tmdb_project_v2.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "b9980f94-eb2f-491e-8e78-39dd9d7878a2",
   "metadata": {},
   "source": [
    "# introdution\n",
    "Here I will investigate TMDB data base for over 10000 movie, Hope to find a succsee formula for movies. Dose high budget movie has a higher chance ?\n",
    "or people have Favourable  geners ?  what is the charcterstics of movies having more revenue ?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "id": "9d5b9e7b-df02-4458-a04f-10feef7cf28b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# imports \n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline\n",
    "sns.set_style('darkgrid')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "id": "ea036c9c-435d-4eb8-a0cc-d596e0059f8a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# adding some fuctions that shorten my work this function  will use pandas.DataFrame.query for selecting on certain condition then  plotting a bar chart\n",
    "def plotting_selection (x, y , x_label, y_label):\n",
    "    median = df['{}'.format(x)].median()\n",
    "    low = df.query('{} < {}'.format(x, median))\n",
    "    high = df.query('{} >= {}'.format(x,median))\n",
    "    mean_low = low['{}'.format(y)].mean()\n",
    "    mean_high = high['{}'.format(y)].mean()\n",
    "    locations = [1, 2]\n",
    "    heights = [mean_low, mean_high]\n",
    "    labels = ['{}'.format(x_label), '{}'.format(y_label)]\n",
    "    plt.bar(locations, heights, tick_label=labels)\n",
    "    return\n",
    "# x = dataFrame column name - str\n",
    "# y = dataFrame column name - str\n",
    "# x_label = label will be represented on x-axis-heights(1) of bar chart -str\n",
    "# y_label = label will be represented on x-axis-heights(2) of bar chart - str"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6f9ed5d8-faee-4fb0-a9f6-ab52391c0098",
   "metadata": {},
   "source": [
    "# Data wrangling \n",
    "here i will load my data and perform some checks for cleanliness, and then dataset for analysis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "id": "abf4b220-8a17-4b50-992d-5ff96d1e814b",
   "metadata": {},
   "outputs": [],
   "source": [
    "#loading csv file \n",
    "df = pd.read_csv('tmdb-movies.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "id": "32b5d252-64dd-4111-b090-35be5a183ad6",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>imdb_id</th>\n",
       "      <th>popularity</th>\n",
       "      <th>budget</th>\n",
       "      <th>revenue</th>\n",
       "      <th>original_title</th>\n",
       "      <th>cast</th>\n",
       "      <th>homepage</th>\n",
       "      <th>director</th>\n",
       "      <th>tagline</th>\n",
       "      <th>...</th>\n",
       "      <th>overview</th>\n",
       "      <th>runtime</th>\n",
       "      <th>genres</th>\n",
       "      <th>production_companies</th>\n",
       "      <th>release_date</th>\n",
       "      <th>vote_count</th>\n",
       "      <th>vote_average</th>\n",
       "      <th>release_year</th>\n",
       "      <th>budget_adj</th>\n",
       "      <th>revenue_adj</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>135397</td>\n",
       "      <td>tt0369610</td>\n",
       "      <td>32.985763</td>\n",
       "      <td>150000000</td>\n",
       "      <td>1513528810</td>\n",
       "      <td>Jurassic World</td>\n",
       "      <td>Chris Pratt|Bryce Dallas Howard|Irrfan Khan|Vi...</td>\n",
       "      <td>http://www.jurassicworld.com/</td>\n",
       "      <td>Colin Trevorrow</td>\n",
       "      <td>The park is open.</td>\n",
       "      <td>...</td>\n",
       "      <td>Twenty-two years after the events of Jurassic ...</td>\n",
       "      <td>124</td>\n",
       "      <td>Action|Adventure|Science Fiction|Thriller</td>\n",
       "      <td>Universal Studios|Amblin Entertainment|Legenda...</td>\n",
       "      <td>6/9/15</td>\n",
       "      <td>5562</td>\n",
       "      <td>6.5</td>\n",
       "      <td>2015</td>\n",
       "      <td>1.379999e+08</td>\n",
       "      <td>1.392446e+09</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       id    imdb_id  popularity     budget     revenue  original_title  \\\n",
       "0  135397  tt0369610   32.985763  150000000  1513528810  Jurassic World   \n",
       "\n",
       "                                                cast  \\\n",
       "0  Chris Pratt|Bryce Dallas Howard|Irrfan Khan|Vi...   \n",
       "\n",
       "                        homepage         director            tagline  ...  \\\n",
       "0  http://www.jurassicworld.com/  Colin Trevorrow  The park is open.  ...   \n",
       "\n",
       "                                            overview runtime  \\\n",
       "0  Twenty-two years after the events of Jurassic ...     124   \n",
       "\n",
       "                                      genres  \\\n",
       "0  Action|Adventure|Science Fiction|Thriller   \n",
       "\n",
       "                                production_companies release_date vote_count  \\\n",
       "0  Universal Studios|Amblin Entertainment|Legenda...       6/9/15       5562   \n",
       "\n",
       "   vote_average  release_year    budget_adj   revenue_adj  \n",
       "0           6.5          2015  1.379999e+08  1.392446e+09  \n",
       "\n",
       "[1 rows x 21 columns]"
      ]
     },
     "execution_count": 149,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#see some data \n",
    "df.head(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "id": "210523c4-e3c6-404e-b850-dc6817fe060f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10866, 21)"
      ]
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     "execution_count": 150,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "# know more about data\n",
    "\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "id": "31b22ecd-629c-4d55-af65-2316f8583f5f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 10866 entries, 0 to 10865\n",
      "Data columns (total 21 columns):\n",
      " #   Column                Non-Null Count  Dtype  \n",
      "---  ------                --------------  -----  \n",
      " 0   id                    10866 non-null  int64  \n",
      " 1   imdb_id               10856 non-null  object \n",
      " 2   popularity            10866 non-null  float64\n",
      " 3   budget                10866 non-null  int64  \n",
      " 4   revenue               10866 non-null  int64  \n",
      " 5   original_title        10866 non-null  object \n",
      " 6   cast                  10790 non-null  object \n",
      " 7   homepage              2936 non-null   object \n",
      " 8   director              10822 non-null  object \n",
      " 9   tagline               8042 non-null   object \n",
      " 10  keywords              9373 non-null   object \n",
      " 11  overview              10862 non-null  object \n",
      " 12  runtime               10866 non-null  int64  \n",
      " 13  genres                10843 non-null  object \n",
      " 14  production_companies  9836 non-null   object \n",
      " 15  release_date          10866 non-null  object \n",
      " 16  vote_count            10866 non-null  int64  \n",
      " 17  vote_average          10866 non-null  float64\n",
      " 18  release_year          10866 non-null  int64  \n",
      " 19  budget_adj            10866 non-null  float64\n",
      " 20  revenue_adj           10866 non-null  float64\n",
      "dtypes: float64(4), int64(6), object(11)\n",
      "memory usage: 1.7+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "id": "94339d58-b667-48de-a7dc-0d1a61391d18",
   "metadata": {},
   "outputs": [],
   "source": [
    "# first i will drop some columns i dont need in my analysis\n",
    "df.drop(['imdb_id', 'homepage', 'keywords', 'tagline', 'overview', 'release_date'], axis= 1, inplace= True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "id": "6b5162ec-7dc6-4894-992b-5bb3cf2f2449",
   "metadata": {},
   "outputs": [
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       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>popularity</th>\n",
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       "      <th>revenue</th>\n",
       "      <th>original_title</th>\n",
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       "      <th>vote_average</th>\n",
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       "      <th>budget_adj</th>\n",
       "      <th>revenue_adj</th>\n",
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       "      <th>10865</th>\n",
       "      <td>22293</td>\n",
       "      <td>0.035919</td>\n",
       "      <td>19000</td>\n",
       "      <td>0</td>\n",
       "      <td>Manos: The Hands of Fate</td>\n",
       "      <td>Harold P. Warren|Tom Neyman|John Reynolds|Dian...</td>\n",
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      "text/plain": [
       "          id  popularity  budget  revenue            original_title  \\\n",
       "10865  22293    0.035919   19000        0  Manos: The Hands of Fate   \n",
       "\n",
       "                                                    cast          director  \\\n",
       "10865  Harold P. Warren|Tom Neyman|John Reynolds|Dian...  Harold P. Warren   \n",
       "\n",
       "       runtime  genres production_companies  vote_count  vote_average  \\\n",
       "10865       74  Horror            Norm-Iris          15           1.5   \n",
       "\n",
       "       release_year     budget_adj  revenue_adj  \n",
       "10865          1966  127642.279154          0.0  "
      ]
     },
     "execution_count": 153,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# re-check \n",
    "df.tail(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "id": "47eda559-eef6-44f1-bcf0-ef861f3f32fc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10866, 15)"
      ]
     },
     "execution_count": 154,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "id": "40b54075-28f3-445b-bcdc-59336032883b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# i saw some data have 0 value at revenue column i will delete them all as they will mislead me \n",
    "df.drop(df[df['revenue'] == 0].index, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "id": "656bdf8a-fe16-4936-81d0-93b4b9b9c3ee",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4850, 15)"
      ]
     },
     "execution_count": 156,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "id": "304e2af0-2fbf-41ba-8f52-f933456bd70f",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
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       "      <th>popularity</th>\n",
       "      <th>budget</th>\n",
       "      <th>revenue</th>\n",
       "      <th>original_title</th>\n",
       "      <th>cast</th>\n",
       "      <th>director</th>\n",
       "      <th>runtime</th>\n",
       "      <th>genres</th>\n",
       "      <th>production_companies</th>\n",
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       "      <th>vote_average</th>\n",
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       "Empty DataFrame\n",
       "Columns: [id, popularity, budget, revenue, original_title, cast, director, runtime, genres, production_companies, vote_count, vote_average, release_year, budget_adj, revenue_adj]\n",
       "Index: []"
      ]
     },
     "execution_count": 157,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "#re-check if there 0 values \n",
    "df.query('revenue == 0')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "id": "e1eaa2de-f57a-4a60-938d-2b5029876933",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 158,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#check for duplicates\n",
    "sum(df.duplicated())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "id": "fe0af447-8f53-4c6d-b765-3885b2f64d75",
   "metadata": {},
   "outputs": [],
   "source": [
    "#droping this duplicate\n",
    "df.drop_duplicates(inplace= True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "id": "75a3bdf8-8026-4017-85c0-9032b572ad75",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 160,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#re-check for duplicates\n",
    "sum(df.duplicated())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "id": "4c7173a6-0681-4ecd-b8da-fe56ecf3a2c1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id                       0\n",
       "popularity               0\n",
       "budget                   0\n",
       "revenue                  0\n",
       "original_title           0\n",
       "cast                     5\n",
       "director                 1\n",
       "runtime                  0\n",
       "genres                   0\n",
       "production_companies    96\n",
       "vote_count               0\n",
       "vote_average             0\n",
       "release_year             0\n",
       "budget_adj               0\n",
       "revenue_adj              0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 161,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#checking for nan-values\n",
    "df.isna().sum()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "id": "0f82a7df-19d7-4d7d-9743-ca1a8e43db5d",
   "metadata": {},
   "outputs": [],
   "source": [
    "#droping NAN values\n",
    "df.dropna(inplace= True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "id": "a0b97cfa-bb19-4523-a76b-a89bf265822e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id                      0\n",
       "popularity              0\n",
       "budget                  0\n",
       "revenue                 0\n",
       "original_title          0\n",
       "cast                    0\n",
       "director                0\n",
       "runtime                 0\n",
       "genres                  0\n",
       "production_companies    0\n",
       "vote_count              0\n",
       "vote_average            0\n",
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       "budget_adj              0\n",
       "revenue_adj             0\n",
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     "execution_count": 163,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "#re-check \n",
    "df.isna().sum()"
   ]
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  {
   "cell_type": "code",
   "execution_count": 164,
   "id": "80cda3ce-9a91-449d-97a4-e5703248981e",
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   "outputs": [
    {
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     "execution_count": 164,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "df.shape"
   ]
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  {
   "cell_type": "code",
   "execution_count": 165,
   "id": "9082a031-9456-4b55-b95e-8d43b60865ef",
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   "outputs": [
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       "      <td>3.927333</td>\n",
       "      <td>0</td>\n",
       "      <td>29355203</td>\n",
       "      <td>Mr. Holmes</td>\n",
       "      <td>Ian McKellen|Milo Parker|Laura Linney|Hattie M...</td>\n",
       "      <td>Bill Condon</td>\n",
       "      <td>103</td>\n",
       "      <td>Mystery|Drama</td>\n",
       "      <td>BBC Films|See-Saw Films|FilmNation Entertainme...</td>\n",
       "      <td>425</td>\n",
       "      <td>6.4</td>\n",
       "      <td>2015</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.700677e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>339527</td>\n",
       "      <td>3.358321</td>\n",
       "      <td>0</td>\n",
       "      <td>22354572</td>\n",
       "      <td>Solace</td>\n",
       "      <td>Abbie Cornish|Jeffrey Dean Morgan|Colin Farrel...</td>\n",
       "      <td>Afonso Poyart</td>\n",
       "      <td>101</td>\n",
       "      <td>Crime|Drama|Mystery</td>\n",
       "      <td>Eden Rock Media|FilmNation Entertainment|Flynn...</td>\n",
       "      <td>474</td>\n",
       "      <td>6.2</td>\n",
       "      <td>2015</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.056620e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>284289</td>\n",
       "      <td>2.272044</td>\n",
       "      <td>0</td>\n",
       "      <td>45895</td>\n",
       "      <td>Beyond the Reach</td>\n",
       "      <td>Michael Douglas|Jeremy Irvine|Hanna Mangan Law...</td>\n",
       "      <td>Jean-Baptiste LÃ©onetti</td>\n",
       "      <td>95</td>\n",
       "      <td>Thriller</td>\n",
       "      <td>Furthur Films</td>\n",
       "      <td>81</td>\n",
       "      <td>5.5</td>\n",
       "      <td>2015</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.222338e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>301875</td>\n",
       "      <td>1.959765</td>\n",
       "      <td>0</td>\n",
       "      <td>2000000</td>\n",
       "      <td>Equals</td>\n",
       "      <td>Kristen Stewart|Nicholas Hoult|Guy Pearce|Jack...</td>\n",
       "      <td>Drake Doremus</td>\n",
       "      <td>101</td>\n",
       "      <td>Drama|Romance|Science Fiction</td>\n",
       "      <td>Scott Free Productions|Infinite Frameworks Stu...</td>\n",
       "      <td>135</td>\n",
       "      <td>5.6</td>\n",
       "      <td>2015</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.839999e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>258509</td>\n",
       "      <td>1.841779</td>\n",
       "      <td>0</td>\n",
       "      <td>233755553</td>\n",
       "      <td>Alvin and the Chipmunks: The Road Chip</td>\n",
       "      <td>Jason Lee|Justin Long|Bella Thorne|Matthew Gra...</td>\n",
       "      <td>Walt Becker</td>\n",
       "      <td>92</td>\n",
       "      <td>Adventure|Animation|Comedy|Family</td>\n",
       "      <td>Regency Enterprises|Fox 2000 Pictures|Sunswept...</td>\n",
       "      <td>278</td>\n",
       "      <td>5.7</td>\n",
       "      <td>2015</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.150550e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10764</th>\n",
       "      <td>11455</td>\n",
       "      <td>0.608679</td>\n",
       "      <td>0</td>\n",
       "      <td>41590893</td>\n",
       "      <td>Up in Smoke</td>\n",
       "      <td>Cheech Marin|Tommy Chong|Strother Martin|Edie ...</td>\n",
       "      <td>Lou Adler</td>\n",
       "      <td>86</td>\n",
       "      <td>Comedy</td>\n",
       "      <td>Paramount Pictures</td>\n",
       "      <td>103</td>\n",
       "      <td>6.5</td>\n",
       "      <td>1978</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.390259e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10766</th>\n",
       "      <td>23916</td>\n",
       "      <td>0.591799</td>\n",
       "      <td>0</td>\n",
       "      <td>7185518</td>\n",
       "      <td>Paradise Alley</td>\n",
       "      <td>Sylvester Stallone|Lee Canalito|Armand Assante...</td>\n",
       "      <td>Sylvester Stallone</td>\n",
       "      <td>107</td>\n",
       "      <td>Action|Adventure|Drama</td>\n",
       "      <td>Image Ten</td>\n",
       "      <td>12</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1978</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.401904e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10812</th>\n",
       "      <td>31948</td>\n",
       "      <td>0.318883</td>\n",
       "      <td>0</td>\n",
       "      <td>5438927</td>\n",
       "      <td>Gray Lady Down</td>\n",
       "      <td>Charlton Heston|David Carradine|Stacy Keach|Ne...</td>\n",
       "      <td>David Greene</td>\n",
       "      <td>111</td>\n",
       "      <td>Thriller|Adventure|History|Drama</td>\n",
       "      <td>Universal Pictures|The Mirisch Corporation</td>\n",
       "      <td>10</td>\n",
       "      <td>6.1</td>\n",
       "      <td>1978</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.818071e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10817</th>\n",
       "      <td>13963</td>\n",
       "      <td>0.064029</td>\n",
       "      <td>0</td>\n",
       "      <td>321952</td>\n",
       "      <td>The Last Waltz</td>\n",
       "      <td>Robbie Robertson|Rick Danko|Levon Helm|Richard...</td>\n",
       "      <td>Martin Scorsese</td>\n",
       "      <td>117</td>\n",
       "      <td>Documentary|Music</td>\n",
       "      <td>FM Productions|Last Waltz Inc.</td>\n",
       "      <td>33</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1978</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.076189e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10819</th>\n",
       "      <td>16214</td>\n",
       "      <td>0.044675</td>\n",
       "      <td>0</td>\n",
       "      <td>78000000</td>\n",
       "      <td>Hooper</td>\n",
       "      <td>Burt Reynolds|Robert Klein|Adam West|Jan-Micha...</td>\n",
       "      <td>Hal Needham</td>\n",
       "      <td>107</td>\n",
       "      <td>Action|Comedy</td>\n",
       "      <td>Warner Bros.</td>\n",
       "      <td>13</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1978</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.607307e+08</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>945 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           id  popularity  budget    revenue  \\\n",
       "30     280996    3.927333       0   29355203   \n",
       "36     339527    3.358321       0   22354572   \n",
       "72     284289    2.272044       0      45895   \n",
       "88     301875    1.959765       0    2000000   \n",
       "95     258509    1.841779       0  233755553   \n",
       "...       ...         ...     ...        ...   \n",
       "10764   11455    0.608679       0   41590893   \n",
       "10766   23916    0.591799       0    7185518   \n",
       "10812   31948    0.318883       0    5438927   \n",
       "10817   13963    0.064029       0     321952   \n",
       "10819   16214    0.044675       0   78000000   \n",
       "\n",
       "                               original_title  \\\n",
       "30                                 Mr. Holmes   \n",
       "36                                     Solace   \n",
       "72                           Beyond the Reach   \n",
       "88                                     Equals   \n",
       "95     Alvin and the Chipmunks: The Road Chip   \n",
       "...                                       ...   \n",
       "10764                             Up in Smoke   \n",
       "10766                          Paradise Alley   \n",
       "10812                          Gray Lady Down   \n",
       "10817                          The Last Waltz   \n",
       "10819                                  Hooper   \n",
       "\n",
       "                                                    cast  \\\n",
       "30     Ian McKellen|Milo Parker|Laura Linney|Hattie M...   \n",
       "36     Abbie Cornish|Jeffrey Dean Morgan|Colin Farrel...   \n",
       "72     Michael Douglas|Jeremy Irvine|Hanna Mangan Law...   \n",
       "88     Kristen Stewart|Nicholas Hoult|Guy Pearce|Jack...   \n",
       "95     Jason Lee|Justin Long|Bella Thorne|Matthew Gra...   \n",
       "...                                                  ...   \n",
       "10764  Cheech Marin|Tommy Chong|Strother Martin|Edie ...   \n",
       "10766  Sylvester Stallone|Lee Canalito|Armand Assante...   \n",
       "10812  Charlton Heston|David Carradine|Stacy Keach|Ne...   \n",
       "10817  Robbie Robertson|Rick Danko|Levon Helm|Richard...   \n",
       "10819  Burt Reynolds|Robert Klein|Adam West|Jan-Micha...   \n",
       "\n",
       "                      director  runtime                             genres  \\\n",
       "30                 Bill Condon      103                      Mystery|Drama   \n",
       "36               Afonso Poyart      101                Crime|Drama|Mystery   \n",
       "72     Jean-Baptiste LÃ©onetti       95                           Thriller   \n",
       "88               Drake Doremus      101      Drama|Romance|Science Fiction   \n",
       "95                 Walt Becker       92  Adventure|Animation|Comedy|Family   \n",
       "...                        ...      ...                                ...   \n",
       "10764                Lou Adler       86                             Comedy   \n",
       "10766       Sylvester Stallone      107             Action|Adventure|Drama   \n",
       "10812             David Greene      111   Thriller|Adventure|History|Drama   \n",
       "10817          Martin Scorsese      117                  Documentary|Music   \n",
       "10819              Hal Needham      107                      Action|Comedy   \n",
       "\n",
       "                                    production_companies  vote_count  \\\n",
       "30     BBC Films|See-Saw Films|FilmNation Entertainme...         425   \n",
       "36     Eden Rock Media|FilmNation Entertainment|Flynn...         474   \n",
       "72                                         Furthur Films          81   \n",
       "88     Scott Free Productions|Infinite Frameworks Stu...         135   \n",
       "95     Regency Enterprises|Fox 2000 Pictures|Sunswept...         278   \n",
       "...                                                  ...         ...   \n",
       "10764                                 Paramount Pictures         103   \n",
       "10766                                          Image Ten          12   \n",
       "10812         Universal Pictures|The Mirisch Corporation          10   \n",
       "10817                     FM Productions|Last Waltz Inc.          33   \n",
       "10819                                       Warner Bros.          13   \n",
       "\n",
       "       vote_average  release_year  budget_adj   revenue_adj  \n",
       "30              6.4          2015         0.0  2.700677e+07  \n",
       "36              6.2          2015         0.0  2.056620e+07  \n",
       "72              5.5          2015         0.0  4.222338e+04  \n",
       "88              5.6          2015         0.0  1.839999e+06  \n",
       "95              5.7          2015         0.0  2.150550e+08  \n",
       "...             ...           ...         ...           ...  \n",
       "10764           6.5          1978         0.0  1.390259e+08  \n",
       "10766           6.0          1978         0.0  2.401904e+07  \n",
       "10812           6.1          1978         0.0  1.818071e+07  \n",
       "10817           8.0          1978         0.0  1.076189e+06  \n",
       "10819           6.0          1978         0.0  2.607307e+08  \n",
       "\n",
       "[945 rows x 15 columns]"
      ]
     },
     "execution_count": 165,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# see budget has 0 value\n",
    "df.query('budget == 0')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "id": "4e70348b-1b11-4de5-8513-65c824e45ac8",
   "metadata": {},
   "outputs": [],
   "source": [
    "#droping\n",
    "df.drop(df[df['budget'] == 0].index, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "id": "f511b86b-9942-4a1e-bad0-d00c288bd8e2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3805, 15)"
      ]
     },
     "execution_count": 167,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "id": "31d84086-d7e5-4635-8f07-91c5bef8cdca",
   "metadata": {},
   "outputs": [],
   "source": [
    "#we have numbers in millions that is dificult to read lits simplify it\n",
    "large_numbers_columns = ['budget', 'revenue', 'budget_adj', 'revenue_adj']\n",
    "for c in large_numbers_columns:\n",
    "    df[c] = df[c].apply(lambda x : x/1000000)\n",
    "    df[c] = df[c].round(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "id": "f875a2ee-4ef7-4393-a369-4b9c89f890d2",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>popularity</th>\n",
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       "       id  popularity  budget   revenue  original_title  \\\n",
       "0  135397   32.985763   150.0  1513.529  Jurassic World   \n",
       "\n",
       "                                                cast         director  \\\n",
       "0  Chris Pratt|Bryce Dallas Howard|Irrfan Khan|Vi...  Colin Trevorrow   \n",
       "\n",
       "   runtime                                     genres  \\\n",
       "0      124  Action|Adventure|Science Fiction|Thriller   \n",
       "\n",
       "                                production_companies  vote_count  \\\n",
       "0  Universal Studios|Amblin Entertainment|Legenda...        5562   \n",
       "\n",
       "   vote_average  release_year  budget_adj  revenue_adj  \n",
       "0           6.5          2015       138.0     1392.446  "
      ]
     },
     "execution_count": 169,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "id": "1f0aa2f8-691a-4bba-a9cc-b5f7b7a08c65",
   "metadata": {},
   "outputs": [
    {
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      ],
      "text/plain": [
       "       id  popularity  budget   revenue  original_title  \\\n",
       "0  135397   32.985763   150.0  1513.529  Jurassic World   \n",
       "\n",
       "                                                cast         director  \\\n",
       "0  [Chris Pratt, Bryce Dallas Howard, Irrfan Khan...  Colin Trevorrow   \n",
       "\n",
       "   runtime                                          genres  \\\n",
       "0      124  [Action, Adventure, Science Fiction, Thriller]   \n",
       "\n",
       "                                production_companies  vote_count  \\\n",
       "0  [Universal Studios, Amblin Entertainment, Lege...        5562   \n",
       "\n",
       "   vote_average  release_year  budget_adj  revenue_adj  \n",
       "0           6.5          2015       138.0     1392.446  "
      ]
     },
     "execution_count": 170,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# some columns has \"|\" character in its value \n",
    "col = ['cast', 'genres', 'production_companies']\n",
    "for co in col:\n",
    "    df[co]= df[co].str.split('|')\n",
    "df.head(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "12a577dd-35dc-46aa-ac43-896969d5d301",
   "metadata": {},
   "source": [
    "# Exploratory Data Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "id": "fa34fb33-4e43-4006-a1e6-e52b8d405da3",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    .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>id</th>\n",
       "      <th>popularity</th>\n",
       "      <th>budget</th>\n",
       "      <th>revenue</th>\n",
       "      <th>runtime</th>\n",
       "      <th>vote_count</th>\n",
       "      <th>vote_average</th>\n",
       "      <th>release_year</th>\n",
       "      <th>budget_adj</th>\n",
       "      <th>revenue_adj</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>3805.000000</td>\n",
       "      <td>3805.000000</td>\n",
       "      <td>3805.000000</td>\n",
       "      <td>3805.000000</td>\n",
       "      <td>3805.000000</td>\n",
       "      <td>3805.000000</td>\n",
       "      <td>3805.000000</td>\n",
       "      <td>3805.000000</td>\n",
       "      <td>3805.000000</td>\n",
       "      <td>3805.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>40040.372668</td>\n",
       "      <td>1.203784</td>\n",
       "      <td>37.607997</td>\n",
       "      <td>108.973387</td>\n",
       "      <td>109.351117</td>\n",
       "      <td>534.159001</td>\n",
       "      <td>6.170565</td>\n",
       "      <td>2001.229172</td>\n",
       "      <td>44.719757</td>\n",
       "      <td>138.715932</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>67585.708616</td>\n",
       "      <td>1.480569</td>\n",
       "      <td>42.321791</td>\n",
       "      <td>177.297597</td>\n",
       "      <td>19.845678</td>\n",
       "      <td>883.757588</td>\n",
       "      <td>0.792437</td>\n",
       "      <td>11.329616</td>\n",
       "      <td>44.886979</td>\n",
       "      <td>216.997314</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>5.000000</td>\n",
       "      <td>0.010335</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>2.200000</td>\n",
       "      <td>1960.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>5925.000000</td>\n",
       "      <td>0.470651</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>14.334000</td>\n",
       "      <td>96.000000</td>\n",
       "      <td>74.000000</td>\n",
       "      <td>5.700000</td>\n",
       "      <td>1995.000000</td>\n",
       "      <td>13.546000</td>\n",
       "      <td>19.254000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>11253.000000</td>\n",
       "      <td>0.810805</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>46.217000</td>\n",
       "      <td>106.000000</td>\n",
       "      <td>209.000000</td>\n",
       "      <td>6.200000</td>\n",
       "      <td>2004.000000</td>\n",
       "      <td>30.384000</td>\n",
       "      <td>62.847000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>38579.000000</td>\n",
       "      <td>1.387163</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>126.070000</td>\n",
       "      <td>119.000000</td>\n",
       "      <td>584.000000</td>\n",
       "      <td>6.700000</td>\n",
       "      <td>2010.000000</td>\n",
       "      <td>60.842000</td>\n",
       "      <td>165.805000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>417859.000000</td>\n",
       "      <td>32.985763</td>\n",
       "      <td>425.000000</td>\n",
       "      <td>2781.506000</td>\n",
       "      <td>338.000000</td>\n",
       "      <td>9767.000000</td>\n",
       "      <td>8.400000</td>\n",
       "      <td>2015.000000</td>\n",
       "      <td>425.000000</td>\n",
       "      <td>2827.124000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  id   popularity       budget      revenue      runtime  \\\n",
       "count    3805.000000  3805.000000  3805.000000  3805.000000  3805.000000   \n",
       "mean    40040.372668     1.203784    37.607997   108.973387   109.351117   \n",
       "std     67585.708616     1.480569    42.321791   177.297597    19.845678   \n",
       "min         5.000000     0.010335     0.000000     0.000000    15.000000   \n",
       "25%      5925.000000     0.470651    10.000000    14.334000    96.000000   \n",
       "50%     11253.000000     0.810805    25.000000    46.217000   106.000000   \n",
       "75%     38579.000000     1.387163    50.000000   126.070000   119.000000   \n",
       "max    417859.000000    32.985763   425.000000  2781.506000   338.000000   \n",
       "\n",
       "        vote_count  vote_average  release_year   budget_adj  revenue_adj  \n",
       "count  3805.000000   3805.000000   3805.000000  3805.000000  3805.000000  \n",
       "mean    534.159001      6.170565   2001.229172    44.719757   138.715932  \n",
       "std     883.757588      0.792437     11.329616    44.886979   216.997314  \n",
       "min      10.000000      2.200000   1960.000000     0.000000     0.000000  \n",
       "25%      74.000000      5.700000   1995.000000    13.546000    19.254000  \n",
       "50%     209.000000      6.200000   2004.000000    30.384000    62.847000  \n",
       "75%     584.000000      6.700000   2010.000000    60.842000   165.805000  \n",
       "max    9767.000000      8.400000   2015.000000   425.000000  2827.124000  "
      ]
     },
     "execution_count": 171,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# lets know more about data statistics\n",
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "id": "03ebe841-6618-4853-b151-99ff52d36760",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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XL+fMmTNkZ2c7+389/fTTZGRkaPBkkf+n0YSs7smYF154ge+++44xY8Zw4403kpCQwMCBA5k3bx4FBQX06dOHnJwc1q9fj8PhIC4ujiFDhjifjJk5cyabN28mOzublJSUtiqbiIh4gJSUlAZ/+zV4ssj3Gk3I9GSMiIiIiPs1mpDZbDaAek/GZGZmuv3JGHXEVBzeHIeIiMilarJTvxlPxqgjpuJoqzhc0RFTRESktRodh6zuyZjExETGjx8PfP9kDEBhYSH9+/cnIiKC4uJiHA4HZWVlFzwZU7duc5+MEREREbmcNNpCpidjRERERNyv0YRMT8aIiIiIuJ+mThIRERExmRIyEREREZMpIRMRERExmRIyEREREZMpIRMRERExmRIyEREREZM1OVK/iDSuqqqKuXPncujQISorK5k+fTo9evQgOTkZi8VCz549SUtLw8fHh/z8fPLy8vD19WX69OkMHz6ciooKEhMTOXHiBDabjczMTEJDQ80uloiItCG1kIm00qZNmwgJCSE3N5eVK1eSnp7OggULSEhIIDc3F8MwKCgo4NixY+Tk5JCXl8eqVavIysqisrKSNWvWEB4eTm5uLqNHj3YOwiwiIpcPtZCJtNKIESOIjo52vrZarezfv58BAwYAEBUVxY4dO/Dx8aFv3774+/vj7+9PWFgYJSUlFBcXM2XKFOe6SshERC4/SshEWslmswFgt9uZNWsWCQkJZGZmYrFYnO+XlZVht9sJCgqq9zm73V5ved26TbFaLYSEdHJ5WdzxnY2xWn3afJst5S2xekucIlKfEjIRFzh8+DAzZswgLi6OUaNG8cILLzjfKy8vJzg4mMDAQMrLy+stDwoKqre8bt2m1NQYnDp1tsH3unQJanB5c1zsO90lJKRTm2+zpbwlVlfH2ZrjSUSaT33IRFrp+PHjTJ48mcTERMaPHw9Ar169KCoqAqCwsJD+/fsTERFBcXExDoeDsrIyDhw4QHh4OJGRkWzfvt25br9+/Uwri4iImEMtZCKttHz5cs6cOUN2draz/9fTTz9NRkYGWVlZdO/enejoaKxWK/Hx8cTFxWEYBrNnz6ZDhw7ExsaSlJREbGwsfn5+LFq0yOQSiYhIW1NCJtJKKSkppKSkXLD81VdfvWBZTEwMMTEx9ZYFBASwZMkSt8UnIiKeT7csRUREREymhExERETEZErIREREREymhExERETEZErIREREREymhExERETEZErIREREREymhExERETEZErIREREREymhExERETEZErIREREREymhExERNrEnj17iI+PB6C0tJTY2Fji4uJIS0ujtrYWgPz8fMaOHUtMTAxbt24FoKKigpkzZxIXF8fUqVM5efKkaWUQcRclZCIi4nYrV64kJSUFh8MBwIIFC0hISCA3NxfDMCgoKODYsWPk5OSQl5fHqlWryMrKorKykjVr1hAeHk5ubi6jR48mOzvb5NKIuJ4SMhERcbuwsDCWLl3qfL1//34GDBgAQFRUFDt37mTv3r307dsXf39/goKCCAsLo6SkhOLiYoYNG+Zcd9euXaaUQcSdmpWQqZlZRERaIzo6Gl9fX+drwzCwWCwA2Gw2ysrKsNvtBAUFOdex2WzY7fZ6y+vWFWlvfJtaYeXKlWzatImAgADg+2bmgQMHMm/ePAoKCujTpw85OTmsX78eh8NBXFwcQ4YMcTYzz5w5k82bN5OdnU1KSorbCyUiIp7Nx+f79oDy8nKCg4MJDAykvLy83vKgoKB6y+vWbQ6r1UJISCfXBg4u+06r1cct8SkG74yhyYSsrpn5qaeeAi5sZt6xYwc+Pj7OZmZ/f/96zcxTpkxxrqv7/iIiAtCrVy+KiooYOHAghYWFDBo0iIiICBYvXozD4aCyspIDBw4QHh5OZGQk27dvJyIigsLCQvr169esbdTUGJw6dbbB97p0CWpweXNc7DsvVUhIJ5d9l2IwN4bWHE91mkzIoqOj+eabb5yv1cwsIiKtlZSURGpqKllZWXTv3p3o6GisVivx8fHExcVhGAazZ8+mQ4cOxMbGkpSURGxsLH5+fixatMjs8EVcrsmE7MfUzOwZTZuKw3PjEJGGde3alfz8fACuv/56Xn311QvWiYmJISYmpt6ygIAAlixZ0iYxipjlkhMyNTN7RvOq4nBNHK5oZhYREWmtS07I1MwsIiIi4lrNSsjUzCzStD179vDiiy+Sk5NDaWkpycnJWCwWevbsSVpaGj4+PuTn55OXl4evry/Tp09n+PDhVFRUkJiYyIkTJ7DZbGRmZhIaGmp2cUREpA1pYFgRF9Ao5CIi0hpKyERcQKOQi4hIayghE3EBjUIuIiKtccmd+kWkae4eHsbTh4ZpLm8aqsRbYvWWOEWkPiVkIm7g7uFhPH1omObylCFTmsNbYnV1nBoaRqRtKCETcQMNDyPSPlVU1bQoST3nqMZ+5pwbIpL2QgmZiItoeBiR9q+jn5Xrkjdf8ue+WngPdjfEI+2HOvWLiIiImEwJmYiIiIjJlJCJiIiImEwJmYiIiIjJlJCJiIiImEwJmYiIiIjJlJCJiIiImEwJmYiIiIjJlJCJiIiImEwJmYiIiIjJlJCJiIiImOyymctSE8KKiIiIp7psEjJNCCsiIiKeSrcsRUREREymhExERETEZErIREREREymhExERETEZJdNp34RERGzXOxJ/6ae/teT/pcPJWQiIiJupif9pSm6ZSkiIiJiMrWQiQigwZNFPJHq5eVDCVkTdN9fLhe6pSLieVQvLx9uT8hqa2uZP38+n332Gf7+/mRkZNCtWzd3b9ZlWloZStJHtOlVTWBwAAEdLv3PWVFVQ0c/6yV/Tgmna3l7PRFxN9URae/cnpC9++67VFZWsnbtWnbv3s3ChQtZtmyZuzdrurZK5H64bkuvonT1Zb7LtZ6INJfqyKW5lFudP1xPF9vmcXtCVlxczLBhwwDo06cP+/btc/cmvVprmqfb0o8re3Mrvip7w7y5nrS0j0vdZ0Waw5vriBl0q9P7WAzDMNy5gaeffpo777yTW265BYBbb72Vd999F19fdV8TqaN6ItI41RFp79w+7EVgYCDl5eXO17W1tapAIj+ieiLSONURae/cnpBFRkZSWFgIwO7duwkPD3f3JkW8juqJSONUR6S9c/sty7onYz7//HMMw+D555/nhhtucOcmRbyO6olI41RHpL1ze0ImIiIiIo3T1EkiIiIiJlNCJiIiImIyr3hExV0jNO/Zs4cXX3yRnJwcSktLSU5OxmKx0LNnT9LS0vDx8SE/P5+8vDx8fX2ZPn06w4cPp6KigsTERE6cOIHNZiMzM5PQ0FB2797Nc889h9VqZejQoTz22GMA/PGPf2Tbtm34+voyd+5cIiIiAKiqqmLu3LkcOnSIyspKpk+fTo8ePdo0jpqaGlJSUjh48CBWq5UFCxZgGEab74s6J06cYOzYsfzpT3/C19fXtDi8kTeMZN6cOme2S6mXZruU+ivm1ZGWnmtcobXnGVdo7XnGlVp6jmkThhfYsmWLkZSUZBiGYXz88cfGtGnTWv2dr7zyijFy5EhjwoQJhmEYxiOPPGJ88MEHhmEYRmpqqvH2228bR48eNUaOHGk4HA7jzJkzzv//6U9/MpYsWWIYhmG88cYbRnp6umEYhnHvvfcapaWlRm1trTFlyhRj3759xr59+4z4+HijtrbWOHTokDF27FhnDOvWrTMyMjIMwzCMkydPGrfcckubx/HOO+8YycnJhmEYxgcffGBMmzbNlH1hGIZRWVlpPProo8add95pfPnll6bF4a3cUU9cqTl1zhM0t156gubWXznPjDrSmnONK7T2POMKrT3PuEprzjFtwSsum9wxQnNYWBhLly51vt6/fz8DBgwAICoqip07d7J371769u2Lv78/QUFBhIWFUVJSUi+eqKgodu3ahd1up7KykrCwMCwWC0OHDmXXrl0UFxczdOhQLBYLP/3pT6mpqeHkyZMAjBgxgscff9wZg9VqbfM4br/9dtLT0wH49ttv6dy5syn7AiAzM5OJEydy1VVXmfY38WaePpJ5c+qcJ2huvfQEza2/cp4ZdaQ15xpXaO15xhVae55xldacY9qCVyRkdrudwMBA52ur1Up1dXWrvjM6OrreoIKGYWCxWACw2WyUlZVht9sJCvp+ShibzYbdbq+3/Ifr/jDGppbX/T8wMBC73c6sWbNISEgwJQ5fX1+SkpJIT08nOjralBg2bNhAaGio88fSrL+JN3NHPXGl5tQ5T9DceukpmlN/5Twz6khrzjWu0NrzjKu05jzjCq09x7QFr0jI2mKE5h/2sSgvLyc4OPiC7ZaXlxMUFFRveWPrNvYddQ4fPsyDDz7Ifffdx6hRo0yLIzMzky1btpCamorD4WjzGNavX8/OnTuJj4/n008/JSkpqV6rVVvuC2/lbSOZN3Sse4rm1EtP0lT9lfM8oY5cym+8q7TmPONKLT3PuEJrzzFtwSsSsrYYoblXr14UFRUBUFhYSP/+/YmIiKC4uBiHw0FZWRkHDhwgPDycyMhItm/f7ly3X79+BAYG4ufnx9dff41hGLz//vv079+fyMhI3n//fWpra/n222+pra0lNDQUgOPHjzN58mQSExMZP368KXFs3LiRFStWABAQEIDFYqF3795tvi9Wr17Nq6++Sk5ODjfddBOZmZlERUW1eRzezNtGMm/oWPcEza2XnqC59VfO84Q6cim/8a7Q2vOMK7T2POMKrT3HtAWvGBjWXSM0f/PNNzzxxBPk5+dz8OBBUlNTqaqqonv37mRkZGC1WsnPz2ft2rUYhsEjjzxCdHQ0586dIykpiWPHjuHn58eiRYvo0qULu3fv5vnnn6empoahQ4cye/ZsAJYuXUphYSG1tbXMmTPH+QOZkZHBW2+9Rffu3Z0xPf3002RkZLRZHGfPnmXOnDkcP36c6upqpk6dyg033NDm++KH4uPjmT9/Pj4+PqbG4W28YSTz5tQ5s11KvTTbpdRfMa+OtPRc4wqtPc+4QmvPM67WknNMW/CKhExERESkPfOKW5YiIiIi7ZkSMhERERGTKSETERERMZkSMhERERGTKSETkXbttddeY/Xq1QCsWbOGV155xeSIREQupIRMRNq14uJiKioqAIiNjeV3v/udyRGJuMfevXuZN2+e2WE0yw8vlOQ8zx3Gux0rKiriueeeo1OnTpSXl/P444+zYsUKqqqq6NixI0lJSURERHDbbbfx8ssv07t3bwASEhIYMGAAcXFxLFu2jLfffpva2lquvfZa0tLSuPrqq4mPj6dPnz784x//4PDhwwwePJj09HS+/fZbRo0axccffwycHxfnh69fe+011qxZQ21tLSEhIaSmpnrcGFZy+flxXamuruatt95yvpeens4bb7zB0qVLOXToEMeOHePQoUNcffXVvPDCC+zZs4f33nuPHTt20LFjR06ePMl3333HvHnzuO222xg5ciQffPABp0+fZsqUKfzjH/9g//79+Pr6smzZMq6++mqOHDnCs88+y+HDh6mqquKee+5h2rRpJu8ZkQt9+eWXHDlyxOwwmqW4uJiePXuaHYZHUUJmki+++IJ3332XqqoqZs6cyX//939zxRVX8MUXX/Db3/6Wt99+m3HjxrFhwwZ69+7N6dOn2bVrF+np6WzcuJHPP/+c1157DV9fX9auXUtKSgorV64E4OuvvyYnJ4ezZ89y11138eGHH9K1a9eLxvLhhx+yceNGVq9eTUBAAO+//z6PPfaY88QnYqa6uvLNN984JyhuyN///nc2btxIYGAg06ZNIy8vj1mzZlFQUEDPnj2ZNGlSvUmeARwOB/n5+bz55pv8/ve/5y9/+Qs33ngjM2bM4C9/+QvTpk0jMTGRhx56iNtuuw2Hw8HUqVMJCwvj7rvvdnfR5TL3+9//nptvvpnJkycDkJuby4cffsjgwYPJycnBx8eHzp07k5qaSseOHVmyZAllZWXMmTOHBQsW8N5777Fs2bJ6F/t9+/ZtdJsHDx5k3rx5nDx5Eh8fH6ZPn87dd9/NF198wbPPPsupU6ewWCxMnjyZ0aNH17swgpZdKE2aNMnt+9IbKCEzyTXXXMO1117L6tWrOXr0KA899JDzPYvFwtdff824ceMYP348ycnJvPHGG9x2220EBQWxdetWPvnkE8aNGwecH3363Llzzs8PHz4cHx8fAgMD6datG6dPn240Idu2bRulpaVMnDjRuezMmTOcOnWKkJAQl5dd5FLU1ZVvvvmm0fUGDBjgnDi6V69enD59usnvvvPOOwH42c9+RufOnbnxxhsBCAsL4/Tp05w9e5aPPvqI06dP89JLLwHnRx0vKSlRQiZuN2HCBJ577jlnQvaXv/yFxx9/nGeeeYa1a9cSGhrKhg0bmDFjBps3b2bWrFls2bKFBQsW8NVXX/Ef//EfDV7sd+rU6aLbfOKJJxg/fjyTJk3i8OHDxMfHExUVxfTp03nqqae48847OXLkCBMmTKBbt25NlqE5F0pynhIyk9RViNraWgYPHszixYud7x0+fJirrroKq9VKr1692LZtGxs2bGDu3LnOz0yZMoW4uDgAKisr6518Onbs6Py/xWJxzmj/w0kZqqqqnP+vra3lvvvuIzEx0fn66NGj/OQnP3F9wUUuUV1daewYhoaP+6b4+/s7/+/n53fB+7W1tRiGQV5eHgEBAQCcPHmSDh06XFohRFpg4MCBOBwOPvnkEwICAjh58iQ7duzg7rvvds6/O3bsWJ577rkLLlh27Nhx0Yv9uguPHzt16hQlJSVMmDABOH8x9O677/Lll1/icDicFzBXX301d955J3/7298YOHBgo2VoyYXS5Uqd+k02ePBgduzYwYEDBwDYvn079957r7MTckxMDCtXruTcuXP069cPgKFDh7Ju3TrsdjsAL730Ek899VSj2wkODqaqqoovv/wSgM2bNzvfGzp0KJs3b+bo0aPA+SfRfvOb37i2oCKtFBoayrfffsuJEycwDKPeMdwYq9VKdXV1i7YZGBhInz59+POf/wycbzmOjY2loKCgRd8nciksFgvjx4/n9ddfZ/369YwfP77BCw3DMC44xusu9l9//XXnv/z8/Eb7bfn6+jq3W+ef//wnNTU19Zb9cJvuuFC6XCkhM1mPHj149tlneeKJJ7j33nt56aWXWLZsGTabDYDbbruNQ4cOOa9Y4Hwz9q233kpMTAz33HMPn332GQsXLmx0O0FBQSQmJjJ16lTGjRtX7wp/6NChTJ06lcmTJzNq1CjeeOMN/vjHP15QAUXM1KNHDyZOnMi4ceOIiYlp9Db8D0VFRZGXl8eKFStatN0XX3yRPXv2MGrUKCZMmMDIkSO59957W/RdIpdqzJgxvPfee2zZsoWxY8cybNgw3nzzTU6ePAnA+vXrCQkJoVu3bvUuPpq62G9IYGAgN998Mxs3bgTO362JjY0lODgYX19f3n77bQCOHDnCli1b+PWvf23KhVJ7pcnFRUREPNhjjz1GdXU1y5cvB2D16tXk5eVRW1tLaGgo8+bNo2fPnpSWljJ16lTCw8P54x//yFtvvcXy5csxDANfX1/mzp1L//79G91WaWkpzzzzDMePH8disTBz5kxuv/12SkpKyMjI4PTp09TU1BAfH09sbCwAmZmZvPXWW3Tp0oVbb72Vt956y9mpv+6pZqDe6y1btrBw4UImTpzII4884t4d6CWUkImIiIiYTJ36RURELhObNm1i1apVDb43atQopkyZ0sYRSR21kImIiIiYTJ36RUREREymhExERETEZB7Zh6y2tpaamobvpFqtlou+503aSznAu8vi52c1O4QWqa2tBfCa/e5tx4ji/Z631hHwrnOJ4mmcp8fjinrikQlZTY3BqVNnG3wvJKTTRd/zJu2lHODdZenSJcjsEFqk7ofAW/a7tx0jivd73lpHwLvOJYqncZ4ejyvqiW5ZioiIiJhMCZmIiIiIyZSQiYiIiJhMCZmIiIiIyTyyU39jamhZ57lzjmrsZ865PiCRy1xgcAABHZr+KWmo3qpeill0LhFP43UJWUc/K9clN282+R/6auE92N0Qj8jlLqCDb4vqJKheinl0LhFPo1uWIiIiIiZTQiYiIiJiMq+7ZSkiIt6npqaGlJQUDh48iNVqZcGCBRiGQXJyMhaLhZ49e5KWloaPjw/5+fnk5eXh6+vL9OnTGT58OBUVFSQmJnLixAlsNhuZmZmEhoaaXSwRl1ELmYiIuN3WrVsByMvLY9asWSxYsIAFCxaQkJBAbm4uhmFQUFDAsWPHyMnJIS8vj1WrVpGVlUVlZSVr1qwhPDyc3NxcRo8eTXZ2tsklEnEttZCJiIjb3X777dx6660AfPvtt3Tu3Jlt27YxYMAAAKKiotixYwc+Pj707dsXf39//P39CQsLo6SkhOLiYqZMmeJcVwmZtDdKyEREpE34+vqSlJTEO++8w5IlS9i6dSsWiwUAm81GWVkZdrudoKDvh6Ow2WzY7fZ6y+vWbYrVaiEkpJPLy+GO77RafdzyvS2leBrnjniUkImISJvJzMzkySefJCYmBofD4VxeXl5OcHAwgYGBlJeX11seFBRUb3nduk1pbHLx1kwG7Y5Jrj198myzeXo8bTK5eE1NDXPmzGHixIlMmjSJr7/+mtLSUmJjY4mLiyMtLY3a2loA8vPzGTt2LDExMc7+AhUVFcycOZO4uDimTp3KyZMnWx20iIh4l40bN7JixQoAAgICsFgs9O7dm6KiIgAKCwvp378/ERERFBcX43A4KCsr48CBA4SHhxMZGcn27dud6/br18+0soi4Q5MtZD/siFlUVOR8MiYhIYGBAwcyb948CgoK6NOnDzk5Oaxfvx6Hw0FcXBxDhgxxdsScOXMmmzdvJjs7m5SUFLcXTEREPMedd97JnDlzmDRpEtXV1cydO5cbbriB1NRUsrKy6N69O9HR0VitVuLj44mLi8MwDGbPnk2HDh2IjY0lKSmJ2NhY/Pz8WLRokdlFEnGpJhMydcQUaVxVVRVz587l0KFDVFZWMn36dHr06KHH+UV+oFOnTrz00ksXLH/11VcvWBYTE0NMTEy9ZQEBASxZssRt8YmYrVnDXtR1xExPTyc6OhrDMNzaEVPEm2zatImQkBByc3NZuXIl6enpepxfREQuSbM79bdlR0xvejKmpTztiZHWaE9laYkRI0YQHR3tfG21Wtm/f79akUVEpNmaTMg2btzIkSNHeOSRRy7oiDlw4EAKCwsZNGgQERERLF68GIfDQWVl5QUdMSMiIprdEdObnoxpKU97YqQ1vLksrngyxmazAWC325k1axYJCQlkZma6/XF+cM+FS1vzxDJ420WGt8UrIhdqMiFTR0yRph0+fJgZM2YQFxfHqFGjeOGFF5zvuetxfrj4hUtbam1S6wll+DFvu8hwZ7yuuGgRkaY1mZCpI6ZI444fP87kyZOZN28egwcPBqBXr15ubUUWEZH2RQPDirTS8uXLOXPmDNnZ2c7+X08//TQZGRlqRRYRkWZRQibSSikpKQ2OradWZBERaa5mDXshIiIiIu6jhExERETEZErIREREREymhExERETEZErIREREREymhExERETEZErIREREREymhExERETEZErIREREREymhExERETEZErIREREREymhExERETEZErIREREREymhExERETEZErIREREREymhEzERfbs2UN8fDwA+/fvZ9iwYcTHxxMfH8+bb74JQH5+PmPHjiUmJoatW7cCUFFRwcyZM4mLi2Pq1KmcPHnStDKIiIg5fM0OQKQ9WLlyJZs2bSIgIACA//3f/+W3v/0tkydPdq5z7NgxcnJyWL9+PQ6Hg7i4OIYMGcKaNWsIDw9n5syZbN68mezsbFJSUswqioiImEAtZCIuEBYWxtKlS52v9+3bx7Zt25g0aRJz587Fbrezd+9e+vbti7+/P0FBQYSFhVFSUkJxcTHDhg0DICoqil27dplVDBERMYlayERcIDo6mm+++cb5OiIiggkTJtC7d2+WLVvGyy+/zI033khQUJBzHZvNht1ux263O5fbbDbKysqa3J7VagEshIR0cnlZ2ponlsFq9fHIuC7G0+Otqqpi7ty5HDp0iMrKSqZPn06PHj1ITk7GYrHQs2dP0tLS8PHxIT8/n7y8PHx9fZk+fTrDhw+noqKCxMRETpw4gc1mIzMzk9DQULOLJeJSSshE3OCOO+4gODjY+f/09HT69+9PeXm5c53y8nKCgoIIDAx0Li8vL3d+rjE1NQZgcOrUWbfEfym6dAlqeqVGeEIZfiwkpJNHxnUx7oy3tX9fgE2bNhESEsILL7zAd999x5gxY7jxxhtJSEhg4MCBzJs3j4KCAvr06aPb+nLZ0i1LETd4+OGH2bt3LwC7du3i5ptvJiIiguLiYhwOB2VlZRw4cIDw8HAiIyPZvn07AIWFhfTr18/M0EVcbsSIETz++OPO11arlf379zNgwADg/K36nTt36ra+XNYabSFTM7NIy8yfP5/09HT8/Pzo3Lkz6enpBAYGEh8fT1xcHIZhMHv2bDp06EBsbCxJSUnExsbi5+fHokWLzA5fxKVsNhsAdrudWbNmkZCQQGZmJhaLxfl+WVlZvdv3dctbelsfzt/ad8etXHd8p6fddlY8jXNHPI0mZGpmFmm+rl27kp+fD8DNN99MXl7eBevExMQQExNTb1lAQABLlixpkxhFzHL48GFmzJhBXFwco0aN4oUXXnC+V3er/oe37+uWt/S2Ppy/tX+xW7mtuRXrjtvDnnabXPE07sfxuOLWfqO3LNXMLCIirXX8+HEmT55MYmIi48ePB6BXr14UFRUB52/V9+/fX7f15bLWaEJms9kIDAys18xsGIbbm5lFRKT9WL58OWfOnCE7O9s5WHJCQgJLly7l/vvvp6qqiujoaLp06eK8rf+b3/ym3m39L774gtjYWNauXctjjz1mdpFEXK7JpyzNaGb2pvv+LeVp98Nboz2VRURcLyUlpcHuKq+++uoFy3RbXy5XjSZkdc3M8+bNY/DgwcD3zcwDBw6ksLCQQYMGERERweLFi3E4HFRWVl7QzBwREXFJzczedN+/pTztfnhreHNZXHHfX0REpLUaTch+2MycnZ0NwNNPP01GRgZZWVl0796d6OhorFarnh4TERERaaFGEzI1M4uIiIi4n0bqFxHTVFTVtOi28TlHNfYz59wQkYiIOZSQiYhpOvpZuS558yV/7quF92B3QzwiImbR1EkiIiIiJlNCJiIiImIyJWQiIiIiJlNCJiIiImIyJWQiIiIiJlNCJiIiImIyJWQiLrJnzx7i4+MBKC0tJTY2lri4ONLS0qitrQUgPz+fsWPHEhMTw9atWwGoqKhg5syZxMXFMXXqVE6ePGlaGURExBxKyERcYOXKlaSkpOBwOABYsGABCQkJ5ObmYhgGBQUFHDt2jJycHPLy8li1ahVZWVlUVlayZs0awsPDyc3NZfTo0c5pykRE5PKhhEzEBcLCwli6dKnz9f79+xkwYAAAUVFR7Ny5k71799K3b1/8/f0JCgoiLCyMkpISiouLGTZsmHPdXbt2mVIGERExjxIyEReIjo7G1/f7iS8Mw8BisQBgs9koKyvDbrcTFPT9NEE2mw273V5ved26IiJyedHUSSJu4OPz/bVOeXk5wcHBBAYGUl5eXm95UFBQveV16zbFarUAFkJCOrk8dm/hzrJbrT5etW+9LV4RuZASMhE36NWrF0VFRQwcOJDCwkIGDRpEREQEixcvxuFwUFlZyYEDBwgPDycyMpLt27cTERFBYWEh/fr1a/L7a2oMwODUqbPuL0wTWjI5uCu4s+whIZ08Yt82lzvjNevvK3K5UUIm4gZJSUmkpqaSlZVF9+7diY6Oxmq1Eh8fT1xcHIZhMHv2bDp06EBsbCxJSUnExsbi5+fHokWLzA5fRETamBIyERfp2rUr+fn5AFx//fW8+uqrF6wTExNDTExMvWUBAQEsWbKkTWIUERHPpE79IiIiIiZTQiYiIiJiMiVkIiIiIiZTQiYiIiJiMiVkIiIiIiZTQiYiIm1iz549xMfHA1BaWkpsbCxxcXGkpaVRW1sLQH5+PmPHjiUmJoatW7cCUFFRwcyZM4mLi2Pq1KmcPHnStDKIuIsSMhERcbuVK1eSkpKCw+EAYMGCBSQkJJCbm4thGBQUFHDs2DFycnLIy8tj1apVZGVlUVlZyZo1awgPDyc3N5fRo0eTnZ1tcmlEXK9ZCZmuakREpDXCwsJYunSp8/X+/fsZMGAAAFFRUezcuZO9e/fSt29f/P39CQoKIiwsjJKSEoqLixk2bJhz3V27dplSBhF3ajIh01WNiIi0VnR0NL6+349FbhgGFosFAJvNRllZGXa7naCg76dqstls2O32esvr1hVpb5ocqb/uquapp54CLryq2bFjBz4+Ps6rGn9//3pXNVOmTHGuq4RMREQAfHy+bw8oLy8nODiYwMBAysvL6y0PCgqqt7xu3eawWi1umXTdHd/paRPEK57GuSOeJhOy6OhovvnmG+frtriq8aZK1FKednC1Rnsqi4i0jV69elFUVMTAgQMpLCxk0KBBREREsHjxYhwOB5WVlRw4cIDw8HAiIyPZvn07ERERFBYW0q9fv2Zto6bGuOik662ZNN0dE7l72oT2iqdxP46nNcdTnUuey7Itrmq8qRK1lKcdXK3hzWVxRSUSkUuXlJREamoqWVlZdO/enejoaKxWK/Hx8cTFxWEYBrNnz6ZDhw7ExsaSlJREbGwsfn5+LFq0yOzwRVzukhOytriqERGR9qdr167k5+cDcP311/Pqq69esE5MTAwxMTH1lgUEBLBkyZI2iVHELJeckOmqRkRERMS1mpWQ6apGRERExH0uuYVMRJpv9OjRzgdbunbtyrRp00hOTsZisdCzZ0/S0tLw8fEhPz+fvLw8fH19mT59OsOHDzc5chERaUtKyETcpG7svpycHOeyadOmkZCQwMCBA5k3bx4FBQX06dOHnJwc1q9fj8PhIC4ujiFDhuDv729W6CIi0saUkIm4SUlJCefOnWPy5MlUV1fzxBNPXNI4fhERESaXQERE2ooSMhE36dixIw8//DATJkzgq6++YurUqZc0jp+IiFw+lJCJuMn1119Pt27dsFgsXH/99YSEhLB//37n+02N49cYq9UCuGcAZW/hzrJ722DH3haviFxICZmIm6xbt47PP/+c+fPnc+TIEex2O0OGDGn2OH6NqakxgIsPoNyWzBpc151l97bBjt0ZrwZPFmkbSshE3GT8+PHMmTOH2NhYLBYLzz//PFdccUWzx/ETEZHLhxIyETfx9/dvcDDk5o7jJyIilw+fplcREREREXdSQiYiIiJiMiVkIiIiIiZTQiYiIiJiMiVkIiIiIiZTQiYiIiJiMiVkIiIiIiZTQiYiIiJiMg0MKyIi0kwVVTUtmk7qnKMa+5lzbohI2gslZCIiIs3U0c/KdcmbL/lzXy28B7sb4pH247JJyHRVIyIiIp7qsknIdFUjIiIinuqySchEpP1Qi7eItDdKyETE66jFW0TaGyVkIiIibtacVt2G3ler7uXD7QlZbW0t8+fP57PPPsPf35+MjAy6devm7s2KeBXVE5HGeXsdUauuNMXtCdm7775LZWUla9euZffu3SxcuJBly5a5e7Muo74q0ha8vZ6IuNvlWkd0Drp8uD0hKy4uZtiwYQD06dOHffv2uXuTLtXSq5qS9BFqnpZm84R6EhgcQECH9t2L4VJObj9cT/XSfJ5QR8yglrXLh9t/fe12O4GBgc7XVquV6upqfH0vvmk/P2ujP5pfLbynRbG09edaIqCDLwEtuBoyW0uu4OR7l1pP/PysgOfs99bUEdVL1/CUY8FdLudzSUs/19pjwtOOqfYej9vnsgwMDKS8vNz5ura2ttEKJHI5Uj0RaZzqiLR3bk/IIiMjKSwsBGD37t2Eh4e7e5MiXkf1RKRxqiPS3lkMwzDcuYG6J2M+//xzDMPg+eef54YbbnDnJkW8juqJSONUR6S9c3tCJiIiIiKNc/stSxERERFpnBIyEREREZN5xSMq3jZC8549e3jxxRfJycmhtLSU5ORkLBYLPXv2JC0tDR8fH/Lz88nLy8PX15fp06czfPhwKioqSExM5MSJE9hsNjIzMwkNDTWlDFVVVcydO5dDhw5RWVnJ9OnT6dGjh1eWpT3x9LrQ0mO/rbX2+G5rNTU1pKSkcPDgQaxWKwsWLMAwDI+N11O1df1p6Dj7t3/7N6ZNm8Z1110HQGxsLHfffXeb/Y6OHj2aoKDzwzV07dqVadOmmfa7vmHDBv7yl78A4HA4+PTTT8nLyzNl/7j6vL17926ee+45rFYrQ4cO5bHHHms6CMMLbNmyxUhKSjIMwzA+/vhjY9q0aSZHdHGvvPKKMXLkSGPChAmGYRjGI488YnzwwQeGYRhGamqq8fbbbxtHjx41Ro4caTgcDuPMmTPO///pT38ylixZYhiGYbzxxhtGenq6aeVYt26dkZGRYRiGYZw8edK45ZZbvLYs7Ykn14XWHPttrbXHd1t75513jOTkZMMwDOODDz4wpk2b5tHxeqq2rj8NHWf5+fnGqlWr6q3XVr+jFRUVxn333Vdvmaf8rs+fP9/Iy8szZf+447x97733GqWlpUZtba0xZcoUY9++fU3G4RW3LL1phOawsDCWLl3qfL1//34GDBgAQFRUFDt37mTv3r307dsXf39/goKCCAsLo6SkpF45o6Ki2LVrlyllABgxYgSPP/6487XVavXasrQnnlwXWnPst7XWHt9t7fbbbyc9PR2Ab7/9ls6dO3t0vJ6qretPQ8fZvn372LZtG5MmTWLu3LnY7fY2+x0tKSnh3LlzTJ48mQcffJDdu3d7xO/6J598wpdffsn9999vyv5x9XnbbrdTWVlJWFgYFouFoUOHNis2r0jILjZCsyeKjo6uN1ihYRhYLBYAbDYbZWVl2O12Z5Nx3XK73V5ved26ZrHZbAQGBmK325k1axYJCQleW5b2xJPrQmuO/bbW2uPbDL6+viQlJZGenk50dLTHx+uJ2rr+NHScRURE8NRTT7F69Wp+9rOf8fLLL7fZ72jHjh15+OGHWbVqFc888wxPPvmkR/yur1ixghkzZgCYsn9cfd7+8XHW3Ni8IiHz5hGafXy+38Xl5eUEBwdfUJ7y8nKCgoLqLa9b10yHDx/mwQcf5L777mPUqFFeXZb2wpvqwqUcL2ZozfFtlszMTLZs2UJqaioOh6NeXJ4Yr6cxo/78+Di744476N27NwB33HEH//u//9tmv6PXX3899957LxaLheuvv56QkBBOnDhRb7tt/bt+5swZ/vnPfzJo0CAAU/dPndae6xpatzmxeUVC5s0jNPfq1YuioiIACgsL6d+/PxERERQXF+NwOCgrK+PAgQOEh4cTGRnJ9u3bnev269fPtLiPHz/O5MmTSUxMZPz48V5dlvbEm+rCpRwvba21x3db27hxIytWrAAgICAAi8VC7969PTZeT9XW9aeh4+zhhx9m7969AOzatYubb765zX5H161bx8KFCwE4cuQIdrudIUOGmPq7/tFHH/HrX//a+drM/VOntee6wMBA/Pz8+PrrrzEMg/fff5/+/fs3uV2vGBjW20Zo/uabb3jiiSfIz8/n4MGDpKamUlVVRffu3cnIyMBqtZKfn8/atWsxDINHHnmE6Ohozp07R1JSEseOHcPPz49FixbRpUsXU8qQkZHBW2+9Rffu3Z3Lnn76aTIyMryuLO2Jp9eFlh77ba21x3dbO3v2LHPmzOH48eNUV1czdepUbrjhBo/dv56qretPQ8dZQkICL7zwAn5+fnTu3Jn09HQCAwPb5He0srKSOXPm8O2332KxWHjyySe54oorTD1H/ed//ie+vr489NBDwPn+W+np6W2+f1x93t69ezfPP/88NTU1DB06lNmzZzcZg1ckZCIiIiLtmVfcshQRERFpz5SQiYiIiJhMCZmIiIiIyZSQiYiIiJhMCZmIiIiIyZSQtZG9e/cyb948s8MQ8WhLly7l2WefNTsMEY9QVFTEyJEjW/Udffv25Ztvvmnx51977TVWr17dqhh+6H/+53+Ij48H4KWXXmLjxo0u+25v55lDfLdDX375JUeOHDE7DBERkWYrLi6mZ8+ebvnuH87zKUrIWuz3v/89N998M5MnTwYgNzeXDz/8kMGDB5OTk4OPjw+dO3cmNTWVjh07smTJEsrKypgzZw4LFizgvffeY9myZVRVVdGxY0eSkpLo27dvo9vcunUrK1asoLKykpMnTzJ69GgSEhIuGsvixYsvup2lS5eye/dujh49ys9//nOSk5OZN28eJ06c4NixY1x77bUsXryYK6+8kr179zJ//nyqqqoICwvj22+/JTk5mYEDB7aoHHL5KSoq4rnnnqNTp06Ul5fz+OOPs2LFikaPmyNHjvDss89y+PBhqqqquOeee5g2bRoAy5cvp6CggIqKCufAjHfccQcHDhzg6aefprKyEsMwGD9+PJMmTQJg2bJlvP3229TW1nLttdeSlpbG1VdffdGYly1bxpdffsmiRYsA+Pvf/05GRgYbN27kH//4By+++CLnzp3Dx8eHxx57jOHDh3P27Fnmz59PaWkpp06dwmaz8eKLL9K9e3fi4+P5yU9+wj//+U9iY2OdrQQijTl79iyzZs2itLSU4OBgnn32WVasWEHPnj15+OGHAUhOTna+/vvf/056ejoWi4Vf/OIX1NbWOr/rlVdeYd26ddhsNvr3709BQQHvvfcelZWVvPjii3z00UfU1NTQq1cvUlJS2LVrF++99x47duygY8eOzrrUkIudn+B8S9hf//pXQkJC6Natm/MzP4xbAENaZNeuXcbIkSOdr8ePH2/87W9/M26//XbjxIkThmEYxvr164277rrLqK2tNdavX2/87ne/MwzDMA4ePGiMHDnSOHnypGEYhvH5558bQ4YMMcrLyy+6vdraWuOBBx4wDh48aBiGYfzf//2fcdNNNxknTpxoMJYdO3Y0up0lS5YY0dHRRlVVlWEYhvFf//VfxooVK5zbmjJlirFq1SqjqqrKiIqKMrZt2+Ys989//nPjgw8+aFE55PL0wQcfGDfeeKPxzTffNHlcPvPMM4ZhGEZ8fLxRUFBgGIZhVFRUGPHx8cbmzZuNb775xoiPjzfOnTtnGIZhvPHGG87jf86cOc7j+OjRo0ZCQoJRU1Nj/OUvfzESEhKcx3teXp4xZcqURmM+fvy4ERkZaXz33XeGYRhGYmKisWbNGuPUqVPGnXfeafzrX/8yDON8XYyKijIOHTpkvPXWW0Z6errzO1JTU41nn33WMAzDeOCBB4w5c+a0el/K5aOu3hQXFxuGcf64HT9+vJGUlGT853/+p3O9utcOh8P49a9/bezcudMwDMP461//aoSHhxv/+te/jMLCQiM6Oto4ffq0UVtba8yZM8cYPny4YRiGsXTpUmPhwoVGbW2tYRiGsWjRIiMtLa3edzemsfPTO++8Y9x9991GWVmZUVVVZfzud78zHnjggWZ/9+VELWQtNHDgQBwOB5988gkBAQGcPHmSHTt2cPfddxMaGgrA2LFjee655y64f79jxw6OHj3qnCoCwGKx8PXXX3PjjTc2uD2LxcLy5cvZtm0bb7zxBgcOHMAwDM6dO9dgLIMHDyY3N/ei2wHo06ePc2Ld3/zmN/z973/nz3/+M1999RVffPEFv/zlL/n8888BuOWWWwAYNGiQs/m6JeWQy9c111zDtddey+rVqxs9LuF8q8BHH33E6dOneemll5zLSkpKuPvuu/nDH/7AX//6V0pLS9mzZ49zIt877riDpKQk9u7dy+DBg0lJScHHx4etW7fyySefMG7cOOD8FDrnzp1rNN4rr7ySW2+9lddff53Ro0fz/vvvk5aWxt///neOHTvGjBkz6sX/2WefMWLECH72s5+Rk5NDaWkpH374Yb2Wv+bMZyfyQz//+c+JjIwEYMyYMcyfP5+rrrqqwXU///xzfH19GTx4MAAjR4509l3evn07I0aMcE5yPWnSJD744AMAtm3bRllZGTt37gSgqqqKK6+8stkxNnZ+2rVrF3fccQeBgYEAjBs3jpycnBbsifZPCVkLWSwWxo8fz+uvv46fnx/jx4/n9OnTF6xnGAbV1dX1ltXW1jJ48GAWL17sXHb48OGLVjI4fzIaM2YMt99+O/3792fcuHG8++67GIbRYCwWi6XR7bzzzjt06tTJufyFF15g7969jBs3joEDB1JdXY1hGFitVowfza5ltVpbXA65fNUdb00dl3XrGIZBXl4eAQEBAJw8eZIOHTqwf/9+Hn30UR566CGGDBnCr371K5555hkAhg8fzpYtW9i5cye7du3i5ZdfZsOGDdTW1jJlyhTi4uKA83P6NVRff2zSpEnMnz8fX19f7rzzTmw2GzU1Ndxwww289tprzvWOHDlCaGgoubm55OfnM2nSJEaNGkVISEi9C7If1jmR5vDxqf/sncViITg4uN7vclVVlfP/P/69rrvo9vX1rfde3e84nK9vc+fOdV54l5eX43A4mh1jY+enH8f0w+1KfXrKshXGjBnDe++9x5YtWxg7dizDhg3jzTff5OTJkwCsX7/eec/carU6E7PBgwezY8cODhw4AJy/crn33nupqKi46LZKS0ux2+0kJCRw2223UVRURGVlpbN/wI9judTtvP/++/zmN79h9OjRXHnllezcudN54vH396ewsBA4/7To559/jsViaVE5RJpz3AQGBtKnTx/+/Oc/A3DmzBliY2MpKCjgo48+onfv3vz2t79lwIABFBQUUFNTA5zv2/nmm29yzz33kJaWRmBgIF9//TVDhw5l3bp12O124HyflqeeeqrJWCMjI/Hx8WHVqlVMnDgRON+yXFpaykcffQTAp59+SnR0NEeOHOH9999nzJgxTJgwgeuvv5733nvPGZtIS3z22Wd8+umnAKxdu5Z+/fpxxRVXsG/fPuD8xcCHH34InG9NMwyD7du3A1BQUOC88Ljlllt4++23KSsrA2DdunXObQwdOpTVq1c7zympqalkZWUB1Dt3XUxj56eoqCj+53/+hzNnzlBbW8vrr7/uwr3TvqiFrBW6dOlCr169qK6u5uqrr+bqq6/moYce4je/+Q21tbWEhoayYsUKfHx86NOnDy+//DKPPfYYf/zjH3n22Wd54oknMAwDX19fli1bhs1mu+i2fv7zn3Prrbdy11134e/vT3h4OD169KC0tJSwsLALYgHo0aNHs7czY8YM/vCHP/DSSy/h5+dHZGQkX3/9Nb6+vixdupS0tDSysrK47rrr6Ny5Mx07dryk7xep09zj5sUXXyQ9PZ1Ro0ZRWVnJyJEjuffeezl+/Dhvv/02d911F7W1tQwfPpzTp09jt9t59NFHefrpp1m7di1Wq5Xbb7+dX/3qV/Tv358jR44QExODxWLhmmuuYeHChc2Kd+zYsbz55pvO2/ChoaEsWbKEP/zhDzgcDgzD4A9/+ANdu3Zl8uTJzJs3z3my69Onj/O2v0hLdO/enT/+8Y/861//4sorr2ThwoX4+Pjw5JNPEh0dTdeuXRk0aBAAfn5+vPzyy8yfP5+srCxuuukm563HwYMHExMTw/3330/Hjh3p2bOns/X50UcfJTMzkzFjxlBTU8NNN91EcnIyAFFRUc668sgjjzQYY2Pnp1tuuYXPPvuMcePGERwczI033sh3333n7t3mlSzGj9s3RX4kMzOThx9+mM6dO3P48GHuu+8+3n33XWdfBJH2qrq6mscee4x7772Xu+++2+xwRFrsk08+4eOPP+bBBx8E4M9//jN79uyp13WgrT322GP8+te/dnYluNyphcyDbNq0iVWrVjX43qhRo5gyZUobR3Tetddey0MPPeTsg5CRkaFkTNqF559/nqKiogbfi4mJYfHixdx+++2MGDGijSMTca3rr7+elStXkp+f72wlTk9Pb/bnXX1+euCBBzh27JizJU7UQiYiIiJiOnXqFxERETGZEjIRERERkykhExERETGZR3bqr62tpabGwGq1UFPjfV3cvDFub4wZWh+3n593DlJYV0ca4q1/y4a0p7KAd5bHW+sIeH49UQztJwZX1BOPTMhqagxOnTpLSEgnTp06a3Y4l8wb4/bGmKH1cXfpEuTCaNpOXR1piLf+LRvSnsoC3lkeb60j4Pn1RDG0nxhcUU90y1JERETEZErIREREREymhExERETEZB7Zh0zaTmBwAAEdzh8Gl3IP/JyjGvuZc+4Kyyvt2bOHF198kZycHEpLS0lOTsZisdCzZ0/S0tLw8fEhPz+fvLw8fH19mT59OsOHD6eiooLExEROnDiBzWYjMzOT0NBQs4sjXuiH9flSXI71uYaW9fu5HPeVtA0lZJe5gA6+XJe8+ZI/99XCe7C7IR5vtXLlSjZt2uScrHfBggUkJCQwcOBA5s2bR0FBAX369CEnJ4f169fjcDiIi4tjyJAhrFmzhvDwcGbOnMnmzZvJzs4mJSXF5BKJN1J9br6OflbtK/EoumUp4gJhYWEsXbrU+Xr//v0MGDAAgKioKHbu3MnevXvp27cv/v7+BAUFERYWRklJCcXFxQwbNsy57q5du0wpg4iImEctZCIuEB0dzTfffON8bRgGFosFAJvNRllZGXa7naCg72+R2Gw27HZ7veV16zbFarUQEtLpIu/5XPQ9b9OeygKeXR5PjUvkcqGETMQNfHy+b3wuLy8nODiYwMBAysvL6y0PCgqqt7xu3aZ4+vhKrtKeygLuL09rxkK6WFzePA6ZiDfRLUsRN+jVqxdFRUUAFBYW0r9/fyIiIiguLsbhcFBWVsaBAwcIDw8nMjKS7du3O9ft16+fmaGLiIgJ1EIm4gZJSUmkpqaSlZVF9+7diY6Oxmq1Eh8fT1xcHIZhMHv2bDp06EBsbCxJSUnExsbi5+fHokWLzA5fRETamBIyERfp2rUr+fn5AFx//fW8+uqrF6wTExNDTExMvWUBAQEsWbKkTWIUERHPpIRMRMTDtHQ8MRHxXqrxIiIepjXjiYmId2pWp/49e/YQHx8PQGlpKbGxscTFxZGWlkZtbS0A+fn5jB07lpiYGLZu3QpARUUFM2fOJC4ujqlTp3Ly5Ek3FUNERETEezWZkK1cuZKUlBQcDgfw/Qjkubm5GIZBQUEBx44dIycnh7y8PFatWkVWVhaVlZXOEchzc3MZPXo02dnZbi+QiIiIiLdpMiHTCOQiIiIi7tVkH7K2HoEcvh+F3JNHtW6Mt8Z9qTyhjJfLvhYRkfbtkjv1u3sEcvh+FHJvHaXbm+J2x8jebam1+1qjkIu4X1VVFXPnzuXQoUNUVlYyffp0evToQXJyMhaLhZ49e5KWloaPjw/5+fnk5eXh6+vL9OnTGT58OBUVFSQmJnLixAlsNhuZmZmEhoaaXSwRl7rkkfo1ArmIiFyKTZs2ERISQm5uLitXriQ9PV39kUV+5JJbyDQCuYiIXIoRI0YQHR3tfG21Wi/oj7xjxw58fHyc/ZH9/f3r9UeeMmWKc10lZNIeNSsh0wjkIiLSUjabDQC73c6sWbNISEggMzOzzfoju5qrvtMT+sAqBs+JQQPDioiI2x0+fJgZM2YQFxfHqFGjeOGFF5zvubs/ckM8of+sJ/Q3VgyuicEV/ZEvuQ+ZiIjIpTh+/DiTJ08mMTGR8ePHA+qPLPJjaiETERG3Wr58OWfOnCE7O9vZ/+vpp58mIyND/ZFF/h8lZCIi4lYpKSmkpKRcsFz9kUW+p1uWIiIiIiZTQiYiIiJiMiVkIiIiIiZTHzIRN9mwYQN/+ctfAHA4HHz66afk5eUxbdo0rrvuOgBiY2O5++67G5wuRkRELh9KyNqJwOAAAjroz+lJxo4dy9ixYwF45plnGDduHP/7v//Lb3/7WyZPnuxcr266mPXr1+NwOIiLi2PIkCH4+/ubFbqIiLQxncHbiYAOvlyXvPmSP/fVwnvcEI380CeffMKXX35JWloaaWlpHDx4kIKCArp168bcuXPZu3dvg9PFREREmB26iIi0EfUhE3GzFStWMGPGDAAiIiJ46qmnWL16NT/72c94+eWXLzpdjIiIXD7UQibiRmfOnOGf//wngwYNAuCOO+5wTvtyxx13kJ6eTv/+/RucLqYxjc3R5wlzsrlKeyoLeHZ5PDUukcuFEjJpkYqqmhbN3XXOUY39zDk3ROSZPvroI3796187Xz/88MOkpqYSERHBrl27uPnmm4mIiGDx4sU4HA4qKyud08U0prE5+jxhXjhXaU9lgeaXxxXz4l0qd8z5KCLNp4RMWqSjn7XFfdYup5txBw8epGvXrs7X8+fPJz09HT8/Pzp37kx6ejqBgYENThcjIiKXDyVkIm40ZcqUeq9vvvlm8vLyLlivoeliRETk8tGihEzjK4mIiIi4TosSMo2vJCIiIuI6rRr2om58pfvvv599+/axbds2Jk2axNy5c7Hb7fXGVwoKCnKOryQiIiIi32tVH7Ifj680YcIEevfuzbJly3j55Ze58cYbWzS+Ut0j/Z78iHhjvDXutuLKfaN9LSIi7UGLEzJ3ja8E3z/S762PvJsRtzc9mu7KfdPafe1N+01ERNqvFt+ybGh8pb179wLUG1+puLgYh8NBWVlZs8ZXEhEREbnctLiFTOMriYiIiLhGixMyja8kIiIi4hqaXFxERETEZErIREREREymhExERETEZErIREREREymhExERETEZK0aqV9ERC4uMDiAgA71f2Y1GLGINEQJmYiImwR08OW65M2X/LmvFt7jhmhExJPplqWIiIiIyZSQiYiIiJhMCZmIiIiIydSHTMSNRo8eTVDQ+U7cXbt2Zdq0aSQnJ2OxWOjZsydpaWn4+PiQn59PXl4evr6+TJ8+neHDh5scuYiItCUlZCJu4nA4AMjJyXEumzZtGgkJCQwcOJB58+ZRUFBAnz59yMnJYf369TgcDuLi4hgyZAj+/v5mhS4iIm1MCZmIm5SUlHDu3DkmT55MdXU1TzzxBPv372fAgAEAREVFsWPHDnx8fOjbty/+/v74+/sTFhZGSUkJERERJpdARETaihIyETfp2LEjDz/8MBMmTOCrr75i6tSpGIaBxWIBwGazUVZWht1ud97WrFtut9sb/W6r1UJISKeLvOdz0fe8TXsqi6fTfhYxlxIyETe5/vrr6datGxaLheuvv56QkBD279/vfL+8vJzg4GACAwMpLy+vt/yHCVpDamoMTp062+B7ISGdLvqet/H2snjTILAX28/eVAYRb9bihEydlUUat27dOj7//HPmz5/PkSNHsNvtDBkyhKKiIgYOHEhhYSGDBg0iIiKCxYsX43A4qKys5MCBA4SHh5sdvoiItKEWJWTqrCzStPHjxzNnzhxiY2OxWCw8//zzXHHFFaSmppKVlUX37t2Jjo7GarUSHx9PXFwchmEwe/ZsOnToYHb48gMNTYEkIuJKLfqFUWdlkab5+/uzaNGiC5a/+uqrFyyLiYkhJiamLcKSFtAUSK6xZ88eXnzxRXJycigtLW32XZWKigoSExM5ceIENpuNzMxMQkNDzS6OiEu1KCFzZ2dl+L7Dsrd26PXWuNuKK/eN9rWId1i5ciWbNm0iICAAgAULFjT7rsqaNWsIDw9n5syZbN68mezsbFJSUkwukYhrtSghc2dnZfi+w7K3dug1I25v6njryn3T2n3tTftNxJuFhYWxdOlSnnrqKYBLuqtSXFzMlClTnOtmZ2ebVg4Rd2nR1Enr1q1j4cKFABd0VgYoLCykf//+REREUFxcjMPhoKysTJ2VRUQuU9HR0fj6ft8GcCl3VX64vG5dkfamRS1k6qwsIiKt4ePzfXtAU3dVfri8bt3maGy8vtZw1Xd6QpcLxeA5MbQoIVNnZRERaY1evXo1ewiYyMhItm/fTkREBIWFhfTr169Z22hsvL7WdFdwVbcLT+iWoxhcE4Mrur/oOW4REWlzSUlJzb6rEhsbS1JSErGxsfj5+TXYICDi7ZSQeRiNdyQi7VXXrl3Jz88Hzj8c1ty7KgEBASxZsqRNYhQxi878HkbjHYmIiFx+WvSUpYiIiIi4jhIyEREREZPplqWIiEgzVVTVtOiJunOOauxnzrkhImkvlJCJiIg0U0c/a4v7+TY9caBcznTLUkRERMRkSshERERETKZbltKmWtr/AtQHQ0RE2i8lZNKmWtr/AtQHQ0RE2i/dshQRERExmRIyEREREZPplqWIm1RVVTF37lwOHTpEZWUl06dP59/+7d+YNm0a1113HQCxsbHcfffd5Ofnk5eXh6+vL9OnT2f48OHmBi8iIm1KCZmIm2zatImQkBBeeOEFvvvuO8aMGcOMGTP47W9/y+TJk53rHTt2jJycHNavX4/D4SAuLo4hQ4bg7+9vYvQiItKWWpSQ6cpfpGkjRowgOjra+dpqtbJv3z4OHjxIQUEB3bp1Y+7cuezdu5e+ffvi7++Pv78/YWFhlJSUEBERYWL07VNgcAABHXQdKiKep0W/TLryF2mazWYDwG63M2vWLBISEqisrGTChAn07t2bZcuW8fLLL3PjjTcSFBRU73N2e+PPk1qtFkJCOl3kPZ+LvudtXF0Wv1aMst7etZdjRsRbtSgh05W/SPMcPnyYGTNmEBcXx6hRozhz5gzBwcEA3HHHHaSnp9O/f3/Ky8udnykvL6+XoDWkpsbg1KmzDb4XEtLpou95G1eXpaVj4F0OLraftc9E2kaLEjJ3XvnD91f/3nql761xe4Mf71dP3tfHjx9n8uTJzJs3j8GDBwPw8MMPk5qaSkREBLt27eLmm28mIiKCxYsX43A4qKys5MCBA4SHh5scvWfTrUcRaW9a/Ivmrit/+P7q31uv9FsTt65GG/fj/draY8Sd+3v58uWcOXOG7OxssrOzAUhOTub555/Hz8+Pzp07k56eTmBgIPHx8cTFxWEYBrNnz6ZDhw5ui6s9COjgq1uPItKutCgh05W/SNNSUlJISUm5YHleXt4Fy2JiYoiJiWmLsERExAO1KCHTlb+IiIiI67QoIdOVv4iIiIjraOokEREREZMpIRMRERExmRIyEREREZNpIB8RERE3q6iqaXCYnaaG3jnnqMZ+5py7whIPooRMRETEzTq2YtqupodTl/ZAtyxFRERETKaETERERMRkSshERERETKY+ZCJimuZMEq75XUXkcqCETERMo0nCRUTOU0LmJs258hcREREBJWRuoyt/uVzo4kNEpPX0KyoirdLSiw/QBYhIUy42oGxTNKCs91FCJl5DI12LyOVGA8pePpSQidfQD1Pz1NCyJxMrqmro6Gd1fUAiItIktydktbW1zJ8/n88++wx/f38yMjLo1q2buzcr4lVcWU9ak7iq36N4Kp1LpL1ze0L27rvvUllZydq1a9m9ezcLFy5k2bJl7t6sy6jDsrQFb68nIu6mOnJpLqXv2Q/XUxcP87g90yguLmbYsGEA9OnTh3379rl7ky6lpyWlLXh7PRFxN9WRS6MuHt7H7QmZ3W4nMDDQ+dpqtVJdXY2vb8s23dIWK/WPEU/m6noi0t6ojrSNlj7V2dJz7DlH9SV/pr2yGIZhuHMDCxYs4Je//CV33303AFFRURQWFrpzkyJeR/VEpHGqI9LeuX1y8cjISGel2b17N+Hh4e7epIjXUT0RaZzqiLR3bm8hq3sy5vPPP8cwDJ5//nluuOEGd25SxOuonog0TnVE2ju3J2QiIiIi0ji337IUERERkcYpIRMRERExmRIyEREREZN5ZEJWVVVFYmIicXFxjB8/noKCArNDarYTJ05wyy23cODAAbNDabYVK1Zw//33M3bsWF577TWzw2lSVVUVv//975k4cSJxcXFeta9ba8+ePcTHxwOwf/9+xo8fT1xcHOnp6dTW1gKwfft2YmJiiImJYf78+RiGQUVFBTNnziQuLo6pU6dy8uRJM4sBNK8sq1atYuzYsYwbN4533nkHwOPK0tDvVWlpKbGxscTFxZGWluYsT35+PmPHjiUmJoatW7cCnlee9qy2tpZ58+Zx//33Ex8fT2lpaZts94fH+qUcG67Q2uPTFWpqapgzZw4TJ05k0qRJfP31120eQ50fnqPNiuGiDA+0bt06IyMjwzAMwzh58qRxyy23mBtQM1VWVhqPPvqoceeddxpffvml2eE0ywcffGA88sgjRk1NjWG3240lS5aYHVKT3nnnHWPWrFmGYRjG+++/bzz22GMmR9Q2XnnlFWPkyJHGhAkTDMMwjDFjxhjFxcWGYRhGVlaWsXHjRqOsrMy45557jBMnTjg/c+LECeNPf/qT82/7xhtvGOnp6eYU4v9pTllOnz5t3HLLLYbD4TBOnTpl3HrrrYZhGB5XloZ+rx555BHjgw8+MAzDMFJTU423337bOHr0qDFy5EjD4XAYZ86ccf7f08rTnm3ZssVISkoyDMMwPv74Y2PatGlu3+aPj/VLOTZcobXHpyu88847RnJysmEY588506ZNa/MYDOPCc7QZMTTGI1vIRowYweOPP+58bbV6xwj7mZmZTJw4kauuusrsUJrt/fffJzw8nBkzZjBt2jRuvfVWs0Nq0vXXX09NTQ21tbXY7fbLZqTusLAwli5d6nx95MgRIiMjgfNjNBUXF/Pxxx8THh5OZmYmcXFxdO7cmdDQ0HrTzkRFRbFr1y5TylCnOWUJCAjgpz/9KefOnePcuXNYLBYAjytLQ79X+/fvZ8CAAcD5GHfu3MnevXvp27cv/v7+BAUFERYWRklJiceVpz0zY/qlHx/rl3JsuEJrj09XuP3220lPTwfg22+/pXPnzm0eA1x4jjYjhsZ4ZEJms9kIDAzEbrcza9YsEhISzA6pSRs2bCA0NNRZ2b3Fd999x759+3jppZd45plnePLJJzE8fCSUTp06cejQIe666y5SU1OdtwLau+jo6HrJ589+9jM+/PBDALZu3cq5c+f47rvvKCoq4sknn2TlypX8f//f/8fBgwex2+0EBZ2fDsVms1FWVmZKGeo0pywA11xzDffccw9jxozhwQcfBPC4sjT0e2UYhjOBrIvxh3HXLbfb7R5XnvbsYtMvudOPj/VLOTZcobXHp6v4+vqSlJREeno60dHRbR5DQ+doM/ZDYzwyIQM4fPgwDz74IPfddx+jRo0yO5wmrV+/np07dxIfH8+nn35KUlISx44dMzusJoWEhDB06FD8/f3p3r07HTp08Pg+LP/1X//F0KFD2bJlC6+//jrJyck4HA6zw2pzzz//PCtWrOB3v/sdV155JVdccQUhISH84he/oEuXLthsNvr378+nn35KYGAg5eXlAJSXlxMcHGxy9PU1VJbCwkKOHj1KQUEB27Zt491332Xv3r0eWZYf/175+Hz/01oX4w/jrlseFBTkkeVpr378N6itrW3zFvZLOTZcpTXHpytlZmayZcsWUlNT6/1mt0UMDZ2jf3iua8v9cDEemZAdP36cyZMnk5iYyPjx480Op1lWr17Nq6++Sk5ODjfddBOZmZl06dLF7LCa1K9fP/72t79hGAZHjhzh3LlzhISEmB1Wo4KDg50V5Cc/+QnV1dXU1NSYHFXb2759O88//zyvvPIKp06dYsiQIfTu3ZvPP/+ckydPUl1dzZ49e+jRoweRkZFs374dgMLCQvr162dy9PU1VJaf/OQndOzYEX9/fzp06EBQUBBnzpzxuLI09HvVq1cvioqKgPMx9u/fn4iICIqLi3E4HJSVlXHgwAHCw8M9rjztmSdMv3Qpx4YrtPb4dIWNGzeyYsUKAAICArBYLPTu3btNY2joHB0VFdWmMTTFIzvfLF++nDNnzpCdnU12djYAK1eupGPHjiZH1v4MHz6cjz76iPHjx2MYBvPmzfP4PnsPPfQQc+fOJS4ujqqqKmbPnk2nTp3MDqvNdevWjd/97ncEBAQwcOBAbrnlFgB+//vfM2XKFOB8/5Hw8HB+9rOfkZSURGxsLH5+fixatMjM0C9wsbLs3LmTmJgYfHx8iIyMZMiQIfTr18+jytLQ79XTTz9NRkYGWVlZdO/enejoaKxWK/Hx8cTFxWEYBrNnz6ZDhw7ExsZ6VHnaszvuuIMdO3YwceJE5/RLbS0pKYnU1NRmHRuu0Nrj0xXuvPNO5syZw6RJk6iurmbu3LnccMMNbbofGtLWf4umaOokEREREZN55C1LERERkcuJEjIRERERkykhExERETGZEjIRERERkykhkxZZtWoVycnJwPkndnbu3GlyRCIiIt7LI4e9EO/y3HPPmR2CiIiIV1MLmYsUFRVx7733MnHiREaNGsW7777LhAkTGD16NBMnTuTjjz+mpqaGW265pd78aQkJCeTm5gKwbNkyxowZw3333cejjz7KkSNHAIiPj2fRokVMmjSJ2267jaeffpra2lq++eYb+vbt6/yuH79+7bXXGDt2LKNHj+ahhx7iwIEDTZZj3bp1zriHDx/ujK2qqoq0tDTuuOMOJk6cyD/+8Q/nZ+Lj4/mf//mf1u1AERGRy5hayFzoiy++4N1336WqqoqZM2fy3//931xxxRV88cUX/Pa3v+Xtt99m3LhxbNiwgd69e3P69Gl27dpFeno6Gzdu5PPPP+e1117D19eXtWvXkpKSwsqVKwH4+uuvycnJ4ezZs9x11118+OGHdO3a9aKxfPjhh2zcuJHVq1cTEBDA+++/z2OPPcZbb7110c+Ul5fz2muv8corr3DFFVewe/dufvvb3xIXF0dubi5fffUVmzdvprq6mgceeKDNppMQERFp75SQudA111zDtddey+rVqzl69CgPPfSQ8z2LxcLXX3/NuHHjGD9+PMnJybzxxhvcdtttBAUFsXXrVj755BPGjRsHnJ9jrW6CZTg/or6Pjw+BgYF069aN06dPN5qQbdu2jdLSUiZOnOhcdubMGU6dOnXRqZFsNhvLly9n+/btfPXVV5SUlHD27FkAdu3axciRI/H398ff359Ro0bx2WeftWJviYiISB0lZC5UN31PbW0tgwcPZvHixc73Dh8+zFVXXYXVaqVXr15s27aNDRs2MHfuXOdnpkyZQlxcHACVlZWcPn3a+fkfThtlsVics9T/cKKFqqoq5/9ra2u57777SExMdL4+evQoP/nJTy4a///93/9x//33ExMTQ79+/RgxYgRbt25tcF1Pn15JRETEm6gPmRsMHjyYHTt2OPtsbd++nXvvvZeKigoAYmJiWLlyJefOnXNOJDx06FDWrVuH3W4H4KWXXuKpp55qdDvBwcFUVVXx5ZdfArB582bne0OHDmXz5s0cPXoUgDVr1vCb3/ym0e/bt28foaGhPProowwdOtSZjNXU1DBs2DA2btyIw+HA4XDw5ptvXupuERERkYtQC5kb9OjRg2effZYnnngCwzDw9fVl2bJl2Gw2AG677TaeeeYZpk6d6vzMhAkTOHLkCDExMVgsFq655hoWLlzY6HaCgoJITExk6tSphIaGMmLECOd7Q4cOZerUqUyePBmLxUJgYCB//OMfsVgsF/2+IUOGsG7dOkaMGIHFYmHAgAGEhoY6b31+/fXXjBw5kpCQELp169bKvSQiIiJ1NLm4tNqYMWNISEjglltuMTsUERERr6QWssvMf/7nf/LXv/61wfcefvhh7r333mZ/V3V1NXfffTeBgYHOW68iIiJy6dRCJiIiImIydeoXERERMZkSMhERERGTKSETERERMZlHduqvra2lpqbhrm1Wq+Wi73mT9lCO9lAGPz8NcCsiIubzyISspsbg1KmzDb4XEtLpou95k/ZQjvZQhi5dNB+niIiYT7csRUREREymhExERETEZErIREREREzmkX3IGlNDy/r9nHNUYz9zzvUBiYiIiLSS1yVkHf2sXJe8+ZI/99XCe7C7IR4RERGR1tItSxERERGTKSETERERMZkSMhERERGTKSETERERMVmTnfprampISUnh4MGDWK1WFixYgGEYJCcnY7FY6NmzJ2lpafj4+JCfn09eXh6+vr5Mnz6d4cOHU1FRQWJiIidOnMBms5GZmUloaGhblE1ERETEKzTZQrZ161YA8vLymDVrFgsWLGDBggUkJCSQm5uLYRgUFBRw7NgxcnJyyMvLY9WqVWRlZVFZWcmaNWsIDw8nNzeX0aNHk52d7fZCiYiIiHiTJlvIbr/9dm699VYAvv32Wzp37sy2bdsYMGAAAFFRUezYsQMfHx/69u2Lv78//v7+hIWFUVJSQnFxMVOmTHGuq4RMREREpL5mjUPm6+tLUlIS77zzDkuWLGHr1q1YLBYAbDYbZWVl2O12goK+H7DVZrNht9vrLa9btylWq4WQkE4tKU+j3PGdLWW1+nhUPC3RHsogIiLiCZo9MGxmZiZPPvkkMTExOBwO5/Ly8nKCg4MJDAykvLy83vKgoKB6y+vWbUpNjcGpU2cbfK8lo/TXudh3miEkpJNHxdMS7aEMrTmeREREXKXJPmQbN25kxYoVAAQEBGCxWOjduzdFRUUAFBYW0r9/fyIiIiguLsbhcFBWVsaBAwcIDw8nMjKS7du3O9ft16+fG4sjIiIi4n0shmEYja1w9uxZ5syZw/Hjx6murmbq1KnccMMNpKamUlVVRffu3cnIyMBqtZKfn8/atWsxDINHHnmE6Ohozp07R1JSEseOHcPPz49FixbRpUuXRoOqqqpptIWspVMnHTvW9O3SttIeWpfaQxnUQiYiIp6gyYTMDErIvEN7KIMSMhER8QQaGFZERETEZErIREREREymhExERETEZErIREREREymhExERETEZErIREREREymhExERETEZErIREREREymhExERETEZErIREREREymhExERETEZErIREREREymhExERETEZErIREREREymhExERETEZErIREREREymhExERETEZErIREREREymhExERETEZErIREREREymhExERETEZErIREREREymhExERETEZErIREREREzm29ibVVVVzJ07l0OHDlFZWcn06dPp0aMHycnJWCwWevbsSVpaGj4+PuTn55OXl4evry/Tp09n+PDhVFRUkJiYyIkTJ7DZbGRmZhIaGtpWZRMRERHxCo22kG3atImQkBByc3NZuXIl6enpLFiwgISEBHJzczEMg4KCAo4dO0ZOTg55eXmsWrWKrKwsKisrWbNmDeHh4eTm5jJ69Giys7PbqlwiIiIiXqPRFrIRI0YQHR3tfG21Wtm/fz8DBgwAICoqih07duDj40Pfvn3x9/fH39+fsLAwSkpKKC4uZsqUKc51lZCJiIiIXKjRhMxmswFgt9uZNWsWCQkJZGZmYrFYnO+XlZVht9sJCgqq9zm73V5ved26zWG1WggJ6dSiAjXGHd/ZUlarj0fF0xLtoQwiIiKeoNGEDODw4cPMmDGDuLg4Ro0axQsvvOB8r7y8nODgYAIDAykvL6+3PCgoqN7yunWbo6bG4NSpsw2+16VLUIPLm+Ni32mGkJBOHhVPS7SHMrTmeBIREXGVRvuQHT9+nMmTJ5OYmMj48eMB6NWrF0VFRQAUFhbSv39/IiIiKC4uxuFwUFZWxoEDBwgPDycyMpLt27c71+3Xr5+biyMiIiLifRptIVu+fDlnzpwhOzvb2f/r6aefJiMjg6ysLLp37050dDRWq5X4+Hji4uIwDIPZs2fToUMHYmNjSUpKIjY2Fj8/PxYtWtQmhRIRERHxJhbDMAyzg/ixqqqaRm9ZXpe8+ZK/86uF93DsWPP6sLWF9nC7rz2UQbcsRUTEE2hgWBERERGTKSETERERMZkSMhERERGTKSETERERMZkSMhERERGTKSETERERMZkSMhERERGTKSETERERMZkSMhERERGTKSETERERMZkSMhERERGTKSETERERMZkSMhERERGTKSETERERMZkSMhERERGTKSETERERMZkSMhERERGTKSETERERMZkSMhERERGTKSETERERMZkSMhERERGTKSETERERMZkSMhERERGTKSETERERMVmzErI9e/YQHx8PQGlpKbGxscTFxZGWlkZtbS0A+fn5jB07lpiYGLZu3QpARUUFM2fOJC4ujqlTp3Ly5Ek3FUNERETEezWZkK1cuZKUlBQcDgcACxYsICEhgdzcXAzDoKCggGPHjpGTk0NeXh6rVq0iKyuLyspK1qxZQ3h4OLm5uYwePZrs7Gy3F0hERETE2zSZkIWFhbF06VLn6/379zNgwAAAoqKi2LlzJ3v37qVv3774+/sTFBREWFgYJSUlFBcXM2zYMOe6u3btclMxRERERLxXkwlZdHQ0vr6+zteGYWCxWACw2WyUlZVht9sJCgpyrmOz2bDb7fWW160rIiIiIvX5Nr1KfT4+3+dw5eXlBAcHExgYSHl5eb3lQUFB9ZbXrdscVquFkJBOlxpak9zxnS1ltfp4VDwt0R7KICIi4gkuOSHr1asXRUVFDBw4kMLCQgYNGkRERASLFy/G4XBQWVnJgQMHCA8PJzIyku3btxMREUFhYSH9+vVr1jZqagxOnTrb4HtdugQ1uLw5LvadZggJ6eRR8bREeyhDa44nERERV7nkhCwpKYnU1FSysrLo3r070dHRWK1W4uPjiYuLwzAMZs+eTYcOHYiNjSUpKYnY2Fj8/PxYtGiRO8ogIiIi4tUshmEYZgfxY1VVNY22kF2XvPmSv/Orhfdw7Jjn9GFrD61L7aEMaiETERFPoIFhRUREREymhExERETEZErIREREREymhExERETEZErIREREREymhExERETEZErIREREREymhExERETEZErIREREREymhExERETEZErIREREREymhExERETEZErIREREREymhExERETEZErIREREREymhExERETEZErIREREREzma3YAbaWiqoYuXYIu+XPnHNXYz5xzQ0QiIiIi5102CVlHPyvXJW++5M99tfAe7G6IR0RERKSOblmKiIiImEwJmYiIiIjJlJCJiIiImEwJmYiIiIjJlJCJiIiImMztT1nW1tYyf/58PvvsM/z9/cnIyKBbt27u3qzLaLgMERERcTe3J2TvvvsulZWVrF27lt27d7Nw4UKWLVvm7s26jIbLEBEREXdz+y3L4uJihg0bBkCfPn3Yt2+fuzcpIiIi4lXc3kJmt9sJDAx0vrZarVRXV+Pr277HpG3Orc6G3q+oqqGjn/WSt6dbpCIiIt7L7VlRYGAg5eXlzte1tbVNJmN+ftZGk5mvFt7Tolja+nMt0ZJkDCCggy8BLejr1lot6V8nIiIi9bn9lmVkZCSFhYUA7N69m/DwcHdvUkRERMSrWAzDMNy5gbqnLD///HMMw+D555/nhhtucOcmRURERLyK2xMyEREREWmcBoYVERERMZkSMhERERGTKSETERERMZlXDAbmTdMv7dmzhxdffJGcnBxKS0tJTk7GYrHQs2dP0tLS8PHxIT8/n7y8PHx9fZk+fTrDhw+noqKCxMRETpw4gc1mIzMzk9DQ0DaPv6qqirlz53Lo0CEqKyuZPn06PXr08LpyiIiIeBOvaCH74fRLv//971m4cKHZITVo5cqVpKSk4HA4AFiwYAEJCQnk5uZiGAYFBQUcO3aMnJwc8vLyWLVqFVlZWVRWVrJmzRrCw8PJzc1l9OjRZGdnm1KGTZs2ERISQm5uLitXriQ9Pd0ryyEiIuJNvCIh85bpl8LCwli6dKnz9f79+xkwYAAAUVFR7Ny5k71799K3b1/8/f0JCgoiLCyMkpKSemWMiopi165dppRhxIgRPP74487XVqvVK8shIiLiTbwiIbvY9EueJjo6ut4sBIZhYLFYALDZbJSVlWG32wkK+n50e5vNht1ur7e8bl0z2Gw2AgMDsdvtzJo1i4SEBK8sh4iIiDfxioSsJdMveQIfn+93b3l5OcHBwReUpby8nKCgoHrL69Y1y+HDh3nwwQe57777GDVqlNeWQ0RExFt4RULmrdMv9erVi6KiIgAKCwvp378/ERERFBcX43A4KCsr48CBA4SHhxMZGcn27dud6/br18+UmI8fP87kyZNJTExk/PjxXlsOERERb+IVI/V70/RL33zzDU888QT5+fkcPHiQ1NRUqqqq6N69OxkZGVitVvLz81m7di2GYfDII48QHR3NuXPnSEpK4tixY/j5+bFo0SK6dOnS5vFnZGTw1ltv0b17d+eyp59+moyMDK8qh4iIiDfxioRMREREpD3ziluWIiIiIu2ZEjIRERERkykhExERETGZEjIRERERkykhExERETGZEjIRERERkykhExERETGZEjIRERERk/3/z/SJmiyyv5YAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 720x720 with 12 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.hist(figsize= (10,10));"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "349c4c41-b011-4406-b245-97d8f3039ddc",
   "metadata": {},
   "source": [
    "## is there in correlation between high vote average and high revenue ?\n",
    "As shown below on the graph. ***yes***, it seems that higher revenue movies has a high voting average. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "id": "e6f4db2e-c7bf-47cf-84a0-4c86b6c45aeb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plotting_selection('vote_average', 'revenue', 'Low', 'Heigh')\n",
    "\n",
    "# Adding some details\n",
    "plt.title('Average voteings by revenue')\n",
    "plt.ylabel('revenue')\n",
    "plt.xlabel('Average voting');"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9078c919-a6c5-4178-b743-4d5838204ca4",
   "metadata": {},
   "source": [
    "## is there in correlation between high budget and high revenue ?\n",
    "As shown below on the graph. yes, it seems that higher revenue movies has a higher budget."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "id": "2466855b-6736-48db-9e57-59756dab7861",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plotting_selection('budget', 'revenue_adj', 'Low budget', 'High budget')\n",
    "#adding details to bar chart\n",
    "plt.title('Average budget by revenue (fig 2)')\n",
    "plt.ylabel('revenue_adj')\n",
    "plt.xlabel('Average budget');"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b4a48d50-1889-41ba-8649-032ccff4cae0",
   "metadata": {},
   "source": [
    "## What kinds of properties are associated with movies that have high revenues?\n",
    "from this simple Statics i will here focus on 25% and 75% as the majorty of this data fall here. so we can see that higher revenue movies seems to be released betwwen 1994 & 2010. and they earn voting average between 5.9 to 7."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "id": "8307b453-cb95-4717-a7f8-fe012375ce97",
   "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>id</th>\n",
       "      <th>popularity</th>\n",
       "      <th>budget</th>\n",
       "      <th>revenue</th>\n",
       "      <th>runtime</th>\n",
       "      <th>vote_count</th>\n",
       "      <th>vote_average</th>\n",
       "      <th>release_year</th>\n",
       "      <th>budget_adj</th>\n",
       "      <th>revenue_adj</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1109.000000</td>\n",
       "      <td>1109.000000</td>\n",
       "      <td>1109.000000</td>\n",
       "      <td>1109.000000</td>\n",
       "      <td>1109.000000</td>\n",
       "      <td>1109.000000</td>\n",
       "      <td>1109.000000</td>\n",
       "      <td>1109.000000</td>\n",
       "      <td>1109.000000</td>\n",
       "      <td>1109.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>29515.623986</td>\n",
       "      <td>2.135908</td>\n",
       "      <td>70.694974</td>\n",
       "      <td>287.409658</td>\n",
       "      <td>116.647430</td>\n",
       "      <td>1201.192065</td>\n",
       "      <td>6.432101</td>\n",
       "      <td>1999.752029</td>\n",
       "      <td>82.663308</td>\n",
       "      <td>369.613230</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>55246.826339</td>\n",
       "      <td>2.218869</td>\n",
       "      <td>56.528811</td>\n",
       "      <td>245.310979</td>\n",
       "      <td>22.464837</td>\n",
       "      <td>1297.990177</td>\n",
       "      <td>0.719705</td>\n",
       "      <td>12.444853</td>\n",
       "      <td>55.141697</td>\n",
       "      <td>287.800565</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>11.000000</td>\n",
       "      <td>0.131526</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>69.000000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>4.100000</td>\n",
       "      <td>1960.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>138.781000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1265.000000</td>\n",
       "      <td>1.011546</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>149.045000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>332.000000</td>\n",
       "      <td>5.900000</td>\n",
       "      <td>1994.000000</td>\n",
       "      <td>39.720000</td>\n",
       "      <td>188.297000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>9268.000000</td>\n",
       "      <td>1.588457</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>209.947000</td>\n",
       "      <td>114.000000</td>\n",
       "      <td>728.000000</td>\n",
       "      <td>6.400000</td>\n",
       "      <td>2003.000000</td>\n",
       "      <td>73.011000</td>\n",
       "      <td>269.859000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>24428.000000</td>\n",
       "      <td>2.523173</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>347.326000</td>\n",
       "      <td>130.000000</td>\n",
       "      <td>1611.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>2010.000000</td>\n",
       "      <td>113.969000</td>\n",
       "      <td>440.960000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>417859.000000</td>\n",
       "      <td>32.985763</td>\n",
       "      <td>380.000000</td>\n",
       "      <td>2781.506000</td>\n",
       "      <td>248.000000</td>\n",
       "      <td>9767.000000</td>\n",
       "      <td>8.300000</td>\n",
       "      <td>2015.000000</td>\n",
       "      <td>368.371000</td>\n",
       "      <td>2827.124000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  id   popularity       budget      revenue      runtime  \\\n",
       "count    1109.000000  1109.000000  1109.000000  1109.000000  1109.000000   \n",
       "mean    29515.623986     2.135908    70.694974   287.409658   116.647430   \n",
       "std     55246.826339     2.218869    56.528811   245.310979    22.464837   \n",
       "min        11.000000     0.131526     0.000000    25.000000    69.000000   \n",
       "25%      1265.000000     1.011546    27.000000   149.045000   100.000000   \n",
       "50%      9268.000000     1.588457    60.000000   209.947000   114.000000   \n",
       "75%     24428.000000     2.523173   100.000000   347.326000   130.000000   \n",
       "max    417859.000000    32.985763   380.000000  2781.506000   248.000000   \n",
       "\n",
       "        vote_count  vote_average  release_year   budget_adj  revenue_adj  \n",
       "count  1109.000000   1109.000000   1109.000000  1109.000000  1109.000000  \n",
       "mean   1201.192065      6.432101   1999.752029    82.663308   369.613230  \n",
       "std    1297.990177      0.719705     12.444853    55.141697   287.800565  \n",
       "min      13.000000      4.100000   1960.000000     0.000000   138.781000  \n",
       "25%     332.000000      5.900000   1994.000000    39.720000   188.297000  \n",
       "50%     728.000000      6.400000   2003.000000    73.011000   269.859000  \n",
       "75%    1611.000000      7.000000   2010.000000   113.969000   440.960000  \n",
       "max    9767.000000      8.300000   2015.000000   368.371000  2827.124000  "
      ]
     },
     "execution_count": 175,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "top_revenue = df.query('revenue_adj > revenue_adj.mean()')\n",
    "top_revenue.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7fc34605-6194-40ba-ae47-ee01b827409b",
   "metadata": {},
   "source": [
    "## is there in correlation between release year and high voting chance ?\n",
    "the following graph is a simple sactterd plots you can see the releas year on x-axis and voting count on y-axis, you can see that the more we go on the way of 2015 the more voting count we have. i followed it by (fig 3) a bar chart represent this correlation.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "id": "09e36fc2-5681-424d-9f63-e14c8e570714",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(df['release_year'], df['vote_count']);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "id": "578dc423-43bc-4c93-b7a8-c740e0ea0f06",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plotting_selection('release_year', 'vote_count', 'Old', 'New')\n",
    "# adding details to  a bar chart\n",
    "plt.title('Average votes by release year (fig 3)')\n",
    "plt.xlabel('release year')\n",
    "plt.ylabel('Average votes');"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8bbfa394-c9e8-4a07-9434-5c265edc2ef8",
   "metadata": {},
   "source": [
    "## which genres associated with high revnue ?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "id": "b0d687ce-c27b-4a4c-bcba-537df66d1809",
   "metadata": {},
   "outputs": [],
   "source": [
    "high_revnue = df.query('revenue_adj > revenue_adj.mean()')\n",
    "genres_dict = {}\n",
    "for i in high_revnue['genres']:\n",
    "    for j in i:\n",
    "        if j not in genres_dict:\n",
    "            genres_dict[j] = 1\n",
    "        else:\n",
    "            genres_dict[j] +=1 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "id": "abdc515d-78b7-4f72-b25d-2a844ace7a32",
   "metadata": {},
   "outputs": [],
   "source": [
    "def to_1D(series):\n",
    "    return pd.Series([x for _list in series for x in _list])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "id": "79480a95-b88e-48e5-aded-ccbca8dd1f9c",
   "metadata": {},
   "outputs": [],
   "source": [
    "list_list = to_1D(high_revnue['genres']).value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "id": "1d40c15d-96f8-416e-a9e2-3906e9109a14",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x648 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#creating numpy array to plotting\n",
    "\n",
    "ind = np.arange(len(to_1D(high_revnue['genres']).value_counts()))\n",
    "width = 0.4\n",
    "\n",
    "# plotting \n",
    "plt.bar(ind, list_list, width, label= list_list.index)\n",
    "\n",
    "\n",
    "# title and labels\n",
    "plt.ylabel('geners')\n",
    "plt.xlabel('count')\n",
    "plt.title('count of most revenued geners (fig 4)')\n",
    "locations = ind + width / 2  # xtick locations\n",
    "labels = list_list.index  # xtick labels\n",
    "plt.xticks(locations, labels);\n",
    "\n",
    "#adjustment\n",
    "plt.xticks(rotation=90)\n",
    "fig = plt.gcf()\n",
    "fig.set_size_inches(14, 9)\n",
    "plt.rcParams.update({'font.size': 12})"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b3aa41e2-096d-4d55-9007-e8e8ba206cf2",
   "metadata": {},
   "source": [
    "## Conclusions"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2d47bc79-930d-4097-aa0c-3fe8c3fd00eb",
   "metadata": {},
   "source": [
    "it seems to be that movies which has high revenue has some characteristics:-  \n",
    "\n",
    "it **costs more**, the more you spend **may** lead to high revenue as seen in *(fig 2)* the gap is very obvious.\n",
    "\n",
    "people seems to **like newest movies** than oldest as seen in *(fig 3)* and when we look at statistics most high revenue movies fall in 1994 to 2010 section and the look of scatter plot of years vs vote count you can detect that people like to review and vote for new movies \n",
    "\n",
    "according to *(fig 4)* its looks like people has more interest in **Action movies** than western or history movies. we can do the same steps to see which company and which cast has high revenue."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7c703638-5f00-47d7-af19-1b7abc94fd04",
   "metadata": {},
   "source": [
    "### Limitations \n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d783de54-fa3a-4bf7-b19a-a77825ef4adc",
   "metadata": {},
   "source": [
    "this statics has few statical operations so the Conclusion is primative we can use it as a guide fore more investigations, but i found a lot of zero revenue movies which limits my data as by cleaning it i lost more than half which lead to no sufficient analysis as those delete was more from old movies . data entery in cast and compny, ...etc , has a weird manner which concume more time to clean it to be useful. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3d97ee55-a0bd-4705-a345-c32342ec4a4a",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}