Add

config.py

@@ -0,0 +1,19 @@
+# ============================================================
+# 프로젝트 공통 설정 — 모델명은 여기서만 변경하세요
+# ============================================================
+
+# Ollama 로컬 LLM 모델
+LLM_MODEL = "gemma2:2b"
+
+# 임베딩 모델 (FAISS 인덱스와 반드시 동일해야 함)
+# 변경 시 FAISS 인덱스 재빌드 필요 → python -c "from llm.vectorization import make_vectorization; make_vectorization()"
+EMBEDDING_MODEL = "intfloat/multilingual-e5-small"
+
+# FAISS 인덱스 경로
+FAISS_PATH = "./faiss"
+
+# PDF 소스 경로
+PDF_SOURCE = "./sources/KCB_Credit_Evaluation_System.pdf"
+
+# FAISS 검색 시 반환할 문서 수 (클수록 정확하지만 프롬프트가 길어져 느려짐)
+RETRIEVER_K = 3

credit_app.py

@@ -0,0 +1,307 @@
+import gradio as gr
+import pandas as pd
+import numpy as np
+import os
+import json
+import time
+
+from langchain_huggingface import HuggingFaceEmbeddings
+from langchain_community.vectorstores import FAISS
+from langchain_ollama import OllamaLLM
+
+# 모듈화된 파일들 불러오기
+from config import LLM_MODEL, EMBEDDING_MODEL, FAISS_PATH, RETRIEVER_K
+from llm.prompt import QA_PROMPT
+from predictors.score_prediction import predictor
+
+# ==========================================
+# 1. 피처 정의
+# ==========================================
+FEATURE_ORDER = [
+    'C1Z001386', 'C1M210000', 'C18210000', 'C1L120001', 'C1L120004', 
+    'L10210000', 'L90210100', 'L90210200', 'L10210B00', 'L10216000', 
+    'L10217000', 'D10110000', 'D10133000', 'PERF1'
+]
+
+FEATURES = {
+    'C1Z001386': '1년내 카드 총 이용금액 (만원)',
+    'C1M210000': '신용카드 건수',
+    'C18210000': '체크카드 건수',
+    'C1L120001': '카드 총 한도금액 (만원)',
+    'C1L120004': '신용카드 개설 후 경과일수',
+    'L10210000': '대출 건수',
+    'L90210100': '은행업종 대출 건수',
+    'L90210200': '카드업종 대출 건수',
+    'L10210B00': '보험업종 대출 건수',
+    'L10216000': '신용 대출 건수',
+    'L10217000': '담보 대출 건수',
+    'D10110000': '연체 건수',
+    'D10133000': '총 연체 상환 금액 (만원)',
+    'PERF1': '1년내 90일 이상 연체 여부',
+}
+
+# ==========================================
+# 2. RAG & Chatbot 로직
+# ==========================================
+class CreditRAGConsultant:
+    def __init__(self):
+        self.embedding_model = HuggingFaceEmbeddings(
+            model_name=EMBEDDING_MODEL
+        )
+        if os.path.exists(FAISS_PATH):
+            self.vectorstore = FAISS.load_local(
+                FAISS_PATH, self.embedding_model, allow_dangerous_deserialization=True
+            )
+            self.retriever = self.vectorstore.as_retriever(search_kwargs={"k": RETRIEVER_K})
+        else:
+            self.vectorstore = None
+            self.retriever = None
+
+        # streaming=True: 토큰 생성 즉시 반환
+        self.llm = OllamaLLM(model=LLM_MODEL, streaming=True)
+
+    def generate_response(self, history, user_message, analysis_report):
+        """
+        RAG 파이프라인을 R → A → G 단계로 직접 분리해서 각 진행상황을 실시간 표시.
+        분석 리포트가 없어도 일반 신용 상담이 가능하도록 예외 없이 처리.
+        """
+        if not user_message:
+            yield history, ""
+            return
+
+        # "생각 중..." 플레이스홀더 제거
+        if history and isinstance(history[-1], dict) and history[-1].get("content") == "생각 중...":
+            history = history[:-1]
+
+        if not self.retriever:
+            history = history + [{
+                "role": "assistant",
+                "content": "⚠️ 시스템: FAISS 인덱스를 로드할 수 없어 문서 검색이 불가합니다."
+            }]
+            yield history, ""
+            return
+
+        # 응답 슬롯 추가
+        history = history + [{"role": "assistant", "content": ""}]
+
+        t0 = time.time()
+
+        # ──────────────────────────────────────────────────
+        # R (Retrieval) — 관련 문서 검색
+        # ──────────────────────────────────────────────────
+        history[-1]["content"] = "📂 **[R] 관련 문서 검색 중...**"
+        yield history, ""
+
+        try:
+            docs = self.retriever.invoke(user_message)
+        except Exception as e:
+            history[-1]["content"] = f"⚠️ 문서 검색 실패: {e}"
+            yield history, ""
+            return
+
+        t_retrieve = time.time() - t0
+        context = "\n\n---\n\n".join(
+            [f"[문서 {i+1}]\n{doc.page_content}" for i, doc in enumerate(docs)]
+        )
+
+        # ──────────────────────────────────────────────────
+        # A (Augmentation) — 분석 리포트 + 사용자 질문으로 프롬프트 구성
+        # ──────────────────────────────────────────────────
+        history[-1]["content"] = (
+            f"📂 **[R] 문서 검색 완료** `{t_retrieve:.1f}s | {len(docs)}개 문서`\n\n"
+            f"🔗 **[A] 프롬프트 구성 중...**"
+        )
+        yield history, ""
+
+        if analysis_report:
+            score_val = int(analysis_report["score"])
+            # 피처 데이터를 읽기 좋은 텍스트로 변환
+            features_text = "\n".join([f"- {FEATURES.get(k, k)}: {v}" for k, v in analysis_report['features'].items()])
+            
+            report_section = (
+                f"■ 현재 분석 결과\n"
+                f"- 예측 신용 점수: {score_val}점\n"
+                f"{features_text}\n"
+            )
+            query_text = (
+                f"{report_section}\n"
+                f"■ 질문: {user_message}"
+            )
+        else:
+            # 분석 미입력: 일반 신용 상담 모드 — 예외 없이 정상 처리
+            query_text = f"■ 질문: {user_message} (현재 분석된 지표 데이터 없음)"
+
+        prompt_text = QA_PROMPT.format(context=context, query=query_text)
+
+        t_augment = time.time() - t0
+
+        # ──────────────────────────────────────────────────
+        # G (Generation) — LLM 스트리밍 응답 생성
+        # ──────────────────────────────────────────────────
+        history[-1]["content"] = (
+            f"📂 **[R] 문서 검색 완료** `{t_retrieve:.1f}s | {len(docs)}개 문서`\n\n"
+            f"🔗 **[A] 프롬프트 구성 완료** `{t_augment - t_retrieve:.2f}s`\n\n"
+            f"💬 **[G] 응답 생성 중...**\n\n"
+        )
+        yield history, ""
+
+        t_gen_start = time.time()
+        answer_buffer = ""
+
+        try:
+            for chunk in self.llm.stream(prompt_text):
+                answer_buffer += chunk
+                history[-1]["content"] = (
+                    f"📂 **[R] 문서 검색 완료** `{t_retrieve:.1f}s | {len(docs)}개 문서`\n\n"
+                    f"🔗 **[A] 프롬프트 구성 완료** `{t_augment - t_retrieve:.2f}s`\n\n"
+                    f"💬 **[G] 응답 생성 중...**\n\n"
+                    f"{answer_buffer}"
+                )
+                yield history, ""
+
+            t_gen = time.time() - t_gen_start
+            t_total = time.time() - t0
+
+            # 최종: 진행상황 헤더 제거하고 답변만 + 타임 요약
+            history[-1]["content"] = (
+                f"{answer_buffer}\n\n"
+                f"---\n"
+                f"⏱️ 검색 `{t_retrieve:.1f}s` → 구성 `{t_augment - t_retrieve:.2f}s` "
+                f"→ 생성 `{t_gen:.1f}s` | **총 `{t_total:.1f}s`**"
+            )
+            yield history, ""
+
+        except Exception as e:
+            import traceback
+            traceback.print_exc()
+            history[-1]["content"] = f"상담 중 오류가 발생했습니다: {e}"
+            yield history, ""
+
+
+consultant = CreditRAGConsultant()
+
+# ==========================================
+# 3. Gradio Interface
+# ==========================================
+with gr.Blocks() as demo:
+    analysis_report = gr.State(None)
+    pending_msg = gr.State("")  # 텍스트박스 클리어 후에도 원본 메시지를 보존하기 위한 State
+
+    gr.Markdown(f"# 🛡️ KCB AI 신용 점수 분석 시스템")
+    gr.Markdown(
+        f"> 🤖 **LLM**: `{LLM_MODEL}` (Ollama 로컬)  |  "
+        f"📚 **임베딩**: `{EMBEDDING_MODEL}`  |  **RAG**: FAISS 검색"
+    )
+    gr.Markdown("금융 지표를 입력하여 AI 예측 점수를 확인하고, 맞춤 상담을 받아보세요. *(분석 없이도 일반 신용 상담 가능)*")
+
+    with gr.Row():
+        with gr.Column(scale=1):
+            gr.Markdown("### 📊 지표 입력")
+            input_list = []
+            ui_input_keys_for_ui = [k for k in FEATURE_ORDER if k in FEATURES and k != 'L10210000']
+            for key in ui_input_keys_for_ui:
+                label = FEATURES[key]
+                if key == 'PERF1':
+                    input_list.append(gr.Checkbox(label=f"{label} (90일 이상 연체)", value=False))
+                else:
+                    input_list.append(gr.Textbox(label=label, placeholder="0"))
+            
+            predict_btn = gr.Button("📈 점수 분석하기", variant="primary", size="lg")
+            
+        with gr.Column(scale=2):
+            with gr.Group():
+                gr.Markdown("### 📈 분석 결과 리포트")
+                result_display = gr.Label(label="예측 신용 점수")
+                result_info = gr.Markdown("버튼을 눌러 분석을 시작하세요.")
+
+            gr.Markdown("### 💬 실시간 신용 상담 챗봇")
+            chatbot = gr.Chatbot(
+                label="상담 챗봇",
+                height=500,
+                layout="bubble",
+                render_markdown=True,
+            )
+            
+            with gr.Row():
+                msg = gr.Textbox(
+                    placeholder="신용 관리에 대해 궁금한 점을 질문해보세요... (예: 점수를 올리려면?)",
+                    show_label=False,
+                    scale=8,
+                    container=False
+                )
+                submit_btn = gr.Button("전송 📤", variant="primary", scale=1)
+            
+            with gr.Row():
+                clear_btn = gr.Button("🗑️ 대화 내역 초기화", variant="secondary", size="sm")
+
+    def handle_predict(*args):
+        try:
+            ui_input_keys = [k for k in FEATURE_ORDER if k in FEATURES and k != 'L10210000']
+            features_dict = {}
+            for i in range(len(args)):
+                key = ui_input_keys[i]
+                if key == 'PERF1':
+                    features_dict[key] = int(args[i])
+                else:
+                    val_str = str(args[i]).strip() if args[i] is not None else ""
+                    if not val_str:
+                        val = 0.0
+                    else:
+                        try:
+                            val = float(val_str.replace(',', ''))
+                        except ValueError:
+                            return None, 0.0, f"⚠️ 오류: '{FEATURES[key]}' 항목에 올바른 숫자만 입력해주세요."
+                    
+                    if val < 0:
+                        return None, 0.0, f"⚠️ 오류: '{FEATURES[key]}' 항목에 0 이상의 숫자를 적어주세요."
+                    features_dict[key] = val
+            
+            features_dict['L10210000'] = (
+                features_dict.get('L90210100', 0.0) +
+                features_dict.get('L90210200', 0.0) +
+                features_dict.get('L10210B00', 0.0)
+            )
+
+            score = predictor.predict(features_dict)
+            
+            if np.isnan(score) or not np.isfinite(score):
+                return None, 0.0, "⚠️ 모델 추론 중 데이터 오류가 발생했습니다."
+            
+            display_score = int(min(max(round(score), 0), 1000))
+            report = {"features": features_dict, "score": float(display_score)}
+            return report, display_score, f"✅ 분석 완료! 당신의 신용 점수는 **{display_score}점**입니다."
+        except Exception as e:
+            return None, 0.0, f"⚠️ 분석 에러: {str(e)}"
+
+    def user_msg(user_message, history):
+        """사용자 메시지를 히스토리에 즉시 추가하고, 텍스트박스는 클리어.
+        원본 메시지는 pending_msg State에 저장해서 generate_response로 전달."""
+        if not user_message:
+            return history, "", ""
+        return history + [
+            {"role": "user", "content": user_message},
+            {"role": "assistant", "content": "생각 중..."}
+        ], "", user_message  # chatbot, msg(클리어), pending_msg(원본 보존)
+
+    predict_btn.click(
+        handle_predict,
+        inputs=input_list,
+        outputs=[analysis_report, result_display, result_info]
+    )
+
+    # pending_msg에서 원본 메시지를 읽어 generate_response에 전달
+    msg.submit(
+        user_msg, [msg, chatbot], [chatbot, msg, pending_msg], queue=False
+    ).then(
+        consultant.generate_response, [chatbot, pending_msg, analysis_report], [chatbot, msg]
+    )
+    submit_btn.click(
+        user_msg, [msg, chatbot], [chatbot, msg, pending_msg], queue=False
+    ).then(
+        consultant.generate_response, [chatbot, pending_msg, analysis_report], [chatbot, msg]
+    )
+
+    clear_btn.click(lambda: [], None, chatbot, queue=False)
+
+if __name__ == "__main__":
+    demo.launch()

evaluation/check.ipynb

@@ -1,830 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "e00459fc",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "STDT          int64\n",
-      "ID           object\n",
-      "GENDER        int64\n",
-      "AGE_BAND      int64\n",
-      "C1Z001373     int64\n",
-      "              ...  \n",
-      "SCORE_6M      int64\n",
-      "PERF1         int64\n",
-      "PERF2         int64\n",
-      "PERF3         int64\n",
-      "PERF4         int64\n",
-      "Length: 157, dtype: object\n",
-      "AL0C00005    87.861277\n",
-      "U81302010     0.007542\n",
-      "dtype: float64\n",
-      "            STDT        GENDER      AGE_BAND     C1Z001373     C1M2B4W03  \\\n",
-      "count  3129036.0  3.129036e+06  3.129036e+06  3.129036e+06  3.129036e+06   \n",
-      "mean    202212.0  1.502075e+00  4.270428e+00  7.055230e+03  4.249793e+03   \n",
-      "std          0.0  4.999958e-01  1.600465e+00  1.083864e+04  7.584188e+03   \n",
-      "min     202212.0  1.000000e+00  1.000000e+00 -7.665200e+04 -4.726000e+04   \n",
-      "25%     202212.0  1.000000e+00  3.000000e+00  1.177500e+02  0.000000e+00   \n",
-      "50%     202212.0  2.000000e+00  4.000000e+00  4.559000e+03  1.778000e+03   \n",
-      "75%     202212.0  2.000000e+00  5.000000e+00  1.043000e+04  6.421000e+03   \n",
-      "max     202212.0  2.000000e+00  9.000000e+00  2.006550e+05  1.669330e+05   \n",
-      "\n",
-      "          C1M2B5W03     C1Z001386     C1M210000     C1M210001     C1M210003  \\\n",
-      "count  3.129036e+06  3.129036e+06  3.129036e+06  3.129036e+06  3.129036e+06   \n",
-      "mean   1.174364e+03  2.723652e+04  1.789889e+00  2.077317e-05  1.050164e-02   \n",
-      "std    2.006592e+03  4.488836e+04  1.687745e+00  4.695859e-03  1.128589e-01   \n",
-      "min   -4.990000e+03 -1.741950e+05  0.000000e+00  0.000000e+00  0.000000e+00   \n",
-      "25%    0.000000e+00  0.000000e+00  0.000000e+00  0.000000e+00  0.000000e+00   \n",
-      "50%    0.000000e+00  1.696900e+04  1.000000e+00  0.000000e+00  0.000000e+00   \n",
-      "75%    2.169000e+03  4.126900e+04  3.000000e+00  0.000000e+00  0.000000e+00   \n",
-      "max    2.372700e+04  8.274750e+05  2.000000e+01  2.000000e+00  8.000000e+00   \n",
-      "\n",
-      "       ...     AL0C00005  AP0910001  AP0910002     AS120G001         SCORE  \\\n",
-      "count  ...  3.129036e+06  3129036.0  3129036.0  3.129036e+06  3.129036e+06   \n",
-      "mean   ... -8.786115e+07        9.0        9.0 -3.904906e+00  8.176420e+02   \n",
-      "std    ...  3.265802e+07        0.0        0.0  5.852996e+00  1.892052e+02   \n",
-      "min    ... -1.000000e+08        9.0        9.0 -9.000000e+00  0.000000e+00   \n",
-      "25%    ... -1.000000e+08        9.0        9.0 -9.000000e+00  7.250000e+02   \n",
-      "50%    ... -1.000000e+08        9.0        9.0 -9.000000e+00  8.820000e+02   \n",
-      "75%    ... -1.000000e+08        9.0        9.0  3.000000e+00  9.510000e+02   \n",
-      "max    ...  5.570000e+03        9.0        9.0  5.000000e+00  1.000000e+03   \n",
-      "\n",
-      "           SCORE_6M         PERF1         PERF2         PERF3         PERF4  \n",
-      "count  3.129036e+06  3.129036e+06  3.129036e+06  3.129036e+06  3.129036e+06  \n",
-      "mean   8.175420e+02  3.623480e-03  4.139614e-03  3.796057e-03  3.486697e-03  \n",
-      "std    1.892047e+02  6.008620e-02  6.420653e-02  6.149511e-02  5.894524e-02  \n",
-      "min    0.000000e+00  0.000000e+00  0.000000e+00  0.000000e+00  0.000000e+00  \n",
-      "25%    7.240000e+02  0.000000e+00  0.000000e+00  0.000000e+00  0.000000e+00  \n",
-      "50%    8.830000e+02  0.000000e+00  0.000000e+00  0.000000e+00  0.000000e+00  \n",
-      "75%    9.520000e+02  0.000000e+00  0.000000e+00  0.000000e+00  0.000000e+00  \n",
-      "max    1.000000e+03  1.000000e+00  1.000000e+00  1.000000e+00  1.000000e+00  \n",
-      "\n",
-      "[8 rows x 152 columns]\n"
-     ]
-    }
-   ],
-   "source": [
-    "\n",
-    "import pandas as pd\n",
-    "\n",
-    "df = pd.read_csv(\"../dataset/personal_cv.csv\")\n",
-    "\n",
-    "# 데이터 타입 확인\n",
-    "print(df.dtypes)\n",
-    "\n",
-    "# 결측값(-99999999) 비율 확인\n",
-    "missing = (df == -99999999).sum() / len(df) * 100\n",
-    "print(missing[missing > 0].sort_values(ascending=False))\n",
-    "\n",
-    "# 기본 통계\n",
-    "print(df.describe())"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "66e6c43d",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0점 제거 후: 321,186행\n",
-      "\n",
-      "신용점수 구간 분포:\n",
-      "SCORE_BIN\n",
-      "고신용(900~1000)    173351\n",
-      "중고신용(700~900)     98714\n",
-      "중저신용(500~700)     39676\n",
-      "저신용(0~500)         9445\n",
-      "Name: count, dtype: int64\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/var/folders/9d/6nccn4756nq7c7030tvhwnj00000gn/T/ipykernel_85303/903746687.py:37: FutureWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n",
-      "  df_sampled = df_clean.groupby('SCORE_BIN', observed=True).apply(\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "샘플링 후: 149,121행\n",
-      "SCORE_BIN\n",
-      "중고신용(700~900)    50000\n",
-      "고신용(900~1000)    50000\n",
-      "중저신용(500~700)    39676\n",
-      "저신용(0~500)        9445\n",
-      "Name: count, dtype: int64\n",
-      "\n",
-      "피처 수: 437개\n",
-      "타겟 분포:\n",
-      "count    149121.000000\n",
-      "mean        788.597917\n",
-      "std         170.132903\n",
-      "min         356.000000\n",
-      "25%         656.000000\n",
-      "50%         814.000000\n",
-      "75%         945.000000\n",
-      "max        1000.000000\n",
-      "Name: PYE_SC0000000, dtype: float64\n",
-      "\n",
-      "train: 119,296개 | test: 29,825개\n",
-      "\n",
-      "========================================\n",
-      "Random Forest\n",
-      "RMSE: 64.83 | MAE: 46.64 | R2: 0.8544\n",
-      "\n",
-      "========================================\n",
-      "XGBoost\n",
-      "RMSE: 61.69 | MAE: 45.22 | R2: 0.8681\n",
-      "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.079039 seconds.\n",
-      "You can set `force_row_wise=true` to remove the overhead.\n",
-      "And if memory is not enough, you can set `force_col_wise=true`.\n",
-      "[LightGBM] [Info] Total Bins 35224\n",
-      "[LightGBM] [Info] Number of data points in the train set: 119296, number of used features: 367\n",
-      "[LightGBM] [Info] Start training from score 788.477032\n",
-      "\n",
-      "========================================\n",
-      "LightGBM\n",
-      "RMSE: 64.32 | MAE: 46.98 | R2: 0.8566\n",
-      "\n",
-      "최종 선정 모델: XGBoost (R2: 0.8681)\n",
-      "사용자 입력 가능 피처: 14개\n",
-      "['AGE', 'SEX', 'JB_TP', 'PYE_ICM', 'PYE_ICM_RT', 'TOT_ASST', 'HOUS_LN_BAL', 'CRDT_LN_BAL', 'CD_USE_AMT', 'OWN_HOUS_CNT', 'OWN_LIV_YN', 'PYE_FAM_CNT', 'PYE_CAR_OWN', 'DAR']\n",
-      "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.004249 seconds.\n",
-      "You can set `force_row_wise=true` to remove the overhead.\n",
-      "And if memory is not enough, you can set `force_col_wise=true`.\n",
-      "[LightGBM] [Info] Total Bins 1668\n",
-      "[LightGBM] [Info] Number of data points in the train set: 119296, number of used features: 14\n",
-      "[LightGBM] [Info] Start training from score 788.477032\n",
-      "\n",
-      "사용자 입력 피처만으로 재학습 결과\n",
-      "RMSE: 108.66 | MAE: 82.22 | R2: 0.5908\n"
-     ]
-    }
-   ],
-   "source": [
-    "import pandas as pd\n",
-    "import numpy as np\n",
-    "from sklearn.model_selection import train_test_split\n",
-    "from sklearn.ensemble import RandomForestRegressor, GradientBoostingRegressor\n",
-    "from sklearn.metrics import mean_squared_error, r2_score, mean_absolute_error\n",
-    "import xgboost as xgb\n",
-    "import lightgbm as lgb\n",
-    "import matplotlib.pyplot as plt\n",
-    "\n",
-    "# ========================\n",
-    "# 1단계: 데이터 로드 & 결측치 50% 이상 컬럼 제거\n",
-    "# ========================\n",
-    "df = pd.read_csv('../dataset/telecom_cb.csv')\n",
-    "threshold = 0.5\n",
-    "df_clean = df[df.columns[df.isna().mean() < threshold]]\n",
-    "\n",
-    "# ========================\n",
-    "# 2단계: 타겟 이상값 제거 (0점 제거)\n",
-    "# ========================\n",
-    "df_clean = df_clean[df_clean['PYE_SC0000000'] > 0].reset_index(drop=True)\n",
-    "print(f\"0점 제거 후: {len(df_clean):,}행\")\n",
-    "\n",
-    "# ========================\n",
-    "# 3단계: 신용점수 구간별 층화 샘플링\n",
-    "# ========================\n",
-    "# 신용점수 구간 생성\n",
-    "df_clean['SCORE_BIN'] = pd.cut(\n",
-    "    df_clean['PYE_SC0000000'],\n",
-    "    bins=[0, 500, 700, 900, 1000],\n",
-    "    labels=['저신용(0~500)', '중저신용(500~700)', '중고신용(700~900)', '고신용(900~1000)']\n",
-    ")\n",
-    "\n",
-    "print(f\"\\n신용점수 구간 분포:\")\n",
-    "print(df_clean['SCORE_BIN'].value_counts())\n",
-    "\n",
-    "# 구간별 균등 샘플링 (각 구간 최대 3000개)\n",
-    "df_sampled = df_clean.groupby('SCORE_BIN', observed=True).apply(\n",
-    "    lambda x: x.sample(n=min(len(x), 50000), random_state=42)\n",
-    ").reset_index(drop=True)\n",
-    "\n",
-    "print(f\"\\n샘플링 후: {len(df_sampled):,}행\")\n",
-    "print(df_sampled['SCORE_BIN'].value_counts())\n",
-    "\n",
-    "# ========================\n",
-    "# 4단계: 피처 & 타겟 분리\n",
-    "# ========================\n",
-    "drop_cols = ['PYE_SC0000000', 'SCORE_BIN', 'PYE_MAX_DLQ_DAY',\n",
-    "             'CUST_ID', 'BASE_YM']\n",
-    "drop_cols = [c for c in drop_cols if c in df_sampled.columns]\n",
-    "\n",
-    "X = df_sampled.drop(columns=drop_cols)\n",
-    "y = df_sampled['PYE_SC0000000']\n",
-    "\n",
-    "X = X.select_dtypes(include=[np.number])\n",
-    "X = X.fillna(X.median())\n",
-    "\n",
-    "print(f\"\\n피처 수: {X.shape[1]}개\")\n",
-    "print(f\"타겟 분포:\\n{y.describe()}\")\n",
-    "\n",
-    "# ========================\n",
-    "# 5단계: train/test 분리\n",
-    "# ========================\n",
-    "X_train, X_test, y_train, y_test = train_test_split(\n",
-    "    X, y, test_size=0.2, random_state=42\n",
-    ")\n",
-    "\n",
-    "print(f\"\\ntrain: {len(X_train):,}개 | test: {len(X_test):,}개\")\n",
-    "\n",
-    "# ========================\n",
-    "# 6단계: 모델 비교\n",
-    "# ========================\n",
-    "models = {\n",
-    "    'Random Forest'  : RandomForestRegressor(n_estimators=100, random_state=42),\n",
-    "    'XGBoost'        : xgb.XGBRegressor(random_state=42),\n",
-    "    'LightGBM'       : lgb.LGBMRegressor(random_state=42)\n",
-    "}\n",
-    "\n",
-    "results = {}\n",
-    "\n",
-    "for name, model in models.items():\n",
-    "    model.fit(X_train, y_train)\n",
-    "    y_pred = model.predict(X_test)\n",
-    "\n",
-    "    rmse = np.sqrt(mean_squared_error(y_test, y_pred))\n",
-    "    mae  = mean_absolute_error(y_test, y_pred)\n",
-    "    r2   = r2_score(y_test, y_pred)\n",
-    "\n",
-    "    results[name] = {'model': model, 'rmse': rmse, 'mae': mae, 'r2': r2}\n",
-    "\n",
-    "    print(f\"\\n{'='*40}\")\n",
-    "    print(f\"{name}\")\n",
-    "    print(f\"RMSE: {rmse:.2f} | MAE: {mae:.2f} | R2: {r2:.4f}\")\n",
-    "\n",
-    "# ========================\n",
-    "# 7단계: 최종 모델 선정\n",
-    "# ========================\n",
-    "best_name  = max(results, key=lambda x: results[x]['r2'])\n",
-    "best_model = results[best_name]['model']\n",
-    "\n",
-    "print(f\"\\n최종 선정 모델: {best_name} (R2: {results[best_name]['r2']:.4f})\")\n",
-    "\n",
-    "# 사용자 입력 가능한 피처만 선택\n",
-    "user_input_cols = [\n",
-    "    'AGE',           # 나이\n",
-    "    'SEX',           # 성별\n",
-    "    'JB_TP',         # 직업군\n",
-    "    'PYE_ICM',       # 연소득\n",
-    "    'PYE_ICM_RT',    # 연소득 백분위\n",
-    "    'TOT_ASST',      # 총자산\n",
-    "    'HOUS_LN_BAL',   # 주택담보대출잔액\n",
-    "    'CRDT_LN_BAL',   # 신용대출잔액\n",
-    "    'CD_USE_AMT',    # 카드소비금액\n",
-    "    'OWN_HOUS_CNT',  # 주택보유건수\n",
-    "    'OWN_LIV_YN',    # 자가거주여부\n",
-    "    'PYE_FAM_CNT',   # 가구원수\n",
-    "    'PYE_CAR_OWN',   # 차량보유여부\n",
-    "    'DAR',           # 자산대비 부채비중\n",
-    "]\n",
-    "\n",
-    "# 실제 데이터에 있는 것만 필터\n",
-    "user_input_cols = [c for c in user_input_cols if c in df_sampled.columns]\n",
-    "\n",
-    "X_user = df_sampled[user_input_cols].copy()\n",
-    "X_user = X_user.select_dtypes(include=[np.number])\n",
-    "X_user = X_user.fillna(X_user.median())\n",
-    "y_user = df_sampled['PYE_SC0000000']\n",
-    "\n",
-    "print(f\"사용자 입력 가능 피처: {X_user.shape[1]}개\")\n",
-    "print(X_user.columns.tolist())\n",
-    "\n",
-    "# train/test 분리\n",
-    "X_tr, X_te, y_tr, y_te = train_test_split(\n",
-    "    X_user, y_user, test_size=0.2, random_state=42\n",
-    ")\n",
-    "\n",
-    "# LightGBM 재학습\n",
-    "model_user = lgb.LGBMRegressor(random_state=42)\n",
-    "model_user.fit(X_tr, y_tr)\n",
-    "y_pred = model_user.predict(X_te)\n",
-    "\n",
-    "rmse = np.sqrt(mean_squared_error(y_te, y_pred))\n",
-    "mae  = mean_absolute_error(y_te, y_pred)\n",
-    "r2   = r2_score(y_te, y_pred)\n",
-    "\n",
-    "print(f\"\\n사용자 입력 피처만으로 재학습 결과\")\n",
-    "print(f\"RMSE: {rmse:.2f} | MAE: {mae:.2f} | R2: {r2:.4f}\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "6fa48db3",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "결측치 50% 이상 제거 후: 446개 컬럼\n",
-      "0점 제거 후: 321,186행\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/var/folders/9d/6nccn4756nq7c7030tvhwnj00000gn/T/ipykernel_85303/1799362720.py:31: FutureWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n",
-      "  df_sampled = df_clean.groupby('SCORE_BIN', observed=True).apply(\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "샘플링 후: 12,000행\n",
-      "SCORE_BIN\n",
-      "저신용(0~500)       3000\n",
-      "중저신용(500~700)    3000\n",
-      "중고신용(700~900)    3000\n",
-      "고신용(900~1000)    3000\n",
-      "Name: count, dtype: int64\n",
-      "\n",
-      "전부 0인 컬럼: 58개 제거\n",
-      "분산 0인 컬럼: 2개 제거\n",
-      "최종 피처 수: 377개\n",
-      "\n",
-      "사용자 입력 가능 피처: 12개\n",
-      "['AGE', 'PYE_ICM', 'PYE_ICM_RT', 'TOT_ASST', 'HOUS_LN_BAL', 'CRDT_LN_BAL', 'CD_USE_AMT', 'OWN_HOUS_CNT', 'PYE_FAM_CNT', 'DAR', 'PYE_CAR_LN_AMT', 'CRDT_LN_BAL_NEW']\n",
-      "\n",
-      "=== 피처별 값 범위 ===\n",
-      "AGE: 고유값 11개 | 범위 20.0 ~ 70.0\n",
-      "PYE_ICM: 고유값 10570개 | 범위 11657.0 ~ 132303.0\n",
-      "PYE_ICM_RT: 고유값 98개 | 범위 1.0 ~ 98.0\n",
-      "TOT_ASST: 고유값 11821개 | 범위 0.0 ~ 2676924.0\n",
-      "HOUS_LN_BAL: 고유값 1504개 | 범위 0.0 ~ 216887.0\n",
-      "CRDT_LN_BAL: 고유값 4905개 | 범위 0.0 ~ 215771.0\n",
-      "CD_USE_AMT: 고유값 7283개 | 범위 0.0 ~ 73952.0\n",
-      "OWN_HOUS_CNT: 고유값 3개 | 범위 0.0 ~ 2.0\n",
-      "PYE_FAM_CNT: 고유값 7개 | 범위 0.0 ~ 6.0\n",
-      "DAR: 고유값 6028개 | 범위 0.0 ~ 5.0\n",
-      "PYE_CAR_LN_AMT: 고유값 1395개 | 범위 0.0 ~ 30825.0\n",
-      "CRDT_LN_BAL_NEW: 고유값 2개 | 범위 0.0 ~ 1.0\n",
-      "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.001097 seconds.\n",
-      "You can set `force_row_wise=true` to remove the overhead.\n",
-      "And if memory is not enough, you can set `force_col_wise=true`.\n",
-      "[LightGBM] [Info] Total Bins 1908\n",
-      "[LightGBM] [Info] Number of data points in the train set: 9600, number of used features: 12\n",
-      "[LightGBM] [Info] Start training from score 710.157187\n",
-      "\n",
-      "=== 최종 모델 성능 ===\n",
-      "RMSE: 126.39 | MAE: 96.84 | R2: 0.6189\n"
-     ]
-    }
-   ],
-   "source": [
-    "import pandas as pd\n",
-    "import numpy as np\n",
-    "from sklearn.model_selection import train_test_split\n",
-    "from sklearn.feature_selection import VarianceThreshold\n",
-    "import lightgbm as lgb\n",
-    "from sklearn.metrics import mean_squared_error, r2_score, mean_absolute_error\n",
-    "\n",
-    "# ========================\n",
-    "# 1단계: 데이터 로드 & 결측치 50% 이상 컬럼 제거\n",
-    "# ========================\n",
-    "df = pd.read_csv('../dataset/telecom_cb.csv')\n",
-    "threshold = 0.5\n",
-    "df_clean = df[df.columns[df.isna().mean() < threshold]]\n",
-    "print(f\"결측치 50% 이상 제거 후: {df_clean.shape[1]}개 컬럼\")\n",
-    "\n",
-    "# ========================\n",
-    "# 2단계: 타겟 이상값 제거 (0점 제거)\n",
-    "# ========================\n",
-    "df_clean = df_clean[df_clean['PYE_SC0000000'] > 0].reset_index(drop=True)\n",
-    "print(f\"0점 제거 후: {len(df_clean):,}행\")\n",
-    "\n",
-    "# ========================\n",
-    "# 3단계: 신용점수 구간별 층화 샘플링\n",
-    "# ========================\n",
-    "df_clean['SCORE_BIN'] = pd.cut(\n",
-    "    df_clean['PYE_SC0000000'],\n",
-    "    bins=[0, 500, 700, 900, 1000],\n",
-    "    labels=['저신용(0~500)', '중저신용(500~700)', '중고신용(700~900)', '고신용(900~1000)']\n",
-    ")\n",
-    "\n",
-    "df_sampled = df_clean.groupby('SCORE_BIN', observed=True).apply(\n",
-    "    lambda x: x.sample(n=min(len(x), 3000), random_state=42)\n",
-    ").reset_index(drop=True)\n",
-    "\n",
-    "print(f\"\\n샘플링 후: {len(df_sampled):,}행\")\n",
-    "print(df_sampled['SCORE_BIN'].value_counts())\n",
-    "\n",
-    "# ========================\n",
-    "# 4단계: 전체 컬럼 대상 분산 0 & 전부 0 제거\n",
-    "# ========================\n",
-    "drop_cols = ['PYE_SC0000000', 'SCORE_BIN', 'PYE_MAX_DLQ_DAY',\n",
-    "             'CUST_ID', 'BASE_YM']\n",
-    "drop_cols = [c for c in drop_cols if c in df_sampled.columns]\n",
-    "\n",
-    "# 수치형만 선택\n",
-    "X_all = df_sampled.drop(columns=drop_cols).select_dtypes(include=[np.number])\n",
-    "X_all = X_all.fillna(X_all.median())\n",
-    "\n",
-    "# 전부 0인 컬럼 제거\n",
-    "zero_cols = [col for col in X_all.columns if (X_all[col] == 0).all()]\n",
-    "print(f\"\\n전부 0인 컬럼: {len(zero_cols)}개 제거\")\n",
-    "X_all = X_all.drop(columns=zero_cols)\n",
-    "\n",
-    "# 분산 0인 컬럼 제거\n",
-    "selector = VarianceThreshold(threshold=0)\n",
-    "selector.fit(X_all)\n",
-    "low_var_cols = X_all.columns[~selector.get_support()].tolist()\n",
-    "print(f\"분산 0인 컬럼: {len(low_var_cols)}개 제거\")\n",
-    "X_all = X_all.drop(columns=low_var_cols)\n",
-    "\n",
-    "print(f\"최종 피처 수: {X_all.shape[1]}개\")\n",
-    "\n",
-    "# ========================\n",
-    "# 5단계: 사용자 입력 가능 피처만 선택\n",
-    "# ========================\n",
-    "user_input_cols = [\n",
-    "    'AGE', 'PYE_ICM', 'PYE_ICM_RT', 'TOT_ASST',\n",
-    "    'HOUS_LN_BAL', 'CRDT_LN_BAL', 'CD_USE_AMT',\n",
-    "    'OWN_HOUS_CNT', 'PYE_FAM_CNT', 'DAR',\n",
-    "    'PYE_CAR_LN_AMT', 'HOUS_LN_BAL_NEW', 'CRDT_LN_BAL_NEW'\n",
-    "]\n",
-    "\n",
-    "# 실제 X_all에 있는 것만 필터\n",
-    "user_input_cols = [c for c in user_input_cols if c in X_all.columns]\n",
-    "X_user = X_all[user_input_cols].copy()\n",
-    "\n",
-    "print(f\"\\n사용자 입력 가능 피처: {X_user.shape[1]}개\")\n",
-    "print(X_user.columns.tolist())\n",
-    "\n",
-    "# 피처별 값 범위 확인\n",
-    "print(\"\\n=== 피처별 값 범위 ===\")\n",
-    "for col in X_user.columns:\n",
-    "    unique_count = X_user[col].nunique()\n",
-    "    col_min = X_user[col].min()\n",
-    "    col_max = X_user[col].max()\n",
-    "    print(f\"{col}: 고유값 {unique_count}개 | 범위 {col_min:.1f} ~ {col_max:.1f}\")\n",
-    "\n",
-    "# ========================\n",
-    "# 6단계: train/test 분리 & 모델 학습\n",
-    "# ========================\n",
-    "y = df_sampled['PYE_SC0000000']\n",
-    "\n",
-    "X_train, X_test, y_train, y_test = train_test_split(\n",
-    "    X_user, y, test_size=0.2, random_state=42\n",
-    ")\n",
-    "\n",
-    "model = lgb.LGBMRegressor(random_state=42)\n",
-    "model.fit(X_train, y_train)\n",
-    "y_pred = model.predict(X_test)\n",
-    "\n",
-    "rmse = np.sqrt(mean_squared_error(y_test, y_pred))\n",
-    "mae  = mean_absolute_error(y_test, y_pred)\n",
-    "r2   = r2_score(y_test, y_pred)\n",
-    "\n",
-    "print(f\"\\n=== 최종 모델 성능 ===\")\n",
-    "print(f\"RMSE: {rmse:.2f} | MAE: {mae:.2f} | R2: {r2:.4f}\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "b803062f",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "=== Permutation Importance ===\n",
-      "            feature  importance       std\n",
-      "1           PYE_ICM    1.237040  0.042766\n",
-      "2        PYE_ICM_RT    0.726069  0.028536\n",
-      "5       CRDT_LN_BAL    0.272620  0.012101\n",
-      "6        CD_USE_AMT    0.088819  0.008274\n",
-      "0               AGE    0.051002  0.003838\n",
-      "7      OWN_HOUS_CNT    0.050263  0.004501\n",
-      "9               DAR    0.014202  0.001999\n",
-      "10   PYE_CAR_LN_AMT    0.011054  0.001366\n",
-      "3          TOT_ASST    0.009042  0.001913\n",
-      "8       PYE_FAM_CNT    0.005112  0.001194\n",
-      "4       HOUS_LN_BAL    0.001540  0.000333\n",
-      "11  CRDT_LN_BAL_NEW    0.001468  0.000416\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/var/folders/9d/6nccn4756nq7c7030tvhwnj00000gn/T/ipykernel_85303/3641905308.py:33: UserWarning: Glyph 44048 (\\N{HANGUL SYLLABLE GAM}) missing from font(s) DejaVu Sans.\n",
-      "  plt.tight_layout()\n",
-      "/var/folders/9d/6nccn4756nq7c7030tvhwnj00000gn/T/ipykernel_85303/3641905308.py:33: UserWarning: Glyph 49548 (\\N{HANGUL SYLLABLE SO}) missing from font(s) DejaVu Sans.\n",
-      "  plt.tight_layout()\n",
-      "/var/folders/9d/6nccn4756nq7c7030tvhwnj00000gn/T/ipykernel_85303/3641905308.py:33: UserWarning: Glyph 47049 (\\N{HANGUL SYLLABLE RYANG}) missing from font(s) DejaVu Sans.\n",
-      "  plt.tight_layout()\n",
-      "/var/folders/9d/6nccn4756nq7c7030tvhwnj00000gn/T/ipykernel_85303/3641905308.py:34: UserWarning: Glyph 44048 (\\N{HANGUL SYLLABLE GAM}) missing from font(s) DejaVu Sans.\n",
-      "  plt.savefig('permutation_importance.png', dpi=150)\n",
-      "/var/folders/9d/6nccn4756nq7c7030tvhwnj00000gn/T/ipykernel_85303/3641905308.py:34: UserWarning: Glyph 49548 (\\N{HANGUL SYLLABLE SO}) missing from font(s) DejaVu Sans.\n",
-      "  plt.savefig('permutation_importance.png', dpi=150)\n",
-      "/var/folders/9d/6nccn4756nq7c7030tvhwnj00000gn/T/ipykernel_85303/3641905308.py:34: UserWarning: Glyph 47049 (\\N{HANGUL SYLLABLE RYANG}) missing from font(s) DejaVu Sans.\n",
-      "  plt.savefig('permutation_importance.png', dpi=150)\n",
-      "/Users/yuje/aiProjects/llm_prj/.venv/lib/python3.10/site-packages/IPython/core/pylabtools.py:170: UserWarning: Glyph 44048 (\\N{HANGUL SYLLABLE GAM}) missing from font(s) DejaVu Sans.\n",
-      "  fig.canvas.print_figure(bytes_io, **kw)\n",
-      "/Users/yuje/aiProjects/llm_prj/.venv/lib/python3.10/site-packages/IPython/core/pylabtools.py:170: UserWarning: Glyph 49548 (\\N{HANGUL SYLLABLE SO}) missing from font(s) DejaVu Sans.\n",
-      "  fig.canvas.print_figure(bytes_io, **kw)\n",
-      "/Users/yuje/aiProjects/llm_prj/.venv/lib/python3.10/site-packages/IPython/core/pylabtools.py:170: UserWarning: Glyph 47049 (\\N{HANGUL SYLLABLE RYANG}) missing from font(s) DejaVu Sans.\n",
-      "  fig.canvas.print_figure(bytes_io, **kw)\n"
-     ]
-    },
-    {
-     "data": {
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",
-      "text/plain": [
-       "<Figure size 1000x600 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "from sklearn.inspection import permutation_importance\n",
-    "import matplotlib.pyplot as plt\n",
-    "import matplotlib.font_manager as fm\n",
-    "\n",
-    "# Permutation Importance 계산\n",
-    "perm_importance = permutation_importance(\n",
-    "    model, X_test, y_test,\n",
-    "    n_repeats=10,\n",
-    "    random_state=42,\n",
-    "    scoring='r2'\n",
-    ")\n",
-    "\n",
-    "# 결과 정리\n",
-    "perm_df = pd.DataFrame({\n",
-    "    'feature'    : X_user.columns,\n",
-    "    'importance' : perm_importance.importances_mean,\n",
-    "    'std'        : perm_importance.importances_std\n",
-    "}).sort_values('importance', ascending=False)\n",
-    "\n",
-    "print(\"=== Permutation Importance ===\")\n",
-    "print(perm_df)\n",
-    "\n",
-    "# 시각화\n",
-    "plt.figure(figsize=(10, 6))\n",
-    "plt.barh(\n",
-    "    perm_df['feature'][::-1],\n",
-    "    perm_df['importance'][::-1],\n",
-    "    xerr=perm_df['std'][::-1],\n",
-    "    color='steelblue'\n",
-    ")\n",
-    "plt.xlabel('Importance (R2 감소량)')\n",
-    "plt.title('Permutation Importance')\n",
-    "plt.tight_layout()\n",
-    "plt.savefig('permutation_importance.png', dpi=150)\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "e04186ff",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Linear Regression         | RMSE: 179.69 | MAE: 152.58 | R2: 0.2299\n",
-      "Ridge                     | RMSE: 179.69 | MAE: 152.58 | R2: 0.2299\n",
-      "Lasso                     | RMSE: 179.82 | MAE: 152.76 | R2: 0.2287\n",
-      "ElasticNet                | RMSE: 181.58 | MAE: 154.96 | R2: 0.2135\n",
-      "Random Forest             | RMSE: 126.58 | MAE: 96.14 | R2: 0.6178\n",
-      "Gradient Boosting         | RMSE: 140.61 | MAE: 113.22 | R2: 0.5284\n",
-      "XGBoost                   | RMSE: 130.61 | MAE: 100.03 | R2: 0.5931\n",
-      "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000623 seconds.\n",
-      "You can set `force_row_wise=true` to remove the overhead.\n",
-      "And if memory is not enough, you can set `force_col_wise=true`.\n",
-      "[LightGBM] [Info] Total Bins 1908\n",
-      "[LightGBM] [Info] Number of data points in the train set: 9600, number of used features: 12\n",
-      "[LightGBM] [Info] Start training from score 710.157187\n",
-      "LightGBM                  | RMSE: 126.39 | MAE: 96.84 | R2: 0.6189\n",
-      "\n",
-      "최종 선정: LightGBM (R2: 0.6189)\n"
-     ]
-    }
-   ],
-   "source": [
-    "from sklearn.linear_model import LinearRegression, Ridge, Lasso, ElasticNet\n",
-    "from sklearn.ensemble import RandomForestRegressor, GradientBoostingRegressor\n",
-    "from sklearn.svm import SVR\n",
-    "from sklearn.neighbors import KNeighborsRegressor\n",
-    "import xgboost as xgb\n",
-    "import lightgbm as lgb\n",
-    "import numpy as np\n",
-    "from sklearn.metrics import mean_squared_error, r2_score, mean_absolute_error\n",
-    "\n",
-    "models = {\n",
-    "    'Linear Regression'   : LinearRegression(),\n",
-    "    'Ridge'               : Ridge(),\n",
-    "    'Lasso'               : Lasso(),\n",
-    "    'ElasticNet'          : ElasticNet(),\n",
-    "    'Random Forest'       : RandomForestRegressor(n_estimators=100, random_state=42),\n",
-    "    'Gradient Boosting'   : GradientBoostingRegressor(random_state=42),\n",
-    "    'XGBoost'             : xgb.XGBRegressor(random_state=42),\n",
-    "    'LightGBM'            : lgb.LGBMRegressor(random_state=42),\n",
-    "}\n",
-    "\n",
-    "results = {}\n",
-    "\n",
-    "for name, model in models.items():\n",
-    "    model.fit(X_train, y_train)\n",
-    "    y_pred = model.predict(X_test)\n",
-    "\n",
-    "    rmse = np.sqrt(mean_squared_error(y_test, y_pred))\n",
-    "    mae  = mean_absolute_error(y_test, y_pred)\n",
-    "    r2   = r2_score(y_test, y_pred)\n",
-    "\n",
-    "    results[name] = {'model': model, 'rmse': rmse, 'mae': mae, 'r2': r2}\n",
-    "    print(f\"{name:25s} | RMSE: {rmse:.2f} | MAE: {mae:.2f} | R2: {r2:.4f}\")\n",
-    "\n",
-    "# 최종 선정\n",
-    "best_name = max(results, key=lambda x: results[x]['r2'])\n",
-    "print(f\"\\n최종 선정: {best_name} (R2: {results[best_name]['r2']:.4f})\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "5ea119e2",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import numpy as np\n",
-    "import tensorflow as tf\n",
-    "from sklearn.tree import DecisionTreeRegressor\n",
-    "from sklearn.ensemble import RandomForestRegressor\n",
-    "from sklearn.preprocessing import StandardScaler\n",
-    "from sklearn.metrics import mean_squared_error, r2_score, mean_absolute_error\n",
-    "import lightgbm as lgb\n",
-    "\n",
-    "results = {}\n",
-    "\n",
-    "# ========================\n",
-    "# 1. Decision Tree\n",
-    "# ========================\n",
-    "dt_model = DecisionTreeRegressor(\n",
-    "    max_depth=10,\n",
-    "    min_samples_split=20,\n",
-    "    random_state=42\n",
-    ")\n",
-    "dt_model.fit(X_train, y_train)\n",
-    "y_pred_dt = dt_model.predict(X_test)\n",
-    "\n",
-    "results['Decision Tree'] = {\n",
-    "    'model' : dt_model,\n",
-    "    'rmse'  : np.sqrt(mean_squared_error(y_test, y_pred_dt)),\n",
-    "    'mae'   : mean_absolute_error(y_test, y_pred_dt),\n",
-    "    'r2'    : r2_score(y_test, y_pred_dt)\n",
-    "}\n",
-    "\n",
-    "# ========================\n",
-    "# 2. Random Forest\n",
-    "# ========================\n",
-    "rf_model = RandomForestRegressor(\n",
-    "    n_estimators=200,\n",
-    "    max_depth=10,\n",
-    "    min_samples_split=20,\n",
-    "    random_state=42\n",
-    ")\n",
-    "rf_model.fit(X_train, y_train)\n",
-    "y_pred_rf = rf_model.predict(X_test)\n",
-    "\n",
-    "results['Random Forest'] = {\n",
-    "    'model' : rf_model,\n",
-    "    'rmse'  : np.sqrt(mean_squared_error(y_test, y_pred_rf)),\n",
-    "    'mae'   : mean_absolute_error(y_test, y_pred_rf),\n",
-    "    'r2'    : r2_score(y_test, y_pred_rf)\n",
-    "}\n",
-    "\n",
-    "# ========================\n",
-    "# 3. LightGBM\n",
-    "# ========================\n",
-    "lgb_model = lgb.LGBMRegressor(\n",
-    "    n_estimators=500,\n",
-    "    learning_rate=0.05,\n",
-    "    max_depth=6,\n",
-    "    num_leaves=31,\n",
-    "    random_state=42\n",
-    ")\n",
-    "lgb_model.fit(X_train, y_train)\n",
-    "y_pred_lgb = lgb_model.predict(X_test)\n",
-    "\n",
-    "results['LightGBM'] = {\n",
-    "    'model' : lgb_model,\n",
-    "    'rmse'  : np.sqrt(mean_squared_error(y_test, y_pred_lgb)),\n",
-    "    'mae'   : mean_absolute_error(y_test, y_pred_lgb),\n",
-    "    'r2'    : r2_score(y_test, y_pred_lgb)\n",
-    "}\n",
-    "\n",
-    "# ========================\n",
-    "# 4. TensorFlow DNN 회귀\n",
-    "# ========================\n",
-    "scaler = StandardScaler()\n",
-    "X_train_scaled = scaler.fit_transform(X_train)\n",
-    "X_test_scaled  = scaler.transform(X_test)\n",
-    "\n",
-    "# 수정된 DNN 모델\n",
-    "tf_model = tf.keras.Sequential([\n",
-    "    tf.keras.layers.Dense(256, activation='relu', input_shape=(X_train.shape[1],)),\n",
-    "    tf.keras.layers.BatchNormalization(),\n",
-    "    # 초기 단계에서는 Dropout을 제외하거나 낮게 설정\n",
-    "    \n",
-    "    tf.keras.layers.Dense(128, activation='relu'),\n",
-    "    tf.keras.layers.BatchNormalization(),\n",
-    "    \n",
-    "    tf.keras.layers.Dense(64, activation='relu'),\n",
-    "    tf.keras.layers.Dense(1) # 회귀를 위한 선형 출력\n",
-    "])\n",
-    "\n",
-    "tf_model.compile(\n",
-    "    optimizer=tf.keras.optimizers.Adam(learning_rate=0.005), # 학습률 상향\n",
-    "    loss='mse',\n",
-    "    metrics=['mae']\n",
-    ")\n",
-    "\n",
-    "# 조기 종료 설정 (교재 반영)\n",
-    "early_stop = tf.keras.callbacks.EarlyStopping(\n",
-    "    monitor='val_loss', \n",
-    "    patience=20, # 충분히 학습하도록 인내심 상향\n",
-    "    restore_best_weights=True\n",
-    ")\n",
-    "\n",
-    "tf_model.fit(\n",
-    "    X_train_scaled, y_train,\n",
-    "    epochs=500,\n",
-    "    batch_size=64, # 배치를 작게 하여 더 자주 가중치 업데이트 [cite: 3079]\n",
-    "    validation_split=0.2,\n",
-    "    callbacks=[early_stop],\n",
-    "    verbose=0\n",
-    ")\n",
-    "y_pred_tf = tf_model.predict(X_test_scaled).flatten()\n",
-    "\n",
-    "results['TensorFlow DNN'] = {\n",
-    "    'model' : tf_model,\n",
-    "    'rmse'  : np.sqrt(mean_squared_error(y_test, y_pred_tf)),\n",
-    "    'mae'   : mean_absolute_error(y_test, y_pred_tf),\n",
-    "    'r2'    : r2_score(y_test, y_pred_tf)\n",
-    "}\n",
-    "\n",
-    "# ========================\n",
-    "# 최종 결과 비교\n",
-    "# ========================\n",
-    "print(f\"\\n{'='*65}\")\n",
-    "print(f\"{'모델':<20} | {'RMSE':>8} | {'MAE':>8} | {'R2':>8}\")\n",
-    "print(f\"{'='*65}\")\n",
-    "\n",
-    "for name, res in sorted(results.items(), key=lambda x: x[1]['r2'], reverse=True):\n",
-    "    print(f\"{name:<20} | {res['rmse']:>8.2f} | {res['mae']:>8.2f} | {res['r2']:>8.4f}\")\n",
-    "\n",
-    "best_name = max(results, key=lambda x: results[x]['r2'])\n",
-    "print(f\"\\n최종 선정: {best_name} (R2: {results[best_name]['r2']:.4f})\")"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": ".venv",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.6"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}

evaluation/model_quantative_eval.ipynb

@@ -1,142 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "520dfcbe",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/yuje/aiProjects/llm_prj/.venv/lib/python3.10/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
-      "  from .autonotebook import tqdm as notebook_tqdm\n",
-      "Using the latest cached version of the dataset since KRX-Data/Won-Instruct couldn't be found on the Hugging Face Hub\n",
-      "Found the latest cached dataset configuration 'default' at /Users/yuje/.cache/huggingface/datasets/KRX-Data___won-instruct/default/0.0.0/1a3648dbef97a48736111d646fa29c4d562f8fd5 (last modified on Sun Apr  5 16:29:07 2026).\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "데이터셋 로드 중...\n",
-      "\n",
-      "🚀 qwen2.5:4b 모델 시험 치는 중...\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "100%|██████████| 50/50 [00:00<00:00, 169.68it/s]\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "🚀 exaone3.5:2.4b 모델 시험 치는 중...\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "100%|██████████| 50/50 [48:27<00:00, 58.15s/it] \n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "🚀 exaone3.5:7.8b 모델 시험 치는 중...\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "  4%|▍         | 2/50 [21:00<7:21:57, 552.45s/it]  "
-     ]
-    }
-   ],
-   "source": [
-    "import ollama\n",
-    "from datasets import load_dataset\n",
-    "from rouge import Rouge # pip install rouge-score\n",
-    "import pandas as pd\n",
-    "from tqdm import tqdm\n",
-    "\n",
-    "# 1. 정석대로 데이터셋 로드\n",
-    "print(\"데이터셋 로드 중...\")\n",
-    "ds = load_dataset(\"KRX-Data/Won-Instruct\", split=\"train\")\n",
-    "\n",
-    "# 2. 평가 모델 리스트 (Ollama)\n",
-    "models = [\"exaone3.5:2.4b\", \n",
-    "        #   \"exaone3.5:7.8b\",\n",
-    "          \"llama3.2:3b\", \n",
-    "        #   \"llama3.1:8b\", \n",
-    "          \"gemma4:e4b\"] # 일단 이거 사용\n",
-    "\n",
-    "# 3. 벤치마크 실행 (샘플 50개)\n",
-    "test_samples = ds.select(range(50)) \n",
-    "rouge = Rouge()\n",
-    "results = []\n",
-    "\n",
-    "for model_name in models:\n",
-    "    print(f\"\\n🚀 {model_name} 모델 시험 치는 중...\")\n",
-    "    total_score = 0\n",
-    "    \n",
-    "    for item in tqdm(test_samples):\n",
-    "        # Won-Instruct의 필드명 사용\n",
-    "        prompt = item['prompt']\n",
-    "        reference = item['original_response'] # 이게 '정답지' 역할을 합니다.\n",
-    "        \n",
-    "        try:\n",
-    "            response = ollama.chat(\n",
-    "                model=model_name, \n",
-    "                messages=[{'role': 'user', 'content': prompt}],\n",
-    "                options={'temperature': 0} # 벤치마크는 결과가 고정되어야 하므로 0\n",
-    "            )\n",
-    "            prediction = response['message']['content']\n",
-    "            \n",
-    "            # 정량 지표 계산 (ROUGE-L)\n",
-    "            if prediction.strip():\n",
-    "                scores = rouge.get_scores(prediction, reference)\n",
-    "                total_score += scores[0]['rouge-l']['f']\n",
-    "        except:\n",
-    "            continue\n",
-    "\n",
-    "    avg_acc = (total_score / len(test_samples)) * 100\n",
-    "    results.append({\"Model\": model_name, \"Benchmark_Score\": f\"{avg_acc:.2f}\"})\n",
-    "\n",
-    "# 4. 결과 출력\n",
-    "print(\"\\n\" + pd.DataFrame(results).to_markdown(index=False))"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": ".venv",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.6"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}

llm/chabot_chain.py

@@ -1,58 +0,0 @@
-import os
-import streamlit as st
-from langchain_huggingface import HuggingFaceEmbeddings
-from langchain_community.vectorstores import FAISS
-from langchain_classic.chains import ConversationalRetrievalChain
-from langchain_ollama import OllamaLLM
-from vectorization import make_vectorization
-
-st.set_page_config(page_title="Page2: RAG chatbot")
-st.title("Langchain + Gemma4:e4b")
-
-@st.cache_resource
-def load_models():
-    embedding_model = HuggingFaceEmbeddings(model_name="intfloat/multilingual-e5-small")
-    llm = OllamaLLM(model="gemma4:e4b")
-    return embedding_model, llm
-
-@st.cache_resource
-def get_vectorstore(_embedding_model):
-    if os.path.exists("./faiss/index.faiss"):
-        vectorstore = FAISS.load_local("./faiss", _embedding_model, allow_dangerous_deserialization=True)
-    else:
-        vectorstore = make_vectorization()
-    retriever = vectorstore.as_retriever(search_kwargs={"k": 3})
-    return vectorstore, retriever
-
-embedding_model, llm = load_models()
-vectorstore, retriever = get_vectorstore(embedding_model)
-qa_chain = ConversationalRetrievalChain.from_llm(llm, retriever)
-
-if "chat_history" not in st.session_state:
-    st.session_state.chat_history = []
-if "messages" not in st.session_state:
-    st.session_state.messages = []
-
-for msg in st.session_state.messages:
-    with st.chat_message(msg["role"]):
-        st.markdown(msg["content"])
-
-if user_question := st.chat_input("Ask your question :)"):
-
-    with st.chat_message("user"):
-        st.markdown(user_question)
-    st.session_state.messages.append({"role": "user", "content": user_question})
-
-    with st.chat_message("assistant"):
-        with st.spinner("Thinking..."):
-            result = qa_chain.invoke({"question": user_question, "chat_history": st.session_state.chat_history})
-            answer = result["answer"]
-            st.markdown(answer)
-
-    st.session_state.messages.append({"role": "assistant", "content": answer})
-    st.session_state.chat_history.append((user_question, answer))
-
-if st.button("Clear Conversation"):
-    st.session_state.chat_history = []
-    st.session_state.messages = []
-    st.rerun()

llm/prompt.py

@@ -0,0 +1,25 @@
+from langchain_core.prompts import PromptTemplate
+
+QA_TEMPLATE = """당신은 KCB 신용평가 전문가이자 따뜻하고 친절한 신용 상담사입니다.
+기계적인 말투를 피하고, 실제 사람과 대화하듯 자연스럽게 상담해 주세요.
+
+[참고 지식]
+{context}
+
+[고객 상황 및 질문]
+{query}
+
+[상담 가이드]
+1. 어조(Tone): 고객의 고민에 공감하며, "~요", "~습니다"를 적절히 섞어 따뜻하고 부드러운 대화체로 답변하세요.
+2. 답변 구조:
+   - 인사 및 공감 (예: "안녕하세요! 현재 점수에 대해 고민이 많으셨군요. 제가 차근차근 설명해 드릴게요.")
+   - 고객의 현재 지표를 바탕으로 한 구체적인 칭찬 또는 원인 분석 (예: "고객님의 경우 연체 건수가 아쉬운 부분입니다.")
+   - [참고 지식]을 활용한 실질적인 점수 상승 팁 제시
+   - 마무리 인사 및 격려
+3. 주의사항:
+   - "사용자 데이터와 질문을 종합적으로 고려하여" 같은 딱딱하고 기계적인 표현은 절대 쓰지 마세요.
+   - 고객이 제공한 데이터 밖의 내용을 지어내지 마세요.
+   - 불필요한 시스템 태그("[사용자 질문]", "답변:")를 출력하지 마세요. 바로 사람에게 말하듯 시작하세요.
+"""
+
+QA_PROMPT = PromptTemplate(template=QA_TEMPLATE, input_variables=["context", "query"])

llm/vectorization.py

@@ -2,24 +2,22 @@ from langchain_community.document_loaders import PyPDFLoader
 from langchain_community.vectorstores import FAISS
 from langchain_huggingface import HuggingFaceEmbeddings
 from langchain_text_splitters import RecursiveCharacterTextSplitter
+from config import EMBEDDING_MODEL, FAISS_PATH, PDF_SOURCE
 
 def make_vectorization():
-  embedding_model = HuggingFaceEmbeddings(model_name="intfloat/multilingual-e5-large-instruct")
+    embedding_model = HuggingFaceEmbeddings(model_name=EMBEDDING_MODEL)
 
-  loader = PyPDFLoader("../sources/KCB_Credit_Evaluation_System.pdf")
-  documents = loader.load()
-
-  text_splitter = RecursiveCharacterTextSplitter(
-    chunk_size = 1000,
-    chunk_overlap = 100,
-    length_function = len,
-    is_separator_regex=False
-  )
-  documents = text_splitter.split_documents(documents)
-  vectorstore = FAISS.from_documents(documents, embedding_model)
-  vectorstore.save_local("../faiss")
-  
-  return vectorstore
-  
+    loader = PyPDFLoader(PDF_SOURCE)
+    documents = loader.load()
 
+    text_splitter = RecursiveCharacterTextSplitter(
+        chunk_size=1000,
+        chunk_overlap=100,
+        length_function=len,
+        is_separator_regex=False
+    )
+    documents = text_splitter.split_documents(documents)
+    vectorstore = FAISS.from_documents(documents, embedding_model)
+    vectorstore.save_local(FAISS_PATH)
 
+    return vectorstore

evaluation/confusion_matrix.png → models/preprocessor.pkl

@@ -1,3 +1,3 @@
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-oid sha256:3368809aae2f19d29534d4bf2ed6794427f6d194a29f084ac97f96a8ef215202
-size 27091
+oid sha256:d0cafaa6a9a854d12ff332ce454529db31d4f8e6982b91afe81826209de3e9b4
+size 3640

evaluation/permutation_importance.png → models/telecom_cb_model.keras

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:1fc38a749d1afeebb0bfb9ce55c6d3ce75d8af3d551ea74ce833d5da1c6d524b
-size 44330
+oid sha256:6535dec164d10666590895d9889acedae334318607a471c192238b6877d847ce
+size 548533

predictors/cb_predictors.ipynb

@@ -1,101 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "9d112a34",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/yuje/aiProjects/llm_prj/.venv/lib/python3.10/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
-      "  from .autonotebook import tqdm as notebook_tqdm\n",
-      "100%|██████████| 23/23 [00:01<00:00, 18.58it/s]1<00:00,  9.42it/s, Describe variable: PYE_SC0000000]   \n",
-      "Summarize dataset: 100%|██████████| 153/153 [00:19<00:00,  7.95it/s, Completed]                           \n",
-      "Generate report structure: 100%|██████████| 1/1 [00:02<00:00,  2.59s/it]\n",
-      "Render HTML: 100%|██████████| 1/1 [00:00<00:00,  1.64it/s]\n",
-      "Export report to file: 100%|██████████| 1/1 [00:00<00:00, 118.82it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "📊 Profiling Report Created\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "import pandas as pd\n",
-    "\n",
-    "df=  pd.read_csv(\"../dataset/telecom_cb.csv\")\n",
-    "\n",
-    "# TRIM csv because the number of column is 160... can't open in PC\n",
-    "keep_columns = [\n",
-    "'SEX',\n",
-    "'AGE',\n",
-    "'JB_TP',\n",
-    "'LIF_STG',\n",
-    "'HB_1ST',\n",
-    "'BUY_LUX_YN',\n",
-    "'CAR_YN',\n",
-    "'VIP_CARD_YN',\n",
-    "'TRAVEL_OS',\n",
-    "'TRAVEL_JJ',\n",
-    "'GOLF_INDOOR',\n",
-    "'PREFER_SPORTS',\n",
-    "'FST_CAR_ELPS',\n",
-    "'TOT_ASST',\n",
-    "'PYE_FAM_CNT',\n",
-    "'OWN_HOUS_CNT',\n",
-    "'OWN_LIV_YN',\n",
-    "'FAM_OWN_HOUS_CNT',\n",
-    "'FAM_OWN_LIV_YN',\n",
-    "'HOUS_LN_BAL',\n",
-    "'CRDT_LN_BAL',\n",
-    "'CD_USE_AMT',\n",
-    "'PYE_SC0000000',\n",
-    "]\n",
-    "df = df[keep_columns]\n",
-    "df.to_csv('../dataset/trimmed_telecom_cb.csv', index=False)\n",
-    "\n",
-    "from ydata_profiling import ProfileReport\n",
-    "profile = ProfileReport(df, title=\"Trimmed CB Dataset Profiling Report\")\n",
-    "\n",
-    "outputfile = '../dataset/report.html'\n",
-    "profile.to_file(outputfile)\n",
-    "print(\"📊 Profiling Report Created\")\n"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": ".venv",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.6"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}

predictors/score_predict.ipynb

@@ -0,0 +1,167 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "executionInfo": {
+     "elapsed": 20954,
+     "status": "ok",
+     "timestamp": 1776342392847,
+     "user": {
+      "displayName": "탁유제",
+      "userId": "06706106398592029119"
+     },
+     "user_tz": -540
+    },
+    "id": "ylszil82Df6M",
+    "outputId": "5e0b6c01-08ed-4d3e-c15d-8810a89f42ac"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Mounted at /content/drive\n"
+     ]
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import seaborn as sns\n",
+    "import joblib\n",
+    "import matplotlib.pyplot as plt\n",
+    "import matplotlib.font_manager as fm\n",
+    "import scipy.stats as stats\n",
+    "import tensorflow as tf\n",
+    "from tensorflow.keras import layers, Sequential, callbacks\n",
+    "from sklearn.compose import ColumnTransformer\n",
+    "from sklearn.preprocessing import StandardScaler, MinMaxScaler\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn.metrics import r2_score, mean_absolute_error\n",
+    "import os\n",
+    "import warnings\n",
+    "\n",
+    "warnings.filterwarnings('ignore')\n",
+    "\n",
+    "# !apt-get -qq install fonts-nanum > /dev/null\n",
+    "font_path = '/usr/share/fonts/truetype/nanum/NanumBarunGothic.ttf'\n",
+    "fe = fm.FontEntry(fname=font_path, name='NanumBarunGothic')\n",
+    "fm.fontManager.ttflist.insert(0, fe)\n",
+    "plt.rc('font', family='NanumBarunGothic')\n",
+    "plt.rcParams['axes.unicode_minus'] = False\n",
+    "\n",
+    "name_mapping = {\n",
+    "    'C1Z001386': '1년내카드총이용금액', 'C1M210000': '신용카드건수',\n",
+    "    'C18210000': '체크카드건수', 'C1L120001': '카드총한도금액',\n",
+    "    'C1L120004': '신용카드개설후경과일수', 'L10210000': '대출건수',\n",
+    "    'L90210100': '은행업종대출건수', 'L90210200': '카드업종대출건수',\n",
+    "    'L10210B00': '보험업종대출건수', 'L10216000': '신용대출건수',\n",
+    "    'L10217000': '담보대출건수', 'D10110000': '연체건수',\n",
+    "    'D10133000': '총연체상환금액', 'PERF1': '1년내90일이상연체여부',\n",
+    "    'SCORE': '신용평가점수'\n",
+    "}\n",
+    "\n",
+    "path = \"/content/drive/MyDrive/AI-Web/LLMPRJ/CB_predict/dataset/telecom_cb.csv\"\n",
+    "use_cols = list(name_mapping.keys())\n",
+    "df = pd.read_csv(path, usecols=use_cols, nrows=300000)\n",
+    "\n",
+    "df['PERF1'] = df['PERF1'].astype(int)\n",
+    "df.replace([np.inf, -np.inf], np.nan, inplace=True)\n",
+    "df.fillna(0, inplace=True)\n",
+    "\n",
+    "log_cols = ['C1Z001386', 'C1L120004', 'D10110000', 'D10133000', 'L90210200',\n",
+    "            'L10216000', 'L10210B00', 'L10217000', 'L90210100', 'L10210000']\n",
+    "df[log_cols] = np.log1p(df[log_cols].clip(lower=0))\n",
+    "\n",
+    "X = df.drop(columns=['SCORE'])\n",
+    "y = df['SCORE']\n",
+    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
+    "\n",
+    "mm_cols = ['C1M210000', 'C18210000']\n",
+    "std_cols = [c for c in X_train.columns if c not in log_cols + mm_cols]\n",
+    "\n",
+    "preprocessor = ColumnTransformer([\n",
+    "    ('log_std', StandardScaler(), log_cols),\n",
+    "    ('minmax', MinMaxScaler(), mm_cols),\n",
+    "    ('std', StandardScaler(), std_cols)\n",
+    "])\n",
+    "\n",
+    "X_train_scaled = preprocessor.fit_transform(X_train)\n",
+    "X_test_scaled = preprocessor.transform(X_test)\n",
+    "\n",
+    "best_lr, best_dr, best_epochs = 0.01, 0.3, 100\n",
+    "tf.keras.backend.clear_session()\n",
+    "\n",
+    "model = Sequential([\n",
+    "    layers.Dense(256, activation='relu', input_shape=(X_train_scaled.shape[1],)),\n",
+    "    layers.BatchNormalization(),\n",
+    "    layers.Dropout(best_dr),\n",
+    "    layers.Dense(128, activation='relu'),\n",
+    "    layers.BatchNormalization(),\n",
+    "    layers.Dropout(best_dr),\n",
+    "    layers.Dense(64, activation='relu'),\n",
+    "    layers.BatchNormalization(),\n",
+    "    layers.Dense(32, activation='relu'),\n",
+    "    layers.Dense(1)\n",
+    "])\n",
+    "\n",
+    "model.compile(optimizer=tf.keras.optimizers.AdamW(learning_rate=best_lr), loss='mse', metrics=['mae'])\n",
+    "\n",
+    "early_stop = callbacks.EarlyStopping(monitor='val_loss', patience=15, restore_best_weights=True)\n",
+    "history = model.fit(X_train_scaled, y_train, epochs=best_epochs, batch_size=128,\n",
+    "                    validation_split=0.2, callbacks=[early_stop], verbose=1)\n",
+    "\n",
+    "plt.figure(figsize=(12, 6))\n",
+    "plt.plot(history.history['loss'], label='Train Loss', color='orange')\n",
+    "plt.plot(history.history['val_loss'], label='Validation Loss', color='green')\n",
+    "plt.title('Training and Validation Loss', fontsize=14)\n",
+    "plt.xlabel('Epochs')\n",
+    "plt.ylabel('Loss')\n",
+    "plt.legend(loc='upper right')\n",
+    "plt.show()\n",
+    "\n",
+    "y_pred = model.predict(X_test_scaled).flatten()\n",
+    "r2 = r2_score(y_test, y_pred)\n",
+    "mae = mean_absolute_error(y_test, y_pred)\n",
+    "\n",
+    "plt.figure(figsize=(15, 6))\n",
+    "sample_range = 440\n",
+    "plt.plot(y_test.values[:sample_range], label='Actual Prices', color='blue', linewidth=1.5)\n",
+    "plt.plot(y_pred[:sample_range], label='Predicted Prices', color='red', linewidth=1.5)\n",
+    "\n",
+    "plt.title(f'KCB Score Prediction\\nR2 Score: {r2:.4f} | MAE: {mae:.2f}', fontsize=14)\n",
+    "plt.xlabel('Sample Datas')\n",
+    "plt.ylabel('KCB Score')\n",
+    "plt.legend(loc='upper left')\n",
+    "plt.show()\n",
+    "\n",
+    "save_dir = \"/content/drive/MyDrive/AI-Web/LLMPRJ/CB_predict/model/\"\n",
+    "os.makedirs(save_dir, exist_ok=True)\n",
+    "model.save(save_dir + \"telecom_cb_model.keras\")\n",
+    "joblib.dump(preprocessor, save_dir + \"preprocessor.pkl\")\n",
+    "print(f\"✅ 모델 및 전처리기 저장 완료: {save_dir}\")\n",
+    "print(f\"📊 최종 성능 - R2: {r2:.4f}, MAE: {mae:.2f}\")"
+   ]
+  }
+ ],
+ "metadata": {
+  "colab": {
+   "authorship_tag": "ABX9TyNVOknhNFA2EiqGiXNtqGAY",
+   "provenance": []
+  },
+  "kernelspec": {
+   "display_name": "Python 3",
+   "name": "python3"
+  },
+  "language_info": {
+   "name": "python"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}

predictors/score_prediction.py

@@ -0,0 +1,60 @@
+import os
+import joblib
+import pandas as pd
+import numpy as np
+import tensorflow as tf
+from typing import Dict, Any
+
+class CreditPredictor:
+    def __init__(self):
+        self.preprocessor_path = "models/preprocessor.pkl"
+        self.model_path = "models/telecom_cb_model.keras"
+        self.preprocessor = None
+        self.model = None
+        self.load_resources()
+
+    def load_resources(self):
+        try:
+            if os.path.exists(self.preprocessor_path):
+                self.preprocessor = joblib.load(self.preprocessor_path)
+                print("Preprocessor loaded successfully.")
+            if os.path.exists(self.model_path):
+                self.model = tf.keras.models.load_model(self.model_path, compile=False)
+                print("Model loaded successfully.")
+        except Exception as e:
+            print(f"Error loading model/preprocessor: {e}")
+
+    def predict(self, features_dict: Dict[str, Any]) -> float:
+        if self.model is None or self.preprocessor is None:
+            return np.nan
+        
+        try:
+            ALL_FEATURES = [
+                'C1Z001386', 'C1M210000', 'C18210000', 'C1L120001', 'C1L120004', 
+                'L10210000', 'L90210100', 'L90210200', 'L10210B00', 'L10216000', 
+                'L10217000', 'D10110000', 'D10133000', 'PERF1'
+            ]
+            
+            full_input = []
+            for col in ALL_FEATURES:
+                val = features_dict.get(col, 0.0)
+                full_input.append(float(val))
+            
+            df = pd.DataFrame([full_input], columns=ALL_FEATURES)
+            
+            log1p_cols = ['C1Z001386', 'C1L120004', 'D10110000', 'D10133000', 'L90210200',
+                          'L10216000', 'L10210B00', 'L10217000', 'L90210100', 'L10210000']
+            
+            valid_log1p = [c for c in log1p_cols if c in df.columns]
+            df[valid_log1p] = np.log1p(df[valid_log1p].clip(lower=0))
+            
+            scaled_data = self.preprocessor.transform(df)
+            score = self.model.predict(scaled_data, verbose=0)[0][0]
+            
+            return float(score)
+        except Exception as e:
+            import traceback
+            traceback.print_exc()
+            return np.nan
+
+predictor = CreditPredictor()