{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "#! pip install tensorflow\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "train = pd.read_csv(\"train.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0                                         poznan.wuoz.gov.pl\n",
       "1                                        vill.okawa.kochi.jp\n",
       "2                                      nationalfinance.co.om\n",
       "3                                     town.ozora.hokkaido.jp\n",
       "4                          open24.ie-news.irish/online/Login\n",
       "                                 ...                        \n",
       "6995051                                           ddht.co.kr\n",
       "6995052                                 www.upstartepoxy.com\n",
       "6995053    employeesalaryschedule70.000webhostapp.com/adb...\n",
       "6995054                                       dekalbtool.com\n",
       "6995055                                   helpinganimals.com\n",
       "Name: URL_clean, Length: 6995056, dtype: object"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 대괄호 [ ] 제거\n",
    "train['URL_clean'] = train['URL'].str.replace(r'[\\[\\]]', '', regex=True)\n",
    "train[\"URL_clean\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "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>ID</th>\n",
       "      <th>URL</th>\n",
       "      <th>label</th>\n",
       "      <th>URL_clean</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>TRAIN_0000000</td>\n",
       "      <td>poznan[.]wuoz[.]gov[.]pl</td>\n",
       "      <td>0</td>\n",
       "      <td>poznan.wuoz.gov.pl</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>TRAIN_0000001</td>\n",
       "      <td>vill[.]okawa[.]kochi[.]jp</td>\n",
       "      <td>0</td>\n",
       "      <td>vill.okawa.kochi.jp</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>TRAIN_0000002</td>\n",
       "      <td>nationalfinance[.]co[.]om</td>\n",
       "      <td>0</td>\n",
       "      <td>nationalfinance.co.om</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>TRAIN_0000003</td>\n",
       "      <td>town[.]ozora[.]hokkaido[.]jp</td>\n",
       "      <td>0</td>\n",
       "      <td>town.ozora.hokkaido.jp</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>TRAIN_0000004</td>\n",
       "      <td>open24[.]ie-news[.]irish/online/Login</td>\n",
       "      <td>1</td>\n",
       "      <td>open24.ie-news.irish/online/Login</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6995051</th>\n",
       "      <td>TRAIN_6995051</td>\n",
       "      <td>ddht[.]co[.]kr</td>\n",
       "      <td>0</td>\n",
       "      <td>ddht.co.kr</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6995052</th>\n",
       "      <td>TRAIN_6995052</td>\n",
       "      <td>www[.]upstartepoxy[.]com</td>\n",
       "      <td>0</td>\n",
       "      <td>www.upstartepoxy.com</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6995053</th>\n",
       "      <td>TRAIN_6995053</td>\n",
       "      <td>employeesalaryschedule70[.]000webhostapp[.]com...</td>\n",
       "      <td>1</td>\n",
       "      <td>employeesalaryschedule70.000webhostapp.com/adb...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6995054</th>\n",
       "      <td>TRAIN_6995054</td>\n",
       "      <td>dekalbtool[.]com</td>\n",
       "      <td>0</td>\n",
       "      <td>dekalbtool.com</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6995055</th>\n",
       "      <td>TRAIN_6995055</td>\n",
       "      <td>helpinganimals[.]com</td>\n",
       "      <td>0</td>\n",
       "      <td>helpinganimals.com</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>6995056 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                    ID                                                URL  \\\n",
       "0        TRAIN_0000000                           poznan[.]wuoz[.]gov[.]pl   \n",
       "1        TRAIN_0000001                          vill[.]okawa[.]kochi[.]jp   \n",
       "2        TRAIN_0000002                          nationalfinance[.]co[.]om   \n",
       "3        TRAIN_0000003                       town[.]ozora[.]hokkaido[.]jp   \n",
       "4        TRAIN_0000004              open24[.]ie-news[.]irish/online/Login   \n",
       "...                ...                                                ...   \n",
       "6995051  TRAIN_6995051                                     ddht[.]co[.]kr   \n",
       "6995052  TRAIN_6995052                           www[.]upstartepoxy[.]com   \n",
       "6995053  TRAIN_6995053  employeesalaryschedule70[.]000webhostapp[.]com...   \n",
       "6995054  TRAIN_6995054                                   dekalbtool[.]com   \n",
       "6995055  TRAIN_6995055                               helpinganimals[.]com   \n",
       "\n",
       "         label                                          URL_clean  \n",
       "0            0                                 poznan.wuoz.gov.pl  \n",
       "1            0                                vill.okawa.kochi.jp  \n",
       "2            0                              nationalfinance.co.om  \n",
       "3            0                             town.ozora.hokkaido.jp  \n",
       "4            1                  open24.ie-news.irish/online/Login  \n",
       "...        ...                                                ...  \n",
       "6995051      0                                         ddht.co.kr  \n",
       "6995052      0                               www.upstartepoxy.com  \n",
       "6995053      1  employeesalaryschedule70.000webhostapp.com/adb...  \n",
       "6995054      0                                     dekalbtool.com  \n",
       "6995055      0                                 helpinganimals.com  \n",
       "\n",
       "[6995056 rows x 4 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "train['dot_count'] = train['URL_clean'].str.count('\\.')\n",
    "train['slash_count'] = train['URL_clean'].str.count('/')\n",
    "train['alpha_count'] = train['URL_clean'].str.count(r'[a-zA-Z]')\n",
    "train['dash_count'] = train['URL_clean'].str.count(r'-')\n",
    "train['underscore_count'] = train['URL_clean'].str.count(r'_')\n",
    "train['digit_count'] = train['URL_clean'].str.count(r'\\d')\n",
    "train['percent_count'] = train['URL_clean'].str.count(r'%')\n",
    "train['equal_count'] = train['URL_clean'].str.count(r'=')\n",
    "train['question_count'] = train['URL_clean'].str.count(r'\\?')\n",
    "train['at_count'] = train['URL_clean'].str.count(r'@')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "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>ID</th>\n",
       "      <th>URL</th>\n",
       "      <th>label</th>\n",
       "      <th>URL_clean</th>\n",
       "      <th>dot_count</th>\n",
       "      <th>slash_count</th>\n",
       "      <th>alpha_count</th>\n",
       "      <th>dash_count</th>\n",
       "      <th>underscore_count</th>\n",
       "      <th>digit_count</th>\n",
       "      <th>percent_count</th>\n",
       "      <th>equal_count</th>\n",
       "      <th>question_count</th>\n",
       "      <th>at_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>TRAIN_0000000</td>\n",
       "      <td>poznan[.]wuoz[.]gov[.]pl</td>\n",
       "      <td>0</td>\n",
       "      <td>poznan.wuoz.gov.pl</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>TRAIN_0000001</td>\n",
       "      <td>vill[.]okawa[.]kochi[.]jp</td>\n",
       "      <td>0</td>\n",
       "      <td>vill.okawa.kochi.jp</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>TRAIN_0000002</td>\n",
       "      <td>nationalfinance[.]co[.]om</td>\n",
       "      <td>0</td>\n",
       "      <td>nationalfinance.co.om</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>TRAIN_0000003</td>\n",
       "      <td>town[.]ozora[.]hokkaido[.]jp</td>\n",
       "      <td>0</td>\n",
       "      <td>town.ozora.hokkaido.jp</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>TRAIN_0000004</td>\n",
       "      <td>open24[.]ie-news[.]irish/online/Login</td>\n",
       "      <td>1</td>\n",
       "      <td>open24.ie-news.irish/online/Login</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>26</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6995051</th>\n",
       "      <td>TRAIN_6995051</td>\n",
       "      <td>ddht[.]co[.]kr</td>\n",
       "      <td>0</td>\n",
       "      <td>ddht.co.kr</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6995052</th>\n",
       "      <td>TRAIN_6995052</td>\n",
       "      <td>www[.]upstartepoxy[.]com</td>\n",
       "      <td>0</td>\n",
       "      <td>www.upstartepoxy.com</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>18</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6995053</th>\n",
       "      <td>TRAIN_6995053</td>\n",
       "      <td>employeesalaryschedule70[.]000webhostapp[.]com...</td>\n",
       "      <td>1</td>\n",
       "      <td>employeesalaryschedule70.000webhostapp.com/adb...</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>41</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6995054</th>\n",
       "      <td>TRAIN_6995054</td>\n",
       "      <td>dekalbtool[.]com</td>\n",
       "      <td>0</td>\n",
       "      <td>dekalbtool.com</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6995055</th>\n",
       "      <td>TRAIN_6995055</td>\n",
       "      <td>helpinganimals[.]com</td>\n",
       "      <td>0</td>\n",
       "      <td>helpinganimals.com</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>17</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>6995056 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                    ID                                                URL  \\\n",
       "0        TRAIN_0000000                           poznan[.]wuoz[.]gov[.]pl   \n",
       "1        TRAIN_0000001                          vill[.]okawa[.]kochi[.]jp   \n",
       "2        TRAIN_0000002                          nationalfinance[.]co[.]om   \n",
       "3        TRAIN_0000003                       town[.]ozora[.]hokkaido[.]jp   \n",
       "4        TRAIN_0000004              open24[.]ie-news[.]irish/online/Login   \n",
       "...                ...                                                ...   \n",
       "6995051  TRAIN_6995051                                     ddht[.]co[.]kr   \n",
       "6995052  TRAIN_6995052                           www[.]upstartepoxy[.]com   \n",
       "6995053  TRAIN_6995053  employeesalaryschedule70[.]000webhostapp[.]com...   \n",
       "6995054  TRAIN_6995054                                   dekalbtool[.]com   \n",
       "6995055  TRAIN_6995055                               helpinganimals[.]com   \n",
       "\n",
       "         label                                          URL_clean  dot_count  \\\n",
       "0            0                                 poznan.wuoz.gov.pl          3   \n",
       "1            0                                vill.okawa.kochi.jp          3   \n",
       "2            0                              nationalfinance.co.om          2   \n",
       "3            0                             town.ozora.hokkaido.jp          3   \n",
       "4            1                  open24.ie-news.irish/online/Login          2   \n",
       "...        ...                                                ...        ...   \n",
       "6995051      0                                         ddht.co.kr          2   \n",
       "6995052      0                               www.upstartepoxy.com          2   \n",
       "6995053      1  employeesalaryschedule70.000webhostapp.com/adb...          2   \n",
       "6995054      0                                     dekalbtool.com          1   \n",
       "6995055      0                                 helpinganimals.com          1   \n",
       "\n",
       "         slash_count  alpha_count  dash_count  underscore_count  digit_count  \\\n",
       "0                  0           15           0                 0            0   \n",
       "1                  0           16           0                 0            0   \n",
       "2                  0           19           0                 0            0   \n",
       "3                  0           19           0                 0            0   \n",
       "4                  2           26           1                 0            2   \n",
       "...              ...          ...         ...               ...          ...   \n",
       "6995051            0            8           0                 0            0   \n",
       "6995052            0           18           0                 0            0   \n",
       "6995053            2           41           0                 0            5   \n",
       "6995054            0           13           0                 0            0   \n",
       "6995055            0           17           0                 0            0   \n",
       "\n",
       "         percent_count  equal_count  question_count  at_count  \n",
       "0                    0            0               0         0  \n",
       "1                    0            0               0         0  \n",
       "2                    0            0               0         0  \n",
       "3                    0            0               0         0  \n",
       "4                    0            0               0         0  \n",
       "...                ...          ...             ...       ...  \n",
       "6995051              0            0               0         0  \n",
       "6995052              0            0               0         0  \n",
       "6995053              0            0               0         0  \n",
       "6995054              0            0               0         0  \n",
       "6995055              0            0               0         0  \n",
       "\n",
       "[6995056 rows x 14 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = train[[\n",
    "    'dot_count', 'slash_count', 'alpha_count',\n",
    "    'dash_count', 'underscore_count', 'digit_count',\n",
    "    'percent_count', 'equal_count', 'question_count', 'at_count'\n",
    "]]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = train['label']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1000x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# 피처 + label 포함해서 상관행렬 계산\n",
    "features = [\n",
    "    'dot_count', 'slash_count', 'alpha_count', \n",
    "    'dash_count', 'underscore_count', 'digit_count',\n",
    "    'percent_count', 'equal_count', 'question_count', 'at_count', 'label'\n",
    "]\n",
    "\n",
    "corr_df = train[features].corr(numeric_only=True)\n",
    "\n",
    "# X축에서 label 제거\n",
    "corr_df = corr_df.drop(columns=['label'])\n",
    "\n",
    "# 히트맵 그리기\n",
    "plt.figure(figsize=(10, 8))\n",
    "sns.heatmap(corr_df, annot=True, cmap='coolwarm', fmt=\".2f\", linewidths=0.5)\n",
    "plt.title(\"Feature Correlation Heatmap (label dropped from X-axis)\", fontsize=14)\n",
    "plt.xticks(rotation=45)\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "# 스케일링\n",
    "scaler = StandardScaler()\n",
    "X_scaled = scaler.fit_transform(X)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "# 1단계: 전체에서 테스트 세트 먼저 떼어내기 (20%)\n",
    "X_train_full, X_test, y_train_full, y_test = train_test_split(\n",
    "    X_scaled, y, test_size=0.2, random_state=42, stratify = y\n",
    ")\n",
    "\n",
    "# 2단계: 남은 80% 중에서 훈련 / 검증으로 다시 나누기 (검증 20% = 전체의 16%)\n",
    "X_train, X_val, y_train, y_val = train_test_split(\n",
    "    X_train_full, y_train_full, test_size=0.2, random_state=42, stratify = y_train_full)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\human\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.11_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python311\\site-packages\\keras\\src\\layers\\core\\dense.py:87: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n",
      "  super().__init__(activity_regularizer=activity_regularizer, **kwargs)\n"
     ]
    }
   ],
   "source": [
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import Dense\n",
    "model = Sequential()\n",
    "model.add(Dense(64, input_dim=10, activation='relu'))\n",
    "model.add(Dense(32, activation='relu'))  \n",
    "model.add(Dense(1, activation='sigmoid'))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 모델 컴파일\n",
    "from tensorflow.keras.optimizers import Adam\n",
    "optimizer = Adam(learning_rate=0.001)  # 학습률을 너무 크게 설정하면 빨리 수렴할 수 있음\n",
    "\n",
    "model.compile(optimizer, loss='binary_crossentropy', metrics=['accuracy'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# EarlyStopping 콜백을 사용하여 검증 손실이 개선되지 않으면 학습을 멈추도록 설정\n",
    "from tensorflow.keras.callbacks import EarlyStopping\n",
    "early_stopping = EarlyStopping(monitor='val_loss', patience=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "\u001b[1m139902/139902\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m244s\u001b[0m 2ms/step - accuracy: 0.8741 - loss: 0.3109 - val_accuracy: 0.8787 - val_loss: 0.3006\n",
      "Epoch 2/10\n",
      "\u001b[1m139902/139902\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m229s\u001b[0m 2ms/step - accuracy: 0.8799 - loss: 0.3000 - val_accuracy: 0.8812 - val_loss: 0.2984\n",
      "Epoch 3/10\n",
      "\u001b[1m139902/139902\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m230s\u001b[0m 2ms/step - accuracy: 0.8809 - loss: 0.2986 - val_accuracy: 0.8809 - val_loss: 0.2974\n",
      "Epoch 4/10\n",
      "\u001b[1m139902/139902\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m235s\u001b[0m 2ms/step - accuracy: 0.8813 - loss: 0.2979 - val_accuracy: 0.8826 - val_loss: 0.2962\n",
      "Epoch 5/10\n",
      "\u001b[1m139902/139902\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m232s\u001b[0m 2ms/step - accuracy: 0.8818 - loss: 0.2970 - val_accuracy: 0.8815 - val_loss: 0.2961\n",
      "Epoch 6/10\n",
      "\u001b[1m139902/139902\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m245s\u001b[0m 2ms/step - accuracy: 0.8822 - loss: 0.2968 - val_accuracy: 0.8824 - val_loss: 0.2962\n",
      "Epoch 7/10\n",
      "\u001b[1m139902/139902\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m244s\u001b[0m 2ms/step - accuracy: 0.8823 - loss: 0.2964 - val_accuracy: 0.8826 - val_loss: 0.2962\n",
      "Epoch 8/10\n",
      "\u001b[1m139902/139902\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m244s\u001b[0m 2ms/step - accuracy: 0.8825 - loss: 0.2958 - val_accuracy: 0.8826 - val_loss: 0.2949\n",
      "Epoch 9/10\n",
      "\u001b[1m139902/139902\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m241s\u001b[0m 2ms/step - accuracy: 0.8829 - loss: 0.2955 - val_accuracy: 0.8828 - val_loss: 0.2949\n",
      "Epoch 10/10\n",
      "\u001b[1m139902/139902\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m240s\u001b[0m 2ms/step - accuracy: 0.8829 - loss: 0.2949 - val_accuracy: 0.8830 - val_loss: 0.2951\n"
     ]
    }
   ],
   "source": [
    "# 모델 학습 (Epoch마다 정확도 출력)\n",
    "history = model.fit(X_train, y_train, \n",
    "                    epochs=10,               # 최대 epoch 수\n",
    "                    batch_size=32,            # 배치 크기\n",
    "                    validation_data=(X_val, y_val),  # 검증 데이터\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m34976/34976\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m40s\u001b[0m 1ms/step - accuracy: 0.8833 - loss: 0.2946\n",
      "Validation Accuracy: 0.8830\n"
     ]
    }
   ],
   "source": [
    "# 모델 평가\n",
    "from sklearn.metrics import accuracy_score\n",
    "val_loss, val_accuracy = model.evaluate(X_val, y_val)\n",
    "print(f'Validation Accuracy: {val_accuracy:.4f}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m34976/34976\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m26s\u001b[0m 747us/step\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import f1_score, accuracy_score\n",
    "\n",
    "# 확률 예측값이 y_val_proba라고 가정\n",
    "# 예: y_val_proba = model.predict(X_val).ravel()\n",
    "\n",
    "# 0.1 ~ 0.89 범위에서 0.01 간격으로 threshold 후보 생성\n",
    "thresholds = np.arange(0.1, 0.9, 0.01)\n",
    "\n",
    "# 각 threshold마다 F1 점수 계산\n",
    "threshold_range = [i * 0.01 for i in range(100)]\n",
    "f1_scores = []\n",
    "accuracies = []\n",
    "thresholds = []\n",
    "\n",
    "y_val_proba = model.predict(X_val).flatten()\n",
    "\n",
    "for t in thresholds:\n",
    "    y_pred = (y_val_proba > t).astype(int)  # 임계값 기준으로 이진 분류\n",
    "    score = f1_score(y_val, y_pred)\n",
    "    f1_scores.append(score)\n",
    "    acc = accuracy_score(y_val, y_pred)   \n",
    "\n",
    "for t in [i * 0.01 for i in range(100)]:\n",
    "    y_pred = (y_val_proba > t).astype(int)\n",
    "\n",
    "    try:\n",
    "        f1 = f1_score(y_val, y_pred)\n",
    "        acc = accuracy_score(y_val, y_pred)\n",
    "\n",
    "        thresholds.append(t)         # 여기서 같이 append!\n",
    "        f1_scores.append(f1)\n",
    "        accuracies.append(acc)\n",
    "    except:\n",
    "        continue"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best Threshold: 0.36\n",
      "Best F1 Score: 0.7238\n",
      "Accuracy at Best Threshold: 0.8736\n"
     ]
    }
   ],
   "source": [
    "# 최고 F1 점수 기준 인덱스\n",
    "best_idx = f1_scores.index(max(f1_scores))\n",
    "best_threshold = thresholds[best_idx]\n",
    "best_f1 = f1_scores[best_idx]\n",
    "best_acc = accuracies[best_idx]  \n",
    "\n",
    "print(f\"Best Threshold: {best_threshold:.2f}\")\n",
    "print(f\"Best F1 Score: {best_f1:.4f}\")\n",
    "print(f\"Accuracy at Best Threshold: {best_acc:.4f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 5))\n",
    "plt.plot(thresholds, f1_scores, label='F1 Score', color='blue')\n",
    "plt.plot(thresholds, accuracies, label='Accuracy', color='green')\n",
    "plt.axvline(x=best_threshold, color='red', linestyle='--', label=f'Best Threshold: {best_threshold:.2f}')\n",
    "plt.xlabel(\"Threshold\")\n",
    "plt.ylabel(\"Score\")\n",
    "plt.title(\"F1 Score & Accuracy by Threshold\")\n",
    "plt.legend()\n",
    "plt.grid(True)\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m43720/43720\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m32s\u001b[0m 729us/step\n"
     ]
    }
   ],
   "source": [
    "# 테스트 데이터 예측 확률\n",
    "y_test_proba = model.predict(X_test).ravel()\n",
    "\n",
    "# 최적 임계값 적용해서 예측값 변환\n",
    "y_test_pred = (y_test_proba > best_threshold).astype(int)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m43720/43720\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m30s\u001b[0m 696us/step\n",
      "Test Accuracy: 0.8676272969781531\n",
      "Test ROC AUC: 0.8988072344759882\n"
     ]
    }
   ],
   "source": [
    "y_test_pred = model.predict(X_test)  # 테스트 데이터에 대해 예측\n",
    "y_test_pred = (y_test_pred > 0.3).astype(int)  # 이진 분류이므로 0.5를 기준으로 0과 1로 변환\n",
    "\n",
    "# 성능 평가\n",
    "from sklearn.metrics import accuracy_score, roc_auc_score\n",
    "\n",
    "print(\"Test Accuracy:\", accuracy_score(y_test, y_test_pred))\n",
    "print(\"Test ROC AUC:\", roc_auc_score(y_test, y_test_proba))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Counter({0: 5430159, 1: 1564897})\n"
     ]
    }
   ],
   "source": [
    "from collections import Counter\n",
    "print(Counter(y))  # 전체 클래스 분포\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Classification Report:\n",
      "\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.80      1.00      0.89    868826\n",
      "           1       1.00      0.15      0.26    250383\n",
      "\n",
      "    accuracy                           0.81   1119209\n",
      "   macro avg       0.90      0.58      0.58   1119209\n",
      "weighted avg       0.85      0.81      0.75   1119209\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report, ConfusionMatrixDisplay\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "# 평가 지표 출력\n",
    "print(\"Classification Report:\\n\")\n",
    "print(classification_report(y_val, y_pred))\n",
    "\n",
    "# 혼동 행렬 시각화\n",
    "ConfusionMatrixDisplay.from_predictions(y_val, y_pred, cmap='Blues')\n",
    "plt.title(\"Confusion Matrix\")\n",
    "plt.show()\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.11.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}