From 88bdd553dddc50865cd6e04e14899a080a2ceaca Mon Sep 17 00:00:00 2001 From: dg492003 Date: Thu, 10 Oct 2024 15:33:42 +0530 Subject: [PATCH 1/4] add new model named credit card estimator used to detect whether the transaction is faulty or not by randomforestcalssifier --- App.py | 3 + form_configs/credit_card_fraud.json | 79 + .../data/creditcardcsvpresent.csv | 3076 +++++++++++++++++ models/credit_card_fraud/model.py | 117 + models/credit_card_fraud/predict.py | 73 + .../transaction_rf_model.pkl | Bin 0 -> 867693 bytes pages/Credit_Card_Fraud_Estimator.py | 4 + pages/pages.json | 22 + 8 files changed, 3374 insertions(+) create mode 100644 form_configs/credit_card_fraud.json create mode 100644 models/credit_card_fraud/data/creditcardcsvpresent.csv create mode 100644 models/credit_card_fraud/model.py create mode 100644 models/credit_card_fraud/predict.py create mode 100644 models/credit_card_fraud/transaction_rf_model.pkl create mode 100644 pages/Credit_Card_Fraud_Estimator.py diff --git a/App.py b/App.py index dd683ffb..b949cbc7 100644 --- a/App.py +++ b/App.py @@ -37,6 +37,9 @@ st.write( "- **Stress Level Detector**: Analyze your mental stress levels based on social media interactions." ) +st.write( + "- **Credit Card Fraud Estimator**: Check Whether your transaction is faulty or not." +) st.write( "- **Parkinson's Disease Detector**: Assess your risk of Parkinson's Disease with advanced machine learning algorithms." ) diff --git a/form_configs/credit_card_fraud.json b/form_configs/credit_card_fraud.json new file mode 100644 index 00000000..7c98f46a --- /dev/null +++ b/form_configs/credit_card_fraud.json @@ -0,0 +1,79 @@ +{ + "Credit Card Fraud Estimator": { + "Average Amount per Transaction per Day": { + "type": "number", + "min_value": 0, + "max_value": 100000, + "default_value": 100, + "step": 100, + "field_name": "avg_amount_per_day" + }, + "Transaction Amount": { + "type": "number", + "min_value": 0, + "max_value": 100000, + "default_value": 3000, + "step": 100, + "field_name": "transaction_amount" + }, + "Is Declined": { + "type": "dropdown", + "options": [ + "Yes", + "No" + ], + "default_value": "No", + "field_name": "Is_declined" + }, + "Total Number of Declines per Day": { + "type": "number", + "min_value": 0, + "max_value": 100, + "default_value": 0, + "step": 1, + "field_name": "no_of_declines_per_day" + }, + "Is Foreign Transaction": { + "type": "dropdown", + "options": [ + "Yes", + "No" + ], + "default_value": "No", + "field_name": "Is_Foreign_transaction" + }, + "Is High-Risk Country": { + "type": "dropdown", + "options": [ + "Yes", + "No" + ], + "default_value": "No", + "field_name": "Is_High_Risk_country" + }, + "Daily Chargeback Average Amount": { + "type": "number", + "min_value": 0, + "max_value": 10000, + "default_value": 0, + "step": 100, + "field_name": "Daily_chargeback_avg_amt" + }, + "6-Month Average Chargeback Amount": { + "type": "number", + "min_value": 0, + "max_value": 10000, + "default_value": 0, + "step": 100, + "field_name": "six_month_avg_chbk_amt" + }, + "6-Month Chargeback Frequency": { + "type": "number", + "min_value": 0, + "max_value": 100, + "default_value": 0, + "step": 1, + "field_name": "six_month_chbk_freq" + } + } +} \ No newline at end of file diff --git a/models/credit_card_fraud/data/creditcardcsvpresent.csv b/models/credit_card_fraud/data/creditcardcsvpresent.csv new file mode 100644 index 00000000..da43c3c0 --- /dev/null +++ b/models/credit_card_fraud/data/creditcardcsvpresent.csv @@ -0,0 +1,3076 @@ +Merchant_id,Transaction date,Average Amount/transaction/day,Transaction_amount,Is declined,Total Number of declines/day,isForeignTransaction,isHighRiskCountry,Daily_chargeback_avg_amt,6_month_avg_chbk_amt,6-month_chbk_freq,isFradulent +3160040998,,100,3000,N,5,Y,Y,0,0,0,Y +3160040998,,100,4300,N,5,Y,Y,0,0,0,Y +3160041896,,185.5,4823,Y,5,N,N,0,0,0,Y +3160141996,,185.5,5008.5,Y,8,N,N,0,0,0,Y +3160241992,,500,26000,N,0,Y,Y,800,677.2,6,Y 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+6665906072,,695.3911956,1390.782391,N,0,N,N,0,0,0,N +6661273529,,1000,25000,Y,0,Y,N,754,585,7,N +6661273532,,500,11000,Y,0,N,N,754,585,7,N +6661273532,,800,0,Y,0,N,N,754,585,7,N +6661273533,,800,20800,Y,0,N,N,754,585,7,N +6661273532,,1500,12000,Y,0,Y,Y,754,585,7,N +6661273533,,1500,36000,Y,0,Y,Y,754,585,7,N diff --git a/models/credit_card_fraud/model.py b/models/credit_card_fraud/model.py new file mode 100644 index 00000000..fb265f7d --- /dev/null +++ b/models/credit_card_fraud/model.py @@ -0,0 +1,117 @@ +# importing libraries +from sklearn.model_selection import train_test_split +from sklearn.ensemble import RandomForestClassifier +import pandas as pd +import warnings +import pickle +import matplotlib.pyplot as plt + + +# reading dataset +warnings.filterwarnings("ignore") + +data = pd.read_csv("/content/creditcardcsvpresent.csv") +df = data.copy(deep=True) + +# df.info() + +# remove transaction_date all values are null +# and also remove merchant id +df = df.drop(columns=['Merchant_id', 'Transaction date'], axis=1) + + +# encoding for qualitative variables +code = { + "N": 0, + "Y": 1 +} + +for obj in df.select_dtypes("object"): + df[obj] = df[obj].map(code) + +# Target and Feature Identification +target = "isFradulent" +features = [col for col in df.columns if col != target] + +X = df[features] # Create a DataFrame for the features +y = df[target] # Create a Series for the target + + +# Split the dataset +X_train, X_test, y_train, y_test = train_test_split( + X, y, test_size=0.2, random_state=42) + + +# Train Random Forest Classifier +rf_model = RandomForestClassifier(n_estimators=100, random_state=42,class_weight='balanced') +rf_model.fit(X_train, y_train) + +# Make predictions +y_pred = rf_model.predict(X_test) + +# Function to prepare input data into a DataFrame +def prepare_input_data( + avg_amount_per_day, + transaction_amount, + Is_declined, + no_of_declines_per_day, + Is_Foreign_transaction, + Is_High_Risk_country, + Daily_chargeback_avg_amt, + six_month_avg_chbk_amt, + six_month_chbk_freq, +): + # Create a DataFrame with the input data + input_data = { + "Average Amount/transaction/day": [avg_amount_per_day], + "Transaction_amount": [transaction_amount], + "Is declined": [Is_declined], + "Total Number of declines/day": [no_of_declines_per_day], + "isForeignTransaction": [Is_Foreign_transaction], + "isHighRiskCountry": [Is_High_Risk_country], + "Daily_chargeback_avg_amt": [Daily_chargeback_avg_amt], + "6_month_avg_chbk_amt": [six_month_avg_chbk_amt], + "6-month_chbk_freq": [six_month_chbk_freq], + } + + return pd.DataFrame(input_data) + +def get_prediction( + avg_amount_per_day, + transaction_amount, + Is_declined, + no_of_declines_per_day, + Is_Foreign_transaction, + Is_High_Risk_country, + Daily_chargeback_avg_amt, + six_month_avg_chbk_amt, + six_month_chbk_freq, +): + # Prepare the input data + input_df = prepare_input_data( + avg_amount_per_day, + transaction_amount, + Is_declined, + no_of_declines_per_day, + Is_Foreign_transaction, + Is_High_Risk_country, + Daily_chargeback_avg_amt, + six_month_avg_chbk_amt, + six_month_chbk_freq, + ) + # Predict using Random Forest + predicted_value = rf_model.predict(input_df) + + # Return "Fraud" if fraud (1), else "Not a Fraud" + return "Fraud" if predicted_value[0] == 1 else "Not a Fraud" + + +# Function to save the model +def save_model(): + # Save the Random Forest model + with open("models/credit_card_fraud/transaction_rf_model.pkl", "wb") as file: + pickle.dump(rf_model, file) + + + +# save_model() diff --git a/models/credit_card_fraud/predict.py b/models/credit_card_fraud/predict.py new file mode 100644 index 00000000..0cd2c2fa --- /dev/null +++ b/models/credit_card_fraud/predict.py @@ -0,0 +1,73 @@ +import pandas as pd +from sklearn.preprocessing import StandardScaler +import pickle + +def load_model(model_path): + """ Load the trained Random Forest model from the specified path. """ + with open(model_path, 'rb') as file: + return pickle.load(file) + +def prepare_input_data( + avg_amount_per_day, + transaction_amount, + Is_declined, + no_of_declines_per_day, + Is_Foreign_transaction, + Is_High_Risk_country, + Daily_chargeback_avg_amt, + six_month_avg_chbk_amt, + six_month_chbk_freq, +): + # Create a DataFrame with the input data + input_data = { + "Average Amount/transaction/day": [avg_amount_per_day], + "Transaction_amount": [transaction_amount], + "Is declined": [Is_declined], + "Total Number of declines/day": [no_of_declines_per_day], + "isForeignTransaction": [Is_Foreign_transaction], + "isHighRiskCountry": [Is_High_Risk_country], + "Daily_chargeback_avg_amt": [Daily_chargeback_avg_amt], + "6_month_avg_chbk_amt": [six_month_avg_chbk_amt], + "6-month_chbk_freq": [six_month_chbk_freq], + } + + return pd.DataFrame(input_data) + +def get_prediction( + avg_amount_per_day, + transaction_amount, + Is_declined, + no_of_declines_per_day, + Is_Foreign_transaction, + Is_High_Risk_country, + Daily_chargeback_avg_amt, + six_month_avg_chbk_amt, + six_month_chbk_freq, +): + # Prepare the input data + input_df = prepare_input_data( + avg_amount_per_day, + transaction_amount, + Is_declined, + no_of_declines_per_day, + Is_Foreign_transaction, + Is_High_Risk_country, + Daily_chargeback_avg_amt, + six_month_avg_chbk_amt, + six_month_chbk_freq, + ) + + # Convert input data to numeric (if necessary) + input_df = input_df.apply(pd.to_numeric, errors='coerce') + + # Fill NaN values (optional: handle as needed) + input_df.fillna(0, inplace=True) + + # Load the model + rf_model = load_model("models/credit_card_fraud/transaction_rf_model.pkl") + + # Predict using Random Forest + predicted_value = rf_model.predict(input_df) + + # Return "Fraud" if fraud (1), else "Not a Fraud" + return "Fraud" if predicted_value[0] == 1 else "Not a Fraud" diff --git a/models/credit_card_fraud/transaction_rf_model.pkl b/models/credit_card_fraud/transaction_rf_model.pkl new file mode 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zi4H*uCrIH0t>N@eFww)_lI;GdT=kgN^d$bvzF&Dz#{CDO=?92Eva6Kob6i*`4e`gRt>sol!-^m4P>;}R8$N990}!YLlX#ANz!$_Lq7r`FzF S(&P4*{etra=O5L(?EeG*K%D*n literal 0 HcmV?d00001 diff --git a/pages/Credit_Card_Fraud_Estimator.py b/pages/Credit_Card_Fraud_Estimator.py new file mode 100644 index 00000000..913e7420 --- /dev/null +++ b/pages/Credit_Card_Fraud_Estimator.py @@ -0,0 +1,4 @@ +from page_handler import PageHandler + +page_handler = PageHandler("pages/pages.json") +page_handler.render_page("Credit Card Fraud Estimator") diff --git a/pages/pages.json b/pages/pages.json index 29e9aca7..e3987787 100644 --- a/pages/pages.json +++ b/pages/pages.json @@ -21,6 +21,28 @@ } ] }, + "Credit Card Fraud Estimator": { + "title": "Credit Card Fraud Estimator", + "page_title": "Credit Card Fraud Estimator", + "page_icon": "\ud83d\udcb0", + "model_predict_file_path": "models/credit_card_fraud/predict.py", + "model_function": "get_prediction", + "form_config_path": "form_configs/credit_card_fraud.json", + "model_detail_function": "model_details", + "tabs": [ + { + "name": "Estimator", + "type": "form", + "form_name": "Credit Card Fraud Estimator" + }, + { + "name": "Model Details", + "type": "model_details", + "problem_statement": "The model predicts whether a credit card transaction is fraudulent based on input features such as transaction amount, card type, and customer profile.", + "description": "This model uses a Random Forest Classifier to classify credit card transactions as fraudulent or non-fraudulent. It learns from historical transaction data with both normal and fraudulent transactions to make accurate predictions." + } + ] + }, "Loan Eligibility Estimator": { "title": "Loan Eligibility Estimator", "page_title": "Loan Eligibility Estimator", From bfafef41bf0d90ac0755a53bec9e55bea0f61c7d Mon Sep 17 00:00:00 2001 From: dg492003 Date: Thu, 10 Oct 2024 15:49:46 +0530 Subject: [PATCH 2/4] moving pickle file to saved models folder --- .../{ => saved models}/transaction_rf_model.pkl | Bin 1 file changed, 0 insertions(+), 0 deletions(-) rename models/credit_card_fraud/{ => saved models}/transaction_rf_model.pkl (100%) diff --git a/models/credit_card_fraud/transaction_rf_model.pkl b/models/credit_card_fraud/saved models/transaction_rf_model.pkl similarity index 100% rename from models/credit_card_fraud/transaction_rf_model.pkl rename to models/credit_card_fraud/saved models/transaction_rf_model.pkl From 8e50b89c8d07f78dbefd4c16151d9a0fbb0138cc Mon Sep 17 00:00:00 2001 From: Yashasvini Sharma <100478608+yashasvini121@users.noreply.github.com> Date: Fri, 11 Oct 2024 00:26:14 +0530 Subject: [PATCH 3/4] Correct model path of credit_card_fraud --- models/credit_card_fraud/predict.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/models/credit_card_fraud/predict.py b/models/credit_card_fraud/predict.py index 0cd2c2fa..e4ad2a8c 100644 --- a/models/credit_card_fraud/predict.py +++ b/models/credit_card_fraud/predict.py @@ -64,7 +64,7 @@ def get_prediction( input_df.fillna(0, inplace=True) # Load the model - rf_model = load_model("models/credit_card_fraud/transaction_rf_model.pkl") + rf_model = load_model("models/credit_card_fraud/saved models/transaction_rf_model.pkl") # Predict using Random Forest predicted_value = rf_model.predict(input_df) From 6a979bdb9e31482c922a3d0d17ca3d70a5ee1f29 Mon Sep 17 00:00:00 2001 From: dg492003 Date: Fri, 11 Oct 2024 02:29:07 +0530 Subject: [PATCH 4/4] adding new svm model for classification inplace of randomforest. also add model_details function and plot some graphs --- models/credit_card_fraud/model.py | 40 ++++--- models/credit_card_fraud/modelEvaluation.py | 108 ++++++++++++++++++ models/credit_card_fraud/predict.py | 30 +++-- .../creditCardFraud_svc_model.pkl | Bin 0 -> 120966 bytes .../saved models/transaction_rf_model.pkl | Bin 867693 -> 0 bytes pages/pages.json | 4 +- 6 files changed, 151 insertions(+), 31 deletions(-) create mode 100644 models/credit_card_fraud/modelEvaluation.py create mode 100644 models/credit_card_fraud/saved models/creditCardFraud_svc_model.pkl delete mode 100644 models/credit_card_fraud/saved models/transaction_rf_model.pkl diff --git a/models/credit_card_fraud/model.py b/models/credit_card_fraud/model.py index fb265f7d..8a710b19 100644 --- a/models/credit_card_fraud/model.py +++ b/models/credit_card_fraud/model.py @@ -1,16 +1,17 @@ # importing libraries from sklearn.model_selection import train_test_split -from sklearn.ensemble import RandomForestClassifier +from sklearn.svm import SVC import pandas as pd import warnings +from sklearn.metrics import accuracy_score, classification_report, confusion_matrix +import pandas as pd import pickle -import matplotlib.pyplot as plt +from models.credit_card_fraud.modelEvaluation import ModelEvaluation +warnings.filterwarnings("ignore") # reading dataset -warnings.filterwarnings("ignore") - -data = pd.read_csv("/content/creditcardcsvpresent.csv") +data = pd.read_csv("models\credit_card_fraud\data\creditcardcsvpresent.csv") df = data.copy(deep=True) # df.info() @@ -23,8 +24,7 @@ # encoding for qualitative variables code = { "N": 0, - "Y": 1 -} + "Y": 1 } for obj in df.select_dtypes("object"): df[obj] = df[obj].map(code) @@ -38,16 +38,14 @@ # Split the dataset -X_train, X_test, y_train, y_test = train_test_split( - X, y, test_size=0.2, random_state=42) +X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42) - -# Train Random Forest Classifier -rf_model = RandomForestClassifier(n_estimators=100, random_state=42,class_weight='balanced') -rf_model.fit(X_train, y_train) +# Train SVM Classifier +svm_model = SVC(kernel='rbf', class_weight='balanced', random_state=42) # RBF kernel (default) is good for non-linear problems +svm_model.fit(X_train, y_train) # Make predictions -y_pred = rf_model.predict(X_test) +y_pred = svm_model.predict(X_test) # Function to prepare input data into a DataFrame def prepare_input_data( @@ -100,7 +98,7 @@ def get_prediction( six_month_chbk_freq, ) # Predict using Random Forest - predicted_value = rf_model.predict(input_df) + predicted_value = svm_model.predict(input_df) # Return "Fraud" if fraud (1), else "Not a Fraud" return "Fraud" if predicted_value[0] == 1 else "Not a Fraud" @@ -109,9 +107,13 @@ def get_prediction( # Function to save the model def save_model(): # Save the Random Forest model - with open("models/credit_card_fraud/transaction_rf_model.pkl", "wb") as file: - pickle.dump(rf_model, file) - - + model_filename = 'creditCardFraud_svc_model.pkl' + with open(model_filename, 'wb') as file: + pickle.dump(svm_model, file) + +# # Function to evaluate accuracy +def get_evaluator(): + evaluator = ModelEvaluation(svm_model, X_train, y_train, X_test, y_test) + return evaluator # save_model() diff --git a/models/credit_card_fraud/modelEvaluation.py b/models/credit_card_fraud/modelEvaluation.py new file mode 100644 index 00000000..dd50b88c --- /dev/null +++ b/models/credit_card_fraud/modelEvaluation.py @@ -0,0 +1,108 @@ +import numpy as np +import pandas as pd +import matplotlib.pyplot as plt +import seaborn as sns +from sklearn.metrics import accuracy_score, confusion_matrix + + +class ModelEvaluation: + def __init__(self, model, train_X, train_Y, test_X, test_Y): + self.model = model + self.train_X = train_X + self.train_Y = train_Y + self.test_X = test_X + self.test_Y = test_Y + self.evaluation_matrix = pd.DataFrame( + np.zeros([1, 8]), + columns=[ + "Train-R2", + "Test-R2", + "Train-RSS", + "Test-RSS", + "Train-MSE", + "Test-MSE", + "Train-RMSE", + "Test-RMSE", + ], + ) + self.random_column = np.random.choice( + train_X.columns[train_X.nunique() >= 50], 1, replace=False + )[0] + + def evaluate(self): + pred_train = self.model.predict(self.train_X) + pred_test = self.model.predict(self.test_X) + + self.update_evaluation_matrix(pred_train, pred_test) + metrics = self.get_metrics() + prediction_plot = self.plot_predictions(pred_train) + error_plot = self.plot_error_terms(pred_train) + + # adding performance graph of the model + performance_plot = self.plot_performance_graph() + + return metrics, prediction_plot, error_plot, performance_plot + + def get_metrics(self): + """Return a dictionary of evaluation metrics for easy integration.""" + pred_train = self.model.predict(self.train_X) + pred_test = self.model.predict(self.test_X) + + metrics = { + "Train_R2": accuracy_score(self.train_Y, pred_train), + "Test_R2": accuracy_score(self.test_Y, pred_test), + "Train_RSS": np.sum(np.square(self.train_Y - pred_train)), + "Test_RSS": np.sum(np.square(self.test_Y - pred_test)) + } + return metrics + + def plot_predictions(self, pred_train): + # Predict on test data + pred_test = self.model.predict(self.test_X) + + # Calculate confusion matrix + cm = confusion_matrix(self.test_Y, pred_test) + + # Plot confusion matrix + fig, ax = plt.subplots(figsize=(10, 6)) + sns.heatmap(cm, annot=True, fmt="d", cmap="Blues", ax=ax) + + ax.set_title("Confusion Matrix") + ax.set_xlabel("Predicted Labels") + ax.set_ylabel("True Labels") + + plt.tight_layout() + return fig + + def update_evaluation_matrix(self, pred_train, pred_test): + return + + # making a separate function for plotting error terms + def plot_error_terms(self, pred_train): + fig, axes = plt.subplots(figsize=(15, 6)) + + # Plotting error distribution + sns.histplot(self.train_Y - pred_train, bins=30, kde=True, ax=axes) + axes.set_title("Error Terms Distribution") + axes.set_xlabel("Errors") + + plt.tight_layout() + return fig # returning figure the is created here + + def plot_performance_graph(self): + # Predict on test data + pred_test = self.model.predict(self.test_X) + + # Calculate confusion matrix + cm = confusion_matrix(self.test_Y, pred_test) + + # Plot confusion matrix + fig, ax = plt.subplots(figsize=(10, 6)) + sns.heatmap(cm, annot=True, fmt="d", cmap="Blues", ax=ax) + + ax.set_title("Confusion Matrix") + ax.set_xlabel("Predicted Labels") + ax.set_ylabel("True Labels") + + plt.tight_layout() + return fig diff --git a/models/credit_card_fraud/predict.py b/models/credit_card_fraud/predict.py index 0cd2c2fa..886416f3 100644 --- a/models/credit_card_fraud/predict.py +++ b/models/credit_card_fraud/predict.py @@ -1,12 +1,14 @@ import pandas as pd -from sklearn.preprocessing import StandardScaler import pickle +from models.credit_card_fraud.model import get_evaluator + def load_model(model_path): """ Load the trained Random Forest model from the specified path. """ with open(model_path, 'rb') as file: return pickle.load(file) + def prepare_input_data( avg_amount_per_day, transaction_amount, @@ -33,6 +35,7 @@ def prepare_input_data( return pd.DataFrame(input_data) + def get_prediction( avg_amount_per_day, transaction_amount, @@ -44,6 +47,12 @@ def get_prediction( six_month_avg_chbk_amt, six_month_chbk_freq, ): + + # Convert "no" to 0 and "yes" to 1 for the relevant fields + Is_declined = 0 if Is_declined.lower() == "no" else 1 + Is_Foreign_transaction = 0 if Is_Foreign_transaction.lower() == "no" else 1 + Is_High_Risk_country = 0 if Is_High_Risk_country.lower() == "no" else 1 + # Prepare the input data input_df = prepare_input_data( avg_amount_per_day, @@ -56,18 +65,19 @@ def get_prediction( six_month_avg_chbk_amt, six_month_chbk_freq, ) - - # Convert input data to numeric (if necessary) - input_df = input_df.apply(pd.to_numeric, errors='coerce') - - # Fill NaN values (optional: handle as needed) - input_df.fillna(0, inplace=True) - + # print(input_df.values) # Load the model - rf_model = load_model("models/credit_card_fraud/transaction_rf_model.pkl") + svm_model = load_model( + "models/credit_card_fraud/saved models/creditCardFraud_svc_model.pkl") + + # print(evaluate_model(rf_model)) # Predict using Random Forest - predicted_value = rf_model.predict(input_df) + predicted_value = svm_model.predict(input_df) # Return "Fraud" if fraud (1), else "Not a Fraud" return "Fraud" if predicted_value[0] == 1 else "Not a Fraud" + +def model_details(): + """Returns model evaluation details.""" + return get_evaluator() diff --git a/models/credit_card_fraud/saved models/creditCardFraud_svc_model.pkl b/models/credit_card_fraud/saved models/creditCardFraud_svc_model.pkl new file mode 100644 index 0000000000000000000000000000000000000000..233cff7f9df91e36cd7103b5009851498a8e2354 GIT binary patch literal 120966 zcmbr{30MtL-vIDRS}ZN3O(;TSEfR`C?-;dFl`_9|*{5p4L&YU^t%y#C?+_@2)TYN;>`u-R~J}$2I*52;3eEi*J 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zi4H*uCrIH0t>N@eFww)_lI;GdT=kgN^d$bvzF&Dz#{CDO=?92Eva6Kob6i*`4e`gRt>sol!-^m4P>;}R8$N990}!YLlX#ANz!$_Lq7r`FzF S(&P4*{etra=O5L(?EeG*K%D*n diff --git a/pages/pages.json b/pages/pages.json index e3987787..6bdad1b2 100644 --- a/pages/pages.json +++ b/pages/pages.json @@ -38,8 +38,8 @@ { "name": "Model Details", "type": "model_details", - "problem_statement": "The model predicts whether a credit card transaction is fraudulent based on input features such as transaction amount, card type, and customer profile.", - "description": "This model uses a Random Forest Classifier to classify credit card transactions as fraudulent or non-fraudulent. It learns from historical transaction data with both normal and fraudulent transactions to make accurate predictions." + "problem_statement": "The model predicts whether a credit card transaction is fraudulent based on input features such as transaction amount, transaction type, and customer profile.", + "description": "This model uses a Support Vector Machine (SVM) to classify credit card transactions as fraudulent or non-fraudulent. It leverages the SVM's ability to create an optimal decision boundary, learning from historical transaction data with both normal and fraudulent transactions to make accurate predictions. The model is particularly useful in identifying complex patterns and borderline cases in high-dimensional data." } ] },