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import pandas as pd |
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from sklearn.tree import DecisionTreeClassifier |
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from sklearn.model_selection import train_test_split |
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from sklearn.metrics import accuracy_score |
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from sklearn.model_selection import cross_val_score |
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import gradio as gr |
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from sklearn.decomposition import PCA |
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import json |
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import pickle |
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with open('fraud_mean_by_country.json', 'r') as json_file: |
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fraud_mean_by_country_dict = json.load(json_file) |
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with open("decision_tree_model.pkl", "rb") as file: |
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clf = pickle.load(file) |
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def predict(transaction_id, customer_id, merchant_id, amount, transaction_time, card_type, location, purchase_category, customer_age, transaction_description, country): |
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data = pd.DataFrame({ |
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'amount': [amount], |
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'country': [country], |
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'customer_age': [customer_age], |
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'card_type': [card_type], |
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'purchase_category': [purchase_category] |
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}) |
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data = pd.get_dummies(data, columns=['card_type', 'purchase_category']) |
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data['country'] = data['country'].map(fraud_mean_by_country_dict) |
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data['Age_group'] = data['customer_age'].apply(lambda x: group(x)) |
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data = pd.get_dummies(data, columns=['Age_group'], drop_first=True, dtype=float) |
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columns_to_add = ['customer_age', 'card_type_Discover', 'card_type_Visa', 'purchase_category_Groceries', 'purchase_category_Restaurant','purchase_category_Retail', 'purchase_category_Travel', 'Age_group_33-60', 'Age_group_60+'] |
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for column in columns_to_add: |
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if column not in data.columns: |
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data[column] = 0 |
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for i in data.columns: |
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data[i] = data[i].astype(int) |
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X_pca = pca.transform(data[['purchase_category_Groceries', 'purchase_category_Retail', 'purchase_category_Travel', 'Age_group_33-60', 'Age_group_60+']]) |
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data['pca1'] = X_pca[:, 0] |
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data['pca2'] = X_pca[:, 1] |
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data.drop(columns=['customer_age', 'card_type_Discover', 'card_type_Visa', 'purchase_category_Groceries', 'purchase_category_Retail', 'purchase_category_Travel', 'Age_group_33-60', 'Age_group_60+'], inplace=True) |
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prediction = clf.predict(data) |
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return "Fraudulent" if prediction[0] == 1 else "Not Fraudulent" |
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card_type_choices = [(val, val) for val in df['card_type'].unique()] |
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location_choices = [(val, val) for val in df['location'].unique()] |
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purchase_category_choices = [(val, val) for val in df['purchase_category'].unique()] |
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country_choices = [(val, val) for val in df['country'].unique()] |
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inputs = [ |
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gr.Textbox(label="Transaction ID"), |
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gr.Textbox(label="Customer ID"), |
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gr.Textbox(label="Merchant ID"), |
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gr.Number(label="Amount"), |
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gr.Textbox(label="Transaction Time"), |
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gr.Dropdown(choices= card_type_choices, label="Card Type"), |
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gr.Dropdown(choices= location_choices, label="Location"), |
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gr.Dropdown(choices= purchase_category_choices, label="Purchase Category"), |
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gr.Number(label="Customer Age"), |
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gr.Textbox(label="Transaction Description"), |
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gr.Dropdown(choices= country_choices, label="Country") |
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] |
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gr.Interface(fn=predict, inputs=inputs, outputs="text").launch() |