Outliers and Correlation-checkpoint.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "08b27478",
   "metadata": {},
   "source": [
    "# Outliers and Correlation"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a421fe51",
   "metadata": {},
   "source": [
    "Outliers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "2f2e3752",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "9104f30f",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv(\"advertising.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "f5b8e00f",
   "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>TV</th>\n",
       "      <th>Radio</th>\n",
       "      <th>Newspaper</th>\n",
       "      <th>Sales</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>230.1</td>\n",
       "      <td>37.8</td>\n",
       "      <td>69.2</td>\n",
       "      <td>22.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>44.5</td>\n",
       "      <td>39.3</td>\n",
       "      <td>45.1</td>\n",
       "      <td>10.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>17.2</td>\n",
       "      <td>45.9</td>\n",
       "      <td>69.3</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>151.5</td>\n",
       "      <td>41.3</td>\n",
       "      <td>58.5</td>\n",
       "      <td>16.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>180.8</td>\n",
       "      <td>10.8</td>\n",
       "      <td>58.4</td>\n",
       "      <td>17.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>195</th>\n",
       "      <td>38.2</td>\n",
       "      <td>3.7</td>\n",
       "      <td>13.8</td>\n",
       "      <td>7.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>196</th>\n",
       "      <td>94.2</td>\n",
       "      <td>4.9</td>\n",
       "      <td>8.1</td>\n",
       "      <td>14.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>197</th>\n",
       "      <td>177.0</td>\n",
       "      <td>9.3</td>\n",
       "      <td>6.4</td>\n",
       "      <td>14.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>198</th>\n",
       "      <td>283.6</td>\n",
       "      <td>42.0</td>\n",
       "      <td>66.2</td>\n",
       "      <td>25.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199</th>\n",
       "      <td>232.1</td>\n",
       "      <td>8.6</td>\n",
       "      <td>8.7</td>\n",
       "      <td>18.4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>200 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        TV  Radio  Newspaper  Sales\n",
       "0    230.1   37.8       69.2   22.1\n",
       "1     44.5   39.3       45.1   10.4\n",
       "2     17.2   45.9       69.3   12.0\n",
       "3    151.5   41.3       58.5   16.5\n",
       "4    180.8   10.8       58.4   17.9\n",
       "..     ...    ...        ...    ...\n",
       "195   38.2    3.7       13.8    7.6\n",
       "196   94.2    4.9        8.1   14.0\n",
       "197  177.0    9.3        6.4   14.8\n",
       "198  283.6   42.0       66.2   25.5\n",
       "199  232.1    8.6        8.7   18.4\n",
       "\n",
       "[200 rows x 4 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "3157d450",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'whiskers': [<matplotlib.lines.Line2D at 0x1b528c822d0>,\n",
       "  <matplotlib.lines.Line2D at 0x1b528c83150>],\n",
       " 'caps': [<matplotlib.lines.Line2D at 0x1b528c83c90>,\n",
       "  <matplotlib.lines.Line2D at 0x1b528c90950>],\n",
       " 'boxes': [<matplotlib.lines.Line2D at 0x1b528c81410>],\n",
       " 'medians': [<matplotlib.lines.Line2D at 0x1b528bb0e90>],\n",
       " 'fliers': [<matplotlib.lines.Line2D at 0x1b528c046d0>],\n",
       " 'means': []}"
      ]
     },
     "execution_count": 23,
     "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": [
    "# Let us plot a boxplot to understand the concept of outliers.\n",
    "# Outliers are the values that skews away from the original dataset. In simple words, the value that are \n",
    "# deviation from original dataset.\n",
    "\n",
    "# Let us first start plotting boxplot for all the columns in data.\n",
    "\n",
    "column = data[\"TV\"]\n",
    "plt.boxplot(column)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "f04edb27",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Let us understand the above o/p (boxplot) now.\n",
    "# So in the above boxplot, the upper and lower horizontal lines are called upper & lower whiskers.\n",
    "# The line inside the box is the median and the box itself indicates how many values lie close to the median.\n",
    "\n",
    "# For the above diagram, we've no outliers. Let us check another column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "d2bbf95e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'whiskers': [<matplotlib.lines.Line2D at 0x1b528d6fdd0>,\n",
       "  <matplotlib.lines.Line2D at 0x1b528d80a50>],\n",
       " 'caps': [<matplotlib.lines.Line2D at 0x1b528d81590>,\n",
       "  <matplotlib.lines.Line2D at 0x1b528d82090>],\n",
       " 'boxes': [<matplotlib.lines.Line2D at 0x1b528d6ca50>],\n",
       " 'medians': [<matplotlib.lines.Line2D at 0x1b528d82c10>],\n",
       " 'fliers': [<matplotlib.lines.Line2D at 0x1b528aa3490>],\n",
       " 'means': []}"
      ]
     },
     "execution_count": 26,
     "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": [
    "\n",
    "column = data[\"Radio\"]\n",
    "plt.boxplot(column)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "0a69992d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'whiskers': [<matplotlib.lines.Line2D at 0x1b528ddba10>,\n",
       "  <matplotlib.lines.Line2D at 0x1b528de8690>],\n",
       " 'caps': [<matplotlib.lines.Line2D at 0x1b528c931d0>,\n",
       "  <matplotlib.lines.Line2D at 0x1b528de9810>],\n",
       " 'boxes': [<matplotlib.lines.Line2D at 0x1b528ddad50>],\n",
       " 'medians': [<matplotlib.lines.Line2D at 0x1b528dea390>],\n",
       " 'fliers': [<matplotlib.lines.Line2D at 0x1b528dead50>],\n",
       " 'means': []}"
      ]
     },
     "execution_count": 27,
     "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": [
    "# Again a decent boxplot without any outliers.\n",
    "# Now let us check the column 'Newspaper'\n",
    "\n",
    "column = data[\"Newspaper\"]\n",
    "plt.boxplot(column)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "78c83069",
   "metadata": {},
   "outputs": [],
   "source": [
    "# As evident from the figure above, there are 2 co-ordinates that skew/deviate from the original data. These co-ordinates\n",
    "# are called as Outliers.\n",
    "# Any value that lies beyound the upper and lower whiskers are called as Outliers.\n",
    "\n",
    "# So now, if we want to eliminate the outlier in this case, we've to consider the data that lies below the threshold of\n",
    "# the upper whisker (which is 90 in this case)\n",
    "\n",
    "data_new = data[data[\"Newspaper\"]< 90]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "d3816f8e",
   "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>TV</th>\n",
       "      <th>Radio</th>\n",
       "      <th>Newspaper</th>\n",
       "      <th>Sales</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>230.1</td>\n",
       "      <td>37.8</td>\n",
       "      <td>69.2</td>\n",
       "      <td>22.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>44.5</td>\n",
       "      <td>39.3</td>\n",
       "      <td>45.1</td>\n",
       "      <td>10.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>17.2</td>\n",
       "      <td>45.9</td>\n",
       "      <td>69.3</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>151.5</td>\n",
       "      <td>41.3</td>\n",
       "      <td>58.5</td>\n",
       "      <td>16.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>180.8</td>\n",
       "      <td>10.8</td>\n",
       "      <td>58.4</td>\n",
       "      <td>17.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>195</th>\n",
       "      <td>38.2</td>\n",
       "      <td>3.7</td>\n",
       "      <td>13.8</td>\n",
       "      <td>7.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>196</th>\n",
       "      <td>94.2</td>\n",
       "      <td>4.9</td>\n",
       "      <td>8.1</td>\n",
       "      <td>14.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>197</th>\n",
       "      <td>177.0</td>\n",
       "      <td>9.3</td>\n",
       "      <td>6.4</td>\n",
       "      <td>14.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>198</th>\n",
       "      <td>283.6</td>\n",
       "      <td>42.0</td>\n",
       "      <td>66.2</td>\n",
       "      <td>25.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199</th>\n",
       "      <td>232.1</td>\n",
       "      <td>8.6</td>\n",
       "      <td>8.7</td>\n",
       "      <td>18.4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>198 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        TV  Radio  Newspaper  Sales\n",
       "0    230.1   37.8       69.2   22.1\n",
       "1     44.5   39.3       45.1   10.4\n",
       "2     17.2   45.9       69.3   12.0\n",
       "3    151.5   41.3       58.5   16.5\n",
       "4    180.8   10.8       58.4   17.9\n",
       "..     ...    ...        ...    ...\n",
       "195   38.2    3.7       13.8    7.6\n",
       "196   94.2    4.9        8.1   14.0\n",
       "197  177.0    9.3        6.4   14.8\n",
       "198  283.6   42.0       66.2   25.5\n",
       "199  232.1    8.6        8.7   18.4\n",
       "\n",
       "[198 rows x 4 columns]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_new"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "0c94f9cd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'whiskers': [<matplotlib.lines.Line2D at 0x1b528e42f50>,\n",
       "  <matplotlib.lines.Line2D at 0x1b528e43b50>],\n",
       " 'caps': [<matplotlib.lines.Line2D at 0x1b528e48650>,\n",
       "  <matplotlib.lines.Line2D at 0x1b528e49150>],\n",
       " 'boxes': [<matplotlib.lines.Line2D at 0x1b528e42350>],\n",
       " 'medians': [<matplotlib.lines.Line2D at 0x1b528e49c50>],\n",
       " 'fliers': [<matplotlib.lines.Line2D at 0x1b528e123d0>],\n",
       " 'means': []}"
      ]
     },
     "execution_count": 30,
     "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": [
    "plt.boxplot(data_new[\"Newspaper\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3ae8f921",
   "metadata": {},
   "source": [
    "Correlation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "b6d7c1b8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Correlation tells how different columns in the dataset are related to each other.\n",
    "# Correlation defn : One unit change in one coulmn will translate to how many units change in another column.\n",
    "# 3 types of Correlation:\n",
    "    # Positive correlation: Direct proportionality b/w 2 columns. \n",
    "                            # If one increases another increases, if one decreases another decreases.\n",
    "    # Negative correlation: Inverse proportionality b/w 2 columns. \n",
    "                            # If one increases another decreases, if one decreases another increases.\n",
    "    # No correlation      : No relation or dependancy b/w 2 columns. \n",
    "                            # One's increase or decrease does not affect the other column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "379a3938",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'TV vs Sales: Positive Correlation')"
      ]
     },
     "execution_count": 38,
     "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": [
    "# Positive Correlation\n",
    "\n",
    "# To check for an example of positive correlation, let us see the following example.\n",
    "\n",
    "column_a = data[\"TV\"]\n",
    "column_b = data[\"Sales\"]\n",
    "\n",
    "plt.scatter(column_a,column_b)\n",
    "plt.xlabel(\"TV\")\n",
    "plt.xlabel(\"Sales\")\n",
    "plt.title(\"TV vs Sales: Positive Correlation\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "ddbab305",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Here as evident from the o/p above there exists are a strong positive correlation b/w TV and Sales."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "86ba51e3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Radio vs Sales')"
      ]
     },
     "execution_count": 35,
     "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": [
    "# Let us check the same for the column \"Radio\" & \"Newspaper\"\n",
    "\n",
    "column_c = data[\"Radio\"]\n",
    "column_d = data[\"Sales\"]\n",
    "\n",
    "plt.scatter(column_c,column_d)\n",
    "plt.xlabel(\"Radio\")\n",
    "plt.xlabel(\"Sales\")\n",
    "plt.title(\"Radio vs Sales\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "d38c4f70",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Newspaper vs Sales')"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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VWlqK5s2be9vxYEwXfHiGWhISEhh8EBERWYyUlAkmnBIREZGuGHwQERGRrhh8EBERka4YfBAREZGuGHwQERGRrhh8EBERka4YfBAREZGuGHwQERGRrhh8EBERka5MV+GUyCkqqwS2F5zGyXNlaBwfjR7pyagTwcUUicj+GHwQGSA7vxCz1+5HYUmZ97GUxGjMHN4WQzNSDNwzIiLtcdiFSGfZ+YWYuHS3T+ABAEUlZZi4dDey8wsN2jMiIn0w+CDSUWWVwOy1+yH8POd5bPba/ais8rcFEZE9MPgg0tH2gtO1ejyqEwAKS8qwveC0fjtFRKQzBh9EOjp5LnDgoWQ7IiIrYvBBpKPG8dGqbkdEZEUMPoh01CM9GSmJ0Qg0odaFq7NeeqQn67lbRES6YvBBpKM6ES7MHN4WAGoFIJ6fZw5vy3ofRGRrDD6IdDY0IwULx3SBO9F3aMWdGI2FY7qwzgcR2R6LjBEZYGhGCga3dbPCKRE5EoMPIoPUiXChd8uGRu8GaYCl84mCY/BBRKQils4nCo05H0REKmHpfCJpGHwQEamApfOJpGPwQUSkApbOJ5KOwQcRkQpYOp9IOgYfREQqYOl8IukYfBARqYCl84mkY/BBRKQCls4nko7BBxE5UmWVwNbDxViddxxbDxerMguFpfOJpGGRMQVYvZD0pOf15pRrW8tCYCydTxSaSwhhqknnpaWlSExMRElJCRISEozenVpYvZD0pOf15pRr21MIrOYXnyc0YA8FkTJy2m8Ou8jA6oWkJz2vN6dc2ywERmQODD4k4pcW6UnP681J1zYLgRGZA4MPifilRXrS83pz0rXNQmBE5sDgQyJ+aZGe9LzenHRtsxAYkTnICj7mzp2L7t27Iz4+Ho0bN8Ztt92Gb7/91mebcePGweVy+fzr1auXqjttBH5pkZ70vN6cdG2zEBiROcgKPjZu3IhJkyZh27ZtyMnJQUVFBYYMGYILFy74bDd06FAUFhZ6/3388ceq7rQR+KVFetLzenPStc1CYETmICv4yM7Oxrhx49CuXTt07NgRixcvxtGjR7Fr1y6f7aKiouB2u73/kpP5pUUkh57Xm9OubRYCIzJeWEXGSkpKAKBWcPHZZ5+hcePGSEpKQv/+/fHiiy+icePGft+jvLwc5eXl3p9LS0vD2SXV+Cu25PnSqlkLwW3DWghkPD2vN6dd2ywERmQsxUXGhBAYOXIkzpw5g88//9z7+IoVK1C/fn2kpaWhoKAAzz77LCoqKrBr1y5ERUXVep9Zs2Zh9uzZtR43sshYqGJLTqkCSebACqdEZAVyiowpDj4mTZqEf/3rX9i8eTOaNWsWcLvCwkKkpaVh+fLlGDVqVK3n/fV8NG/e3LDgg9UPiYiI5JMTfCgadnnkkUewZs0abNq0KWjgAQApKSlIS0vDwYMH/T4fFRXlt0fECKGKLblwtdjS4LZu3g0SEREpJCvhVAiByZMnY+XKlVi/fj3S09NDvqa4uBjHjh1DSor5ewucVGyJSEtarBhLRPYhq+dj0qRJWLZsGVavXo34+HgUFRUBABITExETE4Pz589j1qxZuOOOO5CSkoLvv/8eTz/9NBo1aoTbb79dkwNQk5OKLRFpxSkL1BGRcrJ6PhYuXIiSkhIMGDAAKSkp3n8rVqwAANSpUwf79u3DyJEjcd1112Hs2LG47rrrsHXrVsTHx2tyAGpyUrElIi04ZYE6IgqPrJ6PULmpMTExWLduXVg7ZCRPsaWikjK/eR8uXJ16aIdiS0RqqqwS2PZdMZ78YB9zpogoJK7tUo3Tii0RqSE7vxB9s9bj3r9+ibOXrgTcjjlTROTB4KMGVj8kki7QMEswzJkiorAqnNoVqx8ShRZsanowzJkiIgYfAdSJcKF3y4ZG7waRaYWaml4Tc6aIyIPBB5ECLEMub/iEOVNkVfysa4PBB5FMrGNxlZzhE7suUEf2xs+6dhSv7aIVObXhifTGtX9+Ulkl0DdrfcCp6QCQFBuJBZld0KtlQ9l3i7zjJCPxsy6f5mu7EDkR1/7x5ZmaPnHpbrgAn/PiOfqXRrVHn9aNZL837zjJSPysa49TbYkk4to/tWkxNZ1VUslo/Kxrjz0fRBJx7R//1JyazjtOMgN+1rXH4INIIq79E5haU9Pl3HFyKjxphZ917XHYhUgiz9o/ge63Xbial8A6FsrxjpPMgJ917TH4IABXu7u3Hi7G6rzj2Hq4GJVVppoEZQpc+0d7vOMkM+BnXXscdiHOLJDBk2BZ83yxjoU6uLI0mQU/69pinQ+H41x2ZViDQjueaxLwP32X1yTpiZ916eS03ww+HMxTJCpQgp/nLnPz9IH8sJGu2BtHZD0sMkaScGYBmRVXliayNwYfDsaZBWRmXFmayL4428XBOLOAiIiMwODDwTiXnYiIjMDgw8FCzWUXAEZ3b46P9v7A2h8ysW4KEVFgnO1CfmcWJMVGAgDOXrzifYyzDaThTA0iciJOtSXZqs9l//7URbyWe4C1PxRg3RQicio57TeHXQjATzMLbu2QiuU7jgZcVRS4uqoohxFqC7UiK2Dtc8ehJCJSC6fakg8z1P6wakVBM5w7rXAoiYjUxOCDfBhd+8PKjZzR504rgYaSikrKMHHpbg4lBWHVQJpIaww+yIeRtT+s3sjZsW5KqKEkF64OJQ1u62ajWoOVA2kirTHng3wYVfvDDvkSdqybImcoye7k5Lx4Auma584TSGfnF2q9u0SmxuCDfISq/QEAM4e3Vf0u1w6NnFHnTkt2HUqSKzu/EH2z1iNz0TY8tjwPmYu2oW/Wer9BhB0CaSKtMfigWoZmpGDhmC5wJ/oOD7gTozUb+rBLI2fEudOSHYeS5JLbi2GHQJpIa8z5IL/0XlXUTo2cnVZk9QwlFZWU+b2Td+FqYGWloSQ5lOS82CWQJtISgw8KSOmqokoy/O3WyNllRVbPUNLEpbu9Jfc9rDqUJIeS6dN2CqSJtMLgg1SlNMPfiEaO0yCl8Qwl1fy7uh0wc0NJL4bdAmkiLTD4INWEO1VWz0aO0yDlsdNQkhxKejGc3ltEJAXXdiFVVFYJ9M1aH7CL2nO3t3n6wJBfulr3SHD9FZLKc12H6sXwd10zwCWnkdN+s+eDVKFmaXE18yVqBjJd0xqwaBZJFk4vhlN7i4ikYPBBqjBjhr+/O8/kuEicvnAl4GuMXn+FeSjmE85woF0Sj4nUxuCDVGG2DP9AQyvBAo/qjJgGyW5682IvBpG6GHyQKsyU4R+sNoNUek+DtPq6Nk7AXgwi9bDCKanCTKXFQ+WfBGPE+issxy1v3RQisj72fJBqzFIPQumQiVHTINVM1rUiDjcROQ+DD1KVGcbGpQ6ZJMfVw+kLl70/G1U0y4zJunrhcBORMzH4INUZPTYuNf9k4+9uwK4jZwxPIDRbsq5elKybQkT2wOAjBD2nPtphmqUZjkFqbYZ6dSNMMYxhpmRdPTl9uInIyRh8BKHnWLQdxr3NdAxmyT+RwqnluKUOI2059KMlA3EiCszx5dUvV1ThH1u/x5HTF5GWHItf974W9epGhCzBveCeLmgQV0+VO3w7lPtW8xjU7D0xQ0+MVGYK3vSw9XAxMhdtk7Stnc8DkV3Iab8dHXzM/Xg/Fn1egOqz+iJcwAN9r8VHe4uCdglHuODzOqVfjmquiWIUNY/BaQ1wTVYKlsIVat2U6qwUiBM5lZz227F1PuZ+vB9vbvINPICrAcWiz78PWSei5us82fnZ+YWS96GySmDJlgLJ495mJWfsPhhP70nN91Jybq3Kk6w7slNT9G7Z0LaBBxC8NkxNTql3QuQUjgw+LldUYdHnBaq+p9wvx+z8QvTNWo/f/+sbSe9v1mmWlVUCWw79KGnbQMdQWSWw5eApPPnBPkcX2nIiT26OOzH0TB4rBOJEJI0jE07/sfX7Wj0XapCanR8oPyIYM06z9DdEEoy/Y5D6Hpz5YF+e2jCv5hzA/A2HQm5v1kCciKRzZM/HkdMXNX3/YF+OctcdMaLctxSBhkj8CXQMct7Dgw2PPdWJcKFPq0aStjVjIE5E8jgy+EhLjpW8rZIR92BfjnLWHTHrNEs5AVSgY1C6+BsbHvvy1DsJdKWbNRAnIvkcGXz8uve1CNWWR7iAP2d2rjUWHex1Ur4c5dy5uxOjw87uV2PBrprvse1wseQAKtAxyF38jQ2P/em1OCEXsSMyniNzPurVjcCEX6bjzU2Bk04n/DIdwzum4ub2KT5TH89cKMekZXsAKCsGJfXO/dlbfoFxfdLD+qJVY9qqv/dIiomU9NrJN7TC44Ov83sMcoIwM/cAOWVarF60Lg7n9KncRGbhyOADAJ66+eodlr86HxN+me593t86JQsjXIq/HKWW0lYj8Ah3wa5A73H20hVJ+9CnVaOAxyBn+MSMVUnZiGlHq8UJuYgdkXnIKjI2d+5crFy5Ev/5z38QExOD66+/HllZWfj5z3/u3UYIgdmzZ+Mvf/kLzpw5g549e2LBggVo166dpN9hlgqnodS86+2a1kDyImWeL0HAf++JGkMt4Rb9CvUewch5/2AFppJiIrHg3i7o1cJc9S7sUJHWaexQzI/I7DQrMrZx40ZMmjQJ27ZtQ05ODioqKjBkyBBcuHDBu83LL7+MefPmYf78+dixYwfcbjcGDx6Mc+fOKTsajdWrG4EHftkCz4/MwAO/bCEp8AB8i0GVXLqM/n/YgMxF2/DY8jxkLtqGvlnrAxbFClTbQI0cD0Cdol9yczI85AyRjO7ePGDvjwvAS3e0D9p7YoRQK7ECrEdiRmoVwiMidcgadsnOzvb5efHixWjcuDF27dqFfv36QQiB1157DTNmzMCoUaMAAG+//TaaNGmCZcuW4cEHH1Rvz02gskpg/vpDeDX3QK3nQnXlatW1DEjPpwi2ndT3iI2sg4tXKr0/SxkiCVXbw4zDLB52WonVSTkranwmiEg9YeV8lJSUAACSk6/OQCgoKEBRURGGDBni3SYqKgr9+/fHF198YavgIzu/ELPWfI2i0nK/zwtcvXufvXY/Brd1+/1S95dPogap+RSN46MDNkBS38MTeCTFRGJ8n3RMHtgqaAMWqsDa44OuC/keRrJLI+a0nBU5nwm9OCn4I6pJcfAhhMDUqVPRt29fZGRkAACKiooAAE2aNPHZtkmTJjhy5Ijf9ykvL0d5+U8NeGlpqdJd0o3UCqVG3QVLTWo9c+FyrXFwTwM0uK076HvUVHLpCl7LPYCfu+sHbLxC1fZwAVi+4ygmD2wl4Tcaw4yNmFxOTLyU+pnQayq304I/opoU1/mYPHky9u7di3fffbfWcy6Xb/QuhKj1mMfcuXORmJjo/de8eXOlu6Q6f/UAlBTH0vsuWEq9hBEdUzBpWeBF3HL2F0le9AuQlu9gh3F3owthhVujwqk5K3rVEJGCCygSKQw+HnnkEaxZswYbNmxAs2bNvI+73W4AP/WAeJw8ebJWb4jHU089hZKSEu+/Y8eOKdkl1XkWfquZRDp//SHZiZhG3AUHS2pdcE9nrPmqMGQDNLitW/KiX57XBgse7DBkYWQjFuialNNY2SEAVErrRG8pnBr8EdUka9hFCIFHHnkEH374IT777DOkp6f7PJ+eng63242cnBx07twZAHD58mVs3LgRWVlZft8zKioKUVFRCndfG8G6pf0llwaid1duTYGSWuU0QNXf49/5hfj7Vv/DZ9UFCh7sMGQBaF8Iyx+1hkrMHADqkQOhZaK3FHZKWCYKh6zgY9KkSVi2bBlWr16N+Ph4bw9HYmIiYmJi4HK5MGXKFMyZMwetW7dG69atMWfOHMTGxuKee+7R5ADUJuXORA6jq3L6S2qV2wBVfw8pwUeg4MFs4+7h0LMRC3VNhkpsrs6sAaCeORBaJXpLYebgj0hPsoZdFi5ciJKSEgwYMAApKSnefytWrPBuM23aNEyZMgUPP/wwunXrhuPHj+OTTz5BfHy86juvBaX1LWpK0bErVy6lDVC4+Q5mGndXQ/VaL71balcITc2hEqNzVvxxUg6EWYM/Ir3JCj6EEH7/jRs3zruNy+XCrFmzUFhYiLKyMmzcuNE7G8YKlKw5UtPjg1pj8/SBpgw8AOUNkBrBgxnG3a1GzbtlswWATsuBkPLZcydEoUoILnxHtubYtV0CkXrH8fig1li+45glp8p5GqCJS3fDBXkL5KmR72D0uLvVqH23bETOSiBOy4EI9dkTAMoqqnDvX7/0Pm6V7xUiOWSt7aIHvdd2qSnUmiPV14AAYOkGNJxxdhZI0o+ca1LO38AMf8PVecfx2PK8kNv9cXQnjOzUVPsd0onf1aJjI3H2Yu1FG7lmEFmFnPabPR81yO0VsPLdWDg9EEYm7TlNsGsS//t5dPdrFL2v0X9Dp+ZA1PzsNYqLwm//+RWA2sGH3KRiIitQXGTMzozMSwi3iJRcaidN6r3/ThHomvR4NfeA7JofZmDGBFi9VP/sRUS4UFTqzPor5Ezs+QjAiLwEq5dctvr+m53nmpy//iBezT1Y63krlkcPJ//ITjgFl5yGPR9B6DWVErD+dEOr77+VLN/hvwqwVWeHcAaUc4efyLnY82ECahaRMoLV999K7Do7xOkzoOxUgI9ICvZ8mIDV19uw+v5biZ275/XsaTQbs9VfIdIagw8TsHqDYvX9txJ2z9sXh5/ISTjsYgJWb1Csvv9Wwu55e3P68BM5B4MPHVUv6tSofhQggFMXytEoLgruhGicKLVmg8IGUT9WnB1ihmJmVmKG+itEWmPwoRN/01CrS4qN9CZnWqFBqc6KDaLa9GxgzVQePRROvyYif1heXQeeaajBTrSn0a5ZYtlKX9RObWiMOm6z9ygEuu5ZLpzInuS03ww+NOZZlyPYbBAPF4AmCVF45a5OOHW+3JQNSihmbxDVxgbWv1DXvdL1aIjIvLi2i4mEmoZanQBQVFqOCJfLsotoOWm8mvVNArNrPRIiUgen2mrEs8bJx/vkV/bklFRrcGp9Eynr93D6NREFw54PDYRKLg3FiVNSrThc48QGVmp+i9RruFH9KGw9XGypvzsRhY/Bh8qkJJcG4tQpqVZNVHVafZNA17a/Be2kTL9Oio3Eb9/LQ1FpufdxK/zdiSh8HHZRUbAcAKnsPiW1JisvSOek5eBD5bcAvgvahSoXLgCcuXjFJ/AArPF3J6LwMfhQkZzk0prcCVGOmxkht0EzG7XX45CSS2EUJfktwcqFJ8VGBnwfwNx/dyIKH4ddVKR0bP/WDin44+jOuvd4GJ1nocaMCKOPQa2CX2YfelKa3+KvXHhVlcC9f/sy4HtwJgyR/TH4UJHSsf1dR86ovCehmaGxCzdh0wzHAIS/HoecXAqjhJPfUnP69eq845Ley06JukTki8MuKgqVAxCI3tMx9cizkDKEEE6DZrZcEaXLwWsx9KTF8I2a+S1OS9Qlotoc0/MRTve81NcGW+MkFL3u8vQojCW1R0LpgnR2Ku6ldjEurXqD1Fy/hwsREpEjej6y8wvRN2s9Mhdtw2PL85C5aBv6Zq2XdHcs5bXV7zQTY+phwT2dayXZhaLXXZ7WhbHk9EgoTdg0qriXFj0KatYK0bo3KFgCqZyhIbUTdYnIemzf8xHOeLqU1wLwe6f57C1t0SCuHopKy/D7j77G6QtX4I/ed3lSG7uc/UWyk/2U9EgoSdg0oriXVj0Kag1B6NUbFG5+S/X3scrKvESkPlsHH+F8IUt57ZMr96Hk4hW/wcmkZVeDk9s7N0VMZAQmLt3tfa2HEXd5Uhu7t7Z8j8SYeri2UazkBkbpEELNBq1RXBTgAk6dL8fWw8W1frfeOQNaJoSGMwRRfTjw1Lly3dZSUWv9HrUCGSKyHlsHH3Iawx7pybWmA4Z67dmL/nszagY2ZrrL8zR2UuqRvJp7wPt/KXf54fRIeBq07PxCPPH+V0F7GPTMGZAawA5s0wS7jpyR3YgqzaVQWsLfDDNIjJ4eTUTGs3XwIWeIYep7eT5f5Ekx/osgSVXzTlPNu7xwvrw9jd1D/+uJkUrKXX64PRJSexjUTH4MRWoA22turs/QmpwhGbnBaTgl/I2eQWKW6dFEZCxbBx9yhhhqOnvJf6+GXNUDIDW6q9X48h6akYIH+lyLv/k57kCqT/sMlDcQ7hCCnCEyvXqTpAawNXN65A7JSA1OlZbwN8MMEivUMyEifdg6+AjVGAJAhAvQsoqzmneaan55D2rrlhV8eATLGwinR0JJvogeOQNK/35KkjylBKdKSvir1RsU7nR1u0yPdhoOk5EWbB18hGoMBZQFHp7XJsVG+k049Wyj5p2m2l/eUgKzQIpKAzd+gXokGsRF4oWRGQGDI6X5ImolPwYSznnSoky4kpwNNXqDwu1xU7ueCemDw2SkFdvX+QhWm+CBPtdKeo+a+R/uxGi8MaYLXhrV3u/2WsxiUbu2RbBaC6GcPl8e9PmhGSl49pa2SI6r99NrLlzB7//1TcBaE2apelmzlgcAxefJQ80kT6nH/+wtv8AfR3fCuxN6YfP0gbUaimA1S2o+9/He8OuHGDE9msJjtirCZC+27vnwCNQ9v73gtKShhwX3dkGEy1Wr2zE7vxCJsZG1Zr0kxUZi7qj2qt4ZSP1S3nLoR+/+heouDdRLEUr1oMKf7PxCTFomb3hI7RksSrqKg93l+TtPDePqofjC5ZD7ombAJPU8jeuTHvB4gx0nULtuTYTLf7VeOT1uZgkuSRoOk5HWHBF8AP6756V+kfdqUXutjmAzDs4EmIIbju9PXZS03fwNh/HB7uMY0TEFa74qDNldWj0w23LoFOZvOBTyd7gTYwI+p/RLS80ZLEq6iqXk02yePtAnoOma1gD9/7BB1zLh4Z6nYMcZaAZUsKFJqcMlLKluLRwmI63ZftglGKVlnkPNOPA0sGqU3/b8vne3H5W8fWFJGd7cVCC5u9QTmD0++DqkhCgLH2rxsHCGh9Qo362kq1jq4m4AfBaPq1c3wpAy4UrPk5TjVCpUz1yoz5oAMCzjahCs1ueGlOMwGWnNMT0fgSiZsqn3XcH2gtNBkzylCtVdWv2uOtDdaajGNNwvrXBmsFyuqMLTH+6T3esSzt/TqAJySs6TkpkyUkkZLgl0rlwuQIirU97f2vI9ExpNgMNkpDXHBx+A/C9yve8K1Ly7CBUYBWogpDYIanxpKZnBkp1fiKc/zA+4hg4Q+Nhz9xdJ+h1aBEzhkHuetLhLlTtcUv1c5e4vwt+2fF9rWId1P4zHYTLSGoOP/5HzRa73XYEWdxfBGqJwGlMjvrTkVvysfuzZ+YWS652oHTDpTe3rSOnQUp0IF3qkJ2Pqe3l+n2dCo/H0rCJMzuTonA+lPA1soI+dC6FzI+T+vuS48Mq91xSqIfI0pp78Brnl2wF98iCUVPz0HLvntaGo/fc0SqjrNpSafzI5uTg1qT11nNSnRg4WUSDs+VBA77uCOhEu3N6pqaKKpDXp0V2qZx6EnDyGmscu9bUCP/09rVztUUrRvZr/9/wMAPMzO6NBXJQqx86ERmvgysOkFQYfCumdaKi0HHp1enaXavGl5a/hl9M4VQ8iAOkN2/19rsXQjBS/U3iTYiIxvs+1mDywtSW+kENdt0DtOh9aXNNaDF1aOTA0MysMKZL1MPgIg553BXLKfKckRvut86H1DIya1PzSClS7Y3T35orfU2rDNritO2BeydlLV/Bq7kEs/uJ7vKRyYTmthLpu9bim1c4NYhlwImtxCSFMNam+tLQUiYmJKCkpQUJCgtG7YyqeBhDw32V+f59rMbitW3KFU6sI1PBLWWOn5vbuxGhsnj7Qe376Zq0P2QBu/N0N6P+HDSGHaFwAx8JlCHY9A9LPZbDrQ877WJ1dPu9kXXLabwYfFuO0OzxPgBCo4XcBSJQYfHi8O6GXt0dGSgOYGFMPmYu2SXrvlGrBDYUW7vUs5fpwO+Bv4rTvBTInOe03h10sxmkJYFJmRZy9eAWPD7oOi7cU4Oyl0KXtq+d6SMndWZ13XPL+suS0POFezywDLm1pAAYgZDYMPizISQlgUpNCr20UiwX3dMG9f/sy5LY1cz1CNYBy62NwhoY84VzPTp81wwXgyKoYfJBseo4ty5kVEU4SY7AG0PO+Uqf0suS0fpxeBpw9P2RVDD50YpdkML3HluUEFFrVX/G8b6BVX/3ti17scl0p5fQy4E7v+SHrYvChA7skgxkxtiw3oNCq/srQjBS8MaYLnly5D2cv1s4rMaLktF2uq3A4vQy403t+yLo420VjdpkGaPSsArkNrVY9ApVVAvPXH6qV3Kp3o2+X60otTg3EpE4Xt/tsHzIHTrU1CaMbbDVtPVwsabpp9WmsajPTEIOR+6L2dWWm8xoOuxyHXGrVSyEKF6famoSayWBGf7GaYWzZTLN8jNwXNa8rO/UYmOn60JPeSz0QqYHBh4bUarDN0EBwbNk81LyuWB/CHpxW/4esL8LoHbAzNRpsTwNR807X00Bk5xeGtY9ShVqO3S7LzluBGtdVqPoQwNX6EJVVphqVpSA8PT8jOzVF75YNGXiQqckOPjZt2oThw4cjNTUVLpcLq1at8nl+3LhxcLlcPv969eql1v5aSrgNtpkaCM+sAgC1jscJswrMRI1AUM7QjV4qqwS2Hi7G6rzj2Hq4mIEPkY3JDj4uXLiAjh07Yv78+QG3GTp0KAoLC73/Pv7447B20qrCbbDN1kB4xpbdib531O7EaHbR60iNQNAMOTzVZecXom/WemQu2obHluchc9E29M1ar1vPHhHpS3bOx7BhwzBs2LCg20RFRcHtdiveKSsKlBAaTjKY1C/+3P1FuiXacWzZHMJNMjRTDg9zT4icR5OE088++wyNGzdGUlIS+vfvjxdffBGNGzfW4leZQqiEUKUNttQv/g/zjuPpW/Qb8nDqrAKzCScQNEtlUK5NQuRMqiecDhs2DO+88w7Wr1+PV155BTt27MDAgQNRXl7ud/vy8nKUlpb6/LMSqQmhSpLBeqQnIzmuXsjtTl+4ouvYPBnPkx/x0d4fAAC3dkiVlWRolhwesw0tEpE+VO/5uPvuu73/z8jIQLdu3ZCWloZ//etfGDVqVK3t586di9mzZ6u9G7rQ+q6tToQLt3VKxVtbvg+5rZpj80bXFDGClY5ZranXZqgPYbbcEyLSh+Z1PlJSUpCWloaDBw/6ff6pp57C1KlTvT+XlpaiefPmWu+WKvRYUXJwW7ek4EOtsXkz1BTRm5WOWe38CKNzeMyUe0JE+tG8zkdxcTGOHTuGlBT/X4hRUVFISEjw+WcVety1ecbmA1GzvoZZaoroyUrHrNXUayPrQ7B+DJEzyQ4+zp8/j7y8POTl5QEACgoKkJeXh6NHj+L8+fN44oknsHXrVnz//ff47LPPMHz4cDRq1Ai333672vtuOD3u2jxj8y5oOzZvppoierHaMdsxP8IsuSdEpC/ZwcfOnTvRuXNndO7cGQAwdepUdO7cGc899xzq1KmDffv2YeTIkbjuuuswduxYXHfdddi6dSvi4+NV33mj6XXXpkd9DTs2bKFY7Zjtmh/B+jFEziM752PAgAEIthDuunXrwtohK/HctU1cuhsu+F9RUq27tmBj82okS9q1YQvGasds5/wIo3NPiEhfXFguTHrOGPBXX0OtZEk7N2yBWO2YzVKbQyusH0PkHAw+VGDUXZuaMx/s3rD5Y7Vj1rOnjYhIS1zVViXBZgxUVglsOXQK/2/dt/h/6/6DLQdP4XJFVViLaKmdLOnExD8rHjPzI4jIDlwiWAKHAUpLS5GYmIiSkhJLTbsNJDu/EE+u3IezF6/4PF7zzlXuUMnWw8XIXLQt5HbvTuglqyvbSjUv1GLFY7ZSUTQicgY57TeHXTSUnV+Ih5bu9vtcuEMlWiVLOjHxz4rHzPwIIrIyBh8aqawSmLVmv+Tt5ZZj1zJZ0okNmxOPmYjIKMz50EBllcCSLQUoKpXX6yCnrgQrQxIRkVWx50Nl/vIH5JIyVMKZD9piTgURkXYYfKgo0NRXuaQOlZhhVVI7smICKhGRlXC2i0oqqwT6Zq0Pq8fDU1di8/SBsu6yeZeunkABpOdscjorEZF/nO1igFDrhEghAIzufo3s1zFZUh2haqfISQgmIqLAmHCqErlTWgM1Xa/mHkDfrPWmWsrdKay20JwSlVUirOJ2RERqYM+HSqTmadzWKRV3dm2O7unJWPjZIbyae7DWNp6aHwvu6YIGcfVUG06x4/CMmsdktYXm5GIuCxGZBYMPlUhdJ+SVuzp5V6JdvuOY3/fyvH7yu7tR/cY0nIbCjg2P2sdktYXm5FBzHSAionBx2EUlctcJkZIjUrNH3NNQyB2S8TQ8NX+f0vczAy2Oya61U9ReB4iIKFwMPlQkZ9EvJV33ShoKOzY8Wh2TFReak8IJuSxEZC0cdlGZ1HVClHbdV28opMxwkdPwWGXGjBbH5MkdKa+owpRBrfHu9qMoKi33Pm/l2il2z2WxEjvmXREpweBDA1KmvobKEQlF7QbFSg2P2sfkL3fEnRCNxwddh2sbxVq+kbBzLouV2DHvikgpDrsYJFgXvxRqNyhWanjUPKZAuSMnSsvwWu4BRNWNQO+WDS0beAD2zWWxEjvmXRGFg8GHgQLliARr5+Q2FHZseNQ6Jjvmw/hj11wWq3DKdUYkB4MPgw3NSMHm6QPx7oRe+OPoTnh3Qi/Mz+wMF9RpKMzQ8Khd2EqtY3JSIqacZGh/WJxMOSddZ0RSMefDBPzliCyMcKm2YJyRC9BpNc6txjHZMR8mGKnJ0DUxVyE8TrvOiKRg8GFSShsKvd5PCq0LW4V7THbMhwlF7jpALE4WPideZ2ReZplxxeDDxNReME7PBej0WqQt2DGF+pBJrUprpXwYNXGhPXXwOiOzMFMvJnM+SBNGj3Nn5xeib9Z6ZC7ahseW5yFz0bZaC/apnQ9jt7wIo/+GdmGGvCsis824YvBBmjBynFvOhyzcRMzqvzNUsGM1zFVQj1rXGZESZpxxxWEX0oTU8euDJ85j6+Fi1cYdlQwVhJs7Yte8COYqqMuIvCsiwJyVrhl8kCakVnCdv+EQ5m84pNq4o9IPmdJ8GDvnRTBXQX165l0ReZixF5PDLjZnRB6CJ9Hz5gy35NLxao076v0hs3NeBHMVarNbXg85gxl7MdnzYWNGZDb7+50RLiDUd7RavQR6f8jMeEehJiNrxJiNmWYKEMlhxl5MBh8KVZ/G2SguCnABp86Xe8dxAQQc29VjnrUReQiBfqf43wPDMprg3/knAr5ejXFHvT9kZryjUBtzFeyb10PO4OnFnLh0N1yAz3VsVC8mgw8F/N0BVZcUGwkAOHvxivcxzx0SAM3vnvTOQ6isEth2uBhPfrAv6O/celja0EM4vQR6f8h6pCcjKTbS529dnV3yIpycq2DnvB5yDrP1YjL4kCnQHVB1/hqiopIyPLR0t9/t1b570jOzOVQgVv13nr3kv4GuKdxeAj0/ZDn7iwIGHsDV43ZaXoTdmHGmAJESZurFZPAhQ7A7oFCCvUbtuye98hCkBGI1xdWrg4uXKzUfEtHjQ+a5HoJJio3E4LZu1X4n6c/ueT3kLGbpxWTwIUOoO6BwqHn3pEcegtJAzPW/tl+PIRGtP2RSroezF6+Y4o7YLOs5WJET8nqI9MbgQwY97myKSsuw9XBxWI2EHkmXSgOx8+WVeHzQdVi+46gpxh3DYZU7Ys7SCI8ZZwoQWR2DDxn0uLP5/Udf4/SF2omqchoJPZIuw2lQr20Ui83TB1r+TtwKd8ScpRE+M84UILI6FhmTwXMHpOVXTPXAA1BefEvrtSTCaVAbx0d7h0RGdmqK3i0bWvKLO9T14MLV4NGoO2IzrudgVVybhUhdjuv5CGfsO9gdkFbCSUaVmnSp5JxILZ9enZHd01rkPJj9jpizNNRlppkCRFbnqOBDjbHvQNM4q2sQG3l1ammQKZhyhNNIhEq6VHpO5AZiRjbGWuY8mG3ufHVWyUmxErPMFCCyOpcQwlR9rqWlpUhMTERJSQkSEhJUe99AY9+eZlBu16nUCqdbDv2I+RsOq3IMfxzdCSM7NVXlvQB1zom/ht1f8GVUgqPaf/dAzDibZOvhYmQu2hZyu3cn9GKDSkRhk9N+O6LnQ4sKhVLugHq3bIge6cn4YPfxoJnyyXH1UHzhcsjfqWbiolrnJFBXNBC4vLxe9KxMacY7Ys7SICKzckTCqdSx7yVbClRfrVLKyqC/H5mhe+KimquxVk8e7ZGejO0Fp/HR3h8AALd2SNU9odSz8uirOd/adsVZKbgqLRGZlSN6PqSOaf/+X994/6/mMIGUvICICATMnxAAbs642rvQNa0Bdh05E3aPghb5AGaoJyG13Ht1ds55MHNOChE5lyNyPqSOfVcnNSdAzlh/qG2lLEdf82eljbva+QB65VYo2YdQnJDzYMacFKPwXBBpQ0777Yjgo7JKoG/WelnTQoGfxsQ3Tx/o98tJizt9zxdjzv4ivLXle0n7CChLmA12TkIdu7/3CtTbIOe9lAq1D0btF5mLGXrniOxKTvvtiJyPYGPfwXhyAl7N+bZWHojnLrtmY6e0KFj1fe2Rnox/5xdJ3kdAfrEoNfMB1MwfUUpuuXfmPDiPVp9ZIpLPEcEHELhCoRTzNxxG5qJt6Ju1Htn5hZpXjpTbkCpt3NWq2hhO/ognOTTcRF+5eRuhjlGt/SJzYLVXInNxRMKpR81poafOlfskmYbiuUOaMqi1ppUjlSZAKnmdGlUbla5xomYXuNR9mHxDK/Rp1SjoMWrVNc9cA+Ow2iuRuTgq+AB86zFUVgn8dXOB5FwQT22IxRJyMQDlQYTSeh5KXxdujQol9STUXvBM6j48Pvi6oA2+VguxMdfAWKz2SmQujhl28UdJLogAcPaStLLpSoMBuQvYGb2Amdz8ES26wMPJYfEMsXy45zie/nCf6l3zauYacDhIGSusQEzkJI4OPgDluSBJMZGKi4KFakDkBEVmSZyUkz+iVYKqkhyW7PxC9M1aj8xF2/D4irxaqwqHu19qBlrV9/Wx5Xk+eUgUnNlXICZyGscNu/hTPe9hy6FTmL/hUMjXjO+TjtdyD8hezVRq93ug4lA163yYqViU1PwRqV3bOfuLZA8HyclhUVoXRE7XvFq5BloNBzmF2VcgJnIaBh//48l7uLoWy39D5g5MHtgKP3fXl1U5Um4D4q8hVavCqRJSEib95Y/UfF2juChJv++tLd+jR3qy7EZVSg5LsB6JUOR0zauRa6DnGjV2xmqvRObB4KMGOXdIcu6ylTYg/hpSI7LxlSZM+nudOyEaSbGRPqve+qNloyp3OrNnf+QuxKZGrgFnaqhHjdldRBQ+x+d8+CMnd6D6omrBFlAzQyEupZQmTAZ63YnSspCBB6DtOVE6q0Fu17wauQacqaEuqZ9ZItKO7OBj06ZNGD58OFJTU+FyubBq1Sqf54UQmDVrFlJTUxETE4MBAwbg66+/Vmt/dTM0IwWbpw/EuxN64Y+jO+HdCb2wefpAxV2zVm1AlCZMSunpiatXR9I+aHFO5M5qiHABC+6Rn1ehRiVZztQgIruRHXxcuHABHTt2xPz58/0+//LLL2PevHmYP38+duzYAbfbjcGDB+PcuXNh76ze1LxDsmoDorTHRsrrLlyulLQPWpwTudOZqwTQIK6eot8VbiVZztQgIruRnfMxbNgwDBs2zO9zQgi89tprmDFjBkaNGgUAePvtt9GkSRMsW7YMDz74YHh7a2FKCnGZgdIeG6mvS4qJRMmlK7qfk+q5PVKF0wMTTq4BZ2oQkd2omvNRUFCAoqIiDBkyxPtYVFQU+vfvjy+++MLva8rLy1FaWurzz47UXMhNbcHqjijtsZH6uvF90gEYc048PRLJEns0GsVFhVXgS0pPWqC/hVrr8BARmYGqs12Kiq6uxNqkSROfx5s0aYIjR474fc3cuXMxe/ZsNXfDtMw41S/ULBalPTZSX6dkyrKahmakYGCbJug1NzdggTEXgMTYSPz2n1+hqFS78uih/hacqUFEduESQiiuz+xyufDhhx/itttuAwB88cUX6NOnD3744QekpPz0hTxhwgQcO3YM2dnZtd6jvLwc5eXl3p9LS0vRvHlzlJSUICEhQemumVpllcC274qx9XAxAIHeLRqhlwFZ94Hqjnj2wnNHPffj/XhzU4Hf93BV2y7Q+wP+hwqqv87oRdeC7WugD4i/4wj394f6WxARmVVpaSkSExMltd+q9ny43W4AV3tAqgcfJ0+erNUb4hEVFYWoKGlFp+wiZ3+Rzx3u/A2HdV9kTGrdkaoq4C8BAg8A+L9+6QH3WU5PT7iL24Ur0L42SYhCWUWV36nBahX4YhExInIaVYOP9PR0uN1u5OTkoHPnzgCAy5cvY+PGjcjKylLzV1mW1mWypfYgSJ3F8szq/KBVQNd8VYhpQ38RsFG00lCBv32tEgL3/vXLgK9Ro8AXi4gRkdPIDj7Onz+PQ4d+WvukoKAAeXl5SE5OxjXXXIMpU6Zgzpw5aN26NVq3bo05c+YgNjYW99xzj6o7bkVa3+HKqUIqdebG6QuXgz4vpVE0uldDjpr7ujrvuKTXhTMTxqo1YIiIlJIdfOzcuRM33HCD9+epU6cCAMaOHYslS5Zg2rRpuHTpEh5++GGcOXMGPXv2xCeffIL4+Hj19tqitLzDldujombtDDs3inrUZ7FqDRgrMzrHiMjpZAcfAwYMQLAcVZfLhVmzZmHWrFnh7JctaXWHq6RHRcpslAZxkUGXmPewc6OoR30Wq9aAsSql6xQRkXq4touOtLrDVVKFVErdkRdGZji+sqYe9VnMXAPGbpSuU0RE6mLwoSOtymQr7VEJVbjq5g6pbBShT4EvFhHTntJ1iohIfarOdqHgtCqTHU6PSqjZKGYsjGYEPWbtWGlmkBVxVhGReTD40JkWjXm4OQOhZqOwUbxKj1k7VpoZZDWcVURkHgw+DKB2Y652j0qgmQBsFMnKOKuIyDwYfBhE7cZcrR4VK80E4HRJkoOziozFzytVF9baLlqQUxueagvnA26l9UWsFCSRechZb4jUw8+rM8hpvxl8EICrQUvfrPUBE/I8d4Wbpw80/G7FSkESmQ8bQn3x8+ochi0sR9ZllZkATliEjd3T2mICtX6c8HklZRh8EACgqOSSpO2MnglglSBJKd6V64MJ1Pqw++eVlGORMUJ2fiF+/69vJG1r9EwAO0+XZPVNshs7f14pPAw+HM7T4IVavdYspdTtOl2S1TfJjuz6eaXwMfhwsGANnj9mKKWuVYl6oylZn4fI7Oz6eaXwMfhwsFANnkdyXGStjPTKKoGth4uxOu84th4u1u2O3K6LsLF7muzIrp9XCh8TTh1MakP27K3tfAKP7PxCzFrzNYpKy72PuROiMGtEO12SIu243gy7p8mu7Ph5pfAx+HAwqQ2ZO+Gn7bLzC/HQ/4o0VVdUWo6Hlu7GGzrN2bfbdElW3yQ7s9vnlcLH4MPB5DZ4lVUCT67cF/Q9n1y5T7c5+3aaLqnVisdEZmGnzyuFjzkfDiZ3PHbb4WKcvXgl6HuevXgF2w4Xq7ynzuDpnnYn+vZIuROjWQWSiGyFPR8OJ2c8dut3pyS959bvTqFP60aq76sTsHuaiJyAwQfJaPCkNoBsKMPB7mkisjsGHxYndx2QQNtLafB6t2yI+RsOhdwnNpxERBQMgw8Lk7sOSLjrhvRq0RBJsZFB8z4axEaiVwsGH0REFBgTTi1K7jogaqwbUifChZdGtQ+6zdxR7ZmfQEREQTH4sCC564CouW7I0IwUvDGmi0/tD+BqD4peNT6IiMjaOOxiQXKXqVZ7WWsrz8iQmyNjNlbffyIigMGHJcldB0SLdUPMMCNDbkMcbs6L0ay+/xQYg0pyGgYfFiR3HRA7rhuiJNl24tLdtYaePDkvZi/iZfX9p8AYVJITMefDguQuU223Za3lJs+qmfNiBK3336gVikmdRHAiK2LwYUFyy6LbaVlrJQ2xnJwXM9Jy/7PzC9E3az0yF23DY8vzkLloG/pmrWejpwOrB8VE4WDwYVFy1wFRY90QM9whK2mItch50ZNW+8+7bmNZPSgmCgdzPixM7qyTcGapmGVcWklDbPWcFy32P9RdtwtX77r1WqHYiaweFBOFg8GHxcmddaJkloqSZEetsveVNMSenJeikjK/ja0LV3uAzJrzosX+qz39muSzelBMFA4Ou1BQSsaltcwjUJI8a/WcFy32n3fdxrNbIjiRHAw+KCipd8hLthRgdd5x/DH3oKZ5BEobYjVyXoyk9v7zrtt4Vg+KicLhEkKYKpW6tLQUiYmJKCkpQUJCgtG743ir847jseV5qryXZ3hg8/SBYX+hKs1BsXoxJ7X2v7JKoG/W+qCBZYpKfysKziz5VEThktN+M+eDglLzzlfNPAKlybNmqMwaDrX2v06ECyM6puDNTQUBtxnRMYWBhw6svFwBkVIMPiioUMmOSqiVR2D1QMJIlVUCa74KPgS25qtCTBv6CzaCOuC1TE7DnA8KKti4tFLMIzBeqFwegDUmiEg7DD4opEDJjnIxe988ONuFiIzEYReSpOa49Klz5fj9v76R/Hpm75sLZ7sQkZEYfJBk1celK6sE/rq5QHIuiJvZ+6Zi9cJrRGRtDD5IEU8uyMSlu+ECfBowz8+PD2qNaxvFMXvfhEL9/QD2UhGRdljng8Ji1hoFVq/noRez/v2IyHrktN8MPihsZmvo2aDKY7a/HxFZE4MPcqxAi+B5mlIrlFInIrIiOe03p9ri6p3f1sPFWJ13HFsPF/sskkbWoWQRPCIi0p/jE07ZRW8fXCZeOg61EJGRHB18BOqi96zAyi56a7FK4SyjG34G3ERkNMcGH6G66F242kU/uK2bd4QWYYXCWUY3/Ay4icgMHJvzIaeLnqzBUzgrUKhodHl3T8Nf87rzNPzZ+cEXegsXc2KIyCwcG3wY3UXv9CRXLY4/2CJ4RhfOMkPDz4CbiMzCscMuRnbRG931bjQtj9+zCF7N9ze6vLsZkmGNDriJiDwcG3wYtbaF08fc9Tj+movgmWE2hxkafivkxBCRMzh22MWILnozdL0bSc/j9yyCN7JTU/Ru2dDwpGEzNPxmz4khIudwbPAB/NRF7070/cJ3J0Zr0gPh9DF3Jx+/GRp+M+fEEJGzqD7sMmvWLMyePdvnsSZNmqCoqEjtX6UKPbvozdD1biQnH79ZVpE1a04METmLJjkf7dq1Q25urvfnOnXqaPFrVOPpoteaGbrejeT04zdLw2/GnBgichZNgo+6devC7XZr8daqMKrCpFFJrmbh9OMHzNPw6xVwExH5o0nwcfDgQaSmpiIqKgo9e/bEnDlz0KJFC7/blpeXo7y83PtzaWmpFrvkZeQ0V7N0vRvF6cfvwYafiJxO9YTTnj174u9//zvWrVuHRYsWoaioCNdffz2Ki4v9bj937lwkJiZ6/zVv3lztXfIyusIkoH+Sq9k4/fiJiAhwCSE0ndd54cIFtGzZEtOmTcPUqVNrPe+v56N58+YoKSlBQkKCavtRWSXQN2t9wNkWni7/zdMH6nLnbfTiYkZz+vETEdlNaWkpEhMTJbXfmhcZi4uLQ/v27XHw4EG/z0dFRSEqKkrr3TBFhcnqnN717vTjJyJyMs3rfJSXl+Obb75BSoqx3elOnuZJRERkJqoHH0888QQ2btyIgoICfPnll/jVr36F0tJSjB07Vu1fJYvTp3kSERGZherDLv/973+RmZmJU6dO4Wc/+xl69eqFbdu2IS0tTe1fJQuneRIREZmD6sHH8uXL1X5LVXCaJxERkTk4am0XTvMkIiIynuazXczGLBUmiYiInMpxwQfAaZ5ERERGctSwCxERERmPwQcRERHpisEHERER6YrBBxEREemKwQcRERHpisEHERER6YrBBxEREemKwQcRERHpisEHERER6cqRFU7VUlklWKadiIhIJgYfCmXnF2L22v0oLCnzPpaSGI2Zw9tygToiIqIgOOyiQHZ+ISYu3e0TeABAUUkZJi7djez8QoP2jIiIyPwYfMhUWSUwe+1+CD/PeR6bvXY/Kqv8bUFEREQMPmTaXnC6Vo9HdQJAYUkZthec1m+niIiILITBh0wnzwUOPJRsR0RE5DQMPmRqHB+t6nZEREROw+BDph7pyUhJjEagCbUuXJ310iM9Wc/dIiIisgwGHzLViXBh5vC2AFArAPH8PHN4W9b7ICIiCoDBhwJDM1KwcEwXuBN9h1bcidFYOKYL63wQEREFwSJjCg3NSMHgtm5WOCUiIpKJwUcY6kS40LtlQ6N3g4iIyFI47EJERES6YvBBREREumLwQURERLpi8EFERES6YvBBREREumLwQURERLpi8EFERES6YvBBREREumLwQURERLpihVMdVVYJlmMnIiLHY/Chk+z8Qsxeux+FJWXex1ISozFzeFsuREdERI7CYRcdZOcXYuLS3T6BBwAUlZRh4tLdyM4vNGjPiIiI9MfgQ2OVVQKz1+6H8POc57HZa/ejssrfFkRERPbD4ENj2wtO1+rxqE4AKCwpw/aC0/rtFBERkYEYfGjs5LnAgYeS7YiIiKyOwYfGGsdHq7odERGR1TH40FiP9GSkJEYj0IRaF67OeumRnqznbhERERmGwYfG6kS4MHN4WwCoFYB4fp45vC3rfRARkWMw+NDB0IwULBzTBe5E36EVd2I0Fo7pwjofRETkKCwyppOhGSkY3NbNCqdEROR4DD50VCfChd4tGxq9G0RERIbisAsRERHpisEHERER6YrBBxEREemKwQcRERHpisEHERER6YrBBxEREemKwQcRERHpisEHERER6YrBBxEREenKdBVOhRAAgNLSUoP3hIiIiKTytNuedjwY0wUf586dAwA0b97c4D0hIiIiuc6dO4fExMSg27iElBBFR1VVVfjhhx8QHx8Pl0u9RddKS0vRvHlzHDt2DAkJCaq9rxPw3CnHcxcenj/leO6U47lTRgiBc+fOITU1FRERwbM6TNfzERERgWbNmmn2/gkJCbyYFOK5U47nLjw8f8rx3CnHcydfqB4PDyacEhERka4YfBAREZGuHBN8REVFYebMmYiKijJ6VyyH5045nrvw8Pwpx3OnHM+d9kyXcEpERET25pieDyIiIjIHBh9ERESkKwYfREREpCsGH0RERKQrRwQfr7/+OtLT0xEdHY2uXbvi888/N3qXTGfu3Lno3r074uPj0bhxY9x222349ttvfbYRQmDWrFlITU1FTEwMBgwYgK+//tqgPTavuXPnwuVyYcqUKd7HeO6CO378OMaMGYOGDRsiNjYWnTp1wq5du7zP8/z5V1FRgWeeeQbp6emIiYlBixYt8Pzzz6Oqqsq7Dc/dTzZt2oThw4cjNTUVLpcLq1at8nleyrkqLy/HI488gkaNGiEuLg4jRozAf//7Xx2PwiaEzS1fvlxERkaKRYsWif3794vHHntMxMXFiSNHjhi9a6Zy0003icWLF4v8/HyRl5cnbrnlFnHNNdeI8+fPe7d56aWXRHx8vPjggw/Evn37xN133y1SUlJEaWmpgXtuLtu3bxfXXnut6NChg3jssce8j/PcBXb69GmRlpYmxo0bJ7788ktRUFAgcnNzxaFDh7zb8Pz598ILL4iGDRuKjz76SBQUFIh//vOfon79+uK1117zbsNz95OPP/5YzJgxQ3zwwQcCgPjwww99npdyrh566CHRtGlTkZOTI3bv3i1uuOEG0bFjR1FRUaHz0Vib7YOPHj16iIceesjnsTZt2ognn3zSoD2yhpMnTwoAYuPGjUIIIaqqqoTb7RYvvfSSd5uysjKRmJgo3njjDaN201TOnTsnWrduLXJyckT//v29wQfPXXDTp08Xffv2Dfg8z19gt9xyi7j//vt9Hhs1apQYM2aMEILnLpiawYeUc3X27FkRGRkpli9f7t3m+PHjIiIiQmRnZ+u273Zg62GXy5cvY9euXRgyZIjP40OGDMEXX3xh0F5ZQ0lJCQAgOTkZAFBQUICioiKfcxkVFYX+/fvzXP7PpEmTcMstt2DQoEE+j/PcBbdmzRp069YNd955Jxo3bozOnTtj0aJF3ud5/gLr27cvPv30Uxw4cAAA8NVXX2Hz5s24+eabAfDcySHlXO3atQtXrlzx2SY1NRUZGRk8nzKZbmE5NZ06dQqVlZVo0qSJz+NNmjRBUVGRQXtlfkIITJ06FX379kVGRgYAeM+Xv3N55MgR3ffRbJYvX47du3djx44dtZ7juQvuu+++w8KFCzF16lQ8/fTT2L59Ox599FFERUXhvvvu4/kLYvr06SgpKUGbNm1Qp04dVFZW4sUXX0RmZiYAXntySDlXRUVFqFevHho0aFBrG7Yp8tg6+PBwuVw+Pwshaj1GP5k8eTL27t2LzZs313qO57K2Y8eO4bHHHsMnn3yC6OjogNvx3PlXVVWFbt26Yc6cOQCAzp074+uvv8bChQtx3333ebfj+attxYoVWLp0KZYtW4Z27dohLy8PU6ZMQWpqKsaOHevdjudOOiXniudTPlsPuzRq1Ah16tSpFZGePHmyVnRLVz3yyCNYs2YNNmzYgGbNmnkfd7vdAMBz6ceuXbtw8uRJdO3aFXXr1kXdunWxceNG/OlPf0LdunW954fnzr+UlBS0bdvW57Ff/OIXOHr0KABee8H87ne/w5NPPonRo0ejffv2+PWvf43HH38cc+fOBcBzJ4eUc+V2u3H58mWcOXMm4DYkja2Dj3r16qFr167IycnxeTwnJwfXX3+9QXtlTkIITJ48GStXrsT69euRnp7u83x6ejrcbrfPubx8+TI2btzo+HN54403Yt++fcjLy/P+69atG+69917k5eWhRYsWPHdB9OnTp9a07gMHDiAtLQ0Ar71gLl68iIgI36/xOnXqeKfa8txJJ+Vcde3aFZGRkT7bFBYWIj8/n+dTLsNSXXXimWr7t7/9Tezfv19MmTJFxMXFie+//97oXTOViRMnisTERPHZZ5+JwsJC77+LFy96t3nppZdEYmKiWLlypdi3b5/IzMx07JS9UKrPdhGC5y6Y7du3i7p164oXX3xRHDx4ULzzzjsiNjZWLF261LsNz59/Y8eOFU2bNvVOtV25cqVo1KiRmDZtmncbnrufnDt3TuzZs0fs2bNHABDz5s0Te/bs8ZZekHKuHnroIdGsWTORm5srdu/eLQYOHMiptgrYPvgQQogFCxaItLQ0Ua9ePdGlSxfv9FH6CQC//xYvXuzdpqqqSsycOVO43W4RFRUl+vXrJ/bt22fcTptYzeCD5y64tWvXioyMDBEVFSXatGkj/vKXv/g8z/PnX2lpqXjsscfENddcI6Kjo0WLFi3EjBkzRHl5uXcbnrufbNiwwe/33NixY4UQ0s7VpUuXxOTJk0VycrKIiYkRt956qzh69KgBR2NtLiGEMKbPhYiIiJzI1jkfREREZD4MPoiIiEhXDD6IiIhIVww+iIiISFcMPoiIiEhXDD6IiIhIVww+iIiISFcMPohIV0uWLEFSUpLRu0FEBmLwQUSynDx5Eg8++CCuueYaREVFwe1246abbsLWrVuN3jUisoi6Ru8AEVnLHXfcgStXruDtt99GixYtcOLECXz66ac4ffq00btGRBbBng8ikuzs2bPYvHkzsrKycMMNNyAtLQ09evTAU089hVtuuQUAMG/ePLRv3x5xcXFo3rw5Hn74YZw/fz7o+65duxZdu3ZFdHQ0WrRogdmzZ6OiosL7/KxZs7w9LampqXj00Uc1PU4i0haDDyKSrH79+qhfvz5WrVqF8vJyv9tERETgT3/6E/Lz8/H2229j/fr1mDZtWsD3XLduHcaMGYNHH30U+/fvx5tvvoklS5bgxRdfBAC8//77ePXVV/Hmm2/i4MGDWLVqFdq3b6/J8RGRPriwHBHJ8sEHH2DChAm4dOkSunTpgv79+2P06NHo0KGD3+3/+c9/YuLEiTh16hSAqwmnU6ZMwdmzZwEA/fr1w7Bhw/DUU095X7N06VJMmzYNP/zwA+bNm4c333wT+fn5iIyM1Pz4iEh7DD6ISLaysjJ8/vnn2Lp1K7Kzs7F9+3b89a9/xbhx47BhwwbMmTMH+/fvR2lpKSoqKlBWVobz588jLi6uVvARFxeHqqoq1KlTx/v+lZWVKCsrw4ULF1BcXIw+ffpACIGhQ4fi5ptvxvDhw1G3LlPWiKyKwQcRhe03v/kNcnJysGnTJrRp0wYPPfQQ7r77biQnJ2Pz5s144IEHcObMGSQlJdUKPmJiYjB79myMGjWq1vu2aNECERERuHTpEnJycpCbm4t//vOfSE9Px8aNG9kTQmRRvHUgorC1bdsWq1atws6dO1FRUYFXXnkFERFXU8ree++9oK/t0qULvv32W7Rq1SrgNjExMRgxYgRGjBiBSZMmoU2bNti3bx+6dOmi6nEQkT4YfBCRZMXFxbjzzjtx//33o0OHDoiPj8fOnTvx8ssvY+TIkWjZsiUqKirw5z//GcOHD8eWLVvwxhtvBH3P5557DrfeeiuaN2+OO++8ExEREdi7dy/27duHF154AUuWLEFlZSV69uyJ2NhY/OMf/0BMTAzS0tJ0OmoiUhtnuxCRZPXr10fPnj3x6quvol+/fsjIyMCzzz6LCRMmYP78+ejUqRPmzZuHrKwsZGRk4J133sHcuXODvudNN92Ejz76CDk5OejevTt69eqFefPmeYOLpKQkLFq0CH369EGHDh3w6aefYu3atWjYsKEeh0xEGmDOBxEREemKPR9ERESkKwYfREREpCsGH0RERKQrBh9ERESkKwYfREREpCsGH0RERKQrBh9ERESkKwYfREREpCsGH0RERKQrBh9ERESkKwYfREREpCsGH0RERKSr/w+LePgynNhMQQAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "column_e = data[\"Newspaper\"]\n",
    "column_f = data[\"Sales\"]\n",
    "\n",
    "plt.scatter(column_e,column_f)\n",
    "plt.xlabel(\"Newspaper\")\n",
    "plt.xlabel(\"Sales\")\n",
    "plt.title(\"Newspaper vs Sales\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "6cc5d3b2",
   "metadata": {},
   "outputs": [],
   "source": [
    "# As seen for both the the columns \"Newspaper\" & \"Radio\" there does not exist a strong correlation \n",
    "# b/w these columns wrt Sales."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "452ef798",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# The value of Correlation spans b/w -1 0 and 1\n",
    "# -1 or closer values: Negative correlation\n",
    "#  0 or closer values: No correlation\n",
    "#  1 or closer values: Positive correlation\n",
    "\n",
    "# But to get an exact match of correlation we've Heat Map\n",
    "\n",
    "import seaborn as sns\n",
    "sns.heatmap(data.corr(), annot=True) # annot means annotation, it will assign values to the heatmap."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "de8a7dae",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Let us interpret the image above.\n",
    "# The strip on it's right side gives the value of correlation.\n",
    "# Lighter shade (values closer to 1.0) gives higher correlation and Darker shade (values closer to 0.2) gives \n",
    "# lower correlation\n",
    "# The data in the diagonal is howing 1 always because it shows the correlation of the column with itself.\n",
    "# For e.g Sales (X-axis) = Sales (Y- axis), Newspaper (X-axis) = Newspaper (Y- axis)... and so on....\n",
    "\n",
    "# If we check the correlation of different coulns wrt Sales we get\n",
    "    # 0.16 for Newspaper\n",
    "    # 0.35 for Radio\n",
    "    # 0.9 for TV (close to 1 hence a good positive correlation)\n",
    "    \n",
    "# From the data above one can conclude that if one invests marketing on TV products the Sales will increase for sure."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}