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app.py

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+from flask import Flask, jsonify, request
+import numpy as np
+import pickle
+import pandas as pd
+
+app = Flask(__name__)
+
+
+LABEL = ['Not Claim Loan', 'Claim Loan']
+columns = ['EDUCATION', 'INCOME', 'CREDIT_SCORE', 'ANNUAL_MILEAGE', 'SPEEDING_VIOLATIONS', 'PAST_ACCIDENTS', 'DRIVING_EXPERIENCE', 'VEHICLE_OWNERSHIP', 'MARRIED', 'CHILDREN']
+with open("asuransi.pkl", "rb") as f:
+    model_insurance = pickle.load(f)
+
+@app.route("/")
+def homepage():
+    return "<h1>Backend Pemodelan Car Insurance </h1>"
+
+@app.route("/insurance", methods=["GET", "POST"])
+def insurance_inference():
+    if request.method == 'POST':
+        data = request.json
+        print(data)
+        new_data = [data['EDUCATION'],
+                    data['INCOME'],
+                    data['CREDIT_SCORE'],
+                    data['ANNUAL_MILEAGE'],
+                    data['SPEEDING_VIOLATIONS'],
+                    data['PAST_ACCIDENTS'],
+                    data['DRIVING_EXPERIENCE'],
+                    data['VEHICLE_OWNERSHIP'],
+                    data['MARRIED'],
+                    data['CHILDREN']]
+
+        new_data = pd.DataFrame([new_data],columns=columns)
+
+        res = model_insurance.predict(new_data)
+        print("res :", res )
+        response = {'code':200, 'status':'OK',
+                    'result':{'prediction': str(res[0]),
+                              'classes': LABEL[int(res[0])]}}
+
+
+        return jsonify(response)
+    return "Silahkan gunakan method post untuk mengakses model insurance"
+# app.run(debug=True)

asuransi.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:bdf7e28537b92b4d3cdfd86c41da90ee1b4f812266b6e1c4a05802dd9bb6b244
+size 36918

requirements.txt

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+waitress
+pandas
+flask
+scikit-learn == 1.0.2
+numpy
+feature_engine