ToBigs_9wk_RS_basic.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import os\n",
    "\n",
    "# 경고 방지\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "from datetime import datetime\n",
    "\n",
    "# 그래프 설정\n",
    "plt.rcParams['font.family'] = 'Malgun Gothic'\n",
    "# plt.rcParams['font.family'] = 'AppleGothic'\n",
    "plt.rcParams['font.size'] = 16\n",
    "plt.rcParams['figure.figsize'] = 20, 10\n",
    "plt.rcParams['axes.unicode_minus'] = False\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. 데이터 불러오기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>userId</th>\n",
       "      <th>movieId</th>\n",
       "      <th>rating</th>\n",
       "      <th>timestamp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4.0</td>\n",
       "      <td>964982703</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>4.0</td>\n",
       "      <td>964981247</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>4.0</td>\n",
       "      <td>964982224</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>47</td>\n",
       "      <td>5.0</td>\n",
       "      <td>964983815</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>50</td>\n",
       "      <td>5.0</td>\n",
       "      <td>964982931</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>100831</th>\n",
       "      <td>610</td>\n",
       "      <td>166534</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1493848402</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100832</th>\n",
       "      <td>610</td>\n",
       "      <td>168248</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1493850091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100833</th>\n",
       "      <td>610</td>\n",
       "      <td>168250</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1494273047</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100834</th>\n",
       "      <td>610</td>\n",
       "      <td>168252</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1493846352</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100835</th>\n",
       "      <td>610</td>\n",
       "      <td>170875</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1493846415</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>100836 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        userId  movieId  rating   timestamp\n",
       "0            1        1     4.0   964982703\n",
       "1            1        3     4.0   964981247\n",
       "2            1        6     4.0   964982224\n",
       "3            1       47     5.0   964983815\n",
       "4            1       50     5.0   964982931\n",
       "...        ...      ...     ...         ...\n",
       "100831     610   166534     4.0  1493848402\n",
       "100832     610   168248     5.0  1493850091\n",
       "100833     610   168250     5.0  1494273047\n",
       "100834     610   168252     5.0  1493846352\n",
       "100835     610   170875     3.0  1493846415\n",
       "\n",
       "[100836 rows x 4 columns]"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ratings_data = pd.read_csv(\"ratings.csv\")\n",
    "\n",
    "ratings_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>movieId</th>\n",
       "      <th>title</th>\n",
       "      <th>genres</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Toy Story (1995)</td>\n",
       "      <td>Adventure|Animation|Children|Comedy|Fantasy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Jumanji (1995)</td>\n",
       "      <td>Adventure|Children|Fantasy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Grumpier Old Men (1995)</td>\n",
       "      <td>Comedy|Romance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Waiting to Exhale (1995)</td>\n",
       "      <td>Comedy|Drama|Romance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Father of the Bride Part II (1995)</td>\n",
       "      <td>Comedy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9737</th>\n",
       "      <td>193581</td>\n",
       "      <td>Black Butler: Book of the Atlantic (2017)</td>\n",
       "      <td>Action|Animation|Comedy|Fantasy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9738</th>\n",
       "      <td>193583</td>\n",
       "      <td>No Game No Life: Zero (2017)</td>\n",
       "      <td>Animation|Comedy|Fantasy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9739</th>\n",
       "      <td>193585</td>\n",
       "      <td>Flint (2017)</td>\n",
       "      <td>Drama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9740</th>\n",
       "      <td>193587</td>\n",
       "      <td>Bungo Stray Dogs: Dead Apple (2018)</td>\n",
       "      <td>Action|Animation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9741</th>\n",
       "      <td>193609</td>\n",
       "      <td>Andrew Dice Clay: Dice Rules (1991)</td>\n",
       "      <td>Comedy</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>9742 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      movieId                                      title  \\\n",
       "0           1                           Toy Story (1995)   \n",
       "1           2                             Jumanji (1995)   \n",
       "2           3                    Grumpier Old Men (1995)   \n",
       "3           4                   Waiting to Exhale (1995)   \n",
       "4           5         Father of the Bride Part II (1995)   \n",
       "...       ...                                        ...   \n",
       "9737   193581  Black Butler: Book of the Atlantic (2017)   \n",
       "9738   193583               No Game No Life: Zero (2017)   \n",
       "9739   193585                               Flint (2017)   \n",
       "9740   193587        Bungo Stray Dogs: Dead Apple (2018)   \n",
       "9741   193609        Andrew Dice Clay: Dice Rules (1991)   \n",
       "\n",
       "                                           genres  \n",
       "0     Adventure|Animation|Children|Comedy|Fantasy  \n",
       "1                      Adventure|Children|Fantasy  \n",
       "2                                  Comedy|Romance  \n",
       "3                            Comedy|Drama|Romance  \n",
       "4                                          Comedy  \n",
       "...                                           ...  \n",
       "9737              Action|Animation|Comedy|Fantasy  \n",
       "9738                     Animation|Comedy|Fantasy  \n",
       "9739                                        Drama  \n",
       "9740                             Action|Animation  \n",
       "9741                                       Comedy  \n",
       "\n",
       "[9742 rows x 3 columns]"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movie_data = pd.read_csv(\"movies.csv\")\n",
    "\n",
    "movie_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>userId</th>\n",
       "      <th>movieId</th>\n",
       "      <th>tag</th>\n",
       "      <th>timestamp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>60756</td>\n",
       "      <td>funny</td>\n",
       "      <td>1445714994</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>60756</td>\n",
       "      <td>Highly quotable</td>\n",
       "      <td>1445714996</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>60756</td>\n",
       "      <td>will ferrell</td>\n",
       "      <td>1445714992</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>89774</td>\n",
       "      <td>Boxing story</td>\n",
       "      <td>1445715207</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>89774</td>\n",
       "      <td>MMA</td>\n",
       "      <td>1445715200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>3678</th>\n",
       "      <td>606</td>\n",
       "      <td>7382</td>\n",
       "      <td>for katie</td>\n",
       "      <td>1171234019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3679</th>\n",
       "      <td>606</td>\n",
       "      <td>7936</td>\n",
       "      <td>austere</td>\n",
       "      <td>1173392334</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3680</th>\n",
       "      <td>610</td>\n",
       "      <td>3265</td>\n",
       "      <td>gun fu</td>\n",
       "      <td>1493843984</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3681</th>\n",
       "      <td>610</td>\n",
       "      <td>3265</td>\n",
       "      <td>heroic bloodshed</td>\n",
       "      <td>1493843978</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3682</th>\n",
       "      <td>610</td>\n",
       "      <td>168248</td>\n",
       "      <td>Heroic Bloodshed</td>\n",
       "      <td>1493844270</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3683 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      userId  movieId               tag   timestamp\n",
       "0          2    60756             funny  1445714994\n",
       "1          2    60756   Highly quotable  1445714996\n",
       "2          2    60756      will ferrell  1445714992\n",
       "3          2    89774      Boxing story  1445715207\n",
       "4          2    89774               MMA  1445715200\n",
       "...      ...      ...               ...         ...\n",
       "3678     606     7382         for katie  1171234019\n",
       "3679     606     7936           austere  1173392334\n",
       "3680     610     3265            gun fu  1493843984\n",
       "3681     610     3265  heroic bloodshed  1493843978\n",
       "3682     610   168248  Heroic Bloodshed  1493844270\n",
       "\n",
       "[3683 rows x 4 columns]"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tags_data = pd.read_csv(\"tags.csv\")\n",
    "\n",
    "tags_data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. 데이터 전처리 및 EDA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 100836 entries, 0 to 100835\n",
      "Data columns (total 4 columns):\n",
      " #   Column     Non-Null Count   Dtype  \n",
      "---  ------     --------------   -----  \n",
      " 0   userId     100836 non-null  int64  \n",
      " 1   movieId    100836 non-null  int64  \n",
      " 2   rating     100836 non-null  float64\n",
      " 3   timestamp  100836 non-null  int64  \n",
      "dtypes: float64(1), int64(3)\n",
      "memory usage: 3.1 MB\n"
     ]
    }
   ],
   "source": [
    "ratings_data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 9742 entries, 0 to 9741\n",
      "Data columns (total 3 columns):\n",
      " #   Column   Non-Null Count  Dtype \n",
      "---  ------   --------------  ----- \n",
      " 0   movieId  9742 non-null   int64 \n",
      " 1   title    9742 non-null   object\n",
      " 2   genres   9742 non-null   object\n",
      "dtypes: int64(1), object(2)\n",
      "memory usage: 228.5+ KB\n"
     ]
    }
   ],
   "source": [
    "movie_data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 3683 entries, 0 to 3682\n",
      "Data columns (total 4 columns):\n",
      " #   Column     Non-Null Count  Dtype \n",
      "---  ------     --------------  ----- \n",
      " 0   userId     3683 non-null   int64 \n",
      " 1   movieId    3683 non-null   int64 \n",
      " 2   tag        3683 non-null   object\n",
      " 3   timestamp  3683 non-null   int64 \n",
      "dtypes: int64(3), object(1)\n",
      "memory usage: 115.2+ KB\n"
     ]
    }
   ],
   "source": [
    "tags_data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "9742\n",
      "9742\n"
     ]
    }
   ],
   "source": [
    "# 무비 데이터프레임에 대한 중복값 확인\n",
    "print(len(movie_data))\n",
    "print(len(movie_data[\"movieId\"].unique()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "세 데이터프레임 모두 결측치가 없는 것을 확인할 수 있다."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. 연대별 영화 수 분석"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>movieId</th>\n",
       "      <th>title</th>\n",
       "      <th>genres</th>\n",
       "      <th>year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Toy Story (1995)</td>\n",
       "      <td>Adventure|Animation|Children|Comedy|Fantasy</td>\n",
       "      <td>1995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Jumanji (1995)</td>\n",
       "      <td>Adventure|Children|Fantasy</td>\n",
       "      <td>1995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Grumpier Old Men (1995)</td>\n",
       "      <td>Comedy|Romance</td>\n",
       "      <td>1995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Waiting to Exhale (1995)</td>\n",
       "      <td>Comedy|Drama|Romance</td>\n",
       "      <td>1995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Father of the Bride Part II (1995)</td>\n",
       "      <td>Comedy</td>\n",
       "      <td>1995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9737</th>\n",
       "      <td>193581</td>\n",
       "      <td>Black Butler: Book of the Atlantic (2017)</td>\n",
       "      <td>Action|Animation|Comedy|Fantasy</td>\n",
       "      <td>2017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9738</th>\n",
       "      <td>193583</td>\n",
       "      <td>No Game No Life: Zero (2017)</td>\n",
       "      <td>Animation|Comedy|Fantasy</td>\n",
       "      <td>2017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9739</th>\n",
       "      <td>193585</td>\n",
       "      <td>Flint (2017)</td>\n",
       "      <td>Drama</td>\n",
       "      <td>2017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9740</th>\n",
       "      <td>193587</td>\n",
       "      <td>Bungo Stray Dogs: Dead Apple (2018)</td>\n",
       "      <td>Action|Animation</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9741</th>\n",
       "      <td>193609</td>\n",
       "      <td>Andrew Dice Clay: Dice Rules (1991)</td>\n",
       "      <td>Comedy</td>\n",
       "      <td>1991</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>9742 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      movieId                                      title  \\\n",
       "0           1                           Toy Story (1995)   \n",
       "1           2                             Jumanji (1995)   \n",
       "2           3                    Grumpier Old Men (1995)   \n",
       "3           4                   Waiting to Exhale (1995)   \n",
       "4           5         Father of the Bride Part II (1995)   \n",
       "...       ...                                        ...   \n",
       "9737   193581  Black Butler: Book of the Atlantic (2017)   \n",
       "9738   193583               No Game No Life: Zero (2017)   \n",
       "9739   193585                               Flint (2017)   \n",
       "9740   193587        Bungo Stray Dogs: Dead Apple (2018)   \n",
       "9741   193609        Andrew Dice Clay: Dice Rules (1991)   \n",
       "\n",
       "                                           genres  year  \n",
       "0     Adventure|Animation|Children|Comedy|Fantasy  1995  \n",
       "1                      Adventure|Children|Fantasy  1995  \n",
       "2                                  Comedy|Romance  1995  \n",
       "3                            Comedy|Drama|Romance  1995  \n",
       "4                                          Comedy  1995  \n",
       "...                                           ...   ...  \n",
       "9737              Action|Animation|Comedy|Fantasy  2017  \n",
       "9738                     Animation|Comedy|Fantasy  2017  \n",
       "9739                                        Drama  2017  \n",
       "9740                             Action|Animation  2018  \n",
       "9741                                       Comedy  1991  \n",
       "\n",
       "[9742 rows x 4 columns]"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# movie_data 연도별 컬럼 추가\n",
    "\n",
    "year = movie_data[\"title\"].apply(lambda x : x[-5:-1])\n",
    "\n",
    "movie_data[\"year\"] = year\n",
    "movie_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "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>movieId</th>\n",
       "      <th>title</th>\n",
       "      <th>genres</th>\n",
       "      <th>year</th>\n",
       "      <th>year_term</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Toy Story (1995)</td>\n",
       "      <td>Adventure|Animation|Children|Comedy|Fantasy</td>\n",
       "      <td>1995</td>\n",
       "      <td>1990</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Jumanji (1995)</td>\n",
       "      <td>Adventure|Children|Fantasy</td>\n",
       "      <td>1995</td>\n",
       "      <td>1990</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Grumpier Old Men (1995)</td>\n",
       "      <td>Comedy|Romance</td>\n",
       "      <td>1995</td>\n",
       "      <td>1990</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Waiting to Exhale (1995)</td>\n",
       "      <td>Comedy|Drama|Romance</td>\n",
       "      <td>1995</td>\n",
       "      <td>1990</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Father of the Bride Part II (1995)</td>\n",
       "      <td>Comedy</td>\n",
       "      <td>1995</td>\n",
       "      <td>1990</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9737</th>\n",
       "      <td>193581</td>\n",
       "      <td>Black Butler: Book of the Atlantic (2017)</td>\n",
       "      <td>Action|Animation|Comedy|Fantasy</td>\n",
       "      <td>2017</td>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9738</th>\n",
       "      <td>193583</td>\n",
       "      <td>No Game No Life: Zero (2017)</td>\n",
       "      <td>Animation|Comedy|Fantasy</td>\n",
       "      <td>2017</td>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9739</th>\n",
       "      <td>193585</td>\n",
       "      <td>Flint (2017)</td>\n",
       "      <td>Drama</td>\n",
       "      <td>2017</td>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9740</th>\n",
       "      <td>193587</td>\n",
       "      <td>Bungo Stray Dogs: Dead Apple (2018)</td>\n",
       "      <td>Action|Animation</td>\n",
       "      <td>2018</td>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9741</th>\n",
       "      <td>193609</td>\n",
       "      <td>Andrew Dice Clay: Dice Rules (1991)</td>\n",
       "      <td>Comedy</td>\n",
       "      <td>1991</td>\n",
       "      <td>1990</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>9742 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      movieId                                      title  \\\n",
       "0           1                           Toy Story (1995)   \n",
       "1           2                             Jumanji (1995)   \n",
       "2           3                    Grumpier Old Men (1995)   \n",
       "3           4                   Waiting to Exhale (1995)   \n",
       "4           5         Father of the Bride Part II (1995)   \n",
       "...       ...                                        ...   \n",
       "9737   193581  Black Butler: Book of the Atlantic (2017)   \n",
       "9738   193583               No Game No Life: Zero (2017)   \n",
       "9739   193585                               Flint (2017)   \n",
       "9740   193587        Bungo Stray Dogs: Dead Apple (2018)   \n",
       "9741   193609        Andrew Dice Clay: Dice Rules (1991)   \n",
       "\n",
       "                                           genres  year year_term  \n",
       "0     Adventure|Animation|Children|Comedy|Fantasy  1995      1990  \n",
       "1                      Adventure|Children|Fantasy  1995      1990  \n",
       "2                                  Comedy|Romance  1995      1990  \n",
       "3                            Comedy|Drama|Romance  1995      1990  \n",
       "4                                          Comedy  1995      1990  \n",
       "...                                           ...   ...       ...  \n",
       "9737              Action|Animation|Comedy|Fantasy  2017      2010  \n",
       "9738                     Animation|Comedy|Fantasy  2017      2010  \n",
       "9739                                        Drama  2017      2010  \n",
       "9740                             Action|Animation  2018      2010  \n",
       "9741                                       Comedy  1991      1990  \n",
       "\n",
       "[9742 rows x 5 columns]"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# movie data 연대별 컬럼 추가\n",
    "\n",
    "movie_data[\"year_term\"] = movie_data[\"title\"].apply(lambda x : x[-5:-2] + \"0\")\n",
    "movie_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "year_term\n",
       " Ba0       1\n",
       " Ro0       1\n",
       "0070       2\n",
       "0080       1\n",
       "0110       2\n",
       "0120       1\n",
       "0140       1\n",
       "1900       3\n",
       "1910       7\n",
       "1920      37\n",
       "1930     136\n",
       "1940     197\n",
       "1950     279\n",
       "1960     401\n",
       "1970     499\n",
       "1980    1177\n",
       "1990    2209\n",
       "2000    2847\n",
       "2010    1927\n",
       "9730       1\n",
       "9950       1\n",
       "9980       1\n",
       "9990       1\n",
       "ats0       1\n",
       "ers0       1\n",
       "he 0       1\n",
       "ima0       1\n",
       "irr0       1\n",
       "lig0       1\n",
       "lon0       1\n",
       "osm0       1\n",
       "r O0       1\n",
       "ron0       1\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 연대별 영화 수\n",
    "movie_year_term_count = movie_data[\"year_term\"].value_counts().sort_index()\n",
    "movie_year_term_count"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2000년대 영화가 가장 많을 것을 확인할 수 있다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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O+pIGgMmTJ4NzLms0KeXFixfgnGP8+PFffG10dDRWrFgBJycnBAYGGsUX3J07d8A5x8aNGwHIP7TpB6kuX76MFi1agHOOvn37Kv6lcPHiRXDOxci5dvb0/3/gwAGULFkSnHNZ575SKLsyTDl7REQEMmXKhPbt2+usFkxJScFff/2FmTNnIiAgAGZmZmjXrp2Ygam08PBwuLi4oFOnTrIZUEBaI/TYsWPo3bs3PDw8YGlpiWHDhimUVJcpZ6dzRhl03JVhytlfvXoFR0dHdO7cGREREbLnkpKSsHHjRtSrVw9qtRpWVlaYPXu2Qkl1mer5npycjKZNm8Lf3190ZKempuq0g+/fv49p06bB29sb1tbWWL58uVKRhcTERNSqVQuFCxfG69evAaRl1+6cTE1NxdmzZ9GjRw9YWlrC3d3dKGbcm+pxT0lJQYcOHeDj44Pnz58D0O2wB4APHz5gy5YtKFKkCNRqtWwCnZJM9Zwx1eMu3V9v2LABQMb310Ba52WHDh3AOUfXrl0VHwxcsmQJbG1tRaeelFc79969e1GsWDFwzlG6dGkcPHgQgPIDmYmJiShatCgqVqwIADqrRtPnmzt3LszNzVG6dGmj6XiNj49HhQoVUKpUKYSHhwNIy63dF5acnIxdu3ahdu3a4JyjcOHCOH36tHitIU2YMAHOzs7YsWMHAN1BsujoaCxatAje3t4wNzdHr169RB+Y0ueLNPFz5syZAPSf62/evEGvXr3AOYezszNmzpwpJqQr3RcGAAcOHICNjQ1mzJgh8mj/DOLj47Fx40bUqFFDdNhLA0HGIioqSgzMSv3BUrtA+xiHhoaKc75EiRJiwrwSA7M9evSAjY2N+F76nEePHqFr167gnGPy5MkGSPdlV65cga2tLUaPHi075pKbN29ixIgRYnVv48aNf0jfNQ3w/Edt2bIFnHO0aNECL168+OLr37x5gzJlyiBv3ryIi4tT7OIq/bsVKlSAl5cXLl68KHs8I0uXLgXnHFOnTgWg7Jeb1CFSo0YNUbYkfR7t9/P69Ws0bNgQKpUKJ06cMGjW9O7duwcLCwu0a9cO8fHxehv82r9/+vQpypcvD3t7e/z111+GjitD2ZVhytlPnjwJOzs7zJkzB8Dfn1Ptz2tiYiIuXryILl26QK1WI3/+/Pjzzz8Vyatt3759sLa2xtKlSwHozx4TE4MdO3aIBmj16tXx8OFDRfJqM+XsdM4og467Mkw5+6ZNm2BtbS1mnkuZtW9aIyMj8fvvv+Onn34C5xxdunQRnT9KdjKY8vnevHlz+Pj46H1O+5h+/PgRmzdvRt68eeHk5CQ60pRUq1YtFChQQO9z2tnfv3+P2bNnw97eHn5+fnj8+LGBEmbMVI97165d4ebm9tkJiUDa5/bMmTOoUqUKOOdYs2YNAOU7A031nDHF4x4ZGQlPT08EBQWJQfvP3V9/+PABbdu2BedcDJYodb7MmzcPVlZWYoa8do70Ham//vorVCoVihYtqlOKTgmpqakICQmBjY2NLE/6Y6n9PrZs2QK1Wo0ePXoYLOfnJCcno0KFCihTpoze57XPo4iICAwYMACcc1SpUsWgK3mkYzpgwAB4eHiIzt+MztsnT56gXbt2UKlUaNKkiVGUbD18+DDMzMzE4JT2IJpGo5GdJ6dPn4afnx/s7e2xZcsWg2fNyJ49e8A5x8mTJwFAZ9Be8urVK4wePVqUozt+/DgA5b+XACAhIQGBgYGoUqUKAPnPAZC/J41Gg99++02c8x8/fjRoVsmZM2fEqiipjNnn+nRTUlJQtWpVZMmSRfHVRwCwbds2WFhYiBVT+rZIAdLa+I0bNxal/qT+sO913tAAz39UVFQUGjRoIGau3LlzJ8OTRvrAT5gwAU5OTorOApQyLlu2DJxzNGjQQMz+SL8UWPv1AGSzS5Qi5Wnfvj0451iyZIl4Tt9IuPT6V69ewdnZGV27djVM0AwkJSWhRo0asLW1FUurAf0NaOmxS5cuQa1Wi5J6SqHsyjDl7Ldv3wbn/Ktmsb569QpjxowRMy6UbkRLK6d27doFQLdRoP37e/fuoU2bNuCco1+/fgbNqY8pZ6dzRhl03JVhytkPHToEzrmYOJN+trd29qtXr6JChQpGs7rUlM/3+fPng3OOFStWAPjyLNCzZ8/CwcEB1apVU6xTQTJq1ChwzsVM3PQdIoD8nN+9ezfMzMzQokULOu7/0MaNG8E5x4QJE75qxvDNmzfh4+ODokWLijIuSjLVc8ZUj3uPHj3AORerAwD9HYDSMX/z5g3c3d3RoUMHQ0XUa82aNeCci3JU+vpktH8O27Ztg4ODAypUqICYmBjFO4xPnjwJc3NzVK5cWVbSND0pZ0xMDBo0aAAPDw+dVahK6dy5M6ysrERnavrPavp2gdQX1bt3b4Mf/+HDh8PCwuKr9ksLDw/HkCFDjKbtdenSJVhZWaFv377isfTHT3sy6IMHD1CqVCm4urri9u3bBs2akZMnT8quM186V7Zt2wZnZ2eUKVMGcXFxhoyaodTUVDRv3hwODg5isv+XrjvSdUr7Z2coqampiI2NRZ06dcA5l62qT0lJ0cku/Ux2794Nzrlsj2elHDlyBJxzbN26FYBuO0z7uyouLg79+/cXk8u+Jxrg+Q9LSkpC27ZtoVarUblyZRw+fFinES81LlNSUtC7d29kz57daOq+TpkyBebm5ggICJBtIqfvQw4AtWvXRtmyZY2iwR8dHY3KlSuDc44RI0ZkOPIP/P3hL1myJIKDgzN8f4by+PFj/PTTTzAzM8O8efM+m13i5+eHJk2aGCpihii7Mkw1e0JCAvz8/JA/f348evQIwJc7RSZMmKD4LEbg7xI+lStXznDj3vTZpOXw0kxGpZhydjpnlEHHXRmmnP3WrVuwtrZG69atxWPpv4/S/75JkyZQqVRijwSlmPL5fvfuXbi7u6NAgQK4d++eePxz58348eNhY2Mjq9evhPPnz8Pc3BzVqlVDTEyMePxz502XLl3g4uKi+Ko1Uz3ur169gr+/P9zd3WWThD6Xe/Xq1VCr1YpfYwDTPWdM9bjHx8eLDsDu3bvLjnlGnWmlS5dG9erVkZycrNh18fHjx/Dx8UHBggU/W35IypeYmIhhw4aBc674Kjsgrb+ob9++4JyjRo0aCA0NlQ1Q6luRtHDhQlhbWxtF+XwgbUUy5xwhISGfLe8n5f/48SNq1aqF7NmzG3yQ6vLly7C2tkbTpk1FNZjPiYmJQd26deHu7q54P55Go0GjRo1gbm4uJgd97rVA2gCJMZXaioiIQK5cueDv7y8rM6hvoEqyePFicM6xfft2Q8X8ooMHD4p9arQ/hxlN1nr37h1q1KgBb29vxfpT37x5IyoCNG/eXFZiWXugTTr2p0+fhoODg2j7KunJkydwdXUVq6YA/f1g2o8NHz4cnHMcO3bsu+WgAZ7/KOnLKTIyEuPGjYOHhwfs7OzQs2dPnDlzBm/evJG9/sSJE8iXLx8qV66sRFwZ6SITERGBUaNGwcrKCiqVCr1799bJLbl79y5KliyJSpUqGTKqsHHjRjErR2rwhIaGokSJEuCcIzg4WPbBlVYzSB/w8PBwlCpVCsHBwaIGqRKkTBs2bEDmzJmhVqvRuXNnnc6OlJQUkf3169coXLgwGjZsqOhsNGk2yMaNG00u+6dPn0zmuKevRWuqx116H1KnWM+ePWXPZXRzHhMTg2zZsqFFixaGC5uB3r17g3OOSZMmyR7P6GblwYMHsLOzM4oZXqaYnc4ZZdBxV4Z0I2WK2YG0/C1atICZmZlY1SDJaCagNGsz/Xs1pP/C+b5jxw44OTnB09MT27Ztkz2nb5++0NBQqFQqWUezUubMmQPOOQoWLCjKrUi0zxspu1TKRenBKcC0jnv6zdl9fX1hb2+PRYsWicfTn+/S/1+8eBGWlpZir1Olmeo5Y2rHXfqOOXfuHCpWrAjOOcqWLauzp5H2/fWLFy/EBEqlV9nNmDFDdLZeuXLli4NNcXFxsLa2xrhx4wyU8MsGDx4MlUqF/PnzY8mSJXj27JnseekYf/r0CYMHD4anp6fi5bi19evXD5xz1K1bF1evXpU9p++z+scff4Bz/tlVS9+bRqNBXFycKOE0YcIE0Tf0uWow586dA+ccf/zxh8GyZpTl8OHDsLOzQ65cubBp06avWtVSoEAB/Pzzzz864lfbuXMnLC0tUbRoUVy6dEn2nL7P7qdPn+Di4oLOnTsbKuIXJSUliX1qGjVqhPPnz2c4uCk9vmjRIlhaWopyxYYkXT/OnTuH+vXrg3OOLFmyYO7cuXpfn5KSgsmTJ8Pa2vq7DpD8G5MmTQLnHJ06dZJVxcpoFdujR49gZWX1Xe+ZaIDnf8C7d++wbds21KtXD1ZWVnB3d0eTJk0wevRobN++HWPHjoWvry9sbGwUn7Woz5o1a0R9dE9PT/z2228IDw8XI8txcXFixqi08aIhJSQkwM7ODt7e3jr7HT18+FCUynN0dET37t1x48YNnb9jyZIlsLOzU6xklTQDSntk/K+//kLJkiXBOUdAQAAmTpyot4GzcuVKODg4GM2sC4Cy/0jpZ2lpNxSMPXtGpMZPpUqVZJ9PffsJAUDZsmVRs2ZNJCQkGDKmDmmmDeccv/zyi2w2aEbZCxQogKZNmxoypl6mnB2gc8ZQ0ucxpeOuXcLB1I67NlPMLmW6e/cu8uXLB845fv3118/OYJT4+PigU6dOBsn5JaZ0vgN/tyFjY2MxY8YMODk5wcLCAt26dZPNKk7fSfX777/D3t4ee/fuNWhefaKiokTZjKxZs2LixImyTsz07Z5Zs2bBwcFB0c4FqUP748ePmDVrlkkdd2kwYdOmTfD39xcrBLTvR9MPai5ZskTRPRukz56U6+3btyZ1zkjnwadPn7BhwwaTOe7aXr16JfbXsbS0REhIiN6VLnPmzIG1tbWspJtSUlNTxYSJihUr4tChQzp7IGm3G+7evQsvLy8MGTJEibgy2ufC2rVr4e/vD3NzczRo0ADr16/XWY129OhR5MuXD0FBQYaO+lkvX77Ezz//DM45SpUqhTVr1uDt27fiee3BQQCYOHEinJ2dcfbsWYNnTUxMRN26dcE5R7t27cSeJFJOiXS+XLlyBc7Ozpg/f77Bs+qza9cueHh4wNPTE1OnTsXLly9l7Rbt/XjCw8MRGBiIhg0bIikpSanIgkajQXR0NPr06QPOOQoXLozVq1frrObR/rzGxMTAz88P7dq1+6qyl4aSlJSETp06gXOOEiVKYN26dTqDN1LbLTU1FUOHDoWHh4fiezpGRUVh2LBhcHZ2Bucc/v7+WL58OZ4+fYqXL18CSNuDPXv27ChbtqyiWbW9fPkSNWvWBOcczZo1k1WhAv7+vGp/FvLkyYOOHTt+tww0wPMfon2ifPr0CYmJibKbvPv372PdunVo0aIFHBwcwDkXv0qWLPlVtb6VEhERgf79+yNLlizgnMPJyQlBQUEICgqCj48POOeKjfpLF01zc3O0bdsWCQkJOo35TZs2IVu2bKIh2qBBAyxbtgwLFizAgAEDYGdnB39/f4Nnv3v3LiZNmoSiRYuibNmymDlzpmy5OwDMnj0bbm5u4JwjV65c6Ny5M44cOYIdO3Zg3LhxcHFxQe7cuQ2eXV/jJn1N1NmzZ8Pd3d3osiclJeHAgQOYPXs2Ro0apXdjOGPN3qFDB9SsWVPWUZaYmChrzBhr9vSkzHfu3EFISAg458iePTumTp0qW5qs3Zl2584dBAYGombNmkpE1nHt2jWUL18enHOUKVMGa9eulW0ImpycLD4rt2/fRoECBdCoUSOl4gL4e4bO1atXTTb7gwcPxH4jxnrOSP9++trRpnLOaK9m1a7VbezHHYDejatN5biPHz8eU6dORWpqqjjfTSW7Nuk8OH78uGgntmzZEocPH5a1jbXbazdv3oSfnx9CQkIUySyRPrN3795F69atjf58T0lJEcdUu3Nmz549yJs3Lzjn8PX1xW+//SbKbUi5L126hEqVKiFHjhwGz62dI73169cjU6ZM4JyjcuXKWLp0qaxzBwCuX7+OqlWrImfOnIaIKnxp5v+BAwdEp72xHfcxY8Zg5syZeveqOX/+vOgYsba2RseOHfHo0SOkpKSI9tqZM2dQrlw5ZM+e3aC5tb179w4RERFISEiQvQ9jPmeAtM+mdI+nfQ08deqUGMQ3xuOufY/34MEDWcmqI0eOICAgQPRnVKhQAdOmTcOECRPQrl07WFpaIjAw0OCZ05OOo0ajwezZs+Hh4QFHR0cMHjwYYWFhelc4LFu2DHZ2dkbXP/Pp0ydcvnwZPXv2hKurK6ysrFC0aFF07NgRv//+O7p37w5PT084ODjorJIxFjNmzICzszMcHBzQvn177N69W9amAYCwsDAEBwcjT548Bs2mPQCrfX/q6uqKZcuWZfjn5s+fDxsbG8VLhGn3B4SGhor9DStWrIjt27fjxYsXOgMga9euhaOjI8aPHw9AuRKz+syfPx/29vZwcnJC9+7dcfz4cb3Vdvbu3QtPT08MHTpUgZT6afeVLViwANmyZYONjQ3atGmDXbt2iYESyZkzZ1CkSBGUKlXKIPmSkpJw584dbNiwAQMHDsScOXOwdOlS2cSUS5cuoUWLFmKgx8PDA3nz5oW3tzc45/Dz80NYWJhB8n6tyMhIMZBcpEgR/P7773jy5Il4Xntw89y5c8iZM+d3nVhGAzz/Qdu3b0dwcDDKlSuHevXqYe3atbLnY2NjERkZiQ0bNmD79u3YtWuX4suW03vz5g3u3buH27dvyz4QN2/exOTJkxESEgI/Pz94enqiRIkSWLt2rSKb+N26dQucc/To0QOdOnWCg4MDQkNDxfPpv8DmzJmDUqVKiYuU9KtZs2a4cOGCQbM/evQIRYsWBeccmTJlQtasWcE5x8iRIwFAdk68efMGgwcPRubMmWW5pRlfhq4P/PLlSyxbtgyPHz/W+7z2F9qbN28wfPhwo8n+/PlzNGzYUCeLvg3tjC17VFSU+PfVajWGDx8ue167wWNs2SUajQbh4eH49OmTbOkskLas1sbGRszsWrhwoezmXaPRYNCgQVCpVEZTGkQilU2wsbFBs2bNdMqzAMDIkSOhVqtFOUljYcrZp0+fbrTnTPqOpfTf88Z83O/fv4+QkJAMN5g15uP+5MkTVK1aFaNHj9aZdAAY93G/c+eOmCn3+vVrneeNOTuQ9nN/8uQJEhISZMc9Ojoabdu2hUqlgo+PD4YMGYITJ07o/PmxY8fCzMxMbJBqaNHR0UhNTdVZgTN58mSjPN9jYmKwc+dONGvWDH5+fqhTpw769Omjs5p+xIgRIr+dnR3atGmDESNGYMCAAciaNSssLS2xefNmg+UGdAe+Jdrt9jdv3qB79+5QqVRisGTw4MFYunQpJk+ejLx588LMzMzg15iUlBQ8f/4ckydPxtixY7Fjxw69r/v111+N6rjfvHkTnHMMHjxYtids+mvkwoULReUGqcx1mzZt0KZNG7i6usLOzk6Rz+iHDx+watUqlC5dGk5OTqhWrZrO6qc3b96gW7duRnXOREdHY/Pmzahbty6yZ8+OMmXKoEWLFli6dKnsdXPnzjXK4y7d082dOxelSpXC9OnTZasZgLT9gapVq4YsWbJArVaLiZedO3fWWznDUPR1VEdHR2PHjh2oVasW1Go1cuTIgX79+mHjxo0IDw/H69evxcx0Qw8ufIt3797hzJkzGDhwoBjY5JxDpVIhODjY6O6TAHl58YsXL6JRo0ZQqVRwcnJC1apVMWPGDBw9ehRLly5FkSJFoFKpDFodRjrX37x5g2LFiiE0NBQvXrzA2LFj4eXlBc45ChQogJUrVyIsLEysPvrjjz+QN29eBAQEGCxrevrO9dTUVFy9ehVDhw5FlixZYG5ujqpVq2LcuHE4ePAgrl27hilTpiBr1qzw9vYW798YBni02wgXLlwQe39lz54dnTp1wrZt25CUlIS3b9/ixIkTKFKkCBwcHIxiL3Bt0jGNi4vDqVOn0LZtW9jZ2cHR0RGVKlXCoEGDsHHjRowcORI+Pj6wtrY2SB/NjRs3xIQO7WsH5xz29vbo0qWLrG/39OnTmDp1Ktq3b4+AgADUqFEDEyZMMGj5xK+h3YZcuHAhvL29YW5ujho1augM9Hz48AGtWrWCSqX6roPhNMDzHyFdhFasWAFLS0twzpE7d27xgQkKCsKRI0c++3coeTHVXqK5cOFC5M+fHyqVCm5ubsiZMyd69eol+5BLsyzevn2b4SbuhlChQgX4+vri+vXriImJQb58+ZA7d24x8ixl0/6SiImJwdmzZ3H69Gls3rwZN27cyPBG80eqVq0aPDw8MH/+fCQnJ+PKlSuoW7cuvLy8xAwvjUYja0S/efMGmzZtwpYtWzBv3jwcO3ZMp5PcEObNmwdra2u0b98e+/bt0+nI1EcqVah09jp16sDCwgJt2rTB7t27MW3aNJQrVw6cczRu3Fgcb+3Po7FkB4AhQ4ZArVaLFTpeXl5Yv369eD59J3JUVJTIPnfuXEWzh4aGom3btvDx8YG7uzuqVq2Knj174tChQ+I1z58/F7MupJkirVu3xs8//4xChQqB87T62YaW0THTbkhcvHgR1atXF9n9/PwwbNgw9O7dG0FBQeCco1atWoaKLDx48ADDhw9HvXr1UK5cOSxbtkznmmes2SMjIzF58mR07doVffr0EXWYtb93Xr16hRYtWhjdOZMvXz5YW1vLOvBSUlJkA7HGetxLly4Nf39/nY4Z7fPdWI977dq14e7ujjFjxsgeT3+zWLVqVaM77hUrVoSjoyOsra3RqFEjxMXFyVbyAMZ7zpw6dQohISHw8PCAi4sLhgwZIvucxsfHY/PmzShWrJiY2BIcHIx58+Zh2rRpqF+/PtRqNapVq2bw7BcvXkSXLl0QEBCAzJkzo3bt2jqDUC9evEDTpk2N6nzv1KkTVCoVLC0tkSVLFvj6+op8BQoUwLp168RrX758iSFDhqBkyZLw9PQUrytdurQim+POnDkTW7duzXBymPa5c/v2bbRp0wZeXl6wsrIS2QsWLKhIOZxp06YhZ86cIoetra0oeavRaGSf1/DwcAwbNswojntQUBB8fX11SpZItK/vkZGRmDdvHpo2bSoGHRwcHFCtWjWdPVcMpWPHjlCpVMiSJQuKFSuGLFmywMXFBRcvXgQgb7eHhYWhVatWRnHOdOzYEWq1Gm5ubihbtizKli0r8uTJk0e2Z8fbt28xd+5coznu2ivtbW1t4evri5s3b4rntVcMJiYm4vLlywgLC8OhQ4fw8OFDxTuKpevItm3bMHz4cNnq3nv37mH+/PmoVKkSzMzMwDmHhYUFXF1dwTlH/vz59U5EMEZJSUk4fvw4zp07h5MnTyodR0j/80/fX/Dhwwfs379fp6NZ+mwotR+fNJlm8eLF4rFz586hffv2YqAnU6ZMyJMnj1itmTNnTpw5c0aRvMDf5/qCBQswbNgwWcf7hw8fcODAAfTt21f23WVubi4mrkj34kr0iX2JRqNBZGQk1q1bJ0rRS6uqnJycwDmHt7c3Vq1apXRUnVL6Ujte8uLFCxw6dAgdOnQQE0C02wWfWyX2vWzfvh1+fn5wdHQUVV62b9+ODRs2oHv37rCwsBDt3PR7aJqS5ORkhIWFoUePHrCzswPnHO7u7qhduzaqV68OLy8vmJubo3///t/136UBnv8A6YMcHx8PZ2dnlCtXTlwkd+zYIWbQm5mZoX379rJVD8ZQ51KbtKGcv78/GjZsiObNm4sPhKOjI5YsWaJ0RGH79u3gnGPKlCniOK5atQoqlUrv8kx9m+MqRdpYddy4cbKZoqtWrUKuXLmwcOFC9O7dG4GBgShZsiSaNm2qs8Gckq5cuSK+jNzc3NCnTx9cuHBB9l60l/QbSy3Uffv2gXOOoUOHyr6AHz16hEaNGoFzjl27donHjSm75M6dO8ibNy/KlCmDvn37is9n6dKlZRvGbtq0SbFZ0PocOnQIrq6uUKlUKFq0KIKCgpAjRw5xHlWuXFm2iu7ixYvo2bMnypUrh8yZM0OtViNfvnyYM2eOwTcePHv2LHr16oVz587pLf2U/gbm+PHjqF+/Pnx8fMTPJ3v27Bg9erTOcuwf7eTJk6JEj3TTyjlH0aJFcevWLZ38xpT9woULoiNEagTny5dPDDqkpqbKPp9XrlxBr169jOKciYqKEiVNOU+ru6x9DU9KSpJlP378OOrVq4ccOXIoftx37NgBzjkmT56s9/qXvgSqMR33/fv3g/O0TXGlTtb0N6va2Q8fPow6deoYxfkutWnGjRuH7t27w8bG5rN7cxjTZ/XIkSPw9PSESqVC2bJlUaRIEXDO0atXLyQnJ8v2zHj//j1WrVqFokWLiptI6aarZ8+eePr0qcGzS+30UqVKoW7duqIDxMbGBq1atZINdF66dMkozvfdu3dDpVKhdevWiIyMxNu3bxEREYGjR4+iXr16sLa2Bucc5cuXl+3r8fLlSzx48ADXrl3D7du3dcriGEJMTAzy5s2LXLlyYdCgQRm2H9P//6NHj3D69GkcPXoUx44dQ1RUlME7kPft2wdbW1sUKVIEK1euxPr161G4cGHY2Nh8djPzV69eKXrcpevL1KlTZbPotUmTD7QnIbx//x6xsbF4+PAhIiMjFbtv3bVrFzhP2wtDWgG+Z88eWFpaYuLEiQDSJuJ8+PBBVsL47t27OHnypGLnzK5du6BSqdC+fXskJiaK76OrV6/KBnqCg4NlAycfPnwwiuMuqV+/PvLkySP6N9KfO9rH3FhI5/m9e/fENX7//v2y16SmpuLZs2c4e/YsBg4ciGbNmqFt27aYMmUKHj16pETsb5bRfarSg2vajhw5gp49e6Js2bKoW7eu3knPYWFh2LBhA/744w+sWbMGL1++NOhgg/bxGjx4MMqUKSObcCs5ffo05s2bh5CQEAQEBKBSpUoYMmSI7POrlKSkJFE6q1atWjplKj98+IDXr19j9+7dGDlyJGbMmIEVK1bobA+gBO3VQw8ePMDOnTt1yoVJjh49iq5du6JNmzaoXbs2Ro0aZXTlCLds2YJ69eqhQoUKqFmzpmwyrvReo6KisHPnThw8eBB79uzRW3rue/vw4QOyZ88OPz8/HD9+XO9rnj9/js6dO4tBwDlz5uDTp0+yfjFjur5I4uPj8fTpU1y7dk32eHJyMh48eIAhQ4agUKFCyJUrF9zd3VGlShWsXLnyux93GuD5D5BO8LFjx8LV1VXM1tVoNLhw4QKsrKzQuXNnMcLv7u6u07GvJOmDOm/ePJiZmaF3796yxyMiIjBy5EjY2dlBrVZj0aJFAJT/YGfLlg3ly5fXufD37dsXnHMsXLhQb3kWiZL5x4wZA2trazHTQ7qwSOeLNBOhbNmyyJMnj7gBkDqtlB6o2rt3LywsLNC1a1c0bdoUZmZmyJs3L6ZPn467d+9muD+P9n+V0Lx5cwQEBIgLv/YN07t372BnZ4cmTZroHN/0mZU+/rNmzYKvry/Cw8Nx4cIFMTDLOUfHjh1x4cIFsbRdX+NUCXny5EHRokVx+PBhAGkNjPj4eGzYsEHMOpfyazdGX716hfj4eDx8+FCRjiggbek952l7pc2YMSPDczy9u3fv4vnz57hy5YpiN79+fn7w8vLC/PnzERERgbNnz6Jbt27gnKN48eKiQzL9ezCG7IUKFYKHhwcmTZqEP//8E4sWLYKFhQWaNGny2YHX169fK37OAGllzDhPKxEj7bvXtm1bWf16QL5fjDEc9+zZs8u+W/Vd7/R1EBrDcW/SpAkCAgJw/fp12ePJycnYsWMHBgwYgAEDBmDSpEmyn8PNmzeN5rhL/37x4sWRI0cO3L59G0DG3zvGcM7kzp0bhQsXxsGDBwEADx8+RM2aNZEzZ06xt8G7d+8QHh4uO9/PnTuHc+fO4dChQ+J9Kp09ISEBSUlJmD17tmj3Zs+eHTNnzpSVtZLei1Lne+XKlVGsWLEMO5WOHDmCBg0agPO0fTNnzpxpNPcdAMQqNLVajdy5c3+2/ai911F6hm7bFCxYECVKlJBNqHn48CE8PDxEqbbz589jxYoVOHPmDO7fv28UE4WyZcuGoKAgcb5od5w+e/YMQ4cORbly5UTZldOnT4tBcqXbjwBQpkwZFClSRDbYqtFoULFiRbRu3Ro9evRAQEAAvLy84Ovri65du2a4OsyQ76d8+fKy3NqbyN+/fx8eHh5iVWNAQAB27dplVGWSgLTz28/PD3369JFlSk1NxdatW1GrVi3kzp0bzZo1w/PnzxVMql+NGjWQNWtWsTfK546rMV0jTZ10jTl16hRcXFzAOReTUTjnaNiwoWzygT5KfAYuXbqE8+fP6+zJoa8NZmznS1JSElavXi22InBxcUHLli2xf//+Lx5Lpa830vGdNWuW2LZA+hUSEqK3raN05vSkc17qN5AmXkn/X7hw4S+uCvzR76ldu3Zwd3cXZZy1+0rTT2zev38/smbNiqxZs4qVssZGOuZHjx5F3bp1YWNjAxsbG/z0009Yvny5zuDNhw8fEBkZiYiICL2Tdr8HGuD5j0hISEDDhg1RuHBhWd30bt26iY0cP378KDZqs7KyQubMmWX7xSgpMTER3t7eqFevnqhNmH529NmzZ5EzZ07kyJFD8QbchAkTYGlpKStvIGW9e/eumBl47949pSLqJV1AJ02aBDMzM51VOX379tXZWyUlJQULFy6Eg4MDnJycFNs7Bfj7yzc0NFSsdnnx4gUWLlyIwMBAqFQqVKxYEevXrze62VwajQa1a9dGmTJldJ6TvhyaNWsGLy8vWTku6Wf28eNHPHv2zDBhvyAxMRG+vr5o3749gLT8a9asQenSpcVqQc45fv/9dwBpZduU7GDYvn07rK2tsWDBAr3Pp6SkYNWqVeImt0SJErKybUo7ffq0KJOhUqkQFBSk9xyXzhVj6MwB0jam1HfcU1NTMW7cOHDOMXfuXNlzxpTd1tYWc+bMkT0+YsQIBAUFISkpCTdu3MD8+fMxceJEHDt2zOCrFr4kMTERdevWRdWqVbF69WpRVsDS0lJWdmLcuHHo2bPnD2tofosJEybAwsICq1evFo9J50RUVBRWrFghZvJ26tQJO3fu1LtXjBI+fvyIKlWqoHz58rLH7969Kyv7KP2yt7fHjBkzFEorN2HCBKjValmbZt26dTAzM0O3bt30/hlj+ayuWrUKZmZmWLhwoc7j+fPnx/Dhw1G5cmXY2toiR44cqFy5slGU0QCANWvWwNLSUie7pEGDBvDy8oKjoyPMzMwwcuRInQFaJURGRqJ48eKyUnz6vn9SUlKwaNEiZMqUCTY2Nhm+T0NLTExEzZo14efnh/nz56NYsWJG3X6UbN26FZ6enpg9e7Z4TOo8aNSoEVq2bImhQ4fKrjPVqlXD+fPnlYoMIK0cHudcVgpMyr1mzRpRUtzJyUmsPC1YsKDim4VL7ty5g5w5c6JJkybiMaljVdqXIVOmTKhVq5YoA8w5h4+Pj5ihrMT18sGDB8ibNy+aNm2q9/k3b96gdOnS2LRpEyZPngzOOQIDAw2+ivFLXr9+DU9PT4wYMUI8FhkZiT59+sDa2hr29vaiE7NatWo6+/Mo6eLFi/Dw8MDYsWPFpD7pXvbevXtYtWoV1q5di9DQULF33Ocmh5JvFxAQgDx58ogO5dWrVyNXrlxidUC/fv1k13wlJ1Fu27YNnHPkzZsXXl5eYh8jfZMMjGUgVurDkPZUsbGxQbVq1dC4cWNRgt7LywsDBgzQu7LBGEjH8s6dO1Cr1fDy8sLgwYMxYMAA5MuXT6yoHjZsmNj3SPvPKT3xVltqaiq8vb1RpEgR7N69G0lJSTh27BiaNm0q9idr0qSJ7J7VECt3gLRrnqurK3r27CkmJuk7f7W/Lzdv3gy1Wo2goCCDZPwW0s89Pj5erNIsXbo0SpQoIdpgxYoVw759+z775783GuD5DylWrBjKlSsnfh8fHw87OztZB8KZM2fg6emJChUqgHOu2IzF9Pbs2ZNhZ4f2l5o0Ir127VpDRxRiY2PFzI+MZp+fPXsWLi4uKF68uN6lnUpbsmQJOOfo1KkTPnz4gLt372L16tViloL0vrQv+AcPHgTnHK1bt1a83Nyvv/4KzrlYrSZ1to4ePRre3t6wtrZGq1atcOTIEcX2e9Gndu3aqFGjBgD9jRrpJljfLJH169ejbt26Oo0jQ5NyS4Oc2mXNEhMT0blzZ1FTN1euXNi9e7dSUYW1a9dCrVaLY6d97mo3IsLDw9GrV6/PlhBTwtGjR2FmZoaxY8eKzSiN9RyXJCcno2HDhihSpIgoM6FdWis+Ph7e3t6oWrWq4qU/0ktMTES5cuVQs2ZN0dEhZbx8+TK8vb3x66+/isac1FnfvXt3oygzAPx9jq9evRo2Nja4dOkS3r9/j3HjxonOJ19fX0yePBne3t7InDmz7M8p4f3797C3t0e1atXESgWpEy0iIgJ169YVx9rLywt2dnaws7NDv379jOYmsXTp0qhcubL4fVRUFLp27QozMzP0798f+/fvx7Vr19C/f3+xH8aQIUOQlJSk2HXm/fv3sLS0RIsWLXRKfEmdxdJArDHdxAJp1+a+ffvC2dlZtLWkz+revXvF57NSpUro0qULatWqJR4bOnQoPn78qNhx12g0GDJkCFxdXXHnzh0Ausd36tSp6NKlC27fvo2goCCYmZkZxaBgamoqypQpg0KFCok9X9IfR+3v1vv376N48eJwdnbOsCSHoZUsWRLly5dHbGwsrl27ZvTtRyCt0oGLi4tom0jXvdjYWPj6+sLNzQ1VqlTB5MmTsWLFCgwZMgRubm7IkiWLYrNfExISRHnWkiVLytqMjx8/hqurK7JmzYotW7bg6dOnCAsLExvMZ8qUySj2IImNjYWXlxdq164te/zWrVtQq9WoVasWXrx4IR5//fo1evbsCc55hoMrhhAfH48sWbKIDImJibLPaUJCAqytrcWeC9K9eFBQkFEMJEueP38OZ2dnBAUF4ezZs9i7dy8qVaoEzjl+/vlnhIWFITU1VVTQCAsLUzqysGfPHjg4OMhKgn38+BHbtm2TtSHz5s2Lo0ePKphUP2OofvFPSN+lGzZsgI2NjWzfK2nlWu3atcVeiDly5MCSJUsUXxFz/vx5VK9eXVwzy5UrJ1s1aCxtXX2KFy8OPz8/LF++HEBaWyw2NhZbt25FQECAuLeeOnWqbMKqMZ1bv/zyC/z9/cWKaiDter5gwQIxiTVHjhxYvny5zp7DxkJa9aI9UU6ydetWVKxYUVx3Ro0aZdBsc+bMgZ2dnRi4/JqffXJyMjp06ADO+WfL0CpByj9s2DA4Oztjzpw5ou0rlfFzc3MTbQFDlVGkAR4Tlv5DsWnTJvTt21eUo5g7dy48PDx0akKWK1cOJ0+exJs3bwyW9UuOHTsGzrlYHfK5ToRMmTKhe/fuhoqmIzw8HDNnzszwpkNaeTR69GhwzvHLL7/ozNpRWlJSkphRnDNnTjg5OYnZLNob+gF/b9gaHx+PHDlyoEKFCsqE1nLmzBn89NNPOmVwYmNjERoaivbt28POzg6enp4YMmQIrl69ahSdyHv37sWvv/6a4fNHjhwB51x8ZrVXhZUqVQqZMmUySM6vERsbixw5cmDw4MGyx6UBkrp168Le3l6siNHebNHQdu/eDc45Vq5cCUB3JmX6AcsNGzbAwcEBlStXVrwDEAB27twpanfHxsbi+PHjRnuOa6tatareGTfSse7VqxdcXFzw/v178Zz0nsPDw3Hq1ClFBkxevXoFX19f9OjRQ+e5WbNmgXOOqlWrokePHrh+/To2bNiAtm3billRiYmJRnOtB9JKKUkDy0DaLLuePXvC1tYWnHOoVCrZpppK3UBK35n29vYYN26cbLChWbNmUKvV6Nq1K548eYLnz5/jwIEDaNmypZh4kJSUpNhxl/7dHj16wMnJCREREQDS9gdycnLCgAEDdP7MpUuXUKRIEdja2so6PQ2tWbNm8PDwkN3QSu/nyZMnKFKkCDJnzmx09cUlgwYNglqt1slXo0YNeHp66mzQGhoairx588LR0RGnTp0yYFJdw4YNg7W1tdgbUzru0mewT58+8PLyApC2+Xn9+vXBOf/s3kg/mjS7fPDgweCci7Kn0nPpXyu9p2vXrsHd3R0NGjQwaF59Hj9+DDs7O/j5+YnHYmJivqr9qGRn1LZt21C5cmWdDLt27YK9vT1q1qwpBguBtFIgK1asgEqlUvS+KTQ0VAzQu7u7Y9SoUdBoNGjSpAkyZcokW9kDpA1ESBP6mjdvrlDqNKmpqfj48SPKly8vOqaePHmCzZs3o1SpUnB0dJTtC6P9HdSgQQOo1WpFOu5TUlKQkJCAcuXKIUuWLLL9dyVz586FhYUFQkNDodFo8PHjR/Tu3Rucc8VWsUv3nNL/S2bNmgVbW1uoVCpwzmFra4uZM2fKVh5v2rQJLi4uXyy7ZQhSdmlypLT/RVxcHDp37gwzMzMUKFAAEydOxPLly5E9e3YULlxYVoZTKRld4z5XqtIYaTQa9OjRAz4+PrJBvxkzZsDe3h4ajQZPnjxBw4YNRYe3r6+vwQeVpXtSaZA4JSUFy5cvR6lSpUTVnSlTpuj9M0qTrnfSKouZM2fqfV1UVBR69OgBMzMzZM6cGbVq1cK2bdsMtnLka3Xo0AEtW7bUaYsBaX0xY8aMEf1lFStWxIEDB5SKmqFbt27BwcEB9+/fB6C7b+mHDx8wc+ZMsT+uhYWF3j2pvift7UTs7e3FKqgvXU+k81zqx5EGhoxJcnIymjZtiiZNmshK/ANpFWx27dqFRo0awczMDDY2Nhg5cuQP74OnAR4TktGHQPsir10v+tdff0X27NllM9Bu374Nb29vzJo168eG/UYnT54E5xxjx44FoNuIkP4/IiIC2bJl06nDa2jasxU/l6NPnz7gnKN79+5GM9IvXeT//PNPjB49Gm3atEH//v2xYcMGmJubiyXM+jqL/f39UbFiRZ0LmBK0N05O/zOIiIjA5s2bUa1aNahUKhQsWBDz5883ipJ5nysndPbsWZibm+uUJ5o6dSo459i2bZshIn6RdLwHDRoENzc3scx348aNsLe3Fx04Bw4cEDf1Sq7kefDgAbJkyYLixYvLbp70zTiWHpNWiSndAQj83fG9detW8Zj2Oa5Wq1GgQAHMnz8fDx8+zHCDaEPSaDTo2LEjGjZsKPusalu5ciU452J2mnaJn8mTJyNv3ryKzMRMTExEx44dsWfPHgB/N/Bfv36Nxo0bQ61W4/Dhw6JjQaPR4Pnz52I/MGNZtSm1DdatWwfOuU65m5EjR4rVdtL3lNKdC3PnzhX7BdWpUwfXr1/HoUOHwDlH165ddTpaHzx4gPLly8PBwcEoNiQ+deoUbGxsxEC+NDirXQpB+4bx6tWrUKlUegcTDeHhw4dQqVTo06ePmByU/rN68eJFZMqUCQUKFBAdZ8bSuQCknd9qtRpNmjRBREQEQkNDMWzYMHDOMWDAAHFOa1+H9uzZI1YxK2n9+vUZzqJ8//49XFxc0LFjR3HOnDt3DtbW1vj5558V/xm8fPkSefPmRaZMmbBp0ya99yLpDR06FGq12ijKKk6YMEFMUNHO/rXfrUqIj4/Xuc5FRkaKDkqpQyd9iafq1asja9asis9OX7t2LfLlywdnZ2eUKlUKVlZWGDVqlMilfW1MTU1FlSpV4OjoaBQltw4dOgRfX18xCSFTpkywtbVFQECAznkh3e9JbRypLaGEVatWiT0PN2/ejKSkJERHR2PDhg1wdHREw4YNZWXPw8PDYWtri65duxo056dPn3TOzyFDhmDOnDlITk5GREQE1qxZg/79+2PYsGE6Jcbj4+MxdOhQeHt7K7rZfPr70vDwcOTIkQMqlQrBwcHIly8f1Go1GjduLCaCAGn7UuTOnVvRiVpS/0BiYiLu37+PBQsWYP369dixY4esLJUpad++PQoVKiR7LFeuXOjcubP4fUREBPLly4eff/4ZZmZmBr1flY758+fP4eXlhXnz5sn6vMaPH48cOXKIwSftbNr3rErr2bMnnJycxN5w+nI9ePAAOXPmRKZMmcA5F+VGldxrTfvffPz4MVq1aiXODWnwKf3EsVOnTqFLly4wMzODvb290fTvST5+/IiffvoJS5cuBfB3+yb9ZNb79++jT58+MDc3l+3p9yN16dLlH63EuXv3LjjnOmXTjUXXrl3FeSO1Y7SP9du3b7Fo0SKUKVMGnHNkzpz5h64QpwEeE6LRaPDq1SvMnTsXI0eOxP79+z/7+kmTJoFzjjNnzojH5s6dC1tbW2zYsOFHx9UrfcNHOvkjIiKQJ08e+Pr6ymr/ph/o2bJlC1xdXTF16lQDpv6yjEpTPH36VNRn7ty5s9hfyFhoX3wiIiLg7e2NVq1ayV4jfXEdOXIEmTJlQtu2bQ2aUZ+v2exWo9Hg4cOHmDt3LgoXLgzOuWxvIUP7mobL/fv3YW1tjenTp4vHzp8/j9y5c+vs62AMrl69Cs451qxZg8jISFH+Rnu1TmJiolHMcFm6dCk456hZs+ZXlbmLi4uDra0tBg4c+OPDfcHbt2/Rp08fnb3HtM/x/PnzQ61Wo3r16ti6datRrNC8fPmyzopAbdLMRmkvOOl6dP78eeTKlQvFixc3SE593r17p9P4kgZKhg0bJh7TvoZKeyVJe08Zi8TERAQEBMhmQkdERKBRo0bgnGPy5MkoXry4GOjZtWuXwTNqd7DGxsaic+fO4Jwjd+7cyJo1K8qWLStWbKa/2ZJmemuX4FBKSkoK2rRpA0tLSyxfvhwXLlxAYGCg3v3TpJuA/Pnzo0aNGorU3X/69CnWr18vZv3r+/c1Gg2mTZtmFLPp9fnw4YNYkWxhYSFWJmsPaupro2XJkgV169bV+7yhhIeHw8/PD2q1Gr/99pvotDx58iTq1q0LFxcXnc9j+/bt4erqquiqWOkzuHz5cqjVamTNmhXz58/X2Ycs/b4806dPl5XTU4L2z1q7Yyaj9qP03Vq7dm2sW7dOkcGGzw1eP3/+HLVq1cLQoUMByK+l0vtr0aIF8uXLJ/b4MDTt4/zx40eMGDECLi4uKFCgAM6ePQtA/8+lefPmyJkzJ96+fat4R2ZKSgoOHDiASZMmYdiwYbh69SoaNGiAwoULQ6PR6J10tnDhQqhUKrF6QwnJycmidJlUXihbtmzgnKNIkSKySUzS4ELu3LnRsGFDAIa7Nr5//x6NGjUSJSgfPnwIzjlGjhz52Vn+Ur69e/ciW7Zssn2SlKC9UbvUwbp9+3b4+vrCzs4OOXPmxNKlS2Ul8DQaDX755ReULFlSscFv7Z/zoEGD4OXlJdvLy9/fHzNnzlT8c/itFi9ejLZt24pqAHv37oWzs7NYoSad8xUrVsSYMWMUKwE1YsQIODs7i9Lz2u3cGzduoHPnzrCxsQHnHNWrV5et1DQGgwYNgoWFxRc7refOnYs+ffogNDQUJUqUgKWl5WfvEw1l+vTp8PPzQ4cOHWQVDwDdPksgrbzlmjVrvtgXawjSd/67d+9w8uRJTJgwAQULFkTFihVl1xnpden7Vg05+VnaIuJr+4WkY37kyBGYm5vrrMhXinT8Dhw4gKZNm6JTp06ycqzabYH0x7pfv35iQcOPQgM8JmTevHliI0rp18iRI0UnQfov3RMnTkClUiFnzpzo168funfvDisrKxQpUkSJ+ADSOpoyKqOyYsUKscHj3r17dV5z8+ZN1KxZE87OzoptBh0bG4vXr1/j1q1bSElJkeXIaOnys2fPUKFCBZiZmaFnz56KdbxqZ09OThabmwF/nzvSyPrkyZNlHVJv3rxB48aNoVKpjGbfpq+VmJiIK1euoH///op2iHxJSkoKkpKS4OPjI+p8R0ZGinIJSs5I+5zq1asjODgYs2fPBudcDIh8+vTJqMpUvXv3DiEhIeCco0qVKti7d6+s/Jf0Jaw96Jw9e3b06tVLqcgAvm6mvHSODxkyBJ6ennBwcEC7du1w/PhxxW/GPjcb8caNG7C0tJQ12GJiYsTKR2M651NSUrBjxw5Zybn0nZfS+5HqTxuTRYsWwc7OTpRpmTt3rtj/BUjr6B85ciTc3NwUO+7apVmAtOMpzXbSNzgvtX2klY9LliwxWFZ9pGvHixcvULVqVTg6OqJdu3bIlCmTKD8rlUqQXhsdHY2iRYsiODhYsdVT2rNAP3e9kFY11qtXT2+5HyWFh4djzpw5mDp1KtavX48DBw7AyspK3Hxrz2AE/p7QUrduXcXak5KrV68iICAAKpUKDg4OYo8sadBS+p6SrqXSjHxj6eA5fvw4/Pz8YGVlhbZt2+L48eOyQRAp97t379ClSxd4eXnpDAQZWvrzISPSd+vQoUPh5uYGc3NznX2qfrRdu3ahRo0aGf67qampsolx6T/LkZGRYr8hJVfCpL9HevTokagYoE16TVRUFEqUKIEiRYogMjLSYDm/xdSpU2Fubo5jx46Jx6TzPTIyEu3atYOdnZ1RTO47fPgwqlWrBh8fH5QpUwZ9+/aV7RskfSbevn2LXLlyISQkxKCrSZ49eyY2AK9YsSLy5s2L/Pnz6+ytpu+7KjQ0FDly5IC7u7vss6CU69evg3OO3r17y+61w8LC9FYROHz4MHLlyqXoBErtQXiVSoWqVati/fr1CA0NxYABA8B52mbhSu3l9W9ol4FesGAB3N3dZSXe3717h8DAQIPvR6J9jzxr1iwUKFBAtpI6/T30/v37ZfsIrlmzxqB5P0dql0ycOBGA7uoi6fwaNWoUChYsCCCtbGv+/Pnh5OSkdxKUIc2ZM0fslcI5R9++ffHq1SvxvPZ7Maa+DW2NGzcWpbelXwULFtRZkSblV2IV+J9//gnOOSpXrow3b97oPc/1GTFiBFxdXWXftcZA6qOTtiRYtWqV7P3oGxzUfvxHoQEeE3H+/Hk4OzsjT548mDZtGmbNmoWsWbPCzs4Of/75p+y12ifN0qVLxUZtFhYWqFSpkiKbtCcmJuLQoUNo2rQpfvrpJ7Rq1QqLFy+WzVSJj49Hv379xLLNDh06YM+ePbh9+zY2bdqEQoUKwdraWrGR/uXLlyMgIEDMoChYsCC6deuG9evXy0qW6ftgv3jxAs2bN1dseeHXZr906RKyZ88OKysrBAcHY/jw4Rg9ejTy588PGxsbgzd+vielS1N8iXTeNG7cGJkyZcLDhw9x+vRpODo6Kl5GRh8p77Jly8RyUzc3twy/zAwtoy/PyZMnw8HBAZkzZ8aoUaNw9epV2bkh5d6+fTtcXV0xYcIEg+TVltGKwC81CD58+IDDhw+jTZs24JxjzJgxPyxjRr5mdZ3k4cOHsLe3x8iRI8Vju3fvVuyc17d/hPZ/ExISxI2ivobx7NmzYW9vr8hs3YyyS4MJz58/h7W1NSZOnIibN2+iUKFCcHNz09lvR7vDx1DSZ09f0u/MmTOik0ffdWXq1KmwsLDQ2cfBEDI67pcuXRKrolQqFWrVqqW3k3Lbtm1wcXHR2cvMEPTtRab93/SPP3v2DA0aNADnHB06dBArCpUuq6FPeHg4PDw80KRJE9mgofSepY2XlWjT6Mv++PFjjB8/Hv7+/ihUqBBatWqFLVu26P3zI0aMgJubm8FKamh7/fo1rl27Jjo+pE7X27dv45dffoGVlRW8vLzQo0cPbN26VZzzqampmDRpEuzs7BTbCyZ9du3z/0vtlejoaOzZs0eRDjUPDw9RQjP9Hi9fY8uWLXBwcJCtPDWUZ8+e4dSpU3jw4AESEhIyHDDQ95nYunUr7OzsFLk2SrTzx8XF6dxH3Lx5E25ubsiXLx+2bdsm68yfOHEibGxsFDnfP5f75cuXSExMzPB6v3nzZri4uCjS9j1z5ozYV0+lUqFo0aK4deuWeF5fSapbt27hl19+Qf78+Y2mw3vz5s3IkyePqKCS/rzX/gw/evQIwcHBsLW1VWxwSjqmMTExcHNzQ4MGDWQVAy5fvgzOOcaNGyfOJWPbO0Wb9H70ldNfs2YNOOeyfQ83btwINze3Hz6rPn0+yYoVK3Dw4EG0bNlS7+u1v6sSExOxcOFCBAYGGnSywcGDB7F///4MJyLdv38frq6uyJ07t2ySmHZ7Pjo6Gp06dULp0qXFSp8FCxaAcy7bB1QJnz59QlhYGIYOHQpXV1eoVCp07NgRoaGhsnP9ayZDGZJ0LVm8eLFY3bVo0SL88ccfYjDQ2toa7du3l+1TqUTFACCtr7datWowMzPDwoULZe8jfR7tLSWKFSumaFWPjLx79w5bt25FtWrVxB6DM2bM0Fl1bei+MRrgMREFChSAn5+fbOTyzz//hK2tLdauXYvIyEjMmzcPAwcOxPjx42VLBhMSEvDHH3/gyJEjsnqvhjR69GhYW1uDcw61Wg17e3vY29uL2cPa9u7di0KFCslGoKVl5UrtHTR58mRYWlrCx8cH7dq1Q926dVG8eHGYmZnB3NwcVatWxcaNGz+798WVK1fwyy+/GHyg4WuzSxcdqQap9rHPmTMn5s2bZ/DNt6dPn45GjRrh4cOHBv13v4d/mv3EiROwtLREjhw5ULRoUdjZ2Rl8hvG3ZH/06JGopyvdXCm1Sbu258+f4+bNmzhw4AA+fvwo+9zt3bsXFStWhEqlQkBAAIYMGYI9e/YgNTUVsbGxOHz4MAoWLAhXV1dFZnenz57+eH6pUfby5Uts2rRJkVre35rd399frIp5/vw5GjZsqMg5L/37n8uuj3TdlMpxZs6cWZEb36/JHhISgp9++klMpJBWTiUnJys6IPtvj3vt2rWN5rin/8wNHToUlpaW4Jwjf/78mDFjBh4/fozr16/jjz/+gI+PDzw9PY3yfNd3nUlOTkZISAgsLCzQqFEjxfbjS59d38++devWYm9H7U7Cs2fPomTJknBxcVH8uMfFxekMtOkr0aN9vteqVQt+fn4Gvb4fO3YMbdq0gUqlgq2tLczMzFCrVi3MmjVLlJVLSEjAxo0bxc27SqVClixZUK5cOfj4+ECtVqNkyZIGbwN/LvuFCxc+225X2tChQ2Fubi7agl87QU96H9euXUO1atUM3pY5evSomNQm/SpTpgxGjhyJ0NDQDI+59P9XrlxBpUqV4OjoqMhn9Fvyz5gxA2ZmZvD09ESVKlXQtWtXBAUFwdLSEmXLljXo+a4vd+nSpTFy5EgcOXIkw+MuXV9u3LihWKUM7eugs7Oz2IvP1dUVI0aMkA2epd80/N27d4qv3JG+PyMiIrB//37kyJFD9LlkNFP+8uXLcHZ2hpWVlaw0t1J27NgBKysrzJ07V/Z4njx5ULx4cdFxn5CQgFWrVikyyeDfevHiBbJkyQJHR0eMGTMGU6ZMgZeX1w9vh0VHR+tdybd3715wzpElSxZYWlpi4sSJGebQPue1K1D8aPHx8ciePTs452IfRn3Wr18Pe3t72NnZYcGCBTrPr1q1Cu7u7rJB7zt37sDc3Bzz5s0DoPx3cExMDI4fP4769euLfY9+++03sVesMUpNTUWtWrVQt25dnevgoUOH4O/vL86xMWPGyFYmKeH69euiItWwYcNkK4vTT2hNSEhAt27dxN63xkhaRT137lzkzJkTZmZmqFOnDjZt2iSb2GfIc5sGeEzAwoULRV1/qQEhNRibNGmCsmXLIjg4GJxzsWGyk5MTevXqZRQbmZ4+fRoqlQolS5bEn3/+ibCwMGzevBlBQUHgnIsVOekbQDt27MCwYcPw22+/YdGiRYqVBAkPD4eNjQ0aN24sm0X88uVLnD59Gk2aNBHHvkOHDp/9EjD0cshvyd6+fXtx86jRaPD06VOsXbsWV69eNXhJCom0b4qrqyvGjRtnFButfq1/mj0pKQlDhw4VN2dK7Ofxrdl37NiheCkzSXR0NEaPHi1WLnLO4eDggFatWmH9+vViVtqzZ88wd+5cFC1aVLzO1dUVWbNmhUqlQrZs2Qy+EuNL2b90DVSyYfyt2aXvsn79+sHCwgKbN28We9wYeiD/3x53ABg8eLDeG+Mf7Vuy79u3T7ymWLFiBs2pz/c47kOHDoVKpTL4ytjPZf/jjz9kNa1v3LiBZs2aiddJ+8RIeyAYes+jf3rcpfbL/fv3xeDJypUrDZj827K/fPkS5cqVg7m5OYoVK4ZmzZohJCQErq6u8PDwwLp164wie8uWLbF+/Xrcv39f9np9g65jx46VdYYYgrRJuIODA+rVq4f27dujfPnyYp+j3LlzY8SIEaKNGRMTg5MnT+KXX35B0aJFUaRIERQqVAgzZswweFm5L2XPmzcvhg8f/sV9F5T4bn316hVUKhW6dOmCW7duwd/fH0WKFPmqlXOfPn3CyZMnUaBAATg5OWH16tWGio3w8HBkz54dTk5OotZ87969UaRIETHQPXToUNmgq0Sj0eD+/fuiM1GJcqdfmz8sLEz8mX379qFKlSpitrSDgwNGjRpl0HKnX5N7yJAheo87kLY3Ur58+WBmZib2jlHCxIkTwTnHunXrMG3aNGTOnBmcc/j5+el836SkpGD69Ok4efKkQml1FShQAH369EHFihXFPi9Axp/X5cuXG/Tz+TnSPpLa+zLNmjULnHOdc6JQoUKoU6eOoSNmSGqfXL16FTNmzEClSpUwfPhw7NixQ9bpnZKSgjlz5sj2GMqfP7/Yr+9HadmyJTw8PHD06FHZ47dv38bAgQNRuHBhMRl3/fr1GU66ymg7gB9Fo9Hg48ePGDlypNhEXiKtjpbyxMXFYfr06XB0dATnHD/99BMmTZqEsWPHolu3bjA3N4enp6dsn56tW7fCxsbG4PdOn5Oamoo3b95g7dq14vpZpkwZLFmyRPGBZG3a58GQIUMwYMAA8fv058+cOXNgZ2cn7v9+//13RfsMNmzYIMrJlS9fHmvXrtXJc+/ePYSEhMDOzg5du3ZVKGnG0udNSEjAX3/9hV69esHOzg7Ozs7o0qWLziowQ6ABHiOXkpIiZgVJF0Ttk6RkyZLgnKNZs2ZYvHgxjh07hvnz58PX1xeWlpaKL3kE0jYQzp8/v9hIUxIaGgoHBweEhIQA+LvD73ODIEpcjKZMmQInJyfRCaNvdvG5c+dQokQJsb+HtCxbX7k2Q/rW7JUrVxbZtSk1u1valylHjhziZnz9+vWKz/D4Gv82+86dO2UbthnSt2ZPTU0Vs4mVqOmqrUOHDrCyskKlSpUwe/ZsDB8+HA0bNoSzszNcXFzQsmVL7NmzB0lJSdBoNHjz5g0OHjyINm3aoH79+qhevToGDBigyMy0z2V3dXVFSEgIdu7cKWo0G5N/mv3atWvw8PCASqWCi4sL/P39TSY7kHYz2a5dO6hUKrEpsbFl37FjB+Li4vDu3TsULlwYnHOcP38egLKr7f7NcY+KisKwYcPE95YxZt+xY4dsluXNmzcxYcIEjBw5Et27d8eyZcsUKYn3Pa4zT548wfjx4w2YOs3XZpcmJdy7dw9DhgxBrly5wDmHnZ0dqlatioMHDxpt9oyO+7x588A5R6VKlQyau0WLFvD09JRNeHj37h3+/PNPzJ8/X8wOLVGiBP744w9Z/vfv3yMxMVGxfY6+NnupUqWwdu1a2SoBpTVo0ABeXl5i1uqcOXPAOce0adO++Gc/fPiAjh07wtfX1+CD382bN4enpyc2bNggHktISMC9e/dEiUcrKytUq1YNR48eFTX4pT3YTpw4gT59+mD27NkGzf1P8h8+fFi2h8CtW7dw9epVRfZa/TfHHUgbUJwzZ44ik8q0PXv2DMuXLxfXkSdPnqBz585iJWyVKlVw/PhxAGn3r0WKFDGaMtYPHz5E4cKFxWTbrFmz6nToG/P9661bt2Bubo7evXsDSNu719bWFo0aNZK1VV69eoWCBQuiY8eOit/zAX/3UURHRyMgIEC2gs3W1hb169fXmU1/584drF69Grt37/7h+7/Ex8ejRYsWcHJykpW+03b48GF06NBBTABp166dUVQv0f75aq9GnDNnDnx9fWWDmJJz586hfv36sLW1hUqlEj+L6tWry17/8eNH9OvXD/b29l+cZPE9aX8G4+LicP/+ffz555+4ffs23r59K5779OkTHj9+jAkTJsDLywt2dnaoVKlShoPkhiSd8/fv30e3bt1Qv359MQCnXZVB+x4vNjYWPXr0ENdRpT1+/Bh16tSRVWpq06YNGjVqhGrVqok92fr166dYtYDPkT4P6duN0iqwevXqgXMOf39/DBo0yKCDgzTAY+R+++03cM6RK1cuLF26VHaTdOzYMXh4eKBcuXKyCxKQtmePi4sLcuXKpegqHmn10YIFC8SXhPaXRcuWLXVmEksfFGmvg8ePHytaPmb8+PFwcHAQDYD0gzbaXxRTpkyBvb09AgMDM/wSN6R/kl17hqBSpFzSjP7ly5dj1KhRyJo1KzjnCA4OFh2UxubfZtf+GRm64WzKxx1Ia1RyztGzZ09ZJ9PLly/FpvKcp+0XNGPGDJ3jq+Rm21+b3cvLCzNnzjSaATXg32ffsWMHPD09wTnHkSNHTCr7+fPnUaJECQwcONDgq0y/JfuMGTOg0Whw7949rF27FoCy+2T92+P+8uVLjB8/HkOGDJGtljHG7EpeV9L7X7rOSLNLY2JiEBsbi0uXLiEyMlK2J4+xZk9/3OPj47F9+3ZMnjzZoOf748eP4e3tjY4dO4rzOP2gcFRUFCZMmAAnJye4ubnpzMRVqkPzW7O7u7vLVkYp2RF78uRJcM4xevRoWadamzZtYGlpKfZn+tLn8vXr1wb97N6/fx8eHh7o2rWr2KtB+9/ft28ffH190bdvX9jb28Pf319vmzI+Pl6RyQffmt/Pzw/nzp0zeM70vtdxB5TfP1OSnJwsu1afPn1aVCuRBmW9vb3BOdc7MVEpr1+/xooVK8TEyYIFC2LRokUZbthuTOLj41GhQgW4urri5cuX6Nu3LxwdHbFjxw7Z6zZs2ABXV1fMnDlTmaDpSMezQ4cOMDMzw5AhQ3D16lVs3boVnTp1ks2mP3LkiCJtsvj4eDx48ED8/tmzZ+jfv7+s5FlcXBzWr18v9k5xcnLChAkTZCteDG3kyJHYvHmz7HqcnJyMfv36ia0XmjdvrnfQ4+TJk9i9ezfWrFmDo0eP6kxe2blzJ7y8vNCsWbMf/j4k2p+9HTt2iL0ypQGGWrVqYdmyZbJzJCkpCdevX0fr1q3h7e1tNNdIAOjVq5fInjlzZtl1Xern02g0sp/fX3/9pWg/n0ajkX0/7d27F5UqVYKrq6tY/eXq6oqff/4Z+/btUyxnRh48eICBAwciMDAQXl5eqFOnDoYPHy7KFQNp7zEyMhLr1q1D/vz5wTk36DYpNMBj5JKSktCnTx+d0e+PHz9i4MCB4JyLTbPSN+KlP6dU3Uh9q4+0N7wDgPr16yMkJATz589HgwYNULp0aZQqVQqBgYHInTs3AgMDwTmXbcRlaNLy5M/N+NQ+9qtXr4alpSVq166tyB4Y2kw1u3SOSEv1b9++DSCtvnSbNm3ErJBu3br98Jk334qyK6ddu3bIkSOHuOFOfw5Xq1YN7dq1Q+3atcE5z7CsnBKNt2/NLs2wMwbfI/uzZ88UKQvyvY67EtfLf5tdyU6G73XclbhR/7fXGSU3af1ex90Usvfs2VPv32MK2TM67obu9H7y5Anc3d0xbNgwAPjs4NilS5dQqVIlqFQqTJw40VARM2TK2QsUKIAiRYrgzz//BPD3z/38+fNwd3dHyZIlP1s6V6kB2StXrsDKykqUWU1/vt6+fRtZs2bFkSNHsGvXLuTMmROurq5if1mlB5L/aX5plYapHnclOyy1r8efPn1CRESELH/6PYzWrVuH8uXLI2vWrChSpMhXrWj70fR9pzx79gzDhw+Hq6srbGxs0KpVK+zbt08229sYB3rOnj0Ld3d32Nvbw8zMTLZfCpC2f0aVKlWQKVMmo+roDg8Ph5+fH/r27Sv6nKR9VbVn00t7qly/fl3RvD179gTnHMWLF8fMmTNl99TPnj3DzJkzxYr7gIAAnX2eDeHMmTPgnMPT0xO9e/eWVbZISEjA9u3b0aBBA5iZmcHW1hYjR46UrZL6nKtXr6JEiRLw9vY2aMe3dI2eO3cuHB0d4eXlhY4dO+KXX35BzZo1RX9rq1atdFYVxcTEKL5/TXofP37EnDlzRFnTzJkzY/HixbL2gfQ5TU1N1dnjxphcv34d9+7dw+XLlxEeHm5UGaXvpMOHD8PPzw+cc+TJkwcBAQGijGi2bNkwceJEnX13Hj58iBMnThg0Lw3wGDHtL8779++LC4+1tTWCg4Ph6OgoOg60B06kD+/kyZOhVqtx8eJFw4fH51cfAWl16aUBHGm0tkCBAsiZMycCAgKQJ08eFCxYEAULFlS0EfHs2TNky5YNgYGBOjXS9UlOTkb//v2hUqlw+vRpAyTMmClnT01NFYOY2uX9YmNjsW7dOlStWhWcc7i7u2P69OlGVbaKshteSkoKGjVqJCvxJV0TpS/mFi1aoF69eggLCxN7SPTt21eUa1PK/3L21NRUxa7v3yO7Uh06/zR7v3796JxRMLspH3dTzq5kG/J7HHeljv3bt2/h4ODw1eVir1+/jqCgINja2io+q95Us//+++/gnGPRokV6n9+zZw8sLS1Rq1YtvHz5EoDxrLq4desWOOcYN26c7HHpPJf2+JD2plm5ciWsrKzw888/GzyrPqaa31Rza9u4cSOqV6+O/Pnzo1SpUrL9lzQajWygJzExEX/99RdiY2MVHRTULoP04sULLFu2DOvWrcPOnTvFIP7ly5fRsmVLmJubI0uWLBg0aBAuXbpkNJ9ZbSkpKdBoNFiwYAFsbGzAOUehQoWwbt06XL16FatWrYK/vz/MzMywZs0apePKREZGwsfHRwz4pZ9EERUVhXXr1qFIkSJQqVQoXrw4Fi5cqNjPISYmBpMmTYKLiws456hZsybWr18vu6e+evUqBg0aJMqlK7FPzbZt21CsWDFwzpEvXz5MnjxZVjru9evXmD9/vtgyIleuXFi1apVsf5707Zc///wTtra28Pb2NuheiNLP+vXr17CxsUH16tXFII50Hbly5QoqV64MzjnatGljVIMMn/Py5UsMGjQI5ubmsLKyQsuWLXHw4EFZfmO85gDKT+z4Eu1jmDNnTuTNmxcbN24EkFau8tWrV2LvKTMzM0XKV6dHAzxGLn0H0v79+5E3b14xKDJq1CjZ0k3pgvrx40e0atUKLi4uX9Wx/yPoW320Z88e8fykSZPAOcfQoUNx9epVJCcnIzo6GhqNBrGxsUhKSkJCQoKiF6TU1FQkJyeLWv8hISFiI9mMXg8AL168gIWFhUE3wtWXxVSzS+7du4fFixcDgE6n3rNnzzBt2jSx9LFgwYI6y8iVRNkNr3HjxnB1dUV4eLjscenLuUCBAujQoQOAtPItlStXhpOTEy5cuGDwrOlRdmVQdmVQdmVQdmWYcvZ+/fqJAYev2Sj2zz//hJubmyi7ouTNu6ll12g0qFq1Kpo0aSLqtWt3Lmg0GkRHR6N9+/bgnGPq1Kmy55SWlJSEgIAAeHl54dy5c7JVU6mpqShbtiwKFiwoK1UkTQY0hvK/pprfVHNLA1Br1qyBubk5zMzM8NNPP4l+g4IFC4r9doC0Po70HffGcN6PGjVKdMJLv7Jnz44lS5YASCu9tWvXLlSoUAEqlQr+/v6YNWuWYv0b6Y9ZVFSUzmsePHiABg0awMLCQva+fH19MWPGDENF/SaNGzeWZUv/PjUaDZ48eYLJkyeLPjSlPX78WFzPHRwc0L59e7GyTrJv3z60b99etqejISUkJGD27Nnw8PAA52n7Xq5cuVJspQAAYWFhGDlyJHx8fMA5R1BQEE6ePJnh37dq1aoMn//RunTpgqxZs4rqNulXtaSmpqJv377gnGPKlCmKZMyIdM2IiooSEzy0Xb58WawE9/LywsCBAxVfrWbqpGM+YcIE2NnZYdWqVXpf9+zZM1StWhUWFhaijK5S13ga4DER6W8yZs+eDVtbW3DOUaxYMaxcuVLWoDt48CDc3d3RokULQ0cFkPHqIysrK3Tp0gWrVq1C7ty5xSZf0vvTXkaY/u9R2sCBA2FmZoby5ctnuLpFyittHJ5+NpVSTCl7+gZZfHy8zg2utitXrqBfv35iD4/g4GCDbtanjbIrk12yaNEi0ekhzUSTrF69GpxzrFy5Ujx+//592NnZoWXLlkpFFii7Mii7Mii7Mii7Mkwxu9QmvHjxIjw8PODt7Y1du3aJ5/UNfkj3IU2aNIGfn59hguphytkvXrz4VaW1GzVqBM45Jk+erMi+UulJx3zZsmWwtLREYGAgli9fjqNHj2L37t2oVasWzMzMMHv2bNnxv3TpklF0pJlqflPNrV11JFOmTChXrpwYzNmzZw9y5colBhUaNWok2zPCGM53aXBq+fLlUKvVKFu2LFatWoUDBw6IgWVPT09ZJ3ZERATmz58POzs7TJgwQano4tjv3bsXPXv2RMmSJdGgQQPs2rULb968kZXIu3jxIhYvXozff/8dS5cuxbNnz4xixr32d+iTJ09w7NgxBAcHw8nJCceOHdPZc1hbUlKS4ver6Z08eRJBQUHgnMPHxwcjR45EWFiYeF7pkv9A2iqRnj17wszMDDY2NggJCcGBAwdkrzl27Bg6dOgABwcHmJubo2HDhnoHEJXy9u1blChRAhUrVhSrpfT1ddy5cwe5c+dG2bJlkZqaahQDydI5HRsbixYtWqBJkya4fv263mxbt25FQEAAOOcIDAzEhAkTDFoKLz1juGb8Gx8/fkSZMmVQtmxZvSunpf8/ffo0nJyc0KBBA0VySmiAx8Rof+nGxMSgc+fOogFUr149nDp1SjyuVqt1Zgsakr7VR3ny5AHnHJaWljA3N8epU6fE80psqPktIiMjMWnSJLi6usLCwgL9+vXDkydP9F60Zs2aBUtLS2zfvt3wQfUw5ewZSX9h3b9/P1q0aAHOuUGX/P4TlP3HiI2NRVBQECwsLNCjRw/s378fBw8exNChQ2Fra4uaNWuKFY0pKSlISUlBgwYN4O7urtjMKMpO2Sk7ZafslN1YswPAhQsX4OfnBwsLiwxnPWu3J1u2bInChQvrnWFqaKacXaPR6Ex0k7KeP38euXPnho2NDZYvX65TpltJs2bNQrZs2cT9qVqtBucc48ePF/XppZxxcXGwt7cXZU2MoSPNVPObUm7p35sxYwacnZ1l9w/Xr1+HjY0NunbtimrVqon3M2LECKOa+JmSkgJvb29UrVpVVi3l1q1bUKlUGDZsmFjlIO29k5KSIltJZSjScZN+/hcuXICDgwM451CpVOIYV6tWDfv27cO7d+8y/LuM4TMqvY+VK1eKviXpV5UqVXDq1CmdVZvGkPtzUlNTsWrVKjG4WaZMGcyYMQNv375VOprMxYsXxcTtLFmyYODAgWI/cCDt2rJx40ZUqlQJzs7ORrGnsyQ2Nhb58uVDo0aNAOgfOJP+zJgxY+Do6PhVq38NQfoMd+nSBZxzdO7cOcPXAEB0dDSmTJkCV1dXcM7Fnn6GIH0+X79+bRSDk/9WZGQkChYsKAZuPvc91L9/fzg4OMj2WzM0GuAxQRqNRjYYcuPGDTHq7+DgIGbrDB8+XMGUf0t/szFr1iyx+qhs2bJYtmyZznJyJSUnJ+PQoUPYsGEDVq9eLTbFBdKWlW7duhWVKlUC5xweHh7o378/tm7ditu3b+PFixeYOnUqMmfOjOLFi1P2f5h91apVsuyfo32+REVFYffu3T8qYoYouzLZtUkNstDQUFSvXh1qtRpmZmaiwV+uXDlZ40a65vTs2RPe3t6K7iVE2ZVB2ZVB2ZVB2ZVhitnv3r2LCxcu4NmzZ2LW5+HDh1GuXDlRZz+j0qwXL15EgQIFEBwcbMjIgqlm12g0OH36NLZv345t27bh0qVLX/wzf/31F/z9/eHi4oLVq1cbIKUujUaDM2fOYMeOHdi6dasoB3PmzBnMnTsXISEhGDZsmKxEN/B3G/LQoUNwc3PD2LFjDZ4dMN38pppb26dPnxASEgI/Pz/ZCp3+/fvD29sbQFqHrNShKf2SSisp7fDhw7Czs8PEiRNljxcvXhz+/v5iU/qUlBTs2rULoaGhBs/47Nkz8f/and3FixdHpkyZMHfuXDx//hx//PEH2rZtCw8PD1haWqJ9+/a4cuWKbO8jYyG9j/DwcNja2sLV1RW//vor+vfvj2rVqsHW1hYuLi4YNmwY7t27ZxSD3t/i3bt3GDVqFOzt7cE5x7Vr15SOpEOj0WDTpk2ipGLhwoUxc+ZM2fn2+PFjsQrJkJO4o6Ki8PjxY1lWSWxsLPLnz498+fJ98e/p1KkT8ubNiydPnvyImN9Eum7/9ddfsLKyQq9evcQgrPYAir7VjQ8ePMDChQsNEzSdcuXKoVGjRnj9+rUi//73kpiYiFy5cqFkyZLisfSDh9LPqFevXvDx8cHr168VG1SmAR4Tln5vjC1btiBnzpyi897YfG71Uf369XHkyBEF06W5ceMGGjZsKLv5lsrgLViwQNTEfv78OWbOnCk2ZOWcw9zcXMyGKV68uEFHyv+r2UuUKIFFixbh7t27n12Sr+QmxJRd+RlR6QeFHz16hGXLlmHkyJEICQnBpk2bZLPRpMZ+fHw8atWqhcKFCyu22pGyU/ZvRdkp+7ei7JT9azx8+BCDBw9G5syZwTmHt7c32rdvL/YCunnzJnr16iU6nqTN0I8fP46HDx/i1KlTqFy5MtRqNc6cOWOw3Kae/ebNm2jVqpXY2Fz6VbZsWWzdulWsvpBor+zZvXu3mPHdrVs3g5avyih35cqVsWvXLr2rALTvXePi4jB48GBYWlrKZoAbiqnmN9Xc+tSsWROlSpUSv09JSYGLi4ts4OnRo0fIlSsX6tevD2tra8UzS/766y9wzmUT3NasWQPOOWbPni27P6patSoqV65s0HumW7duwdfXF5MmTRJ9AEDa6p3s2bNjzpw5ste/f/8eu3fvxs8//wwrKyt4enpi3LhxePLkieKTb/WZMmUK/P39ZRVHnj17hpUrV6JKlSrgnMPf3x+LFy9WtDzVPxUWFqZ46coviY2NxcSJE+Hs7AzOOWrWrIkNGzYounqhatWqKFKkCNatWydbAS31Q06YMAGcc0yaNEk2gUaj0Yg22N27d1GuXDmULl3asOG/YMyYMciZM6fOdgvx8fEYOHAggoODUapUKWzdulWhhH/bs2cPLCwsMH36dKPoK/qnpDKbvXv3hkqlwr59+3Reoz3oXLduXRQuXJhW8JB/J/3MhBEjRmDbtm0Kpfm8L60+6tOnz1fVnv5RihYtCjs7O3Ts2BErV67EggUL0LBhQ7F5YpEiRbBkyRLxhfHo0SOcOHECEydORLNmzdCzZ0/FGhL/5exFixbF0qVL8erVK4Nn+xLKrpx169Z9ceaKtvQNjH379sHLy0vvMucfjbJT9m9F2Sn7t6LslP1bSCu8S5cujSZNmqBWrVowNzdH4cKFce/ePfG6Q4cOoU2bNvD29haloKytrcUEMyVWBZhy9pIlS8LGxgYdOnTApk2bsGLFCjRu3BhWVlZiDxLtktbp7/v27dsnJssZQ25pY/b69evL9iBJn3vatGlwdnZWbK8pU81vqrn12b59O3r27CnuTZctW4ZMmTLh7NmzAP6+NpYvXx7r16/XGexU0qNHj2BlZYVffvkFQFpWDw8PVKtWDQ8fPhSv+/DhA0qVKoXmzZsbtNzT9u3boVKpoFarUblyZaxevRoxMTGIjY1F3rx5xeB3+tUVz58/x9KlS1GuXDmYm5sjV65cRtFhDMgnTSxfvhyFCxcWnfTaz925cwdTpkwR+5BUr14d27dvN5pyW/81jx8/Rvv27UV/XvPmzfHhwweD50hMTETbtm1hbm4u9gk6fPiw7DVXrlxBlixZYG9vj5kzZ+Lx48c6A5jDhg0D5xwbN240ZPwMSflGjx4NT09PWdm+3bt3i/aPtC8y51y276ChaLdlb9++jYIFC+LOnTs6zxk76Ttz6dKlcHZ2xtq1a3HkyBGoVCrkyZMHu3bt0ls+eenSpbC2tlZ0ZSxAAzz/Kca+h422z60+KleunCKZlixZAktLS50ZLUlJSTh37hzGjx8PPz8/mJmZ4eeff5Y13gBlS8v9r2Rv3ry5rHax0l8WlF05Z8+ehbu7Oxo0aCAr16DRaERn2ueW5d+8eRMVKlSAg4ODwQc1KTtl/1aUnbJ/K8pO2b/FnDlzoFKpMGTIEABpeSMiIrBixQqxebt2+ZVPnz7h3Llz2Lp1K3r27InOnTtj7NixePjwocFL4phy9gULFsDCwgKzZs2SPZ6QkIC5c+eKQSgrKyudUlDabbFXr14ZdL+mf5MbAI4cOQIfHx8UKlRIkT0mTDW/qeb+WrNmzUKmTJlke2A9fPgQefLkwZgxYxRMpislJQVVq1aFs7MzXr9+jQkTJsDKykpnP9Jdu3bB09Mzwz3AfqQXL16gZ8+eMDMzg42NDVq2bIm1a9cif/78YhBQ6j9Kf2/3119/Yfz48XB1dVV8gCd9thUrVmDnzp1o3ry57HHt/oyUlBScPXsW/fv3h5OTE2xsbBSdVf+/4NSpUyhQoIDim8xfunRJtk/QoEGDZNVprl+/jsDAQJiZmaFmzZqYNm0ajh49io0bN6JBgwZQq9Vo2LChgu9AP2ngafXq1bhy5QqWLVsGBwcH2NjYYPHixXj58iXOnTsHKysrdOrUyeD5pPbThg0bMG/ePPj7+4tys6bUTw2kXXOKFy+OqlWriklCy5cvh5OTE7JkyYIRI0bg0KFDiIuLw7NnzzB79my4urrip59+Ujg5DfD85xhDx+u3SH8jNWzYMEVWH6WmpqJp06bw9/cXHdnJycmyhkJ8fDwuXLiAnj17wtbWFj4+PrIZUoAyx/9/LXvOnDllMxmVQtmV9ebNG7i5uYFzDldXV/Ts2RO3b98Wz6empmZ4TsfExCA4OBienp6YN2+eoSILlJ2yfyvKTtm/FWWn7F8rKSkJAQEBqFWrlqyUD5DWFujbt6+sNMWXbtQN2Z405ezJyckICgpC4cKFZW0x7XujAgUKoGPHjqhRowY45wgJCdEZ+DP0oNT3yB0eHo6NGzfir7/+Mmh2U85vqrm/xcKFC8E5l826//3332FnZ4dly5YpmEy/mzdvIlu2bLC2toZKpRKreSR3795F3bp1YWdnh+joaIVSpu0vJnV4FyhQANmyZUOfPn1kr5GufdrXwKSkJIOXbZdER0fr3f9k7969YsUl5xzLli3TuQZqv4fo6Ghs3LhRVsqN/DipqamIj48HYPjvJm1f2ifo/v376NOnj2zVC+cc9vb26Nevn2yQ2Vi8ePECBQsWhJmZGVQqlSjNmX6vtcDAQLRv316RjBEREeCcI3/+/ChQoAAGDRoke97Y+6q18/Xt21fnOrl3715UqlQJarUa5ubmcHR0hKOjIzjnqFSpklh9qiQa4CFGwRhGdVu2bIncuXN/8XVv3rzBvHnz4ObmBm9vb7H5nZIXLMquDMqunFu3bsHOzg6lSpUSJeV8fX0xZcoUREVFiddl1Lg8ffo0Dhw4YKi4MpSdsn8ryk7ZvxVlp+xfKywsDFmyZMGwYcP0Pv/o0SM4OTmhQ4cOsselSSFKtuFNOXtsbCxKly6NypUr6zyXkpICjUaD6tWrY9CgQXj48CHq1q0Lzjlmzpxp+LBaTDW3xFTzm2rub/H06VN4enrC0dERHTt2RKdOnWBpaYkCBQooHU0vjUaDo0ePImvWrOCcI0+ePNi2bRvOnz+P7du3o3jx4lCr1TorrpTKumHDBtHhzTnHgAED8OLFC/Ea7Yl+St/ntWzZEh4eHjh69Kjs8Tt37mDgwIEIDAyEhYUF8uXLh507d+r8eaXzE+OQfp+gGjVqYMuWLaKM7sOHD7Fy5UoMGzYMy5YtM/gefN/q0qVLGDZsGFq1aoVp06aJwTTJhQsXEBAQgK5duyqUEFi5ciXy588vrjO9e/eWraI2xj29gL9zPX36FAMHDkSLFi3QrFkzAH+XWk5NTcWjR4+wZcsWtGrVCkFBQWjVqhVmzpyJN2/eKJZdGw3wEKOh9Bfxb7/9Jtss8UsXn3Xr1oFzjtatW1P2f4GyK8OUswNpsxCdnJzEjL7ffvsNZmZmoszjhg0bZDmNqTFB2ZVB2ZVB2ZVB2ZVhitnfvHkDzjm2bNkCQH+mIkWKoGrVqjqrfTUaDZYtW4Zp06YZLK82U86empqKChUqIEeOHGJ2v/Z+TfHx8bCyshIbbX/69Am1atWCtbU1jh07pkhm4PvkVvK8N9X8ppr7W92/f1/snWJmZoZq1arh8uXLSsf6rJiYGPTv3x/29vay1QAeHh4YN26c0vFkoqOjMXPmTLHStFatWti4caOso1jp8yQ+Ph4tWrSAk5MTnj9/rvc1hw8fRocOHeDi4gLOOdq3b69Tgh5Q/r0Q46C9T5CdnR06dOhg9IM5ktu3b+PMmTPYvn273pVF0vdAXFwcBg4cCHNzc1y8eNHQMWWk64y7uzs456hatSo2bdpkVNeZjPTs2ROccxQrVgy5c+f+bAUbYyz7SAM8hPy/q1evwtzcHFWrVhWbOGo0Gp2Lj/aNef/+/eHo6IgbN24YNGt6lF0ZlF05R48eBeccPXv2FI+9efMGISEh4saqadOmsi9lfe9PCZRdGZRdGZRdGZRdGaaWXaPRICYmBg0bNsSAAQN0npc6Dpo2bQo/Pz+RU2obXLp0CXnz5kWxYsUMF/r/mXp2ABg/fjw455g7d67s+ZSUFHTv3h329vY4cuSIeP2xY8dgaWkpyp4Y+rwx1dwSU81vqrn/jbCwMJw7d07co5iC169fY8OGDZgxYwZWrlyJBw8eGO0xf/ToETp06ADOORwcHNC+fXucOHFC6VhCfHy8bP/XZ8+eoX///rhy5Yp4LC4uDuvXrxfl55ycnDBhwgRFy+ER43by5EkEBQWBc44cOXJgxIgRCAsLUzqWjLTC+8WLFxgzZgwyZcokSrJxztGgQQMcOXJErECWrjFdunSBg4MD2rZtq1j29B49eoSOHTsa7XVGn5iYGIwbN04Mgjs5OWHx4sX4+PGjeM2nT5+MbrKWhAZ4CNGyYMECmJmZoUSJErJO7PQ106ULqnQjr+RMOgllVwZlV8bVq1eRJ08ebN68GQCQmJgonjt//jyKFy8u9kAYOHCgbFaX0l/ClF0ZlF0ZlF0ZlF0Zppxd2qtD3yrdfv36wc3NDeHh4SJnXFwcBg0aBM45rl69atCs6Zlq9jdv3qBkyZLgnKNatWrYsWMHVqxYgRYtWkCtVqNPnz6y0n4AUKZMGeTPn1+hxGlMNbfEVPObam5ivLQ7vH18fDBmzBij3KNJmlVfvHhx2V4qQNrgz8yZM1G4cGFwzhEQEICNGzcaRcUJYnxSU1OxatUq5MqVC5xzlClTBrNmzdK5diqtfv36UKlUKF++PEaNGoUhQ4agcuXKsLe3h7W1NYYPHy5eu379euTJkwf169fH+/fvlQudAVO5zmh7/PixuO5YWlrqHZxSut2uDw3wEKIlMTFRzI7y8PDAtGnTEBMTI55PP1K+cuVKODg4YMeOHYrk1UbZlUHZlRMVFYW3b9+K36ekpMi+aFevXi024fzpp58wb948o5nVRdmVQdmVQdmVQdmVYcrZM7J48WJwzhEeHi46zfbv3w9XV1e0bt1a4XSfZ+zZIyIi0KhRI1lpJ845OnfuLCvHIp1DVapUQdmyZREfH69o54Kp5paYan5TzU2MV0pKClatWoW8efOCc46ff/5Z6Ug6YmJiMGnSJFGSrWbNmli/fj3i4uLEa65evYpBgwaJ/e/Sr3QjRNu7d+8watQoUVpR2mdYSdLqnSVLlsDMzEysvpS8ffsW69evR6VKlcA5R4cOHZCSkoKoqChcu3YNr1+/ViL2VzGF64w+J0+eRMWKFcXg1KhRo3D79m2lY2WIBngI0eP8+fOiZmTFihWxfv16ndc8fPgQTZs2hZubm1HVX6TsyqDsxkN702SNRoNhw4ZBrVaLG4KMNrc2BpRdGZRdGZRdGZRdGaac/cCBA+D87337Xr9+jebNm8PS0hIfPnxQON3nmUr2GzduYP78+Zg7d67OqiLp3Hj8+DEKFSqEGjVqyPZfUZKp5paYan5TzU2MV1RUFPr374/Dhw8rHSVD2nupSCWf0leW2LdvH9q3by+btEhIRsLCwsTeZcYgNTUV/v7+CA4OFqu805cECwsLQ1BQEMzNzXH27Fmlov4jpnCdSc9UVn0BNMBDSIaSk5MxdepUMTMqICAA06dPx5o1a7Bu3TqULVsWKpUKM2bMUDqqDsquDMpuXLQ70549e4YKFSpg8uTJCib6epRdGZRdGZRdGZRdGaaY/eHDh3ByckLr1q3x6dMnrFy5Emq1GhMnTlQ62heZcvb0JYYWLlwIa2tr/P777wol+jqmmltiqvlNNTch3yp9yaeRI0fK9lJJSkpSMB0h/9zZs2eRKVMmWQk2ifY1/sGDB7Czs0OfPn0MGe9/mjGu+kqPAwAjhGQoOjqa/fbbb2zDhg3s7du3LCUlhTHGmKenJ+vSpQsbNWqUwgkzRtmVQdmNh0ajYQCYWq1WOso3o+zKoOzKoOzKoOzKMLXsnz59Ys2bN2d79+5lEyZMYNu3b2cvXrxgjx8/VjraF5lydm1//PEHmzBhAgPAbt68qXScr2aquSWmmt9UcxPytTQaDVu7di377bff2KNHj1jp0qVZo0aNWEhICHNzc1M6HiH/yL1795ifnx9bvXo1a9WqFUtNTdVpKyYnJzNzc3NWsGBBliNHDrZz506F0v5vunnzJtu3bx8bOHCg0lF00AAPIV8pKiqK3b17lz158oS5ubkxPz8/5u3tzTjnSkf7IsquDMpuPPQ1jkwFZVcGZVcGZVcGZVeGKWV/9OgRq1OnDrt9+zZjjLGdO3eyOnXqKJzq65hydsbS2mT58uVj5ubm7I8//mDlypVTOtJXMdXcElPNb6q5Cfkn3r9/z2bNmsVmzpzJ4uLi2NWrV1mhQoWUjkXIP/L48WPm7+/PGjZsyFasWMEsLCwYY0z0wQBgnHMWGxvLypUrx/z9/dnKlSuZpaWlkrGJkaABHkIIIYQQQgghn3Xz5k3WokULlidPHrZ582al43wTU87OGGMfP35kDx48YAULFlQ6yjcx1dwSU81vqrkJ+aeMeVY9IV8rOTmZBQcHs8uXL7Pdu3ezChUqMMYYS0lJYWZmZkyj0TCVSsX27NnD2rdvzzp27MjGjRuncGpiLGiAhxBCCCGEEELIF6WkpLCEhARmb2+vdJRvZsrZCSGEEPLf99dff7E6deqw8PBwNm3aNNajRw/Z82FhYWzgwIHszJkz7Pnz58zR0VGhpMTY0AAPIYQQQgghhBBCCCGEEKIQAOzYsWOsdevW7PXr16xgwYKsc+fOzN7enr19+5YtWLCAvX79mk2ZMoV16dJF6bjEiNAADyGEEEIIIYQQQgghhBCisLi4OPbrr7+yRYsWsbi4OPF43rx52eDBg1nbtm2VC0eMEg3wEEIIIYQQQgghhBBCCCFGIiIigl26dIlFRUWxXLlyMX9/f+bq6qp0LGKEaICHEEIIIYQQQgghhBBCCCHExKiUDkAIIYQQQgghhBBCCCGEEEK+DQ3wEEIIIYQQQgghhBBCCCGEmBga4CGEEEIIIYQQQgghhBBCCDExNMBDCCGEEEIIIYQQQgghhBBiYmiAhxBCCCGEEEIIIYQQQgghxMTQAA8hhBBCCCGEEEIIIYQQQoiJoQEeQgghhBBCCPlOPn36xK5cucKOHz/O7ty5wwBk+NqgoCCWI0eOf/XvhYaGssaNG7P79+8zxhiLiIhg1atXZ2vXrv1Xfy8hhBBCCCHE+JkpHYAQQgghhBBCTF1SUhIbM2YMmzdvHouLixOPZ8uWjY0ZM4a1a9fum/6+M2fOsISEhAyfL1myJLOzs2NPnjxhW7duZQMGDGC5c+dmCQkJ7ODBg6xkyZL/+L0QQgghhBBCTAMN8BBCCCGEEELIv/Dp0ydWvXp1dvLkSdatWzfWvHlz5u7uzh4+fMhmz57NfvnlF/bo0SM2ePBg2Z9LTU3N8O9s2bIle/r0aYbPX7t2jRUqVOh7vQVCCCGEEEKICaIBHkIIIYQQQgj5F6ZNm8ZCQ0PZsmXL2C+//CIez507NwsODmYdOnRg48aNY+PGjdP5s9mzZ8/w7y1ZsiRbsmSJ3ud8fX3/fXBCCCGEEEKISaMBHkIIIYQQQgj5FxYvXswKFSokG9yRcM7ZtGnT2Lp161jhwoVZ9+7dxXPjx4//bBk2W1tbFhAQ8EMyE0IIIYQQQkwfDfAQQgghhBBCyD/04cMH9vTpU1a/fv0MX+Ps7MwKFizIEhMTWatWrcTjS5cuZU+ePPnxIQkhhBBCCCH/SSqlAxBCCCGEEEKIqZJW4Nja2n72dba2tiwxMdEQkQghhBBCCCH/I2iAhxBCCCGEEEL+IXd3d2Ztbc3u3bv32dfdu3fvs/vtEEIIIYQQQsi3ohJthBBCCCGEEPIPmZmZsZo1a7I9e/aw58+fM29vb53XbN++nb18+ZKVLFmSrV27VjweERHx3XJMnz6deXh4sNjY2O/2dxJCCCGEEEKMGw3wEEIIIYQQQsi/MH78eHbo0CFWq1YttnnzZpY3b17x3N69e9kvv/zCGGNs69atbOvWrbI/+71W9Zw/f55ZWlqylJSU7/L3EUIIIYQQQowflWgjhBBCCCGEkH8hb968bN++fezt27fsp59+YoGBgSw4OJj5+vqy2rVrs9y5c7OnT5+yhIQE2a9y5cp9twybN29mDx48YKGhod/t7ySEEEIIIYQYN1rBQwghhBBCCCH/UtmyZdmDBw/YunXr2NmzZ1lMTAyrVq0aCw4OZnXq1GEqle7cOn2PEUIIIYQQQsjXogEeQgghhBBCCPkObG1tWadOnVinTp2UjkIIIYQQQgj5H0ADPIQQQgghhBDyHcTHx7NNmzaxQoUKsUKFCv3rv+/169ds5cqVjDHGALDk5GSWmJjIoqOjWWRkJJs9e/a//jcIIYQQQgghposGeAghhBBCCCHkO3j37h1r164dGz169HcZ4Ll16xZr164dYyytnJuVlRWztbVlDg4OzM3NjSUmJv7rf4MQQgghhBBiumiAhxBCCCGEEEIUsGHDBpaSkqL3uVu3bjHGGFOr1UytVjMzM7p1I4QQQgghhMjRXQIhhBBCCCGEKMDT0zPD52xsbAyYhBBCCCGEEGKKVEoHIIQQQgghhBBCCCGEEEIIId+GVvAQQgghhBBCyHd09uxZNm3atK9+fXBwMMufP/8PTEQIIYQQQgj5L6IBHkIIIYQQQgj5jg4fPswOHz781a93cnKiAR5CCCGEEELIN6MBHkIIIYQQQgj5DrJmzcoAGPTfbNu2LWvbtq1B/01CCCGEEEKIceAw9B0IIYQQQgghhBBCCCGEEEII+VdUSgcghBBCCCGEEEIIIYQQQggh34YGeAghhBBCCCGEEEIIIYQQQkwMDfAQQgghhBBCCCGEEEIIIYSYGBrgIYQQQgghhBBCCCGEEEIIMTE0wEMIIYQQQgghhBBCCCGEEGJiaICHEEIIIYQQQgghhBBCCCHExNAADyGEEEIIIYQQQgghhBBCiImhAR5CCCGEEEIIIYQQQgghhBATQwM8hBBCCCGEEEIIIYQQQgghJoYGeAghhBBCCCGEEEIIIYQQQkwMDfAQQgghhBBCCCGEEEIIIYSYGBrgIYQQQgghhBBCCCGEEEIIMTH/Bw+bYq6w4P6rAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 2000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 연대별 영화 수 시각화\n",
    "sns.barplot(x = movie_year_term_count.index, y = movie_year_term_count.values, alpha = 0.8)\n",
    "plt.title(\"연대별 영화 수\")\n",
    "plt.xlabel(\"연대\")\n",
    "plt.ylabel(\"영화 수\")\n",
    "plt.xticks(rotation = 60)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2. 장르별 속성 탐색"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "genres\n",
       "Drama                                                  1053\n",
       "Comedy                                                  946\n",
       "Comedy|Drama                                            435\n",
       "Comedy|Romance                                          363\n",
       "Drama|Romance                                           349\n",
       "                                                       ... \n",
       "Action|Crime|Horror|Mystery|Thriller                      1\n",
       "Adventure|Animation|Children|Comedy|Musical|Romance       1\n",
       "Action|Adventure|Animation|Comedy|Crime|Mystery           1\n",
       "Children|Comedy|Fantasy|Sci-Fi                            1\n",
       "Action|Animation|Comedy|Fantasy                           1\n",
       "Name: count, Length: 951, dtype: int64"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movie_data[\"genres\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "movie_data['genres'] = movie_data['genres'].apply(lambda x: x.split('|'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'Adventure': 1263, 'Animation': 611, 'Children': 664, 'Comedy': 3756, 'Fantasy': 779, 'Romance': 1596, 'Drama': 4361, 'Action': 1828, 'Crime': 1199, 'Thriller': 1894, 'Horror': 978, 'Mystery': 573, 'Sci-Fi': 980, 'War': 382, 'Musical': 334, 'Documentary': 440, 'IMAX': 158, 'Western': 167, 'Film-Noir': 87, '(no genres listed)': 34}\n"
     ]
    }
   ],
   "source": [
    "# 장르들을 담을 딕셔너리 생성\n",
    "unique_genre_dict = {}\n",
    "\n",
    "# 영화의 수만큼 반복된다.\n",
    "for genres_list in movie_data['genres']: \n",
    "    # 이미 리스트로 되어 있다면, 각 장르를 직접 사용\n",
    "    for genre_name in genres_list: \n",
    "        # 현재 장르의 이름으로 저장된 데이터가 없다면\n",
    "        if (genre_name in unique_genre_dict) == False:\n",
    "            unique_genre_dict[genre_name] = 1\n",
    "        else:\n",
    "            unique_genre_dict[genre_name] += 1\n",
    "\n",
    "# unique_genre_dict 출력\n",
    "print(unique_genre_dict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = list(unique_genre_dict.keys())\n",
    "y = list(unique_genre_dict.values())\n",
    "sns.barplot(x= x, y = y, alpha=0.8)\n",
    "plt.xlabel(\"영화수\")\n",
    "plt.xticks(rotation = 45)\n",
    "plt.ylabel(\"장르\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "드라마와 코미디가 가장 많은 비중을 차지하고 있다. 대중들이 호불호 없이 접할 수 있는 장르이기 때문에 위와 같은 결과가 나온 것으로 판단된다."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "3. 사용자별 집계 확인"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "userId\n",
      "1    232\n",
      "2     29\n",
      "3     39\n",
      "4    216\n",
      "5     44\n",
      "Name: movieId, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "user_info = ratings_data.groupby(['userId'])['movieId'].count()\n",
    "\n",
    "print(user_info.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count     610.000000\n",
       "mean      165.304918\n",
       "std       269.480584\n",
       "min        20.000000\n",
       "25%        35.000000\n",
       "50%        70.500000\n",
       "75%       168.000000\n",
       "max      2698.000000\n",
       "Name: movieId, dtype: float64"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "user_info.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* 사용자가 매긴 영화수를 확인해보았다. \n",
    "* 위와 같이 한 사람이 최대 2698개의 평점을 매긴 것을 확인할 수 있다.\n",
    "* 따라서 특정 유저들이 많은 영화 평점을 남긴 것으로 보아 편향이 있을 수도 있다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 18.44444444444445, 'user')"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1200x600 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,6))\n",
    "\n",
    "sns.displot(user_info.values)\n",
    "plt.xlabel(\"user\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Memory Based CF"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "import surprise\n",
    "from surprise import SVD, Dataset, Reader, KNNWithMeans, KNNBasic, accuracy\n",
    "from surprise.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = Dataset.load_builtin('ml-100k')\n",
    "train_data, test_data = train_test_split(data, test_size= 0.3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "#테스트\n",
    "#ratings_dict = {\n",
    "#        \"item_Id\" : [1,2,3,1,2,3,2],\n",
    "#        \"user_Id\" : ['a','a','a','b','b','b','c'],\n",
    "#        \"rating\" : [2, 4, 4, 4, 4, 5, 6]\n",
    "#        }\n",
    "#df = pd.DataFrame(ratings_dict)\n",
    "#df"
   ]
  },
  {
   "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>userId</th>\n",
       "      <th>movieId</th>\n",
       "      <th>rating</th>\n",
       "      <th>timestamp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4.0</td>\n",
       "      <td>964982703</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>4.0</td>\n",
       "      <td>964981247</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>4.0</td>\n",
       "      <td>964982224</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>47</td>\n",
       "      <td>5.0</td>\n",
       "      <td>964983815</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>50</td>\n",
       "      <td>5.0</td>\n",
       "      <td>964982931</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100831</th>\n",
       "      <td>610</td>\n",
       "      <td>166534</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1493848402</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100832</th>\n",
       "      <td>610</td>\n",
       "      <td>168248</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1493850091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100833</th>\n",
       "      <td>610</td>\n",
       "      <td>168250</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1494273047</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100834</th>\n",
       "      <td>610</td>\n",
       "      <td>168252</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1493846352</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100835</th>\n",
       "      <td>610</td>\n",
       "      <td>170875</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1493846415</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>100836 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        userId  movieId  rating   timestamp\n",
       "0            1        1     4.0   964982703\n",
       "1            1        3     4.0   964981247\n",
       "2            1        6     4.0   964982224\n",
       "3            1       47     5.0   964983815\n",
       "4            1       50     5.0   964982931\n",
       "...        ...      ...     ...         ...\n",
       "100831     610   166534     4.0  1493848402\n",
       "100832     610   168248     5.0  1493850091\n",
       "100833     610   168250     5.0  1494273047\n",
       "100834     610   168252     5.0  1493846352\n",
       "100835     610   170875     3.0  1493846415\n",
       "\n",
       "[100836 rows x 4 columns]"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# MovieLens 데이터셋\n",
    "ratings_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "min    0.5\n",
       "max    5.0\n",
       "Name: rating, dtype: float64"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 평점 최대 최소 확인\n",
    "ratings_data[\"rating\"].agg([\"min\", \"max\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "최소 0.5, 최대 5.0이기 때문에 rating_scale은 0.5~5.0으로 설정하고자 한다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Computing the msd similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Computing the msd similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Computing the msd similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Evaluating RMSE of algorithm KNNWithMeans on 3 split(s).\n",
      "\n",
      "                  Fold 1  Fold 2  Fold 3  Mean    Std     \n",
      "RMSE (testset)    0.9060  0.9004  0.9083  0.9049  0.0033  \n",
      "Fit time          0.08    0.08    0.09    0.08    0.00    \n",
      "Test time         1.10    0.94    0.96    1.00    0.07    \n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'test_rmse': array([0.90603704, 0.90044931, 0.90831947]),\n",
       " 'fit_time': (0.07551097869873047, 0.0814061164855957, 0.08560943603515625),\n",
       " 'test_time': (1.1002304553985596, 0.9381165504455566, 0.9565482139587402)}"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#reader 생성\n",
    "reader = Reader(rating_scale=(0.5, 5.0))\n",
    "\n",
    "# 데이터 생성\n",
    "a1 = ratings_data[[\"userId\", \"movieId\", \"rating\"]]\n",
    "data = Dataset.load_from_df(a1, reader)\n",
    "\n",
    "# KNNwithMeans\n",
    "model = KNNWithMeans()\n",
    "surprise.model_selection.cross_validate(model, data, measures=['RMSE'], cv=3, verbose=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Computing the msd similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Computing the msd similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Computing the msd similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Evaluating RMSE of algorithm KNNBasic on 3 split(s).\n",
      "\n",
      "                  Fold 1  Fold 2  Fold 3  Mean    Std     \n",
      "RMSE (testset)    0.9559  0.9531  0.9671  0.9587  0.0061  \n",
      "Fit time          0.06    0.08    0.08    0.07    0.01    \n",
      "Test time         1.00    0.86    0.88    0.91    0.07    \n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'test_rmse': array([0.95588347, 0.9530836 , 0.96714957]),\n",
       " 'fit_time': (0.06087231636047363, 0.07695627212524414, 0.07594013214111328),\n",
       " 'test_time': (1.004103183746338, 0.8559927940368652, 0.8785672187805176)}"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# KNNBasics\n",
    "model = KNNBasic()\n",
    "surprise.model_selection.cross_validate(model, data, measures=['RMSE'], cv=3, verbose=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. Model based CF"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Evaluating RMSE of algorithm SVD on 3 split(s).\n",
      "\n",
      "                  Fold 1  Fold 2  Fold 3  Mean    Std     \n",
      "RMSE (testset)    0.8827  0.8790  0.8779  0.8799  0.0020  \n",
      "Fit time          0.61    0.61    0.61    0.61    0.00    \n",
      "Test time         0.25    0.09    0.24    0.19    0.07    \n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'test_rmse': array([0.88265138, 0.87903207, 0.87790086]),\n",
       " 'fit_time': (0.6066701412200928, 0.6135931015014648, 0.6051647663116455),\n",
       " 'test_time': (0.24525833129882812, 0.08995270729064941, 0.23523497581481934)}"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# SVD\n",
    "model = SVD()\n",
    "surprise.model_selection.cross_validate(model, data, measures=['RMSE'], cv=3, verbose=True)"
   ]
  }
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