Modelling.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Modelling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "from scipy.sparse import csr_matrix\n",
    "from sklearn.metrics.pairwise import pairwise_distances\n",
    "from sklearn.metrics.pairwise import cosine_similarity\n",
    "from sklearn.neighbors import NearestNeighbors\n",
    "import ipywidgets as widgets\n",
    "from IPython.display import display\n",
    "from surprise import Reader, Dataset\n",
    "from surprise.model_selection import train_test_split\n",
    "from surprise.model_selection import cross_validate\n",
    "from surprise.prediction_algorithms import SVD\n",
    "from surprise.prediction_algorithms import KNNWithMeans, KNNBasic, KNNBaseline\n",
    "from surprise.model_selection import GridSearchCV\n",
    "from surprise.prediction_algorithms import knns\n",
    "from surprise.similarities import cosine, msd, pearson\n",
    "from surprise import accuracy\n",
    "from surprise.model_selection.validation import cross_validate\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pylab as pylab\n",
    "%matplotlib inline\n",
    "pd.set_option('display.max_columns', 500)\n",
    "mpl.style.use('ggplot')\n",
    "sns.set_style('white')\n",
    "pylab.rcParams['figure.figsize'] = 12,8\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": false
   },
   "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>Anime_id</th>\n",
       "      <th>Title</th>\n",
       "      <th>Genre</th>\n",
       "      <th>Synopsis</th>\n",
       "      <th>Type</th>\n",
       "      <th>Producer</th>\n",
       "      <th>Studio</th>\n",
       "      <th>Rating</th>\n",
       "      <th>ScoredBy</th>\n",
       "      <th>Popularity</th>\n",
       "      <th>Members</th>\n",
       "      <th>Episodes</th>\n",
       "      <th>Source</th>\n",
       "      <th>Aired</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Cowboy Bebop</td>\n",
       "      <td>['Action', 'Adventure', 'Comedy', 'Drama', 'Sc...</td>\n",
       "      <td>In the year 2071, humanity has colonized sever...</td>\n",
       "      <td>TV</td>\n",
       "      <td>['Bandai Visual']</td>\n",
       "      <td>['Sunrise']</td>\n",
       "      <td>8.81</td>\n",
       "      <td>363889.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>704490.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>Original</td>\n",
       "      <td>Apr 3, 1998 to Apr 24, 1999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5</td>\n",
       "      <td>Cowboy Bebop Tengoku no Tobira</td>\n",
       "      <td>['Action', 'Space', 'Drama', 'Mystery', 'Sci-Fi']</td>\n",
       "      <td>Another day, another bounty—such is the life o...</td>\n",
       "      <td>Movie</td>\n",
       "      <td>['Sunrise', 'Bandai Visual']</td>\n",
       "      <td>['Bones']</td>\n",
       "      <td>8.41</td>\n",
       "      <td>111187.0</td>\n",
       "      <td>475.0</td>\n",
       "      <td>179899.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Original</td>\n",
       "      <td>Sep 1, 2001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>6</td>\n",
       "      <td>Trigun</td>\n",
       "      <td>['Action', 'Sci-Fi', 'Adventure', 'Comedy', 'D...</td>\n",
       "      <td>Vash the Stampede is the man with a $$60,000,0...</td>\n",
       "      <td>TV</td>\n",
       "      <td>['Victor Entertainment']</td>\n",
       "      <td>['Madhouse']</td>\n",
       "      <td>8.31</td>\n",
       "      <td>197451.0</td>\n",
       "      <td>158.0</td>\n",
       "      <td>372709.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>Manga</td>\n",
       "      <td>Apr 1, 1998 to Sep 30, 1998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7</td>\n",
       "      <td>Witch Hunter Robin</td>\n",
       "      <td>['Action', 'Magic', 'Police', 'Supernatural', ...</td>\n",
       "      <td>Witches are individuals with special powers li...</td>\n",
       "      <td>TV</td>\n",
       "      <td>['Bandai Visual']</td>\n",
       "      <td>['Sunrise']</td>\n",
       "      <td>7.34</td>\n",
       "      <td>31875.0</td>\n",
       "      <td>1278.0</td>\n",
       "      <td>74889.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>Original</td>\n",
       "      <td>Jul 2, 2002 to Dec 24, 2002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>8</td>\n",
       "      <td>Bouken Ou Beet</td>\n",
       "      <td>['Adventure', 'Fantasy', 'Shounen', 'Supernatu...</td>\n",
       "      <td>It is the dark century and the people are suff...</td>\n",
       "      <td>TV</td>\n",
       "      <td>NaN</td>\n",
       "      <td>['Toei Animation']</td>\n",
       "      <td>7.04</td>\n",
       "      <td>4757.0</td>\n",
       "      <td>3968.0</td>\n",
       "      <td>11247.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>Manga</td>\n",
       "      <td>Sep 30, 2004 to Sep 29, 2005</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Anime_id                           Title  \\\n",
       "0         1                    Cowboy Bebop   \n",
       "1         5  Cowboy Bebop Tengoku no Tobira   \n",
       "2         6                          Trigun   \n",
       "3         7              Witch Hunter Robin   \n",
       "4         8                  Bouken Ou Beet   \n",
       "\n",
       "                                               Genre  \\\n",
       "0  ['Action', 'Adventure', 'Comedy', 'Drama', 'Sc...   \n",
       "1  ['Action', 'Space', 'Drama', 'Mystery', 'Sci-Fi']   \n",
       "2  ['Action', 'Sci-Fi', 'Adventure', 'Comedy', 'D...   \n",
       "3  ['Action', 'Magic', 'Police', 'Supernatural', ...   \n",
       "4  ['Adventure', 'Fantasy', 'Shounen', 'Supernatu...   \n",
       "\n",
       "                                            Synopsis   Type  \\\n",
       "0  In the year 2071, humanity has colonized sever...     TV   \n",
       "1  Another day, another bounty—such is the life o...  Movie   \n",
       "2  Vash the Stampede is the man with a $$60,000,0...     TV   \n",
       "3  Witches are individuals with special powers li...     TV   \n",
       "4  It is the dark century and the people are suff...     TV   \n",
       "\n",
       "                       Producer              Studio  Rating  ScoredBy  \\\n",
       "0             ['Bandai Visual']         ['Sunrise']    8.81  363889.0   \n",
       "1  ['Sunrise', 'Bandai Visual']           ['Bones']    8.41  111187.0   \n",
       "2      ['Victor Entertainment']        ['Madhouse']    8.31  197451.0   \n",
       "3             ['Bandai Visual']         ['Sunrise']    7.34   31875.0   \n",
       "4                           NaN  ['Toei Animation']    7.04    4757.0   \n",
       "\n",
       "   Popularity   Members  Episodes    Source                         Aired  \n",
       "0        39.0  704490.0      26.0  Original   Apr 3, 1998 to Apr 24, 1999  \n",
       "1       475.0  179899.0       1.0  Original                   Sep 1, 2001  \n",
       "2       158.0  372709.0      26.0     Manga   Apr 1, 1998 to Sep 30, 1998  \n",
       "3      1278.0   74889.0      26.0  Original   Jul 2, 2002 to Dec 24, 2002  \n",
       "4      3968.0   11247.0      52.0     Manga  Sep 30, 2004 to Sep 29, 2005  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_anime = pd.read_csv(\"cleaned_data.csv\")\n",
    "#df_anime.drop(\"Unnamed: 0\", axis = 1, inplace = True)\n",
    "df_anime.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Requirement already satisfied: scikit-learn in c:\\\\users\\\\lzdi\\\\anaconda3\\\\envs\\\\learn-env\\\\lib\\\\site-packages (0.23.2)',\n",
       " 'Requirement already satisfied: joblib>=0.11 in c:\\\\users\\\\lzdi\\\\anaconda3\\\\envs\\\\learn-env\\\\lib\\\\site-packages (from scikit-learn) (0.17.0)',\n",
       " 'Requirement already satisfied: scipy>=0.19.1 in c:\\\\users\\\\lzdi\\\\anaconda3\\\\envs\\\\learn-env\\\\lib\\\\site-packages (from scikit-learn) (1.5.0)',\n",
       " 'Requirement already satisfied: numpy>=1.13.3 in c:\\\\users\\\\lzdi\\\\anaconda3\\\\envs\\\\learn-env\\\\lib\\\\site-packages (from scikit-learn) (1.23.4)',\n",
       " 'Requirement already satisfied: threadpoolctl>=2.0.0 in c:\\\\users\\\\lzdi\\\\anaconda3\\\\envs\\\\learn-env\\\\lib\\\\site-packages (from scikit-learn) (2.1.0)']"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "!! pip install scikit-learn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_anime['Synopsis'].isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Metric Based Modelling"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## CONTENT BASED MODEL (First Model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will first build a content based model by using the ```Synopsis``` and ```Title``` column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_anime['Synopsis'] = df_anime['Synopsis'].fillna('')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will use the Term Frequency-Inverse Document Frequency on our model since it is content based."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(12979, 40034)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# creating a sparse matrix for the synopsis column\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "tfidf = TfidfVectorizer(stop_words='english')\n",
    "\n",
    "tfidf_matrix = tfidf.fit_transform(df_anime['Synopsis'])\n",
    "\n",
    "tfidf_matrix.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1.        , 0.23529586, 0.02127464, ..., 0.02133734, 0.00457067,\n",
       "        0.02050118],\n",
       "       [0.23529586, 1.        , 0.04320112, ..., 0.01628467, 0.01529336,\n",
       "        0.        ],\n",
       "       [0.02127464, 0.04320112, 1.        , ..., 0.00264197, 0.        ,\n",
       "        0.        ],\n",
       "       ...,\n",
       "       [0.02133734, 0.01628467, 0.00264197, ..., 1.        , 0.        ,\n",
       "        0.        ],\n",
       "       [0.00457067, 0.01529336, 0.        , ..., 0.        , 1.        ,\n",
       "        0.        ],\n",
       "       [0.02050118, 0.        , 0.        , ..., 0.        , 0.        ,\n",
       "        1.        ]])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# finding similar items\n",
    "from sklearn.metrics.pairwise import linear_kernel\n",
    "\n",
    "cosine_sim = linear_kernel(tfidf_matrix, tfidf_matrix)\n",
    "cosine_sim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Title\n",
       "Cowboy Bebop                            0\n",
       "Cowboy Bebop Tengoku no Tobira          1\n",
       "Trigun                                  2\n",
       "Witch Hunter Robin                      3\n",
       "Bouken Ou Beet                          4\n",
       "                                    ...  \n",
       "Animagear                           12974\n",
       "Magical Halloween MiracleQuartet    12975\n",
       "Orbital Era                         12976\n",
       "Akai Hana Shiroi Hana               12977\n",
       "Arui Tekoteko                       12978\n",
       "Length: 12979, dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#removing duplicate titles\n",
    "indices = pd.Series(df_anime.index, index=df_anime['Title']).drop_duplicates()\n",
    "indices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# creating a function to give the recommendations\n",
    "def get_recommendations(title, cosine_sim=cosine_sim):\n",
    "    \n",
    "    idx = indices[title]\n",
    "\n",
    "    sim_scores = list(enumerate(cosine_sim[idx]))\n",
    "\n",
    "    sim_scores = sorted(sim_scores, key=lambda x: x[1], reverse=True)\n",
    "\n",
    "    # Get the scores of the 15 most similar movies\n",
    "    sim_scores = sim_scores[1:16]\n",
    "    \n",
    "    anime_indices = [i[0] for i in sim_scores]\n",
    "\n",
    "    return df_anime['Title'].iloc[anime_indices]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9604                                      Shounen Maid\n",
       "560                                 Kujibiki Unbalance\n",
       "4521                               Suki Desu Suzukikun\n",
       "5520             Kami nomi zo Shiru Sekai 4nin to Idol\n",
       "3283                       KerakuNoOH King of Pleasure\n",
       "10156                              Kushimitama Samurai\n",
       "5869                                      Sankarea OVA\n",
       "2422                             ef A Tale of Memories\n",
       "2816                                         Kiss yori\n",
       "4199              ef A Tale of Memories  Recollections\n",
       "11903                                   Mirai no Mirai\n",
       "740                                         Jyu Oh Sei\n",
       "2669                                    Hatenkou Yuugi\n",
       "4822     Ultraman Kids Haha wo Tazunete 3000man Kounen\n",
       "5113                                Koguma no Korochan\n",
       "Name: Title, dtype: object"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# testing our function\n",
    "get_recommendations('Sen to Chihiro no Kamikakushi')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "12407    Seishun Buta Yarou wa Yumemiru Shoujo no Yume ...\n",
       "1254                                      Asatte no Houkou\n",
       "5605                                          Hero Herokun\n",
       "9159                                     Yuujou no Kickoff\n",
       "1731                                                  Pops\n",
       "6486                                          Danchi Tomoo\n",
       "4730                        Fortune Arterial Akai Yakusoku\n",
       "12702                               Tomo Tabidachi no Toki\n",
       "6611                         Kakumeiki Valvrave 2nd Season\n",
       "6473                                        Harisu no Kaze\n",
       "8974                         Gakuen Handsome The Animation\n",
       "10173                                      Gakuen Handsome\n",
       "10787                                           Tejina Shi\n",
       "5429                                    VitaminX Addiction\n",
       "5830                             Yajikita Gakuen Douchuuki\n",
       "Name: Title, dtype: object"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_recommendations('Koe no Katachi')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "ename": "IndexError",
     "evalue": "only integers, slices (`:`), ellipsis (`...`), numpy.newaxis (`None`) and integer or boolean arrays are valid indices",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mIndexError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-65-96bd97f76b46>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mget_recommendations\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'Bleach'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m<ipython-input-13-55fe93418958>\u001b[0m in \u001b[0;36mget_recommendations\u001b[1;34m(title, cosine_sim)\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mget_recommendations\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtitle\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcosine_sim\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mcosine_sim\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 4\u001b[1;33m     \u001b[0midx\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mindices\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mtitle\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      5\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      6\u001b[0m     \u001b[0msim_scores\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mlist\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0menumerate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcosine_sim\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0midx\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mIndexError\u001b[0m: only integers, slices (`:`), ellipsis (`...`), numpy.newaxis (`None`) and integer or boolean arrays are valid indices"
     ]
    }
   ],
   "source": [
    "get_recommendations('Bleach')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## CONTENT BASED MODEL(Second Model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Genres, Producer and Studio Based Recommender It goes without saying that the quality of our recommender would be increased with the usage of better metadata. That is exactly what we are going to do in this section. We are going to build a recommender based on the following metadata: the producer, related genres and the studio to see if we will get better recommendations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Anime_id</th>\n",
       "      <th>Title</th>\n",
       "      <th>Genre</th>\n",
       "      <th>Synopsis</th>\n",
       "      <th>Type</th>\n",
       "      <th>Producer</th>\n",
       "      <th>Studio</th>\n",
       "      <th>Rating</th>\n",
       "      <th>ScoredBy</th>\n",
       "      <th>Popularity</th>\n",
       "      <th>Members</th>\n",
       "      <th>Episodes</th>\n",
       "      <th>Source</th>\n",
       "      <th>Aired</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Cowboy Bebop</td>\n",
       "      <td>['Action', 'Adventure', 'Comedy', 'Drama', 'Sc...</td>\n",
       "      <td>In the year 2071, humanity has colonized sever...</td>\n",
       "      <td>TV</td>\n",
       "      <td>['Bandai Visual']</td>\n",
       "      <td>['Sunrise']</td>\n",
       "      <td>8.81</td>\n",
       "      <td>363889.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>704490.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>Original</td>\n",
       "      <td>Apr 3, 1998 to Apr 24, 1999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5</td>\n",
       "      <td>Cowboy Bebop Tengoku no Tobira</td>\n",
       "      <td>['Action', 'Space', 'Drama', 'Mystery', 'Sci-Fi']</td>\n",
       "      <td>Another day, another bounty—such is the life o...</td>\n",
       "      <td>Movie</td>\n",
       "      <td>['Sunrise', 'Bandai Visual']</td>\n",
       "      <td>['Bones']</td>\n",
       "      <td>8.41</td>\n",
       "      <td>111187.0</td>\n",
       "      <td>475.0</td>\n",
       "      <td>179899.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Original</td>\n",
       "      <td>Sep 1, 2001</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Anime_id                           Title  \\\n",
       "0         1                    Cowboy Bebop   \n",
       "1         5  Cowboy Bebop Tengoku no Tobira   \n",
       "\n",
       "                                               Genre  \\\n",
       "0  ['Action', 'Adventure', 'Comedy', 'Drama', 'Sc...   \n",
       "1  ['Action', 'Space', 'Drama', 'Mystery', 'Sci-Fi']   \n",
       "\n",
       "                                            Synopsis   Type  \\\n",
       "0  In the year 2071, humanity has colonized sever...     TV   \n",
       "1  Another day, another bounty—such is the life o...  Movie   \n",
       "\n",
       "                       Producer       Studio  Rating  ScoredBy  Popularity  \\\n",
       "0             ['Bandai Visual']  ['Sunrise']    8.81  363889.0        39.0   \n",
       "1  ['Sunrise', 'Bandai Visual']    ['Bones']    8.41  111187.0       475.0   \n",
       "\n",
       "    Members  Episodes    Source                        Aired  \n",
       "0  704490.0      26.0  Original  Apr 3, 1998 to Apr 24, 1999  \n",
       "1  179899.0       1.0  Original                  Sep 1, 2001  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#previewing data again\n",
    "df_anime.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Genre          0\n",
      "Producer    5779\n",
      "Studio      5611\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# extracting the columns that we need\n",
    "features = ['Genre','Producer', 'Studio']\n",
    "\n",
    "print(df_anime[features].isnull().sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_anime[features] = df_anime[features].fillna('[' ']')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['TV', 'Movie', 'OVA', 'Special', 'ONA', 'Music', 'Unknown'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_anime.Type.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "def clean_data(x):\n",
    "    if isinstance(x, list):\n",
    "        return [str.lower(i.replace(\" \",\"\")) for i in x]\n",
    "    \n",
    "    else:\n",
    "        if isinstance(x, str):\n",
    "            return str.lower(x.replace(\" \",\"\"))\n",
    "        else:\n",
    "            return \"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "features = ['Genre','Producer', 'Studio', 'Type']\n",
    "\n",
    "for feature in features:\n",
    "    df_anime[feature] = df_anime[feature].apply(clean_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "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>Anime_id</th>\n",
       "      <th>Title</th>\n",
       "      <th>Genre</th>\n",
       "      <th>Synopsis</th>\n",
       "      <th>Type</th>\n",
       "      <th>Producer</th>\n",
       "      <th>Studio</th>\n",
       "      <th>Rating</th>\n",
       "      <th>ScoredBy</th>\n",
       "      <th>Popularity</th>\n",
       "      <th>Members</th>\n",
       "      <th>Episodes</th>\n",
       "      <th>Source</th>\n",
       "      <th>Aired</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Cowboy Bebop</td>\n",
       "      <td>['action','adventure','comedy','drama','sci-fi...</td>\n",
       "      <td>In the year 2071, humanity has colonized sever...</td>\n",
       "      <td>tv</td>\n",
       "      <td>['bandaivisual']</td>\n",
       "      <td>['sunrise']</td>\n",
       "      <td>8.81</td>\n",
       "      <td>363889.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>704490.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>Original</td>\n",
       "      <td>Apr 3, 1998 to Apr 24, 1999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5</td>\n",
       "      <td>Cowboy Bebop Tengoku no Tobira</td>\n",
       "      <td>['action','space','drama','mystery','sci-fi']</td>\n",
       "      <td>Another day, another bounty—such is the life o...</td>\n",
       "      <td>movie</td>\n",
       "      <td>['sunrise','bandaivisual']</td>\n",
       "      <td>['bones']</td>\n",
       "      <td>8.41</td>\n",
       "      <td>111187.0</td>\n",
       "      <td>475.0</td>\n",
       "      <td>179899.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Original</td>\n",
       "      <td>Sep 1, 2001</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Anime_id                           Title  \\\n",
       "0         1                    Cowboy Bebop   \n",
       "1         5  Cowboy Bebop Tengoku no Tobira   \n",
       "\n",
       "                                               Genre  \\\n",
       "0  ['action','adventure','comedy','drama','sci-fi...   \n",
       "1      ['action','space','drama','mystery','sci-fi']   \n",
       "\n",
       "                                            Synopsis   Type  \\\n",
       "0  In the year 2071, humanity has colonized sever...     tv   \n",
       "1  Another day, another bounty—such is the life o...  movie   \n",
       "\n",
       "                     Producer       Studio  Rating  ScoredBy  Popularity  \\\n",
       "0            ['bandaivisual']  ['sunrise']    8.81  363889.0        39.0   \n",
       "1  ['sunrise','bandaivisual']    ['bones']    8.41  111187.0       475.0   \n",
       "\n",
       "    Members  Episodes    Source                        Aired  \n",
       "0  704490.0      26.0  Original  Apr 3, 1998 to Apr 24, 1999  \n",
       "1  179899.0       1.0  Original                  Sep 1, 2001  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# previewing data\n",
    "df_anime.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Genre', 'Producer', 'Studio', 'Type']"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# preview of features\n",
    "features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# function to join the two dataframes\n",
    "def create_soup(x):\n",
    "    return \" \".join(x['Genre']) + \" \" + x['Type'] + \" \" + \" \".join(x['Producer']) + \" \" + \" \".join(x['Studio']) + \" \" + x['Synopsis'] + \" \" + \" \".join(x['Studio']) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_anime['soup'] = df_anime.apply(create_soup, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        [ ' a c t i o n ' , ' a d v e n t u r e ' , ' ...\n",
       "1        [ ' a c t i o n ' , ' s p a c e ' , ' d r a m ...\n",
       "2        [ ' a c t i o n ' , ' s c i - f i ' , ' a d v ...\n",
       "3        [ ' a c t i o n ' , ' m a g i c ' , ' p o l i ...\n",
       "4        [ ' a d v e n t u r e ' , ' f a n t a s y ' , ...\n",
       "                               ...                        \n",
       "12974    [ ' k i d s ' , ' m e c h a ' ] ona [ ] [ ] A ...\n",
       "12975    [ ' c o m e d y ' , ' e c c h i ' , ' f a n t ...\n",
       "12976    [ ' a c t i o n ' , ' a d v e n t u r e ' , ' ...\n",
       "12977    [ ' k i d s ' , ' m u s i c ' ] music [ ] [ ] ...\n",
       "12978    [ ' k i d s ' , ' m u s i c ' ] music [ ] [ ] ...\n",
       "Name: soup, Length: 12979, dtype: object"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_anime['soup']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The next steps are the same as what we did with our plot description based recommender. One important difference is that we use the CountVectorizer() instead of TF-IDF. This is because we do not want to down-weight the presence of a producer if he or she has acted or directed in relatively more movies. It doesn't make much intuitive sense."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "# using a count vectorizer\n",
    "count = CountVectorizer(stop_words='english')\n",
    "count_matrix = count.fit_transform(df_anime['soup'])\n",
    "\n",
    "# calculating cosine similarity\n",
    "\n",
    "cosine_sim2 = cosine_similarity(count_matrix, count_matrix)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_anime = df_anime.reset_index()\n",
    "indices = pd.Series(df_anime.index, index=df_anime['Title'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Title\n",
       "Cowboy Bebop                            0\n",
       "Cowboy Bebop Tengoku no Tobira          1\n",
       "Trigun                                  2\n",
       "Witch Hunter Robin                      3\n",
       "Bouken Ou Beet                          4\n",
       "                                    ...  \n",
       "Animagear                           12974\n",
       "Magical Halloween MiracleQuartet    12975\n",
       "Orbital Era                         12976\n",
       "Akai Hana Shiroi Hana               12977\n",
       "Arui Tekoteko                       12978\n",
       "Length: 12979, dtype: int64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "indices"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that we have the cosine similarities, we can use the fuction we created earlier to get recommendations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1                      Cowboy Bebop Tengoku no Tobira\n",
       "3226                             Ginga Senpuu Braiger\n",
       "11619                                          Hiromi\n",
       "4563                                              KOn\n",
       "5950                                   Little Busters\n",
       "1167                        Sol Bianca Taiyou no Fune\n",
       "2533     Saraba Uchuu Senkan Yamato Ai no Senshitachi\n",
       "2411                Code Geass Hangyaku no Lelouch R2\n",
       "1338                              Uchuu Senkan Yamato\n",
       "3841                            Uchuu Kuubo Blue Noah\n",
       "11611                                 Chibikko Cowboy\n",
       "9948                                       Xiao Yeyou\n",
       "6238                              Suisei no Gargantia\n",
       "9016                                Dragon Ball Super\n",
       "4964                     Rokushin Gattai GodMars 1982\n",
       "Name: Title, dtype: object"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# testing the function\n",
    "get_recommendations('Cowboy Bebop', cosine_sim2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9604                                        Shounen Maid\n",
       "740                                           Jyu Oh Sei\n",
       "10156                                Kushimitama Samurai\n",
       "2669                                      Hatenkou Yuugi\n",
       "11903                                     Mirai no Mirai\n",
       "6115                      Majokko Shimai no Yoyo to Nene\n",
       "560                                   Kujibiki Unbalance\n",
       "5113                                  Koguma no Korochan\n",
       "6438           Ashita Genki ni Nare Hanbun no Satsumaimo\n",
       "7374     Kero Kero Keroppi no Yowamushiouji no Daibouken\n",
       "92                               Chou Henshin Cosprayers\n",
       "3124                                    Net Ghost Pipopa\n",
       "5911                            Kyoto Animation Hoshihen\n",
       "999               Fushigi no Umi no Nadia Original Movie\n",
       "10684      Death March kara Hajimaru Isekai Kyousoukyoku\n",
       "Name: Title, dtype: object"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_recommendations('Sen to Chihiro no Kamikakushi', cosine_sim2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3478                             Umineko no Naku Koro ni\n",
       "3809                                   Suteneko Torachan\n",
       "9493                    Tenchi Muyou Ryououki 4th Season\n",
       "8395                                           Hand Soap\n",
       "4233                           Doubutsu Mura no Daisodou\n",
       "2269                                       Umi no Triton\n",
       "8323     Kyoukai no Kanata Movie 2 Ill Be Here  Miraihen\n",
       "6529                                         Golden Time\n",
       "10858                             Haruniwa Ie no 3 Ninme\n",
       "5123                                               Oshin\n",
       "3584                                     Minamike Okaeri\n",
       "6035                           Kono Sekai no Katasumi ni\n",
       "11288                                        Yao Shen Ji\n",
       "298                            Mama wa Shougaku 4 Nensei\n",
       "2993                                     Chis Sweet Home\n",
       "Name: Title, dtype: object"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_recommendations('Mirai no Mirai', cosine_sim2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Title</th>\n",
       "      <th>Rating</th>\n",
       "      <th>Producer</th>\n",
       "      <th>Studio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>178</th>\n",
       "      <td>Sen to Chihiro no Kamikakushi</td>\n",
       "      <td>8.92</td>\n",
       "      <td>['toho']</td>\n",
       "      <td>['studioghibli']</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             Title  Rating  Producer            Studio\n",
       "178  Sen to Chihiro no Kamikakushi    8.92  ['toho']  ['studioghibli']"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(df_anime[['Title', 'Rating', 'Producer', 'Studio']].loc[df_anime['Title'] == 'Sen to Chihiro no Kamikakushi'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our earlier model gives better recommendations in the context where we use ```Cowboy Bebop``` title as an example. The first function gives more recommendations of titles about ```Cowboy Bebop``` than the second model."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# COLLABORATIVE FILTERING\n",
    "\n",
    "## Preprocessing for Collaborative Filtering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "# renaming the anime_id col\n",
    "df_anime = df_anime.rename(columns={'MAL_ID':'anime_id'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(12979, 16)"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#looking at the shape\n",
    "df_anime.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>anime_id</th>\n",
       "      <th>rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>20</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>24</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>79</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>226</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>241</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   user_id  anime_id  rating\n",
       "0        1        20      -1\n",
       "1        1        24      -1\n",
       "2        1        79      -1\n",
       "3        1       226      -1\n",
       "4        1       241      -1"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#loading the cleaned ratings data\n",
    "rating = pd.read_csv('rating.csv')\n",
    "#rating.drop(\"Unnamed: 0\", axis = 1, inplace = True)\n",
    "rating.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ">1 duplicates\n",
      ">0 duplicates\n"
     ]
    }
   ],
   "source": [
    "# confirming there are no duplicates\n",
    "duplicates=rating.duplicated()\n",
    "if duplicates.sum()>0:\n",
    "    print('>{} duplicates'.format(duplicates.sum()))\n",
    "    rating=rating[~duplicates]\n",
    "    \n",
    "print('>{} duplicates'.format(rating.duplicated().sum()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(7813736, 3)"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# checking the entries\n",
    "rating.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " No. of unique anime: 11200 \n",
      " No. of Unique users  73515\n"
     ]
    }
   ],
   "source": [
    "# finding unique users and items\n",
    "unique_users = {int(x): i for i,x in enumerate(rating.user_id.unique())}\n",
    "unique_items = {int(x): i for i,x in enumerate(rating.anime_id.unique())}\n",
    "\n",
    "print(\" No. of unique anime:\",len(unique_items),'\\n',\"No. of Unique users \", len(unique_users))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "# more wrangling\n",
    "n_rating=rating['user_id'].value_counts()\n",
    "rating=rating[rating['user_id'].isin(n_rating[n_rating>=500].index)].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1384631, 3)"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rating.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>anime_id</th>\n",
       "      <th>rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1614</th>\n",
       "      <td>17</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1615</th>\n",
       "      <td>17</td>\n",
       "      <td>19</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1616</th>\n",
       "      <td>17</td>\n",
       "      <td>30</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1617</th>\n",
       "      <td>17</td>\n",
       "      <td>32</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1618</th>\n",
       "      <td>17</td>\n",
       "      <td>43</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      user_id  anime_id  rating\n",
       "1614       17         6       7\n",
       "1615       17        19      10\n",
       "1616       17        30       9\n",
       "1617       17        32      10\n",
       "1618       17        43       8"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# preview of the ratings \n",
    "rating.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Distribution of ratings\n",
    "  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* We will now investigate the distribution of ratings on both the datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "    # ploting the skewness\n",
    "    \n",
    "    plt.figure(figsize = (15, 7))\n",
    "    plt.subplot(1,2,1)\n",
    "    rating[\"rating\"].hist(bins=70)\n",
    "    plt.xlabel(\"Rating\")\n",
    "    plt.ylabel(\"count\")\n",
    "    plt.title(\"Skewness of Rating\")\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "# picking numeric columns\n",
    "new_data = df_anime[['Anime_id','Title', 'ScoredBy',\"Members\", 'Rating']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "# renaming some columns\n",
    "rating=rating.rename(columns={'anime_id':'Anime_id'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "# merging the the two dataframes for modelling\n",
    "new_data=pd.merge(new_data,rating,on='Anime_id')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_data=new_data.sort_values(by='ScoredBy', ascending=False).query('Members>500')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_data=new_data.sort_values(by='ScoredBy', ascending=False).query('Members>500')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1206012, 7)"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Title       0.000002\n",
      "rating      0.000000\n",
      "user_id     0.000000\n",
      "Rating      0.000000\n",
      "Members     0.000000\n",
      "ScoredBy    0.000000\n",
      "Anime_id    0.000000\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "total=new_data.isnull().sum().sort_values(ascending=False)\n",
    "percent=(new_data.isnull().sum()/new_data.isnull().count()).sort_values(ascending=False)\n",
    "print(percent)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Pivot Table\n",
    "We will now create a pivot table so as to make the modelling work much easier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "anime_pivot=new_data.pivot_table(index='Title',columns='user_id',values='rating').fillna(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>user_id</th>\n",
       "      <th>17</th>\n",
       "      <th>54</th>\n",
       "      <th>201</th>\n",
       "      <th>226</th>\n",
       "      <th>271</th>\n",
       "      <th>294</th>\n",
       "      <th>342</th>\n",
       "      <th>392</th>\n",
       "      <th>446</th>\n",
       "      <th>478</th>\n",
       "      <th>661</th>\n",
       "      <th>741</th>\n",
       "      <th>771</th>\n",
       "      <th>786</th>\n",
       "      <th>804</th>\n",
       "      <th>917</th>\n",
       "      <th>940</th>\n",
       "      <th>958</th>\n",
       "      <th>1019</th>\n",
       "      <th>1103</th>\n",
       "      <th>1145</th>\n",
       "      <th>1176</th>\n",
       "      <th>1191</th>\n",
       "      <th>1287</th>\n",
       "      <th>1338</th>\n",
       "      <th>1344</th>\n",
       "      <th>1456</th>\n",
       "      <th>1497</th>\n",
       "      <th>1504</th>\n",
       "      <th>1522</th>\n",
       "      <th>1530</th>\n",
       "      <th>1579</th>\n",
       "      <th>1720</th>\n",
       "      <th>1822</th>\n",
       "      <th>1870</th>\n",
       "      <th>1984</th>\n",
       "      <th>2200</th>\n",
       "      <th>2243</th>\n",
       "      <th>2264</th>\n",
       "      <th>2273</th>\n",
       "      <th>2297</th>\n",
       "      <th>2378</th>\n",
       "      <th>2632</th>\n",
       "      <th>2695</th>\n",
       "      <th>2701</th>\n",
       "      <th>2810</th>\n",
       "      <th>2820</th>\n",
       "      <th>2864</th>\n",
       "      <th>2951</th>\n",
       "      <th>3009</th>\n",
       "      <th>3040</th>\n",
       "      <th>3117</th>\n",
       "      <th>3124</th>\n",
       "      <th>3127</th>\n",
       "      <th>3193</th>\n",
       "      <th>3203</th>\n",
       "      <th>3325</th>\n",
       "      <th>3338</th>\n",
       "      <th>3391</th>\n",
       "      <th>3476</th>\n",
       "      <th>3500</th>\n",
       "      <th>3557</th>\n",
       "      <th>3569</th>\n",
       "      <th>3573</th>\n",
       "      <th>3597</th>\n",
       "      <th>3615</th>\n",
       "      <th>3657</th>\n",
       "      <th>3695</th>\n",
       "      <th>4064</th>\n",
       "      <th>4102</th>\n",
       "      <th>4168</th>\n",
       "      <th>4177</th>\n",
       "      <th>4215</th>\n",
       "      <th>4350</th>\n",
       "      <th>4468</th>\n",
       "      <th>4512</th>\n",
       "      <th>4749</th>\n",
       "      <th>4759</th>\n",
       "      <th>4814</th>\n",
       "      <th>4834</th>\n",
       "      <th>4842</th>\n",
       "      <th>4843</th>\n",
       "      <th>4883</th>\n",
       "      <th>4948</th>\n",
       "      <th>4979</th>\n",
       "      <th>4987</th>\n",
       "      <th>4988</th>\n",
       "      <th>5053</th>\n",
       "      <th>5056</th>\n",
       "      <th>5073</th>\n",
       "      <th>5076</th>\n",
       "      <th>5137</th>\n",
       "      <th>5148</th>\n",
       "      <th>5244</th>\n",
       "      <th>5249</th>\n",
       "      <th>5264</th>\n",
       "      <th>5296</th>\n",
       "      <th>5310</th>\n",
       "      <th>5311</th>\n",
       "      <th>5325</th>\n",
       "      <th>5335</th>\n",
       "      <th>5357</th>\n",
       "      <th>5374</th>\n",
       "      <th>5394</th>\n",
       "      <th>5421</th>\n",
       "      <th>5428</th>\n",
       "      <th>5492</th>\n",
       "      <th>5504</th>\n",
       "      <th>5516</th>\n",
       "      <th>5526</th>\n",
       "      <th>5555</th>\n",
       "      <th>5562</th>\n",
       "      <th>5584</th>\n",
       "      <th>5598</th>\n",
       "      <th>5655</th>\n",
       "      <th>5669</th>\n",
       "      <th>5672</th>\n",
       "      <th>5680</th>\n",
       "      <th>5705</th>\n",
       "      <th>5720</th>\n",
       "      <th>5732</th>\n",
       "      <th>5770</th>\n",
       "      <th>5815</th>\n",
       "      <th>5831</th>\n",
       "      <th>5838</th>\n",
       "      <th>5886</th>\n",
       "      <th>5895</th>\n",
       "      <th>5899</th>\n",
       "      <th>5908</th>\n",
       "      <th>5950</th>\n",
       "      <th>5954</th>\n",
       "      <th>6014</th>\n",
       "      <th>6111</th>\n",
       "      <th>6118</th>\n",
       "      <th>6125</th>\n",
       "      <th>6152</th>\n",
       "      <th>6162</th>\n",
       "      <th>6164</th>\n",
       "      <th>6237</th>\n",
       "      <th>6265</th>\n",
       "      <th>6268</th>\n",
       "      <th>6321</th>\n",
       "      <th>6361</th>\n",
       "      <th>6381</th>\n",
       "      <th>6384</th>\n",
       "      <th>6392</th>\n",
       "      <th>6417</th>\n",
       "      <th>6434</th>\n",
       "      <th>6469</th>\n",
       "      <th>6474</th>\n",
       "      <th>6509</th>\n",
       "      <th>6525</th>\n",
       "      <th>6542</th>\n",
       "      <th>6569</th>\n",
       "      <th>6583</th>\n",
       "      <th>6589</th>\n",
       "      <th>6618</th>\n",
       "      <th>6695</th>\n",
       "      <th>6769</th>\n",
       "      <th>6770</th>\n",
       "      <th>6771</th>\n",
       "      <th>6773</th>\n",
       "      <th>6814</th>\n",
       "      <th>6836</th>\n",
       "      <th>6869</th>\n",
       "      <th>6893</th>\n",
       "      <th>6922</th>\n",
       "      <th>6925</th>\n",
       "      <th>6932</th>\n",
       "      <th>6969</th>\n",
       "      <th>6971</th>\n",
       "      <th>7080</th>\n",
       "      <th>7081</th>\n",
       "      <th>7114</th>\n",
       "      <th>7172</th>\n",
       "      <th>7247</th>\n",
       "      <th>7249</th>\n",
       "      <th>7297</th>\n",
       "      <th>7311</th>\n",
       "      <th>7340</th>\n",
       "      <th>7345</th>\n",
       "      <th>7350</th>\n",
       "      <th>7366</th>\n",
       "      <th>7421</th>\n",
       "      <th>7440</th>\n",
       "      <th>7446</th>\n",
       "      <th>7458</th>\n",
       "      <th>7511</th>\n",
       "      <th>7520</th>\n",
       "      <th>7533</th>\n",
       "      <th>7600</th>\n",
       "      <th>7622</th>\n",
       "      <th>7648</th>\n",
       "      <th>7659</th>\n",
       "      <th>7669</th>\n",
       "      <th>7670</th>\n",
       "      <th>7672</th>\n",
       "      <th>7674</th>\n",
       "      <th>7711</th>\n",
       "      <th>7715</th>\n",
       "      <th>7716</th>\n",
       "      <th>7726</th>\n",
       "      <th>7728</th>\n",
       "      <th>7755</th>\n",
       "      <th>7777</th>\n",
       "      <th>7802</th>\n",
       "      <th>7821</th>\n",
       "      <th>7824</th>\n",
       "      <th>7852</th>\n",
       "      <th>7896</th>\n",
       "      <th>7904</th>\n",
       "      <th>7988</th>\n",
       "      <th>8006</th>\n",
       "      <th>8031</th>\n",
       "      <th>8058</th>\n",
       "      <th>8087</th>\n",
       "      <th>8093</th>\n",
       "      <th>8094</th>\n",
       "      <th>8095</th>\n",
       "      <th>8103</th>\n",
       "      <th>8107</th>\n",
       "      <th>8115</th>\n",
       "      <th>8122</th>\n",
       "      <th>8128</th>\n",
       "      <th>8144</th>\n",
       "      <th>8149</th>\n",
       "      <th>8200</th>\n",
       "      <th>8215</th>\n",
       "      <th>8217</th>\n",
       "      <th>8221</th>\n",
       "      <th>8226</th>\n",
       "      <th>8231</th>\n",
       "      <th>8287</th>\n",
       "      <th>8308</th>\n",
       "      <th>8421</th>\n",
       "      <th>8425</th>\n",
       "      <th>8446</th>\n",
       "      <th>8494</th>\n",
       "      <th>8820</th>\n",
       "      <th>9032</th>\n",
       "      <th>9752</th>\n",
       "      <th>9995</th>\n",
       "      <th>10072</th>\n",
       "      <th>10146</th>\n",
       "      <th>10194</th>\n",
       "      <th>10231</th>\n",
       "      <th>10240</th>\n",
       "      <th>10283</th>\n",
       "      <th>10297</th>\n",
       "      <th>10298</th>\n",
       "      <th>...</th>\n",
       "      <th>60352</th>\n",
       "      <th>60365</th>\n",
       "      <th>60375</th>\n",
       "      <th>60390</th>\n",
       "      <th>60407</th>\n",
       "      <th>60440</th>\n",
       "      <th>60524</th>\n",
       "      <th>60537</th>\n",
       "      <th>60590</th>\n",
       "      <th>60675</th>\n",
       "      <th>60711</th>\n",
       "      <th>60721</th>\n",
       "      <th>60743</th>\n",
       "      <th>60791</th>\n",
       "      <th>60798</th>\n",
       "      <th>60917</th>\n",
       "      <th>60918</th>\n",
       "      <th>60932</th>\n",
       "      <th>60975</th>\n",
       "      <th>60978</th>\n",
       "      <th>60985</th>\n",
       "      <th>60986</th>\n",
       "      <th>61007</th>\n",
       "      <th>61024</th>\n",
       "      <th>61060</th>\n",
       "      <th>61065</th>\n",
       "      <th>61110</th>\n",
       "      <th>61130</th>\n",
       "      <th>61149</th>\n",
       "      <th>61163</th>\n",
       "      <th>61188</th>\n",
       "      <th>61189</th>\n",
       "      <th>61210</th>\n",
       "      <th>61254</th>\n",
       "      <th>61329</th>\n",
       "      <th>61338</th>\n",
       "      <th>61344</th>\n",
       "      <th>61349</th>\n",
       "      <th>61368</th>\n",
       "      <th>61389</th>\n",
       "      <th>61409</th>\n",
       "      <th>61443</th>\n",
       "      <th>61484</th>\n",
       "      <th>61488</th>\n",
       "      <th>61504</th>\n",
       "      <th>61529</th>\n",
       "      <th>61547</th>\n",
       "      <th>61561</th>\n",
       "      <th>61622</th>\n",
       "      <th>61651</th>\n",
       "      <th>61678</th>\n",
       "      <th>61682</th>\n",
       "      <th>61767</th>\n",
       "      <th>61831</th>\n",
       "      <th>61842</th>\n",
       "      <th>61932</th>\n",
       "      <th>62082</th>\n",
       "      <th>62209</th>\n",
       "      <th>62237</th>\n",
       "      <th>62266</th>\n",
       "      <th>62309</th>\n",
       "      <th>62469</th>\n",
       "      <th>62480</th>\n",
       "      <th>62681</th>\n",
       "      <th>62830</th>\n",
       "      <th>63121</th>\n",
       "      <th>63157</th>\n",
       "      <th>63202</th>\n",
       "      <th>63710</th>\n",
       "      <th>64126</th>\n",
       "      <th>64174</th>\n",
       "      <th>64242</th>\n",
       "      <th>64354</th>\n",
       "      <th>64372</th>\n",
       "      <th>64542</th>\n",
       "      <th>64623</th>\n",
       "      <th>64636</th>\n",
       "      <th>64664</th>\n",
       "      <th>65108</th>\n",
       "      <th>65152</th>\n",
       "      <th>65175</th>\n",
       "      <th>65237</th>\n",
       "      <th>65276</th>\n",
       "      <th>65287</th>\n",
       "      <th>65382</th>\n",
       "      <th>65420</th>\n",
       "      <th>65468</th>\n",
       "      <th>65557</th>\n",
       "      <th>65588</th>\n",
       "      <th>65595</th>\n",
       "      <th>65682</th>\n",
       "      <th>65723</th>\n",
       "      <th>65728</th>\n",
       "      <th>65755</th>\n",
       "      <th>65809</th>\n",
       "      <th>65836</th>\n",
       "      <th>65840</th>\n",
       "      <th>65844</th>\n",
       "      <th>65855</th>\n",
       "      <th>65856</th>\n",
       "      <th>65875</th>\n",
       "      <th>65944</th>\n",
       "      <th>65993</th>\n",
       "      <th>66004</th>\n",
       "      <th>66021</th>\n",
       "      <th>66024</th>\n",
       "      <th>66049</th>\n",
       "      <th>66087</th>\n",
       "      <th>66105</th>\n",
       "      <th>66118</th>\n",
       "      <th>66133</th>\n",
       "      <th>66142</th>\n",
       "      <th>66355</th>\n",
       "      <th>66390</th>\n",
       "      <th>66449</th>\n",
       "      <th>66492</th>\n",
       "      <th>66510</th>\n",
       "      <th>66551</th>\n",
       "      <th>66561</th>\n",
       "      <th>66563</th>\n",
       "      <th>66569</th>\n",
       "      <th>66650</th>\n",
       "      <th>66700</th>\n",
       "      <th>66731</th>\n",
       "      <th>66742</th>\n",
       "      <th>66755</th>\n",
       "      <th>66816</th>\n",
       "      <th>66863</th>\n",
       "      <th>66885</th>\n",
       "      <th>66989</th>\n",
       "      <th>67006</th>\n",
       "      <th>67043</th>\n",
       "      <th>67050</th>\n",
       "      <th>67082</th>\n",
       "      <th>67099</th>\n",
       "      <th>67117</th>\n",
       "      <th>67214</th>\n",
       "      <th>67239</th>\n",
       "      <th>67244</th>\n",
       "      <th>67286</th>\n",
       "      <th>67296</th>\n",
       "      <th>67348</th>\n",
       "      <th>67372</th>\n",
       "      <th>67378</th>\n",
       "      <th>67386</th>\n",
       "      <th>67451</th>\n",
       "      <th>67478</th>\n",
       "      <th>67487</th>\n",
       "      <th>67493</th>\n",
       "      <th>67509</th>\n",
       "      <th>67566</th>\n",
       "      <th>67627</th>\n",
       "      <th>67629</th>\n",
       "      <th>67635</th>\n",
       "      <th>67661</th>\n",
       "      <th>67666</th>\n",
       "      <th>67671</th>\n",
       "      <th>67701</th>\n",
       "      <th>67708</th>\n",
       "      <th>67725</th>\n",
       "      <th>67749</th>\n",
       "      <th>67762</th>\n",
       "      <th>67844</th>\n",
       "      <th>67852</th>\n",
       "      <th>67866</th>\n",
       "      <th>67871</th>\n",
       "      <th>67910</th>\n",
       "      <th>67946</th>\n",
       "      <th>67975</th>\n",
       "      <th>67981</th>\n",
       "      <th>68017</th>\n",
       "      <th>68084</th>\n",
       "      <th>68091</th>\n",
       "      <th>68148</th>\n",
       "      <th>68171</th>\n",
       "      <th>68203</th>\n",
       "      <th>68243</th>\n",
       "      <th>68465</th>\n",
       "      <th>68473</th>\n",
       "      <th>68522</th>\n",
       "      <th>68559</th>\n",
       "      <th>68633</th>\n",
       "      <th>68721</th>\n",
       "      <th>68725</th>\n",
       "      <th>68762</th>\n",
       "      <th>68787</th>\n",
       "      <th>68795</th>\n",
       "      <th>68797</th>\n",
       "      <th>68925</th>\n",
       "      <th>68939</th>\n",
       "      <th>69037</th>\n",
       "      <th>69076</th>\n",
       "      <th>69121</th>\n",
       "      <th>69341</th>\n",
       "      <th>69394</th>\n",
       "      <th>69514</th>\n",
       "      <th>69596</th>\n",
       "      <th>69964</th>\n",
       "      <th>70015</th>\n",
       "      <th>70053</th>\n",
       "      <th>70161</th>\n",
       "      <th>70201</th>\n",
       "      <th>70433</th>\n",
       "      <th>70507</th>\n",
       "      <th>70585</th>\n",
       "      <th>70725</th>\n",
       "      <th>70953</th>\n",
       "      <th>71057</th>\n",
       "      <th>71278</th>\n",
       "      <th>71284</th>\n",
       "      <th>71302</th>\n",
       "      <th>71362</th>\n",
       "      <th>71456</th>\n",
       "      <th>71491</th>\n",
       "      <th>71537</th>\n",
       "      <th>71610</th>\n",
       "      <th>71655</th>\n",
       "      <th>71790</th>\n",
       "      <th>71792</th>\n",
       "      <th>71804</th>\n",
       "      <th>71893</th>\n",
       "      <th>72054</th>\n",
       "      <th>72060</th>\n",
       "      <th>72200</th>\n",
       "      <th>72646</th>\n",
       "      <th>72794</th>\n",
       "      <th>72814</th>\n",
       "      <th>72851</th>\n",
       "      <th>72936</th>\n",
       "      <th>72997</th>\n",
       "      <th>73075</th>\n",
       "      <th>73114</th>\n",
       "      <th>73128</th>\n",
       "      <th>73132</th>\n",
       "      <th>73135</th>\n",
       "      <th>73159</th>\n",
       "      <th>73162</th>\n",
       "      <th>73177</th>\n",
       "      <th>73188</th>\n",
       "      <th>73234</th>\n",
       "      <th>73272</th>\n",
       "      <th>73286</th>\n",
       "      <th>73340</th>\n",
       "      <th>73356</th>\n",
       "      <th>73362</th>\n",
       "      <th>73378</th>\n",
       "      <th>73395</th>\n",
       "      <th>73408</th>\n",
       "      <th>73499</th>\n",
       "      <th>73502</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Title</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
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       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
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       "      <th></th>\n",
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       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
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       "      <th></th>\n",
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       "      <th></th>\n",
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       "      <th></th>\n",
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       "      <th></th>\n",
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       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
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       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0008</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>009 ReCyborg</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0091</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 1853 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "user_id       17     54     201    226    271    294    342    392    446    \\\n",
       "Title                                                                         \n",
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       "Title                                                                         \n",
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       "Title                                                                         \n",
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       "Title                                                                         \n",
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       "Title                                                                         \n",
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       "Title                                                                         \n",
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       "Title                                                                         \n",
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       "Title                                                                         \n",
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       "Title                                                                         \n",
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       "user_id       4843   4883   4948   4979   4987   4988   5053   5056   5073   \\\n",
       "Title                                                                         \n",
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       "Title                                                                         \n",
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       "Title                                                                         \n",
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       "Title                                                                         \n",
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       "Title                                                                         \n",
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       "Title                                                                         \n",
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       "Title                                                                         \n",
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       "\n",
       "user_id       6384   6392   6417   6434   6469   6474   6509   6525   6542   \\\n",
       "Title                                                                         \n",
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       "Title                                                                         \n",
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       "0008            0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
       "001             0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
       "009 ReCyborg    8.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
       "0091            0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
       "\n",
       "user_id       6814   6836   6869   6893   6922   6925   6932   6969   6971   \\\n",
       "Title                                                                         \n",
       "0               0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
       "0008            0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
       "001             0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
       "009 ReCyborg    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
       "0091            0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
       "\n",
       "user_id       7080   7081   7114   7172   7247   7249   7297   7311   7340   \\\n",
       "Title                                                                         \n",
       "0               0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
       "0008            0.0    0.0    4.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
       "001             0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
       "009 ReCyborg    0.0    0.0    0.0    0.0    0.0    8.0    0.0    0.0    6.0   \n",
       "0091            0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
       "\n",
       "user_id       7345   7350   7366   7421   7440   7446   7458   7511   7520   \\\n",
       "Title                                                                         \n",
       "0               0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
       "0008            0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
       "001             0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
       "009 ReCyborg    0.0    0.0    8.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
       "0091            0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
       "\n",
       "user_id       7533   7600   7622   7648   7659   7669   7670   7672   7674   \\\n",
       "Title                                                                         \n",
       "0               0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
       "0008            0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
       "001             0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
       "009 ReCyborg    0.0    0.0    0.0    0.0    0.0    0.0    7.0    0.0    0.0   \n",
       "0091            0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
       "\n",
       "user_id       7711   7715   7716   7726   7728   7755   7777   7802   7821   \\\n",
       "Title                                                                         \n",
       "0               0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
       "0008            0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
       "001             0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
       "009 ReCyborg    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
       "0091            0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
       "\n",
       "user_id       7824   7852   7896   7904   7988   8006   8031   8058   8087   \\\n",
       "Title                                                                         \n",
       "0               0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
       "0008            0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
       "001             0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
       "009 ReCyborg    0.0    0.0    0.0    0.0    0.0    7.0    0.0    0.0    0.0   \n",
       "0091            7.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
       "\n",
       "user_id       8093   8094   8095   8103   8107   8115   8122   8128   8144   \\\n",
       "Title                                                                         \n",
       "0               0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
       "0008            0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
       "001             0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
       "009 ReCyborg    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
       "0091            0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
       "\n",
       "user_id       8149   8200   8215   8217   8221   8226   8231   8287   8308   \\\n",
       "Title                                                                         \n",
       "0               0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
       "0008            0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
       "001             0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
       "009 ReCyborg    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
       "0091            0.0    0.0    0.0    0.0   -1.0    0.0    0.0    0.0    0.0   \n",
       "\n",
       "user_id       8421   8425   8446   8494   8820   9032   9752   9995   10072  \\\n",
       "Title                                                                         \n",
       "0               0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
       "0008            0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
       "001             0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
       "009 ReCyborg    0.0    0.0    0.0    0.0    0.0    7.0    0.0    0.0    0.0   \n",
       "0091            0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
       "\n",
       "user_id       10146  10194  10231  10240  10283  10297  10298  ...  60352  \\\n",
       "Title                                                          ...          \n",
       "0               0.0    0.0    0.0    0.0    0.0    0.0    0.0  ...    0.0   \n",
       "0008            0.0    0.0    0.0    0.0    0.0    0.0    0.0  ...    0.0   \n",
       "001             0.0    0.0    0.0    0.0    0.0    0.0    0.0  ...    0.0   \n",
       "009 ReCyborg    0.0    0.0    0.0    0.0    0.0    0.0    0.0  ...    0.0   \n",
       "0091            0.0    0.0    0.0    0.0    0.0    0.0    0.0  ...    0.0   \n",
       "\n",
       "user_id       60365  60375  60390  60407  60440  60524  60537  60590  60675  \\\n",
       "Title                                                                         \n",
       "0               0.0    0.0    0.0    0.0   -1.0    0.0    0.0    0.0    0.0   \n",
       "0008            0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
       "001             0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
       "009 ReCyborg    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
       "0091            0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
       "\n",
       "user_id       60711  60721  60743  60791  60798  60917  60918  60932  60975  \\\n",
       "Title                                                                         \n",
       "0               0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
       "0008            0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
       "001             0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
       "009 ReCyborg    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
       "0091            0.0    0.0    5.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
       "\n",
       "user_id       60978  60985  60986  61007  61024  61060  61065  61110  61130  \\\n",
       "Title                                                                         \n",
       "0               0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
       "0008            0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
       "001             0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
       "009 ReCyborg    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
       "0091            6.0   -1.0    0.0    6.0    0.0    0.0    0.0   10.0    0.0   \n",
       "\n",
       "user_id       61149  61163  61188  61189  61210  61254  61329  61338  61344  \\\n",
       "Title                                                                         \n",
       "0               0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
       "0008            0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
       "001             0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
       "009 ReCyborg    6.0    0.0    0.0    8.0    0.0    0.0    0.0    0.0    0.0   \n",
       "0091            0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
       "\n",
       "user_id       61349  61368  61389  61409  61443  61484  61488  61504  61529  \\\n",
       "Title                                                                         \n",
       "0               0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
       "0008            0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
       "001             0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
       "009 ReCyborg    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
       "0091            0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
       "\n",
       "user_id       61547  61561  61622  61651  61678  61682  61767  61831  61842  \\\n",
       "Title                                                                         \n",
       "0               0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
       "0008            0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
       "001             0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
       "009 ReCyborg    0.0    0.0    0.0    0.0    0.0    0.0    0.0    8.0    0.0   \n",
       "0091            0.0    0.0    0.0    0.0   -1.0    0.0    0.0    0.0    0.0   \n",
       "\n",
       "user_id       61932  62082  62209  62237  62266  62309  62469  62480  62681  \\\n",
       "Title                                                                         \n",
       "0               0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
       "0008            0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
       "001             0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
       "009 ReCyborg    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
       "0091            0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
       "\n",
       "user_id       62830  63121  63157  63202  63710  64126  64174  64242  64354  \\\n",
       "Title                                                                         \n",
       "0               0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
       "0008            0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
       "001             0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
       "009 ReCyborg    0.0    0.0    7.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
       "0091            0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
       "\n",
       "user_id       64372  64542  64623  64636  64664  65108  65152  65175  65237  \\\n",
       "Title                                                                         \n",
       "0               0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
       "0008            0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
       "001             0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
       "009 ReCyborg    0.0    0.0    0.0    0.0    0.0    0.0    0.0    3.0    0.0   \n",
       "0091            0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
       "\n",
       "user_id       65276  65287  65382  65420  65468  65557  65588  65595  65682  \\\n",
       "Title                                                                         \n",
       "0               0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
       "0008            0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
       "001             0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
       "009 ReCyborg    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
       "0091            0.0    0.0    0.0    0.0    0.0   -1.0    0.0    0.0    0.0   \n",
       "\n",
       "user_id       65723  65728  65755  65809  65836  65840  65844  65855  65856  \\\n",
       "Title                                                                         \n",
       "0               0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
       "0008            0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
       "001             0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
       "009 ReCyborg    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
       "0091            0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
       "\n",
       "user_id       65875  65944  65993  66004  66021  66024  66049  66087  66105  \\\n",
       "Title                                                                         \n",
       "0               0.0    0.0    0.0    0.0   -1.0    0.0    0.0    0.0    0.0   \n",
       "0008            0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
       "001             0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
       "009 ReCyborg    0.0    0.0    0.0    0.0   -1.0    0.0    0.0    0.0    0.0   \n",
       "0091            0.0    0.0    0.0    0.0   -1.0    0.0    0.0    0.0    0.0   \n",
       "\n",
       "user_id       66118  66133  66142  66355  66390  66449  66492  66510  66551  \\\n",
       "Title                                                                         \n",
       "0               0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
       "0008            0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
       "001             0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
       "009 ReCyborg    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
       "0091            0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
       "\n",
       "user_id       66561  66563  66569  66650  66700  66731  66742  66755  66816  \\\n",
       "Title                                                                         \n",
       "0               0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
       "0008            0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
       "001             0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
       "009 ReCyborg    0.0    0.0    0.0    0.0    0.0    0.0    7.0    0.0    0.0   \n",
       "0091            0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
       "\n",
       "user_id       66863  66885  66989  67006  67043  67050  67082  67099  67117  \\\n",
       "Title                                                                         \n",
       "0               0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
       "0008            0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
       "001             0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
       "009 ReCyborg    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
       "0091            0.0    0.0    0.0    0.0    0.0    0.0    7.0    0.0    0.0   \n",
       "\n",
       "user_id       67214  67239  67244  67286  67296  67348  67372  67378  67386  \\\n",
       "Title                                                                         \n",
       "0               0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
       "0008            0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
       "001             0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
       "009 ReCyborg    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
       "0091            0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
       "\n",
       "user_id       67451  67478  67487  67493  67509  67566  67627  67629  67635  \\\n",
       "Title                                                                         \n",
       "0               0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
       "0008            0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
       "001             0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
       "009 ReCyborg    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
       "0091            0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
       "\n",
       "user_id       67661  67666  67671  67701  67708  67725  67749  67762  67844  \\\n",
       "Title                                                                         \n",
       "0               0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
       "0008            0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
       "001             0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
       "009 ReCyborg    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
       "0091            0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
       "\n",
       "user_id       67852  67866  67871  67910  67946  67975  67981  68017  68084  \\\n",
       "Title                                                                         \n",
       "0               0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
       "0008            0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
       "001             0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
       "009 ReCyborg    0.0    0.0    9.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
       "0091            0.0    0.0    0.0    0.0    0.0   -1.0    0.0    0.0    0.0   \n",
       "\n",
       "user_id       68091  68148  68171  68203  68243  68465  68473  68522  68559  \\\n",
       "Title                                                                         \n",
       "0               0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
       "0008            0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
       "001             0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
       "009 ReCyborg    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
       "0091            0.0    6.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
       "\n",
       "user_id       68633  68721  68725  68762  68787  68795  68797  68925  68939  \\\n",
       "Title                                                                         \n",
       "0               0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
       "0008            0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
       "001             0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
       "009 ReCyborg    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
       "0091            0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
       "\n",
       "user_id       69037  69076  69121  69341  69394  69514  69596  69964  70015  \\\n",
       "Title                                                                         \n",
       "0               0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
       "0008            0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
       "001             0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
       "009 ReCyborg    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
       "0091            0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
       "\n",
       "user_id       70053  70161  70201  70433  70507  70585  70725  70953  71057  \\\n",
       "Title                                                                         \n",
       "0               0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
       "0008            0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
       "001             0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
       "009 ReCyborg    0.0    0.0    0.0    0.0    8.0    0.0    0.0    0.0    0.0   \n",
       "0091            0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
       "\n",
       "user_id       71278  71284  71302  71362  71456  71491  71537  71610  71655  \\\n",
       "Title                                                                         \n",
       "0               0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
       "0008            0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
       "001             0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
       "009 ReCyborg    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
       "0091            0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
       "\n",
       "user_id       71790  71792  71804  71893  72054  72060  72200  72646  72794  \\\n",
       "Title                                                                         \n",
       "0               0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
       "0008            0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
       "001             0.0    0.0    0.0    0.0    0.0    0.0    0.0   10.0    0.0   \n",
       "009 ReCyborg    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
       "0091            0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   -1.0   \n",
       "\n",
       "user_id       72814  72851  72936  72997  73075  73114  73128  73132  73135  \\\n",
       "Title                                                                         \n",
       "0               0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
       "0008            0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
       "001             0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
       "009 ReCyborg    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
       "0091            0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
       "\n",
       "user_id       73159  73162  73177  73188  73234  73272  73286  73340  73356  \\\n",
       "Title                                                                         \n",
       "0               0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
       "0008            0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
       "001             0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
       "009 ReCyborg    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
       "0091            0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
       "\n",
       "user_id       73362  73378  73395  73408  73499  73502  \n",
       "Title                                                   \n",
       "0               0.0    0.0    0.0    0.0    0.0    0.0  \n",
       "0008            0.0    0.0    0.0    0.0    0.0    0.0  \n",
       "001             0.0    0.0    0.0    0.0    0.0    0.0  \n",
       "009 ReCyborg    0.0    0.0    0.0    0.0    0.0    0.0  \n",
       "0091            0.0    0.0    0.0    0.0    0.0    0.0  \n",
       "\n",
       "[5 rows x 1853 columns]"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "anime_pivot.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that our data is ready for modelling, we will use the item based collaborative filtering model to get recommendations."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Item Based"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "NearestNeighbors(algorithm='brute', metric='cosine')"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# generating a sparse matrix from our pivot table\n",
    "anime_matrix = csr_matrix(anime_pivot.values)\n",
    "\n",
    "# initializing a base model\n",
    "model_knn = NearestNeighbors(metric = 'cosine', algorithm = 'brute')\n",
    "model_knn.fit(anime_matrix)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1086\n"
     ]
    }
   ],
   "source": [
    "query_index = np.random.choice(anime_pivot.shape[0])\n",
    "print(query_index)\n",
    "distances, indices = model_knn.kneighbors(anime_pivot.iloc[query_index,:].values.reshape(1, -1), n_neighbors = 6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "# creating a recommendation function\n",
    "def get_rec(name,it):\n",
    "    distances, indices = model_knn.kneighbors(anime_pivot.loc[name].values.reshape(1, -1), n_neighbors = 250+it)\n",
    "    d1=pd.DataFrame(columns=df_anime.columns)\n",
    "    for i in range(0, len(distances.flatten())):\n",
    "        if i == 0:\n",
    "            print('Recommendations for',name)\n",
    "        else:\n",
    "            d2=df_anime.loc[df_anime[\"Title\"]==anime_pivot.index[indices.flatten()[i]]]\n",
    "            d1=d1.append(d2)\n",
    "    return d1[[\"Title\",\"ScoredBy\"]].sort_values(by=\"ScoredBy\",ascending=False)[:it]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Recommendations for Shingeki no Kyojin\n"
     ]
    },
    {
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       "      <td>Toradora</td>\n",
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       "      <th>2411</th>\n",
       "      <td>Code Geass Hangyaku no Lelouch R2</td>\n",
       "      <td>541989.0</td>\n",
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       "      <td>Sword Art Online II</td>\n",
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      "text/plain": [
       "                                  Title   ScoredBy\n",
       "1239                         Death Note  1006242.0\n",
       "5682                   Sword Art Online   913806.0\n",
       "3567    Fullmetal Alchemist Brotherhood   730784.0\n",
       "8859                      One Punch Man   687965.0\n",
       "7300                        Tokyo Ghoul   656039.0\n",
       "10                               Naruto   645672.0\n",
       "4121                        Angel Beats   640177.0\n",
       "1274     Code Geass Hangyaku no Lelouch   625466.0\n",
       "6891                    No Game No Life   620456.0\n",
       "5435                        Mirai Nikki   591121.0\n",
       "4921                         SteinsGate   561405.0\n",
       "3224                           Toradora   556177.0\n",
       "2411  Code Geass Hangyaku no Lelouch R2   541989.0\n",
       "7252                Sword Art Online II   528708.0\n",
       "5152                     Ao no Exorcist   520377.0"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# testing our function\n",
    "get_rec('Shingeki no Kyojin',15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Recommendations for Fullmetal Alchemist Brotherhood\n"
     ]
    },
    {
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       "      <td>625466.0</td>\n",
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       "      <td>No Game No Life</td>\n",
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       "      <td>Mirai Nikki</td>\n",
       "      <td>591121.0</td>\n",
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       "      <th>4921</th>\n",
       "      <td>SteinsGate</td>\n",
       "      <td>561405.0</td>\n",
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       "    <tr>\n",
       "      <th>3224</th>\n",
       "      <td>Toradora</td>\n",
       "      <td>556177.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2411</th>\n",
       "      <td>Code Geass Hangyaku no Lelouch R2</td>\n",
       "      <td>541989.0</td>\n",
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       "    <tr>\n",
       "      <th>7252</th>\n",
       "      <td>Sword Art Online II</td>\n",
       "      <td>528708.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5152</th>\n",
       "      <td>Ao no Exorcist</td>\n",
       "      <td>520377.0</td>\n",
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      "text/plain": [
       "                                  Title   ScoredBy\n",
       "1239                         Death Note  1006242.0\n",
       "6232                 Shingeki no Kyojin   936784.0\n",
       "5682                   Sword Art Online   913806.0\n",
       "8859                      One Punch Man   687965.0\n",
       "7300                        Tokyo Ghoul   656039.0\n",
       "10                               Naruto   645672.0\n",
       "4121                        Angel Beats   640177.0\n",
       "1274     Code Geass Hangyaku no Lelouch   625466.0\n",
       "6891                    No Game No Life   620456.0\n",
       "5435                        Mirai Nikki   591121.0\n",
       "4921                         SteinsGate   561405.0\n",
       "3224                           Toradora   556177.0\n",
       "2411  Code Geass Hangyaku no Lelouch R2   541989.0\n",
       "7252                Sword Art Online II   528708.0\n",
       "5152                     Ao no Exorcist   520377.0"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_rec('Fullmetal Alchemist Brotherhood',15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Recommendations for Doraemon Movie 07 Nobita to Tetsujin Heidan\n"
     ]
    },
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       "      <td>Dungeon ni Deai wo Motomeru no wa Machigatteir...</td>\n",
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       "      <td>Bakuten Shoot Beyblade 2002</td>\n",
       "      <td>22666.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006</th>\n",
       "      <td>Digimon Adventure 02 Diablomon no Gyakushuu</td>\n",
       "      <td>21737.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>898</th>\n",
       "      <td>Medarot</td>\n",
       "      <td>21122.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2447</th>\n",
       "      <td>Digimon Adventure 02 Movies</td>\n",
       "      <td>18619.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1355</th>\n",
       "      <td>Bakuten Shoot Beyblade G Revolution</td>\n",
       "      <td>17211.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9306</th>\n",
       "      <td>To LOVERu Darkness 2nd OVA</td>\n",
       "      <td>16698.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1993</th>\n",
       "      <td>One Piece Jango no Dance Carnival</td>\n",
       "      <td>14493.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                  Title  ScoredBy\n",
       "9863  Dungeon ni Deai wo Motomeru no wa Machigatteir...   29421.0\n",
       "2005               Digimon Adventure Bokura no War Game   29173.0\n",
       "667                                      Digimon Savers   28566.0\n",
       "1356                        Bakuten Shoot Beyblade 2002   22666.0\n",
       "2006        Digimon Adventure 02 Diablomon no Gyakushuu   21737.0\n",
       "898                                             Medarot   21122.0\n",
       "2447                        Digimon Adventure 02 Movies   18619.0\n",
       "1355                Bakuten Shoot Beyblade G Revolution   17211.0\n",
       "9306                         To LOVERu Darkness 2nd OVA   16698.0\n",
       "1993                  One Piece Jango no Dance Carnival   14493.0"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_rec('Doraemon Movie 07 Nobita to Tetsujin Heidan',10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Recommendations for Naruto\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    <tr style=\"text-align: right;\">\n",
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       "      <th>Title</th>\n",
       "      <th>ScoredBy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1239</th>\n",
       "      <td>Death Note</td>\n",
       "      <td>1006242.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6232</th>\n",
       "      <td>Shingeki no Kyojin</td>\n",
       "      <td>936784.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5682</th>\n",
       "      <td>Sword Art Online</td>\n",
       "      <td>913806.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3567</th>\n",
       "      <td>Fullmetal Alchemist Brotherhood</td>\n",
       "      <td>730784.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8859</th>\n",
       "      <td>One Punch Man</td>\n",
       "      <td>687965.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7300</th>\n",
       "      <td>Tokyo Ghoul</td>\n",
       "      <td>656039.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4121</th>\n",
       "      <td>Angel Beats</td>\n",
       "      <td>640177.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1274</th>\n",
       "      <td>Code Geass Hangyaku no Lelouch</td>\n",
       "      <td>625466.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6891</th>\n",
       "      <td>No Game No Life</td>\n",
       "      <td>620456.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5435</th>\n",
       "      <td>Mirai Nikki</td>\n",
       "      <td>591121.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                Title   ScoredBy\n",
       "1239                       Death Note  1006242.0\n",
       "6232               Shingeki no Kyojin   936784.0\n",
       "5682                 Sword Art Online   913806.0\n",
       "3567  Fullmetal Alchemist Brotherhood   730784.0\n",
       "8859                    One Punch Man   687965.0\n",
       "7300                      Tokyo Ghoul   656039.0\n",
       "4121                      Angel Beats   640177.0\n",
       "1274   Code Geass Hangyaku no Lelouch   625466.0\n",
       "6891                  No Game No Life   620456.0\n",
       "5435                      Mirai Nikki   591121.0"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_rec('Naruto',10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "# saving our function\n",
    "import pickle\n",
    "filename='KNN_model_forCF.sav'\n",
    "pickle.dump(model_knn,open(filename,'wb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Recommendations for Bleach\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Title</th>\n",
       "      <th>ScoredBy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1239</th>\n",
       "      <td>Death Note</td>\n",
       "      <td>1006242.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6232</th>\n",
       "      <td>Shingeki no Kyojin</td>\n",
       "      <td>936784.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5682</th>\n",
       "      <td>Sword Art Online</td>\n",
       "      <td>913806.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3567</th>\n",
       "      <td>Fullmetal Alchemist Brotherhood</td>\n",
       "      <td>730784.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8859</th>\n",
       "      <td>One Punch Man</td>\n",
       "      <td>687965.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7300</th>\n",
       "      <td>Tokyo Ghoul</td>\n",
       "      <td>656039.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Naruto</td>\n",
       "      <td>645672.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4121</th>\n",
       "      <td>Angel Beats</td>\n",
       "      <td>640177.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1274</th>\n",
       "      <td>Code Geass Hangyaku no Lelouch</td>\n",
       "      <td>625466.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6891</th>\n",
       "      <td>No Game No Life</td>\n",
       "      <td>620456.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                Title   ScoredBy\n",
       "1239                       Death Note  1006242.0\n",
       "6232               Shingeki no Kyojin   936784.0\n",
       "5682                 Sword Art Online   913806.0\n",
       "3567  Fullmetal Alchemist Brotherhood   730784.0\n",
       "8859                    One Punch Man   687965.0\n",
       "7300                      Tokyo Ghoul   656039.0\n",
       "10                             Naruto   645672.0\n",
       "4121                      Angel Beats   640177.0\n",
       "1274   Code Geass Hangyaku no Lelouch   625466.0\n",
       "6891                  No Game No Life   620456.0"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_rec('Bleach',10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# HYBRID RECOMMENDATION "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will now build a hybrid recommendation system to get recommendations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "scrolled": false
   },
   "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>Anime_id</th>\n",
       "      <th>Title</th>\n",
       "      <th>Genre</th>\n",
       "      <th>Synopsis</th>\n",
       "      <th>Type</th>\n",
       "      <th>Producer</th>\n",
       "      <th>Studio</th>\n",
       "      <th>Rating</th>\n",
       "      <th>ScoredBy</th>\n",
       "      <th>Popularity</th>\n",
       "      <th>Members</th>\n",
       "      <th>Episodes</th>\n",
       "      <th>Source</th>\n",
       "      <th>Aired</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Cowboy Bebop</td>\n",
       "      <td>['Action', 'Adventure', 'Comedy', 'Drama', 'Sc...</td>\n",
       "      <td>In the year 2071, humanity has colonized sever...</td>\n",
       "      <td>TV</td>\n",
       "      <td>['Bandai Visual']</td>\n",
       "      <td>['Sunrise']</td>\n",
       "      <td>8.81</td>\n",
       "      <td>363889.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>704490.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>Original</td>\n",
       "      <td>Apr 3, 1998 to Apr 24, 1999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5</td>\n",
       "      <td>Cowboy Bebop Tengoku no Tobira</td>\n",
       "      <td>['Action', 'Space', 'Drama', 'Mystery', 'Sci-Fi']</td>\n",
       "      <td>Another day, another bounty—such is the life o...</td>\n",
       "      <td>Movie</td>\n",
       "      <td>['Sunrise', 'Bandai Visual']</td>\n",
       "      <td>['Bones']</td>\n",
       "      <td>8.41</td>\n",
       "      <td>111187.0</td>\n",
       "      <td>475.0</td>\n",
       "      <td>179899.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Original</td>\n",
       "      <td>Sep 1, 2001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>6</td>\n",
       "      <td>Trigun</td>\n",
       "      <td>['Action', 'Sci-Fi', 'Adventure', 'Comedy', 'D...</td>\n",
       "      <td>Vash the Stampede is the man with a $$60,000,0...</td>\n",
       "      <td>TV</td>\n",
       "      <td>['Victor Entertainment']</td>\n",
       "      <td>['Madhouse']</td>\n",
       "      <td>8.31</td>\n",
       "      <td>197451.0</td>\n",
       "      <td>158.0</td>\n",
       "      <td>372709.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>Manga</td>\n",
       "      <td>Apr 1, 1998 to Sep 30, 1998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7</td>\n",
       "      <td>Witch Hunter Robin</td>\n",
       "      <td>['Action', 'Magic', 'Police', 'Supernatural', ...</td>\n",
       "      <td>Witches are individuals with special powers li...</td>\n",
       "      <td>TV</td>\n",
       "      <td>['Bandai Visual']</td>\n",
       "      <td>['Sunrise']</td>\n",
       "      <td>7.34</td>\n",
       "      <td>31875.0</td>\n",
       "      <td>1278.0</td>\n",
       "      <td>74889.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>Original</td>\n",
       "      <td>Jul 2, 2002 to Dec 24, 2002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>8</td>\n",
       "      <td>Bouken Ou Beet</td>\n",
       "      <td>['Adventure', 'Fantasy', 'Shounen', 'Supernatu...</td>\n",
       "      <td>It is the dark century and the people are suff...</td>\n",
       "      <td>TV</td>\n",
       "      <td>NaN</td>\n",
       "      <td>['Toei Animation']</td>\n",
       "      <td>7.04</td>\n",
       "      <td>4757.0</td>\n",
       "      <td>3968.0</td>\n",
       "      <td>11247.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>Manga</td>\n",
       "      <td>Sep 30, 2004 to Sep 29, 2005</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Anime_id                           Title  \\\n",
       "0         1                    Cowboy Bebop   \n",
       "1         5  Cowboy Bebop Tengoku no Tobira   \n",
       "2         6                          Trigun   \n",
       "3         7              Witch Hunter Robin   \n",
       "4         8                  Bouken Ou Beet   \n",
       "\n",
       "                                               Genre  \\\n",
       "0  ['Action', 'Adventure', 'Comedy', 'Drama', 'Sc...   \n",
       "1  ['Action', 'Space', 'Drama', 'Mystery', 'Sci-Fi']   \n",
       "2  ['Action', 'Sci-Fi', 'Adventure', 'Comedy', 'D...   \n",
       "3  ['Action', 'Magic', 'Police', 'Supernatural', ...   \n",
       "4  ['Adventure', 'Fantasy', 'Shounen', 'Supernatu...   \n",
       "\n",
       "                                            Synopsis   Type  \\\n",
       "0  In the year 2071, humanity has colonized sever...     TV   \n",
       "1  Another day, another bounty—such is the life o...  Movie   \n",
       "2  Vash the Stampede is the man with a $$60,000,0...     TV   \n",
       "3  Witches are individuals with special powers li...     TV   \n",
       "4  It is the dark century and the people are suff...     TV   \n",
       "\n",
       "                       Producer              Studio  Rating  ScoredBy  \\\n",
       "0             ['Bandai Visual']         ['Sunrise']    8.81  363889.0   \n",
       "1  ['Sunrise', 'Bandai Visual']           ['Bones']    8.41  111187.0   \n",
       "2      ['Victor Entertainment']        ['Madhouse']    8.31  197451.0   \n",
       "3             ['Bandai Visual']         ['Sunrise']    7.34   31875.0   \n",
       "4                           NaN  ['Toei Animation']    7.04    4757.0   \n",
       "\n",
       "   Popularity   Members  Episodes    Source                         Aired  \n",
       "0        39.0  704490.0      26.0  Original   Apr 3, 1998 to Apr 24, 1999  \n",
       "1       475.0  179899.0       1.0  Original                   Sep 1, 2001  \n",
       "2       158.0  372709.0      26.0     Manga   Apr 1, 1998 to Sep 30, 1998  \n",
       "3      1278.0   74889.0      26.0  Original   Jul 2, 2002 to Dec 24, 2002  \n",
       "4      3968.0   11247.0      52.0     Manga  Sep 30, 2004 to Sep 29, 2005  "
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv('cleaned_data.csv')\n",
    "#data.drop('Unnamed: 0', axis = 1, inplace = True)\n",
    "data.dropna(subset = ['Title'], inplace = True)\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will then use a Term frequency - inverse document frequency to create a search engine so that it is easy to find an anime title and its anime id. The tfdif will enable the computer to find the title that is most similar to the title that we will enter."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "# vector matrix for the anime titles\n",
    "\n",
    "# finding groups of 2 words that are consecutive\n",
    "vectorizer = TfidfVectorizer(ngram_range=(1,2))\n",
    "\n",
    "tfidf = vectorizer.fit_transform(data[\"Title\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will then create a function that will use cosine similarity to compute the similarity between a term that we will enter in our search box and all the anime titles in our dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# creating a search function \n",
    "def search(title):\n",
    "    \"\"\"\n",
    "    A function that takes in a search term of the title that we are \n",
    "    looking for then cleans it and uses the vectorizer\n",
    "    to turn it into a sparse matrix and then uses cosine similarity to find the\n",
    "    most similar titles.\n",
    "    \"\"\"\n",
    "    # cleaning the title entered\n",
    "    query_vec = vectorizer.transform([title])\n",
    "    similarity = cosine_similarity(query_vec, tfidf).flatten()\n",
    "\n",
    "    # find the 5 most similar titles to the searcch term\n",
    "    indices = np.argpartition(similarity, -5)[-10:]\n",
    "\n",
    "    # reverse results so that most similar result is at the top\n",
    "    results = data.iloc[indices].iloc[::-1]\n",
    "    \n",
    "    return results['Title'].to_list()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('search.pkl','wb') as f:\n",
    "    pickle.dump(search,f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Dragon Ball',\n",
       " 'Dragon Ball Kai',\n",
       " 'Dragon Ball Specials',\n",
       " 'Dragon Ball Super',\n",
       " 'Dragon Ball GT',\n",
       " 'Dragon Ball Z The Real 4D',\n",
       " 'Dragon Ball Z Zenbu Misemasu Toshi Wasure Dragon Ball Z',\n",
       " 'Dragon Ball Z Saiyajin Zetsumetsu Keikaku',\n",
       " 'Dragon Ball Super Movie Broly',\n",
       " 'Dragon Ball Episode of Bardock']"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "search('dragon ball')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will use the widgets library to build an interactive search box using our function above. The search box will enable one to type in the name of an anime and see the results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# using the widgets library\n",
    "movie_input = widgets.Text(\n",
    "    # setting default value as Trigun\n",
    "    value='Trigun',\n",
    "    description='Movie Title:',\n",
    "    disabled=False\n",
    ")\n",
    "movie_list = widgets.Output()\n",
    "\n",
    "def on_type(data):\n",
    "    with movie_list:\n",
    "        movie_list.clear_output()\n",
    "        title = data[\"new\"]\n",
    "\n",
    "        # display output when length of title is more than 5\n",
    "        if len(title) > 5:\n",
    "            display(search(title))\n",
    "\n",
    "movie_input.observe(on_type, names='value')\n",
    "\n",
    "\n",
    "display(movie_input, movie_list)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will hardcode an anime id for the 'Cowoy Bebop' anime to find the users who watched and also liked same anime and gave it a rating greater than 6."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# hard coding an anime id to find the users who liked the same movie\n",
    "movie_id = 1\n",
    "\n",
    "#def find_similar_movies(movie_id):\n",
    "anime = data[data[\"Anime_id\"] == movie_id]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We  will now use the ratings dataset to find the users who liked the same movie."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# loadind the clean rating data\n",
    "ratings = pd.read_csv('cleaned_rating.csv')\n",
    "ratings.drop(\"Unnamed: 0\", axis = 1, inplace = True)\n",
    "ratings.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# finding similar users\n",
    "similar_users = ratings[(ratings[\"anime_id\"] == movie_id) & (ratings[\"rating\"] > 6)][\"user_id\"].unique()\n",
    "similar_users"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will then put it into a dataframe to return the records of the similar users."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# similar users dataframe\n",
    "similar_user_recs = ratings[(ratings[\"user_id\"].isin(similar_users)) & (ratings[\"rating\"] > 6)]\n",
    "similar_user_recs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# getting the anime ids from the dataframe\n",
    "similar_user_recs = ratings[(ratings[\"user_id\"].isin(similar_users)) & (ratings[\"rating\"] > 6)][\"anime_id\"]\n",
    "similar_user_recs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will then narrow down the dataframe so that we only get 10% or more of users who are similar and also liked the same movie"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# get value counts for the movie ids\n",
    "similar_user_recs.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Converting to percentage\n",
    "similar_user_recs = similar_user_recs.value_counts() / len(similar_users)\n",
    "\n",
    "# Taking records where only greater than 10% liked\n",
    "similar_user_recs = similar_user_recs[similar_user_recs > .10]\n",
    "similar_user_recs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We now have a set of 326 animes where more than 10% of users liked the anime title. We will now find users who liked animes that are similar to the 'Cowboy' anime. This will be done by finding the users who have rated the animes in our 'similar_user_recs'."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# finding how much all users in the dataset like the animes\n",
    "all_users = ratings[(ratings[\"anime_id\"].isin(similar_user_recs.index)) & (ratings[\"rating\"] > 6)]\n",
    "all_users"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will then find the percentage of all users who recommended the animes that are in ``similar_user_recs``."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# calculating percentage\n",
    "all_user_recs = all_users[\"anime_id\"].value_counts() / len(all_users[\"user_id\"].unique())\n",
    "all_user_recs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will then compare the percentages in ```similar_user_recs``` and ```all_users_recs``` by joining the two pandas series so as to see how users similar to us liked the anime and how much an average person liked the same animes.\n",
    "\n",
    "The goal is to find the animes that have a big difference."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# joining the two pandas series\n",
    "rec_percentages = pd.concat([similar_user_recs, all_user_recs], axis=1)\n",
    "rec_percentages.columns = [\"similar\", \"all\"]\n",
    "rec_percentages"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will then calculate a score by dividing one percentage by the other."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# finding the ratio between the percentages\n",
    "rec_percentages[\"score\"] = rec_percentages[\"similar\"] / rec_percentages[\"all\"]\n",
    "\n",
    "# sorting the recommendations based on the scores\n",
    "rec_percentages = rec_percentages.sort_values(\"score\", ascending=False)\n",
    "rec_percentages"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the above dataframe, the higher the score, the better the recommendation."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will then get the top 10 recommendations and merge that with our ```anime``` dataset so as to get the title of the movies."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# getting top 10 recommendations\n",
    "rec_percentages.head(10).merge(data, left_index=True, right_on=\"Anime_id\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will then put all the code above into one function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def find_similar_movies(movie_id):\n",
    "    \n",
    "    # finding recommendations for users that are similar \n",
    "    similar_users = ratings[(ratings[\"anime_id\"] == movie_id) & (ratings[\"rating\"] > 6)][\"user_id\"].unique()\n",
    "    similar_user_recs = ratings[(ratings[\"user_id\"].isin(similar_users)) & (ratings[\"rating\"] > 6)][\"anime_id\"]\n",
    "\n",
    "    # narrowing them down to 10%\n",
    "    similar_user_recs = similar_user_recs.value_counts() / len(similar_users)\n",
    "    similar_user_recs = similar_user_recs[similar_user_recs > .10]\n",
    "\n",
    "    # finding how common the recommendations are among all users\n",
    "    all_users = ratings[(ratings[\"anime_id\"].isin(similar_user_recs.index)) & (ratings[\"rating\"] > 4)]\n",
    "    all_user_recs = all_users[\"anime_id\"].value_counts() / len(all_users[\"user_id\"].unique())\n",
    "\n",
    "    # concatenating all_user_recs and similar_user_recs\n",
    "    rec_percentages = pd.concat([similar_user_recs, all_user_recs], axis=1)\n",
    "    rec_percentages.columns = [\"similar\", \"all\"]\n",
    "    \n",
    "    # calculating the score\n",
    "    rec_percentages[\"score\"] = rec_percentages[\"similar\"] / rec_percentages[\"all\"]\n",
    "    # sorting values based on score\n",
    "    rec_percentages = rec_percentages.sort_values(\"score\", ascending=False)\n",
    "\n",
    "    # return the top 10 recommendations\n",
    "    return rec_percentages.head(10).merge(data, left_index=True, right_on=\"Anime_id\")[[\"score\", \"Title\", \"Genre\"]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will now create an interactive recommendation widget where one gets to type in an anime title and get recommendations based on the anime title"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# creating an input widget\n",
    "movie_name_input = widgets.Text(\n",
    "    value='Trigun',\n",
    "    description='Movie Title:',\n",
    "    disabled=False\n",
    ")\n",
    "\n",
    "# creating an output widget\n",
    "recommendation_list = widgets.Output()\n",
    "\n",
    "# creating an ontype function\n",
    "def on_type(data):\n",
    "    with recommendation_list:\n",
    "        recommendation_list.clear_output()\n",
    "\n",
    "        # grab title from input widget\n",
    "        title = data[\"new\"]\n",
    "\n",
    "        # display output when length of title is more than 5\n",
    "        if len(title) > 5:\n",
    "            # search title using the search function\n",
    "            results = search(title)\n",
    "            movie_id = results.iloc[0][\"Anime_id\"]\n",
    "            display(find_similar_movies(movie_id))\n",
    "\n",
    "movie_name_input.observe(on_type, names='value')\n",
    "\n",
    "display(movie_name_input, recommendation_list)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Model Based Modelling"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this section, we will use the knnwithmeans and SVD algorithms to build our recommendation model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# reloading and preview of data\n",
    "new_df = pd.read_csv(\"cleaned_anime.csv\")\n",
    "new_df.dropna(subset = ['Title'], inplace = True)\n",
    "new_df = df_anime[['Anime_id', 'Title', 'Rating']]\n",
    "new_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Loading the data set\n",
    "reader = Reader()\n",
    "data = Dataset.load_from_df(new_df,reader)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Print the number of users and items\n",
    "dataset = data.build_full_trainset()\n",
    "print('Number of users: ', dataset.n_users, '\\n')\n",
    "print('Number of items: ', dataset.n_items)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Split the data into train and test set\n",
    "trainset, testset = train_test_split(data, test_size=0.2, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Print the number of users and items\n",
    "print('Number of users_train: ', trainset.n_users, '\\n')\n",
    "print('Number of items_train: ', trainset.n_items, '\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# creating a class to get the top users\n",
    "from collections import defaultdict\n",
    "\n",
    "def get_top_n(predictions, n=10):\n",
    "    # First map the predictions to each user.\n",
    "    top_n = defaultdict(list)\n",
    "    for uid, iid, true_r, est, _ in predictions:\n",
    "        top_n[uid].append((iid, est))\n",
    "\n",
    "    # Then sort the predictions for each user and retrieve the k highest ones.\n",
    "    for uid, user_ratings in top_n.items():\n",
    "        user_ratings.sort(key=lambda x: x[1], reverse=True)\n",
    "        top_n[uid] = user_ratings[:n]\n",
    "\n",
    "    return top_n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#function to use for collaborative Filtering \n",
    "class collab_filtering_based_recommender_model():\n",
    "    def __init__(self, model, trainset, testset, data):\n",
    "        self.model = model\n",
    "        self.trainset = trainset\n",
    "        self.testset = testset\n",
    "        self.data = data\n",
    "        self.pred_test = None\n",
    "        self.recommendations = None\n",
    "        self.top_n = None\n",
    "        self.recommenddf = None\n",
    "\n",
    "    def fit_and_predict(self):        \n",
    "        print('**Fitting the train data...**')\n",
    "        self.model.fit(self.trainset)       \n",
    "\n",
    "        print('**Predicting the test data...**')\n",
    "        self.pred_test = self.model.test(self.testset)        \n",
    "        rmse = round(accuracy.rmse(self.pred_test), 3)\n",
    "        print('**RMSE for the predicted result is ' + str(rmse) + '**')   \n",
    "        \n",
    "        self.top_n = get_top_n(self.pred_test)\n",
    "        self.recommenddf = pd.DataFrame(columns=['CustomerID', 'Description', 'rating'])\n",
    "        for item in self.top_n:\n",
    "            subdf = pd.DataFrame(self.top_n[item], columns=['Description', 'rating'])\n",
    "            subdf['CustomerID'] = item\n",
    "            cols = subdf.columns.tolist()\n",
    "            cols = cols[-1:] + cols[:-1]\n",
    "            subdf = subdf[cols]        \n",
    "            self.recommenddf = pd.concat([self.recommenddf, subdf], axis = 0)        \n",
    "        return rmse\n",
    "        \n",
    "    def cross_validate(self):\n",
    "        print('**Cross Validating the data...**')\n",
    "        cv_result = cross_validate(self.model, self.data, n_jobs=-1)\n",
    "        cv_result = round(cv_result['test_rmse'].mean(),3)\n",
    "        print('**Mean CV RMSE is ' + str(cv_result)  + '**')\n",
    "        return cv_result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Finding the best collaborative Filtering model\n",
    "from surprise.model_selection import RandomizedSearchCV\n",
    "\n",
    "def find_best_model(model, parameters,data):\n",
    "    clf = RandomizedSearchCV(model, parameters, n_jobs=-1, measures=['rmse'])\n",
    "    clf.fit(data)             \n",
    "    print(clf.best_score)\n",
    "    print(clf.best_params)\n",
    "    print(clf.best_estimator)\n",
    "    return clf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# finding the best model between cosine, pearson and pearson_baseline\n",
    "sim_options = {\n",
    "    \"name\": [\"msd\", \"cosine\", \"pearson\", \"pearson_baseline\"],\n",
    "    \"min_support\": [3, 4, 5],\n",
    "    \"user_based\": [True],\n",
    "}\n",
    "params = { 'k': range(30,50,1), 'sim_options': sim_options}\n",
    "clf = find_best_model(KNNWithMeans, params, data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We have an RMSE of 1.7. We will go ahead and fit another model using our collaborative filtering function above to see if we get better results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#fit knnwithmeans model\n",
    "knnwithmeans = clf.best_estimator['rmse']\n",
    "col_fil_knnwithmeans = collab_filtering_based_recommender_model(knnwithmeans, trainset, testset, data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "#fit and predict using the knnmeans model\n",
    "knnwithmeans_rmse = col_fil_knnwithmeans.fit_and_predict()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The rmse does not change much therefore we will go ahead and cross validate our model to see if we have rmse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "#cross validate using knnwith means\n",
    "knnwithmeans_cv_rmse = col_fil_knnwithmeans.cross_validate()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The rmse still does not change much therefore we will use the SVD algorithm to build another model and then cross validate it to see if we have better results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#fitting the SVD model\n",
    "params= {\n",
    "    \"n_epochs\": [5, 10, 15, 20],\n",
    "    \"lr_all\": [0.002, 0.005],\n",
    "    \"reg_all\": [0.4, 0.6]\n",
    "}\n",
    "clf = find_best_model(SVD, params, data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#fitting the SVD model to the train and test data\n",
    "svd = clf.best_estimator['rmse']\n",
    "col_fil_svd = collab_filtering_based_recommender_model(svd, trainset, testset, data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#fitting and prediciting the SVD model\n",
    "svd_rmse = col_fil_svd.fit_and_predict()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#cross validate using SVD\n",
    "svd_cv_rmse = col_fil_svd.cross_validate()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "cross_validate(svd,data,measures=['RMSE', 'MAE'], cv=5, verbose=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# EVALAUTION"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We have built 2 types of recommendation model;\n",
    "* Metric based\n",
    "* Model based.\n",
    "\n",
    "For the metric based model, we have the content-based and the hybrid model. The hybrid model gave better recommendations than than the content-based model.\n",
    "\n",
    "For the Model based, we have used the ```SVD``` and ```knnwithmeans``` algorithms. Both models perform almost the same hence we opted to go with either of the model since they both satisfy our objective of attaining an RMSE of 2.0 and below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "Models=['SVD', 'KNNWithMeans']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# creating a dataframe to compare the algorithms\n",
    "pd.DataFrame({'model':['SVD', 'KNNWithMeans'],'RMSE':[svd_cv_rmse,knnwithmeans_cv_rmse]})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# DEPLOYMENT"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We deployed the hybrid model on streamlit where the app prompts a user to select a title. After selecting the title, the user is given the top 10 recommendations.\n",
    "    \n",
    " * One can use the model through the following [Link](https://anime-deployment.streamlit.app/)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# RECOMMENDATIONS\n",
    "\n",
    "* This app is beneficial to the anime lovers and anime producers.\n",
    "* The recommendation system can be improved by using GridSearch cv. Grid search requires a lot of computational power therefore it is recommended that you have enough computational power so as to do a Grid search."
   ]
  }
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