Movie Recommender System.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# FINAL PROJECT IN LEARNING ANALYTICS\n",
    "\n",
    "BY EIRINI MITSOPOULOU & KONSTANTINA PAPADOPOULOU"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Movie Recommender System"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Recommender systems have become ubiquitous in our lives. The need to build robust movie recommendation systems is extremely important given the huge demand for personalized content of modern consumers. \n",
    "\n",
    "In this project, we attempt to understand the different kinds of recommendation systems and compare their performance on the MovieLens dataset. It includes two of the most commonly used filtering techniques for RS. These are Content–Based Filtering (CBF) and Collaborative Filtering (CF)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import the needed libraries / modules\n",
    "%matplotlib inline\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import ast\n",
    "import datetime\n",
    "from scipy import stats as st\n",
    "from ast import literal_eval\n",
    "import statsmodels.formula.api as smf\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer, CountVectorizer\n",
    "from sklearn.metrics.pairwise import linear_kernel, cosine_similarity\n",
    "from sklearn.neighbors import NearestNeighbors\n",
    "from scipy.sparse import csr_matrix\n",
    "\n",
    "import warnings; warnings.simplefilter('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>adult</th>\n",
       "      <th>belongs_to_collection</th>\n",
       "      <th>budget</th>\n",
       "      <th>genres</th>\n",
       "      <th>homepage</th>\n",
       "      <th>id</th>\n",
       "      <th>imdb_id</th>\n",
       "      <th>original_language</th>\n",
       "      <th>original_title</th>\n",
       "      <th>overview</th>\n",
       "      <th>...</th>\n",
       "      <th>release_date</th>\n",
       "      <th>revenue</th>\n",
       "      <th>runtime</th>\n",
       "      <th>spoken_languages</th>\n",
       "      <th>status</th>\n",
       "      <th>tagline</th>\n",
       "      <th>title</th>\n",
       "      <th>video</th>\n",
       "      <th>vote_average</th>\n",
       "      <th>vote_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>False</td>\n",
       "      <td>{'id': 10194, 'name': 'Toy Story Collection', ...</td>\n",
       "      <td>30000000</td>\n",
       "      <td>[{'id': 16, 'name': 'Animation'}, {'id': 35, '...</td>\n",
       "      <td>http://toystory.disney.com/toy-story</td>\n",
       "      <td>862</td>\n",
       "      <td>tt0114709</td>\n",
       "      <td>en</td>\n",
       "      <td>Toy Story</td>\n",
       "      <td>Led by Woody, Andy's toys live happily in his ...</td>\n",
       "      <td>...</td>\n",
       "      <td>1995-10-30</td>\n",
       "      <td>373554033.0</td>\n",
       "      <td>81.0</td>\n",
       "      <td>[{'iso_639_1': 'en', 'name': 'English'}]</td>\n",
       "      <td>Released</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Toy Story</td>\n",
       "      <td>False</td>\n",
       "      <td>7.7</td>\n",
       "      <td>5415.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>65000000</td>\n",
       "      <td>[{'id': 12, 'name': 'Adventure'}, {'id': 14, '...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8844</td>\n",
       "      <td>tt0113497</td>\n",
       "      <td>en</td>\n",
       "      <td>Jumanji</td>\n",
       "      <td>When siblings Judy and Peter discover an encha...</td>\n",
       "      <td>...</td>\n",
       "      <td>1995-12-15</td>\n",
       "      <td>262797249.0</td>\n",
       "      <td>104.0</td>\n",
       "      <td>[{'iso_639_1': 'en', 'name': 'English'}, {'iso...</td>\n",
       "      <td>Released</td>\n",
       "      <td>Roll the dice and unleash the excitement!</td>\n",
       "      <td>Jumanji</td>\n",
       "      <td>False</td>\n",
       "      <td>6.9</td>\n",
       "      <td>2413.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   adult                              belongs_to_collection    budget  \\\n",
       "0  False  {'id': 10194, 'name': 'Toy Story Collection', ...  30000000   \n",
       "1  False                                                NaN  65000000   \n",
       "\n",
       "                                              genres  \\\n",
       "0  [{'id': 16, 'name': 'Animation'}, {'id': 35, '...   \n",
       "1  [{'id': 12, 'name': 'Adventure'}, {'id': 14, '...   \n",
       "\n",
       "                               homepage    id    imdb_id original_language  \\\n",
       "0  http://toystory.disney.com/toy-story   862  tt0114709                en   \n",
       "1                                   NaN  8844  tt0113497                en   \n",
       "\n",
       "  original_title                                           overview  \\\n",
       "0      Toy Story  Led by Woody, Andy's toys live happily in his ...   \n",
       "1        Jumanji  When siblings Judy and Peter discover an encha...   \n",
       "\n",
       "     ...      release_date      revenue runtime  \\\n",
       "0    ...        1995-10-30  373554033.0    81.0   \n",
       "1    ...        1995-12-15  262797249.0   104.0   \n",
       "\n",
       "                                    spoken_languages    status  \\\n",
       "0           [{'iso_639_1': 'en', 'name': 'English'}]  Released   \n",
       "1  [{'iso_639_1': 'en', 'name': 'English'}, {'iso...  Released   \n",
       "\n",
       "                                     tagline      title  video vote_average  \\\n",
       "0                                        NaN  Toy Story  False          7.7   \n",
       "1  Roll the dice and unleash the excitement!    Jumanji  False          6.9   \n",
       "\n",
       "  vote_count  \n",
       "0     5415.0  \n",
       "1     2413.0  \n",
       "\n",
       "[2 rows x 24 columns]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# read the movies dataset\n",
    "metadata = pd.read_csv(\"movies_metadata.csv\")\n",
    "metadata = metadata[pd.to_numeric(metadata['popularity'], errors='coerce').notnull()]\n",
    "metadata.drop(metadata.index[19730],inplace=True)\n",
    "metadata.drop(metadata.index[29502],inplace=True)\n",
    "metadata.drop(metadata.index[35585],inplace=True)\n",
    "metadata.id =metadata.id.astype(np.int64)\n",
    "metadata['popularity'] = metadata['popularity'].astype(float)\n",
    "metadata.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "<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>110</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1425941529</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>147</td>\n",
       "      <td>4.5</td>\n",
       "      <td>1425942435</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>858</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1425941523</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1221</td>\n",
       "      <td>5.0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
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       "      <td>1246</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1425941556</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "   userId  movieId  rating   timestamp\n",
       "0       1      110     1.0  1425941529\n",
       "1       1      147     4.5  1425942435\n",
       "2       1      858     5.0  1425941523\n",
       "3       1     1221     5.0  1425941546\n",
       "4       1     1246     5.0  1425941556"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# read the ratings dataset\n",
    "ratings = pd.read_csv(\"ratings.csv\")\n",
    "ratings.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Explore data with some basic plots\n",
    "\n",
    "### PLOT #1: Ratings per user."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Below we present the scatterplot and histogram of ratings number per user.\n",
    "\n",
    "From the scatterplot we can see the number of ratings given per user id."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Min number of user ratings:  1\n",
      "Max number of user ratings:  18276\n"
     ]
    },
    {
     "data": {
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X9De/JelJd2+Q9GTwHgAAAABQABmDRTO7W9I/JH1R0nWKPHfx7Ezfc/dnJLXFDXvM3TuCt3+UND7DtMdKGuruz7m7S7pd0rHBx1/Sh0HrbTHDAQAAAAB5CvPojF9IOsndOws87f+QdHfM+ylm9ldJGyTNd/ffS9pFUmvMOK3BMEka4+7vSpK7v2tmOxW4fAAAAADQZ6UMFs3si+7+O0mDJH0p6CrYxd3vzXWiZnaxpA5JzcGgdyVNdPc1ZravpPvN7COKPKojnucwvZmKNGXVxIkTcys0AAAAAPQh6e4sHiDpd5KOSfKZS8opWDSzGZKOlnRQ0LRU7r5N0rbg/+fN7DVJeyhyJzG2qep4Se8E/680s7HBXcWxSpN0x91vVCQ5jxobG7MONgEAAACgr0kZLLr7pcHf0ws1MTM7XNI3JR3g7ptjhtdLanP3TjPbVZFENivcvc3MNprZZyT9P0mnSvpJ8LUHJM2Q9P3g768LVU4AAAAA6Osy9lk0s/OTDF4v6Xl3fyHN9+6UdKCk0WbWKulSRbKf9pf0eNCs9Y9B5tP9JS0wsw5JnZJmuXs0Oc5sRTKrDpT0SPCSIkHiPWb2NUlvSvpKpnkBAAAAAIRjQUvQ1COYLZLUKOnBYNBRkv4saS9Jv3T3Hxa1hAXW2NjoS5YsKXUxAAAAAKAkzOx5d2/MNF6YbKijJH3C3TcFP3yppF8pcjfweUkVFSwCAAAAADLL+JxFSRMlfRDzfrukSe6+RUFSGgAAAABA7xLmzuIiSX80s2gCmWMk3WlmdZKWFq1kAAAAAICSyRgsuvt3zOxhSf+qyHMPZ7l7tNPf9GIWDgAAAABQGmHuLMrdn1ekfyIAAAAAoA8I02cRAAAAANDHECwCAAAAABJkDBbNrM7MqoL/9zCzaWZWW/yiAQAAAABKJcydxWckDTCzXSQ9Kel0SbcWs1AAAAAAgNIKEyyau2+WdLykn7j7cZKmFrdYAAAAAIBSChUsmtl+ijwm46FgWKgsqgAAAACAyhQmWDxX0kWS7nP3l81sV0lPFbdYAAAAAIBSyniH0N0XS1oc836FpG8Us1AAAAAAgNLKGCya2YOSPG7weklLJP3M3bcWo2AAAAAAgNIJ0wx1haRNkm4KXhskrZS0R/AeAAAAANDLhElU83F33z/m/YNm9oy7729mLxerYAAAAACA0glzZ7HezCZG3wT/jw7eflCUUgEAAAAASirMncULJP3BzF6TZJKmSJpjZnWSbitm4QAAAAAApREmG+rDZtYgaS9FgsVXYpLa/E8xCwcAAAAAKI0wdxYlaV9Jk4Px9zYzufvtRSsVAAAAAKCkwjw64w5Ju0l6QVJnMNglESwCAAAAQC8V5s5io6Sp7h7/rEUAAAAAQC8VJhvqS5J2LnZBAAAAAADlI8ydxdGSlprZnyRtiw5092lFKxUAAAAAoKTCBItNxS4EAAAAAKC8hHl0xuKeKAgAAAAAoHyk7LNoZn8I/m40sw0xr41mtiHMj5vZLWb2vpm9FDNspJk9bmbLgr8jguFmZj82s+Vm9qKZfSLmOzOC8ZeZ2YyY4fua2d+D7/zYzCyXhQAAAAAA6C5lsOju/xr8HeLuQ2NeQ9x9aMjfv1XS4XHDviXpSXdvkPRk8F6SjpDUELxmSrpeigSXki6V9GlJn5J0aTTADMaZGfO9+GkBAAAAAHKQMRtq8JzFjMOScfdnJLXFDf6SpNuC/2+TdGzM8Ns94o+ShpvZWEmHSXrc3dvcfa2kxyUdHnw21N2fCx7rcXvMbwEAAAAA8hDm0RkfiX1jZjWS9s1jmmPc/V1JCv7uFAzfRdJbMeO1BsPSDW9NMhwAAAAAkKd0fRYvMrONkvaO7a8oaaWkXxehLMn6G3oOwxN/2GymmS0xsyWrVq3Ko4gAAAAA0Dek67P4PXcfIumKuP6Ko9z9ojymuTJoQqrg7/vB8FZJE2LGGy/pnQzDxycZnmxebnT3RndvrK+vz6PoAAAAANA3ZGyG6u4XmdkIM/uUme0ffeUxzQckRTOaztCHdykfkHRqkBX1M5LWB81UH5V0aFCGEZIOlfRo8NlGM/tMkAX1VBXnjicAAAAA9DkZn7NoZmdIOkeRO3cvSPqMpOckfTHEd++UdKCk0WbWqkhW0+9LusfMvibpTUlfCUZ/WNKRkpZL2izpdEly9zYz+46kPwfjLXD3aNKc2YpkXB0o6ZHgBQAAAADIk0USiaYZwezvkj4p6Y/uvo+Z7SXpv939hJ4oYKE1Njb6kiVLSl0MAAAAACgJM3ve3RszjRcmG+pWd98a/Gh/d39F0p75FhAAAAAAUL4yNkOV1GpmwyXdL+lxM1urFIlkAAAAAAC9Q5gEN8e5+zp3b5L0bUk/l3RssQtWyZqbF2lKw16qqq7WlIa91Ny8qNRFAgAAAICspL2zaGZVkl50949Kkrsv7pFSVbDm5kWafd48DTporiYcO1VbWpdq9nnzJEnTp59U4tIBAAAAQDhhEtw0S7rI3d/smSIVV7ET3Exp2EtbGmdowKS9u4ZtbXlRA5fcpteXvVK06QIAAABAGGET3ITpszhW0stm9idJ7dGB7j4tj/L1Wi0rlmnCsVO7Des/fqpa7llWohIBAAAAQPbCBIv/XfRS9CKTdm3Qltal3e4sbmtdqkm7NpSwVAAAAACQnYzBIv0Us3NZ0yWRPooHzVX/8VO1rXWpNj95ra66+opSFw0AAAAAQgtzZxFZiCaxmd+0QC33LNOkXRt01dVXkNwGAAAAQEXJmOCmtyl2ghsAAAAAKGdhE9ykfM6imT0Z/P1BIQsGAAAAACh/6ZqhjjWzAyRNM7O7JFnsh+7+l6KWDAAAAABQMumCxUskfUvSeEk/ivvMJX2xWIUCAAAAAJRWymDR3X8l6Vdm9m13/04PlgkAAAAAUGJhHp3xHTObJmn/YNDT7v6b4hYLAAAAAFBKKRPcRJnZ9ySdI2lp8DonGAYAAAAA6KXCPGfxKEn7uPsOSTKz2yT9VdJFxSwYAAAAAKB0Mt5ZDAyP+X9YMQoCAAAAACgfYe4sfk/SX83sKUUen7G/uKsIAAAAAL1amAQ3d5rZ05I+qUiw+E13f6/YBQMAAAAAlE6YO4ty93clPVDksgAAAAAAykTYPosAAAAAgD6EYBEAAAAAkCBtsGhmVWb2Uk8VBgAAAABQHtIGi8GzFf9mZhN7qDwAAAAAgDIQJsHNWEkvm9mfJLVHB7r7tKKVCgAAAABQUmGCxf8ueikAAAAAAAXX3LxI85sWqGXFMk3atUGXNV0S+rsZE9y4+2JJb0iqDf7/s6S/5FpYM9vTzF6IeW0ws3PNrMnM3o4ZfmTMdy4ys+Vm9k8zOyxm+OHBsOVm9q1cywQAAFBqzc2LNKVhL1VVV2tKw15qbl5U6iIBqHDNzYs0+7x52tI4QxPOv1dbGmdo9nnzJGlkmO+bu6cfwexMSTMljXT33cysQdIN7n5QvoU3s2pJb0v6tKTTJW1y9yvjxpkq6U5Jn5I0TtITkvYIPn5V0iGSWhUJYr/q7kvTTbOxsdGXLFmSb9EBAAAKJnpCN+igueo/fqq2tS7V5iev1fVXX6Hp008qdfEAVKgpDXtpS+MMDZi0d9ewrS0vauXdF3/gO3b0z/T9MI/OOEvS5yRtkCR3XyZppxzLG+8gSa+5e0uacb4k6S533+bur0tarkjg+ClJy919hbt/IOmuYFwAAICKMr9pgQYdNFcDJu0tq67RgEl7a9BBczW/aUGpi4YKxx3rvq1lxTL1Hz+127D+46dK7v3CfD9MsLgtCMYkSWZWIyn97cjwTlTkrmHUXDN70cxuMbMRwbBdJL0VM05rMCzVcAAAgIqS6oSuZcWyEpUIvUGqJogEjH3HpF0btK21e8PLba1LJbMPUnylmzDB4mIz+y9JA83sEEm/lPRg1iWNY2b9JE0Lfk+Srpe0m6R9JL0r6aroqEm+7mmGJ5vWTDNbYmZLVq1alVe5AQAACi3VCd2kXRtKVCL0BtyxxmVNl2jzk9dqa8uL8s4ObW15UZufvFZyfzvM98MEi9+StErS3yV9XdLDkubnXuQuR0j6i7uvlCR3X+nuncGzHW9SpJmpFLljOCHme+MlvZNmeAJ3v9HdG929sb6+vgBFBwAAKJxUJ3TZZC0E4nHHGtOnn6Trr75CA5fcprd+dLwGLrlN1199hSS1hfl+xgQ3UtddwL0UuXP3z9hmqbkys7skPeruvwjej3X3d4P/z5P0aXc/0cw+ImmRPkxw86SkBkXuLL6qSL/HtxVJcHOSu7+cbrokuAEAAOUoWXp7ktsgH6mSmwxccpteX/ZKCUuGUjOz5929MdN4GZ+zaGZHSbpB0muKBGhTzOzr7v5IHoUbpEgW06/HDP6hme2jSED6RvQzd3/ZzO6RtFRSh6Sz3L0z+J25kh6VVC3plkyBIgAAQLmaPv0kgkMU1GVNl0QekxCXZfeqyJ0lIKMwj854RdLR7r48eL+bpIfcfa8eKF/BcWcRAAAAfQV3rJFMwe4sSno/GigGVkh6P+eSAQAAAOgR3LFGPlIGi2Z2fPDvy2b2sKR7FGki+hVF+gcCAAAAAHqpdHcWj4n5f6WkA4L/V0kakTg6AAAAAKC3SBksuvvpPVkQAAAAAED5CJMNdYqksyVNjh3f3acVr1gAAAAAgFIKk+Dmfkk/l/SgpB3FLQ4AAAAAoByECRa3uvuPi14SAAAAAEDZCBMsXmNml0p6TNK26EB3/0vRSgUAAAAAKKmqEOP8i6QzJX1f0lXB68piFgoAAABA39bcvEhTGvZSVXW1pjTspebmRaUuUp8T5s7icZJ2dfcPil0YAAAAAGhuXqTZ583ToIPmasKxU7WldalmnzdPkjR9+kklLl3fYe6efgSzuyWd7e7v90yRiquxsdGXLFlS6mIAAAAASGFKw17a0jhDAybt3TVsa8uLGrjkNr2+7JUSlqx3MLPn3b0x03hhmqGOkfSKmT1qZg9EX/kXEQAQBs1wAAB9TcuKZeo/fmq3Yf3HT1XLimUlKlHfFCZYvFSRpqiX68M+i1cVs1AAgIhoM5wtjTM04fx7taVxhmafN4+AEQCQs0q4CDlp1wZta13abdi21qWatGtDiUrUN2Vshtrb0AwVQCWhGQ4AoJBi+wL2Hz9V21qXavOT1+r6q68oq76AlVLOShW2GWqYPosbJUVH6iepVlK7uw/Nu5QlQLAIoJJUVVdrwvn3yqo/zEfmnR1660fHa0dnZwlLBgCoRJV0EbK5eZHmNy1Qy4plmrRrgy5ruoRAsUDCBosZs6G6+5C4Hz5W0qfyKBsAIKRJuzZoS+tSdbav1frn7tb2Na2qGTZGo+p3LnXRAAAVqGXFMk04NklfwHvKry/g9OknERyWWJg+i924+/2SvliEsgAA4lzWdInWP3SF1i6+VSMPnqWJF9yrUYefrfYPOsuyjwkAoLzRFxDZyBgsmtnxMa9/M7Pv68NmqQCAIpo+/SQNGVyn0UeepwGT9pZV12jApL019LBzNL9pQamLB/Q5lZAYBEjnsqZLtPnJa7W15UV5Z4e2tryozU9eq8uaLil10VCGMjZDlXRMzP8dkt6Q9KWilAYAkGD1ync0IVn68DJsMgT0ZjwkHL1BdFud37RAb9z9qmoHDlbHlvauC5Bsy4hFNlQAKHOVlIwA6M3YF9GbkG20b8s7G6qZpbsX7e7+nVwLV0oEiwAqDQd0oDyQnRi9CRc/+rZCZENtTzKsTtLXJI2SVJHBIgBUmtgmQy33RNKHX0WgCPS4aHbi2JNrEoOgUlVSVlSUTsoEN+5+VfQl6UZJAyWdLukuSbv2UPkAVBASPxTP9Okn6fVlr2hHZ6deX/YKgSJQAiQGQW9CVlSEkTbBjZmNlHS+pOmSbpP0CXdf2xMFA1BZSPwAoLfjLj96k8uaLokcp+O6OFx19RWlLhrKSLo+i1dIOl6Ru4rXufumnixYsdBnESgO+j4AQN/U3LwoEkCviATQlzVdQgBdIVh3fVfYPovpnrN4gaRxkuZLesfMNgSvjWa2oVAFBdA7tKxYpv7JHu+wgr4PlYgmxQDCiLYq2dI4QxPOv1dbGmdo9nnzqDMqBF0ckEm6PotV7j7Q3Ye4+9CY1xBtAaPdAAAgAElEQVR3H5rvhM3sDTP7u5m9YGZLgmEjzexxM1sW/B0RDDcz+7GZLTezF83sEzG/MyMYf5mZzci3XAByQ9+H3oOTPwBhzW9aoEEHzdWASXvLqms0YNLeGnTQ3K5n9gGobOnuLPaEL7j7PjG3QL8l6Ul3b5D0ZPBeko6Q1BC8Zkq6XurqU3mppE9L+pSkS6MBJoCeReKH3oOTPwBh0aoE6N1KHSzG+5IiiXQU/D02ZvjtHvFHScPNbKykwyQ97u5tQeKdxyUd3tOF7k1oeoZcTZ9+kq6/+goNXHKb3vrR8Rq45DaeA1ihOPlDb8ExrfhoVQL0bqUMFl3SY2b2vJnNDIaNcfd3JSn4u1MwfBdJb8V8tzUYlmo4ckDTM+SLvg+lU8iTYk7+0BtwTOsZtCpBMXHBp/RKGSx+zt0/oUgT07PMbP8041qSYZ5mePcvm800syVmtmTVqlW5lbYP6K1Nz6hoyhPrpXAKfVLMyR96g956TCs3tCpBsXDBpzykfHRGjxbCrEnSJklnSjrQ3d8Nmpk+7e57mtnPgv/vDMb/p6QDoy93/3owvNt4yfDojNSqqqs14fx7ZdUfPn7TOzv01o+O147OzhKWLHexz/6LfYYQB7LSYr0UVjEeW0I6dVS63nhMA/qSTMc2jlP5CfvojJIEi2ZWJ6nK3TcG/z8uaYGkgyStcffvm9m3JI109/80s6MkzZV0pCLJbH7s7p8KEtw8LymaHfUvkvZ197ZU0yZYTK03PievN85Tb8B6KSxOioFE1DNAZUt3bLvj9ju46JynQjxnsZjGSPqDmf1N0p8kPeTuv5X0fUmHmNkySYcE7yXpYUkrJC2XdJOkOZIUBIXfkfTn4LUgXaCI9Hpj0zMSdZQn1kth0ccQSNQbj2lAX5Lu2EYz855TkmDR3Ve4+8eC10fc/bvB8DXufpC7NwR/24Lh7u5nuftu7v4v7r4k5rducffdg9cvSjE/vUVv7HfASXR2eqofIeulsDgpBhL1xmMa0JekO7Zx0bnnlNujM1BivS2bZV85iS5EkNeTHckrfb2UW3IeTor7nnLbBstVbzumobjYr8pLumMbF517kLv3qde+++7r6FsWLmz2ybvv6VZV5ZN339MXLmwudZEKauHCZh9SP87HnHi5T7zwfh9z4uU+pH5c1vM5efc9fcyJl/ukb/6m6zXmxMt98u57Fq3clbheCrW8gVyxDeanUuseFBf7VWVhfeVP0hIPETuVRTbUnkSCG/Q2hUriQJKUcEiagVJjG8wdmZj7tnTZM9mvKg/ZUPNT7gluABRIy4pl6ti4Wu/8fI5afjhN7/x8jjo2rs663T5NOsKhnwRKjW0wdyTF6LsydbVgv6o85dzMvDc1aSZYRNnoTTtWTxpVv7PW/f4OjTx4liZecK9GHjxL635/h0bV75zV71R6P8KeQlCNUmMbzB0BQd+V6UIB+xUKpSdzQPQEgkWUhd62Y/Ukq6rS6CPP63YAHH3kebKq7HZvkqSEQ1CNUmMbzF0lBQSFSlzGRdiITBcKynW/Yh1Wnt7WgoE+iygL9BXIHX0Nex79JFBqbIO5qZQ+i4UoZ6XMa08Jc55RbvsV67AyVcp5Wdg+iwSLKKloxfzG8lc18cL7yn7HKkcE2gAQXrkFBMkUol7n2NBdJQZerMPKVCnrjQQ3KHuxTU9rR00oaNOgvtRso1ybzmSrUtZZpZQTCKuvbdOlTIoRdlkXom8l/TO7q8SuFqzDytRbzsuiCBZRVOkOjLFtuod99gSteeSaguxYfa3/YyUeAOP1xDorVP+fnti2+trJO0qnr9WXpZTNsi5E38pK6p/ZU8o5e2YyrMP89eTxNDqtU049Rf2rpB2Lf1qx52WxaIaKosnU5CO+TXf70sVa/393a/uatzR59z1SNg3K1ISoUm7/40PFXmeFan7UE9tWJTaVQuWivuw52Sxr+ixCYh3mqyeXXyWuK5qhouSyTVNdN/UAjTxklibvvkfKK35hrszSbKPyFHudFSozWU9sW9mUlTuQyBf1ZfZy3e9aXgu/rAvRYqTUrU6amxepfuddVNVvkMyqtNO4CdRRWQqzDjkOpNaTWUnLKQNqobcJgkUUTTHSVIfZGUePGdermm30hQNBsZvaFOqEuCeaBIUtK80HkUy29QXN3LKT637X3LxIVf0HZrWsC9FkslTNLpubF+mM2XO1dst27fTlb2vihfep6oA5OvPs86mjspRuHXIcSK8nL4b11LTS1fHRCzSnzjyroNsEwSKKJtNJSC5XPTPtjM3Ni7RxU7tWP3x1tyB0w6PXVGTH4tgDwcgjzlXr6g06+eSTe90V2rAXDuacNVf96obKrEr96oZqzllzQ/1+oU6Ie6LTetiyltNVTJSHXE4cKzkRQykupOW6381vWqC6jx+d0Dd/zW+urIhlna35TQvUUT0g4RnAQw87hzqqgDgOpJfNsT/f+qQnLrylq+Ojn63b5qqf9p8F3SYIFlE0YU5Csr3qmWlnnN+0QMOOmqcRB5ymtidu0JtXHa81v/2J6vpVJ/x2JTSRiR4IOtvXav0fmjXq8LO7rtD2pquHYS4czDlrrm68bZFGTrtIEy+8TyOnXaQbb1sUKmAs1AlxTzTrSlXWIw49uNuBLJsmbb1VX7jrno1cThxL3VQxV6W6oxLmgmWybbJlxTIN/9xXNXz/U7uOTW2P36CO9rVlv6xz0bJimTrWr+zzdVSxlboZebnXwWGP/fm0GIjO/6aNG7Xh0cIkakwlXR0f/awY+x0JbtKohGcxlbtCL8Nsk+ZIyZ/XGG0is91qNPrI87p+a8Oj1+imn/yobNZzdH7evfUbGnnwrD6dhKJf3VCNnHZRwjJYfe93NGHixIzbWCXtz/FlPeLQg7Xwl/d12+5X3/9djT724j67TVRiMoFiq5QHQRdCqRLzpJvuZU2XpNwm5zct6FOJhKY07KXW1Rs06vCz+8w8l0IpE1RVSh0c5tify3JMNv/rH7pCQwbXafXKd4pynpGsjl/z6E/VvvQp+QdbNfHC+7I6XyTBTZ76UjvwYl4ZKnR/iUxXwbNpwlcJTWSi87N9TWufv0K7ffOmhGXQsXG1vKZ/qP20klKmx5f1kceeSLiaWPfxo7XmN1fmdBWz3K8Gh1FOza/KZXnG1n/tSxfrnZ/P0ZtXHaeaAXUVuY7TKfQdlbDrMN2dinTbZDk19+2J7fWypktU07m113QJKUfNzYu0aeNGrXrghyXZrsqpDk4nzLE/l/ok2fwPO2qe6urqinaeEX+O2/b4Ddq87DntdPy3u55XPmy/wj2KLopgMYVK2QnyVYlBcbodP+wBuRRNZHI5QEfnp2bYmD6fhKJ20OCEZbD+2TsL3ja/HCU7kA3/3FfV0b426+aDlbjPJ1Pq5ldR5bQ8o/XF2mfu0NpnbtfIg2dp4gWRJtuVuI7TKWT/oGzWYboLlum2yXJp7ttT2+v06Sfp5uuv1YiBtXr/f7+jN688TjsW/7SsWu5Usuh6rP7CXI34wn9ozW9/0rWMe2q7yqYOLpcLaqnkUp+U4hgUf4676eWnVH/MvG7PK6+uG6Fh/zq9oNsEzVBT6CvNefJpwlCuzfrCNjlI1USm86lrNXjIELWsWKZR9TvLqqryblKQT3ON5uZFOveCeWrbuFn10/6zrJt7FFO0z+LoY+Z1LYOVd12siRfeV7D9tFy36UI2Neotz9Url/kol3JENTcv0ukzZyVtsl1p6zidQjaBK9Q6LLdtIZlKKGM5KrdjQzmsx7BlqITmqrmUsVTrIHZb9B3e7Rwo7PPKo2iGmqe+kk481ysj0R2rrX4f1YwYrzdee1UzzpgZOjtlMUXvPN5x+x2SpFNOPSXhSlaqJjJtD/5A7R90dmUfXbtlu6oOmJP3Fdh87lRPn36SVr33tm6/8boeuSqdzxXAYl49/Ol112rmjJO0+t7v6K1rTtTKuy5W9YBBJbm70NMK2YStXO7I5atcmvWV2/KcPv0kdWxtL6syFUMh79QVYh2WuklgWOW0vfbU3aZ8p1OOx4ZyWI9h6+Byb6kXDb42rn5XbQ98T29dFa4+KdUxKLZ1XXyLq+jzymsHDS5oM1iCxRRiN4JNL/1Ob//sTK2862K91fq2qqrK8zZ6LnINiuc3LZDt/nltXvq0Rh4Saeo0+tiLddPtd4ZeLpkq8HyDlnSVe6omMsOHDtHQw87RgEl7a8P/+1VCn8ZcK7hCVOzxzW8lFfxAm89BsScOqJ/77Gc1aNgI7fTlSzTxwvtU9/GjtfrBKwpSWZfzAa2QJ8aVdCEsXR1QLs36ynF5lmOZiqFQ/ZDzXV7l0CQwrHLZNnoqACvEdMrx2FAO6zFsHVwOgW0qsdtHtMn+4NE7h7pzXA7HoDNOOzXhHGj1g1fojNNOLeh0aIaaRmzzv8EfO0ztS59W3dQDteXV57S97S1V9x+kmf9xmn563bVFLnXxhL31Ht8Eo+W1ZaoZOV4jD8ktQ2em6ebbbCHX5gGxzY9bfjhNEy8oTFPkQjdXKFazjnzK2RNNMpJNY+0zd2jL3x5Wx9b2vJoH9ZWm55XQJEjKvW4Ku/4L1aysHJdnOZapWHJZj2GyDffE8aYUir1fhZVqmcV2AynEdAuxbsrx2FBJ+3g57x/lXLaw5pw1Vzfferu2b96k2kGDdcZpp4aOS2iGWgDTp5+kwUOGqH7af2rLsudUN/XAvO6klaMwV0aSXZmr6j9Q29veyvlqUaYrdfleycv1Slbs1braUeMLduUu3+YK8XdYzpv3zaJc6cznCmBPXD1Mmehla7t2dHZ2ZSTM5W5rOVypzUfYO/GluhqabUuBMHVAPs/GKtRdjZ5Yntkuu3K44t0T5pw1V6fNOjur9Zhs3S/85X06+SvH5by8yvnOSbxcj/mFvuuXbJl1bFytto2bCzrdQqybcjw2VNI+nuz8Z/1DV6i9vT2hTuvpRDiVtO+m8tPrrtUH7RvkvkMftG8oyg2sPhcstrW1ZbUhRjek7WtateXV5zTqiHO6nbyMOvrCsmimlo9MTXmSnbTVffxoWb+BOVegmXbQfHfgXCv32Ept6Kf/LaFPY64B3vymBTmfjCQ7cK969+2iVHD5HBRz/W42B4d00wh7gpNqeqXof1CoA2O2J3c9/RiRXE4+w9QBuV5UKnSzsmIuz1xP3AtRpnLIYJiqDM3Ni3TjLbdq1NEXZrUeU637Rx57IufllUvdV8plm8sxv9DNLpMts2Jkty5EoJfu2FDO67FcxAe2nU9dK6vpl5APYs5Zc3u8b2g5XggoR30uWGx5qzWrDTG6IdWOGp/XnbRKlupujm/bkvNz3jLtoKPHjMu4A6erpHM98Y+t1Noe+R+NGFirHYt/WpAAb+Ev79NlTZdkXbEnO3DXDN+52/JpX7pY79w8W77D8zpghV1uyZZ9Lss82xPhXJ9xFmZ6PX2ltpBX7wt1clesE59o+Trb1+rdW7+hlXfPV3v7Zs04Y2bKaYU5iKcLKNPNSyVdTS5Vf6lCbJ+FTizSVr9PZJupqtbpM2epc9vmrNdjMdZ9fL209pk7tPr+76rltWUp++OXW8KUWMVYRvHbwhGHHpxQl3ese6/o6yaXi4Cpjg2SirIey+EiTT6SlT82sB085MPcELF12s233t7jdV2u5y2VvH5y0eeCxaohO4XeEJubP8xuNrBhP1m/DzMvFvOBx4XYEAu5Mac6aZu8+x669Yaf5HRynelK3boNG9M+zDdMAptcT/xjK7VV772t9995K+ukMmEDlzC/1fJa4oF72Oe+2pV1b9NLv9Paxbdq1OFna+KF9xW9SV2qZS8p62We7YlwLs84e+O1V7uW8+kzZ6WdXk9eqU0277b753X6zFlZ77eFyuZYrBPYlhXL1LFxtdYFz/4bdeS5sppajT724pTT2m3KRK1K0nH/iEMP7honVd00qn7ntPOS7dXkUp4clCqwzTdILXRikc3/fFablz4d2WYuuFfbt2xS7cgJWd8VKMadhNh66c2rjtPmvz/eVc5k812IZVuM7TH6u6ruX9BlFLbpb/3YXYq6bvK5CJjs2FCMCznZ7Dfldq4Ytvyp6rTtmzf1eF2X7fZRjhd6slmH8eNKGhlmGn0uwU3/nXf3sadd0/U+vpNytFP3G6+9qpq6ERp19IXq2Lha65+9Ux3r3lNV3TAN3juS7Gb0Eeek7Fica+fwQnRaLnTH59jfi10W9WN30dVX/CDnE+pUy2incRNUdcAcdbav1frn7tb2Na2qGTZGA7VNG9aukRSuU3Ku6yDMcsi0XDN1iM/mt/rVDU36zLTV935HEyZOVEvLm9rpy99OWF7D+5tWvfd2zvObSiE7hBcycUCq5Deb//64Rh19ofqPn6o3rzpOEy9I/VzGQm8z6cTPe/vSxVr7zO1p65Vs5j3bdVLMjv7xzzV95+dzNPLg5MmxLmu6ROdeME9r1q3TkE8coy3LntP2Na2qHTVeAxv208hVL3Tbx5PtRwNqqlR1wJyU85Lse+sfukJDBtclPFO10PVpLsuuFAkY8t03C1FHV1VVa0KQZCx+m3nn53M0sGE/bV76tEbF7DNrfnOlbr3hJ5KU9LeLvT7DzHeyZbvppd+p7bHrpc5taeueQpY/dvmPqt9Z7R90auhh50Qu7Pz+Do0+8ryCLKPe9Dy+WMVIfNOTy6oYyztM+aPjxJ+z+NYNGn3sxUWv6/I5zpdbQpxs1mGycVfe8+0d3tlRnWk6PR4smtkESbdL2lnSDkk3uvs1ZtYk6UxJq4JR/8vdHw6+c5Gkr0nqlPQNd380GH64pGskVUu62d2/n2n6tSPH+y4zf9b1PtUJRNvjNyTN9Nn51LVav3Fj2gce57MDFuIAW4yNubk5+YPhU51g5cOsKumD1t+88ji575BU2GAs3TzHLuf29va0J6CxMq2DTJ/HTtt3uKqH7dQtiFj9yDXq3PC+fMcOVVVXa+QR52r9H5q7nTSteuCHuv3G6wp+kC12gJfrtppsna++/7vdDj6ZgpSePFGJn/d0ZQuTXfiM2XPVUT1AHetXqmbYGNV0btXN118buuzFzPjX3LxIJ598ctd+nSrT8JtXHacho8dqy3ZXx/r30gb2sb8dXx+ecuopGecl1Yly/Lqf37SgpCcHpTqBznffzKWOjj2ejKrfWW3r1qv+uMj+G7/NRC+uxGcoP/Dzn9XfXnw54VgVn227WBeFwuxH8cu2felirV18a6jgLNv1kmpe45f/OzfP7rqYEy3Tut8vVMe690I93DvfZZKuvFLywL9Ywm4fxTjXCrusCjHt6IX5MOchYZd7mPJHj1fbrabbNt/24A9U029A0nq4UOs73/q03DLjZrMdxI7bvnRxJFBf/abc3TJNpxTNUDskXeDu/5+kz0g6y8yi952vdvd9glc0UJwq6URJH5F0uKSfmlm1mVVLuk7SEZKmSvpqzO+ktGPj+ynbJsc2Kdje1pr0dviaVe9lfOBxPk0TMjU5yucWfz638qdPP0lWVdWt83ln+1ptt5qsHlof5na51Q5I2hTFagd0vc/UlKgYTaiySSqTqR18pr5WsdOuGb6z6qYeqLYnbtCbVx2vtiduUN3UAzV5tz26lsX6Z+9MSL5UP+0/i9LWv5DNuAqZVCZZc5Id27Z0W87D9jtBqx+5Juc+j4UUP+/b1+TXJ9pq+kWaIl9wr0Ydfraspl9W5SlmR//p00/q1sQsVabh2oGDIy0Y1q8M3cQwWfOwMPMSpg9N9ESplP0b45s4tj3wPW1a/Z7mNy0oatOnfPfNbOvo+OPJum2u/hP/Re/f9129/bMzZTXdm0bWTT0gEij+7WF1rG3V5N320Mz/OE1/euFlrdvmaROlFLO5eZhtL37Zrvv9wpTP9I0/ZibrlpBqe0x3vhC//DvWr+z2u3VTD9C4M66XVVneCZKyadYav26k4vQLTFfmsNMrRlK0sPVwrvVSdJ1YVVXac5pcm1uGrXuHDK5L2OZHHvNN1fWrLmregHyP8+m6PhTjudfxv5lPfRDdZtqXLu7qEtJvzG6hytLjwaK7v+vufwn+3yjpH5J2SfOVL0m6y923ufvrkpZL+lTwWu7uK9z9A0l3BeOmNWnC+JQbYuzOVztqvNY9e6fe+fkctfxwmt75+Ryte/ZOTdq1IePOkM3KSyhfFgfYzf98Vm1P3KCNq9/V6TNn5dwfJ4zm5kUJFcv65+5O+9D6+I06bKarUSNHJPRXXP3w1Ro1ckTXOPkEY2GkSyoT7a/a8sNpkaux9TsnfD9TO/h06yh+2sM/f7LaX/6dRh48SxMvuFcjD54lX/77rnm9rOmSpIkBOjauVkvLmwXv11LsAC/X5lRTGvbSKaeeIkm64/Y79PqyVzRpt4akJ5htD3wvdJ/HYgUF8fNeO3Bw6O0r3vymBQnBztDDzskq0C12Ntirr/hBxkzDHVsiF+JqR43XwD3205q4wH7Nb67MWJ7YvuZh5yX2IBpd9m2P36A3Xnu1JNnykvQr0WVNl2jI6LEaOe2ilH3hCildUo8wJ0WptqcjDj1YUxr20hvLX017POlY9562tv5DVf0GaNThZ2vkobMTthlf/nv94sYbugKLRx57outiQ7H35VQXPsNkzjzl1FPUv0pdydNSJXZ547VXkz62Kuz2mO58If48JcyjorLtGxUt+8hDZ2v1Q1elzEGQTrqT+2L02cvmsVTFSIoWth7ONQNvdJ3UjpyQkCgv9jdyDarCln/1yndS3pAp5IWcfIKrsPO3/qEr1P5BZ0EvaCQL1s+YPVdnnn1+zvVBdJtZ/9zdXTcXZBlvKka4e8lekiZLelPSUElNkt6Q9KKkWySNCMa5VtLJMd/5uaR/C143xww/RdK1KaYzU9ISSUsmTpzoqUzefU8fc+LlPumbv/Ehnzjaq+pG+JgTL/eJF97vY0683KvrRvjsOWf5woXNPqR+XLfPhtSP84ULm33hwmavHlDX9TvR15gTL/fJu++ZctpR6X7b3d2qqnzihff76GPmec2wMV3jDd3vBK8eUOdmVT56zDgfOGJMyt8IU4bJu+/pVlXlk3ffs+t9zfCx3eZL9mFZakdPdFmV146a6DJLOh/VA+p86H4nfDju6Ik+dL8TEpbLwoXNPmDICK8ZPtZlVV4zfKwPGDIiofzJytk1rHZgV1k/LJ957aAhoZZDdDnHrsNRR53v1q/Oq4fWd5uvgSPGhF62sWVPtZ5TTrt2YLd5jVU/dny3dTP6mHkJ5cxmG0i1fMN8lsvv5WrhwmYfPWacVw0annJ/TLc/xYrd/7Pdbwshut3nsn0l22YmXni/W1VV12+HWfaZxst3HcZ+f/SYcV4/dnxCPTPmxMu76reh+53QVadUD6jz2XPOSvv7s+ec5TWDR/qYEy/3UUedH6lDZF4/dnzaeakdNMSH7ndCtzp1zImXe83gkT57zllp6/tibNPJppdsH4/OX+2gIW4Wrgz57Lsy61q+YeqV+GnFLsvaUROTHk+i76sGDE445sTOc7KyR/eD2tETi7ovZ6pXUh2bUn0nVd1TO2hIwvCh+50Qeh3Eni9UDRzW7Zha1X9QVseMZOUfMGREwj4cFTtPyaaf7JieTKq6TWY+pH5c1nVEpvUoWdq6tCeE2UezObZFxa4TWVWkjoyr86oGDU95HhJ2OcTWF6nqpp443qY6D022T9UOGhK6ToxfP/F1c6p5yabuTbZ84uvDbOuD6PKQfbiN99t5d/cw8VqYkYrxkjRY0vOSjg/ej1Gk72GVpO9KuiUYfl2SYPHLkr6SJFj8Sabp7rvvvmk3gNgDWbrAJtXBIN1JRz7BWlR0A4o9GCar5GvrhqWsxDNNO1kFZEkqlprhY1POa7KdRzKvjhu3etgYl1nCfCc7kcy0jGLLPuqo8716aH0kiB6WfeA8efc9k67/fC4EJFvWydZzLpVo/HpLVqlkU86wB6JsgpBsD2xhy5iqAo1W/pm2pWKXMcxBPzpONttXfLCT6nuFmq9iLJ9008gU7CX7btjlFz8vQ/c7wS3u5Dl+Gaaqb/I5WU0m1f4fexIbG0xnU7/lsg7jj4vJyjZ6zLiMJ4fx8xZ/wTPxYqQlBJCZTljjLzYUa1tNtY6iyyFT8BR2H7UU8y+zUPVudJrVQ0YnnCOodqBb/7puAZxq+vvQkfWhyp8puIwNNvIJ3tMF0vmea4U9IS9UEFPoi0vZHl9i65DoOom92F8zfKyPHjPO3fMP5sJcUCn28STZPMQHV0P3O8Gr424MZVuOdBc0Ys9ps7mJk+w3U9WHYesD98gF1dhjXdhgsSSPzjCzWkn/K6nZ3e+VJHdf6e6dHslgcpMizUwlqVXShJivj5f0TprhOYttUrB9zVtqX/p0t6Z/7Uuf1huvvdo1brJ29du3bNLwz31Vw/c/Vasf+pHeuuarWnnXxerctlnP/t//hWoyka4/RfQWeGz/prXP3J607ffWLZs1qn5ntbS8qZNPPlk7jZuQ8bZ4qqYHNQPrVDNktIbvf2pX37kdH2zRxr88mNBXbtTRFyZtC181oE6j48YdfcQ5qh04WHPOmqvTZp3ddXu9+gtztXFTu0aPGaeWFcu69c/p1rzliHPVunqDTj75ZM044+tdZR/80S9qxAGnaeNfftNtmp3ta7Vlu+vkk09Ouw6OOPRgtb/4WNf6H9iwnzb+5UF1bs3+uV6pJNuGpjTspTdeezXr51fGN4fJ93lV2T6zMHY97DRugg4+9FD1qxsqsyr1qxuqOd84N+E5e1u2u869YF636WbTrChaxvgmZ+1LF0f23WkXdW1LWzt2dDVNjSZ3SPYcqEI2KQrT5yN+nLDPjYv/3sCPHanVcY+ZKHRfzGR9zMLsS9mIf87p+NFDtXDhQr3/zlsZ18P8pgWhl1/8vIzY/xR5XP/Wru++tqxrX73j9jskSaeceopOnzlLtvvntXnp0xp5yCxNvOA+jT72YuC4jw0AACAASURBVN10+515LYtUzaFj+3JHmxFtWfZcQp2abt3msi1078uf2K+2Y+NqtW3crLb6fVQ9dKe0zWRj561u6gEauNsnter+yyPJyzav07pHftS1DdcM21k1w8Zk1dQuenysrhuhYf86XWt++xO9eeVx2rH4pwXt+5SsKVt0OaTa39M1c09V98Q3o4/O/+Td9gjVVC+6PHZs3dTtHKGzfa3kO7qa+Eb7OVcPHKx+tbVJfze+/Jm6ocQ2k9y+JnkOiNhcDJmembz2mTv0zs1z1PLDY7T6/u9q+5ZN2vLqc0nPP9I9Di1Tk8TYx1KlakacS7PXYjxyIfYcIppFuqrfIJlVaadxE7q6/rTV76OaEeNltR/2HR223wla88g1qq4bobGn/VhjTrhMA2tN/3NVpKl5ts9ctqrIsb6qKnxz3v5V0vv/+52U+2i+zYxTPSO8o31t17625W8Pa/Qx83Jq5tzcvEj1O+8i1ST2yV337J2qqRvRtb7XbfOU/eLjNTcvUs2AuoRuKdYveZPTsPWBJD3y2BMa8oljurp4KGSS0x4PFs3MFLk7+A93/1HM8LExox0n6aXg/wcknWhm/c1siqQGSX+S9GdJDWY2xcz6KZIE54F8yxfd+WoHDU4Z2CQTPaDWjvowKYNVVWtI4zTVjpog375NP7utOe+KInpQifZvkqTO9e8nPXBt2rxNa7ds105f/rYmXnifqg6YozPPPj/tNFO1596+ZVPXQXjsaT/W0M98RbZje8IJVvvSxWp7/IZulVLUjm3JEwNt37xJN95yq0YdfWGo5DmxD/he/4fmrucLdsb9ft3UA+QffFi+tsdvUNvvbg71PMJHHnuiqwKJPt9rp+Pmd63fXPqVpat4Yg8kEy+4T4P+5RCtvv+7euuq49X5VORRAKecekroiwyTd98j53JK3SvZ6G+svHu+Wlre7Jp+qvWwafB4PfV/SzRy2kWaeOF9GjntIm1oW93tOXvRk5O2jZuTXgQIs49Eyxjf12b9c3enPYFON53p00/SZU2XaNKuDQkXKbIVJriKHye2/ohKdnKcLNip2/vQrPpixj5/MsyBOH6bWPfM7Xk/2zNV0J5Ln5WW15aFSorT3Lwoob+cFLmYlew5utZvQFdigdjtJv5kde3vbtaq+y9Xx6a1OvVrZ2rOWXOzWhZRKRMojBzRdfIWPfnOdBKesIxy6Jcb258z9lnDUeufvVP10/4zVOAaO2/tSxdry2t/Vv2x/6WJF96n0cd/W7ajs6sf3/D+Jtu+Oe3zduPlc7EhGzUD6xJPDhffljapTqY+Zsm2+3z7EUeXh2/fpo6NqyPHgh8cozWP/VRmptFHXdD9HOeoC7SmbW3S34ovf6ZtL7bstSNT94fMVO9Pn36STv5K5NmVsRdlqvsP6rp4kayvcbxkFzdV0y+hXDVDRquuxtT2wPf05pWRhFKf2ucjOveCeTp15lkZj0+pjvPFTKAWzS4ae763bfLndMPNv+h2QWvkoXO69qdBe35Og6Ye2HWeEX9xNJtnLie7UBS9YdC1bn5wjFbdf7neWP6q6nfeRWeefb6qvzBXE865U2NO/K62duxIub5yPWdO+YzwmOAqVcLKZP2FY6cfu8yH7DstIXFe+19/0+2cNlk/6mR5JaLzPfBjR+r9X/9Aaxff2nXONOQTRyc8ezhs39+olhXLum5otT1xgz5Y+Vqo75XizuLnFGky+kUzeyF4HSnph2b2dzN7UdIXJJ0nSe7+sqR7JC2V9FtJZwV3IDskzZX0qCJJcu4Jxi2IaKKFWP3HT1XHlvak40cPqNGrNet+v1CDph7YtZPWjpqg+mNSH0iyMX36SfrFjTd0VcTJsoeuf/ZOVQ8amnDlL13Si+bmRSk7y07ebY+kDx2OPbntyrAUVylFN+rq/oknGttal6p20OCEOwLprlpGl3VsJ91UJ9rVdSO6gqZNLz+l+hRXkOLFnlTFTmfYZ0/QqrgdeNThZ6v9g87Qnf2TVTzJTv5HH3uxRu20s7btULeg+YzZcyMpr9Oc5F/WdInWP3RF1uWMilaysVmzJl5wr3b68re7yp1qPWx766Wu5Rw9gbba/hkztmZzMI298hbd58JmFc2UMKFQV39jL7y0L12stidujHxgptbVG3TG7LkJF2eSZWtd/9AVkce2xKzv+JP+9qWLI48P2LwpIcV5sgNm/FXPtvp9NOOMmV1XhZPNb+zvxK/zXOqzXJd1qgxxVf0HdkuKs+ml3+ntn52plXddrPb29m4H4mSJHXb8/+y9eZRU1bk2/uxz6tQ8dHdVdzN2K4IDKigm917CdQwis6JxESM4EhwiUdTWGDAhTlFxiDcGUVFBUCQOCM2gzINAjAoK0iKIAt0M3V1dQ9c8nLO/P/bZu86pqgaSe9fv/taXr9ZiAd1Vdc7Z+x2f93nfnU4guPIFhDfNR1jI/GJUj5uOO6Y2mJDy5LdbINlcIlgNrZ6NxLdbROJTPW46Xpn3dknCWG7oV/GzPDbjd+j8+IWSgOBPz84UNphPBj2ZoSRd7eHJvN/4mei2RfAMGl0ydIizGE4mcTUmENGtpTLkG9UAl8sFTVXxp2dnwud2QU1E0fb+I6L68Oqfnztu9QHASYMN/2zlIpeKm/Q0vGk+1Hioy8p08bN3lfiVe5b/Ltvh+ut/AU9FJSKb56NqKItFZIcXNJcpe780ly77PcX3z6u+XQGSJqZWqLlLtszJ2P2Vq9aYAm97/QC4zh8NYnUgsmWhyUdVXX47LK7KEt3i+msEN8vFKdHlM5GXrQLsdAwciQ1bPz/hhF2+f13ZtJMBYP/Z1/QZjyAv20XMxAFumkuZAC3OuOIV96r2LzFvziuYP7/AmDDqwYmAO7535YAiS0U3sTeOfoMh+2qEfTyZKtv/RHJ9MjrXlU3kk7m7ur5xzSsvmohKwbpjAEPxJHajrU40bUTLrJsQXv86aq552CQrXE6tgTpAy5viYGugDlRT2f49ezU6PvozaD570uthfF5X/4vR49ZZsHbre1Kf+9+YhvoJpZRQSgdQwzEZlNKJlNJz9Z+PpZQeNXzmcUrpaZTSMyilKw0/X0EpPV3/3eP/nfu681d3mWhznkp/ead62vGdsKv/xai46AbkI8dMStrVURwnO+a42JkZDTHV1BKDl48c+4cnwk2f8Qhc54/uMtgCmBM+5bTTheH2/aQQqBudf7FRcnw+D5Nvuams4uZTiZKKQK6juYCE6tNo87GgOO8n09JUEpwUB9rhTfOh5dIIrngekc0LQLPlKWoHvttbEsBZDFUG43Vc/S8Gsdr/oSScr+3xDA9PGhJNG3Hohetw6Plr0frONHSEw12Oly+mfhodTlejqbu6z2I5GzFsKJJrX0Rk8wJTQJfcuxXxzigmTJggQIrifdB0pK510cMigK4adudxqbFdVXuOR8F0DByJoE6jMVLOuqJqiGnFx6mu/HccVPEaSgZwpGPNKyCK+WiLHLEIcIYHD8Flz4LmMgztfY5VlYnFWlJhD9T2MDme0JpXQNWcKREtns5opHHFvmgUOswDi8BV0447ZdOYxBTvOWcUcF06meDnH11rTvkph+5PbXgQrvNHI9m0gaHly55DeP3rovIpXXynyRFXXDihJOmR7U64+l+C+I7lZStkRqQ8sulN2HqfI2Qt0bShBIgKjGnAnLlvmu7fGEiGqs/DK/PeLnmWLVu3guazZQMCQYd9Yw6Sa1+Eo9/gLo+CKbd+5abElgMjivedtz4YEelDz15tYpGcTOJa3OpxorH98qV3oW7qX1Fzze/gqe6O52c+Vbay8c8ADl3JUjHjo5z/PeW0003HGcV3LAfpAgy1OFyF5yui3U24dhymz3gEkiyLakvx/QAnn/x29bI7nMIX5EItyEdby7J/Mi1NIIqtZK3KTXCtsBGEGs3AqfOsixCKRAXoxO+dahrmzv5z2aT3RMdIlZucCzA6Ic2Ub4Vxnns5Xn1zYdmjr4xAV7k4xeN2mRKZ1L5tCIxpQD7ahf/aX/BPx7NpJwPAnsyrnEwe/H6fKd7jz6j4e5dQx4uPRAH++aNJ+N6VA4p8Q64Te1OcTJ5MbPo/MZ38ZKqjI4YNLWnhCDbORC4VP+71i9ecJ1919y1GPp0ooZDb6wagvXGmACOJrJQFH9qPHhYsrOKYNbptEWqu/A163vYqAqPvA7EoSMcjptMQTvQqTqC1TOqkPve/0rP4/7fXnb+6C6/Me9tEm4slkqb+Ce5UO4LtJk54cUDGy/uWim4mioTUhSM52THHXdEzfti3BwvmvQFJNQcXxGo39XsYaVUWu0ug7MX8fWO/Zblgqxgh48lxaPXsEudfbJRm/eXFLvsyiisCkK0CCeU9o5HN8+Gv7ibWurifpfhYhNRXK1AzbhoqL74J+cgxSDYzdYijO5LTVxLA8USkHIVG7Wz/p6lcXX3G4nAhsmUhOj76C4hsQc3VjEpCc+myFddi6qdxf/ir3Gjq49EejHK24N3FmHDtOFOCx6snNeOmoe7+xbDpxq94HyQ7e5bMkW9FAO0+5zLIvpouzyfqqtpzPAqmEc3rWPE8SDaO6u49TXz8ctWl41VX/jtnVxl7Qw7s3wvVUKlCPlOSuAdGToWay5VUgKvHPgCrw435b87v8vw/qmnC3nSseVkkov6R9wAA0rEIbpw0WdA6i2lcNJcqCSxOJmnjSYzxzDsjo+BElFSjzTlZcMC4vl2h++1HDwvbldq3DVom0eX7bL36F+yWHuy3vf8oJt9yE+h3m7uky3NwhK9XPnxEyJrWBZUpl4yL/xcHkjwILb7HOXPfhG9UA3re9irqH1iKigsnIC/bTfRlHgRVtX8JtbMNwQ8eFf3xdkvBrRv7iW66fQrkS+9C5aW3iOC49d3fI6vS44JPxa0PPCiqf2Apqi6/Hf6qKlPiWtxbNmLYUNO6cL/FqfLG1z86tv+fAXdOJEsnQ1d/bMbvQL/bLHRWyyRAM8mSxD248gXkUnFT8stpd7F4AnMXvvdP9TQd71UumTD6AsXfCxZfLYhiR1Cv9hntpCTLYu/LzRLg/d/txw6jwuspqWZVjyuATkYWzPQZj+CxGb8rSXq7Yj5IFhsm3jIZLcFOoXvFVcxAbXcgmy6hosZ3LC+pRHL/whMbI0BHZAUgLLEt9pu5jhbkY0EQpTwIaQQDjuc/eNxSDMD+o4BkOZkM1PYw+WH+jL7B48tSx7s6ju0fvR++d+WAolzosGhTKk4mTwZYMibXfF9bZt0Eyer4h5gAJ6qOrly1hrVwGM6xtvY4A5LVcdzrW+yu4/ZUG3OC+NfrkPzuU7gHDBNgZFcJM1HsgoVVzJYzym7EwH6pGvvQSSf4pfMtjpxUadJyMm/6v/01Z+6bCIx9CPb6AQAAe/0A1IybhuAHj8Lx+Twc/Os++Ku7QdU05IgVNdc8DFuv/si0NOGXU+4FACGA02c8It4fsjoR2bIQyaYNcJ8/CsGVLyAw4m7x2ejymdDcLkiyXEId49/FlZjfF3QlLhZ4WZKQN/yfqnmomQSCK56H6+zLkGjaYLr2pDvuArFY4b3ibvS+qj9SLU2QWh4XgklzaVRf9duy167v0w+plibxO1f/iyG7KhFa+kdkDD8HyiPLxfe+ZetWvPrmQjjPvRyh1bOR72yDbHcLJ8Sv7zr7MoR3LMfEGybCX90N6WQY7Y0zUT2mQTxXYucqTL7pBsz6y4uQZJkpn2xBdNsi5ILNYg/ysSAim+dDsjrgHz5FXMcYwFkDdQitYUlwe+PTqB7zAGy9+gsDUfyc/FBWXv0spgKmjrM2uVQc+e3LAImIBAuAMBb8/9xYHJ37azh1ZDvX0cLOpet3oUk2ePWJf1Yke7r8plqacMfUBtgtUlk5e/f9F6E43eI7Ek0bTDKRDx+Be8AwJL5ei/alT6N6LFsfW+9zENveaOoXBQDnaT9Ge+NMuAcMY5TJUDOI1QG7zQbvyAbYEmF0rHwBfoOcJte+iGf1s9346+D+feh9VQGscPW/GFTNo/nZqxFMJ9D7+utgDdQhuPw5UDUv7iujP++Ea8dhwbsvAj+9q+Q602c8ctx96uo1fcYjojfEr8tXaM3LolJVjvKVjwVBJAmZdAo11/zOvP56Bfjg94Vn5S9br/5obj+G+W/OZwOC8lkErvxNAUAwrB+vTBhpXMVy1SV98K/mpG1qw4PwjWrQZWGj2Csjo8AoP8V2igc6zp/ehd5X9ceROXec0F4Y19f507sQWzT9uIkcAGiZZAnIUvw+e/0AITvpgzvh+HweZv3lRQz5yU9w8+TbEdmyEKl92wy6NVj0DOY6jooAyPvvPUE1FcRqL/ssirPQ4168l11V1nLJAqLNgwJn/0uQ2rsNB/bvxY2TJuONeXOx/4dDOPj9PgRquiORVeG9wrzvW7ZuxYJ3F7Ne+vBsVF1+u7g/IsnoWPMyoKmoHvvAcWXHaLPvmNqAVN8Lmf52MP2luRQCNd1BDmxBZ7QN8Z0fC1uZaWnCgnfZuhbb/RHDhuLVN5+Bf/T9JXo48YaJZeW+WCa70o/i9xlfJ5Il/tnj+V9ekZk+4xEcWLQXxOqA7PDBedZFCC5/Dlo6AZpLQ3ZXIVDTvex3dch2+K8o+J4uqy3HeZbiV7GOcRvvqfAL+fQNHo/Qmleg5bPsYPalTwNAiZ3csnUrXnl9LgJXTetSt4OtR9BbD15Dq18y+QfT3AGDvwFgkoXHZvyO/Vy3x5EtC5HYuQrUYoNkUVj7xJ5P0Lb4cUg2ViHl7wttXwZYbCLO4vJ76NlxZStd7UufhsVXW/L+TEsTOpY9I8BEow+Q3VWIbJ4Pmk0huPIFuHRdZP7LCZpJimscz8/zZ54wYUIJKyO6dRFyHc2wurzIp1hVqjge5PJmlCPeBx9rPwzJ5kJw+bMIjLpPANyu/hcjc/ibkjgpufZFXH3tOFR364lg61HU/YM6VLx3jn4Xom3JU5BtTuSjrZAcXhBCRILOk0N+377B4xFc8bzYy3K+/rEZv8OkO+5CjlgQGDlVxGyBkUxOQ1sW4sZJkzFx4kSxXnyNysVgXb2YDXkKlRdNFPsR3vQm3IPGoG3JU5AUa9nrR7YsRGz7spLn6Pz4BTxroMpPn/EIDh48hJprHoa9fgA6//YubL3YrIX2JU8i0/w1tHQCkt0FW+9z2DnjrUdFsm+MiXjsaTonEeV9Lp+JUG4tjDadELLrhBuN/1dZxFtvvW1yzvxl69UfajYtEAm3xwOqOI9L6zMiGO3HDuO2W28SZfhiTnPbuzOQK+pFO97kOON9Hfhub8mkSSMS3fO2VwE1j6rLJgGUIvbF0hJaVY5KJSim6/zRaF/8BMIb53aJrhsRsuKqq92qnNRB2Ebks7pbT8xd+B6c514uAhCay0BNhEsMaqJpA1znj4KlsheCrUeRJxLcRYiQa8AwrFy1BoAZsfQNHg9icwrqUGjVS8wAFDnoYsppj1tnoe7+xdCSUYHEVNhISU/RiQ5lPRF3/pTTTgfNJkGLejeLqbXcWOSCx5/W+9ZbbyMWT6BtyZNo/vMEHHzmasR3ryvbs1luci2f7GessBZXT3IdLagYch163TkXrjP/U0w1TB/8CjSTMg0MAYD0oZ2w9TgD8Z2rRIWrZtx0pDojXVZ7yvXndNVXK9kcJjq4ZHOKyoE4kLr9KGa/MgexUNDUB8UPGz/Zw9yL0fsD+/cisXu9MOCdn74HzyBGi9QyCcje6pKqdmTzfNRc8zvQfLZLXTPSTY3PyoMPt8cjEtHjVQiLbYlv8HgElz8vKueRLQtNlO/IloUlQ2GMcnI8RoHx/o2vYgS74sIJJfT5riiUXQ0z4uwAquXR9sHjLAEyBCn8PZxVIRGg8+Ouq1/XX/8LTLrpBiR2roKj32AoVb2Q62hG52cfIpFKIRY8KhBnHkQGRt0LV/9LSwYPBBtnYtJNN4h7LR7uQnQw0bju7UueNMl3dNsiU+973X2L4Tp/NNZv/VzYmnAiI2y58eD1l197wzDF1AwIRLctgmxzCZrTyVSXjRVqS1UPSK4K1Fw9HXX3LRYVJ29VoGxv/j33NZRQ/Re8u1gAhIeeHYfgh4/j3847m11TLk+RLAYSuuzHtbu6rD5wWSrWyeJrnIhlwAfQKA43PIPGQMskEftyJUCIGDISGH0fElnV1GbA97uYll+u2nKiZyl+cdDK6BNJ3wuRiMcRXPE8wpvmI7p1EbRUJwgBasZNg+z0lq2wzpn75gmnC9f36Sf60opjhhNNSzV9p4Gem9ixHIExDaC5jGDRpH/4ApJS2pNXPW4aqobdgdj2ZUJ+eT9x8VpaPAFUeZxwaGkTdZXrTD4Rxs2TbxctGFyXQSkCI6dCCfSGtebUIv81DRZ35Un7+euv/4Wpos7BIMfprKeveJJwcd+lsce9eGCfre4caNk0Oj76sw5wM3tUedkkuAcMMw2ymXDtOLwx/22EU7kuGT1ut88cZ5YZ2MWrVNbvN4MQItosJKsDgTENwsYX0+VlVyUUmheU5nL00OJWms5P3yvZf2PrRLkD60+m2lZsQ/iAvMqLJkIytBwZr8/nSngGjQbScRO13NhTzXMCqJkyLEOJsa+u+i38o6ZCsnuQ2vcpEqkUvFUBU1tbaM1sHHpmHNR4CO1Ln+7S5/KhdZxJ8j85efdfOlnkKFxXvQay3UwvONlpRvw16y8vCoqE6UUBWCxdjuvlr3KOsH3Jk5BcFSWTJk2O6KkxjIbqCaDXnXNLqhqJpo1lm/ErhlwHqjfUHm8qIzcQ6voXBfUpq1E4h99vojh1NQrZSKPg1JvKiybC95PxkOwuWCpKR6ZHty2Cq2hgEM2mUDHkOkGJ6nHrLFQMua7sNDbnGUNgrzsX8Z0fo2ro7aD5dEnwaVTkErpLTXcTEPDqn58z0WmL+xyK95MHWsYJaxOuHYfrr/8F7vzVXWg+dAiQrSX9dpxa2774MVOiSmzO407rndrwIKznDAMAEIsCi8dfUuk73uRaPtnPCHLw6gl/KX6WZLTMugmJPZ+I5vWaqx+GbHfC1vsctC1+HIdf/iUOPj0WuWAzsq3flySsRmfFE/Ta8Y+hvr6uLCqo5nIlSUZwxfNQczkx2Ofwy79ELliggfMme+LwAooNks0JgDCdamvHhBtvwg2TfyVoeu1Ln0bzCz9H6zvTgFwaUxseFDo+dNgwkyFuJZWAbDXRk3ki7TjtxyBWB1xnX2q658jmBcLxdEXJ8Vd3QyyeOO40yIPf7xNBb5dJ2/59ZW2Jls+IwCJuOCamaujtSOxcZaIPTp/xSElQ4ep/MdNFp/ukAvvi4NvV/2JUXDgRbe8/Kvozu5r6y++fI9Kc2tOx5mUQi4Laa/8Aye6CbHPBdfZloGoO7UufLhlWE7j6YeRTcXYsjmGy4oJ3F4vrcVoStzXugSMg2ZzwjmxA3X2L4R40BsHGmSKItNcPgP+KO+E6YwjaFj8u9Hvyjb9gPkB/FQ93YfbIfDxP5si37PtXFnpDi48HSO3bZhogxW15MTVJy6ZE3zcpGu2e62B9a7xX/HjDaYzAyJy5b8J57uVIH9pVosek74XCFxlf5Y6UeOX1uYJK3mPSLARG3w8qKVi/5TM2rXLYHScEEsr1YIY3zUds+zJQqxuUoqR/l8tSZMtC0Gz6uLp1omFA3Jfx47Kqhk4GNLUsoMzbDIy9asX6xHuajvcsN0+ajJoevUuOKeDPd2D/3rIgoppNw9Hnx4VEx9DicLwKt+wpn1AHansImU7sWHZcylyxLBjjJX60g5Geq+qgJL8/DpgYY5bivkMOenAdcJ8/Cm1LnhK+5/DLv0R0+Uxce804wOYQvrAcnY+3YHD/zq/rGzy+rNw7z70cN0++XVBtjZ/tqkeOD/vhrIxyA2JI3wtL+i6NPe7FA/vy4SOoGTeNFQ0ebETVZZO6HGQz+9XXxXCWcv3bwff/gIQKU5xpHNhltAnTZzwCIkkmYNYIhNBcFslvNkGNtgn7qG2chTkvvYi2I82m44iK7b6REmyUp3LgVl62dxmD8R5l3kLmrfQLenQ8FhPgf/zrdSJuAFDi08vFzFo+Ay2bBKVal1OXjaAKl818ZxuqxzSUtBR5RzYgm8sXChKaCjXZCcnpQ83Pfo/KS28pSy02Dq1TqnqX0LD/u5N3/6VpqBzpjn/4x5IyfXvjTLisCgB9SqjVAWL3ItPSxDZXpzUSmxOyqwL5aKtwTECBZlF/GqMlqIkwGzU/4m6Gxpc5r8rWqz8OLiqg8SOGDcUr82ay5upYENEtC6EmI6i5+mFBBQtveAPExiaCJXavg+vsy6DlMnD1vwTB5c8jMGqqoCTwknV405vCUYln6WiBxVcrEsvi8nc5mkBGAzw/GovY9mUCTQYA9zmXCWpXSUn84CF4fjRW0CdBqcloa5kEKn86GeF1r5nK+7mOZoDCMDCoWQQ6x6OD+qu7MfSq9Qjq+/TDiBuvx7vvvyj6D/hzOvtfgoROF25b/EdINnsJteDOX92FlavWiO8lUgFrCR4r0HH4eipVvZALNeOtt97GPfc1oCMchez0AqkEqNWNN+a/jY2bNmLPD4dRffXDaG+cCVuPM0pkMb5zFW679WYReL711ttdU1mScUEtUbLbINtc8A+fgtZF003rJY4+0CmTxrWObFlocsycrtfx8SwzHbeyB+I7VzHEd9S9Zsrw+aMR374ckmKDf/gU2Hox2mG5ITecHmSkQRXL2p2/Ykg37wGruHCimYLb58dI7F6HCRMmQHL5UD1qCkKrZ5soG6E1s0EI0Q+Z1dggByJBsjtBJAv8w6cIPQUAz4+uRGLXWiSyeTiHMypVaMtCHNzUiJpx08XzZpq/hmx3C/kzJoDpQzvhGTQGyaYNcJ19mdB9UIh16ErX7BYJvlHMmfBntfhqUWGTTfTmUPV5CK54XjiQYn2QFBvisRhC+hpzimzNOEYvO/LanagaeruQi+i2RVCTsEdz9AAAIABJREFUEbwy53W8t3gJgq1HQDUK/6ipJffZvvRp3HbTDZi78AWkzizQsySrE7fdepNpn4tp0QBD++vr6wSdifQZAku2lG55YP9eWI49A+e5l0PLsb5JNRGB7KpgQzXWzIba2a7rwga4zvkpOj9vROyLRtRcPd0km9TuRcBAPbfXD0Cq74W4efLtmHjDRFCNQgmn4B9xN9REGIlvNoq1AiDoSp3b/mqS5arLb4e1++kIrXoJ+XQCK1etEf2FgLlVIdfRDC2XNlHOjUkgp8ATi7XEVxj7VuK71wtbzoNqLivEYkNk83zWhrDLTBWXHB5IVqfoFT8etd5Iazz07Dhou9ebgnNu7ySbU9yL8Xv40RrG9TZWrLgt4i0BaiKMzk/fg9oZRNvix0EzSZzS93Q8awi6+dj6vGyHloyK9xHFBsnhETYn09KE4Irncc99DeKzI4YNxezX3kDNuOlQE2ETbZQoVkxteFC0OkQbnwJVGLXO4quFRU3j2ZdeFPtI+l4I0rlMVAGCy54tmyDlMhnktzeKax6d+2vkI8eErY9s+ytywQNwD7iiYCMkK2SXVzzL0QX3Q82lkDllCOTkBlTpemikdyoON6qK6GmBEXejffFjSB/YIYLTo3N/DWKxI7JlYZd2Q3G6kc+kSmh2wRXPo9KhCJmeOHHicSlz/HsLbIpCG8Qrrz8O1/mjhczK7ioB3vMe91ywGVouY5Kt4gSXJ6rc1quJMCS9j9vow99bvATOn96F9Bqzbyim861cNc909nFK7O8zJrkNb3hDb3V4SDzTgndfFEyVe+5rwMSbJ2HChAmo7t4TPxt3pWBScVZGPhY0JSj8ldq7zdQ6wP1qx7JnAIutZPhJcTLj6n8xnGcMQfNzV+OHfXtMFGW6f7oofhDZwqiqHz4BLZ0AZIXNTTDYJns9H9j1Rwz5yU9KqM7t70xDnSGO43sV3vQmqq980CQDobWvIhgMYsKECbjjrinIE8XUDsUp0CtXrREMA6NP7ap1oisa94FFe3HzpMlQZdZCxumkTp0enWlpAl0+E8mPnkEiq5nkzHjNYiot15MTtagADFS5cdJkQelONG2AGu1kej3316UyOKoByRUzEfzgUUbHLmqXIpJcopeJHcsQuGoao393BRz/A5T24te/XLIYCoVEIkE1irqr+oNmEnANHI6291nWTXMZSK4KxGIRoWBUUqBlGK1PUmwIjJyK4LLnAImYDFLbkicZj/qGiSw5GTYUC959EakcNU1G5TQJvvmh1bMR370elFJYXV5MuukGgXC3ffhHEElG9dgH0PrONMbX1vvPJMUOz6AxIigKrZktEKqueu7UaBv8o6aifclTIDofW3Cwv1gq+nXy0TZmQDIJKA433nhltjjQ/ObJt6Nq7EMIrZnd9ZTRRXsFRc/iqoR/9P2g300T/ZM8aC0O6C2eAKqGTkZ4wxtoe/9R5sStDlPQJNlccJw+GO1LnoKk8+SJ4gBV85BsDoZUXlVwEvPfnC+ChXffXwzIFqFsvv+8XgTP0b+9C1BV0F+Ozv01C4QcXrwy720ExjSg6owgwpvnIzByqujFkFoeL+mDiGxZiNxnrbhh8q/YPdud8A+fIhL/dCyCb/ZmRDCrJaOovvI34rgJzmPX9MCTSBIUhxu5ZFwgjAI02DhX7OOROXeIAUsAEZQrS1UPEZwU95kRSUbHR39GPnIMitNdNuiTHB7QfI69L9oKYnWgZtw0tBp6f7gD1dIJABTVows9LBUXTkDHR38u9IMFm9nzZRKApEDbOAuHjh2G4nAjn0oIFGzL1q1s7XWHfHj2rbB4Auhx6yyhO4lvt8DzoytNwAWf1JvvbNMBBxZAE8UGYlFAJAugG+HWRdPZvnzyFoiswHXOT5Fs2gBiURAYXkiEU/u2lVRotXQCIEnIriohU95//xnaljwJmoqh+03/BWugjgXVoRYoVb2R72wz9c4BEOt/St/TcfW14/DSrFmo0505fw9V8zj07DihV0RSQI60wtbzTKT2fYq2JU+xhDiXYSPwZavQh8pYUPQnGYP94gosBxAim+cjc8oQSLG10LIJWDwBQYnhiWuVxwkASMeiyOxcZQI55i58QfSqcVp0rkx/xzk/GoAbbv0l3IPGILF7nckerd/KvrPuqoJ94mAZp/JxexJaPRv5eAcCI+5GcPlzkB0uqLGgSTaj2xaVABac4s7l68icO4StOfzqbWUTI6WqcMD18YLh4h4t/veNt0wqGZRVTIEHgPD6N6BJkuk6vKIf274MNJuEb+htTM6jbdByGdGbfnj2rSxR3L0OgdH3Ih8LChkjNifUTEIAjMXJZKaF9dTncjkE9D40Tp1VO9uh+HsLe+fsfwlAWQtB5U8ni4SB27lyAJFS1dvgZ9pRc83DJh002tDY9kYc3L/PxNK44y42TThQlBRqqVhJr3tg5FS0vjvD5Iu4Dre99wdQLW8KIqWL70RVLIjQ+tcBEFQXJRz8dfD7fbCEU+I4Ef+IuyF7AmUBZYvHzyaUl3m+1r/+HkRRBADFwYhDz/1MPEto9Wy9csR8vKtMv/o99zV02VKjZVLQMinT9bn/tdedWwJQBhtn4pIh/4G1q9egYugvTdequHAiOlb+SXw/B8S5zIbWzGbPbbGaZMrIpuB7o6aTpnggsmkePINGi97A4PLnBYtG1Xvanf0vKUlweVVWS0ZEAF58LVxxN1r1pMY3mB2BpaU6u6x+Gv2t5RjrreWAKwcViayUAKXQadfxZAo5YjHNuHj5tcdQbdjnllk3IbJ5PmR3VYm/NcY7wvYEmwFZAdSwYJRw3eSsqK6SGWMbgOLvBZrPiWfh550ymZhdcn42l6NcMl62/9aYHPK9Kl5fPrWbKFYhE0fm3CH6dgvPeBQvz3sL1WMeQNUZQdGH6f33nwn/WlwAAdAl4EUsVuQpRPJ75LU7S2VjVANCS/+I6rEPCTnzj7jbdE3jv7nuJnYsg5ZJ4dR+Zx63Z9IIqgCsYsnbdMoldvlYEIk8heSsQECPT4qBAKqpaHv/UUDNoL5PP6iZpNDvcvJUXEjh93eyr3+5ZPFgcwuqf/YH04AFxd8LksMD2ekTgwT48I07f30PnMOmIrZoOvwj70Fo9Wy4zhuB0JrZ0DJx1Fzzu0JSEWyG5PIhMOY3JpRpgh748c1W/L3EsBVX/0sQ274CxGJBzbhpwqi8Mm8m1GQE/jNGAVRDtT6Ah0+ajO9eLwL1iiHXCZS7HOICQqAl4wh++ATUdEJQVInVLhLLRNNGxL/8CFRVES8K+oKNMzHpxl+IoI9Tb/j1uONPfL0WarITRJL0hJtNGTUOV5DsLnHNltm3CkfLA3ojOtnztjmiyjLh2nF45fW5QgG0dAKxLz+CZLGIBvjkvm2MYz76vhInccddU4QSQ7aKfTNWOPOxIDJHvgXNlXeq1YZKTLlkMra9UHFKNG1EsmkDLO4q+IdPQdv7j6J67ANI7t2KxLdb4B4wjAV7BoSdI1dVl9+OqstvFyic5PSxQ2/bI3D0vwRa0wa4+l8iGsrVRNg0ICUfbWWyuuZlyA5WDdcySUZxGzAMwWXPQY13dIlC5tMJVF36y5IqkvH5AODg02Nh61Wg8rL1ZM7A86OxJZUXV/+LEfzoRcR3roJ7wDBTYMuDU0dFjRjUwYPtZCxqGrJQeektCC5/Fq5zhiKxay20XFIEUUbgQgQvq18Sep4LNgNgwxxaF00DT6YVfy8xgax10XRB/WtdNK0koC+uaBObE7LDi3z0GPwjpxaCJZtToOS8OssSigWguQw6lhWGe8iuSjgUgpcWLADABomUM/icahKqPg/k8DFIihXW2tOQOfItJKePDVtR7GZnbEAkOz99D1VDb0dozWwR7BsrUxxAOPLanXCdfRlinzeCWG2w1w8UAWX3m/5L6OXAAWdj9mtvQHZXmq7D9e6e+xp0RsHBEp2z+GpBEhGs3/o5tGwKid3rTU6cH0dhtK8AROJGrA4AEPbE95PxCDYy5F9LJ1BzzcMI6RUEHtxxOlsx0yJgAE4qLpyAjlWzWIU92iYSo/iXH+n0ZbBjSijrf4Qur5HNC1j12BBYWyrrcOMtkzDx5kl65coGothBrE5YnBUlSWBx5T8w+l4k9nxiCuZ5RZ9mklD8vZHcuxVqMmqy5wAbflG8pu5zLsOhP41HzTiGQIc3vIHY50t1YEERLAx/dTcQixVqPCLkP7zpTdjrzkX6wJdwnD4Yse3LRF8uZ8xwQIEPl3IPvALxHctLA7uqHsLP8EDIqINGG1ozbrrJHmzZuhXxRLJkKJTr7MtKbA7AAi/J5jT5otCa2Wy4xNFvUTNuOjsS6Ou1qLn6YWGjjWh+aPVsJJo2QEsnMPGmW3Dnr+8BZBtyoWZ0H/JfAphVE5EuAeXQ6tmIbJxn8k+VF00USX8xIMFZN1wXeILLK21G28lBIsnpLRskVnfvyfbQsL4A0LH8eaRbvgFABQho8dVCy2Wx8+tvWEuIAZgDgPTBnSYqbkdbG+IfPA7Z6RXDTWRXhWBEcYCC6y5/cR02xgOBUfcJoDu84Q2osRAAaqqAxb5YCs8FY02gRPybTSBE6vI4J868MbZcEMUKi62bSed4pdDzo7GMzmtI6oMfPg41nUR749OQFIfwFeWSquZQmK3BcHMiVHz2HiRZgFvFiQhnPxltF9/30OrZUFOdaFvyJAhhDKdyQxSNDB3jMCg+5Ci44nnGqtETvPC610zzBopln1gdOLB/b8lAHN+Q69D2weNCjolsYQw23f+piTBCq1+CZPeY/ASvBopzasc0ILjiT3CdfanJjwoZsljR9v4j7HipRsa6489qUdMme8xjCoCY4gIjM8PEAtOBlvC6OVCTUXEdyBYkP3oG8UQKNJcWcbTFXYnAVXrxZstCTLz5FhDFAffAK2AJp0rYMbCYq6SWyh5oW/x42co+Z2Pw4lC5qmYudBgWxYK8mkE8FoNkdSK6ZSGc/S9B/MuPhDwZQTvZVSEKKQYwswon8fqXSxYlT41YcEefC8R0xmLHxxe4M3QMFfpm5UKHQbMpgYS1vjNNoJGusy9DPtZhomNylGnBgpmmSpBv8Hh0rJoFa82piG1fxpLBMQ+Zkg8+2jqycV5RAHwJYl80itH3XIi4kTQG7sVVAtfZl4khMsGVL0DVEzSAccBBCIhFMVGjOP1g5ap5pooip3HwilVs+zJAkiG7fAiMnIrQauaUi0vixiEparQNFUPY5Eoe0JvQyY5mU0Vz7769WK8HTYq/N7RMQjiX9g+fQPVVvxXKZTQEsqsKVNPg/NGNjEr1zDhh0Pj1jsy5U1/rFBR/b6F0re8/ypLfjLkSw5NJgaoXUQvDm96Eq/8lInihuTRzarvXwzNoNJuQmU2ajDJHOyWbkx314aoQAQuvGPO/1UQYkiyzqljRMxOLjcmqpiEfD6G9cSag5lB9FZOxhETEgbnFEx/r+/RjZ64VVZEkh7cEbeSyxhN8EAmSzQlidyG+82MQm7PkGpJiRfWYhrLouEYlBK4oBDLJvVsR62gHtHxJ0plu3o3YF8sAAlMQRYrQVVf/i5ENHkLHMkZhzHcGQfVeLmJ1imRadvuRPvCl0ClODyo25HydjIk0KKCmOlkCoAdWR167EzSfg1JdLwJ9jpwHRt2HfCyI8PrXhUOq7t5T9Lac2u9M5CvqoHXuKqGaxLc3onrcdHa+pM0pZKN6TAOCK1+ARKSyzpi/jCPVO1bNEklDcMXzpipcrqMFWnotQAC33r/nHjBMUOSI1YHLLhqCDZu3svUs05ud2PMJkrEkwmlN0NuNOhf/eh1Cq2ejZkwD2j98Ampnu+ix44mhEbRpW8yO0uVJLpEVaMmoCRwIb3qT9aPp+qZlkmj74HGAQNh3Y1CVjwWhRttK7h2QENveCEtFNzhOH4zOzz6E7PSamAE0n0E6HoFbT7CoRhkFltuEYDNykVZGz/T4ocaCLMgcPgWti6bBP9JM63X0G1y28m+vHwAiSWh97w8ghPXZ1lzzO4YqK1ZkjnyLmmt+J+yAUT/L0du4LWODQJxQ4yF2HltHM9qOsHU/td+ZyFafh/T2ZcKfqNE2ZAFAUpDYvQ40kzT1U/p+UphwCCLBPfCKsusd3bLQBHCJQKhuAGLbl5t8kjFxZPb8KGbPeb1sH36iaQMkp7fE5mjphJj4yn2Rb/B4dHz8F9BcCok9nyDxzUbQbMFGGxkTPJB1nz8KiV1rQbU8cv7TgNiXosecg0EHnx6rD2K5h4GamYSYgmivLzxfcVJYAkhQCmK1CxBWSycE1dJYaePxArE64B40Gp2ffVg2SCSKFW6XC/mIeXAcsTkh25xwnnURUvu2sZ9ZFHguGI32bX/tknr+5it/YfHApMnIU0Cy2YVucGCVxyCMZQKxVtw2RbctMh3hU6yHNJ8DsdohO31CBhPfbAbNpgtxw5rZyIWPibij9Z1pCBbRqsu1XBDJIgBfYxsKrxQaGVqJpo1I7dsGNZ0EsTmZX9Wr5eWCeGF/dNvbuuhhZNt/QPWYB9D2/qOmyjN/ZjUe0gHe2aLo4LmA9S4TIkGpOdVEqeeyJHsCILJF2Hwjm4zHTlyfjZROEWetfRVqMiiqsZLNyWxCJiXaYvgQFu6zQFA2cQHMFE61sx3+kfcIBpuWSUArGpjEWRJc5/jnjGwRx+mDkfh6LSSnz8R8CC5+TCRuitMthonNmftH5JJxKE43HHY70rGMafo3vyZnRWjptch3toEoNgYg6fZUADGLH0eWSqYKcfvixxiV2JDQQrIIm8dlLbz+dazf8hncA6+AdKwDbRxUiRyDmuoEwPwSP7WAF6pAoccf5nYpIyMhsXOViQ3jHjQandv+itTebai+8kGoibCJlchj8uL8JLZoWk+cxOtfL1m02sW/04d2sjH++7aZHB9HWCSbszBqvW4AYtsbTYaaWB0Ir38N1WMf7JKOmdjzCRKZLECJoGQ4zxiCxJ7NSP2wA6AUUHMl9JRMSxNa3/2DcORG5eQJjVGIiGITKFP70qdZklFUJeBCHFzxJzHSnxuuXLAZRLGD5tOmgM1IE/zllHtFRVHQOPJZpA/thOzwAYAwWjzYjmyebzIgPDlSE2GB8gkanq4MzjOGQHaxMfU8gH7rrbexacvf4B44UhhDoFBl4EkoUewlVQAtlzL1HPFeCHMQwr5PsjN6a+e2v6Lz80aAapBsbkD/3mTTBsjuKpFMGmmnvFLNA6vOzxtFPymxOliAmWG0Gy2TgFLV20QNpZoKqqkAwKpDYxpEZYsH+fzvwy/dgsAYvYFZMY8OT+z5BLHty0AsCiSLAtc5Q0XSevjV2xAYOZUh6XqFj1PI8rEgevXrg8MtYaiGKhI3TJaKbqZRzyCSuHfff16PjuXPs3u3OVAzbjpa35lWUqXmAW05dNwY7HJaNlGsAKwl6K+a7ITs8kGNBcUQBsnugvv8USVGNba9ETSTQuqrFaDZFIiVnWPkGTQa8S8/EoCASDTrBiDfGRTvMSLY+c4OsW7G/kP3eSMQ37Va6DgPAqmah+zxo+2DxwCqiepa9JO3Sih/vFeKapRRfK+eLgw+zWUATQWohsSeT6BG2wBChEzwpIf/jL+MwQwbZlQYUmRMGrRcxhTMKf5eyEfbQHMZU0LAKXLpgzuxaekfoWaSUKp6g6q5Elp94psNsFT2QD58pMteNp7os14Ziz6anIFNVM2JSlNy71ZAUwXVuGbcNLS+Mw3E6jDRmgEJ8Z2rQGxuBpCd81Mkvl4HNR40PQcPqvLRNtO9cUp3zbiH0LpoGnxDb2fUZELM55sWVcRtdicyeVUM4fKPuBvBZc9By6Vg63EG0od2Mp1YNE2AEOVovVYti9DSP5Ycn5H4ZjNkp5cFxLpNkOwu5ENHhJwUAyX2ugHIR9tK1p2DOMWU39xnrZCsTlZlBKDoFEse7PHKpWAQ6K0BRn9BbE60L30aWjJast7GaqOxAmivGyCORTBW04vPFOO2Lf7lSkgOjynozkfbWf/8541lbQ73r3yvXf0vRrDxGRCbE4lvNrJz9riN1v04p/kJgO/Lj0C1POx154qAkh/pwJgFjJqvxgqgBx/SkmjaiPjX6wRIZ/QbzX++3gRIOM+6CImmDQJMlnSGAm+7oAa6mfGoiNTebYCaRcWFE03HBokereFTkVzxJ7Fu4Q1vsO/KFKigIhDXYwqTjOr7SzNJ1oPf0QHIFsgOr+g1jX7yFmiW3V943WsgSuGosfYlT5oq5LmOZmG7jfGAvX6AAK9pNgXf5bezs0xzGRCLxRQ3uPpfbKLrKoHecPQbLI5z4pVt9/mjTH2RPNkjsgXxXWtEIsap0MXUfKXmVFahVuxwnTsUMR1EMc484EC85PLBUtENAArJh57kSXaXqDzzAgMH+S2egADxeNGBZhKIbV8OLZcWiXWiaSNS+z9jVdlRU9G6aDoi2/6K7AeP6f7NAVvdQGSbd4pjyfyj72eUzqKkhFisgr6a62hhMVXdAKjH9sJ9XqG9ie8tQMXZskYfG9+5CjSbhu/y68TvlKpeJgZbaM1sQX3ltlbLJE3FDwAmhkSuoxlaUwag1NT3rCbCgD7MjD/L7FfnwOr0wDFwJKD/LJeMQ/ZWi95sTi0NrXlZ6DRjQrH5BOmDO+G5YIxJVgCgYsS9pmtrmULRiLMH1FjQlENEP3kLktXB9Hn3Oli790XmyLdQquuh5dKQPX7kju2HUtUTaipusltH5twh/CMH2nz/eX2hjcBqR83VD0NNhHH45UlQYx2oGHIdEjtXC/pyeN0cgKqClVg8EZvHz6DUipN4yTNmzDiZ9/1f83rkiadneC8YA4BVr2rHPwbZ4UXqwA6oiQ74h0/BsYW/ZXTGEXfD1vtstC97FvnIUdB0HFBzOq3wEWj5LGgmAf/wKQivfRWSswK27qfDUlGLRNNGBPUpbURWINkccJ97OWKffYjw2legxkMgYL1ixGJF9ug++Ef8Gvb6AUju2YzQ6pdAJBkA4Dp3KDq3LYKa7GSUVauNDXDQJ4Rau/VF6ru/A6CApgFUg5YIwz98CogkMcpsshP+KxjPXHZVoPOzxbD1OBPRT99DpmU3ZIcXWjLCKizNO+E88yLko61wD7wCWrITNJtE1ZgHkfruU9h6nAlnv39H7LMlkO1uaMmIjhol4B9+F9re/T3UZBSZlt0IjLoX0FREtr4DW21fxL/8COnmr5E5xI52Se3/O9RUDPEvP0I+fBTJvdsQ3bwA1ekWPPfk4ybqa6ozhNrxj8F7wRgoVT2ROvglaD6L2OdLQEFh73kWUj98gfTBnZDsLKn3D78L0U8WiLUAANlZodM4Yuj8fAn8V9wF//Ap6Px8Cex1A5DYtY7RxSQCAgr3eSOQad6F7NF9cA+8Apmj30KLh6ClCmtKJAn58BFEtr6DTEsTG6BCCOsn3b4M9voByB75FpLdheoxDcge2wfH6YOR/mE7ZHclYtuXI7lvG2SHB4ERdyO+azXs9QOQPrgT9p5nIXtsL6yBevF3bMdy+IdPQXLPZiT3fYrssX3CmEY2vsFkzmJFYOQ98AwchviXH8HWvR9iXzTCP3wKIhvnFgJb/fntPc/CgU0fgNicBVld9wqbYHrVQ0gf+Aq58GFUX/kb2OsHIn3gS3jOH4nolreR/HYriMxkmeZS7PcHv0KNXs0MLnsGnZ99yM5V7HkWkvv+VpjkJkmwVNQitmM5bN3PgKWiFsFlzzCZTIQByYLU/k+hpROIbF0EquZBQFF95W+Qbv4aNJ9Fav/foSWjqB3/GPLRVkQ2z0dk03zkwi2oufIh+IdPgbX7mUh8swlEtkBLhKEE6pA5+i2IJEF2+uAecDkiG+chHz0GLdkJLR1H7c8fg+yuQnDFn5A++BVkuxuucy7TKxeswkYkGZUXTURg5N1QO9sQ2TyfVekVK2w9z0Kuoxn23mcj136Q2Y7Fjxcm30kSMkf2IHFwJ6TTL2bXzaaAfIbZlQ1vQI0FQSxWEEmGZHMi2/Y9ZJ02b63pg+S+vyFzuAmESJDdVbDW9IGlohYAO8uPy2TqwA7Ye5/NdL55F2SnD2oqxvrsxk1D4tstSO77G2y1faFUn4Lknk9AFDvUREjIWnDp0witeRnZo3uR6wxC8feG4/TByBz+BqnvP4O1pg/SLbsR/du7oLkUaJYFOZU//SXC616FtfpUpFt2o33xE6J3jsn3PtBcBu6BwxH7/EO9/+2XSOxaA//wKehY/hxoNoXanz8m9Dn66WKAEOSCB2GtOZUFnLKMmqseQurAdgRG3oPotkWw1vaBGg9BjXeI5xBVHTVvurfQmpcL4/oPfgXPwCtg63EGkt9shn/4FASXPQP/FXcVZPrvi5HXNGgWO2gmbrKzkU/eAs2moCbCoNkU/MOnILZ9GbJH98I94HJhvysuuoFNRv3yY1DFDv/Ygp21VNQiuPRpEJ2uZq8fgNjnS2HrcSYAyvpQ9PviPsFafSpkjx/hta+CqipSP3wOa00fyB4/Ms1fI75zDbKt+1E9Vkef338EmeYmyA433OcNRz7WDkII1HgQteMfQ+yrjxAYeQ/iX61ielTbB2pnOwMdLFZkj34L/xVTkD26D4FR9yLTvIud81u03moiAs+gUYjvWA7Z7Ye1pg8yR/Yg/uVKECIhMOpe2HqexfxjshO59gOw9TgTkU3zxJpHP32PVWnTcSb3h3bCP/zXiO9aBS3ZyXqMR0016Vbq4JfCv3JfpKUTyLZ9z4KotgMAAKLYoMY6WEKUzyJ7eA8S334Cmk4gHzkGLZcGsiloyYhuAweAZpPItOyGq/8l0JKdUGMdpjVJH9oFLR1HZNMCoQfRLQtNti+xeyMyh5sALc9Apx3LUT2mAYmm9ax/uv8lyDTv1gFNCtlViUzz1/CP+LWQQ5rLQk2EoFT1huT0IXv4G3GPwcaZoi9MdlWgY+V/Id28C0RWQCxWgJASW6wmO5E99h3Pjj1gAAAgAElEQVRS338G99mXQak+Bdlje+EeOBz5yDEkE3EAFDSXhpaJm3Qje2wf0ge/YnthsHPRre8wZtTnH7J4yeYCKEVq/9+R2v8ZqJpD+sAOWGv6oPOzxQAILN4A3GdfynRw71a4zxuJTMsepA+y90U/fR/ZI3uEf5fsbkS3LoKk2KHGOpALt0BLxZDtaAaRZHaMkcMLqqmw9zwLnZ8tRvrQLlD9GTp3rBDxkLWmDyKb5sF55oVIfrMZNJsCzaagpTpN8RdV84jvWg0iK6BqHjVX/Ra27qcjue9vyAVbgHwG9vqBaHvvD6xNB4Ct51lI7N4AUA2p/Z9BcngQ37WGtYwkw0JvOv++GJAk1Fz1ENLNX8NWe5q4p8zBr2CvH4jUvk9Bc2l4zh+JfOQYaD6DfOgwiKTgi692IaDLgbXmVFOPt3/4FGZL6s5B5xdLIVmdkKwO5DvbmK0d/xiimxeY9ja+Y6XwiZFN8xBe+ypy7QfgPm8Esq3fwVLZA+mWJsS/Wg0tk2B+Oc78h+zwIrnvb0jt/xTp5t3o/GwJaDYN5LNQ/L1hra5H5sgeJL/ZBHv9QAQbZ4Lms6ge+wBi25ebYrjg0qfhPJMlYf7hU1gr0tfr4Oh/KasYnnkh8uFjAAFkbwCZQ7vgOncoop+8jeTeLaDZNDs+RpJFHBQYNRXxrz5GPtYO/xXsHPJcxyHQdMzk/xK714MoNqEzkU/eQvWVDyLd/DXykaOm9Yp90Yh8tI0Va/Z8gporf4POv78P93kjkP5hO7NRLezkBaPdkp0+NjzQ7mLMx88/ZCCoYoNkc0BLx2CvH4jw2jkgsgWSYoetWz/E92wGkWTQfFZP4FOFtcxlYO/V35SfhNe+AhAJM37/+z+cKHf6lzs6I9/ZKkbkcooZo+5ZQaxOtC95EtCPj7DXD0D26F4Q/aB0xd8bFh9DJrV0HBZXhUC5iN0DLZMUZxmF17+OqqG3s423u2GtPQ3xHcsF8uS5YAwbQpFNwXXWxQLp4kgWkRVUj30Akt2NxO51IA4Po/ZJRNCpnP0vQXDZcwitmgWqZlklTadbGY8DkWwugTaIaYedQWSO7AGRGR3D0ecCvQeIjYNnEwUvE+Pj+VlwWjohxrqribAYv27x1YrDbtPNTYBsgRoPIR8LIvndp3APGIa2xX9kxwj0vwT5yDFUXnoLtFRCjPP2j5oK2ckqlIlEQuyZmDpnLRxrEdn0JjyDRovzldxns3POrN36ghAieocOPTNOVExCq2ej+YWfI9j4DNRUJ2JfNApnlvyWGZB0yzcgFoUFupkkaC7D+kX0vUrsXo+aK38D2VVZMqWQnyOYjxwTZyZWDLkONJtE9uh3IDanOIuKVaqXQU3FocY64LlgDDNcZ1+G4PLnQBSbqGy1L3kKarIT7UufFucVcbmLblsEx6nnm4au8LH4+WhhsISmqYx+o3+Oj+Tn0xPFmVyEIDDqPtBMAmoiBNACzdM46jm0aharcFf1BM3nILsqxBrJnmqGium9n3w0uX/kPQIpKz5PklWS8kJ/tHSC9b/YXai9ZjpcZwxB7ItGQNMgWe2guQzysSAz+skoKi6cCMnmQvuSJwVljNicAqHlZ2kRmfUGQ3GwioJiZ/SzaCsqhlwHYrXDWnsaO2Rbr3IA0PuGH0A+egwVQ66D7PYzCpfTB5rLoL2RHdOQ3Pcpqq/6LTu+IZNCpvlruAcMQ/rQLlEBLqZbhTe9KSr/jtMH6wFbN4FKyw6vOBOPWKyg2SQ0NQc1k0Dr+48CVIO19jRo+SzUdFwcB9Dx8Sw2NCuXRfrgV3APGIZc2w96YH4fHH0uQOdnH4JyCqmaF+eWdqz4E4jVDhBJVO/CG+cWRvJffjuIzQHH6YMZ1fC84YBG0fb+I+j46M9MDlx+XUZ6w+IJwHHaj9H2/iMIr38dzrMuAixWUE1De+NMOPoNFhR/R7/B+lCQAGRfDSJbFrJ+GV12ZS9jBhCJwOKqQPWYBmSav2bBmi6rnNKqRtvERFpu3ztWzQLN5wCwKrjx3vjn+Ijz4MoXILsqDceTtOh9WH9BLtIKYnOAyBZUj2mA7K0x2QQ+YIhmkpD0/kRNTzwqhlwnevv48UOQJXGcktHO5jqaTXRiLZ1A25KnWJ+Wbtv4US2Wyh5oXfR7NP/XdchHjsF11oXQMkn9KBh2HaIogorNfQ2vWvKKlpbPCnvL2xVkV6XQh/ShnSA2pw5ITTUh17mOFjaZVD9Hkus/zaUFq8I3hFUgwutfNx2L4Op/MYjiQHznx2L9OXU0um0RaDYJ+ynnA/kss5fnDNXtpV7lNNi8RNNGdKyaBc+g0cK/chvN+gRTyIeOsOQwl2HBo80p/Er1lQ+yKehWB6P9W+2wVHRje6hXDjs+elHoLj+SQrLahf9RO9sR294I6NR/iydgYiCFVs9GPtYGW48zhK/hPiLX0Yx85Bg6//4hJLsLxGKDluyEb8h1YlgOPxKIUkbzhGJFfOcq8R3GAVYAo2oT/f7ykWPQ1FyJLQaAxO71sNedK87tC62aBWu3fixgtTB5obkMZE+1kBNeCZbd+jAfg8zy++BHXQVG3wcKws4v7vNj0Fwa3h9fBZrLou2Dx9mwulxayInsqmTrs2stiNUGLceO/Uk0rWeJi35eabDxGUCS4DzrIh2YfYAF2IoVSnW9aPGgqor2pU+L4ye474CaNR0nwY8VotmUONYrF2oW+htaMxvxXWtE+wWvJLv6XwzXWReCSITNHFg3ByAE3n8bB5pNIfX9DmZ/FBu0bBr58BG4BwxjA+aqCpNdJZtT6KqWjKG9cSZyHc2siixbEFo3h4GSukxDIvD86Eoo/t6AJEErYrulD+0UPvzQC9dBS8eRPbYfxGKDmuyEmuxkvdD6PRSGsLTozK3SY7a0dILdD5GZXuUykJweeP9tHLRsgbECgPmoVByp775gz29RBNW/7f3H2PPorBDmE9JI7Pmk5Lq5YLOpH5vIFuSjx0RMw4fTSXYv1FgH3AOvEBRyz4+uBLE5dXvRaj6aSO+hTe7dKnrmOUW8YMcyJh3mew5NFX2mfL0Ufy+one1I7d0mPsPXi2aTkGxuFidHW4VNOfj0WHbKgdUBuaKbOO7NPXA4tEwSrnOGguj9iZLNqR8VldcH6nXCddbFjE2VTUJ2+cVaQrKUPU7KUtEdJ/ViY+T/df5YfLVUCdRRotipd/B4avHVUoBQEIm6zxtJidVJAULr7v+QBsY0sP8T9n/PoNGU2FyUOLziM7DYKLF7xPv8o+6lxOqktT9/gnoGjWbvA6GSq5I6+v0HJVYn9Q4eTyWHjxLFLt5LbOxvJVBHa3/+hLim/ZTzKWSb+L3x3mRPgErOCvZ5xS5+r/jrqKPff1DJVUm9g8dTgFBic5l+Jtnd4nf+UfdSi6+WOvr9h/h+/j3eweP19XKI+/MOHk9lT4ASxU5lt589l81FoTgosTnFvfDf1/78icJaglAoTkqs7PtkVxX1Dh5Pid0jnqXu/g/ZNewusX7E4aOQrVRyVVBLRXfxnfx+6x9cRu2nnM/uA4QSu5dKzgpKbGy9Iduo5GLf7x91L7VUdDetJWQbJYqdSk6feHZic1Fic7H7szpNnyEOn9i7+geX0foHlxmejb2X2FzsGX21Qt74Z5RAHZXdfiq7/WI/+HMSh5dCcYi9kb3VYo35PUC2UdlbTUEIld0B073Irir27PrP7KecL74LFvY5/j2yr1asCbGytXOfN7JwLSIJmTXKClHslNhcVHJWUNlbLfYNFpvQD9lbI/aq/sFlVPbVUktFd/YsuozwtZPsbqEzxOYqrDcx7BFIQbb0+zOuqWfQaNP6Gj9r0Z9T7J/NRWW3n+2T1SnklK8tk2UXlb3VVHb7xXfxPeXr7x08nkJxUkiKkGl+PaLYhSyJ77XYxHcExjRQYveI94j75nvA9YVIFERi30OIWF+2Zw4KQkzyIzkrKGSr2AvZW23SZ74Gsq+WrbXBdgg7p987sTqF3TLuV2BMA5WcFVRyVjC7wq9vZfrP5N9jsndc7rje8zXn1yNWh7DJxOahsreaKt36UlhshbVzeNma6foBIgl7xWWSryPbW4fYOyVwivgOLtviO/V14vdu3B9iZdeSnMz2wOKgkqtSyAXfC8nhE9evf3AZlRxeClkR986fX3L6xHMb7bHwH3aPsFuyJ0CJzSlknf9fcvpMOsztMCSFSq5KtsbeGvFz/jNi84i14s/J5cs/6l72nPq9cDvAfY/sCQhdB5GEbeb2l/suJVDHbInFZn4Oq8MkS9wucbvA/QC/HvdxXC64DMtuv7Dzsq+W2TqHl13Paryey6RLRv9oqegu9s9Wf55YDy4rhb8lXa4Lus11n68D112jH5B1fyq5KsU6uc8bKeRGyJfNZbIt3sHjTT7H+Ezc5rE1dIg143KndOtrsn9cl4w2mNtRvm7cThfrNo8Z+M+5bhvlRfbWCHniMsjvk8uneE6Ht/Bcuq+XnBUmf8FtDyy6/dXX3DNoNIWsMP3WbZXRdvJ1rf35E8LGGb+TKHZT7CPssaSY4h2+V8b4CrJV1xdHQT95LMVtnmKnSre+VHJVCt0OjGkQNpT7QRFvWmziWYxyWHf/h4W4zGDf+L4b/R0IETLA98p4DS7rxj2sf3CZ0HPi8DJbresk90uQlYLNt7kosbqo5KoUe0kUu7CV3OdzvZG9NSLuEHZc92HE7hUyQKxOCtkqdNg/6l5K9LjJ6M+FbttcTM50n8xl0Wh7jLrE9ZG/r1wMy57PaVp7Luf+Ufcym23MAwzrSBRHSVzD9YL7EqMNI4pd3Jdx/3iczuLOQhxVHPMRm5uCMLtu/B4uH+J7dRkw2jnJ6TP5X/4+o0xYu/WlJ5M7/a8nb/9f/7F26yuclAiM9MC0WMGIrrA8kJV9tfpmO5jD4oqg/23cROFojQkhNx7uADMGFpvJSMm+2oKTNilsIWGVXVVio7lz5kkpczhFgqgnPEq3vpTYCt9pdJrcyBiTaHGvuiJwg8wMsY/KHn9JQMDvkz8/N7T+UfcKwyq7A3rwpwiDToqCLKZgThEkEqtDGK3ipIU73voHl4lghjtzbgh4UG0M5GVft0ICbmF7xJ2HSI50gyF7qgv7oq8Pu28vlT3+QqIk2wrGzOETxs3suBwiyeNBBP/+QpLpEfcvHIQhwbD4avX9dInk3BiggCi6I3CxJMYQbBKHl+2LbDMHbXpQQhS7CGxLkgBDgCKS+DJJH5cFHowYDbElUM/kXQ9qvYPHM5ngCYwnIO7RmKzzgIi/TwSa3hrhqI3JPNcrIwBT/+AyIQf8/sUf/X64IzcGr9wGcDCFJ/+1P3+C/d5iM92bAHz0NeWOHYQI+ZF0UKCQlBMRrHP7wq/LAwouB9zJGQNoo50KjGmgkt0tnoMHl9wuGGWY6yQLHhyGoKfC5MyLgygO7NjqzxP/9o+6V+w5CwDsYh9EMq4H58ZEj8t0saPnNoTbUf4cYh0MCQ+XU74/xucr2EInhayYnDKXIdntp8RRIWwyT/6MQZF38HgBCojv1/VI7I/iLACD3P7rQQxPBCHrAKMedIrER+yDU+xDcRJoqejOEmiufzwBs7lMOu0ZNFrYM54MMF/nEEG6UV4lp8+053wfJIdX3At/Bp7AcXvHdYsnv8akhdtC2cPkmdl0q/Br/PP8s8a1EjIuWcVayL5uhgTQIfwq98vG4NYIBgmdM/goI7AHWRFgJ/f1PEE26jYHAYz+kt8vA6gK4JrR73Kb6h08vgBQGHw1XyvZWyPkjNsiESOIBN4pEmHuwzloWBzg8sSZgzp83UUwbveYEgy+rsZ9UAJ1wpdzeeF2hd+b0q2v8NUC0CIWsW/CDuk23QgmWyq6i/vgdkt2+5k82g1JgkFmmS13mmy17AkU/LIBmDLabxFDSIoJUOV/c//F14PrMNcN5k9OoZKrwgQG8HiNJ7qSq1IkhCCFoN4YB3IbyD8v9JoUdJ3HA0Z7KfwKTyb1NecJvJBNA/gmAGEdJOX+hce7/D554guLTcQ27vNGUlhsVHJW6LpdiKU4uMllmcfMxliYg9n8mmJdUPCHfG+Jw2fyOQJs0WXJmLAagQNj3GEs2hhjWF4QEtchiinGYAUQZh+NvoUl2UW6IFtN8YvkrCg8H0+MHV6mp8RSiJG5zTfYCAaqOkqST15QML3fkAtIDq+4pjF34D6K6ye3EcW5wv9LFk/0wIApORTCrgcGQgn1SpMQAF2IeaBTbFS5sRBGwuY0KTpHuCwV3UUAYao08mBUF0SGrBWqK9zhB8Y0mKogDAWxmhEiXaCMiBZHx7mD5/fNlUcEWLojNyJEDF13CUReBMq8CmZQbsVfJ5AjSIoIzrghkpw+E+JWHCwzh2th98cDeJASZTI6Hx7QGSuaxYpiTFp5osiDO742RiPGUUIjUis5fSIxEBU+xVlIELnB5Xur2ClRCmg6dwYs6XSIwJ8bDWYUickIimSAFO5fJLL8j+4MmcO2FhyUbC0xnsUVZ+4ICsbaa0JieYWCOzGR3HMk3mB4uCGS3QZE1ohOE7kAWPDgX0+mjYCKMbjgnzHKMQ9ieRWYG3u+B0q3vnqSbE7UA2MaCgidbBMoKg9aZV+3giMn5ioqD3SN12IAQaGCJxJRXqWRC+CDManjoI8RzODBpJAD/VlMiYCh4iy7A3rg5yhxkjzxEgEJ31td57nz5nbAmLAY98EYNBSSNHPlh78/MKaByt5qKulJUHGQJmyEDl4YWQciMNfvmyeaxOYpyLJh/USF1eYp7LFsTvJ5IF9YQ2eJbvFE3Jh4GVkExmCB/+HvV/x1ulMu6DUPBnlSZgQleBBnBF/43sjeaio5fSJA5nslwAm3ubpmZC5AVkzBt1jn/9Pet8bsmp1lXet53uO3DzPtTKdgD9OC/UOwDG2DbTT80NiWVqkihjEqKBoiChFMNSWEpJEED4lnCQQFAkIE5SDEQGqVEn4ILYe0paXSDkcLA7UObSkze3/vYflj3YfrXs/zfntPmb33zOz7Tr69v+99n/d51uFe9+G6r7Xezk4p2KdtWX7aHyWk/SWWmGj/r77mS6wi3fsqQ6iJnaP2woAJeKVE57n5haUDoPDKpAEs8jxFxEPFgZI7vVb9sSZK2ie9t/o3DZR1/Mw/W9C9rMOlex2Ek4RDkxjWc/WXtm4WXmnU57Hf0jFRu6cBZZhnssEvfvN/NWCu+eyV6dewuTKx7Vbl66riwa8L+KrglwJqMYndBvvD/dKYxvRbbMdwdm+zSePKGSU6F8RI0aQ4VG8RbZQmxJxIjVcfCG3kynEbG/dJmiDyGldAOejx+lItS19PXFFsfXMWhcVMAmAyW8l8hsYPpF/WbwHT1dZbskJ+riV7LRlSUJbHWe/NQG0AGuWZVikmsMwYFeO6xXArn99hc9nGj+0rJ2Js+9hPKJhmDI3RE9gATK62zU+XYu00e2tx5tZiYrW7jYFxr69XYrZYgjk6K8TiKlqP+llbdxpX6ufEbhojTUEJGROt8D3/4W+q4+X7wxrX9awxklYjA6C+3AaQS/WMY262YcygUnaDxZVo/o6LM8a2WTnzQ2MpjW10vbEN1Op3Jos36jANnAZVhkqIIW0I5Rk56jNDXjWZMkNeaJEpmipG3wJrMh6qKJPg8crz2ucKJVhkSLmKZejB+syrQeyYZEGowWUDFaomK3eYnDhzO7X9c8iE3ktL6JakScKi1DJD/skwDdursbomqLqOcUvcBTmm4KPRgGIiojQVC5TkPQ7seUzVyeqiNwMn11mgTMGMLmL9MarCMjpYdZQaOGlbFG1niiDrgaNMl8Iz1fDq+LDRsCRcA9YxItFMgzTHqLquNBStfiiQoQZfxq4haUsBRq54UCCVYQ1itdrQjNPoVQJyXr1TNUOq1ZMyhkDBjD8cVVVU19cm0wxf7BXStVK6PHlR44zlmekmz6mhmBLQWjJz5X5bZ1ahkGBDx44RvrK+QnPlQII6rVCZ1XYuYzJQlhvTWdVFQ1a5OiJjrRUMp4Wf+XzJHDO7INiW9ZmBQ7z2h83lOpzd42uBAkKlsfLvoVpJQdp45XmEknvC2uzdIgQfamsU3bXgRSu0Sw9EhrN7AuCg67GBVGv7X5+p9tZsPxxE4SCoLB21NwBLgkYOeKyKso7bEAYBFm2MrGp1ycZRx5cDH9O9DuzzcbjHrlM7F+wRvCrNFS8NxGwNCqBlVPbRg6tA1xUwoqeYGRsBXuW3aigFeKwP7IM0aWdKdg9omX8UO6nV3xAUlqUzTzrwSnW69Y8Ay/WlioVS/BwkGDZXzEYwTdTGlmjpPpYSpAnbx+z1IuqJgm3NJl4JyT4nL0rdZIrZePUBswfsTyyxl1iEK/qDbNlgIHq8fJ+NQ09nNOrzyu0Asxdc/1cWnDOFVQFg1TumSDd2wNLWIMpgDAhlXel4OCMlgpGcuFhgr/MuVZl+zgI9kajkIcGSueBKuMY3XEF3BpWDjxbzLbdh/SsYZLq73ArgecnAYa00R1qlg8qmw8o+IWDJbJUyNIRFxMCnV3CdoVBWZ1Tt2gbGiYI3loCJvWEmSVn6GlNQa7z8XK9Ymk08k6TUk0Lz1VRNLOvoa2w9cgFD7B/bON62YTGPgoViS4bt1RA3si1Te8qghW1ZMF+8caYIFUiYMspxVQMuYuyihRAHwHw7h+qg6l3TiUtmS+zZsu51vHjeOH5kX8MV4+DfZF3pGgg04ue8IJPF2Q4DZnwUAd6+7NW2H0WpSmzsOZgMRltQE510TiLYQWsZuqHuV0xB1AmzEqnjs9dWkeLUgsN7PQGxRDEG+UX3Dyr6TlWzGwUbSgsx5JCCd0WgLeAsxStVUukKNAsQiqj7ApW6p4thdWYBtiadoXrDwYcu/LUnfEYfFIqV0TIkaNXKqwXaEpS5U9/aM6yioY6IUEqdU6fUeaCuutH205xRwO7IICBJ8ujVBA2ebL8GvIpa1petwmyc/dXWdESNCwcQFpxQwGLjRsE306D1RxEum79xbdVE+xmXjVKmjlCDcaIcc1VlrnJlweLEqG3NkOo6GTZXAh1XkwD+vCNrzwv3VGej1YLgZPSzkjQyMKFOloPhYJQvPdfWFT9PaZy6ZhVNbpVMCZiGpfXTx4Yqj8PSKGhGk6EqgyOYnugHpJ1tgCadhSqR23vdLlBAHx35pbZGqV8YVrQ24uecbnjVgkUD36zq6mMV9lxQBcyYFaSLaic00VU6GCcUSttur43OClhuKpaXTBfUpvbUHu6nO/G2/scrQn2UykfQYzh6rgFSC6SckdASqa3tq9M5YcRXASa2nfpab5d1fDUoUj20bRG0Xo3SqAnX5fsNZAzrjhgZVsGjPfLWvi4A0aob25rnP/xNbQ4VMJJANlAqaa+XViY5iB+vPC8klUbDk2fYGJOv5nYxPVLXtIFxq0sGxOi6n/gpAgm4WqdBKAMPWgFo8zTaXPF64rFh5oLqj/nYLribVJJWsb/GCtHtE0ti/GiiqgAZva/VM9YRix+WEUDTPdCcbPHep+AbZL2Gfoiv1vXG+2wNIKFnaWLloGS3d2y5aX7QxjHOP9MvlRas+qZ7cVGGCBYKNbqt77OQWOmYqA+yOZzxYcZ4oPcVoNFKk80bCtmhbWif6SvbVo0BUBxsHlctqRdqNs+BFz68slZWZ/a6AiBA2xqi7eE4UpMSBr5RBkv8JnT3bnsVAzme4Hs/1JZp2zRh4xjOtjOoPoj/NBusQByzKhBjTh2rXlfNXskeXLUZrL8MIPCeVGYI+P5PTcqm67kH/BuIsva9qdpWKJgt2wUUoCTQXIFW8wlkq1WPuMCjrMhghzYEQpQhk8XZDhdfTLp/hDfbWtVKFVEUnjN2q2gsthLQnRmFzaingpibsqI0moclWmMI2iaKLIFdUBBJFsyBrAnhZsekyiEOMRg2RaDWnrDZJvM+UJN7+ibspTknddpWPdL9NuQch40nW2F82UlooEEVTzW8jLaU7b2hWsYOtCFMMSDRoEwDPzYgbW5Xtrhb8rF1o6sLWJNfqeqawVm0w3KYttYC3XWYP168tm8N7kRQRlv0hoqOa6Gk3BtQLL2WdcRofZQcR7oaVZrL6MZ66fsSlZZgaJ8eJgJy2iuibcl8Lu9/iTlRo5JQEK9GsqcymlMdPWkKeyg08CLj3hKdy+4gad9l+39pgIAn+kTnFFRtvHyf08y0AjAsqwZ5gRo1Ou/fdJn3qklbixhmrYr2gTgfzsJr1Pp29YEYYNBeg7LcxD1aVMFUGps+Ryvx+nzui+7ZY2fICPZAVVClDFlw0QVltr40uB9XzdZJRR7DkpD0M6v+WZVPK9FC+Q+VVg4yBO3WpNWAN6kcKtLK66lVJn387J4KbCzWXr2mKqEFsbS+1dYaU2JFDpl8BM8VJ8Q2BhSEG2WPgoUAmFGCoaCTBo3BNyw3BtgozZ11xIKt0QM9vf94z6e1tgl4GEC5DoizpPDsngAEWsB/+f7Qfm5DD6oyCMc+i0EynWcec58jXdPjZCzUH3FgreuEt0cwSNR01sFCDtBtz/g9nxZAMLZrTGluYyZ2R5M0jRHELoU1aeN8yXRDx4yDPt3ioNUaa+v6UrBPCpZa5Y/WqLJ6tKrQ03F5nyonCi1Al4qzVmKFMRTWoc4NgdF2Ddlwj0nWYd0w8NWSFbexVhXn9a2Jh64x8WXKRmqB8JXJYTYGmmm1anR7bDGO2FoPwl1fteq1fvAhjxfHpRUCFGDUeKo/j8Lmcul7rzmQdzuxqcAQ7LNdIwUAZQCF+ZLChQOEnlS5XdXEyuMWr3j6oWGhIscVR5kjnVMrHoj99IrYluzCpQr4YVhNJ6R/muirPbV+bho4qbpAMbgzwIiJQbElbwfRswV0K4u1S9aQ2mMF89zHOdhtZ490esmUU11bHG/0IK/OvRYsGDTUtVakMNCDKO5vth6ryVq7/NAbPDbUrXMUMzCQZ2Ddlftt7e0fatMAACAASURBVDbdzmRxvsM8AUtHKGwjui2uMQRHavwZNVSECihONyIDpImAVUtE0XV/QX9QgSmyJnjyLEPJBcGxfYFmBJcWPAcaEjwgbY6PUBmtIEhgoZ/hioXeY/3gQ7EiIs/mStQkOFhTIq5VCk6mpJ9cHdXyve7LGK7c3xLEre+f1Plqi2w7WQhmFDV5JBQqIqgxALOkSFBnc0SgAF5PNtR9pYhVATPoNn9OGVFwwYzdkk4iU9qtjllZUFC7cr3TsSdDZcZQdKd91ud5cf+Ddn0fZPF+JUt2yJkZFZAPLVIDSgBHoeTa23rFq6IdQnr1NV9iuqxjoeNsaGsfQJNOMj2pdx7j1Qdk7n0N2nNk/G0f0jKuOQsWV2dtHJXOiCK/e+Ks69LGjQJFdQZWHZZ5CKAOARsteaZDCzq9VVSbUUbbXyxj1tsSc9rLbUiQG6jV271LthatPbq+y8LGuQeljFqoAZLqwrjyA01UL6hCzsCUBUQrpzkZ+GFjS5SlMgbkNgTnTP1d+ImDba/x1g9BEn1nHV4/+JDba07K1R6AwJmAlBPlyoC4GTaB2YYzCmrOYgJDbW9/Dy3YMoSe5oDQYgXWeA9WaxcFTVunh7bkz6m6tj4XmzBnvBVCE5qeYhltyja+vliHShgWW7ed48qCVk3yeW/ZxLZ0frLfI9X2BHkFzKjmBMZa4iU66mtx2wVctH5kfm0LyvaqBZ9Obx0rU+g9sBwEpPOTkjXgY1+mdt5B26Xp4NyhIMHfrLvD8648L9yXE1SmkaMsJ21GWfj4if/TcVAaq40jJbvWfqv6DFRNVBurQbokCeKjOLj1NeAgM9vLkHyqvyM/wGwx3kvbknYHydsYDFUr4P7sIfhJW/vKnlHgj3SnB5SsakbJnM6tUr71GZYMr7ZGJ2XgpWem9esN8Co964TNp742+libjnf3s2S9A9sBP9CtnfVwKY6nXKPVSY8F/BRZKywoyMKAkcRqZblpNkoTNnrGpM9SNAgU/bUfeKZtVttq/mSYqS4K28dAnUF10w8nY3CMD2aMdulsuiakYMS+9b43/v12/7CWV+HvADLpfCkgTZV0jgEtLln5/ChAakUli5dGY10kDfXCZBE+OVZFcZRYDx7pJ9smdvAs3wz5yvcqToyN/G6Lid+3QPXMK5H6TENLltRG2vROSqUOLhhdRSvFECkKDfgeivY8Qn3kc+osdP+k0uG4X4p8N+M/muFqjpwChDL6mJbR2wMPdNSYWRCoFNqVVGw1ILzn+cHQTsbTKmGRXqEOm9FE/awFD7TAg8Emwx3uRYGl9tnmAz11S8bYEL6VG0UxPHp8uxlI1SUeSxrTYKiWTNUaKaDTAJ2CV9LRRqFahmu5TbOGmJC8AJx0gQgbbp2LZoxXRuez4EEq7lz9tP4tvBrFOhvWJO2RDeOkr+sBAxRs2F4JGt9IQd9SwrGdubef1NsHMXpfRfnDwQJSiWtzvYogi9J5aZ9We13ndvD2ba+G4NwDDQl8NRmXoJxtSP8/B67D5oqtOatq8RgtNbDZxj2ulLgbgtolxTynGtiHryzhecEQ1yEhxBp0255IAd8CXW65cZsq/eTg08AQXcPrs4Ca2xhJ0Nz2rN/T2fetVSR6/fNAaBV0ugc8dN/iFPBaVd0PZdVb0rF+fxHrgM0T28lOh/UQKgOYumRffdrIBzyx7kt1gH8c7V/52ltt3U9QkD1evs/p1pxAsn4qnc7uxQCVVB+YBYLR2o6ytAPWor6feXAqibvaK74/09H8/a2BGcZmGfw9S0I1oBxX1Sqh2le5fwNIiBkka9bvI2uukN8wn7B2e4noC5VJowevhH2qaosUdNC+9zZBbB6Pg1H9lEGgFGrZU2m6rvOv/sFO8pyxSYj38MRya2CFjXVXHVJ6OesmM1LC1xQoGCq+Tn2R2c8F2XJdAzRXftprS7Cb7XJd6WnawR/IerdkiF530GuYJM3ue8/IRo+0pvyUfEtyZuywAefdfXVOmKGhOhb9mSc6vN99GseehXXCOtTuS+uP/aSN8xh0INphr17buR6gYsXM9eF0XtVFiteaPVrbtieOCbVi3PsPYzDp1/R0SZozxXh/YPTPvBff1g7riiX5XeGJdamLOULfKTZTe8HsOgVE8oCbm0oWy/zgwxMXnSxdlGbgaALMQcmkhY26bAwpiTDjOXbIQufsg9GiChMbxF6peNGOl+9r1TlGTMhp2sEXy0111JoWOdFc1LEYsqrKTYYgJNj6mu436NFLGksbA67OrMnRB8fDhuqs26+5jYvdntGP58aDFjIEOpehTawn6mzXZ55MnjDuNh76bE2K2GFTosDoZa+bbvD12m1MUFgPJgaxT2IowEFL4JVmxYaEgzwbf6IOB920cSNDyI6H1wMFy4EmqPcclnXiZFcOgKgeWpIgwEOYM6NWxKqRr8kNjRvRpwiltip971h7vS3Lrq10j7HXXUIAu6DUg24O1Byx7m2NnaLbJybSf24PBzymg9avwfqg++7UeevvfuDPqrMB02pP/7udyBn02PeJafCjlC0d19lnFLJjdABCA1gomNG1QjpqNkVt9JyTnQnuJuBdv74NPNIEhLcvMMXNgavYJ6eaKRvAkoWZqkmvO+3kyNHXgYE2TMWP99LKUVlufPxt3AQ4VSCjEI2J1poBRsvpOg9ritcO6WWgWFkg6/a/t30GWMxV8JdKtzszUJL1UKthps+6Zy3M/QzKT+3o6ZT606rvnZ3hCoHSsXX8S7Rhoeohn+eKuVU6DVjioHobfg9MItFX/8oBsUU9xZDtRD/2XN0xH9Cd46BrloBuBj2Ykm22R4Nt1s8TFTNuu1WzwvsCGBprIfoFpZwG3TQb1CcXXfwgFceQ8AjwoIF2sC+axKifC/thCdglYF+B+BCf6NjxepTPmu3qtqL4uu/jvAhOBVBcq9irWBVldhFTPoMNWRCYa88koKlbW8w2UlAqJHDLjdFnY78U6FbbOga2iI55tHXtM3rSc+ivruGyqHw4HQOVwX+bHXT7ovPl7JTFZC58b6fY2i0d1EZ+2wC/oJurMC7Rx/fjI4CnHt5Etn1qR+N4A5/aV2cUqbbdNVKGoaICZbVBPb/mry83qLv2d1ltUXfnQD3I3xugVtTddZTlWv5v15flGigFdb9HWSxRDzuUYUQ9HlDGBVBh19Xd9fawcQkcjyjLJerhgDKMQMG0PceDXLcC0OZp8dwXYPd/fwNAQRlH1PNrrX3wz5f1ZdTrnwzPLMt1u+Z4AA57YFwAhyMwjsCx9RP12P5fboDjvl1n7ZHPyxi0y+X6ww7AgLJay9UVKEO7bymA6FjdXQfK0MZpd731YbVG3e/bdYedjXfd7dv4jyPKsGhzU4Y2FmUB1APqboeyGOW+o9z3GspqC9Rje31cAYfzOP46p4C3r1bU/Q6oRx/P3Q5luUI9f6LpxPkTQW+GS/einj/R5j/Mw/U2BuPS+qTXH689DhzO2/ij9bksN6j7c5TFSvo5tr4PC+C4b8887Nv1qD4v46L9XhYoy4U/l2VcoowLa3vr5wFAbWNp47Rsrw2jz81yBaB4m4ahtXe9Rb3+xKR/1u5xhTLImlgufe3o2C02KIN9qI2h9KX19WB61f4+tvvZ+mlzysL6ae0aF7IWj75WlmvvUz9O0vemY4Wudz1putbGG6UAx6ONF1BF96roNNpztd1lAdS96/jhKPNJek82oM3/oa2rWn3tWqdlLe1VT6+39aT3GBcoMq9mG5ZrnxfA+yTPL4s1jo9/HGW1xeK5fwT7//dbNv+6vrytbT0DBRhKHOMyYPPgy/HEh94puqXS9Ldsr6A+8ftiCw/N9owL4HgUPdtj2F7B8fya3Vefa+sABXV3HvWY9IH/bjqxl7Fs81mPB5RhoKZVtyVqm8cx6C/A67uNbz1/Qp5JPkF1UXXT9Kndo4wLHK990tfk8QAcq/kcvl/sw870pH0WqPtzGT/V+yWN4871W8eR3wfEBpFNl79rrRjPruJ47ZOkl0Mbm931putqb0iPrP3kl0y3Zb7bnMlnS2njc/0J8Xti6w47t9+9j9Mx0Hm2vq9QhsH11Pz1OsyhqE9YF22Ni79ebVB3O7FnbEcW0PUU5gGYXWN9e8Nc7M5N78u4wANf9A34yA99o46e2MEqcy4xidpg1Slto9okkc2Dn4MnfvUXTH9Ris+FrE/XWx3DldkXneNmFwdv0+5aXLP6+WMNNtn1ml4n39/HJu4r2rygDM2GomK8dA8On/io6Vvd7XwcVhsalz3pmetNPT8HoG3d+PweDrEfei+JAdrYNj9Z9zuUxQJ1dx33vfFr8djbv0V8Miye0jgl9E3WaFmu7LU2rq5TvKYn9hADyjjIMqsYzu7F8YmPQ21gi4EWrX2rFke0Nh1RVitat0X0rOnDsL6EwycfazHJH3ys2fFhlM/uJrZGxw5oc9raVCl2kM9pjKa6ttq2a2RMy+B2z+ZlsQL257Q2yZaUoX2217EyynzsWgwrMajOs+nhJG5tOrx+8HNw/Tfeg8X9L8b+9x5t63G/R1ksbL0Eu4Uiz/HYq9kCBD1fP/g5uP7h95telNUW9bAXm3SOFiM0ncAocS0GtBdE+dVm25ouHr/148N6fjhabNlivmshFrK1vdrggS/6Bjz+wf+FP/iln2z2vdaCG8jdlyy26g45jtICDQ0oyVio8GLRpKUF0zPOQANmfiYbAgvmiztlMRgWsB4PHkAuN81o1j2w3DR/dtiHia/n58A4oAwSwNajJT5xIWswLw6AE7kQQMo/pVjAZEZnIU5eDcNi6UmjPqeXsSW7ZRxtPIODlfaoEbLXZgM1TqjXlAhcF+NKbQgJyBFAc/Y8fp12BKMeAgwJ9Fvb3DFwEKrGLyY8+3bP41GCwb070lpbv/tgSiahtfUQjbcGNWh6oAZL+29jfFCworpz5IA7GGQNjmVczBC7w2ugxmBG1OZsL3q4WOoQivE8ToJ8vYfpu8yRzYmNl6wznT9zvoM7QArog+5ZAnVofTjuPQFmg8lJIOlVP066tsJ6C3OiCUgN/fJg5CjOXBwRB6TLtSQsMWjQ+bPPdkkHoEDGEnW3N8BkErx2dsL0YhgCcNXGfydzP0ji6fbK9YgSKdbVw4ESCAoC+jVsQamvyxDIylyanh2PrU21X1NuY+yerMNQv1c7h36crKXFfS/E7nd+Ra4fBJwaor6U0UAjHzMJrMoCOO4CsNf0R4EttMD3+uOeYIve4yhggQRD3m5YIjMJEMQOxeRj4XPUBZ89SNj7ouYzuqSJZPOSh/DEh97p4GVnW2XCoP5Qhj1ea0AKgV4zfsLeP9ZmTwqafi8XFrCV1drG04HAFsTa2tr72vCxuk5r99zWLsbRx/1wgNnvcwcntc3NjnTJKgEslgSyz7T1cm7jw+BRWbTn1921Frh+9DfDeDBIpONTxkVMZHl96mfX26ZrHYjQ2id2gf5WG2z6QGvR7zv4HBfVVQlkGWw4HiUGEmBBfAEA858QsLuNx7mtm6l+jti85CFc+433TOZEA+IeMDDwddn5CkusWxwUwA54Im12gmxhACdqafEYg7aA2Yp2b4pXyqiLoo1YBya1z0hyKO1XPxquAwy0tvUnzy7jAlde+YX4xM/8IMpyibJY4fj4JwgEmYI5AVRlH4xiAIHZdE7QQ2HFddRjEbFLkugFn0HPDf5vt4MneoXWpQPYLfHatUSY/I1PIKQdI41duy/7bbO15iZ4bBSI5Hh/NNC76XhtxRQtrpQRGAp9HjFWkj6HNQBAfUQbS+krx7STOH/+XsHW2zOlEKOfVx3V5y9WKOMC4/Yq9h97NJPFOSnDUDF4Vae96Alej0QBoKrDnPOmwJoCFl98xYNAG2oyUAGBqh5Uwh28G3VBDgzNHyZJIAerlvShWNB8ynBMqhrWdwrsgJAQtb81eRobMlWPBpBMEk1bEBt5fnWEjStPmoh0SZvORZsyva+gSdr3MspnJJGQAICRda8cnrvjkbGp5+ctIemStDBOhwOaQS2x3dq+Hpnb7SSpdgc3N/YWPMtYGTAxjF6JJrRoimBSYNG9Z0blwAHfMtyXEyivglRKTDxhmyTx6og7iQYMAalWpE37265lAMEr7qE6wxULQgvZ0U/AG76fOkWq5PRVENdtH6Og75bIi34BFlCHZMMqJCeqsIacbqjvHkzNos2UlEwDpY5NwIlYp8fWH2l7C87bPPrcx2q830eAlePB+hjAlVpDAOAOraH3EfWG2JlDAGsm88KsDk0IqFoXKgZza5d1kYI6D4zHVtXqAnNPdhVZ7yu8Hkx4/xGqgmENMqi0OyemhY5Fb+MAS2RnALdQHQ7BrrIBDg1M1OBa+2Ysgm5seNzMblLFXe26ATUa0Ml6lio0576eoFCVTtZj6w+xIjTI1LGQwN2C7c1l4LgPCQPLVA/PwRV3XjMB8e+rEMQuCIG9+l1OmHWNNgMz0V0LKA8HWcMHsfVq/4YGSh2rVOm9X5M1oEwglFAZm4IzvoaDj9L5gbJpgOYjF0Gv3R+TvbNEUMea7ISuAxJf831V1f0FV+TnbJkDjbQ++4oyWnUawxjXPlVZAjh1E8mTzV0AnbuYTvtJIJXb7Q7EYdaPxHVgUFh/n4kP7BkEpvZ2tmyvtESSEwz2y8Ima3MklUlih8XkTXx9AAGvxWfr+Oi9Q79AwLXbVP072KIAcLptcV326iH7ybmYNegFt4HXPoPVNPf2PInBGeDlGLZVmvfAMDQb1p5gYwoAIVbqxknBHo7n5kDVsNYPEiswW0TaPakA8zgTEM36VA9HjJeu4v43fC1+7x3fjuu/80gmi72UtmeBEBfAAmKqWqgi1P0OzfODAmYRRdaVVoMY1JgDUQM2bQ1QCqHRJ5RdF0wpwRgbBQuIitYh6O2+kdJkgW1XTQ0I6PEYEKbQZ0q0ufoxcaBGJRRKDdCCQqGsGtrDwdTOaVWterWzCmZojwbjUoGxNgc6XIFRNTqnaYjzci1O8xARRHteoUC6uh7QWPi9NKAip9JRoazCfKyTBDAIGxECDvpkKKB21q8pAs6Jfd2dS/C5iElVf4+OYsIBTkziHeGs5+emqyFwl7kPybsFAVRN7yqf5qQP+9m+ma4djzHQokp+CIy5Yij6E8EiDqoWZLA5YACBL4AFZbr+SCe5gu+IPAKoMqGihuobKEGpMXgJlfmjrK0V+uRQ++PUQ7mXgCtWCeicmyckkvzMBVL+gEmgFCqZGvATsKPUsxYs9kHQlFY60WfWW04shobma6Vk0idC7SOFOdqUoDOCrhsrw+bxFDIdqYKza6q383OOXQOLwIgoVomwZxm9uoCp1CHo6wJza2Nnhw2wUPs766NkzRt9W6ujM4G42amWFM5RbJkVEUAsrh7okHOQyHp0POBUsuDMhPMAePXVckuEO9ttibKBTuQ/hzEwXCasCqvM7GPSLXqqvkaTaFsPxxnbAMxU53YW4FrsorqwWhso0SdKfVIMIFBoZ0Fh0v1JUnQChO6BjdmKNyeElJBNgMaO3dIeVOz+el8HgK8FmzOxdZyY6DwoM4sYAmzvjFmk/aSKfE+z1fctxiQQytZsB5rP6fokoWI/tVxO4jjgSHFupCJP2t0DI12SbcC8AFYWo2m1PwAUANuACNIRFbm35cDEPln/+qQtxEHXPZ60GLIDry1Wb/R7Y4ZA7U1HR+4AZR57ZwEO84mn3sPGCKFYEfq52phNjLH0IPO3Mf/VGuCMv1bJpvyhe0aY444lWJZrXHnVm/DEh34au4/+ZlYW56SUoc4F5n0pt6+cAAhBcG+U+irOhOqp97QSM9PnDr4Iz70kPaHNkVMH0CUYHBB0VRjilk+cCye1dVqun6cq0j5KqkJZtVCCtWjYp/vHYr8IJQ57bmTf41DCOM8+AxzILCdOdg6hDNSVLtBg+qNVUynAaBUYRqOJnnHCCHMlw5KUMrT9ilYVXE31ypIQQq/ZOMxVNwALZLy67ToY9hKaw/SAazZQIv2x+QECKt7vlwjBuQUiXllmOo0GCUE3zqnCylRNBgSUQoWuT5LsM51kNtAg9NeSIV1rRqeh/RMzqHSohs9V6nu7M0luMN93+lvnNNCjjD5MtClDYHX+4Imb6JDOt17XpM4+n+cxBAH+btB93p9hSRYhzRPK0olqeY8092vJE+4p3WaScPJYtYsD0GLIOqP74RpPBCcSrnP6XY+GM32bAzwdA5sfoWIyhTz4ANUVTW5lIBxJ933TnMDrXDmIt7dqV2Be6L7bHkAi+iMAslHeBl3XBhZ0dMOJ3WF7bPPQMQT64ebgTccCqpOFaIjd1oCjVq89gW/jxYCd9EHogU3fOv8CdIAO+VgNIgGz8RY8EnA8rXDGvZWtYlQDEA0U8ReyfhjE5bb3waxU7nXfVc8ycfBlSr1sbd2EOWqi8U3UkdZWZw84VXygZIYSaY5pNDEQcV/uDI5eR3qgE8Z6YB2ShEL9G2CAWbBXJ0AUG2MFkZQiX4YJWMuJjPXjFMClY9bv6+fe9FU8es6EjkgVyvCZnnVG7QpjOgeCU1GiBw3CftC+7X3yMsMiC/EyM2lmxq+10Sm6ap9mwXHVD5ln3XeqgI13eiAQBW6niTkT/IH2HQh+yhvK96BtMzwmxFK0JLyUCPZMKKtRYv920QbxFh0GwjvAdbxyP+5/Y1YWT4ruWZQ/pkpHznuKDm0s6DIn0CcBQvkB0BlWhMXCSqfSqphePXSa5AkKmVavVCGYCqABy37nSRVIwUO794KgVJzaCD6p1vGiXq5NyZsxFYrLTGWI+zBHXbTEokfDOHAfhnlkr0uQIfTbUBGygMQpgzou2g9btBwUBXrIIPcQvrlWsJQyzPs8tHIjBsAN2cHmLtJA5dCO1So6AN6vaPRinU9Bj9URtQEznZu2P443V7J0bJrBi3TsWWqrrQnai9RT/PpKSejX3pBvPUwgzDkdxNEfBBVQubk+cZWHx/FEAOqJK++poP0DVDk7CXL0+/e6PZWhok9OUg9YioEhJglNHHsPwsqC0e8pUBLmj+0Q31MD9/35JDEHgFDxp+qLj99ygtz2la1ZPdqdU/upKt3ppffnfBKk9s/gNjEwMmVsXPcAudvPaevx2IAImzejnM1ULq16W4Mu2fh0r/cVF25zqCBQMmB7UXRvn9LJeh/R73frKcZ6oNcc+GFzswu2K9AIh+LJkvTBAuzOP8Yb656i6skL+xDbV4TJnM0nh4jgWh/shr1KZDOZ8qy+U9ge7oNgNj4m3cUTe94XPEsJbokOgxGqL6fWp/lRBozmgmdKgsHxBFU/2JfGiqwCcNq+GdqqrmkCRkMVr6+QKrgqZydEXaXqEG8LCSBIjWAOMYZavyTZ35/b4V4A5Hf5nFH1qPo+tw6PlSjFXbW/B+3oIBmPDze0vtZeEZ9QFxHZIARsBTpuX4Xt5jqwOhjs1z711Gjuq+6n7GiMgdIvDAZQThCBoGlCyPFaAMP6dSH6MgFEJgWNRQB9w3PQxST91iJlqFBMw/shJ7GuJmgcqw0FvZ/vCzAcH0zneTnRp5hMRhZjADwtBsTEFxgORzrRV7ObDLADf6ziT/enMX/gi74Bmwdfjke/62tw/dEPPfuTxVLK6wH8awAjgP9Qa/0nF18/iNbrnqExOkNFAyhQnWxWbTeQ/6sZd6d4CYKnjgcIgdXcfqD2+toUG4IgWsKiDpUP0VDaj1IkAQu426lYssiJ+mVOuaNcmkHkvVm6H3CuohKocE5dnXNuSs9s1Ec/hMYW+N6DmMkplWGARkKGarfQ9FCNmeAyBLUUVDIlpefmhyRZqVN0Yl63d0L3YmmFzeiLtMfA2sNGYQ5Bn+y1mhsXR5B1PJ3GcE6GpML3BrZ9MU4ZOocio/M0Fafq6OtxPDvKnV5HQZ/25xQnPyb4mPSzpyvZa/3+Ra5gcNKsFWne5zNBLZmW5vuutOprewUBd7rtoT43IdHcW5ASx2Xn9xJUOiKdVLnSvbeh+k/7VrWyrtVg1q+Z/WgOfjCYIXPP1VtC/ieBAVGz/d4bavppGuU8Aizrip9DyTE791BJ6QL3cBAUukRJ56xLyk/Rg3jsNRky6SpJoGqqjkV7ZrV1D/i64up6Px6h7x2wBjkgw9kHPb3Snx9pzYXavZswEVwXd/RsYjd0Va8QeIKS6cmaJfSfKsBKfddA3QNTPnG5oD/9WlkDNrZadZ0cuHOE722KB5Vgbi/hzP6oqZ/nfgqI17Fy3O6ID6IDTQIjQNcYEH1yYEpQFVqfP1dZmkm24p71E75UfSjFNvFUc/I36k/2exiQ3CcjOvZ9ZZkp6eEgD0DB1nCYkM3h3tdRf+KqAN/se8yOSLWV2SGBWaIAGDBhV9h88tgx7V/Gm8Ean6/1xBby/sxwArO8Z2Ax7+8jm9yf6GnzTQcs2XRaLCmnYBqVWRN2GMgFgBLapcQwB7cBM8yJUPljavNyDYth2W8oJZuo2hNAESAAIsZGU3Am7m/vixehYq7tN7CB26UxZncoolaTya9NikRUsYXuEzZWldJHlyGOBVqMGFkqcRvGXDEqMBj0GxZ27psn8xLaqIdfnRMVfe48g6ZjL37zD6OMi7sjWSyljAA+CODPAPgwgJ8F8Jdrrb908jPD0CqLFiyXLmDjBEkSLzo0hJFTPQHSDBsdMQ1DnzUoUcQEmFAVUez1iOQr4kP0FUZZ+0CjQ5rMcPSHwTCq2iNuMwubUcNZCkNPA6Hj9C0otpNZ5TCEQHEqHozTJu9JIqB7w3SzeNi/Mwba06QqMfKBKdFI6HMBUAJMn+++NsDm05BBac8kECcHqcFZHxwDYAfhyGO/33Ej/0vliWlOfHDCKSN03Mc9knzSX+/8bU+vzjE75msU0FPwR3PRKoU7c94BaST6bkDDzEFqRXwf9VnpV1xlYwMajKlUIjUB1OC8owZbb8N8FNhBCbwXeSdHdsthQxZwhVP/cLBLlQAAEPFJREFU+n0yMyivsQA6CmqoMiE4WkdeCSHtq/KMGuveUtqTahW5UfdNaUCmVGiqBs/Rg2acfjwpUduhgXl1J6+BhlYD+AhxIAQC+rfrGgf6/WmlPc2J/+4CGm6ijcUYDwjRZKWr/ACYUpp5TnXCuHp1Yi8LP4fBhfCVJwEojPu0+m0SkbJPiROAC/eR7fSreq4HPWnAZ3Ud5bWtSD0fikK67yCJ+9KpDZQgPfRX+iysGqNSqc2drS7HaovrjFZCmBGxoSrqajoes8Hh3vwJgPlgujvlMrI4rk+exWyKltT6qY7tgi4g7BNHDYzpIJfp3k85qdsqbpHaFqqQuvVEkxf2DwEInGFDEMW1D0In+9LCISteKWl2INr9WMXbhzkzvVXgWeM23kbDidiMBKASsGqWA787b5MxRVbRfx7i/nmmsfozdhGI0/kFqJ1tW4lVRYcSCwwyDjoXgfHAFFC1AQRQuG3vEk/VD/56GNTJfPWncPtBZgda04XGe2ZfrX1WD/cpNE+0TYL2PE7uwTEpA7cUS8T53fgZF/tzGINhhqFj1++uwyrhHG8dee3zhwiohceTvt/9EMfdfOie8gbaX96fpE8nz4YxCPTfDigNPohsQrdVjc89uasqi6WU1wB4a631dfL31wFArfUfX/CZqsmZJRCWgIASCkY0xOntab+GfBWDVmt8v9aeKo1OC5mlJtZqwRsvYhNGHsMpXsSvnxxLvSQjQRz8rpLCe/Cc9rc3agZv3NcA1Ix0f2SwnfCmKOAyKr+N5XXwnokgtEE6fM8WZpy50cP0gAE6wCbw7qdHwwMxKPVApCAcTNDvPSFkPraXkCqi6XmQpZXqjsbYgQKR6uWHbHAgH6gNFJRaMEmUyECBnNm7GQ4aUMdjm9eJGtNVK+LY0RiFyvrOK2ETmocicv33WS49STr3PbBK7+FDcHjTd7/3t6+EyoMnwekU9BD6BtGoOTFwmqbTuvjrZiYoM1W9/UFd9ZacvH3HJktnE2Lg5QyI+DU4xQOb/lAnTUz6PVi8j5ppWAxAdXvQDM2X/af+Has0L4cdzD52wX6Yn44eNfkuNBXWJ2obz03cEkBMjoK4HjSBnQuCWV/7w3O0fbL/xWjs3fckTsfVDx5SW2HfNUhVckt2Rq0anwiipE12AJfuH7d9qcdJ/21exC72p8d6RZap13Cq7cw+/mC3lc7W7UllEITHe+p/5m2MBeG8D0sDeabrUzV6ds8W0yl7uiwDCFaZ7CrTEitA2DyA224oCMqHywGBQhbADz2Ii6n4xI64eDzgey87FgzT+Uwv9PtclR2j1OrRv0YqVompekbfBdnG4mBtbboR19xkDx3HT3PVOmZLqFjSHL8qKVTjrVritiJSGcfgE21MO8bEBJzVZEn3/4ftKB6T8AEkXAlrz5hWw2ythHgifjejxjTBX3c0VdbpiS/uQL1JfMg0+9mYYmPzjw7MC3GV2HWfm8FsGW9t4q82ARCruYFhci2sF9+S0sC+cKAS2ze5nmUO6NTPze7Z5MSMgTeAbDsBOF38p7qgwLRfqAUgPniqeN4QQBONBQ4eP7I91gPGyH+FE8DnqLK8HQJ+PgHr13DpXjzvz/2Du2PPYinliwG8vtb6t+Tvvwbgj9dav+rkZ7SyCERH3HGFQ2ANV9J+L53epz/IxoM32H0nCRKmC5GVUTo5RUO6NsUAtVtcc3SyOVqLjkF/uE/XDqbVxI7EQHiuSsD3O7lH5tyD2Ln7cbvnuO+nnnPq89o37QP3O4xBQPPjaZSThJzGzNpP+mUOamavC9MWJ+M8swk+0mDiPtSwJzYEpJ2h7tvAY9XtlwwHQ3QJZ+g3jX8fXPZ0V6Ov8GlkfQBIutcjl30fw3v9HJw6Ee7Evp5Zh0nv9WN5am31MgmUOx3XseLxm1sHPTWd+zO7Jnr67vnU2cy2a2a/yYWHMrCdmFkPPLZ2TfdMXjf6+V4P5tbgqbkO6/2i/vR2gds3YyMm/T0xrnPtZH2IidUFJ1Z29qof934ue6Bt4uO6dTVbfenaOfvMbr32z5u7dnZ/WGeTeO7D6zM6Pgva8PxhatNsvrr7Bp3pxoHHI9g6HnemOwMn527Sbt4/PmOjeTx4riZxABCShcl+yj7ApPGaTXRO+J5T495vazllE2Ztn475nM07Mf+T/cnd+IWxPuFzJ3084dPt+j5J759BY6ljFj4/M6Y38lmzc9T7Yvr8qfV+o3mY9W0XxHn9tXO2wPp8kQ2f8fM8fqds01xb5+zz7HzMxOtzc9bbwn6sJu3t/dicTe7j9rAHdBGS7jn9vNDmde3mcbjyyi+8e05DLaX8JQCv65LFz6u1fnV33VcA+Ar585XdTdr/vTGYSfj+kI2dv5fwyhW5pTcAzBuoyX36Psw9tw98bnTf0F5Hrm9qPOYSh/5+c6rZL5jZtmB+jk4k6SfvB4APirmw7XPv3+i5N3PPOd2ba9Op5/MY9OMRP4Qwf6c+e6M+36g/N+r73Hv2bNKJG43vnC739wTiWPZje+q6U/c/tY5Ojf0coMJyozGee/1G83BRm280f0/m+adeu6h/c/bsRuvlpC4CE31+sjbgwv5YJ+YaF231k773zDrUz59sN/e3a9fcuF9ka29GZy96ffaZ8DGZ2BjMP3/22psY0/71i3Rg7h795y762z5P9rOXi+7B9wFOz92N2n+hjiGO/UV9PmVjJ/2d+Zv7d7PtnrSVX+vswR9mnm605k+us24t8983HMsTbZ08A9Pn67NO+eSL+srX3Mh3n5wvYNa23WgML5rTJ6MTN2PD+3FTudE1N9Lr/jM3ik1u9rOn/P3s2M3Y5FNtuGicwv0R1/ap/tB7q+d/JlAK9h//CA6Pf/xEB1wWN7rgaS4fBvAi+vuFAH67v6jW+m0Avg0ASik/V2t91e1pXsrdKqlnKbdLSik/V4/H1LWUWypp01Jul6SupdwOST27eRlufMnTWn4WwMtKKS8tpawAPAzgR+9wm1JSUlJSUlJSUlJSUp7x8oyuLNZa96WUrwLwNgAjgO+otb7/DjcrJSUlJSUlJSUlJSXlGS/P6GQRAGqtPwbgx57ER77tVrUlJYUk9SzldknqWsrtkNSzlNslqWspt0NSz25SntEH3KSkpKSkpKSkpKSkpKTcGnmm71lMSUlJSUlJSUlJSUlJuQVy1ySLpZTXl1J+uZTySCnlLXe6PSnPDCml/Hop5RdLKe8upfycvPbcUsrbSykfkv+fI6+XUsq/ER17bynlFXSfL5PrP1RK+TJ6/ZVy/0fkszc8wjjl2SGllO8opXyklPI+eu2W69apZ6Q8e+WErr21lPJbYtveXUp5A733daI3v1xKeR29PutH5ZC5d4pOfb8cOIdSylr+fkTef8nt6XHKnZBSyotKKe8opXyglPL+Usrfk9fTrqU8ZXKBnqVNu1VSa33W/6AdfvMrAD4DwArAewB81p1uV/48/X8A/DqA+7vX/hmAt8jvbwHwT+X3NwD4cbRvvHk1gHfK688F8Kvy/3Pk9+fIe+8C8Br5zI8D+II73ef8uW269fkAXgHgfbdTt049I3+evT8ndO2tAN48c+1niY9cA3ip+M7xIj8K4D8DeFh+/1YAXym//x0A3yq/Pwzg++/0WOTPLdWzTwfwCvn9CoAPij6lXcuf26FnadNu0c/dUln8PACP1Fp/tdZ6DuD7ALzpDrcp5ZkrbwLwXfL7dwH48/T6d9cmPwPg3lLKpwN4HYC311ofq7X+HoC3A3i9vHe11vrTtVme76Z7pTzLpdb6UwAe616+Hbp16hkpz1I5oWun5E0Avq/Wer3W+msAHkHzobN+VCo7fwrAD8jne71VXfsBAH862RPPXqm1Plpr/QX5/fcBfADAC5B2LeUplAv07JSkTftDyt2SLL4AwP+hvz+MixUrJUWlAvjvpZSfL6V8hbz2/Frro0AzWgAekNdP6dlFr3945vWUu1duh26dekbK3SdfJfS/7yDa3pPVtfsAfKzWuu9eD/eS9z8u16c8y0XoeZ8L4J1Iu5Zyi6TTMyBt2i2RuyVZnMv68xjYlJuRP1FrfQWALwDwd0spn3/Btaf07Mm+npLSS+pWylMt3wLgMwE8BOBRAP9cXn8qdS318C6UUsplAD8I4GtqrZ+46NKZ19KupdyUzOhZ2rRbJHdLsvhhAC+iv18I4LfvUFtSnkFSa/1t+f8jAH4Yjbbwu0KHgfz/Ebn8lJ5d9PoLZ15PuXvldujWqWek3EVSa/3dWuuh1noE8O/RbBvw5HXto2j0wUX3eriXvH8Pbp4Om/IMlFLKEi2A/95a6w/Jy2nXUp5SmdOztGm3Tu6WZPFnAbxMTjdaoW1K/dE73KaUp7mUUi6VUq7o7wBeC+B9aLqjp7N9GYAfkd9/FMCXyglvrwbwcaHDvA3Aa0spzxFaxGsBvE3e+/1SyquF8/6ldK+Uu1Nuh26dekbKXSQaWIv8BTTbBjT9eFhO/XspgJehHSoy60dl79g7AHyxfL7XW9W1LwbwE3J9yrNQxNZ8O4AP1Fr/Bb2Vdi3lKZNTepY27RbKnT5h53b9oJ269UG0k4++/k63J3+e/j9oJ2S9R37er3qDxk//nwA+JP8/V14vAL5ZdOwXAbyK7vXlaJuqHwHwN+j1V6EZtF8B8O8AlDvd7/y5bfr1n9CoMjs0tPJv3g7dOvWM/Hn2/pzQtf8ouvRetADo0+n6rxe9+WXQCc2n/KjYyneJDv4XAGt5fSN/PyLvf8adHov8uaV69ifRKHnvBfBu+XlD2rX8uU16ljbtFv3oIktJSUlJSUlJSUlJSUlJMblbaKgpKSkpKSkpKSkpKSkpT0IyWUxJSUlJSUlJSUlJSUmZSCaLKSkpKSkpKSkpKSkpKRPJZDElJSUlJSUlJSUlJSVlIpkspqSkpKSkpKSkpKSkpEwkk8WUlJSUlJSniZRSXlJKed+J936ylPKq292mlJSUlJS7VzJZTElJSUlJeRpIKWVxp9uQkpKSkpLCko4pJSUlJSXlU5BSyksA/Lda62fL328GcBnAYwD+NoA9gF+qtT5cSrkE4N8C+GNovvettdYfKaX8dQBvRPuy50toX0au998C+E4AnwXgAwC2t6VjKSkpKSkpIpkspqSkpKSkPLXyFgAvrbVeL6XcK699PYCfqLV+ubz2rlLK/5D3XgPg5bXWxyQBVflKAI/XWl9eSnk5gF+4Te1PSUlJSUkBkDTUlJSUlJSUp1reC+B7Syl/Fa26CACvBfCWUsq7AfwkWiXxxfLe22utj83c5/MBfA8A1FrfK/dNSUlJSUm5bZLJYkpKSkpKyqcme0Q/upH/3wjgmwG8EsDPy17EAuAv1lofkp8X11o/INf/wQXPqE91o1NSUlJSUm5WMllMSUlJSUn51OR3ATxQSrmvlLIG8GfR/OqLaq3vAPAPAdyLto/xbQC+upRSAKCU8rk3cf+fAvBX5PrPBvDyp74LKSkpKSkppyX3LKakpKSkpHwKUmvdlVL+EYB3Avg1AP8bwAjge0op96BVE/9lrfVjpZRvBPCvALxXEsZfR0suL5JvAfCdpZT3Ang3gHfdmp6kpKSkpKTMS6k1GS4pKSkpKSkpKSkpKSkpUZKGmpKSkpKSkpKSkpKSkjKRTBZTUlJSUlJSUlJSUlJSJpLJYkpKSkpKSkpKSkpKSspEMllMSUlJSUlJSUlJSUlJmUgmiykpKSkpKSkpKSkpKSkTyWQxJSUlJSUlJSUlJSUlZSKZLKakpKSkpKSkpKSkpKRMJJPFlJSUlJSUlJSUlJSUlIn8f8QBtui+ceL/AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Scatter plot of all users and individual ratings count.\n",
    "df_1 = ratings[['userId', 'movieId']].groupby('userId').count()\n",
    "df_1.columns=['num_ratings']\n",
    "print(\"Min number of user ratings: \",min(df_1.num_ratings))\n",
    "print(\"Max number of user ratings: \",max(df_1.num_ratings))\n",
    "plt.figure(figsize=(15,5))\n",
    "plt.scatter(df_1.index, df_1.num_ratings, edgecolor='black')\n",
    "plt.xlim(0,len(df_1.index))\n",
    "plt.ylim(0,)\n",
    "plt.title('Ratings per user')\n",
    "plt.xlabel('userId')\n",
    "plt.ylabel('Number of ratings given')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the histogram we can see the number of user ids that correspond to each number of ratings given."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Histogram of ratings counts.\n",
    "plt.figure(figsize=(15,5))\n",
    "plt.hist(df_1.num_ratings, bins=100, edgecolor='black', log=True)\n",
    "plt.title('Ratings per user')\n",
    "plt.xlabel('Number of ratings given')\n",
    "plt.ylabel('Number of userIds')\n",
    "plt.xlim(0,)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Conclusions:**\n",
    "\n",
    "As expected, we get an exponentially declining function, with no users with less than 1 rating given, and some outliers with almost 10.0k and 18.27k ratings.\n",
    "\n",
    "**Insight:**\n",
    "These graphs show us how much dedicated are the users to watching and rating movies."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### PLOT #2: Ratings per movie."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Below we present the scatterplot and histogram of ratings number per movie.\n",
    "\n",
    "From the scatterplot we can see the number of ratings received per movie id."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean number of movies' ratings:  576.8433780339134\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Scatter plot of all users and individual ratings count.\n",
    "df_2 = ratings[['userId', 'movieId']].groupby('movieId').count()\n",
    "df_2.columns=['num_ratings']\n",
    "print(\"Mean number of movies' ratings: \",df_2.num_ratings.mean())\n",
    "plt.figure(figsize=(15,5))\n",
    "plt.scatter(df_2.index, df_2.num_ratings, edgecolor='black')\n",
    "plt.xlim(0,df_2.index.max())\n",
    "plt.ylim(0,)\n",
    "plt.title('Ratings per movie')\n",
    "plt.xlabel('movieId')\n",
    "plt.ylabel('Number of ratings received')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the histogram we can see the number of movie ids that correspond to each number of ratings received."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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byV2eVFW3DzySKXAdP0mSJElqr01Xz4cl+WKSm5L8NMkXkiwbeGSSJEmSpFnRJvH7JHAqsDWwFFjTlEmSJEmSRkCbxG+rqvpkVd3bvI4DthpwXJIkSZKkWdIm8ftZklcmWdS8Xgn816ADm0iSlUlWDzMGSZIkSRoVbRK/P6G3fMONwA3AS5qyoXEBd0mSJElqb9JZPavqWmDfOYhFkiRJkjQAbdbx2w54E7Bt//FV1f1kcNHGJBl392OWLueGddfOYUCSJEmSNHVt1vE7BfgEvdk87x9sOPPMffewzaGnjbv7J0fuM4fBSJIkSdL0tEn87qyqowYeiSRJkiRpINokfh9KcjjwNeCuscKqumhgUUmSJEmSZk2bxO/3gFcBe/Lbrp7VbA9FkpXAymFdX5IkSZJGSZvE78XA9lV196CDaauq1gBrkhw87FgkSZIkab5rs47fJcCWgw5EkiRJkjQYbVr8Hg1ckeR8HjjGr/vLOUzG5R4kSZIkjYA2id/hA49iVLncgyRJkqQRMGniV1Vnz0UgkiRJkqTBaDPGT5IkSZI0wkz8JEmSJKnjxk38knyj+Xnk3IUjSZIkSZptE43xW5Jkd2DfJCcCD5i+sqouGmhkE3ABd0mSJElqb6LE793AYcAy4APr7Stgz0EFNRkXcJckSZKk9sZN/KrqZODkJH9dVe+dw5i6w3X+JEmSJM0DbZZzeG+SfYFnNUVnVdX4i9fpt1znT5IkSdI8MOmsnkn+HngL8MPm9ZamTJIkSZI0AiZt8QNeCDy5qu4HSPIp4GLgHYMMTJIkSZI0O9qu47dl3/stBhHIgtSMARzvtWTZimFHKEmSJKkD2rT4/T1wcZIz6S3p8Cxs7ZsdjgGUJEmSNAfaTO5yQpKzgKfTS/wOraobBx2YJEmSJGl2tGnxo6puAE4dcCySJEmSpAFoO8ZPkiRJkjSiTPwkSZIkqeMmTPySbJTkB3MVTJIXJfl4ki8led5cXXfectZPSZIkSbNgwjF+VXV/kkuSrKiqa6dzgSTHAvsAN1XVE/vK9wI+BCwCjqmqI6rqFOCUJA8H3gd8bTrX7Axn/ZQkSZI0C9pM7rIEuCzJd4E7xgqrat+W1zgO+DDw6bGCJIuAo4HnAuuA85OcWlU/bA55V7NfkiRJkjRDbRK/v53JBarqnCTbrle8K3BVVV0NkOREYL8klwNHAF+pqotmcl1JkiRJUk+bdfzOTrINsENV/VuSh9DrnjkTS4G1fdvrgGcAbwKeA2yR5HFV9bH1T0yyClg1w+tLkiRJ0oIxaeKX5GB6idYjgMfSS9o+Bjx7BtfNBsqqqo4CjproxKpaDaxuYqsZxDD6mslfxvOYpcu5Yd20hmZKkiRJ6pA2XT3fSK9r5nkAVfWjJL8zw+uuA5b3bS8Drp9hnQuPk79IkiRJaqHNOn53VdXdYxtJFgMzbWk7H9ghyXZJNgEOAE5te3KSlUlWzzCGBW/JshUuFyFJkiQtAG1a/M5O8k5g0yTPBd4ArGl7gSQnAHsAj0qyDji8qj6R5BDgDHrjBY+tqsva1llVa4A1TTdUjWeSrqCALYaSJEnSAtAm8TsMOAj4PvCnwOnAMW0vUFUHjlN+elOXBsWuoJIkSZJoN6vn/Uk+RW+MXwFXVtVQJ1VJshJYOcwYJEmSJGlUTDrGL8kLgf+kN9vmh4Grkuw96MAmUlVrqsolHSRJkiSphTZdPd8P/HFVXQWQ5LHAl4GvDDIwzQMuFyFJkiR1QpvE76axpK9xNXDTgOJpxa6ec8QxgpIkSVInjJv4Jdm/eXtZktOBk+iN8XspveUYhsZZPSVJkiSpvYla/Ppb1H4K7N68vxl4+MAikiRJkiTNqnETv6p63VwGMhV29ZQkSZKk9iYd45dkO+BNwLb9x1fVvoMLa2J29ZQkSZKk9tpM7nIK8AlgDXD/YMORJEmSJM22NonfnVV11MAj0ehxuQdJkiRpJLRJ/D6U5HDga8BdY4VVddHAopqEY/zmicmWe3jfi00MJUmSpHmgTeL3e8CrgD35bVfParaHwjF+I8J1ACVJkqR5oU3i92Jg+6q6e9DBaIGxq6gkSZI0J9okfpcAWwI3DTgWLTS2CEqSJElzok3i92jgiiTn88AxfkNbzkGSJEmS1F6bxO/wgUchSZIkSRqYSRO/qjp7LgKZCmf1lCRJkqT2NprsgCS/SHJ787ozyX1Jbp+L4MZTVWuqatUwY5AkSZKkUdGmxW/z/u0kLwJ2HVhE0hhn/ZQkSZJmRZsxfg9QVackOWwQwUgPMMNZP5csW8GN160dd7+JoyRJkhaKSRO/JPv3bW4E7EJvAXdpXrvxurUuFyFJkiTRrsWvfxKVe4EfA/sNJBppLtmVVJIkSQtEmzF+r5uLQKbCWT01K1xAXpIkSQvEuIlfkndPcF5V1XsHEE8rVbUGWJPk4GHFoHlgkhY7SZIkST0TtfjdsYGyzYCDgEcCQ0v8JMAWO0mSJKmlcRO/qnr/2PskmwNvAV4HnAi8f7zzJEmSJEnzy4Rj/JI8AvgL4BXAp4CnVtWtcxGYJEmSJGl2TDTG7x+A/YHVwO9V1S/nLCpJkiRJ0qzZaIJ9fwlsDbwLuD7J7c3rF0lun5vwJEmSJEkzNW7iV1UbVdWmVbV5VT2s77V5VT1sLoOURs2SZStIMu5rybIVww5RkiRJC0ibBdylhWmC5SImW9z9xuvWOuOoJEmS5g0TP2k8EywXYeImSZKkUTKSiV+SlcDKYcchSZIkSaNgosld5q2qWlNVq4YdhyRJkiSNgpFM/CRJkiRJ7Zn4SZIkSVLHjeQYP2noJpjxU5IkSZpvTPyk6Zhgxk+Y/7N+Llm2ghuvWzvu/smWq5AkSdJoMfGT5qFBJ2auMyhJkrSwmPhJ85CJmSRJkmaTk7tIkiRJUseZ+EmSJElSx5n4SZqyJctWkGTc15JlK4YdoiRJkvo4xk/SlDkGUZIkabTMmxa/JNsn+USSk4cdizRwzTqA472Gbr7HJ0mSpCkZaItfkmOBfYCbquqJfeV7AR8CFgHHVNURVXU1cJCJnxaE+b4O4HyPT5IkSVMy6Ba/44C9+guSLAKOBvYGdgYOTLLzgOOQFpTJxuBJkiRpYRloi19VnZNk2/WKdwWualr4SHIisB/ww0HGIi0kjsGTJElSv2GM8VsKrO3bXgcsTfLIJB8DnpLkHeOdnGRVkguSXDDoQCVJkiSpC4Yxq+eG+plVVf0X8PrJTq6q1cBqgCQ1y7FJkiRJUucMI/FbByzv214GXD+VCpKsBFbOZlCSJEmS1FXD6Op5PrBDku2SbAIcAJw6lQqqak1VrRpIdJIkSZLUMQNN/JKcAJwL7JhkXZKDqupe4BDgDOBy4KSqumyQcUiSJEnSQjboWT0PHKf8dOD06dZrV09ptC1ZtoIbr1s77v7HLF3ODeuunfb5izZ5MPfdfee065ckSeqaYYzxm7GqWgOsSXLwsGORNHUzXW6izfkuZyFJkvRbwxjjJ0mSJEmaQyOZ+CVZmWT1sOOQNKIWbUyScV9Llq0YdoQTWrJsxUjHL0mS5p5dPSUtPPfdM9JdQWfaVVaSJC08I9niJ0mSJElqz8RPkiRJkjpuJBM/x/hpwZtkjJqGyzF4kiRpvnGMnzSKRnyMWtc5Bk+SJM03I9niJ0mSJElqz8RPkiRJkjpuJBM/x/hJ89xMxyAOewzjiK/zN1OOUZQkqXsc4ydp9s10DOKwxzAO+/pD5hhFSZK6ZyRb/CRJkiRJ7Zn4SZIkSVLHmfhJkiRJUseNZOLn5C6ShmrAk89MNrnKfK9fGo8TB0nS8Di5iyRN1YAnfxn05CpO3qJh8d6TpOEZyRY/SZIkSVJ7Jn6SJEmS1HEmfpIkSZLUcSZ+kiRJktRxJn6SJEmS1HEjmfi5nIOkkTbg5SCGXf+wp+Sf70sGTBTfsGOTJHWXyzlI0lwb8HIQI1//DM33JQMmim/YsUmSumskW/wkSZIkSe2Z+EmSJElSx5n4SZIkSVLHmfhJkiRJUseZ+EmSJElSx5n4SZIkSVLHmfhJkiRJUseNZOLnAu6S1F3zfQH2gVq08YSfffGDNh3o/pl+t5P92c3UoO+NBX3vSQvYQvm77wLukqR5Zb4vwD5Q990z6Wcf9P6ZGPSf3ajXL2l+Wih/90eyxU+SJEmS1J6JnyRJkiR1nImfJEmSJHWciZ8kSZIkdZyJnyRJkiR1nImfJEmSJHWciZ8kSZIkdZyJnyRJkiR1nImfJEmSJHWciZ8kSZIkdZyJnyRJkiR13OJhBzAmyWbAR4C7gbOq6vghhyRJkiRJnTDQFr8kxya5KckP1ivfK8mVSa5KclhTvD9wclUdDOw7yLgkSZIkaSEZdFfP44C9+guSLAKOBvYGdgYOTLIzsAxY2xx234DjkiRJkqQFY6CJX1WdA9yyXvGuwFVVdXVV3Q2cCOwHrKOX/E0YV5JVSS5IcsEgYpYkzdCijUky7mvxgzadcP9M69cMTPLdLlm2YtgRTmzQ8Q+4/iXLVszo785k++f9n98kJvt+hv39j/L3O+jvdr5ff6EYxhi/pfy2ZQ96Cd8zgKOADyd5IbBmvJOrajWwGiBJDTBOSdJ03HcP2xx62ri7f3LkPpPun2n9mqZR/24HHf+A67/xurUz/rsz0n9+k2jz/Qy6/q5+v4P+buf79ReKYSR+G/rv2KqqO4DXzXUwkiRJktR1w0j81gHL+7aXAddPpYIkK4GVsxmUJEmSJHXVMNbxOx/YIcl2STYBDgBOnUoFVbWmqlYNJDpJkiRJ6phBL+dwAnAusGOSdUkOqqp7gUOAM4DLgZOq6rJBxiFJkiRJC9lAu3pW1YHjlJ8OnD7deu3qKUmSJEntDaOr54zZ1VOSJEmS2hvJxE+SJEmS1N4wZvWcMbt6SpIkSVJ7I9niZ1dPSZIkSWpvJBM/SZIkSVJ7dvWUJEmSpI5LVQ07hmlL8gvgymHHIU3Bo4CfDTsIaYq8bzVqvGc1irxvNV3bVNVWkx00ki1+fa6sql2GHYTUVpILvGc1arxvNWq8ZzWKvG81aI7xkyRJkqSOM/GTJEmSpI4b9cRv9bADkKbIe1ajyPtWo8Z7VqPI+1YDNdKTu0iSJEmSJjfqLX6SJEmSpEmMbOKXZK8kVya5Kslhw45HC0uS5UnOTHJ5ksuSvKUpf0SSryf5UfPz4U15khzV3K+XJnlqX12vaY7/UZLX9JU/Lcn3m3OOSpK5/6TqmiSLklyc5LRme7sk5zX33+eSbNKUP6jZvqrZv21fHe9oyq9M8vy+cp/LmlVJtkxycpIrmuftM33Oar5L8ufN7wY/SHJCkgf7rNV8MJKJX5JFwNHA3sDOwIFJdh5uVFpg7gX+sqqeAOwGvLG5Bw8DvlFVOwDfaLahd6/u0LxWAR+FXqIIHA48A9gVOHzsl5jmmFV95+01B59L3fcW4PK+7SOBf2zu2VuBg5ryg4Bbq+pxwD82x9Hc5wcAv0vvnvxIk0z6XNYgfAj4alXtBDyJ3r3rc1bzVpKlwJuBXarqicAies9Mn7UaupFM/Og9uK+qqqur6m7gRGC/IcekBaSqbqiqi5r3v6D3y8hSevfhp5rDPgW8qHm/H/Dp6vkOsGWSJcDzga9X1S1VdSvwdWCvZt/Dqurc6g3E/XRfXdK0JFkGvBA4ptkOsCdwcnPI+vfs2L18MvDs5vj9gBOr6q6quga4it4z2eeyZlWShwHPAj4BUFV3V9XP8Tmr+W8xsGmSxcBDgBvwWat5YFQTv6XA2r7tdU2ZNOeabhlPAc4DHl1VN0AvOQR+pzlsvHt2ovJ1GyiXZuKDwNuB+5vtRwI/r6p7m+3+++w392az/7bm+Kney9J0bQ/cDHyy6Z58TJLN8DmreayqrgPeB1xLL+G7DbgQn7WaB0Y18dtQH3ynJ9WcS/JQ4AvAW6vq9okO3UBZTaNcmpYk+wA3VdWF/cUbOLQm2ec9q7myGHgq8NGqegpwB7/t1rkh3rMauqYb8X7AdsDWwGb0umWuz2et5tyoJn7rgOV928uA64cUixaoJBvTS/qOr6p/bYp/2nQfovl5U1M+3j07UfmyDZRL0/WHwL5Jfkyva9Ce9FoAt2y6I8ED77Pf3JvN/i2AW5gxv37LAAAHFUlEQVT6vSxN1zpgXVWd12yfTC8R9Dmr+ew5wDVVdXNV3QP8K/AH+KzVPDCqid/5wA7NDEmb0Bv8euqQY9IC0vS//wRweVV9oG/XqcDYjHGvAb7UV/7qZta53YDbmi5KZwDPS/Lw5n8Jnwec0ez7RZLdmmu9uq8uacqq6h1VtayqtqX3zPxmVb0COBN4SXPY+vfs2L38kub4asoPaGai247ehBjfxeeyZllV3QisTbJjU/Rs4If4nNX8di2wW5KHNPfV2H3rs1ZDt3jyQ+afqro3ySH0HuaLgGOr6rIhh6WF5Q+BVwHfT/K9puydwBHASUkOovfwf2mz73TgBfQGZ/8KeB1AVd2S5L30HuQA76mqW5r3fwYcB2wKfKV5SbPtUODEJH8HXEwzkUbz81+SXEXvf58PAKiqy5KcRO8XmXuBN1bVfQA+lzUAbwKOb37BvZres3MjfM5qnqqq85KcDFxE7xl5MbAa+DI+azVk6f2ngiRJkiSpq0a1q6ckSZIkqSUTP0mSJEnqOBM/SZIkSeo4Ez9JkiRJ6jgTP0mSJEnqOBM/SdKUJKkk7+/bfluSv5mluo9L8pLJj5zxdV6a5PIkZ86gjneut/3vM49sbiXZupl6fjbqOivJLrNRlyRp9pn4SZKm6i5g/ySPGnYg/ZIsmsLhBwFvqKo/nkF9D0j8quoPpnD9aWsWKJ+Vf7+r6vqqGniiLUkaPhM/SdJU3UtvQeI/X3/H+i12SX7Z/NwjydlJTkryH0mOSPKKJN9N8v0kj+2r5jlJ/l9z3D7N+YuS/EOS85NcmuRP++o9M8lnge9vIJ4Dm/p/kOTIpuzdwB8BH0vyD+sd/9/qS3JKkguTXJZkVVN2BLBpku8lOX4Dn/WsJCcnuSLJ8UnS7HtBU/atJEclOa0p372p63tJLk6y+Xpxbdu0UH6E3sLQy5M8L8m5SS5K8vkkD22OfXqSf09ySfP9bj7B97dtkh80789L8rt91zwrydOSbJbk2Obci5Ps1+zfNMmJTX2fo7cIuiRpnlo87AAkSSPpaODSJP93Cuc8CXgCcAtwNXBMVe2a5C3Am4C3NsdtC+wOPBY4M8njgFcDt1XV05M8CPh2kq81x+8KPLGqrum/WJKtgSOBpwG3Al9L8qKqek+SPYG3VdUFG4hz/fr+pKpuSbIpcH6SL1TVYUkOqaonj/NZnwL8LnA98G3gD5NcAPwz8KyquibJCX3Hvw14Y1V9u0ng7txAnTsCr6uqNzStre8CnlNVdyQ5FPiLJiH9HPDyqjo/ycOAX9Nr4dzQ91d99Z8IvAw4PMkSYOuqujDJ/wG+WVV/kmRL4LtJ/g34U+BXVfX7SX6fXkIqSZqnbPGTJE1ZVd0OfBp48xROO7+qbqiqu4D/BMYSt+/TS/bGnFRV91fVj+gliDsBzwNeneR7wHnAI4EdmuO/u37S13g6cFZV3VxV9wLHA89qEef69b05ySXAd4DlfdedrI51VXU/8L3m8+0EXN1Xd3/i923gA0neDGzZxLu+n1TVd5r3uwE700vgvge8BtiGXnJ4Q1WdD70/p6auib6/MScBL23evwz4fPP+ecBhzblnAQ8GVtD7Lj/TXOdS4NIW34skaUhs8ZMkTdcH6bXyfLKv7F6a/1Rsujdu0rfvrr739/dt388D/z3qb4Ua2w7wpqo6o39Hkj2AO8aJL5N+gg37TX1N/c8BnllVv0pyFr3EZzL9n/U+ep9v3Hiq6ogkXwZeAHwnyXOq6orx4mrq+npVHdh/QNPytv73N3b8hr6/bftiuC7JfzV1vJxei97Yuf+jqq5c71zGuZYkaR6yxU+SNC1VdQu9VqKD+op/TK9rJcB+wMbTqPqlSTZqxv1tD1wJnAH8WZKNAZI8Pslmk9RzHrB7kkelN1HLgcDZU4xlC+DWJunbiV5L25h7xuJp6Qpg+75k6+VjO5I8tqq+X1VHAhfQax2cyHfodR99XHP+Q5I8vrnG1kme3pRvnmQx7b+/E4G3A1tU1diYyTOAN/WNU3xKU34O8Iqm7InA77f7GiRJw2DiJ0maifcD/bN7fpxesvVd4BmM3xo3kSvpJWhfAV5fVXcCxwA/BC5qJiP5ZybptVJVNwDvAM4ELgEuqqovTTGWrwKLk1wKvJdewjVmNb1xjse3qaiqfg28Afhqkm8BPwVua3a/Nb0JaC6hNybvK5PUdTPwWuCEJrbvADtV1d30Esp/aur6Or0Wyrbf38nAAfQS+jHvpZfAX9qc+96m/KPAQ5vrvx34bpvvQZI0HKmyl4YkSXMhyUOr6pdN69nRwI+q6h+HHZckqfts8ZMkae4c3EySchm9bqT/POR4JEkLhC1+kiRJktRxtvhJkiRJUseZ+EmSJElSx5n4SZIkSVLHmfhJkiRJUseZ+EmSJElSx5n4SZIkSVLH/X/tjWwz6LvSwgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Histogram of ratings counts.\n",
    "plt.figure(figsize=(15,5))\n",
    "plt.hist(df_2.num_ratings, bins=100, edgecolor='black', log=True)\n",
    "plt.title('Ratings per movie')\n",
    "plt.xlabel('Number of ratings received')\n",
    "plt.ylabel('Number of movieIds')\n",
    "plt.xlim(0,)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Conclusions:**\n",
    "\n",
    "From the above we can realize that most movies had a small number of ratings with a mean number of 576.84 ratings per movie. However, the scatterplot presented, also, some outliers over 60000 ratings.\n",
    "\n",
    "**Insight:** Not many users rate the same movies."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### PLOT #3: Average ratings per year"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's show the average value of ratings variation in time."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "ratings_df = ratings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "ratings_df['time_dt'] = ratings_df['timestamp'].apply(lambda x: datetime.datetime.fromtimestamp(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "ratings_df['year'] = ratings_df['time_dt'].dt.year\n",
    "ratings_df['month'] = ratings_df['time_dt'].dt.month\n",
    "ratings_df['day'] = ratings_df['time_dt'].dt.day\n",
    "ratings_df['dayofweek'] = ratings_df['time_dt'].dt.dayofweek"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Last year of dataset:  2017\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "r_df = ratings_df.groupby(['year'])['rating'].mean().reset_index()\n",
    "print(\"Last year of dataset: \",max(ratings_df.year))\n",
    "plt.subplots(ncols=1, figsize=(10,5))\n",
    "plt.plot(r_df['year'],r_df['rating']); plt.xlabel('Year'); plt.ylabel('Average ratings'); plt.title('Average ratings per year')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Coclusions:**\n",
    "\n",
    "We are not observing a special behavior other than variation in time of the ratings averages, with a descending trend from almost 1995 to 1996, also with some peaks in 1995, 1997, 1999, 2008 and 2014 and descending trend since 1999 to 2004, followed by an increase until 2008 and afterwards, an ascending trend from 2009 until 2013 to start decreasing again.\n",
    "\n",
    "We can say that between 2008 and 2004 the users were not giving big ratings to the movies they were watching."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### PLOT #4: Popular Movies"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the plot below we can find the 10 most popular movies. As we can see the top 1 is the \"Minions\" movie and the 10th most popular movie is the \"Gone Girl\"."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Popular Movies')"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pop= metadata.sort_values('popularity', ascending=False)\n",
    "plt.figure(figsize=(10,5))\n",
    "\n",
    "plt.barh(pop['title'].head(10),pop['popularity'].head(10), align='center',color='purple')\n",
    "plt.gca().invert_yaxis()\n",
    "plt.xlabel(\"Popularity\")\n",
    "plt.title(\"Popular Movies\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### PLOT #5: Most profitable movie genre using 'revenue' and 'genre' information."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's plot the most profitable movie genre using 'revenue' and 'genre' information."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "md = pd.read_csv(\"movies_metadata.csv\", dtype='unicode')\n",
    "# covert each item of release_date to datetime.date type entity\n",
    "md['release_date'] = pd.to_datetime(md['release_date'], errors='coerce').apply(lambda x: x.date())\n",
    "# all json columns`\n",
    "json_cols = ['belongs_to_collection', 'genres', 'production_companies', 'production_countries', 'spoken_languages']\n",
    "for col in json_cols:\n",
    "    # use ast because json data has single quotes in the csv, which is invalid for a json object; it should be \" normally\n",
    "    md[col] = md[col].apply(lambda x: np.nan if pd.isnull(x) else ast.literal_eval(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "revOfGenre = {}\n",
    "\n",
    "for i in range(md.shape[0]):\n",
    "    for j in md['genres'][i]:\n",
    "        if 'name' in j and md.iloc[i]['revenue'] is not np.nan:\n",
    "            name = j['name']\n",
    "            rev = int(md.iloc[i]['revenue'])/1000000 #store the revenue in million dollars\n",
    "            if name in revOfGenre:\n",
    "                revOfGenre[name]['total_revenue'] += rev \n",
    "            else:\n",
    "                revOfGenre[name] = {}\n",
    "                revOfGenre[name]['genre'] = name\n",
    "                revOfGenre[name]['total_revenue'] = rev\n",
    "\n",
    "mostProfitableG = pd.DataFrame(None,None,columns=['genre','revenue'])\n",
    "\n",
    "for k,v in  revOfGenre.items():\n",
    "    mostProfitableG =  mostProfitableG.append({'genre':v['genre'],'revenue':v['total_revenue']},ignore_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    " mostProfitableG =  mostProfitableG.sort_values(by='revenue',ascending=False) #Sort by 'revenue'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "scrolled": true
   },
   "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>genre</th>\n",
       "      <th>revenue</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Action</td>\n",
       "      <td>201456.461294</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Adventure</td>\n",
       "      <td>200119.343481</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Comedy</td>\n",
       "      <td>166897.270043</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Drama</td>\n",
       "      <td>160844.199830</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Thriller</td>\n",
       "      <td>129821.552609</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       genre        revenue\n",
       "7     Action  201456.461294\n",
       "3  Adventure  200119.343481\n",
       "1     Comedy  166897.270043\n",
       "6      Drama  160844.199830\n",
       "9   Thriller  129821.552609"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    " mostProfitableG.head()    #Show the first 5 rows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Most profitable movie genre:  201456.461294\n",
      "Less profitable movie genre:  42.0\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1224x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plot\n",
    "plt.figure(figsize=(17,7))\n",
    "ax = sns.barplot(x= mostProfitableG['genre'], y= mostProfitableG['revenue'], color='#8B3A3A')\n",
    "\n",
    "print(\"Most profitable movie genre: \",max( mostProfitableG['revenue']))\n",
    "print(\"Less profitable movie genre: \",min( mostProfitableG['revenue']))\n",
    "\n",
    "x=ax.set_xlabel(\"Movie Genre\")\n",
    "b=ax.set_ylabel(\"Revenue (in Million Dollars)\")\n",
    "c=ax.set_xticklabels(labels=ax.get_xticklabels(),rotation=30)\n",
    "d=ax.set_title(\"Most Profitable Movies by Genre\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Conclusions:**\n",
    "\n",
    "The most profitable movies are the action movies with more than 200k million dollars, followed by the adventure movies with very small difference. The least profitable is the TV Movie genre with 42 million dolars."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### PLOT #6: Most popular genre in terms of popularity points.\n",
    "\n",
    "We can also plot the most popular genre in terms of popularity points."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "md.drop(md.index[35587],inplace=True)    #This row had to be dropped because it contains invalid values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "popularityOfGenre = {}\n",
    "\n",
    "for i,_ in md.iterrows():\n",
    "    for j in md['genres'][i]:\n",
    "        if 'name' in j and md.iloc[i]['popularity'] is not np.nan:\n",
    "            name = j['name']\n",
    "            pop = float(md.iloc[i]['popularity'])\n",
    "            if name in popularityOfGenre:\n",
    "                popularityOfGenre[name]['total_popularity_points'] += pop \n",
    "                popularityOfGenre[name]['total_popularity_counts'] += 1\n",
    "            else:\n",
    "                popularityOfGenre[name] = {}\n",
    "                popularityOfGenre[name]['genre'] = name\n",
    "                popularityOfGenre[name]['total_popularity_points'] = pop\n",
    "                popularityOfGenre[name]['total_popularity_counts'] = 0\n",
    "\n",
    "mostPopularG = pd.DataFrame(None,None,columns=['genre','average_popularity_points'])\n",
    "\n",
    "for k,v in popularityOfGenre.items():\n",
    "    mostPopularG =  mostPopularG.append({'genre':v['genre'],'average_popularity_points':v['total_popularity_points']/v['total_popularity_counts']},ignore_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "mostPopularG = mostPopularG.sort_values(by='average_popularity_points',ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Most popularity points:  5.800850367095867\n",
      "Least popularity points:  0.876306290561383\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1224x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(17,7))\n",
    "ax = sns.barplot(x=mostPopularG['genre'],y=mostPopularG['average_popularity_points'],color='#00C78C')\n",
    "\n",
    "print(\"Most popularity points: \",max(mostPopularG['average_popularity_points']))\n",
    "print(\"Least popularity points: \",min(mostPopularG['average_popularity_points']))\n",
    "\n",
    "x=ax.set_xlabel(\"Movie Genre\")\n",
    "b=ax.set_ylabel(\"Average Popularity Points\")\n",
    "c=ax.set_xticklabels(labels=ax.get_xticklabels(),rotation=30)\n",
    "d=ax.set_title(\"Most Popular Movies by Genre\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Conclusions:**\n",
    "The most popular movie genre in terms of popularity points is the \"Adventure\" genre with 5.8 points and the least popular genre is the \"Foreign\" one with 0.87 points."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### PLOT #7: Average votes for each movie genre.\n",
    "\n",
    "We can also see average votes for each movie genre."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "averageVotesOfG = {}\n",
    "\n",
    "for i,_ in md.iterrows():\n",
    "    for j in md['genres'][i]:\n",
    "        if 'name' in j and md.iloc[i]['vote_average'] is not np.nan:\n",
    "            name = j['name']\n",
    "            voteAv = float(md.iloc[i]['vote_average'])\n",
    "            if name in averageVotesOfG:\n",
    "                averageVotesOfG[name]['total_votes_points'] += voteAv \n",
    "                averageVotesOfG[name]['total_votes_counts'] += 1\n",
    "            else:\n",
    "                averageVotesOfG[name] = {}\n",
    "                averageVotesOfG[name]['genre'] = name\n",
    "                averageVotesOfG[name]['total_votes_points'] = voteAv\n",
    "                averageVotesOfG[name]['total_votes_counts'] = 1\n",
    "\n",
    "highestVotedG = pd.DataFrame(None,None,columns=['genre','average_votes'])\n",
    "\n",
    "for k,v in averageVotesOfG.items():\n",
    "    highestVotedG =  highestVotedG.append({'genre':v['genre'],'average_votes':v['total_votes_points']/v['total_votes_counts']},ignore_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "highestVotedG = highestVotedG.sort_values(by='average_votes',ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "scrolled": true
   },
   "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>genre</th>\n",
       "      <th>average_votes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Animation</td>\n",
       "      <td>6.060858</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>History</td>\n",
       "      <td>6.033047</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>War</td>\n",
       "      <td>5.974225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Drama</td>\n",
       "      <td>5.868718</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Crime</td>\n",
       "      <td>5.864801</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        genre  average_votes\n",
       "0   Animation       6.060858\n",
       "11    History       6.033047\n",
       "14        War       5.974225\n",
       "6       Drama       5.868718\n",
       "8       Crime       5.864801"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "highestVotedG.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Highest vote_average:  6.060858324715603\n",
      "Smallest vore_average:  5.229373368146218\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1224x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(17,7))\n",
    "ax = sns.barplot(x=highestVotedG['genre'], y=highestVotedG['average_votes'], color='#CD6889')\n",
    "\n",
    "print(\"Highest vote_average: \",max(highestVotedG['average_votes']))\n",
    "print(\"Smallest vore_average: \",min(highestVotedG['average_votes']))\n",
    "\n",
    "x=ax.set_xlabel(\"Movie Genre\")\n",
    "b=ax.set_ylabel(\"Votes Average\")\n",
    "c=ax.set_xticklabels(labels=ax.get_xticklabels(),rotation=30)\n",
    "d=ax.set_title(\"Highest Voted Genre\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Conclusions:**\n",
    "\n",
    "The highest voted genre is the \"Animation\" genre with 6.06 in average and the least voted one is the \"TV Movie\" genre with 5.22 in average. Although, the \"Adventure\" and the \"Action\" were the most profitable movie genres and at the top 4 most popular genres, it seems that are not highly voted by the users. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Correlation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Correlation Map"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "f,ax = plt.subplots(figsize=(10, 10))\n",
    "sns.heatmap(metadata.corr(), annot=True, linewidths=.5, fmt= '.1f',ax=ax)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Conclusions:**\n",
    "\n",
    "We can see that popularity, revenue and vote_count are correlated. But vote_average is not correlated with them, meaning that the popularity of the movies can result to higher revenue and number of votes but that does not mean that it drives their ratings up."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Statistical Modelling: Hypothesis Testing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "ratings_stats = ratings"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Choose two sequel movies and take the ratings by users that had rated both the movies\n",
    "\n",
    "We chose two sequel movies, \"Robocop\" and \"Robocop 2\", and we get 23 ratings by the users that rated both of them. Our goal was to observe if there is a significant difference between the two samples, regarding their ratings by the same users."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "scrolled": false
   },
   "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_x</th>\n",
       "      <th>rating_x</th>\n",
       "      <th>movieId_y</th>\n",
       "      <th>rating_y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>772</td>\n",
       "      <td>5549</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5550</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2372</td>\n",
       "      <td>5549</td>\n",
       "      <td>0.5</td>\n",
       "      <td>5550</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8659</td>\n",
       "      <td>5549</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5550</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>20853</td>\n",
       "      <td>5549</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5550</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>40576</td>\n",
       "      <td>5549</td>\n",
       "      <td>3.5</td>\n",
       "      <td>5550</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>46156</td>\n",
       "      <td>5549</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5550</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>56400</td>\n",
       "      <td>5549</td>\n",
       "      <td>2.5</td>\n",
       "      <td>5550</td>\n",
       "      <td>2.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>68586</td>\n",
       "      <td>5549</td>\n",
       "      <td>2.5</td>\n",
       "      <td>5550</td>\n",
       "      <td>2.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>73712</td>\n",
       "      <td>5549</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5550</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>78340</td>\n",
       "      <td>5549</td>\n",
       "      <td>2.5</td>\n",
       "      <td>5550</td>\n",
       "      <td>2.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>79299</td>\n",
       "      <td>5549</td>\n",
       "      <td>3.5</td>\n",
       "      <td>5550</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>83252</td>\n",
       "      <td>5549</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5550</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>86161</td>\n",
       "      <td>5549</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5550</td>\n",
       "      <td>3.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>107720</td>\n",
       "      <td>5549</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5550</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>118638</td>\n",
       "      <td>5549</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5550</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>149103</td>\n",
       "      <td>5549</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5550</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>153362</td>\n",
       "      <td>5549</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5550</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>162974</td>\n",
       "      <td>5549</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5550</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>175794</td>\n",
       "      <td>5549</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5550</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>179792</td>\n",
       "      <td>5549</td>\n",
       "      <td>2.5</td>\n",
       "      <td>5550</td>\n",
       "      <td>2.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>182690</td>\n",
       "      <td>5549</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5550</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>208565</td>\n",
       "      <td>5549</td>\n",
       "      <td>4.5</td>\n",
       "      <td>5550</td>\n",
       "      <td>3.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>265327</td>\n",
       "      <td>5549</td>\n",
       "      <td>2.5</td>\n",
       "      <td>5550</td>\n",
       "      <td>2.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>269621</td>\n",
       "      <td>5549</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5550</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    userId  movieId_x  rating_x  movieId_y  rating_y\n",
       "0      772       5549       4.0       5550       3.0\n",
       "1     2372       5549       0.5       5550       0.5\n",
       "2     8659       5549       3.0       5550       3.0\n",
       "3    20853       5549       4.0       5550       3.0\n",
       "4    40576       5549       3.5       5550       3.0\n",
       "5    46156       5549       3.0       5550       3.0\n",
       "6    56400       5549       2.5       5550       2.5\n",
       "7    68586       5549       2.5       5550       2.5\n",
       "8    73712       5549       3.0       5550       4.0\n",
       "9    78340       5549       2.5       5550       2.5\n",
       "10   79299       5549       3.5       5550       3.0\n",
       "11   83252       5549       2.0       5550       3.0\n",
       "12   86161       5549       4.0       5550       3.5\n",
       "13  107720       5549       2.0       5550       1.0\n",
       "14  118638       5549       3.0       5550       2.0\n",
       "15  149103       5549       1.0       5550       0.5\n",
       "16  153362       5549       1.0       5550       1.0\n",
       "17  162974       5549       2.0       5550       2.0\n",
       "18  175794       5549       2.0       5550       3.0\n",
       "19  179792       5549       2.5       5550       2.5\n",
       "20  182690       5549       3.0       5550       2.0\n",
       "21  208565       5549       4.5       5550       3.5\n",
       "22  265327       5549       2.5       5550       2.5\n",
       "23  269621       5549       3.0       5550       4.0"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r1 = ratings_stats.loc[ratings_stats['movieId'] == 5549]    # select the 'Robocop' movie\n",
    "r2 = ratings_stats.loc[ratings_stats['movieId'] == 5550]    # select the 'Robocop 2' movie\n",
    "r = pd.merge(r1, r2, on='userId')    # merge the two dataframes in one\n",
    "r = r[['userId','movieId_x','rating_x','movieId_y','rating_y']]\n",
    "r"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Present histogram graph for both samples \n",
    "\n",
    "Below we present the histograms for the \"Robocop\" and the \"Robocop 2\" samples."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "n1 = len(r.rating_x.index)\n",
    "h1 = r.hist(column='rating_x',align='mid', bins= 20, grid=False, figsize=(18,8),color='#2BD5E1', zorder=2, rwidth=1)\n",
    "h1 = h1[0]\n",
    "\n",
    "for x1 in h1:\n",
    "    \n",
    "    # Despine\n",
    "    x1.spines['right'].set_visible(False)\n",
    "    x1.spines['top'].set_visible(False)\n",
    "    x1.spines['left'].set_visible(False)\n",
    "    \n",
    "    # Draw horizontal axis lines\n",
    "    vals1 = x1.get_yticks()\n",
    "    for tick1 in vals1:\n",
    "        x1.axhline(y=tick1, linestyle='dashed', alpha=0.4, color='#eeeeee', zorder=1)\n",
    "    # Set title\n",
    "    x1.set_title(\"HISTOGRAM GRAPH of 'Robocop'\")\n",
    "    \n",
    "    # Set x-axis label\n",
    "    x1.set_xlabel(\"Rating\", labelpad=20, weight='bold', size=20)\n",
    "\n",
    "    # Set y-axis label\n",
    "    x1.set_ylabel(\"Number of votes\", labelpad=20, weight='bold', size=20)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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hyAAAAABGQ5ABAAAAjIYgAwAAABgNQQYAAAAwGoIMAAAAYDQEGQAAAMBoCDIAAACA0RBkAAAAAKMhyAAAAABGQ5ABAAAAjIYgAwAAABgNQQYAAAAwGoIMAAAAYDQEGQAAAMBoCDIAAACA0RBkAAAAAKMhyAAAAABGQ5ABAAAAjIYgAwAAABgNQQYAAAAwGoIMAAAAYDQEGQAAAMBoCDIAAACA0RBkAAAAAKMhyAAAAABGQ5ABAAAAjIYgAwAAABgNQQYAAAAwGoIMAAAAYDQEGQAAAMBoCDIAAACA0RBkAAAAAKOxdNgFHLSjR4+esG1xcTGLi4vp7qysrMxtfH19PaurqyeMLy0tZWFhYe7ja2trWVtbO2F8eXk5VTW38SNHjiRJVldXs76+ftxYVWV5eflAxldWVtLdx40vLCxkaWnpQMb1nt7bTO/pvYMY13t6bzO9N57eGzO9N+7ec9zTeyczPsve2vh57sWMDAAAAGA0amuqcwY4475hAADG49LzLjzsEk7KrS964GGXAIxf7WcnMzIAAACA0RBkAAAAAKMhyAAAAABGQ5ABAAAAjIYgAwAAABgNQQYAAAAwGoIMAAAAYDQEGQAAAMBoCDIAAACA0RBkAAAAAKMhyAAAAABGY99BRlV9TlXddbhda9h2q6r6s6r6SFX9e1V99/xKBQAAAM50S1Ps+9gkP5VkPcnnVlUleXmSWyapJNdO8htVdVl3//nMKwUAAADOeNOcWvIVmQQWF3f3FUnulOTzt9nve2dRGAAAAMBW0wQZt07SSf5xeHyn4f4jSR6f5EOZBB1fMbPqAAAAADaZJsj47OH+3cP9Fwz3L+nuX07y3OHx586iMAAAAICtpgkyztry+PMzmaHxtuHxB4f7PtWiAAAAALYzTZBxxXB/76q6Q5I7Do//dbg/Z7j/0CwKAwAAANhqmiDjHzJZA+P2Sf42yTWH7X833N9kuH/PbEoDAAAAON40QcazNn1dw/1fdPflVbWQ5O6ZnFbyhlkVBwAAALDZ0n537O4XVNUtkvxAJutl/GWS7xmGb5vkA0n+I8krZ10kAAAAQDJFkJEk3f3zSX5+m+3/mEmYAQAAADA305xaAgAAAHCopg4yqurOVfUHVfWBqlqtqh8etj1xuF1tHoUCAAAATHVqSVX9P0memkkAUpks7pkkH03y08Pji5O8fHYlAgAAAEzse0ZGVd0+x4cYn9bdb03yr8PD+8ysOgAAAIBNpjm15H8kWRy+fv0246/NJOD48lMtCgAAAGA70wQZd83k1JG/7O6v3Gb8HcP9jU61KAAAAIDtTBNkfM5w/2c7jG+sl3HOyZcDAAAAsLNpgoxPDvdn7zB+m+H+ypMvBwAAAGBn0wQZ/5LJGhgPqaobbx6oqq9Jcv9MZmVcOrvyAAAAAI6Z5vKrL09y+ySfm2NXKEmS/5Xk2jl2OdY/mll1AAAAAJtMMyPjV5J8YPj6rBxbE+M6OXY51vcn+fXZlAYAAABwvH0HGd19RZJ751iYUTkWYCTJfyS5b3d/ZHblAQAAABwzzakl6e43VtWtkjwiyVcnuV6SK5K8Nsmzu/vjsy8RAAAAYGKqICNJuvvKJOcPNwAAAIADs+9TS6rqsuH2XTuM372qnl5VT5tdeQAAAADHTDMj49xMFvi8zg7jX5rkscM+jzu1sgAAAABONM1VS/ZyZIavBQAAAHCCXWdkVNVdt9l8y222XyPJA4av12dRGAAAAMBWe51a8ppMThXZUEm+d7jt5MOnWNNUquq6SZ6R5LaZ1Pqd3f23B1kDAAAAcDD2u0ZG7fD1Zj3cXnVKFU3vaUn+uLu/raqOZDI7BAAAADgN7SfI2Cm42G6/v0vyIydfznSq6tpJ7prkkUnS3UeTHD2o9wcAAAAO1l5BxtcO95XJTItO8htJLtyy30qSy7v7XbMtb0+3SPLBJM+qqi9JcnGSx3X3VTs94ejRE3OOxcXFLC4upruzsrIyt/H19fWsrq6eML60tJSFhYW5j6+trWVtbe2E8eXl5VTV3MaPHJmsA7u6upr19eOXUKmqLC8vH8j4yspKuvu48YWFhSwtLR3IuN7Te5vpPb13EON6T+9tpvfG03tjpvfG3XuOe3rvZMZn2VsbP8+97HrVku7+i+H2mmFTJfm3Tds3bn9zCCFGMglivizJr3f3lya5KsmPb92pqh5TVW+sqjc+4xnPOOgaAQAAgBmpranOmFTVDZK8rrvPHR5/dZIf7+577fK08X7DAACc9i49b+vk53G49UUPPOwSgPHb19IWu87I2PZVq25eVb9cVX9XVW8f7n+5qm4xfY2nprvfn+TdVfWFw6Z7JPnng64DAAAAOBj7vWpJkqSq7pvkd5OctbEpyc2TfHmS76mqb+/ul8y2xD39QJLfGa5YclmSRx3w+wMAAAAHZN9BRlXdPMnvJLl6tj8946xMAoUv7u7LZlTfnrr7zUluf1DvBwAAAByeaU4t+aEk18gkxKgk70zyuuG+hu1XT/K4GdcIAAAAkGS6IOPrhvsrk9y9u2/R3Xfu7lskuXsmVwxJkq+fZYEAAAAAG6YJMj4vk1kXz9p0OdYkyfD4mZnMzLjZrIoDAAAA2GyaIGNxuP/EDuOf2LIfAAAAwExNE2S8L5MZFw+qqrM3D1TV9ZI8eHj4/hnVBgAAAHCcaS6/+jdJzh1u/1ZVL8sktLhBkvskuW4mp5789WxLBAAAAJiYJsg4P8m3D19fN8lDN43Vpq9//VSLAgAAANjOvk8t6e6/TfJzOXap1e38QnebkQEAAADMxTRrZKS7n5jJrIy/HzZtzMT4+yQP6e4nzLA2AAAAgONMc2pJkqS7X5jkhVV19SRnJ/nP7v7kzCsDAAAA2GLfMzKq6v+rqi/YeNzdn+zu9woxAAAAgIMyzaklj09ySVX9ZVU9rKrOmldRAAAAANuZao2MwV2SXJDkfVX1q1X1pbMtCQAAAGB70wQZV2SyuOfG7TpJvi/JG6vq4qr6nqq61hxqBAAAAEgyXZBxgyT3SfKCJJ8Ytm2EGrdL8muZzNJ41kwrBAAAABjsO8jo7tXufnl3PyTJ5yZ5RJI/TbKWY4HGNZI8fB6FAgAAAJzMGhnp7qu6+7ndfc8kX53ksiQ908oAAAAAtlg6mSdV1dlJHpjkoUnuPNOKAAAAAHaw7yCjqo5kskbGQ5Ocl2R5Y2jTbh/PZA0NAAAAgJmbZkbG+zO5UklyfHiRJK9L8owkL+juTwQAAABgDqYJMq6byToYGyHGh5I8L8lvdfclsy4MAAAAYKuTWSPjzzKZffGH3b0y43oAAAAAdjRNkPG/k/x2d79zXsUAAAAA7GbfQUZ3P3GehQAAAADsZeGwCwAAAADYL0EGAAAAMBqCDAAAAGA0BBkAAADAaAgyAAAAgNHY8aolVXWz4csruvvKA6oHAAAAYEe7zch4R5J/T/LdSVJV/15Vl1XVdx1EYQAAAABb7TgjY5Ma7j8vSSe5zvzKAQAAANjZbjMyeri/2S77AAAAAByY3WZkfDST2RePrar7b9r+k1X12F2e1919y5lUBwAAALDJbkHGxUnunsmpJTcdtlWS6w23rXoY723GAAAAAE7ZbqeW/EKS/8qxNTL2st/9AAAAAE7KjjMyuvtVVfXlSb4tyblJHpXJbIuLk7z1QKoDAAAA2GTXq5Z09yVJfjZJqupRw+YXdPdT5l0YAAAAwFb7ufzqho0g4w3zKAQAAABgL/sOMrr72RtfV9X1ktwpk0U/r0jyuu6+YvblAQAAABwzzYyMVNXVkjwtyXcmWdw0tFZVz0ryuO7+1Azrm7mjR4+esG1xcTGLi4vp7qysrMxtfH19PaurqyeMLy0tZWFhYe7ja2trWVtbO2F8eXk5VTW38SNHjiRJVldXs76+ftxYVWV5eflAxldWVtJ9/EV1FhYWsrS0dCDjek/vbab39N5BjOs9vbeZ3htP742Z3ht37znu6b2TGZ9lb238PPey7yCjqirJy5LcIydeoWQpyaOT3KKqvqG3fsIAACRJLj3vwsMu4aTc8qX3P+wSACBJUvvNHKrqoUmek8mVS3a61GoneUR3P2825c2FkAUAODRjDTJufdEDD7uEM4YeAc5gO2UNx1mY4gUftunrVyd5ZJJ7Dvev3mE/AAAAgJmZZo2M22Uym+G13X2PzQNV9dwkf5Hkq4b9AAAAAGZumhkZZw/3f751YFgTY2P7dU+1KAAAAIDtTBNkXDnc32aH8Y3tV518OQAAAAA7m+bUkkuSfGWSB1TV25JckOT9SW6Q5FFJHpDJqSeXzrhGAAAAgCTTBRkvySTISJInDLftvPiUKgIAAADYwTSnlvxakn/f9Lg23Ta8Y9gPAAAAYOb2HWR095VJvjGTU0y2Xtu1kvxzknsO+wEAAADM3DSnlqS7315VX5zkmzK51Or1klyR5K+TvLy712dfIgAAAMDEVEFGkgxhxR8NNwAAAIADM80aGQAAAACHSpABAAAAjIYgAwAAABgNQQYAAAAwGoIMAAAAYDQEGQAAAMBo7Ovyq1X1WUm+dXj44e5++fxKAgAAANjevmZkdPdVSZ413O4914oAAAAAdjDNqSUfHO7fM49CAAAAAPYyTZDxZ0kqye3mVAsAAADArqYJMn4yyX8kuX9V/UBVWSgUAAAAOFD7WuxzcEGSjyb53CRPTfKkqvq3JFdt2a+7+x6zKQ8AAADgmGmCjLsl6eFWSa6X5Owt+9QwDgAAADBz0wQZG2qHrwEAAADmapog4y9jtgUAAABwiPYdZHT33eZYBwAAAMCeXHkEAAAAGI2p18ioqkpynyRfleT6SV6Y5G+TXCdJuvtdsywQAAAAYMNUQUZV3SrJHyb5ok2b35Lk6kl+P8l6VX1Zd//j7EoEAAAAmNj3qSVVdXaSV+ZYiLH5iiUvS/LxYdt9ZlYdAAAAwCbTrJHxQ0luNnx93GVXu3s1yauH7feYTWkAAAAAx5smyLjfcP/+JLfZZvytw/3NthkDAAAAOGXTBBm3TNJJntvdl24z/rHh/ganXBUAAADANqYJMjb2vWqHcQEGAAAAMFfTBBnvG+6/butAVV0jyf2Hh5efalEAAAAA25kmyHhtJot5flVVvWzT9vOSvD6TtTE6yV/NrjwAAACAY6YJMs7PJKhIkm8avq4kd8+xxT87ya/PrDoAAACATfYdZHT33yV5co6/9OpGsLGx7cnd/aYZ1QYAAABwnGlmZKS7fybJA5NshBUbAcabkjyou392hrUBAAAAHGdp2id094uSvKiqrp7k7CT/2d2fnHllAAAAAFtMNSNjG733LvNXVYtV9fdV9UeHXQsAAAAwP1MFGTXx2Kr6pyRXZnKp1Sur6p+q6geq6lSDkZP1uCSXHNJ7AwAAAAdk38FDVR1JclGSpyX5okzWx9i43SbJU5P88bDfgamqmyS5V5JnHOT7AgAAAAdvmjUyfiLJNwxfbz2lZONSrPcY9nvyqZe2b09N8mNJrrWfnY8ePXrCtsXFxSwuLqa7s7KyMrfx9fX1rK6unjC+tLSUhYWFuY+vra1lbW3thPHl5eVU1dzGjxyZZFurq6tZX18/bqyqsry8fCDjKysr6T6+dRcWFrK0tHQg43pP722m9/TeQYzrvc/M3hurlZUVvXdAvTdmjnvj7j2/c/XeyYzPsrc2fp57meZUkIflWIDx0STPTfJLw/1Hh+2V5OFTvOYpqap7J/lAd1+8x36Pqao3VtUbn/EMEzcAAABgrGprqrPjjlWfSrKc5N+S3LG7/3PT2PWSvD7JLZMc7e6z5lDrdjX9QiYBy2qSs5JcO8kfdPdDd3naZ8QCpQDAmenS8y487BJOyq0veuBhl3DG0CPAGaz2s9M0MzLeN9xfuDnESJLuviLJC7fsN3fd/RPdfZPuPjfJg5O8ao8QAwAAABixaYKMCzNJRz57h/FzhvvfP6WKAAAAAHYwTZDx5CRvSPKoqnpIVVXy6UuyfkeSRyV5Sw52oc9P6+7XdPe9D+O9AQAAgIOx41VLquqybTafleRIJgt8PrOqPpzJDI2NJbhvkOTNmayVAQAAADBTu11+9dwcu6zqht607UiSG24Zu36OnWICAAAAMFO7BRnJiSuG7raC6L5WFwViI1wkAAAW7ElEQVQAAAA4WbsFGc8+sCoAAAAA9mHHIKO7H3WQhQAAAADsZZqrlgAAAAAcqr3WyNhWVX1WkrOzQxDS3e86laIAAAAAtjNVkFFVD0vy40luvctuPe3rAgAAAOzHvgOHqnpkkt/eeDiXagAAAAB2Mc0aGT8SAQYAAABwiKY5BeSWmZw28okk5yf51ySr8ygKAAAAYDvTBBkfTHLjJE/v7p+aUz0AAAAAO5rm1JLnZ3Jqyc3mVAsAAADArqaZkfHTSe6a5CFV9bFMgo33ZpvTS1x+FQAAAJiHfQcZ3f3Jqnpmkjsm+d7htu2u07wuAAAAwH5Nc/nVByb5jUyCisQVTAAAAIADNs0aGT+ZY+GFEAMAAAA4cNOcAvIFmczGuDLJryZ5R5Kjc6gJAAAAYFvTBBnvS3Jukl/p7ifMpxwAAACAnU1zaskFmZxScvP5lAIAAACwu2lmZLwgyX2SPLiqPp7kd5O8J8nK1h1dfhUAAACYh2mCjLdlskZGJfnu4bYdl18FAAAA5uJkAgeXXwUAAAAOxbRBhvACAAAAODTTBBmPmlsVAAAAAPuw7yCju589z0IAAAAA9jLN5VcBAAAADtW+Z2RU1cP3u293P+fkygEAAADY2TRrZFyQY1cs2YsgAwAAAJi5k7n86lZbr2Sy37ADAAAAYCqzvPxq7zEOAAAAcEqmCTJuvs22qyW5VZIfTXLXJC9L8oMzqAsAAADgBNNcfvWdOwz9S1W9Msk/Jbl3kt9LstO+AAAAACdtJpdf7e6jSd6UyaklPzSL1wQAAADYaiZBRlXdPMnXDA9vM4vXBAAAANhq36eWVNVl221OcvUk19+07apTLQoAAABgO9Ms9nludr8ySQ+3PznFmgAAAAC2Ne2pJbtdXrWS/EuSHzn5cgAAAAB2Ns2MjGfvsH09yUeSvD7Ji4eFPwEAAABmbprLrz5qnoUAAAAA7GWaGRmnhaNHT5wwsri4mMXFxXR3VlZW5ja+vr6e1dXVE8aXlpaysLAw9/G1tbWsra2dML68vJyqmtv4kSNHkiSrq6tZX18/bqyqsry8fCDjKysr6e7jxhcWFrK0tHQg43pP722m9/TeQYzrvc/M3hurlZUVvXdAvTdmjnvj7j2/c/XeyYzPsrc2fp57OeOCDACYp8vu++LDLuGk3OIl9zvsEgAA9qW2pjrHDVadGHntrbv7Mzkg2fkbBoBTdOl5Fx52CSfl1hc98LBLOGPoEfaiR4Az2G4XGPm0vQKHfb0IAAAAwEHYz+VX9zODofe5HwAAAMBJ22tGxk6XXN3s65PcKIIMAAAAYM52DTJ2u+RqVd0pyf+b5IaZhBiV5JNJnjrLAgEAAAA2TL0oZ1XdMsn/SfItG5uSrCe5IMn/6u73zqw6AAAAgE32HWRU1TlJnpTkMcPzNhYCvSjJj3X3W2dfHgAAAMAxewYZVXVWkscn+dEk18qxAOPiJD/a3a+ZW3UAAAAAm+waZFTVdyV5cibrYGwEGJcl+anufuGcawMAAAA4zl4zMn4rxxby7CTvS/KCJF9YVU/c6Und/TMzqxAAAABgsN81MjYurXrDJD+xj/0FGQAAAMDMLcz49WrvXQAAAABOzn5mZAgnAAAAgM8IewUZNz+QKgAAAAD2Ydcgo7vfeVCFAAAAAOxl1mtkAAAAAMyNIAMAAAAYDUEGAAAAMBqCDAAAAGA0BBkAAADAaAgyAAAAgNEQZAAAAACjIcgAAAAARkOQAQAAAIyGIAMAAAAYDUEGAAAAMBqCDAAAAGA0BBkAAADAaAgyAAAAgNEQZAAAAACjIcgAAAAARkOQAQAAAIyGIAMAAAAYDUEGAAAAMBqCDAAAAGA0BBkAAADAaAgyAAAAgNEQZAAAAACjIcgAAAAARkOQAQAAAIyGIAMAAAAYjVEHGVV106p6dVVdUlVvrarHHXZNAAAAwPwsHXYBp2g1yeO7+01Vda0kF1fVK7v7nw+7MAAAAGD2Rj0jo7vf191vGr7+eJJLktz4cKsCAAAA5mXsMzI+rarOTfKlSV6/235Hjx49Ydvi4mIWFxfT3VlZWZnb+Pr6elZXV08YX1paysLCwtzH19bWsra2dsL48vJyqmpu40eOHEmSrK6uZn19/bixqsry8vKBjK+srKS7jxtfWFjI0tLSgYzrPb23md47vXtvjDb6RO/Nv/fGamVlxXHvgHpvzPzOHXfvjfF3rn/vnV69t/Hz3MtpEWRU1TWT/H6SH+ruj20z/pgkj0mS888/P49+9KMPuMJT8y/3etFhl3BSbvGS+x12CSflsvu++LBLmNoXvuIBh10CAAAHZIz/Xk3G+/fBpeddeNglTG2sn/V+jT7IqKrlTEKM3+nuP9hun+7+zSS/ufFwl9faNQE61fGFhYVTGh+brd/LRlq4k3mP7/V/wcb8f8nm3XunOr6R1h7W+Nh771TH53lc03u7j4/Jdp+j3ptf743R5p+34958e2vM/M49vN4bg3n+zp53b43N5s96DMe9aXt71EfKqqokv53kku5+ymHXAwAAAMzXqIOMJHdJ8rAkd6+qNw+3bzrsogAAAID5GPX8me5+bZI67DoAAACAgzH2GRkAAADAGUSQAQAAAIyGIAMAAAAYDUEGAAAAMBqCDAAAAGA0BBkAAADAaAgyAAAAgNEQZAAAAACjIcgAAAAARkOQAQAAAIyGIAMAAAAYDUEGAAAAMBqCDAAAAGA0BBkAAADAaAgyAAAAgNEQZAAAAACjIcgAAAAARkOQAQAAAIyGIAMAAAAYDUEGAAAAMBqCDAAAAGA0BBkAAADAaAgyAAAAgNEQZAAAAACjIcgAAAAARkOQAQAAAIyGIAMAAAAYDUEGAAAAMBqCDAAAAGA0BBkAAADAaAgyAAAAgNEQZAAAAACjIcgAAAAARkOQAQAAAIyGIAMAAAAYDUEGAAAAMBqCDAAAAGA0BBkAAADAaAgyAAAAgNEQZAAAAACjsXTYBRy0o0ePnrBtcXExi4uL6e6srKzMbXx9fT2rq6snjC8tLWVhYWHH8bHa+KyPHDmSJFldXc36+vpx+1RVlpeXD2R8ZWUl3X3c+MLCQpaWlnYcH4t59d6sxtfW1rK2tnbC+PLycqpqbuNj7r1Zjo/xuDf23hujjT7Re/PvvbFaWVlx3Dug3hszv3MPvvfGZLvPZyz/3hubo0ePjuq4tzG+8fPcixkZAAAAwGiccTMydkt4qmqu4wsLC6c0PjZbv5eNpHon8x7fK00da9qazL/3TnV8I609rPGx996pjo/5uDf23huT7T5HvTe/3hujzT9vx7359taY+Z17eL03BvP8nT3v3hqbzZ/1GI570/b2uI+UAAAAwBlFkAEAAACMhiADAAAAGA1BBgAAADAaggwAAABgNAQZAAAAwGgIMgAAAIDREGQAAAAAoyHIAAAAAEZDkAEAAACMhiADAAAAGA1BBgAAADAaggwAAABgNAQZAAAAwGgIMgAAAIDREGQAAAAAoyHIAAAAAEZDkAEAAACMhiADAAAAGA1BBgAAADAaggwAAABgNAQZAAAAwGgIMgAAAIDREGQAAAAAoyHIAAAAAEZDkAEAAACMhiADAAAAGA1BBgAAADAaggwAAABgNAQZAAAAwGgIMgAAAIDREGQAAAAAoyHIAAAAAEZDkAEAAACMhiADAAAAGA1BBgAAADAaggwAAABgNAQZAAAAwGgIMgAAAIDREGQAAAAAozH6IKOq7llVb6uqt1fVjx92PQAAAMD8jDrIqKrFJOcnOS/JbZJ8e1Xd5nCrAgAAAOZl1EFGkjskeXt3X9bdR5O8IMl9D7kmAAAAYE6WDruAU3TjJO/e9PjyJHfc7QlHjx49Ydvi4mIWFxfT3VlZWZnb+Pr6elZXV08YX1paysLCwo7jY7XxWR85ciRJsrq6mvX19eP2qaosLy8fyPjKykq6+7jxhYWFLC0t7Tg+FvPqvVmNr62tZW1t7YTx5eXlVNXcxsfce7McH+Nxb+y9N0YbfaL35t97Y7WysuK4d0C9N2Z+5x58743Jdp/PWP69NzZHjx4d1XFvY3zj57mXsQcZtc22E/4SrarHJHnM8PCtST41h1rOSfKhObzueF3tsAs4NAffCwsPOtC3Y18cE9gwjl44c4/ZB2UcfbCb/f3bkr2Nvxd2Uv49MoXTtw9243fNVvPrg/F+1n/c3ffca6exBxmXJ7nppsc3SfLerTt1928m+c15FlJVb+zu28/zPRgHvUCiDzhGL5DoA47RCyT6gAl9cPLGvkbGG5LcqqpuXlVHkjw4yUsPuSYAAABgTkY9I6O7V6vqsUn+JMlikmd291sPuSwAAABgTkYdZCRJd78iySsOu47M+dQVRkUvkOgDjtELJPqAY/QCiT5gQh+cpBrrVRoAAACAM8/Y18gAAAAAziCCjClV1T2r6m1V9faq+vFtxh9ZVR+sqjcPt0cfRp3MV1U9s6o+UFX/tMN4VdXThz75x6r6soOukfnbRx/crao+uul48MSDrpGDUVU3rapXV9UlVfXWqnrcNvs4Lpzm9tkHjgunuao6q6r+rqr+YeiDJ2+zz9Wq6oXD8eD1VXXuwVfKvO2zF/ztcIaoqsWq+vuq+qNtxhwTpjT6NTIOUlUtJjk/yddncunXN1TVS7v7n7fs+sLufuyBF8hBuiDJryZ5zg7j5yW51XC7Y5JfH+45vVyQ3fsgSf6qu+99MOVwiFaTPL6731RV10pycVW9csvvB8eF099++iBxXDjd/VeSu3f3lVW1nOS1VXVRd79u0z7fleQ/u/vzq+rBSX4xyYMOo1jmaj+9kPjb4UzxuCSXJLn2NmOOCVMyI2M6d0jy9u6+rLuPJnlBkvseck0cgu7+yyRX7LLLfZM8pydel+S6VXXDg6mOg7KPPuAM0d3v6+43DV9/PJN/qNx4y26OC6e5ffYBp7nhv/Erh4fLw23ronT3TfLs4esXJblHVdUBlcgB2WcvcAaoqpskuVeSZ+ywi2PClAQZ07lxkndvenx5tv8HyrcO04ZfVFU3PZjS+Ayz317h9PeVw5TSi6rqvx12MczfMB30S5O8fsuQ48IZZJc+SBwXTnvDFPI3J/lAkld2947Hg+5eTfLRJJ99sFVyEPbRC4m/Hc4ET03yY0nWdxh3TJiSIGM626ViW1PVlyU5t7u/OMmf5ViyxpllP73C6e9NST6vu78kya8kefEh18OcVdU1k/x+kh/q7o9tHd7mKY4Lp6E9+sBx4QzQ3WvdfbskN0lyh6q67ZZdHA/OEPvoBX87nOaq6t5JPtDdF++22zbbHBN2IciYzuVJNqekN0ny3s07dPeHu/u/hoe/leTLD6g2PrPs2Suc/rr7YxtTSrv7FUmWq+qcQy6LORnOf/79JL/T3X+wzS6OC2eAvfrAceHM0t0fSfKaJPfcMvTp40FVLSW5TpyqeFrbqRf87XBGuEuSb66qd2SyNMHdq+p5W/ZxTJiSIGM6b0hyq6q6eVUdSfLgJC/dvMOW852/OZPzYznzvDTJw4erFNwpyUe7+32HXRQHq6pusHF+Y1XdIZNj7ocPtyrmYfg5/3aSS7r7KTvs5rhwmttPHzgunP6q6vpVdd3h66sn+bokl27Z7aVJHjF8/W1JXtXd/u/raWY/veBvh9Nfd/9Ed9+ku8/N5O/HV3X3Q7fs5pgwJVctmUJ3r1bVY5P8SZLFJM/s7rdW1c8keWN3vzTJD1bVN2eycvkVSR55aAUzN1X1/CR3S3JOVV2e5EmZLOCU7v6NJK9I8k1J3p7kE0kedTiVMk/76INvS/J9VbWa5JNJHuyX0mnrLkkeluQtw7nQSfKTSW6WOC6cQfbTB44Lp78bJnn2cLW7hSQXdvcfbfn34m8neW5VvT2Tfy8++PDKZY720wv+djhDOSacmvK7EwAAABgLp5YAAAAAoyHIAAAAAEZDkAEAAACMhiADAAAAGA1BBgAAADAaggwAAABgNAQZAAAAwGgIMgAAAIDREGQAAAAAoyHIAAAAAEZDkAEAAACMhiADAP7/9u4u1I7qDOP4/wGtMWBVUBFRG7XSarVUQ8HaSGPsl7WkeCNqK0YDit54UwoVRBsrWCxSqF60WqK1FCOithSrF42ioG0RL8Rg/GoPBD/QCz/TmgTzejFzPJPxnMTk7L3DeP4/GJi1Zs2ad++rw3NmrS1JkqTBMMiQJEmSJEmDYZAhSZIkSZIGwyBDkiRJkiQNhkGGJEmSJEkaDIMMSZIkSZI0GAYZkiRJkiRpMAwyJEmSJEnSYBhkSJIkSZKkwTDIkCRJkiRJg2GQIUmSPhOSTCWp9rhjb9cjSZLGwyBDkiTtliRLOoFB99ieZHOSF5P8KcmyMT1v1SjmlSRJw7TP3i5AkiR9ZgRYDHyxPS5Msrqq1k7o+TcAB7bnz07omZIkacIMMiRJ0nw9BawDFgHfAH7Q9gf4VZI7q2r7uIuoqtvG/QxJkrT3ubREkiTN14aq+nVV/bKqzgH+1bl2aHsAkOTSJHcn2ZDkjSRbk7yf5Lkkv0tyQnfiJFPAf3vPW9tdatIdO9seGbMtTUlyVpJ/JHm3XQ7zSJLTZvtwSZYmebAd+16S9UmWt/N0512yh9+fJEnaDb6RIUmSRu2Vzvl24K1O+0pgaW/8vsCX2+PiJN+vqkfHWN9q4Js0b4xMWw6sT3JqVW2c7kyyAngQ2K8z9kzgW22/JEmaMIMMSZI0EkkWAacD3+l031tVWzvtN4G/AS/RBBzbgMOBc4GjaAKDW4CT2vE3AEuAqztzrKNZzrKnlgEbgfuArzGzFGZ/4Crgivbz7AfcxY4hxj3AC8BK4IfzqEGSJO2hVNWuR0mSJLXaJRT95R6zeQBYVVXv9O5fBJwGHAccQPOPlROBSzrDjq6qTXM875KqumOWuqaAL7TNO6tq1Rz3bwK+UlXvtdefBk5prz1dVUvb/vNoQpNpN1bVzzuf4Rng+M71Y6pq6pNfgyRJGiXfyJAkSePwDHDNLCHGVcAa4PO7uP9ImsBhHO6aDjFaLzATZBzc6f96774/TJ9U1QdJ/gxcO54SJUnSXNzsU5IkzddTwM+A3wMftH1fBR5Pctz0oCQrgd+w6xADdlzOMWpTvfaWznn3b6ODeuNe67VfH1VBkiTp0zPIkCRJ87Whqm6qqsuBCzr9BwG/7bTP75xvBs4GFldVgHPGX+bHtvXac62zfbvXPqzXPnw05UiSpN1hkCFJkkamqh4AHu50nZ3kjPb8kE7/f6rqoar6f9vuhhx9/eBh8TzL/LT+3Wv/ePqk3SPjwgnVIUmSOtwjQ5Ikjdoa4Hud9rXAt4HnmflFk5OTrAOepfnp0xU7me8NYCvwubb90ySHAP8DXq6q+0dX+g7+CrwKHNG21yT5Es3GoT9ix40+JUnShPhGhiRJGqmqegJ4tNN1VpJlNPtjvNvpP48m9FgBrN3JfNuAv3S6jgF+AdwErB5N1bM+dwtwETN7aAT4CXANcDLw994t28dViyRJmmGQIUmSxuH6Xvu6qnoZOIMmANhM80bFP4GVwB93Md9lNJuJvgp8ONpS51ZV64FlwEPA+zR1PwZ8F3i8N/ytSdUlSdJClqq59reSJEla2Nq9MLZU7w+mJPvQhDBL266NVXXCpOuTJGkhco8MSZKkuS0Hbk1yN/AczS+ZHAVcykyIAXDz5EuTJGlhMsiQJEnauWOBq3dy/eaqum1SxUiStNAZZEiSJM1tA3ALzd4eRwIH0mz+uQl4Ari9qp7ce+VJkrTwuEeGJEmSJEkaDH+1RJIkSZIkDYZBhiRJkiRJGgyDDEmSJEmSNBgGGZIkSZIkaTAMMiRJkiRJ0mAYZEiSJEmSpMH4CF7TMBdpJlFEAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1296x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "n1 = len(r.rating_y.index)\n",
    "h1 = r.hist(column='rating_y',align='right', bins= 20, grid=False, figsize=(18,8),color='#D748A9', zorder=2, rwidth=1)\n",
    "h1 = h1[0]\n",
    "\n",
    "for x1 in h1:\n",
    "\n",
    "    # Despine\n",
    "    x1.spines['right'].set_visible(False)\n",
    "    x1.spines['top'].set_visible(False)\n",
    "    x1.spines['left'].set_visible(False)\n",
    "    \n",
    "    # Draw horizontal axis lines\n",
    "    vals1 = x1.get_yticks()\n",
    "    for tick1 in vals1:\n",
    "        x1.axhline(y=tick1, linestyle='dashed', alpha=0.4, color='#eeeeee', zorder=1)\n",
    "    # Set title\n",
    "    x1.set_title(\"HISTOGRAM GRAPH of 'Robocop 2'\")\n",
    "    \n",
    "    # Set x-axis label\n",
    "    x1.set_xlabel(\"Rating\", labelpad=20, weight='bold', size=20)\n",
    "\n",
    "    # Set y-axis label\n",
    "    x1.set_ylabel(\"Number of votes\", labelpad=20, weight='bold', size=20)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can observe that the distribution of the graphs that the first graph seems normal whereas the second one does not seem normal. So, we can expect that the second graph will not conform to the normality criterion check below."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Exploring the correlation between the two samples and presenting scatterplot graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "                  PEARSON CORRELATION BETWEEN SAMPLES\n",
      "----------------------------------------------------------------------------\n",
      "\n",
      "Correlation coefficient r =  0.7688113035094101\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "              userId  movieId_x  rating_x  movieId_y  rating_y\n",
      "userId     1.000000        NaN -0.092694        NaN  0.032190\n",
      "movieId_x       NaN        NaN       NaN        NaN       NaN\n",
      "rating_x  -0.092694        NaN  1.000000        NaN  0.768811\n",
      "movieId_y       NaN        NaN       NaN        NaN       NaN\n",
      "rating_y   0.032190        NaN  0.768811        NaN  1.000000\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"\\n                  PEARSON CORRELATION BETWEEN SAMPLES\")\n",
    "print(\"----------------------------------------------------------------------------\\n\")\n",
    "cor, p = st.pearsonr(r[\"rating_x\"],r[\"rating_y\"])\n",
    "print(\"Correlation coefficient r = \",cor)\n",
    "\n",
    "print(\"\\n\\n\\n\\n\",r.corr())\n",
    "plt.scatter(r[\"rating_x\"],r[\"rating_y\"])\n",
    "plt.xlabel(\"Robocop Votes\")\n",
    "plt.ylabel(\"Robocop 2 Votes\")\n",
    "plt.title(\"SCATTERPLOT GRAPH\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In order to explore the correlation between these two samples we used the scipy.stats.pearsonr(x, y) method from which we get that the coefficient between the two samples is positive, meaning that correlation slopes upward, but, it is also strong as well, because the absolute value of the coefficient ≈ 0.77. \n",
    "\n",
    "**Conclusion:** There is a strong and positive correlation between the two samples. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Below we will explore whether the two means of the two related samples are significantly different, regarding their ratings by the same users. \n",
    "\n",
    "**IV (Independent Variable):** The movies that the users rated with two conditions: (a) movie \"Robocop\", (b) movie \"Robocop 2\"\n",
    "\n",
    "**DV (Dependent Variable):** The ratings\n",
    "\n",
    "**Population:** 23 users that rated both of the movies\n",
    "\n",
    "**Null hypothesis:** H0 = There is no significant difference in a user's rating for the \"Robocop\" and the \"Robocop 2\" movie.\n",
    "\n",
    "The first step when applying statistical controls is to check the normality and variance criteria"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Check data for normality criterion**\n",
    "\n",
    "To test for normality, we applied the Shapiro-Wilk test by calling the scipy stats.shapiro() method.\n",
    "This control tests the null hypothesis that the data was drawn from a normal distribution and returns the test statistic ('W') and the probability ('p')."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Normality control probability p for \"Robocop\":  0.3759\n",
      "Normality control probability p for \"Robocop 2\":  0.0236\n",
      "\n",
      "Conclusion: Since p < 0.05 for one or both of the samples, we can not say that  both of them  conform to the normality criterion\n"
     ]
    }
   ],
   "source": [
    "p1 = round(st.shapiro(r.rating_x.dropna())[1],4)\n",
    "p2 =  round(st.shapiro(r.rating_y.dropna())[1],4)\n",
    "print('Normality control probability p for \"Robocop\": ',p1)\n",
    "print('Normality control probability p for \"Robocop 2\": ',p2)\n",
    "if p1>0.05 and p2>0.05:\n",
    "    print(\"\\nConclusion: Since p > 0.05 for one or both of the samples, we can not say that  both of them  conform to the normality criterion\")\n",
    "else:\n",
    "    print(\"\\nConclusion: Since p < 0.05 for one or both of the samples, we can not say that  both of them  conform to the normality criterion\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Check data for variance criterion**\n",
    "\n",
    "To test for variance, we apply the Levene test by calling the scipy stats.levene() method.\n",
    "This control tests the null hypothesis that all samples come from populations with equal variances. It returns the test statistic ('W') and the probability ('p')."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Between Group Variance: p = 0.8232011687036676\n",
      "\n",
      "Conclusion: As p > 0.05 the samples conform to the variance criterion\n"
     ]
    }
   ],
   "source": [
    "# Levene variance test for Control and Treatment groups \n",
    "p = st.levene(r.rating_x.dropna(), r.rating_y.dropna())[1]\n",
    "print('Between Group Variance: p =', p)\n",
    "if p>0.05:\n",
    "    print(\"\\nConclusion: As p > 0.05 the samples conform to the variance criterion\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When the normality criterion is violated (samples do not come from normal distributions) then non-parametric statistical control should be applied.\n",
    "\n",
    "Non-parametric statistics are a branch of statistics making no assumptions about the probability distributions of the variables.\n",
    "\n",
    "**Since the normality criterion is rejected, we proceed with the corresponding non-parametric test, the Wilcoxon signed-rank test to check for significant difference between the groups**.\n",
    "\n",
    "Wilcoxon signed-rank test is the non-parametric counterpart of the paired t-test. We apply the Wilcoxon signed-rank test by calling the scipy stats.wilcoxon() method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wilcoxon signed-rank test: W =  38.0 ,   p-value =  0.3409812623376832\n",
      "\n",
      "Since p > a (0.05), the null hypothesis is accepted!\n",
      "\"Robocop\" mean =  2.6875 ,  \"Robocop 2 mean\" =  2.5208333333333335\n"
     ]
    }
   ],
   "source": [
    "w , p= st.wilcoxon(r.rating_x.dropna(), r.rating_y.dropna())\n",
    "print(\"Wilcoxon signed-rank test: W = \", w, \",   p-value = \" , p )\n",
    "if p<=0.05:   \n",
    "    print(\"\\nSince p < a (0.05), the null hypothesis is rejected!\")\n",
    "else:\n",
    "    print(\"\\nSince p > a (0.05), the null hypothesis is accepted!\")\n",
    "print(\"\\\"Robocop\\\" mean = \", r.rating_x.dropna().mean(),\",  \\\"Robocop 2 mean\\\" = \",r.rating_y.dropna().mean())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Interpretation of results**\n",
    "\n",
    "The p value returned (0.34) is higher than threshold 'a' (0.05). Thus the null hypothesis is accepted and we argue that \"Robocop\" rating is not significantly different compared to the \"Robocop 2\" rating by the users."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Predictive modelling: Regression "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Explore whether \"Robocop\" ratings sample is a predictor of \"Robocop 2\" ratings sample"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Implement Regression Model "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Intercept    0.272189\n",
      "x            0.810651\n",
      "dtype: float64\n",
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                      y   R-squared:                       0.694\n",
      "Model:                            OLS   Adj. R-squared:                  0.675\n",
      "Method:                 Least Squares   F-statistic:                     36.31\n",
      "Date:                Sun, 16 Jun 2019   Prob (F-statistic):           1.76e-05\n",
      "Time:                        12:26:01   Log-Likelihood:                -14.768\n",
      "No. Observations:                  18   AIC:                             33.54\n",
      "Df Residuals:                      16   BIC:                             35.32\n",
      "Df Model:                           1                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "Intercept      0.2722      0.377      0.722      0.481      -0.527       1.072\n",
      "x              0.8107      0.135      6.026      0.000       0.525       1.096\n",
      "==============================================================================\n",
      "Omnibus:                        3.056   Durbin-Watson:                   1.410\n",
      "Prob(Omnibus):                  0.217   Jarque-Bera (JB):                1.518\n",
      "Skew:                           0.691   Prob(JB):                        0.468\n",
      "Kurtosis:                       3.336   Cond. No.                         8.56\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
      "\n",
      "Prediction\n",
      "\n",
      "18    1.893491\n",
      "19    2.298817\n",
      "20    2.704142\n",
      "21    3.920118\n",
      "22    2.298817\n",
      "23    2.704142\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# Split data for training [:18] and testing [18:]\n",
    "rob2_train = r['rating_y'].iloc[:18]\n",
    "rob1_train = r['rating_x'].iloc[:18]\n",
    "\n",
    "rob2_test = r['rating_y'].iloc[18:]\n",
    "rob1_test = r['rating_x'].iloc[18:]\n",
    "\n",
    "# Set training data as DataFrame df\n",
    "# Set predictor x as Independent var and Output y as dependent var\n",
    "df = r[['rating_y','rating_x']].iloc[:18]\n",
    "x = rob1_train \n",
    "y = rob2_train \n",
    "\n",
    "# Fit the model \n",
    "model = smf.ols(formula='y ~ x', data=df).fit()\n",
    "\n",
    "# print params and summary \n",
    "print(model.params)\n",
    "print(model.summary())\n",
    "\n",
    "# predict\n",
    "print('\\nPrediction\\n')\n",
    "ypred = model.predict({'x': rob1_test}) \n",
    "print(ypred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Step 1) Analyzing the correlation of the data: We need to examine if there is correlation between \"Robocop\" ratings sample and  \"Robocop 2\" ratings sample. Pearson correlation coefficient is r~0.77. That means that there is a high positive correlation between \"Robocop\" ratings sample and \"Robocop 2\" ratings sample.\n",
    "\n",
    "Step 2) Estimating predictive analysis: We implement linear regression model in order to find out if \"Robocop\" ratings sample is a predictor of \"Robocop 2\" ratings sample. So, the dependent variable is \"Robocop 2\" ratings sample matrix of row-vectors and the independent variable is \"Robocop\" ratings sample matrix of row-vectors. Our dataframe is 23 rows, so we split data in 18 rows of training data and 5 rows of testing data. We fit the linear regression model is our training data and by using the predict method, we compute the predicted values of our test data.\n",
    "\n",
    "Step 3) Evaluating the validity and usefulness of the model: By taking a look at OLS Regression Results summary table R-squared (coefficient of determination) is interpreted as the proportion of the variance in the dependent variable that is predictable from the independent variable and it ranges from 0 to 1. In our case R-squared = 0.69 means fitted model showed ~ 69% reduction in error over null model. At the same time, Prob (F-statistic) is the probability that the relationship between dependent and independent variable is occurring by chance. Prob (F-statistic) = 1.76e-05 implies that there is statistically significant relationship between \"Robocop\" ratings sample and  \"Robocop 2\" ratings sample (using p < 0.05 as a rejection rule).\n",
    "\n",
    "##### Conslusion: \"Robocop\" ratings sample is a predictor of \"Robocop 2\" ratings sample."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Top Rated Movies Chart according to IMDB's weighted rating formula\n",
    "\n",
    "The Internet Movie Database (IMDB) maintains a chart called the IMDB Top 250, which is a ranking of the top 250 movies according to a certain weighted rating formula.\n",
    "\n",
    "We will use the TMDB Ratings to come up with a Top Movies Chart. We will use IMDB's weighted rating formula to construct a chart. This formula provides a true 'Bayesian estimate', which takes into account the number of votes each title has received, minimum votes required to be on the list, and the mean vote for all titles \n",
    "\n",
    "Weighted Rating (WR) = (v ÷ (v+m)) × R + (m ÷ (v+m)) × C where, \n",
    "\n",
    "* v is the number of votes for the movie\n",
    "* m is the minimum votes required to be listed in the chart\n",
    "* R is the average rating of the movie\n",
    "* C is the mean vote across the whole report"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5.618168818883486"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "C= metadata['vote_average'].mean()\n",
    "C"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So, the mean rating for all the movies is approx 6 on a scale of 10.The next step is to determine an appropriate value for m, the minimum votes required to be listed in the chart. We will use 95th percentile as our cutoff. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "433.1999999999971"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m = metadata['vote_count'].quantile(0.95)\n",
    "m"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2273, 26)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "qualified = metadata.copy().loc[metadata['vote_count'] >= m]\n",
    "qualified.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We see that there are 2273 movies which qualify to be in this list. Now, we need to calculate our metric for each qualified movie. To do this, we will define a function, weighted_rating() and define a new feature wr, of which we'll calculate the value by applying this function to our DataFrame of qualified movies."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "def weighted_rating(x):\n",
    "    v = x['vote_count']\n",
    "    R = x['vote_average']\n",
    "    return (v/(v+m) * R) + (m/(m+v) * C)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "qualified['wr'] = qualified.apply(weighted_rating, axis=1)\n",
    "qualified = qualified.sort_values('wr', ascending=False).head(250)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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>title</th>\n",
       "      <th>vote_count</th>\n",
       "      <th>vote_average</th>\n",
       "      <th>popularity</th>\n",
       "      <th>wr</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>314</th>\n",
       "      <td>The Shawshank Redemption</td>\n",
       "      <td>8358.0</td>\n",
       "      <td>8.5</td>\n",
       "      <td>51.645403</td>\n",
       "      <td>8.357993</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>834</th>\n",
       "      <td>The Godfather</td>\n",
       "      <td>6024.0</td>\n",
       "      <td>8.5</td>\n",
       "      <td>41.109264</td>\n",
       "      <td>8.306664</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12481</th>\n",
       "      <td>The Dark Knight</td>\n",
       "      <td>12269.0</td>\n",
       "      <td>8.3</td>\n",
       "      <td>123.167259</td>\n",
       "      <td>8.208538</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2843</th>\n",
       "      <td>Fight Club</td>\n",
       "      <td>9678.0</td>\n",
       "      <td>8.3</td>\n",
       "      <td>63.869599</td>\n",
       "      <td>8.185101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>292</th>\n",
       "      <td>Pulp Fiction</td>\n",
       "      <td>8670.0</td>\n",
       "      <td>8.3</td>\n",
       "      <td>140.950236</td>\n",
       "      <td>8.172378</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>351</th>\n",
       "      <td>Forrest Gump</td>\n",
       "      <td>8147.0</td>\n",
       "      <td>8.2</td>\n",
       "      <td>48.307194</td>\n",
       "      <td>8.069648</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>522</th>\n",
       "      <td>Schindler's List</td>\n",
       "      <td>4436.0</td>\n",
       "      <td>8.3</td>\n",
       "      <td>41.725123</td>\n",
       "      <td>8.061404</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23670</th>\n",
       "      <td>Whiplash</td>\n",
       "      <td>4376.0</td>\n",
       "      <td>8.3</td>\n",
       "      <td>64.299990</td>\n",
       "      <td>8.058428</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5481</th>\n",
       "      <td>Spirited Away</td>\n",
       "      <td>3968.0</td>\n",
       "      <td>8.3</td>\n",
       "      <td>41.048867</td>\n",
       "      <td>8.036034</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1154</th>\n",
       "      <td>The Empire Strikes Back</td>\n",
       "      <td>5998.0</td>\n",
       "      <td>8.2</td>\n",
       "      <td>19.470959</td>\n",
       "      <td>8.026090</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          title  vote_count  vote_average  popularity  \\\n",
       "314    The Shawshank Redemption      8358.0           8.5   51.645403   \n",
       "834               The Godfather      6024.0           8.5   41.109264   \n",
       "12481           The Dark Knight     12269.0           8.3  123.167259   \n",
       "2843                 Fight Club      9678.0           8.3   63.869599   \n",
       "292                Pulp Fiction      8670.0           8.3  140.950236   \n",
       "351                Forrest Gump      8147.0           8.2   48.307194   \n",
       "522            Schindler's List      4436.0           8.3   41.725123   \n",
       "23670                  Whiplash      4376.0           8.3   64.299990   \n",
       "5481              Spirited Away      3968.0           8.3   41.048867   \n",
       "1154    The Empire Strikes Back      5998.0           8.2   19.470959   \n",
       "\n",
       "             wr  \n",
       "314    8.357993  \n",
       "834    8.306664  \n",
       "12481  8.208538  \n",
       "2843   8.185101  \n",
       "292    8.172378  \n",
       "351    8.069648  \n",
       "522    8.061404  \n",
       "23670  8.058428  \n",
       "5481   8.036034  \n",
       "1154   8.026090  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "qualified[['title','vote_count', 'vote_average','popularity', 'wr']].head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This chart can be considered the simplest of recommenders. It doesn’t take into consideration the tastes of a particular user nor does it try to deduce similarities between different movies. It simply calculates a score for every movie based on a predefined weighted rating formula and outputs a sorted list of movies based on that formula."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Content Based Filtering"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The Content-Based Recommender relies on the similarity of the items being recommended. The basic idea is that if you like an item, then you will also like a “similar” item. So, this is an engine that computes similarity between movies based on certain metrics and suggests movies that are most similar to a particular movie that a user liked. \n",
    "\n",
    "It generally works well when it's easy to determine the context/properties of each item. Most of the recommendation system works with information in text form such as news, books and documents.\n",
    "\n",
    "The content in this system usually uses keywords and the significance of a keyword is measured by TFIDF weight. It denotes the frequency of occurrence of the keyword in the document where as IDF is inverse document frequency of the keyword.\n",
    "\n",
    "## Content Based Recommender based on Movie Overviews and Taglines"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We have to just convert the words or text in vector form and to find the closest recommendation to our given movie input title using cosine similarity.\n",
    "\n",
    "#### We will build a recommender using movie descriptions and taglines. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "metadata['tagline'] = metadata['tagline'].fillna('')\n",
    "metadata['description'] = metadata['overview'] + metadata['tagline']\n",
    "metadata['description'] = metadata['description'].fillna('')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### We define a TF-IDF Vectorizer Object. First, we remove all english stop words such as 'the', 'a'.\n",
    "TF is simply the frequency of a word in a document. IDF is the inverse of the document frequency among the whole corpus of documents. TF-IDF weighting negates the effect of high frequency words in determining the importance of an item (document).\n",
    "\n",
    "We will use TfidfVectorizer function from scikit-learn, which transforms text to feature vectors that can be used as input to estimator."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "tf = TfidfVectorizer(analyzer='word',ngram_range=(1, 2),min_df=0, stop_words='english')\n",
    "tfidf_matrix = tf.fit_transform(metadata['description'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After calculating TF-IDF scores, how do we determine which items are closer to each other, rather closer to the user profile?\n",
    "We need a relative measure.\n",
    "\n",
    "The method of calculating this relative measure is calculated by taking the cosine of the angle between the sentences and the terms. The ultimate reason behind using cosine is that the value of cosine will increase with decreasing value of the angle between which signifies more similarity."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Cosine Similarity\n",
    "\n",
    "We will be using Cosine Similarity to calculate a numeric quantity that denotes the similarity between two movies. Therefore, we will use sklearn's linear_kernel functon."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "cosine_sim = linear_kernel(tfidf_matrix, tfidf_matrix)\n",
    "metadata = metadata.reset_index()\n",
    "titles = metadata['title']\n",
    "indices = pd.Series(metadata.index, index=metadata['title'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We now have a pairwise cosine similarity matrix for all the movies in the dataset. The next step is to write a function that returns the 10 most similar movies based on the cosine similarity score."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Implement a function that returns the 10 most similar movies based on the cosine similarity score."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_recommendations(title):\n",
    "    idx = indices[title]\n",
    "    sim_scores = list(enumerate(cosine_sim[idx]))\n",
    "    sim_scores = sorted(sim_scores, key=lambda x: x[1], reverse=True)\n",
    "    sim_scores = sim_scores[1:11]\n",
    "    movie_indices = [i[0] for i in sim_scores]\n",
    "    recommendations = metadata.iloc[movie_indices][['title', 'vote_average']]\n",
    "    return recommendations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
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       "      <th>vote_average</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>25264</th>\n",
       "      <td>Batman vs Dracula</td>\n",
       "      <td>6.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15511</th>\n",
       "      <td>Batman: Under the Red Hood</td>\n",
       "      <td>7.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21191</th>\n",
       "      <td>Batman Unmasked: The Psychology of the Dark Kn...</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18252</th>\n",
       "      <td>The Dark Knight Rises</td>\n",
       "      <td>7.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41973</th>\n",
       "      <td>Batman Beyond Darwyn Cooke's Batman 75th Anniv...</td>\n",
       "      <td>7.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41967</th>\n",
       "      <td>The Lego Batman Movie</td>\n",
       "      <td>7.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40965</th>\n",
       "      <td>LEGO DC Comics Super Heroes: Batman: Be-Leaguered</td>\n",
       "      <td>6.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40937</th>\n",
       "      <td>Batman: Return of the Caped Crusaders</td>\n",
       "      <td>7.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20229</th>\n",
       "      <td>Batman: The Dark Knight Returns, Part 2</td>\n",
       "      <td>7.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3095</th>\n",
       "      <td>Batman: Mask of the Phantasm</td>\n",
       "      <td>7.4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                   title  vote_average\n",
       "25264                                  Batman vs Dracula           6.5\n",
       "15511                         Batman: Under the Red Hood           7.6\n",
       "21191  Batman Unmasked: The Psychology of the Dark Kn...           8.0\n",
       "18252                              The Dark Knight Rises           7.6\n",
       "41973  Batman Beyond Darwyn Cooke's Batman 75th Anniv...           7.7\n",
       "41967                              The Lego Batman Movie           7.2\n",
       "40965  LEGO DC Comics Super Heroes: Batman: Be-Leaguered           6.1\n",
       "40937              Batman: Return of the Caped Crusaders           7.2\n",
       "20229            Batman: The Dark Knight Returns, Part 2           7.9\n",
       "3095                        Batman: Mask of the Phantasm           7.4"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_recommendations('Batman Returns')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>vote_average</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>32244</th>\n",
       "      <td>Atom Man vs Superman</td>\n",
       "      <td>3.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2529</th>\n",
       "      <td>Superman IV: The Quest for Peace</td>\n",
       "      <td>4.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2527</th>\n",
       "      <td>Superman II</td>\n",
       "      <td>6.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30817</th>\n",
       "      <td>Superman: Brainiac Attacks</td>\n",
       "      <td>6.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16626</th>\n",
       "      <td>All Star Superman</td>\n",
       "      <td>6.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9169</th>\n",
       "      <td>The Batman Superman Movie: World's Finest</td>\n",
       "      <td>7.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19063</th>\n",
       "      <td>Superman/Batman: Public Enemies</td>\n",
       "      <td>6.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21121</th>\n",
       "      <td>Lego Batman: The Movie - DC Super Heroes Unite</td>\n",
       "      <td>6.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2526</th>\n",
       "      <td>Superman</td>\n",
       "      <td>6.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24560</th>\n",
       "      <td>Generation War</td>\n",
       "      <td>8.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                title  vote_average\n",
       "32244                            Atom Man vs Superman           3.8\n",
       "2529                 Superman IV: The Quest for Peace           4.1\n",
       "2527                                      Superman II           6.5\n",
       "30817                      Superman: Brainiac Attacks           6.1\n",
       "16626                               All Star Superman           6.8\n",
       "9169        The Batman Superman Movie: World's Finest           7.1\n",
       "19063                 Superman/Batman: Public Enemies           6.7\n",
       "21121  Lego Batman: The Movie - DC Super Heroes Unite           6.4\n",
       "2526                                         Superman           6.9\n",
       "24560                                  Generation War           8.2"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_recommendations('Superman Returns')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Content Based Recommender based on Movie Overviews, Taglines and Ratings as well\n",
    "One thing that we notice about our recommendation system is that it recommends movies regardless of ratings and popularity. Therefore, we will add a mechanism to remove bad movies and return movies with high vote count and high vote average.\n",
    "\n",
    "We will take the top 25 movies based on similarity scores and calculate the vote of the 60th percentile movie. Then, using this as the value of  m, we will calculate the weighted rating of each movie."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_recommendations(title):\n",
    "    idx = indices[title]\n",
    "    sim_scores = list(enumerate(cosine_sim[idx]))\n",
    "    sim_scores = sorted(sim_scores, key=lambda x: x[1], reverse=True)\n",
    "    sim_scores = sim_scores[1:26]\n",
    "    movie_indices = [i[0] for i in sim_scores]\n",
    "    \n",
    "    movies = metadata.iloc[movie_indices][['title', 'vote_count', 'vote_average']]\n",
    "    vote_counts = movies[movies['vote_count'].notnull()]['vote_count'].astype('int')\n",
    "    vote_averages = movies[movies['vote_average'].notnull()]['vote_average'].astype('int')\n",
    "    C = vote_averages.mean()\n",
    "    m = vote_counts.quantile(0.60)\n",
    "    recommendations = movies[(movies['vote_count'] >= m) & (movies['vote_count'].notnull()) & (movies['vote_average'].notnull())]\n",
    "    recommendations['vote_count'] = recommendations['vote_count'].astype('int')\n",
    "    recommendations['vote_average'] = recommendations['vote_average']\n",
    "    recommendations['wr'] = recommendations.apply(weighted_rating, axis=1)\n",
    "    recommendations = recommendations.sort_values('wr', ascending=False).head(10)\n",
    "    return recommendations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
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       "      <td>8.208538</td>\n",
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       "      <td>6.630444</td>\n",
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       "      <th>1110</th>\n",
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       "      <td>6.372126</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23875</th>\n",
       "      <td>Batman: Assault on Arkham</td>\n",
       "      <td>298</td>\n",
       "      <td>7.3</td>\n",
       "      <td>6.303598</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18218</th>\n",
       "      <td>Happy Feet Two</td>\n",
       "      <td>381</td>\n",
       "      <td>5.8</td>\n",
       "      <td>5.703256</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150</th>\n",
       "      <td>Batman Forever</td>\n",
       "      <td>1529</td>\n",
       "      <td>5.2</td>\n",
       "      <td>5.292320</td>\n",
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       "                                         title  vote_count  vote_average  \\\n",
       "12481                          The Dark Knight       12269           8.3   \n",
       "18252                    The Dark Knight Rises        9263           7.6   \n",
       "41967                    The Lego Batman Movie        1473           7.2   \n",
       "20229  Batman: The Dark Knight Returns, Part 2         426           7.9   \n",
       "15511               Batman: Under the Red Hood         459           7.6   \n",
       "19789  Batman: The Dark Knight Returns, Part 1         410           7.7   \n",
       "1110                        The Wrong Trousers         266           7.6   \n",
       "23875                Batman: Assault on Arkham         298           7.3   \n",
       "18218                           Happy Feet Two         381           5.8   \n",
       "150                             Batman Forever        1529           5.2   \n",
       "\n",
       "             wr  \n",
       "12481  8.208538  \n",
       "18252  7.511457  \n",
       "41967  6.840516  \n",
       "20229  6.749524  \n",
       "15511  6.637739  \n",
       "19789  6.630444  \n",
       "1110   6.372126  \n",
       "23875  6.303598  \n",
       "18218  5.703256  \n",
       "150    5.292320  "
      ]
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     "execution_count": 26,
     "metadata": {},
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    }
   ],
   "source": [
    "get_recommendations('Batman Returns')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
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       "      <th>226</th>\n",
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       "      <td>Justice League: Crisis on Two Earths</td>\n",
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       "    </tr>\n",
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       "      <th>2528</th>\n",
       "      <td>Superman III</td>\n",
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       "    <tr>\n",
       "      <th>2529</th>\n",
       "      <td>Superman IV: The Quest for Peace</td>\n",
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      "text/plain": [
       "                                         title  vote_count  vote_average  \\\n",
       "20229  Batman: The Dark Knight Returns, Part 2         426           7.9   \n",
       "2526                                  Superman        1042           6.9   \n",
       "2527                               Superman II         642           6.5   \n",
       "226                          Dolores Claiborne         183           7.1   \n",
       "21469     Justice League: Crisis on Two Earths         152           7.1   \n",
       "19063          Superman/Batman: Public Enemies         158           6.7   \n",
       "16626                        All Star Superman         121           6.8   \n",
       "19030                       Superman: Doomsday         138           6.5   \n",
       "2528                              Superman III         500           5.3   \n",
       "2529          Superman IV: The Quest for Peace         327           4.1   \n",
       "\n",
       "             wr  \n",
       "20229  6.749524  \n",
       "2526   6.523584  \n",
       "2527   6.144709  \n",
       "226    6.058245  \n",
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       "16626  5.876201  \n",
       "19030  5.831216  \n",
       "2528   5.447697  \n",
       "2529   4.965129  "
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     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_recommendations('Superman Returns')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Pros** of using content-based recommendation:\n",
    "* Can recommend to users with unique tastes.\n",
    "* Can recommend new items.\n",
    "* The user gets recommended the types of items they love.\n",
    "\n",
    "**Cons** of using content-based recommendation:\n",
    "* Finding the appropriate features is hard.\n",
    "* Unable to exploit quality judgments of other users.\n",
    "* The user will never be recommended for different items.\n",
    "* Business cannot be expanded as the user does not try a different type of product."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Nearest Neighbors Collaborative Filtering"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Collaborative Filtering techniques make recommendations for a user based on ratings and preferences data of many users. The main underlying idea is that if two users have both liked certain common items, then the items that one user has liked that the other user has not yet tried can be recommended to him. We see collaborative filtering techniques in action on various Internet platforms such as Amazon.com, Netflix, Facebook. We are recommended items based on the ratings and purchase data that these platforms collect from their user base.\n",
    "\n",
    "Nearest Neighbors Collaborative Filtering: This approach relies on the idea that users who have similar rating behaviors so far, share the same tastes and will likely exhibit similar rating behaviors going forward. The algorithm first computes the similarity between users by using the row vector in the ratings matrix corresponding to a user as a representation for that user. The similarity is computed by using cosine similarity. In order to predict the rating for a particular user for a given movie j, we find the top k similar users to this particular user and then take a weighted average of the ratings of the k similar users with the weights being the similarity values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "      <td>Three Colors: Red</td>\n",
       "      <td>110</td>\n",
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       "      <th>4</th>\n",
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       "      <td>110</td>\n",
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       "      <td>Three Colors: Red</td>\n",
       "      <td>110</td>\n",
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      ],
      "text/plain": [
       "   userId  movieId  rating   timestamp              title   id\n",
       "0       1      110     1.0  1425941529  Three Colors: Red  110\n",
       "1      11      110     3.5  1231676989  Three Colors: Red  110\n",
       "2      22      110     5.0  1111937009  Three Colors: Red  110\n",
       "3      24      110     5.0   979870012  Three Colors: Red  110\n",
       "4      29      110     3.0  1044020005  Three Colors: Red  110"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_ratings = pd.merge(ratings,metadata[['title','id']],left_on='movieId',right_on='id')\n",
    "new_ratings.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create the Ratings Matrix, where the columns represent all the movies and each row vectors represents each user's rating in terms of all the movies. Cells filled with 0.0 means that user has never voted for this certain movie."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
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       "<p>5 rows × 7409 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "title   !Women Art Revolution  $1,000 on the Black  $5 a Day  'Gator Bait  \\\n",
       "userId                                                                      \n",
       "1                         0.0                  0.0       0.0          0.0   \n",
       "2                         0.0                  0.0       0.0          0.0   \n",
       "3                         0.0                  0.0       0.0          0.0   \n",
       "4                         0.0                  0.0       0.0          0.0   \n",
       "5                         0.0                  0.0       0.0          0.0   \n",
       "\n",
       "title   'R Xmas  'Twas the Night Before Christmas  (A)Sexual  \\\n",
       "userId                                                         \n",
       "1           0.0                               0.0        0.0   \n",
       "2           0.0                               0.0        0.0   \n",
       "3           0.0                               0.0        0.0   \n",
       "4           0.0                               0.0        0.0   \n",
       "5           0.0                               0.0        0.0   \n",
       "\n",
       "title   ...And God Created Woman  ...And the Pursuit of Happiness  \\\n",
       "userId                                                              \n",
       "1                            0.0                              0.0   \n",
       "2                            0.0                              0.0   \n",
       "3                            0.0                              0.0   \n",
       "4                            0.0                              0.0   \n",
       "5                            0.0                              0.0   \n",
       "\n",
       "title   00 Schneider - Jagd auf Nihil Baxter      ...       ¡A volar joven!  \\\n",
       "userId                                            ...                         \n",
       "1                                        0.0      ...                   0.0   \n",
       "2                                        0.0      ...                   0.0   \n",
       "3                                        0.0      ...                   0.0   \n",
       "4                                        0.0      ...                   0.0   \n",
       "5                                        0.0      ...                   0.0   \n",
       "\n",
       "title   ¡Three Amigos!  À nos amours  Åke and His World  Æon Flux  Ödipussi  \\\n",
       "userId                                                                        \n",
       "1                  0.0           0.0                0.0       0.0       0.0   \n",
       "2                  0.0           0.0                0.0       0.0       0.0   \n",
       "3                  0.0           0.0                0.0       0.0       0.0   \n",
       "4                  0.0           0.0                0.0       0.0       0.0   \n",
       "5                  0.0           0.0                0.0       0.0       0.0   \n",
       "\n",
       "title   Şaban Oğlu Şaban  Škola princů  Šíleně smutná princezna  Львиная доля  \n",
       "userId                                                                         \n",
       "1                    0.0           0.0                      0.0           0.0  \n",
       "2                    0.0           0.0                      0.0           0.0  \n",
       "3                    0.0           0.0                      0.0           0.0  \n",
       "4                    0.0           0.0                      0.0           0.0  \n",
       "5                    0.0           0.0                      0.0           0.0  \n",
       "\n",
       "[5 rows x 7409 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "if not new_ratings[new_ratings.duplicated(['userId','title'])].empty:\n",
    "    new_ratings = new_ratings.drop_duplicates(['userId','title'])\n",
    "df_for_knn = new_ratings.pivot(index='userId',columns='title',values='rating').fillna(0)\n",
    "df_for_knn.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Fast computation of nearest neighbors is an active area of research in machine learning. The most naive neighbor search implementation involves the brute-force computation of distances between all pairs of points in the dataset.\n",
    "\n",
    "We implement the KNN algorithm that computes the similarity between users by using the row vector in the ratings matrix corresponding to a user as a representation for that user. Algorithm used to compute the nearest neighbors  will use a brute-force search. The similarity is computed by using cosine similarity."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "NearestNeighbors(algorithm='brute', leaf_size=30, metric='cosine',\n",
       "         metric_params=None, n_jobs=None, n_neighbors=5, p=2, radius=1.0)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_for_knn_sparse = csr_matrix(df_for_knn.values)\n",
    "model_knn = NearestNeighbors(metric='cosine',algorithm='brute')\n",
    "model_knn.fit(df_for_knn_sparse) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then, we get the top k similar users to this particular user along with their distance values. Remember: the smallest the distance value is the more similar the two users are."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Nearest Neighbors of User with ID = 120:\n",
      "1: UserId: 79091 -- Distance: 0.4358862071111006\n",
      "2: UserId: 48329 -- Distance: 0.4399554612891232\n",
      "3: UserId: 170179 -- Distance: 0.4451046099840349\n",
      "4: UserId: 165164 -- Distance: 0.4460073130464167\n",
      "5: UserId: 151656 -- Distance: 0.4479670049842581\n",
      "6: UserId: 14127 -- Distance: 0.45085036889186847\n",
      "7: UserId: 249319 -- Distance: 0.4533490133003407\n",
      "8: UserId: 6893 -- Distance: 0.45566191969954795\n",
      "9: UserId: 80096 -- Distance: 0.45597422711617475\n",
      "10: UserId: 100589 -- Distance: 0.45815050773907007\n"
     ]
    }
   ],
   "source": [
    "userid = 120\n",
    "n_neighbors = 11 # for top 10 (the first is the user itself)\n",
    "distances, indices = model_knn.kneighbors(df_for_knn.loc[userid].values.reshape(1,-1),n_neighbors=11) # get the top k similar users to this particular user\n",
    "print(\"Nearest Neighbors of User with ID = {0}:\".format(userid))\n",
    "for i in range(0,len(distances.flatten())):\n",
    "    if i!=0: # avoid the first user that is the user itself\n",
    "        print(\"{0}: UserId: {1} -- Distance: {2}\".format(i,df_for_knn.index[indices.flatten()[i]],distances.flatten()[i])) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In order to predict the rating for a particular user for a given movie j, we compute a weighted average of the ratings of the k similar users with the weights being the similarity values. We assumed that similarity = 1 - distance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The rating that the user with ID = 120 will give in the movie Batman Returns is: 3.4\n"
     ]
    }
   ],
   "source": [
    "sum = 0\n",
    "weightsum = 0\n",
    "movie = 'Batman Returns' # the movie we'll try to predict the rating \n",
    "for i in range(0,len(distances.flatten())):\n",
    "    if i!=0:\n",
    "        indice = df_for_knn.index[indices.flatten()[i]]\n",
    "        weight = 1 - distances.flatten()[i] # computes the weights according to the similarity\n",
    "        sum = sum + df_for_knn.loc[indice, movie]*weight # computes the weighted sum\n",
    "        weightsum = weightsum + weight\n",
    "print (\"The rating that the user with ID = {0} will give in the movie {1} is: {2}\".format(userid,movie,round(sum/weightsum,1)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Cons of using Nearest Neighbors Collaborative Filtering:\n",
    "\n",
    "It suffers issues such as sparsity, cold start and scalability problem.\n",
    "* Sparsity: Ιts a very common problem which usually observed, when a user do not prefer to give feedback or like dislike any item. This creates a difficulty in finding nearest neighbor or similar user, according to which the system makes recommendation.\n",
    "* Cold start: it appears in the early stages of a system or  when new users or items that don't have any ratings enter the system.\n",
    "* Scalability and big data: as the no of users and items are rapidly increasing so the load on the system is also increasing simultaneously. Large datasets acquires more time to process, to extract information which compromises the quality, processing time of the recommender system.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### REFERENCES:\n",
    "PAPERS\n",
    "\n",
    "1. Sappadla, Prateek, Yash Sadhwani, and Pranit Arora. “Movie Recommender System.” Search Engine Architecture, Spring 2017, NYU Courant, (n.d.): 5.\n",
    "\n",
    "2.  Kalra, Nupur, Deepak Yadav, and Gourav Bathla. “Movie Recommender System Using Collaborative Filtering.” International Journal on Future Revolution in Computer Science & Communication Engineering, 4, no. 12 (n.d.): 5, December 2018.\n",
    "\n",
    "3. Arefin, Ahmed Shamsul, Carlos Riveros, Regina Berretta, and Pablo Moscato. “GPU-FS-KNN: A Software Tool for Fast and Scalable KNN Computation Using GPUs.” Edited by Alexandre G. de Brevern. PLoS ONE7, no. 8 (August 28, 2012): e44000.\n",
    "\n",
    "WEBSITES\n",
    "\n",
    "1. https://grouplens.org/datasets/movielens/\n",
    "2. https://towardsdatascience.com/brief-on-recommender-systems-b86a1068a4dd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### EXAMPLES OF WEBSITES THAT USE OUR RECOMMENDER SYSTEMS:\n",
    "- https://www.netflix.com/browse\n",
    "\n",
    "- https://www.imdb.com/?ref_=nv_home\n",
    "\n",
    "- https://openlibrary.org/\n"
   ]
  }
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