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import random
import json
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.metrics import confusion_matrix
from sklearn.svm import SVC
from sklearn.linear_model import LogisticRegression
from sklearn.neighbors import KNeighborsClassifier
from sklearn.naive_bayes import GaussianNB
from sklearn.tree import DecisionTreeClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.linear_model import LinearRegression
def jsonToCSV(numberOfDatas):
name = ["data" + str(numberOfDatas[i]) for i in range(len(numberOfDatas))] #filenames = data + numberofdata's items
fig, axs = plt.subplots(len(numberOfDatas)) #row numbers
fig.suptitle('Epic Prediction')
plt.setp(axs, yticklabels = ['SVC','LR','KN',"GNB",'DT',"RF"]) #label names
y_pos = np.arange(len(['SVC','LR','KN',"GNB",'DT',"RF"])) #customizations
fig.tight_layout()#customizations
for i in range(len(name)): #We will generate random numbers as many as the element in the list numberOfDatas
axs[i].set_yticks(y_pos+1)
def gender():
genderBinary = random.randint(0,1)
return genderBinary
def age():
ageRandom = random.randint(18,65)
return ageRandom
def enteries():
enteriesRandom = random.randint(0,300)
return enteriesRandom
def purchases():
purchasesRandom = random.randint(1,100)
return purchasesRandom
def purchasesAv():
purchasesAvRandom = random.randint(10,1500)
return purchasesAvRandom
def isLeave():
isLeaveRandom = random.randint(0,1)
return isLeaveRandom
limit,index = 0,0
dicts = {}
while True:
dicts[str(index)] = [gender(),
age(),
enteries(),enteries(),enteries(),
purchases(),purchases(),purchases(),
purchasesAv(),purchasesAv(),purchasesAv(),
isLeave()]
index +=1
limit +=1
if limit == numberOfDatas[i]:
break
with open(f'{name[i]}.json', 'w') as outfile: #We saved it in document datax.json (x = numberofdata's items)
json.dump(dicts, outfile)
df = pd.read_json(rf'{name[i]}.json')
df.T.to_csv (rf'{name[i]}.csv', index = None) #We converted it to csv document. + transpose
dataset = pd.read_csv(f'{name[i]}.csv') #we separated it as dependent independent variable.
allOfThem= dataset.iloc[:,1:11].values
willPredict = dataset.iloc[:,11:12].values
X_train, X_test, y_train, y_test = train_test_split(allOfThem, willPredict, test_size = 0.25, random_state = 3)
sc = StandardScaler()
X_train = sc.fit_transform(X_train)
X_test = sc.transform(X_test)
"""
confusion matrix results
[A B
C D]
Accuracy = (A+D/A+B+C+D ) * 100 => %bla bla
"""
classifier = SVC(kernel = 'sigmoid', random_state = 4)
classifier.fit(X_train, y_train)
y_pred = classifier.predict(X_test)
cm_SVC = confusion_matrix(y_test, y_pred)
cm_SVC_C = (cm_SVC[0][0]+cm_SVC[1][1])/len(y_test)*100
#print("SVC: %",cm_SVC_C)
logr = LogisticRegression(random_state=3)
logr.fit(X_train,y_train)
y_pred = logr.predict(X_test)
cm_LR = confusion_matrix(y_test, y_pred)
cm_LR_C = (cm_LR[0][0]+cm_LR[1][1])/len(y_test)*100
#print("LR: %",cm_LR_C)
knn = KNeighborsClassifier(n_neighbors=11, metric='minkowski')
knn.fit(X_train,y_train)
y_pred = knn.predict(X_test)
cm_KNN = confusion_matrix(y_test,y_pred)
cm_KNN_C = (cm_KNN[0][0]+cm_KNN[1][1])/len(y_test)*100
#print("KN: %",cm_KNN_C)
gnb = GaussianNB()
gnb.fit(X_train, y_train)
y_pred = gnb.predict(X_test)
cm_GNB = confusion_matrix(y_test,y_pred)
cm_GNB_C = (cm_GNB[0][0]+cm_GNB[1][1])/len(y_test)*100
#print("GaussianNB: %",cm_GNB_C)
#print("Score:" , gnb.score(y_test, y_pred))
dtc = DecisionTreeClassifier(criterion = 'entropy')
dtc.fit(X_train,y_train)
y_pred = dtc.predict(X_test)
cm_DT = confusion_matrix(y_test,y_pred)
cm_DT_C = (cm_DT[0][0]+cm_DT[1][1])/len(y_test)*100
#print("DT: %",cm_DT_C)
rfc = RandomForestClassifier(n_estimators=10, criterion = 'entropy')
rfc.fit(X_train,y_train)
y_pred = rfc.predict(X_test)
cm_RF = confusion_matrix(y_test,y_pred)
cm_RF_C= (cm_RF[0][0]+cm_RF[1][1])/len(y_test)*100
#print("RF: %",cm_RF_C)
listOfResults_X = [round(cm_SVC_C,4),round(cm_LR_C,4),round(cm_KNN_C,4),round(cm_GNB_C,4),round(cm_DT_C,4),round(cm_RF_C,4)]#we rounded the numbers
listOfResults_Y = [1,2,3,4,5,6]
axs[i].barh(1,cm_SVC_C,height = 1,color = "darkcyan", label='SVC')
axs[i].barh(2,cm_LR_C,height = 1,color = "darkturquoise", label='LR')
axs[i].barh(3,cm_KNN_C,height = 1,color = "steelblue", label='KN')
axs[i].barh(4,cm_GNB_C, height = 1,color = "palevioletred", label='GNB')
axs[i].barh(5,cm_DT_C, height = 1,color = "darkmagenta", label='DT')
axs[i].barh(6,cm_RF_C,height = 1,color = "rosybrown", label='RF')
for x in range(6):
axs[i].text(x = listOfResults_X[x] , y = listOfResults_Y[x] - 0.25, s = listOfResults_X[x], size = 9)
axs[i].set_title("The Number Of Data : " + str(numberOfDatas[i]))
axs[i].set_xlabel('Prediction Rate')
axs[i].set_ylabel('Regression Models')
axs[-1].legend(loc=2)
plt.show()
jsonToCSV([100,1000,5000]) #If you want, you can change these items.
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