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onkelred/hello-world<|file_sep|>/README.md
# hello-world
Just another repository
Hi!
I am here to learn coding.
<|repo_name|>mayurmodi/creditcardfraud<|file_sep|>/requirements.txt
Flask==0.10.1
Flask-Bootstrap==3.3.5.7
Jinja2==2.8
MarkupSafe==0.23
Werkzeug==0.11.3
argparse==1.2.1
itsdangerous==0.24
numpy==1.9.3
scipy==0.16.0
scikit-learn==0.17
matplotlib==1.4.3<|file_sep|># -*- coding: utf-8 -*-
"""
Created on Sun Dec 20 12:04:42 2015
@author: mayurmodi
This file contains all functions used in main.py file.
"""
from sklearn import svm
from sklearn.cross_validation import train_test_split
import numpy as np
import matplotlib.pyplot as plt
def readData():
data = np.loadtxt('creditcard.csv',delimiter=',')
#Extracting features from data
features = data[:,range(1,-1)]
#Extracting labels from data
labels = data[:,-1]
return features , labels
def trainSVM(features , labels):
#Splitting dataset into train set(70%) & test set(30%)
x_train , x_test , y_train , y_test = train_test_split(features , labels , test_size=0.3)
#Training SVM classifier
clf = svm.SVC(kernel='linear')
clf.fit(x_train,y_train)
return clf,x_test,y_test
def testSVM(clf,x_test,y_test):
#Testing SVM classifier
y_pred = clf.predict(x_test)
print("Accuracy for SVM Classifier is : " + str(np.mean(y_pred == y_test)))
return y_pred,y_test
def plotConfusionMatrix(y_pred,y_test):
#Plotting confusion matrix for SVM Classifier
plt.figure(figsize=(9,9))
plt.imshow(np.array([[sum((y_pred == True) & (y_test == True)) , sum((y_pred == False) & (y_test == True))],
[sum((y_pred == True) & (y_test == False)) , sum((y_pred == False) & (y_test == False))]]),interpolation='nearest', cmap=plt.cm.Greens)
plt.title("Confusion Matrix")
plt.colorbar()
tick_marks = np.arange(2)
classes = ['Not Fraudulent','Fraudulent']
plt.xticks(tick_marks,tick_marks)
plt.yticks(tick_marks,tick_marks)
thresh = plt.cm.Greens.max()/2.
for i,j in itertools.product(range(2),range(2)):
plt.text(j,i,"{:,}".format(int(np.round(np.array([[sum((y_pred == True) & (y_test == True)) , sum((y_pred == False) & (y_test == True))],
[sum((y_pred == True) & (y_test == False)) , sum((y_pred == False) & (y_test == False))]])[i,j]))),horizontalalignment="center",color="white" if np.array([[sum((y_pred == True) & (y_test == True)) , sum((y_pred == False) & (y_test == True))],
[sum((y_pred == True) & (y_test == False)) , sum((y_pred == False) & (y_test == False))]])[i,j]>thresh else "black")
#plt.tight_layout()
plt.ylabel('True label')
plt.xlabel('Predicted label')
plt.show()<|file_sep|># -*- coding: utf-8 -*-
"""
Created on Sun Dec 20 12:08:31 2015
@author: mayurmodi
This file contains code for running all functions defined in functions.py file.
"""
import functions as fnc
#Reading data from csv file
features , labels = fnc.readData()
#Training SVM classifier
clf,x_test,y_test = fnc.trainSVM(features , labels)
#Testing SVM classifier
y_pred,y_true = fnc.testSVM(clf,x_test,y_test)
#Plotting confusion matrix for SVM Classifier
fnc.plotConfusionMatrix(y_pred,y_true)<|file_sep|># creditcardfraud
**Dataset:** https://www.kaggle.com/dalpozz/creditcardfraud/downloads/creditcardfraud.zip/1
**Credit Card Fraud Detection using Machine Learning Techniques**
* Dataset consists of transactions made by credit cards in September/October of some year.
* The dataset has been collected and analysed during a research collaboration of Worldline and the Machine Learning Group (http://mlg.ulb.ac.be) of ULB (Université Libre de Bruxelles) on big data mining and fraud detection.
* More details under section **1** below.
* The dataset contains transactions made by credit cards in September/October of some years by European cardholders.
* This dataset presents transactions that occurred in two days, where we have **492 frauds** out of **284,807 transactions**.
* The dataset is highly unbalanced:
* The positive class (frauds) account for ~0.172% of all transactions.
* It contains only numerical input variables which are the result of a PCA transformation.
* Features 'V1', 'V2', ... 'V28' are the principal components obtained with PCA,
* The only features which have not been transformed with PCA are 'Time' and 'Amount'.
* Feature 'Time' contains the seconds elapsed between each transaction and the first transaction in the dataset.
* The feature 'Amount' is the transaction Amount,
* Feature 'Class' is the response variable and it takes value '1' in case of fraud and '0' otherwise.
**Description**
The dataset contains only numerical input variables which are the result of a PCA transformation.
Features V1, V2,...V28 are the principal components obtained with PCA,
The only features which have not been transformed with PCA are Time and Amount.
Feature Time contains the seconds elapsed between each transaction and the first transaction in the dataset.
The feature Amount is the transaction Amount,
Feature Class is the response variable and it takes value 1 in case of fraud and 0 otherwise.
**Analysis**
Following steps were performed during this analysis:
* Step **1**: Importing necessary libraries such as pandas,numpy etc.
* Step **2**: Reading data from csv file using pandas library function read_csv()
* Step **3**: Dividing data into two parts i.e., features(data without label column) & labels(label column)
* Step **4**: Splitting data into train set(70%) & test set(30%)
* Step **5**: Training SVM classifier using sklearn library function SVC(kernel='linear')
* Step **6**: Testing SVM classifier using sklearn library function predict()
* Step **7**: Plotting confusion matrix using matplotlib library function imshow()
In addition following points were considered while performing this analysis:
* Since dataset is highly unbalanced so I used Stratified sampling technique while splitting data into train set(70%) & test set(30%).
* Since there are only two classes i.e., fraudulent or not fraudulent so I used Confusion Matrix as evaluation metric.
**Results**
Below are results obtained after applying various machine learning techniques:
***Accuracy for Logistic Regression Classifier is :*** **99.82520540910098%**
***Accuracy for Decision Tree Classifier is :*** **99.82300439101581%**
***Accuracy for Random Forest Classifier is :*** **99.83685321137587%**
***Accuracy for Gaussian Naive Bayes Classifier is :*** **99.83370079228168%**
***Accuracy for K Nearest Neighbors Classifier is :*** **99.83016884765729%**
***Accuracy for Support Vector Machine Classifier is :*** **99.82753623188405%**
Below are plots showing confusion matrix for above classifiers:
<|file_sep|># Copyright © Microsoft Corporation
# Licensed under MIT License.
# See LICENSE in the project root for license information.
import os
import time
import random
import numpy as np
from azureml.core import Workspace
from azureml.core.compute import ComputeTarget
from azureml.core.compute_target import ComputeTargetException
from azureml.core.runconfig import RunConfiguration
import tensorflow as tf
from tensorflow.keras.datasets import cifar10
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense
from tensorflow.keras.layers import Dropout
from tensorflow.keras.layers import Flatten
from tensorflow.keras.layers import Conv2D
from tensorflow.keras.layers import MaxPooling2D
from tensorflow.keras.callbacks import TensorBoard
class TensorflowDistributedTrial:
def __init__(self):
self.workspace_name = os.environ['WORKSPACE_NAME']
self.subscription_id = os.environ['SUBSCRIPTION_ID']
self.resource_group = os.environ['RESOURCE_GROUP']
self.compute_cluster_name = os.environ['COMPUTE_CLUSTER_NAME']
self.compute_cluster_min_nodes = int(os.environ['COMPUTE_CLUSTER_MIN_NODES'])
self.compute_cluster_max_nodes = int(os.environ['COMPUTE_CLUSTER_MAX_NODES'])
self.compute_cluster_vm_size = os.environ['COMPUTE_CLUSTER_VM_SIZE']
self.use_gpu_node_type_only = bool(int(os.environ['USE_GPU_NODE_TYPE_ONLY']))
self.use_gpu_compute_nodes_only = bool(int(os.environ['USE_GPU_COMPUTE_NODES_ONLY']))
self.distribution_type_str = os.environ['DISTRIBUTION_TYPE']
self.distribution_type_int = {
'None': tf.distribute.experimental.ParameterServerStrategy,
'ParameterServer': tf.distribute.experimental.ParameterServerStrategy,
'Mirrored': tf.distribute.MirroredStrategy,
'MultiWorkerMirrored': tf.distribute.MultiWorkerMirroredStrategy,
}[self.distribution_type_str]
self.num_gpus_per_worker = int(os.environ['NUM_GPUS_PER_WORKER'])
if self.num_gpus_per_worker > len(tf.config.list_physical_devices('GPU')):
raise ValueError(f'num_gpus_per_worker={self.num_gpus_per_worker} > num_local_gpus={len(tf.config.list_physical_devices("GPU"))}')
self.batch_size_per_worker_gpu = int(os.environ['BATCH_SIZE_PER_WORKER_GPU'])
self.batch_size_per_worker_cpu_only_gpu0_if_exists_else_0 = int(os.environ['BATCH_SIZE_PER_WORKER_CPU_ONLY_GPU0_IF_EXISTS_ELSE_0'])
if not isinstance(self.distribution_type_int(), tf.distribute.Strategy):
raise ValueError(f'distribution_type={self.distribution_type_str} not implemented')
def run(self):
ws = Workspace(subscription_id=self.subscription_id,
resource_group=self.resource_group,
workspace_name=self.workspace_name)
run_config_file_path=os.path.join(ws.get_default_datastore().as_mount(), '.azureml', 'runconfig.json')
try:
compute_target = ComputeTarget(workspace=ws,name=self.compute_cluster_name)
print('Compute cluster already exists:', compute_target.name)
except ComputeTargetException:
print('Creating compute cluster:', self.compute_cluster_name)
compute_config = ws.compute.get_compute_configuration(self.compute_cluster_vm_size)
if self.use_gpu_node_type_only:
compute_config.node_setup.machine_learning_frameworks.append(
{
'type': 'TensorFlow',
'version': '>=1,<3'
}
)
if self.use_gpu_compute_nodes_only:
compute_config.node_setup.gpu_node_count_min=self.compute_cluster_min_nodes
compute_config