Jamur (Machine Learning)

Halo semuanya! Pertimbangkan data tentang jamur, prediksi kelayakannya, bangun korelasinya, dan banyak lagi.

Kami akan menggunakan data jamur dari Kaggle (dataframe asli) dari  https://www.kaggle.com/uciml/mushroom-classification , 2 kerangka data tambahan akan dilampirkan ke artikel.

Semua operasi dilakukan di  https://colab.research.google.com/notebooks/intro.ipynb

#  e    
import pandas as pd

#     ,     confusion_matrix:
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import GridSearchCV
from sklearn.metrics import confusion_matrix

#    :
import matplotlib.pyplot as plt
import seaborn as sns

#   
mushrooms = pd.read_csv('/content/mushrooms.csv')

#  
mushrooms.head()
#           :
      
      

        
        
        
      

    
        
        
        
      
      

        
        
        
      

    
    

#  
mushrooms.info()
      
      

        
        
        
      

    
        
        
        
      
      

        
        
        
      

    
    

#     
mushrooms.shape

#    LabelEncoder          (  heatmap)
#     ,           
from sklearn.preprocessing import LabelEncoder
le=LabelEncoder()
for i in mushrooms.columns:
    mushrooms[i]=le.fit_transform(mushrooms[i])

#     
mushrooms.head()
      
      

        
        
        
      

    
        
        
        
      
      

        
        
        
      

    
    

#       heatmap
fig = plt.figure(figsize=(18, 14))
sns.heatmap(mushrooms.corr(), annot = True, vmin=-1, vmax=1, center= 0, cmap= 'coolwarm', linewidths=3, linecolor='black')
fig.tight_layout()
plt.show()
      
      

        
        
        
      

    
        
        
        
      
      

        
        
        
      

    
    

: (veil-color,gill-spacing) = +0.9 (ring-type,bruises) = +0.69 (ring-type,gill-color) = +0.63 (spore-print-color,gill-size) = +0.62 (stalk-root,spore-print-color) = -0.54 (population,gill-spacing) = -0.53 (gill-color,class) = -0.53 , . , , .

#  ,   .
X = mushrooms.drop(['class'], axis=1)
#  ,   .
y = mushrooms['class']

#   RandomForestClassifier.
rf = RandomForestClassifier(random_state=0)

#   ,            
#{'n_estimators': range(10, 51, 10), 'max_depth': range(1, 13, 2),
#             'min_samples_leaf': range(1,8), 'min_samples_split': range(2,10,2)}
parameters = {'n_estimators': [10], 'max_depth': [7],
              'min_samples_leaf': [1], 'min_samples_split': [2]}

#  Random forest  GridSearchCV.
GridSearchCV_clf = GridSearchCV(rf, parameters, cv=3, n_jobs=-1)
GridSearchCV_clf.fit(X, y)

#   ,          
best_clf = GridSearchCV_clf.best_params_

#   .
best_clf
      
      

        
        
        
      

    
        
        
        
      
      

        
        
        
      

    
    

#  confusion matrix ( )  ,          .
y_true = pd.read_csv ('/content/testing_y_mush.csv')
sns.heatmap(confusion_matrix(y_true, predictions), annot=True, cmap="Blues")
plt.show()
      
      

        
        
        
      

    
        
        
        
      
      

        
        
        
      

    
    

Matriks kesalahan ini menunjukkan bahwa kami tidak memiliki kesalahan jenis pertama, tetapi ada kesalahan jenis kedua dalam nilai 3, yang untuk model kami adalah indikator yang sangat rendah yang cenderung ke 0.

Selanjutnya, kita akan melakukan operasi untuk menentukan model dengan akurasi terbaik dari df . kita

#     
from sklearn.metrics import accuracy_score
mr = accuracy_score(y_true, predictions)


#     
from sklearn.model_selection import train_test_split
x_train, x_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 0)

# 
# 
from sklearn.linear_model import LogisticRegression
lr = LogisticRegression(max_iter = 10000)
lr.fit(x_train,y_train)

#  
from sklearn.metrics import confusion_matrix,classification_report
y_pred = lr.predict(x_test)
cm = confusion_matrix(y_test,y_pred)


#  
log_reg = accuracy_score(y_test,y_pred)


#K  
# 
from sklearn.neighbors import KNeighborsClassifier
knn = KNeighborsClassifier(n_neighbors = 5, metric = 'minkowski',p = 2)
knn.fit(x_train,y_train)

#  
from sklearn.metrics import confusion_matrix,classification_report
y_pred = knn.predict(x_test)
cm = confusion_matrix(y_test,y_pred)


#  
from sklearn.metrics import accuracy_score
knn_1 = accuracy_score(y_test,y_pred)


# 
# 
from sklearn.tree import DecisionTreeClassifier
dt = DecisionTreeClassifier(criterion = 'entropy')
dt.fit(x_train,y_train)

#  
from sklearn.metrics import confusion_matrix,classification_report
y_pred = dt.predict(x_test)
cm = confusion_matrix(y_test,y_pred)

#  
from sklearn.metrics import accuracy_score
dt_1 = accuracy_score(y_test,y_pred)


#  
# 
from sklearn.naive_bayes import GaussianNB
nb = GaussianNB()
nb.fit(x_train,y_train)

#  
from sklearn.metrics import confusion_matrix,classification_report
y_pred = nb.predict(x_test)
cm = confusion_matrix(y_test,y_pred)


#  
from sklearn.metrics import accuracy_score
nb_1 = accuracy_score(y_test,y_pred)


#  
plt.figure(figsize= (16,12))
ac = [log_reg,knn_1,nb_1,dt_1,mr]
name = [' ','  ','  ',' ', ' ']
sns.barplot(x = ac,y = name,palette='colorblind')
plt.title("  ", fontsize=20, fontweight="bold")
      
      

        
        
        
      

    
        
        
        
      
      

        
        
        
      

    
    

Kita dapat menyimpulkan bahwa model yang paling akurat untuk prediksi kita adalah pohon keputusan.