data_analysis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "d28e6988",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7b766fc2",
   "metadata": {},
   "source": [
    "### split된 데이터셋 다시 읽어들이기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "a00d6684",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(28014, 18)\n",
      "(28014, 1)\n",
      "(9338, 18)\n",
      "(9338, 1)\n",
      "(9339, 18)\n",
      "(9339, 1)\n"
     ]
    }
   ],
   "source": [
    "df_train_x = pd.read_csv(\"data/splitted/Fraud_Detection_train_features.csv\")\n",
    "df_train_y = pd.read_csv(\"data/splitted/Fraud_Detection_train_target.csv\")\n",
    "df_val_x = pd.read_csv(\"data/splitted/Fraud_Detection_validation_features.csv\")\n",
    "df_val_y = pd.read_csv(\"data/splitted/Fraud_Detection_validation_target.csv\")\n",
    "df_test_x = pd.read_csv(\"data/splitted/Fraud_Detection_test_features.csv\")\n",
    "df_test_y = pd.read_csv(\"data/splitted/Fraud_Detection_test_target.csv\")\n",
    "\n",
    "#shape가 일치하는지 확인\n",
    "print(df_train_x.shape)\n",
    "print(df_train_y.shape)\n",
    "print(df_val_x.shape)\n",
    "print(df_val_y.shape)\n",
    "print(df_test_x.shape)\n",
    "print(df_test_y.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "d190cb6d",
   "metadata": {
    "scrolled": true
   },
   "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>TRANS_YEAR</th>\n",
       "      <th>IS_COVID_YEAR</th>\n",
       "      <th>TRANS_MONTH</th>\n",
       "      <th>TRANS_DAY</th>\n",
       "      <th>TRANS_DAY_SIMPLIFIED</th>\n",
       "      <th>TRANS_HOUR</th>\n",
       "      <th>TRANS_HOUR_SIMPLIFIED</th>\n",
       "      <th>CATEGORY</th>\n",
       "      <th>AMT</th>\n",
       "      <th>GENDER</th>\n",
       "      <th>CITY</th>\n",
       "      <th>CITY_POP</th>\n",
       "      <th>STATE</th>\n",
       "      <th>JOB</th>\n",
       "      <th>AGE</th>\n",
       "      <th>CC_NUM</th>\n",
       "      <th>log_AMT</th>\n",
       "      <th>BC_CITY_POP</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2019</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>23</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>344.01</td>\n",
       "      <td>False</td>\n",
       "      <td>Fulton</td>\n",
       "      <td>355</td>\n",
       "      <td>45</td>\n",
       "      <td>Financial adviser</td>\n",
       "      <td>68</td>\n",
       "      <td>3513618443244549</td>\n",
       "      <td>5.840671</td>\n",
       "      <td>4.329507</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2019</td>\n",
       "      <td>False</td>\n",
       "      <td>12</td>\n",
       "      <td>30</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>92.80</td>\n",
       "      <td>False</td>\n",
       "      <td>Avoca</td>\n",
       "      <td>2036</td>\n",
       "      <td>8</td>\n",
       "      <td>Engineering geologist</td>\n",
       "      <td>52</td>\n",
       "      <td>4198470814557</td>\n",
       "      <td>4.530447</td>\n",
       "      <td>5.164343</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020</td>\n",
       "      <td>True</td>\n",
       "      <td>9</td>\n",
       "      <td>28</td>\n",
       "      <td>2</td>\n",
       "      <td>14</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>6.02</td>\n",
       "      <td>False</td>\n",
       "      <td>Mulberry Grove</td>\n",
       "      <td>1810</td>\n",
       "      <td>43</td>\n",
       "      <td>Race relations officer</td>\n",
       "      <td>48</td>\n",
       "      <td>371226440126102</td>\n",
       "      <td>1.795087</td>\n",
       "      <td>5.112989</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2019</td>\n",
       "      <td>False</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>23</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>1002.17</td>\n",
       "      <td>True</td>\n",
       "      <td>Sacramento</td>\n",
       "      <td>757530</td>\n",
       "      <td>11</td>\n",
       "      <td>Magazine features editor</td>\n",
       "      <td>44</td>\n",
       "      <td>4890424426862856940</td>\n",
       "      <td>6.909923</td>\n",
       "      <td>7.052526</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2019</td>\n",
       "      <td>False</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>22</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>107.33</td>\n",
       "      <td>False</td>\n",
       "      <td>Lawn</td>\n",
       "      <td>213</td>\n",
       "      <td>5</td>\n",
       "      <td>Special educational needs teacher</td>\n",
       "      <td>51</td>\n",
       "      <td>4169759661243568</td>\n",
       "      <td>4.675908</td>\n",
       "      <td>4.053561</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2020</td>\n",
       "      <td>True</td>\n",
       "      <td>10</td>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "      <td>17</td>\n",
       "      <td>2</td>\n",
       "      <td>11</td>\n",
       "      <td>7.64</td>\n",
       "      <td>True</td>\n",
       "      <td>Arcadia</td>\n",
       "      <td>680</td>\n",
       "      <td>19</td>\n",
       "      <td>Designer, jewellery</td>\n",
       "      <td>56</td>\n",
       "      <td>3547560454237873</td>\n",
       "      <td>2.033398</td>\n",
       "      <td>4.658996</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2019</td>\n",
       "      <td>False</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>7.48</td>\n",
       "      <td>False</td>\n",
       "      <td>Hinesburg</td>\n",
       "      <td>4542</td>\n",
       "      <td>39</td>\n",
       "      <td>Claims inspector/assessor</td>\n",
       "      <td>25</td>\n",
       "      <td>6534628260579800</td>\n",
       "      <td>2.012233</td>\n",
       "      <td>5.497430</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2020</td>\n",
       "      <td>True</td>\n",
       "      <td>12</td>\n",
       "      <td>22</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>9.71</td>\n",
       "      <td>False</td>\n",
       "      <td>Grandview</td>\n",
       "      <td>5875</td>\n",
       "      <td>17</td>\n",
       "      <td>Radiographer, therapeutic</td>\n",
       "      <td>31</td>\n",
       "      <td>4908846471916297</td>\n",
       "      <td>2.273156</td>\n",
       "      <td>5.598198</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   TRANS_YEAR  IS_COVID_YEAR  TRANS_MONTH  TRANS_DAY  TRANS_DAY_SIMPLIFIED  \\\n",
       "0        2019          False            1         10                     0   \n",
       "1        2019          False           12         30                     2   \n",
       "2        2020           True            9         28                     2   \n",
       "3        2019          False            9          1                     0   \n",
       "4        2019          False            6          5                     0   \n",
       "5        2020           True           10         12                     1   \n",
       "6        2019          False            3          8                     0   \n",
       "7        2020           True           12         22                     2   \n",
       "\n",
       "   TRANS_HOUR  TRANS_HOUR_SIMPLIFIED  CATEGORY      AMT  GENDER  \\\n",
       "0          23                      3         1   344.01   False   \n",
       "1           7                      1         1    92.80   False   \n",
       "2          14                      2         9     6.02   False   \n",
       "3          23                      3         6  1002.17    True   \n",
       "4          22                      3         8   107.33   False   \n",
       "5          17                      2        11     7.64    True   \n",
       "6          12                      1         6     7.48   False   \n",
       "7          10                      1         0     9.71   False   \n",
       "\n",
       "             CITY  CITY_POP  STATE                                JOB  AGE  \\\n",
       "0          Fulton       355     45                  Financial adviser   68   \n",
       "1           Avoca      2036      8              Engineering geologist   52   \n",
       "2  Mulberry Grove      1810     43             Race relations officer   48   \n",
       "3      Sacramento    757530     11           Magazine features editor   44   \n",
       "4            Lawn       213      5  Special educational needs teacher   51   \n",
       "5         Arcadia       680     19                Designer, jewellery   56   \n",
       "6       Hinesburg      4542     39          Claims inspector/assessor   25   \n",
       "7       Grandview      5875     17          Radiographer, therapeutic   31   \n",
       "\n",
       "                CC_NUM   log_AMT  BC_CITY_POP  \n",
       "0     3513618443244549  5.840671     4.329507  \n",
       "1        4198470814557  4.530447     5.164343  \n",
       "2      371226440126102  1.795087     5.112989  \n",
       "3  4890424426862856940  6.909923     7.052526  \n",
       "4     4169759661243568  4.675908     4.053561  \n",
       "5     3547560454237873  2.033398     4.658996  \n",
       "6     6534628260579800  2.012233     5.497430  \n",
       "7     4908846471916297  2.273156     5.598198  "
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train_x.head(8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "793753aa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>IS_FRAUD</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>True</td>\n",
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       "      <th>1</th>\n",
       "      <td>False</td>\n",
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       "      <th>2</th>\n",
       "      <td>False</td>\n",
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       "      <th>3</th>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>False</td>\n",
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      ],
      "text/plain": [
       "   IS_FRAUD\n",
       "0      True\n",
       "1     False\n",
       "2     False\n",
       "3      True\n",
       "4      True\n",
       "5     False\n",
       "6     False\n",
       "7     False"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train_y.head(8)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2bba07dd",
   "metadata": {},
   "source": [
    "train 데이터셋을 이리저리 뜯어보고 만져보고 시각화하며 train 데이터셋의 인사이트, 패턴을 발견한다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "93cd8d89",
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x_feature = \"TRANS_YEAR\"\n",
    "y_feature = \"TRANS_MONTH\"\n",
    "\n",
    "plt.scatter(x = df_train_x[x_feature], y = df_train_x[y_feature], c = df_train_y[\"IS_FRAUD\"], s = 2)\n",
    "plt.xlabel(x_feature)\n",
    "plt.ylabel(y_feature)\n",
    "plt.legend(df_train_y[\"IS_FRAUD\"])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7c871f58",
   "metadata": {},
   "source": [
    "TRANS_YEAR의 가질 수 있는 값이 단 2개밖에 없어서 분석이 안 된다. 따라서 TRANS_YEAR로는 분석하지 않기로 함."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "7d570826",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x_feature = \"TRANS_MONTH\"\n",
    "y_feature = \"TRANS_DAY\"\n",
    "\n",
    "plt.scatter(x = df_train_x[x_feature], y = df_train_x[y_feature], c = df_train_y[\"IS_FRAUD\"], s = 2)\n",
    "plt.xlabel(x_feature)\n",
    "plt.ylabel(y_feature)\n",
    "plt.legend(df_train_y[\"IS_FRAUD\"])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "879ee2f4",
   "metadata": {},
   "source": [
    "YEAR보다는 더 많은 경우의 수를 보여주지만 여전히 하나의 점에 겹치는게 너무 많아 그래프 분석은 안 된다.\n",
    "## 연속적인 값이나 경우의 수가 매우 많은 이산적인 값으로 시각화를 진행해야 한다"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f51db58d",
   "metadata": {},
   "source": [
    "## 시각화 진행"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "2876c661",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x_feature = \"AMT\"\n",
    "y_feature = \"CITY_POP\"\n",
    "\n",
    "plt.scatter(x = df_train_x[x_feature], y = df_train_x[y_feature], c = df_train_y[\"IS_FRAUD\"], s = 2)\n",
    "plt.xlabel(x_feature)\n",
    "plt.ylabel(y_feature)\n",
    "plt.legend(df_train_y[\"IS_FRAUD\"])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "781e5900",
   "metadata": {},
   "source": [
    "변수의 갯수가 많아서 시각화 하기에 적절하다."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b0b9f238",
   "metadata": {},
   "source": [
    "## 여기서 train feature테이블과 target테이블을 concat함"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "f4aac6fc",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_train = pd.concat([df_train_x, df_train_y],axis = 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "065bad4a",
   "metadata": {},
   "source": [
    "FRAUD가 CITY_POP과는 전혀 연관이 없어 보인다. (CITY_POP이 무엇이든 간에 AMT에 대한 FRAUD의 분포는 거의 동일함)\n",
    "# 결론1: CITY_POP은 모델에서 빼자"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bd05fdd5",
   "metadata": {
    "scrolled": true
   },
   "source": [
    "##### 이제 PLOT그래프를 그려볼 차례이다.\n",
    "##### AMT는 값의 종류가 매우 많기 때문에 일정한 범위로 묶어서 PLOT그래프를 그려야 한다"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f6f25ce3",
   "metadata": {},
   "source": [
    "# 주의!\n",
    "## 한 column에서 가질 수 있는 각각의 값의 갯수가 서로 다르기 때문에, FRAUD가 아니라 FRAUD_RATIO를 사용해야 한다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "7f636fd1",
   "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>FRAUD</th>\n",
       "      <th>NOT_FRAUD</th>\n",
       "      <th>FRAUD_RATIO</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0~250</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>251~500</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>501~750</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>751~1000</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1001~1250</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1251~</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           FRAUD  NOT_FRAUD  FRAUD_RATIO\n",
       "0~250          0          0            0\n",
       "251~500        0          0            0\n",
       "501~750        0          0            0\n",
       "751~1000       0          0            0\n",
       "1001~1250      0          0            0\n",
       "1251~          0          0            0"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#The ratio of FRAUD about AMT\n",
    "\n",
    "amt_group = pd.DataFrame(columns = [\"FRAUD\", \"NOT_FRAUD\", \"FRAUD_RATIO\"], index = [\"0~250\", \"251~500\", \"501~750\", \"751~1000\", \"1001~1250\", \"1251~\"])\n",
    "\n",
    "amt_group = amt_group.fillna(0)\n",
    "\n",
    "amt_group"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "d8f6c982",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "           FRAUD  NOT_FRAUD  FRAUD_RATIO\n",
      "0~250       1462      21650            0\n",
      "251~500     1499        361            0\n",
      "501~750      407        126            0\n",
      "751~1000    1669         28            0\n",
      "1001~1250    715         19            0\n",
      "1251~       5076      22204            0\n"
     ]
    }
   ],
   "source": [
    "for i in range(df_train.shape[0]):\n",
    "    try:\n",
    "        if((df_train.loc[i, \"AMT\"] >= 0) and (df_train.loc[i, \"AMT\"] <= 250)):\n",
    "            if(df_train.loc[i,\"IS_FRAUD\"] == True):\n",
    "                amt_group.loc[\"0~250\", \"FRAUD\"] = amt_group.loc[\"0~250\", \"FRAUD\"] + 1\n",
    "            else:\n",
    "                amt_group.loc[\"0~250\", \"NOT_FRAUD\"] = amt_group.loc[\"0~250\", \"NOT_FRAUD\"] + 1\n",
    "        if((df_train.loc[i, \"AMT\"] >= 251) and (df_train.loc[i, \"AMT\"] <= 500)):\n",
    "            if(df_train.loc[i,\"IS_FRAUD\"] == True):\n",
    "                amt_group.loc[\"251~500\", \"FRAUD\"] = amt_group.loc[\"251~500\", \"FRAUD\"] + 1\n",
    "            else:\n",
    "                amt_group.loc[\"251~500\", \"NOT_FRAUD\"] = amt_group.loc[\"251~500\", \"NOT_FRAUD\"] + 1\n",
    "        if((df_train.loc[i, \"AMT\"] >= 501) and (df_train.loc[i, \"AMT\"] <= 750)):\n",
    "            if(df_train.loc[i,\"IS_FRAUD\"] == True):\n",
    "                amt_group.loc[\"501~750\", \"FRAUD\"] = amt_group.loc[\"501~750\", \"FRAUD\"] + 1\n",
    "            else:\n",
    "                amt_group.loc[\"501~750\", \"NOT_FRAUD\"] = amt_group.loc[\"501~750\", \"NOT_FRAUD\"] + 1\n",
    "        if((df_train.loc[i, \"AMT\"] >= 751) and (df_train.loc[i, \"AMT\"] <= 1000)):\n",
    "            if(df_train.loc[i,\"IS_FRAUD\"] == True):\n",
    "                amt_group.loc[\"751~1000\", \"FRAUD\"] = amt_group.loc[\"751~1000\", \"FRAUD\"] + 1\n",
    "            else:\n",
    "                amt_group.loc[\"751~1000\", \"NOT_FRAUD\"] = amt_group.loc[\"751~1000\", \"NOT_FRAUD\"] + 1\n",
    "        if((df_train.loc[i, \"AMT\"] >= 1001) and (df_train.loc[i, \"AMT\"] <= 1250)):\n",
    "            if(df_train.loc[i,\"IS_FRAUD\"] == True):\n",
    "                amt_group.loc[\"1001~1250\", \"FRAUD\"] = amt_group.loc[\"1001~1250\", \"FRAUD\"] + 1\n",
    "            else:\n",
    "                amt_group.loc[\"1001~1250\", \"NOT_FRAUD\"] = amt_group.loc[\"1001~1250\", \"NOT_FRAUD\"] + 1\n",
    "        else:\n",
    "            if(df_train.loc[i,\"IS_FRAUD\"] == True):\n",
    "                amt_group.loc[\"1251~\", \"FRAUD\"] = amt_group.loc[\"1251~\", \"FRAUD\"] + 1\n",
    "            else:\n",
    "                amt_group.loc[\"1251~\", \"NOT_FRAUD\"] = amt_group.loc[\"1251~\", \"NOT_FRAUD\"] + 1\n",
    "    except:\n",
    "        pass\n",
    "            \n",
    "print(amt_group)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "9d21e246",
   "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>FRAUD</th>\n",
       "      <th>NOT_FRAUD</th>\n",
       "      <th>FRAUD_RATIO</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0~250</th>\n",
       "      <td>1462</td>\n",
       "      <td>21650</td>\n",
       "      <td>0.063257</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>251~500</th>\n",
       "      <td>1499</td>\n",
       "      <td>361</td>\n",
       "      <td>0.805914</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>501~750</th>\n",
       "      <td>407</td>\n",
       "      <td>126</td>\n",
       "      <td>0.763602</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>751~1000</th>\n",
       "      <td>1669</td>\n",
       "      <td>28</td>\n",
       "      <td>0.983500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1001~1250</th>\n",
       "      <td>715</td>\n",
       "      <td>19</td>\n",
       "      <td>0.974114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1251~</th>\n",
       "      <td>5076</td>\n",
       "      <td>22204</td>\n",
       "      <td>0.186070</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           FRAUD  NOT_FRAUD  FRAUD_RATIO\n",
       "0~250       1462      21650     0.063257\n",
       "251~500     1499        361     0.805914\n",
       "501~750      407        126     0.763602\n",
       "751~1000    1669         28     0.983500\n",
       "1001~1250    715         19     0.974114\n",
       "1251~       5076      22204     0.186070"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "amt_group[\"FRAUD_RATIO\"] = amt_group[\"FRAUD\"]/(amt_group[\"NOT_FRAUD\"] + amt_group[\"FRAUD\"])\n",
    "\n",
    "amt_group"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fff3ee0b",
   "metadata": {},
   "source": [
    "### 각 AMT 구간 당 FRAUD의 비율이 정해졌다"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "7995fa28",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot([\"0~250\", \"251~500\", \"501~750\", \"751~1000\", \"1001~1250\", \"1251~\"], amt_group[\"FRAUD_RATIO\"])\n",
    "plt.scatter([\"0~250\", \"251~500\", \"501~750\", \"751~1000\", \"1001~1250\", \"1251~\"], amt_group[\"FRAUD_RATIO\"])\n",
    "plt.xlabel(\"AMT\")\n",
    "plt.ylabel(\"FRAUD_RATIO\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "617f139d",
   "metadata": {},
   "source": [
    "# 결론2: AMT는 M자 모양의 모델을 선정한다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "59ee4a3c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "numpy.ndarray"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(df_train[\"CITY_POP\"].unique())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a148edc8",
   "metadata": {},
   "source": [
    "### 나머지 features에 대하여 FRAUD_RATIO를 구하는 함수를 정의한다.\n",
    "#### PARAMETER: 첫 번째 열은 FRAUD_RATIO를 구하고 싶은 feature가, 두 번재 열은 IS_FRAUD 열이다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "f401fb38",
   "metadata": {},
   "outputs": [],
   "source": [
    "def find_fraud_ratio(df):\n",
    "    obj_column_name = list(df.columns)\n",
    "    obj_column_name = obj_column_name[0]\n",
    "    \n",
    "    print(obj_column_name)\n",
    "    \n",
    "    idx_list = list(df[obj_column_name].unique())\n",
    "    \n",
    "    df_result = pd.DataFrame(columns = [\"INDEX\", \"FRAUD\", \"NOT_FRAUD\", \"FRAUD_RATIO\"], index = idx_list)\n",
    "    df_result = df_result.fillna(0)\n",
    "    df_result[\"INDEX\"] = idx_list\n",
    "    \n",
    "\n",
    "    for i in range(df.shape[0]):\n",
    "        try:\n",
    "            if(df.loc[i, \"IS_FRAUD\"] == True):\n",
    "                df_result.loc[(df.loc[i, obj_column_name]), \"FRAUD\"] += 1\n",
    "            else:\n",
    "                df_result.loc[(df.loc[i, obj_column_name]), \"NOT_FRAUD\"] += 1\n",
    "        except:\n",
    "            pass\n",
    "            \n",
    "    df_result[\"FRAUD_RATIO\"] = df_result[\"FRAUD\"] / (df_result[\"FRAUD\"] + df_result[\"NOT_FRAUD\"])\n",
    "    \n",
    "    df_result = df_result[[\"INDEX\", \"FRAUD_RATIO\"]]\n",
    "    \n",
    "    df_result = df_result.sort_values(\"INDEX\")\n",
    "    \n",
    "    df_result = df_result.rename(columns={\"INDEX\":obj_column_name})\n",
    "    \n",
    "    return df_result"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "174f44f1",
   "metadata": {},
   "source": [
    "리턴값은 1열이 한 feature가 가지는 값의 종류이고, 2열이 FRAUD_RATIO이다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "e2ae764c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['TRANS_YEAR', 'IS_COVID_YEAR', 'TRANS_MONTH', 'TRANS_DAY',\n",
      "       'TRANS_DAY_SIMPLIFIED', 'TRANS_HOUR', 'TRANS_HOUR_SIMPLIFIED',\n",
      "       'CATEGORY', 'AMT', 'GENDER', 'CITY', 'CITY_POP', 'STATE', 'JOB', 'AGE',\n",
      "       'CC_NUM', 'log_AMT', 'BC_CITY_POP', 'IS_FRAUD'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "print(df_train.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "3b887cf0",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TRANS_YEAR\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_trans_year = find_fraud_ratio(df_train[[\"TRANS_YEAR\", \"IS_FRAUD\"]])\n",
    "\n",
    "plt.plot(df_trans_year[\"TRANS_YEAR\"], df_trans_year[\"FRAUD_RATIO\"])\n",
    "plt.xlabel(\"TRANS_YEAR\")\n",
    "plt.ylabel(\"FRAUD_RATIO\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "967defc3",
   "metadata": {},
   "source": [
    "# 결론3: TRANS_YEAR은 종류가 2개 뿐이기 때문에 의미가 없다(제외!)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "120a0bc8",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TRANS_MONTH\n",
      "    TRANS_MONTH  FRAUD_RATIO\n",
      "1             1     0.296580\n",
      "2             2     0.291395\n",
      "3             3     0.232135\n",
      "4             4     0.196737\n",
      "5             5     0.248343\n",
      "6             6     0.200226\n",
      "7             7     0.157340\n",
      "8             8     0.187020\n",
      "9             9     0.209184\n",
      "10           10     0.249048\n",
      "11           11     0.192456\n",
      "12           12     0.128516\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_trans_month = find_fraud_ratio(df_train[[\"TRANS_MONTH\", \"IS_FRAUD\"]])\n",
    "\n",
    "print(df_trans_month)\n",
    "\n",
    "plt.plot(df_trans_month[\"TRANS_MONTH\"], df_trans_month[\"FRAUD_RATIO\"])\n",
    "plt.xlabel(\"TRANS_MONTH\")\n",
    "plt.ylabel(\"FRAUD_RATIO\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b8f416f4",
   "metadata": {},
   "source": [
    "# 결론4: TRANS_MONTH는 4차함수 모양의 모델을 선정한다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "88bdc1cf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TRANS_DAY_SIMPLIFIED\n",
      "   TRANS_DAY_SIMPLIFIED  FRAUD_RATIO\n",
      "0                     0     0.200667\n",
      "1                     1     0.226039\n",
      "2                     2     0.194250\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_trans_day_simplified = find_fraud_ratio(df_train[[\"TRANS_DAY_SIMPLIFIED\", \"IS_FRAUD\"]])\n",
    "\n",
    "print(df_trans_day_simplified)\n",
    "\n",
    "plt.plot(df_trans_day_simplified[\"TRANS_DAY_SIMPLIFIED\"], df_trans_day_simplified[\"FRAUD_RATIO\"])\n",
    "plt.xlabel(\"TRANS_DAY_SIMPLIFIED\")\n",
    "plt.ylabel(\"FRAUD_RATIO\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "865451f6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TRANS_HOUR_SIMPLIFIED\n",
      "   TRANS_HOUR_SIMPLIFIED  FRAUD_RATIO\n",
      "0                      0     0.295506\n",
      "1                      1     0.047840\n",
      "2                      2     0.046963\n",
      "3                      3     0.357904\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_trans_hour_simplified = find_fraud_ratio(df_train[[\"TRANS_HOUR_SIMPLIFIED\", \"IS_FRAUD\"]])\n",
    "\n",
    "print(df_trans_hour_simplified)\n",
    "\n",
    "plt.plot(df_trans_hour_simplified[\"TRANS_HOUR_SIMPLIFIED\"], df_trans_hour_simplified[\"FRAUD_RATIO\"])\n",
    "plt.xlabel(\"TRANS_HOUR_SIMPLIFIED\")\n",
    "plt.ylabel(\"FRAUD_RATIO\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "0d1d0717",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CATEGORY\n",
      "    CATEGORY  FRAUD_RATIO\n",
      "0          0     0.386607\n",
      "1          1     0.385940\n",
      "2          2     0.097325\n",
      "3          3     0.172023\n",
      "4          4     0.120505\n",
      "5          5     0.127660\n",
      "6          6     0.461591\n",
      "7          7     0.246224\n",
      "8          8     0.077751\n",
      "9          9     0.093468\n",
      "10        10     0.068238\n",
      "11        11     0.103281\n",
      "12        12     0.087470\n",
      "13        13     0.064432\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_trans_category = find_fraud_ratio(df_train[[\"CATEGORY\", \"IS_FRAUD\"]])\n",
    "\n",
    "print(df_trans_category)\n",
    "\n",
    "plt.plot(df_trans_category[\"CATEGORY\"], df_trans_category[\"FRAUD_RATIO\"])\n",
    "plt.xlabel(\"CATEGORY\")\n",
    "plt.ylabel(\"FRAUD_RATIO\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "c163fe91",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "STATE\n",
      "    STATE  FRAUD_RATIO\n",
      "0       0     0.175227\n",
      "1       1     0.198992\n",
      "2       2     0.157025\n",
      "3       3     0.129167\n",
      "4       4     0.251524\n",
      "5       5     0.200120\n",
      "6       6     0.210019\n",
      "7       7     0.235602\n",
      "8       8     0.211200\n",
      "9       9     0.150943\n",
      "10     10     0.220541\n",
      "11     11     0.198060\n",
      "12     12     0.222222\n",
      "13     13     0.194274\n",
      "14     14     0.198997\n",
      "15     15     0.235915\n",
      "16     16     0.202952\n",
      "17     17     0.179074\n",
      "18     18     0.215311\n",
      "19     19     0.185972\n",
      "20     20     0.188011\n",
      "21     21     0.166667\n",
      "22     22     0.245810\n",
      "23     23     0.264706\n",
      "24     24     0.137019\n",
      "25     25     0.225000\n",
      "26     26     0.212903\n",
      "27     27     0.233707\n",
      "28     28     0.206122\n",
      "29     29     0.176991\n",
      "30     30     0.203088\n",
      "31     31     0.180060\n",
      "32     32     0.203509\n",
      "33     33     0.212947\n",
      "34     34     0.236111\n",
      "35     35     0.182927\n",
      "36     36     0.225000\n",
      "37     37     0.212891\n",
      "38     38     0.239057\n",
      "39     39     0.198312\n",
      "40     40     0.186732\n",
      "41     41     0.241706\n",
      "42     42     0.203390\n",
      "43     43     0.214737\n",
      "44     44     0.259669\n",
      "45     45     0.210317\n",
      "46     46     0.389610\n",
      "47     47     0.159744\n",
      "48     48     0.225610\n",
      "49     49     0.555556\n",
      "50     50     1.000000\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_trans_state = find_fraud_ratio(df_train[[\"STATE\", \"IS_FRAUD\"]])\n",
    "\n",
    "print(df_trans_state)\n",
    "\n",
    "plt.plot(df_trans_state[\"STATE\"], df_trans_state[\"FRAUD_RATIO\"])\n",
    "plt.xlabel(\"STATE\")\n",
    "plt.ylabel(\"FRAUD_RATIO\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4fbc513a",
   "metadata": {},
   "source": [
    "## (임시테스트)일단 TRANS_DAY를 넣어서 정렬테스트를 진행"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "225862d2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>TRANS_YEAR</th>\n",
       "      <th>IS_COVID_YEAR</th>\n",
       "      <th>TRANS_MONTH</th>\n",
       "      <th>TRANS_DAY</th>\n",
       "      <th>TRANS_DAY_SIMPLIFIED</th>\n",
       "      <th>TRANS_HOUR</th>\n",
       "      <th>TRANS_HOUR_SIMPLIFIED</th>\n",
       "      <th>CATEGORY</th>\n",
       "      <th>AMT</th>\n",
       "      <th>GENDER</th>\n",
       "      <th>CITY</th>\n",
       "      <th>CITY_POP</th>\n",
       "      <th>STATE</th>\n",
       "      <th>JOB</th>\n",
       "      <th>AGE</th>\n",
       "      <th>CC_NUM</th>\n",
       "      <th>log_AMT</th>\n",
       "      <th>BC_CITY_POP</th>\n",
       "      <th>IS_FRAUD</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>8359</th>\n",
       "      <td>2019</td>\n",
       "      <td>False</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>11.74</td>\n",
       "      <td>False</td>\n",
       "      <td>Fort Washakie</td>\n",
       "      <td>1645</td>\n",
       "      <td>20</td>\n",
       "      <td>Information systems manager</td>\n",
       "      <td>37</td>\n",
       "      <td>60416207185</td>\n",
       "      <td>2.463002</td>\n",
       "      <td>5.070778</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23590</th>\n",
       "      <td>2019</td>\n",
       "      <td>False</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>13.17</td>\n",
       "      <td>False</td>\n",
       "      <td>Fort Washakie</td>\n",
       "      <td>1645</td>\n",
       "      <td>20</td>\n",
       "      <td>Information systems manager</td>\n",
       "      <td>37</td>\n",
       "      <td>60416207185</td>\n",
       "      <td>2.577942</td>\n",
       "      <td>5.070778</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1587</th>\n",
       "      <td>2019</td>\n",
       "      <td>False</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>23</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>130.08</td>\n",
       "      <td>False</td>\n",
       "      <td>Fort Washakie</td>\n",
       "      <td>1645</td>\n",
       "      <td>20</td>\n",
       "      <td>Information systems manager</td>\n",
       "      <td>37</td>\n",
       "      <td>60416207185</td>\n",
       "      <td>4.868150</td>\n",
       "      <td>5.070778</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5953</th>\n",
       "      <td>2019</td>\n",
       "      <td>False</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>23</td>\n",
       "      <td>3</td>\n",
       "      <td>9</td>\n",
       "      <td>20.41</td>\n",
       "      <td>False</td>\n",
       "      <td>Fort Washakie</td>\n",
       "      <td>1645</td>\n",
       "      <td>20</td>\n",
       "      <td>Information systems manager</td>\n",
       "      <td>37</td>\n",
       "      <td>60416207185</td>\n",
       "      <td>3.016025</td>\n",
       "      <td>5.070778</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11629</th>\n",
       "      <td>2019</td>\n",
       "      <td>False</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>23</td>\n",
       "      <td>3</td>\n",
       "      <td>12</td>\n",
       "      <td>19.23</td>\n",
       "      <td>False</td>\n",
       "      <td>Fort Washakie</td>\n",
       "      <td>1645</td>\n",
       "      <td>20</td>\n",
       "      <td>Information systems manager</td>\n",
       "      <td>37</td>\n",
       "      <td>60416207185</td>\n",
       "      <td>2.956472</td>\n",
       "      <td>5.070778</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17863</th>\n",
       "      <td>2019</td>\n",
       "      <td>False</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>23</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>594.09</td>\n",
       "      <td>False</td>\n",
       "      <td>Fort Washakie</td>\n",
       "      <td>1645</td>\n",
       "      <td>20</td>\n",
       "      <td>Information systems manager</td>\n",
       "      <td>37</td>\n",
       "      <td>60416207185</td>\n",
       "      <td>6.387031</td>\n",
       "      <td>5.070778</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17722</th>\n",
       "      <td>2020</td>\n",
       "      <td>True</td>\n",
       "      <td>12</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>16</td>\n",
       "      <td>2</td>\n",
       "      <td>13</td>\n",
       "      <td>38.42</td>\n",
       "      <td>False</td>\n",
       "      <td>Fort Washakie</td>\n",
       "      <td>1645</td>\n",
       "      <td>20</td>\n",
       "      <td>Information systems manager</td>\n",
       "      <td>37</td>\n",
       "      <td>60416207185</td>\n",
       "      <td>3.648578</td>\n",
       "      <td>5.070778</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13522</th>\n",
       "      <td>2019</td>\n",
       "      <td>False</td>\n",
       "      <td>11</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>2.08</td>\n",
       "      <td>False</td>\n",
       "      <td>Fort Washakie</td>\n",
       "      <td>1645</td>\n",
       "      <td>20</td>\n",
       "      <td>Information systems manager</td>\n",
       "      <td>37</td>\n",
       "      <td>60416207185</td>\n",
       "      <td>0.732368</td>\n",
       "      <td>5.070778</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16915</th>\n",
       "      <td>2019</td>\n",
       "      <td>False</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>18</td>\n",
       "      <td>2</td>\n",
       "      <td>12</td>\n",
       "      <td>16.54</td>\n",
       "      <td>False</td>\n",
       "      <td>Fort Washakie</td>\n",
       "      <td>1645</td>\n",
       "      <td>20</td>\n",
       "      <td>Information systems manager</td>\n",
       "      <td>37</td>\n",
       "      <td>60416207185</td>\n",
       "      <td>2.805782</td>\n",
       "      <td>5.070778</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19065</th>\n",
       "      <td>2019</td>\n",
       "      <td>False</td>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>16</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>9.14</td>\n",
       "      <td>False</td>\n",
       "      <td>Fort Washakie</td>\n",
       "      <td>1645</td>\n",
       "      <td>20</td>\n",
       "      <td>Information systems manager</td>\n",
       "      <td>37</td>\n",
       "      <td>60416207185</td>\n",
       "      <td>2.212660</td>\n",
       "      <td>5.070778</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4076</th>\n",
       "      <td>2019</td>\n",
       "      <td>False</td>\n",
       "      <td>10</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>14</td>\n",
       "      <td>2</td>\n",
       "      <td>13</td>\n",
       "      <td>10.33</td>\n",
       "      <td>False</td>\n",
       "      <td>Fort Washakie</td>\n",
       "      <td>1645</td>\n",
       "      <td>20</td>\n",
       "      <td>Information systems manager</td>\n",
       "      <td>37</td>\n",
       "      <td>60416207185</td>\n",
       "      <td>2.335052</td>\n",
       "      <td>5.070778</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20572</th>\n",
       "      <td>2019</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "      <td>13</td>\n",
       "      <td>1</td>\n",
       "      <td>22</td>\n",
       "      <td>3</td>\n",
       "      <td>9</td>\n",
       "      <td>3.17</td>\n",
       "      <td>False</td>\n",
       "      <td>Fort Washakie</td>\n",
       "      <td>1645</td>\n",
       "      <td>20</td>\n",
       "      <td>Information systems manager</td>\n",
       "      <td>37</td>\n",
       "      <td>60416207185</td>\n",
       "      <td>1.153732</td>\n",
       "      <td>5.070778</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1880</th>\n",
       "      <td>2020</td>\n",
       "      <td>True</td>\n",
       "      <td>6</td>\n",
       "      <td>15</td>\n",
       "      <td>1</td>\n",
       "      <td>14</td>\n",
       "      <td>2</td>\n",
       "      <td>13</td>\n",
       "      <td>70.21</td>\n",
       "      <td>False</td>\n",
       "      <td>Fort Washakie</td>\n",
       "      <td>1645</td>\n",
       "      <td>20</td>\n",
       "      <td>Information systems manager</td>\n",
       "      <td>37</td>\n",
       "      <td>60416207185</td>\n",
       "      <td>4.251491</td>\n",
       "      <td>5.070778</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9399</th>\n",
       "      <td>2019</td>\n",
       "      <td>False</td>\n",
       "      <td>10</td>\n",
       "      <td>18</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>102.87</td>\n",
       "      <td>False</td>\n",
       "      <td>Fort Washakie</td>\n",
       "      <td>1645</td>\n",
       "      <td>20</td>\n",
       "      <td>Information systems manager</td>\n",
       "      <td>37</td>\n",
       "      <td>60416207185</td>\n",
       "      <td>4.633466</td>\n",
       "      <td>5.070778</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7328</th>\n",
       "      <td>2020</td>\n",
       "      <td>True</td>\n",
       "      <td>5</td>\n",
       "      <td>19</td>\n",
       "      <td>1</td>\n",
       "      <td>20</td>\n",
       "      <td>3</td>\n",
       "      <td>12</td>\n",
       "      <td>13.47</td>\n",
       "      <td>False</td>\n",
       "      <td>Fort Washakie</td>\n",
       "      <td>1645</td>\n",
       "      <td>20</td>\n",
       "      <td>Information systems manager</td>\n",
       "      <td>37</td>\n",
       "      <td>60416207185</td>\n",
       "      <td>2.600465</td>\n",
       "      <td>5.070778</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27969</th>\n",
       "      <td>2020</td>\n",
       "      <td>True</td>\n",
       "      <td>6</td>\n",
       "      <td>21</td>\n",
       "      <td>2</td>\n",
       "      <td>13</td>\n",
       "      <td>2</td>\n",
       "      <td>13</td>\n",
       "      <td>124.66</td>\n",
       "      <td>False</td>\n",
       "      <td>Fort Washakie</td>\n",
       "      <td>1645</td>\n",
       "      <td>20</td>\n",
       "      <td>Information systems manager</td>\n",
       "      <td>37</td>\n",
       "      <td>60416207185</td>\n",
       "      <td>4.825590</td>\n",
       "      <td>5.070778</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17533</th>\n",
       "      <td>2020</td>\n",
       "      <td>True</td>\n",
       "      <td>12</td>\n",
       "      <td>22</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>63.15</td>\n",
       "      <td>False</td>\n",
       "      <td>Fort Washakie</td>\n",
       "      <td>1645</td>\n",
       "      <td>20</td>\n",
       "      <td>Information systems manager</td>\n",
       "      <td>37</td>\n",
       "      <td>60416207185</td>\n",
       "      <td>4.145513</td>\n",
       "      <td>5.070778</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9198</th>\n",
       "      <td>2020</td>\n",
       "      <td>True</td>\n",
       "      <td>8</td>\n",
       "      <td>24</td>\n",
       "      <td>2</td>\n",
       "      <td>18</td>\n",
       "      <td>2</td>\n",
       "      <td>13</td>\n",
       "      <td>126.07</td>\n",
       "      <td>False</td>\n",
       "      <td>Fort Washakie</td>\n",
       "      <td>1645</td>\n",
       "      <td>20</td>\n",
       "      <td>Information systems manager</td>\n",
       "      <td>37</td>\n",
       "      <td>60416207185</td>\n",
       "      <td>4.836837</td>\n",
       "      <td>5.070778</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7490</th>\n",
       "      <td>2019</td>\n",
       "      <td>False</td>\n",
       "      <td>6</td>\n",
       "      <td>25</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>89.63</td>\n",
       "      <td>False</td>\n",
       "      <td>Fort Washakie</td>\n",
       "      <td>1645</td>\n",
       "      <td>20</td>\n",
       "      <td>Information systems manager</td>\n",
       "      <td>37</td>\n",
       "      <td>60416207185</td>\n",
       "      <td>4.495690</td>\n",
       "      <td>5.070778</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11627</th>\n",
       "      <td>2019</td>\n",
       "      <td>False</td>\n",
       "      <td>9</td>\n",
       "      <td>29</td>\n",
       "      <td>2</td>\n",
       "      <td>14</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>67.47</td>\n",
       "      <td>False</td>\n",
       "      <td>Fort Washakie</td>\n",
       "      <td>1645</td>\n",
       "      <td>20</td>\n",
       "      <td>Information systems manager</td>\n",
       "      <td>37</td>\n",
       "      <td>60416207185</td>\n",
       "      <td>4.211683</td>\n",
       "      <td>5.070778</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       TRANS_YEAR  IS_COVID_YEAR  TRANS_MONTH  TRANS_DAY  \\\n",
       "8359         2019          False            3          1   \n",
       "23590        2019          False            3          1   \n",
       "1587         2019          False            3          2   \n",
       "5953         2019          False            3          2   \n",
       "11629        2019          False            3          2   \n",
       "17863        2019          False            3          2   \n",
       "17722        2020           True           12          3   \n",
       "13522        2019          False           11          7   \n",
       "16915        2019          False            3          7   \n",
       "19065        2019          False            9         10   \n",
       "4076         2019          False           10         11   \n",
       "20572        2019          False            1         13   \n",
       "1880         2020           True            6         15   \n",
       "9399         2019          False           10         18   \n",
       "7328         2020           True            5         19   \n",
       "27969        2020           True            6         21   \n",
       "17533        2020           True           12         22   \n",
       "9198         2020           True            8         24   \n",
       "7490         2019          False            6         25   \n",
       "11627        2019          False            9         29   \n",
       "\n",
       "       TRANS_DAY_SIMPLIFIED  TRANS_HOUR  TRANS_HOUR_SIMPLIFIED  CATEGORY  \\\n",
       "8359                      0           2                      0         3   \n",
       "23590                     0           1                      0         3   \n",
       "1587                      0          23                      3         8   \n",
       "5953                      0          23                      3         9   \n",
       "11629                     0          23                      3        12   \n",
       "17863                     0          23                      3         2   \n",
       "17722                     0          16                      2        13   \n",
       "13522                     0          13                      2         7   \n",
       "16915                     0          18                      2        12   \n",
       "19065                     0          16                      2         6   \n",
       "4076                      1          14                      2        13   \n",
       "20572                     1          22                      3         9   \n",
       "1880                      1          14                      2        13   \n",
       "9399                      1          11                      1         1   \n",
       "7328                      1          20                      3        12   \n",
       "27969                     2          13                      2        13   \n",
       "17533                     2           2                      0         3   \n",
       "9198                      2          18                      2        13   \n",
       "7490                      2           4                      0         1   \n",
       "11627                     2          14                      2         9   \n",
       "\n",
       "          AMT  GENDER           CITY  CITY_POP  STATE  \\\n",
       "8359    11.74   False  Fort Washakie      1645     20   \n",
       "23590   13.17   False  Fort Washakie      1645     20   \n",
       "1587   130.08   False  Fort Washakie      1645     20   \n",
       "5953    20.41   False  Fort Washakie      1645     20   \n",
       "11629   19.23   False  Fort Washakie      1645     20   \n",
       "17863  594.09   False  Fort Washakie      1645     20   \n",
       "17722   38.42   False  Fort Washakie      1645     20   \n",
       "13522    2.08   False  Fort Washakie      1645     20   \n",
       "16915   16.54   False  Fort Washakie      1645     20   \n",
       "19065    9.14   False  Fort Washakie      1645     20   \n",
       "4076    10.33   False  Fort Washakie      1645     20   \n",
       "20572    3.17   False  Fort Washakie      1645     20   \n",
       "1880    70.21   False  Fort Washakie      1645     20   \n",
       "9399   102.87   False  Fort Washakie      1645     20   \n",
       "7328    13.47   False  Fort Washakie      1645     20   \n",
       "27969  124.66   False  Fort Washakie      1645     20   \n",
       "17533   63.15   False  Fort Washakie      1645     20   \n",
       "9198   126.07   False  Fort Washakie      1645     20   \n",
       "7490    89.63   False  Fort Washakie      1645     20   \n",
       "11627   67.47   False  Fort Washakie      1645     20   \n",
       "\n",
       "                               JOB  AGE       CC_NUM   log_AMT  BC_CITY_POP  \\\n",
       "8359   Information systems manager   37  60416207185  2.463002     5.070778   \n",
       "23590  Information systems manager   37  60416207185  2.577942     5.070778   \n",
       "1587   Information systems manager   37  60416207185  4.868150     5.070778   \n",
       "5953   Information systems manager   37  60416207185  3.016025     5.070778   \n",
       "11629  Information systems manager   37  60416207185  2.956472     5.070778   \n",
       "17863  Information systems manager   37  60416207185  6.387031     5.070778   \n",
       "17722  Information systems manager   37  60416207185  3.648578     5.070778   \n",
       "13522  Information systems manager   37  60416207185  0.732368     5.070778   \n",
       "16915  Information systems manager   37  60416207185  2.805782     5.070778   \n",
       "19065  Information systems manager   37  60416207185  2.212660     5.070778   \n",
       "4076   Information systems manager   37  60416207185  2.335052     5.070778   \n",
       "20572  Information systems manager   37  60416207185  1.153732     5.070778   \n",
       "1880   Information systems manager   37  60416207185  4.251491     5.070778   \n",
       "9399   Information systems manager   37  60416207185  4.633466     5.070778   \n",
       "7328   Information systems manager   37  60416207185  2.600465     5.070778   \n",
       "27969  Information systems manager   37  60416207185  4.825590     5.070778   \n",
       "17533  Information systems manager   37  60416207185  4.145513     5.070778   \n",
       "9198   Information systems manager   37  60416207185  4.836837     5.070778   \n",
       "7490   Information systems manager   37  60416207185  4.495690     5.070778   \n",
       "11627  Information systems manager   37  60416207185  4.211683     5.070778   \n",
       "\n",
       "       IS_FRAUD  \n",
       "8359       True  \n",
       "23590      True  \n",
       "1587       True  \n",
       "5953       True  \n",
       "11629      True  \n",
       "17863      True  \n",
       "17722     False  \n",
       "13522     False  \n",
       "16915     False  \n",
       "19065     False  \n",
       "4076      False  \n",
       "20572     False  \n",
       "1880      False  \n",
       "9399      False  \n",
       "7328      False  \n",
       "27969     False  \n",
       "17533     False  \n",
       "9198      False  \n",
       "7490      False  \n",
       "11627     False  "
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train = pd.concat([df_train_x, df_train_y], axis = 1)\n",
    "df_train.head()\n",
    "\n",
    "df_train = df_train.sort_values(by=[\"CC_NUM\", \"TRANS_DAY\"] ,ascending=True)\n",
    "\n",
    "df_train.head(20)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e410b744",
   "metadata": {},
   "source": [
    "## 날짜를 2019년 1월 1일을 0DAY라고 간주하고 며칠이 지났는지 표시하는 DAY컬럼을 추가한다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "6934ab98",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>TRANS_YEAR</th>\n",
       "      <th>IS_COVID_YEAR</th>\n",
       "      <th>TRANS_MONTH</th>\n",
       "      <th>TRANS_DAY</th>\n",
       "      <th>TRANS_DAY_SIMPLIFIED</th>\n",
       "      <th>TRANS_HOUR</th>\n",
       "      <th>TRANS_HOUR_SIMPLIFIED</th>\n",
       "      <th>CATEGORY</th>\n",
       "      <th>AMT</th>\n",
       "      <th>GENDER</th>\n",
       "      <th>CITY</th>\n",
       "      <th>CITY_POP</th>\n",
       "      <th>STATE</th>\n",
       "      <th>JOB</th>\n",
       "      <th>AGE</th>\n",
       "      <th>CC_NUM</th>\n",
       "      <th>log_AMT</th>\n",
       "      <th>BC_CITY_POP</th>\n",
       "      <th>IS_FRAUD</th>\n",
       "      <th>DAY</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>8359</th>\n",
       "      <td>2019</td>\n",
       "      <td>False</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>11.74</td>\n",
       "      <td>False</td>\n",
       "      <td>Fort Washakie</td>\n",
       "      <td>1645</td>\n",
       "      <td>20</td>\n",
       "      <td>Information systems manager</td>\n",
       "      <td>37</td>\n",
       "      <td>60416207185</td>\n",
       "      <td>2.463002</td>\n",
       "      <td>5.070778</td>\n",
       "      <td>True</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23590</th>\n",
       "      <td>2019</td>\n",
       "      <td>False</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>13.17</td>\n",
       "      <td>False</td>\n",
       "      <td>Fort Washakie</td>\n",
       "      <td>1645</td>\n",
       "      <td>20</td>\n",
       "      <td>Information systems manager</td>\n",
       "      <td>37</td>\n",
       "      <td>60416207185</td>\n",
       "      <td>2.577942</td>\n",
       "      <td>5.070778</td>\n",
       "      <td>True</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1587</th>\n",
       "      <td>2019</td>\n",
       "      <td>False</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>23</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>130.08</td>\n",
       "      <td>False</td>\n",
       "      <td>Fort Washakie</td>\n",
       "      <td>1645</td>\n",
       "      <td>20</td>\n",
       "      <td>Information systems manager</td>\n",
       "      <td>37</td>\n",
       "      <td>60416207185</td>\n",
       "      <td>4.868150</td>\n",
       "      <td>5.070778</td>\n",
       "      <td>True</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5953</th>\n",
       "      <td>2019</td>\n",
       "      <td>False</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>23</td>\n",
       "      <td>3</td>\n",
       "      <td>9</td>\n",
       "      <td>20.41</td>\n",
       "      <td>False</td>\n",
       "      <td>Fort Washakie</td>\n",
       "      <td>1645</td>\n",
       "      <td>20</td>\n",
       "      <td>Information systems manager</td>\n",
       "      <td>37</td>\n",
       "      <td>60416207185</td>\n",
       "      <td>3.016025</td>\n",
       "      <td>5.070778</td>\n",
       "      <td>True</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11629</th>\n",
       "      <td>2019</td>\n",
       "      <td>False</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>23</td>\n",
       "      <td>3</td>\n",
       "      <td>12</td>\n",
       "      <td>19.23</td>\n",
       "      <td>False</td>\n",
       "      <td>Fort Washakie</td>\n",
       "      <td>1645</td>\n",
       "      <td>20</td>\n",
       "      <td>Information systems manager</td>\n",
       "      <td>37</td>\n",
       "      <td>60416207185</td>\n",
       "      <td>2.956472</td>\n",
       "      <td>5.070778</td>\n",
       "      <td>True</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       TRANS_YEAR  IS_COVID_YEAR  TRANS_MONTH  TRANS_DAY  \\\n",
       "8359         2019          False            3          1   \n",
       "23590        2019          False            3          1   \n",
       "1587         2019          False            3          2   \n",
       "5953         2019          False            3          2   \n",
       "11629        2019          False            3          2   \n",
       "\n",
       "       TRANS_DAY_SIMPLIFIED  TRANS_HOUR  TRANS_HOUR_SIMPLIFIED  CATEGORY  \\\n",
       "8359                      0           2                      0         3   \n",
       "23590                     0           1                      0         3   \n",
       "1587                      0          23                      3         8   \n",
       "5953                      0          23                      3         9   \n",
       "11629                     0          23                      3        12   \n",
       "\n",
       "          AMT  GENDER           CITY  CITY_POP  STATE  \\\n",
       "8359    11.74   False  Fort Washakie      1645     20   \n",
       "23590   13.17   False  Fort Washakie      1645     20   \n",
       "1587   130.08   False  Fort Washakie      1645     20   \n",
       "5953    20.41   False  Fort Washakie      1645     20   \n",
       "11629   19.23   False  Fort Washakie      1645     20   \n",
       "\n",
       "                               JOB  AGE       CC_NUM   log_AMT  BC_CITY_POP  \\\n",
       "8359   Information systems manager   37  60416207185  2.463002     5.070778   \n",
       "23590  Information systems manager   37  60416207185  2.577942     5.070778   \n",
       "1587   Information systems manager   37  60416207185  4.868150     5.070778   \n",
       "5953   Information systems manager   37  60416207185  3.016025     5.070778   \n",
       "11629  Information systems manager   37  60416207185  2.956472     5.070778   \n",
       "\n",
       "       IS_FRAUD  DAY  \n",
       "8359       True    0  \n",
       "23590      True    0  \n",
       "1587       True    0  \n",
       "5953       True    0  \n",
       "11629      True    0  "
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list1 = []\n",
    "for i in range(df_train.shape[0]):\n",
    "    list1.append(0)\n",
    "\n",
    "df_train[\"DAY\"] = list1\n",
    "\n",
    "df_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "71b9b402",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>TRANS_YEAR</th>\n",
       "      <th>IS_COVID_YEAR</th>\n",
       "      <th>TRANS_MONTH</th>\n",
       "      <th>TRANS_DAY</th>\n",
       "      <th>TRANS_DAY_SIMPLIFIED</th>\n",
       "      <th>TRANS_HOUR</th>\n",
       "      <th>TRANS_HOUR_SIMPLIFIED</th>\n",
       "      <th>CATEGORY</th>\n",
       "      <th>AMT</th>\n",
       "      <th>GENDER</th>\n",
       "      <th>CITY</th>\n",
       "      <th>CITY_POP</th>\n",
       "      <th>STATE</th>\n",
       "      <th>JOB</th>\n",
       "      <th>AGE</th>\n",
       "      <th>CC_NUM</th>\n",
       "      <th>log_AMT</th>\n",
       "      <th>BC_CITY_POP</th>\n",
       "      <th>IS_FRAUD</th>\n",
       "      <th>DAY</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2019</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
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       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>344.01</td>\n",
       "      <td>False</td>\n",
       "      <td>Fulton</td>\n",
       "      <td>355</td>\n",
       "      <td>45</td>\n",
       "      <td>Financial adviser</td>\n",
       "      <td>68</td>\n",
       "      <td>3513618443244549</td>\n",
       "      <td>5.840671</td>\n",
       "      <td>4.329507</td>\n",
       "      <td>True</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2019</td>\n",
       "      <td>False</td>\n",
       "      <td>12</td>\n",
       "      <td>30</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>92.80</td>\n",
       "      <td>False</td>\n",
       "      <td>Avoca</td>\n",
       "      <td>2036</td>\n",
       "      <td>8</td>\n",
       "      <td>Engineering geologist</td>\n",
       "      <td>52</td>\n",
       "      <td>4198470814557</td>\n",
       "      <td>4.530447</td>\n",
       "      <td>5.164343</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020</td>\n",
       "      <td>True</td>\n",
       "      <td>9</td>\n",
       "      <td>28</td>\n",
       "      <td>2</td>\n",
       "      <td>14</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>6.02</td>\n",
       "      <td>False</td>\n",
       "      <td>Mulberry Grove</td>\n",
       "      <td>1810</td>\n",
       "      <td>43</td>\n",
       "      <td>Race relations officer</td>\n",
       "      <td>48</td>\n",
       "      <td>371226440126102</td>\n",
       "      <td>1.795087</td>\n",
       "      <td>5.112989</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2019</td>\n",
       "      <td>False</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>23</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>1002.17</td>\n",
       "      <td>True</td>\n",
       "      <td>Sacramento</td>\n",
       "      <td>757530</td>\n",
       "      <td>11</td>\n",
       "      <td>Magazine features editor</td>\n",
       "      <td>44</td>\n",
       "      <td>4890424426862856940</td>\n",
       "      <td>6.909923</td>\n",
       "      <td>7.052526</td>\n",
       "      <td>True</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2019</td>\n",
       "      <td>False</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>22</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>107.33</td>\n",
       "      <td>False</td>\n",
       "      <td>Lawn</td>\n",
       "      <td>213</td>\n",
       "      <td>5</td>\n",
       "      <td>Special educational needs teacher</td>\n",
       "      <td>51</td>\n",
       "      <td>4169759661243568</td>\n",
       "      <td>4.675908</td>\n",
       "      <td>4.053561</td>\n",
       "      <td>True</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   TRANS_YEAR  IS_COVID_YEAR  TRANS_MONTH  TRANS_DAY  TRANS_DAY_SIMPLIFIED  \\\n",
       "0        2019          False            1         10                     0   \n",
       "1        2019          False           12         30                     2   \n",
       "2        2020           True            9         28                     2   \n",
       "3        2019          False            9          1                     0   \n",
       "4        2019          False            6          5                     0   \n",
       "\n",
       "   TRANS_HOUR  TRANS_HOUR_SIMPLIFIED  CATEGORY      AMT  GENDER  \\\n",
       "0          23                      3         1   344.01   False   \n",
       "1           7                      1         1    92.80   False   \n",
       "2          14                      2         9     6.02   False   \n",
       "3          23                      3         6  1002.17    True   \n",
       "4          22                      3         8   107.33   False   \n",
       "\n",
       "             CITY  CITY_POP  STATE                                JOB  AGE  \\\n",
       "0          Fulton       355     45                  Financial adviser   68   \n",
       "1           Avoca      2036      8              Engineering geologist   52   \n",
       "2  Mulberry Grove      1810     43             Race relations officer   48   \n",
       "3      Sacramento    757530     11           Magazine features editor   44   \n",
       "4            Lawn       213      5  Special educational needs teacher   51   \n",
       "\n",
       "                CC_NUM   log_AMT  BC_CITY_POP  IS_FRAUD  DAY  \n",
       "0     3513618443244549  5.840671     4.329507      True    0  \n",
       "1        4198470814557  4.530447     5.164343     False    0  \n",
       "2      371226440126102  1.795087     5.112989     False    0  \n",
       "3  4890424426862856940  6.909923     7.052526      True    0  \n",
       "4     4169759661243568  4.675908     4.053561      True    0  "
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train = df_train.reindex(list(range(df_train.shape[0])))\n",
    "\n",
    "df_train.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c8836c6b",
   "metadata": {},
   "source": [
    "### 각 데이터에 DAY 컬럼의 값을 추가하는 코드"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "6b45f6a9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 28014 entries, 0 to 28013\n",
      "Data columns (total 20 columns):\n",
      " #   Column                 Non-Null Count  Dtype  \n",
      "---  ------                 --------------  -----  \n",
      " 0   TRANS_YEAR             28014 non-null  int64  \n",
      " 1   IS_COVID_YEAR          28014 non-null  bool   \n",
      " 2   TRANS_MONTH            28014 non-null  int64  \n",
      " 3   TRANS_DAY              28014 non-null  int64  \n",
      " 4   TRANS_DAY_SIMPLIFIED   28014 non-null  int64  \n",
      " 5   TRANS_HOUR             28014 non-null  int64  \n",
      " 6   TRANS_HOUR_SIMPLIFIED  28014 non-null  int64  \n",
      " 7   CATEGORY               28014 non-null  int64  \n",
      " 8   AMT                    28014 non-null  float64\n",
      " 9   GENDER                 28014 non-null  bool   \n",
      " 10  CITY                   28014 non-null  object \n",
      " 11  CITY_POP               28014 non-null  int64  \n",
      " 12  STATE                  28014 non-null  int64  \n",
      " 13  JOB                    28014 non-null  object \n",
      " 14  AGE                    28014 non-null  int64  \n",
      " 15  CC_NUM                 28014 non-null  int64  \n",
      " 16  log_AMT                28014 non-null  float64\n",
      " 17  BC_CITY_POP            28014 non-null  float64\n",
      " 18  IS_FRAUD               28014 non-null  bool   \n",
      " 19  DAY                    28014 non-null  int32  \n",
      "dtypes: bool(3), float64(3), int32(1), int64(11), object(2)\n",
      "memory usage: 4.8+ MB\n"
     ]
    }
   ],
   "source": [
    "import datetime as dt\n",
    "\n",
    "for i in range(df_train.shape[0]):\n",
    "    dt1 = dt.datetime(2019, 1, 1)\n",
    "    dt2 = dt.datetime(df_train.loc[i, \"TRANS_YEAR\"], df_train.loc[i, \"TRANS_MONTH\"], df_train.loc[i, \"TRANS_DAY\"])\n",
    "    if(dt1 == dt2):\n",
    "        td = 0\n",
    "        df_train.loc[i, \"DAY\"] = td\n",
    "    else:\n",
    "        td = dt2 - dt1\n",
    "        df_train.loc[i, \"DAY\"] = td\n",
    "        list_temp = (str(df_train.loc[i, \"DAY\"]).split(\" day\"))\n",
    "#         print(list_temp)\n",
    "#         print(type(list_temp[0]))\n",
    "        df_train.loc[i, \"DAY\"] = list_temp[0]\n",
    "\n",
    "df_train[\"DAY\"] = df_train[\"DAY\"].astype(int)\n",
    "\n",
    "df_train.head()\n",
    "df_train.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "9cac2bec",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_train.head()\n",
    "\n",
    "df_train = df_train.sort_values(by=[\"CC_NUM\", \"DAY\"] ,ascending=True)\n",
    "\n",
    "df_train.head(50)\n",
    "\n",
    "df_train.to_csv(\"data/ordered_by_ccnum_and_day.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a342b9e7",
   "metadata": {},
   "source": [
    "### CC_NUM을 기준으로 동일한 CC_NUM에 대하여 날짜가 오름차순으로 정렬되었다!"
   ]
  }
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