
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns


filepath = "Data/creditcard.csv"
df = pd.read_csv(filepath)


df.head(5)


df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 284807 entries, 0 to 284806
Data columns (total 31 columns):
 #   Column  Non-Null Count   Dtype  
---  ------  --------------   -----  
 0   Time    284807 non-null  float64
 1   V1      284807 non-null  float64
 2   V2      284807 non-null  float64
 3   V3      284807 non-null  float64
 4   V4      284807 non-null  float64
 5   V5      284807 non-null  float64
 6   V6      284807 non-null  float64
 7   V7      284807 non-null  float64
 8   V8      284807 non-null  float64
 9   V9      284807 non-null  float64
 10  V10     284807 non-null  float64
 11  V11     284807 non-null  float64
 12  V12     284807 non-null  float64
 13  V13     284807 non-null  float64
 14  V14     284807 non-null  float64
 15  V15     284807 non-null  float64
 16  V16     284807 non-null  float64
 17  V17     284807 non-null  float64
 18  V18     284807 non-null  float64
 19  V19     284807 non-null  float64
 20  V20     284807 non-null  float64
 21  V21     284807 non-null  float64
 22  V22     284807 non-null  float64
 23  V23     284807 non-null  float64
 24  V24     284807 non-null  float64
 25  V25     284807 non-null  float64
 26  V26     284807 non-null  float64
 27  V27     284807 non-null  float64
 28  V28     284807 non-null  float64
 29  Amount  284807 non-null  float64
 30  Class   284807 non-null  int64  
dtypes: float64(30), int64(1)
memory usage: 67.4 MB


df.dtypes

Time      float64
V1        float64
V2        float64
V3        float64
V4        float64
V5        float64
V6        float64
V7        float64
V8        float64
V9        float64
V10       float64
V11       float64
V12       float64
V13       float64
V14       float64
V15       float64
V16       float64
V17       float64
V18       float64
V19       float64
V20       float64
V21       float64
V22       float64
V23       float64
V24       float64
V25       float64
V26       float64
V27       float64
V28       float64
Amount    float64
Class       int64
dtype: object


df.describe()


df.isna().sum()

Time      0
V1        0
V2        0
V3        0
V4        0
V5        0
V6        0
V7        0
V8        0
V9        0
V10       0
V11       0
V12       0
V13       0
V14       0
V15       0
V16       0
V17       0
V18       0
V19       0
V20       0
V21       0
V22       0
V23       0
V24       0
V25       0
V26       0
V27       0
V28       0
Amount    0
Class     0
dtype: int64


# How many fraud transactions are there? Class ==1
fraud_sum = (df['Class']==1).sum()
all_sum = df.shape[0]

print("There are ", all_sum, "transactions, and ", fraud_sum, " fraud transactions")

There are  284807 transactions, and  492  fraud transactions


fraud = df.loc[df['Class']==1]
normal = df.loc[df['Class']==0]


fraud.head(5)


external_factors = ['Time','Amount']

print('\033[1mNumeric Features Distribution'.center(100))

figsize = (12, 4)

n=len(external_factors)
colors = ['g', 'b', 'r', 'y', 'k']

# histogram
plt.figure(figsize=figsize)
for i in range(len(external_factors)):
    plt.subplot(1,n,i+1)
    sns.distplot(df[external_factors[i]],
                 bins=100, 
                 color = colors[i])
plt.tight_layout();

                                 Numeric Features Distribution

/Users/michael/opt/anaconda3/lib/python3.9/site-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).
  warnings.warn(msg, FutureWarning)
/Users/michael/opt/anaconda3/lib/python3.9/site-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).
  warnings.warn(msg, FutureWarning)


print('\033[1m Fraud Numeric Features Distribution'.center(100))
plt.figure(figsize=figsize)
for i in range(len(external_factors)):
    plt.subplot(1,n,i+1)
    sns.distplot(fraud[external_factors[i]],
                 bins=50, 
                 color = colors[i])
plt.tight_layout();

                               Fraud Numeric Features Distribution

/Users/michael/opt/anaconda3/lib/python3.9/site-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).
  warnings.warn(msg, FutureWarning)
/Users/michael/opt/anaconda3/lib/python3.9/site-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).
  warnings.warn(msg, FutureWarning)


print('\033[1m Normal Numeric Features Distribution'.center(100))
plt.figure(figsize=figsize)
for i in range(len(external_factors)):
    plt.subplot(1,n,i+1)
    sns.distplot(normal[external_factors[i]],
                 bins=1000, 
                 color = colors[i])
plt.tight_layout();

                              Normal Numeric Features Distribution

/Users/michael/opt/anaconda3/lib/python3.9/site-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).
  warnings.warn(msg, FutureWarning)
/Users/michael/opt/anaconda3/lib/python3.9/site-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).
  warnings.warn(msg, FutureWarning)


#plot time seris hue=class
plt.figure(figsize = (10,8))
sns.scatterplot(x = df['Time'],y = df['Amount'],hue=df['Class'])

<AxesSubplot:xlabel='Time', ylabel='Amount'>


df.boxplot(column=external_factors, grid=False, figsize=(6,4))

<AxesSubplot:>


fraud.boxplot(column=['Time','Amount'], grid=False, figsize=(6,4))

<AxesSubplot:>


normal.boxplot(column=['Amount'], grid=False, figsize=(6,4))

<AxesSubplot:>


fraud.boxplot(column=['Time'], grid=False, figsize=(6,4))

<AxesSubplot:>


X = df.drop(['Class'], axis=1)
y = df['Class']


from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X,y, test_size=0.2, random_state = 43)


plt.figure(figsize=(12, 10))
cor = X_train.corr()
sns.heatmap(cor, vmin=-1, vmax=1);


keep_columns = np.full(cor.shape[0], True)
for i in range(cor.shape[0] - 1):
    for j in range(i + 1, cor.shape[0] - 1):
        if (np.abs(cor.iloc[i, j]) >= 0.8): # 0.8 is the correlation threshold
            keep_columns[j] = False
selected_columns = X_train.columns[keep_columns]
X_train_reduced = X_train[selected_columns]


print(selected_columns)

Index(['Time', 'V1', 'V2', 'V3', 'V4', 'V5', 'V6', 'V7', 'V8', 'V9', 'V10',
       'V11', 'V12', 'V13', 'V14', 'V15', 'V16', 'V17', 'V18', 'V19', 'V20',
       'V21', 'V22', 'V23', 'V24', 'V25', 'V26', 'V27', 'V28', 'Amount'],
      dtype='object')


selected_columns==X.columns

array([ True,  True,  True,  True,  True,  True,  True,  True,  True,
        True,  True,  True,  True,  True,  True,  True,  True,  True,
        True,  True,  True,  True,  True,  True,  True,  True,  True,
        True,  True,  True])


from sklearn.pipeline import make_pipeline
from sklearn.preprocessing import StandardScaler
from sklearn.metrics import classification_report, confusion_matrix, accuracy_score, roc_curve,RocCurveDisplay
from sklearn.ensemble import RandomForestClassifier
from sklearn.linear_model import LogisticRegression
from xgboost import XGBClassifier
from sklearn.svm import SVC


rf = RandomForestClassifier(random_state=42)
lr = LogisticRegression(max_iter=100000, class_weight="balanced",random_state=42)
xg = XGBClassifier(random_state=42)
svc = SVC(random_state=42)
clf_list = [lr,svc]


# Function to print the accuracy and classification report
def print_acc_and_CR(y_test, model_predictions):
    # Calculate and display the model accuracy
    print('Accuracy: {:.2f}%'.format(accuracy_score(y_test, model_predictions) * 100))
    # Calculate and display the classification report for the model
    print('Classification report: \n', classification_report(y_test, model_predictions))
# Function to display the confusion matrix
def display_confusion_matrix(y_test, model_predictions):
    # Calculate and display the confusion matrix
    model_confusionMatrix = confusion_matrix(y_test, model_predictions)
    # Create variables that will be displayed as text on the plot
    strings2 = np.asarray([['True Negatives \n', 'False Positives \n'], ['False Negatives \n', 'True Positives \n']])
    labels2 = (np.asarray(["{0} {1:g}".format(string, value)
                          for string, value in zip(strings2.flatten(),model_confusionMatrix.flatten())])
             ).reshape(2, 2)
    # Use a heat map plot to display the results
    sns.heatmap(model_confusionMatrix, annot=labels2, fmt='', vmin=0, annot_kws={"fontsize":17})
    plt.xlabel('Predicted value');
    plt.ylabel('Actual value');
    plt.show()


for c in clf_list:
    clf = make_pipeline(StandardScaler(), c)
    clf.fit(X_train_reduced, y_train)
    y_pred = clf.predict(X_test)
    
    print("***************Model************", c)
    print_acc_and_CR(y_test, y_pred)
    y_score = c.decision_function(X_test)
    fpr, tpr, thresholds = roc_curve(y_test, y_score, pos_label=c.classes_[1])

    y_score = clf.decision_function(X_test)

    roc_display = RocCurveDisplay(fpr=fpr, tpr=tpr).plot()
    plt.show()
    display_confusion_matrix(y_test, y_pred)

***************Model************ LogisticRegression(class_weight='balanced', max_iter=100000, random_state=42)
Accuracy: 97.77%
Classification report: 
               precision    recall  f1-score   support

           0       1.00      0.98      0.99     56856
           1       0.07      0.91      0.13       106

    accuracy                           0.98     56962
   macro avg       0.54      0.94      0.56     56962
weighted avg       1.00      0.98      0.99     56962

/Users/michael/opt/anaconda3/lib/python3.9/site-packages/sklearn/base.py:443: UserWarning: X has feature names, but LogisticRegression was fitted without feature names
  warnings.warn(

***************Model************ SVC(random_state=42)
Accuracy: 99.93%
Classification report: 
               precision    recall  f1-score   support

           0       1.00      1.00      1.00     56856
           1       0.94      0.69      0.79       106

    accuracy                           1.00     56962
   macro avg       0.97      0.84      0.90     56962
weighted avg       1.00      1.00      1.00     56962

/Users/michael/opt/anaconda3/lib/python3.9/site-packages/sklearn/base.py:443: UserWarning: X has feature names, but SVC was fitted without feature names
  warnings.warn(


tree_list = [rf, xg]
for c in tree_list:
    clf = make_pipeline(StandardScaler(), c)
    clf.fit(X_train_reduced, y_train)
    y_pred = clf.predict(X_test)
    
    print("***************Model************", c)
    print_acc_and_CR(y_test, y_pred)

    display_confusion_matrix(y_test, y_pred)

***************Model************ RandomForestClassifier(random_state=42)
Accuracy: 99.94%
Classification report: 
               precision    recall  f1-score   support

           0       1.00      1.00      1.00     56856
           1       0.93      0.75      0.83       106

    accuracy                           1.00     56962
   macro avg       0.96      0.88      0.92     56962
weighted avg       1.00      1.00      1.00     56962

***************Model************ XGBClassifier(base_score=0.5, booster='gbtree', callbacks=None,
              colsample_bylevel=1, colsample_bynode=1, colsample_bytree=1,
              early_stopping_rounds=None, enable_categorical=False,
              eval_metric=None, gamma=0, gpu_id=-1, grow_policy='depthwise',
              importance_type=None, interaction_constraints='',
              learning_rate=0.300000012, max_bin=256, max_cat_to_onehot=4,
              max_delta_step=0, max_depth=6, max_leaves=0, min_child_weight=1,
              missing=nan, monotone_constraints='()', n_estimators=100,
              n_jobs=0, num_parallel_tree=1, predictor='auto', random_state=42,
              reg_alpha=0, reg_lambda=1, ...)
Accuracy: 99.95%
Classification report: 
               precision    recall  f1-score   support

           0       1.00      1.00      1.00     56856
           1       0.94      0.77      0.85       106

    accuracy                           1.00     56962
   macro avg       0.97      0.89      0.92     56962
weighted avg       1.00      1.00      1.00     56962


# Set a number of features to consider when looking at rank/importance
n_features = 20

# Use Random Forest to get feature ranks/importances for each feature
importances = rf.feature_importances_
std = np.std([tree.feature_importances_ for tree in rf.estimators_],
             axis=0)
indices = np.argsort(importances)[::-1]

# Print the feature ranking
print("Feature ranking:")

# Look at importance for first 20 features
for f in range(n_features):#range(X_train_reduced.shape[1]):
    print("%d. %s (feature %d) (%f)" %
          (f + 1, X_train_reduced.columns[indices[f]], indices[f], importances[indices[f]]))

# Plot the impurity-based feature importances of the forest
plt.figure()
plt.title("Feature importances")
plt.bar(range(n_features), importances[indices[:n_features]],
        color="r", yerr=std[indices[:n_features]], align="center")
plt.xticks(range(n_features), indices[:n_features])
plt.xlim([-1, n_features]);

Feature ranking:
1. V17 (feature 17) (0.154722)
2. V14 (feature 14) (0.137542)
3. V12 (feature 12) (0.135451)
4. V16 (feature 16) (0.090930)
5. V10 (feature 10) (0.078660)
6. V11 (feature 11) (0.042432)
7. V18 (feature 18) (0.031745)
8. V9 (feature 9) (0.030973)
9. V4 (feature 4) (0.026802)
10. V7 (feature 7) (0.024217)
11. V26 (feature 26) (0.022822)
12. V3 (feature 3) (0.019078)
13. V21 (feature 21) (0.015852)
14. V20 (feature 20) (0.014621)
15. Time (feature 0) (0.013952)
16. V27 (feature 27) (0.013382)
17. V1 (feature 1) (0.013057)
18. V6 (feature 6) (0.013050)
19. V8 (feature 8) (0.013014)
20. Amount (feature 29) (0.012751)


from supervised.automl import AutoML
automl = AutoML(algorithms=['LightGBM','Xgboost','Random Forest','Neural Network'], 
                train_ensemble=False, explain_level=2)
automl.fit(X_train_reduced, y_train)

---------------------------------------------------------------------------
RuntimeError                              Traceback (most recent call last)
RuntimeError: module compiled against API version 0xf but this version of numpy is 0xe

AutoML directory: AutoML_4
The task is binary_classification with evaluation metric logloss
AutoML will use algorithms: ['LightGBM', 'Xgboost', 'Random Forest', 'Neural Network']
AutoML steps: ['simple_algorithms', 'default_algorithms']
Skip simple_algorithms because no parameters were generated.
* Step default_algorithms will try to check up to 4 models

	Time	V1	V2	V3	V4	V5	V6	V7	V8	V9	...	V21	V22	V23	V24	V25	V26	V27	V28	Amount
0	0.0	-1.359807	-0.072781	2.536347	1.378155	-0.338321	0.462388	0.239599	0.098698	0.363787	...	-0.018307	0.277838	-0.110474	0.066928	0.128539	-0.189115	0.133558	-0.021053	149.62
1	0.0	1.191857	0.266151	0.166480	0.448154	0.060018	-0.082361	-0.078803	0.085102	-0.255425	...	-0.225775	-0.638672	0.101288	-0.339846	0.167170	0.125895	-0.008983	0.014724	2.69
2	1.0	-1.358354	-1.340163	1.773209	0.379780	-0.503198	1.800499	0.791461	0.247676	-1.514654	...	0.247998	0.771679	0.909412	-0.689281	-0.327642	-0.139097	-0.055353	-0.059752	378.66
3	1.0	-0.966272	-0.185226	1.792993	-0.863291	-0.010309	1.247203	0.237609	0.377436	-1.387024	...	-0.108300	0.005274	-0.190321	-1.175575	0.647376	-0.221929	0.062723	0.061458	123.50
4	2.0	-1.158233	0.877737	1.548718	0.403034	-0.407193	0.095921	0.592941	-0.270533	0.817739	...	-0.009431	0.798278	-0.137458	0.141267	-0.206010	0.502292	0.219422	0.215153	69.99

	Time	V1	V2	V3	V4	V5	V6	V7	V8	V9	...	V21	V22	V23	V24	V25	V26	V27	V28	Amount	Class
count	284807.000000	2.848070e+05	2.848070e+05	2.848070e+05	2.848070e+05	2.848070e+05	2.848070e+05	2.848070e+05	2.848070e+05	2.848070e+05	...	2.848070e+05	2.848070e+05	2.848070e+05	2.848070e+05	2.848070e+05	2.848070e+05	2.848070e+05	2.848070e+05	284807.000000	284807.000000
mean	94813.859575	3.918649e-15	5.682686e-16	-8.761736e-15	2.811118e-15	-1.552103e-15	2.040130e-15	-1.698953e-15	-1.893285e-16	-3.147640e-15	...	1.473120e-16	8.042109e-16	5.282512e-16	4.456271e-15	1.426896e-15	1.701640e-15	-3.662252e-16	-1.217809e-16	88.349619	0.001727
std	47488.145955	1.958696e+00	1.651309e+00	1.516255e+00	1.415869e+00	1.380247e+00	1.332271e+00	1.237094e+00	1.194353e+00	1.098632e+00	...	7.345240e-01	7.257016e-01	6.244603e-01	6.056471e-01	5.212781e-01	4.822270e-01	4.036325e-01	3.300833e-01	250.120109	0.041527
min	0.000000	-5.640751e+01	-7.271573e+01	-4.832559e+01	-5.683171e+00	-1.137433e+02	-2.616051e+01	-4.355724e+01	-7.321672e+01	-1.343407e+01	...	-3.483038e+01	-1.093314e+01	-4.480774e+01	-2.836627e+00	-1.029540e+01	-2.604551e+00	-2.256568e+01	-1.543008e+01	0.000000	0.000000
25%	54201.500000	-9.203734e-01	-5.985499e-01	-8.903648e-01	-8.486401e-01	-6.915971e-01	-7.682956e-01	-5.540759e-01	-2.086297e-01	-6.430976e-01	...	-2.283949e-01	-5.423504e-01	-1.618463e-01	-3.545861e-01	-3.171451e-01	-3.269839e-01	-7.083953e-02	-5.295979e-02	5.600000	0.000000
50%	84692.000000	1.810880e-02	6.548556e-02	1.798463e-01	-1.984653e-02	-5.433583e-02	-2.741871e-01	4.010308e-02	2.235804e-02	-5.142873e-02	...	-2.945017e-02	6.781943e-03	-1.119293e-02	4.097606e-02	1.659350e-02	-5.213911e-02	1.342146e-03	1.124383e-02	22.000000	0.000000
75%	139320.500000	1.315642e+00	8.037239e-01	1.027196e+00	7.433413e-01	6.119264e-01	3.985649e-01	5.704361e-01	3.273459e-01	5.971390e-01	...	1.863772e-01	5.285536e-01	1.476421e-01	4.395266e-01	3.507156e-01	2.409522e-01	9.104512e-02	7.827995e-02	77.165000	0.000000
max	172792.000000	2.454930e+00	2.205773e+01	9.382558e+00	1.687534e+01	3.480167e+01	7.330163e+01	1.205895e+02	2.000721e+01	1.559499e+01	...	2.720284e+01	1.050309e+01	2.252841e+01	4.584549e+00	7.519589e+00	3.517346e+00	3.161220e+01	3.384781e+01	25691.160000	1.000000

	Time	V1	V2	V3	V4	V5	V6	V7	V8	V9	...	V21	V22	V23	V24	V25	V26	V27	V28	Amount	Class
541	406.0	-2.312227	1.951992	-1.609851	3.997906	-0.522188	-1.426545	-2.537387	1.391657	-2.770089	...	0.517232	-0.035049	-0.465211	0.320198	0.044519	0.177840	0.261145	-0.143276	0.00	1
623	472.0	-3.043541	-3.157307	1.088463	2.288644	1.359805	-1.064823	0.325574	-0.067794	-0.270953	...	0.661696	0.435477	1.375966	-0.293803	0.279798	-0.145362	-0.252773	0.035764	529.00	1
4920	4462.0	-2.303350	1.759247	-0.359745	2.330243	-0.821628	-0.075788	0.562320	-0.399147	-0.238253	...	-0.294166	-0.932391	0.172726	-0.087330	-0.156114	-0.542628	0.039566	-0.153029	239.93	1
6108	6986.0	-4.397974	1.358367	-2.592844	2.679787	-1.128131	-1.706536	-3.496197	-0.248778	-0.247768	...	0.573574	0.176968	-0.436207	-0.053502	0.252405	-0.657488	-0.827136	0.849573	59.00	1
6329	7519.0	1.234235	3.019740	-4.304597	4.732795	3.624201	-1.357746	1.713445	-0.496358	-1.282858	...	-0.379068	-0.704181	-0.656805	-1.632653	1.488901	0.566797	-0.010016	0.146793	1.00	1

Credit Card Fraud Exploration¶

Background: Business Objectives¶

Part I. Data Exploration¶

I - A. Data wrangling¶

I - B. Imbalanced class handling¶

Explore several questions:¶

1. What is the amount distribution of the fraud activities?¶

2. How fraud amount distribution correlated with all other normal transactions?¶

3. Any correlations among v1 to v27?¶

4. Time distribution for fraud and normal transactions.¶

Split data in to X, y¶

remove highly correlated data¶

Basic model selection¶

Make a pipeline to scale data.¶

Choose different models: Logistic Regression, SVM, Random Forest, Gradient Boost, XGBoost classifiers; train the model and compare their performance.¶

Performance evaluation with Confusion matrix, accuracy, precision, recall, and F1 score.¶

XGBoost showed the best precision and recall.¶

Use autoML to find an optimized model¶