Credit-Card-Fraud-Detection

#project for credit card fraud detection import numpy as np import pandas as pd import matplotlib.pyplot as plt import seaborn as sns from matplotlib import gridspec

dataset=pd.read_csv("creditcard.csv") print(dataset.head())

#BELOW PROG FOR FINDING MISSING VALUES IN DATA

#print("Non missing values:",str(dataset.isnull().shape[0])) #print("Missing values:",str(dataset.shape[0]-dataset.isnull().shape[0]))

from sklearn.preprocessing import RobustScaler scaler=RobustScaler().fit (dataset[["Time","Amount"]]) dataset[["Time","Amount"]]=scaler.transform(dataset[["Time","Amount"]])

print(dataset.head())

y=dataset["Class"]#TARGET x=dataset.iloc[:,0:30]#FETAURES

from sklearn.model_selection import _search, train_test_split x_train,x_test,y_train,y_test=train_test_split(x,y,test_size=0.2,random_state=42)

from sklearn.model_selection import StratifiedKFold from sklearn.model_selection import GridSearchCV,cross_val_score,RandomizedSearchCV

kf=StratifiedKFold(n_splits=5,random_state=None,shuffle=False)

#imbalance learn module, only 20 is of test

from imblearn.pipeline import make_pipeline from imblearn.under_sampling import NearMiss from imblearn.over_sampling import SMOTE from sklearn.metrics import roc_curve,roc_auc_score,accuracy_score,recall_score,precision_score,f1_score

#Importing all Classifiers

from sklearn.linear_model import LogisticRegression from sklearn.svm import SVC from sklearn.neighbors import KNeighborsClassifier from sklearn.tree import DecisionTreeClassifier from sklearn.ensemble import RandomForestClassifier

rf=RandomForestClassifier() #rf.fit(Q_train,P_train) #yp=rf.predict(Q_test)

rf.fit(x_train,y_train)

yp = rf.predict(x_test)

print("accuracy is :",accuracy_score(y_test,yp))

print("precision:",precision_score(y_test,yp))

print("recall:",recall_score(y_test,yp))

print("f1 score",f1_score(y_test,yp))

#under sampling function

def get_model_best_estimator(estimator,params,kf=kf,x_train=x_train,y_train=y_train,x_test=x_test,y_test=y_test,is_gridsearch=True,sampling=NearMiss(),scoring="f1",n_jobs=2): if sampling is None: pipeline=make_pipeline(estimator) else: pipeline=make_pipeline(sampling,estimator) estimator_name=estimator.class.name.lower()

new_param = {f"{estimator_name}__{key}": params[key] for key in params}

new_param={f"{estimator_name}__{key}":params [key] for key in params} if is_gridsearch: search=GridSearchCV(pipeline,param_grid=new_param,cv=kf,return_train_score=True,n_jobs=n_jobs,verbose=2) else: search=RandomizedSearchCV(pipeline,param_distributions=new_param,cv=kf,scoring=scoring,return_train_score=True,n_jobs=n_jobs,verbose=1) search.fit(x_train,y_train) cv_score=cross_val_score(search,x_train,y_train,scoring=scoring,cv=kf) y_pred=search.best_estimator_.named_steps[estimator_name].predict(x_test) recall=recall_score(y_test,y_pred,pos_label=1) accuracy=accuracy_score(y_test,y_pred) f1=f1_score(y_test,y_pred) y_prob=search.best_estimator_.named_steps[estimator_name].predict_proba(x_test)[::,1] fpr,tpr,=roc_curve(y_test,y_prob) auc=roc_auc_score(y_test,y_prob) return{ "best_estimator":search.best_estimator, "estimator_name":estimator_name, "cv_score":cv_score, "recall":recall, "accuracy":accuracy, "f1_score":f1, "fpr":fpr, "tpr":tpr, "auc":auc, } res_table=pd.DataFrame(columns=['classifier','fpr','tpr','auc']) result=get_model_best_estimator(estimator=LogisticRegression(),params={"penalty":['l1','l2'],'C':[0.01,0.1,1,100],'solver':['liblinear']},sampling=NearMiss()) #print(result)

print("Estimator_name",search.best_estimator)

print("Accuracy", search.CV_results['mean_test_score'][search.best_index])

print("Recall", search.CV_results['mean_test_recall'][search.best_index])

print("F1 score", search.CV_results['mean_test_f1'][search.best_index])

print("Estimator_name",result["search.best_estimator"])

print("Accuracy",result["accuracy"])

print("recall",result["recall"])

print("f1 score",result["f1_score"]) .

res_table=res_table.add({'classifier':result["estimator_name"],'fpr':result["fpr"],'tpr':result["tpr"],'auc':result["auc"]})

#below starts with over sampling(o_result is over sampling)

res_table=res_table.add({'classifier':result["estimator_name"], 'fpr':result["fpr"], 'tpr':result["tpr"], 'auc':result["auc"]}) o_result=get_model_best_estimator(estimator=LogisticRegression(),params={"penalty":['l1','l2'],'C':[0.01,0.1,1,100,100],'solver':['liblinear']},sampling=SMOTE(random_state=42),scoring='f1',is_gridsearch=False,n_jobs=2) print(o_result) res_table=res_table.add({'classifier':o_result["estimator_name"], 'fpr':o_result["fpr"], 'tpr':o_result["tpr"], 'auc':o_result["auc"]}) print(res_table)