analysis-and-modelling.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Part 2: Analysis and Modelling\n",
    "### *Creating a Hybrid Recommender System <br> using Content-based and Collaborative Filtering*\n",
    "\n",
    "\n",
    "> #### by [Sebastian Einar Røkholt](https://www.linkedin.com/in/sebastianrokholt/)\n",
    ">-------------------------------------------\n",
    "> *January 2021*\n",
    "----------------------------------------------------------------------------\n",
    "<a id=\"top\"></a> \n",
    "![Hybrid Recommender System Graphic](project-graphic.png)\n",
    "\n",
    "\n",
    "### Project Index:\n",
    "1. [GitHub Repo](https://github.com/SebastianRokholt/Hybrid-Recommender-System) <br>\n",
    "2. [cleanup.ipynb](https://github.com/SebastianRokholt/Hybrid-Recommender-System/blob/main/cleanup.ipynb) <br>\n",
    "3. **analysis-and-modelling.ipynb** <br>\n",
    "4. predict.ipynb (Coming soon!) <br>\n",
    "    \n",
    "### Analysis and Modelling Notebook Index:\n",
    "1. [**Exploratory Data Analysis**](#analysis) <br>\n",
    "    1.1       [*Exploring the Users Dataset*](#users)<br>\n",
    "    1.2       [*Exploring the Movies and Ratings Datasets*](#movies-ratings)<br>\n",
    "2. [***First Iteration:* Building a Hybrid Recommender with CBF and CF models**](#1st-iteration)<br>\n",
    "    2.1       [*Splitting the Dataset*](#splitting-the-dataset)<br>\n",
    "    2.2       [*Baseline Model*](#baseline)<br>\n",
    "    2.3       [*Content-based Filtering*](#content-based)<br>\n",
    "    2.4       [*Collaborative Filtering*](#collaborative)<br>\n",
    "    2.5       [*Hybrid Recommender*](#hybrid)<br>\n",
    "3. [**Feature Engineering**](#feature-engineering)<br>\n",
    "    3.1 [*Adding release year*](#feature-engineering-1) <br>\n",
    "    3.1 [*Adding the average age of movie fan*](#feature-engineering-2) <br>\n",
    "4. [***Second Iteration:* Retraining CBF for the Improved Hybrid Recommender**](#2nd-iteration)<br>\n",
    "    4.1       [*Improved Content-based Filtering*](#CBF-improved)<br>\n",
    "    4.2       [*Improved Hybrid Recommender*](#hybrid-improved)<br>\n",
    "5. [**Final Test and Evaluation**](#test)\n",
    "------------"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# ----------------------------------- NOTEBOOK SETUP --------------------------------------------------------- #\n",
    "# Importing libraries\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from math import sqrt\n",
    "import time\n",
    "from scipy.sparse import csr_matrix\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.linear_model import Lasso\n",
    "from sklearn.svm import SVR\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.neighbors import KNeighborsRegressor\n",
    "from sklearn.ensemble import RandomForestClassifier, RandomForestRegressor\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn.preprocessing import minmax_scale\n",
    "\n",
    "# Setting plot style\n",
    "plt.style.use('ggplot')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "------\n",
    "## <a id=\"analysis\"></a>1) Exploratory Data Analysis \n",
    "[*Back to top*](#top)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This section will contain data exploration and visualizations in order to take a closer look at the cleaned datasets.<br>\n",
    "Before I can start building and evaluating different models, I need to answer the following questions: <br>\n",
    "&#9659; **What is the age and gender distribution among the users?** <br>\n",
    "&#9659; **Which genres are the most common in the dataset?** <br>\n",
    "&#9659; **What is the average rating, and how are the different rating scores (1 - 5) distributed across users and genres?** <br>\n",
    "&#9659; **Has the average rating changed over time?**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Loading the cleaned datasets\n",
    "ratings_df = pd.read_csv(\"cleaned_data/rangering.csv\")\n",
    "movies_df = pd.read_csv(\"cleaned_data/film.csv\")\n",
    "users_df = pd.read_csv(\"cleaned_data/bruker.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <a id=\"users\"></a>1.1) Exploring the Users Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>BrukerID</th>\n",
       "      <th>Kjonn</th>\n",
       "      <th>Alder</th>\n",
       "      <th>Jobb</th>\n",
       "      <th>Postkode</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>F</td>\n",
       "      <td>45</td>\n",
       "      <td>6</td>\n",
       "      <td>92103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>M</td>\n",
       "      <td>50</td>\n",
       "      <td>16</td>\n",
       "      <td>55405-2546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>M</td>\n",
       "      <td>18</td>\n",
       "      <td>20</td>\n",
       "      <td>44089</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>M</td>\n",
       "      <td>35</td>\n",
       "      <td>1</td>\n",
       "      <td>33304</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>M</td>\n",
       "      <td>35</td>\n",
       "      <td>6</td>\n",
       "      <td>48105</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   BrukerID Kjonn  Alder  Jobb    Postkode\n",
       "0         0     F     45     6       92103\n",
       "1         1     M     50    16  55405-2546\n",
       "2         2     M     18    20       44089\n",
       "3         3     M     35     1       33304\n",
       "4         4     M     35     6       48105"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Printing the users dataset\n",
    "users_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "        vertical-align: middle;\n",
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       "\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>BrukerID</th>\n",
       "      <th>Alder</th>\n",
       "      <th>Jobb</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>6040.000000</td>\n",
       "      <td>6040.000000</td>\n",
       "      <td>6040.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3020.465894</td>\n",
       "      <td>29.894868</td>\n",
       "      <td>8.200828</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1743.799216</td>\n",
       "      <td>12.484335</td>\n",
       "      <td>10.933051</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1510.750000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3020.500000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>6.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>4530.250000</td>\n",
       "      <td>35.000000</td>\n",
       "      <td>13.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>6040.000000</td>\n",
       "      <td>56.000000</td>\n",
       "      <td>98.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          BrukerID        Alder         Jobb\n",
       "count  6040.000000  6040.000000  6040.000000\n",
       "mean   3020.465894    29.894868     8.200828\n",
       "std    1743.799216    12.484335    10.933051\n",
       "min       0.000000     1.000000     0.000000\n",
       "25%    1510.750000    25.000000     1.000000\n",
       "50%    3020.500000    25.000000     6.000000\n",
       "75%    4530.250000    35.000000    13.000000\n",
       "max    6040.000000    56.000000    98.000000"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "users_df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Grouping user data into male and female age groups\n",
    "user_age_gender_data = users_df[['Kjonn', 'Alder']]\n",
    "female_user_ages = user_age_gender_data.loc[user_age_gender_data['Kjonn'] == 'F'].sort_values('Alder')\n",
    "male_user_ages = user_age_gender_data.loc[user_age_gender_data['Kjonn'] == 'M'].sort_values('Alder')\n",
    "\n",
    "\n",
    "def get_group_count(min_age, max_age, dataset):\n",
    "    age_group = dataset.apply(lambda x: True if max_age > x['Alder'] > min_age else False, axis=1)\n",
    "    count = len(age_group[age_group == True].index)\n",
    "    return count\n",
    "\n",
    "\n",
    "G1_male = get_group_count(0, 18, male_user_ages)\n",
    "G2_male = get_group_count(17, 25, male_user_ages)\n",
    "G3_male = get_group_count(24, 35, male_user_ages)\n",
    "G4_male = get_group_count(34, 45, male_user_ages)\n",
    "G5_male = get_group_count(44, 50, male_user_ages)\n",
    "G6_male = get_group_count(49, 56, male_user_ages)\n",
    "G7_male = get_group_count(55, 200, male_user_ages)\n",
    "\n",
    "G1_female = get_group_count(0, 18, female_user_ages)\n",
    "G2_female = get_group_count(17, 25, female_user_ages)\n",
    "G3_female = get_group_count(24, 35, female_user_ages)\n",
    "G4_female = get_group_count(34, 45, female_user_ages)\n",
    "G5_female = get_group_count(44, 50, female_user_ages)\n",
    "G6_female = get_group_count(49, 56, female_user_ages)\n",
    "G7_female = get_group_count(55, 200, female_user_ages)\n",
    "\n",
    "# Figure 1: Visualizing the userbase by plotting age and gender to a bar chart\n",
    "labels = ['Under 18', '18-24', '25-34', '35-44', '45-49', '50-55', '56+']\n",
    "men_grouped = [G1_male, G2_male, G3_male, G4_male, G5_male, G6_male, G7_male]\n",
    "women_grouped = [G1_female, G2_male, G3_female, G4_female, G5_female, G6_female, G7_female]\n",
    "x = np.arange(len(labels))  # the label locations\n",
    "width = 0.40  # the width of the bars\n",
    "\n",
    "fig1, ax1 = plt.subplots()\n",
    "rects1 = ax1.bar(x - width/2, men_grouped, width, label='Male users', color='royalblue')\n",
    "rects2 = ax1.bar(x + width/2, women_grouped, width, label='Female users', color='darkorange')\n",
    "\n",
    "# Setting the labels for the bar chart\n",
    "ax1.set_ylabel('Number of users', size=12)\n",
    "ax1.set_xlabel('Age', size=12)\n",
    "ax1.set_xticks(x)\n",
    "ax1.set_xticklabels(labels)\n",
    "ax1.set_title('Fig. 1: Users grouped by age and gender', size=15)\n",
    "ax1.legend()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <a id=\"movies-ratings\"></a>1.2) Exploring the Movies and Ratings Datasets\n",
    "[*Back to top*](#top)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>FilmID</th>\n",
       "      <th>Tittel</th>\n",
       "      <th>Action</th>\n",
       "      <th>Adventure</th>\n",
       "      <th>Animation</th>\n",
       "      <th>Children's</th>\n",
       "      <th>Comedy</th>\n",
       "      <th>Crime</th>\n",
       "      <th>Documentary</th>\n",
       "      <th>Drama</th>\n",
       "      <th>Fantasy</th>\n",
       "      <th>Film-Noir</th>\n",
       "      <th>Horror</th>\n",
       "      <th>Musical</th>\n",
       "      <th>Mystery</th>\n",
       "      <th>Romance</th>\n",
       "      <th>Sci-Fi</th>\n",
       "      <th>Thriller</th>\n",
       "      <th>War</th>\n",
       "      <th>Western</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>Autumn in New York (2000)</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>Vie est belle, La (Life is Rosey) (1987)</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>Defying Gravity (1997)</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>Ruthless People (1986)</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>Portraits Chinois (1996)</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   FilmID                                    Tittel  Action  Adventure  \\\n",
       "0       0                 Autumn in New York (2000)       0          0   \n",
       "1       1  Vie est belle, La (Life is Rosey) (1987)       0          0   \n",
       "2       2                    Defying Gravity (1997)       0          0   \n",
       "3       3                    Ruthless People (1986)       0          0   \n",
       "4       4                  Portraits Chinois (1996)       0          0   \n",
       "\n",
       "   Animation  Children's  Comedy  Crime  Documentary  Drama  Fantasy  \\\n",
       "0          0           0       0      0            0      1        0   \n",
       "1          0           0       1      0            0      1        0   \n",
       "2          0           0       0      0            0      1        0   \n",
       "3          0           0       1      0            0      0        0   \n",
       "4          0           0       0      0            0      1        0   \n",
       "\n",
       "   Film-Noir  Horror  Musical  Mystery  Romance  Sci-Fi  Thriller  War  \\\n",
       "0          0       0        0        0        1       0         0    0   \n",
       "1          0       0        0        0        0       0         0    0   \n",
       "2          0       0        0        0        0       0         0    0   \n",
       "3          0       0        0        0        0       0         0    0   \n",
       "4          0       0        0        0        0       0         0    0   \n",
       "\n",
       "   Western  \n",
       "0        0  \n",
       "1        0  \n",
       "2        0  \n",
       "3        0  \n",
       "4        0  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Printing the movies dataset\n",
    "movies_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>FilmID</th>\n",
       "      <th>Action</th>\n",
       "      <th>Adventure</th>\n",
       "      <th>Animation</th>\n",
       "      <th>Children's</th>\n",
       "      <th>Comedy</th>\n",
       "      <th>Crime</th>\n",
       "      <th>Documentary</th>\n",
       "      <th>Drama</th>\n",
       "      <th>Fantasy</th>\n",
       "      <th>Film-Noir</th>\n",
       "      <th>Horror</th>\n",
       "      <th>Musical</th>\n",
       "      <th>Mystery</th>\n",
       "      <th>Romance</th>\n",
       "      <th>Sci-Fi</th>\n",
       "      <th>Thriller</th>\n",
       "      <th>War</th>\n",
       "      <th>Western</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>3883.000000</td>\n",
       "      <td>3883.000000</td>\n",
       "      <td>3883.000000</td>\n",
       "      <td>3883.000000</td>\n",
       "      <td>3883.000000</td>\n",
       "      <td>3883.000000</td>\n",
       "      <td>3883.000000</td>\n",
       "      <td>3883.000000</td>\n",
       "      <td>3883.000000</td>\n",
       "      <td>3883.000000</td>\n",
       "      <td>3883.000000</td>\n",
       "      <td>3883.000000</td>\n",
       "      <td>3883.000000</td>\n",
       "      <td>3883.000000</td>\n",
       "      <td>3883.000000</td>\n",
       "      <td>3883.000000</td>\n",
       "      <td>3883.000000</td>\n",
       "      <td>3883.000000</td>\n",
       "      <td>3883.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1973.687098</td>\n",
       "      <td>0.129539</td>\n",
       "      <td>0.072882</td>\n",
       "      <td>0.027041</td>\n",
       "      <td>0.040948</td>\n",
       "      <td>0.309039</td>\n",
       "      <td>0.054339</td>\n",
       "      <td>0.032707</td>\n",
       "      <td>0.412825</td>\n",
       "      <td>0.017512</td>\n",
       "      <td>0.011074</td>\n",
       "      <td>0.088334</td>\n",
       "      <td>0.029359</td>\n",
       "      <td>0.027041</td>\n",
       "      <td>0.121040</td>\n",
       "      <td>0.071079</td>\n",
       "      <td>0.126706</td>\n",
       "      <td>0.036827</td>\n",
       "      <td>0.017512</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1142.105375</td>\n",
       "      <td>0.335839</td>\n",
       "      <td>0.259976</td>\n",
       "      <td>0.162224</td>\n",
       "      <td>0.198195</td>\n",
       "      <td>0.462157</td>\n",
       "      <td>0.226715</td>\n",
       "      <td>0.177891</td>\n",
       "      <td>0.492405</td>\n",
       "      <td>0.131187</td>\n",
       "      <td>0.104662</td>\n",
       "      <td>0.283816</td>\n",
       "      <td>0.168832</td>\n",
       "      <td>0.162224</td>\n",
       "      <td>0.326216</td>\n",
       "      <td>0.256990</td>\n",
       "      <td>0.332686</td>\n",
       "      <td>0.188362</td>\n",
       "      <td>0.131187</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>985.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1973.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2963.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>3952.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            FilmID       Action    Adventure    Animation   Children's  \\\n",
       "count  3883.000000  3883.000000  3883.000000  3883.000000  3883.000000   \n",
       "mean   1973.687098     0.129539     0.072882     0.027041     0.040948   \n",
       "std    1142.105375     0.335839     0.259976     0.162224     0.198195   \n",
       "min       0.000000     0.000000     0.000000     0.000000     0.000000   \n",
       "25%     985.500000     0.000000     0.000000     0.000000     0.000000   \n",
       "50%    1973.000000     0.000000     0.000000     0.000000     0.000000   \n",
       "75%    2963.500000     0.000000     0.000000     0.000000     0.000000   \n",
       "max    3952.000000     1.000000     1.000000     1.000000     1.000000   \n",
       "\n",
       "            Comedy        Crime  Documentary        Drama      Fantasy  \\\n",
       "count  3883.000000  3883.000000  3883.000000  3883.000000  3883.000000   \n",
       "mean      0.309039     0.054339     0.032707     0.412825     0.017512   \n",
       "std       0.462157     0.226715     0.177891     0.492405     0.131187   \n",
       "min       0.000000     0.000000     0.000000     0.000000     0.000000   \n",
       "25%       0.000000     0.000000     0.000000     0.000000     0.000000   \n",
       "50%       0.000000     0.000000     0.000000     0.000000     0.000000   \n",
       "75%       1.000000     0.000000     0.000000     1.000000     0.000000   \n",
       "max       1.000000     1.000000     1.000000     1.000000     1.000000   \n",
       "\n",
       "         Film-Noir       Horror      Musical      Mystery      Romance  \\\n",
       "count  3883.000000  3883.000000  3883.000000  3883.000000  3883.000000   \n",
       "mean      0.011074     0.088334     0.029359     0.027041     0.121040   \n",
       "std       0.104662     0.283816     0.168832     0.162224     0.326216   \n",
       "min       0.000000     0.000000     0.000000     0.000000     0.000000   \n",
       "25%       0.000000     0.000000     0.000000     0.000000     0.000000   \n",
       "50%       0.000000     0.000000     0.000000     0.000000     0.000000   \n",
       "75%       0.000000     0.000000     0.000000     0.000000     0.000000   \n",
       "max       1.000000     1.000000     1.000000     1.000000     1.000000   \n",
       "\n",
       "            Sci-Fi     Thriller          War      Western  \n",
       "count  3883.000000  3883.000000  3883.000000  3883.000000  \n",
       "mean      0.071079     0.126706     0.036827     0.017512  \n",
       "std       0.256990     0.332686     0.188362     0.131187  \n",
       "min       0.000000     0.000000     0.000000     0.000000  \n",
       "25%       0.000000     0.000000     0.000000     0.000000  \n",
       "50%       0.000000     0.000000     0.000000     0.000000  \n",
       "75%       0.000000     0.000000     0.000000     0.000000  \n",
       "max       1.000000     1.000000     1.000000     1.000000  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movies_df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>BrukerID</th>\n",
       "      <th>FilmID</th>\n",
       "      <th>Rangering</th>\n",
       "      <th>Tidstempel</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>1561</td>\n",
       "      <td>4</td>\n",
       "      <td>959441640</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>1540</td>\n",
       "      <td>3</td>\n",
       "      <td>959441640</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>88</td>\n",
       "      <td>3</td>\n",
       "      <td>959441640</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>620</td>\n",
       "      <td>4</td>\n",
       "      <td>959441640</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>3771</td>\n",
       "      <td>5</td>\n",
       "      <td>959442113</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   BrukerID  FilmID  Rangering  Tidstempel\n",
       "0         0    1561          4   959441640\n",
       "1         0    1540          3   959441640\n",
       "2         0      88          3   959441640\n",
       "3         0     620          4   959441640\n",
       "4         0    3771          5   959442113"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Printing the ratings dataset\n",
    "ratings_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
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       "        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>BrukerID</th>\n",
       "      <th>FilmID</th>\n",
       "      <th>Rangering</th>\n",
       "      <th>Tidstempel</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>900117.000000</td>\n",
       "      <td>900117.000000</td>\n",
       "      <td>900117.000000</td>\n",
       "      <td>9.001170e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2991.868675</td>\n",
       "      <td>1989.666242</td>\n",
       "      <td>3.581491</td>\n",
       "      <td>9.722407e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1736.208380</td>\n",
       "      <td>1126.359558</td>\n",
       "      <td>1.117162</td>\n",
       "      <td>1.214212e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>9.567039e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1458.000000</td>\n",
       "      <td>1037.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>9.653030e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2967.000000</td>\n",
       "      <td>1959.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>9.730170e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>4501.000000</td>\n",
       "      <td>2963.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>9.752206e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>6040.000000</td>\n",
       "      <td>3952.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1.046455e+09</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            BrukerID         FilmID      Rangering    Tidstempel\n",
       "count  900117.000000  900117.000000  900117.000000  9.001170e+05\n",
       "mean     2991.868675    1989.666242       3.581491  9.722407e+08\n",
       "std      1736.208380    1126.359558       1.117162  1.214212e+07\n",
       "min         0.000000       0.000000       1.000000  9.567039e+08\n",
       "25%      1458.000000    1037.000000       3.000000  9.653030e+08\n",
       "50%      2967.000000    1959.000000       4.000000  9.730170e+08\n",
       "75%      4501.000000    2963.000000       4.000000  9.752206e+08\n",
       "max      6040.000000    3952.000000       5.000000  1.046455e+09"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ratings_df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The average rating across all users and movies is **3.58**. <br> \n",
    "My unconfirmed hypothesis for why this number is so high, is that most people watch movies that have been recommended to them, either by friends or by recommender systems. People also tend to do some research into which movie they want to watch, like watching a trailer, reading a review, etc. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Genre</th>\n",
       "      <th>Rated 1</th>\n",
       "      <th>Rated 2</th>\n",
       "      <th>Rated 3</th>\n",
       "      <th>Rated 4</th>\n",
       "      <th>Rated 5</th>\n",
       "      <th>Total nr of ratings</th>\n",
       "      <th>Average rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Action</td>\n",
       "      <td>14884</td>\n",
       "      <td>28359</td>\n",
       "      <td>63691</td>\n",
       "      <td>77911</td>\n",
       "      <td>46969</td>\n",
       "      <td>231814</td>\n",
       "      <td>3.490574</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Adventure</td>\n",
       "      <td>7675</td>\n",
       "      <td>15038</td>\n",
       "      <td>34070</td>\n",
       "      <td>39809</td>\n",
       "      <td>24009</td>\n",
       "      <td>120601</td>\n",
       "      <td>3.476273</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Animation</td>\n",
       "      <td>1886</td>\n",
       "      <td>3264</td>\n",
       "      <td>9871</td>\n",
       "      <td>14258</td>\n",
       "      <td>9719</td>\n",
       "      <td>38998</td>\n",
       "      <td>3.683625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Children's</td>\n",
       "      <td>3500</td>\n",
       "      <td>4776</td>\n",
       "      <td>10932</td>\n",
       "      <td>11917</td>\n",
       "      <td>6554</td>\n",
       "      <td>37679</td>\n",
       "      <td>3.351628</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Comedy</td>\n",
       "      <td>19410</td>\n",
       "      <td>36985</td>\n",
       "      <td>87148</td>\n",
       "      <td>111126</td>\n",
       "      <td>66128</td>\n",
       "      <td>320797</td>\n",
       "      <td>3.522377</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Crime</td>\n",
       "      <td>2879</td>\n",
       "      <td>6726</td>\n",
       "      <td>17580</td>\n",
       "      <td>25579</td>\n",
       "      <td>18800</td>\n",
       "      <td>71564</td>\n",
       "      <td>3.708387</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Documentary</td>\n",
       "      <td>246</td>\n",
       "      <td>425</td>\n",
       "      <td>1293</td>\n",
       "      <td>2723</td>\n",
       "      <td>2405</td>\n",
       "      <td>7092</td>\n",
       "      <td>3.932882</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Drama</td>\n",
       "      <td>10917</td>\n",
       "      <td>26252</td>\n",
       "      <td>76415</td>\n",
       "      <td>118548</td>\n",
       "      <td>86967</td>\n",
       "      <td>319099</td>\n",
       "      <td>3.765894</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Fantasy</td>\n",
       "      <td>2152</td>\n",
       "      <td>4269</td>\n",
       "      <td>9405</td>\n",
       "      <td>10513</td>\n",
       "      <td>6291</td>\n",
       "      <td>32630</td>\n",
       "      <td>3.445051</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Film-Noir</td>\n",
       "      <td>243</td>\n",
       "      <td>764</td>\n",
       "      <td>2877</td>\n",
       "      <td>6186</td>\n",
       "      <td>6358</td>\n",
       "      <td>16428</td>\n",
       "      <td>4.074507</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Horror</td>\n",
       "      <td>8040</td>\n",
       "      <td>10785</td>\n",
       "      <td>19195</td>\n",
       "      <td>19735</td>\n",
       "      <td>10990</td>\n",
       "      <td>68745</td>\n",
       "      <td>3.216016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Musical</td>\n",
       "      <td>1868</td>\n",
       "      <td>3399</td>\n",
       "      <td>9471</td>\n",
       "      <td>13113</td>\n",
       "      <td>9448</td>\n",
       "      <td>37299</td>\n",
       "      <td>3.666881</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Mystery</td>\n",
       "      <td>1563</td>\n",
       "      <td>3588</td>\n",
       "      <td>8994</td>\n",
       "      <td>12740</td>\n",
       "      <td>8956</td>\n",
       "      <td>35841</td>\n",
       "      <td>3.667894</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Romance</td>\n",
       "      <td>5710</td>\n",
       "      <td>13734</td>\n",
       "      <td>36372</td>\n",
       "      <td>48137</td>\n",
       "      <td>28794</td>\n",
       "      <td>132747</td>\n",
       "      <td>3.606952</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Sci-Fi</td>\n",
       "      <td>9908</td>\n",
       "      <td>18189</td>\n",
       "      <td>38582</td>\n",
       "      <td>45602</td>\n",
       "      <td>29229</td>\n",
       "      <td>141510</td>\n",
       "      <td>3.466787</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Thriller</td>\n",
       "      <td>9103</td>\n",
       "      <td>19085</td>\n",
       "      <td>45076</td>\n",
       "      <td>60132</td>\n",
       "      <td>37376</td>\n",
       "      <td>170772</td>\n",
       "      <td>3.571481</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>War</td>\n",
       "      <td>2208</td>\n",
       "      <td>4393</td>\n",
       "      <td>12300</td>\n",
       "      <td>21665</td>\n",
       "      <td>21060</td>\n",
       "      <td>61626</td>\n",
       "      <td>3.892091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Western</td>\n",
       "      <td>958</td>\n",
       "      <td>1757</td>\n",
       "      <td>4833</td>\n",
       "      <td>6634</td>\n",
       "      <td>4452</td>\n",
       "      <td>18634</td>\n",
       "      <td>3.636739</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Genre  Rated 1  Rated 2  Rated 3  Rated 4  Rated 5  \\\n",
       "0        Action    14884    28359    63691    77911    46969   \n",
       "1     Adventure     7675    15038    34070    39809    24009   \n",
       "2     Animation     1886     3264     9871    14258     9719   \n",
       "3    Children's     3500     4776    10932    11917     6554   \n",
       "4        Comedy    19410    36985    87148   111126    66128   \n",
       "5         Crime     2879     6726    17580    25579    18800   \n",
       "6   Documentary      246      425     1293     2723     2405   \n",
       "7         Drama    10917    26252    76415   118548    86967   \n",
       "8       Fantasy     2152     4269     9405    10513     6291   \n",
       "9     Film-Noir      243      764     2877     6186     6358   \n",
       "10       Horror     8040    10785    19195    19735    10990   \n",
       "11      Musical     1868     3399     9471    13113     9448   \n",
       "12      Mystery     1563     3588     8994    12740     8956   \n",
       "13      Romance     5710    13734    36372    48137    28794   \n",
       "14       Sci-Fi     9908    18189    38582    45602    29229   \n",
       "15     Thriller     9103    19085    45076    60132    37376   \n",
       "16          War     2208     4393    12300    21665    21060   \n",
       "17      Western      958     1757     4833     6634     4452   \n",
       "\n",
       "    Total nr of ratings  Average rating  \n",
       "0                231814        3.490574  \n",
       "1                120601        3.476273  \n",
       "2                 38998        3.683625  \n",
       "3                 37679        3.351628  \n",
       "4                320797        3.522377  \n",
       "5                 71564        3.708387  \n",
       "6                  7092        3.932882  \n",
       "7                319099        3.765894  \n",
       "8                 32630        3.445051  \n",
       "9                 16428        4.074507  \n",
       "10                68745        3.216016  \n",
       "11                37299        3.666881  \n",
       "12                35841        3.667894  \n",
       "13               132747        3.606952  \n",
       "14               141510        3.466787  \n",
       "15               170772        3.571481  \n",
       "16                61626        3.892091  \n",
       "17                18634        3.636739  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Generating a new dataframe with rating score count (cols) vs genres (rows)\n",
    "genres = list(movies_df)[2:]\n",
    "merged = pd.merge(ratings_df, movies_df, on='FilmID')\n",
    "\n",
    "cleaned = merged[['FilmID', 'BrukerID', 'Rangering', 'Action', 'Adventure', 'Animation', \"Children's\", 'Comedy',\n",
    "                  'Crime', 'Documentary', 'Drama', 'Fantasy', 'Film-Noir', 'Horror', 'Musical',\n",
    "                  'Mystery', 'Romance', 'Sci-Fi', 'Thriller', 'War', 'Western', 'Tittel']]\n",
    "\n",
    "movie_ratings = cleaned.sort_values('FilmID')\n",
    "rating_scale = [1, 2, 3, 4, 5]\n",
    "\n",
    "genres_rating_count = []\n",
    "for genre in genres:\n",
    "    for rating in rating_scale:\n",
    "        genre_list = movie_ratings.loc[movie_ratings[genre] == 1]\n",
    "        genre_rated_count = len(genre_list.loc[genre_list['Rangering'] == rating])\n",
    "        genres_rating_count.append([genre, rating, genre_rated_count])\n",
    "\n",
    "\n",
    "# Function for extracting the count of a specific rating of all genres\n",
    "def get_rating_count(score):\n",
    "    rating_count = []\n",
    "    for count in genres_rating_count:\n",
    "        if count[1] == score:\n",
    "            rating_count.append(count[2])\n",
    "\n",
    "    return rating_count\n",
    "\n",
    "\n",
    "# Function for extracting the total nr of ratings for all genres\n",
    "def get_total_rating_count():\n",
    "    all_ratings = []\n",
    "    totals = []\n",
    "    for count in genres_rating_count:\n",
    "        all_ratings.append(count[2])\n",
    "\n",
    "    lower = 0\n",
    "    for i in range(lower, len(all_ratings), 5):\n",
    "        lower = i\n",
    "        totals.append(sum(all_ratings[lower:lower+5]))\n",
    "\n",
    "    return totals\n",
    "\n",
    "\n",
    "genre_df = pd.DataFrame({'Genre': [genre for genre in genres], 'Rated 1': get_rating_count(1), 'Rated 2': get_rating_count(2),\n",
    "              'Rated 3': get_rating_count(3), 'Rated 4': get_rating_count(4), 'Rated 5': get_rating_count(5),\n",
    "              'Total nr of ratings': get_total_rating_count()})\n",
    "genre_df[\"Average rating\"] = (genre_df[\"Rated 1\"] + (genre_df[\"Rated 2\"] * 2) + (genre_df[\"Rated 3\"] * 3) + \n",
    "                             (genre_df[\"Rated 4\"] * 4) + (genre_df[\"Rated 5\"] * 5)) / genre_df['Total nr of ratings']\n",
    "genre_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 936x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Figure 2: Visualizing the number of ratings and rating scores for each genre\n",
    "total_ratings = genre_df.iloc[:, 6].values.tolist()\n",
    "fig2, ax2 = plt.subplots()\n",
    "fig2_labels = [genre for genre in genres]\n",
    "x = np.arange(len(fig2_labels))  # the label locations\n",
    "width = 0.7  # the width of the bars\n",
    "ax2.barh(x, total_ratings, width, label='Ratings', color='chocolate')\n",
    "\n",
    "# Setting labels for bar chart\n",
    "ax2.set_xlabel('Count of user ratings', size=16)\n",
    "ax2.set_yticks(x)\n",
    "ax2.set_yticklabels(fig2_labels, fontdict={'fontsize': 14})\n",
    "ax2.set_title('Fig. 2: Count of ratings per genre', size=18)\n",
    "ax2.invert_yaxis()  # Invert y axis so that the genres appear alphabetically\n",
    "ax2.legend(shadow=0.4, prop={\"size\": 13})\n",
    "plt.xticks(fontsize=13)\n",
    "\n",
    "# Adjusting and displaying the graph\n",
    "fig2.set_figheight(8)\n",
    "fig2.set_figwidth(13)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we can see from Figure 2, there are significantly more action, comedy and drama movies in the dataset than anything else. \n",
    "I therefore expect the average rating for these genres to converge around the average rating for all movies, while genres like Western, Documentary or Film-Noir might have somewhat higher or lower average ratings, perhaps making them a lot more or a lot less likely to be recommended than the rest. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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gMFBcXExubu4DnVeJroiIiIjcdyaTiZKSkgc6px5GExERkSrF13ftA5knI2PoA5nnfggLC2Py5MkMGTLE2qHcE63oioiIiFQAgwcPpnHjxgQEBNC8eXOeeOIJPvroozsaIzQ0lE2bNt2nCP8nJiaG7t2706BBA2JiYu77fHdLia6IiIhIBREdHc2RI0dIT08nIiKCiRMncvz4cWuHVUpQUBCvvPIKvXr1snYot6REV0RERKSCcXBwYNiwYRQVFXHgwAFze3x8PI8++iiBgYF06NCBN954g+LiYgBGjBhBRkYGU6dOJSAggMjISACKioqYP38+Xbt2JSgoiEGDBrFv3z7zmEajkZkzZxISEkKbNm1YtGjRbeMbPXo03bp1w83NrZzPvHwp0RURERGpYAoLC1m9ejUATZo0MbfXq1ePhIQEDh06xMqVK0lMTGTt2qs1x6tWrcLX15e4uDiOHDnCunXrAIiLi+Ozzz5jzZo1pKen88wzzzB06FBycnIAWLhwIUlJSXzwwQekpqZy8uRJTp069YDP+P5QoisiIiJSQSxYsICgoCD8/f2ZO3cu8+bNo0WLFubP+/fvT4MGDTAYDAQHBxMeHk5ycvJNxzOZTKxcuZJp06bRsGFD7O3tiYyMpG7dumzbtg2AjRs3MmHCBBo3boyLiwszZsywmb2HteuCiIiISAUxadIkpkyZQk5ODjExMaSkpJhLEADef/99li1bxokTJyguLsZoNNK2bdubjpednU1ubi4jR460SF6NRiNnzpwB4OzZs/j5+Zk/c3V1xcvL6z6c3YOnRFdERESkgnF3dycuLo6wsDC2bt1K7969ycjIYNKkScTHx9O9e3ecnJx49dVXLept7ewsv6z39PTE1dWVxMRE2rRpc8O5fHx8LEoV8vLyyMrKuj8n9oCpdEFERESkAvLw8CAqKoo5c+ZQUlJCXl4eJSUleHp64ujoSFpaWqmtxGrXrm2xS4PBYGD06NHExsZy7NgxAHJzc9m+fTtnz54FIDw8nCVLlvDjjz+Sn5/P7NmzMZlMt4ytsLCQK1euUFxcTHFxMVeuXKGwsLCcr8C904quiIiIVCmV6UUOY8aMIT4+ng0bNjBkyBBiYmIYNWoURqORLl26MGjQIA4ePGjuHx0dzfTp01mxYgXt2rUjISGBmJgYli9fzqhRozhz5gyurq60a9eO2NhYACZOnEhOTg4DBw7E3t6eqKgoi1KGGxk6dCipqanm39evX0/nzp3ZuHHj/bkQd8lgul3KLpXO6dOnrR2C3CVvb28yMzOtHYbcJd2/ykv3rnK71f27dOkSNWrUeMARyfWOHz9Oamoqffv2xcPDw9z+0EMP3dd5VbogIiIiIjZJia6IiIiI2CQluiIiIiJik5ToioiIiIhNUqIrIiIiIjZJia6IiIiI2CTto2uD1vr6WjsEkSpvaEaGtUMQEanytKIrIiIiIjZJia6IiIiIWAgLC+O9996zdhj3TKULIiIiUqVkze78QObxmpZ6+06/MnjwYNLS0nBwcMDe3p769eszefJkBg4cWOYxQkNDefHFFwkPD7/TcMvshx9+YM6cOXzzzTdcunQJX19foqKiGDq04r1aWSu6IiIiIhVEdHQ0R44cIT09nYiICCZOnMjx48etHZaFCxcuEBYWxpYtWzh06BB/+ctfiI2N5ZNPPrF2aKUo0RURERGpYBwcHBg2bBhFRUUcOHDA3B4fH8+jjz5KYGAgHTp04I033qC4uBiAESNGkJGRwdSpUwkICCAyMhKAoqIi5s+fT9euXQkKCmLQoEHs27fPPKbRaGTmzJmEhITQpk0bFi1adMvY2rVrx8iRI/Hx8cFgMNCxY0ceeeQR/vOf/9yHK3FvVLpgg1q9Xt3aIYhUeftXBVo7BLEhrUYctnYI8oAVFhayevVqAJo0aWJur1evHgkJCdSvX58DBw4wbNgw/Pz8ePbZZ1m1atUNSxfi4uJISUlhzZo1+Pn5sX79eoYOHUpycjLu7u4sXLiQpKQkPvjgA3x8fJg1axanTp0qc6z5+fmkpaURExNTfhegnGhFV0RERKSCWLBgAUFBQfj7+zN37lzmzZtHixYtzJ/379+fBg0aYDAYCA4OJjw8nOTk5JuOZzKZWLlyJdOmTaNhw4bY29sTGRlJ3bp12bZtGwAbN25kwoQJNG7cGBcXF2bMmIHBYChTvMXFxUyePJkGDRowePDgezv5+0AruiIiIiIVxKRJk5gyZQo5OTnExMSQkpJiLkEAeP/991m2bBknTpyguLgYo9FI27ZtbzpednY2ubm5jBw50iJ5NRqNnDlzBoCzZ8/i5+dn/szV1RUvL6/bxmo0Gpk4cSI///wzCQkJODo63s0p31da0eXqXzsTJkwgIiKCs2fP3tGxFy5cYP369fzyyy8W7QcOHCAiIoKffvqpPEMVERGRKsDd3Z24uDj+/e9/s3XrVgAyMjKYNGkS0dHR7N27l++//54RI0ZYHGdnZ5naeXp64urqSmJiIv/973/NP0ePHmXixIkA+Pj4WJQq5OXlkZWVdcv4rly5wujRo8nKymLdunXUrFmzPE673CnRBQ4fPsy5c+cASElJuaNjL1y4wMaNG83HX9O4cWNmz56Nj49PucUpIiIiVYeHhwdRUVHMmTOHkpIS8vLyKCkpwdPTE0dHR9LS0ti0aZPFMbVr17bYpcFgMDB69GhiY2M5duwYALm5uWzfvt28uBceHs6SJUv48ccfyc/PZ/bs2ZhMppvGlZuby/DhwzEajfzjH/+gevWK+2yQShe4mtw6OzvToEEDUlJSymXvOVdXVwIDrfMwSp9juVaZV0SkvGTMerCvUPb29iYzM/OBzilSFmPGjCE+Pp4NGzYwZMgQYmJiGDVqFEajkS5dujBo0CAOHjxo7h8dHc306dNZsWIF7dq1IyEhgZiYGJYvX86oUaM4c+YMrq6utGvXjtjYWAAmTpxITk4OAwcOxN7enqioKItShutt2bKF1NRUqlWrRkhIiLn9qaee4i9/+cv9uxh3wWC6VcpeBZSUlDBu3DiCg4Np2bIly5YtIy4ujoYNG5r7nDt3jjVr1rB//34KCgrw8fHhN7/5DYGBgeZl/19bv349Bw4cYNasWcybN48GDRoAUFBQwJo1a0hNTSUvL48GDRrwzDPP0Lp1a/OxM2fOpEaNGoSGhvLee+9x8eJFmjVrxrhx48pULwNgiCpbAbmISEWlRFfuxK3u36VLl6hRo8YDjkiud/z4cVJTU+nbty8eHh7m9oceeui+zlvlV3TT09PNGx83b96c5cuXk5KSYk50L1y4wLRp03BycuLZZ5/Fy8uLkydPkpmZSWhoKJMnT2b+/PmMHj2axo0b33KupUuXsmfPHiIjI/Hx8WHbtm3MmTOHV155hebNm5v7HT16lPPnz/Pcc89RWFjIu+++y7Jly3jppZfu67UQERERsSVVPtFNTk6mevXqtGnTBgcHB0JCQsxPOBoMBrZs2UJeXh5z5swx/wXSqlUr8/HXVmv9/PxuWapw6tQpUlJS+MMf/kC3bt0AaN26NVOnTmXTpk28/PLL5r55eXnExcXh5uYGQE5ODqtWraKwsBAnJ6dSYyclJZGUlATAnDlz7u2CiIhUAN7e3g90PgcHhwc+p5SfW92//Pz8BxyN3IidnR0uLi54enqW+Rvq8lClE12j0cju3bvp0KEDDg5XL0VYWBgLFy7kyJEjBAYGkp6eTuvWrS2W2e/GDz/8gMlkonPn/71f287Ojk6dOvHhhx9a9G3atKk5yQXMdTLZ2dk3fLitZ8+e9OzZ857iExGpSB50GYFKFyq3W92/oqKiBxyN3EhJSQn5+flkZ2dbPOh2v0sXqvSuC3v37iU3N5d27dqRm5tLbm4uLVu2xNHR0bz58qVLl+45yQU4f/481apVw9nZ2aK9Vq1aFBQUYDQazW2urq4Wfa4l4YWFhfcch4iIiEhVUaVXdK9tJfbWW2+V+iw1NZWRI0dSo0YNzp8/f89zeXh4cOXKFQoKCiyS3QsXLuDs7Fyumyzvc2xQbmOJ7fGalmrtEGyWVgVFRCqWKpvoXrlyhW+++YawsLBSX/sfP36c1atXk56eTnBwMP/617/IycnB3d291DjXVlt/vSJ7I/7+/hgMBv7zn//w2GOPAVdfVPGf//zH4kE0ERERESkfVTbR3b17NwUFBfTr14+AgACLz5o1a8bmzZtJSUlh2LBhfPXVV7zyyiv89re/xdvbm1OnTlFQUMCgQYPw9vbGycmJ7du34+rqir29Pf7+/qXm8/PzIywsjBUrVpCfn4+Pjw9JSUmcPn2aqKioB3XaIiIiIlVGla3RTUlJoV69eqWSXLi6Stu5c2e+/vprXFxciI2NpVGjRqxatYo5c+awbds289OdTk5OjBs3juPHj/PKK6/ccguwcePG8dhjj7Fp0ybmzp1LZmYmf/rTn7SiKyIiInIfVPkXRtii/eMb3r6TVFmq0b1/VKNbeeneVW56YUTFpxdGiIiIiDwAvq/4PpB5HvQb/spTWFgYkydPZsiQIdYO5Z4o0bVBIUsmWTuECisjY6i1Q7glrSqJiFRdgwcPJi0tDQcHB+zt7alfvz6TJ09m4MCBZR4jNDSUF198kfDw8PsW5/nz5xk9ejRHjx6loKAALy8vIiIiiI6OxmAw3Ld570aVrdEVERERqWiio6M5cuQI6enpREREMHHiRI4fP27tsCy4urryxhtvkJaWxqFDh0hMTGTz5s2sWbPG2qGVokRXREREpIJxcHBg2LBhFBUVceDAAXN7fHw8jz76KIGBgXTo0IE33niD4uJiAEaMGEFGRgZTp04lICCAyMhI4Orb4ebPn0/Xrl0JCgpi0KBB7Nu3zzym0Whk5syZhISE0KZNGxYtWnTL2JydnWnWrJnFOwDs7Oz44YcfyvMSlAsluiIiIiIVTGFhIatXrwagSZMm5vZ69eqRkJDAoUOHWLlyJYmJiaxduxaAVatW4evrS1xcHEeOHGHdunUAxMXF8dlnn7FmzRrS09N55plnGDp0KDk5OQAsXLiQpKQkPvjgA1JTUzl58iSnTp26bYzPPfcc/v7+dO7cmcuXLzN8+PDyvgz3TImuiIiISAWxYMECgoKC8Pf3Z+7cucybN48WLVqYP+/fvz8NGjTAYDAQHBxMeHg4ycnJNx3PZDKxcuVKpk2bRsOGDbG3tycyMpK6deuybds2ADZu3MiECRNo3LgxLi4uzJgxo0y1tqtXr+bw4cN8+OGHhIeH4+npee8XoJzpYTQbVNEfuBIREZEbmzRpElOmTCEnJ4eYmBhSUlLMJQgA77//PsuWLePEiRMUFxdjNBpp27btTcfLzs4mNzeXkSNHWiSvRqORM2fOAHD27Fn8/PzMn7m6uuLl5VWmeO3t7Xn44YfZtWsXL7/8MosXL77TU76vlOiKiIiIVDDu7u7ExcURFhbG1q1b6d27NxkZGUyaNIn4+Hi6d++Ok5MTr776qkW9rZ2d5Zf1np6euLq6kpiYSJs2bW44l4+Pj0WpQl5eHllZWXcUb1FRUYV7aA5UuiAiIiJSIXl4eBAVFcWcOXMoKSkhLy+PkpISPD09cXR0JC0tjU2bNlkcU7t2bYuE02AwMHr0aGJjYzl27BgAubm5bN++nbNnzwIQHh7OkiVL+PHHH8nPz2f27Nnc6n1iaWlp7Nixg/z8fIqLi/nPf/7D8uXL6d69+324CvdGK7oiIiJSpVSmFzmMGTOG+Ph4NmzYwJAhQ4iJiWHUqFEYjUa6dOnCoEGDOHjwoLl/dHQ006dPZ8WKFbRr146EhARiYmJYvnw5o0aN4syZM7i6utKuXTtiY2MBmDhxIjk5OQwcOBB7e3uioqIsShmuZzQaee211zh27BgGgwEfHx9GjRrFxIkT7/v1uFN6BbANOn36tLVDkLukF0ZUbrp/lZfuXeWmVwBXfHoFsJSbtb4P5tWGIvI/QzMqzwqRiEhVoRpdEREREbFJSnRFRERExCYp0RURERERm6REV0RERERskhJdEREREbFJ2nXBBrV6vbq1QxCpcvavCrR2CGIDWo04bO0QRGyKVnRFRERExCYp0RURERERC2FhYbz33nvWDuOeVZjSBapNE38AACAASURBVJPJxMSJEzl37hzz58/Hx8fntseMHj2a3r17ExER8QAitFRUVMQ///lPOnbsSKNGjR74/CIiInJ3HlSp0Z2WogwePJi0tDQcHBywt7enfv36TJ48mYEDB5Z5jNDQUF588UXCw8PvNNy7cvDgQfr160doaGiFTIwrzIru4cOHOXfuHAApKSlWjub2ioqK2LhxIz/++KO1QxEREREbER0dzZEjR0hPTyciIoKJEydy/Phxa4d1Q0VFRbzwwgt07NjR2qHcVIVJdFNSUnB2diYgIKBSJLrlraSkhKKiImuHISIiIhWAg4MDw4YNo6ioiAMHDpjb4+PjefTRRwkMDKRDhw688cYbFBcXAzBixAgyMjKYOnUqAQEBREZGAlcT0vnz59O1a1eCgoIYNGgQ+/btM49pNBqZOXMmISEhtGnThkWLFpUpxgULFtC6dWtCQ0PL8czLV4UoXSgpKSE1NZX27dvTsmVLli1bxokTJ2jYsKG5z8GDB1m5ciWnT5/Gz8+PUaNGWYyxfv16Pv/8c5YuXYqd3f/y97S0NP7yl79YlENs27aNLVu2cPbsWdzd3enduzeDBg0yH7No0SJOnjzJ0KFDWb16NT///DONGjVi7Nix1K9fH4DnnnsOgMWLF7N48WIAFi5cyLlz55g1axbz5s2jQYMG5jFnzpxJjRo1eOGFFyzmeOqpp0hMTOTMmTPMmDGDoKAgMjMzSUhI4LvvvsNoNBIUFMTvfvc7HnrooTJdzz7Hcst87UVEKquMWRnlNpa3tzeZmZnlNp7IvSosLGT16tUANGnSxNxer149EhISqF+/PgcOHGDYsGH4+fnx7LPPsmrVqhuWLsTFxZGSksKaNWvw8/Nj/fr1DB06lOTkZNzd3Vm4cCFJSUl88MEH+Pj4MGvWLE6dOnXL+P773/+yYcMGtm7dyrJly+7PRSgHFWJFNz09nQsXLhAWFkanTp2wt7e3WNXNzs7mjTfewM3Njeeff54nnniC+fPnU1BQYO7TpUsXLly4wMGDBy3GTk1NpUmTJuYk98MPPyQ+Pp4OHTrw5z//mV69evHee+/x6aefWhyXmZnJP/7xD5566imio6O5ePEif/3rXzGZTADMmDEDgKeeeorZs2cze/ZsPDw87ui8z507x5o1a/jNb37DSy+9RJ06dbh8+TLTp0/n9OnTREVF8cc//pErV64QGxtLYWHhHY0vIiIilcuCBQsICgrC39+fuXPnMm/ePFq0aGH+vH///jRo0ACDwUBwcDDh4eEkJyffdDyTycTKlSuZNm0aDRs2xN7ensjISOrWrcu2bdsA2LhxIxMmTKBx48a4uLgwY8YMDAbDTccsKiri+eefNy/iVWQVYkU3OTmZ6tWr06ZNGxwcHAgJCSElJYXIyEgMBgOffPIJjo6O/PnPf8bZ2RkAZ2dnFixYYB7Dz8+Phg0bsnPnToKDg4GrS/G7d+82/1WTl5fHhg0beOqpp3j66acBCAkJoaCggE2bNtGrVy/zavDly5eJjY2lXr16wNVV53nz5nH69Gl8fX1p2rQpAD4+PgQG3l1R+6VLl5g+fbrFw2yJiYkUFBQQFxeHm5sbAM2aNWPChAn8+9//pk+fPqXGSUpKIikpCYA5c+bcVSwiIpWNt7d3uY3l4OBQruPJg3Wr+5efn/+Ao7k3kyZNYsqUKeTk5BATE2POh655//33zd98FxcXYzQaadu27U3Hy87OJjc3l5EjR1okr0ajkTNnzgBw9uxZ/Pz8zJ+5urri5eV10zEXL15M48aN6dWrV5nPy87ODhcXFzw9PW85dnmzeqJ7LRnt0KEDDg5XwwkLC2PhwoUcOXKEwMBAjh49SkhIiDnJhatPFf460QXo3LkzW7ZsYfTo0djb27N3716uXLlCly5dgKsPvBUUFNC5c2dzPQtAcHAwmzZtIisri9q1awNQu3Ztc5ILmP8FyMrKwtfXt1zO3dPTs9SODfv37yckJAQXFxdzjC4uLjRp0oRjx47dcJyePXvSs2fPcolJRKSyKM9SA5UuVG63un+V9fkXd3d34uLiCAsLY+vWrfTu3ZuMjAwmTZpEfHw83bt3x8nJiVdffdWi3vbX5ZtwNddwdXUlMTGRNm3a3HAuHx8fi1KFvLw8srKybhrbV199xf79+80Li/n5+RQXFxMcHMyOHTtu+A13SUkJ+fn5ZGdnm78dB8pclnm3rJ7o7t27l9zcXNq1a0du7tXa0pYtW+Lo6EhycjKBgYHk5ORY1LsCODk5Ua1aNYu2sLAwEhMTSU9Pp3Xr1uzcuZPAwEDzX3mXLl0C4Pnnn79hLL9OdKtXt3y72LUk3Gg03uMZ/0+tWrVKtV26dIkjR46wc+fOUp+1atWq3OYWERGRis3Dw4OoqCjmzJnDE088QV5eHiUlJXh6euLo6EhaWhqbNm0iICDAfEzt2rUtdmkwGAyMHj2a2NhY4uLiaNKkCbm5uezevZvmzZvj4+NDeHg4S5YsoXPnztStW5fZs2dbJKPXW7p0qUX56LJly/juu+9YtGjRDXMba7J6onutFvett94q9VlqaiojR47E3d2dixcvWnxWWFjIlStXLNrq1q2Lv78/O3fupHnz5qSlpVks918rBfjzn/98wxtRHn9VODo6AqX/grx8+XKpOpYb1b+4ubnRvn37G+5/5+LiUqYY9jk2uH0nEcBrWqq1Q7ApWhUUkfI2ZswY4uPj2bBhA0OGDCEmJoZRo0ZhNBrp0qULgwYNsng+KTo6munTp7NixQratWtHQkICMTExLF++nFGjRnHmzBlcXV1p164dsbGxAEycOJGcnBwGDhyIvb09UVFRFqUM17u+9KBGjRo4OTnd99XZu2HVRPfKlSt88803hIWFlfrq/fjx46xevZr09HT8/f354osvKCgoMJcv7Nq164ZjdunShX/+858EBwdTWFhI586dzZ8FBgbi5OREdnY27dq1u6fYr63wXv+A2LWbn5GRYX5KMjMzk9OnT1uUQtxMcHAwqamp1K9fHycnp3uKUUREREq70xc5PCgbN24s1VajRg2L7cX++Mc/8sc//vGmY/To0YMePXpYtDk4ODBu3DjGjRt3w2OulUC8+uqr5rYJEyaUOe5rO0pVRFZNdHfv3k1BQQH9+vWzWHaHqw9gbd68mZSUFIYMGcLWrVuZM2cOAwYM4Pz582zevPmGiWDnzp1JSEggISGBFi1aWNSJVK9enaeffpp3332XzMxMgoKCMJlMnD59mgMHDjB16tQyx+7g4ECdOnVITU2lQYMGODo60rBhQ7y8vPD39+e9997D2dmZkpISNm/ebF5Nvp0BAwawY8cOZs2aRd++ffH09CQnJ4eDBw/SvHlzunbtWuYYRURERKoyq24vlpKSQr169UoluXA1kezcuTNff/01NWrU4KWXXuLSpUu8+eabbN26lUmTJlk8nHaNt7c3gYGBnD9/3vwQ2q8NGjSIsWPHsnfvXubOncs777xDcnIyQUFBdxx/VFQUly5dIjY2lpdeeons7Gzg6tcG3t7eLFiwgHXr1jF48OAyL+fXrFmT1157DV9fX1atWsXs2bNZs2YNeXl5FvsKi4iIiMitGUy3qjaWSmn/eCXEUjaq0S1fqtGtvHTvKrdb3b9Lly5V+L1eq4Ljx4+TmppK3759Lb5tv991vRXihREiIiIiIuXN6rsuSPkLWTLJ2iFUWBkZQ60dwi1pVUlERKT8aEVXRERERGySEl0RERERsUlKdEVERETEJinRFRERERGbpIfRREREpEpZ6+v7QOYZmpHxQOa5H8LCwpg8eTJDhgyxdij3RImuDaroOwuIiIhIaYMHDyYtLQ0HBwfs7e2pX78+kydPZuDAgWUeIzQ0lBdffJHw8PD7GCn4+vpSrVo17Oz+VxyQlpZGzZo17+u8d0qlCyIiIiIVRHR0NEeOHCE9PZ2IiAgmTpzI8ePHrR3WDa1bt44jR46YfypakgtKdEVEREQqHAcHB4YNG0ZRUREHDhwwt8fHx/Poo48SGBhIhw4deOONNyguLgZgxIgRZGRkMHXqVAICAoiMjASgqKiI+fPn07VrV4KCghg0aBD79u0zj2k0Gpk5cyYhISG0adOGRYsWPdiTvY+U6IqIiIhUMIWFhaxevRqAJk2amNvr1atHQkIChw4dYuXKlSQmJrJ27VoAVq1aha+vL3FxcRw5coR169YBEBcXx2effcaaNWtIT0/nmWeeYejQoeTk5ACwcOFCkpKS+OCDD0hNTeXkyZOcOnXqtjGOGzeO4OBgBgwYwCeffFLel6BcKNEVERERqSAWLFhAUFAQ/v7+zJ07l3nz5tGiRQvz5/3796dBgwYYDAaCg4MJDw8nOTn5puOZTCZWrlzJtGnTaNiwIfb29kRGRlK3bl22bdsGwMaNG5kwYQKNGzfGxcWFGTNmYDAYbhlnYmIiqamp7Nmzh6ioKCZNmsQXX3xRPhehHOlhNBv0oJ4mFZGqoTI/OS5S2UyaNIkpU6aQk5NDTEwMKSkp5hIEgPfff59ly5Zx4sQJiouLMRqNtG3b9qbjZWdnk5uby8iRIy2SV6PRyJkzZwA4e/Ysfn5+5s9cXV3x8vK6ZZyPPPKI+Z8HDRpEcnIy//znP+nevfsdn/P9pERXREREpIJxd3cnLi6OsLAwtm7dSu/evcnIyGDSpEnEx8fTvXt3nJycePXVVy3qbX+9CwKAp6cnrq6uJCYm0qZNmxvO5ePjY1GqkJeXR1ZW1h3Fe7sVYGtR6YKIiIhIBeTh4UFUVBRz5syhpKSEvLw8SkpK8PT0xNHRkbS0NDZt2mRxTO3atS12aTAYDIwePZrY2FiOHTsGQG5uLtu3b+fs2bMAhIeHs2TJEn788Ufy8/OZPXs2JpPppnF9//337N27l8LCQoxGI59++imbNm26o23QHhSt6IqIiEiVUpnKccaMGUN8fDwbNmxgyJAhxMTEMGrUKIxGI126dGHQoEEcPHjQ3D86Oprp06ezYsUK2rVrR0JCAjExMSxfvpxRo0Zx5swZXF1dadeuHbGxsQBMnDiRnJwcBg4ciL29PVFRURalDNfLyspi2rRpnDx5EicnJxo2bMhbb71Fr1697vv1uFMG061SdqmU5lXQrw9EpHKqTEnB3fL29iYzM9PaYchdutX9u3TpEjVq1HjAEcn1jh8/TmpqKn379sXDw8Pc/tBDD93XebWia4NavV7d2iGIiA3ZvyrQ2iFIBdFqxGFrhyByR1SjKyIiIiI2SYmuiIiIiNikCpfomkwmJkyYQEREhPlpwDs1c+ZM3nzzzXKO7OZ27tzJ9u3brR6HiIiIiPxPhavRPXz4MOfOnQMgJSWF8PDwOx5jzJgx2Nvbl3doN5WamsqlS5fo1q2bVeMQERERqYistfdBhVvRTUlJwdnZmYCAAFJSUu5qDD8/P+rVq1fOkVXeOERERKoqe3t7cnNzrZZoVXUmk4nz58+TnZ1tlfkr1IpuSUkJqamptG/fnpYtW5pfcdewYUMAtm/fzuLFi5k3bx6rVq3i8OHDeHl5ERkZSWhoqHmcmTNnUqNGDV544QUA1q9fz9atW/nzn//M8uXLOXnyJE2aNGHSpEk4OzuzdOlS9u/fj7e3N6NHjyY4ONg81pdffklSUpL5jSGNGjVi+PDh+Pv7A7Bo0SJ27doFQEREBACDBw8mIiKiVBwA6enprF27lhMnTuDq6kpoaCjDhw+nWrVqABw4cIBZs2bxyiuvsHXrVvbu3UutWrUYOHAgvXv3LtN17HMs966uv4hIWWXMsq0tx7S9mO1ydXXlypUrHD58GEdHxwr7Bi9bZDKZKC4u5uTJk1y4cAE7OztzvvOgVKhENz09nQsXLhAWFkbz5s1Zvnw5KSkp5kT3mnfeeYeePXvy5JNP8q9//Yu3336bhQsX3vK9zAUFBSxbtownn3wSZ2dnVq5cyYIFC3B0dKRNmzb07t2bDz/8kLfeeoslS5bg7OwMwLlz53jssceoW7cuRUVFJCcn88orr/Dmm29St25dwsPDyczMJC8vj9GjRwPcNI5Tp07x2muvERISwgsvvEBmZiZr167l559/5uWXX7bou3TpUh577DF69uxJcnIyy5cvx9/fn6ZNm97LJRYREalyqlWrxpUrV/j2229xcnKydjhVUkFBAY0aNaraiW5ycjLVq1enTZs2ODg4EBISQkpKCpGRkRZ/gfXv35/HH38cgCZNmhAVFUVaWtot38hRWFjI7373O1q0aAHA+fPnWb58ORERETz55JPA1QT1+eef5+DBg7Rt2xa4ujp7TUlJCSEhIfzwww/s2LGDwYMH4+Pjg5ubGyaTicDAW+81uXHjRmrXrs2f/vQn87uo3dzcePvttzl8+LDF8WFhYeb65BYtWpCWlsauXbuU6IqIiNyF1q1b4+rqytmzZykuLrZ2OFWKwWCgdu3aBAUFPfAV9QqT6BqNRnbv3k2HDh1wcLgaVlhYGAsXLuTIkSMWSWDr1q3N/1yjRg1q1ap129oPBwcHmjdvbv7dx8cHwKJM4Vrbr8c6deoU69at4/Dhw1y4cMHcfvr06Ts+x6NHj9KpUydzkgvQqVMn7O3t+f777296jg4ODtSrV4+srKwbjpuUlERSUhIAc+bMueO4RETulLe3t7VDKFcODg42d05VSVnvn+5x1VNhEt29e/eSm5tLu3btyM29WmPasmVLHB0dSU5OtkgCq1e3fPOXg4MDhYWFtxzfxcXFIsG8lky7urqWajMajQDk5+fz2muvUatWLZ577jlq166No6Mjf/vb38x97sT58+epVauWRZudnR1ubm5cvnzZov3XcV2L7WZz9uzZk549e95xPCIid8vW6llVo1u56f5VXlXmFcDXdlh46623Sn2WmprKyJEjH3BEV7c6y8rKYtq0afj6+prb8/Ly7mo8Dw8Pi1VhuFoOcfnyZdzc3O4pVhERERGxVCES3StXrvDNN98QFhZWamXy+PHjrF69mvT09Ace17VVYkdHR3PboUOHOHfuHE2aNDG33Wq19deaNm3K7t27GTp0qHl1edeuXRQXF1uUVdyrfY4Nym0sKRuvaanlMo5WJSo33T8RkYqlQiS6u3fvpqCggH79+hEQEGDxWbNmzdi8eTMpKSkEBQU90LgCAgKoVq0aS5cu5cknnyQrK4sNGzbg6elp0c/X15c9e/bw9ddf4+XlhYeHR6k+AOHh4bz44ovMnTuXXr16kZ2dzZo1a2jduvVtH2QTERERkTtTIV4YkZKSQr169UoluXB1tbRz5858/fXXd1UXey/c3d15/vnnycnJYe7cuXzyySdERUWZH1q7plevXoSEhLBkyRJeeukl84Nh16tfvz7/93//x8WLF3nzzTdJTEwkLCzMYp9dERERESkfBpNeFWJz9o9vePtOUq5UuiCg+1eZ6d5Vbrp/ldf9fhitQqzoioiIiIiUtwpRoyvlK2TJJGuHUO4yMoZaOwQRERGpZLSiKyIiIiI2SYmuiIiIiNgkJboiIiIiYpOU6IqIiIiITVKiKyIiIiI2Sbsu2CDtUCAiIiKiFV0RERERsVFKdEVERETEJinRFRERERGbpERXRERERGySHkazQWt9fa0dgojVDc3IsHYIIiJiZVrRFRERERGbpERXRERERGySEl0RERERsUlKdEVERETEJulhNBvU6vXq1g5BxOr2rwq0dghSBbUacdjaIYjIr2hFV0RERERskhJdEREREbFJSnRFRERExCZV2ER3165dzJo1i5EjRzJ06FCio6NJTEzk4sWL/PLLL0RERJCWlnbLMdavX8/o0aPNvx84cICIiAh++umnWx63evVqJkyYUC7nUVbXzumXX355oPOKiIiI2KoK+TDa6tWr2bJlC927d6d///64urpy6tQpPv/8c06ePMmIESPKNE6PHj1o3779fY5WRERERCqiCpfo7tmzh48//pjf//73PP744+b2Fi1a0LNnT7777rsyj+Xl5YWXl9f9CJPCwkKcnJzuy9j3qs+xXGuHICJidRmzyv4aaG9vbzIzM+9jNCJiDRUu0d2yZQuNGze2SHKvsbOzo23btuav9wsKCli2bBkpKSm4uLjw+OOPM3jwYOzsrlZkrF+/nq1bt7J8+fKbzpebm0t8fDx79uzBxcWFPn36lOqzfft2Fi9ezOuvv05CQgJHjhzht7/9LYMHD6awsJD169eTkpLChQsX8PX1JTIyknbt2pmPnzBhAqGhoXh6evLxxx9TUFBA69atiYqKonr1m28FtnnzZv7973+TnZ2Nq6srjRo1YsKECbi7u5f5eoqIiIhUVRUq0S0qKuLw4cMMGDCgTP3XrFlDaGgoL7zwAvv372fjxo34+fnRpUuXMs+5ePFiDh48yIgRI3B3d+ejjz7i559/xt7evlTfd955hyeeeILBgwebE9S33nqLo0ePEhERQd26dUlNTWXu3LnMmTOHRo0amY9NTU2lYcOGjB07luzsbFatWoWbmxtjxowBoE6dOqxfv97c/8svv2Tz5s0MGzaM+vXrc+nSJdLT07ly5UqZz01ERESkKqtQie7ly5cxGo14e3uXqX9QUBDPPfccACEhIXz77bd8/fXXZU50T548ye7du5kyZYr5mODgYP7whz/g6upaqn/fvn3p16+f+ff9+/fzzTffMHPmTFq0aAFA69atOXPmDP/85z95/vnnzX0dHByYOnWqOYE+deoUKSkp5kT3ekePHiUkJITevXub20JDQ2/YNykpiaSkJADmzJlTpnMXEbF1Zf1/CVz9b/Sd9JeKRfdPbqZCJbrXGAyGMvULCQmx+N3Pz++Oaqx++OEHAIsH1qpVq0ZISAhHjx4t1f/X5QhwNdF1d3enWbNmFBcXm9uDg4PZvn27Rd+WLVtarBL7+flx8eJFioqKcHAofRsaNWrEv//9b9avX0+7du1o0qSJuSTjej179qRnz563P2ERkSrkTv5/oBrdyk33r/J66KGH7uv4FSrRdXNzw9HRscz/sl5f3+rg4IDRaCzzfDk5Obi4uJR6qKxWrVo37H99+8WLF8nJySEyMrJU3+uT0utXiB0cHDCZTDdNdLt3705+fj7btm1j48aN1KhRgyeeeIKIiIibJrwiIiIi8j8VKtF1cHCgWbNmfPfddzzzzDP3fT53d3fy8/NL7aBw4cKFG/a/fqXZzc0NT09Ppk6dWu6x2dnZMWDAAAYMGEBmZibJycmsW7cOT09PevXqdctj9zk2KPd4xLZ5TUu1dgg2QatKIiIVS4VbGuzXrx8//PBDqa/+AUpKSvj222/LbS5/f3/g6pZm11y5coV9+/aV6fhWrVqRk5NDtWrV8Pf3L/VTXry9vfnNb36Dj48Pp06dKrdxRURERGxZhVrRhav1sgMGDOBvf/sbhw4dokOHDlSrVo2MjAw+//xzateuXeYXRtxO/fr1ad++PX//+9/Jy8vDw8ODDz/8EGdn5zIdHxISQuvWrZk9ezaDBg3Cz8+P/Px8fvzxR4xGI0OHDr3r2JYtW4abmxsBAQG4urpy4MABzp49y7Bhw+56TBEREZGqpMIlugDPPfccgYGBbN26lXfeeYfCwkLq1KnDww8/zJNPPklhYWG5zTV+/Hji4+NZtWoV1apVo3fv3vj7+7Nr167bHmswGIiJiWHz5s1s2bKFzMxM3NzcaNSoEX379r2nuAIDA9m2bRuff/45RqMRHx8fxo0bR8eOHe9pXBEREZGqwmAymUzWDkLK1/7xDa0dglQyqtEtH6rRrbx07yo33b/Kq0rtuiDlI2TJJGuHIDeRkXHrchb9x1pERKT8VLiH0UREREREyoMSXRERERGxSUp0RURERMQmKdEVEREREZukRFdEREREbJJ2XbBBt3uyX0RERKQq0IquiIiIiNgkJboiIiIiYpOU6IqIiIiITVKiKyIiIiI2SQ+j2aC1vr7WDkFEfmVoRoa1QxARqZK0oisiIiIiNkmJroiIiIjYJCW6IiIiImKTlOiKiIiIiE1SoisiIiIiNkm7LtigVq9Xt3YIIvIr+1cFWjsEqWJajThs7RBEKgSt6IqIiIiITVKiKyIiIiI2yaZKF3bt2sWnn37K8ePHKSwspHbt2nTu3Jl+/fpRs2ZNa4d3U6tXr2bXrl0sWrTI2qGIiIiI2AybSXRXr17Nli1b6N69O/3798fV1ZVTp07x+eefc/LkSaZOnWrtEEVERETkAbKJRHfPnj18/PHH/P73v+fxxx83t7do0YKePXvy3XffWTG6B6/PsVxrhyAicscyZlnvVcne3t5kZmZabX4RuT9sItHdsmULjRs3tkhyr7Gzs6Nt27YAXLx4kdWrV/PNN99QWFhI06ZNefbZZ/H39zf3nzBhAqGhodSsWZNPPvmEgoICevTowbPPPsvevXtJSEggMzOT4OBgxo8fj5ubm/nYy5cvs3btWnbv3k1eXh6NGzdmxIgRBAQEmPvk5uYSHx/Pnj17cHFxoU+fPhbxXr58mXHjxhEVFUW3bt3M7SaTiYkTJ9KxY0dGjBhRXpdORERExGZV+kS3qKiIw4cPM2DAgNv2jYuL4+zZszz77LPUqFGDjz76iFmzZjF37lx8fHzM/Xbu3EnTpk0ZP348x44dIzExkZKSEv773/8yZMgQCgsLWbFiBWvXrmXs2LEAGI1GYmNjyc3NZfjw4dSqVYvPPvuM2NhY5s+fj7u7OwCLFy/m4MGDjBgxAnd3dz766CN+/vln7O3tAXBzc6NDhw5s377dItE9cOAA586do3v37uV49URERERsV6VPdC9fvozRaMTb2/uW/b799lsOHTrEzJkzadGiBQDBwcFMmDCBDz/80JywAjg6OvL8889jZ2dHmzZt2L17N59++inz58+nTp06AJw4cYIvv/zSfNyOHTv46aefeOutt6hXrx4ArVq1YsqUKXz00Uc8++yznDx5kt27dzNl3CJoXgAAIABJREFUyhS6dOlijuEPf/gDrq6u5vkff/xxXnvtNX7++Wfq1q0LwPbt22nSpAkNGjQodW5JSUkkJSUBMGfOnLu6jiIi1na7/47fTw4ODladX+6N7p/cTKVPdK8xGAy3/Pzo0aPUrFnTnOQCVKtWjYcffpjvv//eom/Lli2xs/vfzms+Pj5cvnzZnORea7t48SJFRUU4ODiwb98+mjRpQp06dSguLjb3CwoK4tixYwD88MMPALRv394ihpCQEI4ePWpua9WqFd7e3nz55ZdERESQn5/Prl27/p+9O4+v8cz/P/4+2UQWCTmIxBYhliw0trEU0aD1xbSlQdTSUlPFtFM1/VUpqjWpmbb0O6qmNbUvoaMYnSLVmCIoWtG0pJZpSZkkliyCRHJ+f/SbU6dJCBLHufN6Ph55PHKu+z7X/bnP9Z1+365c93U0bNiwUu8tOjpa0dHRN7x/ALjX2XONLGt0HRvj57gCAgIqtX+HD7peXl5ydXW96f+BX7hwQT4+PiXafXx8lJuba9N2/eyq9PO/FD09PUu0WSwWa9DNycnR999/r6FDh5a4RvGs7MWLF1W9enW5ubmVqOF6JpNJUVFR+vzzz/XYY48pKSlJRUVF6tq16w3vEQAAAL9w+KDr4uKi5s2b69ChQxoyZEiZ59WsWVPZ2dkl2rOysmweKLtdXl5eCg4O1pgxY0occ3V1lST5+vrq8uXLys/Ptwm7WVlZJd7To0cPrV27VikpKUpMTFT79u3LXWeya8nlDag6/KYm2buEKotZJQC4txjim9H69u2r48ePKzExscSxoqIiff3112ratKmysrL07bffWo9dvXpVBw8eVIsWLe64hvDwcJ09e1Zms1nBwcE2P8Xraot3d9i/f7/1fVeuXFFycnKJ/sxms1q3bq34+HgdOXLE5sE0AAAA3JzDz+hKP6957devn9577z0dPXpU7du3l7u7u9LS0rRt2zbVrl1bkydPVvPmzTV37lzFxsZad13Iz8/XgAED7riGbt26adu2bZoxY4b69++vunXrKicnR8eOHZOvr6/69eunBg0aqF27dnr//feVl5enmjVrauPGjapWrVqpffbs2VNvvfWW/Pz8FBERccc1AgAAVCWGCLqSNGLECIWEhGjLli2aN2+e8vPzVadOHbVt29YaZCdPnqylS5dqyZIl1n10X3nlFZutxW6Xm5ubpk+frjVr1mjt2rW6ePGifHx81LRpU5uHz5555hl98MEHWrJkidzd3dWnTx8FBwdr7969JfqMjIyUs7OzunfvbvNwHAAAAG7OZLFYLPYuAqU7ePCg3njjDc2bN++WwvjhZxpVYlW417FG135Yo+u4GDvHxvg5LnZdqILOnz+vs2fPauXKlbrvvvsqZMYZAACgqiHo3oMSEhL0j3/8Q0FBQXryySdv+f0RCyZWQlWOKy0t1t4llBuzEgAAVByC7j0oJiZGMTEx9i4DAADAofGEEwAAAAyJoAsAAABDIugCAADAkFija0CO9PAVAABAZWFGFwAAAIZE0AUAAIAhEXQBAABgSARdAAAAGBIPoxnQysBAe5cAAFaxaWn2LgFAFcWMLgAAAAyJoAsAAABDIugCAADAkAi6AAAAMCSCLgAAAAyJXRcMKHy2p71LAACrw0tC7F0CbiJ8ZKq9SwAqBTO6AAAAMCSCLgAAAAyJpQu/snfvXn366ac6efKk8vPzVbt2bXXq1El9+/ZVjRo1ynzf+PHj1bFjR40YMeIuVgsAAICyEHSvs3TpUm3evFlRUVH6n//5H3l4eOj06dPatm2bTp06pcmTJ5f53hdeeEHe3t53sVoAAADcCEH3/+zfv1///Oc/9fTTT6tnz57W9latWik6OlqHDh0q9X35+flyc3NTUFDQ3SoVAAAA5UDQ/T+bN29WUFCQTcgt5uTkpPvuu0/p6emaMGGCJk6cqEOHDmn//v0KDg7WtGnTSixdmD9/vk6dOqWYmBgtW7ZMGRkZCg0N1cSJE5Wbm6uFCxfq2LFjCgwM1Lhx49SoUSPr9YqKirRx40Z99tlnOnfunGrXrq1HHnlEPXr0KNe9PHjiUoV8JgCqrrSZafYu4a4ym83KzMy0dxkAKhhBV9K1a9eUmpqqfv36lev8ZcuWqUOHDnr++efl5FT283yZmZlas2aNhgwZoqtXr+rDDz/UwoULlZGRoQceeEADBgzQypUrNXfuXL311lsymUySpL///e/asWOHBg0apKCgICUnJ2vBggXy9vZW27ZtK+SeAQAAjI6gKyk3N1cFBQUym83lOr9Zs2YaM2ZMufp97bXX5O/vL0n68ccftXHjRo0fP17du3eXJFksFsXFxSktLU3169fX2bNntW3bNo0bN846gxsREaGLFy9q3bp1pQbdhIQEJSQkSJLi4uLKdQ8AcCPl/e+hUbi4uFS5ezYSxg9lIehep3hG9WYiIyPLdV7t2rWtIVeS9fewsLASbefPn1f9+vV1+PBhmUwmdejQQYWFhdbzwsLCtGvXLhUVFZWYRY6OjlZ0dHS5agKA8qhqf8Zn6YJjY/wcV0BAQKX2T9CV5OXlJVdX13L/j8THx6dc53l62n5DmYuLS4n24raCggJJUk5OjoqKijRq1KhS+7xw4YL8/PzKdX0AAICqjKCrn8Nm8+bNdejQIQ0ZMuSm55d35vd2eHl5ydnZWbNmzSr1OuUJ2cmuDSujNFQQv6lJZR5jVsKxMX4AcG/hm9H+T9++fXX8+HElJiaWOFZUVKSvv/76rtQRFhamoqIi5eXlKTg4uMRP8QwwAAAAbozU9H/atWunfv366b333tPRo0fVvn17ubu7Ky0tTdu2bVPt2rU1cuTISq8jICBAvXr10ty5czVgwAAFBweroKBAp06d0pkzZ/T0009Xeg0AAABGQNC9zogRIxQSEqItW7Zo3rx5ys/PV506ddS2bVsNGDBA+fn5d6WO0aNHq169evrss88UHx+v6tWrq379+qXu8QsAAIDSmSwWi8XeRaBiHX6m0c1Pgt2wRte4GD/Hxdg5NsbPcVX2rgus0QUAAIAhsXTBgCIWTLR3CbckLS3W3iUAAAADYkYXAAAAhkTQBQAAgCERdAEAAGBIBF0AAAAYEg+jGRAPdwEAADCjCwAAAIMi6AIAAMCQCLoAAAAwJIIuAAAADImH0QxoZWCgvUsAAFSi2LQ0e5cAOARmdAEAAGBIBF0AAAAYEkEXAAAAhkTQBQAAgCERdAEAAGBI7LpgQOGzPe1dAgCgEh1eEmLvEipE+MhUe5cAg2NGFwAAAIZE0AUAAIAhlWvpQnx8vNatWydJMplM8vDwkL+/vyIiIvTQQw/J19e3Uou8l+3evVv5+fnq0aOHvUsBAADAdcq9RtfDw0NTpkyRJOXl5enkyZPaunWrEhIS9PLLL6tJkyaVVuS9LCkpSTk5OQRdAACAe0y5ly44OzsrJCREISEhatOmjR555BH95S9/Uc2aNfX222+rqKioMuusEiwWi/Lz8+1dBgAAgCHc0a4Lnp6eevzxxzV79mwlJyerTZs2ys7O1tKlS3Xw4EHl5+eradOmGj58uIKDg23em5CQoH/96186e/asPDw81KJFC40bN04eHh6aMWOGvL29NWnSJOv5KSkpmjlzpv7yl7+oYcOGSk9P14QJE/Tss8/q0KFD2rt3r6pXr67Y2Fh169ZNGzZs0CeffKJr164pKipKsbGxcnL6Jdf/+OOPWrFihb777jtJUps2bfTkk09al2EUX2/69OnasmWLvvrqK/n4+Kh///7q06ePJGn+/Pnau3evJCkmJkaSNGjQIMXExOjgwYPavHmzfvjhBxUUFKh+/fqKiYlR69atrTXEx8dry5Ytmjx5spYsWaIffvhBTz/9tFasWKHo6Gg99thjNp/Z9OnT5e3trRdeeOGG4/LgiUu3NI5AVZU2M61C+zObzcrMzKzQPnF3MHaAMd3x9mKhoaFydnZWamqq2rRpoz//+c86e/ashg8fLm9vb23atEkzZ87UnDlz5O/vL0n66KOPFB8fr969e2v48OG6evWqDh48qCtXrsjDw+OWrr9ixQp17dpVkyZN0vbt2zV//nz95z//UUZGhsaNG6cTJ05o9erVCgoKUpcuXSRJZ8+e1bRp0xQcHKwJEyaoqKhIa9as0RtvvKHZs2fLZDJZ+1+4cKG6d++u6Oho7dy5U4sWLVJwcLCaNm2qgQMHKjMzU3l5eRo9erQkyc/PT5KUnp6utm3bqn///nJyctJXX32l2bNna+bMmWrRooW1/6tXr2r+/PkaMGCA6tWrp1q1aql79+5KTEzUoEGDrLX897//1ZEjRzR58uTbHywAAIAq5I6Drqurq7y9vZWVlaWvv/5aR48e1YwZM9SqVStJUlhYmMaPH6+NGzdq7NixunTpktavX6++fftq5MiR1n46dux4W9cPCwtTbGysJKlp06bau3ev9u/fr7lz58rJyUlt2rTRl19+qX379lmD7tq1a+Xr66spU6bIxeXnj6BRo0Z67rnn9NVXXykyMtLaf5cuXTRw4EBJUqtWrXTgwAHt3btXTZs2lb+/v7y8vGSxWBQSYrun4YMPPmj9vaioSKGhoTp9+rS2b99uE3Tz8/M1YsQItW/f3toWFRWljz/+WCkpKQoLC5MkJSYmqkaNGrrvvvtu63MCAACoairkCyMsFosk6dixY6pRo4Y15EqSu7u72rZtqyNHjkiSUlNTlZ+fr6ioqIq4tDUISj8/MFd8/euXKfj7+9v8Serw4cPq3r27TCaTCgsLJUl16tRRnTp1dPz4cZuge/1SAxcXF9WrV0/nzp27aV3nzp3TqlWrdPjwYV28eNH6GTVv3tzmPJPJVCK81qtXTy1btlRiYqLCwsJksVj073//W926dZOzs3OJayUkJCghIUGSFBcXd9PaAPzMbDZXaH8uLi4V3ifuDsbOsTF+KMsdB938/Hzl5ubKx8dHFy5ckI+PT4lzfHx8lJubK0nKycmRpArbkszT0/ZbwFxcXEosf3BxcVFBQYH1dU5OjjZs2KANGzaU6O/XIfZmfZWmqKhIc+bM0eXLlzV48GD5+/urWrVqio+PV1ZWVon6i2eVr9ezZ0998MEHGj16tI4dO6aMjIwy/3EQHR2t6OjoG9YEoKSKXpPJOk/Hxdg5NsbPcQUEBFRq/3ccdFNSUlRYWKiQkBAdO3ZM2dnZJc7JysqSl5eXJMnb21uSdPHiRdWoUaPUPl1dXXXt2jWbtuKgXBG8vLzUvn17PfDAAyWOFdd3J86ePauTJ09qypQpatOmjbW9tB0Vrl8PfL1OnTrpww8/VFJSklJSUtSsWTPVr1+/XNdPdm14e4UbjN/UJHuXcMv4jzUAABXnjr4Z7dKlS1qxYoX1yyOaNm2qrKwsffvtt9Zzih80K16XGhISIjc3NyUmJpbZr5+fn3766SebtuTk5Dsp1UZYWJhOnTqlJk2aKDg42OanTp06t9RXaTO8xYH2+pnajIwM6/KN8nBzc1OXLl20ZcsW7du3j316AQAAblG5Z3QLCwuVmpoqSbpy5YpOnDihrVu36urVq3r55ZetD341b95cc+fOVWxsrHXXhfz8fA0YMEDSz3+qHzhwoFavXq1r164pMjJSBQUFOnjwoB577DHVqlVLHTp00Pbt27V48WJFRkYqJSVFhw4dqrCbfuyxxzRlyhTFxcUpKipK3t7eOn/+vJKTk9WjRw+FhoaWu6/AwEDt379f+/btk5+fn2rWrKnAwED5+flp2bJlGjx4sC5fvqz4+HjVqlXrlurs2bOntm3bZg29AAAAKL9yB928vDxNnTpVJpNJ1atXl7+/v+6///4SXwE8efJkLV26VEuWLLHuo/vKK69YtxaTpEceeUReXl765JNPlJCQIE9PT7Vs2VLu7u6SpMjISA0dOlRbt27V9u3b1a5dO40aNUpz5sypkJsOCAjQ66+/rtWrV2vhwoXKz89XrVq1FB4eblNnefTu3VsnT57UggULdOnSJes+upMmTdKiRYv05ptvys/PT48++qhSUlJ06tSpcvcdHBysWrVqqVWrVre87RoAAEBVZ7IUbweAe87p06f1/PPPa9q0aQoPDy/3+w4/06gSq3IcrNHF3cb4OS7GzrExfo7rnn8YDRUvJydHP/30k9asWaMGDRrYbKEGAACA8iHo3oMOHDigBQsWKCAgQBMnTixzZ4ayRCyYWEmV3bm0tFh7lwAAAKoIgu49qEePHuyyAAAAcIfuaHsxAAAA4F5F0AUAAIAhEXQBAABgSARdAAAAGBIPoxkQOxsAAAAwowsAAACDIugCAADAkAi6AAAAMCSCLgAAAAyJh9EMaGVgoL1LAIDbEpuWZu8SABgIM7oAAAAwJIIuAAAADImgCwAAAEMi6AIAAMCQeBjNgMJne9q7BAC4LYeXhNi7BNym8JGp9i4BKIEZXQAAABgSQRcAAACGRNAFAACAIVW5Nbrx8fFat26dJMlkMsnDw0P+/v6KiIjQQw89JF9fXztXCAAAgIpQ5YKuJHl4eGjKlCmSpLy8PJ08eVJbt25VQkKCXn75ZTVp0sTOFQIAAOBOVcmg6+zsrJCQX57sbdOmjXr37q3p06fr7bff1rx58+TkVHJVR35+vtzc3O5mqbflwROX7F0CgHtc2ky+avd6ZrNZmZmZ9i4DQAWrkkG3NJ6ennr88cc1e/ZsJScnKyAgQBMmTNDEiRN16NAh7d+/X8HBwZo2bZp27NihhIQEnT59WpLUuHFjPf744woODrb2N3/+fJ06dUoxMTFatmyZMjIyFBoaqokTJyo3N1cLFy7UsWPHFBgYqHHjxqlRo0bW927atEm7d+/WTz/9JDc3NwUHB2vUqFHy9/e/658LAACAoyLoXic0NFTOzs5KTU1VQECAJGnZsmXq0KGDnn/+eessb0ZGhrp37666devq2rVr2rlzp6ZPn64333xTdevWtfaXmZmpNWvWaMiQIbp69ao+/PBDLVy4UBkZGXrggQc0YMAArVy5UnPnztVbb70lk8kkSTp37pz69Omj2rVr6/Lly9q2bZumTZumefPmycPD4+5/MAAAAA6IoHsdV1dXeXt7Kysry9rWrFkzjRkzxua8QYMGWX8vKipSRESEjh8/ri+++MLmWG5url577TXrTOyPP/6ojRs3avz48erevbskyWKxKC4uTmlpaapfv74kadSoUSX6HzNmjL788kvr+66XkJCghIQESVJcXNwdfgoAqgKz2WzvEu4pLi4ufCYOjPFDWQi6v2KxWGxeR0ZGljjn9OnTWrVqlVJTU21C8U8//WRzXu3atW2WGxT/HhYWVqLt/Pnz1qCbmpqqNWvW6OTJk8rNzbWee+bMmVJrjo6OVnR0dLnuDwAksR71V1ij69gYP8dV/Bf0ykLQvU5+fr5yc3Pl4+Njbbv+d0m6fPmyXn/9dfn4+GjEiBGqXbu2XF1d9d5776mgoMDmXE9P26/idXFxKdFe3Fb83szMTL3++utq2rSpxo4dq5o1a8rFxUVxcXHKz8+vuJsFAAAwOILudVJSUlRYWGizI0PxutliqampOnfunKZOnarAwEBre15eXoXU8PXXX+vq1auaPHmy3N3dJUmFhYU2M7s3k+zasEJqwc35TU2q0P6YlXBsjB8A3Fv4ZrT/c+nSJa1YscL65RFlKZ5VdXV1tbYdPXpUGRkZFVJHfn6+TCaTnJ2drW1JSUkqLCyskP4BAACqiio5o1tYWKjU1FRJ0pUrV3TixAlt3bpVV69e1csvv1zqHrrFmjVrJnd3dy1cuFADBgzQuXPntHbtWtWqVatCagsLC1NRUZHeffdd9ezZU6dOndKmTZtKLIMAAADAjVXJoJuXl6epU6fKZDKpevXq8vf31/3331+urwD29fXV888/r2XLlmnOnDmqV6+ennrqKW3cuLFCamvYsKGeeeYZrVu3Tvv27VPjxo31/PPPa+7cuRXSPwAAQFVhsvx6mwE4vMPPNLr5SagQrNHF9Rg/x8XYOTbGz3Gx6wJuWcSCifYuoYS0tFh7lwAAAKoYHkYDAACAIRF0AQAAYEgEXQAAABgSQRcAAACGRNAFAACAIbHrggGxwwEAAAAzugAAADAogi4AAAAMiaALAAAAQyLoAgAAwJB4GM2AVgYG2rsEwJBi09LsXQIA4BYwowsAAABDIugCAADAkAi6AAAAMCSCLgAAAAyJoAsAAABDYtcFAwqf7WnvEgBDOrwkxN4lwIGFj0y1dwlAlcOMLgAAAAyJoAsAAABDMtzShfj4eK1bt65Ee3h4uKZNm1Zh17l48aK2bt2qnj17ymw2V1i/AAAAqBiGC7qS5OHhoSlTppRoq0gXL17UunXrFBERQdAFAAC4Bxky6Do7OyskpOo+NPLgiUv2LgEA7CZt5q1/VbPZbFZmZmYlVAPAngwZdG9k48aN2r17t86cOSM3Nzc1a9ZMI0aMkL+/v/WcadOmyc/PT5GRkVq7dq2ys7PVokUL/e53v1OtWrV09uxZ/fGPf5QkvfLKK5IkJycnrV69WpcvX9aKFSt0+PBhnTt3Tj4+PoqMjFRsbKyqV69uvUZCQoI2b96s9PR0ubu7q0GDBhozZozq16+vF198UUFBQXr66adtan/nnXd05swZ/elPf7oLnxQAAIBjM2zQLSwstHnt5OQkk8mkc+fO6aGHHpLZbFZeXp62bt2qV155RfPmzbMJokePHtW5c+c0cuRIXblyRYsXL9b777+vF198UX5+fpowYYL++te/auzYsWrYsKFMJpMk6erVq5KkoUOHqkaNGsrMzNRHH32k9PR0vfTSS5Kkb775RosWLdLgwYPVrFkzXb58WUePHtXly5clSVFRUVq1apWeeOIJVatWTZKUl5enffv2acSIEZX+2QEAABiBIYNuTk6Ohg4datM2depURURE6IknnrC2FRUVKSIiQmPGjNGBAwfUtWtX67ErV67opZdesq7tPX/+vFasWKFr167J1dVVDRs2lCTVr1/fZpmEr6+vxowZY31dWFgos9msGTNm6Pz586pVq5aOHTumoKAgPfzww9bz2rVrZ/39/vvv17Jly7R3715169ZNkrR7925ZLBZ16dKlxP0mJCQoISFBkhQXF3frHxgAGMjtPDfh4uLC8xYOjPFDWQwZdD08PErssBAQECDp55na+Ph4nTx5Urm5udbjP/30k835TZs2tXmArX79+rJYLDp//rzq1Klzw+snJiZq8+bNOnv2rHWGV5LOnDmjWrVqqXHjxlq1apWWLl2q9u3bq1mzZnJx+WUoPD091bFjRyUmJlqDbmJiotq3by9Pz5JfBhEdHa3o6OibfSwAUCXczlpb1ug6NsbPcRXns8piyKDr7Oys4ODgEu3p6el6/fXX1bx5c40dO1Y1a9aUi4uLXn/9dRUUFNic++tAWRxEf33eryUlJendd99Vnz59FBsbK29vb2VmZuqtt96yvrdNmzZ6+umn9emnn2rz5s1yd3dX9+7dNWzYMOtShZ49e2rWrFnKyMhQQUGBUlNTFRMTc9ufCQAAQFVjyKBblq+++koFBQWaPHmy3NzcJEnXrl3TpUsVt0tBUlKSmjdvrtGjR1vbitfeXi8qKkpRUVHKysrS3r17tWTJEnl4eGjIkCGSpNDQUNWpU0c7duxQfn6+/Pz8FBYWVq4akl0bVszNoErzm5pk7xIcDrNKAHBvqVJBNz8/X05OTnJy+uUL4Xbt2iWLxXLLfRXP8Obn55e4hqurq03bF198UWY/Pj4+6t27t/bs2aPTp09b200mk6KiorR9+3Zdu3ZNPXr0sKkbAAAAN1algm54eLiWL1+ud999V1FRUfrxxx+1efNmm90Wyqt27dpydXVVYmKi3N3d5eLioiZNmigiIkJLlizR+vXrFRwcrAMHDui7776zeW/xNmQtW7ZUjRo1dOLECR05cqTEjgo9evRQfHy8LBaLevTocSe3DgAAUOVUqaDbuHFjjRs3TuvWrdPevXsVFBSkSZMm6c9//vMt91WtWjWNHTtWH330kaZPny6LxaLVq1erT58+Sk9P1+bNm1VQUKDWrVtrwoQJNg/HNW3aVJs3b9bOnTt15coVmc1mDRkyRH369LG5Rq1atRQcHCxXV1ebfX4BAABwcybL7fzdHndFdna2xo0bp6eeeuqWZnQPP9Oo8opClcEa3VvHGl3Hxdg5NsbPcbHrQhV0+fJlnT59Wv/85z/l6empzp0727skAAAAh0PQvQd9//33eu2111S7dm1NmDDBukNEeUUsmFhJlRlHWlqsvUsoFbMSAABUHILuPSgiIkLx8fH2LgMAAMChsV8VAAAADImgCwAAAEMi6AIAAMCQWKNrQPfqg1YAAAB3EzO6AAAAMCSCLgAAAAyJoAsAAABDIugCAADAkHgYzYBWBgbauwQAAG5JbFqavUuAATGjCwAAAEMi6AIAAMCQCLoAAAAwJIIuAAAADImgCwAAAENi1wUDCp/tae8SAABQ+MhUe5eAKo4ZXQAAABgSQRcAAACG5FBBNz4+XjExMSV+Zs2apcLCQsXExGjr1q3W86dNm6a5c+dWWj3JycmKiYnRmDFjdPXqVZtjmzdv1pAhQ265z6efflorVqyoqBIBAACqLIdbo+vh4aEpU6aUaHN2dtZrr72munXr3vWasrOztW3bNvXr1++O+3rxxRfl7e1dAVUBAABUbQ4XdJ2dnRUSElLqsbLaK1toaKg2bdqkPn36yNXV9Y76CgoKuuHxoqIiFRUVycWl7KF78MSlO6oBMIq0mXf3K0XNZrMyMzPv6jVRMRg7wJgcaunCjZS2dOHXVq9eraeeekqpqal68cUXNWzYME2fPl0ZGRm6ePGi3njjDQ0fPlx/+MMf9O2335b72r/97W+VnZ2tzz///IbnZWVl6X//93/1xBNP6PHHH9fMmTN14sSExV7QAAAgAElEQVQJm3N+vXThnXfe0csvv6w9e/boD3/4g4YNG1biPQAAACjJIYNuYWGhzY/FYin3e69cuaL3339f/fv317PPPqv09HTNnz9f8+bNU6tWrTRp0iT5+vrqzTffVH5+frn6rF27tu6//35t2LBBhYWFZZ43Z84cffPNNxo5cqSee+45Xbt2TTNnztR///vfG/Z/9uxZrVq1SgMHDtRLL70ks9lc7vsFAACoqhxu6UJOTo6GDh1q0zZ16lSFhoaW6/1Xr17V6NGj1aJFC0lSZmamFi9erCFDhqh///6SpJo1a2ry5Mk6cuSIIiIiytXvww8/rB07duiLL75Qjx49Shw/cOCAvv/+e7366qvWa4eFhWncuHHatGmTxowZU2bfubm5mj59uho2bFjq8YSEBCUkJEiS4uLiylUvUBXc7X8Uuri48A9RB8XYOTbGD2VxuKDr4eGhadOm2bQFBASU+/2urq5q3ry59bW/v7+kn0Pnr9vOnz8v6ed1sdfPGjs5OclkMpWooVOnTvr444/VrVu3Etc9duyYatasaQ25kuTu7q7IyEgdPXr0hjWbzeYyQ64kRUdHKzo6+oZ9AFXR3V5zyTpPx8XYOTbGz3HdSoa7HQ4XdJ2dnRUcHFyi/UZLBq7n4eFhE1KLH+ry9PQs0Va8dCE+Pl7/+Mc/rMcHDx6sgQMHluj70Ucf1eTJk7Vnz54Sxy5cuCAfH58S7T4+PsrNzb1hzaW9DwAAADfmcEHXHnr37q327dtbX9eqVavU8xo2bKi2bdtq/fr16t69u82xmjVrKisrq8R7srKy5OXldcPr/3r2+GaSXcue/XVEflOT7F3CXcOsBAAAFcchH0a722rVqqXg4GDrT82aNcs899FHH9UPP/ygAwcO2LQ3a9ZMFy5csFmmcOXKFX311Vc2SykAAABQMZjRrWBNmzZVRESEkpOT5eT0y78jIiMj1axZM7311luKjY2Vp6enNm7cqMLCQutDcAAAAKg4zOhWgkcffbTU9j/+8Y8KDQ3V4sWL9fbbb8vZ2VnTp0+3y7e5AQAAGJ3Jciub0MIhHH6mkb1LqFCs0YWjYPwcF2Pn2Bg/x1XZuy4wowsAAABDYo2uAUUsmHhH709Li62gSgAAAOyHGV0AAAAYEkEXAAAAhkTQBQAAgCERdAEAAGBIPIxmQDxMBgAAwIwuAAAADIqgCwAAAEMi6AIAAMCQCLoAAAAwJB5GM6CVgYH2LgG4Y7FpafYuAQDg4JjRBQAAgCERdAEAAGBIBF0AAAAYEkEXAAAAhkTQBQAAgCGx64IBhc/2tHcJwB07vCTE3iXAAYWPTLV3CQDuIczoAgAAwJAIugAAADCkKht04+PjNXr06FKPzZ8/X//v//2/u1wRAAAAKlKVDboAAAAwNoJuJcnPz7+l9jvtFwAAALbYdaEc/vOf/2jp0qVKTU2Vq6ur7rvvPo0YMUK+vr6SpPT0dE2YMEETJ07UoUOHtH//fgUHB2vatGkaP368OnbsKA8PDyUkJCgrK0urVq2SJO3evVsfffSRzpw5Ix8fH3Xr1k0xMTFydnaWJCUmJurdd9/V7NmztXz5cn3//fd65JFHNGjQoBvW++CJS5X7gQCAHaTNTLN3CQAcTJUPuoWFhSXaLBaL9ffs7GzNmDFDgYGB+v3vf68rV65o5cqVeu211xQXFycXl18+wmXLlqlDhw56/vnn5eT0y2T5zp071aBBA40ZM8Z6vUOHDmnu3Lnq1q2bhg8frh9++EFr1qxRTk6Oxo4da1PPvHnz1KtXLw0aNEienmwdBgAAUB5VOujm5ORo6NChpR5r0qSJJGnTpk2SpJdfflkeHh6SpICAAE2ZMkV79uxR165dre9p1qyZxowZU2p/L774otzc3Kyv4+PjFRoaqgkTJkiS2rRpI0lauXKlBg4cKD8/P+u5Dz30kPr27VvmfSQkJCghIUGSFBcXd+ObBgAHZTabK61vFxeXSu0flYvxQ1mqdND18PDQtGnTSrSvXbtWFy9elCQdO3ZMERER1pArSU2bNlXt2rV15MgRm6AbGRlZ6nXCwsJsQm5RUZFOnDihUaNG2ZzXuXNnrVixQqmpqerUqdNN+y0WHR2t6OjoG54DAI4uMzOz0vo2m82V2j8qF+PnuAICAiq1/yoddJ2dnRUcHFyi3dvb2xp0L168qAYNGpQ4x9fXV7m5uTZtPj4+pV6neC1vsezsbBUWFpY4v/h1efsFAABA2ap00C0PX19fZWVllWi/ePGigoKCbNpMJlO5+qxRo4acnZ2VnZ1t0158HS8vr9vqt1iya8NbOh+4V/hNTbJ3CXeEWSUAuLewvdhNNGvWTIcOHdLly5etbceOHVNGRoZatGhxW306OTmpSZMmSkqy/X/qSUlJMplMCgkJuaOaAQAAQNC9qX79+kmSXn/9dX355Zf64osv9Oabb6phw4b6zW9+c9v9xsTEKCUlRe+++66+/vprbdy4UWvWrNEDDzxg8yAaAAAAbg9LF26iRo0amj59upYuXap58+bJxcVF9913n0aOHGmztditat26tZ577jl99NFH+uKLL+Tj46N+/fopJiamAqsHAACoukyW6zeNhSEcfqaRvUsAbgtrdGEvjJ1jY/wcV2XvusDSBQAAABgSSxcMKGLBRHuXgHtAWlqsvUsAAMCumNEFAACAIRF0AQAAYEgEXQAAABgSQRcAAACGRNAFAACAIbHrggHxtL3jYi9IAAAqDjO6AAAAMCSCLgAAAAyJoAsAAABDIugCAADAkHgYzYBWBgbauwQABheblmbvEgDgppjRBQAAgCERdAEAAGBIBF0AAAAYEkEXAAAAhsTDaAYUPtvT3iUAMLjDS0LsXQJuQ/jIVHuXANxVzOgCAADAkAi6AAAAMCRDB934+HjFxMTo97//fanHJ06cqJiYGMXHx1fYNVNSUhQTE6Mff/yxwvqUfr6X0aNHV2ifAAAARmbooCtJrq6uSk9P1/Hjx23ajx07pszMTLm6ulbo9YKCgvTaa6/J39+/QvsFAADArTF80HV3d1dYWJh27dpl0757926FhYXJ3d29Qq/n4eGhkJAQubm5VWi/AAAAuDVVYteFzp07a+3atRo+fLhMJpMsFouSkpIUExOjEydOWM+bMWOGvL29NWnSJGtbSkqKZs6cqb/85S9q2LChJGn9+vXavn27zp8/Lw8PDzVu3Fjjx4+Xr69vqecXFRVpw4YNSkxMVEZGhmrUqKHw8HCNHz9eknTw4EFt3rxZP/zwgwoKClS/fn3FxMSodevWt3W/D564dLsfFQCUW9pM43wNsNlsVmZmpr3LAFDBqkTQ7dixoz744AMdOXJELVu21Hfffafs7Gx16NBBy5cvv6W+duzYofXr12vYsGFq0KCBcnJy9M033+jKlStlvudvf/ubduzYod/+9rdq1aqVcnNztWfPHuvx9PR0tW3bVv3795eTk5O++uorzZ49WzNnzlSLFi1u+74BAACqsioRdD09PdWmTRvt2rVLLVu21O7du9W6dWt5et76frPHjh1TRESE+vTpY23r2LFjmeenpaVp+/btGjVqlPr27Wtt79y5s/X3Bx980Pp7UVGRQkNDdfr0aW3fvp2gCwAAcJuqRNCVfg6WS5Ys0ciRI7Vnzx498cQTt9VP48aNtX37dsXHxysyMlJNmjSRk1PZS51TUlIkST169CjznHPnzmnVqlU6fPiwLl68KIvFIklq3rx5uWpKSEhQQkKCJCkuLq6cdwIAd8ZsNtu7hArj4uJiqPupahg/lKXKBN127dpp4cKFWrVqla5evaq2bdveVj9RUVG6fPmyPvvsM61bt07e3t7q1auXYmJiSg28OTk5qlatmjw8PErtr6ioSHPmzNHly5c1ePBg+fv7q1q1aoqPj1dWVla5aoqOjlZ0dPRt3Q8A3C4jrWllja5jY/wcV0BAQKX2X2WCrru7uyIjI7V582Z16tSp1N0WXF1dde3aNZu23Nxcm9dOTk7q16+f+vXrp8zMTO3cuVOrVq1SrVq11Lt37xJ9ent76+rVq8rLyys17J49e1YnT57UlClT1KZNG2t7fn7+7d4qAAAAVIWCriT17t1bBQUF6tWrV6nH/fz89N1339m0JScnl9mf2WzWww8/rM8//1ynT58u9ZywsDBJ0r///W+btbjFigOti8svQ5GRkaEjR46oUaNGN76hMiS7Nryt96Hy+E1NKtd5zEo4NsYPAO4tVSrohoaGKjQ0tMzjHTp00Pbt27V48WJFRkYqJSVFhw4dsjnnb3/7m7y8vNSsWTN5eHgoJSVFZ8+e1bBhw0rtMyAgQNHR0Vq6dKmysrLUsmVL5eXlac+ePXruuecUGBgoPz8/LVu2TIMHD9bly5cVHx+vWrVqVei9AwAAVDVVKujeTGRkpIYOHaqtW7dq+/btateunUaNGqU5c+ZYzwkJCdFnn32mbdu2qaCgQP7+/vrd736nDh06lNnvmDFjZDabtX37dn388cfy8fFRRESEpJ+XS0yaNEmLFi3Sm2++KT8/Pz366KNKSUnRqVOnKv2eAQAAjMpkKX7EH4Zx+JnbW/KAysPShaqB8XNcjJ1jY/wcV2U/jGb4rwAGAABA1cTSBQOKWDDR3iVUmrS0WHuXAAAAHAQzugAAADAkgi4AAAAMiaALAAAAQyLoAgAAwJAIugAAADAkdl0wIHYmAAAAYEYXAAAABkXQBQAAgCERdAEAAGBIBF0AAAAYEg+jGdDKwEB7lwDAYGLT0uxdAgDcMmZ0AQAAYEgEXQAAABgSQRcAAACGRNAFAACAIfEwmgGFz/a0dwkADObwkhB7l4BKED4y1d4lAJWKGV0AAAAYEkEXAAAAhkTQBQAAgCEZKujGx8crJiZGv//970s9PnHiRMXExCg+Pr7CrrlhwwalpKRUWH8AAACoGIYKupLk6uqq9PR0HT9+3Kb92LFjyszMlKura4Veb+PGjQRdAACAe5Dhdl1wd3dXUFCQdu3apeDgYGv77t27FRYWphMnTtixuhu7du2anJyc5OR0Z//+ePDEpQqqCABspc005lcBm81mZWZm2rsMABXMcDO6ktS5c2clJSXJYrFIkiwWi5KSktS5c2frOQcPHtTgwYOVnp5u89709HQNHjxY+/fvlyQdOXJEr7zyikaOHKmRI0dq8uTJSkpKkiSNHz9eOTk5WrdunWJiYhQTE2Od3S0qKtLHH3+siRMnKjY2Vs8++6wSExNtrjVjxgy9+eabSkhI0MSJEzVs2DAdOXJEMTEx+vbbb23OvXLlioYPH65PPvmkQj8rAAAAozLcjK4kdezYUR988IGOHDmili1b6rvvvlN2drY6dOig5cuXS5LatGmjmjVrKjExUTExMdb3JiYmqkaNGrrvvvuUl5enuLg4tWvXToMGDZLFYtGPP/6oS5d+njF94YUXNHPmTP3mN79Rz549JUn169eXJP3973/Xjh07NGjQIAUFBSk5OVkLFiyQt7e32rZta73e0aNH9d///lfDhg2Tm5ubGjdurGbNmunzzz9Xq1atrOclJSWpsLBQXbt2rfTPDwAAwAgMGXQ9PT3Vpk0b7dq1Sy1bttTu3bvVunVreXr+8kUKTk5O6tGjh3bs2KHHHntMJpNJFotFO3bs0P333y9nZ2edOXNGeXl5Gj16tKpXry5Jat26tbWPoKAgOTs7q1atWgoJ+WUz9bNnz2rbtm0aN26cevToIUmKiIjQxYsXtW7dOpuge+nSJc2ZM0e+vr7Wtp49e2rx4sUaPXq03N3dJf0cwNu2basaNWqUuN+EhAQlJCRIkuLi4irgEwSA0pnNZnuXUClcXFwMe29VAeOHshgy6Eo/L19YsmSJRo4cqT179uiJJ54ocU5UVJTWr1+vlJQUhYWFKSUlRRkZGYqKipIk1a1bV+7u7po3b54eeOABtWrVyiYsl+Xw4cMymUzq0KGDCgsLre1hYWHatWuXioqKrOtwmzRpYhNyJalLly5avHixkpKSFBUVpbNnz+rIkSN68cUXS71edHS0oqOjy/3ZAMDtMuo6VtboOjbGz3EFBARUav+GDbrt2rXTwoULtWrVKl29etVmFrVY3bp11apVKyUmJiosLEyff/65mjZtqgYNGkiSvLy8NHXqVK1du1Zvv/22LBaLIiIi9OSTT6pu3bplXjsnJ0dFRUUaNWpUqccvXLggPz8/SZKPj0+J4+7u7urUqZMSExMVFRWlxMRE+fr6qk2bNrfxSQAAAFRNhg267u7uioyM1ObNm9WpUyfrEoBfe+CBB7Rw4ULFxsZq3759Gj58uM3xkJAQvfzyy8rPz1dycrKWLl2qd955R6+//nqZ1/by8pKzs7NmzZolk8lU4vj14ba048V1TZs2TWfOnNG///1vdevWrdy7MSS7NizXebj3NJ/7PbMSDoxZJQC4txg26EpS7969VVBQoF69epV5TocOHbRo0SLNnTtXRUVF6tKlS6nnubm5qV27djp16pQ+/vhja7uLi4sKCgpszg0LC1NRUZHy8vIUERFxW7U3b95cgYGBWrBggTIzM61rfQEAAFA+hg66oaGhCg0NveE5bm5u6tq1q7Zs2aIuXbrYrME9ePCgtm/frg4dOshsNuv8+fNKSEiw6TMgIEAHDx5UmzZt5O7uroCAAAUEBKhXr16aO3euBgwYoODgYBUUFOjUqVM6c+aMnn766XLVHxUVpeXLlyskJESBgYG39yEAAABUUYYOuuXVvn17bdmyxfoQWjF/f3+ZTCatWrVKWVlZqlGjhiIjIxUbG2s9Z/jw4Vq0aJHi4uJ09epVTZ8+XaGhoRo9erTq1aunzz77TPHx8apevbrq169v3YasPIq3Q/t1XQAAALg5k6X4WxWqsOXLl2v37t3661//esffSlaRtmzZouXLl2vhwoXy8PAo9/sOP9OoEqtCZWKNrmNjja7jYuwcG+PnuNh1oRL99NNPOn36tLZu3apBgwbdMyE3PT1dZ86c0fr169WjR49bCrmSFLFgYiVVdvekpcXe/CQAAIAbqNJB929/+5u+//57tWvXTn379rV3OVZr167Vzp071apVKw0ePNje5QAAADgkli4YkMn0F3uXcMeq6owuf35zbIyf42LsHBvj57gqe+nCvfG3egAAAKCCEXQBAABgSARdAAAAGFKVfhjNqKrq+lYAAIDrMaMLAAAAQyLoAgAAwJAIugAAADAkgi4AAAAMiYfRDGhlYKC9SwBwh2LT0uxdAgA4PGZ0AQAAYEgEXQAAABgSQRcAAACGRNAFAACAIRF0AQAAYEjsumBA4bM97V0CgDt0eEmIvUsAJEnhI1PtXQJw25jRBQAAgCERdAEAAGBI9/zShfj4eK1bt8762sfHR8HBwRo6dKgaNWpkx8oAAABwL7vng64keXh4aMqUKZKkjIwMxcfH67XXXtPbb78tLy8vO1cHAACAe5FDBF1nZ2eFhPz8YEZISIhq166tqVOn6uuvv1bXrl3tXN2958ETl+xdAgDctrSZd//rj81mszIzM+/6dQFULocIur/WuHFjSbL5j1J6eroWL16slJQUWSwWtWrVSqNGjZK/v7/1nJiYGI0YMULnz59XYmKiTCaTfvvb32rAgAFKTEzUunXrlJubqw4dOmjMmDFyc3OTJF24cEGrVq3St99+qwsXLshsNqtTp04aNGiQXFxcrNefMGGCnnvuOX3zzTfatWuXqlevrp49e2rQoEFycvplOfQPP/ygVatW6ciRIyosLFT9+vU1dOhQRURESJJyc3O1cuVKffnll8rLy1NQUJBGjhypZs2aVfZHCwAAYBgOGXSLA26dOnUkSQUFBXr11Vfl7Oys3/3ud3JyctLatWs1ffp0vfnmmzbLG/75z38qMjJSzz77rA4cOKDly5crOztbx48f1xNPPKHMzEwtWbJEAQEBevjhhyVJOTk58vLy0ogRI+Tl5aWffvpJa9euVXZ2tsaOHWtT24oVK9SxY0dNmjRJhw8f1rp161S/fn117txZkpSWlqZp06YpICBATz31lLy9vXX8+HHrPRUUFGjWrFm6dOmSHn/8cfn4+Gjr1q2aNWuW3nnnHfn6+lb65wsAAGAEDhN0CwsLJf28RnfRokVq3Lix2rdvL0n6/PPPlZmZqXnz5qlu3bqSpGbNmmnChAnatm2bHnnkEWs/9erVs4bT8PBw7dmzR5999pnmz58vDw8PSVJKSor27dtnDboNGzbUiBEjrH00b95c7u7uWrBggZ588knrrK4ktWzZ0npuRESEvv76a+3bt88adNeuXSsPDw+9+uqr1hnj4plcSfriiy/0448/6q233lK9evWsdT733HPatGmThg8fXuKzSUhIUEJCgiQpLi7u9j5gALhHmM3mu35NFxcXu1wXFYPxQ1kcIujm5ORo6NCh1tfe3t7605/+JFdXV0nSsWPHFBQUZA25kuTn56cWLVroyJEjNn2FhYVZf3dyclKdOnVUrVo1a8iVJH9/f6Wm/rJBtsVi0SeffKKEhASlp6eroKDAeiwzM9NmecT1oVWS6tevb7PEIiUlRffff7815P5acnKymjRpojp16ljDvfRzgD5x4kSp74mOjlZ0dHSpxwDA0dhjrSxrdB0b4+e4AgICKrV/hwi6Hh4emjZtmoqKivTDDz9o2bJlmjdvnmbNmiUnJyddvHhRPj4+Jd7n4+OjjIwMmzZPT9tvDXNxcbEJucVt14fZzZs3a9myZXr44YfVqlUreXp66vjx41q0aJHy8/Nv2v/1feXk5Nxw+UFOTo6+//57m2Bf7PogDwAAgBtziKDr7Oys4OBgST8vSXBzc9Nf//pX7dmzR507d5avr69Onz5d4n1ZWVkVsv3Ynj171KlTJ5vwWdr1ysPb21sXL14s87iXl5eCg4M1ZsyYEseKZ7BvJtm14W3VhnuL39Qke5eAW8SsEgDcWxzym9Huv/9+NWjQQBs2bJD0c/g9ceKE0tPTreecP39eR48eVYsWLe74evn5+TbrcCVp586dt9VXWFiYkpKSSswEFwsPD9fZs2dlNpsVHBxs89OwIQEWAACgvBxiRvfXTCaTHnnkEb3zzjs6fPiwevTooQ0bNmj27NmKiYmx7rrg7e2tXr163fH1IiIi9K9//UvNmjVT3bp1tXPnTp09e/a2+nrsscf00ksvafr06erfv7+8vLz0n//8R15eXurZs6e6deumbdu2acaMGerfv7/q1q2rnJwcHTt2TL6+vurXr98d3w8AAEBV4JBBV5I6d+6stWvXauPGjQoPD9e0adO0ZMkSvffee7JYLAoNDdULL7xQIUsXBg0apOzsbK1evVqS1LFjRz3xxBN64403brmvgIAAvfrqq1q5cqXee+89SbLuoytJbm5umj59utasWaO1a9da1x83bdpU7dq1u+N7AQAAqCpMFovFYu8iULEOP9PI3iWgArBG1/GwRtdxMXaOjfFzXJW964JDrtEFAAAAbsZhly6gbBELJtq7hNuWlhZr7xLsilkJAAAqDjO6AAAAMCSCLgAAAAyJoAsAAABDIugCAADAkHgYzYCq+gNdAAAAEjO6AAAAMCiCLgAAAAyJoAsAAABDIugCAADAkHgYzYBWBgbauwQAvxKblmbvEgCgymFGFwAAAIZE0AUAAIAhEXQBAABgSARdAAAAGBJBFwAAAIbErgsGFD7b094lAPiVw0tC7F0C7Cx8ZKq9SwCqHGZ0AQAAYEgEXQAAABgSSxduIDExUf/617905swZOTs7q3bt2goNDdXIkSPL3cf48ePVsWNHjRgx4obnxcTElNr+6quvqkWLFlq9erU+++wzvf/++7d0DwAAAFUVQbcM69ev15o1azRgwAANGzZM+fn5OnHihL744otbCrovvPCCvL29y3Vuv3799Jvf/MamrUGDBpKkXr16qUOHDuW/AQAAgCqOoFuGTz/9VNHR0YqNjbW2tWvXTo899tgt9RMUFFTuc+vUqaOQkNIfWPHz85Ofn98tXRsAAKAqI+iWIS8vT76+viXaTSaTzev8/HzFx8dr9+7dunjxomrWrKkuXbpYA3J5ly7czK0sXXjwxKU7uhYA2EvazDS7XNdsNiszM9Mu1wZQeQi6ZQgKCtKnn34qs9mstm3blrr8wGKxaM6cOUpNTdXAgQPVpEkTnT9/Xt99991tXbOoqEiFhYXW1yaTSU5OPC8IAABwOwi6ZRg9erT+/Oc/691335XJZFJgYKA6duyo/v37y8PDQ5J06NAhJScn649//KPatWtnfW/37t1v65qLFy/W4sWLra+bN2+uWbNm3fR9CQkJSkhIkCTFxcXd1rUB4F5gNpvtcl0XFxe7XRt3jvFDWQi6ZWjUqJHefvttHTp0SIcOHdI333yjjz76SLt379Ybb7whd3d3ffPNN/Ly8rIJuTdisVhUVFRkff3rGdsBAwaoU6dO1tfVq1cvV7/R0dGKjo4u550BwL3LXssHWLrg2Bg/xxUQEFCp/RN0b8DV1VXt2rWzBtnt27frvffe0/bt29W3b1/l5uaqZs2a5e7v22+/1cyZM62vW7VqpRkzZlhfm81mBQcHV1j9AAAAVRlB9xb07NlTy5cvV1razw9LeHl56cKFC+V+f5MmTfSnP/3J+rq8M7a3Ktm1YaX0C+Pzm5pk7xIcGrNKAHBv4UmnMmRlZZVoy87OVl5ennx8fCRJ4eHhys3N1YEDB8rVZ/Xq1RUcHGz9qezpegAAgKqMGd0yvPDCC2rXrp1at26tGjVqKDMzU5s2bVK1atXUo0cPSVJERIRat26td955x7rrwoULF/Tdd99p7Nix9r0BAACAKo6gW4aBAwdq//79+vDDD5WbmytfX1+FhIToueeeU506dST9/DDZ5MmTtXr1an3yySfKzs5WzZo11bVrVztXDwAAAJPFYrHYuwhUrMPPNLJ3CXBQrNG9M6rIpeAAACAASURBVKzRdVyMnWNj/BxXZS/jZI0uAAAADImlCwYUsWCivUtwSGlpsfYugVkJAAAqEDO6AAAAMCSCLgAAAAyJoAsAAABDIugCAADAkHgYzYDuhYeqAAAA7I0ZXQAAABgSQRcAAACGRNAFAACAIRF0AQAAYEg8jGZAKwMD7V0CgAoSm5Zm7xIAwGExowsAAABDIugCAADAkAi6AAAAMCSCLgAAAAyJoIv/396dx1VZ5/0ff7ErCCogiuBGghvuOqaVlrk0OkhOirZgrmWZ060z3eaMUzblUmMzWWJZ6n3jkltaCo9cIsoylzFTcwXJHVGWRGWJA5zz+4Mf1+1xX9DDuXw/H48eA9/rnO/1vc5nLn37Pd/rukRERERMSXddMKGWU30cPQQRqSB74iMcPQQxkZbPpjp6CCJ3lWZ0RURERMSUFHRFRERExJRMv3QhJibmuq95/fXXycrKYvbs2SxYsIAqVarc9H6WL1/O+vXrmTdvHgD79u3jjTfeYMaMGdSvX98Yy/Dhw3nsscduun8RERERuTmmD7pvvfWW8bPFYuEf//gHf/zjH2nXrp3RHhoaSlZW1m3t59FHH6VDhw631YeIiIiIVBzTB92IiP+7kOO3334DoE6dOnbtt6OkpARXV1cCAgIICAiokD6vxWazUVxcjKen5x3fl4iIiIgzM33QvVmZmZnEx8eTmppKQEAATz75JJ06dTK2T548GV9fX1q3bs3q1avJzMwkLi6O5ORku6ULN2r79u2sXLmSEydO4O3tTbdu3Rg8eDDu7mWlKV8S8corrxAfH8+xY8cYPXo0Xbt2vWqfjx3Ov7WDFxG5Q9LfSHf0EK4pMDCQ7OxsRw9DRCqYgu4lZs6cSY8ePejXrx9r167lvffeY9asWXaztSkpKZw5c4ann34aT09PvL29b2lfmzdvZubMmfTs2ZMnn3ySM2fO8Omnn2K1WhkyZIjxuqKiIuLi4ujXrx/BwcH4+/vf9nGKiIiImJ2C7iX69u1L9+7dAQgLC2PUqFHs2LGDXr16Ga/Jz8/nnXfeoUaNGre8H5vNxqJFi+jWrRsjR4402t3d3Zk3bx79+/fH19cXKFtbPGTIEDp27HjL+xMRERG51yjoXqJ169bGz76+vlSvXp1ff/3V7jVhYWG3FXIBMjIyyM7OpnPnzpSWlhrtkZGRFBcXc+LECZo3bw6Ai4sLbdu2vWpfSUlJJCUlATB9+vTbGpeIyJ0QGBjo6CFck7u7e6Ufo1yd6idXo6B7CR8f+6eKubu7Y7FY7NqqV69+2/s5f/48ANOmTbvi9ovXivn4+Bhrdq+kR48e9OjR47bHJCJyp1T29a9ao+vcVD/nVbdu3Tvav4LuLXBxcbntPqpVqwbAc889R6NGjS7bHhQUdMv7+9mj/u0NTpxKwKQtjh6C/H/6y1ZEpHJR0HWQunXr4u/vT1ZWlmZjRURERO4ABV0HcXV1ZciQIXzwwQcUFhbSpk0b3N3dyczMZPv27YwfPx4vLy9HD1NERETEaSnoOlCXLl2oWrUqn3/+Od988w2urq4EBQXRvn37a67JFREREZHrc7HZbDZHD0Iq1p4XGzh6CHIXaY1u5aE1us5LtXNuqp/zutMXo7ne0d5FRERERBxE34+bUKsPxzp6CHdNevpTjh5ChdKshIiISMXRjK6IiIiImJKCroiIiIiYkoKuiIiIiJiSgq6IiIiImJKCroiIiIiYku66YEJmuxOBiIiIyK3QjK6IiIiImJKCroiIiIiYkoKuiIiIiJiSgq6IiIiImJIuRjOhT0NCHD0EERG5S55KT3f0EEQqLc3oioiIiIgpKeiKiIiIiCkp6IqIiIiIKSnoioiIiIgp6WI0E2o51cfRQxARkbtkT3zEDb+25bOpd3AkIpWPZnRFRERExJQUdEVERETElBR0RURERMSUFHRvU3JyMjExMeTk5Ni1L1q0iJiYGL777ju79t27dxMTE0NKSsrdHKaIiIjIPUdB9zZFRJRdBHBpcE1NTcXLy4vU1NTL2j08PAgLC7trYxQRERG5F+muC7cpJCSEatWqkZqaSpcuXQAoKSnh8OHDdOvW7YoBOCwsDA8Pj1van81mo6Sk5Jrvf+xw/i31LSK3pyiuiOzsbEcPQ25BYGCgaidiQgq6t8nFxYWIiAi7QHv06FFsNhu9e/cmKSmJwsJCqlatitVq5dChQ/To0QOAH3/8kbVr13L06FFKSkoIDQ1l0KBBtGrVyuhr6dKlfP3114wbN474+HhOnDjBiy++yIMPPnjXj1VERETEmSjoVoCIiAhWrFiBxWLB09PTmLWtV68ePj4+HDp0iFatWnHy5EkKCgpo0qQJAJmZmXTo0IF+/frh4uLCTz/9xJQpU3jrrbcIDw83+v/tt9/48MMPiY6Opk6dOvj7+zvqUEVERESchoJuBWjSpAmlpaWkpaXRvHlzUlJSiIiIwMXFhfDwcFJTU2nVqpUx61sedPv06WP0YbVaadGiBcePHyc5Odku6BYVFTFs2DDatWt3xf0nJSWRlJQEwPTp0+/UYYrIdbi7uxMYGOjoYcgtUO2cm+onV6OgWwEaN26Mm5sbqampNG/e3G69bnh4uBFwU1JSCA4Oxs/PD4Ds7GyWLFnC3r17yc3NxWazAWWh92Kurq60adPmqvvv0aOHsRxCRBynpKRE6zydlNboOjfVz3nVrVv3jvavoFsBvLy8aNiwISkpKeTk5JCTk2PcjSEiIoLExESsViupqak0bdoUKAuzb7/9NhaLhcGDB1O7dm28vLxYsmQJhYWFdv37+vri6qobZIiIiIjcDAXdChIREcGmTZtISUmhVq1a1KxZEyib0S0sLGT//v2cPn2a6OhoAE6dOsWxY8eYNGmS3cVnFovltsfys0f92+7jXhUwaYtD969ZCRERkYqjacIK0qRJEy5cuMDGjRuN2VyAqlWrUq9ePRISEozXwf8F2otvE3bmzBkOHTp0F0ctIiIiYl4KuhWkfEnCrl277IIulM327tq1Cx8fH0JCQgAIDQ2lZs2axMfH89NPP7Fp0yamTJmiOyqIiIiIVBAF3Qri7+9PYGAgNpvtikG3vN3FxQUAT09P/vKXvwDw7rvvsmLFCgYMGGDM+IqIiIjI7XGxlV/qL6ax58UGjh6C09IaXbkdqp/zUu2cm+rnvHTXBblprT4c6+ghXFN6+lOOHoKIiIjcA7R0QURERERMSUFXRERERExJQVdERERETElBV0RERERMSUFXRERERExJd10wId3VQEREREQzuiIiIiJiUgq6IiIiImJKCroiIiIiYkoKuiIiIiJiSroYzYQ+DQlx9BBEpJJ7Kj3d0UMQEbnjNKMrIiIiIqakoCsiIiIipqSgKyIiIiKmpKArIiIiIqakoCsiIiIipqS7LphQy6k+jh6CiFRye+IjHD0EqSRaPpvq6CGI3DGa0RURERERU1LQFRERERFTcuqgm5ycTExMDDk5OXbtixYtIiYmhu+++86ufffu3cTExJCSklIh+9+8eTPffvtthfQlIiIiIhXLqYNuRETZGrNLg2tqaipeXl6kpqZe1u7h4UFYWFiF7H/Lli0KuiIiIiKVlFNfjBYSEkK1atVITU2lS5cuAJSUlHD48GG6det2xQAcFhaGh4eHI4Z7TTabjeLiYjw9PW+7r8cO51fAiEREbl76G875aOHAwECys7MdPQwRqWBOHXRdXFyIiIiwC7RHjx7FZrPRu3dvkpKSKCwspGrVqlitVg4dOkSPHj2M127fvp2VK1dy4sQJvL296datG4MHD8bdvexjycnJIT4+nv3791NYWIi/vz8PPPAAgwcPJi4ujm3btgEQExMDwIABA4yfr9f38uXLWb9+Pa+88grx8fEcO3aM0aNHExAQwBtvvMHrr7/O+vXr2blzJ9WrVycqKorevXvflc9VRERExAycOuhC2fKFFStWYLFY8PT0NGZt69Wrh4+PD4cOHaJVq1acPHmSgoICmjRpApStr505cyY9e/bkySef5MyZM3z66adYrVaGDBkCwKxZs7BYLDz33HP4+Phw5swZTp06BcATTzxBdnY2BQUFjBgxAoCAgIAb7hugqKiIuLg4+vXrR3BwMP7+/pw9exaAOXPm0K1bN3r06MGmTZuYN28e9913H40bN75rn62IiIiIM3P6oNukSRNKS0tJS0ujefPmpKSkEBERgYuLC+Hh4aSmptKqVStj1rdJkybYbDYWLVpEt27dGDlypNGXu7s78+bNo3///vj6+pKWlsbLL79Mhw4dAGjRooXx2jp16lCtWjVsNpuxVhi44b4BLBYLQ4YMoWPHjsbryoPuAw88wBNPPAFA8+bN2bFjB9u2bbti0E1KSiIpKQmA6dOn394HKiJyGwIDAx09hFvi7u7utGMX1U+uzumDbuPGjXFzcyM1NZXmzZvbrdcNDw83Am5KSgrBwcH4+flx6tQpsrOz6dy5M6WlpUZfkZGRFBcXc+LECZo3b07Dhg1ZsmQJeXl5REZG3tBJlJGRcUN9Q9nSi7Zt216xn9atWxs/u7u7ExwcfNndJcr16NHDbkmGiIijOOs6V63RdW6qn/OqW7fuHe3f6YOul5cXDRs2JCUlhZycHHJycowZ1oiICBITE7FaraSmptK0aVMAzp8/D8C0adOu2Gf5yTJu3DiWLFlCfHw8+fn5NGjQgCFDhtCyZcurjudG+wbw8fEx1uxeytvb2+53d3d3iouLr7pfEREREbHn9EEXygLtpk2bSElJoVatWtSsWRMom9EtLCxk//79nD59mujoaACqVasGwHPPPUejRo0u6y8oKAgAf39/xowZg9VqJS0tjRUrVvDOO+8we/ZsY/nBpW60byib0b0Tfvaof0f6lYoRMGnLVbdpVsK5qX4iIpWLKYJukyZNWLt2LRs3brRbL1u1alXq1atHQkKC8Toomyb39/cnKyvrhr7yd3V1JSIigoEDBzJp0iSysrLw9fW94izrzfYtIiIiIneGKYJu+ZKEXbt2MXToULttERERfP311/j4+BASEgKUBdchQ4bwwQcfUFhYSJs2bXB3dyczM5Pt27czfvx4SktLmTJlCl27diU4OJiSkhISEhKoUaMGoaGhQNl9fH/88Uf+85//EBAQQM2aNfH3979u315eXnf18xERERG5F5ki6Pr7+xtfGV48owtlQTcpKcm4E0O5Ll26ULVqVT7//HO++eYbXF1dCQoKon379ri7u+Pq6kq9evX48ssvycnJwcvLi/DwcCZNmmQ81KFXr14cOXKEDz/8kPz8fOM+utfrW0RERETuPBebzWZz9CCkYu15sYGjhyDXoDW65qX6OS/Vzrmpfs7rTt91wfWO9i4iIiIi4iD6Ht2EWn041tFDuKPS059y9BBERETECWhGV0RERERMSUFXRERERExJQVdERERETElBV0RERERMSRejmZAu1hIRERHRjK6IiIiImJSCroiIiIiYkoKuiIiIiJiSgq6IiIiImJKCroiIiIiYkoKuiIiIiJiSgq6IiIiImJKCroiIiIiYkoKuiIiIiJiSgq6IiIiImJKCroiIiIiYkoKuiIiIiJiSgq6IiIiImJKCroiIiIiYkoKuiIiIiJiSgq6IiIiImJKCroiIiIiYkoKuiIiIiJiSgq6IiIiImJKCroiIiIiYkoKuiIiIiJiSgq6IiIiImJKCroiIiIiYkoKuiIiIiJiSgq6IiIiImJKCroiIiIiYkoKuiIiIiJiSgq6IiIiImJKCroiIiIiYkovNZrM5ehAiIiIiIhVNM7om8+qrrzp6CHIbVD/npvo5L9XOual+zutO105BV0RERERMSUFXREREREzJbfLkyZMdPQipWGFhYY4egtwG1c+5qX7OS7Vzbqqf87qTtdPFaCIiIiJiSlq6ICIiIiKmpKArIiIiIqbk7ugBSMU4efIk8+fPJzU1FR8fH7p3787AgQNxddW/Ze6206dPs2bNGg4dOsTx48dp1qwZly6Ft9lsfP7553z11VecP3+exo0bM2zYMBo2bGi8Jj09nbi4ONLT02nbti2jR4+mSpUqxvb9+/czc+ZMZs6cadcut27Lli189913HD58mIKCAurWrUtUVBQPPvig3euSkpJYs2YNOTk5hIaG8swzz9CyZUtj+9mzZ5k1axZpaWlEREQwZswYatSoYWw/ffo0EydOZMaMGQQEBNy14zO7rVu3kpiYyKlTpygqKiIwMJCuXbsSHR2Nu3vZX3c695zDr7/+yssvv0xRURELFiwwPmfVr3L69ttvmT179mXtI0eOpFevXoDjaqcUZAJ5eXm8+eabuLi48N///d888cQTJCYmsnz5ckcP7Z504sQJdu7cSXBwMHXr1r3ia7744gtWrlxJdHQ0EyZMoEqVKrz55pvk5uYar5k9ezZ16tRh3LhxnDx5klWrVhnbrFYr8fHxPPXUU/qDugIlJiZSpUoVnn32WSZMmECLFi14//33Wbt2rfGaH374gU8++YSuXbsyceJE6tWrx/Tp0zl+/Ljxmvj4eADGjx9PaWmp8Xu5BQsW0LdvX4XcCnbhwgVatGjB6NGjmThxIo888girVq2y+/x17jmHhQsXXvHzVf0qt9dee4233nrL+K9Tp07GNkfVTjO6JvDVV19hsVj485//jLe3N61ataKwsJAVK1bQr18/vL29HT3Ee0r79u3p2LEjAO+++y4XLlyw226xWPjiiy/o378/jz32GIAx67du3ToGDx7Mb7/9xqFDh5gwYQJ+fn4UFBSQkJBg9JGcnIybmxtdu3a9ewd2Dyj/vMtFRkZy9uxZEhMT+f3vfw/A8uXL6datGwMGDACgefPmHDlyhC+++II//elPAOzZs4eJEyfSuHFjvL29mT59utHnzz//zNGjR/mv//qvu3hk94aePXva/R4ZGUlhYSHr169n+PDhFBcX69xzAgcOHGDXrl3079+fRYsWGe36s7Pya9y48RVDqCNrpxldE9i1axetW7e2C7QPPPAAFouF/fv3O3Bk96brLRdJTU2lsLCQzp07G21VqlShffv27Ny5E4CSkhIAPD09jf8tbysoKGDZsmUMHToUFxeXO3EI96yLQ265Ro0acf78eQDOnDlDRkYGXbp0Mba7urrSuXNndu3aZbSVlJQYtfPy8jJqVz4j8cwzzxjb5c7y9fU1Pn+de5Wf1Wpl/vz5DBgw4LLzUfVzXo6snYKuCaSnp1/2FXlgYCBeXl6cOnXKQaOSq0lPT8fV1ZXg4GC79tDQUKNe1apVIygoiLVr15KXl0dSUpJxn8GVK1fSsmVLIiIi7vrY70UpKSmEhoYCZbUDLjvfQkJCyMvLMwJxWFgYGzZsIC8vj3Xr1nHfffcBsGHDBry9ve2CslQ8q9VKUVERBw8eZO3atfTq1QsXFxede05gw4YNFBcX07t378u2qX6V39ixYxk8eDAvv/wyX331ldHuyNop6JpAfn4+Pj4+l7X7+PiQl5fngBHJteTn51OlSpXLZn59fHwoKioy/gU7YsQIVq1axfDhw8nIyGDgwIGcPn2a5ORknn76aUcM/Z6zZ88efvzxR+Mv3fz8fIDLzrfy38vPt9jYWLZt28bw4cPZvn07sbGx5OXl8dlnnzFs2LC7eAT3ptjYWGJjY3nttddo1qwZzzzzDKBzr7K7cOECy5YtY8iQIcbFgxdT/SqvGjVqMGjQIF566SUmTJhAeHg4n3zyCYmJiYBja6c1uiZxpal8m82mr2cqqavV62Jt27Zl7ty55OTkUKdOHVxdXXn77beNi5jWrVvH6tWrAXj88cevOAMity4zM5P333+fDh068PDDD9ttu9p5Vd4eFhbG7NmzycrKIigoCHd3d+bPn0/79u0JCwtj27ZtLF68mMLCQh599FEGDx58pw/nnvLmm29isVhIS0vjs88+Y/78+YwcORLQuVeZLVmyhPDwcNq1a3fV16h+lVObNm1o06aN8Xvbtm0pLi5m1apV9OnTB3Bc7RR0TcDHx8eYabpYQUHBFWd6xbF8fHwoLCzEarXa/eu2oKAALy8vu5kMLy8v42vyn3/+mWPHjjFu3DiOHj3KsmXLeOuttwCYNGkSTZs2pUGDBnf3YEwqLy+PadOmERgYyNixY4328vMpPz/fbk38lWZ6PTw8jNqdPHmS77//nn//+9/k5uYye/ZsJk6cSFBQEH//+98JDw+nffv2d+PQ7gnlX3c2bdoUX19f4uLi+MMf/qBzrxI7ceIE33zzDW+88YZxPhUVFQFl9XF1dVX9nMz999/Pli1byMrKcmjtFHRNICQkxFg7WC47O5uioqKr3t5KHCckJASr1crp06ft6nOltdblym9RVX4R0/79+4mMjCQkJAQou7p8//79+sO6AhQVFTF9+nRKSkp49dVX7a4gLv+809PTqVWrltGenp5OtWrVrngxG5Tdbiw6OpoaNWqwfft2goODadq0KQCdOnVi3759Crp3SKNGjYCyGXqde5VXRkYGpaWlTJo06bJto0ePpnv37jz44IOqnxNycXFx6LmnNbom0KZNG3bv3k1hYaHRtnnzZjw9PWnevLkDRyZXEhERQdWqVdmyZYvRVlRUxI4dO2jbtu0V3/PVV1/h4+NjdxGTxWKxe/+lXwHJzSstLeVf//oXGRkZTJw4kerVq9ttr127NsHBwWzdutVos1qtbN261e5ru4vt2LGD06dP07dvX6OtfKaq/GfV7s5JSUkBICgoSOdeJda0aVNef/11u/+io6MBmDhxIv369VP9nMy2bdvw9fUlMDDQobXTjK4J9OzZk7Vr1zJjxgyio6PJzMxkxYoV/OEPf9A9dB2gqKjIuF3Kr7/+SmFhoRGM2rZti5eXF48//jgrV67Ex8eHkJAQEhMTsdlsxv0FL5aXl8eKFSv429/+ZrQ1a9aMRYsWkZycDMDevXt56qmn7sLRmdvcuXPZuXMnQ4cOJS8vj9TUVGNbo0aN8PDwYODAgXzwwQfUqlWLJk2asHHjRjIyMox76F6spKSEBQsWEBsbi4eHBwDh4eFkZmayZs0aatWqxQ8//MBLL710147RzKZMmULLli2pV68erq6upKSkkJCQQJcuXahTpw6Azr1Kys/PjxYtWti1ZWVlAWWfefk3K6pf5TRjxgwaN25MgwYNsFqtbN68mc2bNzNs2DBcXV3x9PR0WO1cbPqnjCmcPHmSefPm2T0COCYmRo8AdoDMzMyrBpdZs2YRFBRkPApxw4YNXLhwgfvuu49hw4YZX7NebP78+RQVFfHCCy/YtX/55Zd88cUXAPTv3994oIHcujFjxhh/uV6qvHZQ9gjg1atXk5OTQ7169S57BHC5xMREfvrpJ1577TW79s2bNxsXo/Xo0UN/0VaQpUuXsn37djIzM3Fzc6N27do8/PDD9OzZ87JHAOvcq/zKHyt7pUcAq36Vy6effsq2bdvIycnBZrMRGhpK37597R7u4KjaKeiKiIiIiClpuk9ERERETElBV0RERERMSUFXRERERExJQVdERERETElBV0RERERMSUFXRERERExJQVdERG5Ieno6f/vb33j66acZM2aMo4dz13377bfExsY6ehgichN0H10RcUpxcXFs3LgRADc3NwICAvjd735HTEyMcXP5ymzy5MnUq1ePESNGOHooN+zdd98lPz+f0aNHU6VKFfz8/Bw9pDsmJiaG8ePHc//99xttFouFwsLCyx4NLSKVlx4BLCJOq2XLlowdO5aSkhIOHjzIRx99RFFREaNGjbql/kpKSownaMnlTp8+TceOHY0nxFUWN1o3q9UKcMtPjPT09MTT0/OW3isijqEZXRFxSnFxcVy4cIFXX33VaPvoo4/46aef+Pjjj4GyR2MvXLiQAwcO4OnpSWRkJEOHDqVGjRp2fTRt2pR169ZRUlLC3LlzKSkpYfny5WzatInc3Fz8/f3p06cPffr0ual+W7VqxerVq7FYLHTs2JERI0bg5eVlNxtdbtasWQQGBjJnzhz27t1Lbm4uAQEBPProo0RFRRnhrLS0lIULFxrv79atG8XFxaSnpzN58mSg7FGba9asISkpiV9//ZU6deoQHR1t9zjOS1mtVlatWsXXX3/NuXPnCA4OZvDgwXTs2BEom+G82IABAy5ru1pdli9fzrZt23j33XcBOH78OP/7v//LL7/8gs1mo3bt2jz77LNERkbeVt0u9e233zJv3jzGjRvH4sWLSU9P55133sFisbB06VKOHDlCSUkJ9evXJzY2loiICODyR0HXqlWLuLg4o7+FCxfaHdcf//hHli5dyrlz54iMjGT06NHGbPeN1Gv//v0sXryY48eP4+rqSkhICKNHj6Z+/fpXrZeI3BhNXYiIaXh6elJaWgrA2bNnef3113nkkUeIjY2ltLSUJUuW8PbbbzNlyhQjOO7fvx9vb2/++te/Gv3MmjWLgwcPMnToUBo1akRWVhY5OTk31e+BAweoUaMGf//738nJyeHf//43wcHB9O/fn2HDhpGRkUHdunV56qmnAPDz88NqteLv78+4cePw8/MjLS2Njz/+GF9fX7p37w5AQkICGzdu5Pnnn6d+/fqsX7+eTZs22T0vfunSpWzdupURI0ZQt25dUlNTmTNnDtWqVaNdu3ZX/Oy+/PJLEhISGDVqFGFhYXz//ffMmDGDt99+m4YNG/Lxxx8zefJk2rVrR79+/W5recjMmTNp0KABU6dOxc3NjePHjxszpbdTtyspLi5m1apVjBo1Cj8/P2rWrMkvv/xC165dGTp0KC4uLqxbt45p06Yxc+ZM/Pz8mDZtGiNHjuT555+nffv215wBzszMZPPmzfzlL3+hqKiI9957j6VLl/Lcc88B169XaWkp//znP3nkkUcYO3YspaWlHDly5JZnnUXEnoKuiJhCWloaP/zwgzEruGHDBho0aMAzzzxjvOall15ir5HTuQAABtFJREFU+PDhHD58mMaNGwPg4eHBCy+8gIeHBwAZGRls3ryZv/71r7Rp0waA2rVrG33caL/e3t6MGjUKNzc3QkNDuf/++9m7dy/9+/fH29sbd3d3vLy8jFlKKPtKfdCgQcbvQUFBHDlyhB9++MEIul9++SXR0dHG2tGhQ4eye/du4z2//fYbiYmJTJo0iWbNmhn9pKWlsX79+qsG3YSEBKKionjwwQcBGDRoEAcOHGDNmjX86U9/okaNGri5uVGlShW7Md+K7OxsoqKiCAkJAaBOnTrGtlut29VYrVaGDx9OWFiY0Vb+/5Fyw4cPZ9u2bezatYuuXbsas7E+Pj7XPVar1cqYMWPw9vYGoEePHnzzzTfG9uvVq7CwkPz8fDp06GB8DuWfi4jcPgVdEXFau3btIjY2FqvVSklJCR07dmT48OEAHD58mAMHDlzxKvnTp08bgal+/fp2YenIkSO4uLjQokWLK+7zRvsNDQ3Fzc3N2Obv709aWtp1j2nDhg0kJyeTlZWFxWKhtLSUWrVqAVBQUEBubq6xDwAXFxfuu+8+Y8b55MmTFBcXM3XqVLt+L+7nUgUFBZw9e5YmTZrYtTdt2pSdO3ded8w3q2/fvsyZM4eNGzfSsmVLOnXqZIS7W63b1bi5udGwYUO7tnPnzrFs2TL27dtHbm4uVqsVi8VCdnb2TR9LYGCgEXIBatasyfnz54Ebq1e1atV4+OGHmTJlCpGRkbRs2ZL777+fwMDAmx6LiFxOQVdEnFazZs14/vnncXNzo2bNmnYXJNlsNtq2bcuQIUMue9/FV817eXnd1D5vtN+LQ+7F772WzZs3Ex8fb6wX9fb2Zt26dWzfvv2mxgcwYcKEy8LSlcZ0MRcXlxvez7X6uPQ4y5eTlIuJieGhhx5i586d7N69mxUrVjBq1Ci6d+9e4XVzd3e/bBlAXFwc586d49lnn6VWrVp4eHjwj3/8g5KSkhs9TLv+L+bi4mJc9HajXnzxRfr06cOuXbv48ccfWbJkCa+88orxjYKI3DoFXRFxWl5eXnZfe1+sUaNGbNmyhcDAwJu6k0KjRo2w2Wzs27fvikHjVvu9lLu7+2WB6ODBgzRu3JjHHnvMaDtz5ozxs7e3NzVq1CAtLc34+t1ms/HLL78YX7GHhobi4eFBVlbWZV/RX423tzc1a9bk4MGDdu85ePAgoaGhN3Vcfn5+HDt2zK7t6NGjl70uODiY4OBg+vTpwyeffEJycjLdu3evsM/3Wg4ePMiwYcOMZRy5ubmcPXvW7jVubm43HVgvdSP1KtewYUMaNmzI448/ztSpU9m4caOCrkgF0Gp3ETGl3r17U1BQwHvvvcehQ4c4c+YMP//8M3PmzKGwsPCq7wsODqZz58589NFHbN26lczMTA4cOMB33313W/1eqlatWqSlpZGZmcn58+exWq0EBwdz5MgRdu7cSUZGBp999hn79++3e1+fPn1Ys2YN//nPfzh16hQLFizg7Nmzxmxs1apViYqKYuHChSQnJ3P69GmOHj3Khg0bSEpKuup4+vXrR0JCAps2beLUqVMsW7aMAwcOEBUVdcPHBGXrX48cOWLse/Xq1aSkpBjbLRYLc+fOZd++fWRmZnLo0CG7QF1Rn++1BAcH8/3333Py5EnS0tKYOXPmZaE6KCiIPXv2kJubS15e3i3v63r1yszMZPHixaSkpJCVlcXevXs5duzYTf8DQ0SuTDO6ImJK/v7+vPnmm3z66adMnToVi8VCYGAgrVu3vu7azpdeeolly5bxP//zP1y4cIGAgAD69u172/1eLCoqiri4OMaPH4/FYmHWrFn07NmTo0eP8v7772Oz2ejUqRNRUVF2FzdFRUWRm5vL7NmzcXFx4eGHH+Z3v/sd586dM14zaNAgqlevTkJCAnPnzqVq1ao0bNiQ6Ojoq47n97//PYWFhSxevJjc3Fzq1q3Ln//858vWt15PmzZtGDBgAEuXLqWoqIiHHnqIXr16sWPHDqDsgrv8/Hzi4uLIzc3F19eXdu3aGWtyK+rzvZYXXniBjz/+mAkTJuDv78/AgQONdbXlYmNjWbBgAS+88AL+/v7ExcXd0r6uVy9PT08yMjL417/+xYULF6hevToPPfTQNWslIjdO99EVEXFyEyZMoEmTJsaFeFK5qV4id49mdEVEnEhWVha7d++mefPmlJaWkpSUxNGjR437tkrlonqJOJZmdEVEnEh2djYzZ87k+PHj2Gw2QkNDGTRoEK1bt3b00OQKVC8Rx1LQFRERERFT0l0XRERERMSUFHRFRERExJQUdEVERETElBR0RURERMSUFHRFRERExJT+H/iiELBJEFMRAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 720x864 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Figure 3: Visualizing the ratings for each genre as a proportion of the total number of movies in that genre\n",
    "# function for extracting the count of a specific rating of all genres\n",
    "def get_rating_percentages(score):\n",
    "    rated_count = genre_df.iloc[:, score].values.tolist()\n",
    "    rating_percentages = []\n",
    "    i = 0\n",
    "\n",
    "    for total in total_ratings:\n",
    "        percentage = (100 / total) * rated_count[i]\n",
    "        i += 1\n",
    "        rating_percentages.append(round(percentage, 1))\n",
    "\n",
    "    return rating_percentages\n",
    "\n",
    "\n",
    "genre_rating_percentage_data = {'Genre': [genre for genre in genres], 'Rated 1': get_rating_percentages(1),\n",
    "                                'Rated 2': get_rating_percentages(2), 'Rated 3': get_rating_percentages(3),\n",
    "                                'Rated 4': get_rating_percentages(4), 'Rated 5': get_rating_percentages(5),\n",
    "                                'Total nr of ratings': get_total_rating_count()}\n",
    "genre_rating_percent_df = pd.DataFrame(genre_rating_percentage_data)\n",
    "\n",
    "rated_1_percentage = genre_rating_percent_df.iloc[:, 1].values.tolist()\n",
    "rated_2_percentage = genre_rating_percent_df.iloc[:, 2].values.tolist()\n",
    "rated_3_percentage = genre_rating_percent_df.iloc[:, 3].values.tolist()\n",
    "rated_4_percentage = genre_rating_percent_df.iloc[:, 4].values.tolist()\n",
    "rated_5_percentage = genre_rating_percent_df.iloc[:, 5].values.tolist()\n",
    "\n",
    "fig3, ax3 = plt.subplots()\n",
    "fig3_ylabels = [genre for genre in genres]\n",
    "fig3_xlabels = ['0', '10%', '20%', '30%', '40%', '50%']\n",
    "x = np.arange(len(fig3_ylabels))  # the label locations\n",
    "width = 0.17  # the width of the bars\n",
    "\n",
    "ax3.barh(x + width*2, rated_1_percentage, width, label='Rated 1', color='darkblue')\n",
    "ax3.barh(x + width, rated_2_percentage, width, label='Rated 2', color='chocolate')\n",
    "ax3.barh(x, rated_3_percentage, width, label='Rated 3', color='darkgreen')\n",
    "ax3.barh(x - width, rated_4_percentage, width, label='Rated 4', color='darkgoldenrod')\n",
    "ax3.barh(x - width*2, rated_5_percentage, width, label='Rated 5', color='darkred')\n",
    "\n",
    "# Setting labels for bar chart\n",
    "ax3.set_xlabel('Percentage of user ratings', size=14)\n",
    "ax3.set_xticklabels(fig3_xlabels, fontdict={'fontsize': 15})\n",
    "ax3.set_xlim(right=50)\n",
    "ax3.set_yticks(x)\n",
    "ax3.set_yticklabels(fig3_ylabels, fontdict={'fontsize': 15})\n",
    "ax3.set_title('Fig 3: Genre Popularity', size=16)\n",
    "ax3.invert_yaxis()  # Invert y axis so that the genres appear alphabetically\n",
    "ax3.legend(shadow=0.4, prop={\"size\": 13})\n",
    "\n",
    "# Adjusting and displaying the graph\n",
    "fig3.set_figheight(12)\n",
    "fig3.set_figwidth(10)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Figure 3 shows that a few genres, such as Film-Noir, Documentary and War, are significantly more liked in general than the rest. Since these genres consistently receive very high ratings, movies with these genre tags are very likely to be recommended."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Figure 4: Visualizing the proportional frequency of different rating scores for all genres\n",
    "def get_rating_percentage_total(score):\n",
    "    rated_count = genre_df.iloc[:, score].values.tolist()\n",
    "    total_ratings_count = 0\n",
    "    sum_ratings = 0\n",
    "\n",
    "    for count in rated_count:\n",
    "        sum_ratings += count\n",
    "\n",
    "    for total in total_ratings:\n",
    "        total_ratings_count += total\n",
    "\n",
    "    return round((sum_ratings / total_ratings_count) * 100, 1)\n",
    "\n",
    "\n",
    "ratings_data = [get_rating_percentage_total(5), get_rating_percentage_total(4), get_rating_percentage_total(3),\n",
    "                get_rating_percentage_total(2), get_rating_percentage_total(1)]\n",
    "\n",
    "# Plotting a pie (donut) chart for case distribution visualization\n",
    "labels = [\"Rated 5\", \"Rated 4\", \"Rated 3\", \"Rated 2\", \"Rated 1\"]\n",
    "fig4, ax4 = plt.subplots(subplot_kw=dict(aspect=\"equal\"))\n",
    "wedges, texts, autotext = ax4.pie(ratings_data, autopct='%1.1f%%', pctdistance=0.75, shadow=True,\n",
    "                                  wedgeprops=dict(width=0.5), startangle=100, textprops=dict(color=\"w\"),\n",
    "                                  explode=(0.02, 0.02, 0.02, 0.02, 0.02), colors=['darkred', 'darkgoldenrod',\n",
    "                                                                                  'darkgreen', 'chocolate', 'darkblue'])\n",
    "\n",
    "ax4.legend(wedges, labels, loc=\"upper right\", bbox_to_anchor=(1.0, 0.05, 0.33, 0.9), title='Ratings',\n",
    "           title_fontsize='12', labelspacing=0.8)\n",
    "ax4.set_title(\"Frequency of various ratings for all genres\", size=18)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Figure 4 clearly shows that the MovieLens userbase rate movies highly on average. A score of 4 is very common, while 1's and 2's combined only take up 15% of the total ratings."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "          Rangering\n",
      "BrukerID           \n",
      "0          3.296610\n",
      "1          3.487179\n",
      "2          3.600000\n",
      "3          4.000000\n",
      "4          3.891892\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Figure 5: Visualizing the distribution of average ratings for all users\n",
    "users_avg_rating = pd.DataFrame(ratings_df.groupby('BrukerID')['Rangering'].mean())\n",
    "print(users_avg_rating.head())\n",
    "\n",
    "# Plotting a histogram over the average rating for all users\n",
    "users_avg_rating = users_avg_rating['Rangering']\n",
    "fig5, ax5 = plt.subplots()\n",
    "n, bins, patches = ax5.hist(users_avg_rating, label='Users Average Rating',\n",
    "                            stacked=True, color='royalblue', bins=50, rwidth=0.8)\n",
    "# Set labels for histogram\n",
    "ax5.set_xlabel('Average Rating', size=12)\n",
    "ax5.set_ylabel('Count', size=12)\n",
    "ax5.set_title('The Distribution of Average Rating across All Users', size=12)\n",
    "\n",
    "# Add a line indicating the mean rating for all users\n",
    "plt.axvline(ratings_df[\"Rangering\"].mean(), color='chocolate', linestyle='dotted', dash_capstyle=\"round\",\n",
    "            linewidth=3, label=\"Mean\")\n",
    "ax5.legend(bbox_to_anchor=(1, 0.92))\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Figure 6: Visualizing how the average rating has changed over time\n",
    "def convert_timestamp_to_year(timestamp):\n",
    "    date_string = time.ctime(timestamp)\n",
    "    tokens = date_string.split(sep=\" \")\n",
    "    year = int(tokens[-1])\n",
    "    return year\n",
    "\n",
    "\n",
    "ratings_copy = ratings_df.copy()\n",
    "ratings_copy['Rangeringsår'] = ratings_df[\"Tidstempel\"].map(lambda timestamp: convert_timestamp_to_year(timestamp))\n",
    "yearly_mean_rating = ratings_copy.groupby('Rangeringsår')['Rangering'].mean()\n",
    "\n",
    "# Plotting a bar plot for average rating over time\n",
    "plt.figure()\n",
    "plt.rcParams.update({'font.size': 14})\n",
    "yearly_mean_rating.plot(kind='bar', figsize=(10, 10), title=\"Fig. 6: How the average rating changed over time\",\n",
    "                        subplots=True, color=['darkred', 'darkgoldenrod', 'darkgreen', 'chocolate'],\n",
    "                        ylim=(3.05, 3.7), fontsize=15, label='')\n",
    "plt.xlabel(xlabel='Year of ratings', labelpad=18)\n",
    "plt.ylabel(ylabel='Avg. rating', labelpad=12)\n",
    "\n",
    "# Adding an arrow along the y axis to indicate that the axis continues to 0\n",
    "# and that only an excerpt of the axis has been shown\n",
    "plt.arrow(x=(-0.4), y=3.2, dx=0, dy=(-0.1), width=0.02, length_includes_head=False, head_length=0.037, head_width=0.06,\n",
    "          color='black')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "------\n",
    "## <a id=\"1st-iteration\"></a>2) *First Iteration:* Building a Hybrid Recommender System with CBF and CF models\n",
    "\n",
    "[*Back to top*](#top)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <a id=\"splitting-the-dataset\"></a>2.1) Splitting the Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        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>BrukerID</th>\n",
       "      <th>FilmID</th>\n",
       "      <th>Tidstempel</th>\n",
       "      <th>Rangering</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>708938</th>\n",
       "      <td>4341</td>\n",
       "      <td>1210</td>\n",
       "      <td>976573899</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>371257</th>\n",
       "      <td>1425</td>\n",
       "      <td>3192</td>\n",
       "      <td>970774826</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>845603</th>\n",
       "      <td>5565</td>\n",
       "      <td>2299</td>\n",
       "      <td>976239652</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>667558</th>\n",
       "      <td>3970</td>\n",
       "      <td>83</td>\n",
       "      <td>965770467</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>821518</th>\n",
       "      <td>5320</td>\n",
       "      <td>3843</td>\n",
       "      <td>976249576</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>661055</th>\n",
       "      <td>3915</td>\n",
       "      <td>1846</td>\n",
       "      <td>974698675</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>204614</th>\n",
       "      <td>32</td>\n",
       "      <td>884</td>\n",
       "      <td>974679433</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>476497</th>\n",
       "      <td>2357</td>\n",
       "      <td>101</td>\n",
       "      <td>975126955</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>214539</th>\n",
       "      <td>122</td>\n",
       "      <td>2098</td>\n",
       "      <td>974662091</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>176991</th>\n",
       "      <td>5383</td>\n",
       "      <td>2759</td>\n",
       "      <td>962992451</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>630081 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        BrukerID  FilmID  Tidstempel  Rangering\n",
       "708938      4341    1210   976573899          3\n",
       "371257      1425    3192   970774826          4\n",
       "845603      5565    2299   976239652          5\n",
       "667558      3970      83   965770467          3\n",
       "821518      5320    3843   976249576          4\n",
       "...          ...     ...         ...        ...\n",
       "661055      3915    1846   974698675          4\n",
       "204614        32     884   974679433          4\n",
       "476497      2357     101   975126955          4\n",
       "214539       122    2098   974662091          4\n",
       "176991      5383    2759   962992451          5\n",
       "\n",
       "[630081 rows x 4 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Splitting the ratings dataset into the feature set (X) and target labels (y)\n",
    "X = ratings_df.drop(columns='Rangering')\n",
    "y = ratings_df[\"Rangering\"].values  # The movie ratings are the target variables we want to predict\n",
    "\n",
    "# Preparing train, validation and test datasets.\n",
    "# I have chosen a split ratio of 70%, 15%, 15%, because I want a somewhat large training set at the cost of a\n",
    "# smaller validation and test set. I do not think that a smaller validation (or test) dataset will negatively\n",
    "# impact the generalization ability of the chosen models, because I am only using rather simple ML models\n",
    "# with few hyperparamaters.\n",
    "X_train, X_val_and_test, y_train, y_val_and_test = train_test_split(X, y, test_size=0.3, random_state=101)\n",
    "X_val, X_test, y_val, y_test = train_test_split(X_val_and_test, y_val_and_test, test_size=0.5, random_state=101)\n",
    "\n",
    "# Creating a complete training dataset with X_train and y_train\n",
    "train_df = X_train.copy()\n",
    "train_df[\"Rangering\"] = y_train\n",
    "\n",
    "train_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br><br>\n",
    "### <a id=\"baseline\"></a>2.2) The Baseline Model\n",
    "In order to provide context for the evaluation of more advanced recommendation models, I will begin with constructing a very simple baseline model. I will use a popularity-based approach, where I calculate the average rating for each movie and simply predict that all users rate all movies after the movies' average rating. Even though this model provides no personalization, most users to in fact rate movies close to the average rating for the movies. I therefore expect that the RMSE for this model will prove to be somewhat challenging to beat.\n",
    "<br><br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>FilmID</th>\n",
       "      <th>Predicted rating</th>\n",
       "      <th>Actual rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>2.055556</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>2.055556</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>2.055556</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>2.055556</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>2.055556</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>134982</th>\n",
       "      <td>3952</td>\n",
       "      <td>2.353846</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>134983</th>\n",
       "      <td>3952</td>\n",
       "      <td>2.353846</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>134984</th>\n",
       "      <td>3952</td>\n",
       "      <td>2.353846</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>134985</th>\n",
       "      <td>3952</td>\n",
       "      <td>2.353846</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>134986</th>\n",
       "      <td>3952</td>\n",
       "      <td>2.353846</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>134987 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        FilmID  Predicted rating  Actual rating\n",
       "0            0          2.055556              1\n",
       "1            0          2.055556              4\n",
       "2            0          2.055556              3\n",
       "3            0          2.055556              2\n",
       "4            0          2.055556              2\n",
       "...        ...               ...            ...\n",
       "134982    3952          2.353846              2\n",
       "134983    3952          2.353846              1\n",
       "134984    3952          2.353846              1\n",
       "134985    3952          2.353846              3\n",
       "134986    3952          2.353846              3\n",
       "\n",
       "[134987 rows x 3 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Creating a new dataframe with the average rating for each movie. \n",
    "# The \"prediction\" for the baseline \"model\" will actually just be these averages for each movie.\n",
    "baseline_y_pred = pd.DataFrame(train_df.groupby('FilmID')['Rangering'].mean())\n",
    "\n",
    "# The baseline model has not yet calculated an average for the movies (if any) with no ratings. \n",
    "# We therefore add these movies to the model with a predicted rating equal to the average rating \n",
    "# for all movies in the training dataset.\n",
    "# ADD SOME CODE HERE!!\n",
    "\n",
    "# Creating a dataframe for the validation data (y_true) in order to calculate RMSE of the baseline model\n",
    "val_movies_dict = {'FilmID': X_val[\"FilmID\"], 'Actual rating': y_val}  \n",
    "val_movies_df = pd.DataFrame(val_movies_dict)\n",
    "\n",
    "# Merging the training and validation datasets on the movie ID\n",
    "y_pred_and_y_true = pd.merge(baseline_y_pred, val_movies_df, on='FilmID')\n",
    "baseline_y_pred_vs_y_true = y_pred_and_y_true.rename(columns={\"Rangering\": \"Predicted rating\"})\n",
    "\n",
    "baseline_y_pred_vs_y_true"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE baseline model:  0.981626106076701\n"
     ]
    }
   ],
   "source": [
    "# calculating RMSE for the baseline model\n",
    "print(\"RMSE baseline model: \", sqrt(mean_squared_error(baseline_y_pred_vs_y_true[\"Predicted rating\"], \n",
    "                                                       baseline_y_pred_vs_y_true[\"Actual rating\"])))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br><br>\n",
    "### <a id=\"content-based\"></a>2.3) Content Based Filtering\n",
    "[*Back to top*](#top)\n",
    "\n",
    "For the content-based filtering (CBF) models, I use the information about the users' movie preferences to predict recommendations. In particular, the CBF models use information about what each user has rated, what they rated, and the rated movie's genres to build an individual model for each user. The model then predicts ratings for all of the other movies in the dataset the user has not seen. \n",
    "\n",
    "I want to test a variety of different machine learning algorithms to establish which one is most beneficial for content-based recommendations on this dataset. Even though I suspect that the linear models will perform best, I will try out a few other types of machine learning algorithms as well for my own learning purposes.\n",
    "\n",
    "The main advantage of CBF is that it takes individual user preferences into consideration. For movie recommendations, this is quite fitting, as we often have a few favourite movie genres, actors, directors, etc. that we enjoy more than others. Sadly, the movies dataset from MovieLens contains very few movie features, and content-based filtering models usually need a multitude of item features in order to accurately model user preferences. I will attempt to alleviate these problems with featue engineering in section 3. \n",
    "\n",
    "Another disadvantage with CBF is that these models struggle with very sparse matrices. I will likely encounter this exact issue, as most users only rate a few movies. In contrast to collaborative filtering, CBF models also have a disadvantage in that they don't consider similarities between users, since they only focus on items (content) and individual users. \n",
    "\n",
    "My hypothesis is that a content-based model which only uses the movie genre and user data will not be able to outperform the baseline model. To achieve this, CBF requires more features in the movies dataset. It will however be interesting to see how close I can get, and which model will perform best. The most successful model will be used in the hybrid recommender in chapter 2.5. \n",
    "<br>\n",
    "<br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>BrukerID</th>\n",
       "      <th>Rangering</th>\n",
       "      <th>Action</th>\n",
       "      <th>Adventure</th>\n",
       "      <th>Animation</th>\n",
       "      <th>Children's</th>\n",
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       "<p>630081 rows × 20 columns</p>\n",
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      ],
      "text/plain": [
       "        BrukerID  Rangering  Action  Adventure  Animation  Children's  Comedy  \\\n",
       "0           4341          3       0          0          0           0       0   \n",
       "1           1242          3       0          0          0           0       0   \n",
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       "...          ...        ...     ...        ...        ...         ...     ...   \n",
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       "\n",
       "        Crime  Documentary  Drama  Fantasy  Film-Noir  Horror  Musical  \\\n",
       "0           0            0      1        0          0       0        0   \n",
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       "...       ...          ...    ...      ...        ...     ...      ...   \n",
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       "\n",
       "        Mystery  Romance  Sci-Fi  Thriller  War  Western  \n",
       "0             0        0       0         0    0        0  \n",
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       "\n",
       "[630081 rows x 20 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# ----------- PREPARING TRAINING DATA ----------- #\n",
    "# Adding the movie features (genre, release year) to the training dataset\n",
    "content_train_df = pd.merge(train_df, movies_df, on='FilmID')\n",
    "content_train_df.drop(columns=['Tidstempel', 'FilmID', 'Tittel'], inplace=True)  # Remove useless features\n",
    "\n",
    "content_train_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
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       "       3, 3, 5, 2, 4, 3, 3, 4, 4, 3, 3, 5, 3, 2, 1, 3, 3, 3, 2, 3, 4, 3,\n",
       "       2, 3, 3, 3, 4, 3, 4, 4, 2, 3, 3, 3, 2, 3, 4, 3], dtype=int64)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Creating a list of lists with the target attribute (movie ratings), grouped by userID\n",
    "y_grouped_by_user = content_train_df.groupby([\"BrukerID\"])\n",
    "y_train_listed = []\n",
    "\n",
    "for i, j in y_grouped_by_user:\n",
    "    y_train_listed.append(j[\"Rangering\"].values)  # Extract target feature (ratings) from sorted data\n",
    "    \n",
    "# Target attributes for the first user\n",
    "y_train_listed[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
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     },
     "execution_count": 21,
     "metadata": {},
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    }
   ],
   "source": [
    "# Creating a list of dataframes with the feature set (movie info about genres and release year), grouped by userID\n",
    "content_train_df.drop(columns='Rangering', inplace=True)\n",
    "x_grouped_by_user = content_train_df.groupby([\"BrukerID\"])\n",
    "x_train_listed = []\n",
    "\n",
    "for user_id, group in x_grouped_by_user:\n",
    "    x_train_listed.append(group.drop(columns='BrukerID'))\n",
    "    \n",
    "# The feature set for the first user\n",
    "x_train_listed[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
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       "      Action  Adventure  Animation  Children's  Comedy  Crime  Documentary  \\\n",
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     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "# Creating a new dataframe for the movies data\n",
    "all_movies = movies_df.drop(columns=['Tittel', 'FilmID'])\n",
    "all_movies"
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  },
  {
   "cell_type": "code",
   "execution_count": 23,
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     "execution_count": 23,
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   "source": [
    "# ----------- PREPARING VALIDATION DATA ----------- #\n",
    "# Creating a 2 dimensional matrix for the validation data in order to make it easier to calculate RMSE.\n",
    "\n",
    "# Listing the user ID's in the same order as in the grouped dataframes\n",
    "user_ids = []\n",
    "for user_id, group in x_grouped_by_user:\n",
    "    user_ids.append(user_id)\n",
    "    \n",
    "# Listing the movie IDs in the same order as in the movies dataset\n",
    "movie_ids = movies_df[\"FilmID\"].values\n",
    "\n",
    "# Creating the matrix. Axis 1: User IDs, Axis 2: Movie IDs. Elements: True ratings from validation data\n",
    "df_val = X_val.copy()\n",
    "df_val[\"Rangering\"] = y_val\n",
    "validation_matrix = pd.DataFrame(index=user_ids, columns=movie_ids)  # Starting with an empty matrix\n",
    "for array in df_val.to_records():  # Filling in the true ratings as elements\n",
    "    user = array['BrukerID']\n",
    "    movie = array['FilmID']\n",
    "    true_rating = array['Rangering']\n",
    "    validation_matrix.loc[user][movie] = true_rating\n",
    "    \n",
    "validation_matrix"
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  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
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    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\sebas\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:476: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0, tolerance: 0.0\n",
      "  positive)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE of different content-based filtering models without the year of release feature\n"
     ]
    },
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       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Model      RMSE\n",
       "0  Linear regression  1.071366\n",
       "1              Lasso  1.037082\n",
       "2              KNN_7  1.061543\n",
       "3                RFR  1.080374\n",
       "4                SVR  1.054499"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# ----------- CREATING THE DIFFERENT CONTENT-BASED FILTERING MODELS ----------- #\n",
    "# Assigning the different machine learning algorithms to be implemented in the models (incl. hyperparameters) to a dictionary\n",
    "ml_algorithms = {\"Linear regression\": LinearRegression(), \"Lasso\": Lasso(alpha=1.0, max_iter=10000), \n",
    "                 \"KNN_7\": KNeighborsRegressor(n_neighbors=7),\n",
    "                 \"RFR\": RandomForestRegressor(n_estimators=1000, n_jobs=3, max_features=\"auto\", random_state=0),\n",
    "                 \"SVR\": SVR(C=1.0)}\n",
    "\n",
    "# Saving lists that I later use to construct a dataframe containing the performances of the models\n",
    "CBF_models_listed = []\n",
    "RMSE_CBF_listed = []\n",
    "\n",
    "# For every machine learning algorithm in the dictionary:\n",
    "for name, ml_alg in ml_algorithms.items():\n",
    "    # Create an empty list for predictions\n",
    "    CBF_predictions = []\n",
    "\n",
    "    # For each user in the training dataset:\n",
    "    for i, x in enumerate(x_train_listed):\n",
    "        # Fit a machine learning model\n",
    "        ml_alg.fit(x_train_listed[i], y_train_listed[i])\n",
    "        # Predict all the ratings for this user for all movies\n",
    "        prediction = ml_alg.predict(all_movies)\n",
    "        prediction = np.clip(prediction, 1, 5)  # Predictions must be minimum 1, maximum 5\n",
    "        # Append all the predictions to the predictions list\n",
    "        CBF_predictions.append(prediction)\n",
    "\n",
    "    # Create a dataframe with the predictions\n",
    "    df_predict = pd.DataFrame(CBF_predictions, index=user_ids, columns=movie_ids)\n",
    "\n",
    "    # Create a dataframe with only the predictions for the movies-user combinations that appear in the validation set\n",
    "    num_actual = validation_matrix.to_numpy().flatten()[validation_matrix.notna().to_numpy().flatten()]\n",
    "    num_predict = df_predict.to_numpy().flatten()[validation_matrix.notna().to_numpy().flatten()]\n",
    "\n",
    "    # Calculate the RMSE for the content-based filtering model and add the result to the lists\n",
    "    RMSE_CBF_listed.append(sqrt(mean_squared_error(num_predict, num_actual)))\n",
    "    CBF_models_listed.append(name)\n",
    "\n",
    "\n",
    "# Printing the results\n",
    "RMSE_CBF_df = pd.DataFrame({\"Model\": CBF_models_listed, \"RMSE\": RMSE_CBF_listed})\n",
    "print(\"RMSE of different content-based filtering models without the year of release feature:\")\n",
    "RMSE_CBF_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As expected, the linear models performed better than the KNN classifier, though none of them outperformed the baseline model. Lasso had a sinificantly lower RMSE than the rest, so I will therefore use Lasso for the hybrid recommender in section 2.5. It will however be interesting to see how feature engineering will affect both the classifier and the best linear models. I wonder whether the difference in RMSE between the different models will change or remain the same."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\sebas\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:476: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0, tolerance: 0.0\n",
      "  positive)\n"
     ]
    }
   ],
   "source": [
    "# Running the best content-based filtering model so far\n",
    "model = Lasso(alpha=1.0, max_iter=10000)\n",
    "CBF_predictions = []\n",
    "\n",
    "# For each user in the training dataset:\n",
    "for i, j in enumerate(x_train_listed):\n",
    "    model.fit(x_train_listed[i], y_train_listed[i])\n",
    "    prediction = model.predict(all_movies)\n",
    "    prediction = np.clip(prediction, 1, 5)\n",
    "    CBF_predictions.append(prediction)\n",
    "\n",
    "# Creating a dataframe for the predictions\n",
    "CBF_model = pd.DataFrame(CBF_predictions, index=user_ids, columns=movie_ids)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <a id=\"collaborative\"></a>2.4) Collaborative Filtering\n",
    "[*Back to top*](#top)\n",
    "\n",
    "I now continue to construct collaborative filtering (CF) models using the K-Nearest Neighbours algorithm, \n",
    "which I expect to compliment and outperform the content-based filtering models. I will be using a *user-based* collaborative filtering approach, such that user data (age, gender, location, etc.) will *not* have any influence on the recommendations. The user-based CF models will calculate predictions based on the preferences of the **most similar users** in regards to movie taste. The CF model therefore only needs to know which users have rated which movies, along with the value of each rating. The main disadvantage of user-based CF models, however, is that no contextual information about the rating, nor any information about the user or the items are considered. \n",
    "\n",
    "Similarity in movie preferences are calculated by using the relative difference in movie ratings, where each user's mean rating is subtracted from all of the users movie ratings. As seen in figure 5 in section 1.2, not all users have the same understanding of the rating scale. Some users tend to rate movies a lot higher or lower than others, despite having similar movie tastes. I measure user similary as the Pearson correlation coefficient subtracted from 1. After calculating similarities and distances between all users, a predicted rating for each movie is calculated by taking the average rating given by the K nearest neigbors (e.g. the k most similar users) who have rated the movie in question.\n",
    "\n",
    "Since the dataset consists of ~6000 users and ~4000 movies, I expect that high values (30-100) for the K-hyperparameter will provide better predictions. However, I know that if I keep increasing the number of close users, the predicted rating will converge on the average rating, and thus perform identically to the baseline model. Increasing K will also result in a large increase in the time needed to train the model (and I'm not working on a particularly powerful PC). Seeing as higher K values are likely to give better predictions, I decided to calculate the predicted ratings with a weighted average (weight = 1/Pearson distance) to improve the predictions for the models with high values of K.\n",
    "\n",
    "Similarly to the section on CBF I will train a variety of CF models, though these only vary in hyperparameters instead of algorithmically. I will then use the best performing CF model for the hybrid recommender in the next section."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
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       "        BrukerID  FilmID  Tidstempel  Rangering\n",
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     "execution_count": 26,
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   ],
   "source": [
    "# A quick look at the training data\n",
    "train_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# DATA PREPROCESSING: Calculating the Pearson Distance between all users in the training data\n",
    "# Creating a 2D matrix (user ID vs movie ID) with the ratings as elements\n",
    "user_matrix = train_df.pivot(index='BrukerID', columns='FilmID', values='Rangering')\n",
    "\n",
    "# I subtract each user's average rating to magnify individual preferences\n",
    "user_matrix = user_matrix.sub(user_matrix.mean(axis=1), axis=0)\n",
    "\n",
    "# Replace NaN with 0.0, as this is now the \"neutral\" value\n",
    "user_matrix = user_matrix.fillna(0.0)"
   ]
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  {
   "cell_type": "code",
   "execution_count": 27,
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       "      <td>1.000000</td>\n",
       "      <td>0.978282</td>\n",
       "      <td>0.954219</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.977534</td>\n",
       "      <td>0.981483</td>\n",
       "      <td>0.989847</td>\n",
       "      <td>0.951471</td>\n",
       "      <td>0.941595</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.986514</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6040</th>\n",
       "      <td>0.992078</td>\n",
       "      <td>1.027741</td>\n",
       "      <td>1.029691</td>\n",
       "      <td>1.071963</td>\n",
       "      <td>1.018350</td>\n",
       "      <td>1.030326</td>\n",
       "      <td>0.989035</td>\n",
       "      <td>0.981804</td>\n",
       "      <td>0.975827</td>\n",
       "      <td>0.979975</td>\n",
       "      <td>...</td>\n",
       "      <td>0.994760</td>\n",
       "      <td>0.980172</td>\n",
       "      <td>0.961100</td>\n",
       "      <td>1.000266</td>\n",
       "      <td>0.927586</td>\n",
       "      <td>1.008256</td>\n",
       "      <td>0.969143</td>\n",
       "      <td>0.998009</td>\n",
       "      <td>0.986514</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>6040 rows × 6040 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "BrukerID      0         1         2         3         4         5     \\\n",
       "BrukerID                                                               \n",
       "0         0.000000  1.008851  1.000000  0.870659  1.004774  1.005061   \n",
       "1         1.008851  0.000000  1.000000  1.000000  1.038525  1.215623   \n",
       "2         1.000000  1.000000  0.000000  1.000000  0.954233  1.000000   \n",
       "3         0.870659  1.000000  1.000000  0.000000  1.000000  1.000000   \n",
       "4         1.004774  1.038525  0.954233  1.000000  0.000000  0.966535   \n",
       "...            ...       ...       ...       ...       ...       ...   \n",
       "6036      1.004525  1.000000  0.914610  1.000000  1.020158  1.000000   \n",
       "6037      0.987334  1.008252  1.084354  1.001476  0.936568  1.000000   \n",
       "6038      1.019618  1.011828  1.000000  1.000000  1.076701  1.000000   \n",
       "6039      1.108225  0.992498  0.989900  1.000000  1.008673  1.029731   \n",
       "6040      0.992078  1.027741  1.029691  1.071963  1.018350  1.030326   \n",
       "\n",
       "BrukerID      6         7         8         9     ...      6031      6032  \\\n",
       "BrukerID                                          ...                       \n",
       "0         0.985787  0.993054  0.936473  1.000721  ...  1.000000  0.993009   \n",
       "1         1.013271  0.981892  0.966971  0.972867  ...  1.000000  1.014202   \n",
       "2         1.002765  0.967496  1.024260  0.956310  ...  1.000000  0.939836   \n",
       "3         1.000000  1.000000  0.911999  1.033222  ...  1.000000  1.096116   \n",
       "4         1.079381  0.970525  0.924106  0.932877  ...  0.994337  0.952138   \n",
       "...            ...       ...       ...       ...  ...       ...       ...   \n",
       "6036      1.001341  0.931767  0.978402  0.988251  ...  0.948502  0.975727   \n",
       "6037      1.003874  1.016693  0.986043  1.021241  ...  1.000000  0.928262   \n",
       "6038      0.978866  1.000000  1.028991  0.961786  ...  1.000000  0.981633   \n",
       "6039      0.996649  1.000000  0.978282  0.954219  ...  1.000000  0.977534   \n",
       "6040      0.989035  0.981804  0.975827  0.979975  ...  0.994760  0.980172   \n",
       "\n",
       "BrukerID      6033      6034      6035      6036      6037      6038  \\\n",
       "BrukerID                                                               \n",
       "0         0.919575  1.008068  1.000520  1.004525  0.987334  1.019618   \n",
       "1         0.986063  0.993568  1.000000  1.000000  1.008252  1.011828   \n",
       "2         0.949094  0.938041  0.968789  0.914610  1.084354  1.000000   \n",
       "3         1.000000  0.998262  1.000000  1.000000  1.001476  1.000000   \n",
       "4         0.979018  1.007612  1.046063  1.020158  0.936568  1.076701   \n",
       "...            ...       ...       ...       ...       ...       ...   \n",
       "6036      0.992479  0.984205  0.964859  0.000000  1.067465  1.000000   \n",
       "6037      1.027527  0.927628  1.105656  1.067465  0.000000  1.028313   \n",
       "6038      1.010349  0.987804  1.002550  1.000000  1.028313  0.000000   \n",
       "6039      0.981483  0.989847  0.951471  0.941595  1.000000  1.000000   \n",
       "6040      0.961100  1.000266  0.927586  1.008256  0.969143  0.998009   \n",
       "\n",
       "BrukerID      6039      6040  \n",
       "BrukerID                      \n",
       "0         1.108225  0.992078  \n",
       "1         0.992498  1.027741  \n",
       "2         0.989900  1.029691  \n",
       "3         1.000000  1.071963  \n",
       "4         1.008673  1.018350  \n",
       "...            ...       ...  \n",
       "6036      0.941595  1.008256  \n",
       "6037      1.000000  0.969143  \n",
       "6038      1.000000  0.998009  \n",
       "6039      0.000000  0.986514  \n",
       "6040      0.986514  0.000000  \n",
       "\n",
       "[6040 rows x 6040 columns]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# I calculate the Pearson Correlation between each user,\n",
    "# and subtract this from 1 to get the Pearson Distance between users\n",
    "user_dist_matrix = 1 - user_matrix.T.corr()\n",
    "user_dist_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "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>Model</th>\n",
       "      <th>RMSE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>kNN-5</td>\n",
       "      <td>1.013958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>kNN-10</td>\n",
       "      <td>0.978301</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>kNN-20</td>\n",
       "      <td>0.962699</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>kNN-30</td>\n",
       "      <td>0.959578</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>kNN-40</td>\n",
       "      <td>0.958769</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>kNN-60</td>\n",
       "      <td>0.959030</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Model      RMSE\n",
       "0   kNN-5  1.013958\n",
       "1  kNN-10  0.978301\n",
       "2  kNN-20  0.962699\n",
       "3  kNN-30  0.959578\n",
       "4  kNN-40  0.958769\n",
       "5  kNN-60  0.959030"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# MODELLING: Predicting ratings for every user with K Nearest Neighbours\n",
    "# Models with a different number of neighbors\n",
    "ml_algorithms = {'kNN-5': 5, 'kNN-10': 10, 'kNN-20': 20, 'kNN-30': 30, 'kNN-40': 40, \"kNN-60\": 60}\n",
    "\n",
    "models_CF = []\n",
    "RMSE_CF = []\n",
    "\n",
    "# Training the models and predicting for the users and movies in the validation data\n",
    "for name, num_neighbours in ml_algorithms.items():\n",
    "    predictions = []\n",
    "\n",
    "    # For every rating in the validation data\n",
    "    for index, row in X_val.iterrows():\n",
    "        # If the movie is in the training data\n",
    "        if row[\"FilmID\"] in X_train[\"FilmID\"].unique():\n",
    "            # Extract all user ID's for users who have rated the movie\n",
    "            users_rated_movie = X_train.loc[X_train['FilmID'] == row['FilmID'], 'BrukerID']\n",
    "            # Sort these users by similarity (Pearson distance)\n",
    "            users_sorted = (user_dist_matrix.loc[row['BrukerID'], users_rated_movie].sort_values())\n",
    "            # Select the nearest neighbours\n",
    "            nearest_neighbours = users_sorted[:num_neighbours]\n",
    "            # Extract the nearest neighbours' ratings data\n",
    "            nn_data = train_df.loc[train_df['BrukerID'].isin(nearest_neighbours.index.to_list())]\n",
    "            # Calculate the weighted average of the nearest neighbours' ratings\n",
    "            nearest_neighbours_avg_rating = np.average(nn_data.loc[train_df['FilmID'] == row['FilmID'], 'Rangering'],\n",
    "                                                       axis=0, weights=(1/nearest_neighbours))\n",
    "        else:\n",
    "            # There is a small chance that a few movies in the validation set might not appear in the training set.\n",
    "            # I therefore predict that the user will rate these movies with the average rating for all movies\n",
    "            nearest_neighbours_avg_rating = 4   # Must be changed!\n",
    "\n",
    "        # Appending the prediction to the list of predictions\n",
    "        if not np.isnan(nearest_neighbours_avg_rating):\n",
    "            predictions.append(nearest_neighbours_avg_rating)\n",
    "        else:\n",
    "            predictions.append(3)\n",
    "\n",
    "    models_CF.append(name)\n",
    "    RMSE_CF.append(sqrt(mean_squared_error(y_val, predictions)))\n",
    "\n",
    "\n",
    "# Displaying the results\n",
    "RMSE_CF_dict = {\"Model\": models_CF, \"RMSE\": RMSE_CF}\n",
    "RMSE_CF_df = pd.DataFrame(RMSE_CF_dict)\n",
    "RMSE_CF_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1152x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualizing how the number of neighbors effect the root mean sqaured error\n",
    "fig7, ax7 = plt.subplots()\n",
    "ax7.plot(RMSE_CF_df.Model, RMSE_CF_df.RMSE, label=\"RMSE\", color='darkred', linewidth=2)\n",
    "plt.xlabel(\"Number of nearest neighbors\", labelpad=18)\n",
    "plt.ylabel(\"Root mean squared error\", labelpad=15)\n",
    "plt.title(\"K-value effect on RMSE for collaborative filtering models\")\n",
    "fig7.set_figheight(10)\n",
    "fig7.set_figwidth(16)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The collaborative filtering models outperformed both the content-based and baseline models. With a K-value of 40, the model reached a RMSE of 0.9588. Higher K-values do not seem to lower RMSE any further, as RMSE increased slightly at K=60. I will now proceed with running the best model and storing the results for use in the hybrid recommender."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Rerunning the best model so far (kNN-40) and storing the prediction results\n",
    "best_CF_model = []\n",
    "RMSE_best_CF = []\n",
    "\n",
    "# Training the models and predicting for the users and movies in the validation data\n",
    "CF_predictions = []\n",
    "\n",
    "# For every movie in the validation data\n",
    "for index, row in X_val.iterrows():\n",
    "    # If that movie is in the training data\n",
    "    if row[\"FilmID\"] in X_train[\"FilmID\"].unique():\n",
    "        # Extract all user ID's for users who have rated the movie\n",
    "        users_rated_movie = X_train.loc[X_train['FilmID'] == row['FilmID'], 'BrukerID']\n",
    "        # Sort these users by similarity (Pearson distance)\n",
    "        users_sorted = (user_dist_matrix.loc[row['BrukerID'], users_rated_movie].sort_values())\n",
    "        # Select the nearest neighbours\n",
    "        nearest_neighbours = users_sorted[:40]\n",
    "        # Extract the nearest neighbours' ratings data\n",
    "        nn_data = train_df.loc[train_df['BrukerID'].isin(nearest_neighbours.index.to_list())]\n",
    "        # Calculate the weighted average of the nearest neighbours' ratings\n",
    "        nearest_neighbours_avg_rating = np.average(nn_data.loc[train_df['FilmID'] == row['FilmID'], 'Rangering'],\n",
    "                                                   axis=0, weights=(1/nearest_neighbours))\n",
    "    else:\n",
    "        # There is a small chance that a few movies in the validation set might not appear in the training set.\n",
    "        # I therefore predict that the user will rate these movies with the average rating for all movies\n",
    "        nearest_neighbours_avg_rating = 4   # Must be changed!\n",
    "\n",
    "    # Appending the prediction to the list of predictions\n",
    "    if not np.isnan(nearest_neighbours_avg_rating):\n",
    "        CF_predictions.append(nearest_neighbours_avg_rating)\n",
    "    else:\n",
    "        CF_predictions.append(4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <a id=\"hybrid\"></a>2.5) Hybrid Recommender\n",
    "*Combining the Content-based and Collaborative filtering models to improve results*\n",
    "\n",
    "[*Back to top*](#top)\n",
    "\n",
    "This section will aim to combine both the best CBF and the CF models in an attempt to create a superior hybrid recommender. CF and CBF both have advantages and disadvantages, but their strengths and weaknesses complement each other so that a combination of the two might prove to outperform the CF model by itself.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE combined approach (Lasso and KNN-40):\n",
      "RMSE for combined approach with CBF weighted 0.5 and CF weighted 0.5: \n",
      " 0.9383490270968214 \n",
      "\n",
      "RMSE for combined approach with CBF weighted 0.45 and CF weighted 0.55: \n",
      " 0.9347989553339932 \n",
      "\n",
      "RMSE for combined approach with CBF weighted 0.4 and CF weighted 0.6: \n",
      " 0.9324893533664824 \n",
      "\n",
      "RMSE for combined approach with CBF weighted 0.35 and CF weighted 0.65: \n",
      " 0.9314294489645997 \n",
      "\n",
      "RMSE for combined approach with CBF weighted 0.3 and CF weighted 0.7: \n",
      " 0.9316235074661314 \n",
      "\n",
      "RMSE for combined approach with CBF weighted 0.25 and CF weighted 0.75: \n",
      " 0.9330707464800498 \n",
      "\n",
      "RMSE for combined approach with CBF weighted 0.2 and CF weighted 0.8: \n",
      " 0.9357653515804694 \n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Extracting the validation prediction from the CBF dataframe containing all predictions\n",
    "CBF_predictions = []\n",
    "for index, row in X_val.iterrows():\n",
    "    user_predictions = CBF_model.loc[row[\"BrukerID\"], row[\"FilmID\"]]\n",
    "    CBF_predictions.append(user_predictions)\n",
    "    \n",
    "\n",
    "# Calculating the predictions for the different hybrid \"models\": different weighted averages of CF and CBF filtering\n",
    "print(\"RMSE combined approach (Lasso and KNN-40):\")\n",
    "weighted_avgs = [(0.5, 0.5), (0.45, 0.55), (0.4, 0.6), (0.35, 0.65), (0.3, 0.7), (0.25, 0.75), (0.20, 0.80)]\n",
    "  \n",
    "for weight in weighted_avgs:\n",
    "    combined_predictions = np.array([y_pred * weight[0] for y_pred in np.array(CBF_predictions)]) + np.array([y_pred * weight[1] for y_pred in np.array(CF_predictions)])\n",
    "    print(f\"RMSE for combined approach with CBF weighted {weight[0]} and CF weighted {weight[1]}: \\n\",\n",
    "          sqrt(mean_squared_error(y_val, combined_predictions)), \"\\n\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "------\n",
    "## <a id=\"feature-engineering\"></a>3) Feature Engineering \n",
    "[*Back to top*](#top)\n",
    "\n",
    "The collaborative filtering model I am using for the hybrid recommender does not take any contextual information (such as time of rating) into account. This means that I cannot improve its performance by engineering any new features to the dataset. I could attempt to use a more elaborate method for calculating similarity, or use a completely different collaborative filtering approach such as multidimensional filtering, but that would be outside the scope of this project (at least for now).\n",
    "\n",
    "However, the content-based filtering model predicts new ratings based on the user's movie preferences. By supplying the content-based model with more information about each user's preferences, I aim to improve the performance of the hybrid recommender. I will therefore engineer two new features for the movies dataset, and measure the impact these new features may have on the RMSE of the content-based filtering model and the hybrid recommendations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Re-loading clean datasets\n",
    "ratings_df = pd.read_csv(\"cleaned_data/rangering.csv\")\n",
    "movies_df = pd.read_csv(\"cleaned_data/film.csv\")\n",
    "users_df = pd.read_csv(\"cleaned_data/bruker.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <a id=\"feature-engineering-1\"></a>3.1) Adding release year to the movies dataset\n",
    "Movies that are released around the same time often share similarities such as production style, tone, and cast. This might affect user's preferences, and the linear models in particular may learn the correlation between release year and rating for each user. It might also make it easier for KNN to cluster the rated movies and classify unrated movies released in a similar time period."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>FilmID</th>\n",
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       "      <th>Action</th>\n",
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       "      <th>Mystery</th>\n",
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       "      <th>Thriller</th>\n",
       "      <th>War</th>\n",
       "      <th>Western</th>\n",
       "      <th>Utgivelsesår</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>Vie est belle, La (Life is Rosey) (1987)</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1987.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>Defying Gravity (1997)</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1997.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>Ruthless People (1986)</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1986.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>Portraits Chinois (1996)</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1996.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   FilmID                                    Tittel  Action  Adventure  \\\n",
       "0       0                 Autumn in New York (2000)       0          0   \n",
       "1       1  Vie est belle, La (Life is Rosey) (1987)       0          0   \n",
       "2       2                    Defying Gravity (1997)       0          0   \n",
       "3       3                    Ruthless People (1986)       0          0   \n",
       "4       4                  Portraits Chinois (1996)       0          0   \n",
       "\n",
       "   Animation  Children's  Comedy  Crime  Documentary  Drama  ...  Film-Noir  \\\n",
       "0          0           0       0      0            0      1  ...          0   \n",
       "1          0           0       1      0            0      1  ...          0   \n",
       "2          0           0       0      0            0      1  ...          0   \n",
       "3          0           0       1      0            0      0  ...          0   \n",
       "4          0           0       0      0            0      1  ...          0   \n",
       "\n",
       "   Horror  Musical  Mystery  Romance  Sci-Fi  Thriller  War  Western  \\\n",
       "0       0        0        0        1       0         0    0        0   \n",
       "1       0        0        0        0       0         0    0        0   \n",
       "2       0        0        0        0       0         0    0        0   \n",
       "3       0        0        0        0       0         0    0        0   \n",
       "4       0        0        0        0       0         0    0        0   \n",
       "\n",
       "   Utgivelsesår  \n",
       "0        2000.0  \n",
       "1        1987.0  \n",
       "2        1997.0  \n",
       "3        1986.0  \n",
       "4        1996.0  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Engineering feature nr. 1: Adding release year to movies dataset\n",
    "for index, row in movies_df.iterrows():\n",
    "    title = row[1]\n",
    "    release_year = int(title[-5:-1])\n",
    "    movies_df.loc[index, 'Utgivelsesår'] = release_year\n",
    "    \n",
    "# Printing the movies dataset again\n",
    "movies_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <a id=\"feature-engineering-2\"></a>3.2) Adding the average age of movie fan to the movies dataset\n",
    "Movies, like any product on the market, are designed and marketed to a specific type of audience. From the data analysis in section 1.1, I discovered that the userbase was quite varied across different ages, though not as much as one would expect in regards to the general population. There may yet be a significant correlation between the average age of common viewers and the rating for each movie, especially for children's movies or teenage dramas. However, movies may be designed for a rather narrow audience, but viewed (and rated) by a much larger one. I therefore want to find out whether additional data about the average age of the users who rated each movie highly will improve recommendations. I will call this new feature \"Fan Average Age\", or \"FAA\" for short."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 364 missing values for \"FAA\"\n"
     ]
    },
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>FilmID</th>\n",
       "      <th>Tittel</th>\n",
       "      <th>Action</th>\n",
       "      <th>Adventure</th>\n",
       "      <th>Animation</th>\n",
       "      <th>Children's</th>\n",
       "      <th>Comedy</th>\n",
       "      <th>Crime</th>\n",
       "      <th>Documentary</th>\n",
       "      <th>Drama</th>\n",
       "      <th>...</th>\n",
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       "      <th>Musical</th>\n",
       "      <th>Mystery</th>\n",
       "      <th>Romance</th>\n",
       "      <th>Sci-Fi</th>\n",
       "      <th>Thriller</th>\n",
       "      <th>War</th>\n",
       "      <th>Western</th>\n",
       "      <th>Utgivelsesår</th>\n",
       "      <th>FAA</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>Autumn in New York (2000)</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>30.416667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>Vie est belle, La (Life is Rosey) (1987)</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1987.0</td>\n",
       "      <td>29.833333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>Defying Gravity (1997)</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1997.0</td>\n",
       "      <td>30.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>Ruthless People (1986)</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1986.0</td>\n",
       "      <td>31.884259</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>Portraits Chinois (1996)</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1996.0</td>\n",
       "      <td>29.894868</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   FilmID                                    Tittel  Action  Adventure  \\\n",
       "0       0                 Autumn in New York (2000)       0          0   \n",
       "1       1  Vie est belle, La (Life is Rosey) (1987)       0          0   \n",
       "2       2                    Defying Gravity (1997)       0          0   \n",
       "3       3                    Ruthless People (1986)       0          0   \n",
       "4       4                  Portraits Chinois (1996)       0          0   \n",
       "\n",
       "   Animation  Children's  Comedy  Crime  Documentary  Drama  ...  Horror  \\\n",
       "0          0           0       0      0            0      1  ...       0   \n",
       "1          0           0       1      0            0      1  ...       0   \n",
       "2          0           0       0      0            0      1  ...       0   \n",
       "3          0           0       1      0            0      0  ...       0   \n",
       "4          0           0       0      0            0      1  ...       0   \n",
       "\n",
       "   Musical  Mystery  Romance  Sci-Fi  Thriller  War  Western  Utgivelsesår  \\\n",
       "0        0        0        1       0         0    0        0        2000.0   \n",
       "1        0        0        0       0         0    0        0        1987.0   \n",
       "2        0        0        0       0         0    0        0        1997.0   \n",
       "3        0        0        0       0         0    0        0        1986.0   \n",
       "4        0        0        0       0         0    0        0        1996.0   \n",
       "\n",
       "         FAA  \n",
       "0  30.416667  \n",
       "1  29.833333  \n",
       "2  30.000000  \n",
       "3  31.884259  \n",
       "4  29.894868  \n",
       "\n",
       "[5 rows x 22 columns]"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Feature engineering: Adding average viewer age to movies_df\n",
    "ratings_users_merged = users_df.merge(ratings_df, on=['BrukerID'])\n",
    "movie_fans = {movie_id: [] for movie_id in movies_df['FilmID']}\n",
    "\n",
    "for index, row in ratings_users_merged.iterrows():\n",
    "    rating = row['Rangering']\n",
    "    movie_id = row['FilmID']\n",
    "    user_age = row['Alder']\n",
    "\n",
    "    if int(rating) > 3:  # If the rating the user gave was higher than 3, the user probably enjoyed the movie\n",
    "        movie_fans[movie_id].append(user_age)\n",
    "\n",
    "\n",
    "fan_avg_ages = []\n",
    "\n",
    "for movie_id, ages in movie_fans.items():\n",
    "    if len(ages) > 0:\n",
    "        fan_avg_ages.append(np.mean(ages))\n",
    "    else:\n",
    "        fan_avg_ages.append(np.NaN)\n",
    "\n",
    "movies_df['FAA'] = fan_avg_ages\n",
    "\n",
    "print(f'There are {movies_df.isnull().sum().sum()} missing values for \"FAA\"')\n",
    "\n",
    "# Mean imputation for missing values due to low ratings\n",
    "movies_df['FAA'].fillna(29.894868, inplace=True)\n",
    "movies_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are 364 movies which don't have any ratings greater than 3, so I decided to handle this by imputing the mean user age. I don't think that this will negatively impact recommendations, as these movies wouldn't get recommended anyway. The machine learning algorithms should be able to recognize that the movies with missing FAA are unpopular movies, and might even lower the predicted rating for these movies."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Writing the engineered movies dataframe to a .csv file\n",
    "movies_df.to_csv('movies_feature_engineered.csv', index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "------\n",
    "## <a id=\"2nd-iteration\"></a>4) *Second Iteration:* Retraining the Content-based Filtering Model <br>for an Improved Hybrid Recommender\n",
    " [*Back to top*](#top)\n",
    " \n",
    " Engineering two new features may have improved the content-based filtering model. I will therefore retrain the three most promising CBF models and evaluate them on the validations dataset, before I implement the best one of them in the final hybrid recommender. \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>BrukerID</th>\n",
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       "      <td>32</td>\n",
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       "      <td>4</td>\n",
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      "text/plain": [
       "        BrukerID  FilmID  Tidstempel  Rangering\n",
       "708938      4341    1210   976573899          3\n",
       "371257      1425    3192   970774826          4\n",
       "845603      5565    2299   976239652          5\n",
       "667558      3970      83   965770467          3\n",
       "821518      5320    3843   976249576          4\n",
       "...          ...     ...         ...        ...\n",
       "661055      3915    1846   974698675          4\n",
       "204614        32     884   974679433          4\n",
       "476497      2357     101   975126955          4\n",
       "214539       122    2098   974662091          4\n",
       "176991      5383    2759   962992451          5\n",
       "\n",
       "[630081 rows x 4 columns]"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Redoing the train-val-test split with the same random state to get the same split\n",
    "# Splitting the ratings dataset into the feature set (X) and target labels (y)\n",
    "X = ratings_df.drop(columns='Rangering')\n",
    "y = ratings_df[\"Rangering\"].values  # The movie ratings are the target variables we want to predict\n",
    "\n",
    "# Preparing train, validation and test datasets.\n",
    "X_train, X_val_and_test, y_train, y_val_and_test = train_test_split(X, y, test_size=0.3, random_state=101)\n",
    "X_val, X_test, y_val, y_test = train_test_split(X_val_and_test, y_val_and_test, test_size=0.5, random_state=101)\n",
    "\n",
    "# Creating a complete training dataset with X_train and y_train\n",
    "train_df = X_train.copy()\n",
    "train_df[\"Rangering\"] = y_train\n",
    "\n",
    "train_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <a id=\"CBF-improved\"></a>4.1) Improved Content-based Filtering model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "# For this next iteration of modelling, I will train the two most promising CBF models \n",
    "# on the dataset with three newly engineered features.\n",
    "\n",
    "# ----------- PREPARING TRAINING DATA ----------- #\n",
    "# Adding the movie features (genre, release year) to the training dataset\n",
    "content_train_df = pd.merge(train_df, movies_df, on='FilmID')\n",
    "content_train_df.drop(columns=['Tidstempel', 'FilmID', 'Tittel'], inplace=True)  # Remove useless features\n",
    "\n",
    "# Creating a list of lists with the target attribute (movie ratings), grouped by userID\n",
    "y_grouped_by_user = content_train_df.groupby([\"BrukerID\"])\n",
    "y_train_listed = []\n",
    "\n",
    "for i, j in y_grouped_by_user:\n",
    "    y_train_listed.append(j[\"Rangering\"].values)  # Extract target feature (ratings) from sorted data\n",
    "    \n",
    "# Target attributes for the first user\n",
    "y_train_listed[0]\n",
    "\n",
    "# Creating a list of dataframes with the feature set (movie info about genres and release year), grouped by userID\n",
    "content_train_df.drop(columns='Rangering', inplace=True)\n",
    "x_grouped_by_user = content_train_df.groupby([\"BrukerID\"])\n",
    "x_train_listed = []\n",
    "\n",
    "for user_id, group in x_grouped_by_user:\n",
    "    x_train_listed.append(group.drop(columns='BrukerID'))\n",
    "    \n",
    "    \n",
    "# Creating a new dataframe for the movies data\n",
    "all_movies = movies_df.drop(columns=['Tittel', 'FilmID'])\n",
    "all_movies\n",
    "\n",
    "\n",
    "# ----------- PREPARING VALIDATION DATA ----------- #\n",
    "# Creating a 2 dimensional matrix for the validation data in order to make it easier to calculate RMSE.\n",
    "\n",
    "# Listing the user ID's in the same order as in the grouped dataframes\n",
    "user_ids = []\n",
    "for user_id, group in x_grouped_by_user:\n",
    "    user_ids.append(user_id)\n",
    "    \n",
    "# Listing the movie IDs in the same order as in the movies dataset\n",
    "movie_ids = movies_df[\"FilmID\"].values\n",
    "\n",
    "# Creating the matrix. Axis 1: User IDs, Axis 2: Movie IDs. Elements: True ratings from validation data\n",
    "df_val = X_val.copy()\n",
    "df_val[\"Rangering\"] = y_val\n",
    "validation_matrix = pd.DataFrame(index=user_ids, columns=movie_ids)  # Starting with an empty matrix\n",
    "for array in df_val.to_records():  # Filling in the true ratings as elements\n",
    "    user = array['BrukerID']\n",
    "    movie = array['FilmID']\n",
    "    true_rating = array['Rangering']\n",
    "    validation_matrix.loc[user][movie] = true_rating\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\sebas\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:476: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0, tolerance: 0.0\n",
      "  positive)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE of different content-based filtering models, including the year of release feature\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>Model</th>\n",
       "      <th>RMSE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Lasso</td>\n",
       "      <td>1.020758</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>KNN_7</td>\n",
       "      <td>1.040054</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>SVR</td>\n",
       "      <td>1.070242</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Model      RMSE\n",
       "0  Lasso  1.020758\n",
       "1  KNN_7  1.040054\n",
       "2    SVR  1.070242"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# ----------- CREATING THE DIFFERENT IMPROVED CONTENT-BASED FILTERING MODELS ----------- #\n",
    "ml_algorithms = {\"Lasso\": Lasso(alpha=1.0, max_iter=10000), \"KNN_7\": KNeighborsRegressor(n_neighbors=7),\n",
    "                 \"SVR\": SVR(C=1.0)}\n",
    "\n",
    "# Saving lists that I later use to construct a dataframe containing the performances of the models\n",
    "improved_models_listed = []\n",
    "improved_models_RMSE = []\n",
    "\n",
    "# For every machine learning algorithm in the dictionary:\n",
    "for name, ml_alg in ml_algorithms.items():\n",
    "    # Create an empty list for predictions\n",
    "    CBF_predictions = []\n",
    "\n",
    "    # For each user in the training dataset:\n",
    "    for i, x in enumerate(x_train_listed):\n",
    "        # Fit a machine learning model with the year of release feature\n",
    "        ml_alg.fit(x_train_listed[i], y_train_listed[i])\n",
    "        # Predict all the ratings for this user for all movies\n",
    "        prediction = ml_alg.predict(all_movies)\n",
    "        prediction = np.clip(prediction, 1, 5)  # Predictions must be minimum 1, maximum 5\n",
    "        # Append all the predictions to the predictions list\n",
    "        CBF_predictions.append(prediction)\n",
    "\n",
    "    # Create a dataframe with the predictions\n",
    "    CBF_y_pred_df = pd.DataFrame(CBF_predictions, index=user_ids, columns=movie_ids)\n",
    "\n",
    "    # Create a dataframe with only the predictions for the movies-user combinations that appear in the validation set\n",
    "    num_actual = validation_matrix.to_numpy().flatten()[validation_matrix.notna().to_numpy().flatten()]\n",
    "    num_predict = CBF_y_pred_df.to_numpy().flatten()[validation_matrix.notna().to_numpy().flatten()]\n",
    "\n",
    "    # Calculate the RMSE for the content-based filtering model and add the result to the lists\n",
    "    improved_models_RMSE.append(sqrt(mean_squared_error(num_predict, num_actual)))\n",
    "    improved_models_listed.append(name)\n",
    "\n",
    "\n",
    "# Printing the results\n",
    "RMSE_content_df_improved = pd.DataFrame({\"Model\": improved_models_listed, \"RMSE\": improved_models_RMSE})\n",
    "print(\"RMSE of different content-based filtering models, including the year of release feature\")\n",
    "RMSE_content_df_improved"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Previously, Lasso performed with an RMSE of 1.037. It still outperforms both KNN and SVR after training with new features, with an RMSE reduced by 0.016242. The performance increase is not exactly astounding, but still significant enough to confirm that the feature engineering was successful."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\sebas\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:476: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 0.0, tolerance: 0.0\n",
      "  positive)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE of best content-based filtering model: 1.0207579360879961\n"
     ]
    }
   ],
   "source": [
    "# Running the best improved content-based filtering model\n",
    "model = Lasso(alpha=1.0, max_iter=10000)\n",
    "CBF_improved_predictions = []\n",
    "\n",
    "# For each user in the training dataset:\n",
    "for i, j in enumerate(x_train_listed):\n",
    "    model.fit(x_train_listed[i], y_train_listed[i])\n",
    "    prediction = model.predict(all_movies)\n",
    "    prediction = np.clip(prediction, 1, 5)\n",
    "    CBF_improved_predictions.append(prediction)\n",
    "\n",
    "# Creating a dataframe for the predictions\n",
    "CBF_improved_model = pd.DataFrame(CBF_improved_predictions, index=user_ids, columns=movie_ids)\n",
    "\n",
    "# Creating a dataframe with only the predictions for the movies-user combinations that appear in the validation set\n",
    "num_actual = validation_matrix.to_numpy().flatten()[validation_matrix.notna().to_numpy().flatten()]\n",
    "num_predict = CBF_improved_model.to_numpy().flatten()[validation_matrix.notna().to_numpy().flatten()]\n",
    "print(\"RMSE of best content-based filtering model:\", sqrt(mean_squared_error(num_predict, num_actual)))\n",
    "\n",
    "# Saving the best CBF model's prediction to disk for later use\n",
    "CBF_improved_model.to_pickle(\"./CBF_model.pkl\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <a id=\"hybrid-improved\"></a>4.2) Improved Hybrid Recommender\n",
    "I will now implement the improved CBF model in a new hybrid recommender, and test different weight combinations against the validation set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE combined approach (Lasso and KNN-40):\n",
      "RMSE for combined approach with CBF weighted 0.5 and CF weighted 0.5: \n",
      " 0.9310363505982111 \n",
      "\n",
      "RMSE for combined approach with CBF weighted 0.45 and CF weighted 0.55: \n",
      " 0.9283464888895532 \n",
      "\n",
      "RMSE for combined approach with CBF weighted 0.4 and CF weighted 0.6: \n",
      " 0.9268766505281787 \n",
      "\n",
      "RMSE for combined approach with CBF weighted 0.35 and CF weighted 0.65: \n",
      " 0.926632641189354 \n",
      "\n",
      "RMSE for combined approach with CBF weighted 0.3 and CF weighted 0.7: \n",
      " 0.9276154282369545 \n",
      "\n",
      "RMSE for combined approach with CBF weighted 0.25 and CF weighted 0.75: \n",
      " 0.9298211216418713 \n",
      "\n",
      "RMSE for combined approach with CBF weighted 0.2 and CF weighted 0.8: \n",
      " 0.9332410505126286 \n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Extracting the validation prediction from the improved content dataframe containing all predictions\n",
    "CBF_y_pred = []\n",
    "for index, row in X_val.iterrows():\n",
    "    user_predictions = CBF_improved_model.loc[row[\"BrukerID\"], row[\"FilmID\"]]\n",
    "    CBF_y_pred.append(user_predictions)\n",
    "\n",
    "# Calculating the predictions for the different hybrid \"models\": \n",
    "# different weighted averages of CF and improved CBF filtering\n",
    "print(\"RMSE combined approach (Lasso and KNN-40):\")\n",
    "weighted_avgs = [(0.5, 0.5), (0.45, 0.55), (0.4, 0.6), (0.35, 0.65), (0.3, 0.7), (0.25, 0.75), (0.20, 0.80)]\n",
    "for weight in weighted_avgs:\n",
    "    combined_predictions = ((np.array(CBF_y_pred) * weight[0]) + (np.array(CF_predictions)) * weight[1])\n",
    "    print(f\"RMSE for combined approach with CBF weighted {weight[0]} and CF weighted {weight[1]}: \\n\",\n",
    "          sqrt(mean_squared_error(y_val, combined_predictions)), \"\\n\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "------\n",
    "## <a id=\"test\"></a>5) Final Test and Evaluation\n",
    "\n",
    "[*Back to top*](#top)\n",
    "\n",
    "Finally, I will test the generalization ability of the improved hybrid recommender by making predictions on the test dataset. I will weight the CBF model 0.35 and the CF model 0.65, as this combination has proved to be the most optimal from the test made on the validation set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE KNN_40: 0.958015760014762\n"
     ]
    }
   ],
   "source": [
    "# Building the hybrid recommender: Collaborative Filtering\n",
    "CF_predictions_test = []\n",
    "for index, row in X_test.iterrows():\n",
    "    if row[\"FilmID\"] in X_train[\"FilmID\"].unique():\n",
    "        users_rated_movie = X_train.loc[X_train['FilmID'] == row['FilmID'], 'BrukerID']\n",
    "        users_sorted = (user_dist_matrix.loc[row['BrukerID'], users_rated_movie].sort_values())\n",
    "        n_neighbours = users_sorted[:40]\n",
    "        nn_data = train_df.loc[train_df['BrukerID'].isin(n_neighbours.index.to_list())]\n",
    "        nearest_neighbours_avg_rating = np.average(nn_data.loc[train_df['FilmID'] == row['FilmID'], 'Rangering'],\n",
    "                                                   axis=0, weights=(1/n_neighbours))\n",
    "    else:\n",
    "        nearest_neighbours_avg_rating = train_df[\"Rangering\"].mean()\n",
    "\n",
    "    # appending the prediction to the list\n",
    "    if not np.isnan(nearest_neighbours_avg_rating):\n",
    "        CF_predictions_test.append(nearest_neighbours_avg_rating)\n",
    "    else:\n",
    "        CF_predictions_test.append(4)\n",
    "\n",
    "print(\"RMSE KNN_40:\", sqrt(mean_squared_error(y_test, CF_predictions_test)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE Lasso: 1.019278388038111\n"
     ]
    }
   ],
   "source": [
    "# Building the hybrid recommender: Content-Based filtering\n",
    "# Extracting the predictions for the movies and users in the test data\n",
    "# from the CBF dataframe (which contains predictions for all movies and all users)\n",
    "CBF_predictions_test = []\n",
    "for index, row in X_test.iterrows():\n",
    "    user_predictions = CBF_improved_model.loc[row[\"BrukerID\"], row[\"FilmID\"]]\n",
    "    CBF_predictions_test.append(user_predictions)\n",
    "\n",
    "print(\"RMSE Lasso:\", sqrt(mean_squared_error(y_test, CBF_predictions_test)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE hybrid recommendations (test data): 0.9254351615504374 \n"
     ]
    }
   ],
   "source": [
    "# Calculating the hybrid recommendations\n",
    "hybrid_predictions_test = (np.array([y_pred * 0.35 for y_pred in np.array(CBF_predictions_test)]) \n",
    "                           + np.array([y_pred * 0.65 for y_pred in np.array(CF_predictions_test)]))\n",
    "\n",
    "# Displaying the test results from training the Hybrid Recommender on test data\n",
    "print(f\"RMSE hybrid recommendations (test data): {sqrt(mean_squared_error(y_test, hybrid_predictions_test))} \")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "------------\n",
    "The hybrid recommender model can be expected to perform predictions with a RMSE of 0.925 on test data. The baseline model performed with an RMSE of 0.982, so there is a considerable improvement in recommedations after implementing measures for specific user preferences grouping users with similar tastes. \n",
    "\n",
    "The hybrid recommender requires two new features to be engineered and added to the dataset before training the models. The engineered features are each movie's release year and average fan age.\n",
    "\n",
    "There are multiple unexplored possibilities for me to further improve the hybrid recommender system. A few of them are listed below: \n",
    "   1. Calculating the average fan age as a quantile of the ratings for each movie\n",
    "   2. Using more advanced similarity metrics in collaborative filtering, such as a genetic- or [memetic-based  similarity metrics](https://www.ibm.com/support/knowledgecenter/SSHGWL_1.2.3/analyze-data/markd-jupyter.html).\n",
    "   3. Using more advanced collaborative filtering models, such as [multidimensional CF](https://www.researchgate.net/publication/275026753_A_Multidimensional_Collaborative_Filtering_Fusion_Approach_with_Dimensionality_Reduction)\n",
    "   4. Scraping more information about each movie from the web\n",
    "\n",
    "In the future, I will hopefully make time to implement the hybrid recommender system in a simple website application in order to have fully explored all aspects of the data science process. For now, I will use the hybrid recommender to [predict ratings for all users and all movies](https://github.com/SebastianRokholt/Hybrid-Recommender-System), storing the output to a file.\n",
    "\n",
    "[*Back to top*](#top)"
   ]
  }
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