로지스틱_의사결정나무_[숙제].ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 로지스틱 회귀분석"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import statsmodels.api as sm\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import scipy.stats as stats\n",
    "from sklearn import metrics\n",
    "from sklearn import tree\n",
    "from sklearn.preprocessing import scale"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 숙제 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\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>CHURN</th>\n",
       "      <th>CUSTOMER_ID</th>\n",
       "      <th>GENDER</th>\n",
       "      <th>AGE</th>\n",
       "      <th>CHARGE_TYPE</th>\n",
       "      <th>HANDSET</th>\n",
       "      <th>USAGE_BAND</th>\n",
       "      <th>SERVICE_DURATION</th>\n",
       "      <th>DROPPED_CALLS</th>\n",
       "      <th>PEAK_CALLS_NO</th>\n",
       "      <th>PEAK_CALLS_TIME</th>\n",
       "      <th>WEEKEND_CALLS_NO</th>\n",
       "      <th>WEEKEND_CALLS_TIME</th>\n",
       "      <th>TOTAL_CALLS_NO</th>\n",
       "      <th>TOTAL_CALLS_TIME</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Active</td>\n",
       "      <td>K102990</td>\n",
       "      <td>F</td>\n",
       "      <td>31</td>\n",
       "      <td>CAT 100</td>\n",
       "      <td>ASAD170</td>\n",
       "      <td>Med</td>\n",
       "      <td>33.33</td>\n",
       "      <td>6</td>\n",
       "      <td>218</td>\n",
       "      <td>379.8</td>\n",
       "      <td>9</td>\n",
       "      <td>21.6</td>\n",
       "      <td>366</td>\n",
       "      <td>632.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Active</td>\n",
       "      <td>K103280</td>\n",
       "      <td>M</td>\n",
       "      <td>27</td>\n",
       "      <td>CAT 100</td>\n",
       "      <td>S50</td>\n",
       "      <td>Med</td>\n",
       "      <td>30.87</td>\n",
       "      <td>10</td>\n",
       "      <td>373</td>\n",
       "      <td>656.4</td>\n",
       "      <td>11</td>\n",
       "      <td>28.0</td>\n",
       "      <td>411</td>\n",
       "      <td>810.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Active</td>\n",
       "      <td>K103660</td>\n",
       "      <td>M</td>\n",
       "      <td>27</td>\n",
       "      <td>CAT 100</td>\n",
       "      <td>ASAD170</td>\n",
       "      <td>Med</td>\n",
       "      <td>49.40</td>\n",
       "      <td>11</td>\n",
       "      <td>260</td>\n",
       "      <td>582.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>265</td>\n",
       "      <td>614.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Active</td>\n",
       "      <td>K103730</td>\n",
       "      <td>M</td>\n",
       "      <td>61</td>\n",
       "      <td>CAT 100</td>\n",
       "      <td>ASAD170</td>\n",
       "      <td>Med</td>\n",
       "      <td>48.60</td>\n",
       "      <td>1</td>\n",
       "      <td>294</td>\n",
       "      <td>661.8</td>\n",
       "      <td>14</td>\n",
       "      <td>101.5</td>\n",
       "      <td>326</td>\n",
       "      <td>844.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Active</td>\n",
       "      <td>K104560</td>\n",
       "      <td>F</td>\n",
       "      <td>52</td>\n",
       "      <td>CAT 100</td>\n",
       "      <td>S50</td>\n",
       "      <td>Med</td>\n",
       "      <td>56.03</td>\n",
       "      <td>0</td>\n",
       "      <td>221</td>\n",
       "      <td>555.6</td>\n",
       "      <td>7</td>\n",
       "      <td>25.0</td>\n",
       "      <td>274</td>\n",
       "      <td>673.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    CHURN CUSTOMER_ID GENDER  AGE CHARGE_TYPE  HANDSET USAGE_BAND  \\\n",
       "0  Active     K102990      F   31     CAT 100  ASAD170        Med   \n",
       "1  Active     K103280      M   27     CAT 100      S50        Med   \n",
       "2  Active     K103660      M   27     CAT 100  ASAD170        Med   \n",
       "3  Active     K103730      M   61     CAT 100  ASAD170        Med   \n",
       "4  Active     K104560      F   52     CAT 100      S50        Med   \n",
       "\n",
       "   SERVICE_DURATION  DROPPED_CALLS  PEAK_CALLS_NO  PEAK_CALLS_TIME  \\\n",
       "0             33.33              6            218            379.8   \n",
       "1             30.87             10            373            656.4   \n",
       "2             49.40             11            260            582.0   \n",
       "3             48.60              1            294            661.8   \n",
       "4             56.03              0            221            555.6   \n",
       "\n",
       "   WEEKEND_CALLS_NO  WEEKEND_CALLS_TIME  TOTAL_CALLS_NO  TOTAL_CALLS_TIME  \n",
       "0                 9                21.6             366             632.4  \n",
       "1                11                28.0             411             810.4  \n",
       "2                 0                 0.0             265             614.1  \n",
       "3                14               101.5             326             844.0  \n",
       "4                 7                25.0             274             673.0  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds_tc = pd.read_csv(\"실습파일/통신고객이탈.csv\")\n",
    "ds_tc.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CHURN                 0\n",
       "CUSTOMER_ID           0\n",
       "GENDER                0\n",
       "AGE                   0\n",
       "CHARGE_TYPE           0\n",
       "HANDSET               0\n",
       "USAGE_BAND            0\n",
       "SERVICE_DURATION      0\n",
       "DROPPED_CALLS         0\n",
       "PEAK_CALLS_NO         0\n",
       "PEAK_CALLS_TIME       0\n",
       "WEEKEND_CALLS_NO      0\n",
       "WEEKEND_CALLS_TIME    0\n",
       "TOTAL_CALLS_NO        0\n",
       "TOTAL_CALLS_TIME      0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds_tc.isnull().sum(axis = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train data size:(635, 15)\n",
      "test data size:(295, 15)\n"
     ]
    }
   ],
   "source": [
    "np.random.seed(1234)\n",
    "msk = np.random.rand(ds_tc.shape[0],) < 0.7\n",
    "ds_tc[\"CHURN\"].replace(to_replace = \"Active\", value = 0, inplace = True)\n",
    "ds_tc[\"CHURN\"].replace(to_replace = \"Churned\", value = 1, inplace = True)\n",
    "\n",
    "ds_tc_train = ds_tc[msk]\n",
    "ds_tc_test = ds_tc[~msk]\n",
    "\n",
    "print(\"train data size:{}\".format(ds_tc_train.shape))\n",
    "print(\"test data size:{}\".format(ds_tc_test.shape))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>const</th>\n",
       "      <th>CHURN</th>\n",
       "      <th>CUSTOMER_ID</th>\n",
       "      <th>GENDER</th>\n",
       "      <th>AGE</th>\n",
       "      <th>CHARGE_TYPE</th>\n",
       "      <th>HANDSET</th>\n",
       "      <th>USAGE_BAND</th>\n",
       "      <th>SERVICE_DURATION</th>\n",
       "      <th>DROPPED_CALLS</th>\n",
       "      <th>PEAK_CALLS_NO</th>\n",
       "      <th>PEAK_CALLS_TIME</th>\n",
       "      <th>WEEKEND_CALLS_NO</th>\n",
       "      <th>WEEKEND_CALLS_TIME</th>\n",
       "      <th>TOTAL_CALLS_NO</th>\n",
       "      <th>TOTAL_CALLS_TIME</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>K102990</td>\n",
       "      <td>F</td>\n",
       "      <td>31</td>\n",
       "      <td>CAT 100</td>\n",
       "      <td>ASAD170</td>\n",
       "      <td>Med</td>\n",
       "      <td>33.33</td>\n",
       "      <td>6</td>\n",
       "      <td>218</td>\n",
       "      <td>379.8</td>\n",
       "      <td>9</td>\n",
       "      <td>21.6</td>\n",
       "      <td>366</td>\n",
       "      <td>632.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>K103280</td>\n",
       "      <td>M</td>\n",
       "      <td>27</td>\n",
       "      <td>CAT 100</td>\n",
       "      <td>S50</td>\n",
       "      <td>Med</td>\n",
       "      <td>30.87</td>\n",
       "      <td>10</td>\n",
       "      <td>373</td>\n",
       "      <td>656.4</td>\n",
       "      <td>11</td>\n",
       "      <td>28.0</td>\n",
       "      <td>411</td>\n",
       "      <td>810.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>K103660</td>\n",
       "      <td>M</td>\n",
       "      <td>27</td>\n",
       "      <td>CAT 100</td>\n",
       "      <td>ASAD170</td>\n",
       "      <td>Med</td>\n",
       "      <td>49.40</td>\n",
       "      <td>11</td>\n",
       "      <td>260</td>\n",
       "      <td>582.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>265</td>\n",
       "      <td>614.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>K105530</td>\n",
       "      <td>M</td>\n",
       "      <td>51</td>\n",
       "      <td>CAT 100</td>\n",
       "      <td>S80</td>\n",
       "      <td>Med</td>\n",
       "      <td>49.43</td>\n",
       "      <td>3</td>\n",
       "      <td>289</td>\n",
       "      <td>486.6</td>\n",
       "      <td>1</td>\n",
       "      <td>31.8</td>\n",
       "      <td>427</td>\n",
       "      <td>903.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>K106210</td>\n",
       "      <td>F</td>\n",
       "      <td>30</td>\n",
       "      <td>CAT 100</td>\n",
       "      <td>ASAD170</td>\n",
       "      <td>Med</td>\n",
       "      <td>32.30</td>\n",
       "      <td>6</td>\n",
       "      <td>427</td>\n",
       "      <td>729.6</td>\n",
       "      <td>12</td>\n",
       "      <td>52.8</td>\n",
       "      <td>490</td>\n",
       "      <td>907.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   const  CHURN CUSTOMER_ID GENDER  AGE CHARGE_TYPE  HANDSET USAGE_BAND  \\\n",
       "0    1.0      0     K102990      F   31     CAT 100  ASAD170        Med   \n",
       "1    1.0      0     K103280      M   27     CAT 100      S50        Med   \n",
       "2    1.0      0     K103660      M   27     CAT 100  ASAD170        Med   \n",
       "5    1.0      0     K105530      M   51     CAT 100      S80        Med   \n",
       "6    1.0      0     K106210      F   30     CAT 100  ASAD170        Med   \n",
       "\n",
       "   SERVICE_DURATION  DROPPED_CALLS  PEAK_CALLS_NO  PEAK_CALLS_TIME  \\\n",
       "0             33.33              6            218            379.8   \n",
       "1             30.87             10            373            656.4   \n",
       "2             49.40             11            260            582.0   \n",
       "5             49.43              3            289            486.6   \n",
       "6             32.30              6            427            729.6   \n",
       "\n",
       "   WEEKEND_CALLS_NO  WEEKEND_CALLS_TIME  TOTAL_CALLS_NO  TOTAL_CALLS_TIME  \n",
       "0                 9                21.6             366             632.4  \n",
       "1                11                28.0             411             810.4  \n",
       "2                 0                 0.0             265             614.1  \n",
       "5                 1                31.8             427             903.3  \n",
       "6                12                52.8             490             907.2  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds_tc_train = sm.add_constant(ds_tc_train)\n",
    "ds_tc_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Warning: Maximum number of iterations has been exceeded.\n",
      "         Current function value: 0.261293\n",
      "         Iterations: 35\n",
      "         Function evaluations: 49\n",
      "         Gradient evaluations: 42\n",
      "                           Logit Regression Results                           \n",
      "==============================================================================\n",
      "Dep. Variable:                  CHURN   No. Observations:                  635\n",
      "Model:                          Logit   Df Residuals:                      605\n",
      "Method:                           MLE   Df Model:                           29\n",
      "Date:                Mon, 11 Feb 2019   Pseudo R-squ.:                  0.4660\n",
      "Time:                        09:19:49   Log-Likelihood:                -165.92\n",
      "converged:                      False   LL-Null:                       -310.70\n",
      "                                        LLR p-value:                 9.369e-45\n",
      "==============================================================================================\n",
      "                                 coef    std err          z      P>|z|      [0.025      0.975]\n",
      "----------------------------------------------------------------------------------------------\n",
      "Intercept                     -1.1342      2.139     -0.530      0.596      -5.326       3.058\n",
      "C(GENDER)[T.M]                -0.5785      0.320     -1.810      0.070      -1.205       0.048\n",
      "C(CHARGE_TYPE)[T.CAT 200]     -0.1864      0.629     -0.296      0.767      -1.419       1.046\n",
      "C(CHARGE_TYPE)[T.CAT 50]      -2.4395      1.021     -2.390      0.017      -4.440      -0.439\n",
      "C(CHARGE_TYPE)[T.Play 100]     0.9210      0.703      1.310      0.190      -0.457       2.299\n",
      "C(CHARGE_TYPE)[T.Play 300]    -0.2244      0.796     -0.282      0.778      -1.785       1.336\n",
      "C(HANDSET)[T.ASAD90]           5.4849      0.779      7.039      0.000       3.958       7.012\n",
      "C(HANDSET)[T.BS110]            1.1184      0.691      1.617      0.106      -0.237       2.474\n",
      "C(HANDSET)[T.BS210]           -4.3102      2.838     -1.519      0.129      -9.873       1.253\n",
      "C(HANDSET)[T.CAS30]            5.0987      0.883      5.777      0.000       3.369       6.829\n",
      "C(HANDSET)[T.CAS60]           -1.1870      2.411     -0.492      0.622      -5.912       3.538\n",
      "C(HANDSET)[T.S50]              1.6594      0.658      2.522      0.012       0.370       2.949\n",
      "C(HANDSET)[T.S80]              0.9463      0.715      1.324      0.186      -0.455       2.347\n",
      "C(HANDSET)[T.SOP10]            1.8914      1.128      1.677      0.093      -0.319       4.101\n",
      "C(HANDSET)[T.SOP20]            3.1225      1.259      2.479      0.013       0.654       5.591\n",
      "C(HANDSET)[T.WC95]            -4.7484      4.488     -1.058      0.290     -13.544       4.047\n",
      "C(USAGE_BAND)[T.Low]           1.1177      2.186      0.511      0.609      -3.166       5.401\n",
      "C(USAGE_BAND)[T.Med]          -0.5819      1.324     -0.439      0.660      -3.177       2.014\n",
      "C(USAGE_BAND)[T.MedHigh]      -1.1694      0.949     -1.232      0.218      -3.030       0.691\n",
      "C(USAGE_BAND)[T.MedLow]        1.1007      1.711      0.643      0.520      -2.253       4.454\n",
      "C(USAGE_BAND)[T.None]         -0.3415      2.972     -0.115      0.909      -6.167       5.484\n",
      "AGE                           -0.0317      0.012     -2.626      0.009      -0.055      -0.008\n",
      "SERVICE_DURATION              -0.0283      0.011     -2.636      0.008      -0.049      -0.007\n",
      "DROPPED_CALLS                  0.2097      0.040      5.295      0.000       0.132       0.287\n",
      "PEAK_CALLS_NO                 -0.0008      0.003     -0.300      0.764      -0.006       0.004\n",
      "PEAK_CALLS_TIME                0.0014      0.001      0.983      0.325      -0.001       0.004\n",
      "WEEKEND_CALLS_NO              -0.0110      0.013     -0.874      0.382      -0.036       0.014\n",
      "WEEKEND_CALLS_TIME             0.0073      0.006      1.304      0.192      -0.004       0.018\n",
      "TOTAL_CALLS_NO                 0.0011      0.002      0.458      0.647      -0.003       0.006\n",
      "TOTAL_CALLS_TIME              -0.0012      0.002     -0.765      0.444      -0.004       0.002\n",
      "==============================================================================================\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/pirl/anaconda3/envs/py35/lib/python3.5/site-packages/statsmodels/discrete/discrete_model.py:1724: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "/home/pirl/anaconda3/envs/py35/lib/python3.5/site-packages/statsmodels/discrete/discrete_model.py:1724: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.sum(np.log(self.cdf(q*np.dot(X,params))))\n",
      "/home/pirl/anaconda3/envs/py35/lib/python3.5/site-packages/statsmodels/base/model.py:508: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  \"Check mle_retvals\", ConvergenceWarning)\n"
     ]
    }
   ],
   "source": [
    "log_model_tc = sm.Logit.from_formula(\"\"\" CHURN ~ C(GENDER)+ AGE + C(CHARGE_TYPE) \n",
    "+ C(HANDSET) + C(USAGE_BAND) + SERVICE_DURATION + DROPPED_CALLS + PEAK_CALLS_NO + PEAK_CALLS_TIME \n",
    "+ WEEKEND_CALLS_NO + WEEKEND_CALLS_TIME + TOTAL_CALLS_NO + TOTAL_CALLS_TIME + 1 \"\"\", ds_tc_train)\n",
    "\n",
    "log_result_tc = log_model_tc.fit(method='bfgs')\n",
    "print(log_result_tc.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3    0\n",
       "4    0\n",
       "7    0\n",
       "8    0\n",
       "9    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred_tc = log_result_tc.predict(ds_tc_test)\n",
    "y_pred_class_tc = (y_pred_tc > .5).astype(int)\n",
    "y_pred_class_tc.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.898\n"
     ]
    }
   ],
   "source": [
    "print(\"Accuracy: {0:.3f}\".format(metrics.accuracy_score(ds_tc_test[\"CHURN\"], y_pred_class_tc)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f2c9a20f1d0>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ICDRLS1+xfxVgJTN7jGZo6c3sZ1ZnM7seMBIY3Awt/cu4hK7SH76aJe+LwBJFcmFF/1MnnyRJ0g506jX5GpkvtPQV7EedhG0Y7aulPwW4kVJt/uaSOvmko1j3ucbyYZOk+9GlZ/JBaunbUUtvZhOAsbivQEFhMzuH6Pu0YrkhSZIkaX+6wyCfWvr219KfBBxZ+n0VsI2kr0bbi+IFcxpVDyRJkiRtS3cY5Ast/SMV294vheEr19HLWfRT5Xa10+X2tIXn+/BIUNsV2AwYhT+vNeQWtibplYq2Hg9p2cW4S9vekjaNfUXi3Qt4Ut3eQJ9Ym35GbnlbDrmfh2epT5HbzN4PfIZr5p+peAYPR5sF4+V2sm9Jeh1P/PuppA9i+xRgIVxL/5zcivYsYHW5NW1h0Xuq6qxsAb4jyXC728cB5HbAH+NLFMeF7v89fGb/Q4XFb5IkSdL+dPk1+aa09GY2GR9wq53bR9K0Qust6SpgUGjb92lg7X1RST/BM8o/K4q9RFuT45h1cNe33c3sCeAJYGi5EUkb4Fry3c3sObnpSznb/kvAO7iGfQtzT3pitnxzRZ+eAyYW/Shd43ngO2Y2Ltpfx8zKLwg7UUHcw5w8hdDYE89kMP4Ctb+Fda6kB4Gn8VyFHeL8fg3kOSRJkiTtSHeYybcm9wO1uMw1Jl9bF7gB+H5kvzfEb4CTzOw58JeVCqneAHx9/w5grxr7X8kKeMGcov3KCEBLuKHoj6Q18ATHHNCTJEk6ITnIB6H93g2fqYLP2Msh/v3iuKbkazcCPzezB5q45AbAmLJ2vfQ5ONodhteIb1HBGzwE/7yk6yX9WFItBZCXoSQrxEP9ZanfB8ArEYmoZjcLDcsSgZTQJUmStBddPlzfCiyqunK39+Pr6QDTGwjXNyVfuws4RNLtsZTQKGb2s8ptkvoBb5vZy5JeBS6RtEwkFdaMmZ0YSxBfw5cXBgA7NHHa/5g7XN+v4phh+AC/Ky6/O7hif0OyxKJfKaFLkiRpB3ImH4N5fH5hZp80cXxT8rWiPvzfmmjnaSpkZxXX+FJc40U856BFVrFm9mIsA+wMbCxp2Za0U8HNeEGcl1MilyRJ0nnJQb4Z1Chf+wyfNX9J0olVmikYAvwu2kTSApIGyWvwfwfYsHSNvWhByF7S7pKKojtr4cV63mtuO5VE2drfEsY/SZIkSeckw/UNUw7jA9wGTKdpK1jMbIakPYH7JL1pZudWNm5m40N+9s+Q7Rk+Q94WeDVK9haMBNaTtFL8/ruks+P7K/gLwDohjSs4HJ/9nyXpY+BT4IBalhBqwcyGNbK7UYvfJEmSpH2Y7wf5sk1rxfYFAWKwehIvuPMscFDFdoCZko4u5GuqbwN7ftFmWV4mqS9uxfrtkNmV2TKO3w34I+5T/zpwZFkeGC8h/w2Z4EKxbShhkwv8K7b1iW23ly8iaUvgz8Ai8RleluDFcxgKDI21+T/gRXOK85/C7XR3kPR5SZfjLxOL4ZUIf2Fm71d7vkmSJEnbk+H6pinW7DfAzW4GVWwvPqeWzmnUBlbSRsA1wH5VBvjimA2Ac4Dvmdl6ePLbxNL+dYEFgW0lLd7Ce7sMODQSDDegLqGwIaZQ4Tlf4mLgJTNb08zWACYBF7WwX0mSJEkrkIN882iJjv6IkhytF16d7n46SEcv6eBSf9YArpJ0bo06+puB9SWtU9HmmngS4R9Lm08E+oWWPkmSJOkAcpCvkXnQ0atk7/oasCwwoFYdfSP7W6SjN7NLS/05EXft61Wjjv4zvB797yq2rweMLa/3x/exuA1tPVInnyRJ0j7kIN80RQLeaNxPvZ6OvvQpisJU6ugrB+BCR79gSztU1tHjBjqbSlqmue2Y2Yn4MsAduCLgthpO+wewpaTVmnu90nUvMLN+ZtZv+eWXb2kzSZIkSRPkIN80qaOvf/ynePGf35Y2PwNsEvI/wCWBeJJea5TSTZIkSVpADvKtyHykox8KfBVYHsDMJuImPMeVjjkOeDz2JUmSJB1ADvINIGl2hOkXlTRO0q+LmaqkHYDF5fa0M+SWrqfiA+31kr4labykZ/EB/Uelpv+KS/G+ABwp6fRoc6ikSbG+/ziwOHAY8JCkT3A71xOA8cCrwNckTZX0BF4i9iuSvhnX+LvCUlbSw7Ftnfj9nqRZkibhLnivxX1eAfwEmCGpUBAUz2IyMEdqGNGMR3ADnIIfAmvHs5iJ6/TXldRSc50kSZJkHpnvdfKNMKd2vaQV8LXopYDjY/8tZraHpEXxWexNZvagpI3xAjm7mNmkWLu+U9JGYUc7FDjKzK6R9DXg9NI1y9v/bmYbSbqZOt37HEK3PtzMfh6/d8ST8HYsa+lLLBSDd39gXzP7QNJSQH8zuyza+Al10r/zK84fVlGP/i7gvdDoY2bvSjoa2ApYy8zel7QEMdtPks5Cn6Nvqbp98qm7t3NPkqTtyZl8DZjZW7jX+89L4e1i33Q8i3zl2HQkcLKZTYr9k/CQ/VFVmh5JdUleQ9sb6+MIfEZ/aCOH/Q74SVFv3sw+KAb4oDEL3VpYAfgQmBbtTyueQ5IkSdL+5CBfI2b2El58phyiRtIX8PXskbFpfeaWvo2mipQM+CbweoTL9wSGxPcbqJPqUWyPz1WNdPNx6tvClvu5FLBk3Ee1/asAK0U/lwQeLenpa83cHwe8CUySdGlp+aDyWimhS5IkaQdykG8520oah6+P325mbzTj3GIwPxTYK5YF/l3avyC+xl1wVCnD/4BG2lUj+5piP+BqM7sU2AV4vaSnr8niNrTxXwf2AV7A6+YPrnJcSuiSJEnagVyTrxFJq+PZ528B6wL3x5r8asAjkq42s7G4ZKwvPqst6ItL4gqOqlxjb2J7rWyKJ/XNRazBT5O0egOz+QFAT0nFS0QvSWuZ2YTmdMDMDHgMeEzSncClwODmtJEkbUmuvSfzEzmTrwFJy+OJaOfEIDaHWHM+lTrd+OnAMXJTmMIc5ne4trwt+7g9Hhm4sJHDTgHOjdA9kpaQdGCN0r9a+tBL0malTZsA/21OG0mSJEnrkTP5hikq3S2EO6tdAZzZwLHn43K4PmY2VtJvgZskLQTMAn4Ts/yWMkRSWYP+5fh3P0nb4K5vk4C9zazqTD44D5fCjZI0K/p2BiH9qzi20kJ3vKTP4vvVuJRvoKRvlc75CnC6pF7ADGAqdYY+SZIkSTvTrQd5ScfiOvXZeBGaHwOn4Qlm0+OwiWa2T6wd/wgfmBbG3d/+Kekg4OtmVpa6PQVsIWkR4HbcAnblmBn/HS8U8x7wDvBbM3tU9a1pN5G0Ztm5rsJC9t7o48zoyy3AcWb2XkQGHg9XvKFx/GBgWkj4hgLbA+/ja/RHmNndcQ2TdAlwEiUbXEmPAotI+i6wKJ5nALCb6tvj9gbOxRMG98JL/B5lZp9E7YDJwJ5mdlO0ezOwCl6VL0mSJGlnum24XtJWuAf8Zma2ET7wvhK7Dyglsu1TOu2sSDTbCy8osxA+w91F0mKl4/bBdfEzKy57EZ6ktpaZ9QUOBpaLfY1Z01bjgOj3Rvhgf2Mzbv+ouI/DmFvvPpcNrpltEcf/AdfeF32cXBwT0sHrgBvMbC1gbTwqcFKp7casaJOkw2lII58k3ZVuO8jjM+G3i4HYzN42s9dqOTGSzT4GvhCa8vvw2WvB/njhmTnILVW3wGfcn0U7k8xsnv5fJarL/QZYNQrt1MIhsdRwNrBGSOEOjn0t1cLvBMyI7Psik/5w4AelF6BxwPuSdmlGu0mSJEkb0Z0H+TuAVSS9IOlvkZhWcFVJdz6k8sRIHpsQRXDAB/T9Y18vfBZ7T8Vp61Nht1pBVWvaWog2x9GABr4KF8XM/Hi8Ut0mZnap5rbBrbkPVNH/xwvQy9Qv3HMS9WvYz0Xq5JMkSdqHbrsmb2bTJPUFtgV2BIZH2VXwUPjoKqcdHjPetak/c78F+FtkpX8HuLaRwbwh5pTJbSGFBt4a2F/ePkTSyUBvvMxsQaUN7iW0cta/mY2URCQENnTMBXh1Pvr169fQ/SRJq5PyuWR+ozvP5DGz2WZ2r5kdj1u8NmXHepaZrR/HXSypR7QzHfda70+VUH3wNG7V2mKf+IaINjfENfDv4OY2ZZYBynXljzKztXFZ3yWl7U3Z4DZGof8v92spYFWg0mmuydl8kiRJ0vZ020Fe0joVA1jNmm0z+zdeivag0uZ/AkcAKwIPVznnxTjnhKK+vaQ+kuZp6hDJf6cAr5jZeDObhpfC3Sn2L4NXmXugyunnAAtI2rW5Wni5k57hEkKAu4EvSHpC0ouSxgDPAXea2cfRh10iF+BPeARls2ptJ0mSJO1Dtx3k8czvyyQ9I2k8sB51ldfKa/J3NXD+icARCntZ4E6gF5593lCI+RD8JWCipKdwiVuxrl+5Jt9Udv1V0e+ncNvZsmXrgcDvY0C9BzghXjLqEf38PzxxryEtfEMFbwbgLw6Lx+9F4vssXI64NDACd6MreK1UCncAnvyYJEmSdBBqeLxK5lfkFrHP47kMN5nZOpJ+CGxnZgc1cM5AXE//8+Zcq1+/fjZ6dLX0iCRpfXqOGMsbO85LakySdA4kjTGzfk0d120T75J5Yi/gNjN7QdI7kcC4Pu5y1xhFBb6CrSKfIUmSJOkAcpDvQCRdD6xWsfm3ZnZ7R/SnxADgz/F9GFVC+tH3tYAXzOzbsXl4LTN5SYcSvverrrpqq3Q4SZIkmZsc5DsQM+vf0X2oJBL5dgI2jMS7BXF53gnAdsVxZtZfUj/ckKdZpIQuSZKkfejOiXdJy9gHuMLMvhhZ+Kvg5jcTga9I2rN07GJVW0iSTkquxyfzGzmTTyoZgJv4lLkWrw+wB3CmpLOBN4EP8ez9gso1+Z+a2UNt2dkkSZKkYTK7PulQMrs+SZKk+dSaXd/u4XpJPSUNKwqqSLpV0tqSVgprUiQNlHROxXn3xhowkn4g6UlJ4yU9JWmv0nGfkzS1Uoce20+WNKGkVT+2tH92hY79aBog+vJ8HPdsJJKV928iySR9vWK7STqj9PtIuU0skgZLejXanCDpOknrlY4dVi7uU+pvL0mPxveX496Le+hTcf0rJb0iaeHS36KyWt08I+ksSW9IOqy1206SJElqp13D9VEJ7nrgMjMrDF82xgvI/Ai4sIY2euN2ppuZ2fuh6V6+dMguwAvAvpKOKRWu+T+gJ7Chmc2QtCTuxlbQ3NryB5jZ6EhUe1HS0HCMg7pCMgPwcrgFM4FvSzrFzN5mbs4qfOvlBjb3SNrQzKYC5+FFbX5Upb9bxDkDaVqrbnglvyafdUsxs8MlfdRW7SdJJXffs0bNx+6801x1o5Kk29LeM/kdgVlmNsfj3MzGmdn9eL342xo8s44V8LXgaXH+NDObVNpfyL9eJsxZ5FaoPwJ+YWYz4rwPzWzwPN+RV9b7CJgd1xLu2T4QL/Pao3Tsp3hW+eFNNWpmw3Enve/GpvuBr0qa1xezs4AjVVFjX9ICks6MyMiTkvaJ7V+VdHdEFp6XdHnpnM0l3RcRmf9IWnEe+5YkSZK0Iu09yG9AhV0pgKTVgHcL7/cmGIcnfU2SdKmkOW5xMaB+FbgJrzVf6LvXBF42sw8babe5VrBF2dnngT+WXOm2BiZFmdl7gcra9ecCB0j6fJN36sVnvgQQHvUTgVo95RtiEvAodS8PBfsC60b7uwBnSVoh9m2GG/ysB6wraUtJi+AvU3ubWV/gSuCPtXRAaTWbJEnSLnSW7PqVgPL/2zdop2pms2Ote3NgZ3ww6huz8j2AEWY2XdK1eH33udaF5XayvwKWBbY2s1doebh+eeAhSbeZ2X/xF4thccwwvM78taUb+CBmw78EmqoGp4rfb+H18+d6UWomJwPX4KYzBdsA/4yXlTckPQD0Az4BHjGz1wDk9fL7ADPwKnh3efCCBYEptVw8dfJJa5Mh+CSpTnsP8k/jOuxKpgPlsHajdqqxzv4Y8JikO4FLcfOZAcA2citV8EF8J+BBYFVJS0aY/lLgUrmJzDxZw5rZVEmPA1tImoIvO+wVSX0Cli2uWzrtbHyWfmkTzW+KO9sV9KDpF4Na+vycpGeAbzd5sFOOsMzG/7sRMN7Mtp3X/iRJkiRtQ3uH6+8BFilmft9jAAAgAElEQVRno0vaCFgKnx0WjMILr/SMY/rhLmivRDZ52cJ0E+C/cm/zbYFVS1aqPwMGhBXqxcA5xRp5rEkvPK83FOv9mwIv4pGF8Wa2SvThi/gsvl5lOzP7H3A18MNG2t0b+Br1vevXxl3pWoOTgKNKv+8H9o+1+RWBr1D/BaOSZ4CVJX05+ruwpPVbqW9JkiRJK9DiQV4tkMLFDHw2nvn+oqRX8Sz0vwPLSfpxHPcmnpz2iqQ38ZnvgFiX7gFcJ+kTSdPxRLLn8YH0HuDjYl0d93//TqwfHwu8Djwl6Ql8UPsccHscu3jIy+ZYwaoBKRywPXBHnDcGXyv/Jh5JmKWSFA6v735I6bkVUrgzgOWAP0Q7SwInS5ouaRbwD+Bd4E65L/2K+JLCG5KujP4uXmr3HDwyUO/FJfIW5lqGMLNx0f5ysWkjYFFgPG4fe4SZvVV5Xun8mXhU5szITXiCyPIPDgROq3btJEmSpH1oUbg+MshbKoX7BDgGeAO4D1gvpHAD8KS1ghl4SL4nsG1JCncontA2qCyFM7PLcP/4PRpZWz86PsV93AscWZbCAWsUUjhJp9GwFO594Ktm9rakI4ElzOxgue793gop3J8lLW9mS0jaHviNmf0IWEzStFJ/fx3nDKRCCifpcPxlqOAz/MViWEQltotn+pvyDZvZwaXv36t4HufgyZAFN5rZ2RXn30XJM97MBpW+P46v5VfjcuZO7kuSDmfw4MHz9fWT+YuWzuTbQgr3TzxzviClcPV5D7is9Psj4HxJvfBlgvuKvkf/D5L0WEQU/iZpgdh+iKQXJD0GbNnURSX9Ri6re0rSL2LbMZJ+Gt//KumO+P41SZdJOgsvgzu74ZaTJEmStqalg3ybSOHM7KJop7NI4ZbA19QXBZ6N9naN/e0qhTOzS83s09KmA/GiPx9RP6MfSRvgyxdbR5Tgc/h6e2/g9/hL0zbUn8XPhaQtgANwJcNWwE8lbYi/qBQJd5sBS0c0YVtgpJkdbmZr4n/fau2mhC5JkqQdaO3Eu2ZJ4YCv4+u6L+BSuMGxf44UDh9kv6WK4i3gUrgYeF+RtEpsnm5mm5Q+w5vo8wFmthGwKl4k5ouxfQBwbAySBwFjor3b4wY+wEPSv2yifWhYCjev3IDPmDcDykYwX8UH5tGx3r89sAY+c7/bzN6JJYmrm2h/G+BaM5seL1Y34AP5KGBzSUvjkZhR0Ydt8ReARjGzC8ysn5n1W3755Zs6PEmSJGkhLZXQpRTO6VApHD57HwVcZGbmKwwQ/b3EzH5fPlhRxW5eMbOZkTR5IP43eQFfMviimb3QGtdIkrYi18ST+YmWzuRTCkfHS+HM7CXgOOD8il134aqC5aIPy0paFXgE2EnSMnKTmqYG/fuB/pIWlXsE7EXdTP1+4EhgZHz/GY1L7pIkSZJ2pkWDfMzA++MJZC9Keho4BXgNN2tZM457E68sd2uEjctSuIWA0yU9F/v2i2P7A/dUrOvfCHxTDUvhLotrw9xr8vXc6KpwleqkcEPNbAweSbi+4rhrqcsNKFNI4cocrjoJ3feAncxNZlBJCtdEv+rRiBTuPKtfux8zexI4Aa9GNx5P/FvRzKbgRj2P4M/tmYrmBkuaEp/JZvYY/nIyKs45L9omzl8Rr4b3KjCLGkL1SZIkSTtiZjV9cCnbMHymOwa4FZ+RrgTcHMcMjO3/VzrvXlwOBvAD4Elci/0UsFfpuM/h6/mnVlz3c3gZ1gnA2PgcW9o/u7R9LHB0I/dwL55gNxZ4Fji0Yv8meB7B1yu2G3BG6feRwOD4Phh4NdqcAFyHywKLY4cBa5V+f4YrBnrhNeRnx++ppXvoU3H9K/G178VL286Jfi3djL/hIcDZ8f3/gMMq9k8BlsaXPu6vob0p1a6PJ+tNxJdrNmmsjb59+1qSJEnSPIDRVsP/79e0Jt9MXfxLwOQqbaRFrPMJsLp5pvwWkooIxE1m9oNG+v0S1XXxrY55UmSLy9Wa2VWSjsZfZGa1WseSLsuUoztPkKf3qVmJOZl/qDVc3yxdvIUUroIOs4iVdH2E5PtRF57/Bh2ji//USlI4M+sF/AH4uImmh+FLGtCGuvg453OS3ovvC0o6P5ZV7pB0m6RvlQ4/TNITksZLWru0/XDgDjN7upZrJkmSJK1PrYN8l7aINbP+MdsvEsMWwKMPXckitqgV/3laTxd/VPnZ4S9ilewLrIzbzA6Mtsq8aWabAhfhZYSbJHXySZIk7cO8utClRWx12soithZdPHjxnlfwpYG7zewdAElX4/UACoZYqYxtSAcr2Qa4Ol5WXpN0X8X+6+LfMXh0pEksrWbnOzJEniQdQ60z+aeBvlW2N1sXb2aPmdkp+GC1dxwzAA9pT8YHi0IXP5HQxcf5l8aA/j6toIvHZ9xbxBr33rhZzGTgr8DXi+uWOBuXyy1O42yKJ/YVtKYu/mTgtlLOAtTp4osCQOuY2R9b4Xq1UERxCgvaJEmSpJNQ6yCfunjmC118NR4E9pGzEp7wlyRJknQBapp5mZlJ6g+cLem3uEPcZOAwQhdvZhPN7E1JhS5+ATzJboCZfSap0MX3ivOnAoPwgfQh3EFuc9yI5X9AX0knAOvjsrv/RpsTCF283EVu0VhPXgbP1v8QD48fa2Y3gieS4dr66Xji3XT85eNyfAb/M+CDaOdfZnYSPsjfKOmI0jWG4br4Oe5wweHykrwL4wl078R9XVDo4oGekl6Pe5+DJANux6MWKBztgC/is+Rv4uH3nSQdCBxXSlhcBs/Ufzye1VRJM+IZLYAnDC4Zz2NmXPu5iutfCXwLV0oU/Bn4vLxs7dV4VOVZ4L+4pez7zM2e+MsMko7CZYbTqhyXJEmStBOqH/VtQQM++Pc1s+NaeL6IQb7I3pfL85bCB44HzOxGVbdfvRcfTArb2nryvGIwlLQbPgPuCaxZhLrlhXJ6Mrdt7eDYP83MlqjxPu5lbtvaFYkXCHwA3BrY0swWKp03A38B2dzq29YOjheHaRXyvD/jcsKpctva75nb1lbtb7XnVrH/StxL/mQzK+R5T+AvTOua2XuSljCzaZHH8CiwRSx3lNs5BNjAzA6L32cA3zKzNRp7bv369bPRo7NQXtI0Z+y3R6u08+vhN7dKO0nSkUgaY2b9mjpung1qzOx6qujim0Fb2Na2mzyvAcq2tYVFbCHPW0DSuIhoQMfY1lbSqDwPGCPpYzyZ7yU8UtGgPE/SAfgzrxe1SJIkSdqXVnGha0AXXyttIc97uCQNG4cPONPpANtacxOdLaiT5w0HTjSz10rndXZ53rPAUmbWAx/kG5XnmdlVuPb/zmoXSwldkiRJ+9DaVrOtybzY1i4P3BCZ+H/EJWA30bG2tWV5Xr0a+DZ/2NbOwdJqNkmSpF3oDJKntK115kvb2iSplVxLT5Lm0xlm8inPY76V5yVJkiRtSIcP8jEDT9tapzvZ1iZJkiQdzDxL6FqTmKWfja8BvxebH8XX1S80sz0ak9KFfO0HeKa64S8x1fTyF5vZ0aXzPweciGfAfxSbC708kmbjWv2CYWZWdcCPvqyEh9AXwR3qLijt3wSXqO1mZreVthtwppn9On5XSul+hOcoLB59OQ7YFZfn7YInwT1hZkuU+vsN/AVjEXxpY1HcFhdc3ja5dP0rgWvM7IZq99UcQor4NF7zflkze6+hY1NClyRJ0nxqldB1hjV5oFE72+/ixicXNnJ60cZ8ZWeLl7i9DM+w/w11BX1mlPq7RZwzkEb08q3MIPwl56OmDkyS9ubcQfd0dBf42fk7dXQXkvmEDg/Xl2hIL/9bOpFeXmFbW/HZtYH+lPXybWFnK3Pb2vvxLPhVY3BvlfCMpAUknSnpKUlPFsl2kv4u6Rvx/SZJF8T3QyWdYGZDzGwdPMqQJEmSdBCdaZDvEna2wGrAKRXSutsrjp9LLx/bO7udbSX7AutGu7vgzoEr4C8V28ZLy4rAhnH8tsDIphpNnXySJEn70GnC9Y2QdrbVaSs72zLbAP+Ml5Q3JD0A9MMH+Z/gg/t4vC7/8rh2flBTjVpazSYdSIbKk/mJzjSTTztbp6PtbJskXlhWwOV8I6nLMXjHzHIdPkmSpJPQmQb51MvT8Xr5Cu7HS9guEHK9r1BXiOdRPOIwMo47Kv5NkiRJOgmdZpBvpl7+eOAZSTPxte2p+Np6L+CO0MtPjPPn6OWBv6uuUtuNwJ6SZuEGO2W9/EfAJ9TXy/8v1uVflvSxpCckTZB0u6StS7eyFHC/3M72XeCV0Mv/Hti6nLCHRxTOie+LS5oU37fE9fLfK7V7rKRPJb2Pqw3eBe6UNC3udXXgT+VnKumvksrZ9HtJOr+0/2lJp5X6fbWkWZI+kTQVN6qZgEdP/osnEH4pjr8fn83fDvwNl8uNjXaPkvQyUEtuQZIkSdJGdCqdfEOoZGcbyV61WNPugGvn9yi1MxS42cyuid8/wSV6n5nZ9qXjJsfXb5jZM/FisIeZDayUoknaEZ9R72hmz0p6HviOmY2LiMA60Ua986rcY72+xbZL8TKzDzZ0vlyX/zjwtJldHNumhV5+f2BPM/tu9OUxYKaZbR3HjcLX1p/BdfW/MLNbY9/O+EvO13AL2R9F9ORm3BbXYo3+52Y2tsr9HAKcBazSVjr5Z7+0bovOS+Zv1n3u2aYPSpJOjtrLarY9sPp2tq1hTVswANfDryzX2Jc5A9fcN9W3EXgSWbHMsAIeFcDchW5eKsHdABxQw3Ef4nr5gkIvPxHP6Af3jB8LfCxpKUmLAmvFtu8B9xUDfPT9bjN7FlgPj4JgXlnvI3wJokEiz+APeHLgZzX0P0mSJGkDukJ2PVDPzrY5UrttY7ArWBWfiSJ3mlvJzB6TdDVeCveM0rFXAz8tlgmqXO96XE4HHpZeVtJt+Oz1+Zhh34ZHHApf9f0kbVNqZiszayxZbjReqKcpbgq9PABmNseVTtKCklbCB/uH8Sz8LfHiO0+Y2adyO9mGsvLH4WH+q/HciE2BVfDoAcAV8gp7V5vZyXH9DyWdiEcA5tLKR97FoQCrrrpqDbeXJEmStIQuMZOvkUqpHcD9ZT078O/Svv2os0edy/4VX38eAhxT7WJm1r/U7hHAg2Z2u5mdiMvM7sCXAsqRheEV+vqmsuFbw0b2ITxhrhjkH47vW+NOfE1xIV57YAxwerRX6P73M7MNge2AnSV9t5YOWVrNJkmStAtdZiZfolZr2qYYgOu7i3B4L0lrmdmE0jFX4IN8U1nr9eRsUezmPEkXAlMlLduMfpVpDVncg/iAvh6+9j4VVxZ8ApwXxzxNlL+txMxm4cmLAEh6DC8NjJm9Gv9+IOmfwJeBf8xjf2sm11aTJEkapyvO5GuV2jWIpLVx85eVS5K6U6iYzccAdxaNlJqVtD0eer4wfu8eyYHga96zqTPbaS6tIYt7CPgW8FbUEHgLzxvYAp/Vg7/M7CAvJAR4QqGkdSUtHlJAJO0GTDOzFyQtpDr72YXw6n2tLeFLkiRJ5oEuN8jXKrVrgubYv17M3BGP/UIG9wLwO2DvSFID+D6+Jj8WHzwPKJW1Lc4rPlvTODsCt9RwP3OQtIqk8rLEWLz07MOlbU8DU83sXYCoFbAHbms7QdIzeD3/t3HjnickPYsvSxwUbfQAbpeX7x2HJ0Ze0py+JkmSJG1LlxnkJfWUNEzSi3j9+SWA3fB69BZh9hHAf/AN95pb0w4ttPFmNhDX1R9HXbZ+wb8JT/SY2e8gd4+biQ/ku4eG/lg86eynZra2me0KzJb0aAzsGwP/iLX6c4ArY/thuM3rQaVr3lzSxvcG7ovkvYIfAcdLughPOCxeEqZJKl4kXjazORq0KMM7OjTw4/HiQTuYW9YuIumvuDnPwpJukFSs+b8Qz/IjPCN+Qdzo51NcLWB4fsBecZ0PcXndVGAhfDlgyfhbHYA75M3xDkiSJEnany6xJh/h72o2tCviA2FhQ/sAbqrSGPsCj+Cz9vMr9vWVtF4DsrfhFdr46yTtGDP4y6jQxlc7r8Qm0c5Q5tbGvy7pK/igezuwgJn9XG4v2x/PuH8PrwEwl8Bc0rb44LupmX0irylf/J1Pw6sDrm1e5/9HeARjq9j/oZltEs97GP5srwEOM7Ox8sqBT0i6w8xewF94/mNmp0s6Dre7PRZ/GZgJzKryHJOkQ9nwsg2bPihpVZ486MmO7sJ8S1eZyTdHG/9KE211CW281dW9L6453MzWMLNDmjh/JTwU/0mcN9XMXg/t+veAI4rlAzMr8gi2LzcQSyL3A2ua2WtFsZuQwz2HV7cDn9UX+vzL8LV/zGwIcHAcmyRJknQQXWWQb7Y2XnWlY/csHT9HG4/L5/araPJqYLMa1/XnWL1Sp42/XtKPVd8nvnIdftEm2h2N19lvKbcBa0Q4/9yY2YMnAU4ys2lVrrd+eUMk0n0dr4JX3r46/rcYFZuWjZcR8KWIlWrpoNJqNkmSpF3oEuH6RmhIG19ZyragUht/CfUL4JS18f9p4tpzrF7N7ERJV+Fh8u/i0YIdYne1cH1jzJM2PuRsm+EvCjsC10g6Ek+2a4olS8WD7gOGFjsiVH8tXvq28kWhuX1Mq9mkw8jQcTI/0VUG+dTGN4OofjcCGBGZ8vvhywCrSVqiYpDui6+7Q6zJV7YnaWHgOuBSMytn7r8jafmYza9MLFkkSZIknYOuEq5PbXyNhLa9vNywCfDfyIb/BzBE0gJx7A/wxL77GmlP+Ix+rJn9pWL3v6lTCxyEO/slSZIknQUz6xIfXJr2Mp6xPR0Pa++CJ4jdE8ccBrxZcd5EvBzr8cCpuIXrLGAQbtrybBz3KV4DHjwD/X/AxPh9ES4r+xivFPc28OPSNe6MPhWfKyrOK+87ENeuj8Uz0N+M73cBy+PJar8onT89/t21dN5sXAI4FrgcryX/7zhnczz5cEac+y6wc+xbChgf9/8JPvPerHTPFm0+BQwHFsWXHSyez/TYv2ucs3Y8p+KZfDG2H4Cv0b/e1N+1b9++liRJkjQPYLTVMHZ2iXB9zCbPB062ue1l38EHGfABaFTF6Q8Aj1h9e9lHgAHRVuFXOgXYsJDQRdRgj1IbM6zCXlbSSHMJ3arAlja3hK7eeSUuj3aGMreEbmncw71ggtVl1N8ex9zL3BK6IsGwB+79vobNLaE7Me59U6uT0J2LS+hmA+9bhYTOzP4it+2dDlxg9cP5g/C/SSGhOxQ41syuCv39ESRJZ2Pw5zu6B23L4Pc7ugdJJ6KrhOsbk9BtRFih1kinldDFgHwJdQP2J8AXoxhOrbSqhC5+34fP2CupKqGLnIefU/fylSRJknQAXWWQb0pCVy5q02UldOYJbOdSJ6F7DA/9N6WNL9NmEroqVJXQmdlVwA+BJ6qdlBK6JEmS9qGrDPIN0a72shXUk9DRSexlzQvWbIaH0t/BJXTfr/H0QkI3CniRkoSuNbG0mk2SJGkXusSaPCmhaxbWyhK6RkgJXdL1yDXrZD6iq8zkU0JXI60toWuClNAlSZJ0YrrEIB+JYGkv2wCqby+7BHCF3IXuSTx57sTY9xtc0jdB0kQ8UW7vGtr/F56It56kKZIGxq6TcXe+CcB2+FJHkiRJ0knoKuF6zOw14DuV2yWdAwwEjjOze3Er2TJHA2fj+vH3IgJwGPAhLv9aNyRiT5nZ23GtmZLuAG6O30Ml3YyHo39RTvSTNBkYY2Zrx+998BeH28vdxwfflyr6dj7w55CfLYJr03cChkmaioftF5W0DT5TLjLZVwX+Jel9XJ8+AFg4+jpK0t/xAfdG4F/APRFoWBNPkPsQGG9mB5ae76eSzowXqNnxOdTMRuERizVxTf+HwMg47UN83X8V3GZ2SeDdkBheDCxNkiRJ0mF0mUG+Iczs+obWvyOEXotFbS20h0XtlrjWf2l8wL8fny2PNrNx1GBRa2YPVl6XxvX1xfmNWdSejxfAmSDpl3gk4xB8yeINM1tT0vfw6MoBeOLgJ3gBnSTpVPQ5ulmBsnlm8qm7t+v1kqRMlwjXN4WZNaQjb45FbVO0h77+QTO7oXR8rfayEBa1NRzXEFX19UVX8PwHgM/jyyRQXyd/NV6Vr5DQ7UbTtr9JkiRJG9ItBvlGSIva2mlIXw+ueb9D0hT83v8U21cmBvJ4OfgoKvY1SurkkyRJ2ocuH65vIWlRW4E1YFFrZlfgSoNdzWy0pGNwL4BB83CttJpNOowMnyfzE919Jv80rgOvpCX6+oGRZPdvYCNJa1UccwWeYb5KE23Npa83s/OAnYGNO1pfb2YjzOwPwK+Ab0taCfhSaR1/OFAoBF4l7lduR7u4mbVUOpgkSZK0Mt19kE99fY00pK/Hs+eXK+3bhbqXlLJO/jt4xb8kSZKkk9CtB/nQ1x8KHCdplqTpuKXrwsArkgpjm01wiV2ZbfBs9wHA9ZKWizYGUV9f3xu4NL5fjEvltim18VNJH0v6JM47vqSvPwz4OPr1OPCf0NcX500vfQ4s5QsMAM6N33dFJvxV1OnrtwF+KOkiSbuWztsWD8OPlXR5FX39CEkzoj8/xq13P8FfHp6J7afjyxXgIffd4t7+DlwJIGkd/AVgw6b/SkmSJElb0a3X5GOWnBa1825R+wpwVjnzP9gFeMbM1ggt/2nArWb2vKT+QIvc+JKkLWlvCV1bkHkFSa1065k8aVE7zxa1TZyzF/HiYWYP4L4Ay8fLzDV4db0kSZKkg+jug3xa1NZOYxI6gFMljZd0eiTZQUlCF0wBVo6XmU2Bj6pdKCV0SZIk7UN3H+QbIi1qK2jCovY3+PLE5kBP4MiWXieulVazSZIk7UC3XpMnLWqbRQMWtVeEbwDAzMgHKHIFCgndI/G7d2xLkk5Lrmcn8xPdfSafEroaaURCR2jli0TGvUrX+TdwYOzbBngzlg6SJEmSTkC3HuTTorZxqkjoGrKoHSZpPPAk/oJ0Smy/CXhV0ovAecDPmnP9JEmSpG3pMoO8pJ6ShsVgPUbSrZLWlrSS3AYWSTsU30ucDFwdRi/r48VbnsHlZgMBwqJ2A0nXls67GdjDzE4AnpM0VdITcu/0IXg9d2Jmv6akR4FHgfeByVVu4WPc/W5aKbnva8BCsf894HFJt5nZUDNbHrfIXRmvS79k6bx9gJMKvXucvyehU8dd7HaVNDGe1S2S1jSzHaI07ZGSPgbeM7MiwfBZfOb+GZ43sCIwI/b1BybiLzBfBtaP7V/AIxCf4S9OL8Xf4QDgOWDxKs8hSZIkaSe6xJp8hIlb2zJ2XXz2XKa1LGPXqXZeiVa1jA0J3Zlm9m48q5/jM+5/m9khkjbFn9XEaGcArjr4VukZHA68XHq+XwJmxb6/Rlvfjsz6ItP/WLyAz+mSjsMT9I7FX0qg7iUhmY/pOWJsR3ehHm/suElHdyFJ2o2uMpNvE8vYKud1qN49aLZlbGjai0I1u+CD9QqFhM7MniheGiLH4HPAYOovOaxEKWnOzJ4zs1mSlgG2MLOhsf0TM3s/DitbzV6GvzRgZs/jWfp3N+c+kiRJktalqwzyaRlbO1WfVYkBuAzwXnyJYrnYfjFe/vchSX8sPYPV8az/y2O54gJJi8W+ZUuJdq/iLwpNkjr5JEmS9qFLhOsbIS1jm8/+wDfMbLakG/D1/fPNbIyk1fF7+CowWtKX8f9G+gG/wF8e/gocBZzQ0g6k1ez8RYbHk6Tj6Coz+bSMrZ2GnhWxNr86roOfjOcnzAnZm9mHZnatmf0EfwnaDa9i97KZjQ61wrV40RyAdyIfAHz5o6kyuEmSJEk70lUG+dS7184dwFKSflBskLSxpK/g93pc6f57AatJ6i1pG7nJDZIWwRMT/2tmU4A3S+H7nakznilbzR4E3DgP/U6SJElamS4xyKdlbO2WsaVndULpWd0BLIIvV1xfktAtgSf67Yev5U+WNAOXAa4O3CFpcfxlanzs+zZwavTvH8Af4pn8DH85QtJRcR9pNZskSdKBdIk1+Zglp2VsDZaxpWd1gpldFNs2BRYzsy/G7zkSOjP7ZWz7PXCBmf0mfhcSutl4dv1HkhYCHsajFaPx3IUfmNk1cse7bwEXmtkQST3jd5J0Ku6+Z42O7kLSTdl5pxc7ugtz0SVm8qRlbHMsY3cBphUDfPSpxRI6M/vMzAo3uYXx4j0WLzPbUVcNcI6ETl4MZwCpk0+SJOlQusogn5axtdPaEjokLRzP8k088jAGWB54u1SGdwqefIeZXQX8AbizWgdSQpckSdI+dJVBviHSMrb57A8Mi8G5kNARA/fqeDRjOVxCt3bs+ySe5SrAVyStW7XlGrG0mk2SJGkXusSaPGkZ2xyepi6XoB4VEjrwZLwX8DV8zOxDPKnw2ljb3y32E/vflTQS2BXXyy8nacF4YUib2aRL0BnXTZOkregqM/mU0NVOq0roJK0g6fOxfTG8UM5zMbDfj5vXQErokiRJOh1dYpAPWVhaxjZAFQndXsA3Ss/q/4A3CAldcV4cW0jo1gLul9vMPo5n0d+IvwjcJ2kcniNwi5kVyxFHAb+VNBGX4w1tTr+TJEmStqVLDPIAZvYa8EtcItcD6An8GR+ofy7pZnPL2NNVsps1s4F4+PppMzsaQKGVB7Y2s3XjuD74OvS1ZjbTzHoBN0saGuYsR+GJZR/hIe8/FAO2uXPbgcBM/OXgbEmDK84r+FvMrMfiuv4LJE2KF4C7cMOdvUrHf0lucztHK49byd4rqXixOMnqLGPBE+T6Ax/iMrhewPIhoZsk6VxJT8WAviVwTXxeoc76drY5Y/Goxmd4ouHTpWc7EQ/1G15l7zulPvwGrxGQNU2TJEk6iK6yJt+Y3exSeAb9ec1orrCbHUCsR5foMLvZkNB9BdgrwuvTcYngWzVq5ct8GMlySNodOAmvVvddYFlgIzP7TNKqwAf4QH2amd0X4foRknYxszuByfhLTL2ExMjMPxYvc+jOfIwAABu9SURBVLsg/pJ0E76UYXj0oFMxePDgju5C0sHkfwPJ/ESXmcnTuFZ+bVpoN9uZtPJWZxl7Ax7yH47PiEc0t60KlgLeje8rAa+b2WdxzZfN7D0zm2Zm98W2mcATeDIdZjbJzJ7EZ/NldsOr+71nZu/guRNfM7MhZrYO8PI89jtJkiSZB7rSIJ92s81jybjWc3iU46TYPgz4ttw29vRq4XRJXwC+QdNFhlbGQ/wFc7TyjZE6+SRJkvahy4TrGyHtZqtTDtdvg5fS3dDMXpa0DrBTfEZI6h/5DMhL1w4HzjCz/85jH6piHWg1m6HaJEnmJ7rSTD7tZluImT2A1wRYJn7PMLNbzexI4DQi0S/yHi4GnjKzc2po+lXqP6PUyidJknQiutIgn1r5FiJpfXw9/V1JfSWtFNsXwJ3iihn7KfgLxpE1Nn0bsJukpeOFZmdcp58kSZJ0ArrMIB+a7rSbrcFuFs90/3xxPVx2eHI8w17U2cZ+jBva3CKpD/BbXHo3K9o9GEDSz+KeDwAulzQ+/iZTgZH48sLrwG1m9n6ccz+wFfVVBkmSJEk70mXW5GOWnHazNdjN4lGE982sqGC3O3AEHp1YGrgb+G5ZQmdm78l1/6/g4fpyQt6t+GB+DHB1KAAKCd12uLKgkNB93szeN7NtJb1PknQyphx9f0d3gd6nzmtebZLURpeZyZN2s82xm62kSQldfH+YKtr25kro4j7ux2vjz5qHfidJkiTzQJeZydMCu9nS71WBm+P4ORI6SYWErpxdfzUeXq9VQvfj+F5I6O7F16ovM7PCT32/yHAv2MoacKMzs6mSzqVO9/8Y8Fkzs/MhJHTUVQfcMbYPw8vX7oDP6K+MqnYtoUEJXczkHwBeqjwpIiSHAqy66qotvHSSJEnSFF1pJt8QaTdbnQ/jWl/Clxwuj36+jC8lFNGKETHgtxuWVrNJkiTtQleayafdbAsxswck9ZK0jJn9LyIMtwK3Snobl9Dd24KmX8UTGgt604qqgCRpC3I9PJmf6Eoz+ZTQtZBmSOiaS0rokiRJOjFdZpAP+VfazTZAhYQO6srajsUleQfGM+yJS+aeAp7EIwbnRRtn4mY0S0maIum42L6VpCn487+oQkJ3Cl6G91HgD4WELkmSJOl4Ok24XlJP4Gxc4/4ebpd6GG6XemGUqV0bWMzM1iidNxRPyBsIHIfPgHeVNKhIxjOzosLdGDPbO87bB9gj9p0maSqeOPawpJeAE8ztZpG0JfATPDy9CPAPMxssaaCkG6ir8vYxcBDuSHeapH8C78f+t/GXiSvM7KFodyCwO/CppA/x6nPgs+uXJP0Qt7Z9osojOwcYFUsNE83sFUn9I2lve+AZPDN/36I8raR+eALiLmZ2V2z7HDBTbju7cPTzdOAvIbFbBBiEZ+e/DfzSzEbGuZsDv4r+3GRmhSzwrLjXj6v0O0mSJGknOsUgH2HuajayKwI/IkLijfAYrv+GTmojW7rX1yV9xcwerHL+7SGhGwEMNLPRkvYDTpZ0UUkrT9zfA/HvH2NbQzay1c65q7S9XOd+RTwhcclodxDwiZltGC9iN0vaPKIC5wMH4zP52xXWtGZ2uKS9gS+QErqkk3HGfns0fVCStAO/Hn5zm1+js4TrG9PA700NNrJmVujIO60GPrgBrxzX0LWn4jPm4vdwM1ujPMBLWgrYAn8B2r90eoMa+Fh/3xuPNOwmaeEGrv8mLgv8RWxaj6hBYGZv4JGFTUOK2MPMRsWAfwXwrVJTl+PRkKebeB5JkiRJG9FZBvm0kW0e/YFbzOw54KOIekDjNrLbAs+b2Uv4bH63hho3sxeARSOZbhywl6QFJa2BKwpWoYU2s5BWs0mSJO1FpwjXN0LayFZnAHXr94XWf5w1biM7II4tn3NjI9co7v9CfHliDDAJeAh/ji3GOtBqNknaI0SaJJ2FzjLIpwa+RmLNfntgXUmG/w1nSTrGnLk08JIeBL4N7C7peDyCs7SkxYGZVa6xNvBxlKqFuuQ6JD0GvBD3kDazSZIknZjOEq5PDXzt7AtcYmZfjPvsjcsIt2pEA78LMMrMVolzVgVuInzky0haAZfU/TV+Ly5psfi+GzDNzF4ws1fwrPzN49l8n8YjA0mSJEk70+QgL6mnpGGhTR8j6dYYUJG0kqSbS8d+WdJISc/HuvBFkv6/vTOPlquq0vjvMxDRkIFBRQghikxpgmjSiDK1RhREoR0jKBBal0sZBAQ0jbSyWEakiYJ2aHEGIUhaQIk0YySRoEATIIQhAZOIYCQqghA1gITdf+xz886rd2t4r+pVJfX2b623UnXvufecews90/729/IkNZtVcd8FSdJVaODPAL4l6ffqrYHfQdKDcl33l8lWHyTthCdgmYVnbNtM0v7p3DQ8eOz0tHe/Ld7BQqaBlzQROAv4pKQXJP0D7wAfw21pJ9KjgV+Fu7F9Bvhrdt0TaT/+QUnPVcQLHJDqmSdpCzzI8Da5BexZqc3Ffv7qVMdkYHYRHKekgZfngp8JHCTp1vT84NsaJ1BdA18vP0Chqf8LPijYDXfjG5HuuVjSM+ma0SlqH1xWeA0u1ZtMMrCRNBL4ODBT7qgXBEEQdICay/VphlZN2vYw3tkVs91XAT8GPmzuZlZo0Uc22Ja34wFhK83s6KwNzwJXmdkZki5O9ZKC3/4XOM7M5qZju+OdzS3p8svK9stTIF++Lz4uXX8RmeRN0kHAf+N2qtum++4OjAZWmNnucp35zbjG/+50fR7wVnAJcCzwTuB4fEY+E5icydfOxGfKMyva+xhwaOrk32JmiyUdi+/Lvw8fDJ1jbjRTlkTnyJJ3cBVwVfo6LNU/ysyeSZ+/AXzKzGamAdrOZna8pI/iA7CP4IOIVfiy/fbA9ZJ2MbM1cte8/prqBEFLuOCT1U0pj7vwbW1sSRB0lnoz+VrSNugtbzsOHwzclpW9IkmyapIGEx/EE9ocqN7R6+vwLGwAt9ETwf0R4Laig0/13W9mF9Wrr1HM7HpcOncUvsR/ppk9VVHmBTwYrV7E/lzgo8DX6NGur8XlbAOxkb2lrE5J306R6w9I+kI69g5JuVb/YEk/rrw26+BfgscOFEFxh+G5AsADG9+ZHf+RmT2fYhYexXMRnIdL+yKoLgiCoIPU6+RLpW1QKm+rWjbRS2qGz7gL3gL8JnUUC/AscOsxs+/Kk9BMocdR7p/wmXMt+itvK+MkYAbwCjO7pPJk2q+egi+PA+xYUed+6RmewldOigFSqQa+H7wnqzNnuplNBl6PD5gm4Ilv9lBPsOAxuPKgD5J+iHvKvxZfxYBMLmdmz+OyvTFUkdGZ2clm9jpcU19WR0jogiAI2kAzgXdl8rZa9LJbxfXiBWXyroKXpUHBanyb4Kaym8s17PdLuio73F+L1z6Y2e/x5fhvVpzaMbXrl7hmvZDkraioc2F2TSvkc3NSvf8MfLbk/OGS7sYHQLsBE1JynNnAEZK2BCZRxUjGzI7Cf9sVlCsemsbCajYIgqAt1JPQVZO2QV952wN459GvCOs0Q38/LvX6PK7P3krSSDNbA6w1sz3TjPkGfFvgG6m+/Yv7mNl7UyDfzD6VNM+L6S9nRZW991q0wkJ2atp770MKxDsR2MvM/iLpUnp+o+/jgXPgg5+qWnczWydpDvBpPJag2HdfnYIBR6T7F8cLQkYXbBDEvnsQOPVm8qXStrQE/TC95W2zgKMlvSkr+74UkFeLKcCSTN61A94ZvTcvZGZ/xzudU1Kw22XAPpIOzYq9vE5dHSPFHWxDT3zBYDAKN/R5Ri6lK/bOi+C9J4DpwEUl7XuJpNdmbT0UWJZOz8XT4QJ8iJ5VgLn4ysFweTa8Hai9ZRMEQRC0kZqdfJK2ldm7rjazv5FZvKYAuw/jsqmHJC3FO5k1ddrQsP2rmd0DLAEOT0vv78YlbCsl3YbL8L6UXdJfi9dWULkn/+l0fBJwewrUaxhJN8i1641wN+4+twzPHf/LivOX4bEPD5dcOwy4VO5GtwTYEo9FAM9O92pJy/GI+dPBgzDxXPxL8QQ8x6atgSAIgmADoJGMd/lS9bP4cnqRGGY2nh620Guvoyei+lmSNKsWZnYMgDzP+j3AwSlivgiwK/bkN8HTqh5pyXQl1fdiqnM4sCmu2a5GnwQ2ch18EVA3DjhA7qP+BL78/EEzm5bKnoZHtJ9Nz578cDzS/dg6dV5Fj+nLWGBCun4K8PN0fBtgXZKpgS+7r5+NJ+YkWeEa4Jg8m18alPWRy2XsSxVHPzP7R7ZE/1pgv7RdAv5bFiqJl9Pj+Ae+PP8iPmBcPxiRtBCPP5hIFmwYBEEQtI9mdfK74fvkrdDJ5zaouevc2kxHfjG+Jz8j08mfWkMnXzevvJndRxVr2EInL0+wsy1uuzqZHp38nplO/l/xmXS1vfp5uALhanxwsi7V/+es/jMp0clXMLVEJ1+XNKB4Cu/Eq3ELPjOvXAF4D7C9mb1O0r7ABfhWyda4q98b8QHdIkk/wzv9kbjNbMzsgw2Kpbvu1ra6dlu2tH6hIBhEWqGTPyV9HiydfE5TOnlJEyuW0hdLuqNa+Rbr5E+hZwvicdwRrr+Bezn90skDy83srWb2fA2d/D1m9tuSug7Dl/8xs1txf4BX4E5215nZX9Jg5WbgHWZW+NPfSRUZXRAEQTD41Fuu769O/uKysompaRZYkHdQ63XykhbgOvkr84sznfz30qFGdfJ5nW8eQMd6EvB/wK/r6OS/kA4Vy/gFJ5jZQjN7StJLNXBjm0pq6eSfTCsM8+VJcOYBsyRtlTrjqjr5KlSzlR2Q3WwK5PwEwLhx4+qUDoIgCAZKMy50A9HJr186T515QaVO/ih6OvliT347PMCrqk4ejw142MyKJez+2sD2wcx+L+lmPEd7TtGZG3C1mV0naTy1pXWFTr5eMGIt5sjz3q+kZ48/53BJH8N/221xnfyDcqvcI9K/kygJbGwXFlazQQeJJfRgKBE6+cYYUjr5Ego9/O3pe6GHXwXsnZUbS3MOe0EQBEELCZ18m9jQdfJ1mIuvrpC2P/5gZn/CAyQPljQmbUNMoUomvSAIgqD9tEInv7eky/Hgs7XAdUm3vjRd+4PifsqsaPFZ/xm4actq9baivRI4L83Mi2v3xPfgH6e3Tv5qub3r34Er8AC5giMkPV2cl1vlrreilfQn9c6nP7ryHSSJ3aHAuZKelPQbXBM+XtKydL7gE8CucsvZFyWtldvHXohH5S/BO+Fr6dHTF53nPNKsW9L4dO1iSTfL0/UuBvYCbszaPLyiuUvSPZ4FHsJ/37x9t+FL+IXr3iuze61O7+p5fGvkAUnfStf9DJiczt2Er7RslTr6c/G9+MfxaPrR6d5vxaPu51S+0yAIgqA91N2TT7nbP1Tl9Cy8Y/1ShcRulJktlHQucIWZXS3pOjyArVJitxCP0l7foZvZXHlSFsxs83S4kNitLALgzGyZpL8VZeQSu0KmdjmetKUVErst0/UXUVtiNxV4Bd7RXWO9rWi/iOvxt8cHJ5WWtJcAY82tXcdTsh2g6la0+6bzM4E7gDekKPptcG18wQfwFZjDgXlm9kd65HtfAp4ws/NL3sGLklbgOQwqtwo2BS6yChtaM5sv6UEal1AGLWDixRPrFxri3Hd0WbxqEHQnzRjUADwN/L2OxG6wrWhzNmQr2mfTv+Aa+dEVUfhzaSIYTtJI/P182twpDjNbnQ1IFgM746sSB5esAgyUUhta9STDicC6IAiCDtFsJ787npCmD1UkdoNiRZvqq2tFq0wnD5yFp8T9W1bvYFrRFtK/HfHl7z+n87kVbTMSu53wd/TXKudPBG4xs4fwFZGDB1DHJek9nZ4dK7WhNbP98MDIZytvorCaDYIgaAvNSOjqsUFL7CRNAyZ3ocSuGmV2vv1RQkw1s1WSRgE/kfSImV02kIaEhC4IgqA9NNvJh8TO2RAkdr8GXiNp88rZvKRN8fS3h0j6Ir6CM0bSiBRAWRczW5X+fUbSj/AgwMuoYkM7wGcImiT2m4MgyGl2uT4kdi0ixR0MWGKXBjw/BM5PnXoRPf8B4EDgzuwdjsO3DA5rsG2byvPUFwOGQ+jRw1ezoQ2CIAg6TFOdfIMSu7Ci7c1gWtFOx13vliZ1wlw8OLLhd1iFzYAbJC0B7sUHIkVa3FIb2iAIgmADwMzq/uEzzMuBFXjw3LXAzuncq3E5WFF2L1yi9hCeevUefAY9DZhVcd8F+L548X1PfA/7oIpy64DF+OzxZ8CY7NxO+H540bb5wP7p3DQ8LmBx9jeh5PkmZuefxC1tF+M535cBE7OypwHfwlcp1qZyDwIX4oOm/Hjxd1S6dh6wRfqcl9sqtXM5sDr9FeeGV7R1U+A/U9m78Yj9d2bnJ6d3+Pb0/ZVZO1bjy+vF92HZdSPT77oM3+qYkZ3bDJdKLscVDOOyc2ek48uyOkem+z8P3Ffrv61JkyZZEARB0D+ARdZA/113Tz4tI9eym/0MyaNc5Xaz3yTsZgsuwX3nZ6TvK7LnOt3MvpO08MPwAUoRsZ5zNq7bn2DlWvj8HTashccHBueY2S8kvRQ3tznQzG7Ck/ysNreaXa+Fl7QHvtc/Ad+Xv17SLma2RtLJ+NL9n8veeRAMOmf2yW2Vjj/d3nYEQQdpJPCu1G42O/9+fDYH5Vr4TwH4WKE6mRb+QGChpM3MrI/8Cp9J7pE+l2rhGWD+dPVktztAUvFMzwG/xSP6DyFp4SWt/38QM3tBUmE3W8sZby6e/GdG5Qkz+0729Wkz276kfYUWfrxlWnh8lo2kl+C/x9uAWyUNLxkklGIerPeL9Pk5SffguejB9+6np8//A5yfHf9RqmOFpEfxbYc7zZPh/AEf+ARBEAQdoJE9+f7azXZUC1+vzlpa+DSjnwucZmZ7pr830bgWvghtrtx3b5cWfj/cp34lA9fCI2kL4F346gRU0cLThNVs6OSDIAgGn2YldBu0Fr6szoFgQ0cLX0TPzwG+ama/bW3zHAudfNAOYlk+CBrq5EML73S9Fj5tmXwPuN/McsOgUi28pOJ4QWFBGwRBEGwANLJcH1r4FrEha+ETZ+ODkFMrjlfTws8FDpc0XNKOwA7U3q4JgiAI2kjdTj6F6g9ZLbykbeRWuu8DzpF0raSdcbe5cVm5vdL2w3xggty2dVmq87o0610GjAAW4d7vIySZpPdkVR6B57dH0oL0HtdI+rXcjvdserTwhsv9vgJcSsoTX1wH7IMrA/LVhk+lPfdKS9s9gc/hgYfPSXpe0qNpO+IH9NbC7yTpNykA86f4Fsq9wDbmjnW7pOu27c+7DoIgCFqLvA9v4gbSe4FJZnZG3cIbGWnm/StcMXBhOvZ6vIM+FLjV3Eb3VbjF6xFm9qtUbl9gazP7qZJFLL60PdfMfp727ufjWyarzGzvdN01wEwzW5AGDaea2aK0TH42nlfggFT2r9ZjxZu3O7/umNSuA9O5o3FL2xmpDdeY2e4V159JiaVtdv7W9A4+YWa3S9oS30bZyczGpDKb4Dr5La1GmtvJkyfbokWLqp0OgvWMn17qhdVvHvlKn5jeINjokHSXmU2uV67ZtLaY2U8Y4PLzRkCpfNDcSje30T0eHwj8Kit3q5n9tOJ+95vZz9PndfisfhTwtKQDazUkRbV/FhiXBhqNktvvQpOWthmX46s24DEbVxQn5FK/oueujGMIgiAI2kTTnTyAmX23FfdpB8rsZrO/O6oUL5UEqq90sBEp38nAcam++Wb2GL6/Xejm666EmNk6fFl813ToZRXPMbXksoPwJfXiHs3K+ApuAt4m1+ZPxSPyizrW4PLIZ8zsmcoLQ0IXBEHQHgbTanaDJM9u1wQ1pYNp0DAKuNHMTkyHz6u2/G1mt0gqlvjrkWcVWp8JsITZaYl/c/o+bytkfP8Absdn88NwjXxDhIQuCIKgPQy5Tr6fVJMPlkkH30iSDprZm1LE+7v7UVcxm69qUJOkhhPxQLd6fARfhTgX+C88cLCgGRlfzuV4GuOui8cINjxiLz0I+k9Lluu7mFL5ID5LH5+VuwCYVhG53y8pn5ndCGxBT8reXiTJ3NnAY2a2pMF7GvAfwN6Sdk33aUrGV8ECPLJ/Tp1yQRAEQQeImXwNzMySeuB8SZ/DJWqP4GluV0h6nZktN7PVaT/8HEnb4cvhTwBnZbc7WW7uUlCW030GfRMJzZb0HPBS3MUu170XmQALrjez6fnFZrZW0ldx97yPkVnaqo6fQI6kG4Ajk+FNce8X8ZWCIpq+39x1111PSBqUzHpNsjX+Gw4FhtKzwtB63qH0rDC0nneXRgo1LaEbqmys0kFJX6e3jK+PhK5F9WyCO96NafW924GkRY3IU7qBofSsMLSedyg9Kwyt5230WWO5foBsxNLBShnf6IrVgKaRtAsuoftDK+8bBEEQ9I9Yrm+CjUk6WJBb2iYZXx9L2xbU8RDNKxiCIAiCJomZfBCU8+1ON6CNDKVnhaH1vEPpWWFoPW9Dzxp78kEQBEHQpcRMPgiCIAi6lOjkg6AGkk5JToFbd7otg4mkc5Nr4hJJP5G0UaoiaiHpoOTquFzS9PpXbLxI2l7SfEkPSnpA0on1r9q4kTRM0j3J5KurkTRG0hXpf7NLJb25Wtno5IOgCpK2B94BPNrptrSBm4DdzWwP4GHg3zvcnpaSskVeABwMTAAOlzShs60aVF4ATjGzCcDeuG9GNz8vwIk0lg20G/g6nhdlV+D11Hju6OSDoDrn4c5/XR+4YmY3mlmRUvl2YGwn2zMI7AUsN7OVydHxcnonluoqzOxxM7s7fV6DdwLb1b5q40XSWOAQYKNTPPUXSaOB/YHvgTuU1rLzjk4+CEqQdBiwyszu7XRbOsC/Add1uhEtZjvgsez77+jiTi8nJb16A1DNbbMbOB8fkA8Fa+vX4AZpP0jbE9+VNKJa4dDJB0MWSfPwPP6VfB44HV+q7xpqPa+ZXZ3KfB5f6p3dzrYFg4OkzYErgZPKbJ+7AUnvBv5oZndJ+pdOt6cNbIIbop1gZnekLKbTcZ+S0sJBMCQxs7eXHZc0ER8t35vy+48F7pa0l5mtbmMTW0q15y2QNA13Tpxi3aetXUXvxE9j07GuJZlaXQnMNrOrOt2eQWQf4FBJ78IdNkdJutTMPlrnuo2V3wG/M7NiZeYKvJMvJXTyQVAHSY8Ak82sa40vJB0EfA04wMz+1On2tJrkpfAwMAXv3O8EjjCzBzrasEEiuU1eDDxpZid1uj3tIs3kTzWz/th8b3RIWgh83MweknQmMMLMTisrGzP5IAgAZuFOhzel1YvbzeyTnW1S60iui8cDNwDDgO93awef2Ac4Ergv86Y43cyu7WCbgtZxAu5QOhxYCRxTrWDM5IMgCIKgS4no+iAIgiDoUqKTD4IgCIIuJTr5IAiCIOhSopMPgiAIgi4lOvkgCIIg6FKikw+CIAiCLiU6+SAIgiDoUqKTD4IgCIIu5f8Bh+RCpqmQWfAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ds_logistic_coef = pd.DataFrame({\"Coef\": log_result_tc.params.values[1:]}, index = log_model_tc.exog_names[1:])\n",
    "ds_logistic_coef.plot.barh(y=\"Coef\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/pirl/anaconda3/envs/py35/lib/python3.5/site-packages/ipykernel_launcher.py:5: DataConversionWarning: Data with input dtype int64, float64 were all converted to float64 by the scale function.\n",
      "  \"\"\"\n"
     ]
    },
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CHURN</th>\n",
       "      <th>AGE</th>\n",
       "      <th>SERVICE_DURATION</th>\n",
       "      <th>DROPPED_CALLS</th>\n",
       "      <th>PEAK_CALLS_NO</th>\n",
       "      <th>PEAK_CALLS_TIME</th>\n",
       "      <th>WEEKEND_CALLS_NO</th>\n",
       "      <th>WEEKEND_CALLS_TIME</th>\n",
       "      <th>TOTAL_CALLS_NO</th>\n",
       "      <th>TOTAL_CALLS_TIME</th>\n",
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       "      <td>0.069711</td>\n",
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       "      <td>-0.240702</td>\n",
       "      <td>2.073082</td>\n",
       "      <td>0.618542</td>\n",
       "      <td>-0.088273</td>\n",
       "      <td>-0.309382</td>\n",
       "      <td>-0.601091</td>\n",
       "      <td>0.247156</td>\n",
       "      <td>-0.454921</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.487665</td>\n",
       "      <td>-0.366521</td>\n",
       "      <td>1.070294</td>\n",
       "      <td>2.357157</td>\n",
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       "      <td>-0.232062</td>\n",
       "      <td>-0.983441</td>\n",
       "      <td>-1.374462</td>\n",
       "      <td>-0.328554</td>\n",
       "      <td>-0.811538</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.487665</td>\n",
       "      <td>1.464884</td>\n",
       "      <td>1.072417</td>\n",
       "      <td>0.084552</td>\n",
       "      <td>0.255718</td>\n",
       "      <td>-0.416436</td>\n",
       "      <td>-0.922163</td>\n",
       "      <td>-0.496134</td>\n",
       "      <td>0.310247</td>\n",
       "      <td>-0.286151</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.487665</td>\n",
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       "      <td>-0.248104</td>\n",
       "      <td>0.083895</td>\n",
       "      <td>0.558670</td>\n",
       "      <td>-0.279066</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      CHURN       AGE  SERVICE_DURATION  DROPPED_CALLS  PEAK_CALLS_NO  \\\n",
       "0 -0.487665 -0.061287         -0.066658       0.936779      -0.050954   \n",
       "1 -0.487665 -0.366521         -0.240702       2.073082       0.618542   \n",
       "2 -0.487665 -0.366521          1.070294       2.357157       0.130457   \n",
       "3 -0.487665  1.464884          1.072417       0.084552       0.255718   \n",
       "4 -0.487665 -0.137596         -0.139530       0.936779       0.851786   \n",
       "\n",
       "   PEAK_CALLS_TIME  WEEKEND_CALLS_NO  WEEKEND_CALLS_TIME  TOTAL_CALLS_NO  \\\n",
       "0        -0.622843         -0.431938           -0.777862        0.069711   \n",
       "1        -0.088273         -0.309382           -0.601091        0.247156   \n",
       "2        -0.232062         -0.983441           -1.374462       -0.328554   \n",
       "3        -0.416436         -0.922163           -0.496134        0.310247   \n",
       "4         0.053197         -0.248104            0.083895        0.558670   \n",
       "\n",
       "   TOTAL_CALLS_TIME  \n",
       "0         -0.778293  \n",
       "1         -0.454921  \n",
       "2         -0.811538  \n",
       "3         -0.286151  \n",
       "4         -0.279066  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds_tc_char = ds_tc.select_dtypes(include=\"object\")\n",
    "ds_tc_numeric = ds_tc.select_dtypes(exclude = \"object\")\n",
    "\n",
    "ds_tc_char_train = ds_tc_char[msk]\n",
    "ds_tc_numeric_scaled_train = scale(ds_tc_numeric[msk])\n",
    "ds_tc_numeric_scaled_train = pd.DataFrame(ds_tc_numeric_scaled_train, columns=ds_tc_numeric.columns)\n",
    "ds_tc_numeric_scaled_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>-0.050954</td>\n",
       "      <td>-0.622843</td>\n",
       "      <td>-0.431938</td>\n",
       "      <td>-0.777862</td>\n",
       "      <td>0.069711</td>\n",
       "      <td>-0.778293</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>-0.366521</td>\n",
       "      <td>-0.240702</td>\n",
       "      <td>2.073082</td>\n",
       "      <td>0.618542</td>\n",
       "      <td>-0.088273</td>\n",
       "      <td>-0.309382</td>\n",
       "      <td>-0.601091</td>\n",
       "      <td>0.247156</td>\n",
       "      <td>-0.454921</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>-0.366521</td>\n",
       "      <td>1.070294</td>\n",
       "      <td>2.357157</td>\n",
       "      <td>0.130457</td>\n",
       "      <td>-0.232062</td>\n",
       "      <td>-0.983441</td>\n",
       "      <td>-1.374462</td>\n",
       "      <td>-0.328554</td>\n",
       "      <td>-0.811538</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>1.464884</td>\n",
       "      <td>1.072417</td>\n",
       "      <td>0.084552</td>\n",
       "      <td>0.255718</td>\n",
       "      <td>-0.416436</td>\n",
       "      <td>-0.922163</td>\n",
       "      <td>-0.496134</td>\n",
       "      <td>0.310247</td>\n",
       "      <td>-0.286151</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>-0.137596</td>\n",
       "      <td>-0.139530</td>\n",
       "      <td>0.936779</td>\n",
       "      <td>0.851786</td>\n",
       "      <td>0.053197</td>\n",
       "      <td>-0.248104</td>\n",
       "      <td>0.083895</td>\n",
       "      <td>0.558670</td>\n",
       "      <td>-0.279066</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   CHURN       AGE  SERVICE_DURATION  DROPPED_CALLS  PEAK_CALLS_NO  \\\n",
       "0      0 -0.061287         -0.066658       0.936779      -0.050954   \n",
       "1      0 -0.366521         -0.240702       2.073082       0.618542   \n",
       "2      0 -0.366521          1.070294       2.357157       0.130457   \n",
       "3      0  1.464884          1.072417       0.084552       0.255718   \n",
       "4      0 -0.137596         -0.139530       0.936779       0.851786   \n",
       "\n",
       "   PEAK_CALLS_TIME  WEEKEND_CALLS_NO  WEEKEND_CALLS_TIME  TOTAL_CALLS_NO  \\\n",
       "0        -0.622843         -0.431938           -0.777862        0.069711   \n",
       "1        -0.088273         -0.309382           -0.601091        0.247156   \n",
       "2        -0.232062         -0.983441           -1.374462       -0.328554   \n",
       "3        -0.416436         -0.922163           -0.496134        0.310247   \n",
       "4         0.053197         -0.248104            0.083895        0.558670   \n",
       "\n",
       "   TOTAL_CALLS_TIME  \n",
       "0         -0.778293  \n",
       "1         -0.454921  \n",
       "2         -0.811538  \n",
       "3         -0.286151  \n",
       "4         -0.279066  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds_tc_numeric_scaled_train[\"CHURN\"] = np.where(ds_tc_numeric_scaled_train[\"CHURN\"]>0,1,0)\n",
    "ds_tc_numeric_scaled_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "ds_tc_scaled_train = pd.concat([ds_tc_numeric_scaled_train, ds_tc_char_train.reset_index(drop= True)], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Warning: Maximum number of iterations has been exceeded.\n",
      "         Current function value: 0.262450\n",
      "         Iterations: 35\n",
      "         Function evaluations: 36\n",
      "         Gradient evaluations: 36\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/pirl/anaconda3/envs/py35/lib/python3.5/site-packages/statsmodels/base/model.py:508: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  \"Check mle_retvals\", ConvergenceWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f2c9a103400>"
      ]
     },
     "execution_count": 13,
     "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": [
    "scaled_log_model = sm.Logit.from_formula(\"\"\" CHURN ~ C(GENDER)+ AGE + C(CHARGE_TYPE) \n",
    "+ C(HANDSET) + C(USAGE_BAND) + SERVICE_DURATION + DROPPED_CALLS + PEAK_CALLS_NO + PEAK_CALLS_TIME \n",
    "+ WEEKEND_CALLS_NO + WEEKEND_CALLS_TIME + TOTAL_CALLS_NO + TOTAL_CALLS_TIME + 1 \"\"\", ds_tc_scaled_train)\n",
    "\n",
    "scaled_log_result = scaled_log_model.fit(method='bfgs')\n",
    "\n",
    "ds_logg_scaled_coef = pd.DataFrame({'Coef': scaled_log_result.params.values[1:]},\n",
    "                                  index = scaled_log_model.exog_names[1:])\n",
    "ds_logg_scaled_coef.plot.barh(y=\"Coef\", legend = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 의사결정나무 숙제"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import scipy.stats as stats\n",
    "import statsmodels.api as sm\n",
    "from statsmodels.formula.api import ols\n",
    "from statsmodels.tools.eval_measures import rmse\n",
    "from sklearn.preprocessing import scale, robust_scale, minmax_scale\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import GridSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>diagnosis</th>\n",
       "      <th>mean radius</th>\n",
       "      <th>mean texture</th>\n",
       "      <th>mean perimeter</th>\n",
       "      <th>mean area</th>\n",
       "      <th>mean smoothness</th>\n",
       "      <th>mean compactness</th>\n",
       "      <th>mean concavity</th>\n",
       "      <th>mean concave points</th>\n",
       "      <th>mean symmetry</th>\n",
       "      <th>...</th>\n",
       "      <th>worst radius</th>\n",
       "      <th>worst texture</th>\n",
       "      <th>worst perimeter</th>\n",
       "      <th>worst area</th>\n",
       "      <th>worst smoothness</th>\n",
       "      <th>worst compactness</th>\n",
       "      <th>worst concavity</th>\n",
       "      <th>worst concave points</th>\n",
       "      <th>worst symmetry</th>\n",
       "      <th>worst fractal dimension</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>17.99</td>\n",
       "      <td>10.38</td>\n",
       "      <td>122.80</td>\n",
       "      <td>1001.0</td>\n",
       "      <td>0.11840</td>\n",
       "      <td>0.27760</td>\n",
       "      <td>0.3001</td>\n",
       "      <td>0.14710</td>\n",
       "      <td>0.2419</td>\n",
       "      <td>...</td>\n",
       "      <td>25.38</td>\n",
       "      <td>17.33</td>\n",
       "      <td>184.60</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>0.1622</td>\n",
       "      <td>0.6656</td>\n",
       "      <td>0.7119</td>\n",
       "      <td>0.2654</td>\n",
       "      <td>0.4601</td>\n",
       "      <td>0.11890</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>20.57</td>\n",
       "      <td>17.77</td>\n",
       "      <td>132.90</td>\n",
       "      <td>1326.0</td>\n",
       "      <td>0.08474</td>\n",
       "      <td>0.07864</td>\n",
       "      <td>0.0869</td>\n",
       "      <td>0.07017</td>\n",
       "      <td>0.1812</td>\n",
       "      <td>...</td>\n",
       "      <td>24.99</td>\n",
       "      <td>23.41</td>\n",
       "      <td>158.80</td>\n",
       "      <td>1956.0</td>\n",
       "      <td>0.1238</td>\n",
       "      <td>0.1866</td>\n",
       "      <td>0.2416</td>\n",
       "      <td>0.1860</td>\n",
       "      <td>0.2750</td>\n",
       "      <td>0.08902</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>19.69</td>\n",
       "      <td>21.25</td>\n",
       "      <td>130.00</td>\n",
       "      <td>1203.0</td>\n",
       "      <td>0.10960</td>\n",
       "      <td>0.15990</td>\n",
       "      <td>0.1974</td>\n",
       "      <td>0.12790</td>\n",
       "      <td>0.2069</td>\n",
       "      <td>...</td>\n",
       "      <td>23.57</td>\n",
       "      <td>25.53</td>\n",
       "      <td>152.50</td>\n",
       "      <td>1709.0</td>\n",
       "      <td>0.1444</td>\n",
       "      <td>0.4245</td>\n",
       "      <td>0.4504</td>\n",
       "      <td>0.2430</td>\n",
       "      <td>0.3613</td>\n",
       "      <td>0.08758</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>11.42</td>\n",
       "      <td>20.38</td>\n",
       "      <td>77.58</td>\n",
       "      <td>386.1</td>\n",
       "      <td>0.14250</td>\n",
       "      <td>0.28390</td>\n",
       "      <td>0.2414</td>\n",
       "      <td>0.10520</td>\n",
       "      <td>0.2597</td>\n",
       "      <td>...</td>\n",
       "      <td>14.91</td>\n",
       "      <td>26.50</td>\n",
       "      <td>98.87</td>\n",
       "      <td>567.7</td>\n",
       "      <td>0.2098</td>\n",
       "      <td>0.8663</td>\n",
       "      <td>0.6869</td>\n",
       "      <td>0.2575</td>\n",
       "      <td>0.6638</td>\n",
       "      <td>0.17300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>20.29</td>\n",
       "      <td>14.34</td>\n",
       "      <td>135.10</td>\n",
       "      <td>1297.0</td>\n",
       "      <td>0.10030</td>\n",
       "      <td>0.13280</td>\n",
       "      <td>0.1980</td>\n",
       "      <td>0.10430</td>\n",
       "      <td>0.1809</td>\n",
       "      <td>...</td>\n",
       "      <td>22.54</td>\n",
       "      <td>16.67</td>\n",
       "      <td>152.20</td>\n",
       "      <td>1575.0</td>\n",
       "      <td>0.1374</td>\n",
       "      <td>0.2050</td>\n",
       "      <td>0.4000</td>\n",
       "      <td>0.1625</td>\n",
       "      <td>0.2364</td>\n",
       "      <td>0.07678</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   diagnosis  mean radius  mean texture  mean perimeter  mean area  \\\n",
       "0          0        17.99         10.38          122.80     1001.0   \n",
       "1          0        20.57         17.77          132.90     1326.0   \n",
       "2          0        19.69         21.25          130.00     1203.0   \n",
       "3          0        11.42         20.38           77.58      386.1   \n",
       "4          0        20.29         14.34          135.10     1297.0   \n",
       "\n",
       "   mean smoothness  mean compactness  mean concavity  mean concave points  \\\n",
       "0          0.11840           0.27760          0.3001              0.14710   \n",
       "1          0.08474           0.07864          0.0869              0.07017   \n",
       "2          0.10960           0.15990          0.1974              0.12790   \n",
       "3          0.14250           0.28390          0.2414              0.10520   \n",
       "4          0.10030           0.13280          0.1980              0.10430   \n",
       "\n",
       "   mean symmetry           ...             worst radius  worst texture  \\\n",
       "0         0.2419           ...                    25.38          17.33   \n",
       "1         0.1812           ...                    24.99          23.41   \n",
       "2         0.2069           ...                    23.57          25.53   \n",
       "3         0.2597           ...                    14.91          26.50   \n",
       "4         0.1809           ...                    22.54          16.67   \n",
       "\n",
       "   worst perimeter  worst area  worst smoothness  worst compactness  \\\n",
       "0           184.60      2019.0            0.1622             0.6656   \n",
       "1           158.80      1956.0            0.1238             0.1866   \n",
       "2           152.50      1709.0            0.1444             0.4245   \n",
       "3            98.87       567.7            0.2098             0.8663   \n",
       "4           152.20      1575.0            0.1374             0.2050   \n",
       "\n",
       "   worst concavity  worst concave points  worst symmetry  \\\n",
       "0           0.7119                0.2654          0.4601   \n",
       "1           0.2416                0.1860          0.2750   \n",
       "2           0.4504                0.2430          0.3613   \n",
       "3           0.6869                0.2575          0.6638   \n",
       "4           0.4000                0.1625          0.2364   \n",
       "\n",
       "   worst fractal dimension  \n",
       "0                  0.11890  \n",
       "1                  0.08902  \n",
       "2                  0.08758  \n",
       "3                  0.17300  \n",
       "4                  0.07678  \n",
       "\n",
       "[5 rows x 31 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"실습파일/유방암.csv\", engine = 'python')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>diagnosis</th>\n",
       "      <th>mean radius</th>\n",
       "      <th>mean texture</th>\n",
       "      <th>mean perimeter</th>\n",
       "      <th>mean area</th>\n",
       "      <th>mean smoothness</th>\n",
       "      <th>mean compactness</th>\n",
       "      <th>mean concavity</th>\n",
       "      <th>mean concave points</th>\n",
       "      <th>mean symmetry</th>\n",
       "      <th>...</th>\n",
       "      <th>worst radius</th>\n",
       "      <th>worst texture</th>\n",
       "      <th>worst perimeter</th>\n",
       "      <th>worst area</th>\n",
       "      <th>worst smoothness</th>\n",
       "      <th>worst compactness</th>\n",
       "      <th>worst concavity</th>\n",
       "      <th>worst concave points</th>\n",
       "      <th>worst symmetry</th>\n",
       "      <th>worst fractal dimension</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
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       "      <td>17.99</td>\n",
       "      <td>10.38</td>\n",
       "      <td>122.80</td>\n",
       "      <td>1001.0</td>\n",
       "      <td>0.11840</td>\n",
       "      <td>0.27760</td>\n",
       "      <td>0.3001</td>\n",
       "      <td>0.14710</td>\n",
       "      <td>0.2419</td>\n",
       "      <td>...</td>\n",
       "      <td>25.38</td>\n",
       "      <td>17.33</td>\n",
       "      <td>184.60</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>0.1622</td>\n",
       "      <td>0.6656</td>\n",
       "      <td>0.7119</td>\n",
       "      <td>0.2654</td>\n",
       "      <td>0.4601</td>\n",
       "      <td>0.11890</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>20.57</td>\n",
       "      <td>17.77</td>\n",
       "      <td>132.90</td>\n",
       "      <td>1326.0</td>\n",
       "      <td>0.08474</td>\n",
       "      <td>0.07864</td>\n",
       "      <td>0.0869</td>\n",
       "      <td>0.07017</td>\n",
       "      <td>0.1812</td>\n",
       "      <td>...</td>\n",
       "      <td>24.99</td>\n",
       "      <td>23.41</td>\n",
       "      <td>158.80</td>\n",
       "      <td>1956.0</td>\n",
       "      <td>0.1238</td>\n",
       "      <td>0.1866</td>\n",
       "      <td>0.2416</td>\n",
       "      <td>0.1860</td>\n",
       "      <td>0.2750</td>\n",
       "      <td>0.08902</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>19.69</td>\n",
       "      <td>21.25</td>\n",
       "      <td>130.00</td>\n",
       "      <td>1203.0</td>\n",
       "      <td>0.10960</td>\n",
       "      <td>0.15990</td>\n",
       "      <td>0.1974</td>\n",
       "      <td>0.12790</td>\n",
       "      <td>0.2069</td>\n",
       "      <td>...</td>\n",
       "      <td>23.57</td>\n",
       "      <td>25.53</td>\n",
       "      <td>152.50</td>\n",
       "      <td>1709.0</td>\n",
       "      <td>0.1444</td>\n",
       "      <td>0.4245</td>\n",
       "      <td>0.4504</td>\n",
       "      <td>0.2430</td>\n",
       "      <td>0.3613</td>\n",
       "      <td>0.08758</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>11.42</td>\n",
       "      <td>20.38</td>\n",
       "      <td>77.58</td>\n",
       "      <td>386.1</td>\n",
       "      <td>0.14250</td>\n",
       "      <td>0.28390</td>\n",
       "      <td>0.2414</td>\n",
       "      <td>0.10520</td>\n",
       "      <td>0.2597</td>\n",
       "      <td>...</td>\n",
       "      <td>14.91</td>\n",
       "      <td>26.50</td>\n",
       "      <td>98.87</td>\n",
       "      <td>567.7</td>\n",
       "      <td>0.2098</td>\n",
       "      <td>0.8663</td>\n",
       "      <td>0.6869</td>\n",
       "      <td>0.2575</td>\n",
       "      <td>0.6638</td>\n",
       "      <td>0.17300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>20.29</td>\n",
       "      <td>14.34</td>\n",
       "      <td>135.10</td>\n",
       "      <td>1297.0</td>\n",
       "      <td>0.10030</td>\n",
       "      <td>0.13280</td>\n",
       "      <td>0.1980</td>\n",
       "      <td>0.10430</td>\n",
       "      <td>0.1809</td>\n",
       "      <td>...</td>\n",
       "      <td>22.54</td>\n",
       "      <td>16.67</td>\n",
       "      <td>152.20</td>\n",
       "      <td>1575.0</td>\n",
       "      <td>0.1374</td>\n",
       "      <td>0.2050</td>\n",
       "      <td>0.4000</td>\n",
       "      <td>0.1625</td>\n",
       "      <td>0.2364</td>\n",
       "      <td>0.07678</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   diagnosis  mean radius  mean texture  mean perimeter  mean area  \\\n",
       "0          0        17.99         10.38          122.80     1001.0   \n",
       "1          0        20.57         17.77          132.90     1326.0   \n",
       "2          0        19.69         21.25          130.00     1203.0   \n",
       "3          0        11.42         20.38           77.58      386.1   \n",
       "4          0        20.29         14.34          135.10     1297.0   \n",
       "\n",
       "   mean smoothness  mean compactness  mean concavity  mean concave points  \\\n",
       "0          0.11840           0.27760          0.3001              0.14710   \n",
       "1          0.08474           0.07864          0.0869              0.07017   \n",
       "2          0.10960           0.15990          0.1974              0.12790   \n",
       "3          0.14250           0.28390          0.2414              0.10520   \n",
       "4          0.10030           0.13280          0.1980              0.10430   \n",
       "\n",
       "   mean symmetry           ...             worst radius  worst texture  \\\n",
       "0         0.2419           ...                    25.38          17.33   \n",
       "1         0.1812           ...                    24.99          23.41   \n",
       "2         0.2069           ...                    23.57          25.53   \n",
       "3         0.2597           ...                    14.91          26.50   \n",
       "4         0.1809           ...                    22.54          16.67   \n",
       "\n",
       "   worst perimeter  worst area  worst smoothness  worst compactness  \\\n",
       "0           184.60      2019.0            0.1622             0.6656   \n",
       "1           158.80      1956.0            0.1238             0.1866   \n",
       "2           152.50      1709.0            0.1444             0.4245   \n",
       "3            98.87       567.7            0.2098             0.8663   \n",
       "4           152.20      1575.0            0.1374             0.2050   \n",
       "\n",
       "   worst concavity  worst concave points  worst symmetry  \\\n",
       "0           0.7119                0.2654          0.4601   \n",
       "1           0.2416                0.1860          0.2750   \n",
       "2           0.4504                0.2430          0.3613   \n",
       "3           0.6869                0.2575          0.6638   \n",
       "4           0.4000                0.1625          0.2364   \n",
       "\n",
       "   worst fractal dimension  \n",
       "0                  0.11890  \n",
       "1                  0.08902  \n",
       "2                  0.08758  \n",
       "3                  0.17300  \n",
       "4                  0.07678  \n",
       "\n",
       "[5 rows x 31 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_dummy = pd.get_dummies(df)\n",
    "df_dummy.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>diagnosis</th>\n",
       "      <th>mean radius</th>\n",
       "      <th>mean texture</th>\n",
       "      <th>mean perimeter</th>\n",
       "      <th>mean area</th>\n",
       "      <th>mean smoothness</th>\n",
       "      <th>mean compactness</th>\n",
       "      <th>mean concavity</th>\n",
       "      <th>mean concave points</th>\n",
       "      <th>mean symmetry</th>\n",
       "      <th>...</th>\n",
       "      <th>worst radius</th>\n",
       "      <th>worst texture</th>\n",
       "      <th>worst perimeter</th>\n",
       "      <th>worst area</th>\n",
       "      <th>worst smoothness</th>\n",
       "      <th>worst compactness</th>\n",
       "      <th>worst concavity</th>\n",
       "      <th>worst concave points</th>\n",
       "      <th>worst symmetry</th>\n",
       "      <th>worst fractal dimension</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>17.99</td>\n",
       "      <td>10.38</td>\n",
       "      <td>122.80</td>\n",
       "      <td>1001.0</td>\n",
       "      <td>0.1184</td>\n",
       "      <td>0.2776</td>\n",
       "      <td>0.3001</td>\n",
       "      <td>0.14710</td>\n",
       "      <td>0.2419</td>\n",
       "      <td>...</td>\n",
       "      <td>25.38</td>\n",
       "      <td>17.33</td>\n",
       "      <td>184.60</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>0.1622</td>\n",
       "      <td>0.6656</td>\n",
       "      <td>0.7119</td>\n",
       "      <td>0.2654</td>\n",
       "      <td>0.4601</td>\n",
       "      <td>0.11890</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>19.69</td>\n",
       "      <td>21.25</td>\n",
       "      <td>130.00</td>\n",
       "      <td>1203.0</td>\n",
       "      <td>0.1096</td>\n",
       "      <td>0.1599</td>\n",
       "      <td>0.1974</td>\n",
       "      <td>0.12790</td>\n",
       "      <td>0.2069</td>\n",
       "      <td>...</td>\n",
       "      <td>23.57</td>\n",
       "      <td>25.53</td>\n",
       "      <td>152.50</td>\n",
       "      <td>1709.0</td>\n",
       "      <td>0.1444</td>\n",
       "      <td>0.4245</td>\n",
       "      <td>0.4504</td>\n",
       "      <td>0.2430</td>\n",
       "      <td>0.3613</td>\n",
       "      <td>0.08758</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>11.42</td>\n",
       "      <td>20.38</td>\n",
       "      <td>77.58</td>\n",
       "      <td>386.1</td>\n",
       "      <td>0.1425</td>\n",
       "      <td>0.2839</td>\n",
       "      <td>0.2414</td>\n",
       "      <td>0.10520</td>\n",
       "      <td>0.2597</td>\n",
       "      <td>...</td>\n",
       "      <td>14.91</td>\n",
       "      <td>26.50</td>\n",
       "      <td>98.87</td>\n",
       "      <td>567.7</td>\n",
       "      <td>0.2098</td>\n",
       "      <td>0.8663</td>\n",
       "      <td>0.6869</td>\n",
       "      <td>0.2575</td>\n",
       "      <td>0.6638</td>\n",
       "      <td>0.17300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>20.29</td>\n",
       "      <td>14.34</td>\n",
       "      <td>135.10</td>\n",
       "      <td>1297.0</td>\n",
       "      <td>0.1003</td>\n",
       "      <td>0.1328</td>\n",
       "      <td>0.1980</td>\n",
       "      <td>0.10430</td>\n",
       "      <td>0.1809</td>\n",
       "      <td>...</td>\n",
       "      <td>22.54</td>\n",
       "      <td>16.67</td>\n",
       "      <td>152.20</td>\n",
       "      <td>1575.0</td>\n",
       "      <td>0.1374</td>\n",
       "      <td>0.2050</td>\n",
       "      <td>0.4000</td>\n",
       "      <td>0.1625</td>\n",
       "      <td>0.2364</td>\n",
       "      <td>0.07678</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0</td>\n",
       "      <td>12.45</td>\n",
       "      <td>15.70</td>\n",
       "      <td>82.57</td>\n",
       "      <td>477.1</td>\n",
       "      <td>0.1278</td>\n",
       "      <td>0.1700</td>\n",
       "      <td>0.1578</td>\n",
       "      <td>0.08089</td>\n",
       "      <td>0.2087</td>\n",
       "      <td>...</td>\n",
       "      <td>15.47</td>\n",
       "      <td>23.75</td>\n",
       "      <td>103.40</td>\n",
       "      <td>741.6</td>\n",
       "      <td>0.1791</td>\n",
       "      <td>0.5249</td>\n",
       "      <td>0.5355</td>\n",
       "      <td>0.1741</td>\n",
       "      <td>0.3985</td>\n",
       "      <td>0.12440</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   diagnosis  mean radius  mean texture  mean perimeter  mean area  \\\n",
       "0          0        17.99         10.38          122.80     1001.0   \n",
       "2          0        19.69         21.25          130.00     1203.0   \n",
       "3          0        11.42         20.38           77.58      386.1   \n",
       "4          0        20.29         14.34          135.10     1297.0   \n",
       "5          0        12.45         15.70           82.57      477.1   \n",
       "\n",
       "   mean smoothness  mean compactness  mean concavity  mean concave points  \\\n",
       "0           0.1184            0.2776          0.3001              0.14710   \n",
       "2           0.1096            0.1599          0.1974              0.12790   \n",
       "3           0.1425            0.2839          0.2414              0.10520   \n",
       "4           0.1003            0.1328          0.1980              0.10430   \n",
       "5           0.1278            0.1700          0.1578              0.08089   \n",
       "\n",
       "   mean symmetry           ...             worst radius  worst texture  \\\n",
       "0         0.2419           ...                    25.38          17.33   \n",
       "2         0.2069           ...                    23.57          25.53   \n",
       "3         0.2597           ...                    14.91          26.50   \n",
       "4         0.1809           ...                    22.54          16.67   \n",
       "5         0.2087           ...                    15.47          23.75   \n",
       "\n",
       "   worst perimeter  worst area  worst smoothness  worst compactness  \\\n",
       "0           184.60      2019.0            0.1622             0.6656   \n",
       "2           152.50      1709.0            0.1444             0.4245   \n",
       "3            98.87       567.7            0.2098             0.8663   \n",
       "4           152.20      1575.0            0.1374             0.2050   \n",
       "5           103.40       741.6            0.1791             0.5249   \n",
       "\n",
       "   worst concavity  worst concave points  worst symmetry  \\\n",
       "0           0.7119                0.2654          0.4601   \n",
       "2           0.4504                0.2430          0.3613   \n",
       "3           0.6869                0.2575          0.6638   \n",
       "4           0.4000                0.1625          0.2364   \n",
       "5           0.5355                0.1741          0.3985   \n",
       "\n",
       "   worst fractal dimension  \n",
       "0                  0.11890  \n",
       "2                  0.08758  \n",
       "3                  0.17300  \n",
       "4                  0.07678  \n",
       "5                  0.12440  \n",
       "\n",
       "[5 rows x 31 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "msk = np.random.rand(df_dummy.shape[0]) < 0.7\n",
    "df_train =df_dummy[msk]\n",
    "df_test =df_dummy[~msk]\n",
    "df_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "diagnosis                  0\n",
       "mean radius                0\n",
       "mean texture               0\n",
       "mean perimeter             0\n",
       "mean area                  0\n",
       "mean smoothness            0\n",
       "mean compactness           0\n",
       "mean concavity             0\n",
       "mean concave points        0\n",
       "mean symmetry              0\n",
       "mean fractal dimension     0\n",
       "radius error               0\n",
       "texture error              0\n",
       "perimeter error            0\n",
       "area error                 0\n",
       "smoothness error           0\n",
       "compactness error          0\n",
       "concavity error            0\n",
       "concave points error       0\n",
       "symmetry error             0\n",
       "fractal dimension error    0\n",
       "worst radius               0\n",
       "worst texture              0\n",
       "worst perimeter            0\n",
       "worst area                 0\n",
       "worst smoothness           0\n",
       "worst compactness          0\n",
       "worst concavity            0\n",
       "worst concave points       0\n",
       "worst symmetry             0\n",
       "worst fractal dimension    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train data X size:(402, 30)\n",
      "train data Y size:(402,)\n",
      "test data X size:(167, 30)\n",
      "test data Y size:(167,)\n"
     ]
    }
   ],
   "source": [
    "df_train_y = df_train[\"diagnosis\"]\n",
    "df_train_x = df_train.drop(\"diagnosis\", axis = 1, inplace = False)\n",
    "df_test_y = df_test[\"diagnosis\"]\n",
    "df_test_x = df_test.drop(\"diagnosis\", axis = 1, inplace = False)\n",
    "\n",
    "print(\"train data X size:{}\".format(df_train_x.shape))\n",
    "print(\"train data Y size:{}\".format(df_train_y.shape))\n",
    "print(\"test data X size:{}\".format(df_test_x.shape))\n",
    "print(\"test data Y size:{}\".format(df_test_y.shape))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "Invalid parameter n_estimators for estimator DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,\n            max_features=2, max_leaf_nodes=None, min_impurity_decrease=0.0,\n            min_impurity_split=None, min_samples_leaf=1,\n            min_samples_split=2, min_weight_fraction_leaf=0.0,\n            presort=False, random_state=42, splitter='best'). Check the list of available parameters with `estimator.get_params().keys()`.",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-21-7d764a4f6f24>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     10\u001b[0m                            return_train_score=True)\n\u001b[1;32m     11\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 12\u001b[0;31m \u001b[0mgrid_search\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdf_train_labels\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m~/anaconda3/envs/py35/lib/python3.5/site-packages/sklearn/model_selection/_search.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, X, y, groups, **fit_params)\u001b[0m\n\u001b[1;32m    720\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mresults_container\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    721\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 722\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_run_search\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mevaluate_candidates\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    723\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    724\u001b[0m         \u001b[0mresults\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mresults_container\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/py35/lib/python3.5/site-packages/sklearn/model_selection/_search.py\u001b[0m in \u001b[0;36m_run_search\u001b[0;34m(self, evaluate_candidates)\u001b[0m\n\u001b[1;32m   1189\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_run_search\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mevaluate_candidates\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1190\u001b[0m         \u001b[0;34m\"\"\"Search all candidates in param_grid\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1191\u001b[0;31m         \u001b[0mevaluate_candidates\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mParameterGrid\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mparam_grid\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1192\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1193\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/py35/lib/python3.5/site-packages/sklearn/model_selection/_search.py\u001b[0m in \u001b[0;36mevaluate_candidates\u001b[0;34m(candidate_params)\u001b[0m\n\u001b[1;32m    709\u001b[0m                                \u001b[0;32mfor\u001b[0m \u001b[0mparameters\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mtrain\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtest\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    710\u001b[0m                                in product(candidate_params,\n\u001b[0;32m--> 711\u001b[0;31m                                           cv.split(X, y, groups)))\n\u001b[0m\u001b[1;32m    712\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    713\u001b[0m                 \u001b[0mall_candidate_params\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mextend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcandidate_params\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/py35/lib/python3.5/site-packages/sklearn/externals/joblib/parallel.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, iterable)\u001b[0m\n\u001b[1;32m    915\u001b[0m             \u001b[0;31m# remaining jobs.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    916\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_iterating\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 917\u001b[0;31m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdispatch_one_batch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0miterator\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    918\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_iterating\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_original_iterator\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    919\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/py35/lib/python3.5/site-packages/sklearn/externals/joblib/parallel.py\u001b[0m in \u001b[0;36mdispatch_one_batch\u001b[0;34m(self, iterator)\u001b[0m\n\u001b[1;32m    757\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    758\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 759\u001b[0;31m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_dispatch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtasks\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    760\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    761\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/py35/lib/python3.5/site-packages/sklearn/externals/joblib/parallel.py\u001b[0m in \u001b[0;36m_dispatch\u001b[0;34m(self, batch)\u001b[0m\n\u001b[1;32m    714\u001b[0m         \u001b[0;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_lock\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    715\u001b[0m             \u001b[0mjob_idx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_jobs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 716\u001b[0;31m             \u001b[0mjob\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_backend\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply_async\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbatch\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcallback\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    717\u001b[0m             \u001b[0;31m# A job can complete so quickly than its callback is\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    718\u001b[0m             \u001b[0;31m# called before we get here, causing self._jobs to\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/py35/lib/python3.5/site-packages/sklearn/externals/joblib/_parallel_backends.py\u001b[0m in \u001b[0;36mapply_async\u001b[0;34m(self, func, callback)\u001b[0m\n\u001b[1;32m    180\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mapply_async\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcallback\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    181\u001b[0m         \u001b[0;34m\"\"\"Schedule a func to be run\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 182\u001b[0;31m         \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mImmediateResult\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfunc\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    183\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mcallback\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    184\u001b[0m             \u001b[0mcallback\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/py35/lib/python3.5/site-packages/sklearn/externals/joblib/_parallel_backends.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, batch)\u001b[0m\n\u001b[1;32m    547\u001b[0m         \u001b[0;31m# Don't delay the application, to avoid keeping the input\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    548\u001b[0m         \u001b[0;31m# arguments in memory\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 549\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mresults\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mbatch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    550\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    551\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/py35/lib/python3.5/site-packages/sklearn/externals/joblib/parallel.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    223\u001b[0m         \u001b[0;32mwith\u001b[0m \u001b[0mparallel_backend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_backend\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn_jobs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_n_jobs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    224\u001b[0m             return [func(*args, **kwargs)\n\u001b[0;32m--> 225\u001b[0;31m                     for func, args, kwargs in self.items]\n\u001b[0m\u001b[1;32m    226\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    227\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__len__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/py35/lib/python3.5/site-packages/sklearn/externals/joblib/parallel.py\u001b[0m in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m    223\u001b[0m         \u001b[0;32mwith\u001b[0m \u001b[0mparallel_backend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_backend\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn_jobs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_n_jobs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    224\u001b[0m             return [func(*args, **kwargs)\n\u001b[0;32m--> 225\u001b[0;31m                     for func, args, kwargs in self.items]\n\u001b[0m\u001b[1;32m    226\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    227\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__len__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/py35/lib/python3.5/site-packages/sklearn/model_selection/_validation.py\u001b[0m in \u001b[0;36m_fit_and_score\u001b[0;34m(estimator, X, y, scorer, train, test, verbose, parameters, fit_params, return_train_score, return_parameters, return_n_test_samples, return_times, return_estimator, error_score)\u001b[0m\n\u001b[1;32m    512\u001b[0m     \u001b[0mtrain_scores\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    513\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mparameters\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 514\u001b[0;31m         \u001b[0mestimator\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_params\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mparameters\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    515\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    516\u001b[0m     \u001b[0mstart_time\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtime\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/py35/lib/python3.5/site-packages/sklearn/base.py\u001b[0m in \u001b[0;36mset_params\u001b[0;34m(self, **params)\u001b[0m\n\u001b[1;32m    211\u001b[0m                                  \u001b[0;34m'Check the list of available parameters '\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    212\u001b[0m                                  \u001b[0;34m'with `estimator.get_params().keys()`.'\u001b[0m \u001b[0;34m%\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 213\u001b[0;31m                                  (key, self))\n\u001b[0m\u001b[1;32m    214\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    215\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mdelim\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: Invalid parameter n_estimators for estimator DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,\n            max_features=2, max_leaf_nodes=None, min_impurity_decrease=0.0,\n            min_impurity_split=None, min_samples_leaf=1,\n            min_samples_split=2, min_weight_fraction_leaf=0.0,\n            presort=False, random_state=42, splitter='best'). Check the list of available parameters with `estimator.get_params().keys()`."
     ]
    }
   ],
   "source": [
    "df_train_labels = df_train[\"diagnosis\"]\n",
    "param_grid = [\n",
    "        {'n_estimators': [3, 10, 30], 'max_features': [2, 4, 6, 8, 'auto'], 'random_state': [42]},\n",
    "        {'bootstrap': [False], 'n_estimators': [3, 10], 'max_features': [2, 3, 4], 'random_state': [42]},\n",
    "    ]\n",
    "forest_reg = tree.DecisionTreeClassifier()\n",
    " \n",
    "grid_search = GridSearchCV(forest_reg, param_grid, cv=5,\n",
    "                           scoring='neg_mean_squared_error',\n",
    "                           return_train_score=True)\n",
    " \n",
    "grid_search.fit(df_train, df_train_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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