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| import pandas as pd | |
| import numpy as np | |
| import matplotlib.pyplot as plt | |
| import seaborn as sns | |
| from sklearn.model_selection import train_test_split | |
| from sklearn.linear_model import LogisticRegression | |
| from sklearn.metrics import confusion_matrix, classification_report, accuracy_score, roc_auc_score | |
| from sklearn.preprocessing import StandardScaler | |
| import gradio as gr | |
| def credit_card_fraud_detection(): | |
| # Load the dataset | |
| credit_card_data = pd.read_csv("creditcard.csv") | |
| # Check for missing values | |
| missing_values = credit_card_data.isnull().sum() | |
| # Check class distribution | |
| class_distribution = credit_card_data['Class'].value_counts() | |
| # Visualize class distribution | |
| sns.countplot(x='Class', data=credit_card_data) | |
| plt.title('Class Distribution') | |
| plt.show() | |
| """This Dataset is highly unbalanced | |
| 0 --> Normal Transaction | |
| 1 --> fraudulent transaction | |
| """ | |
| # separating the data for analysis | |
| legit = credit_card_data[credit_card_data.Class == 0] | |
| fraud = credit_card_data[credit_card_data.Class == 1] | |
| # statistical measures of the data | |
| legit_amount_stats = legit.Amount.describe() | |
| fraud_amount_stats = fraud.Amount.describe() | |
| # compare the values for both transactions | |
| class_means = credit_card_data.groupby('Class').mean() | |
| """Under-Sampling | |
| Build a sample dataset containing a similar distribution of normal transactions and Fraudulent Transactions | |
| Number of Fraudulent Transactions --> 492 | |
| """ | |
| legit_sample = legit.sample(n=492) | |
| """Concatenating two DataFrames""" | |
| new_dataset = pd.concat([legit_sample, fraud], axis=0) | |
| class_value_counts = new_dataset['Class'].value_counts() | |
| class_means_new_dataset = new_dataset.groupby('Class').mean() | |
| """Splitting the data into Features & Targets""" | |
| X = new_dataset.drop(columns='Class', axis=1) | |
| Y = new_dataset['Class'] | |
| """Split the data into Training data & Testing Data""" | |
| X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.2, stratify=Y, random_state=2) | |
| """Model Training - Logistic Regression""" | |
| model = LogisticRegression() | |
| # training the Logistic Regression Model with Training Data | |
| model.fit(X_train, Y_train) | |
| """Model Evaluation - Accuracy Score""" | |
| X_train_prediction = model.predict(X_train) | |
| training_data_accuracy = accuracy_score(X_train_prediction, Y_train) | |
| # accuracy on test data | |
| X_test_prediction = model.predict(X_test) | |
| test_data_accuracy = accuracy_score(X_test_prediction, Y_test) | |
| return { | |
| 'missing_values': missing_values, | |
| 'class_distribution': class_distribution, | |
| 'legit_amount_stats': legit_amount_stats, | |
| 'fraud_amount_stats': fraud_amount_stats, | |
| 'class_means': class_means, | |
| 'class_value_counts': class_value_counts, | |
| 'class_means_new_dataset': class_means_new_dataset, | |
| 'training_data_accuracy': training_data_accuracy, | |
| 'test_data_accuracy': test_data_accuracy | |
| } | |
| # Launching the Gradio Interface | |
| iface = gr.Interface(fn=credit_card_fraud_detection, title="Credit Card Fraud Detection") | |
| iface.launch() | |