011. outlier, oversampling with credit card fraud_kaggle.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from xgboost import XGBClassifier, plot_importance as xg_importance\n",
    "from lightgbm import LGBMClassifier, plot_importance as lgb_importance\n",
    "from sklearn.preprocessing import StandardScaler, RobustScaler\n",
    "from sklearn.datasets import load_breast_cancer\n",
    "from sklearn.model_selection import train_test_split, GridSearchCV\n",
    "from sklearn.metrics import accuracy_score, f1_score, precision_score, recall_score, roc_auc_score, confusion_matrix\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def metrics(y_test, pred):\n",
    "    accuracy = accuracy_score(y_test, pred)\n",
    "    precision = precision_score(y_test, pred)\n",
    "    recall = recall_score(y_test, pred)\n",
    "    f1 = f1_score(y_test, pred)\n",
    "    roc_score = roc_auc_score(y_test, pred)\n",
    "    print('정확도 : {0:.2f}, 정밀도 : {1:.2f}, 재현율 : {2:.2f}'.format(accuracy, precision, recall))\n",
    "    print('f1-score : {0:.2f}, auc : {1:.2f}'.format(f1, roc_score))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv('../datas/credit card fraud/creditcard.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(284807, 31)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 284807 entries, 0 to 284806\n",
      "Data columns (total 31 columns):\n",
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      "Amount    284807 non-null float64\n",
      "Class     284807 non-null int64\n",
      "dtypes: float64(30), int64(1)\n",
      "memory usage: 67.4 MB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Time      0\n",
       "V1        0\n",
       "V2        0\n",
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     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    284315\n",
       "1       492\n",
       "Name: Class, dtype: int64"
      ]
     },
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     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.iloc[:, -1].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2c2ff70a780>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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nIc3kz+Z4nO3LTdPAyqH3K4CjizQXSTrnnO0hcRBYneTSJOcBm4E9izwnSTpnnNXLTVX1QpJbgH3AEmBXVR1a5Gmda1zG09nKn80xSNXLlvglSQLO/uUmSdIiMiQkSV2GhCSp66y+ca3xSvIPDL7RvpzB91GOAnuq6vCiTkzSovFKQgAk+SSDP3sS4KcMHj8O8E3/sKLOZkluXOw5vJ75dJMASPI/wLur6v9m1M8DDlXV6sWZmTS7JE9W1SWLPY/XK5ebdMqfgb8FfjWjfnE7Ji2aJA/3DgEXjXMu5xpDQqd8HDiQ5Ah/+aOKlwDvBG5ZtFlJAxcBVwInZ9QD/Nf4p3PuMCQEQFV9P8nfM/jz7MsZ/OObBg5W1YuLOjkJ/g14c1U9NPNAkv8c/3TOHd6TkCR1+XSTJKnLkJAkdRkSkqQuQ0KS1GVISJK6/h96EFvhHkMj3AAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data.iloc[:, -1].value_counts().plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    99.827251\n",
       "1     0.172749\n",
       "Name: Class, dtype: float64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.iloc[:, -1].value_counts() / data.iloc[:, -1].count() * 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = data.iloc[:, :-1]\n",
    "y = data.iloc[:, -1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.25, random_state=0, stratify = y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0    99.827251\n",
      "1     0.172749\n",
      "Name: Class, dtype: float64\n",
      "0    99.827252\n",
      "1     0.172748\n",
      "Name: Class, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "print(y_train.value_counts() / y_train.count() * 100)\n",
    "print(y_test.value_counts() / y_test.count() * 100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "def modeling(model, x_train, x_test, y_train, y_test):\n",
    "    model.fit(x_train, y_train)\n",
    "    pred = model.predict(x_test)\n",
    "    metrics(y_test, pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "정확도 : 1.00, 정밀도 : 0.70, 재현율 : 0.55\n",
      "f1-score : 0.62, auc : 0.78\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "lr = LogisticRegression()\n",
    "modeling(lr, X_train, X_test, y_train, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "정확도 : 1.00, 정밀도 : 0.95, 재현율 : 0.78\n",
      "f1-score : 0.86, auc : 0.89\n"
     ]
    }
   ],
   "source": [
    "lgb = LGBMClassifier(n_estimators = 1000, num_leaves = 64, n_jobs = -1, boost_from_average = False)\n",
    "modeling(lgb, x_train = X_train, x_test = X_test, y_train = y_train, y_test = y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "데이터를 조금 더 살펴보자.\n",
    "\n",
    "Amount와 Time은 연속적인 데이터이다. 로지스틱 회귀와 같은 회귀 종류의 모델들은 정규 분포로 데이터가 분포 되어 있는 것을 선호한다.\n",
    "\n",
    "그래서 데이터 분포를 볼 수 있으면 봐주면서 진행하면 좋다.\n",
    "\n",
    "Amount와 Time에 대해서 봐보자."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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kSlSIiIhI0TCzEuA64ASgFDjLzErzNjsPWOPuA4FrgKvTvqXAaGAIMAq43sxKamjzauAadx8ErEltA7wFfA24PS++nYHLgUOA4cDlZta1fj69iIiIgBIVIiIiUlyGA4vcfbG7bwTGA6fkbXMKcEt6PREYaWaWlo939w3u/gawKLVXaZtpn2NSG6Q2TwVw9yXuPhvYknfszwOPuvtqd18DPEokRURERKSeKFEhIiIixaQn8HbO+7K0rNJt3L0cWAt0q2bfqpZ3A95PbVR1rLrEh5mdb2YzzWzmypUra2hSREREcilRISIiIsXEKlnmBW5TX8urU9A+7j7O3Ye5+7Du3bvX0KSIiIjkUqJCREREikkZ0DvnfS9gaVXbmFlroAuwupp9q1q+CtgptVHVseoSn4iIiGwHJSpERESkmMwABqXZONoSxTEn5W0zCaiYbeM04DF397R8dJoVpD8wCJheVZtpn8dTG6Q2760hvoeB482sayqieXxaJiIiIvVEiQoREREpGqlexIXExf98YIK7zzOzK83s5LTZjUA3M1sEXARckvadB0wAXgEeAi5w981VtZnauhi4KLXVLbWNmR1sZmXA6cAfzWxeOsZq4Coi+TEDuDItExERkXrSuuZNRERERBqPu08GJuctuyzn9XoigVDZvmOBsYW0mZYvJmYFyV8+gxjWUdkxbgJuqvZDiIiISJ2pR4WIiIiIiIiIFI2CEhVmNsrMFpjZIjO7pJL17czszrT+eTPrl7Pu0rR8gZl9Pmf5TWa2wszm5rV1p5m9nB5LzOzltLyfmX2Ss+4Pdf3QIiIiIiIiIlKcahz6YWYlwHXAcUSl6xlmNsndX8nZ7DxgjbsPNLPRwNXAmWZWShSsGgL0AKaY2WB33wzcDPweuDX3eO5+Zs6x/x8xN3qF1939gNp/TBERERERERFpCgrpUTEcWOTui919IzAeOCVvm1OAW9LricBIM7O0fLy7b3D3N4BFqT3c/UliKrFKpf3PAO6oxecRERERERERkSaskERFT+DtnPdlaVml26TK2muJytmF7FuVI4B33X1hzrL+ZvaSmU0zsyMKbEdEREREREREmohCZv2wSpZ5gdsUsm9VzmLb3hTLgD7u/p6ZHQTcY2ZD3P2DbQIxOx84H6DPzjsXeCgRERERERERKQaF9KgoA3rnvO8FLK1qGzNrDXQhhnUUsu+npDa+BNxZsSwNH3kvvX4BeB0YnL+vu49z92HuPqx7x441fjgRERERERERKR6FJCpmAIPMrL+ZtSWKY07K22YSMCa9Pg14zN09LR+dZgXpDwwCphdwzGOBV929rGKBmXVPhT0xswGprcUFtCUiIiIiIiIiTUSNQz/cvdzMLgQeBkqAm9x9npldCcx090nAjcBtZraI6EkxOu07z8wmAK8A5cAFacYPzOwO4ChgFzMrAy539xvTYUfz6SKanwOuNLNyYDPwLXevshiniIiIiIiIiDQ9hdSowN0nA5Pzll2W83o9cHoV+44Fxlay/Kxqjve1SpbdBdxVSLwiIiIiIiIi0jQVMvRDRERERERERKRRKFEhIiIiIiIiIkVDiQoRERERERERKRpKVIiIiIiIiIhI0VCiQkRERERERESKRkGzfoiIiIiIiNSXd9+Fn/wEPvkEVqyAzp23PgYOhHbtso5QRLKkRIWIiIiIiDSaZ56B00+HNWugRw8oK4ONG7eu79sXLr4YSkqyi1FEsqVEhYiIiIiINIhx47a+dofHHoOJE2GXXeCHP4RevWLd+vXwwQfwyitwxx3wyCNwwgnZxCwi2VOiQkREREREGtT69XDbbTBzJuy/P3zta9Chw9b17dvHY9ddYcECuP/+2K5Hj8xCFpEMqZimiIiIiIg0mFWr4Be/gBdegC9+Eb71rW2TFPnOOitqVNxyC2ze3HhxikjxUKJCREREREQaxIoV8Otfw9q18L3vwahR0KqGK5DOnWH0aFiyBKZMaZQwRaTIKFEhIiIiIiL17tVXI0mxaRNcdBHsvXfh+x58MBxwAEyaBMuXN1yMIlKclKgQEREREZF6NXcuHHVUFNC86CLo3bt2+5vB2WdD27Zw660aAiLS0ihRISIiIiIi9WbWLDj66Bji8f3vQ8+edWunSxc480x4/XW49tr6jVFEipsSFSIiIiIiUi+efz6SFO3bw7RpsPvu29feIYfA0KFw+eWwYUP9xCgixU/Tk4qIiIiIyDbGjat5m/PP3/b9n/8MF1wQPSimToX+/eHxx7cvDrNIfFx7LTzyCJx00va1JyJNgxIVIiIiIiJSZxs2wHe/C3/6Exx/PNx+O3TrVn/t7703dO0Kf/+7EhXS8ApJ0lVmyxbYuDGKx5aXQ6dO0LqAq+38hJ8EJSpERERERKRO3n4bTjsNpk+HH/8YrrwSSkrq9xitW8Opp8Jdd0VSpF27+m1fpLZWr46CsXPnwqJFsH79pwu+tmoFu+4aw5/22CMeAwZA9+7ZxNzUKFEhIiIiIiK19uij8OUvx0XaP/4BX/xiwx3r9NPhL3/R8A/JzvLl8MwzkZxYujSWdesW0+h26gRt2sSjbdtI1q1eDcuWxX6zZ0ePC4iExb77wn77ReJCKldQosLMRgG/A0qAP7v7L/LWtwNuBQ4C3gPOdPclad2lwHnAZuA/3P3htPwm4ERghbsPzWnrCuCbwMq06MfuPrm6tkREREREClWX+guy1XvvwcSJ8OKLMSzj7rvjuSGNHAk77aThH9L4Vq+G+++Hf/4zekkMGgSHHhpFXnffPeqo1KS8HN59FxYsiKTF1KmRdNtxx+iN9I1vRJuFtNVS1JioMLMS4DrgOKAMmGFmk9z9lZzNzgPWuPtAMxsNXA2caWalwGhgCNADmGJmg919M3Az8HsiwZHvGnf/dV4c1bUlIiIiIiINaONGeOihuMAyg6uuiulHd9ih4Y/dtm302NDwj6alKScF162DBx+EJ56I98ccAyecEL0naqt16ygy27NntPPJJ/DKK5G0uOsuuPlmKC2Fb34TvvrVT9d4acrfY10V0qNiOLDI3RcDmNl44BQgN1FxCnBFej0R+L2ZWVo+3t03AG+Y2aLU3rPu/qSZ9atFrFW2VYs2REREpMg1UE/OSts0s/7AeGBn4EXgq+6+sapjmFkb4M/AgcR51K3u/r8N9V1IdlrihUFVtmyBF16IC6o1a+Dgg+FLX4JLLmncOCqGfzz6KJx4YuMeWxpOsf1b27w5fscefDCSYiNGRC+enXeuv2PssAMcdFA8zj4b7rwzitH+13/Fv6vTT4cf/AD237/+jtnUtCpgm57A2znvy9KySrdx93JgLdCtwH0rc6GZzTazm8ysay3iwMzON7OZZjZz5bp1BRxKREREikVOT84TgFLgrNSrMte/enIC1xA9OfN7X44CrjezkhravJroyTkIWJParvIYwOlAO3ffl0hi/Hstb7yINBnu8NJLMHZsTD3asWNcPH3jG/V70VaoiuEfEyY0/rGlZZgxA37+8xjOtNdecNllMGZMw/6+d+wI550Hzz0Hs2ZFr4p7743aFyedBM+20NvyhfSoqGykjBe4TSH75rsBuCptdxXw/4BzC23L3ccB4wCG9e1b07FERESkuDRET04qa9PM5gPHAGenbW5J7d5QzTEc2NHMWgM7ABuBD+rrw4sUA/e4YLrvvpjVY7fd4kJq2LAYo5+FirvupaVRp+KQQ6JwYa6W0sNF6t9HH0VS4re/hc6d4dvfjkRBY9tvP7j22pg957rrIp5DD4XBg2PYyT77tJw6FoX8V1MG9M553wtYWtU26Q93F2B1gftuw93fdffN7r4F+BNbTzBq3ZaIiIg0OQ3Rk7Oq5d2A91Mb+ceq6hgTgY+AZcBbwK/dfXXdPqoUi7VrY0jDJ59srczfErlHcmL4cLj++pjN4+tfh8svj2VZJSlyHXRQxDV/ftaRSHPx8MMxC8dvfgP//u9wxRXZJClyde0KP/0pLFkSca1YAb/7Hfz61zEdaktQSI+KGcCgNIbzHaJL5dl520wCxhD1Ik4DHnN3N7NJwO1m9huiAOYgYHp1BzOzPdx9WXr7RWBuzjFq1ZaIiIg0OQ3Rk7Oyy6uaen5WtW44Uf+iB9AVeMrMplT01vhXgGbnA+cD9OnTp5KmpBh88EFMq5nftbpduxhDPngwHHccNPSPMOsx+lu2xPfws59FT4r+/eGcc+Czn41pFovJ3ntDhw5RM2O//bKORhrC2rUxBejChVG4smfPqIuyzz7Qvn39Hef55+EnP4kZOPbaC558Eo44orB/j42lY8eoW9G2bcw68sAD8KtfxYwjp54KvXvX3EZTVWOiwt3LzexC4GGiANVN7j7PzK4EZrr7JOBG4LbUxXI1kcwgbTeB6K5ZDlxQMUuHmd0BHAXsYmZlwOXufiPwSzM7gDgZWAL8e01tiYiISLNRm56cZbXoyVnZ8lXATmbWOvWayN2+qmOcDTzk7puAFWb2DDAM2CZRsc1Q1GHDNBS1yGzZAk89BffcE8XyjjsOdt01Xn/ySdyxX7cOXn45pg6sSFgMHVocvQrqS3l5FPEbOzZ6KAweDLfcEsX9brop6+gq17p1FBh86SXYtOnTwz+k6XGPngNz5sTjrbdieceO8W+xvDyKqJrFv9NBgyJJtc8+cQFfodBk3pw50Vth0iTo3h2uuQa+9a36TYLURiGJkTZt4Mgjo7DnY49FL5Cf/SwSOCedFMOzmptCelTg7pOByXnLLst5vZ4oLlXZvmOBsZUsP6uK7b9aTRyVtiUiIiLNRkP05LTK2kz7PJ7aGJ/avLeGY7wFHGNmfwU6AJ8FftsA34PUQSEn/G++CbffHhdGe+0FZ50Fe+xR+bYffwxPPx0XBtddB7vvDscfX5w9DWpj2bIojvnHP8I778CQIXDHHTHTQFP4XMOGRS+Y+fPVq6I5uPvuuPA2gwEDoqfA0KHQq1ckFVesiN/Td96BsjKYOTP+XbZpE8mK/faLf8tVJa42b4YFC6JQ5uTJUeOkc+e40P/P/4yESFPRti2MGgWf+1xMEzx1anwfhxwCRx8dSZzmoqBEhYiIiEhjaMCenJ9qMx3yYmC8mf0MeCm1TVXHIGYP+QsxNNWAv7j77Ab6OqSezZgBN94Y3cnPOy/uRlZXmK5Dh0hMjBwZQw0efRRuvTWmLfy3f4Nzz407/E2Be/Qiue66GOZRXh6f7frrY6rPuvQUyaqLvIZ/NB8PPRRJisMPjylvd9xx2/UlJZFI3GOPSFBB/O4uXBjDlGbPjgdEbYk+fSLZMWBA/Dt/8cW4kK+YDLJ9e/j85+N3f8cdI2nZFHXoEAmdY46JhMUTT0TS5itfid4iAwdmHeH2ayL/tYqIiEhL0UA9OT/VZlq+mK2Fu3OXV3oMd19X1bGluK1eDX/7W1zAfPe7UYOiUCUlUUzy4IOj2/ikSXDzzXFX//LL4YwzirMnwqpVccf10Ufj8dZbMb3nf/xHdHVvqndfNfyjeXjyyehNcfDB8OUvF54sa906Lsr32QfOPBOWLo0eUn36wOuvw+LFMaxr7dooijlmTBzj4IPjmM1p+FbnznDaaTE0bdkyuOEG+Otfo6fY974XxWebKiUqRERERKRZc4+eEFu2xCwWtUlS5DKLO/j77hsXyc88E/UcLr887m5+4Qtw2GG1u3DesiWmRly3Lh4Vrzdtiguy1q2jvfvvj7vB+Y82bSIJs3w5jB8fF2dr18bF2ttvx2fv0CG6xh99dNyVbtsWHn88Hk3VZz4TiaI33ojaGtK0zJgRvRmGDo1/k3VNHphFsc2ePT9do8L90z2mnn66bscpdl26wA9/CD/6Efzyl/CnP0XC4tBDY3jLF7/Y9BJ6SlSIiIiISLM2bVrUMzj77Ciet73M4MADY9jE3/8eQyB++9uoxt+5c3QrP/bYeD116taLJfdIIqxate2jvLz640HUkyhU+/Yxvv+kk6C0FPr2bV53kSF6g5jBa68pUdHUzJ0bxTH33DOmA22o3kjVDetqrnbfPaYzvfzy+I6vvTZ6nfTsCRdcAN/5TiQ1mgIlKkRERESk2VqxAu66Ky7YP/e5+m27Vau4CDjzTPjww0hKTJ4cj4kTq95vhx1gl12gR4/onZpXkRIAACAASURBVLHTTlHQr2PHGDffsWP0eti8OXpWbNoUSYf162NmkvXrtz42bICdd44LlMcfj+RI7kwIzVWHDpGMee21rCOR2njjDfjDH+J3/8ILW8bvaha6dImhH9/9bvx/9LvfwY9/DFddFT2rRo7cviKiDTldcgUlKkRERESkWdqyJWpJtG4N55zTsHdYO3WK4R+nnrp1usWNG2P6T8+ZoLZz508XDCzEwQfXvM2cObVvtykbNCgKhKpORdPgHsM9OnWKOil1HYIlhSspiSTnSSdFYdFvfjMSF1OnRuL2uOOKt4eFEhUiIiIi0iw98kgU1zv3XOjatfGOawb9+8fr3XdvvOO2NIMHx9Sxb77ZPGY5aO7mzYuCruecEwk7aVwHHhhDbZYujdlWpkyJ2ULOPjtqWRSbZjZaTUREREQE3nkH7rsvii4O/9S8LtIcVMxaouEfxc8dHngghil99rNZR9Oy9egRydurroo6IbfcErOvbNmSdWTbUo8KEREREWl27rorikp++csts6heS9CxYxQJfO21mHFFiterr8ZMNGef3XDFM8eNa5h2m4rafv7u3WMIzvjx0cPi3XdjBpZ27RomvtpSjwoRERERaVZWroxu5kcdFePhpfkaNCgugDdvzjoSqc7kyVE0thiHGLRkJSWRPDr9dHj5Zfj1r+H997OOKihRISIiIiLNypNPxowchx+edSTS0AYPjplP3nwz60ikKgsXRq+X449X0dNiZBbTKX/729Gr4n//N+pYZE1DP0RERESk2di0CZ55Bg44oHELaDa0lt6tvSqqU1H8HnggejYdcUTWkUh19t8ffvjDmMr0xhtjOtOGGqZTCPWoEBEREZFm44UX4KOPYuo9af46d4Y99lCioli98QbMnx/TYLZtm3U0UpPevWMoSFkZPPpotrEoUSEiIiIizcYTT8Buu8Hee2cdiTSWQYNg0SIoL886Esn3wAOw445w5JFZRyKFOvDA6JF2//2wYkV2cWjoh4iIiIg0C2+9FXdwzzijcWb60HCM4jB4cNQleeklOPjgrKORCi+9BHPmwMknxww80nSMHg1XXAF/+xt873vZzJykHhUiIiIi0ixMmxbF+kaMyDoSaUyDB8fztGnZxiHb+vnPI0Fx9NFZRyK11bUrfOlLMa3ss89mE4N6VIiIiIhI0aup98Inn8D06TB8OHTo0DgxSXHo0iWG+0ybBj/4QdbRCMCaNXDvvVErRv8em6YjjoDnn4eJE2Ho0KgH05jUo0JEREREmrxnn4WNG+Goo7KORLIwaBA89RRs3px1JAJwzz0xA4+G4jRdrVrBV74S0/9OmJDB8QvZyMxGmdkCM1tkZpdUsr6dmd2Z1j9vZv1y1l2ali8ws8/nLL/JzFaY2dy8tn5lZq+a2Wwzu9vMdkrL+5nZJ2b2cnr8oa4fWkRERESaD/eoUdCvH/Tpk3U0koXBg2HtWpg1K+tIBODOO6F///g3KU1Xjx4wahTMmAFz59a8fX2qMVFhZiXAdcAJQClwlpmV5m12HrDG3QcC1wBXp31LgdHAEGAUcH1qD+DmtCzfo8BQd98PeA24NGfd6+5+QHp8q7CPKCIiIiLN2WuvwbJl6k3RkqlORfFYuRKmTGm8orbSsEaNiqFVd98dSeHGUkiPiuHAIndf7O4bgfHAKXnbnALckl5PBEaamaXl4919g7u/ASxK7eHuTwKr8w/m7o+4e8XkQs8BvWr5mURERESkBZk2LaZAPOigrCORrHTtCgMGKFFRDP7xjxiCc+aZWUci9aFNGxg5EsrK4M03G++4hSQqegJv57wvS8sq3SYlGdYC3QrctzrnAg/mvO9vZi+Z2TQzO6IW7YiIiIhIM7RhA8yeHUU027bNOhrJ0lFHRZ2KLVuyjqRlu/PO6OFywAFZRyL1ZfjwSFg880zjHbOQREVlHXbyO31UtU0h+1Z+ULOfAOXA39KiZUAfd/8McBFwu5l9qvaomZ1vZjPNbObKdesKOZSIiIiINFHz50fRPl0UyZFHwurVjT+WXrZavjx6tZx5poZ9NCc77ADDhsXMShs2NM4xC0lUlAG9c973ApZWtY2ZtQa6EMM6Ctn3U8xsDHAi8GX3GAmTho+8l16/ALwODM7f193Hufswdx/WvWPHAj6eiIiIiDRVs2fHSfSgQVlHIln73Ofi+ckns42jJZs4MXq0aNhH83P44bB+PbzwQuMcr3UB28wABplZf+Adojjm2XnbTALGAM8CpwGPubub2SSi58NvgB7AIGB6dQczs1HAxcCR7v5xzvLuwGp332xmA1JbiwuIX0RERESaoS1bIlExdCiUlNS8vTRvffvCHnvEVLUXXph1NM3fuHGfXva738VMEc8807jDBKTh7blnFNV8+unGOV6NPSpSzYkLgYeB+cAEd59nZlea2clpsxuBbma2iBiWcUnadx4wAXgFeAi4wN03A5jZHURiYy8zKzOz81Jbvwc6AY/mTUP6OWC2mc0iCnZ+y90/VYxTRERERFqGN96ADz+E/ffPOhIpBmYwYkQkKqTxrVkDixbFEAFpfszgsMPg9ddjyF1DK6RHBe4+GZict+yynNfrgdOr2HcsMLaS5WdVsf3AKpbfBdxVSLwiIiIi0vzNng2tWsGQIVlHIsXi0ENj1ol33427v9J4Zs6MZyUqmq8RI+Cee+Cmm+BXv2rYYxVSo0JEREREpOjMmhW1KTp0yDoSKRYjRsSzelU0vpkzoXdvJYias86dowfbLbfAxo0NeywlKkRERESkyVm5EpYt07AP2daBB8Y0iv/8Z9aRtCyrVsGSJepN0RIcfnj8/3vffQ17HCUqRERERKTJmTUrnvfbL9s4pLi0bx/JCvWoaFwa9tFylJZCr17w5z837HGUqBARERGRJmf27JhdoHv3rCORYnPooXHh3NBd02WrmTOhXz/YZZesI5GG1qoVnHsuPPwwvPVWAx6n4ZoWEREREal/H30ECxdq2IdUbsQIWL8eXn4560hahlWr4O234aCDso5EGsvXvx7Pf/lLwx1DiQoRERERaVLmzYMtWzTsQyqngpqNa+7ceNa/x5ajXz847riY/cO9YY6hRIWIiIiINCmzZkGnTnGyLJKvV6+YfUKJisYxZ04MwdJsHy3LGWfE0I958xqmfSUqREREpKiY2SgzW2Bmi8zskkrWtzOzO9P6582sX866S9PyBWb2+ZraNLP+qY2Fqc22BRxjPzN71szmmdkcM2vfMN+EVKa8PO7g7rdfjJUWqcyIEUpUNIaNG2HBAhg6FMyyjkYa03HHxfMjjzRM+/rvXURERIqGmZUA1wEnAKXAWWZWmrfZecAadx8IXANcnfYtBUYDQ4BRwPVmVlJDm1cD17j7IGBNaru6Y7QG/gp8y92HAEcBm+r1S5BqLVwY9QdUn0KqM2JE3O19552sI2neFiyATZtg332zjkQaW58+sPfeDZeoaN0wzYqIiIjUyXBgkbsvBjCz8cApwCs525wCXJFeTwR+b2aWlo939w3AG2a2KLVHZW2a2XzgGODstM0tqd0bqjnG8cBsd58F4O7v1dsnl4LMmgVt2sA++2QdiRSz3DoVp52WbSzN2dy50LYtDB6cdSTSmMaNi+cePeCxx+C66+L/5Xznn1/3Y6hHhYiIiBSTnsDbOe/L0rJKt3H3cmAt0K2afata3g14P7WRf6yqjjEYcDN72MxeNLMfVfYhzOx8M5tpZjNXrlxZ4EeXmrjHtKT77BMXRyJV+cxnoF07Df9oSO5Rn2LvvSu/SJXmr7Q0etQsXFj/bStRISIiIsWkslHO+TXFq9qmvpZXd4zWwOHAl9PzF81s5Kc2dB/n7sPcfVj37t0raUrqYvlyeO89dTOXmrVtC8OGwT//mXUkzVfFv8ehQ7OORLIyeDCUlMArr9S8bW1p6IeIiIgUkzKgd877XsDSKrYpSzUjugCra9i3suWrgJ3MrHXqNZG7fXXHmObuqwDMbDJwIDC1rh9YCjd/fjyX5lctEWFrd/QKHTrA449v2y19e7qiy7bmzIlnJQ5brnbtYODArf831yf1qBAREZFiMgMYlGbjaEsUx5yUt80kYEx6fRrwmLt7Wj46zdjRHxgETK+qzbTP46kNUpv31nCMh4H9zKxDSmAcybb1M6QBvfoq7LJLPERqsueeMUvMW29lHUnzNHcu9OwJO++cdSSSpdJSKCuDtWvrt10lKkRERKRopJ4NFxIJgfnABHefZ2ZXmtnJabMbgW6pWOZFwCVp33nABCJx8BBwgbtvrqrN1NbFwEWprW6p7eqOsQb4DZH8eBl40d0faJhvQ3Jt3hwzDOy9d9aRSFMxYEA8L16cbRzN0dq1UZdAwz6koodbffeq0NAPERERKSruPhmYnLfsspzX64HTq9h3LDC2kDbT8sVsnRkkd3l1x/grMUWpNKI334xpSTXbhxSqSxfo1k2JioYwZQps2aJhHwK9ekGnTjBvHnz2s/XXrnpUiIiIiEjRe/XVeN5rr2zjkKZlwIBIVHh+SV7ZLg88EDVAKnqtSMvVqlUkkOfPj+RVvbVbf02JiIiIiDSM+fOhd++4cydSqD33hPffhzVrso6k+diyBR58MLr8l5RkHY0Ug9JS+PBDeOed+mtTiQoRERERKWoffxx3xVWfQmqr4o7/669nG0dz8tJLMTWphn1IhYo6FfU5TWlBiQozG2VmC8xskZldUsn6dmZ2Z1r/vJn1y1l3aVq+wMw+n7P8JjNbYWZz89ra2cweNbOF6blrWm5m9n+prdlmdmBdP7SIiIiINB1PPx2zNyhRIbXVqxe0batERX2aPBnMYMiQrCORYtGlS8wA06iJCjMrAa4DTgBKgbPMLH/26vOANe4+ELgGuDrtW0pMATYEGAVcn9oDuDkty3cJMNXdBxFzklckRk4gphkbBJwP3FDYRxQRERGRpmzKlOhiPmhQ1pFIU1NSAv36KVFRnx54AIYP1zAs2VZpKSxaBBs21E97hfSoGA4scvfF7r4RGA+ckrfNKcAt6fVEYKSZWVo+3t03uPsbwKLUHu7+JLC6kuPltnULcGrO8ls9PAfsZGZ7FPIhRURERKTpmjo1uvC3a5d1JNIU7bknlJXFrDGyfVauhOnT4QtfyDoSKTalpdHzbeHC+mmvkERFT+DtnPdlaVml26S5ytcSc5EXsm++3dx9WWprGbBrLeLAzM43s5lmNnPlunU1HEpEREREitl778WYeE1LKnU1cGAUgHzjjawjafoefjhmUFGiQvINHAht2tTf8I9CEhVWybL8CX6q2qaQfQtVUFvuPs7dh7n7sO4dO9bxUCIiIiJSDB5/PC6MVJ9C6mrAgKipoOEf22/yZNh1VzhQ1QIlT9u2MTyvMRMVZUDvnPe9gKVVbWNmrYEuxLCOQvbN927FkI70vKIWcYiIiIhIMzJlSoyF79cv60ikqerQAXr0UKJie23eHD0qTjgBWmnuSKlEaSksW1Y/0wEX8is2AxhkZv3NrC1RHHNS3jaTgDHp9WnAY+7uafnoNCtIf6IQ5vQajpfb1hjg3pzl56TZPz4LrK0YIiIiIiIizdPUqXDUUVEUUaSu9twzprjdvDnrSJqu6dNh9WoN+5CqDR4cz/WRFKwxUZFqTlwIPAzMBya4+zwzu9LMTk6b3Qh0M7NFwEWkmTrcfR4wAXgFeAi4wN03A5jZHcCzwF5mVmZm56W2fgEcZ2YLgePSe4DJwGKiIOefgO9s1ycXERERkaK2ZElUkT/22KwjkaZuzz2jmObcuVlH0nRNnhw9KY47LutIpFj16hV1KhYv3v62WheykbtPJhIFucsuy3m9Hji9in3HAmMrWX5WFdu/B4ysZLkDFxQSr4iIiIg0fVOnxvPIkfDMM9nGIk3bwIHx/PTTsP/+2cbSVD34IBx6KHTtmnUkUqxKSqBv3/opXKvRRSIiIiJSlKZOhd13j3HPItujWzfYaSclvOpq+XJ44YWoTyFSnf794a23YNOm7WtHiQoRERERKTrukagYOTJmbBDZHmYx/EOJirp5+OF4Vn0KqcmAAVBeDmVl29eOEhUiIiIiUnTmzoUVK1SfQurPnnvGnd7tvYBqiSZPhj320LAZqVn//vG8vcM/lKgQERERkaIzZUo8j/xU5TKRutlzz3hWr4raKS+HRx6JYR/q3SQ16do1HttbUFOJChEREREpOlOmxFR3vXtnHYk0F717w447RkFNKdxzz8H772vYhxSuf3/1qBARERGRZmbTJpg2TcM+pH6VlMAhh6hHRW1NngytW+vfoxRuwABYtQrefbfubShRISIiIiJF5fnn4aOPdGEk9e+ww2DWLPjww6wjaTomT47vrUuXrCORpmLAgHh+/vm6t9G6fkIREREREakfU6ZAq1Zw1FFZRyLNzWGHwZYtcQGlRFjN3nknEjtXX511JNKU9O4dPZhuuCGmtq0L9agQERERkaIyZQocdFAUZBOpTyNGRBJMwz8K89BD8XzCCdnGIU1L27aRrNieOhVKVIiIiIhI0fjwQ93tlobTuTPsu68KahZq8mTo1QuGDs06Emlq+veHJUtg8+a67a9EhYiIiIgUjSefjOkQlaiQhnLYYTGTRXl51pEUt02b4NFHNS2p1M2AAbBhAyxdWrf9lagQERERkaIxZQq0bw+HHpp1JNJcHXYYrFsHc+ZkHUlxe+aZ6OGkaUmlLvr3j+e6Dv9QokJEREREisaUKXD44ZGsEGkIhx8ez6pTUb0HHoA2bWDkyKwjkaZol12gUydYvLhu+ytRISIiIiJFYflymDtXwz6kYfXpE3UXlKiomjvcey8cc0xcbIrUlln0qlCPChERERFp0h57LJ6VqJCGdsQRMG1aXJDLp736KixcCKecknUk0pT17x8J6I8+qv2+res/HBERERGRwo0bF8+33AIdOsCMGfDCC9nGJM3byJFwxx0wfz6UlmYdTfG59954PumkbOOQpm3AgHhesgSGDKndvkpUiIiIiEjm3OOice+9oZX6/EoDq+i1M2WKEhX5xo2LR58+MT2pSF316xdDQBYvrn2iQn8GRERERCRzK1bAmjWRqBBpaH37wsCBkaiQba1dG3fADzgg60ikqWvfHnr0qFudioISFWY2yswWmNkiM7ukkvXtzOzOtP55M+uXs+7StHyBmX2+pjbN7Ckzezk9lprZPWn5UWa2NmfdZbX/uCIiIiJSjF59NZ732SfbOKTlOO44eOIJ2LQp60iKy+zZ0cNp//2zjkSag4qCmlu21G6/GhMVZlYCXAecAJQCZ5lZfgep84A17j4QuAa4Ou1bCowGhgCjgOvNrKS6Nt39CHc/wN0PAJ4F/pFznKcq1rn7lbX7qCIiIiJSrObPh513hu7ds45EWopjj4UPP4Tp07OOpLjMmgXdukHPnllHIs3BgAHw8cfRa642CulRMRxY5O6L3X0jMB7Ir/96CnBLej0RGGlmlpaPd/cN7v4GsCi1V2ObZtYJOAa4p3YfSURERJqyRu7J2T+1sTC12bamY6T1fcxsnZn9oP6/gZZnyxZYsCB6U5hlHY20FEcfHb9vGv6x1bp1kTTcf3/9W5T6UVFQs7bDPwpJVPQE3s55X5aWVbqNu5cDa4Fu1exbSJtfBKa6+wc5y0aY2Swze9DMKi3HYWbnm9lMM5u5ct26Aj6eiIiIFIvG7smZ9r3G3QcBa1LbVR4jxzXAg/XzqeWtt+KOm+pTSGPq2hWGDVOiItcjj0B5uepTSP3ZbTdo1y7qntRGIYmKynJp+TMOV7VNbZfnOgu4I+f9i0Bfd98fuJYqelq4+zh3H+buw7p37FjZJiIiIlK8Gq0nZ9rnmNQGqc1TazgGZnYqsBiYV4+fu0WbPz+elaiQxnbccfDcczEERGJa0g4dotCoSH1o1SqK1775Zi33K2CbMqB3zvtewNKqtjGz1kAXYHU1+1bbppl1I04qHqhY5u4fuPu69Hoy0MbMdikgfhEREWk6GrMnZzfg/dRG/rEqPYaZ7QhcDPxPdR9imx6eK1fW8JFlzhzo3Rs6d846Emlpjj02ehBMm5Z1JNkrL4f774d994WSkqyjkeakb18oK4PNmwvfp3UB28wABplZf+Adokvl2XnbTALGEMUvTwMec3c3s0nA7Wb2G6AHMAiYTvSoqK7N04H73X19xQIz2x14N7U7nEiyvFf4RxUREZEmoCF6clZ2Y6amHp5VrfsfYqjIOqtmALe7jwPGAfTtO8zHjatyUwDOP7/69c3ZypWweDF84QtZRyIt0YgRsMMOMfzjxBOzjiZbzzwDq1drtg+pf337xuw6S5dGUroQNSYq3L3czC4EHgZKgJvcfZ6ZXQnMdPdJwI3AbWa2iOhJMTrtO8/MJgCvAOXABe6+GaCyNnMOOxr4RV4opwHfNrNy4BNgtLvnn7iIiIhI01abnpxlBfbkpIrlq4CdzKx16jWRu31VxzgEOM3MfgnsBGwxs/Xu/vu6f+SWbfLkmApxv/2yjkRaovbt4YgjVKcCYthH27YwpNJKgCJ117dvPL/5Zj0mKuBfQy0m5y27LOf1eqIXRGX7jgXGFtJmzrqjKln2e0AnASIiIs1bo/XkTPs8ntoYn9q8t7pjAEdUBGFmVwDrlKTYPvfdB126QJ8+WUciLdWxx8KPfhR3e3v0yDqabLhHomLkyEjeiNSn7t2j59Kbb8Lhhxe2TyE1KkREREQaRerZUNHrcj4woaInp5mdnDa7kagXsQi4CLgk7TsPqOjJ+RCpJ2dVbaa2LgYuSm11S21XeQypXxs2wMMPR2+KVjorlYwcd1w8T52abRxZmjcvhmCdkl+6WKQemEUyujYFNQvqUSEiIiLSWBqzJ6e7LyYKeOcvr/IYOdtcUd16qdm0abBunYZ9SLb22w922SWGf3z1q1lHk417U1+yk06Kgpoi9a1v30gGbtoEbdrUvL0SFSIiIiKSifvui+7AmpZUGktVhW379YNJk+CPf4y7vy2twO0//gHDh7fcoS/S8Pr1i1k/li7dWrOiOupkJyIiIiKNzj0SFcceGwX8RLK0zz7w/vuwfHnWkTS+116DF1+EM8/MOhJpznILahZCiQoRERERaXRz58YJ60knZR2JSCQqAObPzzaOLNx5ZzyfcUa2cUjz1q0b7LijEhUiIiIiUsTuuy+eTzwx2zhEIC6idt215SUq3OGOO2KK1l69so5GmjOz6FWhRIWIiIiIFK377oNhw2CPPbKORCTsvXcMgygvzzqSxjN3biRnRo/OOhJpCfr0gXfeiYKaNVExTRERERFpVCtWwPPPwxVXZB2JyFb77QdPPtm8e1XkFxO955640/3xx1UXGhWpL337wpYtUFYG/ftXv616VIiIiIhIo3rggehyrvoUUkz22Qc6dIDp07OOpHG4w4wZ0ZOkc+eso5GWoF+/eC5k+IcSFSIiIiLSqO67L8bDH3BA1pGIbNW6NRx4IMyaFT0Mmrs334RVq+Dgg7OORFqKrl2hUyclKkRERESkyKxfD488Er0pzLKORmRbw4fDhg3R66e5mzkTSkqUMJTGU5uCmkpUiIiIiEijeeIJ+OgjDfuQ4jRoEHTpEjNhNGdbtkSiYsiQmDJSpLH07QtLl8LGjdVv13ISFR98ECl8EREREcnMfffFhdHRR2cdicintWoFBx0EkyfD2rVZR9NwFi+GNWs07EMaX9++UR/l7ber367lJCquuQbuvjvrKERERERarE2bYOJEGDUK2rfPOhqRylUM/7jnnqwjaTgzZkCbNjHTiUhj6ts3nmsa/tFyEhWrVsVcWCIiIiKSicmT43Tsa1/LOhKRqvXrF1MnNtfhH5s3wwsvRJJCCUNpbDvtFLPMKFEBMQBm40b48MOsIxERERFpsW66CXbfPXpUiBQrMxg9GqZMgZUrs46m/r32WlwWDRuWdSTSUhVSULNlJCrWrYvnDz7INg4RERGRFmr58phJYcyYmAZSpJiddVb0PJg4MetI6t+MGdGTYujQrCORlqpv3/ibUF0JyZaRqKjoSfHhh1HiVkREREQa1W23xYXf17+edSQiNRs6FEpLm9/wj40b4aWXYP/9oW3brKORlqpfv5oLarasRMWWLfDxx9nGIiIiItLCuMewj8MOg732yjoakZqZRa+Kp56qeXaCpuTFF+Ny6LDDso5EWrI+feK5uuEfBSUqzGyUmS0ws0Vmdkkl69uZ2Z1p/fNm1i9n3aVp+QIz+3xNbZrZzWb2hpm9nB4HpOVmZv+Xtp9tZgcWEjuwdegHaPiHiIiISCN77jl49VU499ysIxEp3OjR8TxhQrZx1Kcnn4Rdd4XBg7OORFqyLl2ga1dYsqTqbWpMVJhZCXAdcAJQCpxlZqV5m50HrHH3gcA1wNVp31JgNDAEGAVcb2YlBbT5Q3c/ID1eTstOAAalx/nADTXF/i9KVIiIiIhk5qabYMcd4Ywzso5EpHADB0bByeYy/GPePHj9dTjiiOgxIpKl/v1h8eKq1xfSo2I4sMjdF7v7RmA8cEreNqcAt6TXE4GRZmZp+Xh33+DubwCLUnuFtJnvFOBWD88BO5nZHgXEr0SFiIiISEY++gjGj4czz4SOHbOORqR2zjorpvJ89dWsI9l+48ZFIdtDD806EhEYMADee6/q9YUkKnoCuSOzytKySrdx93JgLdCtmn1ranNsGt5xjZm1q0UcmNn5ZjbTzGaurEhQfPjh1vLSSlSIiIiINJq//z3uGWnYhzRFZ58dM2T84hdZR7J9PvkEbr0VPvMZJQylOOy5Z/XrC5kcqrKOQV7gNlUtryxBUtHmpcByoC0wDrgYuLLAOHD3cWk/hvXtG+vXrYPu3eHdd7cW1hQRERGRBjd2LOy2G8ydG13PRZqS3XeH73wHfvtbuPTSplsMdsIEeP/9GPYhUgx6946+BOXlla8vpEdFGdA7530vYGlV25hZa6ALsLqafats092Xf35++wAAHHhJREFUpeEdG4C/EMNECo2jcuvWQadO0LmzelSIiIiINJLXXoNFi6KrucbES1N18cWwww5wxRVZR1J3f/xjJFlURFOKRZs20Ldv1esLSVTMAAaZWX8za0sUx5yUt80kYEx6fRrwmLt7Wj46zQrSnyiEOb26NivqTqQaF6cCc3OOcU6a/eOzwFp3X1ZA/FsTFZ06KVEhIiIi0khuvhlatYIRI7KORKTudt0V/vM/o9bK7NlZR1N7c+bAs8/C+ecrYSjFZcCAqtfVmKhINScuBB4G5gMT3H2emV1pZienzW4EupnZIuAi4JK07zxgAvAK8BBwgbtvrqrN1NbfzGwOMAfYBfhZWj4ZWEwU5PwT8J2CPj3EcI+OHdWjQkRERKSRbNgQiYohQ2IqOpGm7Ac/iN/jyy/POpLaGzcO2raFMWNq3lakMVWXqCikRgXuPplIFOQuuyzn9Xrg9Cr2HQuMLaTNtPyYKtpx4IJC4t3Gli3w8ceRqNi4EZYWNlpEREREROruj3+EZcs0Jak0D127wve/D5ddBjNnxrSlTcHHH8Ntt8Fpp0G3bllHI7Kt6gpq/v/27j1e6rre9/jrw1oLEAQRvCTgNdHErRGgYnb2bntFTOk8lKRSMXHDVsmsPIqy85RJRZameSEU3F5KMI7i2mppph17tAXEO4jIUvFIpmQYYipy+Zw/vt+RcZjfzCycWfOb33o/H495rN/85nf5Xn4z85vP+l4q6frR2P7xD3APgYpevULrCt9iDE4RERERqZK1a+Gyy+Dww2G//eqdGpHq+MY3wo/973yn3imp3Jw5sGYNTJxY75SIbKlUa7vsBypyU5TmBtPcsCGEFkVERESkJq64Av76V/jhD9UnXrKjd+8wsOZvfwt/+lO9U1OZX/wiBAs124c0muwHKnLTkebGqMhfJyIiIpICS5fClCmhBcKNN8L69fVO0dZbtQp+8hM48UQ4+ODy24s0knPOCdPtNkKriscegwUL4N/+TQFDaTzZD1TkWlTkByo0oKaIiIjU2dtvw4MPwtSpMHgw/OhH8Mor4UfFvvuGgSiT5pdPs6lT4b33wl+RrOnRIwQVH34YHnig3qkp7T/+I3RVGT++3ikRab+KBtNsaPmBii4xLqNAhYiIiNTRI4/A7beHMb932w3GjIGDDgr/U1m8GFpb4WtfC83MjzsutEz493+vd6rLW7ECrr8+pH3ffeudGpHamDABrr4aTj0VFi6E3Xevd4q29MgjIZDy4x9v/l+tSCPpXC0qevUKywpUiIiIpJaZjTSzZWbWZmaTi7zezczmxNcXmNkeea9dFNcvM7Njyh3TzPaMx1gej9m11DnM7Cgze9zMno1/i85WVsobb8Add8CgQWGqwylT4Mgjw6BiZnDAAXDxxXD22WFKwZtugquuCmM+pN0ll0BTU2NO4ShSqW7dQjBx3To4/vj09Sp3D60pPvGJ0FVFpBFlP1Cxdi107w4tLSFYYaZAhYiISEqZWRNwLXAsMBj4spkNLthsPPCWu+8NXAlMi/sOBsYC+wMjgevMrKnMMacBV7r7IOCteOzEcwBvAse7+wHAOODW9uRv0ya4+eZwW3LGGdC/f1I5wKc/HYIYp54KbW0wbFj4721aPfss3HYbfP3rMHBgvVMjUlv77Qe//jU89xx85SuwcWO9U7TZAw/AH/8YghU9etQ7NSJbJ/uBinfeCQEKCF0/clOUioiISBodDLS5+0vu/gEwGxhdsM1o4Oa4PBc4wswsrp/t7uvc/WWgLR6v6DHjPofHYxCP+cVS53D3J939tbh+CdDdzLpVmrmHH4YXX4QvfQn69Cm/fZcu8LnPhS4gXbqEkftvuKHSs3Wsiy8OrUImb9EGRiSbjjoqdAG55x644IJ6pybItabYffcw3o1Io+ocY1TkAhUQAhVqUSEiIpJWA4BX856vBA5J2sbdN5jZGqBfXD+/YN8BcbnYMfsBf3f3DUW2TzrHm3nHORF40t3XFWbCzCYAEwD69t0NCLNh3HVX6NoxYkSJEihit93g8cfhq18N/eMXLIBrrgmNRtPg7rvDj7Uf/hD69q13akQ+vhkzym8zYULoorV0aZiSd7/94Mwza5+2UubNg0WLYNas0HVMpFF1jhYVubEpIIwmo0CFiIhIWhWbRM8r3KZa68umw8z2J3QHmVhkO9x9hrsPd/fh226744ddPpqbQ7Bha6YK7NcP7r03TIs4c2YIdixZ0v7jVNvzz4fuKcOGwXnn1Ts1Ih3vyivhmGPgrLPg97+vXzo2bgyfD/vuG96TIo0s+y0q1q6FAQM2P+/VK/xLQ0RERNJoJbBr3vOBwGsJ26w0s2ZgO2B1mX2LrX8T6GNmzbFVRf72SefAzAYCdwGnufuLlWTqD38I40ycfjpsv30lexTX1ASXXgqHHBJm1hg+PIzqP2nS5uBHpf8JroY1a+CLXwwtO+66Kz0tPEQ6UnMzzJkDn/0sjBoVZr4544yOT8ecOSF4OXt2SJNII8v+JVzY9SPXosJ96/6dISIiIrX0GDDIzPYE/kwYHPMrBdu0EgayfBQ4CXjI3d3MWoFfmdkVQH9gELCQ0Dpii2PGfR6Ox5gdj3l3mXP0Ae4FLnL3P1WSoQ0bwo/4f/qn9nf5SHLccWHwyvHj4dxzQ0uLm26CXXapzvErsWkTnHJKGHPjwQdh113L7yOSVdttF6YEPfnk8L58/PHQ0qKjul+sXw/f/GYYyPattyoLWIqkWbYDFe7hXVsYqFi/PswnpLC/iIhIqsTxICYB9wNNwCx3X2JmlwKL3L0VmAncamZthFYOY+O+S8zsDuA5YANwjrtvBCh2zHjKC4HZZnYZ8GQ8NknnACYBewPfMbPvxHVHu3tic83Vq0NLiFNOqe7/SHbeGf7rv2D6dPj2t+HAA+EXv6je8cv53vfCuBQ//zn8y7903HlF0qpfP/jtb8PAspdfDk8/DaNHhyBGKdVo4XTFFaHR+Nlnh4F3RRpdtgMVuXmCCseogNCqQoEKERGR1HH3+4D7CtZdkrf8PjAmYd+pwNRKjhnXv0SYFaRwfdFzuPtlwGVlM5Hn/ffDj5WP0+UjiVnoF//5z4dAyIknhsE6v/Ql2Gmn6p8vZ9680AXl9NPhnHNqdx6RRtPcHLpjDR0aun8sWQITJ8Jee9XunPfdBxddFMaJOfDA2p1HpCNlO962aVP4WzjrB2hATREREekQZrVvcbDffjB/Pvz0p7B8eWjtcPfd8MEH1T/XE0/AaafBQQeFvvjqSSuypbFj4dFHQ+Di8suhtTV0A6u2pUvhy1+GIUNg3Di9HyU7st2ioligIr9FhYiIiEiN9ewZHrXW0gLf+lZoUDp3bvgv6/z5MGYMfOYzH/8HjDtcfTVccAHsuCPceSfcckt10i7SiCoZB+Lii8PglvfeG7qCnH569cZzWb0aTjghNBKfNy90OxHJimy3qMh1/SgWqFi7tuPTIyIiIp1O7tajo2y3XRjM79vfhm22CeNWTJ0aBvfL/Q+nvVatCgN4nndemIbxqafCoH0iUlrPnuH9eNZZ4f+kP/hBGFsm9zNla23YEAbufOWVMFjvbrtVJ70iadE5WlTkj1Ghrh8iIiLSgeo1TeA++8CUKbBgQfhP64wZYQDOY48NU5u2tFR2nPvvD03K16yBa68NP7jUvFykfYYMgb33DlOI3nNPCPaNGQOf+tTWHe/888NsOzNnhmlRRbKmoq9OMxsJXEUYKftGd/9RwevdgFuAYcDfgJPdfUV87SJgPLARONfd7y91TDP7JTAcWE+YUmyiu683s88Tpgx7OZ72Tne/tGTCN24Mw95us83mdU1NIbSpQIWIiIg0kK2ZbrCpKfyIGTECnnwydAf5z/+Ehx4KLSQOOSQ89tln80wB774LixbBVVdBWxs88wz07x+6fDQ3ww03VDVbIp3GttuG1hXDhoXuIFdeGQIVn/50eB9WYs2aMFjnVVeFFk5nnFHbNIvUS9lAhZk1AdcCRwErgcfMrNXdn8vbbDzwlrvvbWZjgWnAyWY2mDCd1/6E+cwfNLN94j5Jx/wlcErc5lfAmcD18fkf3f0LFedu06bwiVAY9u/dW4EKERER6TS6dAk/joYOhcWLw4Cbt90WBsOE0F1k+HB4663Qjz7XLH2nneDoo+H446Fr1/qlXyRLhgyB/feHRx6B3/wmBBJHj4bvfz/M2lPMG2/Az34G110XfsaMHRsG6RTJqkpaVBwMtMXpuzCz2cBowhzlOaOB78blucA1ZmZx/Wx3Xwe8HOciz00BVvSYcfow4vqFwNb3gNy4sfjExb17a4wKERER6XTMwg+hn/883CY9/zwsXBi6hyxaBH36wOTJ4YfTCy98dJgvEamelhY44gg47LDws+Tyy8PUonvsEVo45R577RWCGTNnwrp1obvI5MlhgFyRLKskUDEAeDXv+UqgsHHSh9u4+wYzWwP0i+vnF+w7IC6XPKaZtQCnAt/IW32omT0NvAac7+5LChNrZhOACQAHNjcX/4bt3RtWrNhyvYiIiEgn0dQU/qu7//5hzIpCW9PVRETap3t3OPfcMPbLDTeErlYvvBBm1Mk1AG9pCVMCX3BBCF6IdAaVBCqKDZfkFW6TtL7YbCOFx7wOeMTd/xifPwHs7u7vmNkoYB4waIuDuM8AZgAMb2nxooGKXr3U9UNERERERFKhb1+48MLNz93DbDvLl4dWFf371y9tIvVQSaBiJZA/2+9AQouGYtusNLNmYDtgdZl9E49pZv8b2BGYmFvn7m/nLd9nZteZ2Q7u/mZiynNjVBTq3Tu0nfrgg8RdRURERERE6sEszNKz8871TolIfRRr2VDoMWCQme1pZl0Jg2O2FmzTCoyLyycBD7m7x/Vjzaybme1JaAGxsNQxzexM4Bjgy+7+4WzfZvaJOO4FZnZwTPvfSqZ806aPTk2ak5vQXK0qRERERERERFKlbIuKOObEJOB+wlSis9x9iZldCixy91ZgJnBrHCxzNSHwQNzuDsLAmxuAc9x9I0CxY8ZTTgdeAR6NcYncNKQnAWeZ2QbgPWBsDIaUltSiAhSoEBEREREREUmZSrp+EGfiuK9g3SV5y+8DYxL2nQpMreSYcX3RNLn7NcA1laT3I5LGqAAFKkRERKRT0kCZIumh96PIlirp+tHY1KJCREREREREpGFkP1BRbIyK3Lq1azs2LSIiIiIiIiJSUvYDFcVaVLS0QI8ealEhIiIiIiIikjKdM1ABoVWFAhUiIiIiIiIiqZLtQIUZNCeMF9q7twIVIiIiIiIiIimT7UBFU1Pya717a4wKERERERERkZTJdqCiS4nsqeuHiIiIiIiISOpkO1BRrkXFe+/B++93XHpEREREREREpKRsByrKtagAWLWqY9IiIiIiIiIiImVlO1BRrkUFwOuvd0xaRERERERERKSsbAcqSrWoGDgwvH7ttR2XHhEREREREREpKduBilItKnbYAUaOhFtugfvu67g0iYiIiIiIiEiibAcqSrWoABg1CgYPhokTNQOIiIiIiIiISApkO1BRqkUFQEsLzJoFr70GF17YMWkSERERERERkUTZDlSUa1EBcMghcN55MH06/OEPNU+SiIiIiIiIiCTLdqCiXIuKnO9/Hz75STjzTHj33dqmSUREREREREQSZTtQYVbZdj16wI03wosvwqRJ8Le/1TZdIiIiIiIiIlJUc70TUHczZmxePvJIuOkmuPVWGDIEDjsMPvWp0IVkwoT6pVFERERERESkk1CgIt+YMTBiBPz3f8OCBbBoEWy/fRjH4tBD4YAD6p1CERERERERkUyrqOuHmY00s2Vm1mZmk4u83s3M5sTXF5jZHnmvXRTXLzOzY8od08z2jMdYHo/Ztdw5qmrXXeHkk2HatNCKon9/eOABOPDAEKj4wQ9g2TJYt64mpxcREens0n7fkXQOERERqY6yLSrMrAm4FjgKWAk8Zmat7v5c3mbjgbfcfW8zGwtMA042s8HAWGB/oD/woJntE/dJOuY04Ep3n21m0+Oxr086x8ctgEQtLTBsWHisXQvbbAO33w5TpoQHwLbbwo47hke/fqH1Re7Rpw/07Bn26949/M1/9OgR/jY1gftHH83N0LVrSENLS3heON6G2ZYPCPvn69Llo6/ny9+20vE8REREaijt9x1J53D3jbUrFRERkc6lkq4fBwNt7v4SgJnNBkYD+TcMo4HvxuW5wDVmZnH9bHdfB7xsZm3xeBQ7ppktBQ4HvhK3uTke9/qkc7gX/jKvgV69wt9TT4VRo2DpUnj7bXjnnRDEeOcdeP31MGPIu+/Ce+9tGTBIgy5dNgdDiikX9Mh/LX/bXDAkJ7df/t9i5yw8VuFyqXSWOn6xAE5S2kqlqZz885Yrq0bTnuu3EfMnImmW6vuOEud4tFoFICIi0tlVEqgYALya93wlcEjSNu6+wczWAP3i+vkF+w6Iy8WO2Q/4u7tvKLJ90jnezE+ImU0AciNfrrOJExdXkMfOYdOm0q+XCmIk24GCOpCqUxnXnsq49lTGtdfeMt69Vgn5mNJ+31HqHB8qvB+ZONGydj+Sxfe08tQYspanrOUHlKdGkZY8Fb0fqSRQUezfpYW/ZpO2SVpfbGyMUttXmg7cfQYwA8DMFrn78CL7SZWojGtPZVx7KuPaUxnXXobKOO33HbofQXlqFMpT+mUtP6A8NYq056mSwTRXArvmPR8IvJa0jZk1A9sBq0vsm7T+TaBPPEbhuZLOISIiItmR9vuOStInIiIiH0MlgYrHgEFxVOyuhAGkWgu2aQXGxeWTgIfi2BGtwNg4cvaewCBgYdIx4z4Px2MQj3l3mXOIiIhIdqT9viPpHCIiIlIlZbt+xH6Zk4D7gSZglrsvMbNLgUXu3grMBG6NA0qtJtwAELe7gzAA1gbgnNyo2MWOGU95ITDbzC4DnozHJukcZcyoYBv5eFTGtacyrj2Vce2pjGsvE2Wc9vuOUucoIRN1U0B5agzKU/plLT+gPDWKVOfJ1ChBRERERERERNKikq4fIiIiIiIiIiIdQoEKEREREREREUmNzAYqzGykmS0zszYzm1zv9DQSM1thZs+a2VNmtiiu62tmvzOz5fHv9nG9mdnVsZyfMbOheccZF7dfbmbjks7XGZjZLDNbZWaL89ZVrUzNbFiss7a4b7Hp8zItoYy/a2Z/jtfyU2Y2Ku+1i2J5LTOzY/LWF/3siIPwLYhlPycOyNepmNmuZvawmS01syVm9o24XtdylZQoY13LDSrN9yNZfk+bWZOZPWlm98TnRa97C4OizonpW2Bme+Qdo13vrRrnp4+ZzTWz52N9Hdro9WRm34zX3WIzu93MujdaPVmd7u+SzlGj/Fwer7tnzOwuM+uT91pVvn9K1W8t8pT32vlm5ma2Q3ye+joqlScz+3os9yVm9uO89amvp6LcPXMPwkBZLwJ7AV2Bp4HB9U5XozyAFcAOBet+DEyOy5OBaXF5FPAbwrzyI4AFcX1f4KX4d/u4vH2981bHMv1nYCiwuBZlShhx/tC4z2+AY+ud55SU8XeB84tsOzh+LnQD9oyfF02lPjuAO4CxcXk6cFa981yHMt4FGBqXewEvxLLUtVz7Mta13ICPUvWQhkeW39PAt4BfAffE50Wve+BsYHpcHgvMicvtfm/VOD83A2fG5a5An0auJ2AA8DKwTV79nN5o9USd7u+SzlGj/BwNNMflaXn5qdr3T1L91ipPcf2uhAGWXyH+7mmEOipRT/8KPAh0i893aqR6KvbIaouKg4E2d3/J3T8AZgOj65ymRjea8CVJ/PvFvPW3eDCfMB/9LsAxwO/cfbW7vwX8DhjZ0YlOC3d/hDBqfL6qlGl8rbe7P+rhk+OWvGN1GgllnGQ0MNvd17n7y0Ab4XOj6GdHjI4fDsyN++fXV6fh7n9x9yfi8lpgKeFmU9dylZQo4yS6ltMt1fcjWX1Pm9lA4Djgxvi81HWfn9e5wBFx+3a9t2qcn96EHyYzAdz9A3f/Ow1eT4TZB7cxs2agB/AXGqye6nh/l3SOqufH3R9w9w3x6XxgYF4aqvX9k1S/NclTdCVwAZA/s0Tq66hEns4CfuTu6+I2q/LSkfp6KiargYoBwKt5z1dS+kZPPsqBB8zscTObENft7O5/gXBjA+wU1yeVteqgvGqV6YC4XLhegkmx+d6svGZ37S3jfsDf876oO30Zx+Z+nwEWoGu5JgrKGHQtN6KG+S7M2Hv6Z4QfIJvi81LX/Ydpj6+vidun6f5mL+CvwE0WurPcaGY9aeB6cvc/Az8B/h8hQLEGeJzGrqecjqiXpHPU2hmEVgNQ3e+fpPqtCTM7Afizuz9d8FIj19E+wP+IXTL+r5kdFNc3bD1lNVBRLLKjeVgrd5i7DwWOBc4xs38usW1SWasOtl57y1Rlnex64JPAEMKN0E/jepXxx2Bm2wL/BzjP3d8utWmRdSrnChQpY13LjakhyjtL72kz+wKwyt0fz19dIh2pzxOh5cFQ4Hp3/wzwD0JT8iSpz1MMto4mNEXvD/Qk3HcmpSP1eapAQ+fBzKYAG4Bf5lYV2Wxr89NheTWzHsAU4JJiLyekoxHqqJnQLWUE8L+AO2Jrh4asJ8huoGIlod9RzkDgtTqlpeG4+2vx7yrgLkLToDdi8ybi31xzoqSyVh2UV60yXcnmZnj56zs9d3/D3Te6+ybgBsK1DO0v4zcJzf+aC9Z3OmbWQvhB80t3vzOu1rVcRcXKWNdyw0r9d2EG39OHASeY2QpCU+bDCS0skq77D9MeX9+O0KQ6Tfc3K4GV7p5rXTWXELho5Ho6EnjZ3f/q7uuBO4HP0tj1lNMR9ZJ0jpqIg0d+Afhq7OJAmXS39/snqX5r4ZOEANnT8XNiIPCEmX2iRNpTX0cxLXfGbisLCS3KdiiT9jTXU2YDFY8Bg+KIpV0Jg3201jlNDcHMeppZr9wyYQCdxYTyGxc3GwfcHZdbgdMsGAGsic2b7geONrPtY9T86LhONqtKmcbX1prZiBg5PS3vWJ1a7gsi+p+EaxlCGY+NoxfvCQwiDIZU9LMjfik/DJwU98+vr04jXl8zgaXufkXeS7qWqySpjHUtN6xU349k8T3t7he5+0B334NQ3g+5+1dJvu7z83pS3N5p53urxnl6HXjVzPaNq44AnqOB64nQ5WOEmfWI58zlqWHrKU9H1EvSOarOzEYCFwInuPu7eS9V8/snqX6rzt2fdfed3H2P+DmxkjCo8Os0aB1F8wiBWcxsH8IAmW/SoPUEZHPWD988ausLhNFMp9Q7PY3yIPSDfDo+luTKjtD/6PfA8vi3b1xvwLWxnJ8Fhucd6wzCgC1twNfqnbc6l+vthOba6wkfiOOrWabAcMIPlxeBawCrd55TUsa3xjJ8hvDhukve9lNieS0jb3TzpM+O+N5YGMv+18RRlTvTA/gcoYnfM8BT8TFK13KHlLGu5QZ9JNVDGh5Zf08Dn2fzrB9Fr3uge3zeFl/fK2//dr23apyXIcCiWFfzCE28G7qegO8Bz8fz3kqYlaCh6ok63d8lnaNG+WkjjEuQ+4yYvrVlvzX1W4s8Fby+gs2zfqS+jkrUU1fgtpiWJ4DDG6meij1yBSkiIiIiIiIiUndZ7fohIiIiIiIiIg1IgQoRERERERERSQ0FKkREREREREQkNRSoEBEREREREZHUUKBCRERERERERFJDgQoRERERERERSQ0FKkREREREREQkNf4/yFzBkfFt2HMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1296x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, 2, figsize=(18, 4))\n",
    "amount_val = data['Amount'].values\n",
    "time_val = data['Time'].values\n",
    "\n",
    "sns.distplot(amount_val, ax=ax[0], color='r')\n",
    "ax[0].set_title(\"Distribution of Transation Amount\")\n",
    "ax[0].set_xlim([min(amount_val), max(amount_val)])\n",
    "\n",
    "sns.distplot(time_val, ax=ax[1], color='b')\n",
    "ax[1].set_title(\"Distribution Of Transaction Time\")\n",
    "ax[1].set_xlim([min(time_val), max(time_val)])\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "그럼 이 데이터 분포를 변환해보자."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler = StandardScaler()\n",
    "\n",
    "data['scaled_amount'] = scaler.fit_transform(data['Amount'].values.reshape(-1, 1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['loged_amount'] = np.log1p(data['Amount'].values.reshape(-1, 1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, 2, figsize=(18, 4))\n",
    "loged_amount = data['loged_amount'].values\n",
    "scaled_amount = data['scaled_amount'].values\n",
    "\n",
    "sns.distplot(loged_amount, ax=ax[0], color='r')\n",
    "ax[0].set_xlim([min(loged_amount), max(loged_amount)])\n",
    "\n",
    "sns.distplot(scaled_amount, ax=ax[1], color='b')\n",
    "ax[1].set_xlim([min(scaled_amount), max(scaled_amount)])\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "뭔가 log보단 standardscaler가 좋은 것 같다.\n",
    "\n",
    "Time도 변형해보자."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "rob_scaler = RobustScaler()\n",
    "\n",
    "data['scaled_time'] = rob_scaler.fit_transform(data['Time'].values.reshape(-1, 1))\n",
    "data['loged_time'] = np.log1p(data['Time'].values.reshape(-1, 1))\n",
    "data['std_time'] = scaler.fit_transform(data['Time'].values.reshape(-1, 1))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x288 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, 3, figsize=(18, 4))\n",
    "scaled_time = data['scaled_time'].values\n",
    "loged_time = data['loged_time'].values\n",
    "std_time = data['std_time'].values\n",
    "\n",
    "sns.distplot(scaled_time, ax=ax[0], color='r')\n",
    "ax[0].set_xlim([min(scaled_time), max(scaled_time)])\n",
    "\n",
    "sns.distplot(loged_time, ax=ax[1], color='b')\n",
    "ax[1].set_xlim([min(loged_time), max(loged_time)])\n",
    "\n",
    "\n",
    "sns.distplot(std_time, ax=ax[2], color='b')\n",
    "ax[2].set_xlim([min(std_time), max(std_time)])\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "데이터 분포가 비슷하고, 범위가 많이 작아진 rob_scaler가 좋겠다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "data.drop(['loged_time','std_time','loged_amount','Time','Amount'], axis = 1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "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>V1</th>\n",
       "      <th>V2</th>\n",
       "      <th>V3</th>\n",
       "      <th>V4</th>\n",
       "      <th>V5</th>\n",
       "      <th>V6</th>\n",
       "      <th>V7</th>\n",
       "      <th>V8</th>\n",
       "      <th>V9</th>\n",
       "      <th>V10</th>\n",
       "      <th>...</th>\n",
       "      <th>V22</th>\n",
       "      <th>V23</th>\n",
       "      <th>V24</th>\n",
       "      <th>V25</th>\n",
       "      <th>V26</th>\n",
       "      <th>V27</th>\n",
       "      <th>V28</th>\n",
       "      <th>Class</th>\n",
       "      <th>scaled_amount</th>\n",
       "      <th>scaled_time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-1.359807</td>\n",
       "      <td>-0.072781</td>\n",
       "      <td>2.536347</td>\n",
       "      <td>1.378155</td>\n",
       "      <td>-0.338321</td>\n",
       "      <td>0.462388</td>\n",
       "      <td>0.239599</td>\n",
       "      <td>0.098698</td>\n",
       "      <td>0.363787</td>\n",
       "      <td>0.090794</td>\n",
       "      <td>...</td>\n",
       "      <td>0.277838</td>\n",
       "      <td>-0.110474</td>\n",
       "      <td>0.066928</td>\n",
       "      <td>0.128539</td>\n",
       "      <td>-0.189115</td>\n",
       "      <td>0.133558</td>\n",
       "      <td>-0.021053</td>\n",
       "      <td>0</td>\n",
       "      <td>0.244964</td>\n",
       "      <td>-0.994983</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.191857</td>\n",
       "      <td>0.266151</td>\n",
       "      <td>0.166480</td>\n",
       "      <td>0.448154</td>\n",
       "      <td>0.060018</td>\n",
       "      <td>-0.082361</td>\n",
       "      <td>-0.078803</td>\n",
       "      <td>0.085102</td>\n",
       "      <td>-0.255425</td>\n",
       "      <td>-0.166974</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.638672</td>\n",
       "      <td>0.101288</td>\n",
       "      <td>-0.339846</td>\n",
       "      <td>0.167170</td>\n",
       "      <td>0.125895</td>\n",
       "      <td>-0.008983</td>\n",
       "      <td>0.014724</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.342475</td>\n",
       "      <td>-0.994983</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-1.358354</td>\n",
       "      <td>-1.340163</td>\n",
       "      <td>1.773209</td>\n",
       "      <td>0.379780</td>\n",
       "      <td>-0.503198</td>\n",
       "      <td>1.800499</td>\n",
       "      <td>0.791461</td>\n",
       "      <td>0.247676</td>\n",
       "      <td>-1.514654</td>\n",
       "      <td>0.207643</td>\n",
       "      <td>...</td>\n",
       "      <td>0.771679</td>\n",
       "      <td>0.909412</td>\n",
       "      <td>-0.689281</td>\n",
       "      <td>-0.327642</td>\n",
       "      <td>-0.139097</td>\n",
       "      <td>-0.055353</td>\n",
       "      <td>-0.059752</td>\n",
       "      <td>0</td>\n",
       "      <td>1.160686</td>\n",
       "      <td>-0.994972</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.966272</td>\n",
       "      <td>-0.185226</td>\n",
       "      <td>1.792993</td>\n",
       "      <td>-0.863291</td>\n",
       "      <td>-0.010309</td>\n",
       "      <td>1.247203</td>\n",
       "      <td>0.237609</td>\n",
       "      <td>0.377436</td>\n",
       "      <td>-1.387024</td>\n",
       "      <td>-0.054952</td>\n",
       "      <td>...</td>\n",
       "      <td>0.005274</td>\n",
       "      <td>-0.190321</td>\n",
       "      <td>-1.175575</td>\n",
       "      <td>0.647376</td>\n",
       "      <td>-0.221929</td>\n",
       "      <td>0.062723</td>\n",
       "      <td>0.061458</td>\n",
       "      <td>0</td>\n",
       "      <td>0.140534</td>\n",
       "      <td>-0.994972</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-1.158233</td>\n",
       "      <td>0.877737</td>\n",
       "      <td>1.548718</td>\n",
       "      <td>0.403034</td>\n",
       "      <td>-0.407193</td>\n",
       "      <td>0.095921</td>\n",
       "      <td>0.592941</td>\n",
       "      <td>-0.270533</td>\n",
       "      <td>0.817739</td>\n",
       "      <td>0.753074</td>\n",
       "      <td>...</td>\n",
       "      <td>0.798278</td>\n",
       "      <td>-0.137458</td>\n",
       "      <td>0.141267</td>\n",
       "      <td>-0.206010</td>\n",
       "      <td>0.502292</td>\n",
       "      <td>0.219422</td>\n",
       "      <td>0.215153</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.073403</td>\n",
       "      <td>-0.994960</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         V1        V2        V3        V4        V5        V6        V7  \\\n",
       "0 -1.359807 -0.072781  2.536347  1.378155 -0.338321  0.462388  0.239599   \n",
       "1  1.191857  0.266151  0.166480  0.448154  0.060018 -0.082361 -0.078803   \n",
       "2 -1.358354 -1.340163  1.773209  0.379780 -0.503198  1.800499  0.791461   \n",
       "3 -0.966272 -0.185226  1.792993 -0.863291 -0.010309  1.247203  0.237609   \n",
       "4 -1.158233  0.877737  1.548718  0.403034 -0.407193  0.095921  0.592941   \n",
       "\n",
       "         V8        V9       V10  ...       V22       V23       V24       V25  \\\n",
       "0  0.098698  0.363787  0.090794  ...  0.277838 -0.110474  0.066928  0.128539   \n",
       "1  0.085102 -0.255425 -0.166974  ... -0.638672  0.101288 -0.339846  0.167170   \n",
       "2  0.247676 -1.514654  0.207643  ...  0.771679  0.909412 -0.689281 -0.327642   \n",
       "3  0.377436 -1.387024 -0.054952  ...  0.005274 -0.190321 -1.175575  0.647376   \n",
       "4 -0.270533  0.817739  0.753074  ...  0.798278 -0.137458  0.141267 -0.206010   \n",
       "\n",
       "        V26       V27       V28  Class  scaled_amount  scaled_time  \n",
       "0 -0.189115  0.133558 -0.021053      0       0.244964    -0.994983  \n",
       "1  0.125895 -0.008983  0.014724      0      -0.342475    -0.994983  \n",
       "2 -0.139097 -0.055353 -0.059752      0       1.160686    -0.994972  \n",
       "3 -0.221929  0.062723  0.061458      0       0.140534    -0.994972  \n",
       "4  0.502292  0.219422  0.215153      0      -0.073403    -0.994960  \n",
       "\n",
       "[5 rows x 31 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "맨 뒤에 있으니 얘내를 관리하게 편하게 맨 앞으로 보내자"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "scaled_amount = data['scaled_amount']\n",
    "scaled_time = data['scaled_time']\n",
    "data.drop(['scaled_time','scaled_amount'], axis = 1, inplace=True)\n",
    "data.insert(0, 'scaled_amount', scaled_amount)\n",
    "data.insert(1, 'scaled_time', scaled_time)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "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>scaled_amount</th>\n",
       "      <th>scaled_time</th>\n",
       "      <th>V1</th>\n",
       "      <th>V2</th>\n",
       "      <th>V3</th>\n",
       "      <th>V4</th>\n",
       "      <th>V5</th>\n",
       "      <th>V6</th>\n",
       "      <th>V7</th>\n",
       "      <th>V8</th>\n",
       "      <th>...</th>\n",
       "      <th>V20</th>\n",
       "      <th>V21</th>\n",
       "      <th>V22</th>\n",
       "      <th>V23</th>\n",
       "      <th>V24</th>\n",
       "      <th>V25</th>\n",
       "      <th>V26</th>\n",
       "      <th>V27</th>\n",
       "      <th>V28</th>\n",
       "      <th>Class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.244964</td>\n",
       "      <td>-0.994983</td>\n",
       "      <td>-1.359807</td>\n",
       "      <td>-0.072781</td>\n",
       "      <td>2.536347</td>\n",
       "      <td>1.378155</td>\n",
       "      <td>-0.338321</td>\n",
       "      <td>0.462388</td>\n",
       "      <td>0.239599</td>\n",
       "      <td>0.098698</td>\n",
       "      <td>...</td>\n",
       "      <td>0.251412</td>\n",
       "      <td>-0.018307</td>\n",
       "      <td>0.277838</td>\n",
       "      <td>-0.110474</td>\n",
       "      <td>0.066928</td>\n",
       "      <td>0.128539</td>\n",
       "      <td>-0.189115</td>\n",
       "      <td>0.133558</td>\n",
       "      <td>-0.021053</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.342475</td>\n",
       "      <td>-0.994983</td>\n",
       "      <td>1.191857</td>\n",
       "      <td>0.266151</td>\n",
       "      <td>0.166480</td>\n",
       "      <td>0.448154</td>\n",
       "      <td>0.060018</td>\n",
       "      <td>-0.082361</td>\n",
       "      <td>-0.078803</td>\n",
       "      <td>0.085102</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.069083</td>\n",
       "      <td>-0.225775</td>\n",
       "      <td>-0.638672</td>\n",
       "      <td>0.101288</td>\n",
       "      <td>-0.339846</td>\n",
       "      <td>0.167170</td>\n",
       "      <td>0.125895</td>\n",
       "      <td>-0.008983</td>\n",
       "      <td>0.014724</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.160686</td>\n",
       "      <td>-0.994972</td>\n",
       "      <td>-1.358354</td>\n",
       "      <td>-1.340163</td>\n",
       "      <td>1.773209</td>\n",
       "      <td>0.379780</td>\n",
       "      <td>-0.503198</td>\n",
       "      <td>1.800499</td>\n",
       "      <td>0.791461</td>\n",
       "      <td>0.247676</td>\n",
       "      <td>...</td>\n",
       "      <td>0.524980</td>\n",
       "      <td>0.247998</td>\n",
       "      <td>0.771679</td>\n",
       "      <td>0.909412</td>\n",
       "      <td>-0.689281</td>\n",
       "      <td>-0.327642</td>\n",
       "      <td>-0.139097</td>\n",
       "      <td>-0.055353</td>\n",
       "      <td>-0.059752</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.140534</td>\n",
       "      <td>-0.994972</td>\n",
       "      <td>-0.966272</td>\n",
       "      <td>-0.185226</td>\n",
       "      <td>1.792993</td>\n",
       "      <td>-0.863291</td>\n",
       "      <td>-0.010309</td>\n",
       "      <td>1.247203</td>\n",
       "      <td>0.237609</td>\n",
       "      <td>0.377436</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.208038</td>\n",
       "      <td>-0.108300</td>\n",
       "      <td>0.005274</td>\n",
       "      <td>-0.190321</td>\n",
       "      <td>-1.175575</td>\n",
       "      <td>0.647376</td>\n",
       "      <td>-0.221929</td>\n",
       "      <td>0.062723</td>\n",
       "      <td>0.061458</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.073403</td>\n",
       "      <td>-0.994960</td>\n",
       "      <td>-1.158233</td>\n",
       "      <td>0.877737</td>\n",
       "      <td>1.548718</td>\n",
       "      <td>0.403034</td>\n",
       "      <td>-0.407193</td>\n",
       "      <td>0.095921</td>\n",
       "      <td>0.592941</td>\n",
       "      <td>-0.270533</td>\n",
       "      <td>...</td>\n",
       "      <td>0.408542</td>\n",
       "      <td>-0.009431</td>\n",
       "      <td>0.798278</td>\n",
       "      <td>-0.137458</td>\n",
       "      <td>0.141267</td>\n",
       "      <td>-0.206010</td>\n",
       "      <td>0.502292</td>\n",
       "      <td>0.219422</td>\n",
       "      <td>0.215153</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   scaled_amount  scaled_time        V1        V2        V3        V4  \\\n",
       "0       0.244964    -0.994983 -1.359807 -0.072781  2.536347  1.378155   \n",
       "1      -0.342475    -0.994983  1.191857  0.266151  0.166480  0.448154   \n",
       "2       1.160686    -0.994972 -1.358354 -1.340163  1.773209  0.379780   \n",
       "3       0.140534    -0.994972 -0.966272 -0.185226  1.792993 -0.863291   \n",
       "4      -0.073403    -0.994960 -1.158233  0.877737  1.548718  0.403034   \n",
       "\n",
       "         V5        V6        V7        V8  ...       V20       V21       V22  \\\n",
       "0 -0.338321  0.462388  0.239599  0.098698  ...  0.251412 -0.018307  0.277838   \n",
       "1  0.060018 -0.082361 -0.078803  0.085102  ... -0.069083 -0.225775 -0.638672   \n",
       "2 -0.503198  1.800499  0.791461  0.247676  ...  0.524980  0.247998  0.771679   \n",
       "3 -0.010309  1.247203  0.237609  0.377436  ... -0.208038 -0.108300  0.005274   \n",
       "4 -0.407193  0.095921  0.592941 -0.270533  ...  0.408542 -0.009431  0.798278   \n",
       "\n",
       "        V23       V24       V25       V26       V27       V28  Class  \n",
       "0 -0.110474  0.066928  0.128539 -0.189115  0.133558 -0.021053      0  \n",
       "1  0.101288 -0.339846  0.167170  0.125895 -0.008983  0.014724      0  \n",
       "2  0.909412 -0.689281 -0.327642 -0.139097 -0.055353 -0.059752      0  \n",
       "3 -0.190321 -1.175575  0.647376 -0.221929  0.062723  0.061458      0  \n",
       "4 -0.137458  0.141267 -0.206010  0.502292  0.219422  0.215153      0  \n",
       "\n",
       "[5 rows x 31 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "다시 모델 성능을 봐볼까?\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_X_y_data(data):\n",
    "    X = data.iloc[:, :-1]\n",
    "    y = data.iloc[:, -1]\n",
    "    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.25, random_state=0, stratify = y)\n",
    "    return X_train, X_test, y_train, y_test\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = get_X_y_data(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "정확도 : 1.00, 정밀도 : 0.87, 재현율 : 0.66\n",
      "f1-score : 0.75, auc : 0.83\n"
     ]
    }
   ],
   "source": [
    "lr = LogisticRegression()\n",
    "modeling(lr, X_train, X_test, y_train, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "정확도 : 1.00, 정밀도 : 0.95, 재현율 : 0.78\n",
      "f1-score : 0.86, auc : 0.89\n"
     ]
    }
   ],
   "source": [
    "lgb = LGBMClassifier(n_estimators = 1000, num_leaves = 64, n_jobs = -1, boost_from_average = False)\n",
    "modeling(lgb, x_train = X_train, x_test = X_test, y_train = y_train, y_test = y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "로지스틱 회귀에서 아래와 같이 변화\n",
    "    - 정밀도 : 0.70 -> 0.87\n",
    "    - 재현율 : 0.55 -> 0.66\n",
    "    - f1-score : 0.62 -> 0.75\n",
    "    \n",
    "LightGBM은 크게 변화가 없다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "attachments": {
    "image.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 이상치 데이터 처리\n",
    "\n",
    "이상치 데이터(outlier)는 전체 데이터의 패턴에서 벗어난 이상 값을 가진 데이터를 뜻함  \n",
    "이상치 데이터가 있으면 모델의 성능에 영향을 줌. 그래서 이 이상치 탐지는 정말 중요함  \n",
    "대표적인 방법이 IQR이라는 방법임. Inter Quantile Range의 약자로써 사분위 값의 편차를 이용. 그리고 boxplot으로도 시각화 가능  \n",
    "\n",
    "사분위는 데이터를 값이 높은 순으로 정렬하고 1/4씩 쪼갠 것을 말함.  \n",
    "25% : Q1  \n",
    "50% : Q2  \n",
    "75% : Q3\n",
    "\n",
    "IQR는 여기서 Q1 - Q3의 구간을 뜻함. 즉, IQR = Q3 - Q1  \n",
    "\n",
    "IQR을 이용해 이상치를 탐지하는 것은 IQR에 1.5를 곱해서 구하는 방법임.  \n",
    "1.5를 곱해서 Q3에 더한 값을 최대값, 1.5를 곱해서 Q1에 뺀 값을 최소값이라고 함   \n",
    "\n",
    "\n",
    "근데 이 값보다 크거나, 작은 애들을 이상치라고 함.\n",
    "\n",
    "boxplot으로 보면 아래와 같음  \n",
    "\n",
    "\n",
    "![image.png](attachment:image.png)\n",
    "\n",
    "\n",
    "이제 이 이상치를 제거해야 함\n",
    "\n",
    "너무 많은 feature를 제거하면 오히려 모델 성능이 떨어지기 때문에 label과 연관성이 높은 feature를 기준으로 진행한다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>scaled_amount</th>\n",
       "      <th>scaled_time</th>\n",
       "      <th>V1</th>\n",
       "      <th>V2</th>\n",
       "      <th>V3</th>\n",
       "      <th>V4</th>\n",
       "      <th>V5</th>\n",
       "      <th>V6</th>\n",
       "      <th>V7</th>\n",
       "      <th>V8</th>\n",
       "      <th>...</th>\n",
       "      <th>V20</th>\n",
       "      <th>V21</th>\n",
       "      <th>V22</th>\n",
       "      <th>V23</th>\n",
       "      <th>V24</th>\n",
       "      <th>V25</th>\n",
       "      <th>V26</th>\n",
       "      <th>V27</th>\n",
       "      <th>V28</th>\n",
       "      <th>Class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>scaled_amount</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.010596</td>\n",
       "      <td>-2.277087e-01</td>\n",
       "      <td>-5.314089e-01</td>\n",
       "      <td>-2.108805e-01</td>\n",
       "      <td>9.873167e-02</td>\n",
       "      <td>-3.863563e-01</td>\n",
       "      <td>2.159812e-01</td>\n",
       "      <td>3.973113e-01</td>\n",
       "      <td>-1.030791e-01</td>\n",
       "      <td>...</td>\n",
       "      <td>3.394034e-01</td>\n",
       "      <td>1.059989e-01</td>\n",
       "      <td>-6.480065e-02</td>\n",
       "      <td>-1.126326e-01</td>\n",
       "      <td>5.146217e-03</td>\n",
       "      <td>-4.783686e-02</td>\n",
       "      <td>-3.208037e-03</td>\n",
       "      <td>2.882546e-02</td>\n",
       "      <td>1.025822e-02</td>\n",
       "      <td>0.005632</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>scaled_time</th>\n",
       "      <td>-0.010596</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.173963e-01</td>\n",
       "      <td>-1.059333e-02</td>\n",
       "      <td>-4.196182e-01</td>\n",
       "      <td>-1.052602e-01</td>\n",
       "      <td>1.730721e-01</td>\n",
       "      <td>-6.301647e-02</td>\n",
       "      <td>8.471437e-02</td>\n",
       "      <td>-3.694943e-02</td>\n",
       "      <td>...</td>\n",
       "      <td>-5.086602e-02</td>\n",
       "      <td>4.473573e-02</td>\n",
       "      <td>1.440591e-01</td>\n",
       "      <td>5.114236e-02</td>\n",
       "      <td>-1.618187e-02</td>\n",
       "      <td>-2.330828e-01</td>\n",
       "      <td>-4.140710e-02</td>\n",
       "      <td>-5.134591e-03</td>\n",
       "      <td>-9.412688e-03</td>\n",
       "      <td>-0.012323</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>V1</th>\n",
       "      <td>-0.227709</td>\n",
       "      <td>0.117396</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>4.697350e-17</td>\n",
       "      <td>-1.424390e-15</td>\n",
       "      <td>1.755316e-17</td>\n",
       "      <td>6.391162e-17</td>\n",
       "      <td>2.398071e-16</td>\n",
       "      <td>1.991550e-15</td>\n",
       "      <td>-9.490675e-17</td>\n",
       "      <td>...</td>\n",
       "      <td>1.036959e-16</td>\n",
       "      <td>-1.755072e-16</td>\n",
       "      <td>7.477367e-17</td>\n",
       "      <td>9.808705e-16</td>\n",
       "      <td>7.354269e-17</td>\n",
       "      <td>-9.805358e-16</td>\n",
       "      <td>-8.621897e-17</td>\n",
       "      <td>3.208233e-17</td>\n",
       "      <td>9.820892e-16</td>\n",
       "      <td>-0.101347</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>V2</th>\n",
       "      <td>-0.531409</td>\n",
       "      <td>-0.010593</td>\n",
       "      <td>4.697350e-17</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>2.512175e-16</td>\n",
       "      <td>-1.126388e-16</td>\n",
       "      <td>-2.039868e-16</td>\n",
       "      <td>5.024680e-16</td>\n",
       "      <td>3.966486e-16</td>\n",
       "      <td>-4.413984e-17</td>\n",
       "      <td>...</td>\n",
       "      <td>-9.309954e-16</td>\n",
       "      <td>8.444409e-17</td>\n",
       "      <td>2.500830e-16</td>\n",
       "      <td>1.059562e-16</td>\n",
       "      <td>-8.142354e-18</td>\n",
       "      <td>-4.261894e-17</td>\n",
       "      <td>2.601622e-16</td>\n",
       "      <td>-4.478472e-16</td>\n",
       "      <td>-3.676415e-16</td>\n",
       "      <td>0.091289</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>V3</th>\n",
       "      <td>-0.210880</td>\n",
       "      <td>-0.419618</td>\n",
       "      <td>-1.424390e-15</td>\n",
       "      <td>2.512175e-16</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>-3.416910e-16</td>\n",
       "      <td>-1.436514e-15</td>\n",
       "      <td>1.431581e-15</td>\n",
       "      <td>2.168574e-15</td>\n",
       "      <td>3.433113e-16</td>\n",
       "      <td>...</td>\n",
       "      <td>-9.429297e-16</td>\n",
       "      <td>-2.971969e-17</td>\n",
       "      <td>4.648259e-16</td>\n",
       "      <td>2.115206e-17</td>\n",
       "      <td>-9.351637e-17</td>\n",
       "      <td>4.771164e-16</td>\n",
       "      <td>6.521501e-16</td>\n",
       "      <td>6.239832e-16</td>\n",
       "      <td>7.726948e-16</td>\n",
       "      <td>-0.192961</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>V4</th>\n",
       "      <td>0.098732</td>\n",
       "      <td>-0.105260</td>\n",
       "      <td>1.755316e-17</td>\n",
       "      <td>-1.126388e-16</td>\n",
       "      <td>-3.416910e-16</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>-1.940929e-15</td>\n",
       "      <td>-2.712659e-16</td>\n",
       "      <td>1.556330e-16</td>\n",
       "      <td>5.195643e-16</td>\n",
       "      <td>...</td>\n",
       "      <td>-3.223123e-16</td>\n",
       "      <td>-9.976950e-17</td>\n",
       "      <td>2.099922e-16</td>\n",
       "      <td>6.002528e-17</td>\n",
       "      <td>2.229738e-16</td>\n",
       "      <td>5.394585e-16</td>\n",
       "      <td>-6.179751e-16</td>\n",
       "      <td>-6.403423e-17</td>\n",
       "      <td>-5.863664e-17</td>\n",
       "      <td>0.133447</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>V5</th>\n",
       "      <td>-0.386356</td>\n",
       "      <td>0.173072</td>\n",
       "      <td>6.391162e-17</td>\n",
       "      <td>-2.039868e-16</td>\n",
       "      <td>-1.436514e-15</td>\n",
       "      <td>-1.940929e-15</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>7.926364e-16</td>\n",
       "      <td>-4.209851e-16</td>\n",
       "      <td>7.589187e-16</td>\n",
       "      <td>...</td>\n",
       "      <td>2.076048e-16</td>\n",
       "      <td>-1.368701e-16</td>\n",
       "      <td>5.060029e-16</td>\n",
       "      <td>1.637596e-16</td>\n",
       "      <td>-9.286095e-16</td>\n",
       "      <td>5.625102e-16</td>\n",
       "      <td>9.144690e-16</td>\n",
       "      <td>4.465960e-16</td>\n",
       "      <td>-3.299167e-16</td>\n",
       "      <td>-0.094974</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>V6</th>\n",
       "      <td>0.215981</td>\n",
       "      <td>-0.063016</td>\n",
       "      <td>2.398071e-16</td>\n",
       "      <td>5.024680e-16</td>\n",
       "      <td>1.431581e-15</td>\n",
       "      <td>-2.712659e-16</td>\n",
       "      <td>7.926364e-16</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.429426e-16</td>\n",
       "      <td>-1.707421e-16</td>\n",
       "      <td>...</td>\n",
       "      <td>1.898638e-16</td>\n",
       "      <td>-1.575903e-16</td>\n",
       "      <td>-3.362902e-16</td>\n",
       "      <td>-7.232186e-17</td>\n",
       "      <td>-1.261867e-15</td>\n",
       "      <td>1.081933e-15</td>\n",
       "      <td>-2.378414e-16</td>\n",
       "      <td>-2.623818e-16</td>\n",
       "      <td>4.813155e-16</td>\n",
       "      <td>-0.043643</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>V7</th>\n",
       "      <td>0.397311</td>\n",
       "      <td>0.084714</td>\n",
       "      <td>1.991550e-15</td>\n",
       "      <td>3.966486e-16</td>\n",
       "      <td>2.168574e-15</td>\n",
       "      <td>1.556330e-16</td>\n",
       "      <td>-4.209851e-16</td>\n",
       "      <td>1.429426e-16</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>-8.691834e-17</td>\n",
       "      <td>...</td>\n",
       "      <td>1.744242e-16</td>\n",
       "      <td>1.938604e-16</td>\n",
       "      <td>-1.058131e-15</td>\n",
       "      <td>2.327911e-16</td>\n",
       "      <td>-2.589727e-17</td>\n",
       "      <td>1.174169e-15</td>\n",
       "      <td>-7.334507e-16</td>\n",
       "      <td>-5.886825e-16</td>\n",
       "      <td>-6.836764e-17</td>\n",
       "      <td>-0.187257</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>V8</th>\n",
       "      <td>-0.103079</td>\n",
       "      <td>-0.036949</td>\n",
       "      <td>-9.490675e-17</td>\n",
       "      <td>-4.413984e-17</td>\n",
       "      <td>3.433113e-16</td>\n",
       "      <td>5.195643e-16</td>\n",
       "      <td>7.589187e-16</td>\n",
       "      <td>-1.707421e-16</td>\n",
       "      <td>-8.691834e-17</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.095534e-16</td>\n",
       "      <td>-2.412439e-16</td>\n",
       "      <td>5.475559e-16</td>\n",
       "      <td>3.897104e-16</td>\n",
       "      <td>-1.802967e-16</td>\n",
       "      <td>-1.390791e-16</td>\n",
       "      <td>-1.209975e-16</td>\n",
       "      <td>1.733633e-16</td>\n",
       "      <td>-4.484325e-16</td>\n",
       "      <td>0.019875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>V9</th>\n",
       "      <td>-0.044246</td>\n",
       "      <td>-0.008660</td>\n",
       "      <td>2.169581e-16</td>\n",
       "      <td>-5.728718e-17</td>\n",
       "      <td>-4.233770e-16</td>\n",
       "      <td>3.859585e-16</td>\n",
       "      <td>4.205206e-16</td>\n",
       "      <td>1.114447e-16</td>\n",
       "      <td>7.933251e-16</td>\n",
       "      <td>2.900829e-16</td>\n",
       "      <td>...</td>\n",
       "      <td>-4.340941e-16</td>\n",
       "      <td>4.578389e-17</td>\n",
       "      <td>2.871855e-17</td>\n",
       "      <td>5.929286e-16</td>\n",
       "      <td>-2.346385e-16</td>\n",
       "      <td>1.099645e-15</td>\n",
       "      <td>-1.388725e-15</td>\n",
       "      <td>-2.287414e-16</td>\n",
       "      <td>9.146779e-16</td>\n",
       "      <td>-0.097733</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>V10</th>\n",
       "      <td>-0.101502</td>\n",
       "      <td>0.030617</td>\n",
       "      <td>7.433820e-17</td>\n",
       "      <td>-4.782388e-16</td>\n",
       "      <td>6.289267e-16</td>\n",
       "      <td>6.055490e-16</td>\n",
       "      <td>-6.601716e-16</td>\n",
       "      <td>2.850776e-16</td>\n",
       "      <td>3.043333e-17</td>\n",
       "      <td>9.051847e-17</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.141888e-15</td>\n",
       "      <td>8.089504e-16</td>\n",
       "      <td>-6.707598e-16</td>\n",
       "      <td>3.809732e-16</td>\n",
       "      <td>-4.032806e-17</td>\n",
       "      <td>-2.863813e-16</td>\n",
       "      <td>-2.554293e-16</td>\n",
       "      <td>-3.103239e-16</td>\n",
       "      <td>-1.515934e-16</td>\n",
       "      <td>-0.216883</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>V11</th>\n",
       "      <td>0.000104</td>\n",
       "      <td>-0.247689</td>\n",
       "      <td>2.438580e-16</td>\n",
       "      <td>9.468995e-16</td>\n",
       "      <td>-5.501758e-17</td>\n",
       "      <td>-2.083600e-16</td>\n",
       "      <td>7.342759e-16</td>\n",
       "      <td>4.865799e-16</td>\n",
       "      <td>-1.084105e-15</td>\n",
       "      <td>1.954747e-16</td>\n",
       "      <td>...</td>\n",
       "      <td>-2.310189e-16</td>\n",
       "      <td>-3.911893e-16</td>\n",
       "      <td>-3.811640e-17</td>\n",
       "      <td>2.232007e-16</td>\n",
       "      <td>1.219849e-15</td>\n",
       "      <td>-4.567635e-16</td>\n",
       "      <td>-1.110976e-16</td>\n",
       "      <td>-2.635827e-16</td>\n",
       "      <td>-3.091914e-16</td>\n",
       "      <td>0.154876</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>V12</th>\n",
       "      <td>-0.009542</td>\n",
       "      <td>0.124348</td>\n",
       "      <td>2.422086e-16</td>\n",
       "      <td>-6.588252e-16</td>\n",
       "      <td>2.206522e-16</td>\n",
       "      <td>-5.657963e-16</td>\n",
       "      <td>3.761033e-16</td>\n",
       "      <td>2.140589e-16</td>\n",
       "      <td>1.510045e-15</td>\n",
       "      <td>-6.266057e-17</td>\n",
       "      <td>...</td>\n",
       "      <td>1.276976e-16</td>\n",
       "      <td>3.229576e-16</td>\n",
       "      <td>-5.903992e-16</td>\n",
       "      <td>1.392162e-16</td>\n",
       "      <td>4.901644e-16</td>\n",
       "      <td>5.053736e-16</td>\n",
       "      <td>-5.759321e-16</td>\n",
       "      <td>-2.312619e-16</td>\n",
       "      <td>7.327446e-16</td>\n",
       "      <td>-0.260593</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>V13</th>\n",
       "      <td>0.005293</td>\n",
       "      <td>-0.065902</td>\n",
       "      <td>-2.115458e-16</td>\n",
       "      <td>3.854521e-16</td>\n",
       "      <td>-6.883375e-16</td>\n",
       "      <td>-1.506129e-16</td>\n",
       "      <td>-9.578659e-16</td>\n",
       "      <td>-2.268061e-16</td>\n",
       "      <td>-9.892325e-17</td>\n",
       "      <td>-2.382948e-16</td>\n",
       "      <td>...</td>\n",
       "      <td>2.345665e-18</td>\n",
       "      <td>9.499130e-17</td>\n",
       "      <td>-2.659710e-17</td>\n",
       "      <td>-5.884304e-16</td>\n",
       "      <td>-5.470547e-16</td>\n",
       "      <td>8.066738e-17</td>\n",
       "      <td>-2.121518e-16</td>\n",
       "      <td>-4.520414e-16</td>\n",
       "      <td>1.049541e-15</td>\n",
       "      <td>-0.004570</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>V14</th>\n",
       "      <td>0.033751</td>\n",
       "      <td>-0.098757</td>\n",
       "      <td>9.352582e-16</td>\n",
       "      <td>-2.541036e-16</td>\n",
       "      <td>4.271336e-16</td>\n",
       "      <td>-8.522435e-17</td>\n",
       "      <td>-3.634803e-16</td>\n",
       "      <td>3.452801e-16</td>\n",
       "      <td>-1.729462e-16</td>\n",
       "      <td>-1.131098e-16</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.789433e-16</td>\n",
       "      <td>1.634141e-17</td>\n",
       "      <td>3.439699e-16</td>\n",
       "      <td>7.620728e-17</td>\n",
       "      <td>2.335749e-16</td>\n",
       "      <td>-2.606783e-16</td>\n",
       "      <td>-6.580254e-18</td>\n",
       "      <td>1.285770e-16</td>\n",
       "      <td>2.503271e-15</td>\n",
       "      <td>-0.302544</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>V15</th>\n",
       "      <td>-0.002986</td>\n",
       "      <td>-0.183453</td>\n",
       "      <td>-3.252451e-16</td>\n",
       "      <td>2.831060e-16</td>\n",
       "      <td>1.122756e-16</td>\n",
       "      <td>-1.507718e-16</td>\n",
       "      <td>-5.132620e-16</td>\n",
       "      <td>-6.368111e-18</td>\n",
       "      <td>1.936832e-17</td>\n",
       "      <td>2.021491e-16</td>\n",
       "      <td>...</td>\n",
       "      <td>3.240475e-16</td>\n",
       "      <td>1.947458e-17</td>\n",
       "      <td>-8.936817e-16</td>\n",
       "      <td>1.119827e-16</td>\n",
       "      <td>-4.589689e-16</td>\n",
       "      <td>3.869740e-16</td>\n",
       "      <td>3.761094e-16</td>\n",
       "      <td>-1.265235e-15</td>\n",
       "      <td>-1.063286e-15</td>\n",
       "      <td>-0.004223</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>V16</th>\n",
       "      <td>-0.003910</td>\n",
       "      <td>0.011903</td>\n",
       "      <td>6.308789e-16</td>\n",
       "      <td>4.934097e-17</td>\n",
       "      <td>1.183364e-15</td>\n",
       "      <td>-6.939204e-16</td>\n",
       "      <td>-3.517076e-16</td>\n",
       "      <td>-2.477917e-16</td>\n",
       "      <td>2.893672e-16</td>\n",
       "      <td>5.027192e-16</td>\n",
       "      <td>...</td>\n",
       "      <td>3.903456e-16</td>\n",
       "      <td>-3.927401e-16</td>\n",
       "      <td>3.878384e-17</td>\n",
       "      <td>8.519670e-16</td>\n",
       "      <td>-4.289239e-16</td>\n",
       "      <td>-6.644104e-16</td>\n",
       "      <td>-5.186503e-16</td>\n",
       "      <td>7.820038e-16</td>\n",
       "      <td>8.637186e-16</td>\n",
       "      <td>-0.196539</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>V17</th>\n",
       "      <td>0.007309</td>\n",
       "      <td>-0.073297</td>\n",
       "      <td>-5.011524e-16</td>\n",
       "      <td>-9.883008e-16</td>\n",
       "      <td>4.576619e-17</td>\n",
       "      <td>-4.397925e-16</td>\n",
       "      <td>1.425729e-16</td>\n",
       "      <td>3.567582e-16</td>\n",
       "      <td>1.149692e-15</td>\n",
       "      <td>-3.508777e-16</td>\n",
       "      <td>...</td>\n",
       "      <td>-9.887431e-16</td>\n",
       "      <td>-7.753967e-16</td>\n",
       "      <td>-8.389703e-16</td>\n",
       "      <td>5.367784e-16</td>\n",
       "      <td>-5.543631e-17</td>\n",
       "      <td>4.822068e-16</td>\n",
       "      <td>4.870302e-16</td>\n",
       "      <td>8.844373e-16</td>\n",
       "      <td>-2.182692e-16</td>\n",
       "      <td>-0.326481</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>V18</th>\n",
       "      <td>0.035650</td>\n",
       "      <td>0.090438</td>\n",
       "      <td>2.870125e-16</td>\n",
       "      <td>2.636654e-16</td>\n",
       "      <td>5.427965e-16</td>\n",
       "      <td>1.493667e-16</td>\n",
       "      <td>1.109525e-15</td>\n",
       "      <td>2.811474e-16</td>\n",
       "      <td>-1.116789e-16</td>\n",
       "      <td>-4.093852e-16</td>\n",
       "      <td>...</td>\n",
       "      <td>-4.942764e-16</td>\n",
       "      <td>-1.140973e-15</td>\n",
       "      <td>-8.662635e-17</td>\n",
       "      <td>-3.624236e-16</td>\n",
       "      <td>-1.126043e-16</td>\n",
       "      <td>-2.310856e-16</td>\n",
       "      <td>3.183964e-16</td>\n",
       "      <td>2.435170e-16</td>\n",
       "      <td>8.844995e-16</td>\n",
       "      <td>-0.111485</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>V19</th>\n",
       "      <td>-0.056151</td>\n",
       "      <td>0.028975</td>\n",
       "      <td>1.818128e-16</td>\n",
       "      <td>9.528280e-17</td>\n",
       "      <td>2.576773e-16</td>\n",
       "      <td>-2.656938e-16</td>\n",
       "      <td>-3.138234e-16</td>\n",
       "      <td>2.717167e-16</td>\n",
       "      <td>-2.874017e-16</td>\n",
       "      <td>-5.339821e-16</td>\n",
       "      <td>...</td>\n",
       "      <td>2.938450e-16</td>\n",
       "      <td>4.032541e-16</td>\n",
       "      <td>-9.690436e-16</td>\n",
       "      <td>5.733798e-16</td>\n",
       "      <td>3.126716e-17</td>\n",
       "      <td>7.415355e-16</td>\n",
       "      <td>5.614354e-16</td>\n",
       "      <td>-1.113035e-16</td>\n",
       "      <td>-1.375843e-15</td>\n",
       "      <td>0.034783</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>V20</th>\n",
       "      <td>0.339403</td>\n",
       "      <td>-0.050866</td>\n",
       "      <td>1.036959e-16</td>\n",
       "      <td>-9.309954e-16</td>\n",
       "      <td>-9.429297e-16</td>\n",
       "      <td>-3.223123e-16</td>\n",
       "      <td>2.076048e-16</td>\n",
       "      <td>1.898638e-16</td>\n",
       "      <td>1.744242e-16</td>\n",
       "      <td>-1.095534e-16</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>-1.120828e-15</td>\n",
       "      <td>1.105842e-15</td>\n",
       "      <td>4.986739e-16</td>\n",
       "      <td>1.637488e-16</td>\n",
       "      <td>-1.518242e-16</td>\n",
       "      <td>-2.975081e-16</td>\n",
       "      <td>-1.446069e-15</td>\n",
       "      <td>-1.133579e-16</td>\n",
       "      <td>0.020090</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>V21</th>\n",
       "      <td>0.105999</td>\n",
       "      <td>0.044736</td>\n",
       "      <td>-1.755072e-16</td>\n",
       "      <td>8.444409e-17</td>\n",
       "      <td>-2.971969e-17</td>\n",
       "      <td>-9.976950e-17</td>\n",
       "      <td>-1.368701e-16</td>\n",
       "      <td>-1.575903e-16</td>\n",
       "      <td>1.938604e-16</td>\n",
       "      <td>-2.412439e-16</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.120828e-15</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>3.905948e-15</td>\n",
       "      <td>6.127323e-16</td>\n",
       "      <td>1.298254e-16</td>\n",
       "      <td>-2.826293e-16</td>\n",
       "      <td>-4.907301e-16</td>\n",
       "      <td>-1.033403e-15</td>\n",
       "      <td>5.132234e-16</td>\n",
       "      <td>0.040413</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>V22</th>\n",
       "      <td>-0.064801</td>\n",
       "      <td>0.144059</td>\n",
       "      <td>7.477367e-17</td>\n",
       "      <td>2.500830e-16</td>\n",
       "      <td>4.648259e-16</td>\n",
       "      <td>2.099922e-16</td>\n",
       "      <td>5.060029e-16</td>\n",
       "      <td>-3.362902e-16</td>\n",
       "      <td>-1.058131e-15</td>\n",
       "      <td>5.475559e-16</td>\n",
       "      <td>...</td>\n",
       "      <td>1.105842e-15</td>\n",
       "      <td>3.905948e-15</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>3.130812e-16</td>\n",
       "      <td>1.150829e-17</td>\n",
       "      <td>-6.078986e-16</td>\n",
       "      <td>-8.477050e-16</td>\n",
       "      <td>-1.294910e-16</td>\n",
       "      <td>-3.021376e-16</td>\n",
       "      <td>0.000805</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>V23</th>\n",
       "      <td>-0.112633</td>\n",
       "      <td>0.051142</td>\n",
       "      <td>9.808705e-16</td>\n",
       "      <td>1.059562e-16</td>\n",
       "      <td>2.115206e-17</td>\n",
       "      <td>6.002528e-17</td>\n",
       "      <td>1.637596e-16</td>\n",
       "      <td>-7.232186e-17</td>\n",
       "      <td>2.327911e-16</td>\n",
       "      <td>3.897104e-16</td>\n",
       "      <td>...</td>\n",
       "      <td>4.986739e-16</td>\n",
       "      <td>6.127323e-16</td>\n",
       "      <td>3.130812e-16</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>-4.411271e-17</td>\n",
       "      <td>-9.938362e-16</td>\n",
       "      <td>8.848700e-16</td>\n",
       "      <td>5.524044e-16</td>\n",
       "      <td>9.029821e-16</td>\n",
       "      <td>-0.002685</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>V24</th>\n",
       "      <td>0.005146</td>\n",
       "      <td>-0.016182</td>\n",
       "      <td>7.354269e-17</td>\n",
       "      <td>-8.142354e-18</td>\n",
       "      <td>-9.351637e-17</td>\n",
       "      <td>2.229738e-16</td>\n",
       "      <td>-9.286095e-16</td>\n",
       "      <td>-1.261867e-15</td>\n",
       "      <td>-2.589727e-17</td>\n",
       "      <td>-1.802967e-16</td>\n",
       "      <td>...</td>\n",
       "      <td>1.637488e-16</td>\n",
       "      <td>1.298254e-16</td>\n",
       "      <td>1.150829e-17</td>\n",
       "      <td>-4.411271e-17</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.557318e-15</td>\n",
       "      <td>3.129195e-16</td>\n",
       "      <td>-3.736529e-16</td>\n",
       "      <td>-2.259275e-16</td>\n",
       "      <td>-0.007221</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>V25</th>\n",
       "      <td>-0.047837</td>\n",
       "      <td>-0.233083</td>\n",
       "      <td>-9.805358e-16</td>\n",
       "      <td>-4.261894e-17</td>\n",
       "      <td>4.771164e-16</td>\n",
       "      <td>5.394585e-16</td>\n",
       "      <td>5.625102e-16</td>\n",
       "      <td>1.081933e-15</td>\n",
       "      <td>1.174169e-15</td>\n",
       "      <td>-1.390791e-16</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.518242e-16</td>\n",
       "      <td>-2.826293e-16</td>\n",
       "      <td>-6.078986e-16</td>\n",
       "      <td>-9.938362e-16</td>\n",
       "      <td>1.557318e-15</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>2.810884e-15</td>\n",
       "      <td>-6.107118e-16</td>\n",
       "      <td>3.399375e-16</td>\n",
       "      <td>0.003308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>V26</th>\n",
       "      <td>-0.003208</td>\n",
       "      <td>-0.041407</td>\n",
       "      <td>-8.621897e-17</td>\n",
       "      <td>2.601622e-16</td>\n",
       "      <td>6.521501e-16</td>\n",
       "      <td>-6.179751e-16</td>\n",
       "      <td>9.144690e-16</td>\n",
       "      <td>-2.378414e-16</td>\n",
       "      <td>-7.334507e-16</td>\n",
       "      <td>-1.209975e-16</td>\n",
       "      <td>...</td>\n",
       "      <td>-2.975081e-16</td>\n",
       "      <td>-4.907301e-16</td>\n",
       "      <td>-8.477050e-16</td>\n",
       "      <td>8.848700e-16</td>\n",
       "      <td>3.129195e-16</td>\n",
       "      <td>2.810884e-15</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>-3.383861e-16</td>\n",
       "      <td>-3.751403e-16</td>\n",
       "      <td>0.004455</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>V27</th>\n",
       "      <td>0.028825</td>\n",
       "      <td>-0.005135</td>\n",
       "      <td>3.208233e-17</td>\n",
       "      <td>-4.478472e-16</td>\n",
       "      <td>6.239832e-16</td>\n",
       "      <td>-6.403423e-17</td>\n",
       "      <td>4.465960e-16</td>\n",
       "      <td>-2.623818e-16</td>\n",
       "      <td>-5.886825e-16</td>\n",
       "      <td>1.733633e-16</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.446069e-15</td>\n",
       "      <td>-1.033403e-15</td>\n",
       "      <td>-1.294910e-16</td>\n",
       "      <td>5.524044e-16</td>\n",
       "      <td>-3.736529e-16</td>\n",
       "      <td>-6.107118e-16</td>\n",
       "      <td>-3.383861e-16</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>-3.770124e-16</td>\n",
       "      <td>0.017580</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>V28</th>\n",
       "      <td>0.010258</td>\n",
       "      <td>-0.009413</td>\n",
       "      <td>9.820892e-16</td>\n",
       "      <td>-3.676415e-16</td>\n",
       "      <td>7.726948e-16</td>\n",
       "      <td>-5.863664e-17</td>\n",
       "      <td>-3.299167e-16</td>\n",
       "      <td>4.813155e-16</td>\n",
       "      <td>-6.836764e-17</td>\n",
       "      <td>-4.484325e-16</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.133579e-16</td>\n",
       "      <td>5.132234e-16</td>\n",
       "      <td>-3.021376e-16</td>\n",
       "      <td>9.029821e-16</td>\n",
       "      <td>-2.259275e-16</td>\n",
       "      <td>3.399375e-16</td>\n",
       "      <td>-3.751403e-16</td>\n",
       "      <td>-3.770124e-16</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>0.009536</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Class</th>\n",
       "      <td>0.005632</td>\n",
       "      <td>-0.012323</td>\n",
       "      <td>-1.013473e-01</td>\n",
       "      <td>9.128865e-02</td>\n",
       "      <td>-1.929608e-01</td>\n",
       "      <td>1.334475e-01</td>\n",
       "      <td>-9.497430e-02</td>\n",
       "      <td>-4.364316e-02</td>\n",
       "      <td>-1.872566e-01</td>\n",
       "      <td>1.987512e-02</td>\n",
       "      <td>...</td>\n",
       "      <td>2.009032e-02</td>\n",
       "      <td>4.041338e-02</td>\n",
       "      <td>8.053175e-04</td>\n",
       "      <td>-2.685156e-03</td>\n",
       "      <td>-7.220907e-03</td>\n",
       "      <td>3.307706e-03</td>\n",
       "      <td>4.455398e-03</td>\n",
       "      <td>1.757973e-02</td>\n",
       "      <td>9.536041e-03</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>31 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               scaled_amount  scaled_time            V1            V2  \\\n",
       "scaled_amount       1.000000    -0.010596 -2.277087e-01 -5.314089e-01   \n",
       "scaled_time        -0.010596     1.000000  1.173963e-01 -1.059333e-02   \n",
       "V1                 -0.227709     0.117396  1.000000e+00  4.697350e-17   \n",
       "V2                 -0.531409    -0.010593  4.697350e-17  1.000000e+00   \n",
       "V3                 -0.210880    -0.419618 -1.424390e-15  2.512175e-16   \n",
       "V4                  0.098732    -0.105260  1.755316e-17 -1.126388e-16   \n",
       "V5                 -0.386356     0.173072  6.391162e-17 -2.039868e-16   \n",
       "V6                  0.215981    -0.063016  2.398071e-16  5.024680e-16   \n",
       "V7                  0.397311     0.084714  1.991550e-15  3.966486e-16   \n",
       "V8                 -0.103079    -0.036949 -9.490675e-17 -4.413984e-17   \n",
       "V9                 -0.044246    -0.008660  2.169581e-16 -5.728718e-17   \n",
       "V10                -0.101502     0.030617  7.433820e-17 -4.782388e-16   \n",
       "V11                 0.000104    -0.247689  2.438580e-16  9.468995e-16   \n",
       "V12                -0.009542     0.124348  2.422086e-16 -6.588252e-16   \n",
       "V13                 0.005293    -0.065902 -2.115458e-16  3.854521e-16   \n",
       "V14                 0.033751    -0.098757  9.352582e-16 -2.541036e-16   \n",
       "V15                -0.002986    -0.183453 -3.252451e-16  2.831060e-16   \n",
       "V16                -0.003910     0.011903  6.308789e-16  4.934097e-17   \n",
       "V17                 0.007309    -0.073297 -5.011524e-16 -9.883008e-16   \n",
       "V18                 0.035650     0.090438  2.870125e-16  2.636654e-16   \n",
       "V19                -0.056151     0.028975  1.818128e-16  9.528280e-17   \n",
       "V20                 0.339403    -0.050866  1.036959e-16 -9.309954e-16   \n",
       "V21                 0.105999     0.044736 -1.755072e-16  8.444409e-17   \n",
       "V22                -0.064801     0.144059  7.477367e-17  2.500830e-16   \n",
       "V23                -0.112633     0.051142  9.808705e-16  1.059562e-16   \n",
       "V24                 0.005146    -0.016182  7.354269e-17 -8.142354e-18   \n",
       "V25                -0.047837    -0.233083 -9.805358e-16 -4.261894e-17   \n",
       "V26                -0.003208    -0.041407 -8.621897e-17  2.601622e-16   \n",
       "V27                 0.028825    -0.005135  3.208233e-17 -4.478472e-16   \n",
       "V28                 0.010258    -0.009413  9.820892e-16 -3.676415e-16   \n",
       "Class               0.005632    -0.012323 -1.013473e-01  9.128865e-02   \n",
       "\n",
       "                         V3            V4            V5            V6  \\\n",
       "scaled_amount -2.108805e-01  9.873167e-02 -3.863563e-01  2.159812e-01   \n",
       "scaled_time   -4.196182e-01 -1.052602e-01  1.730721e-01 -6.301647e-02   \n",
       "V1            -1.424390e-15  1.755316e-17  6.391162e-17  2.398071e-16   \n",
       "V2             2.512175e-16 -1.126388e-16 -2.039868e-16  5.024680e-16   \n",
       "V3             1.000000e+00 -3.416910e-16 -1.436514e-15  1.431581e-15   \n",
       "V4            -3.416910e-16  1.000000e+00 -1.940929e-15 -2.712659e-16   \n",
       "V5            -1.436514e-15 -1.940929e-15  1.000000e+00  7.926364e-16   \n",
       "V6             1.431581e-15 -2.712659e-16  7.926364e-16  1.000000e+00   \n",
       "V7             2.168574e-15  1.556330e-16 -4.209851e-16  1.429426e-16   \n",
       "V8             3.433113e-16  5.195643e-16  7.589187e-16 -1.707421e-16   \n",
       "V9            -4.233770e-16  3.859585e-16  4.205206e-16  1.114447e-16   \n",
       "V10            6.289267e-16  6.055490e-16 -6.601716e-16  2.850776e-16   \n",
       "V11           -5.501758e-17 -2.083600e-16  7.342759e-16  4.865799e-16   \n",
       "V12            2.206522e-16 -5.657963e-16  3.761033e-16  2.140589e-16   \n",
       "V13           -6.883375e-16 -1.506129e-16 -9.578659e-16 -2.268061e-16   \n",
       "V14            4.271336e-16 -8.522435e-17 -3.634803e-16  3.452801e-16   \n",
       "V15            1.122756e-16 -1.507718e-16 -5.132620e-16 -6.368111e-18   \n",
       "V16            1.183364e-15 -6.939204e-16 -3.517076e-16 -2.477917e-16   \n",
       "V17            4.576619e-17 -4.397925e-16  1.425729e-16  3.567582e-16   \n",
       "V18            5.427965e-16  1.493667e-16  1.109525e-15  2.811474e-16   \n",
       "V19            2.576773e-16 -2.656938e-16 -3.138234e-16  2.717167e-16   \n",
       "V20           -9.429297e-16 -3.223123e-16  2.076048e-16  1.898638e-16   \n",
       "V21           -2.971969e-17 -9.976950e-17 -1.368701e-16 -1.575903e-16   \n",
       "V22            4.648259e-16  2.099922e-16  5.060029e-16 -3.362902e-16   \n",
       "V23            2.115206e-17  6.002528e-17  1.637596e-16 -7.232186e-17   \n",
       "V24           -9.351637e-17  2.229738e-16 -9.286095e-16 -1.261867e-15   \n",
       "V25            4.771164e-16  5.394585e-16  5.625102e-16  1.081933e-15   \n",
       "V26            6.521501e-16 -6.179751e-16  9.144690e-16 -2.378414e-16   \n",
       "V27            6.239832e-16 -6.403423e-17  4.465960e-16 -2.623818e-16   \n",
       "V28            7.726948e-16 -5.863664e-17 -3.299167e-16  4.813155e-16   \n",
       "Class         -1.929608e-01  1.334475e-01 -9.497430e-02 -4.364316e-02   \n",
       "\n",
       "                         V7            V8  ...           V20           V21  \\\n",
       "scaled_amount  3.973113e-01 -1.030791e-01  ...  3.394034e-01  1.059989e-01   \n",
       "scaled_time    8.471437e-02 -3.694943e-02  ... -5.086602e-02  4.473573e-02   \n",
       "V1             1.991550e-15 -9.490675e-17  ...  1.036959e-16 -1.755072e-16   \n",
       "V2             3.966486e-16 -4.413984e-17  ... -9.309954e-16  8.444409e-17   \n",
       "V3             2.168574e-15  3.433113e-16  ... -9.429297e-16 -2.971969e-17   \n",
       "V4             1.556330e-16  5.195643e-16  ... -3.223123e-16 -9.976950e-17   \n",
       "V5            -4.209851e-16  7.589187e-16  ...  2.076048e-16 -1.368701e-16   \n",
       "V6             1.429426e-16 -1.707421e-16  ...  1.898638e-16 -1.575903e-16   \n",
       "V7             1.000000e+00 -8.691834e-17  ...  1.744242e-16  1.938604e-16   \n",
       "V8            -8.691834e-17  1.000000e+00  ... -1.095534e-16 -2.412439e-16   \n",
       "V9             7.933251e-16  2.900829e-16  ... -4.340941e-16  4.578389e-17   \n",
       "V10            3.043333e-17  9.051847e-17  ... -1.141888e-15  8.089504e-16   \n",
       "V11           -1.084105e-15  1.954747e-16  ... -2.310189e-16 -3.911893e-16   \n",
       "V12            1.510045e-15 -6.266057e-17  ...  1.276976e-16  3.229576e-16   \n",
       "V13           -9.892325e-17 -2.382948e-16  ...  2.345665e-18  9.499130e-17   \n",
       "V14           -1.729462e-16 -1.131098e-16  ... -1.789433e-16  1.634141e-17   \n",
       "V15            1.936832e-17  2.021491e-16  ...  3.240475e-16  1.947458e-17   \n",
       "V16            2.893672e-16  5.027192e-16  ...  3.903456e-16 -3.927401e-16   \n",
       "V17            1.149692e-15 -3.508777e-16  ... -9.887431e-16 -7.753967e-16   \n",
       "V18           -1.116789e-16 -4.093852e-16  ... -4.942764e-16 -1.140973e-15   \n",
       "V19           -2.874017e-16 -5.339821e-16  ...  2.938450e-16  4.032541e-16   \n",
       "V20            1.744242e-16 -1.095534e-16  ...  1.000000e+00 -1.120828e-15   \n",
       "V21            1.938604e-16 -2.412439e-16  ... -1.120828e-15  1.000000e+00   \n",
       "V22           -1.058131e-15  5.475559e-16  ...  1.105842e-15  3.905948e-15   \n",
       "V23            2.327911e-16  3.897104e-16  ...  4.986739e-16  6.127323e-16   \n",
       "V24           -2.589727e-17 -1.802967e-16  ...  1.637488e-16  1.298254e-16   \n",
       "V25            1.174169e-15 -1.390791e-16  ... -1.518242e-16 -2.826293e-16   \n",
       "V26           -7.334507e-16 -1.209975e-16  ... -2.975081e-16 -4.907301e-16   \n",
       "V27           -5.886825e-16  1.733633e-16  ... -1.446069e-15 -1.033403e-15   \n",
       "V28           -6.836764e-17 -4.484325e-16  ... -1.133579e-16  5.132234e-16   \n",
       "Class         -1.872566e-01  1.987512e-02  ...  2.009032e-02  4.041338e-02   \n",
       "\n",
       "                        V22           V23           V24           V25  \\\n",
       "scaled_amount -6.480065e-02 -1.126326e-01  5.146217e-03 -4.783686e-02   \n",
       "scaled_time    1.440591e-01  5.114236e-02 -1.618187e-02 -2.330828e-01   \n",
       "V1             7.477367e-17  9.808705e-16  7.354269e-17 -9.805358e-16   \n",
       "V2             2.500830e-16  1.059562e-16 -8.142354e-18 -4.261894e-17   \n",
       "V3             4.648259e-16  2.115206e-17 -9.351637e-17  4.771164e-16   \n",
       "V4             2.099922e-16  6.002528e-17  2.229738e-16  5.394585e-16   \n",
       "V5             5.060029e-16  1.637596e-16 -9.286095e-16  5.625102e-16   \n",
       "V6            -3.362902e-16 -7.232186e-17 -1.261867e-15  1.081933e-15   \n",
       "V7            -1.058131e-15  2.327911e-16 -2.589727e-17  1.174169e-15   \n",
       "V8             5.475559e-16  3.897104e-16 -1.802967e-16 -1.390791e-16   \n",
       "V9             2.871855e-17  5.929286e-16 -2.346385e-16  1.099645e-15   \n",
       "V10           -6.707598e-16  3.809732e-16 -4.032806e-17 -2.863813e-16   \n",
       "V11           -3.811640e-17  2.232007e-16  1.219849e-15 -4.567635e-16   \n",
       "V12           -5.903992e-16  1.392162e-16  4.901644e-16  5.053736e-16   \n",
       "V13           -2.659710e-17 -5.884304e-16 -5.470547e-16  8.066738e-17   \n",
       "V14            3.439699e-16  7.620728e-17  2.335749e-16 -2.606783e-16   \n",
       "V15           -8.936817e-16  1.119827e-16 -4.589689e-16  3.869740e-16   \n",
       "V16            3.878384e-17  8.519670e-16 -4.289239e-16 -6.644104e-16   \n",
       "V17           -8.389703e-16  5.367784e-16 -5.543631e-17  4.822068e-16   \n",
       "V18           -8.662635e-17 -3.624236e-16 -1.126043e-16 -2.310856e-16   \n",
       "V19           -9.690436e-16  5.733798e-16  3.126716e-17  7.415355e-16   \n",
       "V20            1.105842e-15  4.986739e-16  1.637488e-16 -1.518242e-16   \n",
       "V21            3.905948e-15  6.127323e-16  1.298254e-16 -2.826293e-16   \n",
       "V22            1.000000e+00  3.130812e-16  1.150829e-17 -6.078986e-16   \n",
       "V23            3.130812e-16  1.000000e+00 -4.411271e-17 -9.938362e-16   \n",
       "V24            1.150829e-17 -4.411271e-17  1.000000e+00  1.557318e-15   \n",
       "V25           -6.078986e-16 -9.938362e-16  1.557318e-15  1.000000e+00   \n",
       "V26           -8.477050e-16  8.848700e-16  3.129195e-16  2.810884e-15   \n",
       "V27           -1.294910e-16  5.524044e-16 -3.736529e-16 -6.107118e-16   \n",
       "V28           -3.021376e-16  9.029821e-16 -2.259275e-16  3.399375e-16   \n",
       "Class          8.053175e-04 -2.685156e-03 -7.220907e-03  3.307706e-03   \n",
       "\n",
       "                        V26           V27           V28     Class  \n",
       "scaled_amount -3.208037e-03  2.882546e-02  1.025822e-02  0.005632  \n",
       "scaled_time   -4.140710e-02 -5.134591e-03 -9.412688e-03 -0.012323  \n",
       "V1            -8.621897e-17  3.208233e-17  9.820892e-16 -0.101347  \n",
       "V2             2.601622e-16 -4.478472e-16 -3.676415e-16  0.091289  \n",
       "V3             6.521501e-16  6.239832e-16  7.726948e-16 -0.192961  \n",
       "V4            -6.179751e-16 -6.403423e-17 -5.863664e-17  0.133447  \n",
       "V5             9.144690e-16  4.465960e-16 -3.299167e-16 -0.094974  \n",
       "V6            -2.378414e-16 -2.623818e-16  4.813155e-16 -0.043643  \n",
       "V7            -7.334507e-16 -5.886825e-16 -6.836764e-17 -0.187257  \n",
       "V8            -1.209975e-16  1.733633e-16 -4.484325e-16  0.019875  \n",
       "V9            -1.388725e-15 -2.287414e-16  9.146779e-16 -0.097733  \n",
       "V10           -2.554293e-16 -3.103239e-16 -1.515934e-16 -0.216883  \n",
       "V11           -1.110976e-16 -2.635827e-16 -3.091914e-16  0.154876  \n",
       "V12           -5.759321e-16 -2.312619e-16  7.327446e-16 -0.260593  \n",
       "V13           -2.121518e-16 -4.520414e-16  1.049541e-15 -0.004570  \n",
       "V14           -6.580254e-18  1.285770e-16  2.503271e-15 -0.302544  \n",
       "V15            3.761094e-16 -1.265235e-15 -1.063286e-15 -0.004223  \n",
       "V16           -5.186503e-16  7.820038e-16  8.637186e-16 -0.196539  \n",
       "V17            4.870302e-16  8.844373e-16 -2.182692e-16 -0.326481  \n",
       "V18            3.183964e-16  2.435170e-16  8.844995e-16 -0.111485  \n",
       "V19            5.614354e-16 -1.113035e-16 -1.375843e-15  0.034783  \n",
       "V20           -2.975081e-16 -1.446069e-15 -1.133579e-16  0.020090  \n",
       "V21           -4.907301e-16 -1.033403e-15  5.132234e-16  0.040413  \n",
       "V22           -8.477050e-16 -1.294910e-16 -3.021376e-16  0.000805  \n",
       "V23            8.848700e-16  5.524044e-16  9.029821e-16 -0.002685  \n",
       "V24            3.129195e-16 -3.736529e-16 -2.259275e-16 -0.007221  \n",
       "V25            2.810884e-15 -6.107118e-16  3.399375e-16  0.003308  \n",
       "V26            1.000000e+00 -3.383861e-16 -3.751403e-16  0.004455  \n",
       "V27           -3.383861e-16  1.000000e+00 -3.770124e-16  0.017580  \n",
       "V28           -3.751403e-16 -3.770124e-16  1.000000e+00  0.009536  \n",
       "Class          4.455398e-03  1.757973e-02  9.536041e-03  1.000000  \n",
       "\n",
       "[31 rows x 31 columns]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr = data.corr()\n",
    "corr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x187090b3518>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,10))\n",
    "sns.heatmap(corr, cmap='coolwarm_r')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "보아하니 V14, V17이 조금 높게 나온다.\n",
    "\n",
    "얘내들을 좀 살펴보자"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'V14 vs Class')"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, axes = plt.subplots(ncols=2, figsize=(15,8))\n",
    "\n",
    "sns.boxplot(x=\"Class\", y=\"V17\", data=data, ax=axes[0])\n",
    "axes[0].set_title('V17 vs Class')\n",
    "\n",
    "sns.boxplot(x=\"Class\", y=\"V14\", data=data, ax=axes[1])\n",
    "axes[1].set_title('V14 vs Class')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "뭔지는 모르겠지만 이 두개는 0과 1을 잘 구분해지는 역할을 한다.\n",
    "\n",
    "그럼 나머지 feature는 어떻게 보일까?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'V21 vs Class')"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, axes = plt.subplots(ncols=2, figsize=(15,8))\n",
    "\n",
    "sns.boxplot(x=\"Class\", y=\"V20\", data=data, ax=axes[0])\n",
    "axes[0].set_title('V20 vs Class')\n",
    "\n",
    "sns.boxplot(x=\"Class\", y=\"V21\", data=data, ax=axes[1])\n",
    "axes[1].set_title('V21 vs Class')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "비슷한 경향을 보인다.\n",
    "\n",
    "그럼 저 V14, V20의 아웃라이어를 제거하자.\n",
    "\n",
    "일단, data.copy를 통해 복사 후 진행. 원본 데이터 유지를 위해서!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_copy = data.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "def remove_outlier_test(d_cp, column):\n",
    "    fraud_column_data = d_cp[column]\n",
    "    quan_25 = np.percentile(fraud_column_data.values, 25)\n",
    "    quan_75 = np.percentile(fraud_column_data.values, 75)\n",
    "    \n",
    "    iqr = quan_75 - quan_25\n",
    "    iqr = iqr * 1.5\n",
    "    lowest = quan_25 - iqr\n",
    "    highest = quan_75 + iqr\n",
    "    outlier_index = fraud_column_data[(fraud_column_data < lowest) | (fraud_column_data > highest)].index\n",
    "    print(len(outlier_index))\n",
    "    d_cp.drop(outlier_index, axis = 0, inplace=True)\n",
    "    print(d_cp.shape)\n",
    "    return d_cp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "14149\n",
      "(270658, 31)\n"
     ]
    }
   ],
   "source": [
    "data_copy = remove_outlier_test(data_copy, 'V14')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "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>scaled_amount</th>\n",
       "      <th>scaled_time</th>\n",
       "      <th>V1</th>\n",
       "      <th>V2</th>\n",
       "      <th>V3</th>\n",
       "      <th>V4</th>\n",
       "      <th>V5</th>\n",
       "      <th>V6</th>\n",
       "      <th>V7</th>\n",
       "      <th>V8</th>\n",
       "      <th>...</th>\n",
       "      <th>V20</th>\n",
       "      <th>V21</th>\n",
       "      <th>V22</th>\n",
       "      <th>V23</th>\n",
       "      <th>V24</th>\n",
       "      <th>V25</th>\n",
       "      <th>V26</th>\n",
       "      <th>V27</th>\n",
       "      <th>V28</th>\n",
       "      <th>Class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.244964</td>\n",
       "      <td>-0.994983</td>\n",
       "      <td>-1.359807</td>\n",
       "      <td>-0.072781</td>\n",
       "      <td>2.536347</td>\n",
       "      <td>1.378155</td>\n",
       "      <td>-0.338321</td>\n",
       "      <td>0.462388</td>\n",
       "      <td>0.239599</td>\n",
       "      <td>0.098698</td>\n",
       "      <td>...</td>\n",
       "      <td>0.251412</td>\n",
       "      <td>-0.018307</td>\n",
       "      <td>0.277838</td>\n",
       "      <td>-0.110474</td>\n",
       "      <td>0.066928</td>\n",
       "      <td>0.128539</td>\n",
       "      <td>-0.189115</td>\n",
       "      <td>0.133558</td>\n",
       "      <td>-0.021053</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.342475</td>\n",
       "      <td>-0.994983</td>\n",
       "      <td>1.191857</td>\n",
       "      <td>0.266151</td>\n",
       "      <td>0.166480</td>\n",
       "      <td>0.448154</td>\n",
       "      <td>0.060018</td>\n",
       "      <td>-0.082361</td>\n",
       "      <td>-0.078803</td>\n",
       "      <td>0.085102</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.069083</td>\n",
       "      <td>-0.225775</td>\n",
       "      <td>-0.638672</td>\n",
       "      <td>0.101288</td>\n",
       "      <td>-0.339846</td>\n",
       "      <td>0.167170</td>\n",
       "      <td>0.125895</td>\n",
       "      <td>-0.008983</td>\n",
       "      <td>0.014724</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.160686</td>\n",
       "      <td>-0.994972</td>\n",
       "      <td>-1.358354</td>\n",
       "      <td>-1.340163</td>\n",
       "      <td>1.773209</td>\n",
       "      <td>0.379780</td>\n",
       "      <td>-0.503198</td>\n",
       "      <td>1.800499</td>\n",
       "      <td>0.791461</td>\n",
       "      <td>0.247676</td>\n",
       "      <td>...</td>\n",
       "      <td>0.524980</td>\n",
       "      <td>0.247998</td>\n",
       "      <td>0.771679</td>\n",
       "      <td>0.909412</td>\n",
       "      <td>-0.689281</td>\n",
       "      <td>-0.327642</td>\n",
       "      <td>-0.139097</td>\n",
       "      <td>-0.055353</td>\n",
       "      <td>-0.059752</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.140534</td>\n",
       "      <td>-0.994972</td>\n",
       "      <td>-0.966272</td>\n",
       "      <td>-0.185226</td>\n",
       "      <td>1.792993</td>\n",
       "      <td>-0.863291</td>\n",
       "      <td>-0.010309</td>\n",
       "      <td>1.247203</td>\n",
       "      <td>0.237609</td>\n",
       "      <td>0.377436</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.208038</td>\n",
       "      <td>-0.108300</td>\n",
       "      <td>0.005274</td>\n",
       "      <td>-0.190321</td>\n",
       "      <td>-1.175575</td>\n",
       "      <td>0.647376</td>\n",
       "      <td>-0.221929</td>\n",
       "      <td>0.062723</td>\n",
       "      <td>0.061458</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.073403</td>\n",
       "      <td>-0.994960</td>\n",
       "      <td>-1.158233</td>\n",
       "      <td>0.877737</td>\n",
       "      <td>1.548718</td>\n",
       "      <td>0.403034</td>\n",
       "      <td>-0.407193</td>\n",
       "      <td>0.095921</td>\n",
       "      <td>0.592941</td>\n",
       "      <td>-0.270533</td>\n",
       "      <td>...</td>\n",
       "      <td>0.408542</td>\n",
       "      <td>-0.009431</td>\n",
       "      <td>0.798278</td>\n",
       "      <td>-0.137458</td>\n",
       "      <td>0.141267</td>\n",
       "      <td>-0.206010</td>\n",
       "      <td>0.502292</td>\n",
       "      <td>0.219422</td>\n",
       "      <td>0.215153</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   scaled_amount  scaled_time        V1        V2        V3        V4  \\\n",
       "0       0.244964    -0.994983 -1.359807 -0.072781  2.536347  1.378155   \n",
       "1      -0.342475    -0.994983  1.191857  0.266151  0.166480  0.448154   \n",
       "2       1.160686    -0.994972 -1.358354 -1.340163  1.773209  0.379780   \n",
       "3       0.140534    -0.994972 -0.966272 -0.185226  1.792993 -0.863291   \n",
       "4      -0.073403    -0.994960 -1.158233  0.877737  1.548718  0.403034   \n",
       "\n",
       "         V5        V6        V7        V8  ...       V20       V21       V22  \\\n",
       "0 -0.338321  0.462388  0.239599  0.098698  ...  0.251412 -0.018307  0.277838   \n",
       "1  0.060018 -0.082361 -0.078803  0.085102  ... -0.069083 -0.225775 -0.638672   \n",
       "2 -0.503198  1.800499  0.791461  0.247676  ...  0.524980  0.247998  0.771679   \n",
       "3 -0.010309  1.247203  0.237609  0.377436  ... -0.208038 -0.108300  0.005274   \n",
       "4 -0.407193  0.095921  0.592941 -0.270533  ...  0.408542 -0.009431  0.798278   \n",
       "\n",
       "        V23       V24       V25       V26       V27       V28  Class  \n",
       "0 -0.110474  0.066928  0.128539 -0.189115  0.133558 -0.021053      0  \n",
       "1  0.101288 -0.339846  0.167170  0.125895 -0.008983  0.014724      0  \n",
       "2  0.909412 -0.689281 -0.327642 -0.139097 -0.055353 -0.059752      0  \n",
       "3 -0.190321 -1.175575  0.647376 -0.221929  0.062723  0.061458      0  \n",
       "4 -0.137458  0.141267 -0.206010  0.502292  0.219422  0.215153      0  \n",
       "\n",
       "[5 rows x 31 columns]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_copy.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = get_X_y_data(data_copy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "정확도 : 1.00, 정밀도 : 0.33, 재현율 : 0.06\n",
      "f1-score : 0.11, auc : 0.53\n"
     ]
    }
   ],
   "source": [
    "lr = LogisticRegression()\n",
    "modeling(lr, X_train, X_test, y_train, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "정확도 : 1.00, 정밀도 : 1.00, 재현율 : 0.06\n",
      "f1-score : 0.12, auc : 0.53\n"
     ]
    }
   ],
   "source": [
    "lgb = LGBMClassifier(n_estimators = 1000, num_leaves = 64, n_jobs = -1, boost_from_average = False)\n",
    "modeling(lgb, x_train = X_train, x_test = X_test, y_train = y_train, y_test = y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "이렇게 하니까 정밀도, 재현율, f1-score가 전부 망가짐  \n",
    "**이유**는 전체 데이터 셋에서 outlier를 구하니까 데이터 셋 전부가 망가져버림  \n",
    "즉, 전체 데이터 셋 기준 outlier이므로 데이터 셋이 틀어진 것 같다.  \n",
    "\n",
    "label이 0일 때와 1일 때를 각각 나누어서 outlier 제거를 해보자."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "def remove_outlier(d_cp, column):\n",
    "    #fraud_column_data = d_cp[d_cp['Class']==1][column]\n",
    "    fraud_column_data = d_cp[d_cp['Class']==0][column]\n",
    "    quan_25 = np.percentile(fraud_column_data.values, 25)\n",
    "    quan_75 = np.percentile(fraud_column_data.values, 75)\n",
    "    \n",
    "    iqr = quan_75 - quan_25\n",
    "    iqr = iqr * 1.5\n",
    "    lowest = quan_25 - iqr\n",
    "    highest = quan_75 + iqr\n",
    "    outlier_index = fraud_column_data[(fraud_column_data < lowest) | (fraud_column_data > highest)].index\n",
    "    print(len(outlier_index))\n",
    "    d_cp.drop(outlier_index, axis = 0, inplace=True)\n",
    "    print(d_cp.shape)\n",
    "    return d_cp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_copy = data.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4\n",
      "(284803, 31)\n"
     ]
    }
   ],
   "source": [
    "data_copy = remove_outlier(data_copy, 'V14')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = get_X_y_data(data_copy)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**아래는 class=1일 때**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "정확도 : 1.00, 정밀도 : 0.86, 재현율 : 0.67\n",
      "f1-score : 0.76, auc : 0.84\n",
      "정확도 : 1.00, 정밀도 : 0.95, 재현율 : 0.81\n",
      "f1-score : 0.88, auc : 0.91\n"
     ]
    }
   ],
   "source": [
    "modeling(lr, X_train, X_test, y_train, y_test)\n",
    "modeling(lgb, X_train, X_test, y_train, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**아래는 class=0일 때**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "13800\n",
      "(271007, 31)\n",
      "정확도 : 1.00, 정밀도 : 0.99, 재현율 : 0.87\n",
      "f1-score : 0.93, auc : 0.93\n",
      "정확도 : 1.00, 정밀도 : 1.00, 재현율 : 0.86\n",
      "f1-score : 0.93, auc : 0.93\n"
     ]
    }
   ],
   "source": [
    "data_copy = data.copy()\n",
    "data_copy = remove_outlier(data_copy, 'V14')\n",
    "X_train, X_test, y_train, y_test = get_X_y_data(data_copy)\n",
    "modeling(lr, X_train, X_test, y_train, y_test)\n",
    "modeling(lgb, X_train, X_test, y_train, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "attachments": {
    "image.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# oversampling, undersampling\n",
    "\n",
    "이 데이터 셋은 데이터가 매우매우 불균형하다. 왜냐하면 사기를 치는 경우가 보통의 경우보다 매우 적기 때문이다.\n",
    "\n",
    "즉, 사기를 친 case가 매우 적은데 이렇게 데이터가 불균형하면 모델이 다양한 유형을 학습하지 못한다.\n",
    "그렇게 되면 성능이 좋은 모델이 나오기가 힘들다.\n",
    "\n",
    "그래서 지도학습에서 이러한 불균형한 데이터 셋을 맞추기 위해 oversampling 기법과 undersampling 기법을 제공한다.\n",
    "\n",
    "![image.png](attachment:image.png)\n",
    "\n",
    "\n",
    "언더 샘플링은 많은 데이터 세트를 적은 데이터 세트 수준으로 감소시키는 방법이다. 즉, 정상 데이터 1만건, 비정상 데이터 100건이 있으면 정상 데이터를 100건으로 줄이는 방법이다. 이 방법은 너무 많은 정상 데이터를 감소시키기 때문에 오히려 학습이 되지 않아 잘 사용하지 않는다.\n",
    "\n",
    "오버 샘플링은 적은 데이터 세트를 충분히 확호하는 방법이다. 동일한 데이터를 증식하는 방법은 overfitting이 되기 때문에 좋지 못하다. 그래서 원본 데이터의 피처 값들을 아주 약간만 변경하여 증식하는 방법은 사용한다. 대표적으로 SMOTE(Synthetic Minority Over-sampling Technique) 방법이 있다. SMOTE는 적은 데이터 세트에 있는 개별 데이터들의 K Nearest Neighbor를 찾아서 이 데이터와 K개 이웃들의 차이를 일정 값으로 만들어 기존 데이터와 약간 차이가 나는 새로운 데이터를 생성한다."
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![image.png](attachment:image.png)\n",
    "\n",
    "출처 : https://joonable.tistory.com/27\n",
    "\n",
    "사이킷런에서는 이와 같은 라이브러리를 제공해준다.\n",
    "imbalanced-learn 패키지가 그와 같다.\n",
    "\n",
    "pip install imbalanced-learn 으로 설치할 수 있다.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SMOTE 전 학습 데이터 셋 :  (203255, 30) (203255,)\n",
      "SMOTE 후 학습 데이터 셋 :  (405772, 30) (405772,)\n"
     ]
    }
   ],
   "source": [
    "from imblearn.over_sampling import SMOTE\n",
    "\n",
    "smote = SMOTE(random_state = 0)\n",
    "X_train_over, y_train_over = smote.fit_sample(X_train, y_train)\n",
    "print(\"SMOTE 전 학습 데이터 셋 : \", X_train.shape, y_train.shape)\n",
    "print(\"SMOTE 후 학습 데이터 셋 : \", X_train_over.shape, y_train_over.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x18703d7bac8>"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_train.value_counts().plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x18703de9780>"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pd.Series(y_train_over).value_counts().plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "정확도 : 0.98, 정밀도 : 0.07, 재현율 : 0.95\n",
      "f1-score : 0.12, auc : 0.96\n"
     ]
    }
   ],
   "source": [
    "lr = LogisticRegression()\n",
    "modeling(lr,X_train_over, X_test, y_train_over, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "재현율이 크게 올랐지만, 정밀도가 박살이 났다. 이정도로 정밀도가 낮으면 사용할 수 없는 모델이다.\n",
    "\n",
    "이렇게 된 이유는 오버 샘플링으로 인해 실제 원본 데이터의 유형보다 너무 많은 class =1 을 학습하면서 실제 테스트 데이터 ㅅ트에서 예측을 지나치게 class=1로 적용해 정밀도가 떨어진 것이다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "정확도 : 1.00, 정밀도 : 0.98, 재현율 : 0.88\n",
      "f1-score : 0.93, auc : 0.94\n"
     ]
    }
   ],
   "source": [
    "lgb = LGBMClassifier(n_estimators=1000, num_leaves = 64, n_jobs = -1, boost_from_average = False)\n",
    "modeling(lgb, X_train_over, X_test, y_train_over, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "LightGBM 에서는 Logistic Regression과 다르게 재현율이 89%까지 상승했다. 정밀도도 97%가 유지되고 f1-score도 93%를 유지한다.\n",
    "\n",
    "일반적으로 SMOTE를 적용하면 재현율을 높아지나 정밀도가 낮아지는일이 많이 발생한다.\n",
    "그래서 재현율을 증가시키고 정밀도 감소율을 낮추는 것이 중요하다. 좋은 SMOTE 패키지일 수록 이러한 특성을 잘 반영한다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}