machine-learning-with-classification-algorithms.ipynb

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    "# INTRODUCTION\n",
    "\n",
    "\n",
    "<br>Content:\n",
    "1. [İmport Data](#1)\n",
    "1. [Normalization](#2)\n",
    "1. [Logistic Regression Classification](#3)\n",
    "1. [K-Nearest Neighbors Classification](#4)\n",
    "1. [Naive Bayes Classification](#5)\n",
    "1. [Random Forrest Classification](#6)\n",
    "1. [Support Vector Machines Classification](#7)\n",
    "1. [Decision Tree Classification](#8)\n",
    "\n"
   ]
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   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
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   "source": [
    "# İmport Data\n",
    "<a id=\"1\"></a>"
   ]
  },
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   "source": [
    "data=pd.read_csv(\"/kaggle/input/biomechanical-features-of-orthopedic-patients/column_2C_weka.csv\")"
   ]
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pelvic_incidence</th>\n",
       "      <th>pelvic_tilt numeric</th>\n",
       "      <th>lumbar_lordosis_angle</th>\n",
       "      <th>sacral_slope</th>\n",
       "      <th>pelvic_radius</th>\n",
       "      <th>degree_spondylolisthesis</th>\n",
       "      <th>class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>63.027817</td>\n",
       "      <td>22.552586</td>\n",
       "      <td>39.609117</td>\n",
       "      <td>40.475232</td>\n",
       "      <td>98.672917</td>\n",
       "      <td>-0.254400</td>\n",
       "      <td>Abnormal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>39.056951</td>\n",
       "      <td>10.060991</td>\n",
       "      <td>25.015378</td>\n",
       "      <td>28.995960</td>\n",
       "      <td>114.405425</td>\n",
       "      <td>4.564259</td>\n",
       "      <td>Abnormal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>68.832021</td>\n",
       "      <td>22.218482</td>\n",
       "      <td>50.092194</td>\n",
       "      <td>46.613539</td>\n",
       "      <td>105.985135</td>\n",
       "      <td>-3.530317</td>\n",
       "      <td>Abnormal</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>69.297008</td>\n",
       "      <td>24.652878</td>\n",
       "      <td>44.311238</td>\n",
       "      <td>44.644130</td>\n",
       "      <td>101.868495</td>\n",
       "      <td>11.211523</td>\n",
       "      <td>Abnormal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>49.712859</td>\n",
       "      <td>9.652075</td>\n",
       "      <td>28.317406</td>\n",
       "      <td>40.060784</td>\n",
       "      <td>108.168725</td>\n",
       "      <td>7.918501</td>\n",
       "      <td>Abnormal</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
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       "   pelvic_incidence  pelvic_tilt numeric  lumbar_lordosis_angle  sacral_slope  \\\n",
       "0         63.027817            22.552586              39.609117     40.475232   \n",
       "1         39.056951            10.060991              25.015378     28.995960   \n",
       "2         68.832021            22.218482              50.092194     46.613539   \n",
       "3         69.297008            24.652878              44.311238     44.644130   \n",
       "4         49.712859             9.652075              28.317406     40.060784   \n",
       "\n",
       "   pelvic_radius  degree_spondylolisthesis     class  \n",
       "0      98.672917                 -0.254400  Abnormal  \n",
       "1     114.405425                  4.564259  Abnormal  \n",
       "2     105.985135                 -3.530317  Abnormal  \n",
       "3     101.868495                 11.211523  Abnormal  \n",
       "4     108.168725                  7.918501  Abnormal  "
      ]
     },
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   "source": [
    "data.head()"
   ]
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    "data[\"class\"]=[1 if each ==\"Abnormal\" else 0 for each in data[\"class\"] ]"
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       "      <th></th>\n",
       "      <th>pelvic_incidence</th>\n",
       "      <th>pelvic_tilt numeric</th>\n",
       "      <th>lumbar_lordosis_angle</th>\n",
       "      <th>sacral_slope</th>\n",
       "      <th>pelvic_radius</th>\n",
       "      <th>degree_spondylolisthesis</th>\n",
       "      <th>class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>63.027817</td>\n",
       "      <td>22.552586</td>\n",
       "      <td>39.609117</td>\n",
       "      <td>40.475232</td>\n",
       "      <td>98.672917</td>\n",
       "      <td>-0.254400</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>39.056951</td>\n",
       "      <td>10.060991</td>\n",
       "      <td>25.015378</td>\n",
       "      <td>28.995960</td>\n",
       "      <td>114.405425</td>\n",
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       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>68.832021</td>\n",
       "      <td>22.218482</td>\n",
       "      <td>50.092194</td>\n",
       "      <td>46.613539</td>\n",
       "      <td>105.985135</td>\n",
       "      <td>-3.530317</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
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       "      <td>24.652878</td>\n",
       "      <td>44.311238</td>\n",
       "      <td>44.644130</td>\n",
       "      <td>101.868495</td>\n",
       "      <td>11.211523</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>49.712859</td>\n",
       "      <td>9.652075</td>\n",
       "      <td>28.317406</td>\n",
       "      <td>40.060784</td>\n",
       "      <td>108.168725</td>\n",
       "      <td>7.918501</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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       "   pelvic_incidence  pelvic_tilt numeric  lumbar_lordosis_angle  sacral_slope  \\\n",
       "0         63.027817            22.552586              39.609117     40.475232   \n",
       "1         39.056951            10.060991              25.015378     28.995960   \n",
       "2         68.832021            22.218482              50.092194     46.613539   \n",
       "3         69.297008            24.652878              44.311238     44.644130   \n",
       "4         49.712859             9.652075              28.317406     40.060784   \n",
       "\n",
       "   pelvic_radius  degree_spondylolisthesis  class  \n",
       "0      98.672917                 -0.254400      1  \n",
       "1     114.405425                  4.564259      1  \n",
       "2     105.985135                 -3.530317      1  \n",
       "3     101.868495                 11.211523      1  \n",
       "4     108.168725                  7.918501      1  "
      ]
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   "source": [
    "data.head()"
   ]
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>pelvic_incidence</th>\n",
       "      <th>pelvic_tilt numeric</th>\n",
       "      <th>lumbar_lordosis_angle</th>\n",
       "      <th>sacral_slope</th>\n",
       "      <th>pelvic_radius</th>\n",
       "      <th>degree_spondylolisthesis</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>63.027817</td>\n",
       "      <td>22.552586</td>\n",
       "      <td>39.609117</td>\n",
       "      <td>40.475232</td>\n",
       "      <td>98.672917</td>\n",
       "      <td>-0.254400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>39.056951</td>\n",
       "      <td>10.060991</td>\n",
       "      <td>25.015378</td>\n",
       "      <td>28.995960</td>\n",
       "      <td>114.405425</td>\n",
       "      <td>4.564259</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>68.832021</td>\n",
       "      <td>22.218482</td>\n",
       "      <td>50.092194</td>\n",
       "      <td>46.613539</td>\n",
       "      <td>105.985135</td>\n",
       "      <td>-3.530317</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>69.297008</td>\n",
       "      <td>24.652878</td>\n",
       "      <td>44.311238</td>\n",
       "      <td>44.644130</td>\n",
       "      <td>101.868495</td>\n",
       "      <td>11.211523</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>49.712859</td>\n",
       "      <td>9.652075</td>\n",
       "      <td>28.317406</td>\n",
       "      <td>40.060784</td>\n",
       "      <td>108.168725</td>\n",
       "      <td>7.918501</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      "text/plain": [
       "   pelvic_incidence  pelvic_tilt numeric  lumbar_lordosis_angle  sacral_slope  \\\n",
       "0         63.027817            22.552586              39.609117     40.475232   \n",
       "1         39.056951            10.060991              25.015378     28.995960   \n",
       "2         68.832021            22.218482              50.092194     46.613539   \n",
       "3         69.297008            24.652878              44.311238     44.644130   \n",
       "4         49.712859             9.652075              28.317406     40.060784   \n",
       "\n",
       "   pelvic_radius  degree_spondylolisthesis  \n",
       "0      98.672917                 -0.254400  \n",
       "1     114.405425                  4.564259  \n",
       "2     105.985135                 -3.530317  \n",
       "3     101.868495                 11.211523  \n",
       "4     108.168725                  7.918501  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y=data[\"class\"].values\n",
    "x_data= data.drop([\"class\"],axis=1)\n",
    "x_data.head()\n",
    "\n"
   ]
  },
  {
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   "id": "a0e21e6f",
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    "tags": []
   },
   "source": [
    "# Normalization\n",
    "\n",
    "<a id=\"2\"></a>"
   ]
  },
  {
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   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.10/site-packages/numpy/core/fromnumeric.py:84: FutureWarning: In a future version, DataFrame.min(axis=None) will return a scalar min over the entire DataFrame. To retain the old behavior, use 'frame.min(axis=0)' or just 'frame.min()'\n",
      "  return reduction(axis=axis, out=out, **passkwargs)\n",
      "/opt/conda/lib/python3.10/site-packages/numpy/core/fromnumeric.py:84: FutureWarning: In a future version, DataFrame.max(axis=None) will return a scalar max over the entire DataFrame. To retain the old behavior, use 'frame.max(axis=0)' or just 'frame.max()'\n",
      "  return reduction(axis=axis, out=out, **passkwargs)\n",
      "/opt/conda/lib/python3.10/site-packages/numpy/core/fromnumeric.py:84: FutureWarning: In a future version, DataFrame.min(axis=None) will return a scalar min over the entire DataFrame. To retain the old behavior, use 'frame.min(axis=0)' or just 'frame.min()'\n",
      "  return reduction(axis=axis, out=out, **passkwargs)\n"
     ]
    }
   ],
   "source": [
    "x=(x_data - np.min(x_data))/(np.max(x_data)- np.min(x_data))"
   ]
  },
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    "execution": {
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     "iopub.status.busy": "2023-10-27T09:25:15.302897Z",
     "iopub.status.idle": "2023-10-27T09:25:15.316964Z",
     "shell.execute_reply": "2023-10-27T09:25:15.316114Z"
    },
    "papermill": {
     "duration": 0.029325,
     "end_time": "2023-10-27T09:25:15.319349",
     "exception": false,
     "start_time": "2023-10-27T09:25:15.290024",
     "status": "completed"
    },
    "tags": []
   },
   "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>pelvic_incidence</th>\n",
       "      <th>pelvic_tilt numeric</th>\n",
       "      <th>lumbar_lordosis_angle</th>\n",
       "      <th>sacral_slope</th>\n",
       "      <th>pelvic_radius</th>\n",
       "      <th>degree_spondylolisthesis</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.355688</td>\n",
       "      <td>0.519900</td>\n",
       "      <td>0.229180</td>\n",
       "      <td>0.250857</td>\n",
       "      <td>0.307461</td>\n",
       "      <td>0.025148</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.124501</td>\n",
       "      <td>0.296783</td>\n",
       "      <td>0.098578</td>\n",
       "      <td>0.144629</td>\n",
       "      <td>0.476649</td>\n",
       "      <td>0.036365</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.411666</td>\n",
       "      <td>0.513932</td>\n",
       "      <td>0.322995</td>\n",
       "      <td>0.307661</td>\n",
       "      <td>0.386097</td>\n",
       "      <td>0.017523</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.416151</td>\n",
       "      <td>0.557414</td>\n",
       "      <td>0.271260</td>\n",
       "      <td>0.289436</td>\n",
       "      <td>0.341826</td>\n",
       "      <td>0.051838</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.227272</td>\n",
       "      <td>0.289479</td>\n",
       "      <td>0.128129</td>\n",
       "      <td>0.247022</td>\n",
       "      <td>0.409579</td>\n",
       "      <td>0.044173</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   pelvic_incidence  pelvic_tilt numeric  lumbar_lordosis_angle  sacral_slope  \\\n",
       "0          0.355688             0.519900               0.229180      0.250857   \n",
       "1          0.124501             0.296783               0.098578      0.144629   \n",
       "2          0.411666             0.513932               0.322995      0.307661   \n",
       "3          0.416151             0.557414               0.271260      0.289436   \n",
       "4          0.227272             0.289479               0.128129      0.247022   \n",
       "\n",
       "   pelvic_radius  degree_spondylolisthesis  \n",
       "0       0.307461                  0.025148  \n",
       "1       0.476649                  0.036365  \n",
       "2       0.386097                  0.017523  \n",
       "3       0.341826                  0.051838  \n",
       "4       0.409579                  0.044173  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "563abac5",
   "metadata": {
    "papermill": {
     "duration": 0.011313,
     "end_time": "2023-10-27T09:25:15.342071",
     "exception": false,
     "start_time": "2023-10-27T09:25:15.330758",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Logistic Regression Classification\n",
    "<a id=\"3\"></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "7a5708cd",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-10-27T09:25:15.367240Z",
     "iopub.status.busy": "2023-10-27T09:25:15.366795Z",
     "iopub.status.idle": "2023-10-27T09:25:16.674649Z",
     "shell.execute_reply": "2023-10-27T09:25:16.673635Z"
    },
    "papermill": {
     "duration": 1.323422,
     "end_time": "2023-10-27T09:25:16.677250",
     "exception": false,
     "start_time": "2023-10-27T09:25:15.353828",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.10/site-packages/scipy/__init__.py:146: UserWarning: A NumPy version >=1.16.5 and <1.23.0 is required for this version of SciPy (detected version 1.23.5\n",
      "  warnings.warn(f\"A NumPy version >={np_minversion} and <{np_maxversion}\"\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "x_train, x_test, y_train, y_test = train_test_split(x,y,test_size = 0.2,random_state=42)\n",
    "x_train = x_train.T\n",
    "x_test = x_test.T\n",
    "y_train = y_train.T\n",
    "y_test = y_test.T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "ad439a32",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-10-27T09:25:16.702789Z",
     "iopub.status.busy": "2023-10-27T09:25:16.701848Z",
     "iopub.status.idle": "2023-10-27T09:25:16.826311Z",
     "shell.execute_reply": "2023-10-27T09:25:16.825177Z"
    },
    "papermill": {
     "duration": 0.140167,
     "end_time": "2023-10-27T09:25:16.829066",
     "exception": false,
     "start_time": "2023-10-27T09:25:16.688899",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "test accury 77.41935483870968\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "lr=LogisticRegression()\n",
    "lr.fit(x_train.T, y_train.T)\n",
    "\n",
    "print(\"test accury {}\".format(lr.score(x_test.T, y_test.T)*100))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cd41464d",
   "metadata": {
    "papermill": {
     "duration": 0.011441,
     "end_time": "2023-10-27T09:25:16.852216",
     "exception": false,
     "start_time": "2023-10-27T09:25:16.840775",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### test accury 77.42"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a6f578df",
   "metadata": {
    "papermill": {
     "duration": 0.011707,
     "end_time": "2023-10-27T09:25:16.875328",
     "exception": false,
     "start_time": "2023-10-27T09:25:16.863621",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# K-Nearest Neighbors Classification\n",
    "<a id=\"4\"></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "2570ec90",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-10-27T09:25:16.900664Z",
     "iopub.status.busy": "2023-10-27T09:25:16.900277Z",
     "iopub.status.idle": "2023-10-27T09:25:16.907189Z",
     "shell.execute_reply": "2023-10-27T09:25:16.906182Z"
    },
    "papermill": {
     "duration": 0.022343,
     "end_time": "2023-10-27T09:25:16.909638",
     "exception": false,
     "start_time": "2023-10-27T09:25:16.887295",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "x_train,x_test,y_train,y_test = train_test_split(x,y,test_size=0.3,random_state=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "20a9f55a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-10-27T09:25:16.935799Z",
     "iopub.status.busy": "2023-10-27T09:25:16.935425Z",
     "iopub.status.idle": "2023-10-27T09:25:17.087752Z",
     "shell.execute_reply": "2023-10-27T09:25:17.086354Z"
    },
    "papermill": {
     "duration": 0.168445,
     "end_time": "2023-10-27T09:25:17.090319",
     "exception": false,
     "start_time": "2023-10-27T09:25:16.921874",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "13 knn score: 78.49462365591397\n"
     ]
    }
   ],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "\n",
    "knn = KNeighborsClassifier(n_neighbors = 13)# n_neighbors = k\n",
    "\n",
    "knn.fit(x_train,y_train)\n",
    "\n",
    "prediction = knn.predict(x_test)\n",
    "\n",
    "\n",
    "print(\"{} knn score: {}\".format(\"13\",knn.score(x_test, y_test)*100))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "61e3c520",
   "metadata": {
    "papermill": {
     "duration": 0.011199,
     "end_time": "2023-10-27T09:25:17.113183",
     "exception": false,
     "start_time": "2023-10-27T09:25:17.101984",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "###  accury: 78.50"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "2e782fb6",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-10-27T09:25:17.139701Z",
     "iopub.status.busy": "2023-10-27T09:25:17.138565Z",
     "iopub.status.idle": "2023-10-27T09:25:17.530581Z",
     "shell.execute_reply": "2023-10-27T09:25:17.529349Z"
    },
    "papermill": {
     "duration": 0.408076,
     "end_time": "2023-10-27T09:25:17.532992",
     "exception": false,
     "start_time": "2023-10-27T09:25:17.124916",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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dSjqShX9uOYuqWr3YpdAtPN47FP/+aw+7WUZeXl2LHefyAQAjejjO8Nf17gn3xpDOgfjl3FUs+yUVK8b3Fq0W+/hb4SCC67s+OcWVEARB5GqI6E58sj8Tf998muHHDnxz7DKe/+IYanT28d/ql3NXUaMzIKaNO7qGeIldjsXMqZ8LlF9eA62IXVR2gKwozMcNAFBeo4OmqhbebgqRKyKi1lizJx0Ltp4DADw7KBqvPNwREjjeMIUj2HuhAM99cQzJZ69i2mcp+PDJWJs/T8u4+mtEjxCHHP4y6hLiha0vDULnYE9R3ycDkBW5KmTw91CisKIG2cVVDEBEduSDHRewJDkVAPDC4HZ4OaGDQ39J2buHOgdi/VP3YsqnR7DrfAEmf3IEaybGwU1hm197pZVa7EktAOAYmx/eThcb6HBxCMzKwn1dAQDZJeIvASSi2xMEAe/+fN4Ufl4e2qGu88PwY/MGtvfHJ0/3gbtCht/SijBp/RFU1OjELqtZP/2eB51BQOdgL7QL8BS7HKfAAGRl4fXDYLawBwIR3ZogCFi07Q+s2Fm3cdvcYZ3w4kPtRa6KWiM+xg+fTo6Hp1KOw5nFSFx3CJqqWrHLasK4+ssZuj+2ggHIytgBIrIPBoOAf245g9X1J1j/c0QXTLu/rchV0Z2IjfTBF8/GQ+3qguNZpZiw9iBKrmnFLsskv7waBy4WAXDc1V+2SPQAtHLlSkRHR0OlUiE2NhZ79+696WMnTZoEiUTS5Na1a9dGjystLcWMGTMQHBwMlUqFzp07Y+vWrZZ+Ky3S0AGyjbNQiKgpg0HAvO9O45MDlyCRAAv/0h2TBkSLXRbdhR5h3vjy2b7wc1fg98tlGLfmIAorbONk8m2n82AQ6paIh/u6iV2O0xA1ACUlJWHWrFmYN28ejh8/jkGDBmHYsGHIyspq9vHLly9Hbm6u6ZadnQ1fX1+MGjXK9BitVouhQ4ciMzMTX3/9Nc6fP481a9YgNDTUWm/rlox/udkBIrJNOr0Br3x1El8ezoZUArzzRE+Mj48Quywygy4hXtg0tS/aeCrxR145xnx0AFfLqsUuCz+cNA5/sftjTaIGoKVLl2Ly5MmYMmUKOnfujGXLliE8PByrVq1q9vFqtRpBQUGm29GjR1FSUoKnn37a9Jj169ejuLgY3333HQYMGIDIyEgMHDgQPXv2tNbbuiVjByinpAoGA/cCIrIltXoDZiWdwDfHL0MmlWD52F54IjZM7LLIjNoHeuK/0/ohWK3CxYJrGPPRAVwpFa8jf7m0CkcvlUAiqTv8lKxHtACk1WqRkpKChISERvcnJCRg//79LbrGunXrMGTIEERGRpru27JlC/r164cZM2YgMDAQ3bp1w8KFC6HX28ZGWMHeKkglgFZnQIGNtF+JqO5oixlfHMOPp3LhIpPgP+N781/kDira3x3/ndYPYT6uyCyqxOiPDoi2MOV/9ZOf+0T58ogkKxMtABUWFkKv1yMwMLDR/YGBgcjLy7vt83Nzc7Ft2zZMmTKl0f3p6en4+uuvodfrsXXrVrz22mtYsmQJFixYcNNr1dTUoKysrNHNUlxkUgSr6ydCcyUYkU2ortVj2mcp2H72KhRyKT5KjMWfugWJXRZZULivG/47rR+i/NyQU1KF0R8dQHpBhdXr2MLhL9GIPgn6xr00BEFo0f4aGzZsgLe3N0aOHNnofoPBgICAAKxevRqxsbEYO3Ys5s2bd9NhNQBYtGgR1Gq16RYeHn5H76WluBKMyHZUanWY8slR7DpfAJWLFOueisODnQJv/0SyeyHervjvtH5oF+CBXE01xqw+iAtXy632+ukFFfj9chlkUgmGMXBbnWgByN/fHzKZrEm3Jz8/v0lX6EaCIGD9+vVITEyEQtF4N+Xg4GB06NABMlnDluedO3dGXl4etNrmlz3OnTsXGo3GdMvOzr7Dd9UyXAlGZBsqanSY9PER7EsrhLtChg1P98Gg9m3ELousKMBLhU1T+6JTkCcKymswZvVBnL1iuVGA6/14qu7oi4Ht/OHnobTKa1ID0QKQQqFAbGwskpOTG92fnJyM/v373/K5u3fvRlpaGiZPntzkZwMGDEBaWhoMhoYD1lJTUxEcHNwkLBkplUp4eXk1ulmSaSUYh8CIRKOpqkXiukM4nFEMT6Ucn06OR98YP7HLIhH4eyjx5bN90T1UjeJrWoxbcxCnckot+pqCIHD4S2SiDoHNmTMHa9euxfr163Hu3DnMnj0bWVlZmD59OoC6zszEiRObPG/dunWIj49Ht27dmvzsueeeQ1FREWbOnInU1FT873//w8KFCzFjxgyLv5+W4hAYkbhKrmkxYe1BHM8qhdrVBV88G4/YSB+xyyIR+bgr8PmUePSK8IamqhYT1hxCyqUSi73e+avlSMuvgEImRUJXDrmKQdQANGbMGCxbtgzz58/HPffcgz179mDr1q2mVV25ublN9gTSaDTYvHlzs90fAAgPD8f27dtx5MgR9OjRAy+99BJmzpyJV1991eLvp6U4BEYknsKKGoxbcxC/Xy6Dn7sCXz7bFz3CvMUui2yA2tUFn02OR59oX5TX6JC47hAOphdZ5LWMe/880LENvFQuFnkNujWJIAjcjOYGZWVlUKvV0Gg0FhkOu1pWjfiFOyCVAOffGgYXmehz0YmcQn5ZNcavPYS0/Aq08VRi45R4tA/kwZPUWJVWj2c/PYp9aYVQuUixduK9GNje32zXFwQB97+zC1nFlfhgXC8OgZlRa76/+c0rgjYeSijkUhgEILdU/F1IiZzBldK6pc5p+RUIVqvw32n9GH6oWa4KGdY+FYfBHdugutaAZz45gp1/5Jvt+qdyNMgqroSriwwPdQ4w23WpdRiARCCVShDmw3lARNaSXVy32V1mUSXCfOqWPkf7u4tdFtkwlYsMHybGIqFLILQ6A6Z+dhQ//X77Pepawjj8NaRLINwUcrNck1qPAUgkEVwJRmQVGYXXMPqjA8gpqUKUX93mdzxwklpCKZfhPxN64889glGrFzBj4zFTeLlTBoNgWv4+ogePvhATA5BITBOh2QEispgLV8sx+qMDyNVUo12AB/47rR9CvF3FLovsiItMiuVje+Hx3qHQGwTM3HQcm1Ny7vh6Ry+VIK+sGp4qOe7vyD2nxMQAJBLTUniuBCOyiLNXyjBm9UEUlNegU5AnNk3tiwAvnrVErSeTSvDuEz0x9t5wGATgla9P4svDWbd/YjOMHaSHuwZBKZfd5tFkSQxAImEHiMhyTuWUYtyagyi+pkX3UDW+fLYv/LnTLt0FqVSChX/pjqf6RUIQgLnfnMYn+zNbdQ2d3oCtp+uHv7jyS3QMQCJp2A2aHSAic0q5VIIJaw5BU1WLXhHe+HxKPHzcm98Fnqg1pFIJ/vloV0y9LwYA8MaWM1i952KLn7//YhGKrmnh665A/7bcdVxsDEAiMXaACitqUKXVi1wNkWM4lF6EiesOobxGhz5RvvhscjzUrtxkjsxHIpFg7rBOePHBdgCAhVv/wAc7LrToucbhr0e6B3H/NxvA/wIiUbu5wFNVt/wxh8NgRHdt34VCPPXxYVzT6jGgnR82PHMvPJRcYkzmJ5FI8HJCR7w8tAMAYElyKt79+Txuta9wjU6Pn87ULaMf0YPDX7aAAUhExi5QFpfCE92VnX/k45lPjqC61oDBHdtg3VP3cn8VsrgXH2qPeY90BgCs2JmGhVvP3TQE7UktRHm1DoFeStwb5WvNMukmGIBE1LASjAGI6E799Hsepn52FFqdAQldAvFhYixULlxdQ9bx7H0xmP9YVwDAmr0ZeGPLGRgMTUOQcfjrzz1CIJVKrFojNY8BSEQNK8E4EZroTvxw8gpmbDyGWr2A4T2C8Z8Jvbm0mKxuYr8oLHq8OyQS4NMDl/CPb083CkGVWh2Sz14FwNVftoQBSETh3A2a6I5tTsnBzE3HoTcIeLxXKJaPuYcTS0k04/pEYMmonpBKgE1HsvHKVyeh0xsAAL/+kY+qWj3CfV3RM0wtcqVkxN8WIjINgbEDRNQqXx7Owitfn4RBAMbeG453R/WEnOGHRPZ47zC8P64XZFIJvjl+GTOTTqBWbzANf43oEQKJhMNftoKzBEVkHALLKa6EIAj8PwZRC3yyPxNvbDkDAHiqXyTeGNGVcyrIZvy5RwhcZFK8sPEY/ncqF1VaPfalFQLg8Jet4T+ZRBRWH4DKa3TQVNWKXI140gsq8OsfV8Uug+zAmj3ppvDz7KBo/PNRhh+yPQ93DcLqxDgo5FL8+kc+tDoD2gV4oFOQp9il0XUYgETkqpCZtud35h2hX/zyOJ7ZcBTb6reIJ2rOqZxSLNh6DgDw4oPt8I9HOrNrSjZrcKcArH/qXqhc6r5mOfxlexiARNYwD8g5J0LX6g04n1cOAFianAp9M8tHiQBgyfZUAMBj94Tg5YSO/DIhmzewvT+SpvbDtPtjMHlQtNjl0A0YgERmWgrvpCvBckqqoKsPPRfyK0yTBYmudySzGLtTCyCXSjCnfvddInvQM9wbc4d15q7kNogBSGTO3gHKKKxo9Odlv6Sitn7pKBEACIKAd38+DwAYFReOSD93kSsiIkfAACSyhg6Qc84ByiisC373d2gDP3cFMosq8c2xHJGrIluy/2IRDmUUQyGTmg6gJCK6WwxAIjNthujkHaBuoV547oG2AID3d6ShRqcXsyyyEYIg4N3tdd2f8fERCPF2FbkiInIUDEAiM+0FVFLV7Pkxji6j8BoAIMrPHU/2jUSQlwqXS6uQdCRb5MrIFuw8n4/jWaVQuUjx/OC2YpdDRA6EAUhkwd4qSCWAVmdAQUWN2OVYXUZBXQCKaeMOlYsML9QPcXzwaxqqtOwCOTODQcC7P9et/HqqfxQCPFUiV0REjoQBSGQuMimC1c55Knx1rR5XNNUAgGh/DwDA6LhwhPm4oqC8Bp8dzBSxOhLbT2fycDa3DB5KOabfx+4PEZkXA5ANcNaVYJlFdd0ftasLfNxcAAAKuRQzH2oPAFi16yIqanSi1Ufi0RsELE2u6/5MHhgNH3eFyBURkaNhALIBzroSzDj8FeXv3mhTu7/0CkWMvztKKmvx8b4MscojEW05eRlp+RVQu7pwAzkisggGIBtgWgnmZENg6fUToGP8G+/rIpdJMat+s7vVe9OhqXTec9KcUa3egGW/XAAATLs/Bl4qF5ErIiJHxABkA5x2CKw+AEX7N93Y7s/dg9Ex0BPl1Tqs2Ztu7dJIRJtTcnCpqBL+HgpM6h8ldjlE5KAYgGyA0w6B3SIASaUSzEmo6wKt/y0DRU64Qs4Z1ej0eH9HXffnuQfawU3B4wOIyDIYgGxARP0QWK6myqmOgbhVAAKAhC6B6BGmRqVWjw93X7RmaSSSTYezcUVTjSAvFSbER4hdDhE5MAYgG9DGUwmlXAqDAOSWVotdjlVoqmpRdE0LoG4SdHMkEgleTugIAPj0wCVcLXOOz8ZZVWn1WLEzDQDwwoPtoHKRiVwRETkyBiAbIJFIEObjXPOAjPN/AjyVtzwl+b72/rg3ygc1OgNW/JpmrfJIBJ8eyERBeQ3CfV0xOi5c7HKIyMExANkIZ1sJdrvhL6Pru0CbjmQ5zefjbMqra03DnDMf6gCFnL+aiMiy+FvGRpgmQjtJByi9hQEIAPrG+GFgO3/U6gV88OsFS5dGIvj4t0yUVNYipo07Rt4TInY5ROQEGIBshGkpvJOsBGtpB8jIuCJs87HLSC+osFhdZH2llVqs2VO31cHsIR0gl/HXEhFZHn/T2Ahn6wDdag+g5vSO8MFDnQKgNwhYvoNdIEeyZm86ymt06BTkieHdg8Uuh4icBAOQjWiYA+T4HSBBEEwdoJg2LQtAQEMXaMvJKzifV26R2si6Citq8PFvmQCAOUM7QCqV3PoJRERmwgBkI4wdoMKKGlRqHfsA0IKKGlTU6CCVNAS/lugaosbw7sEQBOC9+oMyyb59uOsiKrV69AxTY2iXQLHLISInwgBkI9RuLvBU1S0Hzylx7C6Q8RDUUB9XKOWt2+tl9tD2kEqAn87k4XSOxhLlkZXkaarx6cFLAICXEzo2OhCXiMjSGIBsSMORGI49DyizyDj/x6PVz20X4ImR94QCAJYknzdrXWRdK3ZegFZnQJ8oXwxq7y92OUTkZBiAbEjDSjDHDkA3OwW+pWYOaQ+ZVIJd5wtwNLPYnKWRlWQXVyLpSDYA4OWEDuz+EJHVMQDZkIaVYM4xBBbl1/L5P9eL9HPH6LgwAMCS7ZwLZI/e33EBtXoBg9r7Iz7GT+xyiMgJMQDZEGfZDdq0B1Cb1g+BGb3wYHsoZFIcSC/C/rRCc5VGVpBeUIHNx3IA1K38IiISAwOQDTENgTlwB0hvEHCpPuDd6RAYAIR6u2J8/Wnh724/D0EQzFIfWd6yXy7AIABDOgegV4SP2OUQkZNiALIhxiGwnOJKh/1Cv1JaBa3OAIVMihBv17u61vOD20LlIsWxrFLsOl9gpgrJkv7IK8MPp64AAGaz+0NEImIAsiFh9QGovEYHTVWtyNVYhnH4K8LPDbK73PQuwFOFp/pHAWAXyF68l5wKQQCGdw9G1xC12OUQkRNjALIhrgoZ/D2UABx3R+jWngF2O9PvawsPpRxnrpThp9/zzHJNsoxTOaX4+cxVSCV1+zkREYmJAcjGNMwDcsyJ0Bl3uQT+Rj7uCjwzMBoAsDQ5FXoDu0C2yrhib2SvULQL8BS5GiJydgxANsbRN0M0dwcIACYPjIba1QUX8ivww8krZruumPZdKMToDw/g+xOXxS7FLI5kFmN3agHkUglmPsTuDxGJjwHIxjhLByjKjAFI7eqCqffFAACW/ZKKWr3BbNcWw45zV/HMhiM4nFmMWUknsPFQltgl3RVBEPDuz3W7do+KC0ekn/n+2xMR3SkGIBvT0AFyvDlAWp0BOSV3vwS+OZP6R8HPXYHMokp8U7/HjD366fdcTP88BVq9ARG+bhAE4B/fnsaG3zLELu2O7b9YhEMZxVDIpHjxwXZil0NEBMAGAtDKlSsRHR0NlUqF2NhY7N2796aPnTRpEiQSSZNb165dm338pk2bIJFIMHLkSAtVb36mzRAdsAOUVVwJgwC4K2Ro46k067XdlXI890BbAMD7O9JQo9Ob9frW8P2Jy5ix8Thq9QJG9AzBjpfvx7T6ztY/fziLj3ZfFLnC1hMEAe9ur+v+jI+PuOutD4iIzEXUAJSUlIRZs2Zh3rx5OH78OAYNGoRhw4YhK6v5lv/y5cuRm5trumVnZ8PX1xejRo1q8thLly7hlVdewaBBgyz9NszKtBdQSRUMDjaht2EHaHeLnP30ZN9IBHopcbm0ynTOlL346mg2ZiWdgN4g4K+9w7BszD1wkUnx6rBOeKm+a7Jo2x94f8cFkSttnZ3n83E8qxQqFymeH9xW7HKIiExEDUBLly7F5MmTMWXKFHTu3BnLli1DeHg4Vq1a1ezj1Wo1goKCTLejR4+ipKQETz/9dKPH6fV6TJgwAf/6178QExNjjbdiNsHeKkgldcNFBRU1YpdjVhmFFQCAKAvNAVG5yPDig3UTbD/4NQ1VWvvoAn1x6BL+9vUpCAIwrk843nmih2mPJIlEgjkJHfFKQt2mgUuTU/HOz3/YxZ5HBoNgWvn1VP8oBHiqRK6IiKhBqwNQVFQU5s+ff9MuTUtptVqkpKQgISGh0f0JCQnYv39/i66xbt06DBkyBJGRkY3unz9/Ptq0aYPJkye36Do1NTUoKytrdBOLi0yKYLVjngpv7iXwzRkdF44wH1cUlNfgs4OZFnsdc1m/LwPzvv0dQN08poV/6Q5pMxtEvvBge8x7pDMA4D87L2LB/87ZfAj66Uwezlwpg4dSjun3sftDRLal1QHo5Zdfxvfff4+YmBgMHToUmzZtQk1N6zsVhYWF0Ov1CAwMbHR/YGAg8vJuv6Fdbm4utm3bhilTpjS6/7fffsO6deuwZs2aFteyaNEiqNVq0y08PLzFz7UER10Jdv0QmKUo5FLTMutVuy6iokZnsde6Wx/uvoj5P54FAEy7LwZvjOhyy6HBZ++LwfzH6ua7rd2Xgf/7/ozNDpPqDQKWJtd1fyYPjIaPu0LkioiIGmt1AHrxxReRkpKClJQUdOnSBS+99BKCg4Pxwgsv4NixY60u4MZf+IIgtGh+yIYNG+Dt7d1ognN5eTmefPJJrFmzBv7+/i2uYe7cudBoNKZbdra480ccdSVYwx5Ad34KfEv8pVcoYvzdUVJZi4/32d7qKUEQsPyXC3h72x8AgJcebIdXh3Vq0d/7if2i8Pbj3SGRAJ8dvIR/fHvaJjd/3HLyMtLyK6B2dcHkQdFil0NE1MQdzwHq2bMnli9fjsuXL+ONN97A2rVrce+996Jnz55Yv379bdvz/v7+kMlkTbo9+fn5TbpCNxIEAevXr0diYiIUioZ/WV68eBGZmZkYMWIE5HI55HI5Pv30U2zZsgVyuRwXLza/ikapVMLLy6vRTUwRvo63GeK1Gh2ultV1CqMtvA+MXCbFrPqDNlfvTYem0nbOVRMEAe/8fB7v/VLXHXkloQPmJHRs1aTwsX0isGRUT0glwKYj2Xjlq5PQ2dDeR7V6A5b9UjdZe9r9MfBSuYhcERFRU3ccgGpra/Hf//4Xjz76KF5++WXExcVh7dq1GD16NObNm4cJEybc8vkKhQKxsbFITk5udH9ycjL69+9/y+fu3r0baWlpTeb4dOrUCadPn8aJEydMt0cffRSDBw/GiRMnRB/aailHXApv7P74uiugdrP8F+KfuwejY6Anyqt1WLM33eKv1xKCIOCt/53Dyl11Qfy14Z3xwoN3tivy473D8P64XpBJJfj2+GXM3HTCZjaA3JySg0tFlfD3UGBS/WG1RES2Rt7aJxw7dgwff/wxvvzyS8hkMiQmJuK9995Dp06dTI9JSEjAfffdd9trzZkzB4mJiYiLi0O/fv2wevVqZGVlYfr06QDqhqYuX76MTz/9tNHz1q1bh/j4eHTr1q3R/SqVqsl93t7eANDkfltmmgPkQENgmUXmPwLjVqRSCeYkdMC0z1Kw/rcMPD0gCn4e5t17qDUMBgH/t+V3fH6wbvHA/Me6YmK/qLu65p97hMBFJsULG4/hf6dzodUbsGJ8LyjlMjNUfGdqdHrTUv3nHmgHN0Wrf8UQEVlFqztA9957Ly5cuIBVq1YhJycH7777bqPwAwBdunTB2LFjb3utMWPGYNmyZZg/fz7uuece7NmzB1u3bjWt6srNzW2y2kyj0WDz5s0tXuFlj4xzgHI1VTbzr/q7lVFg3QAEAAldAtEjTI1KrR4firiJoN4gYO43p/H5wSxIJMDbj3e/6/Bj9HDXIKyeGAeFXIrks1cx7bMUVNeKt/x/0+FsXNFUI8hLhQnxEaLVQUR0OxKhlWtpL1261GTZuaMpKyuDWq2GRqMRZT6QIAjo9PpPqNEZsOdvgxHh52b1GsxtTtIJfHP8Mv72cEfMGGy94xB2pxbgqfWHoZRLsef/DUagl3X3otHpDXjlq5P47sQVSCXAu6N64vHeYWZ/nd/SCjH5kyOorjWgf1s/rH0qzurdlyqtHve9sxMF5TVY8JdumBDv2L8niMj2tOb7u9UdoPz8fBw6dKjJ/YcOHcLRo0dbezlqhkQiQZiPYy2FT7fAKfAtcV97f9wb5YManQErfk2z6mvX6g14adNxfHfiCuRSCd4f18si4QcABrTzxydP94G7Qob9F4vw1PrDKK+27uTvTw9koqC8BuG+rhgVax/z7YjIebU6AM2YMaPZZeKXL1/GjBkzzFIUXTcR2kFWgll7DpCRRCLBywkdAQCbjmRZ7fOs0enx3OfHsPV0HlxkEqyc0Bt/7hFi0deMj/HDZ1Pi4amS40hmCRLXHYamyjohqLy61jTMOPOhDlDIRT9mkIjollr9W+rs2bPo3bt3k/t79eqFs2fPmqUoum4vIAfoAJVc06K0fim6pY7BuJW+MX4Y2M4ftXoBH/xq+bO0qmv1mPppCn45dxUKuRSrJ8YhoWuQxV8XAHpH+GDjlL7wdnPBiexSTFh7ECXXtBZ/3Y9/y0RJZS1i2rhj5D2WDXpERObQ6gCkVCpx9erVJvfn5uZCLueKD3NxpJVgxuGvYLUKrgpxVijNqT9La/Oxy0gvqLDY61RqdXj64yPYnVoAVxcZPp50LwZ3DLDY6zWne5gaXz7bF37uCvx+uQzj1hxEQbnlzpUrrdRizZ66rQZmD+kAuYzdHyKyfa3+TTV06FDTzslGpaWl+Mc//oGhQ4eatThnZuwAZTnAEFiGSPN/rtc7wgcPdQqA3iBguYVOVC+vrsVT6w/jQHoR3BUyfPJMHwxo1/Idyc2pc7AXkqb1RYCnEn/klWPs6gO4WlZtkddaszcd5TU6dAryxPDuwRZ5DSIic2t1AFqyZAmys7MRGRmJwYMHY/DgwYiOjkZeXh6WLFliiRqdknEOUI4DDIFl2kAAAhq6QFtOXsH5vHKzXltTWYsn1x3GkcwSeKrk+GxKPPpE+5r1NVqrXYAnkqb1Q4hahYsF1zD6owO4XGrejmJhRQ0+/i0TADBnaIdmD3IlIrJFrQ5AoaGhOHXqFBYvXowuXbogNjYWy5cvx+nTp+1mp2V7YOwAFVZoUam13QM9W8IWOkAA0DVEjeHdgyEIwHv1B3WaQ/E1LcavPYiT2aXwdnPBxil90TvCx2zXvxvR/u5ImtYP4b6uuFRUidEfHkBWkflC9Ye7LqJSq0fPMDWGdrn1ETZERLbkjibtuLu7Y+rUqeauha6jdnOBp0qO8modckqq0CHQU+yS7phYS+CbM3toe2z7PRc/ncnD6RwNuoep7+p6BeU1eHLtIZy/Wg4/dwU+nxKPzsHiniV3o3BfNyRN7YcJaw8ho7CuE7Tx2XjEtLm7Q2nzNNX47OAlAMDLrTzPjIhIbHc8W/Hs2bP46aefsGXLlkY3Mp+GU+HtdxhMEASbGQID6oaFRt4TCgBYknz+rq6Vp6nGmNUHcP5qOQI8lUia1tfmwo9RiLcrkqb2RbsAD+SVVWP0RweRevXuhgFX7LyAGp0BfaJ8Mai9OHOdiIjuVKs7QOnp6fjLX/6C06dPQyKRmE59N/7rT68Xbxt+RxPu64qzuWV2HYCultWgqlYPmVRimtcktplD2uP7k1ew63wBjmYWIy6q9XN1ckoqMWHtIVwqqkSIWoWNz/ZFlA0EvFsJ8FJh09S+eHLtofqJ0Qfx2eQ+6BrS+i5YdnElko7U7Qf2ckIHdn+IyO60ugM0c+ZMREdH4+rVq3Bzc8OZM2ewZ88exMXFYdeuXRYo0Xk17AVkv0vh0wvrlpxH+LrBxUaWR0f6uWN0XN2OzEu2t34u0KWiaxjz0UFcKqpEuK8rkqb1s/nwY+TvocSmqX3RPVRdN3dpzSGczC5t9XXe33EBtXoBg9r7Iz7Gz/yFEhFZWKu/kQ4cOID58+ejTZs2kEqlkEqlGDhwIBYtWoSXXnrJEjU6LUfYDdo4ATrKxs4ze+HB9lDIpDiQXoT9aYUtft7FggqM+eggLpdWIdrfHf+d1s9mOlst5e2mwBfPxqN3hDc0VbV4cu0hpFwqbvHz0wsqsPlYDoC6lV9ERPao1QFIr9fDw6Nu8qS/vz+uXLkCAIiMjMT583c3p4IaM22GaMcdoIZT4O9uwq25hXq7Ynz9aeXvbj+PlpwJnHq1HGM+Ooi8smq0D/BA0tS+CFa7WrpUi/BSueDTyXVL9ctrdEhcdxgH04ta9Nxlv1yAQQCGdA5ALxtZ7UZE1FqtDkDdunXDqVOnAADx8fFYvHgxfvvtN8yfPx8xMTFmL9CZGYfAcoorW/QFbYtMZ4C1sb0houcHt4XKRYpjWaXYdb7glo89c0WDsasPorCiBp2DvbBpal8EWPlkeXPzUMrxydN9MLCdPyq1ekz6+DD2pN76c/gjrww/nKr7R89sdn+IyI61OgC99tprMBgMAIC33noLly5dwqBBg7B161a8//77Zi/QmYXVB6DyGp3VDrU0N+MS+BgbnCMT4KnCU/2jANy6C3QyuxTjVh9E8TUteoSp8eWz8fDzUFqxUstxVciw9qk4DO7YBtW1Bkz55Ch2nGt61I3Re8mpEARgeI/gO5o8TURkK1odgB5++GE8/vjjAICYmBicPXsWhYWFyM/Px4MPPmj2Ap2Zq0IG//ovWns8E0ynN5g23bOFJfDNmX5fW3go5ThzpQw/n8lr8vOjmcWYsPYQyqp16B3hjc+nxMPbTSFCpZajcpHho8Q4PNw1EFq9AdM/T8FPv+c2edzpHA1+PnMVUgkwe0h7ESolIjKfVgUgnU4HuVyO33//vdH9vr6+XAZrIQ3zgOxvInROSRV0BgFKuRRBNjpc5OOuwDMDowHUrQjTGxq6QAcuFmHi+sOoqNEhPtoXn06Oh5fKRaxSLUohl2LF+N74c49g1OoFzNh4HFtOXmn0mHe3183xG9krFO0C7HdjTiIioJUBSC6XIzIyknv9WJE9b4aYUdSwAaItnxE1eWA01K4uuJBfgR/qv/T3pBZg0seHUanVY1B7f2x4ug88lHe0cbrdcJFJsXxsLzzeOxR6g4BZm47j65S61V5HMouxO7UAcqkEMx9i94eI7N8dzQGaO3cuiotbvmyW7pw9d4AaVoDZ5vCXkdrVBVPvq5vAv+yXVPx8Jg9TPjmKGp0BD3YKwJqJcXBVyESu0jpkUgnefaInxvUJh0EAXvnqJDYeysK7P9d1f0bFhSPSz7b/exIRtUSr/0n7/vvvIy0tDSEhIYiMjIS7e+NfhseOHTNbcXR9B8j+5gDZyiGoLTGpfxTW78tAZlElpn2WAgB4uGsgPhjXGwq5bWzgaC1SqQQL/9IdSrkMG/Zn4h/fngYAKGRSvPhgO5GrIyIyj1YHoJEjR1qgDLoZ02aI9tgBMm6CaAcByF0px3MPtMVb/zsHABjRMwRLR/e0md2rrU0ikeCNEV2gkEuxek86AGB8fARCvO1z3yMiohu1OgC98cYblqiDbsK0F1BJFQwGwabn0twow4aXwDfnyb6RSL1ajgBPFWYP7QCZHX3WliCRSDB3WCcEeamQklXCuT9E5FAce1anAwj2VkEqAbQ6AwoqahBoo6upblRdq8cVTd2wnT0MgQF1y8EXP9FT7DJsikQiwTMDo/EMosUuhYjIrFrd35dKpZDJZDe9kXm5yKSmYQd7Wgl2qagSggB4qeTwdXesfXOIiMj+tboD9O233zb6c21tLY4fP45PPvkE//rXv8xWGDUI93FDTkkVsksqERflK3Y5LZJRfwp8tL8794giIiKb0+oA9NhjjzW574knnkDXrl2RlJSEyZMnm6UwahDu64oD6fa1Eiyj0LZ3gCYiIudmtiUu8fHx+OWXX8x1ObqOPW6G2NABsq1T4ImIiAAzBaCqqip88MEHCAsLM8fl6Ab2uBTetAeQDZ4CT0RE1OohMB8fn0ZzOgRBQHl5Odzc3PD555+btTiqY9oN2q6GwOoDEHcNJiIiG9TqAPTee+81CkBSqRRt2rRBfHw8fHx8zFoc1TEOgeVqqlCrN9j85nxl1bUorNACAKL83USuhoiIqKlWB6BJkyZZoAy6lTaeSijlUtToDMgtrUaEn22Hisz67k8bTyU8HfT0dCIism+tbiV8/PHH+Oqrr5rc/9VXX+GTTz4xS1HUmEQiQZiP/RyKak9ngBERkXNqdQB6++234e/v3+T+gIAALFy40CxFUVPGidBZdrASLL2A83+IiMi2tToAXbp0CdHRTbfFj4yMRFZWllmKoqbsaSl8ZhFXgBERkW1rdQAKCAjAqVOnmtx/8uRJ+Pn5maUoasq0EqzE9leCcQiMiIhsXasD0NixY/HSSy9h586d0Ov10Ov1+PXXXzFz5kyMHTvWEjUS7KcDJAgCMgrs6xR4IiJyPq1eBfbWW2/h0qVLeOihhyCX1z3dYDBg4sSJnANkQcY5QDk2Pgm6sEKL8hodJJKGmomIiGxNqwOQQqFAUlIS3nrrLZw4cQKurq7o3r07IiMjLVEf1TN2gAortKjU6uCmaPV/Oqswzv8J9XaFykUmcjVERETNu+Nv0fbt26N9+/bmrIVuQe3mAk+VHOXVOuSUVKFDoKfYJTXLOPzF+T9ERGTLWj0H6IknnsDbb7/d5P533nkHo0aNMktR1Dx7mAeUXsj5P0REZPtaHYB2796N4cOHN7n/T3/6E/bs2WOWoqh5DWeC2W4AMp4CH8UARERENqzVAaiiogIKhaLJ/S4uLigrKzNLUdQ8UwfIhpfCcwk8ERHZg1YHoG7duiEpKanJ/Zs2bUKXLl3MUhQ1z7iqylY7QAaDgMyiutpi/D1EroaIiOjmWj0J+vXXX8df//pXXLx4EQ8++CAAYMeOHdi4cSO+/vprsxdIDWx9M8QrmipodQa4yCQIrT+7jIiIyBa1OgA9+uij+O6777Bw4UJ8/fXXcHV1Rc+ePfHrr7/Cy8vLEjVSPeMQWE5xJQRBgEQiEbmixozDXxG+bpBJbas2IiKi67V6CAwAhg8fjt9++w3Xrl1DWloaHn/8ccyaNQuxsbHmro+uE1YfgMprdNBU1YpcTVMN8384/EVERLbtjgIQAPz666948sknERISghUrVuCRRx7B0aNHzVkb3cBVIYO/hxIAkF1se8NgxgAUw0NQiYjIxrVqCCwnJwcbNmzA+vXrce3aNYwePRq1tbXYvHkzJ0BbSbivKworapBdUonuYWqxy2mEK8CIiMhetLgD9Mgjj6BLly44e/YsPvjgA1y5cgUffPCBJWujZtjyZojGABTlxwBERES2rcUdoO3bt+Oll17Cc889xyMwRNSwEsy2ApBWZzCFMg6BERGRrWtxB2jv3r0oLy9HXFwc4uPjsWLFChQUFFiyNmpGQwfItuYAZZdUwiAAbgoZAjyVYpdDRER0Sy0OQP369cOaNWuQm5uLadOmYdOmTQgNDYXBYEBycjLKy8vvqICVK1ciOjoaKpUKsbGx2Lt3700fO2nSJEgkkia3rl27mh6zZs0aDBo0CD4+PvDx8cGQIUNw+PDhO6rNFpk2Q7SxDtD1h6Da2vJ8IiKiG7V6FZibmxueeeYZ7Nu3D6dPn8bLL7+Mt99+GwEBAXj00Udbda2kpCTMmjUL8+bNw/HjxzFo0CAMGzYMWVlZzT5++fLlyM3NNd2ys7Ph6+vb6BDWXbt2Ydy4cdi5cycOHDiAiIgIJCQk4PLly619qzYpoj4A5ZRUwWAQRK6mgWn+DydAExGRHbjjZfAA0LFjRyxevBg5OTn48ssvW/38pUuXYvLkyZgyZQo6d+6MZcuWITw8HKtWrWr28Wq1GkFBQabb0aNHUVJSgqefftr0mC+++ALPP/887rnnHnTq1Alr1qyBwWDAjh077vh92pJgtQoyqQRanQEFFTVil2PCU+CJiMie3FUAMpLJZBg5ciS2bNnS4udotVqkpKQgISGh0f0JCQnYv39/i66xbt06DBkyBJGRkTd9TGVlJWpra+Hr69vi2myZXCZFsFoFwLZWgmVyCTwREdkRswSgO1FYWAi9Xo/AwMBG9wcGBiIvL++2z8/NzcW2bdswZcqUWz7u1VdfRWhoKIYMGXLTx9TU1KCsrKzRzZY1nApvOwGIewAREZE9ES0AGd04YbalZ1xt2LAB3t7eGDly5E0fs3jxYnz55Zf45ptvoFKpbvq4RYsWQa1Wm27h4eEtrl8MpqXwNrIS7FqNDnll1QAYgIiIyD6IFoD8/f0hk8madHvy8/ObdIVuJAgC1q9fj8TERCgUimYf8+6772LhwoXYvn07evToccvrzZ07FxqNxnTLzs5u3ZuxMlvbDDGzqK774+PmAm+35v97EBER2RLRApBCoUBsbCySk5Mb3Z+cnIz+/fvf8rm7d+9GWloaJk+e3OzP33nnHbz55pv46aefEBcXd9talEolvLy8Gt1sma0thc8srKuD3R8iIrIXrToLzNzmzJmDxMRExMXFoV+/fli9ejWysrIwffp0AHWdmcuXL+PTTz9t9Lx169YhPj4e3bp1a3LNxYsX4/XXX8fGjRsRFRVl6jB5eHjAw8MxTim3tSGwjMIKADwFnoiI7IeoAWjMmDEoKirC/PnzkZubi27dumHr1q2mVV25ublN9gTSaDTYvHkzli9f3uw1V65cCa1WiyeeeKLR/W+88Qb++c9/WuR9WJtxCCxXU4VavQEuMnGncqWbJkC7iVoHERFRS4kagADg+eefx/PPP9/szzZs2NDkPrVajcrKmw/9ZGZmmqky29XGUwmlXIoanQFXSqsQKfLhow0rwNgBIiIi+yD6KjBqPYlEgjAf2xkG4x5ARERkbxiA7JStTIQuuaZFSWUtACCKQ2BERGQnGIDslK0shc+oXwIf5KWCm0L0EVUiIqIWYQCyU6aVYCXiDoFdfwo8ERGRvWAAslO20gEyboIY3YYBiIiI7AcDkJ0yzgHKEXkOEE+BJyIie8QAZKeMHaDCCi0qtTrR6jAOgUWJvBSfiIioNRiA7JTazQWeqrpJxzkizQMSBIFDYEREZJcYgOyY2POA8strUKnVQyaVmGohIiKyBwxAdqzhTDBxAlB6/fBXuI8rFHL+VSIiIvvBby07ZuoAiTQElsEdoImIyE4xANkx027QInWAjKfARzEAERGRnWEAsmNib4aYUVgXvLgEnoiI7A0DkB0zDoHlFFdCEASrv76xA8RT4ImIyN4wANmxsPoAVF6jg6aq1qqvrdMbkFU/9MYl8EREZG8YgOyYq0IGfw8lACC72LrDYJdLq1CrF6CUSxHspbLqaxMREd0tBiA71zAPyLoToY0rwKL83CGVSqz62kRERHeLAcjOibUZIpfAExGRPWMAsnMRxqXwInWAOP+HiIjsEQOQnWvYDdq6c4BMAYiHoBIRkR1iALJzDbtBswNERETUUgxAds64G3ROSRUMBuvsBVRdq8fl0rqOE+cAERGRPWIAsnPBahVkUgm0OgMKKmqs8ppZxZUQBMBTJYefu8Iqr0lERGRODEB2Ti6TIlhdtw+PtVaCGU+Bj/Z3h0TCJfBERGR/GIAcgLXnAWUWcQk8ERHZNwYgB2DtlWAZBQxARERk3xiAHIC1N0PkJohERGTvGIAcQLiVN0NMZwAiIiI7xwDkAKw5BFZeXYvC+tVmUQxARERkpxiAHIBxCCxXU4VavcGir5VZWNdl8vdQwkvlYtHXIiIishQGIAfQxlMJpVwKgwBcKbVsFyi9sAIAEMPuDxER2TEGIAcgkUgQ5mOdYTDjBOgofzeLvg4REZElMQA5CGtNhM40TYD2sOjrEBERWRIDkIOw1lJ4LoEnIiJHwADkIEwrwUosNwQmCIJpCXwMT4EnIiI7xgDkIKzRASq6pkV5tQ4SCRDhyzlARERkvxiAHIRxDlCOBecAGef/hKhdoXKRWex1iIiILI0ByEEYO0CFFVpUanUWeQ0OfxERkaNgAHIQajcXeKrkAIAcC80D4gRoIiJyFAxADsTS84CMp8BH+TEAERGRfWMAciANZ4JZJgBlFtV3gDgERkREdo4ByIGYOkAWGAIzGATTEBiPwSAiInvHAORATLtBW6ADlFtWjRqdAS4yCUK9Xc1+fSIiImtiAHIgltwM0Tj/J9zXDXIZ/9oQEZF94zeZAzEOgeUUV0IQBLNeO6OIw19EROQ4GIAcSFh9ACqv0UFTVWvWaxs7QFwCT0REjoAByIG4KmRo46kEAGQXm3cYLKOwAgBPgSciIsfAAORgwn2M84DMOxHauAIsyp9ngBERkf1jAHIwllgJVqs3mCZWx7ADREREDoAByME07AVkvgCUXVwJvUGAq4sMgV5Ks12XiIhILAxADqZhN2jzzQG6/gwwiURitusSERGJhQHIwViiA8RDUImIyNGIHoBWrlyJ6OhoqFQqxMbGYu/evTd97KRJkyCRSJrcunbt2uhxmzdvRpcuXaBUKtGlSxd8++23ln4bNsM4ByinpAoGg3n2AmIAIiIiRyNqAEpKSsKsWbMwb948HD9+HIMGDcKwYcOQlZXV7OOXL1+O3Nxc0y07Oxu+vr4YNWqU6TEHDhzAmDFjkJiYiJMnTyIxMRGjR4/GoUOHrPW2RBWsVkEmlUCrM6CgosYs12QAIiIiRyMRzL1lcCvEx8ejd+/eWLVqlem+zp07Y+TIkVi0aNFtn//dd9/h8ccfR0ZGBiIjIwEAY8aMQVlZGbZt22Z63J/+9Cf4+Pjgyy+/bFFdZWVlUKvV0Gg08PLyauW7Et/Af/+KnJIqfD29H+KifO/6ev0W7UCuphrfPN8fvSN8zFAhERGR+bXm+1u0DpBWq0VKSgoSEhIa3Z+QkID9+/e36Brr1q3DkCFDTOEHqOsA3XjNhx9++JbXrKmpQVlZWaObPTPnPKAqrR65mmoAQLQfO0BEROQYRAtAhYWF0Ov1CAwMbHR/YGAg8vLybvv83NxcbNu2DVOmTGl0f15eXquvuWjRIqjVatMtPDy8Fe/E9phzJVhm/Rlg3m4u8HFX3PX1iIiIbIHok6BvXFYtCEKLllpv2LAB3t7eGDly5F1fc+7cudBoNKZbdnZ2y4q3UcYOUJYZNkPk/B8iInJEcrFe2N/fHzKZrElnJj8/v0kH50aCIGD9+vVITEyEQtG4KxEUFNTqayqVSiiVjrPBnzl3g2YAIiIiRyRaB0ihUCA2NhbJycmN7k9OTkb//v1v+dzdu3cjLS0NkydPbvKzfv36Nbnm9u3bb3tNR2IcAsspufshsHTjKfCc/0NERA5EtA4QAMyZMweJiYmIi4tDv379sHr1amRlZWH69OkA6oamLl++jE8//bTR89atW4f4+Hh069atyTVnzpyJ++67D//+97/x2GOP4fvvv8cvv/yCffv2WeU92QLjEFiupgq1egNcZHeec41zgKLbMAAREZHjEDUAjRkzBkVFRZg/fz5yc3PRrVs3bN261bSqKzc3t8meQBqNBps3b8by5cubvWb//v2xadMmvPbaa3j99dfRtm1bJCUlIT4+3uLvx1a08VRCKZeiRmfAldIqRN5F94ZDYERE5IhE3QfIVtn7PkAA8NCSXbhYcA2fT47HwPb+d3SN0kot7plfN5x45l8Pw10pal4mIiK6JbvYB4gsyzQR+i72AjJ2fwK9lAw/RETkUBiAHJRpM8S7WAlmmv/D4S8iInIwDEAOyrQZ4l2sBMswrgDz9zBLTURERLaCAchBmaMDlF4/BBbDDhARETkYBiAHZZwDlGOGOUAcAiMiIkfDAOSgjB2gwgotKrW6Vj9fEARk1gegKAYgIiJyMAxADkrt5gJPVd3KrTvZEbqgvAbXtHpIJUBEfTeJiIjIUTAAObC7mQdknP8T7usGhZx/TYiIyLHwm82BmVaC3UEA4vwfIiJyZAxADszUAbqDITDT/B8egkpERA6IAciBmXaDvoshsBgegkpERA6IAciB3c1miBwCIyIiR8YA5MCMq7dyiivRmjNv9QYBl3gMBhEROTAGIAcWVj8HqLxGB01VbYufd7mkCrV6AQq5FCFqV0uVR0REJBoGIAemcpGhjacSAJBd3PJhsIwi4wRoN0ilEovURkREJCYGIAcX7mOcB9TyidAZBRUAOPxFRESOiwHIwd3JSrCGCdA8BZ6IiBwTA5CDa9gLqOUBKN0UgHgEBhEROSYGIAfXsBt0y+cAZRaxA0RERI6NAcjBtbYDVKPTmw5P5RwgIiJyVAxADs44ByinpAoGw+33AsoqqoQgAJ5KOfw9FJYuj4iISBQMQA4uWK2CTCqBVmdAQUXNbR9vnP8T5e8OiYRL4ImIyDExADk4uUyKYLUKAJDVgpVgmTwCg4iInAADkBMwzQNqQQDiGWBEROQMGICcQGtWgvEUeCIicgYMQE6gNSvBjB2gKD8GICIiclwMQE6gpbtBV9ToUFBeN1E6ikNgRETkwBiAnIBxCMy4v8/NGCdA+3sooHZ1sXhdREREYmEAcgLGIbBcTRVq9YabPi6dE6CJiMhJMAA5gTaeSijlUhgE4ErpzbtAGQWc/0NERM6BAcgJSCQShPncfiWY6QwwrgAjIiIHxwDkJEwToW+xEsy0BJ5DYERE5OAYgJzE7TZDFAQBGQUVAHgKPBEROT4GICdh2gzxJivBiq9pUVatAwBE+rlZrS4iIiIxMAA5idt1gIzzf0K9XaFykVmtLiIiIjEwADkJ4xygnJvMAUov4BJ4IiJyHgxATsLYASqs0KJSq2vycx6CSkREzoQByEmo3VzgqZIDaH5HaNMZYAxARETkBBiAnMit5gFlcAk8ERE5EQYgJ2JaCXZDADIYhIZNEBmAiIjICTAAOZEI02aIjYfA8sqqUV1rgFzasGM0ERGRI2MAciKm3aBv6AAZh78ifN0gl/GvBBEROT5+2zkR0xygGzpAXAFGRETOhgHIiRjnAOUUV0IQBNP9DEBERORsGICcSFh9B6i8RgdNVa3pflMA4inwRETkJBiAnIjKRYY2nkoAQHZxwzCYKQD5MQAREZFzYAByMuE+xkNR6yZC1+oNpknR7AAREZGzYAByMjeuBMspqYLOIMDVRYZAT5WYpREREVkNA5CTaVgJVheAMgorANQdgSGVSkSri4iIyJoYgJxMw27QdXOAGk6BdxOtJiIiImtjAHIyN3aAeAQGERE5I9ED0MqVKxEdHQ2VSoXY2Fjs3bv3lo+vqanBvHnzEBkZCaVSibZt22L9+vWNHrNs2TJ07NgRrq6uCA8Px+zZs1FdXW3Jt2E3jHOAcoqrYDAI1+0B5CFmWURERFYlF/PFk5KSMGvWLKxcuRIDBgzARx99hGHDhuHs2bOIiIho9jmjR4/G1atXsW7dOrRr1w75+fnQ6XSmn3/xxRd49dVXsX79evTv3x+pqamYNGkSAOC9996zxtuyacFqFWRSCbR6A/LLa5BRwA4QERE5H1ED0NKlSzF58mRMmTIFQF3n5ueff8aqVauwaNGiJo//6aefsHv3bqSnp8PX1xcAEBUV1egxBw4cwIABAzB+/HjTz8eNG4fDhw9b9s3YCblMimC1CjklVbiQX44rmrrOGAMQERE5E9GGwLRaLVJSUpCQkNDo/oSEBOzfv7/Z52zZsgVxcXFYvHgxQkND0aFDB7zyyiuoqmrY1G/gwIFISUkxBZ709HRs3boVw4cPt9ybsTPGeUD7LhQCANSuLvBxcxGzJCIiIqsSrQNUWFgIvV6PwMDARvcHBgYiLy+v2eekp6dj3759UKlU+Pbbb1FYWIjnn38excXFpnlAY8eORUFBAQYOHAhBEKDT6fDcc8/h1VdfvWktNTU1qKmpMf25rKzMDO/QdoX7uuJAOrA7tQBAXfdHIuESeCIich6iT4K+8YtXEISbfhkbDAZIJBJ88cUX6NOnDx555BEsXboUGzZsMHWBdu3ahQULFmDlypU4duwYvvnmG/z444948803b1rDokWLoFarTbfw8HDzvUEbZOwA/ZFXDgCI4fAXERE5GdECkL+/P2QyWZNuT35+fpOukFFwcDBCQ0OhVqtN93Xu3BmCICAnJwcA8PrrryMxMRFTpkxB9+7d8Ze//AULFy7EokWLYDAYmr3u3LlzodFoTLfs7GwzvUvbZFwJZhTFAERERE5GtACkUCgQGxuL5OTkRvcnJyejf//+zT5nwIABuHLlCioqKkz3paamQiqVIiwsDABQWVkJqbTx25LJZBAEAYIgNHtdpVIJLy+vRjdHZtwM0YgToImIyNmIOgQ2Z84crF27FuvXr8e5c+cwe/ZsZGVlYfr06QDqOjMTJ040PX78+PHw8/PD008/jbNnz2LPnj3429/+hmeeeQaurnVf6iNGjMCqVauwadMmZGRkIDk5Ga+//joeffRRyGQyUd6nrTEOgRkxABERkbMRdRn8mDFjUFRUhPnz5yM3NxfdunXD1q1bERkZCQDIzc1FVlaW6fEeHh5ITk7Giy++iLi4OPj5+WH06NF46623TI957bXXIJFI8Nprr+Hy5cto06YNRowYgQULFlj9/dmqNp5KKOVS1OjqhgQZgIiIyNlIhJuNCzmxsrIyqNVqaDQahx0Oe2jJLlwsuIYATyUOzxsidjlERER3rTXf36KvAiNxGCdCs/tDRETOiAHISUUwABERkRMTdQ4QiWd0XDjSC65hXJ/mz1wjIiJyZAxATqpbqBqfT4kXuwwiIiJRcAiMiIiInA4DEBERETkdBiAiIiJyOgxARERE5HQYgIiIiMjpMAARERGR02EAIiIiIqfDAEREREROhwGIiIiInA4DEBERETkdBiAiIiJyOgxARERE5HQYgIiIiMjpMAARERGR05GLXYAtEgQBAFBWViZyJURERNRSxu9t4/f4rTAANaO8vBwAEB4eLnIlRERE1Frl5eVQq9W3fIxEaElMcjIGgwFXrlyBp6cnJBKJ2OVYTFlZGcLDw5GdnQ0vLy+xy7EJ/Eya4mfSPH4uTfEzaYqfSfMs9bkIgoDy8nKEhIRAKr31LB92gJohlUoRFhYmdhlW4+Xlxf9j3oCfSVP8TJrHz6UpfiZN8TNpniU+l9t1fow4CZqIiIicDgMQEREROR0GICemVCrxxhtvQKlUil2KzeBn0hQ/k+bxc2mKn0lT/EyaZwufCydBExERkdNhB4iIiIicDgMQEREROR0GICIiInI6DEBOZtGiRbj33nvh6emJgIAAjBw5EufPnxe7LJuyaNEiSCQSzJo1S+xSRHf58mU8+eST8PPzg5ubG+655x6kpKSIXZZodDodXnvtNURHR8PV1RUxMTGYP38+DAaD2KVZ1Z49ezBixAiEhIRAIpHgu+++a/RzQRDwz3/+EyEhIXB1dcUDDzyAM2fOiFOsldzqM6mtrcXf//53dO/eHe7u7ggJCcHEiRNx5coV8Qq2gtv9PbnetGnTIJFIsGzZMqvVxwDkZHbv3o0ZM2bg4MGDSE5Ohk6nQ0JCAq5duyZ2aTbhyJEjWL16NXr06CF2KaIrKSnBgAED4OLigm3btuHs2bNYsmQJvL29xS5NNP/+97/x4YcfYsWKFTh37hwWL16Md955Bx988IHYpVnVtWvX0LNnT6xYsaLZny9evBhLly7FihUrcOTIEQQFBWHo0KGmY4Yc0a0+k8rKShw7dgyvv/46jh07hm+++Qapqal49NFHRajUem7398Tou+++w6FDhxASEmKlyuoJ5NTy8/MFAMLu3bvFLkV05eXlQvv27YXk5GTh/vvvF2bOnCl2SaL6+9//LgwcOFDsMmzK8OHDhWeeeabRfY8//rjw5JNPilSR+AAI3377renPBoNBCAoKEt5++23TfdXV1YJarRY+/PBDESq0vhs/k+YcPnxYACBcunTJOkWJ7GafSU5OjhAaGir8/vvvQmRkpPDee+9ZrSZ2gJycRqMBAPj6+opcifhmzJiB4cOHY8iQIWKXYhO2bNmCuLg4jBo1CgEBAejVqxfWrFkjdlmiGjhwIHbs2IHU1FQAwMmTJ7Fv3z488sgjIldmOzIyMpCXl4eEhATTfUqlEvfffz/2798vYmW2RaPRQCKROHVH1WAwIDExEX/729/QtWtXq78+zwJzYoIgYM6cORg4cCC6desmdjmi2rRpE44dO4YjR46IXYrNSE9Px6pVqzBnzhz84x//wOHDh/HSSy9BqVRi4sSJYpcnir///e/QaDTo1KkTZDIZ9Ho9FixYgHHjxoldms3Iy8sDAAQGBja6PzAwEJcuXRKjJJtTXV2NV199FePHj3fq88H+/e9/Qy6X46WXXhLl9RmAnNgLL7yAU6dOYd++fWKXIqrs7GzMnDkT27dvh0qlErscm2EwGBAXF4eFCxcCAHr16oUzZ85g1apVThuAkpKS8Pnnn2Pjxo3o2rUrTpw4gVmzZiEkJARPPfWU2OXZFIlE0ujPgiA0uc8Z1dbWYuzYsTAYDFi5cqXY5YgmJSUFy5cvx7Fjx0T7e8EhMCf14osvYsuWLdi5c6dTnXzfnJSUFOTn5yM2NhZyuRxyuRy7d+/G+++/D7lcDr1eL3aJoggODkaXLl0a3de5c2dkZWWJVJH4/va3v+HVV1/F2LFj0b17dyQmJmL27NlYtGiR2KXZjKCgIAANnSCj/Pz8Jl0hZ1NbW4vRo0cjIyMDycnJTt392bt3L/Lz8xEREWH6vXvp0iW8/PLLiIqKskoN7AA5GUEQ8OKLL+Lbb7/Frl27EB0dLXZJonvooYdw+vTpRvc9/fTT6NSpE/7+979DJpOJVJm4BgwY0GSLhNTUVERGRopUkfgqKyshlTb+d6NMJnO6ZfC3Eh0djaCgICQnJ6NXr14AAK1Wi927d+Pf//63yNWJxxh+Lly4gJ07d8LPz0/skkSVmJjYZL7lww8/jMTERDz99NNWqYEByMnMmDEDGzduxPfffw9PT0/Tv9LUajVcXV1Frk4cnp6eTeZAubu7w8/Pz6nnRs2ePRv9+/fHwoULMXr0aBw+fBirV6/G6tWrxS5NNCNGjMCCBQsQERGBrl274vjx41i6dCmeeeYZsUuzqoqKCqSlpZn+nJGRgRMnTsDX1xcRERGYNWsWFi5ciPbt26N9+/ZYuHAh3NzcMH78eBGrtqxbfSYhISF44okncOzYMfz444/Q6/Wm372+vr5QKBRilW1Rt/t7cmMIdHFxQVBQEDp27GidAq223oxsAoBmbx9//LHYpdkULoOv88MPPwjdunUTlEql0KlTJ2H16tVilySqsrIyYebMmUJERISgUqmEmJgYYd68eUJNTY3YpVnVzp07m/098tRTTwmCULcU/o033hCCgoIEpVIp3HfffcLp06fFLdrCbvWZZGRk3PR3786dO8Uu3WJu9/fkRtZeBs/T4ImIiMjpcBI0EREROR0GICIiInI6DEBERETkdBiAiIiIyOkwABEREZHTYQAiIiIip8MARERERE6HAYiIiIicDgMQETm1Bx54ALNmzRK7DCKyMgYgIhLVpEmTMHLkyEb3ff3111CpVFi8eLE4RRGRw+NhqERkU9auXYsZM2bgP//5D6ZMmSJ2OUTkoNgBIiKbsXjxYrzwwgvYuHHjbcPPzz//DJVKhdLS0kb3v/TSS7j//vsBAEVFRRg3bhzCwsLg5uaG7t2748svv7zldSUSCb777rtG93l7e2PDhg2mP1++fBljxoyBj48P/Pz88NhjjyEzM9P08127dqFPnz5wd3eHt7c3BgwYgEuXLt32/ROR9TAAEZFNePXVV/Hmm2/ixx9/xF//+tfbPn7IkCHw9vbG5s2bTffp9Xr897//xYQJEwAA1dXViI2NxY8//ojff/8dU6dORWJiIg4dOnTHdVZWVmLw4MHw8PDAnj17sG/fPnh4eOBPf/oTtFotdDodRo4cifvvvx+nTp3CgQMHMHXqVEgkkjt+TSIyPw6BEZHotm3bhu+//x47duzAgw8+2KLnyGQyjBkzBhs3bsTkyZMBADt27EBJSQlGjRoFAAgNDcUrr7xies6LL76In376CV999RXi4+PvqNZNmzZBKpVi7dq1plDz8ccfw9vbG7t27UJcXBw0Gg3+/Oc/o23btgCAzp0739FrEZHlsANERKLr0aMHoqKi8H//938oLy9v8fMmTJiAXbt24cqVKwCAL774Ao888gh8fHwA1HWEFixYgB49esDPzw8eHh7Yvn07srKy7rjWlJQUpKWlwdPTEx4eHvDw8ICvry+qq6tx8eJF+Pr6YtKkSXj44YcxYsQILF++HLm5uXf8ekRkGQxARCS60NBQ7N69G7m5ufjTn/7U4hDUp08ftG3bFps2bUJVVRW+/fZbPPnkk6afL1myBO+99x7+3//7f/j1119x4sQJPPzww9BqtTe9pkQigSAIje6rra01/W+DwYDY2FicOHGi0S01NRXjx48HUNcROnDgAPr374+kpCR06NABBw8ebM1HQkQWxiEwIrIJERER2L17NwYPHoyEhAT8/PPP8PLyuu3zxo8fjy+++AJhYWGQSqUYPny46Wd79+7FY489ZgpFBoMBFy5cuOWQVJs2bRp1bC5cuIDKykrTn3v37o2kpCQEBATcsr5evXqhV69emDt3Lvr164eNGzeib9++t30/RGQd7AARkc0ICwvDrl27UFRUhISEBGg0mts+Z8KECTh27BgWLFiAJ554AiqVyvSzdu3aITk5Gfv378e5c+cwbdo05OXl3fJ6Dz74IFasWIFjx47h6NGjmD59OlxcXBq9nr+/Px577DHs3bsXGRkZ2L17N2bOnImcnBxkZGRg7ty5OHDgAC5duoTt27cjNTWV84CIbAwDEBHZFONwWGlpKYYOHdpkmfuN2rdvj3vvvRenTp0yrf4yev3119G7d288/PDDeOCBBxAUFNRk08UbLVmyBOHh4bjvvvswfvx4vPLKK3BzczP93M3NDXv27EFERAQef/xxdO7cGc888wyqqqrg5eUFNzc3/PHHH/jrX/+KDh06YOrUqXjhhRcwbdq0O/1IiMgCJMKNg91EREREDo4dICIiInI6DEBEZLOMy8ybu+3du1fs8ojIjnEIjIhsVlpa2k1/FhoaCldXVytWQ0SOhAGIiIiInA6HwIiIiMjpMAARERGR02EAIiIiIqfDAEREREROhwGIiIiInA4DEBERETkdBiAiIiJyOgxARERE5HT+P08iKIYMf+J0AAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "score_list=[]\n",
    "\n",
    "for each in range(1,15):\n",
    "    knn2=KNeighborsClassifier(n_neighbors=each)\n",
    "    knn2.fit(x_train, y_train)\n",
    "    score_list.append(knn2.score(x_test,y_test))\n",
    "    \n",
    "plt.plot(range(1,15),score_list)\n",
    "plt.xlabel(\"K_values\")\n",
    "plt.ylabel(\"Accuracy\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "137a18f2",
   "metadata": {
    "papermill": {
     "duration": 0.011953,
     "end_time": "2023-10-27T09:25:17.557286",
     "exception": false,
     "start_time": "2023-10-27T09:25:17.545333",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### Best choice for knn its a thirteen "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ba94f93c",
   "metadata": {
    "papermill": {
     "duration": 0.011859,
     "end_time": "2023-10-27T09:25:17.581172",
     "exception": false,
     "start_time": "2023-10-27T09:25:17.569313",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Naive Bayes Classification\n",
    "<a id=\"5\"></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "1f901fdb",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-10-27T09:25:17.606764Z",
     "iopub.status.busy": "2023-10-27T09:25:17.606333Z",
     "iopub.status.idle": "2023-10-27T09:25:17.613385Z",
     "shell.execute_reply": "2023-10-27T09:25:17.612456Z"
    },
    "papermill": {
     "duration": 0.022535,
     "end_time": "2023-10-27T09:25:17.615509",
     "exception": false,
     "start_time": "2023-10-27T09:25:17.592974",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "x_train, x_test, y_train, y_test = train_test_split(x,y,test_size = 0.3,random_state=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "ae5226b0",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-10-27T09:25:17.641857Z",
     "iopub.status.busy": "2023-10-27T09:25:17.641075Z",
     "iopub.status.idle": "2023-10-27T09:25:17.661627Z",
     "shell.execute_reply": "2023-10-27T09:25:17.660320Z"
    },
    "papermill": {
     "duration": 0.036703,
     "end_time": "2023-10-27T09:25:17.664288",
     "exception": false,
     "start_time": "2023-10-27T09:25:17.627585",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " naive bayes Accuracy rate :  81.72043010752688\n"
     ]
    }
   ],
   "source": [
    "from sklearn.naive_bayes import GaussianNB\n",
    "nb = GaussianNB()\n",
    "nb.fit(x_train,y_train)\n",
    "\n",
    "print(\" naive bayes Accuracy rate : \",nb.score(x_test,y_test)*100)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "82bb8fe7",
   "metadata": {
    "papermill": {
     "duration": 0.011851,
     "end_time": "2023-10-27T09:25:17.688190",
     "exception": false,
     "start_time": "2023-10-27T09:25:17.676339",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "###  naive bayes Accuracy rate :  81.72"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2fab62f8",
   "metadata": {
    "papermill": {
     "duration": 0.011815,
     "end_time": "2023-10-27T09:25:17.712323",
     "exception": false,
     "start_time": "2023-10-27T09:25:17.700508",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Random Forrest Classification\n",
    "<a id=\"6\"></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "bb2c8b39",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-10-27T09:25:17.739675Z",
     "iopub.status.busy": "2023-10-27T09:25:17.739264Z",
     "iopub.status.idle": "2023-10-27T09:25:17.746331Z",
     "shell.execute_reply": "2023-10-27T09:25:17.745147Z"
    },
    "papermill": {
     "duration": 0.02441,
     "end_time": "2023-10-27T09:25:17.748768",
     "exception": false,
     "start_time": "2023-10-27T09:25:17.724358",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "x_train, x_test,y_train, y_test = train_test_split(x,y,test_size = 0.15,random_state = 42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "8e42f155",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-10-27T09:25:17.775701Z",
     "iopub.status.busy": "2023-10-27T09:25:17.775325Z",
     "iopub.status.idle": "2023-10-27T09:25:18.208183Z",
     "shell.execute_reply": "2023-10-27T09:25:18.206995Z"
    },
    "papermill": {
     "duration": 0.44943,
     "end_time": "2023-10-27T09:25:18.210809",
     "exception": false,
     "start_time": "2023-10-27T09:25:17.761379",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "random forest algo result:  85.1063829787234\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "rf = RandomForestClassifier(n_estimators = 100,random_state = 1)\n",
    "rf.fit(x_train,y_train)\n",
    "print(\"random forest algo result: \",rf.score(x_test,y_test)*100)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c16fda09",
   "metadata": {
    "papermill": {
     "duration": 0.01208,
     "end_time": "2023-10-27T09:25:18.235581",
     "exception": false,
     "start_time": "2023-10-27T09:25:18.223501",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### random forest algo result:  85.1"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3fb1d7ea",
   "metadata": {
    "papermill": {
     "duration": 0.01226,
     "end_time": "2023-10-27T09:25:18.260169",
     "exception": false,
     "start_time": "2023-10-27T09:25:18.247909",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Support Vector Machines Classification\n",
    "<a id=\"7\"></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "28921837",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-10-27T09:25:18.288922Z",
     "iopub.status.busy": "2023-10-27T09:25:18.288317Z",
     "iopub.status.idle": "2023-10-27T09:25:18.294392Z",
     "shell.execute_reply": "2023-10-27T09:25:18.293443Z"
    },
    "papermill": {
     "duration": 0.02207,
     "end_time": "2023-10-27T09:25:18.296615",
     "exception": false,
     "start_time": "2023-10-27T09:25:18.274545",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "x_train, x_test, y_train, y_test = train_test_split(x,y,test_size = 0.3,random_state=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "3cce058f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-10-27T09:25:18.323156Z",
     "iopub.status.busy": "2023-10-27T09:25:18.322702Z",
     "iopub.status.idle": "2023-10-27T09:25:18.337644Z",
     "shell.execute_reply": "2023-10-27T09:25:18.336145Z"
    },
    "papermill": {
     "duration": 0.030894,
     "end_time": "2023-10-27T09:25:18.340022",
     "exception": false,
     "start_time": "2023-10-27T09:25:18.309128",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " accuracy of svm algo:  80.64516129032258\n"
     ]
    }
   ],
   "source": [
    "from sklearn.svm import SVC\n",
    " \n",
    "svm = SVC(random_state = 1)\n",
    "svm.fit(x_train,y_train)\n",
    "\n",
    "print(\" accuracy of svm algo: \",svm.score(x_test,y_test)*100)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d8bf24c6",
   "metadata": {
    "papermill": {
     "duration": 0.012098,
     "end_time": "2023-10-27T09:25:18.364914",
     "exception": false,
     "start_time": "2023-10-27T09:25:18.352816",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### accuracy of svm algo:  80.65"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9bfc7f51",
   "metadata": {
    "papermill": {
     "duration": 0.011976,
     "end_time": "2023-10-27T09:25:18.389179",
     "exception": false,
     "start_time": "2023-10-27T09:25:18.377203",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Decision Tree Classification\n",
    "<a id=\"8\"></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "4fb1d1fa",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-10-27T09:25:18.415533Z",
     "iopub.status.busy": "2023-10-27T09:25:18.415099Z",
     "iopub.status.idle": "2023-10-27T09:25:18.421922Z",
     "shell.execute_reply": "2023-10-27T09:25:18.420904Z"
    },
    "papermill": {
     "duration": 0.022421,
     "end_time": "2023-10-27T09:25:18.424074",
     "exception": false,
     "start_time": "2023-10-27T09:25:18.401653",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# train test split\n",
    "from sklearn.model_selection import train_test_split\n",
    "x_train, x_test,y_train, y_test = train_test_split(x,y,test_size = 0.15,random_state = 42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "ea223d8d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-10-27T09:25:18.450706Z",
     "iopub.status.busy": "2023-10-27T09:25:18.450320Z",
     "iopub.status.idle": "2023-10-27T09:25:18.462358Z",
     "shell.execute_reply": "2023-10-27T09:25:18.461246Z"
    },
    "papermill": {
     "duration": 0.028085,
     "end_time": "2023-10-27T09:25:18.464536",
     "exception": false,
     "start_time": "2023-10-27T09:25:18.436451",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " Decision Tree Classifier of Accuracy rate:  74.46808510638297\n"
     ]
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "dt = DecisionTreeClassifier()\n",
    "dt.fit(x_train,y_train)\n",
    "\n",
    "print(\" Decision Tree Classifier of Accuracy rate: \", dt.score(x_test,y_test)*100)\n"
   ]
  }
 ],
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