{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "3d15dbb2",
   "metadata": {},
   "source": [
    "# 해상운임 머신러닝 10단계 실습 — 교육생용\n",
    "\n",
    "## Goal\n",
    "\n",
    "2025년 교육용 가상 선적 데이터로 다음 능력을 익힙니다.\n",
    "\n",
    "1. 데이터 품질을 진단하고 정제 규칙을 설명한다.\n",
    "2. 견적 시점에서 사용할 수 없는 데이터 누출 변수를 식별한다.\n",
    "3. 같은 날짜가 겹치지 않는 시간순 70/15/15 분할을 수행한다.\n",
    "4. 업무 기준선, Ridge, Random Forest를 MAPE·RMSE·R² 등으로 비교한다.\n",
    "5. 유가·혼잡·홍해 우회·계약 전환 시나리오를 조건부 추정한다.\n",
    "6. 지연 및 통관보류 분류에서 재현율과 오분류 비용을 해석한다.\n",
    "7. B/L OCR 추출값을 원문과 대조하고 HS코드는 후보로만 다룬다.\n",
    "8. 심층 신경망 구조와 epoch별 학습 과정을 시각적으로 설명한다.\n",
    "\n",
    "모든 값은 교육용 가상값입니다. 실제 견적, 계약, 통관 또는 투자 판단에 사용하지 마세요."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4ee4c9ff",
   "metadata": {},
   "source": [
    "## Setup\n",
    "\n",
    "Colab에서는 이 노트북을 연 뒤 첫 코드 셀을 실행하고, 데이터 폴더를 찾지 못할 때 배포 ZIP을 업로드하세요.\n",
    "로컬에서는 노트북 파일이 있는 Colab_10단계_실습 폴더에서 실행하면 됩니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "d8d3825a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-08-10T21:28:52.920485Z",
     "iopub.status.busy": "2026-08-10T21:28:52.920345Z",
     "iopub.status.idle": "2026-08-10T21:28:55.071733Z",
     "shell.execute_reply": "2026-08-10T21:28:55.071186Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Python: 3.13.9\n",
      "pandas: 2.3.3 / scikit-learn: 1.7.2\n",
      "DATA_DIR: G:\\내 드라이브\\한국생산성본부_AI강의_26년도\\해상운임_ML_실습_1년\\Colab_10단계_실습\\data\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>단계</th>\n",
       "      <th>파일명</th>\n",
       "      <th>행수</th>\n",
       "      <th>열수</th>\n",
       "      <th>학습주제</th>\n",
       "      <th>목표변수</th>\n",
       "      <th>난이도</th>\n",
       "      <th>데이터성격</th>\n",
       "      <th>난수시드</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>01_ocean_freight_raw.csv</td>\n",
       "      <td>1669</td>\n",
       "      <td>23</td>\n",
       "      <td>문제 정의·스키마·기초 프로파일링</td>\n",
       "      <td>해상운임_USD</td>\n",
       "      <td>초급</td>\n",
       "      <td>교육용 가상 데이터</td>\n",
       "      <td>20260722</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>02_freight_quality_challenge.csv</td>\n",
       "      <td>1681</td>\n",
       "      <td>23</td>\n",
       "      <td>결측·중복·논리오류·이상치 탐지</td>\n",
       "      <td>해상운임_USD</td>\n",
       "      <td>초급</td>\n",
       "      <td>교육용 가상 데이터</td>\n",
       "      <td>20260722</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>03_freight_eda_features.csv</td>\n",
       "      <td>1669</td>\n",
       "      <td>29</td>\n",
       "      <td>EDA·그룹 비교·날짜/업무 파생변수</td>\n",
       "      <td>해상운임_USD</td>\n",
       "      <td>초급</td>\n",
       "      <td>교육용 가상 데이터</td>\n",
       "      <td>20260722</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>04_freight_leakage_trap.csv</td>\n",
       "      <td>1669</td>\n",
       "      <td>27</td>\n",
       "      <td>예측시점 기준 데이터 누출 식별</td>\n",
       "      <td>해상운임_USD</td>\n",
       "      <td>중급</td>\n",
       "      <td>교육용 가상 데이터</td>\n",
       "      <td>20260722</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>05_freight_time_split_drift.csv</td>\n",
       "      <td>1669</td>\n",
       "      <td>25</td>\n",
       "      <td>무작위 분할과 시간순 분할·드리프트</td>\n",
       "      <td>해상운임_USD</td>\n",
       "      <td>중급</td>\n",
       "      <td>교육용 가상 데이터</td>\n",
       "      <td>20260722</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>06_freight_baseline.csv</td>\n",
       "      <td>1669</td>\n",
       "      <td>6</td>\n",
       "      <td>그룹 중앙값 기준선·미등장 조합 fallback</td>\n",
       "      <td>해상운임_USD</td>\n",
       "      <td>중급</td>\n",
       "      <td>교육용 가상 데이터</td>\n",
       "      <td>20260722</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>07_freight_regression.csv</td>\n",
       "      <td>1669</td>\n",
       "      <td>26</td>\n",
       "      <td>Ridge·Random Forest 모델 비교</td>\n",
       "      <td>해상운임_USD</td>\n",
       "      <td>중급</td>\n",
       "      <td>교육용 가상 데이터</td>\n",
       "      <td>20260722</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>08_freight_scenarios.csv</td>\n",
       "      <td>54</td>\n",
       "      <td>23</td>\n",
       "      <td>5개 업무 시나리오 조건부 추정</td>\n",
       "      <td>예측운임_USD</td>\n",
       "      <td>중급</td>\n",
       "      <td>교육용 가상 데이터</td>\n",
       "      <td>20260722</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>09_shipment_delay_classification.csv</td>\n",
       "      <td>1669</td>\n",
       "      <td>21</td>\n",
       "      <td>3일 이상 지연 분류와 오분류 비용</td>\n",
       "      <td>지연3일이상_여부</td>\n",
       "      <td>중급</td>\n",
       "      <td>교육용 가상 데이터</td>\n",
       "      <td>20260722</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>10_customs_hold_bl_ocr.csv</td>\n",
       "      <td>1669</td>\n",
       "      <td>36</td>\n",
       "      <td>B/L OCR 검증·통관보류 불균형 분류</td>\n",
       "      <td>통관보류여부</td>\n",
       "      <td>고급</td>\n",
       "      <td>교육용 가상 데이터</td>\n",
       "      <td>20260722</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   단계                                   파일명    행수  열수  \\\n",
       "0   1              01_ocean_freight_raw.csv  1669  23   \n",
       "1   2      02_freight_quality_challenge.csv  1681  23   \n",
       "2   3           03_freight_eda_features.csv  1669  29   \n",
       "3   4           04_freight_leakage_trap.csv  1669  27   \n",
       "4   5       05_freight_time_split_drift.csv  1669  25   \n",
       "5   6               06_freight_baseline.csv  1669   6   \n",
       "6   7             07_freight_regression.csv  1669  26   \n",
       "7   8              08_freight_scenarios.csv    54  23   \n",
       "8   9  09_shipment_delay_classification.csv  1669  21   \n",
       "9  10            10_customs_hold_bl_ocr.csv  1669  36   \n",
       "\n",
       "                         학습주제       목표변수 난이도       데이터성격      난수시드  \n",
       "0          문제 정의·스키마·기초 프로파일링   해상운임_USD  초급  교육용 가상 데이터  20260722  \n",
       "1           결측·중복·논리오류·이상치 탐지   해상운임_USD  초급  교육용 가상 데이터  20260722  \n",
       "2        EDA·그룹 비교·날짜/업무 파생변수   해상운임_USD  초급  교육용 가상 데이터  20260722  \n",
       "3           예측시점 기준 데이터 누출 식별   해상운임_USD  중급  교육용 가상 데이터  20260722  \n",
       "4         무작위 분할과 시간순 분할·드리프트   해상운임_USD  중급  교육용 가상 데이터  20260722  \n",
       "5  그룹 중앙값 기준선·미등장 조합 fallback   해상운임_USD  중급  교육용 가상 데이터  20260722  \n",
       "6   Ridge·Random Forest 모델 비교   해상운임_USD  중급  교육용 가상 데이터  20260722  \n",
       "7           5개 업무 시나리오 조건부 추정   예측운임_USD  중급  교육용 가상 데이터  20260722  \n",
       "8         3일 이상 지연 분류와 오분류 비용  지연3일이상_여부  중급  교육용 가상 데이터  20260722  \n",
       "9      B/L OCR 검증·통관보류 불균형 분류     통관보류여부  고급  교육용 가상 데이터  20260722  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from pathlib import Path\n",
    "import json, math, os, sys, warnings, zipfile\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import sklearn\n",
    "from IPython.display import display\n",
    "from sklearn.base import clone\n",
    "from sklearn.compose import ColumnTransformer, TransformedTargetRegressor\n",
    "from sklearn.ensemble import RandomForestRegressor, RandomForestClassifier\n",
    "from sklearn.impute import SimpleImputer\n",
    "from sklearn.linear_model import Ridge, LinearRegression, LogisticRegression\n",
    "from sklearn.metrics import (\n",
    "    accuracy_score, average_precision_score, confusion_matrix, f1_score,\n",
    "    mean_absolute_error, mean_squared_error, precision_score, r2_score, recall_score,\n",
    ")\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.neural_network import MLPRegressor\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.preprocessing import OneHotEncoder, StandardScaler\n",
    "\n",
    "SEED = 20260722\n",
    "np.random.seed(SEED)\n",
    "warnings.filterwarnings(\"ignore\", message=\"Glyph .* missing from font\")\n",
    "pd.set_option(\"display.max_columns\", 50)\n",
    "pd.set_option(\"display.float_format\", lambda value: f\"{value:,.3f}\")\n",
    "\n",
    "def find_data_dir():\n",
    "    candidates = [\n",
    "        Path(\"data\"),\n",
    "        Path(\"Colab_10단계_실습/data\"),\n",
    "        Path(\"/content/Colab_10단계_실습/data\"),\n",
    "    ]\n",
    "    for candidate in candidates:\n",
    "        if (candidate / \"dataset_manifest.csv\").exists():\n",
    "            return candidate\n",
    "    if \"google.colab\" in sys.modules:\n",
    "        from google.colab import files\n",
    "        print(\"배포 ZIP 파일을 업로드하세요.\")\n",
    "        uploaded = files.upload()\n",
    "        for name, payload in uploaded.items():\n",
    "            path = Path(name)\n",
    "            path.write_bytes(payload)\n",
    "            if path.suffix.lower() == \".zip\":\n",
    "                with zipfile.ZipFile(path) as archive:\n",
    "                    archive.extractall(Path(\"/content/Colab_10단계_실습\"))\n",
    "        matches = list(Path(\"/content\").rglob(\"dataset_manifest.csv\"))\n",
    "        if matches:\n",
    "            return matches[0].parent\n",
    "    raise FileNotFoundError(\"data/dataset_manifest.csv를 찾을 수 없습니다. README_교육생용.md를 확인하세요.\")\n",
    "\n",
    "DATA_DIR = find_data_dir()\n",
    "OUTPUT_DIR = Path(\"outputs/day2-freight-automl\")\n",
    "OUTPUT_DIR.mkdir(parents=True, exist_ok=True)\n",
    "manifest = pd.read_csv(DATA_DIR / \"dataset_manifest.csv\", encoding=\"utf-8-sig\")\n",
    "\n",
    "print(\"Python:\", sys.version.split()[0])\n",
    "print(\"pandas:\", pd.__version__, \"/ scikit-learn:\", sklearn.__version__)\n",
    "print(\"DATA_DIR:\", DATA_DIR.resolve())\n",
    "display(manifest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "3e83a311",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-08-10T21:28:55.073659Z",
     "iopub.status.busy": "2026-08-10T21:28:55.073479Z",
     "iopub.status.idle": "2026-08-10T21:28:55.081871Z",
     "shell.execute_reply": "2026-08-10T21:28:55.081369Z"
    }
   },
   "outputs": [],
   "source": [
    "def load_lab(filename, dates=()):\n",
    "    return pd.read_csv(\n",
    "        DATA_DIR / filename,\n",
    "        encoding=\"utf-8-sig\",\n",
    "        parse_dates=list(dates),\n",
    "    )\n",
    "\n",
    "def split_by_unique_date(frame, date_column=\"선적일\"):\n",
    "    ordered = frame.sort_values([date_column, \"선적ID\"]).reset_index(drop=True)\n",
    "    unique_dates = pd.Series(ordered[date_column].drop_duplicates().sort_values().to_numpy())\n",
    "    train_cut = pd.Timestamp(unique_dates.iloc[int(len(unique_dates) * 0.70)])\n",
    "    valid_cut = pd.Timestamp(unique_dates.iloc[int(len(unique_dates) * 0.85)])\n",
    "    train = ordered[ordered[date_column] < train_cut].copy()\n",
    "    valid = ordered[(ordered[date_column] >= train_cut) & (ordered[date_column] < valid_cut)].copy()\n",
    "    test = ordered[ordered[date_column] >= valid_cut].copy()\n",
    "    date_sets = [set(part[date_column].dt.date) for part in (train, valid, test)]\n",
    "    assert date_sets[0].isdisjoint(date_sets[1])\n",
    "    assert date_sets[0].isdisjoint(date_sets[2])\n",
    "    assert date_sets[1].isdisjoint(date_sets[2])\n",
    "    assert train[date_column].max() < valid[date_column].min() < test[date_column].min()\n",
    "    return train, valid, test\n",
    "\n",
    "def period(frame, date_column=\"선적일\"):\n",
    "    return f\"{frame[date_column].min().date()} ~ {frame[date_column].max().date()}\"\n",
    "\n",
    "def regression_metrics(actual, predicted):\n",
    "    actual = np.asarray(actual, dtype=float)\n",
    "    predicted = np.asarray(predicted, dtype=float)\n",
    "    nonzero = actual != 0\n",
    "    absolute_error = np.abs(actual - predicted)\n",
    "    return {\n",
    "        \"MAPE_%\": float(np.mean(absolute_error[nonzero] / np.abs(actual[nonzero])) * 100) if nonzero.any() else np.nan,\n",
    "        \"MAPE_제외건수\": int((~nonzero).sum()),\n",
    "        \"RMSE_USD\": float(mean_squared_error(actual, predicted) ** 0.5),\n",
    "        \"R2\": float(r2_score(actual, predicted)),\n",
    "        \"MAE_USD\": float(mean_absolute_error(actual, predicted)),\n",
    "        \"WAPE_%\": float(absolute_error.sum() / np.abs(actual).sum() * 100) if np.abs(actual).sum() else np.nan,\n",
    "        \"편향_USD\": float(np.mean(predicted - actual)),\n",
    "        \"P90_절대오차_USD\": float(np.percentile(absolute_error, 90)),\n",
    "    }\n",
    "\n",
    "def baseline_predict(reference, target, keys, target_column):\n",
    "    medians = reference.groupby(keys)[target_column].median()\n",
    "    fallback = float(reference[target_column].median())\n",
    "    predictions, fallback_count = [], 0\n",
    "    for row in target[keys].itertuples(index=False, name=None):\n",
    "        if tuple(row) in medians.index:\n",
    "            predictions.append(medians.loc[tuple(row)])\n",
    "        else:\n",
    "            predictions.append(fallback)\n",
    "            fallback_count += 1\n",
    "    return np.asarray(predictions, dtype=float), fallback_count\n",
    "\n",
    "def make_regression_pipeline(estimator, categorical, numeric):\n",
    "    prep = ColumnTransformer([\n",
    "        (\"cat\", Pipeline([\n",
    "            (\"impute\", SimpleImputer(strategy=\"most_frequent\")),\n",
    "            (\"onehot\", OneHotEncoder(handle_unknown=\"ignore\")),\n",
    "        ]), categorical),\n",
    "        (\"num\", Pipeline([\n",
    "            (\"impute\", SimpleImputer(strategy=\"median\")),\n",
    "            (\"scale\", StandardScaler()),\n",
    "        ]), numeric),\n",
    "    ])\n",
    "    return Pipeline([(\"prep\", prep), (\"model\", estimator)])\n",
    "\n",
    "def make_classification_pipeline(estimator, categorical, numeric):\n",
    "    return make_regression_pipeline(estimator, categorical, numeric)\n",
    "\n",
    "def classification_metrics(actual, probability, threshold=0.50):\n",
    "    predicted = (np.asarray(probability) >= threshold).astype(int)\n",
    "    tn, fp, fn, tp = confusion_matrix(actual, predicted, labels=[0, 1]).ravel()\n",
    "    return {\n",
    "        \"정확도\": accuracy_score(actual, predicted),\n",
    "        \"정밀도\": precision_score(actual, predicted, zero_division=0),\n",
    "        \"재현율\": recall_score(actual, predicted, zero_division=0),\n",
    "        \"F1\": f1_score(actual, predicted, zero_division=0),\n",
    "        \"PR_AUC\": average_precision_score(actual, probability),\n",
    "        \"TN\": int(tn), \"FP\": int(fp), \"FN\": int(fn), \"TP\": int(tp),\n",
    "    }"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "32d6616a",
   "metadata": {},
   "source": [
    "## Steps\n",
    "\n",
    "### 1. 문제 정의와 데이터 프로파일링\n",
    "\n",
    "미션: 예측 단위, 목표변수, 화폐 단위, 기준일을 먼저 적고 행 수·기간·결측·중복을 확인하세요.\n",
    "\n",
    "토론: 견적일과 선적일 중 어느 시점을 실제 업무 예측시점으로 삼을지에 따라 어떤 변수가 달라질까요?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "a60bb83f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-08-10T21:28:55.083887Z",
     "iopub.status.busy": "2026-08-10T21:28:55.083740Z",
     "iopub.status.idle": "2026-08-10T21:28:55.104370Z",
     "shell.execute_reply": "2026-08-10T21:28:55.103821Z"
    },
    "tags": [
     "solution"
    ]
   },
   "outputs": [
    {
     "data": {
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>선적ID</th>\n",
       "      <th>견적일</th>\n",
       "      <th>선적일</th>\n",
       "      <th>연월</th>\n",
       "      <th>항로</th>\n",
       "      <th>거리_NM</th>\n",
       "      <th>컨테이너규격</th>\n",
       "      <th>TEU</th>\n",
       "      <th>화물중량_톤</th>\n",
       "      <th>화물부피_CBM</th>\n",
       "      <th>선사</th>\n",
       "      <th>계약유형</th>\n",
       "      <th>견적리드타임_일</th>\n",
       "      <th>성수기여부</th>\n",
       "      <th>유가인덱스</th>\n",
       "      <th>환율_KRW_USD</th>\n",
       "      <th>항만혼잡도</th>\n",
       "      <th>홍해우회여부</th>\n",
       "      <th>항만파업여부</th>\n",
       "      <th>예정운송일수</th>\n",
       "      <th>실제운송일수</th>\n",
       "      <th>지연일수</th>\n",
       "      <th>해상운임_USD</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>SHP-00001</td>\n",
       "      <td>2024-12-16</td>\n",
       "      <td>2025-01-01</td>\n",
       "      <td>2025-01</td>\n",
       "      <td>부산-상하이</td>\n",
       "      <td>500</td>\n",
       "      <td>40GP</td>\n",
       "      <td>2.000</td>\n",
       "      <td>7.800</td>\n",
       "      <td>51.700</td>\n",
       "      <td>MAERSK</td>\n",
       "      <td>스팟</td>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "      <td>103.650</td>\n",
       "      <td>1,359.460</td>\n",
       "      <td>17.100</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>706.270</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>SHP-00002</td>\n",
       "      <td>2024-12-14</td>\n",
       "      <td>2025-01-01</td>\n",
       "      <td>2025-01</td>\n",
       "      <td>부산-뉴욕</td>\n",
       "      <td>9800</td>\n",
       "      <td>40HC</td>\n",
       "      <td>2.000</td>\n",
       "      <td>13.500</td>\n",
       "      <td>50.100</td>\n",
       "      <td>HMM</td>\n",
       "      <td>장기계약</td>\n",
       "      <td>18</td>\n",
       "      <td>0</td>\n",
       "      <td>101.310</td>\n",
       "      <td>1,358.000</td>\n",
       "      <td>39.800</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>31</td>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "      <td>3,821.400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>SHP-00003</td>\n",
       "      <td>2024-12-16</td>\n",
       "      <td>2025-01-01</td>\n",
       "      <td>2025-01</td>\n",
       "      <td>부산-싱가포르</td>\n",
       "      <td>2500</td>\n",
       "      <td>40HC</td>\n",
       "      <td>2.000</td>\n",
       "      <td>18.700</td>\n",
       "      <td>56.600</td>\n",
       "      <td>ONE</td>\n",
       "      <td>장기계약</td>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "      <td>100.420</td>\n",
       "      <td>1,368.830</td>\n",
       "      <td>18.200</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>1,087.360</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>SHP-00004</td>\n",
       "      <td>2024-12-19</td>\n",
       "      <td>2025-01-01</td>\n",
       "      <td>2025-01</td>\n",
       "      <td>부산-로테르담</td>\n",
       "      <td>10800</td>\n",
       "      <td>40HC</td>\n",
       "      <td>2.000</td>\n",
       "      <td>14.700</td>\n",
       "      <td>56.200</td>\n",
       "      <td>MSC</td>\n",
       "      <td>장기계약</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>101.550</td>\n",
       "      <td>1,341.380</td>\n",
       "      <td>25.800</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>30</td>\n",
       "      <td>37</td>\n",
       "      <td>7</td>\n",
       "      <td>4,155.080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>SHP-00005</td>\n",
       "      <td>2024-12-23</td>\n",
       "      <td>2025-01-01</td>\n",
       "      <td>2025-01</td>\n",
       "      <td>부산-함부르크</td>\n",
       "      <td>11000</td>\n",
       "      <td>40GP</td>\n",
       "      <td>2.000</td>\n",
       "      <td>6.100</td>\n",
       "      <td>66.300</td>\n",
       "      <td>MSC</td>\n",
       "      <td>장기계약</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>98.630</td>\n",
       "      <td>1,379.980</td>\n",
       "      <td>33.900</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>32</td>\n",
       "      <td>34</td>\n",
       "      <td>2</td>\n",
       "      <td>3,210.390</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        선적ID        견적일        선적일       연월       항로  거리_NM 컨테이너규격   TEU  \\\n",
       "0  SHP-00001 2024-12-16 2025-01-01  2025-01   부산-상하이    500   40GP 2.000   \n",
       "1  SHP-00002 2024-12-14 2025-01-01  2025-01    부산-뉴욕   9800   40HC 2.000   \n",
       "2  SHP-00003 2024-12-16 2025-01-01  2025-01  부산-싱가포르   2500   40HC 2.000   \n",
       "3  SHP-00004 2024-12-19 2025-01-01  2025-01  부산-로테르담  10800   40HC 2.000   \n",
       "4  SHP-00005 2024-12-23 2025-01-01  2025-01  부산-함부르크  11000   40GP 2.000   \n",
       "\n",
       "   화물중량_톤  화물부피_CBM      선사  계약유형  견적리드타임_일  성수기여부   유가인덱스  환율_KRW_USD  항만혼잡도  \\\n",
       "0   7.800    51.700  MAERSK    스팟        16      0 103.650   1,359.460 17.100   \n",
       "1  13.500    50.100     HMM  장기계약        18      0 101.310   1,358.000 39.800   \n",
       "2  18.700    56.600     ONE  장기계약        16      0 100.420   1,368.830 18.200   \n",
       "3  14.700    56.200     MSC  장기계약        13      0 101.550   1,341.380 25.800   \n",
       "4   6.100    66.300     MSC  장기계약         9      0  98.630   1,379.980 33.900   \n",
       "\n",
       "   홍해우회여부  항만파업여부  예정운송일수  실제운송일수  지연일수  해상운임_USD  \n",
       "0       0       0       3       3     0   706.270  \n",
       "1       0       0      31      31     0 3,821.400  \n",
       "2       0       0       8       8     0 1,087.360  \n",
       "3       1       0      30      37     7 4,155.080  \n",
       "4       0       0      32      34     2 3,210.390  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# TODO 1: 원본을 불러오고 행·열·기간·결측·중복·운임 요약을 계산하세요.\n",
    "raw = load_lab(\"01_ocean_freight_raw.csv\", dates=[\"견적일\", \"선적일\"])\n",
    "display(raw.head())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "49c67f64",
   "metadata": {},
   "source": [
    "### 2. 결측·중복·논리오류·이상치\n",
    "\n",
    "미션: 문제를 한꺼번에 삭제하지 말고 결측, 중복, 논리 오류, 통계적 이상치를 구분하세요.\n",
    "높은 운임이 정상적인 성수기·우회 신호일 수도 있으므로 업무 맥락을 확인합니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "bc9025ac",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-08-10T21:28:55.106241Z",
     "iopub.status.busy": "2026-08-10T21:28:55.106060Z",
     "iopub.status.idle": "2026-08-10T21:28:55.123869Z",
     "shell.execute_reply": "2026-08-10T21:28:55.123350Z"
    },
    "tags": [
     "solution"
    ]
   },
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>선적ID</th>\n",
       "      <th>견적일</th>\n",
       "      <th>선적일</th>\n",
       "      <th>연월</th>\n",
       "      <th>항로</th>\n",
       "      <th>거리_NM</th>\n",
       "      <th>컨테이너규격</th>\n",
       "      <th>TEU</th>\n",
       "      <th>화물중량_톤</th>\n",
       "      <th>화물부피_CBM</th>\n",
       "      <th>선사</th>\n",
       "      <th>계약유형</th>\n",
       "      <th>견적리드타임_일</th>\n",
       "      <th>성수기여부</th>\n",
       "      <th>유가인덱스</th>\n",
       "      <th>환율_KRW_USD</th>\n",
       "      <th>항만혼잡도</th>\n",
       "      <th>홍해우회여부</th>\n",
       "      <th>항만파업여부</th>\n",
       "      <th>예정운송일수</th>\n",
       "      <th>실제운송일수</th>\n",
       "      <th>지연일수</th>\n",
       "      <th>해상운임_USD</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>SHP-00346</td>\n",
       "      <td>2025-03-09</td>\n",
       "      <td>2025-03-15</td>\n",
       "      <td>2025-03</td>\n",
       "      <td>부산-상하이</td>\n",
       "      <td>500</td>\n",
       "      <td>40HC</td>\n",
       "      <td>2.000</td>\n",
       "      <td>24.400</td>\n",
       "      <td>61.900</td>\n",
       "      <td>MAERSK</td>\n",
       "      <td>장기계약</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>105.940</td>\n",
       "      <td>1,369.180</td>\n",
       "      <td>22.600</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>744.540</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>SHP-00192</td>\n",
       "      <td>2025-01-18</td>\n",
       "      <td>2025-02-11</td>\n",
       "      <td>2025-02</td>\n",
       "      <td>부산-LA</td>\n",
       "      <td>5200</td>\n",
       "      <td>40HC</td>\n",
       "      <td>2.000</td>\n",
       "      <td>12.100</td>\n",
       "      <td>45.700</td>\n",
       "      <td>MSC</td>\n",
       "      <td>장기계약</td>\n",
       "      <td>24</td>\n",
       "      <td>0</td>\n",
       "      <td>104.850</td>\n",
       "      <td>1,360.230</td>\n",
       "      <td>24.700</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>2,205.470</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>SHP-00223</td>\n",
       "      <td>2025-01-24</td>\n",
       "      <td>2025-02-17</td>\n",
       "      <td>2025-02</td>\n",
       "      <td>부산-로테르담</td>\n",
       "      <td>10800</td>\n",
       "      <td>40HC</td>\n",
       "      <td>2.000</td>\n",
       "      <td>8.600</td>\n",
       "      <td>49.100</td>\n",
       "      <td>ONE</td>\n",
       "      <td>장기계약</td>\n",
       "      <td>24</td>\n",
       "      <td>0</td>\n",
       "      <td>107.140</td>\n",
       "      <td>1,354.270</td>\n",
       "      <td>35.800</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30</td>\n",
       "      <td>30</td>\n",
       "      <td>0</td>\n",
       "      <td>3,366.640</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>SHP-01433</td>\n",
       "      <td>2025-10-20</td>\n",
       "      <td>2025-11-10</td>\n",
       "      <td>2025-11</td>\n",
       "      <td>부산-함부르크</td>\n",
       "      <td>11000</td>\n",
       "      <td>40GP</td>\n",
       "      <td>2.000</td>\n",
       "      <td>6.500</td>\n",
       "      <td>53.400</td>\n",
       "      <td>MSC</td>\n",
       "      <td>장기계약</td>\n",
       "      <td>21</td>\n",
       "      <td>1</td>\n",
       "      <td>105.790</td>\n",
       "      <td>1,315.530</td>\n",
       "      <td>42.600</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>32</td>\n",
       "      <td>32</td>\n",
       "      <td>0</td>\n",
       "      <td>3,782.690</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>SHP-01245</td>\n",
       "      <td>2025-09-09</td>\n",
       "      <td>2025-09-30</td>\n",
       "      <td>2025-09</td>\n",
       "      <td>부산-로테르담</td>\n",
       "      <td>10800</td>\n",
       "      <td>40GP</td>\n",
       "      <td>2.000</td>\n",
       "      <td>22.900</td>\n",
       "      <td>46.200</td>\n",
       "      <td>CMA_CGM</td>\n",
       "      <td>스팟</td>\n",
       "      <td>21</td>\n",
       "      <td>1</td>\n",
       "      <td>97.080</td>\n",
       "      <td>1,313.170</td>\n",
       "      <td>22.400</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>30</td>\n",
       "      <td>38</td>\n",
       "      <td>8</td>\n",
       "      <td>6,336.490</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        선적ID        견적일        선적일       연월       항로  거리_NM 컨테이너규격   TEU  \\\n",
       "0  SHP-00346 2025-03-09 2025-03-15  2025-03   부산-상하이    500   40HC 2.000   \n",
       "1  SHP-00192 2025-01-18 2025-02-11  2025-02    부산-LA   5200   40HC 2.000   \n",
       "2  SHP-00223 2025-01-24 2025-02-17  2025-02  부산-로테르담  10800   40HC 2.000   \n",
       "3  SHP-01433 2025-10-20 2025-11-10  2025-11  부산-함부르크  11000   40GP 2.000   \n",
       "4  SHP-01245 2025-09-09 2025-09-30  2025-09  부산-로테르담  10800   40GP 2.000   \n",
       "\n",
       "   화물중량_톤  화물부피_CBM       선사  계약유형  견적리드타임_일  성수기여부   유가인덱스  환율_KRW_USD  \\\n",
       "0  24.400    61.900   MAERSK  장기계약         6      0 105.940   1,369.180   \n",
       "1  12.100    45.700      MSC  장기계약        24      0 104.850   1,360.230   \n",
       "2   8.600    49.100      ONE  장기계약        24      0 107.140   1,354.270   \n",
       "3   6.500    53.400      MSC  장기계약        21      1 105.790   1,315.530   \n",
       "4  22.900    46.200  CMA_CGM    스팟        21      1  97.080   1,313.170   \n",
       "\n",
       "   항만혼잡도  홍해우회여부  항만파업여부  예정운송일수  실제운송일수  지연일수  해상운임_USD  \n",
       "0 22.600       0       0       3       3     0   744.540  \n",
       "1 24.700       0       0      15      15     0 2,205.470  \n",
       "2 35.800       0       0      30      30     0 3,366.640  \n",
       "3 42.600       0       0      32      32     0 3,782.690  \n",
       "4 22.400       1       1      30      38     8 6,336.490  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# TODO 2: 결측, 중복, 음수 중량, 0~100 밖 혼잡도, 음수 리드타임, IQR 이상치를 집계하세요.\n",
    "quality = load_lab(\"02_freight_quality_challenge.csv\", dates=[\"견적일\", \"선적일\"])\n",
    "display(quality.head())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "259dea24",
   "metadata": {},
   "source": [
    "### 3. EDA와 업무 파생변수\n",
    "\n",
    "미션: 항로·월·컨테이너·계약유형별 운임을 비교하고 관찰한 패턴을 세 문장으로 기록하세요.\n",
    "상관관계는 인과효과가 아닙니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "ff6ef536",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-08-10T21:28:55.125755Z",
     "iopub.status.busy": "2026-08-10T21:28:55.125578Z",
     "iopub.status.idle": "2026-08-10T21:28:55.140419Z",
     "shell.execute_reply": "2026-08-10T21:28:55.139923Z"
    },
    "tags": [
     "solution"
    ]
   },
   "outputs": [],
   "source": [
    "# TODO 3: 항로·월·컨테이너·계약유형별 운임 분포를 표와 그래프로 비교하세요.\n",
    "eda = load_lab(\"03_freight_eda_features.csv\", dates=[\"견적일\", \"선적일\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c3e066b9",
   "metadata": {},
   "source": [
    "### 4. 데이터 누출 함정\n",
    "\n",
    "미션: 견적 시점에 알 수 있는 변수와 운송·정산 후 확정되는 변수를 나누세요.\n",
    "성능이 지나치게 좋으면 먼저 누출을 의심합니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "0a01b530",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-08-10T21:28:55.142483Z",
     "iopub.status.busy": "2026-08-10T21:28:55.142337Z",
     "iopub.status.idle": "2026-08-10T21:28:55.156270Z",
     "shell.execute_reply": "2026-08-10T21:28:55.155669Z"
    },
    "tags": [
     "solution"
    ]
   },
   "outputs": [],
   "source": [
    "# TODO 4: 컬럼을 견적 시점 사용 가능/불가로 분류하고 누출 포함·제외 성능을 비교하세요.\n",
    "leakage = load_lab(\"04_freight_leakage_trap.csv\", dates=[\"견적일\", \"선적일\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "62f62a6d",
   "metadata": {},
   "source": [
    "### 5. 무작위 분할과 시간순 분할\n",
    "\n",
    "미션: 같은 선적일을 한 구간에 유지한 70/15/15 분할과 무작위 분할을 비교하세요.\n",
    "무작위 분할은 미래 시장국면을 학습에 섞어 낙관적 결과를 만들 수 있습니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "9e285ecd",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-08-10T21:28:55.158382Z",
     "iopub.status.busy": "2026-08-10T21:28:55.158198Z",
     "iopub.status.idle": "2026-08-10T21:28:55.172005Z",
     "shell.execute_reply": "2026-08-10T21:28:55.171430Z"
    },
    "tags": [
     "solution"
    ]
   },
   "outputs": [],
   "source": [
    "# TODO 5: 날짜 집합이 겹치지 않는 시간순 분할과 무작위 분할을 각각 수행하고 성능 차이를 설명하세요.\n",
    "drift = load_lab(\"05_freight_time_split_drift.csv\", dates=[\"견적일\", \"선적일\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4e03a687",
   "metadata": {},
   "source": [
    "### 6. 업무 기준선\n",
    "\n",
    "미션: Training의 항로×컨테이너규격×계약유형 중앙값을 사용하세요.\n",
    "Validation에 처음 등장한 조합은 Training 전체 중앙값으로 대체하고 fallback 건수를 기록합니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "99276e7a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-08-10T21:28:55.174160Z",
     "iopub.status.busy": "2026-08-10T21:28:55.174020Z",
     "iopub.status.idle": "2026-08-10T21:28:55.183237Z",
     "shell.execute_reply": "2026-08-10T21:28:55.182728Z"
    },
    "tags": [
     "solution"
    ]
   },
   "outputs": [],
   "source": [
    "# TODO 6: Training 그룹 중앙값 기준선과 전체 중앙값 fallback을 구현하세요.\n",
    "baseline_data = load_lab(\"06_freight_baseline.csv\", dates=[\"선적일\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8889c3f4",
   "metadata": {},
   "source": [
    "### 7. 회귀 모델 비교와 Test 1회 평가\n",
    "\n",
    "미션: Validation에서 기준선·Ridge·Random Forest를 비교한 뒤 모델을 확정합니다.\n",
    "그 다음 Training+Validation으로 재학습하고 Test를 한 번만 평가합니다.\n",
    "\n",
    "RF_TREE_COUNT 슬라이더로 Random Forest의 트리 수를 조정할 수 있습니다.\n",
    "트리 수가 많을수록 결과는 안정적이지만 학습 시간이 늘어납니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "0be83836",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-08-10T21:28:55.185354Z",
     "iopub.status.busy": "2026-08-10T21:28:55.185180Z",
     "iopub.status.idle": "2026-08-10T21:28:55.199815Z",
     "shell.execute_reply": "2026-08-10T21:28:55.199267Z"
    },
    "tags": [
     "solution"
    ]
   },
   "outputs": [],
   "source": [
    "# TODO 7: 기준선·Ridge·Random Forest를 Validation에서 비교하고 최종 Test를 한 번만 평가하세요.\n",
    "# 아래 슬라이더는 Random Forest 트리 개수만 조정합니다. 최종 Test 평가는 고정 1회입니다.\n",
    "RF_TREE_COUNT = 220 #@param {type:\"slider\", min:50, max:1000, step:50}\n",
    "regression = load_lab(\"07_freight_regression.csv\", dates=[\"견적일\",\"선적일\",\"특성기준일\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "565d8260",
   "metadata": {},
   "source": [
    "### 7-B. 딥러닝 신경망 구조와 학습 과정 시각화\n",
    "\n",
    "교육용 심층 신경망 MLP를 Training 구간에서 학습하고 Validation에서만 확인합니다.\n",
    "DEEP_EPOCHS 슬라이더로 최대 학습 횟수를 조정할 수 있으며, 조기 종료되면 실제 epoch는 더 작을 수 있습니다.\n",
    "구조도는 입력 특성 → 은닉층 1 → 은닉층 2 → 운임 1개 출력의 연결을 보여줍니다.\n",
    "\n",
    "작은 표 형태 데이터에서는 Random Forest가 신경망보다 더 잘 작동할 수 있습니다. 이 단계는 딥러닝 구조와 학습 과정을 이해하기 위한 실습이며 최종 Test 1회 원칙에는 영향을 주지 않습니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "350b3b8b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-08-10T21:28:55.201977Z",
     "iopub.status.busy": "2026-08-10T21:28:55.201811Z",
     "iopub.status.idle": "2026-08-10T21:28:56.258516Z",
     "shell.execute_reply": "2026-08-10T21:28:56.258129Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1300x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>모델</th>\n",
       "      <th>은닉층</th>\n",
       "      <th>요청_최대_epoch</th>\n",
       "      <th>실제_epoch</th>\n",
       "      <th>MAPE_%</th>\n",
       "      <th>RMSE_USD</th>\n",
       "      <th>R2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>DeepMLP</td>\n",
       "      <td>32-16</td>\n",
       "      <td>250</td>\n",
       "      <td>111</td>\n",
       "      <td>9.419</td>\n",
       "      <td>305.506</td>\n",
       "      <td>0.969</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        모델    은닉층  요청_최대_epoch  실제_epoch  MAPE_%  RMSE_USD    R2\n",
       "0  DeepMLP  32-16          250       111   9.419   305.506 0.969"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x420 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "최대 epoch=250, 조기 종료 후 실제 학습 epoch=111\n",
      "딥러닝은 Validation까지만 확인했으며 최종 Test는 기존 선택 모델로 1회만 평가합니다.\n"
     ]
    }
   ],
   "source": [
    "DEEP_EPOCHS = 250 #@param {type:\"slider\", min:50, max:500, step:50}\n",
    "DEEP_HIDDEN_1 = 32 #@param {type:\"slider\", min:8, max:128, step:8}\n",
    "DEEP_HIDDEN_2 = 16 #@param {type:\"slider\", min:4, max:64, step:4}\n",
    "\n",
    "# TODO 7과 독립적으로 실행되도록 딥러닝용 데이터를 준비합니다.\n",
    "deep_data = load_lab(\n",
    "    \"07_freight_regression.csv\",\n",
    "    dates=[\"견적일\", \"선적일\", \"특성기준일\"],\n",
    ")\n",
    "deep_train, deep_valid, _ = split_by_unique_date(deep_data)\n",
    "DEEP_TARGET = \"해상운임_USD\"\n",
    "DEEP_CATEGORICAL = [\"항로\", \"컨테이너규격\", \"선사\", \"계약유형\"]\n",
    "DEEP_NUMERIC = [\n",
    "    \"거리_NM\", \"TEU\", \"화물중량_톤\", \"화물부피_CBM\", \"견적리드타임_일\",\n",
    "    \"성수기여부\", \"유가인덱스\", \"환율_KRW_USD\", \"항만혼잡도\",\n",
    "    \"홍해우회여부\", \"항만파업여부\", \"예정운송일수\", \"월\", \"요일\",\n",
    "]\n",
    "DEEP_FEATURES = DEEP_CATEGORICAL + DEEP_NUMERIC\n",
    "\n",
    "# 신경망 연결 구조를 예시 이미지처럼 그립니다.\n",
    "def draw_neural_network(layer_sizes, output_path):\n",
    "    x_positions = np.linspace(0.0, 4.5, len(layer_sizes))\n",
    "    max_display_nodes = [8, 8, 8, 1]\n",
    "    layer_names = [\n",
    "        f\"Input layer\\n{layer_sizes[0]} features\",\n",
    "        f\"Hidden layer 1\\n{layer_sizes[1]} neurons · ReLU\",\n",
    "        f\"Hidden layer 2\\n{layer_sizes[2]} neurons · ReLU\",\n",
    "        \"Output layer\\n1 freight value (USD)\",\n",
    "    ]\n",
    "    layer_colors = [\"#4C78A8\", \"#F2A541\", \"#E07B39\", \"#59A14F\"]\n",
    "    positions = []\n",
    "    for size, maximum in zip(layer_sizes, max_display_nodes):\n",
    "        shown = min(size, maximum)\n",
    "        positions.append(np.linspace(0.16, 0.84, shown))\n",
    "\n",
    "    fig, ax = plt.subplots(figsize=(13, 6))\n",
    "    for layer_index in range(len(layer_sizes) - 1):\n",
    "        for left_y in positions[layer_index]:\n",
    "            for right_y in positions[layer_index + 1]:\n",
    "                ax.plot(\n",
    "                    [x_positions[layer_index] + 0.07, x_positions[layer_index + 1] - 0.07],\n",
    "                    [left_y, right_y], color=\"#64748B\", alpha=0.18, linewidth=0.65, zorder=1,\n",
    "                )\n",
    "\n",
    "    for layer_index, (x_value, y_values, color) in enumerate(zip(x_positions, positions, layer_colors)):\n",
    "        for y_value in y_values:\n",
    "            ax.add_patch(plt.Circle(\n",
    "                (x_value, y_value), 0.065, facecolor=color, edgecolor=\"#1F2937\",\n",
    "                linewidth=1.2, zorder=3,\n",
    "            ))\n",
    "        if layer_sizes[layer_index] > len(y_values):\n",
    "            ax.text(x_value, 0.09, \"···\", ha=\"center\", va=\"center\", fontsize=16)\n",
    "        ax.text(x_value, 1.03, layer_names[layer_index], ha=\"center\", va=\"bottom\", fontsize=11)\n",
    "\n",
    "    weight_labels = [\n",
    "        f\"W1  [{layer_sizes[0]}, {layer_sizes[1]}]\",\n",
    "        f\"W2  [{layer_sizes[1]}, {layer_sizes[2]}]\",\n",
    "        f\"W3  [{layer_sizes[2]}, {layer_sizes[3]}]\",\n",
    "    ]\n",
    "    for index, label in enumerate(weight_labels):\n",
    "        ax.text((x_positions[index] + x_positions[index + 1]) / 2, 0.02, label,\n",
    "                ha=\"center\", va=\"center\", fontsize=10, color=\"#374151\")\n",
    "    ax.annotate(\"Data flow\", xy=(4.65, 0.93), xytext=(-0.15, 0.93),\n",
    "                arrowprops={\"arrowstyle\":\"->\", \"linewidth\":1.4, \"color\":\"#374151\"},\n",
    "                ha=\"left\", va=\"center\", fontsize=10, color=\"#374151\")\n",
    "    ax.set_xlim(-0.35, 4.85)\n",
    "    ax.set_ylim(-0.04, 1.15)\n",
    "    ax.axis(\"off\")\n",
    "    ax.set_title(\"Ocean Freight Deep Neural Network Architecture\", fontsize=15, pad=24)\n",
    "    plt.tight_layout()\n",
    "    plt.savefig(output_path, dpi=180, bbox_inches=\"tight\")\n",
    "    plt.show()\n",
    "\n",
    "network_layers = [len(DEEP_FEATURES), DEEP_HIDDEN_1, DEEP_HIDDEN_2, 1]\n",
    "draw_neural_network(network_layers, OUTPUT_DIR / \"deep_learning_architecture.png\")\n",
    "\n",
    "# 범주형은 dense one-hot, 수치형은 표준화하고 목표값도 표준화합니다.\n",
    "deep_preprocessor = ColumnTransformer([\n",
    "    (\"cat\", Pipeline([\n",
    "        (\"impute\", SimpleImputer(strategy=\"most_frequent\")),\n",
    "        (\"onehot\", OneHotEncoder(handle_unknown=\"ignore\", sparse_output=False)),\n",
    "    ]), DEEP_CATEGORICAL),\n",
    "    (\"num\", Pipeline([\n",
    "        (\"impute\", SimpleImputer(strategy=\"median\")),\n",
    "        (\"scale\", StandardScaler()),\n",
    "    ]), DEEP_NUMERIC),\n",
    "])\n",
    "mlp = MLPRegressor(\n",
    "    hidden_layer_sizes=(DEEP_HIDDEN_1, DEEP_HIDDEN_2),\n",
    "    activation=\"relu\", solver=\"adam\", batch_size=64,\n",
    "    learning_rate_init=0.001, max_iter=DEEP_EPOCHS,\n",
    "    early_stopping=True, validation_fraction=0.15,\n",
    "    n_iter_no_change=25, random_state=SEED,\n",
    ")\n",
    "deep_model = Pipeline([\n",
    "    (\"prep\", deep_preprocessor),\n",
    "    (\"model\", TransformedTargetRegressor(regressor=mlp, transformer=StandardScaler())),\n",
    "])\n",
    "deep_model.fit(deep_train[DEEP_FEATURES], deep_train[DEEP_TARGET])\n",
    "deep_prediction = deep_model.predict(deep_valid[DEEP_FEATURES])\n",
    "deep_metrics = regression_metrics(deep_valid[DEEP_TARGET], deep_prediction)\n",
    "fitted_mlp = deep_model.named_steps[\"model\"].regressor_\n",
    "epochs_run = fitted_mlp.n_iter_\n",
    "loss_curve = fitted_mlp.loss_curve_\n",
    "\n",
    "deep_result = pd.DataFrame([{\n",
    "    \"모델\":\"DeepMLP\", \"요청_최대_epoch\":DEEP_EPOCHS, \"실제_epoch\":epochs_run,\n",
    "    \"은닉층\":f\"{DEEP_HIDDEN_1}-{DEEP_HIDDEN_2}\", **deep_metrics,\n",
    "}])\n",
    "deep_result.to_csv(OUTPUT_DIR / \"deep_learning_validation_metrics.csv\", index=False, encoding=\"utf-8-sig\")\n",
    "display(deep_result[[\"모델\", \"은닉층\", \"요청_최대_epoch\", \"실제_epoch\", \"MAPE_%\", \"RMSE_USD\", \"R2\"]])\n",
    "\n",
    "fig, axes = plt.subplots(1, 2, figsize=(12, 4.2))\n",
    "axes[0].plot(np.arange(1, len(loss_curve) + 1), loss_curve, color=\"#4C78A8\", linewidth=1.8)\n",
    "axes[0].axvline(epochs_run, color=\"#E45756\", linestyle=\"--\", linewidth=1.0)\n",
    "axes[0].set(title=\"Deep learning training curve\", xlabel=\"Epoch\", ylabel=\"Scaled training loss\")\n",
    "axes[0].grid(alpha=0.2)\n",
    "axes[1].scatter(deep_valid[DEEP_TARGET], deep_prediction, alpha=0.55, s=22, color=\"#59A14F\")\n",
    "low = min(deep_valid[DEEP_TARGET].min(), deep_prediction.min())\n",
    "high = max(deep_valid[DEEP_TARGET].max(), deep_prediction.max())\n",
    "axes[1].plot([low, high], [low, high], \"--\", color=\"#374151\", linewidth=1.0)\n",
    "axes[1].set(title=\"Deep MLP: actual vs predicted\", xlabel=\"Actual USD\", ylabel=\"Predicted USD\")\n",
    "plt.tight_layout()\n",
    "plt.savefig(OUTPUT_DIR / \"deep_learning_training.png\", dpi=180, bbox_inches=\"tight\")\n",
    "plt.show()\n",
    "print(f\"최대 epoch={DEEP_EPOCHS}, 조기 종료 후 실제 학습 epoch={epochs_run}\")\n",
    "print(\"딥러닝은 Validation까지만 확인했으며 최종 Test는 기존 선택 모델로 1회만 평가합니다.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "df3d810b",
   "metadata": {},
   "source": [
    "### 8. 5개 견적 시나리오\n",
    "\n",
    "미션: 유가 +10%, 혼잡 +20점, 홍해 우회, 계약유형 전환, 복합 악화를 기준 조건과 비교하세요.\n",
    "결과는 인과효과가 아니라 학습 데이터 관계를 적용한 조건부 추정입니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "f2af2f99",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-08-10T21:28:56.260288Z",
     "iopub.status.busy": "2026-08-10T21:28:56.260097Z",
     "iopub.status.idle": "2026-08-10T21:28:56.268498Z",
     "shell.execute_reply": "2026-08-10T21:28:56.268040Z"
    },
    "tags": [
     "solution"
    ]
   },
   "outputs": [],
   "source": [
    "# TODO 8: 기준/변경 쌍을 예측하고 변화 USD와 변화율을 계산하세요.\n",
    "scenarios = load_lab(\"08_freight_scenarios.csv\", dates=[\"견적일\",\"선적일\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "53557bd6",
   "metadata": {},
   "source": [
    "### 9. 3일 이상 지연 분류\n",
    "\n",
    "미션: 정확도만 보지 말고 지연 양성 재현율·정밀도·F1·PR-AUC를 함께 비교하세요.\n",
    "거짓 음성은 위험 지연을 놓치는 비용, 거짓 양성은 불필요한 수동검토 비용입니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "2b340cd9",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-08-10T21:28:56.270217Z",
     "iopub.status.busy": "2026-08-10T21:28:56.270085Z",
     "iopub.status.idle": "2026-08-10T21:28:56.282165Z",
     "shell.execute_reply": "2026-08-10T21:28:56.281693Z"
    },
    "tags": [
     "solution"
    ]
   },
   "outputs": [],
   "source": [
    "# TODO 9: Logistic과 Random Forest의 재현율·정밀도·F1·PR-AUC·혼동행렬을 비교하세요.\n",
    "delay = load_lab(\"09_shipment_delay_classification.csv\", dates=[\"견적일\",\"선적일\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c5bf9612",
   "metadata": {},
   "source": [
    "### 10. B/L OCR 검증과 통관보류 분류\n",
    "\n",
    "미션 A: 송하인·수하인·품목·수량·Incoterms·도착지·선적일을 원문과 대조하세요.\n",
    "미션 B: 통관보류 양성 재현율을 중심으로 분류 모델을 비교하고 사람 검토 큐를 설계하세요.\n",
    "HS코드는 교육용 후보만 제시하며 전문가 확인 필요를 반드시 표시합니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "d2a4408d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-08-10T21:28:56.284094Z",
     "iopub.status.busy": "2026-08-10T21:28:56.283930Z",
     "iopub.status.idle": "2026-08-10T21:28:56.299567Z",
     "shell.execute_reply": "2026-08-10T21:28:56.299108Z"
    },
    "tags": [
     "solution"
    ]
   },
   "outputs": [],
   "source": [
    "# TODO 10: OCR 필드별 일치율을 계산하고 통관보류 분류의 양성 재현율을 비교하세요.\n",
    "customs = load_lab(\"10_customs_hold_bl_ocr.csv\", dates=[\"견적일\",\"선적일\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b528dc09",
   "metadata": {},
   "source": [
    "## Checks\n",
    "\n",
    "아래 셀은 핵심 산출물을 저장합니다. 정답 노트북에서는 반드시 모든 셀 실행 후 파일 6종 이상이 생성되어야 합니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "c211ef0a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-08-10T21:28:56.301542Z",
     "iopub.status.busy": "2026-08-10T21:28:56.301384Z",
     "iopub.status.idle": "2026-08-10T21:28:56.305695Z",
     "shell.execute_reply": "2026-08-10T21:28:56.305290Z"
    },
    "tags": [
     "student-check"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "교육생용 노트북: 아직 완료하지 않은 단계가 있어 최종 보고서 생성을 건너뜁니다.\n",
      "- TODO 7 회귀 모델: reg_train, reg_valid, reg_test, model_comparison, best_name, test_metrics\n",
      "- TODO 8 시나리오: scenario_results\n",
      "- TODO 9 지연 분류: delay_comparison, DELAY_TARGET\n",
      "- TODO 10 통관·OCR: ocr_rates, sample_review, customs_comparison, CUSTOMS_TARGET\n",
      "오류가 아닙니다. TODO를 완성하거나 정답 포함 노트북에서 전체 결과를 확인하세요.\n"
     ]
    }
   ],
   "source": [
    "required_by_step = {\n",
    "    \"TODO 7 회귀 모델\": [\"reg_train\", \"reg_valid\", \"reg_test\", \"model_comparison\", \"best_name\", \"test_metrics\"],\n",
    "    \"TODO 8 시나리오\": [\"scenario_results\"],\n",
    "    \"TODO 9 지연 분류\": [\"delay_comparison\", \"DELAY_TARGET\"],\n",
    "    \"TODO 10 통관·OCR\": [\"ocr_rates\", \"sample_review\", \"customs_comparison\", \"CUSTOMS_TARGET\"],\n",
    "}\n",
    "missing_by_step = {\n",
    "    step: [name for name in names if name not in globals()]\n",
    "    for step, names in required_by_step.items()\n",
    "}\n",
    "missing_by_step = {step: names for step, names in missing_by_step.items() if names}\n",
    "\n",
    "if missing_by_step:\n",
    "    print(\"교육생용 노트북: 아직 완료하지 않은 단계가 있어 최종 보고서 생성을 건너뜁니다.\")\n",
    "    for step, names in missing_by_step.items():\n",
    "        print(f\"- {step}: {', '.join(names)}\")\n",
    "    print(\"오류가 아닙니다. TODO를 완성하거나 정답 포함 노트북에서 전체 결과를 확인하세요.\")\n",
    "else:\n",
    "    print(\"TODO 7~10의 핵심 변수가 모두 준비되었습니다.\")\n",
    "    print(\"정답 포함 노트북의 Checks 결과와 비교해 보세요.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9b0a3007",
   "metadata": {},
   "source": [
    "## Next Steps\n",
    "\n",
    "1. 교육생용 노트북의 TODO를 먼저 해결한 뒤 정답 포함 노트북과 비교합니다.\n",
    "2. Test를 보고 모델을 바꾸지 말고, 필요하면 새로운 미래 홀드아웃 또는 롤링 백테스트를 만듭니다.\n",
    "3. 실제 데이터로 전환할 때는 견적 시점 as-of 스냅샷과 label available 시점을 기록합니다.\n",
    "4. 세그먼트별 오차, 예측구간, 드리프트 감지, 사람 승인 절차를 추가합니다."
   ]
  }
 ],
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