12.Cluster_Analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 12. Cluster Analysis\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from scipy.cluster.hierarchy import dendrogram, linkage\n",
    "from scipy.spatial.distance import pdist\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from gensim.models import Word2Vec\n",
    "from sklearn.cluster import KMeans\n",
    "import seaborn as sns\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "################  Recommendations (all data, positive recommendations only) ################\n",
      "Users: 12,663,134\n",
      "Games: 37,419\n",
      "Number of users 2,000\n",
      "First five users id [11187923 12792392 11703922  9531989 11022607]\n",
      "################  Recommendations (Sample) ################\n",
      "Minimum number of recommendations to be included in the sample: 20\n",
      "Number of randomly selected users: 2000\n",
      "Users: 1,991\n",
      "Games: 11,366\n",
      "################ User-Game Matrix ################\n",
      "Number of Rows: 1,992\n",
      "Numbeer of Columns: 11,367\n",
      "Number of stored values: 74,405\n"
     ]
    }
   ],
   "source": [
    "%run \"01.Recommendation_Loading.ipynb\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 50872 distinct games\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 50872 entries, 0 to 50871\n",
      "Data columns (total 3 columns):\n",
      " #   Column       Non-Null Count  Dtype \n",
      "---  ------       --------------  ----- \n",
      " 0   app_id       50872 non-null  int64 \n",
      " 1   description  50872 non-null  object\n",
      " 2   tags         50872 non-null  object\n",
      "dtypes: int64(1), object(2)\n",
      "memory usage: 1.2+ MB\n",
      "There are 50872 distinct games\n",
      "There are 441 tags\n",
      "The top 10 tags with their respective count are: [('Indie', 27957), ('Singleplayer', 22566), ('Action', 21897), ('Adventure', 20183), ('Casual', 17844)]\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 49628 entries, 10 to 2515460\n",
      "Columns: 441 entries, 1980s to eSports\n",
      "dtypes: int8(441)\n",
      "memory usage: 21.3 MB\n"
     ]
    }
   ],
   "source": [
    "%run \"04.Games_Exploration.ipynb\"\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
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       "tags  app_id  1980s  1990's  2.5D  2D  2D Fighter  2D Platformer  360 Video  \\\n",
       "0         10      1       1     0   0           0              0          0   \n",
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       "\n",
       "[5 rows x 442 columns]"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "matrix_games_tags = matrix_games_tags.reset_index()\n",
    "games_to_keep = recommendations.app_id.unique()\n",
    "matrix_games_tags = matrix_games_tags[matrix_games_tags[\"app_id\"].isin(games_to_keep)]\n",
    "matrix_games_tags.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
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       "    app_id  app_id_categorical\n",
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       "1   381210                3122\n",
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     "execution_count": 37,
     "metadata": {},
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   ],
   "source": [
    "games_idx = pd.read_csv('matrix/games_idx.csv')\n",
    "games_idx.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
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       "   app_id_categorical  1980s  1990's  2.5D  2D  2D Fighter  2D Platformer  \\\n",
       "0                2500      0       0     0   0           0              0   \n",
       "1                 238      0       0     0   0           0              0   \n",
       "2                4100      0       0     0   0           0              0   \n",
       "3                9629      0       0     0   0           0              0   \n",
       "4                1570      0       0     0   0           0              0   \n",
       "\n",
       "   360 Video  3D  3D Fighter  ...  Well-Written  Werewolves  Western  \\\n",
       "0          0   0           0  ...             0           0        0   \n",
       "1          0   0           0  ...             0           0        0   \n",
       "2          0   0           0  ...             0           0        0   \n",
       "3          0   1           0  ...             0           0        0   \n",
       "4          0   0           0  ...             0           0        0   \n",
       "\n",
       "   Wholesome  Word Game  World War I  World War II  Wrestling  Zombies  \\\n",
       "0          0          0            0             0          0        0   \n",
       "1          0          0            0             0          0        0   \n",
       "2          0          0            0             0          0        0   \n",
       "3          0          0            0             0          0        0   \n",
       "4          0          0            0             0          0        0   \n",
       "\n",
       "   eSports  \n",
       "0        0  \n",
       "1        0  \n",
       "2        0  \n",
       "3        0  \n",
       "4        0  \n",
       "\n",
       "[5 rows x 442 columns]"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "matrix_games_tags = pd.merge(games_idx, matrix_games_tags, left_on=\"app_id\", right_on=\"app_id\").drop(columns=\"app_id\")\n",
    "matrix_games_tags.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>app_id</th>\n",
       "      <th>helpful</th>\n",
       "      <th>funny</th>\n",
       "      <th>date</th>\n",
       "      <th>is_recommended</th>\n",
       "      <th>hours</th>\n",
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       "      <th>review_id</th>\n",
       "      <th>user_id_categorical</th>\n",
       "      <th>app_id_categorical</th>\n",
       "      <th>...</th>\n",
       "      <th>Well-Written</th>\n",
       "      <th>Werewolves</th>\n",
       "      <th>Western</th>\n",
       "      <th>Wholesome</th>\n",
       "      <th>Word Game</th>\n",
       "      <th>World War I</th>\n",
       "      <th>World War II</th>\n",
       "      <th>Wrestling</th>\n",
       "      <th>Zombies</th>\n",
       "      <th>eSports</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>337000</td>\n",
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       "      <td>38767195</td>\n",
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       "      <td>0</td>\n",
       "      <td>2019-08-20</td>\n",
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       "      <td>59.2</td>\n",
       "      <td>725645</td>\n",
       "      <td>14952611</td>\n",
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       "      <td>2500</td>\n",
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       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>337000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2022-10-30</td>\n",
       "      <td>1</td>\n",
       "      <td>330.8</td>\n",
       "      <td>1396964</td>\n",
       "      <td>17137785</td>\n",
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       "      <th>3</th>\n",
       "      <td>337000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2016-08-26</td>\n",
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       "      <td>0</td>\n",
       "      <td>2016-08-26</td>\n",
       "      <td>1</td>\n",
       "      <td>70.9</td>\n",
       "      <td>2498931</td>\n",
       "      <td>17101142</td>\n",
       "      <td>217</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 451 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   app_id  helpful  funny       date  is_recommended  hours  user_id  \\\n",
       "0  337000       19      6 2021-07-11               1   26.0    12039   \n",
       "1  337000        0      0 2019-08-20               1   59.2   725645   \n",
       "2  337000        0      0 2022-10-30               1  330.8  1396964   \n",
       "3  337000        0      0 2016-08-26               1   23.3  2152134   \n",
       "4  337000        0      0 2016-08-26               1   70.9  2498931   \n",
       "\n",
       "   review_id  user_id_categorical  app_id_categorical  ...  Well-Written  \\\n",
       "0   38767195                    0                2500  ...             0   \n",
       "1   14952611                   77                2500  ...             0   \n",
       "2   17137785                  146                2500  ...             0   \n",
       "3   19289194                  197                2500  ...             0   \n",
       "4   17101142                  217                2500  ...             0   \n",
       "\n",
       "   Werewolves  Western  Wholesome  Word Game  World War I  World War II  \\\n",
       "0           0        0          0          0            0             0   \n",
       "1           0        0          0          0            0             0   \n",
       "2           0        0          0          0            0             0   \n",
       "3           0        0          0          0            0             0   \n",
       "4           0        0          0          0            0             0   \n",
       "\n",
       "   Wrestling  Zombies  eSports  \n",
       "0          0        0        0  \n",
       "1          0        0        0  \n",
       "2          0        0        0  \n",
       "3          0        0        0  \n",
       "4          0        0        0  \n",
       "\n",
       "[5 rows x 451 columns]"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "matrix_games_tags = pd.merge(recommendations, matrix_games_tags, left_on=\"app_id_categorical\", right_on=\"app_id_categorical\")\n",
    "matrix_games_tags.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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      "text/plain": [
       "                      app_id  helpful  funny       date  is_recommended  \\\n",
       "user_id_categorical                                                       \n",
       "0                    1782210      100     80 2022-03-28               1   \n",
       "1                    1139900        5      2 2022-06-14               1   \n",
       "2                    1808360        2      0 2022-02-05               1   \n",
       "3                    1853200        2      2 2022-12-04               1   \n",
       "4                    1570070       13      5 2022-06-04               1   \n",
       "\n",
       "                     hours  user_id  review_id  app_id_categorical  1980s  \\\n",
       "user_id_categorical                                                         \n",
       "0                     41.0    12039   41042676               10847      0   \n",
       "1                    106.5    20798   39854648                8548      0   \n",
       "2                     33.6    24787   31123930               10906      0   \n",
       "3                    130.4    39739   41086653               10991      0   \n",
       "4                    203.7    52992   39699084               10349      1   \n",
       "\n",
       "                     ...  Well-Written  Werewolves  Western  Wholesome  \\\n",
       "user_id_categorical  ...                                                 \n",
       "0                    ...             0           0        0          0   \n",
       "1                    ...             0           0        0          0   \n",
       "2                    ...             0           0        0          0   \n",
       "3                    ...             0           0        0          0   \n",
       "4                    ...             0           0        0          0   \n",
       "\n",
       "                     Word Game  World War I  World War II  Wrestling  Zombies  \\\n",
       "user_id_categorical                                                             \n",
       "0                            0            0             1          0        0   \n",
       "1                            0            1             1          0        1   \n",
       "2                            0            1             1          0        0   \n",
       "3                            0            1             1          0        0   \n",
       "4                            0            0             0          0        0   \n",
       "\n",
       "                     eSports  \n",
       "user_id_categorical           \n",
       "0                          0  \n",
       "1                          0  \n",
       "2                          0  \n",
       "3                          0  \n",
       "4                          0  \n",
       "\n",
       "[5 rows x 450 columns]"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "matrix_games_tags = matrix_games_tags.groupby(\"user_id_categorical\").max()\n",
    "matrix_games_tags.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['app_id',\n",
       " 'helpful',\n",
       " 'funny',\n",
       " 'date',\n",
       " 'is_recommended',\n",
       " 'hours',\n",
       " 'user_id',\n",
       " 'review_id',\n",
       " 'app_id_categorical']"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "columns_to_drop = list(recommendations.columns)\n",
    "columns_to_drop.remove(\"user_id_categorical\")\n",
    "columns_to_drop"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1986</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1987</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1988</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1989</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1991 rows × 441 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                     1980s  1990's  2.5D  2D  2D Fighter  2D Platformer  \\\n",
       "user_id_categorical                                                       \n",
       "0                        0       0     1   1           0              0   \n",
       "1                        0       1     1   0           0              0   \n",
       "2                        0       0     0   1           0              1   \n",
       "3                        0       0     0   0           0              0   \n",
       "4                        1       1     1   1           0              1   \n",
       "...                    ...     ...   ...  ..         ...            ...   \n",
       "1986                     0       1     0   1           0              1   \n",
       "1987                     0       1     0   1           0              0   \n",
       "1988                     0       0     0   0           0              0   \n",
       "1989                     1       1     0   1           0              1   \n",
       "1990                     0       1     0   1           0              1   \n",
       "\n",
       "                     360 Video  3D  3D Fighter  3D Platformer  ...  \\\n",
       "user_id_categorical                                            ...   \n",
       "0                            0   1           0              1  ...   \n",
       "1                            0   1           0              1  ...   \n",
       "2                            0   0           0              0  ...   \n",
       "3                            0   1           0              0  ...   \n",
       "4                            0   0           0              0  ...   \n",
       "...                        ...  ..         ...            ...  ...   \n",
       "1986                         0   1           0              1  ...   \n",
       "1987                         0   0           0              0  ...   \n",
       "1988                         0   1           0              0  ...   \n",
       "1989                         0   0           0              0  ...   \n",
       "1990                         0   0           0              1  ...   \n",
       "\n",
       "                     Well-Written  Werewolves  Western  Wholesome  Word Game  \\\n",
       "user_id_categorical                                                            \n",
       "0                               0           0        0          0          0   \n",
       "1                               0           0        0          0          0   \n",
       "2                               0           0        0          0          0   \n",
       "3                               0           0        0          0          0   \n",
       "4                               0           0        0          0          0   \n",
       "...                           ...         ...      ...        ...        ...   \n",
       "1986                            1           0        0          1          0   \n",
       "1987                            0           0        0          1          0   \n",
       "1988                            0           0        0          0          0   \n",
       "1989                            0           0        0          0          0   \n",
       "1990                            0           0        0          0          0   \n",
       "\n",
       "                     World War I  World War II  Wrestling  Zombies  eSports  \n",
       "user_id_categorical                                                          \n",
       "0                              0             1          0        0        0  \n",
       "1                              1             1          0        1        0  \n",
       "2                              1             1          0        0        0  \n",
       "3                              1             1          0        0        0  \n",
       "4                              0             0          0        0        0  \n",
       "...                          ...           ...        ...      ...      ...  \n",
       "1986                           0             0          0        0        0  \n",
       "1987                           0             0          0        1        0  \n",
       "1988                           0             0          0        1        0  \n",
       "1989                           0             0          0        1        0  \n",
       "1990                           0             0          0        0        0  \n",
       "\n",
       "[1991 rows x 441 columns]"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "matrix_games_tags = matrix_games_tags.drop(columns=columns_to_drop)\n",
    "matrix_games_tags"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "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",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>user_id_categorical</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>...</th>\n",
       "      <th>1981</th>\n",
       "      <th>1982</th>\n",
       "      <th>1983</th>\n",
       "      <th>1984</th>\n",
       "      <th>1985</th>\n",
       "      <th>1986</th>\n",
       "      <th>1987</th>\n",
       "      <th>1988</th>\n",
       "      <th>1989</th>\n",
       "      <th>1990</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1980s</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990's</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2.5D</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2D</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2D Fighter</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>World War I</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>World War II</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wrestling</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Zombies</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>eSports</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>441 rows × 1991 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "user_id_categorical  0     1     2     3     4     5     6     7     8     \\\n",
       "1980s                   0     0     0     0     1     0     0     0     1   \n",
       "1990's                  0     1     0     0     1     1     0     0     1   \n",
       "2.5D                    1     1     0     0     1     1     1     0     0   \n",
       "2D                      1     0     1     0     1     1     1     1     1   \n",
       "2D Fighter              0     0     0     0     0     1     0     0     0   \n",
       "...                   ...   ...   ...   ...   ...   ...   ...   ...   ...   \n",
       "World War I             0     1     1     1     0     0     0     0     0   \n",
       "World War II            1     1     1     1     0     0     0     0     0   \n",
       "Wrestling               0     0     0     0     0     0     0     0     0   \n",
       "Zombies                 0     1     0     0     0     1     1     0     0   \n",
       "eSports                 0     0     0     0     0     1     0     0     0   \n",
       "\n",
       "user_id_categorical  9     ...  1981  1982  1983  1984  1985  1986  1987  \\\n",
       "1980s                   0  ...     0     0     0     0     0     0     0   \n",
       "1990's                  0  ...     1     0     1     0     0     1     1   \n",
       "2.5D                    0  ...     0     0     0     0     0     0     0   \n",
       "2D                      1  ...     1     0     1     0     1     1     1   \n",
       "2D Fighter              0  ...     0     0     1     0     0     0     0   \n",
       "...                   ...  ...   ...   ...   ...   ...   ...   ...   ...   \n",
       "World War I             0  ...     0     0     0     0     0     0     0   \n",
       "World War II            0  ...     1     1     1     0     0     0     0   \n",
       "Wrestling               0  ...     0     0     0     0     0     0     0   \n",
       "Zombies                 1  ...     1     0     1     1     0     0     1   \n",
       "eSports                 1  ...     1     0     0     0     0     0     0   \n",
       "\n",
       "user_id_categorical  1988  1989  1990  \n",
       "1980s                   0     1     0  \n",
       "1990's                  0     1     1  \n",
       "2.5D                    0     0     0  \n",
       "2D                      0     1     1  \n",
       "2D Fighter              0     0     0  \n",
       "...                   ...   ...   ...  \n",
       "World War I             0     0     0  \n",
       "World War II            0     0     0  \n",
       "Wrestling               0     0     0  \n",
       "Zombies                 1     1     0  \n",
       "eSports                 0     0     0  \n",
       "\n",
       "[441 rows x 1991 columns]"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "matrix_games_tags.T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Principal Component Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Standardize the data\n",
    "scaler = StandardScaler()\n",
    "game_user_matrix_standardized = scaler.fit_transform(matrix_games_tags.T)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reduced Game Tags Dimensions:\n",
      " [[  0.5047312   -2.45003828  -2.19796052 ...   9.52829596   0.08728552\n",
      "   -4.22574991]\n",
      " [ 12.17646662  -1.24598007  -3.59728218 ...  16.37128309  -1.67886414\n",
      "   -4.47147515]\n",
      " [  7.52380558 -10.04233453   2.40278287 ...  -5.97131263   1.51949363\n",
      "    3.06318352]\n",
      " ...\n",
      " [-27.33124618   1.61715316  -2.09303689 ...  -0.79188299  -2.31689134\n",
      "    0.6517085 ]\n",
      " [ 36.82347638   9.73078864  -9.21026701 ...   7.43765046  -1.7929957\n",
      "   -7.01348757]\n",
      " [ -2.50499374   7.32804254  -5.94578536 ...   4.86202279   2.21214837\n",
      "    6.91065887]]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(441, 10)"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Specify the number of components (dimensions) you want after reduction\n",
    "num_components = 10\n",
    "\n",
    "# Apply PCA\n",
    "pca = PCA(n_components=num_components)\n",
    "game_tags_reduced = pca.fit_transform(game_user_matrix_standardized)\n",
    "\n",
    "# Print the reduced dimensions for each game tag\n",
    "print(\"Reduced Game Tags Dimensions:\\n\", game_tags_reduced)\n",
    "\n",
    "game_tags_reduced.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>0</th>\n",
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       "      <th>9</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1980s</th>\n",
       "      <td>0.5</td>\n",
       "      <td>-2.5</td>\n",
       "      <td>-2.2</td>\n",
       "      <td>-5.4</td>\n",
       "      <td>13.8</td>\n",
       "      <td>3.1</td>\n",
       "      <td>-0.3</td>\n",
       "      <td>9.5</td>\n",
       "      <td>0.1</td>\n",
       "      <td>-4.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990's</th>\n",
       "      <td>12.2</td>\n",
       "      <td>-1.2</td>\n",
       "      <td>-3.6</td>\n",
       "      <td>-2.7</td>\n",
       "      <td>14.9</td>\n",
       "      <td>4.3</td>\n",
       "      <td>-1.2</td>\n",
       "      <td>16.4</td>\n",
       "      <td>-1.7</td>\n",
       "      <td>-4.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2.5D</th>\n",
       "      <td>7.5</td>\n",
       "      <td>-10.0</td>\n",
       "      <td>2.4</td>\n",
       "      <td>2.7</td>\n",
       "      <td>7.1</td>\n",
       "      <td>-1.6</td>\n",
       "      <td>5.5</td>\n",
       "      <td>-6.0</td>\n",
       "      <td>1.5</td>\n",
       "      <td>3.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2D</th>\n",
       "      <td>59.9</td>\n",
       "      <td>-5.2</td>\n",
       "      <td>8.8</td>\n",
       "      <td>-6.2</td>\n",
       "      <td>-3.2</td>\n",
       "      <td>-2.1</td>\n",
       "      <td>-1.5</td>\n",
       "      <td>2.3</td>\n",
       "      <td>-8.9</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2D Fighter</th>\n",
       "      <td>-7.2</td>\n",
       "      <td>-1.5</td>\n",
       "      <td>-2.1</td>\n",
       "      <td>-3.2</td>\n",
       "      <td>6.6</td>\n",
       "      <td>-10.0</td>\n",
       "      <td>13.6</td>\n",
       "      <td>-1.1</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>5.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>World War I</th>\n",
       "      <td>-18.4</td>\n",
       "      <td>5.4</td>\n",
       "      <td>-3.1</td>\n",
       "      <td>-1.8</td>\n",
       "      <td>-2.4</td>\n",
       "      <td>3.7</td>\n",
       "      <td>0.4</td>\n",
       "      <td>3.5</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>-0.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>World War II</th>\n",
       "      <td>6.3</td>\n",
       "      <td>14.7</td>\n",
       "      <td>-5.9</td>\n",
       "      <td>6.7</td>\n",
       "      <td>-0.4</td>\n",
       "      <td>8.4</td>\n",
       "      <td>4.8</td>\n",
       "      <td>8.9</td>\n",
       "      <td>1.2</td>\n",
       "      <td>-3.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wrestling</th>\n",
       "      <td>-27.3</td>\n",
       "      <td>1.6</td>\n",
       "      <td>-2.1</td>\n",
       "      <td>-1.4</td>\n",
       "      <td>-1.6</td>\n",
       "      <td>-3.1</td>\n",
       "      <td>-1.8</td>\n",
       "      <td>-0.8</td>\n",
       "      <td>-2.3</td>\n",
       "      <td>0.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Zombies</th>\n",
       "      <td>36.8</td>\n",
       "      <td>9.7</td>\n",
       "      <td>-9.2</td>\n",
       "      <td>1.2</td>\n",
       "      <td>8.9</td>\n",
       "      <td>1.5</td>\n",
       "      <td>-1.8</td>\n",
       "      <td>7.4</td>\n",
       "      <td>-1.8</td>\n",
       "      <td>-7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>eSports</th>\n",
       "      <td>-2.5</td>\n",
       "      <td>7.3</td>\n",
       "      <td>-5.9</td>\n",
       "      <td>-8.4</td>\n",
       "      <td>8.0</td>\n",
       "      <td>-7.1</td>\n",
       "      <td>10.2</td>\n",
       "      <td>4.9</td>\n",
       "      <td>2.2</td>\n",
       "      <td>6.9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>441 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 0     1    2    3    4     5    6    7    8    9\n",
       "1980s          0.5  -2.5 -2.2 -5.4 13.8   3.1 -0.3  9.5  0.1 -4.2\n",
       "1990's        12.2  -1.2 -3.6 -2.7 14.9   4.3 -1.2 16.4 -1.7 -4.5\n",
       "2.5D           7.5 -10.0  2.4  2.7  7.1  -1.6  5.5 -6.0  1.5  3.1\n",
       "2D            59.9  -5.2  8.8 -6.2 -3.2  -2.1 -1.5  2.3 -8.9  0.0\n",
       "2D Fighter    -7.2  -1.5 -2.1 -3.2  6.6 -10.0 13.6 -1.1 -1.0  5.2\n",
       "...            ...   ...  ...  ...  ...   ...  ...  ...  ...  ...\n",
       "World War I  -18.4   5.4 -3.1 -1.8 -2.4   3.7  0.4  3.5 -2.0 -0.4\n",
       "World War II   6.3  14.7 -5.9  6.7 -0.4   8.4  4.8  8.9  1.2 -3.9\n",
       "Wrestling    -27.3   1.6 -2.1 -1.4 -1.6  -3.1 -1.8 -0.8 -2.3  0.7\n",
       "Zombies       36.8   9.7 -9.2  1.2  8.9   1.5 -1.8  7.4 -1.8 -7.0\n",
       "eSports       -2.5   7.3 -5.9 -8.4  8.0  -7.1 10.2  4.9  2.2  6.9\n",
       "\n",
       "[441 rows x 10 columns]"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "comp_df = pd.DataFrame(data=game_tags_reduced, index=matrix_games_tags.T.index)\n",
    "comp_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Component: 0\n",
      "Top Game Tags in this component:\n",
      "Singleplayer       71.3\n",
      "Action             71.3\n",
      "Adventure          71.0\n",
      "Atmospheric        70.4\n",
      "Multiplayer        70.4\n",
      "Indie              69.6\n",
      "Great Soundtrack   69.0\n",
      "Story Rich         68.2\n",
      "Co-op              67.9\n",
      "Open World         67.9\n",
      "Name: 0, dtype: float64\n",
      "\n",
      "Component: 1\n",
      "Top Game Tags in this component:\n",
      "Military                24.7\n",
      "Team-Based              23.6\n",
      "Massively Multiplayer   22.4\n",
      "War                     22.4\n",
      "PvP                     20.2\n",
      "Realistic               19.6\n",
      "RTS                     19.5\n",
      "Moddable                19.4\n",
      "Tactical                18.6\n",
      "Sandbox                 17.6\n",
      "Name: 1, dtype: float64\n",
      "\n",
      "Component: 2\n",
      "Top Game Tags in this component:\n",
      "Roguelike               26.5\n",
      "Roguelite               24.8\n",
      "Procedural Generation   22.4\n",
      "Action Roguelike        21.1\n",
      "Building                20.4\n",
      "Management              20.2\n",
      "Resource Management     19.9\n",
      "Turn-Based Strategy     19.6\n",
      "Dungeon Crawler         19.5\n",
      "Turn-Based              19.3\n",
      "Name: 2, dtype: float64\n",
      "\n",
      "Component: 3\n",
      "Top Game Tags in this component:\n",
      "Dark Fantasy          22.6\n",
      "Action RPG            19.5\n",
      "Turn-Based            18.1\n",
      "Magic                 17.7\n",
      "Medieval              16.3\n",
      "Steampunk             15.2\n",
      "Turn-Based Combat     15.0\n",
      "Turn-Based Strategy   15.0\n",
      "Isometric             14.9\n",
      "Hack and Slash        14.5\n",
      "Name: 3, dtype: float64\n",
      "\n",
      "Component: 4\n",
      "Top Game Tags in this component:\n",
      "Fast-Paced          19.4\n",
      "Old School          18.3\n",
      "Aliens              16.2\n",
      "Top-Down Shooter    15.9\n",
      "1990's              14.9\n",
      "Blood               14.3\n",
      "Arcade              14.2\n",
      "Local Multiplayer   14.2\n",
      "1980s               13.8\n",
      "Arena Shooter       13.7\n",
      "Name: 4, dtype: float64\n",
      "\n",
      "Component: 5\n",
      "Top Game Tags in this component:\n",
      "Management            17.6\n",
      "Resource Management   16.5\n",
      "Point & Click         14.7\n",
      "Economy               14.6\n",
      "Walking Simulator     13.4\n",
      "Relaxing              13.3\n",
      "Building              13.2\n",
      "Base Building         12.4\n",
      "City Builder          12.0\n",
      "Minimalist            11.8\n",
      "Name: 5, dtype: float64\n",
      "\n",
      "Component: 6\n",
      "Top Game Tags in this component:\n",
      "Fighting            17.6\n",
      "Competitive         17.4\n",
      "Local Multiplayer   15.4\n",
      "Beat 'em up         14.4\n",
      "2D Fighter          13.6\n",
      "Sports              12.8\n",
      "Racing              12.6\n",
      "Dating Sim          12.2\n",
      "War                 10.9\n",
      "Comic Book          10.5\n",
      "Name: 6, dtype: float64\n",
      "\n",
      "Component: 7\n",
      "Top Game Tags in this component:\n",
      "1990's               16.4\n",
      "Dating Sim           14.4\n",
      "Visual Novel         14.1\n",
      "Top-Down Shooter     12.4\n",
      "Romance              11.9\n",
      "Military             11.7\n",
      "Hentai               11.4\n",
      "Silent Protagonist   10.9\n",
      "1980s                 9.5\n",
      "Mod                   9.4\n",
      "Name: 7, dtype: float64\n",
      "\n",
      "Component: 8\n",
      "Top Game Tags in this component:\n",
      "Combat         22.3\n",
      "3D             17.8\n",
      "PvE            16.4\n",
      "Realistic      15.6\n",
      "Early Access   14.4\n",
      "Stylized       12.6\n",
      "PvP            12.3\n",
      "Team-Based     10.7\n",
      "Swordplay      10.3\n",
      "Souls-like      9.2\n",
      "Name: 8, dtype: float64\n",
      "\n",
      "Component: 9\n",
      "Top Game Tags in this component:\n",
      "Competitive           15.8\n",
      "Team-Based            12.4\n",
      "PvP                   11.4\n",
      "Tactical              11.1\n",
      "Card Game              9.7\n",
      "Board Game             8.7\n",
      "Turn-Based Strategy    8.6\n",
      "Narration              8.6\n",
      "Deckbuilding           8.6\n",
      "Turn-Based Tactics     8.5\n",
      "Name: 9, dtype: float64\n",
      "\n"
     ]
    }
   ],
   "source": [
    "for comp in comp_df.columns:\n",
    "    print(\"Component:\", comp)\n",
    "    print(\"Top Game Tags in this component:\")\n",
    "    print(comp_df[comp].sort_values(ascending=False)[:10])\n",
    "    print(\"\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's assign names to each component"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "comp_df.columns = [\"Adventure\", \"War Muliplayer\", \"Management\", \"Strategy\", \"Simulation\",\n",
    "                  \"RPG\",\"Romantic\", \"Driving\", \"Realistic\", \"Horror\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "      <th>Driving</th>\n",
       "      <th>Realistic</th>\n",
       "      <th>Horror</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1980s</th>\n",
       "      <td>0.5</td>\n",
       "      <td>-2.5</td>\n",
       "      <td>-2.2</td>\n",
       "      <td>-5.4</td>\n",
       "      <td>13.8</td>\n",
       "      <td>3.1</td>\n",
       "      <td>-0.3</td>\n",
       "      <td>9.5</td>\n",
       "      <td>0.1</td>\n",
       "      <td>-4.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990's</th>\n",
       "      <td>12.2</td>\n",
       "      <td>-1.2</td>\n",
       "      <td>-3.6</td>\n",
       "      <td>-2.7</td>\n",
       "      <td>14.9</td>\n",
       "      <td>4.3</td>\n",
       "      <td>-1.2</td>\n",
       "      <td>16.4</td>\n",
       "      <td>-1.7</td>\n",
       "      <td>-4.5</td>\n",
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       "    <tr>\n",
       "      <th>2.5D</th>\n",
       "      <td>7.5</td>\n",
       "      <td>-10.0</td>\n",
       "      <td>2.4</td>\n",
       "      <td>2.7</td>\n",
       "      <td>7.1</td>\n",
       "      <td>-1.6</td>\n",
       "      <td>5.5</td>\n",
       "      <td>-6.0</td>\n",
       "      <td>1.5</td>\n",
       "      <td>3.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2D</th>\n",
       "      <td>59.9</td>\n",
       "      <td>-5.2</td>\n",
       "      <td>8.8</td>\n",
       "      <td>-6.2</td>\n",
       "      <td>-3.2</td>\n",
       "      <td>-2.1</td>\n",
       "      <td>-1.5</td>\n",
       "      <td>2.3</td>\n",
       "      <td>-8.9</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>2D Fighter</th>\n",
       "      <td>-7.2</td>\n",
       "      <td>-1.5</td>\n",
       "      <td>-2.1</td>\n",
       "      <td>-3.2</td>\n",
       "      <td>6.6</td>\n",
       "      <td>-10.0</td>\n",
       "      <td>13.6</td>\n",
       "      <td>-1.1</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>5.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Adventure  War Muliplayer  Management  Strategy  Simulation   RPG  \\\n",
       "1980s             0.5            -2.5        -2.2      -5.4        13.8   3.1   \n",
       "1990's           12.2            -1.2        -3.6      -2.7        14.9   4.3   \n",
       "2.5D              7.5           -10.0         2.4       2.7         7.1  -1.6   \n",
       "2D               59.9            -5.2         8.8      -6.2        -3.2  -2.1   \n",
       "2D Fighter       -7.2            -1.5        -2.1      -3.2         6.6 -10.0   \n",
       "\n",
       "            Romantic  Driving  Realistic  Horror  \n",
       "1980s           -0.3      9.5        0.1    -4.2  \n",
       "1990's          -1.2     16.4       -1.7    -4.5  \n",
       "2.5D             5.5     -6.0        1.5     3.1  \n",
       "2D              -1.5      2.3       -8.9     0.0  \n",
       "2D Fighter      13.6     -1.1       -1.0     5.2  "
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "comp_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Adventure', 'War Muliplayer', 'Management', 'Strategy', 'Simulation',\n",
       "       'RPG', 'Romantic', 'Driving', 'Realistic', 'Horror'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "comp_df.columns\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Adventure</th>\n",
       "      <th>War Muliplayer</th>\n",
       "      <th>Management</th>\n",
       "      <th>Strategy</th>\n",
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       "      <th>Military</th>\n",
       "      <td>21.0</td>\n",
       "      <td>24.7</td>\n",
       "      <td>-8.3</td>\n",
       "      <td>6.4</td>\n",
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       "      <th>Team-Based</th>\n",
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       "      <th>Massively Multiplayer</th>\n",
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       "      <th>War</th>\n",
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       "      <td>12.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>9.7</td>\n",
       "      <td>10.9</td>\n",
       "      <td>8.1</td>\n",
       "      <td>2.2</td>\n",
       "      <td>5.0</td>\n",
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       "    <tr>\n",
       "      <th>PvP</th>\n",
       "      <td>42.4</td>\n",
       "      <td>20.2</td>\n",
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       "      <td>-8.5</td>\n",
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       "      <th>Point &amp; Click</th>\n",
       "      <td>28.0</td>\n",
       "      <td>-20.4</td>\n",
       "      <td>0.3</td>\n",
       "      <td>-2.4</td>\n",
       "      <td>-4.9</td>\n",
       "      <td>14.7</td>\n",
       "      <td>1.8</td>\n",
       "      <td>1.2</td>\n",
       "      <td>-6.8</td>\n",
       "      <td>4.6</td>\n",
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       "    <tr>\n",
       "      <th>Interactive Fiction</th>\n",
       "      <td>3.4</td>\n",
       "      <td>-21.1</td>\n",
       "      <td>0.3</td>\n",
       "      <td>-1.8</td>\n",
       "      <td>-10.0</td>\n",
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       "      <td>1.8</td>\n",
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       "      <td>6.1</td>\n",
       "      <td>2.9</td>\n",
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       "    <tr>\n",
       "      <th>Emotional</th>\n",
       "      <td>15.2</td>\n",
       "      <td>-21.7</td>\n",
       "      <td>-1.2</td>\n",
       "      <td>3.9</td>\n",
       "      <td>-8.3</td>\n",
       "      <td>7.0</td>\n",
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       "      <td>2.8</td>\n",
       "      <td>5.1</td>\n",
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       "    <tr>\n",
       "      <th>Mystery</th>\n",
       "      <td>31.0</td>\n",
       "      <td>-22.7</td>\n",
       "      <td>-5.3</td>\n",
       "      <td>1.3</td>\n",
       "      <td>-6.8</td>\n",
       "      <td>10.0</td>\n",
       "      <td>-4.4</td>\n",
       "      <td>2.1</td>\n",
       "      <td>4.6</td>\n",
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       "    <tr>\n",
       "      <th>Visual Novel</th>\n",
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       "                       Adventure  War Muliplayer  Management  Strategy  \\\n",
       "Military                    21.0            24.7        -8.3       6.4   \n",
       "Team-Based                  22.4            23.6        -5.4      -7.6   \n",
       "Massively Multiplayer       22.3            22.4        -3.1     -10.6   \n",
       "War                         27.3            22.4        -6.2      12.0   \n",
       "PvP                         42.4            20.2        -1.5      -8.5   \n",
       "...                          ...             ...         ...       ...   \n",
       "Point & Click               28.0           -20.4         0.3      -2.4   \n",
       "Interactive Fiction          3.4           -21.1         0.3      -1.8   \n",
       "Emotional                   15.2           -21.7        -1.2       3.9   \n",
       "Mystery                     31.0           -22.7        -5.3       1.3   \n",
       "Visual Novel                15.4           -23.3         1.3      -1.8   \n",
       "\n",
       "                       Simulation  RPG  Romantic  Driving  Realistic  Horror  \n",
       "Military                      1.6 10.9       9.1     11.7        3.5     5.3  \n",
       "Team-Based                    5.6  0.5       7.3      8.6       10.7    12.4  \n",
       "Massively Multiplayer        -1.4 -1.4       9.3      8.4        9.2     1.9  \n",
       "War                           0.3  9.7      10.9      8.1        2.2     5.0  \n",
       "PvP                          -0.2 -6.4       5.9      5.8       12.3    11.4  \n",
       "...                           ...  ...       ...      ...        ...     ...  \n",
       "Point & Click                -4.9 14.7       1.8      1.2       -6.8     4.6  \n",
       "Interactive Fiction         -10.0  6.5       1.8      5.3        6.1     2.9  \n",
       "Emotional                    -8.3  7.0       4.5      1.0        2.8     5.1  \n",
       "Mystery                      -6.8 10.0      -4.4      2.1        4.6     7.4  \n",
       "Visual Novel                -17.5 -2.8       9.9     14.1        1.7    -3.5  \n",
       "\n",
       "[441 rows x 10 columns]"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "comp_df.sort_values(by=\"War Muliplayer\", ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Wrestling</th>\n",
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      "text/plain": [
       "              Adventure  War Muliplayer  Management  Strategy  Simulation  \\\n",
       "1980s               0.5            -2.5        -2.2      -5.4        13.8   \n",
       "1990's             12.2            -1.2        -3.6      -2.7        14.9   \n",
       "2.5D                7.5           -10.0         2.4       2.7         7.1   \n",
       "2D                 59.9            -5.2         8.8      -6.2        -3.2   \n",
       "2D Fighter         -7.2            -1.5        -2.1      -3.2         6.6   \n",
       "...                 ...             ...         ...       ...         ...   \n",
       "World War I       -18.4             5.4        -3.1      -1.8        -2.4   \n",
       "World War II        6.3            14.7        -5.9       6.7        -0.4   \n",
       "Wrestling         -27.3             1.6        -2.1      -1.4        -1.6   \n",
       "Zombies            36.8             9.7        -9.2       1.2         8.9   \n",
       "eSports            -2.5             7.3        -5.9      -8.4         8.0   \n",
       "\n",
       "               RPG  Romantic  Driving  Realistic  Horror Assigned_Component  \n",
       "1980s          3.1      -0.3      9.5        0.1    -4.2         Simulation  \n",
       "1990's         4.3      -1.2     16.4       -1.7    -4.5            Driving  \n",
       "2.5D          -1.6       5.5     -6.0        1.5     3.1          Adventure  \n",
       "2D            -2.1      -1.5      2.3       -8.9     0.0          Adventure  \n",
       "2D Fighter   -10.0      13.6     -1.1       -1.0     5.2           Romantic  \n",
       "...            ...       ...      ...        ...     ...                ...  \n",
       "World War I    3.7       0.4      3.5       -2.0    -0.4     War Muliplayer  \n",
       "World War II   8.4       4.8      8.9        1.2    -3.9     War Muliplayer  \n",
       "Wrestling     -3.1      -1.8     -0.8       -2.3     0.7     War Muliplayer  \n",
       "Zombies        1.5      -1.8      7.4       -1.8    -7.0          Adventure  \n",
       "eSports       -7.1      10.2      4.9        2.2     6.9           Romantic  \n",
       "\n",
       "[441 rows x 11 columns]"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Function to assign each tag to the component with the highest loading\n",
    "def assign_to_component(row):\n",
    "    return row.idxmax()\n",
    "\n",
    "# Apply the function to each row (tag)\n",
    "comp_df['Assigned_Component'] = comp_df.apply(assign_to_component, axis=1)\n",
    "\n",
    "# Display the resulting DataFrame with assigned components\n",
    "comp_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Assigned_Component</th>\n",
       "      <th>Adventure</th>\n",
       "      <th>Driving</th>\n",
       "      <th>Horror</th>\n",
       "      <th>Management</th>\n",
       "      <th>RPG</th>\n",
       "      <th>Realistic</th>\n",
       "      <th>Romantic</th>\n",
       "      <th>Simulation</th>\n",
       "      <th>Strategy</th>\n",
       "      <th>War Muliplayer</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Assigned_Component  Adventure  Driving  Horror  Management  RPG  Realistic  \\\n",
       "0                           1        1       0           1    1          1   \n",
       "1                           1        1       0           1    1          1   \n",
       "2                           1        1       0           1    1          0   \n",
       "3                           1        1       0           1    1          1   \n",
       "4                           1        1       1           1    1          1   \n",
       "\n",
       "Assigned_Component  Romantic  Simulation  Strategy  War Muliplayer  \n",
       "0                          1           1         1               1  \n",
       "1                          1           1         1               1  \n",
       "2                          1           1         0               1  \n",
       "3                          1           1         1               1  \n",
       "4                          1           1         1               1  "
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "matrix_games_tags_comp =pd.merge(matrix_games_tags.T,comp_df[\"Assigned_Component\"], left_index=True, right_index=True)\\\n",
    "            .groupby(\"Assigned_Component\").max().T\n",
    "matrix_games_tags_comp.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>Adventure</th>\n",
       "      <th>Driving</th>\n",
       "      <th>Horror</th>\n",
       "      <th>Management</th>\n",
       "      <th>RPG</th>\n",
       "      <th>Realistic</th>\n",
       "      <th>Romantic</th>\n",
       "      <th>Simulation</th>\n",
       "      <th>Strategy</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1986</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1987</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1988</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1989</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1991 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Adventure  Driving  Horror  Management  RPG  Realistic  Romantic  \\\n",
       "0             1        1       0           1    1          1         1   \n",
       "1             1        1       0           1    1          1         1   \n",
       "2             1        1       0           1    1          0         1   \n",
       "3             1        1       0           1    1          1         1   \n",
       "4             1        1       1           1    1          1         1   \n",
       "...         ...      ...     ...         ...  ...        ...       ...   \n",
       "1986          1        1       0           1    1          1         1   \n",
       "1987          1        1       0           1    1          1         1   \n",
       "1988          1        1       0           1    0          1         0   \n",
       "1989          1        1       1           1    1          1         1   \n",
       "1990          1        1       1           1    1          1         1   \n",
       "\n",
       "      Simulation  Strategy  War Muliplayer  \n",
       "0              1         1               1  \n",
       "1              1         1               1  \n",
       "2              1         0               1  \n",
       "3              1         1               1  \n",
       "4              1         1               1  \n",
       "...          ...       ...             ...  \n",
       "1986           1         1               1  \n",
       "1987           1         1               1  \n",
       "1988           1         1               1  \n",
       "1989           1         1               1  \n",
       "1990           1         1               1  \n",
       "\n",
       "[1991 rows x 10 columns]"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "matrix_games_tags_comp = matrix_games_tags_comp.rename_axis('', axis=1)\n",
    "matrix_games_tags_comp"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Hierarchical Cluster Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Grouping users of similar preference"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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5O51z+umnc/rpp7N69WpOOOGEnfa//e1vZ968edxzzz2ceuqpO+0/66yzeM1rXkN3dzdnnHHGTvs/8IEPcOyxx7J8+XLOPPPMnfZ/7GMf40UvehHXX38973vf+3baf/7553PEEUdwzTXXcO655+60/8ILL6StrY3vfOc7fOpTn9pp/6WXXsqBBx7I4sWLueCCC3baf/nll7PvvvuyaNEiFi1atNP+zs5Opk6dyuc//3mWLFmy0/7rrrsOgPPOO4+Ojo4e+6ZMmcJVV10FwEc+8hG+//3v99g/c+ZMvvGNonfE2WefzQ033NBj/wEHHMBll10GwJlnnsny5ct77D/ssMO46KKLAFh22Sc4+msP9dh/xBFHcP755wNwyimncO+99/bYf9RRR/Hxj38cgDe84Q2sWbOmx/5jjjmGD37wgwAcf/zxbNq0qcf+uXPn8p73vAdgp88d+NmbKJ+9BQsWsGLFih77/ez52QM/ewN99h59+A9cf+H7Ofpr03vs97PnZw/8d8/P3vIe+5v92RtMZnZHxO8pQugyitbSd1EE1pcCXcBLgNof3AHA/sDSXpdaTBH4ng3c2Met2srzFg6pwMH9FngY+GpE/Bfwv5n5hwbOOwbYDlwREfWZ7/vAyXUtygA31YfT0nLg4xExE7g2M+/elYcYCsegSpIkSRrPfgS8NCIOBA4Cfly37RkUXXB/VB67X/n9d72uUXs9o597zCy/PzAiFZcycx3w58AewBJgVURcGRGHDnLqvsAkiu66W+q+FlE0Uu5Xd2zvZwWYRxHo/wO4qxz3eswuPErDIjNH4z4NmT17di5btqzVZWiUzbuw+E3c4jOOanElkqRG+O+2pFaKiJsyc3Yf21cDn8vMc3ptfxfwfuDdwNmZeXhEvA44n6Jb7ieBGZm5LSIOohgvOiczr6q7xsspurk+PzN3akEtg+7twGvrusX2PuYciq66+5av3wh8DZiZmWvrjnsI+GJmvqfX+VOAYynGxq7JzBf299wR8XbgMxTjS7f3Uc4tmflYRFwHrM7MnbsmsKPr8vMpuj2/FDgoM4fWjD1EtqBKkiRJGg8eAyb3sf1HFK2kC4Af1m07GHgTcH1dd9d7gfuBE3td4yRgPXBrP/fuBu6jmJSoUbX+zM+obYiIFwDT+zo4MzeV4ffLwDPrdvX13NdStKDunZnL+vh6rJECM3N7Obb1w8BUivesqSo1BlWSJEmShqkLeHVEXE0xbrO7nFhoOUW4fBlwAUBmro2I28tt769dIDO3ly2dF0bEGuB7wMspxp++r68JkurOey/wlYj4CkXLaFLM4vu1zOyrm+jPKULtZyLigxTdh99b1gpARLwa+GuKWXXvBp4KnEERQAd67u6I+ALw9Yj4JEV33cnAs4DDMvOt/b2J5YRQ/0MxI/EK4AnAWcCDwB39nTdSbEGVJEmSNB78I/AIcCXFREbPgyI8AteXx/yw7vjauNMf118kM78I/D3FEi4dwMnAWQMtMVOe91XgDRTLsVxOEfDagVX9HP9YeY/t5fFnUQThdXWH/YYi6H6MYvbeTwJXU4TWAZ8b+FuK5W5OAzopxp++utd70JfNFC3F76KYhfhiiuVn/jwzNw104khwDKpazrFMkjS2+O+2pFbqbwyqxgdbUCVJkiRJlWBAlSRJkiRVggFVkiRJklQJTQuoEdFWLuha+1ofEWc2636SJEmSpLGtacvMZGY3cARAREyimEL5imbdT5IkSZI0to1WF99jgN9m5l2jdD9JkiRJ0hgzWgH1jRSL1UqSJEmS1KemB9SI2BN4LbC0n/0LImJZRCxbtarPNWwlSZIkSRPAaLSgHg/cnJm/62tnZl6UmbMzc/asWbNGoRxJkiRJUhWNRkA9Gbv3SpIkSZIG0dSAGhFTgVcB32zmfSRJkiRJY1/TlpkByMyNwMxm3kOSJEmSND6M1iy+kiRJkiQNyIAqSZIkSaoEA6okSZIkqRIMqJIkSZKkSjCgSpIkSZIqwYAqSZIkSaoEA6okSZIkqRIMqJIkSZKkSjCgSpIkSZIqwYAqSZIkSaoEA6okSZIkqRIMqJIkSZKkSjCgSpIkSZIqwYAqSZIkSaoEA6okSZIkqRIMqJIkSZKkSjCgSpIkSZIqwYAqSZIkSaoEA6okSZIkqRIMqJIkSZKkSjCgSpIkSZIqwYAqSZIkSaoEA6okSZIkqRIMqJIkSZKkSjCgSpIkSZIqwYAqSZIkSaoEA6okSZIkqRIMqJIkSZKkSjCgSpIkSZIqwYAqSZIkSaoEA6okSZIkqRIMqJIkSZKkSjCgSpIkSZIqwYAqSZIkSaqE3VtdgCRp5Hz1Z3fzreX3tboMjXO3P7AegHkX3tDiSjQRvO6Ip/KmFxzU6jIkjRJbUCVpHPnW8vt2hAepWZ6533Seud/0VpehCeD2B9b7SzdpgrEFVZLGmWfuN53FZxzV6jIkaZfZSi9NPLagSpIkSZIqwYAqSZIkSaoEA6okSZIkqRIMqJIkSZKkSjCgSpIkSZIqoakBNSKeFBGXR0RXRNwREU4rKUmSJEnqU7OXmfk0cHVmnhARewJTm3w/SZIkSdIY1bSAGhHTgZcBpwNk5mPAY826nyRJkiRpbGtmF99DgVXAwoj4RUT8V0Q8sYn3kyRJkiSNYc0MqLsDRwIXZOZzgUeAf+59UEQsiIhlEbFs1apVTSxHkiRJklRlzQyo9wL3ZubPyteXUwTWHjLzosycnZmzZ82a1cRyJEmSJElV1rSAmpkPAvdERFu56Rjg9mbdT5IkSZI0tjV7Ft+/A75SzuC7Epjf5PtJkiRJksaopgbUzFwOzG7mPSRJkiRJ40Mzx6BKkiRJktQwA6okSZIkqRIMqJIkSZKkSjCgSpIkSZIqwYAqSZIkSaoEA6okSZIkqRIMqJIkSZKkSjCgSpIkSZIqwYAqSZIkSaoEA6okSZIkqRIMqJIkSZKkSjCgSpIkSZIqYfdWFyBJkqTWWbd4Ces7OlpdRp8e3fcVANx16gUtrmRn0+fOZZ95J7W6DGncMaBKkiRNYOs7Otjc1cXk9vZWl7KTT6/+QatL6NPmri4AA6rUBAZUSZKkCW5yezsHX3pJq8sYM+469bRWlyCNW45BlSRJkiRVggFVkiRJklQJBlRJkiRJUiUYUCVJkiRJlWBAlSRJkiRVggFVkiRJklQJBlRJkiRJUiUYUCVJkiRJlWBAlSRJkiRVggFVkiRJklQJBlRJkiRJUiUYUCVJkiRJlbB7qwuQpGZZumIpnSs7W13GqOpe+3IA5l99UYsrGT1zDp3DiYed2OoyJEnSCDCgShq3Old20r22m7YZba0uZdQ897n/2+oSRlX32m4AA6okSeOEAVXSuNY2o42Fxy1sdRlqkvlXz291CZIkaQQ5BlWSJEmSVAkGVEmSJElSJRhQJUmSJEmVYECVJEmSJFWCAVWSJEmSVAkGVEmSJElSJRhQJUmSJEmVYECVJEmSJFWCAVWSJEmSVAkGVEmSJElSJRhQJUmSJEmVYECVJEmSJFXC7s28eETcCWwAtgFbM3N2M+8nSZIkSRq7mhpQS6/IzNWjcB9JkiRJ0hg2aBffKJwSEf9Svj4oIp7f/NIkSZIkSRNJI2NQPw8cBZxcvt4A/GeD10/guxFxU0QsGEZ9kiRJkqQJopEuvi/IzCMj4hcAmbkuIvZs8Povzsz7I+LJwPcioiszf1h/QBlcFwAcdNBBQ6ldkiRJkjSONNKCuiUiJlG0hhIRs4DtjVw8M+8vv/8euALYqWtwZl6UmbMzc/asWbMaLlySJEmSNL40ElA/QxEunxwRHwV+DHxssJMi4okRMa32M/DnwK92oVZJkiRJ0jg2aBffzPxKRNwEHAME8JeZeUcD1/4j4IqIqN3nq5l59a4UK0mSJEkavwYNqBHxQuC2zPzP8vW0iHhBZv5soPMycyXwpyNTpiRJkiRpvGuki+8FwMN1rx8pt0mSJEmSNGIaCaiRmVl7kZnbaWz2X0mSJEmSGtZIQF0ZEX8fEXuUX+8CVja7MEmSJEnSxNJIQP0b4EXAfcC9wAso1y2VJEmSJGmkNDKL7++BN45CLZIkSZKkCayRWXxnAW8DDqk/PjP/unllSZIkSZImmkYmO/oW8CPgGmBbc8uRJEmSJE1UjQTUqZn5T02vRJIkSZI0oTUySVJHRMxpeiWSJEmSpAmtkYD6LoqQuiki1kfEhohY3+zCJEmSJEkTSyOz+E4bjUIkSZIkSRNbI2NQiYh9gKcDk2vbMvOHzSpKkiRJkjTxNLLMzFspuvkeACwHXgjcALyyqZVJkiRJkiaURlpQ3wX8GfDTzHxFRLQDH25uWZKk3pauWErnys5Wl1EpXWu7AJh/9fwWV1Idcw6dw4mHndjqMiRJGpZGJknanJmbASLiCZnZBbQ1tyxJUm+dKzvpXtvd6jIqpX1GO+0z2ltdRmV0r+32lxiSpDGtkRbUeyPiScB/A9+LiHXA/c0sSpLUt7YZbSw8bmGry1BF2ZIsSRrrGpnF9/Xlj+dExA+AvYGrmlqVJEmSJGnCGbSLb0RcWvs5M/83M78NfLmpVUmSJEmSJpxGxqA+q/5FREwCntecciRJkiRJE1W/ATUizo6IDcBzImJ9+bUB+D3wrVGrUJIkSZI0IfQbUDPz45k5Dfi3zJxefk3LzJmZefYo1ihJkiRJmgAa6eLbERFPBIiIUyLi3yPi4CbXJUmSJEmaYBoJqBcAGyPiT4H3AncBlzS1KkmSJEnShNNIQN2amQm8Dvh0Zn4amNbcsiRJkiRJE82g66ACGyLibOAU4GXlLL57NLcsSZIkSdJE00gL6jzgUeAtmfkg8FTg35palSRJkiRpwhm0BbUMpf9e9/puHIMqSZIkSRph/QbUiPhxZr6kXPs063cBmZnTm16dJEmSJGnC6DegZuZLyu9OiCRJkiRJarqBWlBnDHRiZq4d+XIkSZKk5lq3eAnrOzqGff7mri4A7jr1tF2qY/rcuewz76RduoY03gw0BvUmiq69ARwErCt/fhJwN/DHzS5OkiRJGmnrOzrY3NXF5Pb2YZ0/3PPq1UKuAVXqaaAuvn8MEBFfAL6dmZ3l6+OBY0enPEmSJGnkTW5v5+BLWzfv5662vkrjVSPLzPxZLZwCZOZVwMubV5IkSZIkaSIadJkZYHVEfAC4jKLL7ynAmqZWJUmSJEmacBppQT0ZmAVcUX7NKrdJkiRJkjRiBm1BLWfrfdco1CJJkiRJmsAaaUGVJEmSJKnpDKiSJEmSpEoYMKBGxKSIePdoFSNJkiRJmrgGDKiZuQ143SjVIkmSJEkaQERkRLxzhK/5/Ig4ZySvOVyNdPH9SUR8LiJeGhFH1r6aXpkkSZIkaTQ8H/hQq4uAxtZBfVH5/V/rtiXwypEvR5IkSZI0lkXElMzcNJxzB21BzcxX9PHVcDgtx7H+IiI6hlOgJEmSJA0mIl4bETdFxCMRsS4ifhYRL6/bv1tE/HNE/CYiHo2IFRHx5rr9H46IByNit17XnVt2q31a3ba3RsRt5XXuioj39jpnUUQsi4hXRcQtZU0/johnNfAcMyPiwoh4ICI2R0R3RJw5wPF3RsR5vbadXta8V/l6j4g4LyLuLmu+PyKuiIg9I+J04LPlcVl+XVd3rcMj4sqI2FB+LY2Ip9TtP7o85y8i4tsR8TDwuXLfW8r3aVNErI6I/x3sPRg0oEbEH0XElyLiqvL1MyPiLYOdV+ddwB1DOF6SJEmSGhYRfwJcDlwLvAb4K6ADmFF32GeBDwAXAa8GrgC+HBFzy/1fB/4IeDk9nQTclJm/Ke/1j8AFwH8Dc8ufP9LHuNCDgH8DPgqcDDwZWBIRMcBzTAGuA/4S+AgwB/gUsP+gb8LAzqZ4Tz4IvAo4E3gImARcWd4D4Kjy6x1lPU8DfgJMBk4FTgeeBXynj+f4EvBL4LXAlyLiZcAXgMuA44G/Bq4H9h6o0Ea6+C4CFgLvL1+vABaXBQwoIg6g+MP/KPAPDdxLkiRJkobqucCGzPzHum2dtR/KoPV2YH5mXlxuviYi9qMYe9mRmXdExC3APOAH5XlPoJg09iPl6+nl8edm5ofL63wvIqYCH4iIC8qJZqEIxy/OzF+X5+5GEYrbgK5+nuM0igB4ZGYuL7ddO+R3Y2fPB75a9+wAS8rvmyLiToDM/Gmv8z4EPAgcn5mPAZTvURdFeL6y7tilmfnB2ouIeA9wS2Z+vO6Ybw9WaCMBdd/MXBIRZ5dFb42IbYOdVDofeC8wrZGDu7u7Ofroo3tsO+mkk3jHO97Bxo0bmTNnzk7nnH766Zx++umsXr2aE044Yaf9b3/725k3bx733HMPp5566k77zzrrLF7zmtfQ3d3NGWecsdP+D3zgAxx77LEsX76cM888c6f9H/vYx3jRi17E9ddfz/ve976d9p9//vkcccQRXHPNNZx77rk77b/wwgtpa2vjO9/5Dp/61Kd22n/ppZdy4IEHsnjxYi644IKd9l9++eXsu+++LFq0iEWLFu20v7Ozk6lTp/L5z3+eJUuW7LT/uuuuA+C8886jo6NnL+wpU6Zw1VVXAfCRj3yE73//+z32z5w5k2984xsAnH322dxwww099h9wwAFcdtllAJx55pksX768x/7DDjuMiy66CIBll32Co7/2UI/9RxxxBOeffz4Ap5xyCvfee2+P/UcddRQf/3jxeX/DG97AmjVreuw/5phj+OAHi78jxx9/PJs29ewGP3fuXN7znvcA7PS5Az974+Wz97Mv/IyjP3F0j/31n70FCxawYsWKHvur+tnrWlv8t2zRg4v87I2Bz95g/+4147O3/qD1/Omb/hTw3z0/e8t77B/os7f5ji6eOWMfvly+rtK/ezUj/dnbfEfx7+nkspZWfPbqa5ionz3Y9X/36twK7B0RFwNfAX6SmY/U7T8G2A5cERH1Gej7wMkRMakMlouBd0fEOzNzK0XL3zQeD3NHAU8Elva6zrUUrZMHAHeV2+6shdPS7eX3A+g/oL4S+EVdOB0py4G3R8TvgKuBWzMzGzjvWOBiYHvd8/4fcCcwm54B9cqep7Ic+GRE/AdFMP9pLeQOpJFZfB+JiJkUEyMRES+kaA4eUNlU/vvMvGmQ4xaU/bOXbdmypYFyJEmSJOlxmdlN0dJ5KEXL6eqI+GpEzCoP2ZeiO+tDwJa6r0UUjXb7lcd9vTy2NufOPOCGzLy77joAt/W6zg/K7QfWlfWHXmXWwtnkAR5lJvDAAPuH61zgPym67v4SuCci3tXAefsC/0TPZ91C8T4f2OvY39W/yMxrgPnAyyi6La+OiM9HxBMHumEMFpzLJWU+CxwO/AqYBZyYmb8c5LyPU/RT3krxhzAd+GZmntLfObNnz85ly5YNWI/Gn3kXFr+JW3zGUS2uROPN/KvnA7DwuIUtrmRkjLfn0cjzM6LhuOvU0wA4+NJLWlzJ6KnCM1ehhrEqIm7KzNkD7N+bYpjh+cC1mfnGiHg78BngxRQtqb3dUteF9UbgFuDvgN8D78vMz5T7jqcIwHPpFchK3Zm5ISIWAYfX1xkRh1C0Pr4mM/ucQDYiFgNPy8znDfB8CfxdZtYmIuoCvpuZf193zD9QjCudlpkP9zr/6cDfUAzBPD4zry7Hz342M6PXsb+jaP38rz5KWZ2Zd0bE0RQB/dmZ+at+ap4F/D/gP4DPZOY/9/d8jXTxvY1ioHAbEEA3jc3+ezbFYFzKot8zUDiVJEmSpF2VmQ8BX41iBt9aC8i1FC2oe2fm9wa5xNcp5t+5FpgCLK3bdwOwCdg/M3t3aR0J3wdOjIjnZOYtDZ5zL/CMXtte1d/Bmfnrcnzo3wLPpOjyWwvnkzNzc696DqeYJKqRLsH93XMVcGFE/L/ynv1qJKDekJlHUgRVACLiZuDI4RYoSZIkSSMlIs6gCKNXA/cDTwdOBC6BogtwRHwB+HpEfBJYRtHL81nAYZn51rrLLaGYffffgB9m5o4ut5n5h4g4B/h0RBwM/JCi8e4w4BWZ+fpdfJRLKILjd8v7dAN/XNbYX6vjFcBnI+J9wI0ULZU9lnKJiCuAm4BfUATsEyiy4A/LQ2pjYt8VEdcC68tu0+cAPweujIgvA6uBp1IE4EWZeV1/DxIRH6aYKOq68rznUjR89tt6CgME1HJtm6cCUyLiuRStp1B01Z060EV7Kwu/bijnSJIkSVKDbqFY3uTfKULRA8AXgX+pO+ZvKVYkeRvwr8B6iomLeqxOkpn3RMT1FN2BP0wvmfnJiLgfeDdwFrCZx1c62SWZuTkiXgl8oqxxOsWERJ8f4LSLgD8B/h54AkXIPRe4sO6Y6ynG0/4jRaC+HXhDZtbGV/6IIpC/C/g4RXA9OjNXlHMQnVveZwpwH0XL6m8GeZwbKd6jN1JMNHUXReD99EAnDdSC+hcU69wcQNF/uRZQNwA7T18mSZIkSS2QmTdQjDsd6JikGJd6fgPXe8kg+y+jWN+zv/2n97HtTh7PVANdew1FiH5bP/uj1+stFONJey/reVHdMbUW4f7umRSrr7y3j31dFC2u/Z17HX08VznOts+xtgPpN6CWa+RcHBFvyMxvDPXCkiRJkiQNRSPLzBwQEdOj8F8RcXNE/HnTK5MkSZIkTSiNBNS/zsz1wJ8DT6ZYy+YTTa1KkiRJkjThNBJQa/2J5wALy/VPB+07LUmSJEnSUDQSUG+KiO9SBNT/iYhp9L24rSRJkiRJw9bIOqhvAY4AVmbmxoiYSdHNV5IkSZKkEdNIQK1NsfycCHv2SpIkSZKao5GA+o91P08Gng/cBLyyKRVJkiRJkiakQQNqZr6m/nVEHAh8smkVSZIkSZImpEYmSertXuDwkS5EkiRJkjSxDdqCGhGfBbJ8uRvFhEm/bGJNkiRJkqQJqJExqMvqft4KfC0zf9KkeiRJkiRJE1QjY1AvHo1CJEmSJEkTW78BNSJu5fGuvT12AZmZz2laVZIkSZKkCWegFtS5o1aFJEmSJGnCGyig7gH8Ue/xphHxUuD+plYlSZIkSZpwBlpm5nxgQx/bN5X7JEmSJEkaMQMF1EMy85beGzNzGXBI0yqSJEmSJE1IA3XxnTzAvikjXYgkSZI0lq1bvIT1HR0NHbu5qwuAu049bdBjp8+dyz7zTtql2qSxYqAW1Bsj4m29N0bEW4CbmleSJEmSNPas7+jYETwHM7m9ncnt7YMet7mrq+HQK40HA7WgnglcERF/xeOBdDawJ/D6JtclSZIkjTmT29s5+NJLRux6jbSwSuNJvwE1M38HvCgiXgEcXm6+MjOvHZXKJEmSJEkTykAtqABk5g+AH4xCLZIkSZKkCWygMaiSJEmSJI0aA6okSZIkqRIMqJIkSZKkSjCgSpIkSZIqwYAqSZIkSaoEA6okSZIkqRIGXWZGkjRxLF2xlM6Vna0uQ8PUtbYLgPlXz29xJdoVcw6dw4mHndjqMiSpJWxBlSTt0Lmyk+613a0uQ8PUPqOd9hntrS5Du6B7bbe/JJI0odmCKknqoW1GGwuPW9jqMqQJydZvSROdLaiSJEmSpEowoEqSJEmSKsGAKkmSJEmqBAOqJEmSJKkSDKiSJEmSpEowoEqSJEmSKsGAKkmSJEmqBAOqJEmSJKkSDKiSJEmSpEpoWkCNiMkR8fOI+GVE3BYRH27WvSRJkiRJY9/uTbz2o8ArM/PhiNgD+HFEXJWZP23iPSVJkiRJY1TTAmpmJvBw+XKP8iubdT9JkiRpLFi3eAnrOzoaOnZzVxcAd516WkPHT587l33mnTTs2qRWa+oY1IiYFBHLgd8D38vMnzXzfpIkSVLVre/o2BE8BzO5vZ3J7e0NHbu5q6vh4CtVVTO7+JKZ24AjIuJJwBURcXhm/qr+mIhYACwAOOigg5pZjiRJklQJk9vbOfjSS0b0mo22skpVNiqz+GbmH4DrgOP62HdRZs7OzNmzZs0ajXIkSZIkSRXUzFl8Z5Utp0TEFOBYoLG+DJIkSZKkCaeZXXz3Ay6OiEkUQXhJZtopXpIkSZLUp2bO4nsL8NxmXV+SJEmSNL6MyhhUSZIkSZIGY0CVJEmSJFVCU5eZkSRJ0uhZt3jJkNfBrK3HOZwlSqbPncs+804a8nmS1B9bUCVJksaJ9R0dOwJnoya3tzO5vX3I99rc1TXkMCxJg7EFVZIkaRyZ3N7OwZde0vT7DKfFVZIGYwuqJEmSJKkSDKiSJEmSpEqoVhff1b+Gha9udRUabQ++rvi+8NzW1qHWePYJMHt+v7uXrlhK58rOYV26a20xDmv+1f1ffzBzDp3DiYedOOzzJUmS1LhqBdStm1pdgVpg8UHfanUJapUHby2+DxBQO1d20r22m7YZbUO+fPuMoU/6Ua97bTeAAVWSJGmUVCug7j4F5l/Z6iokjZYGe0y0zWhj4XELm1zMznal5VWSJElD5xhUSZIkSVIlGFAlSZIkSZVgQJUkSZIkVYIBVZIkSZJUCQZUSZIkSVIlGFAlSZIkSZVgQJUkSZIkVYIBVZIkSZJUCQZUSZIkSVIlGFAlSZIkSZVgQJUkSZIkVYIBVZIkSZJUCQZUSZIkSVIlGFAlSZIkSZVgQJUkSZIkVYIBVZIkSZJUCQZUSZIkSVIlGFAlSZIkSZVgQJUkSZIkVYIBVZIkSZJUCQZUSZIkSVIlGFAlSZIkSZWwe6sLkCRJUuutW7yE9R0dDR+/uasLgLtOPW1I95k+dy77zDtpSOdImjhsQZUkSRLrOzp2hM5GTG5vZ3J7+5Dusbmra0ghWNLEYwuqJEmSgCJ0HnzpJU27/lBbWyVNPLagSpIkSZIqwRZUSRPC0hVL6VzZOaRzutYWXd3mXz1/SOfNOXQOJx524pDOkaSxZKjjVWuGO24VHLsqTRS2oEqaEDpXdtK9tntI57TPaKd9xtDGV3Wv7R5yEJaksWao41VrhjNuFRy7Kk0ktqBKmjDaZrSx8LiFTb3HUFtbJWmsavZ41XqOXZUmDltQJUmSJEmVYECVJEmSJFWCAVWSJEmSVAlNC6gRcWBE/CAi7oiI2yLiXc26lyRJkiRp7GvmJElbgbMy8+aImAbcFBHfy8zbm3hPSRqTGlkGp9Flb1zmRpIkjVVNC6iZ+QDwQPnzhoi4A3gqYECVpF5qy+C0zWjr95hGlrypLaVjQJU0kQ22Tutg67G65qrUOqOyzExEHAI8F/jZaNxPksaikVgGx2VuJOnxdVr7W3N1oLVYa+HVgCq1RtMDakTsBXwDODMz1/exfwGwAOA5+09pdjmSJEmaAIa7Tqtrrkqt1dRZfCNiD4pw+pXM/GZfx2TmRZk5OzNn77HHHs0sR5IkSZJUYc2cxTeALwF3ZOa/N+s+kiRJkqTxoZktqC8GTgVeGRHLy685TbyfJEmSJGkMa+Ysvj8GolnXlyRJkiSNL00dgypJkiRJUqMMqJIkSZKkSjCgSpIkSZIqoenroEqSJElVt27xEtZ3dLC5qwt4fD3U6XPnss+8k1pZmjShGFAlaYxYumIpnSs7Bzyma23xP1bzr54/6PXmHDqHEw87cURqk6SxrhZOJ7e379hWC6sGVGn0GFAlaYzoXNlJ99pu2ma09XtM+4z2fvfV617bDWBAlaQ6k9vbOfjSS3a8rrWiSho9BlRJGkPaZrSx8LiFu3ydRlpYJUmSRpuTJEmSJEmSKsGAKkmSJEmqBAOqJEmSJKkSDKiSJEmSpEowoEqSJEmSKsGAKkmSJEmqBJeZkaQWWbpiKZ0rOwHoWlssBl9b/mXOoXNco1SSKmzd4iWs7+josW1zV/Fvee/1U6fPncs+804atdqkscyAKkkt0rmyk+613bTNaKN9RvuO7d1ruwEMqJLUoN5hcTSC4vqODjZ3dTG5/fF/v+t/7l2LAVVqjAFVklqobUYbC49b2GNbrRVVktSY3mFxJILiusVLdpyzbvGSPs+b3N7OwZdeMuB1eodkSQMzoEqSJGnMGywsDjUo1rfIru/osAVUGiUGVEmSpBapH4sOO49HB8ekt1JfLbGSmstZfCVJklqkNha9pn1G+05j0usDrCSNd7agSpIktVBfY9FrHJMuaaIxoEqSJEkjqH5W4d4zCk+fO7dldUljgQFVkiRJ41J/QbHZ65LWzypcP461VoOk/hlQJUmSNC71FRR7LzdTC7GDLSkzVH3NKuySM9LgDKiSJEljUH3rYE3v7qRA01sLq653UOwdEutD7OauLpeUkVrMWXwlSZLGoPpWv5q+upT2DrHaWS3EuqyM1Hq2oEqSJI1RfXUjrWeXUkljjS2okiRJkqRKMKBKkiRJkirBgCpJkiRJqgQDqiRJkiSpEpwkSdLoWrYQbr28+PnBW4rvC19dfH/2CTB7fmvqkiRJUsvZgippdN16OTx4a/HzU55TfEGxrRZcJUmSNCHZgipp9D3l2TD/yp7baq2oaqqlK5bSubKTrrXF2onzr57PnEPncOJhJ7a4MkmSJAOqJFVe71C5dMXSYQfKzpWddK/tpn1GsRh999puAAOqJEmqBLv4SlLF1YfKIOhc2blL12ub0cbC4xay8LiFtM1oG6EqJUmSdp0tqJI0BtRC5fyrnURKGu9qvSYAu+NLmnAMqJIqaSS7tUrSWFLrNdE2o23Cdsdft3gJ6zs6drze3FX8t+CuU08DYPrcuewz76SW1CapuQyokiqpvltr99puOld2Tpj/MZOkWq+JmonWe2J9Rwebu7qY3F4E9Np3eDysGlCl8cmAKqmy7NYqSRPX5PZ2Dr70kp2211pRJY1PTpIkSZIkSaoEA6okSZLUoHWLl3DXqaexuauLzV1d3HXqaaxbvKTVZUnjhl18JUmSxonBJhcCJxjaVb3HxzomVhpZTQuoEfFlYC7w+8w8vFn3kSQNXe9ZkmtLWEga2waaXAgMUyOlfnysY2KlkdXMFtRFwOeAnUe3S5Jaqn6WZHh8CQtJY19/kwuBYUpS9TUtoGbmDyPikGZdX5K0a+qXsXCmZKl6aj0dgB69HWr+dtMqZk2Z1ZLa1LdaF+v6rtW1LtXrFi/ZsX3SzJnsMcs/O6kvjkGVJEmqoFpPh7YZbTt6O9R0r+1mzaatEzqg1o+37T3WtlXjbAcan1o/NnjbmjUGVKkfLQ+oEbEAWADwnP2ntLgaSWqtpSuW7mgpWbpiKScedmKLK5LUDL3Hgff3972+p0O9oiX1tmaXWWn1YbB+rO1IjrPdsmoV29asAR5vDR3MQONTewdXSTtreUDNzIuAiwBmH7J3trgcSaNt2UK49XJ48Jbi9cJXw7NPaG1NLdS5spMgdvxsQJXGp/px4N1ru/37Pkx9jbcdyXG2tXA6ub3dUCmNkpYHVEkT3K2Xw4O3wlOeU7x+8Nbi+35Pbl1NLdY2o63VJUgaBbXWUceAt96WVat2rG0KRcjdsmoV8HgIdoIpaXQ0c5mZrwFHA/tGxL3AhzLzS826n6Qx7CnPhvlXFj8vfHVra5EkVVLvCYhGcqKhbWvWsG3NGrvgShWwW7MunJknZ+Z+mblHZh5gOJWkx9XGmnat7WL+1fNZumJpq0uSpErrPQFRrfvtSKm1lB586SU7rR9bm4G31qoqqXns4itJLVAba9o2o23HGqSOP5M0UupnuK3Xe7bbeltWrar8zLL13W1Hs5VzfUcH2zds2Cm4Shp5TWtBlSQNrDb+zDGnkkZafVfYer1nvK3Z3NU14i2SE12t1XVzVxfrFi9pdTnSmGELqiRJ0jjU1wy3/RntFslW6D2GtdaK3Kxuu/Ut2H21ZkvqmwFV0ujoazkZgA0PwLT9WleXJGlC6D2GFZo/GZJdgqWhM6BKGh29l5OBckmZNKCOktrETDOnzGTWlGqPM5OkZujdqjwRWo6lscaAKmn01C8nA0Uraq1FVU3XubKTh7c8TPsMf6MvVUXtF0cA86+ez5xD57S4IklqLQOqJElSi/Q1o7ckTWQGVElqofrWk9paqKs2rWLNpjXMnDKzlaVJGiW1Gb3nXz2/1aWMK7VZdGs/N/Mek2b677U0UlxmRpJaqNZ6EgSdKzsBWLNpDQ9vedhxopK0C0ZjFt3a+qhVXz9WGktsQZWkFnMdVElqDmfRlcYeA6okSdIo615XjDdt26fnL6hWbVzFms1rAJxxW9KEZECVJEkaZRse29Dn9jWb1xBE8fOmNQbUcWbLqlVsW7OGfOyxVpciVZZjUCVVy4YHiqVnNjzY6kokqSXaZrTZ9X8U1CY4GkpY3NzVxZZVq4Z9z21r1rB9wwZizz2HfQ1pvLMFVVL1PLoe1t8H057S6kokaUyptdBNmjnTiXtK/b0ntQmOdps2raHr7DZtGts3FC3fQ31vt/7+92xdu9aWU6kBtqBKqpZHyt9MT/K3y5I0VHvMmsX2DRvYtmbNkM7b3N3N9o0bm1RVaw33PelPPvYYm7u6hrR0zda1a205lRpkC6okVURtcpQt27c09T5da7ucfEVSD9vXr291CWNG7Lkn2zdsaNrSNdJEZwuqJFXEms3F+qd77LZH0+6x1x578fCWh1mzqWdLwqpNq+ha28WqTcMfWyVJo2E4Y0cljR0GVEkSs6bM6jO4SqqepSuW0rW2q+m9LaqqNnbU7rLS+GRAlSTtaDmdqP/DK40lnSs7m97bQs0xXsf5SiPJgCpJ2tFy6v/wSpromtqFeNu2kb+mNM4YUCVJkjRubO7uZnN397DPH+kuxFtWrbLlVBoCA6okSZLGje3r14/4rMS7sgzPHrNm2XIqDYHLzEhSRXWt7WLpiqWjes9NWzeN6v0kaSxwGR5p9NiCKkkVVFsOpnNl56jed1v6W35JGkhtbOqWVS7LJTWDLaiSWmPZQnjwFtjmOnYA3eu6d7Re2oopTRyrNq1i1pRZO21bs2mNs2oPolXjOnPrVqDsuitpxNmCKqk1br0cHl0Pk+omoVi2EB57pHU1tdCGxzbsaL3clttsyZTGub322Augz7WH12xas8vLyEyISXmGOK5zQrwn0jhgC6qk6rj1cqgFs8cegQ0PtraecajWImPLjDTOOSnPzvp5T7Zv3Ejs7v8SS1VhC6qk6olJsH0rPOL4npFWC6audypJsNu0abBt245uu5Jaz4AqSZIkSaoEA6okSZIkqRIMqJIkSZKkSjCgSpIkSZIqwYAqSZIkSaoEA6okSZIkqRIMqJIkSZKkSjCgSpIkSZIqwYAqSZIkSaoEA6okSZIkqRIMqJIkSZKkSjCgSpIkSZIqwYAqSZIkSaoEA6okSZIkqRKaGlAj4riI6I6I30TEPzfzXpIkSZKksa1pATUiJgH/CRwPPBM4OSKe2az7SZIkSZLGtma2oD4f+E1mrszMx4CvA69r4v0kSZIkSWNYZGZzLhxxAnBcZr61fH0q8ILMfGd/58zef/dc9nf7NaUeSRXw2COw5xMf/zm3QUwqXte2P7oegPlPeTI3T57M1D33on1GOzf//mam7j51WLdtn9FO19ouNm7dOOxrDOVeQ61149aNbM/tfe7bLXbjyCcfueOau/JebNy6EYDtuZ3dovj9ZO069TXsFrs1/X2SBA9vebjH38Xa3+/6v4u1ffVqf1/PWzKFg+7cyG5Ti/2T29vZ3NXF9g0bigMnTdqxbzBDPWdyezsbb7654esPx/aNG3tcf/vGjY/v3Lat+N5HvbVn2W3atB3X6P18k9vbAdh4883FtSYV/y3aberU4j7lttq1d5xfb9Ikph55JBtvvLHnseU969+j/s4fyfev/pma+efSapPb2znksktvyszZra5FzdHMgHoi8Be9AurzM/Pveh23AFhQvmwDuptSkCRJkqTx4ODMnNXqItQcuzfx2vcCB9a9PgC4v/dBmXkRcFET65AkSZIkjQHNHIN6I/D0iPjjiNgTeCPw7SbeT5IkSZI0hjWtBTUzt0bEO4H/ASYBX87M25p1P0mSJEnS2NbUdVAzszMzD8vMP8nMjzbzXpKkkRUR74+I2yLilohYHhEvaPL9rouIhie9iIh/jYhjh3iPOyNi36FXJ0mSRkMzx6BKksaoiDgKmAscmZmPlqFuzxaX1UNm/kura5AkSSOrqS2okqQxaz9gdWY+CpCZqzPzfoCI+JeIuDEifhURF0VElNuvi4j/iIgfRsQdEfFnEfHNiPh1RJxbHnNIRHRFxMVly+zlEbHTeggR8ecRcUNE3BwRSyNirz6OWVQuaVZrGf1wefytEdFebp8ZEd+NiF9ExIVA1J1/SkT8vGwdvjAiJpU13xIRkyPiiWUL8uEj//ZKkqS+GFAlSX35LnBgRKyIiM9HxMvr9n0uM/8sMw8HplC0tNY8lpkvA74AfAv4W+Bw4PSImFke0wZclJnPAdYD76i/cdla+wHg2Mw8ElgG/EMDNa8uj78AeE+57UPAjzPzuRQT9R1U3uMZwDzgxZl5BLAN+KvMvLE87lzgk8BlmfmrBu4tSZJGgAFVkrSTzHwYeB7FOtWrgMURcXq5+xUR8bOIuBV4JfCsulNrs7XfCtyWmQ+UrbAreXzpsXsy8yflz5cBL+l1+xcCzwR+EhHLgTcDBzdQ9jfL7zcBh5Q/v6y8B5l5JbCu3H5M+Xw3lvc4Bji03PevwKuA2RQhVZIkjRLHoEqS+pSZ24DrgOvKMPrmiPg68HlgdmbeExHnAJPrTnu0/L697ufa69p/c7L3rXq9DuB7mXnyEEuu3W8bPf/71vv6tXtcnJln97FvBrAXsAfFsz0yxDokSdIw2YIqSdpJRLRFxNPrNh0B3MXjYXR1OS70hGFc/qByEiaAk4Ef99r/U+DFEfG0spapEXHYMO4D8EPgr8rrHA/sU27/PnBCRDy53DcjImqttBcBHwS+Avx/w7yvJEkaBltQJUl92Qv4bEQ8CdgK/AZYkJl/iIgvUnThvRO4cRjXvoOiNfZC4NcUY0Z3yMxVZXfir0XEE8rNHwBWDONeHy6vczPwv8Dd5T1uj4gPAN+NiN2ALcDflmNtt2bmVyNiEnB9RLwyM68dxr0lSdIQRWZfPZ8kSRp5EXEI0FFOsCRJktSDXXwlSZIkSZVgC6okSZIkqRJsQZUkSZIkVYIBVZIkSZJUCQZUSZIkSVIlGFAlSZIkSZVgQJUkSZIkVYIBVZIkSZJUCf8/NkQDhhBKTeAAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 1008x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "from sklearn.preprocessing import normalize\n",
    "from scipy.cluster.hierarchy import linkage, dendrogram, fcluster\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Assuming X is your user tags matrix\n",
    "# Replace this with your actual data\n",
    "X = matrix_games_tags_comp\n",
    "\n",
    "# Normalize the data\n",
    "X_normalized = normalize(X)\n",
    "\n",
    "# Perform hierarchical clustering\n",
    "linkage_matrix = linkage(X_normalized, method='ward')\n",
    "\n",
    "# Increase the size of the plot by setting figsize\n",
    "fig, ax = plt.subplots(figsize=(14, 8))\n",
    "\n",
    "\n",
    "# Plot the dendrogram\n",
    "dendrogram(linkage_matrix, labels=np.arange(1, len(X) + 1))\n",
    "plt.title('Hierarchical Clustering Dendrogram')\n",
    "plt.xlabel('User')\n",
    "plt.ylabel('Distance')\n",
    "plt.xticks(visible=False)\n",
    "\n",
    "# Mark the cuts in the tree that signify two or three clusters\n",
    "ax = plt.gca()\n",
    "bounds = ax.get_xbound()\n",
    "ax.plot(bounds, [7.25, 7.25], '--', c='k')\n",
    "ax.plot(bounds, [4, 4], '--', c='k')\n",
    "ax.text(bounds[1], 7.25, ' two clusters', va='center', fontdict={'size': 15})\n",
    "ax.text(bounds[1], 4, ' seven clusters', va='center', fontdict={'size': 15})\n",
    "plt.xlabel(\"Sample index\")\n",
    "plt.ylabel(\"Cluster distance\")\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "num_clusters = 7"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([4, 4, 4, ..., 6, 1, 1], dtype=int32)"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "labels = fcluster(linkage_matrix, num_clusters, criterion='maxclust')\n",
    "labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1991,)"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "labels.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "matrix_games_tags_comp[\"Cluster\"] = labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "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>Adventure</th>\n",
       "      <th>Driving</th>\n",
       "      <th>Horror</th>\n",
       "      <th>Management</th>\n",
       "      <th>RPG</th>\n",
       "      <th>Realistic</th>\n",
       "      <th>Romantic</th>\n",
       "      <th>Simulation</th>\n",
       "      <th>Strategy</th>\n",
       "      <th>War Muliplayer</th>\n",
       "      <th>Cluster</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1986</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1987</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1988</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1989</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1991 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Adventure  Driving  Horror  Management  RPG  Realistic  Romantic  \\\n",
       "0             1        1       0           1    1          1         1   \n",
       "1             1        1       0           1    1          1         1   \n",
       "2             1        1       0           1    1          0         1   \n",
       "3             1        1       0           1    1          1         1   \n",
       "4             1        1       1           1    1          1         1   \n",
       "...         ...      ...     ...         ...  ...        ...       ...   \n",
       "1986          1        1       0           1    1          1         1   \n",
       "1987          1        1       0           1    1          1         1   \n",
       "1988          1        1       0           1    0          1         0   \n",
       "1989          1        1       1           1    1          1         1   \n",
       "1990          1        1       1           1    1          1         1   \n",
       "\n",
       "      Simulation  Strategy  War Muliplayer  Cluster  \n",
       "0              1         1               1        4  \n",
       "1              1         1               1        4  \n",
       "2              1         0               1        4  \n",
       "3              1         1               1        4  \n",
       "4              1         1               1        1  \n",
       "...          ...       ...             ...      ...  \n",
       "1986           1         1               1        4  \n",
       "1987           1         1               1        4  \n",
       "1988           1         1               1        6  \n",
       "1989           1         1               1        1  \n",
       "1990           1         1               1        1  \n",
       "\n",
       "[1991 rows x 11 columns]"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "matrix_games_tags_comp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "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>Adventure</th>\n",
       "      <th>Driving</th>\n",
       "      <th>Horror</th>\n",
       "      <th>Management</th>\n",
       "      <th>RPG</th>\n",
       "      <th>Realistic</th>\n",
       "      <th>Romantic</th>\n",
       "      <th>Simulation</th>\n",
       "      <th>Strategy</th>\n",
       "      <th>War Muliplayer</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cluster</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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>666.0</td>\n",
       "      <td>663.0</td>\n",
       "      <td>651.0</td>\n",
       "      <td>663.0</td>\n",
       "      <td>666.0</td>\n",
       "      <td>660.0</td>\n",
       "      <td>666.0</td>\n",
       "      <td>666.0</td>\n",
       "      <td>666.0</td>\n",
       "      <td>635.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>141.0</td>\n",
       "      <td>109.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>141.0</td>\n",
       "      <td>141.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>141.0</td>\n",
       "      <td>141.0</td>\n",
       "      <td>141.0</td>\n",
       "      <td>141.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>98.0</td>\n",
       "      <td>79.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>93.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>87.0</td>\n",
       "      <td>92.0</td>\n",
       "      <td>96.0</td>\n",
       "      <td>97.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>601.0</td>\n",
       "      <td>590.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>599.0</td>\n",
       "      <td>600.0</td>\n",
       "      <td>595.0</td>\n",
       "      <td>597.0</td>\n",
       "      <td>601.0</td>\n",
       "      <td>558.0</td>\n",
       "      <td>600.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>163.0</td>\n",
       "      <td>113.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>162.0</td>\n",
       "      <td>163.0</td>\n",
       "      <td>138.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>160.0</td>\n",
       "      <td>163.0</td>\n",
       "      <td>157.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>76.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>59.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>73.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>246.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>242.0</td>\n",
       "      <td>236.0</td>\n",
       "      <td>246.0</td>\n",
       "      <td>230.0</td>\n",
       "      <td>201.0</td>\n",
       "      <td>245.0</td>\n",
       "      <td>246.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Adventure  Driving  Horror  Management   RPG  Realistic  Romantic  \\\n",
       "Cluster                                                                      \n",
       "1            666.0    663.0   651.0       663.0 666.0      660.0     666.0   \n",
       "2            141.0    109.0    49.0       141.0 141.0        0.0     141.0   \n",
       "3             98.0     79.0    17.0         0.0  93.0       68.0      87.0   \n",
       "4            601.0    590.0    16.0       599.0 600.0      595.0     597.0   \n",
       "5            163.0    113.0    49.0       162.0 163.0      138.0       0.0   \n",
       "6             76.0     45.0    21.0        67.0   0.0       59.0      60.0   \n",
       "7            246.0     20.0    63.0       242.0 236.0      246.0     230.0   \n",
       "\n",
       "         Simulation  Strategy  War Muliplayer  \n",
       "Cluster                                        \n",
       "1             666.0     666.0           635.0  \n",
       "2             141.0     141.0           141.0  \n",
       "3              92.0      96.0            97.0  \n",
       "4             601.0     558.0           600.0  \n",
       "5             160.0     163.0           157.0  \n",
       "6              72.0      67.0            73.0  \n",
       "7             201.0     245.0           246.0  "
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "matrix_games_tags_clus = matrix_games_tags_comp.groupby(\"Cluster\").sum()\n",
    "matrix_games_tags_clus"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Cluster'>"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(matrix_games_tags_clus.T, annot=True, cmap=\"YlGnBu\", fmt=\"g\", cbar_kws={'label': 'Occurrences'})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Applying Word2Vec to Gaems-Tag (An exploration)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "# !pip install gensim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\riccardoricci\\AppData\\Roaming\\Python\\Python39\\site-packages\\sklearn\\cluster\\_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "ename": "AttributeError",
     "evalue": "'NoneType' object has no attribute 'split'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "Input \u001b[1;32mIn [64]\u001b[0m, in \u001b[0;36m<cell line: 19>\u001b[1;34m()\u001b[0m\n\u001b[0;32m     17\u001b[0m \u001b[38;5;66;03m# Apply K-means clustering\u001b[39;00m\n\u001b[0;32m     18\u001b[0m kmeans \u001b[38;5;241m=\u001b[39m KMeans(n_clusters\u001b[38;5;241m=\u001b[39mnum_clusters, random_state\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m42\u001b[39m)\n\u001b[1;32m---> 19\u001b[0m tag_clusters \u001b[38;5;241m=\u001b[39m \u001b[43mkmeans\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfit_predict\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtag_vectors_standardized\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m     21\u001b[0m \u001b[38;5;66;03m# Print the clusters for each tag\u001b[39;00m\n\u001b[0;32m     22\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m tag, cluster \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mzip\u001b[39m(game_tags, tag_clusters):\n",
      "File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python39\\site-packages\\sklearn\\cluster\\_kmeans.py:1033\u001b[0m, in \u001b[0;36m_BaseKMeans.fit_predict\u001b[1;34m(self, X, y, sample_weight)\u001b[0m\n\u001b[0;32m   1010\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mfit_predict\u001b[39m(\u001b[38;5;28mself\u001b[39m, X, y\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, sample_weight\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m):\n\u001b[0;32m   1011\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"Compute cluster centers and predict cluster index for each sample.\u001b[39;00m\n\u001b[0;32m   1012\u001b[0m \n\u001b[0;32m   1013\u001b[0m \u001b[38;5;124;03m    Convenience method; equivalent to calling fit(X) followed by\u001b[39;00m\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m   1031\u001b[0m \u001b[38;5;124;03m        Index of the cluster each sample belongs to.\u001b[39;00m\n\u001b[0;32m   1032\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[1;32m-> 1033\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfit\u001b[49m\u001b[43m(\u001b[49m\u001b[43mX\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43msample_weight\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43msample_weight\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241m.\u001b[39mlabels_\n",
      "File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python39\\site-packages\\sklearn\\cluster\\_kmeans.py:1455\u001b[0m, in \u001b[0;36mKMeans.fit\u001b[1;34m(self, X, y, sample_weight)\u001b[0m\n\u001b[0;32m   1453\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m   1454\u001b[0m     kmeans_single \u001b[38;5;241m=\u001b[39m _kmeans_single_lloyd\n\u001b[1;32m-> 1455\u001b[0m     \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_check_mkl_vcomp\u001b[49m\u001b[43m(\u001b[49m\u001b[43mX\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mX\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mshape\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   1457\u001b[0m best_inertia, best_labels \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m, \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m   1459\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m i \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_n_init):\n\u001b[0;32m   1460\u001b[0m     \u001b[38;5;66;03m# Initialize centers\u001b[39;00m\n",
      "File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python39\\site-packages\\sklearn\\cluster\\_kmeans.py:911\u001b[0m, in \u001b[0;36m_BaseKMeans._check_mkl_vcomp\u001b[1;34m(self, X, n_samples)\u001b[0m\n\u001b[0;32m    909\u001b[0m n_active_threads \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mint\u001b[39m(np\u001b[38;5;241m.\u001b[39mceil(n_samples \u001b[38;5;241m/\u001b[39m CHUNK_SIZE))\n\u001b[0;32m    910\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m n_active_threads \u001b[38;5;241m<\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_n_threads:\n\u001b[1;32m--> 911\u001b[0m     modules \u001b[38;5;241m=\u001b[39m \u001b[43mthreadpool_info\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    912\u001b[0m     has_vcomp \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mvcomp\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01min\u001b[39;00m [module[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mprefix\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;28;01mfor\u001b[39;00m module \u001b[38;5;129;01min\u001b[39;00m modules]\n\u001b[0;32m    913\u001b[0m     has_mkl \u001b[38;5;241m=\u001b[39m (\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmkl\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mintel\u001b[39m\u001b[38;5;124m\"\u001b[39m) \u001b[38;5;129;01min\u001b[39;00m [\n\u001b[0;32m    914\u001b[0m         (module[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124minternal_api\u001b[39m\u001b[38;5;124m\"\u001b[39m], module\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mthreading_layer\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m))\n\u001b[0;32m    915\u001b[0m         \u001b[38;5;28;01mfor\u001b[39;00m module \u001b[38;5;129;01min\u001b[39;00m modules\n\u001b[0;32m    916\u001b[0m     ]\n",
      "File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python39\\site-packages\\sklearn\\utils\\fixes.py:162\u001b[0m, in \u001b[0;36mthreadpool_info\u001b[1;34m()\u001b[0m\n\u001b[0;32m    160\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m controller\u001b[38;5;241m.\u001b[39minfo()\n\u001b[0;32m    161\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m--> 162\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mthreadpoolctl\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mthreadpool_info\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[1;32mc:\\Users\\riccardoricci\\Anaconda3\\lib\\site-packages\\threadpoolctl.py:124\u001b[0m, in \u001b[0;36mthreadpool_info\u001b[1;34m()\u001b[0m\n\u001b[0;32m    107\u001b[0m \u001b[38;5;129m@_format_docstring\u001b[39m(USER_APIS\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mlist\u001b[39m(_ALL_USER_APIS),\n\u001b[0;32m    108\u001b[0m                    INTERNAL_APIS\u001b[38;5;241m=\u001b[39m_ALL_INTERNAL_APIS)\n\u001b[0;32m    109\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mthreadpool_info\u001b[39m():\n\u001b[0;32m    110\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"Return the maximal number of threads for each detected library.\u001b[39;00m\n\u001b[0;32m    111\u001b[0m \n\u001b[0;32m    112\u001b[0m \u001b[38;5;124;03m    Return a list with all the supported modules that have been found. Each\u001b[39;00m\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m    122\u001b[0m \u001b[38;5;124;03m    In addition, each module may contain internal_api specific entries.\u001b[39;00m\n\u001b[0;32m    123\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[1;32m--> 124\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_ThreadpoolInfo\u001b[49m\u001b[43m(\u001b[49m\u001b[43muser_api\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m_ALL_USER_APIS\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241m.\u001b[39mtodicts()\n",
      "File \u001b[1;32mc:\\Users\\riccardoricci\\Anaconda3\\lib\\site-packages\\threadpoolctl.py:340\u001b[0m, in \u001b[0;36m_ThreadpoolInfo.__init__\u001b[1;34m(self, user_api, prefixes, modules)\u001b[0m\n\u001b[0;32m    337\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39muser_api \u001b[38;5;241m=\u001b[39m [] \u001b[38;5;28;01mif\u001b[39;00m user_api \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;28;01melse\u001b[39;00m user_api\n\u001b[0;32m    339\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mmodules \u001b[38;5;241m=\u001b[39m []\n\u001b[1;32m--> 340\u001b[0m     \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_load_modules\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    341\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_warn_if_incompatible_openmp()\n\u001b[0;32m    342\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n",
      "File \u001b[1;32mc:\\Users\\riccardoricci\\Anaconda3\\lib\\site-packages\\threadpoolctl.py:373\u001b[0m, in \u001b[0;36m_ThreadpoolInfo._load_modules\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    371\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_find_modules_with_dyld()\n\u001b[0;32m    372\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m sys\u001b[38;5;241m.\u001b[39mplatform \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mwin32\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[1;32m--> 373\u001b[0m     \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_find_modules_with_enum_process_module_ex\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    374\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m    375\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_find_modules_with_dl_iterate_phdr()\n",
      "File \u001b[1;32mc:\\Users\\riccardoricci\\Anaconda3\\lib\\site-packages\\threadpoolctl.py:485\u001b[0m, in \u001b[0;36m_ThreadpoolInfo._find_modules_with_enum_process_module_ex\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    482\u001b[0m         filepath \u001b[38;5;241m=\u001b[39m buf\u001b[38;5;241m.\u001b[39mvalue\n\u001b[0;32m    484\u001b[0m         \u001b[38;5;66;03m# Store the module if it is supported and selected\u001b[39;00m\n\u001b[1;32m--> 485\u001b[0m         \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_make_module_from_path\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfilepath\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    486\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[0;32m    487\u001b[0m     kernel_32\u001b[38;5;241m.\u001b[39mCloseHandle(h_process)\n",
      "File \u001b[1;32mc:\\Users\\riccardoricci\\Anaconda3\\lib\\site-packages\\threadpoolctl.py:515\u001b[0m, in \u001b[0;36m_ThreadpoolInfo._make_module_from_path\u001b[1;34m(self, filepath)\u001b[0m\n\u001b[0;32m    513\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m prefix \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mprefixes \u001b[38;5;129;01mor\u001b[39;00m user_api \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39muser_api:\n\u001b[0;32m    514\u001b[0m     module_class \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mglobals\u001b[39m()[module_class]\n\u001b[1;32m--> 515\u001b[0m     module \u001b[38;5;241m=\u001b[39m \u001b[43mmodule_class\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfilepath\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mprefix\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43muser_api\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43minternal_api\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    516\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mmodules\u001b[38;5;241m.\u001b[39mappend(module)\n",
      "File \u001b[1;32mc:\\Users\\riccardoricci\\Anaconda3\\lib\\site-packages\\threadpoolctl.py:606\u001b[0m, in \u001b[0;36m_Module.__init__\u001b[1;34m(self, filepath, prefix, user_api, internal_api)\u001b[0m\n\u001b[0;32m    604\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39minternal_api \u001b[38;5;241m=\u001b[39m internal_api\n\u001b[0;32m    605\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_dynlib \u001b[38;5;241m=\u001b[39m ctypes\u001b[38;5;241m.\u001b[39mCDLL(filepath, mode\u001b[38;5;241m=\u001b[39m_RTLD_NOLOAD)\n\u001b[1;32m--> 606\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mversion \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_version\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    607\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mnum_threads \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mget_num_threads()\n\u001b[0;32m    608\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_get_extra_info()\n",
      "File \u001b[1;32mc:\\Users\\riccardoricci\\Anaconda3\\lib\\site-packages\\threadpoolctl.py:646\u001b[0m, in \u001b[0;36m_OpenBLASModule.get_version\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    643\u001b[0m get_config \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mgetattr\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_dynlib, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mopenblas_get_config\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[0;32m    644\u001b[0m                      \u001b[38;5;28;01mlambda\u001b[39;00m: \u001b[38;5;28;01mNone\u001b[39;00m)\n\u001b[0;32m    645\u001b[0m get_config\u001b[38;5;241m.\u001b[39mrestype \u001b[38;5;241m=\u001b[39m ctypes\u001b[38;5;241m.\u001b[39mc_char_p\n\u001b[1;32m--> 646\u001b[0m config \u001b[38;5;241m=\u001b[39m \u001b[43mget_config\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msplit\u001b[49m()\n\u001b[0;32m    647\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m config[\u001b[38;5;241m0\u001b[39m] \u001b[38;5;241m==\u001b[39m \u001b[38;5;124mb\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mOpenBLAS\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[0;32m    648\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m config[\u001b[38;5;241m1\u001b[39m]\u001b[38;5;241m.\u001b[39mdecode(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mutf-8\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
      "\u001b[1;31mAttributeError\u001b[0m: 'NoneType' object has no attribute 'split'"
     ]
    }
   ],
   "source": [
    "game_tags = list(matrix_games_tags.columns)\n",
    "\n",
    "# Load Word2Vec model\n",
    "sentences = [tag.split() for tag in game_tags]\n",
    "model = Word2Vec(sentences, vector_size=20, window=5, min_count=1, workers=4)\n",
    "\n",
    "# Get vectors for each tag, handling out-of-vocabulary cases\n",
    "tag_vectors = [model.wv[tag] if tag in model.wv else np.zeros(model.vector_size) for tag in game_tags]\n",
    "\n",
    "# Standardize the data\n",
    "scaler = StandardScaler()\n",
    "tag_vectors_standardized = scaler.fit_transform(tag_vectors)\n",
    "\n",
    "# Specify the number of clusters\n",
    "num_clusters = 10\n",
    "\n",
    "# Apply K-means clustering\n",
    "kmeans = KMeans(n_clusters=num_clusters, random_state=42)\n",
    "tag_clusters = kmeans.fit_predict(tag_vectors_standardized)\n",
    "\n",
    "# Print the clusters for each tag\n",
    "for tag, cluster in zip(game_tags, tag_clusters):\n",
    "    print(f\"{tag}: Cluster {cluster}\")\n",
    "\n",
    "tag_cluster_df = pd.DataFrame([tag_clusters], columns=game_tags).T\\\n",
    "                .reset_index()\\\n",
    "                .rename({0:\"Cluster\", \"index\":\"Tag\"}, axis=1)\n",
    "tag_cluster_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Tag</th>\n",
       "      <th>Cluster</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>BMX</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>Combat</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>Crime</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110</th>\n",
       "      <td>Deckbuilding</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>117</th>\n",
       "      <td>Diplomacy</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>134</th>\n",
       "      <td>Episodic</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150</th>\n",
       "      <td>First-Person</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>187</th>\n",
       "      <td>Hunting</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>235</th>\n",
       "      <td>Minimalist</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>237</th>\n",
       "      <td>Mod</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>260</th>\n",
       "      <td>Nonlinear</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>294</th>\n",
       "      <td>Psychological</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>303</th>\n",
       "      <td>RTS</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>308</th>\n",
       "      <td>Realistic</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>311</th>\n",
       "      <td>Remake</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>326</th>\n",
       "      <td>Sailing</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>332</th>\n",
       "      <td>Sequel</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>335</th>\n",
       "      <td>Shooter</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>345</th>\n",
       "      <td>Sniper</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>346</th>\n",
       "      <td>Snooker</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>347</th>\n",
       "      <td>Snow</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>350</th>\n",
       "      <td>Software</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>387</th>\n",
       "      <td>Tile-Matching</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>394</th>\n",
       "      <td>Touch-Friendly</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>413</th>\n",
       "      <td>Unforgiving</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>433</th>\n",
       "      <td>Western</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Tag  Cluster\n",
       "43              BMX        0\n",
       "86           Combat        0\n",
       "99            Crime        0\n",
       "110    Deckbuilding        0\n",
       "117       Diplomacy        0\n",
       "134        Episodic        0\n",
       "150    First-Person        0\n",
       "187         Hunting        0\n",
       "235      Minimalist        0\n",
       "237             Mod        0\n",
       "260       Nonlinear        0\n",
       "294   Psychological        0\n",
       "303             RTS        0\n",
       "308       Realistic        0\n",
       "311          Remake        0\n",
       "326         Sailing        0\n",
       "332          Sequel        0\n",
       "335         Shooter        0\n",
       "345          Sniper        0\n",
       "346         Snooker        0\n",
       "347            Snow        0\n",
       "350        Software        0\n",
       "387   Tile-Matching        0\n",
       "394  Touch-Friendly        0\n",
       "413     Unforgiving        0\n",
       "433         Western        0"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tag_cluster_df[tag_cluster_df[\"Cluster\"]==0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# matrix_games_tags[matrix_games_tags[\"user_id_categorical\"]==0]"
   ]
  }
 ],
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