Project_Machine_Learning.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data mining"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " Author: mortezagholi79\n",
    " \n",
    "Email: *mortezagholi79@gmail.com*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Import module"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn import preprocessing\n",
    "from sklearn.metrics import accuracy_score, classification_report, confusion_matrix\n",
    "from matplotlib import pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn.preprocessing import StandardScaler"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Load datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1553, 13)"
      ]
     },
     "execution_count": 152,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "EURUSD_df = pd.read_csv(\"Datasets/Data_EURUSD.csv\")\n",
    "\n",
    "EURUSD_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>lag1</th>\n",
       "      <th>lag2</th>\n",
       "      <th>lag3</th>\n",
       "      <th>lag4</th>\n",
       "      <th>lag5</th>\n",
       "      <th>RSI</th>\n",
       "      <th>StdDev</th>\n",
       "      <th>Momentum</th>\n",
       "      <th>macd</th>\n",
       "      <th>atr</th>\n",
       "      <th>sar</th>\n",
       "      <th>candelstatus</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2016.01.08</td>\n",
       "      <td>0.01491</td>\n",
       "      <td>0.00385</td>\n",
       "      <td>-0.00883</td>\n",
       "      <td>-0.00425</td>\n",
       "      <td>-0.00663</td>\n",
       "      <td>53.980341</td>\n",
       "      <td>0.007385</td>\n",
       "      <td>100.729961</td>\n",
       "      <td>0.001760</td>\n",
       "      <td>0.009561</td>\n",
       "      <td>1.097535</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2016.01.11</td>\n",
       "      <td>-0.00032</td>\n",
       "      <td>0.01491</td>\n",
       "      <td>0.00385</td>\n",
       "      <td>-0.00883</td>\n",
       "      <td>-0.00425</td>\n",
       "      <td>53.304457</td>\n",
       "      <td>0.007288</td>\n",
       "      <td>100.053144</td>\n",
       "      <td>0.001487</td>\n",
       "      <td>0.009937</td>\n",
       "      <td>1.070710</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2016.01.12</td>\n",
       "      <td>-0.00608</td>\n",
       "      <td>-0.00032</td>\n",
       "      <td>0.01491</td>\n",
       "      <td>0.00385</td>\n",
       "      <td>-0.00883</td>\n",
       "      <td>49.097754</td>\n",
       "      <td>0.006899</td>\n",
       "      <td>99.107363</td>\n",
       "      <td>0.001160</td>\n",
       "      <td>0.009863</td>\n",
       "      <td>1.071234</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2016.01.13</td>\n",
       "      <td>-0.00018</td>\n",
       "      <td>-0.00608</td>\n",
       "      <td>-0.00032</td>\n",
       "      <td>0.01491</td>\n",
       "      <td>0.00385</td>\n",
       "      <td>48.866270</td>\n",
       "      <td>0.006218</td>\n",
       "      <td>99.502264</td>\n",
       "      <td>0.000835</td>\n",
       "      <td>0.009872</td>\n",
       "      <td>1.071748</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2016.01.14</td>\n",
       "      <td>0.00215</td>\n",
       "      <td>-0.00018</td>\n",
       "      <td>-0.00608</td>\n",
       "      <td>-0.00032</td>\n",
       "      <td>0.01491</td>\n",
       "      <td>50.456963</td>\n",
       "      <td>0.006051</td>\n",
       "      <td>99.306101</td>\n",
       "      <td>0.000523</td>\n",
       "      <td>0.010026</td>\n",
       "      <td>1.072251</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Date     lag1     lag2     lag3     lag4     lag5        RSI  \\\n",
       "0  2016.01.08  0.01491  0.00385 -0.00883 -0.00425 -0.00663  53.980341   \n",
       "1  2016.01.11 -0.00032  0.01491  0.00385 -0.00883 -0.00425  53.304457   \n",
       "2  2016.01.12 -0.00608 -0.00032  0.01491  0.00385 -0.00883  49.097754   \n",
       "3  2016.01.13 -0.00018 -0.00608 -0.00032  0.01491  0.00385  48.866270   \n",
       "4  2016.01.14  0.00215 -0.00018 -0.00608 -0.00032  0.01491  50.456963   \n",
       "\n",
       "     StdDev    Momentum      macd       atr       sar  candelstatus  \n",
       "0  0.007385  100.729961  0.001760  0.009561  1.097535             0  \n",
       "1  0.007288  100.053144  0.001487  0.009937  1.070710             0  \n",
       "2  0.006899   99.107363  0.001160  0.009863  1.071234             0  \n",
       "3  0.006218   99.502264  0.000835  0.009872  1.071748             1  \n",
       "4  0.006051   99.306101  0.000523  0.010026  1.072251             0  "
      ]
     },
     "execution_count": 153,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "EURUSD_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Date', 'lag1', 'lag2', 'lag3', 'lag4', 'lag5', 'RSI', 'StdDev',\n",
       "       'Momentum', 'macd', 'atr', 'sar', 'candelstatus'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 154,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    " EURUSD_df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['lag1', 'lag2', 'lag3', 'lag4', 'lag5', 'RSI', 'StdDev', 'Momentum',\n",
       "       'macd', 'atr', 'sar', 'candelstatus'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 155,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "EURUSD_df = EURUSD_df.drop(columns=['Date'])\n",
    "EURUSD_df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>lag1</th>\n",
       "      <th>lag2</th>\n",
       "      <th>lag3</th>\n",
       "      <th>lag4</th>\n",
       "      <th>lag5</th>\n",
       "      <th>RSI</th>\n",
       "      <th>StdDev</th>\n",
       "      <th>Momentum</th>\n",
       "      <th>macd</th>\n",
       "      <th>atr</th>\n",
       "      <th>sar</th>\n",
       "      <th>candelstatus</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.01491</td>\n",
       "      <td>0.00385</td>\n",
       "      <td>-0.00883</td>\n",
       "      <td>-0.00425</td>\n",
       "      <td>-0.00663</td>\n",
       "      <td>53.980341</td>\n",
       "      <td>0.007385</td>\n",
       "      <td>100.729961</td>\n",
       "      <td>0.001760</td>\n",
       "      <td>0.009561</td>\n",
       "      <td>1.097535</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.00032</td>\n",
       "      <td>0.01491</td>\n",
       "      <td>0.00385</td>\n",
       "      <td>-0.00883</td>\n",
       "      <td>-0.00425</td>\n",
       "      <td>53.304457</td>\n",
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       "      <td>0.001487</td>\n",
       "      <td>0.009937</td>\n",
       "      <td>1.070710</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.00608</td>\n",
       "      <td>-0.00032</td>\n",
       "      <td>0.01491</td>\n",
       "      <td>0.00385</td>\n",
       "      <td>-0.00883</td>\n",
       "      <td>49.097754</td>\n",
       "      <td>0.006899</td>\n",
       "      <td>99.107363</td>\n",
       "      <td>0.001160</td>\n",
       "      <td>0.009863</td>\n",
       "      <td>1.071234</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.00018</td>\n",
       "      <td>-0.00608</td>\n",
       "      <td>-0.00032</td>\n",
       "      <td>0.01491</td>\n",
       "      <td>0.00385</td>\n",
       "      <td>48.866270</td>\n",
       "      <td>0.006218</td>\n",
       "      <td>99.502264</td>\n",
       "      <td>0.000835</td>\n",
       "      <td>0.009872</td>\n",
       "      <td>1.071748</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.00215</td>\n",
       "      <td>-0.00018</td>\n",
       "      <td>-0.00608</td>\n",
       "      <td>-0.00032</td>\n",
       "      <td>0.01491</td>\n",
       "      <td>50.456963</td>\n",
       "      <td>0.006051</td>\n",
       "      <td>99.306101</td>\n",
       "      <td>0.000523</td>\n",
       "      <td>0.010026</td>\n",
       "      <td>1.072251</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      lag1     lag2     lag3     lag4     lag5        RSI    StdDev  \\\n",
       "0  0.01491  0.00385 -0.00883 -0.00425 -0.00663  53.980341  0.007385   \n",
       "1 -0.00032  0.01491  0.00385 -0.00883 -0.00425  53.304457  0.007288   \n",
       "2 -0.00608 -0.00032  0.01491  0.00385 -0.00883  49.097754  0.006899   \n",
       "3 -0.00018 -0.00608 -0.00032  0.01491  0.00385  48.866270  0.006218   \n",
       "4  0.00215 -0.00018 -0.00608 -0.00032  0.01491  50.456963  0.006051   \n",
       "\n",
       "     Momentum      macd       atr       sar  candelstatus  \n",
       "0  100.729961  0.001760  0.009561  1.097535             0  \n",
       "1  100.053144  0.001487  0.009937  1.070710             0  \n",
       "2   99.107363  0.001160  0.009863  1.071234             0  \n",
       "3   99.502264  0.000835  0.009872  1.071748             1  \n",
       "4   99.306101  0.000523  0.010026  1.072251             0  "
      ]
     },
     "execution_count": 156,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "EURUSD_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Check missing values "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 157,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "EURUSD_df.shape[0] - EURUSD_df.dropna().shape[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data normalization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "list_df = []\n",
    "\n",
    "for df in [EURUSD_df]:\n",
    "    columns = df.columns\n",
    "\n",
    "    y = df[\"candelstatus\"].copy()\n",
    "    X = df.drop(columns=[\"candelstatus\"]).copy()\n",
    "    scaler = StandardScaler()\n",
    "    X = pd.DataFrame(scaler.fit_transform(X))\n",
    "    X[\"candelstatus\"] = np.array(y)\n",
    "    X.columns = columns\n",
    "    list_df.append(X)\n",
    "    \n",
    "EURUSD_df = list_df[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Corralation matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>lag1</th>\n",
       "      <th>lag2</th>\n",
       "      <th>lag3</th>\n",
       "      <th>lag4</th>\n",
       "      <th>lag5</th>\n",
       "      <th>RSI</th>\n",
       "      <th>StdDev</th>\n",
       "      <th>Momentum</th>\n",
       "      <th>macd</th>\n",
       "      <th>atr</th>\n",
       "      <th>sar</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>lag1</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.007744</td>\n",
       "      <td>0.011555</td>\n",
       "      <td>-0.011621</td>\n",
       "      <td>-0.012631</td>\n",
       "      <td>0.350008</td>\n",
       "      <td>-0.005304</td>\n",
       "      <td>0.258972</td>\n",
       "      <td>-0.007602</td>\n",
       "      <td>-0.015390</td>\n",
       "      <td>-0.088667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lag2</th>\n",
       "      <td>-0.007744</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.008421</td>\n",
       "      <td>0.011000</td>\n",
       "      <td>-0.012228</td>\n",
       "      <td>0.317634</td>\n",
       "      <td>0.003791</td>\n",
       "      <td>0.256290</td>\n",
       "      <td>0.002976</td>\n",
       "      <td>-0.012359</td>\n",
       "      <td>-0.089120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lag3</th>\n",
       "      <td>0.011555</td>\n",
       "      <td>-0.008421</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.007187</td>\n",
       "      <td>0.012476</td>\n",
       "      <td>0.298527</td>\n",
       "      <td>0.013321</td>\n",
       "      <td>0.253319</td>\n",
       "      <td>0.019192</td>\n",
       "      <td>-0.011459</td>\n",
       "      <td>-0.074985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lag4</th>\n",
       "      <td>-0.011621</td>\n",
       "      <td>0.011000</td>\n",
       "      <td>-0.007187</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.006348</td>\n",
       "      <td>0.273546</td>\n",
       "      <td>0.014181</td>\n",
       "      <td>0.261249</td>\n",
       "      <td>0.039874</td>\n",
       "      <td>-0.003227</td>\n",
       "      <td>-0.061980</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lag5</th>\n",
       "      <td>-0.012631</td>\n",
       "      <td>-0.012228</td>\n",
       "      <td>0.012476</td>\n",
       "      <td>-0.006348</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.250988</td>\n",
       "      <td>0.016851</td>\n",
       "      <td>0.269084</td>\n",
       "      <td>0.064127</td>\n",
       "      <td>0.001833</td>\n",
       "      <td>-0.032040</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RSI</th>\n",
       "      <td>0.350008</td>\n",
       "      <td>0.317634</td>\n",
       "      <td>0.298527</td>\n",
       "      <td>0.273546</td>\n",
       "      <td>0.250988</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.089176</td>\n",
       "      <td>0.845943</td>\n",
       "      <td>0.638840</td>\n",
       "      <td>0.130709</td>\n",
       "      <td>0.015656</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>StdDev</th>\n",
       "      <td>-0.005304</td>\n",
       "      <td>0.003791</td>\n",
       "      <td>0.013321</td>\n",
       "      <td>0.014181</td>\n",
       "      <td>0.016851</td>\n",
       "      <td>0.089176</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.058699</td>\n",
       "      <td>0.086920</td>\n",
       "      <td>0.608300</td>\n",
       "      <td>-0.053347</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Momentum</th>\n",
       "      <td>0.258972</td>\n",
       "      <td>0.256290</td>\n",
       "      <td>0.253319</td>\n",
       "      <td>0.261249</td>\n",
       "      <td>0.269084</td>\n",
       "      <td>0.845943</td>\n",
       "      <td>0.058699</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.420508</td>\n",
       "      <td>0.037559</td>\n",
       "      <td>-0.047431</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>macd</th>\n",
       "      <td>-0.007602</td>\n",
       "      <td>0.002976</td>\n",
       "      <td>0.019192</td>\n",
       "      <td>0.039874</td>\n",
       "      <td>0.064127</td>\n",
       "      <td>0.638840</td>\n",
       "      <td>0.086920</td>\n",
       "      <td>0.420508</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.172637</td>\n",
       "      <td>0.248710</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>atr</th>\n",
       "      <td>-0.015390</td>\n",
       "      <td>-0.012359</td>\n",
       "      <td>-0.011459</td>\n",
       "      <td>-0.003227</td>\n",
       "      <td>0.001833</td>\n",
       "      <td>0.130709</td>\n",
       "      <td>0.608300</td>\n",
       "      <td>0.037559</td>\n",
       "      <td>0.172637</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.039402</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sar</th>\n",
       "      <td>-0.088667</td>\n",
       "      <td>-0.089120</td>\n",
       "      <td>-0.074985</td>\n",
       "      <td>-0.061980</td>\n",
       "      <td>-0.032040</td>\n",
       "      <td>0.015656</td>\n",
       "      <td>-0.053347</td>\n",
       "      <td>-0.047431</td>\n",
       "      <td>0.248710</td>\n",
       "      <td>-0.039402</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              lag1      lag2      lag3      lag4      lag5       RSI  \\\n",
       "lag1      1.000000 -0.007744  0.011555 -0.011621 -0.012631  0.350008   \n",
       "lag2     -0.007744  1.000000 -0.008421  0.011000 -0.012228  0.317634   \n",
       "lag3      0.011555 -0.008421  1.000000 -0.007187  0.012476  0.298527   \n",
       "lag4     -0.011621  0.011000 -0.007187  1.000000 -0.006348  0.273546   \n",
       "lag5     -0.012631 -0.012228  0.012476 -0.006348  1.000000  0.250988   \n",
       "RSI       0.350008  0.317634  0.298527  0.273546  0.250988  1.000000   \n",
       "StdDev   -0.005304  0.003791  0.013321  0.014181  0.016851  0.089176   \n",
       "Momentum  0.258972  0.256290  0.253319  0.261249  0.269084  0.845943   \n",
       "macd     -0.007602  0.002976  0.019192  0.039874  0.064127  0.638840   \n",
       "atr      -0.015390 -0.012359 -0.011459 -0.003227  0.001833  0.130709   \n",
       "sar      -0.088667 -0.089120 -0.074985 -0.061980 -0.032040  0.015656   \n",
       "\n",
       "            StdDev  Momentum      macd       atr       sar  \n",
       "lag1     -0.005304  0.258972 -0.007602 -0.015390 -0.088667  \n",
       "lag2      0.003791  0.256290  0.002976 -0.012359 -0.089120  \n",
       "lag3      0.013321  0.253319  0.019192 -0.011459 -0.074985  \n",
       "lag4      0.014181  0.261249  0.039874 -0.003227 -0.061980  \n",
       "lag5      0.016851  0.269084  0.064127  0.001833 -0.032040  \n",
       "RSI       0.089176  0.845943  0.638840  0.130709  0.015656  \n",
       "StdDev    1.000000  0.058699  0.086920  0.608300 -0.053347  \n",
       "Momentum  0.058699  1.000000  0.420508  0.037559 -0.047431  \n",
       "macd      0.086920  0.420508  1.000000  0.172637  0.248710  \n",
       "atr       0.608300  0.037559  0.172637  1.000000 -0.039402  \n",
       "sar      -0.053347 -0.047431  0.248710 -0.039402  1.000000  "
      ]
     },
     "execution_count": 159,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr_EURUSD = EURUSD_df.drop(columns=[\"candelstatus\"]).corr()\n",
    "corr_EURUSD"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Heatmap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1800 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(15, 25))\n",
    "ax = fig.add_subplot(111)\n",
    "mask=np.zeros_like(corr_EURUSD)\n",
    "mask[np.triu_indices_from(mask)] = True\n",
    "sns.heatmap(corr_EURUSD,\n",
    "            xticklabels=corr_EURUSD.columns,\n",
    "            yticklabels=corr_EURUSD.columns,\n",
    "           ax=ax, mask=mask, square=True,\n",
    "            cbar_kws={\"shrink\": 0.3},\n",
    "           cmap=\"binary\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Class Distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    779\n",
       "0    774\n",
       "Name: candelstatus, dtype: int64"
      ]
     },
     "execution_count": 161,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "EURUSD_df[\"candelstatus\"].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Class distribution plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\morte\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  \n"
     ]
    }
   ],
   "source": [
    "EURUSD_class = EURUSD_df[[\"candelstatus\"]]\n",
    "EURUSD_class[\"dataset_name\"] = [\"EURUSD\" for i in range(EURUSD_df.shape[0])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "merge_df = pd.concat([EURUSD_class], ignore_index=True)\n",
    "g = sns.catplot(x='dataset_name', hue='candelstatus', \n",
    "            kind='count', data=merge_df, ci=False, aspect=1,\n",
    "               legend=False)\n",
    "plt.xlabel(\"$Dataset$ $Name$\")\n",
    "plt.ylabel(\"$Count$\")\n",
    "plt.legend(labels=[\"Down (0)\", \"Up (1)\"],\n",
    "          bbox_to_anchor=[0.7,1], ncol=5, frameon=False)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Histogram plot of features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2520x2520 with 12 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = EURUSD_df.drop(columns=[\"candelstatus\"]).hist(bins=100, alpha=0.5, label=\"Full\", grid=False, figsize=(35, 35))\n",
    "EURUSD_df[EURUSD_df[\"candelstatus\"] == 0].hist(bins=100, ax=ax.ravel()[:12],grid=False, color=\"Red\", alpha=0.5, label=\"0\")\n",
    "EURUSD_df[EURUSD_df[\"candelstatus\"] == 1].hist(bins=100, ax=ax.ravel()[:12], color=\"gold\",grid=False, alpha=0.5, label=\"1\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Test and Trian "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(EURUSD_df.drop(columns=[\"candelstatus\"]) ,EURUSD_df.candelstatus\n",
    "                                                    ,stratify=None,test_size=0.2, shuffle=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1242, 11)"
      ]
     },
     "execution_count": 166,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(311, 11)"
      ]
     },
     "execution_count": 167,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test.shape\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# k-nearest neighbors algorithm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "error = []\n",
    "\n",
    "# Calculating error for K values between 1 and 50\n",
    "for i in range(1, 50):\n",
    "    knn = KNeighborsClassifier(n_neighbors=i)\n",
    "    knn.fit(X_train, y_train)\n",
    "    pred_i = knn.predict(X_test)\n",
    "    error.append(np.mean(pred_i != y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Mean Error')"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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IiIjIBky03TRxIvDTn7odBRERERHZgIm2m0pLgRUr3I6CiIiIiGzARNtNaWks70dERETUTDHRdlN6uinv10xm5yQiIiKiOky03ZSeDhw9ClRWuh1J81BcDM/YfFSmZaI2Lh6VaZnwjM0Hiovdjiw28fEiIiKyFRNtN/XsCVx4IXDkiNuRNH0LF6Kidx4KZrVGblkhktSD3LJCFMxqjYreecDChW5HGFv4eBEREdlOtJl0W+jXr5+uXLnS7TDIDcXFqOidh0sOz8cyDDju5jwsxeKUoUhduwzIznYhwBjDx4uIiCgqIrJKVfs1th5btKnJ80yZjhnVowMmjQCwDAMws3oUPNOedTiy2MTHi4iIyBlMtN20Zg3QqxewZInbkTRpta/NwXPVdwRdZ2b1KNS8OsehiGIbHy8iIiJnMNF2U1wcUFQE7N7tdiRNWnL5XpSgW9B1tqErWpXvdSii2MbHi4iIyBlMtN2Unm7+spZ2VDxtMtANJUHX6YptqGqT4VBEsY2PFxERkTOYaLvJl2gfOuRuHE1c3M0jcGfii0HXuStxFuJHjnAootjGx4uIiMgZTLTd1LYtIMJEO0rJ947D2MQXkIelAW/Pw1LclTgLyfl3OxxZbOLjRURE5Awm2m6KiwOuu44l1KKVnY3UebOxOGUoJidORA8UIwHV6IFiTE6caErVzZvNx9kn6OP1EB8vIiIii7CONjUfxcXwPPZb1Lz/AVqV7UWVtEb82DGmZZZJ4/GKi+GZ9ixqZvwJrbQKVUltEX/7z5B83wQ+XkREREGEWkebiTY1L1ddBWzZYs4UPPmkmd4+KcntqGJXZSWQkmIeq1/+0u1oiIiImgROWNNUXHMNMHSo21E0H6tXA337mhbZ2lqgJHh1jRbvwAFTy71HD6CqylwnIiIiSzDRdpvHA3z7rdtRNA+7dgE7dwJ9+gA9e5plRUXuxhTrOnUCvv4a+OlPzf8PP+x2RERERM0GE223paez6ohVVq82f32J9kknAWVl7sbUVIgAp5wCbNzodiRERETNBhNttzHRts6qVebvWWcBmZnAd98BN9zgbkyxbtYs4JJLgOpqICeHiTYREZGFmGi7jYm2dW68EZgzx9Qnp9B8+aX5gZKYaBLt774D9u1zOyoiIqJmgYm22/LygP/5H6Cmxu1Imr5evYCbbqq7PnWqGWxKDSstBbp0Mf/n5Ji/mza5Fw8REVEzwkTbbcOGAS++CMTHux1J03boEPDaa8CePXXL9u4FFiwAjh51L65YV1oKnHyy+b9vX+CZZ4CuXd2NiYiIqJlgoh0LVM2FIvf558DIkcD69XXLevY0fY+3b3cvrljn36KdmQncey8TbSIiIosw0XbbggWmf6yvYgZFxvf4nXVW3TKW+AuuthbIzQXOPLNu2Y4dwPLl7sVERETUjNiaaIvIFSKyWUSKROShALffKiJ7RORL72WU320/E5FvvJef2Rmnq1JSTP/s7793O5KmbdUqM+nKCSfULWOiHVxcHLB4MTB2bN2yX/7SdGciIiKiqNmWaItIPIBnAQwBkAPgJhHJCbDqm6p6pvcyy7ttewCPAugP4BwAj4pIO7tidVV6uvnLyiPRWb3a1M/217EjMHAgkJrqTkxNUU6OadXm65GIiChqdrZonwOgSFW3qOoRAG8AuDrEbS8H8Imq7lfVAwA+AXCFTXG6i4l29A4eBLZsMYP5/IkA//43cMst7sQV6955Bzj11GOnqWflESIiIsvYmWh3BuA/Cq3Uu6y+60RkrYjME5GTw9lWRMaIyEoRWbnHv9pEU8JEO3rp6aYV9o473I6kadmyxUy/3s7vZJEv0ebENURERFGzM9GWAMvql9b4AECWqvYGsBjAK2FsC1V9XlX7qWq/Dh06RBWsa9LTgXHjgNNPdzuSpksE6NQJCPQaKCgwVTVqa52PK9aVlprJfdLS6pZlZQGtWjHRJiIisoCdiXYpgJP9rncBsNN/BVXdp6oe79UXAPQNddtmIyEB+OMfgQsvdDuSpmv6dFOLPJDkZNPavWOHszE1Bf6l/Xzi402XEv8Bkk4rLoZnbD4q0zJRGxePyrRMeMbmA8XF7sVEREQUATsT7RUAeolIdxFJAnAjgPn+K4hIR7+rQwH4OoYuAnCZiLTzDoK8zLuseTpyBCgvdzuKpmv6dODDDwPfxsojDQuUaAPAkCGmgosbFi5ERe88FMxqjdyyQiSpB7llhSiY1RoVvfOAhQvdiYuIiCgCtiXaqnoUwDiYBHkTgLmqukFEJonIUO9qE0Rkg4isATABwK3ebfcDeBwmWV8BYJJ3WfPUuzdw++1uR9E0lZWZfsb1K474MNFu2MCBwKWXHr982zbghReAigpn4ykuRsXwW3DJ4fl4oPr/sAXZqEECtiAbD1T/Hy45PB8Vw29hyzYRETUZCXbuXFUXAFhQb9kjfv9PBDCxgW1fAvCSnfHFjLQ01tGO1Jdfmlk161cc8enSBUhKYqIdyNSpgZevWgWMGWMm/+nXz7FwPFOmY0b1aCzDgIC3L8MAzKwehfHTnkXy9AZiJyIiiiGcGTIWpKez6kikfDNCNtSiHR8PjBoF/OhHzsXUFKiaSyAuVR6pfW0OnqsOXjlmZvUo1Lw6x6GIiIiIosNEOxYw0Y7c7t1At27AD37Q8DrPPsta2vUtX24m8vnHP46/LTsbSEx0PNFOLt+LEnQLus42dEWr8r0ORURERBQdJtqxgIl25J58MrQ+u4cPN9yC2xKVlgKVlcCJJx5/W0ICcMopjifanjYZ6IaSoOt0xTZUtclwKCIiIqLoMNGOBddcA+Tnux1F0xUfH/z2l14yrbe7djkTT1Ow3TsfVOdAc0jBdB/ZsMG5eADE3TwCdyY2UKbR667EWYgfOcKhiIiIiKLDRDsWXHUVcN99bkfR9KxcCVx0UeMJoS+Z5IDIOqWlZmKa9u0D3z51KrBihaMhJd87DmMTX0Aelga8PQ9LcVfiLCTn3+1oXERERJFioh0LqqpMSbWjR92OpGn5/HPTx9g3jX1DWOLveL4a2hJoElaY2xpKwu2SnY3UebOxOGUoJidORA8UIwHV6IFiTI5/AItThiJ13mzTh5yIiKgJYKIdC+bMMQP6SkvdjqRpWbXKTLveUPcHn27dTL9jJtp1Lr44eO32sjLgsceAf/3LsZAAAEOGIHXtMowf48G6tEHwxLXGOumN8T0WIHXtMjOZDhERURPBRDsW+FpkWUs7PKtXm/rZDbXK+iQkAFlZTLT9jR4NTAxYwt5ISgKeeAJY5MKErNnZSJ76FFKK1yPuaDVSbrgKyYcPujdbJRERUYSYaMcCX6LNyiOhq6oyfbMbqp9dX34+cO219sbUVKgC+/cHr8KSnGy63DhceeS/fGcrFi4EBg8GduwAtmxxJxYiIqII2TozJIWIiXb49u833R/OPTe09ceOtTeepuS774COHU198WCPS06Oe4n21q3mb1YWcMYZpntQsFrpREREMYiJdixgoh2+Tp2Ajz4Kff3qapO8nXyyqbbRkvnGAjTWtz0nB5g/HzhyxHQlcZIv0e7WzZRmbCxWIiKiGMSuI7GgY0dTTi3UbhAE1NSEt/7HH5tJWL74wp54mhJfot2lS/D1cnJMFxJfzW0nlZQAGRkmyQaATZuAggLn4yAiIooCE+1Y0Lat6UN82mluR9J0DBgQvGpGfSzxVyfURPv66031ETfK6W3darqN+CxeDPz853Ut3URERE0Au47Eiq+/BlJSGk9+yHRlWLMGuPDC0LfJygLi4phoAybRTkw0gw2DSUx0Jp5ARo82z7PP4MHm72efHZuAExERxTAm2rFi4EDghhuAGTPcjiT2bdhgkrBwutokJwNduzLRBoDLLgNOPNH88GjMww+b8omTJtkfl7/rrjv2em6umUBnyRLgllucjYWIiChC7DoSK9LTORgyVKtXm7/h9mnPzmaiDZhp6++/P7R116wB3n3X3njqq6w0fekrKuqWxcUB551nEm0iIqImgol2rGCiHbrVq4G0tPD7Dt9/P/Doo/bE1JRs2hT6ay0nB9i8GTh61N6Y/K1bZ35E/e1vxy4fPNh0e9m717lYiIiIosBEO1Yw0Q7dhRcCDz0UWtcHf5dfDvz4x/bE1FSoAmedZWZ9DEVOjimNWFxsb1z+SkrM3/p9sUeNAg4eNNVIiIiImgD20Y4V6emc+S5Uw4dHtl1FBfD556a/70knWRtTU7FvH+DxhD7oNifH/N24ETj1VPvi8udfQ9tf27bOHJ+IiMgibNGOFRMmhN7K2JIdPGgSsWDThzdk61Yzm+Tf/251VE1HqKX9fH74Q6BXL9Oq7ZSSEuCEE+omcvI3ezZw003OxUJERBQFJtqx4qKLgKFD3Y4i9r3/PtC9O/DVV+Fv26OH+duSB0Tu2GH+hppot2ljSk/ecIN9MdVXUnJ8a7bPd98Bb7wB7NrlXDxEREQRYqIdK3buBD79NLKW2pZk1SozW+App4S/bevWJsFsyYl2uC3abpg4Efjd7wLf5l9Pm4iIKMYx0Y4Vr79uBvn5lzSj461eDZx5JhAfH9n2PXs2jUS7uBiesfmoTMtEbVw8KtMy4RmbH/2gxAsuAP70JyAzM/Rtnn/e/LAJd9r7SA0caAauBnLWWeaHlltl/ux6Xoiak0jeJ3xv2Y+PsSuYaMcKX39UVh5pWE0N8OWX4dfP9tcUEu2FC1HROw8Fs1ojt6wQSepBblkhCma1RkXvPGDhwsj3feqpwJgxQEIY46ATEoBvvnFm+vOKCmD+fGD37sC3JyYCgwa5k2jb+bwQNReRvE/43rIfH2P3qGqzuPTt21ebtDfeUAVUN2xwO5LYtWmTeYz+/OfI97Fhg+rnn1sXk9WKirQ8JUPzUKimH9GxlzwUanlKhmpRUWT7X7pUdfPm8LcBVOfPj+yY4Vi92hzr7bcbXqegQHXoUNUjR+yPx8fu54WoOYjkfcL3lv34GNsCwEoNIT9li3asYIt24zp2BObOBS65JPJ95OQA55xjXUwW80yZjhnVo7EMAwLevgwDMLN6FDzTno3sALfdBvzqV+Ftc9pp5u/GjZEdMxy+VvP6NbT9jR9vBsUmJtofj5ftzwtRMxDW++Sbb4DNm+F5+AnMODKK7y0b8fPLXaLNZPBdv379dOXKlW6HEbnCQnNKfOFC4Ior3I6m+aqoAObNA/r2NfW0Y0xlWiZyywqxBQ3PetkDxViXNggph8KsvKFqalGPHg1Mmxbetl26mNKIr7wS3nbhmjYNuOceM/vjiScGX7eqCmjVyt54vGx9XoiaibDeJ3oYKCtDJVohF+v53rIRP7/sISKrVLVfY+uxRTtWnHYa8O67ZrAXBfbOO2Z67mjU1gK33gp8+KElIVktuXwvStBAaTuvbeiKVuURTEP+/ffmh0YkFUduusmZHyYlJaakYPv2wde7/XZgQODWGTvY+rwQNRNhvU9efBGYMwfJOML3ls34+eUuJtqxol074JprwqsG0ZLU1ppuDzNnRreftm2BH/wgZgdEetpkoBtKgq7TFdtQ1SaCacijKe03eTJw//3hbxeurVtNtxGR4OtlZQFr1gAHDtgfE2x+XoiaibDeJ9dfD9x0Ezxt+d6yGz+/3MVEO1bU1gILFkQ2EUtLUFxsWmT79o1+XzFceSTu5hG4M/HFoOvclTgL8SNHhL9zX6LduXMEkQE4etT+GSKnTAFefrnx9QYPNl1h/vlPe+PxsvV5IWomInmfhLRNwgt8b0WBn18uC2XEZFO4NPmqI7W1qnFxqr/+tduRxCZfVZbVq6Pf1623qnbuHP1+7GDn6PC9e1X/+lfVgwfD33bFCtWkJNUFC8Lf1g6VlarJyar33uvM8Thqn6hxdlUdkVRW5IoGP79sgVioOiIiV4jIZhEpEpGHgqw3XERURPp5ryeJyMsisk5E1ojIBXbGGRNEgLQ0Vh1pyOrVQFIS8KMfRb+vnj3NVOSHD0e/L6tlZyN13mwsThmKyQkPogeKkYBq9EAxJidOxOKUoUidNxvIbnhQS4NOPBH48Y/rKtyEo3t34MgReyuPVFQAzz4b2tmGVq2A/v2dq6ft/7zgPmufF6Lmwv99End/aO8T/20SJx6/TasrkTrx56bUb+t1AAAgAElEQVRiFEXG9xgnDsFk3MvPL6eFko1HcgEQD6AYQA8ASQDWAMgJsF5bAJ8BWAagn3fZ3QBe9v5/EoBVAOKCHa/Jt2irqnbrpnrLLW5HEZsuuUTVqud4zx7Vb781ZxFiVVGRVl1+lVagtdZIvFakZWrV3fnRtTh8+qnqJ59Evv1JJ6nefnvk2zdmzRrTvPLmm6Gt//bbqi+9ZF88gXzzjVbFt9aKxDTrnhei5qaoSKs6dNaK+DZaExfi+6SoSKvuzteKtMyGt1mwQHX7dvvjb45qa1W7d9eqzt21IjVDaxCnFakZ/PyKAkJs0Q5jeriwnQOgSFW3AICIvAHgagD1m8QeB/A0gPv8luUA+BsAqOpuETkIoB+A5TbG6770dLZoN+TttxueLTBcGU1gwEd2NpIvvxBY9AGwbz9S0tNNH+mkpMj3+dRTpmxepHXIc3LsbdEu8Q7W6RZ8dPx/DRtmXywNSUtDck0lUFMJnHoqUjimguh4GRlI3vetqdk/aRJSQtkmOxvJ06cC06cCwPHblJUBI0eawgFLlgCdOlkcdAvwzDNIbtvWfAeIhPa8UNTs7DrSGcB2v+ul3mX/JSJnAThZVevXWlsD4GoRSRCR7gD6Aji5/gFEZIyIrBSRlXv27LE2ejekpZkBf04rLoZnbD4q0zJRGxePyrRMeMbmmwGIbvKP64R2qOwzyJq4amqAJ59sGlPO9uhhulSkpwOvvhrdvkpLI6s44uNLtNWm2vuhTFZTX3ExsGKFHdEE5vsx0Lkz0Bw+c4js8O9/mwH+gwdbt8+2bYEPPgB27QIuvBD49tvY/e6KRSKmceLSSxuv6kSWsjPRDvRM/vcbWkTiAEwDcG+A9V6CScxXAvg9gEIAR4/bmerzqtpPVft16NDBkqBd9Yc/mIuTFi5ERe88FMxqjdyyQiSpB7llhSiY1RoVvfPcS0btjCs+3kyM8v771sVrh3zvF0anTuaDcfXq6PYXbaJ97bXAQw/ZV3mkpMT0vT7ppNC3ueUWYMIEe+IJxJdon302sH+/OctARMf67DMzc6vVte4HDDCf/Tt2AP37o+L0/rH33RWrZswAtmypuz5+PPC737kXT0sSSv+SSC4ABgBY5Hd9IoCJftfTAewFsNV7qQKwE95+2vX2VYgA/bv9L82ij7bTYnUkshNx9e+vevHF1sVst8GDTcyRKiszD95TT1kWkuWGD1c99dTwtpk4UTUhQbW83J6Y6qusNNUPpkwxj+euXc4cl6gpef11eysCvf66liMl9r67YtWmTeaBKSioW3b22aqXXeZeTM0AYqDqyAoAvUSku4gkAbgRwHy/BP+QqmaoapaqZsEMhhyqqitFJEVEUgFARC4FcFRVbewcGiO++AJ4/XXHDueZMh0zqkdjGQK3OizDAMysHgXPtGcdi8mxuGK4lvZ/XXll3RmOPn3MBC2RtqDu2GH+RtOirWpO1/rqcVvtpZeAjz4Kb5vBg81jUlhoT0z1tWplutD4apGz+wjR8UaMAJ55xrbde/61AjMSJsTcd1fMevtt89d/XEuXLvZ9ltMxbEu0VfUogHEAFgHYBGCuqm4QkUkiMrSRzU8CsFpENgF4EMBIu+KMKW++aWY/tKsPbD21r83Bc9V3BF1nZvUo1Lw6x5F4fByJq2dPYNs2wOOJfB92qqkBFi2qGwDapw9QVRX5hEZZWcDatcAVV0QXV24u8MQT0e2jIW3bhtc/GwAGDjRdgZwq8/fyy8Dcuaa04AsvmFlGiajO7t1m0LWNal+bg+eOjgq6jhvfXTFr3jzzWek/WRkTbcfYWkdbVReo6imqmq2qT3qXPaKq8wOse4GqrvT+v1VVT1XV01T1ElUNPndoc5Gebvq/OpT8JZfvRQmCV3jYhq5oVW7vh2Z9jsTVs6fpQxirHzS7d5uWWl8L9LnnAg8/bAbMRiI5GTj9dFNLO1Ii9lUeOXwYePBBYNWq8LZr29bMFupUoj1tmjnrlJUFjBrVNCrYEDmpoADo2NHWeQpi9bsrJhUXA19+CQwffuzyLl1M8QU3CjC0MJyCPZb4JhJxqMSfp00GuiH4b5iu2IaqNs4mE47EdcMN5osgVgv0+34A+BLtrCxg0iSga9fI9vfpp8DMmdGfLcnJATZssP6sS0kJ8PTTwObN4W/7wgumxcZuqqYySlaWOeOwYoU5K0JEdZYsMWfgUuwrHher310xyTfZ23XXHbu8Z09zhpIlhW3HRDuWOJxox908Ancmvhh0nbsSZyF+5AhH4vFxJK6kJNPlIFbVT7QB0/LwxReR7e/NN02LeLRlnXJyTLUNq/sm+0r7hVpD21/v3kBmpqXhBHTwoKnl262bKV12zjmmKwkRGZWVwPLl1pb1CyBWv7ti0vXXA/v2Hd9IM2wYsG4dcPJxlZPJYky0Y4mvW4BDiXbyveMwNvEF5GFpwNvzsBR3Jc5Ccv7djsTjeFz33w9Mnx7dPuySlGRKyPl/CP7618B555nW1HBFW9rPxzcNstXdR3xl88Lto+3zhz+YHxN28q/znZhoJs7gYEiiOsuWAUeO2J5ox+p3V8zxnXls08bdOFo4Jtqx5PzzTWWJ3FxnjpedjdR5s7G49VWYjHvRA8VIQDV6oBiT4x7AYlyM1PP6At27OxNP/bhShmJy3P3HxpU4EYtThiJ13uzou3384x/Ah/XnSooRP/mJaRny7wPcp4+ZvOabb8LfX2mpNS0XffsCs2cDp50W/b78lZSY5LVjx8i2nz0beO45a2Oqb7t3/i1fq3uHDky0ifwtWQLExZkxJXby/45InGjfd0RT9/vfA4MGBe4vX1Njco5nWZnFbky0Y0l6ujkN3rq1c8ccMgSp6z7H+DFHsK7tIHjiWmNd2iCMv7MaqWNvM5Uvxo51rBLKMXGtXYbx7edgXcJZdXGN8SB17TJgyJDoj9EUSvz569vX/A13wCBgXYt2+/ZmGmSru2rs2mV+CMRF+JE0eLBpTbNzIPHQoeZsU+/e5joTbaJj/exnwBtv1HWDtJPvO2KMB+vSBtnzHdHUvfWW6c4TqL98fLypYrV2rfNxtTBMtGNJRYX5den0Cz87G8l/+iNSvt+FuJqjSDm0C8nPTjPdKh580Aw087XmOalHDyR7ypBy1611cU2fal0rRc+epjuAXTMdRmPYMGD06GOXnXaaqeMc7gyRVVWm3JYViTYAbNpkfoBZ6eWXo3vdDx5s7ufy5dbFFEhamml5B5hoE9XXvbvpE+yU7GwkT5+KlEPe765vt1j7HdGUlZYCS5ceX23EX5cudXMskG2CJtoiEici650KpsU7cgQYNw74+9+dO6aqeSPOnXv8bSLAb39rEqCuXesm3nLKnj1m8FnPnvbsv2dPc/qsJAarR65Zc/zpvoQE05oabqKdnGwGMN5tUX/FZ54xU59bLTU18m3PO8+8Xu0s8zdlSt0EQgDwwAOmlBkRmdbRP/8ZKC935/i//KWpE+302ddY9c475m9jiXaslrhtRoIm2qpaC2CNiERYU4zC4vBgSADAf/5jZo1qqGVOBOjUyXx4PfKIGUBYVATP2HxUpmWiNi4elWmZ8IzNN/U6reTr1mFXon3KKWZg2/799uw/UqoNd/WYPDn8GddEzMC9E06IPrbiYng2bUHl7u+te+49HjOTXDQ/MA8cgCejEyonTbbvNfnaa8DHH9ddHzAAuPBC6/ZP1JTNm2cmXDtyxJ3jd+9uKgP95z/uHD/WzJtn5k445ZSG12Gi7YhQuo50BLBBRP4mIvN9F7sDa5Hi483oYCcTbV8LYCijxA8cAKZMQcVp/VAwqxVyywqRpB7klhWiYFZrVPTOAxYutC62qipT5aJXL+v26W/gQPOhfM459uw/Unv3mi+rQIn2+efX9dUO1dKlprUn2tfVwoWo6J2HguX9kYv11j3327YBf/lL5B/4vrgOjkRu9Wr7XpMlJcdWRdmxA3jvPdMHkqilW7LEJHbt27tz/D59zN9IxrA0N6qm8eKBB4Kv17ev+f6LpJIVhU5Vg14ADA50aWw7py99+/bVZqFzZ9Xbb3fueD/7meqJJ6rW1DS+7jffaHlCmuahUOv6kdRd8lCo5SkZqkVFtofdrK1ebR7Qt98+/raqKtU33lD98svQ9/fUU2Z/338feUxFRVqekmHPc//JJ2Ynn34aW3H5O3TI7PB3v6tb9uqrZtlXX0W3b6Km7sgR1ZQU1XHj3Iuhqko1MVH1oYfci4FaFAArNYT8tNEWbVVdAuArAG29l03eZWSHtDRnp0RdssS0koZQ7cEz9VnMkLFYhgEBb1+GAZhZPQqeaU2oXNCECcBdd7kdxbESE81gyFNPPf42EVP14y9/CX1/paWmCkDbthGH5JkyHTOqR9vz3PvXp46luPz5+vH7T6jToYP5ywGR1NKtXGnGlNhcPzuo5GRTGjfcMSzN0YcfmjOjFBMaza5E5AYAywFcD+AGAJ+LSJDe9RSVjz8Gnn/emWMdPmySuRDLINW+NgfPVY8Kus7M6lGoeXWOFdEBV19t+oTbaedOU087luTmmn7zP/rR8bclJZnTs+GcHrWgtJ957u8Iuk7Ez31Jiek21blzbMXlb/9+8yPY/8cAE20iwzdj7fnnuxtHfr49A7Wbkt27zXen/8Dthmzfbj7Twmm4obAlhLDOrwCcraq7AUBEOgBYDGCenYG1WFaVYAtFSgrw0Uchr55cvhclCD5F9jZ0Ratyi35J//OfESVfYenZE5g/3/RRi5Up2VWDT5Xet69JxBtbz8eCRNvW5766GvjhD01VlViKy9/gwaaPu39FAybaRMbYsaa6xUknuRvHyJHuHj8WvPceUFsbWpnF9u1NQ0csVt5qRkIZDBnnS7K99oW4HUXigw9C+yVqhTAn9/C0yUA3BH9DdsU2VLXJCLpOSPbvN4Mv7ao44tOzp0n03KgT3pDbbgs+4LFPH/P4hPrhuH9/1Im2rc/9U08B6yOrIuroaxI49ocNE22iOm4n2YBJML/6KrY+z502b54pIHD66Y2vm5pqKlKx8oitQkmYPxKRRSJyq4jcCuCvABbYG1YLNn++STyccMYZpo9yiOJuHoE7E18Mus5dibMQP3JEtJHZX9rPxzexQSzNELl9u+lv2BDf6Hrf6drGFBUBM2ZEFZKjz30YHIvrkUeOH8HfqpUpSXjrrdHtm6gp+/JL4Nprga+/djsS03iUm+tc98tYs2+f+UwaPjy0s50AS/w5IJTBkPcD+BOA3gDOAPC8qj5od2AtVnq6M+X9vv0W2Lz52MFdjUi+dxzGJr6APCwNeHseluKuxFlIzrdgYhSnEu1TTjGTncRKtxGg8a4eZ55pBhBec01o+xMxfbujYNtzX11tumW8/XZsxVXfRx+ZSYTqu/BC+7s3EcWyxYtNd4UoBltbpnVrM4NuSx0QuWSJ6QYZbJKa+pho266xmSHjRWSxqr6jqveoar6qvutUcC1Serqpy2v3tODh1M/2yc5G6rzZWJwyFJMTJ6IHipGAavRAMSYnTsTilKFInTfbmulv27UDLrvMTEJgp86dgc8+i52JR4JNVuOTlGR+IIXSYrF+vRkc9M030cUV9Ll/KPLnvrTUPP6R/rh06jVZUhL4R+nf/266exG1VEuWmK4KHTu6HYnRt68ZLN4SZ4gcNszMDXHWWaFvM2QIcNFF9sVEjc4MWQPgsIikOxQPOTU75KefmhaIM88Mb7shQ5C6dhnGj/FgXdogeNAK66Q3xo/xIHXtspArmIRyHCxaZFooWpKDB001mMb6VH/yiSlL2NiXycaNwKuvWjNbW/3nXlpjHU7H+Bt2R/7cByqbF21cca2xLm2Qda/Jw4fNSP5A5QenTQMefTS6/RM1VTU1ZtC6m2X96uvTB/juO3PWtiXKygq92wgAjB8PPP20beFQaH20qwCsE5EXRaTAd7E7sBYr3fubxu5a2kuWAOeeG1GlB2RnI3n6VKQc2oW43/0WKe1bI/l3j1vTku1TW2vdvhrz858DeXnOHS+Y2loTT//+wdfbvBl47jkzO2EwvkFBVlWz8X/uN65HSmsg+cZhkT/3UdTQbjCu++5FSnItkqdPteY1uW2b+Rvox0CHDhwMSS3XunWmUSjWEm2g5XUfefNN06J94ED429bWcnZIG4WSaP8VwMMAPgOwyu9CdrjxRpNk29llQtUkc3feGf2+7rnHJBqpqdHvy9/JJwMTJ1q7z4YkJgJr1zqb3DfkxBOB3/8eGDQo+Hq+qiSNfZmUlprnxnemxEqnnmpa4K+8MvJ9lJSY1peTT7Yuro4dzWvSqgS4osIMsAo0XsCXaLfE09REZWVAv36xl2i//z4wcKDbkThrzhwzcdAJJ4S33ZIlZvD90sDjXCh6jfbRBnCpqr5S/+JQfC1Pq1amS0c4p37CJWKS7KFDo99XQoL1sX7/vZlIpl07a/cbSHExPCvWorJSUZuQiMq0THjG5gPFxcG3GZuPyrRM1MbFh7ZNqMrLQyu72Lu3mc2zsYlrfP297Xg9WTDIEm3bmkkuot2Pv5wc83fjRmv217evabkLdJahQwfzfJWXW3Msaj7s/JyIFeedB6xYYe0P5WilpJjvtvbt3Y7EXvVfX/M/gaddJrBlS3j76dABOHrU3QGRzfy9Ekof7Q4iYuG3IAW1axdw772mZJJdVqyoOx1uhTvuMK2wVvG9ueyuOLJwISp656Hg332Qi/VIUg9yywpRMKs1KnrnAQsXNrzNrNbILSsMbZtwTJ5svigaGwybmmomeWmsRTs+PvBU7lYpLATOPruuC0i47rnHjBewktWJdjCspU2B2P05EQtUrRn7YYcNG6IuaRrTAr2+sA4FGy8O//Xl61boVqLdMt4rGvQCU9pvBUz3kXt8l8a2c/rSt29fbRa++UYVUJ09275jnH666qWXWre/M89UHTLEuv3NnWsegzVrrNtnfUVFWp6SoXkoVPONcewlD4VanpKhWlQU3Tbhuv121Y4dQ1v3Zz9TveiiyI9lhXXrzJ3/85/djcNfba1q27aq48ZZs7+f/1z1ppsC37Zvn+qmTapHjlhzLGr6nPiciAXr1qm2bq26YIHbkRxvyhTzYO/a5XYk1rPj9dW2rfmcc1oTf68AWKkh5Keh9NHeCeBDmNbvtn4XsoNvMKRdVUf27TOnwa3sU5eTY23roa+GtpWDK+vxTJmOGdWjsQwDAt6+DAMws3oUPNOejWqbsJWWhn4a9uWXgb/9LfJjWSEnx/Qrj6RVuqbGlAWzenIJEeBXvwIuvtia/S1fbs40BdK+vTmzkJhozbGoyXPkcyIWLFliStH+8IduR3K8cCf1akJseX25VEu7xbxXQsnG618AJESynZ2XZtOiXVVlfso98YQ9+3/3XbP/f/7Tun0+8YTZZ1mZNftbsED1F7+wZl8NONz2JO2BooC/on2XHijSiuQTzFkGVT2cmhHaNmmZkQeWk6M6bJg1d3LXLtULLlD929+s2V9Drr1WtXv38Lfbts08aM89Z31MVurUSfW22wLfVlGhOnWq6sqVzsZEMSvkz5ZoPidiwfXXq558sjmDFGsOHrT3e9RFtry+pk1Tfekl+4JuQFN/ryDaFm0R+Zff/6/Wu3m5PWk/ITnZXOwq77dkiRlwefbZ1u3T1yf2q6+s2d+QIaY+sY2Sy/eiBMFrN29DV7TyHPrv1MLJh/eHtk353sgDa2yyGn9HjwKXXw784Q+Bby8pMS3Nhw9HHk8oBg82kyT4SgmGyldDO9rSfoHU1JjnrbIyuv14PGZgbkMxqpp+5osXR3ccajZC/myJ5nPCbarmu2TwYHsH7kcqPd2M8WmGJf5seX394hfAbbdFGVn4WsR7BcEHQ/rXa8utd1sMvrOakRNOsC85WrIEGDDAJPNWyc0FzjjDuph37LC91J6nTQa6oSToOl2xDVVpJwFXXBHeNm0yIguqthZ4+OHQq8EkJJgE1zfLZ32+U4FW1dBuyMUXA9dea8rghcM3gDKayWoasmiRGQTaWFWWxvh+PDQUY2qqmVSJgyHJy/bPiViwebOZxCmWyvrV16cPsGaN21FYzpbXV02NmeDH4RK3LeK9guCJtjbwf6DrZKUdO4A//tGefc+fDxRYPN9Qr16mSsr550e/r4oKkxg+9VT0+woi7uYRuDPxxaDr3JU4C/EjR5gyeuFuE1FQccB994XXt9g33XAgTiXaubnAO++E31fT16Ldtav1MVlVeeToUXOGxbe/QDhpDfmx/XMiFqSlAY8/Dlx6qduRNKygwIxHamZseX3NmgV06mTO3jmoRbxXgIb7aAPYAuBaANd5/x/mvVwHoDiUfilOXppNH+2Wbs0a0zHrjTfsPU4sVh3Zv1+1uFi1ujr0bZ5+2hx8z57jb7vvPtXkZOf6UH73XXjrz56tOny4PbHU1KimpNje119VVfv2tbbqDjVtTbySAsU4O15fH35oNl661L64A2ni7xVYUHVkCYChAK70/n+V93IlzCyRZJeCAuA3v7F+v6+9Zn2FB5/HHrNmGnOnamhnZyN13mwsThmKyYkT0QPFSEA1eqAYkxMnYnHKUKTOm31s5ZNItgnHe++ZbcOpce6bITLQ6Pp27ZzrQ/n880Bmpjn9GKqRI4G33rInnrg44LTTnKmlnZHBFm2qc8znxEPHfk7gvug/J9ymarpm2TWOyCq1tcADD5ipyZsT3+sr/nJMxn3WfA+5VUvb/70Sd/+x9yXhoab/XvEJJRtvCpdm1aJ97bWqP/qR9fs9+2zV886zfr+qqo89piqievhwdPvxtdAeOGBNXI0pKtKqu/O1Ii1Ta+LitSItU6vuzg/+C9q3TWqG1kC0ovWJjW8TikmTzH2vrAx9m/37TS3tv/89umNHa/ny8M9E1NTYF4+q6siRql26RLeP229XPffc4Ovs3q36/ffRHYean/qfLakZWoVE1Ycfdjuy6Pjmepgxw+1IGpeVpXrDDW5HYb3vv1ft1Uur8s4P77urIfv2med02jTrYw1FUZFWZZ2qFXGpWiPxWtG6vVb97/iYbcn2gYV1tCMmIleIyGYRKRKRh4KsN1xEVET6ea8nisgrIrJORDaJyEQ744w56enW19EuKzMjsO0avJKTY1o6Nm+Obj9FRaaF8IQTrImrMdnZSJ4+FSmHdiGu5ihSDu1C8vSpwX9B+7b5bivioEj55S8a3yYUO3aY/r6tWoW+Tbt2ppb2hRdGd+xonXWWmU69oYGZ9dXWmuf4//7PvpjuvNOcHdIohpR8881/++g3qEMHc9+J/GVnI3nkT5Hy1KOIqzyMlPI9SB48EHjxRaCqyu3oIud7j8fyQEifPn2aZeURtG0LfPUVkpd8Et53V0PatTODut2aHTI7G8mJtUgZNgRxtUeRcngfkp8raPot2V62JdoiEg/gWQBDAOQAuElEjhtRJCJtAUwA8Lnf4usBJKvq6QD6AvhfEcmyK9aYY0ei/e9/m5HFdibaQPSn6m+4Afjtb6OPxwmpqabsm1XdE8Ip7Vdf/S/u2lrznPzpT9HHFYqEBGDQoNAT7e++Mz/+fBM02WHgQFMNJZquMyUljVdFWbYMuP/+6EsJUvPz/vvAhAnm/QGYqkI7d5rJppqqJUvMj8vTTnM7ksb17Wsab+yaAM4NpaXm/sTFAUlJ1uxTBPjd74Arr7Rmf+GqqjLdRn15xK5dwJNPhtcVMYbZ2aJ9DoAiVd2iqkcAvAHg6gDrPQ7gaQD+mYICSBWRBACtARwBEOMdwiyUlmaSECtL7SxZYj7sBwSegSlqvXoB8fHRJ50XXwyMGmVNTE6wclbMSBPt5583LRwHDtQt27MH2LQJqK62JrZQDB5sHotQ+ivbWUPbp6YG+Mc/zOMQiepq85w0lmivXw8884wpd0bkr7QU6Ny57qzIRReZH4ArVliz/+JieMbmozItE7Vx8ahMy4RnbH7dWBer+B/n1ddQeaAKnrvvsf44VrNihkinHuNQTZhgziDW1Fi73/HjgQsusHafoVIFXnkFGDbMXN+/H/j1r001q2YgpERbRAaKyAgRucV3CWGzzgD8Z7Ao9S7z3+9ZAE5W1Q/rbTsPQAWAbwFsA/CMqu4PJdZmISPDXMKtSxzMd98B/fubVlg7JCUBt99uEu5IHTkCFBbG/iAbf1ddZV23jd/8xnyIhisry5Sh+/LLumVOlfbzN2yYaakLpeuLnTW0/Q0ZArz0UmTb+uq5N/ZjoEMH85cDIqm++j+eRYCPP478Nelv4UJU9M5DwazWyC0rRJJ6kFtWiIJZrVHROw9YuDD6YwQ6Do4g9+gX1h/HDn36ACeeGPl706nHOFRr1wLvvgvccotp2LLSvn3Hfoc4qXVr4OabzXwcgGnAOu00YN48d+KxWmOduAG8CqAQwAwAf/ReCkLY7noAs/yujwTwR7/rcQA+BZDlvf4pgH7e/wcBeB1AIoCTAGwG0CPAMcYAWAlgZdeuXe3r8d5chFM2zg0bN5oBGa+/7nYkTcuePeZxmzy5btl775llK1a4F1cwTz1l4rN7EOEZZ6j++MeRbVtSojpmjOqqVcHX+/e/zX1ZsCCy41Dz1auX6k9/Gvi2khLVI0ci269TZdGaePk1VY28vGks3vfrr1dt29YMgrfaxImqCQn2D1IP5Isvjv+uevhh1bg41V27nI8nRLBwMGQ/AINUdayqjvdeQml2KwVwst/1LgD8q6G3hZlx8lMR2QogD8B874DIEQA+UtVqVd0N4N/eOI6hqs+raj9V7dfB16pEDfP1E7RTZWXkp7SKiszfpjYAoqYm+v65hw6Z7j2R9CXMyDCTvvgP+tmxw/x1skUbMDNVvvFG4+v17g2MG2f/IMJouvZ07Wr6uPtOPzeELdoUiGrD3cHWrTMlTF97LaJde6ZMx4zq0ViGwF0Bl2EAZlaPgmfasxHt3+nj2CrCMRoxd983bjQtvBMmmMGLVuvSxZwZdTD78ugAACAASURBVKML3JNPAv/zP8cuGz7cnFF87z3n47FYKIn2egA/iGDfKwD0EpHuIpIE4EYA8303quohVc1Q1SxVzQKwDMBQVV0J013kIjFSYZLwryKIoWnasMGchl+/3pr9PfWUOYVu9/Sq779vuqZE2ifWl2jbXUPbShUVQJs2wB/+EN1+vvjC9I+LdMrw+qPrMzOByy8HTjopurjC9frrwIgRpo9dMEOG2Df7qb+cHNNNJZJuWBUVof1o9CXa/n3kiQDzg3digKJZubnA6aebBOPo0bB3W/vaHDxXfUfQdWZWj0LNq3PC3rcbx7HVe++Zx7qsLKzNYu6+f/CB+X7Nz7dn/27V0gbMj4j6s++efjrwox+5VwnFQqEk2hkANorIIhGZ77s0tpGqHgUwDsAiAJsAzFXVDSIySUSGNrL5swDawCT5KwC8rKprQ4i1eSgvN/2wwpm4JJiFC03/q8bKlEUrK8u04kTaglhUZEq+tW9vaVi2Sk01fQCjHRC53TucIdIW6FtvBe66q66U3XXXAR99ZP9zXt/gwSaGf/4z+Hp79tj/ww+o+/D+KoLf6ePHh3Z2JT3dnNH4+c/DPwY1XyKm5fHEEwPf9vDDZkDdX/4S9q6Ty/eiBMHHN2xDV7Qq3xv2vt04jq0SEkyj1Zo1YW0Wc/f9wQfN51ig15MV3Eq0q6uBr78+voqNiOkz/vjjzsZjg1D6EjwW6c5VdQGABfWWPdLAuhf4/V8O08e7ZfKVPLOiJFFVFfD55+Y0vd1OOcUkdtEk2j17OjOToZWsqDzi+3Dr3Dn4eg25OlBBHxecc44ZDLlkScMxqZofZXfeCUyZYm88F14ILF9uWkbCtXUr0KlT4+uJhFf7nFqGNWuAuXPNqf7MzONvHzrUdKF64glzFiiMwW2eNhnoVlaCLWj4h2BXbENVmwykRBK7w8exla/r1+rVwLnnhrxZTN33AwfMj7ZIvx9C4VaiXVRkzurUb9EG6rq7VlcDiYnOxmWhRpu7VHVJoIsTwbVYaWnmrxWJ9rJlgMfjzOQCrVsDPXpEnnQ+8QQwebK1MTkhJ8e0NETTQltaaj5Io6kKs307sGWL+b9378gqmEQrORnIywteT3vfPuDwYdMH2m7t2gFnnx1ZIhxKDW2fyZPt/9FATcvnn5sJmRoqsRkXV9eqHWa5v7ibR+DOxBeDrnNX4izEjxwR1n7dOo6tOnUCfvCDsLvlxcx9Lyoy92HuXHuPk5FhqkZddpm9x6nPly8ESrQB8yP0mmuci8cGjSbaIpInIitEpFxEjohIjYg0ofprTZCvRduKMndLlpgWt/POi35foTjttMgT7bPPdq+OZzRyckx/3u3bG1+3IdFMVuPTvz/w2GOmxbioyLrJDMI1eLBpzWuoT6RTpf18PvwQmD07vG1qakzXrVDrfH/8MfD222GHRs1YaalJpn8QZIjTsGHmvZqXF9auk0fegLEJzyMPSwPenoeluCtxFpLz7w5rv8cd595xGJv4gu3HsV0EM0TGzH33TeBm93d4XJzpgnjKKfYep75LLjHzHTSUaHfpAnzySZMeAxNKB87pAG4C8A3M5DGjvMvILikpQPfupnUwWtnZZgIYp6Y0v+02YOzY8Lfbu9f8Yt8bw339GnL++aYfWTTP16RJwPQo31a+L5MDB0yfYacrjvjcfbeZ2auhiiJOTFbjb/bs8Pv5ffutOZ0Z6o+BDh1YdYSOVVoKdOwYvNpTXFzd+6C8PLT97toF3H47UjukYnHKUExOnIgeKEYCqtEDxZgc9wAWJ/8EqfNmR1/BKTsbqXP/jMWJQzA5/oFjj5M4EYtThlpzHLv95CemIcc3hiUU2dlIfesVLJbLMBn3uXPf//Mf8/k1Zox5Ldlt40bgs8/sP46/9HTTwNbQWcfhw81ZoQ8+cDQsSzVW/w/eOoEA1votKwyldqCTl759+4ZXAJFiy4IFpjjpv/7ldiRN1yOPmLqjhYXmsXzrLbcjCuyZZ0x8dtSCDeTRR83jcvhw6Nvs3q36+OOqa9eGtv6ECarp6RGFR83UpZeqnnNOaOuOG6fau3fjNYy/+041J0c1JUX1s89Ui4q06u58rUjL1Jq4eK1Iy9Sq9j9Qzc5WPXo0+vvgE+g4d+fHdv1sK7z/viqgVRcPMfdd4rUiNcO5+z5mjGpSkmppqf3HUlW96SbVnj2dOZbPc8+Z76yG1Naqnnyy6lVXORdTiGBhHe3D3vJ8X4rI0yKSD8Cm6QXJUgcOhF3SKGq1taaf8K5d4W3XFEv7+du9G9i8ObJtq6tNPd3//Ce6GPr0MY//Au/4Y7datAFTS/ueewLfdu65pj++U2dZcnLM4/L116Fv06GDmQL49NNDX//QITO7KRFgxiKE+h7s39/M+jc/SEGvvXuBiy82nxN//avpSpCdjeTpU5FyaBfiao4i5dAuJD9XYPp9W9GVac0a4K23gO7djz/O9Kmx35LtT9WMDQln/UmTgB49kLzwfXPfa48ipXyPM/f94EHzvTBqlL2DIP117mzOxITT8h+No0fNWKJ33214HRHTqr1oUdOaNdpPKIn2SO9642CmRT8ZwHV2BkUwVUIeeCC6fcycaUrlWTGoMlSVleYDaNas8LYrKjL1qJ2u+2yVG280/dsisWMHMHKk6acWDd/o+tJS4PbbTfcjt6xfDxQUBP6h178/8KtfOVddxtf3L5yxAzt3mu4jocrMNGW3Dh4MLzZqvlauBOaEWGP5xhtNI8OkSQ0nOfffbz4nP/gg+FiW664zY2Uefzz6EpoPPwz87/9GVoc+1uTkhDdAvKrK9J1/9NG6ihf79pkfMI3NE2CFE04wn6MPP2z/sXy6dDH3e98+Z463ZYtpnGiof7bPrbcC06Y5X67WIqFUHSkBIAA6qupvVPUeVS2yP7QWbv16M2o9XMXF8IzNR2VaJmp/9WtU1ibDM/Ex08LhhNRU0+cw3AGRTbW0n4+vxF8kLQG+ckrRtkB7PPBcdiUq5/0VtS//GZW9esMzNt+5597f4MFmQGFh4fG3rVvnzBeVT69epnRaOC3ajz0GnHFG6OuPHm1aHJvqD0Wynkjo4zYSEsyPzy++gOcnw8znd1w8KtMy697D06aZQWEXXxx8X3Fx5mzM+vXRzar3xRcmqb/nHvtncHVC/dlzG9O6tRk3c8stdcs2bjStq8uXWx+f/3e377mfXODsjxynS/w1VnHEp3dvM/arTRv7Y7JBKFVHrgLwJYCPvNfPDGXCGopSenr4LdELF6Kidx4KZrVGblkhknAEubVrUDCrNSp655mJa5wQSV1pX6LdVOXkmNNaO3eGv60VifbChag4YwAK/nE6csuXIkk9yC0rdP659xk40CQPgcr8nXeeaSVySnKyeV4eCVjCP7CtW50brEnNz9atJkFbG8Y8ayeeiApJRcFHPc3nt+89/KdE8x5eujT0OtA//alpJffNWhqJxx8330Pjx0e+j1jSp4/58eHxNL7uihWBJ92K5OxYKOp/d//387uVs5/fbiXa9SerCeTAAeDFF0MfNBxLGuvEDWAVgHQAX/gtWxtKB3AnL81uMOTNN6t27x76+kVFWp6SoXkoVNOseuwlD4VanpLhzACO++5TTU4ObzDOf/6j+s03toVku3/8wzzQH38c/raTJ5ttDx2K7Nix9Nz7699fdeDAY5cdOGCCeuYZZ2MJ1ymnqA4fHvr6u3apDhum+skn9sVETceiReZ1/tlnoa0fa+/hNWvMgR991JnjOeGtt8x9Wrky+Hq1taqDBql26aJ65Mjxt590kuodd1gXVyw992VlqosXq+7bZ/+xVFVHjjQDHUOxZIl5QObOtTemMMDCwZBHVdXBTr4EIOwWbc+U6ZhRPRrLMCDg7cswADOrR8Ez7VmrImxYTo5pNQhncF9WVtNv0QYia+nYvt2cmvVNVBSmmHru/V12mSnZ5N9P1Oka2j5LlgA/+1logxVra00JwnBatOPjgXfeATZsiDhEakbCPEtl23t4zx7g97////buPD6q8vof+OeEhJBVkSCiQAJRkRgRC2oQFbWo4IK2xaUorQtQFhUpbrS1danayk9oKQhfhVpRsWLqglZcwIIohFUExS2hLGFtQIRsQ0ie3x9nxgxhMpnl3pk7M5/365VXMjN37jw3987Muc99nnOCH9K2dy9w1lnAuHHBPc/JPHNYWipcs3gx8MknwAMP+K5GaEUlYC+O+vzOzNShSccdZ/9rAcDf/65F9QLRr5/OhSkutrdNdmgpEgcwG8BQAOsBnALgbwBmBhLFR/In7nq0//KXo3sD/ajOOt50Q6nPM2LPTzeUmqrsDjY22m3rVu09CLSHduNG7eGsqLC3XXZqaDBmzpzQeh127TJmzZqQX9pR+74lb7yhDVq1KrKv++KL+rpffNHysjt36rLTpgW+/vp6TSH429+G3kaKHw8/rMdQbW1Ai9v2Hp41S5/8zjvBb0NDQ/DPcbKGBn1/rlzpf7mLLjKmY0djamp8Pz5mjKbytOj/47jP7wUL9IqME40aZUxGhjFVVdFuiTHG2h7tOwGcDsAF4GUABwDcbUfQT17GjdOz6gClVlZgC/z3Em5FF7SpjEBBmM6ddcJIoD20ixcD99yjs51jlYhmDgkl5VOHDo29LSFw1L73xbs3zdOjHenxz8FccUhPB/7xD61YFqikJM06wqI1BGiP9vHHBzwZ0rb38LBhevXo4YcD79V+5x1NgxerE9ObI6JpRc8+u/llli7V76P772++gMq99wZdzt0fx31+P/ww8OST9r/O9u3A6NHBXQUcMkQnh773nn3tskEgWUeqjTG/NcacbYzp4/47hiOi+OTKzEEutvhdpgu2ojYzJzINWrkS+PDDwJYtLdUZ3pGofGWnzZuBl18O/jLt3/6mE51C5Lh9723kSODyyxtvDxqklwvbtYtsO7p31y/aQALt7GwdZtK9e3CvweqQ5C2I48e293Dr1sDEiZrB6oMPWl7+66+Bq68G/vzn4F4nVtTU6P+irs734zt3AqefrlmEmpOXpx0qFp2IOO7zu1OnyEyGXLcOmDkzuKQP/fsDOTk6WTWGNBtoi8h8fz+RbGRCWrRIx8gFmJot6eahGJUy2+8yo1NmodWwoVa0rmW/+532CgSitFQ/uGI0R+YP5s8Hhg4Fdu8O/DmHDwN33x3WrHLH7XtvWVla0tdzteLUU4Fbb418b1l6uuYVDyTQ/vpr/TIO9oSpoEDnVhA980xQpaxtfQ/fcosGT4H0aj/+uPbkjh0b/OvEgjff1NzYzX0OXH+9ph9NT29+HfX1wF//qt/RFnDc57cn0A728y9YwWQc8UhO1jStjz9uT5ts4i+y6QugE4ClAP4fgKea/JCdXC4946sI7HJR6oQ7MCblWRTBd89oEZZjdMospI6P0AdoQQHw5ZeBFUyI9dR+HqFMiNy1S/9HYaT2c9y+99a/vx7LnpzwCxcGl8/aSr16BTYZ8q9/Ba64IviTgVdfBZ57LrS2UUKz9T2cmqq92h07+q+MWFoKvPSSXs6P13zwniF6vvJpf/ihBtEtve+TkrSw0Lx5ljTJcZ/fnTrp8Ay7C91t3KjHZNu2wT0v2OUdwF+gfQKA3wAoBPBXAJcCqDDGLDHG+EiOS5by9IwFerDn5yOjeA4Wpg/GJExAN5QhGXXohjJMSpmIhemDkVE8J3IlcwsK9M26bZv/5YzRIReJGmhbkUPbe9+nTIz+vvd2wQX6xeXJp33DDZoFIRqKizUzSEu2bIl8VhSKH9XVwCWXaLGXQNn9Hh49Wo//jIzml3niCc2ycc89ob1GLDj5ZL3K1nSM9apVmm3j//6v5XWIWJt5xLPv0652xnd3pHJpb9zYcqGa5gwfrhVLY0SzgbYxpt4Y864x5pcAigCUAlgsInGSvd7hPBMJgzmrHDQIGa88hzvxN2xIOweupDRsyO6HO0e6kLG+RMfHRkqgQaeIVgn83e/sb5PdOnbUE6RIB9qA7vv1JbhzpAsbsvtFd997a9tWq3p99JGWY9+3L3pBbKA91Js3h9bGuXOBvn11OBAlrvJy4D//AfbvD+55dr6HPcf+V19p0Zam6uu1HPbIkcAJJ4T+Ok6XlKRDMpv2aD/6qKa0GzYssPWEUwnYl0GDkPHUo/rdnVEU3c/vyy/X4XPBzlEJVk2NjocPRX098MorgV2hdIBkfw+KSCqAKwH8HEAegKkAAugSorAF26PtsWMHUlEHfLkWyM2Fn5Fm9vKMu9q4seUPidTUwEsVO1koPR1WBdoAkJ+P1GmTgWmTASB6+76psWN1jPYW94SfaAXa27bpJMf77gMGDvS9jDHazssuC379+/ZpTti9ezWTDCWmcN7Tdr6HDx/WnvbTTz96YmSrVjp0orlJgvHkRz/SnuvDh3XMr6fU/B//GHip+YICfb//73/WDbPZsQOprRqA/20F0tKi9/l97LH6Y7f16wMbWurLkCGaGWrRouh1IgXB32TI5wEsA/AjAA+7s448aozZHrHWJbK2bRtn2AZjxAgtFBPtS9/t2um43JEj/S/33ns6GbCqKjLtstvs2Zp5JFBjxwJbt8bkuLOAjRihZZw9gXa0Spu3bas9jf5Sc+3bp8diKO8fT7lrZh5JbFaePFspORmYMEHnSSxb1nj/nj06gVtEs5TEuxEjdPK5p5f/0Uc1sLzjjsDX4bliW1pqXbvWrNH1pqVZt85QTZ0KvP++/a8TagKEAQP0qn+MFK/xt5XDAJwKYByAZSJywP1zUEQORKZ5CSwrS/N5XnttcM8TiV4g09Q557TcQ/Dhh8CMGc3nLI01PXoEl6YwJUXzjsdbzlpvZWVw3TYKNT8ZigYIai4dDNeY8QFn1LFMZqYG0P6uOGRkaC9JsO87oDHQDnACc8wpK4NrzHjUZHdAQ1Ir1GR3iM5+tIKd2+IJtE86Kfx1WW3UKKBtW7hu/GXjtnc6Ba4uJwOffRbt1tmvrAyuac+i5urr0ZDSWvf7h5/okJFgMgb17w9UVgLnnWdd29auDauegqWeeMKyyZ4+Pf+8ppEMtXZGaipw8cVwvfRqTHwe+RujnWSMyXL/ZHv9ZBljQqsVTfb6+mvgppv0txOsWgU89JD/cWylpUC3bnrpMh7s2aM5aL/6KrDl/9//07G98WrBAlT1LMLU57JQWLcWrXEIhZXLMXVWGqp6FoWV1jAkLQ3tadNGL6+HcrIazz3anv04Kw2FB5ehtXGh8OCy6O3HcNi9LZmZwLnn+k8RFy0ffYSqygZM3XZN47bXrcXUutGoOm9AbO3HYDW336tvQ9Xsl4Pb9tat/U8sDcX69ZrNxAnszqX98cd6xTvUDrYFC1D13lJMrRsVG59HgZSPjIWfuCvBboyWgr377sCXnz5d67WWldnXpmB42lNe3vwyPXsac9VVkWuT3bZs0W2eOTOw5bt0MWbYMHvbFC2lpaYyPccUYZnPssJFWGYq03NCK1sfqgkTjGnTxpjDh30/vnq1Ma++qiXVg7VnjzF9+hjz1lvhtdFpnLgfQxVP2xIsbru12z59ujETJ9rX5mi69lpjCgvtW/955xnTv39oz3XQcQwLS7BTtFRUNI5rDcSSJXq5smtX+9oUjJYyjxijl3niIbWfR+fO2qMVyITIhgZgxw7njeW0iOupaXi6bgRK0Nfn4yXoixl1w+GaMj1yjTr3XL3s29wk4zlzgNtuC20oT/v2ehXnqqvCa6PDOHI/hiietiVY3HaLt33NGq1ya4XiYr0SaneRmEDZ2aNtTFip/WLxOGag7WTZ2YFnHTFGA+3+/Z0z3relQHv/fh3DHU+BtoiO0w4k0N6zR2e+x2mg3fDiXMysu93vMjPqhqP+hQgOnbnuOuDddzWVly+bN+uwEae8hxzAkfsxRBHZlqIi4Cnn1XSLp/0YLFu2vaBAJ5Hu3Rtm6wC88IJm0XDK506nTvr9XFlp/bp37dJ1hxhox+JxzEDbyY45BjgQ4LzTb77RN33//va2KRjt22v2keaCzrZtgZ07gTFjItsuuwWa4s+p2QksklpZgS3wn71jK7qgTWUUJg8213MUag5tj5/9LLjsBTHA0fsxSLZvi6cKqr8KjFEST/sxWLZsuydQ/PLLMFrmtnYt0Lt3+OuxytixGmRnZlq/7u+/B84/Xyv1hiAWj2MG2k52zDGB92hXVGh+VCcF2p680ps2tbxcPCko0AlxBw/6X27PHt12J2YnsIArMwe58D/0qQu2ojYzyBSW4erfXyuL+bJlS3hZe3bvtq5inEM4dj+GwPZt2bFDfzvw5Dme9mOwbNn2UCoB+7Jnj3a6OCXjCKABttWTPT1OOw1YulSD7RDE4nHMQNvJ+vXTDAiBLvv55/ZXcwrWW281n4/z6ae1JLdTxqVZZexYzcXcUmrDK67QHrAQz+ydLunmoRiVMtvvMqNTZqHVsKERapFb69bAhg1H379/v57YhtOj3b593GUdcex+DIHt2+Lgq1TxtB+DZcu2d+6snxU1NeE1zlOl0kk92gcOAPfco8NRHSYmj+NAZkzGwk9cZh0JVEND81kUnOyGG4w5+eRot4Ls4qDZ4Ue46y5jMjL0fePt8GFjvv7amF27Ql/3iBHGdOgQXvucxqn7MRR2b8vcubqijRutbbcV4mk/BsvJ2z5rlmZC2r8/8q/dnJoa/cf88Y/Wr3vgQGNuuy305ztoX4JZRxLIpk06Fvrtt6PdkqNt3apZHFavPvqx0tL4mgjp7b77tMyvP5MnA3/4Q2TaEw35+cgonoOF6YMxKWUiuqEMyahDN5RhUspELEwfjIziOUB+fmTbVVCgVxy2bTvy/latgFNPDa98evv2Oowr1NLCTuTU/RgK722Re63flmOPBS691JE92nG1H4Pl5G2//XYdZhhMwRy7tWmjVantyDyyenV4dTOcvC+bE0g0Hgs/cdmj/dxzxmRlGbN7t//lZs/WU7nPP49Is4Kyfbu2bdq0I+9vaDDmmGOMueOO6LTLbr166Zm7P/37G3PBBRFpTlSVlpraseNNVXYHU5/UylRldzC1Y8dHr+fso4/0mFyw4Mj7lywxZsoUYw4dCn3d8+YZc801xlRVhddGJxoyxNSeda7uR0kyVUgztbeNis0e0NJSU5t7iqlCmqlHkqnKyInuMRlJTns/RpLV2/7mm8aceaazeqOt0quX9TUu9uzRz94pU8JflwOOY7BHOw60aqVnui1NiPzoIz37DDFdjq06dtQz9aYTRvbu1e1y0lmnlQLJPFJeHrcTIY+Qn4/UaZOR/v0uJNUfRvr3u5A6bXL09v3pp+tVlpwmk2XeeAP4zW+A5OTQ133ddboeJ1YFDEdlJfD220g9/xzdjx8tQTpqkHrVZbH5Hs7PR+qx6Ui/egCSTD3SK/8X3WMykpz2fowkq7ddREvXh5p5ZO9enRS4cGFoz7eTHbm0Pd+JVsQqMXQc2xpoi8hAEflaREpF5AE/yw0RESMifdy3bxKRdV4/DSISnzPG/Ml2V7pvKcXfkiXAhRc6M3uHJ/NI06Bz/37g7LM16IlHBQU6bKa5PKTG6IeYEy8xx7vjjgNmzwb69Dnyfk/GESe+j6LtnXeA2lpgyBC9ffbZenn5o4+i265wjBwJDBtm7Tr79wduucXadZJzhZt55NNPgU8+sa49VurUSYfYWclzQuLETkEb2RZoi0grANMBDAJQAODnInLUf1dEsgDcBWCF5z5jzEvGmF7GmF4AhgHYbIxZZ1dbHcszZstfj/bWrZr710lp/ZryFWiffDKwcqWOZ4xHng+Sr77y/fjevZpxhIF2dDQ0aCo+b1u2hJdxBND9fcIJwPz54a3HaYqLdex6v356OzUV6NvX+qwEZWVwjRmPmuwOaEhqhZrsDnCNGa8VZK02Zoxegbj3Xus+P0tLwxt/SrElL09POEMNtD0ZR5yU2s9j+nStz2GlvDw9uU2EK7le7OzRPgdAqTFmkzHmEIB/ArjGx3KPAngSQG0z6/k5gJftaaLDBRJoJyfr5e7LL49Mm0LRs6f2Ilp9duxkBQUaRO/b5/vxffv0w6ZLl8i2i9Tdd2sqTOOVWtJTFTIcmZkawO/aFd56nKS6Gvj3v4Gf/vTIILJ/fy04VdvcR3eQFixAVc8iTJ2VhsKDy9DauFB4cBmmzkpDVc8iYMECa14H0G3atAmoq9MrGCtWaJXWcNTV6f+DJ8+Jo1UrzQsdaqC9Zo1+5jRXqTaakmwIDwcOBObMSbirhnYG2icB8J7WX+6+7wcichaAzsYYf+kybkCiBtodO+plSH9nfyeeCDz2mPPyZ3u76y69ZOSdAH/sWP3ijlfdu2tWi8su8/34qafq0JGf/CSy7SLVvbuewO7cqberqvQqQ7g92u3b6+94yqVdXQ2MGAHcdNOR999/vxZoadMm/NcoK0PVkF9gQPV83Ff3ODYhH/VIxibk4766xzGgej6qhvzCup7tlSt1LOfSpXpS7HIB//1veOvctUtP3BhoJ5Yrrgj9+9dpFSG9ffut1rn49FPr1tlcx1OcszPQ9nXK8kP3kYgkAZgCYEKzKxA5F0C1MebzZh4fKSKrRWT1/+Lpi83jhBOA554Dzjmn+WU++ST8hPnRsGZN4OXliazWdGxlRoYG22PHhrfe1FQtVBRPn0c5OcBf/tI4bMSjTRvLeqZcT03D03UjUIK+Ph8vQV/MqBsO15TplrzeEYVlrKrw5+BiNWSjxx4DpkwJ/nmHD+uxF2hRukhraADmzbOu0u2+fZqGeOpUa9YXQ+wMtMsBdPa63QnADq/bWQAKASwWkc0AigDM90yIdLsRfnqzjTHPGGP6GGP6tPf0JMUbY4D6et+P7dihM5ZnzIhsm0Lx058CDz/ceDuec2h7PPVU8z3aM2Zob7b30AWKHF/BVXp6y9U8AxFP1SFra7XXt7nPoEmTdJxzmBpenIuZdbf7XWZG3XDUvzA3DwqAcQAAIABJREFU7NcC0BgUn3QS0KOH/h1uQJGdDdx6q7OvLpJ9gs2dn5wMvPmmzhVwIs+VdKsyj3gmQsb7974PdgbaqwCcIiJdRaQ1NGj+YYaQMeZ7Y0yOMSbPGJMHoATAYGPMauCHHu/roGO7E9dxx+klWl88E5GcPBHSo7wc+Phj/fu77/Qyfby/4aqqNG2TrysOK1Zo4v4EG6vmGMcfr+8tT3D13nvAhAnWXB268UadKBgPPvhAMxotWuT78QMHgNdf1zSkYUitrMAW+B+2sxVd0KayIqzX+UF5OdC2rV7JyMrSIXrhpgU7/XTg738HunWzpIkUI7Zu1ZPrl14K7nnNnbw6RWamFmCyKtC2MrVfjLEt0DbGHAZwB4D3AHwJYJ4x5gsReUREBgewigsBlBtjNtnVxpiQltb8ZMglS7QXpVcMZD70zjziGWcZ74F2jx7aY/3110c/xtR+0SUC/PnPwPXX6+0PPwSmTdOhH+F67DHgjjvCX48TFBdrQHrxxb4f799fA4YwU5S5MnOQiy1+l+mCrajNzPG7TMCavv+ee67xWAhVdTWvUCWijh31OzrYKyK//KWexDqZlbm0N27Uq4YJmADA1jzaxph3jDGnGmPyjTGPue/7vTHmqNxXxpiLPL3Z7tuLjTFFdrYvJmRnNz+WeckSHToSC+mkCgp0qMv+/UBKig6biNcc2h7+xn4y0I6+4cMbx0du3qxfAFbNtHd6b1UgDh3SS9vXXKPvWV/69tVL4GGm+Uu6eShGpcz2u8zolFloNWxoWK/zgzFjgN///sj7KiuDv/zv7dZbgTPOCK9dFHtSUnRye7CB9po1ehLrZKefrp19Vti4UTuf7Mhm4nCJt8Wx5phjfPdo796tOXudfkbs4Qk6v/wSOPNM4LXXgFNOiW6b7HbKKXoS1PQDmMVqnKGqSntiKysbi9VY4Q9/0Muusd67uWiRfvZ4itT4kpGhxWvCDLRTJ9yBMSnPogjLfT5ehOUYnTILqePDnKzqcdllR27X3Lk6hGTz5tDXWV6uucYp8QRSCdhbZaVe6XRqxhGPf/5T3xtWGD4c+PWvrVlXjGGg7XTNBdrt2gElJUen3HKqM84ABgzQv8PNVxsrWrfWL/OOHY+8v7ZW/x8JOFbNUZYt0ytCq1drgBVuaj+PrCzdx2GOW466t97SK2qe921zrr9eT57DObHIz0dG8RwsTB+MSZiAbihDMurQDWWYlDIRC9MHI6N4jjXllevq9MRg797G+7p21d/hTIjkyXPiKijQvOyBzvFYt07fL04sVGOX664Dhlp0RSrGJEe7AdSC66/3XeglORk499zItydUubk6sQrQ4Ob447VXO97908dc3rQ0YLnvnjuKIM+Jzrp1OtTDqkDbO5d2drY164yGv/4VGD265XHrd99tzesNGoSMP/4Gd/76fozJmIM2Nd+hNjMHrYYNRer4EmuCbADYvh246CJg1izgdnemE+/MI1ddFfw66+t1aBwD7cT04x/r93RtbWBDLZxcEdLbokXAgw9qmr9wju2KCn3fFRQ0PwwtjjHQdrrhw33f/8QTOmykaW5bp2to0NR+p50W7ZZEjqenjxlGnOXEEzUQLi3VoDic8bnevANtq4LDaEhJCXzMsTGNeXLDsXQpUk9sr8WeHnwQ6e++C0ybHN46m/KV7/rYY/V4CLVHe88evVLHQDsxXXCB/gTqjDOAceOOvtrpNHV12im0ZUt4x/bbb+schm++if8hoz5w6IjTHT58dDWligotu754cVSaFLJ77tEvs9274z/jiMe772ow98UXjffNnQucdVb85FqOVSLak+kJrqyapBMP1SEfeUSzpwRq4MDwq5xWVmqZ9Z/9TPeFMcD69dZPLPUE2p07H3l/sONsvaWk6OTKeEnrSMGrrtbqoIG4+GItAuX0zhdPcB1u5pGNG/XKmGeIVoJhoO10jz2mvUTe45qXLtXfsZA/26OsDK6lK1Gz+3s0QFDzyCS4xoy3rqSyU3XsqAGE9xf4N98An32m4+8pesrK4Pq+BjX/WY4GSUJN9vHWHJO5uTqcIpJprMrK4BozHjXZHdCQ1Ao12R1C35bDh4G//Q343GdBXt969tTc8OHkIV+/XicPeyYp5uVpW3bs8Pu0oDVXwXHkSOBXvwptnTk5WpArFlKtkj369Ams+MyhQ/odYNUVNDt5TkatCLS7d9chrwmIgbbTeYIx7xR/S5Zo+eOzz45Om4K1YAGqehZh6przUIjP0RqHUFizElNnpaGqZ5H2YsWrU0/V3jnvQNuTnaB16+i1K9F5jsnSQY3H5MHl1hyTxx+vJZnPPNO69vrj2ZZZaSg8uAytjQuFB5eFvi1Ll+pVM3/ZRpq68EINIFasCO61vJ13nl4F8AyH84yZDycTiC/l5Tphten4+euuaxyzHayKCh0+EuuZZih03lfH/Fm7VoPO+UdlOXae7GzNoGRFoJ3Ak/8ZaDtdc4F2377WFNewW1kZqob8AgOq5+O++j9hE/JRj2RsQj7uq3scA6rno2rIL+K3ZzstTSvFeX8Ab9/OsZzR5H1MHrbpmKyubr7QlJW8t6XucWu2pbhYC0sMGhT4cy64QC+Dh5rmzxOgpqU11gXwpFu0OtD+1a98pyyrr9eext27g1/npElHD0WhxFJQoPM9XC7/y8XKREhA39MDBmjnQaiqqvQ9zECbHMsTaHu+tGtrdRxYjAwbcT01DU/XjUAJfI9dLEFfzKgbDteU6RFuWQQ1HfvJNGBRFZFjskcP4K67Qn9+gCzflvp6zQZ0xRUabAfq2GO1Bz/UQPv11/X5W7yqQ3bp4js9Zrh69PCdWeS777SnMZS8wZ73tNPH3JJ9Cgr0/fPtt/6XW7tWhxrFyonZ668DEyeG/vyUFL2qdsMN1rUpxjDQdrqmgXabNjpm8f77o9emIDS8OBcz6/xfjp1RNxz1L1iUFN+JbrjhyA+Zc86JnUJDcSgix2ROTkQmQ1q+Ld9/ryfxoeS7fegh4N57g38eoL3o27cDJ53UeF9aGvDqqy3n8Q7Wv/6lxb6aysnRiayhTIjkyTP5qwTsbc0a7c1OlJOy1q2Byy/XYZQJioG20516qmYA8D77FdGAOwakVlZgC/znJ96KLmhTWRGhFkXB0KGai9Rj9mxg/PjotSfBReSYbN8+IoG25dty3HGa+z2UDCLXXBPccBOP2lotjvOTn/ieLHXoUPDrbM7hw1qboLle61AzjzDQpu7dNZOIvyEhLpdOMo6FYSMezzyj2UJCLTS3dCnw3nvWtinGMNB2us6dNUjzpMUZMgR48snotikIrswc5GKL32W6YCtqM3Mi1KIo+e67o9M0UlRE5JiMUKBt6bZ4ctyHo6REK24G4/33NTOPr8mXt98OFBaG1yZvu3bpdjYXFHsC7WAmNRrDQJu082vcuJZT1776auxUdAb0+N68ObS5CwDw1FMJW3rdg4G20zU06If4d99pSefXX4+p0s5JNw/FqJTZfpcZnTILrYbFcWnW6mpN0fj001ppKycHWLUq2q1KWBE5JiMUaFu6LStWaDGJN98MvUEjRugQkmAUFwNt2wKXXHL0Y+3a6bhtq1KhNZfaz6OgANi/P/B8yICOy50+Pfw84hT7duwA/vOf5h9PTQWuvdbak0e7hZtLO8EzjgAMtJ3v0CHt1Z45E/jkE/3CiZGJkACQOuEOjEl5FkXwXXK8CMsxOmUWUsePjXDLIig9XVOVbdyoH1Z79+oleoqKiByTV1+teZVtzpVr6bYUF+vEpYsuCr1B/ftrj3ZdXeDPueoqDc59lWbOy9PPwGACX39aCrSvvBJ4442jU//5k5ysFXyLisJvH8W2qVN1PHJzwywWLdKrPrEknEC7tlYzHjHQJkdLTdUvoAMHdEZ/cnJsVR/Lz0dG8RwsTB+MSSkT0Q1lSEYduqEMk1ImYmH6YGQUz4ntUtWB8FyS9nxYeU/6osiKxDF58cVaCdWqapPN8WxL6yswCROabMsDjdty4on+12OMBtqXXRZeIaX+/TWdlyeFWSCuv775DC2eXNpb/A+PCVhLgXbXrjrWPCMj8HXu3q3ba+VYcopNBQV6ktlcOs0HHgB++9vItilc4QTansI8DLTJ0UT0i+/77zXQPvvs4L4EnGDQIGSsL8GdI13YkN0PrqQ0bMjuhztHupCxviS0CVSxpqBAMx1s2aJDR2JkMmvcsvuYPHRIxztXVlrTXn8GDULGlRfjztRZTbblkG7LySfrpOrXX29+HatXA1u3BlekxhdPNp1A0/x98AGwbVvzj3tyaVsVaA8bBnz8sQ5Vac4nnwCLFwe+ztdeA3r31itVlNj8ZR6pq9Pqp7E0ERLQq6/XXhvaHATP/yHBA+3ErIcZazyB9lln6RjKWJSfj9Rpk4FpkwEAQWTojQ8FBTrjfOlSTppyCjuPydWrtcLhO+9E5kTy00+ReuUATV2HJtty4IAec9dfr48PHnz08//1L71a5uuxYHToAJx2mgaz993nf9m6OuDGGzVn9wsv+F4mL08z9LQ0wSxQ7do1Vp5szv33a9GcQE8Wysv1fxdOUQ+KD6edpr83bjx6zP7GjXoC3rt35NsVDhH/J+n+/PSnmmUlgVP7AQy0Y4Mn0H7ppWi3hEJ10UXA//2f9hpmZUW7NWS39u31dwQmROLwYf/ZObKzgXffBS69VHusX39dxyJ7u/9+nYxoxdyBt94K7GRy8WLNxOOvFz0jA5g8Ofw2ebz4InDCCf5zcxcUBBdYlJfr0BxPRUtKXJmZjfNxmlqzRn/HWo+2hzHB5/5u3Ro4/XR72hNDOHTEycrK4BozHjVfbkbDvxegJrsDXGPGx2+58nhVVgbXpKmouedBNDz+BGoem8z9GO88vZuRCLSTk4Hf/U4v7zbnmGM0l+0ZZ2gv07x5+tmS3QENSa1Qk3saXG8ssOaYPPnkwIZGFRdrYHLZZf6Xq6nRYjZWePBBYM4c/8sUFAAVFYHvu/JyzrmgRi++qLUvmlq7VjtZrLo6EyllZXCd0Qc1rTL1s6KlOMQTt2R3QIO0Qk1Gu4T/vmOg7VQLFqCqZxGmzkpDYc1KtIYLhQeXYeqsNFT1LNKSpuR83vvx4DK0NtyPCSE7WycxRyLQ/vxzHR7SkrZtdUz0pZei6tax9h2T9fU66euVV5pf5vBh7TW+6iqtAOnPzTdrb3y4Gho0YG+ptz3QCn8ezKFN3s4/3/dE6iefBJYvt3+CtJU8318bL0GhWd/yZ0XT7zu4UFi9kt93xpi4+Ondu7eJG6WlpjI9xxRhmdHrNUf+FGGZqUzPMaa0NNotJX+4HxPbiScac/vt9r/OyScbM3hwYMtG6phsqU2rVxsjYkxxccvrGj/emLQ0YxoawmvTzp26kdOm+V9u2zZd7umnA1vvwoXGLF8eXtsofuzYYcyMGcbs2hXtloQn2M+KBPy+A7DaBBCfxtCpVeJwPTUNT9eNQAl8p/ErQV/MqBsO15TpEW4ZBYP7McE9+STwi1/Y+xo7dmh2kwBz60fsmOzfXyf+NpdHvHdvbXvTseK+5OXp8JGKAMvIN6el1H4eJ50ErFwZ+L778Y+ZQ5sabdkCjB6tx5DHpk06D8Kq7DkRENBnxaHb4Xr4CWDzZrgeehxP1w3n950vgUTjsfATTz3a1VnHm24o9XlW6PnphlJTld0h2k0lP7gfyXZz5+qBtHp1QItH7JicM0dXtm5deOsxxpg33tB1rVoV3npefz2o/1VA/vc/bd/evdatk2Lb/v16nP3pT433Pf+83vfFF9FrV5AC/qxAmjGAqUabhPu+A3u0Y1dqZQW2INfvMlvRBW0qw+zhIVtxPya48nIta26nJUt0PHivXgEtHrFj0l8+7U8+0ep5mzYFti5P0ZrNm8Nr05VXao9iIOWvly3zPaGtqVWrdBLq11+H1zaKH8cco1dFvMf4r12rFYK7d49eu4IU8GeFHAKeew6pOMTvu2Yw0HYgV2YOcuH/ElMXbEVtZk6EWkSh4H5McJMmaUBppyVLdPJVgKnlInZM5uZq8F9dffRj8+Zpuz0pEFty8sma4q9nz/DalJICdOmi1XZbsmwZ8Ic/aPpBf1jplXzxVAL2WLtW3w8xlAIy4M+KrBzgllvgyuL3XXMYaDtQ0s1DMSpltt9lRqfMQqthQyPUIgoF92OCa99e89/bWZr7lVeARx8NePGIHpNr12r2EW8NDVocZ+DAwPPJZ2Zq0Zpwi148/7zmsg+EJ/PIl1/6X668XHMLd+wYXtsovhQU6LFjjB7zn34ac/mzg/2s4PedH4GML4mFn3gao52Is3fjEvdjYps5U3d0eXm0W9IoGsekd7aQZcv0hV54Ibh1bN4c/njv/v2NOf/8wJbdtEnb+cwz/pe77TZjOnYMr10Uf/bs0bHaxmgWm+OOM+bvf49um4LFrCMtAsdox7D8fGQUz8HC9MGYlDIR3VCGZNShG8owKWUiFqYPRkbxHN+5Osk5uB8Tm93VIf/5T+C114J7TiSPye++0569mTMb7ysu1iEcV18d3LruvBMYNiy89gST7zo3V/N7t5RLmzm0yZf27XWsNqDHR0VF+MdvpAX7WcHvu2Yx0HaqQYOQsb4Ed450YUN2P7iS0rAhux/uHOlCxvoSYNCgaLeQAsH9mLjsDrQfe+zIIDZQkTomjz0WOHhQS617FBQAd93VGIQEKi9PJzIaE1pbjAkuKE5KAnr0aHkC5rRpgQ9HocRRX6/VWufP19siWsE11gT7WcHvO5/EhPrB5TB9+vQxq1evjnYziIjUvn3Af/6jkxU7dLB23RUVGsj/8Y/Ab39r7bqtdNNNwKJFwM6dGmyEavJkYMIE/Z+2bRv88z3/r7/8BRg3LrDnHDyo48PDaTclrhNO0MqnIjoJ98EHo90ispiIrDHG9GlpOfZoExHZ4bjjgJ/9zPogG9BiMEDAhWqipn9/YPdu4JtvgPXrgcrK0NbjSfEXasGPXbs04AlmmEdWlv8gu7oamD4d+Pbb0NpE8a2gAPjiC+DVV7U4EyUsWwNtERkoIl+LSKmIPOBnuSEiYkSkj9d9PUVkuYh8ISIbRKSNnW0lIrLchx9qgGm1JUuANm2As8+2ft1W6toVLrRGTa++aDizF2radoRrzHigrCy49eTl6e9Qc2kXFgIuV3Bjw0tLdVzthg2+H9+yBbjjDs2lTeStrAyu/32PmpJ1aPj+AGr+8Upoxz3FBdsCbRFpBWA6gEEACgD8XEQKfCyXBeAuACu87ksG8CKAUcaY0wFcBKDOrrYSEdnixhu119Nq334L9O0bWE7oaFmwAFXXDsVUGYfC2lVojUMoPLwOU2eloapnEbBgQeDr6t5dewbPPTf09qSkAK1bB/ecF18EmhuSGGhJd0osCxagqmcRpm4cgEJ8rsd97arQjnuKC3aOzj8HQKkxZhMAiMg/AVwDoOk07kcBPAngHq/7LgOw3hjzGQAYY/ba2E4iInu0b2/PZMh//zv0YRiRUFaGqiG/wIDq+ShB3x/u3oR83Ff3OF6ruxoLhwzWCVKBZCHIzASGDAm9PS+9BKxcCfz1r4E/p2tXPZFpLvMIA21qyurjnuKCnUNHTgKwzet2ufu+H4jIWQA6G2PebvLcUwEYEXlPRNaKyH02tpOIyB52BdqABp8O5XpqGp6uG3FEsOGtBH0xo244XFOC6O1fuVInVobigw+CT4XYqhVw2mktB9onnhhamyju2HLcU8yzM9D2NYvkhxQnIpIEYAqACT6WSwZwPoCb3L9/IiI/PuoFREaKyGoRWf0/u77MiIhCZUeg/ac/AbfcEnqquwhoeHEuZtbd7neZGXXDUf/C3MBX+vDDwL33htagUPNdNy2l7W37dt2/bTh9iJQtxz3FPDsD7XIAnb1udwLgPfU2C0AhgMUishlAEYD57gmR5QCWGGMqjDHVAN4BcFT9UmPMM8aYPsaYPu09OWuJiJzCjkD7tdeATZscnXYutbICW5Drd5mt6II2lRWBrzQ3N/SsI6EG2r16afaROh9ThCZNApYvD609FJdsOe4p5tkZaK8CcIqIdBWR1gBuBDDf86Ax5ntjTI4xJs8YkwegBMBgY8xqAO8B6Cki6e6Jkf1x9NhuIiJnGzcOeOcd69Z34ACwdq3j0/q5MnOQC/9BcRdsRW1mTuArzc3VPNoHDwbXmGCL1Xi77z7NGpOScvRjWVkcZ0tHsOW4p5hnW6BtjDkM4A5o0PwlgHnGmC9E5BERGdzCc78DMBkarK8DsNYY82+72kpEZIvu3cPLlNHUJ59o1TmHB9pJNw/FqJTZfpcZnTILrYYNDXylnhR/wfZqV1drlcouXYJ7XkseeujIqpeU8Gw57inmsTIkEZFdduwA3n8fuPLKxpLs4XjgAeCpp4D9+4GMjPDXZ5eyMlT1LDoq+4JHEZZjYXqQ2RdKSjSl4VtvacW9SDAGGDgQuOQS4P77G++vqQHS04HHHgN+85vItIWcz47jnhyLlSGJiKLtq6+AW2/VCnFW6NhRy5o7OcgGgPx8ZBTPwcL0wZiUMhHdUIZk1KEbyjApZaIGG8Vzggs2zjhDg+1I9uaL6LCTpmOxt2/X30ztR97sOO4p5jHQJiKyi6cX26oJkePGAf/4hzXrstugQchYX4I7R7qwIbsfXElp2JDdD3eOdGmP3qBBwa0vI0OH4WRlBfe8N98ErrkG+O674J7n4SvzCHNoU3OsPu4p5tlZsIaIKLFZGWhXVmoqueQY+tjOz0fqtMnAtMkAgPRw1zd/PnDoUHDFa9at0+EmoV4FKCjQTC+1tY2p/Bhokz9WH/cU09ijTURkl3bt9LcVgfaf/6yBe21t+OuKVdOmAU8+GdxzysuBDh2CL7/uUVAANDQA33zTeN+uXfr7pJN8P4eIyI2BNhGRXVJSgLZtrQm0lywBTjklsQuk5OUFn3Uk1NR+HmeeCVx+uWZ78bjnHuD7750/Vp6Ioi6GrkESEcWgjz7SHtVw1NQAK1YAd91lTZtiVW4usGePpuxLD/CCfHk5cPLJob/maacB77579P3Z2aGvk4gSBnu0iYjsVFgYfmq/FSt0bLLD82fbLtdddW/r1sCf07GjZiwJl3eP9u9+Bzz3XPjrJKK4x0CbiMhOH3wAPPNMeOtYskRTzZ1/vjVtilWeojWbNwf+nPffBx55JLzXHT1ay7F7zJrF8utEFBAOHSEislNxsaaYGzky9HVcdZVOrDz2WOvaFYv69NEc1iecENnXPe44zYl+6JDe3r2bGUeIKCDs0SYislP79kBFhWauCFXv3sAdd1jXpljVpg1w4olAUoBfXR9/DPzoR8Dnn4f3ugUFwOHDQGmpVvsEGGgTUUAYaBMR2SknR8f37t8f2vP/+19g4cLG3tREN3Mm8OyzgS1bVgZ8+imQlhbeaxYU6O+NG5lDm4iCwkCbiMhO4RatmTsXuPRS4MAB69oUy+bNC3wioicoDjffdffuOkZ+40ZN65edzUCbiALCQJuIyE7hBtpLlmjmkpwc69oUy3JzA8+lXV6u/7dwc4+npwP33qtDeK68UoPtHj3CWycRJQROhiQistOFF2olwVAC5bo6YNky4JZbLG9WzMrL03HSLheQmup/2XCL1Xj785+PvC1izXqJKK4x0CYislObNqH3qK5ZA1RVMX+2N0+Kv23bWi5E06OHFpyxQkODvuazz+oJUNPAm4jIBwbaRER2MgZ46CGgqAgYNCi45y5dqr8ZaDfKzQWSk4GdO1sOtJ980rrXffFF4Je/BNq21TSDREQB4BhtIiK7lJXBNfbXqHlkEhquuAo12R3gGjNes2H4e86Y8ajJ7oCG+x9ATUYOXA894f85ieSCC4DaWv0dKWVlcM1/DzVog4bv9qNm8YqW9yMRERhoExHZY8ECVPUswtRZaSjEBrSGC4UHl2HqrDRU9SwCFizw/5yDy9DauFBYVeL/OYmmVSv9aUlZmU5EffPN8F7Ps0/md0EhPkdrHEJh3VruEyIKiBhjot0GS/Tp08esXr062s0gIgLKylDVswgDquejBH2PergIy7EwfTAy1pcA+fmhPydRPfigVsmcMKH5ZZYsAS66CPjgA2DAgNBeh/uEiJohImuMMS2OI2OPNhGRxVxPTcPTdSN8BmcAUIK+mFE3HK4p08N6TsJasgSYP9//Mp4c2p07h/wy3CdEFC4G2kREFmt4cS5m1t3ud5kZdcNRP/sfwFNPAbNmBf6cF+Za2NIYlZsLbN7sfxkLitVwnxBRuJh1hIjIYqmVFdiCXL/LbEUXtKndD9xzD9C1a+DPqaywsqmxKS8PePll4PBhzUDiS3m5Di/JzAz5ZbhPiChc7NEmIrKYKzMHufBfvbALtqI2q72WVl+/PvDnZLJCJHJzgfr6xl5rX370o7AL/XCfEFG4GGgTEVks6eahGJUy2+8yo1NmodUvbgKysoDMzMCfM2yolU2NTd26abC9f3/zy9x6KzBlSlgvw31CROFi1hEiIqsx60j0BVKivSXcJ0TUDGYdISKKlvx8ZBTPwcL0wZiUMhHdUIZk1KEbyjApZaIGZ8VzjgzOQnkO+XboEJCWBjzxRHjr4T4hojAx0CYissOgQchYX4I7R7qwIbsfXElp2JDdD3eOdGkPqK9y7KE8J1Hdcgvwhz/4fmznTsAYLVgTLu4TIgoDh44QEVHsOe887bVetOjoxz75BDj/fK3aOHBg5NtGRHGPQ0eIiCh++cul7clG0qlTxJpDROQLA20iIoo9eXnAtm2a5q8pBtpE5BAMtImIKPbk5gJ1dToeu6nevbUQ0DHHRL5dRERebA20RWSgiHwtIqUi8oCf5YaIiBGRPu7beSJSIyLr3D8z7WwnERHFmB49gH79gOrqox+76CJg0iRAJOLNIiLyZlsJdhFpBWA6gEsBlANYJSLzjTEbmyyXBeAuACuarKLMGNPLrvYREVEM698lO0O/AAAMKklEQVQf+Phj34+VlwPt2ulkSSKiKLKzR/scAKXGmE3GmEMA/gngGh/LPQrgSQC1NraFiIgSxXnnAWPGRLsVRES2BtonAdjmdbvcfd8PROQsAJ2NMW/7eH5XEflURJaIyAU2tpOIiGLRj38M/PrXR95XXw/s2MGJkETkCLYNHQHga3DcD0m7RSQJwBQAt/hYbieALsaYvSLSG8AbInK6MebAES8gMhLASADo0qWLVe0mIqJYcPAg8MUXR963a5cG2yed5Ps5REQRZGePdjmAzl63OwHY4XU7C0AhgMUishlAEYD5ItLHGOMyxuwFAGPMGgBlAE5t+gLGmGeMMX2MMX3aW1EBjIiIYoevXNpM7UdEDmJnoL0KwCki0lVEWgO4EcB8z4PGmO+NMTnGmDxjTB6AEgCDjTGrRaS9ezIlRKQbgFMAbLKxrUREFGvy8oCtW7XcugcDbSJyENsCbWPMYQB3AHgPwJcA5hljvhCRR0RkcAtPvxDAehH5DEAxgFHGmH12tZWIiGJQbi5QWwvs2dN43xlnAJMnA926Ra9dRERudo7RhjHmHQDvNLnv980se5HX3/8C8C8720ZERDHurLOAG2/UwjUep56qP0REDmBroE1ERGSbfv30x9vGjUB6ug4rISKKMgbaREQU2w4fBpLdX2ejRgFJScDixVFtEhERYHMJdiIiIlvl5wPjxjXeLi/nREgicgwG2kREFLuysxtT/DU0ANu3M9AmIsdgoE1ERLErLw/YskX/rqgADh1ioE1EjsFAm4iIYldurgbaxgDbtul9DLSJyCEYaBMRUezKywMqK4F9+4CuXYFXXwXOPTfarSIiAsCsI0REFMvOOw+4917t0W7XDhgyJNotIiL6AQNtIiKKXeecoz8A8OmnwMGDwIUXRrdNRERuDLSJiCi2VVbqJMjJk4GlSxuzkBARRRkDbSIiil3GACeeCNx6K3NoE5HjcDIkERHFLpHGzCMMtInIYRhoExFRbMvL0+EiDLSJyGEYaBMRUWzLzQU++wyorWWgTUSOwjHaREQU2/Ly9Pe77wKnnx7VphAReWOPNhERxa6yMrhWfoaa1GPRMOgK1BT0hmvMeKCsLNotIyJioE1ERDFqwQJU9SzC1Dc6o9C1Gq2NC4UHl2HqrDRU9SwCFiyIdguJKMGJMSbabbBEnz59zOrVq6PdDCIiioSyMlT1LMKA6vkoQd+jHi7CcixMH4yM9SVAfn4UGkhE8UxE1hhj+rS0HHu0iYgo5riemoan60b4DLIBoAR9MaNuOFxTpke4ZUREjRhoExFRzGl4cS5m1t3ud5kZdcNR/8LcCLWIiOhoDLSJiCjmpFZWYAty/S6zFV3QprIiQi0iIjoaA20iIoo5rswc5GKL32W6YCtqM3Mi1CIioqMx0CYiopiTdPNQjEqZ7XeZ0Smz0GrY0Ai1iIjoaAy0iYgo5qROuANjUp5FEZb7fLwIyzE6ZRZSx4+NcMuIiBox0CYiotiTn4+M4jlYmD4Yk1ImohvKkIw6dEMZJqVM1NR+xXOY2o+IooqBNhERxaZBg5CxvgR3jnRhQ3Y/uJLSsCG7H+4c6dL82YMGRbuFRJTgWLCGiIiIiCgILFhDRERERBRFDLSJiIiIiGzAQJuIiIiIyAYMtImIiIiIbGBroC0iA0XkaxEpFZEH/Cw3RESMiPRpcn8XEakUkXvsbCcRERERkdVsC7RFpBWA6QAGASgA8HMRKfCxXBaAuwCs8LGaKQAW2NVGIiIiIiK72NmjfQ6AUmPMJmPMIQD/BHCNj+UeBfAkgFrvO0XkWgCbAHxhYxuJiIiIiGxhZ6B9EoBtXrfL3ff9QETOAtDZGPN2k/szANwP4GEb20dEREREZJtkG9ctPu77oTqOiCRBh4bc4mO5hwFMMcZUivhazQ/rGAlgpPtmpYh8HXJrG+UAqLBgPRR7uO8TF/d94uK+T0zc74nLqn2fG8hCdgba5QA6e93uBGCH1+0sAIUAFruD6RMAzBeRwQDOBTBERJ4EcCyABhGpNcZM834BY8wzAJ6xstEisjqQSj8Uf7jvExf3feLivk9M3O+JK9L73s5AexWAU0SkK4DtAG4EMNTzoDHme+hZBQBARBYDuMcYsxrABV73PwSgsmmQTURERETkZLaN0TbGHAZwB4D3AHwJYJ4x5gsRecTda01EREREFLfs7NGGMeYdAO80ue/3zSx7UTP3P2R5w/yzdCgKxRTu+8TFfZ+4uO8TE/d74orovhdjTMtLERERERFRUFiCnYiIiIjIBgy0vQRaMp5in4j8XUT2iMjnXvcdJyIfiMi37t9to9lGsp6IdBaR/4jIlyLyhYiMc9/PfR/nRKSNiKwUkc/c+/5h9/1dRWSFe9+/IiKto91WsoeItBKRT0Xkbfdt7vsEICKbRWSDiKwTkdXu+yL2mc9A2y3QkvEUN/4BYGCT+x4AsMgYcwqARe7bFF8OA5hgjOkBoAjAWPf7nPs+/rkAXGKMORNALwADRaQIwJ+hdRtOAfAdgNuj2Eay1zhocgYP7vvEcbExppdXWr+IfeYz0G4UaMl4igPGmI8A7Gty9zUAnnf//TyAayPaKLKdMWanMWat+++D0C/dk8B9H/eMqnTfTHH/GACXACh23899H6dEpBOAKwHMct8WcN8nsoh95jPQbtRiyXiKex2MMTsBDcgAHB/l9pCNRCQPwFkAVoD7PiG4hw6sA7AHwAcAygDsd6ejBfi5H8/+AuA+AA3u2+3AfZ8oDID3RWSNu6I4EMHPfFvT+8UYvyXjiSh+iEgmgH8BuNsYc8BdnZbinDGmHkAvETkWwOsAevhaLLKtIruJyFUA9hhj1ojIRZ67fSzKfR+f+hljdojI8QA+EJGvIvni7NFu1FLJeIp/u0WkIwC4f++JcnvIBiKSAg2yXzLGvOa+m/s+gRhj9gNYDB2nf6yIeDqd+Lkfn/oBGCwim6HDQi+B9nBz3ycAY8wO9+890BPscxDBz3wG2o1+KBnvnnl8I4D5UW4TRdZ8AL90//1LAG9GsS1kA/e4zNkAvjTGTPZ6iPs+zolIe3dPNkQkDcAA6Bj9/wAY4l6M+z4OGWMmGmM6GWPyoN/tHxpjbgL3fdwTkQwRyfL8DeAyAJ8jgp/5LFjjRUSugJ7ltgLwd2PMY1FuEtlERF4GcBGAHAC7AfwBwBsA5gHoAmArgOuMMU0nTFIME5HzASwFsAGNYzV/Ax2nzX0fx0SkJ3TSUytoJ9M8Y8wjItIN2st5HIBPAdxsjHFFr6VkJ/fQkXuMMVdx38c/9z5+3X0zGcBcY8xjItIOEfrMZ6BNRERERGQDDh0hIiIiIrIBA20iIiIiIhsw0CYiIiIisgEDbSIiIiIiGzDQJiIiIiKyAQNtIqIYJCKVXn9fISLfikgXr/vyRKRcRJKaPG+diJzjZ723iMg0e1pNRJRYGGgTEcUwEfkxgL8BGGiM2eq53xizGcA2ABd4LXsagCxjzMpIt5OIKBEx0CYiilEicgGAZwFcaYwp87HIy9BKeB43uu+DiFwtIitE5FMRWSgiHXys/x8iMsTrtncv+r0iskpE1ovIw1ZtExFRPGGgTUQUm1KhZYOvNcZ81cwy8wBcKyLJ7ts3QCvhAcDHAIqMMWe577sv0BcWkcsAnALgHAC9APQWkQuD3wQioviW3PIiRETkQHUAlgG4HcA4XwsYY3aJyBcAfiwiuwHUGWM+dz/cCcArItIRQGsA/w3itS9z/3zqvp0JDbw/CnoriIjiGHu0iYhiUwOA6wGcLSK/8bOcZ/jID8NG3P4GYJox5gwAvwLQxsdzD8P9PSEiAg3IAUAAPGGM6eX+OdkYMzusrSEiikMMtImIYpQxphrAVQBuEpHbm1nsXwCuwJHDRgDgGADb3X//spnnbgbQ2/33NQBS3H+/B+A2EckEABE5SUSOD2UbiIjiGYeOEBHFMGPMPhEZCOAjEakwxrzZ5PH9IlICoIMxxnt4yEMAXhWR7QBKAHT1sfpnAbwpIisBLAJQ5V7n+yLSA8By7ehGJYCbAeyxduuIiGKbGGOi3QYiIiIiorjDoSNERERERDZgoE1EREREZAMG2kRERERENmCgTURERERkAwbaREREREQ2YKBNRERERGQDBtpERERERDZgoE1EREREZIP/DwmNxOGCiL9uAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12, 6))\n",
    "plt.plot(range(1, 50), error, color='red', linestyle='dashed', marker='o',\n",
    "         markerfacecolor='blue', markersize=10)\n",
    "plt.title('Error Rate K Value')\n",
    "plt.xlabel('K Value')\n",
    "plt.ylabel('Mean Error')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Predict by KNN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "classifierKNN = KNeighborsClassifier(n_neighbors=29)\n",
    "classifierKNN.fit(X_train, y_train)\n",
    "y_predKNN = classifierKNN.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import classification_report, confusion_matrix\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cm=confusion_matrix(y_test,y_predKNN)\n",
    "plt.figure(figsize=(12,6))\n",
    "plt.title(\"Confusion Matrix\")\n",
    "sns.heatmap(cm, annot=True,fmt='d', cmap='Blues',linewidths=5,linecolor='black', cbar=False )\n",
    "plt.ylabel(\"Actual Values\")\n",
    "plt.xlabel(\"Predicted Values\")\n",
    "plt.savefig('confusion_matrix.png')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The Accuracy for Test Set is 55.62700964630225\n"
     ]
    }
   ],
   "source": [
    "test_acc = accuracy_score(y_test, y_predKNN)\n",
    "print(\"The Accuracy for Test Set is {}\".format(test_acc*100))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.56      0.59      0.57       157\n",
      "           1       0.56      0.52      0.54       154\n",
      "\n",
      "   micro avg       0.56      0.56      0.56       311\n",
      "   macro avg       0.56      0.56      0.56       311\n",
      "weighted avg       0.56      0.56      0.56       311\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, y_predKNN))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Decision Tree for Classification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "classifierDT = DecisionTreeClassifier()\n",
    "classifierDT.fit(X_train, y_train)\n",
    "y_predDT = classifierDT.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cm=confusion_matrix(y_test,y_predDT)\n",
    "plt.figure(figsize=(12,6))\n",
    "plt.title(\"Confusion Matrix\")\n",
    "sns.heatmap(cm, annot=True,fmt='d', cmap='Purples',linewidths=5,linecolor='black', cbar=False)\n",
    "plt.ylabel(\"Actual Values\")\n",
    "plt.xlabel(\"Predicted Values\")\n",
    "plt.savefig('confusion_matrix.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.49      0.56      0.53       157\n",
      "           1       0.48      0.42      0.45       154\n",
      "\n",
      "   micro avg       0.49      0.49      0.49       311\n",
      "   macro avg       0.49      0.49      0.49       311\n",
      "weighted avg       0.49      0.49      0.49       311\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, y_predDT))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The Accuracy for Test Set is 48.87459807073955\n"
     ]
    }
   ],
   "source": [
    "test_acc = accuracy_score(y_test, y_predDT)\n",
    "print(\"The Accuracy for Test Set is {}\".format(test_acc*100))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Random Forest Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "from  sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.model_selection import cross_val_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "classifierRF = RandomForestClassifier( random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    " grid_param = {\n",
    "    'n_estimators':[20,50,100,150,200,250],\n",
    "    'criterion': ['gini', 'entropy'],\n",
    "    'bootstrap': [True, False],\n",
    "    'max_depth':[3,5,6,10]\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "gd_sr = GridSearchCV(estimator=classifierRF,\n",
    "                     param_grid=grid_param,\n",
    "                     scoring='accuracy',\n",
    "                     cv=5,\n",
    "                     n_jobs=-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5, error_score='raise-deprecating',\n",
       "       estimator=RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
       "            max_depth=None, max_features='auto', max_leaf_nodes=None,\n",
       "            min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "            min_samples_leaf=1, min_samples_split=2,\n",
       "            min_weight_fraction_leaf=0.0, n_estimators='warn', n_jobs=None,\n",
       "            oob_score=False, random_state=0, verbose=0, warm_start=False),\n",
       "       fit_params=None, iid='warn', n_jobs=-1,\n",
       "       param_grid={'n_estimators': [20, 50, 100, 150, 200, 250], 'criterion': ['gini', 'entropy'], 'bootstrap': [True, False], 'max_depth': [3, 5, 6, 10]},\n",
       "       pre_dispatch='2*n_jobs', refit=True, return_train_score='warn',\n",
       "       scoring='accuracy', verbose=0)"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gd_sr.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'bootstrap': True, 'criterion': 'entropy', 'max_depth': 5, 'n_estimators': 150}\n"
     ]
    }
   ],
   "source": [
    "best_parameters = gd_sr.best_params_\n",
    "print(best_parameters)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Predict : RandomForest "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_predRF = gd_sr.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cm=confusion_matrix(y_test,y_predRF)\n",
    "plt.figure(figsize=(12,6))\n",
    "plt.title(\"Confusion Matrix\")\n",
    "sns.heatmap(cm, annot=True,fmt='d', cmap='Greens',linewidths=5,linecolor='black', cbar=False)\n",
    "plt.ylabel(\"Actual Values\")\n",
    "plt.xlabel(\"Predicted Values\")\n",
    "plt.savefig('confusion_matrix.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The Accuracy for Test Set is 51.76848874598071\n"
     ]
    }
   ],
   "source": [
    "test_acc = accuracy_score(y_test, y_predRF)\n",
    "print(\"The Accuracy for Test Set is {}\".format(test_acc*100))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.52      0.56      0.54       157\n",
      "           1       0.51      0.47      0.49       154\n",
      "\n",
      "   micro avg       0.52      0.52      0.52       311\n",
      "   macro avg       0.52      0.52      0.52       311\n",
      "weighted avg       0.52      0.52      0.52       311\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, y_predRF)) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Support Vector Machines (SVM)  classifiers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.svm import SVC\n",
    "from sklearn import svm"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Choose the best parameter for SVM and Training the Algorithm "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'C': 1.0, 'gamma': 'scale', 'kernel': 'rbf'}\n"
     ]
    }
   ],
   "source": [
    "classifierSVM = svm.SVC() \n",
    "# defining parameter range \n",
    "param_grid_svm = {'C': [0.001, 0.01, 0.05, 0.1, 0.5, 1.0, 10.0, 100],  \n",
    "              'gamma':['scale', 'auto'],\n",
    "              'kernel': ['linear','rbf','sigmoid']}  \n",
    "   \n",
    "gridSVM = GridSearchCV(estimator=classifierSVM,\n",
    "                     param_grid = param_grid_svm,\n",
    "                     scoring='accuracy',\n",
    "                     cv=10,\n",
    "                     refit=True,\n",
    "                     n_jobs=-1)\n",
    "   \n",
    "# fitting the model for grid search \n",
    "gridSVM.fit(X_train, y_train) \n",
    " \n",
    "# print best parameter after tuning \n",
    "print(gridSVM.best_params_) \n",
    "grid_predictions = gridSVM.predict(X_test) \n",
    "   \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# print classification report : Support Vector"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cm=confusion_matrix(y_test,grid_predictions)\n",
    "plt.figure(figsize=(12,6))\n",
    "plt.title(\"Confusion Matrix : Support Vector\")\n",
    "sns.heatmap(cm, annot=True,fmt='d', cmap='Reds',linewidths=5,linecolor='black',cbar=False)\n",
    "plt.ylabel(\"Actual Values\")\n",
    "plt.xlabel(\"Predicted Values\")\n",
    "plt.savefig('confusion_matrix.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The Accuracy for Test Set is 52.73311897106109\n"
     ]
    }
   ],
   "source": [
    "test_acc = accuracy_score(y_test, grid_predictions)\n",
    "print(\"The Accuracy for Test Set is {}\".format(test_acc*100))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 216,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.53      0.64      0.58       157\n",
      "           1       0.53      0.42      0.47       154\n",
      "\n",
      "   micro avg       0.53      0.53      0.53       311\n",
      "   macro avg       0.53      0.53      0.52       311\n",
      "weighted avg       0.53      0.53      0.52       311\n",
      "\n"
     ]
    }
   ],
   "source": [
    "\n",
    "print(classification_report(y_test, grid_predictions)) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Results table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 308,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Accuracy for Test Set</th>\n",
       "      <th>Accuracy for  Predicting 0</th>\n",
       "      <th>Accuracy for  Predicting 1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>KNN</th>\n",
       "      <td>55.63</td>\n",
       "      <td>0.59</td>\n",
       "      <td>0.52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DecisionTree</th>\n",
       "      <td>48.87</td>\n",
       "      <td>0.56</td>\n",
       "      <td>0.42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RandomForest</th>\n",
       "      <td>51.77</td>\n",
       "      <td>0.56</td>\n",
       "      <td>0.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SVM</th>\n",
       "      <td>52.73</td>\n",
       "      <td>0.64</td>\n",
       "      <td>0.42</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Accuracy for Test Set  Accuracy for  Predicting 0  \\\n",
       "KNN                           55.63                       0.59   \n",
       "DecisionTree                  48.87                       0.56   \n",
       "RandomForest                  51.77                       0.56   \n",
       "SVM                           52.73                       0.64   \n",
       "\n",
       "             Accuracy for  Predicting 1  \n",
       "KNN                               0.52   \n",
       "DecisionTree                      0.42   \n",
       "RandomForest                      0.47   \n",
       "SVM                               0.42   "
      ]
     },
     "execution_count": 308,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "index= ['KNN', 'DecisionTree', 'RandomForest', 'SVM']\n",
    "cols = ['Accuracy for Test Set ', 'Accuracy for  Predicting 0', 'Accuracy for  Predicting 1']\n",
    "\n",
    "\n",
    "\n",
    "df = pd.DataFrame(np.array([  \n",
    "    \n",
    "                                 [round(accuracy_score(y_test, y_predKNN)*100,2),classification_report(y_test, y_predKNN)[84:88],(classification_report(y_test, y_predKNN)[138:143])]\n",
    "                            \n",
    "                                ,[ round(accuracy_score(y_test, y_predDT)*100,2),classification_report(y_test, y_predDT)[84:88], \n",
    "                                  classification_report(y_test, y_predDT)[138:143]]\n",
    "                            \n",
    "                             , [ round(accuracy_score(y_test, y_predRF)*100,2),classification_report(y_test, y_predRF)[84:88],\n",
    "                                classification_report(y_test, y_predRF)[138:143]]\n",
    "                            \n",
    "                             ,  [ round(accuracy_score(y_test, grid_predictions)*100,2),classification_report(y_test, grid_predictions)[84:88], \n",
    "                                 classification_report(y_test, grid_predictions)[138:143]]\n",
    "                       ]),index=index, columns=cols)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.12"
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 },
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