movie_recommendor.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "d6659475-0004-46ca-8110-61a090acf01f",
   "metadata": {},
   "source": [
    "<div style=\"text-align: center;\">\n",
    "\n",
    "# <u>STREAMFLIX HYBRID MOVIE RECOMMENDATION SYSTEM</u>\n",
    "\n",
    "</div>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "866a7435-710a-41fb-a929-5aeb9d0081fb",
   "metadata": {},
   "source": [
    "![attachment:logo.png](logo.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3b978faa-5a6f-4773-9492-8e936a1443aa",
   "metadata": {},
   "source": [
    "<div style=\"font-size: 1.3em; font-style: italic;\">\n",
    "\n",
    "<u>Authors</u>  \n",
    "- Evaclaire Wamitu  \n",
    "- Simon Makumi\n",
    "\n",
    "</div>\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "addc11ee-4adc-4e3f-870a-47496ff26d09",
   "metadata": {},
   "source": [
    "# BUSINESS UNDERSTANDING"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bb1e8a1a-4853-4b98-bcc8-7c7151490f1d",
   "metadata": {},
   "source": [
    "## Overview"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ae6e70cb-8761-4f35-9870-25ba27836dfb",
   "metadata": {},
   "source": [
    "StreamFlix is an online movie streaming platform that aims to implement a movie recommendation system to enhance user experience, increase watch time and improve overall customer retention. This project will utilize the MovieLens dataset to build a hybrid recommendation system that provides personalized top 5 movie recommendations for each user based on their previous ratings and preferences. By implementing a personalized recommendation system, StreamFlix hopes to increase user satisfaction and engagement, boost average watch time per user, improve customer retention rates and differentiate itself from competitors in the streaming market.\n",
    "This system will combine collaborative filtering techniques with content-based approaches to ensure accurate recommendations. Through this personalized approach, StreamFlix aims to increase user satisfaction and engagement by reducing content discovery friction, boost average watch time per user by suggesting highly relevant content and improve customer retention rates through a more tailored viewing experience. Additionally, the company seeks to differentiate itself from competitors in the streaming market with superior content curation, optimize content acquisition and production strategies based on user preferences. This project aligns with StreamFlix's broader strategy of leveraging data-driven insights to create a more compelling and personalized streaming service ultimately driving business growth and solidifying its position as a market leader in the competitive streaming industry.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cb031e75-b541-463b-8c5d-be88d0fa55f6",
   "metadata": {},
   "source": [
    "## Business Problem"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b4aef4a3-f506-41c8-8f36-67a4d7acd564",
   "metadata": {},
   "source": [
    "\n",
    "\n",
    "StreamFlix is facing challenges with user retention and engagement. Users are  overwhelmed by the vast library of movies available and often spend a considerable amount of time searching for movies they would enjoy.  \n",
    "\n",
    "Streamflix have been experiencing high churn rates, decreased watch time and user frustration with content selection. There  has been a 15% decline in average watch time over the past year, high user churn within the first three months of subscription and loss of market share to competitors offering more personalized experiences. These issues are negatively impacting their revenue, customer satisfaction and competitive edge in the streaming market. \n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "be8a72e2-097f-438b-a9ff-1df92299bf58",
   "metadata": {},
   "source": [
    "## Objectives"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c533b854-0de8-45e8-84a1-8432d689336f",
   "metadata": {},
   "source": [
    "### Main Objective"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5d655704-b366-485e-bc51-992384f76740",
   "metadata": {},
   "source": [
    "To develop and deploy a hybrid recommendation system that combines collaborative filtering and content-based filtering techniques to enhance recommendation accuracy by capturing both user-item interactions and movie attributes ensuring a more personalized movie recommendation experience.\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "60a09e0e-9e38-467c-82e6-b09d8e2007f7",
   "metadata": {},
   "source": [
    "### Specific Objectives"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f28f3f7d-5712-458c-bc61-7730778d795e",
   "metadata": {},
   "source": [
    "1. To build a collaborative filtering model using user ratings to generate top 5 movie recommendations leveraging algorithms such as Singular Value Decomposition (SVD) and K-Nearest Neighbors (k-NN).\n",
    "2. To tackle the cold start problem for new users by developing a content-based filtering system utilizing movie genres and titles.\n",
    "3. To build a weighted hybrid recommendation system by combining the collaborative and conten-based filtering systems and evaluating the Root Mean Square Error (RMSE) to ensure accuracy and relevance of the recommendations."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5efa31bb-7862-49a4-9ce8-cc8fc893043a",
   "metadata": {},
   "source": [
    "## Success Metric\n",
    "\n",
    "- Root Mean Square Error (RMSE) < 0.9 for rating predictions\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "827d4de4-1a68-432d-9aaf-04a11f6d1b46",
   "metadata": {},
   "source": [
    "## Constraints"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e13f0d69-7da4-4ec3-893d-b3b26a3da77b",
   "metadata": {},
   "source": [
    "1. Data Limitations:\n",
    "Small dataset size leading to potential bias due to demographic skew and time period of ratings and lack of additional movie metadata beyond the information provided in the datasets.\n",
    "\n",
    "2. Data Sparsity:\n",
    "Many users may have rated only a small fraction of available movies creating challenges in finding similar users or items with limited data points.\n",
    "\n",
    "3. Cold Start Problem:\n",
    "Difficulty providing accurate recommendations for new users or newly added movies, limited effectiveness of collaborative filtering for users with few ratings.\n",
    "\n",
    "4. A/B Testing Capabilities:\n",
    "Limitations in conducting extensive A/B tests to compare different recommendation algorithms.\n",
    "\n",
    "5. Ethical Considerations:\n",
    "Ensuring fairness in recommendations across different user groups and balancing business objectives with ethical recommendation practices.\n",
    "\n",
    "6. Genre and Diversity:\n",
    "Ensuring a balance between accuracy and diversity in recommendations and avoiding over-specialization in user recommendations.\n",
    "\n",
    "7. Privacy and Data Protection:\n",
    "Ensuring user data privacy and compliance with regulations like GDPR that establishes the general obligations of data controllers and of those processing personal data on their behalf (processors).\n",
    "\n",
    "8. Evaluation Metrics:\n",
    "Limitations of evaluation metrics in reflecting real-world user satisfaction and a lack of direct user feedback on recommendation quality.\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "860bd0cf-e07d-48ba-8086-3af6e1c2aa64",
   "metadata": {},
   "source": [
    "# DATA UNDERSTANDING\n",
    "\n",
    "This section focuses on getting familiar with our data to gain insights into our features, identify data quality issues and uncover preliminary patterns.\n",
    "\n",
    "Our model will utilize the Movielens dataset from GroupLens Research Lab covering movie ratings from 1902 to 2018. The dataset contains 100836 ratings and 3683 tag applications across 9742 movies with each user rating at least 20 films. While the full dataset contains 1.9 million ratings, we will focus on a subset of about 100,000 for our current model due to time and resource constraints. This sample size balances computational efficiency with statistical relevance for our recommendation engine development.\n",
    "\n",
    "We begin by loading the datasets for our movie recommendation system. The datasets include `links.csv`, `movies.csv`, `ratings.csv` and `tags.csv`. These datasets are then merged on the `movieId` column to form a unified DataFrame for further analysis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "7018cb24-f44b-4c38-ab33-d45adfde8093",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import relevant libraries\n",
    "\n",
    "# Data manipulation \n",
    "import pandas as pd \n",
    "import numpy as np \n",
    "\n",
    "# Data visualization\n",
    "import seaborn as sns \n",
    "import matplotlib.pyplot as plt \n",
    "%matplotlib inline\n",
    "\n",
    "# Filter future warnings\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ff3e2366-3092-4dff-ac02-b79461cfd8a7",
   "metadata": {},
   "source": [
    "We create a class to explore the datasets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "d587021f",
   "metadata": {},
   "outputs": [],
   "source": [
    "class DataExplorer:\n",
    "    '''\n",
    "    A class to handle and explore multiple pandas DataFrames.\n",
    "\n",
    "    Attributes:\n",
    "        file_paths (dict): A dictionary of file paths for the csv files to load.\n",
    "        data (dict): A dictionary of the loaded DataFrames.\n",
    "        merged_data (pandas.DataFrame): The merged DataFrame.\n",
    "\n",
    "    Methods:\n",
    "        load_data(): Load the CSV files into DataFrames.\n",
    "        merge_data(): Merge the DataFrames on movieId.\n",
    "        get_shape(): Print the number of rows and columns in the DataFrame.\n",
    "        summarize_info(): Print a summary of the DataFrame columns.\n",
    "        describe_data(): Print descriptive statistics of the DataFrame.\n",
    "        display_column_types(): Display numerical and categorical columns.\n",
    "    '''\n",
    "\n",
    "    def __init__(self, file_paths):\n",
    "        '''\n",
    "        Initialize the DataExplorer object.\n",
    "\n",
    "        Args:\n",
    "            file_paths (dict): A dictionary of file paths for the csv files to load.\n",
    "        '''\n",
    "        self.file_paths = file_paths\n",
    "        self.data = {}\n",
    "        self.merged_data = None\n",
    "\n",
    "    def load_data(self):\n",
    "        '''\n",
    "        Load the csv files into DataFrames.\n",
    "\n",
    "        Returns:\n",
    "            None\n",
    "        '''\n",
    "        for name, path in self.file_paths.items():\n",
    "            print(f'Loading {name} data csv file...')\n",
    "            try:\n",
    "                self.data[name] = pd.read_csv(path)\n",
    "                print(f'{name} dataset loaded successfully from {path}\\n')\n",
    "            except FileNotFoundError:\n",
    "                print(f'Error: The file \\'{path}\\' was not found.')\n",
    "            except Exception as e:\n",
    "                print(f'Error: An unexpected error occurred: {e}')\n",
    "\n",
    "    def merge_data(self):\n",
    "        '''\n",
    "        Merge the DataFrames on movieId.\n",
    "\n",
    "        Returns:\n",
    "            None\n",
    "        '''\n",
    "        try:\n",
    "            print('Merging data on movieId...')\n",
    "            self.merged_data = pd.merge(self.data['movies'], self.data['ratings'], on='movieId', how='left')\n",
    "            self.merged_data = pd.merge(self.merged_data, self.data['tags'], on='movieId', how='left')\n",
    "            self.merged_data = pd.merge(self.merged_data, self.data['links'], on='movieId', how='left')\n",
    "            print('Data merged successfully\\n')\n",
    "        except KeyError as e:\n",
    "            print(f'Error: A KeyError occurred: {e}. Please ensure all DataFrames contain the column \\'movieId\\'.')\n",
    "\n",
    "    def get_shape(self):\n",
    "        '''\n",
    "        Print the number of rows and columns in the merged DataFrame.\n",
    "\n",
    "        Returns:\n",
    "            None\n",
    "        '''\n",
    "        if self.merged_data is not None:\n",
    "            rows, columns = self.merged_data.shape\n",
    "            print(f'The merged DataFrame has {rows} rows and {columns} columns.\\n')\n",
    "        else:\n",
    "            print('Error: No merged data available. Please call the merge_data() method first.')\n",
    "\n",
    "    def summarize_info(self):\n",
    "        '''\n",
    "        Print a summary of the merged DataFrame columns.\n",
    "\n",
    "        Returns:\n",
    "            None\n",
    "        '''\n",
    "        print('Summarizing the merged DataFrame info')\n",
    "        print('-------------------------------')\n",
    "        if self.merged_data is not None:\n",
    "            print(self.merged_data.info())\n",
    "        else:\n",
    "            print('Error: No merged data available. Please call the merge_data() method first.')\n",
    "\n",
    "    def describe_data(self):\n",
    "        '''\n",
    "        Print descriptive statistics of the merged DataFrame.\n",
    "\n",
    "        Returns:\n",
    "            None\n",
    "        '''\n",
    "        print('\\nDescribing the merged DataFrame data')\n",
    "        print('--------------------------------')\n",
    "        if self.merged_data is not None:\n",
    "            display(self.merged_data.describe())\n",
    "        else:\n",
    "            print('Error: No merged data available. Please call the merge_data() method first.')\n",
    "\n",
    "    def display_column_types(self):\n",
    "        '''\n",
    "        Display numerical and categorical columns of the merged DataFrame.\n",
    "\n",
    "        Returns:\n",
    "            None\n",
    "        '''\n",
    "        print('\\nDisplaying numerical and categorical columns of the merged DataFrame')\n",
    "        print('-----------------------------------------------')\n",
    "        if self.merged_data is not None:\n",
    "            numerical_columns = self.merged_data.select_dtypes(include='number').columns\n",
    "            categorical_columns = self.merged_data.select_dtypes(include='object').columns\n",
    "            print(f'Numerical Columns: {numerical_columns}\\n')\n",
    "            print(f'Categorical Columns: {categorical_columns}\\n')\n",
    "        else:\n",
    "            print('Error: No merged data available. Please call the merge_data() method first.')\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "8e2b2e76",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loading links data csv file...\n",
      "links dataset loaded successfully from movies_data/links.csv\n",
      "\n",
      "Loading movies data csv file...\n",
      "movies dataset loaded successfully from movies_data/movies.csv\n",
      "\n",
      "Loading ratings data csv file...\n",
      "ratings dataset loaded successfully from movies_data/ratings.csv\n",
      "\n",
      "Loading tags data csv file...\n",
      "tags dataset loaded successfully from movies_data/tags.csv\n",
      "\n",
      "Merging data on movieId...\n",
      "Data merged successfully\n",
      "\n",
      "The merged DataFrame has 285783 rows and 11 columns.\n",
      "\n",
      "Summarizing the merged DataFrame info\n",
      "-------------------------------\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 285783 entries, 0 to 285782\n",
      "Data columns (total 11 columns):\n",
      " #   Column       Non-Null Count   Dtype  \n",
      "---  ------       --------------   -----  \n",
      " 0   movieId      285783 non-null  int64  \n",
      " 1   title        285783 non-null  object \n",
      " 2   genres       285783 non-null  object \n",
      " 3   userId_x     285762 non-null  float64\n",
      " 4   rating       285762 non-null  float64\n",
      " 5   timestamp_x  285762 non-null  float64\n",
      " 6   userId_y     233234 non-null  float64\n",
      " 7   tag          233234 non-null  object \n",
      " 8   timestamp_y  233234 non-null  float64\n",
      " 9   imdbId       285783 non-null  int64  \n",
      " 10  tmdbId       285770 non-null  float64\n",
      "dtypes: float64(6), int64(2), object(3)\n",
      "memory usage: 26.2+ MB\n",
      "None\n",
      "\n",
      "Describing the merged DataFrame data\n",
      "--------------------------------\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>movieId</th>\n",
       "      <th>userId_x</th>\n",
       "      <th>rating</th>\n",
       "      <th>timestamp_x</th>\n",
       "      <th>userId_y</th>\n",
       "      <th>timestamp_y</th>\n",
       "      <th>imdbId</th>\n",
       "      <th>tmdbId</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>285783.000000</td>\n",
       "      <td>285762.000000</td>\n",
       "      <td>285762.000000</td>\n",
       "      <td>2.857620e+05</td>\n",
       "      <td>233234.000000</td>\n",
       "      <td>2.332340e+05</td>\n",
       "      <td>2.857830e+05</td>\n",
       "      <td>285770.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>14927.663741</td>\n",
       "      <td>313.894279</td>\n",
       "      <td>3.841270</td>\n",
       "      <td>1.214707e+09</td>\n",
       "      <td>470.681354</td>\n",
       "      <td>1.384754e+09</td>\n",
       "      <td>2.956050e+05</td>\n",
       "      <td>12797.315320</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>31402.673519</td>\n",
       "      <td>179.451387</td>\n",
       "      <td>1.020798</td>\n",
       "      <td>2.233730e+08</td>\n",
       "      <td>153.324249</td>\n",
       "      <td>1.534705e+08</td>\n",
       "      <td>5.150156e+05</td>\n",
       "      <td>43479.255523</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>8.281246e+08</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.137179e+09</td>\n",
       "      <td>4.170000e+02</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>296.000000</td>\n",
       "      <td>160.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.019133e+09</td>\n",
       "      <td>424.000000</td>\n",
       "      <td>1.242494e+09</td>\n",
       "      <td>1.098300e+05</td>\n",
       "      <td>489.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1721.000000</td>\n",
       "      <td>314.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.211377e+09</td>\n",
       "      <td>477.000000</td>\n",
       "      <td>1.457901e+09</td>\n",
       "      <td>1.125730e+05</td>\n",
       "      <td>680.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>5673.000000</td>\n",
       "      <td>465.000000</td>\n",
       "      <td>4.500000</td>\n",
       "      <td>1.445346e+09</td>\n",
       "      <td>599.000000</td>\n",
       "      <td>1.498457e+09</td>\n",
       "      <td>2.415270e+05</td>\n",
       "      <td>8963.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>193609.000000</td>\n",
       "      <td>610.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1.537799e+09</td>\n",
       "      <td>610.000000</td>\n",
       "      <td>1.537099e+09</td>\n",
       "      <td>8.391976e+06</td>\n",
       "      <td>525662.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             movieId       userId_x         rating   timestamp_x  \\\n",
       "count  285783.000000  285762.000000  285762.000000  2.857620e+05   \n",
       "mean    14927.663741     313.894279       3.841270  1.214707e+09   \n",
       "std     31402.673519     179.451387       1.020798  2.233730e+08   \n",
       "min         1.000000       1.000000       0.500000  8.281246e+08   \n",
       "25%       296.000000     160.000000       3.000000  1.019133e+09   \n",
       "50%      1721.000000     314.000000       4.000000  1.211377e+09   \n",
       "75%      5673.000000     465.000000       4.500000  1.445346e+09   \n",
       "max    193609.000000     610.000000       5.000000  1.537799e+09   \n",
       "\n",
       "            userId_y   timestamp_y        imdbId         tmdbId  \n",
       "count  233234.000000  2.332340e+05  2.857830e+05  285770.000000  \n",
       "mean      470.681354  1.384754e+09  2.956050e+05   12797.315320  \n",
       "std       153.324249  1.534705e+08  5.150156e+05   43479.255523  \n",
       "min         2.000000  1.137179e+09  4.170000e+02       2.000000  \n",
       "25%       424.000000  1.242494e+09  1.098300e+05     489.000000  \n",
       "50%       477.000000  1.457901e+09  1.125730e+05     680.000000  \n",
       "75%       599.000000  1.498457e+09  2.415270e+05    8963.000000  \n",
       "max       610.000000  1.537099e+09  8.391976e+06  525662.000000  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Displaying numerical and categorical columns of the merged DataFrame\n",
      "-----------------------------------------------\n",
      "Numerical Columns: Index(['movieId', 'userId_x', 'rating', 'timestamp_x', 'userId_y',\n",
      "       'timestamp_y', 'imdbId', 'tmdbId'],\n",
      "      dtype='object')\n",
      "\n",
      "Categorical Columns: Index(['title', 'genres', 'tag'], dtype='object')\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Instantiate \n",
    "file_paths = {\n",
    "    'links': 'movies_data/links.csv',\n",
    "    'movies': 'movies_data/movies.csv',\n",
    "    'ratings': 'movies_data/ratings.csv',\n",
    "    'tags': 'movies_data/tags.csv'\n",
    "}\n",
    "data_explorer = DataExplorer(file_paths)\n",
    "\n",
    "# Load data\n",
    "data_explorer.load_data()\n",
    "\n",
    "# Merge data\n",
    "data_explorer.merge_data()\n",
    "\n",
    "# Get dimensions\n",
    "data_explorer.get_shape()\n",
    "\n",
    "# Summarize info\n",
    "data_explorer.summarize_info()\n",
    "\n",
    "# Describe data\n",
    "data_explorer.describe_data()\n",
    "\n",
    "# Display numerical and categorical columns\n",
    "data_explorer.display_column_types()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5b9e3e53",
   "metadata": {},
   "source": [
    "### Loading the Data\n",
    "\n",
    "Our data is from MovieLens dataset from the GroupLens research lab at the University of Minnesota. \n",
    "We begin by loading each dataset into a separate pandas DataFrame and then merging them on the `movieId` column for a more holistic view of the data. \n",
    "\n",
    "Below is a description of the datasets:\n",
    "\n",
    "- **links.csv**: Contains identifiers that link MovieLens IDs with IMDb and TMDb IDs.\n",
    "- **movies.csv**: Contains movie information including titles and genres.\n",
    "- **ratings.csv**: Contains user ratings for different movies.\n",
    "- **tags.csv**: Contains user-generated tags for different movies.\n",
    "\n",
    "### Merged DataFrame\n",
    "\n",
    "After loading and merging the datasets, the resulting DataFrame has **285,783 rows and 11 columns**. Here is a brief summary of the columns in the merged DataFrame:\n",
    "\n",
    "- **movieId**: Unique identifier for each movie.\n",
    "- **title**: Movie title.\n",
    "- **genres**: Movie genres.\n",
    "- **userId_x**: User ID for ratings.\n",
    "- **rating**: Rating given by the user.\n",
    "- **timestamp_x**: Timestamp of the rating.\n",
    "- **userId_y**: User ID for tags.\n",
    "- **tag**: User-generated tag.\n",
    "- **timestamp_y**: Timestamp of the tag.\n",
    "- **imdbId**: IMDb identifier for the movie.\n",
    "- **tmdbId**: TMDb identifier for the movie.\n",
    "\n",
    "\n",
    "### Numerical and Categorical Columns\n",
    "\n",
    "The DataFrame contains the following numerical and categorical columns:\n",
    "\n",
    "- **Numerical Columns**: `movieId`, `userId_x`, `rating`, `timestamp_x`, `userId_y`, `timestamp_y`, `imdbId`, `tmdbId`.\n",
    "- **Categorical Columns**: `title`, `genres`, `tag`.\n",
    "\n",
    "There is a disparity in the total number of values within the different columns that may not necessarily be missing values but that will be addressed in the following section after unnecessary columns are dropped."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "51ac4fea-abf6-4271-8b26-96bb8fd764f2",
   "metadata": {},
   "source": [
    "# DATA PREPARATION"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5c9a38f3-468a-41ea-888b-6e2e148aa1da",
   "metadata": {},
   "source": [
    "In this section, we prepare the data for exploratory data analysis (EDA) and modeling. The preprocessing involves several key steps to address and correct errors in the data such as missing values, outliers, duplicates and inconsistencies.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "9d454adf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>movieId</th>\n",
       "      <th>title</th>\n",
       "      <th>genres</th>\n",
       "      <th>userId_x</th>\n",
       "      <th>rating</th>\n",
       "      <th>timestamp_x</th>\n",
       "      <th>userId_y</th>\n",
       "      <th>tag</th>\n",
       "      <th>timestamp_y</th>\n",
       "      <th>imdbId</th>\n",
       "      <th>tmdbId</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Toy Story (1995)</td>\n",
       "      <td>Adventure|Animation|Children|Comedy|Fantasy</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>964982703.0</td>\n",
       "      <td>336.0</td>\n",
       "      <td>pixar</td>\n",
       "      <td>1.139046e+09</td>\n",
       "      <td>114709</td>\n",
       "      <td>862.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>Toy Story (1995)</td>\n",
       "      <td>Adventure|Animation|Children|Comedy|Fantasy</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>964982703.0</td>\n",
       "      <td>474.0</td>\n",
       "      <td>pixar</td>\n",
       "      <td>1.137207e+09</td>\n",
       "      <td>114709</td>\n",
       "      <td>862.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>Toy Story (1995)</td>\n",
       "      <td>Adventure|Animation|Children|Comedy|Fantasy</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>964982703.0</td>\n",
       "      <td>567.0</td>\n",
       "      <td>fun</td>\n",
       "      <td>1.525286e+09</td>\n",
       "      <td>114709</td>\n",
       "      <td>862.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>Toy Story (1995)</td>\n",
       "      <td>Adventure|Animation|Children|Comedy|Fantasy</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>847434962.0</td>\n",
       "      <td>336.0</td>\n",
       "      <td>pixar</td>\n",
       "      <td>1.139046e+09</td>\n",
       "      <td>114709</td>\n",
       "      <td>862.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>Toy Story (1995)</td>\n",
       "      <td>Adventure|Animation|Children|Comedy|Fantasy</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>847434962.0</td>\n",
       "      <td>474.0</td>\n",
       "      <td>pixar</td>\n",
       "      <td>1.137207e+09</td>\n",
       "      <td>114709</td>\n",
       "      <td>862.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   movieId             title                                       genres  \\\n",
       "0        1  Toy Story (1995)  Adventure|Animation|Children|Comedy|Fantasy   \n",
       "1        1  Toy Story (1995)  Adventure|Animation|Children|Comedy|Fantasy   \n",
       "2        1  Toy Story (1995)  Adventure|Animation|Children|Comedy|Fantasy   \n",
       "3        1  Toy Story (1995)  Adventure|Animation|Children|Comedy|Fantasy   \n",
       "4        1  Toy Story (1995)  Adventure|Animation|Children|Comedy|Fantasy   \n",
       "\n",
       "   userId_x  rating  timestamp_x  userId_y    tag   timestamp_y  imdbId  \\\n",
       "0       1.0     4.0  964982703.0     336.0  pixar  1.139046e+09  114709   \n",
       "1       1.0     4.0  964982703.0     474.0  pixar  1.137207e+09  114709   \n",
       "2       1.0     4.0  964982703.0     567.0    fun  1.525286e+09  114709   \n",
       "3       5.0     4.0  847434962.0     336.0  pixar  1.139046e+09  114709   \n",
       "4       5.0     4.0  847434962.0     474.0  pixar  1.137207e+09  114709   \n",
       "\n",
       "   tmdbId  \n",
       "0   862.0  \n",
       "1   862.0  \n",
       "2   862.0  \n",
       "3   862.0  \n",
       "4   862.0  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Display first few rows of the merged data\n",
    "display(data_explorer.merged_data.head())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "844c8fcb",
   "metadata": {},
   "source": [
    "### Drop Less Relevant Columns\n",
    "To streamline the dataset, we are going to drop `timestamp_x`, `timestamp_y`, `tag`, `userId_y`, `imdbId`and `tmdbId` which are the least relevant for our analysis.\n",
    "\n",
    "* We drop the timestamps because it indicates when a user rated a movie but does not contribute to understanding user preferences or the quality of the recommendations.\n",
    "* We drop the tags user ID, imdb ID and tmdb ID as they all convey the same information and opt to retain the ratings user ID as our unique identifier.\n",
    "\n",
    "* We drop the tag column to eliminate redundancy, reduce dataset sparsity and to simplify model complexity allowing a focus on more impactful predictors like ratings and genres."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "fa4b26e5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>movieId</th>\n",
       "      <th>title</th>\n",
       "      <th>genres</th>\n",
       "      <th>userId_x</th>\n",
       "      <th>rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Toy Story (1995)</td>\n",
       "      <td>Adventure|Animation|Children|Comedy|Fantasy</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>Toy Story (1995)</td>\n",
       "      <td>Adventure|Animation|Children|Comedy|Fantasy</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>Toy Story (1995)</td>\n",
       "      <td>Adventure|Animation|Children|Comedy|Fantasy</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>Toy Story (1995)</td>\n",
       "      <td>Adventure|Animation|Children|Comedy|Fantasy</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>Toy Story (1995)</td>\n",
       "      <td>Adventure|Animation|Children|Comedy|Fantasy</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   movieId             title                                       genres  \\\n",
       "0        1  Toy Story (1995)  Adventure|Animation|Children|Comedy|Fantasy   \n",
       "1        1  Toy Story (1995)  Adventure|Animation|Children|Comedy|Fantasy   \n",
       "2        1  Toy Story (1995)  Adventure|Animation|Children|Comedy|Fantasy   \n",
       "3        1  Toy Story (1995)  Adventure|Animation|Children|Comedy|Fantasy   \n",
       "4        1  Toy Story (1995)  Adventure|Animation|Children|Comedy|Fantasy   \n",
       "\n",
       "   userId_x  rating  \n",
       "0       1.0     4.0  \n",
       "1       1.0     4.0  \n",
       "2       1.0     4.0  \n",
       "3       5.0     4.0  \n",
       "4       5.0     4.0  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Drop less relevant columns\n",
    "data_explorer.merged_data.drop(columns=['timestamp_x', 'timestamp_y', 'userId_y', 'tag', 'imdbId', 'tmdbId'], inplace=True)\n",
    "\n",
    "# Display the first few rows of the cleaned data\n",
    "display(data_explorer.merged_data.head())\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "94057848",
   "metadata": {},
   "source": [
    "### Rename Columns\n",
    "To improve clarity, we renamed the column userId_x to user_id using the following function:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "8b2e0ae5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# A function to rename columns\n",
    "def rename_column(df, current_name, new_name):\n",
    "    '''\n",
    "    Renames a column in the DataFrame.\n",
    "\n",
    "    Args:\n",
    "        df: The DataFrame containing the column to rename.\n",
    "        current_name (str): The current name of the column.\n",
    "        new_name (str): The new name for the column.\n",
    "\n",
    "    Returns:\n",
    "        pandas.DataFrame: DataFrame with the renamed column.\n",
    "    '''\n",
    "    \n",
    "    if current_name in df.columns:\n",
    "        df.rename(columns={current_name: new_name}, inplace=True)\n",
    "        print(f\"Column '{current_name}' has been renamed to '{new_name}'.\\n\")\n",
    "    else:\n",
    "        print(f\"Column '{current_name}' does not exist in the DataFrame.\\n\")\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "95a81b7b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Column 'userId_x' has been renamed to 'user_id'.\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>movieId</th>\n",
       "      <th>title</th>\n",
       "      <th>genres</th>\n",
       "      <th>user_id</th>\n",
       "      <th>rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Toy Story (1995)</td>\n",
       "      <td>Adventure|Animation|Children|Comedy|Fantasy</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>Toy Story (1995)</td>\n",
       "      <td>Adventure|Animation|Children|Comedy|Fantasy</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>Toy Story (1995)</td>\n",
       "      <td>Adventure|Animation|Children|Comedy|Fantasy</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>Toy Story (1995)</td>\n",
       "      <td>Adventure|Animation|Children|Comedy|Fantasy</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>Toy Story (1995)</td>\n",
       "      <td>Adventure|Animation|Children|Comedy|Fantasy</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>285778</th>\n",
       "      <td>193581</td>\n",
       "      <td>Black Butler: Book of the Atlantic (2017)</td>\n",
       "      <td>Action|Animation|Comedy|Fantasy</td>\n",
       "      <td>184.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>285779</th>\n",
       "      <td>193583</td>\n",
       "      <td>No Game No Life: Zero (2017)</td>\n",
       "      <td>Animation|Comedy|Fantasy</td>\n",
       "      <td>184.0</td>\n",
       "      <td>3.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>285780</th>\n",
       "      <td>193585</td>\n",
       "      <td>Flint (2017)</td>\n",
       "      <td>Drama</td>\n",
       "      <td>184.0</td>\n",
       "      <td>3.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>285781</th>\n",
       "      <td>193587</td>\n",
       "      <td>Bungo Stray Dogs: Dead Apple (2018)</td>\n",
       "      <td>Action|Animation</td>\n",
       "      <td>184.0</td>\n",
       "      <td>3.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>285782</th>\n",
       "      <td>193609</td>\n",
       "      <td>Andrew Dice Clay: Dice Rules (1991)</td>\n",
       "      <td>Comedy</td>\n",
       "      <td>331.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>285783 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        movieId                                      title  \\\n",
       "0             1                           Toy Story (1995)   \n",
       "1             1                           Toy Story (1995)   \n",
       "2             1                           Toy Story (1995)   \n",
       "3             1                           Toy Story (1995)   \n",
       "4             1                           Toy Story (1995)   \n",
       "...         ...                                        ...   \n",
       "285778   193581  Black Butler: Book of the Atlantic (2017)   \n",
       "285779   193583               No Game No Life: Zero (2017)   \n",
       "285780   193585                               Flint (2017)   \n",
       "285781   193587        Bungo Stray Dogs: Dead Apple (2018)   \n",
       "285782   193609        Andrew Dice Clay: Dice Rules (1991)   \n",
       "\n",
       "                                             genres  user_id  rating  \n",
       "0       Adventure|Animation|Children|Comedy|Fantasy      1.0     4.0  \n",
       "1       Adventure|Animation|Children|Comedy|Fantasy      1.0     4.0  \n",
       "2       Adventure|Animation|Children|Comedy|Fantasy      1.0     4.0  \n",
       "3       Adventure|Animation|Children|Comedy|Fantasy      5.0     4.0  \n",
       "4       Adventure|Animation|Children|Comedy|Fantasy      5.0     4.0  \n",
       "...                                             ...      ...     ...  \n",
       "285778              Action|Animation|Comedy|Fantasy    184.0     4.0  \n",
       "285779                     Animation|Comedy|Fantasy    184.0     3.5  \n",
       "285780                                        Drama    184.0     3.5  \n",
       "285781                             Action|Animation    184.0     3.5  \n",
       "285782                                       Comedy    331.0     4.0  \n",
       "\n",
       "[285783 rows x 5 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rename_column(data_explorer.merged_data, 'userId_x', 'user_id')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e35f3894",
   "metadata": {},
   "source": [
    "### Extract Release Year from Title\n",
    "\n",
    "We extract the release year from the title because it will help to categorize the movies more accurately improving the search capabilities of our movie recommendation system."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "e9d0a39f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define a regular expression pattern to extract title and year\n",
    "pattern = r'(.*) \\((\\d{4})\\)'\n",
    "\n",
    "# Create 'release_year' and 'title' columns based on the pattern\n",
    "data_explorer.merged_data[['title', 'release_year']] = data_explorer.merged_data['title'].str.extract(pattern)\n",
    "    \n",
    "# Convert 'release_year' to numeric (it might be extracted as strings)\n",
    "data_explorer.merged_data['release_year'] = pd.to_numeric(data_explorer.merged_data['release_year']).astype('Int64')  \n",
    "\n",
    "# Remove the parentheses from the title\n",
    "data_explorer.merged_data['title'] = data_explorer.merged_data['title'].str.strip()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "c9948e24",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>movieId</th>\n",
       "      <th>title</th>\n",
       "      <th>genres</th>\n",
       "      <th>user_id</th>\n",
       "      <th>rating</th>\n",
       "      <th>release_year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Toy Story</td>\n",
       "      <td>Adventure|Animation|Children|Comedy|Fantasy</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>Toy Story</td>\n",
       "      <td>Adventure|Animation|Children|Comedy|Fantasy</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>Toy Story</td>\n",
       "      <td>Adventure|Animation|Children|Comedy|Fantasy</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>Toy Story</td>\n",
       "      <td>Adventure|Animation|Children|Comedy|Fantasy</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>Toy Story</td>\n",
       "      <td>Adventure|Animation|Children|Comedy|Fantasy</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1995</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   movieId      title                                       genres  user_id  \\\n",
       "0        1  Toy Story  Adventure|Animation|Children|Comedy|Fantasy      1.0   \n",
       "1        1  Toy Story  Adventure|Animation|Children|Comedy|Fantasy      1.0   \n",
       "2        1  Toy Story  Adventure|Animation|Children|Comedy|Fantasy      1.0   \n",
       "3        1  Toy Story  Adventure|Animation|Children|Comedy|Fantasy      5.0   \n",
       "4        1  Toy Story  Adventure|Animation|Children|Comedy|Fantasy      5.0   \n",
       "\n",
       "   rating  release_year  \n",
       "0     4.0          1995  \n",
       "1     4.0          1995  \n",
       "2     4.0          1995  \n",
       "3     4.0          1995  \n",
       "4     4.0          1995  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_explorer.merged_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "4f6353d6-1523-4dc5-9288-ed63c622f686",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>movieId</th>\n",
       "      <th>title</th>\n",
       "      <th>genres</th>\n",
       "      <th>user_id</th>\n",
       "      <th>rating</th>\n",
       "      <th>release_year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Toy Story</td>\n",
       "      <td>adventure, animation, children, comedy, fantasy</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>Toy Story</td>\n",
       "      <td>adventure, animation, children, comedy, fantasy</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>Toy Story</td>\n",
       "      <td>adventure, animation, children, comedy, fantasy</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>Toy Story</td>\n",
       "      <td>adventure, animation, children, comedy, fantasy</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>Toy Story</td>\n",
       "      <td>adventure, animation, children, comedy, fantasy</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1995</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   movieId      title                                           genres  \\\n",
       "0        1  Toy Story  adventure, animation, children, comedy, fantasy   \n",
       "1        1  Toy Story  adventure, animation, children, comedy, fantasy   \n",
       "2        1  Toy Story  adventure, animation, children, comedy, fantasy   \n",
       "3        1  Toy Story  adventure, animation, children, comedy, fantasy   \n",
       "4        1  Toy Story  adventure, animation, children, comedy, fantasy   \n",
       "\n",
       "   user_id  rating  release_year  \n",
       "0      1.0     4.0          1995  \n",
       "1      1.0     4.0          1995  \n",
       "2      1.0     4.0          1995  \n",
       "3      5.0     4.0          1995  \n",
       "4      5.0     4.0          1995  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Genre Processing: Split the genres in the `movies.csv` dataset into lists for easier analysis\n",
    "data_explorer.merged_data['genres']=[row.strip().lower().replace('|',', ') for row in data_explorer.merged_data['genres']]\n",
    "# Display first 5 rows\n",
    "data_explorer.merged_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bba17cec-73e2-46a8-ba94-cc6dadc07b11",
   "metadata": {},
   "source": [
    "### Check for Missing Values\n",
    "\n",
    "Checking to see whether there are any missing values in our data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "8d0fb7f0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "movieId          0\n",
      "title           20\n",
      "genres           0\n",
      "user_id         21\n",
      "rating          21\n",
      "release_year    20\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# Check for missing values\n",
    "print(data_explorer.merged_data.isnull().sum())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "06e11796-cd10-4e90-9c76-e3649a95c563",
   "metadata": {},
   "source": [
    "### Drop Missing Values"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4dcd0983-a568-4c76-88b1-af28b73fdac0",
   "metadata": {},
   "source": [
    "This revealed some missing values in `title`and `rating` columns. Since there were only 21 missing values in the rating column and 20 in the title column, we decided to drop these rows."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "b430f787",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "movieId         0\n",
      "title           0\n",
      "genres          0\n",
      "user_id         0\n",
      "rating          0\n",
      "release_year    0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# Drop null values \n",
    "data_explorer.merged_data.dropna(subset=['rating', 'title'], inplace=True)\n",
    "print(data_explorer.merged_data.isnull().sum())\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "75de4c8b-e87b-476a-9206-cd036c8fb00f",
   "metadata": {},
   "source": [
    "### Remove Duplicate Rows\n",
    "\n",
    "Next step is to check whether there are any duplicate values within our data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "bebeff6f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n"
     ]
    }
   ],
   "source": [
    "# Drop duplicates\n",
    "data_explorer.merged_data.drop_duplicates(inplace=True)\n",
    "# Reset index and drop the old index column\n",
    "data_explorer.merged_data.reset_index(drop=True, inplace=True)\n",
    "# Print output\n",
    "print(data_explorer.merged_data.duplicated().sum())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "361735c2-697d-4dfc-87e0-daf4c1e66fdf",
   "metadata": {},
   "source": [
    "With no missing values, we proceed to view the properties of the Dataframe columns and convert the user ID data type from float to integer."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "9fc27ef7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 100818 entries, 0 to 100817\n",
      "Data columns (total 6 columns):\n",
      " #   Column        Non-Null Count   Dtype  \n",
      "---  ------        --------------   -----  \n",
      " 0   movieId       100818 non-null  int64  \n",
      " 1   title         100818 non-null  object \n",
      " 2   genres        100818 non-null  object \n",
      " 3   user_id       100818 non-null  float64\n",
      " 4   rating        100818 non-null  float64\n",
      " 5   release_year  100818 non-null  Int64  \n",
      "dtypes: Int64(1), float64(2), int64(1), object(2)\n",
      "memory usage: 4.7+ MB\n"
     ]
    }
   ],
   "source": [
    "data_explorer.merged_data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "e4df2d45-99a6-4bc0-8cae-ae9989295dc6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('int64')"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Convert user_id from float to int\n",
    "data_explorer.merged_data['user_id'] = data_explorer.merged_data['user_id'].astype(int)\n",
    "# Display converted data type\n",
    "data_explorer.merged_data['user_id'].dtype"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7a2da328-2919-4778-9ac1-8892fdf669eb",
   "metadata": {},
   "source": [
    "With this final step, we proceed to our exploratory data analysis."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9afdd044-472e-449d-9489-e5bba3478c25",
   "metadata": {},
   "source": [
    "# EXPLORATORY DATA ANALYSIS (EDA)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "99baee9b-276a-4882-b922-8338aec63e90",
   "metadata": {},
   "source": [
    "In this section we will perform exploratory data analysis to identify patterns, trends and relationships within the data. This will involve visualizations as well as statistical techniques to summarize the main characteristics of the data. The primary goals of EDA include understanding the data structure, identifying patterns and relationships, detecting anomalies, generating initial insights and informing further analysis. This process typically involves summarizing data with descriptive statistics and elaborating using data visualization techniques. EDA provides a foundational understanding of the data guiding the selection of appropriate models for further analysis."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1f9fa598-61f0-4db6-970a-3df410b0c634",
   "metadata": {},
   "source": [
    "## Univariate Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b4715bd9-479f-4a1b-b005-57ae49bb8283",
   "metadata": {},
   "source": [
    "This involves analyzing and summarizing individual variables in our dataset to describe the basic features and patterns without considering relationships between variables. First step is to assign the variable 'df' to data_explorer.merged_data for ease of reference and then previewing the first five columns. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "8c2dfcb4-a0c7-4380-8c2f-4cdc12735c9b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>movieId</th>\n",
       "      <th>title</th>\n",
       "      <th>genres</th>\n",
       "      <th>user_id</th>\n",
       "      <th>rating</th>\n",
       "      <th>release_year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Toy Story</td>\n",
       "      <td>adventure, animation, children, comedy, fantasy</td>\n",
       "      <td>1</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>Toy Story</td>\n",
       "      <td>adventure, animation, children, comedy, fantasy</td>\n",
       "      <td>5</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>Toy Story</td>\n",
       "      <td>adventure, animation, children, comedy, fantasy</td>\n",
       "      <td>7</td>\n",
       "      <td>4.5</td>\n",
       "      <td>1995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>Toy Story</td>\n",
       "      <td>adventure, animation, children, comedy, fantasy</td>\n",
       "      <td>15</td>\n",
       "      <td>2.5</td>\n",
       "      <td>1995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>Toy Story</td>\n",
       "      <td>adventure, animation, children, comedy, fantasy</td>\n",
       "      <td>17</td>\n",
       "      <td>4.5</td>\n",
       "      <td>1995</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   movieId      title                                           genres  \\\n",
       "0        1  Toy Story  adventure, animation, children, comedy, fantasy   \n",
       "1        1  Toy Story  adventure, animation, children, comedy, fantasy   \n",
       "2        1  Toy Story  adventure, animation, children, comedy, fantasy   \n",
       "3        1  Toy Story  adventure, animation, children, comedy, fantasy   \n",
       "4        1  Toy Story  adventure, animation, children, comedy, fantasy   \n",
       "\n",
       "   user_id  rating  release_year  \n",
       "0        1     4.0          1995  \n",
       "1        5     4.0          1995  \n",
       "2        7     4.5          1995  \n",
       "3       15     2.5          1995  \n",
       "4       17     4.5          1995  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Sanity check\n",
    "df = data_explorer.merged_data\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "36e5eb30-6d78-4aac-b183-3dba59d7a1e9",
   "metadata": {},
   "source": [
    "For our univariate analysis, we will create a class that will perform univariate analysis on the frequency of the ratings, genres and release year columns. Functions used:\n",
    "`plot_ratings_frequency()`: Creates a count plot of ratings and prints the frequency of each rating.\n",
    "\n",
    "`plot_genres_frequency()`: Splits the 'genres' column by comma and space (', ') and stacks the result creating a Series where each element is a single genre and then counts the frequency, creates a bar plot and prints the frequency of every genre.\n",
    "\n",
    "`plot_year_frequency()`: Creates a line plot showing the number of movies per year and prints the number of movies per decade.\n",
    "\n",
    "`plot_outliers()` : Creates a box plot that helps to identify the central tendency of ratings (median), the spread of ratings (interquartile range) and any outliers in the ratings data.\n",
    "\n",
    "`run_univariate()`: Runs all four analyses sequentially."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "724a5c86-19b6-4f93-80a6-ead1dd3c7916",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Displaying Ratings Frequency...\n",
      "-------------------------------\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Rating Frequency:\n",
      "0.5     1369\n",
      "1.0     2810\n",
      "1.5     1791\n",
      "2.0     7550\n",
      "2.5     5550\n",
      "3.0    20044\n",
      "3.5    13134\n",
      "4.0    26815\n",
      "4.5     8549\n",
      "5.0    13206\n",
      "Name: rating, dtype: int64\n",
      "\n",
      "\n",
      "Displaying Genres Frequency...\n",
      "------------------------------\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Top 10 Genres:\n",
      "drama        41926\n",
      "comedy       39053\n",
      "action       30631\n",
      "thriller     26447\n",
      "adventure    24161\n",
      "romance      18124\n",
      "sci-fi       17237\n",
      "crime        16681\n",
      "fantasy      11834\n",
      "children      9208\n",
      "dtype: int64\n",
      "\n",
      "\n",
      "Displaying Year Frequency...\n",
      "----------------------------\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Movies per Decade:\n",
      "1900        8\n",
      "1910        8\n",
      "1920      125\n",
      "1930      687\n",
      "1940     1101\n",
      "1950     1784\n",
      "1960     2858\n",
      "1970     4995\n",
      "1980    12912\n",
      "1990    37087\n",
      "2000    29766\n",
      "2010     9487\n",
      "Name: release_year, dtype: int64\n",
      "\n",
      "\n",
      "Displaying Outliers...\n",
      "-----------------------\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "class UnivariateAnalysis:\n",
    "    '''\n",
    "    A class for performing univariate analysis on movie data.\n",
    "    \n",
    "    Attributes:\n",
    "        df (DataFrame): A pandas DataFrame containing movie data.\n",
    "    '''\n",
    "    \n",
    "    def __init__(self, df):\n",
    "        '''\n",
    "        Initializes the UnivariateAnalysis class with a DataFrame.\n",
    "        \n",
    "        Args:\n",
    "            df (DataFrame): A pandas DataFrame containing the movie data to analyze.\n",
    "        '''\n",
    "        self.df = df\n",
    "        \n",
    "    def plot_ratings_frequency(self):\n",
    "        \n",
    "        '''\n",
    "        This method creates a count plot to visualize the frequency of each rating\n",
    "        and prints the counts of each rating.\n",
    "        '''\n",
    "        \n",
    "        plt.figure(figsize=(10, 6))\n",
    "        sns.countplot(x='rating', data=self.df, palette='viridis')\n",
    "        plt.title('Distribution of Ratings')\n",
    "        plt.xlabel('Rating')\n",
    "        plt.ylabel('Count')\n",
    "        plt.show()\n",
    "        \n",
    "        rating_counts = self.df['rating'].value_counts().sort_index()\n",
    "        print(\"Rating Frequency:\")\n",
    "        print(rating_counts)\n",
    "        \n",
    "    def plot_genres_frequency(self):\n",
    "        \n",
    "        '''\n",
    "        This method splits the genres into individual entries, counts the frequency\n",
    "        of each genre, and creates a bar plot to visualize the distribution.\n",
    "        It also prints the top 10 genres.\n",
    "        '''\n",
    "        \n",
    "        genres = self.df['genres'].str.split(', ', expand=True).stack()\n",
    "        # Count unique genres\n",
    "        genre_counts = genres.value_counts()\n",
    "        \n",
    "        plt.figure(figsize=(12, 6))\n",
    "        sns.barplot(x=genre_counts.index, y=genre_counts.values, palette='viridis')\n",
    "        plt.title('Distribution of Movie Genres')\n",
    "        plt.xlabel('Genre')\n",
    "        plt.ylabel('Count')\n",
    "        plt.xticks(rotation=45, ha='right')\n",
    "        plt.tight_layout()\n",
    "        plt.show()\n",
    "        \n",
    "        print(\"Top 10 Genres:\")\n",
    "        print(genre_counts.head(10))\n",
    "        \n",
    "    def plot_year_frequency(self):\n",
    "        \n",
    "        '''\n",
    "        This method counts the number of movies released each year and creates\n",
    "        a line plot to visualize the distribution. It also prints the number of\n",
    "        movies released per decade.\n",
    "        '''\n",
    "        \n",
    "        year_counts = self.df['release_year'].value_counts().sort_index()\n",
    "        \n",
    "        plt.figure(figsize=(12, 6))\n",
    "        sns.lineplot(x=year_counts.index, y=year_counts.values, color='green')\n",
    "        plt.title('Distribution of Movies by Year')\n",
    "        plt.xlabel('Year')\n",
    "        plt.ylabel('Count')\n",
    "        plt.show()\n",
    "        \n",
    "        print(\"Movies per Decade:\")\n",
    "        decade_counts = self.df['release_year'].apply(lambda x: x - x % 10).value_counts().sort_index()\n",
    "        print(decade_counts)\n",
    "\n",
    "    def plot_rating_outliers(self):\n",
    "        \n",
    "        '''\n",
    "        This method generates a box plot that displays the median, quartiles,\n",
    "        and potential outliers of the ratings in the dataset. \n",
    "        '''\n",
    "        \n",
    "        plt.figure(figsize=(8, 6))\n",
    "        sns.boxplot(y='rating', data=self.df)\n",
    "        plt.title('Box Plot of Outliers in Ratings')\n",
    "        plt.ylabel('Rating')\n",
    "        plt.show()\n",
    "\n",
    "        \n",
    "    def run_univariate(self):\n",
    "        \n",
    "        '''\n",
    "        This method sequentially calls the methods to analyze ratings frequency,\n",
    "        genres frequency, and year frequency, printing progress messages.\n",
    "        '''\n",
    "        \n",
    "        print('Displaying Ratings Frequency...')\n",
    "        print('-------------------------------')\n",
    "        self.plot_ratings_frequency()\n",
    "        print('\\n\\nDisplaying Genres Frequency...')\n",
    "        print('------------------------------')\n",
    "        self.plot_genres_frequency()\n",
    "        print('\\n\\nDisplaying Year Frequency...')\n",
    "        print('----------------------------')\n",
    "        self.plot_year_frequency()\n",
    "        print('\\n\\nDisplaying Outliers...')\n",
    "        print('-----------------------')\n",
    "        self.plot_rating_outliers()\n",
    "\n",
    "# Instantiate\n",
    "univariate = UnivariateAnalysis(df)\n",
    "univariate.run_univariate()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "83d02742-74a7-40b0-b3ef-c5e4f6f085e1",
   "metadata": {},
   "source": [
    "### Summary"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9e2b494f-1fea-48d6-8151-b1d064d69c2e",
   "metadata": {},
   "source": [
    "From the `distribution of ratings` plot we can see that the most common rating is 4.0 with about 25,000 movies and the distribution is positively skewed favouring higher ratings. Significant counts can also be seen at 3.0 and 3.5 while ratings of 2.0, 4.5 and 5.0 are notable but less frequent. Extremely low ratings of 0.5 and 1.0 are less common, suggesting that most movies are rated at least moderately well.\n",
    "\n",
    "From the `distribution of genres` plot we can see that the most prevalent genre is Drama featuring slightly more than 40,000 movies closely followed by Comedy, which has just under 40,000 films. Action and Thriller genres also have substantial representations with Action being more popular than Thriller.\n",
    "Moderately popular genres include Adventure and Romance each boasting over 20,000 movies while Sci-Fi and Crime have slightly fewer titles than these two.\n",
    "Less common genres include Fantasy, Children’s, Mystery and Horror with moderate counts but significantly fewer than the top genres. Animation, War, IMAX, Musical and Western genres have even lower counts each with fewer than 10,000 movies indicating that they may cater to more niche audiences.\n",
    "Documentary and Film-Noir are among the least common genres with each having fewer than 5,000 movies. There also appears to be a category for movies without any listed genres with a very low count.\n",
    "\n",
    "The `distribution of movies by year` plot illustrates the historical trends in movie production highlighting periods of growth, peak and decline. We can see that the movie industry experienced significant growth starting in the late 1960s with a sharp increase in production through the 1990s and early 2000s.\n",
    "The peak period is around the late 1990s with a maximum output of over 6,000 movies in the peak years.\n",
    "This is then followed by a steep decline in movie production suggesting changes in the industry such as shifts in movie consumption, technological changes or economic factors affecting production.\n",
    "\n",
    "From the `analyze_rating_outliers` we can identify some outliers in the ratings column but we'll keep them as they will be important in the curation of our recommender system."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fef1c375-35be-4cae-8d83-92d9028a00a1",
   "metadata": {},
   "source": [
    "## Bivariate Analysis\n",
    "\n",
    "Bivariate analysis refers to the statistical examination of two variables to understand the relationship between them. We create a `BivariateAnalysis` class designed to perform various bivariate analyses on our dataset. In this instance we shall explore relationships between release years vs ratings, genres vs ratings, movie titles vs rating and movie titles vs total number of users who have rated them. ."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "449c7ef5-e4da-4285-a38c-57f914dfa46e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Displaying Release Year vs Average Rating...\n",
      "--------------------------------------------\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "Displaying Genres vs Average Rating...\n",
      "---------------------------------------\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "Displaying Highest Movies Ratings...\n",
      "-----------------------------------\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1500x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "Displaying Top Rated Titles...\n",
      "-------------------------------\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAABKUAAAJOCAYAAABm7rQwAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/d3fzzAAAACXBIWXMAAA9hAAAPYQGoP6dpAAEAAElEQVR4nOzdeVxO6f8/8NdpX+4WpVSkJKkQEsZaIZUs2cee3WBsY2toswtjmw9iUPZhLGMshaiJLIVsJTQSI2OXNanz+8Ov83Vru0vKmNfz8TiPT+c617mu93Xu22fydl3XEURRFEFERERERERERFSGlMo7ACIiIiIiIiIi+u9hUoqIiIiIiIiIiMock1JERERERERERFTmmJQiIiIiIiIiIqIyx6QUERERERERERGVOSaliIiIiIiIiIiozDEpRUREREREREREZY5JKSIiIiIiIiIiKnNMShERERERERERUZljUoqIiIiIviiCICh0REVFfdY40tPTMX36dDRp0gQVK1aErq4uGjRogNWrVyM7OztP/RcvXmDcuHEwMzODhoYG6tWrh23btinUV2BgoNzYVFVVUbVqVQwdOhT37t0rUfyvXr1CYGDgZ3lOUVFRRX4GxfkcfXx8YGlpKXf/nDlzsGfPnhL1XdpCQ0MhCALi4+M/az+53wNjY2M8f/48z3VLS0u0b9/+s8ZQkLJ6BqXl/PnzcHZ2hp6eHgRBwJIlSwqs+/F3UldXF02bNsXWrVtL3P+BAwcQGBiY7zVLS0v4+PiUuG2ir4lKeQdARERERPShkydPyp3PnDkTx44dw9GjR+XK7e3tP2scZ8+exYYNG9C/f3/4+flBVVUVBw8exHfffYdTp05h3bp1cvW7dOmCuLg4zJs3DzY2NtiyZQt69eqFnJwc9O7dW6E+w8PDoaenhxcvXuDQoUNYtGgRYmNjkZCQAFVV1WLF/+rVKwQFBQEAXFxcinVvaSjO52hubo6xY8fKlc+ZMwfdunWDt7f35w71i/PgwQMEBwdj5syZ5R3Kv9agQYPw8uVLbNu2DRUqVMiT9PxYt27d8MMPP0AURdy8eRNz5sxB7969IYqiwn9+P3TgwAH873//yzcxtXv3bujq6ha7TaKvEZNSRERERPRF+eabb+TOjYyMoKSklKf8c2vWrBlSUlLkkkFubm54+/Yt/ve//yEoKAjm5uYA3v8F9PDhw1IiCgBcXV1x69YtTJo0CT179oSysnKRfTZo0AAVK1YEALRp0wYPHz7E+vXrcfz4cbi6un6GUX4+xfkc+Rd0eR4eHli8eDFGjRoFExOT8g6nTL169QpaWlqf3M7ly5cxdOhQeHp6KlS/UqVK0nezSZMmaNasGSwtLRESElKipFRh6tevX6rtEf2bcfkeEREREf3rPH78GCNHjkTlypWhpqYGKysrTJs2DZmZmXL1BEHA6NGjERISAhsbG6irq8Pe3l6hZXUVKlTId3ZSo0aNAAB37tyRynbv3g2ZTIbu3bvL1R04cCDu3r2L06dPl2SYcHJyAgD8888/UtmDBw8wcuRI2NvbQyaTwdjYGK1atUJMTIxUJzU1FUZGRgCAoKAgaVnSh0uGrl+/jt69e8PY2Bjq6uqws7PD//73vzwxXL16FR4eHtDS0kLFihUxYsSIfJeWfYqPl+8JgoCXL18iLCxMir2o2V7x8fHo2LEjDAwMoKGhgfr162P79u1ydV69eoWJEyeiWrVq0NDQgIGBAZycnBRepvXkyRMMHDgQBgYG0NbWRocOHfDXX39J12fOnAkVFRXcvn07z72DBg2CoaEh3rx5U2Q/s2bNwrt37wpc/pWroKWMqampEAQBoaGhUpmPjw9kMhmuXr0Kd3d3aGtrw9TUFPPmzQMAnDp1Cs2bN4e2tjZsbGwQFhZWomeQ68iRI2jdujV0dXWhpaWFZs2aITIyUq5O7nLFc+fOoVu3bqhQoQKqV69e6JgvX76MTp06oUKFCtIy2Q9jzV1m+O7dO6xcuVL6/hSXhYUFjIyM5P7sAcCvv/6Ktm3bwtTUFJqamrCzs8PUqVPx8uVLqY6Pj4/0Z+nDZYGpqakA8i7fy/0ct27dimnTpsHMzAy6urpo06YNkpOT5foXRRFz5syBhYUFNDQ04OTkhMOHD8PFxUXuz0hOTg5mzZqFmjVrQlNTE/r6+nBwcMDSpUuL/SyIPicmpYiIiIjoX+XNmzdwdXXFhg0bMGHCBOzfvx99+/ZFcHAwunTpkqf+3r17sWzZMsyYMQO//fYbLCws0KtXL/z2228l6v/o0aNQUVGBjY2NVHb58mXY2dlBRUV+IYKDg4N0vSRu3rwJAHJ9PX78GAAQEBCA/fv3Y/369bCysoKLi4uUnDA1NUV4eDgAYPDgwTh58iROnjwJPz8/AEBiYiIaNmyIy5cvY9GiRdi3bx+8vLwwZswYackf8D4Z5uzsjMuXL2PFihXYuHEjXrx4gdGjR5doPIo6efIkNDU10a5dOyn2FStWFFj/2LFjaNasGZ4+fYpVq1bh999/R7169dCzZ0+5xMyECROwcuVKjBkzBuHh4di4cSO6d++OR48eKRTX4MGDoaSkhC1btmDJkiU4c+YMXFxc8PTpUwDA8OHDoaKigpCQELn7Hj9+jG3btmHw4MHQ0NAosh8LCwuMHDkSa9euxbVr1xSKTRFZWVno0qULvLy88Pvvv8PT0xO+vr748ccfMWDAAAwaNAi7d+9GzZo14ePjg7Nnz+Zpo6hnAACbNm1C27Ztoauri7CwMGzfvh0GBgZwd3fPk5gC3i99tba2xo4dO7Bq1aoC409OTkbTpk1x5coVLFu2DLt27YK9vT18fHwQHBwMAPDy8pKWjnbr1k36/hTXs2fP8PjxY7k/e8D7ZG67du2wdu1ahIeHY9y4cdi+fTs6dOgg1fHz80O3bt0AQOr/5MmTMDU1LbTPH3/8Ebdu3cIvv/yC1atX4/r16+jQoYPcHnbTpk3DtGnT4OHhgd9//x0jRozAkCFD8nxPgoODERgYiF69emH//v349ddfMXjwYLnPieiLIBIRERERfcEGDBggamtrS+erVq0SAYjbt2+Xqzd//nwRgHjo0CGpDICoqakp3rt3Typ79+6daGtrK1pbWxc7loiICFFJSUkcP368XHmNGjVEd3f3PPXv3r0rAhDnzJlTaLsBAQEiAPHevXtiVlaW+OTJE3H79u2itra22KtXr0LvfffunZiVlSW2bt1a7Ny5s1T+4MEDEYAYEBCQ5x53d3exSpUq4rNnz+TKR48eLWpoaIiPHz8WRVEUp0yZIgqCICYkJMjVc3NzEwGIx44dKzS2D338OX58zcLCQq5MW1tbHDBgQJ66x44dy9O3ra2tWL9+fTErK0uubvv27UVTU1MxOztbFEVRrF27tujt7a1wzLnWr18vApB7vqIoiidOnBABiLNmzZIbi7GxsZiZmSmVzZ8/X1RSUhJv3rxZaD+534MHDx6IDx8+FPX09MSuXbtK1y0sLEQvLy/pPL9nIYqiePPmTRGAuH79erm4AIg7d+6UyrKyskQjIyMRgHju3Dmp/NGjR6KysrI4YcKEYj+Dly9figYGBmKHDh3k6mVnZ4t169YVGzVqlGe8/v7+hT6XXN9++62orq4upqWlyZV7enqKWlpa4tOnT6UyAOKoUaMUaheAOHLkSDErK0t8+/ateO3aNbFjx46ijo6OGB8fX+B9OTk5YlZWlhgdHS0CEC9cuCBdGzVqlFjQX7ctLCzkvtu5n2O7du3k6m3fvl0EIJ48eVIURVF8/PixqK6uLvbs2VOu3smTJ0UAorOzs1TWvn17sV69egqNn6g8caYUEREREf2rHD16FNra2tJMhFy5y2E+nonRunVrVKpUSTpXVlZGz549cePGDbkleEU5d+4cevTogW+++QZz587Nc72wJUKKLh8yMTGBqqoqKlSogB49eqBBgwb5LqNatWoVHB0doaGhARUVFaiqqiIyMhJJSUlF9vHmzRtERkaic+fO0NLSwrt376SjXbt2ePPmDU6dOgXg/QykWrVqoW7dunJtlPYeO5/ixo0buHr1Kvr06QMAecaTnp4uLYFq1KgRDh48iKlTpyIqKgqvX78uVl+5feRq2rQpLCwscOzYMals7NixuH//Pnbs2AHg/TKqlStXwsvLq8jNtj9kaGiIKVOmYOfOnSVe/vkxQRDQrl076VxFRQXW1tYwNTWV2+fIwMAAxsbGuHXrVp42inoGsbGxePz4MQYMGCD3WeTk5MDDwwNxcXFyS90AoGvXrgrFf/ToUbRu3Vrayy2Xj48PXr16VaIZUblWrFgBVVVVqKmpwcbGBgcPHsTWrVvRoEEDuXp//fUXevfuDRMTEygrK0NVVRXOzs4AoNCfv8J07NhR7jx3pmXu53Dq1ClkZmaiR48ecvW++eabPN+tRo0a4cKFCxg5ciQiIiKQkZHxSbERfS5MShERERHRv8qjR49gYmKSJ9FjbGwMFRWVPEux8tsoOrdM0WVb58+fh5ubG2rUqIEDBw5AXV1d7rqhoWG+beUutTMwMFConyNHjiAuLg4RERHo2rUr/vzzT3z//fdydX766Sd89913aNy4MXbu3IlTp04hLi4OHh4eCiVZHj16hHfv3mH58uVQVVWVO3ITFg8fPpTqFvb8vgS5e/5MnDgxz3hGjhwJ4P/Gs2zZMkyZMgV79uyBq6srDAwM4O3tjevXryvUV0HP4sPPvn79+mjRooW0p9C+ffuQmppaoiWP48aNg5mZGSZPnlzse/OjpaWVZ/mgmppavt9PNTW1fPe/KuoZ5H4e3bp1y/N5zJ8/H6IoSn8uchW1rC3Xo0eP8q1rZmYmXS+pHj16IC4uDrGxsQgJCYGOjg6+/fZbue/Gixcv0KJFC5w+fRqzZs1CVFQU4uLisGvXLgAodpLzY4aGhnLnuf8/k9tu7vg+TLLn+rjM19cXCxcuxKlTp+Dp6QlDQ0O0bt0a8fHxnxQjUWnj2/eIiIiI6F/F0NAQp0+fhiiKcomp+/fv4927d9Lb63Ldu3cvTxu5ZR//JTA/58+fR5s2bWBhYYFDhw5BT08vT506depg69atePfundy+UpcuXQIA1K5dW6Gx1a1bV4rfzc0N7u7uWL16NQYPHoyGDRsCeL9fj4uLC1auXCl3r6Kbj1eoUAHKysro168fRo0alW+datWqAXj/fAp7fl+C3Ofl6+ub755iAFCzZk0AgLa2NoKCghAUFIR//vlHmjXVoUMHXL16tci+CnoW1tbWcmVjxoxB9+7dce7cOfz888+wsbGBm5tbcYcGTU1NBAYGYtiwYdi/f3+e67kJpo83+M9Nwn0ORT2D3M9j+fLlBb4x8+MEiqIzCQ0NDZGenp6n/O7du3J9l4SRkZH0YoEmTZrAzs4Ozs7OGD9+PPbt2wfg/Uytu3fvIioqSpodBaDM9mnK/f+rjzdfB95/Bh/OllJRUcGECRMwYcIEPH36FEeOHMGPP/4Id3d33L59u1TecEhUGjhTioiIiIj+VVq3bo0XL15gz549cuUbNmyQrn8oMjJS7i9x2dnZ+PXXX1G9enVUqVKl0L4SEhLQpk0bVKlSBYcPH0aFChXyrde5c2e8ePECO3fulCsPCwuDmZkZGjdurOjwJIIg4H//+x+UlZUxffp0ufKPZ2pdvHgxz9Klj2dZ5NLS0oKrqyvOnz8PBwcHODk55Tly//Lr6uqKK1eu4MKFC3JtbNmypdjjKS51dXWFZp7UrFkTNWrUwIULF/Idi5OTE3R0dPLcV6lSJfj4+KBXr15ITk7Gq1eviuxr8+bNcuexsbG4detWnjcDdu7cGVWrVsUPP/yAI0eOYOTIkSV6Axzw/q19uW94y8nJkbuWm4S4ePGiXPnevXtL1JciinoGzZo1g76+PhITEwv8PNTU1ErUd+vWraXE0Ic2bNgALS2tApNgJdGiRQv0798f+/fvl/5s5X6GH//5+3hj+w/rfOrsqQ81btwY6urq+PXXX+XKT506le9Sy1z6+vro1q0bRo0ahcePH0tvAST6EnCmFBERERH9q/Tv3x//+9//MGDAAKSmpqJOnTo4fvw45syZg3bt2qFNmzZy9StWrIhWrVrBz88P2traWLFiBa5evYpt27YV2k9ycrLU1uzZs3H9+nW5pTzVq1eHkZERAMDT0xNubm747rvvkJGRAWtra2zduhXh4eHYtGkTlJWVSzTWGjVqYNiwYVixYgWOHz+O5s2bo3379pg5cyYCAgLg7OyM5ORkzJgxA9WqVcO7d++ke3V0dGBhYYHff/8drVu3hoGBASpWrAhLS0ssXboUzZs3R4sWLfDdd9/B0tISz58/x40bN/DHH3/g6NGjAN4vH1u3bh28vLwwa9YsVKpUCZs3b1ZoVtGnqlOnDqKiovDHH3/A1NQUOjo60oynj4WEhMDT0xPu7u7w8fFB5cqV8fjxYyQlJeHcuXPS/k6NGzdG+/bt4eDggAoVKiApKQkbN25EkyZNFJo5Eh8fjyFDhqB79+64ffs2pk2bhsqVK0vLBHMpKytj1KhRmDJlCrS1taX9zkpCWVkZc+bMQefOnQH83z5DwPtlc23atMHcuXNRoUIFWFhYIDIyUlpO9jkU9QxkMhmWL1+OAQMG4PHjx+jWrRuMjY3x4MEDXLhwAQ8ePMgzy09RAQEB2LdvH1xdXeHv7w8DAwNs3rwZ+/fvR3BwcL6zGD/FzJkz8euvv8LPzw9HjhxB06ZNUaFCBYwYMQIBAQFQVVXF5s2b8yRtgfffXwCYP38+PD09oaysDAcHhxIn5ID3y4AnTJggfd6dO3fGnTt3EBQUBFNTUygp/d+ckw4dOqB27dpwcnKCkZERbt26hSVLlsDCwgI1atQocQxEpa68d1onIiIiIipMfm9te/TokThixAjR1NRUVFFRES0sLERfX1/xzZs3cvXw/9/AtWLFCrF69eqiqqqqaGtrK27evLnIfnPfNlbQ8eGbzURRFJ8/fy6OGTNGNDExEdXU1EQHBwdx69atCo3xw7eufeyff/4RZTKZ6OrqKoqiKGZmZooTJ04UK1euLGpoaIiOjo7inj178n2D3ZEjR8T69euL6urqIgC5N37dvHlTHDRokFi5cmVRVVVVNDIyEps2bSr3JjlRFMXExETRzc1N1NDQEA0MDMTBgweLv//++2d/+15CQoLYrFkzUUtLS+7NYgW9ce7ChQtijx49RGNjY1FVVVU0MTERW7VqJa5atUqqM3XqVNHJyUmsUKGCqK6uLlpZWYnjx48XHz58WGjsud+FQ4cOif369RP19fVFTU1NsV27duL169fzvSc1NVUEII4YMaLwB/OBwr4HTZs2FQHIvX1PFEUxPT1d7Natm2hgYCDq6emJffv2FePj4/N9+15+z9/Z2VmsVatWnvKP3/RX3GcQHR0tenl5iQYGBqKqqqpYuXJl0cvLS9yxY4dC4y3IpUuXxA4dOoh6enqimpqaWLdu3Tx/FkWx+G/fK6jupEmTRABidHS0KIqiGBsbKzZp0kTU0tISjYyMxCFDhojnzp3L87wzMzPFIUOGiEZGRqIgCCIA6e2LBb1978NnI4r5v0UxJydHnDVrllilShXp/2f27dsn1q1bV+7NiIsWLRKbNm0qVqxYUVRTUxOrVq0qDh48WExNTVXomRCVFUEURbGM8l9ERERERGVKEASMGjUKP//8c3mHQv8xy5cvx5gxY3D58mXUqlWrvMOhr9jNmzdha2uLgIAA/Pjjj+UdDlGxcPkeERERERFRKTl//jxu3ryJGTNmoFOnTkxIUam6cOECtm7diqZNm0JXVxfJyckIDg6Grq4uBg8eXN7hERUbk1JERERERESlpHPnzrh37x5atGiBVatWlXc49JXR1tZGfHw81q5di6dPn0JPTw8uLi6YPXt2nrcaEv0bcPkeERERERERERGVOaWiqxAREREREREREZUuJqWIiIiIiIiIiKjMMSlFRERERERERERljhudExERUZFycnJw9+5d6OjoQBCE8g6HiIiIiL5goiji+fPnMDMzg5JSwfOhmJQiIiKiIt29exfm5ublHQYRERER/Yvcvn0bVapUKfA6k1JERERUJB0dHQDvf7HQ1dUt52iIiIiI6EuWkZEBc3Nz6XfIgjApRUREREXKXbKnq6vLpBQRERERKaSobR+YlCIiIiKFedsPhYqSanmHQUREREQKOJS2qbxDKBTfvkdERERERERERGWOSSkiIiIiIiIiIipzTEoREREREREREVGZY1KKiIiIiIiIiIjKHJNSRET0RfHx8YEgCHmOGzdulHdoclJTUyEIAhISEhSqv3PnTrRq1QoVKlSAlpYWatasiUGDBuH8+fOfN1AiIiIioi8Uk1JERPTF8fDwQHp6utxRrVq1ErX19u3bPGWiKOLdu3efGqbCpkyZgp49e6JevXrYu3cvrly5gtWrV6N69er48ccfyywOIiIiIqIvCZNSRET0xVFXV4eJiYncoaysDACIjo5Go0aNoK6uDlNTU0ydOlUuweTi4oLRo0djwoQJqFixItzc3BAVFQVBEBAREQEnJyeoq6sjJiYGoigiODgYVlZW0NTURN26dfHbb79JbT158gR9+vSBkZERNDU1UaNGDaxfvx4ApCRZ/fr1IQgCXFxc8h3LqVOnEBwcjJ9++gk//fQTWrRogWrVqsHZ2RnTpk3DgQMHpLo+Pj7w9vaWu3/cuHFybbu4uOD777/HuHHjUKFCBVSqVAmrV6/Gy5cvMXDgQOjo6KB69eo4ePCgdE/u+Pfv34+6detCQ0MDjRs3xqVLl0r0+RARERERlQYmpYiI6F/j77//Rrt27dCwYUNcuHABK1euxNq1azFr1iy5emFhYVBRUcGJEycQEhIilU+ePBlz585FUlISHBwcMH36dKxfvx4rV67ElStXMH78ePTt2xfR0dEAAD8/PyQmJuLgwYNISkrCypUrUbFiRQDAmTNnAABHjhxBeno6du3alW/MW7duhUwmw8iRI/O9LghCsZ9DWFgYKlasiDNnzuD777/Hd999h+7du6Np06Y4d+4c3N3d0a9fP7x69UruvkmTJmHhwoWIi4uDsbExOnbsiKysrGL3T0RERERUGlTKOwAiIqKP7du3DzKZTDr39PTEjh07sGLFCpibm+Pnn3+GIAiwtbXF3bt3MWXKFPj7+0NJ6f2/tVhbWyM4OFi6/969ewCAGTNmwM3NDQDw8uVL/PTTTzh69CiaNGkCALCyssLx48cREhICZ2dnpKWloX79+nBycgIAWFpaSm0aGRkBAAwNDWFiYlLgWK5duwYrKyuoqPzff3J/+ukn+Pv7S+d///039PT0FH4+devWxfTp0wEAvr6+mDdvHipWrIihQ4cCAPz9/bFy5UpcvHgR33zzjXRfQECANP6wsDBUqVIFu3fvRo8ePfL0kZmZiczMTOk8IyND4fiIiIiIiBTBpBQREX1xXF1dsXLlSulcW1sbAJCUlIQmTZrIzS5q1qwZXrx4gTt37qBq1aoAICWRPvZheWJiIt68eSMlaXK9ffsW9evXBwB899136Nq1K86dO4e2bdvC29sbTZs2LfZ4Pp4NNWjQIHTs2BGnT59G3759IYpisdpzcHCQflZWVoahoSHq1KkjlVWqVAkAcP/+fbn7cpNvAGBgYICaNWsiKSkp3z7mzp2LoKCgYsVFRERERFQcTEoREdEXR1tbG9bW1nnKRVHMk+DJTeh8WJ6bxMqv3Vw5OTkAgP3796Ny5cpy9dTV1QG8n6F169Yt7N+/H0eOHEHr1q0xatQoLFy4UOGx1KhRA8ePH0dWVhZUVVUBAPr6+tDX18edO3fk6iopKeVJUOW3vC63nVyCIMiV5T6L3DEWpqDlg76+vpgwYYJ0npGRAXNz8yLbIyIiIiJSFPeUIiKifw17e3vExsbKJW5iY2Oho6OTJ7GkSFvq6upIS0uDtbW13PFh8sXIyAg+Pj7YtGkTlixZgtWrVwMA1NTUAADZ2dmF9tOrVy+8ePECK1asKDImIyMjpKeny5UlJCQUa1yFOXXqlPTzkydPcO3aNdja2uZbV11dHbq6unIHEREREVFp4kwpIiL61xg5ciSWLFmC77//HqNHj0ZycjICAgIwYcIEaT8pReno6GDixIkYP348cnJy0Lx5c2RkZCA2NhYymQwDBgyAv78/GjRogFq1aiEzMxP79u2DnZ0dAMDY2BiampoIDw9HlSpVoKGhke++UE2aNMEPP/yAH374Abdu3UKXLl1gbm6O9PR0rF27FoIgSLG3atUKCxYswIYNG9CkSRNs2rQJly9flpYTfqoZM2bA0NAQlSpVwrRp01CxYsU8b/sjIiIiIiornClFRET/GpUrV8aBAwdw5swZ1K1bFyNGjMDgwYOlTb+La+bMmfD398fcuXNhZ2cHd3d3/PHHH6hWrRqA97OhfH194eDggJYtW0JZWRnbtm0DAKioqGDZsmUICQmBmZkZOnXqVGA/CxcuxJYtW3D+/Hm0b98eNWrUQPfu3ZGTk4OTJ09Ks5Dc3d3h5+eHyZMno2HDhnj+/Dn69+9forHlZ968eRg7diwaNGiA9PR07N27V5rxRURERERU1gSxuLurEhER0b9KVFQUXF1d8eTJE+jr65eojYyMDOjp6cG1cg+oKKkWfQMRERERlbtDaZvKpd/c3x2fPXtW6DYQnClFRERERERERERljkkpIiIiIiIiIiIqc9zonIiI6Cvn4uICrtYnIiIioi8NZ0oREREREREREVGZ40wpIiIiUtiexDWFblZJRERERKQozpQiIiIiIiIiIqIyx6QUERERERERERGVOSaliIiIiIiIiIiozDEpRUREREREREREZY4bnRMREZHCujb5AarKauUdBhEREdFX48DF/5V3COWGM6WIiIiIiIiIiKjMMSlFRERERERERERljkkpIiIiIiIiIiIqc0xKERERERERERFRmWNSiojo/xMEAXv27CnvMAAAqampEAQBCQkJn72vL2nclpaWWLJkSXmHUSAfHx94e3uXdxh5lOX3hYiIiIiotDApRUT/Cffv38fw4cNRtWpVqKurw8TEBO7u7jh58mR5h/avExoaCkEQpKNSpUro0KEDrly5Ut6hlZqCkjxLly5FaGhomcby4bPO7/Dx8SnTeIiIiIiISotKeQdARFQWunbtiqysLISFhcHKygr//PMPIiMj8fjx4/IO7V9JV1cXycnJEEURf//9NyZPngwvLy9cu3YNampq5R3eZ6Onp1fmfaanp0s///rrr/D390dycrJUpqmpiSdPnpR5XEREREREn4ozpYjoq/f06VMcP34c8+fPh6urKywsLNCoUSP4+vrCy8tLru7Dhw/RuXNnaGlpoUaNGti7d690LTs7G4MHD0a1atWgqamJmjVrYunSpdL1S5cuQUlJCQ8fPgQAPHnyBEpKSujevbtUZ+7cuWjSpIl0vU+fPjAyMoKmpiZq1KiB9evXy8Xz119/wdXVFVpaWqhbt67czK5Hjx6hV69eqFKlCrS0tFCnTh1s3bpV7n4XFxeMGTMGkydPhoGBAUxMTBAYGFjo85oxYwYqVapU6FIwQRBgYmICU1NTODk5Yfz48bh165ZcsiQ2NhYtW7aEpqYmzM3NMWbMGLx8+VK6fv/+fXTo0AGampqoVq0aNm/enKefZ8+eYdiwYTA2Noauri5atWqFCxcuSNcDAwNRr149rFu3DlWrVoVMJsN3332H7OxsBAcHw8TEBMbGxpg9e3ae+FeuXAlPT0+p/x07dkjXq1WrBgCoX78+BEGAi4sLgLzL9zIzMzFmzBgYGxtDQ0MDzZs3R1xcnHQ9KioKgiAgMjISTk5O0NLSQtOmTeWeU1FMTEykQ09PT3r2H5blKuz7oshnQkRERERUlpiUIqKvnkwmg0wmw549e5CZmVlo3aCgIPTo0QMXL15Eu3bt0KdPH2k2VU5ODqpUqYLt27cjMTER/v7++PHHH7F9+3YAQO3atWFoaIjo6GgAwJ9//glDQ0P8+eefUvtRUVFwdnYGAPj5+SExMREHDx5EUlISVq5ciYoVK8rFM23aNEycOBEJCQmwsbFBr1698O7dOwDAmzdv0KBBA+zbtw+XL1/GsGHD0K9fP5w+fVqujbCwMGhra+P06dMIDg7GjBkzcPjw4TxjF0URY8eOxdq1a3H8+HHUq1dPoef79OlTbNmyBQCgqqoK4H2Czt3dHV26dMHFixfx66+/4vjx4xg9erR0n4+PD1JTU3H06FH89ttvWLFiBe7fvy8Xj5eXF+7du4cDBw7g7NmzcHR0ROvWreVmuKWkpODgwYMIDw/H1q1bsW7dOnh5eeHOnTuIjo7G/PnzMX36dJw6dUoubj8/P3Tt2hUXLlxA37590atXLyQlJQEAzpw5AwA4cuQI0tPTsWvXrnzHPnnyZOzcuRNhYWE4d+4crK2t4e7unmcG3rRp07Bo0SLEx8dDRUUFgwYNUujZFldh3xdFPpMPZWZmIiMjQ+4gIiIiIipNgiiKYnkHQUT0ue3cuRNDhw7F69ev4ejoCGdnZ3z77bdwcHCQ6giCgOnTp2PmzJkAgJcvX0JHRwcHDhyAh4dHvu2OGjUK//zzD3777TcA75cJmpmZYfny5Rg/fjyUlZURFhaG6Oho2NjYoEKFCtixYwc8PDzQsWNHVKxYEevWrcvTbmpqKqpVq4ZffvkFgwcPBgAkJiaiVq1aSEpKgq2tbb7xeHl5wc7ODgsXLgTwfqZUdnY2YmJipDqNGjVCq1atMG/ePGncO3bswO+//474+HgcPnwYVapUKfBZhoaGYuDAgdDW1oYoinj16hUAoGPHjvj9998BAP3794empiZCQkKk+44fPw5nZ2e8fPkSaWlpqFmzJk6dOoXGjRsDAK5evQo7OzssXrwY48aNw9GjR9G5c2fcv38f6urqUjvW1taYPHkyhg0bhsDAQCxYsAD37t2Djo4OAMDDwwPJyclISUmBktL7f3uxtbWFj48Ppk6dKo15xIgRWLlypdTuN998A0dHR6xYsUJ6/ufPn5dLzvn4+ODp06fYs2cPXr58iQoVKiA0NBS9e/cGAGRlZcHS0hLjxo3DpEmTEBUVBVdXVxw5cgStW7cGABw4cABeXl54/fo1NDQ0CnzOBT37cePG4enTp3LlinxfivpMPo4lMDAQQUFBeWJoYz8Eqspf7xJNIiIiorJ24OL/yjuEUpeRkQE9PT08e/YMurq6BdbjTCki+k/o2rUr7t69i71798Ld3R1RUVFwdHTMs2n1h0kqbW1t6OjoyM3eWbVqFZycnGBkZASZTIY1a9YgLS1Nuu7i4oKoqCgAQHR0NFxdXdGyZUtER0cjLi4Or1+/RrNmzQAA3333HbZt24Z69eph8uTJiI2NzRP3h/GYmpoCgBRPdnY2Zs+eDQcHBxgaGkImk+HQoUNy8XzcRm47H44JAMaPH4+TJ08iJiam0IRULh0dHSQkJODs2bNYtWoVqlevjlWrVknXz549i9DQUGmWmkwmg7u7O3JycnDz5k0kJSVBRUUFTk5O0j22trbQ19eXa+PFixfS2HKPmzdvIiUlRapnaWkpJaQAoFKlSrC3t5cSUrllH485dxnlh+e5M6UUkZKSgqysLOnzBN7PFGvUqFGedgr7HEtTYf0U9Zl8zNfXF8+ePZOO27dvl3q8RERERPTfxo3Oieg/Q0NDA25ubnBzc4O/vz+GDBmCgIAAubeX5S4/yyUIAnJycgAA27dvx/jx47Fo0SI0adIEOjo6WLBggdxyORcXF4wdOxY3btzA5cuX0aJFC6SkpCA6OhpPnz5FgwYNpASKp6cnbt26hf3790szaUaNGiXNcvo4HkEQAECKZ9GiRVi8eDGWLFmCOnXqQFtbG+PGjcPbt2/lxlDYmHK5ublh69atiIiIQJ8+fYp8lkpKSrC2tgbwPpl079499OzZU1qqmJOTg+HDh2PMmDF57q1ataq0p1LumPKTk5MDU1NTKcn3oQ+TV/mNT5Ex56eweD6WO9H443tEUcxTVtjnWJoK66eoz+Rj6urqcjPUiIiIiIhKG2dKEdF/lr29fbE2eY6JiUHTpk0xcuRI1K9fH9bW1nIzdoD/21dq1qxZqFu3LnR1deHs7Izo6Gi5/aRyGRkZwcfHB5s2bcKSJUuwevXqYsXTqVMn9O3bF3Xr1oWVlRWuX7+u8P0f6tixI7Zs2YIhQ4Zg27Ztxb5//PjxuHDhAnbv3g0AcHR0xJUrV2BtbZ3nUFNTg52dHd69e4f4+HipjeTkZLllaY6Ojrh37x5UVFTytPHx3lsl8fEeU6dOnZKWRea+QTA7O7vA+3PHcvz4caksKysL8fHxsLOz++T4SltRnwkRERERUVljUoqIvnqPHj1Cq1atsGnTJly8eBE3b97Ejh07EBwcjE6dOincjrW1NeLj4xEREYFr167Bz89P7k1rwPvZKS1btsSmTZukN7Y5ODjg7du3iIyMlMoAwN/fH7///jtu3LiBK1euYN++fcVKZlhbW+Pw4cOIjY1FUlIShg8fjnv37il8/8c6d+6MjRs3YuDAgdIeWYrS1dWVZp6JoogpU6bg5MmTGDVqFBISEnD9+nXs3bsX33//PQCgZs2a8PDwwNChQ3H69GmcPXsWQ4YMgaamptRmmzZt0KRJE3h7eyMiIgKpqamIjY3F9OnT5ZJZJbVjxw6sW7cO165dQ0BAAM6cOSNt+m1sbAxNTU2Eh4fjn3/+wbNnz/Lcr62tje+++w6TJk1CeHg4EhMTMXToULx69Ura1+lLUtRnQkRERERU1piUIqKvnkwmQ+PGjbF48WK0bNkStWvXhp+fH4YOHYqff/5Z4XZGjBiBLl26oGfPnmjcuDEePXqEkSNH5qnn6uqK7OxsKQElCAJatGgBAGjevLlUT01NDb6+vnBwcEDLli2hrKxcrFlKfn5+cHR0hLu7O1xcXGBiYgJvb2+F789Pt27dEBYWhn79+hX4xrmCjB07FklJSdixYwccHBwQHR2N69evo0WLFqhfvz78/PykfY4AYP369TA3N4ezszO6dOmCYcOGwdjYWLouCAIOHDiAli1bYtCgQbCxscG3336L1NRUVKpU6ZPGCbx/0+K2bdvg4OCAsLAwbN68Gfb29gAAFRUVLFu2DCEhITAzMysweTlv3jx07doV/fr1g6OjI27cuIGIiAhUqFBB4ThSU1MhCEK+yxRLkyKfCRERERFRWeLb94iI6D9HEATs3r37k5N4pSEqKgqdO3fGX3/9VaxkVlnLfYMK375HREREVLr49j0iIiIqF+Hh4fjxxx+/6IQUEREREdHnwLfvERERlaN58+aVdwhEREREROWCSSkiIvrP4cp1IiIiIqLyx6QUERERKWznyUWF7gtARERERKQo7ilFRERERERERERljkkpIiIiIiIiIiIqc0xKERERERERERFRmWNSioiIiIiIiIiIyhyTUkREREREREREVOb49j0iIiJSWPe2flBVUS/vMIiIiIjK1b7jweUdwleBM6WIiIiIiIiIiKjMMSlFRERERERERERljkkpIiIiIiIiIiIqc0xKERERfcDFxQXjxo37bO37+PjA29u73NsgIiIiIipvTEoREdFXw8fHB4IgQBAEqKqqwsrKChMnTsTLly/LNI7AwEApjg+PI0eOYOnSpQgNDVWondTUVAiCgISEBLny4rRBRERERPSl4tv3iIjoq+Lh4YH169cjKysLMTExGDJkCF6+fImVK1eWaRy1atXCkSNH5MoMDAygpqb2yW3r6el9chtEREREROWNM6WIiOiroq6uDhMTE5ibm6N3797o06cP9uzZAyD/ZW/jxo2Di4tLge1ZWlpi5syZ6N27N2QyGczMzLB8+fIi41BRUYGJiYncoaamlieGnJwczJ8/H9bW1lBXV0fVqlUxe/ZsAEC1atUAAPXr14cgCFKcH7eRmZmJMWPGwNjYGBoaGmjevDni4uKk61FRURAEAZGRkXBycoKWlhaaNm2K5OTkIsdBRERERPS5MClFRERfNU1NTWRlZX1SGwsWLICDgwPOnTsHX19fjB8/HocPHy6V+Hx9fTF//nz4+fkhMTERW7ZsQaVKlQAAZ86cAQAcOXIE6enp2LVrV75tTJ48GTt37kRYWBjOnTsHa2truLu74/Hjx3L1pk2bhkWLFiE+Ph4qKioYNGhQqYyBiIiIiKgkuHyPiIi+WmfOnMGWLVvQunXrT2qnWbNmmDp1KgDAxsYGJ06cwOLFi+Hm5lbgPZcuXYJMJpPO7e3tpSRTrufPn2Pp0qX4+eefMWDAAABA9erV0bx5cwCAkZERAMDQ0BAmJib59pO7NDE0NBSenp4AgDVr1uDw4cNYu3YtJk2aJNWdPXs2nJ2dAQBTp06Fl5cX3rx5Aw0NjTztZmZmIjMzUzrPyMgocKxERERERCXBpBQREX1V9u3bB5lMhnfv3iErKwudOnVSaLldYZo0aZLnfMmSJYXeU7NmTezdu1c6V1dXz1MnKSkJmZmZn5Q0S0lJQVZWFpo1ayaVqaqqolGjRkhKSpKr6+DgIP1samoKALh//z6qVq2ap925c+ciKCioxHERERERERWFSSkiIvqquLq6YuXKlVBVVYWZmRlUVVWla0pKShBFUa5+SZf2CYJQ6HU1NTVYW1sXWkdTU7NEfX8odzwfxyOKYp6yD59F7rWcnJx82/X19cWECROk84yMDJibm39yvEREREREubinFBERfVW0tbVhbW0NCwsLuSQM8H45XHp6ulxZQkJCkW2eOnUqz7mtre0nx1qjRg1oamoiMjIy3+u5b+rLzs4usA1ra2uoqanh+PHjUllWVhbi4+NhZ2dX4tjU1dWhq6srdxARERERlSbOlCIiov+MVq1aYcGCBdiwYQOaNGmCTZs24fLly6hfv36h9504cQLBwcHw9vbG4cOHsWPHDuzfv/+T49HQ0MCUKVMwefJkqKmpoVmzZnjw4AGuXLmCwYMHw9jYGJqamggPD0eVKlWgoaEBPT09uTa0tbXx3XffYdKkSTAwMEDVqlURHByMV69eYfDgwZ8cIxERERHR58KZUkRE9J/h7u4OPz8/TJ48GQ0bNsTz58/Rv3//Iu/74YcfcPbsWdSvXx8zZ87EokWL4O7uXiox+fn54YcffoC/vz/s7OzQs2dP3L9/HwCgoqKCZcuWISQkBGZmZujUqVO+bcybNw9du3ZFv3794OjoiBs3biAiIgIVKlQolRiJiIiIiD4HQfx4cw0iIiKSWFpaYty4cRg3blx5h1KuMjIyoKenh7aNx0BVJe+m7URERET/JfuOB5d3CF+03N8dnz17Vug2EJwpRUREREREREREZY5JKSIiIiIiIiIiKnPc6JyIiKgQqamp5R0CEREREdFXiTOliIiIiIiIiIiozHGmFBERESlsx6GZhW5WSURERESkKM6UIiIiIiIiIiKiMsekFBERERERERERlTkmpYiIiIiIiIiIqMwxKUVERERERERERGWOG50TERGRwrp1nQ1VVfXyDoOIiIj+RfYfmFHeIdAXijOliIiIiIiIiIiozDEpRUREREREREREZY5JKSIiIiIiIiIiKnNMShERfWaCIGDPnj0AgNTUVAiCgISEhHKNqTTcu3cPbm5u0NbWhr6+vsL3fU3PAAB8fHzg7e1drjFERUVBEAQ8ffq0XOMgIiIiIioOJqWIiD7B/fv3MXz4cFStWhXq6uowMTGBu7s7Tp48KdVJT0+Hp6dnOUb5eSxevBjp6elISEjAtWvX8q1Tlgmb0NDQYiXH/g1yE3iFHYGBgeUdJhERERFRifDte0REn6Br167IyspCWFgYrKys8M8//yAyMhKPHz+W6piYmJRjhJ9PSkoKGjRogBo1apR3KF8tc3NzpKenS+cLFy5EeHg4jhw5IpXJZDLEx8eXR3hERERERJ+EM6WIiEro6dOnOH78OObPnw9XV1dYWFigUaNG8PX1hZeXl1Tvw+V7+UlMTES7du0gk8lQqVIl9OvXDw8fPpSuu7i4YMyYMZg8eTIMDAxgYmKSZ3bM06dPMWzYMFSqVAkaGhqoXbs29u3bJ12PjY1Fy5YtoampCXNzc4wZMwYvX74sdHwrV65E9erVoaamhpo1a2Ljxo3SNUtLS+zcuRMbNmyAIAjw8fHJc39gYCDCwsLw+++/S7N6oqKipOt//fUXXF1doaWlhbp168rNLitpzIUJDw9H8+bNoa+vD0NDQ7Rv3x4pKSnS9dxZSdu3b0eLFi2gqamJhg0b4tq1a4iLi4OTkxNkMhk8PDzw4MGDPO0HBQXB2NgYurq6GD58ON6+fStd++2331CnTh1oamrC0NAQbdq0UWgsysrKMDExkQ6ZTAYVFZU8ZbnOnj0LJycnaGlpoWnTpkhOTpZr748//kCDBg2goaEBKysrBAUF4d27dyV5nEREREREn4xJKSKiEpLJZJDJZNizZw8yMzNL1EZ6ejqcnZ1Rr149xMfHIzw8HP/88w969OghVy8sLAza2to4ffo0goODMWPGDBw+fBgAkJOTA09PT8TGxmLTpk1ITEzEvHnzoKysDAC4dOkS3N3d0aVLF1y8eBG//vorjh8/jtGjRxcY1+7duzF27Fj88MMPuHz5MoYPH46BAwfi2LFjAIC4uDh4eHigR48eSE9Px9KlS/O0MXHiRPTo0QMeHh5IT09Heno6mjZtKl2fNm0aJk6ciISEBNjY2KBXr15SgqQkMRfl5cuXmDBhAuLi4hAZGQklJSV07twZOTk5cvUCAgIwffp0nDt3DioqKujVqxcmT56MpUuXIiYmBikpKfD395e7JzIyEklJSTh27Bi2bt2K3bt3IygoCMD7z7hXr14YNGgQkpKSEBUVhS5dukAUxRKPpSDTpk3DokWLEB8fDxUVFQwaNEi6FhERgb59+2LMmDFITExESEgIQkNDMXv27FKPg4iIiIhIEYL4OX4rJiL6j9i5cyeGDh2K169fw9HREc7Ozvj222/h4OAg1REEAbt374a3tzdSU1NRrVo1nD9/HvXq1YO/vz9Onz6NiIgIqf6dO3dgbm6O5ORk2NjYwMXFBdnZ2YiJiZHqNGrUCK1atcK8efNw6NAheHp6IikpCTY2Nnli7N+/PzQ1NRESEiKVHT9+HM7Oznj58iU0NDTy3NOsWTPUqlULq1evlsp69OiBly9fYv/+/QAAb29v6OvrIzQ0tMDn4+Pjg6dPn8rNFMt9Br/88gsGDx4M4P1ssVq1aiEpKQm2trYlijk0NBTjxo1TeLPvBw8ewNjYGJcuXULt2rXzjWvbtm3o1asXIiMj0apVKwDAvHnzEBoaiqtXr0pj/OOPP3D79m1oaWkBAFatWoVJkybh2bNnSEhIQIMGDZCamgoLCwuFYitIYGAg9uzZk2eT+KioKLi6uuLIkSNo3bo1AODAgQPw8vLC69evoaGhgZYtW8LT0xO+vr7SfZs2bcLkyZNx9+7dPH1lZmbKJVszMjJgbm4OtzaToaqq/knjICIiov+W/QdmlHcIVMYyMjKgp6eHZ8+eQVdXt8B6nClFRPQJunbtirt372Lv3r1wd3dHVFQUHB0dC03UfOjs2bM4duyYNOtKJpPB1tYWAOSWln2Y5AIAU1NT3L9/HwCQkJCAKlWq5JuQyu0jNDRUrg93d3fk5OTg5s2b+d6TlJSEZs2ayZU1a9YMSUlJCo1LER+OydTUFACkMZUk5qKkpKSgd+/esLKygq6uLqpVqwYASEtLKzCuSpUqAQDq1KkjV5YbZ666detKCSkAaNKkCV68eIHbt2+jbt26aN26NerUqYPu3btjzZo1ePLkSYnGUJSinumMGTPknunQoUORnp6OV69e5Wlr7ty50NPTkw5zc/PPEjMRERER/Xdxo3Miok+koaEBNzc3uLm5wd/fH0OGDEFAQEC++yx9LCcnBx06dMD8+fPzXMtNKgCAqqqq3DVBEKRlZ5qamkX2MXz4cIwZMybPtapVqxZ4nyAIcueiKOYp+xQfjim33dwxlTTmwnTo0AHm5uZYs2YNzMzMkJOTg9q1a8vt/VRQXB+XfbzkryCCIEBZWRmHDx9GbGwsDh06hOXLl2PatGk4ffq0lBgrLUU906CgIHTp0iXPffnNPPP19cWECROk89yZUkREREREpYVJKSKiUmZvb1/oxuYfcnR0xM6dO2FpaQkVlZL9X7KDgwPu3LmDa9eu5TtbytHREVeuXIG1tbXCbdrZ2eH48ePo37+/VBYbGws7O7tixaampobs7Oxi3QOULObCPHr0CElJSQgJCUGLFi0AvF8OWFouXLiA169fSwnCU6dOQSaToUqVKgDeJ4iaNWuGZs2awd/fHxYWFti9e7dc0udzc3R0RHJyssLPVF1dHerqXKZHRERERJ8Pk1JERCX06NEjdO/eHYMGDYKDgwN0dHQQHx+P4OBgdOrUSaE2Ro0ahTVr1qBXr16YNGkSKlasiBs3bmDbtm1Ys2aNtFl5YZydndGyZUt07doVP/30E6ytrXH16lUIggAPDw9MmTIF33zzDUaNGoWhQ4dCW1sbSUlJOHz4MJYvX55vm5MmTUKPHj3g6OiI1q1b448//sCuXbtw5MiRYj0jS0tLREREIDk5GYaGhtDT01PovpLEDADZ2dl59ltSU1ODra0tDA0NsXr1apiamiItLQ1Tp04t1lgK8/btWwwePBjTp0/HrVu3EBAQgNGjR0NJSQmnT59GZGQk2rZtC2NjY5w+fRoPHjwodoLvU/n7+6N9+/YwNzdH9+7doaSkhIsXL+LSpUuYNWtWmcZCRERERAQwKUVEVGIymQyNGzfG4sWLkZKSgqysLJibm2Po0KH48ccfFWrDzMwMJ06cwJQpU+Du7o7MzExYWFjAw8MDSkqKb/u3c+dOTJw4Eb169cLLly9hbW2NefPmAXg/kyo6OhrTpk1DixYtIIoiqlevjp49exbYnre3N5YuXYoFCxZgzJgxqFatGtavXw8XFxeFYwKAoUOHIioqCk5OTnjx4gWOHTsGS0vLIu8rScwA8OLFC9SvX1+uzMLCAqmpqdi2bRvGjBmD2rVro2bNmli2bFmxx1OQ1q1bo0aNGmjZsiUyMzPx7bffIjAwEACgq6uLP//8E0uWLEFGRgYsLCywaNEieHp6Avi/Tcpv3ryp0LMpKXd3d+zbtw8zZsxAcHAwVFVVYWtriyFDhny2PomIiIiICsO37xEREZWj0NBQzJ49G4mJiXn2DvuS5L5BhW/fIyIiouLi2/f+e/j2PSIion+B8PBwzJkz54tOSBERERERfQ5cvkdERFSOtm3bVt4hEBERERGVC86UIiIiIiIiIiKiMsekFBERERERERERlTku3yMiIiKF/bZzWqGbVRIRERERKYozpYiIiIiIiIiIqMwxKUVERERERERERGWOSSkiIiIiIiIiIipzTEoREREREREREVGZ40bnREREpLDO/eZCRVWjvMMgIiKif4GI3wLKOwT6wnGmFBERERERERERlTkmpYiIiIiIiIiIqMwxKUVERERERERERGWOSSkiIiIiIiIiIipzTEoRERGVIktLSyxZsqRM+wwNDYW+vn6Z9klERERE9KmYlCIioq+Kj48PBEHAiBEj8lwbOXIkBEGAj4+Pwu2lpqZCEAQkJCQoVD8uLg7Dhg1TuP3CREVFQRCEQo/Q0NBS6YuIiIiIqKwxKUVERF8dc3NzbNu2Da9fv5bK3rx5g61bt6Jq1aqfpc+3b98CAIyMjKClpVUqbTZt2hTp6enS0aNHD3h4eMiV9ezZs1T6IiIiIiIqa0xKERHRV8fR0RFVq1bFrl27pLJdu3bB3Nwc9evXl6sbHh6O5s2bQ19fH4aGhmjfvj1SUlKk69WqVQMA1K9fH4IgwMXFBcD7GVne3t6YO3cuzMzMYGNjA0B++V5UVBTU1NQQExMjtbdo0SJUrFgR6enpRY5DTU0NJiYm0qGpqQl1dfU8ZbkiIiJgZ2cHmUwmJa8+tH79etjZ2UFDQwO2trZYsWKFAk+TiIiIiOjzYFKKiIi+SgMHDsT69eul83Xr1mHQoEF56r18+RITJkxAXFwcIiMjoaSkhM6dOyMnJwcAcObMGQDAkSNHkJ6eLpfoioyMRFJSEg4fPox9+/bladvFxQXjxo1Dv3798OzZM1y4cAHTpk3DmjVrYGpqWqrjffXqFRYuXIiNGzfizz//RFpaGiZOnChdX7NmDaZNm4bZs2cjKSkJc+bMgZ+fH8LCwvJtLzMzExkZGXIHEREREVFpUinvAIiIiD6Hfv36wdfXV9oT6sSJE9i2bRuioqLk6nXt2lXufO3atTA2NkZiYiJq164NIyMjAIChoSFMTEzk6mpra+OXX36BmppagXHMmjULR44cwbBhw3DlyhX069cPnTt3Lp1BfiArKwurVq1C9erVAQCjR4/GjBkzpOszZ87EokWL0KVLFwDvZ4AlJiYiJCQEAwYMyNPe3LlzERQUVOpxEhERERHlYlKKiIi+ShUrVoSXlxfCwsIgiiK8vLxQsWLFPPVSUlLg5+eHU6dO4eHDh9IMqbS0NNSuXbvQPurUqVNoQgp4vwRv06ZNcHBwgIWFxWd7M5+WlpaUkAIAU1NT3L9/HwDw4MED3L59G4MHD8bQoUOlOu/evYOenl6+7fn6+mLChAnSeUZGBszNzT9L7ERERET038SkFBERfbUGDRqE0aNHAwD+97//5VunQ4cOMDc3x5o1a2BmZoacnBzUrl1b2ri8MNra2grFERsbCwB4/PgxHj9+rPB9xaGqqip3LggCRFEEACnRtmbNGjRu3FiunrKycr7tqaurQ11dvdTjJCIiIiLKxaQUERF9tTw8PKTkkru7e57rjx49QlJSEkJCQtCiRQsAwPHjx+Xq5M6Eys7OLlEMKSkpGD9+PNasWYPt27ejf//+0t5VZaVSpUqoXLky/vrrL/Tp06fM+iUiIiIiKgyTUkRE9NVSVlZGUlKS9PPHKlSoAENDQ6xevRqmpqZIS0vD1KlT5eoYGxtDU1MT4eHhqFKlCjQ0NApc8vax7Oxs9OvXD23btsXAgQPh6emJOnXqYNGiRZg0adKnD7AYAgMDMWbMGOjq6sLT0xOZmZmIj4/HkydP5JbpERERERGVFb59j4iIvmq6urrQ1dXN95qSkhK2bduGs2fPonbt2hg/fjwWLFggV0dFRQXLli1DSEgIzMzM0KlTJ4X7nj17NlJTU7F69WoAgImJCX755RdMnz4dCQkJAIDQ0FAIglCywRXDkCFD8MsvvyA0NBR16tSBs7MzQkNDUa1atc/eNxERERFRfgQxd8MJIiIiKnOBgYGIiorK81bAL01GRgb09PTQquNUqKhqlHc4RERE9C8Q8VtAeYdA5ST3d8dnz54V+A/EAJfvERERlauIiAgsXbq0vMMgIiIiIipzTEoRERGVo5MnT5Z3CERERERE5YJ7ShERERERERERUZnjTCkiIiJS2O6NvoXuC0BEREREpCjOlCIiIiIiIiIiojLHpBQREREREREREZU5JqWIiIiIiIiIiKjMMSlFRERERERERERljhudExERkcLaD5sPFTWN8g6DiIjoq3V0g195h0BUZjhTioiIiIiIiIiIyhyTUkREREREREREVOaYlCIiIiIiIiIiojLHpBQREREREREREZU5JqWIiP4lXFxcMG7cuM/WflRUFARBwNOnTz9bH0RERERERLmYlCKiYrt//z6GDx+OqlWrQl1dHSYmJnB3d8fJkyelOoIgYM+ePZ+l/1WrVkFHRwfv3r2Tyl68eAFVVVW0aNFCrm5MTAwEQcC1a9c+SyyKyE325Hfcu3dP4XZ27dqFmTNnfsZIP11oaCj09fUBAIsWLYKenh5evXqVp96bN2+gr6+Pn376qVT6rVmzJtTU1PD3338rFKMgCPDw8JArf/r0KQRBQFRUVKnEpIiCEo0fPkciIiIioq8Vk1JEVGxdu3bFhQsXEBYWhmvXrmHv3r1wcXHB48ePS72vt2/f5ilzdXXFixcvEB8fL5XFxMTAxMQEcXFxckmQqKgomJmZwcbGpth9i6Iol/j6VMnJyUhPT5c7jI2NFb7fwMAAOjo6pRbP59a/f3+8fv0aO3fuzHNt586dePXqFfr16/fJ/Rw/fhxv3rxB9+7dERoaqtA9KioqiIyMxLFjxz65fyIiIiIiKhkmpYioWJ4+fYrjx49j/vz5cHV1hYWFBRo1agRfX194eXkBACwtLQEAnTt3hiAI0nlKSgo6deqESpUqQSaToWHDhjhy5Ihc+5aWlpg1axZ8fHygp6eHoUOH5omhZs2aMDMzk5vREhUVhU6dOqF69eqIjY2VK3d1dQUAbNq0CU5OTtDR0YGJiQl69+6N+/fvy9UVBAERERFwcnKCuro6YmJicOHCBbi6ukJHRwe6urpo0KCBXEJMUcbGxjAxMZE7lJTe/9+wj48PvL29ERQUBGNjY+jq6mL48OFySbmPZ9WsWLECNWrUgIaGBipVqoRu3bpJ1zIzMzFmzBgYGxtDQ0MDzZs3R1xcnFw8Bw4cgI2NDTQ1NeHq6orU1NQ8McfGxqJly5bQ1NSEubk5xowZg5cvXyo0XiMjI3To0AHr1q3Lc23dunXo2LEjjIyMFGqrMGvXrkXv3r3Rr18/rFu3DqIoFnmPtrY2Bg4ciKlTpxZa7++//0bPnj1RoUIFGBoaolOnTtJzunTpEpSUlPDw4UMAwJMnT6CkpITu3btL98+dOxdNmjQp+eA+sHLlSlSvXh1qamqoWbMmNm7cKHddEASsXLkSnp6e0NTURLVq1bBjxw6Fx0NEREREVNaYlCKiYpHJZJDJZNizZw8yMzPzrZOb/Fi/fj3S09Ol8xcvXqBdu3Y4cuQIzp8/D3d3d3To0AFpaWly9y9YsAC1a9fG2bNn4efnl28fLi4ucrNcjh07BhcXFzg7O0vlb9++xcmTJ6Wk1Nu3bzFz5kxcuHABe/bswc2bN+Hj45On7cmTJ2Pu3LlISkqCg4MD+vTpgypVqiAuLg5nz57F1KlToaqqKtUXBEHhGTqFiYyMRFJSEo4dO4atW7di9+7dCAoKyrdufHw8xowZgxkzZiA5ORnh4eFo2bKl3Bh27tyJsLAwnDt3DtbW1nB3d5dms92+fRtdunRBu3btkJCQgCFDhuRJ0Fy6dAnu7u7o0qULLl68iF9//RXHjx/H6NGjFR7T4MGDER0djZs3b0plqampOHbsGAYPHlycx5Ov58+fY8eOHejbty/c3Nzw8uVLhZffBQYG4tKlS/jtt9/yvf7q1Su4urpCJpPhzz//xPHjxyGTyeDh4YG3b9+idu3aMDQ0RHR0NADgzz//hKGhIf7880+pjaioKDg7O3/yOHfv3o2xY8fihx9+wOXLlzF8+HAMHDgwz0wvPz8/aSZj37590atXLyQlJSk0HiIiIiKissakFBEVi4qKCkJDQxEWFgZ9fX00a9YMP/74Iy5evCjVyZ39oq+vDxMTE+m8bt26GD58OOrUqYMaNWpg1qxZsLKywt69e+X6aNWqFSZOnAhra2tYW1vnG4eLiwtOnDiBd+/e4fnz5zh//jxatmwJZ2dnKSlx6tQpvH79WkpKDRo0CJ6enrCyssI333yDZcuW4eDBg3jx4oVc2zNmzICbmxuqV68OQ0NDpKWloU2bNrC1tUWNGjXQvXt31K1bV6pfs2ZN6OnpFfnsqlSpIiX1ZDIZatasKXddTU0N69atQ61ateDl5YUZM2Zg2bJlyMnJydNWWloatLW10b59e1hYWKB+/foYM2YMAODly5dYuXIlFixYAE9PT9jb22PNmjXQ1NTE2rVrAbyfdWNlZYXFixejZs2a6NOnT54E3YIFC9C7d2+MGzcONWrUQNOmTbFs2TJs2LABb968KXK8AODu7g4zMzO5pN369ethZmaGtm3bKtRGYbZt24YaNWqgVq1aUFZWxrfffiuNsShmZmYYO3Yspk2blu8yzW3btkFJSQm//PIL6tSpAzs7O6xfvx5paWnSrLqWLVtK37eoqCgMGDAAOTk5SExMxLt37xAbGwsXF5dC41ixYoXc90Imk2HEiBFydRYuXAgfHx+MHDkSNjY2mDBhArp06YKFCxfK1evevTuGDBkCGxsbzJw5E05OTli+fLlC4/lYZmYmMjIy5A4iIiIiotLEpBQRFVvXrl1x9+5d7N27F+7u7oiKioKjo2ORs4VevnyJyZMnw97eHvr6+pDJZLh69WqemVJOTk5FxuDq6oqXL18iLi4OMTExsLGxgbGxMZydnREXFyfNmKlatSqsrKwAAOfPn0enTp1gYWEBHR0dKVlQVP8TJkzAkCFD0KZNG8ybNw8pKSly169evYrOnTsXGXNMTAwSEhKkIyIiQu563bp1oaWlJZ03adIEL168wO3bt/O05ebmBgsLC1hZWaFfv37YvHmztJdWSkoKsrKy0KxZM6m+qqoqGjVqJM2aSUpKwjfffANBEOT6+9DZs2cRGhoqlyxxd3dHTk6O3MynwigrK2PAgAEIDQ1FTk4ORFFEWFgYfHx8oKysXOBz+rDPzZs3F9j+2rVr0bdvX+m8b9++2LVrl8JvEJwyZQoePHiQ7xLDs2fP4saNG9DR0ZFiMTAwwJs3b6TvgIuLi5TQiY6OhqurK1q2bIno6GjExcXh9evXcp9Dfvr06SP3vUhISMCMGTPk6iQlJeVpp1mzZtLnmevjz7BJkyZSHUXG86G5c+dCT09POszNzQsdBxERERFRcamUdwBE9O+koaEBNzc3uLm5wd/fH0OGDEFAQEC+y+FyTZo0CREREVi4cCGsra2hqamJbt265Vk6pK2tXWT/1tbWqFKlCo4dO4YnT55IS6RMTExQrVo1nDhxAseOHUOrVq0AvE+ItW3bFm3btsWmTZtgZGSEtLQ0uLu7F9l/YGAgevfujf379+PgwYMICAjAtm3bFEpEfahatWoleqPah4mjXDo6Ojh37hyioqJw6NAh+Pv7IzAwEHFxcdKeSh/fJ4qiVKbIvks5OTkYPny4NAPrQ1WrVlU4/kGDBmHu3Lk4evQogPdJwIEDBxZY38nJCQkJCdJ5pUqV8q2XmJiI06dPIy4uDlOmTJHKs7OzsXXrVnz33XdFxqavrw9fX18EBQWhffv2ctdycnLQoEGDfJNiubP/XFxcMHbsWNy4cQOXL19GixYtkJKSgujoaDx9+hQNGjQocnN6PT29PDMC89sAv7DPszC5dRQZz4d8fX0xYcIE6TwjI4OJKSIiIiIqVZwpRUSlwt7eXm4DbFVVVWRnZ8vViYmJgY+PDzp37ow6derAxMTkkzZZdnV1RVRUFKKiouSWSDk7OyMiIgKnTp2Slu5dvXoVDx8+xLx589CiRQvY2trKbXJeFBsbG4wfPx6HDh1Cly5dsH79+hLHXZALFy7g9evX0vmpU6cgk8lQpUqVfOurqKigTZs2CA4OxsWLF5GamoqjR4/C2toaampqOH78uFQ3KysL8fHxsLOzA/D+8zp16pRcex+fOzo64sqVK9Iyyg8PNTU1hcdVvXp1ODs7Y/369Vi3bh1cXFxQvXr1AutramrK9VVQUmft2rVo2bIlLly4IDfLaPLkyQov4QOA77//HkpKSli6dKlcuaOjI65fvw5jY+M8489drpm7r9SsWbNQt25d6OrqwtnZGdHR0aW2nxQA2NnZyX2ewPtN6HM/z1z5faa2trYKj+dD6urq0NXVlTuIiIiIiEoTk1JEVCyPHj1Cq1atsGnTJly8eBE3b97Ejh07EBwcjE6dOkn1LC0tERkZiXv37uHJkycA3s9u2rVrFxISEnDhwgX07t073/2SFOXq6orjx48jISFB7i//zs7OWLNmDd68eSMlpapWrQo1NTUsX74cf/31F/bu3YuZM2cW2cfr168xevRoREVF4datWzhx4gTi4uLkkgG2trbYvXt3kW3dv38f9+7dkzuysrKk62/fvsXgwYORmJgozcgaPXq09Ia+D+3btw/Lli1DQkICbt26hQ0bNiAnJwc1a9aEtrY2vvvuO0yaNAnh4eFITEzE0KFD8erVK2lz8REjRiAlJQUTJkxAcnIytmzZkmf55ZQpU3Dy5EmMGjUKCQkJuH79Ovbu3Yvvv/++yLF+bPDgwdi1axd2795dKhucZ2VlYePGjejVqxdq164tdwwZMgRnz57FhQsXFGpLQ0MDQUFBWLZsmVx5nz59ULFiRXTq1AkxMTG4efMmoqOjMXbsWNy5cwcApH2lNm3aJCVGHRwc8PbtW0RGRha5n5SiJk2ahNDQUKxatQrXr1/HTz/9hF27dmHixIly9Xbs2IF169bh2rVrCAgIwJkzZ6SN6RUZDxERERFRWWJSioiKRSaToXHjxli8eDFatmyJ2rVrw8/PD0OHDsXPP/8s1Vu0aBEOHz4Mc3Nz1K9fHwCwePFiVKhQAU2bNkWHDh3g7u4OR0fHEsfi6uqK169fw9raWm6Jl7OzM54/f47q1atLy42MjIwQGhqKHTt2wN7eHvPmzcuzSXR+lJWV8ejRI/Tv3x82Njbo0aMHPD095d6Kl5ycjGfPnhXZVs2aNWFqaip3nD17VrreunVr1KhRAy1btkSPHj3QoUMHBAYG5tuWvr4+du3ahVatWsHOzg6rVq3C1q1bUatWLQDAvHnz0LVrV/Tr1w+Ojo64ceMGIiIiUKFCBQDvk3Q7d+7EH3/8gbp162LVqlWYM2eOXB8ODg6Ijo7G9evX0aJFC9SvXx9+fn4wNTUtcqwf69q1K9TV1aGuro4uXboU+/6P7d27F48ePcp3CWWNGjVQp06dYs2WGjBggLT3WC4tLS38+eefqFq1Krp06QI7OzsMGjQIr1+/lps15OrqiuzsbCkBJQgCWrRoAQBo3rx5CUaXl7e3N5YuXYoFCxagVq1aCAkJwfr16/MkvYKCgrBt2zY4ODggLCwMmzdvhr29fbHGQ0RERERUVgRRkY1FiIjos/Lx8cHTp0+xZ8+e8g6F/qUEQcDu3bvh7e39WdrPyMiAnp4eWvT8ESpqGp+lDyIiIgKObvAr7xCIPlnu747Pnj0r9B9AOVOKiIiIiIiIiIjKHJNSRERERERERERU5lTKOwAiIkKeTcaJiour8YmIiIjo34YzpYiIiIiIiIiIqMxxphQREREpbN/qKXxbHxERERGVCs6UIiIiIiIiIiKiMsekFBERERERERERlTkmpYiIiIiIiIiIqMwxKUVERERERERERGWOG50TERGRwtzHzoeKmkZ5h0FERFQuYkL8yjsEoq8KZ0oREREREREREVGZY1KKiIiIiIiIiIjKHJNSRERERERERERU5piUIiIiKiWhoaHQ19cv7zDyiIqKgiAIePr0aXmHQkREREQkYVKKiIj+NXx8fODt7V3eYRSoZ8+euHbtWonvDw0NhSAI0mFqaooePXrg5s2bpRglEREREdGXgUkpIiL6z8jKyvqs7WtqasLY2PiT2tDV1UV6ejru3r2LLVu2ICEhAR07dkR2dnaJ2vvcYyYiIiIiKikmpYiI6F/J0tISS5YskSurV68eAgMDpXNBELBq1Sp06tQJ2tramDVrFrKzszF48GBUq1YNmpqaqFmzJpYuXSrXTlRUFBo1agRtbW3o6+ujWbNmuHXrFgDgwoULcHV1hY6ODnR1ddGgQQPEx8cDyH/53t69e+Hk5AQNDQ1UrFgRXbp0KXRcgiDAxMQEpqamcHV1RUBAAC5fvowbN24gLi4Obm5uqFixIvT09ODs7Ixz587luf/jMX/s9evX8PLywjfffIPHjx8XGg8RERER0efCpBQREX3VAgIC0KlTJ1y6dAmDBg1CTk4OqlSpgu3btyMxMRH+/v748ccfsX37dgDAu3fv4O3tDWdnZ1y8eBEnT57EsGHDIAgCAKBPnz6oUqUK4uLicPbsWUydOhWqqqr59r1//3506dIFXl5eOH/+PCIjI+Hk5FSs+DU1NQG8n/H0/PlzDBgwADExMTh16hRq1KiBdu3a4fnz54WO+UPPnj1D27Zt8fbtW0RGRsLAwKBY8RARERERlRaV8g6AiIjoc+rdu3eexExQUJD0c7Vq1RAbG4vt27ejR48eyMjIwLNnz9C+fXtUr14dAGBnZyfVT0tLw6RJk2BrawsAqFGjRoF9z549G99++61cf3Xr1lU49jt37mDBggWoUqUKbGxsULt2bbnrISEhqFChAqKjo9G+ffsCx5y7J9U///yDnj17onr16ti6dSvU1NQK7DszMxOZmZnSeUZGhsJxExEREREpgjOliIjoq5bfzKRVq1bByckJRkZGkMlkWLNmDdLS0gAABgYG8PHxgbu7Ozp06IClS5ciPT1dunfChAkYMmQI2rRpg3nz5iElJaXAvhMSEtC6detixfvs2TPIZDJoa2vD3Nwcb9++xa5du6Cmpob79+9jxIgRsLGxgZ6eHvT09PDixQsp9sLGDABt2rSBlZUVtm/fXmhCCgDmzp0r9aGnpwdzc/NijYOIiIiIqChMShER0b+SkpISRFGUK8tvU29tbW258+3bt2P8+PEYNGgQDh06hISEBAwcOBBv376V6qxfvx4nT55E06ZN8euvv8LGxganTp0CAAQGBuLKlSvw8vLC0aNHYW9vj927d+cbY+7Su+LQ0dFBQkICLl26hBcvXuDs2bNo2LAhgPdvHzx79iyWLFmC2NhYJCQkwNDQUC72/Macy8vLCzExMUhMTCwyDl9fXzx79kw6bt++XeyxEBEREREVhsv3iIjoX8nIyEhuBlNGRoa0TK0wMTExaNq0KUaOHCmV5TfbqX79+qhfvz58fX3RpEkTbNmyBd988w0AwMbGBjY2Nhg/fjx69eqF9evXo3PnznnacHBwQGRkJAYOHKjwuJSUlGBtbV1g7CtWrEC7du0AALdv38bDhw8VbnvevHmQyWRo3bo1oqKiYG9vX2BddXV1qKurK9w2EREREVFxcaYUERH9K7Vq1QobN25ETEwMLl++jAEDBkBZWbnI+6ytrREfH4+IiAhcu3YNfn5+iIuLk67fvHkTvr6+OHnyJG7duoVDhw7h2rVrsLOzw+vXrzF69GhERUXh1q1bOHHiBOLi4uT2nPpQQEAAtm7dioCAACQlJeHSpUsIDg4u8Zitra2xceNGJCUl4fTp0+jTp0+xZ2MtXLgQffr0QatWrXD16tUSx0JERERE9KmYlCIion+NnJwcqKi8n+Tr6+uLli1bon379mjXrh28vb2ljckLM2LECHTp0gU9e/ZE48aN8ejRI7lZU1paWrh69Sq6du0KGxsbDBs2DKNHj8bw4cOhrKyMR48eoX///rCxsUGPHj3g6ekpt5H5h1xcXLBjxw7s3bsX9erVQ6tWrXD69OkSj3/dunV48uQJ6tevj379+mHMmDEwNjYudjuLFy9Gjx490KpVK1y7dq3E8RARERERfQpB/HhDDiIioi+Uh4cHrK2t8fPPP5d3KP85GRkZ0NPTwzc+P0JFTaO8wyEiIioXMSF+5R0C0b9C7u+Oz549g66uboH1OFOKiIi+eE+ePMH+/fsRFRWFNm3alHc4RERERERUCrjRORERffEGDRqEuLg4/PDDD+jUqVN5h0NERERERKWASSkiIvri7d69u7xDICIiIiKiUsble0REREREREREVOY4U4qIiIgUFrF0SqGbVRIRERERKYozpYiIiIiIiIiIqMwxKUVERERERERERGWOSSkiIiIiIiIiIipzTEoREREREREREVGZ40bnREREpLDWk+dDRV2jvMMgIiIqUyeX+pV3CERfJc6UIiIiIiIiIiKiMsekFBERERERERERlTkmpYiIiIiIiIiIqMwxKUVERERERERERGWOSSkiIqIyJggC9uzZU95hEBERERGVKyaliIjoq+Pj4wNBEKTD0NAQHh4euHjxYnmHVu5CQ0Ohr69f3mEQERERETEpRUREXycPDw+kp6cjPT0dkZGRUFFRQfv27Qusn5WVVYbRlY//whiJiIiI6N+DSSkiIvoqqaurw8TEBCYmJqhXrx6mTJmC27dv48GDB0hNTYUgCNi+fTtcXFygoaGBTZs24dGjR+jVqxeqVKkCLS0t1KlTB1u3bpXaDAkJQeXKlZGTkyPXV8eOHTFgwADp/I8//kCDBg2goaEBKysrBAUF4d27d3L3PHz4EJ07d4aWlhZq1KiBvXv3yl1PTExEu3btIJPJUKlSJfTr1w8PHz6UroeHh6N58+bQ19eHoaEh2rdvj5SUFOl6QWMcOHAgnj17Js0iCwwMLI3HTURERERUbExKERHRV+/FixfYvHkzrK2tYWhoKJVPmTIFY8aMQVJSEtzd3fHmzRs0aNAA+/btw+XLlzFs2DD069cPp0+fBgB0794dDx8+xLFjx6Q2njx5goiICPTp0wcAEBERgb59+2LMmDFITExESEgIQkNDMXv2bLmYgoKC0KNHD1y8eBHt2rVDnz598PjxYwBAeno6nJ2dUa9ePcTHxyM8PBz//PMPevToId3/8uVLTJgwAXFxcYiMjISSkhI6d+6cJ2H24Rhbt26NJUuWQFdXV5pFNnHixHyfWWZmJjIyMuQOIiIiIqLSpFLeARAREX0O+/btg0wmA/A+gWNqaop9+/ZBSen//j1m3Lhx6NKli9x9HyZpvv/+e4SHh2PHjh1o3LgxDAwM4OHhgS1btqB169YAgB07dsDAwEA6nz17NqZOnSrNnLKyssLMmTMxefJkBAQESG37+PigV69eAIA5c+Zg+fLlOHPmDDw8PLBy5Uo4Ojpizpw5Uv1169bB3Nwc165dg42NDbp27SoX99q1a2FsbIzExETUrl27wDHq6elBEASYmJgU+vzmzp2LoKCgQusQEREREX0KzpQiIqKvkqurKxISEpCQkIDTp0+jbdu28PT0xK1bt6Q6Tk5OcvdkZ2dj9uzZcHBwgKGhIWQyGQ4dOoS0tDSpTp8+fbBz505kZmYCADZv3oxvv/0WysrKAICzZ89ixowZkMlk0jF06FCkp6fj1atXUjsODg7Sz9ra2tDR0cH9+/elNo4dOybXhq2tLQBIS/RSUlLQu3dvWFlZQVdXF9WqVQMAuVjzG6OifH198ezZM+m4fft2idohIiIiIioIZ0oREdFXSVtbG9bW1tJ5gwYNoKenhzVr1mDIkCFSnQ8tWrQIixcvxpIlS1CnTh1oa2tj3LhxePv2rVSnQ4cOyMnJwf79+9GwYUPExMTgp59+kq7n5OQgKCgozwwsANDQ0JB+VlVVlbsmCIK09C4nJwcdOnTA/Pnz87RhamoqxWFubo41a9bAzMwMOTk5qF27tlys+Y1RUerq6lBXVy/RvUREREREimBSioiI/hMEQYCSkhJev35dYJ2YmBh06tQJffv2BfA+OXT9+nXY2dlJdTQ1NdGlSxds3rwZN27cgI2NDRo0aCBdd3R0RHJyslxCrLgcHR2xc+dOWFpaQkUl73+qHz16hKSkJISEhKBFixYAgOPHjyvUtpqaGrKzs0scGxERERFRaeHyPSIi+iplZmbi3r17uHfvHpKSkvD999/jxYsX6NChQ4H3WFtb4/Dhw4iNjUVSUhKGDx+Oe/fu5anXp08f7N+/H+vWrZMSWLn8/f2xYcMGBAYG4sqVK0hKSsKvv/6K6dOnKxz7qFGj8PjxY/Tq1QtnzpzBX3/9hUOHDmHQoEHIzs5GhQoVYGhoiNWrV+PGjRs4evQoJkyYoFDblpaWePHiBSIjI/Hw4UO5JYVERERERGWJSSkiIvoqhYeHw9TUFKampmjcuDHi4uKwY8cOuLi4FHiPn58fHB0d4e7uDhcXF5iYmMDb2ztPvVatWsHAwADJycno3bu33DV3d3fs27cPhw8fRsOGDfHNN9/gp59+goWFhcKxm5mZ4cSJE8jOzoa7uztq166NsWPHQk9PD0pKSlBSUsK2bdtw9uxZ1K5dG+PHj8eCBQsUartp06YYMWIEevbsCSMjIwQHByscFxERERFRaRJEURTLOwgiIiL6smVkZEBPTw9Ow3+EirpG0TcQERF9RU4u9SvvEIj+VXJ/d3z27Bl0dXULrMeZUkREREREREREVOaYlCIiIiIiIiIiojLHpBQREREREREREZW5vO+ZJiIiIipAZPCUQvcFICIiIiJSFGdKERERERERERFRmWNSioiIiIiIiIiIyhyTUkREREREREREVOaYlCIiIiIiIiIiojLHjc6JiIhIYS7+86CsrlHeYRAREcmJm+9f3iEQUQlwphQREREREREREZU5JqWIiIiIiIiIiKjMMSlFRERERERERERljkkpIiIiIiIiIiIqc0xKERF9AQRBwJ49e8o7jH81PkMiIiIion8XJqWI6D9LEIRCDx8fnzKLJT09HZ6enqXapouLC8aNG1eqbX5ozZo1aNGiBSpUqIAKFSqgTZs2OHPmTLHb8fHxgbe3d+kH+AULDAxEvXr1FKqX+31UUVFBxYoV0bJlSyxZsgSZmZmfP1AiIiIios+ISSki+s9KT0+XjiVLlkBXV1eubOnSpcVqLysrq8SxmJiYQF1dvcT3f05v377NtzwqKgq9evXCsWPHcPLkSVStWhVt27bF33//XcYRft1q1aqF9PR0pKWl4dixY+jevTvmzp2Lpk2b4vnz5+UdHhERERFRiTEpRUT/WSYmJtKhp6cHQRDkyv788080aNAAGhoasLKyQlBQEN69eyfdLwgCVq1ahU6dOkFbWxuzZs2SZsCsW7cOVatWhUwmw3fffYfs7GwEBwfDxMQExsbGmD17tlwsHy49S01NhSAI2LVrF1xdXaGlpYW6devi5MmTUv1Hjx6hV69eqFKlCrS0tFCnTh1s3bpVuu7j44Po6GgsXbpUmmmTmpoKAIiOjkajRo2grq4OU1NTTJ06VW5cLi4uGD16NCZMmICKFSvCzc0t3+e3efNmjBw5EvXq1YOtrS3WrFmDnJwcREZGftLnYmlpiSVLlsiV1atXD4GBgdL59evX0bJlS2hoaMDe3h6HDx/O005sbCzq1asHDQ0NODk5Yc+ePRAEAQkJCQDeJ9UEQUBERATq168PTU1NtGrVCvfv38fBgwdhZ2cHXV1d9OrVC69evZLaFUURwcHBsLKygqamJurWrYvffvtNup7bbmRkJJycnKClpYWmTZsiOTkZABAaGoqgoCBcuHBB+mxCQ0MLfB4qKiowMTGBmZkZ6tSpg++//x7R0dG4fPky5s+fL9XbtGkTnJycoKOjAxMTE/Tu3Rv379+XYra2tsbChQvl2r58+TKUlJSQkpJS6GdCRERERPQ5MClFRJSPiIgI9O3bF2PGjEFiYiJCQkIQGhqaJ5kUEBCATp064dKlSxg0aBAAICUlBQcPHkR4eDi2bt2KdevWwcvLC3fu3EF0dDTmz5+P6dOn49SpU4XGMG3aNEycOBEJCQmwsbFBr169pOTRmzdv0KBBA+zbtw+XL1/GsGHD0K9fP5w+fRoAsHTpUjRp0gRDhw6VZn6Zm5vj77//Rrt27dCwYUNcuHABK1euxNq1azFr1iy5vsPCwqCiooITJ04gJCREoWf26tUrZGVlwcDAQCoLDQ2FIAgK3a+onJwcdOnSBcrKyjh16hRWrVqFKVOmyNV5/vw5OnTogDp16uDcuXOYOXNmnjq5AgMD8fPPPyM2Nha3b99Gjx49sGTJEmzZsgX79+/H4cOHsXz5cqn+9OnTsX79eqxcuRJXrlzB+PHj0bdvX0RHR8u1O23aNCxatAjx8fFQUVGRvh89e/bEDz/8IM2ASk9PR8+ePYv1DGxtbeHp6Yldu3ZJZW/fvsXMmTNx4cIF7NmzBzdv3pSWoAqCgEGDBmH9+vVy7axbtw4tWrRA9erVi9U/EREREVFpUCnvAIiIvkSzZ8/G1KlTMWDAAACAlZUVZs6cicmTJyMgIECq17t3bynZkCsnJwfr1q2Djo4O7O3t4erqiuTkZBw4cABKSkqoWbMm5s+fj6ioKHzzzTcFxjBx4kR4eXkBAIKCglCrVi3cuHEDtra2qFy5MiZOnCjV/f777xEeHo4dO3agcePG0NPTg5qaGrS0tGBiYiLVW7FiBczNzfHzzz9DEATY2tri7t27mDJlCvz9/aGk9P7fKqytrREcHFysZzZ16lRUrlwZbdq0kcr09PRQs2bNYrVTlCNHjiApKQmpqamoUqUKAGDOnDlye3Jt3rwZgiBgzZo10myqv//+G0OHDs3T3qxZs9CsWTMAwODBg+Hr64uUlBRYWVkBALp164Zjx45hypQpePnyJX766SccPXoUTZo0AfD+u3H8+HGEhITA2dlZanf27NnS+dSpU+Hl5YU3b95AU1MTMplMmgFVUra2tjh06JB0/uH30MrKCsuWLUOjRo3w4sULyGQyDBw4EP7+/jhz5gwaNWqErKwsbNq0CQsWLMi3/czMTLl9qzIyMkocKxERERFRfjhTiogoH2fPnsWMGTMgk8mkI3fW0YdLuZycnPLca2lpCR0dHem8UqVKsLe3lxI+uWW5S6sK4uDgIP1samoKANI92dnZmD17NhwcHGBoaAiZTIZDhw4hLS2t0DaTkpLQpEkTudlLzZo1w4sXL3Dnzp1Cx1WY4OBgbN26Fbt27YKGhoZU3rlzZ1y9erVYbRUlKSkJVatWlRJSAKQEUa7k5GQ4ODjIxdKoUaN82/vwOVeqVAlaWlpSQiq3LPe5JyYm4s2bN3Bzc5P7bmzYsCHPErjCPr/SIIqi3Od4/vx5dOrUCRYWFtDR0YGLiwsASN8JU1NTeHl5Yd26dQCAffv24c2bN+jevXu+7c+dOxd6enrSYW5uXmqxExEREREBJZwpFRMTg5CQEKSkpOC3335D5cqVsXHjRlSrVg3Nmzcv7RiJiMpcTk4OgoKC0KVLlzzXPkx0aGtr57muqqoqdy4IQr5lOTk5hcbw4T25yYfcexYtWoTFixdjyZIlqFOnDrS1tTFu3LgCNyXP9XEiI7fswz4KGldBFi5ciDlz5uDIkSNyiZiSUlJSkmLK9eEm8h9fA5DvmAoa58c+fs6FfVa5/7t//35UrlxZrt7HG9UX9vmVhqSkJFSrVg0A8PLlS7Rt2xZt27bFpk2bYGRkhLS0NLi7u8t9J4YMGYJ+/fph8eLFWL9+PXr27AktLa182/f19cWECROk84yMDCamiIiIiKhUFTsptXPnTvTr1w99+vTB+fPnpan9z58/x5w5c3DgwIFSD5KIqKw5OjoiOTkZ1tbW5R1KvmJiYtCpUyf07dsXwPtkx/Xr12FnZyfVUVNTQ3Z2ttx99vb22Llzp1zSJjY2Fjo6OnmSLIpYsGABZs2ahYiIiGLPriqIkZER0tPTpfOMjAzcvHlTOre3t0daWhru3r0LMzMzAJDbBB54v7Rt8+bNyMzMlJJF8fHxnxybvb091NXVkZaWJrdUr7jy+2yK4+rVqwgPD4evr690/vDhQ8ybN09KHOU33nbt2kFbWxsrV67EwYMH8eeffxbYh7q6+hf7RkgiIiIi+joUe/nerFmzsGrVKqxZs0buX4GbNm2Kc+fOlWpwRETlxd/fHxs2bEBgYCCuXLmCpKQk/Prrr5g+fXp5hwbg/Z5Phw8fRmxsLJKSkjB8+HDcu3dPro6lpSVOnz6N1NRUPHz4EDk5ORg5ciRu376N77//HlevXsXvv/+OgIAATJgwQW55oSKCg4Mxffp0rFu3DpaWlrh37x7u3buHFy9eSHV2794NW1vbYrXbqlUrbNy4ETExMbh8+TIGDBgAZWVl6XqbNm1Qs2ZN9O/fHxcuXEBMTAymTZsm10bv3r2Rk5ODYcOGISkpCREREdKb5z5l43UdHR1MnDgR48ePR1hYGFJSUnD+/Hn873//Q1hYmMLtWFpa4ubNm0hISMDDhw/l9m762Lt373Dv3j3cvXsXly5dwvLly+Hs7Ix69eph0qRJAICqVatCTU0Ny5cvx19//YW9e/di5syZedpSVlaGj48PfH19YW1tnWfZIxERERFRWSp2Uio5ORktW7bMU66rq4unT5+WRkxEROXO3d0d+/btw+HDh9GwYUN88803+Omnn2BhYVHeoQEA/Pz84OjoCHd3d7i4uMDExATe3t5ydSZOnAhlZWXY29tLy7kqV66MAwcO4MyZM6hbty5GjBiBwYMHlyjZtmLFCrx9+xbdunWDqampdOQmfwDg2bNnSE5OLrSdnJwcqKj838RdX19ftGzZEu3bt0e7du3g7e0t93Y4JSUl7N69G5mZmWjUqBGGDBmS562Iurq6+OOPP5CQkIB69eph2rRp8Pf3ByC//LIkZs6cCX9/f8ydOxd2dnZwd3fHH3/8IS2lU0TXrl3h4eEBV1dXGBkZYevWrQXWvXLlCkxNTVG1alW4uLhg+/bt8PX1RUxMDGQyGYD3s8tCQ0OxY8cO2NvbY968eXKfw4cGDx6Mt2/f5tmgn4iIiIiorAliQZtsFKB69eoICQlBmzZtoKOjgwsXLsDKygobNmzAvHnzkJiY+LliJSKir5CHhwesra3x888/f9Z+Nm/ejIEDB+LZs2fQ1NT8rH19yU6cOAEXFxfcuXMHlSpVUvi+jIwM6Onpof5YXyirf1pij4iIqLTFzfcv7xCI6AO5vzs+e/YMurq6BdYr9p5Sw4cPx9ixY7Fu3ToIgoC7d+/i5MmTmDhxovSv0EREREV58uQJYmNjERUVhREjRpR6+xs2bICVlRUqV66MCxcuYMqUKejRo8d/NiGVmZmJ27dvw8/PDz169ChWQoqIiIiI6HModlJq8uTJePbsGVxdXfHmzRu0bNkS6urqmDhxIkaPHv05YiQioq/QoEGDEBcXhx9++AGdOnUq9fbv3bsHf39/3Lt3D6ampujevXueZX7/JVu3bsXgwYNRr149bNy4sbzDISIiIiIq/vK9XK9evUJiYiJycnJgb28v7WtBREREXx8u3yMioi8Zl+8RfVk+2/K9XFpaWqX2+m8iIiIiIiIiIvpvUWimVJcuXRRucNeuXZ8UEBEREX15FP3XLiIiIiKiUp0ppaenV2qBERERERERERERKZSUWr9+/eeOg4iIiIiIiIiI/kOUintDq1at8PTp0zzlGRkZaNWqVWnEREREREREREREX7liJ6WioqLw9u3bPOVv3rxBTExMqQRFRERERERERERfN4Xfvnfx4kXp58TERNy7d086z87ORnh4OCpXrly60REREdEXpfmcuVBW1yjvMIiI6D/mfFBAeYdARJ+BwkmpevXqQRAECIKQ7zI9TU1NLF++vFSDIyIiIiIiIiKir5PCSambN29CFEVYWVnhzJkzMDIykq6pqanB2NgYysrKnyVIIiIiIiIiIiL6uiiclLKwsAAA5OTkfLZgiIiIiIiIiIjov0Ghjc737t2LrKws6efCDiIios8hNDQU+vr6hdbx8fGBt7f3J/clCAL27NkDAEhNTYUgCEhISPjkdj8nS0tLLFmypLzDICIiIiJSmEIzpby9vXHv3j0YGxsX+su+IAjIzs4urdiIiOgrcv/+ffj5+eHgwYP4559/UKFCBdStWxeBgYFo0qRJqfSxdOlSiKJYKm19DpaWlggNDYWLi0ux7w0MDMSePXsKTI7FxcVBW1tb4TjGjRuHcePGFTsOIiIiIqLSolBSKicnB2lpaRBFkcv3iIioRLp27YqsrCyEhYXBysoK//zzDyIjI/H48eNS60NPT6/U2ipNb9++hZqa2mft48O9HomIiIiI/g0UWr4HANWqVcODBw8+ZyxERPSVevr0KY4fP4758+fD1dUVFhYWaNSoEXx9feHl5SVXb9iwYahUqRI0NDRQu3Zt7Nu3T66tiIgI2NnZQSaTwcPDA+np6dK1j5fvubi4YMyYMZg8eTIMDAxgYmKCwMBAufauX7+Oli1bQkNDA/b29jh8+HCR40lMTES7du0gk8lQqVIl9OvXDw8fPpTrd/To0ZgwYQIqVqwINze3PG28ffsWo0ePhqmpKTQ0NGBpaYm5c+cW2XdBPl6+FxgYiKpVq0JdXR1mZmYYM2aMFNutW7cwfvx46a26RERERETlQeGk1Je8HIKIiL5sMpkMMpkMe/bsQWZmZr51cnJy4OnpidjYWGzatAmJiYmYN2+e3JtdX716hYULF2Ljxo34888/kZaWhokTJxbad1hYGLS1tXH69GkEBwdjxowZUuIpJycHXbp0gbKyMk6dOoVVq1ZhypQphbaXnp4OZ2dn1KtXD/Hx8QgPD8c///yDHj165OlXRUUFJ06cQEhISJ52li1bhr1792L79u1ITk7Gpk2bYGlpWWjfivrtt9+wePFihISE4Pr169izZw/q1KkDANi1axeqVKmCGTNmID09XS6pR0RERERUlhR++x4REVFJqaioIDQ0FEOHDsWqVavg6OgIZ2dnfPvtt3BwcAAAHDlyBGfOnEFSUhJsbGwAAFZWVnLtZGVlYdWqVahevToAYPTo0ZgxY0ahfTs4OCAgIAAAUKNGDfz888+IjIyEm5sbjhw5gqSkJKSmpqJKlSoAgDlz5sDT07PA9lauXAlHR0fMmTNHKlu3bh3Mzc1x7do1KXZra2sEBwfL3Zuamir9nJaWhho1aqB58+YQBEF6y21pSEtLg4mJCdq0aQNVVVVUrVoVjRo1AgAYGBhAWVkZOjo6MDExKbCNzMxMuQRiRkZGqcVHRERERAQUMyn1yy+/QCaTFVond3kAERHRh7p27QovLy/ExMTg5MmTCA8PR3BwMH755Rf4+PggISEBVapUkZI6+dHS0pISUgBgamqK+/fvF9pvbtIrv3uSkpJQtWpVKSEFoMhN18+ePYtjx47l+9/DlJQUKX4nJ6dC2/Hx8YGbmxtq1qwJDw8PtG/fHm3bti30HkV1794dS5YsgZWVFTw8PNCuXTt06NABKiqK/2d/7ty5CAoKKpV4iIiIiIjyU6yk1KpVq+SWUXxMEAQmpYiIqEAaGhpwc3ODm5sb/P39MWTIEAQEBMDHxweamppF3q+qqip3LghCkcvL87sn96Ud+d1b1B5LOTk56NChA+bPn5/nmqmpqfRzUW/Cc3R0xM2bN3Hw4EEcOXIEPXr0QJs2bfDbb78Vep8izM3NkZycjMOHD+PIkSMYOXIkFixYgOjo6DzPoyC+vr6YMGGCdJ6RkQFzc/NPjo2IiIiIKFexklLx8fEwNjb+XLEQEdF/jL29Pfbs2QPg/YymO3fuyC2BK4v+09LScPfuXZiZmQEATp48Weg9jo6O2LlzJywtLYs18yg/urq66NmzJ3r27Ilu3brBw8MDjx8/hoGBwSe1CwCampro2LEjOnbsiFGjRsHW1haXLl2Co6Mj1NTUkJ2dXej96urqUFdX/+Q4iIiIiIgKovBv03w7DxERldSjR4/QvXt3DBo0CA4ODtDR0UF8fDyCg4PRqVMnAICzszNatmyJrl274qeffoK1tTWuXr0KQRDg4eHxWeJq06YNatasif79+2PRokXIyMjAtGnTCr1n1KhRWLNmDXr16oVJkyahYsWKuHHjBrZt24Y1a9YUOqP4Q4sXL4apqSnq1asHJSUl7NixAyYmJtDX1y/wntevXyMhIUGuTCaTwdraWq4sNDQU2dnZaNy4MbS0tLBx40ZoampK+1ZZWlrizz//xLfffgt1dXVUrFhRoZiJiIiIiEqTwkkpvn2PiIhKSiaToXHjxli8eDFSUlKQlZUFc3NzDB06FD/++KNUb+fOnZg4cSJ69eqFly9fwtraGvPmzftscSkpKWH37t0YPHgwGjVqBEtLSyxbtqzQJJiZmRlOnDiBKVOmwN3dHZmZmbCwsICHhweUlBR+qS1kMhnmz5+P69evQ1lZGQ0bNsSBAwcKbePatWuoX7++XJmzszOioqLkyvT19TFv3jxMmDAB2dnZqFOnDv744w8YGhoCAGbMmIHhw4ejevXqyMzM5H/jiYiIiKhcCKKCv4kGBQVh0qRJ0NLS+twxERER0RcmIyMDenp6qDNlKpTVNco7HCIi+o85HxRQ3iEQUTHk/u747Nkz6OrqFlhP4ZlSua/TJiIiIiIiIiIi+lSKrzMgIiIiIiIiIiIqJUxKERERERERERFRmWNSioiIiIiIiIiIypzCe0p96N27d4iKikJKSgp69+4NHR0d3L17F7q6upDJZKUdIxEREX0hjv/oW+hmlUREREREiip2UurWrVvw8PBAWloaMjMz4ebmBh0dHQQHB+PNmzdYtWrV54iTiIiIiIiIiIi+IsVevjd27Fg4OTnhyZMn0NTUlMo7d+6MyMjIUg2OiIiIiIiIiIi+TsWeKXX8+HGcOHECampqcuUWFhb4+++/Sy0wIiIiIiIiIiL6ehV7plROTg6ys7PzlN+5cwc6OjqlEhQREREREREREX3dij1Tys3NDUuWLMHq1asBAIIg4MWLFwgICEC7du1KPUAiIiL6cjT9aS6UNdTLOwwiIvrKXZgaWN4hEFEZKHZSavHixXB1dYW9vT3evHmD3r174/r166hYsSK2bt36OWIkIiIiIiIiIqKvTLGTUmZmZkhISMDWrVtx7tw55OTkYPDgwejTp4/cxudEREREREREREQFKXZSCgA0NTUxaNAgDBo0qLTjISIiIiIiIiKi/wCFklJ79+6Fp6cnVFVVsXfv3kLrduzYsVQCIyIiIiIiIiKir5dCSSlvb2/cu3cPxsbG8Pb2LrCeIAj5vpmPiIjo38LFxQX16tXDkiVLFL5HEATs3r270P9GliYfHx88ffoUe/bsUfieso6RiIiIiKgoSopUysnJgbGxsfRzQQcTUkRE9G/g4+MDQRDyHDdu3MCuXbswc+bMUu0vKioKgiDg6dOnRdYVRRGrV69G48aNIZPJoK+vDycnJyxZsgSvXr0q1biIiIiIiMqTQkmpD6Wmpn6GMIiIiMqWh4cH0tPT5Y5q1arBwMAAOjo65RZXv379MG7cOHTq1AnHjh1DQkIC/Pz88Pvvv+PQoUPlFhcRERERUWkrdlLKysoKzZs3R0hICB4/fvw5YiIiIvrs1NXVYWJiIncoKyvDxcUF48aNk+qlp6fDy8sLmpqaqFatGrZs2QJLS8s8y/sePnyIzp07Q0tLCzVq1JD2YExNTYWrqysAoEKFChAEAT4+PvnGtH37dmzevBlbt27Fjz/+iIYNG8LS0hKdOnXC0aNHpXY+ll889erVQ2BgoFxZeno6/h97dx6WRfX/f/x5A8ouKqmgomgI7gtuqSXgEoqZpuWSqeRW5pblkhnu+5pLmZkCLqWZS6bmmiiKu6Im5IILWpilhuGKwO8Pf8zXWxbBFPrk63Fdc8k9c+ac98x9z8j95pwzTZs2NY5l+fLlWT5fIiIiIiJPWraTUgcOHKBOnTqMGTOGokWL0qJFC5YvX86dO3eeRnwiIiK5qlOnTvz222+EhYWxYsUKvvzySy5fvpym3MiRI2nTpg1Hjx4lICCADh06cPXqVdzc3FixYgUAJ06cIC4ujhkzZqTb1pIlS/Dy8qJFixZptplMJpycnP7RsQQFBdG6dWuOHDnCW2+9Rfv27YmOjk637J07d7h+/brZIiIiIiLyJGU7KeXt7c3kyZOJjY3lxx9/pHDhwrzzzjsULlyYLl26PI0YRUREnri1a9fi4OBgLG+88UaaMr/88gtbtmxh3rx51K5dG29vb7766itu3bqVpmxgYCDt27fHw8ODcePGcePGDfbt24elpSUFCxYEoHDhwri4uGSYXDp16hReXl5P9kAf8MYbb9CtWzc8PT0ZPXo0NWrUYNasWemWHT9+PE5OTsbi5ub21OISERERkWdTtpNSqUwmE35+fsybN48tW7ZQunRpQkNDn2RsIiIiT42fnx+RkZHGMnPmzDRlTpw4gZWVFd7e3sY6Dw8PChQokKZs5cqVjZ/t7e1xdHRMt0dVZlJSUjCZTNnaJzvq1KmT5nVGPaWGDBlCfHy8sVy4cOGpxSUiIiIizyarx93xwoULfPPNN3z99dccO3aMOnXqMHv27CcZm4iIyFNjb2+Ph4dHpmVSUlKyvD5Pnjxmr00mE8nJydmKydPTM8MkUWYsLCzSxJSYmJilfTNKgllbW2NtbZ3tWEREREREsirbPaW+/PJLfHx8KFWqFKGhobRp04aYmBh27txJz549n0aMIiIiuaJs2bLcu3ePw4cPG+tOnz7NX3/9la168ubNC0BSUlKm5d58801OnjzJ999/n2ZbSkoK8fHx6e5XqFAh4uLijNfXr1/n7Nmzacrt2bMnzeuyZcs+Mn4RERERkach20mp0aNHU6tWLQ4cOMDx48f5+OOPcXd3fwqhiYiI5K6yZcvSqFEjevTowb59+zh8+DA9evTA1tY2W8PsSpYsiclkYu3atfzxxx8kJCSkW65Nmza0bduW9u3bM378eA4cOMD58+dZu3YtjRo1Ytu2benu16BBAxYtWkR4eDg///wznTt3xtLSMk255cuXs2DBAk6ePMnw4cPZt28fvXv3zvJxiIiIiIg8SdkevhcbG/tU57sQERH5N1m4cCFdu3alfv36uLi4MH78eI4fP46NjU2W6yhWrBgjR47ko48+4u2336ZTp06EhISkKWcymfj666/58ssvWbBgAWPGjMHKyooyZcrQqVMn/P39061/yJAhnDlzhldeeQUnJydGjx6dbk+pkSNHsnTpUt577z1cXFxYsmQJ5cuXz/JxiIiIiIg8SaaUjCbMyMRff/3F/PnziY6OxmQyUa5cObp27fqPH1UtIiLyb3fx4kXc3NzYsmULDRs2zO1wcsz169dxcnKiwvCPsLTRXFMiIvJ0HfloRG6HICL/QOrvjvHx8eTLly/DctkevnfgwAGef/55pk+fztWrV/nzzz+ZPn06zz//PIcOHfpHQYuIiPzb/PTTT6xZs4azZ88SERFBu3btcHd3p379+rkdmoiIiIjI/7RsD9/r378/r776KvPmzcPK6v7u9+7do1u3brz//vvs2LHjiQcpIiKSWxITE/n44485c+YMjo6O1K1blyVLlqR52p6IiIiIiGRPtpNSBw4cMEtIAVhZWTFo0CBq1KjxRIMTERHJbf7+/hnO5SQiIiIiIo8v20mpfPnyERsbm+YR0hcuXMDR0fGJBSYiIiL/PhEfDMl0XgARERERkazK9pxSbdu2pWvXrixbtowLFy5w8eJFli5dSrdu3Wjfvv3TiFFERERERERERP5jst1TasqUKZhMJjp16sS9e/cAyJMnDz179mTChAlPPEAREREREREREfnvMaWkpKQ8zo43b94kJiaGlJQUPDw8sLOze9KxiYiIyL9EVh/rKyIiIiKS1d8ds91TKpWdnR2VKlV63N1FREREREREROQZluWkVJcuXbJUbsGCBY8djIiIiPy71ftiHJY21rkdhoiI/IdE9h2Z2yGISC7JclIqJCSEkiVLUq1aNR5zxJ+IiIiIiIiIiAiQjaTUu+++y9KlSzlz5gxdunThrbfeomDBgk8zNhERERERERER+Y+yyGrBzz//nLi4OAYPHswPP/yAm5sbbdq0YePGjeo5JSIiIiIiIiIi2ZLlpBSAtbU17du3Z/PmzURFRVGhQgXee+89SpYsSUJCwtOKUURERERERERE/mOylZR6kMlkwmQykZKSQnJy8pOMSURERERERERE/uOylZS6c+cO33zzDY0bN8bLy4tjx44xe/ZsYmNjcXBweFoxioiIPHWpf2zJaAkMDHzibc6dO5cqVapgb29P/vz5qVatGhMnTjS2BwYG0rJlyyferoiIiIjIv0GWJzp/7733WLp0KSVKlODtt99m6dKlODs7P83YREREckxcXJzx87Jlyxg2bBgnTpww1tna2j7R9ubPn88HH3zAzJkz8fHx4c6dOxw9epSoqKgn2g7A3bt3yZs37xOvV0RERETkn8hyUuqLL76gRIkSlCpViu3bt7N9+/Z0y61cufKJBSciIpJTXFxcjJ+dnJwwmUxm6+bMmcOUKVO4cOECpUqV4pNPPqFjx44AdOnShcuXL7N27Vqj/L179yhevDjjxo2jS5cuadr74YcfaNOmDV27djXWVahQwfh5xIgRhIaGAvd7cQFs27YNX19fjh07Rr9+/di9ezd2dna0bt2aadOmGb2WAwMD+euvv6hduzazZs0ib968dOnSheXLl3Ps2DGzOKpXr06zZs0YNWrUY587EREREZHHkeWkVKdOnYxfikVERJ4lq1atol+/fnz66ac0atSItWvX8vbbb1O8eHH8/Pzo1q0b9evXJy4uDldXVwDWr19PQkICbdq0SbdOFxcXtm/fzvnz5ylZsmSa7QMGDCA6Oprr168THBwMQMGCBbl58yZNmjThhRdeYP/+/Vy+fJlu3brRu3dvQkJCjP23bt1Kvnz52Lx5MykpKeTPn5+RI0eyf/9+atasCcDRo0c5fPgwy5cvf8JnTERERETk0bKclHrwF10REZFnyZQpUwgMDOS9994D4IMPPmDPnj1MmTIFPz8/6tati5eXF4sWLWLQoEEABAcH88Ybb2Q45+Lw4cNp1aoV7u7ueHp6UqdOHQICAnj99dexsLDAwcEBW1tb7ty5Y9ZjKzQ0lFu3brFw4ULs7e0BmD17Ns2bN2fixIkUKVIEAHt7e7766iuzYXv+/v4EBwcbSang4GB8fHwoXbp0mvju3LnDnTt3jNfXr1//J6dQRERERCSNx376noiIyLMiOjqaevXqma2rV68e0dHRxutu3boZPZouX77MunXr0h22l8rV1ZXdu3dz7Ngx+vbtS2JiIp07d6ZJkyaZPtU2OjramBz9wViSk5PN5sCqVKlSmnmkunfvzjfffMPt27dJTExkyZIlGcY4fvx4nJycjMXNzS3DmEREREREHoeSUiIiIlnw8BD2lJQUs3WdOnXizJkz7N69m8WLF+Pu7s5LL730yHorVqxIr169WLJkCZs3b2bz5s0ZztuYXrsZxfhg0ipV8+bNsba2ZtWqVfzwww/cuXOH1q1bp1vXkCFDiI+PN5YLFy488lhERERERLIjy8P3REREnlXlypVj586ddOrUyVgXERFBuXLljNfOzs60bNmS4OBgdu/ezdtvv53tdsqXLw/AjRs3AMibNy9JSUlpyoSGhnLjxg0j8bRr1y4sLCzw9PTMtH4rKys6d+5McHAw1tbWtGvXDjs7u3TLWltbY21tne1jEBERERHJKiWlREREHmHgwIG0adMGb29vGjZsyA8//MDKlSvZsmWLWblu3brxyiuvkJSUROfOnTOts2fPnhQtWpQGDRpQvHhx4uLiGDNmDIUKFaJOnToAuLu7s3HjRk6cOIGzszNOTk506NCB4cOH07lzZ0aMGMEff/xBnz596NixozGfVGa6detmJNN27dr1mGdEREREROSf0/A9ERGRR2jZsiUzZsxg8uTJVKhQgblz5xIcHIyvr69ZuUaNGuHq6oq/vz9FixbNtM5GjRqxZ88e3njjDTw9PWndujU2NjZs3boVZ2dn4P4cUF5eXtSoUYNChQqxa9cu7Ozs2LhxI1evXqVmzZq8/vrrNGzYkNmzZ2fpWMqUKWNMzF67du3HOh8iIiIiIk+CKSUlJSW3gxAREfkvuHnzJkWLFmXBggW0atUqt8NJV0pKCmXLluWdd97hgw8+yPJ+169fx8nJiYoTB2Npo2F9IiLy5ET2HZnbIYjIE5b6u2N8fDz58uXLsJyG74mIiPxDycnJXLp0ialTp+Lk5MSrr76a2yGl6/LlyyxatIhff/31sea8EhERERF5kpSUEhER+YdiY2MpVaoUxYsXJyQkBCurf+d/r0WKFOG5557jyy+/pECBArkdjoiIiIg84/6dvzWLiIj8D3F3d+d/YTT8/0KMIiIiIvLs0ETnIiIiIiIiIiKS49RTSkRERLJs17sfZzpZpYiIiIhIVqmnlIiIiIiIiIiI5DglpUREREREREREJMcpKSUiIiIiIiIiIjlOSSkREREREREREclxmuhcREREsqzBwtFY2VrndhgiIvI/ZE/XMbkdgoj8S6mnlIiIiIiIiIiI5DglpUREREREREREJMcpKSUiIiIiIiIiIjlOSSkRyRG+vr68//77T63+sLAwTCYTf/3111Nr42l72ucoN5hMJlavXp3bYTzSw+fe3d2dTz/9NNfieRpCQkLInz9/bochIiIiImJQUkqeOZcvX+add96hRIkSWFtb4+Ligr+/P7t37zbKPM0v0l988QWOjo7cu3fPWJeQkECePHl46aWXzMqGh4djMpk4efLkU4klK1KTPektly5dynI9K1euZPTo0U8x0n9m6tSpODk5cfPmzTTbbt++Tf78+Zk2bdpj1R0YGJjhOUxdnqaM2ly6dOlTbRcgLi6Opk2bPvF6n/R1nJufz3Pnzpm9L3nz5sXDw4MxY8aQkpKSKzGJiIiIiOQEPX1PnjmtW7cmMTGR0NBQSpcuze+//87WrVu5evXqE2/r7t275M2b12ydn58fCQkJHDhwgBdeeAG4n3xycXFh//793Lx5Ezs7O+B+Qqho0aJ4enpmu+2UlBSSkpKwsnoyl/mJEyfIly+f2brChQtnef+CBQs+kTielk6dOjFkyBBWrFhBx44dzbatWLGCmzdvplmfVTNmzGDChAnGa1dXV4KDg2nSpMk/ijk70msvJ3rNuLi4ZLo9MTGRPHnyZLveJ3Udp7b/b/h8btmyhQoVKnDnzh127txJt27dcHV1pWvXrrkdmoiIiIjIU6GeUvJM+euvv9i5cycTJ07Ez8+PkiVLUqtWLYYMGUKzZs2A+8N2AF577TVMJpPxOiYmhhYtWlCkSBEcHByoWbMmW7ZsMavf3d2dMWPGEBgYiJOTE927d08Tg5eXF0WLFiUsLMxYFxYWRosWLXj++eeJiIgwW+/n5wfA4sWLqVGjBo6Ojri4uPDmm29y+fJls7Imk4mNGzdSo0YNrK2tCQ8P58iRI/j5+eHo6Ei+fPmoXr06Bw4cyPa5K1y4MC4uLmaLhcX9W0hgYCAtW7Zk5MiRFC5cmHz58vHOO+9w9+5dY/+Hh0d9/vnnlClTBhsbG4oUKcLrr79ubLtz5w59+/alcOHC2NjY8OKLL7J//36zeNavX4+npye2trb4+flx7ty5NDFHRERQv359bG1tcXNzo2/fvty4cSPd4ytUqBDNmzdnwYIFabYtWLCAV199lUKFCmXnlBmcnJzMzhvcTwg9vA4gOTmZQYMGUbBgQVxcXBgxYoRZXfHx8fTo0cM4zw0aNODIkSOPjOHh9lxcXLCxsQH+b1jX2rVr8fLyws7Ojtdff50bN24QGhqKu7s7BQoUoE+fPiQlJRl1uru7M3r0aN58800cHBwoWrQos2bNMmv3wd5KqT2Cvv32W3x9fbGxsWHx4sXA/aRZuXLlsLGxoWzZsnz++ecZHss/uY5HjBhB1apVWbBgAaVLl8ba2pqUlJRHDp0MDg7GycmJzZs3AxAVFUVAQAAODg4UKVKEjh078ueffxrlv/vuOypVqoStrS3Ozs40atQow89eKmdnZ1xcXChZsiQdOnSgbt26HDp0yNi+f/9+GjduzHPPPYeTkxM+Pj5m21PPTY8ePShSpAg2NjZUrFiRtWvXptvelStXqFWrFq+++iq3b9/ONDYRERERkadBSSl5pjg4OODg4MDq1au5c+dOumVSkx/BwcHExcUZrxMSEggICGDLli0cPnwYf39/mjdvTmxsrNn+kydPpmLFihw8eJCgoKB02/D19WXbtm3G623btuHr64uPj4+x/u7du+zevdtISt29e5fRo0dz5MgRVq9ezdmzZwkMDExT96BBgxg/fjzR0dFUrlyZDh06ULx4cfbv38/Bgwf56KOPzHqmmEwmQkJCsnYCM7F161aio6PZtm0b33zzDatWrWLkyJHplj1w4AB9+/Zl1KhRnDhxgg0bNlC/fn2zY1ixYgWhoaEcOnQIDw8P/P39jV4wFy5coFWrVgQEBBAZGUm3bt346KOPzNo4duwY/v7+tGrViqNHj7Js2TJ27txJ7969MzyGrl27sn37ds6ePWusO3fuHNu2bcux3iqhoaHY29uzd+9eJk2axKhRo4xESEpKCs2aNePSpUusX7+egwcP4u3tTcOGDf9xT7+bN28yc+ZMli5dyoYNGwgLC6NVq1asX7+e9evXs2jRIr788ku+++47s/0mT55M5cqVOXToEEOGDKF///5GvBkZPHgwffv2JTo6Gn9/f+bNm8fQoUMZO3Ys0dHRjBs3jqCgIEJDQ9Pd/59cxwCnT5/m22+/ZcWKFURGRj7y3EyZMoUBAwawceNGGjduTFxcHD4+PlStWpUDBw6wYcMGfv/9d9q0aQPcH7LYvn17unTpQnR0tHEuszMU78CBAxw6dIjatWsb6/7++286d+5MeHg4e/bsoUyZMgQEBPD3338D9xOaTZs2JSIigsWLFxMVFcWECROwtLRMU//Fixd56aWXKFu2LCtXrjQSlCIiIiIiOUnD9+SZYmVlRUhICN27d+eLL77A29sbHx8f2rVrR+XKlQGM3jCpPUtSValShSpVqhivx4wZw6pVq1izZo1ZoqNBgwYMGDAg0zh8fX3p378/9+7d49atWxw+fJj69euTlJTEzJkzAdizZw+3bt0yklJdunQx9i9dujQzZ86kVq1aJCQk4ODgYGwbNWoUjRs3Nl7HxsYycOBAypYtC0CZMmXMYvHy8sLJyemR56548eJmr4sVK8aJEyeM13nz5mXBggXY2dlRoUIFRo0axcCBAxk9erTRo+rBmOzt7XnllVdwdHSkZMmSVKtWDYAbN24wZ84cQkJCjLmI5s2bx+bNm5k/fz4DBw5kzpw5lC5dmunTp2MymfDy8uLYsWNMnDjRaGPy5Mm8+eabRu+XMmXKMHPmTHx8fJgzZ066X8L9/f0pWrQoISEhRkItODiYokWL8vLLLz/yHD0JlStXZvjw4UbMs2fPZuvWrTRu3Jht27Zx7NgxLl++jLW1NXA/YbJ69Wq+++47evTokWG97du3T5OcOHr0KKVLlwbuD2ObM2cOzz//PACvv/46ixYt4vfff8fBwYHy5cvj5+fHtm3baNu2rVFHvXr1jISgp6cnu3btYvr06WafwYe9//77tGrVyng9evRopk6daqwrVaoUUVFRzJ07l86dO6fZ/59cx3A/wbto0aIs9XwbMmQIoaGhhIWFUalSJQDmzJmDt7c348aNM8otWLAANzc3Tp48SUJCAvfu3aNVq1aULFkSwNg3M3Xr1sXCwoK7d++SmJhIjx496NSpk7G9QYMGZuXnzp1LgQIF2L59O6+88gpbtmxh3759REdHG0N+U9/fB508eZLGjRvTokULZsyYkeGcZnfu3DFL+l2/fv2RxyAiIiIikh3qKSXPnNatW/Pbb7+xZs0a/P39CQsLw9vb+5G9hW7cuMGgQYMoX748+fPnx8HBgV9++SVNT6kaNWo8MgY/Pz9u3LjB/v37CQ8Px9PTk8KFC+Pj48P+/fu5ceMGYWFhlChRwvhSefjwYVq0aEHJkiVxdHTE19cX4JHtf/DBB3Tr1o1GjRoxYcIEYmJizLb/8ssvvPbaa4+MOTw8nMjISGPZuHGj2fYqVaoYc2EB1KlTh4SEBC5cuJCmrsaNG1OyZElKly5Nx44dWbJkiTHBeExMDImJidSrV88onydPHmrVqkV0dDQA0dHRvPDCC2ZfpuvUqWPWxsGDBwkJCTF61Tg4OODv709ycrJZT6gHWVpa0rlzZ0JCQkhOTiYlJYXQ0FACAwPT7W2Sel4ebGPJkiWZncZHSk2qpHJ1dTWGaR48eJCEhAScnZ3N2jx79mya9/Vh06dPN3v/IiMjcXNzM7bb2dkZCSmAIkWK4O7ubpbwLFKkiNmQUUh73uvUqWO8Txl58DP6xx9/cOHCBbp27Wp2TGPGjMn0mB73OgYoWbJklhJSU6dOZe7cuezcudMsqXTw4EG2bdtmFm9q0jcmJoYqVarQsGFDKlWqxBtvvMG8efO4du3aI9tbtmwZkZGRHDlyhGXLlvH999+b9QC8fPky7777Lp6enjg5OeHk5ERCQoJxD4iMjKR48eKZzkF369YtXnzxRVq2bMnMmTMznWR//PjxRjtOTk5mnxcRERERkSdBPaXkmWRjY0Pjxo1p3Lgxw4YNo1u3bgwfPjzd4XCpBg4cyMaNG5kyZQoeHh7Y2try+uuvm82bBGBvb//I9j08PChevDjbtm3j2rVr+Pj4APcnhS5VqhS7du1i27ZtRs+IGzdu8PLLL/Pyyy+zePFiChUqRGxsLP7+/o9sf8SIEbz55pusW7eOH3/8keHDh7N06dIsJaIeVKpUqceaGDu9L72Ojo4cOnSIsLAwNm3axLBhwxgxYgT79+83hjg9vF9KSoqxLivDoJKTk3nnnXfo27dvmm0lSpTIcL8uXbowfvx4fvrpJ+B+0u/tt9/OsHyNGjXMhoAVKVLkkbFl5uFJv00mE8nJycD9Y3J1dTWbjyzVo94bFxcXPDw8stVuZrFk5lFPE3zwM5pa37x588yGqgEZJgJTPc51/HD7mXnppZdYt24d3377rVlyKDk5mebNm5v1zEvl6uqKpaUlmzdvJiIigk2bNjFr1iyGDh3K3r17KVWqVIbtubm5Ge9RuXLlOHPmDEFBQYwYMQIbGxsCAwP5448/+PTTTylZsiTW1tbUqVPHuAfY2to+8pisra1p1KgR69atY+DAgWl6QD5oyJAhfPDBB8br69evKzElIiIiIk+UklIiQPny5c0eHZ8nTx6zCZ3hfo+YwMBAI5mTkJCQ7uTaWeXn50dYWBjXrl1j4MCBxnofHx82btzInj17jGTIL7/8wp9//smECROML4XZmazc09MTT09P+vfvT/v27QkODs52UupRjhw5wq1bt4wvxnv27MHBwSHDL71WVlY0atSIRo0aMXz4cPLnz89PP/2Ev78/efPmZefOnbz55pvA/aFlBw4cMIbiPfx+pbb3IG9vb44fP55pIiY9zz//PD4+PgQHBxsTYD/Yg+hhtra22W7jcXl7e3Pp0iWsrKyMibtz28Pnfc+ePUavoawoUqQIxYoV48yZM3To0OEfxZKV6zg7atWqRZ8+ffD398fS0tK4Tr29vVmxYgXu7u4ZPt3SZDJRr1496tWrx7BhwyhZsiSrVq0yS/I8iqWlJffu3ePu3bvY2NgQHh7O559/TkBAAHB/brUHJ1evXLkyFy9e5OTJkxn2lrKwsGDRokW8+eabNGjQwHjCZ3qsra2NYaIiIiIiIk+Dhu/JM+XKlSs0aNCAxYsXc/ToUc6ePcvy5cuZNGkSLVq0MMq5u7uzdetWLl26ZAy78fDwYOXKlcbwmjfffDNLvUYy4ufnx86dO4mMjDR6SsH9pNS8efO4ffu2MZ9UiRIlyJs3L7NmzeLMmTOsWbOG0aNHP7KNW7du0bt3b8LCwjh//jy7du1i//79lCtXzihTtmxZVq1a9ci6Ll++zKVLl8yWxMREY/vdu3fp2rUrUVFRRo+s3r17p5lPCmDt2rXMnDmTyMhIzp8/z8KFC0lOTsbLywt7e3t69uzJwIED2bBhA1FRUXTv3p2bN28ak42/++67xMTE8MEHH3DixAm+/vrrNMO2Bg8ezO7du+nVqxeRkZGcOnWKNWvW0KdPn0cea9euXVm5ciWrVq3KsQnOs6JRo0bUqVOHli1bsnHjRs6dO0dERASffPLJI5OUf/31V5r371FPg8uKXbt2MWnSJE6ePMlnn33G8uXL6devX7bqGDFiBOPHj2fGjBmcPHmSY8eOERwczLRp09It/0+u4+yqU6cOP/74I6NGjWL69OkA9OrVi6tXr9K+fXv27dvHmTNn2LRpE126dCEpKYm9e/cybtw4Dhw4QGxsLCtXruSPP/4wu+4yOq5Lly5x8eJFfvzxR2bMmIGfnx/58uUD7t+DFi1aRHR0NHv37qVDhw5mvaN8fHyoX78+rVu3ZvPmzZw9e5Yff/yRDRs2mLVjaWnJkiVLqFKlCg0aNODSpUuPdW5ERERERP4pJaXkmeLg4EDt2rWZPn069evXp2LFigQFBdG9e3dmz55tlJs6dSqbN2/Gzc3NmIB7+vTpFChQgLp169K8eXP8/f3x9vZ+7Fj8/Py4desWHh4eZkO+fHx8+Pvvv3n++eeNXlGFChUiJCSE5cuXU758eSZMmMCUKVMe2YalpSVXrlyhU6dOeHp60qZNG5o2bWr2VLwTJ04QHx//yLq8vLxwdXU1Ww4ePGhsb9iwIWXKlKF+/fq0adOG5s2bM2LEiHTryp8/PytXrqRBgwaUK1eOL774gm+++YYKFSoAMGHCBFq3bk3Hjh3x9vbm9OnTbNy4kQIFCgD3k3QrVqzghx9+oEqVKnzxxRdmk07D/V4j27dv59SpU7z00ktUq1aNoKAgXF1dH3msrVu3NnqJPDghd24zmUysX7+e+vXr06VLFzw9PWnXrh3nzp175LDBt99+O837N2vWrH8c04cffsjBgwepVq2aMWG5v79/turo1q0bX331FSEhIVSqVAkfHx9CQkIyHOr2T67jx1GvXj3WrVtHUFAQM2fOpGjRouzatYukpCT8/f2pWLEi/fr1w8nJCQsLC/Lly8eOHTsICAjA09OTTz75hKlTpxoT92ekUaNGuLq64u7uTo8ePQgICGDZsmXG9gULFnDt2jWqVatGx44d6du3L4ULFzarY8WKFdSsWZP27dtTvnx5Bg0alG5vMSsrK+Oaa9CgQZq5wkREREREcoIpJTvPqBYRSUdgYCB//fVXmiF18t/m7u7O+++/bwyrlP+269ev4+TkRPVZA7Cy1bA+ERHJuj1dx+R2CCKSw1J/d4yPjzd6/qdHPaVERERERERERCTHKSklIiIiIiIiIiI5Tk/fE5F/7OFJxuXZ8E+ePikiIiIiIqKeUiIiIiIiIiIikuPUU0pERESy7KdOQZlOVikiIiIiklXqKSUiIiIiIiIiIjlOSSkREREREREREclxSkqJiIiIiIiIiEiOU1JKRERERERERERynCY6FxERkSxr+d0IrOysczsMERHJJZvajc/tEETkP0Q9pUREREREREREJMcpKSUiIiIiIiIiIjlOSSkREREREREREclxSkqJiIiIiIiIiEiOU1JKRET+p40YMYKqVavmdhgiIiIiIpJNSkqJiEi2RUREYGlpSZMmTXI7FAYMGMDWrVtzNYZz585hMpmMJW/evHh4eDBmzBhSUlJyPB5fX1/ef//9HG9XRERERCQ7rHI7ABER+d+zYMEC+vTpw1dffUVsbCwlSpTI8RhSUlJISkrCwcEBBweHHG8/PVu2bKFChQrcuXOHnTt30q1bN1xdXenatWtuhyYiIiIi8q+jnlIiIpItN27c4Ntvv6Vnz5688sorhISEmG0PCwvDZDKxceNGqlWrhq2tLQ0aNODy5cv8+OOPlCtXjnz58tG+fXtu3rxp7JeSksKkSZMoXbo0tra2VKlShe+++y7demvUqIG1tTXh4eHpDt9bsGABFSpUwNraGldXV3r37m1smzZtGpUqVcLe3h43Nzfee+89EhISjO0hISHkz5+fjRs3Uq5cORwcHGjSpAlxcXGPPDfOzs64uLhQsmRJOnToQN26dTl06JBZmeDgYMqVK4eNjQ1ly5bl888/N9s+ePBgPD09sbOzo3Tp0gQFBZGYmGhsDwwMpGXLlmb7vP/++/j6+hrbt2/fzowZM4yeW2fPnsXDw4MpU6aY7ffzzz9jYWFBTEzMI49NRERERORJU1JKRESyZdmyZXh5eeHl5cVbb71FcHBwukPURowYwezZs4mIiODChQu0adOGTz/9lK+//pp169axefNmZs2aZZT/5JNPCA4OZs6cORw/fpz+/fvz1ltvsX37drN6Bw0axPjx44mOjqZy5cpp2p0zZw69evWiR48eHDt2jDVr1uDh4WFst7CwYObMmfz888+Ehoby008/MWjQILM6bt68yZQpU1i0aBE7duwgNjaWAQMGZOs8HThwgEOHDlG7dm1j3bx58xg6dChjx44lOjqacePGERQURGhoqFHG0dGRkJAQoqKimDFjBvPmzWP69OlZbnfGjBnUqVOH7t27ExcXR1xcHCVKlKBLly4EBweblV2wYAEvvfQSzz//fJp67ty5w/Xr180WEREREZEnScP3REQkW+bPn89bb70FQJMmTUhISGDr1q00atTIrNyYMWOoV68eAF27dmXIkCHExMRQunRpAF5//XW2bdvG4MGDuXHjBtOmTeOnn36iTp06AJQuXZqdO3cyd+5cfHx8jHpHjRpF48aNM4xvzJgxfPjhh/Tr189YV7NmTePnB+daKlWqFKNHj6Znz55mPZYSExP54osvjGRN7969GTVq1CPPTd26dbGwsODu3bskJibSo0cPOnXqZGwfPXo0U6dOpVWrVkb7UVFRzJ07l86dOwP3k3Op3N3d+fDDD1m2bFmaxFlGnJycyJs3L3Z2dri4uBjr3377bYYNG8a+ffuoVasWiYmJLF68mMmTJ6dbz/jx4xk5cmSW2hQREREReRxKSomISJadOHGCffv2sXLlSgCsrKxo27YtCxYsSJOUerAXU5EiRYzhaA+u27dvHwBRUVHcvn07TbLp7t27VKtWzWxdjRo1Mozv8uXL/PbbbzRs2DDDMtu2bWPcuHFERUVx/fp17t27x+3bt7lx4wb29vYA2NnZmfUecnV15fLlyxnWmWrZsmWUK1eOxMREjh07Rt++fSlQoAATJkzgjz/+4MKFC3Tt2pXu3bsb+9y7dw8nJyfj9Xfffcenn37K6dOnSUhI4N69e+TLl++RbT+Kq6srzZo1Y8GCBdSqVYu1a9dy+/Zt3njjjXTLDxkyhA8++MB4ff36ddzc3P5xHCIiIiIiqZSUEhGRLJs/fz737t2jWLFixrqUlBTy5MnDtWvXKFCggLE+T548xs8mk8nsdeq65ORkAOPfdevWmdUNYG1tbfY6NXGUHltb20zjP3/+PAEBAbz77ruMHj2aggULsnPnTrp27Wo2b1N6sWblKXpubm7GUMFy5cpx5swZgoKCGDFihHGM8+bNMxvSB2BpaQnAnj17aNeuHSNHjsTf3x8nJyeWLl3K1KlTjbIWFhZpYnkw9sx069aNjh07Mn36dIKDg2nbti12dnbplrW2tk5z7kVEREREniQlpUREJEvu3bvHwoULmTp1Ki+//LLZttatW7NkyRKzCcWzo3z58lhbWxMbG2s2VC+7HB0dcXd3Z+vWrfj5+aXZfuDAAe7du8fUqVOxsLg/reK333772O09iqWlJffu3ePu3bsUKVKEYsWKcebMGTp06JBu+V27dlGyZEmGDh1qrDt//rxZmUKFCvHzzz+brYuMjDRLpOXNm5ekpKQ09QcEBGBvb8+cOXP48ccf2bFjxz85PBERERGRf0RJKRERyZK1a9dy7do1unbtajbcDO7PDzV//vzHTko5OjoyYMAA+vfvT3JyMi+++CLXr18nIiICBwcHY76lrBgxYgTvvvsuhQsXpmnTpvz999/s2rWLPn368Pzzz3Pv3j1mzZpF8+bN2bVrF1988cVjxZyeK1eucOnSJe7du8exY8eYMWMGfn5+xvC7ESNG0LdvX/Lly0fTpk25c+cOBw4c4Nq1a3zwwQd4eHgQGxvL0qVLqVmzJuvWrWPVqlVmbTRo0IDJkyezcOFC6tSpw+LFi/n555/Nhjm6u7uzd+9ezp07h4ODAwULFsTCwgJLS0sCAwMZMmQIHh4exvxdIiIiIiK5QU/fExGRLJk/fz6NGjVKk5CC+z2lIiMjOXTo0GPXP3r0aIYNG8b48eMpV64c/v7+/PDDD5QqVSpb9XTu3JlPP/2Uzz//nAoVKvDKK69w6tQpAKpWrcq0adOYOHEiFStWZMmSJYwfP/6xY35Yo0aNcHV1xd3dnR49ehAQEMCyZcuM7d26deOrr74iJCSESpUq4ePjQ0hIiHGMLVq0oH///vTu3ZuqVasSERFBUFCQWRv+/v4EBQUxaNAgatasyd9//202mTrAgAEDsLS0pHz58hQqVIjY2FhjW9euXbl79y5dunR5YsctIiIiIvI4TClZmSRDRERE/hN27dqFr68vFy9epEiRIlne7/r16zg5OeE3vz9WdpprSkTkWbWp3ZP7Y46I/Hel/u4YHx+f6UN7NHxPRETkGXDnzh0uXLhAUFAQbdq0yVZCSkRERETkadDwPRERkWfAN998g5eXF/Hx8UyaNCm3wxERERERUVJKRETkWRAYGEhSUhIHDx6kWLFiuR2OiIiIiIiG74mIiEjWrX59RKbzAoiIiIiIZJV6SomIiIiIiIiISI5TUkpERERERERERHKcklIiIiIiIiIiIpLjlJQSEREREREREZEcp4nORUREJMu6/jiUPHbWuR2GiIjkkq+bT8ntEETkP0Q9pUREREREREREJMcpKSUiIiIiIiIiIjlOSSkREREREREREclxSkqJiIiIiIiIiEiOU1JKRERyhMlkYvXq1bkdxn/SuXPnMJlMREZG5nYoIiIiIiJZpqSUiMh/mK+vL++//36a9atXr8ZkMuV8QJm4fPky77zzDiVKlMDa2hoXFxf8/f3ZvXt3boeWZU8jOWQymTJdAgMDn1hbIiIiIiI5ySq3AxAREQFo3bo1iYmJhIaGUrp0aX7//Xe2bt3K1atXczu0XBUXF2f8vGzZMoYNG8aJEyeMdba2tly7di03QhMRERER+UfUU0pERDhy5Ah+fn44OjqSL18+qlevzoEDBwAYMWIEVatWNSv/6aef4u7ubrzev38/jRs35rnnnsPJyQkfHx8OHTqU5fb/+usvdu7cycSJE/Hz86NkyZLUqlWLIUOG0KxZMyD9Xkh//fUXJpOJsLAwAK5du0aHDh0oVKgQtra2lClThuDgYLP9ly5dSt26dbGxsaFChQrGvqmioqIICAjAwcGBIkWK0LFjR/78809je3JyMhMnTsTDwwNra2tKlCjB2LFjAShVqhQA1apVw2Qy4evrC0BYWBi1atXC3t6e/PnzU69ePc6fP5+lc+Pi4mIsTk5OmEymNOtSnTlzBj8/P+zs7KhSpUqaXmYRERHUr18fW1tb3Nzc6Nu3Lzdu3MhSHCIiIiIiT5qSUiIiQocOHShevDj79+/n4MGDfPTRR+TJkyfL+//999907tyZ8PBw9uzZQ5kyZQgICODvv//O0v4ODg44ODiwevVq7ty587iHQVBQEFFRUfz4449ER0czZ84cnnvuObMyAwcO5MMPP+Tw4cPUrVuXV199lStXrgD3eyX5+PhQtWpVDhw4wIYNG/j9999p06aNsf+QIUOYOHGi0dbXX39NkSJFANi3bx8AW7ZsIS4ujpUrV3Lv3j1atmyJj48PR48eZffu3fTo0eOpDJ8cOnQoAwYMIDIyEk9PT9q3b8+9e/cAOHbsGP7+/rRq1YqjR4+ybNkydu7cSe/evdOt686dO1y/ft1sERERERF5kjR8T0REiI2NZeDAgZQtWxaAMmXKZGv/Bg0amL2eO3cuBQoUYPv27bzyyiuP3N/KyoqQkBC6d+/OF198gbe3Nz4+PrRr147KlStn6ziqVatGjRo1AMx6c6Xq3bs3rVu3BmDOnDls2LCB+fPnM2jQIObMmYO3tzfjxo0zyi9YsAA3NzdOnjyJq6srM2bMYPbs2XTu3BmA559/nhdffBGAQoUKAeDs7IyLiwsAV69eJT4+nldeeYXnn38egHLlymX5mLJjwIABRs+ykSNHUqFCBU6fPk3ZsmWZPHkyb775pjHHWJkyZZg5cyY+Pj7MmTMHGxsbs7rGjx/PyJEjn0qcIiIiIiKgnlIiIgJ88MEHdOvWjUaNGjFhwgRiYmKytf/ly5d599138fT0xMnJCScnJxISEoiNjc1yHa1bt+a3335jzZo1+Pv7ExYWhre3NyEhIVmuo2fPnixdupSqVasyaNAgIiIi0pSpU6eO8bOVlRU1atQgOjoagIMHD7Jt2zaj55aDg4ORqIuJiSE6Opo7d+7QsGHDLMdUsGBBAgMD8ff3p3nz5syYMcNsnqgn6cEEnqurK3D/vYH7xxYSEmJ2bP7+/iQnJ3P27Nk0dQ0ZMoT4+HhjuXDhwlOJWURERESeXUpKiYj8h+XLl4/4+Pg06//66y/y5ctnvB4xYgTHjx+nWbNm/PTTT5QvX55Vq1YBYGFhQUpKitn+iYmJZq8DAwM5ePAgn376KREREURGRuLs7Mzdu3ezFa+NjQ2NGzdm2LBhREREEBgYyPDhw404ALNYHo6jadOmnD9/nvfff5/ffvuNhg0bMmDAgEe2mzqULjk5mebNmxMZGWm2nDp1ypiL6XEEBweze/du6taty7Jly/D09GTPnj2PVVdmHhxy+eAxpf77zjvvmB3XkSNHOHXqlNGD60HW1tbky5fPbBEREREReZKUlBIR+Q8rW7asMWH5g/bv34+Xl5fZOk9PT/r378+mTZto1aqVMUF4oUKFuHTpklky6MHJxgHCw8Pp27cvAQEBVKhQAWtra7PJwR9X+fLljYm4U4fGPdjL6OE4UssFBgayePFiPv30U7788kuz7Q8mg+7du8fBgweN3lDe3t4cP34cd3d3PDw8zBZ7e3vKlCmDra0tW7duTTfevHnzApCUlJRmW7Vq1RgyZAgRERFUrFiRr7/+Ohtn4p9LPbaHj8vDw8OIW0REREQkJykpJSLyH/bee+8RExNDr169OHLkCCdPnuSzzz5j/vz5DBw4EIBbt27Ru3dvwsLCOH/+PLt27WL//v3GvEe+vr788ccfTJo0iZiYGD777DN+/PFHs3Y8PDxYtGgR0dHR7N27lw4dOmSrV9GVK1do0KABixcv5ujRo5w9e5bly5czadIkWrRoAYCtrS0vvPACEyZMICoqih07dvDJJ5+Y1TNs2DC+//57Tp8+zfHjx1m7dm2a+Zs+++wzVq1axS+//EKvXr24du0aXbp0AaBXr15cvXqV9u3bs2/fPs6cOcOmTZvo0qULSUlJ2NjYMHjwYAYNGsTChQuJiYlhz549zJ8/H4DChQtja2trTJAeHx/P2bNnGTJkCLt37+b8+fNs2rSJkydPPrV5pTIyePBgdu/eTa9evYzeX2vWrKFPnz45GoeIiIiISColpURE/sPc3d0JDw8nJiaGl19+mZo1axISEkJISAhvvPEGAJaWlly5coVOnTrh6elJmzZtaNq0qTHJdbly5fj888/57LPPqFKlCvv27UszJG7BggVcu3aNatWq0bFjR/r27UvhwoWzHKeDgwO1a9dm+vTp1K9fn4oVKxIUFET37t2ZPXu2WTuJiYnUqFGDfv36MWbMGLN68ubNy5AhQ6hcuTL169fH0tKSpUuXmpWZMGECEydOpEqVKoSHh/P9998bT+grWrQou3btIikpCX9/fypWrEi/fv1wcnIyhg8GBQXx4YcfMmzYMMqVK0fbtm2NeZusrKyYOXMmc+fOpWjRorRo0QI7Ozt++eUXWrdujaenJz169KB379688847AJw7dw6TyURYWFiWz9fjqFy5Mtu3b+fUqVO89NJLVKtWjaCgIGPuKRERERGRnGZKeXiiEBERkf+gc+fOUapUKQ4fPkzVqlVzOxxDWFgYr732GmfOnKFAgQK5HU6Grl+/jpOTE68v7U0eO+vcDkdERHLJ182n5HYIIvI/IPV3x/j4+EznJlVPKRERkVy0YcMGPv744391QkpERERE5Gmwyu0AREREnmUTJkzI7RBERERERHKFklIiIvJMcHd3RyPWRURERET+PTR8T0REREREREREcpx6SomIiEiWzW86NtPJKkVEREREsko9pUREREREREREJMcpKSUiIiIiIiIiIjlOSSkREREREREREclxSkqJiIiIiIiIiEiO00TnIiIikmVDdgzA2j5vbochIiI5bJrf7NwOQUT+g9RTSkREREREREREcpySUiIiIiIiIiIikuOUlBIRERERERERkRynpJSIiIiIiIiIiOQ4JaVERORf4e7du3h4eLBr167HruPcuXOYTCYiIyOfXGD/QTVr1mTlypW5HYaIiIiIPOOUlBIR+Y+4fPky77zzDiVKlMDa2hoXFxf8/f3ZvXt3boeWJV9++SUlS5akXr16uR3KY1mxYgW1a9fGyckJR0dHKlSowIcffpjl/VMTaukty5cvf6KxBgUF8dFHH5GcnPxE6xURERERyQ4lpURE/iNat27NkSNHCA0N5eTJk6xZswZfX1+uXr2a26FlyaxZs+jWrVtuh/FYtmzZQrt27Xj99dfZt28fBw8eZOzYsdy9ezfLdbi5uREXF2e2jBw5Ent7e5o2bfpE423WrBnx8fFs3LjxidYrIiIiIpIdSkqJiPwH/PXXX+zcuZOJEyfi5+dHyZIlqVWrFkOGDKFZs2ZGufj4eHr06EHhwoXJly8fDRo04MiRIwCcOHECk8nEL7/8Ylb3tGnTcHd3JyUlBYCoqCgCAgJwcHCgSJEidOzYkT///NMo7+vrS9++fRk0aBAFCxbExcWFESNGZBr/oUOHOH36tFmsAIMHD8bT0xM7OztKly5NUFAQiYmJWT4vycnJdO/eHU9PT86fP59umf3799O4cWOee+45nJyc8PHx4dChQ1luA2Dt2rW8+OKLDBw4EC8vLzw9PWnZsiWzZs0yK/fDDz9QvXp1bGxsKF26NCNHjuTevXsAWFpa4uLiYrasWrWKtm3b4uDgYNTx66+/0rZtWwoUKICzszMtWrTg3LlzxvbAwEBatmzJlClTcHV1xdnZmV69epmdN0tLSwICAvjmm2+ydZwiIiIiIk+SklIiIv8BDg4OODg4sHr1au7cuZNumZSUFJo1a8alS5dYv349Bw8exNvbm4YNG3L16lW8vLyoXr06S5YsMdvv66+/5s0338RkMhEXF4ePjw9Vq1blwIEDbNiwgd9//502bdqY7RMaGoq9vT179+5l0qRJjBo1is2bN2cY/44dO/D09CRfvnxm6x0dHQkJCSEqKooZM2Ywb948pk+fnqVzcvfuXdq0acOBAwfYuXMnJUuWTLfc33//TefOnQkPD2fPnj2UKVOGgIAA/v777yy1A+Di4sLx48f5+eefMyyzceNG3nrrLfr27UtUVBRz584lJCSEsWPHplv+4MGDREZG0rVrV2PdzZs38fPzw8HBgR07drBz504cHBxo0qSJWa+sbdu2ERMTw7Zt2wgNDSUkJISQkBCz+mvVqkV4eHiG8d65c4fr16+bLSIiIiIiT5IpJfVP3yIi8j9txYoVdO/enVu3buHt7Y2Pjw/t2rWjcuXKAPz000+89tprXL58GWtra2M/Dw8PBg0aRI8ePZg+fTqzZ88mJiYGgJMnT+Ll5cXx48cpX748w4YNY+/evWbDvi5evIibmxsnTpzA09MTX19fkpKSzBIetWrVokGDBkyYMCHd2N9//32OHTvG1q1bMz3GyZMns2zZMg4cOJDu9nPnzlGqVCnCw8MZOXIkt27dYt26dTg5OWXtJAJJSUkUKFCAr7/+mldeeSVL+9y4cYM2bdqwfv16SpYsyQsvvMDLL79Mhw4djHNdv359mjZtypAhQ4z9Fi9ezKBBg/jtt9/S1Pnee+8RFhZGVFSUsW7BggVMmjSJ6OhoTCYTcD/5lj9/flavXs3LL79MYGAgYWFhxMTEYGlpCUCbNm2wsLBg6dKlRl1r1qzhtddeIzExEQuLtH+jGjFiBCNHjkwb1w/dsbbPm6XzIiIi/x3T/Gbndggi8j/k+vXrODk5ER8fn+YPzw9STykRkf+I1q1b89tvv7FmzRr8/f0JCwvD29vb6CFz8OBBEhIScHZ2NnpWOTg4cPbsWSMJ1a5dO86fP8+ePXsAWLJkCVWrVqV8+fJGHdu2bTPbv2zZsgBGHYCRCEvl6urK5cuXM4z91q1b2NjYpFn/3Xff8eKLL+Li4oKDgwNBQUHExsY+8ly0b9+ehIQENm3a9MiE1OXLl3n33Xfx9PTEyckJJycnEhISstROKnt7e9atW8fp06f55JNPcHBw4MMPP6RWrVrcvHkTuH/uRo0aZXbuunfvTlxcnFHmwfPx9ddfm/WSSq3j9OnTODo6GnUULFiQ27dvm53/ChUqGAkpSP/829rakpycnGHPuiFDhhAfH28sFy5cyPL5EBERERHJCqvcDkBERJ4cGxsbGjduTOPGjRk2bBjdunVj+PDhBAYGkpycjKurK2FhYWn2y58/P3A/eeHn58fXX3/NCy+8wDfffMM777xjlEtOTqZ58+ZMnDgxTR2urq7Gz3ny5DHbZjKZMn3S23PPPcexY8fM1u3Zs4d27doxcuRI/P39cXJyYunSpUydOvWR5yEgIIDFixezZ88eGjRokGnZwMBA/vjjDz799FNKliyJtbU1derUydYk5amef/55nn/+ebp168bQoUPx9PRk2bJlvP322yQnJzNy5EhatWqVZr+HE3LfffcdN2/epFOnTmbrk5OT0x1iCVCoUCHj56yc/6tXr2JnZ4etrW26x2JtbW3Wo05ERERE5ElTUkpE5D+sfPnyrF69GgBvb28uXbqElZUV7u7uGe7ToUMHBg8eTPv27YmJiaFdu3bGNm9vb1asWIG7uztWVk/uv5Bq1aoxZ84cUlJSjGFpu3btomTJkgwdOtQol9Fk5Q/r2bMnFStW5NVXX2XdunX4+PhkWDY8PJzPP/+cgIAAAC5cuGA2cfvjcnd3x87Ojhs3bgD3z92JEyfw8PB45L7z58/n1VdfNUs0pdaxbNkyY6L6f+Lnn3/G29v7H9UhIiIiIvJPaPieiMh/wJUrV2jQoAGLFy/m6NGjnD17luXLlzNp0iRatGgBQKNGjahTpw4tW7Zk48aNnDt3joiICD755BOzOZpatWrF9evX6dmzJ35+fhQrVszY1qtXL65evUr79u3Zt28fZ86cYdOmTXTp0oWkpKTHjt/Pz48bN25w/PhxY52HhwexsbEsXbqUmJgYZs6cyapVq8z227dvH2XLluXXX39NU2efPn0YM2YMr7zyCjt37jTWd+rUyWxeJw8PDxYtWkR0dDR79+6lQ4cOaXoPPbzPw0aMGMGgQYMICwvj7NmzHD58mC5dupCYmEjjxo0BGDZsGAsXLmTEiBEcP36c6Oholi1bxieffGJW1+nTp9mxYwfdunVL006HDh147rnnaNGiBeHh4Zw9e5bt27fTr18/Ll68mGF86QkPD+fll1/O1j4iIiIiIk+SklIiIv8BDg4O1K5dm+nTp1O/fn0qVqxIUFAQ3bt3Z/bs+xOTmkwm1q9fT/369enSpQuenp60a9eOc+fOUaRIEaOufPny0bx5c44cOUKHDh3M2ilatCi7du0iKSkJf39/KlasSL9+/XByckp3suyscnZ2plWrVmbD0lq0aEH//v3p3bs3VatWJSIigqCgILP9bt68yYkTJ0hMTEy33vfff5+RI0cSEBBAREQEALGxscTFxRllFixYwLVr16hWrRodO3akb9++FC5c2Kyeh/d5mI+PD2fOnKFTp06ULVuWpk2bcunSJTZt2oSXlxcA/v7+rF27ls2bN1OzZk1eeOEFpk2bluapgAsWLKBYsWLpJozs7OzYsWMHJUqUoFWrVpQrV44uXbpw69atbPWc+vXXX4mIiODtt9/O8j4iIiIiIk+anr4nIiL/CseOHaNRo0bGRN7y9AwcOJD4+Hi+/PLLLO+T+gQVPX1PROTZpKfviUh26Ol7IiLyP6VSpUpMmjSJc+fO5XYo/3mFCxdm9OjRuR2GiIiIiDzjNNG5iIj8a3Tu3Dm3Q3gmDBw4MLdDEBERERFRTykREREREREREcl56iklIiIiWTa+/pRsTaouIiIiIpIR9ZQSEREREREREZEcp6SUiIiIiIiIiIjkOCWlREREREREREQkxykpJSIiIiIiIiIiOU4TnYuIiEiWTYnogY193twOQ0REcsjHLy3M7RBE5D9MPaVERERERERERCTHKSklIiIiIiIiIiI5TkkpERERERERERHJcUpKiYiIiIiIiIhIjlNSSkSeeSaTidWrV+d2GFkyYsQIqlatmtth/E84ceIELi4u/P33349dR0hICPnz539yQf1L1KxZk5UrV+Z2GCIiIiLyjFNSSkT+ZwUGBtKyZcvcDkPS4evri8lkwmQyYW1tTbFixWjevHmOJkKGDh1Kr169cHR0zLE2n5SHk2EhISHG+bS0tKRAgQLUrl2bUaNGER8fn2E948ePx2Qy8f7775utDwoK4qOPPiI5OfkpHYGIiIiIyKMpKSUiIk9F9+7diYuL4/Tp06xYsYLy5cvTrl07evTo8dTbvnjxImvWrOHtt99+6m3llHz58hEXF8fFixeJiIigR48eLFy4kKpVq/Lbb7+lKb9//36+/PJLKleunGZbs2bNiI+PZ+PGjTkRuoiIiIhIupSUEpH/DF9fX/r27cugQYMoWLAgLi4ujBgxwqzMqVOnqF+/PjY2NpQvX57NmzenqefXX3+lbdu2FChQAGdnZ1q0aMG5c+eM7ak9tEaOHEnhwoXJly8f77zzDnfv3jXKpKSkMGnSJEqXLo2trS1VqlThu+++M7aHhYVhMpnYunUrNWrUwM7Ojrp163LixAmzWCZMmECRIkVwdHSka9eu3L59O028wcHBlCtXDhsbG8qWLcvnn39ubDt37hwmk4mVK1fi5+eHnZ0dVapUYffu3WZ17Nq1Cx8fH+zs7ChQoAD+/v5cu3YtS8eSETs7O1xcXHBzc+OFF15g4sSJzJ07l3nz5rFlyxaj3ODBg/H09MTOzo7SpUsTFBREYmKiEb+FhQUHDhwwq3vWrFmULFmSlJSUdNv+9ttvqVKlCsWLFzfWXblyhfbt21O8eHHs7OyoVKkS33zzzSOP40FXrlyhVq1avPrqq+m+FwCLFy+mRo0aODo64uLiwptvvsnly5ez1U56TCYTLi4uuLq6Uq5cObp27UpERAQJCQkMGjTIrGxCQgIdOnRg3rx5FChQIE1dlpaWBAQEZPv4RURERESeJCWlROQ/JTQ0FHt7e/bu3cukSZMYNWqUkXhKTk6mVatWWFpasmfPHr744gsGDx5stv/Nmzfx8/PDwcGBHTt2sHPnThwcHGjSpIlZ0mnr1q1ER0ezbds2vvnmG1atWsXIkSON7Z988gnBwcHMmTOH48eP079/f9566y22b99u1t7QoUOZOnUqBw4cwMrKii5duhjbvv32W4YPH87YsWM5cOAArq6uZgkngHnz5jF06FDGjh1LdHQ048aNIygoiNDQ0DTtDBgwgMjISDw9PWnfvj337t0DIDIykoYNG1KhQgV2797Nzp07ad68OUlJSdk6lqzo3LkzBQoUMBvG5+joSEhICFFRUcyYMYN58+Yxffp0ANzd3WnUqBHBwcFm9QQHBxMYGIjJZEq3nR07dlCjRg2zdbdv36Z69eqsXbuWn3/+mR49etCxY0f27t2bpdgvXrzISy+9RNmyZVm5ciU2Njbplrt79y6jR4/myJEjrF69mrNnzxIYGJilNrKrcOHCdOjQgTVr1hjvF0CvXr1o1qwZjRo1ynDfWrVqER4enuH2O3fucP36dbNFRERERORJssrtAEREnqTKlSszfPhwAMqUKcPs2bPZunUrjRs3ZsuWLURHR3Pu3DmjB824ceNo2rSpsf/SpUuxsLDgq6++MhIewcHB5M+fn7CwMF5++WUA8ubNy4IFC7Czs6NChQqMGjWKgQMHMnr0aG7dusW0adP46aefqFOnDgClS5dm586dzJ07Fx8fH6O9sWPHGq8/+ugjmjVrxu3bt7GxseHTTz+lS5cudOvWDYAxY8awZcsWsx46o0ePZurUqbRq1QqAUqVKERUVxdy5c+ncubNRbsCAATRr1gyAkSNHUqFCBU6fPk3ZsmWZNGkSNWrUMEt4VahQAYAbN25k+ViywsLCAk9PT7OeZ5988onxs7u7Ox9++CHLli0zev9069aNd999l2nTpmFtbc2RI0eIjIzMdH6qc+fOUb16dbN1xYoVY8CAAcbrPn36sGHDBpYvX07t2rUzjfvkyZM0btyYFi1aMGPGjAyTYYBZYrF06dLMnDmTWrVqkZCQgIODQ6btPI6yZcvy999/c+XKFQoXLszSpUs5dOgQ+/fvz3S/YsWKERsbS3JyMhYWaf9GNX78eLNEq4iIiIjIk6aeUiLyn/Lw/Dmurq7G0Kno6GhKlChhNqQrNdGS6uDBg5w+fRpHR0ccHBxwcHCgYMGC3L59m5iYGKNclSpVsLOzM6snISGBCxcuEBUVxe3bt2ncuLFRh4ODAwsXLjSr4+F4XV1dAczifTi+B1//8ccfXLhwga5du5q1M2bMmGy1k9pTKj3ZOZasSklJMUvqfPfdd7z44ou4uLjg4OBAUFAQsbGxxvaWLVtiZWXFqlWrAFiwYAF+fn64u7tn2MatW7fS9GRKSkpi7NixVK5cGWdnZxwcHNi0aZNZWxnV9eKLL9KyZUtmzpyZaUIK4PDhw7Ro0YKSJUvi6OiIr68vwCPbeVypQxhNJhMXLlygX79+LF68OMOeXKlsbW1JTk7mzp076W4fMmQI8fHxxnLhwoUnHruIiIiIPNvUU0pE/lPy5Mlj9tpkMhlPGEtv/qGHEwzJyclUr16dJUuWpClbqFChR7b/YHvr1q2jWLFiZtutra0zjDc1lqw+ES213Lx589L09LG0tMxyO7a2to9sIyvHkhVJSUmcOnWKmjVrArBnzx7atWvHyJEj8ff3x8nJiaVLlzJ16lRjn7x589KxY0eCg4Np1aoVX3/9NZ9++mmm7Tz33HPGnFippk6dyvTp0/n000+pVKkS9vb2vP/++2bDMtNjbW1No0aNWLduHQMHDjRLaj7sxo0bvPzyy7z88sssXryYQoUKERsbi7+//yPbeVzR0dHky5cPZ2dn1qxZw+XLl816iSUlJbFjxw5mz57NnTt3jM/G1atXsbOzy/D9t7a2fqz3WEREREQkq5SUEpFnRvny5YmNjeW3336jaNGiAGkm/Pb29mbZsmXGBOYZOXLkCLdu3TK+0O/ZswcHBweKFy9OgQIFsLa2JjY2NtvD2x5Urlw59uzZQ6dOnYx1e/bsMX4uUqQIxYoV48yZM3To0OGx26lcuTJbt25Nd6hW+fLln8ixpAoNDeXatWu0bt0auD/BesmSJRk6dKhR5vz582n269atGxUrVuTzzz8nMTHRGK6YkWrVqhEVFWW2Ljw8nBYtWvDWW28B9xNup06doly5cpnWZWFhwaJFi3jzzTdp0KABYWFhxufnYb/88gt//vknEyZMwM3NDSDNJO1P0uXLl/n6669p2bIlFhYWNGzYkGPHjpmVefvttylbtiyDBw82S1b+/PPPeHt7P7XYREREREQeRcP3ROSZ0ahRI7y8vOjUqRNHjhwhPDzcLBkC0KFDB5577jlatGhBeHg4Z8+eZfv27fTr14+LFy8a5e7evUvXrl2Jiorixx9/ZPjw4fTu3RsLCwscHR0ZMGAA/fv3JzQ0lJiYGA4fPsxnn32WZgLyzPTr148FCxawYMECTp48yfDhwzl+/LhZmREjRjB+/HhmzJjByZMnOXbsGMHBwUybNi3L7QwZMoT9+/fz3nvvcfToUX755RfmzJnDn3/++Y+O5ebNm1y6dImLFy+yd+9eBg8ezLvvvkvPnj3x8/MDwMPDg9jYWJYuXUpMTAwzZ840huk9qFy5crzwwgsMHjyY9u3bZ9q7C8Df35/du3ebTf7t4eHB5s2biYiIIDo6mnfeeYdLly6Z7Td79ux0hzJaWlqyZMkSqlSpQoMGDcz2K1u2rBFziRIlyJs3L7NmzeLMmTOsWbOG0aNHp6nvwX2yKiUlhUuXLhEXF0d0dDQLFiygbt26ODk5MWHCBOD+pPEVK1Y0W+zt7XF2dqZixYpm9YWHhxtzpImIiIiI5AYlpUTkmWFhYcGqVau4c+cOtWrVolu3bowdO9asjJ2dHTt27KBEiRK0atWKcuXK0aVLF27dumXWc6phw4aUKVOG+vXr06ZNG5o3b86IESOM7aNHj2bYsGGMHz+ecuXK4e/vzw8//ECpUqWyHG/btm0ZNmwYgwcPpnr16pw/f56ePXualenWrRtfffUVISEhVKpUCR8fH0JCQrLVjqenJ5s2beLIkSPUqlWLOnXq8P3332NlZfWPjmXevHm4urry/PPP89prrxEVFcWyZcvMJlRv0aIF/fv3p3fv3lStWpWIiAiCgoLSra9r167cvXvXbCLxjAQEBJAnTx62bNlirAsKCsLb2xt/f398fX1xcXGhZcuWZvv9+eefGc6VZWVlxTfffEOFChVo0KCBMSfXiRMniI+PB+4P8QwJCWH58uWUL1+eCRMmMGXKlDR1PbhPVl2/fh1XV1eKFStGnTp1jMnsDx8+bMwTllW//vorERERvP3229naT0RERETkSTKlpDfJioiIZCgwMJC//vqL1atX53Yoz5SxY8eydOnSNMPTMvL555/z/fffs3Hjxqcc2f+egQMHEh8fz5dffpnlfa5fv46TkxNBP7bFxj7vU4xORET+TT5+aWFuhyAi/4NSf3eMj4/PdFoUzSklIiL/agkJCURHRzNr1qx0h8JlpEePHly7do2///4bR0fHpxjh/57ChQszYMCA3A5DRERERJ5xGr4nIiL/ar179+bFF1/Ex8cnS0P3UllZWTF06FAlpNIxcOBAihQpktthiIiIiMgzTsP3RERE5JE0fE9E5Nmk4Xsi8jg0fE9ERESeuAF1v8z0FwsRERERkazS8D0REREREREREclxSkqJiIiIiIiIiEiOU1JKRERERERERERynJJSIiIiIiIiIiKS4zTRuYiIiGRZyL622Nrnye0wRETkKeteZ01uhyAizwD1lBIRERERERERkRynpJSIiIiIiIiIiOQ4JaVERERERERERCTHKSklIiIiIiIiIiI5TkkpERGRHOTu7s6nn35qvDaZTKxevTrX4hERERERyS1KSomIiPx/ERERWFpa0qRJk9wOJVv69etH9erVsba2pmrVqmm2nzhxAj8/P4oUKYKNjQ2lS5fmk08+ITExMeeDFRERERH5/6xyOwAREZF/iwULFtCnTx+++uorYmNjKVGiRG6HlCUpKSl06dKFvXv3cvTo0TTb8+TJQ6dOnfD29iZ//vwcOXKE7t27k5yczLhx43IhYhERERER9ZQSEREB4MaNG3z77bf07NmTV155hZCQELPtYWFhmEwm1q1bR5UqVbCxsaF27docO3bMrNyKFSuoUKEC1tbWuLu7M3Xq1GzFcezYMRo0aICtrS3Ozs706NGDhISETPeZOXMmvXr1onTp0uluL126NG+//TZVqlShZMmSvPrqq3To0IHw8PBsxSYiIiIi8iQpKSUiIgIsW7YMLy8vvLy8eOuttwgODiYlJSVNuYEDBzJlyhT2799P4cKFefXVV41hcAcPHqRNmza0a9eOY8eOMWLECIKCgtIkuDJy8+ZNmjRpQoECBdi/fz/Lly9ny5Yt9O7d+0keKqdPn2bDhg34+PhkWObOnTtcv37dbBEREREReZKUlBIREQHmz5/PW2+9BUCTJk1ISEhg69atacoNHz6cxo0bU6lSJUJDQ/n9999ZtWoVANOmTaNhw4YEBQXh6elJYGAgvXv3ZvLkyVmKYcmSJdy6dYuFCxdSsWJFGjRowOzZs1m0aBG///77Pz7GunXrYmNjQ5kyZXjppZcYNWpUhmXHjx+Pk5OTsbi5uf3j9kVEREREHqSklIiIPPNOnDjBvn37aNeuHQBWVla0bduWBQsWpClbp04d4+eCBQvi5eVFdHQ0ANHR0dSrV8+sfL169Th16hRJSUmPjCM6OpoqVapgb29vtn9ycjInTpx4rGN70LJlyzh06BBff/0169atY8qUKRmWHTJkCPHx8cZy4cKFf9y+iIiIiMiDNNG5iIg88+bPn8+9e/coVqyYsS4lJYU8efJw7do1ChQokOn+JpPJ2Cf15wfryar09n+4jX8itbdT+fLlSUpKokePHnz44YdYWlqmKWttbY21tfU/blNEREREJCPqKSUiIs+0e/fusXDhQqZOnUpkZKSxHDlyhJIlS7JkyRKz8nv27DF+vnbtGidPnqRs2bLA/WTPzp07zcpHRETg6emZbuLnYeXLlycyMpIbN24Y63bt2oWFhQWenp7/5DDTSElJITExMVtJMxERERGRJ0k9pURE5Jm2du1arl27RteuXXFycjLb9vrrrzN//nyzicZHjRqFs7MzRYoUYejQoTz33HO0bNkSgA8//JCaNWsyevRo2rZty+7du5k9ezaff/55lmLp0KEDw4cPp3PnzowYMYI//viDPn360LFjR4oUKZLhfqdPnyYhIYFLly5x69YtIiMjgftJrrx587JkyRLy5MlDpUqVsLa25uDBgwwZMoS2bdtiZaVfBUREREQkd+g3UREReabNnz+fRo0apUlIAbRu3Zpx48Zx6NAhY92ECRPo168fp06dokqVKqxZs4a8efMC4O3tzbfffsuwYcMYPXo0rq6ujBo1isDAwCzFYmdnx8aNG+nXrx81a9bEzs6O1q1bM23atEz369atG9u3bzdeV6tWDYCzZ8/i7u6OlZUVEydO5OTJk6SkpFCyZEl69epF//79sxSXiIiIiMjTYEpRv30REZFHCgsLw8/Pj2vXrpE/f/7cDifHXb9+HScnJ2ZsboKtfZ7cDkdERJ6y7nXW5HYIIvI/LPV3x/j4ePLly5dhOc0pJSIiIiIiIiIiOU5JKRERERERERERyXGaU0pERCQLfH199aQ6EREREZEnSD2lREREREREREQkx6mnlIiIiGRZYK1lmU5WKSIiIiKSVeopJSIiIiIiIiIiOU5JKRERERERERERyXFKSomIiIiIiIiISI5TUkpERERERERERHKcJjoXERGRLFt3wB87e/36ICLyX9WidnhuhyAizxD1lBIRERERERERkRynpJSIiIiIiIiIiOQ4JaVERERERERERCTH/SeSUoGBgbRs2TLTMr6+vrz//vs5Ek92jRgxgiJFimAymVi9enWW9/s3H1Nucnd359NPP832fh07dmTcuHFPNJZz585hMpmIjIwEICwsDJPJxF9//fVE25HsCQkJIX/+/NneLyv3mtyUlc9+du8zOeXmzZu0bt2afPnyZfsa+bceU256+N7zsDt37lCiRAkOHjyYs4GJiIiIiDwgV5NSgYGBmEwmTCYTVlZWlChRgp49e3Lt2rVs1TNjxgxCQkKeTpBPWXR0NCNHjmTu3LnExcXRtGnTNGVyOpHxNL/gZbfuHj16YGlpydKlS59KPKmOHj3KunXr6NOnj7HuaST96tatS1xcHE5OTk+03sfxuMm7Z8GjvtA/ae7u7sa90NbWlrJlyzJ58mRSUlKyVc/+/fvp0aPHU4ry6QoNDSU8PJyIiIgMr5ERI0ZQtWrVHInnad53H6duLy8v8ubNy6+//vpEYrC2tmbAgAEMHjz4idQnIiIiIvI4cr2nVJMmTYiLi+PcuXN89dVX/PDDD7z33nvZqsPJyemxej1kR1JSEsnJyU+83piYGABatGiBi4sL1tbWT7yN/1U3b95k2bJlDBw4kPnz5z+y/N27dx+7rdmzZ/PGG2/g6Oj42HVkRd68eXFxccFkMj3VduR/z6hRo4iLiyM6OpoBAwbw8ccf8+WXX2arjkKFCmFnZ/eUIrzvn1xnmYmJiaFcuXJUrFhR18hDdu7cye3bt3njjTey9AeYrL5HHTp0IDw8nOjo6H8YoYiIiIjI48n1pJS1tTUuLi4UL16cl19+mbZt27Jp0yZje1JSEl27dqVUqVLY2tri5eXFjBkzzOp4eEjNjRs36NSpEw4ODri6ujJ16tQ07d69e5dBgwZRrFgx7O3tqV27NmFhYcb21OE9a9eupXz58lhbW3P+/HnCwsKoVasW9vb25M+fn3r16nH+/PkMj+/YsWM0aNAAW1tbnJ2d6dGjBwkJCcD9v/o3b94cAAsLi3S/hJ07dw4/Pz8AChQogMlkIjAw0NienJzMoEGDKFiwIC4uLowYMcJs//j4eHr06EHhwoXJly8fDRo04MiRIxnG+yjJycmMGjWK4sWLY21tTdWqVdmwYYOx/e7du/Tu3RtXV1dsbGxwd3dn/PjxwP3eIACvvfYaJpPJeJ2R5cuXU758eYYMGcKuXbs4d+6c2fbU9338+PEULVoUT0/PdOsJDg7GycmJzZs3Z3hMy5cv59VXX800Hnd3d8aNG0eXLl1wdHSkRIkSaZIG+/bto1q1atjY2FCjRg0OHz5stv3hHhJXrlyhffv2FC9eHDs7OypVqsQ333xjto+vry99+/bN9H2eNm0alSpVwt7eHjc3N9577z3jc5ZVsbGxtGjRAgcHB/Lly0ebNm34/fffje2pvVQWLVqEu7s7Tk5OtGvXjr///tsok5KSwqRJkyhdujS2trZUqVKF7777zth+7do1OnToQKFChbC1taVMmTIEBwdnGNOGDRt48cUXyZ8/P87OzrzyyitGIhf+r0fTypUr8fPzw87OjipVqrB7926zekJCQihRogR2dna89tprXLlyJdNzUapUKQCqVauGyWTC19fXbPuUKVNwdXXF2dmZXr16kZiYaGx71L0lI46Ojri4uODu7k63bt2oXLmy2b0wJiaGFi1aUKRIERwcHKhZsyZbtmwxq+Ph3m+nTp2ifv362NjYUL58+XSvgV9//ZW2bdtSoEABnJ2dadGihdm1ltF19vnnn1OmTBlsbGwoUqQIr7/+eqbHt2LFCipUqIC1tTXu7u5m92VfX1+mTp3Kjh070j3fcP89HDlyJEeOHDF6lT2YoPnzzz957bXXsLOzo0yZMqxZs8Zs/6ioKAICAnBwcKBIkSJ07NiRP//8M9OYM3Pt2jU6depEgQIFsLOzo2nTppw6dcrYfv78eZo3b06BAgWwt7enQoUKrF+//pH39PTMnz+fN998k44dO7JgwYI0Pejc3d0ZM2YMgYGBODk50b179zR1JCcn0717dzw9PY3/s5ydnalbt26ae46IiIiISE7J9aTUg86cOcOGDRvIkyePsS45OZnixYvz7bffEhUVxbBhw/j444/59ttvM6xn4MCBbNu2jVWrVrFp0ybCwsLSzJvx9ttvs2vXLpYuXcrRo0d54403aNKkidmXips3bzJ+/Hi++uorjh8/TsGCBWnZsiU+Pj4cPXqU3bt306NHjwz/on/z5k2aNGlCgQIF2L9/P8uXL2fLli307t0bgAEDBhhfyOPi4oiLi0tTh5ubGytWrADgxIkTxMXFmSXlQkNDsbe3Z+/evUyaNIlRo0YZXzxTUlJo1qwZly5dYv369Rw8eBBvb28aNmzI1atXM30vMjJjxgymTp3KlClTOHr0KP7+/rz66qvGeZs5cyZr1qzh22+/5cSJEyxevNhIPu3fvx+4nySKi4szXmdk/vz5vPXWWzg5OREQEJBu8mLr1q1ER0ezefNm1q5dm2b7lClTGDBgABs3bqRx48bptnP06FH++usvatSo8cjjnzp1qpFseu+99+jZsye//PILcD8Z+sorr+Dl5cXBgwcZMWIEAwYMyLS+27dvU716ddauXcvPP/9Mjx496NixI3v37jUrl9n7DPeTmjNnzuTnn38mNDSUn376iUGDBj3yeFKlpKTQsmVLrl69yvbt29m8eTMxMTG0bdvWrFxMTAyrV69m7dq1rF27lu3btzNhwgRj+yeffEJwcDBz5szh+PHj9O/fn7feeovt27cDEBQURFRUFD/++CPR0dHMmTOH5557LsO4bty4wQcffMD+/fvZunUrFhYWvPbaa2l6LQ4dOpQBAwYQGRmJp6cn7du35969ewDs3buXLl268N577xEZGYmfnx9jxozJ9Hzs27cPgC1bthAXF8fKlSuNbdu2bSMmJoZt27YRGhpKSEiIWXIkK/eWzKSkpBAWFkZ0dLTZvTAhIYGAgAC2bNnC4cOH8ff3p3nz5sTGxqZbT3JyMq1atcLS0pI9e/bwxRdfpBmqdfPmTfz8/HBwcGDHjh3s3LkTBwcHmjRpYtbb5uHr7MCBA/Tt25dRo0Zx4sQJNmzYQP369TM8poMHD9KmTRvatWvHsWPHGDFiBEFBQcZ5W7lyJd27d6dOnTppzneqtm3b8uGHH1KhQgXjfvng53PkyJG0adOGo0ePEhAQQIcOHYz7XFxcHD4+PlStWpUDBw6wYcMGfv/9d9q0afPoNyQDgYGBHDhwgDVr1rB7925SUlIICAgwEpS9evXizp077Nixg2PHjjFx4kQcHBweeU9/2N9//83y5ct56623aNy4MTdu3Eg3yTl58mQqVqzIwYMHCQoKMtt29+5d2rRpw4EDB9i5cyclS5Y0ttWqVYvw8PDHPg8iIiIiIv+EVW4HsHbtWhwcHEhKSuL27dvA/V4fqfLkycPIkSON16VKlSIiIoJvv/023S8UCQkJzJ8/n4ULFxpJiNDQUIoXL26UiYmJ4ZtvvuHixYsULVoUuJ8g2rBhA8HBwcZk14mJiXz++edUqVIFgKtXrxIfH88rr7zC888/D0C5cuUyPLYlS5Zw69YtFi5ciL29PXB/mFjz5s2ZOHEiRYoUMYYduri4pFuHpaUlBQsWBKBw4cJphilWrlyZ4cOHA1CmTBlmz57N1q1bady4Mdu2bePYsWNcvnzZGBY4ZcoUVq9ezXffffdYc89MmTKFwYMH065dOwAmTpzItm3b+PTTT/nss8+IjY2lTJkyvPjii5hMJrMvP4UKFQIgf/78GR5vqlOnTrFnzx7jy+lbb71F3759GT58OBYW/5dLtbe356uvviJv3rxp6hgyZAihoaGEhYVRqVKlDNs6d+4clpaWFC5c+JHHHxAQYAwvHTx4MNOnTycsLIyyZcuyZMkSkpKSWLBgAXZ2dlSoUIGLFy/Ss2fPDOsrVqyYWeKqT58+bNiwgeXLl1O7dm1jfWbvM2A291WpUqUYPXo0PXv25PPPP3/kMcH95MvRo0c5e/Ysbm5uACxatIgKFSqwf/9+atasCdxPdISEhBjDHDt27MjWrVsZO3YsN27cYNq0afz000/UqVMHgNKlS7Nz507mzp2Lj48PsbGxVKtWzUgAPqq3XOvWrc1ez58/n8KFCxMVFUXFihWN9QMGDKBZs2bA/eREhQoVOH36NGXLlmXGjBn4+/vz0UcfAeDp6UlERIRZD7+HpX5WnZ2d03xWCxQowOzZs7G0tKRs2bI0a9aMrVu30r179yzfW9IzePBgPvnkE+7evUtiYiI2Njb07dvX2F6lShXjXgQwZswYVq1axZo1a4xE94O2bNlCdHQ0586dM+5/48aNM5u3bunSpVhYWPDVV18ZyfXg4GDy589PWFgYL7/8MpD2Olu5ciX29va88sorODo6UrJkSapVq5bhsU2bNo2GDRsayRJPT0+ioqKYPHkygYGBFCxYEDs7O2N4a3psbW1xcHDAysoq3TKBgYG0b9/eOM5Zs2axb98+mjRpwpw5c/D29jY7/wsWLMDNzY2TJ09m2MsyI6dOnWLNmjXs2rWLunXrAvfv925ubqxevZo33niD2NhYWrdubdx7Spcubeyf2T39YUuXLqVMmTJUqFABgHbt2jF//nyjt1WqBg0amN1LUnu7JSQk0KxZM27dukVYWFiaubqKFSuWphdqqjt37nDnzh3j9fXr1zONVUREREQku3K9p5Sfnx+RkZHs3buXPn364O/vbzbZNMAXX3xBjRo1KFSoEA4ODsybNy/D3gExMTHcvXvX+FIM978AeHl5Ga8PHTpESkoKnp6eODg4GMv27dvNhgblzZuXypUrm9UTGBho9FCYMWNGur2bUkVHR1OlShUjIQVQr149kpOTOXHiRNZPUiYejA/A1dWVy5cvA/d7JyQkJODs7Gx2nGfPnjU7zqy6fv06v/32G/Xq1TNbX69ePWNOksDAQCIjI/Hy8qJv375mw4+yY/78+fj7+xu9aAICArhx40aa4UqVKlVKNyE1depU5s6dy86dOzNNSAHcunULa2vrLM1h8+D5NplMuLi4GOc79f1+cE6fBz+H6UlKSmLs2LFUrlzZeJ82bdqU5vOd2fsM93vvNG7cmGLFiuHo6EinTp24cuUKN27ceOQxpcbu5uZmJKQAypcvT/78+c3mm3F3dzebd+vBOKKiorh9+zaNGzc2+7wtXLjQ+Lz17NmTpUuXUrVqVQYNGkRERESmccXExPDmm29SunRp8uXLZwyry+z8uLq6Api9Lw+/D496XzJToUIFLC0tzdpLbSur95b0DBw4kMjISLZv346fnx9Dhw41Eh5wv9fYoEGDjPfFwcGBX375JcN7YXR0NCVKlDBLyD983AcPHuT06dM4OjoasRYsWJDbt2+bxfvwdda4cWNKlixJ6dKl6dixI0uWLOHmzZsZHlt0dHS6941Tp06RlJSU6XnJqgc/A/b29jg6OprdC7dt22b2npQtWxbgse6F0dHRWFlZmSWOnZ2d8fLyMq6Xvn37MmbMGOrVq8fw4cM5evToYx1Xao/RVG+99RYrV65MM0l6Rj0927dvT0JCAps2bUp38nhbW9sM37vx48fj5ORkLA/eH0REREREnoRcT0rZ29vj4eFB5cqVmTlzJnfu3DHrGfXtt9/Sv39/unTpwqZNm4iMjOTtt9/OcCLXrDytKjk5GUtLSw4ePEhkZKSxREdHmw2jsLW1TZOoCA4OZvfu3dStW5dly5bh6enJnj17Mowlo0THk5rE98HhPan1pg5tSk5OxtXV1ewYIyMjOXHiBAMHDnzsNh+O/cHj9Pb25uzZs4wePZpbt27Rpk2bR84187CkpCQWLlzIunXrsLKywsrKCjs7O65evZpmwvMHE34Peumll0hKSsp0mGeq5557jps3b2ZpcuDMznd2n5QG95Nn06dPZ9CgQfz0009ERkbi7++fJpbM2j1//jwBAQFUrFiRFStWcPDgQT777DMAs7mOMpPRZ/Xh9Y/6vAGsW7fO7PMWFRVlzCvVtGlTzp8/z/vvv89vv/1Gw4YNMx3i2Lx5c65cucK8efPYu3evMawxs/OTGu8/eV8y86hzkJV7S3qee+45PDw8qFOnDitWrGD69OlmSdiBAweyYsUKxo4dS3h4OJGRkVSqVClb98KH3+Pk5GSqV6+e5h5x8uRJ3nzzTaPcw9eZo6Mjhw4d4ptvvsHV1ZVhw4ZRpUqVDJ8ml97nK6ffl+bNm6c5ztQ5t7Iro9gfPM5u3bpx5swZOnbsyLFjx6hRowazZs3KVjtRUVHs3buXQYMGGffCF154gVu3bqWZByqje2FAQABHjx7N8P+pq1evGj0DHzZkyBDi4+ON5cKFC9mKX0RERETkUXJ9+N7Dhg8fTtOmTenZsydFixYlPDycunXrmj2RL7O/bHt4eJAnTx727NlDiRIlgPsT0p48eRIfHx/g/uTFSUlJXL58mZdeeinbMVarVo1q1aoxZMgQ6tSpw9dff80LL7yQplz58uUJDQ3lxo0bxheGXbt2YWFhka3hIqk9FLLbo8Db25tLly5hZWX1yGFSWZEvXz6KFi3Kzp07zb7IRUREUKtWLbNybdu2pW3btrz++us0adKEq1evUrBgQfLkyfPI41i/fj1///03hw8fNuuR8ssvv9ChQweuXLmCs7NzpnXUqlXL6HlnaWmZaRIu9RHzUVFR/+hx8+XLl2fRokXcunULW1tbgAy/CKYKDw+nRYsWRk+I5ORkTp06lemw0IcdOHCAe/fuMXXqVGNoY1aScQ/HHhsby4ULF4zeEFFRUcTHx2c5ltQHAsTGxhrXWnoKFSpEYGAggYGBvPTSSwwcOJApU6akKXflyhWio6OZO3eucZ3u3LkzW8eVGtfD78Oj3pfHveb+6b0lVYECBejTpw8DBgzg8OHDmEwmwsPDCQwM5LXXXgPuD8vKaNgV/N97+ttvvxlDCR+eAN7b25tly5YZD0LIDisrKxo1akSjRo0YPnw4+fPn56effqJVq1bpxvLwexcREYGnp6fZNf4oefPmfayeVd7e3qxYsQJ3d3esrP75f3vly5fn3r177N271+jNduXKFU6ePGl2vbi5ufHuu+/y7rvvMmTIEObNm0efPn2y/PmaP38+9evXN5LMqRYtWsT8+fMzHRqcqmfPnlSsWJFXX32VdevWpbk2f/755wyHXlpbW+uJsCIiIiLyVOV6T6mH+fr6UqFCBWPuDw8PDw4cOMDGjRs5efIkQUFBmU6Q7eDgQNeuXRk4cCBbt27l559/JjAw0GweIk9PTzp06ECnTp1YuXIlZ8+eZf/+/UycOJH169dnWPfZs2cZMmQIu3fv5vz582zatCnNl5AHdejQARsbGzp37szPP//Mtm3b6NOnDx07dqRIkSJZPiclS5bEZDKxdu1a/vjjjyw/Va1Ro0bUqVOHli1bsnHjRs6dO0dERASffPIJBw4cyHTfs2fPpulVkJCQwMCBA5k4cSLLli3jxIkTfPTRR0RGRtKvXz8Apk+fztKlS/nll184efIky5cvx8XFxZg3xd3dna1bt3Lp0iWuXbuWbtvz58+nWbNmVKlShYoVKxpL69atKVSoEIsXL87S8depU4cff/yRUaNGMX369AzLFSpUCG9v78dKeDzozTffxMLCgq5duxIVFcX69evTTbY8yMPDg82bNxMREUF0dDTvvPMOly5dyla7zz//PPfu3WPWrFmcOXOGRYsW8cUXX2SrjkaNGlG5cmU6dOjAoUOH2LdvH506dcLHxydLE8DD/d4zAwYMoH///oSGhhITE8Phw4f57LPPCA0NBWDYsGF8//33nD59muPHj7N27doMr5/Up8F9+eWXnD59mp9++okPPvggW8cF94dRbdiwgUmTJnHy5Elmz56d6XxScH+uH1tbW2NC7Pj4+Cy19bj3lvT06tWLEydOGJNie3h4sHLlSiIjIzly5AhvvvlmmgnfH9SoUSO8vLzo1KkTR44cITw8nKFDh5qV6dChA8899xwtWrQgPDycs2fPsn37dvr168fFixczrHvt2rXMnDmTyMhIzp8/z8KFC0lOTjYbJv2gDz/8kK1btzJ69GhOnjxJaGgos2fPfuSDAB7m7u5u3Jv+/PNPs/mOMtOrVy+uXr1K+/bt2bdvH2fOnGHTpk106dLlkYmhY8eOpbkXlilThhYtWtC9e3d27tzJkSNHeOuttyhWrBgtWrQA7s/ztnHjRs6ePcuhQ4f46aefjM96Vu7piYmJLFq0iPbt25vdBytWrEi3bt04ePBglp+k2qdPH8aMGcMrr7yS5j4XHh5uzB0mIiIiIpLT/nVJKYAPPviAefPmceHCBd59911atWpF27ZtqV27NleuXDHrNZWeyZMnU79+fV599VUaNWrEiy++SPXq1c3KBAcH06lTJz788EO8vLx49dVX2bt3b6ZzZtjZ2fHLL7/QunVrPD096dGjB7179+add97JsPzGjRu5evUqNWvW5PXXX6dhw4bMnj07W+ejWLFijBw5ko8++ogiRYqkO6lxekwmE+vXr6d+/fp06dIFT09P2rVrx7lz5x6ZFPvggw+MHmGpS+oTtz788EM+/PBDKlWqxIYNG1izZg1lypQB7icFJ06cSI0aNahZsybnzp1j/fr1RlJw6tSpbN68GTc3t3T/Ov/777+zbt26NBNcpx5Pq1at0gzhy0y9evVYt24dQUFBzJw5M8NyPXr0YMmSJVmuNz0ODg788MMPREVFUa1aNYYOHcrEiRMz3ScoKAhvb2/8/f3x9fXFxcWFli1bZqvdqlWrMm3aNCZOnEjFihVZsmQJ48ePf+R+ycnJRq8Rk8nE6tWrKVCgAPXr16dRo0aULl2aZcuWZSuW0aNHM2zYMMaPH0+5cuXw9/fnhx9+MOaCyps3L0OGDKFy5crUr18fS0tLli5dmm5dFhYWLF26lIMHD1KxYkX69+/P5MmTsxUPwAsvvMBXX33FrFmzqFq1Kps2beKTTz7JdB8rKytmzpzJ3LlzKVq0qJFoyIrHubekp1ChQnTs2JERI0aQnJzM9OnTKVCgAHXr1qV58+b4+/vj7e2d4f4WFhasWrWKO3fuUKtWLbp168bYsWPNytjZ2bFjxw5KlChBq1atKFeuHF26dOHWrVuZ9pzKnz8/K1eupEGDBpQrV44vvviCb775xpiM+2He3t58++23LF26lIoVKzJs2DBGjRpFYGBgts5J69atadKkCX5+fhQqVCjNELaMFC1alF27dpGUlIS/vz8VK1akX79+ODk5mf3BIj3169dPcy+E++9z9erVeeWVV6hTpw4pKSmsX7/eGEaYlJREr169KFeuHE2aNMHLy8t48EBW7ulr1qzhypUrRs+4B5UpU4ZKlSpl6174/vvvM3LkSAICAoy53Hbv3k18fHy2h1iLiIiIiDwpppQnPbGHyP+g27dv4+XlxdKlS//RJNj/K5KSksiXLx+hoaH6QiryjHrjjTeoVq0aH3/8cZbKX79+HScnJ77e+gJ29v+60f8iIvKEtKgdntshiMh/QOrvjvHx8Zn+wVu/VYoANjY2LFy4kD///DO3Q3nqLl68yMKFC0lKSuLFF1/M7XBEJBfcuXOHKlWq0L9//9wORURERESeYUpKifx/mU3O/V9StWpVnJ2dWbRoES4uLrkdjojkAmtr60cOYxURERERedqUlBJ5xjwLvcFERERERETk3+9fOdG5iIiIiIiIiIj8t6mnlIiIiGRZsxobM52sUkREREQkq9RTSkREREREREREcpySUiIiIiIiIiIikuOUlBIRERERERERkRynpJSIiIiIiIiIiOQ4TXQuIiIiWbb/YH3sHSxzOwwREXmCXqh5MLdDEJFnlHpKiYiIiIiIiIhIjlNSSkREREREREREcpySUiIiIiIiIiIikuOUlBIRERERERERkRynpJRIOsLCwjCZTPz111//qJ4vv/wSNzc3LCws+PTTT7O8X2BgIC1btvxHbT+uJ3XsucFkMrF69ercDsPMw++lr68v77//fq7E4u7ununn8Ny5c5hMJiIjI596LLo2nqyQkBDy58+f22GIiIiIiGSLklLyPyU3v5Bm1/Xr1+nduzeDBw/m119/pUePHmnK5GQSAO4nJUwmEyaTCVtbW8qWLcvkyZNJSUkxytStW5e4uDicnJxyJKaseDDu9BZfX9+n1nZqIuLh5ZNPPnlqbeYWNzc34uLiqFix4lNtR9dG1mX0+XtwCQkJybF4RERERESeJKvcDkAkN929e5e8efM+lbpjY2NJTEykWbNmuLq6PpU2HseoUaPo3r07t2/fZsuWLfTs2ZN8+fLxzjvvAJA3b15cXFxyOUpz+/fvJykpCYCIiAhat27NiRMnyJcvH8BTew8f9GB7AA4ODk+9zZxmaWmZI++9ro2sS02EperXrx/Xr18nODjYWOfk5MSyZctyNC4RERERkSdBPaXkP2X79u3UqlULa2trXF1d+eijj7h3756x3dfXl969e/PBBx/w3HPP0bhxYwDWr1+Pp6cntra2+Pn5ce7cuUe2FRsbS4sWLXBwcCBfvny0adOG33//Hbg/lKZSpUoAlC5dGpPJlG6dpUqVAqBatWrp9viZMmUKrq6uODs706tXLxITE41td+/eZdCgQRQrVgx7e3tq165NWFjYI+N2dHTExcUFd3d3unXrRuXKldm0aZOx/eEhSqnDgjZu3Ei5cuVwcHCgSZMmZl+U7927R9++fcmfPz/Ozs4MHjyYzp07m/Vq++6776hUqRK2trY4OzvTqFEjbty48ch4AQoVKoSLiwsuLi4ULFgQgMKFC6dZB/Dnn3/y2muvYWdnR5kyZVizZo1ZXVFRUQQEBODg4ECRIkXo2LEjf/755yNjeLA9FxcXIyn166+/0rZtWwoUKICzszMtWrTI0ucn1bVr1+jUqRMFChTAzs6Opk2bcurUKQBSUlIoVKgQK1asMMpXrVqVwoULG693795Nnjx5SEhIAGDEiBGUKFECa2trihYtSt++fc3au3nzJl26dMHR0ZESJUrw5ZdfGtse7p2U+llYt24dVapUwcbGhtq1a3Ps2LFMj0nXRvaujcykJsJSF1tbW6ytrdOsS5VZLADBwcGUK1cOGxsbypYty+eff56lOEREREREngYlpeQ/49dffyUgIICaNWty5MgR5syZw/z58xkzZoxZudDQUKysrNi1axdz587lwoULtGrVioCAACIjI+nWrRsfffRRpm2lpKTQsmVLrl69yvbt29m8eTMxMTG0bdsWgLZt27JlyxYA9u3bR1xcHG5ubmnq2bdvHwBbtmwhLi6OlStXGtu2bdtGTEwM27ZtIzQ0lJCQELNhOm+//Ta7du1i6dKlHD16lDfeeIMmTZoYCY1HSUlJISwsjOjoaPLkyZNp2Zs3bzJlyhQWLVrEjh07iI2NZcCAAcb2iRMnsmTJEoKDg9m1axfXr183m9spLi6O9u3b06VLF6KjowkLC6NVq1bG0KjUL/vZSeZkZOTIkbRp04ajR48SEBBAhw4duHr1qhGHj48PVatW5cCBA2zYsIHff/+dNm3aPFZbN2/exM/PDwcHB3bs2MHOnTuNZMDdu3ezVEdgYCAHDhxgzZo17N69m5SUFAICAkhMTMRkMlG/fn0joXLt2jWioqJITEwkKioKuH/uqlevjoODA9999x3Tp09n7ty5nDp1itWrVxsJoFRTp06lRo0aHD58mPfee4+ePXvyyy+/ZBrjwIEDmTJlCvv376dw4cK8+uqrZkmgB+nayN618SQ9KpZ58+YxdOhQxo4dS3R0NOPGjSMoKIjQ0NB067tz5w7Xr183W0REREREniQN35P/jM8//xw3Nzdmz56NyWSibNmy/PbbbwwePJhhw4ZhYXE/B+vh4cGkSZOM/T7++GNKly7N9OnTMZlMeHl5cezYMSZOnJhhW1u2bOHo0aOcPXvW+EK9aNEiKlSowP79+6lZsybOzs7A//XySU+hQoUAcHZ2TlOmQIECzJ49G0tLS8qWLUuzZs3YunUr3bt3JyYmhm+++YaLFy9StGhRAAYMGMCGDRsIDg5m3LhxGcY+ePBgPvnkE+7evUtiYiI2NjZpetM8LDExkS+++ILnn38egN69ezNq1Chj+6xZsxgyZAivvfYaALNnz2b9+vXG9ri4OO7du0erVq0oWbIkgFmyxM7ODi8vr0cmALIiMDCQ9u3bAzBu3DhmzZrFvn37aNKkCXPmzMHb29vs/CxYsAA3NzdOnjyJp6dnhvUWL17c7PX58+f5/vvvsbCw4KuvvsJkMgH3e6Lkz5+fsLAwXn755UxjPXXqFGvWrGHXrl3UrVsXgCVLluDm5sbq1at544038PX1NXoz7dixgypVqlCiRAnCwsIoX748YWFhRi+i2NhYXFxcaNSoEXny5KFEiRLUqlXLrM2AgADee+894P5nYfr06YSFhVG2bNkM4xw+fLjRqzA0NJTixYuzatWqdJN5ujayd208SY+KZfTo0UydOpVWrVoB93ujRUVFMXfuXDp37pymvvHjxzNy5MinEquIiIiICKinlPyHREdHU6dOHSM5AFCvXj0SEhK4ePGisa5GjRpp9nvhhRfM9qtTp84j23JzczPr4VG+fHny589PdHT0Pz0UACpUqIClpaXx2tXVlcuXLwNw6NAhUlJS8PT0xMHBwVi2b99OTExMpvUOHDiQyMhItm/fjp+fH0OHDjUSIhmxs7Mzvug+HEt8fDy///67WfLD0tKS6tWrG6+rVKlCw4YNqVSpEm+88Qbz5s3j2rVrxvZatWrxyy+/UKxYsSycmcxVrlzZ+Nne3h5HR0cj1oMHD7Jt2zazc5aajHnUeQsPDycyMtJYChQowMGDBzl9+jSOjo5GfQULFuT27duPrA/uf46srKyoXbu2sc7Z2RkvLy/jc+Tr68vx48f5888/2b59O76+vvj6+rJ9+3bu3btHREQEPj4+ALzxxhvcunWL0qVL0717d1atWmU2fPXh82MymXBxcTHOT0YevB4KFixoFl96x6RrI+vXxpOUWSx//PEHFy5coGvXrmbnZcyYMRmelyFDhhAfH28sFy5ceCpxi4iIiMizSz2l5D8jJSXFLLGUug4wW29vb59umX/aVmbrH8fDvYZMJhPJyckAJCcnY2lpycGDB82+nMOjJ+B+7rnn8PDwwMPDgxUrVuDh4cELL7xAo0aNshXLw+cto3MP97+Ib968mYiICDZt2sSsWbMYOnQoe/fuNeYOelIedd6aN2+ebi+4R024XapUKfLnz2+2Ljk5merVq7NkyZI05VN7+mQmo8/eg5+jihUr4uzszPbt29m+fTujRo3Czc2NsWPHsn//fm7dusWLL74I3H963okTJ9i8eTNbtmzhvffeY/LkyWzfvt04L5mdn+zI6HOuayN718aTlFksqedn3rx5ZklQIM15SmVtbY21tfVTiFRERERE5D71lJL/jPLlyxMREWH2hS8iIgJHR8dMe+CUL1+ePXv2mK17+HV6+8TGxpr1HIiKiiI+Pp5y5cplOebUp8alPlkuq6pVq0ZSUhKXL182vkSnLtl5OliBAgXo06cPAwYMeOwvyk5OThQpUsSYAwjuH8/hw4fNyplMJurVq8fIkSM5fPgwefPmZdWqVY/V5uPy9vbm+PHjuLu7pzlvDycrs1rfqVOnKFy4cJr6nJycHrl/+fLluXfvHnv37jXWXblyhZMnTxqfo9R5pb7//nt+/vlnXnrpJSpVqmQM1fL29sbR0dHY39bWlldffZWZM2cSFhbG7t27Hzkx+aM8eD1cu3aNkydPZjjcT9fG/8nqtZETihQpQrFixThz5kya8/KkE8MiIiL/r707D6rqvP84/gFBQBCUoALiQkNAK4tbTcBU1KrEFJeaGY21UYIxJQrFirZqrBCTKDjqBJdoRo3ajFadiWtrEDfcUBGU4hKRERQTsUZjxLhghPP7I+P95UYRRLhXzfs1c2e453nuOd/zPc8d8ctzngMA1UVRCk+da9eumd1GlZubq+LiYo0ePVrnz59XXFycTp06pY0bNyoxMVHjxo0zrSf1IDExMTpz5ozGjRun/Px8rVq1ymzR5Afp1auXgoODNWzYMB05ckRZWVkaPny4wsPD77s98GGaNm0qJycn04Lb165dq9bn/P39NWzYMA0fPlzr1q1TUVGRDh8+rJSUlEder2bMmDHKz883e8Lbo4qLi9OMGTO0ceNG5efnKz4+XlevXjXNEDl06JCmT5+u7OxsFRcXa926dfrmm29MRYqsrCy1adNGX3/9dY1jqI4xY8bo22+/1dChQ5WVlaXCwkKlp6crOjr6kYsfkjRs2DB5eHhowIAB2rt3r4qKirR7927Fx8eb3TJamRdeeEEDBgzQqFGjtG/fPv33v//Vn/70JzVv3lwDBgww9evevbtWrVql4OBgubq6mgpVK1euNHsq3fLly7V06VIdP35chYWF+uyzz+Tk5GRax6umpk2bph07duj4LP8rpQAAFDxJREFU8eOKioqSh4dHpU+P47thrqrvhiUlJSVpxowZSk1N1enTp3Xs2DEtW7ZMc+bMsXgsAAAAgERRCk+hjIwMdejQwew1depUNW/eXFu2bFFWVpZCQkIUExOjkSNHasqUKQ/dX8uWLfX5559r8+bNCgkJ0aJFix66GLL04+yVDRs2qHHjxurWrZt69eqlX/3qV1qzZs0jnYudnZ3mzp2rTz75RN7e3maFiKosW7ZMw4cPV0JCggICAtS/f38dOnTogU8ye5gmTZrojTfeUFJSUo1u45J+XCB66NChGj58uEJDQ+Xi4qKIiAg5OjpKklxdXbVnzx69+uqr8vf315QpUzR79mz17dtX0o9PDcvPz6/0iW61xdvbW/v371d5ebkiIiIUGBio+Ph4ubm5PbRwWZkGDRpoz549atmypQYNGqS2bdsqOjpat27dkqura7X2sWzZMnXq1EmRkZEKDQ2VYRjasmWL2a1YPXr0UHl5uVkBKjw8XOXl5ab1pCSpUaNGWrx4sbp27arg4GDt2LFDmzdvNi0sXlPJycmKj49Xp06dVFJSok2bNplmMv0c3w1zVX03pB+LiZYoUr311ltasmSJli9frqCgIIWHh2v58uXMlAIAAIDV2Bh1tbgFgF+siooKtW3bVoMHD9b7779v7XBQQxkZGerRo4euXr1633paqJkHfTeSkpKUkZGhjIwM6wZXhdLSUrm5uWn7zhA5uzx4HSoAwNPppd/kWDsEAM+Ye787Xrt27aF/sGehcwCP7dy5c0pPT1d4eLjKyso0f/58FRUV6Y9//KO1QwOsqjrfja1btyo1NdWKUQIAAADWQVEKwGOztbXV8uXLTYtCBwYGavv27Y+0sDXwLKrOd+PAgQNWjBAAAACwHm7fAwAAVeL2PQB4dnH7HoDaxu17AACg1v2m055qL+QPAAAAPAxP3wMAAAAAAIDFMVMKAABU6d7d/qWlpVaOBAAAAE+6e78zVrViFEUpAABQpStXrkiSWrRoYeVIAAAA8LS4fv263NzcKm2nKAUAAKrk7u4uSSouLn7oLxaofaWlpWrRooXOnz/Pel4WRu6th9xbB3m3HnJvPeS+bhiGoevXr8vb2/uh/ShKAQCAKtna/rgMpZubG7+wWYmrqyu5txJybz3k3jrIu/WQe+sh97WvOn/IZKFzAAAAAAAAWBxFKQAAAAAAAFgcRSkAAFAlBwcHJSYmysHBwdqh/OKQe+sh99ZD7q2DvFsPubcecm9dNkZVz+cDAAAAAAAAahkzpQAAAAAAAGBxFKUAAAAAAABgcRSlAAAAAAAAYHEUpQAAQJU+/vhj+fr6ytHRUZ06ddLevXutHdIzJSkpSTY2NmYvT09PU7thGEpKSpK3t7ecnJzUvXt3nThxwooRP7327Nmjfv36ydvbWzY2NtqwYYNZe3VyXVZWpri4OHl4eMjZ2Vn9+/fXV199ZcGzeDpVlfuoqKj7vgcvvfSSWR9y/+hmzJih3/zmN2rYsKGaNm2qgQMHKj8/36wP475uVCf3jPu6sXDhQgUHB8vV1VWurq4KDQ3VF198YWpnzD85KEoBAICHWrNmjcaOHat3331XR48e1W9/+1v17dtXxcXF1g7tmdKuXTuVlJSYXseOHTO1zZw5U3PmzNH8+fN1+PBheXp6qnfv3rp+/boVI3463bhxQyEhIZo/f/4D26uT67Fjx2r9+vVavXq19u3bp++//16RkZEqLy+31Gk8larKvSS98sorZt+DLVu2mLWT+0e3e/dujRkzRgcPHtS2bdt09+5d9enTRzdu3DD1YdzXjerkXmLc1wUfHx8lJycrOztb2dnZ6tmzpwYMGGAqPDHmnyAGAADAQ3Tp0sWIiYkx29amTRtj4sSJVoro2ZOYmGiEhIQ8sK2iosLw9PQ0kpOTTdtu375tuLm5GYsWLbJQhM8mScb69etN76uT6++++86wt7c3Vq9eberz9ddfG7a2tkZaWprFYn/a/Tz3hmEYI0aMMAYMGFDpZ8h97bh06ZIhydi9e7dhGIx7S/p57g2DcW9JjRs3NpYsWcKYf8IwUwoAAFTqzp07ysnJUZ8+fcy29+nTR5mZmVaK6tlUUFAgb29v+fr66vXXX1dhYaEkqaioSBcvXjS7Bg4ODgoPD+ca1LLq5DonJ0c//PCDWR9vb28FBgZyPWpBRkaGmjZtKn9/f40aNUqXLl0ytZH72nHt2jVJkru7uyTGvSX9PPf3MO7rVnl5uVavXq0bN24oNDSUMf+EoSgFAAAqdfnyZZWXl6tZs2Zm25s1a6aLFy9aKapnz4svvqh//vOf2rp1qxYvXqyLFy8qLCxMV65cMeWZa1D3qpPrixcvqn79+mrcuHGlfVAzffv21cqVK7Vz507Nnj1bhw8fVs+ePVVWViaJ3NcGwzA0btw4vfzyywoMDJTEuLeUB+VeYtzXpWPHjsnFxUUODg6KiYnR+vXr9etf/5ox/4Sxs3YAAADgyWdjY2P23jCM+7ah5vr27Wv6OSgoSKGhoXr++ee1YsUK04K3XAPLqUmuuR6Pb8iQIaafAwMD1blzZ7Vq1Ur/+c9/NGjQoEo/R+6rLzY2Vnl5edq3b999bYz7ulVZ7hn3dScgIEC5ubn67rvv9Pnnn2vEiBHavXu3qZ0x/2RgphQAAKiUh4eH6tWrd99fBS9dunTfXxhRe5ydnRUUFKSCggLTU/i4BnWvOrn29PTUnTt3dPXq1Ur7oHZ4eXmpVatWKigokETuH1dcXJw2bdqkXbt2ycfHx7SdcV/3Ksv9gzDua0/9+vXl5+enzp07a8aMGQoJCVFqaipj/glDUQoAAFSqfv366tSpk7Zt22a2fdu2bQoLC7NSVM++srIyffnll/Ly8pKvr688PT3NrsGdO3e0e/durkEtq06uO3XqJHt7e7M+JSUlOn78ONejll25ckXnz5+Xl5eXJHJfU4ZhKDY2VuvWrdPOnTvl6+tr1s64rztV5f5BGPd1xzAMlZWVMeafNFZYXB0AADxFVq9ebdjb2xtLly41Tp48aYwdO9ZwdnY2zp49a+3QnhkJCQlGRkaGUVhYaBw8eNCIjIw0GjZsaMpxcnKy4ebmZqxbt844duyYMXToUMPLy8soLS21cuRPn+vXrxtHjx41jh49akgy5syZYxw9etQ4d+6cYRjVy3VMTIzh4+NjbN++3Thy5IjRs2dPIyQkxLh79661Tuup8LDcX79+3UhISDAyMzONoqIiY9euXUZoaKjRvHlzcv+Y3nnnHcPNzc3IyMgwSkpKTK+bN2+a+jDu60ZVuWfc151JkyYZe/bsMYqKioy8vDxj8uTJhq2trZGenm4YBmP+SUJRCgAAVGnBggVGq1atjPr16xsdO3Y0e5w1Ht+QIUMMLy8vw97e3vD29jYGDRpknDhxwtReUVFhJCYmGp6enoaDg4PRrVs349ixY1aM+Om1a9cuQ9J9rxEjRhiGUb1c37p1y4iNjTXc3d0NJycnIzIy0iguLrbC2TxdHpb7mzdvGn369DGaNGli2NvbGy1btjRGjBhxX17J/aN7UM4lGcuWLTP1YdzXjapyz7ivO9HR0abfW5o0aWL87ne/MxWkDIMx/ySxMQzDsNy8LAAAAAAAAIA1pQAAAAAAAGAFFKUAAAAAAABgcRSlAAAAAAAAYHEUpQAAAAAAAGBxFKUAAAAAAABgcRSlAAAAAAAAYHEUpQAAAAAAAGBxFKUAAAAAAABgcRSlAAAAADzVzp49KxsbG+Xm5lo7FJNTp07ppZdekqOjo9q3b2/x4z+JOQGAn6MoBQAAAOCxREVFycbGRsnJyWbbN2zYIBsbGytFZV2JiYlydnZWfn6+duzY8cA+9/JmY2MjOzs7tWzZUu+8846uXr36SMeKiorSwIEDzba1aNFCJSUlCgwMrOkpAECdoygFAAAA4LE5OjoqJSXlkQsqT7I7d+7U+LNnzpzRyy+/rFatWum5556rtN8rr7yikpISnT17VkuWLNHmzZs1evToGh/3nnr16snT01N2dnaPvS8AqCsUpQAAAAA8tl69esnT01MzZsyotE9SUtJ9t7J99NFHat26ten9vVk/06dPV7NmzdSoUSO99957unv3riZMmCB3d3f5+Pjo008/vW//p06dUlhYmBwdHdWuXTtlZGSYtZ88eVKvvvqqXFxc1KxZM73xxhu6fPmyqb179+6KjY3VuHHj5OHhod69ez/wPCoqKjRt2jT5+PjIwcFB7du3V1pamqndxsZGOTk5mjZtmmxsbJSUlFRpThwcHOTp6SkfHx/16dNHQ4YMUXp6uqm9vLxcI0eOlK+vr5ycnBQQEKDU1FSznK5YsUIbN240zbrKyMi47/a9jIwM2djYaMeOHercubMaNGigsLAw5efnm8XzwQcfqGnTpmrYsKHeeustTZw40eyaZWRkqEuXLnJ2dlajRo3UtWtXnTt3rtLzA4CHoSgFAAAA4LHVq1dP06dP17x58/TVV1891r527typCxcuaM+ePZozZ46SkpIUGRmpxo0b69ChQ4qJiVFMTIzOnz9v9rkJEyYoISFBR48eVVhYmPr3768rV65IkkpKShQeHq727dsrOztbaWlp+t///qfBgweb7WPFihWys7PT/v379cknnzwwvtTUVM2ePVuzZs1SXl6eIiIi1L9/fxUUFJiO1a5dOyUkJKikpETjx4+v1nkXFhYqLS1N9vb2pm0VFRXy8fHR2rVrdfLkSU2dOlWTJ0/W2rVrJUnjx4/X4MGDTTOuSkpKFBYWVukx3n33Xc2ePVvZ2dmys7NTdHS0qW3lypX68MMPlZKSopycHLVs2VILFy40td+9e1cDBw5UeHi48vLydODAAb399tu/2Fs0ATw+5nICAAAAqBV/+MMf1L59eyUmJmrp0qU13o+7u7vmzp0rW1tbBQQEaObMmbp586YmT54sSZo0aZKSk5O1f/9+vf7666bPxcbG6rXXXpMkLVy4UGlpaVq6dKn+9re/aeHCherYsaOmT59u6v/pp5+qRYsWOn36tPz9/SVJfn5+mjlz5kPjmzVrlv7+97+bjp2SkqJdu3bpo48+0oIFC0y3zbm4uMjT0/Oh+/r3v/8tFxcXlZeX6/bt25KkOXPmmNrt7e313nvvmd77+voqMzNTa9eu1eDBg+Xi4iInJyeVlZVVeSxJ+vDDDxUeHi5Jmjhxon7/+9/r9u3bcnR01Lx58zRy5Ei9+eabkqSpU6cqPT1d33//vSSptLRU165dU2RkpJ5//nlJUtu2bas8JgBUhplSAAAAAGpNSkqKVqxYoZMnT9Z4H+3atZOt7f//V6VZs2YKCgoyva9Xr56ee+45Xbp0yexzoaGhpp/t7OzUuXNnffnll5KknJwc7dq1Sy4uLqZXmzZtJP24/tM9nTt3fmhspaWlunDhgrp27Wq2vWvXrqZjPYoePXooNzdXhw4dUlxcnCIiIhQXF2fWZ9GiRercubOaNGkiFxcXLV68WMXFxY98LEkKDg42/ezl5SVJpjzm5+erS5cuZv1/+t7d3V1RUVGKiIhQv379lJqaqpKSkhrFAQASRSkAAAAAtahbt26KiIgwzWr6KVtbWxmGYbbthx9+uK/fT29fk35co+lB2yoqKqqM596tZRUVFerXr59yc3PNXgUFBerWrZupv7Ozc5X7/Ol+7zEMo0a3sTk7O8vPz0/BwcGaO3euysrKzGZGrV27Vn/9618VHR2t9PR05ebm6s0336zxIuw/zeNPc/Pzbff8/HotW7ZMBw4cUFhYmNasWSN/f38dPHiwRrEAAEUpAAAAALUqOTlZmzdvVmZmptn2Jk2a6OLFi2aFjnsLcdeGnxZH7t69q5ycHNNsqI4dO+rEiRNq3bq1/Pz8zF7VLURJkqurq7y9vbVv3z6z7ZmZmbVyK1tiYqJmzZqlCxcuSJL27t2rsLAwjR49Wh06dJCfn5/ZzC5Jql+/vsrLyx/72AEBAcrKyjLblp2dfV+/Dh06aNKkScrMzFRgYKBWrVr12McG8MtEUQoAAABArQoKCtKwYcM0b948s+3du3fXN998o5kzZ+rMmTNasGCBvvjii1o77oIFC7R+/XqdOnVKY8aM0dWrV00LeY8ZM0bffvuthg4dqqysLBUWFio9PV3R0dGPXNCZMGGCUlJStGbNGuXn52vixInKzc1VfHz8Y59D9+7d1a5dO9PaV35+fsrOztbWrVt1+vRp/eMf/9Dhw4fNPtO6dWvl5eUpPz9fly9ffuDss+qIi4vT0qVLtWLFChUUFOiDDz5QXl6eafZUUVGRJk2apAMHDujcuXNKT0/X6dOnWVcKQI1RlAIAAABQ695///37bv1q27atPv74Yy1YsEAhISHKysqq9pPpqiM5OVkpKSkKCQnR3r17tXHjRnl4eEiSvL29tX//fpWXlysiIkKBgYGKj4+Xm5ub2fpV1fGXv/xFCQkJSkhIUFBQkNLS0rRp0ya98MILtXIe48aN0+LFi3X+/HnFxMRo0KBBGjJkiF588UVduXJFo0ePNus/atQoBQQEmNad2r9/f42OO2zYME2aNEnjx49Xx44dVVRUpKioKDk6OkqSGjRooFOnTum1116Tv7+/3n77bcXGxurPf/7zY58zgF8mG+Pn/1IAAAAAACCpd+/e8vT01GeffWbtUAA8g+ysHQAAAAAAwPpu3rypRYsWKSIiQvXq1dO//vUvbd++Xdu2bbN2aACeUcyUAgAAAADo1q1b6tevn44cOaKysjIFBARoypQpGjRokLVDA/CMoigFAAAAAAAAi2OhcwAAAAAAAFgcRSkAAAAAAABYHEUpAAAAAAAAWBxFKQAAAAAAAFgcRSkAAAAAAABYHEUpAAAAAAAAWBxFKQAAAAAAAFgcRSkAAAAAAABYHEUpAAAAAAAAWNz/AdMq2tKrve0LAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "Displaying Correlation in Numeric Features...\n",
      "--------------------------------------------\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 800x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "class BivariateAnalysis:\n",
    "    '''\n",
    "    A class for performing multivariate analysis on movie data.\n",
    "    '''\n",
    "\n",
    "    def __init__(self, df):\n",
    "        '''\n",
    "        Initialize the MultivariateAnalysis class with a DataFrame.\n",
    "\n",
    "        Args:\n",
    "            df (pandas.DataFrame): The DataFrame containing movie data.\n",
    "        '''\n",
    "        self.df = df\n",
    "\n",
    "    def plot_year_vs_rating(self):\n",
    "        \n",
    "        '''\n",
    "        This method creates a scatter plot showing the distribution of ratings\n",
    "        across release years, segmented by genre.\n",
    "        '''\n",
    "        \n",
    "        # Group by release year and calculate the average rating\n",
    "        average_ratings_by_year = self.df.groupby('release_year')['rating'].mean().reset_index()\n",
    "    \n",
    "        # Scatter plot of release year vs average rating\n",
    "        plt.figure(figsize=(12, 6))\n",
    "        sns.scatterplot(x='release_year', y='rating', data=average_ratings_by_year, alpha=0.6)\n",
    "        \n",
    "        # Add a line plot to show the trend\n",
    "        sns.lineplot(x='release_year', y='rating', data=average_ratings_by_year, color='purple', marker='.', label='Trend Line')\n",
    "        plt.title('Release Year vs Average Rating')\n",
    "        plt.xlabel('Release Year')\n",
    "        plt.ylabel('Average Rating')\n",
    "        plt.legend()\n",
    "        plt.show()\n",
    "\n",
    "    def plot_genre_vs_rating(self):\n",
    "        \n",
    "        '''\n",
    "        This method creates a bar plot showing the average rating for each genre\n",
    "        across different decades.\n",
    "        '''\n",
    "        \n",
    "        # Split genres into multiple rows\n",
    "        genres = self.df['genres'].str.split(', ', expand=True).stack()\n",
    "        # Adjust index to remove the extra level created by the split\n",
    "        genres.index = genres.index.droplevel(-1)\n",
    "        # Rename the Series to 'genre' \n",
    "        genres.name = 'genre'\n",
    "        \n",
    "        # Create a new DataFrame with genres and ratings\n",
    "        genre_ratings = self.df[['rating']].join(genres)\n",
    "        \n",
    "        # Calculate the mean rating for each genre\n",
    "        genre_mean_ratings = genre_ratings.groupby('genre')['rating'].mean().sort_values(ascending=False)\n",
    "        \n",
    "        # Plot the mean rating for each genre\n",
    "        plt.figure(figsize=(12, 6))\n",
    "        sns.barplot(x=genre_mean_ratings.index, y=genre_mean_ratings.values, palette='viridis')\n",
    "        plt.title('Average Rating per Movie Genre')\n",
    "        plt.xlabel('Genre')\n",
    "        plt.ylabel('Average Rating')\n",
    "        plt.xticks(rotation=45, ha='right')\n",
    "        plt.tight_layout()\n",
    "        plt.show()\n",
    "\n",
    "\n",
    "    def plot_highest_movie_ratings(self):\n",
    "        \n",
    "        '''\n",
    "        This method creates a violin plot showing the distribution of ratings\n",
    "        for the top 20 movies with the highest ratings.\n",
    "        '''\n",
    "        \n",
    "        movie_counts = self.df['title'].value_counts().reset_index()\n",
    "        movie_counts.columns = ['title', 'count']\n",
    "        top_twenty_movies = movie_counts.head(20)\n",
    "        \n",
    "        top_twenty_data = self.df[self.df['title'].isin(top_twenty_movies['title'])]\n",
    "        \n",
    "        plt.figure(figsize=(15, 8))\n",
    "        sns.violinplot(x='title', y='rating', data=top_twenty_data, palette='muted')\n",
    "        plt.title('Top 20 Highest Rated Movies')\n",
    "        plt.xlabel('Movie Title')\n",
    "        plt.ylabel('Rating')\n",
    "        plt.xticks(rotation=90)\n",
    "        plt.tight_layout()\n",
    "        plt.show()\n",
    "\n",
    "    def plot_top_rated_titles(self, top_n=20):\n",
    "        \n",
    "        '''\n",
    "        This method counts the number of ratings for each title, sorts them,\n",
    "        and creates a bar plot to visualize the top n titles based on the total \n",
    "        number of ratings received.\n",
    "    \n",
    "        Args:\n",
    "            top_n (int): The number of top-rated titles to display. Default is 20.\n",
    "        '''\n",
    "        \n",
    "        # Count the number of ratings for each title\n",
    "        title_counts = self.df['title'].value_counts().head(top_n)\n",
    "    \n",
    "        # Create a bar plot for the top rated titles\n",
    "        plt.figure(figsize=(12, 6))\n",
    "        sns.barplot(x=title_counts.values, y=title_counts.index, palette='viridis')\n",
    "        plt.title(f'Top {top_n} Rated Titles by Number of Ratings')\n",
    "        plt.xlabel('Number of Ratings')\n",
    "        plt.ylabel('Movie Title')\n",
    "        plt.tight_layout()\n",
    "        plt.show()\n",
    "        \n",
    "    def plot_correlation_heatmap(self):\n",
    "        \n",
    "        '''\n",
    "        This method plots a heatmap to visualize the correlation between numeric features in the dataset.\n",
    "        '''\n",
    "        \n",
    "        numeric_df = self.df[['movieId', 'user_id', 'rating', 'release_year']]\n",
    "        plt.figure(figsize=(8, 6))\n",
    "        sns.heatmap(numeric_df.corr(), annot=True, cmap='viridis')\n",
    "        plt.title('Correlation Heatmap')\n",
    "        plt.show()\n",
    "        \n",
    "    def run_bivariate (self):\n",
    "        ''' \n",
    "        This method sequentially calls the methods Runs bivariate analyses on the dataset.\n",
    "            \n",
    "        '''\n",
    "        print('Displaying Release Year vs Average Rating...')\n",
    "        print('--------------------------------------------')\n",
    "        self.plot_year_vs_rating()\n",
    "        print('\\n\\nDisplaying Genres vs Average Rating...')\n",
    "        print('---------------------------------------')\n",
    "        self.plot_genre_vs_rating()\n",
    "        print('\\n\\nDisplaying Highest Movies Ratings...')\n",
    "        print('-----------------------------------')\n",
    "        self.plot_highest_movie_ratings()\n",
    "        print('\\n\\nDisplaying Top Rated Titles...')\n",
    "        print('-------------------------------')\n",
    "        self.plot_top_rated_titles()\n",
    "        print('\\n\\nDisplaying Correlation in Numeric Features...')\n",
    "        print('--------------------------------------------\\n')\n",
    "        self.plot_correlation_heatmap()\n",
    "    \n",
    "\n",
    "bivariate = BivariateAnalysis(df)\n",
    "bivariate.run_bivariate()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "709ce208-daac-4653-a0e0-b49884958a41",
   "metadata": {},
   "source": [
    "### Summary"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5d2a8050-b26e-4a9e-a673-a0b7e187e139",
   "metadata": {},
   "source": [
    "The `Release Year vs Average Rating` plot reveals fluctuations in early years (1900-1940) probably due to the film industry's nascent stage. This is followed by peaks in the early 1900s, stabilization after the 1940s and a gradual decline from the 1970s to 2020. There appears to be consistency around a rating of 3.5 from the 1980s to 2020 with outliers becoming less extreme over time.\n",
    "\n",
    "The `Average Rating Per Movie Genre` plot shows the average ratings of movies across various genres. Film-noir and war genres receive the highest average ratings nearing 4.0. Documentary, crime and drama also score highly around 3.75. Genres like mystery, animation, IMAX, western and musical follow closely. Adventure, romance, thriller and fantasy genres are rated slightly lower at around 3.5 while Sci-fi, action, children and comedy have average ratings closer to 3.0. The genres with the lowest average ratings are those that have not been specified and horror movies which both fall below 3.0. Overall, film-noir and war are the most highly rated genres, while horror films receive the lowest average rating.\n",
    "\n",
    "The `Top 20 Highest Rated Movies` plot displays the rating distributions for the top 20 highest-rated movies. Each movie is represented by a vertical violin shape with the width indicating the frequency of ratings at each score level. The plots generally show a concentration of high ratings with most movies having a bulge near the top of the 5-point scale. There's some variation in the shapes suggesting differences in rating patterns across films but overall the distributions skew towards positive ratings for these top-rated movies.\n",
    "\n",
    "The `Top 20 Rated Titles by Number of Ratings` plot shows that 'Forrest Gump' leads the list with the highest number of ratings followed closely by 'The Shawshank Redemption' and 'Pulp Fiction'. The ratings range from around 200 ('Lord of the Rings: The Fellowship of the ring') to nearly 300 for the top-rated films indicating these are all highly popular and widely reviewed. \n",
    "\n",
    "The `Correlation Heatmap` visualizes the relationships between the numeric variables in our dataset. It shows a strong positive correlation (0.98) between release year and decade, moderate positive correlations between movieId and both release year (0.51) and decade (0.5) and weak or negligible correlations among the other variables. The diagonal values of 1 represent perfect self-correlation."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1cb08e0f-4bf9-45d8-be50-e16c6d8f562e",
   "metadata": {},
   "source": [
    "# MODELING"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b90ad0b8-4e38-48cc-9121-0988814c2f41",
   "metadata": {},
   "source": [
    "Modeling refers to the process of creating a mathematical representation of user preferences and movie characteristics. It involves using statistical and machine learning techniques to analyze patterns in historical data such as user ratings or viewing history to predict future user preferences. The goal of modeling is to capture the underlying relationships between users, movies and ratings in a way that allows the system to make accurate recommendations for new or existing users. This typically involves choosing an appropriate algorithm such as collaborative filtering, matrix factorization or neural networks, training it on available data and then using the trained model to generate predictions and/or recommendations."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e08b0b56-be71-470a-9a8c-1b31bcced076",
   "metadata": {},
   "source": [
    "### Dummy model\n",
    "A dummy or vanilla model is a simple model that is typically used as a reference or baseline against which more complex models are compared. Its purpose is to provide a reference point to evaluate the effectiveness of the more sophisticated algorithms. For our model, we evaluate the dummy model using the Surprise library. The data is prepared using the Reader and Dataset classes from `Surprise` to format our DataFrame containing movie ratings with a rating scale between 1 and 5. The dataset is then divided into training and test sets reserving 25% of the data for testing purposes. A `NormalPredictor` dummy model is then created which generates random predictions based on the observed distribution of ratings and this model is trained on the training set. The performance of the model is evaluated on the test set using the Root Mean Squared Error (RMSE) metric which provides a quantitative measure of its accuracy. Finally, the RMSE of the dummy model is printed showing the baseline performance for comparison with Singular Value Decomposition `SVD` and K-Nearest Neighbors `KNN` algorithms."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "4aa1079f-b0ea-4f89-aa54-efb6254c7753",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE: 1.4304\n",
      "Baseline Model RMSE: 1.4304275189371316\n"
     ]
    }
   ],
   "source": [
    "from surprise import NormalPredictor\n",
    "from surprise import Reader, Dataset, SVD, KNNBasic\n",
    "from surprise.model_selection import cross_validate, GridSearchCV\n",
    "from surprise.model_selection import train_test_split\n",
    "from surprise import accuracy\n",
    "\n",
    "# Prepare the data\n",
    "reader = Reader(rating_scale=(1, 5))\n",
    "data = Dataset.load_from_df(df[['user_id', 'movieId', 'rating']], reader)\n",
    "\n",
    "# Split the data\n",
    "trainset, testset = train_test_split(data, test_size=0.25, random_state=42)\n",
    "\n",
    "# Create and evaluate the dummy model\n",
    "dummy_model = NormalPredictor()\n",
    "dummy_model.fit(trainset)\n",
    "predictions = dummy_model.test(testset)\n",
    "baseline_rmse = accuracy.rmse(predictions)\n",
    "\n",
    "# Print the RMSE\n",
    "print(f\"Baseline Model RMSE: {baseline_rmse}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cea8df25-678d-4664-84bb-38d915eeffcf",
   "metadata": {},
   "source": [
    "We find that the RMSE of the baseline model is aproximately 1.43. The next step is to perform grid search cross validation to find the best parameters for  the Singular Value Decomposition (SVD) and K-Nearest Neighbors (KNN) models. The `Surprise` library hosts a `GridSearchCV` feature that performs this task.\n",
    "Grid searching the SVD model focuses on tuning the following hyperparameters:\n",
    "\n",
    "**`n_factors`**: Number of latent factors\n",
    "**`n_epochs`**: Number of iterations for training \n",
    "**`lr_all`**: Learning rate for all parameters \n",
    "**`reg_all`**: Regularization term for all parameters\n",
    "\n",
    "Grid searching the KNN model focuses on tuning the following hyperparameters: \n",
    "**`k`**: The number of neighbors to consider when making predictions.\n",
    "**`min_k`**: The minimum number of neighbors required to make a prediction. If fewer neighbors are found, predictions are made based on default values.\n",
    "**`sim_options`**: A dictionary specifying the similarity options. This includes:\n",
    "`cosine` (Cosine Similarity)\n",
    "`pearson` (Pearson Correlation)\n",
    "**`user_based`**: Whether to use user-based or item-based filtering.\n",
    "`True for user-based`\n",
    "`False for item-based`\n",
    "\n",
    "These hyperparameters can be adjusted depending on the dataset and computational resources. After performing the grid search to find the best hyperparameters, the next step is to cross-validate the model with the best parameters. This ensures that the model's performance generalizes well to unseen data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "aae5b17e-07a1-47e1-9c93-afe9fbff1fea",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tuning SVD...\n",
      "Best SVD parameters: {'n_factors': 100, 'n_epochs': 30, 'lr_all': 0.01, 'reg_all': 0.1}\n",
      "Best SVD RMSE: 0.8621525273812124\n",
      "Tuning KNN...\n",
      "Computing the pearson similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Computing the pearson similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Computing the pearson similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Computing the pearson similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Computing the pearson similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Computing the pearson similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Computing the cosine similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Computing the cosine similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Computing the cosine similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Computing the cosine similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Computing the cosine similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Computing the cosine similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Computing the pearson similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Computing the pearson similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Computing the pearson similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Computing the pearson similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Computing the pearson similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Computing the pearson similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Computing the cosine similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Computing the cosine similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Computing the cosine similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Computing the cosine similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Computing the cosine similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Computing the cosine similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Computing the pearson similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Computing the pearson similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Computing the pearson similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Computing the pearson similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Computing the pearson similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Computing the pearson similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Computing the cosine similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Computing the cosine similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Computing the cosine similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Computing the cosine similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Computing the cosine similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Computing the cosine similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Computing the pearson similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Computing the pearson similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Computing the pearson similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Computing the pearson similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Computing the pearson similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Computing the pearson similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Computing the cosine similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Computing the cosine similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Computing the cosine similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Computing the cosine similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Computing the cosine similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Computing the cosine similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Computing the pearson similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Computing the pearson similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Computing the pearson similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Computing the pearson similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Computing the pearson similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Computing the pearson similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Computing the cosine similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Computing the cosine similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Computing the cosine similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Computing the cosine similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Computing the cosine similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Computing the cosine similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Computing the pearson similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Computing the pearson similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Computing the pearson similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Computing the pearson similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Computing the pearson similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Computing the pearson similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Computing the cosine similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Computing the cosine similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Computing the cosine similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Computing the cosine similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Computing the cosine similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Computing the cosine similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Computing the pearson similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Computing the pearson similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Computing the pearson similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Computing the pearson similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Computing the pearson similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Computing the pearson similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Computing the cosine similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Computing the cosine similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Computing the cosine similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Computing the cosine similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Computing the cosine similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Computing the cosine similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Computing the pearson similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Computing the pearson similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Computing the pearson similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Computing the pearson similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Computing the pearson similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Computing the pearson similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Computing the cosine similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Computing the cosine similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Computing the cosine similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Computing the cosine similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Computing the cosine similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Computing the cosine similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Computing the pearson similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Computing the pearson similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Computing the pearson similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Computing the pearson similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Computing the pearson similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Computing the pearson similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Computing the cosine similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Computing the cosine similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Computing the cosine similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Computing the cosine similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Computing the cosine similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Computing the cosine similarity matrix...\n",
      "Done computing similarity matrix.\n",
      "Best KNN parameters: {'k': 30, 'min_k': 5, 'sim_options': {'name': 'cosine', 'user_based': True}}\n",
      "Best KNN RMSE: 0.974567962502892\n",
      "\n",
      "Best model: SVD\n",
      "\n",
      "Best RMSE: 0.8621525273812124\n"
     ]
    }
   ],
   "source": [
    "def grid_search_models(data):\n",
    "    '''\n",
    "    Performs a grid search to tune hyperparameters for SVD and KNN models, evaluates their performance using RMSE, and returns the best model based on RMSE.\n",
    "\n",
    "    Parameters:\n",
    "    data (Dataset): The Surprise Dataset object containing the rating data.\n",
    "\n",
    "    Returns:\n",
    "    tuple: A tuple containing the name of the best model ('SVD' or 'KNN') and its corresponding RMSE score.\n",
    "    '''\n",
    "    # Define parameter grids\n",
    "    svd_param_grid = {\n",
    "        'n_factors': [20, 50, 100],\n",
    "        'n_epochs': [10, 15, 30], \n",
    "        'lr_all': [0.002, 0.005, 0.01],\n",
    "        'reg_all': [0.02, 0.05, 0.1]\n",
    "    }\n",
    "    \n",
    "    knn_param_grid = {\n",
    "        'k': [10, 20, 30],\n",
    "        'min_k': [1, 5, 10],\n",
    "        'sim_options': {'name': ['pearson', 'cosine'], 'user_based': [True, False]}\n",
    "    }\n",
    "    \n",
    "    # Perform grid search for SVD\n",
    "    print('Tuning SVD...')\n",
    "    gs_svd = GridSearchCV(SVD, svd_param_grid, measures=['rmse'], cv=3)\n",
    "    gs_svd.fit(data)\n",
    "    \n",
    "    svd_best_params = gs_svd.best_params['rmse']\n",
    "    svd_best_score = gs_svd.best_score['rmse']\n",
    "    \n",
    "    print(f\"Best SVD parameters: {svd_best_params}\")\n",
    "    print(f\"Best SVD RMSE: {svd_best_score}\")\n",
    "    \n",
    "    # Perform grid search for KNN\n",
    "    print('Tuning KNN...')\n",
    "    gs_knn = GridSearchCV(KNNBasic, knn_param_grid, measures=['rmse'], cv=3)\n",
    "    gs_knn.fit(data)\n",
    "    \n",
    "    knn_best_params = gs_knn.best_params['rmse']\n",
    "    knn_best_score = gs_knn.best_score['rmse']\n",
    "    \n",
    "    print(f\"Best KNN parameters: {knn_best_params}\")\n",
    "    print(f\"Best KNN RMSE: {knn_best_score}\")\n",
    "    \n",
    "    # Determine the best overall model\n",
    "    if svd_best_score < knn_best_score:\n",
    "        best_model_class = SVD(**svd_best_params)\n",
    "        best_model_params = svd_best_params\n",
    "        best_model_name = 'SVD'\n",
    "        best_score = svd_best_score\n",
    "    else:\n",
    "        best_model_class = KNNBasic(**knn_best_params)\n",
    "        best_model_params = knn_best_params\n",
    "        best_model_name = 'KNN'\n",
    "        best_score = knn_best_score\n",
    "        \n",
    "    # Print the best model and best RMSE score\n",
    "    print(f\"\\nBest model: {best_model_name}\")\n",
    "    print(f\"\\nBest RMSE: {best_score}\")\n",
    "    \n",
    "    return best_model_name, best_score\n",
    "\n",
    "# Instantiate\n",
    "best_model_name, best_score = grid_search_models(data)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aaab006c-6092-4bf0-bff0-6e49289c48c3",
   "metadata": {},
   "source": [
    "### Summary"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0c954d06-e38c-447b-901f-d4bdd36aad59",
   "metadata": {},
   "source": [
    "The output reveals the results of tuning and evaluating recommendation models using grid search. For the SVD model, the optimal parameters were identified as having 100 factors, 30 epochs, a learning rate of 0.01 and regularization of 0.1 achieving the best RMSE of approximately 0.862. In contrast, the KNN model required extensive computation of similarity matrices for various configurations including Pearson, cosine and MSD (Mean Squared Difference) similarities. The best parameters for the KNN model were found to be 30 neighbors, a minimum of 10 neighbors and using the cosine similarity metric with a non-user-based approach resulting in a higher RMSE of about 0.917. Consequently, the SVD model emerged as the superior choice with the lowest RMSE and we therefore selected it as the best model overall."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3504e6c1-96da-4765-b42d-6de6f0594e14",
   "metadata": {},
   "source": [
    "### Cross-validation of the best model"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c90a5694-30a2-47fb-8617-bd405616c182",
   "metadata": {},
   "source": [
    "The cross_validate_best_model function evaluates the performance of the best model identified from the previous grid search through cross-validation. The best model was identified as the `SVD` model. It initializes the model with the corresponding optimal parameters and performs cross-validation using 5 folds and computes the mean RMSE from the results. The function prints out the mean RMSE and returns the model name, the best score from the grid search and the mean RMSE. The mean RMSE is "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "04358685-5ec8-4d2a-99e5-fca4203c4911",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Evaluating RMSE of algorithm SVD on 5 split(s).\n",
      "\n",
      "                  Fold 1  Fold 2  Fold 3  Fold 4  Fold 5  Mean    Std     \n",
      "RMSE (testset)    0.8654  0.8591  0.8506  0.8529  0.8548  0.8566  0.0052  \n",
      "Fit time          8.94    8.49    8.08    8.03    8.22    8.35    0.33    \n",
      "Test time         0.11    0.12    0.11    0.12    0.12    0.11    0.00    \n",
      "SVD Model Mean RMSE: 0.8565943146879842\n",
      "SVD Model Standard Deviation RMSE: 0.0052232662741193365\n"
     ]
    }
   ],
   "source": [
    "# Initialize SVD model with specified hyperparameters\n",
    "svd_model = SVD(n_factors=100, n_epochs=30, lr_all=0.01, reg_all=0.1)\n",
    "\n",
    "# Perform cross-validation on the SVD model using 5 folds\n",
    "# Measures RMSE (Root Mean Square Error) for evaluation\n",
    "cross_val_results = cross_validate(svd_model, data, measures=['RMSE'], cv=5, verbose=True)\n",
    "\n",
    "# Print the mean RMSE from the cross-validation results\n",
    "print(f\"SVD Model Mean RMSE: {np.mean(cross_val_results['test_rmse'])}\")\n",
    "\n",
    "# Print the standard deviation of RMSE from the results\n",
    "print(f\"SVD Model Standard Deviation RMSE: {np.std(cross_val_results['test_rmse'])}\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cccdc46d-7e10-4265-9deb-7b8a532dd9b5",
   "metadata": {},
   "source": [
    "The cross-validation results for the SVD model indicate strong and consistent performance. The model achieved an average RMSE of approximately 0.857 across five folds with a very low standard deviation of 0.005 demonstrating stable performance across different data splits. Overall these metrics suggest that the SVD model not only provides reliable predictions with low error but also maintains efficient and consistent training and prediction times. The next step is to build a class that will provide the top 5 recommendations."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4346b53e-4007-4f6d-bae0-8c57d222f574",
   "metadata": {},
   "source": [
    "## Collaborative Filtering"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b39ef000-bec1-43fe-88e7-a993094af5e5",
   "metadata": {},
   "source": [
    "Collaborative filtering is a technique used in recommendation systems to predict a user's preferences or interests based on their past behaviour and the behavior of similar users. It operates on the assumption that users who have agreed in the past will likely agree in the future and that users tend to like similar things. There are two main types of collaborative filtering: `user-based` collaborative filtering which finds users with similar tastes and recommends items they liked and `item-based` collaborative filtering which finds items similar to those a user has liked in the past. This method is widely used in streaming services to suggest movies, music, or products that a user might enjoy based on the collective behaviour and preferences of other users."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "133e166f-e332-4457-8d10-6c5003684986",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Movie: Before Sunrise (1995)\n",
      "Genre: drama, romance\n"
     ]
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "Rate this movie from 1 to 5 (or 'n' if you haven't seen it):  n\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Movie: To Wong Foo, Thanks for Everything! Julie Newmar (1995)\n",
      "Genre: comedy\n"
     ]
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "Rate this movie from 1 to 5 (or 'n' if you haven't seen it):  5\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Movie: Ferris Bueller's Day Off (1986)\n",
      "Genre: comedy\n"
     ]
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "Rate this movie from 1 to 5 (or 'n' if you haven't seen it):  5\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Movie: Miracle on 34th Street (1994)\n",
      "Genre: drama\n"
     ]
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "Rate this movie from 1 to 5 (or 'n' if you haven't seen it):  4\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Movie: Toy Story (1995)\n",
      "Genre: adventure, animation, children, comedy, fantasy\n"
     ]
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "Rate this movie from 1 to 5 (or 'n' if you haven't seen it):  5\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Recommended movies:\n",
      "1. Dr. Strangelove or: How I Learned to Stop Worrying and Love the Bomb (1964) - Predicted rating: 4.31\n",
      "Genre: comedy, war\n",
      "2. Philadelphia Story, The (1940) - Predicted rating: 4.24\n",
      "Genre: comedy, drama, romance\n",
      "3. Pulp Fiction (1994) - Predicted rating: 4.22\n",
      "Genre: comedy, crime, drama, thriller\n",
      "4. Princess Bride, The (1987) - Predicted rating: 4.22\n",
      "Genre: action, adventure, comedy, fantasy, romance\n",
      "5. Amelie (Fabuleux destin d'Amélie Poulain, Le) (2001) - Predicted rating: 4.21\n",
      "Genre: comedy, romance\n"
     ]
    }
   ],
   "source": [
    "import pickle \n",
    "\n",
    "# Create collab df\n",
    "collab_df = df[['user_id', 'movieId', 'rating']].copy()\n",
    "\n",
    "# Save to a CSV file\n",
    "collab_df.to_csv('collab_movies.csv', index=False)\n",
    "\n",
    "class CollabBasedModel:\n",
    "    def __init__(self, collab_df):\n",
    "        '''\n",
    "        Initializes the MovieRecommender with a DataFrame containing movie data.\n",
    "\n",
    "        Args:\n",
    "            df (pd.DataFrame): DataFrame containing movie information with columns 'user_id', 'movieId', 'rating', 'title', 'release_year', and 'genres'.\n",
    "        '''\n",
    "        self.df = collab_df\n",
    "        self.model = None\n",
    "\n",
    "    def train_model(self):\n",
    "        '''\n",
    "        Trains the SVD model on the movie ratings data. Splits the data into training and test sets and fits the model.\n",
    "        '''\n",
    "        # Create a Reader object to parse the ratings data with a specified rating scale\n",
    "        reader = Reader(rating_scale=(1, 5))\n",
    "        \n",
    "        # Load the data from the DataFrame into a Surprise Dataset object\n",
    "        data = Dataset.load_from_df(self.df[['user_id', 'movieId', 'rating']], reader)\n",
    "        \n",
    "        # Split the data into a training set and a test set with 80% of the data for training\n",
    "        trainset, _ = train_test_split(data, test_size=0.2)\n",
    "        \n",
    "        # Initialize the SVD (Singular Value Decomposition) model for collaborative filtering\n",
    "        self.model = SVD()\n",
    "        \n",
    "        # Train the SVD model on the training set\n",
    "        self.model.fit(trainset)\n",
    "\n",
    "\n",
    "    def get_user_ratings(self, num_movies=5):\n",
    "        '''\n",
    "        Collects ratings from the user for a specified number of movies.\n",
    "\n",
    "        Args:\n",
    "            num_movies (int): Number of movies to rate.\n",
    "\n",
    "        Returns:\n",
    "            list: List of tuples containing movieId and user rating.\n",
    "        '''\n",
    "        \n",
    "        # Initialize an empty list to store user ratings\n",
    "        user_ratings = []\n",
    "        \n",
    "        # Loop to collect ratings for a specified number of movies\n",
    "        for _ in range(num_movies):\n",
    "            # Randomly sample one movie from the DataFrame\n",
    "            movie = self.df.sample(1).iloc[0]\n",
    "            \n",
    "            # Display the movie details to the user\n",
    "            print(f\"\\nMovie: {movie['title']} ({movie['release_year']})\")\n",
    "            print(f\"Genre: {movie['genres']}\")\n",
    "            \n",
    "            # Prompt the user to rate the movie or indicate they haven't seen it\n",
    "            rating = input(\"Rate this movie from 1 to 5 (or 'n' if you haven't seen it): \")\n",
    "            \n",
    "            # If the user has watched the movie and provided a rating, add it to the list\n",
    "            if rating.lower() != 'n':\n",
    "                user_ratings.append((movie['movieId'], float(rating)))\n",
    "        \n",
    "        # Return the list of user ratings\n",
    "        return user_ratings\n",
    "\n",
    "\n",
    "    def get_recommendations(self, user_ratings, n=5, genre=None):\n",
    "        '''\n",
    "        Provides movie recommendations based on user ratings and optional genre filtering.\n",
    "\n",
    "        Args:\n",
    "            user_ratings (list): List of tuples containing movieId and user rating.\n",
    "            n (int): Number of recommendations to provide.\n",
    "            genre (str, optional): Genre to filter recommendations by.\n",
    "\n",
    "        Returns:\n",
    "            list: List of recommended movies with their predicted ratings.\n",
    "        '''\n",
    "    \n",
    "        # Generate a unique user ID for a new user who is providing ratings for the first time\n",
    "        new_user_id = self.df['user_id'].max() + 1\n",
    "        \n",
    "        # Identify movies that the new user has not yet rated\n",
    "        movies_to_predict = self.df[~self.df['movieId'].isin([x[0] for x in user_ratings])]['movieId'].unique()\n",
    "        \n",
    "        # Predict ratings for each of these movies\n",
    "        predictions = [\n",
    "            (movie_id, self.model.predict(new_user_id, movie_id).est)  # Predict rating for the movie\n",
    "            for movie_id in movies_to_predict\n",
    "        ]\n",
    "        \n",
    "        # Sort predictions in descending order of estimated ratings\n",
    "        recommendations = sorted(predictions, key=lambda x: x[1], reverse=True)\n",
    "        \n",
    "        # If a genre filter is specified\n",
    "        if genre:\n",
    "            # Filter recommendations to include only those that match the specified genre\n",
    "            genre_recommendations = [\n",
    "                (movie_id, rating) for movie_id, rating in recommendations\n",
    "                if genre.lower() in self.df[self.df['movieId'] == movie_id]['genres'].iloc[0].lower()\n",
    "            ]\n",
    "            return genre_recommendations[:n]  # Return top-n genre-specific recommendations\n",
    "        else:\n",
    "            return recommendations[:n]  # Return top-n general recommendations\n",
    "\n",
    "\n",
    "    def print_recommendations(self, recommendations):\n",
    "        '''\n",
    "        Prints the recommended movies with their predicted ratings.\n",
    "\n",
    "        Args:\n",
    "            recommendations (list): List of recommended movies with their predicted ratings.\n",
    "        '''\n",
    "        # Enumerate through the sorted recommendations with an index starting at 1\n",
    "        for i, (movie_id, predicted_rating) in enumerate(recommendations, 1):\n",
    "            # Retrieve the movie details from the DataFrame using the movie_id\n",
    "            movie = self.df[self.df['movieId'] == movie_id].iloc[0]\n",
    "            \n",
    "            # Print the recommendation number, movie title, release year, and predicted rating formatted to two decimal places\n",
    "            print(f\"{i}. {movie['title']} ({movie['release_year']}) - Predicted rating: {predicted_rating:.2f}\")\n",
    "            \n",
    "            # Print the genre(s) of the movie\n",
    "            print(f\"Genre: {movie['genres']}\")\n",
    "\n",
    "    def recommend_movies(self, num_ratings=5, num_recommendations=5, genre=None):\n",
    "        '''\n",
    "        Recommends movies based on user input ratings and optionally filters by genre.\n",
    "        \n",
    "        Args:\n",
    "            num_ratings (int): Number of movies to rate.\n",
    "            num_recommendations (int): Number of recommendations to provide.\n",
    "            genre (str, optional): Genre to filter recommendations by.\n",
    "        '''\n",
    "        \n",
    "        # Retrieve the user's ratings based on the number of ratings specified\n",
    "        user_ratings = self.get_user_ratings(num_ratings)\n",
    "        \n",
    "        # Get movie recommendations based on the user's ratings, desired number of recommendations, and optional genre filter\n",
    "        recommendations = self.get_recommendations(user_ratings, num_recommendations, genre)\n",
    "        \n",
    "        # Print a header for the recommended movies\n",
    "        print(\"\\nRecommended movies:\")\n",
    "        \n",
    "        # Print the list of recommended movies\n",
    "        self.print_recommendations(recommendations)\n",
    "\n",
    "\n",
    "# Instantiate\n",
    "recommender = CollabBasedModel(df)\n",
    "recommender.train_model()\n",
    "\n",
    "# Save the trained model using pickle\n",
    "with open('collaborative_model.pkl', 'wb') as f:\n",
    "    pickle.dump(recommender.model, f)\n",
    "    \n",
    "# Get recommendations\n",
    "recommender.recommend_movies(num_ratings=5, num_recommendations=5, genre='Comedy')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8f9045e0-ca85-4ebf-a75d-a68c2a73006f",
   "metadata": {},
   "source": [
    "### Summary"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "675c21e7-e54e-4d8a-a958-cb572905d592",
   "metadata": {},
   "source": [
    "The recommendation process involves getting user ratings for a few sample movies then uses the ratings to predict the user's preferences for unseen movies. The system can filter recommendations by genre if specified. The recommend_movies method chains all steps together prompting the user for ratings, generating recommendations and provides the output. In this case, the `recommend_movies` method uses the collected ratings to predict how the user would rate movies they haven’t rated yet. The method then sorts these predictions to find the highest-rated movies. The genre filteer applied in this case is 'Comedy' and the recommendations are further filtered to include only movies that match the specified genre as illustrated by the output above."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "db412a64-bbf2-4a51-8d39-4cca89fd7c29",
   "metadata": {},
   "source": [
    "## Content Based"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5682dd7a-ffa5-4238-828d-1eda7c95e082",
   "metadata": {},
   "source": [
    "A content based recommender system is a type of recommendation algorithm that suggests items to users based on the characteristics or features of the items they have previously liked or interacted with. It analyzes the content or attributes of items such as movies to find similarities and make recommendations. This content-based filtering approach recommends movies based on the similarity of their genres. It utilizes TF-IDF to represent genres as vectors, calculates cosine similarity to assess the similarity between movies and retrieves movies that are most similar to a given movie. \n",
    "The TfidfVectorizer from sklearn.feature_extraction.text is used to convert the genre descriptions into numerical vectors. Each genre is transformed into a TF-IDF (Term Frequency-Inverse Document Frequency) matrix which captures the importance of genres across the dataset. The TF-IDF matrix represents how important each genre is in relation to the other genres in the dataset.\n",
    "The cosine similarity matrix is then computed using cosine_similarity from sklearn.metrics.pairwise. This matrix measures the similarity between movies based on their genre vectors with values ranging from 0 (no similarity) to 1 (identical genre profiles)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "0660dda9-1dd6-42b7-9e9c-0b64deef12aa",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "41657                                       Antz\n",
      "51710                                Toy Story 2\n",
      "57060    Adventures of Rocky and Bullwinkle, The\n",
      "59397                  Emperor's New Groove, The\n",
      "64009                             Monsters, Inc.\n",
      "Name: title, dtype: object\n"
     ]
    }
   ],
   "source": [
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "from sklearn.metrics.pairwise import cosine_similarity\n",
    "import pickle\n",
    "\n",
    "# Drop duplicates to get unique movie titles\n",
    "content_df = df.drop_duplicates(subset='title')[['movieId', 'title', 'genres', 'release_year']]\n",
    "# Save to a CSV file\n",
    "content_df.to_csv('content_movies.csv', index=False)\n",
    "\n",
    "class ContentBasedModel:\n",
    "    '''\n",
    "    A content-based recommender system for movies based on genre similarity.\n",
    "    '''\n",
    "\n",
    "    def __init__(self, df):\n",
    "        '''\n",
    "        Initialize the ContentBasedModel with a dataframe of movie information.\n",
    "\n",
    "        Args:\n",
    "            df (pd.DataFrame): Dataframe containing movie information.\n",
    "        '''\n",
    "        self.df = content_df\n",
    "        self.tfidf_matrix = None\n",
    "        self.cosine_sim = None\n",
    "        self.indices = None\n",
    "\n",
    "    def train_model(self):\n",
    "        '''\n",
    "        Train the content-based model by creating a TF-IDF matrix and calculating cosine similarity.\n",
    "\n",
    "        Args: \n",
    "            self (ContentBasedModel): The instance of the ContentBasedModel class.\n",
    "        '''\n",
    "        # Define the TF-IDF vectorizer\n",
    "        tfidf = TfidfVectorizer(stop_words='english')\n",
    "        \n",
    "        # Fit and transform the genres\n",
    "        self.tfidf_matrix = tfidf.fit_transform(self.df['genres'])\n",
    "        \n",
    "        # Calculate the cosine similarity matrix\n",
    "        self.cosine_sim = cosine_similarity(self.tfidf_matrix, self.tfidf_matrix)\n",
    "        \n",
    "        # Create a reverse mapping of indices and movie titles\n",
    "        self.indices = pd.Series(self.df.index, index=self.df['title']).drop_duplicates()\n",
    "\n",
    "    def get_recommendations(self, title, k=5):\n",
    "        '''\n",
    "        Get movie recommendations based on genre similarity to the input movie.\n",
    "\n",
    "        Args:\n",
    "            title (str): Title of the movie to base recommendations on.\n",
    "            k (int): Number of recommendations to return.\n",
    "\n",
    "        Returns:\n",
    "            pd.Series: Series of recommended movie titles.\n",
    "        '''\n",
    "        # Get the index of the movie that matches the title\n",
    "        idx = self.indices[title]\n",
    "        \n",
    "        # Get the pairwise similarity scores of all movies with that movie\n",
    "        sim_scores = list(enumerate(self.cosine_sim[idx]))\n",
    "        \n",
    "        # Sort the movies based on the similarity scores\n",
    "        sim_scores = sorted(sim_scores, key=lambda x: x[1], reverse=True)\n",
    "        \n",
    "        # Get the scores of the k most similar movies\n",
    "        sim_scores = sim_scores[1:k+1]\n",
    "        \n",
    "        # Get the movie indices\n",
    "        movie_indices = [i[0] for i in sim_scores]\n",
    "        \n",
    "        # Return the top k most similar movies\n",
    "        return self.df['title'].iloc[movie_indices]\n",
    "\n",
    "# Instantiate\n",
    "content_recommender = ContentBasedModel(df)\n",
    "# Train the model\n",
    "content_recommender.train_model()\n",
    "# Get recommendations for a specific movie\n",
    "recommendations = content_recommender.get_recommendations('Toy Story')\n",
    "# Print the recommendations\n",
    "print(recommendations)\n",
    "\n",
    "# Save the trained TF-IDF matrix and cosine similarity matrix using pickle\n",
    "content_model = {\n",
    "    'tfidf_matrix': content_recommender.tfidf_matrix,\n",
    "    'cosine_sim': content_recommender.cosine_sim,\n",
    "    'indices': content_recommender.indices\n",
    "}\n",
    "\n",
    "with open('contentbased_model.pkl', 'wb') as f:\n",
    "    pickle.dump(content_model, f)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0e1fd17e-6eb7-49f5-9bb5-b6cb231f45b5",
   "metadata": {},
   "source": [
    "### Summary"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ba4d1bad-c35d-4932-bdf9-4ded5bd19984",
   "metadata": {},
   "source": [
    "The class above implements a content based movie recommender system using genres as the primary feature. It begins by importing necessary libraries and preparing the dataset ensuring only unique movie titles are retained. The `ContentBasedModel` class encapsulates the core functionality. Within its `train_model` method, movie genres are converted into numerical features using TF-IDF (Term Frequency-Inverse Document Frequency) vectorization creating a matrix of genres. The cosine similarity between all pairs of movies is then calculated based on these TF-IDF representations producing a similarity matrix that identifies movies with similar genres. The `get_recommendations` method uses this similarity matrix to provide the top 5 most similar movies to a given title. The code demonstrates the class's usage by creating an instance, training the model and getting recommendations for 'Toy Story'. Finally, it saves the trained model including the TF-IDF matrix, cosine similarity matrix and movie indices to a pickle file for future use.\n",
    "The recommender system outputs the top 5 movies similar to 'Toy Story' based on genre similarity i.e. 'Antz','Toy Story 2', 'The Adventures of Rocky and Bullwinkle', 'The Emperor's New Groove' and 'Monsters, Inc.'. Each recommendation shares key characteristics with 'Toy Story' being that they are family-friendly animations with a focus on adventure and comedy. These recommendations are evidently relevant and appealing to fans of Toy Story.\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fcd47967-e4a5-4915-a4ad-5ba0ebf8cae4",
   "metadata": {},
   "source": [
    "## Hybrid System"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c07a19f0-fcde-450c-8fd0-eac2c0eac9f7",
   "metadata": {},
   "source": [
    "A hybrid recommendation system integrates multiple techniques such as collaborative filtering and content-based filtering to enhance recommendation accuracy. Collaborative filtering relies on user interactions to suggest items based on similar users' preferences while content-based filtering recommends items based on features and past user preferences. The hybrid system combines these approaches to address their individual weaknesses—like the cold start problem in collaborative filtering and limited feature scope in content-based filtering. It can use methods such as weighted hybrids where recommendations from both techniques are averaged with specific weights, switching hybrids which chooses methods based on conditions or feature augmentation where one technique is enhanced with features from another. This integration aims to provide more accurate and personalized recommendations by leveraging the strengths of each method and mitigating their limitations.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "29fa9bcb-c197-453e-b5f5-0bf14cc8943b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Movie: Dick (1999)\n",
      "Genre: comedy\n"
     ]
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "Rate this movie from 1 to 5 (or 'x' if you haven't watched it):  3\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Movie: Girl Who Kicked the Hornet's Nest, The (Luftslottet som sprängdes) (2009)\n",
      "Genre: action, crime, mystery\n"
     ]
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "Rate this movie from 1 to 5 (or 'x' if you haven't watched it):  5\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Movie: Return to Treasure Island (1988)\n",
      "Genre: adventure, animation, comedy\n"
     ]
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "Rate this movie from 1 to 5 (or 'x' if you haven't watched it):  5\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Movie: Center Stage (2000)\n",
      "Genre: drama, musical\n"
     ]
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "Rate this movie from 1 to 5 (or 'x' if you haven't watched it):  x\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Movie: Sleepaway Camp (1983)\n",
      "Genre: horror\n"
     ]
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "Rate this movie from 1 to 5 (or 'x' if you haven't watched it):  3\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Hybrid Recommended movies:\n",
      "1. Shawshank Redemption, The (1994) - Hybrid score: 4.20\n",
      "   Genre: crime, drama\n",
      "2. Lawrence of Arabia (1962) - Hybrid score: 4.19\n",
      "   Genre: adventure, drama, war\n",
      "3. Cool Hand Luke (1967) - Hybrid score: 4.14\n",
      "   Genre: drama\n",
      "4. Raiders of the Lost Ark (Indiana Jones and the Raiders of the Lost Ark) (1981) - Hybrid score: 3.63\n",
      "   Genre: action, adventure\n",
      "5. Dr. Strangelove or: How I Learned to Stop Worrying and Love the Bomb (1964) - Hybrid score: 3.14\n",
      "   Genre: comedy, war\n"
     ]
    }
   ],
   "source": [
    "class HybridModel:\n",
    "    '''\n",
    "    A hybrid recommender system that combines collaborative filtering and content-based filtering.\n",
    "    '''\n",
    "\n",
    "    def __init__(self, collab_df, content_df):\n",
    "        '''\n",
    "        Initialize the HybridModel with collaborative and content-based dataframes.\n",
    "\n",
    "        Args:\n",
    "            collab_df (pd.DataFrame): Dataframe for collaborative filtering.\n",
    "            content_df (pd.DataFrame): Dataframe for content-based filtering.\n",
    "        '''\n",
    "        self.collab_model = CollabBasedModel(collab_df)\n",
    "        self.content_model = ContentBasedModel(content_df)\n",
    "        self.df = pd.merge(collab_df, content_df, on='movieId').drop_duplicates(subset=['movieId'])\n",
    "\n",
    "    def train_models(self):\n",
    "        '''\n",
    "        Train both collaborative and content-based models.\n",
    "        '''\n",
    "        self.collab_model.train_model()\n",
    "        self.content_model.train_model()\n",
    "\n",
    "    def get_user_ratings(self, num_movies=5):\n",
    "        '''\n",
    "        Get user ratings for a specified number of random movies.\n",
    "\n",
    "        Args:\n",
    "            num_movies (int): Number of movies to rate. Defaults to 5.\n",
    "\n",
    "        Returns:\n",
    "            list: List of tuples containing movie IDs and ratings.\n",
    "        '''\n",
    "        # Initialize an empty list to store user ratings\n",
    "        user_ratings = []\n",
    "\n",
    "        # Loop to collect ratings for a specified number of movies\n",
    "        for _ in range(num_movies):\n",
    "            \n",
    "            # Randomly sample one movie from the DataFrame\n",
    "            movie = self.df.sample(1).iloc[0]\n",
    "            \n",
    "            # Display the movie details to the user\n",
    "            print(f\"\\nMovie: {movie['title']} ({movie['release_year']})\")\n",
    "            print(f\"Genre: {movie['genres']}\")\n",
    "            \n",
    "            # Prompt the user to rate the movie or indicate they haven't watched it\n",
    "            rating = input(\"Rate this movie from 1 to 5 (or 'x' if you haven't watched it): \")\n",
    "            \n",
    "            # If the user has watched the movie and provided a rating, add it to the list\n",
    "            if rating.lower() != 'x':\n",
    "                user_ratings.append((movie['movieId'], float(rating)))\n",
    "        \n",
    "        # Return the list of user ratings\n",
    "        return user_ratings\n",
    "\n",
    "\n",
    "    def get_hybrid_recommendations(self, user_ratings, n=5, collab_weight=0.5):\n",
    "        '''\n",
    "        Get hybrid recommendations based on user ratings.\n",
    "\n",
    "        Args:\n",
    "            user_ratings (list): List of tuples containing movie IDs and ratings.\n",
    "            n (int): Number of recommendations to return.\n",
    "            collab_weight (float): Weight for collaborative filtering (0 to 1).\n",
    "\n",
    "        Returns:\n",
    "            list: List of tuples containing recommended movie IDs and hybrid scores.\n",
    "        '''\n",
    "        # Generate a new user ID by incrementing the maximum user ID in the DataFrame\n",
    "        new_user_id = self.df['user_id'].max() + 1\n",
    "        \n",
    "        # Get collaborative filtering recommendations\n",
    "        collab_recommendations = self.collab_model.get_recommendations(user_ratings, n)\n",
    "        \n",
    "        # Extract movie IDs from collaborative filtering recommendations\n",
    "        collab_movie_ids = [rec[0] for rec in collab_recommendations]\n",
    "        \n",
    "        # Extract scores from collaborative filtering recommendations\n",
    "        collab_scores = np.array([rec[1] for rec in collab_recommendations])\n",
    "        \n",
    "        # Initialize a list to store content-based scores\n",
    "        content_scores = []\n",
    "        \n",
    "        # Loop through each movie ID from collaborative filtering recommendations\n",
    "        for movie_id in collab_movie_ids:\n",
    "            \n",
    "            # Get the title of the movie corresponding to the movie ID\n",
    "            title = self.df[self.df['movieId'] == movie_id]['title'].values[0]\n",
    "            \n",
    "            # Get the top content-based recommendation for the movie\n",
    "            content_rec = self.content_model.get_recommendations(title, k=1)\n",
    "            \n",
    "            # Calculate the average rating of the content-based recommendation\n",
    "            content_score = self.df[self.df['title'] == content_rec.iloc[0]]['rating'].mean()\n",
    "            \n",
    "            # Append the content-based score to the list\n",
    "            content_scores.append(content_score)\n",
    "            \n",
    "        # Convert the list of content-based scores to a NumPy array\n",
    "        content_scores = np.array(content_scores)\n",
    "        \n",
    "        # Combine collaborative and content-based scores using a weighted average\n",
    "        hybrid_scores = collab_weight * collab_scores + (1 - collab_weight) * content_scores\n",
    "        \n",
    "        # Combine movie IDs with their hybrid scores and sort them in descending order of scores\n",
    "        hybrid_recommendations = sorted(zip(collab_movie_ids, hybrid_scores), key=lambda x: x[1], reverse=True)\n",
    "        \n",
    "        # Return the top n hybrid recommendations\n",
    "        return hybrid_recommendations[:n]\n",
    "\n",
    "\n",
    "    def print_recommendations(self, recommendations):\n",
    "        '''\n",
    "        Print the recommended movies with their hybrid scores.\n",
    "\n",
    "        Args:\n",
    "            recommendations (list): List of tuples containing movie IDs and hybrid scores.\n",
    "        '''\n",
    "        # Loop through the recommendations and print the details of each recommended movie\n",
    "        for i, (movie_id, score) in enumerate(recommendations, 1):\n",
    "            \n",
    "            # Retrieve the movie details based on the movie ID\n",
    "            movie = self.df[self.df['movieId'] == movie_id].iloc[0]\n",
    "            \n",
    "            # Print the movie rank, title, release year and hybrid score\n",
    "            print(f\"{i}. {movie['title']} ({movie['release_year']}) - Hybrid score: {score:.2f}\")\n",
    "            \n",
    "            # Print the genre of the movie\n",
    "            print(f\"   Genre: {movie['genres']}\")\n",
    "\n",
    "\n",
    "    def recommend_movies(self, num_ratings=5, num_recommendations=5, collab_weight=0.5):\n",
    "        '''\n",
    "        Get user ratings and provide hybrid movie recommendations.\n",
    "\n",
    "        Args:\n",
    "            num_ratings (int): Number of movies to rate.\n",
    "            num_recommendations (int): Number of recommendations to provide.\n",
    "            collab_weight (float): Weight for collaborative filtering (0 to 1).\n",
    "            \n",
    "        Returns:\n",
    "        list: A list of recommended movies based on the hybrid model.\n",
    "        '''\n",
    "        # Get user ratings for a specified number of movies\n",
    "        user_ratings = self.get_user_ratings(num_ratings)\n",
    "        \n",
    "        # Generate hybrid recommendations based on the user ratings\n",
    "        recommendations = self.get_hybrid_recommendations(user_ratings, num_recommendations, collab_weight)\n",
    "        \n",
    "        # Print the hybrid recommended movies\n",
    "        print(\"\\nHybrid Recommended movies:\")\n",
    "        self.print_recommendations(recommendations)\n",
    "\n",
    "\n",
    "# Instantiate\n",
    "hybrid_model = HybridModel(collab_df, content_df)\n",
    "# Train both the collaborative filtering and content-based models\n",
    "hybrid_model.train_models()\n",
    "\n",
    "# Recommend movies using the hybrid model specifying the number of user ratings to collect,\n",
    "# the number of movie recommendations to generate and the weight for collaborative filtering in the hybrid model\n",
    "hybrid_model.recommend_movies(num_ratings=5, num_recommendations=5, collab_weight=0.5)\n",
    "\n",
    "# Save the hybrid model\n",
    "with open('hybrid_model.pkl', 'wb') as f:\n",
    "    pickle.dump(hybrid_model, f)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "79b94085-598d-41f5-9ed1-3eb9e3492352",
   "metadata": {},
   "source": [
    "### Summary\n",
    "The `HybridRecommender` class combines two types of recommendation systems namely content-based and collaborative filtering into a hybrid model to enhance the top-5 movie recommendations. This class takes in two datasets (one for each recommendation model) and provides a unified recommendation list based on both models. It initializes with separate dataframes for each filtering type and merges them for a comprehensive recommendation system. The class trains both collaborative and content-based models, allows users to rate movies on a scale of 1 to 5 and 'x' if they have not seen the movie then generates hybrid recommendations by blending the two models' scores based on a specified weight in this case 0.5 meaning half of each model's. The model is designed for user interaction, training and evaluation with capabilities for saving and restoring its state."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "edacbc2c-dcaf-40ec-b840-1805755548c4",
   "metadata": {},
   "source": [
    "#### Evaluating RMSE of the hybrid model\n",
    "Evaluating the RMSE (Root Mean Squared Error) of the hybrid model involves measuring the accuracy of its predictions by comparing the predicted ratings to actual ratings. RMSE quantifies the average magnitude of the prediction errors, where a lower RMSE indicates better predictive accuracy. To calculate RMSE, you first predict ratings for a set of items, then compute the difference between these predicted ratings and the actual ratings, square these differences, average them and finally take the square root of the result. For a hybrid model, RMSE helps assess how well the model integrates these methods in predicting user preferences with the goal of achieving a lower RMSE to reflect more precise and reliable recommendations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "e4244309-de25-4da0-b248-561f2cfbc79e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Starting prediction generation...\n",
      "Finished prediction generation.\n",
      "Root Mean Square Error (RMSE): 1.2463\n"
     ]
    }
   ],
   "source": [
    "from surprise import Dataset, Reader, accuracy\n",
    "from surprise.model_selection import train_test_split\n",
    "\n",
    "def evaluate_rmse(hybrid_model, test_size=0.2, collab_weight = None, random_state=42):\n",
    "    '''\n",
    "    Evaluate the RMSE of the hybrid model.\n",
    "\n",
    "    Args:\n",
    "        hybrid_model: The hybrid recommendation model to be evaluated.\n",
    "        test_size (float): The proportion of the dataset to include in the test split.\n",
    "        collab_weight (float, optional): The weight for collaborative filtering in the hybrid model. Defaults to None.\n",
    "        random_state (int, optional): The seed used by the random number generator for reproducibility. Defaults to 42.\n",
    "\n",
    "    Returns:\n",
    "        float: The RMSE of the hybrid model.\n",
    "    '''\n",
    "    # Extract collaborative data from the model\n",
    "    collab_df = hybrid_model.collab_model.df\n",
    "    \n",
    "    reader = Reader(rating_scale=(1, 5))\n",
    "    data = Dataset.load_from_df(collab_df[['user_id', 'movieId', 'rating']], reader)\n",
    "    \n",
    "    # Split the data into training and test sets\n",
    "    trainset, testset = train_test_split(data, test_size=test_size, random_state=random_state)\n",
    "    \n",
    "    # Train both models\n",
    "    hybrid_model.train_models()\n",
    "\n",
    "    # Generate predictions for the test set\n",
    "    true_ratings = []\n",
    "    pred_ratings = []\n",
    "\n",
    "    print('Starting prediction generation...')\n",
    "    for idx, (user_id, movie_id, true_rating) in enumerate(testset):\n",
    "        recommendations = hybrid_model.get_hybrid_recommendations([(movie_id, true_rating)], n=1, collab_weight=0.5)\n",
    "        pred_rating = recommendations[0][1] if recommendations else 0\n",
    "        true_ratings.append(true_rating)\n",
    "        pred_ratings.append(pred_rating)\n",
    "\n",
    "    print('Finished prediction generation.')\n",
    "\n",
    "    # Calculate RMSE using numpy vectorized operations\n",
    "    true_ratings = np.array(true_ratings)\n",
    "    pred_ratings = np.array(pred_ratings)\n",
    "    rmse = np.sqrt(np.mean((true_ratings - pred_ratings) ** 2))\n",
    "\n",
    "    return rmse\n",
    "\n",
    "# Instantiate the hybrid model\n",
    "hybrid_model = HybridModel(collab_df, content_df)\n",
    "# Evaluate RMSE\n",
    "rmse = evaluate_rmse(hybrid_model, collab_weight = 0.5)\n",
    "print(f\"Root Mean Square Error (RMSE): {rmse:.4f}\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d10a43f7-781a-4e35-816c-2ecd91c60743",
   "metadata": {},
   "source": [
    "With an RMSE of 1.2463, the hybrid model's performance shows that on average its predictions deviate from the actual ratings by approximately 1.25 units. While RMSE provides a measure of accuracy, a lower RMSE is generally preferred. An RMSE of 1.2463 suggests that the model's predictions are somewhat accurate but there is room for improvement. It indicates that while the hybrid model integrates multiple recommendation techniques, its predictions still have a notable level of error and further refinement or optimization may be needed to enhance accuracy. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b9dd9949-f5af-40fc-97a7-6274b47ae5fb",
   "metadata": {},
   "source": [
    "#### Optimizing hybrid model parameters"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "358c0076-07da-4785-bbfc-e033f45b9c57",
   "metadata": {},
   "source": [
    "In this next step, different collaborative filtering weights are tested to determine their impact on the hybrid model's performance. The weights list contains values representing the proportion of influence from collaborative filtering in the hybrid model. The loop iterates over each weight, adjusting the model's configuration accordingly and evaluates the RMSE for each setting using the `evaluate_rmse` function. The RMSE values are then printed alongside their corresponding weight providing insights into how varying the balance between collaborative and content-based filtering affects the model's prediction accuracy. This process helps identify the optimal weight for achieving the best performance of the hybrid recommendation system."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "3b94a93f-7012-4b75-b72e-63b04b8ae40b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Starting prediction generation...\n",
      "Finished prediction generation.\n",
      "RMSE with collab_weight 0.2: 1.2559\n",
      "Starting prediction generation...\n",
      "Finished prediction generation.\n",
      "RMSE with collab_weight 0.4: 1.2523\n",
      "Starting prediction generation...\n",
      "Finished prediction generation.\n",
      "RMSE with collab_weight 0.6: 1.1263\n",
      "Starting prediction generation...\n",
      "Finished prediction generation.\n",
      "RMSE with collab_weight 0.8: 1.1221\n"
     ]
    }
   ],
   "source": [
    "# List of different collaborative filtering weights to test\n",
    "weights = [0.2, 0.4, 0.6, 0.8]\n",
    "\n",
    "# Iterate over each weight value\n",
    "for weight in weights:\n",
    "    \n",
    "    # Evaluate the RMSE of the hybrid model with the current collaborative filtering weight\n",
    "    rmse = evaluate_rmse(hybrid_model, collab_weight=weight)\n",
    "    \n",
    "    # Print the RMSE result with the corresponding collaborative filtering weight\n",
    "    print(f\"RMSE with collab_weight {weight}: {rmse:.4f}\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "215ef4ab-e717-40f1-9599-d86b5adbf0cf",
   "metadata": {},
   "source": [
    "### Summary\n",
    "The `evaluate_rmse` function evaluates the performance of the hybrid recommender model by calculating the Root Mean Squared Error (RMSE). It prepares test data, generates predictions using the hybrid model with a specified weight for collaborative filtering and then merges these predictions with the true ratings. By computing the RMSE, the function quantifies the accuracy of the model's predictions. As the weight assigned to collaborative filtering increases, starting from 0.2 up to 0.8, the RMSE values decrease indicating improved prediction accuracy. With a weight of 0.2 the RMSE is 1.2559 and with a weight of 0.4 it slightly improves to 1.2523. The RMSE significantly drops to 1.1263 with a weight of 0.6 and further decreases to 1.1221 with a weight of 0.8. This suggests that higher collaborative filtering weight tends to enhance the model's accuracy leading to lower prediction errors and helps in identifying the most effective balance between collaborative and content-based filtering for better performance.\n",
    "The function below plots the different RMSE scores against the different collaborative weights in order to visualize the outcome.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "4a98a25c-2ed9-419c-9f04-e04670db469c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_rmse_vs_weight(weights, rmse_scores):\n",
    "    '''\n",
    "    Plot RMSE against collaborative filtering weights.\n",
    "\n",
    "    Args:\n",
    "        weights (list): A list of collaborative filtering weights.\n",
    "        rmse_scores (list): A list of RMSE scores corresponding to the weights.\n",
    "\n",
    "    Returns:\n",
    "        None\n",
    "    '''\n",
    "    plt.figure(figsize=(10, 6))\n",
    "    plt.plot(weights, rmse_scores, marker='o', linestyle='--', color='purple')\n",
    "\n",
    "    # Add titles and labels\n",
    "    plt.title('RMSE vs. Collaborative Filtering Weight')\n",
    "    plt.xlabel('Collaborative Filtering Weight')\n",
    "    plt.ylabel('RMSE Score')\n",
    "    plt.grid(True)\n",
    "\n",
    "    # Show the plot\n",
    "    plt.show()\n",
    "\n",
    "# Initializing weights and scores\n",
    "weights = [0.2, 0.4, 0.6, 0.8]\n",
    "rmse_scores = [1.2559, 1.2523, 1.1263, 1.1221]\n",
    "plot_rmse_vs_weight(weights, rmse_scores)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "53328243-2794-41fc-aff0-0899b13c0652",
   "metadata": {},
   "source": [
    "## Deployment on Streamlit"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1c0548a9-2cec-43b9-9cb1-03523fa09506",
   "metadata": {},
   "source": [
    "Streamlit is a Python library that simplifies the creation of web applications for data science and machine learning projects. In this deployment, Streamlit is used to create an interactive movie recommendation system. The app features the collaborative filtering model using the SVD algorithm which was trained on user-movie ratings data. The model was chosen due to its better accuracy score as continuous improvement is performed on the hybrid model. The live demo of the application is linked here:\n",
    "\n",
    "[![Streamflix Recommender System](https://img.shields.io/badge/Streamlit-App-brightgreen)](https://eva-claire-streamflix-recommender-system-app-owstvv.streamlit.app/)\n",
    "\n",
    "#### Features\n",
    "\n",
    "- Personalized movie recommendations\n",
    "- Search for movies and view details\n",
    "- Get movie trailers and posters\n",
    "- Browse movies by genre\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "986c0d7d-209c-4860-83fa-cec0add9783d",
   "metadata": {},
   "source": [
    "# CONCLUSIONS"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f7cdfb43-e9bf-44c6-b095-8e41e7d91c6a",
   "metadata": {},
   "source": [
    "After evaluating the collaborative filtering, content-based filtering and hybrid models, we can conclude that:\n",
    "\n",
    "The collaborative filtering model, with an RMSE of 0.86, performs exceptionally well by accurately predicting user preferences based on interaction data. In contrast, the hybrid model which leverages all the features included in both the collaborative and content based models shows higher RMSE indicating it may not capture user preferences as effectively. The hybrid model, in theory, benefits from integrating collaborative filtering more heavily achieving the lower RMSE scores as collaborative weight increases. This confirms that a hybrid approach with a focus on collaborative filtering provides superior accuracy and recommendation quality compared to content-based filtering alone.\n",
    "By refining the collaborative filtering weight and exploring additional evaluation methods, the recommendation system can be further optimized to deliver more accurate and relevant movie recommendations."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8b70bea7-8f23-449c-929b-439f1f9c67a4",
   "metadata": {},
   "source": [
    "# RECOMMENDATIONS"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "71a175f9-408c-4272-b74e-ed1406e7d38f",
   "metadata": {},
   "source": [
    "1. Experiment further with collaborative filtering weights in finer increments around the optimal value (e.g., between 0.6 and 0.8) to provide additional insights into achieving even better performance.\n",
    "\n",
    "2. Implement cross-validation to ensure that the observed improvements in RMSE are consistent across different subsets of the data. This helps in verifying that the results are not due to random chance or overfitting.\n",
    "\n",
    "3. Enhance the content-based model by incorporating more detailed item features such as plot summaries which could provide value especially for users with limited interaction history.\n",
    "\n",
    "4. Explore other hyperparameters and configurations for both collaborative filtering and content-based components of the hybrid model to potentially enhance performance.\n",
    "\n",
    "5. Evaluate the model using additional metrics such as Mean Average Precision (MAP) or Precision@K to gain a more comprehensive understanding of its recommendation quality.\n",
    "\n",
    "6. Incorporate user feedback and real-world testing to validate the model's effectiveness in practical scenarios and ensure it aligns with user preferences and expectations.\n",
    "\n",
    "7. Regularly evaluate the recommendation system with updated data and metrics to ensure it adapts to changing user preferences and content.\n",
    "\n",
    "8. Integrate additional techniques such as deep learning-based model to further enhance the system's capabilities and address any remaining limitations."
   ]
  }
 ],
 "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": "3.11.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}