data.ipynb

{
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  "metadata": {
    "colab": {
      "name": "data.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "toc_visible": true,
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/saurabhgssingh/bikesharing/blob/master/data.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Iex-uLrdaAUn",
        "colab_type": "text"
      },
      "source": [
        "# The Movies Dataset\n",
        "\n",
        "<div align=\"left\">\n",
        "<a href=\"https://github.com/madewithml/incubator/blob/master/datasets/movies/data.ipynb\" role=\"button\"><img class=\"notebook-badge-image\" src=\"https://img.shields.io/static/v1?label=&amp;message=View%20On%20GitHub&amp;color=586069&amp;logo=github&amp;labelColor=2f363d\"></a>&nbsp;\n",
        "<a href=\"https://colab.research.google.com/github/madewithml/incubator/blob/master/movies/data.ipynb\"><img class=\"notebook-badge-image\" src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>\n",
        "</div>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Jwa6uZd9agOh",
        "colab_type": "text"
      },
      "source": [
        "## Download\n",
        "\n",
        "Download the data from the source to your file system so you can start exploring it. The official dataset is located [here](https://www.kaggle.com/rounakbanik/the-movies-dataset/data) but we **highly** recommend using our slightly cleaned version [here](https://drive.google.com/drive/folders/1ZGp7ORu9nA6l3PyNK_H0MGTXNl4sNMsA?usp=sharing) (where we combined movies, keywords and credits into one DataFrame)."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-4BAxsphbOUw",
        "colab_type": "text"
      },
      "source": [
        "We didn't do much preprocessing (because we want you to learn how to do that) but we did make sure the data formats are easier to handle etc. as the original dataset is a bit unconventionally stored (ex. they had a string of lists of dicts as values in a Pandas Dataframe which is hopefully something you never see in the real world)."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "M01lUNomaPJn",
        "colab_type": "code",
        "outputId": "ca2886ad-d4d9-45c7-c28e-7e3f06184ade",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "# We saved our data to our Google Drive so we need to connect. \n",
        "# You can choose to do the same or just use in your local computer.\n",
        "from google.colab import drive\n",
        "drive.mount('/content/drive', force_remount=True)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mounted at /content/drive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-vw-ImpjVUpP",
        "colab_type": "code",
        "outputId": "cb069277-4a9e-4f35-b6a6-af149a56c7e3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 121
        }
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Go to this URL in a browser: https://accounts.google.com/o/oauth2/auth?client_id=947318989803-6bn6qk8qdgf4n4g3pfee6491hc0brc4i.apps.googleusercontent.com&redirect_uri=urn%3aietf%3awg%3aoauth%3a2.0%3aoob&response_type=code&scope=email%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdocs.test%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive.photos.readonly%20https%3a%2f%2fwww.googleapis.com%2fauth%2fpeopleapi.readonly\n",
            "\n",
            "Enter your authorization code:\n",
            "··········\n",
            "Mounted at /content/drive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qTQqSkYibgij",
        "colab_type": "text"
      },
      "source": [
        "## Features\n",
        "\n",
        "Some light feature exploration on the different data CSVs."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pAvaOpZqbiSd",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import math\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "from pylab import rcParams"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OaRBr7WKbu-f",
        "colab_type": "text"
      },
      "source": [
        "### Movies\n",
        "\n",
        "Rich metadata about each movie including it's title, description, genres, etc."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bFdztqiJbzlG",
        "colab_type": "code",
        "outputId": "57dcfc79-62a5-4e1a-cfe5-514d3d8098f9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 421
        }
      },
      "source": [
        "# Load movies\n",
        "movies = pd.read_csv(\"/content/drive/My Drive/Incubator/movies.csv\", header=0)\n",
        "movies = movies.replace({np.nan: None}) # replace NaN with None\n",
        "movies.head()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "error",
          "ename": "FileNotFoundError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-2-030585bc4705>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# Load movies\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mmovies\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread_csv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"/content/drive/My Drive/Incubator/movies.csv\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mheader\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m \u001b[0mmovies\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmovies\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreplace\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m{\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnan\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m# replace NaN with None\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mmovies\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhead\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36mparser_f\u001b[0;34m(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, squeeze, prefix, mangle_dupe_cols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, dayfirst, cache_dates, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, doublequote, escapechar, comment, encoding, dialect, error_bad_lines, warn_bad_lines, delim_whitespace, low_memory, memory_map, float_precision)\u001b[0m\n\u001b[1;32m    674\u001b[0m         )\n\u001b[1;32m    675\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 676\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0m_read\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    677\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    678\u001b[0m     \u001b[0mparser_f\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__name__\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36m_read\u001b[0;34m(filepath_or_buffer, kwds)\u001b[0m\n\u001b[1;32m    446\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    447\u001b[0m     \u001b[0;31m# Create the parser.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 448\u001b[0;31m     \u001b[0mparser\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mTextFileReader\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfp_or_buf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    449\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    450\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mchunksize\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0miterator\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, f, engine, **kwds)\u001b[0m\n\u001b[1;32m    878\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moptions\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"has_index_names\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mkwds\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"has_index_names\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    879\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 880\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_make_engine\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mengine\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    881\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    882\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mclose\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36m_make_engine\u001b[0;34m(self, engine)\u001b[0m\n\u001b[1;32m   1112\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_make_engine\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mengine\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"c\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1113\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mengine\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"c\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1114\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mCParserWrapper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moptions\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1115\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1116\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mengine\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"python\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, src, **kwds)\u001b[0m\n\u001b[1;32m   1889\u001b[0m         \u001b[0mkwds\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"usecols\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0musecols\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1890\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1891\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_reader\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mparsers\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTextReader\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msrc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1892\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0munnamed_cols\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_reader\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0munnamed_cols\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1893\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32mpandas/_libs/parsers.pyx\u001b[0m in \u001b[0;36mpandas._libs.parsers.TextReader.__cinit__\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;32mpandas/_libs/parsers.pyx\u001b[0m in \u001b[0;36mpandas._libs.parsers.TextReader._setup_parser_source\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] File /content/drive/My Drive/Incubator/movies.csv does not exist: '/content/drive/My Drive/Incubator/movies.csv'"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zTEajfD9cWff",
        "colab_type": "code",
        "outputId": "d5249e95-0c09-4e15-d0ae-8e22f082092f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "# Total\n",
        "print (f\"{len(movies)} movies in total.\")"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "46628 movies in total.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7suejNB4cZ7y",
        "colab_type": "code",
        "outputId": "b289b657-c08b-4828-8ee2-7b71b3535221",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 320
        }
      },
      "source": [
        "# Genres\n",
        "all_genres = [s.split(\", \") for s in movies[movies.genres.notnull()].genres]\n",
        "genres = [item for l in all_genres for item in l ]\n",
        "unique_genres = set(genres)\n",
        "print (f\"total of {len(unique_genres)} unique genres from {len(genres)} occurances.\")\n",
        "pd.Series(genres).value_counts().plot(kind='bar', figsize=(10, 3))\n",
        "plt.title(\"# of movies per genre\")\n",
        "plt.ylabel(\"# of movies\")\n",
        "plt.xlabel(\"genre\")\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "total of 20 unique genres from 93342 occurances.\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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mZmZmNjiDqYE7ELhW0g3A5KW0ImLfYqUyMzMzs44Gk8D9AbgMuIPUB87MzMzMumgwTaizRsTXIuK4iDihdRnoRZKOlfSEpDsr2xaSNEbShHy9YN4uSYdLuk/S7ZLWrLxmz/z8CZL2rGxfS9Id+TWHKy/UamZmZjbcDSaBu0jSKEmL5wRsIUkLDeJ1xwNb9dl2AHBpRKwIXJrvA2wNrJgvo4AjISV8wEHAusAHgINaSV9+zucqr+sby8zMzGxYGkwT6m75+sDKtgGnEYmIKyWN7LN5B2CTfPsEYCzw7bz9xIgI4HpJC0haPD93TGvOuTwX3VaSxgLzRcT1efuJwI54hQgzMzObAQxmJYblaow3IiIez7f/CYzIt5cEHq4875G8rb/tj7TZ3pakUaSaPZZZZpnpKL6ZmZlZ93VtUfpc2xYNxToqItaOiLUXXXTRJkKamZmZFdN0Avev3DRKvn4ib38UWLryvKXytv62L9Vmu5mZmdmw1zGBk7Rhvp69xnijgdZI0j2Bcyvb98ijUdcDns9NrZcAW0haMA9e2AK4JD/2gqT18ujTPSr7MjMzMxvW+quBOzxfXzctO5Z0an7tSpIekfQZ4OfA5pImAP+V7wNcCEwE7gP+CHwJIA9e+DFwU778qDWgIT/n6Pya+/EABjMzM5tB9DeI4XVJRwFLSjq874MDrcQQEbt1eGizNs8NYJ8O+zkWOLbN9nHAqv2VwczMzGw46i+B25ZUS7YlML6Z4piZmZnZQDomcBHxFHCapL9HxG0NlsnMzMzM+jGYUahPSzonL4v1hKSzJS018MvMzMzMrITBJHDHkUaJLpEv5+VtZmZmZtYFg0ng3pUXsn8jX44HPBuumZmZWZcMJoF7StLukmbOl92Bp0sXzMzMzMzaG0wC92ngY6S1Sx8Hdgb2LlkoMzMzM+tsMIvZPwRs30BZzMzMzGwQuraYvZmZmZlNGydwZmZmZj3GCZyZmZlZjxmwD5yk70XEIfn27BHxavliWd1GHnDBNL3uwZ9vU3NJzMzMbHp1rIGT9G1J65NGnbZcV75IZmZmZtaf/mrg7gF2AZaXdFW+v7CklSLi3kZKZ2ZmZmZv018fuOeA7wD3AZsAv8vbD5B0beFymZmZmVkH/dXAbQn8AFgB+DVwO/BiRHgSXzMzM7Mu6lgDFxHfiYjNgAeBk4CZgUUlXS3pvIbKZ2ZmZmZ9DDgKFbgkIsYB4yR9MSI2krRI6YJZb/OoVzMzs3IGnAcuIr5VubtX3vZUqQKZmZmZWf8GUwM3WUTcVqogZtPDNX5mZjYj8UoMZmZmZj3GCZyZmZlZj3ECZ2ZmZtZjnMCZmZmZ9RgncGZmZmY9xgmcmZmZWY9xAmdmZmbWY5zAmZmZmfWYriRwkh6UdIekWyWNy9sWkjRG0oR8vWDeLkmHS7pP0u2S1qzsZ8/8/AmS9uzGsZiZmZk1rZs1cB+KiDUiYu18/wDg0ohYEbg03wfYGlgxX0YBR0JK+ICDgHWBDwAHtZI+MzMzs+FsKDWh7gCckG+fAOxY2X5iJNcDC0haHNgSGBMRz0TEs8AYYKumC21mZmbWtG4lcAH8VdJ4SaPythER8Xi+/U9gRL69JPBw5bWP5G2dtr+NpFGSxkka9+STT9Z1DGZmZmZd8Y4Ws6/RRhHxqKR3AWMk3VN9MCJCUtQVLCKOAo4CWHvttWvbr5mZmVk3dCWBi4hH8/UTks4h9WH7l6TFI+Lx3ET6RH76o8DSlZcvlbc9CmzSZ/vYwkU3A2DkARdM0+se/Pk2NZfEzMxmRI03oUqaW9K8rdvAFsCdwGigNZJ0T+DcfHs0sEcejboe8Hxuar0E2ELSgnnwwhZ5m5mZmdmw1o0auBHAOZJa8U+JiIsl3QScIekzwEPAx/LzLwQ+DNwHvATsDRARz0j6MXBTft6PIuKZ5g7DzMzMrDsaT+AiYiKwepvtTwObtdkewD4d9nUscGzdZTQbatxka2ZmVUNpGhEzMzMzGwQncGZmZmY9plvTiJjZEOYmWzOzoc01cGZmZmY9xgmcmZmZWY9xE6qZdZ2bbM3M3hnXwJmZmZn1GCdwZmZmZj3GTahmNsNxk62Z9TrXwJmZmZn1GCdwZmZmZj3GTahmZoW5ydbM6uYaODMzM7Me4xo4M7NhxjV+ZsOfEzgzM5tmThbNusNNqGZmZmY9xgmcmZmZWY9xE6qZmfUMN9maJa6BMzMzM+sxroEzMzPrwDV+NlQ5gTMzMxsinDDaYDmBMzMzm0E5YexdTuDMzMysEU4Y6+NBDGZmZmY9xjVwZmZmNiwN5xo/18CZmZmZ9RjXwJmZmZnVoMkaP9fAmZmZmfWYnk/gJG0l6V5J90k6oNvlMTMzMyutpxM4STMD/wNsDawC7CZple6WyszMzKysnk7ggA8A90XExIh4DTgN2KHLZTIzMzMrqtcTuCWBhyv3H8nbzMzMzIYtRUS3yzDNJO0MbBURn833PwWsGxFf7vO8UcCofHcl4N5pCLcI8NR0FHcoxxvOx+Z4jud43Ys3nI/N8RyvqXjLRsSifTf2+jQijwJLV+4vlbdNJSKOAo6ankCSxkXE2tOzj6Eabzgfm+M5nuN1L95wPjbHc7xux+v1JtSbgBUlLSdpNuDjwOgul8nMzMysqJ6ugYuINyR9GbgEmBk4NiLu6nKxzMzMzIrq6QQOICIuBC5sINR0NcEO8XjD+dgcz/Ecr3vxhvOxOZ7jdTVeTw9iMDMzM5sR9XofODMzM7MZjhM4MzMzsx7T833gzMymV16WbwSV78SI+Ef3SmRm1j/XwA0RkhZuMNZMkjZoKl6f2HN1I25J+e/5sW6Xo5Sc3DQZT5J2l/SDfH8ZSR8oGO8rwL+AMcAF+XJ+wXiHSXpvqf23ibedJH/X26A09Xkf7t+bAJJGSDpG0kX5/iqSPlPb/j2IoTNJ2wDvBeZobYuIHxWKNQG4FTgOuCgK/2Mk3RIR7y8Zo0+8DYCjgXkiYhlJqwOfj4gvFYq3IXAwsCypVkVARMTyheI1PSHkXMDXgWUi4nOSVgRWiojaEw9JE4GzgeMi4u66998m3pHAW8CmEfEfkhYE/hoR6xSKdx9pBZenS+y/TbzPAnuT3pfHAadGxPMF4/0JWJ/0Pzw2Iu4pFSvHGw8cC5wSEc+WjFWJuQEwkqlrUE8cDvEkjQF2iYjn8v0FgdMiYstC8Rr7vHfhe3Mn4FDgXaTfhNbvwnyF4l1E+ox/NyJWlzQLcEtEvK+O/fusrANJ/wvsCnyF9E/ehZQMlPIe0hDjTwETJP1U0nsKxrtU0kclqWCMqt8AWwJPA0TEbcB/Fox3DPBrYCNgHWDtfF3K3yR9Q9LSkhZqXQrGOw54lfTDDGkFkkMKxVod+D/gaEnXSxolqcgXXrZuROwDvAKQk4DZCsZ7GCiWQPUVEUdHxIbAHqQk4HZJp0j6UKF4uwPvB+4Hjpd0Xf4fzlsiHul7cwngJkmnSdqy5PeMpJOAXzHls976vA+LeMAireQNJn8e3lUwXpOf96a/N38BbB8R80fEfBExb6nkLVskIs4gnZASEW8Ab9a294jwpc0FuL3P9TzAVQ3F/hDpB/k54Apg/QIxJuU31evAC/n+CwWP6YZ8fUtl222l4zX4fnmgzWViwXjjmvx7VmJsnN+bLwInAO8u8b8jTcx9c76/aPU4C8Q7BrgaOBD4WutS+O84M7AD8BdgPPBt4DxSzUqpmAsD+wMPAhcBE4CvFIw3E7B9fr/8A/ghsFCBOH8ntyY1celCvPGkmvbW/WVbn40GYhf9vHfhe/Oapv5vOd7Y/LlrfZetB1xR1/49iKGzl/P1S5KWINUcLV4qWO4DtzupBu5fpJq/0cAawJnAcnXGi4hSZ9+dPJybHULSrMB+pC/CUi6X9Evgz6SaKgAi4uYSwSKi1v/PILwmaU4gACStQOU465T7xGxDavYbCRwGnAx8kDSJdt01xYcD5wDvkvQTYGfgezXHqPpHvsxG2Zo+ACT9BtgOuBT4aUTcmB86VNK9BeLtAOwFvBs4EfhARDyRm+HvBo4oEHM10vvlw6TmuJNJNVaXkb7T6nQnsBjweM37HSrxvgtcLekKUmvQB4FRpYI1+XnvwvfmOEmnk06cqr8Lfy4U72uk3/EVJF1DOhndua6dO4Hr7HxJCwC/BG4m/VAeXTDedcBJwI4R8Uhl+7jcnFur3KTxSWC5iPixpKWBxSs/JnX7AvA7YEnSGd1fgX0KxQJYN19XmzYC2LREsJyUfpEpzcJjgT9ExOsl4gEHARcDS0s6GdiQ9CNdwgTgcuCXEXFtZftZkmptBs+d7R8AvgVsRvrB2jEiiiX7EfHDHHuefP/fpWJltwPfi4gX2zxWYrDGR4DfRMSV1Y0R8VKdHapbch+450g1mwdEROuH8obcN7VuiwB3S7qRqX+Uty8Qq/F4EXGxpDVJtTcA+0fEUyViZY193gEkrQqswtR9zUv1X5wPeAnYorItSCf6tYuImyVtDKxE+i67t87fBA9iGARJswNzRNmOxooG/xlNdxQf7iQdDcxKamaAVJP6ZkR8tmDMhUlf6gKuL/Glns/GvxuFBu90iNn0AJtVSSdPrb43TwF7RM3rKucf4Y5K1A7n/9/fIqJI/7oOMZePiIkNxtu43faIuKKX40laOSLu6fS+Kfh+aezzLukgYBNSAnchsDVwdUTUVktViTUzcGhEfKPufbeJtWlEXJYHTbxNXTV+roHroFKNPJL8d5JERPy65jjnMaUZ7G2PFzyLXDci1pR0S47zrKRizUeSTgD2i6lHUh0WEZ8uFG9+Ui1V64zxCuBHBZPwdSJi9cr9yyTdVihWy5KkvlSzAP+Z35+1nklGxJuStgUaS+DIA2yAPzd0UnMUqc/b5QCSNgH+CNQ91c5h/TxWpHY4///ekjR/yRPQPraTdBypX+3RpAEUB0TEX0sEi4grJI1gyiClGyPiiRKxWvFK7buPr5GaStu9b0q+X5r8vO9MGjRxS0Tsnf+PfyoRKB9biRrgdjYmdRfYrl1RqKnGzwlcZ+eRRsHdQR5BUsiv8vVOpH4VrTfvbqS+cKW8npPUVvK4KGWPc7XoM5JKUslalmNJfVVa8wx9ijRys+0ZUQ3elLRCRNwPqRaCOkcb9SHpWGA14C6m/N9KNQVcI+m/gdNJnZlTsEL9CYHPk3683pD0CoWH+gNzt5I3UqCxkuauO0iTtWB9/Bu4Q2k6iur/b99C8T4dEb+TtCWwIOmzdxKp20TtlOYS+yWp24KAIyR9MyLOKhRvEvl7s+J5YBzw9bpqHyNiVL5u+n3T5Of9lYh4S9IbeaTrE8DSBeK03CppNKlfefXY6j7xPSjf/GxEFPsdcALX2VIRsVrpIK2zOUmHxdTz4ZwnaVzB0E13FJ9J0oKR54XKQ8VLvv9WiIiPVu7/UNKtBeN9gzRwYiLpR2RZUifgUtaLiFUK7r+q1em8elZerD9hFwbYTJT0fVKSAWkwUe1NgE01q7TxZwr18emg1ZTwYeCkiLir5DQipE7+67Rq3fLJ6N+AIgkc8FvgEeAU0rF+HFiB1Ff6WFKTYG0k7QJcHBGTJH0PWBP4cUTcUmeciiY/7zflvuZ/JI22/TepP3gpc5AGJFaPpVgfOOABSReTkuHL6m5RcALX2UWStihV7d/G3NW+I5KWA2qvBWiJiJNzZ+NGOoqTmgGuk3Rmjrcz8JOC8V6WtFFEXA2TJ/Z9eYDXTJNck7k6sCKpsyqkzqpFRoVm10laJRqYWLfpGoBOHaX7dsKv0adJU1y0vsSvytvq1kizytt2HHFCHrG8TETUPsq1jfGS/koaOX+g0nxzJWv3Z+rTZPo0Zec43b5Pd4mjJN0aEd+W9J0C8b4fEWdK2gj4L1Jt4/8yZaBWrRr+vM9HmmN1LGlQ1nwRcXupYBFR8qS6nZWBbUkD9o6RdD5pqqCr69i5BzF0IOkjpObMmUhzpZWesXkrUl+cag3O5yPikprj9DtJYkQ8U2e8PrHfS5rjDtLZSLHkQ9IapAEF85P+ns8Ae0WaQLhEvBsjothyT23ibUwanv5P0ki41vuzSK2xml2V5LzK3TlIIzPHR0SRGr/hTtJ2pK4as0XEcvmz8aNS/WvzSOI1SBtuIsUAABpPSURBVPN5PZcH2yxZ6odZabqg1YBT86ZdSfN3frtQvOtIE5O3avh2JvWhXC8ncrVOk9Ia1CPpZ8AdEXFKyYE+TfYfVpq8+oP5sgJwC3BlRPyu7lg53nuAI4EREbGq0nQ320dEqUnQq7EXJM3E8MmIqGW5MidwHUh6gDTR5h1NjQ7No11XznfvKVGDk48rmNLMQeV+RKGlpnLsxhcMz/0qiIgXCsf5DWkUaiP9xJSWf/oaffpoRsRDBWL9LzAXKfk+mvSDdWNE1D4FRYf4SwO/7dMkXsd+fxsR+1cHElUVTHAWYMoqDNXPQpE+abmmfVNgbOtHX9KdEbFqiXh5/wuSaqSrCX+pGlTyoJdWB/WrIuKcgrGWJ/0Qr09631wPfJU0PdJaddWuVOKdn/e9Oan59GXS52/1fl847fHOJvUfro6oXz0iivQfzr8L65C+X74AvBwRK/f/qmmOdQXwTdIUT019FjYmnVRsReoneXpEnF3Lvp3AtSfpSmCTiChZ9V+NNxfpB3nZKLy2ZTcoLRh+EGlgxpsUqjGStHtE/EnS19o9HjWPIq7EvbzN5ihVayTpuohYf+Bn1hLr9ohYrXI9D2m93g82FF/AXXX3+ZO0VkSMV/PTUFxL+tHvm3yf0PFF0xfv+lw7dEvlR+v2grW1nyVN1L0UaX3n9YDrXIM6bfJvw1akyoQJkhYH3leqe0+7WsQSNYt5v5eSugpdR+q6cHUUHEEs6aaIWKfPZ6HIseV9P0iqVTwDGB3t536cZu4D19lEYKzSYrTVyRqLJACkEZLjmXptyzOBWhM4dWEuqmw/UkJaesHwVr/Bdh3hi5yt5DPI0RHxmxL77+AWSaeQRkuXnlG86VVJjmDK/6rVHFf7+zIixueba/RtspG0H6npqIQ5IqLtCUYhd0n6BDBzPjHcF7h2gNdMj/1INSrXR8SHJK0M/LTuIJKujoiN9PZRoUW6u0j6VkT8os/7c7JSNaikiYPH5TIsk7fdUygWNNh/mDSp9VrAqqSRvM/lk9NS8Z5SWrWmNfvCzpRdUWO1kq0/TuA6a63L1sjyOqRRk7tK2g0mz5JeYuRW43NRZY0sGB4Rf8g3/xYR11QfU6E5gCLNL7QbqV9MU+YkJW5NzCje9Kok1dHXbwCn9v1f1mxPUpNY1V5tttXlJEmfI52cVZPvUv1Pv0IaqfkqaeTkJcCPC8WCNDXEK5KQNHukyWhXGvhl70xEbJSvmxq13BrkVXJ2gHYuYEo3lzlIg0PuJfVJLeELwIm5LxzAs6TPSO0i4qsAeaDLXqSKjMWA2UvEIw0mOApYWdKjpN/4TxaKBTCf0hyok5v3SfOhPtLPawbNTahDRG5W2Yy02O6a+Szh1CY7xpck6RjSCM0LaKBGU9LNEbHmQNtqjNdoH7gm5R/hV1u3ST8ir5Too5lj7NeuRqzujs056f4EaY3OqyoPzQu8FRGb1RmvEncf0gjs55hSk1Os/6mkXSLizIG21RjvHNIUOvuTTgifBWaNiA8XindSRHxqoG0F4s4VES+VjNEh7prAl6LQKi+SlouIB6r9h1vbCsT6MmkAw1rAg6TP4VURcVndsXK81rHNTRq9PKnUseV4Y0gnTdUpij4ZEZvXsn8ncO0pzSX0Ld4+8q5Un6bNSfOwrUKa8HJD0qjJsTXH6cpcVEpLprSL98Oa46xPmkF/f6auEZsP+EjBjr+N9IHrRjNOF5LhdvFqH3UnaVlSbcbPgAMqD00ijWJ8o854lbgTSQvKl1zPshqv0f9fnzgbk0aCXxSF1gXueyySZiH9/4rMk5i/Y44B5omIZSStTpox4Esl4nUowx0R8b5C+273fhkfEWsViPUNUtI2vtTnrU+8xo4t77tof0I3oXZ2Mqk2ZVtSlfKewJOlgkXEGEk3M2Vty/0KfcF3ay6qWhO1fswGzEN6b1ebVl4gjZ4sIpqbO6mxZhxJi5GW65pTadWMVpP+fKRRqXXHa9WILac0W3rLvKRpYGoVacTuQ5I+CTwWEa/kcsxJ6oD/YN0xs/tIC2oXJWlr0mS6S0o6vPLQfKSm6VJxJ9d+xZSJyk8ijWasM86BwHdI789WPyMBr5GayUr5LbAlaRofIuI2FVjkvUVTD8iaiVRb9ViBOCuTKizm73OCPx+VSow6RcSvBn7W9OvGsWVPS9qdKVPc7EbqQ1wLJ3CdLRwRx+SmmyuAKyTdVDhmE2tbHqQ0T9NFEXFGnfvuT1M1mpX/1fFRYEqNTtTQ3EkRcV4eNPG+KL8o85akfilLAdWm7kmkH866XUvqULwIU/fVnETq7FzKGUy97umbpAFE67R/+nR7kbSkz+VM3Z2g7trTx0iJ/vakAVItk0jTXpQyVd+s/H6tvYYjIn4G/EzSzyLiwLr3P0Dsh/t0US62XBLpBKZV2/4GaeBSLdNQ9LESqcJiAaY+wZ8EfK5AvCZ169g+DRxBag0K0ndcbZMJuwm1A00Zen8Jadmpx4CzImKFQvHarm0Z5RZ7HxdTL91VlNLM7KeTlpyaXKMZ5SbbHAPsEnn9VaV5qU6LiC0LxWt67qQmpxH5aNQ0b9FQ1KGZ47aCze1tO4RHuWlEZm01X+bPwdJRYFLdao0YU2oYJ9eIlUyy1OC8c5LOIp3Q/DdpNYT9gLUj4uOF4q1D+ruOZEqlS0S5aWDWj4iSy1l1zXA7NidwHUjaltQ2vzQpg54P+GFEjO73hdMe7+5SfTY6xPs58BRv73RfZCRcq5+BKvNPKc/JUyje2/pMlehHVdl3Y3Mn5X0fSaqxLbooc441O/BR3j7xbKmVGHYCDgXeRUoASq+CMgY4ovXZlrQDsG+pQQxNkzSWVAs3C6km7gng2sgjAAvEa7RGTA3POydpEdII5f8ivTf/SuryUmSKJEn3kk5876TwpN053i+AQ0hTh1xMqlj4akT8qUS8JjV9bEpLYn6Ft3931jJJuJtQ28hV/itGmkT3eaYs/1RSY2tbZrvm630q2wIotRJDqwPz40rLMj0G9Lus13R6S9IykVd6kDSSQvPAZU3OnQTNLsp8LulzMJ5Kk19BvwC2i7Jr81Z9AThZ0n+TfpAfJq2UUITSXGw/Iw1YqtYYlfrszZ9HEn4WODF3oyi53uSBkpYkLQdY/dEqtRJDI/POweTfht9FRMmpJ/p6MiLOG/hptdkiIr6ltJzkg8BOwJWkpSV7XdPH9hfSgJfzKLAesBO4NqI783qdSEriGlnbMiKWK7HffhyS+4l9nSk1miX74XwXuFpp6RSRhqqPKhjvi8AJamDupGwm0ll/tYm4vzn+psdSEbFVoX23868Gkzci4n5gPaUVJoiIfxcOeRypv+RvSCeHe1N28fVZlGbv/xjpc1FUrt3/OHA3U/qGBemHsoRG5p2Dyb8Ny0qaLSJeKxGjjYMkHQ1cSvlJuyFNhwSwDXBmRDyvIlOSdkXTx/ZKRBw+8NOmjRO4zq7JZ+RNzet1DKnf1FTL65QkaQPeXrV7YolYMWVJsEZqNCPiYklrk5K2W0hnQiVrxP5OqjlagdRR9nlgR8p1vl+tlbwBRMSzeaRoCddKel9E3FFo/32Nk3Q66X9W7AdLHZZda32hR7lVV+aMiEslKTeDHay0XukPCsX7EWny3qsj4ialtTwnFIoF8BHSqitN1NYCPKI00fRfgDGSngVKDmCaSPp9GM3Uvw2l3i97k9bInpVK/2gKzRgAnCfpHtL35RfzALRXCsVqWtPH9julKbT+ytTfZbXkEU7gOmv1XWpNfyHKrlTwZKn+de3kYf0rkPqMVM+SiyRwpfsCtInXtl8M5f5/55ImZr2ZtAxaaTNJWjAingWQtBDlPs8bAXtJeoAGaodJtbMvUX6VidZUKE3N5N/yah4JPkFpItNHSVPflHJZVCbtjYiJpD6NpUwkJRuNJHAR8ZF88+A8snd+Uv+mUu7Pl5lo5r2zTkQUqVFsJyIOyH3Fns81ji8COzQVv6Q2x/YSZY/tfaSKmU2ZOvmu5XfICVwflbPx85myfElLyT5UTa5tCbA2sEo0N4qlaF+ANhrrF5M13cx4GKnJvfXDvAtpdv8Sti6037YiorZh9gNojSi/OwqtSlClKfOj/YWUPO5LWtJqU8o2t18v6VZS0+1FDXzmXyJNk9K3ya/UWqGTR9eSpoWYRFpbs0hrSeQ5LRtscr+2if7RajPJe5/mxVK/RY2RNBfwJWAZUuvMEqQpRmpdc7xiF2D5Us3tTuDernVGtRIpATiXlMRtB9xYMG6Ta1tCGtG0GGUX8q0q2hegQ7xG+sVkjTYzRsSJksYx5Uxup1Jf8BHxkKSNSAN7jsvNDsVqjCS9BzgSGBERq0paDdg+Ig6pOdSHJR0AHEgazVvaWpKWIK29+EdSovP1BuK+hzRi8tPA4ZLOAI6PiP8rFG90vjRC0o9J8xVOpEAtR5t4q5KWRloo338K2CMi7ioRj9R6cGsDNeDVSd6rSX6r9annEzjSScx4psz7+Cjps18qgbuT1KXmiRI79zQiHUi6EtgmIibl+/MCF0REsRm3myDpPNKHcV5SM/GNTH2WXKpJ8xOkeZqK9AVoE6+R9Rgl3UH6e85COr6JNNPM2Jjch2NtUr+m9+Qk5MyI2HCAl05rvCuAbwJ/aE37IunOiFi15ji/JE3iOQ9Tr4xQZNoSSfuSBrssT/rhaP0wtuKVGoVaLcOHSCPu5gZuAw6IHp8XK0+z8b6mBhUorVv93Yi4PN/fBPhpRGzQ7wunPd6y7bYXnEZkDt4+bVCUmjaoScrzn6oypZTKzvk4ljRVyU0U+J11DVxnI0gTULa8lrcVIWkp0ujM1o/iVaRRho/UHOpXpB+MQ0md7CcXIW8rpWhfgL4a7BezbYF9DjUfAd5PbpKKiMfyCU0pc0XEjX2ab2pf+ikivgl8U9K5EVG8j0+ugT5c0pER8cXS8VokLUxaRHsP4J+kvqijSSdwZ5LWg60zXtPTpBSt5Whj7lbyBhARY5UWRy+iVKLWj78wpT9vq4P/cKnpeU1pqbwAkLQCZftqtl0DvC5O4Do7Ebgx1+RASnaOLxjvOOAUUps5pC/c44DN6wwSU9YmnLV1uyW/sUsp2hegP32Ps+Z9N/3l2g2vRURIan3pFfuxyp7KX6yteDtTsKm/ieStT7zGkrfsOlKT3/YRUR1gM07S/xaI1/Q0KT8j9SG+kwZaE4CJkr5P+ptC+q6eWChWNzTdn7dJB5FO5JeWdDKpwmSvUsFK/vaAm1D7JWlN0vxhAFdGxC0FYzUyk7+kL5I6cS5PGknVMi9wTUTsXme8Sty/AKMioqmzZKuJpG+Qmoc3J/1Yfho4JSKOKBRvedJi5BuQmr4fAD45gyTLtdOUpZj6Tqxbaimm1qord0TE+6rbCsW7C/gDfaZgqvvHszUIJQ90G0kanQ1pfrsftkaE9zpJR5FWJmlq2qDGSPoTaWqnl0lJ9w0R8VR3SzXtnMANEXnE1nHAqXnTbsDeUfNyPkoTzS5I+iE+oPLQpCi0jFaOO5aCfQGsLEmbkwbYCLgkIsYUjDVzHuI/NzBTqx+qTRs1vxTTtaTk5ixSp/hHgZ+XmgpDBZfk6xPnbtJgkItINYutPoxAuWUImzKD9Of9EKlS5oOkUei3kCpnftfVgk0jJ3BDRO6oegSwPulDdC3wlYh4uKsFq4mkjdttL13FbPWRNB9T1+CUWjf3H6RmjtNJc5gV/5LK3QeWiYh7S8dqmqSrI2KjgZ853XFaNVTfAn5P6pf2Y1L/019ExPWF4v6alGiMpuAAqTaDUCY/REODUErqNFiiZbjUgCsth7YOKQn/AvByRKxcKNZOpMGPRfrZOYEbIiSdAOwfU0/M+quI+HR3S1YfSSNIHxyAG92c2hskfZ40ofUrpBqcoj9Yea6mbUnLMa1JGuJ/WuR1ZgvE2440uGe2iFhO0hrAj4ZL7bCkzUg1+kWXYupTQ7UJU8+hWTLhv7zN5ohyi9k3OgjF6pNbuuYm9Qu9irQ6SbHfIUnHkQbqXUk6Ib04ImobkOUEboioDmvub1uvkvQx4JfAWKasTfrNiDirm+WygUmaAKzfjb4ieYLW35H6wM1cKMZ40pfs2MrUApP7b/W63O9nZeAuKiPA6z45HArTpJj1R9JvgLVIJzLXkBKr6yKi2DKLkmYlTYa+K6lrwZiI+Gwd+/Yo1KGjyaWRuuG7pCVhngDIk8H+jdRPxoa2+5l6nrTicpP7rsBWwDjSQuylvB5vX9R6OJ3ZNrIUU9PTpKjDWraV8pRam9R6VER8FSbP67oXqd/5YsDsBWO+Luki0nfKnKQZLZzADTNNLo3UDTP1qap+mrJTC1h9DiStNHEDDSyNJOlBUufiM0i1tC/2/4rpdleeaHrmPIfZvqQ+qMNFI0sxtTTYvNiazqbptWytRymtPfxBUi3cg8CxpKbUUvFaNW+bkFqfjqbGk1E3oQ4hklZhysS2lzX1hVuaUtXGMcCSTBlluytwe0R8u2sFs0GRdCNwNW+fpuGEQvHmi4gXSuy7Q7y5SDXErWXsLgEOiYhXOr+qd0j6O2nEXemlmMyGtDwl0lXA+Dr7ovUT71RS37eLSgxkcAJnjciTbP6AKXMnXRUR5/TzEhsimuqLKekI+mm6LFXjN9w1vRRT03J3jM8x9dJPDKcBYNabJB3at5Ki3bZp5SZUa8p44OGIaNtfxYa0iySNAs5j6ibUukcVjsvXG5KWYTo9398FKFYbLWkMsEtEPJfvL0ga9bplqZhNGi6JWj/OJdWq/A14s8tlMavaHOibrG3dZts0cQ2cNULSPcC7gYeAyX2a3Iwz9El6oM3mktOIXA9s1GriyKO4roqI9QrFG9YjwIe7EivWmE2PyopHKwD3VR6qdcUj18BZU4ZFbcaMKCJqXex8EBYE5gNaNXzz5G2lvCVpmYj4B0xucvSZbe84X9KHI+LCbhfELDuFNB9i0RWPXANnZv2StEe77RFxYqF4ewMHA5eTOtz/J3BwwUETW5HWXr2CKXMUjoqIS0rEs3pJmkQakfoq8DpTBmnM19WCmRXmBM7M+pUHF7TMAWwG3BwROxeMuQTwKeDvwFzAYxFxZcF4iwCtJtrre3mB6xlRnjdzRdL7E/AyfTb8OYEzs3dE0gKkTv5bFdr/Z4H9gKWAW0mJ1XWllkbKMZcElmXqUYzFEkarT4f3y7URsVlXC2ZWmPvAmdk79SJQsl/cfqQ1c6+PiA9JWhn4aalgkg4lzUs41VJTpGV2bOhr9P1iNlQ4gTOzfkk6jymd+mciTfFxRsGQr0TEK5KQNHtE3COp5FJQOwIrlZho0xrR9PvFbEhwAmdmA/lV5fYbwEMR8UjBeI/kZtq/AGMkPUuafqaUicCsVOa4s57S9PvFbEhwHzgz65ek5YDHW0tLSZoTGBERDzYQe2NgfuDiiHitUIyzgdWBS2lgrVcrp4n3i9lQ4QTOzPolaRywQesHUdJspMko1+luyeohac9220tNW2JmVgc3oZrZQGap1mZExGs5iRsWIuKEXKu4TETc2+3ymJkNxkzdLoCZDXlPStq+dUfSDsCwmSdN0nak6ScuzvfXkDS6u6UyM+ufm1DNrF+SVgBOBpbMmx4GPhUR93evVPWRNB7YFBjbWv9U0p0RsWp3S2Zm1pmbUM2sXzlRW0/SPPn+v7tcpLq9HhHPS6pue6vTk83MhgI3oZpZvyTNL+nXwFhgrKTDJM3f5WLV6S5JnwBmlrRiXjrs2m4XysysP07gzGwgxwKTgI/lywvAcV0tUb2+AryXNIXIqaTj27+rJTIzG4D7wJlZvyTdGhFrDLTNzMya4z5wZjaQlyVtFBFXA0jaEHi5y2WabpJ+GxH791kqbLKI2L7Ny8zMhgQncGY2kC8AJ1b6vT0LtJ38tseclK9/1e+zzMyGIDehmllbkr5WvQvMnW+/CERE/Lr5UtVP0tzAyxHxVr4/MzB7RLzU3ZKZmXXmQQxm1sm8+bI2qRZuPtI6k58H1uxiuep2KTBX5f6cwN+6VBYzs0FxE6qZtRURPwSQdCWwZkRMyvcPBi7oYtHqNkd1bruI+Lekufp7gZlZt7kGzswGMgJ4rXL/tbxtuHhR0uQaRUlrMQwGaZjZ8OYaODMbyInAjZLOyfd3BI7vXnFqtz9wpqTHSH39FgN27W6RzMz650EMZjagXEP1wXz3yoi4pZvlqZukWYGV8t17I+L1bpbHzGwgTuDMbIYmaRfg4oiYJOl7pAEah0TEzV0umplZR+4DZ2Yzuu/n5G0jYDPgGODILpfJzKxfTuDMbEb3Zr7eBvhjRFwAzNbF8piZDcgJnJnN6B6V9AfSwIULJc2OvxvNbIhzHzgzm6HlOd+2Au6IiAmSFgfeFxF/7XLRzMw6cgJnZmZm1mPcTGBmZmbWY5zAmZmZmfUYJ3BmZmZmPcYJnJlZYZK8bKGZ1coJnJlZH5K+L+leSVdLOlXSNyStIOliSeMlXSVp5fzc4yUdLulaSRMl7Zy3b5KfNxq4W9LMkn4p6SZJt0v6fFcP0sx6ms8KzcwqJK0DfBRYHZgVuBkYDxwFfCFPNbIu8Htg0/yyxYGNgJWB0cBZefuawKoR8YCkUcDzEbFOnmvuGkl/jYgHmjo2Mxs+nMCZmU1tQ+DciHgFeEXSecAcwAbAmZJaz5u98pq/RMRbpJq2EZXtN1YStC2A1Vo1dMD8wIqAEzgze8ecwJmZDWwm4LmIWKPD469Wbqty+8U+278SEZfUXTgzm/G4D5yZ2dSuAbaTNIekeYBtgZeAByTtAqBk9Xe430uAL0qaNe/jPZLmrrPgZjbjcA2cmVlFRNyUBx7cDvwLuAN4HvgkcKSk75H6xp0G3PYOdn00MBK4Wakd9klgxxqLbmYzEC+lZWbWh6R5IuLfeZ3UK4FREXFzt8tlZtbiGjgzs7c7StIqpMELJzh5M7OhxjVwZmZmZj3GgxjMzMzMeowTODMzM7Me4wTOzMzMrMc4gTMzMzPrMU7gzMzMzHqMEzgzMzOzHvP/f/oWRaU7M5AAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 720x216 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oosg8FCnfWVI",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def get_year(date):\n",
        "    year = None\n",
        "    if date:\n",
        "        year = date[:4]\n",
        "    return year"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aiO9z6NwfNdW",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Year (example of basic feature engineering)\n",
        "movies['year'] = movies.date.apply(get_year) # same as movies.date.apply(lambda date: get_year(date))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pMpHNrOzcy1q",
        "colab_type": "code",
        "outputId": "8704c92d-76cc-4ca6-f01f-50113bdd88d7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 334
        }
      },
      "source": [
        "# Years\n",
        "years = movies[movies.year.notnull()].year # get rows where year is not None\n",
        "print (f\"Total of {len(set(years))} uinque years from {min(years)} to {max(years)}\")\n",
        "pd.Series(years).value_counts().sort_index().plot(kind='bar', figsize=(30, 5))\n",
        "plt.title(\"# of movies over time\")\n",
        "plt.ylabel(\"# of movies\")\n",
        "plt.xlabel(\"year\")\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Total of 135 uinque years from 1874 to 2020\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
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\n",
            "text/plain": [
              "<Figure size 2160x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "x6zna8Lxlc8U",
        "colab_type": "code",
        "outputId": "92995dca-2f0f-47c9-b59d-1951c0ae82f7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 85
        }
      },
      "source": [
        "# Sort by popularity\n",
        "sorted_by_popularity = movies.sort_values(by='popularity', ascending=False)\n",
        "print (\"Most popular movies:\\n\", sorted_by_popularity['title'].values[:10])"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Most popular movies:\n",
            " ['Minions' 'Wonder Woman' 'Beauty and the Beast' 'Baby Driver'\n",
            " 'Big Hero 6' 'Deadpool' 'Guardians of the Galaxy Vol. 2' 'Avatar'\n",
            " 'John Wick' 'Gone Girl']\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3MUTBmsE7ZzG",
        "colab_type": "code",
        "outputId": "8f65a559-cb80-4916-d3bc-28fdda01d280",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 299
        }
      },
      "source": [
        "# Get poster image\n",
        "from IPython.core.display import display, HTML\n",
        "prefix_url = 'http://image.tmdb.org/t/p/'\n",
        "poster_sizes = [\"w92\", \"w154\", \"w185\", \"w342\", \"w500\", \"w780\", \"original\"]\n",
        "full_url = prefix_url + poster_sizes[2] + movies.poster_url[0]\n",
        "display(HTML(f'<img src=\"{full_url}\">'))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<img src=\"http://image.tmdb.org/t/p/w185/rhIRbceoE9lR4veEXuwCC2wARtG.jpg\">"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SJuApUPxnZNR",
        "colab_type": "text"
      },
      "source": [
        "### Ratings\n",
        "\n",
        "User ratings for each movie. We are using the smaller set of ratings (subset of 100,000 ratings from 700 users on 9,000 movies.) but you can download the full version (26 million ratings from 270,000 users for all 45,000 movies) from the [source](https://www.kaggle.com/rounakbanik/the-movies-dataset/data). "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OONPWedbmvWL",
        "colab_type": "code",
        "outputId": "b4b18ff8-cbfc-4499-c986-3d6ed82f77a7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 195
        }
      },
      "source": [
        "# Load ratings\n",
        "ratings = pd.read_csv(\"/content/drive/My Drive/Incubator/ratings_small.csv\", header=0)\n",
        "ratings.head()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>userId</th>\n",
              "      <th>movieId</th>\n",
              "      <th>rating</th>\n",
              "      <th>timestamp</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>31</td>\n",
              "      <td>2.5</td>\n",
              "      <td>1260759144</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>1029</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1260759179</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>1061</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1260759182</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>1129</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1260759185</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1</td>\n",
              "      <td>1172</td>\n",
              "      <td>4.0</td>\n",
              "      <td>1260759205</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   userId  movieId  rating   timestamp\n",
              "0       1       31     2.5  1260759144\n",
              "1       1     1029     3.0  1260759179\n",
              "2       1     1061     3.0  1260759182\n",
              "3       1     1129     2.0  1260759185\n",
              "4       1     1172     4.0  1260759205"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DuKBFhYM1705",
        "colab_type": "text"
      },
      "source": [
        "The first row in the dataframe above can be read as the following: userId `1` voted `2.5` on movieId `31` at timestamp `1260759144`."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "I2WaidX8n3VS",
        "colab_type": "code",
        "outputId": "7fa3e4d9-c56f-4dd6-ad60-31d867a3a5a0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 346
        }
      },
      "source": [
        "# Distribution of ratings\n",
        "print (ratings['rating'].describe())\n",
        "pd.Series(ratings['rating']).value_counts().sort_index().plot(kind='bar', figsize=(5, 2))\n",
        "plt.title(\"Distribution of ratings\")\n",
        "plt.ylabel(\"# of movies\")\n",
        "plt.xlabel(\"rating\")\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "count    100004.000000\n",
            "mean          3.543608\n",
            "std           1.058064\n",
            "min           0.500000\n",
            "25%           3.000000\n",
            "50%           4.000000\n",
            "75%           4.000000\n",
            "max           5.000000\n",
            "Name: rating, dtype: float64\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 360x144 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sJSQZp3Z0ZF5",
        "colab_type": "text"
      },
      "source": [
        "## Project ideas\n",
        "\n",
        "Details and resources for different project directions using this dataset. When you're developing your models, start with simple approaches first and then slowly add complexity. You should clearly document (README, articles and [Weights and Biases reports](https://www.youtube.com/watch?v=o2dOSIDDr1w)) and save your progression from simple to more complex models so your audience can see the improvements. The ability to write well and document your thinking process is a core skill to have in research and industry."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3rRBPthW0cLb",
        "colab_type": "text"
      },
      "source": [
        "- **Moderate Projects** \n",
        "\n",
        "    - **[Exploratory analysis](https://madewithml.com/projects/160/programming-probabilistically/)**: relationships between staff/revenue, popularity/genre, etc. These could be simple statistical tests to validate some hypothesis (ex. action movies get better ratings than horror movies). This project can also involve a lot of exploratory analysis. \n",
        "        - People should not dismiss this project because this kind of statistical analysis is **very prevalent** in the industry. Not everything is about using deep learning to predict classes and it's kind of analysis that many marketing, advertising, science, etc. industries utilize every day to make their decisions.\n",
        "\n",
        "    - **[Text classification](https://madewithml.com/topics/text-classification/)**: multi-class classification of movie genres using title, ratings, etc. You can also be very creative here and try to get more metadata like movie descriptions from online using the ids provided in the dataset.\n",
        "        - When you're developing your models, start with simple approaches first and then slowly add complexity. For example, start with analyzing the text as tokens and using [logistic regression](https://madewithml.com/topics/logistic-regression/). Then explore [embeddings](https://madewithml.com/topics/embeddings/) with [CNNs](https://madewithml.com/topics/convolutional-neural-networks/) and [RNNs](https://madewithml.com/topics/recurrent-neural-networks/) and eventually attempt to use contextualized embeddings, pretrained [Transformers](https://madewithml.com/topics/transformers/) from [HuggingFace](https://madewithml.com/search-results/?tags=huggingface), etc.\n",
        "\n",
        "    - **Miscellaneous**: predict numerical statistics like revenue, popularity, etc. given title, genre, production companies, etc. Here you can start with simple models like [linear regression]() and then try out ensemble methods like [random forests]() and evetually use neural networks. If you have time, you can even explore more recent methods that offer the performance of large networks with the interpretability of simples models - for example: [TabNet](https://madewithml.com/projects/1001/tabnet-attentive-interpretable-tabular-learning/) or [Neural Backed Decision Trees](https://madewithml.com/projects/659/neural-backed-decision-trees/)."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IsxRjRtt1SLJ",
        "colab_type": "text"
      },
      "source": [
        "- **Challenging projects**\n",
        "    - [**Recommendation systems**](https://madewithml.com/topics/recommendation-systems/): recommend a movie based on liked movies. There are a lot of different approaches here but the most common technique will be content based. Ask the user to select movies that they like (from the dataset) and then recommend similar movies (based on genres, ratings, cast, crew, etc.) that they'd enjoy. \n",
        "\n",
        "    - [**Text generation**](https://madewithml.com/topics/text-generation/): generate the `tagline` or `overview` from movie title, genre, poster image, etc. or vice versa. You should definitely leverage pretrained language models here and then you can fine-tune using text from this dataset. \n",
        "\n",
        "    - [**Named entity recognition**](https://madewithml.com/topics/named-entity-recognition/): identify entities in movie titles and descriptions. This dataset doesn't come with any labeled entities however you can use a trained NER model to extract relevant entites that model can handle. This isn't much of an application so if you do choose this project, we suggest extending this to things like few-shot NER using embeddings, etc.\n",
        "\n",
        "    - [**Few-shot learning**](https://madewithml.com/topics/few-shot-learning/): identify genres using one or just a few samples, also check out [self-supervision](https://madewithml.com/search-results/?tags=self-supervised-learning&tags=natural-language-processing) techniques in NLP where you can create meaningful tasks with the data that extract relevant signal from the latent space which can then be used for classification with very few samples. "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IgCBLj5S1jhZ",
        "colab_type": "text"
      },
      "source": [
        "---\n",
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        "             "
      ]
    }
  ]
}