UGotTheJob.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 364,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sbs\n",
    "from collections import Counter\n",
    "from joblib import  dump"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 365,
   "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>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ssc_percentage</th>\n",
       "      <td>215.0</td>\n",
       "      <td>67.303395</td>\n",
       "      <td>10.827205</td>\n",
       "      <td>40.89</td>\n",
       "      <td>60.600</td>\n",
       "      <td>67.0</td>\n",
       "      <td>75.700</td>\n",
       "      <td>89.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hsc_percentage</th>\n",
       "      <td>215.0</td>\n",
       "      <td>66.333163</td>\n",
       "      <td>10.897509</td>\n",
       "      <td>37.00</td>\n",
       "      <td>60.900</td>\n",
       "      <td>65.0</td>\n",
       "      <td>73.000</td>\n",
       "      <td>97.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>degree_percentage</th>\n",
       "      <td>215.0</td>\n",
       "      <td>66.370186</td>\n",
       "      <td>7.358743</td>\n",
       "      <td>50.00</td>\n",
       "      <td>61.000</td>\n",
       "      <td>66.0</td>\n",
       "      <td>72.000</td>\n",
       "      <td>91.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>emp_test_percentage</th>\n",
       "      <td>215.0</td>\n",
       "      <td>72.100558</td>\n",
       "      <td>13.275956</td>\n",
       "      <td>50.00</td>\n",
       "      <td>60.000</td>\n",
       "      <td>71.0</td>\n",
       "      <td>83.500</td>\n",
       "      <td>98.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mba_percent</th>\n",
       "      <td>215.0</td>\n",
       "      <td>62.278186</td>\n",
       "      <td>5.833385</td>\n",
       "      <td>51.21</td>\n",
       "      <td>57.945</td>\n",
       "      <td>62.0</td>\n",
       "      <td>66.255</td>\n",
       "      <td>77.89</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     count       mean        std    min     25%   50%     75%  \\\n",
       "ssc_percentage       215.0  67.303395  10.827205  40.89  60.600  67.0  75.700   \n",
       "hsc_percentage       215.0  66.333163  10.897509  37.00  60.900  65.0  73.000   \n",
       "degree_percentage    215.0  66.370186   7.358743  50.00  61.000  66.0  72.000   \n",
       "emp_test_percentage  215.0  72.100558  13.275956  50.00  60.000  71.0  83.500   \n",
       "mba_percent          215.0  62.278186   5.833385  51.21  57.945  62.0  66.255   \n",
       "\n",
       "                       max  \n",
       "ssc_percentage       89.40  \n",
       "hsc_percentage       97.70  \n",
       "degree_percentage    91.00  \n",
       "emp_test_percentage  98.00  \n",
       "mba_percent          77.89  "
      ]
     },
     "execution_count": 365,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"Job_Placement_Data.csv\")\n",
    "\n",
    "df.describe().transpose()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 366,
   "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>gender</th>\n",
       "      <th>ssc_percentage</th>\n",
       "      <th>ssc_board</th>\n",
       "      <th>hsc_percentage</th>\n",
       "      <th>hsc_board</th>\n",
       "      <th>hsc_subject</th>\n",
       "      <th>degree_percentage</th>\n",
       "      <th>undergrad_degree</th>\n",
       "      <th>work_experience</th>\n",
       "      <th>emp_test_percentage</th>\n",
       "      <th>specialisation</th>\n",
       "      <th>mba_percent</th>\n",
       "      <th>status</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>M</td>\n",
       "      <td>67.00</td>\n",
       "      <td>Others</td>\n",
       "      <td>91.00</td>\n",
       "      <td>Others</td>\n",
       "      <td>Commerce</td>\n",
       "      <td>58.00</td>\n",
       "      <td>Sci&amp;Tech</td>\n",
       "      <td>No</td>\n",
       "      <td>55.0</td>\n",
       "      <td>Mkt&amp;HR</td>\n",
       "      <td>58.80</td>\n",
       "      <td>Placed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>M</td>\n",
       "      <td>79.33</td>\n",
       "      <td>Central</td>\n",
       "      <td>78.33</td>\n",
       "      <td>Others</td>\n",
       "      <td>Science</td>\n",
       "      <td>77.48</td>\n",
       "      <td>Sci&amp;Tech</td>\n",
       "      <td>Yes</td>\n",
       "      <td>86.5</td>\n",
       "      <td>Mkt&amp;Fin</td>\n",
       "      <td>66.28</td>\n",
       "      <td>Placed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>M</td>\n",
       "      <td>65.00</td>\n",
       "      <td>Central</td>\n",
       "      <td>68.00</td>\n",
       "      <td>Central</td>\n",
       "      <td>Arts</td>\n",
       "      <td>64.00</td>\n",
       "      <td>Comm&amp;Mgmt</td>\n",
       "      <td>No</td>\n",
       "      <td>75.0</td>\n",
       "      <td>Mkt&amp;Fin</td>\n",
       "      <td>57.80</td>\n",
       "      <td>Placed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>M</td>\n",
       "      <td>56.00</td>\n",
       "      <td>Central</td>\n",
       "      <td>52.00</td>\n",
       "      <td>Central</td>\n",
       "      <td>Science</td>\n",
       "      <td>52.00</td>\n",
       "      <td>Sci&amp;Tech</td>\n",
       "      <td>No</td>\n",
       "      <td>66.0</td>\n",
       "      <td>Mkt&amp;HR</td>\n",
       "      <td>59.43</td>\n",
       "      <td>Not Placed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>M</td>\n",
       "      <td>85.80</td>\n",
       "      <td>Central</td>\n",
       "      <td>73.60</td>\n",
       "      <td>Central</td>\n",
       "      <td>Commerce</td>\n",
       "      <td>73.30</td>\n",
       "      <td>Comm&amp;Mgmt</td>\n",
       "      <td>No</td>\n",
       "      <td>96.8</td>\n",
       "      <td>Mkt&amp;Fin</td>\n",
       "      <td>55.50</td>\n",
       "      <td>Placed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>210</th>\n",
       "      <td>M</td>\n",
       "      <td>80.60</td>\n",
       "      <td>Others</td>\n",
       "      <td>82.00</td>\n",
       "      <td>Others</td>\n",
       "      <td>Commerce</td>\n",
       "      <td>77.60</td>\n",
       "      <td>Comm&amp;Mgmt</td>\n",
       "      <td>No</td>\n",
       "      <td>91.0</td>\n",
       "      <td>Mkt&amp;Fin</td>\n",
       "      <td>74.49</td>\n",
       "      <td>Placed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>211</th>\n",
       "      <td>M</td>\n",
       "      <td>58.00</td>\n",
       "      <td>Others</td>\n",
       "      <td>60.00</td>\n",
       "      <td>Others</td>\n",
       "      <td>Science</td>\n",
       "      <td>72.00</td>\n",
       "      <td>Sci&amp;Tech</td>\n",
       "      <td>No</td>\n",
       "      <td>74.0</td>\n",
       "      <td>Mkt&amp;Fin</td>\n",
       "      <td>53.62</td>\n",
       "      <td>Placed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>212</th>\n",
       "      <td>M</td>\n",
       "      <td>67.00</td>\n",
       "      <td>Others</td>\n",
       "      <td>67.00</td>\n",
       "      <td>Others</td>\n",
       "      <td>Commerce</td>\n",
       "      <td>73.00</td>\n",
       "      <td>Comm&amp;Mgmt</td>\n",
       "      <td>Yes</td>\n",
       "      <td>59.0</td>\n",
       "      <td>Mkt&amp;Fin</td>\n",
       "      <td>69.72</td>\n",
       "      <td>Placed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>213</th>\n",
       "      <td>F</td>\n",
       "      <td>74.00</td>\n",
       "      <td>Others</td>\n",
       "      <td>66.00</td>\n",
       "      <td>Others</td>\n",
       "      <td>Commerce</td>\n",
       "      <td>58.00</td>\n",
       "      <td>Comm&amp;Mgmt</td>\n",
       "      <td>No</td>\n",
       "      <td>70.0</td>\n",
       "      <td>Mkt&amp;HR</td>\n",
       "      <td>60.23</td>\n",
       "      <td>Placed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>214</th>\n",
       "      <td>M</td>\n",
       "      <td>62.00</td>\n",
       "      <td>Central</td>\n",
       "      <td>58.00</td>\n",
       "      <td>Others</td>\n",
       "      <td>Science</td>\n",
       "      <td>53.00</td>\n",
       "      <td>Comm&amp;Mgmt</td>\n",
       "      <td>No</td>\n",
       "      <td>89.0</td>\n",
       "      <td>Mkt&amp;HR</td>\n",
       "      <td>60.22</td>\n",
       "      <td>Not Placed</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>215 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    gender  ssc_percentage ssc_board  hsc_percentage hsc_board hsc_subject  \\\n",
       "0        M           67.00    Others           91.00    Others    Commerce   \n",
       "1        M           79.33   Central           78.33    Others     Science   \n",
       "2        M           65.00   Central           68.00   Central        Arts   \n",
       "3        M           56.00   Central           52.00   Central     Science   \n",
       "4        M           85.80   Central           73.60   Central    Commerce   \n",
       "..     ...             ...       ...             ...       ...         ...   \n",
       "210      M           80.60    Others           82.00    Others    Commerce   \n",
       "211      M           58.00    Others           60.00    Others     Science   \n",
       "212      M           67.00    Others           67.00    Others    Commerce   \n",
       "213      F           74.00    Others           66.00    Others    Commerce   \n",
       "214      M           62.00   Central           58.00    Others     Science   \n",
       "\n",
       "     degree_percentage undergrad_degree work_experience  emp_test_percentage  \\\n",
       "0                58.00         Sci&Tech              No                 55.0   \n",
       "1                77.48         Sci&Tech             Yes                 86.5   \n",
       "2                64.00        Comm&Mgmt              No                 75.0   \n",
       "3                52.00         Sci&Tech              No                 66.0   \n",
       "4                73.30        Comm&Mgmt              No                 96.8   \n",
       "..                 ...              ...             ...                  ...   \n",
       "210              77.60        Comm&Mgmt              No                 91.0   \n",
       "211              72.00         Sci&Tech              No                 74.0   \n",
       "212              73.00        Comm&Mgmt             Yes                 59.0   \n",
       "213              58.00        Comm&Mgmt              No                 70.0   \n",
       "214              53.00        Comm&Mgmt              No                 89.0   \n",
       "\n",
       "    specialisation  mba_percent      status  \n",
       "0           Mkt&HR        58.80      Placed  \n",
       "1          Mkt&Fin        66.28      Placed  \n",
       "2          Mkt&Fin        57.80      Placed  \n",
       "3           Mkt&HR        59.43  Not Placed  \n",
       "4          Mkt&Fin        55.50      Placed  \n",
       "..             ...          ...         ...  \n",
       "210        Mkt&Fin        74.49      Placed  \n",
       "211        Mkt&Fin        53.62      Placed  \n",
       "212        Mkt&Fin        69.72      Placed  \n",
       "213         Mkt&HR        60.23      Placed  \n",
       "214         Mkt&HR        60.22  Not Placed  \n",
       "\n",
       "[215 rows x 13 columns]"
      ]
     },
     "execution_count": 366,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Preparation\n",
    "\n",
    "## Data Cleaning\n",
    "Non sono presenti variabili con valori nulli nel dataset\n",
    "\n",
    "## Feature Selection\n",
    "\n",
    "### Influenza degli attributi per il piazzamento nel lavoro"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 367,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 215 entries, 0 to 214\n",
      "Data columns (total 13 columns):\n",
      " #   Column               Non-Null Count  Dtype  \n",
      "---  ------               --------------  -----  \n",
      " 0   gender               215 non-null    object \n",
      " 1   ssc_percentage       215 non-null    float64\n",
      " 2   ssc_board            215 non-null    object \n",
      " 3   hsc_percentage       215 non-null    float64\n",
      " 4   hsc_board            215 non-null    object \n",
      " 5   hsc_subject          215 non-null    object \n",
      " 6   degree_percentage    215 non-null    float64\n",
      " 7   undergrad_degree     215 non-null    object \n",
      " 8   work_experience      215 non-null    object \n",
      " 9   emp_test_percentage  215 non-null    float64\n",
      " 10  specialisation       215 non-null    object \n",
      " 11  mba_percent          215 non-null    float64\n",
      " 12  status               215 non-null    object \n",
      "dtypes: float64(5), object(8)\n",
      "memory usage: 22.0+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 368,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='ssc_percentage', ylabel='Density'>"
      ]
     },
     "execution_count": 368,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "numerical_features = df.select_dtypes(exclude=\"object\").columns.tolist()\n",
    "categorical_features = df.select_dtypes(exclude=\"float64\").columns.tolist()\n",
    "\n",
    "sbs.kdeplot(data=df,x=numerical_features[0],hue=\"status\",palette=\"Greens\",fill=True)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Da queso grafico si può notare che la variabile ssc_percentage influisce, in quanto più è alto il valore è più c'è possibilità di essere piazzati. Alla fine abbiamo deciso di  non rimuoverla. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Adesso valutiamo il grado d'influenza della variabile hsc_percentage:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 369,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='hsc_percentage', ylabel='Density'>"
      ]
     },
     "execution_count": 369,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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HSUlJOHDgABISEvD222/j/fffx4QJEwAAS5cuRY8ePTrqIyuKBRIRBZ3y+lIY1UYY1O0/p41BY4RFZ0WBPZ8FEgWEVatWITQ0FB6PB7Is43e/+x3mzZuHFStWNGpnt9sxb948fPHFFyguLobX60V9fb3/DlJOTg7UajXGjx/f5Hl27NiB77//3n9H6dfy8vJQX18Pt9vdaEmuiIgIDBjQ+olaAxkLJCIKOhUNZTB3YCfqGGM8Dtbug8NjR4j29F8WRJ3pkksuwRtvvAGdToeEhARoNE3/6n7ooYeQmZmJf/zjH+jbty+MRiNuuOEG/+M4o/HsAxrsdjuuueYavPjii6e9Fx8fj0OHDrX9wwQR9kEioqDi9rlR57HBrLN02Dmi9NFQS2oU2vM77BxEzRUSEoK+ffsiOTn5jMURAKxfvx4zZ87Eddddh7S0NMTFxeHIkSP+99PS0iDLMtauXdvk/ueffz727NmDlJQU9O3bt9ErJCQEffr0gVarxaZNm/z7VFdX48CBA+32WQMJCyQiCio17ioAgFnbcQWSWqVGlCEGBXX54GpMFCz69euHjz/+GDk5OdixYwd+97vfQZZl//spKSmYMWMG/vjHP2LlypXIz8/HmjVr8N///hcAcPfdd6OqqgrTpk3Dli1bkJeXh6+//hq33norfD4fQkNDMWvWLDz88MP47rvvsHv3bsycObPReqldCR+xEVFQqWqogEbSwNCO8x81JdYYj9L6YlQ0lCHaGNuh5yJlOb2OLnGel19+GX/84x8xduxYREVF4dFHH4XNZmvU5o033sDjjz+Ou+66C5WVlUhOTvZPOpmQkID169fj0UcfxWWXXQaXy4WePXvi8ssv9xdBL730kv9RXFhYGB588EHU1tZ26OdSCherbSUuVkukjA0la+H2uXFexJAOPY8QAtkVmxFljMGI6NEdei7qWGdauDRYJoqklmuPxWp5B4mIgoYQAlWuCsSbEjv8XJIkIdoYi+OOQngjh0Oj0nb4OalzmTQhmNjjyoBeaoSUwwKJiIKG3VsHj+xBWAf2P/q1GGMcCu35OO44hp4c8t8lmTQhLFioSV2zZxURdUnVDZUAgDBtWKecz6A2wKILR6H9cKecj4gCBwskIgoaVa5KmNSmTn3cFWOMQ0VDORwee6edk4iUxwKJiIJGjasKIZ109+ikk3MiHbUf6dTzEpGyWCARUVAQQkatpxahnVwgqVVqRBqiUWDnnEhE3QkLJCIKCnWeOsjChxBN5y/9EWuMg9PrQKWrotPPTUTKYIFEREGh1l0NAAhVYG00s9YKg9qAwjouPULUXbBAIqKgUOOqhkFtUGQ+IkmSEG2IwzFHIbyyt9PPT0SdjwUSEQWFWnc1QjSd2//o12KNcfAJL447jiqWgSgQzJw5E5MnT1Y6BiRJwsqVKzvs+JwokogCnhACNe4axJsSFMtg0Bhh0YWjoO4wJ43sQkrsFahx2c7dsJ1Y9WbEhUY1u/3MmTOxdOlSzJ8/H4899ph/+8qVK3Hddde1aOBASkoK7r//ftx///3nbFdQUAAAMJlMGDBgAObMmYMbb7yx2efqClggEVHAq/c54ZHdit5BAk7cRTpQuw92T12nj6aj9ldir8DNK+9Hg8/daec0qHVYPnlhi4okg8GAF198EXfccQfCw8M7MN0vnnnmGdx2222w2WxYsGABpk6disTERIwdO7ZTzh8IWCARUcCrcSnXQfvXIg3R0NgOoqAuv8MXy6WOV+OyocHnxoy06xAXGt3h5yuxl2Pprk9Q47K1qECaOHEiDh06hPnz5+Pvf//7Gdv973//w9y5c3Ho0CHEx8fj3nvvxYMPPggAuPjii1FQUIAHHngADzzwAACc9e5TWFgY4uLiEBcXh9dffx3vv/8+Pv/88yYLpNWrV+O5557D7t27oVarMWbMGLz66qvo06ePv82xY8fw8MMP4+uvv4bL5UJqaipef/11ZGRkAAA+/fRTPP3009i7dy8SEhIwY8YM/PWvf4VGc6JMOXjwIGbNmoXNmzejd+/eePXVV5v982stFkhEFPBs7hpoVVroVHpFc6glNaKNsSiwH0Zq+GCoJHbj7AriQqORbI5XOsYZqdVqPP/88/jd736Hv/zlL+jRo8dpbbZt24abbroJ8+bNw9SpU7FhwwbcddddiIyMxMyZM/Hxxx9j6NChuP3223Hbbbe16PwajQZarRZud9N32hwOB2bPno0hQ4bAbrdj7ty5uO6665CTkwOVSgW73Y7x48cjMTERn332GeLi4pCdnQ1ZlgEAP/zwA6ZPn45FixbhwgsvRF5eHm6//XYAwFNPPQVZlnH99dcjNjYWmzZtQm1t7TkfE7YHFkhEFPBsnlqYNCGQJEnpKIg1xqPYeRwlziIkhJz+i4qoI1x33XVIT0/HU089hbfffvu0919++WVMmDABTz75JACgf//+2Lt3L1566SXMnDkTERERUKvV/jtDzeV2u7FgwQLU1tbiN7/5TZNtpkyZ0ujP77zzDqKjo7F3714MHjwYy5YtQ3l5ObZs2YKIiAgAQN++ff3tn376aTz22GOYMWMGAKB379549tln8cgjj+Cpp57Ct99+i/379+Prr79GQsKJfojPP/88rrjiimZ/jtbgX3+IKODZ3LUBs+J6qDYMYVozjtTlKR2FupkXX3wRS5cuxb59+057b9++fbjgggsabbvgggtw8OBB+Hy+Fp/r0UcfRWhoKEwmE1588UW88MILuOqqq5pse/DgQUybNg29e/eG2WxGSkoKAKCwsBAAkJOTg2HDhvmLo1Pt2LEDzzzzDEJDQ/2v2267DcXFxXA6ndi3bx+SkpL8xREAjBkzpsWfqaV4B4mIAposfLB76hBjbP7fejtarDEeh2y5cHodAVO4Udd30UUXYdKkSZgzZw5mzpzZoed6+OGHMXPmTISGhiI2Nvasd2+vueYa9OzZE2+99RYSEhIgyzIGDx7sfyRnNBrPei673Y6nn34a119//WnvGQyGtn2QNmCBREQBze6pg4AIqEIk2hCD/LpDOGLLwyB21qZO9MILLyA9PR0DBgxotD01NRXr169vtG39+vXo378/1Go1AECn0zX7blJUVFSjx2BnUllZidzcXLz11lu48MILAQA//vhjozZDhgzB//3f/6GqqqrJu0jnn38+cnNzz3i+1NRUHD16FMXFxYiPP9FX7KeffmrW52gLPmIjooBmc9cCQEAVSGqVBjHGOOTX5cEnWv74gqi10tLScMstt2DRokWNtj/44IPIysrCs88+iwMHDmDp0qV47bXX8NBDD/nbpKSkYN26dTh+/DgqKtpnXcHw8HBERkbizTffxKFDh/Ddd99h9uzZjdpMmzYNcXFxmDx5MtavX4/Dhw/jf//7HzZu3AgAmDt3Lt577z08/fTT2LNnD/bt24fly5fjiSeeAHBiFF///v0xY8YM7NixAz/88AP++te/tkv+s+EdJCIKaDZPLXQqPbQKLDFyNvGmRBQ7j6PIcRRJoSlKx6E2KLGXB9V5nnnmGXz44YeNtp1//vn473//i7lz5+LZZ59FfHw8nnnmmUaP4p555hnccccd6NOnD1wuV4smmTwTlUqF5cuX4y9/+QsGDx6MAQMGYNGiRbj44ov9bXQ6Hb755hs8+OCDuPLKK+H1ejFo0CC8/vrrAIBJkyZh1apVeOaZZ/Diiy9Cq9Vi4MCB+NOf/uQ/xyeffIJZs2Zh1KhRSElJwaJFi3D55Ze3Of/ZSKI9fkLdkM1mg8ViQW1tLcxms9JxiLqsn0p/QL3XicER6UpHOc3uqhxIkgoXJ1yqdBQ6i4aGBuTn56NXr16N+rQEy0SR1HJnuuZA839/8w4SEQU0m7sWFp1V6RhNijclYl/NblS7KhGuj1Q6DrVQXGgUlk9eGNBLjZByWCARUcDyyV44vHbEmxKVjtKkCH0UDGojDtbsx6jYC869AwWcuNAoFizUJHbSJqKAVeepAxBYHbR/TZIkJJh6oMh5DA6PXek4RNSOWCARUcCq8wTeCLZTxRrjoZY0yLMdUDoKEbUjFkhEFLDqPHXQqfTQqAK3N4BapUa8KQFH6g7D7XMpHYeI2gkLJCIKWHaPDUbN2WfhDQTxph4QELyLFOA4aLv7aJcpDNohBxFRh7C5bTCqTUrHOCedWod4YwIO1R6AuxOHjFPzaLUn5tByOp0KJ6HOcvJan7z2rRG4962JqFsTQobDW4coQ7TSUZolMSQJxc7jOGw7gIHhg5WOQ7+iVqthtVpRVlYGADCZTGddW4yClxACTqcTZWVlsFqt/mVWWoMFEhEFJKfXCVnIMGkC/w4SAOjUesSZEnCwNhe9zP2gV+uVjkS/Ehd3YrHjk0USdW1Wq9V/zVuLBRIRBaQ6z4nJ+4wBPILtVD1CeqK0vhgHavYiLXKY0nHoVyRJQnx8PGJiYuDxeJSOQx1Iq9W26c7RSSyQiCgg2T11UEkq6FXBcydGp9YhMSQJh20H0cfcHyZt8BR33YVarW6XX57U9bGTNhEFpDrPiQ7awdZXJNGUBLVKgz3VO5WOQkRtEBAF0uuvv46UlBQYDAZkZGRg8+bNZ22/YsUKDBw4EAaDAWlpafjyyy/973k8Hjz66KNIS0tDSEgIEhISMH36dBQVFTU6RkpKCiRJavR64YUXOuTzEVHLnRjiHxz9j35NrdKgZ2gvHHMUoKKe/V2IgpXiBdKHH36I2bNn46mnnkJ2djaGDh2KSZMmnbEj3YYNGzBt2jTMmjUL27dvx+TJkzF58mTs3r0bwImhfdnZ2XjyySeRnZ2Njz/+GLm5ufjtb3972rGeeeYZFBcX+1/33ntvh35WImq+Ok9dUAzxb0qsMR5hWjN2VG6DLGSl4xBRK0hC4ZmzMjIyMHLkSLz22msAAFmWkZSUhHvvvRePPfbYae2nTp0Kh8OBVatW+beNHj0a6enpWLx4cZPn2LJlC0aNGoWCggIkJycDOHEH6f7778f999/fqtw2mw0WiwW1tbUwm82tOgYRNc0ju7Gq4GMMsAxCtDFW6TitYvfUIadyKwZHpKOfZaDScYjoZ839/a3oHSS3241t27Zh4sSJ/m0qlQoTJ07Exo0bm9xn48aNjdoDwKRJk87YHgBqa2shSRKsVmuj7S+88AIiIyMxbNgwvPTSS/B6vWc8hsvlgs1ma/Qioo5h/3mR2mB8xHZSqDYMCaYe2Fu90z8ij4iCh6IFUkVFBXw+H2JjG/8NMTY2FiUlJU3uU1JS0qL2DQ0NePTRRzFt2rRGleJf/vIXLF++HN9//z3uuOMOPP/883jkkUfOmHX+/PmwWCz+V1JSUnM/JhG10MkCyaAO/GVGzqZnWG/oVHpkl2+C4KM2oqDSpYf5ezwe3HTTTRBC4I033mj03uzZs/3/PmTIEOh0Otxxxx2YP38+9PrThxXPmTOn0T42m41FElEHcXjs0Kp0Ab1IbXOoJTX6WQZiV9V2HKjdhwHW85SORETNpOgdpKioKKjVapSWljbaXlpaesYZMOPi4prV/mRxVFBQgMzMzHP2E8rIyIDX68WRI0eafF+v18NsNjd6EVHHsHvqYAzyu0cnWXRWJIX0xN7qXaho4Kg2omChaIGk0+kwfPhwZGVl+bfJsoysrCyMGTOmyX3GjBnTqD0AZGZmNmp/sjg6ePAgvv32W0RGRp4zS05ODlQqFWJiYlr5aYiovdg9dUH/eO3XkkNTYNZZsaVsI1y+BqXjEFEzKH7/evbs2ZgxYwZGjBiBUaNGYeHChXA4HLj11lsBANOnT0diYiLmz58PALjvvvswfvx4LFiwAFdddRWWL1+OrVu34s033wRwoji64YYbkJ2djVWrVsHn8/n7J0VERECn02Hjxo3YtGkTLrnkEoSFhWHjxo144IEH8Pvf/x7h4eHK/CCIyM/utSPBlKh0jHYjSSoMsAxCTuVW/FT6I8bFXwK1xNmciQKZ4gXS1KlTUV5ejrlz56KkpATp6elYvXq1vyN2YWEhVKpfbnSNHTsWy5YtwxNPPIHHH38c/fr1w8qVKzF48InVs48fP47PPvsMAJCent7oXN9//z0uvvhi6PV6LF++HPPmzYPL5UKvXr3wwAMPNOpjRETKcPtc8MhuGIJ0DqQz0av1SLUOxq6q7cip2Irzo0YF3SzhRN2J4vMgBSvOg0TUMaoaKrG2OBPpkSMQqg1TOk67K60vwcHafUi1pmFgODttE3W25v7+VvwOEhHRr9m9XWOI/5nEGuPg8jVgX80uGDRGpIT1VjoSETWBBRIRBRSHp65LDPE/m6SQnnD7XNhesRlalRaJIZwyhCjQKL4WGxHRr9k99i4zxP9MJElCH3N/RBlisKVsA0qdxUpHIqJTsEAiooBi99TBoOnaBRJwokjqb0lFuD4CP5X9gPL60nPvRESdhgUSEQUUh7frTBJ5LipJhYHW82DWWrCxdB0qGyqUjkREP2OBREQB48QQf0+X7aDdFJWkRmp4GkK0YdhQsgY1riqlIxERWCARUQBxeO0A0C0esf2aWlJjkDUNRo0JP5asgc1dq3Qkom6PBRIRBQyHxwEA3eYR269pVBqcFz4EOpUO60vWwOl1KB2JqFtjgUREAcPhrYNG0kCj0iodRREalRaDwocAEFhfsgZun0vpSETdFgskIgoYDo+92z1eO5Vercd54UPR4GvAprIfIQuf0pGIuiUWSEQUMBxee7fqoH0mRo0JqdbBqGyowPaKreCKUESdjwUSEQUMu8cOg9qgdIyAYNFZ0dcyAIX2fBypy1M6DlG3wwKJiAKCT/jQ4KvnHaRfiTXGI96UiJ2V2ah2VSodh6hbYYFERAHB+fMINhZIjfUK64sQbSg2l66HR/YoHYeo22CBREQBobvOgXQuKkmFAZZBcMku7KzcpnQcom6DBRIRBQSHxw4JEvQqvdJRAo5BY0TvsH4otB/BccdRpeMQdQsskIgoIJwcwSZJktJRAlKMMQ6R+ijsqNgKt8+tdByiLo8FEhEFBIeHQ/zPRpIk9Db3h1d4sad6h9JxiLo8FkhEFBAcXjsMGg7xPxu9Wo+eob1xpC4PFQ3lSsch6tJYIBGR4oQQcHodnAOpGeJNiQjVhmFnZTYnkCTqQCyQiEhxLtkFn/BBz0ds5yRJEnqF9UWtuxqF9iNKxyHqslggEZHinJ6fh/jzDlKzWHRWRBlisKd6B7ycG4moQ7BAIiLFObycJLKlUkJ7w+1zIc92UOkoRF0SCyQiUpzTa4dG0kKj0igdJWgYNEbEmRJwsHYfPDKH/RO1NxZIRKQ4h8fBEWyt0COkJ3yyD4dqc5WOQtTlsEAiIsU5vHboVSyQWkqv1iPOlIhDtbmcPJKonbFAIiLFOb28g9RaPUKSIAsZ+XXsi0TUnlggEZGiZCGj3utkB+1W0qn1iDHG4VDtAfhkr9JxiLoMFkhEpKh6rxMCgkP82yAxJBlu2YUCe77SUYi6DBZIRKQoJ4f4t5lRY0SUIQYHa/dDCFnpOERdAgskIlKUw3tikkg97yC1SaIpCU6vAyXOYqWjEHUJLJCISFFOjwN6lR4qiV9HbRGmMyNMa0ae7YDSUYi6BH4jEZGiHF4H7x61k3hTD5Q3lMLmrlU6ClHQY4FERIpyeu0skNpJlCEaOpUOh7n8CFGbsUAiIkU5vQ6OYGsnKkmFWGM8jtqPcBFbojZigUREivEJHxp8DRzB1o5iTQnwCi+OOQqVjkIU1FggEZFiTg7x5yO29mNQGxCui8ARW57SUYiCGgskIlKM03NyDiQWSO0p1pSAancVat01SkchCloskIhIMSfvIOnUeoWTdC0R+khoVToU1B1WOgpR0GKBRESKcf48xJ9zILUvlaRCjCEWR+1HIHNmbaJW4bcSESmGI9g6TowxDm7ZjVLOrE3UKiyQiEgxDq8DehULpI4Qog1FiCYUhVzAlqhVWCARkWKcnEW7Q8UY41DiLILb51I6ClHQYYFERIrwyV64fA18xNaBog2xEBA47jiqdBSioMMCiYgU4fQ6AQAGDQukjqJT62DRheOovUDpKERBhwUSESnil0kiOYt2R4o2xKDSVY76nwtSImqegCiQXn/9daSkpMBgMCAjIwObN28+a/sVK1Zg4MCBMBgMSEtLw5dfful/z+Px4NFHH0VaWhpCQkKQkJCA6dOno6ioqNExqqqqcMstt8BsNsNqtWLWrFmw2+0d8vmI6HROrwMSJOhVOqWjdGmRhihIkPiYjaiFFC+QPvzwQ8yePRtPPfUUsrOzMXToUEyaNAllZWVNtt+wYQOmTZuGWbNmYfv27Zg8eTImT56M3bt3AwCcTieys7Px5JNPIjs7Gx9//DFyc3Px29/+ttFxbrnlFuzZsweZmZlYtWoV1q1bh9tvv73DPy8RneDwOqBX6yFxDqQOpVFpEaGP5GM2ohaShBBCyQAZGRkYOXIkXnvtNQCALMtISkrCvffei8cee+y09lOnToXD4cCqVav820aPHo309HQsXry4yXNs2bIFo0aNQkFBAZKTk7Fv3z4MGjQIW7ZswYgRIwAAq1evxpVXXoljx44hISHhnLltNhssFgtqa2thNptb89GJurUtZRtQ57EhLWKY0lG6vPL6UuTW7sVlPa5GiDZU6ThEimru729F/+rmdruxbds2TJw40b9NpVJh4sSJ2LhxY5P7bNy4sVF7AJg0adIZ2wNAbW0tJEmC1Wr1H8NqtfqLIwCYOHEiVCoVNm3a1IZPRETNxTmQOk+EPhIqScXHbEQtoGiBVFFRAZ/Ph9jY2EbbY2NjUVJS0uQ+JSUlLWrf0NCARx99FNOmTfNXiiUlJYiJiWnUTqPRICIi4ozHcblcsNlsjV5E1HqcA6nzqFUahOsiWSARtUCXfvjv8Xhw0003QQiBN954o03Hmj9/PiwWi/+VlJTUTimJuh+f7OMcSJ0s0hCNGneVf/QgEZ2dogVSVFQU1Go1SktLG20vLS1FXFxck/vExcU1q/3J4qigoACZmZmNnjPGxcWd1gnc6/WiqqrqjOedM2cOamtr/a+jR/k3MaLWcvpODvFngdRZIvSRkCChyHFM6ShEQUHRAkmn02H48OHIysryb5NlGVlZWRgzZkyT+4wZM6ZRewDIzMxs1P5kcXTw4EF8++23iIyMPO0YNTU12LZtm3/bd999B1mWkZGR0eR59Xo9zGZzoxcRtc7JOXl4B6nzaFQahOsj+JiNqJk0SgeYPXs2ZsyYgREjRmDUqFFYuHAhHA4Hbr31VgDA9OnTkZiYiPnz5wMA7rvvPowfPx4LFizAVVddheXLl2Pr1q148803AZwojm644QZkZ2dj1apV8Pl8/n5FERER0Ol0SE1NxeWXX47bbrsNixcvhsfjwT333IObb765WSPYiKhtHJ4Td5B0ar3CSbqXSH00Dtr2o8FbD4OGE3QSnY3iBdLUqVNRXl6OuXPnoqSkBOnp6Vi9erW/I3ZhYSFUql9udI0dOxbLli3DE088gccffxz9+vXDypUrMXjwYADA8ePH8dlnnwEA0tPTG53r+++/x8UXXwwA+OCDD3DPPfdgwoQJUKlUmDJlChYtWtTxH5iIUP9zB20V50DqVBGGSMAGlNQXISWsj9JxiAKa4vMgBSvOg0TUelvLNqLWU4shnAOp0+2syoZRHYKxcRcpHYVIEUExDxIRdU8n5kDi4zUlROqjUF5fAq/sUToKUUBjgUREnc7pdbCDtkIi9NGQIaO0vuk534joBBZIRNSpZOFDg6+eQ/wVYtQYYdKEoNh5XOkoRAGNBRIRdSonh/grLkIfhRJnEYSQlY5CFLBYIBFRpzo5BxLvICknQh8Jj+xGlatK6ShEAYsFEhF1KoeXs2grLUxrhlalRYmzSOkoRAGLBRIRdap6rwM6lZ5zIClIkiRYdREoYT8kojPiNxQRdSoHR7AFhAh9JGyeWi5eS3QGLJCIqFM5PQ7oucSI4sL1EZAgodRZrHQUooDEAomIOpXz52VGSFkalRZmnYXD/YnOgAUSEXUaWcicAymAhOsiUNFQBp/sUzoKUcBhgUREnabBVw8BwT5IASJcHwmf8KHSVa50FKKAwwKJiDqN08Mh/oHEpAmBTqVnPySiJrBAIqJO4+QcSAFFkiSE6yNQWs8CiehULJCIqNM4vQ5oVVqoJbXSUehn4foI1HlsHO5PdIpWFUiHDx9u7xxE1A04vU4Y1EalY9CvWHXhHO5P1IRWFUh9+/bFJZdcgvfffx8NDQ3tnYmIuijnz7NoU+DQqLQI05pRVl+idBSigNKqAik7OxtDhgzB7NmzERcXhzvuuAObN29u72xE1MU4OYt2QLLqw1HeUApZyEpHIQoYrSqQ0tPT8eqrr6KoqAjvvPMOiouLMW7cOAwePBgvv/wyyss5ZJSIGhNCoN7rZAftAGTVRcAje1DjqlI6ClHAaFMnbY1Gg+uvvx4rVqzAiy++iEOHDuGhhx5CUlISpk+fjuJiPtMmohMafA2QIbNACkBh2jBoJA0fsxH9SpsKpK1bt+Kuu+5CfHw8Xn75ZTz00EPIy8tDZmYmioqKcO2117ZXTiIKcidHSfERW+CRJBUsunCUskAi8tO0ZqeXX34Z7777LnJzc3HllVfivffew5VXXgmV6kS91atXLyxZsgQpKSntmZWIgpjTawfAOZAClVUfjsO2g/DIbmhVOqXjECmuVQXSG2+8gT/+8Y+YOXMm4uPjm2wTExODt99+u03hiKjrcHqd0EhaaFSt+tqhDhaui4CAQHl9GRJCeigdh0hxrfqmyszMRHJysv+O0UlCCBw9ehTJycnQ6XSYMWNGu4QkouDHEWyBzaAxwqA2oryhlAUSEVrZB6lPnz6oqKg4bXtVVRV69erV5lBE1PU4PQ7o1ZwDKZBZdeEoc7IfEhHQygJJCNHkdrvdDoOBf0MkotM5vQ72PwpwFl047N461HudSkchUlyLHrHNnj0bwIkFDufOnQuTyeR/z+fzYdOmTUhPT2/XgEQU/IQQcPociFbHKh2FzsKqtwIAyutLkRzGpwHUvbWoQNq+fTuAE192u3btgk73y0gHnU6HoUOH4qGHHmrfhEQU9FyyC7KQ2QcpwGlVOoRqwlDWwAKJqEUF0vfffw8AuPXWW/Hqq6/CbDZ3SCgi6lqcnhNzIPERW+Cz6Kwoqy+BEAKSJCkdh0gxreqD9O6777I4IqJm4ySRwcOqD4fL14A6j03pKESKavYdpOuvvx5LliyB2WzG9ddff9a2H3/8cZuDEVHX4fQ6oJE00Ki0SkehczBrrZAgoaKhDGadRek4RIppdoFksVj8t1stFv5PQ0TNxxFswUOtUiNMa0ZZfSl6m/spHYdIMc0ukN59990m/52I6FxYIAUXiy4cJfXH2Q+JurVW9UGqr6+H0/nLPBkFBQVYuHAhvvnmm3YLRkRdBwuk4GLVW+GRPah11ygdhUgxrSqQrr32Wrz33nsAgJqaGowaNQoLFizAtddeizfeeKNdAxJRcBNC/LzMCGfRDhZhWgtUUKG8oVTpKESKaVWBlJ2djQsvvBAA8NFHHyEuLg4FBQV47733sGjRonYNSETBzS274BM+6NVGpaNQM6kkFcw6C8rrWSBR99WqAsnpdCIsLAwA8M033+D666+HSqXC6NGjUVBQ0K4BiSi4cYh/cLLorKhoKIcsZKWjECmiVQVS3759sXLlShw9ehRff/01LrvsMgBAWVkZ50ciokYcHhZIwciiC4dPeFHjrlY6CpEiWlUgzZ07Fw899BBSUlKQkZGBMWPGADhxN2nYsGHtGpCIgpvT64Ba0kAttWjiflJYqDYMakmNivoypaMQKaJV31g33HADxo0bh+LiYgwdOtS/fcKECbjuuuvaLRwRBb8THbQNHC4eZFSSCmatBeUNpeiPVKXjEHW6Vv+VLi4uDnFxcY22jRo1qs2BiKhr4RD/4GXWWXHMUQhZyFBJrXrgQBS0WlUgORwOvPDCC8jKykJZWRlkuXEnvsOHD7dLOCIKfg6vHWFazr4fjKw6Kwrsh1HjqkKEIUrpOESdqlUF0p/+9CesXbsWf/jDHxAfH89b50TUpBNzIDkRbYhVOgq1QsjP/ZDKG8pYIFG306oC6auvvsIXX3yBCy64oL3zEFEX4pZdkIWPI9iC1Ml+SBX1ZRhgHaR0HKJO1aqHyuHh4YiIiGjvLETUxZwc4s8+SMHLorOi0sX5kKj7aVWB9Oyzz2Lu3LmN1mMjIjoVJ4kMfmadFT7hQ42L8yFR99KqR2wLFixAXl4eYmNjkZKSAq1W2+j97OzsdglHRMHN6XVAI2mgUWnP3ZgCkn8+pIYyRBgilY5D1GladQdp8uTJePDBB/HQQw/hhhtuwLXXXtvo1RKvv/46UlJSYDAYkJGRgc2bN5+1/YoVKzBw4EAYDAakpaXhyy+/bPT+xx9/jMsuuwyRkZGQJAk5OTmnHePiiy+GJEmNXn/+859blJuIzo1D/IOfSlIhTGtBRQMnjKTupVV3kJ566ql2OfmHH36I2bNnY/HixcjIyMDChQsxadIk5ObmIiYm5rT2GzZswLRp0zB//nxcffXVWLZsGSZPnozs7GwMHjwYwIkpCMaNG4ebbroJt9122xnPfdttt+GZZ57x/9lkMrXLZyKiXzg8dj5e6wIsOguOO45yPiTqViQhhGjNjjU1Nfjoo4+Ql5eHhx9+GBEREcjOzkZsbCwSExObdYyMjAyMHDkSr732GgBAlmUkJSXh3nvvxWOPPXZa+6lTp8LhcGDVqlX+baNHj0Z6ejoWL17cqO2RI0fQq1cvbN++Henp6Y3eu/jii5Geno6FCxe27EP/is1mg8ViQW1tLdefIzqDb46ugkVnRW9zP6WjUBvY3DXYWbUdFydchnA9B+hQcGvu7+9W/VVg586d6N+/P1588UX84x//QE1NDYATj7fmzJnTrGO43W5s27YNEydO/CWMSoWJEydi48aNTe6zcePGRu0BYNKkSWdsfzYffPABoqKiMHjwYMyZM+ecHc5dLhdsNlujFxGdmRAC9V4nDGqj0lGojUK1ZqgkFR+zUbfSqgJp9uzZmDlzJg4ePAiD4Zfb51deeSXWrVvXrGNUVFTA5/MhNrbxBHKxsbEoKSlpcp+SkpIWtT+T3/3ud3j//ffx/fffY86cOfj3v/+N3//+92fdZ/78+bBYLP5XUlJSi85J1N00+OohQ2YfpC7A3w+JC9dSN9KqPkhbtmzBv/71r9O2JyYmtrhYUcLtt9/u//e0tDTEx8djwoQJyMvLQ58+fZrcZ86cOZg9e7b/zzabjUUS0Vk4OMS/S7HoLCh2HocQgqsnULfQqjtIer2+yUdMBw4cQHR0dLOOERUVBbVajdLS0kbbS0tLT1sE96S4uLgWtW+ujIwMAMChQ4fO2Eav18NsNjd6EdGZOT12ACyQugqz1gqP7IHNU6t0FKJO0aoC6be//S2eeeYZeDweAIAkSSgsLMSjjz6KKVOmNOsYOp0Ow4cPR1ZWln+bLMvIysrCmDFjmtxnzJgxjdoDQGZm5hnbN9fJqQDi4+PbdBwi+oXT64BWpYVa1aob1RRgwnRmSJD4mI26jVYVSAsWLIDdbkd0dDTq6+sxfvx49O3bF2FhYfjb3/7W7OPMnj0bb731FpYuXYp9+/bhzjvvhMPhwK233goAmD59eqNO3/fddx9Wr16NBQsWYP/+/Zg3bx62bt2Ke+65x9+mqqoKOTk52Lt3LwAgNzcXOTk5/kd/eXl5ePbZZ7Ft2zYcOXIEn332GaZPn46LLroIQ4YMac2Pg4ia4PA62EG7C1FLaoRpzeyoTd1Gq/5qZ7FYkJmZifXr12PHjh2w2+04//zzTxthdi5Tp05FeXk55s6di5KSEqSnp2P16tX+jtiFhYVQqX6p4caOHYtly5bhiSeewOOPP45+/fph5cqV/jmQAOCzzz7zF1gAcPPNNwM4MXfTvHnzoNPp8O2332LhwoVwOBxISkrClClT8MQTT7TmR0FEZ+Dw2NlBu4sx66woqy9hPyTqFlo8D5Isy1iyZAk+/vhjHDlyBJIkoVevXrjhhhvwhz/8odv8T8N5kIjObnXhZ4g0RCElrOmBDxR8ql2V2FO9ExMTr0SYjt97FJw6ZB4kIQR++9vf4k9/+hOOHz+OtLQ0nHfeeSgoKMDMmTNx3XXXtTk4EQU/Wcio93EOpK4mTGs50Q+Jj9moG2jRI7YlS5Zg3bp1yMrKwiWXXNLove+++w6TJ0/Ge++9h+nTp7drSCIKLs6fh/jzEVvXolFpEKINRWVDOXqZ+yodh6hDtegO0n/+8x88/vjjpxVHAPCb3/wGjz32GD744IN2C0dEwcnJOZC6LLPWivKGMrRylSqioNGiAmnnzp24/PLLz/j+FVdcgR07drQ5FBEFN4eHd5C6KovOggZfvb8IJuqqWlQgVVVVnbbUx6/Fxsaiurq6zaGIKLg5vSdGsHHl967HrLMCACobypUNQtTBWvTt5fP5oNGcuduSWq2G1+ttcygiCm5Or4OP17oorUoLkyYEFSyQqItrUSdtIQRmzpwJvV7f5Psul6tdQhFRcLNzDqQuzaKzciQbdXktKpBmzJhxzjYcwUZEDq8dZl2i0jGog5i1JxaubfDWw6DhVA7UNbWoQHr33Xc7KgcRdREe2Q2P7OYcSF2Y5WQ/JFc5EjXJyoYh6iDsQUlE7erkCDb2Qeq6dGo9jGoj+yFRl8YCiYjalcNrBwAY+eilSzPrrKioZz8k6rpYIBFRu3J6HVBLamgkrdJRqAOZdRbYPLVw+zg4h7omFkhE1K4cHjsMamO3Wbi6u7JorQCASleFskGIOggLJCJqVycKJPY/6ur0agN0Kj0njKQuiwUSEbUrh9cOPUewdXmSJMGis7AfEnVZLJCIqN0IIXMW7W7ErLOixl0Nr+xROgpRu2OBRETtpt5bDwHBEWzdhEVnhYBAlatS6ShE7Y4FEhG1m5ND/DlJZPdgVJugVWk5HxJ1SSyQiKjdnCyQuA5b9yBJEsxazodEXRMLJCJqNycXqVVJ/GrpLsw6C6pdlfAJn9JRiNoVv8WIqN2cnAOJug+LzgoZMqpdVUpHIWpXLJCIqN3YvXUwskDqVkI0oVBLGlQ28DEbdS0skIioXQghTtxB4gi2bkWSJJh1FlTUs6M2dS0skIioXbhlF3zCy0ds3ZBFa0GlqxyykJWOQtRuWCARUbuwe06MYOMjtu7HrLPCJ3yocVUrHYWo3bBAIqJ24fDUAeAcSN1RqDYMakmNCvZDoi6EBRIRtQuH1w6dSg+1Sq3I+YUA6r0ulDmrUFlfAx8f93QalaRCmNbMAom6FI3SAYioa7B77IqswVZQexw/Hd+BXeUHUNVQ69+ullRIsSZiZNwQjEpIg0Gj7/Rs3YlFZ8Vxx1EIIUPiPFjUBbBAIqJ2YffUdeoItmN1pVh5IBN7K/Jg0YeilzUJGYlDEaIxwCdk1LjqcKTmGP67/yt8kbcG1/afgDEJ6ZAkqdMydidmnRUF9nzUuGsQro9QOg5Rm7FAIqJ24fDaYdEldvh5fMKHrw//iK8Or4NFF4Yr+1yEvuEpUJ1S+PQEMDRmIOrcDqw/lo33d3+G7JI9+OOQKTBp2U+qvYVpzVBBhYqGMhZI1CXwPigRtZnb54ZHdnd4B22Hpx6vbX0fX+atw/C4wfjd4GvQP6LXacXRr4XpQnB57wtxXf9LkV9zHC9ufAtlDq4+395UkgphOjPXZaMugwUSEbXZyUVqO/IRW5mzCn/f+BYKbSW4fsClGJs4DBqp+R3Ce1oScPOgK+ETMl7ZshRlTi6N0d4sOisqXeUQ7CBPXQALJCJqM7vHBgAwqk0dcvxiezle2fwufELGzYOuRI+wuFYdx6IPw5SBk6CWVFi4ZSmq6mvPvRM1m0UXDo/sQa2bP1cKfiyQiKjN7B47tCodNKr279ZYXFeOVzYvgU6txQ0DL4dFH9am44VojZgy8DIIIWPx9v/A5XW3U1I62Q+pvKFU6ShEbcYCiYjazO7pmEVqK501WLTt3zBq9bh+wCSYtO0zjUCI1oRr+v0GZY4q/Hv3pxCiXQ7b7Z3oh2RhPyTqElggEVGb2T02GDXt+3itzuXAoq3vQSVJmNz/UhjbeR6jKGM4Lut9AbJL92Ld0c3teuzuzKKzoqKB/ZAo+LFAIqI2EUK0+x0kt8+LN7b/B05vAyb3n4iQDhqW3ze8J9JjB+Lj3EwU13E1+vZg0VnhFR7UuGuUjkLUJiyQiKhN3LILXuGFoZ3uIAkhsHTXxzheV4rf9pvQ5j5H53JBj+Ew60Pxzq7/wSv7OvRc3UGY1gyVpOKyIxT0WCARUZvU/bxIbXvdQfoibw1ySvfj8j4XIjYksl2OeTZalQaTeo9Dsb0c3x7Z0OHn6+pUkgpmrQXl9eyoTcGNBRIRtYn95wKpPeZA2layB1/mrcPYHsPQx5rc5uM1V4wpEufHDsKXeWs5P1I7sOjCUdFQDpn9kCiIsUAiojZxeOqgVxugbsGkjU05VleCf+/6FAMiemFE3OB2Std8GYlDEaI14T97Pueotjay6sLhE15Uu1hsUvBigUREbdIeHbQd7nr8a/uHsBrCMLHXWEUWlNWqNLik5yjkVh1BTtneTj9/VxKqDYVa0vAxGwU1FkhE1CZ1bRziLwuBt3d+BKenAVf3uwTaDphssrlSLD3Qy9ID/9ufCY/sVSxHsJMkFSw6KyeMpKDGAomIWk0IAYfX3qZFaj87mIXcynxc0ecimHWh7ZiudS5KHoFalw3fFfykdJSgZtFZUdVQAR8LTQpSLJCIqNWcXgdkIcPUyjtI20v24pv89RiXNBzJ5vh2Ttc64QYLhsQMwOq8H1DndiodJ2hZdeGQIaPSVaF0FKJWYYFERK1m9w/xb3mBdLyuDEt3r8SAiF44P3ZQe0drk1EJQwEIfH34B6WjBC2TJgRalY79kChoKV4gvf7660hJSYHBYEBGRgY2bz77lP8rVqzAwIEDYTAYkJaWhi+//LLR+x9//DEuu+wyREZGQpIk5OTknHaMhoYG3H333YiMjERoaCimTJmC0lL+T0zUUnUeG1RQQa9u2RppDk89Fm//D6x65Tpln41Ro8f5cYOx7ugWVNZzZfrWkCQJVl04SutLlI5C1CqKFkgffvghZs+ejaeeegrZ2dkYOnQoJk2ahLKypmdg3bBhA6ZNm4ZZs2Zh+/btmDx5MiZPnozdu3f72zgcDowbNw4vvvjiGc/7wAMP4PPPP8eKFSuwdu1aFBUV4frrr2/3z0fU1Z3soN2SAscnfPi/nBUB0Sn7bIbFDoJercOXh75XOkrQsurCUeuuhsvnUjoKUYtJQig340dGRgZGjhyJ1157DQAgyzKSkpJw77334rHHHjut/dSpU+FwOLBq1Sr/ttGjRyM9PR2LFy9u1PbIkSPo1asXtm/fjvT0dP/22tpaREdHY9myZbjhhhsAAPv370dqaio2btyI0aNHNyu7zWaDxWJBbW0tzGZzSz86UZewrigLkiRhoPW8Zu/z4d4v8eOxbbhuwKXoERbXgenabnvpPvxwdCvmjrsbMaYIpeMEHZfPhS3lGzAyeix6hHbexJ9EZ9Pc39+K3UFyu93Ytm0bJk6c+EsYlQoTJ07Exo0bm9xn48aNjdoDwKRJk87Yvinbtm2Dx+NpdJyBAwciOTm5Rcchop/vILWg/9Haws1Ye3QLLu6ZEfDFEQCkRfeHSWPA6rx1SkcJSnq1HiZNCMr4mI2CkGIFUkVFBXw+H2JjYxttj42NRUlJ0/8zlZSUtKj9mY6h0+lgtVpbdByXywWbzdboRdSduX0uuGVXs0ew7SzLxYr9qzEsNhVp0f07OF370KjUGBE/GJuLd3EJklay6MJRVl8CBR9WELWK4p20g8X8+fNhsVj8r6SkJKUjESnKv0itJuScbQtqj+OdHf9Db2sSLkwa2dHR2tVg3kVqk3BdOOp9Tti9dUpHIWoRxQqkqKgoqNXq00aPlZaWIi6u6VvvcXFxLWp/pmO43W7U1NS06Dhz5sxBbW2t/3X06NFmn5OoK7J7TtxFNZ5jkdpiezle2/Y+okzhmNT7QqgCbMTauWhUagyPOw+bi3ehsr5G6ThBx6KzQoKEMicfs1FwUaxA0ul0GD58OLKysvzbZFlGVlYWxowZ0+Q+Y8aMadQeADIzM8/YvinDhw+HVqttdJzc3FwUFhae9Th6vR5ms7nRi6g7q/PYYDjHIrUVzmos2vpvmLRG/LbfbwJ2xNq5DI7uD71Gh2/zNygdJeioVRpYdFaU1hcrHYWoRRT9tpo9ezZmzJiBESNGYNSoUVi4cCEcDgduvfVWAMD06dORmJiI+fPnAwDuu+8+jB8/HgsWLMBVV12F5cuXY+vWrXjzzTf9x6yqqkJhYSGKiooAnCh+gBN3juLi4mCxWDBr1izMnj0bERERMJvNuPfeezFmzJhmj2AjIqDOffYO2mXOKizcshQqScLk/pfCoNF3Yrr2pVVrMCwmFeuPZ59YEkWv/JIowcSqj0Ch/Qh8shfqIC2SqftRtA/S1KlT8Y9//ANz585Feno6cnJysHr1an9H7MLCQhQX//K3jrFjx2LZsmV48803MXToUHz00UdYuXIlBg8e7G/z2WefYdiwYbjqqqsAADfffDOGDRvWaBqAV155BVdffTWmTJmCiy66CHFxcfj444876VMTdQ1nW6S2uK4cr2xeAhUk3DBwEkK0rV+rLVAMiRkItaRCFtdoa7FwXSRk4UNFQ7nSUYiaTdF5kIIZ50Gi7swn+/BZwQr0NQ9AnCmh0XsHqwqwePtyhOqMmNz/0i5RHJ3047Ft2F1+EH8b/wCMQXxHrLMJIbC1fCN6hPbEkMjzlY5D3VzAz4NERMGr7ucO2qZfjWAT4kQB8c9t/0a0KQI3DLyiSxVHAJAekwqP7MGPR7cpHSWoSJIEqz4CpU72Q6LgwQKJiFqsznNifbKTBZLL68b7uz/Fsj2rkBrVB9f2nwC9WqtkxA4RqjMhNaIPviv4CV7Zp3ScoBKuj4TdW+df4Jgo0LFAIqIWs7lroVfpoVFpkFuZj2fXv4GtJbsxqdc4TOg5Bmqp6361nB93HmpdddhcvFPpKEElXBcOCRJKnEVKRyFqlq77LUZEHcbmroVG0uGtnP/i1a3vwaQ14PeDf4vUqD5KR+twEUYL+liTkJW/EezB2XxqlQZWXTiKnceVjkLULCyQiKjZhBDYW3EIBbZCbCvZj0PVhZjUexyuH3AZLPowpeN1mmFx56HYUY69lYeUjhJUIgxRqGwoh0d2Kx2F6Jw4IQURnZVX9mFvxSFsKtqBzPwNKLKXYvYFv0WsKQZj0kYH7eSPbZEYGoO4kCh8m78B50X1VTpO0AjXR0LgAEqdJegRmqx0HKKz6n7fbETk55V9cHjq4XA7YXPbUeuyo6q+BmXOShyzlSCv5igOVRfA7fPApDXgvKh++G3/8ZAkF3pbkrtlcQScGJU1LG4Qvspbh2N1JegR1vzljrozg9qAEE0oSpzHWSBRwOue325E3URVfS0OVOUjr7oQx+pKUeIoR4WzBjUuG+rcDjR4XU3uF6I1ItoUgWhTJK7p+xv0svZAT0sCVJIKDm81Kj3HoJZ0nfxpAku/8BSs12Uj68hPmJE2Wek4QSNCH4mS+iLIwgfVWZapIVIaCySiLsQr+5BTug9rCjdjc9EOFNpOzDujV+sQbYpAhMGC2JBI9AlPQojWCKPWAKPGAINGD5PGAJPWCLM+FLqzDNH3CBdU0EDVhUeqNYdKkjA0diA2HM/G5P4TYeHyI80SaYjGUUcByuvLEGuKVzoO0RmxQCLqAiqc1fjkQCY+PZCFivpqRBisGBTdBxNSxiLFkogIoxUqSWqXc7nlBmikrjfHUWsMjuqHTcd3YG3hFvy23yVKxwkKIZpQGNRGFDmPskCigMYCiSiIVdbXYOmuT/BJbibUkgojEtIwNnEYks0JkNqpIDqVR26AXnXmRWq7E71Gh/Oi+2Ld0c24vPe4s955oxMkSUKkIQpFjuNIj5QhdfM7kRS4WCARBSGv7MNH+1fjrZz/QkBgUu9xuDg5A0atoUPP6xNe+OCBppv3P/q19NhU5JTux6aiHbgwaYTScYJClD4axx1HUdlQgShjjNJxiJrEAokoyBTWFuGpH/6J/ZWHMS5pOK7p+xuE6DpnzTOP3AAALJB+xaIPQ5/wZHxXsAnjegzvsDt3XUmo1gy9So/jzqMskChg8d4mURD5Km8d/vD5I6isr8GDGX/EzYOu6rTiCAA84kSBpGYfpEaGxQ1CqaMCeyvylI4SFE48ZovBMXshZCErHYeoSbyDRBQEvLIXC7e8h4/2r8bohHTclHoF9JrOv4tzooO2jndJTpEQEo3YkChkFWzEedGcOLI5YoyxKHIeRXl9KTtrU0DiHSSiAOf0NOChrBfxSe43uHnQVfj94N8qUhwBvxRI1JgkSRgWm4r9lYdRVFemdJygEKIJhVFtwjFHgdJRiJrEAokogFU32PDn1XORU7Yfdw2/BRcmjVDs7o0QAh7BAulM+kWkIFQbgu8KNykdJShIkoRoYwyKHMfgk71KxyE6DQskogBVWV+Du1bPQ4m9HA+MnImBkb0VzeMVLgCCBdIZqCUVhsYOwOaiHahzOZSOExSiDbHwCi+KnUVKRyE6DQskogBUVV+Lu1bPQ1VDLe4bORM9zMqv9eXmCLZzSovuDwkSfji2VekoQcGoMSFMa0aB/bDSUYhOwwKJKMDYXHb8JfM51LjqcP/IGYgLjVI6EoATI9hUUHP9rLMwaPRIjeqDtYVb4JV9SscJCrHGeJTVl8Dp5V03CiwskIgCSIPXhdlZ81FsL8c9w3+PmJBIpSP5ueV63j1qhmGxqahzO7CleJfSUYJCtCEGakmNgrp8paMQNcICiShA+GQZT657FQeqjuDu4b9DQljgTKAnhPi5QNIrHSXghRss6G1NQtaRjRBC6TSBT63SIMoQg4K6PAjOiUQBhAUSUYBYuGUJ1h/LxqyhN6KnJVHpOI34hAcyfNCqeAepOc6PG4Qiexn2V7FvTXPEGRNQ76tHaX2J0lGI/FggEQWAj/avxor9q3FT6hUYHN1P6TincYt6AOAdpGZKDI1FTEgkso5sUDpKUAjVhiFUG4ZDtblKRyHyY4FEpLDNRTvx8uYluKRnRsAuduqWf+6gDXbQbg5JknB+7CDsrcjjxJHNIEkSEkxJKG8ohc1do3QcIgAskIgUdbyuFH9d+woGRvbGdf0vUzrOGbllJ5cYaaF+ESkI04Ugq2Cj0lGCQpQhGjqVHodqDygdhQgACyQixdR7GvDI9y/BqDHg1iFToFYF7v+OXGKk5dSSCumxqdhcvBO1LrvScQKeSlIh3pSIo/YjaPA1KB2HiAUSkRKEEHh+w79wzFaC29JvgklrUDrSGXmFBzK80KrY/6ilBkf1g1pSYy2XH2mWeFMCJEmFgzX7lI5CxAKJSAn/y/0amUfW45bzrgmo4fxNccvsoN1aeo0OadH9sbZwKxq8bqXjBDyNSosEUw8crjuIBm+90nGom2OBRNTJdpcfxMItS3Fx8igMjx+sdJxzcstOdtBug/TYVLh9bmw4tk3pKEEhMaQHVFDhQC3vIpGyWCARdaJalx1/XfMyksLicd2AwO2U/Wsu2QmNpGcH7VYK04VgQGQvfFvwE5cfaQaNSouEkB7Itx2C3VOndBzqxlggEXUSIQSe/fF12D1O/HHoDdCoAv+OzMkZtNn/qG3OjxuMmgYbtnL5kWZJNCVDq9JhV+V2paNQN8YCiaiTLN/7BX48tg1/GHwtIowWpeM0i1e4ICBDy/5HbRJltKK3NQnf5K+H4Poj56RWqdHL3Acl9UUocRYpHYe6KRZIRJ1gX0UeXs/+AL/pORppMQOUjtNsLtkJgB2028OI+MEocVRgVznn+WmOSH00LLpw7KjYCo/sUToOdUMskIg6mMPtxF/XvoLEsDhc23+i0nFa5MQCtVqoJH5VtFVCaAx6hMXiq7x1XMS2GSRJQj/zALhkF3ZUsIM7dT5+6xF1ICEEXtj4FqobavHHIVOCot/Rr53soE3tY2T8EBTYipDLRWybxaAxore5H446juCovUDpONTNsEAi6kBf5K1F5pH1uHnQ1YgyhSsdp0Vk4YNHNLD/UTtKNscjNiQKX+X9oHSUoBFjiEO0IRbZFZtQ2VChdBzqRlggEXWQgtoi/OOn/8OYxGEYEQTzHZ3q5ASRWilwZ/kONpIkYVR8Gg5WH8Gh6kKl4wQFSZLQzzIAoZow/FS6DnVum9KRqJtggUTUAdw+D55Y+wqsBjNuHHi50nFapUF2QIIKakmrdJQupZc1CdGmcHyZt1bpKEFDJamRGp4GjUqLdcXfoqqhUulI1A2wQCLqAK9v+wD5tccwc8gU6DXBucirS3ZAKxk4QWQ7U0kSRsUPxf7Kwzhcc0zpOEFDq9IiLWIYDGojfiz5Dvm2PE6ZQB2KBRJRO/vx6DZ8uO9LXNf/UiSZ45SO0yonJoh0Qqfi47WO0Cc8GVHGcHyZt0bpKEFFq9LivIihiDLEIKdyC9aXrEG1i3eTqGOwQCJqR2WOKjyz/nWkRffH+ORRSsdpNbeoh4BgB+0OopIkZCQMwd6KPOTVsC9SS6glNfpZBmJQ+BDYPXVYU5SJdUXfIq/2AGzuWgghKx2RugiN0gGIugqfLOOpH16FWlLh94OvDepHUy6fAxIkDvHvQH3CeyLaFI7PD3yP+0fNUDpO0InQRyI8KgIVDeUoqy/GrqrtEBBQSWqY1Cbo1QZoVFqoJTVUkgRJUkEtnehTp1frYFCbEKoNg0VnhUbFX4V0Ov5XQdRO3t35P+SU7sd9I6cjVGdSOk6bNMgOLlDbwVSShNGJ6fj84PfIrczHgMheSkcKOpIkIdoYg2hjDLyyF3ZPHRxeO1y+BnhkDzyyGy7hg4CAEAICAj7ZB49w+2fnliAhXB+JxJAk9AhJhkFjVPhTUaBggUTUDrJL9uKdnR/hij4XoV9EitJx2uRk/yODKkzpKF1eb0sS4kKi8OnBLDwc8SewHm09jUoDqz4cVn3z5hvzyT7U+5ywuWtR667G7qoc7K7KQVJoTwywnodQLf/77+5YIBG1UXWDDXPXvYq+4T1xRZ+LlI7TZh7RABk+dtDuBJIkYWyP8/Fx7jfYUbYf6bEDlY7UbahVaoSqwhCqDUNCSA94ZQ9K60tw3FGIo/YC9LekYoB1ENR8/NZtsZM2URvIQsa8H/4Jl8+NGWnXd4k1yxp8dkiQOEFkJ0k2xyPZnIDPDmbBxw7GitGotEgMScLw6NHoEdITB2r34buir2Fz1yodjRQSEN/mr7/+OlJSUmAwGJCRkYHNmzeftf2KFSswcOBAGAwGpKWl4csvv2z0vhACc+fORXx8PIxGIyZOnIiDBw82apOSkgJJkhq9XnjhhXb/bNS1vbdrJTYX7cSMtOtgNXSNW/L1ch3nP+pk43qcjxJHBX46nqN0lG5PLanRM6wXhkWNhCxkrCn6BsfsHGnYHSleIH344YeYPXs2nnrqKWRnZ2Po0KGYNGkSysrKmmy/YcMGTJs2DbNmzcL27dsxefJkTJ48Gbt37/a3+fvf/45FixZh8eLF2LRpE0JCQjBp0iQ0NDQ0OtYzzzyD4uJi/+vee+/t0M9KXcu2kj14M+dDTOo9DqlRfZSO0y6EkH+e/4gdVTtTTEgkBkT0wqpD38PldSsdhwCYNCEYGjkcEfoobCnfgEO1uUpHok4mCYWnIs3IyMDIkSPx2muvAQBkWUZSUhLuvfdePPbYY6e1nzp1KhwOB1atWuXfNnr0aKSnp2Px4sUQQiAhIQEPPvggHnroIQBAbW0tYmNjsWTJEtx8880ATtxBuv/++3H//fe3KrfNZoPFYkFtbS3MZnOrjkHBq8JZjT98/ghiTBG4Z8Tvu8SjNeDE47Uydz7CNQnQqjjEvzPZ3Ha8t2slJvUah6v6Xqx0HPqZEAIF9sM45ijEQOt5SA1PUzoStVFzf38r+q3udruxbds2TJw40b9NpVJh4sSJ2LhxY5P7bNy4sVF7AJg0aZK/fX5+PkpKShq1sVgsyMjIOO2YL7zwAiIjIzFs2DC89NJL8Hq9Z8zqcrlgs9kavah78spePLF2IYQQuHXIlC5THAFAg2yHBBU0UnAujxLMzLpQDItNRWb+BtQ08PslUEiShJSwPkgJ7Y39NXuwv3qP0pGokyj6zV5RUQGfz4fY2NhG22NjY1FSUtLkPiUlJWdtf/Kf5zrmX/7yFyxfvhzff/897rjjDjz//PN45JFHzph1/vz5sFgs/ldSUlLzPyh1Ka9t+wC7ynNx69ApCNOHKB2nXTX47NCpjOx/pJAR8UOgVavxyYFMpaPQKXqE9kTP0F7YV7MLebUHlI5DnaDbjl+cPXu2/9+HDBkCnU6HO+64A/Pnz4def/qjhTlz5jTax2azsUjqhjLz12P53i9w48DL0Tc8Wek47conPHCLeoSpopSO0m3p1VqM7TEcmfnrMa7HCPSL6Kl0JPqVpNAUeGUvdlZlw6gxISGkh9KRqAMpegcpKioKarUapaWljbaXlpYiLq7pRT7j4uLO2v7kP1tyTOBEXyiv14sjR440+b5er4fZbG70ou7lYNURPLf+DYyMTwvqddbOpN5nBwDoVcE9C3iwS43sg/iQaPx3/1cc9h+AUsL6IMoQgy3lG1DtqlI6DnUgRQsknU6H4cOHIysry79NlmVkZWVhzJgxTe4zZsyYRu0BIDMz09++V69eiIuLa9TGZrNh06ZNZzwmAOTk5EClUiEmJqYtH4m6qJoGGx7+7iXEhETid+dd0yUfQdX7bNBIeqgktdJRujWVJOHinhkoqivD2sKzT3lCnU+SJPS3DESIJhQ/lf6ABm+90pGogyjeu3T27Nl46623sHTpUuzbtw933nknHA4Hbr31VgDA9OnTMWfOHH/7++67D6tXr8aCBQuwf/9+zJs3D1u3bsU999wD4MR/vPfffz+ee+45fPbZZ9i1axemT5+OhIQETJ48GcCJjt4LFy7Ejh07cPjwYXzwwQd44IEH8Pvf/x7h4c2bpp66D6/sxeNrXoHd48Rt6TdBp9YqHandCSGjQbZDz+H9ASE2JBJDYvrj84Pfo7qBExUGGpWkxkDrYMjCh01lP0IWPqUjUQdQvA/S1KlTUV5ejrlz56KkpATp6elYvXq1v5N1YWEhVKpf6rixY8di2bJleOKJJ/D444+jX79+WLlyJQYPHuxv88gjj8DhcOD2229HTU0Nxo0bh9WrV8NgODEzsF6vx/LlyzFv3jy4XC706tULDzzwQKM+RkQnvbx5CXaU7cO9I6Yj0mhVOk6HcMlOCMjQSXy8FijGJp6PvOqj+O++1bhj2FSl49Ap9Go9BloHY1fVduyu2oEhkecrHYnameLzIAUrzoPUPXy0fzX+sekd/O68a3BBj677BVjtLoLDV4NIbVKXfHwYrA5WFeCLvDW4Lf1GDIsdpHQcakKR4xgO1x3EqJixSAzpWgM3uqqgmAeJKJBtOLYdL29+F5f0zOjSxZEQAk6fDXqVicVRgOkbnoy+4clYvvdL2N1OpeNQE+JNiYgyxCC7fDMcHrvScagdsUAiasLBqgL8de0rOC+qH64fcJnScTqUW9TDBw/0qq41p1NXIEkSLuk5Gl7Zhw/3fXnuHajTSZKEvuYB0Ki02Fy2nv2RuhAWSESnKHVU4IFvn0eUKRwzu9hM2U1x+mohQQWtZFA6CjUhRGvE+ORR2FayB1tLOItzINKoNBhgHYRadw32VO1UOg61k679zU/UQnVuBx74dj4EBO48fxoMmq695MaJx2u10KtC+HgtgA2ISMGAiBT8Z88qVNVzVFsgCtOakRLWG4dsuSh1Fisdh9oBCySin7l8bjzy3d9R4ijHXef/DhZ9mNKROpxHNMAnPDDw8VpAO/moTatSY+muTyBzbE1ASjAlwaqLwLbyn+DyNSgdh9qIBRIRAJ8sY+66V7G7/CD+PGwa4kKjlY7UKRy+Gj5eCxIGjR6X9R6HQ9WF+CpvrdJxqAknJ5GUIWNb+SZwkHhwY4FE3Z4QAvM3/gs/HN2GWUNvRJ8utsbamQgh4PDWwKAK5eO1INEjLA5jEtPxZd467KvIUzoONUGn1qOfeSBK64txuO6g0nGoDVggUbcmhMCrW9/DqkPf4w+Dr0VaTH+lI3WaBtkOGV4YVKFKR6EWGBGfhp7meLyz83+orK9ROg41IcIQhXhTInZX5sDmrlE6DrUSCyTq1v5vxwos3/sFpqZeiVEJQ5SO06ns3mqoJS00UtfuiN7VqCQJk/pcBI1Kg8Xbl8PldSsdiZqQEtYHBo0Rm8s2wCd7lY5DrcACibqtpbs+wds7PsLk/hNxUfJIpeN0Kln4UC/b+HgtSBk1elzT7xKUO6rw3u6V7OsSgNSSGgMsg+Dw2LG7aofScagVWCBRt/T+7s/wRvZ/cGWf8bi01wVKx+l0Dl8NAMHHa0EsyhiOy3pfgJzS/Vh5MEvpONSEEG0oepn74nDdQRQ5jikdh1qIBRJ1Ox/s/gyvbXsfl/e+EFf2Ga90nE4nhECdtxJ6yQS1pPh61dQGfcN74qKkEcjMX491hVuVjkNNiDMmIFIfjeyKTXB6HUrHoRZggUTdynu7VuKf297HpN7jcHXfS7rl4yWX7IBXuGBUc5HlriA9NhXDYlPx4b4vsbVkt9Jx6BSSJKGfZQBUkhqbyzZAFrLSkaiZWCBRt/HOjv/h/2UvwxV9LsI1fX/TLYsjAKjzVkINLec+6iIkScKFSSORGtUbS3d+gp1luUpHolNoVFoMsAxCjasKe6q5FEmwYIFEXZ4QAm9kL8ObOR/i6r6XdNs7RwDgFR7UyzYY1WHd9mfQFakkCRNTLkAvaxLe2vFfFkkByKyzICWsDw7V7md/pCDBAom6NFnIeGXLEizdtRLXD7gMV/S5SOlIiqrzVkCCCgZV119GpbtRSRKu7HMReluS8FbOf7G9dK/SkegUCaYeiNRHY1v5T6hz25SOQ+fAAom6LK/sw3Pr38CKfatx86CrMCFljNKRFOUTXti9lTCqwqCS+L9+V6SSVLiiz0XoG94Tb+/4COuPZSsdiX7lRH+kgdCp9fip7Ad4ZI/Skegs+C1JXZLL58bja17G14d/wMwh1+HCpBFKR1Kc3VsFAcCktigdhTqQSlJhUu8LkRbdHx/s+RxfHFoLTpMUODQqDQZaB6PeW48tZRsh2Gk7YLFAoi7H7nbi/sy/4afjObh92FSMiE9TOpLiZOFDnbcCRlUoVJJa6TjUwVSShIuTMzA2cRi+yFuDJbv+Bw9ncw4YJo0JA6yDUFpfxEkkAxgnQaEupdxZhQe+nY8ieynuHfEH9A5PUjpSQKjzVkCGDJPaqnQU6iSSJGFUwhBYDWZ8k/8jSh1VuD39JkQYeQcxEEToI9E7rB8O2XIRqg1DL3NfpSPRKXgHibqMI7XH8acvn0BlfQ0eGHUri6Of+YQXNm8FjKowTgzZDfWPSMFNqVeg1mXD/I3/wp7yQ0pHop/FmxIRb+qBnMqtHNkWgFggUZeQU7oft335BNSSCg+OuhUJoTFKRwoYNm85AIEQ3j3qtmJMkbh50NWIMUXg9ewP8L/cb+CVfUrH6vYkSULvsL6IMsRgS/kGVNSXKR2JfoUFEgW9b49swL3fPIP40Gg8MOpWhPMRgp9HdqHOWwGT2sK+R93ciQVuJ2B80kisKdiEFza+iaO2EqVjdXuSJKG/ZSDMWgs2lK5DVUOF0pHoZyyQKGgJIbB01yd4Yu1CpMem4q7ht8Ck5ezQJwkhUO0pghoamFQsGulE5+1hcYNw86Cr4JG9+PtPb+Gzg1lw+9iBW0kqSY1UaxpCNCFYX7KWRVKAkITgANDWsNlssFgsqK2thdnMNa06m8fnxYs/vYVVh77HFX0uwlV9LubM0Kdw+myocBfAoomBXhWidBwKMD4hY0vRTmwp2YUIgwU3pV6J86LYUVhJXtmLvdU74fA6MDZuPKIM0UpH6pKa+/ubBVIrsUBSTm1DHR5bswC7ynPxu/OuQUbCUKUjBRxZ+FDUcAAaSQuLJpbFI51RVX0tvi/8CUdtJRgaMxDXD7gU0aYIpWN1Wz7Zi701u1DnqcOomLGINyUqHanLYYHUwVggKSO/5hgezHoBNpcdtw2bir7hyUpHCkiV7qNw+myI0CZy5BqdkxACB6qP4Mej21DvbcDFyaMwqfeFCNEalY7WLcnCh9yafahyVSA9agRSwvooHalLYYHUwVggdb4fj27D3HWvwmow445hNyPKFK50pIBU77Oh3F2AMHUUjGquuUbN55G9yC7Zg23Fe6BWqXBZ73G4OGkU9Bqd0tG6HSEE8mwHUFJfhL6WgRgcPgQSlwhqFyyQOhgLpM5zsjP2v7Z/iCEx/TE97ToYNHqlYwUkr/CgpOEgNJIeFk0MH61Rqzg89dhSvBO7yg7ApDViUq8LMC5pJHRq3o3sTEIIFDmPIb/uEGKN8RgRPRo6Nb/72ooFUgdjgdQ5HG4nnln/OtYWbsGVfcbjij7joeIv/SYJIVDmOgyPcCFCm8hh/dRmNrcdm47vxL7KPIRojbis1wUY12M47yh1smpXFXJr9kKn1mFUzAUI17OPWFuwQOpgLJA63uHqo3hszT9Q7qzC9LTrMCRmgNKRAlq1uwh1vkpYNfHQqTjdAbWfWlcdNhftwv7KPBg1BvwmZTQuSh4Jk4b/nXWWBm899tfugcNjx+CIdPQx9+cd4lZigdTBWCB1rK8P/4j5GxYjwmjFn9JvRGxIlNKRAprdW4Uqz3GEqiNhUvO/R+oYNrcdW4t3Y2/FIWgkNS5MGolLUjJg1bOvW2eQhYwjdXkoch5DjCEO50dnwKhhR/qWYoHUwVggdYwGrxuvbH4Xnx7Mwqj4Ibh50FW8nX8O9b46lLuPwKgKQ5iGhSR1PIenHjmle7GzLBde4UNG/FBMSBmD+FDO29MZql2VOFi7HwICQyOHo0dIT95NagEWSB2MBVL7O1x9FE+uW4hCWzFuTL0CYxOH8X/6c3D5HChz50MrGdkpmzqdy+fBrvJc5JTsh93jQFp0f0xMGYu+4T3B/xQ7lkd247DtEMobShFnTEB61AgYNSalYwUFFkgdjAVS+xFC4H+532DR1vcQabTi1iFTkBgWq3SsgOeSnShz5UMj6WDVxHIIMCnGK3zIrczH9pK9qKivRrI5HhNTxmBY3CCoOVigQ1U2lCPPdgCykHFexFD0CuvD74JzYIHUwVggtY8KZzX+tuENbDyegwuTRuD6AZdBp9YqHSvgnbhzdOTnmbLjoOIXIgUAIQQKbEXYXrIXBbYihBvMuCQ5A2OTzmeH7g7klT04UncYJfVFsOrCkR41kiPdzoIFUgdjgdQ2Qgh8nf8jFmx6B5Ik4ZbzfovB0f2UjhUUnD4bKt2FP891FMviiAJSRX01skv2IrfyMNQqNcYmDsPFPTMQw2VMOozNXYs82wE4vHakhPXBoPAh0HPepNOwQOpgLJBar9RRgZd+ehs/HtuGEXGDcWPqFQjV8dn5uQghYPdVotpTDL1kglkTzVvpFPAcHid2lOViV/kBNHhcGBzdD5f0zMCAiN7sp9QBhJBR5DyOQvsRqCQVBoWnISWsD/8i9SsskDoYC6SW88o+/G//11i8fTl0ai1uSr0C6bGpSscKCkLIqPIUweGrhlFlRqg6gh2yKah4ZC8OVOYjp2wfyp3ViAuJwsXJGchIGMKRqh3A7XOjwH4YpfXFCNOaMSTyfMQY45SOFRBYIHUwFkgts71kL/6x+R0crj6KcUnDcW2/CTBq2SehOTyyCxXuQniEC2HqSK6vRkFNCIHjdSXIKduPvOqj0Gt0GJOYjouSRiI2JFLpeF1OnceGfNsh2Dy1iDXGY3DEUJh1VqVjKYoFUgdjgdQ8R20l+H/ZH+D7gk1IsSTiptQr0dOSoHSsoHDikVoVajzFUEEDsyYaWhX7E1DXYXPbsassF7srDqHe04ABESkYlzQCQ2MGQqPi6Lf2IoRApascR+oOo8FXj+TQXkgNHwyTJkTpaIpggdTBWCCdXZmjCkt2fYxPD2TBrA/Bb/tNwIj4NK6j1kwe2YUqz3G4ZAcMqjCEqiPYh4C6LK/sw6HqAuwqO4Dj9lKEak0Yk5iOMT2GIY6z6LcbWcgocRbhqOMIfLIPvcx90d86CAZ197qbzwKpg7FAalqJvQIf7PkMKw98C51ai0t7XYDxyaM4dL+ZZOGDzVsOm7cCaqgRqomEXsUO7NR9VNTXYE/5AeyvzEe9twG9LD2QkTAUw+PPQ4iWy2q0B6/sRZHzGI47jkJAoLe5H/qZB8DQTZYtYYHUwVggNba/8jCW7/0CmfkbYNDocEnP0bg4eRT7GTWTEDLsvirUesogQ4ZJZUGI2sJRatRteYUP+dVHsbcyDwW1RZAkYHBUPwyPH4y0qP7s2N0OPLIHRY6jKHIehxAyeob1Rl/LAIRqu3Y/RxZIHYwFElDvaUBWwU9YeSATu8sPIsJgxSU9MzC2x/kw8MurWWThg91XjTpPOXzwwqAKRYg6HGpJo3Q0ooDh8NTjQNUR5FYeRomjAjqVFoOi+yI9NhWDo/rCxDtLbeKVPSh2HkeR8xg8sgfxpkT0MfdHlKFrLl/EAqmDddcCySt7sa14D77O/xHfF2xCvbcBqZF9MC5pONKiB0Ct4h2P5vDIDbB7q2D3VUNAhkEVCpPaAo3EwpLobGpddThYXYBDVQUocVRAJanQx5qMwdH9MCiqLxJCYzi/Uiv5hA/l9aUoch6D0+tAiCYUKWF9kBya0qUevwVVgfT666/jpZdeQklJCYYOHYp//vOfGDVq1Bnbr1ixAk8++SSOHDmCfv364cUXX8SVV17pf18IgaeeegpvvfUWampqcMEFF+CNN95Av36/zNRcVVWFe++9F59//jlUKhWmTJmCV199FaGhoc3K3J0KJJvLjs3FO7HhWDZ+OLoNdW4HYkyRGBmfhlEJQxBlClc6YlDwCg/qfbVweGvgFvWQoIJRFQaj2sw7RkStUOd2Ir/mKPJrj+GYrQQe2YswXQgGRvZGv4ie6GNNRlxINAumFhJCoNZdg9L6YlQ2lEOGjBhDHBJDkxBvSoQ+yDt1B02B9OGHH2L69OlYvHgxMjIysHDhQqxYsQK5ubmIiYk5rf2GDRtw0UUXYf78+bj66quxbNkyvPjii8jOzsbgwYMBAC+++CLmz5+PpUuXolevXnjyySexa9cu7N27FwbDiQt7xRVXoLi4GP/617/g8Xhw6623YuTIkVi2bFmzcnflAqnCWY09FQexo3Q/skv3IrcyHwICPcJiMTi6P9JjU9EjLK5L3nptT0LIcMn1aJDtqPfVwSPqAQA6yQSDOhR6ycSfIVE78co+FNnLUGgrwjFbCcqcVZCFDJPWgBRLIlIsiUg2J6BHWBzCDRYWTc3klT2oaChHeUMpat01AIAIfSRijQmINsYiXB98I2yDpkDKyMjAyJEj8dprrwEAZFlGUlIS7r33Xjz22GOntZ86dSocDgdWrVrl3zZ69Gikp6dj8eLFEEIgISEBDz74IB566CEAQG1tLWJjY7FkyRLcfPPN2LdvHwYNGoQtW7ZgxIgRAIDVq1fjyiuvxLFjx5CQcO55erpCgVTrsuN4XQkKaouQX3MMh6oLcaAqHxX11QCACIMVvcOTMCCiF1IjeyPcaFE4ceASQoZHuOGR6+EWDXD5nHCLegACElTQqYzQSyboVEaouLo5UYdz+zwocVSgqK4UpY5KlDor4PQ0AACMGj3iQ2MQFxKF2JBIRJkiEG0KR4TRykV1z8Ltc6PKVYFqVxVq3FXwCR/Ukhrh+giE6yNh1UXArLMgVBsW0EVTc39/K3pf3+12Y9u2bZgzZ45/m0qlwsSJE7Fx48Ym99m4cSNmz57daNukSZOwcuVKAEB+fj5KSkowceJE//sWiwUZGRnYuHEjbr75ZmzcuBFWq9VfHAHAxIkToVKpsGnTJlx33XXt+Ck7hxACbtkDh7seDk89HB4nbC47bG47ahpsqG6wocJZjXJnNUodFSh1VMDucfr3DzdYkBgag/PjBiHZnIAUSyILIpz4ucrwQRa+n//phU944RMeeIUHXuGGV7jhEx7/PipooFXpEaoKh1YyQCPpeKeIqJPp1Fokm+ORbI4H8PPEq556lDurUFlfjYr6GhyuOYqtxbvhln/5/9eg0cOqD4PVYIZFH4YwnQmhOhNCdSEwaYwwaQ0wagwwaHQwqPXQa3TQqrTd4o6UTq1DnCkBcaYECCGjzlMHm6cWdW4bCu1HcNC3HwAgQUKIJhQh2lCYNCEwaUJgUBug//mlU+mgVeugkTQB/d2oaIFUUVEBn8+H2NjYRttjY2Oxf//+JvcpKSlpsn1JSYn//ZPbztbm1Md3Go0GERER/jancrlccLlc/j/X1tYCOFGJtrcPdn+Gt3f+r92Peyq9WodIgxXRpgjoNVrAC1RUV6KiuhLZ2NXh51dKstWKpHBrux+3weuDy+uBT5bb/dhE1H7UAGK1BsRqDUBYDHxChtvrgVf2Qj75UMXnRYOzGg3OapR3cJ7UqD4w64N7VmstdIAsod7rQIOvAQ44AJS26ZgqqDAu/hJY9O3bz/Xk7+1zPUBjz9Bmmj9/Pp5++unTticlJSmQhoiIiNqirq4OFsuZn5QoWiBFRUVBrVajtLRxlVlaWoq4uKZXHY6Liztr+5P/LC0tRXx8fKM26enp/jZlZWWNjuH1elFVVXXG886ZM6fRo72amhr07NkThYWFZ/0Bk/JsNhuSkpJw9OjRoO0v1p3wegUXXq/gwWt1ghACdXV15+xvrGiBpNPpMHz4cGRlZWHy5MkATnTSzsrKwj333NPkPmPGjEFWVhbuv/9+/7bMzEyMGTMGANCrVy/ExcUhKyvLXxDZbDZs2rQJd955p/8YNTU12LZtG4YPHw4A+O677yDLMjIyMpo8r16vh15/+kKhFoulW/+HFkzMZjOvVRDh9QouvF7Bg9cKzbqxofgjttmzZ2PGjBkYMWIERo0ahYULF8LhcODWW28FAEyfPh2JiYmYP38+AOC+++7D+PHjsWDBAlx11VVYvnw5tm7dijfffBMAIEkS7r//fjz33HPo16+ff5h/QkKCvwhLTU3F5Zdfjttuuw2LFy+Gx+PBPffcg5tvvrlZI9iIiIioa1O8QJo6dSrKy8sxd+5clJSUID09HatXr/Z3si4sLITqV7Mzjx07FsuWLcMTTzyBxx9/HP369cPKlSv9cyABwCOPPAKHw4Hbb78dNTU1GDduHFavXu2fAwkAPvjgA9xzzz2YMGGCf6LIRYsWdd4HJyIiooCl+DxIwcrlcmH+/PmYM2dOk4/eKHDwWgUXXq/gwusVPHitWoYFEhEREdEpAneqSyIiIiKFsEAiIiIiOgULJCIiIqJTsEA6i/nz52PkyJEICwtDTEwMJk+ejNzc3EZtGhoacPfddyMyMhKhoaGYMmXKaRNZUud74YUX/FM+nMRrFViOHz+O3//+94iMjITRaERaWhq2bt3qf18Igblz5yI+Ph5GoxETJ07EwYMHFUzcffl8Pjz55JPo1asXjEYj+vTpg2effbbRUg28XspZt24drrnmGiQkJECSJP/apCc159pUVVXhlltugdlshtVqxaxZs2C32zvxUwQeFkhnsXbtWtx999346aefkJmZCY/Hg8suuwwOh8Pf5oEHHsDnn3+OFStWYO3atSgqKsL111+vYGrasmUL/vWvf2HIkCGNtvNaBY7q6mpccMEF0Gq1+Oqrr7B3714sWLAA4eG/rLn097//HYsWLcLixYuxadMmhISEYNKkSWhoaFAweff04osv4o033sBrr72Gffv24cUXX8Tf//53/POf//S34fVSjsPhwNChQ/H66683+X5zrs0tt9yCPXv2IDMzE6tWrcK6detw++23d9ZHCEyCmq2srEwAEGvXrhVCCFFTUyO0Wq1YsWKFv82+ffsEALFx40alYnZrdXV1ol+/fiIzM1OMHz9e3HfffUIIXqtA8+ijj4px48ad8X1ZlkVcXJx46aWX/NtqamqEXq8X//nPfzojIv3KVVddJf74xz822nb99deLW265RQjB6xVIAIhPPvnE/+fmXJu9e/cKAGLLli3+Nl999ZWQJEkcP36807IHGt5BaoHa2loAQEREBABg27Zt8Hg8mDhxor/NwIEDkZycjI0bNyqSsbu7++67cdVVVzW6JgCvVaD57LPPMGLECNx4442IiYnBsGHD8NZbb/nfz8/PR0lJSaPrZbFYkJGRweulgLFjxyIrKwsHDhwAAOzYsQM//vgjrrjiCgC8XoGsOddm48aNsFqtGDFihL/NxIkToVKpsGnTpk7PHCgUn0k7WMiyjPvvvx8XXHCBf9bukpIS6HQ6WK3WRm1jY2NRUlKiQMrubfny5cjOzsaWLVtOe4/XKrAcPnwYb7zxBmbPno3HH38cW7ZswV/+8hfodDrMmDHDf01Ozqh/Eq+XMh577DHYbDYMHDgQarUaPp8Pf/vb33DLLbcAAK9XAGvOtSkpKUFMTEyj9zUaDSIiIrr19WOB1Ex33303du/ejR9//FHpKNSEo0eP4r777kNmZmajJWUoMMmyjBEjRuD5558HAAwbNgy7d+/G4sWLMWPGDIXT0an++9//4oMPPsCyZctw3nnnIScnB/fffz8SEhJ4vajL4iO2ZrjnnnuwatUqfP/99+jRo4d/e1xcHNxuN2pqahq1Ly0tRVxcXCen7N62bduGsrIynH/++dBoNNBoNFi7di0WLVoEjUaD2NhYXqsAEh8fj0GDBjXalpqaisLCQgDwX5NTRxnyeinj4YcfxmOPPYabb74ZaWlp+MMf/oAHHnjAv4g4r1fgas61iYuLQ1lZWaP3vV4vqqqquvX1Y4F0FkII3HPPPfjkk0/w3XffoVevXo3eHz58OLRaLbKysvzbcnNzUVhYiDFjxnR23G5twoQJ2LVrF3JycvyvESNG4JZbbvH/O69V4LjgggtOmzLjwIED6NmzJwCgV69eiIuLa3S9bDYbNm3axOulAKfT2WjRcABQq9WQZRkAr1cga861GTNmDGpqarBt2zZ/m++++w6yLCMjI6PTMwcMpXuJB7I777xTWCwWsWbNGlFcXOx/OZ1Of5s///nPIjk5WXz33Xdi69atYsyYMWLMmDEKpqaTfj2KTQheq0CyefNmodFoxN/+9jdx8OBB8cEHHwiTySTef/99f5sXXnhBWK1W8emnn4qdO3eKa6+9VvTq1UvU19crmLx7mjFjhkhMTBSrVq0S+fn54uOPPxZRUVHikUce8bfh9VJOXV2d2L59u9i+fbsAIF5++WWxfft2UVBQIIRo3rW5/PLLxbBhw8SmTZvEjz/+KPr16yemTZum1EcKCCyQzgJAk693333X36a+vl7cddddIjw8XJhMJnHdddeJ4uJi5UKT36kFEq9VYPn888/F4MGDhV6vFwMHDhRvvvlmo/dlWRZPPvmkiI2NFXq9XkyYMEHk5uYqlLZ7s9ls4r777hPJycnCYDCI3r17i7/+9a/C5XL52/B6Kef7779v8nfVjBkzhBDNuzaVlZVi2rRpIjQ0VJjNZnHrrbeKuro6BT5N4JCE+NVUqERERETEPkhEREREp2KBRERERHQKFkhEREREp2CBRERERHQKFkhEREREp2CBRERERHQKFkhEREREp2CBRERERHQKFkhE1CYXX3wx7r//fqVjEBG1KxZIRESdbN68eUhPT1c6BhGdBQskIqKfud1upSMQUYBggUREbSbLMh555BFEREQgLi4O8+bNAwAIITBv3jwkJydDr9cjISEBf/nLX/z7uVwuPProo0hKSoJer0ffvn3x9ttvn/N8a9asgSRJ+OKLLzBkyBAYDAaMHj0au3fvbtTuxx9/xIUXXgij0YikpCT85S9/gcPh8L+fkpKCZ599FtOnT4fZbMbtt98OAFi/fj0uvvhimEwmhIeHY9KkSaiurvZ/1vnz56NXr14wGo0YOnQoPvroo9OyZWVlYcSIETCZTBg7dixyc3MBAEuWLMHTTz+NHTt2QJIkSJKEJUuWAABefvllpKWlISQkBElJSbjrrrtgt9sbfaa33noLSUlJMJlMuO666/Dyyy/DarU2avPpp5/i/PPPh8FgQO/evfH000/D6/We8+dKRL+i8GK5RBTkxo8fL8xms5g3b544cOCAWLp0qZAkSXzzzTdixYoVwmw2iy+//FIUFBSITZs2iTfffNO/70033SSSkpLExx9/LPLy8sS3334rli9ffs5znly9PDU1VXzzzTdi586d4uqrrxYpKSnC7XYLIYQ4dOiQCAkJEa+88oo4cOCAWL9+vRg2bJiYOXOm/zg9e/YUZrNZ/OMf/xCHDh0Shw4dEtu3bxd6vV7ceeedIicnR+zevVv885//FOXl5UIIIZ577jkxcOBAsXr1apGXlyfeffddodfrxZo1axply8jIEGvWrBF79uwRF154oRg7dqwQQgin0ykefPBBcd5554ni4mJRXFwsnE6nEEKIV155RXz33XciPz9fZGVliQEDBog777zTn/fHH38UKpVKvPTSSyI3N1e8/vrrIiIiQlgsFn+bdevWCbPZLJYsWSLy8vLEN998I1JSUsS8efNaeYWJuicWSETUJuPHjxfjxo1rtG3kyJHi0UcfFQsWLBD9+/f3Fy2/lpubKwCIzMzMFp/zZBHy62KqsrJSGI1G8eGHHwohhJg1a5a4/fbbG+33ww8/CJVKJerr64UQJwqkyZMnN2ozbdo0ccEFFzR53oaGBmEymcSGDRsabZ81a5aYNm1ao2zffvut//0vvvhCAPCf96mnnhJDhw495+dcsWKFiIyM9P956tSp4qqrrmrU5pZbbmlUIE2YMEE8//zzjdr8+9//FvHx8ec8HxH9go/YiKjNhgwZ0ujP8fHxKCsrw4033oj6+nr07t0bt912Gz755BP/o56cnByo1WqMHz++1ecdM2aM/98jIiIwYMAA7Nu3DwCwY8cOLFmyBKGhof7XpEmTIMsy8vPz/fuNGDGi0TFzcnIwYcKEJs936NAhOJ1OXHrppY2O+9577yEvL++MP5P4+HgAQFlZ2Vk/z7fffosJEyYgMTERYWFh+MMf/oDKyko4nU4AQG5uLkaNGtVon1P/vGPHDjzzzDON8t12220oLi72H4eIzk2jdAAiCn5arbbRnyVJgizLSEpKQm5uLr799ltkZmbirrvuwksvvYS1a9fCaDR2aCa73Y477rijUZ+nk5KTk/3/HhIS0ui9s+U62R/oiy++QGJiYqP39Hp9oz//+mciSRKAE/2XzuTIkSO4+uqrceedd+Jvf/sbIiIi8OOPP2LWrFlwu90wmUxn3PfUjE8//TSuv/76094zGAzNOgYRsUAiog5mNBpxzTXX4JprrsHdd9+NgQMHYteuXUhLS4Msy1i7di0mTpzYqmP/9NNP/mKnuroaBw4cQGpqKgDg/PPPx969e9G3b98WHXPIkCHIysrC008/fdp7gwYNgl6vR2FhYZvufOl0Ovh8vkbbtm3bBlmWsWDBAqhUJ27u//e//23UZsCAAdiyZUujbaf++fzzz0dubm6LPzcRNcYCiYg6zJIlS+Dz+ZCRkQGTyYT3338fRqMRPXv2RGRkJGbMmIE//vGPWLRoEYYOHYqCggKUlZXhpptuatbxn3nmGURGRiI2NhZ//etfERUVhcmTJwMAHn30UYwePRr33HMP/vSnPyEkJAR79+5FZmYmXnvttTMec86cOUhLS8Ndd92FP//5z9DpdPj+++9x4403IioqCg899BAeeOAByLKMcePGoba2FuvXr4fZbMaMGTOalTslJQX5+fnIyclBjx49EBYWhr59+8Lj8eCf//wnrrnmGqxfvx6LFy9utN+9996Liy66CC+//DKuueYafPfdd/jqq6/8d6gAYO7cubj66quRnJyMG264ASqVCjt27MDu3bvx3HPPNSsfEYGj2IiobcaPHy/uu+++RtuuvfZaMWPGDPHJJ5+IjIwMYTabRUhIiBg9enSjzsv19fXigQceEPHx8UKn04m+ffuKd95555znPNkR+vPPPxfnnXee0Ol0YtSoUWLHjh2N2m3evFlceumlIjQ0VISEhIghQ4aIv/3tb/73e/bsKV555ZXTjr9mzRoxduxYodfrhdVqFZMmTRLV1dVCCCFkWRYLFy4UAwYMEFqtVkRHR4tJkyaJtWvXNsp2sr0QQmzfvl0AEPn5+UKIE529p0yZIqxWqwAg3n33XSGEEC+//LKIj48XRqNRTJo0Sbz33nunHevNN98UiYmJwmg0ismTJ4vnnntOxMXFNcq/evVqMXbsWGE0GoXZbBajRo1qNHqQiM5NEkIIRSs0IqIWWrNmDS655BJUV1efNgdQd3Pbbbdh//79+OGHH5SOQtSl8BEbEVEQ+cc//oFLL70UISEh+Oqrr7B06VL8v//3/5SORdTlcJg/EQWcP//5z42Gqf/69ec//1npeIravHkzLr30UqSlpWHx4sVYtGgR/vSnPykdi6jL4SM2Igo4ZWVlsNlsTb5nNpsRExPTyYmIqLthgURERER0Cj5iIyIiIjoFCyQiIiKiU7BAIiIiIjoFCyQiIiKiU7BAIiIiIjoFCyQiIiKiU7BAIiIiIjoFCyQiIiKiU/x/GYnuJwO2kg0AAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sbs.kdeplot(data=df,x=numerical_features[1],hue=\"status\",palette=\"Greens\",fill=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Da questo grafico invece si può vedere che la variabile hsc_percentage influisce, quindi abbiamo deciso di non rimuoverla.\n",
    "\n",
    "Adesso valutiamo l'influenza della variabile degree_percentage:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 370,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='degree_percentage', ylabel='Density'>"
      ]
     },
     "execution_count": 370,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sbs.kdeplot(data=df,x=numerical_features[2],hue=\"status\",palette=\"Greens\",fill=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Da questo grafico si nota che la variabile degree_percentage influisce discretamente, ma nonostante questo non la elimineremo.\n",
    "\n",
    "Continuiamo adesso valutando l'influenza della variabile emp_test_percentage:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 371,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='emp_test_percentage', ylabel='Density'>"
      ]
     },
     "execution_count": 371,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sbs.kdeplot(data=df,x=numerical_features[3],hue=\"status\",palette=\"Greens\",fill=True)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Da questo grafico si nota che la variabile emp_test_percentage influisce discretamente. Da questa osservazione abbiamo deciso di eliminare questa variabile\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 372,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(columns=\"emp_test_percentage\", inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "Continuiamo adesso valutando l'influenza della variabile mba_percent"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 373,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='mba_percent', ylabel='Density'>"
      ]
     },
     "execution_count": 373,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sbs.kdeplot(data=df,x=numerical_features[4],hue=\"status\",palette=\"Greens\",fill=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Da questo grafico si può notare che la variabile mba_percent influisce discretamente, nonostante questo abbiamo deciso di eliminarla per due motivi:\n",
    "1. Scarsa influenza\n",
    "2. Non è necessario per il nostro modello \n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 374,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(columns=[\"mba_percent\"], inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Adesso calcoliamo il grado di influenza della variabile gender"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 375,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='gender', ylabel='count'>"
      ]
     },
     "execution_count": 375,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sbs.countplot(data=df,x=categorical_features[0],hue=\"status\",palette=\"Greens\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Da questo grafico si può notare che il gender influensce di molto. Oltrettutto ci fa capire che le predizioni del nostro modello di machine learning sono influenzate in modo negativo eticamente, causando una discrimazione per un sesso rispetto ad un altro. Abbiamo così deciso di eliminare questa variabile dal dataset per evitare questo tipo di problema, rendendolo quanto più neutrale possibile."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 376,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(columns=['gender'], inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "Adesso calcoliamo il grado di influenza della variabile ssc_board"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 377,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='ssc_board', ylabel='count'>"
      ]
     },
     "execution_count": 377,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sbs.countplot(data=df,x=categorical_features[1],hue=\"status\",palette=\"Greens\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Da questo grafico si può notare che la variabile ssc_board influisce discretamente, nonostante questo riteniamo di non eliminare la variabile per due motivi:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Adesso osserviamo l'influenza della variabile hsc_board:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 378,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='hsc_board', ylabel='count'>"
      ]
     },
     "execution_count": 378,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sbs.countplot(data=df,x=categorical_features[2],hue=\"status\",palette=\"Greens\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Da questo grafico si può notare che la variabile hsc_board influisce notevolmente, nonostante questo riteniamo di non eliminare la variabile"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Adesso osserviamo l'influenza della variabile hsc_subject:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 379,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='hsc_subject', ylabel='count'>"
      ]
     },
     "execution_count": 379,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sbs.countplot(data=df,x=categorical_features[3],hue=\"status\",palette=\"Greens\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Da questo grafico si può notare che la variabile hsc_subject influisce notevolmente, quindi abbiamo ritenuto di non eliminare la variabile in questione.\n",
    "\n",
    "Adesso verifichiamo l'influenza della variabile undegrad_degree:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 380,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='undergrad_degree', ylabel='count'>"
      ]
     },
     "execution_count": 380,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sbs.countplot(data=df,x=categorical_features[4],hue=\"status\",palette=\"Greens\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Da questo grafico si può notare che la variabile undergrad_degree influenza notevolmente, quindi  abbiamo ritenuto di non rimuovere questa variabile.\n",
    "\n",
    "Adesso analizziamo l'influenza della variabile work_experience:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 381,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='work_experience', ylabel='count'>"
      ]
     },
     "execution_count": 381,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sbs.countplot(data=df,x=categorical_features[5],hue=\"status\",palette=\"Greens\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Da questo grafico si può notare subito che la variabile work_experience influisce, quindi abbiamo deciso di non rimuoverla."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Adesso analizziamo l'influenza della variabile specialisation:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 382,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='specialisation', ylabel='count'>"
      ]
     },
     "execution_count": 382,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sbs.countplot(data=df,x=categorical_features[6],hue=\"status\",palette=\"Greens\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Da questo grafici si può notare che la variabile specialisation influisce, quindi abbiamo ritenuto di non rimuoverla."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Adesso mostriamo il dataset dopo la selezioni delle variabili:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 383,
   "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>ssc_percentage</th>\n",
       "      <th>ssc_board</th>\n",
       "      <th>hsc_percentage</th>\n",
       "      <th>hsc_board</th>\n",
       "      <th>hsc_subject</th>\n",
       "      <th>degree_percentage</th>\n",
       "      <th>undergrad_degree</th>\n",
       "      <th>work_experience</th>\n",
       "      <th>specialisation</th>\n",
       "      <th>status</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>67.00</td>\n",
       "      <td>Others</td>\n",
       "      <td>91.00</td>\n",
       "      <td>Others</td>\n",
       "      <td>Commerce</td>\n",
       "      <td>58.00</td>\n",
       "      <td>Sci&amp;Tech</td>\n",
       "      <td>No</td>\n",
       "      <td>Mkt&amp;HR</td>\n",
       "      <td>Placed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>79.33</td>\n",
       "      <td>Central</td>\n",
       "      <td>78.33</td>\n",
       "      <td>Others</td>\n",
       "      <td>Science</td>\n",
       "      <td>77.48</td>\n",
       "      <td>Sci&amp;Tech</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Mkt&amp;Fin</td>\n",
       "      <td>Placed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>65.00</td>\n",
       "      <td>Central</td>\n",
       "      <td>68.00</td>\n",
       "      <td>Central</td>\n",
       "      <td>Arts</td>\n",
       "      <td>64.00</td>\n",
       "      <td>Comm&amp;Mgmt</td>\n",
       "      <td>No</td>\n",
       "      <td>Mkt&amp;Fin</td>\n",
       "      <td>Placed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>56.00</td>\n",
       "      <td>Central</td>\n",
       "      <td>52.00</td>\n",
       "      <td>Central</td>\n",
       "      <td>Science</td>\n",
       "      <td>52.00</td>\n",
       "      <td>Sci&amp;Tech</td>\n",
       "      <td>No</td>\n",
       "      <td>Mkt&amp;HR</td>\n",
       "      <td>Not Placed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>85.80</td>\n",
       "      <td>Central</td>\n",
       "      <td>73.60</td>\n",
       "      <td>Central</td>\n",
       "      <td>Commerce</td>\n",
       "      <td>73.30</td>\n",
       "      <td>Comm&amp;Mgmt</td>\n",
       "      <td>No</td>\n",
       "      <td>Mkt&amp;Fin</td>\n",
       "      <td>Placed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>210</th>\n",
       "      <td>80.60</td>\n",
       "      <td>Others</td>\n",
       "      <td>82.00</td>\n",
       "      <td>Others</td>\n",
       "      <td>Commerce</td>\n",
       "      <td>77.60</td>\n",
       "      <td>Comm&amp;Mgmt</td>\n",
       "      <td>No</td>\n",
       "      <td>Mkt&amp;Fin</td>\n",
       "      <td>Placed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>211</th>\n",
       "      <td>58.00</td>\n",
       "      <td>Others</td>\n",
       "      <td>60.00</td>\n",
       "      <td>Others</td>\n",
       "      <td>Science</td>\n",
       "      <td>72.00</td>\n",
       "      <td>Sci&amp;Tech</td>\n",
       "      <td>No</td>\n",
       "      <td>Mkt&amp;Fin</td>\n",
       "      <td>Placed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>212</th>\n",
       "      <td>67.00</td>\n",
       "      <td>Others</td>\n",
       "      <td>67.00</td>\n",
       "      <td>Others</td>\n",
       "      <td>Commerce</td>\n",
       "      <td>73.00</td>\n",
       "      <td>Comm&amp;Mgmt</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Mkt&amp;Fin</td>\n",
       "      <td>Placed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>213</th>\n",
       "      <td>74.00</td>\n",
       "      <td>Others</td>\n",
       "      <td>66.00</td>\n",
       "      <td>Others</td>\n",
       "      <td>Commerce</td>\n",
       "      <td>58.00</td>\n",
       "      <td>Comm&amp;Mgmt</td>\n",
       "      <td>No</td>\n",
       "      <td>Mkt&amp;HR</td>\n",
       "      <td>Placed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>214</th>\n",
       "      <td>62.00</td>\n",
       "      <td>Central</td>\n",
       "      <td>58.00</td>\n",
       "      <td>Others</td>\n",
       "      <td>Science</td>\n",
       "      <td>53.00</td>\n",
       "      <td>Comm&amp;Mgmt</td>\n",
       "      <td>No</td>\n",
       "      <td>Mkt&amp;HR</td>\n",
       "      <td>Not Placed</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>215 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     ssc_percentage ssc_board  hsc_percentage hsc_board hsc_subject  \\\n",
       "0             67.00    Others           91.00    Others    Commerce   \n",
       "1             79.33   Central           78.33    Others     Science   \n",
       "2             65.00   Central           68.00   Central        Arts   \n",
       "3             56.00   Central           52.00   Central     Science   \n",
       "4             85.80   Central           73.60   Central    Commerce   \n",
       "..              ...       ...             ...       ...         ...   \n",
       "210           80.60    Others           82.00    Others    Commerce   \n",
       "211           58.00    Others           60.00    Others     Science   \n",
       "212           67.00    Others           67.00    Others    Commerce   \n",
       "213           74.00    Others           66.00    Others    Commerce   \n",
       "214           62.00   Central           58.00    Others     Science   \n",
       "\n",
       "     degree_percentage undergrad_degree work_experience specialisation  \\\n",
       "0                58.00         Sci&Tech              No         Mkt&HR   \n",
       "1                77.48         Sci&Tech             Yes        Mkt&Fin   \n",
       "2                64.00        Comm&Mgmt              No        Mkt&Fin   \n",
       "3                52.00         Sci&Tech              No         Mkt&HR   \n",
       "4                73.30        Comm&Mgmt              No        Mkt&Fin   \n",
       "..                 ...              ...             ...            ...   \n",
       "210              77.60        Comm&Mgmt              No        Mkt&Fin   \n",
       "211              72.00         Sci&Tech              No        Mkt&Fin   \n",
       "212              73.00        Comm&Mgmt             Yes        Mkt&Fin   \n",
       "213              58.00        Comm&Mgmt              No         Mkt&HR   \n",
       "214              53.00        Comm&Mgmt              No         Mkt&HR   \n",
       "\n",
       "         status  \n",
       "0        Placed  \n",
       "1        Placed  \n",
       "2        Placed  \n",
       "3    Not Placed  \n",
       "4        Placed  \n",
       "..          ...  \n",
       "210      Placed  \n",
       "211      Placed  \n",
       "212      Placed  \n",
       "213      Placed  \n",
       "214  Not Placed  \n",
       "\n",
       "[215 rows x 10 columns]"
      ]
     },
     "execution_count": 383,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Feature Scaling\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Balancing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Analizziamo ora come è bilanciato il numero di esempi disponibili per il modello:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 384,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='status', ylabel='Count'>"
      ]
     },
     "execution_count": 384,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sbs.histplot(df['status'], legend=True, color=\"green\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Come si può notare dal grafico non abbiamo un dataset bilanciato perché abbiamo una notevole quantità di esempi \"Placed\" rispetto a quelli \"Not Placed\". Da quest'analisi siamo arrivati alla conclusione di bilanciare il dataset e di utilizzare una famosa tecnica chiamata \"Oversampling\", nel quale vengono aggiunte casualmente un numero di istanze del dataset nella classe di minoranza (ovvero \"Not Placed\")."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 385,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Placed        148\n",
       "Not Placed     67\n",
       "Name: status, dtype: int64"
      ]
     },
     "execution_count": 385,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.status.value_counts()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Oversampling con RandomOverSampler"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Utilizziamo una tecnica di ecoding per codificare le nostre feature categoriche"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 386,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.model_selection import train_test_split\n",
    "from imblearn.over_sampling import RandomOverSampler\n",
    "from sklearn.metrics import classification_report\n",
    "le = LabelEncoder()\n",
    "df['specialisation']=le.fit_transform(df['specialisation'])\n",
    "df['undergrad_degree']=le.fit_transform(df['undergrad_degree'])\n",
    "df['hsc_subject']=le.fit_transform(df['hsc_subject'])\n",
    "df['work_experience']=le.fit_transform(df['work_experience'])\n",
    "df['ssc_board'] = le.fit_transform(df['ssc_board'])\n",
    "df['hsc_board'] = le.fit_transform(df['hsc_board'])"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Adesso dividiamo il nostro dataset, salvando in una variabile \"X\" i valori delle variabili indipendenti e nella variabile \"y\" i valori della variabile dipedentente"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 387,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df.drop('status',axis=1)\n",
    "y = df['status']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Vediamo le istanze per l'allenamento prima dell'incremento di istanze"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 388,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[('Not Placed', 67), ('Placed', 148)]\n"
     ]
    }
   ],
   "source": [
    "print(sorted(Counter(y).items()))"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Adesso utilizziamo la funzione RandomOverSampler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 389,
   "metadata": {},
   "outputs": [],
   "source": [
    "rd = RandomOverSampler(random_state=0)\n",
    "X, y = rd.fit_resample(X, y)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 390,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='status', ylabel='Count'>"
      ]
     },
     "execution_count": 390,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sbs.histplot(y, legend=True, color=\"green\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Come si può vedere adesso abbiamo un numero di istanze uguali."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Modelling"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Per l'obiettivo che vogliamo raggiungere dovremo affrontare un problema di classificazione. I classificatori che abbiamo ipotizzato di utilizzare  sono i seguenti:\n",
    "- Naive Bayes\n",
    "- Decision Tree\n",
    "- Random Forest\n",
    "- K-Nearest Neighbors\n",
    "\n",
    "Andiamo ad analizzare i seguenti modelli attraverlo l'utilizzo delle seguenti metriche:\n",
    "- accuratezza\n",
    "- precisione\n",
    "- recall"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Validazione del modello"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Dividiamo adesso il dataset "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 391,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train , X_test , y_train , y_test = train_test_split(X , y , test_size=0.33, random_state=0)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "E analizziamo i vari modelli"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Decision Tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 392,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "  Not Placed       0.84      0.89      0.86        54\n",
      "      Placed       0.85      0.80      0.82        44\n",
      "\n",
      "    accuracy                           0.85        98\n",
      "   macro avg       0.85      0.84      0.84        98\n",
      "weighted avg       0.85      0.85      0.85        98\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "\n",
    "tc = DecisionTreeClassifier()\n",
    "decision_model = tc.fit(X_train, y_train)\n",
    "decision_train_prediction = decision_model.predict(X_train)\n",
    "decision_test_prediction = decision_model.predict(X_test)\n",
    "\n",
    "print(classification_report(y_test,decision_test_prediction))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Naive Bayes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 393,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "  Not Placed       0.72      0.70      0.71        54\n",
      "      Placed       0.64      0.66      0.65        44\n",
      "\n",
      "    accuracy                           0.68        98\n",
      "   macro avg       0.68      0.68      0.68        98\n",
      "weighted avg       0.68      0.68      0.68        98\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.naive_bayes import MultinomialNB\n",
    "\n",
    "nb = MultinomialNB()\n",
    "nb_model = nb.fit(X_train, y_train)\n",
    "nb_prediction = nb_model.predict(X_test)\n",
    "\n",
    "print(classification_report(y_test, nb_prediction))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 394,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "  Not Placed       0.89      0.87      0.88        54\n",
      "      Placed       0.84      0.86      0.85        44\n",
      "\n",
      "    accuracy                           0.87        98\n",
      "   macro avg       0.87      0.87      0.87        98\n",
      "weighted avg       0.87      0.87      0.87        98\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "rf = RandomForestClassifier()\n",
    "random_model = rf.fit(X_train, y_train)\n",
    "random_prediction = random_model.predict(X_test)\n",
    "\n",
    "print(classification_report(y_test, random_prediction))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### K-Nearest Neighbors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 395,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "  Not Placed       0.88      0.78      0.82        54\n",
      "      Placed       0.76      0.86      0.81        44\n",
      "\n",
      "    accuracy                           0.82        98\n",
      "   macro avg       0.82      0.82      0.82        98\n",
      "weighted avg       0.82      0.82      0.82        98\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "\n",
    "kn = KNeighborsClassifier(n_neighbors=3)\n",
    "kn_model = kn.fit(X_train, y_train)\n",
    "kn_prediction = kn_model.predict(X_test)\n",
    "\n",
    "print(classification_report(y_test, kn_prediction))"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Come abbiamo potuto notare solo due modelli come \"Random Forest\" e \"Decision Tree\" offrono diversi valori metrici buoni. La decisione però è ricaduta sul modello \"Random Forest\"."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 396,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['job_prevision.joblib']"
      ]
     },
     "execution_count": 396,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dump(rf, \"job_prevision.joblib\")"
   ]
  }
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