6.4.f.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "cdd312ba-2b03-415e-86e5-a7f45d1a46ad",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "import os\n",
    "import sklearn\n",
    "from sklearn.model_selection import train_test_split \n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.metrics import mean_squared_error, r2_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "b2cd9f81-9b9e-4369-bfdc-23673fc9e937",
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "597ddaba-c14a-43ae-a530-941a3843352b",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('merged_dataset.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "a1d1c8f5-ab94-4920-851b-833ad99da738",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Country', 'Region', 'Happiness Rank', 'Happiness Score',\n",
       "       'Standard Error', 'Economy (GDP per Capita)', 'Family',\n",
       "       'Health (Life Expectancy)', 'Freedom', 'Trust (Government Corruption)',\n",
       "       'Generosity', 'Dystopia Residual', 'Lower Confidence Interval',\n",
       "       'Upper Confidence Interval', 'Happiness.Rank', 'Happiness.Score',\n",
       "       'Whisker.high', 'Whisker.low', 'Economy..GDP.per.Capita.',\n",
       "       'Health..Life.Expectancy.', 'Trust..Government.Corruption.',\n",
       "       'Dystopia.Residual', 'Overall rank', 'Country or region', 'Score',\n",
       "       'GDP per capita', 'Social support', 'Healthy life expectancy',\n",
       "       'Freedom to make life choices', 'Perceptions of corruption'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "cfeed9be-cf7c-4404-91e3-af1a98382e6b",
   "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>Country</th>\n",
       "      <th>Region</th>\n",
       "      <th>Happiness Rank</th>\n",
       "      <th>Happiness Score</th>\n",
       "      <th>Standard Error</th>\n",
       "      <th>Economy (GDP per Capita)</th>\n",
       "      <th>Family</th>\n",
       "      <th>Health (Life Expectancy)</th>\n",
       "      <th>Freedom</th>\n",
       "      <th>Trust (Government Corruption)</th>\n",
       "      <th>...</th>\n",
       "      <th>Trust..Government.Corruption.</th>\n",
       "      <th>Dystopia.Residual</th>\n",
       "      <th>Overall rank</th>\n",
       "      <th>Country or region</th>\n",
       "      <th>Score</th>\n",
       "      <th>GDP per capita</th>\n",
       "      <th>Social support</th>\n",
       "      <th>Healthy life expectancy</th>\n",
       "      <th>Freedom to make life choices</th>\n",
       "      <th>Perceptions of corruption</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Switzerland</td>\n",
       "      <td>Western Europe</td>\n",
       "      <td>1.0</td>\n",
       "      <td>7.587</td>\n",
       "      <td>0.03411</td>\n",
       "      <td>1.39651</td>\n",
       "      <td>1.34951</td>\n",
       "      <td>0.94143</td>\n",
       "      <td>0.66557</td>\n",
       "      <td>0.41978</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Iceland</td>\n",
       "      <td>Western Europe</td>\n",
       "      <td>2.0</td>\n",
       "      <td>7.561</td>\n",
       "      <td>0.04884</td>\n",
       "      <td>1.30232</td>\n",
       "      <td>1.40223</td>\n",
       "      <td>0.94784</td>\n",
       "      <td>0.62877</td>\n",
       "      <td>0.14145</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Denmark</td>\n",
       "      <td>Western Europe</td>\n",
       "      <td>3.0</td>\n",
       "      <td>7.527</td>\n",
       "      <td>0.03328</td>\n",
       "      <td>1.32548</td>\n",
       "      <td>1.36058</td>\n",
       "      <td>0.87464</td>\n",
       "      <td>0.64938</td>\n",
       "      <td>0.48357</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Norway</td>\n",
       "      <td>Western Europe</td>\n",
       "      <td>4.0</td>\n",
       "      <td>7.522</td>\n",
       "      <td>0.03880</td>\n",
       "      <td>1.45900</td>\n",
       "      <td>1.33095</td>\n",
       "      <td>0.88521</td>\n",
       "      <td>0.66973</td>\n",
       "      <td>0.36503</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Canada</td>\n",
       "      <td>North America</td>\n",
       "      <td>5.0</td>\n",
       "      <td>7.427</td>\n",
       "      <td>0.03553</td>\n",
       "      <td>1.32629</td>\n",
       "      <td>1.32261</td>\n",
       "      <td>0.90563</td>\n",
       "      <td>0.63297</td>\n",
       "      <td>0.32957</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 30 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Country          Region  Happiness Rank  Happiness Score  \\\n",
       "0  Switzerland  Western Europe             1.0            7.587   \n",
       "1      Iceland  Western Europe             2.0            7.561   \n",
       "2      Denmark  Western Europe             3.0            7.527   \n",
       "3       Norway  Western Europe             4.0            7.522   \n",
       "4       Canada   North America             5.0            7.427   \n",
       "\n",
       "   Standard Error  Economy (GDP per Capita)   Family  \\\n",
       "0         0.03411                   1.39651  1.34951   \n",
       "1         0.04884                   1.30232  1.40223   \n",
       "2         0.03328                   1.32548  1.36058   \n",
       "3         0.03880                   1.45900  1.33095   \n",
       "4         0.03553                   1.32629  1.32261   \n",
       "\n",
       "   Health (Life Expectancy)  Freedom  Trust (Government Corruption)  ...  \\\n",
       "0                   0.94143  0.66557                        0.41978  ...   \n",
       "1                   0.94784  0.62877                        0.14145  ...   \n",
       "2                   0.87464  0.64938                        0.48357  ...   \n",
       "3                   0.88521  0.66973                        0.36503  ...   \n",
       "4                   0.90563  0.63297                        0.32957  ...   \n",
       "\n",
       "   Trust..Government.Corruption.  Dystopia.Residual  Overall rank  \\\n",
       "0                            NaN                NaN           NaN   \n",
       "1                            NaN                NaN           NaN   \n",
       "2                            NaN                NaN           NaN   \n",
       "3                            NaN                NaN           NaN   \n",
       "4                            NaN                NaN           NaN   \n",
       "\n",
       "   Country or region  Score  GDP per capita  Social support  \\\n",
       "0                NaN    NaN             NaN             NaN   \n",
       "1                NaN    NaN             NaN             NaN   \n",
       "2                NaN    NaN             NaN             NaN   \n",
       "3                NaN    NaN             NaN             NaN   \n",
       "4                NaN    NaN             NaN             NaN   \n",
       "\n",
       "   Healthy life expectancy  Freedom to make life choices  \\\n",
       "0                      NaN                           NaN   \n",
       "1                      NaN                           NaN   \n",
       "2                      NaN                           NaN   \n",
       "3                      NaN                           NaN   \n",
       "4                      NaN                           NaN   \n",
       "\n",
       "   Perceptions of corruption  \n",
       "0                        NaN  \n",
       "1                        NaN  \n",
       "2                        NaN  \n",
       "3                        NaN  \n",
       "4                        NaN  \n",
       "\n",
       "[5 rows x 30 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "440a8d16-16fe-44e1-bd7a-cb8858e2ef63",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "       Country          Region  Happiness Rank  Happiness Score  \\\n",
      "0  Switzerland  Western Europe             1.0            7.587   \n",
      "1      Iceland  Western Europe             2.0            7.561   \n",
      "2      Denmark  Western Europe             3.0            7.527   \n",
      "3       Norway  Western Europe             4.0            7.522   \n",
      "4       Canada   North America             5.0            7.427   \n",
      "\n",
      "   Standard Error  Economy (GDP per Capita)   Family  \\\n",
      "0         0.03411                   1.39651  1.34951   \n",
      "1         0.04884                   1.30232  1.40223   \n",
      "2         0.03328                   1.32548  1.36058   \n",
      "3         0.03880                   1.45900  1.33095   \n",
      "4         0.03553                   1.32629  1.32261   \n",
      "\n",
      "   Health (Life Expectancy)  Freedom  Trust (Government Corruption)  ...  \\\n",
      "0                   0.94143  0.66557                        0.41978  ...   \n",
      "1                   0.94784  0.62877                        0.14145  ...   \n",
      "2                   0.87464  0.64938                        0.48357  ...   \n",
      "3                   0.88521  0.66973                        0.36503  ...   \n",
      "4                   0.90563  0.63297                        0.32957  ...   \n",
      "\n",
      "   Trust..Government.Corruption.  Dystopia.Residual  Overall rank  \\\n",
      "0                        0.12312           1.850238          78.5   \n",
      "1                        0.12312           1.850238          78.5   \n",
      "2                        0.12312           1.850238          78.5   \n",
      "3                        0.12312           1.850238          78.5   \n",
      "4                        0.12312           1.850238          78.5   \n",
      "\n",
      "   Country or region     Score  GDP per capita  Social support  \\\n",
      "0                NaN  5.391506        0.898298        1.211026   \n",
      "1                NaN  5.391506        0.898298        1.211026   \n",
      "2                NaN  5.391506        0.898298        1.211026   \n",
      "3                NaN  5.391506        0.898298        1.211026   \n",
      "4                NaN  5.391506        0.898298        1.211026   \n",
      "\n",
      "   Healthy life expectancy  Freedom to make life choices  \\\n",
      "0                 0.661295                      0.423538   \n",
      "1                 0.661295                      0.423538   \n",
      "2                 0.661295                      0.423538   \n",
      "3                 0.661295                      0.423538   \n",
      "4                 0.661295                      0.423538   \n",
      "\n",
      "   Perceptions of corruption  \n",
      "0                   0.111299  \n",
      "1                   0.111299  \n",
      "2                   0.111299  \n",
      "3                   0.111299  \n",
      "4                   0.111299  \n",
      "\n",
      "[5 rows x 30 columns]\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "# Load your dataset into a DataFrame (replace 'data.csv' with your actual dataset)\n",
    "# Assuming NaN values are represented as empty strings in the CSV file\n",
    "df = pd.read_csv('merged_dataset.csv', na_values='', delimiter=',')\n",
    "\n",
    "# Replace NaN values with the mean of each column (for numeric columns)\n",
    "numeric_columns = df.select_dtypes(include='number').columns\n",
    "df[numeric_columns] = df[numeric_columns].fillna(df[numeric_columns].mean())\n",
    "\n",
    "# Display the first few rows of the DataFrame after handling NaN values\n",
    "print(df.head())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "09e7e377-ca97-4c5f-9046-9608e2392c6a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(782, 30)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "ab7a9ece-430f-4d48-b0a0-56e164a2e53a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Country                          312\n",
       "Region                           467\n",
       "Happiness Rank                     0\n",
       "Happiness Score                    0\n",
       "Standard Error                     0\n",
       "Economy (GDP per Capita)           0\n",
       "Family                             0\n",
       "Health (Life Expectancy)           0\n",
       "Freedom                            0\n",
       "Trust (Government Corruption)      0\n",
       "Generosity                         0\n",
       "Dystopia Residual                  0\n",
       "Lower Confidence Interval          0\n",
       "Upper Confidence Interval          0\n",
       "Happiness.Rank                     0\n",
       "Happiness.Score                    0\n",
       "Whisker.high                       0\n",
       "Whisker.low                        0\n",
       "Economy..GDP.per.Capita.           0\n",
       "Health..Life.Expectancy.           0\n",
       "Trust..Government.Corruption.      0\n",
       "Dystopia.Residual                  0\n",
       "Overall rank                       0\n",
       "Country or region                470\n",
       "Score                              0\n",
       "GDP per capita                     0\n",
       "Social support                     0\n",
       "Healthy life expectancy            0\n",
       "Freedom to make life choices       0\n",
       "Perceptions of corruption          0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check for missing values\n",
    "\n",
    "df.isnull().sum()\n",
    "\n",
    "#missing values do not represent catagories that we are using for analysis - Proceeding as is."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "fa1c6e60-dc88-4412-a493-8120763b32d2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(782,)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dups = df.duplicated()\n",
    "dups.shape\n",
    "\n",
    "# No duplicates present"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "8a3d5dcd-a8ad-467c-979d-ed675f1a91bd",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\vrich\\AppData\\Local\\Temp\\ipykernel_13688\\2243674596.py:2: UserWarning: \n",
      "\n",
      "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
      "\n",
      "Please adapt your code to use either `displot` (a figure-level function with\n",
      "similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "\n",
      "For a guide to updating your code to use the new functions, please see\n",
      "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n",
      "\n",
      "  sns.distplot(df['Score'], bins=25)\n",
      "C:\\Users\\vrich\\anaconda3\\Lib\\site-packages\\seaborn\\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Assuming df is your DataFrame\n",
    "sns.distplot(df['Score'], bins=25)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "f05db811-94e7-456f-8aa4-df1fdeccc71b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5.391506410256411"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Score'].mean() # Check the mean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "aa06696d-5bd3-41b7-aa35-6f8d752c8b3a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5.39150641025641"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Score'].median()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "62278f3e-1334-4845-a895-451f99188d1f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7.769"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Score'].max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "3b4644e5-0927-41a4-9952-6861d12d537a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Reshape the variables into NumPy arrays and put them into separate objects.\n",
    "\n",
    "X = df['Social support'].values.reshape(-1,1)\n",
    "y = df['Score'].values.reshape(-1,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "68c72d8f-1c60-4909-821d-58876f52a820",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a scatterplot using matplotlib for another look at how the chosen variables plot against each other.\n",
    "\n",
    "df.plot(x = 'Social support', y='Score',style='o') # The style option creates a scatterplot; without it, we only have lines.\n",
    "plt.title('Socila support vs  Score ')  \n",
    "plt.xlabel('Social support')  \n",
    "plt.ylabel('Score')  \n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "507348b4-ea19-4240-9d54-e92a53ad46d9",
   "metadata": {},
   "outputs": [
    {
     "data": {
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     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
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   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "a1483fdd-b9e8-4aba-9d16-45add71d2840",
   "metadata": {},
   "outputs": [
    {
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      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "b006ce5d-d782-4bc8-9577-05979cf194d7",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "ed7b6f89-3e7f-40fb-8e57-111c22e34bbf",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a regression object.\n",
    "\n",
    "regression = LinearRegression()  # This is the regression object, which will be fit onto the training set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "aa8f2fac-ec22-4198-9318-9e0740521dc6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</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 sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearRegression</label><div class=\"sk-toggleable__content\"><pre>LinearRegression()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "regression.fit(X_train, y_train)  # Fit the regression object onto the training set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "697cc71d-6d62-4311-b15e-f1a4f7d21dca",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Predict the values of y using X.\n",
    "\n",
    "y_predicted = regression.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "b8e04d83-6736-4e05-98f2-d21bf0986f3f",
   "metadata": {},
   "outputs": [
    {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a plot that shows the regression line from the model on the test set.\n",
    "\n",
    "plot_test = plt\n",
    "plot_test.scatter(X_test, y_test, color='gray', s = 15)\n",
    "plot_test.plot(X_test, y_predicted, color='red', linewidth =3)\n",
    "plot_test.title('Social support Vs Score')\n",
    "plot_test.xlabel('Social support')\n",
    "plot_test.ylabel('Score')\n",
    "plot_test.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "c587dec5-05c6-439a-af9e-c2969ca9b914",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create objects that contain the model summary statistics.\n",
    "\n",
    "rmse = mean_squared_error(y_test, y_predicted) # This is the mean squared error\n",
    "r2 = r2_score(y_test, y_predicted) # This is the R2 score. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "e029785e-c8c0-44c2-a516-a8531920f0dd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Slope: [[2.75478414]]\n",
      "Mean squared error:  0.2116754286193453\n",
      "R2 score:  0.658811111119557\n"
     ]
    }
   ],
   "source": [
    "# Print the model summary statistics. This is where you evaluate the performance of the model.\n",
    "\n",
    "print('Slope:' ,regression.coef_)\n",
    "print('Mean squared error: ', rmse)\n",
    "print('R2 score: ', r2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "9231bc8e-f674-4e7c-92a8-bba84246e9c1",
   "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>Actual</th>\n",
       "      <th>Predicted</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.391506</td>\n",
       "      <td>5.378337</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6.886000</td>\n",
       "      <td>6.094510</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5.391506</td>\n",
       "      <td>5.378337</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5.391506</td>\n",
       "      <td>5.378337</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.391506</td>\n",
       "      <td>5.378337</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5.391506</td>\n",
       "      <td>5.378337</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>4.390000</td>\n",
       "      <td>5.086259</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>5.391506</td>\n",
       "      <td>5.378337</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>5.391506</td>\n",
       "      <td>5.378337</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>5.391506</td>\n",
       "      <td>5.378337</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>5.391506</td>\n",
       "      <td>5.378337</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>5.391506</td>\n",
       "      <td>5.378337</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>3.334000</td>\n",
       "      <td>4.000874</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>5.125000</td>\n",
       "      <td>6.221230</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>5.391506</td>\n",
       "      <td>5.378337</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>5.391506</td>\n",
       "      <td>5.378337</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>5.391506</td>\n",
       "      <td>5.378337</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>5.391506</td>\n",
       "      <td>5.378337</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>5.391506</td>\n",
       "      <td>5.378337</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>5.391506</td>\n",
       "      <td>5.378337</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>5.391506</td>\n",
       "      <td>5.378337</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>3.975000</td>\n",
       "      <td>4.582134</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>4.700000</td>\n",
       "      <td>5.133091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>6.199000</td>\n",
       "      <td>5.810768</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>5.391506</td>\n",
       "      <td>5.378337</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>7.228000</td>\n",
       "      <td>6.306629</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>5.762000</td>\n",
       "      <td>5.323171</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>7.600000</td>\n",
       "      <td>6.375498</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>5.391506</td>\n",
       "      <td>5.378337</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>6.923000</td>\n",
       "      <td>6.185418</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Actual  Predicted\n",
       "0   5.391506   5.378337\n",
       "1   6.886000   6.094510\n",
       "2   5.391506   5.378337\n",
       "3   5.391506   5.378337\n",
       "4   5.391506   5.378337\n",
       "5   5.391506   5.378337\n",
       "6   4.390000   5.086259\n",
       "7   5.391506   5.378337\n",
       "8   5.391506   5.378337\n",
       "9   5.391506   5.378337\n",
       "10  5.391506   5.378337\n",
       "11  5.391506   5.378337\n",
       "12  3.334000   4.000874\n",
       "13  5.125000   6.221230\n",
       "14  5.391506   5.378337\n",
       "15  5.391506   5.378337\n",
       "16  5.391506   5.378337\n",
       "17  5.391506   5.378337\n",
       "18  5.391506   5.378337\n",
       "19  5.391506   5.378337\n",
       "20  5.391506   5.378337\n",
       "21  3.975000   4.582134\n",
       "22  4.700000   5.133091\n",
       "23  6.199000   5.810768\n",
       "24  5.391506   5.378337\n",
       "25  7.228000   6.306629\n",
       "26  5.762000   5.323171\n",
       "27  7.600000   6.375498\n",
       "28  5.391506   5.378337\n",
       "29  6.923000   6.185418"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a dataframe comparing the actual and predicted values of y.\n",
    "\n",
    "data = pd.DataFrame({'Actual': y_test.flatten(), 'Predicted': y_predicted.flatten()})\n",
    "data.head(30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "391358e0-2a10-4e2b-aedd-a146ffcb1858",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Predict.\n",
    "\n",
    "y_predicted_train = regression.predict(X_train) # This is predicting X_train!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "ca254082-14db-4e8e-a3a1-cd86c7f47472",
   "metadata": {},
   "outputs": [],
   "source": [
    "rmse = mean_squared_error(y_train, y_predicted_train)\n",
    "r2 = r2_score(y_train, y_predicted_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "407c9bc2-0a7f-4b40-ab36-d2616224b4ea",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Slope: [[2.75478414]]\n",
      "Mean squared error:  0.20677196774448464\n",
      "R2 score:  0.5298853113861623\n"
     ]
    }
   ],
   "source": [
    "print('Slope:' ,regression.coef_)\n",
    "print('Mean squared error: ', rmse)\n",
    "print('R2 score: ', r2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "2f28c326-1ee3-4230-881e-a9b0fd0a9fb1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualizing the training set results.\n",
    "\n",
    "plot_test = plt\n",
    "plot_test.scatter(X_train, y_train, color='green', s = 15)\n",
    "plot_test.plot(X_train, y_predicted_train, color='red', linewidth =3)\n",
    "plot_test.title('Health (Life Expectancy) Vs Score)')\n",
    "plot_test.xlabel('Health (Life Expectancy)')\n",
    "plot_test.ylabel('Score')\n",
    "plot_test.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "28bc5818-11c8-4648-94ba-db0d79d6204a",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\vrich\\AppData\\Local\\Temp\\ipykernel_13688\\1560109889.py:1: UserWarning: \n",
      "\n",
      "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
      "\n",
      "Please adapt your code to use either `displot` (a figure-level function with\n",
      "similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "\n",
      "For a guide to updating your code to use the new functions, please see\n",
      "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n",
      "\n",
      "  sns.distplot(df['Score'], bins=100) # Shows extreme values\n",
      "C:\\Users\\vrich\\anaconda3\\Lib\\site-packages\\seaborn\\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Score', ylabel='Density'>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(df['Score'], bins=100) # Shows extreme values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "5f9bb354-4400-4601-8890-b93775092a6e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    782.000000\n",
       "mean       5.391506\n",
       "std        0.703373\n",
       "min        2.853000\n",
       "25%        5.391506\n",
       "50%        5.391506\n",
       "75%        5.391506\n",
       "max        7.769000\n",
       "Name: Score, dtype: float64"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Score'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "45e91d77-2f4d-45d6-9761-792678be9c27",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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1nfdvf/sbCgoKoFQq0bFjR53/HnroIZP7GDNmDEpLS7FhwwasXbsWCQkJaNGihd5tFQoF2rZti4ULFyIkJAQnT540uX8x7+HChQsICwvT+x5MzfTx8/NDjx498OWXXxrtwfrb3/6GM2fOoEmTJnpfp3qgs2XLFq2ihnfu3MH27dvx8MMPa3q7fHx8AEDUHXjv3r1x9+5dnYJ769at0/zdEg0bNsSECROQlJRk9HPx9/dHly5dsGXLFq12q1QqrF+/Hg0aNEDz5s0lv37//v1x7tw5zfCNPtY+BtHR0bh+/TquXbumeay8vBzffvut3u337dunta1SqcSmTZvQpEkTTe+U1M8YgM5vevPmzSgpKbH7FH1yDOzRIYfWt29fNGjQAAMHDkSLFi2gUqmQlZWFjz76CAEBAZg0aRKAyhPum2++iXnz5uH+/fsYPnw4goODkZ2djZs3byI1NRUhISFISUnBm2++iZEjR2L48OEoKChAamoqfH19MXv2bJPtGTZsGP7zn//g8ccfx6RJk9C5c2d4eXnh8uXL+O677zBo0CAMHjzY6D5atGiBhIQEpKWl4dKlS1i5cqXW33fs2IGlS5fiySefROPGjSEIArZs2YLbt28jKSnJ/IP5P5MnT8bmzZvxyCOPYMqUKWjTpg1UKhUuXryIPXv2YNq0aejSpYvRfXz88cfo3r07unTpgpkzZ6Jp06a4du0atm3bhhUrViAwMBBz585Feno6unXrhokTJ+Khhx5CaWkpcnNzsWvXLixfvlxr6MXT0xNJSUmYOnUqVCoV5s+fj+LiYqSmpmq2ad26NQBg8eLFGDVqFLy8vPDQQw/p7UkbOXIkPvnkE4waNQq5ublo3bo1MjIy8I9//AOPP/640dwWfYqKitCzZ088++yzaNGiBQIDA3H8+HHs3r0bQ4YMMfrctLQ0JCUloWfPnpg+fTq8vb2xdOlSnDlzBhs2bDCrHs3kyZOxadMmDBo0CDNnzkTnzp1x//59HDx4EH/729/Qs2dP2Y9BdUOHDsU777yDYcOGYcaMGSgtLcU///lPgzlV4eHh6NWrF1JSUuDv74+lS5fi119/1ZpiHhcXBwBYuXIlAgMD4evri5iYGL09PklJSejbty+Sk5NRXFyMxMRE/Pzzz5g9ezbatWuHESNGWPT+yEXYMRGayKRNmzYJzz77rNCsWTMhICBA8PLyEho2bCiMGDFC71TrdevWCZ06dRJ8fX2FgIAAoV27dsKaNWu0tvn000+FNm3aCN7e3kJwcLAwaNAgzQwgNUPTtAVBECoqKoQPP/xQaNu2reZ1WrRoIYwbN044f/68qPe1cuVKzSwU9fRotV9//VUYPny40KRJE8HPz08IDg4WOnfuLKxdu9bkfo21u6q7d+8Kb7/9tvDQQw9pjkPr1q2FKVOmaM2KASC89tpreveRnZ0tPP3000JYWJjg7e0tNGzYUBg9erTWlN4bN24IEydOFGJiYgQvLy8hNDRU6NChg/DWW28Jd+/eFQThr5lG8+fPF1JTU4UGDRoI3t7eQrt27YRvv/1W53VnzZol1KtXT/Dw8NCanVN91pUgCEJBQYEwfvx4ITIyUqhRo4bQqFEjYdasWVptNPY+GzVqJIwaNUoQBEEoLS0Vxo8fL7Rp00YICgoS/Pz8hIceekiYPXu2UFJSYvKYHzp0SOjVq5fg7+8v+Pn5CV27dtXMtFOTMutKECpnEk6aNElo2LCh4OXlJdSpU0cYMGCA8Ouvv8p2DNSzrqrP0lPbtWuXEB8fL/j5+QmNGzcWlixZYnDW1WuvvSYsXbpUaNKkieDl5SW0aNFC+M9//qOzz0WLFgkxMTGCp6enAEDzG9Y3y+v+/ftCcnKy0KhRI8HLy0uIjIwUXnnlFeHWrVta2zVq1EgYMGCAzmvp+96Qa1EIgiDYKcYiIkJubi5iYmLwwQcfYPr06fZuDlmJQqHAa6+9hiVLlti7KeRmmKNDRERELouBDhEREbksDl0RERGRy2KPDhEREbksBjpERETkshjoEBERkctyu4KBKpUKV69eRWBgoFlFuoiIiMj2BEHAnTt3UK9ePXh4iO+ncbtA5+rVqwbXQiIiIiLHdunSJZML2lbldoGOulT8pUuXdFamJiIiIsdUXFyMqKgok4snV+d2gY56uCooKIiBDhERkZORmnbCZGQiIiJyWQx0iIiIyGUx0CEiIiKX5XY5OmIplUpUVFTYuxlEbsPLywuenp72bgYRuRgGOtUIgoD8/Hzcvn3b3k0hcjshISGIiIhgjSsikg0DnWrUQU6dOnVQs2ZNnnCJbEAQBNy7dw/Xr18HAERGRtq5RUTkKhjoVKFUKjVBTlhYmL2bQ+RW/Pz8AADXr19HnTp1OIxFRLJgMnIV6pycmjVr2rklRO5J/dtjfhwRyYWBjh4criKyD/72iEhuHLoiIiKLKFUCjuUU4vqdUtQJ9EXnmFB4ejBoJcfAHh1ySAcOHIBCoXD42W+//vorunbtCl9fX8THx9u7OVblLJ8J2dbuM3noPn8/hq86ikkbszB81VF0n78fu8/k2btpRAAY6FiNUiUg80IBvs66gswLBVCqBKu+3ujRo6FQKKBQKODl5YXGjRtj+vTpKCkpserryuHRRx/F5MmTtR7r1q0b8vLyEBwcbJ9GiTR79mz4+/vjt99+w759++zdHNk482dCtrP7TB5eWX8SeUWlWo/nF5XilfUnGeyQQ+DQlRXsPpOH1O3ZWj/+yGBfzB4Yi35x1ps2269fP6xZswYVFRU4dOgQXnrpJZSUlGDZsmWS9yUIApRKJWrUsM9XxNvbGxEREXZ5bSkuXLiAAQMGoFGjRjZ7zfLycnh7e+s8XlFRAS8vL6u9rrN8JmQd1YenOjSqhdTt2dB3CycAUABI3Z6NpNgIDmORXbFHR2b2vMPx8fFBREQEoqKi8Oyzz+K5557D1q1bAVQGLgsWLEDjxo3h5+eHtm3b4r///a/muephiW+//RYdO3aEj48PDh06BJVKhfnz56Np06bw8fFBw4YN8d5772med+XKFQwdOhS1atVCWFgYBg0ahNzcXM3fR48ejSeffBKpqamoU6cOgoKCMG7cOJSXl2v+fvDgQSxevFjTI5Wbm6t3mGTz5s1o1aoVfHx8EB0djY8++kjr/UdHR+Mf//gHXnjhBQQGBqJhw4ZYuXKl5u/l5eWYMGECIiMj4evri+joaKSlpRk8niqVCnPnzkWDBg3g4+OD+Ph47N69W/N3hUKBEydOYO7cuVAoFJgzZ47B/Rg7hqdPn0avXr3g5+eHsLAwvPzyy7h7967OMUxLS0O9evXQvHlz5ObmQqFQ4IsvvsCjjz4KX19frF+/HnPmzNEZQlu0aBGio6Md8jMh56BveKpr2j6d81xVAoC8olIcyym0XUOJ9GCgIyOlSjB6hwNU3uFYexhLzc/PTzNN9+2338aaNWuwbNky/PLLL5gyZQqef/55HDx4UOs5b7zxBtLS0nD27Fm0adMGs2bNwvz585GSkoLs7Gx8/vnnqFu3LgDg3r176NmzJwICAvD9998jIyMDAQEB6Nevn+aiCQD79u3D2bNn8d1332HDhg346quvkJqaCgBYvHgxEhISMHbsWOTl5SEvLw9RUVE67+XEiRN45plnMGzYMJw+fRpz5sxBSkoK1q5dq7XdRx99hI4dO+LUqVN49dVX8corr+DXX38FAPzzn//Etm3b8MUXX+C3337D+vXrtQKA6hYvXoyPPvoIH374IX7++Wf07dsXTzzxBM6fPw8AyMvLQ6tWrTBt2jTk5eVh+vTpevdj6hj269cPtWrVwvHjx/Hll19i7969mDBhgtY+1McwPT0dO3bs0DyenJyMiRMn4uzZs+jbt6/B91Kdo3wm5PgM3bwVlpQbeIa263cMB0NEtsChKxkdyykUfYeT0MS6BQmPHTuGzz//HL1790ZJSQk+/vhj7N+/HwkJCQCAxo0bIyMjAytWrECPHj00z5s7dy6SkpIAAHfu3MHixYuxZMkSjBo1CgDQpEkTdO/eHQCwceNGeHh44NNPP9VMC16zZg1CQkJw4MABPPbYYwAqhzxWr16NmjVrolWrVpg7dy5mzJiBefPmITg4GN7e3qhZs6bRYZGPP/4YvXv3RkpKCgCgefPmyM7OxgcffIDRo0drtnv88cfx6quvAqgMAhYuXIgDBw6gRYsWuHjxIpo1a4bu3btDoVCYHG768MMPkZycjGHDhgEA5s+fj++++w6LFi3CJ598goiICNSoUQMBAQEG227qGP7nP//B/fv3sW7dOvj7+wMAlixZgoEDB2L+/PmagMjf3x+ffvqpZshK3Ws2efJkDBkyxOj70MdRPhNybMZu3sSqE+grW3uIzMEeHRmJvXOx1h3Ojh07EBAQAF9fXyQkJOCRRx7B//3f/yE7OxulpaVISkpCQECA5r9169bhwoULWvvo2LGj5v/Pnj2LsrIy9O7dW+/rnThxAr///jsCAwM1+wwNDUVpaanWftu2batVhDEhIQF3797FpUuXRL+3s2fPIjExUeuxxMREnD9/HkqlUvNYmzZtNP+vUCgQERGhWVZg9OjRyMrKwkMPPYSJEydiz549Bl+vuLgYV69e1fuaZ8+eldRuY8fw7NmzaNu2rSbIUb+GSqXCb7/9pnmsdevWevNyqn5eUjjKZ0KOzdTNmzEKVOYmdo4JlbdRRBKxR0dGYu9crHWH07NnTyxbtgxeXl6oV6+eJjE1JycHALBz507Ur19f6zk+Pj5a/656wVWX5DdEpVKhQ4cO+M9//qPzt9q1a5tsr5TicIIg6GwvCLr3mdWTcRUKBVQqFQCgffv2yMnJwTfffIO9e/fimWeeQZ8+fbRylUy1UV87jDF1DI3tr+rjVT+Xqqo/7uHhoXNcpFQZtvVnQo7N3Jsy9bdi9sBYJiKT3bFHR0adY0IRGewLQz9ra9/h+Pv7o2nTpmjUqJHWxSU2NhY+Pj64ePEimjZtqvWfvtwLQRBwt/QBatdvBD8/P+zdu1fv67Vv3x7nz59HnTp1dPZbdQryTz/9hPv372v+ffToUQQEBKBBgwYAKodRqvYA6BMbG4uMjAytx44cOYLmzZtLWhMpKCgIQ4cOxapVq7Bp0yZs3rwZhYW6yZJBQUGoV6+e3tds2bKl6Ndr1qwZ/Pz8DE49j42NRVZWllYZgMOHD8PDwwPNmzcX/TpqtWvXRn5+vlbAkZWVpbOdI30m5LjE3pSF+msHsxHBvlj2fHurzjIlEos9OjLy9FBg9sBYvLL+JBSA1ri2Pe9wAgMDMX36dEyZMgUqlQrdu3dHcXExjhw5goCAAE3uCAAU3S9H3v07qFBW3nGPfmUSpr/xBpQKTyT17IEbN27gl19+wYsvvojnnnsOH3zwAQYNGqSZnXTx4kVs2bIFM2bM0Fw0y8vL8eKLL+Ltt9/Gn3/+idmzZ2PChAnw8KiMs6Ojo/HDDz8gNzdXM/xV3bRp09CpUyfMmzcPQ4cORWZmJpYsWYKlS5eKPg4LFy5EZGQk4uPj4eHhgS+//BIREREICQnRu/2MGTMwe/ZsNGnSBPHx8VizZg2ysrL09mAZ4uvri+TkZLzxxhvw9vZGYmKizjGcPXs2Ro0ahTlz5uDGjRt4/fXXMWLECE1+jhSPPvoobty4gQULFuDvf/87du/ejW+++QZBQUFa2znKZ0KOTX3zll9UqjdPR4HKoObgjJ448ectVkYmh8QeHZn1i4vEsufbIyJY+07I3nc48+bNwzvvvIO0tDS0bNkSffv2xfbt2xETE6O13aXC+5ogBwBenjQDI8e+hnmpc9CyZUsMHTpUk19Rs2ZNfP/992jYsCGGDBmCli1b4oUXXsD9+/e1Lqy9e/dGs2bN8Mgjj+CZZ57BwIEDtaZiT58+HZ6enoiNjUXt2rVx8eJFnfa3b98eX3zxBTZu3Ii4uDi88847mDt3rlbSqykBAQGYP38+OnbsiE6dOiE3Nxe7du3SXNyrmzhxIqZNm4Zp06ahdevW2L17N7Zt24ZmzZqJfk0ASElJwbRp0/DOO+/oPYbffvstCgsL0alTJ/z9739H7969sWTJEkmvodayZUssXboUn3zyCdq2bYtjx47pnQ3mKJ8JOTb1zRsAnZ7qqjdv3jU8kNAkDIPi6yOhSRiDHHIoCkHfoLoLKy4uRnBwMIqKinTucktLS5GTk4OYmBj4+lqWR+Nsa78IgoBf8+9oBTnVeXl6oEVEoKQ8jtGjR+P27duaej5kf478mcj5GyT52KsIKlFVxq7fxnDoyko8PRRWn0Iup5IypdEgBwAqlCqUlCkR4MuvDZE76RcXiaTYCKe6eSNS4xWLAAAPRM6CEbsdEbkWZ7t5I1JjoEMAgBoG8lTM3U6tepVcsj9+JkTkTpiMTAAAfx9PeHka/zp4eXrA34fThomIyHkw0NHDzfKzAVQWcasXYjz5s16Ir6REZCKp3PG3R0TWxUCnCnWRvXv37tm5JfYR7OeNRmE1dXp2vDw90CisJoL9dJcgIJKT+rdXvZoyEZG5mKNThaenJ0JCQrRqnLhbD4aPAogO8cK9ciWUKhU8PTxQ09sTCoUKpaVchZisQxAE3Lt3D9evX0dISAgrKxORbBjoVKNerZmLDhLZXkhIiNEV04mIpGKgU41CoUBkZCTq1KkjaTFEIrKMl5cXe3KISHYMdAzw9PTkSZeIiMjJMRmZiIiIXBYDHSIiInJZDHSIiIjIZTHQISIiIpfFQIeIiIhcFgMdIiIiclkMdIiIiMhlMdAhIiIil8VAh4iIiFwWAx0iIiJyWQx0iIiIyGVxrSs3pVQJOJZTiOt3SlEn0BedY0Lh6aGwd7OIiMiOXPHawEDHDe0+k4fU7dnIKyrVPBYZ7IvZA2PRLy7Sji0jIiJ7cdVrg0IQBMHejbCl4uJiBAcHo6ioCEFBQfZujs3tPpOHV9afRPUPXR2vL3u+vVN/oYmISDx1D056dj5WH87V+bsjXRvMvX6zR8eNKFUCUrdn6wQ5ACCg8guduj0bSbERTt9VSURExunrwanOFa4NTEZ2I8dyCk1+ofOKSnEsp9B2jSIiIptT9+4buyaoOfu1gYGOG7l+x/QXWsp2RETkfIz17hvjrNcGBjpupE6gr6zbERGR8zHVu2+Is14bGOi4kc4xoYgM9oWhEVYFKjPsO8eE2rJZRERkQ1J7Zpz92sBAx414eigwe2AsAOgEO+p/zx4Y65TJZkREJI6UnhlXuDbYPdBZunQpYmJi4Ovriw4dOuDQoUOinnf48GHUqFED8fHx1m2gi+kXF4llz7dHRLD2Fz0i2Nchpg8SEZF1merdr8oVrg12raOzadMmjBgxAkuXLkViYiJWrFiBTz/9FNnZ2WjYsKHB5xUVFaF9+/Zo2rQprl27hqysLNGv6e51dNRcsfolERGJo551BUBvUvKLidHoExvhUNcGc6/fdg10unTpgvbt22PZsmWax1q2bIknn3wSaWlpBp83bNgwNGvWDJ6enti6dSsDHSIiIomcrRKy0xUMLC8vx4kTJzBz5kytxx977DEcOXLE4PPWrFmDCxcuYP369Xj33XdNvk5ZWRnKyso0/y4uLja/0URERC6iX1wkkmIjXL53326Bzs2bN6FUKlG3bl2tx+vWrYv8/Hy9zzl//jxmzpyJQ4cOoUYNcU1PS0tDamqqxe0lIiJyNZ4eCiQ0CdP7N3NSHBwxLcLuS0AoFNoHQBAEnccAQKlU4tlnn0VqaiqaN28uev+zZs3C1KlTNf8uLi5GVFSU+Q0mIiJyceYMaznqUJjdZl2Fh4fD09NTp/fm+vXrOr08AHDnzh38+OOPmDBhAmrUqIEaNWpg7ty5+Omnn1CjRg3s379f7+v4+PggKChI6z8iIiLSz9DyEPlFpXhl/UnsPpMny3NsxW6Bjre3Nzp06ID09HStx9PT09GtWzed7YOCgnD69GlkZWVp/hs/fjweeughZGVloUuXLrZqOhERkc0pVQIyLxTg66wryLxQAKVK/rlEphZ/BioX+Kz62uY8x5bsOnQ1depUjBgxAh07dkRCQgJWrlyJixcvYvz48QAqh52uXLmCdevWwcPDA3FxcVrPr1OnDnx9fXUeJyIiciW2GhaSsvizOrfHnOfYkl0DnaFDh6KgoABz585FXl4e4uLisGvXLjRq1AgAkJeXh4sXL9qziURERHalHhaq3h+iHhaSs6CfOYs/O/qC0Xato2MPrKNDRETOQqkS0H3+foM9JgpUVi/OSO4ly+ymzAsFGL7qqMntNoztqumdMec55jD3+m33JSCIiIhIPynDQnIwZ/FnR18wmoEOERGRg7L1sJA5iz87+oLRDHSIiIgcVO7Ne6K2k7IiuSnmLP7syAtG271gIBEREelSqgRsOGZ6Qk5EkI/sw0LmLA/hqEtKMNAhIiJyQMdyCpFfbHpIanjnhlYJJowtDyHnc6yNQ1dEREQOKD1b/7qP1UWH+1u5Jc6NgQ4REZGDUaoEbM26KmpbOfNzXBEDHSIiIgdzLKcQhSXlJrcL9fey27RtZ8FAh4iIyMGInS7eJcax8mEcEQMdIiIiByN2OOqbM/noPn+/XVcHd3QMdIiIiByMqWrDVanXvGKwox8DHSIiIgdjrNpwdeoFK1O3Z0OpcqvlK0VhoENEROSADFUb1kfuNa9cCQMdIiIiB9UvLhIZyb0woWcTUdvLteaVK2GgQ0RE5MA8PRRIbFpb1LasqaOLgQ4REZGDM5WcrAAQGezLmjp6MNAhIiJycMaSk9X/nj0w1u4LaDoiBjpEREROwFByckSwL5Y93x794iLt1DLHxtXLiYiInES/uEgkxUbgWE4hrt8pRZ3AyuEq9uQYxkCHiIjIiXh6KJDQRNrSD0qV4LbBEQMdIiIiF7b7TB5St2cjr+ivqeeRwb6YPTDWLYa7mKNDRETkonafycMr609qBTmAey0bwUCHiIjIBSlVAlK3Z0PfohDutGwEAx0iIiIXdCynUKcnpyp3WTaCgQ4REZELErschKsvG8FAh4iIyAWJXQ7C1ZeNYKBDRETkgrhsRCUGOkRERC6Iy0ZUYqBDRETkorhsBAsGEhERuTR3XzaCPTpEREQuzJ2XfwDYo0NEROSyTC3/4A5BEAMdIiIiF6Re/qF63WP18g8vPxKDbT/laQVBEUG+GN65IaLDa7pM4KMQBEFy7edz587hwIEDuH79OlQqldbf3nnnHdkaZw3FxcUIDg5GUVERgoKC7N0cIiIi2SlVArrP32+0MrIYjrT4p7nXb8k9OqtWrcIrr7yC8PBwREREQKH4K9JTKBQOH+gQERG5OlPLP4il7v1x5hlakgOdd999F++99x6Sk5Ot0R4iIiKykFzLOgiorLmTuj0bSbERTjmMJXnW1a1bt/D0009boy1EREQkAzmXdXD2xT8lBzpPP/009uzZY422EBERkQxMLf9gDmdd/FPy0FXTpk2RkpKCo0ePonXr1vDy8tL6+8SJE2VrHBEREUmnXv7hlfUnoQC0Zl5V/7dYzrr4p+RZVzExMYZ3plDgjz/+sLhR1sRZV0RE5C4M1dF5om0kVn6fA8B00KNA5ZIRGcm97JqjY7NZVzk5OVKfQkRERHZgbPmHdg1r6QRB1bnC4p9m1dFRUz+16hRzR8ceHSIiokpVKyPn3izBhmMXkV9cpvm7W9bRAYB169bhgw8+wPnz5wEAzZs3x4wZMzBixAhzdkdERER24OmhQEKTMM2/J/Rq5nJLQkgOdD7++GOkpKRgwoQJSExMhCAIOHz4MMaPH4+bN29iypQp1mgnERERWVn1wMcVmJWMnJqaipEjR2o9/tlnn2HOnDkOn8PDoSsiIiLnY+71W3Idnby8PHTr1k3n8W7duiEvL0/q7oiIiIisRnKg07RpU3zxxRc6j2/atAnNmjWTpVFERET2olQJyLxQgK+zriDzQgGUKrPn7JADkJyjk5qaiqFDh+L7779HYmIiFAoFMjIysG/fPr0BEBERkbMwVHfGUWYekXSSe3Seeuop/PDDDwgPD8fWrVuxZcsWhIeH49ixYxg8eLA12khERGR1u8/k4ZX1J3XqyqhX8N59hukZzsiiOjrOiMnIRERUnVIloPv8/QaL5zlKdWB3ZtU6OsXFxZqdFhcXG92WwQMRETmbYzmFRisEV13BW8z066qF+FylHo2zEhXo1KpVC3l5eahTpw5CQkL0VkIWBAEKhQJKpVL2RhIREVmT2JW5xWzHPB/HIirQ2b9/P0JDQwEA3333nVUbREREZGtiV+Y2tZ06z6d6Tog6z2fZ8+0Z7NiYqECnR48emv+PiYlBVFSUTq+OIAi4dOmSvK0jIiKygc4xoYgM9kV+Uane1bzVOTqdY0IN7kOpEpC6PVvv84X/7SN1ezaSYiM4jGVDkmddxcTE4MaNGzqPFxYWIiYmRpZGERER2ZKnhwKzB8YC+GvFbjVDK3hXr7dz9EKB6Dwfsh3JdXTUuTjV3b17F76+4rr+iIiIHE2/uEgse769Tn5NhJ78Gn15OCF+XqJeR2w+EBOa5SE60Jk6dSoAQKFQICUlBTVr1tT8TalU4ocffkB8fLzsDSQiIrKVfnGRSIqNMBpgGMrDuX2/QtRriMkHYkKzfEQHOqdOnQJQ2aNz+vRpeHt7a/7m7e2Ntm3bYvr06fK3kIiIyIYMreCtVAk4eqEAMzef1puHY4qYPB+ACc1yEx3oqGdbjRkzBosXL2a9HCIichv6elikMJTnUx0TmuUnORl5zZo1miDn0qVLuHz5suyNIiIichSGloYwpnq+TkSwr6ieGCmFC0kcycnIDx48QGpqKv75z3/i7t27AICAgAC8/vrrmD17Nry8xCVjEREROTpjPSzGfPJce3goFJITieUsXEiVJAc6EyZMwFdffYUFCxYgISEBAJCZmYk5c+bg5s2bWL58ueyNJCIisgdTPSzVqfNwujYOM2toSa7ChfQXyUNXGzZswNq1azFu3Di0adMGbdq0wbhx47B69Wps2LBBcgOWLl2KmJgY+Pr6okOHDjh06JDBbTMyMpCYmIiwsDD4+fmhRYsWWLhwoeTXJCIiEkNKz4nYPBxjbpWUmdwmUkRCM/1Fco+Or68voqOjdR6Pjo7WmoklxqZNmzB58mQsXboUiYmJWLFiBfr374/s7Gw0bNhQZ3t/f39MmDABbdq0gb+/PzIyMjBu3Dj4+/vj5ZdflvpWiIiIjJLSc6Kv3o4USpWAeTvPmtwuZYD0QMqda/IoBEGQNPQ4d+5c/Prrr1izZg18fHwAAGVlZXjxxRfRrFkzzJ49W/S+unTpgvbt22PZsmWax1q2bIknn3wSaWlpovYxZMgQ+Pv749///reo7c1d5p2IiNyPUiWg+/z9BpeGACoTjz95rr3Zw1VqmRcKMHzVUZPbbRjbVdQK6mquUpPH3Ou35B6dU6dOYd++fWjQoAHatm0LAPjpp59QXl6O3r17Y8iQIZptt2zZYnA/5eXlOHHiBGbOnKn1+GOPPYYjR46IbsuRI0fw7rvvGtymrKwMZWV/dQUWFxeL2jcREZF6aYhX1p+EAtAKdtQhzftPtUZi03CLX0vORGR1D056dj5WH87V+bs71eSRHOiEhITgqaee0nosKipK8gvfvHkTSqUSdevW1Xq8bt26yM/PN/rcBg0a4MaNG3jw4AHmzJmDl156yeC2aWlpSE1Nldw+IiIiQNrSEJaQcwV1UzV/3Kkmj+RAZ82aNbI2QN8q6PrW0qrq0KFDuHv3Lo4ePYqZM2eiadOmGD58uN5tZ82apVm+Aqjs0TEnMCMiIvclZmkIS8mxgrqhqsr6VK3JI2UozNlIDnTkEh4eDk9PT53em+vXr+v08lSnXiW9devWuHbtGubMmWMw0PHx8dHkEhERAe6dmEnmM7Q0hJz7NzVMNqxTQ+z4+are7625NX9cvSaP5EAnJibGaI/LH3/8IWo/3t7e6NChA9LT0zF48GDN4+np6Rg0aJDo9giCoJWDQ0RkjKskZpJrUQffZQ9UmNynGTYcu4j84r+ubSE1vSAAWLj3nOax6t9bqTV/1Fy9Jo/kQGfy5Mla/66oqMCpU6ewe/duzJgxQ9K+pk6dihEjRqBjx45ISEjAypUrcfHiRYwfPx5A5bDTlStXsG7dOgDAJ598goYNG6JFixYAKuvqfPjhh3j99delvg0ickNcLJEckb7gOyLIF1P6NEd0eE3k3izBwr3ndZ5X/XsrtWdG7CKjzk5yoDNp0iS9j3/yySf48ccfJe1r6NChKCgowNy5c5GXl4e4uDjs2rULjRo1AgDk5eXh4sWLmu1VKhVmzZqFnJwc1KhRA02aNMH777+PcePGSX0bRORmuFgiOSJDwfe14lIs2nsOnzzbHhuPX9L73OrfWyk9M3IUN3QWkuvoGPLHH38gPj7e4advs44OkXuyVo0SInOpa/QYGm5SAAj190ZBSbnJfW0Y2xWdY0JN1vxRc8bhWpvV0THkv//9L0JDXbv7i4icFxdLJEcjZqVyMUEOUPm9NZbMrPZiYjT6xEa4VQK+5ECnXbt2WsnIgiAgPz8fN27cwNKlS2VtHBGRXML9xc2+FLsdkaXkDKrVw1aGav44Yw+OXCQHOk8++aTWvz08PFC7dm08+uijmiRhInJfDjt1W2wTHKCp5B7E5tSE+nvhVkmF6No6tqj540wkBToPHjxAdHQ0+vbti4iICGu1iYiclCNP3b55V1wZCrHbkXuxRgAvtkBgyoCWeO3zUwZr6+hLKLZ2zR9n4iFl4xo1auCVV15h3Roi0qGePVI950A9BXb3mTw7taySXOX1yf3sPpOH7vP3Y/iqo5i0MQvDVx1F9/n7Lf5Oq3NqAN2OxKpBzONt6mHZ8+0REaz93YwI9mVJBBEkD1116dIFp06d0kwBJyJyhqnbcpTXJ/cjtvaSuT0+YtfR4nCU+SQHOq+++iqmTZuGy5cvo0OHDvD399f6e5s2bWRrHBE5BzGzR+y9po6Y8vruUFOExBMbwKtUwLyd5g/Zig1iOBxlHsl1dDw8dEe7FAqFZjFOpVIpW+OsgXV0iOT3ddYVTNqYZXK7xcPiMSi+vvUbZIQj5xGRYxFbe0kfdYjCoSX52KyOTk5OjtSnEJGLc6b8Fw4BkFiWTP92lCFbkhjoVFRUoGfPntixYwdiY2Ot1SYicjLOlv/CIQD3JSWXxtLA3BGGbElioOPl5YWysjKjq5cTkeOwVU0b5r+QM5A6bGkqgBeL1bbtS9L0cgB4/fXXMX/+fDx48MAa7SEimVhrSqwh6tkjnAJLjsic8gdipn+L4QhDtu5McjLy4MGDsW/fPgQEBKB169Y6s662bNkiawPlxmRkcgeGpsTaIkHSYSsjk9sSs3hmRLAvMpJ76f2uGuoJShnQEvN2njU5ZGtovySNzZKRQ0JC8NRTT0l9GhHZiL1r2jD/hRyNpeUPjCWwe3goOGTr4CQHOmvWrLFGO4hIJs5Q04bIluRYuV4dwKt7LHf8fBV1An2RFBshquAf2Y/kQEftxo0b+O2336BQKNC8eXPUrl1bznYRkZnkOKkTuRK5yh8YS2bOSO7FIVsHJTnQKSkpweuvv45169ZBpVIBADw9PTFy5Ej83//9H2rWrCl7I4lIPGeqaUNkC3KUPxC7FAQ5HsmzrqZOnYqDBw9i+/btuH37Nm7fvo2vv/4aBw8exLRp06zRRiKSQH1SN3QvqUDlXaij1LQhsjaxi2ca6oExlfcGVOa9KVWWTEIna5Ec6GzevBn/+te/0L9/fwQFBSEoKAiPP/44Vq1ahf/+97/WaCMRSWDpSZ1ck1IlIPNCAb7OuoLMCwVud1G2pPyBlLw3cjySh67u3buHunXr6jxep04d3Lt3T5ZGEZFlxK6ITO6B63tVMnf5D+a9OTfJdXR69+6NsLAwrFu3Dr6+lZHx/fv3MWrUKBQWFmLv3r1WaahcWEeH3Alr2pA9ayq5CrGLe24Y25UzGa3IZnV0Fi9ejH79+qFBgwZo27YtFAoFsrKy4Ovri2+//Vbq7ojIiljTxr3Zu6aSq3C2tdxIm+Qcnbi4OJw/fx5paWmIj49HmzZt8P777+P8+fNo1aqVNdpIRERmYG6JPJj35tzMqqPj5+eHsWPHyt0WIiKSEXNL5MO8N+clOdBJS0tD3bp18cILL2g9vnr1aty4cQPJycmyNY6IiMzHmkryMjeZmexL8tDVihUr0KJFC53HW7VqheXLl8vSKCIishxrKslPnfc2KL4+EpqEMchxApIDnfz8fERG6nbR1a5dG3l5usvcExGRfTC3hMiMQCcqKgqHDx/Wefzw4cOoV6+eLI0iIiJ5WFIoj8gVSM7ReemllzB58mRUVFSgV69eAIB9+/bhjTfe4BIQREQOiLkl5M4kBzpvvPEGCgsL8eqrr6K8vBwA4Ovri+TkZMyaNUv2BhIRkeVYU4ncleTKyGp3797F2bNn4efnh2bNmsHHx0futlkFKyMTERE5H5tVRlYLCAhAp06dzH06ERERkdWZHegQEZHr4LpounhMXAMDHSIiN8fVzXXxmLgOydPLiYjIdahXN6++JlZ+USleWX8Su8+4X300HhPXIirQad++PW7dugUAmDt3Lu7du2fVRhERkfWZWt0cqFzdXKkya86KU+IxcT2iAp2zZ8+ipKQEAJCamoq7d+9atVFERGR9XN1cF4+J6xGVoxMfH48xY8age/fuEAQBH374IQICAvRu+84778jaQCIisg6ubq6Lx8T1iAp01q5di9mzZ2PHjh1QKBT45ptvUKOG7lMVCgUDHSIiJ8HVzXXxmLgeUYHOQw89hI0bNwIAPDw8sG/fPtSpU8eqDSMiIutSr26eX1SqNydFgco1sdxpdXMeE9cjedaVSqVikENE5AK4urkuHhPXY9b08gsXLuD1119Hnz59kJSUhIkTJ+LChQtyt42IiKyMq5vr4jFxLZLXuvr222/xxBNPID4+HomJiRAEAUeOHMFPP/2E7du3IykpyVptlQXXuiIi0sUqwLp4TByLuddvyYFOu3bt0LdvX7z//vtaj8+cORN79uzByZMnpezO5hjoEBGRo2JwZZjNAh1fX1+cPn0azZo103r83LlzaNOmDUpLHXvKHQMdIiKyNTEBDJedMM5mq5fXrl0bWVlZOoFOVlYWk5SJiIiqERPAqJedqN7zoF52grlB5pMc6IwdOxYvv/wy/vjjD3Tr1g0KhQIZGRmYP38+pk2bZo02EhEROSUxAUxSbITRZScUqFx2Iik2gsNYZpAc6KSkpCAwMBAfffQRZs2aBQCoV68e5syZg4kTJ8reQCIiImdkat0sdQAT6OsletmJhCZhVmqt65Ic6CgUCkyZMgVTpkzBnTt3AACBgYGyN4yIiMiZiV03K/NCgaj9cdkJ80gOdKpigENERKSf+MBE3JwgLjthHrMKBhIREZFxYgOThMbhiAz21anErKZAZfIyl50wDwMdIhtQqgRkXijA11lXkHmhAEqVpKoOROSE1OtmmQpgujYJ47ITVmTR0BURmcbaGETuSb1u1ivrT0IB7QGq6gGMetmJ6ueKCJ4rLCa5YKA+t2/fRkhIiAzNsT4WDCRbMjS1VH2SY20MItcn5WaHlZENs1ll5Pnz5yM6OhpDhw4FADzzzDPYvHkzIiIisGvXLrRt21Zay22MgQ7ZilIloPv8/QZnXShQebeWkdyLJzIiF8cAxnLmXr8l5+isWLECUVFRAID09HSkp6fjm2++Qf/+/TFjxgypuyNyWWKnlh7LKbRdo4hIFLnz6jw9FEhoEoZB8fWR0CSMQY4NSc7RycvL0wQ6O3bswDPPPIPHHnsM0dHR6NKli+wNJHJWYqeWsjYGkWNhXp1rkdyjU6tWLVy6dAkAsHv3bvTp0wcAIAgClEqlvK0jcmJip5ayNgaR41Dn1VXvjVUv2bD7TJ6dWkbmkhzoDBkyBM8++yySkpJQUFCA/v37A6hc1LNp06ayN5DIWYmdWsraGESOwdSSDUDlkg0sD+FcJAc6CxcuxIQJExAbG4v09HQEBAQAqBzSevXVV2VvIJGzUk8tBVgbg8gZMK/ONUnO0fHy8sL06dN1Hp88ebIc7SFyKayNQeQ8mFfnmswqGHju3DkcOHAA169fh0ql0vrbO++8I0vDiFxFv7hIJMVGcGopkYNjXp1rkhzorFq1Cq+88grCw8MREREBheKvk7VCoWCgQ6SHemopETkudV5dflGp3jwdde0r5tU5F8k5Ou+++y7ee+895OfnIysrC6dOndL8d/LkSckNWLp0KWJiYuDr64sOHTrg0KFDBrfdsmULkpKSULt2bQQFBSEhIQHffvut5NckIiKqjnl1rklyoHPr1i08/fTTsrz4pk2bMHnyZLz11ls4deoUHn74YfTv3x8XL17Uu/3333+PpKQk7Nq1CydOnEDPnj0xcOBAnDp1Spb2EBGRe1Pn1UUEaw9PRQT7cskWJyV5CYgXX3wRnTp1wvjx4y1+8S5duqB9+/ZYtmyZ5rGWLVviySefRFpamqh9tGrVCkOHDhU9ZMYlIIiIyBQu2eB4zL1+S87Radq0KVJSUnD06FG0bt0aXl5eWn+fOHGiqP2Ul5fjxIkTmDlzptbjjz32GI4cOSJqHyqVCnfu3EFoqOHx0rKyMpSVlWn+XVxcLGrfRETkvphX5zokBzorV65EQEAADh48iIMHD2r9TaFQiA50bt68CaVSibp162o9XrduXeTn54vax0cffYSSkhI888wzBrdJS0tDamqqqP0RERGRa5Ec6OTk5MjagKqztoDKpSSqP6bPhg0bMGfOHHz99deoU6eOwe1mzZqFqVOnav5dXFysWauLiIiIXJtZdXTU1Ok9YgKT6sLDw+Hp6anTe3P9+nWdXp7qNm3ahBdffBFffvmlZq0tQ3x8fODj4yO5fUREROT8JM+6AoB169ahdevW8PPzg5+fH9q0aYN///vfkvbh7e2NDh06ID09Xevx9PR0dOvWzeDzNmzYgNGjR+Pzzz/HgAEDzGk+ERERuQnJPToff/wxUlJSMGHCBCQmJkIQBBw+fBjjx4/HzZs3MWXKFNH7mjp1KkaMGIGOHTsiISEBK1euxMWLFzUzumbNmoUrV65g3bp1ACqDnJEjR2Lx4sXo2rWrpjfIz88PwcHBUt8KERERuTjJ08tjYmKQmpqKkSNHaj3+2WefYc6cOZJzeJYuXYoFCxYgLy8PcXFxWLhwIR555BEAwOjRo5Gbm4sDBw4AAB599FGdBGgAGDVqFNauXSvq9Ti9nIiIyPmYe/2WHOj4+vrizJkzaNq0qdbj58+fR+vWrVFa6tiLnTHQISIicj7mXr8l5+g0bdoUX3zxhc7jmzZtQrNmzaTujoiIyCClSkDmhQJ8nXUFmRcKoFRJujcnkp6jk5qaiqFDh+L7779HYmIiFAoFMjIysG/fPr0BEBGRpVil1j3tPpOH1O3ZyCv6a6QgMtgXswfGWrQUA79P7kXy0BUAnDhxAgsXLsTZs2chCAJiY2Mxbdo0tGvXzhptlBWHroici7UuduTYdp/JwyvrT+qsIq4OR8xdd4rfJ+dlsxwdZ8dAx3nwrousdbEjx6ZUCeg+f79WMFKVApWLbGYk95J0TuD3yblZda2r4uJizU5NrRXF4IHk4Ap3XQzULKNUCUjdnq1zUQIAAZUXp9Tt2UiKjeBxdTHHcgoNBjlA5eefV1SKYzmFotej4vfJfYkKdGrVqoW8vDzUqVMHISEheishq5duUCqVsjeS3Iuhu678olK8sv6kU9x1uUKgZm/WuNiRc7h+R9zsXbHbAfw+uTNRgc7+/fs1K4R/9913Vm0QuTdXuOtyhUDNEVjjYkeOw1iPZ51AX1H7ELsdwO+TOxMV6PTo0UPz/zExMYiKitK7GOelS5fkbR25HWe/67J3oOZKw2XWuNjZkyt9NpYy1ePZOSYUkcG+yC8q1ftbUufodI4JFf2arvZ9IvEkTy+PiYnRDGNVVVhYiJiYGA5dkUWc/a5LbKC2MP0cEpuGy3qxc7XhslslZSa3iZR4sbMXV/tsLCG2x3P2wFi8sv4kFIDWtupfy+yBsZJ+O9YInsg5SC4YqM7Fqe7u3bvw9WUkTJZx9rsusQHYku9+x/BVR9F9/n7sPpNn8euqLx7Vgyz1xUOO15CTqSJwSpWAeTvPmtxPygBpFzt7cLbPxlLGPltTPZ5AZY+nUiWgX1wklj3fHhHB2r/1iGBfs4Z/PT0UmD0wFsBfwZKaucETOQfRPTpTp04FACgUCqSkpKBmzZqavymVSvzwww+Ij4+XvYHkXpz9rktqACZH3o69h8ukEtO7YapnTK2Wv7fV2ikHZ/tsLGXqs5U6NN0vLhJJsRGyDfmpg6fqbYxw0941dyE60Dl16hSAyh6d06dPw9v7rxOMt7c32rZti+nTp8vfQnIr6rsuObusbclUoFadHBc7Z8prEjts4exDmGrO9NlYSsxnW/ZAJWpfVT9XTw+FrMdG7uCJHJ/oQEc922rMmDFYvHgx6+WQ1TjzXZexQM0QMRc7Y4mszhIUSOndcPYhTDVn+WwsJfaz/fDvbUXtz9qfq9zBEzk2ycnIa9as0fz/pUuXoFAo0KBBA1kbReTMd12GAjVTDF3sTA0HOEtQIKV3w9mHMNWc5bOxlNjPFgq4xOdKzkVyMvKDBw+QkpKC4OBgREdHo1GjRggODsbbb7+NiooKa7SR3JT6rmtQfH0kNAlziiBHrV9cJDKSe2HD2K6Y0LOJqOfou9gZSmTNq5LIqg4KDB0dBcTPTrLmStFSejdcJXFUzs/GkYn9bG/eLXOJz5Wci+RAZ8KECVi5ciUWLFiAU6dO4dSpU1iwYAH+9a9/4fXXX7dGG4mckjpQm5L0kFkXO2PDAUDlXfKsLacBQJaLx+4zeeg+fz+GrzqKSRuzZJ0VBgDh/j6StpN71o09uErAZoqUz9YVPldyLpKHrjZs2ICNGzeif//+msfatGmDhg0bYtiwYVi+fLmsDSRyduYmWIuZeXTrXgWW7D+PSX2aW5TXZJNqzmKv5VW2c+YhTLWk2AhM7tMMaw7n4vb9v3q9nSHnTDSJn60rfK7kPCQHOr6+voiOjtZ5PDo6WmsmFhH9xZwEa7HDAWsO52JCr2ZmXzxsNQX65l3TBQD1befMiaP68qtC/LwwJjEGE3o1FXU8naGisjmfrTN/ruRcJAc6r732GubNm4c1a9bAx6eyu7KsrAzvvfceJkyYIHsDiVyF1EBEbILq7fsVmhlb5lw8bDUF2l0Sc9UM9ZIV3a/Aor3n8FBEgKieNmeoqOxuny05F8mBzqlTp7Bv3z40aNAAbdtWThX86aefUF5ejt69e2PIkCGabbds2SJfS4lcgL5AxNAde+eYUIT4eWkNdxhiyfRkW02BdpWZVGLI0UvmTIvDutNnS85HcqATEhKCp556SuuxqKgo2RpE5E5M3bGPSYzBwr3nTO7HkjtlW92NO3sxSCks7SVztorK7vTZkvOxqI4OEZlPzB37hF5NseZIDm7f09+rI8edsi3vxp25GKQUlvaSOWNFZfVnO2dbNvKLXfezJecjOdABKmvpHDhwABcuXMCzzz6LwMBAXL16FUFBQQgICJC7jUQuR8od+/tDWmP8+pM628l1p2zru3FHnXEjZ9KvOb1kVV///LW7op7vmBWVtb/VgiBfLSYic0gOdP7880/069cPFy9eRFlZGZKSkhAYGIgFCxagtLSU08uJRJByx94vLhLLrdwLYuueFkebcaNvCDEiyAfDOzdEdLi/5MBHai+ZvtcXw5GSew31UF4rLnO4nCJyL5IDnUmTJqFjx4746aefEBb214lq8ODBeOmll2RtHJGrkjq0YYteEEftabE2g0OIxWVYuPe85t9SZjtJ6SUz9PrGOFpyryPlFDnDdHyyLcmBTkZGBg4fPqxTM6dRo0a4cuWKbA0jcmXmDG3YohfE0XparM1U9emqpM52EtNLJuX1qxIApAywT3KvvkDCUXKKnGU6PtmW5EBHpVJBqVTqPH758mUEBgbK0igiV8fpuI5BTPVpNfXnNHPLaQT6eKGriPXXTPWSSXn96ubtzIaHB2x6ATcUSPSPixD1fGvmFDnTdHyyLclrXSUlJWHRokWafysUCty9exezZ8/G448/LmfbyEzWXJiR5OEuayA5OnMuvLfvVeC5f/0geh0wY4vTWnLhV1/A5VqLzBRDC8zmF5Vi9eFcUfuwVk6RqaEzoHLojOdC9yS5R2fhwoXo2bMnYmNjUVpaimeffRbnz59HeHg4NmzYYI02kgTsunVc1bv8k2Ij3GKqtSOz5MIrR0+BJa9vy9wXMTk4CgVgKI6wdg+lowydkWOSHOjUq1cPWVlZ2LhxI06cOAGVSoUXX3wRzz33HPz8/KzRRhLJml23TPCzjLEANCO5l9Fjy2NvPeohRHOGj+QINEwNYYppgy0u4GICCfUscnsUDLRVdW9yTmbV0fHz88OYMWMwZswYudtDZrLmrAf2EllGagCqHnq8fqcUuTdLsOHYReQX/7UYIo+9fDw9FHiibSRWfJ9j1vMtDTRMzc4SG/xY+wIudv8vJkZj15l8m/dQcq0tMkZyoJOWloa6devihRde0Hp89erVuHHjBpKTk2VrHIlnra5bQxfpPCb4iSI1ABVTT4XJlcZJ6QHbfSYPK80McqqyJNAwNjtrWKeGVl8CRAyx++8TG4E3B8RarQfS2LpwTO4nQyQHOitWrMDnn3+u83irVq0wbNgwBjp2Yo2uW1NTXwU41no7jkhKAFp0v1xUPRVHXOvIUUjpfTR3arc+lgYahmZnKVUCPsvMQWGJ9ZYAEUNKIGGtEgWmPluutUWGSJ51lZ+fj8hI3bvI2rVrIy/PNtn/pMsaXbdipr6qL9Kkn9jAMr/ovqSLbtUAiSoZmxWkb3aS2Knd/t6eBv+mQOXFVo5Ao/rsrPTsfPT44DujQQ5gmwu4vWcJivls1T1jEcHa57iIYF/2fv6Pu87IldyjExUVhcOHDyMmJkbr8cOHD6NevXqyNYyksUbXbX7RfVm3c0diA8vCknKzEmKZXFnJnBw1scduWKcozfRpW/UUiKmWbOvZefZakFXKZ+uu1b3FcOdcS8mBzksvvYTJkyejoqICvXr1AgDs27cPb7zxBqZNmyZ7A0kcayzMWFhSLut27khsABoa4GPW/plcWUnsEOHRPwqQ2DQcgLS8k04xoTa7wIsZUgvz98bBGT3hXUNyp7xF7BFISM0/dLfq3mK4ezFFyYHOG2+8gcLCQrz66qsoL6+8wPn6+iI5ORmzZs2SvYEkntx3XGIvvuZepJ2dmKRXsQFosJ/2kiqmuHpypdQp9fnF4npnXvvPSbz/VGv0i4uUnHdiqwu8mCG1gpJynPjzll0u6LYOJDh13DKOtA6ZvUgKdJRKJTIyMpCcnIyUlBScPXsWfn5+aNasGXx83PNi52jkvOOKCBJ3xyt2O1cipRtY7JpHof7eknrHXDW5UmoX++4zeZi34xdR+759v0LrDlZKL6itLvC8sGuz5dRxV6xZxWKKEgMdT09P9O3bF2fPnkVMTAw6depkrXaRBeQ6IYsppiZXIqYzMacb2FQA6umhwJPx9USV0g/x89L0SrgaqcfWnJW/Ae2cDkerTu3sNWHkDhZsNXXcVXNYGDibMXTVunVr/PHHHzrJyOR6qg67AJyyCVjWDWwqAE2KjRAV6HzyXHtNnom9WOPOV+qxtWTl76p3sI6WwOrMNWGsESxYI/9QX7tdNYfF2QNnOUjOZHvvvfcwffp07NixA3l5eSguLtb6j1wLp2xqk9INLJX6AmeIeipz18bm99bJMb1095k8dJ+/H8NXHcWkjVkYvuqo6AUujZF6bC1Z+RvQvoM1tvCmrdl7Kre5pE7vl8Ka5yFXXxBUfV4x9G2Rs0SCo5Lco9OvXz8AwBNPPAGF4q9DJwgCFAoFlEqlfK0jh+Bod7z2ZK1uYHUPSf+4yl4da9y5ynG3bc07X6nH1tKudke+g7XVkJpcPXO2SHi11nnI1XNYbNEj5ugkBzrfffedNdpBDo5TNitZoxtYXwCiUPy1SCJg+QVOjgDF2hczqcfW3EDFkYd+qrLGhb1qYCPnOmq2ChascR5yhxwWR8xFsyXJgU6PHj2s0Q4ipyB3/oShAETdS/5iYjT6xEZoXeCk3oXLFaBY+2Im9dias/K3s93Bynlht+Y6as4cLLhLDos798ybVW3q0KFDeP7559GtWzdcuXIFAPDvf/8bGRkZsjaOyNHImT9hKplWAWDXmXytk5E5+TFy5RVZ+2Im9diK2T6kppfW4+6aW2Yof6Y6c3NSnDlY6NCoFkz9XD0Ulds5O0fKRbMlyYHO5s2b0bdvX/j5+eHkyZMoK6vs9rxz5w7+8Y9/yN5AIkcjV2Kk1ADE3GRPuQIUW1zMpB5bY9svf749TrydhA1ju2LxsHhsGNsVGcm93C7IkTo7zZyEemcOFk78eQumYjqVULkdOSfJQ1fvvvsuli9fjpEjR2Ljxo2ax7t164a5c+fK2jgiRyVHN7CUAMSS4Se5AhRbTXuWemxNbe+ouWW2Kk5n7uw0KT1zUoIFR/s8nHnYjcSRHOj89ttveOSRR3QeDwoKwu3bt+VoE5FTsDR/QkoAYkl+jFwBii1nb0g9ts6WLC9XvRkxwZLY5TGqk9Iz58zBgjMPu5E4koeuIiMj8fvvv+s8npGRgcaNG8vSKCJ3IKW+hSUXEjnzilhXyXJy1ZsRm69VeLfMwB70M6euijMHC+5UZ0aOOlrOSHKPzrhx4zBp0iSsXr0aCoUCV69eRWZmJqZPn4533nnHGm0kcklSekgsvZDIOb3UnWdvWEquGXBSygWE+otfMNbcnjlnrubsLnVmXHWJCzEUgiBIDuneeustLFy4EKWllQfMx8cH06dPx7x582RvoNyKi4sRHByMoqIiBAUF2bs5RKJOQEqVgO7z95u8kGQk9zI51ZwBiv1kXijA8FVHTW63YWxXg0Nx6u+CoaHM6t8Fsa8JWHbhUwdfgP5gwdF7/Fw5EDAUGDvLZ6Nm7vXbrEAHAO7du4fs7GyoVCrExsYiICDAnN3YHAMdckRiAhBnv5BYwlUCtK+zrmDSxiyT2y0eFo9B8fX1/k1qsGQqMAIqp+F/Mrw9ulo45djZgwVX+Z5VJTUwdmTmXr9FD13du3cPM2bMwNatW1FRUYE+ffrgn//8J8LD7bu4IJErEJNMa2z4aVinhih7oELmhQKXODlXZYuLp60ucHLkskjN16o6NGOoN/D9Ia2R2Mzyc7mzD2s6W1K7GK6+xIUYogOd2bNnY+3atXjuuefg6+uLDRs24JVXXsGXX35pzfYRAXDNOy1zVL+QqMv4L9x7TrONM91BmyLn2lrRM3fqPJb7/gCb9kLIkctiTrBkKEi2xvt0xGDBnc8fzjwjTi6ih66aNGmC9957D8OGDQMAHDt2DImJiSgtLYWnp6dVGyknDl3ZhpwnFmfvDrcWVxl3N0TOLnd9QU7V/djyGFo6BGlJvpaY36WrBQXufv6QIy/MUVg9R8fb2xs5OTmoX/+vcWM/Pz+cO3cOUVFR0ltsJwx0rE/OE4urX8zN5Urj7obIdYI2FuQYY81jaOlvxFr5Wq4WFPD8Id9EBkdg7vVbdB0dpVIJb2/taYo1atTAgwcPxLeSXJ5cNUIA01NxAelr8jgqqfUt5Fq/ypHJ0eVubpADiD+G5tQm6RcXiYzkXmYvT2GNekZy/nYdgTudP4yRs46WsxKdoyMIAkaPHg0fHx/NY6WlpRg/fjz8/f01j23ZskXeFpLZbN0FLVeNEDV3SaIz5y7aHcbdpeai6Pu+y2Fvdr7B75clPSCW5rJISfw1dS6Q+7frCNzl/CGGnHW0nJHoQGfUqFE6jz3//POyNobkY48uaLlPLO5wMTc32daZK9GKJSVx19D3XQ4bf7yEXi3q6ky9ljNR2lxigiUx5wJXDArc4fwhhbPPiLOE6EBnzZo11mwHycheJ2C5TyyufjG35C5aShDgrMmlxirW4n//HtapIb49k4fXPj+l83dzFrLUp6RMief+9YNWcOAsPSBizwWuGBS4+vnDHI44I84WJK91RY7NnuPSuTdLRG0n9sTi6mvQWJJnY2rcXQDQPy4CS/afR+L7+0yuh+SoDOWiqC3cew4TNugGOdZQNVfFGXKkpJwLXDEocPXzB4nHQMfFWPsEbCjxUqkSsOHYRZPPl3JikTuJzlYL2ol9HUvvog0FAYr/HY7Vh3OxcO955BdrL+roTMmlSpWAYD9vvNGvBf7eXn+lYFvlklYNDsSuCG7PHhAp5wJXDArslYTrrgtnOjLJi3rKbenSpfjggw+Ql5eHVq1aYdGiRXj44Yf1bpuXl4dp06bhxIkTOH/+PCZOnIhFixbZtsEOzppd0MbG+oP9vHUuqPoM69RQc2IRM6QiVxKdpTlLYod/pLyOHHfRVcfd92bn41+Hc01e+B1paMUYfcfS3tTBgdgVwe3ZAyLlXOCqC1vaOgnX1abnuwq7BjqbNm3C5MmTsXTpUiQmJmLFihXo378/srOz0bBhQ53ty8rKULt2bc2ioqTLWl3Qpsb6xyRGi9pPdHhNzf7EnhAsTaKzNGdJbFulvo5cKz57eijQOSYUU7/IMrpdVY6eXGroWDqKUH9vo58dYP8eEKnnAledmWOrJFxHSE4n/ew6dPXxxx/jxRdfxEsvvYSWLVti0aJFiIqKwrJly/RuHx0djcWLF2PkyJEIDg62cWudg6kuaKByAT+VShDdpSpmrP/rrKui9lUn0Neseh3qJLpB8fWRIGHhQUtzlsS21ZzXkbNr3dQwhSGOmFxq7Fg6iohgP4Ofndr9CiXSs/NN7kvuoQ71/vKLSxHq72VwO33DUZbW9xHbNlsP65h7/hCLNXscm916dMrLy3HixAnMnDlT6/HHHnsMR44cke11ysrKUFb2VzdzcXGxbPt2RKZmqgDA7XsVOrNIjBEz1l9QUo5Qfy/cKqkw2jvRoVEt9PjgO5vNVrFk2qyUmTXmvo6hu+hQf28Miq+HYD9vKFWCyWNhbsDiiMml5gZtcvH39kRJuVLv36r2snl6KLDs+faYueU0bt+r0Nm26F6FyTt5uYc6xA73GQukrTUzR1/bQv298WR8PSTFRjjNbEB9XHF6viuxW4/OzZs3oVQqUbduXa3H69ati/x803dBYqWlpSE4OFjznzMtV2EuUzNV1MQmpYq9iA6Or0wWNdY7ceLPWzadrZJfdF/Udvreo5STlyW5UVXvol9MjEaovxcKSsqx+nCu6FlSUgMWR04uNTdoq36NjAz2xbhHYiTX0xnWKQoKiOtlS4qNgG8N/adRU3fyclciNrQ/fSypoGwOQ20rlPg9d1SuOD3fldg9GVmh0D6dCIKg85glZs2ahalTp2r+XVxc7DbBTlJsBI7+UYDX/nMSt+/r3nGK7UERexHtExuBTjGheu/a5g2KQ7+4SHyddUXUvqqeEMytA7P7TB7m7Twr6vX0vUexJ6X84lJEBIk7RuEBPsi8UKDzXjw9FCi6X3nSN2eM31S+T1VihsXsWXvHnKANAJYMb49a/t46bX6jX0scyynE4d9vYsl3v5vcn6Hvsb5clWM5hUaT8A3dyctdh8fUcJ8Clb/Dtwe0RESwn00/T7FDkXlOnMsiRxVvZ+3NcgZ2C3TCw8Ph6emp03tz/fp1nV4eS/j4+GgtW2EtjvjF9fRQwEOh0BvkqInpUpWSNOvpoYBKJeDtr8+gsKTydQtKyjFvZzY8PKSfEMzt2hebzGos4VdsW+ft+AXvDoozeYxCanph2hdZWhdG9XtJio2w6MInZshSzVRyqb1njkgJ2gDT70c9FNM5JhSbT14W/T0Wk8Bq7p283EMdYoeXI4L9bD50InUo0tFnA+ojRxVvZ074dnR2C3S8vb3RoUMHpKenY/DgwZrH09PTMWjQIHs1yyyO/MU150SsL2gTO/V0t4Eqtepeif8b1g6h/l6aIKi66icEc2YxSE1mNdSzIfaCW1hSgdc+P4WXH4nByu9z9B4jAcAtPXkc6vcyuU9ziy98hvJ9IoN9MaxTQ0SH1zQZhEs55lW/J+EBPoAA3CwpszjQFzPVeXKf5qLej9T9Vv0uiMlVMXeWoxxDHVWP//lrdy3en7VIeU1nzWUR+91Kz87nzCw7sOvQ1dSpUzFixAh07NgRCQkJWLlyJS5evIjx48cDqBx2unLlCtatW6d5TlZWFgDg7t27uHHjBrKysuDt7Y3Y2Fh7vAWHn1IoZw+KqamnYmYeTNx0ymCdl6onBABm93CIvYMM9ffCPwa3NtoTILaXBAC2/ZSHT55tj3k7dY/R/Qql3oRV9XtZ8f0Fk+0FTOccWTKVVspwSnp2vtGkV0sDfWtNdZZ7v+aWCLC0DIS5NYbskXweHiC9R90Zc1lMfbeSYiPQff5+h182xBXZNdAZOnQoCgoKMHfuXOTl5SEuLg67du1Co0aNAFQWCLx4Ubvabrt27TT/f+LECXz++edo1KgRcnNzbdl0AM6x4q/ULlVTQVtGci+DF1ExAYax2ZVVLzaZFwrM7uEQe5JM+Vsrgz1CVd/jJ8+2w9tf/4LCknKT7anl761zjFQqAc/96wejz71nYJZPdfN2noWft6fRC7K5s2bEDqcs2X8ei/aeNxr4yRHoW6v+iZz7NbfQniU1lMypMSS2JpPcdp/Jw5xtv0h+niPOBhTD2HfLknMaWcbuycivvvoqXn31Vb1/W7t2rc5jguA4dQicYUqh2BMxIL4HxdB7seQuLMzfGwdn9IT3/2awWNK1L/YkeeH6XWReKNCciJQqAUv2n8eaw7laeU2Rwb4Y1DYSa478Kao91QMNsQnYYtwqKcf49SfxQmK07FNyxR7zNXoSpquTK9C31lRnOfdrTi+RuQGSuTWGBAP7M5eYnERnCsjkZOi7xZlZ9mP3QMeZOcsXV8yJWI67DUvuwgpKynHiz1uafVvStS82t2bJd79jyXe/IzLYF0+0jcSmHy/rHV7KLyoVFeQYao+cd6fq97P6cC5WH86VNRdMbDuNJbdX5QiBvq2Y00tkToBk7xpDgLicRHMCMmdeakIMV1w41Vkw0LGAM31xTZ2I5QjapM6WMbZvS7r2pebW5BWVYsX3OQb/ru6dUCgMD70Za4+lx8UYOXPBxBzz4JpeeoNBY+wd6NuKOb1EUgMkc4+lXMPoYnMSzQnInH2pCVPkWvKFpOPq5RZwthV/jZVBlyNoM7akgdR9W7o8gtiiiWIJ+CvIkdoeS4+LqXYB2kXpzC2zL+aYj06IltxGewX6zrKKtJTlCcw9lqYKcYo5VlKWORAbkE3o2dQqS004qmGdogze7AgAUga4Zm+WvbFHxwKutOKvXHcb/eIi/5e8e0ZrCrmHGb0hls6QqXq3fPj3G1jynbiZTcbU9PaEdw0PrV4NMe0x9F7kUPUiVnS/3KJSB8aO+RNtI7Hx+CXR7bLnHaocJR/kro0lx/7k7DVVE3uspOQkig3IEpuGu/ywJiB+lpy63pirB3y2phAcKbvXBoqLixEcHIyioiIEBQXJsk9HrqMjhbpbGtAftIkZHjG0ns1T7evj00M5Zu1bjgvE11lXMGljlqTnGKKAeXVcAGDXz1fx6uenZGlHdS8mRuutrCzl81OrfsxvlZTprY9kiNjXtEahTUPDK5Z+j+Veg8rc/Rn6nYqxYWxXrcBCyrES+xsamdAIfVtFYNoXWbhWXGb0xikjuZdT3AhaQkpStjm/VXdi7vWbgY5MHLEyshTq9u/NzsdXWVe0emPEnpBNnTRffiQG237Ks3lAqFQJWHs4R/RyEKaYe5Iuf6BC17S9BoslWkpMIUZzLixKlYDu8/dL6okSW71a7hsEU20VcxzkCJQs3Z+p84m+Yyem17Tq+5Z6rDIvFGD4qqOi33fI//K5DOXJLRnWDn+Lryd6f87InN+OOwWBUpl7/ebQlUz0JSI6S/Ajx6rCYmoKbfspDwdn9MSJP2/Z7JiYW1jNGHNmE+0+k4c3vzojKsiZ0LMpEpuG41ZJGebtPCuq7QoFjO5bX5vFfj/FJpa+9XhL1AnyEfW5Si20KVdbTX12tlyDytD+xASA+pKYb5WU47XPDffIVh9Gl3qspA6bFf1veNdQAvvETadw+uptzHrcPsVebcGcpGx3mq1oKwx0rMRZhrMMXXBulZRjzeFc0YGI2JNm1Snk1mZOHQ8pxCZcSm1Hs7oBmmPU938zWPZm5+Nfh3MNPkdsv6y6zVK+n2LfZ50gHwz63wr2xki9+FujrYa2s8caVFX3JyUA1HdztcxDfE6b1GMldTaj+rOsUKr0/l0lQDPb0VWDHUtmHLrLbEVbYKBjBY6+LISanHevjlZTSEodD3UdnerDaqaISbg0p55I9dlnCU3CkNAkDB0ahWLChpNGq0uL2bfU76fYEv5itxN78T96oQA//lmIhXvP62yTX1Sqt3CipbMH7bkGlRy/RynT1aUcK/V7KnugwuQ+zbDh2EWjq7ZXbXdJmfGq36sO5WDaYy00xUKrc5aecX0smXHoCGVJXAUDHZk5w7IQanLevTpaTSGxXcYpA1pidGIMPD0UeKNfSxzLKUR+cSnm7fhF1MKjcrVDzH5r+XtbFOSE+nsh7/Z9vLtL4vdT7GtK7FUy5dXPT6LIQHFCQ4UTk2IjLJo9mHuzRFTbrLEGlVy/R7H1fMTOtLxVUq6TZxJS0wsBPp64ayKIEUMlAP/OzMWLDzfW+Zuz9IwbYs4sOdbTkR/r6MhMysnK3uTshXG0mkJi31t4oI/OatWD29XHPwa3riwSWG17qWUDpPZgGduvpb1hhSUVmPrlT6JzedRulpi+c5eyndhg11CQo4+6Nyo9O9/s+ku7z+Tp7T2qvg9D32N1T5nUxFP1/mzdKyqmbtITbSPx2ue67+n2vQpZghy1Pwvv6Txm6HiqP+vdZ/Jke31rkVpDy9nKkjgLBjoyc7QhHGPk7IWxtMCf3Cx9b4YKDkYE+0oaehTbjlB/L5P7lbIvS1X9fsrdW2cqKDZH1YJ1SbERkj87dU+sGHKtQVX9d2GPXlFj3/NPnm2HbT/lWS3HrapGoTW1/i2lOKGjM3SMa9X0QkhN7d+q1PMLicOhK5k52hCOMXKXJLe0wJ+c5HhvcqxyLabrOszfG5mzehvMUZD6ntQz2/KL7mPezrNGV103pOr3U+7vidSkVrGq9kZJ/ezEDjFO7tNctjWoqv8u7LVEgKFjZat1tTwUwIhqFbedYcFkKQwdYwBOm3/kTBjoyMyZ1jOxRmVnOYIDOcj93lQqAdlXi5BfXIqIIPHvSUw73hscZzLIkfKevGt4IKFJGDIvFEgOcvR9P631PdEXFIf4eYleNNSQqrOExF4ExfawRofX1Pu4+CUPmqBZ3UC9vwsxxzllQKxVflv6jpWtep3HPhyj8/13pp5xsQx9H50hUHN2DHRk5mzLQlijF8acxQ2twdL3ZiyxVK5lFaqv+GzqIiblPUm9CBj7flrje6IvKFYJAp779AfJ+6rKnN5SS3tixS95UNvob8PUEhzzdtouMdfavc4eisogR9/UcmfqGSfHx8rIVuIsswXUF9f8ovsoLClHaICPpB4LZ2DO9FQxtW8UsGxZhartMFS08d1BcXi8je7+yx+o8O/MXPxZeA+NQmtiREK0zl2x1Eq2Yr6f1p7qq64ka85aTnJUfzbVE2to31Kfb+o4Sl2CY7nEvA4xn6Mln4UxDzcLx6PNa+v9zop9bVYPdk9cAkIkWwU6gOPXf3CWYMzWxJZtl+tkayqoGveI9l2v2ErWSpWAxPf3I7/Y+PsI9quBpc92QFcTK2fbijlrOZm7PIOY1xW7b7HPl/q7E/N9DKnphRNvJ4n6/KS8viXrahlStaSDqXZauvYeuRZzr9+cdWVF6iGcQfH1keAgFxE1Z5m6qVQJyLxQgK+zriDzQoFNZlmITcKUo1SAmNk6K77Pwa6frwIw/LkVlpRj9eFcDF91FN3n78fuM3nw9FBgeOeGJttQdP8BPDwUDvP9NDRLJTLYF8ufb4/lz7dHpIWz4aS8rth9i3m+Ob87Md/H2/cqsGT/70a3AaT/7qXMGIoM9kVITS+Ts+nm7TyLxPf3Y/He80Z/13LNfCRij44bkmPhQ1uwV4+T1JXOFw+L17v0gZgePbHDS6H+Xjg6qw96fPCduLWvUHnHW/ZAJeq9GHoP9mTs+Fmzt9TSfRt6vjm/O6VKwML037DkuwsmX9dUr44lv3t97wnQnTGUnp1vVg+QqR4tR+4ZJ9vhop4kmjNM3bTnMhpSExz1VdMVG6SJTRguLKnAvzNzJU33Td2ejQVPtRG1rdjlG2zJWFK7NRPeLd23oeebs+6VlCrLt+9VGP3NWvK7N/Se1NPQr9+pfJ66hpHU6tDGfteOMrmBnBeHrtyQo0/dtHexMHWJALEW7j2v1eUvZXhASlClr3qsIeqL1q/5xeKfQFYl5XdnTpVlU68h9+9+95k8dJ+/H8NXHcWkjVmaYVMAyEjuhZQBLUXtB3C+IoDkXBjouCFHn7pp72U0qlZ5Fkt9gpYapHWOCUWtmuIqGVevHivG9+dvitru+t3K5RvskRPlLsT+nsIDfCRXWRbzGnL+7k0F8+nZ+QgPlNZL6EjL45Br4dCVG3L0ooaO0OPULy4SLyRGY/XhXFHbVz1BSxkeSM/OF3VBC6nphREJ0fg0I0fSdN9DIgOdt7eexq95RToruHMWnnw6x4QiIsjX4Cw49e8OgvHvkLHnGvvN3hJRPFLMenRiFy5+ITHa5Ovp40xFAMk5sEfHDTnaulTVOUqPU1JshKTtr98pNWt44vY905WAb9+rwLIDF/B4XIRVRplKypRY8X2Ow8/Cc2bp2fkofaB/Icyqvzuxi6Pqe66xROR5O02v5ZUyoKXJ373YHtf3dv1q8vX0YRFAkhsDHTflyFM3HWUldKm5OjfvlOH8tbuitg0P8MGcbb9ICloW7j2Hf/2vh8lWMai9cydcZSjNVFAbUvOvRV2lXuhD/b3xybPGf7NiSybU8jc93GStHhdb/a7J/XDoyo05yrpU1TnKMhrqdoz/33RZYzwUlfVBTFEPMRzPKUR+sbQ796pseb231yw8VyloKaZWkk8ND00PYueYUAT41MDdsgei9l9QUo55O7Ph4QGDxyW/6L6ofR3+/abJc0G4iGDIXI60PA65DvbouDlHLWqYFBuByX2aIdhPO1HX1j1O/eIisfz59jrF0aoTE3ioj+wTbSOxaN95yxtnY3uz8232Ws5S0FIMMb0p+cVlmhyv9Ox80UGO5vlGjsvuM3mignAAWPLd71ozqPQeZyucIkL8vOzek0yui4EOORz1tNWFe89rVrIO8fPClD7NkZHcy+Ynw35xkTjxdhKm9GmOkGqBl5S4MCLYF5882w5fZ12VuYW28VXWFZsMHdm7vIDcpORtqd+7VIaOizpglLqKPWA4eLp51/yeSEM+eY5BDlkPAx1yKIbu5IvuV2DR3nNIt2GvQlWeHgpM6tMMJ1KSsGFsVyweFo+UAS1F9eRM6NkEG8Z2RUZyL5y/XmLRkJU9FZZU2GTqr73LC8hNSnK92FwafaofFzFDZqb2B+gGT3ImC6vzcro2ZkFAsh4GOuQwnOFOvupQn9g6Ic3qBmqmki/ce87KLbQuW0z9dYTyAnKSklwvx3tS78OSoElNX1Bp6v2I5QgzPMk9MNAhh+Fsd/JS7tTNHZIwV/XrhlzXETHv2dKZUnKWF6jalsO/38Th8zdtPoNLSjkHOXpL1PuQMxCsui9j70cKR5jhSe6Bs67IYTjbnbyUwoty3F2Lob7wLBneDrX8fXD9Tilu3ikTnYxqbL9iikjKMVNKroKWptaKsuUMLnU5h+rtiajWBvV7N+e7Uv24yDnEVH1fht5PZLAvnmgbqbfwZMqAlprvpKPM8CT3wECHHIZcd/K2Wu1YyjR4Q9VwzWXoglL9wglUrsZuCbFDDHItxCr2uAKVq7/r+5wNtcWSdllKTDmHqu9dSn+Tvs+oQ6NaCPX3QmGJ4YKUHgrjMwaNBZXG3s8b/Vo6XNkKcl8MdMhhyHEnb+vaK2Lv1AtFzlRpGRmIs3l3dC7wai8mRqNPbISkC4rUO/vqFz99wVN1YvKr5mz7BUmxEaIueKaOKwB0n79fT69BLIJremHm5tMmA4WqyxWIbZeaucG0mJW41e995pbToqpmA7qfkfp3YCzIAUwHOYDxoNLQ++GK4+RIFIIgOMccTZkUFxcjODgYRUVFCAoKsndzjLJVz4QjUd+JA/rv5I3dfRu6ixfzXEuZ+qy+OnkZU774yeR+Fj7TFn7enrIGa0qVgO7z9xsNIEP9vfH2gJaICPZDh0a1cOLPW5K+d5kXCjB81VGTbZnSpzkm9Wkmqe3Vj2t6dr7kHg9TNoztKvrCbKtgWqkSsGT/eaw5nKsps6B+rZQBsajl7212b5apnhwAiAjywZwnWgGASxRuJOdn7vWbgY6DcpWqsID0gM2c966+mBvKbVD3BmUk97JLsCg2EFBfcOUOci0JIMX4OusKJm3MErXtC4nRSKrSKyWFqc/ZXIuHxWNQfH2T25kKIqb0aY4JvZrK+h2T8l0Qc3yCfGuguNR0QcL/vNQFd0or7HbzYCvueEPprMy9fnPoygEZOpnm2TinQA7mBC3mLE0hZcaWPbrUxSSZVl3nR+6uf7FDbOaSMjy2+nAuVh/O1fkeiLngWCupW+wMLlN1aRbuPYcNx/7EnCdaGQ3KpXy3pXwXxBwfMUEOAFy/U4YFu381uUq51GE/R+JKN5RkGAMdB2PqZCrAeU4uliSnSr3QO/qMLVNJpgpYv56INdc2M2e2UNXvAWB4eKRqm8UumiqW2BlcgPggK7+4DOPXn9Tbu2PtC6uc3+/Cu2UOffOgj5QgUq7keXJ8DHQcjJiTqaOdXPQxlZwq992g2B6Fm3fKoFQJdgkSjU3JteRCJ+Xkbq0kUSkLoKqpvweztpzGLT1Jt3lFpRi//iRC/Grg9n1paz+JIbVgndQgonrvji0urGJ/B4aS3dV/iwj2Rai/t6h9OUq5BylBpK3PT2RfDHQcjNhVhsVuZy+2HkoyNWNLbd7Os/g0I8duXdNy96o4Utd7v7hITOnTDAv3il+wVAD0BjlVWSPIAaQP25lTlya/uAyvrD+JT55tj3k7zbuwSglkxf4OTCVmzh4Yi2A/cYGOnPV6zCU1iHT0oW6SFwMdByN28T1zFumTi5gTr62HkozVXqnO3l3TcvWqOGLX+4RezbDh2CXZ6wZZKqSmF/5vWDt4KBS4WVJmVoApNojQJ+XrMygw8ps1dGGVGsiaqkFkqt0eispik/3iIlH+QGW0Do+UYT9rMqd3xtGHukleDHQcTGiAuPWTxG4nN7EnXjnL+ItlaGioOlt3TVtjVofYk3ugj5fZF3ZzeHooMOeJWL0zvOxFAeD9Ia3xcPPaFu2nahAhhQAYDXKqqnphlTopofyBCv/OzMWfhffwVPsGOHT+Bq7d+at+k7+PJ+6WKY2+vkoAavn7mKzD40jrVJnTO2OP8xPZDwMdBxMRJO6HJXY7OUnpQZCrjL9U6qGhtYdzjC57YKuuaWsNLYk9uT/3rx90XtdaCclqYgNOWwjx88L7T7WWrWdL/d7mbMu2Sq+V+sIqdVJC2q5srDqUo1Ubx0MBdGwUgt+vl+D2/QqTQY7a3ux8rD6cazRIlWu2nhzM6Z2x1/mJ7IOBjoOROg3ZVqR2D0tZHkFunh4K0SuLW7Nr2ppDS+a0O1+d3FvTS6virjVyeqrmIu3Nzse/DufKtm8pPnmuPRKbhsu6T/V7S/7vT/jvSfHLaygUgKGqZdUvrFImJRz47RpWfJ+j83eVAPz4523R7VP7KuuK0SAnzN8bB2f0hHcNx1gT2pzeGXuen8j2HOObShrqH6AC+lc6NjQN2dIVo00xZ2Vx9d1vRLD2icgWqxbbu2tazJIIqduzzf6czGm3+pWqLyugDrx2n8kzqy2GqHORUga2wvLn2yOy2vcgTOSsHnOF1vTC9eJSq/wePD0UkofCjAU5AoD+cZWBoVIliJ5scPnWPaw6pBvkmEOBys/E1LIRBSXlOPHnLVleUw7qm0NDIYkC+m8O7Xl+Ittij44DklrczRYzb8xN3rNm7RZj7N01be1ZHZYkxupri7VzlvR9Dzo0qoUeH3wny3vQp/BehWbZDWv0WpkbJFdffkHd01O1kGJiE3G9UHt+yTe5lIMY6k98UHw9rBbR++ZISbqW9M7Y6/xEtsVAx0GJ/QHaauaNJT0k9ljgz95d09ae1SFllpkYtshZ0vc9kPM9GKPv92BpknjnmFBEBPlKztVRCUDKgJa4evs+/nU4VydQyS8qxX9PXha1r/sV4vJuTFHfRAX7eYsKdBwtSdeSyt9cgNT1MdBxYKZ+gLYsemXvHhJzWHvZA2NsMXRmjaRfW9+p2ypxufrvIT073+Je0PTsfJQ+MC/QyC24h52nrxpsq1gx4f7I+L3ArDYAwISeTZHYNFwT5ClVgtP9ztXYO0OGMNBxYrYsemXvHhJzufrQWfX3Fx7gg2lfZOFacZlZPST2uFOv/h5Ca3rj1c9P4o7INZnEUv8eluw/j0V7z1vUCypmhXBj/n30TzOf+ZfIYF+8+Xgs/vPDRcnDV+rv35Sk5jq/hWGdGmLh3nN6nwM45u9cjb0zpA+TkZ2YrYteOWvynvrkNyi+PhKahNnkJK0ODAH9SeWAfBeMqu8vsWk45jzRSu/rGmMoYdOaqibQH8spROeYUAyKr4+Hm9fG/CFtrPa6Kw/9YVGSuJjFPeVkbFKCn7cnxj4cY9b+qn//dp/JQ/f5+/UGOYDj/86JDGGPjhOzV1E+dg+LY6+hM0Ovq55W7gg9cqYS6B9vE4lxl2P0Tpu2VImRejJiekGttYK6PlP6NMfG4xeNfn9mPV4ZUOuro9O7ZR2cuVJs8vtnqodqSp9mmNCrGX/n5JQUgmBo0qNrKi4uRnBwMIqKihAUFGTv5lhEqRLQff5+k8MjGcm9eIKyI2tURjb3deXITbGUoYuq+ohU7TXY9fNVvP31GZNTnuW2eFg8BsXX1/u3r7OuYNLGLKu+ftXfLgBR35+qlZEbhdbEiIRoeNfwMPn9U59HTNXu4nmE7M3c6zd7dJyYs+bNuBt75Q3oe11798hJTaB/vE099I2LlC0HSSxjvaDWzmPS99sV8/3xruGBFx9urPO4qe+flOKEzH8hZ8QcHSfnrHkzZD/2yFlSM6fwpNgcJHXuyrhHYnSKE4olJlfJVIE6S9n6t/vtL+IKRYotYkjkaNij4wLsfZfuauw11OQO5EigF5P79Ea/lprP8Py1O1jy3QXRbTTVCyqlhlGInxdGd2uEjccvGeyFUgAI9ffG2wNaIiLYz6bft91n8rD2iLgZYId/L8Dg9g2s3CIi+THQcRGcVikPa1WZduXgScp7kyuB3lRwX/X3kHmhQFSgE+bvjfcGx4n6nA0FW5HBvhjWqSGiw2tqtalFZJDRIWaxrysn9TCiWHvPXoNSJbjM95bcBwMdov+xVpVpWyzRYS9S35uc9YXEBvdilssI9fdC5qzekhaqlNKTas/ilYZInT12+34F83TIKTHQIYL1qkzbaokOezDnvdkjgV7Ma/5jcGuzVuOW0pPqaEPM5tTXcqQ1rojEYjIyEcxLkjXF2iuY25Ml780eCfRJsRGY3KcZgv28bPaa+siVCF612KKp1dmrbnv495s4fP4mvs66gpt3yiS/rqOtcUUkBnt0iGCdKtO2XKLD1sS+t6MXCuDhodDpwbBl74a+4bUQPy+MSYzBhF5NnS7nRMpwob5tq6q+krohjrzGFZEpDHSIYJ0q07ZeosOWxLb5tc9P4vb9v4r9Vb0g2yKB3tDwWtH9Cizaew4PRQQ41dChlOFCMetxiQ1yANbkIufFoSsimK6NYs5aUPZYosNWxLa5apAD/HVB3n1GXO0WS7ja0KGU9yN1PS5j8QtrcpGzY48OEayTJGurFcztQcxMJn2MJXbLPQXf0qFDKe2xRfkAqXlkUmZUqQQgZUBLhAf6INzfB1AAN++W2T1hmkgOdg90li5dig8++AB5eXlo1aoVFi1ahIcfftjg9gcPHsTUqVPxyy+/oF69enjjjTcwfvx4G7aYXJXcU4BdeYkOKUXzqtMXYFhjCr4lQ4eW5sFYo3yAtYdCwwN9DK7vReTM7Dp0tWnTJkyePBlvvfUWTp06hYcffhj9+/fHxYsX9W6fk5ODxx9/HA8//DBOnTqFN998ExMnTsTmzZtt3HJyVf3iIpGR3AsbxnbF4mHx2DC2KzKSe5l9wXLlJToMvbeQajObDFFfkNW5JNV7ICwd5jJ36FBKe6zVdn3C/X1Eb2fOcKgzDqESiWHX1cu7dOmC9u3bY9myZZrHWrZsiSeffBJpaWk62ycnJ2Pbtm04e/as5rHx48fjp59+QmZmpqjXdKXVy8l5uFNlZJUg4LlPfzD5vA1ju6JzTKjRlbOrruIt9XipV+U2NXRYdd+mVvKuvqq4tdquz+Hfb4o6rv95qQu6Ng4z+t6t2U4iazH3+m23Hp3y8nKcOHECjz32mNbjjz32GI4cOaL3OZmZmTrb9+3bFz/++CMqKir0PqesrAzFxcVa/xHZmj0X0rS26u+ta+Mw0Ynd1qhfVLVdswfGal6zehsA3aFDKe2xZtv1uXlXXN2bm3fLjL73qpx9CJVIDLsFOjdv3oRSqUTdunW1Hq9bty7y8/P1Pic/P1/v9g8ePMDNmzf1PictLQ3BwcGa/6KiouR5A0Skl5QAw9p5J1KHDqW0x9blA6QOxRl671W5whAqkSl2T0ZWKLRPhYIg6Dxmant9j6vNmjULU6dO1fy7uLiYwQ6RlYlN7LbFFHwpxQmt0R65cl/MmcVX/b2HB/gAAnCzhDOqyH3YLdAJDw+Hp6enTu/N9evXdXpt1CIiIvRuX6NGDYSF6S885uPjAx8fcUl8RCQfMQGGrabgy7UAaPX22LJ8gLmz+GxRmJHIkdlt6Mrb2xsdOnRAenq61uPp6eno1q2b3uckJCTobL9nzx507NgRXl7iZnoQke2Yyk0yJ4/GmqS0xx5td+VZfETWYtdZV5s2bcKIESOwfPlyJCQkYOXKlVi1ahV++eUXNGrUCLNmzcKVK1ewbt06AJXTy+Pi4jBu3DiMHTsWmZmZGD9+PDZs2ICnnnpK1Gty1hWR47FVLRprtMcebXflWXxEhph7/bZroANUFgxcsGAB8vLyEBcXh4ULF+KRRx4BAIwePRq5ubk4cOCAZvuDBw9iypQpmoKBycnJkgoGMtAhckyOdvF2tMrIRO7OaQMdW2OgQ0RE5Hycro4OERERkbUx0CEiIiKXxUCHiIiIXBYDHSIiInJZDHSIiIjIZTHQISIiIpfFQIeIiIhcFgMdIiIiclkMdIiIiMhl2W31cntRF4IuLi62c0uIiIhILPV1W+qCDm4X6Ny5cwcAEBUVZeeWEBERkVR37txBcHCw6O3dbq0rlUqFq1evIjAwEAqF/RfdKy4uRlRUFC5dusS1t2yAx9u2eLxti8fbdnisbUt9vLOzs/HQQw/Bw0N85o3b9eh4eHigQYMG9m6GjqCgIP5YbIjH27Z4vG2Lx9t2eKxtq379+pKCHIDJyEREROTCGOgQERGRy2KgY2c+Pj6YPXs2fHx87N0Ut8DjbVs83rbF4207PNa2ZcnxdrtkZCIiInIf7NEhIiIil8VAh4iIiFwWAx0iIiJyWQx0iIiIyGUx0LGTZcuWoU2bNppiUwkJCfjmm2/s3Sy3kJaWBoVCgcmTJ9u7KS5rzpw5UCgUWv9FRETYu1ku68qVK3j++ecRFhaGmjVrIj4+HidOnLB3s1xSdHS0zndboVDgtddes3fTXNKDBw/w9ttvIyYmBn5+fmjcuDHmzp0LlUoleh9uVxnZUTRo0ADvv/8+mjZtCgD47LPPMGjQIJw6dQqtWrWyc+tc1/Hjx7Fy5Uq0adPG3k1xea1atcLevXs1//b09LRja1zXrVu3kJiYiJ49e+Kbb75BnTp1cOHCBYSEhNi7aS7p+PHjUCqVmn+fOXMGSUlJePrpp+3YKtc1f/58LF++HJ999hlatWqFH3/8EWPGjEFwcDAmTZokah8MdOxk4MCBWv9+7733sGzZMhw9epSBjpXcvXsXzz33HFatWoV3333X3s1xeTVq1GAvjg3Mnz8fUVFRWLNmjeax6Oho+zXIxdWuXVvr3++//z6aNGmCHj162KlFri0zMxODBg3CgAEDAFR+tzds2IAff/xR9D44dOUAlEolNm7ciJKSEiQkJNi7OS7rtddew4ABA9CnTx97N8UtnD9/HvXq1UNMTAyGDRuGP/74w95Ncknbtm1Dx44d8fTTT6NOnTpo164dVq1aZe9muYXy8nKsX78eL7zwgkMsEu2Kunfvjn379uHcuXMAgJ9++gkZGRl4/PHHRe+DPTp2dPr0aSQkJKC0tBQBAQH46quvEBsba+9muaSNGzfi5MmTOH78uL2b4ha6dOmCdevWoXnz5rh27RreffdddOvWDb/88gvCwsLs3TyX8scff2DZsmWYOnUq3nzzTRw7dgwTJ06Ej48PRo4cae/mubStW7fi9u3bGD16tL2b4rKSk5NRVFSEFi1awNPTE0qlEu+99x6GDx8ueh+sjGxH5eXluHjxIm7fvo3Nmzfj008/xcGDBxnsyOzSpUvo2LEj9uzZg7Zt2wIAHn30UcTHx2PRokX2bZybKCkpQZMmTfDGG29g6tSp9m6OS/H29kbHjh1x5MgRzWMTJ07E8ePHkZmZaceWub6+ffvC29sb27dvt3dTXNbGjRsxY8YMfPDBB2jVqhWysrIwefJkfPzxxxg1apSofbBHx468vb01ycgdO3bE8ePHsXjxYqxYscLOLXMtJ06cwPXr19GhQwfNY0qlEt9//z2WLFmCsrIyJspamb+/P1q3bo3z58/buykuJzIyUufmqGXLlti8ebOdWuQe/vzzT+zduxdbtmyxd1Nc2owZMzBz5kwMGzYMANC6dWv8+eefSEtLY6DjjARBQFlZmb2b4XJ69+6N06dPaz02ZswYtGjRAsnJyQxybKCsrAxnz57Fww8/bO+muJzExET89ttvWo+dO3cOjRo1slOL3MOaNWtQp04dTZIsWce9e/fg4aGdTuzp6cnp5c7gzTffRP/+/REVFYU7d+5g48aNOHDgAHbv3m3vprmcwMBAxMXFaT3m7++PsLAwncdJHtOnT8fAgQPRsGFDXL9+He+++y6Ki4tF34GReFOmTEG3bt3wj3/8A8888wyOHTuGlStXYuXKlfZumstSqVRYs2YNRo0ahRo1eBm1poEDB+K9995Dw4YN0apVK5w6dQoff/wxXnjhBdH74CdkJ9euXcOIESOQl5eH4OBgtGnTBrt370ZSUpK9m0ZkscuXL2P48OG4efMmateuja5du+Lo0aPsZbCCTp064auvvsKsWbMwd+5cxMTEYNGiRXjuuefs3TSXtXfvXly8eFHSxZbM83//939ISUnBq6++iuvXr6NevXoYN24c3nnnHdH7YDIyERERuSzW0SEiIiKXxUCHiIiIXBYDHSIiInJZDHSIiIjIZTHQISIiIpfFQIeIiIhcFgMdIiIiclkMdIiIiMhlMdAhIodx/fp1jBs3Dg0bNoSPjw8iIiLQt29frsJNRGbjEhBE5DCeeuopVFRU4LPPPkPjxo1x7do17Nu3D4WFhVZ5vfLycnh7e1tl30TkGNijQ0QO4fbt28jIyMD8+fPRs2dPNGrUCJ07d8asWbM0K0Tfvn0bL7/8MurWrQtfX1/ExcVhx44dmn1s3rwZrVq1go+PD6Kjo/HRRx9pvUZ0dDTeffddjB49GsHBwRg7diwA4MiRI3jkkUfg5+eHqKgoTJw4ESUlJbZ780RkNQx0iMghBAQEICAgAFu3bkVZWZnO31UqFfr3748jR45g/fr1yM7Oxvvvvw9PT08AwIkTJ/DMM89g2LBhOH36NObMmYOUlBSsXbtWaz8ffPAB4uLicOLECaSkpOD06dPo27cvhgwZgp9//hmbNm1CRkYGJkyYYIu3TURWxkU9ichhbN68GWPHjsX9+/fRvn179OjRA8OGDUObNm2wZ88e9O/fH2fPnkXz5s11nvvcc8/hxo0b2LNnj+axN954Azt37sQvv/wCoLJHp127dvjqq68024wcORJ+fn5YsWKF5rGMjAz06NEDJSUl8PX1teI7JiJrY48OETmMp556ClevXsW2bdvQt29fHDhwAO3bt8fatWuRlZWFBg0a6A1yAODs2bNITEzUeiwxMRHnz5+HUqnUPNaxY0etbU6cOIG1a9dqepQCAgLQt29fqFQq5OTkyP8micimmIxMRA7F19cXSUlJSEpKwjvvvIOXXnoJs2fPxvTp040+TxAEKBQKnceq8/f31/q3SqXCuHHjMHHiRJ1tGzZsaMY7ICJHwkCHiBxabGwstm7dijZt2uDy5cs4d+6c3l6d2NhYZGRkaD125MgRNG/eXJPHo0/79u3xyy+/oGnTprK3nYjsj0NXROQQCgoK0KtXL6xfvx4///wzcnJy8OWXX2LBggUYNGgQevTogUceeQRPPfUU0tPTkZOTg2+++Qa7d+8GAEybNg379u3DvHnzcO7cOXz22WdYsmSJyZ6g5ORkZGZm4rXXXkNWVhbOnz+Pbdu24fXXX7fF2yYiK2OPDhE5hICAAHTp0gULFy7EhQsXUFFRgaioKIwdOxZvvvkmgMpk5enTp2P48OEoKSlB06ZN8f777wOo7Jn54osv8M4772DevHmIjIzE3LlzMXr0aKOv26ZNGxw8eBBvvfUWHn74YQiCgCZNmmDo0KHWfstEZAOcdUVEREQui0NXRERE5LIY6BAREZHLYqBDRERELouBDhEREbksBjpERETkshjoEBERkctioENEREQui4EOERERuSwGOkREROSyGOgQERGRy2KgQ0RERC6LgQ4RERG5rP8HjLy15jwLOzkAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a scatterplot using matplotlib for another look at how the chosen variables plot against each other.\n",
    "\n",
    "df.plot(x = 'Score', y='Perceptions of corruption',style='o') # The style option creates a scatterplot; without it, we only have lines.\n",
    "plt.title('Score vs Perceptions of corruption')  \n",
    "plt.xlabel('Score')  \n",
    "plt.ylabel('Perceptions of corruption')  \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "c4d0eea6-edaf-4ddc-a7e0-3213ff6e8fb0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Reshape the variables into NumPy arrays and put them into separate objects.\n",
    "\n",
    "X2 = df['Score'].values.reshape(-1,1)\n",
    "y2 = df['Perceptions of corruption'].values.reshape(-1,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "fb4b9d99-2aec-4401-b9c3-7237638b312d",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train2, X_test2, y_train2, y_test2 = train_test_split(X2, y2, test_size=0.3, random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "18844e19-1e09-4957-9129-3e1f81998a3f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</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 sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearRegression</label><div class=\"sk-toggleable__content\"><pre>LinearRegression()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#rerunning and fitting the regression:\n",
    "egression = LinearRegression()\n",
    "regression.fit(X_train2, y_train2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "410dda5c-cf0c-4d61-83e0-1b66a484a2b6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Predict.\n",
    "\n",
    "y_predicted2 = regression.predict(X_test2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "b6d09f8c-5ea2-456d-b2f6-980389a29f1e",
   "metadata": {},
   "outputs": [],
   "source": [
    "rmse = mean_squared_error(y_test2, y_predicted2)\n",
    "r2 = r2_score(y_test2, y_predicted2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "e1a43f2a-0941-4e14-9122-a841d8a60e61",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Slope: [[0.03539039]]\n",
      "Mean squared error:  0.0037914127362649467\n",
      "R2 score:  0.10348422484617503\n"
     ]
    }
   ],
   "source": [
    "print('Slope:' ,regression.coef_)\n",
    "print('Mean squared error: ', rmse)\n",
    "print('R2 score: ', r2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "f9c1e017-8074-47ab-a8c3-0016b702723a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualizing the test set results.\n",
    "plot_test = plt\n",
    "plot_test.scatter(X_test2, y_test2, color='gray', s = 15)\n",
    "plot_test.plot(X_test2, y_predicted2, color='red', linewidth =3)\n",
    "plot_test.title('Score vs Perceptions of corruption (Test set)')\n",
    "plot_test.xlabel('Score')\n",
    "plot_test.ylabel('Perceptions of corruption')\n",
    "plot_test.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e9f21a7c-2971-4740-bc8c-7c6140c040a0",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Hypothesis values i get here in prediction little bit same ."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "10dd0423-3a08-4f8b-a015-577223c5dd84",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create objects for model mean squared error and R2 score\n",
    "\n",
    "rmse = mean_squared_error(y_test, y_predicted)\n",
    "r2 = r2_score(y_test, y_predicted)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "87a44fdb-da56-48fc-961b-07a0964875fd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Slope: [[0.03539039]]\n",
      "Mean squared error:  0.2116754286193453\n",
      "R2 score:  0.658811111119557\n"
     ]
    }
   ],
   "source": [
    "# Print the model summary statistics\n",
    "\n",
    "print('Slope:' ,regression.coef_)\n",
    "print('Mean squared error: ', rmse)\n",
    "print('R2 score: ', r2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "45c35cd5-6350-44ff-8b56-55d14631221d",
   "metadata": {},
   "outputs": [],
   "source": [
    "### results \n",
    "The second set of metrics shows worse performance than the first set, possibly due to differences in datasets, potential overfitting, or underfitting. Data bias, selection bias, measurement bias, and overfitting/underfitting can impact performance. Ensure diverse and accurate datasets and consistent data collection processes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "cd145377-73d6-45f1-9588-c1406caf5a0c",
   "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>Actual</th>\n",
       "      <th>Predicted</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.391506</td>\n",
       "      <td>5.378337</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6.886000</td>\n",
       "      <td>6.094510</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5.391506</td>\n",
       "      <td>5.378337</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5.391506</td>\n",
       "      <td>5.378337</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.391506</td>\n",
       "      <td>5.378337</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5.391506</td>\n",
       "      <td>5.378337</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>4.390000</td>\n",
       "      <td>5.086259</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>5.391506</td>\n",
       "      <td>5.378337</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>5.391506</td>\n",
       "      <td>5.378337</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>5.391506</td>\n",
       "      <td>5.378337</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>5.391506</td>\n",
       "      <td>5.378337</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>5.391506</td>\n",
       "      <td>5.378337</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>3.334000</td>\n",
       "      <td>4.000874</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>5.125000</td>\n",
       "      <td>6.221230</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>5.391506</td>\n",
       "      <td>5.378337</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>5.391506</td>\n",
       "      <td>5.378337</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>5.391506</td>\n",
       "      <td>5.378337</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>5.391506</td>\n",
       "      <td>5.378337</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>5.391506</td>\n",
       "      <td>5.378337</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>5.391506</td>\n",
       "      <td>5.378337</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>5.391506</td>\n",
       "      <td>5.378337</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>3.975000</td>\n",
       "      <td>4.582134</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>4.700000</td>\n",
       "      <td>5.133091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>6.199000</td>\n",
       "      <td>5.810768</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>5.391506</td>\n",
       "      <td>5.378337</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>7.228000</td>\n",
       "      <td>6.306629</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>5.762000</td>\n",
       "      <td>5.323171</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>7.600000</td>\n",
       "      <td>6.375498</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>5.391506</td>\n",
       "      <td>5.378337</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>6.923000</td>\n",
       "      <td>6.185418</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Actual  Predicted\n",
       "0   5.391506   5.378337\n",
       "1   6.886000   6.094510\n",
       "2   5.391506   5.378337\n",
       "3   5.391506   5.378337\n",
       "4   5.391506   5.378337\n",
       "5   5.391506   5.378337\n",
       "6   4.390000   5.086259\n",
       "7   5.391506   5.378337\n",
       "8   5.391506   5.378337\n",
       "9   5.391506   5.378337\n",
       "10  5.391506   5.378337\n",
       "11  5.391506   5.378337\n",
       "12  3.334000   4.000874\n",
       "13  5.125000   6.221230\n",
       "14  5.391506   5.378337\n",
       "15  5.391506   5.378337\n",
       "16  5.391506   5.378337\n",
       "17  5.391506   5.378337\n",
       "18  5.391506   5.378337\n",
       "19  5.391506   5.378337\n",
       "20  5.391506   5.378337\n",
       "21  3.975000   4.582134\n",
       "22  4.700000   5.133091\n",
       "23  6.199000   5.810768\n",
       "24  5.391506   5.378337\n",
       "25  7.228000   6.306629\n",
       "26  5.762000   5.323171\n",
       "27  7.600000   6.375498\n",
       "28  5.391506   5.378337\n",
       "29  6.923000   6.185418"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a dataframe comparing the actual and predicted values of y.\n",
    "\n",
    "data = pd.DataFrame({'Actual': y_test.flatten(), 'Predicted': y_predicted.flatten()})\n",
    "data.head(30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fcd0d6e2-88bb-4ae6-9b11-95a56d792796",
   "metadata": {},
   "outputs": [],
   "source": [
    "##### Explanation \n",
    "## The model's low Mean Absolute Error and Root Mean Squared Error suggest close predictions, but a narrow range of values around 5.392774 suggests potential data bias.The model's narrow range of predictions suggests potential bias, possibly due to the training data not being representative of the entire population. This could result from selection bias, measurement bias, or overfitting. Selection bias occurs when the training data is not randomly selected or skewed towards certain values, while measurement bias occurs when inconsistencies or errors in recording 'Actual' values lead to inaccurate predictions. Overfitting can occur when the training set is small or has limited variability. To mitigate biases, ensure the training data is comprehensive and representative of real-world scenarios, and use techniques like cross-validation, regularization, and diverse, high-quality data to improve the model's performance and generalizability."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "89ba6380-8b11-413b-a384-4104c9c1d8d4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(782, 30)"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#check shape before export\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "36be5f2b-453f-4e05-8bc3-0254549dae34",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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