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import gradio as gr |
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import pandas as pd |
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from sklearn.tree import DecisionTreeClassifier |
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df = pd.read_csv("bankloan.csv") |
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df = df.drop(columns=["ID", "ZIP.Code"]) |
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X = df.drop(columns=["Personal.Loan"]) |
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y = df["Personal.Loan"] |
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model = DecisionTreeClassifier(random_state=42) |
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model.fit(X, y) |
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def predict(age, exp, income, family, ccavg, edu, mort, sec, cd, online, credit): |
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input_data = pd.DataFrame([{ |
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"Age": age, |
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"Experience": exp, |
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"Income": income, |
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"Family": family, |
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"CCAvg": ccavg, |
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"Education": edu, |
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"Mortgage": mort, |
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"Securities.Account": sec, |
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"CD.Account": cd, |
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"Online": online, |
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"CreditCard": credit |
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}]) |
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pred = model.predict(input_data)[0] |
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return "核貸 ✅" if pred == 1 else "不核貸 ❌" |
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demo = gr.Interface( |
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fn=predict, |
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inputs=[ |
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gr.Number(label="年齡 Age"), |
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gr.Number(label="工作年資 Experience"), |
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gr.Number(label="收入 Income"), |
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gr.Number(label="家庭人數 Family"), |
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gr.Number(label="信用卡平均月花費 CCAvg"), |
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gr.Number(label="教育程度 Education"), |
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gr.Number(label="房貸金額 Mortgage"), |
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gr.Number(label="是否有證券帳戶 (0/1)"), |
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gr.Number(label="是否有定存帳戶 (0/1)"), |
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gr.Number(label="是否使用線上服務 (0/1)"), |
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gr.Number(label="是否持有信用卡 (0/1)") |
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], |
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outputs="text", |
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title="Loan Approval Predictor", |
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description="使用 bankloan.csv 資料訓練的決策樹模型，預測是否核貸" |
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) |
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demo.launch() |
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