가짜 데이터 추가.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import tensorflow as tf\n",
    "import pandas as pd\n",
    "import random"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 시그모이드 함수\n",
    "def sigmoid(x):\n",
    "    return 1/(1+np.exp(-x).astype(np.float64))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "def tanh(x):  # 하이퍼볼릭 탄젠트 함수\n",
    "    return (np.exp(x)-np.exp(-x))/(np.exp(x)+np.exp(-x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "def relu(x):  # relu 함수\n",
    "    return max(0,x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\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>CertificateNums</th>\n",
       "      <th>VolunteerHours</th>\n",
       "      <th>Donation</th>\n",
       "      <th>YearsOfService</th>\n",
       "      <th>FineNums</th>\n",
       "      <th>ShoppingNums</th>\n",
       "      <th>ShoppingAmount</th>\n",
       "      <th>Stock</th>\n",
       "      <th>RealEstateSearch</th>\n",
       "      <th>ElectricCar</th>\n",
       "      <th>BMI</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>TravelNums</th>\n",
       "      <th>SNSFriends</th>\n",
       "      <th>BookNums</th>\n",
       "      <th>UseTrafficNums</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.87</td>\n",
       "      <td>56.87</td>\n",
       "      <td>1</td>\n",
       "      <td>4.52</td>\n",
       "      <td>-9</td>\n",
       "      <td>4.66</td>\n",
       "      <td>92695.82</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>19.19</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>1.60</td>\n",
       "      <td>148</td>\n",
       "      <td>2.03</td>\n",
       "      <td>14.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.54</td>\n",
       "      <td>44.34</td>\n",
       "      <td>0</td>\n",
       "      <td>3.40</td>\n",
       "      <td>-3</td>\n",
       "      <td>4.48</td>\n",
       "      <td>104661.87</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>24.16</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1.33</td>\n",
       "      <td>142</td>\n",
       "      <td>1.99</td>\n",
       "      <td>16.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3.98</td>\n",
       "      <td>60.37</td>\n",
       "      <td>0</td>\n",
       "      <td>4.49</td>\n",
       "      <td>-5</td>\n",
       "      <td>4.69</td>\n",
       "      <td>91292.12</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>24.49</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1.55</td>\n",
       "      <td>47</td>\n",
       "      <td>2.01</td>\n",
       "      <td>15.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2.79</td>\n",
       "      <td>46.33</td>\n",
       "      <td>1</td>\n",
       "      <td>4.22</td>\n",
       "      <td>-2</td>\n",
       "      <td>4.47</td>\n",
       "      <td>115118.65</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>24.76</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>1.62</td>\n",
       "      <td>395</td>\n",
       "      <td>1.99</td>\n",
       "      <td>20.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2.28</td>\n",
       "      <td>42.92</td>\n",
       "      <td>1</td>\n",
       "      <td>5.81</td>\n",
       "      <td>-4</td>\n",
       "      <td>5.77</td>\n",
       "      <td>106202.76</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>19.54</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1.45</td>\n",
       "      <td>309</td>\n",
       "      <td>2.02</td>\n",
       "      <td>13.54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>995</th>\n",
       "      <td>2.33</td>\n",
       "      <td>52.84</td>\n",
       "      <td>0</td>\n",
       "      <td>4.54</td>\n",
       "      <td>-6</td>\n",
       "      <td>7.92</td>\n",
       "      <td>116831.41</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>21.44</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>1.44</td>\n",
       "      <td>82</td>\n",
       "      <td>2.01</td>\n",
       "      <td>16.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>996</th>\n",
       "      <td>2.71</td>\n",
       "      <td>58.24</td>\n",
       "      <td>1</td>\n",
       "      <td>3.57</td>\n",
       "      <td>-8</td>\n",
       "      <td>4.78</td>\n",
       "      <td>96511.36</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>24.52</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>1.51</td>\n",
       "      <td>280</td>\n",
       "      <td>1.99</td>\n",
       "      <td>15.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>997</th>\n",
       "      <td>2.26</td>\n",
       "      <td>46.84</td>\n",
       "      <td>0</td>\n",
       "      <td>6.57</td>\n",
       "      <td>-9</td>\n",
       "      <td>6.12</td>\n",
       "      <td>95559.36</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>24.33</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>1.45</td>\n",
       "      <td>204</td>\n",
       "      <td>1.98</td>\n",
       "      <td>15.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>998</th>\n",
       "      <td>3.51</td>\n",
       "      <td>55.81</td>\n",
       "      <td>1</td>\n",
       "      <td>5.86</td>\n",
       "      <td>-5</td>\n",
       "      <td>5.70</td>\n",
       "      <td>98984.89</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>21.76</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>1.42</td>\n",
       "      <td>315</td>\n",
       "      <td>2.01</td>\n",
       "      <td>11.81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999</th>\n",
       "      <td>2.30</td>\n",
       "      <td>49.89</td>\n",
       "      <td>1</td>\n",
       "      <td>5.10</td>\n",
       "      <td>-1</td>\n",
       "      <td>5.40</td>\n",
       "      <td>95246.31</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>25.78</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1.60</td>\n",
       "      <td>48</td>\n",
       "      <td>2.01</td>\n",
       "      <td>16.13</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1000 rows × 17 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     CertificateNums  VolunteerHours  Donation  YearsOfService  FineNums  \\\n",
       "0               1.87           56.87         1            4.52        -9   \n",
       "1               1.54           44.34         0            3.40        -3   \n",
       "2               3.98           60.37         0            4.49        -5   \n",
       "3               2.79           46.33         1            4.22        -2   \n",
       "4               2.28           42.92         1            5.81        -4   \n",
       "..               ...             ...       ...             ...       ...   \n",
       "995             2.33           52.84         0            4.54        -6   \n",
       "996             2.71           58.24         1            3.57        -8   \n",
       "997             2.26           46.84         0            6.57        -9   \n",
       "998             3.51           55.81         1            5.86        -5   \n",
       "999             2.30           49.89         1            5.10        -1   \n",
       "\n",
       "     ShoppingNums  ShoppingAmount  Stock  RealEstateSearch  ElectricCar  \\\n",
       "0            4.66        92695.82      0                 7            1   \n",
       "1            4.48       104661.87      0                 1            1   \n",
       "2            4.69        91292.12      1                 0            0   \n",
       "3            4.47       115118.65      1                 5            1   \n",
       "4            5.77       106202.76      1                 8            0   \n",
       "..            ...             ...    ...               ...          ...   \n",
       "995          7.92       116831.41      0                 9            0   \n",
       "996          4.78        96511.36      1                 3            0   \n",
       "997          6.12        95559.36      0                 1            0   \n",
       "998          5.70        98984.89      1                 6            0   \n",
       "999          5.40        95246.31      1                 6            0   \n",
       "\n",
       "       BMI  Sex  Age  TravelNums  SNSFriends  BookNums  UseTrafficNums  \n",
       "0    19.19    0    8        1.60         148      2.03           14.16  \n",
       "1    24.16    0    2        1.33         142      1.99           16.83  \n",
       "2    24.49    0    2        1.55          47      2.01           15.98  \n",
       "3    24.76    1    9        1.62         395      1.99           20.09  \n",
       "4    19.54    1    2        1.45         309      2.02           13.54  \n",
       "..     ...  ...  ...         ...         ...       ...             ...  \n",
       "995  21.44    0    8        1.44          82      2.01           16.85  \n",
       "996  24.52    1    5        1.51         280      1.99           15.95  \n",
       "997  24.33    1    7        1.45         204      1.98           15.99  \n",
       "998  21.76    0    9        1.42         315      2.01           11.81  \n",
       "999  25.78    0    2        1.60          48      2.01           16.13  \n",
       "\n",
       "[1000 rows x 17 columns]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv(\"MOCK_DATA.csv\")\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'CertificateNums'"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.columns[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-2"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "random.randint(-20,20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "M = 17       # 입력 데이터의 차원\n",
    "K = 5      # 출력 클래스의 수\n",
    "n = 200     # 각 클래스에 속한 데이터 수\n",
    "N = n * K   # 전체 데이터 수\n",
    "\n",
    "# 샘플 데이터 생성\n",
    "X1 = data[data.columns[0]]\n",
    "X2 = data[data.columns[1]]\n",
    "X3 = data[data.columns[2]]\n",
    "X4 = data[data.columns[3]]\n",
    "X5 = data[data.columns[4]]\n",
    "X6 = data[data.columns[5]]\n",
    "X7 = data[data.columns[6]]\n",
    "X8 = data[data.columns[7]]\n",
    "X9 = data[data.columns[8]]\n",
    "X10 = data[data.columns[9]]\n",
    "X11 = data[data.columns[10]]\n",
    "X12 =  data[data.columns[11]]\n",
    "X13 =  data[data.columns[12]]\n",
    "X14 =  data[data.columns[13]]\n",
    "X15 = data[data.columns[14]]\n",
    "X16 = data[data.columns[15]]\n",
    "X17 = data[data.columns[16]]\n",
    "\n",
    "w1,w2,w3,w4,w5,w6,w7,w8,w9,w10,w11,w12,w13,w14,w15,w16,w17 = np.random.uniform(-2,10,17)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3.634412730601584 5.176592206727888 -1.0213687732543293 7.703833833835581 1.937533038099704 1.5795544583938166 5.869938351434783 -0.9646346844738072 0.8925350302352277 3.2268427250627356 -0.733470286778978 1.186847235900332 4.396328347065602 -1.6482039941732456 1.749848329252532 9.814675134261652 9.544152142207258\n"
     ]
    }
   ],
   "source": [
    "print(w1,w2,w3,w4,w5,w6,w7,w8,w9,w10,w11,w12,w13,w14,w15,w16,w17)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = w1*X1 + w2*X2 + \\\n",
    "            w3 * X3 + tanh(w4*X4) + w5*X5 + w6*X6 + \\\n",
    "            w7*X7 +  w8*X8 + w9*X9 + w10*X11 + w11*X11 + w12*X12 + \\\n",
    "            sigmoid(w12*X12) + w13*X13 + w14*X14 + w15*X15 +  w16*X16 + w17*X17 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "y +=  np.random.uniform(-100,100,1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      544982.229194\n",
       "1      615132.697778\n",
       "2      536556.429325\n",
       "3      676974.160314\n",
       "4      624417.594528\n",
       "           ...      \n",
       "995    686425.384274\n",
       "996    567525.043190\n",
       "997    561755.192343\n",
       "998    582081.338888\n",
       "999    559714.792284\n",
       "Length: 1000, dtype: float64"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "q10 = np.percentile(y,100-3)\n",
    "q9 = np.percentile(y, 100 -11 ) # return 50th percentile, e.g median.\n",
    "q8 = np.percentile(y, 100 - 21 ) # return 50th percentile, e.g median.\n",
    "q7 = np.percentile(y, 100 - 33) # return 50th percentile, e.g median.\n",
    "q6 = np.percentile(y, 100-  45) # return 50th percentile, e.g median.\n",
    "q5 = np.percentile(y, 45) # return 50th percentile, e.g median.\n",
    "q4 = np.percentile(y, 33) # return 50th percentile, e.g median.\n",
    "q3 =np.percentile(y, 21)\n",
    "q2 = np.percentile(y, 1)\n",
    "q1 = np.percentile(y, 3) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.series.Series"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "score = []\n",
    "for i in y:\n",
    "    if i < q1:\n",
    "        score.append(1)\n",
    "    elif i <q2:\n",
    "        score.append(2)\n",
    "    elif i <q3:\n",
    "        score.append(3)\n",
    "    elif i <q4:\n",
    "        score.append(4)\n",
    "    elif i <q5:\n",
    "        score.append(5)\n",
    "    elif i <q6:\n",
    "        score.append(6)\n",
    "    elif i <q7:\n",
    "        score.append(7)\n",
    "    elif i <q8:\n",
    "        score.append(8)\n",
    "    elif i <q9:\n",
    "        score.append(9)\n",
    "    else:\n",
    "        score.append(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
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     "execution_count": 39,
     "metadata": {},
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  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['score'] = score"
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  },
  {
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   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
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       "        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>CertificateNums</th>\n",
       "      <th>VolunteerHours</th>\n",
       "      <th>Donation</th>\n",
       "      <th>YearsOfService</th>\n",
       "      <th>FineNums</th>\n",
       "      <th>ShoppingNums</th>\n",
       "      <th>ShoppingAmount</th>\n",
       "      <th>Stock</th>\n",
       "      <th>RealEstateSearch</th>\n",
       "      <th>ElectricCar</th>\n",
       "      <th>BMI</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>TravelNums</th>\n",
       "      <th>SNSFriends</th>\n",
       "      <th>BookNums</th>\n",
       "      <th>UseTrafficNums</th>\n",
       "      <th>score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.87</td>\n",
       "      <td>56.87</td>\n",
       "      <td>1</td>\n",
       "      <td>4.52</td>\n",
       "      <td>-9</td>\n",
       "      <td>4.66</td>\n",
       "      <td>92695.82</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>19.19</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>1.60</td>\n",
       "      <td>148</td>\n",
       "      <td>2.03</td>\n",
       "      <td>14.16</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.54</td>\n",
       "      <td>44.34</td>\n",
       "      <td>0</td>\n",
       "      <td>3.40</td>\n",
       "      <td>-3</td>\n",
       "      <td>4.48</td>\n",
       "      <td>104661.87</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>24.16</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1.33</td>\n",
       "      <td>142</td>\n",
       "      <td>1.99</td>\n",
       "      <td>16.83</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3.98</td>\n",
       "      <td>60.37</td>\n",
       "      <td>0</td>\n",
       "      <td>4.49</td>\n",
       "      <td>-5</td>\n",
       "      <td>4.69</td>\n",
       "      <td>91292.12</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>24.49</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1.55</td>\n",
       "      <td>47</td>\n",
       "      <td>2.01</td>\n",
       "      <td>15.98</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2.79</td>\n",
       "      <td>46.33</td>\n",
       "      <td>1</td>\n",
       "      <td>4.22</td>\n",
       "      <td>-2</td>\n",
       "      <td>4.47</td>\n",
       "      <td>115118.65</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>24.76</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>1.62</td>\n",
       "      <td>395</td>\n",
       "      <td>1.99</td>\n",
       "      <td>20.09</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2.28</td>\n",
       "      <td>42.92</td>\n",
       "      <td>1</td>\n",
       "      <td>5.81</td>\n",
       "      <td>-4</td>\n",
       "      <td>5.77</td>\n",
       "      <td>106202.76</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>19.54</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1.45</td>\n",
       "      <td>309</td>\n",
       "      <td>2.02</td>\n",
       "      <td>13.54</td>\n",
       "      <td>8</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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>995</th>\n",
       "      <td>2.33</td>\n",
       "      <td>52.84</td>\n",
       "      <td>0</td>\n",
       "      <td>4.54</td>\n",
       "      <td>-6</td>\n",
       "      <td>7.92</td>\n",
       "      <td>116831.41</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>21.44</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>1.44</td>\n",
       "      <td>82</td>\n",
       "      <td>2.01</td>\n",
       "      <td>16.85</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>996</th>\n",
       "      <td>2.71</td>\n",
       "      <td>58.24</td>\n",
       "      <td>1</td>\n",
       "      <td>3.57</td>\n",
       "      <td>-8</td>\n",
       "      <td>4.78</td>\n",
       "      <td>96511.36</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>24.52</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>1.51</td>\n",
       "      <td>280</td>\n",
       "      <td>1.99</td>\n",
       "      <td>15.95</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>997</th>\n",
       "      <td>2.26</td>\n",
       "      <td>46.84</td>\n",
       "      <td>0</td>\n",
       "      <td>6.57</td>\n",
       "      <td>-9</td>\n",
       "      <td>6.12</td>\n",
       "      <td>95559.36</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>24.33</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>1.45</td>\n",
       "      <td>204</td>\n",
       "      <td>1.98</td>\n",
       "      <td>15.99</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>998</th>\n",
       "      <td>3.51</td>\n",
       "      <td>55.81</td>\n",
       "      <td>1</td>\n",
       "      <td>5.86</td>\n",
       "      <td>-5</td>\n",
       "      <td>5.70</td>\n",
       "      <td>98984.89</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>21.76</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>1.42</td>\n",
       "      <td>315</td>\n",
       "      <td>2.01</td>\n",
       "      <td>11.81</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999</th>\n",
       "      <td>2.30</td>\n",
       "      <td>49.89</td>\n",
       "      <td>1</td>\n",
       "      <td>5.10</td>\n",
       "      <td>-1</td>\n",
       "      <td>5.40</td>\n",
       "      <td>95246.31</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>25.78</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1.60</td>\n",
       "      <td>48</td>\n",
       "      <td>2.01</td>\n",
       "      <td>16.13</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1000 rows × 18 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     CertificateNums  VolunteerHours  Donation  YearsOfService  FineNums  \\\n",
       "0               1.87           56.87         1            4.52        -9   \n",
       "1               1.54           44.34         0            3.40        -3   \n",
       "2               3.98           60.37         0            4.49        -5   \n",
       "3               2.79           46.33         1            4.22        -2   \n",
       "4               2.28           42.92         1            5.81        -4   \n",
       "..               ...             ...       ...             ...       ...   \n",
       "995             2.33           52.84         0            4.54        -6   \n",
       "996             2.71           58.24         1            3.57        -8   \n",
       "997             2.26           46.84         0            6.57        -9   \n",
       "998             3.51           55.81         1            5.86        -5   \n",
       "999             2.30           49.89         1            5.10        -1   \n",
       "\n",
       "     ShoppingNums  ShoppingAmount  Stock  RealEstateSearch  ElectricCar  \\\n",
       "0            4.66        92695.82      0                 7            1   \n",
       "1            4.48       104661.87      0                 1            1   \n",
       "2            4.69        91292.12      1                 0            0   \n",
       "3            4.47       115118.65      1                 5            1   \n",
       "4            5.77       106202.76      1                 8            0   \n",
       "..            ...             ...    ...               ...          ...   \n",
       "995          7.92       116831.41      0                 9            0   \n",
       "996          4.78        96511.36      1                 3            0   \n",
       "997          6.12        95559.36      0                 1            0   \n",
       "998          5.70        98984.89      1                 6            0   \n",
       "999          5.40        95246.31      1                 6            0   \n",
       "\n",
       "       BMI  Sex  Age  TravelNums  SNSFriends  BookNums  UseTrafficNums  score  \n",
       "0    19.19    0    8        1.60         148      2.03           14.16      4  \n",
       "1    24.16    0    2        1.33         142      1.99           16.83      7  \n",
       "2    24.49    0    2        1.55          47      2.01           15.98      3  \n",
       "3    24.76    1    9        1.62         395      1.99           20.09     10  \n",
       "4    19.54    1    2        1.45         309      2.02           13.54      8  \n",
       "..     ...  ...  ...         ...         ...       ...             ...    ...  \n",
       "995  21.44    0    8        1.44          82      2.01           16.85     10  \n",
       "996  24.52    1    5        1.51         280      1.99           15.95      5  \n",
       "997  24.33    1    7        1.45         204      1.98           15.99      5  \n",
       "998  21.76    0    9        1.42         315      2.01           11.81      6  \n",
       "999  25.78    0    2        1.60          48      2.01           16.13      4  \n",
       "\n",
       "[1000 rows x 18 columns]"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "data.to_csv('y까지_씨드배정.csv')"
   ]
  }
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