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Python Task on A to J one Prototype

Python Task on A to J one Prototype

  • 20th May, 2022
  • 16:41 PM

import pandas as pd
import numpy as np
import pandas as pd
import numpy as np
import random
alp=['a','b','c','d','e','f','g','h','i','j']
d={}
ls=[]
for k in range(0,10):
    matrix=[]
    ls=[]
    for i in range(7):
        row=[] 
        for j in range(7): 
            row.append(random.randint(0,1))
        matrix.append(row)
        d[alp[k]]=np.squeeze(np.asarray(matrix))
        
d

s=pd.DataFrame(columns=['0','1','2','3','4','5','6','label'],index=list(range(70)))
i=0
for j in range(len(d)):
    for m in range(7):
        for k in range(7):
                s.iloc[i,k]=(d[list(d.keys())[j]][m][k])
        s.iloc[i,k+1]=list(d.keys())[j]
        i=i+1
     #   i=i+7
        
x=s[['0','1','2','3','4','5','6']]
y=s['label']

# Random Forest

from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier
X_train, X_test, y_train, y_test = train_test_split(x, y, test_size=0.3)
clf = RandomForestClassifier(max_depth=50, random_state=0)

clf.fit(X_train,y_train)
y_pred=clf.predict(X_test)

from sklearn import metrics
# Model Accuracy, how often is the classifier correct?
print("Accuracy:",metrics.accuracy_score(y_test, y_pred))

#SVM Classifier

from sklearn.svm import SVC
svclassifier = SVC(kernel='linear')
svclassifier.fit(X_train, y_train)

y_pred = svclassifier.predict(X_test)


from sklearn.metrics import classification_report, confusion_matrix
print(confusion_matrix(y_test,y_pred))
print(classification_report(y_test,y_pred))

from sklearn import metrics
# Model Accuracy, how often is the classifier correct?
print("Accuracy:",metrics.accuracy_score(y_test, y_pred))

#MLP Classifier

from sklearn.neural_network import MLPClassifier
#Initializing the MLPClassifier
classifier = MLPClassifier(hidden_layer_sizes=(150,100,50), max_iter=300,activation = 'relu',solver='adam',random_state=1)
#Fitting the training data to the network
classifier.fit(X_train, y_train)
#Predicting y for X_val
y_pred = classifier.predict(X_test)

from sklearn import metrics
# Model Accuracy, how often is the classifier correct?
print("Accuracy:",metrics.accuracy_score(y_test, y_pred))

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