add new model named credit card estimator used to detect whether the …

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."
 )

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"
+        }
+    }
+}

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()

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"

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")

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",