scaler.transform(X_test)



    











In [42]:



    


clf = RandomForestClassifier(n_estimators=1)
clf = clf.fit(X_train, y_train)

accuracy = clf.score(X_train, y_train)
print ' اداء النموذج في عينة التدريب بدقة ', accuracy*100

accuracy = clf.score(X_test, y_test)
print ' اداء النموذج في عينة الفحص بدقة ', accuracy*100

ypredict = clf.predict(X_train)
print '\n Training classification report\n', classification_report(y_train, ypredict)
print "\n Confusion matrix of training \n", confusion_matrix(y_train, ypredict)

ypredict = clf.predict(X_test)
print '\n Training classification report\n', classification_report(y_test, ypredict)
print "\n Confusion matrix of training \n", confusion_matrix(y_test, ypredict)



    


















    






 اداء النموذج في عينة التدريب بدقة  86.5921787709
 اداء النموذج في عينة الفحص بدقة  65.8008658009

 Training classification report
             precision    recall  f1-score   support

          0       0.87      0.94      0.90       345
          1       0.87      0.74      0.80       192

avg / total       0.87      0.87      0.86       537


 Confusion matrix of training 
[[323  22]
 [ 50 142]]

 Training classification report
             precision    recall  f1-score   support

          0       0.72      0.79      0.76       155
          1       0.48      0.38      0.42        76

avg / total       0.64      0.66      0.65       231


 Confusion matrix of training 
[[123  32]
 [ 47  29]]

تجربة تحسين اداء النموذج بطريقة

min-max scaler





In [54]:



    


columns = ['Pregnancies', 'Glucose', 'BloodPressure', 'SkinThickness', 'Insulin', 'BMI', 'DiabetesPedigreeFunction', 'Age']
labels = df['Outcome'].values
features = df[list(columns)].values

X_train, X_test, y_train, y_test = train_test_split(features, labels, test_size=0.30)

scaler = preprocessing.MinMaxScaler()

scaler.fit(X_train)
X_train = scaler.transform(X_train)
# apply same transformation to test data
X_test = scaler.transform(X_test)

clf = RandomForestClassifier(n_estimators=1)
clf = clf.fit(X_train, y_train)

accuracy = clf.score(X_train, y_train)
print ' اداء النموذج في عينة التدريب بدقة ', accuracy*100

accuracy = clf.score(X_test, y_test)
print ' اداء النموذج في عينة الفحص بدقة ', accuracy*100

ypredict = clf.predict(X_train)
print '\n Training classification report\n', classification_report(y_train, ypredict)
print "\n Confusion matrix of training \n", confusion_matrix(y_train, ypredict)

ypredict = clf.predict(X_test)
print '\n Training classification report\n', classification_report(y_test, ypredict)
print "\n Confusion matrix of training \n", confusion_matrix(y_test, ypredict)



    


















    






 اداء النموذج في عينة التدريب بدقة  88.0819366853
 اداء النموذج في عينة الفحص بدقة  66.6666666667

 Training classification report
             precision    recall  f1-score   support

          0       0.91      0.91      0.91       341
          1       0.84      0.84      0.84       196

avg / total       0.88      0.88      0.88       537


 Confusion matrix of training 
[[309  32]
 [ 32 164]]

 Training classification report
             precision    recall  f1-score   support

          0       0.77      0.74      0.75       159
          1       0.47      0.51      0.49        72

avg / total       0.68      0.67      0.67       231


 Confusion matrix of training 
[[117  42]
 [ 35  37]]

















In [55]:



    


columns = ['Pregnancies', 'Glucose', 'BloodPressure', 'SkinThickness', 'Insulin', 'BMI', 'DiabetesPedigreeFunction', 'Age']
labels = df['Outcome'].values
features = df[list(columns)].values

X_train, X_test, y_train, y_test = train_test_split(features, labels, test_size=0.30)

clf = RandomForestClassifier(n_estimators=5)
clf = clf.fit(X_train, y_train)

accuracy = clf.score(X_train, y_train)
print ' اداء النموذج في عينة التدريب بدقة ', accuracy*100

accuracy = clf.score(X_test, y_test)
print ' اداء النموذج في عينة الفحص بدقة ', accuracy*100

ypredict = clf.predict(X_train)
print '\n Training classification report\n', classification_report(y_train, ypredict)
print "\n Confusion matrix of training \n", confusion_matrix(y_train, ypredict)

ypredict = clf.predict(X_test)
print '\n Testing classification report\n', classification_report(y_test, ypredict)
print "\n Confusion matrix of training \n", confusion_matrix(y_test, ypredict)



    


















    






 اداء النموذج في عينة التدريب بدقة  97.0204841713
 اداء النموذج في عينة الفحص بدقة  74.8917748918

 Training classification report
             precision    recall  f1-score   support

          0       0.97      0.98      0.98       339
          1       0.96      0.95      0.96       198

avg / total       0.97      0.97      0.97       537


 Confusion matrix of training 
[[332   7]
 [  9 189]]

 Training classification report
             precision    recall  f1-score   support

          0       0.81      0.84      0.82       161
          1       0.59      0.54      0.57        70

avg / total       0.74      0.75      0.75       231


 Confusion matrix of training 
[[135  26]
 [ 32  38]]

















In [ ]:

Content source: Faris137/MachineLearningArabic

Similar notebooks:

notebook.community | gallery | about