DataCheckpoint_Group080_WI24.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gxp75OuOKsod"
      },
      "source": [
        "**If you lost points on the last checkpoint you can get them back by responding to TA/IA feedback**  \n",
        "\n",
        "Update/change the relevant sections where you lost those points, make sure you respond on GitHub Issues to your TA/IA to call their attention to the changes you made here.\n",
        "\n",
        "Please update your Timeline... no battle plan survives contact with the enemy, so make sure we understand how your plans have changed."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DsstcPVGKsoh"
      },
      "source": [
        "# COGS 108 - EDA Checkpoint"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qN7nnxMXKsoh"
      },
      "source": [
        "# Names\n",
        "\n",
        "- Celeste Walstrom-Vangor\n",
        "- Rui Wang\n",
        "- Jheel Gandhi\n",
        "- Howard Ma\n",
        "- Kenny Qiu"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0-MJWoy1Ksoi"
      },
      "source": [
        "# **Research Question**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9XvtCVa_Ksoi"
      },
      "source": [
        "Can we predict the Letterboxd ratings for upcoming movies based solely on actors in those movies and the ratings of their previous films, focusing on the ratings of Gen Z?"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IY98TkGTKsoi"
      },
      "source": [
        "## Background and Prior Work"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iLKJ-FeSKsoi"
      },
      "source": [
        "By figuring out if there is a correlation between actors' previous movies and their forcasted Letterboxd movie rating, we will be able to determine whether a new movie can be determined as highly rated based only on the actors who are starring in it. Although there are many factors that go into the ratings of a movie, if we can create an accurate algorithm that focuses on actors alone it will be highly convinient so people can determine whether a movie will be highly rated before going to see it. As busy college students, we want to dedicate our time to helping people avoid going to a low rated movies before the ratings come out. If we can find a correlation between the ratings of previous movies with actors and the ratings of new movies with those same actors it could save people a lot of time.\n",
        "\n",
        "Here are some prior work we found online:\n",
        "\n",
        "1. Vox Analysis on the the actors and actresses who most consistently appear in terrible movies\n",
        "\n",
        "  One previous analysis from Vox was done on the actors who starred in the best and worst rated films based on metacritic. From which they restricted the data set to only include actors that meet the following criteria:\n",
        "    1. The actor/actress must have performed in at least 10 films (writing, directing, and producing credits were omitted).\n",
        "    2. At least one of these films had to have grossed $30 million or more at the box office, adjusted for inflation.\n",
        "    3. At least one of these films had to be within the past five years (we only wanted semi-active performers).\n",
        "\n",
        "  From the blog post analysis of the top 10 worst rated actors, we can see familiar names like Adam Sandler and Jennifer Love Hewitt who are often known for being in worse rated movies. On the other hand, for the top 10 best rated actors, familiar names that are known for having good movies like Leonardo Di Caprio and Jennifer Lawerence make the list.\n",
        "\n",
        "  Something interesting to note is that genres like action and comedy are rated more harshly than genres like documentaries and drama. This is hypothesized to be due to the subjective nature of comedy films.\n",
        "\n",
        "  This analysis does only look at single actors rather than taking into account all the actors in a film, but it does conclude that there’s at least some correlation between an actor and their movie’s rating.\n",
        "\n",
        "2.\n",
        "The Hustle - The actors who are the best (and worst) at their job:\n",
        "\n",
        "  The Hustle did a similar analysis on actors and the critical ratings they get.\n",
        "  Here are the datasets used by the blog post:\n",
        "    1. Average Metacritic scores (a measurement of critical ratings) across all films for 35k+ actors\n",
        "    2. Average domestic box office data across all films an actor has played a prominent role in over their career\n",
        "\n",
        "  They chose domestic box office because international films are biased towards franchise films, and the box office dataset is not adjusted for inflation so it favors newer films.\n",
        "\n",
        "  Similar to the first analysis, critics are biased against comedy and love films, with those films getting lower than average ratings.\n",
        "\n",
        "  They also included a beloved actors matrix where they plotted the average box office of an actor (how loved they are by the audience) with the average metacritic score (how loved they are by the critics).\n",
        "\n",
        "  They then did a composite score taking into account both the percentiles of the box office and their metacritic. In this final list, actors like Leonardo DiCaprio and Tom Hanks make the list of the best combination and actors like Bella Thorne and Chad Michael Murray make the list of the worst combination.\n",
        "\n",
        "  This analysis also seems to support the fact that some actors are more likely to have better rated movies, but this study only takes in single actors as well as the first analysis did, so there’s more work to be done on analyzing multiple actors for a single film.\n",
        "\n",
        "  Something to note here is that just because a movie has bad critic ratings does not mean that the audience doesn’t like it, as supported by big box offices for some of the worst rated actors.\n",
        "\n",
        "\n",
        "\n",
        "References:\n",
        "* https://www.vox.com/2016/4/11/11381206/worst-actors-hollywood\n",
        "* https://thehustle.co/the-actors-who-are-the-best-and-worst-at-their-job/"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "isi8Ikl7Ksoj"
      },
      "source": [
        "# **Hypothesis**\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KzEKerE7Ksoj"
      },
      "source": [
        "We predict that there exists a positive correlation between the Letterboxd ratings of past movies with the top 1000 most relevant actors and the ratings of their upcoming films. This assumption stems from the idea that actors with a history of highly rated performances are likely to continue appearing in well-received films, contributing to positive Letterboxd reviews."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1vwYiAtdm4qW"
      },
      "source": [
        "# **Data**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pQExPTH-m4qW"
      },
      "source": [
        "## Data overview\n",
        "\n",
        "For each dataset include the following information\n",
        "- Dataset #1\n",
        "  - Dataset Name:actors\n",
        "  - Link to the dataset:https://www.kaggle.com/datasets/gsimonx37/letterboxd?select=actors.csv\n",
        "  - Number of observations:5523327\n",
        "  - Number of variables:2\n",
        "- Dataset #2\n",
        "  - Dataset Name:genres\n",
        "  - Link to the dataset: https://www.kaggle.com/datasets/gsimonx37/letterboxd?select=genres.csv\n",
        "  - Number of observations:990770\n",
        "  - Number of variables:2\n",
        "- Dataset #3\n",
        "  - Dataset Name:language\n",
        "  - Link to the dataset: https://www.kaggle.com/datasets/gsimonx37/letterboxd?select=languages.csv\n",
        "  - Number of observations:988826\n",
        "  - Number of variables:3\n",
        "- Dataset #4\n",
        "  - Dataset Name:movies\n",
        "  - Link to the dataset: https://www.kaggle.com/datasets/gsimonx37/letterboxd?select=movies.csv\n",
        "  - Number of observations:896400\n",
        "  - Number of variables:7\n",
        "\n",
        "\n",
        "We are planning to utilize four datasets: **actors.csv**, **genres.csv**, **language.csv**, and **movies.csv**.\n",
        "\n",
        "**actors.csv**: This dataset comprises two variables: **id** and **name**. The **id** column contains numerous instances of identical numbers each associated with different **name** entries, representing actors. We intend to consolidate identical **id** numbers and compile a corresponding list of actor names. This process will involve groupby() to handle repeated **id** values.\n",
        "\n",
        "**genres.csv**: Similar to the **actors.csv**, it contains two variables: **id** and **genre**. We will merge identical **id** numbers, creating a comprehensive list of genres associated with each unique identifier. This dataset will help us understand the distribution of movie genres and their relationships with other variables.\n",
        "\n",
        "**language.csv**: This dataset includes three variables: **id**, **type**, and **language**. Our approach will not only groupby() the same **id** but also filter entries in the **type** column to retain only 'Language' and 'Primary language'.\n",
        "\n",
        "**movies.csv**: Containing seven variables, we plan to discard **tagline**, **description**, and **minute** from our analysis.  We will focus on **id**, **name**, **date**, and **rating**, which are more aligned with our objectives.\n",
        "\n",
        "For data cleaning, we will include movies with a **date** later than 1970, with 'English' among their languages, and we will eliminate any datas containing **'NaN'** to maintain data integrity.\n",
        "\n",
        "The combination of these datasets will involve merging based on the **id** field, ensuring that each movie's data is enriched with corresponding actors, genres, and language information. The final dataset will feature variables such as **id**, **movie**, **date**, **rating**, **actors**, **genre**, **language**, and **director**. This cohesive dataset will form the foundation of our project's analysis.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Onsj3MkBm4qW"
      },
      "outputs": [],
      "source": [
        "# Imports\n",
        "import pandas as pd"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3bPoPtVEm4qY"
      },
      "source": [
        "## Dataset # actors"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "PTpeIBvFm4qY",
        "outputId": "7cd97a5a-b84c-4073-d1aa-80b8a601909d"
      },
      "outputs": [
        {
          "data": {
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>id</th>\n",
              "      <th>name</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1000001</td>\n",
              "      <td>Margot Robbie</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1000001</td>\n",
              "      <td>Lewis Easter</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1000001</td>\n",
              "      <td>Onyemachi Ejimofor</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1000001</td>\n",
              "      <td>Cameron Everitt</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1000001</td>\n",
              "      <td>Luke Field-Wright</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5523322</th>\n",
              "      <td>1896377</td>\n",
              "      <td>Nikita Dzhigurda</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5523323</th>\n",
              "      <td>1896382</td>\n",
              "      <td>Marine Petrosyan</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5523324</th>\n",
              "      <td>1896387</td>\n",
              "      <td>Rebecca Jim</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5523325</th>\n",
              "      <td>1896391</td>\n",
              "      <td>Ko Shibasaki</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5523326</th>\n",
              "      <td>1896393</td>\n",
              "      <td>Atsuhiro Inukai</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5523327 rows × 2 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "              id                name\n",
              "0        1000001       Margot Robbie\n",
              "1        1000001        Lewis Easter\n",
              "2        1000001  Onyemachi Ejimofor\n",
              "3        1000001     Cameron Everitt\n",
              "4        1000001   Luke Field-Wright\n",
              "...          ...                 ...\n",
              "5523322  1896377    Nikita Dzhigurda\n",
              "5523323  1896382    Marine Petrosyan\n",
              "5523324  1896387         Rebecca Jim\n",
              "5523325  1896391        Ko Shibasaki\n",
              "5523326  1896393     Atsuhiro Inukai\n",
              "\n",
              "[5523327 rows x 2 columns]"
            ]
          },
          "execution_count": 4,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df1 = pd.read_csv('actors.csv')\n",
        "df1"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rwpgUYNzm4qZ"
      },
      "source": [
        "## Dataset # genres"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "TdKcUb5jm4qZ",
        "outputId": "2d368c73-ad42-4cf8-af97-c7d4a2736638"
      },
      "outputs": [
        {
          "data": {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>id</th>\n",
              "      <th>genre</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1000001</td>\n",
              "      <td>Comedy</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1000001</td>\n",
              "      <td>Fantasy</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1000001</td>\n",
              "      <td>Adventure</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1000002</td>\n",
              "      <td>Comedy</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1000002</td>\n",
              "      <td>Thriller</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>990765</th>\n",
              "      <td>1896380</td>\n",
              "      <td>Crime</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>990766</th>\n",
              "      <td>1896380</td>\n",
              "      <td>Drama</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>990767</th>\n",
              "      <td>1896382</td>\n",
              "      <td>Drama</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>990768</th>\n",
              "      <td>1896391</td>\n",
              "      <td>Music</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>990769</th>\n",
              "      <td>1896393</td>\n",
              "      <td>TV Movie</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>990770 rows × 2 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "             id      genre\n",
              "0       1000001     Comedy\n",
              "1       1000001    Fantasy\n",
              "2       1000001  Adventure\n",
              "3       1000002     Comedy\n",
              "4       1000002   Thriller\n",
              "...         ...        ...\n",
              "990765  1896380      Crime\n",
              "990766  1896380      Drama\n",
              "990767  1896382      Drama\n",
              "990768  1896391      Music\n",
              "990769  1896393   TV Movie\n",
              "\n",
              "[990770 rows x 2 columns]"
            ]
          },
          "execution_count": 5,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df5 = pd.read_csv('genres.csv')\n",
        "df5"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sqFJvemhm4qa"
      },
      "source": [
        "## Dataset # language"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "nchXy8Ydm4qa",
        "outputId": "0578b553-bc3b-475e-f5c9-29f137663d60"
      },
      "outputs": [
        {
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>id</th>\n",
              "      <th>type</th>\n",
              "      <th>language</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1000001</td>\n",
              "      <td>Language</td>\n",
              "      <td>English</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1000002</td>\n",
              "      <td>Primary language</td>\n",
              "      <td>Korean</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1000002</td>\n",
              "      <td>Spoken language</td>\n",
              "      <td>English</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1000002</td>\n",
              "      <td>Spoken language</td>\n",
              "      <td>German</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1000002</td>\n",
              "      <td>Spoken language</td>\n",
              "      <td>Korean</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>988821</th>\n",
              "      <td>1896389</td>\n",
              "      <td>Language</td>\n",
              "      <td>English</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>988822</th>\n",
              "      <td>1896390</td>\n",
              "      <td>Language</td>\n",
              "      <td>English</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>988823</th>\n",
              "      <td>1896391</td>\n",
              "      <td>Language</td>\n",
              "      <td>Japanese</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>988824</th>\n",
              "      <td>1896392</td>\n",
              "      <td>Language</td>\n",
              "      <td>Chinese</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>988825</th>\n",
              "      <td>1896393</td>\n",
              "      <td>Language</td>\n",
              "      <td>Japanese</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>988826 rows × 3 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "             id              type  language\n",
              "0       1000001          Language   English\n",
              "1       1000002  Primary language    Korean\n",
              "2       1000002   Spoken language   English\n",
              "3       1000002   Spoken language    German\n",
              "4       1000002   Spoken language    Korean\n",
              "...         ...               ...       ...\n",
              "988821  1896389          Language   English\n",
              "988822  1896390          Language   English\n",
              "988823  1896391          Language  Japanese\n",
              "988824  1896392          Language   Chinese\n",
              "988825  1896393          Language  Japanese\n",
              "\n",
              "[988826 rows x 3 columns]"
            ]
          },
          "execution_count": 6,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df7 = pd.read_csv('languages.csv')\n",
        "df7"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qqUzd0Lsm4qa"
      },
      "source": [
        "## Dataset # movies"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "B7zqsHrum4qa",
        "outputId": "de3c63bd-0a7a-4fa0-f3ac-5ea75735eb84"
      },
      "outputs": [
        {
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              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>id</th>\n",
              "      <th>name</th>\n",
              "      <th>date</th>\n",
              "      <th>tagline</th>\n",
              "      <th>description</th>\n",
              "      <th>minute</th>\n",
              "      <th>rating</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1000001</td>\n",
              "      <td>Barbie</td>\n",
              "      <td>2023.0</td>\n",
              "      <td>She's everything. He's just Ken.</td>\n",
              "      <td>Barbie and Ken are having the time of their li...</td>\n",
              "      <td>114.0</td>\n",
              "      <td>3.91</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1000002</td>\n",
              "      <td>Parasite</td>\n",
              "      <td>2019.0</td>\n",
              "      <td>Act like you own the place.</td>\n",
              "      <td>All unemployed, Ki-taek's family takes peculia...</td>\n",
              "      <td>133.0</td>\n",
              "      <td>4.57</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1000003</td>\n",
              "      <td>Everything Everywhere All at Once</td>\n",
              "      <td>2022.0</td>\n",
              "      <td>The universe is so much bigger than you realize.</td>\n",
              "      <td>An aging Chinese immigrant is swept up in an i...</td>\n",
              "      <td>140.0</td>\n",
              "      <td>4.32</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1000004</td>\n",
              "      <td>Fight Club</td>\n",
              "      <td>1999.0</td>\n",
              "      <td>Mischief. Mayhem. Soap.</td>\n",
              "      <td>A ticking-time-bomb insomniac and a slippery s...</td>\n",
              "      <td>139.0</td>\n",
              "      <td>4.27</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1000005</td>\n",
              "      <td>Interstellar</td>\n",
              "      <td>2014.0</td>\n",
              "      <td>Mankind was born on Earth. It was never meant ...</td>\n",
              "      <td>The adventures of a group of explorers who mak...</td>\n",
              "      <td>169.0</td>\n",
              "      <td>4.32</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>896395</th>\n",
              "      <td>1896389</td>\n",
              "      <td>伝七捕物帖 銀蛇呪文</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Kofu duty guard Yamauchi Hinata-kami cultivate...</td>\n",
              "      <td>98.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>896396</th>\n",
              "      <td>1896390</td>\n",
              "      <td>太阳的子民 Sa'icelen</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>The government's discriminatory policies, wars...</td>\n",
              "      <td>38.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>896397</th>\n",
              "      <td>1896391</td>\n",
              "      <td>柴咲コウ CONCERT TOUR 2023 ACTOR'S THE BEST</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>KO SHIBASAKI CONCERT TOUR 2023 - ACTOR'S THE BEST</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>896398</th>\n",
              "      <td>1896392</td>\n",
              "      <td>相知相守民歌45演唱会</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>262.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>896399</th>\n",
              "      <td>1896393</td>\n",
              "      <td>絶対BLになる世界VS絶対BLになりたくない男 2024</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>One day, the protagonist realizes that he live...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>896400 rows × 7 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "             id                                     name    date  \\\n",
              "0       1000001                                   Barbie  2023.0   \n",
              "1       1000002                                 Parasite  2019.0   \n",
              "2       1000003        Everything Everywhere All at Once  2022.0   \n",
              "3       1000004                               Fight Club  1999.0   \n",
              "4       1000005                             Interstellar  2014.0   \n",
              "...         ...                                      ...     ...   \n",
              "896395  1896389                               伝七捕物帖 銀蛇呪文     NaN   \n",
              "896396  1896390                          太阳的子民 Sa'icelen     NaN   \n",
              "896397  1896391  柴咲コウ CONCERT TOUR 2023 ACTOR'S THE BEST     NaN   \n",
              "896398  1896392                              相知相守民歌45演唱会     NaN   \n",
              "896399  1896393             絶対BLになる世界VS絶対BLになりたくない男 2024     NaN   \n",
              "\n",
              "                                                  tagline  \\\n",
              "0                        She's everything. He's just Ken.   \n",
              "1                             Act like you own the place.   \n",
              "2        The universe is so much bigger than you realize.   \n",
              "3                                 Mischief. Mayhem. Soap.   \n",
              "4       Mankind was born on Earth. It was never meant ...   \n",
              "...                                                   ...   \n",
              "896395                                                NaN   \n",
              "896396                                                NaN   \n",
              "896397                                                NaN   \n",
              "896398                                                NaN   \n",
              "896399                                                NaN   \n",
              "\n",
              "                                              description  minute  rating  \n",
              "0       Barbie and Ken are having the time of their li...   114.0    3.91  \n",
              "1       All unemployed, Ki-taek's family takes peculia...   133.0    4.57  \n",
              "2       An aging Chinese immigrant is swept up in an i...   140.0    4.32  \n",
              "3       A ticking-time-bomb insomniac and a slippery s...   139.0    4.27  \n",
              "4       The adventures of a group of explorers who mak...   169.0    4.32  \n",
              "...                                                   ...     ...     ...  \n",
              "896395  Kofu duty guard Yamauchi Hinata-kami cultivate...    98.0     NaN  \n",
              "896396  The government's discriminatory policies, wars...    38.0     NaN  \n",
              "896397  KO SHIBASAKI CONCERT TOUR 2023 - ACTOR'S THE BEST     NaN     NaN  \n",
              "896398                                                NaN   262.0     NaN  \n",
              "896399  One day, the protagonist realizes that he live...     NaN     NaN  \n",
              "\n",
              "[896400 rows x 7 columns]"
            ]
          },
          "execution_count": 7,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df4 = pd.read_csv('movies.csv')\n",
        "df4"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **EDA part 1: Data cleaning**"
      ],
      "metadata": {
        "id": "5tMbNyvBAng9"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Now our raw data consists of multiple datasets and isn't very clean, so we will need to do some wrangling. First let's load in the datasets"
      ],
      "metadata": {
        "id": "K3cVIb9ZWmAl"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "8802b96e"
      },
      "outputs": [],
      "source": [
        "# Imports\n",
        "%matplotlib inline\n",
        "\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "import ast\n",
        "import seaborn as sns\n",
        "sns.set()\n",
        "sns.set_context('talk')\n",
        "from collections import Counter\n",
        "import warnings\n",
        "warnings.filterwarnings('ignore')\n",
        "\n",
        "import patsy\n",
        "import statsmodels.api as sm\n",
        "import scipy.stats as stats\n",
        "from scipy.stats import ttest_ind, chisquare, normaltest\n",
        "# Note: the statsmodels import may print out a 'FutureWarning'. Thats fine."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "9118d8e7",
        "outputId": "c18bdafc-cf47-4e12-e5e6-51276cd0d0d0"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>id</th>\n",
              "      <th>name</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1000001</td>\n",
              "      <td>[Margot Robbie, Lewis Easter, Onyemachi Ejimof...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1000002</td>\n",
              "      <td>[Rosie Peralta, Anna Elisabeth Rihlmann, Andre...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1000003</td>\n",
              "      <td>[Randall Archer, Boon Pin Koh, Efka Kvaracieju...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1000004</td>\n",
              "      <td>[Leonard Termo, Greg Bronson, Michael Arturo, ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1000005</td>\n",
              "      <td>[Lena Georgas, Jeff Hephner, Elyes Gabel, Broo...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        id                                               name\n",
              "0  1000001  [Margot Robbie, Lewis Easter, Onyemachi Ejimof...\n",
              "1  1000002  [Rosie Peralta, Anna Elisabeth Rihlmann, Andre...\n",
              "2  1000003  [Randall Archer, Boon Pin Koh, Efka Kvaracieju...\n",
              "3  1000004  [Leonard Termo, Greg Bronson, Michael Arturo, ...\n",
              "4  1000005  [Lena Georgas, Jeff Hephner, Elyes Gabel, Broo..."
            ]
          },
          "execution_count": 25,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df1 = pd.read_csv('actors.csv')\n",
        "grouped1 = df1.groupby('id')['name'].apply(list).reset_index()\n",
        "grouped1.head(5)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "062dc7e4",
        "outputId": "6e729192-3b54-49ef-ed13-035b4c902f7c"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
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              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>id</th>\n",
              "      <th>genre</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1000001</td>\n",
              "      <td>[Comedy, Fantasy, Adventure]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1000002</td>\n",
              "      <td>[Comedy, Thriller, Drama]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1000003</td>\n",
              "      <td>[Science Fiction, Action, Adventure]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1000004</td>\n",
              "      <td>[Drama]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1000005</td>\n",
              "      <td>[Science Fiction, Drama, Adventure]</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        id                                 genre\n",
              "0  1000001          [Comedy, Fantasy, Adventure]\n",
              "1  1000002             [Comedy, Thriller, Drama]\n",
              "2  1000003  [Science Fiction, Action, Adventure]\n",
              "3  1000004                               [Drama]\n",
              "4  1000005   [Science Fiction, Drama, Adventure]"
            ]
          },
          "execution_count": 26,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df5 = pd.read_csv('genres.csv')\n",
        "grouped5 = df5.groupby('id')['genre'].apply(list).reset_index()\n",
        "grouped5.head(5)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "664af1aa",
        "outputId": "d91058f3-0a86-43e9-b7fa-eaba8dd6cb31"
      },
      "outputs": [
        {
          "data": {
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>id</th>\n",
              "      <th>type</th>\n",
              "      <th>language</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1000001</td>\n",
              "      <td>Language</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1000002</td>\n",
              "      <td>Primary language</td>\n",
              "      <td>Korean</td>\n",
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              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>1000003</td>\n",
              "      <td>Primary language</td>\n",
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              "    <tr>\n",
              "      <th>9</th>\n",
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              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>1000005</td>\n",
              "      <td>Language</td>\n",
              "      <td>English</td>\n",
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              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>1000006</td>\n",
              "      <td>Language</td>\n",
              "      <td>English</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>1000007</td>\n",
              "      <td>Primary language</td>\n",
              "      <td>English</td>\n",
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              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>1000008</td>\n",
              "      <td>Primary language</td>\n",
              "      <td>English</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>1000009</td>\n",
              "      <td>Language</td>\n",
              "      <td>English</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>1000010</td>\n",
              "      <td>Primary language</td>\n",
              "      <td>English</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>1000011</td>\n",
              "      <td>Language</td>\n",
              "      <td>English</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>1000012</td>\n",
              "      <td>Primary language</td>\n",
              "      <td>English</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>1000013</td>\n",
              "      <td>Language</td>\n",
              "      <td>English</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29</th>\n",
              "      <td>1000014</td>\n",
              "      <td>Language</td>\n",
              "      <td>English</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>30</th>\n",
              "      <td>1000015</td>\n",
              "      <td>Primary language</td>\n",
              "      <td>English</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>33</th>\n",
              "      <td>1000016</td>\n",
              "      <td>Primary language</td>\n",
              "      <td>English</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>36</th>\n",
              "      <td>1000017</td>\n",
              "      <td>Primary language</td>\n",
              "      <td>English</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>44</th>\n",
              "      <td>1000018</td>\n",
              "      <td>Primary language</td>\n",
              "      <td>English</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>46</th>\n",
              "      <td>1000019</td>\n",
              "      <td>Primary language</td>\n",
              "      <td>English</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>49</th>\n",
              "      <td>1000020</td>\n",
              "      <td>Language</td>\n",
              "      <td>English</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         id              type language\n",
              "0   1000001          Language  English\n",
              "1   1000002  Primary language   Korean\n",
              "5   1000003  Primary language  English\n",
              "9   1000004          Language  English\n",
              "10  1000005          Language  English\n",
              "11  1000006          Language  English\n",
              "14  1000007  Primary language  English\n",
              "16  1000008  Primary language  English\n",
              "19  1000009          Language  English\n",
              "20  1000010  Primary language  English\n",
              "24  1000011          Language  English\n",
              "26  1000012  Primary language  English\n",
              "28  1000013          Language  English\n",
              "29  1000014          Language  English\n",
              "30  1000015  Primary language  English\n",
              "33  1000016  Primary language  English\n",
              "36  1000017  Primary language  English\n",
              "44  1000018  Primary language  English\n",
              "46  1000019  Primary language  English\n",
              "49  1000020          Language  English"
            ]
          },
          "execution_count": 27,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df7 = pd.read_csv('languages.csv')\n",
        "filtered_df7 = df7[df7['type'].isin(['Language', 'Primary language'])]\n",
        "filtered_df7.head(20)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "df48ddf9"
      },
      "outputs": [],
      "source": [
        "df4 = pd.read_csv('movies.csv')\n",
        "df4.rename(columns={'name': 'movie'}, inplace=True)\n",
        "merged_df14 = pd.merge(df4, grouped1, on='id', how='left')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "2122fe59"
      },
      "outputs": [],
      "source": [
        "merged_df145 = pd.merge(merged_df14, grouped5, on='id', how='left')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "c09027ac",
        "outputId": "3493cc8e-7b14-4a9b-b75f-08034e94e531"
      },
      "outputs": [
        {
          "data": {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>id</th>\n",
              "      <th>movie</th>\n",
              "      <th>date</th>\n",
              "      <th>tagline</th>\n",
              "      <th>description</th>\n",
              "      <th>minute</th>\n",
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              "      <th>name</th>\n",
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              "      <th>language</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1000001</td>\n",
              "      <td>Barbie</td>\n",
              "      <td>2023.0</td>\n",
              "      <td>She's everything. He's just Ken.</td>\n",
              "      <td>Barbie and Ken are having the time of their li...</td>\n",
              "      <td>114.0</td>\n",
              "      <td>3.91</td>\n",
              "      <td>[Margot Robbie, Lewis Easter, Onyemachi Ejimof...</td>\n",
              "      <td>[Comedy, Fantasy, Adventure]</td>\n",
              "      <td>Language</td>\n",
              "      <td>English</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1000002</td>\n",
              "      <td>Parasite</td>\n",
              "      <td>2019.0</td>\n",
              "      <td>Act like you own the place.</td>\n",
              "      <td>All unemployed, Ki-taek's family takes peculia...</td>\n",
              "      <td>133.0</td>\n",
              "      <td>4.57</td>\n",
              "      <td>[Rosie Peralta, Anna Elisabeth Rihlmann, Andre...</td>\n",
              "      <td>[Comedy, Thriller, Drama]</td>\n",
              "      <td>Primary language</td>\n",
              "      <td>Korean</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1000003</td>\n",
              "      <td>Everything Everywhere All at Once</td>\n",
              "      <td>2022.0</td>\n",
              "      <td>The universe is so much bigger than you realize.</td>\n",
              "      <td>An aging Chinese immigrant is swept up in an i...</td>\n",
              "      <td>140.0</td>\n",
              "      <td>4.32</td>\n",
              "      <td>[Randall Archer, Boon Pin Koh, Efka Kvaracieju...</td>\n",
              "      <td>[Science Fiction, Action, Adventure]</td>\n",
              "      <td>Primary language</td>\n",
              "      <td>English</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1000004</td>\n",
              "      <td>Fight Club</td>\n",
              "      <td>1999.0</td>\n",
              "      <td>Mischief. Mayhem. Soap.</td>\n",
              "      <td>A ticking-time-bomb insomniac and a slippery s...</td>\n",
              "      <td>139.0</td>\n",
              "      <td>4.27</td>\n",
              "      <td>[Leonard Termo, Greg Bronson, Michael Arturo, ...</td>\n",
              "      <td>[Drama]</td>\n",
              "      <td>Language</td>\n",
              "      <td>English</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1000005</td>\n",
              "      <td>Interstellar</td>\n",
              "      <td>2014.0</td>\n",
              "      <td>Mankind was born on Earth. It was never meant ...</td>\n",
              "      <td>The adventures of a group of explorers who mak...</td>\n",
              "      <td>169.0</td>\n",
              "      <td>4.32</td>\n",
              "      <td>[Lena Georgas, Jeff Hephner, Elyes Gabel, Broo...</td>\n",
              "      <td>[Science Fiction, Drama, Adventure]</td>\n",
              "      <td>Language</td>\n",
              "      <td>English</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        id                              movie    date  \\\n",
              "0  1000001                             Barbie  2023.0   \n",
              "1  1000002                           Parasite  2019.0   \n",
              "2  1000003  Everything Everywhere All at Once  2022.0   \n",
              "3  1000004                         Fight Club  1999.0   \n",
              "4  1000005                       Interstellar  2014.0   \n",
              "\n",
              "                                             tagline  \\\n",
              "0                   She's everything. He's just Ken.   \n",
              "1                        Act like you own the place.   \n",
              "2   The universe is so much bigger than you realize.   \n",
              "3                            Mischief. Mayhem. Soap.   \n",
              "4  Mankind was born on Earth. It was never meant ...   \n",
              "\n",
              "                                         description  minute  rating  \\\n",
              "0  Barbie and Ken are having the time of their li...   114.0    3.91   \n",
              "1  All unemployed, Ki-taek's family takes peculia...   133.0    4.57   \n",
              "2  An aging Chinese immigrant is swept up in an i...   140.0    4.32   \n",
              "3  A ticking-time-bomb insomniac and a slippery s...   139.0    4.27   \n",
              "4  The adventures of a group of explorers who mak...   169.0    4.32   \n",
              "\n",
              "                                                name  \\\n",
              "0  [Margot Robbie, Lewis Easter, Onyemachi Ejimof...   \n",
              "1  [Rosie Peralta, Anna Elisabeth Rihlmann, Andre...   \n",
              "2  [Randall Archer, Boon Pin Koh, Efka Kvaracieju...   \n",
              "3  [Leonard Termo, Greg Bronson, Michael Arturo, ...   \n",
              "4  [Lena Georgas, Jeff Hephner, Elyes Gabel, Broo...   \n",
              "\n",
              "                                  genre              type language  \n",
              "0          [Comedy, Fantasy, Adventure]          Language  English  \n",
              "1             [Comedy, Thriller, Drama]  Primary language   Korean  \n",
              "2  [Science Fiction, Action, Adventure]  Primary language  English  \n",
              "3                               [Drama]          Language  English  \n",
              "4   [Science Fiction, Drama, Adventure]          Language  English  "
            ]
          },
          "execution_count": 30,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "merged_df1457 = pd.merge(merged_df145, filtered_df7, on='id', how='left')\n",
        "merged_df1457.head(5)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "0672afd7"
      },
      "outputs": [],
      "source": [
        "df6 = pd.read_csv('crew.csv')\n",
        "directors_df6 = df6[df6['role'] == 'Director']\n",
        "directors_df6.rename(columns={'name': 'Director'}, inplace=True)\n",
        "directors_df6.drop(columns='role', inplace=True)\n",
        "directors_df6.head(5)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "8062c288",
        "outputId": "dd527f0e-80e0-46bc-d6cd-8828ecd1e30c"
      },
      "outputs": [
        {
          "data": {
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              "      <th>Director</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1000001</td>\n",
              "      <td>[Greta Gerwig]</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1000002</td>\n",
              "      <td>[Bong Joon-ho]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1000003</td>\n",
              "      <td>[Daniel Kwan, Daniel Scheinert]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1000004</td>\n",
              "      <td>[David Fincher]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1000005</td>\n",
              "      <td>[Christopher Nolan]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>1000006</td>\n",
              "      <td>[Todd Phillips]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>1000007</td>\n",
              "      <td>[Rodney Rothman, Peter Ramsey, Bob Persichetti]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>1000008</td>\n",
              "      <td>[Rian Johnson]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>1000009</td>\n",
              "      <td>[Damien Chazelle]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>1000010</td>\n",
              "      <td>[Quentin Tarantino]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>1000011</td>\n",
              "      <td>[Matt Reeves]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>1000012</td>\n",
              "      <td>[Christopher Nolan]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>1000013</td>\n",
              "      <td>[Damien Chazelle]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>1000014</td>\n",
              "      <td>[Jordan Peele]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>1000015</td>\n",
              "      <td>[Ari Aster]</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         id                                         Director\n",
              "0   1000001                                   [Greta Gerwig]\n",
              "1   1000002                                   [Bong Joon-ho]\n",
              "2   1000003                  [Daniel Kwan, Daniel Scheinert]\n",
              "3   1000004                                  [David Fincher]\n",
              "4   1000005                              [Christopher Nolan]\n",
              "5   1000006                                  [Todd Phillips]\n",
              "6   1000007  [Rodney Rothman, Peter Ramsey, Bob Persichetti]\n",
              "7   1000008                                   [Rian Johnson]\n",
              "8   1000009                                [Damien Chazelle]\n",
              "9   1000010                              [Quentin Tarantino]\n",
              "10  1000011                                    [Matt Reeves]\n",
              "11  1000012                              [Christopher Nolan]\n",
              "12  1000013                                [Damien Chazelle]\n",
              "13  1000014                                   [Jordan Peele]\n",
              "14  1000015                                      [Ari Aster]"
            ]
          },
          "execution_count": 12,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "grouped6 = directors_df6.groupby('id')['Director'].apply(list).reset_index()\n",
        "grouped6.head(15)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "1a60cd5e",
        "outputId": "55ae8f26-821a-4cab-8a8c-915c4e0768dc"
      },
      "outputs": [
        {
          "data": {
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              "      <td>1000001</td>\n",
              "      <td>Barbie</td>\n",
              "      <td>2023.0</td>\n",
              "      <td>She's everything. He's just Ken.</td>\n",
              "      <td>Barbie and Ken are having the time of their li...</td>\n",
              "      <td>114.0</td>\n",
              "      <td>3.91</td>\n",
              "      <td>[Margot Robbie, Lewis Easter, Onyemachi Ejimof...</td>\n",
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              "      <th>1</th>\n",
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              "      <td>Parasite</td>\n",
              "      <td>2019.0</td>\n",
              "      <td>Act like you own the place.</td>\n",
              "      <td>All unemployed, Ki-taek's family takes peculia...</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1000003</td>\n",
              "      <td>Everything Everywhere All at Once</td>\n",
              "      <td>2022.0</td>\n",
              "      <td>The universe is so much bigger than you realize.</td>\n",
              "      <td>An aging Chinese immigrant is swept up in an i...</td>\n",
              "      <td>140.0</td>\n",
              "      <td>4.32</td>\n",
              "      <td>[Randall Archer, Boon Pin Koh, Efka Kvaracieju...</td>\n",
              "      <td>[Science Fiction, Action, Adventure]</td>\n",
              "      <td>Primary language</td>\n",
              "      <td>English</td>\n",
              "      <td>[Daniel Kwan, Daniel Scheinert]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1000004</td>\n",
              "      <td>Fight Club</td>\n",
              "      <td>1999.0</td>\n",
              "      <td>Mischief. Mayhem. Soap.</td>\n",
              "      <td>A ticking-time-bomb insomniac and a slippery s...</td>\n",
              "      <td>139.0</td>\n",
              "      <td>4.27</td>\n",
              "      <td>[Leonard Termo, Greg Bronson, Michael Arturo, ...</td>\n",
              "      <td>[Drama]</td>\n",
              "      <td>Language</td>\n",
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              "      <td>[David Fincher]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1000005</td>\n",
              "      <td>Interstellar</td>\n",
              "      <td>2014.0</td>\n",
              "      <td>Mankind was born on Earth. It was never meant ...</td>\n",
              "      <td>The adventures of a group of explorers who mak...</td>\n",
              "      <td>169.0</td>\n",
              "      <td>4.32</td>\n",
              "      <td>[Lena Georgas, Jeff Hephner, Elyes Gabel, Broo...</td>\n",
              "      <td>[Science Fiction, Drama, Adventure]</td>\n",
              "      <td>Language</td>\n",
              "      <td>English</td>\n",
              "      <td>[Christopher Nolan]</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        id                              movie    date  \\\n",
              "0  1000001                             Barbie  2023.0   \n",
              "1  1000002                           Parasite  2019.0   \n",
              "2  1000003  Everything Everywhere All at Once  2022.0   \n",
              "3  1000004                         Fight Club  1999.0   \n",
              "4  1000005                       Interstellar  2014.0   \n",
              "\n",
              "                                             tagline  \\\n",
              "0                   She's everything. He's just Ken.   \n",
              "1                        Act like you own the place.   \n",
              "2   The universe is so much bigger than you realize.   \n",
              "3                            Mischief. Mayhem. Soap.   \n",
              "4  Mankind was born on Earth. It was never meant ...   \n",
              "\n",
              "                                         description  minute  rating  \\\n",
              "0  Barbie and Ken are having the time of their li...   114.0    3.91   \n",
              "1  All unemployed, Ki-taek's family takes peculia...   133.0    4.57   \n",
              "2  An aging Chinese immigrant is swept up in an i...   140.0    4.32   \n",
              "3  A ticking-time-bomb insomniac and a slippery s...   139.0    4.27   \n",
              "4  The adventures of a group of explorers who mak...   169.0    4.32   \n",
              "\n",
              "                                                name  \\\n",
              "0  [Margot Robbie, Lewis Easter, Onyemachi Ejimof...   \n",
              "1  [Rosie Peralta, Anna Elisabeth Rihlmann, Andre...   \n",
              "2  [Randall Archer, Boon Pin Koh, Efka Kvaracieju...   \n",
              "3  [Leonard Termo, Greg Bronson, Michael Arturo, ...   \n",
              "4  [Lena Georgas, Jeff Hephner, Elyes Gabel, Broo...   \n",
              "\n",
              "                                  genre              type language  \\\n",
              "0          [Comedy, Fantasy, Adventure]          Language  English   \n",
              "1             [Comedy, Thriller, Drama]  Primary language   Korean   \n",
              "2  [Science Fiction, Action, Adventure]  Primary language  English   \n",
              "3                               [Drama]          Language  English   \n",
              "4   [Science Fiction, Drama, Adventure]          Language  English   \n",
              "\n",
              "                          Director  \n",
              "0                   [Greta Gerwig]  \n",
              "1                   [Bong Joon-ho]  \n",
              "2  [Daniel Kwan, Daniel Scheinert]  \n",
              "3                  [David Fincher]  \n",
              "4              [Christopher Nolan]  "
            ]
          },
          "execution_count": 13,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "merged_df14576 = pd.merge(merged_df1457, grouped6, on='id', how='left')\n",
        "merged_df14576.head(5)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "scrolled": true,
        "id": "78c47804",
        "outputId": "0f469d17-8c3f-41db-c43b-5604056a3a70"
      },
      "outputs": [
        {
          "data": {
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              "      <th>1</th>\n",
              "      <td>1000002</td>\n",
              "      <td>Parasite</td>\n",
              "      <td>2019.0</td>\n",
              "      <td>4.57</td>\n",
              "      <td>[Rosie Peralta, Anna Elisabeth Rihlmann, Andre...</td>\n",
              "      <td>[Comedy, Thriller, Drama]</td>\n",
              "      <td>Korean</td>\n",
              "      <td>[Bong Joon-ho]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1000003</td>\n",
              "      <td>Everything Everywhere All at Once</td>\n",
              "      <td>2022.0</td>\n",
              "      <td>4.32</td>\n",
              "      <td>[Randall Archer, Boon Pin Koh, Efka Kvaracieju...</td>\n",
              "      <td>[Science Fiction, Action, Adventure]</td>\n",
              "      <td>English</td>\n",
              "      <td>[Daniel Kwan, Daniel Scheinert]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1000004</td>\n",
              "      <td>Fight Club</td>\n",
              "      <td>1999.0</td>\n",
              "      <td>4.27</td>\n",
              "      <td>[Leonard Termo, Greg Bronson, Michael Arturo, ...</td>\n",
              "      <td>[Drama]</td>\n",
              "      <td>English</td>\n",
              "      <td>[David Fincher]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1000005</td>\n",
              "      <td>Interstellar</td>\n",
              "      <td>2014.0</td>\n",
              "      <td>4.32</td>\n",
              "      <td>[Lena Georgas, Jeff Hephner, Elyes Gabel, Broo...</td>\n",
              "      <td>[Science Fiction, Drama, Adventure]</td>\n",
              "      <td>English</td>\n",
              "      <td>[Christopher Nolan]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>1000006</td>\n",
              "      <td>Joker</td>\n",
              "      <td>2019.0</td>\n",
              "      <td>3.83</td>\n",
              "      <td>[Danny Schoch, Graham Mabry, Steven Elson, Rog...</td>\n",
              "      <td>[Thriller, Drama, Crime]</td>\n",
              "      <td>English</td>\n",
              "      <td>[Todd Phillips]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>1000007</td>\n",
              "      <td>Spider-Man: Into the Spider-Verse</td>\n",
              "      <td>2018.0</td>\n",
              "      <td>4.43</td>\n",
              "      <td>[Donna Lynn Leavy, Lex Lang, Harrison Knight, ...</td>\n",
              "      <td>[Adventure, Animation, Science Fiction, Action]</td>\n",
              "      <td>English</td>\n",
              "      <td>[Rodney Rothman, Peter Ramsey, Bob Persichetti]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>1000008</td>\n",
              "      <td>Knives Out</td>\n",
              "      <td>2019.0</td>\n",
              "      <td>4.00</td>\n",
              "      <td>[Raúl Castillo, Shyrley Rodriguez, Kerry Franc...</td>\n",
              "      <td>[Mystery, Comedy, Crime]</td>\n",
              "      <td>English</td>\n",
              "      <td>[Rian Johnson]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>1000009</td>\n",
              "      <td>La La Land</td>\n",
              "      <td>2016.0</td>\n",
              "      <td>4.05</td>\n",
              "      <td>[Carol Connors, Daniel Gaymon, Patrick Cook, A...</td>\n",
              "      <td>[Drama, Comedy, Music, Romance]</td>\n",
              "      <td>English</td>\n",
              "      <td>[Damien Chazelle]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>1000010</td>\n",
              "      <td>Pulp Fiction</td>\n",
              "      <td>1994.0</td>\n",
              "      <td>4.27</td>\n",
              "      <td>[Brad Blumenthal, Carl Allen, Don Blakely, Ric...</td>\n",
              "      <td>[Crime, Thriller]</td>\n",
              "      <td>English</td>\n",
              "      <td>[Quentin Tarantino]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>1000011</td>\n",
              "      <td>The Batman</td>\n",
              "      <td>2022.0</td>\n",
              "      <td>3.99</td>\n",
              "      <td>[Akie Kotabe, Philip Shaun McGuinness, Craige ...</td>\n",
              "      <td>[Thriller, Mystery, Crime]</td>\n",
              "      <td>English</td>\n",
              "      <td>[Matt Reeves]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>1000012</td>\n",
              "      <td>Oppenheimer</td>\n",
              "      <td>2023.0</td>\n",
              "      <td>4.26</td>\n",
              "      <td>[Harrison Gilbertson, Jack Cutmore-Scott, Oliv...</td>\n",
              "      <td>[Drama, History]</td>\n",
              "      <td>English</td>\n",
              "      <td>[Christopher Nolan]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>1000013</td>\n",
              "      <td>Whiplash</td>\n",
              "      <td>2014.0</td>\n",
              "      <td>4.43</td>\n",
              "      <td>[Jimmie Kirkpatrick, April Grace, Daniel Weidl...</td>\n",
              "      <td>[Drama, Music]</td>\n",
              "      <td>English</td>\n",
              "      <td>[Damien Chazelle]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>1000014</td>\n",
              "      <td>Get Out</td>\n",
              "      <td>2017.0</td>\n",
              "      <td>4.16</td>\n",
              "      <td>[Richard Herd, Erika Alexander, Jeronimo Spinx...</td>\n",
              "      <td>[Horror, Mystery, Thriller]</td>\n",
              "      <td>English</td>\n",
              "      <td>[Jordan Peele]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>1000015</td>\n",
              "      <td>Midsommar</td>\n",
              "      <td>2019.0</td>\n",
              "      <td>3.78</td>\n",
              "      <td>[Anders Back, Dag Andersson, Tomas Engström, L...</td>\n",
              "      <td>[Mystery, Drama, Horror]</td>\n",
              "      <td>English</td>\n",
              "      <td>[Ari Aster]</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         id                              movie    date  rating  \\\n",
              "0   1000001                             Barbie  2023.0    3.91   \n",
              "1   1000002                           Parasite  2019.0    4.57   \n",
              "2   1000003  Everything Everywhere All at Once  2022.0    4.32   \n",
              "3   1000004                         Fight Club  1999.0    4.27   \n",
              "4   1000005                       Interstellar  2014.0    4.32   \n",
              "5   1000006                              Joker  2019.0    3.83   \n",
              "6   1000007  Spider-Man: Into the Spider-Verse  2018.0    4.43   \n",
              "7   1000008                         Knives Out  2019.0    4.00   \n",
              "8   1000009                         La La Land  2016.0    4.05   \n",
              "9   1000010                       Pulp Fiction  1994.0    4.27   \n",
              "10  1000011                         The Batman  2022.0    3.99   \n",
              "11  1000012                        Oppenheimer  2023.0    4.26   \n",
              "12  1000013                           Whiplash  2014.0    4.43   \n",
              "13  1000014                            Get Out  2017.0    4.16   \n",
              "14  1000015                          Midsommar  2019.0    3.78   \n",
              "\n",
              "                                                 name  \\\n",
              "0   [Margot Robbie, Lewis Easter, Onyemachi Ejimof...   \n",
              "1   [Rosie Peralta, Anna Elisabeth Rihlmann, Andre...   \n",
              "2   [Randall Archer, Boon Pin Koh, Efka Kvaracieju...   \n",
              "3   [Leonard Termo, Greg Bronson, Michael Arturo, ...   \n",
              "4   [Lena Georgas, Jeff Hephner, Elyes Gabel, Broo...   \n",
              "5   [Danny Schoch, Graham Mabry, Steven Elson, Rog...   \n",
              "6   [Donna Lynn Leavy, Lex Lang, Harrison Knight, ...   \n",
              "7   [Raúl Castillo, Shyrley Rodriguez, Kerry Franc...   \n",
              "8   [Carol Connors, Daniel Gaymon, Patrick Cook, A...   \n",
              "9   [Brad Blumenthal, Carl Allen, Don Blakely, Ric...   \n",
              "10  [Akie Kotabe, Philip Shaun McGuinness, Craige ...   \n",
              "11  [Harrison Gilbertson, Jack Cutmore-Scott, Oliv...   \n",
              "12  [Jimmie Kirkpatrick, April Grace, Daniel Weidl...   \n",
              "13  [Richard Herd, Erika Alexander, Jeronimo Spinx...   \n",
              "14  [Anders Back, Dag Andersson, Tomas Engström, L...   \n",
              "\n",
              "                                              genre language  \\\n",
              "0                      [Comedy, Fantasy, Adventure]  English   \n",
              "1                         [Comedy, Thriller, Drama]   Korean   \n",
              "2              [Science Fiction, Action, Adventure]  English   \n",
              "3                                           [Drama]  English   \n",
              "4               [Science Fiction, Drama, Adventure]  English   \n",
              "5                          [Thriller, Drama, Crime]  English   \n",
              "6   [Adventure, Animation, Science Fiction, Action]  English   \n",
              "7                          [Mystery, Comedy, Crime]  English   \n",
              "8                   [Drama, Comedy, Music, Romance]  English   \n",
              "9                                 [Crime, Thriller]  English   \n",
              "10                       [Thriller, Mystery, Crime]  English   \n",
              "11                                 [Drama, History]  English   \n",
              "12                                   [Drama, Music]  English   \n",
              "13                      [Horror, Mystery, Thriller]  English   \n",
              "14                         [Mystery, Drama, Horror]  English   \n",
              "\n",
              "                                           Director  \n",
              "0                                    [Greta Gerwig]  \n",
              "1                                    [Bong Joon-ho]  \n",
              "2                   [Daniel Kwan, Daniel Scheinert]  \n",
              "3                                   [David Fincher]  \n",
              "4                               [Christopher Nolan]  \n",
              "5                                   [Todd Phillips]  \n",
              "6   [Rodney Rothman, Peter Ramsey, Bob Persichetti]  \n",
              "7                                    [Rian Johnson]  \n",
              "8                                 [Damien Chazelle]  \n",
              "9                               [Quentin Tarantino]  \n",
              "10                                    [Matt Reeves]  \n",
              "11                              [Christopher Nolan]  \n",
              "12                                [Damien Chazelle]  \n",
              "13                                   [Jordan Peele]  \n",
              "14                                      [Ari Aster]  "
            ]
          },
          "execution_count": 14,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "merged_df14576.drop(columns=['tagline', 'description', 'minute', 'type'], inplace=True)\n",
        "merged_df14576.head(15)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "27ce29c6",
        "outputId": "6cc83054-e178-42cb-a16d-273e914824ec"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>id</th>\n",
              "      <th>movie</th>\n",
              "      <th>date</th>\n",
              "      <th>rating</th>\n",
              "      <th>name</th>\n",
              "      <th>genre</th>\n",
              "      <th>language</th>\n",
              "      <th>Director</th>\n",
              "    </tr>\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1000001</td>\n",
              "      <td>Barbie</td>\n",
              "      <td>2023.0</td>\n",
              "      <td>3.91</td>\n",
              "      <td>[Margot Robbie, Lewis Easter, Onyemachi Ejimof...</td>\n",
              "      <td>[Comedy, Fantasy, Adventure]</td>\n",
              "      <td>English</td>\n",
              "      <td>[Greta Gerwig]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1000002</td>\n",
              "      <td>Parasite</td>\n",
              "      <td>2019.0</td>\n",
              "      <td>4.57</td>\n",
              "      <td>[Rosie Peralta, Anna Elisabeth Rihlmann, Andre...</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1000003</td>\n",
              "      <td>Everything Everywhere All at Once</td>\n",
              "      <td>2022.0</td>\n",
              "      <td>4.32</td>\n",
              "      <td>[Randall Archer, Boon Pin Koh, Efka Kvaracieju...</td>\n",
              "      <td>[Science Fiction, Action, Adventure]</td>\n",
              "      <td>English</td>\n",
              "      <td>[Daniel Kwan, Daniel Scheinert]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1000004</td>\n",
              "      <td>Fight Club</td>\n",
              "      <td>1999.0</td>\n",
              "      <td>4.27</td>\n",
              "      <td>[Leonard Termo, Greg Bronson, Michael Arturo, ...</td>\n",
              "      <td>[Drama]</td>\n",
              "      <td>English</td>\n",
              "      <td>[David Fincher]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1000005</td>\n",
              "      <td>Interstellar</td>\n",
              "      <td>2014.0</td>\n",
              "      <td>4.32</td>\n",
              "      <td>[Lena Georgas, Jeff Hephner, Elyes Gabel, Broo...</td>\n",
              "      <td>[Science Fiction, Drama, Adventure]</td>\n",
              "      <td>English</td>\n",
              "      <td>[Christopher Nolan]</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>895437</th>\n",
              "      <td>1895407</td>\n",
              "      <td>Loving You is My Destiny</td>\n",
              "      <td>1971.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>[Levent Dönmez, Sacide Keskin, Timuçin Caymaz,...</td>\n",
              "      <td>[Drama, Romance]</td>\n",
              "      <td>Turkish</td>\n",
              "      <td>[Orhan Aksoy]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>895438</th>\n",
              "      <td>1895408</td>\n",
              "      <td>Our Round Earth: What It's Like</td>\n",
              "      <td>1971.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>[Documentary]</td>\n",
              "      <td>English</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>895439</th>\n",
              "      <td>1895409</td>\n",
              "      <td>Salgari Of Our Childhood</td>\n",
              "      <td>1971.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>[Omar Salgari]</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Italian</td>\n",
              "      <td>[Corrado Farina]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>895440</th>\n",
              "      <td>1895410</td>\n",
              "      <td>Small Smoke at Blaze Creek</td>\n",
              "      <td>1971.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>[Documentary]</td>\n",
              "      <td>English</td>\n",
              "      <td>[Michael J.F. Scott]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>896394</th>\n",
              "      <td>1896364</td>\n",
              "      <td>Winterland</td>\n",
              "      <td>2019.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>[Cody Townsend, Robin Van Gyn, Mark Carter, Cl...</td>\n",
              "      <td>[Documentary]</td>\n",
              "      <td>English</td>\n",
              "      <td>[Todd Jones, Jon Klaczkiewicz, Steve Jones]</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>680141 rows × 8 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "             id                              movie    date  rating  \\\n",
              "0       1000001                             Barbie  2023.0    3.91   \n",
              "1       1000002                           Parasite  2019.0    4.57   \n",
              "2       1000003  Everything Everywhere All at Once  2022.0    4.32   \n",
              "3       1000004                         Fight Club  1999.0    4.27   \n",
              "4       1000005                       Interstellar  2014.0    4.32   \n",
              "...         ...                                ...     ...     ...   \n",
              "895437  1895407           Loving You is My Destiny  1971.0     NaN   \n",
              "895438  1895408    Our Round Earth: What It's Like  1971.0     NaN   \n",
              "895439  1895409           Salgari Of Our Childhood  1971.0     NaN   \n",
              "895440  1895410         Small Smoke at Blaze Creek  1971.0     NaN   \n",
              "896394  1896364                         Winterland  2019.0     NaN   \n",
              "\n",
              "                                                     name  \\\n",
              "0       [Margot Robbie, Lewis Easter, Onyemachi Ejimof...   \n",
              "1       [Rosie Peralta, Anna Elisabeth Rihlmann, Andre...   \n",
              "2       [Randall Archer, Boon Pin Koh, Efka Kvaracieju...   \n",
              "3       [Leonard Termo, Greg Bronson, Michael Arturo, ...   \n",
              "4       [Lena Georgas, Jeff Hephner, Elyes Gabel, Broo...   \n",
              "...                                                   ...   \n",
              "895437  [Levent Dönmez, Sacide Keskin, Timuçin Caymaz,...   \n",
              "895438                                                NaN   \n",
              "895439                                     [Omar Salgari]   \n",
              "895440                                                NaN   \n",
              "896394  [Cody Townsend, Robin Van Gyn, Mark Carter, Cl...   \n",
              "\n",
              "                                       genre language  \\\n",
              "0               [Comedy, Fantasy, Adventure]  English   \n",
              "1                  [Comedy, Thriller, Drama]   Korean   \n",
              "2       [Science Fiction, Action, Adventure]  English   \n",
              "3                                    [Drama]  English   \n",
              "4        [Science Fiction, Drama, Adventure]  English   \n",
              "...                                      ...      ...   \n",
              "895437                      [Drama, Romance]  Turkish   \n",
              "895438                         [Documentary]  English   \n",
              "895439                                   NaN  Italian   \n",
              "895440                         [Documentary]  English   \n",
              "896394                         [Documentary]  English   \n",
              "\n",
              "                                           Director  \n",
              "0                                    [Greta Gerwig]  \n",
              "1                                    [Bong Joon-ho]  \n",
              "2                   [Daniel Kwan, Daniel Scheinert]  \n",
              "3                                   [David Fincher]  \n",
              "4                               [Christopher Nolan]  \n",
              "...                                             ...  \n",
              "895437                                [Orhan Aksoy]  \n",
              "895438                                          NaN  \n",
              "895439                             [Corrado Farina]  \n",
              "895440                         [Michael J.F. Scott]  \n",
              "896394  [Todd Jones, Jon Klaczkiewicz, Steve Jones]  \n",
              "\n",
              "[680141 rows x 8 columns]"
            ]
          },
          "execution_count": 16,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "#We want to eliminate movies that are too old, and we decided that 1970 is a good year to draw the line\n",
        "merged_df14576['date'] = pd.to_numeric(merged_df14576['date'], errors='coerce')\n",
        "filtered_df = merged_df14576[merged_df14576['date'] > 1970.0]\n",
        "filtered_df"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "205e8fbf",
        "outputId": "a938dfbd-d386-4b17-b685-c23fb8ef936f"
      },
      "outputs": [
        {
          "data": {
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              "      <th>0</th>\n",
              "      <td>1000001</td>\n",
              "      <td>Barbie</td>\n",
              "      <td>2023.0</td>\n",
              "      <td>3.91</td>\n",
              "      <td>[Margot Robbie, Lewis Easter, Onyemachi Ejimof...</td>\n",
              "      <td>[Comedy, Fantasy, Adventure]</td>\n",
              "      <td>English</td>\n",
              "      <td>[Greta Gerwig]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1000002</td>\n",
              "      <td>Parasite</td>\n",
              "      <td>2019.0</td>\n",
              "      <td>4.57</td>\n",
              "      <td>[Rosie Peralta, Anna Elisabeth Rihlmann, Andre...</td>\n",
              "      <td>[Comedy, Thriller, Drama]</td>\n",
              "      <td>Korean</td>\n",
              "      <td>[Bong Joon-ho]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1000003</td>\n",
              "      <td>Everything Everywhere All at Once</td>\n",
              "      <td>2022.0</td>\n",
              "      <td>4.32</td>\n",
              "      <td>[Randall Archer, Boon Pin Koh, Efka Kvaracieju...</td>\n",
              "      <td>[Science Fiction, Action, Adventure]</td>\n",
              "      <td>English</td>\n",
              "      <td>[Daniel Kwan, Daniel Scheinert]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1000004</td>\n",
              "      <td>Fight Club</td>\n",
              "      <td>1999.0</td>\n",
              "      <td>4.27</td>\n",
              "      <td>[Leonard Termo, Greg Bronson, Michael Arturo, ...</td>\n",
              "      <td>[Drama]</td>\n",
              "      <td>English</td>\n",
              "      <td>[David Fincher]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1000005</td>\n",
              "      <td>Interstellar</td>\n",
              "      <td>2014.0</td>\n",
              "      <td>4.32</td>\n",
              "      <td>[Lena Georgas, Jeff Hephner, Elyes Gabel, Broo...</td>\n",
              "      <td>[Science Fiction, Drama, Adventure]</td>\n",
              "      <td>English</td>\n",
              "      <td>[Christopher Nolan]</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>153113</th>\n",
              "      <td>1153114</td>\n",
              "      <td>Cinema Chupista Maava</td>\n",
              "      <td>2015.0</td>\n",
              "      <td>3.17</td>\n",
              "      <td>[Raj Tarun, Avika Gor, Rao Ramesh]</td>\n",
              "      <td>[Comedy, Romance, Family]</td>\n",
              "      <td>Telugu</td>\n",
              "      <td>[Trinadha Rao Nakkina]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>153293</th>\n",
              "      <td>1153294</td>\n",
              "      <td>Los Angeles/Tehran</td>\n",
              "      <td>2018.0</td>\n",
              "      <td>2.24</td>\n",
              "      <td>[Parviz Parastouei, Mahnaz Afshar, Gohar Kheyr...</td>\n",
              "      <td>[Comedy]</td>\n",
              "      <td>Persian (Farsi)</td>\n",
              "      <td>[Tina Pakravan]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>154615</th>\n",
              "      <td>1154616</td>\n",
              "      <td>Kutti Puli</td>\n",
              "      <td>2013.0</td>\n",
              "      <td>2.89</td>\n",
              "      <td>[G. Gnanasambandam, Raja Simman, Balasaravanan...</td>\n",
              "      <td>[Action, Thriller]</td>\n",
              "      <td>Tamil</td>\n",
              "      <td>[M. Muthaiah]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>156022</th>\n",
              "      <td>1156023</td>\n",
              "      <td>To Steal a Thief</td>\n",
              "      <td>1996.0</td>\n",
              "      <td>2.94</td>\n",
              "      <td>[Eva Dahlman, Agneta Guldberg, Per-Erik Bergqu...</td>\n",
              "      <td>[Comedy, Crime]</td>\n",
              "      <td>Swedish</td>\n",
              "      <td>[Clas Lindberg]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>158773</th>\n",
              "      <td>1158774</td>\n",
              "      <td>As Seen On Tv</td>\n",
              "      <td>2001.0</td>\n",
              "      <td>3.16</td>\n",
              "      <td>[Cecilia Frode, Anna Carlsten, Magnus Härensta...</td>\n",
              "      <td>[Comedy]</td>\n",
              "      <td>Swedish</td>\n",
              "      <td>[Fredrik Lindström]</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>68318 rows × 8 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "             id                              movie    date  rating  \\\n",
              "0       1000001                             Barbie  2023.0    3.91   \n",
              "1       1000002                           Parasite  2019.0    4.57   \n",
              "2       1000003  Everything Everywhere All at Once  2022.0    4.32   \n",
              "3       1000004                         Fight Club  1999.0    4.27   \n",
              "4       1000005                       Interstellar  2014.0    4.32   \n",
              "...         ...                                ...     ...     ...   \n",
              "153113  1153114              Cinema Chupista Maava  2015.0    3.17   \n",
              "153293  1153294                 Los Angeles/Tehran  2018.0    2.24   \n",
              "154615  1154616                         Kutti Puli  2013.0    2.89   \n",
              "156022  1156023                   To Steal a Thief  1996.0    2.94   \n",
              "158773  1158774                      As Seen On Tv  2001.0    3.16   \n",
              "\n",
              "                                                     name  \\\n",
              "0       [Margot Robbie, Lewis Easter, Onyemachi Ejimof...   \n",
              "1       [Rosie Peralta, Anna Elisabeth Rihlmann, Andre...   \n",
              "2       [Randall Archer, Boon Pin Koh, Efka Kvaracieju...   \n",
              "3       [Leonard Termo, Greg Bronson, Michael Arturo, ...   \n",
              "4       [Lena Georgas, Jeff Hephner, Elyes Gabel, Broo...   \n",
              "...                                                   ...   \n",
              "153113                 [Raj Tarun, Avika Gor, Rao Ramesh]   \n",
              "153293  [Parviz Parastouei, Mahnaz Afshar, Gohar Kheyr...   \n",
              "154615  [G. Gnanasambandam, Raja Simman, Balasaravanan...   \n",
              "156022  [Eva Dahlman, Agneta Guldberg, Per-Erik Bergqu...   \n",
              "158773  [Cecilia Frode, Anna Carlsten, Magnus Härensta...   \n",
              "\n",
              "                                       genre         language  \\\n",
              "0               [Comedy, Fantasy, Adventure]          English   \n",
              "1                  [Comedy, Thriller, Drama]           Korean   \n",
              "2       [Science Fiction, Action, Adventure]          English   \n",
              "3                                    [Drama]          English   \n",
              "4        [Science Fiction, Drama, Adventure]          English   \n",
              "...                                      ...              ...   \n",
              "153113             [Comedy, Romance, Family]           Telugu   \n",
              "153293                              [Comedy]  Persian (Farsi)   \n",
              "154615                    [Action, Thriller]            Tamil   \n",
              "156022                       [Comedy, Crime]          Swedish   \n",
              "158773                              [Comedy]          Swedish   \n",
              "\n",
              "                               Director  \n",
              "0                        [Greta Gerwig]  \n",
              "1                        [Bong Joon-ho]  \n",
              "2       [Daniel Kwan, Daniel Scheinert]  \n",
              "3                       [David Fincher]  \n",
              "4                   [Christopher Nolan]  \n",
              "...                                 ...  \n",
              "153113           [Trinadha Rao Nakkina]  \n",
              "153293                  [Tina Pakravan]  \n",
              "154615                    [M. Muthaiah]  \n",
              "156022                  [Clas Lindberg]  \n",
              "158773              [Fredrik Lindström]  \n",
              "\n",
              "[68318 rows x 8 columns]"
            ]
          },
          "execution_count": 17,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df_cleaned = filtered_df.dropna()\n",
        "df_cleaned"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "fd547eb1",
        "outputId": "c5846ed1-5ba6-49ee-b74c-e3595adf523e"
      },
      "outputs": [
        {
          "data": {
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              "  <thead>\n",
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              "      <th>id</th>\n",
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              "      <th>Director</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
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              "      <th>0</th>\n",
              "      <td>1000001</td>\n",
              "      <td>Barbie</td>\n",
              "      <td>2023.0</td>\n",
              "      <td>3.91</td>\n",
              "      <td>[Margot Robbie, Lewis Easter, Onyemachi Ejimof...</td>\n",
              "      <td>[Comedy, Fantasy, Adventure]</td>\n",
              "      <td>English</td>\n",
              "      <td>[Greta Gerwig]</td>\n",
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              "      <th>2</th>\n",
              "      <td>1000003</td>\n",
              "      <td>Everything Everywhere All at Once</td>\n",
              "      <td>2022.0</td>\n",
              "      <td>4.32</td>\n",
              "      <td>[Randall Archer, Boon Pin Koh, Efka Kvaracieju...</td>\n",
              "      <td>[Science Fiction, Action, Adventure]</td>\n",
              "      <td>English</td>\n",
              "      <td>[Daniel Kwan, Daniel Scheinert]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1000004</td>\n",
              "      <td>Fight Club</td>\n",
              "      <td>1999.0</td>\n",
              "      <td>4.27</td>\n",
              "      <td>[Leonard Termo, Greg Bronson, Michael Arturo, ...</td>\n",
              "      <td>[Drama]</td>\n",
              "      <td>English</td>\n",
              "      <td>[David Fincher]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1000005</td>\n",
              "      <td>Interstellar</td>\n",
              "      <td>2014.0</td>\n",
              "      <td>4.32</td>\n",
              "      <td>[Lena Georgas, Jeff Hephner, Elyes Gabel, Broo...</td>\n",
              "      <td>[Science Fiction, Drama, Adventure]</td>\n",
              "      <td>English</td>\n",
              "      <td>[Christopher Nolan]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>1000006</td>\n",
              "      <td>Joker</td>\n",
              "      <td>2019.0</td>\n",
              "      <td>3.83</td>\n",
              "      <td>[Danny Schoch, Graham Mabry, Steven Elson, Rog...</td>\n",
              "      <td>[Thriller, Drama, Crime]</td>\n",
              "      <td>English</td>\n",
              "      <td>[Todd Phillips]</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>126882</th>\n",
              "      <td>1126883</td>\n",
              "      <td>Bangaram</td>\n",
              "      <td>2006.0</td>\n",
              "      <td>2.97</td>\n",
              "      <td>[Ashutosh Rana, Trisha Krishnan, L. B. Sriram,...</td>\n",
              "      <td>[Drama, Action]</td>\n",
              "      <td>English</td>\n",
              "      <td>[Dharani]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>127954</th>\n",
              "      <td>1127955</td>\n",
              "      <td>Emu War - OverSimplified</td>\n",
              "      <td>2018.0</td>\n",
              "      <td>3.53</td>\n",
              "      <td>[Stuart Webster]</td>\n",
              "      <td>[Documentary, History]</td>\n",
              "      <td>English</td>\n",
              "      <td>[Stuart Webster]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>127968</th>\n",
              "      <td>1127969</td>\n",
              "      <td>Lee Evans: Wired and Wonderful</td>\n",
              "      <td>2002.0</td>\n",
              "      <td>3.56</td>\n",
              "      <td>[Lee Evans]</td>\n",
              "      <td>[Comedy]</td>\n",
              "      <td>English</td>\n",
              "      <td>[Tom Poole]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>128915</th>\n",
              "      <td>1128916</td>\n",
              "      <td>The Return of 'Allo 'Allo!</td>\n",
              "      <td>2007.0</td>\n",
              "      <td>3.62</td>\n",
              "      <td>[Gorden Kaye, Vicki Michelle, Arthur Bostrom, ...</td>\n",
              "      <td>[Documentary]</td>\n",
              "      <td>English</td>\n",
              "      <td>[Dennis Martin]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>144326</th>\n",
              "      <td>1144327</td>\n",
              "      <td>Gunnar Rehlin - A Small Film About Hurting Som...</td>\n",
              "      <td>1999.0</td>\n",
              "      <td>3.51</td>\n",
              "      <td>[Tommy Bergqvist, Monte Reid, Martin Luuk, Kri...</td>\n",
              "      <td>[Comedy, TV Movie]</td>\n",
              "      <td>English</td>\n",
              "      <td>[Tomas Alfredson]</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>36364 rows × 8 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "             id                                              movie    date  \\\n",
              "0       1000001                                             Barbie  2023.0   \n",
              "2       1000003                  Everything Everywhere All at Once  2022.0   \n",
              "3       1000004                                         Fight Club  1999.0   \n",
              "4       1000005                                       Interstellar  2014.0   \n",
              "5       1000006                                              Joker  2019.0   \n",
              "...         ...                                                ...     ...   \n",
              "126882  1126883                                           Bangaram  2006.0   \n",
              "127954  1127955                           Emu War - OverSimplified  2018.0   \n",
              "127968  1127969                     Lee Evans: Wired and Wonderful  2002.0   \n",
              "128915  1128916                         The Return of 'Allo 'Allo!  2007.0   \n",
              "144326  1144327  Gunnar Rehlin - A Small Film About Hurting Som...  1999.0   \n",
              "\n",
              "        rating                                               name  \\\n",
              "0         3.91  [Margot Robbie, Lewis Easter, Onyemachi Ejimof...   \n",
              "2         4.32  [Randall Archer, Boon Pin Koh, Efka Kvaracieju...   \n",
              "3         4.27  [Leonard Termo, Greg Bronson, Michael Arturo, ...   \n",
              "4         4.32  [Lena Georgas, Jeff Hephner, Elyes Gabel, Broo...   \n",
              "5         3.83  [Danny Schoch, Graham Mabry, Steven Elson, Rog...   \n",
              "...        ...                                                ...   \n",
              "126882    2.97  [Ashutosh Rana, Trisha Krishnan, L. B. Sriram,...   \n",
              "127954    3.53                                   [Stuart Webster]   \n",
              "127968    3.56                                        [Lee Evans]   \n",
              "128915    3.62  [Gorden Kaye, Vicki Michelle, Arthur Bostrom, ...   \n",
              "144326    3.51  [Tommy Bergqvist, Monte Reid, Martin Luuk, Kri...   \n",
              "\n",
              "                                       genre language  \\\n",
              "0               [Comedy, Fantasy, Adventure]  English   \n",
              "2       [Science Fiction, Action, Adventure]  English   \n",
              "3                                    [Drama]  English   \n",
              "4        [Science Fiction, Drama, Adventure]  English   \n",
              "5                   [Thriller, Drama, Crime]  English   \n",
              "...                                      ...      ...   \n",
              "126882                       [Drama, Action]  English   \n",
              "127954                [Documentary, History]  English   \n",
              "127968                              [Comedy]  English   \n",
              "128915                         [Documentary]  English   \n",
              "144326                    [Comedy, TV Movie]  English   \n",
              "\n",
              "                               Director  \n",
              "0                        [Greta Gerwig]  \n",
              "2       [Daniel Kwan, Daniel Scheinert]  \n",
              "3                       [David Fincher]  \n",
              "4                   [Christopher Nolan]  \n",
              "5                       [Todd Phillips]  \n",
              "...                                 ...  \n",
              "126882                        [Dharani]  \n",
              "127954                 [Stuart Webster]  \n",
              "127968                      [Tom Poole]  \n",
              "128915                  [Dennis Martin]  \n",
              "144326                [Tomas Alfredson]  \n",
              "\n",
              "[36364 rows x 8 columns]"
            ]
          },
          "execution_count": 19,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "filtered_df = df_cleaned[df_cleaned['language'].apply(lambda x: 'English' in x)]\n",
        "filtered_df.to_csv('filtered_2.csv', index=False)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Now we have merged our loose datasets and dropped the null values, here's what the final dataset look like"
      ],
      "metadata": {
        "id": "B9htNGE-ZGjm"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.read_csv('filtered_2.csv')\n",
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "knhK2QgYU59V",
        "outputId": "51fed59a-5c3d-4b33-9ce4-0b1796e041d1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        id                              movie    date  rating  \\\n",
              "0  1000001                             Barbie  2023.0    3.91   \n",
              "1  1000003  Everything Everywhere All at Once  2022.0    4.32   \n",
              "2  1000004                         Fight Club  1999.0    4.27   \n",
              "3  1000005                       Interstellar  2014.0    4.32   \n",
              "4  1000006                              Joker  2019.0    3.83   \n",
              "\n",
              "                                                name  \\\n",
              "0  ['Margot Robbie', 'Lewis Easter', 'Onyemachi E...   \n",
              "1  ['Randall Archer', 'Boon Pin Koh', 'Efka Kvara...   \n",
              "2  ['Leonard Termo', 'Greg Bronson', 'Michael Art...   \n",
              "3  ['Lena Georgas', 'Jeff Hephner', 'Elyes Gabel'...   \n",
              "4  ['Danny Schoch', 'Graham Mabry', 'Steven Elson...   \n",
              "\n",
              "                                        genre language  \\\n",
              "0          ['Comedy', 'Fantasy', 'Adventure']  English   \n",
              "1  ['Science Fiction', 'Action', 'Adventure']  English   \n",
              "2                                   ['Drama']  English   \n",
              "3   ['Science Fiction', 'Drama', 'Adventure']  English   \n",
              "4              ['Thriller', 'Drama', 'Crime']  English   \n",
              "\n",
              "                              Director  \n",
              "0                     ['Greta Gerwig']  \n",
              "1  ['Daniel Kwan', 'Daniel Scheinert']  \n",
              "2                    ['David Fincher']  \n",
              "3                ['Christopher Nolan']  \n",
              "4                    ['Todd Phillips']  "
            ],
            "text/html": [
              "\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1000001</td>\n",
              "      <td>Barbie</td>\n",
              "      <td>2023.0</td>\n",
              "      <td>3.91</td>\n",
              "      <td>['Margot Robbie', 'Lewis Easter', 'Onyemachi E...</td>\n",
              "      <td>['Comedy', 'Fantasy', 'Adventure']</td>\n",
              "      <td>English</td>\n",
              "      <td>['Greta Gerwig']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1000003</td>\n",
              "      <td>Everything Everywhere All at Once</td>\n",
              "      <td>2022.0</td>\n",
              "      <td>4.32</td>\n",
              "      <td>['Randall Archer', 'Boon Pin Koh', 'Efka Kvara...</td>\n",
              "      <td>['Science Fiction', 'Action', 'Adventure']</td>\n",
              "      <td>English</td>\n",
              "      <td>['Daniel Kwan', 'Daniel Scheinert']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1000004</td>\n",
              "      <td>Fight Club</td>\n",
              "      <td>1999.0</td>\n",
              "      <td>4.27</td>\n",
              "      <td>['Leonard Termo', 'Greg Bronson', 'Michael Art...</td>\n",
              "      <td>['Drama']</td>\n",
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              "    </tr>\n",
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              "      <th>3</th>\n",
              "      <td>1000005</td>\n",
              "      <td>Interstellar</td>\n",
              "      <td>2014.0</td>\n",
              "      <td>4.32</td>\n",
              "      <td>['Lena Georgas', 'Jeff Hephner', 'Elyes Gabel'...</td>\n",
              "      <td>['Science Fiction', 'Drama', 'Adventure']</td>\n",
              "      <td>English</td>\n",
              "      <td>['Christopher Nolan']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1000006</td>\n",
              "      <td>Joker</td>\n",
              "      <td>2019.0</td>\n",
              "      <td>3.83</td>\n",
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              "      <td>['Thriller', 'Drama', 'Crime']</td>\n",
              "      <td>English</td>\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 36364,\n  \"fields\": [\n    {\n      \"column\": \"id\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 27577,\n        \"min\": 1000001,\n        \"max\": 1144327,\n        \"num_unique_values\": 36364,\n        \"samples\": [\n          1001667,\n          1041424,\n          1050511\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"movie\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 34282,\n        \"samples\": [\n          \"The Fever\",\n          \"American Symphony\",\n          \"Velvet Goldmine\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"date\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 13.937266977420396,\n        \"min\": 1971.0,\n        \"max\": 2024.0,\n        \"num_unique_values\": 54,\n        \"samples\": [\n          1991.0,\n          1988.0,\n          1978.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"rating\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.4762973293189504,\n        \"min\": 0.89,\n        \"max\": 4.68,\n        \"num_unique_values\": 347,\n        \"samples\": [\n          1.28,\n          2.88,\n          4.5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 35828,\n        \"samples\": [\n          \"['David Blue', 'Jax Connolly', 'Ashley Jones', 'Preston James Hillier', 'Paul Boesing']\",\n          \"['Chipaul Cao', 'Millie Cao', 'Maksym Kapitanchuk', 'Elena Krifuks']\",\n          \"['Scott Patey', 'Andrew Stehlin', 'Karen Elizabeth Austin', 'Richard Stroh', 'Sarah-Jane Redmond', 'John DeSantis', 'Jackson Berlin', 'Stephen Huszar', 'Katharine Isabelle', 'Jody Thompson', 'Christopher De-Schuster', 'Donovan Cerminara', 'James Pizzinato', 'Marco Soriano', 'Mia Kirshner', 'Diora Baird', 'Rhys Coiro', 'Harold Perrineau', 'Kiele Sanchez', 'Ben Cotton', 'Stacey Roy', 'Katie Keating', 'Troy Ruptash', 'Monique Ganderton']\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"genre\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 5061,\n        \"samples\": [\n          \"['Romance', 'Drama', 'Comedy']\",\n          \"['Thriller', 'Mystery', 'Action']\",\n          \"['Action', 'Thriller', 'TV Movie', 'Horror', 'Science Fiction']\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"language\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"English\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Director\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 17216,\n        \"samples\": [\n          \"['Mars Callahan']\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 95
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "rename third column to make it easier to read"
      ],
      "metadata": {
        "id": "ip1QhJeNAYr4"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df = df.rename(columns={'name': 'actors'})"
      ],
      "metadata": {
        "id": "5d-btH5LAYal"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Let's also drop all the null values in columns 'movie', 'rating', and 'actors'"
      ],
      "metadata": {
        "id": "e8Vu7ghTZuYK"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Drop rows with NaN values in 'movie', 'rating', or 'actors' columns\n",
        "df = df.dropna(subset=['movie', 'rating', 'actors'])\n",
        "\n",
        "# Display the first few rows of the cleaned DataFrame\n",
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "_ahglpaZDgGn",
        "outputId": "7f19c860-2341-426f-e21b-07659a927c6b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        id                              movie    date  rating  \\\n",
              "0  1000001                             Barbie  2023.0    3.91   \n",
              "1  1000003  Everything Everywhere All at Once  2022.0    4.32   \n",
              "2  1000004                         Fight Club  1999.0    4.27   \n",
              "3  1000005                       Interstellar  2014.0    4.32   \n",
              "4  1000006                              Joker  2019.0    3.83   \n",
              "\n",
              "                                              actors  \\\n",
              "0  ['Margot Robbie', 'Lewis Easter', 'Onyemachi E...   \n",
              "1  ['Randall Archer', 'Boon Pin Koh', 'Efka Kvara...   \n",
              "2  ['Leonard Termo', 'Greg Bronson', 'Michael Art...   \n",
              "3  ['Lena Georgas', 'Jeff Hephner', 'Elyes Gabel'...   \n",
              "4  ['Danny Schoch', 'Graham Mabry', 'Steven Elson...   \n",
              "\n",
              "                                        genre language  \\\n",
              "0          ['Comedy', 'Fantasy', 'Adventure']  English   \n",
              "1  ['Science Fiction', 'Action', 'Adventure']  English   \n",
              "2                                   ['Drama']  English   \n",
              "3   ['Science Fiction', 'Drama', 'Adventure']  English   \n",
              "4              ['Thriller', 'Drama', 'Crime']  English   \n",
              "\n",
              "                              Director  \n",
              "0                     ['Greta Gerwig']  \n",
              "1  ['Daniel Kwan', 'Daniel Scheinert']  \n",
              "2                    ['David Fincher']  \n",
              "3                ['Christopher Nolan']  \n",
              "4                    ['Todd Phillips']  "
            ],
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              "                                                    [key], {});\n",
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              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
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              "      --bg-color: #E8F0FE;\n",
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              "  [theme=dark] .colab-df-quickchart {\n",
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              "\n",
              "  .colab-df-quickchart {\n",
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              "    background-color: var(--hover-bg-color);\n",
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              "  .colab-df-quickchart-complete:disabled,\n",
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              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-716b16f9-ed61-4b8c-b15a-d4a80626e579 button');\n",
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              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 36364,\n  \"fields\": [\n    {\n      \"column\": \"id\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 27577,\n        \"min\": 1000001,\n        \"max\": 1144327,\n        \"num_unique_values\": 36364,\n        \"samples\": [\n          1001667,\n          1041424,\n          1050511\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"movie\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 34282,\n        \"samples\": [\n          \"The Fever\",\n          \"American Symphony\",\n          \"Velvet Goldmine\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"date\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 13.937266977420396,\n        \"min\": 1971.0,\n        \"max\": 2024.0,\n        \"num_unique_values\": 54,\n        \"samples\": [\n          1991.0,\n          1988.0,\n          1978.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"rating\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.4762973293189504,\n        \"min\": 0.89,\n        \"max\": 4.68,\n        \"num_unique_values\": 347,\n        \"samples\": [\n          1.28,\n          2.88,\n          4.5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"actors\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 35828,\n        \"samples\": [\n          \"['David Blue', 'Jax Connolly', 'Ashley Jones', 'Preston James Hillier', 'Paul Boesing']\",\n          \"['Chipaul Cao', 'Millie Cao', 'Maksym Kapitanchuk', 'Elena Krifuks']\",\n          \"['Scott Patey', 'Andrew Stehlin', 'Karen Elizabeth Austin', 'Richard Stroh', 'Sarah-Jane Redmond', 'John DeSantis', 'Jackson Berlin', 'Stephen Huszar', 'Katharine Isabelle', 'Jody Thompson', 'Christopher De-Schuster', 'Donovan Cerminara', 'James Pizzinato', 'Marco Soriano', 'Mia Kirshner', 'Diora Baird', 'Rhys Coiro', 'Harold Perrineau', 'Kiele Sanchez', 'Ben Cotton', 'Stacey Roy', 'Katie Keating', 'Troy Ruptash', 'Monique Ganderton']\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"genre\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 5061,\n        \"samples\": [\n          \"['Romance', 'Drama', 'Comedy']\",\n          \"['Thriller', 'Mystery', 'Action']\",\n          \"['Action', 'Thriller', 'TV Movie', 'Horror', 'Science Fiction']\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"language\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"English\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Director\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 17216,\n        \"samples\": [\n          \"['Mars Callahan']\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 97
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Remove columns so we only have movie name, rating, name of actors"
      ],
      "metadata": {
        "id": "RTQ2GLAW-jv4"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "columns_to_keep = ['movie', 'rating', 'actors']\n",
        "\n",
        "# Drop columns that are not in the 'columns_to_keep' list\n",
        "df = df[columns_to_keep]\n",
        "\n",
        "# Display the first few rows of the modified DataFrame\n",
        "df.head()\n",
        "print(len(df))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "d8yE_F2X-sRP",
        "outputId": "20377c49-07d4-48ae-f94a-4002102a9289"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "36364\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "seems like there's duplicate entries looking at row 2 and 3 of the previous table, let's dedupe"
      ],
      "metadata": {
        "id": "4On_9UEB_K9N"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df = df.drop_duplicates(subset='movie', keep='first')\n",
        "\n",
        "# Display the first few rows of the DataFrame after removing duplicates\n",
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "lPL-8CL__m4y",
        "outputId": "e1dd0711-f84f-4d0a-9445-f6d22ecb72e5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                               movie  rating  \\\n",
              "0                             Barbie    3.91   \n",
              "1  Everything Everywhere All at Once    4.32   \n",
              "2                         Fight Club    4.27   \n",
              "3                       Interstellar    4.32   \n",
              "4                              Joker    3.83   \n",
              "\n",
              "                                              actors  \n",
              "0  ['Margot Robbie', 'Lewis Easter', 'Onyemachi E...  \n",
              "1  ['Randall Archer', 'Boon Pin Koh', 'Efka Kvara...  \n",
              "2  ['Leonard Termo', 'Greg Bronson', 'Michael Art...  \n",
              "3  ['Lena Georgas', 'Jeff Hephner', 'Elyes Gabel'...  \n",
              "4  ['Danny Schoch', 'Graham Mabry', 'Steven Elson...  "
            ],
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              "      <th>2</th>\n",
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              "      <td>4.27</td>\n",
              "      <td>['Leonard Termo', 'Greg Bronson', 'Michael Art...</td>\n",
              "    </tr>\n",
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              "      <th>3</th>\n",
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              "      background-color: #3B4455;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
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              "      buttonEl.style.display =\n",
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              "        const element = document.querySelector('#df-85f1a826-57eb-4df7-a988-b5336135f9fd');\n",
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              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "            title=\"Suggest charts\"\n",
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              "\n",
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              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
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              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-5b088ecd-cb74-4b4f-9f58-9cdc1faf4f50 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 34282,\n  \"fields\": [\n    {\n      \"column\": \"movie\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 34282,\n        \"samples\": [\n          \"The Fever\",\n          \"American Symphony\",\n          \"Velvet Goldmine\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"rating\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.47844409776735825,\n        \"min\": 0.89,\n        \"max\": 4.68,\n        \"num_unique_values\": 347,\n        \"samples\": [\n          1.48,\n          2.88,\n          4.5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"actors\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 33762,\n        \"samples\": [\n          \"['Mercedes Colon', 'Alexandra Quint', 'Cassie Tauer', 'Kaylin Mally', 'Jacqueline Walker', 'Julia Sanford', 'Lawrence J. Hughes', 'Matthew Sato', 'Hannah Hueston', 'Grace Patterson', 'Aleia Theriza', 'Alexa Sutherland', 'Ashley Scott', 'Alie Urquhart', 'Tommi Rose', 'Karla Mosley', 'Jazzy Kae', 'Mena Suvari']\",\n          \"['Markas Sagaitis', 'Titas Rukas', 'Markas Eimontas', 'Matvej Buravkov', 'Edmund Dehn', 'Marijus Demiskis', 'Richard Brake', 'Rosy McEwen', 'Eddie Marsan', 'Raffiella Chapman', 'Melanie Gaydos']\",\n          \"['Ray Charles', 'Steve Burns', 'Traci Paige Johnson']\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 99
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "That looks better and can be used for analysis!"
      ],
      "metadata": {
        "id": "rSyUQTl3gjbZ"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **EDA part 2: Data Exploring and visualization**"
      ],
      "metadata": {
        "id": "KWhAexdAgW3v"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Now let's create a new table with two columns, actor name and the average rating of all the movies they were in\n",
        "\n",
        "First let's have a dictionary actor_ratinngs which stores rating info for each actor"
      ],
      "metadata": {
        "id": "SQ-LNlX5_9O3"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from ast import literal_eval\n",
        "# Create an empty dictionary to store the sum and count of ratings for each actor\n",
        "actor_ratings = {}\n",
        "\n",
        "# Iterate through each row of the DataFrame\n",
        "for index, row in df.iterrows():\n",
        "  # Extract the list of actors from the 'actors' column\n",
        "  actors_list_str = row['actors']\n",
        "  actors_list_arr = literal_eval(actors_list_str)\n",
        "  row['actors'] = actors_list_arr\n",
        "  # Extract the rating for the movie\n",
        "  rating = row['rating']\n",
        "\n",
        "  # Check if the 'actors' column is a list\n",
        "  if isinstance(actors_list_arr, list):\n",
        "      # Iterate through each actor in the list\n",
        "    for actor in actors_list_arr:\n",
        "      # If the actor is not in the dictionary, add a new entry\n",
        "      if actor not in actor_ratings:\n",
        "          actor_ratings[actor] = {'sum': rating, 'count': 1}\n",
        "      else:\n",
        "          # If the actor is already in the dictionary, update the sum and count\n",
        "          actor_ratings[actor]['sum'] += rating\n",
        "          actor_ratings[actor]['count'] += 1"
      ],
      "metadata": {
        "id": "tu0FssTjANiG"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Then let's convert it into a table"
      ],
      "metadata": {
        "id": "yFIk0bVjDGsB"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Create a list of dictionaries for actor names and their average ratings\n",
        "data_list = [{'actor_name': actor, 'average_rating': round(data['sum'] / data['count'], 2), 'movie_count': data['count']} for actor, data in actor_ratings.items()]\n",
        "\n",
        "# Create a new DataFrame using pandas.concat\n",
        "average_ratings_df = pd.concat([pd.DataFrame(data_list)])\n",
        "\n",
        "# Drop duplicate rows\n",
        "average_ratings_df = average_ratings_df.drop_duplicates()\n",
        "\n",
        "# Filter actors with more than 10 movies\n",
        "average_ratings_df_filtered = average_ratings_df[average_ratings_df['movie_count'] > 20]\n",
        "\n",
        "# Display the new table with actor names and their average ratings\n",
        "average_ratings_df_filtered.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "SWNOHNejDI0h",
        "outputId": "460dc206-7d74-4c49-b56d-890b274fa004"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "          actor_name  average_rating  movie_count\n",
              "0      Margot Robbie            3.15           28\n",
              "88         John Cena            2.75           40\n",
              "100       Rob Brydon            3.31           37\n",
              "108     Ryan Gosling            3.47           34\n",
              "109  America Ferrera            3.37           25"
            ],
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              "\n",
              "  <div id=\"df-cd19bc9a-8d80-4416-879c-55d964c1ef4a\" class=\"colab-df-container\">\n",
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              "  <thead>\n",
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              "      <th></th>\n",
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              "      <td>Margot Robbie</td>\n",
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              "      <td>28</td>\n",
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              "    <tr>\n",
              "      <th>88</th>\n",
              "      <td>John Cena</td>\n",
              "      <td>2.75</td>\n",
              "      <td>40</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>100</th>\n",
              "      <td>Rob Brydon</td>\n",
              "      <td>3.31</td>\n",
              "      <td>37</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>108</th>\n",
              "      <td>Ryan Gosling</td>\n",
              "      <td>3.47</td>\n",
              "      <td>34</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>109</th>\n",
              "      <td>America Ferrera</td>\n",
              "      <td>3.37</td>\n",
              "      <td>25</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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              "    <div class=\"colab-df-buttons\">\n",
              "\n",
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              "      display:flex;\n",
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              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
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              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-cd19bc9a-8d80-4416-879c-55d964c1ef4a button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-cd19bc9a-8d80-4416-879c-55d964c1ef4a');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
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              "\n",
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              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-e35947d0-2fa0-4394-8d69-e67d0a3c03d2')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
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              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
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              "      --hover-bg-color: #434B5C;\n",
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              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
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              "    height: 32px;\n",
              "    padding: 0;\n",
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              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
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              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-e35947d0-2fa0-4394-8d69-e67d0a3c03d2 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "average_ratings_df_filtered",
              "summary": "{\n  \"name\": \"average_ratings_df_filtered\",\n  \"rows\": 3673,\n  \"fields\": [\n    {\n      \"column\": \"actor_name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 3673,\n        \"samples\": [\n          \"Michael O'Neill\",\n          \"Colin Quinn\",\n          \"AnnaSophia Robb\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"average_rating\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.19378137654121688,\n        \"min\": 2.43,\n        \"max\": 3.94,\n        \"num_unique_values\": 126,\n        \"samples\": [\n          3.42,\n          3.18,\n          2.46\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"movie_count\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 17,\n        \"min\": 21,\n        \"max\": 279,\n        \"num_unique_values\": 102,\n        \"samples\": [\n          24,\n          86,\n          43\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 101
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Top 10 actors/actresses are below"
      ],
      "metadata": {
        "id": "bL1ROhNWO3Iq"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Sort the DataFrame by 'average_rating' in descending order and get the top 10\n",
        "top_10_high_ratings = average_ratings_df_filtered.sort_values(by='average_rating', ascending=False).head(10)\n",
        "\n",
        "# Display the top 10 actors with the highest ratings\n",
        "print(\"Top 10 actors with the highest ratings:\")\n",
        "top_10_high_ratings"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 381
        },
        "id": "sXZjDxhBO5NS",
        "outputId": "dd307ea6-04af-4a37-b26e-a70170a790aa"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Top 10 actors with the highest ratings:\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                   actor_name  average_rating  movie_count\n",
              "95359      David Attenborough            3.94           38\n",
              "82193                   Jay-Z            3.75           26\n",
              "8577             Roger Taylor            3.73           22\n",
              "47967              Dave Grohl            3.71           34\n",
              "75971             Jonas Mekas            3.70           24\n",
              "46069  Martin Luther King Jr.            3.69           44\n",
              "22724               Spike Lee            3.67           30\n",
              "10780        Gregg Turkington            3.67           27\n",
              "13224                Yoko Ono            3.66           32\n",
              "43054       Bruce Springsteen            3.66           28"
            ],
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              "  }\n",
              "\n",
              "  @keyframes spin {\n",
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              "    async function quickchart(key) {\n",
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              "</div>\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "top_10_high_ratings",
              "summary": "{\n  \"name\": \"top_10_high_ratings\",\n  \"rows\": 10,\n  \"fields\": [\n    {\n      \"column\": \"actor_name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 10,\n        \"samples\": [\n          \"Yoko Ono\",\n          \"Jay-Z\",\n          \"Martin Luther King Jr.\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"average_rating\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.08363943780033167,\n        \"min\": 3.66,\n        \"max\": 3.94,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          3.75,\n          3.69,\n          3.94\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"movie_count\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 6,\n        \"min\": 22,\n        \"max\": 44,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          32,\n          26,\n          44\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 102
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Here's Ryan Gosling's number"
      ],
      "metadata": {
        "id": "GoO1W-kqgrPO"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "average_ratings_df_filtered[average_ratings_df_filtered['actor_name'] == 'Ryan Gosling']"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 81
        },
        "id": "OfOFsPhcgqgD",
        "outputId": "1f233a5a-c303-4dc4-d06d-b28faca120f8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       actor_name  average_rating  movie_count\n",
              "108  Ryan Gosling            3.47           34"
            ],
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              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"average_ratings_df_filtered[average_ratings_df_filtered['actor_name'] == 'Ryan Gosling']\",\n  \"rows\": 1,\n  \"fields\": [\n    {\n      \"column\": \"actor_name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"Ryan Gosling\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"average_rating\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": 3.47,\n        \"max\": 3.47,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          3.47\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"movie_count\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": 34,\n        \"max\": 34,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          34\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 115
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Bottom 10 actors"
      ],
      "metadata": {
        "id": "D8b64Y8nQo1u"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Sort the DataFrame by 'average_rating' in ascending order and get the bottom 10\n",
        "top_10_low_ratings = average_ratings_df_filtered.sort_values(by='average_rating', ascending=True).head(10)\n",
        "\n",
        "# Display the top 10 actors with the highest ratings\n",
        "print(\"Top 10 actors with the lowest ratings:\")\n",
        "top_10_low_ratings"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 381
        },
        "id": "ovObkWGYQq9g",
        "outputId": "56fccaaf-0750-483f-a960-ee94cf514592"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Top 10 actors with the lowest ratings:\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "            actor_name  average_rating  movie_count\n",
              "30490   Jackie Sandler            2.43           33\n",
              "42366     Bella Thorne            2.44           28\n",
              "39223  Natassia Malthe            2.46           28\n",
              "34105    Sadie Sandler            2.47           21\n",
              "81277    Steven Seagal            2.48           49\n",
              "83129  Diana Terranova            2.50           22\n",
              "56894      Ryan Hansen            2.50           22\n",
              "34100    Sunny Sandler            2.50           22\n",
              "60107   Arielle Kebbel            2.51           25\n",
              "6254     Chris Coppola            2.51           21"
            ],
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              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
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              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
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              "        const charts = await google.colab.kernel.invokeFunction(\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "top_10_low_ratings",
              "summary": "{\n  \"name\": \"top_10_low_ratings\",\n  \"rows\": 10,\n  \"fields\": [\n    {\n      \"column\": \"actor_name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 10,\n        \"samples\": [\n          \"Arielle Kebbel\",\n          \"Bella Thorne\",\n          \"Diana Terranova\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"average_rating\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.02905932629027108,\n        \"min\": 2.43,\n        \"max\": 2.51,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          2.43,\n          2.44,\n          2.5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"movie_count\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 8,\n        \"min\": 21,\n        \"max\": 49,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          33,\n          28,\n          25\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 103
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Let's see the general trend of movie ratings"
      ],
      "metadata": {
        "id": "Ef-EnR_5cjtA"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "sns.histplot(x= df['rating'], bins = 10)\n",
        "plt.title('Ratings vs number of movies')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 521
        },
        "id": "VYFfhZdpb93g",
        "outputId": "061987bc-7bfd-4ecd-fe95-db0c608f1b60"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Ratings vs number of movies')"
            ]
          },
          "metadata": {},
          "execution_count": 104
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "We can see that the movies are pretty normally distributed"
      ],
      "metadata": {
        "id": "3sLrbrMJcsq-"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "now let's look at each actor's average movie rating vs their movie counts"
      ],
      "metadata": {
        "id": "baRAa0xrebmz"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "f1PWuGVAKsom",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 495
        },
        "outputId": "6433f282-3cf8-4a97-f176-3e34d63e22ea"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='average_rating', ylabel='movie_count'>"
            ]
          },
          "metadata": {},
          "execution_count": 105
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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YUV6u9ft5/SkqKhgAcOVKVSOPpOmje8UP3Sf+6F7xR/eKH7pP/Lm6V2FhSkgk3qeV+YPXM3RxcXHIzMy0/XvPnj0Qi8Xo3Lmz2+cIhUKoVCq0bdsWY8eOdQqq+FCpVB4f79u3Lz788EOUl5ejrKwMkZGRAIDff/8dANCzZ0+X17333nuxZs0abN26FVqt1pYTp9FobDt3x48f7/S8+Ph49OzZEzt27EBOTk69BHSEEEIIIXx4HdCNHTsWY8eOtf07JSUFoaGhWLZsmV8H5i29Xm/7f7lcbvv/gwcPAgC6devm8nkdO3aEVCqFwWDAsWPH0LVrVwDAsWPHYDQaIZVK3c4mdu3aFTt27MChQ4f89CoIIYQQQrzn86aIt956CzKZzB9j8cn69esBWAJM+9m8wsJCAEDr1q1dPk8ikSA2Nhbnzp1DQUGBLaArKCgAALRo0cLtMq/1nNZj/U0iEdmmdJu6QBlnU0D3ih+6T/zRveKP7hU/dJ/4ayr3yueAzn62rrHk5eXhxx9/BABMnz7d4bGKigoAQGhoqNvnWx+rrKys0/OsxxJCCCGENIYGr0Pnb1evXsUTTzwBk8mE22+/HSNGjHB43GAwAIDbWTYAtjIn9su23jzPeqy/0aaImwvdK37oPvFH94o/ulf80H3iL+A3Rbiza9curF+/HidOnEB5eTlMJpPbYwUCAf766y+fr1lVVYVp06ahuLgYaWlpePvtt52Okclk0Ol0YBjG7XmMRiMAx9w76zIyn+c1hSVnQgghhNy6fA7oOI7DSy+9hNWrV9v+XRu+ZU480Wg0mDp1KvLz89G2bVt8/fXXLnfChoSEQKfTeVwWtT4WEhJi+xqf5VQ+y7KEEEIIIfXN54Bu2bJlWLVqFQAgLS0NAwcORPPmzSEW199qrk6nw6OPPoqDBw8iPj4eixcvRrNmzVweGx8fj9LSUpw7d87l4wzDoLi42Has/fMAoLi4GAzDuFx6PX/+vNPzCCGEEEIams9R18qVKyEQCHDPPffgjTfe8MeYPDIYDJgxYwb27NmDuLg4LFmyBFFRUW6P79y5M3bt2oV9+/a5fPzw4cNgGAYymcyhU0SHDh0gkUhgNBpx+PBh2+5Xe9ZzeqrBRwghhBBS34S+nsBaFmT27Nm+nqpWDMPgiSeeQG5uLqKjo7F06VLExsZ6fM6QIUMAWHL8XM3SLV++HIClMHFQUJDt6yqVCr179wYA/PTTT07PKywsxM6dOwEAQ4cOrdsLIoQQQgjxA58DOplMhpCQkHrPIzObzZg9eza2bt2KqKgoLF26FK1atar1eWlpaRgwYADMZjOeeuopXL58GYAl12/58uVYs2YNhEIhZsyY4fTcmTNnQiAQYM2aNVi+fLktP/Dy5ct4+umnwbIsBg8eTF0iCCGEENKovO7lWtNDDz2E/fv3Y/fu3Q4zXP7266+/2mYB4+LiEB0d7fbYOXPmIDU11fbvsrIy3H///SgsLIRUKkVycjKuX7+OkpISCAQCvPzyy3jooYdcnmvJkiV4++23wXEcYmNj0axZM5w+fRpGoxEJCQn4/vvvER4e7t8XW43Kltxc6F7xQ/eJP7pX/NG94ofuE383XdmSCRMmYM+ePVixYgUefvhhf4zJJWuJEAAoKipCUVGR22OrqhzfiOHh4VixYgUWLVqEnJwcnD59GkqlEn379sWUKVPQs2dPt+eaOHEi2rdvj2+++QaHDx/GtWvX0KJFCwwdOhTTp0+v1yCWEEIIIYQPn2foAGDu3Ln4+eef8cYbb2DMmDF+GBYBaIbuZkP3ih+6T/zRveKP7hU/dJ/4u+lm6F588UUAgEKhwIsvvoiPP/4Y6enpHmeuBAIB/vOf//h6aUIIIYQQAj8EdKtWrYJAILBtGCguLrbVdavJehwFdIQQQggh/uNzQDdmzBi/dH4ghBBCCCF143NA56p/KiGEEEIIaTg+16EjhBBCCCGNiwI6QgghhJAARwEdIYQQQkiA8zmHbtCgQV4/RyAQ4K+//vL10oQQQgghBH4I6Dx1bLBnX9qEdsUSQgghhPiPzwHdW2+95fHxqqoqHDp0CH/++SeUSiWefPJJKBQKXy9LCCGEEEKq+RzQjR07ltdxZ86cwaRJk7Bu3TosW7bM18sSQgghhJBqDbYpIikpCa+++ioOHDiApUuXNtRlCSGEEEJueg26y7V///6QSCRYs2ZNQ16WEEIIIeSm1qABnVgshlQqxYULFxrysoQQQgghN7UGDegKCgqg0WggFvucukcIIYQQQqo1WEBXWlqKF198EQKBAOnp6Q11WUIIIYSQm57PU2Uvvviix8eNRiNKSkpw5MgRmEwmCAQCTJ482dfLEkIIIYSQaj4HdKtWrXIoGuyJUqnESy+9hD59+vh6WUIIIYQQUs3ngG7MmDEeOz+IRCKEhIQgJSUFAwcOhEql8vWShBBCCCHEjs8B3dtvv+2PcRBCCCGEkDpq0F2uhBBCCCHE/yigI4QQQggJcH4tCJefn49169YhLy8PZWVlAIDw8HBkZGRg5MiRSE1N9eflCCGEEEII/BTQabVazJkzBxs2bAAAhx2vZ86cwd69e7F48WIMHz4cc+fOhVKp9MdlCSGEEEII/BDQsSyLmTNnYteuXeA4DlFRUejZsydiYmIAAJcuXcKuXbtw+fJlbNiwAWVlZfjmm2887owlhBBCCCH8+RzQrV69Gjt37oRYLMYLL7yABx54AEKhY2oey7L44Ycf8NZbb2Hnzp1Ys2YNxowZ4+ulCSGEEEII/LApYu3atRAIBHjuuefw4IMPOgVzACAUCjFhwgQ899xz4DgOq1ev9vWyhBBCCCGkms8B3fHjxyESiTB+/Phajx0/fjzEYjGOHTvm62UJIYQQQkg1nwM6jUaDoKAgyOXyWo+Vy+UICgqCVqv19bKEEEIIIaSazwFds2bNUFVVhWvXrtV67LVr11BZWYmwsDBfL0sIIYQQQqr5HNB17twZHMfhk08+qfXYjz/+GBzH4bbbbvP1soQQQgghpJrPAd2ECRPAcRyWL1+OZ599FufOnXM65ty5c3jmmWewfPlyCAQCTJgwwdfLEkIIIYSQaj6XLenRowceeeQRLF26FL/++it+/fVXxMbGonnz5gCA0tJSXLp0yXb8xIkT0b17d18vSwghhBBCqvmlU8SLL76IVq1a4ZNPPkFFRQWKi4tRXFzscExYWBieeOIJmp0jhBBCCPEzv/VyffDBB3HPPfdg+/btyMvLs22SiIiIQHp6Onr16gWZTOavyxFCCCGEkGp+C+gAQCaTYeDAgRg4cKA/T0sIIYQQQjzweVMEIYQQQghpXD7P0F27dg3r169HeHg4Ro4c6fHYtWvXory8HCNHjkR4eLivlyaEEEIIIfBTL9e33nrLZbmSmo4fP4633noLv/76q6+XJYSQOhGJhJBIRBCJaIGCEHLz8Pk32qZNmwAAQ4cOrfXYMWPGgOM4bNy40dfLEkIIbwIBIJdLoAqWgxUKcF3LgBUKEBQsh1wugUDQ2CMkhBDf+Lzkev78eUilUiQlJdV6bLt27SCTyXDhwgVfL0sIIbyIRALIlTLsyS/Fyi2ncbaowvZYYlwoxvVPRmZqNPRaA8xmrhFHSgghdefzDN21a9egUCh4H69QKHD16lVfL0sIucXUZalUIADkShmW/JqP977b5xDMAcDZogq8990+LF2fD7lS1mAzdbTsSwjxN59/m6hUKlRVVcFgMNR6rMFgQFVVlVcBICHk1uXrUqlMJsGe/FL8llvo8bgNOwqxN78UUpnEf4OvgZZ9CSH1yecl17Zt22Lv3r3YvHlzrXl0mzZtgtlsRkJCgq+XJYTc5PyxVCqWiLBi8yle11u55TQyU6Nh0DN+Gb89WvYlhNQ3n2foBg4cCI7j8N///helpaVujystLcV///tfCAQCDB482NfLEkJucr4ulYpEQmj0DAqKK3ld70xRBTR6xu/LoE112ZcQcnPx+TfXfffdh5iYGJSUlGDMmDFYsmQJCgsLYTQaYTQaUVhYiMWLF2PMmDEoKSlBdHQ0HnjgAX+MnRBykzKbWZ+XSoVCAaq03s22qbUMhEL/RlRNadmXEHLz8nnJVaFQ4NNPP8XUqVNx/fp1vPPOO3jnnXecjuM4Ds2aNcNnn30GpVLp62UJITcxxsz6vFTKshyCld4FRyqlBCzr3yXPprLsSwi5ufmll2taWhpWrVqF999/H7/99htMJpPD4xKJBCNGjMBTTz2F6OjoOl/nypUr2L59O/Ly8nDkyBEcO3YMBoMB3bt3x7Jly9w+b+DAgSgqKvJ47sOHD0Mmk7l87MKFC1i4cCG2b9+OsrIyREREoFevXpgxYwZatWpV59dDCHFNo6vbUqnZzNq+bjazUCnlSIwLdVrmdCUpLhRKuQSaKn2dx12TL8u+9q+FEEJq45eADgBiYmLw7rvv4o033kBeXh6uXLkCgUCAqKgopKenQy6X+3yN9evX46233qrz89u1aweVSuXyMYGbxJUDBw5g8uTJ0Gq1CA0NRbt27XDhwgWsWLECOTk5WLJkCTp27FjnMRFCnKnrsFQappTAbHb8uokxY1z/ZLz33b5azzGufzJMjLnW47whFApQ4afXQgghnvgtoLNSKBTIzMz0+nmLFy+GRqPBv/71L7fHqFQqZGdnIyMjAxkZGcjPz8fChQt5X+OVV15Bjx49eB+v1WrxxBNPQKvV4q677sJrr70GmUwGg8GA//u//8PKlSvxxBNP4Pfff/dLwEoIsVD5aanUYGCQmRqNYVnxHnPYhmfHo1tqNLRq/83OAU1n2ZcQcvPze0BXV19//TWuXbvmMaC7++67cffdd9v+7WlXrT/89NNPuHLlCtq0aYPXX38dEonlF7NMJsPrr7+OvXv34vz58/j555/x0EMP1etYCLmVBCkkflkq5ThArzVg4shUpCdGYIWLkiF39U9Gt+qSIZyf46imsOxLCLk1NJmArinKyckBAIwdO9YWzFlJpVKMGzcOH330EX777TcK6AjxgUgkhFAosM1MSURCvy2Vms0ctGo9OreNRGZqNDR6BmotA5VSAqVcAhNjhlat93swZ9XYy76EkFvDLRXQ/fjjj/jmm2+g1+sRGRmJbt26YdSoUS7z6sxmM/Ly8gDA7RJyt27dAABHjhyB2WyGSCSqv8ETcpMRCCwlPcQSETR6BhVaBsFKCfQGEyRioV+XSjkOMOgZGKo3HIRVL2s2xExYYy/7EkJuDbdUQLdhwwaHf//666+YP38+3n//ffTq1cvhsaKiIjCMJZnZ3U7W1q1bAwCMRiOKi4v9vuNVIhEhKirYr+esL4EyzqaA7pWF3mjC7qOXsGKzi2XQAcnonhaDKaPTkJEUiV82n3I65u4BbZGZFg25VAylvOnXbptcj6+F3lP80b3ih+4Tf03lXt0SAV337t3Rs2dPZGRkoEWLFmAYBvv27cPHH3+M/Px8zJgxAz/88APS0tJszykvL7f9f1hYmMvzhoaG2v6/oqKCSpgQwpPeaMI3647itx2FTo+dLarAu//bh+HZ8Zg0Kg3ZHWORmRYNjY6BWsdApZAgSC6BRCwMqOb2cqn4pnkthJCm55YI6N5++22HfysUCgwYMABZWVl44IEHcPToUbz77rtYsmSJ7Rij0Wj7/5r5c1ZSqdT2/3q9/5dJGMaM8nKt38/rT9a/TK5cqWrkkTR9DXmvai5nVlUvZyrlEpgZMwwGpt5yxmojl0uw/9RVl8GcvQ07CpGWEIHObSNtS6WhcjFYM4uqSl3DDLae+Ou10M8ff3Sv+KH7xJ+rexUWpoRE0jjpV7dEQOeOXC7HrFmzMG3aNOzatQsVFRW2WTf7YI1hGJdFh+2DPipbQpqKpt4Inm/nBKFQgJ15JeiRFgMTY4bZzN40tdluptdCCGkabumADgBuu+02AADLsrhw4YItoLNfTi0vL3fZ4aKi4sYHpf3xhDQW+0bwrhLwrY3gh2fH45ERqfW6u9MVPp0TVAoJhmcnYGhWGxgYM4qvaRCslCJIKW302UVCCGmqbvmAzn451Wz3J3NcXBwkEgkYhsH58+ddBnTnz58HYJnNa9GiRf0PlpBaeNMI3n45s6HU1jkhNjIIr0/LwtGz1/Dm4t1NbnaREEKaqls+C/fkyZO2/4+JibH9v1gsRnp6OgBg7969Lp9r/XpGRgaVLCFNgreN4Bs618NT5wSVQoI3pmdh5eZTmL/8gFMhXuvs4tL1+ZArZXDTrY8QQm5Jt3xAt2jRIgBAcnKy0yzckCFDAACrVq2ylTCxMhqNWLlyJQBg6NChDTBSQjzzpRF8QzGbWQTJLV0gahqenYC8M9eQs/Ocx3Ns2FGIvfmlkMqafqkSQghpKE0moOPqKSnm66+/xrJly3D9+nWHr1+/fh2vvvoqfv/9dwDAk08+6fTce++9F1FRUTh37hxee+01GAwGAIDBYMBrr72G8+fPo3nz5rjnnnvqZeyEeEMoFKCqDo3ghcKGneqydk6wJxQKMDSrDdZuO8PrHI0xu0gIIU1Zk8mhmzJlCrRazyU6SkpKMGbMGNu/rbtM9+/fjx49eti+PnXqVEybNg0AcOnSJXz77beYN28e4uLiEB4eDr1ej7Nnz8JkMkEoFOLpp5+2zcbZUyqVmD9/PqZOnYoVK1bgr7/+QsuWLXHx4kVUVFRAqVTik08+gUKh8MMdIMQ3gdII3lXnhNbRwTAw5jrNLprNbD2OlhBCAoNfAzqWZZGXl4fi4mLo9XqH4Ks2kydPrvUYs9nsUPDXymQyOXzdvibciBEjAACHDx9GcXExjh8/DpFIhJYtW6J79+544IEH0KFDB7fX7Nq1K9asWYOFCxdi+/btOHnyJJo1a4Zx48Zh5syZVEyYNBmB0gie4wC91oCJI1ORnhiBFVtOQy4VoVJjrP3JdtRaBmFKSaOV/7DvP0tBJSGksfktoFu2bBk+++wzh6VN+4CuoqICEyZMgMlkwv/+9z9ERkZ6fY2WLVvixIkTXj2nc+fO6Ny5s9fXste6dWun4sSENEWB0gjebOagVevRuW2kZdeq0QSTl7tWG2N20V3/2SClnEqqEEIalV8Cutdffx0//vgjOI6DSqWCVqt1yokLDQ1Famoq1q1bh5ycHDz44IP+uDQhxE4gNYLnOMCgZ2xdIFRKaZOeXWzqBZsJIbc2nzdF/P333/jhhx+gVCqxYMEC7N27F+Hh4S6PHTlyJDiOw44dO3y9LCHEBfvlzGcndHXaTZoYF4pnJ3TFIyNSodcamsxsktnMutws4U5Dzy7aF2x+77t9VFKFENLk+DxD9+OPP0IgEODJJ5/E4MGDPR7bpUsXAI613wgh/lVzOVOjZ6DWMlBV93I1MeYG7xDBh212MTveY5/XxphdbOoFmwkhxOeA7vDhwwCAu+66q9Zjg4ODoVKpcPXqVV8vSwjxoOZyZlh1vllDb4DwhnV2cfKoNGQkRuKXzaecljXv6p+MbtXLmt4EpL5uYPC2YHNmajQFdISQBuVzQFdeXm4L1PgQCoVgWdoRRkhDCaRG8GYzB7lUjOyOsejWoblPs4v+2sDgS8Fm2v1KCGkoPgd0KpUKlZWVYBjGoS+qK+Xl5aiqqkLz5s19vSwh5CYmEgmhLtPUeXbRnxsYaus/60pjl1QhhNx6fN4U0a5dO3Ach0OHDtV67Pr168FxnK1HKiGEeGI2s2AYs1czXf7ewBAoBZsJIbc2nwO6IUOGgOM4LFiwwONS6vHjx/HRRx9BIBDYiv0SQoi/ebOBgU9PWE/9Z12xllSh5VZCSEPyOaAbP348kpOTsWvXLkyaNAmbN2+GuXqdobCwENu3b8ebb76J++67D1VVVejUqROGDRvm88AJIcSVmhsYhEIB4mNDkJoQjvjYEIfetXx7wjblkiqEEAL4IYdOIpHgiy++wNSpU7Fr1y7s3r3b9ph94MZxHNq1a4dPPvkEAirSRAipB/YbGFQKCYZnJ2BoVhsYGDMqNUaEBEkhFYvw+85z2LCjgPcGhkAq2EwIuTX5pVNEXFwcVq5ciW+++QYrVqxAcXGxw+PR0dG45557MHnyZCiVSn9ckhBCnFg3MMRGBuH1aVk4evYa3ly822lTxKjeifhgVj+8tiiX1wYGV/1n/VVSpb5Rz1lCbg0CrmaPLj8oLS3F5cuXwbIsIiMjERcX5+9L3BIYxozycm1jD8OjqKhgAMCVK1WNPJKmryHvVSB/iPtyn0QiITiRAIyJw4pNp5Cz85zbY4dmxWNc/2RIJQIIzPzuk0AASGUSSKpLodQsqWJs4F6u7u5VzZItVdUlW5RyyS3bc5Z+V/FD94k/V/cqLEzJK42jPvhlhq6m6OhoREdH18epCSFuUON4ywaGkCAF/jlc7DGYA4Cc3EJ0iG+G7IwWqKrU8Tp/IBRspp6zhNya6iWgI4Q0LPoQv4EFsObvM7yOXfv3WWRltKjTdZpiwWb7ki2ucv2sJVuGZ8fjkRGpTbIFHCGkbnze5UoIaVzUOP4GkUgIjc7oXVcHnREi0c3xq9DfJVsIIYHDqxm6Dh06AAASExOxfv16h695QyAQID8/3+vnEUKcUeP4G1x1dRAKBWgdHQylXAyt3oTzpVUORX9vpq4O1HOWkFuXVwGddf+E/T6KethTQQjxAn2I32Df1YFP2RK1jqnXrg4NuTmFes4ScmvzKqD79ttvAQByudzpa4SQhkcf4o7MZhYqpRw90mIwZXR6rWVLvl6bB6Vc4tdNDY21OYV6zhJya/MqoOvevTuvrxFCGgZ9iDszMWb8+94u+HZDvsudrmeLKjB/+QEMzYrHk/d28WtXh8bcnEI9Zwm5td0cmcCE3KLoQ9wFAXDgxGVeZUsOnrwMf+0RaezNKdRzlpBbm88B3WeffebUGYIQ0jDoQ9yZWCzCL3xzCjefhlgq9ktwVdcdpiKREBKJyC87bannLCG3Lp9/g8yfPx+DBw/Gww8/jJUrV0KtVvtjXIQQnuhD/Ia65BRWaYwICpZDJPItqvN2c4pEKoYqWA5WKMB1LQNWKEBQsBxyuaTOAaZ9z1lPrD1njQbn5Xp/BpiEkIbjc2HhFi1aoLi4GLt378aePXvwxhtvYNCgQbjzzjvRu3dvCIX0S4GQ+kSN42+oS07h1Qoddhwuxh0929S50G5dA8nFvx7FP4durHD4mmdX156z1GWEkMDnc0C3adMm7NmzB6tXr8Yff/yBqqoqbNiwARs2bEBERARGjRqFO++8EykpKf4YLyGkhkBuHO9vdckpDAmSYuPeCwgPkde5Rl9dA8mySsfg2h+dHMxmDlq1Hp3bRiIzNdplz1n781KXEUJuDn5p/ZWZmYnMzEy89tpr2LhxI9asWYN//vkHV69exZIlS7BkyRK0bdsWY8aMwahRoxAVFeWPyxJCqnn7IX6zspYtSYwLddqU4EpSXCikYhHOX6r0qUZfXQNJrd7k8jFfi0Dz7TlLrcIIuXn4dT1UKpVi2LBh+Pzzz/H333/j5ZdfRnp6OjiOw8mTJ/Huu++if//+mDp1qj8vSwjBjQ9xdZUeQpZDmFICYfWHuEF/6yyZeZNTOKpPInJ2FoLlHGv0easum1OsgaQ7K7echkQi8nosrsbGMGaXG2GoVRghN496S3ALDw/HQw89hJ9//hm//fYbHnvsMcTGxsJsNmP79u31dVlCCDx/iN/s+G4MGJoVj9SECPy2o9D2NbWWgVBYtx0JdQ0k3fElwOTL640cfggwCSH1o0F2LGg0GqjVauj1N28yNiGkabDmFE4amYpnH+zqNGuWGBeKWfd1wbj+yXhtUS7UuhtLmr7U6PMlkHTHlwCzNr50GSGEND1+yaFzpaSkBGvXrsWaNWtQUFAAwNL3VSKRoH///vV1WUIIgdnMQaPWo0dqDNISI6AzmBx6uebsLMRXa/Icgjlrjb66tgHjtTllQDLatmrmFEi6U59FoKnLCCE3F78GdBqNBjk5OVizZg327t0LjuPAVSfudOrUCaNHj8aIESMQFhbmz8sSQogTjgOMRhPyzlzDL5tOQSkXQ6s34fylSpdLnf6o0Vfb5hSRQID/+3onSq5qaj2XrwFmbajLCCE3F58DOpZlsW3bNqxduxYbN26EwWCwBXEtWrTA6NGjMWbMGMTHx/t6KUII8Yp1GfTo2WsNVqPP0w5TuVyCoT3jkXfmWq3nqe8i0HXZEVyfASYhxDc+B3R9+/bFtWuWX04cx0GlUmHIkCEYM2YMMjMzfR4gIYTUVWPX6DObWYflyaZWBNq6keO97/bVeuzN3mWEkEDnc0B39epViEQi9OrVC2PGjMGgQYMgk8n8MTZCCPFZU6rR19gBZk1NLcAkhNSdzwHdCy+8gFGjRiEiIsIf4yGEEL/jW2i3IdQlwBSJhBAKBWBZzq+laJpagEkIqTufA7qJEyf6YRiEENIwai6DNgY+ASaf/qr+0JRmMAkhdVcvZUuKiopseXURERGIi4urj8sQQkjAcxVg8u2v6i9NaQaTEFI3fgvoLl++jC+//BLr169HeXm5w2NhYWEYOXIkpk2bhubNm/vrkoSQBlRfy36BPpaafB2bN/1VJ41Kg1zq37/Lm8IMJiHEe34p+b1v3z6MHj0a3333Ha5fv26rP2f97/r16/jf//6HO++8E/v37/fHJQkhDUAgAORyCVTBcrBCAa5rGbBCAYKC5ZDLJRDUTxODJj+W+hybN/1V9xwtbXIBrSsikRASiYi6TBBSj3z+0+7atWuYOXMmKioqoFKpcN9996FXr16IjrYsB5SWlmLHjh1Yvnw5rl+/jhkzZmDDhg20iYKQJo7vsp9ea4DZXL8JVg0xlrrOrPl7bN70V12x+RQy0/y39OpPfHIAKS+PEP/xOaD75ptvUFFRgcTERCxevNgWyFklJiYiKysLDz74ICZNmoSCggIsXrwYzzzzjK+XJoTUE2+W/R4ZkVqvSfP1ORZfgw5/j61O/VV1lry3pjRT15T+GCDkVuFzQLd161YIBALMnTvXKZizFx0djblz52LChAnYsmULBXSENGHeLPulJUSgc9tIGPT+2XXZUGPxR9Dh77HVqb+qjkGoXNxk8t6a0h8DhNxKfE5oKCoqgkKhQNeuXWs9tmvXrlAoFCgqKvL1soSQeuTNst/KLachkYgCaiz2Qcd73+1zan1lDTqWrs+HXClzmwPn77HVqb+qomn1V/UmyN2bXwqpzLvXSwhxjTJUCSEO6rTsV13uIlDGUtegwz65vz7GZjazCJJLkBgXyuucSXGhCJJLYDazTWbjQVP6Y4CQW4nPP/lxcXHQ6XQ4ePBgrcceOHAAOp2O6tIR0oQJhQJUebvsp2UgFPp/m2l9jcWboCNnZyFkUrHTDlaFUgpAAJWC/wwTn7FZ+6vyce/gdhCJBE1m529T+mOAkFuNzzl0ffr0wenTpzFnzhwsXboU4eHhLo+7du0aXn31VQgEAvTt29fXyxJC6kmdlv2U9bPsVx9j8SboiI0MwhPju2Dn0Utu8+w+mNUPry3KRclVjc9jA/j3V73/jnbo3L45co+UYMXmprHxoE45gFoGYUpJk8kBJCRQ+RzQTZkyBStWrMDp06cxbNgw3H///cjKyrJtkLh06RJyc3OxfPlylJeXIyQkBJMnT/Z54ISQ+mE2s1Ap5UiMC3XKLXMlKS4USrmkXroK+Gss9iVJ+AYdKoUEb0zPwopNp5Cz85zT49Y8u6FZ8Xh9Whae/mgr1Dr35+V7n/j0V713UFt0SYnG4l+P4rcdhW7H1tAbD5rSHwOE3Gp8DugiIyOxYMEC/Otf/0JFRQW++OILfPHFF07HcRyHkJAQfPrpp4iMjPT1soSQemRd9nvvu321HjuufzJMTP1Nr9R1LO5KksjlEkRKRVApJC4DMKFQgNbRwRjZKwGFxZX4Y/d5j9fMyS1ESptmGJYdj583ul/G9eY+1dZfFRyH3UcvuQzm7DXELmTHcTedPwYIudX4pWdM9+7dsXbtWnz22WfIyclBRYXjD3JoaCiGDx+Oxx57zGNpk9pcuXIF27dvR15eHo4cOYJjx47BYDCge/fuWLZsmcfnMgyDpUuXYu3atTh//jwkEglSUlLw0EMP4Y477vD43Pz8fHz55ZfYs2cPKisr0bx5cwwYMAAzZ850u8RMSCDju+w3PDse3VKjoVXXzweySCQEy7Lo7uVYaitJclf/ZMyf3R+vfL7DtlSqUkgwPDsBQ7PawMCYwZhYSCUiLHpxMH7feQ4bdhS4nYFbt+0sXp7UHSs2nYKryaa63CdP/VVVwXKvNh5kpkY3SEAHNK0/Bgi5lQg4zv8T8RcuXEBZWRkAIDw8HK1atfLLeZcsWYK33nrL6eu1BXQGgwGTJk3Cvn37IBKJkJycDJ1Oh/PnLX95T5s2zW1dvD/++ANPP/00GIZBREQEYmJiUFBQAK1Wi6ioKPzwww9+e301MYwZ5eXaejm3v0RFBQMArlypauSRNH2Bdq+sQdHe/FKXy3539U9Gt3rI0YqKCobZzMLImKHRM6jSMghVSaGQSbAn/5LLfDH7sbAsB6VK7rYOmtWw7HiM7ZeMpz/aiuAgKV6floWjZ69h3T9nnc4/qnci0hIjPObKff3y7Vj861H8c6jY7dj8cZ9EIiFYoQCT5v7J+zmL59wOYQP1vRUIwOv+N/RycKD9/DUWuk/8ubpXYWHKRtu57d+uztVatWpVL0GOSqVCdnY2MjIykJGRgfz8fCxcuLDW57377rvYt28fWrZsiUWLFiExMREAsHHjRsyaNQuLFi3CbbfdhoEDBzo8r7S0FM899xwYhsHMmTPx+OOPQywWo6qqCk899RS2bduGWbNm4ZdffoGgMRtJElIPalv2MzFmv38Yi0QC6I0m7D7qHLilJ0Vg6uh0vP14b2h0DNRao9NYhEIhFEp+JUl+21GIDvHhuLNfIvrf1spjrtz85QdqzZXTGkyYdW8XTBmdVq/3qalvPOCTA2gf5FJRYUL8w68BndlsRmFhISorK2EymTwem5mZ6fX57777btx99922f5eWltb6nKtXr+LHH38EAMybN88WzAHAoEGDMHXqVCxcuBALFixwCui++uor6HQ6ZGZm4t///rft68HBwXj//fcxaNAg5OXlYfPmzU7PJeRm4GnZz9+sxX6/Wec60T/vzDXM+nArRmTH45GRqRCDA8tagk6ZTIIglRwaPQOBUMh7OXLN1jN4a2Yv7DhS4jKYs1dbrpxKIYZGY4AQqPU+1bVvLBAYGw/MZg4GnRFdU6LQPS0aal39/zFAyK3OLwHdpUuX8MEHH+DPP/+EXl/7L3qBQID8/Hx/XLpWmzZtAsMwiI+PR8+ePZ0ev++++7Bw4UIcPXoU58+fR+vWrW2P/f777wCA8ePHOz0vNDQUQ4cOxc8//4zffvuNAjpy0zOb2Xqd4bEV+60l0X/9jkKkVif6s6wJSpXclivHshyef7gb7zpoBSWVAARY8/cZXsev23YWr07pib3HSnHuUpUtSKqZ3O/qPnnqG8uxLIwGE0ym2oO7przxoOZrvFyuR3B1EKeQimA0mGgDBCH1xOeA7sKFC7j//vtx7do18E3Hq4e0PbesBY/dtSaLjo5Gy5YtcfHiRRw8eNAW0JWUlNhmAN3NJnbr1g0///wzDh065P+BE3KL8bbDQGZqNMQSkUOuVmpCOCo1Rt7XbB0dDLXO6FUhXDPL4uVJ3SGAwLZZorbkfr59Y+UCwGQ0w2BgPM5eNcWNB3xfI9eAdfEIuZX4HNB98MEHuHr1KsLDwzF79mz07t0bkZGREImaRjuXwsJCAHCYeaupdevWuHjxIgoKCpyeJ5FIEBMT4/J51jzBCxcugGEYSCT+7UkokYhsSZdNXaCMsymge+XatQqdl4EVh/0nLjvkymn1JoQESXlfUykXe6wd58qVch2Wrs+H3mjGqN6J+PS5gQiSiyGTioFgucvn6I0mrPn7DPYfv2yrhWed3bOvZ3fPoLY4feE6bkuJhlzq+ddzj/QYDMuO9zijOTw7Ht3TYyzncjM2f9EbTW6Xy+3r4k0alVbra6tP9PPHD90n/prKvfL5p2rHjh0QCAT44IMPXC5pNjZrCZXQUPe9Ea2PVVbe+DApLy+3PeZuw0NYWBgAgGVZqNVqNGvWzA8jJqRpMZtZMGa2eiOCJQ8qSC6BRCz0a8smtReJ/kKhAEKhAL9scpzRO19aBZlExHs5MiJEjmAl/wAQAEKCpNDqTSgsqcT85QdsQYo7jMlSAqVXxxbISIpESJAUUrHIqRSKNUcvLFgOvcEMtZaBRuf+fsulYkwelYaMxEj8svmU04zY3QPaIjPNOTCsj++n2czyrouXnhiJ7I6x1O6LED/zOaAzGo2Qy+VNMpgDLCVLAHicPZNKLb/Q7fP/vHme/fH+RGVLbi6Bdq/4LqH5oxyHSCREkIL/r6PW0cHQ6k1OM3osyyEn9xxG9U7E/OUHaj1Ptw7RkMv4B4BJcaGQikU4f+nGdd0V762t7Muo3olObcPWbTuLuY9l47Uvc8GYWCjlYmj1JohFQozpl+R0v6OigpHdMRbdOjR3uQtZXalDFec4nvr4fqqC5U7BtTsrNp9Ctw7NoS6rvVWaPwXaz19jofvEX1MrW+Lzn0gtW7Zs0Jw4b8lkMgCWwsLuGI2WnBu5/MaShDfPsz+ekJuBdcfpkl/z8d53+5yCHesS2tL1+ZArZT43gWdZFkEKKRLj3M+k22vbKgxqnetcuQ07CpCWGIGhPdt4PMfQrHh0SIjAHzvPYVz/ZF7XHdUnETk7C52KB6/cchoSqdh2H+zv37tu7t/85QewcstpvD4tCyqF5Q/HM0UV0OgYvDK5O55/uBseGZGK5x/uhhcfycT5S1X48Y8TTvdbJBJCXaWHkOUQppRAWL271qC/kYdXn99Pb3rj2l5j9a5pQoj/+PwTNWzYMBgMBuTm5vpjPH4XEhICAE7dK+xZH7MeC9xYhq2oqHAbsFqXZYVCIVQqlT+GS0i9EomEkEhEtX6Y2nac1lLLLWfnORwrKINCKXV7ztquKRIJoFTJUXxFjdF9El0eU1Ovji2gUjgulQqFAsTHhqB1TDAWrTmCcQPaYtZ9XZyCxMS4UDwzoSvG9U/Ga4ty8fPGU0iJD8ewrHiP1xyaFY/UhAiXy4pniipQpTEiKFgOqVQEhVLK7/7lFiK/4BqGZd+4tkbHYNlvxzHjnU14fsE/mPHOJsxbshuxkUEYlp2AvDPXIJU5rxyYzSwYxuyyDArf7+eGHYXYm1/q8vzuCIUCVNWhLp5QSLU7CfEnn5dcJ0+ejD///BNz5szB4sWL661rQl3Fx8dj//79OHfOfY0pa8eI+Ph4h+cBlhm6kpIStGjRwul5Fy5cAGCZpfT3hghC/MVTuQwz43pHZW07Tu3bZBlNLK5U6KFS3Din0chAKq39mvYzR9sOFuGDWf0wtGcbjzXhhmfHo0NCOASwBGeXy7QOLbsqNUaEBEkhFgnQLFiGOZO7w8CwMJlYBCnEUMolMLMspv9noy1/bc4XO/DmY9lIS4xwuRw5uk8iUhMsnSLcbaKo1BihkIvBcAJIvKiFV7NtmEwqwukL5Q7H2Bc3vmdQW0gkIq9aedVlBzHf8wdCXTxCbgU+B3RyuRyLFy/GnDlzMHr0aAwZMgQZGRkICgry+LwxY8b4emleOnfujJUrV2L//v0uHy8tLcXFixdtx1q1aNECzZs3x+XLl7F3716MHj3a6bl79+51eh4hTUld8qZqW0KLjQyytcl6c/Ful+fsnhaDI6ev4rvfj3u8pkQidpg5em1RLl6floWU+HCs3ebcguuuAcno1uHGcycMSUGr6GC3YxnVOxGxkSpcLdciMlQBlUICrcYAhVKG5uFKqKuPb97M8lhcVBBendIDeqMZDGOGWCyEVCxCzs5CfLUmz+OO2OAgKRb+cggXL6u9qoV3pqgCBsaM1jEhEAkFTjl69qwbJ7qnRfNesvRlSZRP0eOmXBePkFuJX/aOFxUV4erVq9DpdFizZg3WrFnj8XiBQNBgAd2gQYMwd+5cFBYWYufOnU6bN6xdJFJTU9GmjWPOzZAhQ7Bs2TL89NNPTgFdRUUFcnJyAABDhw6tx1dASN3Yz365WmqzLyVh31PTU2splUKCN6ZneWyT9d53+zAsKx5j+yfjcpnW5ePWawoAh5mjkqsaPP3RVgzLjscrk7o7zLgpZGKo5BJUVenAcQDHMchIjsQ3a/M8tuwalhWPiSNT8eT7W9A1pTkmjkwDWBbjB7XF29/uRXS4Ei9N7I7F644iZ+c5CAVAfItQvD6tJ75YdQQ7Dhc75czVlBQXCgGAHYeLkRLvXS08wDK7p5SLcUePNi5z9Oyt23YWWRmxMPNcsmyIVmFNsS4eIbcan3Pojh8/joceeshWwFcikaB58+aIjY11+5+7um71ITIyEvfeey8A4OWXX8bZs2dtj23atAlfffUVAODxxx93eu6UKVMgl8uxZ88ezJ8/H+bq325VVVWYPXs2qqqqkJqaSl0iSJNU17wpT0tow6tzuGprk/Wbi9wwp2seK4WJZZ1mjtQ6Bj9vPIWp8/7EO9/uxdL1+Xjn272YPPcPVOmMEAotv7akUgl2H73Eayy5R0rQt0scNuwoRO6RYhRf0+K2lGjcd3s7zLyrE3bZnYflLMHgum0F6JoSXWswBzhulvC2Fh5gKYXSpX1ztzl69s4UVUCrN/HuH90QS6IGA4PM1Oha8xCHZ8ejW2o0jAbvAkxCSO18nqH75JNPoNVq0apVK8ydOxfdu3e3/cL1t5KSEoeZPesu0/3796NHjx62r0+dOhXTpk2z/fvZZ5/F0aNHceDAAYwcORJt27aFVqu15c5NnjwZgwcPdrpebGws3nnnHcyePRsLFy7E8uXLERMTg4KCAmi1WkRGRuKjjz7i/YuVkIZU17wpd0toQqEAQ7PaYO43u3ids2ZumNM1N59Gtw7REArg8nGWAwpLagZ7JoQpxDCbvXt99mNZ+7fl/5f+ehSPjLTUj3t+wTan52zYUcArp8+6WeKrNXkAvK+FlxQXilCVDP26tPSYo2dPo2MQalfmRSoVw2h03T+7IZZEOQ7Qaw2YODIV6YkRLsu03NU/Gd2ql9qbcGEEQgKWzwHdgQMHIBAI8OGHHyI9Pd0fY3LLbDbbdpbaM5lMDl+v2U9WLpfj22+/xZIlS7Bu3ToUFhZCIpGge/fuePDBBzFkyBC31xw6dChatWqFL774Anv37sXJkyfRvHlzjBs3DjNnzkRERIS/Xh4hfuNr3pSrJbTW0cEwMOY65YbVDMysj2v1JrePu6JSSMCyLKRSMTQu6tDxGYv1/48WlCG/4BraxIS4PI9ax+C1Rbl487FsZCRHYsVm1zmI7Vo3cwjEvK2FN25AMqQiIb5YddhWj662exAeKodYJMTVch2qtEYEK6VQhSgAloVWa4BAILR1o3D3/XQ7njouiZrNHLRqPTq3jURmarRTXTyOZWHQGantFyH1xOeATqfTQaFQ1HswB1h2k544caJOz5VKpZg+fTqmT5/u9XPT0tLw8ccf1+m6hDQGX/Om7JfQrEu2Srm4zrlh7mh0DJJbhfEK6DKSIqBSSMBxgIExoarKfTFvoVCA1tHBtsK850urHMZi/f/th4oREeq+JVbJVQ0+/GE/XprYHR3iw51y+owmFk9/uNVpVq3m7J6r8bAsh6FZ8UiJD8fe46V4cnwXvLDwH49BXWxkEN58LBv7j192ucllbL8k9EiPhYEx4XqlwWFncc3vpyvWJVGt2vJHsUjkGBjWhuMAg56BQc9ALBaieZgcQpEQah2DKutO52D3u6sJIXXnc0DXpk0bnD17Fmazucn0byXkVudr3pSrJbS65oZp9a6XAgFLkNgzLQZ/7T7v8TyxkUF44ZFM7Dp6CSu3nAbLcnj+4W7Or8GunIp98CUViyCR3EgFsY7r1IVyp3p2NbEsh0vXNHhm/t9oHRNiC8oEAuC5h7q5XCK1zu69MT0Ld/Rog/BQOXQGk0MwWFahR3CQFK98vgMlVzUYlh2P16dl4emPnANE62t7Y3oWft54Ejm5rjeBvP/9ftsmkHmLd6O0TGu38zgak3guiXpb5qYmkUgAmULaIF1GCCEWPgd0Y8aMwdtvv41Nmzbh9ttv98eYCCE+8kfelKslNKVc4lObrJqPA0DrmBCPeWoqhQRvP94b//vtuG12SSgUOOWp8SmnMuu+2/D12rwb4xIIoJSLPb4mrd4EhUzslNPnagyuXLisxoJfDjmN586+SUhNCLd97fed59CtQzQeGZGK9dsLbLN4VsOzE5BfcM1lMGfvt9xCpCaG41/3dMKcL3KddhZ3cbEkGiSXABwHE2OqU5kb+1m8uu6uJoT4xufdCw899BB69uyJV199FQcO1J4vQghpGNa8KT7c5U1Zl9CsraU4lvW5TZb947/lFuK1RbkeuzrMfTQLR05fdQgO7PPUgBuzVys3n8L85QfctrZaueU0nry3MzbtOw+Ws5zHzHIY28/9a7Lf5GCv5hjs2Zd3+fCH/S7H8+EP+7Fi82m8MT0LDw5NwVcvDUZclAopbZrhhUcysejFwRg/qB1UCgmSW4ZhTP8krN56xu047a3ecgbt24RDbPcb3rqbmQOgrtJDDA6xEUqolFJo9CZcuq6DwQwIAAQrpdDUmCWs2R5MrpBAFSwHKxTgupYBKxQgKFiOoCAZDpy4XC9dKQgh7vk8Q/fZZ5+hc+fOyM/PxwMPPICuXbvyKiz8r3/9y9dLE0I8cJUH50rNvCl3zGYWOq2R1zmH1dj5WZP9zlC1jnFbe06lkEAhE+OXzadss0BW9nlqIUEyXuVUcnILkZ4YAVSfJikuFCKhAD3SY1zOElpz3/Yfv4wJQ9rju99PQC4VQW80AxxwpqgcQ7LaOD2Xb3kX63i6tG/uclZxbP9kfP3K7WBZDnqjdxtSNDoGPTNi8c+hEtvXrbuZWbMZMoUMuXklWLX1jNN1x/RLwsez++ObdXm4UKp2yPs7cPIKJgzlcODkVZezeHf1T0ZKQjhiI4Nq3eThbVcKQoh7As5do1KeUlJSbGU7rKfiU8bj2LFjvlz2lsAwZpSXa2s/sBFFRQUDAK5cqWrkkTR9jXGvrJ0i9uaX1po3xTeXqdZzDkhGZmoMDp++gu9/P+EiSElC99QY/GfJbhw6ddXh3EIBkBIfjgFdWyIzNQZ6oxlGxgxJdceG33eew4YdBbYcM+syq0opwcufbecV8CTFheLlSd0xdd6fmP1AV3RuG2kLcHYdLcGqLWcc2okxZhYSkRAqhRQaPWMLNq2bRAQCDsFKGXYdvYTVW8+gsKQSX700GHO/2eX1eFzNZg7NisfkUWm4dFWDJz/YUuv5rD6Z3R+78y9h2W/HHb6+9LXboZBJ3RZktuYhjuqTCJOZxZXrOgQHSSAVi7Bp3wUM6NoSv2x0XVjafszj+ie7zQe0t3jO7RDy3HThC/pdxQ/dJ/5c3auwMCUkksbZT+DzDF1mZqY/xkEIqQe1lZIwMWavc5hqO6dQIMDPG0+BA+c042Zto3X6QgUev7sznv5oK7QGk20HaJBcgmljMnD07DW88fUul628PpjVD68tykXJVQ1Krmrw0Y/78dT9t3ldwmTC0BTbzCTHAWazDj06RKNneixYlsOe/FJ8seoIpoxOx6FTV7HuH+dWZKN6JyItMQILVxzCmL5JePvx3qjSGMEBfivvkpNbiLSEcHTtEI342BCn3Dp3VEopjIxzkCQVi7Err8RlQGafh/jaolyXM3cKmRiHTl91em7NMae0aYZh2fH4eaPnWoHedqUghLjmc0C3bNkyf4yDEFJP7EtJiERChFXvZvWll6a7c5oZM/acuoqfNp4EAKzYdMphZ+j5S5W2WaiEFiF45sFuaB2tAstxMBjMiGym8JhMb21QX3M3aFmld6/FwJgxuk8StJobwSzLAhqNAUEqOZasz8e2A0X48Kl+HtucWcczYUgHPP3RVvTpHIcHhqSgtKz2enLAjSVdk4lFUlyo22Bt9dYz6J4WgxceyYREJHSaqawpKS4UQQoJ9h6/5PB1sVgIkUiIlVtOOz2HT1u3D77f7/L+u1JbYWnbdb3sSkEIca1+WjoQQpoks5kFw5hhNrMQiYSQSES8m7zzOWfN7g3WnaH5BWUoLLkRzAmFAhw9ew3pCeGQiIVgTCykUhGvVmU5NdqK1aWcSmiQDDoXy8wymQS780vx245CDM9OwNEz13D83HWkJoQjPjYEQhf9U+3H81tuIU6cK0OoSubx+iqFBOMHtcNXLw3G8w93AwfgnsHtHDZC2DtTVIGySj3eXroH85bsRmxkED6Y1Q+xka5zla3LpVyNCbrRvROg0bkuOO1N3p+ntm72Y7bOPLpj3V1d38utjclfP2eE1IbeYYTcQgQCQC53vTtRLpfAly52IpEQjMmMDvHh6NUxFmKx868XayDz9cu34/4hKbh4RQ2N3gSpRIQguRi/5Rbwuta6bWcxLCseQoH7XajuWIIIMUwm5yDCGpCGBEkxpl8iOrePwvMPd8MjI1Lx/MPd3AZc9uNZ/udJhAZJ3Y7HPhh7c/FuzHhnE558fwsee3ujx2DNWgzZOjO4cstpS/5gjbEMy45Hx+QoVGmMSGgR6hCE9u4UhyqtY3FooVCAxBahGNErAWu38dtFa/96PamtsHRdu1I0dfX5c0aIOz4vuRJCAoN1M4O/i70KhYBSKQOEQmh0DIb0jEewUoIn7+2C44XXsXDFIZSWaR3ys+Z+45wfN65/Mv597222/DhPauad5eSew519E/HhD/xabQk4DgIBHHIHre3S9EYzPnyqH/afuOLyPtXM47OOx8xySIkPR35BGfSMGWP6JuGDH/Y7XJvPsqa7ZeWaRZpr5qmlJ0Vgyqg0tIhS4XqlAUaTGfcMbocHhqTg953nwJjMaBWjglprso3FuvGD5TiYWNZveX9WngpL891dHWjq6+eMkNpQQEdINW/bHDU1nsZfX8VeJRKhZXdoLeUvPvxhP6aMTvcYyLz33T7e+VmAZfZHpRCjd6cWyEiKQPv4ZjheWIbfcj232kpLjMDpixVIbh0GnebGh6pYLES5hrF0Y/jrpNcBl0bH4NkHu+Glz7ajvNLgshSKN8ua9sFaUlwogpVSpyLN67adxbwZvXBHj9YICbIEEZ/87FzE2NoSjDEwUCkk6JEWgymj021FmOVSER4ZkepxTK7uv6fZt6S4UISqZBCJHKej7HdXG/UMxGKR03u2Pn8Wrf2K6wMVVSaNiQI6cksTCOBzm6PGxHf8MpmEV37ahh2FSEuIQOe2kbXWBhMKAZlC5rb8hTWJflhWPGY/0BXbDxd7Fcis2HzaZVAGWGaXWjVXYc6UntDqTbhepUdZpQETR6bhnkHtIBIJoNU7ttq6XqlHiygVKjUGiMRClFcZERYst+yGEAigN5rQLESGXUdLvQ64AEAmFeGvPefx+rQsCIXAgp8P4rFxnZCeFImVW06jsKQSQ7PaYO43uzye28p+U8Hovokul+nOFFWgQmOAXCrC4nVH3X4f3v9+vy2I0BkY/PveLvh2Q77t+PjYEL+3dRvXPxkiAfDWjF5OO6EF1T9UJuBGj1el3PJ1gcBvP4v2Px/XKnRQaxkEKcT19vNdHz9nhPBFAR25ZQX60og346+5WcFezdms1VvP8Cr2qlTK3Ja/sD/3iXPXYWJZrPmbf37W69OzMCwr3qnkye87z2HfiVK8NLE7Dpy44rIO3th+SUhNjMAbX++yLYlai/SGBsvx6pc7nb6emhCO//tyJ97/dx+v8sisAVdCC0ubsx9+P47mzRTo1iEaKoUUWj2DkqtqvDKpuy1w8HZZ8/4hKegQH4EqrdFhidP6fWPNHEquaPFHLf1wN+woRLvWzRARKkeF2ujwfbPPQ/RHWzf75VSOg20ntDUo3X3sssN7NjYyCG8+lo38s9dczvTW5WexMX6+Pf2c1URFlYm/+VxYmNQfKixcfwQCQKmSu10asfLn0og/75U34584IhVaA4OJb/zp8JinRvYKuQgmg8nlxgHb80MUeH7BNpcBSs38LKFAgMlv/uniLK599fLtWPLrUfxzqNj2NWv+WnbHWKzccgrL/3T/wemusK2nr48f1A5mlsW0//zFe5yfPT8Q73y7F2P6JaHoitq2PDpvRi9Uaoy2PDuhALhnUFv06hSHJ9/fwvv8Hz3dD6FBMrz02XbMuq8Llq7Px/lLVQ7fN8bEQi4VQSSsvZxJ25ZhmDcjG89/+o/T9238oHaIjQzC/OW15yE+M6ErWkQGuexRe9eAZHTr4BwouXvPqhQSj+VhrLz5WWyMn2+RSAhWKMCkufzf5w1VVNkbgfo7vTHcdIWFCQlEgb404tX4EyOQmhDh8PW45iq8Md3SI9VdI/vuqdEuS3sAgFQqhkbH4NylKsTHhjgsi0aHK22bH+qan3WtQudUW86av3bywnWM7ZeM9f8UOgQu9jONxwvLcKy6tIZ9YVt3BW9zcgvRMy0GISrvlh0rNUYM6NbKoc3ZmaIKVGmN+OjH/baZQJYD9hy7jKFZCV6dP1gpxbv/24uSqxqEBEkRJJfgg1n9bPeWz4YNe4yZhdpN2RL7Vmq1dYFo36YZtuy/gFcmda/uictCKBRALBKivMrg8nnu3rN88wqt7+Wu7aNg0Js8BkGN8fMtFApQofXuHFRUmfgTBXTklhToSyNejX/zaWR2iIZQACjlEozpl4RRfRJ5JW5PHJEKo4GB2ew4iyAUCgAzh69eGuwwuyeTiCARC/FdzvF6y8/6bUch2re+EZS5m2lUyMTITI3G77mFqLT7oHVX8HbjnvOYPDrdq3FGhCrQp5MSL3++3SG4LKvUQygUOAS7F6+oEaSQeLWsqVJIcPJcGZLiQiGTiDB9bEaddshaKeViVLkJOtQ6Bq8tysXr07KQEh+OtducO2OM6ZeElDbheOfbPbitfTQAQABAozdBIRNDAA47DpeAMZlx3x3tHWa+XL1nhUKBV3mFKzefRrcO0ZYSIB7y4Brj55tlOQQrJbUfaIeKKhN/ooCO3HKs5Sm8anRe3RGhKSyN1G38JozolYDRfZOg1hqRe6TEu9k9IWwfoAxjglgiwv5TzrlJj47NQJBCUq/5WcCNoGzH4WK8NjXL7YzVuP7J+PDp/njl8x0OJUZcldzYfqQE/xrf2atxyqUizHhno1OQFB4ix7MPdnXYmKGUiWFkzBjVO5HXsuaoPolgTCzatQnHHT3a4EJpFcoqDXXasGGl1Zs8Bh0lVzV4+qOtGJYdb2vbZjKxkEhEkEstwfr/LcrFcw9lepwlTEuMQN6Za0hpHWbrJuLqPds6OhgGxuzVe7msUo93vt0LoVDgMg+usX6+zWYWKqXcq/ePUi7xqWMLIfaosDC55QiFArezFO6otYzLLgGNoS7j1+oZPDA0Bau2nEaoSsZ7g8LKzafBcRymvPknXly4HYfPXoMiSI7Fv+bjve/2OXxwCYUC9EiLwaoabaVYlkNO7jmM6p3I65qj+iQiZ2ehx3ZRZ4oqYDSZMffRbKzcfArzlx9w+hC1zjT+vPGUUwFeVyU3WJZDpcaI0X34j3PN32ecvhfWEiPvfbcPM97ZhOcX/IMZ72zCspzjKKvQIy0xAkN7tvF47qFZ8UhNiEBZhR4vT+qOjKRItIoJ8bnwr0QktM0SuqPWMfh54ylMnfcnvs85jlCVDP/9dg8mz/0DVVojXnyku8d7bi16HN8ixJZL5O49q5SLUakxOn3dE/sCy+99tw9L1+dDrpTZNlw05s+3iTFjXP9kXsferEWVSeOhgI7ccgJ9aUQkEiJY6d0SZniIDPuPXUZ+QZnXMyLW2ayzRRU4W1SJ3CPFLmf34mNDYDS5Lk67YUcBr0DG2uXgbFGF21ZbViKhEIdPX61Tqyp3S7pmlkVGciTvgOu3HYVOj43um4iiK2o8cU8Xh24Ppy+UQyEX47VFuRg3oC1m3dfFKbBKjAvFrPu6YFz/ZLy2KBcSiRA5uYUIDpIAHFen75u9Eb0TcLywjFdwzXJA1w7RWPfPWZwttrRtk0nEyC8o43XPj5y+ChNraTHn7meuLm3ban7vNuwoxN78UkhllvNbruV4Tuvyt7sWbiql1C8/3wYDg8zUaAzLivd4nHUXsNHQdNI4SOCjJVdyywnEpRH7bgx6gwkqhQS9O7XAjiMltX4QtW0ZBpFIhF82n/JpRsRdvpM1h21UnwRcd5MQX1t+VnpSBKbdmY6YiCBcrzRg/OB2DqVKau7cFAoFCAmSelUKpWaJEfslXaFQgF4ZsVDKJFjwy0FMHZ3hNo9sdJ9EpCZE4LVFuU45akOz4tEhPgJPf7QVvTvHOeSyXbyiRmh1bp/9sqbRxEKtNUKllEIitgRwX63JQ3S4ElKxCN/lHEdSy7Ba+8PWZC28bM3j69K+OVITIjDnix2Y+2i2y80P9htLrMdbN3sIhQIEycW8c9PWbTuLrIxYmKsLBAcrpE7vWX8tx9vnwZnNLFRBMiTGheJymdbtTm7r+yo6XIkghQTqSh2v1+UJxwF6rQETR6YiPTHCZVkda1FlvdbQpGtcksBDAR25JVmXRt77bl+txzb20oi1G0Px5SqEhcihM5hwtUKHiSPTMGlkWq2lKiYMTbE1ZPdlg4KrfCf7dl5frDqCCUNT3J6nZn6W0cSCMZkREiSDQibGnqOXMH/5QV47N3tlxEKrN9VpxmpU30TsO1GK1jEhKKvUY2jP+OryKsDVCh0mjUyDWCRAu9bN8OZj2ahQG1CpMSI0SIZmITIUXVbj458OOOwitd8wYA30/th1Dt1SmuOREalYv70AQoEA+uocuq/X5kEAyyyRWCSATCKCWCQAONi+br/0vHG3dxs2VAoJ4qJUePbBTOiMJugMJoSqZBAKgJl3dYJcKsSkUWm2osf2gY/RxNqOF8CyCzVnZyEGZ7aCxst7rtVb/vhQ6xiUXNO4fM9al+P55hW6Wo63z4MDAJPZjPGD2iExLrTWHcEFxRUwmc1+y5E1mzlo1Xp0bhuJzNRop6LKJsZMHSJIvaCAjtyS7JdGaqtT1Zj9JoVCSyshrd6EgpIqrHPR1slTqYrh2fFIT4pAcfXXfZkRSYkPd5jdq9mXVCgUYPKoNLfnFgoFaB0TjOhmSohEAog5oeX1VXc5cLfj1tXOzcE9WuO6lzOmlRojBnVrhaz0WJwvrcLLkzIREiTDzrxLbjdUpCdFYMmv+ajSGqHVm3CtQoehWfF4+v7bnHbUSsRCzHh7I1jOUtPNGhw1D1ciPSkCiupNEZ3aRuLT5wZi//HLbgONT58bCJOJtc2OebNhw1qk9+BJ171ox/RLAmNW4Y3PtqNL++Z4bUoPyGVi7M4vdTmeO/smYdFLg2FkWK/veYXGgO9+P46/7Ioe279nF60+gjNF5RiS1YZXuRT7GcOarCVAAKC80oAu7aM8ds+Yv/wAhmXFY+LIVJSV66CSi92WD/G2FRnHAQY9Y9sQEladskEbIEh9ooCO3JICZWlEqZTByLD44ffjXpWqsB8/Y2RsGwLsNyjUZUYkPERue6xm/TClTIwLpWqnmU/rkuyw7HhwACrUBugMZkhEQpRc0eLombJad9za79ys0jLoEB+OSo13+UeRoQrE3BaEWR9uRZXGiA+f6ufxA99auuXpB27Dum1nsXrrGduGgRWbTqF1jF39vUuV+PS5gWjfJhzTxmS4nRUaP6gturRv7rFkzPzlBzA8Ox7jBrS1fd1+w8ZHP7r/vlmD7J83nkROrvt2bEOz4vHcQ5mY88UO3NGjDX74w/19+PCH/bbCyxKxd2nXMokIpy+Uu32NzzzYtbqlG+swY+jNMrfttdvluYaFyJB7xHMXEwD4LbcQ7ds0Q/e0aDA1cir91RbQbGapzhxpEBTQkVtWICyNCEVC7Mnj1wM1PTECC54dAI7jHMYvFAqhUkptszveFJC1zojERgZh1n232XZI1uxLal16PXHuOlITw23ntl+SnfvNLocP6uSWYXjj0Sy8/Nl2Xvdi3bazmPtYNtRaBq98tgOvTu3p1UxjkEKCKq1lhtGbYrbt24QjOyMWA7u1xpwvdqDoihosB4eSJ4AlT3Dm3Z2w/M8TLnPTWJZDldaIXXmXeJWMadfasfSI/YYNd+Menp2A/IJrLoM5e9YAecZdnXjdh5zcQnRo0wxdO0T7rfyM9TVaO2ykJ0Vg6uh0vP14b2h0DDQ6BmKxEBKREDk7LXmF7oK5mnmuCpHIq/zK7I4toDffmH0O9LaA5NZEu1zJLc26NKKu0kPIcghTSiCsXhox6P3buNtbUqkYLAteH0xCoQC78kqgUkiglIocxm82sxAKBLZyHNYNCp52Wj77YFeMG5CMRauPoH2bZvjPjF5YteUUVm85g1G9Ex3y6ayzQis3n8L73+/DK5/vwLgBbfHMhK6Y95j7siImM4sKtcG7emE6Bh8vPwCjicWeY5d4l4gY1ScRKzafworNp/H6tCwMzY7nXQJk7d9nEBGmAAB8MKsvJgxNcSiBYhUdrsShU1dswZFQKEBqQjj+dXcnLJ5zO55/uBv6dG6JVVtPOz3XFfvSI0lxoZCIRPi/L3MxfnB7zH00C707tXDYrZncMgxj+idh9Vb+gUxqfDOs+4fnfdh2FkIBMLp3oscdo1Z8ys/Yv8a8M9cw68OtWPrrUQQpxIgKlaHkqgbT/vMnft54ym0wBzjmuYpEQlvOKB/W95U1/04gsKQ5LHFRmge4MXtbs1wKIY2NZugIqdbUlkbEYiHUOqPHD6aaXRKKr2oQrJQiKNhxWchoNCEzNcY2u+OqgGylxohmwXKEBctwrUIHiUiAyaPToDOYYGY53Hd7exw4eQWd2kahSmuw5dNZZ7v+2H3etqPyox/3497b23kscVGXHbdqLYPnH+4GtY6BgTEjNiKo1jxI+5lGtY5Bt5TmaBMb4vUHPstx0BlM6JbSHP1va4lXv7yRs9gpOQJyqRhr/j7jdolZLhXDaDLjyvUbuyntd5VaW6dZlw3tN3KMH9wWBsaM1x/NhsnEIkwlw5TR6XhifGdUaowwsywU1e3YvHldaj0Dluc+gNIyLUwsh6yOsUhNjMD1Kr3bnci15bvZj6Fmkef1OwqRWt2OKzUhHEN6epfnamnB5e37ymhrwRXobQHJrYsCOkKaKI6Dx1kJ++VMd0n91mUhg4FBULAc9w9JcSjHYc0Hy+7YAoMzWyMuytK+6fSFCqza6rj7UWc0o02spa7Z0Kx4GE0sQoKkGJbdBrlHSpzagIWHyPHCp/+4HX9dapAFB0nxxaoj+OdQse0ezHssG+lJEVixmV/u1a6jl9A8XOnVda+U67B0fT70Rssu1dtSmuPNR7Px7w+2IDhIihcmdkelxgi90WzrtVpziTkxLhRj+yfjg1n98N9lltZZnsppqHUMKjVGjOyTgM7tmmP30UtYtfWMy9eYkRyJb9blYXgvfkWRrdRaxqnAsivW99qhU1exysUSpHWTw9dr89CncxzatW7mMd/Nnqsiz9YyJLo65Ln6Wmcy0NsCklsXBXSENFFGo8ltAeGaO0xrsk/qf2REKrRqPXQaA+RKGRJiQ/DqlB7QGUwOuzQrqgyQSkRYseUUfvzjJGIjgzw2gr+rfzI+eWYA5DIRFDKJwzHxsSF44ZFMj7NFddlxKwCw43Cx7WslVzV477t9eOHhTHSID3cKjlzlXp26UA6Vlx/41tIthSWVtk0oDw1LwT0Dk9G/W2v8ues8OiSE1/o9ef+7fRiaFY95M3ph91HXu0rtdy1HhiqQ0CIE36x13rRgzcv7Y9c5XCnX4eERaWBM3k0xq5QStz1zbcfweK9ZNznMfuA2MGYWj761kVcwB7gu8mwtQyKEwOs817rUmQySS6DTGgO+LSC5tVFAR0gTZV8gteYHkzdJ/fbLQlq1HpGhckgkIggFgEgogFwqgkwqhlIqwv7jpfjxj5O8PsTfrQ4Y7729Pb5eeyNoEgoFSIoLBcOYER8b4rCMaM/XHbf2Sq5p8OKn/9h2nuqNZnAcB4VMjObNlGgebimPotWbcPGKGkFyiU/J/Tm5hegQ3wzDeyVi99FLOHGuDIO7t8LRM2X4w65Ehys5uYVITQjHhdIqt62zhmbF483HsqGUibH9kONuzZrL7LagXCqGSiFBepKljyqf1xUkl0Ik8pwE5s17LTUhAj3TYhATEQSTmXW5lFxzDO42TljLkJjN3pcA8arO5IBkaPUmcEIhVAoJLl3T1Pocd+MkpDFRQEdIE2ZmTLirfzLetftgctexwR37ZaGa9bEUYiFYEwuNQQ9VsBzL/zoJwLsPcetuzN92FDoUpzUyZjz/cDe33R4sz/d+x21N1qVblgOuluvsxmCGSCi0zQBdr9JDIRNDKhaB47haS4BYuQsk/9p9Hr0yWiCjbSQSW4biWoUerWKCsejFwbUWe16z9Yytc4WrADUntxBpCeHonhbjsHmDzzL7i49k4pmPtznVJKxp3IBkmFgWY/u5D3y8fa+t2nIaPdJi8MajWbaCzJ46fngK0l212+Ob58q3zuTQrHi0bdUM/3pvM9Q6Br07tcDEkWm8XquncRLSGCigI6QJ0+urP5iqAyYALjs2eOJuWcj+w9F+qcnbD/F1287i1Sk9cHv3NrVW5a9Z/Ni64/bNx7KRkRzpMg9uXP9kjzlZ1qXbHmkxmDI63da1YsrodOSduWbpReqiHlxWRiyOF3ruS+oukIyNDMIT47tg59FLLstaeCr2DLjeDFDT6q1n0D0tBueqH/dmmf3tx3vj8f9uchtQWgOZpz/c6rYNGFC391qlxojFvx615Tm6uyeegnRf2+3xqTPpKr9yx5ESj8Wx/T1OQvyJAjpCmjCOA3RaAyaNSEVaQgRWbjkNuVRUp92hnpaFLDsDLR9q3n6Il5ZpIZeJPRantV9GXPjLIVyr0NuW4UquanCsoAwqpQSvT8sCY2ZxtVyHsGAZQoOkqNIaMeuDrW6DE5blsGnvBTx5bxcs25CPfw4V48On+nkMfN7+di/uvb0dJo1K87pnK9+csprFnmtytRnAnnV3rTXo82qZPTECcx/NwicuOovYv67SMq3HHrvJrcJgMHq3lni1QoeySscAp+Z74FhBmccg3R/t9mrWmdQaTFDrGCikYohFAuzOL8VHP+5HaZnW9hxv0wBqlkvxppsEIf5GAR0hTZzZzEGvM+K2lOZITQgHy3EQeFn8yros5O5Dx35noLflRIZnJ2B3finv4sePjusIs5m1LcMZGDM6JITjWEEZdAYTFq05Ao2OgVZvwj2D2iIjKRJ6xoxeHWMRqpKhQm3ArvxSmEw3xs8BOHDiMnJ2nsP4Qe14BT7L/zyJVs2DEREqx1sze1XvUjVBLBK63VBhfb18i/Fau1tYiwPbc7UZwMpazsRkZpEUF4qLV9TeLbNvPo03Z2Tjlck9wLEc1DoGQQoJBIDT66pZwoblgEqNAUqZBOGhMmh0njdNePO6cnILkZEUAZVS4jbQ9We7PfsUg6ioYKgUEmgNpupAORhPjO/stBzMNw3AOk6zyQxVsNynbhKE+AMFdIQEAJOJhVwpxLwlu8GxwGted0mQAhzn8KGjCpKDM7MwGEwwmW7sDPSmnEhdcqxentQdj721CfEtQjG2XxJ6pMdCbzDh3KVKfL7ysMOHfE5uIXqkxeD714dBrTOiqnrsT97bBccLr2PhikO4Uq7DoG6tMPebXXUaz+vTs7DjcAn2HS/Fv+/tgvnLD2DH4WKXeV11WY52lSvnbjNAzQ0PeqMZ997eDg8O6wCRSOBQw86TM0UVKK8y4N1le9GqeTCeuLczPvxxP3IPl7h8XfYtzXp1bIHpYzPw2pe5CFKI8cpk795rnrpDAJZgc+5j2YiOUEJ9sfYyJP6Y+RKJBNAbTdiVx2+J3Dpr6S4N4K7+ychMiwbHAftPXqVuEqRJoICOkABhYkwY18+yQWL99gL+y0IDklF8RY2PfzroUFeurNIAvcGEUJUMQcGWgG9c/2R88MN+3uVEWkcHw2hivcqxsuaOnS2qwPvVfUXH9U/GbzsKHYI5W56amw/hsf2S8fHs/lj861HbEnF8bAgMjBnnLlXZihx72mV5pqgCjInF/uOlkElEqFAb0C0lGhcvq10+ty45Za5y5VxtBqhtw4O1hp27vLyaKjVGyGVinCutQpWWwfZDJbU+h+WAbYeK8cDQFLAccLTgOsxm1ucNJPbOFFXAYDTj7Zm9oNa5LkMCAHK5731UgRudH75Zd9SWh2rP1RK5ddZywbMDkJkaA43O6DROjoPHnrw1ywbRTB2pbxTQERIgDHYbJLxZFkpPisTj/92E4CCpx7py4we1Rfe0GAzp2YZ3HpFSLobO4N2SXM3cMVdLk3zy1N7/3vKBOXlUOio1BqgUEkSEyiEWCZ2KHHvaaavWMZg+NgNGkxkKmdhjJ4S6dLeo+XqHZcUjKyMWT76/xfY1Xq+3uoadp7w8e9alT6VcbOth6+2YWZbDhh2FGJYd79NO5JrUWiPE4Gzt9uzLkPi7j6qt84OLYM5ezfehWseAZTkwBsZpnHI5dZMgTQ8FdIQECOsGiYkjLDv3vl6bhymj090m9d81IBmd2zXHMx//DQBuAwbrctbabWdxXW05/4ETl9G2dbNaP8S7pjRHmErm1etwlWNVc2nSmw0A7ds0Q5uYECx4dgA4Djh69ppXO0+VMjF++uskRvVJwv7jVzw+d90/ZxERqqjT67XfkHD49FX07RJnC2D9lZdnZV36vHi5Cj3SYtAsWO6xJqC7MQOW3bYDurZy6jJixWcnck0qpRQCAWA2mx026tj3UfXXzJc3nR/WbTuLlyf3wIpNp5DQIhRBCgn0LoJh6iZBmiIK6AgJIGYzB4POiK7to5DZwbJzr3O7KGRntLBU0dcxUCnEUMolEAkEeO0rS/DiaqOAfb4Wy3HQ6U1QyMXgOCAjORImE4tJo9KQnhTpMsgZ3ScRGUmRPhfpBSzLcGaWQ0p8OI6fu+5Vntrav8/ilcndIRQI8MNfJ3gvq6l1jKX7hAAY3TeJVyeER0akgjGxXr3eZsFyWz0+64aE6HAl5j6Wjf0nLqOguNIveXn2RvVJxIXLanz54mAYTSzK1Xq88EgmJCJhrTXyan6P1DoGr3yxA/NmZCMpLgRvz+yFcrXxxgymRAS5VIS3lu7htRScFBcKhUwMnYGBKuRGHifHATK52K8zX5ZyPCavlsjNZhatY0IwbkAyREIBFEqpU+6pRufdOambBGkIFNARUkcNWaZAILAsHVlzii6X6y0fLkoJwHIwGhkIWRZhCssymV5rBCsU4OjZMpeJ/LGRQXhjehaqNEYIhQIYDGZoDSaIxULojSYoZWK8vXQPzCyH/l1bOrUKk0lEOF+qhlAIGBgT76r8nnKsNDoGzz7YDQtXHPKYB1ezoX1BSSVEIiH2H7/s9bLaqD6JuFBahbJKgy2Yq3l+63WtRZSbBctwZ99EfPgDj5yyvon4++BFbNheiPOXKm2vW11dkuTVKT3AmFhwgM95eVZDq5d0+bQWcxWAufoelVzV4NS56+jaIRo78y7hl02nbtyfS5W4e2A7DOrWmleHilF9EnH83HW0jlbheqUBBqMZwUFSKBViCAUC/JZbwOs+8Jn5EgoFqNR5t9ysN5owqk8CMlNjXOZvDu7eGiN7JXh1TuomQRoCBXSEeKFmYNUQZQpq5hTZb2y4VnFjY4NSKYWZMcNkYiAWi9zWlVMpJJj3WDZEIiHOXarCgl+ca5Xd2TcJL1R3HFj4y2F8Ljhsa6sVpJBg2p0ZuF6px7cb8nG5TOtztwcAkElF+GvPeTx+dycwJtYpD04mEeFCqRqtYoJhMJoc8uNkEhHW/H3G5Xlrss5wqbUMUhMiIBYLsWT9Trcttexz6NZtO4u5j2VDJBRgaE8eRYnjI9zmu5VV6rFsQz6iw4MwNCue19itDEYzkluFOQR01mX2bh2isXLzKSz/y3lJsLYaecOzLcHgum1noVJIbI8Nz45H5/bNwXLAis2nnAJJbzp+eAo2x/VPxr/vvY3Xxg8+M18CgQAqBb8d21YqhRT9urTE1242UZy+UA6ZVOTdOambBGkAFNARwpO/k7X5qJlTFBsZ5HJjg1AoQHZGLIb0aIP28c1g0DFu68qN6ZcEmVSMZRvy3S4xfvjDfqeOA4UllVApJC6L9noqTuupSK+VdZlv874LGNHLkk9mf49jI4Pw5mPZqNAYsPSbfIfXfWffRIzplwyOu5EP6MmZogpwHHDPoHb4bOUhTB6VBr3R7PG+DurWGkOz4jHnix3Q6Bh8uyEfDw1LrfPrBSx5amqdCeqiCstMqxeCFBI8NCwFdw1IhsnEQiIRIUQphUgowNGz11wGc/ZqzlTaj/mD7/ejZ3osPpjVD1+vzUPfLnHI7BANxmiCkXU9k2jt+OHpPWD9+fAUbL7n5caP2ma+OI6D0suUAIVcjMLiSvzuJjC1diahbhKkqaGAjhAe6iNZmw/bDr3cQpc7IV3NKpVXGREWLIMCQMekSAQHSRAZpkB8bAguXlFjeHY89uRfwvFz15GaEO62rIe148C8x7Ix/6eDOFtUgeHZCTh65prTc2sWpzUwZjAmFkq562K2NY3qk4hNe8/j9WlZ+HZ9vlOu3xvTs/DzXyeRs/MchEIBUhPCMaBrS2SmxsBgNKO8ylBr31h7Wj2DJevzoavuHsDnvprMLD6Y1RdVWiO0ehM+/ukApo1Ox9uP90Z5lQEGxgyZRAiRSIicXM+v1z5PTaWUIlgp9SpAEAoEmDL3D7SMDsHsB27Dz3+dxLlLlZg3oxd2HCmu9RyAZaZy3oxeGNitFZQysa1zwvFz17Hr6CUMz47H7AldoTMw0BoYqORSXPPQuN7de0AmESFYKYVQKMCe/FKvg01Papv5YlkOZpblXeJnVJ9EmM0swkLkbnvy+tJNgpD6RAEdCUgN3WbHPrDyxJtkbT7jt99NVzOYsi59uitDclf/ZLwypQcq1HpodAxeeCQTcqkIUokIndpFoV2bZrWW9Vi5+TTeerw33n68N7R6BkFyCTR6Bu3jXT/3542nbA3aOySEY0iPNvhm7VH8VktplayMWJw6X47jhdedctlG9kpAYXEldhwpwfhB7TCiVzwUcjGOF17HV2vysONIie1DvbYcMes5w4JlkEtFuF5pQHS4EvuOXbZdt7Z6cOP6J+O5h7pZypxIxTBX5/WJhAJIJSKYzRwE8NzJw5qnFh1hudblMq3Xtd5MLFBYUgmxWIgzRRUoLKmEVm+Czk2XhprOFFVUb6KxLK266pyQlhiBIIUU7/1vLzq3i6q1cb19geLWMSGYM6UHvlmbB43OiJcm9fRqt+mrU3pi77FSnLvkemeuu5kv+58tlmUhFonQsW0kr+XgjORIGIxmTP/Pn4hv4f695G03CX90vSCkNhTQkYDRGPlrVv4qU2A2s7zbBFl26DEoKK5ESJAUY/olwmhibcFUVDMlxCIBSq5qcNmuHyVgmTF8t3r5alz/ZLy0cDuCg6R4a2Yvt4V6XX14nSmqgFprhEIuhkImxi4Pzeg/fKofco+UoHenFrZZLa3ehEmj0pDmYaesdZmvT+c4pCaGI6llKLq2j7bNjplMLKRSERa9NBgaHQOFTILyKgNkUhEmDE3BpJFptgDEXY6Y/YybZdenAQ8NT4VELIRcKsbabZb8Oz714KzLgpNHpWHvsVL8tPGUVxsPhmVbcgl/+OME3nwsGys2ncI/h4rx0dP9MSwr3uMfDTXzEGvuSNXoGFSoDW6fX5Naa8SP2wvw1+7zLsf/9do8/Pu+LvhgVj+8/tVOCAXgNZPIcoBIKAA4oKCkEm8+ammt5tVuU5bFy5O6QwCByz827Ge+XP1uCFVJESSTofiKGqVlWowb0LbWJfLTF8px+mI5WM5zvqH9ErO7XeCuul4QUp8EHEdvtaaKYcwoL9fWfmAjiooKBgBcuVJVr9dpjPy1G9cWghUKMGnun7yfs3jO7RDazb6JRAIoVXLsPnrJZSshV+OXSES4rmXw1tI9tobm7oKptMQIt4nks+7rgivlWvS/rZXbQMXKGgDaf3h9+uwAhARJkZNbiAMnr7hcoo2NDMLbj/fGkdPObZDSkyIw/c50xESqoNYy0OgsAaIAAuTsLHToEHHv7e0wrn8ydhwuwbp/zjrl0OUXlGEVz3sw674uKLqixj+Him0zbvbnBIDenVpg4sg0TJ1n+d6OH9QOsZFBvJbSnn7gNlworXK7LFjzXloDh6yMWKzcfAocJ7BdS6WQ4OPZ/SESCXHo1BWs3nrGY15ezddoHcPiOXfg9a92utz96spnzw/EO9/udbtbdlz/ZLAci20Hi9GvS0vkHilGRKiC147mZyZ0xdUKHXp1bIEdh4uREh+O5xf8w2tcAPDOv3pj6fp86I1mp+/viF6W1AaDjoFAAEiqZ9Ct7z37XM9/DhXjg1n9sP6fs5BKRRiWFe+06SVnZyGMDIsRvRJc5u7VvM9WKoUEX740CGKh5Y+vmt0kjAHYy7WhfqffDFzdq7AwJSQS7zbN+AvN0JEmr7Hy16zEYiHURrNXhVntk7X5tB5yNX6W5RAS5Jg/5uq5nnYtApblq7mPZmHX0VKvC9eqFBJEhSlgYjn07hyHjm2jnJZZAUvR4h9+P+7y/IXFlbheZURkMxZGk6U/qUQigkQkcFiaVCkkGJzZGot/PYqcXM85dPblTApLKl3eA2uR2Dt6tHEbyJZV6nGtQmc756g+Cfh81WFemyvWbD3jsR5cTm4h0hMj8MFT/WA2s7bA4aXPtuP5hzKhUkrw8mfbAViW0w+duoqv1+bhzr6JeGtmL1TY13qzq2Fn/f66mq1TysUQiTwv9wKWpedeGbEIkksgFgmQ2CIUcpnIIVi3vhfSEiNw4MRlRIUpkBQXioS4UF7Ll13aN0eVxoC8M9ewed9FdE+LqXVc9qzFje2/v/Nm9IJaY0RMZBA0tt6+UnCMGecvVdlmqmsWarbOpuUXXMO8JbthNnO2949IJMCo3p43sbir/RcdroRIKIS6Sg+RSOjU9YKQhkQBHWny/J2/xofjEo4JBqPZq6R7+2Rtvq2Hao7fbGYREqTAP4eLfeogYK3TZl1W9EQoFGDfsVJMH5uBc5cq8djYjtiTX4oVHpZod+aVuO1yYM1HO3n+Ol75fIfbcyxacwQ9UmNQWFyJP3addziH9cP5n0PFGD+onceyIvb3wFoktrC40jY2sViIHqnRCFXJUKE2oErDIDYyCF+9NBiMicX1KgMeHNoBk0em1/p9rq0eHACs2nIar07piTe+3QOW5aCQiWE2c/h4+QH8+74uKCiudKgTqNYx+O73E9i8/yLefLQXdHoT1m07ix2Hi22BhLU8SdtWjp0Zxg1IBjhgbD/3NQEdi0kD16v0eHlSD9tOaDPLQiJyLNOSnRELgQBY/08B5s3Ixnvf7cPUOzNqXb78ePkBzJ7QFev+2YvzpVVeb/wIVkodClDn5BYiIykCcVEqvPDpP27fS69/tdOp7qL9ho2XJ3aH0cSCYcwQi4Uug2VX32tr4ev8gjLb1+2Xfc1mlurMkUZ1ywR0n3zyCRYsWODxmP/7v//D/fff7/R1hmGwdOlSrF27FufPn4dEIkFKSgoeeugh3HHHHfU1ZFKtodvs1La8W1th1prJ2r6MnwW8rq9WcxahdXQw1DrGY/5SzV2d16sMmDGuE0QiAc7ZzXxYWWcGh2XHY+KIVLzwqfNSWm35aJfLtCi5qkG3DtGYcVcnqLVGSCUih92FWoMJQ7Pa4POVhz32obXP+Xp0bIbtHhiMZuw8WoLocCVm3tUJHeKbQa1jUKVlEKaSQiGXYM/RSx4DVk810Wr2aa3pTFEFjCYzXp3SA1r9jdp5QXIJrlbobN8f+zqBAFB8RYMn39+MYdmWXL0JQ1Mc6+5JRfjwh/22cQ3NikdKfDgOnLyM7mkxGJrVxmGWE6h9s4d1WfPL1UdsZUteW5QLjd6E2Q90w2crD0GtY7A7vxT5BWUOu1lrLl9agyO1zgi2es+PUADeGz9G902EwMVE48rNp/HypO4oLHYMCu1nquc+mg0D49zJwX7DxoCurTCmXxLe+XavQ8FnT6yFr1/6bDtKrmpowwNpcm6ZgM4qIiICbdq0cflYVFSU09cMBgMmTZqEffv2QSQSITk5GTqdDrt378bu3bsxbdo0PPPMM/U97FuW/cYAPnxts8Nnebe2JU77v9p9GT8AaHTeJZK7mjFSysXQeJhN5PNB7y6wOVZQhiqt69fnqT+p/TVfW5TrfuZu9REYTWZMG5NRa2uuoVnxmDI6HUbTjXugVEggFgnx8ez+2HX0El5YuN0hx+qHtXl1XsoGXPelrUmjY/DTxgKcOl9uW+ZrFa3CIyMsO0Zr1gm0qrlj1L4zwwez+uFf93TG5ysPI7tjLNKTIvHql7kouqzGfXe0w6SRaUhpc2MGjc9mD/t7+PRHW9G7cxxen5YFrZ7BqQvlmHlXJ9s4rWOz7mi2znjuOnoJJrscVrWWgVIuRuvoYFRqjUhNiOC1XNshPgJVWqPTe/lMUQUYE4usjFjk5l1yWhbPyS1Ez7QYhKjcFxNmOct5xGIh71xD4Ebh6zcfy8bp89fRuX1z2vBAmpRbLqDr27cv3n77bd7Hv/vuu9i3bx9atmyJRYsWITExEQCwceNGzJo1C4sWLcJtt92GgQMH1teQm4SGLhNiJRQKbB0P+PKlzQ7f5V13S5w1/2r3ZfwAUOnlc13NGEWEyBGsdP0B580HvavARqUQQ6tjnOrZuWo3VpdrzrirI1iOw5HT/BvXpyZGQCkXW2ZKZWJMHpWGxeuOOjzfU7Dp6pyulrLd9aWt+VrDQ+WYMCTFoXWaVCKCQipCelIE1FoGIUFSty3HWA5OgYdMKsKmvecxe0JXVGgMYExmvDEtyzKzub0Q7VuHo2VzFV6bapkZFIuEOHHuutevN6VNM2SmRuPk+etQKSRIahlme12uumpMHuW4VK1SSqDVm6CUi1GhNuKjHw/wLkA9674uLmc/DYwZk0enO13LauOe85g8Ot3j66xLcWCpWIQffj+OFpFB6JbS3ClXt7F+RxJidcsFdN64evUqfvzxRwDAvHnzbMEcAAwaNAhTp07FwoULsWDBgpsyoONTJqS+sSxn63jAly9tdrxZHrVf4oxvcaNMAWOwtN6y1MGq2/gBQKaQQOHl54KrGaNuHaIhEQtdfnjVNbCxfqAP7xUPluPwyIhUh3y2vLNXnZYR63LNDvHNkJ3Rglf+H2D5nmRlxEJnMOHOfkkQCYHcvEsO1/IUbLo7p6ulbE99aYEbO3MPn3Le+Wttr/biI5n4v692IjRIim9eud0x6HOTr2kNLr7LOY6IUIVt96X1nIteGoxKrRE6vQlavQlyqRiyOr6v1207i+yOLXD3wHZYs/U0OreLwqjeCRjVJ6nWrhpfrj6MILkEIpEAWr0JIUFSl8WH3S3Xupv9lIiFmPv1LgiFAoccTI2OgVZvQu7RS/jX+M4egzVviwPbf69Xbj6NzA7R4LjGLaVESE0U0HmwadMmMAyD+Ph49OzZ0+nx++67DwsXLsTRo0dx/vx5tG7duhFGWT/4lgmpb2YzC5VS3iBtduqyPAoAS169AzKpGOA4CAAwHKp331nKF4gEAqQnRfBqXG4dPwAs+TUfdw9s6/Usgv2M0dCseKQlRUKjZ3BX/2S8a5csX9fAZvvh4uodg2V44+tdLmdZRvZJwKVrziV3vL3m2r/PIiu9Bc7xXBo7U1QBrd6EXh1bIDUhAhwEWLXltMMxrnLWajtnzaVsay05d31pbTtzN550ymUDbrRXu/+Odpj3aDZ2Hb2EVS5Klbha7rYPLuyDL+s57WsPag0m3Nk3ESN7J9bp9ZaWaWEys5BKhLizfzJKr2nx4LAODikJ7rpqPP1AV5RV6DGmbzIWrTnisCnC3VKyNTh2N/tp/3WlXOKQg3nlutYWGDImFuP6u98cAtgVB3aRb2iv5m5ia1qERCKCVC5plFJKhLhyywV0x48fx+zZs3HlyhUEBQWhffv2GDFiBNq2bet07MGDBwEAXbt2dXmu6OhotGzZEhcvXsTBgwdvmoDOmzIhk0alQS6t37eRiTHX+svZypc2O3VZHtXoGUSoZACAPccuu/3F/uIj3fHMx3/X2nB8XP9kgOOw59hlbNhRCJVCWqdZhMS4UIzpl4SMpEgEKcTYd+wyUhLCHfKX6hLYmFkOb83shR//OOn2vfHRjwfw0LAUDOzm/PNQl2uqdc65VJ5UaY0YlNkaKzadwriBbZ2u5S5nzRO9wYyIUDmEQsGNnqRbTrndFTk8OwH5Bdc8BgoqhQQDu7XGNzXKtFi5Wu7u3TnOKbioGWxaZ1OfebAbWkerwMGyjO+NSo0RraKD8dLE7th3/LKt9p+1Tp/1e19b/uWYvknokR6Dzu2jnLphuFpKtnI3+1mzw4a7HEzr9+jewW3dthpT6xhs3ncek0elIz3Rc+HrmuVMtDoTopopsLiRSikR4sotF9AdO3YMx44ds/1706ZN+Pzzz/Hwww/j+eefh0h0oyBgYWEhAHgM1Fq3bo2LFy+ioKCg3sbc0LwpE5KeGInsjrH1Oh6DgUFmanStVfR93XVWp+VRhQRiqQiL19X+i92+0b0r1vELYCl3ER8bgjNF5RiS1YZXi6GsjFicL63CwG6toJCJoZSJwQH4Zq0lf8z64WvNX5JLRV4HNjqDCZeuGmp9b3z3+wkMy05wml2sSzCl0TEed5LWpJCJ8e7/9iI+NgRVLq5lXf7zRkiQFM8/3A1anQlCoQALfj6IiSPT0Do6xKlQdHLLMIzpn2SrMeeObenZQ9AH3Fh6fmN6FlRKqVNw4Spvct22s5j7WDbmfLEDZjOH5x/u5vXrnTwqDcv/POHQis1+dpVPLuQHP+zH8Ox43Ht7e7yzbC/mPprNa1OEq9lP69ftO2zU1s1j0shUtIoOweqtZ2Ays7bZQLFIiLH9kpCZGg2BgEOX9lFITQj3uARsr1moDLsbuJQSIbW5ZQK65s2b48knn0SfPn3QsmVLqFQqFBQU4Pvvv8ePP/6IpUuXQiwW47nnnrM9p6LC8ks6NDTU7Xmtj1VW8t8txZdEIrJVom5IeqOJd77Nis2nkJkW3SDjnDw6DRlJkfhls3OrpbsHtEVmWjTkUrFtybIu9EaTV0ucKoUUu/MvufzFbp/knl9QhrwzVx0a3bsbv1prxMt2+UUsy2LSqDRkJEe67DJhnUV473/7oNEztuWrr1653aEGW838JZazlJLwRrBSip82nnAo7Ouq2DLLcti6/6LTzGpdgimVUorwEDmvYy3fEwkiQuWo1BhdBuh1SYgHgMXr8nHPoLYQALh4WY2nPtyKBc8MQFpiBExmDpUaAxQyMUKUUhiMnmch67L0/Pr0LDz+7iY0C5Y7bEJxlWt2pqgCFWoDOBYQCgRQyiRe14Dbk+9YiLrm7CrfXMgNOwrRrnUz9O0SZyvw625TxNj+SUhNiMArn++wBVE1Z8r6dWnJOwczIykCt7WPQve0GGh0DKq0RgQrpQhSWAoqS8TVf8BzJrz6Za6t4LDeYAbAQS4TIzJMAa3BZHuPt20ZBrHIMSfR3YYWwFKKqHt6DEKC+b2Hm4rG+OwJVE3lXt0yAd29997r9LX27dvj9ddfR8uWLfHee+9h6dKleOCBB9CyZUsAlpIlACCRuA8QpFLLh5Nef/PUItLUUrPM3pmiCmh0TL0vuwKAXCpGdsdYZKZFQ6NjbD06g+QSSMRCW6kPX0hEQtw1IBnv/q/25d27BrSFQGBJkrYPcMoq9RjaM95pB6BCJoZKIcE7/+rtdvx6own7jjsv3aYnRWDK6HS8NbMXrlcaUKExOMwifLPuKMJD5DdmagQCaHUmbD9c7DBma7mJNX+fweRRaRjUrTV6d2rh0ODeFaFQgEFdWyJYKcHU0Rm8kve/yzmOr1+53SFHqS7BVJBCgp7psfjnULHT4zU/SMf2T4bWYMLkUelQysUQCARO16prQvxvuYVITQxH53bN8dxDliLTBsaML1YdRjOVDFPuTIfeaIaeMXssEwPUbelZZzDh49n9HerZKWRiSMRClFXXtLNSVZdreW1qT2gNDIwm/mkLo/pYasDV3IhiP7vqy8aSOV/swIy7OuHtmb2gtmuXFSSXQCQUgOWA/8zohSqtEZIahX+tdQn5XDc2MshSm+/EFZd1Bq3LsiKhACKRAOMHtcUnPx1y6PlbpTEiOEgKiViI33Mt7/EHh6VAW51r6263r/3PREP+jiS3NnqHAZg8eTK+/fZbXL58GZs2bcLDDz8MAJDJLLlRDOP+l7PRaPkFJ5f7/6+vxujlKpGIvC6VodYxEHEcmDrmrtWFSCREqFwM1syiqlJX+xN4Eggsu0J5Le92aA7GZHaYTQsPkSEkSIadeZdc5hSN65+M7qnREHGcw/gFAkCpkrvNW8w7cw1PfbgVw7Licc+gdvjpr5O4VqFHWYUOQ7MS8NFT/Zw+UBRysVPOnkOBXT2DkmsaTByZ5tDg3rEsieUDa0SveMhlYuzMu8Sr2LL1eQAwaWSaQ45STu45LwrMJkEIS0Brv1Tn6oO0WbAcoSop1m47izVbz6B5uBJP3NPJZSBjS4j3cvlv9ZYzuK19czz3yTZENVNiXP9k/OuezjCZOeytzqEEgDmTe3h8XXVdel7+VwH+2n2jk4Z1Z+t7/75x761L6yfOXbfd8+hwJT6e3b/W1zssy7J0r3ZR/9B+drWuG0u6dojGtDszcPTsNbz42XanFlxj+yUjNTEcW/ddQP+urfD6V7scNkvEx4bwum7NdnE12S/L3jOoLU6dv46uHaLx2fODsP/EZZc/u3f2TcLC5wZCKRej2O4+86nfWKWtn9+RNXfZ2m/GqusuW+rlyh/1cm2CRCIROnXqhD///BPnzt344Q8JCQFwY+nVFetj1mMDXV3zyNgGrrtUX212OA7Qaw2YODIV6YkRLv+yv6t/MjLTogEO2Hf8ilND8Jo1z6w8JUrzzVv8LbcQ7ds0Q2JcKIqvavDev913T7irfzJm3Xeb7YO+U9tIvDSxO3YdvYSXPtvhlFM0oleCQ1BmHxiYWc7j67JP3n/3u7144eFMHDl9FS8u3I7LZVrbMq/JzMJsZhERpsTxwrJag4ue6TFQ641Y93cBxg1oi5T4cOQeKcGU0eluX/foPomYP7s/Xv0yF3O+yMWnzw10CtDVOgavLcrFm49lIz2Jf0K8ZabMjPlPD8DLn2/H5ysP49PnBmLFJssmEUvpjhgo5WKPs5B1WXpWyMTQ6RmHPrP2O1tfn5aFOV/scMhrs/aoHdErHgdPXrHdQ0814PLOXkNCrPPvM/vZ1brkX1ZpGTx+dyf8+McJt99368/HXQMsm9SE1TN2VnwDYW/L8Vy5rrXk5/15wm2/5Q+r8wEnjUy19VjmW79RLBJAppCAZVm/7XjlW4mAdtneOiigq2ZdVjWZbuSixMfHY//+/Q5BXk3nz5+3HXszqEuZkCC5xK+zZN6SSsW2Dzmj0XPVfj7MZg5atR6d20YiMzUaGrtlIaVcAhNjBsfBaTbNm5wi+0RpgQAQS8Ve1Ql7ZXJ3j03nzxZV4N3qGYjXp2Xh7aW78dLE7vg+5zhkUjFempjpcono3KVKzHusF75Zl4cpo9Px018nEKyUwWRmeX04piWE4z+P9cI364463Bv7lksPDeuAAycu8wouDp68gvatm6FcbcDTH23FPQOTMXtCVyyucX771/3RjwcwPDseH/y7r2VzwNc78fr0bKQlRjh88JVc1WDlltO4q38yUhM810Szd61Ch+PnruP1aVn4+8BF7D9+GdsOFtn6zDImFlq9yeMSZ12WnkNVMjw4rIPLgrrWwGTmXR1d9r1tFizHiwv/wZXrulprwEWHK/Gfmb0gFMAxmJKJcaFUjXH9k/HLplMeA1JXOWXNgmU4db6c189HakIEQlUyp2VxPoFwXZaDX5+exavfcs7Oc+ieFoP0xAj8c4hfj+UO8c3QrUM0lv6aj4f9tOPVm0oEtMv21kEBXbVTpywfpjExMbavde7cGStXrsT+/ftdPqe0tBQXL160HXuz8KZMyF0D2kIi9j13zVtCIaBUygCh0JLsXGVAsFIKVYgCYFlotQZbD8m64DjAoGdgqG7DFVZdrFhTpYdc7jyb5u2HiLVnq4kxQREkQ6XGcYnL/gNRbzQDHCCXiaDVm1BQUgmRSIhDpy7znoF45sFuOHz6Kkb0TnQ7s3Vn3yT0TI+BVm/CvYPbwcxyuHtgO8ikIsz5Ygev17V66xl07RCN33cWOj3GckBBcSUUcjF+/PME7+Dircd7o0prgFrHIL5FGHbmlfDahLIrrwRd2jdH3y4t8eWqw3hifGd0T4uB3mCCzmgpuKuQiaDRmTDlzT/c1kSrKSRIis17LyAyVG5ZVvthHz6ePQDnSiqx5Nd8XLysRrMQGWbe3cntEqc1j+/Ovon48Ifal57H9EvC8XNl+L9FO93Wp1v/TwHmzcjGe9/tc+h7y7KWXa7W91dtNeDU1bX8sju2sOUtxkWpMPfRbFwsrUJ6UgSOnr3qMiB1l1OmkIkRJJdgzTbHmoDurNpyGnMfy0araBWGZ8djQ3WgxScQrstyMGNicfTsVbfH2L8uo4kFy/Lvsbz277PomhJtycH0045XbyoR0C7bWwcFdAC2bNliC+h69epl+/qgQYMwd+5cFBYWYufOnU7Fha1dJFJTU932hw1E3pQJyUyL9stmBG9IJELIFDLsyitxWYx1bL8k9EiPhUFnAMP4vhRcc3nXVTeJunyIaPQMgoJk2Lj7AtrHNwPg/IGoM5jQLFhuW2oysywkIhHkEhHvD5Tfthcgu2MLyKUi/OQhp8i+KO2LC7dDrWPQu1MLTByZ5tXrqtR4qBsnsMyyeBNcVGmMeObBbnjn2z3oEN8Mz3/6j8MpPQURUrEQP/11Cg8O6wCjyQxAADPLwWg0QwCA4zioFBKkJvIv/GwtbLty82lkpsbg6Qe6olJjRGiwFJNGpdm+Vyxn2Z2cmhiB1S7ep61jgpGZGo2hPT0vPQ/NikdqYgSEAsuuZHft2BgzC63B5NT3NjUh3GmZ0lMNOAAorzJgULfWOHjyCu7sl4TRfRJRoTZCJhOBMZkxcWQaiq+oHXIha8spG9c/GU+Ov81lT+CazhRVQG+wpAI8MiIVgzMt9fqsZV48bWipS35ihcaACUM74GhBmdPYar6umgFybc4UVdj60lr/kPM1uPKmo42/rkmavlsioDt16hSWLVuGBx54ACkpKbavsyyLDRs24LXXXgMADBgwAB07drQ9HhkZiXvvvRf/+9//8PLLLzv0ct20aRO++uorAMDjjz/egK+m/vHNI+uWGt3gO7eEQkCmkOEbD03V3/9+P4ZlWYoem806n2bqanLXTaIuHyJqnQmXr+uQ3akFACCzQzSmjcnwmGSdlhiB1VtP485+yTh3qarW8iFCoQCpieHgOA5nLlbgD7uEeldqtvgqq9TjWoV3y+kVaoPbunFyqQjXa3TxqC24uFqhw/bDxXj+4Uyoa+zA5hNE3NkvCXKpyOXu4RuFnzPxzMfbag007AvenqkONsViAV798kaQYv+9WvjLIYztn4y3H++N8ioDrlfpHWYhV289jeceyqx16fmVz3fg1Sk9XBYQtn6vlHIxOA5Oy/51KxUjQXiIHJ8+NxAHT17Biwu3u9x1nd2xBU6cu45tB4tqzSl7zy4FoGZPYFeulOuwdH0+9EYz7uybhDmTe6BSY0C52ojW0cE4ef66yz846/J6ZRIR/tpz3mlsrmrtuQqQa2OtFZhfUAZN9ax/Xfu91qWjja/XJIHhlgjoTCYTli9fjuXLlyMsLAwtWrSASCTC+fPnbZsaunXrhv/+979Oz3322Wdx9OhRHDhwACNHjkTbtm2h1WptuXOTJ0/G4MGDG/T1NAQ+eWRatd6h5ltDNKdWKi0zc7UtNVpLTHTvEA2N2uC367vrJlGnD02FBDHhClyvMsLImPHMg1095sRYZ2Usy6EsvnppsNtSCQBss1YmlsPl61q0ignGohcHu9zNas++xERdi/C66sEJWO6TQubdr52QICk27b2A+NhgNLOr5cWnsK01j+je29vj85WHnV6zN4WfXRW8vVqdT/fmY9lY+MshXKvQo7CkEvOXH8Cw7Hg8MiINh09fwbUKHb7LOWFbNrefhaxZG9Baz65mHl+V1rHAslAowL5jpZg+NgP7jpVCLASCFBL8+s9Zh0D/4hW11/l6IUFSqHUMftl4yun9KBQKoNYyWLT6CPp2icPEkam4o0drHD3rfU9gT6zvo8KSStvs8T2D2uLHP05AKBBg2pgMtG/TzCkQFouECFXJvG6Z98PvxxEVpsCw7His2HwaraODMaJXvNPr8vVnQq1lEKaUOMz6e/O7sy4dbVxdk9x8bomALi4uDrNmzcLBgwdx5swZnDt3DkajEaGhoejbty9GjhyJkSNHOnSJsJLL5fj222+xZMkSrFu3DoWFhZBIJOjevTsefPBBDBkypBFeUcPwlEdmz2xmoQqWN0xzaqHQVhqiNqu3nEGPNN+7WNj/snW3C7guSe4yqQgvLNyO0mtaPPtgNxSWVNaaE2MtlhoXpcLHLgoUj+qdiPlP9wcH4Mjpq7WWU3A1I2XfTqourytYKXXqwWlVl/NZlzj/3HUek0en2x7ztrCtpyBiw45CpCVGYO5jWfjkp0Mul/G7p8XgP0t2OwR81ny6iBA5Hh3XEWYzC5lEhAularSKCQbLckiKC4VEIsLLk7rbgmn7iVRrbcAVm06hV8cWmD42A699mYvCEsc8vmbBMmj1Jqcl5utVBjz/SCbCVDJwHIdXJneH3ugY6F8oVWNsv2S8/z2/OnQcx+HI6asO70d3S9ssy6FF9fuRD/s/GNzlKbrq5WoNBhPjQvHzxlM4VljmkINpMrNQKSxFgzVaI+/SOPazrhv3nMeLj2RiWFa8w2YSe2WVeoe+tLWp+VpU1b9Ha5Yc4fu7s06VCKqvSSwaYvKhMdwSAV1ISAhmzJhR5+dLpVJMnz4d06dP9+OoAourMiEikQB6owm7j15y2cGgtm3z3v5QSaXiuhU9lkug9zJ/xO0vW4UEAL9itZ6qx9/ZLwmrtpyGVm/CB7P6QaWU1Noqymrl5tN4eVJ3FBY7fpicLarA12vz8OlzA/HjHydqnenztPRlXSLytgjv6L6JEHpoP1HXor4sB2w/UoInxndBcsswsByHUX0S8Pmqww5lPNzhE0Ss3Hwar0////bOOzyKav3j39m+m0ZCIAmRkMam01sCQijSu+0qKlUERa+FnwrivaLY71WKCoiCoKJypasgIEjvPQkE0ijphZTdbJ/5/bGZZXZ3tiYoCefzPD6GnZkzZ945c+ad97wl1WGQRs7NGjz3UBeLzLgv6s1/mu/JwlXH8fYzaahR67B2dZZHyjTNAAfPF+Hx4fGgmdsRpgIBhb4pYWjlK0VIkBzzp/RyuMQ8YUAMEqNb4+2vj9stAad1CnNa2xS4bYUEKCsfTWdL2974WdrWn+UiEFB4bGgczmSXITIsAKDMS/X1WiN+PZSPeVN6YuPeq1aKcESoPxZM6QVfuQhrtmfiwLlCj/MMhgX74PlHuuJMtjkVEZ+vHCsH27q0zuCO4ZjwAChkYmjrdVD4ynAyq5S3LNn4hrJkfHOnN5kIFDKx3Yf4vYa3CnRz4p5Q6AhNDxs2v3p7psO8TXxh8415qAQCCnV1ni2f1tXrIfKTwcfP/YfWNr9TWVW9xTJRVauDRCRwmqx23P3RkEpEDrPH640mxHcIwvc7L2PRrDT8efoG+nUJb5IX4si0KJy5XOaWpc/Z0hd3iciTJLwJka2h0RkdB0Vw2huR2gE7nNQxtV3iVEhFMNI03n4mFbVqPW7V6fDE8ARMG22fxsMWV0oEu09dvR4ff3sKNAPeII2odv4Wmdn60+mNJrzzTJrTwBNPlGmuRYxmgOJKNWZN7AyhkEJxhRplVfV27f93/Rm79tnzZl+/Zalt6izv3qqtF/Hsg50t49HV0rY3fpZ89WfZ6x1zfzSMNA2jkUb/ruGo1xpxq05rWYYWiQSIjwxCVn4VALPiW1Gtgb+vBCcyS/Fbw9j/96qjeNfNPIMAXPrKceVw6HyRV4mpJ6bHwmgwQaaQ4offsyERCx2mEMq9WY1/DI3jTTniSSYC9pz3MvdKzj6i0BG8whI27yJvEzds3mgwev1QCYUUJDIh/Eye+a6IRQK8ufIIBALKrYfWNr9TWLCPVQoINoEw32Su0hjw+c/nMH9KL5zI5K8UwV26Y2tSnr1Sjk4d23h0XXwvxMaUY+JarWyXiNgkvAufTnX4chzXPxrJMcH415dH8fqTPTC+fzR2nbhuZZnkWixPZ5eaK0jEOK9Pyyb1ZS0jZxwENrhaRgYAjc7oMFiDK1eZVGRRFhzJTFVvsPOnEwoEuJBT4ZEfGeurxbXi+vtI4CMT2407vutdteXi7fq9DXJ2pKyzv7fyk2LB1F7QG2kYjCaIhAKrEnKj+kbBYDQhMsy85O5qadsbn7LWAXKr+rzhbX3x7qw0XMypxGf/O2dJHL39EF+911jMndQdb6w4YkmAvWhWGow0g01/Wpfh+2T9Gbz6ZA8kRgU5zTP4yGCly2ASWzm4qks7MT0WyohAyxgemRaJHokhMBlNyMitxMi+US4DoDJyKxEf0couQtWTTAQ9EkNQr7p3rXP3Us4+otARvMKbsHmRWOjxQ0VRgFQmhkgsQlWNDgG+3vmu0AxcPrRCoQCyhki0349fc2iZ4Co43MncVy7GnIe7OK2owFpQnnuoC8QiCm9/fRwMg0YHHggEFFKTQ2Ew0o229HEtTyzFFWos23AWC6b2RmJUEAxGGjqDCX4KCRQyEWrUehiMJnz4XD9IxUL4+UjQoV2AJXUI61OmN5ig1RkR4CsFA6B3UiiSY1pb1Se1rU/bPb4tnnuoc6MtX4F+Mmh1zhNPOwvoYGVGM8DDg5V4Y8Vhy3kEAgr+PhK3U8mwyWxZXy3b2qzPTEzBz384r0IwMi0Sc5/ojopbGkilQlCg8Puxa9h5rMAqWKKg5PZSP6uQznxvN16f3Au36nT49XC+VQk5BuYyY689Za5X6yoPoTd+kTKJENPGJOGJEfEQCQXw95FizfZMHDxXiE9fGuA00OW/nGhZtjLGoXM3MbBHhFUZPn8fCSRiIURCCp/+cAaRYQEI8pejqlaDX48UQN+Q0sjRhxD3ugqKa+32Ka5QWwW02NVtlokx491dCGqlwP9N6o4eDR+TcoUUUe383RrPDw/uCLFYaKfQeZKJQFuva7YKSlNwL+XsIwodwWO8C5s3uuX078qi98hgpVf+V3zt67QGiEQCSKQiCATmayqr0SIs2Aer5g3BzTIVLhfc4p10bSdzukH5vJTvPKcY0GApiQxEN2Vb5BfVQiCgvA4U4C7LMTBHs3mCraWPL5KTZWjvDhAKKZhoGoH+UmTlV2HDnis4cqEINIOGsldhGNIzAlHhAdj8Zw40WiNmP9gJtWodFvH4lE1Mj0XXuLZYve28xdLEVS70RhoiAYUsd+XqYBnZ7EckgrP3Gp8jPr/MdPjh98uQioVIjApCvdaIDiF+Vvn1XMEq078cysPWA3kWhSs6PAAvPNIFhWUq9yoqRLdGfEQgjCYGcqkAw/p0wIODYnGrVodbdTq8+lRPiIUCq0LxOoMJjw6NR0SoPxb/uB9+PhKHJeT4/OP4fEM98R8dc380th7IxZELRWZ/wzodMnIrseNoAa+ljA/2Xs9+sDPyi2pwf5f7cP5qBTbbKDY9E0Mwe2InzJvSB6p6vaXW6T+GxuNywS18sfE85FIRbw5Jrr/n1gO50BlMvKmCHOVS/HrBUHz+6iCIREJLVgCxWAQjzeD8Vfctub0acn3a+hm7m4lAIBBALBa0uAAAd7mXcvYRhY7gMd6Ezas1Bhw6X+jWvs4set4WVbdrPykUEokQNAOnS8CJ0UEIC/bhXcbjOmX369wOcx7p6n75rgN56JMUZi6v5GWgQEhr/oSnnsBapBzVLmVhk9uaaAZikRCrt922QlqUyrRImEw0NDojTDSDZx/qDCFF4fudl7GFx3LFtcrOeaQLnvtoL/x8JPjviwOQlV+FRWtOoKC4Fl/NH+JRWTS+ZeQx90ejRq13mjJl4sBY3ChTQSETO/TF85WLEdxKjuce7gKN7rZVMchPhpIq5znsbFFrDBjdLxqj+0ZbFK6C4lr4+0iwxM3r3bwvB29M6413Vh3G28+kISu/kjfZNndJWmcwYVifDnjtM3P0prv+cY6iXCUiIfadvoHkmNYu/Ud1BpO5ZuqubCyalYaf/7iChwcrsex/57xyGXj/uX5Q1et5rV2dOwZj7qTuOJ5ZwpvYefyAGCx9JR3f77zkMK8cO99odAaIhAKnqYJsfTNV9Xq0CZChXqWFVCqGj68MOoMJQgGFbQfdt+SmpoTBJKB4U444ykTAPWdLDQBwh3stZx9R6O4hmipU25uweR+5GLk3+a1Ptl/z+cW1MNI0zlwut7PoOVruZLF17o4I9bOzEOQW1kBVr4dAQDksFO5JIlSaAa6XqqDS6D2aOOp1BkS2CwBNM8gtrMbw1Ei3AwXYFyL3ReyNpS/QT4bXn+qJQH8pCstU+GzDOSvLk0gowOh+UZbktv994X6cv1puOSfr25aVX4lFq4/zvjRH9o3C8awSh75tbMqQ92anwd9Xij0nruNMdhlomkFkmL/HFThsl5FZmdE07XA51RzQEYTMvCorXzzu2PSRiTH7wU64cLXC7gOAtWR5glgkwDtfH4dAQFn5xHl6vTRNux2MsWhWGiQiAQQU8K/pvSESCpB97ZbVGOI+i1qdCf4+EpcJnMf0i4ZYKMDjw+M98h+9VHDLcr3e3Gu1xoDcmzV21x0SpMD8Kb2cuj980pCAfMroRKgdWGS4frGOPvwc+W/6KiQwmWhLNOumP3MQ1lqB6WOTPZsntEaIKceR4yxsJgKhkLI6Z0sNAHCHey1nH1HoWjh3IlTbm7B5uVRktZQlEFCI7xCIQd3bo2dSiJ0PlUjg2Exuu9xpNDHQ6o0QiwSQS0Uor66HVGz20eH7klZpDDDRjMcO7M4SoSpkIo+XO+s1Rrw9MxV19XrUqs3JhaeMTsIDvTpYyhyxRIcH4MGBseiibIPFP57Fw4M6oqCoFlduVFuUL76lL2eM6x8Drc6ID9adhFZvxOyJnbBgWi9LEls2Tcvlgiq8ufIIyqs1EAoF2HbAnLi2dYAMcx7ugg17snmVUPal6Y5SvPNoAf49ow/0Rhp9O7dDSmywJTigwssISq5y//W2DPzz0a4QCq1fjNwXnF5vgk5vxNGLxXh7Zir2n7mJQT3ag2YY6HQmBAfK8c2vWbyBQEcuFmPamCSv/TtZhWv2g51QWeuZA7tIKMC5K+VujeXk6NZIim4NbUMtW6lYiM1/5iAxKggDu9+HnomhVpZHiVgIuVSI92an4afdjhVGNl2OMx9ZPv9RboUVb6qtqDUGHMsstvv92Qc743hmidsJyPskhfHeO3f8Yvn8N2PCA8wpjhgGazgyUchELitk8F1jgFzk1gf5vRQA4A73Ws4+otC1YO5kqLZHYfMDY0EzNGjm9rLNiLRIMDCXidLoTJCIhDh9qQy/HclHF2Ubl3mtuMudA7u3x8wJydAbzUldNVoTVm29iCMXi618k9gv6YVfHfPYgd1VDjOt3hwg4AlyqQgrNl2wFEBn+8ktc1RVq4O/jwQBPlJIxALQDDBzQgpEAgEUMhFi27eySunALn25WpIemRaJ3smh0BtMEAkpvD0zDZl5lXjdpsQTqxS992xfrPs1CyaasTies0ur/3ggDjHhrbD39A1cvnbLbjJ0pRSz+b+OZZQ0yvLFWpfaBfvg1Sd7wGRiLJGMsx/sBIOJxqJn0qDRm1Cr0kFmCdiog1prhN5gQkz7VgjwlUIuFWF0vygYjTTqNAZIJEKczCrFpfwqKwWaG717+nKpV7nJuHJKiAxEV2Vbpz5otteskIrcHsus8vbcR3sxqEd7TBmThH9N7w2pVISTWaV4+2t7Kyvr53g+x3Hxek/S5bDXyFrluAFB3MhXd+Cz/ItEAt6av45gE5DzfQiNTItCZm4lLl+7ZXffba+LO8bH3B8No4lG9jXr8mSqeoPHlVJ85GLI5GZF0NUH+b0UAOAO91rOPqLQtVDu9JeaJWw+LdJp6pKRaZHokRACCuaIRjYdwTs8y3OswvXtjiy381qFtPbBP4bGoayqHiFBPqiu00MqEWLS8HhMHZ1kscpxv6TfezbNKwd2ZznM7mvjAx+Z2KOJAwCOXCiy+j2vsMaqzNGGPVfQKzEEA3tE4FhGCQ6eK8T0sck4l1POm9JhXP8YBPiai8S7yr/1yfozGNj9Prw7u69TC8TiH8/isaFKPPdwF5zILLHLzafRmxWhbg33eceRAruccI6UYlc5ztyxfHF9u/RGGrfqdJCIhRALBaBAYXDPCCREBkEsEkCjM+KPk9dx9ko5fGRiPD0+BVW1Oqz9jV+R6ZkYAgXDQC4RISU2GDH3BVisV2xFiIhQX+gMJku0pu1L3BZn/p3bDuQhNaUdVi94wNpSZmNhZumbEga1F2O5e0IIHh8Wj4s5FYiLCMQ3Tu6/K9cDT33fth3IQ+8G/1E2kpSdG3zknj1DtpZ/AOidGGJX89cZ7NJtz8S2Vh9C/j4SjB8QDb2RRlxkoMt7YZvS5lat1s5v+HppHYRCgVcVZbiKq6MP8nspAMBd7qWcfUSha6Hc6S81Nmx+2pgkpEQH4+d9V52GzYvFIvzz0a5Y91uW3YuDXUbYfeIaKmo0mDI6CQaj64fKVy7Gu7PSIBQKkJVfhU9/5C+FxfVv2Xm0AGkpoQjw9cwSwJf3jdv/Uf2ioTMaG2Wh4cJaMiaPTEBIax+s3paBQ+eLXKZ0+PSHM7hUEIkHB8aiokaD95/tixqV3q4oPJt/q0Oo2W+Jrz3WStQ6QIahvSOx5pcM7DhyjTc3H4sjnyJHSrGrHGeuAkYc+XaxEbdDe0cgPjIIa37JxGND43H+agW+/z0bvnKxS1myiszU0Yk4nV2G//1hP8bHD4gBRflBKhbh/NVyHL1YjOljk3lrjLoKPGHlVKfWY80vmXaWW9vcc1qdCaP7RUPPyRnnzlKRWmPAC490wfc7L8PfRwqtjv/+c9l1/Bp6xLfF5FGJ+PVwvtW5IkL8PPZ94yaf3nvqBl54tCu+/S0L/j5St5+hsf1joDeY7J6hAF8p6jx0f6ir16NtK7nlQ+jguULMnJBiqRrh7hjnprR5d3aanfWQphnsPFLg9jWyFWXYdthnUiYR4ue9V3GpoBJPjUy0RLPeSwEA7nIv5ewjCl0L5a/4UjOZGMgkIqR1CkOPhLYOw+YZBhBLgLPZZVYvDkdRc3KJOUt+ckxrKz8yW8YPiIFUIsK3PEoi4Ni/xbYmqDvw5SezymxvolFbp0dqShiyr99yarV0ZqHhsu1AHhbNTsORC8XYeeyaZykdIgORHN0aF65W4Ifd2ZbSSdyqB44sK7b3hV1afWigEkH+cgzsfp/LHGl8Fh1bpZg9/7trTtilguAqJo4im/mse3xjqrpOj6mjkyGggIy8cggElNt1YHceLUBiVBBulNbZWVTKqupRVlUPhUyMNdszLS+LzLxKu9xkgX4yBPhKsO1ALl5uUKYdUVGjQZWNH51t7rm6ej38FOYxqarXW3LGuaqYAZiT+ubcrMGuE9fx1fwhTi1rttbPtkEKJMe0tjqXN75vKo0ese1boaC4FgzMc8Oh80V4ZHBHpHVqh8sFztPUjEiNRJ/kUGh0BjtrV41K57HflJ9CAiNNQ6ejkRLbGr2SQq3uKRfnY1yH73dehkIqgkIqtrMe+srFkIgF5nnChSV3RFok4jsE4cvNF51GGJdUqBEcIANtou+pAAB3uZdy9hGFrgXiKlSbL6qU/VJjt3sSCSsUCqCqUluFzWsbokgFAvPXn0gkxM8cBdNV1NzE9FjMm9wTc5ce5I2ONL+UI3Eiy7Xjs61/C1sT1NNoUD+f2y8JNjt9Vl4l/r3qqKUd9vfEqCBs/tM+VYJt9nhn5BbWQKszITOvwvOUDgfy8N6zfeGrkOCNKfxF4VnLCje3FrsM6ei+jOsfA7lU5NSfCrgtc/M9KoVCJkLrABmC/KUWXyRfuQgikcA+GayNYuKoUoWtUubOmJo5vhOeHJ4IUUMFEXfYuj/XbrmYPZeqXo8jF4qs/aRsaoyyz9n4ATHQG2mX991ZcuOzV8oxaTiNzLwqbD3gPD0J33PD5uP7dkeWS8uaO5Gtn7w4AKu2XvQ4MbZMIkLvxFDsPXUDg3u0x4pNF/DJiwOgqtfjbHYZHhkShz7JYdhz8rqdLyxr5Tx3pRxSicjOens8qxT/fNSz59tHLka91mC2+NECXMwp8ap8nlwqQmWNFuPT7a2HXHl+sv6MU0vugwNjER8ZhAUrjsDPR4KFT6fiWnEt1u/KRs6NastHDyuPsDa+0Gv191QAgCe4m7OvOStzAFHoWiSOQrUdfeVJxULIJCIIBVSjImFpmoZYLIZUZh9Ra6RplN+6nc/Kmd8U17/vg+f64bmP9tq9BCND/SEUChw6g9sqrb8eyseCab1x6lIprpXUoV5r8Ghp51adFv+a1hvVKh2qVXpEhPjx+icWV6jxz//+iRFp5q93o8n8AheLBJBJhNAbacxdcgBB/jKnTtYstWodJg1PQI1a7/GyVlWtFh+uO2WdFmPrRag15gS+bVrJrHJrqTQGh9cFWPv3uYpaBRryhD3bFwN7tIeq3gChQIDnH+mKWrUeNM2glZ8UxRVqfLUtwy6i11YxYSObl76Sjl6J/VBXr4evQozXGxzf3R1Tw1Mj8Y8HlI1KhcKea9O+q5YcanzQDCzLywIB5bB6Axc2+vVmmX0CW4VUhLdnpuLb3y55XTFjbP9o6PQm5BXW2tUq5eKOPC2RuRM7QSYVeeZ4LhWhU8dgPD40DnqjCU+PT8Gvh/LwyBAlggJk0OiM8PeVYPrYZDz/SBfUqvUw0TTEwtsuAyFBCrwxrTfaBftYWW+NRhqXCm5h/IAYfLL+jMv+jE+PQZ1aj3e/aVzew6h25nuXEBmExMjWMNG0RSZ88nRmyTWaaDzz/h/wkYvx33/2B8MA94X4wt83Ag8OjLX66Fn841lcuX4LU0YlQgLcMwEAnuIoZ19Lun6i0LVA+EK1XX1tTxgQg8Roc64x9svek0hYdyJq2Rf0/Z3D3Vru+u1IARKjWuPdWWlYssHaP65XcihvYIOzpQmxiMKCab0AhoJEIkCnjsFuRYP2SQ41OzMLBGb/HLUexzKKHX7Bcy00i2aloV0bX7y16hh85CLMm9wLi18a4NQixUUhE2PPyet47qHOdstwrmCXOK+X1KG4Qo0eCSGY/WBnlN+qR5C/FP4+9lU4bpaqvLJM8JFbWIOqOh0+XHfKothw61R+sv4M+iSH4YVHulpZlBwpJiqNATqDCftO5WJcegxq6m7n/fNkCTUhMhDJMcEeSNJ6uZg9FzeHmiNsxyNf9QbuPR9zfzRulKnw5Tz7BLY3y1QeVVGwvT8j0yKRmhyGokqznJ3VYPVEnikxrZEQGeT2B9LEgbEoq9bgx13ZmDWxExiGwZXr1RjTP8ahz9rY+6OREhuMf608gsJyc/9VhTXQ6Y1YvT0TT49LsbLefrHxPJa+ku7y+R6RGoneSWHYeSQfZVX1XvkDssr+2P7RqFHpMCE91jLPsdZDPnk6suROSI9BQlRr+PlI8OGcfjiTXcabGJn70cPmcuwR3/aeCQBoDGzOvpYGUehaILah2u58bf+XJ1+YJ5Gw7kTUsu2zOajcYfOfOXj/uX4ca5cevnIJ/BRiFFdaLym5VFrTY5EUFYSVWy6if5dwxEcG4eHBSiRGt+adMFkl98VP91uUjdj7WuHtZ1Kx7H/nXfadZoB9p29izP3R0BlMmD+1F85fKef14bC1SAkEFPqmhKGVrxS5N6sx9v5oBPhK3ZIZi7+PBH4KCZa8ko6LORWWpWE2IIAb2epNln5XqVwAsz8TqwjZBr9MH5uMlxfvR78u4bwWJVvFJCY8AFKxEN//fhnKDoEWeXgTZdknuaFKB8ef0FmaEHYZlHsuV75j7i5Zsvd8eGokUlPCcCKz1G5/dty9ueKIU39DFu79iWx3e5ny2x2XMG5ArNkdgqKgkFpHlQoEFCJD/TGqbxTe+uqoW/LctC8HC6b1QmKUe+lyOsUGY/GPZ1FZo8Wn60/hjWl9oIxo5TQx8uIfzQr+v2fY+2WqNQYs23AWr0/uicSoINAMA43WCK3eiGljk5EU05rX/YFNdLx6ewbiIoIaIuwdV41wRK1aj8E92qNPchi2HczF1v15UGkMFt/PEakdnI5PriUXuD0+356Zih9+z3bbj2/Tvhz0TAhpsgCAlh4s0RIhCl0LhRuq7cnXNt+XvatIWJOJdiui1raGqTvkFtagrl4PkZCCgAKkYiGEAgAUA1/5beuCW0prg1LJVSQeGtQRvRND0Sk2GFq9Cap6PVoHyCESUth2MA+rtmZYLY/SNIMalc7t/l+9UQ0/hfvLV2/PTMWfZ25icI/2oBmgpFKNZyZ0AgD4KyQuA0VYYsID4K+Q4OXHu9kp2nzjwRPLhEBAwUQzYACkpoThaEaJwyVj1gL0yGClndXUVy7G/z3RHR9/d9oy7jbuy7EoVlqdCWcvl2HG+GScvlSKiQNjUViuAs0Au45dswS2eGNVqdcakdapHc5dKXdo0WUtaCFBCksS4IjQ29UMbHOocfFkyXLRrDRcvX4LXePaYu+p6zh4rsiS346Vq0QsgNFEu/Q35F4jRVH4cv4QUKAsy5QCAYXHhsZh9YIHAJgt6zPHp+CDdScxtJdZ8aAZBkaa9kieWr0J3/yShZkTUpAQFYStB/j9wnokhICigFkTO8FE0/CRiiGkHNfq5SralwuqcCm/0mp+YhXthwZ1RM6NarQP8WtING6CWGcCwzDoHNsGvZPCoNYYLMEkPnIxKqrrMe/zQ8grqsXOo9cwIi0SM8YlQ6vn9190RHCAHO26++Dlxfst1kPAbIFb+NUxvPdsXwDmJT/uPXUmTzaAyVNruVprgAjwOACAm4C+skYDVb0BPnLRPVcqrLlDFLoWChuqPTItskksL84iYQ1G2rsapm5OEHX1evx2uAA3y+oslSVq1Qb4yiQW64K3SmtKTGv0SAgBTTMQCSkoZGJQlPmakqODMSItCjr97XxgcqkIBiMNX7njep9crpfWwVcuweELRS77dv5qOZ4cEY+2gQqHAQnOAkW4TEiPhVZvwsVL1oq2I2uWO5GKtsuHqnoDnhqZiGljknmVipjwAMglIrz4j25OAxU+eXEA1v9+CTPHp2BEaiT0RhOEAoHlZX2rTod5U3ohwFcKhmHQyleCW3VaS84ymUTosVWluk6HkWlReGpkoksLWn5RDXYeKwAoCjHhAZaKIGwONT6fJU+XLHsnhUJvpNEzMRTJ0cGQy0QQCwU4mVWKCznlmDI6ibf4vLNAiBq1Dq39ZXj980MoLFcjLNgH7zyTCq3eBF+5BCqNHpUqLcKCffDlvCE43lCyK9BPiknD4z2Sp1ZnwpyHO0NnMKFTxzZITQmDWmNEdZ0OMqkQcqkIYpEAz7y/B8kxwRjcIwJR4f4ABZgYBkcziqyUHYcR8FIReiaGYPfxa0iJCYafQoKHB3dEj4QQHLlQjLW/XbILULqYV44tB3JhMjEWy6ZQSGF032i8PrkXPvz2JLrHhWB4agcYjDQCfKUO/dC4CqZWb8J9bXwQ4CtBflEtSm/dzp3J7b9Wby6h5kkkskpjwLGMEo+tsSqNEa3kIug0enSPb4NeSeacfM4CAO5kAnrCXwtR6Foo3FDtOrVnuYn48oXx5SwSCinoDGZlx9Maps6S9NriIxPjieHxMDZYxzQ6E2RiIQQCYFz/aCz56ZzXSuumfTnoGtcW735zwpLriX0R1Kh1WLs6y6k/oCvFCgAYwGUmf9ai48zh/dMfzjgNFGEZmRaJlNhgmGjG7ryOrFlyqQjBreQO++fp8iFgViqFQgqbdrsOVJj9YGeczS7DvtM3MX1sMjJyK3kTJz+YHosv5w8BA6CoXIWx90djy/5cj6Ms/RRihLf1weptzks6jUiNxNQxSfBXSDAiLQo0TYNqqKvpKEeep0vAO48WoGdCCPQGEwxGGgYTDV+BADKpCN3i26JHYggEAgo3SupQVlXP20++QAipWIhdx69h4cw0vLH8MD54ti9kUpFVsXp2+f2H3y9j5zFzjsEFU3uD9sAc4ysXo02QHDTNwGCkUVljznmokIktAVfzlx/GwqdTseSVdGh0RggF5jJ9ZoW9Hk8MT8C00eYPgzPZpXj1yZ68Yy05pjVmjE3GynlDoNIYUFWrRWJUa+gNJhRXqC3yYZ+n/zlYxgXM5dYefUCJ92b3xZELxZZzPTJYaXdP+VL5BPrJoJCJUFdvQJtAOdYvHI5LBbewce8VzHmkq0fPCp9MnxgRb1cS0ZE1VmcwIT4yCK0DZBA1BLeVVWvh16DEySVC6HVGuwAAUiqsZUEUuhaMycRApzGg3sP8co6S6HJzFrETwW+HCxDXIdCj9tUaoyUHlStiwgPgIxPjzS+P2GVKf2RwR6SmtEPZrXqvHZlzC2tQo7r95ensRcBaEH47ko/iSrXlBVqvMzr0v+qbYl7qcdU3dy06bKDI2zNT8dnP53mVzW5xbfHpj2cwbYx9+TQ+S1znjsHmUlkNaRD46lm6u3zIyqRfl3B0VrbBqaxSt3O9lVaqMWNcstPzfNzwcnn0gTi8v/Yk3pvdF5U1GjtfMGfEhAfAVyHBiUzXfdtxtADJMa3RLtgHi1Yft0RBsufiy5HnyRIwW/bshJPC70nR5lq0fZLDHCoCfP6GEpEQ3++8jNDWPnj5sa6Qy8RYvS3D6pq5484y9v/IxsODlW7Jk/34OZddji37c2E00ZbnQCQUYFTfKHSLb4t3ZqZBrTFg1/FCjEiNwrXiWvxxyj4lyYQBsXhwUCxv5ZKwYB+80KAoLd3gPIm4O4FXvnIxhvSMwJpfMrGTU4vY9p668zGTFN0ayzeeQb/O7bBgeh9s3HsFP+2xXrWw9SF1FInMVod4c6X9nOdIGdTqjHjtyR44e7mMd5mVtbIxNlY2UiqsZUEUuhYOTTPw9TA3kaM8WNycRexEkH2tCr2TQj1qXyEToW+ndthz4rrLfccNiMHm/Tl2GdfzCmvwwbpT+MdQJR5M74ib5SqP+sBVWjU6o10EozsJkH0VYnz+6iDQDAOtgzJNg3tGQFXvfDnQU4vO5j9zsHBmKhZM7QW9kYbBaC47JREJIRRS+O73S1BrDLzLkLaRjSFBCsyf0gtrtmfC30fqsJ6lJxGkb89MhZ+PBAIK2HbQvRqjW/fnYuHMVJx0QwFkldqFT5vPM6hHBEw0jYnpsfh571WXS1Rj7o+GiXbfTWDTvhy8MbUXCorMlQC4Vjlujrz4yCBsO5jHuwTMF3TBpiFxR1F2FUACWFueJw6MxY0yFRQyMbb8mYv3n+uLoxeLrc5jm9h5dN8oFBTV4vfj1+Gn4B8LXFgFcNuBXEjFIsyf0pPXv2/Tvhw8MkQJH7kIDw9Solqlg0Iu4i3Pd6O0DjRD28nD048KZ4FX7L0Y1TcSmXmVVsocALu8h4lRQZZycba+jXz3KKugChMGxOLXwwVQaQyO5w+ODyn3XtpWh3B0jewY8JWL0a6Nr51iyj2Oz8omFApIqbAWBlHoWjjeFCdmHcBtf2dzFgmFgEQqQnJMa8TcF+DU54SvfZFQgIhQP9cpBTiZ0h3x464riIsIQkSon8tzc/H3kUCrN5dMatNKgeAAGWLDW9lF97mTrDYhKghvf33cKt3LmH7R+PzVQRAJKegNziPFvHHqr6vX4+NvT4FmgDen98bqbRk4erEYXy14AFl5VVDIRAjyl9kpEjfLVVZ+X88+2BknMkssFhpba5M3EaTvzEqDTm+CRufZNRmMNE5dLnG5b1iwDxKignA5vwoffWeuKztuQAzG3h8NZUSgVZkz2yWq4anm5ei6es/cBLhuCLYWnNKqeiz+8QwGdm+Pf8/obXF+jwzzR1WtFsP7RFqCDTRaI+QyEShQXqchcZQyhu3nY8PiER8ZhKy8Knzy4gB8ve0iaMb8QuYS3yHQKrGzwUhDIhZi1bwh2Hv6BpJjnEetjkyLQn5RDUb1jXZpvbp8rQrB/jK79ENcq9PCr445HGse+cjyBF6JRAIM6NIOfZLbIa5DINRaAwL9ZJj3xSHedoor1Hhz5RH8e0YfyKQi9OsSjk4d2zgcV7b3KC7C/Peh80Uu5w+uxW1kmrmKzJebLzqMvLY916Th8eYPIR5ljgtrZeuZ0BagKGj1Rs9r3t4DpcKaM0ShuwfwpDixoxqjEwfGgmIYyGRCiCQSHMsoscpf5uprnmVCeix0BhMoMJg6JslhyhBupnRXwQfrd17Gu7PTPFIq/RQSi4WrWqXF1DHJ8JGLoNLczm3nSbJa23QvbJmmKaOTIKAop33zpnRSrVoPmVSErPwqVFRrcKtOh6h2AVBIxdAbTXjjsV4I8JHwFnm/UarChAGx+Hp7BhKigvDaZwcBgNfaRNOM58vZehPOXylDWBtfj65JVa/HrVqd031sl8S5dWXnfXHYobLw9bYM3N8l3GxhMJjMlms3A1sAs9N/6wAZCoprLXJ6e2YqhvbugKAAGbR6s1+YTGJ++daodJg/pScCfKXmpMpCAXRaA+p1RohEZv+xhMhALPz6mFvn51rfnKWM0elNGNIzAvOXH7akQnnhka52y/5hwT6YO6m700ALiqLw8GClZSzYplEZnx4Drc7oNN0Ia1F6eHBHCCigoMix1emdZ9KgM9jnlvSmSgobeNUmUIFnH+yMxKhA0AxwIqsUb311DDTN4LWnerisknHl+i23a7na3qMF03phaO8Obs0fi2al4VJ+ldn31URj3IAYDO7R3mFEM3uuzX/mYEC3+/DG8sNuyWbTnznoGt8Wb644DKlYiMmjEt06juVeKBXWnCEK3T2Au8WJHdUYHZ4aifjIIGQWVCIpug1Wb89w6nPiiBGpkVBGBOLlT82KT3JMa0wdnYT3n+2LW7U61Kh18PeRoJWvFAyAme/tceuFe/VmNbQGk9uJTSekx6K0qh6f2/igDekVgVF9oyz/HpkWZV6SaUS6l/jIIAT6SZ0q1M4SvDqCuyzO/v3w4I4w0TQWPZMGoVCAoxklDstDsfV3bQNa2IoMbAZ7BrBEdbpLjVqHi7mViIsM8ug4c/kl5ykj+Hy+XC3DjUyLxMuPd8O2g7mYvugC2gYpPA5s8fOR4LmHuuCNFYet9r9RpsJPe67wBnKw/mWZ+ZW8Hy0T02Pxz0e7udUHW79PvsAlVoYff3fK0t7OowUY2jsCooayfsBtpdgdRezBgbEor663qmbg7yOBQmp+dZy/WuH289Etrq3DYKidRwvQJykU/r72z4FXaWl0BjzQOwLTxiTj9OUy1NUbrK63qapkcD/kbO+RUCjA+atlbsknJaY1fBVivPP1MSycmeow0p2rSOoMJsya2Ak6vWeyYX2G602ezzv3Sqmw5orA9S6E5g434vX/JnVHdHiA1fbo8AC8MqkbHh7c0arGaHR4AF78R1dMTI/FghVHIKCEOJ5R7NDnZOLAjnjxH1152587qTsesmk/I7cSryw5gG9+yYJELMSGPVdw6HwRtHoTKqo1bltPAKC0qh6dYs2VH5zBRoD++8ujdhaznBvVkEmEABqsAmmRLqNTWbYfzMOItCgIKOvft+7PRXS7APRKCnXYN276C3fgLouzfydGBUEZEQiRQACpRIQffr+MT384Y3eN7Mto7a9ZoBkGlTUau/bZDPYz3t2N1dsyLDJxF5lEhPyiWo+vyUduX8ycC2upYf3yPAkkOXqxGHoDbZUwe9OfOVj4dCp85c59TGPCA0AB+N8fV7Dw6VSEBCksL/xVWy5ixrhkbNp3FUt+OmuRN9eS+Ml6/vvgSR8Aa79PvsClmPAACCgKlwuqrH4/dK4QforbL25Pli8z8yqhN9CY8e5ufLjuFNb+moUP153Cf78/BaGActtHcvvBPEjEQvjIHdsQ/jh5HYF+MrvfvbFgqzUGzBibgjXbM1FQVGuneDZVlYyshrx4LOx9EQgoSERCt+ePTftyEBHqj9ee6olvf7vk9Nllx4zBSCOtUxjKq+2fYWdYKsh4Me8oZGKy3HoXQxS6ewRuceIPnu2LNW8+gGWvpGPNmw/g/Wf7IjUpFHKpCP+a3hsfzumH5a8NwhtTeqGwXIWXF+9HebUGCZGBdn44LKxlp7BchQVTe2HF64OxbG46lr82CP+a3htd49rgpU/+5LVE7DhagIu55Zg0LB4Dut6HLzaet3oBuYOvXIzlGy84VSpffrwbJg2Px+ufH+JVFrkTXESIH0wmz5Krmkw0IkL97X6nBBTOXSlz2LfIMH9U1+kwMT3WrXNxl8XH9o+Gql6PBwd1xFtfmZfvTmaVuKXkXMqvQpC//QuUhWaAoxklEIsEHk36YqEABcU1luABd6/JaKIR2c7xebiWGlvlzhXbD+ZhRGqklcLN90J21Ledxwqwo2H/2Q92trzwHb38G6MUOILPKsvXT1sDyrZD+Zacfd7KDTBXM8jKr0JBcS1i2wfylt5zBGs1a+Wk2snhi8VQyER2Y80bC7aPXIyLuRXYdeI67/U6UmYaO67Y+xIR4od6rWf+aSYTjYKiWrfHTHArOQ6cLURrJ88wH2wfuWl33OFeLhXWXCAK3T0EW5xYVaeFgGbQSiGGoKE4Mc0AC1YctvoKf/q93fjfH1eh0hjQOzHEpQMt17LzwdqTUMjE+G7HJZy7Yk5pUOtk6W7bgTy0DVLglSX7UV2rs7yA3IG1Up2+XGqlVC5/bRA+ebE/1rw51Ky0JofhvW9OOFze4k5wvgoxNDrPMsZr9Ub42lggBAIKQgGFH3Zl2/WNqzifzi5FXIdAlxZGdll8x5ECc23OlHYIDpTjn//9E1KREKBc57xj+Wn3FQT4SJzK2dNJf0J6rEWp+O1IPpKiW7u8phFpt6/J2Xm4lprG1NzkwqfoceHKm90/MTIQ2w/lOnz5N4WyaQufVZZrzbTtJxejkYbeYMKEATFNJrdOsW1cRm/botYYUKNy7CNJ0wxq1XqMvd96DHhjSfKVS/DdzksOr9fRuG6MfLj3pWP7Vh6tMADm+eNYZrFb+24/mAeRUICB3dvDz8UzzMV27Lj7jLKlwvQ6EuF6N0MUunsUk4mGwWCCyURDKBRArTUgr7DW6iuc+6Uf4CtFnZu+VGxtwopqDZQdghy+aLiwkZutA+QYPyAGTEMaCncYNyDGokRwlcoP153CV1sz8MfJ69DqTaiq0yIzr8ppW+wEN6BLuFvLYFx85GK7eqvmL3WzJcO2b1zF+fud2fjXl+Zl67kOlsXZ5e+vt2Xg6fHJeGhQRxy7WISqGi1UGgOi2vl7FLXG9T10RybDU10rmz0TQyz32p2l+P97ojumjErEHyevYcv+XKcvF66lxttAEttlytzCGjAMkNapnV3fWHlz3QRyC2ug0hpA045f/k2lNHHhWt+4fzvqpy1qrR69kkIxsPt9jZbb8NRIRIT6Qyb1zAVbJjEHHTnDRNNIiQ22GmueflSM7R8Dg9GEvMJap+OET5lpzLji3pc+KWGQeygfH7nYLlWJI3ILa6DWGKCt18OoN3pl3QfcfEYndcfkUYlWpcIIdyckKIIAgYBCjQtlrUalg5+H+eyCA+RI76bA658fdutrlS1y3SMxBBq11u1Ajt5JoZA0LAuyfid0Q+qIEamRSIw2B3qM7hflsB0WdoJ7b3ZfyBuWf9yNnFVIxai2sUCYv9StXxC2xbhZ2GXrT14agH9P74N6nQEGIw0/hQQKmQganRFGE42Z41Ow81gBVm3JQHArOd6c3tvShtpDqwDX99DRUo9KY8Afp65j6ugkJEcH80b9scXftToj2gYpoGrYbhtkYVvGyUcmxrzlh/DqEz0RFuyLr7dlYPrYZN7ISpFQYEmR09hAEi71WgOemZCCScPjrSIK2fqntmNXVW+w8mWzpamUTRZusJLZKhuGimoNBveMgFgocNhPlpjwAIgEQnyw9gTmTemFWrVnY4SVGzf6/M2VR/DOLM8iy4UCyqmPpHm5Xojdx69h6ugkxHe4PQbcDbwamRaJ1ORQlDWU4XI2Tviiur0dV13j2lru0Yi0SKTEBMNooj2eP5zJx67/9Xq0UogbHfTG94waTTR85eZ5x7ZUGOHuhSh0BNA041JZO55Vin8+2tWz1CA+Ejz97i7UuPkCCQ6QIyrVHxq1DjR9O5DDYZHpgbHoomyDBSuO4K2n+yA+Msii9IhFQkhEAlwvrYOAAiaPSrCUbHJFcYUaH393Cgum9XY7HcuY+6OhN5igtrFA9E4Kha/c/ReESmOAyUTjrW9OgGnIMff1tosQUBReeLQrFv94BkcuFFu+sOt1Rvg2LE/nF9d67Xv49PgUXiWKm2tv/vLD+Pf0PkiOaW2VCiXIX4Y/Tt7Ay4v3Y2RalJ3MWMvkxr1XERF6uzbl+PQYdFO2Rc6NGssL5ZkJKdAbTUiMbo3UlLCG0lBayKUiSERCiAQUJqTH4tMfzjiso8qHo/yKAOCjEOPfXx4FzeB2zq+SWoe1hn0VYitfNlu8UQqCA+R2/oxcRfnrbRmYMS4ZiVGt8Z/vTqN3UigCfKV4f+0JlzWRWavM2SsV+Od/9+OTl/p7JLdAPxnmTe4JsUgArd6Iq9dvobBchZ1HCtyOLB9zfzR2HLX37+Mytn80alQ6DOphTrvSLa6t1UcARZlTHaXEBmPjPp5qCANjoWwfiF8P52FA9/YAnNfbBeyVGb2RRqCfZ3k1A3ykGND1Pny9LQPPTExBr8RQ/Hf9aYxMjXQ/XVR/8/zhSQApG3HKDXpzNlc6SwPFfUb7dmqHf/6jK9QqnV2pMMLdDVHoCG4lHzYaaVwquIXxA2LwyfozLtscnx4DCkDrVgrUqN1XANV1GsuXIDeQo2diCNRaA9Qacx4vsUgAuVQIrd6EmPAAbN2fh7BgH2w9kIvRfaOsXnYCCogMC8C/Z/Rxe6I2GGiIhAJ06ujcegXcTlZrNNFWCsOINPPSlNjGeuhKDhKRENeKaxHVLgBggCMXivHSY91gohkrZQ4AosL8YTTRGNMvGsv+d86rc52+XIpLi6scWtHEIgFmf/AH2gQqYDDSeOb93bgv5LZi1jsxFKHBPlBpDE4tKVzL5PDUSCRGtoaJvm3FsFX6NDojKAAyKVtKisJ7z/ZD76RQDO0VwVtH1RGOAgbY0nKUgEKBG8tdrH+WUEhZRfJy5e1KieBr089Hghce6YrpY5Oh1hjgIxdDIROhRq0HTd+2yrKWuEsF5qTBQ/t0cJpQ1tYqU1ypxuZ9uW7LbWz/aBw4dxO/HS7A9ZJaKGTm6igjUiM9spp1jWtrZxmy3adPchi2HczF1v155jQgN2vsPgL8FWL8a0Yf9EwIgUrLFp03V11gaAbvrDmOy9duYVTfaIv8XY0TrjIzf0ovqOr1biuqEwfGAhQgFgvwyqTuqKzR4IX//onSqnpk5laaZZUW6dTlhO9ZcAU30TvAP1eaZWOu5UoxDM5cKXeZHodmgNSUdhCR5MHNEuJDRwBwO/mwM77YeB69naTfYBmRGoneSWHYfcKzCCqD3mhn1rcN5GgTIEVxhRpPv7sbW/7Mw5Vr1Zg4sCMiQv3QqWMw4jsE4ptfs9AhzB9De5v7STNAXlENfj2c73Z/RvWLQva1KhQU1Tr1L2H9l3JuVFssEOzvU0Yl4o9T17DjaIFXPi7s38P6RKJ3chh2Hs23U0jG3B9t8QUa2ivC63M58u/jBrRMHBiLGpUOEWEBVr6WmQWV6JVkXvJxxyeH6wtYU6fDg5z+skpfVn4V8otqkVd0+zwT0mORfa0KKzadx7SxyYgK97co3M5wFjAw5v5oXCq4hdF93YzG7R+NayU1GNMv2qFvl8fRgwNjUa8xQCAE9p68hpyb1ZBJhdh+IBeLfziDD9ZaBygBt5fBp41JxiuPd3MY1f3kiHg73zp3HeGHp0YiIbI11v16CQXFtYhsF4CZE1IgEQnw4KCOmDEuGV9vy3AZWf7Y0DgoZCLMnJDiMKXREyMS8PLi/fh+Z7ZVX7njoaC4Fg/06YCMvEo8/uZv2H4wD0YjDR+ZCC8t/hMb9+VgcI8Iq49PT653aB/zB9jH351GYpR78kmJCUZJhRpSsRB1aj3+b+lBlFbVIzo8ALMndoJCKsTU0Ul4ZRL/PeI+C7UqvdWz4Ay+iFNnQW9qtQ5d49paIpYdMTItEj2TQiAUEtWgOUIxDFkZv1sxGEyorq7/S85FUYDCV4Zvfsly6ocxc3wShvSKxPHMYmz+0z5Z6oT0GPROCsOKTedxIrMUn786CD/uznb6hWpbY9Ddfh48V4hPXhyAXw/lQSIRYnTfKMikIpzMKsXBc4WYPjYZWfmVlmVEtrTVpn38CUNZhjcslby58gj+88L92LgvB2KRACNSI+0yt+88VgCjkcY/hsbhemkdfOViy+8KmRCj+sbg9OVSxHcIwoY9V1z6uExMj7XU7Hx4cEdcuXYL3ePbol5rxJz/7LN62XH39/ORWDLbJ0W3dnkutsD9cx/tdeh7xW2/f9dwTBmViKIKNVr5Sa2WXOVSEerUerQNUuBkQ5H5sqp6jEiLxIjUSBiMNHQGk8UXkLU6iYVCHDp3EyP6RmPN9kyX/Z08KtFi/Yhu54+5T3RHm0AFaJqxnNeRbx9f8l7uff7o+fvx465st2T26rKDeOeZNGzadxWHzhfxjil3xxpbTeRiThk6d2wDmqFw+nIpEiKDQFEUzmaX8S6Dj70/Gl3j2kIuFaKo3PqeBPhIEegvRWmlGr4+EmzYbZ9AmK2EwH0+uO1PTI9Fr6RQaHTGBv9ZCURCCjuPFiC9e3v8ctD8zI1IjYTeaIJAIEBAg6/drTot/BQS+PtIUFqpRmiwL/48fR3l1VreZ+jg+UKMTI3E6u2ZLhOTTx2ThBtldfBTSBDgI8GOowUY1L09ftidjYNnCy0yP3ulHEtfSceahjZdXe+E9BgkRrXGghVHUFyhtiSFzsqrxGYHSaHZqiO3arQIa+MLjc6AWpXeYhUzGkzQ6wygKECukIISCKDWGKDS6C3+aeZIegZyqXl/kVjocg72ZL7kIhRSkCmkOJVVyr8smx6LHokhUMjM7jfl5XXuN36P0qaNueQkV1atWikgFnuWu7OpIArdXcxfqdAB7j/weq0OMrkUDCioNQbU1evhp5DARy5GRXU9/vPdaYCi8GB6LHomhQAMcNJFm9p6c/ZyT/t5gKO4bT+UB6lYiPTu96FXYih0BhPEIgF8ZRLzEoRGD38fKeRSEU5mlfD64XB9ltI6haFzxzYAzEmQN+/PgcnEWJZ/hEIK4weYXwQrNl6AWmuw+/3HXZcxtE8HRIYFgKYZnLpU6vK8bIkqijI74FMCYP4XR6xqxdq+gAAgOaY1nh6bjPC2vjA5ORf7sqYo4FgGv2Ju2x+2JFF1nQ5BATIYTXRDXVIx/BRiFFWosP73bESG+WNEaiSMJhpqjREBvhL4+UhQVa3Bz3/mIOdGNeTS2/KbMCAWvZNDwDAUTmSW8Cpl7MvzvW9O4PzVCrvrEAkY0AwFpsHSyB2PxeUqrNqWgYzcSofX169zu4YlcwYZuRUOFajkmGCIhBQu5lTg0Pkiy7g7erHY7uMBuF0pIiu/klfGDw6MRc/EUFAUA4ORgVBAwaA3QSQWQCgUgKYZFDtQoKvrdGjXxhdavdln1Ecmhk5vgkpjgEQsgFQkBAOzT6DJgcKbHNMaM8Ymo10bX6vnWCETQaM1wsTQkIiEUGkMCG4lg6reiPNXy62ud/uhPMszodEZcV9bXwzt1QFxkYEAgDq1AQIBAFDIyK3AlgO5ds/QxPRY9E4KBc3A8YfigBj0SgrFtgO5UHYIRESIP1ZuvoD7O4dbzTHc+eDytSpMHZ2EE5kl2Lw/1+ojQ2+kodUbEeAjhY9cDFW9HmKhAAaaQa1KB5lUZPHBjQj1h95gstpfpzcCDCCRCC2Km0AggEBAgaYZh8uVEonIah/b/d2dgz2ZL7lQFCCRiiEWC+2WZdnrCA62V1II/BCFjuA2f7VCB7j3wHNHTJs25gS8JtqEWjX//oBnbXraz3qdAWAoKGQiqLVGiw+Sj1wEvZ4GAxoyqbkgOmAukA5KAKlE2PDFbIBvg88Sm3tOLhU17MdAIRVDQFHmMlgaA1T1evg2KAwUzE7JrH+fj1wMH5kYNEND0PBFzvbH30cMChRMNAMV53fueRUyMYwmExgaEIuFEFDmZSdbxVkoAGi6oT8N/fdp8CMSCCjoG5RZiqLs+mw00TAYTWbfGtxu395yYJaDiWYgEgjMcgagkIqh1Ruh05sglQghk4hAUYyVgt/KVwqFTGSWOAXzeWz7KxNDZ7j9YqQabqzapr/mwUFZlHJfufl3mqZhNNCQSEXmfSgKJhMNo4mBSEhBKBRYyY9P3o6uz3w/newvFKBee3t/y7EysZUsfeRiCChYlE3u72yIjkbHkaVUCL2BBsMwFvmZaPM+Gr0RcokIMqkIQoG5TXO/KMgkQut2JEIIBGYlV280QSzkjAWe8c7tZ73W+lys/LnycSQrdnwxNAORSMArt9v3whzlDIaBwWACQEMqk/Iq5uyYlUluP5esVct2jjH3jYJCKoZGb4BYJLScl23TV2GWP8Mw0OqMAEVBKhFBpzeCoihIxQKoNMYGWYnM481EQ68zu4W4Utwag6dzsLcIhfwKKJ+SQuCHKHQEt/k7FDoujh54LtwB7c7+7uzTmH4C4P2bey7u/rb9cXUs9wtbrzd61GZQkI9FVtx2RCIBhEIKJhMDo5HmPVYsFlr20WoNvP3h7s/tg1RqvQ/bjrnf5r8FAsqyD0Xd/pthGMvf3H20WiOvrNhroRqUK9vziMVC3raNxtv98mso/2QwmK+ToigwDAO9/vY+3OvT6YwuZcmVgUDAP0Z0OpPlWIPBZPldoZDw9lkkonj7xj2vQEBZyZhP9ub7ZS8f7nXJZLf7X1t7O/KQff5qajS8Fh8A8PWV8spZJLotQ43GYPlbLhfz7s/ts23/XT1Djp4JR3OAQiGxtM+Vg6Pn0tF8wP07IEBukZWjZ5fbvjvnutPcifnSFUShc5+7TaEjUa4Eh5hfyE27v6dtenNeR3/z7W87Sbo6lvsi8KZNvnb0TlKWOWrTUX/49nfU5zuBs2sBYKWMOoJV6KqrPU+Z4Oj83siAlWXdX5i6wZF8XPWf3c431lROqjNwYY91d39X7XD/dueZ4FJvU4XC1XNpva/jZxHge2Y8mz/+Su7EfElouZBQFgKBQCAQCIRmDlHoCAQCgUAgEJo5RKEjEAgEAoFAaOYQhY5AIBAIBAKhmUMUOgKBQCAQCIRmDlHoCAQCgUAgEJo5RKEjEAgEAoFAaOaQxMJ3MQxjTux5N8MmUDQYSLIkVxBZuQeRk/sQWbkPkZV7EDm5D5+sRA0Vev4OiEJHIBAIBAKB0MwhS64EAoFAIBAIzRyi0BEIBAKBQCA0c4hCRyAQCAQCgdDMIQodgUAgEAgEQjOHKHQEAoFAIBAIzRyi0BEIBAKBQCA0c4hCRyAQCAQCgdDMIQodgUAgEAgEQjOHKHQEAoFAIBAIzRyi0BEIBAKBQCA0c4hCRyAQCAQCgdDMIQodgUAgEAgEQjOHKHQEAoFAIBAIzRyi0BEIBAKBQCA0c4hCRyAQCAQCgdDMIQodgUAgEAgEQjOHKHQEAoFAIBAIzRyi0BEIBAKBQCA0c0R/dwcIdx6GYXD27Fns3bsXp0+fRl5eHlQqFfz8/JCYmIjx48djzJgxoCjKo3Zff/11bN682ek+q1atQv/+/Xm3qdVqfPnll/j9999RVFQEhUKBzp07Y9q0aejdu7dHfWkq7oSsjh8/jqeeesqtfZ9//nnMmTPH6re4uDinxwQHB+Pw4cNu96cp2bFjB44cOYLMzEyUlZWhuroaYrEYkZGRGDBgACZPnozAwECv2v7999/x3Xff4fLlyzAYDOjQoQPGjh2Lp556CmKx2OFxlZWVWL58Ofbt24eysjL4+/ujZ8+eeOaZZ5CQkODtpTaKppaTyWTCsWPH8Oeff+Ls2bMoKCiAVqtFq1atkJKSgkcffRTp6em8x968eRODBw922n7nzp2xYcMGTy6xybgTY6olzlVA08uqJc9Vtuzfvx8zZ84EAISHh2Pv3r1etXM3zVMUwzCMV0cSmg1Hjx7FlClTLP9u3749/P39UVhYiOrqagBAeno6li1bBolE4na77CQZFhaGsLAwh/t07tzZ7veqqio8/vjjyM/Ph0QiQWxsLKqqqlBSUgKKovDmm29i0qRJHl1nU3AnZJWVlYV33nnH4XaVSoUrV64AAFavXo2+fftabWcnyeTkZN5ztmrVCsuXL3erL03NuHHjcPnyZUgkErRp0waBgYGoqqpCUVERAKB169ZYvXo14uPjPWr3ww8/xOrVqwEAERERkMvlyMnJgclkQs+ePbF69WpeWVy7dg2PP/44KioqoFAoEBUVhZKSElRWVkIsFmPJkiUulZk7QVPL6X//+x8WLFgAABAIBIiIiICPjw+uXbsGlUoFAHj00UexcOFCu48PrkLXrVs33vY7duyIt99+26trbSx3Yky1xLkKaHpZteS5iotarcbo0aMtcvJWobvr5imG0OI5fPgwM2jQIGbt2rVMRUWF1bbNmzczycnJjFKpZD766COP2n3ttdcYpVLJLF261OM+zZo1i1EqlcyECROYkpIShmEYhqZp5scff2SUSiWTkJDAZGVledxuY7lTsnLGsmXLGKVSyQwYMIAxmUx225VKJaNUKpkbN2402Tmbip9++ok5ceIEo9frrX6/fPkyM3r0aEapVDIjR470qM1du3YxSqWSSU5OZvbs2WP5PScnhxk0aBCjVCqZ999/3+44mqaZcePGMUqlkpk+fTpTW1vLMAzDGAwGZvHixYxSqWS6dOnClJaWenGljaOp5bRhwwZmzJgxzIYNGyzXyTDma/3qq6+YuLg4RqlUMt9//73dsTdu3LCMqbuROzGmWuJcxTB3RlbOaM5zFZd33nmHUSqVzOzZsxmlUskMHDjQ4zbuxnmKKHT3AHV1dXYPPJfly5czSqWS6dWrF+9D6ghvJ8nMzExGqVQy8fHxTEFBgd32//u//2OUSiUzZ84cj9ptCu6UrBxB0zQzePBgRqlUMp988gnvPs1lkrTl/Pnzlr7n5OS4fdzYsWMZpVLJLFmyxG7bkSNHLJNoZWWl1bbdu3czSqWS6d69O1NdXW137KRJkxxOsn8n3sjp1q1bDE3TDrcvWLCAUSqVzNixY+223e0KnTO8HVMtca5yhbeyckRLmavOnj3LxMfHM7Nnz2Y2btzotUJ3N85TJCjiHsDX19fpWj7rN1JdXY2qqqo73p/ff/8dANCnTx906NDBbvujjz4KwOzjUF9ff8f7w+WvltXJkydx48YNAMDEiRMb3d7dRHR0tOVvjUbj1jEFBQW4fPkygNvjgEtqaio6dOgAvV6PP/74w2rbjh07AADDhw9HQECA3bGPPPKI1X53C97IqVWrVk79ONlxmp+f37jO3WV4I6vGcDfPVa5oalm1hLnKYDDgzTffhEwmw7/+9S+v27lb5ykSFEGAVqu1/C2TyTw+/vjx47h69Sqqq6vh7++PpKQkjB07FuHh4bz7nzt3DgDQo0cP3u2dOnWCRCKBTqfDpUuX0L17d4/7dKdorKxsYR21u3fvzvvC4PLFF1+grKwMJpMJISEh6NOnD0aOHOmR3+NfyenTpwHA4iPiDuzYaN++PUJCQnj36d69O65du4bz58/j4Ycftvx+/vx5AI7HFft7SUkJSktLHbb/V+ONnFzBjlO5XO50v0WLFiEvLw8URSE8PBz9+vXDkCFDIBDcnd/6jZXVvTRXNfW4aglz1cqVK3HlyhXMmzcPoaGhXrdzt85TRKEj4NdffwUAxMfHw9fX1+PjT548afXv3bt34/PPP8c///lPPP3003b7FxQUADA7kfIhFosRFhaGa9euIT8//66aJBsrKy719fXYuXMnAGDChAku99+4caPVvzdv3oylS5di2bJlSEpKalRfmgqaplFeXo7Dhw/jP//5DwBg7ty58PHxcet4V2ODu41rfdLr9SgsLHR6bFhYGMRiMQwGA/Ly8v5Wha6xcnIFO05dPTvffvut1b9/+uknJCQkYNmyZWjfvn2T9KWxNKWsWvpcdafGVUuYq3Jzc7Fy5UokJSXhySefbFRbd+s8RRS6e5yMjAz8+OOPAGAJ4XaXDh064PXXX0efPn0QHh4OiUSC7OxsrF69Gjt37sR//vMfKBQKuwiwmpoaAOA1N7Ow22praz3q052kMbLiY+fOnaivr4dcLseIESMc7jd48GCMGzcO8fHxCA0NhVqtxtGjR/Hpp5/ixo0bmDZtGrZs2eIweu+v4JtvvsH7779v9VunTp3wwQcfOEwFwYe3Y0OlUoGmaafHUhQFf39/VFZW/m3jqqnk5Iw9e/Zg3759oCgKM2bMsNsuEokwduxYjBo1CrGxsWjbti1u3bqF/fv3Y/Hixbh06RKmT5+OTZs2NfqjpTE0paxa+lx1p8dVc5+rGIbBggULYDQasXDhQgiFwka1d7fOU3enXZ3wl1BRUYHnn38eRqMRDzzwAEaNGuXR8bNnz8bUqVORkJAAf39/yGQydO7cGUuWLMHjjz8OAFi8eDHUarXVcTqdDgCc+qqxpnnuEuffSWNlxQe7hDF06FCnL84vvvgCw4YNQ4cOHSCVShEUFIRRo0Zhw4YNaNeuHaqrq/HZZ581uj+NISQkBN26dUPnzp3Rpk0bUBSFS5cuYevWrR5NSt6ODfY47nZ3j/0raSo5OSI3Nxevv/46AGDy5Mm8aUlCQ0Px8ccfIz09Hffddx8kEglCQkLwyCOP4IcffoCvry+uXbuGdevWNbo/jaEpZdXS56o7Pa6a+1y1fv16nDlzBpMmTUJKSkqj27tb5ymi0N2j1NXV4emnn0ZRURGSkpLwwQcfNGn7L7/8MsRiMWpra3Hs2DGrbVKpFIDZQdURer0eQNP4qTWWOyGrGzduWJZ/3FnC4CMoKMhiKdyzZw+YvzGl5IgRI/DDDz9gw4YNOHToELZs2YLOnTvjl19+wVNPPQWTyeRWO96ODfY47nZ3j/0raSo58VFcXIwZM2agrq4OAwYMwNy5cz1uo0OHDnjssccAmJcj/07upKy4tIS56k7KqrnPVaWlpfjkk08QEhKCF198sUnavFvnKaLQ3YOo1WrMmDEDWVlZ6NixI77++usmX1rx8/NDx44dAZiTKHLx9/cHcNtszQe7jd337+JOyWrLli1gGAbh4eHo06eP1+107doVgDnqlk18fDcQHx+PlStXIjAwEJcuXbL4dLnC27Hh6+trceR3dCzDMBZrxd89rli8lZMt5eXlmDJlCoqKitCrVy8sW7bMqfXAGeyYYv2E7haaSla2tJS5iktTyqq5z1XvvPMOVCoVFixY0GTvubt1niIK3T2GRqPBM888g3PnziEyMhJr1qzxujSTK9gXitFotPo9MjISgP3kyWIwGCwZvNl9/w7ulKwYhsGWLVsAAOPHj/e45BoX7ku7qSwWTYWvry969eoFAMjMzHTrGDYaz9HYAIDr168DsB4bEokE7dq1s9puS3FxseWLuqmiSZsCb+TEpbKyEpMnT0ZBQQG6du2KFStWWFkCPIUdU3fbeAIaLytHNPe5io+mkFVLmKuysrIAAAsXLkTfvn2t/nv33XcBmOcG9rczZ864bPNunaeIQncPodPpMHv2bJw8eRLh4eH45ptv0KZNmztyLqPRiLy8PACwCw/v0qULgNth9bZcuHABBoMBUqn0b6u9eSdldeLECdy8eRMURXm9hMFy9epVAGZTfqtWrZqgd00L+4J0dwJnSy/dvHkTpaWlvPuw44YdRyzsv0+dOsV7HPt7aGhoo1IW3Ak8lRNLdXU1pk6ditzcXCQlJWHVqlWNjmhkx9TdJiMWb2XlrL3mPFc5o7GyaklzVUVFhd1/bJk8mqYtvzlbRmW5W+cpotDdIxgMBjz//PM4evQoQkJCsHbt2jsaafTTTz+hrq4OIpHIzkw/bNgwAOacUHxfOD/99BMAc3LUpkrj4Al3Wlasg3GPHj0alRrCaDRizZo1AMyJT0Wiuytovbq6GidOnAAAt192UVFRUCqVAG6PAy5Hjx7FtWvXIBaL7WodsuNq586dvMsZbLH54cOHu38RfwHeyAkwR8xNmzYN2dnZUCqV+Prrr+Hn59eovqjVaqxfvx4A7Op03g14KytnNOe5yhlNIauWMFft3bsX2dnZvP+xkcHh4eGW33r37u2yzbt1niIK3T2AyWTCK6+8gv3796NNmzZYu3at2w/noEGDMGjQIEsOIpbDhw/j448/tvOz0ev1+Pbbby0Pyj/+8Q+0bdvWap+kpCQMHDgQJpMJL730EsrKygCYzfs//fQTtm7dCoFAgNmzZ3t5xd5zJ2TFRa1WW7LPu5Nt/T//+Q82b95s+ZJkKS4uxgsvvIBz585BJBLhueeec6uPTcmJEyfwxRdf4ObNm3bbMjMzMX36dNTV1SEkJMRucnImqzlz5gAAVq1aZVUwOy8vz1KQ/vHHH0dQUJDVcUOGDEFcXBzq6uowd+5c1NXVATDf0yVLluDkyZOQy+WYNm1a4y7cQ+6EnDQaDWbOnInMzExER0fjm2++cdsd4M0338SuXbvsnLJzc3MxY8YM3Lx5EwqFAtOnT/fwShvPnZBVS52r7tTzx9KS5ipvaW7z1N31SU+4I+zYscPyYEokEsyfP9/hvm+++SYSExMt/2aTINqWtdFoNPjqq6/w1VdfITg42JL8MD8/37LvsGHD8Nprr/Ge57333sNjjz2GzMxMDB48GLGxsbh16xaKi4tBURTmz5//tySgvBOy4vL777+jvr4eCoXC8qXmjLy8PKxatQpvvPEG2rdvj4CAANTV1SE/Px8Mw0AqlWLRokWWJYC/ktraWixZsgRLlixBmzZt0LZtWwiFQhQXF6O8vByAOZ3CypUr7awXzmQ1bNgwTJ48GWvXrsXs2bMREREBhUKBq1evwmQyoXv37njllVfsjhMIBFiyZAkmTZqEAwcOoH///oiKikJJSQkqKyshFovx8ccf/+UJhe+EnNatW2e1DMi+XPhYunSplbvAhQsXsGHDBojFYkRERMDX1xe3bt2y+PQEBARg8eLFuO+++xp34V5wJ2TVUueqO/X8sbSkucpbmts8RRS6ewDul3hhYaFlkPLBfi24IikpCc8++yzOnTtnyZJuMBgQFBSEfv36YcKECRg0aJDD44OCgrBx40asWrUKO3fuRE5ODhQKBfr374/p06c3KpqqMdwJWXFhlzCGDRvm1hLNY489huDgYGRkZKCsrAyFhYUQi8Xo2LEjUlNT8cQTTzjNVn4n6dq1K+bNm4fjx48jJycHBQUF0Ov18Pf3R+/evTFo0CA89NBDXkWWzZ8/H127dsX69etx6dIllJWVISYmBmPHjsWUKVMcRnBGRUVh27ZtWL58Ofbt24crV67A398fw4YNw6xZs6wU8L+KOyEn7jhl/b8cwc19BQDPPPMMDh48iIyMDFRUVODatWuQyWRISkpC//79MWnSpDvmW+uKOyGrljpX3cnnD2hZc9Wd4m6bpyjm70xeRSAQCAQCgUBoNMSHjkAgEAgEAqGZQxQ6AoFAIBAIhGYOUegIBAKBQCAQmjlEoSMQCAQCgUBo5hCFjkAgEAgEAqGZQxQ6AoFAIBAIhGYOUegIBAKBQCAQmjlEoSMQCAQCgUBo5hCFjkAgEAgEAqGZQxQ6AoFAIBAIhGYOUegIBAKB0OQsW7YMcXFxePLJJ//urhAI9wSiv7sDBAKBQGg+XLp0CXv27IGfnx+mTJnyd3eHQCA0QCx0BAKBQHCbS5cu4bPPPsO6deuc7hcYGIioqCiEhYX9RT0jEO5tiIWOQCAQCE3OE088gSeeeOLv7gaBcM9ALHQEAoFAIBAIzRyKYRjm7+4EgUD4e6mpqcGuXbtw6NAh5OXlobS0FBqNBsHBwejWrRuefPJJdOnSxeqYsWPHIjs7G1OmTMG8efMctn306FFMmTIFFEVh7969aNeundX2mzdvYu3atThy5AiKiopA0zTCwsLQr18/TJs2zW5/ANi0aRPmzZuH8PBw7N27F8eOHcO6detw4cIFVFZWYty4cfjggw8AADdu3MCOHTtw/Phx3Lx5E6WlpaAoCmFhYejbty+mTp3Kew4WhmGwadMmbNiwAVeuXIFAIEB0dDQeeughPPLII5g3bx42b96MCRMmWM5py5UrV/Dtt9/i+PHjKC0thUAgwH333YdBgwZh8uTJCAoKcnh+T4iLiwMArFu3DrGxsfjyyy/x559/oqSkBFqtFtnZ2QAAjUaDP/74AwcOHEB2djZKS0uhUqnQqlUrdOrUCY8++igGDBjgsH1HzJkzB88//zwAc1DEZ599hl69euHbb7+12u/111+3ktnOnTvx/fffIzs7GzqdDpGRkZg4cSKefPJJCAT8doemuC8EQkuCLLkSCASsW7cOn332GQBAKBTC19cXAFBUVISioiL8+uuvmD9/Pp566inLMePGjcNHH32EX3/9Fa+++iqEQiFv29u2bQMA9OzZ005x2rZtG9544w3o9XoAgEQigUAgQH5+PvLz87Fp0yYsXboU/fr1c9j3tWvX4v333wfDMPDz87Prx/z583HixAkAgFgsho+PD2pra5Gbm4vc3Fxs3rwZK1asQI8ePezaNplMmDt3Ln777TcAAEVR8Pf3R0ZGBi5cuIATJ05ALBY7FiyAVatW4ZNPPgFN0wAAuVwOg8GAK1eu4MqVK9i4cSO+/PJLJCYmOm3HE65fv46XX34ZFRUVkEqlEImsp/odO3ZYlHCKouDr6wuRSITy8nL88ccf+OOPPzBt2jS89tprVscFBwdDq9VCpVJBIBDYKaIKhcLjvr799tv4/vvvIRAI4OvrC61Wi8uXL+O9995DVlYWPvzwQ7tjmuK+EAgtDoZAINzz/Pjjj8zSpUuZixcvMjqdjmEYhqFpmrl+/TqzaNEiJi4ujklISGAyMzMtx5SWljIJCQmMUqlk9u/fz9uuRqNhunbtyiiVSubnn3+22nbo0CEmPj6eSUxMZD766CPmxo0bDE3TDE3TTG5uLvPCCy8wSqWS6datG1NYWGh17MaNGxmlUsmkpKQwCQkJzOuvv84UFRUxDMMwRqORuXbtmmXfRYsWMd999x2Tn5/PmEwmhmEYxmAwMOfPn2emT5/OKJVKpl+/foxGo7Hr/8qVKxmlUskolUrm/fffZ6qqqhiGYZi6ujpmxYoVTFxcHNOzZ09GqVQyr732mt3xGzZsYJRKJdOlSxdm+fLlTFlZmaWPFy9eZJ566ilGqVQy/fv3Z1QqlfOb5AZsX7t06cIMGzaMOXLkiOWa8/LyLPvt3r2b+eCDD5hTp04x9fX1lt9LS0uZZcuWMUlJSYxSqWT27Nljdw5W9gMHDnTal6VLlzJKpZJ54okn7La99tprjFKpZHr27MkkJSUxa9asYerq6hiGYZiqqirmjTfesFzLkSNH7I5v7H0hEFoiRKEjEAguWbhwIaNUKpn58+db/T5t2jRGqVQyL7/8Mu9x27dvZ5RKJdOpUyfLC5thGMZkMjFDhw5llEol8+OPPzo876xZsxilUsksWrTI6ndWqVAqlcycOXO8vi6j0ciMGTOGUSqVzJYtW6y2qdVqplu3brzXzcIqLXyKQ11dHdOjRw9GqVQyBw4c4D3eYDAwEyZMYJRKJbNmzRqvr4OF7Uu3bt2Y4uJir9v56quvGKVSyUyePNluW1MqdEqlktm4cSPv8axc3njjDavfG3tfCISWCgmKIBAILmH9qU6fPm31+7hx4wAAe/bsgUqlsjtu69atAIAhQ4ZYlnEB4OTJkygoKEBgYCAefvhhh+cdP348AODQoUMO95k5c6Z7F8GDUCjE/fffD8D+2g4fPmy5plmzZvEeP3XqVMjlct5tu3btQm1tLRITEy3nsEUkEmH06NEAnF+jp4wbNw6hoaFeH5+eng4AOHfuHEwmUxP1yp6wsDBMmDCBd9ugQYMAwOL3x9LY+0IgtFSIDx2BQABgDh5Yv349jh8/juvXr0OtVlv8vlhKS0ut/v3AAw/Ax8cHarUau3btwsSJEy3bKioqcPjwYQC3FT+WM2fOAABUKpVDZQcADAYDALMvHx8ymQxJSUkur+3UqVP4+eefce7cOZSWlqK+vt5uH9try8zMBAC0a9cO7du3523X19cXSUlJOHXqlN029hpzc3PRt29fh33TarUAHF+jN3Tr1s3lPhUVFVi/fj0OHz6MgoIC1NXV2SlvGo0GNTU1TRa0YUtKSgooiuLdFhISAsAcsMOlsfeFQGipEIWOQCBg9+7dePnlly3BCYD5pSiVSkFRFAwGA2pqauwUIblcjmHDhmHTpk3YunWrlUL3yy+/wGQyoU2bNnYKTVlZGQCzwlZRUeGyf6zSY0urVq0cRkGyfPzxx/jqq68s/xYKhQgICLA4zdfX11v+41JVVQUAaNu2rdP2WcXDFvYadToddDqd0zYAx9foDa1bt3a6/ezZs5g5cyZqa2stvykUCsjlclAUBZPJhFu3bgEwK3V3Ch8fH4fb2OAWo9Fo9Xtj7wuB0FIhCh2BcI9z69YtvP7669Dr9ejTpw+ee+45dOrUCTKZzLIPm3qEj3HjxmHTpk04ceIEiouLLZUB2OjW0aNH20Wespagzp07Y8OGDV733VFkLcvhw4ctytzjjz+Oxx57DDExMVbHLV68GMuXL3fYhiMLkivYaxw5ciQ+/fRTr9rwFmdKrtFoxCuvvILa2lokJCTgpZdeQvfu3a2WxK9fv44HHngAgDk9yN2It/eFQGipEIWOQLjH2b9/P1QqFQICArBixQpe36Py8nKHx/fu3RthYWEoLi7G9u3bMXPmTOTk5FiWxlg/OC5t2rQB0LTLjHz8+uuvAIB+/frh3//+N+8+jiyE7DIja2lzhO1SLctfdY2ecu7cORQWFkIoFGLlypW8lixn9/vvprH3hUBoqZCgCALhHqekpAQAEBUV5dCR/OjRow6PpygKY8eOBXA7CIL9f1xcHOLj4+2OYX28ysvLcfHiRe877wL22hzleGMYBseOHePdxvrmFRYW4ubNm7z7qNVqi+JqC3uNmZmZLpWPv5Li4mIAZsXI0bKks/vNWv/+LstdY+8LgdBSIQodgXCP4+fnBwAoKCjg9fW6dOkStm/f7rQNNughJycHFy9etOzPZ50DzFa9Dh06AADef/99K989Pqqrq51udwS7jHj58mXe7T/88ANu3LjBu61v376W41euXMm7zzfffOPQx2z48OHw9/eHwWDABx984FQBomnayp/tTsLe74qKCl7rZElJiV1lBy6sTP6q/trS2PtCILRUiEJHINzj9O3bFwKBANXV1Zg7d65lqUqv1+O3337DtGnTnDqvA0BMTAySk5MBAG+99RaKi4shFAotKTlsEYlEWLhwIUQiEU6fPo0nnngCR48etUS1Auao2x9++AEPPvgg1q9f79W1sRG0Bw4cwOeff24JfKitrcWKFSuwaNEitGrVivdYhUKBp59+GgCwYcMGfPTRRxbFUqVS4csvv8Rnn32GgIAA3uP9/f0xf/58AOal35kzZ+L8+fOWyGGappGbm4vVq1dj1KhR2Ldvn1fX6Cndu3eHQqEAwzB48cUXkZ+fD8Ds83fw4EE8+eSTTo/v2LEjALMM2EoNfyWNvS8EQkuF+NARCPc4kZGRmD59OlatWoVdu3Zh165d8PPzg1arhcFgwH333YcXX3wRc+fOddrO+PHjkZGRgYyMDABAamqq00jE1NRULFmyBK+++irOnz+PKVOmWEpz1dfXW1nthgwZ4tW1jR8/Hlu2bMGpU6ewdOlSLFu2DP7+/qirqwNN00hPT0dCQoLDoIgZM2YgKysLv//+O77++musWbMGfn5+UKlUMJlMGDduHCiKwpYtWyCRSOyOnzBhArRaLd59910cOHAABw4cgEQigUKhgFqttlJg/yonfz8/P7z66qt46623cPLkSQwfPhwKhQImkwk6nQ6BgYF4//33MXv2bN7jO3TogNTUVBw9ehQvvfQSFixYYFGKn3rqKYfBM01JY+8LgdASIRY6AoGAuXPn4sMPP7REtxqNRkRERGDWrFnYsmWLyxQRADBq1Cir+pmOllu5DBkyBLt378acOXPQqVMnKBQK1NXVQSKRID4+Hg8//DA+//xzTJ8+3avrEovFWL16NebMmYPIyEiIRCIwDINOnTrhrbfewvLly51GyopEIixZsgSLFi2ykk1ycjIWLVqEjz76yLL06O/vz9vGY489hp07d2LatGmIj4+HRCJBXV0dFAoFkpOT8eSTT2LNmjUOrZl3gsceewxffvklevXqZVHmQkJC8OSTT2Lr1q1QKpVOj1+6dCmmTJmCyMhIGI1GFBYWorCwEHV1dX9J/5vivhAILQ2KuVtj0gkEAuEuh2EYpKeno6SkBB9++KFbSizhzkPuC+FehFjoCAQCwUu2bt2KkpISiEQipKWl/d3dITRA7gvhXoQodAQCgeCEl19+GTt37rRUKADMEaJffvklFixYAMAc5evOsjSh6SD3hUCwhiy5EggEghN69Ohh8Q2Ty+UQiURWvmI9evTAypUrrSotEO485L4QCNYQhY5AIBCcsGXLFhw4cABZWVmoqqpCfX09/Pz8kJCQgJEjR2LcuHFWwSCNxbburTscPny4yc7fXPir7wuBcLdDFDoCgUC4i4iLi/P4mOzs7DvQEwKB0JwgCh2BQCAQCARCM4cERRAIBAKBQCA0c4hCRyAQCAQCgdDMIQodgUAgEAgEQjOHKHQEAoFAIBAIzRyi0BEIBAKBQCA0c4hCRyAQCAQCgdDMIQodgUAgEAgEQjOHKHQEAoFAIBAIzZz/BwGoDej90UHdAAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "sns.scatterplot(x=average_ratings_df_filtered['average_rating'], y=average_ratings_df_filtered['movie_count'])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "E7Z2O6uBKsom"
      },
      "source": [
        "### How accurate are our predictions if we compare it with the actual rating a movie received\n",
        "\n",
        "\n",
        "we want to come up with rating predictions then compare it with the actual rating to see how accurate we are\n",
        "\n",
        "***We first want to look at the relationships between a movie's rating vs the average ratings of every actor in the movie***"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Add column to original df where we get the average rating of all the actors in that row by using our dictionary to get the average rating of each actor"
      ],
      "metadata": {
        "id": "FamsJpXiBVgp"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "for index, row in average_ratings_df_filtered.iterrows():\n",
        "    actor_name = row['actor_name']\n",
        "    if actor_name in actor_ratings:\n",
        "        actor_ratings[actor_name]['average_rating'] = row['average_rating']"
      ],
      "metadata": {
        "id": "KCXGudi2BVTp"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Now let's create a new column for average rating of actors for a movie"
      ],
      "metadata": {
        "id": "LxqYIm9_EGN3"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df['avg_rating_of_actors'] = df['actors'].apply(lambda actors:\n",
        "    sum(actor_ratings[actor]['average_rating'] for actor in actors if actor_ratings[actor]['average_rating'] is not None) / len([actor for actor in actors if actor_ratings[actor]['average_rating'] is not None])\n",
        "    if isinstance(actors, list) and len(actors) > 0 else None\n",
        ")\n",
        "\n",
        "for index, row in df.iterrows():\n",
        "  # Extract the list of actors from the 'actors' column\n",
        "  actors_list_str = row['actors']\n",
        "  actors_list_arr = literal_eval(actors_list_str)\n",
        "  row['actors'] = actors_list_arr\n",
        "  # Extract the rating for the movie\n",
        "  rating = row['rating']\n",
        "  actor_num = 0\n",
        "  actor_rating = 0\n",
        "  # Check if the 'actors' column is a list\n",
        "  if isinstance(actors_list_arr, list):\n",
        "    for actor in actors_list_arr:\n",
        "      if 'average_rating' in actor_ratings[actor]:\n",
        "        actor_rating += actor_ratings[actor]['average_rating']\n",
        "        actor_num += 1\n",
        "      avg_rating = None\n",
        "      if actor_num > 0:\n",
        "        avg_rating = round(actor_rating / actor_num, 2)\n",
        "    df['avg_rating_of_actors'][index] = avg_rating\n",
        "\n",
        "df = df.dropna(subset=['avg_rating_of_actors', 'rating'])\n",
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "sZ6qt1BfEJQA",
        "outputId": "6be2f277-df4e-4125-c9c7-05ddc57799f0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                               movie  rating  \\\n",
              "0                             Barbie    3.91   \n",
              "1  Everything Everywhere All at Once    4.32   \n",
              "2                         Fight Club    4.27   \n",
              "3                       Interstellar    4.32   \n",
              "4                              Joker    3.83   \n",
              "\n",
              "                                              actors avg_rating_of_actors  \n",
              "0  ['Margot Robbie', 'Lewis Easter', 'Onyemachi E...                 3.14  \n",
              "1  ['Randall Archer', 'Boon Pin Koh', 'Efka Kvara...                 3.06  \n",
              "2  ['Leonard Termo', 'Greg Bronson', 'Michael Art...                 3.17  \n",
              "3  ['Lena Georgas', 'Jeff Hephner', 'Elyes Gabel'...                 3.23  \n",
              "4  ['Danny Schoch', 'Graham Mabry', 'Steven Elson...                  3.2  "
            ],
            "text/html": [
              "\n",
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          "metadata": {},
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    },
    {
      "cell_type": "code",
      "source": [
        "import patsy\n",
        "import statsmodels.api as sm\n",
        "\n",
        "# Use patsy to create design matrices\n",
        "outcome_1, predictors_1 = patsy.dmatrices('rating ~ avg_rating_of_actors', df)\n",
        "\n",
        "# Create an OLS model\n",
        "mod_1 = sm.OLS(outcome_1, predictors_1)\n",
        "\n",
        "# Fit the model\n",
        "res_1 = mod_1.fit()\n",
        "\n",
        "# Display the summary statistics\n",
        "print(res_1.summary())\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eRhcoeSIA_r4",
        "outputId": "a7fce9a1-8d6f-47d0-de7c-78e769352811"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                            OLS Regression Results                            \n",
            "==============================================================================\n",
            "Dep. Variable:                 rating   R-squared:                       0.310\n",
            "Model:                            OLS   Adj. R-squared:                  0.307\n",
            "Method:                 Least Squares   F-statistic:                     89.72\n",
            "Date:                Sat, 24 Feb 2024   Prob (F-statistic):               0.00\n",
            "Time:                        00:22:30   Log-Likelihood:                -12772.\n",
            "No. Observations:               25468   AIC:                         2.580e+04\n",
            "Df Residuals:                   25340   BIC:                         2.684e+04\n",
            "Df Model:                         127                                         \n",
            "Covariance Type:            nonrobust                                         \n",
            "================================================================================================\n",
            "                                   coef    std err          t      P>|t|      [0.025      0.975]\n",
            "------------------------------------------------------------------------------------------------\n",
            "Intercept                        1.9300      0.401      4.818      0.000       1.145       2.715\n",
            "avg_rating_of_actors[T.2.44]     0.0875      0.448      0.195      0.845      -0.790       0.965\n",
            "avg_rating_of_actors[T.2.46]    -0.0300      0.566     -0.053      0.958      -1.140       1.080\n",
            "avg_rating_of_actors[T.2.48]     0.4083      0.433      0.944      0.345      -0.440       1.256\n",
            "avg_rating_of_actors[T.2.5]      0.5175      0.448      1.156      0.248      -0.360       1.395\n",
            "avg_rating_of_actors[T.2.51]     0.5000      0.463      1.081      0.280      -0.407       1.407\n",
            "avg_rating_of_actors[T.2.52]     0.6100      0.491      1.243      0.214      -0.352       1.572\n",
            "avg_rating_of_actors[T.2.54]     0.2500      0.566      0.441      0.659      -0.860       1.360\n",
            "avg_rating_of_actors[T.2.55]     0.3580      0.439      0.816      0.415      -0.502       1.218\n",
            "avg_rating_of_actors[T.2.56]     0.5637      0.425      1.327      0.185      -0.269       1.396\n",
            "avg_rating_of_actors[T.2.57]     0.7867      0.463      1.701      0.089      -0.120       1.693\n",
            "avg_rating_of_actors[T.2.58]     0.7000      0.566      1.236      0.217      -0.410       1.810\n",
            "avg_rating_of_actors[T.2.59]     0.3462      0.425      0.815      0.415      -0.486       1.179\n",
            "avg_rating_of_actors[T.2.6]      0.6257      0.428      1.461      0.144      -0.214       1.465\n",
            "avg_rating_of_actors[T.2.61]     0.8186      0.428      1.912      0.056      -0.021       1.658\n",
            "avg_rating_of_actors[T.2.62]     0.5838      0.410      1.424      0.154      -0.220       1.387\n",
            "avg_rating_of_actors[T.2.63]     0.6960      0.414      1.682      0.092      -0.115       1.507\n",
            "avg_rating_of_actors[T.2.64]     0.7967      0.422      1.887      0.059      -0.031       1.624\n",
            "avg_rating_of_actors[T.2.65]     0.5144      0.422      1.218      0.223      -0.313       1.342\n",
            "avg_rating_of_actors[T.2.66]     0.6167      0.412      1.499      0.134      -0.190       1.423\n",
            "avg_rating_of_actors[T.2.67]     0.3745      0.410      0.915      0.360      -0.428       1.177\n",
            "avg_rating_of_actors[T.2.68]     0.7750      0.415      1.869      0.062      -0.038       1.588\n",
            "avg_rating_of_actors[T.2.69]     0.6429      0.412      1.560      0.119      -0.165       1.451\n",
            "avg_rating_of_actors[T.2.7]      0.6672      0.408      1.633      0.102      -0.133       1.468\n",
            "avg_rating_of_actors[T.2.71]     0.6562      0.406      1.615      0.106      -0.140       1.453\n",
            "avg_rating_of_actors[T.2.72]     0.7854      0.406      1.933      0.053      -0.011       1.582\n",
            "avg_rating_of_actors[T.2.73]     0.7645      0.407      1.881      0.060      -0.032       1.561\n",
            "avg_rating_of_actors[T.2.74]     0.6836      0.408      1.677      0.094      -0.115       1.483\n",
            "avg_rating_of_actors[T.2.75]     0.6877      0.406      1.696      0.090      -0.107       1.483\n",
            "avg_rating_of_actors[T.2.76]     0.7733      0.405      1.910      0.056      -0.020       1.567\n",
            "avg_rating_of_actors[T.2.77]     0.7113      0.404      1.761      0.078      -0.080       1.503\n",
            "avg_rating_of_actors[T.2.78]     0.5494      0.404      1.359      0.174      -0.243       1.342\n",
            "avg_rating_of_actors[T.2.79]     0.7188      0.402      1.786      0.074      -0.070       1.508\n",
            "avg_rating_of_actors[T.2.8]      0.8366      0.403      2.078      0.038       0.047       1.626\n",
            "avg_rating_of_actors[T.2.81]     0.6803      0.403      1.690      0.091      -0.109       1.469\n",
            "avg_rating_of_actors[T.2.82]     0.6474      0.402      1.610      0.108      -0.141       1.436\n",
            "avg_rating_of_actors[T.2.83]     0.7772      0.402      1.934      0.053      -0.010       1.565\n",
            "avg_rating_of_actors[T.2.84]     0.7801      0.402      1.941      0.052      -0.008       1.568\n",
            "avg_rating_of_actors[T.2.85]     0.7893      0.402      1.965      0.049       0.002       1.577\n",
            "avg_rating_of_actors[T.2.86]     0.7612      0.402      1.894      0.058      -0.027       1.549\n",
            "avg_rating_of_actors[T.2.87]     0.7883      0.402      1.962      0.050       0.001       1.576\n",
            "avg_rating_of_actors[T.2.88]     0.7747      0.401      1.930      0.054      -0.012       1.561\n",
            "avg_rating_of_actors[T.2.89]     0.8316      0.401      2.072      0.038       0.045       1.618\n",
            "avg_rating_of_actors[T.2.9]      0.7610      0.401      1.896      0.058      -0.026       1.548\n",
            "avg_rating_of_actors[T.2.91]     0.7723      0.401      1.924      0.054      -0.014       1.559\n",
            "avg_rating_of_actors[T.2.92]     0.7958      0.401      1.984      0.047       0.010       1.582\n",
            "avg_rating_of_actors[T.2.93]     0.8321      0.401      2.073      0.038       0.046       1.619\n",
            "avg_rating_of_actors[T.2.94]     0.8031      0.401      2.002      0.045       0.017       1.589\n",
            "avg_rating_of_actors[T.2.95]     0.8014      0.401      1.998      0.046       0.015       1.588\n",
            "avg_rating_of_actors[T.2.96]     0.8962      0.401      2.235      0.025       0.110       1.682\n",
            "avg_rating_of_actors[T.2.97]     0.8585      0.401      2.141      0.032       0.072       1.645\n",
            "avg_rating_of_actors[T.2.98]     0.8887      0.401      2.216      0.027       0.103       1.675\n",
            "avg_rating_of_actors[T.2.99]     0.9005      0.401      2.246      0.025       0.115       1.686\n",
            "avg_rating_of_actors[T.3.0]      0.9152      0.401      2.283      0.022       0.129       1.701\n",
            "avg_rating_of_actors[T.3.01]     0.9306      0.401      2.321      0.020       0.145       1.717\n",
            "avg_rating_of_actors[T.3.02]     0.9796      0.401      2.443      0.015       0.194       1.765\n",
            "avg_rating_of_actors[T.3.03]     0.9805      0.401      2.446      0.014       0.195       1.766\n",
            "avg_rating_of_actors[T.3.04]     1.0034      0.401      2.503      0.012       0.218       1.789\n",
            "avg_rating_of_actors[T.3.05]     1.0463      0.401      2.610      0.009       0.261       1.832\n",
            "avg_rating_of_actors[T.3.06]     1.0811      0.401      2.697      0.007       0.295       1.867\n",
            "avg_rating_of_actors[T.3.07]     1.0946      0.401      2.731      0.006       0.309       1.880\n",
            "avg_rating_of_actors[T.3.08]     1.1098      0.401      2.769      0.006       0.324       1.895\n",
            "avg_rating_of_actors[T.3.09]     1.1415      0.401      2.848      0.004       0.356       1.927\n",
            "avg_rating_of_actors[T.3.1]      1.1659      0.401      2.909      0.004       0.380       1.952\n",
            "avg_rating_of_actors[T.3.11]     1.1625      0.401      2.900      0.004       0.377       1.948\n",
            "avg_rating_of_actors[T.3.12]     1.2149      0.401      3.031      0.002       0.429       2.000\n",
            "avg_rating_of_actors[T.3.13]     1.2119      0.401      3.024      0.003       0.426       1.998\n",
            "avg_rating_of_actors[T.3.14]     1.2412      0.401      3.097      0.002       0.456       2.027\n",
            "avg_rating_of_actors[T.3.15]     1.2694      0.401      3.167      0.002       0.484       2.055\n",
            "avg_rating_of_actors[T.3.16]     1.2590      0.401      3.141      0.002       0.473       2.045\n",
            "avg_rating_of_actors[T.3.17]     1.3159      0.401      3.283      0.001       0.530       2.102\n",
            "avg_rating_of_actors[T.3.18]     1.3395      0.401      3.341      0.001       0.554       2.125\n",
            "avg_rating_of_actors[T.3.19]     1.3612      0.401      3.396      0.001       0.575       2.147\n",
            "avg_rating_of_actors[T.3.2]      1.3700      0.401      3.418      0.001       0.584       2.156\n",
            "avg_rating_of_actors[T.3.21]     1.3817      0.401      3.447      0.001       0.596       2.167\n",
            "avg_rating_of_actors[T.3.22]     1.3994      0.401      3.490      0.000       0.614       2.185\n",
            "avg_rating_of_actors[T.3.23]     1.4224      0.401      3.547      0.000       0.636       2.208\n",
            "avg_rating_of_actors[T.3.24]     1.4766      0.401      3.682      0.000       0.691       2.263\n",
            "avg_rating_of_actors[T.3.25]     1.4481      0.401      3.611      0.000       0.662       2.234\n",
            "avg_rating_of_actors[T.3.26]     1.4570      0.401      3.632      0.000       0.671       2.243\n",
            "avg_rating_of_actors[T.3.27]     1.4758      0.401      3.678      0.000       0.689       2.262\n",
            "avg_rating_of_actors[T.3.28]     1.4901      0.401      3.714      0.000       0.704       2.276\n",
            "avg_rating_of_actors[T.3.29]     1.5372      0.401      3.831      0.000       0.751       2.324\n",
            "avg_rating_of_actors[T.3.3]      1.5204      0.401      3.787      0.000       0.733       2.307\n",
            "avg_rating_of_actors[T.3.31]     1.5113      0.402      3.763      0.000       0.724       2.299\n",
            "avg_rating_of_actors[T.3.32]     1.5208      0.402      3.787      0.000       0.734       2.308\n",
            "avg_rating_of_actors[T.3.33]     1.4787      0.401      3.683      0.000       0.692       2.265\n",
            "avg_rating_of_actors[T.3.34]     1.5528      0.402      3.862      0.000       0.765       2.341\n",
            "avg_rating_of_actors[T.3.35]     1.5276      0.402      3.800      0.000       0.740       2.316\n",
            "avg_rating_of_actors[T.3.36]     1.5928      0.403      3.955      0.000       0.804       2.382\n",
            "avg_rating_of_actors[T.3.37]     1.5372      0.402      3.823      0.000       0.749       2.325\n",
            "avg_rating_of_actors[T.3.38]     1.5084      0.402      3.753      0.000       0.721       2.296\n",
            "avg_rating_of_actors[T.3.39]     1.6311      0.403      4.052      0.000       0.842       2.420\n",
            "avg_rating_of_actors[T.3.4]      1.6214      0.403      4.020      0.000       0.831       2.412\n",
            "avg_rating_of_actors[T.3.41]     1.5576      0.403      3.861      0.000       0.767       2.348\n",
            "avg_rating_of_actors[T.3.42]     1.5964      0.404      3.950      0.000       0.804       2.389\n",
            "avg_rating_of_actors[T.3.43]     1.5616      0.403      3.874      0.000       0.772       2.352\n",
            "avg_rating_of_actors[T.3.44]     1.5868      0.403      3.933      0.000       0.796       2.378\n",
            "avg_rating_of_actors[T.3.45]     1.6634      0.404      4.120      0.000       0.872       2.455\n",
            "avg_rating_of_actors[T.3.46]     1.5958      0.405      3.945      0.000       0.803       2.389\n",
            "avg_rating_of_actors[T.3.47]     1.6374      0.407      4.024      0.000       0.840       2.435\n",
            "avg_rating_of_actors[T.3.48]     1.7406      0.404      4.306      0.000       0.948       2.533\n",
            "avg_rating_of_actors[T.3.49]     1.6203      0.407      3.980      0.000       0.822       2.418\n",
            "avg_rating_of_actors[T.3.5]      1.5930      0.403      3.957      0.000       0.804       2.382\n",
            "avg_rating_of_actors[T.3.51]     1.6350      0.405      4.038      0.000       0.841       2.429\n",
            "avg_rating_of_actors[T.3.52]     1.7307      0.405      4.274      0.000       0.937       2.524\n",
            "avg_rating_of_actors[T.3.53]     1.6235      0.407      3.990      0.000       0.826       2.421\n",
            "avg_rating_of_actors[T.3.54]     1.6678      0.407      4.100      0.000       0.871       2.465\n",
            "avg_rating_of_actors[T.3.55]     1.6315      0.407      4.013      0.000       0.835       2.428\n",
            "avg_rating_of_actors[T.3.56]     1.7085      0.405      4.221      0.000       0.915       2.502\n",
            "avg_rating_of_actors[T.3.57]     1.6855      0.405      4.164      0.000       0.892       2.479\n",
            "avg_rating_of_actors[T.3.58]     1.7519      0.407      4.305      0.000       0.954       2.550\n",
            "avg_rating_of_actors[T.3.59]     1.7723      0.416      4.264      0.000       0.958       2.587\n",
            "avg_rating_of_actors[T.3.6]      1.8708      0.417      4.488      0.000       1.054       2.688\n",
            "avg_rating_of_actors[T.3.61]     1.7383      0.417      4.170      0.000       0.921       2.555\n",
            "avg_rating_of_actors[T.3.62]     1.7608      0.417      4.224      0.000       0.944       2.578\n",
            "avg_rating_of_actors[T.3.63]     1.7066      0.405      4.213      0.000       0.913       2.501\n",
            "avg_rating_of_actors[T.3.64]     1.8400      0.566      3.248      0.001       0.730       2.950\n",
            "avg_rating_of_actors[T.3.65]     2.1264      0.415      5.129      0.000       1.314       2.939\n",
            "avg_rating_of_actors[T.3.66]     1.7873      0.418      4.272      0.000       0.967       2.607\n",
            "avg_rating_of_actors[T.3.67]     1.8250      0.448      4.075      0.000       0.947       2.703\n",
            "avg_rating_of_actors[T.3.69]     1.8629      0.428      4.350      0.000       1.024       2.702\n",
            "avg_rating_of_actors[T.3.7]      1.8044      0.422      4.274      0.000       0.977       2.632\n",
            "avg_rating_of_actors[T.3.71]     1.9337      0.413      4.684      0.000       1.125       2.743\n",
            "avg_rating_of_actors[T.3.72]     1.9450      0.491      3.965      0.000       0.983       2.907\n",
            "avg_rating_of_actors[T.3.73]     1.8282      0.418      4.370      0.000       1.008       2.648\n",
            "avg_rating_of_actors[T.3.75]     1.5300      0.463      3.308      0.001       0.623       2.437\n",
            "avg_rating_of_actors[T.3.94]     2.0444      0.407      5.026      0.000       1.247       2.842\n",
            "==============================================================================\n",
            "Omnibus:                      772.216   Durbin-Watson:                   1.826\n",
            "Prob(Omnibus):                  0.000   Jarque-Bera (JB):             1169.695\n",
            "Skew:                          -0.305   Prob(JB):                    1.01e-254\n",
            "Kurtosis:                       3.855   Cond. No.                     1.82e+03\n",
            "==============================================================================\n",
            "\n",
            "Notes:\n",
            "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
            "[2] The condition number is large, 1.82e+03. This might indicate that there are\n",
            "strong multicollinearity or other numerical problems.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(14, 8))\n",
        "sns.boxplot(x='avg_rating_of_actors', y='rating', data=df, palette='viridis')\n",
        "plt.title('Box Plot of avg_rating_of_actors vs. Rating', fontsize=16)\n",
        "plt.xlabel('avg_rating_of_actors', fontsize=14)\n",
        "plt.ylabel('Rating', fontsize=14)\n",
        "plt.xticks(rotation=45, ha='right')\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 783
        },
        "id": "HC4CIJhlI_nI",
        "outputId": "41881982-e0ff-4c6c-fa08-8a2cfd7ec32d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1400x800 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Let's look at the relationship between average rating of actors in a movie vs the movie's rating"
      ],
      "metadata": {
        "id": "u_Vxj5UOnhA7"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.scatter(df['avg_rating_of_actors'], df['rating'])\n",
        "plt.xlabel('Average Rating of Actors')\n",
        "plt.ylabel('Movie Rating')\n",
        "plt.title('Average Rating of Actors vs Movie Rating')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 503
        },
        "id": "OYHpg9zBIpQi",
        "outputId": "e17bf375-999f-415b-8917-2c1eb2d0268e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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ihd5rN3vd6NevH9OnTx9mx44dTHV1NcMwDJOTk8MsXrxY/Rm8c+eO1vavv/66xmeUvTYVFhYyb7/9NiOXy5m+ffuKcm0yhAIrhmHmzJnDyOVyZt26dRqP//HHH4xcLmfGjRuntc2ePXsYuVzOdO/enSkvL9d6vqamhunTpw8jl8uZP/74Q+O5devWqT+AbLDR1MGDBxm5XM4MGDCAUSqVGs+xX4zevXszOTk5fP9cte+//56Ry+XM3LlztZ5jg8JBgwZpXEBZt27dYrp166b3h5u9cL344ot6j//TTz8xcrmcmTRpEu+26wus2AvUxIkT1W07ePAg7/3n5uYy3bp1YyIjI5msrCyt58UMrObMmaPzIrpp0yZGLpcz0dHRWp8BY9+f5ixYsEB9s9E4KGelpKSoA9fvv/9e63mxAqvY2Fh1ANdYeXk5Ex0dzcjlciY5OVlrO6VSqQ58Hn/8cfWPBBdc3ttvvvlG/aOdmpqq9Xx5ebn6M/rUU0/pPYah7zD7A//QQw9xbjsX8+fPN/jd/PPPP9Wfu8bXtm+//ZaRy+XM/PnzRW1P08Cqurqa6d27t1bQcurUKY3zYSiwSk5OVj/3+++/6zwuG3gNGDCAqampUT/e+Nqtb1v2B/zxxx/Xek5fYHX37l2mS5cuTHR0tM5gm2EaPjfDhw8XdMNpKLAqLCxUB3VCr7kMwzCrV69m5HI58+qrr2o9J2ZgJZfLmVOnTmk9X1tbywwdOpSRy+XM+vXrNZ7LyspS34h8+eWXBo9t6sDK4Sev3717F2fPntU5XDd+/Hi4ubnh9u3bWqtURo8eDS8vL9TW1qpz6zR25MgRlJeXw9XVFePGjdN4jh3jnjdvnt6hkTFjxsDLywvFxcVISUnR+ZrJkycjNDSU89/aFDuEmZycrNEtzDAMDh48CAB4/PHHdc7faN++PcaPH69zv42HVhctWqT3+Oz5vn79unr4ha+//voLQ4YMwZAhQzB48GD06NEDTzzxhHpV2pw5czBmzBje+w0JCUHnzp3BMAwuXLggqG1cPf3003By0v4qssMQNTU1yMjIUD9u7PvTnLKyMvWKuwULFugccunatat61ZMpl76zw4BNv5teXl7q91XXJPYzZ86ok7KuWrUKMplM1Hax3/mxY8dCLpdrPe/l5YWFCxcCAOLi4lBeXq5zP4a+wz4+PgCAyspKVFVVidFs9TGBhtV1FRUVWs/v2rULwD/XoKbtKSoqMulqaTc3N0yYMAEMw2gMO7L/zU7ANuSvv/4CAISGhuKxxx7T+Zp///vfABpSeSQkJKgfb3zNZs9FYwqFQv3+8xkGZFeZDxs2DJ07d9b5msaf65MnT3Led1OPPvqo+rrYu3dvDBo0CF999RWAhnQHH330kaD9snNyz507J7htXPTu3RsDBw7Uelwmk2Ho0KEAtOeNHjx4EPX19XB3d8e8efN07vfZZ58Vva26OPwcq+3bt4NhGPTr109rXgD7Id+zZw+2bduG3r17q59zc3PD2LFjsX37duzevVvry8uOv48ePVojn0leXp56cu9//vMfvPHGG3rbxl5Ms7Ky0KNHD63nG7dHn4KCAmzevBkJCQlIT09HeXm51kWxuroapaWl6knAd+/eRVlZGYCGORb69O/fX+eF58qVK+o5LQsWLGi2jUDDGH7jeWxc1dbWauQeY8lkMnzyyScGlzzX19dj79692Lt3L65fv46ioiKd+2Ln0JhKdHS0zseDg4PV/11SUqL+b2Pfn+akpKSo52IMHjxY7+uGDBmCffv2ITU1FUqlUvRcXRkZGfj777913vQADSvI9uzZo5730TgAZIPhoKAgdO/eXdR2KRQK9UV90KBBel83ZMgQAA2fs5SUFJ0/FIa+w9HR0fD390d+fj6mT5+OmTNnYtCgQWjfvj0kEong9j/44IPw9PREZWUlDh48qDHBuqCgQB1kND3ngwYNgqurK65evYrZs2dj2rRpGDhwoN4J+saYOnUqtmzZgp07d+L5559HVVUVDh48CKlUyimYuXLlCgBgwIABOm9aAKBDhw4ICQlBXl4erly5orFIKSYmBlu3bsX58+dx9+5djb/x2LFjKCsrg6urK6+bF/bmPCEhQf3Z0IW97mdnZ3Ped1PFxcU6Hx8yZAi++OILrUUnjd29exebN2/GmTNnkJmZicrKSq15qnl5eYLbxoWu3zsWe10sLS3VeJztgOjWrZve1cgREREICwtDTk6OSC3VzaEDq/r6evUqIH1f1piYGOzZswf79u3Df/7zH43VGjExMdi+fbt6kjo72bCoqEh9t9F08mXjD6S+D39TNTU1Oh9vPKlalwsXLuCpp55S/wgDDRMl3d3dIZFIoFKp1G1gl8ez7Wc1/nFvSt9qtfv376v/m2tPVOPj89F4ZYlCoUB6ejp++ukn7NixA//973/Rtm1bdOrUSefxFi9ejDNnzqgfc3FxUWcIB6CegC20bVzpu8g1nshcV1en/m9j35/mNN6/oX2wz9XV1aG0tFRQYGwIe9PTp08fnT/egwcPVv8w7t+/X71UHwDy8/MBQGPitVhKS0vVNyeGzk/jnqjG57QxQ99hHx8ffPLJJ1i+fDnS0tLw9ttvAwC8vb3Rt29fjB8/Hg8//DDvgNbd3R1jx47Fjh07sGvXLo3Aas+ePVCpVAgKCtL68Y+IiMA777yD//73v7hw4YI6eA0ICMCAAQMwceJEjB492qigj9WzZ0906NABt27dwqlTp5CVlYXq6mqMGDGC0+e6sLAQQPPfgdDQUOTl5alfz+rTpw9at26Ne/fuYffu3RorutmblZEjR6p78bhgr4vsJO/m6Lvuc9F4FXJhYSFOnTqFDz/8EAkJCfjkk0/03tAfOnQIL774onrBCtBwfXJ1dYVEIoFSqURpaamoPai6GFoVyV4XG18TgX++Y4auiUDDZ4ICKxM6efKkujfitddew2uvvab3tVVVVdi3bx8effRR9WP9+vVDq1atkJWVhd27d+OZZ54B0DA0UldXhxYtWmhdnBpH/n/99ZfGiiq+9N2JAQ0fuuXLl6OsrAxdunTBsmXL0KdPH40f8czMTHWPDtNotUhjQi6Sjf/GS5cuCV56zZdMJoNcLseaNWsANAwdvPDCC4iNjdValfL111/jzJkzcHNzw7Jly/DQQw8hLCxM4++dNWsWzp07p/fcWAMxfsSsEbtMHmgYdmi6fLupbdu2aQRWtnJeDH2HgYbg8ciRIzh48CBOnz6NCxcuID09HceOHcOxY8fw3Xff4YcffuAdRE+ePBk7duzA2bNnkZOTg7CwMAD/9LRPnDhR54rBSZMmYfjw4di/fz/OnDmjTlWyb98+7Nu3D3379sU333xjsEeEq6lTp+LDDz/Ejh071MO65spdxfaSfvnllxqBVXFxMeLi4gDwGwYE/lmFt2jRIrz00kviNtiAwMBATJw4EV26dMHUqVPx66+/olu3blrnsri4GCtXroRCocDAgQOxZMkSREdHa1w72TQc1swavvsOPceKb8mapnM5JBIJHnnkEQCaY/Hsf0+YMEFr+XTju3pjunqbk5ycjKysLEilUnzzzTcYMWKE1sWOvatvqnFeoMa9T03p6w5u/DdaqizIypUr4e3tjdu3b2Pjxo1az7PzgpYsWYJ58+ahZcuWWl9IofO+TM3Y94fP/g0Ng7L7d3Z21lpebay4uDiDf1tTf//9N9LT09X/Zj+DpviO+fr6qoMOQ+en8XPN5doyxMPDAzExMXj//fdx4MABxMXF4aWXXoKrq6tGTxYfAwYMQFhYGOrr6/Hnn38CaMhrxA6nNO1pb8zPzw8zZ87Ep59+iuPHj+PQoUN46qmnIJFI8PfffzebW4qryZMnw9nZGQcOHEBycjL8/Py0cgrqw/YENjeMzz6vq+eQDZzS09ORnJwMoOFmWKlUqvPM8cGWcDHldd+QDh06qKdmfPjhh1rz606cOIGKigr4+vri66+/Rv/+/bVuSPX9ZlgD9jvW3HXD1MOYgAMHVkVFRepkf1988QXOnz+v939bt24F0DC0dvv2bY39sBegO3fu4NKlS7h9+zYuX76s8VxjrVu3Vt9dHjt2zER/HdRdnQEBAXrvZvWVegkPD1d3cbM5YHTR91z37t3VwxOm/BsN8fX1xdy5cwEA3377rdZ4PHtB7dKli87t7927pzFhvCk2CLNEb5ax709zoqKi1D0phsoBJSYmAmhIdin2/Cr2JubBBx80+N08f/48oqKiAGjeKLFzl/Lz89XfR66ae2/ZhINAQw4yfdjz4+TkpG6jGEJCQrBo0SJ1/q7GE6+5kkgkmDRpEoB/bgTZ/4+MjNQ7uVqXiIgILF++HBMnTgTwz99trKCgIAwbNkydp+2RRx7hvAihW7duABoWMejLY3fr1i31j6yueXht2rRRJzBmzw3bo6frprk57GcyMTFR51xOc3jyySfh7e2NoqIi/PDDDxrPsdfEdu3a6c0RZuh6YMlrIgD1d+zKlSt6hyrv3r1r8mFAwIEDq127dkGpVMLb2xsjR46Ep6en3v9FR0ejffv2ALR7rdq1a6eeaLdr1y71F1Aul6Nr1646jz19+nT1vq5evWqwnY0nLfPBTpgvKCjQ2fOSm5urkaSuMYlEoh4i/P3337WCEqDhLk7Xakig4Q6b7cn77rvvmr1DE/o3NmfOnDnw8PBARUUFfvzxR43n2N6769ev69z2448/Nrhvdnt9q71Mydj3pzk+Pj7qlTc//PCDzjlm169fV69MZH9QxVJQUIDjx48DaKgFaei76enpqV7B1bi254ABA9TzstasWaMxZ6Q5XN7bhx9+GABw4MAB9QrUxiorK/H9998DaFhJJaQgb3NtZnsTmhtO1Iftkbl58yYuX76s7rnS11vFtT1iDsU8/fTTmD9/PubPn4/Zs2dz3m7ChAkAoE60q8sXX3wBoCHxq75FGuw5+uuvv5CWlqbuuTLUo6fPtGnT4OzsjOLiYvWx9VEoFKisrOR9jOZ4e3urz+PGjRs15vmyn9H09HSdgd+1a9fUnxFd2O9N4zm95vTggw/CyckJVVVV2LRpk87XsCsjTc1hAys2QBo9ejSnu6DGy2+bTppjv3x79+5Vf/DYu0FdnnzyScjlctTW1uKJJ57AL7/8ovEBLysrw4kTJ/DKK6/wupg01qdPH3h4eIBhGCxdulRdF0qlUuHkyZPqQtP6LF68GG5ubigoKMD8+fPVASDDMDh16pTeZfisZcuWITg4GMXFxZgxYwZ27typ0fVcVFSEAwcOYMmSJSYrxskOWQANGYUbTyAeNmwYgIYv2sGDB9Xv6d27d7F8+XLs27fP4PAWOyH+zz//NPnkdl2MfX+as3TpUri4uCAjIwMLFixQr4Krr6/HiRMnsGjRItTV1SEiIkIjg70Ydu7cibq6Ori5uemtatAYuzKrcW1PqVSK119/HRKJBOfOncO8efPw999/q3svFAoFzpw5g5deegk3b97U2B+X9/bxxx9H69atoVQqsWjRIpw4cUK979TUVCxYsAD37t2DTCbD0qVLhZwGfPvtt1i4cCF27typMaSlUCjw119/qXscuJwjXTp06KDu2XnzzTeRk5MDqVSqN1B+66238O9//xsHDhzQmOxdWVmJ3377DTt37jSqPbr07NkTK1aswIoVK7TKrxgSHR2tznj/9ttv45dfflG/l/n5+Xjttdewf/9+AA1pF/TNA2UXB5SUlGDlypUAgI4dO6rPGx8RERHqebjff/89XnnlFY2gvK6uDteuXcO6devw0EMP4dq1a7yPwcXcuXPh7u6OyspKjV6rIUOGwMnJCSUlJXjppZfUvXns523+/PkGJ5WzaUdu3rxpsIi2qbRq1Uo9B/qLL77ADz/8oA5Oi4uLsWbNGmzfvp3XggOhHHLyenJysvpi2jTHlD7jxo3D+vXr1XfTjXMjPfzww1izZg2Ki4tRXFwMJycng4GVp6cnvv/+e7zwwgtITk7G22+/jXfeeQfe3t6or6/XCEDatGkj6G/09vbGK6+8gjfffBNJSUkYN24cPDw8oFKpUFtbC39/f6xZs0b9RW+qTZs2+N///ofly5fjypUrmDJlCjw9PVFfX4/q6mqEhIRg5cqVenMEBQcHY8OGDXj22WeRnp6OFStWwMnJCT4+PlAoFBpdtYaW9Btr/vz5+PXXX1FVVYXvvvtOXb5i6dKlSExMREFBAZ5//nk4OzvD3d1d3Uvx4osvIj4+Xu9w2syZM3H+/HkcOHAAR48eRUBAAJydnRESEoLffvvNZH8Py9j3pzlRUVH44IMP8Morr+DcuXOYNGkSvLy8oFQq1XezYWFh+Prrr0Wva8YO6Y0YMYJTEefw8HBERUUhJSVFo7bniBEj8P777+P111/HuXPnMHv2bMhkMnUvJhtMN00JwuW99fLywldffaUuafPUU0/B1dUVLi4u6u+vTCbDhx9+yGtYrTGGYXDy5En1CmM3Nze4ubmhtLRUPdzSoUMH9Q++EDExMbhy5Yo6PcGgQYP0rqqqq6vD/v371QGJh4cHnJ2dNXoo+vTpg6efflpwe8T07rvvori4GGfPnsXbb7+NNWvWwNPTE2VlZerzN3/+fDz++ON69+Hr64uRI0fi4MGD6nPEd9J6Y0uWLIFKpcJXX32lHuFg39emqXBMNQk7ICAAjz32GDZt2oRff/0VTz75JAIDA9G2bVssWLAA3333HQ4ePIiDBw/C29sbNTU1UCqVaN26NZYuXap34n3//v3Rrl073LlzB48//jh8fX3VvVivvPIK599aY6xcuRK3bt3CuXPn8MEHH+Djjz+Gl5eX+j1/5pln8PfffyMpKcmki6ocsseK7a3y9vY2mE+kscjISPUKvqbDgf7+/hrFjAcNGtTsKp2QkBBs3rwZn3zyCUaNGoWgoCBUV1dDqVSiVatWGDlyJF599VX88ssvfP40DY8//ji+/fZb9O/fXx1UhYSEYM6cOdi1a5fOxIaNjRs3Dtu3b8e4ceMQEBAAhUKBwMBAPPHEE9i5c6e661jfHUCHDh3w559/4q233sLQoUPh7++PiooKMAyDNm3aYNy4cXj77bfx2WefCf4bmxMUFKROKLh582b15MtWrVph+/btePTRR9U/JK6urhg5ciR++OGHZmtKsTXX+vTpAzc3N+Tn5yMrK8ssEyNZxr4/zXn44Yexd+9ezJgxAxEREVAoFJBKpejSpQuef/557Nmzx6hVrbqcO3dOPY+Rz4WYfS1b25MVExODffv2Ye7cuejYsSOcnZ1RW1uLli1bYsyYMfjggw+0/gau761cLsfevXvx/PPPo0uXLpBKpVAoFIiIiMDMmTOxd+9eo35Mpk+fjrfffhsTJ06EXC6Hm5ubenJx37598eqrryI2NlY9KVqICRMmaMyPMzTE9eyzz+K1117Dgw8+iPbt28PZ2VldVH3IkCHqGnhcgmFz8Pb2xoYNG/Duu++if//+8PT0RFVVFVq0aIGxY8di06ZNeuukNtY4kGruprk5EokE//73v7F7927MmjULHTp0gJOTEyoqKuDj44NevXphwYIF+P3339GnTx/Bx2nOggUL4OLigqqqKnz77bfqx1966SX873//U68GZHuln376aezcudNgKgNnZ2ds3LgRjz32GFq3bo2qqipkZWUhKyvL5OkZWJ6entiwYQNeeeUV9dxPNk/lunXrsHTpUvWNgJDhea4kjDWvJSdW7dNPP8XXX3+NgQMH6lx5RyyL3h9CCPlHZWUlBgwYAKVSiV9//RV9+/Y1yXEcsseKGK+oqEg9KZSdr0SsB70/hBCi6aeffoJSqYSfn5/oFRkao8CK6LVp0yZ8++23yMjIUM9HUSgUOHHiBGbPno3CwkIEBARwqt1FxEfvDyGE/KOiogLLli1DXFycxty/rKws/O9//8O6desAAE888YRJ51jRUCDR691331UvW5VKpfD29taY9Ovt7Y0vv/wSAwYMsGQzHRa9P4QQ8o+ysjKN+qnswprGqSvGjh2LTz75hHceMj4cclUg4WbKlCmQSqVISkpCXl4eSkpK4Orqig4dOmDo0KGYO3eu4Hp0xHj0/hBCyD88PDzwxhtvICEhAWlpaSgqKkJtbS2CgoLQrVs3xMTEYOzYsSYve0M9VoQQQgghIqE5VoQQQgghIqHAihBCCCFEJDTHigOGYVBXp7uQp7VwcZECAJRKVTOvJHSuuKHzxB2dK+7oXHFD54k7XefK2dnJ5HOp9KHAioO6unqUlJgnc6xQQUENWWStvZ3WgM4VN3SeuKNzxR2dK27oPHGn61z5+XmoAy5zo6FAQgghhBCRUGBFCCGEECISCqwIIYQQQkRCgRUhhBBCiEgosCKEEEIIEQkFVoQQQgghIqHAihBCCCFEJJTHihBi1UorFYi7mI0bmcWoUajgJpMiMsIfw3q0hK+nzNLNI4QQDRRYEUKskkKpwubDaUi4nANVvWat+JT0YuyKv4Oh0WGYNaYTXJwtkwiQEEKaosCKEGJ1FEoVPv3jIlLvluh9jaqewYnkbOQWVmHZ9B6QWSjLMiGENEZzrAghVmfz4TSDQVVjqXdL8NuRNNM2iBBCOKIeK0KIVSmtqEXC5Rxe28RfykHMsPaC5lzRHC5CiJgosCKEWJW4S9pzqpqjqmdw8mI2Jg5uy3kbmsNFCDEFCqwIIVblRmaxoO1SM4s5B1bmnMNFPWKEOBYKrAghVqVGoTL5dnzncL3yVSLCg714BUTUI0aIbvZ+s0GBFSHEqrjJhAUZzW3HXsxTbhfixr1SXvsuq1IiJb2Yc0Bkih4xe/8xIvbPUW42KLAihFgVeYQ/UtL5DwdGRvjrfNzQxVwILgGRkFWNc8d11vm8o/wYEfvmSClUKN0CIcSqDI8Og9RJwmsbqZMEw3q01HqcvZjHXcwWJahqTF+aB6GrGksrFVqPc2k/+2P0yZaLUCiFDaMSYmqOlEKFAitCiFXx9XLFkO5hvLYZGh2mcziMz8VcCF0BkdBVjZ9vuwhlnWZg5Eg/RsR+iXmzYQtoKJAQYhUazyGqqq2Dh6szqmrrmt2uha8bJgxqq70/ARdzvtg0Dx3bBqofE7qqMT2nHJ9suageAjF3Pi97RPPSrIO5UqhYCwqsCCEWZewcqILSGqz65pTWPCMhF3MhUpsEUkJXNQKa860c7cdITDQvzbqYI4WKNaHAihBiMVwmtHKha9Kr0Is5X00DKaGrGllsr5M5foxsvUdHV/s7tvLFlfQi3Moq07udvUySthUV1c33POvcrkbYdpZGgRUhxGLEngOVercEG/dfR2igJ27n6P9hFVPTQEroqkYW2+tkyh8jW+/Raa79XDW3IpOIo5rDkL7O7SiwIoQQ7kw1B+pUSp7o+zSkaZqH4dFh2B1/x6hhyNTMYpP9GNn6snexejlZpp6XZuu9guIQ+l0w/VC+KVBgRQixCHPNgTIlXWkefL1c0a9LME4bEeA1DC+a5sdIzBxbliB2L6ep5qXZeq+gmNxdhYUaQrezNEq3QAixCHPNgTKlpmkeFEoVNuy7jrNXjes1c5NJTfJjZOvL3k3Vy9l0AYKxKP+YJi93F7NuZ2kUWBFCLMKY1XPWIDLcD7PGdFL/u7bRj6mxHXGREf4m+TEyZqWhNTBVL6fYn0XKP6ZJrqcqQnP0VVOwdrbZz0YIsXnGrp6zFKmTROcQznc7L4syRMUOLzLgNxGbZejHyNaXvZuql1PXZ1Ho3CjKP6atZ4dAxMbd5r9dpxYmaI3pUWBFCLEIY1fPmYuHqzNCAtzh4eqs94e1uKwGR5IyRTne0OgwuMukyCmo5L2tvtI+LKE9M9bSu2iqdlTW1kFZp4KLs9TouVGUf0xb8q1CYdulFaB1kJfIrTE9CqwIIWanUKoEBQ6WUFVbh4gQb4MTuA+ezUCdyvghKg9XZ2TkluPFdQmcss43pa+0D0toL6HYvYvF5TX4MzGdd2+QqXo52az3S6Z0w5exV4xaMWnrvYKm4GjnhAIrQohZib1c3hyaG6q5IvCOvKmq2jqk55YL2rbpnC9dhPYSijXXRaFUYd3WZBxJytQKRLn0BpmylzP1bgne++U8couqOL9e14pJW+8VNAVHOycUWBFCzMrUhZFNobmhGqE5p8QyKCoE88Z3RlWtCnFn7+rtCRoeHYZdJ2/zmlzvJIHB4UWuxMifJUaOMEO4BlUsXQG3tfQKWhNHOycUWBFCzEbocnlvDxeUVylN0CLu/kxMR/ylHLi7OcPLrWG+VcfWvtiTmI6bFg4Ug/3c8eshbvOCgv09eAUQIQEeokyqFiN/lq+XK4Z0D0OclaxS1BVwW7pX0BpFhHoLOidtQr1N0BrTo8CKEGI2QpfLWzqoAgBlXT3ul1Sr/21NE+8P/X0XVbX6h03YnqC7eeXIb/Q3cHG/uBqllQqjgisxV8rNGtMJeUVVVtPr2XQekJBeteYWHdg6iQW2tCQKrAghZmMPSUGtkaGgqrHbOfznb6nqGaz+6SzCAjxQVVv3/8OeEo2eu+YmnYu5Uk7mIsWSKd2w5tfzyCnkN3RnCk3nAQnpVWtu0YGtyxA4bzAj1zz1PsVGgRUhxGxsdTKqoyupUKCkQnf2dS6TzsVcFaZQqvBl7BWrCKoA3fOA+PSqcVl0YOscbfI6ZV4nhJiNrU5GJYY1V55FzB9Wa1v8oGtulMxFimXTe2BEz5Zw0jOa5SQBRvRsiRdn9LD7WoE0eZ0Q4nB0ZZnu3SUUDw6IEPU4tpIUlAiTercEG/dfx6JHojQeF+uH1VS1AoVqbm4UwwASiaThP5qQSGxz/pAQjjahnwIrQhxYc1mmfzt4HUO66x/iMaRxsFZRrUR1bZ3RNfSI9TuVkgdAgnnjI9WfGbF+WE1VK5AVGsBvxaS+uVFipJawJ8Ojw7Az7jb4vHMSiJPmwxIosCLEQXG5+Nep+F38SysVOHb+HuIu5qCkolbkFhNbcSolF0VlNerPjFgr5Uy5+CEy3A9Pjo/Eq9+d4XQD4CQBJg1pq/M5MVJL2BNfL1e4uUpRzXGRBQC4uzrb7IR+CqwIcVB8L/4b96ciNNBDZ/JJd5lUb8+XMTq09MGdnDLq6bJBjQMGsVbKmWIyc+Oi2v/9MYnzZ62eAT76PRnvLhqo8TgVYdZ2N6+cV1AFNFQhuJdfQbUCCSG2QcjF/1RKrtZjKenF2HnyNrw9ZCit1L1qTIjGP3a/HkqzmoSQ9iLA2xVF5abvUWwcMPBZKdfC1w0TBrXVetzYycwtfN0Q4u+uMyv93bxy3pnXcwqrtH78qQizti3Hbgra7vcjaXhpZi+RW2N6tCqQEAck5lyVegaiBVWR4b6YOrw9PloyBHPHdYaLsxSzxnRCZLifKPsnDXNX+nYJMsuKKzZgAP5ZKTd2YBs4Sw1P3C4orcGqb05h4/7rUNb909MhN3Iyc7C/O5bP7IX/PNEXy2f2wsTBbdW9RMb8+DdmTGoJeyU8j5Ww7SyNAitCHJC1JuqMaheo8WMH/PODPCgqxIItsx8MgINn75ktR1DjgEHmIsVzj/XE1yvHoIWvm8HtmqZwUChVyCmoNKotKlW93ufE+vEXel5v55RhT2K6qD2/1qJpwW1Tb2dpFFgR4oCsNfHelTtFOh+XuUgRGuhp5tYQMej6rG09cgMFpTWctk+9W4JfDt3Ap39cxOmreUa1xdAPtVg//kJ7AqtrVdgRdxsvfZmAjfuv68wHZqua66EUeztLo8CKEAdkrYn3CgzUsbPWXjZiWNPPWnFZDY4kZfLaR8KlHFGSghr63Iv142/scCXbU/fGN4motZPgSmgxZVstwkyBFSEOyNiLv6no+yGpqFLghhVl2ybcNc1FdfBsBu/eIbEGhCpr6jTmbDXWMkhYj2irJtsNjw6DVF+6dR5S7hThmx2XjN6PNZgxsqOg7WaOts1SPxRYEeKAxLr4i83FWfuSVFGlwIpvTkFpo/MtHFnjXFSllQr8mZiOXSduWaw96bnlesvuRAhc1h8epNmrwqaWEMPhpEy7mHMVHuKNEH93XtuEBrjbZKoFgAIrQhwK++P2/Z6r8HC1vmwrzk7al6T3fjnPOwcOsQ5Do8PgLpNiw77reOnLBMTG3UZ5ldKibWLzazXFN9XCP9tpT6gXayUrwwCHku4avR9r0KGlD8/X+5qoJaZHgRUhDkChVGn8uKWkF6O82rI/cLo0jauE5BYi1iEy3A+PjmiPT/+4iLiL2SYtRcNX/KUcrZ4gMQtFNy7CbGzP8N/X7xu1vTUorajFmWv8/o7TV/NstreOAitC7Bxbusbaftx0cW/Ui6ZQqvDp1osWbA0Ryt/bFS/O6IGtx2+LMulcbI3za7HEKhTNkrlIMXdcZ3y0ZAimDm8PofFVebVtBheNGZM01RZZ31gAIURUfErXWJqXuwuAf4LBkgrb/1FxRAHerqiqqUP8Jev9YUzNLNbIdC5WoWhAswA5m+XdyUmCegedJ5iiJ41Kc67cKbLJbPQUWBFix4SUrrEkdtXWhn3XbSYYJNoy71fgte+5FTPmQiJpmG8kpqZDeD07BCI27jbv/fTs1EL93wqlSvSamd7/f7Nhy/INpFExxFD6FWtGQ4GE2DExS9eYQ3puOV76MtHoRJDEspR19aisqRNlX+6uUgyMChVlX401HcJLvlUoaD/JaQUATDfk3rdzsGj7shShyU5tNY8XBVaE2DFbTKppjZPqieVU16pQr6pvtgQOX02H8Iyt8WeKIXeJBHiwX4So+7QEVxdh89eEbmdpNBRIiB2z1tI1hPDBd0VZcxrn12JVVAvrYauoqTPZkPvofhEadTNtVQtfNxSV1/LeLshP3GDaXKjHihA7Zq2lawixpKHRYVoBS3WtsMCquqbOJEPuXdsF4Jmp0aLu01Ki2gcK266dsO0sjQIrQuyYtZauIcRSIsP9MGuMdqkUlcDZ8SqGEXXI3UkCjOjZEm8vHgyZjQ6FNTU8Wlgm+qa9iraChgIJsWPDo8OwO/6OTU1gJ8RU/L1k6NzGD1W1Kvg6awYtdXX1gvapqqsXPOQe4O2KsEAPdUqGyAh/DOvREr6eMrsJqgCgTGCiz/IqhU0OhVJgRYgJ6Mpj0/iiaS5s3bI4G020R4iYiisU2BWfjj2JGRgaHYZZYzrB5f8DrDqVsMCqokaJsEAPQdvWKlUWuS6Y25ZjNwVt9/uRNLw0s5fIrTE9CqwIEUlppQLHzt/DyYvZKNaR2DIlvRg74m7D39sVw3uE4YFerc1yMZ01phNyCiqRllUqeB9OEoiWk4gQS1PVMziRnI3cwiosm94DMhcpXF2kglJE1KkY5JfWCGpHZU0ddsTdxq74O1qBnj3JyC0363aWRoEVIUZikwLGX8rmFHwUl9fqvWsWS+Mes6raOuQJrLcndZIgPNgL94urUEWFkImdYQsyzx3XWfDKNQAoKK2BBIDQew9dgZ49qROYcV7odpZGgRUhRmCTAgrJX2OKi6kYmZ+93J0RHuyNunoGt7NKkG6jd42EcBF/KQcxw9ojqn0gbtwT3qsrRgjQONCzJ85SCWoFpKdzlhpXwNpSaFUgIUYQIykgezE1lliZnyuq65CVX4G0uyUQOO2EEJvBFvsdHh0muFCymOIv5aBU4GRva9Um1Nus21kaBVaECCRmUkAxLqZiZn4uq6Ls58RxpGYWw9fLFUOjLb+8nw307MmMkR0FbTdztHZaDFtAgRUhAomZFNDYi6mtFVsmxJqw6RJmjemEyHA/yzYG/5TJsRfhId4I8XfntU1ogDtaB3mZqEWmRYEVIQKJXYfPmIuprRVbJsSasBUKZC5SLJveA/7erhZtjz2WourQ0ofn631N1BLTs+nA6sSJE4iMjERkZCRGjRpl6eYQByP2xU/o/hRKFY6fvydqWwhxJI0LMstcpHigVysLtsb+SlGVVtTyrvd4+mqezc41s9lVgZWVlXjzzTct3QziwMS++AnZn0Kpwse/J+vMm0UI4eb4hSzEX8qGu6szvNxd0CbU26K52yLtrBSVkB51dnrExMFtTdMoE7LZHqtPP/0U2dnZGD16tKWbQhyU2HX4hFxMf/zrmlGJPwkhQFF5Le6X1CAjrwIp6cX463QmBJYONJrUSWKzNfL0SblTJGi7KwK3szSbDKySk5Px66+/YvTo0RgzZoylm0Mc1PDoMEhFWp/N92KqUKrw7e4UnOXZvU4I4cZSMxb7dwm2u/I2+SXVgrYrELidpdlcYKVUKvH666/Dzc0Nb7zxhqWbQxwYW4dPDEOjwzhfTNl8Vaev5olybEKI9Th77T427r8OhdJ+JrDXKPiXCjJmO0uzuTlW33zzDW7cuIFVq1YhNDTU0s0hDm7WmE7IK6oyKn9Uh5Y+8POS4ePfL3Aq2CxmvipCiHVhKzIUltfizUWDLN0cUdQLLE1jqyudbSqwunXrFr755htERUVhzpw5lm4OIerl2b8dSUM8zwmaThIgJMAD6bnluJVdpvFcSnqxzsKslK+KEMdw5VYhvtt5GTMe6GDpphhNKTCwUtZRYGVSDMPgtddeQ11dHVavXg2p1HzLUV1cpAgKso3U+rbSTmsg5rl6aU4/LCivwaEzmbh8qwDVtXVwd3VG9w4t0D8qBGdT8jQe79I2ABdS7+N6hv7cVU3vXF1dpDhqZLkaQojtOHw2A7PHdYa/t5ulm2IURuCMNQYMr+u0tfz+2UxgtXnzZpw/fx5z5sxB9+7dLd0cQrT4e7th+hg5po+Raz3XNsxX4/F1W5MNBlWNsXeuzz3WE1duFYrWXkKIdVPVA4fOZOq8phDrZROBVV5eHj755BOEhIRg6dKlZj++UqlCSUmV2Y/LBxup5+eXW7gl1s/S56q0ohaHz2by2ubw2UyM6xeO8opaUdvi6eYMTzcX3LfR1TeE2LuzV7Ixsoc4i2QsxVnqBGUd/4ruzlInTtdpXdd0Pz8PuLhYJtGqTawKfPvtt1FRUYHXXnsNXl62WTuIEKBhRd/n2y4JSpZ3/MI9FJaJG1jVKOpQb6mEPYSQZuWX1Fi6CUZrFypsiK5dmHUM7fElYRjrv6qOGjUKWVlZaNGihdZzNTU1qKiogJOTEwICAgAAa9euRe/evUU7PvVY2RdLnSs2TYLQFX0ers6oqrXN5ceEEGE83ZyxdulwSzfDKFfvFOGjLcm8t3tlVi905pA42dp6rGxiKJBVUFCg97n6+nr180ql0lxNIhyVVioQdzEbNzKLOaUUsEXN/Y3GpkmgoIoQx+NqoeBATFczBGZev13EKbCyNjYRWB09elTvczt27MCqVavQqlUrg68jlqFQqrD5cBoSLmunItCXUsDWcPkb+3cJxhlK6EkI4cnP29XSTTDa39fzBW53H4/aYLoJmwisiG3iMvTFphTILazCsuk9ILOxuzOuf+OpFAqqCCH8iV3s3RLKqoTNDRW6naXZxOR1Ypv4DH2l3i3Bb0fSTNsgE6As6IQQU6oTmFzTmgj9G2z1b6ceK2ISQjKEx1/KQcyw9lY/54qdS5Vypwg3KKgihJiQSsU/TYG1kUgkEFLWumE722PzgdXUqVMxdepUSzeDNBHHs7wL0DBkdvJiNiYObmuaRhnJ0FwqQggxBXsYCvR0c0ZJhULQdraIhgKJSdzI5JZVvKlUgduZGjuXKo5KyhBCzKiNwBxQ1iTQR1hJHqHbWZpthoPE6tUoVGbdztRoLhUhxBLOXrsPN5mzTaemcRXY6yZ0O0ujwIqYhNDua1N2e7Nzo+7klqO6tg7OEnDKpSVkvpiYWvi6oaDU9rMvE0L4KyitwY642zadmqZGYA6+WoVt5u6jwIqYhDzCHynp/If1Ik2QDM7YXFpC5ouJwUkCDOvREhMGtcGqb07TECQhDsyWU9MUCSzFVVhK6RYIURseHQapE78VHVInCYb1aClqO7jMjWIvWJ9suQiFUnsoUuh8MX3CgzwNnht/LxkmD22LN+f3R4CPGzbuuw4PG53ESQgRly2mpqmoEVYNReh2lkZXa2ISvl6uGNI9DHEXszlvMzQ6TPQ5BEJyac0d11njcbHnfRWV12Js/3AAEmTklmmVv3GXSbH5cBpW/5REvVSEEC22kpqGVS/wOiZ0O0ujwIqYzKwxnZBXVMUpsIkM98OsMZ147b+52nxi5dISe95XZU0d/jqdCamTRGsI0thCzYQQ+2ftqWkcHQVWxGRkLlIsm94Dvx1JQ7yeeUq6govmcJ0z5eclEyWXltD5YlyO1XTOBK0+JIRwkZpZbDOBlZOTBPUCsqg78ZxOYi0osCImJXORYu64zogZ1h4nL2YjVU/vEld86g96uAr7eDe9YA2PDsPu+DsmG5ZjhyBjhraz6OpDQojtsNbUNLr4ebkKWtns52WbBagpsCJm4espw8TBbY2+w+LTo1MlcIlv0wuWkPlifMVfyoGnmwvNqSKEcGJLGdn7dwnGX6czBWwXYoLWmB6tCiQ2w1z5pHRdsGaN6YTIcD+THVNVz+Dv6/dNtn9CiH0xRWoaUxkgMEAaGEWBFSEmZa58UrouWOx8MVcX031lyqv519IihDgeNsedrThzLU/QdqdThG1naRRYEZshdj4pXQzl0pK5SNEm1MfkbSCEEENCAjxsJtUCAJy5Jqw3/qzAgMzSKLAiNsMckzWby6UV1S7AZMeuE7BqhhDieO4XV6O00nZ6uEsrhGVQLxG4naXR5HVids3ln9JH6GRND1dnThPZueTSMuUKQWVdvej7JITYH1vLYyX0ptFWbzYpsCJmY2zNPqH5pB7s2xollQqjcmk1DgY93JxRXmWbpRYIIfbBlvJYORoKrIhZ8Mk/pa/IqJDeIqmTBA/0bg1fT5k6l9bt3HJU19bBWYJme8oMBYOEEGIptpTHytFQYEXMQoyafcbWH2RzaQUFeQMA8vPLDW5L5WUIIdbKlvJYSZ0AlYCZDlIbnQVuo80mtkRozT5dkzP55JMSUn+wMSovQwixVraUx4prubKmZAK3szTBPVY7d+7k9XqZTAYfHx906tQJISG2mfSLCCMk/5S+yZmmqj/YlLmSkRJCCF+G0sJYIyeJsJp/EoHbWZrgwGrlypWC/+iOHTviqaeewiOPPCL08MSGCM0/pW9yZnP1B3t2aoELaQX4YtslwXUJzZWMlBBC+GouLYy1qWeEXUuFbmdpggOrli0bouXi4mJUV1c37MzZGX5+fgCAkpIS1NU1LHF3d3eHv78/ysvLUV5ejrS0NLzyyiu4cuUKVq1aZeSfQKyd0EmWzW3XtP4gO9F89U9JBlcdvjCzt9bE+KbMkYyUEEL4MnaKgyUI7Xiy0Q4r4YHV0aNH8fvvv+Pdd9/FgAED8Mwzz6B3796QyRqiaIVCgfPnz+Prr7/G+fPn8cwzz+Cxxx5DRkYGvv76a8TGxmLTpk0YOXIkBg4cKNofRKyP0EmWfLbjs+qwsLwWS2f2xt7EdL25tGjFDSHEmogxxcFSvN1dUF3L/5rq7e5igtaYnuDA6tSpU3jrrbcwfvx4fPTRR1rDgjKZDAMHDsTAgQOxfPlyvPnmm2jXrh369u2LNWvWAABiY2OxdetWCqzsnND8U3wmZ/KZaH7lViEWvXcITXuZG/dqyZxpXQchxHI83V3QKsgTLk4S3lMZrE239oE4ej6L93bd2weaoDWmJ/jX46effgLDMHjllVeanWv18ssvQ6VS4fvvv1c/tmjRIgDAhQsXhDaB2Ijh0WGQOvHr0+UzObO0ohbxl7inYACgFVSx2F6tvOJqXvsjhBAxVVYr0a6lL5bP7IWJg9vabFAFCB+1cJXZZkYowYHVlStX4OPjw2mFX2hoKHx8fHDp0iX1Y+3bt4e7uzsKCwuFNoHYCDb/FB98JmfGXcqB2PPMi8pts0YVIcR+HEnKtKmagPpk5BrOGah/uzKRW2IeggOryspKVFVVQalsvrSHQqFAVVUVKisrNR53dnaGVGpbY8VEGFPln1IoVTj6910jWkYIIdapTtWQdsbWmWoBk7USHFi1atUKdXV12LNnT7Ov3bt3L+rq6tCqVSv1Y5WVlSgvL0dgoG2OoRJ+2PxTI3q21DssKHWSYETPlnhxRg9OkzMVShU+/j0ZpVS3jxBip1LtYIWyORYwWRPBA5jjxo3D+vXr8dZbb8HFxQUTJ07U+bq9e/firbfegkQiwfjx49WPX7t2DQDQrl07oU0gNqa5/FN8J2f+fPAG0rJKTdhiQgixLFvttWmsTai3oAVMbUJ9TNAa0xMcWC1atAgHDhzArVu38PLLL+Ozzz5Dv379EBwcDAC4f/8+kpKSkJWVBYZh0KFDByxcuFC9PZu5ffDgwcb9BcTmNM0/1ZzSSgXiLmZrpEZoE+qNRMqMTgixc7baa9OY8CmwDpYg1N3dHT///DNeeeUVxMfH4969e8jK0lxOyfz/0qshQ4bgf//7H9zd3dXPzZ8/H//6178QHh4utAnEzrEJPxMua2dBF3L3QwghtsaWagLqkyl48rqw7SzNqLWMAQEB+P7773Hu3Dns378fV69eRVFRkfq5rl27YuzYsejbt6/Wtu3btzfm0MQG6Opp4jrkxyXhJyGE2DNnqW3VBNTH0Savi5Ikok+fPujTp48YuyJ2oLmeJjYJp6EMwnwSfhJCiD0a3S/CpvNXsRxt8jqllyaiYnua4i5m6y1izCbh/GTLRSiU2nckQhJ+EkKIPenWIRBPxXS3dDNEIRc4nGmrw6AUWBFR8elpSr1bgt+OpGk9boqEn4QQYgvYtDOrFw1qtli8rejZQVhapZ6dWojcEvMweiiwoqICx48fR2pqKkpLSw0mDJVIJHjvvfeMPSSxUqUVtUjguVIv/lIOYoa11+juTrlN2fgJIY7F3VWK8QPaqOeg2ktQBQDJt4Rd05PTCtA6yEvk1pieUYHVjh078O6776Kqqkr9GKOjCJtEIgHDMBRY2bm4S9pzqpqjqm/ILNw49UJBaY3ILSOEEOvWPsyHcwoaW3NDYJLT1MximzwnggOrkydP4j//+Q8YhoGrqyt69uyJ4OBgODvbZtFEYjxjvzzsKsISO6iNRQghfNjqfCIuaFUgR99//z0YhkHPnj2xfv16BAQEiNkuYoOEfgmqauuwYd91nasICSHE3kmd7COtgj60KpCjlJQUSCQSvP/++xRUEQDCvwR5RdUGVxESQog9GxodZhdpFfShVYEcqVQqeHh4oG3btiI2h9gyoV+eqto6kVtCCCG2ITLcD7PGdLJ0M0xqeHQYpE4SXtvYci+e4KHA8PBw3LlzByqVClKpbXbXOTpjMqPrMjw6DLvj71DPEyGEcCB1kuDFGT30Jkq2F75erhjSPQxxF7nnJ7TlXjzBgdWkSZPw8ccfIy4uDiNHjhSzTcTExMiMrouvlyuC/NyRW1TV/IsJIcTByVyc7D6oYs0a0wl5RVWc8hzaei+e4KHAuXPnonv37li9ejXS09NFbBIxJTEyo+tTWlGL+8UUVBFCCBfe7i6WboLZyFykWDa9B0b0bKl3WJBNjmrrvXgSRlfiKQ527tyJyspKfPHFF6ipqcHYsWPRo0cPeHp6GtwuJiZGyOEsSqlUoaTEugOGoCBvAEB+vuFq4Bv2XefVHTuiZ0vMHde52dcplCq8/+t5pNtoNXJCCDG3hwdG4NEHOup8jus13RaVVipw8mI2UkWahqLrXPn5ecDFQklWBQdWnTt3hkTSEHWyyT+bPZhEgqtXrwo5nEXZS2BVWlGLl9Yn8poDJXWS4KMlQwx+2NleMCqaTAgh3D08sA0ycst0Bhf2HFiJzdoCK8FzrFq2tM3Z+o5MrMzoTfGpD2gpUidAVW/pVhBCSAMJgL9OZ2g81niO6wsze9tVWRtHIjiwOnr0qJjtIGZgirICQuoDWgIFVYQQU5I6SRAR4oU7Odx6mPTd4rJzXAtKa7B68WDxGkjMRvDkdWJ7TFFWQEgvGCGE2Juh0WFYMas3RvRsCZ4pm3RKuVOEb3ZcMn5HxOyosJ8DMUVZAaG9YIQQYi/Y9AAuzlLMHdcZMcPaa03ObhPqjX2nM/X2VOlyOCkTDw+IsNl8To6KAisHIo/wR0o6/0DIUFkBWy2SSQghxpI6SXTm/PP1lGHi4LYaUyi2Hb/JK6gCAIYBDiXdxaMPdBCnwcQsOAVW69atAwD4+/tj9uzZGo/x9dxzzwnajhhPSGb05soK2GqRTEII4SPYzw1Bfu6C0wP8fT1f0HH/vn6fAisbwzmwkkgkaNeunUZgxSXFQlMUWFmOKcoKCO0FI4QQWzI0uqXB1dHNKa9WmHU7YjmcAqt+/foB0EyxwD5GbIvYZQWoPiAhxN7ZckFgYn6cAquff/6Z02PE+rFlBX47koZ4PSv69M0b0EVILxghhNgSMQoCe7u7oLqW/5xURyp7Yy9o8roDkrnoX7kipKwAn14wvjxcnVFVWyf6fgkhhAuxCgL36RyMfaczeW/Xt3Ow0ccm5iW4pE1SUhJcXFzQs2dPTq+/dOkSamtrbXII0V5K2piSQqky2AsmxKCoEOQWVXFOuEcIIWLy95Lh/acHiVIQuLSiFi+uS+C1MlAiAT55biilW2iG3ZS0mTNnDoKCgnDy5ElOr1+6dClyc3NtslYgaV5zvWDZhZU4nZLHeX+DokKx6JGu+Pj3CyZsNSGE6Deyd2tRgiqgYdpEsL878oqrOW/TsoUnBVU2yKihQL6dXQI7x4iVK61UIO5iNm4YGFJUKFUoLqvlPGl+3vhIALTqkBBiGWJPWC+tqEV+CfegCgByCytRWqmg4MrGmG2OVXV1NZydaUqXPVEoVdh8OA0Jl7WH/xoXE501ppPgSfO06pAQYgliTFhvLO5SDvhexlT1wMmL2UaleSDmZ5ZI5/bt2yguLkZoaKg5DkfMQKFU4dM/LhrsgWKLieYWVmHZ9B6CJs37erliYFSoTRR6JoRYloerM2QuTiipMC73k1gT1hsTWv4rNbOYAisbwzmwOnz4MI4cOaLxWEVFBVatWmVwu7KyMvz999+QSCTo06ePsFYSq7P5cBrnVYCpd0vw25E0zB3XGYDucg+GiFDPlBDiAB7o1RIHzt4VvD2fVDN8CS3/RWXDbA/nwOr69euIjY2FRCJRz5WqqalBbGwsp+0DAgIo67qdKK2o5d2DFH8pBzHD2vPuWi+tqMWplFxe2xBCHNPf1/MFTRsI9nPD0OiWvFPN8CG0/BeVDbM9nAOrzp07Y8qUKep/x8bGws3NDePHj9e7jUQigZeXFzp16oSxY8fCx8fHuNYSqxAnIKWCqp4RNFdAyLEIIY6prErYEGCQn7vJh9uELsSJjPA3QWuIKXEOrMaMGYMxY8ao/x0bGwsvLy+sWbPGJA0j1succwWEHosQ4nisebhNyEIcZymV0rFFgievb9q0CS4ulGrfEZnz4lVRrRR0LEII4cocw21Cyn+N7hdBqRZskODAqn///mK2g9gQc84VqKZyNoQQEzPXcBuf8l/dOgTiqZjuKLXyqh9Em5OlG0Bsj1zgRaiypg7KOr69VrQmkBBiOmInAjWEzec3omdLSJ10X9ukThKM6NkSqxcNgsxCJVmIcYzOY5Wfn4/t27fj3LlzyM3NRXV1td4M6xKJBIcPHzb2kMTChCbtTM8txydbLqpzWnHh7kpJZQkhpiN2ItDmcM3nR0GV7TLqV+vQoUNYsWJFs8EU+5xEQr0Ptqxx6RoPN2eUV/Gf/9Q0p1VzvNwpsCKEmIYpEoFyxTefH7Edgn+1bt68ieXLl0OhUOCBBx7AiBEjsHr1anh7e2PFihUoKChAYmIizp49C39/fzz33HPw8PAQs+3ETAyVrhHiRHI2cgqr0K1dQLN5Y6hWICFEbKZMBEqIhBFYGfm1117Dtm3bMGnSJHzwwQcAGnJdtWjRAvHx8erXnThxAkuXLkXHjh2xefNmwSsJ9+3bh8TERKSkpOD+/fsoKSmBi4sL2rZtixEjRmDu3Lnw9zfNBESlUoUSK59AGBTkDQDIzy8Xdb9cStcYo7kL3NU7RfhoS7JJjk0IcRwdWvoYLJ9lbUx1TbdHus6Vn58HXCw0nCp48vrZs2chkUiwePFig68bMWIEVqxYgcuXL2Pjxo1CD4evv/4af/zxB9LS0iCTyRAZGQk/Pz9cvXoVX331FSZMmIDr168L3j/RjU/pGiHYeoKfbLkIhVJ7YvtfZzJMdmxCiOP4zxN9sXxmL0wc3Nbqgypi2wQHVnl5eZBKpejQoYP6MYlEAqVSe97N5MmTIZVK8ddffwk9HGbPno1ffvkF58+fx9GjR7F9+3YcO3YMu3fvhlwuR2FhIZYvXy54/0SbkNI1QrFzr5rKyKW7NUKIcfQswCPEJAQHVi4uLvDy8tJ4zMPDA+Xl5air08w95O7uDk9PT2RmZgo9HKZPn45+/fppDSVGRkbi3XffBdAw7+vWrVuCj0E0mbuczInkbPzv13PYk5iO0sqG0hRKVb3Zjk8IsU9+Xq6WbgJxIIInrwcHByMzMxP19fVwcmqIz1q1aoW0tDRcv34d3bp1U7+2tLQUZWVlcHU1zYe7ffv26v+urq42yTFsQXF5Df5MTMcNPct3DWm84o/dNr+kxkwt/0fq3VKk3i3Frvg7CA/2gkJJgRUhxDgDo0It3QTiQAQHVm3btsWdO3dw+/ZtdOzYEQDQu3dv3LhxAz/++CM++eQT9Ws/++wzAEC7du2Ma60e586dA9DQY2aqY1gzhVKFdVuTcSQpE3UqzR6mlPRi7Iq/o3eCuNgr/sSiqmeQTsOAhBARPNgv3NJNIA5EcGA1aNAgHD16FCdPnlQHVjNnzsQff/yBffv24caNG4iMjMSNGzdw8+ZNSCQSTJs2TbSG19fXIz8/HwkJCfjoo48AAC+99BI8PT1FOwbLxUWqXnVgbWqVKrz53SlcuVWo9zXsBPHC8lq8uWgQXP9/pQSXbQkhxJY92D8CHdsGWroZglnrb481spZzJTiwGj9+PK5evYra2lr1Y507d8arr76KNWvW4ObNm7h586b6uQkTJmDOnDnGtRbAhg0bsGbNGo3HoqOj8f7772P48OFG79/WfLfzMufA6MqtQny38zKee6wn720JIcTcJBIgwMcNhaXCpiVEtQvA01OjRW4VIYYJzmNlyK1bt3DgwAHk5ubCy8sLw4YNw6BBg0TZ9759+7Bp0yaoVCpkZ2ejoKAAzs7OGDt2LP773//Cx8dHlOM0Zq15rEoravHS+kReQ3hSJwk+WjIEYBje2xJCiDm5yZwwfmBbxMbd5r1t21BvrPpXb5tNAEp5rLiztjxWJqkX0qFDBzz77LOm2DXGjx+P8ePHq/99/fp1vP3229izZw9u3bqF7du3Qyq1zS8SX0JW7anqGZy8mA3m//+bEEKsVZ2KwcGzwlaTe7o522xQRWyb2QqxKZVKbNmyBf/6179E3W/nzp3xzTffYMyYMbh27Rr27t2LSZMmiXoMa3UjU1ipl1SB2xFCiDnVqRjUqeqaf6EONQrthMO2gF2hfSe3HNW1dXCWwGayxZMGJg+sVCoVtm3bhq+//hp5eXmiB1YA4OXlhf79++PAgQNISUlxmMBK6IXDVi84hBDClZvMtnqrDK3Qbm51N7EuggKr6upqZGRkQKVSoXXr1vD19dV6DcMwiI2Nxfr165GVlQWGYSCRmC79LZuUVKVynKBB6IXD1i44hBDCV2SEaWrHmgKXmqzs6u7cwiosm94DMgvNHyLN4xVYlZeX45133sG+ffvUpWskEglGjRqFN954A8HBwQCAM2fO4J133sHNmzfVAdXo0aPx9NNPi/8XACgpKcHZs2cBAF26dDHJMayRPMIfKen8h/Uqa+oQ3TFQ0LaEEMJVkJ878kvMn7RZ6iTBsB4tzX5cofjUZGXLf80d19m0jSKCcS5pU1dXhyeffBK7d++GQqEAwzBgGAb19fU4cuQI5s+fD4VCgR9//BFPPvkk0tLS4OTkhIkTJ2L37t348ssv0b17d0GNPHv2LNavX4979+5pPZeSkoIFCxagvLwcISEhGDdunKBj2KLh0WGQCiiClZ5bjpTbRVQ/ixBiMh1a+qCw1DKVMIZGh9nMfCQhNVnjL+Woy34R68O5xyo2NhZXrlwBAAwcOBDDhg0DwzCIj4/H6dOncevWLfz3v/9FbGwsJBIJYmJisGTJEoSHG5/xtqysDJ9//jk+//xzBAUFITg4GFKpFDk5OcjPzwcAhISE4JtvvjFJglBr5evliiHdwxB3MZv3treyyxAa4IHcIutLI0EIsV0SAMN7toSvlwy3ssvMfvzIcD/MGtPJ7McVypjV3RMHtzVNo4hROOexWrBgARITE/HYY4/hrbfe0nju9ddfx9atWyGRSODj44O1a9eif//+ojWysLAQf/75J86cOYObN2+isLAQCoUCPj4+6NixI0aNGoVHH31Uqyi0WKw1jxUAVFQpsOKbU6iu5T+3TOokgYuzBDUKqsdHCDFeC183PBPTDVfuFOHg2UxU1ghb0SfUoKgQzBvf2aYmd3/8+wVB0zKi2vpj+cxeJmiR7bHZPFY3btwAAJ35qZ599lls3boVALB8+XJRgyoACAwMxLx58zBv3jxR92sPtp24LSioAhruelQKymVFCDGen5cMnSP88d7P5yyWIy8s0NOmgiqAVnfbI85zrEpKSuDm5obQUO0q4WFhYXB3dwcAjBo1SrzWEYOEjM0TQogpOEudEG/hYu62mKOPVnfbH86BlVKpNDh/iX2uRYsWxreKcCJkbJ4QQsQmAVAgsJ6fmGyxF0cuMC2ELaWTcDRmy7xONLHZdW9kFqNGoYKbTMo7u67QzOuEECIqCQAR7vGM3Y0t9uIMjw7D7vg7vGu+2lI6CUdDgZWZiZld1xbvzggh9sVFKoFSJU7P+ZDoMOQXV3PO6dSULfbiCFndbUvpJBwRr8CqsLCw2QSchp6XSCS4evUqn0PaFbGz69ri3RkhxL6IFVRFhvthzkNyMAywcf91nErJ47W9LffizBrTCXlFVZwCSltLJ+GIOM+xAqBOCmrM/xyZkOy6hggdmyeEEGshdZJgRM+WeHFGD7g4SyFzkWLRI1EYGBXCaz+23Isjc5Fi2fQeGNGzpd6kz03PE7FenHusnnvuOVO2w+4Jza4bM6y93ouFkLF5VoeWPriTUwaa+04IsQRPN2eM7R+hd17pvHGdUVxW6zC9ODIXKeaO64yYYe1x8mI2bueWo7q2Ds4S8J5/SyyLc4JQRyZGgtA/E9MRG3eb93ZTh7fXmV2Xnat18mI2r8mebFbkWWM64ddDaYKythNCiLH0XdsaUyhV+O1IGuL1rICWOkk4z0m1NbqSXhLdbDZBKDGO0BV8qZnFWhcfLnO1dGnh64ZXZvVGC183APzG9QkhRCzOUm7zoZr24qQasYqaEHOhwMpMxMyuy2euFgBIJMDwHi217urYcX1Dd4SEECK20f0ieAVEvp4yTBzclmrjEZtAgZWZiJVdV8hcLQmAmGHtdXaV67ojrKhWorq2DkXltagTacUPIYQAQLcOgZg+Wo4/T94yKo8fIdaKAiszkUf4Cyq02TQvi5Bs6/UMmq2EruuOUGhxUEIIaUrqJMGgbqFwc3PB4vcPa9208c3jR4i14pVugQg3PDpM7zJafXTlZTFmrhZflM6BEGKsYD83TB3eHq/O6YPrGcU4fDZTb084m8fvky0XoVBSAmRimyiwMhM2uy4fuvKymLMSupBgkBBCWFInCV6a2QsFpTV4Z+PfnOsJcsnjR4i1osDKjGaN6YTIcD9Or9WXl8WcldCFBIOEEMIa3C0UP+y9hjieaWGAhjx+pZUKk7SLEFOiOVZmxGUVnr68LGzR5vwSYRXkm6uhpa8o9PCeYbiaXmQVlesJIbYjMtwPDAPB6VxU9Uyzc0MJsUYUWJkZ37wshoo2c2WohlZzRaF3xAk6JCHEgbUN9caCCV2w6tvTRu1HVx4/QqwdBVYWwiUvi9BEoE3pq6El1v4JIYTlJAH+/VgPxF3MNjo3ntA5pYRYkiiB1fXr1xEfH4/s7GzU1NTgvffeUz+nVCpRVFQEiUSC4OBgMQ7nMPgmAtXFUA0tMfZPCCGNsb3uQlcwNyZ0TikhlmRUYFVeXo5XX30Vhw8fBgAwDAOJRKIRWNXV1WHy5MkoKyvDrl270KmTbRfKNBchiUAba66GlrH7J4SQphrfyInR29Tc3FBCrJHgVYFKpRKLFi3C4cOH4ebmhhEjRsDV1VXrde7u7pg6dSrq6+tx4MABoxrrSIQkAgX+yRnz0ZIhmDuus94ke0L3TwghTUmdJBjRsyVenNFDfc0xtrfJ0NxQQqyZ4B6rbdu2ITk5GREREfj5558REhKCoUOHorCwUOu1Y8eOxY8//oikpCSjGutIhHajV9bUmXT/hBACAN4eLogI9tJbikZotQmWvrmhhFg7wYHV3r17IZFIsGrVKoSEhBh8bZcuXeDk5ITbt28LPZzDEdqNXllThx1xt5stDVFRrTS2iYQQBxUZ7qfRO6XL8Ogw7I6/I6hn3NDcUEKsneDA6saNG5BIJBgyZEizr5XJZPD29kZJSYnQwzkcY7vR2dIQuYVVWDa9B2QumvujwIoQwldzczcbYxMMx13M5rx/iQQY3qOl3jx+VLSZ2ALBgVV1dTU8PT0hk3H7UCuVSjg7U3YHroztRmexpSHmjuus8TgtYyaEcBXs546h0WG8A5lZYzohr6iK0+rjFr5uWDGrFwJ93dWPNZdnj4o2E2skePK6v78/KioqUFlZ2exr7969i6qqKkq3wIOYdfp0lYagieuEEK6C/NwwcXBb3r1DbLWJET1b6r2esRPf3100QCuo+vSPiwbzYVHRZmKNBAdWPXr0AACcOHGi2df+8ssvAIA+ffoIPZzDEbNOH1saojEJ1VYmhHBkTA83W23ioyVDMHV4e/SUByGyjT+i2vobXMHMJ88eFW0m1kTw2Ny0adNw6NAhfP755+jTp4/eCey///47Nm3aBIlEghkzZghuqCPi043enKalIbzdXVBdS3d4hJDmiZGok602ERTkDQDIzy/X+1ohefbiL+UgZlh7mnNFLE5wYPXAAw/goYcewsGDBzFt2jQ88sgjqKlpKNS7ZcsWZGdn49ixY0hLSwPDMJg+fbq6l4tww6VoM1dN7zj7dA7GvtOZxjaREOIAzJ2oU0iePSraTKyFUbPJP/zwQ7i6uuLPP//Ehg0b1I+/+eabABoysQMNvVtvvPGGMYdyWE2LNh84m8k5V1VjTe84H+objv2nM8E3VOvU2hfKunqk5+q/2ySE2A9LJOoUmmePijYTa2BUYOXq6ooPP/wQM2bMwLZt23DhwgXcv38fKpUKLVq0QO/evTFjxgz069dPrPY6LLYbnQEQG8c/H1jTO05fL1cE+7sjr7ia8z5C/N3hJJFQUEWIA7FEok6hc7potTOxBqLkP+jbty/69u0rxq5IM4Qk3dN1x1laUYv8Eu5BFQDkFVfzCsQIIbbNUok6hc7poqLNxBpQYikb0ThBnoebM8qruCf41HXHGXcpB5RxgRCiiwTA8J7aiTrNRWgePyraTKwBBVZWzlCCPC703XFSrUBCiC4tfN3wyqzeaOHrZrE2iNUzT4glcAqs2OLJbm5u6N69u8ZjfNF8K+7YBHlC0i00V3qC5iIQ4lgiQrzQJsQbiVdydQYsfMrVmJqQcjhUtJlYC06B1Zw5cyCRSNC+fXvs3btX4zE+JBIJrl69yr+VDopPgjwA8PWSoXULT041tGguAiGOw0kCLJveE76eMkwd0QEnL2Yj1UDdPWuozccnjx8VbSbWhPNQIMMwqK+v13qMD76vd2RCEuRVViux8JEoThc+sWoREkKs34CuoerrArvCWFdaAmuqzcclj5819bIRwpIwFO00S6lUoaSkyqzH/DMxXVBahanD23PK41JaUYuX1idSzUBCHICTBBjWw/BkdD5TDyLD/bBseg/IXPgHM1wyrzdVWqlotpfN3gg5T45K17ny8/OAi4DPpxho8rqVMnWCPCFzGAghtqmeAU4kZyO3sEpvQCSkNt/ccZ1FbqluhnrZCLE2goswE9MyR4K8CYPbWHTlDyHEvPQVKxZam6+0UiFW0wixG4IDqyeffBK7du1CdTUljDQFUybIUyhV2LDvOl795jQKSmsEHYcQYpt0BUTG1OYjhGgSHFidOnUKK1euxJAhQ7By5UokJiaK2S6HFxHqLWi7Ns1sx86jiLuYTfOrCHFAugIiY6YeEEI0CQ6sJk2aBDc3N1RVVWHXrl1YsGABRowYgY8//hhpadpdzYQffoksuG/JN4WDIQHervB0o2l6hNiapgER1eYjRDyCA6sPPvgAiYmJ+OCDDzB48GA4OTkhLy8P33//PSZNmoSpU6di06ZNKCoqErO9DiNDYKHjjNwyvc+VVtQi/pJ4XfdhgR545fFeou2PEKLNxdkJLs5OkLk4wdPNWZSbmaYBEdXmI0Q8Rn1D3d3dMWnSJEyaNAn5+fn4888/sXv3bly/fh1Xr17FtWvX8MEHH2Do0KGYNGkSxowZA5nMPpfGis0Ud5Bi1weMjPBHeIg33F2lqK6lO1fiuCQSAAxgisF1Zd0/+QMVynoDr+TOWarZs90m1FtQXrs2oT6itIcQeyLaOE5QUBDmz5+P+fPn48aNG9i5cyf27t2LvLw8HD9+HCdOnIC3tzfOnj0r1iHtminuIFNuFwptjha2LldpRS0FVcThMUxDrqg2Id4or1SgsLzW0k0ySCrVHKwQHhDSPE1CmjJJugW5XI5XXnkFx48fx08//YRu3bqBYRiUl1OiM67kAqu0G6ruLuYKQLYuV9wlfku0CbFX9QyQnluOYhtIQVCn0gyIMgVPPaBrOiFNmWzm8f3797Fnzx7s2rULN27cMNVh7FbPDoGCMq9fvJkPADozEtcqxelZalyXS8xeMELsQb0NrLZVqTSHFGnyOiHiETWwqqmpwcGDB7Fr1y6cPn0a9fX16vqAXbt2RUxMjJiHs2vJt4QFLLeyy3Eru1xnTS+ZixSVNXWC26SrLhflwSLE9jSdMkCT1wkRjyiBVWJiInbt2oVDhw6hurpaHUyFhobikUceweTJk9GxY0cxDuUwhOaVYanqGa0SFkF+7igWMPfDw9UZ4wZEaPSClVYqEHcxGyU2MOxBCNHUdMqA0KLshqYeEOKoBAdWN27cwK5du7Bnzx7cv38fAMAwDDw8PPDQQw9h8uTJGDhwICQS4RmZHJlYXeyNa3pFtQvADQE5rMYNiFDX6FIoVdh8OA0Jl/lnaiaEWB678KSx4dFh2B1/h9d3Wtd+CCFGBFaTJk2CRCIBwzBwcnLCwIEDMXnyZDz00ENwd3cXs40OScwu9vhLOYgZ1t7oiyebtV2sBKOEEPNjF540JqQou679EEKMHArs2LEjJk+ejEceeQQhISFitYmgoaSNkK55XVT1DNb8/DeGRrdEvy7BOJ2Sx3nbxhdPMbO2E0LMr/HCk6ZmjemEvKIqTt9xQ/shxNEJDqxiY2PRpUsXMdtCGhF7APV+SQ12xN2GkwTw9ZRxqkrf+OJZWlGLhMuUWoEQW6Rr4UlTMhcplk3vgd+OpCFeT1FmLvshxNEJDqwoqDItU+WHqWcaJp77espQUa3UOyzo7+2Kzm38UFWrgq+zFHF6LrSEEOsVGe6LqHaBOtOv6CJzkWLuuM6IGdYeJy9mIzWzGDUKFdxkUkRG+HPeDyGOTLR0C2lpabhy5QoKCxvSBAQGBqJ79+60GlAgU+eHKa1UwNPdGR4yKcqr67SOV1xei13x6diTmIGh0WG4X1xt0vYQQsQX1S5QvfCED19PGSYObitoW0IcndGB1cmTJ/Hhhx8iLS1N5/NyuRwvv/wyhg4dauyhHIo58sNUVtehstpwXis2bQPlqyHE9qRmFlNwRIiZGVXS5pdffsHixYuRlpamXh0YGBiIwMBASKVSMAyD1NRULFq0CL/++qtYbXYIQkvamAplWCbE9tD3lhDzExxYXb9+He+99x7q6+sRHR2Nb7/9FhcuXEB8fDzi4+Nx/vx5fPvtt+jZsycYhsF7772H69evi9l2uzY8OgxSJ8oBRggRjnqaCTE/wYHVTz/9hPr6eowcORKbN2/G8OHDIZP9M6lRJpNh+PDh+PXXXzFy5EioVCps3LhRlEY7AjavDCGECFVZWwdlHfVaEWJOggOrpKQkSCQS/Oc//4FUqv+uSCqV4tVXXwUAnDlzRujhHNKsMZ0QGe5n6WYQQmxUek45PtlyEQqRCrATQponOLAqKCiAt7c3Wrdu3exrw8PD4ePjg4KCAqGHc0hsXpkRPVvSsCAhRBC2rBUhxDwErwp0c3NDdXU16urq4OxseDd1dXWorq6mUjeNsEWMbzSTJ0ZXXpmq2jrkFVWjqtbwij5CCAH+KWtFOagIMT3BgVX79u1x8eJFHDhwABMmTDD42v3790OpVCIqKkro4eyGoSLGKenF2BV/R2dm46Z5ZRRKlcEMyYQQwlLVMzh5MZtSLxBiBoKHAseNGweGYbB69WqcOnVK7+sSExOxevVqSCQSjB8/Xujh7AJbxDjuYrbeYIjNG9XcvAi2J+ujJUMwdXh79JQHQR7hBw9X0XK+EkLsSGqmOLVHCSGGSRiGEdTdoVAoMG3aNKSlpUEikaBnz54YPHiwuhhzbm4uTp06heTkZDAMg06dOmH79u0aKwdthVKpQklJldH72bDvOq/q8SN6tsTccZ05vTYoyBsAkJVdQj1ZhBAtHVr64D9P9LV0M9TXqvx805Ttshd0nrjTda78/Dzg4mKZdCOCuzdkMhm+//57PP/887h06RIuXLiA5ORkjdewMVuPHj3wxRdf2GRQJRYhRYyFzIvQNScr834FyquUfJtMCLEjlNOKEPMwatwoJCQEv//+O/bv3499+/Zp1Qrs1q0bHn74YYwdOxZOTkYlebd5QooYGzMvovGcLHYIMvVuCe/9SCSAsD5NQogptPB1Q0FpDe/tIq2smgMh9sroCTlOTk54+OGH8fDDD4vRHrt1Q+D8BjFqfbFpG/gMEfp7u2J4jzBk5Vfg71RKk0GIpUmdJBgaHYYJA9tg1bened2oSZ0kGNajpQlbRwhh0UxnMxFas0usWl/sEOGEgW3wwW8Xmr3jLS6vxZXbRbidUybK8QkhwgT7uWFodEuNVCxDuofxmq85NDqMUi0QYiYUWJmJ0PkNYs+L2HMqg/Mwwq1sCqoIsSSpkwSr5vTVCopmjemEvKIqTsP7keF+mDWmk4laSAhpinNglZ3N/e7IkJYt+XdHMwyDCxcu4OjRozh37hxu376NiooKeHt7o2vXroiJicEjjzwCicR6s5PLI/yRks5/OJCdF8E1oaghQibQE0IsR19PE5fhfXbosGlOPEKIaXFOt9C5c2ejAxeJRIKrV6/y3u7UqVOYN2+e+t9siZysrCyUlJQAAB544AGsXbvWJCsPxUi3UFpRi5fWJ/KeF/He4oHYm5ihM6Eo+5qh0WF4YWZvyFykBpfm/pmYjti424LaLxYJAJoLT0jzIsP98OKMHs0GRaWVCvUKYKE3XeZEaQS4ofPEnU2nWxCY8spoDMOgdevWmDt3LiZMmIDAwED1czt37sTrr7+O48eP4/PPP8fLL79skTY2x9fLlfe8iEHdQvHjnmsGu/vZhKKF5bV4c9Egg/sTOoFeTBRUEUcmdZJgTJ/WOHstD8UVCr2v4dPT1LQqAyHEsngFVhKJBK1atcKUKVPQr18/U7VJS3R0NPbv3w8XFxet52JiYpCbm4tPP/0U27Ztw/Lly602tQPfeREAOKdIuHKrEN/tvIwZD3TQ+xqxJsIT4oiC/dxRWaNEZY3wGp2qegae7i74+LmhNtfTRAjhhvNQ4FNPPYWEhASoVCpIJBKEh4dj6tSpmDJlijrbuqVcvXoVU6ZMAQAkJCSgRYsWou5frMzrQPM1/tRLqge1wapv+C2pdpZK8OGzQ/RelD/+/YKgeV6EEGDq8PaoUdThr9OZRu0nqq0/ls/sJVKrbA8NcXFD54k7mx0K/Pbbb3H//n3s3LkTO3bsQHp6Oj7//HOsXbsWgwYNwrRp0zB69GiLZFevqflnlZubm5vZj8+Hrszouu5W/0xM551QtE5lOKGo0An0hDg6Ng/UwSTjgiqAeo4JsXe8hgKDg4Px1FNP4amnnsK5c+ewfft27N+/H/Hx8UhISICPjw8eeeQRTJkyBVFRUaZqs5a9e/cCaJhg7+XlJfr+XVyk6ojYGMXlNTh4JgNXbhWiurYO7q7O6N0lFA8OiIC/t2ZAeCdX2F3K7dxyvW2dMrIT/ky4gzoVzXQihI+wFp7o2DYQGw+kGr0vby9XUa4nto7OATd0nrizlnMlOI9Vnz590KdPH7z++uv466+/sGPHDpw7dw6//PILfv31V3Tq1AmPPvooHnnkEfj7m66UwpUrV/D7778DaBiutEa1ShW+23kZR5IytYKa5Bv5+O3gdYzuF4GnYrpD9v9dl9W1wuZxGNrO38cNo/tF4MDpDM77G9M/HNn5lbh6p0hQe/TxdHdBZTXVLyS2IaegAsXlNYK/l4117yDuVAVCiHUxOkGou7s7pk2bhmnTpiEzMxPbt2/Hrl27cOPGDaxZswa5ubl45ZVXxGirloKCAjz//POoq6vDgw8+iAkTJpjkOMbMseJSp69OxeDA6QykZ5Vi2fQekLlI4Swws4WzxPCY/JQhbZGeVcp5Av1jw9uDYYDfjqTh5MVs8Byd1GtM71aIu5itd2UUIdZEVQ/sPJom+HvJkjpJ0LtjoEPPm6G5Q9zQeeLO2uZYibp8LiIiAtOmTcPEiRN1ruATU3l5ORYtWoTs7GxERUXh/fffN+nxhNp8OI3zyr7UuyXYuP86/kxMR35JtaDjNVdolU0sOKJnS0iddP9KSJ0kGNGzpTqHDjsv7OPnhmLq8PaIauuPDi19ENXWHw8PjNC7H32kThI80Ls1Anysez4cIY0dO38PHVr7GrUPKi1DiP0TpaRNdXU19u3bh+3bt+P8+fMAGnJPyeVyDBpkOLeSEJWVlVi4cCGuXr2KTp064YcffjDJ3CpjCcl0fiolT/DxnKXcCq1ynUDflL58ORXVdYLqlkml1pkWgxBdiisUSLldBCcJBPXcUmkZQhyDUYHV33//je3bt+PAgQOorq4GwzDw9fXFhAkTMHXqVHTr1k2sdqpVV1dj8eLFSE5ORtu2bfHTTz+ZdA6XMeL0pFQwldH9InjdDYuVWFBo3TJnqfWWICJEl1vZZQgN8EBuEfepARIJMLxHSyotQ4iD4B1Y5eXlITY2FrGxscjMzATDMHBycsLgwYMxbdo0jBkzxmQpF2pra/HMM88gKSkJrVq1woYNGxAUFGSSY4nBnJnOu3UIxFMx3VEqUr4tPoTWLaurqzd3UwkxWn5JNTq09OFUpLyFrxtWzOqFQF93M7SMEGINOAdW7Mq/U6dOob6+HgzDICIiAlOmTMGUKVMQGhpqynZCqVTi+eefx6lTpxASEoKNGzciLCzMpMc0ljny1TStFWgpQoYXa5UUWBHbo6pn0K19AFoHe1EBZEKIFs6B1YsvvgiJRAI3NzeMGzcO06ZNQ9++fU3ZNjWVSoXly5fjxIkTCAoKwsaNGxEeHm6WYxvDTWa6C2qwnzuGRoepAxZLBlWN8RleFGPpOiGWcPNeKZbP7KW+kbidW47q2jo4S2CWsjSllQrEXczGDSqHQ4jV4T0U6O7ujrNnz+Ls2bO8DyaRSHD48GHe2+3btw8HDhwAAMhkMrz66qt6X/v666+ja9euvI9hCqbMdB7k52YHRVcpUSmxTWxvNHsjYa6l8QqlCpsPpyHhsnZPWUp6MXbF36GeMkIsjFdgxTAMioqEJ4qUSIRNVlYo/sl1lJWVhaysLL2vLS+3npwfw6PDsDv+jkkmsNtDWQx3V1EWpRJidqbsjdaHS048VT2DE8nZyC2sUufEI4SYF+dftueee86U7TBo6tSpmDp1qsWOL5SvlyuGdA/jlYqAK0tc2MXm5W7aXGeEmEpz+eJMgW9OvN+OpGHuuM6mbRQhRItNBFa2jE8qAj4scWE3hq45ITQQSGwRW5DZnITkxIu/lIOYYe1pzhUhZkZjMSbGJRUBX5a4sAtlaE4IIbYo2N/d7MGKkJx4qnoGJy9m28FcTEJsCwVWZtBcKoLswkqc5pFx3drKYuhboTSwawh+2HtN9N46QoQK9HVDTW0dKmuEr0jNK6pCaaXCrN9BoTnxUjOLKbAixMwosDIjfakIFEoVistqeWcut7TmVijFnrwNhjqpiBUZ0CUYKXeKUVkjfJFLPQOz9wQJXaxiD4tcCLE1VKzNCggpjGxp7AqluIvZeocoKKgi1uZEcrYo+dNSzVhVARC+WMUeFrkQYmuox8pKCC2MbCl8VigRYi0qa4wbBmSZuydIaE48W1vkQog9oMDKyohVGNmUhKxQIsSemLsnSEhOPFta5EKIPaGhQMKbkBVKhNgTc/cEsTnx+LC2RS6EOArqsbIQW671JXSFEiH2wFI9QXxy4lnTIhdCHA0FVmZmD7W+aKURcWSW6gnikhNP6iSx+usHIfaOAiszsvVaX2wvW25RlaWbQgicJICLsxS1SvMF+pbuCbK1RS6EOCIKrMzIVmt9UfZ0Yi2cpU7o1MoH1QoV7t6vMGtQ5e/tajXpTmxhkQshjooCKzOx9lpflD2d2AKVqh4KVT3Sc4Un+BRqZK9WVhFUEUKsGwVWZmKttb4oezqxJQyAW1llZj8upS4ghHBF6RbMxJhaX6ZiyezpkeG+mDq8PV5/og9a+Lpx2sbDVdh9gNDtCGFR6gJCCFcUWJmJNdb6smT29Kh2gZg4uC3atfTFOwsHcCrn82C/1oKONap3S4QGeBjTXOLALD1hnRBiW+hW3kysrdaXpbOnp2YWq4c4ua50Kq2oxe6EdN69aEfOZaGaUkQQAdqGelvNhHVCiG2gwMpMrK3Wl6Wzp+vqieO00klAkymoIkJ5ujlTUEUI4YUCKzOxtlpfls6eXl6lwMe/X+CVgyfuUo6QuIoQwSgZLiGELwqszISt9RV3MZvzNqacMGvpH4z7JTW4X1Kj/jeXrPOWDgaJ4zF3seWmbLn0FSGOigIrM7KmWl+W/sHQpbms85YOBol4PFydUVVbZ+lmNMvcxZZZ9lD6ihBHRasCzYit9cVlBZypJ8zKLfSDwQWbdb4pawwGiTAP9m0NPy/r7nGxVO4qLmlQ2JuQT7ZchMKM2ecJIc2jHiszs5ZaX0LmfJmTrqzzQhcAEOviJAEUqnqzlqMRwlK5q2y19BUhpAEFVhZi6VpfQuZ8SaB7UZ7USYJB3UIhAZB4JVeUYE1X1nlrDwYJN/UMsO90pqWbYZClcldZe+krQkjzaCjQgc0a0wmR4X6cXmvoos0wDKROEvzrITk+WjIEU4e3R1Rbf3Ro6YOotv4I9nMX1L6mWefZYJAQUzHXULw+xpS+IoRYB+qxcmDsnK/fjqQhXs8FXeokgZe7C0orFXr3U89AY9J50564dzf9jfsl1bzbp2uyOp8FAIRw5eEqxbgBbSy+2s6Y0leW6v0mhGiiwMrBNTfnK7ugEqev5nHaV+rdEmzcfx2hgZ4ay8PLq5SC2qZrsrrMRYolU7rhvV/OI7eoStB+CWkqLNDTKgITayx9RQjhhwIrAkD3nK/Silq8tD6R135OpXALwrjQtdRdoVThy9grFFQRUVnLilNrK31FCOGPAisLMUXiv+LyGvyZmC7aPi1Z9kbfUndLFo4m9stS+aqasrbSV4QQ/iiwMjNTJP5TKFVYtzUZR5IyUacSL5mgJTOde7o54/s/U7SKMFuycDSxT5bKV6WLtZW+IoTwR4GVGbGJ/wz1uDSXfdwc+2RZct5GWZUSKenFGoGhn5eMUi0Q0VkqX5Uu1lb6ihDCH6VbMCMhif8ssU+WtczbYAPDQ0n3LN0UYmcsla/KED5pUKyx/YQ4OuqxMhNTJP4zdTJBU2Y6D/ZzQ5CfOzLvV3BeNWgLteWIdTGU1NYStfa4zK3kmgaFagUSYp0osDITYxL/6VsGbop9NmbKTOfeHjIsnNiV96pDQriKDPfDgoldcTol12Klo1h851ZaS+krQgh/FFiZiSkS/5k6maCQ+R5cucmkFl11SOxX094cS5aOAoybB2np0leEEP4osDITUyT+M0cyQVNlOo+M8NcqWUMIH4OiQhAW6Gn1vTlUVJkQx0KBlZmYIvGfOZIJcpnvwRe7PPzizQKj98VX17b+cHJywpXbhWY/NhFPZLgf5o3vrO6RslZUVJkQx0OrAs1ELjCBn6HEf6bYpy7sfA9dBZanDm+PgVEhvPbXv0sI4i5mC86e7uEq7H5A6iTBokei8MqcvpBIBO2CWIFBUSEWK5LMFxVVJsTxUI+VmZgi8Z+5kwnqm++hUKpQXFbLabjD11OGM1dzYUzH14N9W6OkUoG45GydK770YfP9HDybAYamdtms0koFvth2yaqH/1hUVJkQx0OBlZmYIvGfsfsUq6wO1+XhXu4uKK1UcN6vPpFt/NE5wh8PD2qDDzdfQEFpTfPbNMr3c+UWDQPasqtNUoAYU13A1KioMiGOhwIrM+IzEZxr4j8h+zRFWZ3mlodnF1Ti9FVxCjTvSUxH5wh/BPm6452FA3jn+6mmfFh2p/GquvkTu+B0Sp6odTiFoqLKhDgeCcPQoEhzlEoVSkqEzQdqSqFUiZ74T6FUITYhXWetwKb7ZBg0u/SbFRnux6sEjj6lFbV4aX2iaKkVPN2csXbpcM1jVCo45/v5YsdlJN/IF6UtRJhgPzcAEtwvqRZ935ZIChoU5A0AyM8v13j8z8R0xMbd5r2/qcPb2+1QoL5zRTTReeJO17ny8/OAi5G/XUJRj5WZmSLxn8xFiuce64nZ4zpj59E0g/vcsO+62Zd+i52vSlfwyCffT7cOgRRYWZDUSYI+nYNx7vp9k+xf3ydNaM1MY1BRZUIcDwVWFmKKxH/+3m4G92mppd9CJ/Dq4yw1bknfQ/3b4PeDqToDNGJ69fUM9p3OtNjxzZkriooqE+J4KN2CA7HU0m+xJ+K2CfU2ant/HzeM7hchUmsIX9YQzsZfyhFlIQUXVFSZEMdCgZUDMWbptzHEnog7c7TxPzyLYrpz/rEjxrO2vGHmzBXFrpod0bMlpE66T4TUSYIRPVvaTH4uQoh+NBToQCy19Fse4Y+UdHGGA8MCPdA6yMvo/bj+/4/d+7+eR3qudU4OlTpJMKhbKC7eLEB5ldLSzeHFw9UZIQHu8HB1RptQb+w/k2kVPVWNmTNXFBVVJsRxUGDlQCy19FvIBF5d3F2l+M+cPkbtozGZixSebtbzFQj2c4O3h0zrx/bj3y+IFpiaS1VtHSJCvDF3XGf8mZguKCFssJ87Wvi5aeWtEoslckVRUWVC7B8NBToQc5XAaYqdwGuMsEAPfPjMYHi4uRi1n6asJRGj1EmCVXP64j9P9MXymb0wcXBbdQ+G0PfN0th5TEKHoIP83LBoYle9w2fGolxRhBBToMDKgQyPDuP9IyXW0m8+E3idJIDM2Qmebs7o2tYfby3oj3cXDRQ9qAKs58fV0EowIe+bNWDnMRkzBC1GUK6PsTcMhBCii/WMgxCTs+TSb65lb8xdlkTM+V9CucmkuF9UhT2J6Trn2gh536xFamax0UPQfKoLcCWRgHJFEUJMggIrB2OKsjpcWdsEXoVShZyCSpMfRyKBwaLPNQoVrmWW4Fpmid5yQqYILsyhRqFCdMcWgoJXtkeJS1DOm7XNpCeE2A0qacOBmCVtTIVP+QNTlNWxJUFB3qhVqvCfL+NNHqhEhvth4cQuOJWSh2sZxbiTU8ZpaExXOaHm3jdrFNXWHwsnduVd0kjqJMFHS4ZoBdq6Shcx0C7MzIWYZWOo/Ah3dK64ofPEHZW0IRZnbT1HlvDdzssmDaqaBqcTB7dFQWkNrmVwCwB0ZQc39L6xKQ2sLd6KjPAXdQha16q6j3+/IKht5ky3QAhxHBRYOTBHXfpdXFaDI0nilVQJ9nNDkJ+7weBUzHJC+t63iuo6q5uH1bNTCwCmHYK2VH42QgjRhVYFEodz8GyGqHUCW/i5N/sac5QT4rPy0lyS0woAmDb7uKXysxFCiC7UY0UczpVbhaLur+n8npT0Yq1J6MaUE+Lao9h4kndccrZVzM9u3H5TDUELXdlJ6RYIIaZAgRVxONW1dSY/hqqewYnkbOQWVmHZ9B5mG65ig5eHB7XBh5svoKC0RtBxxaKr/WIPQQvJ7C9WfjZCCGmKhgKJwyitVODPxHRk51eY7ZjsJHRzD1cF+brjnYUD0DbUW9D2YjHHcJuQJKJi5WcjhJCmqMeK2D2FUoXNh9OQcNkyaQriL+VgbP8Isw9XiVUL0cXZCTJnJ1TW8O/pM9dwmyXzsxFCSGPUY0XsmkKpwqd/XETcxWyjgyp5uB+6tuUfKDQcl7FIOSExVr5FBHvhnYUDLFYOiQtTTo4nhBA+qMeK2LXNh9NEyVc1omdLzB3XWXDOpIzccgyMCuWVcmFQt1Cjh6vEGIpzk0ktWg6JK8rPRgixBhRYEbslJHeULo2HjoyZhM63jLIYZZfFqIXIDufZynCbo+ZnI4RYBwqsiN0SkjuqMV2lfYT2AEmdJDiVkstrm8QruZg6ooPBXpbSSgXiLmbjhp7eGSEr5hpzlv4znGethbQJIcSaUGBF7JbQ3FGebs4Y2z9C59CR0B4gZ2cnwQlCdfW8GJqQ3zSPFt8hvMZG94vQOAc03EYIIYZRYEXsltBhu9AAD73DSEJzJgnN9K4rQSg7Id/QkFzjPFpLpnTjPITXWLcOgXgqpjtKdRQgp+E2QgjRjVYFErtlitxRQnMmqVT1gtqiKzjkMyE/9W4JtsfdxpIp3RAa4MFpG3b13OpFgyCzUHV4QgixVRRYEbslF5hDqbncS3xq8rGTuMUK8oQWc/582yXkFmn3PDXVwtcNa54aiLnjOlNQRQghAlBgRezW8Ogwk+ReEpIzSawgT2gx51vZZZxeW1Bag72nM3jtnxBCyD9ojhWxW6bMvcR3ErdY9eyETsjnI/5SDmKGtUdQkMkPRQghdsdmAqv8/HwkJCTgypUruHz5Mq5du4ba2lr0798fP//8s6WbR6yUqXMvcZ3ELVaQJ0Ym9eawqxE7tg00+bEIIcTe2ExgtXfvXqxZs8bSzSA2xppyL4kR5JmjqDHQsBqREEIIfzYTWHl5eWHw4MHo3r07unfvjqtXr2L9+vWWbhaxAU2H7W7nlqO6tg7OEpg195IYQZ4YmdS5MEfPGCGE2CObCaweffRRPProo+p/5+XlWbA1xBaxw3ZBQd4AgPz8crO3wdgEm8ZmUufKXD1jhBBib2wmsCLEnghNsClkrpYQzaWcIIQQohulWyDExvDJo9WhpQ94ZpzglHKCEEKIbhRYEWJj+OTRemVWLwyN5hckcU05QQghRBsNBXLg4iJVz8uxdrbSTmtg6+fqpTn9sKC8BofOZOLyrQJU19bB3dUZ3Tu0wIMDIuDv7QYAeOHx3iiqqMWVW4XN7rNbh0C8MLO3RtZ1Wz9P5kTnijs6V9zQeeLOWs4VBVaE2DB/bzdMHyPH9DFyva9xdZHizUWD8N3OyziSlKmzILSzVILR/SLwVEx3KmVDCCFGoMCKA6VShZKS5uusWZIlV7rZGkc9VzMe6IBx/cINrkYsbfQ5d9TzJASdK+7oXHFD54k7XefKz88DLha6SaTAihALKK1UIO5iNm7wTLdgLKGrEQkhhHBDgRUhZqRQqrD5cBoSLmsnCE1JL8au+DtmyQJPCCHENCiwIsRMFEoVPv3josGSNqp6BieSs5FbWIVl03vQfCdCCLExlG6BEDPZfDiNU51AAEi9W4LfjqSZtkGEEEJER4EVIWZQWlGLhMs5vLaJv5SD0kqFiVpECCHEFGxmKDAnJwcxMTHqfysUDT8458+fx4ABA9SPL1y4EIsWLTJ38wgxKE5P0WVDVPUMTl7MponmhBBiQ2wmsFKpVCgpKdF6vK6uTuPxmpoa8zWKEI5uZBYL2i41s5gCK0IIsSE2E1i1bt0aqamplm4GIYLUKFRm3Y4QQohl0BwrQszATSZsdZ/Q7QghhFgGBVaEmIE8wl/QdpECtyOEEGIZFFgRYgbDo8MgdZLw2kbqJMGwHi1N1CJCCCGmQIEVIWbg6+WKId3DeG0zNDrMpOVtCCGEiI8CK0LMZNaYTogM9+P02shwP8wa08m0DSKEECI6CqwIMROZixTLpvfAiJ4t9Q4LSp0kGNGzJV6c0YNqBRJCiA2ymXQLhNgDmYsUc8d1Rsyw9jh5MRupmcWoUajgJpMiMsIfw3q0pOE/QgixYRRYEWIBvp4yTBzclpJ/EkKInaGhQEIIIYQQkVBgRQghhBAiEgqsCCGEEEJEQoEVIYQQQohIKLAihBBCCBEJBVaEEEIIISKhwIoQQgghRCQShmEYSzfC2jEMg7q6eks3wyAXl4Ys3UqlysItsX50rrih88QdnSvu6FxxQ+eJO13nytnZCRIJv8L3YqHAihBCCCFEJDQUSAghhBAiEgqsCCGEEEJEQoEVIYQQQohIKLAihBBCCBEJBVaEEEIIISKhwIoQQgghRCQUWBFCCCGEiIQCK0IIIYQQkVBgRQghhBAiEgqsCCGEEEJEQoEVIYQQQohIKLAihBBCCBEJBVaEEEIIISKhwIoQQgghRCQUWBFCCCGEiIQCK0IIIYQQkVBgRQghhBAiEgqsCCGEEEJE4mzpBjgShmFw4cIFHD16FOfOncPt27dRUVEBb29vdO3aFTExMXjkkUcgkUh47XflypWIjY01+JrvvvsOw4cP1/lcZWUlvv32Wxw4cADZ2dnw8PBAjx49MH/+fAwYMIBXW8RiinN15swZPPHEE5xe+/zzz+O5557TeCwyMtLgNi1atEBCQgLn9ohp3759SExMREpKCu7fv4+SkhK4uLigbdu2GDFiBObOnQt/f39B+z5w4AB++eUXXL9+HUqlEm3atMGkSZPwxBNPwMXFRe92hYWF+Oqrr3Ds2DHcv38fPj4+6NevHxYvXowuXboI/VONIvZ5UqlUOH36NI4fP44LFy4gPT0dNTU18PPzQ/fu3TFjxgw88MADOre9d+8eRo8ebXD/PXr0wB9//MHnTxSNKT5T9nitEvs82fN1SpcTJ07gqaeeAgC0atUKR48eFbQfa7pOSRiGYQRtSXg7deoU5s2bp/53eHg4fHx8kJWVhZKSEgDAAw88gLVr10Imk3HeL3uxCgsLQ1hYmN7X9OjRQ+vxoqIizJo1C3fu3IFMJkPHjh1RVFSE3NxcSCQSvP7665g9ezavv1MMpjhXV69exdtvv633+YqKCty4cQMA8OOPP2LIkCEaz7MXrG7duuk8pp+fH7766itObRHb5MmTcf36dchkMgQFBcHf3x9FRUXIzs4GAAQGBuLHH39E586dee33f//7H3788UcAQEREBNzd3XHz5k2oVCr069cPP/74o85zkZGRgVmzZqGgoAAeHh5o164dcnNzUVhYCBcXF3z++efNBhWmIPZ52rp1K1577TUAgJOTEyIiIuDp6YmMjAxUVFQAAGbMmIHVq1dr3QQ0Dqx69+6tc/+dOnXCW2+9JehvNZYpPlP2eK0S+zzZ83WqqcrKSkycOFF9roQGVlZ3nWKI2SQkJDCjRo1iNm7cyBQUFGg8Fxsby3Tr1o2Ry+XMBx98wGu/K1asYORyOfPFF1/wbtPTTz/NyOVyZsqUKUxubi7DMAxTX1/P/P7774xcLme6dOnCXL16lfd+jWWqc2XI2rVrGblczowYMYJRqVRaz8vlckYulzN3794V7Zhi2bJlC3P27FlGoVBoPH79+nVm4sSJjFwuZx5++GFe+zx48CAjl8uZbt26MYcPH1Y/fvPmTWbUqFGMXC5n1qxZo7VdfX09M3nyZEYulzMLFixgysrKGIZhGKVSyXz22WeMXC5nevbsyeTl5Qn4S40j9nn6448/mEceeYT5448/1H8nwzT8rd9//z0TGRnJyOVy5tdff9Xa9u7du+rPlDUyxWfKHq9VpjhPhtjydaqpt99+m5HL5cwzzzzDyOVyZuTIkbz3YY3XKQqszKi8vFzry9fYV199xcjlcqZ///46vzD6CL1YpaSkMHK5nOncuTOTnp6u9fzLL7/MyOVy5rnnnuO1XzGY6lzpU19fz4wePZqRy+XMJ598ovM1tnTBauzixYvqtt+8eZPzdpMmTWLkcjnz+eefaz2XmJiovpgVFhZqPHfo0CFGLpczffr0YUpKSrS2nT17tt6LnSUJOU/FxcVMfX293udfe+01Ri6XM5MmTdJ6ztoDK0OEfqbs8VpliNDzpI89XacuXLjAdO7cmXnmmWeY7du3Cw6srPE6RZPXzcjLy8vgWC87r6CkpARFRUUmb8+BAwcAAAMHDkSbNm20np8xYwaAhjHwqqoqk7enMXOfq6SkJNy9excAMHXqVKP3Z03at2+v/u/q6mpO26Snp+P69esA/vkcNDZo0CC0adMGCoUCR44c0Xhu3759AIBx48bB19dXa9vp06drvM5aCDlPfn5+Buf5sZ/TO3fuGNc4KyPkXBnDmq9Vhoh9nuzlOqVUKvH666/Dzc0Nb7zxhuD9WOt1iiavW5Gamhr1f7u5ufHe/syZM0hLS0NJSQl8fHwQFRWFSZMmoVWrVjpfn5ycDADo27evzuejo6Mhk8lQW1uLa9euoU+fPrzbZCrGnqum2Am1ffr00Xnhbmz9+vW4f/8+VCoVQkJCMHDgQDz88MO85sWZ07lz5wBAPYeAC/azER4ejpCQEJ2v6dOnDzIyMnDx4kU89thj6scvXrwIQP/nin08NzcXeXl5evdvbkLOU3PYz6m7u7vB173zzju4ffs2JBIJWrVqhaFDh2LMmDFwcrLOe19jz5WjXKvE/kzZy3Xqm2++wY0bN7Bq1SqEhoYK3o+1XqcosLIie/fuBQB07twZXl5evLdPSkrS+PehQ4fw5Zdf4t///jcWLVqk9fr09HQADZP9dHFxcUFYWBgyMjJw584dq7lYAcafq8aqqqqwf/9+AMCUKVOaff327ds1/h0bG4svvvgCa9euRVRUlFFtEUt9fT3y8/ORkJCAjz76CADw0ksvwdPTk9P2zX02Gj/XuDdGoVAgKyvL4LZhYWFwcXGBUqnE7du3LRpYGXuemsN+Tpv77vz8888a/96yZQu6dOmCtWvXIjw8XJS2GEvMc2XP1ypTfabs5Tp169YtfPPNN4iKisKcOXOM2pe1XqcosLISV65cwe+//w4A6qWnXLVp0wYrV67EwIED0apVK8hkMqSmpuLHH3/E/v378dFHH8HDw0NrxUxpaSkA6OwGZbHPlZWV8WqTKRlzrnTZv38/qqqq4O7ujvHjx+t93ejRozF58mR07twZoaGhqKysxKlTp/Dpp5/i7t27mD9/Pnbu3Kl3tZM5bNiwAWvWrNF4LDo6Gu+//77eJey6CP1sVFRUoL6+3uC2EokEPj4+KCwstNjnSqzzZMjhw4dx7NgxSCQSLFy4UOt5Z2dnTJo0CRMmTEDHjh0RHByM4uJinDhxAp999hmuXbuGBQsWYMeOHUbfPBhDzHNlz9cqU3+m7OE6xTAMXnvtNdTV1WH16tWQSqVG7c9ar1PW2c/sYAoKCvD888+jrq4ODz74ICZMmMBr+2eeeQZPPvkkunTpAh8fH7i5uaFHjx74/PPPMWvWLADAZ599hsrKSo3tamtrAcDgXCa227jx0JslGXuudGG71x966CGDP2Dr16/H2LFj0aZNG7i6uiIgIAATJkzAH3/8gZYtW6KkpATr1q0zuj3GCAkJQe/evdGjRw8EBQVBIpHg2rVr2LVrF6+Lg9DPBrtd4+e5bmtOYp0nfW7duoWVK1cCAObOnasznUJoaCg+/PBDPPDAA2jdujVkMhlCQkIwffp0/Pbbb/Dy8kJGRgY2bdpkdHuMIea5sudrlak/U/Zwndq8eTPOnz+P2bNno3v37kbvz1qvUxRYWVh5eTkWLVqE7OxsREVF4f333xd1/y+++CJcXFxQVlaG06dPazzn6uoKoGEioT4KhQKAOPOYjGWKc3X37l31sASX7nVdAgIC1D1nhw8fBmPB1HDjx4/Hb7/9hj/++APx8fHYuXMnevTogT179uCJJ56ASqXitB+hnw12u8bPc93WnMQ6T7rk5ORg4cKFKC8vx4gRI/DSSy/x3kebNm3w+OOPA2gYJrMkU56rxmz9WmXK82QP16m8vDx88sknCAkJwdKlS0XZp7VepyiwsqDKykosXLgQV69eRadOnfDDDz+I3uXv7e2NTp06AWhIhtaYj48PgH+6U3Vhn2NfaymmOlc7d+4EwzBo1aoVBg4cKHg/vXr1AtCwSpFNYGoNOnfujG+++Qb+/v64du2aes5Pc4R+Nry8vNQTrvVtyzCM+g7e0p8rltDz1FR+fj7mzZuH7Oxs9O/fH2vXrjV4N20I+5li55FYC7HOVVP2cq1iiXme7OE69fbbb6OiogKvvfaaaL9z1nqdosDKQqqrq7F48WIkJyejbdu2+OmnnwSXHGkOe2Gvq6vTeLxt27YAtC9iLKVSqc6Iy77WEkx1rhiGwc6dOwEAMTExvEsJNdb4x1OsO3ixeHl5oX///gCAlJQUTtuwK5j0fTYAIDMzE4DmZ0Mmk6Fly5YazzeVk5OjvsMUa/WdGIScp8YKCwsxd+5cpKeno1evXvj666817oz5Yj9T1vZ5Aow/V/rY+rWqKTHOk71cp65evQoAWL16NYYMGaLxv3fffRdAw7WBfez8+fPN7tNar1MUWFlAbW0tnnnmGSQlJaFVq1bYsGEDgoKCTHKsuro63L59GwC0lrX27NkTwD9Lgpu6dOkSlEolXF1dLVbbzZTn6uzZs7h37x4kEong7nVWWloagIYuZj8/PxFaJy72h4rrxZQtKXLv3j3k5eXpfA37uWE/Ryz233///bfO7djHQ0NDjVpqbQp8zxOrpKQETz75JG7duoWoqCh89913Rq8CYz9T1naOWELPlaH92fK1Sh9jz5O9XacKCgq0/seWf6qvr1c/Zmh4j2Wt1ykKrMxMqVTi+eefx6lTpxASEoKNGzeadHXGli1bUF5eDmdnZ60u5LFjxwJoyCmjK+LfsmULgIYkh2ItP+fD1OeKnQzat29fo5a019XV4aeffgLQkMDQ2dm6FtuWlJTg7NmzAMD5R6ddu3aQy+UA/vkcNHbq1ClkZGTAxcVFq5YW+7nav3+/zm52tqjwuHHjuP8RZiDkPAENK4zmz5+P1NRUyOVy/PDDD/D29jaqLZWVldi8eTMAaNWCswZCz5Uhtnyt0keM82Qv16mjR48iNTVV5//Y1ZStWrVSP8alqLa1XqcosDIjlUqF5cuX48SJEwgKCsLGjRs5f1FGjRqFUaNGqfOYsBISEvDhhx9qzcNQKBT4+eef1R/YmTNnIjg4WOM1UVFRGDlyJFQqFZYtW4b79+8DaOh63rJlC3bt2gUnJyc888wzAv9i4UxxrhqrrKxUZ3PmksH4o48+QmxsrPrOipWTk4MXXngBycnJcHZ2xpIlSzi1UUxnz57F+vXrce/ePa3nUlJSsGDBApSXlyMkJETrImHoXD333HMAgO+++06jMOrt27fVhYdnzZqFgIAAje3GjBmDyMhIlJeX46WXXkJ5eTmAhvf0888/R1JSEtzd3TF//nzj/nCeTHGeqqur8dRTTyElJQXt27fHhg0bOA9Tv/766zh48KDW5Nlbt25h4cKFuHfvHjw8PLBgwQKef6nxTHGu7PFaZarvHsuerlPGsLXrlHXdWtu5ffv2qb8kMpkMr776qt7Xvv766+jatav632wys6blGqqrq/H999/j+++/R4sWLdRJzO7cuaN+7dixY7FixQqdx3nvvffw+OOPIyUlBaNHj0bHjh1RXFyMnJwcSCQSvPrqqxZJJmeKc9XYgQMHUFVVBQ8PD/WdiyG3b9/Gd999h//85z8IDw+Hr68vysvLcefOHTAMA1dXV7zzzjvqrmlzKisrw+eff47PP/8cQUFBCA4OhlQqRU5ODvLz8wE0LAX/5ptvtO7mDZ2rsWPHYu7cudi4cSOeeeYZREREwMPDA2lpaVCpVOjTpw+WL1+utZ2TkxM+//xzzJ49G3FxcRg+fLhW1fgPP/zQ7IlBTXGeNm3apDE8xV7kdfniiy80hrEvXbqEP/74Ay4uLoiIiICXlxeKi4vVcz58fX3x2WefoXXr1sb94QKY4lzZ47XKVN89lj1dp4xha9cpCqzMqPGdaVZWlvrDogsbPTcnKioKzz77LJKTk9VZh5VKJQICAjB06FBMmTIFo0aN0rt9QEAAtm/fju+++w779+/HzZs34eHhgeHDh2PBggVGrUAxhinOVWNs9/rYsWM5DR08/vjjaNGiBa5cuYL79+8jKysLLi4u6NSpEwYNGoR//etfBrP/mlKvXr2watUqnDlzBjdv3kR6ejoUCgV8fHwwYMAAjBo1Co8++qiglTivvvoqevXqhc2bN+PatWu4f/8+OnTogEmTJmHevHl6V7y1a9cOu3fvxldffYVjx47hxo0b8PHxwdixY/H0009rBMLmYorz1Phzys4P0qdx7hwAWLx4MU6ePIkrV66goKAAGRkZcHNzQ1RUFIYPH47Zs2ebbO5lc0xxruzxWmXK7x5gX9cpU7K265SEsWTSHUIIIYQQO0JzrAghhBBCREKBFSGEEEKISCiwIoQQQggRCQVWhBBCCCEiocCKEEIIIUQkFFgRQgghhIiEAitCCCGEEJFQYEUIIYQQIhIKrAghhBBCREKBFSGEEEKISCiwIoQ4nHv37iEyMhKRkZG4d++epZtjdqmpqfj3v/+NoUOHomvXroiMjMTkyZMt3SxC7AIVYSY2obS0FMOGDVMXsj1w4ADatm1r2UYRnUaNGqWzaLaHhwdCQkLQq1cvzJgxAz179jTJ8deuXQsAmDJlClq3bm2SY9iyu3fv4vHHH0dlZSUAwM/PD87OzvD39xe8z6VLl2Lfvn0AgKeffhrLli0Tpa1N0XtLbAEFVsQm/Pnnn+qgCgC2b9+O5cuXW7BFpDmurq7w9vYGADAMg+LiYty5cwd37txBbGwsnnvuOTz33HOiH3fdunUAgP79++v98XVxcUG7du3U/+1ItmzZgsrKSrRp0wY///wzQkJCjNpfcXExDh8+rP53bGwsXnjhBUilUmObqoXLe0uIpdFQILEJ27ZtAwDMmTMHQMPFW6VSWbJJpBkPP/wwEhISkJCQgMTERFy6dAmbNm2CXC4HwzBYu3YtTp06ZZG2hYSEYP/+/di/f7/RgYWtuXHjBgBg9OjRovztu3fvhlKpxIgRIxAREYG8vDzEx8cbvV9CbBUFVsTqpaSk4Nq1a/Dx8cHLL7+M1q1bIz8/HydOnLB00wgPLi4uGDBgANavXw9n54bOcjZgJuZTXV0NoGFoVgzsexgTE6Oep0XvK3FkNBRIrB57kR4/fjxcXV0RExODdevWYfv27Rg1apTGazds2IA1a9YgMDAQcXFx6h/wphiGwejRo5GVlYUXXngBS5Ys0XheoVBg69at2L9/P27cuIHKykr4+voiOjoaM2fOxIgRI3TuNzIyEgCwadMmdOzYEd9++y2OHz+O3Nxc1NTUIDU1FUDDj9uRI0cQFxeH1NRU5OXloaKiAn5+foiOjsaMGTP0HoOVmpqKr776CklJSSgrK0NwcDAeeOABPPvss7h58yaeeOIJ9et0qaiowObNm3HkyBHcuXMHVVVVCAwMRO/evfHEE0+gV69eBo8vVHh4ONq2bYubN28iLS1N52uSk5Nx6NAhXLhwATk5OSgoKICrqyvat2+PMWPGYPbs2fD09NTYZuXKlYiNjVX/m/37Wa1atcLRo0cBNExeHz16NADgyJEjGsNKZ86c0Th3GRkZ+Prrr5GYmIjCwkIEBARg+PDheP755w32+Bj7/jQnMzMTP/zwA06dOoXc3Fw4OzujTZs2GD16NObNmwcvLy+N1zed+7Zu3Tr10BrQ8JkdMGAArzZcunQJN27cgLe3N0aPHo3u3btj3bp1OHbsGIqKihAQEGBw+5ycHPz8889ISEjAvXv3oFQqERwcjE6dOmHs2LHq7zyf95ZVXl6OjRs34siRI8jIyEBdXR1CQ0MxaNAgLFy4EOHh4TrbxPU7DAAnT57Eli1bcOnSJRQVFUEmk8Hf3x9t2rTBkCFDMG3aNPj5+fE5pcQOUGBFrFptbS327NkDoOGOmP3/L7/8EsePH0dBQQFatGihfv3EiRPxwQcfoLCwEPHx8XjggQd07jcpKQlZWVmQSCRaq6GysrKwePFi9Y++RCKBl5cXCgoKcPToURw9ehQzZ87E6tWr9bY7MzMTL774ojogaBrg7du3D6tWrdLYv7OzM/Lz83HkyBEcOXIE8+fPx4oVK3Tu/9ChQ1i2bBmUSiWAht6H/Px8/PLLLzhw4ABefPFFvW0DgGvXruHpp59Gbm4uAEAqlcLNzQ25ubn466+/sG/fPixbtgyLFy82uB+hGIYBANTX1+t8fsaMGer/dnd3h7u7O0pLS3Hx4kVcvHgRu3btwqZNmxAYGKh+nZeXF1q0aIGCggIAgK+vr8b8KSGTs0+fPo1nnnkGVVVV8PT0BMMwyMvLw9atW3HixAls27ZNZ3Bl7PvTnL/++gsrVqyAQqEAAHh6ekKpVOLq1au4evUqtm3bhh9++AEdOnRQb+Pv74/a2lqUlpZCqVTCw8NDo9dKyFyzpjc94eHh6Nu3L5KSkrBr1y48+eSTerfduXMn3njjDfXcSRcXF3h6eiInJwd3797F0aNHERkZiS5duvB+b9PS0rBw4UL155v9DmZkZCAjIwM7duzARx99hLFjx+ptX3Pf4XXr1qkn0wMNn1OGYXDv3j3cu3cPCQkJ6NatG+9gldgBhhArtmvXLkYulzMPPvigxuOzZs1i5HI5891332lts3DhQkYulzNLly7Vu99XX32VkcvlzOzZszUer6ysZMaNG8fI5XLmX//6F3PmzBmm9v/aO/OgqI6uD//YhhEYCFZ0IrhFklZE0YhEAS1KRUA0BsqEYBQ1IqIxsVCMS9TEKt8qSFIaS624YVSSkERFRRkR1KAQFGSRUoi4IYgYEQLCIDLD0t8ffLffGWZhWHyD2k8VVcPt232777l97+nuc04rFJRSSmtra+mBAwfo6NGjKSGEHjx4UKNcQgglhNDRo0dTHx8feunSJdrc3EwppbSoqIidd/bsWRoVFUWzs7NpfX09O15eXk537NhBnZycKCGEnjt3TuMa9+/fp6NGjaKEEBoQEECvX79OKaW0paWFpqen00mTJlFXV1dWl7aUl5dTNzc3Sgihn332Gb1+/TpVKpWUUkorKyvptm3b6PDhwykhhJ49e1bnPdTFpEmTKCGErlmzRmv6/fv3WflhYWFazwkLC6MymYw+fvyYHXv27BlNTk6mPj4+lBBCly1bpjWv0O6MjAyddSwtLWXnlZaWqqVlZGSwNFdXV7pkyRJ6584dSimlCoWCymQy+s4771BCCP3iiy+0tq8r8mmP/Px89nwEBQXRwsJCSimlzc3N9Pz589TDw4MSQqiXlxetq6vTyD937lxKCKHbt2/v8LVVqa+vp2PGjKGEEJqVlcWOHzlyhBJC6PTp03XmTUlJoUOHDmVtyMrKYv1EoVDQrKwsumHDBnr79m21fIbIVi6X08mTJ1NCCJ04cSK9cOECK/vGjRs0MDCQEkLoiBEj6I0bNzTyG9KHHzx4QIcNG0YJITQyMpI+evSI5a+traVZWVl006ZNTPacVwuuWHF6NMHBwZQQQnfu3Kl2/PDhw5QQQn19fTXyJCQkUEIIHTlyJJXL5RrpDQ0N1MXFhRJC6OHDh9XSdu7cyZQqQdloS3JyMiWE0HHjxtHGxka1NOGlPGbMGPr33393tLmM6OhoSgih8+fP10gTlEI3NzdaXV2tkX737l06YsQInR/udevWUUIIXblypc7rHzhwgBJC6MyZMztcd12KlVKppBkZGXTGjBmsbsnJyR0u/9GjR3TEiBF06NChtKysTCO9OxWr4OBg9lFVJSYmhhJCqLOzs8Yz0FX5tEdISAgbbKgq5QIFBQVMcY2OjtZI7y7F6vjx40yBU0Uul1NnZ2dKCKF5eXka+RobG5niM3v2bDZwMQRDZLtnzx5KCKFOTk705s2bGulyuZw9o4sXL9Z5DX19WCaTUUII9fb2NrjunFcHbrzO6bGUlpbiypUrWpfrpk2bBrFYjKKiIuTm5qqlTZkyBVZWVlAoFCy2jirnz5+HXC6Hubk5fH191dLi4uIAAAsWLNC5NOLl5QUrKytUV1ejoKBA6znvv/8+3njjDYPb2hZhCTMvL0/N+5FSiuTkZADA7NmztdpvDBkyBNOmTdNarurSamhoqM7rC/e7sLCQLb90lNOnT8PDwwMeHh5wd3fHqFGjMG/ePOaVFhwcDC8vrw6XK5VKMWzYMFBKcfXq1U7VzVCWLFkCY2PN16Rgn9XQ0ICSkhJ2vKvyaY/a2lrmcRcSEoJevXppnDN8+HBMnToVACCTyTp1HUMQlgHb9k0rKysmV21G7JmZmSwo67p16yASibq1XkKf9/HxASFEI93KygqLFi0CAKSmpkIul2stR18ftra2BgA8ffoU9fX13VFtzksEt7Hi9Fji4uJAKYWrq6tGzBrh5Z2QkICjR49izJgxLE0sFsPHxwdxcXE4efIkPvzwQ7W8J0+eBND6cRTiLAFAeXk5M+5dv349vvrqK511E16mZWVlGDVqlEa6an10UVlZidjYWKSnp6O4uBhyuVwjhMSzZ89QU1PDjIBLS0tRW1sLAHB1ddVZ9rvvvov4+HiN4/n5+cymJSQkpN06AsDDhw/V7NgMRaFQqMUeExCJRNi6dSv7+GujpaUFMpkMMpkMhYWFqKqq0lqWYEPzvHB2dtZ6vG/fvuz3kydP2O+uyqc9CgoKmH2au7u7zvM8PDyQmJiImzdvorGxsdtjdZWUlCA7O1vroAdoDeCZkJAAmUyGL7/8Uk0BFJThPn36YOTIkd1aL6VSyYzL3dzcdJ7n4eEBoPU5KygowPjx4zXO0deHnZ2dYWtri4qKCgQGBiIoKAhubm4YMmQIjIyMutgKzosOV6w4PZKWlhbmBaRrqw1/f38kJCQgMTER69evV/MS8/f3R1xcHDNSt7e3BwBUVVUhLS2NnaNKeXk5+11dXW1QPRsaGrQeVzWq1sbVq1exePFi9hEGWg2ce/XqBSMjIzQ3N7M6CO7xQv0FVD/ubdHlrfb48WP229CZKNXrd4SAgABERUUBaP3gFRcX48CBAzh27Bi+/vprDB48GG+//bbW64WFhSEzM5MdMzMzYxHCATAD7M7WzVDaetYJqBoyNzU1sd9dlU97qJavrwwhrampCTU1NZ1SjPUhDHpcXFy0ete5u7tDKpWivLwcZ86cQUBAAEurqKgAANjZ2XVrnYDW50IYnOi7P6ozUar3VBV9fdja2hpbt25FREQEbt++jc2bNwMAJBIJxo4di2nTpsHPz++VCz7LaYUrVpweSVpaGpuN2LBhAzZs2KDz3Pr6eiQmJuKDDz5gx1xdXWFvb4+ysjKcPHkSS5cuBdC6NNLU1ITXX3+djVoFVD3UTp8+reZR1VG0LR8JNDU1ISIiArW1tXB0dMSKFSvg4uKi9hG/f/8+m9ERZija0pmRsWobr127BnNz8w6X0RlEIhEIIYiMjAQAHDt2DMuXL8fx48chFovVzt29ezcyMzMhFouxYsUKeHt7o1+/fmrt/fjjj5GTk6Pz3vQEXtaZi+bmZjboycnJYeEJdHH06FE1xepFuS/6+jDQqjyeP38eycnJyMjIwNWrV1FcXIyUlBSkpKRg37592L9//ysXgJbDA4RyeiiCrZOhtLXlMDIywnvvvQcAaksuwu/p06druE+rjuofPnzYoet3hLy8PJSVlcHExAR79uyBp6enxsyIMKpvi2pcINXZp7aozr6potpGbfv5/S9Yu3YtJBIJioqKcOjQIY10wS5o2bJlWLBgAezs7DQ+xp21+3redFU+HSlf3zKoUL6pqSlsbGw6dS1dpKam6m1bW7Kzs1FcXMz+F57B59HHbGxs2FY6+u6Palp7sbb0YWFhAX9/f0RFRSEpKQmpqalYtWoVzM3N1WayOK8WXLHi9DiqqqpYsL/t27cjNzdX59+RI0cAtC6tFRUVqZUjLPXdu3cP165dQ1FREa5fv66Wpkr//v3Z6DIlJeU5ta41KCLQ+kLXNZrVtdXLgAEDmOHslStXdF5DV9rIkSPZ8sTzbKM+bGxsMH/+fADA3r17UVNTo5YufPQcHR215n/w4IGawXhbBCXs35jN6qp82sPJyYnNpOjbDujSpUsAWoNddvdylDCImTp1qt6+mZubCycnJwDqAyXBdqmiooL1R0NpT7YikYjNoGVkZOgsR7g/xsbGrI7dgVQqRWhoKIvflZ6e3m1lc14cuGLF6XHEx8ejsbEREokEkyZNgqWlpc4/Z2dnDBkyBIDmrNWbb77JDMvj4+PZbBUhBMOHD9d67cDAQFbWX3/9pbeeqkbLHUEwmK+srNQ68/Lo0SP89NNPWvMaGRmxJcLffvtNQykBgOLiYq3ekEDrCFuYydu3b1+7swadbWN7BAcHw8LCAnV1dfjxxx/V0oTZu8LCQq15t2zZordsIb8ub6/nSVfl0x7W1taYMGECAGD//v1abcwKCwuZZ+KMGTM6dR1dVFZW4sKFCwBa94LU1zctLS2Z163q3p7jxo1jdlmRkZEsyKkhGCJbPz8/AEBSUhLzQFXl6dOniI6OBgB4enqqObAYSnt1Fpa321tO5LyccKlzehyCgjRlyhSDXLGFl3d8fLyaITHwX8N3mUyGU6dOAQBmzpyps6xPPvkEhBAoFArMmzcPP//8s5ohe21tLS5evIjVq1djzpw5HWvY/+Pi4gILCwtQShEeHo579+4BaLVdSUtLYxtN6yIsLAxisRiVlZVYuHAhUwAppbh8+bJON3yBFStWoG/fvqiursZHH32EEydOoK6ujqVXVVUhKSkJy5YtQ0RERKfa2B6vvfYagoKCALRuHaJqQDxx4kQAwK5du5CcnMxkWlpaioiICCQmJupd3hIM4k+dOvXcjdu10VX5tEd4eDjMzMxQUlKCkJAQ5gXX0tKCixcvIjQ0FE1NTRg4cKBaBPvu4MSJE2hqaoJYLNa5q4EqQlgJ1b09TUxMsHHjRhgZGSEnJwcLFixAdnY2s/9TKpXIzMzEqlWrcOfOHbXyDJHt7Nmz0b9/fzQ2NiI0NBQXL15kZd+8eRMhISF48OABRCIRwsPDO3MbsHfvXixatAgnTpxQW1ZUKpU4ffo09u/fDwAG3SPOywc3Xuf0KPLy8tjLtG2MKV34+vrihx9+YKNp1dhIfn5+iIyMRHV1Naqrq2FsbKxXsbK0tER0dDSWL1+OvLw8bN68Gf/5z38gkUjQ0tKipoAMGjSoU22USCRYvXo1Nm3ahKysLPj6+sLCwgLNzc1QKBSwtbVFZGQkM7hvy6BBg/DNN98gIiIC+fn5CAgIgKWlJVpaWvDs2TNIpVKsXbtWZ4ygvn374uDBg/j0009RXFyMNWvWwNjYGNbW1lAqlWpxefS59HeVhQsX4pdffkF9fT327dvHtu8JDw/HpUuXUFlZic8//xympqbo1asXm6VYuXIl/vzzT53LaUFBQcjNzUVSUhL++OMP9O7dG6amppBKpfj111+fW3sEuiqf9nBycsK3336L1atXIycnBzNnzoSVlRUaGxtZSIp+/fph9+7dGvspdhVhSc/T09OgTZwHDBgAJycnFBQUqO3t6enpiaioKGzcuBE5OTmYM2cORCIRm8UUlOm2IUEMka2VlRV27drFtrRZvHgxzM3NYWZmxvqvSCTCd999h2HDhnXqPlBKkZaWxjyMxWIxxGIxampq2DKlg4MD1q5d26nyOS82XLHi9CiE2SqJRKLhtaeLoUOHwsHBAXfv3sXRo0fVFCtbW1t4enri3LlzAFpj27TnpSOVShEbG4szZ84gISEB+fn5TCmzt7cHIQRubm6dDvIItI6q7ezsEB0djfz8fDQ3N0MqlcLT0xOhoaFsjzld+Pr6YvDgwdi1axeuXLkCuVwOqVSKyZMnY+nSpcjJyQHw30CGbXFwcMCpU6dw/PhxJCcn48aNG6ipqYGZmRkGDRoER0dHeHh46N1Lrav06dMHs2bNQmxsLGJjY7Fw4UL06dMH9vb2iIuLw44dO5CamoqqqiqYm5tj7NixmDt3LiZMmMCCZGpDmKX8/fffcevWLVRUVOjck/B50VX5tIefnx+cnJw0NmF2dHSEl5eX1k2Yu0pOTg6zYzR00COcW1BQoLG3p7+/P8aOHYuYmBikp6fj4cOHUCgUsLOzAyEE3t7eGp65hsqWEAKZTIZDhw7h3LlzKCkpgVKpxMCBA+Hu7o6QkBAMHDiws7cCgYGBkEqlyMzMxK1bt/D48WPU1dXBxsYGb731Fry9vREUFPQ/87rl9CyMaE/2V+ZwOJ3i+++/x+7duzF+/HitnnecfxcuHw7n5YXbWHE4LxlVVVXMW1KwV+L0HLh8OJyXG74UyOG8gMTExKChoQE+Pj6wt7eHqakplEolLl++jKioKPzzzz/o3bs3Zs2a9W9X9ZWEy4fDeXXhihWH8wJSWlqKmJgYbNmyBSYmJpBIJGpGvxKJBNu2bYOtre2/XNNXEy4fDufVhStWHM4LSEBAAExMTJCVlYXy8nI8efIE5ubmcHBwwIQJEzB//ny+lca/CJcPh/Pqwo3XORwOh8PhcLoJbrzO4XA4HA6H001wxYrD4XA4HA6nm+CKFYfD4XA4HE43wRUrDofD4XA4nG6CK1YcDofD4XA43QRXrDgcDofD4XC6Ca5YcTgcDofD4XQTXLHicDgcDofD6Sb+D0exj8bA7ZQZAAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Seems like there are some signs of correlation happening here but is it strong enough?\n",
        "\n",
        "## ***Let's train a linear regression Model***\n"
      ],
      "metadata": {
        "id": "xiyS9rLKJU9N"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.linear_model import LinearRegression\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import mean_squared_error\n",
        "\n",
        "X = df['avg_rating_of_actors'].values.reshape(-1, 1)\n",
        "y = df['rating']\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1, random_state=42) #Split the dataset into training-testing\n",
        "model = LinearRegression()\n",
        "model.fit(X_train, y_train)\n",
        "y_pred = model.predict(X_test)\n",
        "# Plot data points and regression line\n",
        "plt.scatter(X_test, y_test, label='Testing Data')    # Plot testing data points\n",
        "plt.plot(X_test, y_pred, color='red', label='Linear Regression')    # Plot regression line\n",
        "plt.xlabel('Average rating of actor')\n",
        "plt.ylabel('Rating')\n",
        "plt.title('Year released vs rating')\n",
        "plt.show()\n",
        "print(\"Coefficient(s):\", model.coef_)\n",
        "print(\"Intercept:\", model.intercept_)\n",
        "mse = mean_squared_error(y_test, y_pred)\n",
        "print(\"Mean Squared Error:\", mse)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 557
        },
        "id": "1skhjnD4lRzv",
        "outputId": "81aee5b1-f3d2-45d4-eca8-e94d18b94880"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Coefficient(s): [1.56917309]\n",
            "Intercept: -1.7849436501096538\n",
            "Mean Squared Error: 0.16553406510901802\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Let's try to make some predictions based on one actor!**"
      ],
      "metadata": {
        "id": "jbdU7ztcvqKb"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def One_actor_prediction():\n",
        "  name = input(\"Enter an actor of your choice: \")\n",
        "  score = average_ratings_df_filtered.loc[average_ratings_df_filtered['actor_name'] == name, 'average_rating'].iloc[0]\n",
        "  score_list = [score]\n",
        "  data_points = []\n",
        "  data_points.append(score_list)\n",
        "  predictions = model.predict(data_points)\n",
        "  return predictions"
      ],
      "metadata": {
        "id": "YIhBL29svO4Q"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "for i in range(3):\n",
        "  prediction = One_actor_prediction()\n",
        "  print(\"Predicted score of this actor:\", prediction)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zi49XR0Jy-Af",
        "outputId": "12f87f8c-a291-40e4-bf2f-e331759485ee"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Enter an actor of your choice: Ryan Gosling\n",
            "Predicted score of this actor: [3.66008697]\n",
            "Enter an actor of your choice: Emma Stone\n",
            "Predicted score of this actor: [3.29917716]\n",
            "Enter an actor of your choice: Andrew Garfield\n",
            "Predicted score of this actor: [3.76992909]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "This is the prototype of our model and it only takes one datapoint for now, but this will improve and take in more actors"
      ],
      "metadata": {
        "id": "2XF6ZJp5yQQt"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uepEQGlsKsom"
      },
      "source": [
        "# **Ethics & Privacy**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WfWgPrlPKson"
      },
      "source": [
        "As a group of students enrolled at UCSD, we all acknowledge that we have aspects of our lives in common that could create biases in our research, and therefore data. We are all members of Gen Z, students, live/lived in San Diego, privileged enough to receive a high education, etc. These aspects of our lives could create an underlying bias that we need to address. Knowing this, we need to ensure that we are accessing data that reaches beyond our generation and using sources that address movies and actors of all ages and backgrounds, basing their popularity on one designated, impartial scale. Additionally, we chose to use Letterboxd for the rating of the movies in our data sets. We know that half of Letterboxd users are under the age of 35, and more than half are between 16 and 24 (https://variety.com/2023/film/news/letterboxd-martin-scorsese-younger-audience-classic-films-1235804153/). Since we know this to be true, we have decided to make our intentions about being able to predict the movie ratings of only Letterboxd users, meaning mostly Generation Z movie ratings. If this is made clear to the audience, we will avoid a bias, as it is not going to skew the data, but be a clear intention in the research process. We are also aware of the fact that choosing Letterboxd in the first place is likely a result of our generation. This could therefore mean that the scale we are using to rate movies is based more than half on the younger portion of the population. Although this is true, Letterboxd will have an impartial scale and vast amount of data to access. As a warning, we will provide this context to the audience so they are well informed that the ratings may primarily reflect the views of the younger generations, and may not accurately depict how the elder populations feel about the movies.\n",
        "\n",
        "We have intentionally chosen to work with a dataset that is public\n",
        "information accessable from Kaggle to avoid issues with privacy. Since this is a public data set, we will not have to worry about terms of use privacy issues."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vGY4RlH7Kson"
      },
      "source": [
        "# **Team Expectations**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Xm9_f-nGKson"
      },
      "source": [
        "\n",
        "\n",
        "*   We decide to meet at least once a week when necessary and previously decided upon\n",
        "*   We want to ensure that there is a hybrid meeting system available so group members can join from anywhere\n",
        "*   We want to make sure that deadlines are clearly communicated and that people are gently reminded to meet those deadlines\n",
        "*   Judgment free zone!\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JY4jF0XFKson"
      },
      "source": [
        "# **Project Timeline Proposal**"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from IPython.display import Image\n",
        "Image('/content/Screenshot 2024-02-25 at 6.31.49 PM.png')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 251
        },
        "id": "b5UI67I0QSpi",
        "outputId": "66476808-e538-47ef-89bb-c0bb1bbf414a"
      },
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {},
          "execution_count": 1
        }
      ]
    }
  ],
  "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": "3.9.7"
    },
    "colab": {
      "provenance": []
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}