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