{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "26189da1",
   "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>battery_power</th>\n",
       "      <th>blue</th>\n",
       "      <th>clock_speed</th>\n",
       "      <th>dual_sim</th>\n",
       "      <th>fc</th>\n",
       "      <th>four_g</th>\n",
       "      <th>int_memory</th>\n",
       "      <th>m_dep</th>\n",
       "      <th>mobile_wt</th>\n",
       "      <th>n_cores</th>\n",
       "      <th>...</th>\n",
       "      <th>px_height</th>\n",
       "      <th>px_width</th>\n",
       "      <th>ram</th>\n",
       "      <th>sc_h</th>\n",
       "      <th>sc_w</th>\n",
       "      <th>talk_time</th>\n",
       "      <th>three_g</th>\n",
       "      <th>touch_screen</th>\n",
       "      <th>wifi</th>\n",
       "      <th>price_range</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>842</td>\n",
       "      <td>0</td>\n",
       "      <td>2.2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>0.6</td>\n",
       "      <td>188</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>20</td>\n",
       "      <td>756</td>\n",
       "      <td>2549</td>\n",
       "      <td>9</td>\n",
       "      <td>7</td>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1021</td>\n",
       "      <td>1</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>53</td>\n",
       "      <td>0.7</td>\n",
       "      <td>136</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>905</td>\n",
       "      <td>1988</td>\n",
       "      <td>2631</td>\n",
       "      <td>17</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>563</td>\n",
       "      <td>1</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>41</td>\n",
       "      <td>0.9</td>\n",
       "      <td>145</td>\n",
       "      <td>5</td>\n",
       "      <td>...</td>\n",
       "      <td>1263</td>\n",
       "      <td>1716</td>\n",
       "      <td>2603</td>\n",
       "      <td>11</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>615</td>\n",
       "      <td>1</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>0.8</td>\n",
       "      <td>131</td>\n",
       "      <td>6</td>\n",
       "      <td>...</td>\n",
       "      <td>1216</td>\n",
       "      <td>1786</td>\n",
       "      <td>2769</td>\n",
       "      <td>16</td>\n",
       "      <td>8</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1821</td>\n",
       "      <td>1</td>\n",
       "      <td>1.2</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>1</td>\n",
       "      <td>44</td>\n",
       "      <td>0.6</td>\n",
       "      <td>141</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>1208</td>\n",
       "      <td>1212</td>\n",
       "      <td>1411</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>15</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   battery_power  blue  clock_speed  dual_sim  fc  four_g  int_memory  m_dep  \\\n",
       "0            842     0          2.2         0   1       0           7    0.6   \n",
       "1           1021     1          0.5         1   0       1          53    0.7   \n",
       "2            563     1          0.5         1   2       1          41    0.9   \n",
       "3            615     1          2.5         0   0       0          10    0.8   \n",
       "4           1821     1          1.2         0  13       1          44    0.6   \n",
       "\n",
       "   mobile_wt  n_cores  ...  px_height  px_width   ram  sc_h  sc_w  talk_time  \\\n",
       "0        188        2  ...         20       756  2549     9     7         19   \n",
       "1        136        3  ...        905      1988  2631    17     3          7   \n",
       "2        145        5  ...       1263      1716  2603    11     2          9   \n",
       "3        131        6  ...       1216      1786  2769    16     8         11   \n",
       "4        141        2  ...       1208      1212  1411     8     2         15   \n",
       "\n",
       "   three_g  touch_screen  wifi  price_range  \n",
       "0        0             0     1            1  \n",
       "1        1             1     0            2  \n",
       "2        1             1     0            2  \n",
       "3        1             0     0            2  \n",
       "4        1             1     0            1  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd \n",
    "\n",
    "df = pd.read_csv('mobile price.csv')\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "eac16ee0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['battery_power', 'blue', 'clock_speed', 'dual_sim', 'fc', 'four_g', 'int_memory', 'm_dep', 'mobile_wt', 'n_cores', 'pc', 'px_height', 'px_width', 'ram', 'sc_h', 'sc_w', 'talk_time', 'three_g', 'touch_screen', 'wifi', 'price_range']\n"
     ]
    }
   ],
   "source": [
    "print(df.columns.tolist())\n",
    "# print(len(df.columns.tolist()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "08492051",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\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>battery_power</th>\n",
       "      <th>blue</th>\n",
       "      <th>clock_speed</th>\n",
       "      <th>dual_sim</th>\n",
       "      <th>fc</th>\n",
       "      <th>four_g</th>\n",
       "      <th>int_memory</th>\n",
       "      <th>m_dep</th>\n",
       "      <th>mobile_wt</th>\n",
       "      <th>n_cores</th>\n",
       "      <th>...</th>\n",
       "      <th>px_height</th>\n",
       "      <th>px_width</th>\n",
       "      <th>ram</th>\n",
       "      <th>sc_h</th>\n",
       "      <th>sc_w</th>\n",
       "      <th>talk_time</th>\n",
       "      <th>three_g</th>\n",
       "      <th>touch_screen</th>\n",
       "      <th>wifi</th>\n",
       "      <th>price_range</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>2000.000000</td>\n",
       "      <td>2000.0000</td>\n",
       "      <td>2000.000000</td>\n",
       "      <td>2000.000000</td>\n",
       "      <td>2000.000000</td>\n",
       "      <td>2000.000000</td>\n",
       "      <td>2000.000000</td>\n",
       "      <td>2000.000000</td>\n",
       "      <td>2000.000000</td>\n",
       "      <td>2000.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>2000.000000</td>\n",
       "      <td>2000.000000</td>\n",
       "      <td>2000.000000</td>\n",
       "      <td>2000.000000</td>\n",
       "      <td>2000.000000</td>\n",
       "      <td>2000.000000</td>\n",
       "      <td>2000.000000</td>\n",
       "      <td>2000.000000</td>\n",
       "      <td>2000.000000</td>\n",
       "      <td>2000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1238.518500</td>\n",
       "      <td>0.4950</td>\n",
       "      <td>1.522250</td>\n",
       "      <td>0.509500</td>\n",
       "      <td>4.309500</td>\n",
       "      <td>0.521500</td>\n",
       "      <td>32.046500</td>\n",
       "      <td>0.501750</td>\n",
       "      <td>140.249000</td>\n",
       "      <td>4.520500</td>\n",
       "      <td>...</td>\n",
       "      <td>645.108000</td>\n",
       "      <td>1251.515500</td>\n",
       "      <td>2124.213000</td>\n",
       "      <td>12.306500</td>\n",
       "      <td>5.767000</td>\n",
       "      <td>11.011000</td>\n",
       "      <td>0.761500</td>\n",
       "      <td>0.503000</td>\n",
       "      <td>0.507000</td>\n",
       "      <td>1.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>439.418206</td>\n",
       "      <td>0.5001</td>\n",
       "      <td>0.816004</td>\n",
       "      <td>0.500035</td>\n",
       "      <td>4.341444</td>\n",
       "      <td>0.499662</td>\n",
       "      <td>18.145715</td>\n",
       "      <td>0.288416</td>\n",
       "      <td>35.399655</td>\n",
       "      <td>2.287837</td>\n",
       "      <td>...</td>\n",
       "      <td>443.780811</td>\n",
       "      <td>432.199447</td>\n",
       "      <td>1084.732044</td>\n",
       "      <td>4.213245</td>\n",
       "      <td>4.356398</td>\n",
       "      <td>5.463955</td>\n",
       "      <td>0.426273</td>\n",
       "      <td>0.500116</td>\n",
       "      <td>0.500076</td>\n",
       "      <td>1.118314</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>501.000000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>500.000000</td>\n",
       "      <td>256.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>851.750000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.700000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>109.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>282.750000</td>\n",
       "      <td>874.750000</td>\n",
       "      <td>1207.500000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1226.000000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>1.500000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>141.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>564.000000</td>\n",
       "      <td>1247.000000</td>\n",
       "      <td>2146.500000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1615.250000</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>2.200000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>48.000000</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>170.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>947.250000</td>\n",
       "      <td>1633.000000</td>\n",
       "      <td>3064.500000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1998.000000</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>64.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>1960.000000</td>\n",
       "      <td>1998.000000</td>\n",
       "      <td>3998.000000</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       battery_power       blue  clock_speed     dual_sim           fc  \\\n",
       "count    2000.000000  2000.0000  2000.000000  2000.000000  2000.000000   \n",
       "mean     1238.518500     0.4950     1.522250     0.509500     4.309500   \n",
       "std       439.418206     0.5001     0.816004     0.500035     4.341444   \n",
       "min       501.000000     0.0000     0.500000     0.000000     0.000000   \n",
       "25%       851.750000     0.0000     0.700000     0.000000     1.000000   \n",
       "50%      1226.000000     0.0000     1.500000     1.000000     3.000000   \n",
       "75%      1615.250000     1.0000     2.200000     1.000000     7.000000   \n",
       "max      1998.000000     1.0000     3.000000     1.000000    19.000000   \n",
       "\n",
       "            four_g   int_memory        m_dep    mobile_wt      n_cores  ...  \\\n",
       "count  2000.000000  2000.000000  2000.000000  2000.000000  2000.000000  ...   \n",
       "mean      0.521500    32.046500     0.501750   140.249000     4.520500  ...   \n",
       "std       0.499662    18.145715     0.288416    35.399655     2.287837  ...   \n",
       "min       0.000000     2.000000     0.100000    80.000000     1.000000  ...   \n",
       "25%       0.000000    16.000000     0.200000   109.000000     3.000000  ...   \n",
       "50%       1.000000    32.000000     0.500000   141.000000     4.000000  ...   \n",
       "75%       1.000000    48.000000     0.800000   170.000000     7.000000  ...   \n",
       "max       1.000000    64.000000     1.000000   200.000000     8.000000  ...   \n",
       "\n",
       "         px_height     px_width          ram         sc_h         sc_w  \\\n",
       "count  2000.000000  2000.000000  2000.000000  2000.000000  2000.000000   \n",
       "mean    645.108000  1251.515500  2124.213000    12.306500     5.767000   \n",
       "std     443.780811   432.199447  1084.732044     4.213245     4.356398   \n",
       "min       0.000000   500.000000   256.000000     5.000000     0.000000   \n",
       "25%     282.750000   874.750000  1207.500000     9.000000     2.000000   \n",
       "50%     564.000000  1247.000000  2146.500000    12.000000     5.000000   \n",
       "75%     947.250000  1633.000000  3064.500000    16.000000     9.000000   \n",
       "max    1960.000000  1998.000000  3998.000000    19.000000    18.000000   \n",
       "\n",
       "         talk_time      three_g  touch_screen         wifi  price_range  \n",
       "count  2000.000000  2000.000000   2000.000000  2000.000000  2000.000000  \n",
       "mean     11.011000     0.761500      0.503000     0.507000     1.500000  \n",
       "std       5.463955     0.426273      0.500116     0.500076     1.118314  \n",
       "min       2.000000     0.000000      0.000000     0.000000     0.000000  \n",
       "25%       6.000000     1.000000      0.000000     0.000000     0.750000  \n",
       "50%      11.000000     1.000000      1.000000     1.000000     1.500000  \n",
       "75%      16.000000     1.000000      1.000000     1.000000     2.250000  \n",
       "max      20.000000     1.000000      1.000000     1.000000     3.000000  \n",
       "\n",
       "[8 rows x 21 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "5b30a8d5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 2, 3, 0])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['price_range'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "7013d599",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(2000, 21)\n"
     ]
    }
   ],
   "source": [
    "print(df.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "ff8874a8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "price_range\n",
       "1    500\n",
       "2    500\n",
       "3    500\n",
       "0    500\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['price_range'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "ce2fdd57",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "\n",
    "label_counts = df['price_range'].value_counts()\n",
    "\n",
    "temp1 = label_counts.index\n",
    "temp2 = label_counts.values\n",
    "\n",
    "labels = [\n",
    "    f'{temp1[0]} ({temp2[0]})',\n",
    "    f'{temp1[1]} ({temp2[1]})',\n",
    "    f'{temp1[2]} ({temp2[2]})',\n",
    "    f'{temp1[3]} ({temp2[3]})'\n",
    "]\n",
    "\n",
    "colors = ['#246B10', '#351C6B', '#8142AB', '#5EAB90']\n",
    "\n",
    "plt.figure(1, (8,8))\n",
    "plt.pie(label_counts, explode=[0.04, 0.04, 0.04 ,0.04], startangle=80, labels=labels, colors=colors, shadow=True, autopct='%1.1f%%')\n",
    "plt.title('counts of price range', fontsize=18)\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "6283eb42",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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76xf2dmOZuhQGEYXrKljhL4dt0F5rTiw1KLWmR3YDi/mtJqjEuhr4m/dYgxJ9kr/Whs2bub/4z5NSWI4Va2UcD1yxB9inMZ0Bd63SAyxuS2k1O+6agI+1TtH2mTjWvaaOe3ATExODJk2a2MWXtSwMDBg0hMPahO3bC0+aQnzO10vDIII1JMGPP4I1P88++6wFZZdffjluu+02ex5w2mmn4a9//SsaN25sj1AvvfSS1fqwCa5z586Wa3TNNdfY8ujhhx/G008/jfPOO88SbPnvnXfeiddee+2Qy3jHHXcUvZ8/acAam7/97W+W0NyoUSMrMwOOTz/9tNj72rZta+tnDhRrn1i+sWP9VdUMcli79N5779kxYg0OA7BgrDHy+XwW7LF2iLVPb7/9NjZs2IAJEw5QRSpyPLFHTXLJGxG4UuBKutF6N/l54Eo4B66YbkW5G8XE9reLlyvpryUvfDlf+5ONeeEJlXANnLxf/EFU/PlwMp4FYvtagnIJMT2DuoOzSHXsIuaKqgZE1f/twlnsPd3swseeVnbBCcVk24IlcMWdaQmvJbdrIODdAofNOkx0TbjU9lnJ7RgGJ2/ybzU3TPBlrzP2agriSr7HarsdXtjjL4ST9S5cKfeFuYBGWy8dywUi5n8kDisMCEKwZ1LOFLiS77beZqFcyfdas5q/OYqVJjlwpfCYh1xwXfFwJf/V3/OMvaKiW/6W3xTEyVsIV8q/w1xCY60M3CY2f3Fb2ZvK/mW36ayP4Eq81RLBbTnZnxROCy1wIlzJf7N9wh5KTFIuWfMRV7iu9/z5N3nz4PgcuJLv9/emY1AREybFg+eIpx5c7ipwJd8JFGwC4s4uOZ+nLlxx/azXnyv5rjBlTIIr6S5/LlaJ2hseu38UHttH4bhTgRN9nBteHJknEw5zcXix5cU7gBfd0nJ0jobu3bsXi0C5bgYjrNkgBgQHwloqNkPx17BDMd+IAcR1112HG264oeh11uowIDpUoWVg2VgDxGCGNUesReE+Dh2JmcFNMAZGgd99Wr58uQV2wYFk165di80/f/58rFq1ympugrHmKLjZTaQssfFs4s+GK6Y9nMwP/Um5Mb38X+w5v/gvbrxQM6HVVRUu3y64Utgde6W/Ky4viOwK7qkJJ/MDu9N2VXzfX2ORP8d/kWDQwgtPheeBvNmFXbErW6BgF93sEf67W/bCiRsKJ+tHuFKfBPIX+bvxciyR+Ev8vWcyXvOXj116GQCxlxW782Z+YBdx672S/YX/PXbRirbEXgYvrgovAkwmzZ3sD96YfMsLrTsVTs5YuFIeAAo2+rufc7sYyCDOn8jrSrDuv+w+zV40yJsDx3oxcTsu849bk8myneKvGWCuEpvbEv/iT1j11IQr7lzrggx2BUe2vwu5dzOc/KVwVXwbTs4YwLsKiGpqtVeOqyJcyf8o7Aqe6R+/ptJHcHLGWSK0rTvuLP+oNOxSjwS4Kv3Pn2Nk3e2rF5aN3aqHA3HnwxV/FhxXKhweR1vnaIBdmKPbWC2Gk7fYjq9/3BYPXMl/t9o0Kxt7drGHk9UQzYCr0gh/8rfVnLS1Y+Jk/1DYbf90OJnvWxd4J7oTXJU+hpM9yr+fK77rDwSZ6M0u9pW+8Pewsi7lrf1DCfiygbzv/b2rkOx/P4clYE0J18Uau+xRVnvjSvyzP9nbXc3e56r0gZ2b7Opvyex8n+OzbbfaMFeyfwwlHh/WyvE8iu0Ph0MIOBn+msPolnAy37LkcSeqPVyVPvf3zOLnI7odXHED/WPvFGyCq+I7hftxrb+beuyp/m7uqU/AcVWD2xMmGI7k4IZNRhzTpl69ekhPT7eu3bzD//HHH206aw9Y68AaHWJTC3NJGEwMGTLEaj9mzZplTSRlBZt1DuW3lMLJyOBgXcD//vc/dOvWrdi03/ObS6FlYPd61oY999xzVqPC6QwQQ5uKQgMuBnEMNg8Vy88aODathapa9dif3CKHyuVOBtytgNTH/HkZHOOFNzGJ9YCEi/wXNV7oOLMnBY5TH3BaA3HnwMW7Vpd/gDakPGSDnNn7mUTMcVQsR6agcCA8Fxx3C4DjvXD51g3aBXD8Gg4O53LBFd0ETnR7OGyuiTvL39vIFWVBGDlWRq9/cDbr+hsFRDcG4s/2rzfGDYe1NJzHcjpyCpvEvP5xTaKaAnEXFA7Wxo3nYHp5QPzF/ufRXa2pi8vigIM2OB+7/NoAdpadXDiGzkn+Wh8LLWL925My3Jo6bLA4a26KBRJYK3154fr8OSYuDy89Lv8F3F0DiGrnf08SmwjZo4sDIzr2fodJuKltAB+3h/vMC8RdavveplseTbR/oD03gyYfEHcNEHexBWRFo52k/Mc/Fo4NyhgFuBvC4b5JauNvanIS4biygfgWcCzJuXC72DvL0wqIGeivSQnkEMXVty33d8Hm/BzAzgMkNbLgzeH7kthtnducb8cLrNVzOGAix0aq498OG7wwH0hkDQ63hfuPzV45QMKt/vcxwHBqAomsQeExifcvP+FG/zlhR67wcu5m4BINJDayQSX9NTi9/EncNpqgjTLpPxeZ98PgNoq5rAX+Wr7AdnA9SRy3yL8Mh9OTfhviwIJZliF2CByObcSy2WiEfH8eENUGLg8HIDw+jmtww1oBBjDsKs2aCdYcMLDp37+/TWdzhtv9W7Vfz549LQB64IEHcN9991nzCZNZj2U3a+abBGO3dZbjUIMPbiNzW5ifEhpMMDm6Vq1aWLNmjTV5HSnMfWFi7xVXXFFUO7ZixYpSE7fDYbMZ85XYDBhI6v711+K9P9gtn01T/AHMP9r8J3I8WKBgPTwCzz1hcyv8r4d8cTMAKTFj4XuDlxkVJomUtSJBT91ujvcUGPOp+GfJbaMah5aHF8Pfvk/chYGQXyC3xD9Srs1ftOyA0JuuQBmL56X4K60PlAQbPH9wGcLf1Lk8xWt5/eIOMYmitBtJD1xR0WGmJ4UpV+Dv5AOU83B/a/Bg74v/He8Pt61l6TcQE1HWHNfg5s033zzg9HB5GhdeeKE9jhcGXHfddRf+/Oc/2+CBL7zwgtUkHSr2tOJ7LrnkEqu5YlDHAIlNPAwgHn30Ueu1xNeZF8PmI9ZO7d2719Z7OBh8ff755zYIYsWKFS0JmkHK7wluGHAyh4gJ30w2Zk0bg0wKNNMxIGMtEQMpJiPXqVMH69evx5dffmm9sPhcREQk4hOKyxvWNGVnZ1swcsstt1hTGXsfHarKlStbLyk24bCJjc04bIYK1OJcf/31lpDLRFw2IXGed955x5KDDxeDENaqsCcTu3wzb4Y9n34P1kyxlozl7tKli5Uz0FsqMKowc3gmTpxozYxMaGZCMfOHmHOjmhwRETkhfn7hePgjP7/AwIA9jpi7Iv7mLvaaYhJxuJ5hh0o/vyAiIkfy5xfKXG8pKbs4wCLHrWEzFwMa1lr16tXrDwU2IiIiR5qapcoRdudmcBHucaBfUj9SmGfDpjiOmnz11Vdb89RXX3111NcrIiLye6hZqhw1j+zZs8cepXUxZ7f58qis73cRETn+1CwVofhbTXyIiIhI6dQsJSIiIhFFwY2IiIhEFAU3IiIiElEU3IiIiEhEUXAjIiIiEUXBjYiIiEQUBTdlWPreDGxYthlLZ6zExuWb7fmx8NJLL6FBgwY25ky3bt0wc+bMY7JeERGRI0E/v1BG7dy4C09f/ypmj5lf9FrnM9rhrv/diKp1qxy19X7yySf26+OvvvqqBTb8HS3+4Oby5ctRrVq1o7ZeERGRI0U1N2UQa2hCAxua9dN8PHPDq0e1BueZZ57BDTfcgGuuuQYtW7a0IIe/9v3WW28dtXWKiIgcSQpuyqC92/eXCGyCAxxOPxry8vIwe/Zs9OvXr+g1t9ttz6dNm3ZU1ikiInKkKbgpgzL3Zx1welbagacfrl27dsHr9aJ69erFXufzbdu2HZV1ioiIHGkKbsqgxNSEA05PSDnwdBERkROZgpsyqGL1VEseDoevc/rRUKVKFXg8Hmzfvr3Y63xeo0aNo7JOERGRI03BTRmUXDHJekWFBjj+3lI32fSjISYmBp06dcLYsWOLXvP5fPa8R48eR2WdIiIiR5q6gpdR7O5938d3WPIwc2zYFMUam6MV2ASwG/iwYcPQuXNndO3a1bqCZ2ZmWu8pERGR8kDBTRnGQOZoBzOhLr74YuzcuRMPPfSQJRG3b98eo0ePLpFkLCIiUlYpuJESbr31VnuIiIiUR8q5ERERkYii4EZEREQiioIbERERiSgKbkRERCSiKLgRERGRiKLgRkRERCKKghsRERGJKApuREREJKIouBEREZGIouBGREREIoqCmzIsLTMH67btwcK1W+1fPj/aJk6ciLPOOgu1atWCy+XCqFGjjvo6RUREjiT9tlQZtW1POh7/4CdMW7qh6LUeLerjwSv6o0al5KO2Xv4CeLt27XDttdfivPPOO2rrEREROVoU3JRBrKEJDWxo2tL1ePyDMRh+3WCkJMYdlXUPGjTIHiIiIuWVmqXKoD3pWSUCm+AAh9NFREQkPAU3ZVB6du4Bp2ccZLqIiMiJTMFNGZQcH3vA6UkHmS4iInIiU3BTBlVKTrDk4XD4OqeLiIhIeApuyiAmC7NXVGiAw+cPXdn/qCUTi4iIRAL1liqj2N2bvaKYPMwcGzZFscbmaAc2GRkZWLVqVdHztWvXYt68eahUqRLq1at3VNctIiJyJCi4KcMYyBzrWppZs2bh1FNPLXp+11132b/Dhg3DO++8c0zLIiIicjgU3Egxffv2heM42isiIlJuKedGREREIspxDW6GDx+OLl26IDk5GdWqVcM555yD5cuXH/A9bBrhbx4FP+LilGArIiIiZSC4+eWXX3DLLbdg+vTpGDNmDPLz83HGGWfY7xsdSEpKCrZu3Vr0WL9+/TErs4iIiJRtxzXnZvTo0SVqZViDM3v2bPTu3bvU97G2pkaNGseghCIiIlLelKmcm/3799u/7HZ8sO7K9evXR926dTF06FAsXry41Hlzc3ORlpZW7CEiIiKRq8wENz6fD3fccQd69eqF1q1blzpf8+bN8dZbb+Grr77CBx98YO/r2bMnNm3aVGpeT2pqatGDAZGIiIhELpdTRvr93nTTTfjhhx8wefJk1KlT55DfxzydFi1a4NJLL8Xjjz8etuaGjwDW3DDAYS0Rc3eC5eTk2KB1DRs2VJLyMaT9LiIiB8PrNyspwl2/y+Q4N7feeiu+/fZbTJw48XcFNhQdHY0OHToUG1U3WGxsrD1ERETkxHBcm6VYacTAZuTIkRg3bpzVmPxeXq8XCxcuRM2aNY9KGUVERKR8Oa7BDbuBM2/mo48+srFutm3bZo/s7Oyiea666irce++9Rc8fe+wx/PTTT1izZg3mzJmDK664wrqCX3/99cdpKyLH4Yw7JCIiUtYc1+DmlVdesbYzDvnPmpfA45NPPimaZ8OGDTaWTcDevXtxww03WJ7N4MGDrQ1u6tSpaNmyJSJNWl4W1mXsxKJ9G7E+Y6c9L4vjDomIiJQlZSahuCwkJJWlxNbt2fvxr0VfYvrulUWvda/cFPe3Pg/V41OPSRl27txpNTgMeg407tAfVZb2u4iIlP+E4jLTFVx+wxqa0MCG+JyvH+0anN877pCIiEhZouCmDNqTl1kisAng65xeVsYdEhERKWvKRFdwKS6jIOcPTT8SmHuzaNEiG3dIRESkPFFwUwYlRcX9oenHc9whERGR403NUmVQpZhESx4Oh69zelkdd0hEROR4U3BTBqXEJFivqNAAJ9BbitOP17hDIiIiZZ2apcoodvf+Z7uLLXmYOTZsimKNzdEKbALjDhHHHQr29ttv4+qrrz5q6xURETmSFNyUYQxkjmYwE+oEG/JIREQilJqlREREJKIouBEREZGIouBGREREIoqCGxEREYkoCm5EREQkoii4ERERkYii4EZEREQiioIbERERiSgKbkRERCSiKLgRERGRiKLgRor9tlTbtm2RkpJijx49euCHH37QHhIRkXJFvy1Vhjne/YCzG/ClA+5kwFUZLk/qUVtfnTp18MQTT6Bp06b2O1Pvvvsuhg4dirlz56JVq1ZHbb0iIiJHkoKbMsrxboWz/34gb/JvL8acAqT+Ey5PzaOyzrPOOqvY83/9619WmzN9+nQFNyIiUm6oWaqM1tiUCGwobxKc/Q/4a3SOMq/XixEjRiAzM9Oap0RERMoL1dyURWyKCg1sAvIm+afj6DRPLVy40IKZnJwcJCUlYeTIkWjZsuVRWZeIiMjRoJqbsog5Nn9k+h/QvHlzzJs3DzNmzMBNN92EYcOGYcmSJUdtfSIiIkeaam7KIiYP/5Hpf0BMTAyaNGlif3fq1Am//vornn/+ebz22mtHbZ0iIiJHkmpuyiJXZX/ycDh8ndOPEZ/Ph9zc3GO2PhERkT9KNTdlkHX3Tv2nJQ9bjk1AzClwWW+po5Nvc++992LQoEGoV68e0tPT8dFHH2HChAn48ccfj8r6REREjgYFN2WUdfdOfeaYjnOzY8cOXHXVVdi6dStSU1NtQD8GNv379z9q6xQRETnSFNyUYf5A5ugFM6HefPPNY7YuERGRo0U5NyIiIhJRFNyIiIhIRFFwIyIiIhFFwY2IiIhEFAU3YfAXseXY0f4WEZEjScFNkOjoaPs3KyvriO5kObDA/g7sfxERkT9CXcGDeDweVKhQwcZ7oYSEBLhcrj+0g+XANTYMbLi/ud+5/0VERP4oBTchatSoYf8GAhw5+hjYBPa7iIjIH6XgJgRramrWrIlq1aohPz//D+9gOTA2RanGRkREjiQFN6XgBVcXXRERkfJHCcUiIiISURTciIiISERRcCMiIiIRRcGNiIiIRBQFNyIiIhJR1FtKBEC+z4v9eVmAix8KNwocHyrEJCDK7R9Y0OvzYl9+FvjDHBVjEuFxlbwvyC7Ix768DBQ4XiRGxaFSbNJB921aXhbyfAWIi4pBUlRc0LLykFGQg2h3FFKi47AzNx0FPi/i3BzF2UF8VCwSomJt3p05acjz5SPBE4uUmATbDs7rwIELLls+y5voicHe/Cz7u0J0om1rZkGObWu0ywOOV8l/fQ6Q6+TD5zi2vgRPDHK9+djvzYYbbtSIr4Acbx4y83Pgg2PzxbijkBQdZ2Xam5th683y5tpAjbGeaGR782zZMZ4oZHvz4YELcZ4YpOdnw3HB1uN2uRDl8iCtINu2K9ETa9uR6xTYe6NcbmT58pDsiUOWN8/WHeuKgtvltu3nMA6xbv/yOS3eHW3l8MFXuFddyHcK4HV8iHfH2Hq5XdzHca5o5KPA3sthO1mebNunMfA6DryO17aFezXOHQUX3Mh3vHYsuLw4dwxyfXm2f3iOeFwuK5cXPju3OJ37N4/Ld7FMDjxwI9btQZa3cF97ouGFF/k+H1I88cj25dl74z3RyPNxihex7hjEujx2zHJ8+UXnIcvG/ceycvksr+1vdxQyvbmIdntQ4ONaHVuG/9yAnRv8m+cO18XjHuPyINoVZWXkdP6bFBWLfK8XuT7uLx4Lj+2LGB4Tb56tJ5fnsTvaysXjkO/4Cvc1j4ODLG8Boj0eKyu3nUvmdqdExSO9INv2V3JUrO0P/371/wwO9yXLwWMZ5XbD6/OXPcbWlWfliXVHWzl4fLndKVEJdt74HC/c8K8zzylAvq/Azkefz8dxP2zZ3Jc8j3isbP8VDgmSGp2AvXkZtg38bLKs3N8si53Pbg9So+OR4y0o3I+w/cT9wHO1YmwS0vOzkJ6f4z8vXI7t1+DvFZ/js89LZoF/edXjU7Ete5+tM9ETZ2Vm2ROj/J/twLyxHg+qxqUWfV/YcvIywV3G8yWrsHwVYhKRVZCLzILcoudcHufle1KiE+wzGYzbwu8lHo/g77DAdyBViE6Ap3AbyqrjGtwMHz4cX375JZYtW4b4+Hj07NkTTz75JJo3b37A93322Wd48MEHsW7dOjRt2tTeM3jw4GNWboksW7L24PMN0zF2+yK70J1Roy1apNbG0v2bcXadzvZF+/Wm2fh+y1y7gJxZuxOG1OqA6vEV7P28QDLAmLd3PT5ePwX78zLRoVJDXNbgZFSLS0Hl2OQS62QAsjxtC95YNQ5bsvegaUpN3NDkdNSNr2SBzNtrJmDunrX25XJp/V623heXj0bD5Oq4ttGpWJOxHV0qN8a6zJ14b81EbM3eiybJNXBb80HYl5dp8/N/o7fOx9Sdy+0LfWidzuhRpSn+ufhL/LlJPwts3lnzi5W9eUotW0/VuBRM37USX26cacFJ9yrN7H28oD6z/Dv70r3zpCHYmLnb9tVnG6ZhfeYu1E+sgmsan4qqhds6Z89arEjbhr7VW2Dkxl+xeP9G2w8X1OtmFwkGSIv3bcR3W+baMk+r3hrn1O2CHzbPw9y9a3FTs/5Ic2VhzNaF+GXHEtuewbU64PQabfDZxhn4fsucomPVrUoTu3gwuPp0/TSM37HYpg2s2R6nVm9pF9w8nw978tLx4bpJWJG21S4iwxr1RXpeNuolVkZSdDy+3TQHY7cvtHWdUbMdBtRsh7m716Jpak18sn4aJu9cZhcZlqN3tRZYtG8jPl4/Ffvzs9ChYgOcVbsTJu1chq6VG2Nzlv+Y5hYwcPEhNSYRb60Zj5VpW23bL21wMpom18D4rYsxctOvdg71qNIMZ9fpZPtj4r4lOCmlDkNHzN2zDqO3zrP9dnXDPrbcd9ZMsPOzWlwq/ty0n12I5u1Zi5YV6uKLDdOLtpHnCi+y3C+z9qxBclQchtbpYufBtuy9eHnlGFxQt5sFpe+vm4RdOWl27l/R4BRUjEnC4ws/x1WN+2BHTpoda14ked6dW7crUi2AcLAnPwOjN89Dv1ptMXrLXPt87MxNw0frphQurw6uadzXbhpeWDEOS/dvQpW4FJxbp4vdCOzNzcRpNVpbkMBzMcbjsUCL0QIDjnl712H0lvl2PnK/96/ZFrkFDDBykRqTYBfzXJ8XH66dhCX7N9l5dmWj3ijwevHT1vm446Qh2JqzD++umVB0rl7dqC8y8rOxNWc/WleoY5/HD9dNtuPWtXITXN7wFHy8bgomBJ1LA2u2s+P00frJmLl7NVql1sWwRn3w5cYZmLZrpe3bc+p0sZuf99dOwj0tz8aW7L1IiY7H/L3r8fO2BRZI9avRFufX64o4VxT25Gfho3Vc3iq7ITqnTmc0Sapu4fjXm2dh3p51FpBwua0q1LH989TSbyzYuqR+L/Ss2szOix+3zsOq9G0YUrsjvto0Cwv2rrfvjssbnIJYTxSeXvINKsUm46pGvVE5JhnDF4+0QL5v9Za4qH5P1I6vaJ/bzdl7MGLdFPyyY6kFvBfV72H7nOfkN5tn4/vNc22+M2t3tHXxXC6rXM5x/NXCgQMH4pJLLkGXLl1QUFCA++67D4sWLcKSJUuQmJgY9j1Tp05F7969LTA688wz8dFHH1lwM2fOHLRu3fqg60xLS0Nqair279+PlJSUo7BVUt4Cm2unv4o9eRnFXm+WXNOCE95lPbnkK2zN3ldser2Eynipy3UW4KxJ3443V4/DmG0Li83Du9dnOg2zZfFLOCCnIA+fb5yB/y7/odj8DEb+3e4Suygs3L+h2DR+uTJQem/tRJvvvR43Y9KOZXh99diiec6v2w214iva3Vj1+BQ8NP8z7MvPLLacFim18WDr8/HlppkW0AW7r9W5+GHLXMzdu67Y6/xy/ne7S+0On3e2322ajdYV6+LZZd+X2J9cxs6c/fhh6zzc0mwAHpj/iX0xBuPFlEHUKyvHFHu9WmwK7mt9rl2o+bU0fPEobMsJ2e+JVXBz0zPwj3kfFb3WNLkmhjXsbReBO2a/axfMgJNSauNf7S6x4OqRBZ9ZLUSw6xqfakHMbbPexvac/cWmNU+uiUfaXoQbZrxmtWjBGMzwIvXU0q+LHe/h7S/DvxZ9iZuanYFvNs3CLc0GYlduGu6f/4ntv4AHWp9XFPQFY20By8t5n1z8Ff7eaqiVjZok1cCVjU7Bows+L9qONhXq2YWGx/LyBifjsYVfFE3jxfbxdhfj3nkfW01DsO5Vmtp+HLd9EXK8+Rixfmqx6ayh4DFvnFQNTy75Gr/uWV1sOoO8Fzpdiw1Zu/CfJV/hiQ6X2XruaXE2Fu3fiM9Czi0u79lOw+xCzuA54Ly6XW3a7D1r8VjbC5GWn409uRkWqLJ2YeTGmViwb0OJ8/Gf7S6xQGTp/o24pMHJuHLqiyXOsysbnoKhtTtb2bkNof7Rcig2Z+3F6oxtFrA9v/yHwnJehUcWfB7mO6GWLfPBBZ/YvmUZeB6G27cMDvkZP71Ga7yzegJWZ2wvNk+V2GS83OV6XDftFaR7fzu3qsam4MHW5+Fvcz+wGpRg/Wq0Qd9qLbEsbQs+WDfJXjul6kl2Hr6y8ifc1WII7ps3otj5Tzy/q8el2ncH8XzhPuT3DLH26Z3uN9t5c/W0l5Eecq53rNgQZ9XphEcXfl7s9ToJlfBKl+uLbvKOhd9z/T6uOTejR4/G1VdfjVatWqFdu3Z45513sGHDBsyePbvU9zz//PMWFN19991o0aIFHn/8cXTs2BEvvvjiMS27lH/53gJ8umF6iS8xWpG+FfGeGCzYu6FEYEMbsnZj8s7lVkvCqtrQwIZYPf2/VWPtSzrY7rwMvLzipxLz84L2f0u/sbu6ULxzb1+xgTUHcD63y4M314wvms6A5+RqJ9ld66J9G/Dz1oUlAhtamrYZazN3YFV6yS9bXpxDAxviBYd3bXneAvy6ezVOq9kar6/8LagK9uyy79AkuSYurNfd5gm94BC/9Jul1ILbGgB+syM3Dbtz07EqbStm71lTIrChDZm7sClrN05KqVX02sr0rdifn21l451osGVpm5GWn2X7OzSwobdXT7DaiNDAhk5KrY23Vo8rEdgQ91NSdKzdpQcfb96x80LAwG1o3S62755e9l2xwIYXGt5thwY2xFogBoZbsvaicXJ1q7XhcacL6nfHqyvHFNsO1oTxtQvr9bB1Bk/jnTVrnEIvvsQAg9t8evXWJQIb4nF7eeVPVp5wnw/us0xvDp5e+g0G1eqALzbMsFqqRsnVSwQ2geXx3L6mUd9ir7OGsGfV5liXscMePCem7lyBhfs22D4LDWyI+/S7zXOsFqxrlWZWAxqoeQjGfZznePHC8tEIh68PqNUWU3etsBoIBmwMFsZvX1LKd8IWaz7jRZ37lvuttH3L2r8d2fstUAsNbGhXbjpGbpqJtpXqF3udtZevrfq5RGBDP29baM3XnSo1KmqOZE1hpdhEq2F5c/X4EoEN/bh1ftF3B327eQ66VG5StAx+dlir9+aqcSUCG5qzd619UivHFG9m35S1BxN3LLMbkbKoTCUUMxqjSpUqlTrPtGnT0K9fv2KvDRgwwF4PJzc316K94IeInW/5Wfh564JSdwa/yCbtXFrqdDaNZBXkYfbuNaXOwyp1XlyDsao63JdQIPBhLko4vGNjE0rVmGRrygoOHFgTwgs/8zYYOEzZubzUMrGq/oyabYq91rZCfUzdVfp7Ju9YZrVPE3cssVCKeRzh8MueF9jqcRUsiCrN6vRtqJVQ8oLEL1x+VTJwLM3EHUvRoWLDYq+x7MwD4hd/uIthuECJWNaV6duKBSkBXBbXVZqZu1ahdWrdYq+xSY21RQzSWK3P48y/g/FCE1x7EW5fsymkY6WGdvHiv4FandBAm3knDJ55Jx4aoLWrWB8zDrCeGbtWYU9I4B1sfeZOu9gF1h+KuRyc3r5SA0zZucKaTdgsVBqen9yuUAxq2LyWlp9j50/D5GpIy8s+6D5qWaGO5bb8tHWBNQOHal2hrn3GmX8SDs/hwMV81u41VqvZsVKjA352OI3nHj8vB9q3E7Yv8QdOB1jWL9uXlNi3jZOqY/EB9iGbclkTWCehctFr3E98H4O80ixN22y1nsE3BMEBIZutJ9hnOzw2u7WpWDwQo++2zLHPV1lUZoIbJnjdcccd6NWr1wGbl7Zt24bq1asXe43P+Xo4bL5iNVbgUbdu8S8jOXExaTCQ2Bd2OhMYC+92wolyRVmSaGhCXjDeibKZpfj7DpyIx7u+cCwp1PFZIiv/DsZAh68xOODfB9ouXmCYXFr8/UxAjTrgexh0+NcRvnwB/mTaA8/D5YWr1eE6eMceun3BYsK8l0mkPGLhlhku+ftgy6ND2Y+h7wsEZ8R9EHrsA8s92PZxH/BYx7g9ResIt98Dr4Xb32xCPNC2swwHO06B8ymcwLYxp4jLYgIxy37g5bkOuB+5TC6Pn03/MS2t7P73sHzcR9xXoVihwMTlA5ancHrgHAgkYh9svZYQfoB9y2CBn7Hfex4HvncOtH5LVA+6OeJyuL8OJDroPAq8J3ifsfbloGVlHlSY1w92DuFED25uueUWy7cZMWLEEV3uvffeazVCgcfGjSWrguXExBwNJkaWOj06AWfUbFvq9Ivqd7ceDWyTLk2vqs0tbydYjfhUa/IKh1XeO3OK3+kHvvCYMMxEXjaDsW2etTQBrB2oEVfB/mXCJtvnS8PE11EbZxZ7jTkPvaqdVOp7+tVsYz01BtRsb3fsoVXUAawB4d03E1qZDxIOLwq8iwzX3McvpBhX1AHLz2mhd9d9qrdEcnQcJoWpaWGtBu9sw+GXc4OkqmHvPlk7wOTh0jC3IrQZ7+SqzTFr92rLyWISLkM1/h3s192rcHLVA+zrGm3gOD5M3rHc/mY5aGPWbktCDsZmD+ZZsTawUcg2Ttu53BJ1S8NkVO6b0i7SrJVi4iqbicJhMybPQ5aTuSWstWF+WWnLa5VaB/vYIzEI52X5mYBcOTbJzuk16TusJrLXAfZR/5ptsHDvequZYRNRuFoe1qyENh0G4zkcCMY6VW5kNSasMeS2lKZvtRa23IPtWyayM9G4b/VWpc7D5jzmzQVj4jET5EvDZG8mCrNJKKBblaaYvXs1ulRqHPY9LrgsL401Z4EbrvqJVe2cCW7CYyL4gc4VdhIIxebn5Oh4lEVlIri59dZb8e2332L8+PGoU6fOAeetUaMGtm8v3obJ53w9nNjYWEs8Cn6IBL5YB9Vsb4maodj2vjl7r1X/tkkteZHuXKkx2lWoj5SYeGsuuKHxaSXmqRSThGsbn2pBUrAqsSl4tO2FJXJOeLF4qM0F1twV6vomp2HM1gWFXXnZFdmfvBt8l/f1pllW08AvVDapNEysVmI5p1VvZRc05oUEY14Jk6uZbBiqbkJlnFqtFTxuf1DCi90dLYaUqIHi/nykzYXW84SJoEzWDe7eHnB780FYuq9k1Tt7bMV4olEnsTIaJla1i2EoJn5yG9mrI4CBAu/22QzDXizBmFPCQPL2kwaXCCi577gP2Q043Lq4Ty6u39MuvqHOrt3JeqcE51zwYnl+vW4Yu20Rbm0+0Jr/eOzvb32edVUOYI7Djpz9GBAmcOZFhwExe1fx7rxBUrWinI3P1k/HjU37W/f0AOZ93N58MEZumImbm51RbBuZozGkdgdr8gnFRF6eB5+sm4a/tjizxHQeNy6PtSIsTyjuE87D85jBF28CasZXwLSdK6w3XSjms/ytxVl4acWPxV7nOpjPxd5b9RKrWhPbkDod7Txjc264mwuejyxTj6rNLe+mXkIV68UU6qTUWohyRVsCfWjAxXOXSd0fr5tsvY7m71lneS7M9WJQHi4YPrV6K5uHTccH27fMmTq1emvkegusF1yo5sm10K9Ga2tiCsZ9cX3j0/xDNYRgzyzmNf24ZX7Ra1c0ONlueEZt+hVXNDzFkoNDXdf4VGt+D+R9/alpP+s4EMB9zeCO5zpvrkIxKTsjP6dEU3Snio2K8sHKouPaW4qrvu222zBy5EhMmDDBunUfzMUXX4ysrCx88803Ra+xC3nbtm3x6quvHvT96i0loXih4R0Tv1gYOLAbMDH4aFuRgY3L7kp5wWYV7Hl1u1leRZU4f7dnXli3Ze+35XyxcYZ9QTP46VO9hSXqhruz4UVxa9ZeS6hkbgoDKH5Z1oyrgB256Ri/fZG1c/POmF+WTGBm0MO8APZ+YFfwthUaIK0gC19umIlN2btt2tA6nZCZz3FeOF6KFysztlueDC+u59btYoHYB2sm4vJGvS1fgXeXW7L32R1hn2otbcwRBg5fbfrVchV4EWlfoYFdHN5a7U9gZrdejv8R5Y7CuG2LrPwMEFn+RHesjUXD2qXpO1dYbRC7tLOGg4m0vFtdablDVazZhF/KrC4fVKu9da1dvH+DdUM9v7B7MgMIroP7/aw6nVEnvhJWp2/H91vn2l03j1X12FQbA4Zdl5lA/MOWeXYcua6GSdWsyZvzslxMtF6Stgm14yvZfuXdaPOUmpaIzSRydnVltT+3hYEGk0LrJlbGnD3rrCsvy8Sy1k+oil156RZQ8ngz6LLy79uINhXrWdd8XoRZbt4xszv0T1vmY8H+DbYNPIZcJ++e2WuK47WcUrUF2lSoa2P/MGGXzZ08htw/TALlecYyc9/x4sp9ymX5hysAFu7dgGaptTB91wqrheD6z67d2S5qzP3i+cSLH+/QGZwwAXrE+ilomlTTznOei8xNaluhHk6v3sb22UsrRttFml232cWcAR8v1swViY+KsSYcdin+ZftitK5YD2vTd1iQw9oFbhfzgHgTwJo/NtPwvGaSMGsv+1Vva+cxc69YHo6twm7r/GzwfOW6GIAyuZdDGjCnikMGsKaN4yTx/OE8qVHxiI+OxdhtC/3Ljqtg+4m1h8yB+lPj/sj25VrX6jUZO9AoqRrOqt0Zq9O2ISE61rahQmwivts0B+uydtr53q8ma6I2WzIu98OQWh3RLLmGNeVwP07ZuczOISbych1MQmYge2adTvY9wNodBiMsM2uruI8Y7PL97O7PnB3WULLmiceLuVUMFplQzN5r/M7guTRj9yqreWINIntL8rwasX6aHccL6nW32jrmdXFbf9m+FGfUamsdCmbuXm29D8+r1w37cjMxYsNU1IhLtedsiuNnmeMo8TPCY8nPJm3P3odf96zBj1vmWdd0bh9vNHgOcn8wCZq3U/7vwFp2XhxLv+f6fVyDm5tvvtm6cn/11VfFxrZh4TnuDV111VWoXbu25c4EuoL36dMHTzzxBIYMGWLNWP/+97/VFVz+sJyC/KL2YxtULeq3u20K3B2yPT0cXnz4xcSBwphMGn2AXJwAtmNzgDZeyILvLvmx5GBoNnid22N3bDZIGgd646BjHn/bO+V78+3LkwP7sd2c5WRZGJTZwHPMTXC7LZjh2DZst+c2MHjjnSUzCDyFg/jxNb6T7+c+4Ppjo6KRW5Bn62BZ2E2X5eZFiBcfyyEpnI9l4vZbXoKPA/wx7yDKLt6B2gt+wVvuheNYngb5B2WLsoHBMvNy4HL7cy54QfS5OEigfyC2HF+BDcDHwdr4xeV2XIiO8ucDMBBggm2B4+9p4nIAt9tt+zYzL9fKzgui4/IP4scy+gd9i7Jl2zYX3t1y3YHcJu4HPljIwEBufMqaFQZRPE4cqC7bBm9jbzaf7Qduo6twgEE+Z30by1+0bF6i3S7/mC6WF8EB6vxDDlreDXv7cH772xeU0+IfBC/Hybdl8B1cgu0vx2vH2fax5bD4X+Nx5SBythEc8M/tLhxHxp/lFI3fBu2zfAzblx7rbeQfMcmxHLPAgJBcFi/O3FY7Jp4YG8QyzhNlveq4fJbfBsgrzAmxoMVyUfz73Mrl4kCGUTbgXqAmkKcG9wmLy7OTZ6qVkzk0bpf9ze3hPuB+5PHlacTl+bffg4SoGJvP8neCPgccSyfew2NWYDV2PLe4XO4z+wwXHn+ejwnRccjKz7V1cCwd/56yD4cF/hbcFQ6OaDkwrt/yaLhdgf3IgQ/5WeAAhBz8MfR7Jb+gwMbw4Wc7NirGmn25n5PcsfAPR+kfJJPnL5fHdXPsptBcP37uWb7AORld+N0R+l1i6+Rn1Oez3ldhvwu9eUXb8Hu+A4+2chPclJYE9fbbb1sXcerbty8aNGhg3cSDB/F74IEHigbx+89//nPIg/ip5kZERKT8KTfBzfGg4EZERKT8KTeD+ImIiIgcaQpuREREJKIouBEREZGIouBGREREIoqCGxEREYkoCm5EREQkoii4ERERkYii4EZEREQiioIbERERiSgKbkRERCSiKLgRERGRiKLgRkRERCLKYQc3BQUF+Pnnn/Haa68hPT3dXtuyZQsyMjKOZPlEREREfpcoHIb169dj4MCB2LBhA3Jzc9G/f38kJyfjySeftOevvvrq4SxWRERE5PjU3Nx+++3o3Lkz9u7di/j4+KLXzz33XIwdO/aPl0pERETkWNbcTJo0CVOnTkVMTEyx1xs0aIDNmzcfbllEREREjk/Njc/ng9frLfH6pk2brHlKREREpFwFN2eccQaee+65oucul8sSiR9++GEMHjz4SJZPRERE5HdxOY7j/L63+GtoBgwYAL515cqVln/Df6tUqYKJEyeiWrVqKKvS0tKQmpqK/fv3IyUl5XgXR0RERI7w9fuwgptAV/ARI0ZgwYIFVmvTsWNHXH755cUSjMsiBTciIiLlz++5fh9WQrG9MSoKV1xxxeG+XUREROSoOKzg5uuvvw77OnNv4uLi0KRJEzRs2PCPlk1ERETk2AQ355xzjgUyoS1agdf478knn4xRo0ahYsWKh7MKERERkWPXW2rMmDHo0qWL/cu2Lz74d7du3fDtt99aUvHu3bvxt7/97fBKJSIiInIsa244QvHrr7+Onj17Fr12+umnW5PUn/70JyxevNi6il977bWHWy4RERGRY1dzs3r16rCZynxtzZo19nfTpk2xa9euwyuViIiIyLEMbjp16oS7774bO3fuLHqNf99zzz3WXEUc96Zu3bqHWy4RERGRY9cs9eabb2Lo0KGoU6dOUQCzceNGNGrUCF999ZU959g3DzzwwOGVSkREROQwHfYgfvx9qZ9++gkrVqyw582bN0f//v3hdh9WZdAxo0H8REREyp9jMkJxeaXgRkREpPw5JiMUjx071h47duywWpxgb7311uEuVkREROQPOazg5tFHH8Vjjz1mP5hZs2ZNG7RPREREpNwGN6+++ireeecdXHnllUe+RCIiIiJ/wGFl/+bl5RUbwE9ERESkXAc3119/PT766KMjXxoRERGR49EslZOTYz+/8PPPP6Nt27aIjo4uNv2ZZ575o+USEREROXbBzYIFC9C+fXv7e9GiRcWmKblYREREyl1wM378+CNfEhEREZEjoGwPJywiIiLyOx32IH6zZs3Cp59+ig0bNljvqWBffvnl4S5WRERE5NjX3IwYMcK6gi9duhQjR45Efn4+Fi9ejHHjxtnQyCIiIiLlKrj597//jWeffRbffPMNYmJi8Pzzz2PZsmW46KKLUK9evSNfShEREZGjGdysXr0aQ4YMsb8Z3GRmZlovqTvvvNO6iIuIiIiUq+CmYsWKSE9Pt79r165d1B183759yMrKOrIlFBERETnaCcW9e/fGmDFj0KZNG1x44YW4/fbbLd+Gr51++umHs0gRERGR41dz8+KLL+KSSy6xv++//37cdddd2L59O84//3y8+eabh7yciRMn4qyzzkKtWrWsWWvUqFEHnH/ChAk2X+hj27Zth7MZIiIiEoF+d81NQUEBvv32WwwYMMCeu91u/OMf/zislTNXp127drj22mtx3nnnHfL7li9fjpSUlKLn1apVO6z1i4iISOT53cFNVFQUbrzxRusG/kcNGjTIHr8Xg5kKFSr84fWLiIhI5DmsZqmuXbti3rx5OF74u1Y1a9ZE//79MWXKlAPOm5ubi7S0tGIPERERiVyHlVB88803W57Nxo0b0alTJyQmJhabzl8KPxoY0Lz66qvo3LmzBS1vvPEG+vbtixkzZqBjx45h3zN8+HA8+uijR6U8IiIiUva4HMdxfu+bmGdTYkEuF7go/uv1en9/QVwuG+34nHPO+V3v69Onjw0c+P7774edziCIjwDW3NStWxf79+8vlrcjIiIiZRev3/wVhEO5fh9Wzc3atWtRVrCJbPLkyaVOj42NtYeIiIicGA4ruKlfv/4hzcdRjNl0xOako4W5P0dz+SIiInKC/Cr4oY5jk52dXer0jIwMrFq1qliNEIOVSpUqWVPTvffei82bN+O9996z6c899xwaNmyIVq1aIScnxwInDh74008/Hc3NEBERkXLkqAY3BzNr1iyceuqpRc+ZpEzDhg3DO++8g61bt2LDhg1F0/Py8vDXv/7VAp6EhARLXP7555+LLUNERERObIeVUHyokpOTMX/+fDRq1AjlMSFJREREyt/1+7DGuREREREpqxTciIiISERRcCMiIiIR5agGN/fdd5/1fBIREREp88ENRwTu1asXatWqhfXr1xd11f7qq6+K5mFXbv3ApYiIiJT54OaVV16xbtuDBw/Gvn37in5ugYEMAxwRERGRchXcvPDCC/jf//6H+++/Hx6Pp+h1/qDlwoULj2T5RERERI5+cMORhDt06FDidf6GU2Zm5uEsUkREROT4BTf8CQT+TEKo0aNHo0WLFkeiXCIiIiLH7ucXmG9zyy232O87cYDjmTNn4uOPP8bw4cPt955EREREylVwc/311yM+Ph4PPPAAsrKycNlll1mvqeeffx6XXHLJkS+liIiIyLH6bSkGN/x172rVqqE80G9LiYiIlD+/5/oddbgJxQUFBWjatKn9OjcftHLlSkRHR6NBgwaHV3IRERGR45FQfPXVV2Pq1KklXp8xY4ZNExERESlXwc3cuXNtdOJQ3bt3D9uLSkRERKRMBzculwvp6eklXmc7WGC0YhEREZFyE9z07t3bun0HBzL8m6+dfPLJR7J8IiIiIr/LYSUUP/nkkxbgNG/eHKeccoq9NmnSJMtkHjdu3OEsUkREROT41dy0bNkSCxYswEUXXYQdO3ZYE9VVV12FZcuWoXXr1kemZCIiIiLHY5yb8kbj3IiIiJQ/R2WcG9bUsFbG7Xbb3wfStm3bQy+tiIiIyBF0yMFN+/btsW3bNhuJmH+zx1S4Sh++rh5TIiIiUuaDG45KXLVq1aK/RURERMp1cFO/fn37Nz8/H48++igefPBBNGzY8GiWTUREROTo95bib0d98cUXv39NIiIiImW1K/g555yDUaNGHfnSiIiIiByPQfz4a+CPPfYYpkyZgk6dOiExMbHY9L/85S9/tFwiIiIix26cmwPl2rC31Jo1a1BWaZwbERGR8ueojHMTLLi3VCA2YlAjIiIiUi5zbujNN9+0Qf3i4uLswb/feOONI1s6ERERkd/psGpuHnroITzzzDO47bbb0KNHD3tt2rRpuPPOO7FhwwbLxxEREREpNzk3HMzvv//9Ly699NJir3/88ccW8OzatQtllXJuREREyp/fc/0+rGYpDuTXuXPnEq+z51RBQcHhLFJERETkiDis4ObKK6/EK6+8UuL1119/HZdffvmRKJeIiIjIscu5CSQU//TTT+jevbs9nzFjhuXbXHXVVbjrrruK5mNujoiIiEiZDm4WLVqEjh072t+rV6+2f6tUqWIPTgtQ93AREREpF8HN+PHjj3xJRERERI7nODciIiIiZZGCGxEREYkoCm5EREQkoii4ERERkYii4EZEREQiioIbERERiSgKbkRERCSiHPYIxXJkZeflIyMrF1EeNyomJxSblpfvxf7MbHjcnBb/uwdH3JuRjZzcfMREu5GT54XX50NsdBRioz2okJSAzOw8W79/XQVwu12okBSPuJjoomXsS89Gdn4+HJ+DKI8HSQmxSIj1T2fZWMa4mCjk5BfA43IjOSEW+zKzkV/gtXJzu3Ly8lHg9SExLgZenwO3y4UojwteH8Dfb42OcsOBC7mF80VHeex9yfGx4M+77k7PBBygYlI8vI4Pe9OzbV/ERnng8biR7/XZfPn8fTO+Hu1BQYEXSfEsS46tIzE2Gj4H9sjNz7eycTty83/7TTSWjfNymsftQkJsFNKy8+H1ehHFMrncKHB8yC/wISk+Gtm5BfA5PsR4PIiLjUZmTr7//R637S9yFW5rbv5v+5/bz+3kNP+DZSqw17ntXHdmTp7tb+7bjOw8xMdE2Xu8joMot8u2g/PxtaS4GOQWFCCvwGevkc/nICbaY8eB2xXtccPtccNb4IMD/zEo8DpIiotCRk4+YqKi+HO6tm0sp/8ccOy4xkVH23ZxG3is4qKjrLzc7/Gx0ci3bXNsv9t2uN3+Y+D12Xu4/XFRbuT5eIy8iI/x73fuC54bPgDVKiRib1o24PKXnTweF6LcbnsP18X5ua9Ybi6bx9//3Gf7PK/Aa/PERHmQlpVr+4J/5+QVIDbGA58PVpbsvAI7X5Liogv/9p/b3HXcHu5v7gMeC643hvvO7bL9yzdGR3vsfMwt8PqfR3nghsu2o8DL7fN/HvhZ4DnCI5Lv9dp2JcTFwufz7zce2/TMXGTm5lkZUpPikZIQh73pWf7jmhBr+3p3WpbtL2/h+yokxCEtO9e2i8fD6/XB5zhIiI0p9h3C9e3NyLJp/OxwG4nLSsvKsc9rpZSQ75yCAuzPzIHb5UblkGki5cFxDW4mTpyIp556CrNnz8bWrVsxcuRInHPOOQd8z4QJE+znHRYvXoy6devigQcewNVXX43yil+Cm3bux9s//YqZyzYgNTEeV/XrhG4t6qFScgI270rDx+PnYML81XahubhvO5zargmqVkg66LL3pGVi5vKNGD9vFc47pS1G/7oMM5ZuQGJ8DM7u0QrdT6pnX2BzV21BSmIcPv1lPtZt24O6VSvg8tM7oHHNyoiNibLybdixD19OXogd+zLQqkF1XH5aR1StkIh12/bi59krMKR7S3w7Y4nNd8vZvTBp4RqMnbvKvnTP7NYSvds2wnNfTMSabXtwUt1quHZgV2zbm47UhFi8+9Mse/3PZ3ZHakI83hszy6adVKcqrh3U1b7o3/95NqYtWY9B3ZqjX4dmeOOHmVi8bhsa16qM2889Bb8u34gmtavgl/mrMXHhGguKzuvVGqd2aIpRUxdj1JRF9mXeu00jXNSnPT6bNB/j5q6yL+4rTu+IpnWqYsuuNNu+kVMWYXdaJto1qolrBnTFyi2ZePGrKdiXkY2OTWrjvJPbwOUGEmNj8daPM+24sYxDe7a292zcuRcvjJqCLs3r2mvLNmxH+ya1sWLTTnw2cQF27c9E24Y1MewM/4/PMgDIzM3H/76bjlVbdqNW5RRc2a8TaldJxX1vfW8ByCWndrBlfzpxCX6ctdwuwP06NkPHpnUwZ8Um9G7bGBnZuZi8aC0qpSTaxfaLyb9txwW922Hk5IW2Xy87rYMdU27nGZ2boX2jWliyfjsGdjkJu9IzsXrLbnw0bq5tb4cmta0sG3bsxaSFa3FOr9b4ec4KnNWjla3jx9kr7DziOTRmznILtk5t1xg9WzXA57/Mx9BerbFldxre/vFXNKldGdcP6oblG3agdtUKWL1lF5rXrYapi9dh/PzVtk1n92yN/h2b4pvpS/DTrBV2IR/YuTkGdGmOl76agmUbd+KkulVx3aBudvxZzn4dm+K0Dk2RmZ2Lr6ctwYV92tpF/L0xs+1zw3NwUJeT0KJedUxcuBrD+nfBmDmr8NXUxRZU9WrdEBf1aYc3vp+O2pUr4MweLfHT7BX4csoiC9pPbt0A/Ts1w8ad+1C9QrIFIwyIvZkOVm7ehc8n+Y9pm4Y1cOmpHZCVl4+tu9Nsn53bqw2+nzAPLerVwPw1WzBm9goLWPq2a4yTWzfEt9OXYFj/zti8az/eHTMbW/ekoWntqvjTkG6YvnQ9vpuxFM/eeDZjKPtscns37dpv5z3Pn+lL1iM3rwAnt2mID8fNxarNu+y8uX5gV7SoX93O+dGzltux53oHdG6Oi/q0teCVy+L+YaB0Sd/2VqYqqYl2bnw0bg5+WbDGpl16Wgf0adPokL5zRMoKl8NbhePkhx9+wJQpU+zXxM8777yDBjdr165F69atceONN+L666/H2LFjcccdd+C7777DgAEDjvhPph8LvOAN+8/HdmcbjF+mtw7thSuf+MjuPoN1aFwLT94wBFVSS/+y4YXp2S8mYty8VXjy+iH4+xvfWS1AMF58uY712/fioXd/LLGMey7qi0rJ8Zi9crNdlIOd26u11e7w9SeuG4y7//etfcn+95Zz8Mh7P2FXWmax+ZvWrmJfxg+8Pdqed25aB2f3bFW03qE9W1nNA7+gg/HC/tjVAywA4p37Py45Dbe+MBIFvP3mb5fdeLYFTXdf3BePvj/GLjIBj1x1Bj6ZMB9LN2wvtkyW+1/XDMLtL4+yL3x67KozMHf1FrvgB2OQNPy6wXjuy0l2AUJh7cLTN56NG57+zO7Mg53SuiEGd2thF6LXvpuOPm0b4eK+7e1iOSp02W43nr9lqNV2/OWlUSX2/5+HdLeg6Ob/foHnbhqK4R+Ps+AkWMMalXDjmT2snC+MmmwX6vSsHLvIh67r39cNxn9HTrKL47UDu1jgzECJy/j3tYOwcssuTFm41gKWYKz1eOHWczFl0Vp8NnE+3r3nUuxJz7Jz6r5LT8er307D2m17ir2nRsVk3Hvpabj95a/wp8HdsG7HXgtWWMPy/t8vs+P+l3NOxn8+HW/BTwDP1XDLq14xydZ1xytfWW0Lz4unbzwLX09bjPHzVqNOlVQ7N7Jy8ix45/JZ0xGsWZ2qdq4+8v5PWLBma7FpKQmxeP3OCy2wYyC9bOOOYtN5o/H41QPw8fi5uPCUdnbcGXyUOF/cbvtsMlg8pU1DPPjOj7j57J5444cZtuxgVVMT8X9/PsvK/85Pvxabxv301A1nolqFJKtpmbRoDV75ZlrIGQI8dEU/uyH46+vflJh2x3mnWK3q2z/OKnqNgfTzN59jn9f0kO8VBu4PXdkfVzzxsQXKwTo3q2PnTxUGziLHye+5fh/XnJtBgwbhn//8J84999xDmv/VV19Fw4YN8fTTT6NFixa49dZbccEFF+DZZ59FecQq4ac+nVAisCHe4W3Ztd/uHEPxIsw7/APZuS/D7n4Hdz3JLkihgQ2xtoNfYryYhPPfkZPRsEZluzMNdVqHJhZwnNW9JT4aPxfZufl2J8oam9DAhniHy1qiBtUr2vPze7fFU59NKJp+eoem+Hj8vBLvY1X6S19NxcV92ttF4slPxhcFNm0b1bSLUP3qFTF50bpigU3NSikWDIUGNoHA76fZy60GjNhswaaA0AsVMfh5/bvpuLB326LXBnVpYTUzoYENTVq01pp+WjaobkEB9w+Dl1Hhlu3z4enPJ2LLbn/QFOqtH39FcnwMzuzWAnNXbS4R2BCDAO571irw0bV53RKBTWBdr383DRf2aWfP3/tptp0bgWUsXr8NdatUKBHYEJt5Xv56qtWe9GjZAAvWbrFzq3rFZAv4QgMRYllnr9yErifVs+0Y0rWFvc7awDkrN6FCUpwtJziwYQDM5+GWt31vhi2vS/N6RecFPzus9SAGbIvWbbMmINZGhAY2xGYeLjs0sCHeQHzw82yrNQsNbIjBHGv62GxXtWKS1WiEPV98PqvlY/Mx29bYPMbzMjSwoZ37M63J6P2ffws+AhjA8Vxnkyqbq978YSZK+4zmF34eQr309VT0btO42GtndGqOL6csLBHY0JxVm7F8405rAgs1a8UmrN1a8riIlFXlKqF42rRp6NevX7HXWGPD10uTm5tr0V7wo6zgFwy/sEszbel6u9sMh3esB6p0Y3MUsdliyqJ1pc7H5oAGNSqFncaL9+70khcJNuXwrp8XmPaNa1nVOHVqVscu7qWZvHCtlYeYzxL4guVdMavjubxwOC01KQ5VU5OKXfg6Na1jTSVcL5tjQu9Cpywufbv976ttf7MZjjVopWFgVqtyatHzVg1qWFNUaXiR2LE3A41rVUFsTDRWHmDZa7buRuVS7oYZnPFi36dtY2siKE16dq5NZ3MEy1qaQJNX4CLMXKxA3tTS9Tswa4X/nAmHTSoM9Hq0rG8XeO5v7v/Q/V5iHzetbe9jYMvmHDtmhe/l9GD+86f07eT8rEEICA6MiM2RzHsJXW5Au8a18OPs5aUun0FRIFcpHC6f279xx76wwUoAz9FqFZIteOvbvnGpnwkGLgxIGfyGw+CHNVEMjhhghsNcMtZilXb+8NxgbU3wPj7w98EqtG1cM+w0NqGJlBflKrjZtm0bqlevXuw1PmfAkp2dHfY9w4cPt2qswIN5OmUFv5QO9GXKO/9ALUUo3l0dKLGYSaTEnIWoqNIPM5MxmYhYGgYhoTEH52ceAzEgYRLnb6//9kUaigmYgXUxMbNoeUzaLExyLE0g4bZYOSzZ012UKBus4CDLtH3r9W9YIGn0QIIPkyWeukvfp5YM7GKiNJOF/c8Ptm2lYZNEAbfvQMvgOjxuqwX4PetiU1YgaZfHIybMHXvwueou3Ca+g/uWCbFMsC4NyxK4cPNv7jfbFiY0h9nnVn7PQZYXcq7y/Aw+poHE9HD43lgmTJe2/Gi+v9TJtn8YqDEZubR1BHB/cl32mQg5NwNs/5UyLcDl9idTH2xdpeHyg4Mn/2et9OXFHOD7gLVVIuVFuQpuDse9995r7XOBx8aNpd+dHmupiXHoW9g0Ek7Plg2wPEwVOTGx80C6Flbf8y62f8dmpc7HRMzNu/aVWj4+Qr9cebdYOTnRLjasHTm9g38beId6wHW1a4IpS/x3jcwLYT4B8a6e1fj8Yg2HzRWbdvrvltkUFcBtO6NTM0xcsMZylILNWLYBfdo1KrUsnJ9NaLRx5340qlm51Dvgjk1rY0lQ8xaXzWa50rDWiE0XTO5kE1jDmpVKXTaTeUvb/+xVVr1CEr6ausgSQUtTKSnB9gObZpg/U9q6mBwcaKazXjrspVTYtMYcKE4vTa9WDayt5Oc5K+E4Ptt/kxevQ78OTUt9zxmF+5i1Q7zIsvl16uK1lggdOHbBWAt0+kGWx9qTANZqsmdaAJfLgJVNL+H8umITzuzubx4Lh01npdUeEs/tsXNXonaVCtYUWtqNCc9R1taxafL7mUtLPVeycvOtRpA1WuGwCZf7rkJyvOWjhcPmV/aELO38YfAXyCsj7vd+Ifs9GPfd7BXha5PP7N6y1PeJlDXlKripUaMGtm8vnkPB50wsio9nG3dJsbGxNj34UVYkxMXgL+eeHLarJZNJmdMQ3BwScHaPlqhbrcIBl10lJcF6ETGhmBfGmpWSS8wztEcrxES5cf9l/UrcRfOL+8HL+2HNlt249ZxeJd773fQluP/S0/HTrOWWV8KET+aFnFSvmgUj4S6O7G4eyIv5YtICPDpsQNF6v5i0ELefd0qJ2hl+ud82tBc+mTAPL38zFXdf2Neq8wPV/+ylxPc0r1O1WBMegwrmXfQLc2FhAMBmkZnLf2taYk4Sk6tDMdH0mgFdiuVXsBnuhsHdrGdJqEtObW8Bw4R5q+1C+f2MpUiMjbF8oVDMD7nzgt5o26hWsaYDYoDy94tPtYCFvcRqV05B6wY1SiyDOTbcrzyXup1Uz3rX3HhWj7DrYhIxe7xxf911fm/rHUd8X/VKydaL6cYzu5d4L5sN/3xmD7w/bo41bbVuUNN6vzFgYa0ByxCqZf3qluTLHI6/XtjHjh+x+z57y/H8ZO8b9hoLbmbidrM8odjTictbumFH0Xlx36Wn4eVvpthzNo/WqZJiOUCtG9ZA01qVwwad3JaBXUoGP/WqVbAeYEvWb7PeXqHYO4lBC88dNmHyNL357JLnC89NfnZ5U8CgvXblVOuZ1KNF/RLz8pzlcAR3nt+7xHnPgIefvw3b91qQcvdFfUsErbwZ+NuFfawbd7jzh5+vmUuLN58y2DyjYzPUrVrye4VJ/QzamHAfij0Euf9Fyovj2lsqtLr8YL2l/v73v+P777/HwoULi1677LLLsGfPHowe7e+FU956SxG7jfKOlG3+DHTYu6Z+tYrWPXv73nTMWr4R389choQ4dgVvb7UM/JI+lIRl5gd8N3Mp+rZtbMHALwtW2xfwkG4t7QuOX5AL1261HAG2t/Pi1ahGRQzu1hJVUhOsan37vgzsz8i2MrCrNC8S7AnCL0G26Y/+dbldkFhTwcTDi/q2swRMXtgZvDD44ZfmJ7/Mt67i7JrMO/QVG3dYF2wud/mmnZbg2rRWFUtg3rw7Da3r18Cgrs2tjDOWbcS4eSvRqn51K/uYOSusF1edyim47LSO2Lx7v32hs6wM6Fj1zh5dDWtWtn3AYIrjgfTr1LQoV4R3seyxckHvtpb4GUhC/eHXZRaEdW5eF6e3b2LdhT8YO8emd25W13J12C2flxoGdMx34cVsSLcWqFEp2XrKcJ/0adcYretXt/ynU9o0soCLeQvch1zOqe393bdZi8I7bE5bsmEH6lZJxTknt7YaM/aASk6Iw9ndW9jYJQx22IWZ8w/o3MyaSjhOUa0qydZrZsueNOvS3qBGRfz463LbH+wVxwcDG24nE5SnLd1gXal5oWdTEPMzWNPAsV1y8vPxxcSFdgz5Pvb44nZyLBhe4L+fscyWwWO7aN1WKxfHR/qxsOs2g2meuzxv2XNs0dqt+HnuKrSoV83ex/UyGOP+Y/kZ8PB48vixRrJRzUpYvH57UVdwDiXQrG5VC5DWbd9rARHLzfycOSs2Wy0SPxPb96Rj2abtltjOIRWYJ8R9wPNnUGGtzJrNu6yr94ad+zBy8iJLxmZX8jYNa1piL3PCWtWvYcfo84nzLdeFtav8rHCsGzY3suyBXCXmLTGgtP3crA66t6hv6+H+4jzM81mzdY8FgtzH7J3G5TDA4H5bvG4rBndtgYycXAvwed63acDz/iRMmL8Ks1dstqCfwQtz1Jhrt2HnfrSsV8267rMmiYE55+fQCzz3Glav6A9GqlZAVm4eVmzahU9/mWfrPadnK7RrXNuaxNih4IeZy5Bo3ysdbL+zTNyPM5ZvsKEj+F3BbuIM6kLH3xI51n7P9fu4BjcZGRlYtWqV/d2hQwc888wzOPXUU1GpUiXUq1fPmpQ2b96M9957r1hX8FtuuQXXXnstxo0bh7/85S/luit4AA8DL778EgyXN8GqZ7eHg5H9/qGJOJAdBxrjoG/MeeBgdC4HiI+PsZwO5jpwPIzAIHMcfI0DkAXnZ3BQL7bFs4abtTpxQdM5Vg/vTnnh5HKYg+MfNI2D2fmnB/IlWEXO5hBWlHNbAzkMbE7gezjIGU9IDgjnzwFxigYTtB5fLHdstN3p8sue/3IAPf88/sHs8lkGtxvMDuE6GKDxvdzHHLfHBhZ0cTA55h8w78lto/pxH3HbuUyWlYPHMeXJ8hB8ju0D/s1B8PLyOHCfY3fVrKnhYH18nU02LrhsG3gh5DzcB/5tZA7Ob/kmPu5Mlz+w5z7isjlAHGtDuGcZoDGgCAzox/OD+8RVmLvCclouEv/nYjndyMotKJaj5M9jcSOP+9UGqePAgoDj8udqcLrtl8KB9IqaIFmmAn9OCM87Hl8un9vC+bi/eYHkcriPuB85sKDP68/BChzXwKB+bD6y5hfHXzaWh/uH6+F5yePN/cdALjMr16ZbTpB9Pfn3KIMf/wB90ba/AuXn8lhs/0B73If+ASW5eAYvPL48rhzckfuPx5bbzPcxHYXrJ5afuJ1RHv+5bDlGhQM6cnl83X++8/zkwHncJP8x5vv8B7Qwv4eDGRYOVsjnLJ9/ID//QJbcR8yN47JZzgIvzykgOT7Oto2fW84b+Kyxlic3z38cORhhXEyMbasNvOnyn3NcjzVHheTVcB7uy+CBOQ/2vcLjzP1WWnOxyLFWboIbDsjHYCbUsGHD8M4779jgfOvWrbP5gt9z5513YsmSJahTpw4efPDB3zWIX1kNbkRERCQCgpvjQcGNiIhI+VNuBvETEREROdIU3IiIiEhEUXAjIiIiEUXBjYiIiEQUBTciIiISURTciIiISERRcCMiIiIRRcGNiIiIRBQFNyIiIhJRFNyIiIhIRFFwIyIiIhFFwY2IiIhEFAU3IiIiElEU3IiIiEhEUXAjIiIiEUXBjYiIiEQUBTciIiISURTciIiISERRcCMiIiIRRcGNiIiIRBQFNyIiIhJRFNyIiIhIRFFwIyIiIhFFwY2IiIhEFAU3IiIiElEU3IiIiEhEUXAjIiIiEUXBjYiIiEQUBTciIiISURTciIiISERRcCMiIiIRRcGNiIiIRBQFNyIiIhJRFNyIiIhIRFFwIyIiIhFFwY2IiIhEFAU3IiIiElEU3IiIiEhEUXAjIiIiEUXBjYiIiEQUBTciIiISURTciIiISERRcCMiIiIRRcGNiIiIRBQFNyIiIhJRykRw89JLL6FBgwaIi4tDt27dMHPmzFLnfeedd+ByuYo9+D4RERGRMhHcfPLJJ7jrrrvw8MMPY86cOWjXrh0GDBiAHTt2lPqelJQUbN26teixfv36Y1pmERERKbuOe3DzzDPP4IYbbsA111yDli1b4tVXX0VCQgLeeuutUt/D2poaNWoUPapXr35MyywiIiJl13ENbvLy8jB79mz069fvtwK53fZ82rRppb4vIyMD9evXR926dTF06FAsXry41Hlzc3ORlpZW7CEiIiKR67gGN7t27YLX6y1R88Ln27ZtC/ue5s2bW63OV199hQ8++AA+nw89e/bEpk2bws4/fPhwpKamFj0YEImIiEjkOu7NUr9Xjx49cNVVV6F9+/bo06cPvvzyS1StWhWvvfZa2Pnvvfde7N+/v+ixcePGY15mEREROXaicBxVqVIFHo8H27dvL/Y6nzOX5lBER0ejQ4cOWLVqVdjpsbGx9hAREZETw3GtuYmJiUGnTp0wduzYotfYzMTnrKE5FGzWWrhwIWrWrHkUSyoiIiLlxXGtuSF2Ax82bBg6d+6Mrl274rnnnkNmZqb1niI2QdWuXdtyZ+ixxx5D9+7d0aRJE+zbtw9PPfWUdQW//vrrj/OWiIiISFlw3IObiy++GDt37sRDDz1kScTMpRk9enRRkvGGDRusB1XA3r17res4561YsaLV/EydOtW6kYuIiIi4HMdxTqTdwK7g7DXF5GIOBigiIiKRdf0ud72lRERERA5EwY2IiIhEFAU3IiIiElEU3IiIiEhEUXAjIiIiEUXBjYiIiEQUBTciIiISURTciIiISERRcCMiIiIRRcGNiIiIRBQFNyIiIhJRFNyIiIhIRFFwIyIiIhFFwY2IiIhEFAU3IiIiElEU3IiIiEhEUXAjIiIiEUXBjYiIiEQUBTciIiISURTciIiISERRcCMiIiIRRcGNiIiIRBQFNyIiIhJRFNyIiIhIRFFwIyIiIhFFwY2IiIhEFAU3IiIiElEU3IiIiEhEUXAjIiIiEUXBjYiIiEQUBTciIiISURTciIiISERRcCMiIiIRRcGNiIiIRBQFNyIiIhJRFNyIiIhIRFFwIyIiIhFFwY2IiIhEFAU3IiIiElEU3IiIiEhEUXAjIiIiEUXBjYiIiEQUBTciIiISURTciIiISESJOt4FiBTeAi/2bN+L3Mw8uD1upFROQn5uATL3ZwEuFxJTE5CblWv/ZuzNtPljE2JQkO9FdHQUfI4PLpfL3puXUwA4jv3tjnbDm+e1+WLiouHz+uAt8CGpYiJyMnOQn5OPqJgoRMdEwev1oSAvH9GxMfD5vPDm++Byu+CJcQM+F/Jz8wG4bL1W5nwv3FFu5GXnwe12wxPtgeNzkJ9fgNi4aCs3l8/XomIL11Hgs+VEx0ZZmaKiPCgo4Lq8tm15uXkoyPUiNjHW/17HsfLl5eYjNi7G1pGdlgUHQHxSPHxeL1weNxyvA8fns/k8Hg/ikmKRk5kLT5TbluOJjrJ1+l/z2Ha5XS5Ex0Uhc38OAAfRMdHw+XyIS4qz9+Rk+edNSI5DdnqOlYXPuf3chrwcbkd04fHzwcvtTohBfHIc9u9Mt/0fEx+D3Ixc+BwHcQkx3CXwOTw8/A/s2NkxdrtQoWoysjNyuYuRk5GLqGgPkisl2npSq6bYdtG29Tvg9rjgK3BQkFeAuMQ4/3qycpCbnW/nAcvBfROXEGvnUV5ugS2PZYqNj0ZuVj5i4qORXDGp6Bzktu/bmebfjzkFcLtdqFKnEjL3ZdkyeB54c31WPp6LPBacnp2WbeuNS4y1Y7hvx347P5MrJSEmzn+uhAqsizsjpUoyoqJLfpWk78lAXk4e4pPjkZAcX/pnx+vFfi4LLqRWTS7aTyIi5Tq4eemll/DUU09h27ZtaNeuHV544QV07dq11Pk/++wzPPjgg1i3bh2aNm2KJ598EoMHD8bxsnXNdvuin/r1r5j02TS0PqUFLrjrLHz+9NeY8/NCJFZIxJl/7o+uA9tj8ZTl+OQ/o7Bt7Q40aFUXVzx4AQq8XsQnxmHrmh2o1bg6Rr3wA1r2aI6mnRraxfybV3/EoknLkFolBeffdaZN27p6O0b+9zssnLjULkLn3T4Ezbs2wc6NOxEbH4sv//s9ls9cha6DOuDMG8/AF89+i/kTFttFdvD1/dCqV3Pk5xVgxBMjbb5TLz0ZPc/ugk//7yusnL0GVetWwXl3DMHuLXvw9Us/4pTzu+GUC3rYBXX3pj1YNnMVajSsZoHa6LfGYf+uNLTr28q2e/2SjXC53fjqxR+wY8MuNGpXHxfdfQ5SqyRh5As/4Ncf5lnAMfDa09B1cEe43LAA6ptXx2D617Psonvj08NQqUYFfPDPz608VWpXwvl3nIlq9avi7Qc/xkV/Oxu1m9XEmPcn4qd3J1iQ0O3MTuhzYQ/bH+NHTMH4jydb0HbaZSejzSkt8Prd76NJh4a2XXN+XoBGbetbsMBt/OLZ77B3+z607NEMF/5tqF3ceaH/5MlRmPnDPCSmxOOMq/ui/amtkcAgLjsPHo8bn7z8I6Z/Mwux8TEYcO1p6DW0Cz761xeYO24RKlRL9R+v7s2wYOIMNOvUGMtmrkTtJjUsiBv1wvdYMnWFzXfBX8+yQOal299CxWqpOOumAWjbuwWmjlmAL5//3vZz2z4tccbVp2L5rytxUtem+PHt8fa8aUf/eTJ//CJUrl3JjkfgvDjntkFIqpCI1/72Li68eyg6nNoa3//vZ0wZ9asFjv2v6oOuQzrhP1e9gCp1KuPie4Zi1o/zMO7jKeg+pCPOv+ss1GxUzfZjwM5NuzF+xGR8/7+xFtT2vbQXhtzQDzUaVLPp+3enYen0lfjon1/YvM27NMGVD12IOs1r2rkZbPv6nRj99nj8/P4vto4BV/dFv6v6oFrdKsf8cywikcPl2C3o8fPJJ5/gqquuwquvvopu3brhueees+Bl+fLlqFbN/2UZbOrUqejduzeGDx+OM888Ex999JEFN3PmzEHr1q0Pur60tDSkpqZi//79SElJ+cPl37xqG9Yv3og37/sIG5ZusgvJU2Mfxp29H7KalWBdh3RE+76t7CIb7P6P78SqOavQ7tQ2mPrVr8jOyEGvc7parcLjFz1jd78BDHAe+PROPDBkOHKzf3udep3bFVc9chFu6nCP3VlXrlkRd71xIx457/8Ka2386resg6sfu8SWzfl4kWdQ8n/XvmzPg51/55nI2JuBH9+ZYBflhz7/G5665iUr3+p5azF55Mxi8w+4+lQLet59+JOi11gz8cgXf8PjFz/jr+UI0rZ3S9z+6p/w8Dn/waYVW+y1Vj2b4/IHzscDZz1hNVXBzr1tENqd1hqPnPsUrnviciyZshzTvplVNJ0B0d/fuw33DvxXsW2p3bQmrh9+OR694P/sInzvh7dj0eRl2Lh8swUDwVgL8Z+xD+H5G1/H+iWbik1jcMNAhEHonac8ZEFdsGadGuHsmwfi/657uei1M4b1xWX3n4fX734P599xFjL2Z+KfFz1jwWWwUy/pZfvu4+Ej/fvymlMtQPn86W9+25cJsXjws7/ixVvfwO2v/AkPn/sfPDXuYbzxjw9x0d+G4vGLni5xXnQ/s5M9TurWFA+cORy7Nu8pNr1eizq47cXrcPfpj9rz21+5AT+/PxGLpy63GpcXZwxH3ZNq27Rdm3bjvsH/xtpFG4oto3KtSnhu8uNW3s+e+hof/uuLYtMZuAwffT869mtb9NqODTvx176PYNu6HcXm5bF66ueHUbVu5WKvi8iJLe13XL+Pe87NM888gxtuuAHXXHMNWrZsaUFOQkIC3nrrrbDzP//88xg4cCDuvvtutGjRAo8//jg6duyIF1988ZiXndXprAHYtHKrBTZ09T8vwVv3f1QisKGZ381B9XpVkZCSUOz1F297E93P7mp33wwazr19CDYs3YwRT44qFtjQwGtPxYePf1HiAkZTRs605hc2cdDgP/XDB//8olhgQ2ffMhCv3/N+0cV/6K0D8c5DI0oENjTy+e/R+8Ke9jfD4NXz1lnZGrapVyKwoVPO7473H/us2GusOfn29TElAhtaMHGJBYc9h3Yueu3KRy7Ci7e9VSKwoVEvjrZ9yCatDx79zAKAYHu27bMatC6D2hd7fTOP0bLNaNa5MU6/4hS8ce+HaHVy8xKBDRXkF+Dl299Gvyv7lJg2b/wiCzC+fO67EoENrZi9xs6LavV+q3lgzRKbaFjzxdqhDx//vERgQ6xtYq0cm/+INTPt+7a22qUANrV9+dy3FsiOfnsczv3LYCybsQotujfDZ09/Hfa8mP7tbDTr2hg/fzCxRGBDPHe3rN5mx5TeefATqzmirPRsvPvIJxZw0/yJS0oENsTar9FvjkPGvix89O8vS0znufXsn1/D7q17i55P+HRqicAmcKymfze7xOsiIofquAY3eXl5mD17Nvr16/dbgdxuez5t2rSw7+HrwfPTgAEDSp0/NzfXor3gx5GStjvDLnAzgr6IW3Rtilk/zi/1PfN/WWzNCMG4DOZZ8ELPixObWOq1qI3FU5aVeD+bNniBLc2M7+egSYcG9neDVvWwdNqKEvOw2YNNaQHM22DzUTi8CPEOmzVGbXq3wC+fTbWL4NLpJZfLWqs92/aWCErYJDTjuzmllpk1L93P/C24SUiKs4ttOKxoXDF7NTqc3tr2leUNBTWZ2D74do41kYW7yLOpp3G7BkjblWbNcaVZNXet1VSFwwAjXGAXMPP7ubbNwdis2LxzY8s/YgBUmuWzVqFO81pFz1fPX1eiHGzqbNG9OaZ/OwftT2tt5x+XzWbH0uzZsg8zvi09YBj74SR0PqN90fkYyEWiKSN/Rca+TGRn5ljAVRoGZ3u37fXnI4XBplgGecR/x34wqdRljWFAuNc/r4hIuQpudu3aZXe51atXL/Y6nzP/Jhy+/nvmZ/MVq7ECj7p16x6x8jNhkwJ32sTvdSatloYXDeZzlFiWx23T3FEey4XwL7/k4QkkxZaGuR+84BOTX8MtI7gmINzzEmWOCSQye21b+Tcv0qE4PVxiKecP3kfh94nvkMsTExtdVBvFeUMvpkw8Dl5e2NeZDBx0AQ+HAWepZT7g9kSVOMaWDO5z7Jw50HK5bcFlD7ctPP5MGuY0n9efsH2w844J2OGOWfB6WWMV7hgE3seyH2i7OV+4861Y2QuXe7Dy2GfhIMsSESlNxH973HvvvdY+F3hs3LjxiC2btRnVG1S15oaAad/OQp+Lfnseql2fViVqDJgDkrk/0/JPXA5QkOfFyjlr0HVwhxLvnzN2IXqc/VstR6guA9tj5Zy19vfCSUtLNM8Qm2eYWBvAWpwGrf1NEqHYW4Z5FLyLnv3TAvS/qq/VJrTo1qzEvGy6SEiJtwArGGsWDrRP2JQ15r0JRc+ZhMrclXAYPDVq18BqL1jjxCAnNLjpc1FPS/INxWTjmd/PsRqO2s1qoEn7BqUGGh1Oa23Jv+EkFCYXl6bHWV2sfAFcR4sezTDzh7lW29Th9DZh38f5GrdviI3LNttzXtzrt6hTohaLTVK//jjPtpM1adx/c36ejx5ndSq1TEzIZk5PaQZefzqmjJpZdD7u27m/aNoZw/pYIjqTgdmkWZozb+yPSjUrlhoAscYsubK/d1dKpWQMPcCy2FTKhG4RkXIX3FSpUsW6fW7f/lsTCfF5jRrhmwT4+u+ZPzY21hKPgh9HUuteJ1m37ECiJJMpL733XLuYhGJyLgOO4HwL3qHe/c4tWP7rauv+PP6TyZb/0umMdvYFz540wcZ9NBmX/P0cSxYOxfWyuzVrCQK5HszfqVC1+DZ/8/KPuOnZq4u657J31rX/vNQSf0Mvtjf85wp89/oYe163eS3LJelxdhfLibjwb2eXKAPLd9cbNxULGtgzh3k7TJYNxWTbGg2qWmJvAJNjb3vp+rDdh296dhgWT1tuAc1tL11nva+CsWdWq14nWTJsMPY0Yi4SE8Bn/zQfwx69xC7mNzx5RYl1MJi78ZmrLYAIxRwfdqPuP6wv6hUm2QY7+dyu2LVlD9J2pxe9dt3wy+wc2bJyK5IqJuHiv5+DlMrJJd7LpOOJn00tCtaue+IyjP1oUonE3UHXnmYBco+zOlvTDoOGrLQsDPlTf0uoDnXu7YMx+YsZ1vQXLmjsPKC91aiwaZLn45+eutJyrah6/aq46O6hVrNDTTs2suTkUGxqPeW8btb1ncc/VHxSHP729s2oUPW387lT/7bWay9cYMljKCJSbntLsYcUu32z+3cgx6NevXq49dZb8Y9//KPE/BdffDGysrLwzTe/9SDp2bMn2rZta8nIx7q3FG1du9266rJGZMKIKahYI9W6E/MiOu3rWXaxHHz96ajZuLrlGvzwxjhsXrUVTTs1Qv8r+1gicXSMB2sWbUSzjo0wd+wC5Od57U6c87NbMWsbKlZPtS7lyZWT4SvwWX7N3LELkVol2RJAeWFhsmedZrUsH2PRpKU4qWsTnHb5KZg1ep7VHPAOvP+Vva3bLwOQyV/OsICrzckt0OvcLpj4+XQLDJjnwdqJ+ROWYNHkpVZLwFwbNn1sWbnNmjAy07LsTn3CiMnYuz3Nup137N/WysoLLrsLb1qx1fKEep7TxbpTs4fSpC+mIz4lHgOvOc0CGzZPsOmF62F5+Pyie4YipWISxn8yxbpLV61bCUNu6G+5H+yazW3gWELrFm/ED2+OtXFdGFiw2zLLtG7xJsvbYPMHu5yzNuHrV35C10Ht0bF/OywYv9hyWzgeUExcLL597Sfs2brPcmVOuaA7tq3djpoNq9u+YXnZ/bvfFb3tGPJCn747wwIWbs8vn061wIndoWs3q4WJn07FvAmLUalmBZz55zPs3+3rdqFGgyqW0Ov2eKxJiXlIXH7lWhWtuz5zXUb99wcLUAZceyqq1qlsZfr6ldFI25Vh+5a5NUya5jFf8MsSS9au3qAastOzbV+zS/2S6SvsvKhYLQWD/9Qfu7fstX3U7/JT0LJnc5vO85THkknnDH7fe/QzNO3QCH0v6YlJn0+3nKq+F/eydYZ2y96zfR/WzF+Pr18ebflhg6473RKaAwF9dka2DWvw9Ss/YsuqbVZbyXJWq++/mQlNRGZgzwCax+qsG8+wAIrHUETkcK/fZaIr+LBhw/Daa69ZkMOu4J9++imWLVtmuTTsJl67dm3LnQl0Be/Tpw+eeOIJDBkyBCNGjMC///3v49YVPFhWepaNX+J2eyxYoZzC8VBcHpfleXjczBFhonNeUX4M8xssj4V3xz6f1exwGXwDB9kryM8HHH++Qx6nweXPU/F67eLiH1CvwC5WHPAvKp6DAxbA5bNVwhPttoseH1w5B8Tj+ngxYR4I8zfYBMJTwdaX6x/MjgPhMQDi29heFh3tz3XhAHQ2OA38y7TlFPhsPRzfJs8GhIvxD0YIx2qSOLgdB9fjwrgMvicwaCHXy/JwUD6Wx3KFuE8Kl839EcMAiDuO5ebghi6+z19ur9exIMXlciMqyg2HYxZ6HRTYYIP+GgcuJ7D9sQmxNgBhblYeYuKiLJCMjvYgNzffkplZHm4DB7tjs1xudq4tm9vnY95RbLQNwOdlYBQTbYm2zEdhwMZBHHk8MtOybdlsyuF2sEYtIDszF978fDs2PP4cPI/HJD8nzz/YHnNqwFwaDzyeKNsfuQyA42NsG7h8DgzIICs0z4bnH7fRBkOM8tj4STwXuA/5Pi6fy8vP5XZEWc8vnke2vLhoCz7y8/It2OS0A+E6eO6Ejl0TwLwjG7wxPuag+TO5OXnc3aUOGigikvY7rt/HfRA/1sTs3LkTDz30kCUFt2/fHqNHjy5KGt6wYUOxL0bW0nBsmwceeAD33XefDeI3atSoQwpsjraE5AQkhLQ2sMtyOIk4vHyCY5mFEJ8YpuwHGGk2ILFwH8QnlrKsQ1jG0cYkaV74S9u3cQm/TTvYRZ7BUtFyK/iDqdIu+BSfyGklp3O0YySXXt7iywifPBxoPgoOEpinFEj0jory/xsXtLEMaIKXx3VFH0KMEVhXaSy4OkCSc7Dg4E9E5I867jU3x9rRrLkRERGRo6NcDeInIiIiciQpuBEREZGIouBGREREIoqCGxEREYkoCm5EREQkoii4ERERkYii4EZEREQiioIbERERiSgKbkRERCSiHPefXzjWAgMyc6RDERERKR8C1+1D+WGFEy64SU9Pt3/r1q17vIsiIiIih3Ed588wHMgJ99tSPp8PW7ZsQXJysv0qdSREsgzUNm7ceML8VtaJts3a3sh2oh3fE3Gbtb1HBsMVBja1atUq9oPa4ZxwNTfcIXXq1EGk4RfEifAlcSJvs7Y3sp1ox/dE3GZt7x93sBqbACUUi4iISERRcCMiIiIRRcFNORcbG4uHH37Y/j1RnGjbrO2NbCfa8T0Rt1nbe+ydcAnFIiIiEtlUcyMiIiIRRcGNiIiIRBQFNyIiIhJRFNyIiIhIRFFwUwY98sgjNnpy8OOkk04qmp6Tk4NbbrkFlStXRlJSEs4//3xs37692DI2bNiAIUOGICEhAdWqVcPdd9+NgoIClBUTJ07EWWedZSNNcvtGjRpVbDrz3B966CHUrFkT8fHx6NevH1auXFlsnj179uDyyy+3gbEqVKiA6667DhkZGcXmWbBgAU455RTExcXZiKj/+c9/UBa39+qrry5xzAcOHFhut3f48OHo0qWLjQTO8++cc87B8uXLi81zpM7jCRMmoGPHjtYjpUmTJnjnnXdQFre3b9++JY7xjTfeWC6395VXXkHbtm2LBqXr0aMHfvjhh4g8toeyvZF0bMN54oknbJvuuOOO8nOM2VtKypaHH37YadWqlbN169aix86dO4um33jjjU7dunWdsWPHOrNmzXK6d+/u9OzZs2h6QUGB07p1a6dfv37O3Llzne+//96pUqWKc++99zplBct0//33O19++SV76zkjR44sNv2JJ55wUlNTnVGjRjnz5893zj77bKdhw4ZOdnZ20TwDBw502rVr50yfPt2ZNGmS06RJE+fSSy8tmr5//36nevXqzuWXX+4sWrTI+fjjj534+Hjntddec8ra9g4bNsy2J/iY79mzp9g85Wl7BwwY4Lz99ttWjnnz5jmDBw926tWr52RkZBzR83jNmjVOQkKCc9dddzlLlixxXnjhBcfj8TijR48uc9vbp08f54Ybbih2jHnMyuP2fv311853333nrFixwlm+fLlz3333OdHR0bb9kXZsD2V7I+nYhpo5c6bToEEDp23bts7tt99e9HpZP8YKbspocMOLWDj79u2zD9Vnn31W9NrSpUvtgjlt2jR7zpPI7XY727ZtK5rnlVdecVJSUpzc3FynrAm92Pt8PqdGjRrOU089VWy7Y2Nj7YJN/CDwfb/++mvRPD/88IPjcrmczZs32/OXX37ZqVixYrFt/vvf/+40b97cOZ5KC26GDh1a6nvK8/bSjh07rPy//PLLET2P77nnHrsRCHbxxRdbsFGWtjdwAQy+OIQqz9tLPPfeeOONiD+2odsbycc2PT3dadq0qTNmzJhi21gejrGapcooNsGwCaNRo0bWFMHqPZo9ezby8/OtmSaATVb16tXDtGnT7Dn/bdOmDapXr140z4ABA+zH2xYvXoyybu3atdi2bVuxbeTviXTr1q3YNrJppnPnzkXzcH7+dtiMGTOK5unduzdiYmKK7Qc2F+zduxdlDatnWXXbvHlz3HTTTdi9e3fRtPK+vfv377d/K1WqdETPY84TvIzAPIFllJXtDfjwww9RpUoVtG7dGvfeey+ysrKKppXX7fV6vRgxYgQyMzOtuSbSj23o9kbysb3lllusWSm0XOXhGJ9wP5xZHvAiznZHXuS2bt2KRx991PIoFi1aZBd9Xrx4oQvGE4jTiP8Gn1CB6YFpZV2gjOG2IXgbGQgEi4qKsotJ8DwNGzYssYzAtIoVK6KsYH7NeeedZ+VdvXo17rvvPgwaNMg+5B6Pp1xvr8/ns7b6Xr162Rd/oDxH4jwubR5+gWZnZ1u+VlnYXrrssstQv359u2lhbtTf//53Czy//PLLA25LYFpZ296FCxfaxZ25F8y5GDlyJFq2bIl58+ZF5LEtbXsj8dgSA7g5c+bg119/Rajy8PlVcFMG8aIWwCQ2Bjv84Hz66afH5ctajr5LLrmk6G/e7fC4N27c2GpzTj/99HJ9CHj3x8B88uTJOBGUtr1/+tOfih1jJsvz2DKY5bEub3jzxUCGtVSff/45hg0bhl9++QWRqrTtZYATacd248aNuP322zFmzBjrnFAeqVmqHGB03KxZM6xatQo1atRAXl4e9u3bV2weZqlzGvHf0Kz1wPPAPGVZoIzhtiF4G3fs2FFsOrPw2aMoEvYDmyNZxc1jXp6399Zbb8W3336L8ePHo06dOkWvH6nzuLR52KPleNwIlLa94fCmhYKPcXnaXt65s3dLp06drLdYu3bt8Pzzz0fssS1teyPx2M6ePdu+b9iLiTXEfDCQ++9//2t/s3alrB9jBTflALv78g6AdwP8YEVHR2Ps2LFF01n9yZycQPsv/2UVavDFkBE4T5hANWpZxqYVnvTB28hqSuaWBG8jP1j8EAaMGzfOmgQCXyych12w2TYcvB94B1aWmqTC2bRpk+Xc8JiXx+1l3jQv9Ky6ZzlDm8uO1HnMeYKXEZgnOBeiLGxvOKwFoOBjXF62Nxyei7m5uRF3bA+2vZF4bE8//XQrL7cj8GC+H/M/A3+X+WP8h1OS5Yj761//6kyYMMFZu3atM2XKFOtKxy507IER6ILHbqbjxo2zLng9evSwR2gXvDPOOMO6pbJbXdWqVctUV3Bm4bN7IB88DZ955hn7e/369UVdwStUqOB89dVXzoIFC6wnUbiu4B06dHBmzJjhTJ482bL6g7tGM6OfXaOvvPJK67I5YsQI63Z4PLpGH2h7Oe1vf/ub9TLgMf/555+djh072vbk5OSUy+296aabrCs/z+Pg7rFZWVlF8xyJ8zjQlfTuu++23hovvfTScek+e7DtXbVqlfPYY4/ZdvIY87xu1KiR07t373K5vf/4xz+sJxi3hZ9PPmfPvZ9++iniju3BtjfSjm1pQnuElfVjrOCmDGJXuJo1azoxMTFO7dq17Tk/QAG8wN98883WFZEnxrnnnmtfpMHWrVvnDBo0yMY5YWDEgCk/P98pK8aPH28X+dAHu0QHuoM/+OCDdrFmF/DTTz/dxpcItnv3bru4JyUlWffCa665xgKFYBwj5+STT7ZlcF8yaCpr28sLIL8A+MFn98r69evbmBnBXSjL2/aG21Y+OBbMkT6PuW/bt29vnxdeVILXUVa2d8OGDXaxq1Spkh0bjlHEL/TgsVDK0/Zee+21dp6yDDxv+fkMBDaRdmwPtr2RdmwPNbgp68fYxf/88fofERERkbJBOTciIiISURTciIiISERRcCMiIiIRRcGNiIiIRBQFNyIiIhJRFNyIiIhIRFFwIyIiIhFFwY2IiIhEFAU3IiIiElEU3IiIiEhEUXAjIuVKXl7e8S6CiJRxCm5EpEzr27cvbr31Vtxxxx2oUqUKBgwYgGeeeQZt2rRBYmIi6tati5tvvhkZGRlF73nnnXdQoUIFfPvtt2jevDkSEhJwwQUXICsrC++++y4aNGiAihUr4i9/+Qu8Xu9x3T4ROfIU3IhImceAJCYmBlOmTMGrr74Kt9uN//73v1i8eLFNGzduHO65555i72Egw3lGjBiB0aNHY8KECTj33HPx/fff2+P999/Ha6+9hs8///y4bZeIHB36VXARKfM1N2lpaZgzZ06p8zBAufHGG7Fr166imptrrrkGq1atQuPGje01TmdAs337diQlJdlrAwcOtFocBkwiEjmijncBREQOplOnTsWe//zzzxg+fDiWLVtmgU9BQQFycnKstoZNUMR/A4ENVa9e3QKZQGATeG3Hjh06ACIRRs1SIlLmMbcmYN26dTjzzDPRtm1bfPHFF5g9ezZeeumlEsnG0dHRxZbhcrnCvubz+Y56+UXk2FLNjYiUKwxmGJA8/fTTlntDn3766fEuloiUIaq5EZFypUmTJsjPz8cLL7yANWvWWB6NcmZEJJiCGxEpV9q1a2ddwZ988km0bt0aH374oeXfiIgEqLeUiIiIRBTV3IiIiEhEUXAjIiIiEUXBjYiIiEQUBTciIiISURTciIiISERRcCMiIiIRRcGNiIiIRBQFNyIiIhJRFNyIiIhIRFFwIyIiIhFFwY2IiIggkvw/LSwiNIpCQoEAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.scatterplot(data=df, x='ram', y='price_range', hue='price_range', palette='viridis')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "c53395be",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.DataFrame'>\n",
      "RangeIndex: 2000 entries, 0 to 1999\n",
      "Data columns (total 21 columns):\n",
      " #   Column         Non-Null Count  Dtype  \n",
      "---  ------         --------------  -----  \n",
      " 0   battery_power  2000 non-null   int64  \n",
      " 1   blue           2000 non-null   int64  \n",
      " 2   clock_speed    2000 non-null   float64\n",
      " 3   dual_sim       2000 non-null   int64  \n",
      " 4   fc             2000 non-null   int64  \n",
      " 5   four_g         2000 non-null   int64  \n",
      " 6   int_memory     2000 non-null   int64  \n",
      " 7   m_dep          2000 non-null   float64\n",
      " 8   mobile_wt      2000 non-null   int64  \n",
      " 9   n_cores        2000 non-null   int64  \n",
      " 10  pc             2000 non-null   int64  \n",
      " 11  px_height      2000 non-null   int64  \n",
      " 12  px_width       2000 non-null   int64  \n",
      " 13  ram            2000 non-null   int64  \n",
      " 14  sc_h           2000 non-null   int64  \n",
      " 15  sc_w           2000 non-null   int64  \n",
      " 16  talk_time      2000 non-null   int64  \n",
      " 17  three_g        2000 non-null   int64  \n",
      " 18  touch_screen   2000 non-null   int64  \n",
      " 19  wifi           2000 non-null   int64  \n",
      " 20  price_range    2000 non-null   int64  \n",
      "dtypes: float64(2), int64(19)\n",
      "memory usage: 328.3 KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "dcc7315e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1800x1800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(1, (18,18))\n",
    "sns.heatmap(df.corr(), annot=True, cmap='Greens')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "c362fd57",
   "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>battery_power</th>\n",
       "      <th>blue</th>\n",
       "      <th>clock_speed</th>\n",
       "      <th>dual_sim</th>\n",
       "      <th>fc</th>\n",
       "      <th>four_g</th>\n",
       "      <th>int_memory</th>\n",
       "      <th>m_dep</th>\n",
       "      <th>mobile_wt</th>\n",
       "      <th>n_cores</th>\n",
       "      <th>pc</th>\n",
       "      <th>px_height</th>\n",
       "      <th>px_width</th>\n",
       "      <th>ram</th>\n",
       "      <th>sc_h</th>\n",
       "      <th>sc_w</th>\n",
       "      <th>talk_time</th>\n",
       "      <th>three_g</th>\n",
       "      <th>touch_screen</th>\n",
       "      <th>wifi</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>842</td>\n",
       "      <td>0</td>\n",
       "      <td>2.2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>0.6</td>\n",
       "      <td>188</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>20</td>\n",
       "      <td>756</td>\n",
       "      <td>2549</td>\n",
       "      <td>9</td>\n",
       "      <td>7</td>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1021</td>\n",
       "      <td>1</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>53</td>\n",
       "      <td>0.7</td>\n",
       "      <td>136</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>905</td>\n",
       "      <td>1988</td>\n",
       "      <td>2631</td>\n",
       "      <td>17</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>563</td>\n",
       "      <td>1</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>41</td>\n",
       "      <td>0.9</td>\n",
       "      <td>145</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>1263</td>\n",
       "      <td>1716</td>\n",
       "      <td>2603</td>\n",
       "      <td>11</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>615</td>\n",
       "      <td>1</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>0.8</td>\n",
       "      <td>131</td>\n",
       "      <td>6</td>\n",
       "      <td>9</td>\n",
       "      <td>1216</td>\n",
       "      <td>1786</td>\n",
       "      <td>2769</td>\n",
       "      <td>16</td>\n",
       "      <td>8</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1821</td>\n",
       "      <td>1</td>\n",
       "      <td>1.2</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>1</td>\n",
       "      <td>44</td>\n",
       "      <td>0.6</td>\n",
       "      <td>141</td>\n",
       "      <td>2</td>\n",
       "      <td>14</td>\n",
       "      <td>1208</td>\n",
       "      <td>1212</td>\n",
       "      <td>1411</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>15</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   battery_power  blue  clock_speed  dual_sim  fc  four_g  int_memory  m_dep  \\\n",
       "0            842     0          2.2         0   1       0           7    0.6   \n",
       "1           1021     1          0.5         1   0       1          53    0.7   \n",
       "2            563     1          0.5         1   2       1          41    0.9   \n",
       "3            615     1          2.5         0   0       0          10    0.8   \n",
       "4           1821     1          1.2         0  13       1          44    0.6   \n",
       "\n",
       "   mobile_wt  n_cores  pc  px_height  px_width   ram  sc_h  sc_w  talk_time  \\\n",
       "0        188        2   2         20       756  2549     9     7         19   \n",
       "1        136        3   6        905      1988  2631    17     3          7   \n",
       "2        145        5   6       1263      1716  2603    11     2          9   \n",
       "3        131        6   9       1216      1786  2769    16     8         11   \n",
       "4        141        2  14       1208      1212  1411     8     2         15   \n",
       "\n",
       "   three_g  touch_screen  wifi  \n",
       "0        0             0     1  \n",
       "1        1             1     0  \n",
       "2        1             1     0  \n",
       "3        1             0     0  \n",
       "4        1             1     0  "
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = df.drop(columns=['price_range'])\n",
    "\n",
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "781a20e9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1\n",
       "1    2\n",
       "2    2\n",
       "3    2\n",
       "4    1\n",
       "Name: price_range, dtype: int64"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y = df['price_range']\n",
    "\n",
    "y.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "434fcccb",
   "metadata": {},
   "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=0.2, random_state=42, stratify=y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "86522133",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\Khalilavi\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\sklearn\\linear_model\\_logistic.py:406: ConvergenceWarning: lbfgs failed to converge after 13595 iteration(s) (status=1):\n",
      "STOP: TOTAL NO. OF F,G EVALUATIONS EXCEEDS LIMIT\n",
      "\n",
      "You might also want to scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-4 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: #000;\n",
       "  --sklearn-color-text-muted: #666;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "}\n",
       "\n",
       "#sk-container-id-4.light {\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: black;\n",
       "  --sklearn-color-background: white;\n",
       "  --sklearn-color-border-box: black;\n",
       "  --sklearn-color-icon: #696969;\n",
       "}\n",
       "\n",
       "#sk-container-id-4.dark {\n",
       "  --sklearn-color-text-on-default-background: white;\n",
       "  --sklearn-color-background: #111;\n",
       "  --sklearn-color-border-box: white;\n",
       "  --sklearn-color-icon: #878787;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-4 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-4 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-4 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-4 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: flex;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "  align-items: center;\n",
       "  justify-content: center;\n",
       "  gap: 0.5em;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 label.sk-toggleable__label .caption {\n",
       "  font-size: 0.6rem;\n",
       "  font-weight: lighter;\n",
       "  color: var(--sklearn-color-text-muted);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-4 div.sk-toggleable__content {\n",
       "  display: none;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  overflow: visible;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-4 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-4 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-4 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-4 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-4 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 0.5em;\n",
       "  text-align: center;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-3) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  border: var(--sklearn-color-fitted-level-0) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-0);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  border: var(--sklearn-color-fitted-level-0) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-0);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-4 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-4 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".estimator-table {\n",
       "    font-family: monospace;\n",
       "}\n",
       "\n",
       ".estimator-table summary {\n",
       "    padding: .5rem;\n",
       "    cursor: pointer;\n",
       "}\n",
       "\n",
       ".estimator-table summary::marker {\n",
       "    font-size: 0.7rem;\n",
       "}\n",
       "\n",
       ".estimator-table details[open] {\n",
       "    padding-left: 0.1rem;\n",
       "    padding-right: 0.1rem;\n",
       "    padding-bottom: 0.3rem;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table {\n",
       "    margin-left: auto !important;\n",
       "    margin-right: auto !important;\n",
       "    margin-top: 0;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table tr:nth-child(odd) {\n",
       "    background-color: #fff;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table tr:nth-child(even) {\n",
       "    background-color: #f6f6f6;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table tr:hover {\n",
       "    background-color: #e0e0e0;\n",
       "}\n",
       "\n",
       ".estimator-table table td {\n",
       "    border: 1px solid rgba(106, 105, 104, 0.232);\n",
       "}\n",
       "\n",
       "/*\n",
       "    `table td`is set in notebook with right text-align.\n",
       "    We need to overwrite it.\n",
       "*/\n",
       ".estimator-table table td.param {\n",
       "    text-align: left;\n",
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       "    padding: 0;\n",
       "}\n",
       "\n",
       ".user-set td {\n",
       "    color:rgb(255, 94, 0);\n",
       "    text-align: left !important;\n",
       "}\n",
       "\n",
       ".user-set td.value {\n",
       "    color:rgb(255, 94, 0);\n",
       "    background-color: transparent;\n",
       "}\n",
       "\n",
       ".default td {\n",
       "    color: black;\n",
       "    text-align: left !important;\n",
       "}\n",
       "\n",
       ".user-set td i,\n",
       ".default td i {\n",
       "    color: black;\n",
       "}\n",
       "\n",
       "/*\n",
       "    Styles for parameter documentation links\n",
       "    We need styling for visited so jupyter doesn't overwrite it\n",
       "*/\n",
       "a.param-doc-link,\n",
       "a.param-doc-link:link,\n",
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       "    text-decoration: underline dashed;\n",
       "    text-underline-offset: .3em;\n",
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       "    padding: .5em;\n",
       "}\n",
       "\n",
       "/* \"hack\" to make the entire area of the cell containing the link clickable */\n",
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       "    position: absolute;\n",
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       "    inset: 0;\n",
       "}\n",
       "\n",
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       "    position: absolute;\n",
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       "    left: 0;\n",
       "    padding: .5ex;\n",
       "    margin-left: 1.5em;\n",
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       "    box-shadow: .3em .3em .4em #999;\n",
       "    width: max-content;\n",
       "    text-align: left;\n",
       "    max-height: 10em;\n",
       "    overflow-y: auto;\n",
       "\n",
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       "    border: thin solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       "/* Fitted state for parameter tooltips */\n",
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       "    border: thin solid var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
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       "    display: block;\n",
       "}\n",
       "\n",
       ".copy-paste-icon {\n",
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       "    background-repeat: no-repeat;\n",
       "    background-size: 14px 14px;\n",
       "    background-position: 0;\n",
       "    display: inline-block;\n",
       "    width: 14px;\n",
       "    height: 14px;\n",
       "    cursor: pointer;\n",
       "}\n",
       "</style><body><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression(max_iter=1000000, random_state=202020)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" checked><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>LogisticRegression</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html\">?<span>Documentation for LogisticRegression</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"\">\n",
       "        <div class=\"estimator-table\">\n",
       "            <details>\n",
       "                <summary>Parameters</summary>\n",
       "                <table class=\"parameters-table\">\n",
       "                  <tbody>\n",
       "                    \n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('penalty',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=penalty,-%7B%27l1%27%2C%20%27l2%27%2C%20%27elasticnet%27%2C%20None%7D%2C%20default%3D%27l2%27\">\n",
       "            penalty\n",
       "            <span class=\"param-doc-description\">penalty: {'l1', 'l2', 'elasticnet', None}, default='l2'<br><br>Specify the norm of the penalty:<br><br>- `None`: no penalty is added;<br>- `'l2'`: add a L2 penalty term and it is the default choice;<br>- `'l1'`: add a L1 penalty term;<br>- `'elasticnet'`: both L1 and L2 penalty terms are added.<br><br>.. warning::<br>   Some penalties may not work with some solvers. See the parameter<br>   `solver` below, to know the compatibility between the penalty and<br>   solver.<br><br>.. versionadded:: 0.19<br>   l1 penalty with SAGA solver (allowing 'multinomial' + L1)<br><br>.. deprecated:: 1.8<br>   `penalty` was deprecated in version 1.8 and will be removed in 1.10.<br>   Use `l1_ratio` instead. `l1_ratio=0` for `penalty='l2'`, `l1_ratio=1` for<br>   `penalty='l1'` and `l1_ratio` set to any float between 0 and 1 for<br>   `'penalty='elasticnet'`.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">&#x27;deprecated&#x27;</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('C',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=C,-float%2C%20default%3D1.0\">\n",
       "            C\n",
       "            <span class=\"param-doc-description\">C: float, default=1.0<br><br>Inverse of regularization strength; must be a positive float.<br>Like in support vector machines, smaller values specify stronger<br>regularization. `C=np.inf` results in unpenalized logistic regression.<br>For a visual example on the effect of tuning the `C` parameter<br>with an L1 penalty, see:<br>:ref:`sphx_glr_auto_examples_linear_model_plot_logistic_path.py`.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">1.0</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('l1_ratio',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=l1_ratio,-float%2C%20default%3D0.0\">\n",
       "            l1_ratio\n",
       "            <span class=\"param-doc-description\">l1_ratio: float, default=0.0<br><br>The Elastic-Net mixing parameter, with `0 <= l1_ratio <= 1`. Setting<br>`l1_ratio=1` gives a pure L1-penalty, setting `l1_ratio=0` a pure L2-penalty.<br>Any value between 0 and 1 gives an Elastic-Net penalty of the form<br>`l1_ratio * L1 + (1 - l1_ratio) * L2`.<br><br>.. warning::<br>   Certain values of `l1_ratio`, i.e. some penalties, may not work with some<br>   solvers. See the parameter `solver` below, to know the compatibility between<br>   the penalty and solver.<br><br>.. versionchanged:: 1.8<br>    Default value changed from None to 0.0.<br><br>.. deprecated:: 1.8<br>    `None` is deprecated and will be removed in version 1.10. Always use<br>    `l1_ratio` to specify the penalty type.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">0.0</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('dual',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=dual,-bool%2C%20default%3DFalse\">\n",
       "            dual\n",
       "            <span class=\"param-doc-description\">dual: bool, default=False<br><br>Dual (constrained) or primal (regularized, see also<br>:ref:`this equation <regularized-logistic-loss>`) formulation. Dual formulation<br>is only implemented for l2 penalty with liblinear solver. Prefer `dual=False`<br>when n_samples > n_features.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">False</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('tol',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=tol,-float%2C%20default%3D1e-4\">\n",
       "            tol\n",
       "            <span class=\"param-doc-description\">tol: float, default=1e-4<br><br>Tolerance for stopping criteria.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">0.0001</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('fit_intercept',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=fit_intercept,-bool%2C%20default%3DTrue\">\n",
       "            fit_intercept\n",
       "            <span class=\"param-doc-description\">fit_intercept: bool, default=True<br><br>Specifies if a constant (a.k.a. bias or intercept) should be<br>added to the decision function.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">True</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('intercept_scaling',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=intercept_scaling,-float%2C%20default%3D1\">\n",
       "            intercept_scaling\n",
       "            <span class=\"param-doc-description\">intercept_scaling: float, default=1<br><br>Useful only when the solver `liblinear` is used<br>and `self.fit_intercept` is set to `True`. In this case, `x` becomes<br>`[x, self.intercept_scaling]`,<br>i.e. a \"synthetic\" feature with constant value equal to<br>`intercept_scaling` is appended to the instance vector.<br>The intercept becomes<br>``intercept_scaling * synthetic_feature_weight``.<br><br>.. note::<br>    The synthetic feature weight is subject to L1 or L2<br>    regularization as all other features.<br>    To lessen the effect of regularization on synthetic feature weight<br>    (and therefore on the intercept) `intercept_scaling` has to be increased.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">1</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('class_weight',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=class_weight,-dict%20or%20%27balanced%27%2C%20default%3DNone\">\n",
       "            class_weight\n",
       "            <span class=\"param-doc-description\">class_weight: dict or 'balanced', default=None<br><br>Weights associated with classes in the form ``{class_label: weight}``.<br>If not given, all classes are supposed to have weight one.<br><br>The \"balanced\" mode uses the values of y to automatically adjust<br>weights inversely proportional to class frequencies in the input data<br>as ``n_samples / (n_classes * np.bincount(y))``.<br><br>Note that these weights will be multiplied with sample_weight (passed<br>through the fit method) if sample_weight is specified.<br><br>.. versionadded:: 0.17<br>   *class_weight='balanced'*</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">None</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('random_state',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=random_state,-int%2C%20RandomState%20instance%2C%20default%3DNone\">\n",
       "            random_state\n",
       "            <span class=\"param-doc-description\">random_state: int, RandomState instance, default=None<br><br>Used when ``solver`` == 'sag', 'saga' or 'liblinear' to shuffle the<br>data. See :term:`Glossary <random_state>` for details.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">202020</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('solver',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=solver,-%7B%27lbfgs%27%2C%20%27liblinear%27%2C%20%27newton-cg%27%2C%20%27newton-cholesky%27%2C%20%27sag%27%2C%20%27saga%27%7D%2C%20%20%20%20%20%20%20%20%20%20%20%20%20default%3D%27lbfgs%27\">\n",
       "            solver\n",
       "            <span class=\"param-doc-description\">solver: {'lbfgs', 'liblinear', 'newton-cg', 'newton-cholesky', 'sag', 'saga'},             default='lbfgs'<br><br>Algorithm to use in the optimization problem. Default is 'lbfgs'.<br>To choose a solver, you might want to consider the following aspects:<br><br>- 'lbfgs' is a good default solver because it works reasonably well for a wide<br>  class of problems.<br>- For :term:`multiclass` problems (`n_classes >= 3`), all solvers except<br>  'liblinear' minimize the full multinomial loss, 'liblinear' will raise an<br>  error.<br>- 'newton-cholesky' is a good choice for<br>  `n_samples` >> `n_features * n_classes`, especially with one-hot encoded<br>  categorical features with rare categories. Be aware that the memory usage<br>  of this solver has a quadratic dependency on `n_features * n_classes`<br>  because it explicitly computes the full Hessian matrix.<br>- For small datasets, 'liblinear' is a good choice, whereas 'sag'<br>  and 'saga' are faster for large ones;<br>- 'liblinear' can only handle binary classification by default. To apply a<br>  one-versus-rest scheme for the multiclass setting one can wrap it with the<br>  :class:`~sklearn.multiclass.OneVsRestClassifier`.<br><br>.. warning::<br>   The choice of the algorithm depends on the penalty chosen (`l1_ratio=0`<br>   for L2-penalty, `l1_ratio=1` for L1-penalty and `0 < l1_ratio < 1` for<br>   Elastic-Net) and on (multinomial) multiclass support:<br><br>   ================= ======================== ======================<br>   solver            l1_ratio                 multinomial multiclass<br>   ================= ======================== ======================<br>   'lbfgs'           l1_ratio=0               yes<br>   'liblinear'       l1_ratio=1 or l1_ratio=0 no<br>   'newton-cg'       l1_ratio=0               yes<br>   'newton-cholesky' l1_ratio=0               yes<br>   'sag'             l1_ratio=0               yes<br>   'saga'            0<=l1_ratio<=1           yes<br>   ================= ======================== ======================<br><br>.. note::<br>   'sag' and 'saga' fast convergence is only guaranteed on features<br>   with approximately the same scale. You can preprocess the data with<br>   a scaler from :mod:`sklearn.preprocessing`.<br><br>.. seealso::<br>   Refer to the :ref:`User Guide <Logistic_regression>` for more<br>   information regarding :class:`LogisticRegression` and more specifically the<br>   :ref:`Table <logistic_regression_solvers>`<br>   summarizing solver/penalty supports.<br><br>.. versionadded:: 0.17<br>   Stochastic Average Gradient (SAG) descent solver. Multinomial support in<br>   version 0.18.<br>.. versionadded:: 0.19<br>   SAGA solver.<br>.. versionchanged:: 0.22<br>   The default solver changed from 'liblinear' to 'lbfgs' in 0.22.<br>.. versionadded:: 1.2<br>   newton-cholesky solver. Multinomial support in version 1.6.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">&#x27;lbfgs&#x27;</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('max_iter',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=max_iter,-int%2C%20default%3D100\">\n",
       "            max_iter\n",
       "            <span class=\"param-doc-description\">max_iter: int, default=100<br><br>Maximum number of iterations taken for the solvers to converge.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">1000000</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('verbose',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=verbose,-int%2C%20default%3D0\">\n",
       "            verbose\n",
       "            <span class=\"param-doc-description\">verbose: int, default=0<br><br>For the liblinear and lbfgs solvers set verbose to any positive<br>number for verbosity.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">0</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('warm_start',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=warm_start,-bool%2C%20default%3DFalse\">\n",
       "            warm_start\n",
       "            <span class=\"param-doc-description\">warm_start: bool, default=False<br><br>When set to True, reuse the solution of the previous call to fit as<br>initialization, otherwise, just erase the previous solution.<br>Useless for liblinear solver. See :term:`the Glossary <warm_start>`.<br><br>.. versionadded:: 0.17<br>   *warm_start* to support *lbfgs*, *newton-cg*, *sag*, *saga* solvers.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">False</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('n_jobs',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.8/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=n_jobs,-int%2C%20default%3DNone\">\n",
       "            n_jobs\n",
       "            <span class=\"param-doc-description\">n_jobs: int, default=None<br><br>Does not have any effect.<br><br>.. deprecated:: 1.8<br>   `n_jobs` is deprecated in version 1.8 and will be removed in 1.10.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">None</td>\n",
       "        </tr>\n",
       "    \n",
       "                  </tbody>\n",
       "                </table>\n",
       "            </details>\n",
       "        </div>\n",
       "    </div></div></div></div></div><script>function copyToClipboard(text, element) {\n",
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       "        return 'light';\n",
       "    }\n",
       "\n",
       "    // Guess based on a parent element's color\n",
       "    const color = window.getComputedStyle(element.parentNode, null).getPropertyValue('color');\n",
       "    const match = color.match(/^rgb\\s*\\(\\s*(\\d+)\\s*,\\s*(\\d+)\\s*,\\s*(\\d+)\\s*\\)\\s*$/i);\n",
       "    if (match) {\n",
       "        const [r, g, b] = [\n",
       "            parseFloat(match[1]),\n",
       "            parseFloat(match[2]),\n",
       "            parseFloat(match[3])\n",
       "        ];\n",
       "\n",
       "        // https://en.wikipedia.org/wiki/HSL_and_HSV#Lightness\n",
       "        const luma = 0.299 * r + 0.587 * g + 0.114 * b;\n",
       "\n",
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       "        // Otherwise fall back to the next heuristic.\n",
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       "    return window.matchMedia('(prefers-color-scheme: dark)').matches ? 'dark' : 'light';\n",
       "}\n",
       "\n",
       "\n",
       "function forceTheme(elementId) {\n",
       "    const estimatorElement = document.querySelector(`#${elementId}`);\n",
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       "forceTheme('sk-container-id-4');</script></body>"
      ],
      "text/plain": [
       "LogisticRegression(max_iter=1000000, random_state=202020)"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "model = LogisticRegression(max_iter=1000000, random_state=202020)\n",
    "\n",
    "model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "5335485b",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "14ffda7b",
   "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>battery_power</th>\n",
       "      <th>blue</th>\n",
       "      <th>clock_speed</th>\n",
       "      <th>dual_sim</th>\n",
       "      <th>fc</th>\n",
       "      <th>four_g</th>\n",
       "      <th>int_memory</th>\n",
       "      <th>m_dep</th>\n",
       "      <th>mobile_wt</th>\n",
       "      <th>n_cores</th>\n",
       "      <th>pc</th>\n",
       "      <th>px_height</th>\n",
       "      <th>px_width</th>\n",
       "      <th>ram</th>\n",
       "      <th>sc_h</th>\n",
       "      <th>sc_w</th>\n",
       "      <th>talk_time</th>\n",
       "      <th>three_g</th>\n",
       "      <th>touch_screen</th>\n",
       "      <th>wifi</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>501</th>\n",
       "      <td>1773</td>\n",
       "      <td>1</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0</td>\n",
       "      <td>16</td>\n",
       "      <td>1</td>\n",
       "      <td>32</td>\n",
       "      <td>0.6</td>\n",
       "      <td>170</td>\n",
       "      <td>6</td>\n",
       "      <td>18</td>\n",
       "      <td>1215</td>\n",
       "      <td>1472</td>\n",
       "      <td>3566</td>\n",
       "      <td>17</td>\n",
       "      <td>6</td>\n",
       "      <td>13</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1924</th>\n",
       "      <td>1982</td>\n",
       "      <td>1</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>0.2</td>\n",
       "      <td>80</td>\n",
       "      <td>5</td>\n",
       "      <td>20</td>\n",
       "      <td>287</td>\n",
       "      <td>593</td>\n",
       "      <td>1824</td>\n",
       "      <td>13</td>\n",
       "      <td>3</td>\n",
       "      <td>14</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1710</th>\n",
       "      <td>548</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>56</td>\n",
       "      <td>0.4</td>\n",
       "      <td>146</td>\n",
       "      <td>4</td>\n",
       "      <td>14</td>\n",
       "      <td>657</td>\n",
       "      <td>1657</td>\n",
       "      <td>562</td>\n",
       "      <td>17</td>\n",
       "      <td>14</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>547</th>\n",
       "      <td>1117</td>\n",
       "      <td>1</td>\n",
       "      <td>0.6</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>31</td>\n",
       "      <td>0.2</td>\n",
       "      <td>146</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>1258</td>\n",
       "      <td>1627</td>\n",
       "      <td>2003</td>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>496</th>\n",
       "      <td>1554</td>\n",
       "      <td>1</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>124</td>\n",
       "      <td>3</td>\n",
       "      <td>12</td>\n",
       "      <td>395</td>\n",
       "      <td>1579</td>\n",
       "      <td>3635</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>18</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</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",
       "      <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>1204</th>\n",
       "      <td>1524</td>\n",
       "      <td>0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>41</td>\n",
       "      <td>1.0</td>\n",
       "      <td>96</td>\n",
       "      <td>4</td>\n",
       "      <td>20</td>\n",
       "      <td>184</td>\n",
       "      <td>740</td>\n",
       "      <td>1936</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1937</th>\n",
       "      <td>1396</td>\n",
       "      <td>1</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>37</td>\n",
       "      <td>0.7</td>\n",
       "      <td>134</td>\n",
       "      <td>4</td>\n",
       "      <td>15</td>\n",
       "      <td>560</td>\n",
       "      <td>1177</td>\n",
       "      <td>2694</td>\n",
       "      <td>18</td>\n",
       "      <td>3</td>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>412</th>\n",
       "      <td>1478</td>\n",
       "      <td>1</td>\n",
       "      <td>0.8</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>48</td>\n",
       "      <td>0.7</td>\n",
       "      <td>99</td>\n",
       "      <td>5</td>\n",
       "      <td>17</td>\n",
       "      <td>694</td>\n",
       "      <td>882</td>\n",
       "      <td>1141</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>243</th>\n",
       "      <td>1918</td>\n",
       "      <td>0</td>\n",
       "      <td>1.9</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>0.6</td>\n",
       "      <td>110</td>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "      <td>1170</td>\n",
       "      <td>1543</td>\n",
       "      <td>1717</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>16</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>724</th>\n",
       "      <td>696</td>\n",
       "      <td>0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>51</td>\n",
       "      <td>0.3</td>\n",
       "      <td>197</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>649</td>\n",
       "      <td>907</td>\n",
       "      <td>2630</td>\n",
       "      <td>18</td>\n",
       "      <td>8</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>400 rows × 20 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      battery_power  blue  clock_speed  dual_sim  fc  four_g  int_memory  \\\n",
       "501            1773     1          2.5         0  16       1          32   \n",
       "1924           1982     1          1.6         0   2       1          12   \n",
       "1710            548     0          1.0         1   0       1          56   \n",
       "547            1117     1          0.6         1   0       0          31   \n",
       "496            1554     1          0.5         1  11       0           5   \n",
       "...             ...   ...          ...       ...  ..     ...         ...   \n",
       "1204           1524     0          2.5         1  11       1          41   \n",
       "1937           1396     1          0.6         0   0       0          37   \n",
       "412            1478     1          0.8         1  12       0          48   \n",
       "243            1918     0          1.9         0   2       0          10   \n",
       "724             696     0          0.5         0   6       0          51   \n",
       "\n",
       "      m_dep  mobile_wt  n_cores  pc  px_height  px_width   ram  sc_h  sc_w  \\\n",
       "501     0.6        170        6  18       1215      1472  3566    17     6   \n",
       "1924    0.2         80        5  20        287       593  1824    13     3   \n",
       "1710    0.4        146        4  14        657      1657   562    17    14   \n",
       "547     0.2        146        2   7       1258      1627  2003     8     7   \n",
       "496     1.0        124        3  12        395      1579  3635     7     4   \n",
       "...     ...        ...      ...  ..        ...       ...   ...   ...   ...   \n",
       "1204    1.0         96        4  20        184       740  1936     7     0   \n",
       "1937    0.7        134        4  15        560      1177  2694    18     3   \n",
       "412     0.7         99        5  17        694       882  1141     5     2   \n",
       "243     0.6        110        5  10       1170      1543  1717     7     1   \n",
       "724     0.3        197        3   8        649       907  2630    18     8   \n",
       "\n",
       "      talk_time  three_g  touch_screen  wifi  \n",
       "501          13        1             0     1  \n",
       "1924         14        1             1     1  \n",
       "1710          8        1             1     1  \n",
       "547          14        0             1     1  \n",
       "496          18        0             0     0  \n",
       "...         ...      ...           ...   ...  \n",
       "1204          9        1             1     1  \n",
       "1937         19        0             0     1  \n",
       "412           6        0             0     1  \n",
       "243          16        1             1     1  \n",
       "724           9        0             0     1  \n",
       "\n",
       "[400 rows x 20 columns]"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "a1f00c2e",
   "metadata": {},
   "outputs": [
    {
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       "       0, 2, 3, 2, 3, 2, 2, 2, 2, 2, 3, 0, 3, 0, 2, 3, 2, 0, 3, 3, 3, 0,\n",
       "       0, 2, 3, 1, 2, 3, 2, 3, 3, 3, 2, 1, 3, 3, 1, 1, 0, 1, 3, 0, 0, 1,\n",
       "       3, 3, 3, 3, 0, 1, 2, 0, 0, 3, 1, 1, 2, 1, 3, 0, 0, 1, 1, 3, 2, 1,\n",
       "       1, 3, 0, 3, 1, 2, 2, 3, 0, 1, 0, 1, 1, 1, 0, 2, 1, 2, 3, 0, 1, 2,\n",
       "       1, 1, 1, 3, 2, 2, 0, 0, 3, 1, 2, 2, 1, 3, 2, 0, 0, 3, 1, 2, 3, 3,\n",
       "       0, 3, 2, 1, 1, 1, 1, 2, 0, 1, 1, 3, 0, 0, 1, 0, 1, 2, 1, 2, 2, 3,\n",
       "       0, 2, 3, 2, 2, 3, 3, 2, 1, 0, 3, 3, 1, 3, 2, 1, 2, 1, 3, 2, 2, 1,\n",
       "       0, 0, 3, 2, 2, 3, 2, 0, 0, 0, 1, 2, 3, 2, 3, 1, 3, 3, 2, 2, 1, 0,\n",
       "       2, 2, 2, 3, 1, 0, 3, 2, 1, 1, 1, 1, 2, 1, 1, 3, 1, 2, 2, 3, 1, 3,\n",
       "       1, 2, 0, 0, 2, 3, 3, 2, 3, 2, 0, 1, 0, 2, 2, 3, 0, 0, 2, 1, 0, 1,\n",
       "       1, 0, 0, 0, 0, 0, 2, 1, 2, 0, 1, 2, 3, 3, 3, 0, 3, 1, 2, 2, 2, 0,\n",
       "       2, 2, 1, 0, 0, 2, 2, 2, 3, 1, 2, 0, 3, 3, 3, 2, 1, 1, 2, 0, 0, 2,\n",
       "       2, 0, 0, 0, 2, 1, 2, 1, 0, 3, 0, 3, 3, 3, 2, 2, 0, 2, 2, 1, 0, 1,\n",
       "       2, 1, 2, 2])"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "008b1182",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "501     3\n",
       "1924    1\n",
       "1710    0\n",
       "547     2\n",
       "496     3\n",
       "       ..\n",
       "1204    1\n",
       "1937    2\n",
       "412     1\n",
       "243     2\n",
       "724     1\n",
       "Name: price_range, Length: 400, dtype: int64"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "8458975f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "score model 0.75\n",
      "------------------------------------------------------------\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.95      0.89      0.92       100\n",
      "           1       0.68      0.64      0.66       100\n",
      "           2       0.57      0.67      0.61       100\n",
      "           3       0.85      0.80      0.82       100\n",
      "\n",
      "    accuracy                           0.75       400\n",
      "   macro avg       0.76      0.75      0.75       400\n",
      "weighted avg       0.76      0.75      0.75       400\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score, classification_report\n",
    "\n",
    "print(f'score model {accuracy_score(y_test, y_pred)}')\n",
    "print('-'*60)\n",
    "print(classification_report(y_test, y_pred))"
   ]
  }
 ],
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