will_the_ad_be_clicked (1).ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Will the Ad be clicked?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Installing libraries, dependencies, and data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import the required libraries and dependencies\n",
    "import pandas as pd\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import OneHotEncoder, OrdinalEncoder, StandardScaler\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.cluster import KMeans\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.metrics import r2_score, classification_report\n",
    "from imblearn.over_sampling import SMOTE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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>Age</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Income</th>\n",
       "      <th>Location</th>\n",
       "      <th>Ad Type</th>\n",
       "      <th>Ad Topic</th>\n",
       "      <th>Ad Placement</th>\n",
       "      <th>Clicks</th>\n",
       "      <th>Click Time</th>\n",
       "      <th>Conversion Rate</th>\n",
       "      <th>CTR</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>9995</th>\n",
       "      <td>7</td>\n",
       "      <td>Male</td>\n",
       "      <td>69925.29</td>\n",
       "      <td>Rural</td>\n",
       "      <td>Text</td>\n",
       "      <td>Fashion</td>\n",
       "      <td>Social Media</td>\n",
       "      <td>5</td>\n",
       "      <td>2023-05-31 20:45:56.927349</td>\n",
       "      <td>0.2460</td>\n",
       "      <td>0.1045</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9996</th>\n",
       "      <td>35</td>\n",
       "      <td>Male</td>\n",
       "      <td>42182.75</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Native</td>\n",
       "      <td>Technology</td>\n",
       "      <td>Website</td>\n",
       "      <td>9</td>\n",
       "      <td>2023-09-01 20:45:56.927349</td>\n",
       "      <td>0.2664</td>\n",
       "      <td>0.0407</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9997</th>\n",
       "      <td>42</td>\n",
       "      <td>Male</td>\n",
       "      <td>55084.12</td>\n",
       "      <td>Suburban</td>\n",
       "      <td>Text</td>\n",
       "      <td>Finance</td>\n",
       "      <td>Search Engine</td>\n",
       "      <td>3</td>\n",
       "      <td>2023-11-15 20:45:56.927349</td>\n",
       "      <td>0.2354</td>\n",
       "      <td>0.0390</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9998</th>\n",
       "      <td>34</td>\n",
       "      <td>Male</td>\n",
       "      <td>39382.80</td>\n",
       "      <td>Rural</td>\n",
       "      <td>Native</td>\n",
       "      <td>Technology</td>\n",
       "      <td>Social Media</td>\n",
       "      <td>4</td>\n",
       "      <td>2023-05-23 20:45:56.927349</td>\n",
       "      <td>0.1725</td>\n",
       "      <td>0.0455</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9999</th>\n",
       "      <td>54</td>\n",
       "      <td>Female</td>\n",
       "      <td>66441.59</td>\n",
       "      <td>Suburban</td>\n",
       "      <td>Banner</td>\n",
       "      <td>Technology</td>\n",
       "      <td>Search Engine</td>\n",
       "      <td>2</td>\n",
       "      <td>2023-11-21 20:45:56.927349</td>\n",
       "      <td>0.0307</td>\n",
       "      <td>0.0323</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Age  Gender    Income  Location Ad Type    Ad Topic   Ad Placement  \\\n",
       "9995    7    Male  69925.29     Rural    Text     Fashion   Social Media   \n",
       "9996   35    Male  42182.75     Urban  Native  Technology        Website   \n",
       "9997   42    Male  55084.12  Suburban    Text     Finance  Search Engine   \n",
       "9998   34    Male  39382.80     Rural  Native  Technology   Social Media   \n",
       "9999   54  Female  66441.59  Suburban  Banner  Technology  Search Engine   \n",
       "\n",
       "      Clicks                  Click Time  Conversion Rate     CTR  \n",
       "9995       5  2023-05-31 20:45:56.927349           0.2460  0.1045  \n",
       "9996       9  2023-09-01 20:45:56.927349           0.2664  0.0407  \n",
       "9997       3  2023-11-15 20:45:56.927349           0.2354  0.0390  \n",
       "9998       4  2023-05-23 20:45:56.927349           0.1725  0.0455  \n",
       "9999       2  2023-11-21 20:45:56.927349           0.0307  0.0323  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Read csv file as DataFrame, with the first column as row index\n",
    "df = pd.read_csv(\"Dataset_Ads.csv\")\n",
    "\n",
    "# Preview the dataset\n",
    "df.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Cleanup and Preparation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Age                  int64\n",
       "Gender              object\n",
       "Income             float64\n",
       "Location            object\n",
       "Ad Type             object\n",
       "Ad Topic            object\n",
       "Ad Placement        object\n",
       "Clicks               int64\n",
       "Click Time          object\n",
       "Conversion Rate    float64\n",
       "CTR                float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking the data types\n",
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Age                0.0\n",
       "Gender             0.0\n",
       "Income             0.0\n",
       "Location           0.0\n",
       "Ad Type            0.0\n",
       "Ad Topic           0.0\n",
       "Ad Placement       0.0\n",
       "Clicks             0.0\n",
       "Click Time         0.0\n",
       "Conversion Rate    0.0\n",
       "CTR                0.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking for null values\n",
    "df.isna().sum() / len(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th {\n",
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       "    }\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>Age</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Income</th>\n",
       "      <th>Location</th>\n",
       "      <th>Ad Type</th>\n",
       "      <th>Ad Topic</th>\n",
       "      <th>Ad Placement</th>\n",
       "      <th>Clicks</th>\n",
       "      <th>Click Time</th>\n",
       "      <th>Conversion Rate</th>\n",
       "      <th>CTR</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>9994</th>\n",
       "      <td>49</td>\n",
       "      <td>Female</td>\n",
       "      <td>72810.17</td>\n",
       "      <td>Rural</td>\n",
       "      <td>Banner</td>\n",
       "      <td>Technology</td>\n",
       "      <td>Website</td>\n",
       "      <td>9</td>\n",
       "      <td>2023-07-24 20:45:56.927349</td>\n",
       "      <td>0.0679</td>\n",
       "      <td>0.0443</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9996</th>\n",
       "      <td>35</td>\n",
       "      <td>Male</td>\n",
       "      <td>42182.75</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Native</td>\n",
       "      <td>Technology</td>\n",
       "      <td>Website</td>\n",
       "      <td>9</td>\n",
       "      <td>2023-09-01 20:45:56.927349</td>\n",
       "      <td>0.2664</td>\n",
       "      <td>0.0407</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9997</th>\n",
       "      <td>42</td>\n",
       "      <td>Male</td>\n",
       "      <td>55084.12</td>\n",
       "      <td>Suburban</td>\n",
       "      <td>Text</td>\n",
       "      <td>Finance</td>\n",
       "      <td>Search Engine</td>\n",
       "      <td>3</td>\n",
       "      <td>2023-11-15 20:45:56.927349</td>\n",
       "      <td>0.2354</td>\n",
       "      <td>0.0390</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9998</th>\n",
       "      <td>34</td>\n",
       "      <td>Male</td>\n",
       "      <td>39382.80</td>\n",
       "      <td>Rural</td>\n",
       "      <td>Native</td>\n",
       "      <td>Technology</td>\n",
       "      <td>Social Media</td>\n",
       "      <td>4</td>\n",
       "      <td>2023-05-23 20:45:56.927349</td>\n",
       "      <td>0.1725</td>\n",
       "      <td>0.0455</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9999</th>\n",
       "      <td>54</td>\n",
       "      <td>Female</td>\n",
       "      <td>66441.59</td>\n",
       "      <td>Suburban</td>\n",
       "      <td>Banner</td>\n",
       "      <td>Technology</td>\n",
       "      <td>Search Engine</td>\n",
       "      <td>2</td>\n",
       "      <td>2023-11-21 20:45:56.927349</td>\n",
       "      <td>0.0307</td>\n",
       "      <td>0.0323</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Age  Gender    Income  Location Ad Type    Ad Topic   Ad Placement  \\\n",
       "9994   49  Female  72810.17     Rural  Banner  Technology        Website   \n",
       "9996   35    Male  42182.75     Urban  Native  Technology        Website   \n",
       "9997   42    Male  55084.12  Suburban    Text     Finance  Search Engine   \n",
       "9998   34    Male  39382.80     Rural  Native  Technology   Social Media   \n",
       "9999   54  Female  66441.59  Suburban  Banner  Technology  Search Engine   \n",
       "\n",
       "      Clicks                  Click Time  Conversion Rate     CTR  \n",
       "9994       9  2023-07-24 20:45:56.927349           0.0679  0.0443  \n",
       "9996       9  2023-09-01 20:45:56.927349           0.2664  0.0407  \n",
       "9997       3  2023-11-15 20:45:56.927349           0.2354  0.0390  \n",
       "9998       4  2023-05-23 20:45:56.927349           0.1725  0.0455  \n",
       "9999       2  2023-11-21 20:45:56.927349           0.0307  0.0323  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Remove ages that are below 18; This is under the assumption that users below 18 are not going to be converted into sales.\n",
    "# Asked XPert Learning Assistant for code\n",
    "df = df[df[\"Age\"] >= 18]\n",
    "df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    }\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>Age</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Income</th>\n",
       "      <th>Location</th>\n",
       "      <th>Ad Type</th>\n",
       "      <th>Ad Topic</th>\n",
       "      <th>Ad Placement</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>61</td>\n",
       "      <td>Male</td>\n",
       "      <td>35717.43</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Banner</td>\n",
       "      <td>Travel</td>\n",
       "      <td>Social Media</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>41</td>\n",
       "      <td>Male</td>\n",
       "      <td>47453.25</td>\n",
       "      <td>Rural</td>\n",
       "      <td>Video</td>\n",
       "      <td>Travel</td>\n",
       "      <td>Search Engine</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>49</td>\n",
       "      <td>Female</td>\n",
       "      <td>68126.35</td>\n",
       "      <td>Rural</td>\n",
       "      <td>Text</td>\n",
       "      <td>Food</td>\n",
       "      <td>Social Media</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>68</td>\n",
       "      <td>Female</td>\n",
       "      <td>64585.73</td>\n",
       "      <td>Suburban</td>\n",
       "      <td>Text</td>\n",
       "      <td>Health</td>\n",
       "      <td>Website</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>63</td>\n",
       "      <td>Male</td>\n",
       "      <td>21109.40</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Native</td>\n",
       "      <td>Fashion</td>\n",
       "      <td>Search Engine</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9994</th>\n",
       "      <td>49</td>\n",
       "      <td>Female</td>\n",
       "      <td>72810.17</td>\n",
       "      <td>Rural</td>\n",
       "      <td>Banner</td>\n",
       "      <td>Technology</td>\n",
       "      <td>Website</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9996</th>\n",
       "      <td>35</td>\n",
       "      <td>Male</td>\n",
       "      <td>42182.75</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Native</td>\n",
       "      <td>Technology</td>\n",
       "      <td>Website</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9997</th>\n",
       "      <td>42</td>\n",
       "      <td>Male</td>\n",
       "      <td>55084.12</td>\n",
       "      <td>Suburban</td>\n",
       "      <td>Text</td>\n",
       "      <td>Finance</td>\n",
       "      <td>Search Engine</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9998</th>\n",
       "      <td>34</td>\n",
       "      <td>Male</td>\n",
       "      <td>39382.80</td>\n",
       "      <td>Rural</td>\n",
       "      <td>Native</td>\n",
       "      <td>Technology</td>\n",
       "      <td>Social Media</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9999</th>\n",
       "      <td>54</td>\n",
       "      <td>Female</td>\n",
       "      <td>66441.59</td>\n",
       "      <td>Suburban</td>\n",
       "      <td>Banner</td>\n",
       "      <td>Technology</td>\n",
       "      <td>Search Engine</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8694 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Age  Gender    Income  Location Ad Type    Ad Topic   Ad Placement\n",
       "0      61    Male  35717.43     Urban  Banner      Travel   Social Media\n",
       "1      41    Male  47453.25     Rural   Video      Travel  Search Engine\n",
       "2      49  Female  68126.35     Rural    Text        Food   Social Media\n",
       "3      68  Female  64585.73  Suburban    Text      Health        Website\n",
       "4      63    Male  21109.40     Urban  Native     Fashion  Search Engine\n",
       "...   ...     ...       ...       ...     ...         ...            ...\n",
       "9994   49  Female  72810.17     Rural  Banner  Technology        Website\n",
       "9996   35    Male  42182.75     Urban  Native  Technology        Website\n",
       "9997   42    Male  55084.12  Suburban    Text     Finance  Search Engine\n",
       "9998   34    Male  39382.80     Rural  Native  Technology   Social Media\n",
       "9999   54  Female  66441.59  Suburban  Banner  Technology  Search Engine\n",
       "\n",
       "[8694 rows x 7 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Creating a DataFrame for the X (features) value\n",
    "X = df.drop(columns=[\"Clicks\", \"Click Time\", \"Conversion Rate\",\"CTR\"])\n",
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       3\n",
       "1       5\n",
       "2       4\n",
       "3       6\n",
       "4       5\n",
       "       ..\n",
       "9994    9\n",
       "9996    9\n",
       "9997    3\n",
       "9998    4\n",
       "9999    2\n",
       "Name: Clicks, Length: 8694, dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Creating DataFrames for the y (target) values\n",
    "y_clicks = df[\"Clicks\"]\n",
    "y_conversion = df[\"Conversion Rate\"]\n",
    "y_ctr = df[\"CTR\"]\n",
    "y_clicks\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       0.0981\n",
       "1       0.0937\n",
       "2       0.1912\n",
       "3       0.1122\n",
       "4       0.1426\n",
       "         ...  \n",
       "9994    0.0679\n",
       "9996    0.2664\n",
       "9997    0.2354\n",
       "9998    0.1725\n",
       "9999    0.0307\n",
       "Name: Conversion Rate, Length: 8694, dtype: float64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_conversion"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       0.0737\n",
       "1       0.0592\n",
       "2       0.0563\n",
       "3       0.0232\n",
       "4       0.0539\n",
       "         ...  \n",
       "9994    0.0443\n",
       "9996    0.0407\n",
       "9997    0.0390\n",
       "9998    0.0455\n",
       "9999    0.0323\n",
       "Name: CTR, Length: 8694, dtype: float64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_ctr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Creating a OneHotEncoder column function\n",
    "def oheEncodeColumn(X_df, X_column):\n",
    "    ohe = OneHotEncoder(handle_unknown=\"ignore\", sparse_output=False)\n",
    "    encoded_column = ohe.fit(X_df[X_column].values.reshape(-1, 1))\n",
    "    return encoded_column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Location\n",
       "Rural       2960\n",
       "Suburban    2877\n",
       "Urban       2857\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# This is a column that might contain variables with a specific order\n",
    "X[\"Location\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Creating an OrdinalEncoder column function\n",
    "def ordEncodeColumn(X_df, X_column):\n",
    "    ord = OrdinalEncoder(categories=[[\"Rural\", \"Suburban\", \"Urban\"]], encoded_missing_value=-1, handle_unknown=\"use_encoded_value\", unknown_value=-1)\n",
    "    encoded_column = ord.fit(X_df[X_column].values.reshape(-1, 1))\n",
    "    return encoded_column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Creating an encode function\n",
    "def X_preprocess(X_data):\n",
    "    # Creating an encoder for each X column\n",
    "    # and Transforming them into numpy arrays\n",
    "\n",
    "    # Gender column\n",
    "    gender_ohe = oheEncodeColumn(X_data, \"Gender\")\n",
    "    gender_encoded = gender_ohe.transform(X_data[\"Gender\"].values.reshape(-1,1))\n",
    "    # Location column\n",
    "    location_ord = ordEncodeColumn(X_data, \"Location\")\n",
    "    location_encoded = location_ord.transform(X_data[\"Location\"].values.reshape(-1,1))\n",
    "    # Ad Type column\n",
    "    type_ohe = oheEncodeColumn(X_data, \"Ad Type\")\n",
    "    type_encoded = type_ohe.transform(X_data[\"Ad Type\"].values.reshape(-1,1))\n",
    "    # Ad Topic column\n",
    "    topic_ohe = oheEncodeColumn(X_data, \"Ad Topic\")\n",
    "    topic_encoded = topic_ohe.transform(X_data[\"Ad Topic\"].values.reshape(-1,1))\n",
    "    # Ad Placement column\n",
    "    placement_ohe = oheEncodeColumn(X_data, \"Ad Placement\")\n",
    "    placement_encoded = placement_ohe.transform(X_data[\"Ad Placement\"].values.reshape(-1,1))\n",
    "\n",
    "    # Turning each numpy array into a DataFrame\n",
    "    gender_df = pd.DataFrame(gender_encoded, columns=gender_ohe.get_feature_names_out())\n",
    "    type_df = pd.DataFrame(type_encoded, columns=type_ohe.get_feature_names_out())\n",
    "    topic_df = pd.DataFrame(topic_encoded, columns=topic_ohe.get_feature_names_out())\n",
    "    placement_df = pd.DataFrame(placement_encoded, columns=placement_ohe.get_feature_names_out())\n",
    "\n",
    "    # Creating an encoded DataFrame\n",
    "    out_df = pd.concat([gender_df, type_df, topic_df, placement_df], axis=1)\n",
    "    out_df[\"Location\"] = location_encoded\n",
    "    out_df[\"Age\"] = df[\"Age\"]\n",
    "    out_df[\"Income\"] = df[\"Income\"]\n",
    "    # Return the DataFrame\n",
    "    return out_df\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Question 1: Which features have the strongest influence on clicking of an Ad?\n",
    "Which features have the strongest influence on clicking of an Ad?\n",
    "\"Age\" and \"Income\" has the strongest influence on PCA1 and PCA2."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.90930384, -0.71067545],\n",
       "       [ 0.26112309, -0.12495413],\n",
       "       [ 0.92039539,  0.90681658],\n",
       "       ...,\n",
       "       [ 0.34353213,  0.25589384],\n",
       "       [-0.31574017, -0.52774104],\n",
       "       [ 1.33244058,  0.82273214]])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Scale the X data by using StandardScaler()\n",
    "X_scaled = StandardScaler().fit_transform(X [[\"Age\", \"Income\"]])\n",
    "X_scaled\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "           Age    Income\n",
       "0     1.909304 -0.710675\n",
       "1     0.261123 -0.124954\n",
       "2     0.920395  0.906817\n",
       "3     2.486167  0.730108\n",
       "4     2.074122 -1.439745\n",
       "...        ...       ...\n",
       "8689  0.920395  1.140581\n",
       "8690 -0.233331 -0.387999\n",
       "8691  0.343532  0.255894\n",
       "8692 -0.315740 -0.527741\n",
       "8693  1.332441  0.822732\n",
       "\n",
       "[8694 rows x 2 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_scaled = pd.DataFrame(X_scaled, columns=[\"Age\", \"Income\"])\n",
    "X_scaled"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
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       "8693      0.0       0.0        1.0        0.0      0.0       0.0         0.0   \n",
       "\n",
       "      x0_Finance  x0_Food  x0_Health  x0_Technology  x0_Travel  \\\n",
       "0            0.0      0.0        0.0            0.0        1.0   \n",
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       "3            0.0      0.0        1.0            0.0        0.0   \n",
       "4            0.0      0.0        0.0            0.0        0.0   \n",
       "...          ...      ...        ...            ...        ...   \n",
       "8689         0.0      0.0        0.0            1.0        0.0   \n",
       "8690         0.0      0.0        0.0            1.0        0.0   \n",
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       "8693         0.0      0.0        0.0            1.0        0.0   \n",
       "\n",
       "      x0_Search Engine  x0_Social Media  x0_Website  Location   Age    Income  \n",
       "0                  0.0              1.0         0.0       2.0  61.0  35717.43  \n",
       "1                  1.0              0.0         0.0       0.0  41.0  47453.25  \n",
       "2                  0.0              1.0         0.0       0.0  49.0  68126.35  \n",
       "3                  0.0              0.0         1.0       1.0  68.0  64585.73  \n",
       "4                  1.0              0.0         0.0       2.0  63.0  21109.40  \n",
       "...                ...              ...         ...       ...   ...       ...  \n",
       "8689               0.0              0.0         1.0       0.0  51.0  48175.94  \n",
       "8690               0.0              0.0         1.0       2.0  40.0  39445.38  \n",
       "8691               1.0              0.0         0.0       1.0  24.0  56537.25  \n",
       "8692               0.0              1.0         0.0       0.0  33.0  50267.70  \n",
       "8693               1.0              0.0         0.0       1.0  42.0  39510.01  \n",
       "\n",
       "[8694 rows x 18 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "preprocess_X = X_preprocess(X)\n",
    "preprocess_X = preprocess_X.drop(columns=\"x0_Female\")\n",
    "preprocess_X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "      x0_Male  x0_Other  x0_Banner  x0_Native  x0_Text  x0_Video  x0_Fashion  \\\n",
       "0         1.0       0.0        1.0        0.0      0.0       0.0         0.0   \n",
       "1         1.0       0.0        0.0        0.0      0.0       1.0         0.0   \n",
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       "...       ...       ...        ...        ...      ...       ...         ...   \n",
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       "8693      0.0       0.0        1.0        0.0      0.0       0.0         0.0   \n",
       "\n",
       "      x0_Finance  x0_Food  x0_Health  x0_Technology  x0_Travel  \\\n",
       "0            0.0      0.0        0.0            0.0        1.0   \n",
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       "...          ...      ...        ...            ...        ...   \n",
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       "8692         0.0      0.0        0.0            1.0        0.0   \n",
       "8693         0.0      0.0        0.0            1.0        0.0   \n",
       "\n",
       "      x0_Search Engine  x0_Social Media  x0_Website  Location       Age  \\\n",
       "0                  0.0              1.0         0.0       2.0  1.909304   \n",
       "1                  1.0              0.0         0.0       0.0  0.261123   \n",
       "2                  0.0              1.0         0.0       0.0  0.920395   \n",
       "3                  0.0              0.0         1.0       1.0  2.486167   \n",
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       "...                ...              ...         ...       ...       ...   \n",
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       "8693               1.0              0.0         0.0       1.0  1.332441   \n",
       "\n",
       "        Income  \n",
       "0    -0.710675  \n",
       "1    -0.124954  \n",
       "2     0.906817  \n",
       "3     0.730108  \n",
       "4    -1.439745  \n",
       "...        ...  \n",
       "8689  1.140581  \n",
       "8690 -0.387999  \n",
       "8691  0.255894  \n",
       "8692 -0.527741  \n",
       "8693  0.822732  \n",
       "\n",
       "[8694 rows x 18 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "preprocess_X[\"Age\"] = X_scaled[\"Age\"]\n",
    "preprocess_X[\"Income\"] = X_scaled[\"Income\"]\n",
    "preprocess_X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.86177399,  0.9072693 ],\n",
       "       [ 0.18190138,  0.18044123],\n",
       "       [ 0.95844367, -0.78382252],\n",
       "       ...,\n",
       "       [ 0.38196305, -0.20603586],\n",
       "       [-0.44966716,  0.48297395],\n",
       "       [ 1.43407004, -0.65035443]])"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# #Using PCA for feature analysis\n",
    "# PCA weight for the desired number of components (e.g., 2)\n",
    "pca = PCA(n_components=2)\n",
    "pca_info = pca.fit_transform(preprocess_X)\n",
    "pca_info"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.190281  , 0.19010573])"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate the PCA explained variance ratio\n",
    "pca.explained_variance_ratio_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PCA1</th>\n",
       "      <th>PCA2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
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       "      <td>1.861774</td>\n",
       "      <td>0.907269</td>\n",
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       "      <th>1</th>\n",
       "      <td>0.181901</td>\n",
       "      <td>0.180441</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.958444</td>\n",
       "      <td>-0.783823</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2.548659</td>\n",
       "      <td>-0.399946</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.954916</td>\n",
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       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "       PCA1      PCA2\n",
       "0  1.861774  0.907269\n",
       "1  0.181901  0.180441\n",
       "2  0.958444 -0.783823\n",
       "3  2.548659 -0.399946\n",
       "4  1.954916  1.668575"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create the PCA DataFrame\n",
    "pca_df = pd.DataFrame(\n",
    "    pca_info, \n",
    "    columns=[\"PCA1\", \"PCA2\"]\n",
    ")\n",
    "\n",
    "# Review the PCA DataFrame\n",
    "pca_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "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>k</th>\n",
       "      <th>inertia</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>17405.319302</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>11568.781805</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>7534.332345</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>6094.649462</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>5071.539950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>4237.574742</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>3684.633835</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>3361.027882</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>3015.905748</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>2641.984979</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    k       inertia\n",
       "0   1  17405.319302\n",
       "1   2  11568.781805\n",
       "2   3   7534.332345\n",
       "3   4   6094.649462\n",
       "4   5   5071.539950\n",
       "5   6   4237.574742\n",
       "6   7   3684.633835\n",
       "7   8   3361.027882\n",
       "8   9   3015.905748\n",
       "9  10   2641.984979"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a a list to store inertia values and the values of k\n",
    "inertia = []\n",
    "k = list(range(1, 11))\n",
    "\n",
    "# Create a for-loop where each value of k is evaluated using the K-means algorithm\n",
    "# Fit the model \n",
    "# Append the value of the computed inertia from the `inertia_` attribute of the KMeans model instance\n",
    "for i in k:\n",
    "    k_model = KMeans(n_clusters=i, n_init='auto', random_state=0)\n",
    "    k_model.fit(pca_df)\n",
    "    inertia.append(k_model.inertia_)\n",
    "\n",
    "# Define a DataFrame to hold the values for k and the corresponding inertia\n",
    "elbow_data = {\"k\": k, \"inertia\": inertia}\n",
    "\n",
    "# Create the DataFrame from the elbow data\n",
    "df_elbow = pd.DataFrame(elbow_data)\n",
    "\n",
    "# Review the DataFrame\n",
    "df_elbow\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: title={'center': 'Elbow Curve'}, xlabel='k'>"
      ]
     },
     "execution_count": 22,
     "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": [
    "# Plot the DataFrame\n",
    "df_elbow.plot.line(\n",
    "    x=\"k\", \n",
    "    y=\"inertia\", \n",
    "    title=\"Elbow Curve\", \n",
    "    xticks=k\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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>PCA1</th>\n",
       "      <th>PCA2</th>\n",
       "      <th>Ad Encounter</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.861774</td>\n",
       "      <td>0.907269</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.181901</td>\n",
       "      <td>0.180441</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.958444</td>\n",
       "      <td>-0.783823</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2.548659</td>\n",
       "      <td>-0.399946</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.954916</td>\n",
       "      <td>1.668575</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8689</th>\n",
       "      <td>0.986720</td>\n",
       "      <td>-0.991920</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8690</th>\n",
       "      <td>-0.220544</td>\n",
       "      <td>0.349798</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8691</th>\n",
       "      <td>0.381963</td>\n",
       "      <td>-0.206036</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8692</th>\n",
       "      <td>-0.449667</td>\n",
       "      <td>0.482974</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8693</th>\n",
       "      <td>1.434070</td>\n",
       "      <td>-0.650354</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8694 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          PCA1      PCA2  Ad Encounter\n",
       "0     1.861774  0.907269             0\n",
       "1     0.181901  0.180441             1\n",
       "2     0.958444 -0.783823             0\n",
       "3     2.548659 -0.399946             0\n",
       "4     1.954916  1.668575             0\n",
       "...        ...       ...           ...\n",
       "8689  0.986720 -0.991920             0\n",
       "8690 -0.220544  0.349798             1\n",
       "8691  0.381963 -0.206036             0\n",
       "8692 -0.449667  0.482974             1\n",
       "8693  1.434070 -0.650354             0\n",
       "\n",
       "[8694 rows x 3 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Define the model Kmeans model using the optimal value of k for the number of clusters.\n",
    "model = KMeans(n_clusters=3, n_init='auto', random_state=0)\n",
    "\n",
    "# Fit the model\n",
    "model.fit(pca_df)\n",
    "\n",
    "# Make predictions\n",
    "k_3 = model.predict(pca_df)\n",
    "\n",
    "# Create a copy of the customers_pca_df DataFrame\n",
    "pca_predictions_df =pca_df.copy()\n",
    "\n",
    "# Add a class column with the labels\n",
    "pca_predictions_df[\"Ad Encounter\"] = k_3\n",
    "pca_predictions_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='PCA1', ylabel='PCA2'>"
      ]
     },
     "execution_count": 24,
     "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": [
    "# Plot the data points based on the Clicks\n",
    "pca_predictions_df.plot.scatter(\n",
    "    x=\"PCA1\", \n",
    "    y=\"PCA2\",\n",
    "    c=\"Ad Encounter\", \n",
    "    colormap='winter')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
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       "      x0_Male  x0_Other  x0_Banner  x0_Native  x0_Text  x0_Video  x0_Fashion  \\\n",
       "0         1.0       0.0        1.0        0.0      0.0       0.0         0.0   \n",
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       "      x0_Search Engine  x0_Social Media  x0_Website  Location       Age  \\\n",
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       "\n",
       "        Income  Ad Encounter  \n",
       "0    -0.710675             0  \n",
       "1    -0.124954             1  \n",
       "2     0.906817             0  \n",
       "3     0.730108             0  \n",
       "4    -1.439745             0  \n",
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       "\n",
       "[8694 rows x 19 columns]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Define the model Kmeans model using the optimal value of k for the number of clusters.\n",
    "model = KMeans(n_clusters=3, n_init='auto', random_state=0)\n",
    "\n",
    "# Fit the model\n",
    "model.fit(preprocess_X)\n",
    "\n",
    "# Make predictions\n",
    "k_3 = model.predict(preprocess_X)\n",
    "\n",
    "# Create a copy of the customers_pca_df DataFrame\n",
    "X_predictions_df =preprocess_X.copy()\n",
    "\n",
    "# Add a class column with the labels\n",
    "X_predictions_df[\"Ad Encounter\"] = k_3\n",
    "X_predictions_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>-0.001257</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>x0_Search Engine</th>\n",
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       "      <td>0.004404</td>\n",
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       "      <td>-0.016909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Age</th>\n",
       "      <td>0.990236</td>\n",
       "      <td>0.124050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Income</th>\n",
       "      <td>0.122818</td>\n",
       "      <td>-0.991866</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "                      PCA1      PCA2\n",
       "x0_Male          -0.001891  0.003800\n",
       "x0_Other          0.000067  0.002085\n",
       "x0_Banner         0.003312 -0.001139\n",
       "x0_Native        -0.000203 -0.002428\n",
       "x0_Text           0.001308  0.001052\n",
       "x0_Video         -0.004416  0.002515\n",
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       "x0_Finance       -0.000695 -0.001854\n",
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       "x0_Technology    -0.001224 -0.000654\n",
       "x0_Travel        -0.002707 -0.001257\n",
       "x0_Search Engine  0.010166  0.004404\n",
       "x0_Social Media  -0.006066 -0.016714\n",
       "x0_Website       -0.004100  0.012310\n",
       "Location          0.064162 -0.016909\n",
       "Age               0.990236  0.124050\n",
       "Income            0.122818 -0.991866"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Determine which feature has the stronger influence on each principal component. \n",
    "# Use the columns from the original DataFrame. FYI: The data has already been scaled and fitted.\n",
    "pca_component_weights = pd.DataFrame(pca.components_.T, columns=['PCA1', 'PCA2'], index=preprocess_X.columns)\n",
    "pca_component_weights"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Income', ylabel='Age'>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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jUATJn3/Cbbc5jRwQQ+Dxx2Hp0pKbV6hZvBieeMJp5IC850mT4K+/imcOsbEwa5aEviwW+WsyQaVKstyIZ56BL790X3bmDAwbJuE0haLUoZKRixRl6CgUQfL++8Zl0mYzvP128c+nqHjnnfzVUyDLPvig+OYxbhxs3y7VV2PGwAsvwK5d0KFD/nVtNmmA6qnObLdDWpqq1FIoLkRU6EqhCJIDB9y9HA5sNjh4sNinU2QcOGBc1WS1wqFDxTuXVq3gf//zv15GhnevjcUCCQmhnZdCERJU6KpIUR4dhSJIOnUy9uhYLNC5c7FPxy+6LqGcAQNE4+a66+Dvv/1v17mzd89VYiK0aAF9+sDs2cb9rfyRlAQPPyxVU506wcsvi6FSGGJioG5d49dyckR0sKywbBlcfrl8ZtdcI4nVinKKCl0VKZquXzjdZ1JTU4mNjSUlJYVKlSqV9HQUZZS9eyVskp3tvMBrmjzWrYOLLirZ+Xny+OMwbZrkttjtTuPl++/lQuqN9esleVfXnSXejjHMZvH2OJ7fdht8+GHgczp2TLqdHz/u9BqZTHLsfv1V9HgKynvvweTJ7svMZqhXTyqxCjN2cfHyy/DYY87jbLHIcf76a7jqqpKe3YVDUV8zHOOz6k2IiSrcYGmZcMn96vpmgPLoKBRB0ry5tCBo2tS5rG5dWLSo9Bk5Bw/CSy/J/w6jzGqVi+c99/hustmrFyxcKDo4DsLDxaBzGCeOMT/6COLjA5/Xyy+7GzmOsdavL3wezV13yfgVKzqX9ekDv/1WNoycEyfgySflf8fxsVrls7r7biWSWC5RgoFFijJ0FIoC0K+feAd27JCO3QcPwpVXlvSs8vPDD8bLdV3m7y/XZtQoWWfrVtiwQaqujIwjsxm++y7weS1ZYnzBNpng228DH8cITYNHHhFD6u+/Zf6rVkGjRoUbt7j4+WfjHDBdF0/Ytm3FPydFEaNCV0WKSkZWKAqIrsPZs/KoWRNq1CjpGeXHYvHttfHVZHPrVjh8GNq0gfbtIT3d/74CxaiaC8RICVV386io0pkz5Q9/7191f1cogkN5dBSKAvD335Ik2qcPDB8uoasHHyxYUm5RMny494Tizp0lb8WTo0ehZ0/o2FF6VjVtKn2qdF1yeozaOdhs4v0JlHHjjI0dmw2uvjrwccojgwaJkeaJySSfRZs2xT8nRRGjQldFijJ0FIogSUmRlg+HDzuXWa3w+uvwxhslNy8jatWCV1+V/x0Gj9ksCsdGWji6LsbRpk3uy5cskdyXJ5/0bsytWxf4vB5+WKq2tFxvu8N4GjlSKowuZGJjRcPI1btlsUgLjo8/dh4zRTlCha6KFOUEVSiCZN486XNlFBJ67TWYMqV0XYzuuw+6dJGE4SNHoGtXqUoyyllZt844qdhmk/ddv76z0soVTROD6pZbAptTXJwoTM+cKdVfkZEiBujN03OhMXGilMLPmCE5Ru3bSyJy8+YlPTOFouyhDB2FIkj27ZM77Jyc/K8lJkrZeWmr7unbVx7+2LfP+2s2myTCGhk6ug779wc3p4oVRe34/vuD2+5CoUcPeSguEFToqchQoSuFwoU//oDx4yX35s47pfWAJy1aGFfFgHgmBgyQ0u09e7zv5+hR0Um5+GLRRfnmG99Jw8VFixbeX7NYRD/IKHSlaUXnbUhOlhL5Sy6BIUNEoNDb8VcoyiQqdFWkKMFAhSKXTz6BW2+V0InV6syP+PZbGDzYuV5qKjRpIhdgzxJpV1E+iwVWrMjvSdm5Uwyp1FTZ3iEK9/DD8MorRfoW/aLrIuQXH+/+3kwmuOkmER5s1ky6iXsaPHPmyDqh5NQpSYw+cED25zi+I0ZI01EV5lIUJcUmGLjybahQSMHA9Ey4/B51fTNAeXQUCuDcOclb0XWnt8AhrDdpkvtFv1Il0TpxFQx04CrKl50Nt9+e31Pz4INOIwecf//3v5LXSNE08S716eO+bOxYePddSW5esUJydRxERooBNH586OczbZpoFDmOq+PvN99IgrRCoVD4Qxk6CgViuGRm5l+u65LAu2WL+/KOHaWD9oYN4okxwm6Xdf7917ns/HkR8TMSyzObxUtR0tSuLQJ7O3fKXA8ehC++gOhoeb13b8nHWb8eVq6UnlWPPVY0Cdhfflm6j5VCERJU6KpIUcnICgX+ZfW95aV06+bfC+O6rWvfqED3A5IkfOYMtG0LFSr43p8vjh8Xw6VRIxE59EWrVvIwwmSSkFJRkJgolUZNmvjWJVKtEBTlBtW9vEhRHh2FArj0UuNKKU0Tg6BTJ+/bDhrkPVekWjX3JN2oKElW9iaWN2KE+7K9e6V/VvPm8rdmTQnnBJtZl5YGN9wgfat69pS/N97oX+24OElNlRLzunWlz1adOhIm9HasRo4s/jkqFIqyhzJ0FApE1+V//5P/HRdWi0UMnffe8y27X6cOPPqo8WunTsH8+e7LXntNjCrHfhxiebfdJno3DjIyoH9/d/G+9HTpRj5jRsBvDYCbb4YFC9xzXebPhwkTghunKLnuOmmM6jDibDapXAsPdzd2NE2Oy7XXlsg0FYrQo0JXRYoydBSKXO65R7qSDx0KrVuLQu+6dYG1JKhe3ThHxWRydg930LmzVDXddpvI+fftC59+Cu+/777e119LE0ejEE0wXp0DB8SA8BzHZpPu5AcPBjZOUbJrlwgHes7Rbpe8pjvvFAG9Hj1EffqHH0QpWKEoF5RAC4jVq1czYsQI6tSpg6ZpLPGT3f/bb7+haVq+x65du9zWW7hwIW3atCEiIoI2bdqwuBQk06kcHYXChUGD5BEs27c7y9JdsdslqdeT5s39e2W2b5eLuZEw4ZEjkjztSBD2hdH+Hei6vF7Snb137PD+mq6L0fnOO8U3H4WivJOenk7Hjh255ZZbuCaIviu7d+92K1+vXr163v/r169n7NixPP/881x11VUsXryYMWPGsHbtWi666KKQzj8YlKGjKJWcOCF9fTZtkiqgW28t3Z2oGzb07mFxLcUOdkxvwnhVqhg3fvQ2ji98GTl2OyxbJp6fnBwYNkxKzQur/JyUJC0p4uMlJ6d3b9/rN2jg/bVTp+Rc2bhROshPnChJ4kYcOyb73bpVPpdJk8RTpFCUKKEIPQW5/dChQxk6dGjQu6lRowaVK1c2fO3NN9/k8ssvZ+rUqQBMnTqVVatW8eabbzJv3ryg9xUqlKGjKHXs2CHhHEc/KbNZ8mTef190aUojEybACy+IYeBp8Nx3X8HGvO46KdtOT3evPtI0CbMFWs7dtq0oMP/xh7vhZLGIgdG6tfF2Npv0nvr6a/kMdF3yfN5/H376KTBvkhFbtojKcVqavC+zGd5+Gxo3hoQE9zmazZIobqRZBJLD06ePVKQ5zpUZM2S8u+92X3fTJkkEz8hw3+9nn8H11xfsvSgUIaEMVV117tyZ8+fP06ZNG5588kkGDBiQ99r69et54IEH3NYfPHgwb775ZvFMzgsqR0dR6rjjDukQ7jAarFb5e/fd4ukpjdSrJ56PuDjnMk2De+/Nf8ENlLg4yUVx8QyjaWJUPflkcGN9+WX+yrHOncVw8cbXX8sDxOhxGFt//glvvRXc/l2ZNEmMHJvNXaDx6NH8RlePHvD5597HmjwZzp51P1dAjMujR53r6bo0HE1Pd9+v3S7zSU0t+PtRKEoTqampbo+srKyQjFu7dm0+/PBDFi5cyKJFi2jZsiUDBw5k9erVeeskJSVR00O3ombNmiQlJYVkDgWlzHh0ZsyYwYwZMziYmznZtm1bnnrqqQK53hSllxMnYO1a49esVlHDLa1enUGD5OL600+itHzxxQUPWzno3Vu8HD//LF6Lnj1FXyZYateGv/6Sx759kiPUvbtvr9CCBcYNPO12ERB87LHg53H4sISYjMjOFtXoFi1EZLFVK+m07m2OyclyrL2xaJF4vkDG++cf4/UyM2H5cgnJKRQlQghDV/U9fnSefvppnnnmmcKNDbRs2ZKWLVvmPe/VqxcJCQm8+uqr9OvXL2+55vGF1XU937LipswYOvXq1eOll16iWbNmAMyZM4crr7ySzZs307Zt2xKenSJUnD9fuNdLmshIGD48NGNlZ4uRU62aNLMsLJomWjyOnMDERKl26thRcn48OX/eu2BfRoYYnocPQ+XKxts7SE4WI61ePf+fX1aWaOj06iXPT58Wb0uDBvn1dE6f9j3WuXNScRYT43+/RqrYCmPOnJHPtEED37ILiiAIYegqISHBLVk4orAJdT7o2bMnc+fOzXteq1atfN6bEydO5PPyFDdlJnQ1YsQIhg0bRosWLWjRogUvvvgiMTEx/PHHHyU9NUUIqV9fmkYa3QDoesEqosoaug6vvirigM2aQdWqIvZ39mxoxneEiOrUkfyXqlXFsPAM3wwebPw5mM1ykatTR3JnqleXDuye3ukzZyTPqFo1Wa9WLVi61D0U58l778HmzVJVNny4rNukiZwXn3wi66SnS7jJV6d1u13Ce02aSILy/ffLXyM0DQYO9D6WQkhKglGjnJ9n3br5JREUBSSEOjqVKlVyexSlobN582Zq166d97xXr16sXLnSbZ0VK1bQ21+1QRFTJu1xm83GV199RXp6Or0ct36KcoGmwZtviuqtyST5FJomF/877vDekqA88dpr7v2zbDYJI+3fL7o+hfEC5+SIB8fTG/LHHxIWcy3zvvVW+OADSfh16NtYLFLt9euvzvXsdmmyuXu3VDNZLPJ5DR0qCcCObc+ehUce8d1xfNs26NdPPERHjzoTuxMTpZoqMlJydr7/3r+OkOvrv/0GsbFy7BznlSMs9/DDhQ8xlnesVjGK9+51HtcTJ+Cuu0TQceLEkp2fInjS0tLYt29f3vMDBw4QHx9PlSpVaNCgAVOnTuXo0aN8+umngFRUNWrUiLZt25Kdnc3cuXNZuHAhCxcuzBvjvvvuo1+/frz88stceeWVLF26lJ9++om13vIRioky49EB+Oeff4iJiSEiIoI777yTxYsX06ZNG6/rZ2Vl5UvMUpR+rrhCmkoOHix39B06SCXNe++V9MyKnuxs+L//y7/cZhNjxCXvr0B89ZX3kM/OndKk1EFMDPz+u+TNNGggeT6TJkHFisbz27kTvv1Wnv/2m+QDGYkd+upRZbOJx+bw4fzraRo88QR8913wLTBsNjG07rtPKq+qV5fk7E8+yS/oqMjPN9/I52skd/Dcc8F/HgoPSkAwcOPGjXTu3JnOubodU6ZMoXPnzjz11FMAJCYmcvjw4bz1s7Ozeeihh+jQoQN9+/Zl7dq1fPfdd1ztoqjau3dv5s+fzyeffEKHDh2YPXs2CxYsKFENHQBN18vOKZqdnc3hw4dJTk5m4cKFfPzxx6xatcqrsfPMM8/w7LPP5luekpLiFsNUKEoLBw54TzY2mSSk5VG9GRS33SaaM96YPl0qxbyRnOxeWeZKWJgkKD/3nHilHnnEd1NOXzi8eKEkLEw8DyrcEjxPPgmvvGIsXglyXsTGFuuUioXU1FRiY2OL7JrhGJ8lH0CFAIWxvJGeCaPuUNc3A8pU6Co8PDwvGblbt25s2LCB6dOn88EHHxiuP3XqVKZMmZL3PDU1NV9GuuLCJT5eqofS0uQuf9SowrUVSEuTsMrmzeL9mDDBv1ifJ1WrSujH6M7ZbpdxfWGzSVjnxx8lpHDVVXDypFRtRUf7F/pr0EASd7/6SsJkVapI809H2XeFChI+MkrutVqd86tdu+BGjlGllwNvxyYQHMdv+3aYO1cuzhdfLD2zXI9LsOfF33/D009LaLFZMzH0OnYs2BxLK3XqeD/u0dFyXigUpZUy5dHxZODAgdSvX5/Zs2cHtH5RW+eKssMLL8B//+usGrFaRbPlp5+MQzP+2L9fRA4TE53ieiaTNM4MpFeWKzfeKNu5hm5MJqluOnLEuyJyVpYk8P70kzNPxjW3BnwbCdHRkoMxcKBUY4WFOcd4913pN/XAA+L18UTTZF4JCWIcZWRIlZVDD8n1fQRiAIWFOfWTQoGmyefy+ONiiLgej3btJNRWtWrw58Vzz4mR48nLL4tHq7xw5ozkMWVmun8mJpOU8JewHlyRUWwencUh8uhcpTw6RpSZHJ3HH3+cNWvWcPDgQf755x+eeOIJfvvtN2644YaSnpqijLFpk1zMQC5kjov/pk3w/PMFG/O22+D4cacQnc0mbv4bb5SLfTC8846zBNzR2bxyZcl/8dX2Yfp0+OUX+d8xBweu7xPyJzSHh8OKFZIDs3evLMvJcRdr/PRTYyPHsf3Spc4y8+homa9DKd7xPnr1gqeekufeKrqmTxcNnEBbXDioVMm7ZyEiQsJpjs/X9Xjs3CkGULDnxY4dxkYOSDd7lzzPMk+VKqJh5Ti+js/zssuMc8oUQaK6lxcpZSZ0dfz4ccaPH09iYiKxsbF06NCBH374gcsvv7ykp6YoY3z+uXEIxGaDOXMkFyEYjh93GhieZGaKAXDTTYGPV7myiCauWSNhkdq1pQrN34V/9uzAvCUWi4TVqlUTz02PHjBlirPdglGysKaJAWI2G7/euLFc9Fzp3Vs8UEuXSmly164SKtI0MQy//Vbm26aNs4HpyJFShg7SVb1WLf/vqVYt8ShceaXMbelSCdfFxUkCcmyshJ+mT3dWXLlis0kbiOjo4M4LX4rNIF65YBWsSzOXXy4eyyVLJKG9Z085d0pYC06h8EuZMXRmzpxZ0lNQlBPOnQvsNUcFUMWKvn/M09K8v6ZpBWsvoGlSZu0iOOqXQPej65JjM22ac1lGhizLzvY+H0dvKiO8HdOoKOmX5Um9ehIKs9tl3H79nF4CBzk5gRlu1aq5qxp761uVmmps6IAYpL48b0bvz5+nLlhPXlkgJka8lIoQU4Z6XZVFykzoSqEIFZdcYpyrYjZD//5ywX/kEfEIxMZKQvGHH3rPF2nUSJI1jdB1GbM4uOyywJRqbTY5BiAl6337SkiiShWn1ownVqv3xpoWi9ztB4PVCs88I0ZKbKwcv9dfl+P1ww9S+l23rv9xzObA992/v3HVkMkkIbVLL/V9XriSkCAl1/72p1AERAmUl19IlOlk5GBRycgKkKTdHj0kXOK4uzeb5bF2rXg6li7N70144w1R2DVi7lwYP9492VbTxJvxxRdF9lbc2L1bwkPnz3vXqjGbRVF40ybJxenRw91z4itZWNNErfnkSffjFhUl/atc2uD45bbbYObM/MbjjTdKSEjT/HtzzGYxkuLjAxP8s9nEc/Tnn+7zB0k27tXL93nRvbssS0sTI8yXBy0iQhJ4C9rhXVE6KLZk5K8/DE0y8rW3q+ubAcqjo7jgiIiQKpvJk+VCGRYmlUZr1shrixcbX2SffVaMJCNuvFG269RJPBx160oFz5w5RflO3GnZUjw0w4dLcnBMjJSXDxwo7zE2VtSl16wR4+Sll5wdvB34Mi40Td7XLbfI2BERMGIErF8fnJFz6JCxkQO+jRxNk31aLFLiPnq0GC2BKkaYzVJ2P2WKVFiFhUnO0C+/iPfF13nhMHJAxCv9hQmzssRYVigCRiUiFxnKo6NQuPDhh2IMeGPbNigvPWQbNJAQjBHeBPu85bgEw9dfi5ESDBaLqDLPmFG4fYeCyy4TXSJfhIXJefT228UzJ0XRUGwena8+LLz7LyMDRiuPjhFlJhlZoSgOfHXh9nzdZpN8kj//FA/BuHES2ikIyclSpXP0qLS8uPxy8RDt3y+hpmuvda+6OnwYvvxSkmT795eHpkmVkWOciAhJLq5YUQyLmjVFCHDfPhG2i4uTqihPg8ZX4nVkpFQSNW0KY8bIsi+/9D5PI/wdYyN0XbZbvlxKz48cEVG+m26Sqi1/2O1SPu8QQRw71iluqOviyfnyS6n0atNGwpCdOhmP5a05qOf+qlYN8M0ZcP48LFwoVXGNG8uxjokJbNvERDkHzp6FPn3kXPJM9FaUMkLhlVFeHa8oj45C4UJmpoRnPIXuHKXXK1bI8+RkuYBs3CjeBrtd1imIQOCqVdLfKyNDxsrJcebKhIXJ81q1JMTSujV89JFULIGsZ7WKl+GBB8Sgychw98iYTPJ/hQqSX+IYMybGe8WYrxYMjhJzh0ZOcrL7PH/91XfzVZtNLt7HjgXuHdI0aN9emoZ6MnUqvPiidwMtNRWGDJEQm+tn9dlnUs4+cqTk6Hhy553SX81z3Ph4yG0P5BWTSZqhekvg9sWePaLIfOyYUzgxLg5WroQuXXxvO28e3Hyzs2mp1Sq5Rz/+WDAhzAudYvPofPlRaDw6Y25T1zcDlJ2vULgQFeUUq9M0p/R/o0Ywa5ZzvQcflFYP4MxzsVrFq3PiROD7y8yUPBqH4qyjKshhZDmenzwpd/U7d0pIxG537hPECLrqKmdrBlcjxW6X5w6jxjFmZqbz4mexOBNzn31WjClvhoPDOElOlofnPEeP9q1obDbLMfZ34bVY5GJtMkkS8bZtxutNmyaeNW9MnSoNRsH5WTnEHB991HsY6v33xXDwJCbGt9fLbJbjVxAjR9flHDp+XJ7n5MiylBT5fH0ZhkeOiIfLkVzuODf++ksEERWlGCUYWKQoQ0eh8KB/f8ldefddePhhWLBAVHDr1ZPXs7OlysrzouNolzB/fuD7+u47CTH4qzCy2eRC/9prToPEFbtd5hVMfymbTUJfM2c6WyPs2iXKxbfeGliIxts8jTwvrnTrJl4GX1x3nSRM79snF2tf1WCffOJ9Pp98Ymwg2O3ymi+jzKgB6ty53kNBUVHSmHXiRO9j+mLnTjGgjUQNDx+WxGhvfPGF8TGy2cRIL2jvMUUxoMrLixSVo6NQGBAXB3fdZfxaZqZ3YT2TCU6dCnw/p08HN6+kpODWD4RatYwvzAUROnQQyDEwagzqypVXwjXXyPHOzPS+nq5796JlZ3vf1mwWb78vjMY9fdp7UnZWltMgLgj+jpuv8+X0aXlPRgZNRoYci8jIgs9NoSirKI+OQhEEKSnS38jbHb0jJ8KIbdukFUFUlJQv33knNG8e+L7DwmDw4IJ37zbCYhHtHSMuusjYe+SPsDDvibwOjh2DDz7w/rqmiabNX39JyMYXJpOUiRsRFSXJxUahppwcqTzzNYewMBE1DA+XHK21a+W4GAkPghgZERGSJ3PkiCQGT5okfbiioyVZe+dO7/vs0MF7h3lNcy9z98TbvDRNGpeazZLLVK+evJ8ePfyLHiqKCRW6KlKUoaNQBEh2tlzs3nnH+K7ZbJaLx+DB+V/btUt6A337rXgyUlMlLHL77TBsWGD9gu67Ty6azZoFb4AYja9p4rXyVin2zDPkdWEPhvvv911xdPasHIsvv/Q+10mTxEjo29c4UdiVSpXgP//x/voLL8j7cD0GZrMkTB896nvsrVvFU5KTA6tXS5JwrVqyrbfPICdHGqB27y7nw5w5EiLMzJQ+UT17wr//Gm9buTI89FD+5Zom4URfhtnIkVKJ5jovR1L5889LJdl//yvvOSdHRCNHjgwu1KooIlToqkhRho5CESCLF0uTTaOQhckkQno//mhsGPzf/0lYw3Vbm03KsgcO9B3uqFhRclVeflk8FGvWSEjHH45EarNZ1n/kEacBUqWKXPzeeMP79pdcIkm+HTo436PD21C5suTyPPWUhPlAPB8vvSQPX3z4oVxsjTxT4eFyMX7vPfGc2Wy+E3Avu0yqqby14ADxCC1cKOXvjn3cdJN407zlrdSqJQaC6+s2mzx/5hmplDPq4eVKUlL+92mzSRjJV+PY556D//3PmSNVubIcE38aQhaLJKXfdJO8RxAhx4ULJZl+wYL8SeogCdkqf0dRnlHl5QpFgNx5pyTuGl2gw8K85+2AaLYY5ddomlyIly41vqBbLFJl9c477sv/+ktCFd6YOVMMr/R0mZvDQLHbxbsQExOcV+jcObl4hoXl395mk4quihUD8/4MGeI9EblJE6e3IyrKOI/HbJaKqRkz/Gv2uKLrMvfISHkvcXHOqjFXTCYxiv7913uIymoVI6Zhw8D370qjRpK07IuCflYg5+L5886GtNOniyK0N4Pm4MGCv5fyTLGVl38+KzTl5TdMVNc3A1QysqLMY7OJvs2ePRLWGTKkYLkl/vD12+EQc8vMhGXLpDy4aVPJRfGVBGo2y8WoQgXvyb9G+3V4a7yxY4eo8kZHS/m02SwXui5dJD/IiKNHJbSm6xJ2+ftvudBffrlclHfuFENk6FD34+voORUolSo5tXg8iYmRefuqLnKUyhvlsuTkiKjggQNyXNPT5Ri0ayeaRxERkuRcvbrsy8jQcYS4vHmSoqPFGKpQwbfekDc0LbDjZTIFd1xdCQ93enVAjoUvr02FCgXbjyJEhCL0dMG4LIJHeXQUZZqDB2HQIGlQ6RDZa9pUDJ8mTUK7L29CcWYz3HOPJJqOHCnNHF3xdzFcsUKMoxkzjC+u//wjF2oH69ZJjynP/QTCFVeIMePJK6+I3oyue5+r4/g2aCBzDqa/lStLl0pSthEWS+DJ1u3bi2fIoXC8e7ecC4cP+94uLEykAw4flpBiMGEbs1lyZRyJ1EOGiJBfMGNomsgEPPBA4NsUljNnJLzn2avNUwhT4U6xeXTmhsijc6Py6BihcnQUZRZdF+PCEQJwXGwOHZKclFCb8J06yYUR5IJsyfWHduwoejvDhxt7CPzNo3JlyctwGDOuY7/yiruRk5HhfT8OfCU2f/cdvPqq+7KVK515Gr7m6ji+R4+KV6Sgx3fkSEnCBvf36lDyDZQdOyTB1jG3kSP9JxeDeH3uuEOMlJ49ZVlYmHMevo5fq1bOcwBEVNBbftCECU4Fa9f3OXiwNA4tTqpUkQRpRzd2i0XeZ61avqvfFMWEqroqUpRHR1Fm2bpVjAxv/P23f6n+grBli3TZdvSZuvpqqVy56abgx7JYxEPw/vsS4lq4UBJdY2PhhhucicAOPv9c8lOMCDSMUr++u9dj9GhJtA62Wefvv0Pv3sFt40DXpVR74UIxPDRNvCwF4cABKeXu2zfwbSwWMbamTxdv2sqVEtZ67z3veTm33SZhNc+QWVqaiAguWybK0G3bSn5Uv37y+rp10sg0O1sq7IYOLbneUwcOSBVYYqKEMW+4IfAeWhcixebR+fST0Hh0brpFXd8MUDk6ijJLYqLv148dKxpDp2PH/AZWYqL3vBNfWK0yT5Cciuuuk4c3fO1H1wML/Xh6gxISCtaR3DHvgqBpYpg4jJP//rfgYyUm+j8XPHEcd4tFDNWrr5Zk8enTjde3WCRZ1ygvKCZGPDcO740nffrIozTQuLFUjSlKGaqpZ5GiQleKIicrS0qjW7USV/n110veyYcfSjioZk0Jx/z+u+8xXnnFOcZ110m4wded8TvvyLrt2skFzJsBsHat7L9mTTGMPvww+HLbzp0LZiyYzf6NsfPnpZ9Ty5YSNvG2n4oVAwv9ePZg6t7dGVYJhrvvhjff9L3PefPEQLBY5POqWlWqnapXl79Vqoj3yjUcFAyaJoabP4FCI/btk/PQQfXq3tteWK1FYzQrFIqiR4WuFEWK3S6u+p9+choPFouzL5Qj3GI2y99vvhH3vucYw4dL4qnrGGFhUhH07bfuhommOR+OfQCMHSv9gFxzML77TnI7XNfVdRHSe++94N5n9+4S1jIyRIzCSmazeAN27PCe52GzSYLtb7/5N75efllECPfv9210/fyzJKA62LtXPFRZWQXTUxk9WjRaPHNbXnpJEpyLGpNJEpxnzIDvvw98O7NZvGhr1jjVoS+7zLjJZ4MGclyLoppPUXopttDV7NmhCV1NmKCubwYoj46iSPnxR6nocL2AWq3OC7Hj4m+zyf/335/fIPjpJykZ9hwjO1u+2/fd5yzfjox03pW77kPXJY/G0cXasfy++5xGl+t8ZsyQKp5AMZnkfV5zjdPL5HpRbNJEBPCaNXMu69ZN8nF8id19/72IwPkyQCpXFoG5hx8Wg8iXHsqYMe5GDohw3s8/SxWTA9dyeH+qzV99BX/84b7s7FkREywOHOdN/fre1zHy/Nlscg45OnsfOiTH2ghveTsKRUhQychFisrRURQpK1YEXjKs6+JdOHLE/aL144/GY9hscmH68UdR+U1MFD0Qb4aD2SzrOoT2EhK8S/FrWvAl1FWrimfj7FlpG9CggfzNyhLjQ9PE2Dl0SDwJvgwc1/ceFmZ8oQ0LE29QgwZOzZQ6ddz1UzzxVpLeq5d0zT56VIyq+vXleB486D+/xGSSebr2+Fq7tviMA12Xz9GzdNqVRo0kAdvoHHJ4G3/+2Xsyd2Ki6DS1bh2yaSsUimJCGTqKIsVbg8JgtvHVcTksTAyIChXEW5KZ6dR78UTXRfRu/XopK/ZlEOi65H7Mmye5O5dc4jtskZLivFAOHOj03Dg0XkB0eHbskITQnj1FzO7zz8Vrk5kpfZSmTHGfV2Sk94tvRIS7h8hmE4+Ot27dJpO7kvD58zLn3bvFOLPbxbsTFiYGU79+gefuhIWJ0Xn8uFTzlESXbF+ijOHh3o+j2SyVU/v3+x6/IOeyQhEQSjCwSFGGjqJIGTNGckcCwWwWr4BnQujo0cbJqmazjO8aWomKknye777Ln6dit0v4av58yUn55hvxVvzxh3FOi2vopVEjKR92De84+PBDCYE52hVERMDrrzsbTZ45I1U9q1Y5t2nQQLwnrvtdsQKeeEKMH0cfpTFjRFzO6L27Vmdt3izaNgkJ+dd1ff9jx8r/P/wg2/vS42ncWN5z9+6wYYPvcWfMgCefdC4bNUqSo8+d876dP3r0kMaT/pK8zWbx0g0dKh4zTyPXZhODzds4OTkS/vNlCLVrF3oBSoUiD1V1VaSoHB1FkdKlCzz2mPzvECnTNGdzSccyk0laA7z/fv4xOnUSA8BzjHr1jBtIvvmmVNCYTM51Pdm+XQyD994LTHsmIUGSgj3DI2vWiPica0+mrCwRhHPke9x8s4RyXDl82PjCa7eLAXL6tDzv0cPZzdr1vTdqJOE6kDylQYO8l3s7PFGjRokBdfiwvHdfRg5IiG3wYPFW+aJixfx9vJYtc+rIFJTt28UAcXyOrt4lz/Pmgw8kT6dbN3ndZHK+72bNxJPmC19GToUKMGtW4d6LQqEoOZRHR1HkTJsmF8zPPpOLa79+Iqi2b580n3SIl91+u/fy3hdekAqrTz+VMfr2lTGMegE1biwXyZkzJTRj1EDSahUvyF9/BVZpZLPJxXzZMvEwOXj3XeP8IbNZxOWaNTNuueAPRwdvkLL6IUPE05OSIiKFEyaIgQGwaBGcOmU8jtks6tGjR4uhYzbLRTuQnCm73b9WzoMPGnuc7HbJffEWRgQxYNPTJS/LiPR0kQU4cUI8SjVrwsSJcrw//ljOm86d5bypWVO2Wb1awo3ffy/hqhEj5FgVpJpM0yTB+557AsunUigKjApdFSnK0FEUC/37y8OVLl3kESiXXCKPQKhSRS5SF13kvVM2uOuo+MNsFi+HK//+a2w02GyS8+Gv75I39u1z/q9pkvczcKDxugcPek/4ttnEWHAYAo71/VVSue7bl8crI8O7gKGv5GBNE0XojRu9GzoguUNGJereVJQjIsSwmTBBnh8+bNwBPRB0XQwrZeQoihwVuipSVOhKUeZxlI4PHCi5FHfeKRUyAC1a+BYVDCa8YrNBmzbuy9q3956wm5gIf/5ZMO0VfwJ4O3aIodCunZR3+/LQdOoEb7wh///zD2zbFri4oS8jx2yWFhD+unx7G7dNG9/GEEjV2/vvS/J27doiANmypey3TRvx7H30kff3X7OmewJ2MFSoAHXrui/bsUPyreLiRAOpc2fR8ClKNmwQkc22bcVDpRpwKhTBoTw6ijLPffdJmMgRJtm9W0Jcq1dLzsaNN0ovItfwhaNr8zXXSLXT6tX+L/6aJjkjnvv+7DNjz8epU5Jf06CBlMwHEz7ZsUPWNzIUfv9djDqbTS7wjnW8eV+SkqSa64cf4Ndfgw/jVK0q4ULX42MySUNNX3k+GRnGniOLRYyUb74RA9UXL74o4UXX93X8uPN/TZP8p+XLpZ+U5/F65BHvVWi+0DS49153Dbd168Qr6Vo2Hx8vIcFnny0a3aClS8WwcjQ83b1bQqHTp8v8FOUEFboqUpRHR1Gm2bZNjBxwXsAdYoIPPCDP339fEoIdnhVNk2TcL7+U5wsXyp2y46LszQOjaZKT4krHjnIxMgpvOC7Ohw+L4q7r/v0Jl373nRgmRtx9t1xsHV4Mx/v2l1S9YoVsF2yritOnxRh0PT4TJkju1cMP+97WaE4XXyx5Rd76Srny55++35fjtcWLJSfIle3b4a23jLeLipLwpgPX0vTwcElsfu45920cx92IZ54RYzaU2Gyi0K3rzs/a8dk9/LCE9RTlhVCIBarQlTeUoaMo03z7rbFhYrPJnX5KilzUZs2SxNp166SCauFCp3cmLk4ulIcPy+tPPmnsSbHbpRTdU3Rv2DDJ3fGm42KxiD5NYqKMv2sXpKb6fl8WiyQ+e5KUJF4EI6+MpknSd0HwF15r00aO27p1chxnzpRjUZD8l4cekoq0UDafMTpe3s4NEC/Prl3iLYqPF+/TgQPy/pKSRB7ANSR5/Lgkr3tD18WrFEq2bpVzxug4ZWfnN+wUZRg9RA+FIcrQUZRp/CXLuhosNWqITo9n3oWDevXkdUc1k699emI2+xbX0zQpefe1/0D24y+JuCBhGvCdx+TYb926kiuzZ49Ugv39d8H2lZMT+p5RdrsYXbNnSxXWvHn+twkLk+TlEyfkPe3bJ8s8w5MQWPK2v3XS0qTR7PTp/kv7AxnP32emUPhi9erVjBgxgjp16qBpGkuWLPG5/qJFi7j88supXr06lSpVolevXvzoUekxe/ZsNE3L9zhf0IqAEKG+Kooyi67LHa+Rd8NslnwKf0aLEVde6X3Miy8WD5AR11xjfAG3WuGqq5zPK1RwD2UZ4bkNiPieQ4TQCF2XXKOC4K9dQ82aYgzUrSsJwI8+6tTxCZarrhKvUKCVX4Fgt4uX6ZZbJDn5+utFe8koTOcQGBw4UEKPgwZBq1YiX9C9u/wfH+++TY0aomnkDZMJrrjC++uPPSbhynvukbBYlSoSlvJFhw6S32V0nKKi5BxSlBNKoNdVeno6HTt25J133glo/dWrV3P55Zfz/fffs2nTJgYMGMCIESPY7OHqrFSpEomJiW6PyJKQSndBdS9XlFlmzoRJk4xfq1hRQlcdOhRs7Keekgu5o3TabJaLy5o13iuiDh+WC+iJE85EYrtdknbnzHG/YG3bJkZTaqqxR6pyZcmNcb1rHz1aQmzB5tiEikCUjh3vMRS/KhaLhAPT0go/luvnGB0t7+XECeNqLbNZ9JkOHHDPpdq4UYw8o5vT//3PKezoyfz57irWrrzzjohLeuPHH0XpG2SuDhmBjz7yfu4rQkexdS+f8TlEFbJ7eWYG3HVDgeaqaRqLFy9m1KhRQW3Xtm1bxo4dy1O5mfizZ8/m/vvvJzkQl2Uxojw6ijLLK6949wrce2/BjRyQRNTvvhPvTq9eUl31zz++y74bNIAtWyTHp08fyZf54gsJp3jOs107MZq8kZws1VUOHHlFJWXkQGDtHMzmwodUoqOlkumbb0Jj5AwfDiNHyud4//3w6qviUfJWkm6zSaLv55+7L+/WTRKcJ0wQD1eVKmL4/PyzdyMHpC2FN4xam7gyeLDkBt1yi4QNx4wRA14ZOQpvpKamuj2y/Gk4FBC73c65c+eo4prVD6SlpdGwYUPq1avH8OHD83l8SgJVXq4os/z7r7HnICzMu1JwMAwbJo9gqFFDSo2ffdb/ujabb8/Hnj1yIQXJHykLvtdAFJd9ERYmLTVef904GbsgnDsnzU4dvPKKd5FDBxaLU4vJlSZN4JNPgtu/L3XpQM7Tdu2kn5qiHBNCwcD69eu7LX766ad55plnCje2Aa+99hrp6emMGTMmb1mrVq2YPXs27du3JzU1lenTp9OnTx+2bNlC8+bNfY5ntVr5/PPPGTx4MLVq1QrpXJWhoyizNG5sbOzYbNC0qe9tv/tO8l2OHZNy4iFDoGFD8cg0bCgJrV26yF39okUy5siRUqZuJEBns4mOy/z5khA8aJDcdfvyIG/d6nuO778vHoSUFKkQKgtEREi+T0FaLoBs++WXErK6/PLQzMlV5HHFCjEa/HnGcnJE2frOO0W5uVUrOV/atjVef9cuUWvesUPaftx1l9P7V7Omd8+Ux81wgTl7Vt7Xzz9LWO7GG8UrFso8qEBISJDWJRs2iOTCpEmF73l2QRBCHZ2EhAS30FVEREQhB87PvHnzeOaZZ1i6dCk1XPr29OzZk549e+Y979OnD126dOHtt9/mLW9aD7lYLBbuuusudu7cGfL5qhwdRZnl/ffzJ3SaTGKI7N/vvW/WSy8ZtxVw4MiDaNFC7upNJqcx1b27lEZXqOBc326X5NcFC5x5OZomF7x166Batfz7eOQRyesIBn8VZuUJTZM8mWrV3NthFIT9+8Uo9ve5e5uHrjsr6hYtEs0lV378UZY59G4sFjkHvvhCusV/9hncdJPx+K+/7tR7KijHjklYziFK6fBWTZwoPcGKy9jZvFlatGRkODvGW60SKvTUnyorFFuOzrtfhCZHZ/L1RZ6js2DBAm655Ra++uorrvCVgZ/LbbfdxpEjR1gegP7CgAEDuP/++7nyyisDmXbAqBwdRZnljjuksiYszLmsdm258Hgzcs6dg8cf9z2uI/ziCF3Y7XIR03VJSHU023Tw/fdi5DjWBVl3/37jHIwDB4I3chxjhpKwMPeqtOK++/eFrkuido0a/r1z3jCbJa+pcWNJPPb3uXubB8g5YbNJ2w3XCjWHQWG1Os8bq1W2u/128e6NH2+sYnzzzYU3ckBygI4dc557Dm/VrFmwalXhxw+UyZOdRg44j8cjj8DRo8U3jzJJCVRdFYR58+YxYcIEvvjii4CMHF3XiY+Pp3bt2gGN/5///IcpU6bwzjvvsH79erZu3er2KCgqdKUos2iadDW//34Rr6tUSZKAfZVtr1xZOIPBbhejxlUReOFC46aaNpuEsl5/3X354sUF338o0DTo2lXCOBUrStJzWpqEZ5o1K9m5uWK3i0fs7FlpfbBokQjlLV6cv7mqJ3feKarIDiP4u+98f+6Rkf7FD3UdTp6E9eud4ZhNm4xzcByG2q+/Sp7X9OmSt/X++3Je3Habd2M8WL780jg3ymKRc7N//9DsxxcnTshxMULXRT3clzTCBU8JtIBIS0tjn4u79MCBA8THx1OlShUaNGjA1KlTOXr0KJ9++ikgRs5NN93E9OnT6dmzJ0lJSQBERUWJVwp49tln6dmzJ82bNyc1NZW33nqL+Ph43vXWhdeDsWPHAnCvy52Bpmnouo6madgKWI2hDB1FmadaNWcJrj8KmywLzjv6kycljJWU5P0i6ro/XRcvUahbBRSEuDh5nDolXq7YWPe+TqUJm03K9i+6SJ5/953/bapXl/flyIHxpxMUTE7R7t3y2Xfp4v98ct1v5cqip+Pg6FEZp3lz91BosPj67Q/F+R4I/vZTXPMos5RA9/KNGzcyYMCAvOdTpkwB4Oabb2b27NkkJiZy+PDhvNc/+OADrFYrkydPZrKLJoJjfYDk5GRuv/12kpKSiI2NpXPnzqxevZoevkSoXDhw4EBQ7yFQVI6O4oLi1Cm5CBYUs1nyPP74w78Ev9ksYY0PP3SWBO/eXfB9h5K4uPwl7pGR4mUItgFpUdK1q4QLXXnkEfGSBXJzd9NNMGOGeFNeeaXw8/HMk2rZUgzdlBTj9S+5RBrK1qvnXHbsmJSor1wpzytUEAHGJ58sWPhw3DhJhDc6Ht98E/hNQGHQdWjfHnbuND539uwRg66sUWw5Om/ND02Ozr3j1PXNAJWjo7igqFatcD+4DRtKf6RAjJwqVeTi9e+/UoW1d6//8b2pLkNoc2jOns2v43P+vIge+tuPZ/PTomxF8MIL+Zc99JBU9ASy308/lRL9UBg5kN9zt3u388JudNzWroVLL3V6dmw2UWT+9VfnOunpIlD56qsFm9Pzz0vY1jVkq2kwdGjw8ggFRdOkua7J5Ezcdnw+U6aUTSOnWFG9rvL47LPP6NOnD3Xq1OFQboz6zTffZOnSpQUeUxk6iguKo0cLV8Xz4YfOO3EjatSQH/W775ZeUA0aSPJyICXXjz4qoTBv6Lp76KOo8OfxciQIT5wouRctWxbdXIw+qxo1xMvz8MNSzu6PgvbkcsWX8XfunMzFNSnegc0mBu6338rz5culFN0olPPyywUL8TRvLu/xjjskx6pLF3jjDViypHj7YQ0YIDcBo0eL3lDv3uLNKqgBd0FRRpKRi5oZM2YwZcoUhg0bRnJycl5OTuXKlXnzzTcLPK7K0VFcUOzYUbhk5M2bfW/ftm1+Y2XLlsAuYHfdJcmtviiOljEnTvh+vV07d4/We+9JyCLUaJqoURtRo4aUi3/4IRSR8Ksb/s6Zc+ckUdoIi0Xex1VXyV9vYoWnT8uxr1Mn+Pk1aiQ6PiVN585SVq9QFIS3336bjz76iFGjRvHSSy/lLe/WrRsP+ZIf90OZ8ehMmzaN7t27U7FiRWrUqMGoUaPYXVoSHhTFQkaG9Pi58Uap4HBtkRAoHqKhQdOune/XGzfOv6xhQ9+dzUFEC6Oi5I7YF4sW+X49FPjzPG3fLoZGzZrSoqAI9MgAMS42bJA+UcOGieeiTRspp3a0oyjs5xloONDfek2bek/mtlqd86xXz3tuUWSku4Dg339LWfoNN0hYKDU1sLkqyiAqdAVIMnLnzp3zLY+IiCA9Pb3A45YZQ2fVqlVMnjyZP/74g5UrV2K1Whk0aFCh3ryi7HDqlCSm3n67lGx/9JE0xXzyyeDGadVKSoP9GR7e+PJLSbr0xnPP5V92yy2+PTqaJj2M+vTxn0tSCCmJkJGUJNVCJ05IiXqoWjUYER8vn/fy5RLG2rlTjnHTphKGNNKnCZSIiMC8e2az5Fh5IzxcNHHuuCN/qMhkkmqr0aPl+TXXiDHjKYFgMkmyusNj9+abcr7PmCFyBvfdJ0Z2QkKg705RplCGDgCNGzcmPj4+3/Lly5fTxlXiPEjKjKHzww8/MGHCBNq2bUvHjh355JNPOHz4MJv8+foV5YKnnnIm89psTsPhxRf9h3s8mT/ft7Hii08+kaorz1YsmibGV926+bfx19NO18VDceDAhaN8HCjejsfJk6KfNHFiwVsMRESIiKCrcRIRIaX2rgwd6i6s6MnHH0te0//9nzSBdaVaNTHSYmLkeXQ0/PBDfrXsK66QHB2Q8yC30jdPqFDXITHRuVyhKI88/PDDTJ48mQULFqDrOn/99Rcvvvgijz/+OA+7ipcFSZnN0UnJref07JzqSlZWllvn1lTl+y2zfP65scvfYhHDpWvXwMeqXVuMo99/Fw/Kd98FXk5tNsv6iYkSRvr2WwlHPPKI82LmyWef+R7TZJIxS7IzeVlk0SIxBAqqupuaKgm0kydLzlF4uBg10dGScJ6UJB3LW7f2HpYymZxelshImdO2bRJyq1ZNQnvh4e7bdO8u26xYIV6xbt3cDe8vv5RxPc8Hq1XEErOyii5cqCghSkBHpzRyyy23YLVaeeSRR8jIyOD666+nbt26TJ8+nXHjxhV43DJp6Oi6zpQpU7j44otp5yNpYtq0aTwbSBtpRZGRliYaI7Vq5XfX2+1yMalQQe6iz52Ti0/t2vlDAJmZ3veRkRH4fFz3cfHFUpmyfHngho7dLuXAAFdfLQ8HZ85IQmrNmu45Hf7mZ7eXHt2asoTdLhd/X+eGPw4dkuqgq66S8yImRs5T17LszEzvoUeTyXk+OGjXzn8uV1iYeHGMyMz0nhNks0kFXygMnexsMbSqVjVuVKsoRkpAGbm0ctttt3Hbbbdx6tQp7Ha7W9PQglJmQleu3H333WzdupV58+b5XG/q1KmkpKTkPRJUgLvYOHtWxNri4sTjUbeu5Bs4QhELFkieRd26sk7dumLs1KsnFSS5quN5XHaZcWsHq1XKaf1x4oQIqznm07AhzJ4t4wZb0uvZVXvnTvEMVK0qBlSHDu5VSa1aBTe+IjA6d5YLdAAtd7wyaZLT0HacF5984r5OVJSoMhuValutoeuy7mDgQONz0mQS7483z2Gg2O2iT1SjhiRJV6kiuU6FMRgVilBw6aWXkpycDEC1atXyjJzU1FQuvfTSAo9b5pSR77nnHpYsWcLq1atpbFTi4gOljFw82O1yl7xxY373+3vvSflsAE1ymTdPjBOQCpSePY2l/Js2lXCBt9JrqxU6dRL9Es/5PPecVPEE+i1o1Urel0Oy//hxqQRKSXGOrWlilP3+uxyLiy9WYamiYNUqyc9JTBT9mGA8e/749FNpxulg9WoxQHTd/XMeNkzUh0Mp5qjr8v349lunp89sln2sWCFGdWF4/HGYNs19mckkXq2vvy7c2OWNYlNG/t9XoVFGfnh0mb6+mUwmkpKS8nlxTpw4Qd26dcnx18vF27ihmFxxoOs6d999N4sWLeKXX34J2shRFB+//gp//ml8cX/uOXjmGf9CZpomBoiDLl3E02PEv//CV195H+ubb6Qk2mg+L7/suwmoJw8/7N6X6KOPIDnZfWyH0TRtmnH38vJEdLQYBIX1MhSEmjXlb+3a8vmGMm/lmWfcjd9+/UTleMgQ8f40bixekUWLQt/1XdPkfH7xRRHei42VXJ/Vqwtv5KSmipigJ3a7NADdtatw4ysKyAUuGOjanXzHjh1uHcs3b97MzJkzqWtU6REgZSZHZ/LkyXzxxRcsXbqUihUr5nVOjY2NJUoFmEsVGzZ4F0VLSpKHPxwNMDMzJXRgt3tvoRAWJl4W1ztwVzZulHWMbgaCUScIC8svjLdxo3F+jdXqNPbKszfn7bel8gnk+Hom3hYlmzY5VZljY0MrHLh/vxgFrhVYF13kVDguasLDRQU71ErYO3f67tK+aZMKtZYIF3iOTqdOndA0DU3TDENUUVFRvP322wUev8wYOjNmzACgf//+bss/+eQTJkyYUPwTKsMcOSKVQElJUq00Zkxwirt//y13nNnZcoc7cKC7h6ZmTe8X97AwKdU9c8b/fmJinHfpmib5NWfP5l/PapU5/for9O8vF9yFC8XQqFpVKrO8zUfT5BFIMrDNJu9t7Vqpftm1y3dp+/nzxSvBXxLccYe0rhg+XLwP3gzKosDh0dmxQ1oNeDOuC0J4uDQCDQ+XhPMePeTcmztX9HyaNpX9//mnGOLjxjkrp7Ky5Pz76y+pvLrxRu/eSCO2bpUctsxMyf8ZPNj3eXTggMzr9GkJ7159tXeD019eZ2Ki9BKzWJzvW6Eoag4cOICu6zRp0oS//vqL6i59aMLDw6lRowbmYFzvHpS5HJ3CoHJ0xNU+bpxc2E0muSg1aSJucX+eQUevpVdecQruWa1ykVu0yNnrJzVVxsrIcDcgzGZRea1XT+T7fRkXJpMkSLq62adOlX0bbee4yA0fLqGsnTtlPna7zNtRrut6tptM0nDRX4NOV/r0KZgisyJ0mExyDu3fL+fHww/L5+/4rEOF6zk+erSUnKekyHKHMefInbFaxTC6807pWL5rl/P8A5gzR859fzz7rITNXPc9cKB4koxuRubMEY+apjm/z23bwm+/5dfqcdC/vxjrrkahySQ3FZmZ7vu++254663Qh+fKCsWWo/Py1xBZwf8GvjifDo9ee0Ff37yhDJ0LiLNnJRHY03XtKKf1p3C7cqWxQqymSeM+VzGzX36RhMpz55xGSM+eUsodGSkXjm+/zX8X7nh+2WXSMNJVv+T8eVGW/f57+WH2ZigZvWY2y4UnK8tp9AwYIO/5scekT5DjrlnX5S49lMmtitBRpYqzn1inTsW3X1/nnIPLL5e5eXqWLBbpDF+7tvdt160TQ9pov88+m18F/MgR8RR57stsFi/S7NnG+zlyRL5fu3c7v28VKzrbaniydCmMHOl93uWZYjN0XgqRofNY2Td09uzZw2+//caJEyewe3zhnnrqqQKNqQydC4iZM6Wc1giTSVzflSt73/7mm42F+zRNdEM82xOcOycufIco2oABzjtDXRe3/5o1kgcxcKCEns6ckYqtPn2M7yId291+u1RaBXr2mkzw/PMSbjh9Gnr1kmooxz527hQjLCxMEk+L+gI6ZYrcjWdkyIW7oKJ3FyIPPiiG9cMPS6uEgnT8LgocIVKjc9Jkgtdfl1YO3rjrLlFZNno/jRuLB8uV114ToUoj4yssTPLPjDqqg+xj+XIJ+zVsKGNt2pR/7maz3LBcqNVYytApXj766CPuuusuqlWrRq1atdBcLgKapvH3338XaNwyk6OjKDzJyd7vSu12EffzZeh4Vhc50HXj3JmKFcFb+pSmiYenZ0/nsqZNve/bc7saNYILU5hM4s259VbnxUjT5G9Ojqjftm4t63peUIqCm2+WiqzwcDEevSVSK/KTliZ/c+U2Sg3+Qmf+5puc7N1jlCsEn299b9/nnBw5370ZOhYLjBghD5AWK0Zzt9mMv9uKEKOUkQF44YUXePHFF3n00UdDOm45T5VUuNK3r/cf0vr1Jazlb3ujpEiLpfBlr8FyySXBJfparaK1cu21kosQHi53stHR8rx9e1FJBgkvFGW5dFSUhDgiI+W479174eZAFIS+fcUTuGxZ6fHmgHy3vH2OdruUiz/2mHdjqG9f49fMZuPvV9++3t9/u3bBncOXXmoss2AySU6PoohRTT0BOHv2LKMdHXBDiDJ0LiC6d5c7OFcDwfHD/NJL/g2HW2+VJGPXzt9msxgNU6eGfr6+uPNO8eoEkoivaRKqevBBMWYcHp3Dh535Stu3i2DawoXihfJWdh4WVvhKqsxMCeeBhKyeew46dizcmBcSP/8sF1/HMSxN+PLo5OSIbtMDDxi/ftNNUhjgek6bzfJ988zPAd/5Qs8/H9h8HTz8sBj9nvuuUUO+awpFcTB69GhWrFgR8nGVoXMBoWnSMPCxxyQvBMSTsXAhXH+9/+3j4iRhctw48YKYTJKc/PvvzrBPcVG9OvzxR2B3m45w15kz3suPHaGsBx+UY2R0wYqMlPygYBqIBso//wS2nvL8SG5TSfcGq1IFmjcv2LbvvGNsSMfEyHfpppvkXDOZxJOzZo1xzthTT3k3uoP1dDVtKt/tYcOcifujR8t3zKXSV1FUXOCCgQ6aNWvGf//7XyZMmMBrr73GW2+95fYoKCoZ+QLGUWJeEHTdWbZdkrz8MjzxhH/9lM6dYfPmwu9v+3a5UH30UejDJoFU9fzxh7wX1b265HBUGV59tSQJF4Q//hABQm/4+37Z7d69mWFh0pHdSAE5EBwhOGVUF2My8guLQpOM/OTVZfr65qvjgaZp7C9gAqVKRr6A8fYjarNJCfeWLZK3M3q0JBa7Ulp+CK3WwETiUlKcyce+8GdsVK4snq2iIBAvxXXXwX/+I3f8vhRuFUWHrss5UJhricOjasS5c1LldPSoNIgdNsw9XLx/v+hWhYeLaKcnNhucOiUtKly1fAYNcnojrVb5jm/dKppE117rzOkpiZuXU2TwFds5Qya9qU9/GqFRCn5gFMXKgQMHimRc5dFRuJGUJBob27c7y2UrVoTvvpNy7NLEiy/Cf/8bOpE4k0kMGSPVZrNZEqB//llK0du0Cc0+C0ogRpuiaIiOhmPHxNBo0ECMCk98GaKaJt+noUPzv+YIH6WmyjlntUpY+OefJUnecc47Pn9f54Cr0e74/7rrxAs6aJCIGlosso/YWCk379Ur+ONRWBaxk+tZSDY2TGjY0LmEhnzL9cRQjD1FDCg2j87zi0Pj0fnvVer6ZoDy6CjcuP12Z2M/R2gmLQ2uvFKExkpLW7FffzVO0CwIjh/7ihWNy3hBPFuzZsn/DjXckjQ0lJFTcF57TdocFPQYfvKJGAaPPuq9lcm774r68CefGL9+zTXyfXL17Jw/L8J8587J3Bzfvz17RP/qoYec53wgc3f1EDr+nz8f4uOdfeMc+zh3TvZ95EjxhkUTOcc4viYHmaAtt3RoLYd5gp+ZjoE1WB65wHtdOZjoaJznhVmOH+EgUcnIijxOnhS1Ys9QkN0uP+jffVcy8zJi1izvOQo1aogwWyANJm+4Ae6/X3p/pad7D4M98YSUo4Mkw5Z0bpKi4OTkSJuQ558XA/6qq4K7uDuah374oXG40WSSnlHvvececnKg62LUfPWV+/LvvxcxS88xbTbxtrz7bmBVhr7QdfFIGn3HT52S/RQnX/BPnnHjig2dmWzGRglnnRcXKhkZkPJy18eJEyf45ZdfWLRoEcmFEM5SHh1FHmfOeL9T1LTSVc57/Lh3o6RCBSmXnz7d/zj9+okX69gx78nFZrO72NvJk6UjP0lRMA4fliRiV4+gkUFihMkkn7/d7l0A0GyW78q5c77PqZMn3Zd5PndF1+UcDVXTUm/4mkOR7I8MzGjYDYyddHLIxkaUuh+/YFi8eHG+ZXa7nf/85z80adKkwOOqM0iRR6NG3pWRdd23anJxc9FFxne3Fou0jwgPd3a39sWjj8o49ep5X8dmk7vzTz6R41CnTukSqlMEx5w5cp5omuTb3HWXCDcGgsPzMWaM93VyciRRedw47wax1Zq/M7ivTuGxsZIjVliPjsnk26gr7m7lPaibF7ZyRQNaUY0ovEg7lzeUYKBXTCYTDzzwAG8UtIwQZegoXIiI8J33cuutEt8vDdx1l1xMXH/4TSZ5PPywdF0+ftz/OA7ZfX85D4cPS5foyZMlmVNRdnENUWZmwvvvw8GDgW8/bZpoTxlhNouo5osvwqpV3pWOu3eXpH9XOneGK64wDos+/jjcc0/+c95hSAUSSnV0OL/99vyvmUwwfHjxC1eOpCXtqIHZo8JKB56jf/FOpiRRoSuf/Pvvv1gLcXdZYEMnOzub3bt3F2rnitLHlCnSh8mInBwRKSsN1KkjyZ6XXOJc1rWrdI6uUEEuXkXB++/LcfBGaUnWVpQMNWs6y86Nwkxms/Q1+/FHY+Pkyy9FPsBxHtWoIY1LH37Y+Jzv1k3Oye7dnctatnSK/Lnuo317ycF55x3R2KlRQ5ZHRYkBv2BBgd92gbFg4hduYixtseRejhpTmS+4mtG0Lf4JKUqUKVOmuD0eeOABxo0bx9ixYxk7dmyBxw26vDwjI4N77rmHOXPmANJSvUmTJtx7773UqVOHxx57rMCTKWpUeXlg3HabhGmMfqijo723RygpUlNlrg59G19d2osKi0W0SCpUkP0rLkyiosRLZITZLK1SAmnPkJUlFYBVqxqHqzzPeZDmmyaThLkcIbZKlcQwz86WCi/XUJrNJsnPsbGlQ4AygxzSyaYa0aVGQ6fYysufWhqa8vLnrizT17cBHk3dTCYT1atX59JLL2XixIlYAk2m8yDoraZOncqWLVv47bffGDJkSN7yyy67jKeffrpUGzqKwKhQwXtuQXS0JFouXy4/lEOG+G8GakR6ulRxJSdLc8LCtJDw/E5nZBR8rIJit8tFJzq6+PetKD2Yzd5FJ+12OHAAvvlGQqFnz4rx0bWr5MZomqzz889SFdaypXhvMjLku3LmjOSftWtnLFboavSYTE6PTWSk97k61ikNRBNG9IWSk+OJ6l4OwK+//lok4wZt6CxZsoQFCxbQs2dPNJerYZs2bfj3339DOjlFyXD99cYVS2az/MjWretMxjWZ4Nlng9O0+eEHSeY8d859n7Nni5JrQdF1cfG/9lrBxygodru0hfj44+Lft6L0kJbm/TVdh88/l4cnl1wi37kxY0Q3x0HjxuJ1SU11LrvmGhmjNHhhFIqi4OTJk+zevRtN02jRogXVC9lwLegcnZMnT1LD4DYgPT3dzfBRlF169BDdGJCQjMNb2KgR/Pabe8WR3S5KrcuWBTZ2UhKMGpX/gjB/viR5FobZswM3copCB8dqlfyKBx8MbP+a5rwzV7o8FzZr10qDWs97xQMH3I0cgMWL4ZlnimtmimJBVV0BYkdMnDiR2rVr069fP/r27UudOnW49dZbySiEqz7on9fu3bvznYtynMO4+eijj+hVEvrhiiLhhRek8eBdd0ny5GefiYvdKERqNsOMGYGN+9lnkjPgmRlmt0uSZGF45x3/+jaffSahgTvukKTru+7y3XcoWDIyxFD87Te49FIRGaxXT+7Mu3SRHI0HH5Rj+uabEsLYvt13uXJZxlHpU95o1Ci049lsEsYNRCfHbpfvW0l3cFeEEFV1BUgy8qpVq/jmm29ITk4mOTmZpUuXsmrVKh4M5A7SC0GHrqZNm8aQIUPYsWMHVquV6dOns337dtavX8+qVasKPBFF6eOii9w7LM+YYawfY7PBoUOBjXnkiBhGRj/SDiG2gl4YExL8l4k3bSr9fC691LlszRrvUv4FYccOMVx+/jmw9WNjYcQI8WqVN1aulGqfQDSNyhK+QlTFQUqKqCurnDBFeWLhwoV8/fXX9O/fP2/ZsGHDiIqKYsyYMcwI9I7ag6AvKb179+b3338nIyODpk2bsmLFCmrWrMn69evp6miNqyj1LNl1kv6zN9PgjfUMnbuFn/b7v9J36WLs0bFYnF2R/dGxo3F5tqZBq1aBGTkLF4qHxGKRH/px46SqpHNn3x6dsDBo0SL/cm/vq6C88YZ0gq5SRYyq1q2lGicyEtq2Fel/mw0++EDKg6tWFY2i8sjgwdL4srx5dU6fLtn9N2pUMlIGqznECObRgDfoyywWsA09yJiJjs4CttGXWTTgDUYwj9UEeKdUXimB0NXq1asZMWIEderUQdM0lixZ4nebVatW0bVrVyIjI2nSpAnvG+h4LFy4kDZt2hAREUGbNm0M1Y69kZGRQU2Du6IaNWoUb+gKoH379syZM4dt27axY8cO5s6dS/v27Qs8CUXx8uq6w1y1YDtrDqeQkJrFyv1nufyzrXy6JcnndvfeKwaB60XLYVgE6lUcN05COZ4ls7ouuT7+mDlTyrgPHhRjITNT9D9atpS+Rb48OnfeKUaFJ465hyrFLDVVqsrOnpXw365dcvedleX09nTrJvPZtEm8Sd46XZd1bDZ53+UtzFLSTVUdHcyLk4XsoD+zWc5eEkhlHUcYx0KeJThP/rOsYhwLWccREkhlOXvpz2wWsqOIZl4GKIHQVXp6Oh07duSdAHMGDhw4wLBhw+jbty+bN2/m8ccf595772Whi3rm+vXrGTt2LOPHj2fLli2MHz+eMWPG8Oeffwa0j169evH0009z3uUHMTMzk2effbZQqTFB6+ikembGOQbSNCIiIggPpJNiCaF0dOBMZg61X1tHti3/x14lysKxKb2JsHi3f9euFWXVnTvledOm0h5h0KDA57B/v3gwfvtNnletKkqyd9zhf9tKldyrtVxp0QL27TO+qI4bB59+6r2q66efJF9n3z553rRp/sRQRfmipDvQB4rZ7MzdiYuTKsd77ineOdiwU583SCR/zM6MRgIPUJuKfsc5xjka8IZhI886VOQw92MuRYL9xaaj89i3odHReWl4geaqaRqLFy9m1KhRXtd59NFHWbZsGTsdP/7AnXfeyZYtW1i/fj0AY8eOJTU1leUu3WGHDBlCXFwc8+bN8zuPbdu2MWTIEM6fP0/Hjh3RNI34+HgiIyP58ccfadu2YCKSQTvsK1eu7LO6ql69ekyYMIGnn34aU3nzV5cDVh1MNjRyAM5kWvk78Ry96sd63f7iiyV5dv9+MSiaNQv+zrJJE/j1Vzh6VLwfTZsG1mn85EnvRg64l+W6YrHIPH2Vrl92mWy/b594rDZtgkIIcSpKOa1bw4YNEoLKyICNGyVBvLjxprnjis0m4dpWreQ8Lol7yR2cNDRyQDqN/8R+xuO/f8TP7Dc0ckCMoB2cpD3lLKGrmPF0RkRERBARAi2C9evXM8jjjnbw4MHMnDmTnJwcwsLCWL9+PQ888EC+dd58882A9tGuXTv27t3L3Llz2bVrF7quM27cOG644QaiChGrDdrQmT17Nk888QQTJkygR48e6LrOhg0bmDNnDk8++SQnT57k1VdfJSIigscff7zAE1MUDeFm38anv9dBDJumTQs/l7p15REoBc2jsdvFO/Pjj6JX4hBQ27YNdu+W99Kpk7yv5s2drynKL2az6Dl17gzVqknSdGmmdm1o06bk9h+O726i/l4P9Tjlj1BUTcn29T061D799NM8EwI9gqSkpHz5MzVr1sRqtXLq1Clq167tdZ2kJN9pEa5ERUVx2223FXq+rgR96ZgzZw6vvfYaY1zqYUeOHEn79u354IMP+Pnnn2nQoAEvvviiMnRKIQMaV6ZShJnULPc6VpMG9SpF0Ll2TAnNzD9xcVK9461ZZ69e8Oef+e+Q7XaYN08ecXFShj5zpvTFct120SKoVUueX3aZJBSXdHWNomjYtk1yvUqaQHKXatcu/q7inrSgKq2oxh5OY/fwyERhYQjNAhpnCM2IwkIm7uWbJjRaUpUWGCTRXQiEQgcnd/uEhAS30FUovDkOPKM5jswX1+VG6wSqsTdt2jRq1qzJxIkT3ZbPmjWLkydP8uijjxZk2sEHQ9evX0/nzp3zLe/cuXNenO7iiy/m8OHDBZqQomiJDjMzc2RLTBpYTHLyWTQIM2l8cmUrTKVc9PHzz41DZf37w9y5/sttk5Phxhud+UEONmwQxVkHFSqI0nEpPxyKcozJJGGq2bON+10VJxoaMxlJBOa8TuMWTGjADK4gFi99JjyIJZIZXIGWuz1Ijk8EZj5mZKnpcVWWqVSpktsjVIZOrVq18nlmTpw4gcVioWpulYe3dYwqqYz44IMPaNWqVb7lbdu2NazwCpSgDZ169eox06Br4cyZM/NcZqdPnybOtfGKolRxbZsaxN/RjTu61mZIsyrce1E9tv2nO5c2Lv2f2cCBkiM0cKB0aG7SRNSQf/1V/o/1nl4ESPKprue/k7ZaYd062LLFuWzcOCmbD8bYueYa8Q6FslxdUX4I5Fxq1Ehy4e69F/75J7hE/6KkN/XZwWQepBdDaMatdGYjt3MznYIa52Y6sZHbuZXODKEZD9KLHUymN/X9b1xeKQOCgb169WKlR4x3xYoVdOvWjbDcBEhv6/Tu3TugfSQlJVG7du18y6tXr05iYmIBZ16A0NWrr77K6NGjWb58Od27d0fTNDZs2MDOnTvzysw2bNhQqJbqiqKnfc0Y3hlmICpTBmjdWqqkPNF1SXAuDAcOiNaPg6Sk4CpzHnoIevYUj5CRuKLiwiaQc2n5ckk8Lo00ojIvc3mhx+lCbd5neAhmVE4IYegqUNLS0tjnKDNFysfj4+OpUqUKDRo0YOrUqRw9epRPP/0UkAqrd955hylTpnDbbbexfv16Zs6c6VZNdd9999GvXz9efvllrrzySpYuXcpPP/3E2rVrA5pT/fr1+f3332ncuLHb8t9//506BekenUvQhs7IkSPZs2cPM2bMYM+ePei6ztChQ1myZAnJyckA3HXXXQWekKJ8kpBynul/HmHVwRSqRluY0KkWY9vWCLo/2p9/wltvSXl769bQvbuEoRISRJumQYPAFJK9cccd0qahd28JG3hRUzBE0ySvx2aTvyXRRV1RetE0ybc5dsz3OnffDZMmSdVfMF8PHZ1l7OYj/iaJNHpRj0E0ZRG72EoSTYhjMj3oT6O8bTZyjOn8yXZO0JKq3MNFF7Zn5QJi48aNDBgwIO/5lClTALj55puZPXs2iYmJbikojRs35vvvv+eBBx7g3XffpU6dOrz11ltc4xLz7927N/Pnz+fJJ5/kv//9L02bNmXBggVc5Cqx74NJkyZx//33k5OTw6W58vU///wzjzzySKFaQASto+NJcnIyn3/+ObNmzSI+Ph5bIM1aSgilo1My7DqVTu+Zm0nNsmLTJfHZrsPtXWvzwfCWAY+zYAFcd53kK1itztJchx6KI1wUiCelKDRUNA1q1JDE0W++Ce3YirKNySTNbAcOhMmT/a9rt4te1QcfBL6PqfzES/yOGQ0bOiY07Oh5zy1oWNF5j2HcRXe+Zgdj+RoTGlbsWDBhw85sRnFTAKXiFwLFpqPz0HKIKKSOTlY6vDq0TF/fdF3nscce46233iI7OxuAyMhIHn30UZ566qkCj1tgQ+eXX35h1qxZLFq0iIYNG3LNNddwzTXXGCYqlxaUoVMyjJz3D9/vPY2RfM9fk7rQva7/zyIrS+6Gz571vZ6mSe7OuXOimuxrvaIQiwtEF0Vx4dG6tXgjY2KkYe6LL8o57Y8//wys4moPp2lJYAq3kVg4xP204V1Ok/9LUpFwkniIaHwIT10gFJuh82CIDJ3Xyrah4yAtLY2dO3cSFRVF8+bNC51QHVQy8pEjR3jhhRdo0qQJ1113HXFxceTk5LBw4UJeeOGFUm3kKEoGq93Od16MHItJY8muUwGNs369fyMHxHg5cQL++st3k0xdB48KxpCgjJzyjaZBQX5zrVaoWFG2/+9/5RxdtQqWLPHeB8xigaVLAxv/G3bn1kD55zxWPmCToZEDcI5s1XtKUaLExMTQvXt32rVrF5KqsYBzdIYNG8batWsZPnw4b7/9NkOGDMFsNheq5Euh8Idd14lPSuOfZDtYYsDqpc5W06FWGljskBjDvn1mv41GY0qvZJCiFGOxBOaNcSUzU3qaVakizytVgn79QpewHqxz8iC+7xqCbdSpKCShqJoq4qqr4iA9PZ2XXnqJn3/+mRMnTmD3uHPcv39/gcYN2NBZsWIF9957L3fddRfNHfKxCoUfLCYTw5pXZbmBV8dq17myVTWv265LSGH84p3sP5vb4O1hM6xsAhs95JQbJsNVO6Fy7tUn08xVzzWj+vlYwmpo5JyIBI+7XZNJ+gV99JHvEJdC4YquS8PWYDlyRMQo77kHXnnFqYtjsYgcwe+/59/GapVGtYEwghY8TODyzrOI9/paDOFc4pKwrCgGSqDqqjQyadIkVq1axfjx46ldu3bQxSreCNjQWbNmDbNmzaJbt260atWK8ePHqxJyRUC8clkT1h5O5lyWzS0ZeVKX2nSvY9wI8GhqFpd/toXzVheLPsIGw/dCWjjsqi7LKmfCjVvB7LJepA2u3M3JDAskVYDPO+aK55jE86NrvPCC6O5UqVL4knSFIhBycuCNNySE5VDkP3YM/v7beP2BA6WqMBBaUo1H6cPLPpKRNXxfCx3JyG8zVOXnFDfK0AFg+fLlfPfdd/Tp0yek4waco9OrVy8++ugjEhMTueOOO5g/fz5169bFbrezcuVKzvnqtqi4oGldvQJb7uzOfRfVo0utGAY2juOLq1vz4fAWXi32j/9OJMtqx+755bUDfRKcz7smipHjeiZruetFW6FJCtyxETolQa1z0OI0lW/dzqOP6fzyizJyFMWLrsObb0JuQQkff+z83xVNk4T6YG5opzGQRYxhME3pTC3uohtLGMsNdKAjNX3m8DSmMtfQmjXcwoQgBQAVilARFxdHFUd8N4QEraMTHR3NxIkTmThxIrt372bmzJm89NJLPPbYY1x++eUsW7Ys5JNUlH0axEby2uDA+uEA7DyVbnyDYgKquQjUVMvwjEo519OR12pkwEhna/NkIC3bxs6dliKrvlKUDaKji1/vKCUFTp6Uhra7dhmff7ourwWDhsZVtOYqWrstv5JWHCKZRkw33M6Mxn/ozkMEpl6rKAJUjg4Azz//PE899RRz5swh2l8/nyAolFB9y5YteeWVV5g2bRrffPMNs2bNCtW8FGWEXafSeX/jMfadyaRF1Wju6laH5lULd4JuP5HOvjOZxgaIHTjr0lcnOVKWmV1fj4KtNeBkNLQ5BW1Ounl84iItfL3jBLMOnkfXmxRqroqyS3S0dLIvbkMnOhpyWwPRuLGx10bTJLTqi+Ok8QGb2MBRalCBW+mCBRMf8zf/cpZsbERgpilx3ERHIrFwnvzZzzZ0muBs/3KIZN5nI1s5Tg52MsnhOOlEYqEn9ZhMdzpSqzCHQOGJMnQAeO211/j333+pWbMmjRo1ymst4eBvb3FeP4SkI4/ZbGbUqFGMGjUqFMMpygjf7D7F1V9uBySx+Md9Z3jnr6N8c117BjcrmPtx4Y6TjP1axvTq0fmjnvPp5trYLzpC3gYakrcz4BD8Xg++bgstTsHY7WCWPIWKEWZuXbYHzlRy2UvZ/5FQBEdGRvEbOSYT3HmnGFggCsj/+5/IErga9roO99/vfZydnORiPiGF87ligKa8BGNHTo6D3zjIR/zNABrxG4fcuo+b0ahFDMORdjDrSWAgn5KFFSOlhH84wcf8zadcxY10KOBRUCiMKSobQrUeVBSIbJudW5buwmZ3FqJaddB0nQlLd5HwQE8s3gRCvJCZY2Pisl2GmjsgujsdUxqy6R9nJ9w64RWoXi2WzWdSnLaKw7vT5whsqwl7qsH26tDhBJ1rxfB3UprYN8scDYWUkaMoLA4r2zuaJsre//d/zmWNG4tWzo03Svk5SCXWE0/ATTd5H2sy3+cZOYCbWWLzuEVwPF/DYa6kBYvZnfdaE+JYyjjCMaOjM5FlZGEzNHJc3+ntfMNIWlKJ0HTGvuBRycgAPP3000UyrjJ0FAVi7eEUTmfmd4PrQFJaNn8eOUefBn5aiXvwy4FkUrO8txD54YYODGwSR8J9ohhbtSr07atT4SUvDalsmoStkmJge3W0Dic4nZkjr52MhjOhiwErLlCqpcH730O3o7C/CpyOgpppsLsanKjA/Z0bMjKyGadPQ5cuxuGooUOl+uq336R0vW9fUff2RjLn+ZWDQU81BztX0or/MYjNJFGLGHpTPy9JeTen2UVgAp6ZWPmRfYymbdDzUBhwgYeu/vrrL7p27Yo5V3dB13W3QpWsrCyWLl3KmDFjCjS+MnQUBSLb5lsCeM/pDDrXjiE6zIvAXwHGjA4TD1H9+mCpnEVKlhUbkVjzlWa5YLaL9ZUZBulh5FTMXVcHqmbAmagy/QOhKGEqZcM1O+X/hrkG99lIqJYJ9VOIjTHTltrUIL+8fzY29nOWOCKpGRHD4MG+d3UeKwf8CP35I5E07OhcTet8VVjZBNenMNj1FQpv9OrVi8TERGrUqAFAbGws8fHxNMm9M0hOTua6664rsKETXGyhhFm9ejUjRoygTp06aJrGkiVLSnpKFyy968cSZfF++kxctpvq//udJ3/Zj82XIeLCJY0qE242NjriIi10qV2Rg8mZDPw0njqvr6f1uxuo98YfNKjkxX1u1mFfbq7Q4cro/+tD5uImYNWgZgbc8xfc/we0ORHQ/BSKfCRVgHNhYjifC4ebr4QaD0GbyVDjYZ59NIya1tcYyTyOkwaI6vBb/EktXqU171KL1xjKXI5g7JnU0fk/1lCT/9GG92jDewG3e/BkKj/Tgndoylt84xLCAmhDdWoTmFy4BRMDUYn8IUMP0aOM4tly06gFZ2H6j5cpQyc9PZ2OHTvyzjuBNa9TFB2VIiy8fJn80HmxTcjIsfN/aw7z318PBDRmlagwXhjQGBBRQdex3xjcDLuuc8nseFYdTM7b5lRGDgdTchWRHd8Dx5d+d1XYH0de7kSFbM722Qsmly9MpSwYvQMan6VM/1IoSgYTkGOSU2zMtfB5B2ebksww+F9vePhyvmcvl/EZNux8xN/cxw+c5XzeMCvZzwDmGHpJXuF3nuAXUnEK7th9nKveTCDX5YdIZhQLWI9Tk8qCiekMQcP/heFJ+lIrQKNIEQCO0FVhH+WYwqgklylDZ+jQobzwwgtcffXVJT0VBXDPRfVYNq4d/RpWpnq0sZKqDrz5xxHSsgNr6vNwnwYsGtOWi+vHUrdiOAObxLHixg7c3KkWX+04yeGULK/JynlCgWnhsLoBfOWuJ0KXRFFX9hQX1IH+B2jSykadOgFNU1FOiIoq5ADpYfBBd/inBvzQHGweP6m6Bu91x5YczjZOsJy9vMDqfMPY0NnHGZbiLp6TjY2XMOgPYUBv6jGTkXSnLtWIphYxVCeaOlTMp4rsSJ1+2WPs0bTlV25mKM2pTQz1qUQckURhoQJhdKMO87mGp7gkoDkpFKWBcp2jk5WVRZZL973UVC9Jq4oCM6JlNUa0rMZHm45x+7d7DNfJtNrZf/Y8HWoGdgd4VevqXNU6fzZmfFIaYSaNHF+hMBNQMRsuOgq/erjWa6V53cbcKJV/Z1kYMUISQxUXBjab9J2yFSbdZEtNqJ/i/fVsC+ypiqVHEn9xjAQvIaowTGzhuFuCbyLnSHbx/PgilWwm0pmJdHZbPpTPOUZ+5XobOn+TmG/5JTRSva6KG1V1xY4dO0hKSgIkTLVr1y7S0uQ3+9SpwJLkvVGuDZ1p06bx7LPPlvQ0LgjqesuTQe4ca8WEe339TGYOc+KT2HI8jWybjqZBtMVE9QrhnEjPJtJiYnSbGtSpGI4t0DhtmB3CrXKRcZAa4fXHwK5DvdfXkdrBBLXCYF19OBYDybmVWVUzpI1ETDYkxsCWWpBVrr8+FwRG7Re8cvcf0OU4bKwNn3WEcxESBq17Th6+qJ2GFTtz2ZJP58ZBDnZqeiQtVyUaCxpWP1cxDbzm19SjIhZM+ZRxTGjUo1Le8+OkMYvN7OEMTYljIp2pg3EvOkWIucCrrgAGDhzoloczfPhwQEJWnlVYwVKuf6mnTp3KlClT8p6npqZSv379EpxR+WVQ0zjqVYog8Zx7aMmswciW1ahRwdjQ2X4inUtmb+ZMbqm658+5BphNGu9uOMbNHWsSbjaRZfWVoeDYsS59sP6o5/wB2FkVeh4xXF0Hjp7LhjCg7nkYs0PK01/tDc3OSHd0x4qdgL6HYVZnUWFWlG80HTonwmsr5YS8OR4eXwsXT4DDlWHi39D6FDQ9AwdjweZSaWixwWX7ob54cQ7gw/MDPMcqJtKZCsj3JYEUzJiw+qlw0hGxv384Tntqur12O135mM35trGjMxnpGvoHR7icz8ggJ697yv+xhu+5gf7Ku1P0XOAenQMHAsvjLChlKkcnWCIiIqhUqZLbQ1E0WEwmvr++fT7PTfc6lfhoREuv292ydBfJ561ev+c65JWPz9lynCf7NiAmPMCS9Uv3Q4vTzpE6nPD/Y6DhIjyow4TNcOUu8jI0zbmvR2fD8N3eRlGUC3JPluan4esvIdwunkITUCMN3v8OZi+Btqdk2bdfQL1z7tt2OA5zlgS8x1NkcivOfoGTWOZFozg/mViZwNJ8y7tTlw8YTpjLz70GPEofrqc9dnSuZyEZ5GBHx4qODZ0sbFzHwoD3r1AUlIYNGwb0KCjl2qOjKF7a14zhwH09+WHfGY6kZtGhZgy961fy6nI8mJzJhmOBd703a7DrdCaJD/bm2i+38eO/Z33aLfddXIdHH63I+9+e5LnPz0gycrCmfc0MY9FbM9A0WQyeDO9hOUXZwmSCfp9u5Lf4LDhWEa7eCaN2OdW2HYTpMGi/+3nR6jTsewtWNIVDlaHtCfH8Belx/xbJdUsghXUYeyCNcOTc/MsZmuLeguV2unINrfmWPWRjYxBNaUhlADZylAMk5xvPjk4SaazhEANoHNybUARPGQ89lWbKlKGTlpbGvn378p4fOHCA+Ph4qlSpQoMGDUpwZgoQwb+U81aGNa+K2eT80tp1nTOZOVQMtxDhor2Tlh1cBqhdh3NZVjJybNSKCcek4b0CC+hTP5bqFcIIr5sB3fMnXQaM0e+PHSkftqi73fKE3Q7NBiWz9vr1WM+GSw6ON+PY6Lyw6DBsn8ELgeMoMU8np0DbnyUz7/8MckgghQbEUpVobqZTvvXT8J2o5O91RQi4wENXRU2ZMnQ2btzIgAED8p478m9uvvlmZs+eXUKzUmTm2HjilwN8sOkYGTl2alQIY+rFDbi3R10+3pzEc6sOcvRcNhFmjQmdavG/y5tSMcJCy6rRVI8O42RGYD/oOrB83xlqvLqOmHCzTyPHBKxLSOG2b3aT4qOtRNDowMY6sLqhJKOqX5dyx8c1BkLkJXA+DDolwuYPi3X/Odi5noW8yiBqEUMSXqoFvdCP2dxER7aQxB8czVt+CQ1ZxnX5+lN1ow6RmDlvkAdkQaM3Kq9RUbbR9MLIDZYxUlNTiY2NJSUlReXrhJCr5m9j2Z5TeFZ9j2xRlWV7TrstM2vQt2FlfrmpI5qmMTs+kVuW7s6n8+GJv9c96VSzAluOp4fWDNEBuwZvXQQpEahmoOUZl3jlF1/D2O3u+VtFjBmN5lTlEXozkWWY0PJEAjWgNdXZwcmgvxfNqcJu7kbzeCNteJedBn2uBtKYn/DRXbScU9TXDMf43PYLhBdSgDE7DT66VF3fDCjXyciKomfbiTSW7M5v5AB842HkgISafjuYzLoEqUKZ0Kk2S8a2o3vdikSaNSqGm4i0mKgQZqJR5Qiiw0zERpi9/phbNKgYbsKEqCk3qhzBSwObsPVEiI0cyL3Q6XDREZSRU95x+XxvugqmDoRDsaKCXAzY0NnFKaIIYxnj6EEdorDQlDheZzDx3MFbDKEJcUGNu5cz/IJ7hcsWkgyNHIANHCWzgCE0RRBc4C0gipoyFbpSlD7+Ouo9mdjb986kwZ9HU/O6m1/ZqhpXtqrmdZz5245z3cKdhq9Zdfjxxo70qh+bt+y7PacNDS8HP97Ygblbj/PFP8d9hr+MJ09eqbDiAsFqhlcuhlf6wJW7YcmCYtltGCb+4iivM5gR5K9cvIeLGEgT2vJeUOOu4pBbn6oNeFfITCWbfZzJV7KuUISKzp07B6yR8/fffxdoH8rQURQKb60ffGHX82+n6zq/Hkzmw03HOJKaRdfaFbn3ono0rRJF9WjfVU3bTqQzY8NREs5l0656NF1q+xY5W3s4hR0nC+jxcbSYMKLlSTgeA8lKW6fUUzELxv0DAw6C1QTTL4JNdX1soIs3r8mZ4pohVuzs5TSnyKAaIlxpR+dLtvMyv3OKdJpTJejw1SJ2sIbDDKIJd9Itb2xv+HtdEQIuYMHAUaNG5f1//vx53nvvPdq0aUOvXr0A+OOPP9i+fTv/+c9/CrwPlaOjKBTZNjv1Xl/P6cwcNy+KRYOq0WGcyshx85poQEy4mWMP9iImXOzsHJudqxZs47u9+S8ibw9txl3d6tJo+h+GYoRRFhNpOfkrnyqGmzlnUNVl0kDXRYTQGmBX9Xx80Rb2uLSo0HQIt8GD66QKa05HOBRcSEFRjHRMgp8+hSqZYuRY7FJB90kHeGiwqGl7XjQ0HW7fCO9+n7/UvAjRgCjC+IbruIi69ORjtnHScL2CnM1RWFjODVzLV5wh07BZ6IcM5za6FmD0sk+x5ehM/DU0OTqzBpTp69ukSZOoXbs2zz//vNvyp59+moSEBGbNmlWgcVWOjqJQhJtNLB3XjkoRYrRYcsvKG8dF8eONHWhbo0Lecg2IDjOxeGy7PCMHYPqfRwyNHIB7lu9j16kMlo5rR+VI931UCDeTbmDkAIZGDog3yVWE0AgNeGlgYzrVqpD/xWwTJEfK/yY7oEOYDcZtE0G51AhRy813wbhg7idKN5oOX30Flc/Lr194rgBghBUmbIVPFzmFIM02+YzNdpjxLcwoXiMH5Kw5j5Vr+JLH+dnQyHGsVxAysTKGr1nIGCxeLgd38h37KD5PluLC5auvvuKmm/Inv994440sXLiwwOOq0JWi0PSqH0vCAz35avtJElKzaF+jAiNaVsViMvH37d34Yd8ZNiWeo1ZMOGPaVqdypHvYatbmJK9ja8BnW5N46bKmHLq/Fwt3nuRg8nnaVI/m1qW7QmY+XNmyKrViwmlWJYprWlencVwUaw6nsCXJI8QVbofr/4GkipAUIw1E256AyFzDalsNl5l7vhMj5UFFsdL9KDQ3uGhbdIjJgat3wehdsL4e/NJYepuN3uG/l1URYkcnmfN8RPD5CRpQj0peG4kCnCCd8+QQgTlPw8dzjM/ZytP0D3r/igC5gENXrkRFRbF27VqaN2/utnzt2rVERkYWeFxl6ChCQky4hVs613Zbpus6Jg2uaFGVK1pU9brt6UzvVR06cDa3D1aFcDM3dayVN/bor3YUfuK53NyxJqNaVc9rIAdwKiPH2JDSkdYSrfJXlZEZJl4Dwx+dgvwQKeMoNOSWpcRl+F7N8YvY+4g8ShHnsQa9jQUTA2jEp2z1ud5pMr0KA5rQOOMiQqgoApRgIAD3338/d911F5s2baJnz56A5OjMmjWLp556qsDjKkNHEXJ2nkznsZ/38/3eM5g0uLpVdaZd1phGlY2TdC9pWJmvdhi75AFWHUpm96kMWlaTpMh/z2Ty4IrCqc96cvWXYjRFWkyct9qpGmXhnDehwcrZ3m2P+inwu5FKt47ZAjYr+DVczHboewi6H4MKOXC8AqxqCDtq+N5OQX7DUIcIG9y5EZ77VTw0doyD9q4XilJoWxbkOpaDnctpyhJ2k0qW1/V6U58e1GUDx/Ll6eRgpw9Keb5IUR4dAB577DGaNGnC9OnT+eKLLwBo3bo1s2fPZsyYMQUeVyUjK0LKgbOZdPpgI+nZtrzEYYtJo2qUhX/u6k51gy7m8Unn6PbhJp+l3jHhZrbd1Z0ws0aHGRs4nRn83W3IMXK22IGPu0Bixfw/PHVTZLndi/KcyS6vXbsD2px0XowdF+bFrWBLrRC/iQsEh/7R2k+krQM4PwLHeef6vOxfMzAhwoIjaMFL/O51vUl05iNGspJ/GcxcNMhr42lGow3V2cjthBd3glIpoNiSkW9eFZpk5DmXlNvrW3x8PJ06dSrQtioZWRFSXl2XQEaO3c1osdp1TmXk8N4GY72OVtWiiQrz/SOanm3j9fUJvPXnEc6eLwVGDhhfDE3ATVug6zFJZnUlJRLGbPM+nt0ENdKh3Un3b6YJufhetl8u2Irg0TX4oz4sb+ZMl0pzyRVz/SwLWsJUymhMHIsZy2us97rOQ/RiBsMBuJymLOcGOiEh6Egs3EJnfmPCBWnkFCtKMNCQlJQU3nvvPbp06ULXrgWv/FOGjiKk/HIw2bCiSRSRzxpus+tUht8Gn3ru2L8eTPYpBlgqiLTB8L3wiMddtFmXrte+3AWVzxv/YGlI4nPl8yGc6AWGxSY9ykB++aJz8Cr6W8QencE04QyP8AqXBb2tGY1xtOUUDzGCFl6neh4r/3KWHIwrEwEuo4lbtdVgmrGJ2znPE6QxlY8YQRWULlSR4whdFfZRTvjll1+44YYbqF27Nm+//TbDhg1j48aNBR5P5egofLI58Ry/HEimQriJq1tXp4ZB6MmVuEiL4Q2xSYO4qDBOZWSzaOcpUrOs9G9UmW51KhEbEdhpGBdpITrMVDZuuHXE+9L/AERnQ91UMVTsGrQ/DjurieKuJ1bN90U2S91ZB0TnRBhwQMQdF7WGUxXk2Mfm5qnoiLFTArd6GnCcDPZwmliPBpuBcphUFrGLKCyYMWE1MGbs6Jz3077hH06wheM0pjIjaUlE7iUhopguDfs5y7fsQUdnBC2DbmmhKD8cOXKE2bNnM2vWLNLT0xkzZgw5OTksXLiQNm3aFGpslaOjMCTHZuemJTuZv+1knsiexaTx0YiW3NzJe57Ih5uOcce3ewxf61WvIpsS08ix6WiaaNpc3boa865pwyWfxLPhWKrPPJ2ZI1sSZTFx/SLjdhDBYAIf97khwsh541iWYYG5HeBYJY8XdJj0t5Qze257Lgxe620wqCIPiw3mLoKxO8CmibFpNcHEK2FeO9j3NjROLulZ5jXprEkFjpMe9PaOM8CCyafHRgMqE0kKWW5JxhrkGUhmNGzo1CKGlYynHUWf9K6j8zS/8QKrc+ejoaPzBH15jgH5mo6WFMWWo3Pj6tDk6MztF9Rc33vvPf73v/+RmJhI27ZtefPNN+nbt6/huhMmTGDOnDn5lrdp04bt27cDMHv2bG655ZZ862RmZvosDx82bBhr165l+PDh3HDDDQwZMgSz2UxYWBhbtmwptKGjQlcKQ15ff4QF26QSyiGyl2PXmbhsF7tOef9hnti5Fj3rGbdgWH/kHNk2XZqA5/7mLt55ipfWHmbOVa0ME5UdjG5TnZs71mJsuxrc4sPQAudFoEZumwmzy2+mOddhEhtp4elLjCtJQvYT66vCPNIqejx5eTwuK8/pKFaYp9H3j6q68o0OD60T3RuQUKEJCLPDnCUwdyE0Si7B+TlxGB2+jByTj8u9IyUjB3veOiaDtXXgLOeJQjytYblrmXINC5AGogAnSedK5huqI4eab9nD86zOex/23Nm8wBq+wfhGqVxTAqGrBQsWcP/99/PEE0+wefNm+vbty9ChQzl8+LDh+tOnTycxMTHvkZCQQJUqVRg9erTbepUqVXJbLzEx0a8GzooVK5g0aRLPPvssV1xxBWZzaD3XKnSlMGTGxqOGP3cmDWbHi4CfERaTiQph5oDDSzrw/sZjPHVJI/be04Mv/jnBbweT2XM6g2ybncaVo7ivZ10GNIrLa/w2c2RLUrOsLNp5ynAf1aLD+H1iZ+rHRvD1jpP8dfQcMeFmLCaNs5lWWlaL4ob2NYmLCuOmDrV4Yc1hNielUSPaQpsaFbDZ4UjqeRbvMtDJCRUmRKCu2VnY7drQVIMcCxyoDM2S3bf5vSHKm+OJLtVqL/8EC9vAfX8aazVqOlyxD9LD5LiXAYbQjCZUBjTs2JlNPBkGWjomNG6jK9+wm6PkFza0YOIKmtOHBuzjDLFE8AJr8q1nQ2c/Z1nDIS6hUejfkAsf8neeJ8kVMxofsomRBk1MFaHl9ddf59Zbb2XSpEkAvPnmm/z444/MmDGDadOm5Vs/NjZWvE+5LFmyhLNnz+bz4GiaRq1awVWHrlmzhlmzZtGtWzdatWrF+PHjGTt2bAHelTHK0FEYcjLDy8VAh6Q0Y2ExB0lp2UHdEzr2FRNu4faudbi9ax2f62uahtmkoeWG1Dyx6TrNq4rmzo0danFjB+9fuiZVopl1Zat8y59bdZBv9pwpeD+sQInxcizTDHI3MoJvoFr+0UTV+KE/5OENE1DJ93lbmtCA1lTjVQYBEuqZgXEypgkNM5rXyigrdk6Tyb1cBMDvHDY0dBwkkVa4yQdAIufyGTkgxpaRsVbuCaFgYGqquwp2REQEERHuvyfZ2dls2rSJxx57zG35oEGDWLduXUC7mzlzJpdddhkNGzZ0W56WlkbDhg2x2Wx06tSJ559/ns6dO/scq1evXvTq1Yvp06czf/58Zs2axZQpU7Db7axcuZL69etTsaLvZs2+UIaOwpCutSvye0JKvgonmw7d6lRkxb9n+L81h9hyPJ26FcO5u0ddbu9aB5OmcVG9Suw+nRmQkWACutQKPDat6zpztiSx5pD36qvULBtVX1lLsypRJGdaOZGRQ/saFXi0T30OpWTx6roEElLPo+tg0jRsuo5dB4tJcgUcOUlFbuQAHPPy5f2xKSxtJZ6Iqhkw8ADUOic6PH69OmVcCKZmGjy5OjcEpUsy8Qv9nPlMl/8L/10FfRLgXDh81dq7CGAZRQfe4y/CMfEsAwjDTCdqsYXjhoJ+TYmjJ/VIINUwMXkth+nJxzxCHwbQiHAv7R4AulDbcLk/UjjPNNYyl62cx8oQmvEUl9CCqqSTzcv8zmziSSeHOCIxQ74ZWDDRi3oF2n+ZJoSCgfXr13db/PTTT/PMM8+4LTt16hQ2m42aNWu6La9ZsyZJSd5b8jhITExk+fLleaJ+Dlq1asXs2bNp3749qampTJ8+nT59+rBly5Z8bR2MiI6OZuLEiUycOJHdu3czc+ZMXnrpJR577DEuv/xyli1b5ncMI1QyssKQlf+eYfBckY13nCBmDWpUCOeFSxtx67I9mDUxfBxhqnt61OWtoc3ZcTKdLh9sJMeu5xkjJg2vhsnsK1tyc6fAflz/+8sBXlhzKOj345hrqeJUJLxzEfmUfMFgmQY9jsBf/i4CLknNZdLY0WHmMtEisuQeixwNTsRA5zukkmrBQqdhU1bfZhAMztW3WcZuRrHAcJ06VOQzrmIIc7GhG+bZOBKg32YoB0nmdda7rWVCYzRtmM+1Qc/xPNbczuon8jw1ZjQqEM46JnI73/AHR/Pm5QhbuYa4TWhEYCaeO2mB95YxxUmxJSNftzY0ycjzLiYhIcFtrkYenWPHjlG3bl3WrVtHr1698pa/+OKLfPbZZ+zatcvnrqZNm8Zrr73GsWPHCA/3nltpt9vp0qUL/fr146233irQ27LZbHzzzTfMmjWrwIZOOboHUoSSy5tWYdHYtjSJkyQyDbi8SRyrJnTiv78eBJyGg+OH6p2/jrL/bCZtqlfg55s60cnFU1OvonEZrQmITwqs6uR4WjYv/W6cKOePUmfkAFQ5D/0PSudsyE1MNlJNzn2+2VfcO/cNVsiGyLITojFk2sWSSOwgTBcvz/3r4fUVztJwKBdGjreu4Q5+5F9+J4EracU1tDZc5zhp/MIBVjCetlQ3XMdhZEzlZ56iH09xCZVyy9sjsXA33ZnNqAK9h3n8wxaOu4WjbOhkkMNdfMc6jrgZXzZ0zGhu5fVdqc0v3FxqjJxiJRRigbmHt1KlSm4PTyMHoFq1apjN5nzemxMnTuTz8uSbqq4za9Ysxo8f79PIATCZTHTv3p29e/f6XM8XZrOZUaNGFdjIARW6UvhgVKvqXNmyGsfOZRMdZiIuKoydJ9M5ds74QqoDvxw4S5O4KPo0iGXT7d04npaNpsENi3ZyODV/rx07sHzfad6gmd/5rDpkLEZYZjEB/Q/BxYchPRz+rgWrG3l3Yef4+rpqcNUOaJgCb/bysV5pR4N9VSWk59ox3KLDyD0l2kW8qLBipzO12Iz3kMFP7OdiGnDESxdyGzo/sI8XuJQt3MmnbGECSw3XTSObeI7zDP2ZysWcIJ1qRBNFwXPAVrLfMLnYip0NHMNioPVjQycLG8d5CIAaVCjw/ss8xdzrKjw8nK5du7Jy5UquuuqqvOUrV67kyiuv9LntqlWr2LdvH7feeqv/Kek68fHxtG/fPuC5FQXK0ClH7D+byZpDKVSKMDOkWRW/bRXOZubw479nsNnh8qZxbmKAm46dY+vxNOrHRjCgURxmk3yJov2M6fl6zRgZs0KYyTB8pfkZ03WO9vIaZbXoImQX6Vsd2hgdqqdBp+NSOl1eDlGkR3WRDdEeKoeY0cjy05n8X86QSQ4VCDOsaNSA6FxDRUOjDr4TN6NchAHrE+v22gHOsppDVCKCITQLyACK9rGOkZHjIBJLSA0cHZ0/OcpOTtKEOPrRsNRo8vgkhMnIgTJlyhTGjx9Pt27d6NWrFx9++CGHDx/mzjvvBGDq1KkcPXqUTz/91G27mTNnctFFF9GuXbt8Yz777LP07NmT5s2bk5qayltvvUV8fDzvvvtugd9WKCifvxwXGDa7zn++38NHmxLzzvXKkRbmXdOaIc2M3cAfbTrGPcv3kpUb07GYNJ4f0Ig7utbh6i+38dvBlLx1m8ZF8t31HWhZLZqGlSPpXqcifyeeyxcOirKYuKK58f7GtavB0t35y7V1oGpUGLqu55WPO/j472Pc/b1zjmYNws0a2cUchyo2JebWJ2GFcdk+5BpDKZ56FLnl6K1PQWyup+3meFjQFs6HuW9fFn7wNV3ycKpmeiwHZnUBfTN0TXTm75QDbOjs4JTPdebyD9+wh1voxC8cNFznBpx3zZfQiKpEcYbMfHk4DYilK/krG23YuZvlfMDGvG1iiWAe1zAU74mkaWTzj0fYyoEZjWtoxRy2Gr52Ix28jhssJ0hnBPP4i6N5y9pQne+4nkZUDtl+ygtjx47l9OnTPPfccyQmJtKuXTu+//77vCqqxMTEfJo6KSkpLFy4kOnTpxuOmZyczO23305SUhKxsbF07tyZ1atX06NHjyJ/P75QycjlgFfXHeaRlfvdfmY0xCjYf19P6njkx/xxJIVeMzcbjtW7fiX+POKuUGzWoGHlSPbcfRFmk8bW42n0nx1PSpbchZo0DbuuM/eq1lzX3ji+a7PrXPHFVn7896zh63Ovas0NHZzb+ppjcRNpMXHeWuQ6ysKfdWF5c/KZVhY73LwFvmwD5zyMHc0uhsHkDeS1nt5RHb5um7uCHePcn5LEm+Glw7vfwX82ibKxjhg1PzSFq8dA07Ow4SOIyPV+laa3VAyEY+ISGrGS/bm5PTpWdIbSjKWMI8ylxPxH9jGS+dhys2M0IBwzKxjPxeQXy3yd9TzEiny/I2GY+Zd7qYfxb+btfMMsNhsaOp2oySpu4UVW8wrrsGBCR8eGTmuqsYZbqEp0oY6Jg0F8xq8cdPMeWTDRhurEc0eBPDvFlow8Zh2EFTIZOScNvuxd7q5voUB5dMoBb/2ZX9xPR5SMZ8cn8Xhfd52D9zcew2LS8uW7mDRYl5A/B8Cmw/6z5/nlwFkub1qFDjVj2H13D2ZuTmTL8XTqVYzg1i61aFXNuwvabNJoFhfFSu1svvCVSYO3/jriZuh4m2NJUGxGDsBFR6FBCvxRF45WEgOn2RlZbtfyGzkAukl6OR2OlRwdE9DmpPTYygiTHluRVjhZgdJjGXibhwb3DIUNdeCtHyDcJh3H57WDSZvhlngIK91GTn0qkeAll6awWNG5hIbcRTeWshsdGEVLRtISs0dS82CasYvJfMzf/MtZWlGNSXTxarC8xZ+GvyO2XLHCJ+mXb5s0spnDFkMjB+Bh+lCJCF7mcq6gBV/wD+fIZgCNuIH2hcoLcmU/Z1nJ/nzLrdjZynE2cIwe1A3JvoqEEghdXUgoQ6cccPRc/iRfALOmkZCS/7UDyecNDQh/NkWCSzJx9QrhPHZxQx9rG2x/LstwH3YdDnvM86CXOV4Q1E6Dq3bnX57g5y4txcVzZ0IMnIxwMOnyt7RaBp6YgR01oGKuaOWIvfIoA2hAN+pwhNQiue6Y0TjKOZ6gH1d5qcBypTFxvMjAgMY+5kWoT0MjgRTD106R4VWPx4TmJj7Yj4b0I7jfjEDxNj8Hh0kp3YaOokhRhk45oE31Cmw/kZ7vhzXHrrP6UDIr/j3DoKZV8pZ3qhnDuoQUPB0VFhPY7d6bXbarUfCkwWPnsjjuRVHZrEHHmu5jd6wZw+8Gc7ygqZsKt2+ELbVgYx2weZQl13Ap09eBJmdEq6dWmnRK319ZvD+lhYrn4fE1cOUe6cr+RXuwWOHhP6RMHspMapEDHWhF1SK7uc7BzvfspTIvYUenHpW4k27cSTevysiB0prq/MPxfHO3Yffa6LM2MVQiglSMKir1YmkQCtCKaoZVXw6Kax4Fppirri40StGvnqKgPNG3gdcf1t2nMxg8dyvvb3Qm6N3doy4Wk1RBOdAQz8qYttXzXVfMGvRtEEv3OgWT4E5IOU+XDzay8ZjxHaNdh0f7uOcMGM0xFDhO+FCPWyyYEG/PkH1w3T+SuAvyt8kZqOWhRzRoP1y7E/oclofXH0JvfvNQ+NO9oUubi7/qQqtT0OE4vPQTvPgbxJ2HcLu79mEZ4n8EJqFfUA6RQgpZnCObnZziPn5gVG4uTmF4gr75DrUZjapEM56OhttEYOEh8ssZWDDRgZpcRpNCzSlQahLDBDrla2xqRmM4LWhFNS9blhJCqKOjyI8ydMoB49rV5L1hzakckf+OzpFU/OCKf0nLluTh5lWj+eGGDjSJi8pbr16lCBaPbcenV7XmvovqEpHb8lsDRrasxpJx7fJVRQXK/605zKmMHEPRvtgIM/OuacOAxnFuy43mGAra1qjA3Kta0aiy7266pRZHTnGzs9DyFKBDvRQYvd14vdanocE5aJIM1+yUBphu6NApCZqfJt8vZaUsyREqEnLvYBe3gV8byS+RCXfvjebxt4xgLYErznL28T2FC++NoS3vcwVxOL8bXajNKiZQGe/flyfox5P0zStZB7icJvzIjYYd1YuKdxjGbXTJE2A0ozGWdnzB1cU2B0XpRFVdlSNW/nuGQXPzl3E6+P769gx1Kf/WdZ1dpzKw2nXaVK+Qp5UDkHLeyr9nM6lTMZxaMcaqxoFS+7V1ho1ATRqMaVOdede2NdhKsNrshL+w2u+lw6KBNYAz+T/davPuFS3RdZ2dpzJISDnPkM//8b9haUMHUsPhVLSI6AWiwWNHjIo1jeR5jwQRLDTpUsV0LlweIJVOduCD7kUy/TwsNrjnL1E8VhjyCzfxG4d4nlVevwdmNG6lMx8wotD7y8LKTk5RiQiaEOd/g1zSyGYPp6lJBep6SXguDs6SyQGSqU8lqhdSo6fYqq6uXh+aqqtFvcrt9a0wqBydcoQ/MT+LR7xG0zRaVzf+IYiNtNCldsG7xbrhxZbWgDCzyWU1nb8Tz/HVjhOct+pc1bo6fRvEYgphnyrH/jRNo031ClQy8IKVCTRENyc2iHYPGtD4DByqDAmxEG2V9hNmxHCqlO3s8H06Cv4N/CJXKIrMa1T20YCLqMdOPzo74L+VhIPdnGIvZ2hKHK0N2kVEYKETvtqNGBNDeIEbgoaSOKKII7Se4CJH5egUKcrQKUf0qFuRGhXCOJmek+/OLy7SQt+GsYbbFRU5Njt3L9/L8fQcw9dtOlzTWn5oD5zNZNDcrew74xSKm/7nUSqFmwIKBATizQHxILlSr1IknWpVCLjfVplGA5qkQpN4yLRIbo/Z5TWA82bpFr6nmHIarGa4emfx7KuUYUHzG+aqQBhhmEgizeeaNnSuoY3Psc6SyTgWsoJ/85ZdSiMWMJpqIdKyUShKIypHpxwRZjYxc2RLzCYtz3tjMWmYNPhwRAsiLcXrvXh21UE3tWZPrmldjREtq2Kz61z22RY3I8dBarbdb9l7MOw6nX8fFfx4wsocgRyvSKtTdA8gOTc8ubQl7A1FU0V/WZK5y/7zl2gEXYBYcxtb+iKNHHoyk+dZ7XO9CXRkAI18rnMji/nZQ2tmFYcYy1cBzVdRhDg8OoV9KAxRHp1yxvAW1dh8R1fe23CMXacyaF4liru616FTrRCFoQIk22Y3FDJ0cHf3Orw5pDkmTWP5vtPsP3u+WOb13KqDTOzsdK/bdZ31BiKJZRpHpZKv3z3P12Z3hDrnYGd1PxsGSJsTUDcNLjoiQoZftoXzFufYVTPgxi3w6k9lLtk4lNhyBQBXccjrOn+T6LX4rBGx/I9BXE1rn8q//3LGMFnZhs4vHGQHJ2njpeu5ohhQgoFFijJ0yiHtasTw3hUtSnQOZzOtnMs2TpC1mDRqxUTkJT8beXKKCs/O6+eyCluUW0oJxniwA8nR8ggFYTa4dhc8+5tz2V91YZfLhfTgdIgxDmleaFxNK5+GDhhfwzSgC3W41k/ICuBfjFuvOF8/owydkkTl6BQpytBRFAlVoizERphJycpv7FjtOnvPZHDpnHjMJmjto3VEqKlXSUI020+k885fR9lyPM3PFhcAJqBjolRuHakI6eGSw5NVQHn+HDN83xR6HIVhe+WK3OoU7K0CNdOlygrKnBhgURHP8QJtpwN27Ojofvs4NaeKn9eNw5U27MxnG5/zD2lkM4im3EW3AvWnSiWLD9jId+wlAjNjaMtNdHTrz6VQFAWqvFxRZDz96wGeX33Io3symEwaNrvudp2LsGicDzSjuBDMGdWKWjHhXPHFP4B+4SovOw61ownoV20lbKXpTgNE08FuwrdF4vla7nOTDexmUT5+4RdY0wDuvALWfpKrz3PB/Oz4xYSGGY2cAvoWb6cr73OFX2NnJPP4nr1u6sFmNC6lMSsYn299HZ3rWch8tmNCw46OCY26VOQPJlGHwMPhZ8ikNzPZy5m8cezoDKEp33B9wBVjxU2xlZeP+Cs05eXf9FDXNwNK59mlKBf895KGTO5R162svUZMGHZdz/updYSmz1t1aseE5xsjLtJMTHjhT1MNeLR3fW7sUJNJy3Zjs1/ARg64NzPfWzU3N4dc93cwyY2e6+Q+t+fepf9fX9hRDfodhpWfKSPHADs6dnQqUzC9qg/ZxB8c8bveZ1zFMJq7LbucpsznWsP1f+Rf5rM9b46Ov8c4x3OsCmqOr7KOfblGjut4P/AvX7Ld16YXBioZuUhRoStFkWExmXh7aHP+268h8Ulp1KgQxoQlu0hKy5+boQHta1bgxxvbs3jXaTJybFzVqhoX1YslI8fGH0dSsdl1jqae55Zle7zu857udbi8aRUqRphpUCmSZXtOER1mYmzbGsRGhhGfdM6tOakC2FlNFJPtHgalrkFUNvQ7CD82p0BxJrMdlrWEtqckOVlhiA2dFIN+UYFgwcRidtGL+j7XiyWSZVzHfs6yl9M0pQrNfIS0FrMTCyasHp4mGzpfsYP3GR7wHL9ku2EfKhMai9nF9bQPeCyFIliUoaMocmpUCM9rKupL+M+uS1f0wU2r0DgukhoVxMMTHWbm0sZx6LrOyv2+kyrb1KjAiJaiAXPgbCb1K0ZwIiOHdQmpxEVaSMmyhuZNlSd0zXvFhkbhPTDqTjMgCnOU7R5bZ2PjH44TgYW2VHcLazUhLk/x+AipHCWVZlTJl3fjOaYrVi8dy73hrdkmud6sCx5VdVWkKENHUaxc3aoaO06m59PG0YHTGdnUeW09OtJI9IYONZlxRQuiw8xsOnaO8Yt3svNUhs/xX1h9iEoRFt7+8wh/HDVuIhpp1siy6ep3wUGL09IR3RPNDq1PQbdEyAiH3xr5NlqMvEI2E4zw7oFTOGlKHAdJ9mEUGGPFzgicVZafsoUp/MhppJqxBVWZzZVuHp/TZDCBpXzHHnTEK3Q7XXmDwXld0IfTgo/ZbLjPTKy8y19MpkdAc7yaVkznz3zvzQ5uc79gUVVXRYrK0VEUK5N71MHspTno5qT0vJ9Bmw6fbz3OpGW7OZ6WzaWfxrPbj5EDkJiWzQ2Ldno1cgBl5HjS6pR0P3e9rdR0aRHR75B4dfodgt4J5L9tzN2m70EIt0nvKnA2D538F7Q/UQxvouzzL2e5ogAX/cE0pR8NAfiRfdzMkjwjB2AfZ7iMzziC6EXp6FzJfJazN+/TtGLnfTYyhR/zthtOC4bRzHCfOdi5m+XMZ1tAc3yEPtSlkptAogb0oT7X0S6Id6tQBI8ydBTFyrqEVHIClDq26TB/2wleX59AWnZgejeBDK2MHA/MOlz/DwzdB3VToUYa9EqAOzdCbG7eiAb0OSwNQN3ITV6+JR4eXQsXHxaxwH6H4POF8Pby4n0vZZxNHCM6CEe7CY3aVMwLTb3C7/nUlu3oZGHlAzYCsIFj/E6CgXdF5yP+5myukWTGxBLGcQ2tDfetAdNYG9A8axLDRm7jMS6mAzXoRh1eZRArGU+ECizkFxIv6ENhiDrDFMXK9hMZWEwa1gCNHR3YmHhOya0UNRZdWjH4ascQbYUoq+jseDJxlHhzJm2GXz8tsmmWdxJJCypnxY7ONhcdnn84YRj6sqOznZMA7Mj9a0Q2NvZzlq65TTHD/r+9+45vus4fOP76JmmbDrpLW6C0UKBQ9qYgCCogcgq40FMQz3E4QbyfynmO8+7k3LhAuSEqJ6IioicquBgyjlGG7F1GSwfdK03y/f2RNjRkNG2Thrbv5+PRB/Sb7/gkbfN95zPeb7SEo3c4KVkFDrhRbLRGDMH8lSv4K1e4fUyrIUNXXiWBjmgS2aUG/rkjk5UHc90OcmrEBzcwcZ3wLINiKeOgNVsKcU46aMmCXOIPbSogogKSXU8WF66FEkARFW5n1NGhIbnWyqkkwsmj3C5Y0qIhiXAAEnFe3FfBMkH5HbZRQCUjSCCWYJwN9iYg+VrEpa/ZBToLFizgpZdeIjMzk549ezJ//nxGjhzp62YJF/bllDLyvXQKKow0JD3lZ/vd/9QovMioBT8jLP8UrjkCRsUy7OVObS3hliIqHQY5zmpdGTHzAIOt389iKLezwm4/FZV7GADA5STRjSiOct4ueWAKUUxmGTo0mDCznH3Eu0gMKPNrPERWXXlVs5qjs2zZMmbPns2TTz5Jeno6I0eOZMKECWRkZPi6acKFmf89RGGFEbPasL/FKrNKZKAfoU4SB9a+vwbqNDxzeSJtg5pdDH/pCzTCC99b5vKAZbir5sWXIMcjnA1baVF4jOEE1CqXoEfHO0xkZPVEZIDf0ptnuNwm03Ab/PmYG+lRXctKg8IqfmtX9mEQ7dhXPRRlrG6JCpyjxOlKsK20zsrzHicJA72qWZWAGDp0KAMGDGDhwoXWbT169GDy5MnMmzevzuOlBETTyy41EPvyRo+ca/2d/ThfbqS40kiATkOlUaVvXDDxIQH8cDwfraIwNjmC0AAdRrOZGz/Zy8qDeR65tqg2bRckFTSzj0gtw34eIJZgvucYCgpj6UwYeof7nqOEnziBHh1j6Uww9vOqVFTWk0EGhfSmLcvZz/Osr9fydgUo4AlCG5jV+VLXZCUgxu7wTAmINQPk/uZAs/nYazAY2L59O0888YTN9nHjxrFxo+MbaWVlJZWVF7KNFhUVebWNwl6lh+ssXFedDLDG8fxy9uWUMq5zBOGBF+by6DQaIvXN5te7+dCZpffGRyowEkEgN9Gzzn1jCeEWF8NKZVSRSTEDibcuTf8Pe6pXb7kf6KhYJjALcSlrNp/LcnNzMZlMxMbG2myPjY0lKyvL4THz5s0jLCzM+pWQ4DpFuvC8DqEBdIkMbPS9MSxAy8D4C3MFtp0posOrG+n8xhZGLd5JxIu/MPaDXRRWXMh8fN+gdo28qrBzJFLmAvhANEH0rB56aowqTDzGGqJ5kS68STQv8jDfUIGRcSTbrawCy1CXIxqgD7FEVa/QEo2g4oGhK18/iUtXswl0aigXJZtTVdVuW425c+dSWFho/Tp16lRTNFHUoigK88d3QVEs2Y6hYR0CL45NJtDPMj/hZEEFI95L50yxwWaf74/nc8X7O6kZjR3cIYzUmCC7c4mGUiEr2PnMWNEorip4z2c8frXm5zTULL7lZTZSjuUDQQUm3mYrd7GSK+nERLra/H3q0OCHhgl0sakDq0VBg4ZXGVdn1XThJsmh4zXNJtCJjo5Gq9Xa9d5kZ2fb9fLUCAgIIDQ01OZLNL2J3aJYO6Mf45MjiQnyo3dsMPPHd2FAXLBbxz9/RSfuHXihd+b1LacxOCmatSOrhM2nLwxR7rlvMJNTotDIe7EHKDD0rKyw8iB/tKQQxRS6O+xNAUsJh9vo0+hrnaOEf7DD7p5oRmUpv3KSQj5nKn/nKlKIoi3BXE93tnA3X3Err3M1PYkhhiAm0o313MmVdG50u4TwtmYzicHf35+BAweyZs0apkyZYt2+Zs0aJk2a5MOWCXdc1jGcr28Lt9m2eJfjIcfaNAoE6Gzj8Y2nCl0esyOzhLSEsOrjFVbc0psqkxn/v66rX6OFvXbFzejj0aXvFnrxPpO5l68cJuUDOI5nchP9SrbTYEoFdpJFEuE8xggeY4TdPg8xlIcY6pG2iItIwkCvajaBDsCcOXOYNm0agwYNIi0tjUWLFpGRkcHMmTN93TRRT3vOlVBcaaxzFMSswtniSkxmFa1GocJowlxHwsHXNp/i6Z+PE+ynpWdMEJGBOjJLDGjA7URswolif9AbpUfHQzoSykZOsYdzOCtycnFVcYB0MlnKr2RQSCVGYgjmchK5kVSOkc+H7CafckbQkZtIJQAd7VzkwwGIQM9idrKF00QRxDT6kEK0y2OEh0geHa9qVsvLwZIw8MUXXyQzM5NevXrx2muvMWrUKLeOleXll4aXfsngse+PoVHcq00FMCYpnPcmpTBuyW4O5ZXXfYDwjsFnLHl0agc6MpTVYLfSi6X8ihbF6bJuBfiQKdbhq7+wlqf5GQ2KXd6deELIogRtdbebETOpxLCWGYQSQCwvUUCl3fm7EoUZM0fIR4cGFRUzKou4lrurEw22Rk22vPyKdNC5DkTrZCyGH/vL/c2BZhfoNIYEOr73a3YJvRduq/dxGgV6tw3m1+xSnEzPEU1Chd8cgoFZlu6x2jNUhdf4oeEMczhBAUP4Z72O1aJwB/3oTVseqVWdvLbBtCOdTIwXBU4aFE4wiwQXZSNasiYLdMbs9Eyg81M/ub85IKPtokn9Z3c2OiczgwO0MCYxzOHEYbMKu845D3I6hDooNCm8QIH/psDbg+C7ZPhFUjY0BSNmlrOf/7DH5eosR0yo/IfdvEe6w5hUAbZx1i7IqbGMvfVvsKgfqV7uVRLoiCZ1vrzK6WMmVSFUr6t3PSytAkF+jV96K+ohNxj+1wF2O17xKDxLQeEcpRRdNOzkrkpMFFHp8F7o6h6pQWnwNYW4VDSrycii+dpzroQ5q4/y/THHK0gULMNTzko26DQKIf4aCivsZzKYVNDJ8EnTU8zQWaqVNwUzKs/yM0H4OV055Yo/WhIJ5zTFTpMCOqqzZcTMaJIa0mRRH7LqyqukR0d43YmCckb8O52fjju+KWqoTiXvZFxKq4C/VuGKpAinnzwPygTlJqZCgAmGnfZ1Q1qVMiw9os6yFTtjwMRaTqJDg9bBsTVBTu1HNChcRSfGSKDjfT4aulqwYAGdOnVCr9czcOBA1q9f73Tfn3/+GUVR7L4OHDhgs9/y5ctJTU0lICCA1NRUVqxYUf+GeZgEOsLr5m8+TVmVyeH8Go0CUUF+9g/UMrFbFD/d0Y/vnQRKgExQ9oXkPAiqkrkBPhCAllAC0KA4DFycaYM/l9eqdn4xbfX5YgjiCUbwJbdK5uOm4IPq5cuWLWP27Nk8+eSTpKenM3LkSCZMmEBGRobL4w4ePEhmZqb1q2vXrtbHNm3axNSpU5k2bRq7du1i2rRp3HzzzWzZsqVBL4unSKAjvG5DRqHTQKR/XAhBfq5/DVfe0psArYaiSikeeOlQICMC/KXIpy+UY2Qnv8fE09xFf7cnKOdQxhV0cvq4EZVfuZ9s/o+/cSWBuP4QIpqvV199lbvuuou7776bHj16MH/+fBISEli4cKHL49q2bUtcXJz1S6u9MD9y/vz5jB07lrlz59K9e3fmzp3LlVdeyfz58738bFyTQEdY7c0u5W/rTvKXtSdIzyx2ue+R82X8fcNJnv35OJtOFeIqS0F0kJ/DlVRaBWKC/AgLcD5VTAPc8tlefrdyv7tPQzSVSq1kYPQRBQjGstIwHL3D+TXOjgtH7/LxCBePCy/x4NBVUVGRzVdlpf1kcoPBwPbt2xk3bpzN9nHjxrFx40aXTe3fvz/x8fFceeWV/PTTTzaPbdq0ye6c48ePr/Oc3iaBjkBVVR5fc5ReC7fyzM/H+fPaEwxYtJ3f//cgZgcBzAsbMuj25v/404/H+dv6DIb/O52bP9uH0ez4rndX/3iHiQFNKvyufzz/N9z5EmUzsGxvDjuySt16LtK50IQMOtgfI8GOD6jAVXzAPnL4nP1uBzoq8ATfE4K/3TwfLQrjSSaWEM83WLjmwaGrhIQEwsLCrF/z5s2zu1xubi4mk8muTmRsbKxdPcka8fHxLFq0iOXLl/P555+TkpLClVdeybp1F0rrZGVl1eucTUVWXQm+PpzHixstld1rDzEt2p7JZQlhTOsbZ9226VQhT/xw7MK+1YHQ8n05vJUQyuxh9kHLjakxPDikPW/974y1grlJhQeHtOfG1BgUReGzfTmsPOR4xZU7FOCvV3TipY2nKKgwNvg8wh21UiGv6gIxpdC2zKctao32kcPVLOEsrntfL1aGkRD80KOjnCp0aKjCTCci+AfXeam1oqmcOnXKJmFgQECA030VxTbYVVXVbluNlJQUUlJSrN+npaVx6tQpXn75ZZvqBPU5Z1ORQEfwXnoWWsV+Qq9GgX+lZ9oEOot3ZqHTKBgddNH8Y3umw0BHURTenNCVu/vHs/JgLgCTUqLpG3fhk+MXt/bmp+P5vPBLBjsyi8ktc5a+zDEV+G3vtjw4pD3Lfs3mUF4ZZ4oMfHkwl1KjdDl4Vq03rTJ/eGcQDMuAcSd81qLWyITKKYrqfZwZlSIMfMgUiqnkNEX0I45JdMcfyUflEx6sdRUaGlpnZuTo6Gi0Wq1dT0t2drZdj4wrw4YNY8mSJdbv4+LiGn1Ob5BAR5BdanA4WdisQnapbYK/vPIqTA6CHBXIKXOeDBCgb1yITXBzsTGdIhjTKYJxH+5ijZN8O67kllWRFB7IPQPbWbd1fn0zxwsq6n0uUQ+qAmdbZ4mA5qwCI/cx2NfNENDkeXT8/f0ZOHAga9asYcqUKdbta9asYdKkSW6fJz09nfj4eOv3aWlprFmzhkceecS6bfXq1QwfPtztc3qDBDqCER3D2HS6yC7Y0WkURiXa3sCGtg9lxYFcu+zFWgVGdrTse6qwgqd+Os6yvdkYjCooEOynZVJKFGVGM98cPo+iQGJYAGeLDZQbzYzqGMZzYzqRlhBGWodQfjyeX68l44E6DT2ig1l/soCnfz7BLxmFtAnQ1lnpXDSEChoVtKolwo0vhnFHfd0oUU9pdKhznypMvMxGFrCNc5TQh1ie5nKuI6XOY8Wlbc6cOUybNo1BgwaRlpbGokWLyMjIYObMmQDMnTuXM2fO8MEHHwCWFVVJSUn07NkTg8HAkiVLWL58OcuXL7eec9asWYwaNYoXXniBSZMmsXLlSr7//ns2bNjgk+dYQwIdwYND2rNoeyZFlUZrcFGTpG9Omu1Q1N0D4nlt82mbXiCNAhpF4Y8jE8kpNTD0nztse4lUKDaYWLIn2+Zc+3MvJPn76UQBoxbvZO2Mftw3uB1vbz1DfoXR7ermZlVl5cEcpq+wJK8yqXC+3PFcnZqSEcUGWa7eYGaNZRJyYj5M22MJfESzoEFhMt3pSds6953BSpayxzqqkk4Wk/iYJbWqqQsP8ODQlbumTp1KXl4ezz33HJmZmfTq1YtVq1aRmGjJs5SZmWmTU8dgMPCHP/yBM2fOEBgYSM+ePfn666+55pprrPsMHz6cjz/+mD/96U889dRTJCcns2zZMoYOHdrIJ9c4Ur1cALAvp5Q53x1h9dF8VGB0UjivjEtmQLx9Rd0TBeU88t1RvjyYi1mFYe1DeXFsZ0YmhvPsz8f567qTDUrgp1VgVGI4P97Rj0N5ZTzy7RFWHTlvs49OAaODc2sVyzL2nLKqOoOjhNAAvp/elzu/OMDG0/Wf4yBqmZEOCUWyfrOZCMaP+xnMXxhDQB2fc38lm944zqnSnjacZDbaFv6Db7Lq5UP3eqZ6+Zaecn9zQHp0BACpMcF8e3tfyqsstaRcFclMCg9kxdReVBhNGM0qIf4Xfo1+qOeQU20mFdaeLEBVVbpFBfH1bX0orzJhVsFgMmM0q9z++X5WO5i/Y1LhXKnrOUI1dBqFblFBHMiTlUKNo0LHIlnT34ys5BaupLNb+/7MCRQcdxScoZgTFJBMpEfbJ4Q3SKAjbATWowq4Xme/b1iADo2C20NOFwvx19osRaxpT3D1apAwvc7hCrH68NcqpGcWO5xULeqpSmPJjiyahQgC3d43lACXoyEh1QkLhQf4YOiqNWnZ/Y6iyd3eJ7bBQY5Wgel94lzuc1vv2EbXtTqYV86ARdsplJISjaTA7lhJGNhMtKcN/XH991XbdaQQiM6uw06LwhV0ksSCnuSDWletiQQ6wqNu7tmWO/q6zpmg0yjWkhAKWJMI9mwbzHNjklwee11KFFd1DnerLfJn3wR+6AQ5QZb/yyfKS9okUupVoDMcPR8yBW11xXNddS7lWEJYxG+819DWyEfVy1sLGboSHqVRFN6b1J0Z/eJYvi+HI+fL0Wk1dIsM5JZeMRRWmlh5IA9Fgd6xwRzOLaPIYOLyxHBuSI3BX+s69lYUBVWlzuExBegcoadnTBBfHTov7wHeUuEH/xgAv/0VOhf4ujWtQnvaEEMQOzlXr+O+5BBvM7Fex9xAKodpx/vs5CzF9Cee2+hNG5xn2xXiUiOBjvA4RVEYnRTB6KQIh49f1blxExjPFBnqHB5Tscz3iQryR+skk7PwEJMGToZLoNME/NBwHSl0JoI9fI+pHiH8OUoadM0kwnmG0Q06VrhJ5uh4lQQ6osFUVeXD3ed4Y8tpThVV0jc2hCcu68gVnRwHOI58ujeb1zaf5sj5clJjgnhkaAdWHMzlk705VBjNRAXpuLZrFAfyyq37xLfx50h+eZ3By65zpew6514xUNEYCsTVr96SaBgjZvoTR2ci6hXkaFBIIYpprGAVh6nAiAIkE8mDDOYuBtgV+RRNyRNzbOTn54zk0REN9sT3R3nhl1NosMxH1VYPJy29IZWpvepORvbChgye+OGYdRjKndVUNftoFUs9UZkHewkIMsCjm2TGXxNoTxsO8CBB+DGEf7CTLLuAJ5QASjHYbdejw4DJYaXz+xnM21xjt721a7I8OgP3g7aReXRMxbC9h9zfHJC3JtEgpworePEXS8XzmmDDVF0R4JHvjtS5dPt8eRVP/3zccrx64fi61OwToNPQNcr9pbLCi648Lu8kHqBB4VZ6EeViCfg9DCAEfzQofMft3EAPa09MOHpeYiw7uJfLSbIe04FQBhFPlZMgB2ABWzlEnkefj6gHWXXlVTJ0JRrkx+MFTjvOM0sM7M8tpVdb58tPN2QUYmjEOvGyKjOLftONpIhA0jOLmbxsr9vHOkuCJhpChTNtYEBW3bsKO5NJ4c+MoQoT3YkmGH+SeZ08yu32VYCtnLV+H0UQy7iJfMrJo5yOhFmrj//AdLIppQQDiYTRhnkuh7oUYDVH6UaUp5+icIfM0fEqCXREgwToXH960Otcf8QPqGN1lTv0flo6hunJLK6s13HyfuBhOhlAbCgdGg6SyzHySSfLZZ4bFdhPDjvJpC9xbOMshzlPVyIZRDu7peNB+LGRU2znLLo6utxULENbQrRE8pstGmRClygCdRrKjbY3Oa1iyYeTHOF6WGl0UjgReh0FFcYGBx4r9udQUmnk5s/2NfAMovEU6Jnj60Y0Wys4wGfsd3v/YxTQn0UE40cpF0qeDKE9K7mFuOokfh/zK3fzpXUfTXUY5OxvzQ8Nk6Qiue94YuhJhq6ckpF10SBheh3/uDYFjWJJAFiT+C/QT8u/rkuxKePgSIBOw/uTu6PVKDbHa+rxt/r3X04x4aM95Fc4rlIumsCg05ainqJB6rNyqrbaQQ7ADjKZyqcA7OYct/G5zT5mVIdXUqq/3uYaYghuUFuEB0jCQK+SHh3RYLf1iaVvXDD/2J7JqaJKercN5t6B7Wgf6l4ysWtTotl7/2AWbT/L0fwKukcF8ftB8Ww+XcTz6zPILauie3QQkYE6Pt+f6/DvuDHzfByJDtSSWy6lIeqkmOHqIzAoU1a1XgKMmFlHBvvI4V22oUGxm3isQaET4XQnmrMUE4QffWjLPQykP/E+arkQ3ieBjmiUXm1DeH1C1wYf3y0qiJfHdbHZlhQeyC29LpSRuO3zfWgaWcjTXZVNcZGWQNVAZIX0CV9iTlLAcQowOki8YEalEhP/5bc+aJlwSYauvEoCHeEzPx7P540tpzmcV05ShJ5gPy37c8uIDNRxZ784pveNQ6Mo9IgOctrt7umwpMQgE2vdlhMEnfMl2LmEdCeansSwmqN2w2JaFLoTzZ/5mZUcRIuGqfTkPgYRLJXIfUtWXXmVBDrCJ97ddpaZXx+yJgDcl1tmfUyjwLqThfx0vIDFk7tz94B4XvzlFKVVJpvSDyrQxl9LiaGhMx3syXtFPfySAIPPgqLK8JWPaVGYTHc6EcF9DOYttqJelDfHjMo+cviR49bt2znLp+zlZ2YQiJ+vmi+kR8er5LOYaHJFlUYe+e4I4Hg4qiaY+WD3OTafLiIuJIAfpvelW60EgeF6HYt+040l13dvUHASWMfyeOGG0gBId12pXnhHEmHWRIEaFG6mJ4uZDEBnIviW20gi3Lp/NEFMIoVzlNgEPyqW3Dzvs6sJWy9E05IeHdHk1p4osFuW7ohOo/DlwTzSEsIY3D6UffcPYW9OKWVVZvrGhhCg0/DKxlNuXTM6UMd/f9ub/Tll9I8PoU9sCN8czmPi0l8b+3RaN630gTW195jEDPqRRQnHyacTEdZl5TUuJ4nDPMQezmHARD/i6Me7Tld5reQAMxnUFM0XjsjQlVdJoFNPhRVGtp0tpk2AlkHt2qCpYxm1sGVWVQ6ft8/66kztl1dRFGu2ZVVV2ZFZzJHzZU6OvOi6wNAOYQztEGbdFhEoXfWNJ7//Te04+ZykgETC7QKc2gyYKKACDQoqrrvvKzCxhqMkE0ln3C/K64wZlW2cpZhKBtGOMPSNPmeLJkNXXiWBjptUVWXehgz+su4kFdW9EZ0j9Cy9IZUh7aWAmju2ny1m6md7OZpf4db+RrPKlO7Rdtv35ZRy06d72ZfjXpADcL7cSNQLG/jlrv50jw6mxGDklU3u9QYJF3KCfN2CVuc51vEX1nE9PVjMZEIcTCRewm4eYhUFWLKGRxPEWDqzj1y7Zecq8DMn+JkTAEykK0u4nvAGBidbOcMtLOcY+YAl4/KfGMkfGWmXvVmIpiBzdNz03s4snvzxuDXIAThZUMHYD3eRW2bwYcuah/PlVVz14S5OFNQd5Gir3wvvG9SOwRcFkWVVJq54fycHc90PcqxtqDAy9J87MKsq9351iM/359b7HOIiilk6dXxABb7gAHex0u6xdZxkOiusQQ5AHmV8wl46EW79cTn7sX3LEW7j8wa1K5cyruJDTlBg3VaBkT/xE/8mvUHnbBUkYaBXSaDjppd+ybB7YzCpUGIw8f7Ocz5pU3Py4a5zFFUaHU4+DtAq3NqrLXf1j2N4h1AmdI3is5t68vY19vl5PtmbzbnSqgbn1CmqNPGP7Wf5+NdseV/whOFn5A3WBZ0Xo0ATKp+yjzPYZqZ+jc1oL7quCigoXEZHXmYco0hkGB2cnncVhznC+Xq36X12UoLBrtdIAV5iY73P12pI9XKvkqErNx3Nr3D4fq5RFA7m1b93obU5lFeGVlEwq/avosGk8p/re9RZNgLgcF45fhqFKnPD764/nsiXe7OnBBqlR8cFLRqMeC/Ttgoc4TztudDzeYAcHFWQM2LmCOdZzGTmkMY3HOYaPnJ67kPk0YXIerXnEHkOszKrwNHqoSwhmpoEOm5KCtdz5Hy53duHWVXpEum6gKWALpGBmBwEOWB5bZ0FOVUmM//Zc47P9mZzpthAbllVo4IcgFWH6/9JVVxMhZQ8yyxvDRLsOGFykKHYkxQsy8n3ks0CtnKAPMoxokWxW2GlQ0M3ojhDEQvYyk/Vc3KcSW7ApOQuRNoFOTXtrL3c/SeO8y/SyaKEobTnfgbbBGutjqy68ioJdNz0aFoCM78+ZLNNo0CgTsMdfeN81KrmY1rfWJ75+YRd0j+AOWmOu9ANJjPX/Gc3Pxwv8GhbJPtxY6nwm0MwMMsS6EiQ41RNz4o3sngD9CSGnWRxPZ9UX8/ssEdFwbISajxd6MkCSjBgQnXYLi0KY0giBfuFAHW5g378mbWUY7TL1/MoaQA8z3qe5Ed0aDBi5mdOsIBtrOdOetG23tdsEWTVlVfJHB033TswnqdGJeJXq7x2fIg/397eh9gQSZ9el+ggf767vQ/t21wo+KnTKMy9rCMPDG7v8Jglu895PMgRHtCx0BLkgLyDNJFwJyUafiWHO1mJCbO1vpWjHpUw9CzlBhaw1RrkgOPgazRJfMyNDWpnW4L5jtuJr7Xs3Q8NTzKS3zOQ4+TzJ34EsLbXhEoxlTzIqgZdU4i6SI+OmxRF4bkxnXh4aHu2nLbk0RmREIZWI1G0u9ISwjg+axgbTxVSWGlkaPtQYoKdB4nL9+WgUbDrARI+lpoLJsVxskDLrFdxERX4G1fwIbs4QF69jy/A+crOPJznpXqeKxhEO0aSSBlVTOUzh/spwF30Zw5p9CCm3u2rbQQdOclsfuEURVQyjA5EY0lD8AUHUFBQLwqxTKis5SQFVDR4WXuzJkNXXiWBTj1FB/kzsVuUr5txyThTVElOmYFuUUEE+WkpqzJxOK+cyEAdCWH2b1hajcLIxHDr9xmFFeSXG0mJDkSv09rsazCrOJnWI3xJIz+UhjhPOQaMHj1nXUNiA4hnLMkAFNdabm5/HoUUot0OcsyoHCAXDQopRNnlx9GiYRSJdsc5qqpen8dbLBm68ioJdESDnCmqZMbKA3x/zLKSIthPw/CEMDafLqLYYFllMiYpnMWTu9PRQcBzLL+cO1bsZ8Mpy9LYsAAtz45OYtbQDtaJydd2i+KHY7JS45JzONJSzNMRea916hU2efycKuCPFoODlV3B+DGCjtbvYwimP3Hs4pzd8JYZlYnYp3Nw5GsOcT+ryKAQgK5EsohrGU1SncdeQ1ce43u77RoU+hNn7flpdaRHx6uazQj73/72N4YPH05QUBDh4eG+bk6rZjSbufKDnfx8osC6rbTKzJpj+dYgB2D9yQKueH8nVSbbT2nlVSYuX7yTTacv5P8orDTxyHdH+Xd6lnXbXf3j6R0b7Ha7dJoLyQaFFx2OhEORkqTsEnAFSQ6DHIC/c5Vd1uTXuRodGnTVb/01hUEfZIhbvTnbOMskPuZUdZADcJTzXM0SDlJ3As6etOV+Bttcu6Y987m6zuOFaIhmE+gYDAZuuukm7rvvPl83pdVbdfg8B/PKMdYxecaoWvIPrTxo+wb46b4cThdV2iX9U4C/rT+JWj1eFeyvZd2MfoQG2A5p1dYzJoj+cSF0jtBze+9YNv6uf72eS4BERg2gwLKeUBBQ967Ca95kAgddzPeJwj7txUgS2co9TKUnnYkgjQ68z2TecDPIeJVN1XNsLjBjmWPzFv9z6xxvMYH3mUwaHehMBLfQi63cw2W1ep9aHRUPJAz09ZO4dDWboas///nPACxevNi3DRHsPleCTqPUGegA+GkUdp8r5cbUC9v2nCt1mPRPBY4XVGAwqQToLAGIRlEoqnT8idVPo3BdSjTPX9nZpm31UdnQFMutnVkDoQYZqvKhzkRwhmKHj/mhYQ/Z3OrgsT7EsoTrG3TNHWQ6nEdjxMxOshwcYU9BYTp9mU7fBrWhxZK3Iq9pNoFOQ1RWVlJZeWECXlFRkYu9xcX2nCvhvZ1ZZJUYGNSuDXf2iyMi0I+EUL1bQQ5YCnN2DLP95N8hNMBp8sBQfy1zfzhGVomBAfEhTO8TR1iAlkIHwU6VWeXnEwV8cziPq7tEoigK8fVc6q/TgLGVzn9skPZF0PccBBihUiuZkX1oAVsJQEulg6ErI2YSvJCAL5FwjnDeYTLCxFoJAWuYq8tJrGA/RlR+Q1em0MM6dCZEU2jRv23z5s0jLCzM+pWQkODrJjUb/9qRSd93tvHm/86wbG82/7fmKN3f/h+H88q4MTWGCL2OulbWaxRoE6Dl5p62ScBu69MWvU5jd7wCFBlM1ms+/v0xei7cytSebZ1e639nirjmoz3cvmI/ZlUlJtif/nHuz+uRpev1MOok3J0OAzKhV7YlyBE+8w1HHM7P0aAQjD+30tvj13yQwXZBDlgCq/sYZLPNhJlb+IxrWcoH7OYjdnMzn3E1S6j08OqzZk9qXXmVTwOdZ599FkVRXH5t27atweefO3cuhYWF1q9Tp055sPUt17kSA/d9fQgVS4+MWbUEBHllVdz/9SGC/bV8e3sfYoL8bI5rd1FvSlSgH9/c1ofQANuOw+ggf/57a2/CL9peUwWi9jXzy6s4kFvGTamOJ0rWjDx9tCebFdXVyK/p6v7yfwl03NS2BMacsPxfq9qWfZDXsMECG9GpbrbLRmMRgZ5V/NYr+WiuJYV5XGnTI+OPloVMtFnhBfAJe/mUfYAlEKrJEv0jx3mHhr+vt0g+ql6+YMECOnXqhF6vZ+DAgaxfv97pvp9//jljx44lJiaG0NBQ0tLS+O6772z2Wbx4scP7eEVFRf0b50E+Hbp68MEHueWWW1zuk5SU1ODzBwQEEBAgEybra8WBHIdDUyYVvj9eQF5ZFUPah5LxSBqrj54np7SKwe3b0KttCPtyStlyuojoID/Gd4nEX+s4lh7TKYIzj6bx7ZHz5JcbOVNcyVM/nXB4zXUZhZz7w3CeuTyJcR/u4nSxffI0jQIf/3qOG1Jj+GxfTqNfA3GRXjmOkwTKh0i3tacNqUSjx48BxHMHfelAKKs5SjalHCCXl9jY4LhxNkMZQyfGk0yAF9/an+Ay7qQfaziGBoWr6UKkg4nPH7HHaYHPJexhFsO81kZRt2XLljF79mwWLFjAiBEjePfdd5kwYQL79u2jY0f7ieHr1q1j7NixPP/884SHh/Pee+9x7bXXsmXLFvr3v7AIJDQ0lIMHD9ocq9f7NgmkTwOd6OhooqPrX09F1E9umQFVxWUW4trKq8woCk6T9VVUT2rx12r4TTfbn19qTDCpMe4NHel1WiZ3t/TUvLnltMvkZ+VVZnrEBBOgcxw4mVUoqbJ045cYvFctutXSOXhNK7RQpYUQmZTsjm5EsZrpdtsn0o1iKnme9WgcFON012V05DpS6nXMecrJo4wg/IinjXXJd11iCeF2+rjcp5Qqh+UoLI85z/TcKvkgYeCrr77KXXfdxd133w3A/Pnz+e6771i4cCHz5s2z23/+/Pk23z///POsXLmSr776yibQURSFuLhLq/5js5mMnJGRwfnz58nIyMBkMrFz504AunTpQkhIiOuDW6ltZ4t4aNURNp+xTMIe3K4Nb0zowrAOYS6Pu7JzhMMhHQVLFfJ2bTxf2+vKzhFO3947hAaQUD2h+eoukSzYetZuXwW4vGM49351kMwSeRP1uOMRkHbG8v8if1jVFQ5FWd5cw8vhqmPQs+48Kq2ZoyAklzIeYhWfsq/BAQ5YJgOPdJCJ2JkD5HI3X/ILF4bz2xLEa1zNbz00t2ccyazlpF2wo0VhAl08co0Wo4kTBhoMBrZv384TTzxhs33cuHFs3LjRrXOYzWaKi4uJjIy02V5SUkJiYiImk4l+/frxl7/8xSYQ8oVmMxn56aefpn///jzzzDOUlJTQv39/+vfv36g5PC3ZsfxyRi/eydazF1aabc8s5or3d3Egt9TlsX1iQ5jeJ9bms11NuplXxiVbMxd7UmpMMFd3iXD4WKnBRHH1qqsKo+PPiCH+WlYfy+ff6Zky78YbDkfCsXCo1MB7/Szf13yCLNDDZz3hoJRGcWUw7Wy+N2HmCt53GeTU9LDU9UY9l8toi3s9qTmUchn/tglyALIp4zY+Z3n1vJrGmskgEglDW+udRIeGKIJ4lOEeuYawV1RUZPNVe+VxjdzcXEwmE7GxsTbbY2NjycpyL03AK6+8QmlpKTfffLN1W/fu3Vm8eDFffvklS5cuRa/XM2LECA4fPty4J9VIzSbQWbx4Maqq2n2NHj3a1027JL2++TSVRrNNUj6zalmS/dqm03Ue/+9J3Xl1fDIpUYGEBWgZkxTOj3f05doU7w01lleZHXacF1QYeX9XFnllVXyw65zDY4sNJn46UWCXhFB4igJLe8GKHpbAxqyxfUxRYa37PQqtjRaFt9lqs+1rDrOHbIdBjg4NYQQwls4sZCLj6eJyUGl8dT0rd/yDHZx3UQj0Wda6fS5XwtGzmbu5n8G0JZhoArmDvmzlHtrRxiPXaDE8uOoqISHBZrWxo2GoGhd/aFVV1a0PskuXLuXZZ59l2bJltG17YVXtsGHDuP322+nbty8jR47kk08+oVu3brz55psNfGE8o9kMXYn62XymCKODm77RrNqUXnBGq1GYPSyB2cOabkn+9sxih59rNQpsO1tM39gQuySDogkZtVDqZynqab7ozVBVIEuGkJ0xobIZ2w8Y2ziLDo3TBHwnmG1dOXUVnemK45uFAmwn027VkzPbsB/6re1XsjFi9kium7YE8wYTeIMJjT5Xi+bBoatTp04RGnohh5KjBTnR0dFotVq73pvs7Gy7Xp6LLVu2jLvuuotPP/2Uq666yuW+Go2GwYMH+7xHRwKdFio+xB+tgl0Ph1bBK3NsGqLEYOS1Taf576E8AnQa9DoNJQb7N32zCnuzS1l99LwPWins1AxZacyQmgsdCyC0EgzOS3UIKMHAb1lOKAEkE0EgOkxOqnXr0DCWD9Ci4Xf043pSHe4Hlvvbek5yjHwuoyOTSKEYAx+yi2PkE4iOSsxosExYzsH10HU4epvhJtEEPDgZOTQ01CbQccTf35+BAweyZs0apkyZYt2+Zs0aJk2a5PS4pUuX8rvf/Y6lS5cyceLEupukquzcuZPevT2f06k+JNBpoe4d2I6VB+3r4JhUy2O+diivjEGLttsUAXVGBXaeKyE9qwRt9Wqw2rcHrQKJ4XqKKk2cL6+ymaOjVaBn22B2n3P95i7cdL56mWhQJdyxG9qW2X4SVZEVWE7kUMZSfgUsc2/80Vp7dC7+MG/EzDYyAdjCGR5ltctzL2c/OjS8zha6EUkmJRRjQMOFvxUtCq+y2eV5FOBeBqDID7HFmzNnDtOmTWPQoEGkpaWxaNEiMjIymDlzJmDJQ3fmzBk++OADwBLkTJ8+nddff51hw4ZZe4MCAwMJC7MscPnzn//MsGHD6Nq1K0VFRbzxxhvs3LmTt99+2zdPslqzmaMj6uearlE8c3kiCpahn5rMwo+PSGBKd98v6b/xk71uBTk1zOqF3l1N9W9tzQTptsH+fHlLb766tRdh1UkIax7r2TaYNdP6cnmi65Vmwk1lARBXDOOOQVSZZZty0ZeokxmVSoz4o0WPDgVc9qKUUOXyfCpQVR3SHOI8xdXLt2t/IHBnVdd4knmW0XXuJzzMBwkDp06dyvz583nuuefo168f69atY9WqVSQmWubaZWZmkpGRYd3/3XffxWg08sADDxAfH2/9mjVrlnWfgoIC7r33Xnr06MG4ceM4c+YM69atY8iQIQ15VTxGUVVn2VJanqKiIsLCwigsLKyza6+lOJZfzpcHc1FVuDYlii6RQb5uEsfyy0l+Y0ujzjHvik4oikKnCD2TUqKt+XVKDSZWHMjhTJGBvnHBjEuORKMo/PGHY7ywIcPJQIGoF40Z/rjBPnmgaJAPmYIBE7mU8SQ/Opyz42030INHSWMYHaQ3pxZv3zNqzk/8adA08vzmIsjs0Krub+6SoasWrnNEYJNOKHbErKoYzao1S3K2B/Lc9IkLYUKXSMqrTFQaTfhrLanGg/213N7HkqyqymS2JD1ULCu3FFcZCYX7dGYJcjyoChO/oz8qKo/zvU/a8Ft6k4bUAhQtkwQ6wmvOl1fx+PfHWLL7HBVGM10jA8ktqyK/onEF/XQahVWHcrlu6R7rZGutAvcOiOftid3YkFHIE98fY+PpIvy1ClO6R5NVYpCl555i0EJOIESVy+C3B6ziMNeRQhRBxBNCJiVNen0NCsPo0KTXFBdp4oSBrY0EOsIrDCYzoxfvZF9OqTXAOHzeee6O2mrm1zgLTLpG6nl7W6bNNpMKC7dncqKwkjXH8jFXj8gaTCrL9krtK89S4OdEuPGAZRJI7WBHJiPX2woOsJ9c5nElWU0c5IBlXo7ktfExH5SAaE3k85jwis/357Anu7TevShaBcYmRzAqMRwF8NMohPhZfk0TQgP442Ud2Z/rPGD65sh5VFWV7Mjeti8WlneH7Oo5XzL5qcFMqOwlh4f51mmMOIsh9OZCYjZ3a1K54wC5TuqgC9EySI+O8IpfMorw0yj1TvBnUmFfdhknH0nDrKqWhTyKgllV0SgK76VnunUO0QT2toVyP7h9z4WPTPKhskF0aDhBgcPH/NCgRcNu7kNFxYSKDg1D+AdbOdvoax+ngFzKiHGzhITwAhm68ioJdITHlBiMfLYvh9NFlWSXGjA1sFvFYDYz+9vDBOq0tA/156bUtsSGWJIcRgTKr+wlpVx+Hp5gwlL+xNFfjBEz/+MML/EL15FCCpb0ENEEoUFxWiHcXQqwjL3cRm8iCGzUuUQDydCVV8nycuERm08XMuE/eyioMKLTKBg9NHakYJl8vHhyd37bO5YKo4m4lzdSWOl+Dh7hZcEGuH8rBDVukrlwz2MM5+9cxTL2civLXe6rRaETERwjv86AKAg/PuMmJtDVk81t1ppseXnMWc8sL89pJ/c3B2SOjmg0g8nMdUt/pajScqOrCXJqkhRqlYb/oqlYCpFOX7GfEwXl6HVaVkzthZ9GPr1cMkr9YUV3X7ei1XiRjazkIBUukgjWJB/sQiTfcBtDaF/necuo4kY+Jd9FwU/hJT5IGNiaSKAjGu3bI+fJKauymwBc8/2jaQk8OjyB/97am8WTupMYZl9kzh0fVlcuH9MpgrOPpvHYiAQ6hjbsXMLDjkVAlbyduKsxk4m1KPyTHSxih8PzKEBXIvmYG9jNfXQhkg3cyT+5ts5zl1PFp+xrcNtEA3mwermwJwPsotFySl2np7+tTyx9Yi9Utv41u5TXt5yu10RljaKQXes60UH+vHBVMjf2iGHIP3fUv9HCs8waqNSCnyy/ckdj5tWYUMmihHwqHJ5HBdoTylR6Wbdp0dCXuDrPrUWps+in8AKZjOxVEuiIRhvS3nkOjjb+WrpEBvLVwVyeX5/B7uySBq3GqjKrLNp+lre2nkGnUfhNtyje/U03ukcHEaBVqJSlVj6kWqqXB7sOeIX76krivZ1Mp93xGmAX5wjib6QQzSyGcJR8/kl6ndc1ovIJ+3ieDcQQxEwG8Shp+OGZyvQqKu+yndfZTAZFpBLDE4zgBhfV2YVoLJmMLDxi8sd7+OpQnt3w1d+u6ESH0ADu+OIAGgWP5reJD/Fn/wNDeHXTKZ5bd9JzJxb1UJ0h8NqDMCDL141pETQo+KGhCrNHVlSp1B04OTqm5v83kMqn3NSodtT4P1bzMpus16hZNfYOE/k9gzxyDU9qssnIEVmemYycHyf3NwdkUF14xNIbUnlwcHsCq4trxgT58eq4ZP5veAKPrTkK1B3k3D8onm5RtstbXf2CZpYY+OeOTJ4ZncTzV3SyZlRuKAXQ62Scu15CK+E6CXI8pSOh/MB01jKDoW5MIK6LetG/9Tmm5v+fsY9tHsjXc4YiXmWzzTVqArkn+IEKWvmqPZmI7DUydCU8ItBPy+sTuvLi2GQKK41EBfqh1SjszS7lXB1zeGpM6BrFW9d0I6+8Cr1WQ4XJzKubTjNvQ4bTY749ep5Hhycwd2Qi+RVGXt10qt4JA69ODuf9yT1oE6Aj+Y0tZHqg6Gir0CcLJh2Uj0uNpEHhEA8Shp5ogqzbN3IX+ZSTTiZX8qHP2qdF4QeOMYh2jTrPOk467aEqoII9nGOwB4I7IS4mgY7wqACdhrY6f+v3If7uj+2H+GtRFIXoIMvxIUCE3vWvaKi/FqPZzOqj+ew5V9KgobGiSjP/PXyesZ0jJLFvfaiNyBsgrPzREEMwwfixmqMcI5/uRHM5iYSh5xRFPm2fGZUQ/OvesQ7BdZyjrmvsIJMfOEYGhfSmLdeSQnxLqdGlKjQ6rbisunJKAh3hVYnheoa0a8P2zGKnPS0axTLUdVnHMLvHbu7Zlse/P+a0Zza/oorkN7aQUVjZ4DZuPF3ExtO+vZk0PyocC5cinnXQAMlEcpTzTsuBVWAilpcJR29T1LMH0aioHCCvCdqpoEODEZNdO7VoPDJZeCydCUdPIRU2f89aFLoTTffqjM8XK8XAjXzKtxyx2X4fX/M3ruQJLmt023zOE0NPMnzllHweE17370ndCXfRM6Oq8NH1PdBp7H8dE8P1vDY+2emxP50o5HQjghzREKoluJl8UIKcOiQSzhfcQkfCXe5XgdGucvl+cusd5HQhkgC0aKsDFw0K/tUrpnRo0FW/5StcCG601fu8xyTiaWPJRl79mAK8w0TiCHF6TXcF4sd/uN56zZr2heDPh0xBcfLL9AfWsJqjdtvNwFx+YBWHG9020bJJj47wup5tg1kzrS8DFm13+LgK6P2cD3HNGpbAhoxCPtuf6/BxX2Zu0SrQvk0AGUWtKdhS4NbdkJzv64Zc0jQopNGBVGL4lfv4D3v4koN8zzEq8WwJk6G05z4G8Vt6c45S/k06R8mnO1H8jv6coZgP2UUBlYykI2NI4mN+5QB5JBPBXfSnPaFcSzc+ZDf/4wyxBDODfvQgxmPtvIauHOYh/k06JyikJzH8jv42c5NqK6eK90h3OrdHARayjWuae9kKGbryKgl0RJMorXL9xn66jkDB4Ml16R6k0yh8dnPP1pe0sH2x9ObUwYzKaYoBy/yUexnIvQykDc97PND5D9eTTCQAHQjlaS63eTyWEAYQb7NtLiPtztOGAO5nMPcz2KPtqy2RcP7MGLf2LaDC5WulAiedVH1vVmToyqsk0BF2Vh7IZf6W0xw9X05qTBB/GJ7AVZ0jG3XO7tFBLot9fnUwlxEJYbSvLulgVlXeS8/ine1nOVdiIMRfiwbf9t5cTKNA79gQukYFtr6khW8NhpgyGHYGejjuaROwnpP48RcC0BKBnhtIpTsx7CCz0TlyagSgZRIfU4yBjoSSTwVFVDKCBB7nMvq5kRHZk37iOC+xkV/JphMRzGIo19OjQeeKIZhogsilzOHjGhQGNnI1mGj5JGGgsPHqplM8uvooWgVMKtZ/35uUwox+8XWfwIUHVh1i4dazDt/edQpEBvmx/d6BdAjV8/v/HmTR9kxrYrGadtQOduqTBM2Rxh4P8MXUXkzqHs2c744wf/Pp1vWhSlEt3eVXHoPLTsnEZDdoUQglgHwqPHZOZ7/HNXNgfmA6l9HRY9dz5SP2cDufo0HBhIq2+t95jZg0/CqbeJTVDh/TobCD39Ob2MY026kmSxgYkgNKI8+vFkFJjNzfHJDJyMIqv7yKP/5wDMC6Qqrm30e+O0qlsXH9KfPHd2HW0PboFPu7oVGF82VV/H1DBnvOlbBoeyZw4Q3cVD3/NUB34Vc2ISyAqzqFN/je2tigpF2IP9elRAHw4tjO/GF4Av6NzVrYnNTMCfgpCcqkc9gdJlSKqGQ8ycQ3coJvTYVyZ7/HRswYMTOH7xp1HXcZMDGLb1GxPE9q/fs0PzntlanLIwxjHlcShJ/N9k6E8y23ey3IaVJSvdyr5N1JWK07Weh0+KWgwsi2s8WMcLAE3F1+Wg2vXd2V4wUVfHkwz+7v0qjCyoN5JIbpHZaLUIFyo5lt9wwgxF9H16hAkuZv9vjft0aB5Ag9h8+7/tR9tsTA2WID7UMD0Gk0vDg2macvT2TH2WIuf3+Xh1t1CTNr4Hg49JQhLHeYUDlIHhk8whHOM5rFnGtAIc0g/CjHiNHFgK4Zla2cpYAKwtE3ptl12kWW02CmCjM/cZyb6Fnv8yooPMFlzGYYRzlPMQaiCKQLkU5XajU7MhnZqyTQEVZ+dfRGeKq3ItBPi0bBYV4df62Cv9Z1R2NyZCDhesunu7ra3FB6rcatoa2Lrx/ir6N3bOOX4jY7Wvk4WR/+aNGhoTvRdj0V7tK52SGv1GPfxvCvo/BnXY/XRY+OnrRt1DlE6yRDV8JqTFI4YQH2b0YaBTqEBjAg3jNZSG9KjXEY5GgVuLVXW6b0iHYYYWgVSxtrghyA3/aKbXCNK6X6nBdTVbhrQLzLIEcBRiSE0jbYPptrRKAfo5PCXbYryK8F/elpzNC59Sw1b2xorUHhVnpZv7+VXtZhqPqcYwrdXfbmgGV4axzJHslsXJfexNKJ8OrsO7ZC8OcqOnu9Dc2WDF15VQt6txWNFein5V/XdUerWJZNA9b/vzcpBa3GM70nk7tHc2OqJTeHRrHcOBQgNSaY/xvekY5hel4Ya3lTrGmHRoHQAB1vXWObL+MPwxNIjQm2nqN2EzVcuClpuBDU1Jzz6csTCdPrLmyv/vf5Kzvx4JAOTKqef+NIiL+WBRO7OX387Wu6Ehqg4+KXTKNYvv51XQpPjEhwevylou6fuAoTD4H/pbQeznP6Vc//qAlEFCCZCDrS8MmefYjlUdKs3z/GCHoQc+F3uPpaIfg7ff37EcdrjGdmdcVvR4GSBoVw9LzO1Q1ua31oUHiPSfhXJyyECxOi/8G1dZaAaNVUxTNfwiFZdSXs/JpdwjvbznI0v4LU6CDuG9yOLpGOE3o1lMms8vn+HD7dl0O50cw1XSK5o18cQbUSB27IKODf6VlklRgY0r4NMwe1Iy4kwO5cZVUm3t+Zxaoj59HrNNyYGoMW+HR/DmVVZsZ1jmBccgQf7DpHelYJ7dsEcM/AeIa0D+VciYF3t59l8+kiYkP8ubNfHKMSw61t/HRfNgu2nuVAbilVZpXoID8mpUQzJy2Bdm3s21JbVkkl72w7y88nCsgrMxLir2FAfBt+P6gdfaqHt74+lMujq49yoqACf62GkYlhtA3y44fjBZjMKglhAYQFaMkpreJ4QQXFBpN17pJWgSCdhkA/LQWVRgwmFaU6cKyZvK1RIC7En9ToII7kV1BWZUKngXMlVRir9wnx0xAe6EepwYTBZCbYX0u7NgGE63V0iwpkUrco/u/7Y+zLKbP50BgUYSTi+uMEdaggGB3HKKAEg9OK2VoU/NAQjD9xhKBHRwmVZFNGQXVZgJobfc0kVi2W3C6diaANAWzlDBUY0aIhmiBSiaaEKk5QgBmVGILRo+UY+RRjwFy98sdyw9VYAl4UjKhUYsKEGQ0KYQTgj4YiDGjRMJh2/IlRjCaJrzjER+yhBANX0Znf0Z8Q/JjDav7JDsqwFK2tyTTcjUjuZgDfcoR0slCBCPQkEcFkUphGX7vhqlIMfMAuVnGEQHTcTE+uohMfsJvvOEo+5ahADIH8hhSm0YdA/FBR+ZrDfMQeiqgkkTDyqaCACobRgd8zkFgPZDWuj2Pks5Ct7CWHToTzewbRp5lOGG6yVVf6PM+suqqIkvubAxLoCCGEEA40WaAT4KFAp1ICHUdkMrIQQgjhS7Lqyqtkjo4QQgghWizp0RFCCCF8SWpdeZUEOkIIIYQvydCVV8nQlRBCCCFaLOnREUIIIXxJhq68SgIdIYQQwpdk6MqrJNARQgghfEl6dLxK5ugIIYQQrdCCBQvo1KkTer2egQMHsn79epf7r127loEDB6LX6+ncuTPvvPOO3T7Lly8nNTWVgIAAUlNTWbFihbea7zYJdIQQQghf8kGtq2XLljF79myefPJJ0tPTGTlyJBMmTCAjI8Ph/sePH+eaa65h5MiRpKen88c//pGHH36Y5cuXW/fZtGkTU6dOZdq0aezatYtp06Zx8803s2XLlka9PI0lJSCEEEIIB5qsBASF0IhCsdVnA9xv69ChQxkwYAALFy60buvRoweTJ09m3rx5dvs//vjjfPnll+zfv9+6bebMmezatYtNmzYBMHXqVIqKivjmm2+s+1x99dVERESwdOnSRjy3xmlVc3RqYrqioiIft0QIIcSlruZe4f3+AE/ckyznuPj+FhAQQECAbQFig8HA9u3beeKJJ2y2jxs3jo0bNzo8+6ZNmxg3bpzNtvHjx/Ovf/2Lqqoq/Pz82LRpE4888ojdPvPnz2/IE/KYVhXoFBcXA5CQkODjlgghhGguiouLq3tePMvf35+4uDiysjxzTwoJCbG7vz3zzDM8++yzNttyc3MxmUzExtpWlY+NjSUrK8vhubOyshzubzQayc3NJT4+3uk+zs7ZVFpVoNOuXTtOnTpFmzZtUJRLYyleUVERCQkJnDp1SobTPExeW++R19a75PX1nvq8tqqqUlxcTLt27bzSFr1ez/HjxzEYDB45n6qqdve2i3tzart4X0fH17X/xdvre86m0KoCHY1GQ4cOHXzdDIdCQ0PlDc1L5LX1HnltvUteX+9x97X1Rk9ObXq9Hr1e79VrXCw6OhqtVmvX05KdnW3XI1PD0vNkv79OpyMqKsrlPs7O2VRk1ZUQQgjRivj7+zNw4EDWrFljs33NmjUMHz7c4TFpaWl2+69evZpBgwbh5+fnch9n52wqrapHRwghhBAwZ84cpk2bxqBBg0hLS2PRokVkZGQwc+ZMAObOncuZM2f44IMPAMsKq7feeos5c+Zwzz33sGnTJv71r3/ZrKaaNWsWo0aN4oUXXmDSpEmsXLmS77//ng0bNvjkOdaQQMfHAgICeOaZZ1yOo4qGkdfWe+S19S55fb1HXluLqVOnkpeXx3PPPUdmZia9evVi1apVJCYmApCZmWmTU6dTp06sWrWKRx55hLfffpt27drxxhtvcMMNN1j3GT58OB9//DF/+tOfeOqpp0hOTmbZsmUMHTq0yZ9fba0qj44QQgghWheZoyOEEEKIFksCHSGEEEK0WBLoCCGEEKLFkkBHCCGEEC2WBDqXoMrKSvr164eiKOzcudPXzWn2Tpw4wV133UWnTp0IDAwkOTmZZ555xmPZSFujBQsW0KlTJ/R6PQMHDmT9+vW+blKzN2/ePAYPHkybNm1o27YtkydP5uDBg75uVos0b948FEVh9uzZvm6KaAIS6FyCHnvsMa+lHG+NDhw4gNls5t1332Xv3r289tprvPPOO/zxj3/0ddOapWXLljF79myefPJJ0tPTGTlyJBMmTLBZiirqb+3atTzwwANs3ryZNWvWYDQaGTduHKWlpb5uWouydetWFi1aRJ8+fXzdFNFEZHn5Jeabb75hzpw5LF++nJ49e5Kenk6/fv183awW56WXXmLhwoUcO3bM101pdoYOHcqAAQNYuHChdVuPHj2YPHky8+bN82HLWpacnBzatm3L2rVrGTVqlK+b0yKUlJQwYMAAFixYwF//+lf69evn88rawvukR+cScu7cOe655x4+/PBDgoKCfN2cFq2wsJDIyEhfN6PZMRgMbN++nXHjxtlsHzduHBs3bvRRq1qmwsJCAPk99aAHHniAiRMnctVVV/m6KaIJSWbkS4SqqsyYMYOZM2cyaNAgTpw44esmtVhHjx7lzTff5JVXXvF1U5qd3NxcTCaTXZG+2NhYu2J+ouFUVWXOnDlcdtll9OrVy9fNaRE+/vhjduzYwdatW33dFNHEpEfHy5599lkURXH5tW3bNt58802KioqYO3eur5vcbLj72tZ29uxZrr76am666SbuvvtuH7W8+VMUxeZ7VVXttomGe/DBB9m9e7dNHSHRcKdOnWLWrFksWbKkySuFC9+TOTpelpubS25urst9kpKSuOWWW/jqq69sbhYmkwmtVsttt93G+++/7+2mNjvuvrY1b2xnz55lzJgxDB06lMWLF6PRSJxfXwaDgaCgID799FOmTJli3T5r1ix27tzJ2rVrfdi6luGhhx7iiy++YN26dXTq1MnXzWkRvvjiC6ZMmYJWq7VuM5lMKIqCRqOhsrLS5jHRskigc4nIyMigqKjI+v3Zs2cZP348n332GUOHDqVDhw4+bF3zd+bMGcaMGcPAgQNZsmSJvKk1wtChQxk4cCALFiywbktNTWXSpEkyGbkRVFXloYceYsWKFfz888907drV101qMYqLizl58qTNtjvvvJPu3bvz+OOPy/BgCydzdC4RHTt2tPk+JCQEgOTkZAlyGuns2bOMHj2ajh078vLLL5OTk2N9LC4uzocta57mzJnDtGnTGDRoEGlpaSxatIiMjAxmzpzp66Y1aw888AAfffQRK1eupE2bNtY5T2FhYQQGBvq4dc1bmzZt7IKZ4OBgoqKiJMhpBSTQES3e6tWrOXLkCEeOHLELGqVDs/6mTp1KXl4ezz33HJmZmfTq1YtVq1aRmJjo66Y1azXL9UePHm2z/b333mPGjBlN3yAhWggZuhJCCCFEiyWzMYUQQgjRYkmgI4QQQogWSwIdIYQQQrRYEugIIYQQosWSQEcIIYQQLZYEOkIIIYRosSTQEUIIIUSLJYGOEEIIIVosCXSEaAZmzJjB5MmTfd0MIYRodiTQEUIIIUSLJYGOEM3M6NGjefjhh3nssceIjIwkLi6OZ5991mafgoIC7r33XmJjY9Hr9fTq1Yv//ve/1seXL19Oz549CQgIICkpiVdeecXm+KSkJP76178yffp0QkJCSExMZOXKleTk5DBp0iRCQkLo3bs327Ztszlu48aNjBo1isDAQBISEnj44YcpLS312mshhBB1kUBHiGbo/fffJzg4mC1btvDiiy/y3HPPsWbNGgDMZjMTJkxg48aNLFmyhH379vH3v/8drVYLwPbt27n55pu55ZZb2LNnD88++yxPPfUUixcvtrnGa6+9xogRI0hPT2fixIlMmzaN6dOnc/vtt7Njxw66dOnC9OnTrYVR9+zZw/jx47n++uvZvXs3y5YtY8OGDTz44INN+toIIURtUtRTiGZgxowZFBQU8MUXXzB69GhMJhPr16+3Pj5kyBCuuOIK/v73v7N69WomTJjA/v376datm925brvtNnJycli9erV122OPPcbXX3/N3r17AUuPzsiRI/nwww8ByMrKIj4+nqeeeornnnsOgM2bN5OWlkZmZiZxcXFMnz6dwMBA3n33Xet5N2zYwOWXX05paSl6vd4rr40QQrgiPTpCNEN9+vSx+T4+Pp7s7GwAdu7cSYcOHRwGOQD79+9nxIgRNttGjBjB4cOHMZlMDq8RGxsLQO/eve221Vx3+/btLF68mJCQEOvX+PHjMZvNHD9+vKFPVQghGkXn6wYIIerPz8/P5ntFUTCbzQAEBga6PFZVVRRFsdvm6ho1+zvaVnNds9nM73//ex5++GG7c3Xs2NFlm4QQwlsk0BGihenTpw+nT5/m0KFDDnt1UlNT2bBhg822jRs30q1bN+s8noYYMGAAe/fupUuXLg0+hxBCeJoMXQnRwlx++eWMGjWKG264gTVr1nD8+HG++eYbvv32WwAeffRRfvjhB/7yl79w6NAh3n//fd566y3+8Ic/NOq6jz/+OJs2beKBBx5g586dHD58mC+//JKHHnrIE09LCCEaRAIdIVqg5cuXM3jwYG699VZSU1N57LHHrPNvBgwYwCeffMLHH39Mr169ePrpp3nuueeYMWNGo67Zp08f1q5dy+HDhxk5ciT9+/fnqaeeIj4+3gPPSAghGkZWXQkhhBCixZIeHSGEEEK0WBLoCCGEEKLFkkBHCCGEEC2WBDpCCCGEaLEk0BFCCCFEiyWBjhBCCCFaLAl0hBBCCNFiSaAjhBBCiBZLAh0hhBBCtFgS6AghhBCixZJARwghhBAtlgQ6QgghhGix/h+WE41BJVQq7gAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the data points based on Ad Encounter (who clicked the Ad and what the Ad was for - this section is to verify that our predictions match the PCA)\n",
    "X_predictions_df.plot.scatter(\n",
    "    x=\"Income\", \n",
    "    y=\"Age\",\n",
    "    c=\"Ad Encounter\", \n",
    "    colormap='winter')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Question 2: Can a Regression Model predict the Click Through Rate of an Ad?\n",
    "Based on the results of the principal component analysis Age, and Income have a high loading, possibly implicating that they are significant variables in the dataset. Next, train test split was run on the data solely analyzing click through rate (CTR) on the full and select variables and then scaled the select data in order to perform linear regression on both sets of data.\n",
    "R2 values range from 0 to1 with a 1 indicating that the regression model predictions fit the data perfectly. The r2 values of -0.005 and -0.002. This indicates that the linear regression model does not explain the variability of the data. Overall, the changes in the dependent variable CTR are not completely explained by the changes in the independent variables, demonstrating that a Regression Model cannot predict the click through rate of an ad."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "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>Age</th>\n",
       "      <th>Income</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>61</td>\n",
       "      <td>35717.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>41</td>\n",
       "      <td>47453.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>68</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>63</td>\n",
       "      <td>21109.40</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>9994</th>\n",
       "      <td>49</td>\n",
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       "    <tr>\n",
       "      <th>9996</th>\n",
       "      <td>35</td>\n",
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       "    <tr>\n",
       "      <th>9997</th>\n",
       "      <td>42</td>\n",
       "      <td>55084.12</td>\n",
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       "    <tr>\n",
       "      <th>9998</th>\n",
       "      <td>34</td>\n",
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       "    <tr>\n",
       "      <th>9999</th>\n",
       "      <td>54</td>\n",
       "      <td>66441.59</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8694 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Age    Income\n",
       "0      61  35717.43\n",
       "1      41  47453.25\n",
       "2      49  68126.35\n",
       "3      68  64585.73\n",
       "4      63  21109.40\n",
       "...   ...       ...\n",
       "9994   49  72810.17\n",
       "9996   35  42182.75\n",
       "9997   42  55084.12\n",
       "9998   34  39382.80\n",
       "9999   54  66441.59\n",
       "\n",
       "[8694 rows x 2 columns]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_full = X\n",
    "X_sel = X[[\"Age\", \"Income\"]]\n",
    "X_sel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_full_train, X_full_test, X_sel_train, X_sel_test, y_train, y_test = train_test_split(X_full, X_sel,  y_ctr, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.50185341, -1.53278454],\n",
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       "       ...,\n",
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       "       [ 1.08968008, -1.50220395]])"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "X_full_train_scaled = StandardScaler().fit_transform(X_full_train[[\"Age\", \"Income\"]])\n",
    "X_full_test_scaled = StandardScaler().fit_transform(X_full_test[[\"Age\", \"Income\"]])\n",
    "X_full_train_scaled"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
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       "           Age    Income\n",
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     "execution_count": 31,
     "metadata": {},
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   "source": [
    "X_full_train_scaled = pd.DataFrame(X_full_train_scaled, columns=[\"Age\", \"Income\"])\n",
    "X_full_test_scaled = pd.DataFrame(X_full_test_scaled, columns=[\"Age\", \"Income\"])\n",
    "X_full_train_scaled"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
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       "[6520 rows x 19 columns]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_full_train_preprocess = X_preprocess(X_full_train)\n",
    "X_full_test_preprocess = X_preprocess(X_full_test)\n",
    "X_full_train_preprocess"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
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       "[6520 rows x 19 columns]"
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     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_full_train_preprocess[\"Age\"] = X_full_train_scaled[\"Age\"]\n",
    "X_full_train_preprocess[\"Income\"] = X_full_train_scaled[\"Income\"]\n",
    "X_full_test_preprocess[\"Age\"] = X_full_test_scaled[\"Age\"]\n",
    "X_full_test_preprocess[\"Income\"] = X_full_test_scaled[\"Income\"]\n",
    "X_full_train_preprocess"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.50185341, -1.53278454],\n",
       "       [ 0.67750675, -0.00926443],\n",
       "       [ 0.84237608,  1.21667452],\n",
       "       ...,\n",
       "       [-0.31170924, -0.18338886],\n",
       "       [ 0.01802942,  1.5780552 ],\n",
       "       [ 1.08968008, -1.50220395]])"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_sel_train_preprocess = StandardScaler().fit_transform(X_sel_train)\n",
    "X_sel_test_preprocess = StandardScaler().fit_transform(X_sel_test)\n",
    "X_sel_train_preprocess"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "lr1 = LinearRegression()\n",
    "lr2 = LinearRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;}#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": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr1.fit(X_full_train_preprocess, y_train)\n",
    "lr2.fit(X_sel_train_preprocess, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "predicted1 =lr1.predict(X_full_test_preprocess)\n",
    "predicted2 = lr2.predict(X_sel_test_preprocess)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The r2 is -0.007.\n",
      "The r2 is -0.001.\n"
     ]
    }
   ],
   "source": [
    "r21 = round(r2_score(y_test, predicted1),3)\n",
    "r22 = round(r2_score(y_test, predicted2),3)\n",
    "\n",
    "print(f\"The r2 is {r21}.\")\n",
    "print(f\"The r2 is {r22}.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Question 3: Can a Classification model predict if someone clicks on an ad?\n",
    "[According to a blog post from Neil Patel (n.d.)](https://neilpatel.com/blog/what-is-the-optimum-number-of-clicks-before-conversions-start-happening/#:~:text=It%E2%80%99s%20a%20good%20strategy%20to%20directly%20pitch%20your,product%20page%2C%20will%20lead%20to%20a%20website%20conversion.), it takes at least 2 clicks to convert a user into a customer. The code uses a slice of the DataFrame that only contains rows with the amount of clicks being from 0-3. After doing a train-test-split, the X data is scaled and encoded and the y data is the clicks column encoded as \"Yes\" for rows with 2-3 clicks and \"No\" for rows with 0-1 clicks. I used the Random Forest Classifier as my model, and I calculated the maximum depth that produces the most accurate results looping through max depths 1-15. The model trained with a max depth of 8 had an accuracy score of 85%. I made several attemps to resample the data, and I when I did it with the SMOTE sampler it produced an accuracy of 81%, but with a higher accuracy for \"No\" results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Clicks\n",
       "4     1528\n",
       "5     1512\n",
       "6     1266\n",
       "3     1202\n",
       "7      884\n",
       "2      721\n",
       "8      597\n",
       "9      344\n",
       "1      278\n",
       "10     167\n",
       "0       72\n",
       "11      72\n",
       "12      34\n",
       "13       8\n",
       "14       6\n",
       "15       2\n",
       "17       1\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"Clicks\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<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",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
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       "      <th>Clicks</th>\n",
       "      <th>Click Time</th>\n",
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       "      <th>CTR</th>\n",
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       "      <th>8</th>\n",
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       "      <td>Search Engine</td>\n",
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       "      <td>2023-09-07 20:45:56.898459</td>\n",
       "      <td>0.1816</td>\n",
       "      <td>0.0588</td>\n",
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       "    <tr>\n",
       "      <th>18</th>\n",
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       "      <td>Male</td>\n",
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       "      <td>Suburban</td>\n",
       "      <td>Native</td>\n",
       "      <td>Food</td>\n",
       "      <td>Search Engine</td>\n",
       "      <td>2</td>\n",
       "      <td>2024-03-13 20:45:56.898459</td>\n",
       "      <td>0.2154</td>\n",
       "      <td>0.0710</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>9982</th>\n",
       "      <td>36</td>\n",
       "      <td>Male</td>\n",
       "      <td>73173.74</td>\n",
       "      <td>Rural</td>\n",
       "      <td>Banner</td>\n",
       "      <td>Food</td>\n",
       "      <td>Social Media</td>\n",
       "      <td>1</td>\n",
       "      <td>2024-01-09 20:45:56.927349</td>\n",
       "      <td>0.2495</td>\n",
       "      <td>0.0657</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9984</th>\n",
       "      <td>22</td>\n",
       "      <td>Female</td>\n",
       "      <td>43235.45</td>\n",
       "      <td>Rural</td>\n",
       "      <td>Banner</td>\n",
       "      <td>Travel</td>\n",
       "      <td>Search Engine</td>\n",
       "      <td>3</td>\n",
       "      <td>2024-02-19 20:45:56.927349</td>\n",
       "      <td>0.1734</td>\n",
       "      <td>0.0750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9987</th>\n",
       "      <td>35</td>\n",
       "      <td>Male</td>\n",
       "      <td>37732.04</td>\n",
       "      <td>Suburban</td>\n",
       "      <td>Banner</td>\n",
       "      <td>Fashion</td>\n",
       "      <td>Search Engine</td>\n",
       "      <td>3</td>\n",
       "      <td>2023-10-13 20:45:56.927349</td>\n",
       "      <td>0.0417</td>\n",
       "      <td>0.0216</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9997</th>\n",
       "      <td>42</td>\n",
       "      <td>Male</td>\n",
       "      <td>55084.12</td>\n",
       "      <td>Suburban</td>\n",
       "      <td>Text</td>\n",
       "      <td>Finance</td>\n",
       "      <td>Search Engine</td>\n",
       "      <td>3</td>\n",
       "      <td>2023-11-15 20:45:56.927349</td>\n",
       "      <td>0.2354</td>\n",
       "      <td>0.0390</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9999</th>\n",
       "      <td>54</td>\n",
       "      <td>Female</td>\n",
       "      <td>66441.59</td>\n",
       "      <td>Suburban</td>\n",
       "      <td>Banner</td>\n",
       "      <td>Technology</td>\n",
       "      <td>Search Engine</td>\n",
       "      <td>2</td>\n",
       "      <td>2023-11-21 20:45:56.927349</td>\n",
       "      <td>0.0307</td>\n",
       "      <td>0.0323</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2273 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Age  Gender    Income  Location Ad Type    Ad Topic   Ad Placement  \\\n",
       "0      61    Male  35717.43     Urban  Banner      Travel   Social Media   \n",
       "8      33    Male  25917.02     Rural   Video        Food  Search Engine   \n",
       "10     37    Male  23818.88     Rural  Banner        Food  Search Engine   \n",
       "13     36    Male  56217.30     Rural  Native     Fashion  Search Engine   \n",
       "18     39    Male  83984.08  Suburban  Native        Food  Search Engine   \n",
       "...   ...     ...       ...       ...     ...         ...            ...   \n",
       "9982   36    Male  73173.74     Rural  Banner        Food   Social Media   \n",
       "9984   22  Female  43235.45     Rural  Banner      Travel  Search Engine   \n",
       "9987   35    Male  37732.04  Suburban  Banner     Fashion  Search Engine   \n",
       "9997   42    Male  55084.12  Suburban    Text     Finance  Search Engine   \n",
       "9999   54  Female  66441.59  Suburban  Banner  Technology  Search Engine   \n",
       "\n",
       "      Clicks                  Click Time  Conversion Rate     CTR  \n",
       "0          3  2024-01-18 20:45:56.898459           0.0981  0.0737  \n",
       "8          3  2023-10-05 20:45:56.898459           0.0347  0.0488  \n",
       "10         2  2023-11-24 20:45:56.898459           0.2038  0.0530  \n",
       "13         3  2023-09-07 20:45:56.898459           0.1816  0.0588  \n",
       "18         2  2024-03-13 20:45:56.898459           0.2154  0.0710  \n",
       "...      ...                         ...              ...     ...  \n",
       "9982       1  2024-01-09 20:45:56.927349           0.2495  0.0657  \n",
       "9984       3  2024-02-19 20:45:56.927349           0.1734  0.0750  \n",
       "9987       3  2023-10-13 20:45:56.927349           0.0417  0.0216  \n",
       "9997       3  2023-11-15 20:45:56.927349           0.2354  0.0390  \n",
       "9999       2  2023-11-21 20:45:56.927349           0.0307  0.0323  \n",
       "\n",
       "[2273 rows x 11 columns]"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Creating a new DataFrame that only has instances of users interacting with an ad with 0-2 clicks\n",
    "clicks_df = df[df[\"Clicks\"] <= 3]\n",
    "clicks_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
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       "      <td>Social Media</td>\n",
       "    </tr>\n",
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       "      <th>9984</th>\n",
       "      <td>22</td>\n",
       "      <td>Female</td>\n",
       "      <td>43235.45</td>\n",
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       "    </tr>\n",
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       "      <td>35</td>\n",
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       "      <td>Suburban</td>\n",
       "      <td>Banner</td>\n",
       "      <td>Fashion</td>\n",
       "      <td>Search Engine</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9997</th>\n",
       "      <td>42</td>\n",
       "      <td>Male</td>\n",
       "      <td>55084.12</td>\n",
       "      <td>Suburban</td>\n",
       "      <td>Text</td>\n",
       "      <td>Finance</td>\n",
       "      <td>Search Engine</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9999</th>\n",
       "      <td>54</td>\n",
       "      <td>Female</td>\n",
       "      <td>66441.59</td>\n",
       "      <td>Suburban</td>\n",
       "      <td>Banner</td>\n",
       "      <td>Technology</td>\n",
       "      <td>Search Engine</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2273 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Age  Gender    Income  Location Ad Type    Ad Topic   Ad Placement\n",
       "0      61    Male  35717.43     Urban  Banner      Travel   Social Media\n",
       "8      33    Male  25917.02     Rural   Video        Food  Search Engine\n",
       "10     37    Male  23818.88     Rural  Banner        Food  Search Engine\n",
       "13     36    Male  56217.30     Rural  Native     Fashion  Search Engine\n",
       "18     39    Male  83984.08  Suburban  Native        Food  Search Engine\n",
       "...   ...     ...       ...       ...     ...         ...            ...\n",
       "9982   36    Male  73173.74     Rural  Banner        Food   Social Media\n",
       "9984   22  Female  43235.45     Rural  Banner      Travel  Search Engine\n",
       "9987   35    Male  37732.04  Suburban  Banner     Fashion  Search Engine\n",
       "9997   42    Male  55084.12  Suburban    Text     Finance  Search Engine\n",
       "9999   54  Female  66441.59  Suburban  Banner  Technology  Search Engine\n",
       "\n",
       "[2273 rows x 7 columns]"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Creating a DataFrame for the X (features) value\n",
    "clicks_X = clicks_df.drop(columns=[\"Clicks\", \"Click Time\", \"Conversion Rate\",\"CTR\"])\n",
    "clicks_X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       3\n",
       "8       3\n",
       "10      2\n",
       "13      3\n",
       "18      2\n",
       "       ..\n",
       "9982    1\n",
       "9984    3\n",
       "9987    3\n",
       "9997    3\n",
       "9999    2\n",
       "Name: Clicks, Length: 2273, dtype: int64"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Creating DataFrames for the y (target) values\n",
    "clicks_y = clicks_df[\"Clicks\"]\n",
    "clicks_y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Income</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1704.000000</td>\n",
       "      <td>1704.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>37.481808</td>\n",
       "      <td>49902.143644</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>11.800690</td>\n",
       "      <td>19680.688781</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>18.000000</td>\n",
       "      <td>-18644.310000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>29.000000</td>\n",
       "      <td>36549.147500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>36.000000</td>\n",
       "      <td>50049.285000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>45.000000</td>\n",
       "      <td>62443.987500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>86.000000</td>\n",
       "      <td>126635.800000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Age         Income\n",
       "count  1704.000000    1704.000000\n",
       "mean     37.481808   49902.143644\n",
       "std      11.800690   19680.688781\n",
       "min      18.000000  -18644.310000\n",
       "25%      29.000000   36549.147500\n",
       "50%      36.000000   50049.285000\n",
       "75%      45.000000   62443.987500\n",
       "max      86.000000  126635.800000"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Split the data into training and testing sets\n",
    "X_train, X_test, y_train, y_test = train_test_split(clicks_X, clicks_y, random_state=42)\n",
    "X_train.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-0.54943486, -0.5134637 ],\n",
       "       [-0.54943486,  0.60082737],\n",
       "       [ 2.2478328 , -0.47606785],\n",
       "       ...,\n",
       "       [-0.80373192,  2.07492741],\n",
       "       [-0.2951378 ,  1.19398222],\n",
       "       [ 0.63728475,  1.4619585 ]])"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train_scaled = StandardScaler().fit_transform(X_train[[\"Age\", \"Income\"]])\n",
    "X_test_scaled = StandardScaler().fit_transform(X_test[[\"Age\", \"Income\"]])\n",
    "X_train_scaled"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Age</th>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-1.481857</td>\n",
       "      <td>0.612500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.382988</td>\n",
       "      <td>-0.778784</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <th>1699</th>\n",
       "      <td>2.078301</td>\n",
       "      <td>-0.447035</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1700</th>\n",
       "      <td>1.230645</td>\n",
       "      <td>0.701104</td>\n",
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       "    <tr>\n",
       "      <th>1701</th>\n",
       "      <td>-0.803732</td>\n",
       "      <td>2.074927</td>\n",
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       "    <tr>\n",
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       "      <td>-0.295138</td>\n",
       "      <td>1.193982</td>\n",
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       "    <tr>\n",
       "      <th>1703</th>\n",
       "      <td>0.637285</td>\n",
       "      <td>1.461958</td>\n",
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       "  </tbody>\n",
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       "<p>1704 rows × 2 columns</p>\n",
       "</div>"
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       "           Age    Income\n",
       "0    -0.549435 -0.513464\n",
       "1    -0.549435  0.600827\n",
       "2     2.247833 -0.476068\n",
       "3    -1.481857  0.612500\n",
       "4     0.382988 -0.778784\n",
       "...        ...       ...\n",
       "1699  2.078301 -0.447035\n",
       "1700  1.230645  0.701104\n",
       "1701 -0.803732  2.074927\n",
       "1702 -0.295138  1.193982\n",
       "1703  0.637285  1.461958\n",
       "\n",
       "[1704 rows x 2 columns]"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train_scaled = pd.DataFrame(X_train_scaled, columns=[\"Age\", \"Income\"])\n",
    "X_test_scaled = pd.DataFrame(X_test_scaled, columns=[\"Age\", \"Income\"])\n",
    "X_train_scaled"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
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       "      <td>47.0</td>\n",
       "      <td>48621.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1703</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>68388.40</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1704 rows × 19 columns</p>\n",
       "</div>"
      ],
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       "      x0_Female  x0_Male  x0_Other  x0_Banner  x0_Native  x0_Text  x0_Video  \\\n",
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       "1702        0.0      1.0       0.0        0.0        0.0      1.0       0.0   \n",
       "1703        1.0      0.0       0.0        0.0        1.0      0.0       0.0   \n",
       "\n",
       "      x0_Fashion  x0_Finance  x0_Food  x0_Health  x0_Technology  x0_Travel  \\\n",
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       "3            0.0         0.0      1.0        0.0            0.0        0.0   \n",
       "4            0.0         1.0      0.0        0.0            0.0        0.0   \n",
       "...          ...         ...      ...        ...            ...        ...   \n",
       "1699         0.0         0.0      0.0        0.0            0.0        1.0   \n",
       "1700         0.0         0.0      0.0        1.0            0.0        0.0   \n",
       "1701         0.0         0.0      0.0        0.0            0.0        1.0   \n",
       "1702         0.0         0.0      0.0        0.0            1.0        0.0   \n",
       "1703         0.0         0.0      0.0        0.0            1.0        0.0   \n",
       "\n",
       "      x0_Search Engine  x0_Social Media  x0_Website  Location   Age     Income  \n",
       "0                  0.0              0.0         1.0       1.0  61.0   35717.43  \n",
       "1                  0.0              0.0         1.0       0.0  41.0   47453.25  \n",
       "2                  0.0              1.0         0.0       0.0  49.0   68126.35  \n",
       "3                  0.0              0.0         1.0       2.0  68.0   64585.73  \n",
       "4                  0.0              0.0         1.0       1.0  63.0   21109.40  \n",
       "...                ...              ...         ...       ...   ...        ...  \n",
       "1699               1.0              0.0         0.0       0.0  44.0  111757.88  \n",
       "1700               1.0              0.0         0.0       1.0   NaN        NaN  \n",
       "1701               0.0              1.0         0.0       1.0  27.0   86915.35  \n",
       "1702               0.0              0.0         1.0       2.0  47.0   48621.82  \n",
       "1703               0.0              0.0         1.0       2.0  24.0   68388.40  \n",
       "\n",
       "[1704 rows x 19 columns]"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Encode the X data with the X_preprocess function\n",
    "X_train_encoded = X_preprocess(X_train)\n",
    "X_test_encoded = X_preprocess(X_test)\n",
    "X_train_encoded"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr>\n",
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       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.637285</td>\n",
       "      <td>1.461958</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1704 rows × 19 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      x0_Female  x0_Male  x0_Other  x0_Banner  x0_Native  x0_Text  x0_Video  \\\n",
       "0           0.0      0.0       1.0        0.0        0.0      1.0       0.0   \n",
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       "\n",
       "      x0_Fashion  x0_Finance  x0_Food  x0_Health  x0_Technology  x0_Travel  \\\n",
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       "      x0_Search Engine  x0_Social Media  x0_Website  Location       Age  \\\n",
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       "1                  0.0              0.0         1.0       0.0 -0.549435   \n",
       "2                  0.0              1.0         0.0       0.0  2.247833   \n",
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       "...                ...              ...         ...       ...       ...   \n",
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       "1703               0.0              0.0         1.0       2.0  0.637285   \n",
       "\n",
       "        Income  \n",
       "0    -0.513464  \n",
       "1     0.600827  \n",
       "2    -0.476068  \n",
       "3     0.612500  \n",
       "4    -0.778784  \n",
       "...        ...  \n",
       "1699 -0.447035  \n",
       "1700  0.701104  \n",
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       "1703  1.461958  \n",
       "\n",
       "[1704 rows x 19 columns]"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train_encoded[\"Age\"] = X_train_scaled[\"Age\"]\n",
    "X_train_encoded[\"Income\"] = X_train_scaled[\"Income\"]\n",
    "X_test_encoded[\"Age\"] = X_test_scaled[\"Age\"]\n",
    "X_test_encoded[\"Income\"] = X_test_scaled[\"Income\"]\n",
    "X_train_encoded"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Clicks\n",
       "Yes    1439\n",
       "No      265\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Encoding the clicks column using a custom function\n",
    "def encode_clicks(clicks):\n",
    "    if clicks >= 2:\n",
    "        return \"Yes\"\n",
    "    else:\n",
    "        return \"No\"\n",
    "# Applying the function to y_train and y_test\n",
    "y_train_encoded = y_train.apply(encode_clicks)\n",
    "y_test_encoded = y_test.apply(encode_clicks)\n",
    "y_train_encoded.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "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>train</th>\n",
       "      <th>test</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>depth</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.844484</td>\n",
       "      <td>0.850615</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.844484</td>\n",
       "      <td>0.850615</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.844484</td>\n",
       "      <td>0.850615</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.844484</td>\n",
       "      <td>0.850615</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.844484</td>\n",
       "      <td>0.850615</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          train      test\n",
       "depth                    \n",
       "1      0.844484  0.850615\n",
       "2      0.844484  0.850615\n",
       "3      0.844484  0.850615\n",
       "4      0.844484  0.850615\n",
       "5      0.844484  0.850615"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a loop to vary the max_depth parameter\n",
    "# Make sure to record the train and test scores \n",
    "# for each pass.\n",
    "\n",
    "# Depths should span from 1 up to 15 in steps of 1\n",
    "depths = range(1, 15)\n",
    "\n",
    "# The scores dataframe will hold depths and scores\n",
    "# to make plotting easy\n",
    "scores = {\"train\":[], \"test\":[], \"depth\":[]}\n",
    "\n",
    "# Loop through each depth\n",
    "for depth in depths:\n",
    "    clf = RandomForestClassifier(max_depth=depth)\n",
    "    clf.fit(X_train_encoded, y_train_encoded)\n",
    "\n",
    "    train_score = clf.score(X_train_encoded, y_train_encoded)\n",
    "    test_score = clf.score(X_test_encoded, y_test_encoded)\n",
    "\n",
    "    scores[\"depth\"].append(depth)\n",
    "    scores[\"train\"].append(train_score)\n",
    "    scores[\"test\"].append(test_score)\n",
    "\n",
    "# Create a dataframe from the scores dictionary and\n",
    "# set the index to depth\n",
    "scores_df = pd.DataFrame(scores).set_index(\"depth\")\n",
    "scores_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='depth'>"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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lPLV0M3YD7o9qxZAeJj0nrxY0iJluXV1d8fb25vjx47i5ueHi0iB62lVjGAYFBQVkZWUREBDgKIAiImIewzCY8tlWjuYWEtbUmxeGdDU7Uo1qEIXFYrEQHBzMwYMHOXz4sNlx6qyAgAA96VlExEl8vOEICdsycXWx8ObwnjTyqN8f6fX76H7F3d2dDh066LRQNbm5uWlkRUTESRw4fpoXvtwBwITYjvQIDTA3UC1oMIUFwMXFRTO0iohInVZcamfcks0UFNvo27Ypj9/QzuxItUIXc4iIiNQhcxP3sC09lwBvN+YN64HVxWJ2pFqhwiIiIlJHrN6bzbsrDwDw6r3dCfb3MjlR7VFhERERqQNOnClmwr82A/BQn9bcHtmwboJQYREREXFyhmHwzLKtZOUX0a6ZD9Pv7GJ2pFqnwiIiIuLk/rEule92HcPd6sJfRvTEy73h3bWpwiIiIuLEUo7l8/JXOwF45vZOdA3xNzmROVRYREREnFRhiY0nP9pEUamdGzo24w8x4WZHMo0Ki4iIiJOa9c1udmfm09THndce6I5LA7mFuTIqLCIiIk5oZcpx3v/pEACvPdCD5r4Ne+JTFRYREREnU2qzM+Pc1Pu/79uGAZ2bm5zIfCosIiIiTuZfG46w//gZmvi4M3FgJ7PjOIVqFZb58+cTHh6Op6cnUVFRrFq16pLbv/POO0RERODl5UWnTp348MMPK2xz6tQpxo4dS3BwMJ6enkRERJCQkFCdeCIiInXWmaJSXv8uBYAnb26Pn6ebyYmcQ5Uffrh06VLGjx/P/PnziYmJ4d1332XQoEHs3LmT1q1bV9g+Pj6eKVOm8N5773HttdeSlJTEY489RuPGjRk8eDAAxcXF3HbbbTRv3pxly5bRqlUr0tLS8PX1vfIjFBERqUMWrjrI8fwi2jT15qE+bcyO4zQshmEYVdmhT58+9OrVi/j4eMe6iIgIhg4dSlxcXIXto6OjiYmJYc6cOY5148ePZ8OGDaxevRqAv/71r8yZM4fdu3fj5la9JpmXl4e/vz+5ubn4+flV6zVERETMdDy/iBvnrKCg2MbbD/Xkru4hZkeqcZf7+V2lU0LFxcUkJycTGxtbbn1sbCxr1qypdJ+ioiI8Pctf2ezl5UVSUhIlJSUAfPHFF/Tt25exY8cSFBREZGQkM2fOxGazXTRLUVEReXl55RYREZG67M3vUygottGjlT93dgs2O45TqVJhyc7OxmazERQUVG59UFAQmZmZle4zcOBAFi5cSHJyMoZhsGHDBhYtWkRJSQnZ2dkAHDhwgGXLlmGz2UhISGDatGnMnTuXV1555aJZ4uLi8Pf3dyyhoaFVORQRERGnsv/4aT5KSgNgyh0RWCwNd86VylTrotsL30TDMC76xk6fPp1BgwZx/fXX4+bmxt13383o0aMBsFrLnoVgt9tp3rw5CxYsICoqiuHDhzN16tRyp50uNGXKFHJzcx1LWlpadQ5FRETEKcz+Zjc2u8GtEc25vm1Ts+M4nSoVlsDAQKxWa4XRlKysrAqjLud5eXmxaNEiCgoKOHToEKmpqYSFheHr60tgYCAAwcHBdOzY0VFgoOy6mMzMTIqLiyt9XQ8PD/z8/MotIiIiddGGQyf4dscxXCww+fbOZsdxSlUqLO7u7kRFRZGYmFhufWJiItHR0Zfc183NjVatWmG1WlmyZAl33XUXLi5lPz4mJoZ9+/Zht9sd26ekpBAcHIy7u3tVIoqIiNQphmEwM2EXAMOuDaVDkO6QrUyVTwlNmDCBhQsXsmjRInbt2sVTTz1FamoqTzzxBFB2qmbUqFGO7VNSUvjHP/7B3r17SUpKYvjw4Wzfvp2ZM2c6tvnjH/9ITk4O48aNIyUlhf/85z/MnDmTsWPHXoVDFBERcV7f7jjGxtRTeLlZGX9rR7PjOK0qz8MybNgwcnJymDFjBhkZGURGRpKQkECbNmX3imdkZJCamurY3mazMXfuXPbs2YObmxsDBgxgzZo1hIWFObYJDQ1l+fLlPPXUU3Tv3p2WLVsybtw4Jk+efOVHKCIi4qRKbHZmf7MbgMf6hxPk17CfF3QpVZ6HxVlpHhYREalr/u/nw0z/fDtNfdxZ+cwAGnlUeRyhzquReVhERETk6jhdVMqb56bgH3drhwZZVqpChUVERMQEC348QPbpYsIDfRhxXcVH20h5KiwiIiK1LCuvkPd+PADAMwM74WbVx/Fv0TskIiJSy17/bi9nS2z0bB3A7ZEtzI5TJ6iwiIiI1KJ9WfksXV92N+2fNQX/ZVNhERERqUWvfr0HuwGxXYK4NqyJ2XHqDBUWERGRWrLuQA7f7TqG1cXCM5qCv0pUWERERGqBYRjM/Lpskrjh14bSvnkjkxPVLSosIiIitSBhWyZb0k7h7W5l3K0dzI5T56iwiIiI1LDiUjtzvi0bXfmfG9rS3FdT8FeVCouIiEgN+ygplUM5BQQ28uCx/m3NjlMnqbCIiIjUoPzCEt78fi8A42/tgI+m4K8WFRYREZEa9O7KA5w4U0zbZj4MuzbU7Dh1lgqLiIhIDcnMLWTh6rIp+Cff3llT8F8BvXMiIiI15PXEFApL7PRu05jYLkFmx6nTVFhERERqwJ7MfD5OTgNgiqbgv2IqLCIiIjVg1je7sRswKLIFUW0amx2nzlNhERERucrW7M/mv7uzcHWxMGlgJ7Pj1AsqLCIiIleR3W7w6rkp+B/q05q2zTQF/9WgwiIiInIVfbUtg61HcvFxt/LkLZqC/2pRYREREblKikptjin4H7+xHYGNPExOVH+osIiIiFwli39OJe3EWZr5evBo/3Cz49QrKiwiIiJXQe7ZEt76b9kU/BNu64i3u6bgv5pUWERERK6Cv67cz8mCEto3b8QDUa3MjlPvqLCIiIhcoaOnzrJo9UEAnr29M66agv+q0zsqIiJyheYlplBUaue68CbcEtHc7Dj1kgqLiIjIFdiVkccnG48A8GdNwV9jVFhERESuwKtf78Yw4M7uwVwTGmB2nHpLhUVERKSaVu/NZmXKcdysFp7RFPw1SoVFRESkGux2g7ivdwHwuz5taNPUx+RE9ZsKi4iISDV8seUoO47m0cjDlT/d3N7sOPWeCouIiEgVlU3BvweAP97Ujqaagr/GqbCIiIhU0f+tPUz6qbME+XnwhxhNwV8bVFhERESqILeghLf+uw+Aibd1wsvdanKihkGFRUREpArm/7CP3LMldAxqxH2agr/WqLCIiIhcpiMnC3h/zSEApgyKwOqiSeJqS7UKy/z58wkPD8fT05OoqChWrVp1ye3feecdIiIi8PLyolOnTnz44YcX3XbJkiVYLBaGDh1anWgiIiI1Zt7yFIpL7fRt25SbOjUzO06DUuVnXy9dupTx48czf/58YmJiePfddxk0aBA7d+6kdevWFbaPj49nypQpvPfee1x77bUkJSXx2GOP0bhxYwYPHlxu28OHD/P000/Tv3//6h+RiIhIDdienstnm9MBmHJHZ03BX8sshmEYVdmhT58+9OrVi/j4eMe6iIgIhg4dSlxcXIXto6OjiYmJYc6cOY5148ePZ8OGDaxevdqxzmazceONNzJmzBhWrVrFqVOn+Pzzzy87V15eHv7+/uTm5uLn51eVQxIREflNI/+2jlV7sxnSI4S/jOhpdpx643I/v6t0Sqi4uJjk5GRiY2PLrY+NjWXNmjWV7lNUVISnp2e5dV5eXiQlJVFSUuJYN2PGDJo1a8YjjzxyWVmKiorIy8srt4iIiNSEH1OOs2pvNm5WC5M0Bb8pqlRYsrOzsdlsBAUFlVsfFBREZmZmpfsMHDiQhQsXkpycjGEYbNiwgUWLFlFSUkJ2djYAP/30E3/729947733LjtLXFwc/v7+jiU0NLQqhyIiInJZbHaDuK93AzDy+jBCm3ibnKhhqtZFtxeetzMM46Ln8qZPn86gQYO4/vrrcXNz4+6772b06NEAWK1W8vPzefjhh3nvvfcIDAy87AxTpkwhNzfXsaSlpVXnUERERC7p803p7MrIw9dTU/CbqUoX3QYGBmK1WiuMpmRlZVUYdTnPy8uLRYsW8e6773Ls2DGCg4NZsGABvr6+BAYGsnXrVg4dOlTuAly73V4WztWVPXv20K5duwqv6+HhgYeHpkIWEZGaU1hiY+7ysin4//em9jT2cTc5UcNVpREWd3d3oqKiSExMLLc+MTGR6OjoS+7r5uZGq1atsFqtLFmyhLvuugsXFxc6d+7Mtm3b2Lx5s2MZMmQIAwYMYPPmzTrVIyIipvlgzSGO5hYS7O/JmJgws+M0aFW+rXnChAmMHDmS3r1707dvXxYsWEBqaipPPPEEUHaqJj093THXSkpKCklJSfTp04eTJ08yb948tm/fzgcffACAp6cnkZGR5X5GQEAAQIX1IiIiteXkmWLeXnFuCv7YTni6aQp+M1W5sAwbNoycnBxmzJhBRkYGkZGRJCQk0KZNGwAyMjJITU11bG+z2Zg7dy579uzBzc2NAQMGsGbNGsLCwq7aQYiIiFxt76zYR35hKZ1b+HJPz5Zmx2nwqjwPi7PSPCwiInK1pJ0o4Ja5Kym22fngD9dxY0fNaltTamQeFhERkYbgteV7KLbZ6dc+kBs6XP4drFJzVFhERER+ZduRXP69+SgAzw7SFPzOQoVFRETkHMMwmJmwC4B7erYksqW/yYnkPBUWERGRc77dkcnaAzm4W12YcFtHs+PIr6iwiIiIALkFJUz/9w4AHrshXFPwOxkVFhEREWBmwi6O5xfRtpkPf7q5g9lx5AIqLCIi0uCt2ZfN0g1lz6R79d7umiTOCamwiIhIg3a22Mazn24D4OHrW3NdeBOTE0llVFhERKRBe/27FFJPFBDs78nk2zubHUcuQoVFREQarK1HTrFw1QEAXh4aia+nm8mJ5GJUWEREpEEqsdl5ZtlW7AYM7hHCLRFBZkeSS1BhERGRBmnBjwfYnZlPgLcbzw/uYnYc+Q0qLCIi0uDsP36aN7/fC8Bzd3UhsJGHyYnkt6iwiIhIg2K3Gzz7yVaKS+3c0LEZ9/RsaXYkuQwqLCIi0qAsTkpl/aGTeLtbmXlPpB5uWEeosIiISIORkXuWWV/vBmDSwE60aqzp9+sKFRYREWkQDMNg2mfbOV1USs/WAYzqG2Z2JKkCFRYREWkQvtyawfe7s3CzWph1X3esLjoVVJeosIiISL138kwxL35R9iTmsQPa0zHI1+REUlUqLCIiUu+99NVOcs4U0zGoEf97U3uz40g1qLCIiEi99sOeLD7dlI7FAq/e1x13V3301UX6ryYiIvXWmaJSpn62HYDR0WH0at3Y5ERSXSosIiJSb835dg/pp87SMsCLp2M7mR1HroAKi4iI1EvJh0/ywdpDAMTd2w0fD1dzA8kVUWEREZF6p6jUxrOfbMUw4N5eLbmhYzOzI8kVUmEREZF6Z/6K/ezNOk1TH3em36knMdcHKiwiIlKvpBzLZ/4P+wB4YUhXGvu4m5xIrgYVFhERqTdsdoNnlm2lxGZwa0Rz7uoebHYkuUpUWEREpN74YM0hNqedopGHKy8N1ZOY6xMVFhERqRfSThQw59s9ADw7qDPB/l4mJ5KrSYVFRETqPMMw+PNn2zhbYuO68CY8dF1rsyPJVabCIiIidd6nG9NZtTcbd1cXXr23Gy56EnO9o8IiIiJ1WvbpIl76z04Axt3SgbbNGpmcSGqCCouIiNRpL3yxg1MFJXQJ9uN/bmhrdhypISosIiJSZ3238xhfbc3AxQKz7uuOm1Ufa/WV/suKiEidlFdYwrTPy57E/Fj/tnRr5W9yIqlJ1Sos8+fPJzw8HE9PT6Kioli1atUlt3/nnXeIiIjAy8uLTp068eGHH5b7/nvvvUf//v1p3LgxjRs35tZbbyUpKak60UREpIGY9fVuMvMKadPUm/G3djQ7jtSwKheWpUuXMn78eKZOncqmTZvo378/gwYNIjU1tdLt4+PjmTJlCi+88AI7duzgxRdfZOzYsXz55ZeObX744QdGjBjBihUrWLt2La1btyY2Npb09PTqH5mIiNRb6w7ksHhd2edO3L3d8HK3mpxIaprFMAyjKjv06dOHXr16ER8f71gXERHB0KFDiYuLq7B9dHQ0MTExzJkzx7Fu/PjxbNiwgdWrV1f6M2w2G40bN+btt99m1KhRl5UrLy8Pf39/cnNz8fPzq8ohiYhIHVJYYuOON1dxIPsMw68N5dX7upsdSa7A5X5+V2mEpbi4mOTkZGJjY8utj42NZc2aNZXuU1RUhKenZ7l1Xl5eJCUlUVJSUuk+BQUFlJSU0KRJk4tmKSoqIi8vr9wiIiL131++38uB7DM09/Vgyh0RZseRWlKlwpKdnY3NZiMoKKjc+qCgIDIzMyvdZ+DAgSxcuJDk5GQMw2DDhg0sWrSIkpISsrOzK93n2WefpWXLltx6660XzRIXF4e/v79jCQ0NrcqhiIhIHbTjaC7v/ngAgBl3R+Lv5WZyIqkt1bro9sKHSRmGcdEHTE2fPp1BgwZx/fXX4+bmxt13383o0aMBsFornnOcPXs2H330EZ9++mmFkZlfmzJlCrm5uY4lLS2tOociIiJ1RKnNzuRPtmKzGwyKbMHtkS3MjiS1qEqFJTAwEKvVWmE0JSsrq8Koy3leXl4sWrSIgoICDh06RGpqKmFhYfj6+hIYGFhu29dee42ZM2eyfPlyune/9DlJDw8P/Pz8yi0iIlJ//W31Qban5+Hn6cqLd3c1O47UsioVFnd3d6KiokhMTCy3PjExkejo6Evu6+bmRqtWrbBarSxZsoS77roLF5dffvycOXN46aWX+Oabb+jdu3dVYomISD13KPsM8xJTAJh2Zxea+158BF7qJ9eq7jBhwgRGjhxJ79696du3LwsWLCA1NZUnnngCKDtVk56e7phrJSUlhaSkJPr06cPJkyeZN28e27dv54MPPnC85uzZs5k+fTr//Oc/CQsLc4zgNGrUiEaN9EwIEZGGzDAMpny6jaJSOzHtm/JA71ZmRxITVLmwDBs2jJycHGbMmEFGRgaRkZEkJCTQpk0bADIyMsrNyWKz2Zg7dy579uzBzc2NAQMGsGbNGsLCwhzbzJ8/n+LiYu6///5yP+v555/nhRdeqN6RiYhIvbB0fRprD+Tg6eZC3D3dL3rNpNRvVZ6HxVlpHhYRkfrnWF4ht85bSX5hKdPujODR/nq4YX1TI/OwiIiI1Kbn/r2d/MJSerTyZ0xMuNlxxEQqLCIi4pS+3pbBtzuO4epi4dX7umN10amghkyFRUREnE5uQQnPfbEDgD/e1I6IYJ3qb+hUWERExOm8krCT4/lFtGvmw/+7ub3ZccQJqLCIiIhT+WlfNv/acASLBWbd1x0PVz2JWVRYRETEiZwttjHl020AjLy+Db3DLv4QXGlYVFhERMRpzEvcQ+qJAkL8PXnm9s5mxxEnosIiIiJOYUvaKf62+iAAr9zTjUYeVZ7bVOoxFRYRETFdybknMdsNuPuaEAZ0bm52JHEyKiwiImK6d1fuZ3dmPo293Xjuri5mxxEnpMIiIiKmyswt5K3/7gPg+cFdadrIw+RE4oxUWERExFRvfp9CUamd3m0ac/c1IWbHESelwiIiIqbZf/w0/9pwBIBnB3XWk5jlolRYRETENK99uweb3eDWiOaac0UuSYVFRERMsTntFF9vz8RigUkDNeeKXJoKi4iI1DrDMJj19W4A7u3Zik4tfE1OJM5OhUVERGrdj3uzWXsgB3erC0/d1sHsOFIHqLCIiEitstt/GV0Z2bcNrRp7m5xI6gIVFhERqVVfbj3Kzow8fD1cGTugvdlxpI5QYRERkVpTXGpn7vIUAP7nhrY08XE3OZHUFSosIiJSa5asTyX1RAGBjTx4pH+42XGkDlFhERGRWnGmqJS/fF82Bf+4W9rj7a6nMcvlU2EREZFasWj1QbJPF9G6iTfDrm1tdhypY1RYRESkxp04U8y7Px4AYGJsR9xd9fEjVaPfGBERqXHvrNjH6aJSuob4Mbi7HnAoVafCIiIiNerIyQL+b+1hAJ65vTMuLnrAoVSdCouIiNSo1xP3Umyz07dtU27oEGh2HKmjVFhERKTG7MnM59NNRwCYPKgzFotGV6R6VFhERKTGzPl2N4YBgyJbcE1ogNlxpA5TYRERkRqx/tAJvtuVhdXFwtMDO5kdR+o4FRYREbnqDOOXBxw+2LsV7Zo1MjmR1HUqLCIictV9vyuLDYdP4uHqwrhbOpodR+oBFRYREbmqbHaD2d+Wja6MiQmnhb+nyYmkPlBhERGRq+qzTemkHDuNn6crf7yxndlxpJ5QYRERkaumsMTG64kpAPzvgPb4e7uZnEjqCxUWERG5ahavSyX91FmC/DwYHR1mdhypR6pVWObPn094eDienp5ERUWxatWqS27/zjvvEBERgZeXF506deLDDz+ssM0nn3xCly5d8PDwoEuXLnz22WfViSYiIibJLyzhnRX7ABh/a0c83awmJ5L6pMqFZenSpYwfP56pU6eyadMm+vfvz6BBg0hNTa10+/j4eKZMmcILL7zAjh07ePHFFxk7dixffvmlY5u1a9cybNgwRo4cyZYtWxg5ciQPPvgg69atq/6RiYhIrXrvxwOcOFNM22Y+PBDVyuw4Us9YDMMwqrJDnz596NWrF/Hx8Y51ERERDB06lLi4uArbR0dHExMTw5w5cxzrxo8fz4YNG1i9ejUAw4YNIy8vj6+//tqxze23307jxo356KOPLitXXl4e/v7+5Obm4ufnV5VDEhGRK3Q8v4gb56ygoNhG/O96MahbsNmRpI643M/vKo2wFBcXk5ycTGxsbLn1sbGxrFmzptJ9ioqK8PQsf0ubl5cXSUlJlJSUAGUjLBe+5sCBAy/6mudfNy8vr9wiIiLmeOu/eykottEjNIDbI1uYHUfqoSoVluzsbGw2G0FBQeXWBwUFkZmZWek+AwcOZOHChSQnJ2MYBhs2bGDRokWUlJSQnZ0NQGZmZpVeEyAuLg5/f3/HEhoaWpVDERGRq+Rwzhn+ua7ssoDJt3fSAw6lRlTrotsLfxkNw7joL+j06dMZNGgQ119/PW5ubtx9992MHj0aAKv1lwuyqvKaAFOmTCE3N9expKWlVedQRETkCs1dnkKp3eCGjs2Ibhdodhypp6pUWAIDA7FarRVGPrKysiqMkJzn5eXFokWLKCgo4NChQ6SmphIWFoavry+BgWW/2C1atKjSawJ4eHjg5+dXbhERkdq1PT2XL7YcBeAZPeBQalCVCou7uztRUVEkJiaWW5+YmEh0dPQl93Vzc6NVq1ZYrVaWLFnCXXfdhYtL2Y/v27dvhddcvnz5b76miIiYa/a3ewAY0iOEyJb+JqeR+sy1qjtMmDCBkSNH0rt3b/r27cuCBQtITU3liSeeAMpO1aSnpzvmWklJSSEpKYk+ffpw8uRJ5s2bx/bt2/nggw8crzlu3DhuuOEGZs2axd13382///1vvvvuO8ddRCIi4nzW7M/mx5TjuLpYmBirBxxKzapyYRk2bBg5OTnMmDGDjIwMIiMjSUhIoE2bNgBkZGSUm5PFZrMxd+5c9uzZg5ubGwMGDGDNmjWEhYU5tomOjmbJkiVMmzaN6dOn065dO5YuXUqfPn2u/AhFROSqMwyDWd+Uja481Kc1bZr6mJxI6rsqz8PirDQPi4hI7fl6WwZ/XLwRb3crKycNoJmvh9mRpI6qkXlYRERESm125iwvG115tF+4yorUChUWERGpko+Tj3Dg+Bkae7vx2A1tzY4jDYQKi4iIXLazxTbe+C4FgLED2uPr6WZyImkoVFhEROSyfbD2EMfyimgZ4MXD17cxO440ICosIiJyWXILSpi/Yh8AT93WEU8362/sIXL1qLCIiMhliV+5n7zCUjoF+XJPz5Zmx5EGRoVFRER+U2ZuIe//dBCASQM7YXXRAw6ldqmwiIjIb3rz+xSKSu30btOYWyKamx1HGiAVFhERuaT9x0/zrw1HAHh2UGcsFo2uSO1TYRERkUt67ds92OwGt0Y0p3dYE7PjSAOlwiIiIhe1Oe0UX2/PxGKBSQM7mx1HGjAVFhERqZRhGMz6ejcA9/ZsRacWviYnkoZMhUVERCr1495s1h7Iwd3qwlO3dTA7jjRwKiwiIlKB3f7L6MrIvm1o1djb5ETS0KmwiIhIBV9uPcrOjDx8PVwZO6C92XFEVFhERKS84lI7c5eXPeDwf25oSxMfd5MTiaiwiIjIBZasTyX1RAGBjTz4Q79ws+OIACosIiLyK2eKSvnL92UPOHzylvb4eLianEikjAqLiIg4LFp9kOzTRbRu4s3wa1ubHUfEQYVFREQAOHGmmHd/PADAxNiOuLvqI0Kch34bRUQEgHdW7ON0USldQ/wY3D3E7Dgi5aiwiIgIR04W8H9rDwPwzO2dcXHRAw7FuaiwiIgIryfupdhmp2/bptzQIdDsOCIVqLCIiDRwezLz+XTTEQAmD+qMxaLRFXE+KiwiIg3cnG93YxgwKLIF14QGmB1HpFIqLCIiDdj6Qyf4blcWVhcLTw/sZHYckYtSYRERaaAM45cHHD7YuxXtmjUyOZHIxamwiIg0UN/vymLD4ZN4uLow7paOZscRuSQVFhGRBiivsITnv9gBwJiYcFr4e5qcSOTSVFhERBqgl7/aSfqps7Ru4s2fbm5vdhyR36TCIiLSwHy/6xj/2nAEiwVee6CHHnAodYIKi4hIA3LyTDHPfroNgEf7hXNdeBOTE4lcHhUWEZEG5LkvdnA8v4h2zXyYGKvbmKXuUGEREWkgvtp6lC+3HMXqYmHug9fg6WY1O5LIZVNhERFpALLyC5n++XYA/vemdprRVuocFRYRkXrOMAz+/Ol2ThaUEBHsx59u7mB2JJEqq1ZhmT9/PuHh4Xh6ehIVFcWqVasuuf3ixYvp0aMH3t7eBAcHM2bMGHJycspt88Ybb9CpUye8vLwIDQ3lqaeeorCwsDrxRETkVz7ZmM53u47hZrUw78EeuLvq/1Wl7qnyb+3SpUsZP348U6dOZdOmTfTv359BgwaRmppa6farV69m1KhRPPLII+zYsYOPP/6Y9evX8+ijjzq2Wbx4Mc8++yzPP/88u3bt4m9/+xtLly5lypQp1T8yERHh6KmzvPhl2QRx42/tSESwn8mJRKqnyoVl3rx5PPLIIzz66KNERETwxhtvEBoaSnx8fKXb//zzz4SFhfHkk08SHh5Ov379ePzxx9mwYYNjm7Vr1xITE8NDDz1EWFgYsbGxjBgxotw2IiJSNYZhMPmTreQXlnJNaACP39DW7Egi1ValwlJcXExycjKxsbHl1sfGxrJmzZpK94mOjubIkSMkJCRgGAbHjh1j2bJl3HnnnY5t+vXrR3JyMklJSQAcOHCAhISEcttcqKioiLy8vHKLiIj8YvG6VFbtzcbD1YW5D/bA1apTQVJ3VWl6w+zsbGw2G0FBQeXWBwUFkZmZWek+0dHRLF68mGHDhlFYWEhpaSlDhgzhrbfecmwzfPhwjh8/Tr9+/TAMg9LSUv74xz/y7LPPXjRLXFwcL774YlXii4g0GIdzzjAzYRcAk2/vrCcxS51XrbptsVjKfW0YRoV15+3cuZMnn3yS5557juTkZL755hsOHjzIE0884djmhx9+4JVXXmH+/Pls3LiRTz/9lK+++oqXXnrpohmmTJlCbm6uY0lLS6vOoYiI1Ds2u8Gkj7dSUGyjT3gTRkeHmR1J5IpVaYQlMDAQq9VaYTQlKyurwqjLeXFxccTExDBp0iQAunfvjo+PD/379+fll18mODiY6dOnM3LkSMeFuN26dePMmTP8z//8D1OnTsXFpWKv8vDwwMPDoyrxRUQahPd/OkjSoRP4uFt57YEeuLhU/j+UInVJlUZY3N3diYqKIjExsdz6xMREoqOjK92noKCgQuGwWstmVzQM45LbGIbh2EZERH7bvqx8Zn+7B4Bpd3UhtIm3yYlEro4qP6JzwoQJjBw5kt69e9O3b18WLFhAamqq4xTPlClTSE9P58MPPwRg8ODBPPbYY8THxzNw4EAyMjIYP3481113HSEhIY5t5s2bR8+ePenTpw/79u1j+vTpDBkyxFFuRETk0kptdib8awvFpXZu7NiM4deGmh1J5KqpcmEZNmwYOTk5zJgxg4yMDCIjI0lISKBNmzYAZGRklJuTZfTo0eTn5/P2228zceJEAgICuPnmm5k1a5Zjm2nTpmGxWJg2bRrp6ek0a9aMwYMH88orr1yFQxQRaRjif9jP1iO5+Hm6Muu+7he9tlCkLrIY9eScS15eHv7+/uTm5uLnp4mRRKRh2XE0l7vf/olSu8Hrw3pwT89WZkcSuSyX+/mtm/JFROq4olIbE/+1hVK7wcCuQQy9pqXZkUSuOhUWEZE67s3v9rI7M58mPu68ck83nQqSekmFRUSkDtuYepK/rtwPwMx7IglspOkepH5SYRERqaPOFtt4+l9bsBsw9JoQbo8MNjuSSI1RYRERqaPmfLuHA9lnCPLz4MUhkWbHEalRKiwiInXQ2v05LPrpIACv3tcdf283kxOJ1CwVFhGROuZ0USmTlm0BYMR1oQzo1NzkRCI1T4VFRKSOeeU/uzhy8iytGnsx9c4uZscRqRUqLCIidcgPe7L4KKlsNvE59/egkUeVJywXqZNUWERE6ojcghImf7IVgDExYfRt19TkRCK1R4VFRKSOeOHLHRzLK6JtoA/PDOxsdhyRWqXCIiJSB3yzPYPPNqXjYoHXHuyBl7ueZC8NiwqLiIiTyz5dxNTPtgPw+I3t6NW6scmJRGqfCouIiBMzDINpn20n50wxnYJ8GX9rB7MjiZhChUVExIn9e/NRvtmRiauLhbkP9sDDVaeCpGFSYRERcVKZuYU89++yU0FP3tKByJb+JicSMY8Ki4iIEzIMg8mfbCWvsJTurfz5403tzI4kYioVFhERJ7R0fRorU47j7urC3Ad64GbVX9fSsOlPgIiIk0k7UcBLX+0EYFJsJzoE+ZqcSMR8KiwiIk7EbjeYtGwLZ4ptXBvWmD/0Czc7kohTUGEREXEiH6w9xM8HTuDlZuW1B3pgdbGYHUnEKaiwiIg4if3HT/Pq17sB+POdEbRp6mNyIhHnocIiIuIESm12nv54C0Wldvp3COThPq3NjiTiVFRYREScwIJVB9iUegpfD1dm3dcdi0WngkR+TYVFRMRkuzPzeD0xBYDnBnchJMDL5EQizkeFRUTERMWldiYs3UKJzeDWiObcH9XK7EgiTkmFRUTERG//dy87M/II8HZj5r3ddCpI5CJUWERETLIl7RTv/LAfgJeHRtLc19PkRCLOS4VFRMQEhSU2Jn68BZvd4K7uwdzVPcTsSCJOTYVFRMQEc5fvYV/WaQIbefDS3ZFmxxFxeiosIiK1bP2hEyxcfRCAV+/tRmMfd5MTiTg/FRYRkVqUlV/I0x9vwTDggahW3NolyOxIInWCq9kBREQaAsMw+GLLUZ7/YgenCkoI8fdk+uAuZscSqTNUWEREaljO6SKm/3s7CdsyAega4sebw3vi5+lmcjKRukOFRUSkBn2zPZOpn20j50wxri4W/t/N7Rk7oD1uVp2RF6kKFRYRkRpwqqCYF77YweebjwLQKciXuQ/2ILKlv8nJROqmalX8+fPnEx4ejqenJ1FRUaxateqS2y9evJgePXrg7e1NcHAwY8aMIScnp9w2p06dYuzYsQQHB+Pp6UlERAQJCQnViSciYqoVu7OIff1HPt98FBcL/O9N7fjiTzEqKyJXoMqFZenSpYwfP56pU6eyadMm+vfvz6BBg0hNTa10+9WrVzNq1CgeeeQRduzYwccff8z69et59NFHHdsUFxdz2223cejQIZYtW8aePXt47733aNmyZfWPTESkluUVlvDMsi2M+ft6svKLaNvMh0/+GM0zt3fGw9VqdjyROs1iGIZRlR369OlDr169iI+Pd6yLiIhg6NChxMXFVdj+tddeIz4+nv379zvWvfXWW8yePZu0tDQA/vrXvzJnzhx2796Nm1v1LkLLy8vD39+f3Nxc/Pz8qvUaIiLVtXpvNs8s28LR3EIsFngkJpynB3bC001FReRSLvfzu0ojLMXFxSQnJxMbG1tufWxsLGvWrKl0n+joaI4cOUJCQgKGYXDs2DGWLVvGnXfe6djmiy++oG/fvowdO5agoCAiIyOZOXMmNpvtolmKiorIy8srt4iI1LYzRaVM+3wbD/9tHUdzC2ndxJul/9OXaXd1UVkRuYqqVFiys7Ox2WwEBZWf6CgoKIjMzMxK94mOjmbx4sUMGzYMd3d3WrRoQUBAAG+99ZZjmwMHDrBs2TJsNhsJCQlMmzaNuXPn8sorr1w0S1xcHP7+/o4lNDS0KociInLF1h3IYdCbq/jHz2WnxEde34avx/XnuvAmJicTqX+qddHthY8/Nwzjoo9E37lzJ08++STPPfccycnJfPPNNxw8eJAnnnjCsY3dbqd58+YsWLCAqKgohg8fztSpU8uddrrQlClTyM3NdSznTy+JiNS0whIbL321k+Hv/UzqiQJaBnix+NE+vDQ0Eh8P3XwpUhOq9CcrMDAQq9VaYTQlKyurwqjLeXFxccTExDBp0iQAunfvjo+PD/379+fll18mODiY4OBg3NzcsFp/GT6NiIggMzOT4uJi3N0rPmfDw8MDDw+PqsQXEblim1JPMvHjLRw4fgaAYb1DmXZXBL6aBE6kRlVphMXd3Z2oqCgSExPLrU9MTCQ6OrrSfQoKCnBxKf9jzheT89f7xsTEsG/fPux2u2OblJQUgoODKy0rIiK1rajUxqxvdnNf/BoOHD9Dc18P3h99LbPu766yIlILqnxKaMKECSxcuJBFixaxa9cunnrqKVJTUx2neKZMmcKoUaMc2w8ePJhPP/2U+Ph4Dhw4wE8//cSTTz7JddddR0hICAB//OMfycnJYdy4caSkpPCf//yHmTNnMnbs2Kt0mCIi1bc9PZchb/1E/A/7sRtwT8+WLH/qBgZ0bm52NJEGo8onW4cNG0ZOTg4zZswgIyODyMhIEhISaNOmDQAZGRnl5mQZPXo0+fn5vP3220ycOJGAgABuvvlmZs2a5dgmNDSU5cuX89RTT9G9e3datmzJuHHjmDx58lU4RBGR6imx2Xn7v/t4Z8U+Su0GgY3ceXloN26PbGF2NJEGp8rzsDgrzcMiIlfT7sw8Jv5rCzuOlk2ZcEe3Frx0dyRNG+naOZGr6XI/v3U5u4jIr5Ta7Lz74wHe/G4vxTY7Ad5uzLg7ksHdgy96N6SI1DwVFhGRc/YfP83Ef21hc9opAG6NaM7Me7vR3NfT3GAiosIiImK3Gyz66SBzvt1DUakdX09Xnh/clft6tdSoioiTUGERkQbtcM4ZJn28laRDJwDo3yGQ2fd3J9jfy+RkIvJrKiwi0iDZ7QaL1x1mZsJuzpbY8Ha3MvXOCB66rrVGVUSckAqLiDQ46afO8syyLfy0LweA69s2Yc79PQht4m1yMhG5GBUWEWkwDMPg4w1HmPHVTk4XleLp5sLk2zvz+75huLhoVEXEmamwiEiDkJlbyJRPt7Jiz3EAerUO4LUHetC2WSOTk4nI5VBhEZF6yzAM1h7I4aOkNL7dnkmxzY671YWJsR15tH9brBpVEakzVFhEpN7JPl3EsuQjLElK5VBOgWN9VJvGxN3bjY5BviamE5HqUGERkXrBbjdYsz+Hj5JSWb4zkxJb2VNHGnm4cvc1IYy4rjWRLf1NTiki1aXCIiJ1WlZ+IR9vOMLS9WmknvhlNOWa0ABGXBfKXd1D8PHQX3UidZ3+FItInWO3G6zal81H61L5btcxSu1loym+Hq7c06slw69tTZcQPQRVpD5RYRGROuNYXiEfb0hjyfo0jpw861jfq3UAI65rzZ3dg/F2119rIvWR/mT/lsUPwNFNZqcQabAMoKTUTmGpDWupnWHAMMDF04KHqwueblZcz1hgBWVLXeHdFPxDwb8VBISe+/dzX/sGg1V/PYv8mv5E/Jazp+DMcbNTiDRYFsD93MKFdyGXnlvqojPH4fjuyr9nsYJfyAWFphX4tz73z1bgofljpGFRYfkt974LJYVmpxBpEErtdtYfOsnX2zNIOniCc5em4Ovpym1dghjUtQVtmvqYG/JqMOxwJgtyj8CptLJ/5qadW9LBXvLL1xfj1bh8ibmw1Pg0AxeX2jsmkRqmwvJbmrQ1O4FIvZd+6ixL16fxr/VpZOYVAj6AD33Cm/BQn9YM7NoCTzer2TFrh90Op4/9UljKFZpzBacoF86eLFsyt1X+OlYP8G/5y6kmR6EJ/WWUxtWjdo9N5AqosIiIKUptdv67O4uPklL5IeU4xrnRlCY+7tzXqyXDr2tNu4Y4bb6LC/gFly2h11W+TWHuuRJzBE6lViw0+RlgK4ITB8qWi2kUVHa9jIuTfBS4eV3kNFjLsu9Jg+Ykv6Ui0lCknSgoG03ZkEZWfpFjfXS7poy4rjWxXYPwcG0goynV5elftgR1rfz7thLIS/9VqUn71Smnc1+Xni0byTl9rHazV5dPs1+NEF04YhQK3k3Aokct1GcqLCJS40psdr7fdYx/JqWxau8voylNfdy5v3crhl/bmvDAenBtirOwukHjsLKlMoYBBScgNxXyj5VdU+MMivJ/KVW/LlclZ8ouUj5z/OJ3bbp5lz/ldeGdV34hZe+L1FkqLCJSI04XlbIjPZeVKcf514YjZJ/+ZTSlf4dARlzXmlsjgnB31YWhtc5iAZ+mZYuzM4yya3V+XWAuHC06kwUlBZCdUrZUxuJSdvrrYndeBYSCh54x5cxUWETkiuUXlrA9PY/t6blsS89le3ouB3POOEZSAAIbefBg71YMuza0ftzpI7XDYik73ePdBIJ7VL5NSeG5U2AXXqR87uu8dLAVl/0zLx0udvOVp/8vBaZFJLTuW3YdkYqMU7AYxq//Sqm78vLy8Pf3Jzc3Fz8/TcktUlPyCkvYfq6UbDtXUg5mn6l02xB/T7q3CmBozxBuiQjCzarRFDGB/de3kVdykXJuGhSeqnxfiwu06A5tossKTOu+0KhZrcav7y7381uFRUQuKreghO1Hy0ZNzo+cHM4pqHTblgFeRLb0o1tLfyJb+tOtpT9NG+m2WakjivJ/uUj55CFIT4bDa+DU4YrbNu0Ara//pcQ0DtMFv1dAhUVEquRUQTHb0/McxWRbem65px//WqvGXuWKSWRLf5r4uNdyYpFakJsOqWvLlsNrIWtHxW18g8uKy/kC07yLJu2rAhUWEbmok2eKHaMmO86NoKSdOFvptqFNLignIf40VjmRhursSUhdB6lrygrM0U1lMxP/mqc/hF4PbfpC62gI6Qmu+jNzMSosIgLAiXPlZHt6LtuOlJWT9FOVl5M2Tb2JPFdKykqKHwHe+otW5KKKC8pOH6WuLTuFlJZUdhv2r7l6Qsve5wqMLuS9kArLVbJ0fSoZuXqWkNQtpTaDvVn5bE/Pu2g5CTtXTrqdW7qG+OPvrXkqRK6IrRQyt/5SYFLXQkFO+W0sVmjRTRfynqPCcpXcO/8nNqaeumqvJ2KG8ECfc+XEj8jz5cRL5USkxhkGZO/95RRS6pqyO5Uu1LTDL6eQ2vSFgDYN5kJeFZar5N2V+0k7WfmFhyLOyoLFcXqna4gfvp4qJyJO4/yFvOdHYLJ2VtzGN+SXU0htoqFZRL29kFeFRUREpC4oOAFp634pMEc3gb20/DaeAWW3Up8vMMHX1JsLeVVYRERE6qLiAkjf8MsppLT1lVzI6wWtep8rMH2h1XXgUTefbq7CIiIiUh/YSsou5D289pc5YSq7kDe4+y/XwLTuCz6B5uStIhUWERGR+sgwyh7yeP4U0uG1ZU/evlBgx/IT2gW0dsoLeVVYREREGorcI7+cQjq8Fo7vqriNX8tfTiG1joZmnZ3iQt7L/fyuVtL58+cTHh6Op6cnUVFRrFq16pLbL168mB49euDt7U1wcDBjxowhJyen0m2XLFmCxWJh6NCh1YkmIiLS8Pi3gu4PwF2vw9if4ZmDMPwjiP5T2aR1Lq5lT6revgz+MxHi+8LscPjncPjpzbLrZEqLzT6KS6ryCMvSpUsZOXIk8+fPJyYmhnfffZeFCxeyc+dOWrduXWH71atXc+ONN/L6668zePBg0tPTeeKJJ+jQoQOfffZZuW0PHz5MTEwMbdu2pUmTJnz++eeXnUsjLCIiIhdRfAaObPjlduoj66Hkgik7zl/Ie/4UUqtra+VC3ho7JdSnTx969epFfHy8Y11ERARDhw4lLi6uwvavvfYa8fHx7N+/37HurbfeYvbs2aSlpTnW2Ww2brzxRsaMGcOqVas4deqUCouIiEhNsJVAxtZfTWi3Fs6eKL+NxQrBPc4VmOtr7ELeGjklVFxcTHJyMrGxseXWx8bGsmbNmkr3iY6O5siRIyQkJGAYBseOHWPZsmXceeed5babMWMGzZo145FHHrmsLEVFReTl5ZVbRERE5DJY3aBVVNkpoxH/hEn74X/XlZ1S6vYg+IeCYYOjG2Ht27D0YZjTDnYnmBbZtSobZ2dnY7PZCAoKKrc+KCiIzMzMSveJjo5m8eLFDBs2jMLCQkpLSxkyZAhvvfWWY5uffvqJv/3tb2zevPmys8TFxfHiiy9WJb6IiIhUxsUFmncuW3r/oWzdqbTyM/Ie31024mJWxOrsZLngtijDMCqsO2/nzp08+eSTPPfccyQnJ/PNN99w8OBBnnjiCQDy8/N5+OGHee+99wgMvPyhpilTppCbm+tYfn16SURERK5QQCh0fxAGvwFj15VdyOvf0rQ4VRphCQwMxGq1VhhNycrKqjDqcl5cXBwxMTFMmjQJgO7du+Pj40P//v15+eWXOXbsGIcOHWLw4MGOfex2e1k4V1f27NlDu3btKryuh4cHHh4eVYkvIiIi1eXdxNQfX6URFnd3d6KiokhMTCy3PjExkejo6Er3KSgowOWC+7ytVitQNjLTuXNntm3bxubNmx3LkCFDGDBgAJs3byY0NLQqEUVERKQeqtIIC8CECRMYOXIkvXv3pm/fvixYsIDU1FTHKZ4pU6aQnp7Ohx9+CMDgwYN57LHHiI+PZ+DAgWRkZDB+/Hiuu+46QkJCAIiMjCz3MwICAipdLyIiIg1TlQvLsGHDyMnJYcaMGWRkZBAZGUlCQgJt2rQBICMjg9TUX6YIHj16NPn5+bz99ttMnDiRgIAAbr75ZmbNmnX1jkJERETqNU3NLyIiIqap0an5RURERGqTCouIiIg4PRUWERERcXoqLCIiIuL0VFhERETE6amwiIiIiNNTYRERERGnp8IiIiIiTk+FRURERJxelafmd1bnJ+zNy8szOYmIiIhcrvOf27818X69KSz5+fkAerqziIhIHZSfn4+/v/9Fv19vniVkt9s5evQovr6+WCwWs+PUiLy8PEJDQ0lLS9Pzks7Re1I5vS8V6T2pSO9J5fS+VFST74lhGOTn5xMSEoKLy8WvVKk3IywuLi60atXK7Bi1ws/PT3+ILqD3pHJ6XyrSe1KR3pPK6X2pqKbek0uNrJyni25FRETE6amwiIiIiNNTYalDPDw8eP755/Hw8DA7itPQe1I5vS8V6T2pSO9J5fS+VOQM70m9uehWRERE6i+NsIiIiIjTU2ERERERp6fCIiIiIk5PhUVEREScngpLHRAXF8e1116Lr68vzZs3Z+jQoezZs8fsWE4lLi4Oi8XC+PHjzY5iqvT0dB5++GGaNm2Kt7c311xzDcnJyWbHMlVpaSnTpk0jPDwcLy8v2rZty4wZM7Db7WZHqzU//vgjgwcPJiQkBIvFwueff17u+4Zh8MILLxASEoKXlxc33XQTO3bsMCdsLbnUe1JSUsLkyZPp1q0bPj4+hISEMGrUKI4ePWpe4FryW78rv/b4449jsVh44403aiWbCksdsHLlSsaOHcvPP/9MYmIipaWlxMbGcubMGbOjOYX169ezYMECunfvbnYUU508eZKYmBjc3Nz4+uuv2blzJ3PnziUgIMDsaKaaNWsWf/3rX3n77bfZtWsXs2fPZs6cObz11ltmR6s1Z86coUePHrz99tuVfn/27NnMmzePt99+m/Xr19OiRQtuu+02xzPa6qNLvScFBQVs3LiR6dOns3HjRj799FNSUlIYMmSICUlr12/9rpz3+eefs27dOkJCQmopGWBInZOVlWUAxsqVK82OYrr8/HyjQ4cORmJionHjjTca48aNMzuSaSZPnmz069fP7BhO58477zT+8Ic/lFt37733Gg8//LBJicwFGJ999pnja7vdbrRo0cJ49dVXHesKCwsNf39/469//asJCWvfhe9JZZKSkgzAOHz4cO2EcgIXe1+OHDlitGzZ0ti+fbvRpk0b4/XXX6+VPBphqYNyc3MBaNKkiclJzDd27FjuvPNObr31VrOjmO6LL76gd+/ePPDAAzRv3pyePXvy3nvvmR3LdP369eP7778nJSUFgC1btrB69WruuOMOk5M5h4MHD5KZmUlsbKxjnYeHBzfeeCNr1qwxMZlzyc3NxWKxNPgRS7vdzsiRI5k0aRJdu3at1Z9dbx5+2FAYhsGECRPo168fkZGRZscx1ZIlS9i4cSPr1683O4pTOHDgAPHx8UyYMIE///nPJCUl8eSTT+Lh4cGoUaPMjmeayZMnk5ubS+fOnbFardhsNl555RVGjBhhdjSnkJmZCUBQUFC59UFBQRw+fNiMSE6nsLCQZ599loceeqjBPwxx1qxZuLq68uSTT9b6z1ZhqWP+3//7f2zdupXVq1ebHcVUaWlpjBs3juXLl+Pp6Wl2HKdgt9vp3bs3M2fOBKBnz57s2LGD+Pj4Bl1Yli5dyj/+8Q/++c9/0rVrVzZv3sz48eMJCQnh97//vdnxnIbFYin3tWEYFdY1RCUlJQwfPhy73c78+fPNjmOq5ORk3nzzTTZu3GjK74ZOCdUhf/rTn/jiiy9YsWIFrVq1MjuOqZKTk8nKyiIqKgpXV1dcXV1ZuXIlf/nLX3B1dcVms5kdsdYFBwfTpUuXcusiIiJITU01KZFzmDRpEs8++yzDhw+nW7dujBw5kqeeeoq4uDizozmFFi1aAL+MtJyXlZVVYdSloSkpKeHBBx/k4MGDJCYmNvjRlVWrVpGVlUXr1q0df+8ePnyYiRMnEhYWVuM/XyMsdYBhGPzpT3/is88+44cffiA8PNzsSKa75ZZb2LZtW7l1Y8aMoXPnzkyePBmr1WpSMvPExMRUuN09JSWFNm3amJTIORQUFODiUv7/zaxWa4O6rflSwsPDadGiBYmJifTs2ROA4uJiVq5cyaxZs0xOZ57zZWXv3r2sWLGCpk2bmh3JdCNHjqxwveDAgQMZOXIkY8aMqfGfr8JSB4wdO5Z//vOf/Pvf/8bX19fxf0L+/v54eXmZnM4cvr6+Fa7h8fHxoWnTpg322p6nnnqK6OhoZs6cyYMPPkhSUhILFixgwYIFZkcz1eDBg3nllVdo3bo1Xbt2ZdOmTcybN48//OEPZkerNadPn2bfvn2Orw8ePMjmzZtp0qQJrVu3Zvz48cycOZMOHTrQoUMHZs6cibe3Nw899JCJqWvWpd6TkJAQ7r//fjZu3MhXX32FzWZz/L3bpEkT3N3dzYpd437rd+XC4ubm5kaLFi3o1KlTzYerlXuR5IoAlS7vv/++2dGcSkO/rdkwDOPLL780IiMjDQ8PD6Nz587GggULzI5kury8PGPcuHFG69atDU9PT6Nt27bG1KlTjaKiIrOj1ZoVK1ZU+nfI73//e8Mwym5tfv75540WLVoYHh4exg033GBs27bN3NA17FLvycGDBy/69+6KFSvMjl6jfut35UK1eVuzxTAMo+ZrkYiIiEj16aJbERERcXoqLCIiIuL0VFhERETE6amwiIiIiNNTYRERERGnp8IiIiIiTk+FRURERJyeCouIiIg4PRUWEalVN910E+PHj6/xn2OxWPj8889r/OeISO1QYRGROu2FF17gmmuuMTuGiNQwFRYRERFxeiosIlJjzpw5w6hRo2jUqBHBwcHMnTu33PeLi4t55plnaNmyJT4+PvTp04cffvjB8f2///3vBAQE8Pnnn9OxY0c8PT257bbbSEtLc3z/xRdfZMuWLVgsFiwWC3//+98d+2dnZ3PPPffg7e1Nhw4d+OKLL2rjsEWkBqiwiEiNmTRpEitWrOCzzz5j+fLl/PDDDyQnJzu+P2bMGH766SeWLFnC1q1beeCBB7j99tvZu3evY5uCggJeeeUVPvjgA3766Sfy8vIYPnw4AMOGDWPixIl07dqVjIwMMjIyGDZsmGPfF198kQcffJCtW7dyxx138Lvf/Y4TJ07U3hsgIldPrTwTWkQanPz8fMPd3d1YsmSJY11OTo7h5eVljBs3zti3b59hsViM9PT0cvvdcsstxpQpUwzDMIz333/fAIyff/7Z8f1du3YZgLFu3TrDMAzj+eefN3r06FHh5wPGtGnTHF+fPn3asFgsxtdff301D1NEaomryX1JROqp/fv3U1xcTN++fR3rmjRpQqdOnQDYuHEjhmHQsWPHcvsVFRXRtGlTx9eurq707t3b8XXnzp0JCAhg165dXHfddZfM0L17d8e/+/j44OvrS1ZW1hUdl4iYQ4VFRGqEYRiX/L7dbsdqtZKcnIzVai33vUaNGpX72mKxVNi/snUXcnNzq7CP3W7/zf1ExPnoGhYRqRHt27fHzc2Nn3/+2bHu5MmTpKSkANCzZ09sNhtZWVm0b9++3NKiRQvHPqWlpWzYsMHx9Z49ezh16hSdO3cGwN3dHZvNVktHJSJmUWERkRrRqFEjHnnkESZNmsT333/P9u3bGT16NC4uZX/tdOzYkd/97neMGjWKTz/9lIMHD7J+/XpmzZpFQkKC43Xc3Nz405/+xLp169i4cSNjxozh+uuvd5wOCgsL4+DBg2zevJns7GyKiopMOV4RqVkqLCJSY+bMmcMNN9zAkCFDuPXWW+nXrx9RUVGO77///vuMGjWKiRMn0qlTJ4YMGcK6desIDQ11bOPt7c3kyZN56KGH6Nu3L15eXixZssTx/fvuu4/bb7+dAQMG0KxZMz766KNaPUYRqR0W47dONIuImOTvf/8748eP59SpU2ZHERGTaYRFREREnJ4Ki4iIiDg9nRISERERp6cRFhEREXF6KiwiIiLi9FRYRERExOmpsIiIiIjTU2ERERERp6fCIiIiIk5PhUVEREScngqLiIiIOL3/D98+5rlmOTkeAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the scores dataframe with the plot method\n",
    "scores_df.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {color: black;}#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>RandomForestClassifier(max_depth=8, random_state=42)</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\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier(max_depth=8, random_state=42)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "RandomForestClassifier(max_depth=8, random_state=42)"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Instantiate a RandomForestClassifier instance\n",
    "model = RandomForestClassifier(max_depth=8, random_state=42)\n",
    "\n",
    "# Fit the traning data to the model\n",
    "model.fit(X_train_encoded, y_train_encoded)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = model.predict(X_test_encoded)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Classification Report - Original Data\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "          No       0.00      0.00      0.00        85\n",
      "         Yes       0.85      1.00      0.92       484\n",
      "\n",
      "    accuracy                           0.85       569\n",
      "   macro avg       0.43      0.50      0.46       569\n",
      "weighted avg       0.72      0.85      0.78       569\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\MJSmi\\anaconda3\\envs\\dev\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1469: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "c:\\Users\\MJSmi\\anaconda3\\envs\\dev\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1469: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "c:\\Users\\MJSmi\\anaconda3\\envs\\dev\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1469: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n"
     ]
    }
   ],
   "source": [
    "# Print classification reports\n",
    "print(f\"Classification Report - Original Data\")\n",
    "print(classification_report(y_test_encoded, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Instantiate the SMOTE instance \n",
    "# Set the sampling_strategy parameter equal to auto\n",
    "smote_sampler = SMOTE(random_state=42, sampling_strategy='auto')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Fit the training data to the smote_sampler model\n",
    "X_resampled, y_resampled = smote_sampler.fit_resample(X_train_encoded, y_train_encoded)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Clicks\n",
       "Yes    1439\n",
       "No     1439\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Count distinct values for the resampled target data\n",
    "y_resampled.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-3 {color: black;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 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-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 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-3 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-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 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-3 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-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 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-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 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-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 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-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier(max_depth=8, random_state=42)</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-3\" type=\"checkbox\" checked><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier(max_depth=8, random_state=42)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "RandomForestClassifier(max_depth=8, random_state=42)"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Instantiate a new RandomForestClassier model \n",
    "smote_model = RandomForestClassifier(max_depth=8, random_state=42)\n",
    "\n",
    "# Fit the resampled data to the new model\n",
    "smote_model.fit(X_resampled, y_resampled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Predict labels for resampled testing features\n",
    "smote_y_pred = smote_model.predict(X_test_encoded)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Classification Report - Original Data\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "          No       0.00      0.00      0.00        85\n",
      "         Yes       0.85      1.00      0.92       484\n",
      "\n",
      "    accuracy                           0.85       569\n",
      "   macro avg       0.43      0.50      0.46       569\n",
      "weighted avg       0.72      0.85      0.78       569\n",
      "\n",
      "---------\n",
      "Classification Report - Resampled Data - SMOTE\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "          No       0.13      0.05      0.07        85\n",
      "         Yes       0.85      0.94      0.89       484\n",
      "\n",
      "    accuracy                           0.81       569\n",
      "   macro avg       0.49      0.50      0.48       569\n",
      "weighted avg       0.74      0.81      0.77       569\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\MJSmi\\anaconda3\\envs\\dev\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1469: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "c:\\Users\\MJSmi\\anaconda3\\envs\\dev\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1469: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "c:\\Users\\MJSmi\\anaconda3\\envs\\dev\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1469: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n"
     ]
    }
   ],
   "source": [
    "# Print classification reports\n",
    "print(f\"Classification Report - Original Data\")\n",
    "print(classification_report(y_test_encoded, y_pred))\n",
    "print(\"---------\")\n",
    "print(f\"Classification Report - Resampled Data - SMOTE\")\n",
    "print(classification_report(y_test_encoded, smote_y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Question 4: What is the most desirable ad?\n",
    "We found that more men over the age of 30 years old who live in Rural , Suburban and Urban under both test data and train data found they are more intersted into the topics of Finance, Travel, and Fashion vs females. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['Age', 'Gender', 'Income', 'Location', 'Ad Type', 'Ad Topic',\n",
      "       'Ad Placement', 'Clicks', 'Click Time', 'Conversion Rate', 'CTR'],\n",
      "      dtype='object')\n",
      "      Age  Gender    Income  Location Ad Type    Ad Topic   Ad Placement  \\\n",
      "0      61    Male  35717.43     Urban  Banner      Travel   Social Media   \n",
      "1      41    Male  47453.25     Rural   Video      Travel  Search Engine   \n",
      "2      49  Female  68126.35     Rural    Text        Food   Social Media   \n",
      "3      68  Female  64585.73  Suburban    Text      Health        Website   \n",
      "4      63    Male  21109.40     Urban  Native     Fashion  Search Engine   \n",
      "...   ...     ...       ...       ...     ...         ...            ...   \n",
      "9994   49  Female  72810.17     Rural  Banner  Technology        Website   \n",
      "9996   35    Male  42182.75     Urban  Native  Technology        Website   \n",
      "9997   42    Male  55084.12  Suburban    Text     Finance  Search Engine   \n",
      "9998   34    Male  39382.80     Rural  Native  Technology   Social Media   \n",
      "9999   54  Female  66441.59  Suburban  Banner  Technology  Search Engine   \n",
      "\n",
      "      Clicks                  Click Time  Conversion Rate     CTR  \n",
      "0          3  2024-01-18 20:45:56.898459           0.0981  0.0737  \n",
      "1          5  2023-04-24 20:45:56.898459           0.0937  0.0592  \n",
      "2          4  2024-02-24 20:45:56.898459           0.1912  0.0563  \n",
      "3          6  2023-12-13 20:45:56.898459           0.1122  0.0232  \n",
      "4          5  2023-07-02 20:45:56.898459           0.1426  0.0539  \n",
      "...      ...                         ...              ...     ...  \n",
      "9994       9  2023-07-24 20:45:56.927349           0.0679  0.0443  \n",
      "9996       9  2023-09-01 20:45:56.927349           0.2664  0.0407  \n",
      "9997       3  2023-11-15 20:45:56.927349           0.2354  0.0390  \n",
      "9998       4  2023-05-23 20:45:56.927349           0.1725  0.0455  \n",
      "9999       2  2023-11-21 20:45:56.927349           0.0307  0.0323  \n",
      "\n",
      "[8694 rows x 11 columns]\n"
     ]
    }
   ],
   "source": [
    "print(df.columns)\n",
    "print(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total Males: 4316\n",
      "Total Females: 3462\n"
     ]
    }
   ],
   "source": [
    "# Count the total number of males\n",
    "total_males = df[df['Gender'] == 'Male'].shape[0]\n",
    "\n",
    "# Count the total number of females\n",
    "total_females = df[df['Gender'] == 'Female'].shape[0]\n",
    "\n",
    "# Create X and y variables\n",
    "X = total_males\n",
    "y = total_females\n",
    "\n",
    "print(\"Total Males:\", X)\n",
    "print(\"Total Females:\", y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "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>Age</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Income</th>\n",
       "      <th>Location</th>\n",
       "      <th>Ad Type</th>\n",
       "      <th>Ad Topic</th>\n",
       "      <th>Ad Placement</th>\n",
       "      <th>Clicks</th>\n",
       "      <th>Click Time</th>\n",
       "      <th>Conversion Rate</th>\n",
       "      <th>CTR</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>61</td>\n",
       "      <td>Male</td>\n",
       "      <td>35717.43</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Banner</td>\n",
       "      <td>Travel</td>\n",
       "      <td>Social Media</td>\n",
       "      <td>3</td>\n",
       "      <td>2024-01-18 20:45:56.898459</td>\n",
       "      <td>0.0981</td>\n",
       "      <td>0.0737</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>41</td>\n",
       "      <td>Male</td>\n",
       "      <td>47453.25</td>\n",
       "      <td>Rural</td>\n",
       "      <td>Video</td>\n",
       "      <td>Travel</td>\n",
       "      <td>Search Engine</td>\n",
       "      <td>5</td>\n",
       "      <td>2023-04-24 20:45:56.898459</td>\n",
       "      <td>0.0937</td>\n",
       "      <td>0.0592</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>63</td>\n",
       "      <td>Male</td>\n",
       "      <td>21109.40</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Native</td>\n",
       "      <td>Fashion</td>\n",
       "      <td>Search Engine</td>\n",
       "      <td>5</td>\n",
       "      <td>2023-07-02 20:45:56.898459</td>\n",
       "      <td>0.1426</td>\n",
       "      <td>0.0539</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>20</td>\n",
       "      <td>Male</td>\n",
       "      <td>39576.44</td>\n",
       "      <td>Suburban</td>\n",
       "      <td>Video</td>\n",
       "      <td>Health</td>\n",
       "      <td>Social Media</td>\n",
       "      <td>5</td>\n",
       "      <td>2024-02-19 20:45:56.898459</td>\n",
       "      <td>0.1824</td>\n",
       "      <td>0.0424</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>33</td>\n",
       "      <td>Male</td>\n",
       "      <td>25917.02</td>\n",
       "      <td>Rural</td>\n",
       "      <td>Video</td>\n",
       "      <td>Food</td>\n",
       "      <td>Search Engine</td>\n",
       "      <td>3</td>\n",
       "      <td>2023-10-05 20:45:56.898459</td>\n",
       "      <td>0.0347</td>\n",
       "      <td>0.0488</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Age Gender    Income  Location Ad Type Ad Topic   Ad Placement  Clicks  \\\n",
       "0   61   Male  35717.43     Urban  Banner   Travel   Social Media       3   \n",
       "1   41   Male  47453.25     Rural   Video   Travel  Search Engine       5   \n",
       "4   63   Male  21109.40     Urban  Native  Fashion  Search Engine       5   \n",
       "5   20   Male  39576.44  Suburban   Video   Health   Social Media       5   \n",
       "8   33   Male  25917.02     Rural   Video     Food  Search Engine       3   \n",
       "\n",
       "                   Click Time  Conversion Rate     CTR  \n",
       "0  2024-01-18 20:45:56.898459           0.0981  0.0737  \n",
       "1  2023-04-24 20:45:56.898459           0.0937  0.0592  \n",
       "4  2023-07-02 20:45:56.898459           0.1426  0.0539  \n",
       "5  2024-02-19 20:45:56.898459           0.1824  0.0424  \n",
       "8  2023-10-05 20:45:56.898459           0.0347  0.0488  "
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_male = df[df['Gender'] == 'Male']\n",
    "df_female = df[df['Gender'] == 'Female']\n",
    "\n",
    "df_male.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Assign the counts to X and y\n",
    "# To get the records that are only males say somthing like\n",
    "\n",
    "\n",
    "\n",
    "# Assigning counts to X and y \n",
    "X_df_male = df_male.drop(\"Clicks\",axis=1)  # Total Males\n",
    "X_df_female = df_female.drop(\"Clicks\",axis=1)  # Total Females\n",
    "\n",
    "y_df_male = df_male[\"Clicks\"]\n",
    "y_df_female = df_female[\"Clicks\"]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_train shape: (3237, 10)\n",
      "X_test shape: (1079, 10)\n",
      "y_train shape: (3237,)\n",
      "y_test shape: (1079,)\n"
     ]
    }
   ],
   "source": [
    "# Splitting the data into training and testing sets\n",
    "X_train, X_test, y_train, y_test = train_test_split(X_df_male, y_df_male, random_state=42)\n",
    "\n",
    "\n",
    "# Displaying the shapes of the resulting arrays\n",
    "print(\"X_train shape:\", X_train.shape)\n",
    "print(\"X_test shape:\", X_test.shape)\n",
    "print(\"y_train shape:\", y_train.shape)\n",
    "print(\"y_test shape:\", y_test.shape)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_train shape: (2596, 10)\n",
      "X_test shape: (866, 10)\n",
      "y_train shape: (2596,)\n",
      "y_test shape: (866,)\n"
     ]
    }
   ],
   "source": [
    "# Splitting the data into training and testing sets (Female)\n",
    "X_train_f,X_test_f, y_train_f, y_test_f = train_test_split(X_df_female, y_df_female, random_state=42)\n",
    "\n",
    "\n",
    "# Displaying the shapes of the resulting arrays\n",
    "print(\"X_train shape:\", X_train_f.shape)\n",
    "print(\"X_test shape:\", X_test_f.shape)\n",
    "print(\"y_train shape:\", y_train_f.shape)\n",
    "print(\"y_test shape:\", y_test_f.shape)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ad Topic\n",
      "Finance       1492\n",
      "Technology    1453\n",
      "Food          1450\n",
      "Fashion       1450\n",
      "Travel        1448\n",
      "Health        1401\n",
      "Name: count, dtype: int64\n",
      "\n",
      "Total counts for each click type:\n",
      "Clicks\n",
      "4     1528\n",
      "5     1512\n",
      "6     1266\n",
      "3     1202\n",
      "7      884\n",
      "2      721\n",
      "8      597\n",
      "9      344\n",
      "1      278\n",
      "10     167\n",
      "0       72\n",
      "11      72\n",
      "12      34\n",
      "13       8\n",
      "14       6\n",
      "15       2\n",
      "17       1\n",
      "Name: count, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# Display total counts for each unique value in 'Ad_types' column\n",
    "ad_types_total = df['Ad Topic'].value_counts()\n",
    "print(ad_types_total)\n",
    "\n",
    "# Display total counts for each unique value in 'clicks' column\n",
    "clicks_total = df['Clicks'].value_counts()\n",
    "print(\"\\nTotal counts for each click type:\")\n",
    "print(clicks_total)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "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>Ad Topic</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Clicks</th>\n",
       "      <th>Location</th>\n",
       "      <th>Age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Travel</td>\n",
       "      <td>Male</td>\n",
       "      <td>3</td>\n",
       "      <td>Urban</td>\n",
       "      <td>61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Travel</td>\n",
       "      <td>Male</td>\n",
       "      <td>5</td>\n",
       "      <td>Rural</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Food</td>\n",
       "      <td>Female</td>\n",
       "      <td>4</td>\n",
       "      <td>Rural</td>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Health</td>\n",
       "      <td>Female</td>\n",
       "      <td>6</td>\n",
       "      <td>Suburban</td>\n",
       "      <td>68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Fashion</td>\n",
       "      <td>Male</td>\n",
       "      <td>5</td>\n",
       "      <td>Urban</td>\n",
       "      <td>63</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Ad Topic  Gender  Clicks  Location  Age\n",
       "0   Travel    Male       3     Urban   61\n",
       "1   Travel    Male       5     Rural   41\n",
       "2     Food  Female       4     Rural   49\n",
       "3   Health  Female       6  Suburban   68\n",
       "4  Fashion    Male       5     Urban   63"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Display what is the most Desiable or hihg Ad? using the Test data ( useing the most accurate modle find the correlation between each column and the amount of clicks the ad gets)\n",
    "\n",
    "Ad_topics = df[['Ad Topic','Gender','Clicks', 'Location','Age']]\n",
    "\n",
    "\n",
    "# Create X and y variables\n",
    "X = Ad_topics\n",
    "\n",
    "\n",
    "Ad_topics.head()\n",
    "\n",
    "\n",
    "# Display what is the most Desiable Ad? using the Train data \n",
    "\n",
    "# Encode the X data with the X_preprocess function\n",
    "# show most desiable ad.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Gender\n",
       "Female    17446\n",
       "Male      21721\n",
       "Other      4592\n",
       "Name: Clicks, dtype: int64"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Clicks in total for Genders on Ad Topics\n",
    "Gender_Group = Ad_topics.groupby('Gender')['Clicks'].sum()\n",
    "Gender_Group.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Gender\n",
      "Female    866\n",
      "dtype: int64\n",
      "Gender\n",
      "Male    1079\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# Show the Male vs Females total for the Test data \n",
    "Female = X_test_f.groupby('Gender').size()\n",
    "\n",
    "Male = X_test.groupby('Gender').size()\n",
    "print(Female)\n",
    "print(Male)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Gender\n",
      "Male    3237\n",
      "dtype: int64\n",
      "Gender\n",
      "Male    3237\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# Show the Male vs Females total for the Train data \n",
    "Female = X_train.groupby('Gender').size()\n",
    "Male = X_train.groupby('Gender').size()\n",
    "print(Female)\n",
    "print(Male)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Location\n",
       "Rural       15060\n",
       "Suburban    14413\n",
       "Urban       14286\n",
       "Name: Clicks, dtype: int64"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# show the location of the customers who are click from the top ad form Df\n",
    "Location = Ad_topics.groupby('Location')['Clicks'].sum()\n",
    "Location.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\MJSmi\\AppData\\Local\\Temp\\ipykernel_26056\\1161659475.py:4: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  Ad_topics['Age_Category'] = Ad_topics['Age'].apply(lambda x: '30 or Below' if int(x) < 31 else '30 or More')\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Age_Category\n",
       "30 or Below    13632\n",
       "30 or More     30127\n",
       "Name: Clicks, dtype: int64"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# show the Ages form 30 below vs 31 and upperwards who is click what age is most inerested in this ad form the top data from the Test_data \n",
    "#Age = Ad_topics.groupby('Age')['Clicks'].sum()\n",
    "#Age.head()\n",
    "Ad_topics['Age_Category'] = Ad_topics['Age'].apply(lambda x: '30 or Below' if int(x) < 31 else '30 or More')\n",
    "Age = Ad_topics.groupby('Age_Category')['Clicks'].sum()\n",
    "Age.head()"
   ]
  }
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