Create README.md

README.md

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+# -*- coding: utf-8 -*-
+"""Final_project_of_Credit_Card_Fraud_Detection(1).ipynb
+
+Automatically generated by Colaboratory.
+
+Original file is located at
+    https://colab.research.google.com/drive/1PSHcV_bp0wcT0Kl_f2n5QwtlOZj3M5BV
+"""
+
+import pandas as pd
+import seaborn as sns
+import matplotlib.pyplot as plt
+
+data=pd.read_csv('/content/data4.csv')
+
+data.head()
+
+data.shape
+
+data.isnull().sum().sum()
+
+data.keys()
+
+data.info()
+
+data=data.drop(['Unnamed: 0','nameOrig','nameDest'],axis=1)
+
+data.shape
+
+data['isFraud'].value_counts()
+
+plt.pie(data['isFraud'].value_counts(),labels=['Not_Fraud','Fraud'],autopct='%0.2f%%')
+plt.show()
+
+#sns.countplot('isFraud',data=data)
+sns.countplot(data=data, x="type", hue="isFraud")
+plt.show()
+
+plt.figure(figsize=(6,8))
+sns.countplot(data=data, x="isFraud", hue="type")
+plt.show()
+
+data.tail()
+
+data['type'].value_counts()
+
+
+
+dict1={'CASH_OUT':0,'TRANSFER':1,'PAYMENT':2,'CASH_IN':3,'DEBIT':4}
+
+data['type']=data['type'].map(dict1)
+
+data.head()
+
+X=data.drop('isFraud',axis=1)
+
+X
+
+y=data['isFraud']
+
+y
+
+from sklearn.model_selection import train_test_split
+
+X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=0.30,random_state=0)
+
+print(X_train.shape)
+print(X_test.shape)
+print(y_train.shape)
+print(y_test.shape)
+
+from sklearn.preprocessing import StandardScaler
+
+sc=StandardScaler()
+
+X_train_sc=sc.fit_transform(X_train)
+X_test_sc=sc.transform(X_test)
+
+X_train_sc
+
+X_test_sc
+
+from sklearn.linear_model import LogisticRegression
+
+model1=LogisticRegression()
+
+model1.fit(X_train_sc,y_train)
+
+y_pred1=model1.predict(X_test_sc)
+
+from sklearn.metrics import classification_report
+
+print(classification_report(y_test,y_pred1))
+
+from sklearn.naive_bayes import GaussianNB
+
+model2=GaussianNB()
+
+model2.fit(X_train_sc,y_train)
+
+y_pred2=model2.predict(X_test_sc)
+
+print(classification_report(y_test,y_pred2))
+
+from sklearn.neighbors import KNeighborsClassifier
+
+model3=KNeighborsClassifier()
+
+model3.fit(X_train_sc,y_train)
+
+y_pred3=model3.predict(X_test_sc)
+
+print(classification_report(y_test,y_pred3))
+
+from sklearn.tree import DecisionTreeClassifier
+
+model4=DecisionTreeClassifier()
+
+model4.fit(X_train_sc,y_train)
+
+y_pred4=model4.predict(X_test_sc)
+
+print(classification_report(y_test,y_pred4))
+
+from sklearn import tree
+
+plt.figure(figsize=(10,10))
+tree.plot_tree(model4,filled=True)
+plt.show()
+
+from sklearn.ensemble import RandomForestClassifier,AdaBoostClassifier
+
+model5=RandomForestClassifier()
+
+model5.fit(X_train_sc,y_train)
+
+y_pred5=model5.predict(X_test_sc)
+
+print(classification_report(y_test,y_pred5))
+
+model6=AdaBoostClassifier()
+
+model6.fit(X_train_sc,y_train)
+
+y_pred6=model6.predict(X_test_sc)
+
+print(classification_report(y_test,y_pred6))
+
+model5.predict([[239,2,5178.72,400705.00,395526.28,0.00,0.00]])
+
+model5.predict([[369,0,89596.79,89596.79,0.0,0.00,89596.79]])
+