KHUDA 토이_프로젝트_Bayesian Network CF.ipynb

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   "id": "0a55f7bc",
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   "source": [
    "import pandas as pd\n",
    "\n",
    "#제품데이터 불러오기\n",
    "book_data= pd.read_csv(\"../data/book/books.csv\", low_memory = False) # csv 파일이 커서 low_memory는 False로 설정함.  \n",
    "                                                                      # book_data는 해당 알고리즘에서 굳이 필요없을 것 같다는 판단.                     \n",
    "#유저데이터 불러오기                                                 \n",
    "user_data = pd.read_csv(\"../data/book/Users.csv\")\n",
    "\n",
    "#별점 데이터 불러오기\n",
    "rating_data = pd.read_csv(\"../data/book/Ratings.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "ad4937c4",
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       "      <th></th>\n",
       "      <th>ISBN</th>\n",
       "      <th>Book-Title</th>\n",
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       "      <td>0195153448</td>\n",
       "      <td>Classical Mythology</td>\n",
       "      <td>Mark P. O. Morford</td>\n",
       "      <td>2002</td>\n",
       "      <td>Oxford University Press</td>\n",
       "      <td>http://images.amazon.com/images/P/0195153448.0...</td>\n",
       "      <td>http://images.amazon.com/images/P/0195153448.0...</td>\n",
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       "      <td>Clara Callan</td>\n",
       "      <td>Richard Bruce Wright</td>\n",
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       "      <td>HarperFlamingo Canada</td>\n",
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       "      <td>Gina Bari Kolata</td>\n",
       "      <td>1999</td>\n",
       "      <td>Farrar Straus Giroux</td>\n",
       "      <td>http://images.amazon.com/images/P/0374157065.0...</td>\n",
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       "      <td>The Mummies of Urumchi</td>\n",
       "      <td>E. J. W. Barber</td>\n",
       "      <td>1999</td>\n",
       "      <td>W. W. Norton &amp;amp; Company</td>\n",
       "      <td>http://images.amazon.com/images/P/0393045218.0...</td>\n",
       "      <td>http://images.amazon.com/images/P/0393045218.0...</td>\n",
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       "      <td>0440400988</td>\n",
       "      <td>There's a Bat in Bunk Five</td>\n",
       "      <td>Paula Danziger</td>\n",
       "      <td>1988</td>\n",
       "      <td>Random House Childrens Pub (Mm)</td>\n",
       "      <td>http://images.amazon.com/images/P/0440400988.0...</td>\n",
       "      <td>http://images.amazon.com/images/P/0440400988.0...</td>\n",
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       "      <td>0525447644</td>\n",
       "      <td>From One to One Hundred</td>\n",
       "      <td>Teri Sloat</td>\n",
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       "      <td>Dutton Books</td>\n",
       "      <td>http://images.amazon.com/images/P/0525447644.0...</td>\n",
       "      <td>http://images.amazon.com/images/P/0525447644.0...</td>\n",
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       "      <td>Christine Wicker</td>\n",
       "      <td>2004</td>\n",
       "      <td>HarperSanFrancisco</td>\n",
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       "      <td>Republic (World's Classics)</td>\n",
       "      <td>Plato</td>\n",
       "      <td>1996</td>\n",
       "      <td>Oxford University Press</td>\n",
       "      <td>http://images.amazon.com/images/P/0192126040.0...</td>\n",
       "      <td>http://images.amazon.com/images/P/0192126040.0...</td>\n",
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       "      <td>A Guided Tour of Rene Descartes' Meditations o...</td>\n",
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       "      <td>McGraw-Hill Humanities/Social Sciences/Languages</td>\n",
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       "              ISBN                                         Book-Title  \\\n",
       "0       0195153448                                Classical Mythology   \n",
       "1       0002005018                                       Clara Callan   \n",
       "2       0060973129                               Decision in Normandy   \n",
       "3       0374157065  Flu: The Story of the Great Influenza Pandemic...   \n",
       "4       0393045218                             The Mummies of Urumchi   \n",
       "...            ...                                                ...   \n",
       "271355  0440400988                         There's a Bat in Bunk Five   \n",
       "271356  0525447644                            From One to One Hundred   \n",
       "271357  006008667X  Lily Dale : The True Story of the Town that Ta...   \n",
       "271358  0192126040                        Republic (World's Classics)   \n",
       "271359  0767409752  A Guided Tour of Rene Descartes' Meditations o...   \n",
       "\n",
       "                 Book-Author Year-Of-Publication  \\\n",
       "0         Mark P. O. Morford                2002   \n",
       "1       Richard Bruce Wright                2001   \n",
       "2               Carlo D'Este                1991   \n",
       "3           Gina Bari Kolata                1999   \n",
       "4            E. J. W. Barber                1999   \n",
       "...                      ...                 ...   \n",
       "271355        Paula Danziger                1988   \n",
       "271356            Teri Sloat                1991   \n",
       "271357      Christine Wicker                2004   \n",
       "271358                 Plato                1996   \n",
       "271359   Christopher  Biffle                2000   \n",
       "\n",
       "                                               Publisher  \\\n",
       "0                                Oxford University Press   \n",
       "1                                  HarperFlamingo Canada   \n",
       "2                                        HarperPerennial   \n",
       "3                                   Farrar Straus Giroux   \n",
       "4                             W. W. Norton &amp; Company   \n",
       "...                                                  ...   \n",
       "271355                   Random House Childrens Pub (Mm)   \n",
       "271356                                      Dutton Books   \n",
       "271357                                HarperSanFrancisco   \n",
       "271358                           Oxford University Press   \n",
       "271359  McGraw-Hill Humanities/Social Sciences/Languages   \n",
       "\n",
       "                                              Image-URL-S  \\\n",
       "0       http://images.amazon.com/images/P/0195153448.0...   \n",
       "1       http://images.amazon.com/images/P/0002005018.0...   \n",
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       "3       http://images.amazon.com/images/P/0374157065.0...   \n",
       "4       http://images.amazon.com/images/P/0393045218.0...   \n",
       "...                                                   ...   \n",
       "271355  http://images.amazon.com/images/P/0440400988.0...   \n",
       "271356  http://images.amazon.com/images/P/0525447644.0...   \n",
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       "271358  http://images.amazon.com/images/P/0192126040.0...   \n",
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       "\n",
       "                                              Image-URL-M  \\\n",
       "0       http://images.amazon.com/images/P/0195153448.0...   \n",
       "1       http://images.amazon.com/images/P/0002005018.0...   \n",
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       "3       http://images.amazon.com/images/P/0374157065.0...   \n",
       "4       http://images.amazon.com/images/P/0393045218.0...   \n",
       "...                                                   ...   \n",
       "271355  http://images.amazon.com/images/P/0440400988.0...   \n",
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       "\n",
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       "\n",
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  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "b7a3a96f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 문자열을 정수로 매핑할 딕셔너리 생성\n",
    "unique_strings = book_data['Book-Title'].unique()\n",
    "string_to_int_mapping = {string: idx for idx, string in enumerate(unique_strings)}\n",
    "\n",
    "# 'A' 열의 문자열을 정수로 인코딩\n",
    "book_data['Book-Title_encoded'] = book_data['Book-Title'].map(string_to_int_mapping)\n",
    "\n",
    "# 문자열을 정수로 매핑할 딕셔너리 생성\n",
    "unique_strings = book_data['Book-Author'].unique()\n",
    "string_to_int_mapping = {string: idx for idx, string in enumerate(unique_strings)}\n",
    "\n",
    "# 'A' 열의 문자열을 정수로 인코딩\n",
    "book_data['Book-Author_encoded'] = book_data['Book-Author'].map(string_to_int_mapping)\n",
    "\n",
    "# 문자열을 정수로 매핑할 딕셔너리 생성\n",
    "unique_strings = book_data['Publisher'].unique()\n",
    "string_to_int_mapping = {string: idx for idx, string in enumerate(unique_strings)}\n",
    "\n",
    "# 'A' 열의 문자열을 정수로 인코딩\n",
    "book_data['Publisher_encoded'] = book_data['Publisher'].map(string_to_int_mapping)\n"
   ]
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  {
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   "execution_count": 45,
   "id": "e7c4084b",
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       "      <td>0195153448</td>\n",
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       "      <td>http://images.amazon.com/images/P/0060973129.0...</td>\n",
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       "      <td>The Mummies of Urumchi</td>\n",
       "      <td>E. J. W. Barber</td>\n",
       "      <td>1999</td>\n",
       "      <td>W. W. Norton &amp;amp; Company</td>\n",
       "      <td>http://images.amazon.com/images/P/0393045218.0...</td>\n",
       "      <td>http://images.amazon.com/images/P/0393045218.0...</td>\n",
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       "      <td>1988</td>\n",
       "      <td>Random House Childrens Pub (Mm)</td>\n",
       "      <td>http://images.amazon.com/images/P/0440400988.0...</td>\n",
       "      <td>http://images.amazon.com/images/P/0440400988.0...</td>\n",
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       "    <tr>\n",
       "      <th>271357</th>\n",
       "      <td>006008667X</td>\n",
       "      <td>Lily Dale : The True Story of the Town that Ta...</td>\n",
       "      <td>Christine Wicker</td>\n",
       "      <td>2004</td>\n",
       "      <td>HarperSanFrancisco</td>\n",
       "      <td>http://images.amazon.com/images/P/006008667X.0...</td>\n",
       "      <td>http://images.amazon.com/images/P/006008667X.0...</td>\n",
       "      <td>http://images.amazon.com/images/P/006008667X.0...</td>\n",
       "      <td>242132</td>\n",
       "      <td>17770</td>\n",
       "      <td>303</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>271358</th>\n",
       "      <td>0192126040</td>\n",
       "      <td>Republic (World's Classics)</td>\n",
       "      <td>Plato</td>\n",
       "      <td>1996</td>\n",
       "      <td>Oxford University Press</td>\n",
       "      <td>http://images.amazon.com/images/P/0192126040.0...</td>\n",
       "      <td>http://images.amazon.com/images/P/0192126040.0...</td>\n",
       "      <td>http://images.amazon.com/images/P/0192126040.0...</td>\n",
       "      <td>242133</td>\n",
       "      <td>832</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>271359</th>\n",
       "      <td>0767409752</td>\n",
       "      <td>A Guided Tour of Rene Descartes' Meditations o...</td>\n",
       "      <td>Christopher  Biffle</td>\n",
       "      <td>2000</td>\n",
       "      <td>McGraw-Hill Humanities/Social Sciences/Languages</td>\n",
       "      <td>http://images.amazon.com/images/P/0767409752.0...</td>\n",
       "      <td>http://images.amazon.com/images/P/0767409752.0...</td>\n",
       "      <td>http://images.amazon.com/images/P/0767409752.0...</td>\n",
       "      <td>242134</td>\n",
       "      <td>102022</td>\n",
       "      <td>642</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>271360 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              ISBN                                         Book-Title  \\\n",
       "0       0195153448                                Classical Mythology   \n",
       "1       0002005018                                       Clara Callan   \n",
       "2       0060973129                               Decision in Normandy   \n",
       "3       0374157065  Flu: The Story of the Great Influenza Pandemic...   \n",
       "4       0393045218                             The Mummies of Urumchi   \n",
       "...            ...                                                ...   \n",
       "271355  0440400988                         There's a Bat in Bunk Five   \n",
       "271356  0525447644                            From One to One Hundred   \n",
       "271357  006008667X  Lily Dale : The True Story of the Town that Ta...   \n",
       "271358  0192126040                        Republic (World's Classics)   \n",
       "271359  0767409752  A Guided Tour of Rene Descartes' Meditations o...   \n",
       "\n",
       "                 Book-Author Year-Of-Publication  \\\n",
       "0         Mark P. O. Morford                2002   \n",
       "1       Richard Bruce Wright                2001   \n",
       "2               Carlo D'Este                1991   \n",
       "3           Gina Bari Kolata                1999   \n",
       "4            E. J. W. Barber                1999   \n",
       "...                      ...                 ...   \n",
       "271355        Paula Danziger                1988   \n",
       "271356            Teri Sloat                1991   \n",
       "271357      Christine Wicker                2004   \n",
       "271358                 Plato                1996   \n",
       "271359   Christopher  Biffle                2000   \n",
       "\n",
       "                                               Publisher  \\\n",
       "0                                Oxford University Press   \n",
       "1                                  HarperFlamingo Canada   \n",
       "2                                        HarperPerennial   \n",
       "3                                   Farrar Straus Giroux   \n",
       "4                             W. W. Norton &amp; Company   \n",
       "...                                                  ...   \n",
       "271355                   Random House Childrens Pub (Mm)   \n",
       "271356                                      Dutton Books   \n",
       "271357                                HarperSanFrancisco   \n",
       "271358                           Oxford University Press   \n",
       "271359  McGraw-Hill Humanities/Social Sciences/Languages   \n",
       "\n",
       "                                              Image-URL-S  \\\n",
       "0       http://images.amazon.com/images/P/0195153448.0...   \n",
       "1       http://images.amazon.com/images/P/0002005018.0...   \n",
       "2       http://images.amazon.com/images/P/0060973129.0...   \n",
       "3       http://images.amazon.com/images/P/0374157065.0...   \n",
       "4       http://images.amazon.com/images/P/0393045218.0...   \n",
       "...                                                   ...   \n",
       "271355  http://images.amazon.com/images/P/0440400988.0...   \n",
       "271356  http://images.amazon.com/images/P/0525447644.0...   \n",
       "271357  http://images.amazon.com/images/P/006008667X.0...   \n",
       "271358  http://images.amazon.com/images/P/0192126040.0...   \n",
       "271359  http://images.amazon.com/images/P/0767409752.0...   \n",
       "\n",
       "                                              Image-URL-M  \\\n",
       "0       http://images.amazon.com/images/P/0195153448.0...   \n",
       "1       http://images.amazon.com/images/P/0002005018.0...   \n",
       "2       http://images.amazon.com/images/P/0060973129.0...   \n",
       "3       http://images.amazon.com/images/P/0374157065.0...   \n",
       "4       http://images.amazon.com/images/P/0393045218.0...   \n",
       "...                                                   ...   \n",
       "271355  http://images.amazon.com/images/P/0440400988.0...   \n",
       "271356  http://images.amazon.com/images/P/0525447644.0...   \n",
       "271357  http://images.amazon.com/images/P/006008667X.0...   \n",
       "271358  http://images.amazon.com/images/P/0192126040.0...   \n",
       "271359  http://images.amazon.com/images/P/0767409752.0...   \n",
       "\n",
       "                                              Image-URL-L  Book-Title_encoded  \\\n",
       "0       http://images.amazon.com/images/P/0195153448.0...                   0   \n",
       "1       http://images.amazon.com/images/P/0002005018.0...                   1   \n",
       "2       http://images.amazon.com/images/P/0060973129.0...                   2   \n",
       "3       http://images.amazon.com/images/P/0374157065.0...                   3   \n",
       "4       http://images.amazon.com/images/P/0393045218.0...                   4   \n",
       "...                                                   ...                 ...   \n",
       "271355  http://images.amazon.com/images/P/0440400988.0...               19469   \n",
       "271356  http://images.amazon.com/images/P/0525447644.0...              242131   \n",
       "271357  http://images.amazon.com/images/P/006008667X.0...              242132   \n",
       "271358  http://images.amazon.com/images/P/0192126040.0...              242133   \n",
       "271359  http://images.amazon.com/images/P/0767409752.0...              242134   \n",
       "\n",
       "        Book-Author_encoded  Publisher_encoded  \n",
       "0                         0                  0  \n",
       "1                         1                  1  \n",
       "2                         2                  2  \n",
       "3                         3                  3  \n",
       "4                         4                  4  \n",
       "...                     ...                ...  \n",
       "271355                  377               1460  \n",
       "271356               102021                140  \n",
       "271357                17770                303  \n",
       "271358                  832                  0  \n",
       "271359               102022                642  \n",
       "\n",
       "[271360 rows x 11 columns]"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "book_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "id": "c41de4c6",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_book_title = book_data['Book-Title_encoded']\n",
    "y_book_Author = book_data['Book-Author_encoded']\n",
    "y_book_Publisher = book_data['Publisher_encoded']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "id": "86a10dd8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 203,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_book_title.hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "id": "57e2d157",
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 204,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_book_Author.hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "id": "df300e75",
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 205,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_book_Publisher.hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "id": "d5abf60f",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_all = y_book_title + y_book_Author + y_book_Publisher"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "id": "6aa4d10b",
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 207,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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MKSkpioiIUFRUlGbNmqVLly75zBQWFmrIkCGy2Wzq1auXcnNz66wlOztbPXv2VHh4uBITE7Vnz56GRAEAABbXoJLTrVs3Pf/88youLtbevXt177336v7779ehQ4ckSTNnztS7776rjRs3avv27frqq6/0wAMPmNevrq5WSkqKqqqqtGvXLr366qvKzc1VVlaWOXP8+HGlpKTonnvuUUlJiWbMmKEnn3xSW7duNWfWr1+vjIwMzZs3T59++qkGDhwop9OpioqKH3p/AAAAi2hQybnvvvs0btw43X777brjjjv029/+Vm3bttXu3bt15swZvfLKK1q2bJnuvfdeJSQkaM2aNdq1a5d2794tScrPz9fhw4f12muvadCgQRo7dqwWLVqk7OxsVVVVSZJycnIUFxenpUuXqm/fvkpPT9cvfvELLV++3FzHsmXLNHXqVE2ePFnx8fHKyclRRESEVq9e7ce7BgAAtGQ3/J6c6upqbdy4UefOnZPD4VBxcbG8Xq+SkpLMmT59+qh79+4qKirS8OHDVVRUpP79+ys6OtqccTqdmj59ug4dOqTBgwerqKjI5xi1MzNmzJAkVVVVqbi4WJmZmeb+4OBgJSUlqaioqN41ezweeTwe83JlZaUkyev1yuv13uhdUUftsWzBht+O2VLUZg607IGaWwrc7I2V258/ixpD7fqa+zobQ6Bmb465r3ctDS45Bw4ckMPh0MWLF9W2bVu99dZbio+PV0lJicLCwhQZGekzHx0dLbfbLUlyu90+Bad2f+2++mYqKyt14cIFnTp1StXV1VecOXLkSL1rX7x4sRYsWFBne35+viIiIq4dvoEWDa3x+zFbikDNHqi5pcDN7u/cmzdv9uvxGovL5WrqJTSZQM3enHKfP3/+uuYaXHJ69+6tkpISnTlzRm+88YZSU1O1ffv2Bi+wKWRmZiojI8O8XFlZqdjYWCUnJ8tut/vtdrxer1wul57bGyxPjf/OumgJbMGGFg2tCbjsgZpbCtzsjZX74Hyn347VGGp/vo0ePVqhoaFNvZybKlCzN8fcta/EXEuDS05YWJh69eolSUpISNAnn3yilStXauLEiaqqqtLp06d9ns0pLy9XTEyMJCkmJqbOWVC1Z19dPvP9M7LKy8tlt9vVunVrhYSEKCQk5Ioztce4GpvNJpvNVmd7aGhoozxwnpogv55a2pIEavZAzS0FbnZ/5779uXy/Hasx1J46P/i328zcgXbae2P9zmjumlPu613HD/4wwJqaGnk8HiUkJCg0NFQFBQXmvtLSUpWVlcnhcEiSHA6HDhw44HMWlMvlkt1uV3x8vDlz+TFqZ2qPERYWpoSEBJ+ZmpoaFRQUmDMAAAANeiYnMzNTY8eOVffu3fXtt99q3bp1Kiws1NatW9W+fXtNmTJFGRkZ6tixo+x2u375y1/K4XBo+PDhkqTk5GTFx8fr0Ucf1ZIlS+R2uzV37lylpaWZz7BMmzZNq1at0uzZs/XEE09o27Zt2rBhg/Ly/vmhXhkZGUpNTdXQoUM1bNgwrVixQufOndPkyZP9eNcAAICWrEElp6KiQo899phOnDih9u3ba8CAAdq6datGjx4tSVq+fLmCg4M1YcIEeTweOZ1Ovfjii+b1Q0JCtGnTJk2fPl0Oh0Nt2rRRamqqFi5caM7ExcUpLy9PM2fO1MqVK9WtWze9/PLLcjr/+Tr1xIkT9fXXXysrK0tut1uDBg3Sli1b6rwZGQAABK4GlZxXXnml3v3h4eHKzs5Wdnb2VWd69OhxzbMHRo4cqX379tU7k56ervT09HpnAABA4OIPdAIAAEui5AAAAEui5AAAAEui5AAAAEui5AAAAEui5AAAAEui5AAAAEui5AAAAEui5AAAAEui5AAAAEui5AAAAEui5AAAAEui5AAAAEui5AAAAEui5AAAAEui5AAAAEui5AAAAEui5AAAAEui5AAAAEui5AAAAEui5AAAAEui5AAAAEui5AAAAEui5AAAAEui5AAAAEui5AAAAEui5AAAAEui5AAAAEui5AAAAEui5AAAAEui5AAAAEui5AAAAEui5AAAAEui5AAAAEtqUMlZvHixfvzjH6tdu3aKiorS+PHjVVpa6jMzcuRIBQUF+XxNmzbNZ6asrEwpKSmKiIhQVFSUZs2apUuXLvnMFBYWasiQIbLZbOrVq5dyc3PrrCc7O1s9e/ZUeHi4EhMTtWfPnobEAQAAFtagkrN9+3alpaVp9+7dcrlc8nq9Sk5O1rlz53zmpk6dqhMnTphfS5YsMfdVV1crJSVFVVVV2rVrl1599VXl5uYqKyvLnDl+/LhSUlJ0zz33qKSkRDNmzNCTTz6prVu3mjPr169XRkaG5s2bp08//VQDBw6U0+lURUXFjd4XAADAQlo1ZHjLli0+l3NzcxUVFaXi4mKNGDHC3B4REaGYmJgrHiM/P1+HDx/W+++/r+joaA0aNEiLFi3SnDlzNH/+fIWFhSknJ0dxcXFaunSpJKlv377auXOnli9fLqfTKUlatmyZpk6dqsmTJ0uScnJylJeXp9WrV+uZZ55pSCwAAGBBDSo533fmzBlJUseOHX22r127Vq+99ppiYmJ033336bnnnlNERIQkqaioSP3791d0dLQ573Q6NX36dB06dEiDBw9WUVGRkpKSfI7pdDo1Y8YMSVJVVZWKi4uVmZlp7g8ODlZSUpKKioquul6PxyOPx2NerqyslCR5vV55vd4buAeurPZYtmDDb8dsKWozB1r2QM0tBW52cv8ztz9/fjZntTkDJW+t5pj7etdywyWnpqZGM2bM0E9/+lP169fP3P7II4+oR48e6tq1q/bv3685c+aotLRUb775piTJ7Xb7FBxJ5mW3213vTGVlpS5cuKBTp06purr6ijNHjhy56poXL16sBQsW1Nmen59vljB/WjS0xu/HbCkCNXug5pYCNzu5pc2bNzfhSm4+l8vV1EtoEs0p9/nz569r7oZLTlpamg4ePKidO3f6bH/qqafMf/fv319dunTRqFGjdOzYMd122203enN+kZmZqYyMDPNyZWWlYmNjlZycLLvd7rfb8Xq9crlcem5vsDw1QX47bktgCza0aGhNwGUP1NxS4GYn9z9zH5zvbOJV3Ry1P9tHjx6t0NDQpl7OTdMcc9e+EnMtN1Ry0tPTtWnTJu3YsUPdunWrdzYxMVGSdPToUd12222KiYmpcxZUeXm5JJnv44mJiTG3XT5jt9vVunVrhYSEKCQk5IozV3svkCTZbDbZbLY620NDQxvlgfPUBMlTHTg//C4XqNkDNbcUuNnJrWbzi+9maazfGc1dc8p9veto0NlVhmEoPT1db731lrZt26a4uLhrXqekpESS1KVLF0mSw+HQgQMHfM6Ccrlcstvtio+PN2cKCgp8juNyueRwOCRJYWFhSkhI8JmpqalRQUGBOQMAAAJbg57JSUtL07p16/TOO++oXbt25nto2rdvr9atW+vYsWNat26dxo0bp06dOmn//v2aOXOmRowYoQEDBkiSkpOTFR8fr0cffVRLliyR2+3W3LlzlZaWZj7LMm3aNK1atUqzZ8/WE088oW3btmnDhg3Ky8sz15KRkaHU1FQNHTpUw4YN04oVK3Tu3DnzbCsAABDYGlRyXnrpJUnffeDf5dasWaPHH39cYWFhev/9983CERsbqwkTJmju3LnmbEhIiDZt2qTp06fL4XCoTZs2Sk1N1cKFC82ZuLg45eXlaebMmVq5cqW6deuml19+2Tx9XJImTpyor7/+WllZWXK73Ro0aJC2bNlS583IAAAgMDWo5BhG/adKxsbGavv27dc8To8ePa75bvyRI0dq37599c6kp6crPT39mrcHAAACD3+7CgAAWBIlBwAAWBIlBwAAWBIlBwAAWBIlBwAAWBIlBwAAWBIlBwAAWBIlBwAAWBIlBwAAWBIlBwAAWBIlBwAAWBIlBwAAWBIlBwAAWBIlBwAAWBIlBwAAWBIlBwAAWBIlBwAAWBIlBwAAWBIlBwAAWBIlBwAAWBIlBwAAWBIlBwAAWBIlBwAAWBIlBwAAWBIlBwAAWBIlBwAAWBIlBwAAWBIlBwAAWBIlBwAAWBIlBwAAWBIlBwAAWBIlBwAAWBIlBwAAWBIlBwAAWFKDSs7ixYv14x//WO3atVNUVJTGjx+v0tJSn5mLFy8qLS1NnTp1Utu2bTVhwgSVl5f7zJSVlSklJUURERGKiorSrFmzdOnSJZ+ZwsJCDRkyRDabTb169VJubm6d9WRnZ6tnz54KDw9XYmKi9uzZ05A4AADAwhpUcrZv3660tDTt3r1bLpdLXq9XycnJOnfunDkzc+ZMvfvuu9q4caO2b9+ur776Sg888IC5v7q6WikpKaqqqtKuXbv06quvKjc3V1lZWebM8ePHlZKSonvuuUclJSWaMWOGnnzySW3dutWcWb9+vTIyMjRv3jx9+umnGjhwoJxOpyoqKn7I/QEAACyiVUOGt2zZ4nM5NzdXUVFRKi4u1ogRI3TmzBm98sorWrdune69915J0po1a9S3b1/t3r1bw4cPV35+vg4fPqz3339f0dHRGjRokBYtWqQ5c+Zo/vz5CgsLU05OjuLi4rR06VJJUt++fbVz504tX75cTqdTkrRs2TJNnTpVkydPliTl5OQoLy9Pq1ev1jPPPPOD7xgAANCyNajkfN+ZM2ckSR07dpQkFRcXy+v1KikpyZzp06ePunfvrqKiIg0fPlxFRUXq37+/oqOjzRmn06np06fr0KFDGjx4sIqKinyOUTszY8YMSVJVVZWKi4uVmZlp7g8ODlZSUpKKioquul6PxyOPx2NerqyslCR5vV55vd4bvBfqqj2WLdjw2zFbitrMgZY9UHNLgZud3P/M7c+fn81Zbc5AyVurOea+3rXccMmpqanRjBkz9NOf/lT9+vWTJLndboWFhSkyMtJnNjo6Wm6325y5vODU7q/dV99MZWWlLly4oFOnTqm6uvqKM0eOHLnqmhcvXqwFCxbU2Z6fn6+IiIjrSN0wi4bW+P2YLUWgZg/U3FLgZie3tHnz5iZcyc3ncrmaeglNojnlPn/+/HXN3XDJSUtL08GDB7Vz584bPcRNl5mZqYyMDPNyZWWlYmNjlZycLLvd7rfb8Xq9crlcem5vsDw1QX47bktgCza0aGhNwGUP1NxS4GYn9z9zH5zvbOJV3Ry1P9tHjx6t0NDQpl7OTdMcc9e+EnMtN1Ry0tPTtWnTJu3YsUPdunUzt8fExKiqqkqnT5/2eTanvLxcMTEx5sz3z4KqPfvq8pnvn5FVXl4uu92u1q1bKyQkRCEhIVecqT3GldhsNtlstjrbQ0NDG+WB89QEyVMdOD/8Lheo2QM1txS42cmtZvOL72ZprN8ZzV1zyn2962jQ2VWGYSg9PV1vvfWWtm3bpri4OJ/9CQkJCg0NVUFBgbmttLRUZWVlcjgckiSHw6EDBw74nAXlcrlkt9sVHx9vzlx+jNqZ2mOEhYUpISHBZ6ampkYFBQXmDAAACGwNeiYnLS1N69at0zvvvKN27dqZ76Fp3769Wrdurfbt22vKlCnKyMhQx44dZbfb9ctf/lIOh0PDhw+XJCUnJys+Pl6PPvqolixZIrfbrblz5yotLc18lmXatGlatWqVZs+erSeeeELbtm3Thg0blJeXZ64lIyNDqampGjp0qIYNG6YVK1bo3Llz5tlWAAAgsDWo5Lz00kuSpJEjR/psX7NmjR5//HFJ0vLlyxUcHKwJEybI4/HI6XTqxRdfNGdDQkK0adMmTZ8+XQ6HQ23atFFqaqoWLlxozsTFxSkvL08zZ87UypUr1a1bN7388svm6eOSNHHiRH399dfKysqS2+3WoEGDtGXLljpvRgYAAIGpQSXHMK59qmR4eLiys7OVnZ191ZkePXpc8934I0eO1L59++qdSU9PV3p6+jXXBAAAAg9/uwoAAFgSJQcAAFgSJQcAAFgSJQcAAFgSJQcAAFgSJQcAAFgSJQcAAFgSJQcAAFgSJQcAAFgSJQcAAFhSg/6sAwAA39fzmbxrDzUzXzyf0tRLwE3AMzkAAMCSKDkAAMCSKDkAAMCSKDkAAMCSKDkAAMCSKDkAAMCSKDkAAMCSKDkAAMCSKDkAAMCSKDkAAMCSKDkAAMCSKDkAAMCSKDkAAMCSKDkAAMCSKDkAAMCSKDkAAMCSKDkAAMCSKDkAAMCSKDkAAMCSKDkAAMCSKDkAAMCSKDkAAMCSKDkAAMCSGlxyduzYofvuu09du3ZVUFCQ3n77bZ/9jz/+uIKCgny+xowZ4zNz8uRJTZo0SXa7XZGRkZoyZYrOnj3rM7N//37dfffdCg8PV2xsrJYsWVJnLRs3blSfPn0UHh6u/v37a/PmzQ2NAwAALKrBJefcuXMaOHCgsrOzrzozZswYnThxwvz685//7LN/0qRJOnTokFwulzZt2qQdO3boqaeeMvdXVlYqOTlZPXr0UHFxsX73u99p/vz5+tOf/mTO7Nq1Sw8//LCmTJmiffv2afz48Ro/frwOHjzY0EgAAMCCWjX0CmPHjtXYsWPrnbHZbIqJibnivs8++0xbtmzRJ598oqFDh0qSXnjhBY0bN06///3v1bVrV61du1ZVVVVavXq1wsLCdOedd6qkpETLli0zy9DKlSs1ZswYzZo1S5K0aNEiuVwurVq1Sjk5OQ2NBQAALKbBJed6FBYWKioqSh06dNC9996r3/zmN+rUqZMkqaioSJGRkWbBkaSkpCQFBwfr448/1s9//nMVFRVpxIgRCgsLM2ecTqf+67/+S6dOnVKHDh1UVFSkjIwMn9t1Op11Xj67nMfjkcfjMS9XVlZKkrxer7xerz+im8eTJFuw4bdjthS1mQMte6DmlgI3O7lbdu4b+Zlfex1//r5oCZpj7utdi99LzpgxY/TAAw8oLi5Ox44d07PPPquxY8eqqKhIISEhcrvdioqK8l1Eq1bq2LGj3G63JMntdisuLs5nJjo62tzXoUMHud1uc9vlM7XHuJLFixdrwYIFdbbn5+crIiLihvLWZ9HQGr8fs6UI1OyBmlsK3Ozkbpl+yHs4XS6XH1fScjSn3OfPn7+uOb+XnIceesj8d//+/TVgwADddtttKiws1KhRo/x9cw2SmZnp8+xPZWWlYmNjlZycLLvd7rfb8Xq9crlcem5vsDw1QX47bktgCza0aGhNwGUP1NxS4GYnd8vOfXC+s8HXqf3ZPnr0aIWGhjbCqpqn5pi79pWYa2mUl6sud+utt6pz5846evSoRo0apZiYGFVUVPjMXLp0SSdPnjTfxxMTE6Py8nKfmdrL15q52nuBpO/eK2Sz2epsDw0NbZQHzlMTJE91y/0h8EMEavZAzS0FbnZyt0w/5Gd+Y/3OaO6aU+7rXUejf07O3//+d33zzTfq0qWLJMnhcOj06dMqLi42Z7Zt26aamholJiaaMzt27PB5zc3lcql3797q0KGDOVNQUOBzWy6XSw6Ho7EjAQCAFqDBJefs2bMqKSlRSUmJJOn48eMqKSlRWVmZzp49q1mzZmn37t364osvVFBQoPvvv1+9evWS0/ndU4N9+/bVmDFjNHXqVO3Zs0cfffSR0tPT9dBDD6lr166SpEceeURhYWGaMmWKDh06pPXr12vlypU+LzU9/fTT2rJli5YuXaojR45o/vz52rt3r9LT0/1wtwAAgJauwSVn7969Gjx4sAYPHixJysjI0ODBg5WVlaWQkBDt379fP/vZz3THHXdoypQpSkhI0IcffujzMtHatWvVp08fjRo1SuPGjdNdd93l8xk47du3V35+vo4fP66EhAT96le/UlZWls9n6fzkJz/RunXr9Kc//UkDBw7UG2+8obffflv9+vX7IfcHAACwiAa/J2fkyJEyjKufOrh169ZrHqNjx45at25dvTMDBgzQhx9+WO/Mgw8+qAcffPCatwcAAAIPf7sKAABYEiUHAABYEiUHAABYEiUHAABYEiUHAABYEiUHAABYEiUHAABYEiUHAABYEiUHAABYEiUHAABYEiUHAABYEiUHAABYEiUHAABYEiUHAABYEiUHAABYUqumXgAAADdbz2fyGnwdW4ihJcOkfvO3ylMd1Airqt8Xz6fc9Nts6XgmBwAAWBIlBwAAWBIlBwAAWBIlBwAAWBIlBwAAWBIlBwAAWBIlBwAAWBIlBwAAWBIlBwAAWBIlBwAAWBIlBwAAWBIlBwAAWBIlBwAAWBIlBwAAWBIlBwAAWBIlBwAAWBIlBwAAWFKDS86OHTt03333qWvXrgoKCtLbb7/ts98wDGVlZalLly5q3bq1kpKS9Pnnn/vMnDx5UpMmTZLdbldkZKSmTJmis2fP+szs379fd999t8LDwxUbG6slS5bUWcvGjRvVp08fhYeHq3///tq8eXND4wAAAItqcMk5d+6cBg4cqOzs7CvuX7Jkif7whz8oJydHH3/8sdq0aSOn06mLFy+aM5MmTdKhQ4fkcrm0adMm7dixQ0899ZS5v7KyUsnJyerRo4eKi4v1u9/9TvPnz9ef/vQnc2bXrl16+OGHNWXKFO3bt0/jx4/X+PHjdfDgwYZGAgAAFtSqoVcYO3asxo4de8V9hmFoxYoVmjt3ru6//35J0v/8z/8oOjpab7/9th566CF99tln2rJliz755BMNHTpUkvTCCy9o3Lhx+v3vf6+uXbtq7dq1qqqq0urVqxUWFqY777xTJSUlWrZsmVmGVq5cqTFjxmjWrFmSpEWLFsnlcmnVqlXKycm5oTsDAABYh1/fk3P8+HG53W4lJSWZ29q3b6/ExEQVFRVJkoqKihQZGWkWHElKSkpScHCwPv74Y3NmxIgRCgsLM2ecTqdKS0t16tQpc+by26mdqb0dAAAQ2Br8TE593G63JCk6Otpne3R0tLnP7XYrKirKdxGtWqljx44+M3FxcXWOUbuvQ4cOcrvd9d7OlXg8Hnk8HvNyZWWlJMnr9crr9V53zmupPZYt2PDbMVuK2syBlj1Qc0uBm53cgZVbavrs/vw9dSO321S3fyXXuxa/lpzmbvHixVqwYEGd7fn5+YqIiPD77S0aWuP3Y7YUgZo9UHNLgZud3IGnqbI39ck1LperSW//cufPn7+uOb+WnJiYGElSeXm5unTpYm4vLy/XoEGDzJmKigqf6126dEknT540rx8TE6Py8nKfmdrL15qp3X8lmZmZysjIMC9XVlYqNjZWycnJstvtDYlaL6/XK5fLpef2BstTE+S347YEtmBDi4bWBFz2QM0tBW52cgdWbqnpsx+c77zptyn983fa6NGjFRoa2iRr+L7aV2Kuxa8lJy4uTjExMSooKDBLTWVlpT7++GNNnz5dkuRwOHT69GkVFxcrISFBkrRt2zbV1NQoMTHRnPmP//gPeb1e8w51uVzq3bu3OnToYM4UFBRoxowZ5u27XC45HI6rrs9ms8lms9XZHhoa2igPnKcmSJ7qwPohUCtQswdqbilws5M78DRV9qYuGI31u/JGXO86GvzG47Nnz6qkpEQlJSWSvnuzcUlJicrKyhQUFKQZM2boN7/5jf7yl7/owIEDeuyxx9S1a1eNHz9ektS3b1+NGTNGU6dO1Z49e/TRRx8pPT1dDz30kLp27SpJeuSRRxQWFqYpU6bo0KFDWr9+vVauXOnzLMzTTz+tLVu2aOnSpTpy5Ijmz5+vvXv3Kj09vaGRAACABTX4mZy9e/fqnnvuMS/XFo/U1FTl5uZq9uzZOnfunJ566imdPn1ad911l7Zs2aLw8HDzOmvXrlV6erpGjRql4OBgTZgwQX/4wx/M/e3bt1d+fr7S0tKUkJCgzp07Kysry+ezdH7yk59o3bp1mjt3rp599lndfvvtevvtt9WvX78buiMAAIC1NLjkjBw5UoZx9XeWBwUFaeHChVq4cOFVZzp27Kh169bVezsDBgzQhx9+WO/Mgw8+qAcffLD+BQMAgIDE364CAACWRMkBAACWRMkBAACWRMkBAACWRMkBAACWRMkBAACWRMkBAACWRMkBAACWRMkBAACWRMkBAACWRMkBAACWRMkBAACWRMkBAACWRMkBAACWRMkBAACWRMkBAACWRMkBAACWRMkBAACWRMkBAACWRMkBAACWRMkBAACWRMkBAACWRMkBAACWRMkBAACW1KqpFwAAAK6t5zN5TXK7thBDS4ZJ/eZvlac6qEHX/eL5lEZa1fXhmRwAAGBJlBwAAGBJlBwAAGBJlBwAAGBJlBwAAGBJlBwAAGBJlBwAAGBJlBwAAGBJlBwAAGBJfi858+fPV1BQkM9Xnz59zP0XL15UWlqaOnXqpLZt22rChAkqLy/3OUZZWZlSUlIUERGhqKgozZo1S5cuXfKZKSws1JAhQ2Sz2dSrVy/l5ub6OwoAAGjBGuWZnDvvvFMnTpwwv3bu3Gnumzlzpt59911t3LhR27dv11dffaUHHnjA3F9dXa2UlBRVVVVp165devXVV5Wbm6usrCxz5vjx40pJSdE999yjkpISzZgxQ08++aS2bt3aGHEAAEAL1Ch/u6pVq1aKiYmps/3MmTN65ZVXtG7dOt17772SpDVr1qhv377avXu3hg8frvz8fB0+fFjvv/++oqOjNWjQIC1atEhz5szR/PnzFRYWppycHMXFxWnp0qWSpL59+2rnzp1avny5nE5nY0QCAAAtTKOUnM8//1xdu3ZVeHi4HA6HFi9erO7du6u4uFher1dJSUnmbJ8+fdS9e3cVFRVp+PDhKioqUv/+/RUdHW3OOJ1OTZ8+XYcOHdLgwYNVVFTkc4zamRkzZtS7Lo/HI4/HY16urKyUJHm9Xnm9Xj8kl3k8SbIFG347ZktRmznQsgdqbilws5M7sHJLgZv9h+T25+/WGzmu30tOYmKicnNz1bt3b504cUILFizQ3XffrYMHD8rtdissLEyRkZE+14mOjpbb7ZYkud1un4JTu792X30zlZWVunDhglq3bn3FtS1evFgLFiyosz0/P18RERE3lLc+i4bW+P2YLUWgZg/U3FLgZid34AnU7DeSe/PmzY2wEun8+fPXNef3kjN27Fjz3wMGDFBiYqJ69OihDRs2XLV83CyZmZnKyMgwL1dWVio2NlbJycmy2+1+ux2v1yuXy6Xn9gbLU9OwP0vf0tmCDS0aWhNw2QM1txS42ckdWLmlwM3+Q3IfnN84byGpfSXmWhrl5arLRUZG6o477tDRo0c1evRoVVVV6fTp0z7P5pSXl5vv4YmJidGePXt8jlF79tXlM98/I6u8vFx2u73eImWz2WSz2epsDw0NVWho6A3lq4+nJkie6sD5H+FygZo9UHNLgZud3IEnULPfSO7G+N3akOM2+ufknD17VseOHVOXLl2UkJCg0NBQFRQUmPtLS0tVVlYmh8MhSXI4HDpw4IAqKirMGZfLJbvdrvj4eHPm8mPUztQeAwAAwO8l59e//rW2b9+uL774Qrt27dLPf/5zhYSE6OGHH1b79u01ZcoUZWRk6IMPPlBxcbEmT54sh8Oh4cOHS5KSk5MVHx+vRx99VH/961+1detWzZ07V2lpaeazMNOmTdPf/vY3zZ49W0eOHNGLL76oDRs2aObMmf6OAwAAWii/v1z197//XQ8//LC++eYb3XLLLbrrrru0e/du3XLLLZKk5cuXKzg4WBMmTJDH45HT6dSLL75oXj8kJESbNm3S9OnT5XA41KZNG6WmpmrhwoXmTFxcnPLy8jRz5kytXLlS3bp108svv8zp4wAAwOT3kvP666/Xuz88PFzZ2dnKzs6+6kyPHj2u+Y7skSNHat++fTe0RgAAYH387SoAAGBJlBwAAGBJlBwAAGBJlBwAAGBJlBwAAGBJlBwAAGBJlBwAAGBJlBwAAGBJlBwAAGBJlBwAAGBJlBwAAGBJlBwAAGBJlBwAAGBJlBwAAGBJlBwAAGBJlBwAAGBJlBwAAGBJlBwAAGBJlBwAAGBJlBwAAGBJlBwAAGBJlBwAAGBJlBwAAGBJlBwAAGBJlBwAAGBJlBwAAGBJlBwAAGBJlBwAAGBJlBwAAGBJlBwAAGBJlBwAAGBJlBwAAGBJlBwAAGBJlBwAAGBJLb7kZGdnq2fPngoPD1diYqL27NnT1EsCAADNQIsuOevXr1dGRobmzZunTz/9VAMHDpTT6VRFRUVTLw0AADSxFl1yli1bpqlTp2ry5MmKj49XTk6OIiIitHr16qZeGgAAaGKtmnoBN6qqqkrFxcXKzMw0twUHByspKUlFRUVXvI7H45HH4zEvnzlzRpJ08uRJeb1ev63N6/Xq/PnzauUNVnVNkN+O2xK0qjF0/nxNwGUP1NxS4GYnd2DllgI3+w/J/c033zTKmr799ltJkmEY9Q8aLdQ//vEPQ5Kxa9cun+2zZs0yhg0bdsXrzJs3z5DEF1988cUXX3xZ4OvLL7+styu02GdybkRmZqYyMjLMyzU1NTp58qQ6deqkoCD/tfLKykrFxsbqyy+/lN1u99txW4JAzR6ouaXAzU7uwMotBW725pjbMAx9++236tq1a71zLbbkdO7cWSEhISovL/fZXl5erpiYmCtex2azyWaz+WyLjIxsrCXKbrc3m2+Imy1Qswdqbilws5M78ARq9uaWu3379tecabFvPA4LC1NCQoIKCgrMbTU1NSooKJDD4WjClQEAgOagxT6TI0kZGRlKTU3V0KFDNWzYMK1YsULnzp3T5MmTm3ppAACgibXokjNx4kR9/fXXysrKktvt1qBBg7RlyxZFR0c36bpsNpvmzZtX56WxQBCo2QM1txS42ckdWLmlwM3eknMHGca1zr8CAABoeVrse3IAAADqQ8kBAACWRMkBAACWRMkBAACWRMlpBNnZ2erZs6fCw8OVmJioPXv2NPWSrmr+/PkKCgry+erTp4+5/+LFi0pLS1OnTp3Utm1bTZgwoc4HMJaVlSklJUURERGKiorSrFmzdOnSJZ+ZwsJCDRkyRDabTb169VJubm6dtTTm/bZjxw7dd9996tq1q4KCgvT222/77DcMQ1lZWerSpYtat26tpKQkff755z4zJ0+e1KRJk2S32xUZGakpU6bo7NmzPjP79+/X3XffrfDwcMXGxmrJkiV11rJx40b16dNH4eHh6t+/vzZv3tzgtfgz++OPP17ne2DMmDEtPvvixYv14x//WO3atVNUVJTGjx+v0tJSn5nm9P19PWvxV+6RI0fWecynTZvWonNL0ksvvaQBAwaYH1rncDj03nvvNei2rJjbqo/3dfHDn5HCZV5//XUjLCzMWL16tXHo0CFj6tSpRmRkpFFeXt7US7uiefPmGXfeeadx4sQJ8+vrr78290+bNs2IjY01CgoKjL179xrDhw83fvKTn5j7L126ZPTr189ISkoy9u3bZ2zevNno3LmzkZmZac787W9/MyIiIoyMjAzj8OHDxgsvvGCEhIQYW7ZsMWca+37bvHmz8R//8R/Gm2++aUgy3nrrLZ/9zz//vNG+fXvj7bffNv76178aP/vZz4y4uDjjwoUL5syYMWOMgQMHGrt37zY+/PBDo1evXsbDDz9s7j9z5owRHR1tTJo0yTh48KDx5z//2WjdurXxxz/+0Zz56KOPjJCQEGPJkiXG4cOHjblz5xqhoaHGgQMHGrQWf2ZPTU01xowZ4/M9cPLkSZ+Zlpjd6XQaa9asMQ4ePGiUlJQY48aNM7p3726cPXvWnGlO39/XWos/c//Lv/yLMXXqVJ/H/MyZMy06t2EYxl/+8hcjLy/P+N///V+jtLTUePbZZ43Q0FDj4MGD13VbVs1t1cf7elBy/GzYsGFGWlqaebm6utro2rWrsXjx4iZc1dXNmzfPGDhw4BX3nT592ggNDTU2btxobvvss88MSUZRUZFhGN/9Ag0ODjbcbrc589JLLxl2u93weDyGYRjG7NmzjTvvvNPn2BMnTjScTqd5+Wbeb9//RV9TU2PExMQYv/vd78xtp0+fNmw2m/HnP//ZMAzDOHz4sCHJ+OSTT8yZ9957zwgKCjL+8Y9/GIZhGC+++KLRoUMHM7dhGMacOXOM3r17m5f/9V//1UhJSfFZT2JiovFv//Zv170Wf2Y3jO9Kzv3333/V61gle0VFhSHJ2L59u3ns5vL9fT1r8Vduw/jul97TTz991etYIXetDh06GC+//HLAPN7fz20YgfV4fx8vV/lRVVWViouLlZSUZG4LDg5WUlKSioqKmnBl9fv888/VtWtX3XrrrZo0aZLKysokScXFxfJ6vT55+vTpo+7du5t5ioqK1L9/f58PYHQ6naqsrNShQ4fMmcuPUTtTe4ymvt+OHz8ut9vtc/vt27dXYmKiT87IyEgNHTrUnElKSlJwcLA+/vhjc2bEiBEKCwszZ5xOp0pLS3Xq1Clzpr774nrW0hgKCwsVFRWl3r17a/r06frmm2/MfVbJfubMGUlSx44dJTWv7+/rWYu/ctdau3atOnfurH79+ikzM1Pnz58391khd3V1tV5//XWdO3dODocjYB7v7+euZfXH+2pa9CceNzf/7//9P1VXV9f5xOXo6GgdOXKkiVZVv8TEROXm5qp37946ceKEFixYoLvvvlsHDx6U2+1WWFhYnT9iGh0dLbfbLUlyu91XzFu7r76ZyspKXbhwQadOnWrS+612nVe6/cszREVF+exv1aqVOnbs6DMTFxdX5xi1+zp06HDV++LyY1xrLf42ZswYPfDAA4qLi9OxY8f07LPPauzYsSoqKlJISIglstfU1GjGjBn66U9/qn79+pm311y+v69nLf7KLUmPPPKIevTooa5du2r//v2aM2eOSktL9eabb7b43AcOHJDD4dDFixfVtm1bvfXWW4qPj1dJSYmlH++r5Zas/XhfCyUnwI0dO9b894ABA5SYmKgePXpow4YNat26dROuDDfLQw89ZP67f//+GjBggG677TYVFhZq1KhRTbgy/0lLS9PBgwe1c+fOpl7KTXW13E899ZT57/79+6tLly4aNWqUjh07pttuu+1mL9OvevfurZKSEp05c0ZvvPGGUlNTtX379qZeVqO7Wu74+HhLP97XwstVftS5c2eFhITUead4eXm5YmJimmhVDRMZGak77rhDR48eVUxMjKqqqnT69GmfmcvzxMTEXDFv7b76Zux2u1q3bt3k91vtbdR3+zExMaqoqPDZf+nSJZ08edIv98Xl+6+1lsZ26623qnPnzjp69Ki5ppacPT09XZs2bdIHH3ygbt26mdub0/f39azFX7mvJDExUZJ8HvOWmjssLEy9evVSQkKCFi9erIEDB2rlypWWf7yvlvtKrPR4Xwslx4/CwsKUkJCggoICc1tNTY0KCgp8Xhttzs6ePatjx46pS5cuSkhIUGhoqE+e0tJSlZWVmXkcDocOHDjg80vQ5XLJbrebT5U6HA6fY9TO1B6jqe+3uLg4xcTE+Nx+ZWWlPv74Y5+cp0+fVnFxsTmzbds21dTUmD8wHA6HduzYIa/Xa864XC717t1bHTp0MGfquy+uZy2N7e9//7u++eYbdenSxVxzS8xuGIbS09P11ltvadu2bXVeTmtO39/XsxZ/5b6SkpISSfJ5zFta7qupqamRx+Ox7ON9rdxXYuXHu45GeTtzAHv99dcNm81m5ObmGocPHzaeeuopIzIy0udd683Jr371K6OwsNA4fvy48dFHHxlJSUlG586djYqKCsMwvjvdr3v37sa2bduMvXv3Gg6Hw3A4HOb1a089TE5ONkpKSowtW7YYt9xyyxVPPZw1a5bx2WefGdnZ2Vc89bAx77dvv/3W2Ldvn7Fv3z5DkrFs2TJj3759xv/93/8ZhvHdqcuRkZHGO++8Y+zfv9+4//77r3gK+eDBg42PP/7Y2Llzp3H77bf7nEZ9+vRpIzo62nj00UeNgwcPGq+//roRERFR5zTqVq1aGb///e+Nzz77zJg3b94VT6O+1lr8lf3bb781fv3rXxtFRUXG8ePHjffff98YMmSIcfvttxsXL15s0dmnT59utG/f3igsLPQ5dfb8+fPmTHP6/r7WWvyV++jRo8bChQuNvXv3GsePHzfeeecd49ZbbzVGjBjRonMbhmE888wzxvbt243jx48b+/fvN5555hkjKCjIyM/Pv67bsmJuKz/e14OS0wheeOEFo3v37kZYWJgxbNgwY/fu3U29pKuaOHGi0aVLFyMsLMz40Y9+ZEycONE4evSouf/ChQvGv//7vxsdOnQwIiIijJ///OfGiRMnfI7xxRdfGGPHjjVat25tdO7c2fjVr35leL1en5kPPvjAGDRokBEWFmbceuutxpo1a+qspTHvtw8++MCQVOcrNTXVMIzvTl9+7rnnjOjoaMNmsxmjRo0ySktLfY7xzTffGA8//LDRtm1bw263G5MnTza+/fZbn5m//vWvxl133WXYbDbjRz/6kfH888/XWcuGDRuMO+64wwgLCzPuvPNOIy8vz2f/9azFX9nPnz9vJCcnG7fccosRGhpq9OjRw5g6dWqdctkSs18psySf773m9P19PWvxR+6ysjJjxIgRRseOHQ2bzWb06tXLmDVrls/nprTE3IZhGE888YTRo0cPIywszLjllluMUaNGmQXnem/Larmt/HhfjyDDMIzGeY4IAACg6fCeHAAAYEmUHAAAYEmUHAAAYEmUHAAAYEmUHAAAYEmUHAAAYEmUHAAAYEmUHAAAYEmUHAAAYEmUHAAAYEmUHAAAYEmUHAAAYEn/HySWRTmJdBE7AAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_all.hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "ab60bcd3",
   "metadata": {},
   "outputs": [],
   "source": [
    "string_to_int_mapping_book_data = string_to_int_mapping.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "41edbbdb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>User-ID</th>\n",
       "      <th>Location</th>\n",
       "      <th>Age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>nyc, new york, usa</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>stockton, california, usa</td>\n",
       "      <td>18.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>moscow, yukon territory, russia</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>porto, v.n.gaia, portugal</td>\n",
       "      <td>17.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>farnborough, hants, united kingdom</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>278853</th>\n",
       "      <td>278854</td>\n",
       "      <td>portland, oregon, usa</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>278854</th>\n",
       "      <td>278855</td>\n",
       "      <td>tacoma, washington, united kingdom</td>\n",
       "      <td>50.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>278855</th>\n",
       "      <td>278856</td>\n",
       "      <td>brampton, ontario, canada</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>278856</th>\n",
       "      <td>278857</td>\n",
       "      <td>knoxville, tennessee, usa</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>278857</th>\n",
       "      <td>278858</td>\n",
       "      <td>dublin, n/a, ireland</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>278858 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        User-ID                            Location   Age\n",
       "0             1                  nyc, new york, usa   NaN\n",
       "1             2           stockton, california, usa  18.0\n",
       "2             3     moscow, yukon territory, russia   NaN\n",
       "3             4           porto, v.n.gaia, portugal  17.0\n",
       "4             5  farnborough, hants, united kingdom   NaN\n",
       "...         ...                                 ...   ...\n",
       "278853   278854               portland, oregon, usa   NaN\n",
       "278854   278855  tacoma, washington, united kingdom  50.0\n",
       "278855   278856           brampton, ontario, canada   NaN\n",
       "278856   278857           knoxville, tennessee, usa   NaN\n",
       "278857   278858                dublin, n/a, ireland   NaN\n",
       "\n",
       "[278858 rows x 3 columns]"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "user_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "ff615f62",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>User-ID</th>\n",
       "      <th>Location</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>nyc, new york, usa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>stockton, california, usa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>moscow, yukon territory, russia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>porto, v.n.gaia, portugal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>farnborough, hants, united kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>278853</th>\n",
       "      <td>278854</td>\n",
       "      <td>portland, oregon, usa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>278854</th>\n",
       "      <td>278855</td>\n",
       "      <td>tacoma, washington, united kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>278855</th>\n",
       "      <td>278856</td>\n",
       "      <td>brampton, ontario, canada</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>278856</th>\n",
       "      <td>278857</td>\n",
       "      <td>knoxville, tennessee, usa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>278857</th>\n",
       "      <td>278858</td>\n",
       "      <td>dublin, n/a, ireland</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>278858 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        User-ID                            Location\n",
       "0             1                  nyc, new york, usa\n",
       "1             2           stockton, california, usa\n",
       "2             3     moscow, yukon territory, russia\n",
       "3             4           porto, v.n.gaia, portugal\n",
       "4             5  farnborough, hants, united kingdom\n",
       "...         ...                                 ...\n",
       "278853   278854               portland, oregon, usa\n",
       "278854   278855  tacoma, washington, united kingdom\n",
       "278855   278856           brampton, ontario, canada\n",
       "278856   278857           knoxville, tennessee, usa\n",
       "278857   278858                dublin, n/a, ireland\n",
       "\n",
       "[278858 rows x 2 columns]"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# age 칼럼 삭제\n",
    "user_data2 = user_data.drop(columns = ['Age'])\n",
    "user_data2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "cb79d41f",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>User-ID</th>\n",
       "      <th>ISBN</th>\n",
       "      <th>Book-Rating</th>\n",
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       "      <td>276726</td>\n",
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       "      <td>276727</td>\n",
       "      <td>0446520802</td>\n",
       "      <td>0</td>\n",
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       "      <th>1149775</th>\n",
       "      <td>276704</td>\n",
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       "      <td>9</td>\n",
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       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>1149777</th>\n",
       "      <td>276709</td>\n",
       "      <td>0515107662</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1149778</th>\n",
       "      <td>276721</td>\n",
       "      <td>0590442449</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1149779</th>\n",
       "      <td>276723</td>\n",
       "      <td>05162443314</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1149780 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         User-ID         ISBN  Book-Rating\n",
       "0         276725   034545104X            0\n",
       "1         276726   0155061224            5\n",
       "2         276727   0446520802            0\n",
       "3         276729   052165615X            3\n",
       "4         276729   0521795028            6\n",
       "...          ...          ...          ...\n",
       "1149775   276704   1563526298            9\n",
       "1149776   276706   0679447156            0\n",
       "1149777   276709   0515107662           10\n",
       "1149778   276721   0590442449           10\n",
       "1149779   276723  05162443314            8\n",
       "\n",
       "[1149780 rows x 3 columns]"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rating_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "88719a83",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         User-ID  Book-Rating\n",
      "1         276726            5\n",
      "3         276729            3\n",
      "4         276729            6\n",
      "6         276736            8\n",
      "7         276737            6\n",
      "...          ...          ...\n",
      "1149773   276704            5\n",
      "1149775   276704            9\n",
      "1149777   276709           10\n",
      "1149778   276721           10\n",
      "1149779   276723            8\n",
      "\n",
      "[433671 rows x 2 columns]\n"
     ]
    }
   ],
   "source": [
    "rating_data2 = rating_data.drop(columns = ['ISBN'])\n",
    "rating_data2\n",
    "\n",
    "rating_data3 = rating_data2[(rating_data2['Book-Rating'] != 0)]\n",
    "print(rating_data3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "ad9998ad",
   "metadata": {},
   "outputs": [],
   "source": [
    "# user_data 중 rating_data에 있는 유저들만 빼내어 unique_user 데이터에 할당 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "ff0e6703",
   "metadata": {},
   "outputs": [],
   "source": [
    "user_ids_rating = rating_data2['User-ID'].tolist()\n",
    "unique_user = user_data2[user_data2['User-ID'].isin(user_ids_rating)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "c42ff726",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    }\n",
       "\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>User-ID</th>\n",
       "      <th>Location</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>stockton, california, usa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>washington, dc, usa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>timmins, ontario, canada</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>germantown, tennessee, usa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>albacete, wisconsin, spain</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>278845</th>\n",
       "      <td>278846</td>\n",
       "      <td>toronto, ontario, canada</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>278848</th>\n",
       "      <td>278849</td>\n",
       "      <td>georgetown, ontario, canada</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>278850</th>\n",
       "      <td>278851</td>\n",
       "      <td>dallas, texas, usa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>278851</th>\n",
       "      <td>278852</td>\n",
       "      <td>brisbane, queensland, australia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>278853</th>\n",
       "      <td>278854</td>\n",
       "      <td>portland, oregon, usa</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>105283 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        User-ID                         Location\n",
       "1             2        stockton, california, usa\n",
       "6             7              washington, dc, usa\n",
       "7             8         timmins, ontario, canada\n",
       "8             9       germantown, tennessee, usa\n",
       "9            10       albacete, wisconsin, spain\n",
       "...         ...                              ...\n",
       "278845   278846         toronto, ontario, canada\n",
       "278848   278849      georgetown, ontario, canada\n",
       "278850   278851               dallas, texas, usa\n",
       "278851   278852  brisbane, queensland, australia\n",
       "278853   278854            portland, oregon, usa\n",
       "\n",
       "[105283 rows x 2 columns]"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "unique_user"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "f8db555d",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/hy/_k7gcypd00s_2mmm5h2jktx00000gn/T/ipykernel_1657/3778636167.py:4: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  unique_user['City'] = spliting[0].str.strip()\n",
      "/var/folders/hy/_k7gcypd00s_2mmm5h2jktx00000gn/T/ipykernel_1657/3778636167.py:5: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  unique_user['State'] = spliting[1].str.strip()\n",
      "/var/folders/hy/_k7gcypd00s_2mmm5h2jktx00000gn/T/ipykernel_1657/3778636167.py:6: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  unique_user['Country'] = spliting[2].str.strip()\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "    }\n",
       "\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>User-ID</th>\n",
       "      <th>Location</th>\n",
       "      <th>City</th>\n",
       "      <th>State</th>\n",
       "      <th>Country</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>stockton, california, usa</td>\n",
       "      <td>stockton</td>\n",
       "      <td>california</td>\n",
       "      <td>usa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>washington, dc, usa</td>\n",
       "      <td>washington</td>\n",
       "      <td>dc</td>\n",
       "      <td>usa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>timmins, ontario, canada</td>\n",
       "      <td>timmins</td>\n",
       "      <td>ontario</td>\n",
       "      <td>canada</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>germantown, tennessee, usa</td>\n",
       "      <td>germantown</td>\n",
       "      <td>tennessee</td>\n",
       "      <td>usa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>albacete, wisconsin, spain</td>\n",
       "      <td>albacete</td>\n",
       "      <td>wisconsin</td>\n",
       "      <td>spain</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>278845</th>\n",
       "      <td>278846</td>\n",
       "      <td>toronto, ontario, canada</td>\n",
       "      <td>toronto</td>\n",
       "      <td>ontario</td>\n",
       "      <td>canada</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>278848</th>\n",
       "      <td>278849</td>\n",
       "      <td>georgetown, ontario, canada</td>\n",
       "      <td>georgetown</td>\n",
       "      <td>ontario</td>\n",
       "      <td>canada</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>278850</th>\n",
       "      <td>278851</td>\n",
       "      <td>dallas, texas, usa</td>\n",
       "      <td>dallas</td>\n",
       "      <td>texas</td>\n",
       "      <td>usa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>278851</th>\n",
       "      <td>278852</td>\n",
       "      <td>brisbane, queensland, australia</td>\n",
       "      <td>brisbane</td>\n",
       "      <td>queensland</td>\n",
       "      <td>australia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>278853</th>\n",
       "      <td>278854</td>\n",
       "      <td>portland, oregon, usa</td>\n",
       "      <td>portland</td>\n",
       "      <td>oregon</td>\n",
       "      <td>usa</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>105283 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        User-ID                         Location        City       State  \\\n",
       "1             2        stockton, california, usa    stockton  california   \n",
       "6             7              washington, dc, usa  washington          dc   \n",
       "7             8         timmins, ontario, canada     timmins     ontario   \n",
       "8             9       germantown, tennessee, usa  germantown   tennessee   \n",
       "9            10       albacete, wisconsin, spain    albacete   wisconsin   \n",
       "...         ...                              ...         ...         ...   \n",
       "278845   278846         toronto, ontario, canada     toronto     ontario   \n",
       "278848   278849      georgetown, ontario, canada  georgetown     ontario   \n",
       "278850   278851               dallas, texas, usa      dallas       texas   \n",
       "278851   278852  brisbane, queensland, australia    brisbane  queensland   \n",
       "278853   278854            portland, oregon, usa    portland      oregon   \n",
       "\n",
       "          Country  \n",
       "1             usa  \n",
       "6             usa  \n",
       "7          canada  \n",
       "8             usa  \n",
       "9           spain  \n",
       "...           ...  \n",
       "278845     canada  \n",
       "278848     canada  \n",
       "278850        usa  \n",
       "278851  australia  \n",
       "278853        usa  \n",
       "\n",
       "[105283 rows x 5 columns]"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Spliting the columns 'Location' to create the columns 'City', 'State' and 'Country' \n",
    "\n",
    "spliting = unique_user['Location'].str.split(pat=',',expand=True)\n",
    "unique_user['City'] = spliting[0].str.strip()\n",
    "unique_user['State'] = spliting[1].str.strip()\n",
    "unique_user['Country'] = spliting[2].str.strip()\n",
    "\n",
    "unique_user"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "d6ab0204",
   "metadata": {},
   "outputs": [],
   "source": [
    "unique_user= unique_user.drop('Location', axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "52fb851f",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "        User-ID        City       State    Country  City_encoded  \\\n",
      "1             2    stockton  california        usa             0   \n",
      "6             7  washington          dc        usa             1   \n",
      "7             8     timmins     ontario     canada             2   \n",
      "8             9  germantown   tennessee        usa             3   \n",
      "9            10    albacete   wisconsin      spain             4   \n",
      "...         ...         ...         ...        ...           ...   \n",
      "278845   278846     toronto     ontario     canada           180   \n",
      "278848   278849  georgetown     ontario     canada          1022   \n",
      "278850   278851      dallas       texas        usa           105   \n",
      "278851   278852    brisbane  queensland  australia           255   \n",
      "278853   278854    portland      oregon        usa            14   \n",
      "\n",
      "        State_encoded  Country_encoded  \n",
      "1                   0                0  \n",
      "6                   1                0  \n",
      "7                   2                1  \n",
      "8                   3                0  \n",
      "9                   4                2  \n",
      "...               ...              ...  \n",
      "278845              2                1  \n",
      "278848              2                1  \n",
      "278850             23                0  \n",
      "278851             80                8  \n",
      "278853             12                0  \n",
      "\n",
      "[105283 rows x 7 columns]\n"
     ]
    }
   ],
   "source": [
    "# 문자열을 정수로 매핑할 딕셔너리 생성\n",
    "unique_strings = unique_user['City'].unique()\n",
    "string_to_int_mapping = {string: idx for idx, string in enumerate(unique_strings)}\n",
    "\n",
    "# 'A' 열의 문자열을 정수로 인코딩\n",
    "unique_user['City_encoded'] = unique_user['City'].map(string_to_int_mapping)\n",
    "\n",
    "# 문자열을 정수로 매핑할 딕셔너리 생성\n",
    "unique_strings = unique_user['State'].unique()\n",
    "string_to_int_mapping = {string: idx for idx, string in enumerate(unique_strings)}\n",
    "\n",
    "# 'A' 열의 문자열을 정수로 인코딩\n",
    "unique_user['State_encoded'] = unique_user['State'].map(string_to_int_mapping)\n",
    "# 문자열을 정수로 매핑할 딕셔너리 생성\n",
    "unique_strings = unique_user['Country'].unique()\n",
    "string_to_int_mapping = {string: idx for idx, string in enumerate(unique_strings)}\n",
    "\n",
    "# 'A' 열의 문자열을 정수로 인코딩\n",
    "unique_user['Country_encoded'] = unique_user['Country'].map(string_to_int_mapping)\n",
    "\n",
    "# 결과 출력\n",
    "print(unique_user)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "id": "9dce612d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x3420f7490>,\n",
       " <matplotlib.lines.Line2D at 0x337ebe170>]"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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iyZZuycaiDUdkl0GkKQwwREQSzP/2IFZsPy67DCLNYoAhInIjPmiRyDl4IzsiIjcQQmD38WKGFyIn4QgMEZGLnS6rxnUv8Xb/RM7EAENE5EKLNx7BeynZsssg8joMMERELpCSVYhJy3fLLoPIazHAEBE5ESfpErkHAwwRkZM8/90hLNuWI7sMIp/AAENE1AJvJx/F57tykV/KO+gSuRMDDBFRM+zLK8HdS7bJLoPIZzHAEBE5oKSyBkOeT5JdBpHPY4AhIrLDwg2H8cGWX2WXQUS/Y4AhIrqELFMZ4t5MlV0GEV2EjxIgImrCpkMFDC9EHoojMEREFzlbUYNFG47gyz15skshoiYwwBAR/Y43oSPSDp5CIiICkPrLaYYXIg3hCAwR+bT9J0pw17u8nwuR1jDAEJFPqrVYcdWzG2SXQUTNxABDRD6lsqYOX+zKw/NrD8kuhYhagAGGiHzC6bJqXPfSJtllEJGTMMAQkdcb9nIyTGY+bJHImzDAEJFX+qWgDCPf4E3oiLwVAwwReZXjZypwy6spsssgIhdjgCEir2CxCizfloMX1x2WXQoRuQEDDBFp2m9FFbj5lRTZZRCRm/FOvESkWf9JP8HwQuSjOAJDRJrU7el1sksgIok4AkNEmpKWXcTwQkQcgSEibTCVVmHYwmTZZRCRh2CAISKPVmex4tHP92JDpkl2KUTkQRhgiMjjFJVXY+iLvO0/ETWNc2CIyKPkFVcyvBDRZXEEhog8Qp3Fip7PbpBdBhFpBEdgiEg6q1UwvBCRQxhgiEiqovJqdH9mvewyiEhjeAqJiKQQQuD6l5NxuqxadilEpEEMMETkFkIIZJ+uQEllDe5bmia7HCLSOAYYInKpZVtzkFtciRXbj8suhYi8CAMMEblE3BupyCook10GEXkpBhgicqqSyhoMeT5JdhlE5OV4FRIROU36b8UML0TkFhyBISKn4BOiicidOAJDRC1SXFHD8EJEbscRGCJqFqtV8AZ0RCSNwyMwqampuPPOOxEREQFFUbBmzRqb9ZMmTYKiKDavUaNG2bQpLi7G+PHjERQUhJCQEEyePBnl5eU2bfbv34+bbroJAQEBiIyMxOLFix0/OiJyCYYXIpLN4QBTUVGBwYMHY8mSJU22GTVqFPLz89XX559/brN+/PjxOHjwIJKSkrB27VqkpqZi6tSp6nqz2YyRI0eia9euSE9PxyuvvIL58+fjww8/dLRcInKyypo6hhciks7hU0ijR4/G6NGjL9nGaDQiPDy80XWHDx/Gxo0bsXv3blx77bUAgHfeeQe33347Xn31VUREROCzzz5DTU0Nli1bBoPBgP79+yMjIwOvv/66TdAhIvd6Ye0hfLQ1R3YZRESumcSbkpKC0NBQ9O7dG9OnT0dRUZG6Li0tDSEhIWp4AYDY2FjodDrs3LlTbTN8+HAYDAa1TVxcHLKysnD27NlG91ldXQ2z2WzzIiLnuf6lTQwvROQxnD6Jd9SoUbj33nsRFRWF7OxsPPPMMxg9ejTS0tKg1+thMpkQGhpqW4SfH9q3bw+TyQQAMJlMiIqKsmkTFhamrmvXrl2D/S5cuBALFixw9uEQ+SQhBA7nl+Hn3LM4fqYC/2JwISIP4/QAM3bsWPXrgQMHYtCgQejRowdSUlIwYsQIZ+9OlZiYiJkzZ6rfm81mREZGumx/RN6m1mLFS+sO85lFRKQJLr+Munv37ujQoQOOHTuGESNGIDw8HIWFhTZt6urqUFxcrM6bCQ8PR0FBgU2b+u+bmltjNBphNBpdcARE3q974jpYhewqiIjs5/Ib2Z04cQJFRUXo1KkTACAmJgYlJSVIT09X22zevBlWqxXR0dFqm9TUVNTW1qptkpKS0Lt370ZPHxGR42rqrDhWWI5uTzO8EJH2ODwCU15ejmPHjqnf5+TkICMjA+3bt0f79u2xYMECjBkzBuHh4cjOzsZTTz2Fnj17Ii4uDgDQt29fjBo1ClOmTMHSpUtRW1uLGTNmYOzYsYiIiAAAjBs3DgsWLMDkyZMxe/ZsZGZm4q233sIbb7zhpMMm8m28cy4RaZ3DIzB79uzB1VdfjauvvhoAMHPmTFx99dWYO3cu9Ho99u/fj7vuugu9evXC5MmTMXToUPz00082p3c+++wz9OnTByNGjMDtt9+OG2+80eYeL8HBwfjhhx+Qk5ODoUOH4oknnsDcuXN5CTVRC5Weq2V4ISKvoAghvHLw2Gw2Izg4GKWlpQgKCpJdDpF0QghEJfIGdETkPMcXxTt9m/Z+fvNhjkReTgiB1F9OM7wQkVfhwxyJvNSxwjLEvp4quwwiIpfgCAyRlxFC4JuMkwwvROTVOAJD5EUOnipF/NtbZZdBRORyDDBEXiC/9BxiFm6WXQYRkdswwBBplBACCSt/xvoDJtmlEBG5HQMMkQaVV9dhwLzvZZdBRCQNJ/ESaYzVKhheiMjnMcAQaci2Y2fQ/Rnez4WIiKeQiDRixsqfsXZ/vuwyiIg8AgMMkYf7ak8eZv1nv+wyiIg8CgMMkQfjgxeJiBrHOTBEHqiq1sLwQkR0CQwwRB4mr7gSfZ7bKLsMIiKPxlNIRB7AahW8uoiIyAEMMEQSCSGwaMMRfJD6q+xSiIg0hQGGSBJzVS0Gzf9BdhlERJrEAEPkZrtyivHnD9Jkl0FEpGkMMERuYrEK9OA8FyIip+BVSERucPBUKcMLEZETMcAQuVhlTR3i394quwwiIq/CAEPkQtuPnUG/uXxyNBGRs3EODJELvLXpKN7Y9IvsMoiIvBZHYIicbM/xYoYXIiIXY4AhcqIfjxTivqW8RJqIyNUYYIic5HC+GQ+t2C27DCIin8A5MEQttD37DMb9c6fsMoiIfApHYIha4Lt9pxheiIgk4AgMUTP8M/VXvLT+sOwyiIikqqq1IMBfL2XfDDBEDur29DrZJRAReYTKGnkBhqeQiOy09egZhhciIg/BERiiyxBCICqRzzEiIvIkHIEhuoRvMk4yvBAReSAGGKIm7D9Rgn98kSG7DCIiagQDDFET7np3m+wSiIioCQwwRI2Y/mm67BKIiOgSGGCILpL49X5syDTJLoOIiC6BVyER/e6p/+zDqj0nZJdBRKQZisR9M8CQzztWWI7Y17fILoOIiBzAU0jk0zLyShheiIg0iAGGfNa3+07hniW80oiISIt4Col80tc/n8DMVftkl0FERM3EAEM+5+4l27Avr0R2GURE1AIMMORT+DBGIiLvwDkw5DMYXoiIvAcDDPmED7Zkyy6BiIiciKeQyOvd+moKcs5UyC6DiMjrKBLvZMcAQ15LCIGoxPWyyyAiIhfgKSTySv/66VeGFyIiL8YRGPIqp0rO4Q+LNssug4iIXIwjMOQ1SiprGF6IiNxIkfg4R47AkOZxrgsRke/hCAxp2r68EoYXIiIfxABDmrX92BnczYcxEhH5JJ5CIs2xWAV6PMNRFyIi6STeB4YjMKQpmw4VMLwQEZHjASY1NRV33nknIiIioCgK1qxZY7NeCIG5c+eiU6dOCAwMRGxsLI4ePWrTpri4GOPHj0dQUBBCQkIwefJklJeX27TZv38/brrpJgQEBCAyMhKLFy92/OjIq6RkFeKRf++RXQYREXkAhwNMRUUFBg8ejCVLljS6fvHixXj77bexdOlS7Ny5E61bt0ZcXByqqqrUNuPHj8fBgweRlJSEtWvXIjU1FVOnTlXXm81mjBw5El27dkV6ejpeeeUVzJ8/Hx9++GEzDpG8wZw1BzBp+W7ZZRARkYdQhBCi2W9WFKxevRr33HMPgPOjLxEREXjiiSfw5JNPAgBKS0sRFhaGFStWYOzYsTh8+DD69euH3bt349prrwUAbNy4EbfffjtOnDiBiIgIvP/++3j22WdhMplgMBgAAE8//TTWrFmDI0eO2FWb2WxGcHAwSktLERQU1NxDJA8w9sM07Pi1WHYZRER0kX3zRiI40N+p27T389upc2BycnJgMpkQGxurLgsODkZ0dDTS0tIAAGlpaQgJCVHDCwDExsZCp9Nh586dapvhw4er4QUA4uLikJWVhbNnzza67+rqapjNZpsXad/2Y2cYXoiIqAGnXoVkMpkAAGFhYTbLw8LC1HUmkwmhoaG2Rfj5oX379jZtoqKiGmyjfl27du0a7HvhwoVYsGCBcw6EpBsw73uUV9fJLoOIiDyU11yFlJiYiNLSUvWVl5cnuyRqpm5Pr2N4ISKiS3JqgAkPDwcAFBQU2CwvKChQ14WHh6OwsNBmfV1dHYqLi23aNLaNC/dxMaPRiKCgIJsXaUtlTR26Pb1OdhlERGQnxVvuAxMVFYXw8HAkJyery8xmM3bu3ImYmBgAQExMDEpKSpCenq622bx5M6xWK6Kjo9U2qampqK2tVdskJSWhd+/ejZ4+Iu/Qb+73sksgIiKNcDjAlJeXIyMjAxkZGQDOT9zNyMhAbm4uFEXBY489hhdffBHffvstDhw4gAcffBARERHqlUp9+/bFqFGjMGXKFOzatQvbtm3DjBkzMHbsWERERAAAxo0bB4PBgMmTJ+PgwYP48ssv8dZbb2HmzJlOO3DyLBx5ISIiRzg8iXfPnj249dZb1e/rQ8XEiROxYsUKPPXUU6ioqMDUqVNRUlKCG2+8ERs3bkRAQID6ns8++wwzZszAiBEjoNPpMGbMGLz99tvq+uDgYPzwww9ISEjA0KFD0aFDB8ydO9fmXjHkPRheiIjIUS26D4wn431gtGHHr0UY++EO2WUQEVEz7J8/EkEBcu4Dw4c5kjRP/3c/vtjNq8WIiMhxDDDkdrUWK656doPsMoiISMO85j4wpA0HT5UyvBARUYsxwJBbxb+9VXYJRETkJBJvA8MAQ+6xYlsOrzYiIvIyisQ72XEODLkcgwsRETkbR2DIpRheiIjIFRhgyGW+/vmE7BKIiMhLMcCQSxRX1GDmqn2yyyAiIi/FAENOV11nwTUvJMkug4iIvBgDDDnV5iMF6D1no+wyiIjIy/EqJHIKIQSiEtfLLoOIiHwER2DIKRheiIh8D29kR5r17b5TvFSaiIjcjqeQqFmsVoHuz3DUhYiI5OAIDDms1mJleCEiIqkYYMghQgg+TZqIiKRjgCGHcLIuERF5AgYYshsn6xIRkadggCG7/DedzzUiIiLPwauQ6JKEEBj3z51I+7VIdilERORhFIk3gmGAoSbV1FnRaw4n7BIRkefhKSRqEsMLERF5KgYYaqC8uo4TdomIyKMxwFADA+Z9L7sEIiKiS2KAIRv78kpkl0BERBqhSHycIyfxkoqnjYiISCs4AkMAGF6IiEhbGGCI4YWIiJpF5n1gGGB83OtJv8gugYiIyGGcA+Ojai1WPlWaiIg0iwHGxyzbmoPn1x6SXQYREVGL8BSSDzldVs3wQkREXoEjMD6CE3WJiMibcATGB9yzZJvsEoiIiJyKIzBezGIV6PHMetllEBEROR1HYLyUEAwvRETkvRhgvFRUIsMLERG5Vlp2kbR9M8B4oRGvpcgugYiIfEBRRY20fXMOjJfpnrgOViG7CiIiItfiCIwXOXiqlOGFiIh8AgOMl/hP+gnEv71VdhlERERuwQDjBb7ddwpPfrVPdhlERERuwwCjcesP5OPvn++VXQYREZFbMcBomKm0Cn/77GfZZRAREbkdr0LSqOfWZOKTHb/JLoOIiHyYInHfDDAaNOrNVBwxlckug4iIfJwiMcHwFJLGCCEYXoiIyOcxwGjMwPk/yC6BiIhIOgYYDXlx7SGUV9fJLoOIiEg6BhiN+Hj7cfxra47sMoiIiDwCA4xGzPv2oOwSiIiIPAYDjAZ0e3qd7BKIiIg8CgOMhxOCT2ckIiK6GAOMh4tKXC+7BCIiIo/DAOPBqusssksgIiLySAwwHspqFeg9Z6PsMoiIiDyS0wPM/PnzoSiKzatPnz7q+qqqKiQkJOCKK65AmzZtMGbMGBQUFNhsIzc3F/Hx8WjVqhVCQ0Mxa9Ys1NX51v1Puj/DU0dERERNccmzkPr3749Nmzb9byd+/9vN448/jnXr1uGrr75CcHAwZsyYgXvvvRfbtm0DAFgsFsTHxyM8PBzbt29Hfn4+HnzwQfj7++Pll192Rbkeh1cdERERXZpLAoyfnx/Cw8MbLC8tLcVHH32ElStX4rbbbgMALF++HH379sWOHTswbNgw/PDDDzh06BA2bdqEsLAwDBkyBC+88AJmz56N+fPnw2AwuKJkj8GrjoiIiC7PJXNgjh49ioiICHTv3h3jx49Hbm4uACA9PR21tbWIjY1V2/bp0wddunRBWloaACAtLQ0DBw5EWFiY2iYuLg5msxkHDzZ9M7fq6mqYzWablxbxqiMiIqLLc3qAiY6OxooVK7Bx40a8//77yMnJwU033YSysjKYTCYYDAaEhITYvCcsLAwmkwkAYDKZbMJL/fr6dU1ZuHAhgoOD1VdkZKRzD8zFhBA8dURERGQnp59CGj16tPr1oEGDEB0dja5du2LVqlUIDAx09u5UiYmJmDlzpvq92WzWVIh5cNku2SUQERFphssvow4JCUGvXr1w7NgxhIeHo6amBiUlJTZtCgoK1Dkz4eHhDa5Kqv++sXk19YxGI4KCgmxeWvLT0TOySyAiItIMlweY8vJyZGdno1OnThg6dCj8/f2RnJysrs/KykJubi5iYmIAADExMThw4AAKCwvVNklJSQgKCkK/fv1cXa4UvxVVyC6BiIioEQLhKGqwtItSgC5KQSPt3cfpp5CefPJJ3HnnnejatStOnTqFefPmQa/X44EHHkBwcDAmT56MmTNnon379ggKCsKjjz6KmJgYDBs2DAAwcuRI9OvXDxMmTMDixYthMpkwZ84cJCQkwGg0Ortcj3DzKymySyAiIh9lRA2s0OGPuj04I4KxyvgCACDL2hm9dScAAG/X3YPX6/4MADCgFqnGxwEAayw/yykaLggwJ06cwAMPPICioiJ07NgRN954I3bs2IGOHTsCAN544w3odDqMGTMG1dXViIuLw3vvvae+X6/XY+3atZg+fTpiYmLQunVrTJw4Ec8//7yzS/UImSdLZZdAREReQg8LWuMcauCPgUoO5vh/ih8s1yJSKcRrdX/GAF0OpujXYVbtXzFQl4PX/N9Ha6W60W3VhxcA+LvfGjXAtMY5dbm/pdy1B3QJivDSG4+YzWYEBwejtLTUo+fD8MojIiJqjgBU41PDQqy1DMMKyyhcidPYFvAPAECJaI0QxXZ6wo+WwbhVvw8AsNXSHzfqm741SWO6Va0EALSDGXsDpgEA1o1KRfywwS09FBv2fn675EZ2ZB+GFyIiappAZ+UMXvN/H2ssNyDZcg3+7vc1OitncMvvQQQArtX9gk8sf1TDC4AG4QUAeiqn1K+vUMqcUqECxSnbaQ4GGEn2HC+WXQIREbmNgA4C7VCGGvjjet1htFfKsMFyPaphwDT9t8gSkfjQ8AaqhT+ur16CN/zfw236DABAtO4Izvl9gkClptkVROpOO+lYPAMDjCT3LU2TXQIREdkpANW4XncEtfBDmrU/AGCg8iv66HLxleVm4PeRiHH6ZLzs/xFm1U7FV5Zb4Ic6rDS8hOt1WY1u9zbdXuy19sQT/v9RlxmVWsz0+0oNL/VaEl4a0v7sEQYYCbJPy5v0RETk2wTaowzFCEIHlCJYKUdHpRRXKScQgBr8y3I7BHQYohzD8/7LMUiX02ALN1S9hZPoiO+McwAAp0UIUqxDAAAv+38EAHjF/0N8ZbkFI3Q/NxleAGC0fjcqEdBgeRulygnH6t0YYCQY8doW2SUQEXkNA2pRBz3+rE/Beks0zGiN1jiHsfrNGKY7jD5KHp6pm4w7dDtwv18KAGByzRP4yPBag211UQrxXN3DWGOc2+T+onQmnLR2VL/vqZxECoY02tYflmYelfZHSFyNAYaIiNzOiBoIKL9f7vsr+uuO44i1C67RHcUKSxysjdxntS0qMcvvS5hEe8ToDmJ+3UQkG2fZtFnk/y90q1qJF/yX4179VnX5J4ZFNu0aCy8AMMFvE56re9gJR9gyYy6o3RXkTb11HgYYN7NYmaqJyLMZUYNrdEeRbu2FGviry69WjiJYqUCKdQgCUI3pft/he8u1OCS62bz/AX0yeiknsKDuQTT8qBTYZ5yCYKUSABBd9a56KqZeFQxYaRnRoK4n/b7Eg35J6vfJ+lkN2tQbqdtj38E6wX36VPzLEu+2/dF5DDBuZLEK9HhmvewyiMjHBaIKHZRSWIQevXV5OC7C8ZLfR/iD/hB2WvsgWncEAPAfy3A8WTtNfd9q4zwA5+eATPDbhGl+3+Effl+r9wept/D3eSDfW6/DDqvtI2B6KSfU8AIAQ3W/NKivn3K80bq7K/mOH6wLKBed3umjy5NUyf8IB8dUvOGf0gwwbsTwQkSuoMAK8fsplyBUwIgajNX/iNXWm3BCdMQQ5Rj+ZXgVIShHYt0jeMX/wya3VR9egPMjCxcGmHqdlKImQ4YCq/p1W1Q2WG/PnBBnnN64OGSQreb0sT/qUOtBscFzKiEi8hkCVykncUxEqMEDAAYp2ShGEE6Ijk2+s4dyUp33cUJ0QIUIQGflNBJq/4GfrT2xP2Cq2naC2ITrq9+zmZB6qfBir6Y+/FqhCpuMT17m3eKi77Q3G0OLNTvDa/7v4++1j8ouQ8UA4yb7T5TILoGInE6g6+9P5LVCwRj9TygRbVAHPYJQgZ3WvvivcQEAIL76JRwUUQCAI8ZJCFBqcdgaidE1/wfg/NN9vzU+BwANTskA5+eldFfyscGYqC7rrJxR08QKw2JMqnnK5j2hSolTj7ZeU6Mbd+m3I0L53006m/tB7+mjJ86uz6/ZVyq51136NAYYX3TXu9tkl0BEdhPQw4q+ym/4q99apFn7Y4L+B/TV5WFizWxssQ7Grbq9WG54xe4tfmB4AzdWvw0ACFBqAQB9L5g70VfJVb9+w38J5tY+hDK0Upd9aXgBQ3TZl9zHIOVXu+ux11y/fyNHhOMTy0h1WVOxZJH/vxzeviMhx5G2njJG8he/TZdtc7d+e4v3487Qd7XumNv2dSkMMETkM67EaSiKQG8lD7usfTFYl40gVGC3tQ8S/NbgK8vNWGd8tsH77tTvUL/+2PB/6Fa10qHwAgAG1Nnd9k/6bSgRbbCgbiKA86dmLhdeAGDmBXdzdZaH/TYCgG2AUZr/YWnfB61nj8DYq5uSj2G6w27ZV5xut1v2AwDLDK+6bV+XwgDjYkIIRCVy8i6RK+lghR8saIUqTPP7DtkiAh1Rgv9YbsYsvy9RiBAk+H1r855061UYqjsKADhqvRJX6U5ikt8PLqvR0X8hh/9+Kma0bifeN7zlipKazd5jcVZUCUQV+ih5DvWhqyNQD+XkZdu0h3MemGiPSMW7nnNkDwYYF2N4IWoeBVb4w4Ja6NFVKUChaIfrdFnYbu2PTw0vo07oUQUDRuj3NrmNp/xXNbmuPrwAwFW6y38YuUpHnEUPXX6DD9z6UyD/54RJt57EntNAF7f43PAihugcOz3m6lNIF99AT2v0F1wtplUMMC6UdKhAdglEHkig/uMlGOWIUky4W78ND/l9DwAoEm3xh+p38JnhZQxSsvGN5Qb8P79U9d151o5e9VTd3QEJAIB/1/1RciX2s3ck5CPDa9houQ7Tah9HAKqx2jAPJaKNw/tzNLy4Wv3E7ctx59VKjo7wyQztzsIA40JT/u2+O0ESeQI/1KEOfmiFKozU7UGy9Rrcpd+O6X7fnr9iBkChCMEd1S9BDyvSAhpe0XCFUoa79Ntx7e83OLswvADQcHg5/wHTX2n4cEAAuEGXafO9J1+J40hto/S7YaytwT36beiry22kRcMP+SsUcwuqc70+SmPHIZenTFp2JwYYFzlW6L5zn0SyhKEYOwNmoEr4q1fWjK9JxGeGhU2+J1QpQYLfGuSKsCbbeMPwdmOilPxGJwlrjaMflgKKQ5cKj9SnA7UO7uQinhAAffV+Me7CAOMCFqtA7Oupl29I5LEEOqIUNfBDHyUPd+u3wow22GC5Dg/6/YDFtWOxM2CG2ro+vAC4ZHipp/U/6yHNnJw5sInRl8Z4wgdwUxyt7VIf5J57lE3zxGDiyT8vrsIA4wJ8ZAB5GgNqEaYUIwLFeNhvA1ZY4nCLLgNv1Y1BJQLUdkv930At9DaXDV9omt93AIC+ivxnv8iUEfBX2SW43bv+b6tf++KH5YU8McDIioKKxK5ggCHSHIEA1OB23U5Uw4B11mENWvzbfyGG6w9gk+VqDNFlo8NFcwri9OfnZ+lhxYt1EwAAvZQ8jNLbdy+Jq5QTLToCBcJDPwQ8h6f1zh0XhFrHTyFdap2nHak2+WIvMsA4WezrW2SXQF6gu3IKm39/psyDNbORah2MQFThJ+NjDcLIseoIZIkuNsuG6w8AAGIvcYkxcP7JwPXu07vvtKcC4XP/ivemDxhv/38XozsouwSHtVXOyS7B7RhgnOxYYbnsEshjCVyjHEWmiEIQKvGw3wbkiY743DICD+s34CH9RuwWvRGMCpt7m/z79zu/jtNvbhBeAOAq5WSDANO86jzHPXrve/RGY//vLs2T/o/YujjA9FBOIltc2WT7S8+BcU20a0nI+tzwUrPeF44im+/d+X+w/vSuL2GAcaKVOz3v0jqSQ4EVCoCOKMGDfj+gQgTiKf8v1fXFog3aK+fDbqlojbn+nwAAItH0JcIBqGliX875MxnYxPZdQcGlP7jcdft1ap6Lf+b+6f8abqt5XVI1jbtwYrmzDdftb3T5g35JLtsnNcQA40TPrD4guwRykzAUwwoFZrTGK/4f4C59GopEW8RUv4s5fp9e9g9ZfXgB0MS9MRpqKqY469+v7vzj6+2nIJzBk/soSjHZfB+hFDXR8vKaOso5fp9gjeUGZIruzd62q3R00VO+yTEMMESNUGBFOM5imO4QihGEAUoO2ill6K/8hp66E+j4++mAw9ZI9YnCVyhlGKdP5r/CyOu540Zzj/htwCN+G9CtaqXL9+Wopv7R0NTjIMg1GGCcpM7inTfe8kZtUInb9TtxxNoFVykn8V/rTYjX7cQ7/u/gpbrxeM7/U7u3VR9e6oUorpwDZe+fTfsJKOisFOKk6NDsbTR3z3F2XvHkTYbrGz/10BhP/vBzdHRID2uTE2N5FRI1FwOMk7zyfZbsEugibVCJCgTgEf16dFfy8YDfj422q6wxYonh/D0uHAkv7tbUH/qWnGq4Wb8fW/WPYWXdrc3eRnMoAKJ1R9y6T0/gziu9XMnRyDFR/z3i9btcUosMTf3GXfw76smnAb0BA4yTfJDqWQ8b81Xj9Zvwkv8yh94zUGf/3VFdwd4/cfFN3FzuLcN7KKlpiy3Wwc2uYVwT4Y6oMW1RiaG/P6vKHvfqf2pynRZHYFor1Xa188YA0waVsktQMcA4wZZftPpwOa0SiFaOoK1SiSBU4KToiF66PLzgv6JZW5P9R8beP98DdMebXPfx75daa4XsPqeWGe+X7OA7tBdSmuPiMLaymZdje7LH/f4ruwQVA4wTTFzmPUOjnkIHK8JRjCsUM970X4Ieunx8a4nB32sfxSHjw2hl57+A7CH7T+sAB56P4ylaGj/8Ffsf7OcrfPX0gzcfpSsv5ZYl4venynsCBhjyCAbU4v/pt+Al/2X4yTIAN+kzG7S5S5+Gv9c+6tTw4glu1e+TXYLbXa/43vwXX6bF00Tk+RhgWkgIb/73g2u0QhW6KgU4I4KwOyChwfrGwku9sfrNriyN3ERR+HtzOd40AnPpI/GecOM9/8ea5klhlAGmhR79/NLPmvFlISjDbbq9CFEq8IXlVvxZn4L5/v9u0TYX+f/LOcWRVDrwtgMX86bAcrGeyqlLrPXe4/ZGt3vQ1WQMMC20dn++7BKkaYUqRCqFNs/h6abkY5RuN572/8Kmbf2t8j1RLyXv8o3s5Dn/NvFs7KfLq++j1qiSWoczGL1wLkhjPGl0whcwwJBd/FCHjijF437/wSDdr/jUEosX/ZcDACbWzFYv4U0xPiGzzGa5TZ8hu4TLukax/5JVLeiIEtkleJymPvz0PN2mCTpYcYeu8VsdkGswwNAl9VZy8b3x6QbLX9QtV79+yu8LbKlp/j1I6PK+Ns6XXYINQwuvIvJTeArpcrz5lJIt7xi1+H/6Leih890ReRkYYFogt8hzbujTcgJBqIAZbRCOInxiWISrdCdlF6U5g5Rs2SWQRvH0g7b9oYlHJZDrMMC0wF8+2im7hBa5V5eKv/qtRW/dCXXZwzVPYpnhVYlVaZsvXhJNzhF40e0BtDQCc/5+J82r11uCm3b+b3kPBpgWyC32/BGYK3Ead+rTMNlvPVZaYvFG3X14wW8ZJvhtarR988OLwHCd/Q+qI+ebrF+PfdbuOCy6yi6FmqGzB90grDlG61x1dYo2okEvhSPW7sYA40WCUI6x+h/RQ8lHlC4fvZQTCFEq1PX/8PsaVyknnH4ZnAJghO5nfGR4zanbJcfUP4jSLFpJroQ8SWucc8t+mvt08Un6jZdc/4XhxWZt19366X6TXYIUiiJvBI0BRuM64ixG6XcjUjmNqX7rLtveVdfw38Dzvx4jSPH8kUFyn4MBk92yn+aeCrrcaddhusPN2i55PwaYZpqz5oC0fRtRg0X+/0QfJRd9dc67h0lz9dP9hr3WnrLLIPIqWpoDA3jPXBZyzJ7jZzHqejn71snZrfZ9uiPXjXsTeFi/AZnGh/EXfRKyAibhT/ptHhFe6jn+dFoiItK6tfsudZdl1+IIjIdSYEWkchr/9l+EbroCdXn9zeOIiDzJvfqtsksgCawSnwfIAONBJuh/wL36rYhQziBMKZFdDhER0SVZrAwwPquzchrXKUfwhuF92aUQkQfR2hwYIndjgGmGjLySZr1PgRXj9ckYp9/ss5fcERG52kT997JLIDdggGmGe5Zsc6i9P+rwqN/X+LvfGtcUREReRwEQjiLZZWjSAv+PZZdAbsAA40J/0GVipeFl2WUQkQYN1f2CHQGPyi6DyGMxwLjANP23eNr/C9llEJGGnX++EJFn63XBs/TcjQHGib4zPIOBuuOyyyAiInKLmyU+A48Bxgl0sOLXgL/ILoOIiMitZF4rxzvxOij1l9MNljG8EBERuRcDjIMeXGb7MMRX/ZdKqoSIiEgumc/A4imkZuJpIyIi8nU8hdSEJUuWoFu3bggICEB0dDR27dp1+Te5wU26/QwvRETk82SOwHhsgPnyyy8xc+ZMzJs3Dz///DMGDx6MuLg4FBYWSq3LiBp8YlgktQYiIiJf57EB5vXXX8eUKVPw0EMPoV+/fli6dClatWqFZcuWSa3rXf+3pe6fiIjIUxgh735FHhlgampqkJ6ejtjYWHWZTqdDbGws0tLSGn1PdXU1zGazzcsV/qj/2SXbJSIi0popfuul7dsjA8yZM2dgsVgQFhZmszwsLAwmk6nR9yxcuBDBwcHqKzIy0h2lEhER+ayqVhHS9u2RAaY5EhMTUVpaqr7y8vJcsp/0Pk+6ZLtERERaY5wg77E5HnkZdYcOHaDX61FQUGCzvKCgAOHh4Y2+x2g0wmg0ury2oWOfA/Ccy/dDRETk6eRdg+ShIzAGgwFDhw5FcnKyusxqtSI5ORkxMTESKyMiIiJP4JEjMAAwc+ZMTJw4Eddeey2uv/56vPnmm6ioqMBDDz0kuzQiIiKSzGMDzP3334/Tp09j7ty5MJlMGDJkCDZu3NhgYi8RERH5HkUIIfNOwC5jNpsRHByM0tJSBAUFyS6HiIiI7GDv57dHzoEhIiIiuhQGGCIiItIcBhgiIiLSHAYYIiIi0hwGGCIiItIcBhgiIiLSHAYYIiIi0hwGGCIiItIcBhgiIiLSHI99lEBL1d9g2Gw2S66EiIiI7FX/uX25BwV4bYApKysDAERGRkquhIiIiBxVVlaG4ODgJtd77bOQrFYrTp06hbZt20JRFKdt12w2IzIyEnl5eXzGUjOxD1uOfdhy7MOWYx+2HPuwISEEysrKEBERAZ2u6ZkuXjsCo9Pp0LlzZ5dtPygoiD9sLcQ+bDn2YcuxD1uOfdhy7ENblxp5qcdJvERERKQ5DDBERESkOQwwDjIajZg3bx6MRqPsUjSLfdhy7MOWYx+2HPuw5diHzee1k3iJiIjIe3EEhoiIiDSHAYaIiIg0hwGGiIiINIcBhoiIiDSHAcZBS5YsQbdu3RAQEIDo6Gjs2rVLdkkuN3/+fCiKYvPq06ePur6qqgoJCQm44oor0KZNG4wZMwYFBQU228jNzUV8fDxatWqF0NBQzJo1C3V1dTZtUlJScM0118BoNKJnz55YsWJFg1q00v+pqam48847ERERAUVRsGbNGpv1QgjMnTsXnTp1QmBgIGJjY3H06FGbNsXFxRg/fjyCgoIQEhKCyZMno7y83KbN/v37cdNNNyEgIACRkZFYvHhxg1q++uor9OnTBwEBARg4cCDWr1/vcC0yXK4PJ02a1ODnctSoUTZtfL0PFy5ciOuuuw5t27ZFaGgo7rnnHmRlZdm08aTfX3tqcSd7+u+WW25p8HM4bdo0mza+2n8uJ8huX3zxhTAYDGLZsmXi4MGDYsqUKSIkJEQUFBTILs2l5s2bJ/r37y/y8/PV1+nTp9X106ZNE5GRkSI5OVns2bNHDBs2TPzhD39Q19fV1YkBAwaI2NhYsXfvXrF+/XrRoUMHkZiYqLb59ddfRatWrcTMmTPFoUOHxDvvvCP0er3YuHGj2kZL/b9+/Xrx7LPPiq+//loAEKtXr7ZZv2jRIhEcHCzWrFkj9u3bJ+666y4RFRUlzp07p7YZNWqUGDx4sNixY4f46aefRM+ePcUDDzygri8tLRVhYWFi/PjxIjMzU3z++eciMDBQfPDBB2qbbdu2Cb1eLxYvXiwOHTok5syZI/z9/cWBAwccqkWGy/XhxIkTxahRo2x+LouLi23a+HofxsXFieXLl4vMzEyRkZEhbr/9dtGlSxdRXl6utvGk39/L1eJu9vTfzTffLKZMmWLzc1haWqqu9+X+czUGGAdcf/31IiEhQf3eYrGIiIgIsXDhQolVud68efPE4MGDG11XUlIi/P39xVdffaUuO3z4sAAg0tLShBDnP4h0Op0wmUxqm/fff18EBQWJ6upqIYQQTz31lOjfv7/Ntu+//34RFxenfq/V/r/4w9dqtYrw8HDxyiuvqMtKSkqE0WgUn3/+uRBCiEOHDgkAYvfu3WqbDRs2CEVRxMmTJ4UQQrz33nuiXbt2ah8KIcTs2bNF79691e///Oc/i/j4eJt6oqOjxV//+le7a/EETQWYu+++u8n3sA8bKiwsFADEli1bhBCe9ftrTy2yXdx/QpwPMP/4xz+afA/7z3V4CslONTU1SE9PR2xsrLpMp9MhNjYWaWlpEitzj6NHjyIiIgLdu3fH+PHjkZubCwBIT09HbW2tTb/06dMHXbp0UfslLS0NAwcORFhYmNomLi4OZrMZBw8eVNtcuI36NvXb8Kb+z8nJgclksjmW4OBgREdH2/RZSEgIrr32WrVNbGwsdDoddu7cqbYZPnw4DAaD2iYuLg5ZWVk4e/as2uZS/WpPLZ4sJSUFoaGh6N27N6ZPn46ioiJ1HfuwodLSUgBA+/btAXjW7689tch2cf/V++yzz9ChQwcMGDAAiYmJqKysVNex/1zHax/m6GxnzpyBxWKx+SEEgLCwMBw5ckRSVe4RHR2NFStWoHfv3sjPz8eCBQtw0003ITMzEyaTCQaDASEhITbvCQsLg8lkAgCYTKZG+61+3aXamM1mnDt3DmfPnvWa/q8/5saO5cL+CA0NtVnv5+eH9u3b27SJiopqsI36de3atWuyXy/cxuVq8VSjRo3Cvffei6ioKGRnZ+OZZ57B6NGjkZaWBr1ezz68iNVqxWOPPYYbbrgBAwYMAACP+v21pxaZGus/ABg3bhy6du2KiIgI7N+/H7Nnz0ZWVha+/vprAOw/V2KAocsaPXq0+vWgQYMQHR2Nrl27YtWqVQgMDJRYGfmysWPHql8PHDgQgwYNQo8ePZCSkoIRI0ZIrMwzJSQkIDMzE1u3bpVdiiY11X9Tp05Vvx44cCA6deqEESNGIDs7Gz169HB3mT6Fp5Ds1KFDB+j1+gYzugsKChAeHi6pKjlCQkLQq1cvHDt2DOHh4aipqUFJSYlNmwv7JTw8vNF+q193qTZBQUEIDAz0qv6vr/dSxxIeHo7CwkKb9XV1dSguLnZKv164/nK1aEX37t3RoUMHHDt2DAD78EIzZszA2rVr8eOPP6Jz587qck/6/bWnFlma6r/GREdHA4DNz6Gv95+rMMDYyWAwYOjQoUhOTlaXWa1WJCcnIyYmRmJl7ldeXo7s7Gx06tQJQ4cOhb+/v02/ZGVlITc3V+2XmJgYHDhwwObDJCkpCUFBQejXr5/a5sJt1Lep34Y39X9UVBTCw8NtjsVsNmPnzp02fVZSUoL09HS1zebNm2G1WtU/kDExMUhNTUVtba3aJikpCb1790a7du3UNpfqV3tq0YoTJ06gqKgInTp1AsA+BM5f3j1jxgysXr0amzdvbnC6zJN+f+2pxd0u13+NycjIAACbn0Nf7T+Xkz2LWEu++OILYTQaxYoVK8ShQ4fE1KlTRUhIiM3scm/0xBNPiJSUFJGTkyO2bdsmYmNjRYcOHURhYaEQ4vyle126dBGbN28We/bsETExMSImJkZ9f/1lhCNHjhQZGRli48aNomPHjo1eRjhr1ixx+PBhsWTJkkYvI9RK/5eVlYm9e/eKvXv3CgDi9ddfF3v37hW//fabEOL8ZbchISHim2++Efv37xd33313o5dRX3311WLnzp1i69at4qqrrrK5BLikpESEhYWJCRMmiMzMTPHFF1+IVq1aNbgE2M/PT7z66qvi8OHDYt68eY1eAny5WmS4VB+WlZWJJ598UqSlpYmcnByxadMmcc0114irrrpKVFVVqdvw9T6cPn26CA4OFikpKTaX+VZWVqptPOn393K1uNvl+u/YsWPi+eefF3v27BE5OTnim2++Ed27dxfDhw9Xt+HL/edqDDAOeuedd0SXLl2EwWAQ119/vdixY4fsklzu/vvvF506dRIGg0FceeWV4v777xfHjh1T1587d0787W9/E+3atROtWrUSf/rTn0R+fr7NNo4fPy5Gjx4tAgMDRYcOHcQTTzwhamtrbdr8+OOPYsiQIcJgMIju3buL5cuXN6hFK/3/448/CgANXhMnThRCnL/09rnnnhNhYWHCaDSKESNGiKysLJttFBUViQceeEC0adNGBAUFiYceekiUlZXZtNm3b5+48cYbhdFoFFdeeaVYtGhRg1pWrVolevXqJQwGg+jfv79Yt26dzXp7apHhUn1YWVkpRo4cKTp27Cj8/f1F165dxZQpUxqEWV/vw8b6D4DN75Yn/f7aU4s7Xa7/cnNzxfDhw0X79u2F0WgUPXv2FLNmzbK5D4wQvtt/rqYIIYT7xnuIiIiIWo5zYIiIiEhzGGCIiIhIcxhgiIiISHMYYIiIiEhzGGCIiIhIcxhgiIiISHMYYIiIiEhzGGCIiIhIcxhgiIiISHMYYIiIiEhzGGCIiIhIcxhgiIiISHP+Pxc+p8+sq9LkAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(unique_user[['State_encoded','Country_encoded']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "id": "c3dcda30",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler  # 표준화 패키지 라이브러리 \n",
    "x = unique_user[['State_encoded','Country_encoded','City_encoded']]\n",
    "#y = df['target'].values # 종속변인 추출\n",
    "\n",
    "x = StandardScaler().fit_transform(x) # x객체에 x를 표준화한 데이터를 저장\n",
    "\n",
    "features = ['State_encoded','Country_encoded','City_encoded']\n",
    "scaling_encode_data = pd.DataFrame(x, columns=features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "id": "5164d6c9",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.decomposition import PCA\n",
    "pca = PCA(n_components=) # 주성분을 몇개로 할지 결정\n",
    "printcipalComponents = pca.fit_transform(x)\n",
    "principalDf = pd.DataFrame(data=printcipalComponents, columns = ['principal component1', 'principal component2'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "id": "b01a27a2",
   "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>principal component1</th>\n",
       "      <th>principal component2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.708531</td>\n",
       "      <td>0.500029</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.705899</td>\n",
       "      <td>0.500436</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.678079</td>\n",
       "      <td>0.518376</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.700636</td>\n",
       "      <td>0.501248</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.647628</td>\n",
       "      <td>0.536722</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>105278</th>\n",
       "      <td>-0.658428</td>\n",
       "      <td>0.477363</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>105279</th>\n",
       "      <td>-0.565471</td>\n",
       "      <td>0.283361</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>105280</th>\n",
       "      <td>-0.638952</td>\n",
       "      <td>0.490480</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>105281</th>\n",
       "      <td>-0.277181</td>\n",
       "      <td>0.632478</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>105282</th>\n",
       "      <td>-0.676731</td>\n",
       "      <td>0.504443</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>105283 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        principal component1  principal component2\n",
       "0                  -0.708531              0.500029\n",
       "1                  -0.705899              0.500436\n",
       "2                  -0.678079              0.518376\n",
       "3                  -0.700636              0.501248\n",
       "4                  -0.647628              0.536722\n",
       "...                      ...                   ...\n",
       "105278             -0.658428              0.477363\n",
       "105279             -0.565471              0.283361\n",
       "105280             -0.638952              0.490480\n",
       "105281             -0.277181              0.632478\n",
       "105282             -0.676731              0.504443\n",
       "\n",
       "[105283 rows x 2 columns]"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "principalDf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "id": "0eff21ad",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.48165047, 0.30426857])"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pca.explained_variance_ratio_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "id": "39f41b77",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
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       "    .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>State_encoded</th>\n",
       "      <th>Country_encoded</th>\n",
       "      <th>City_encoded</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.388557</td>\n",
       "      <td>-0.219561</td>\n",
       "      <td>-0.743566</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.384741</td>\n",
       "      <td>-0.219561</td>\n",
       "      <td>-0.743306</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.380924</td>\n",
       "      <td>-0.178867</td>\n",
       "      <td>-0.743046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.377107</td>\n",
       "      <td>-0.219561</td>\n",
       "      <td>-0.742787</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.373290</td>\n",
       "      <td>-0.138173</td>\n",
       "      <td>-0.742527</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>105278</th>\n",
       "      <td>-0.380924</td>\n",
       "      <td>-0.178867</td>\n",
       "      <td>-0.696802</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>105279</th>\n",
       "      <td>-0.380924</td>\n",
       "      <td>-0.178867</td>\n",
       "      <td>-0.478051</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>105280</th>\n",
       "      <td>-0.300770</td>\n",
       "      <td>-0.219561</td>\n",
       "      <td>-0.716287</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>105281</th>\n",
       "      <td>-0.083209</td>\n",
       "      <td>0.105991</td>\n",
       "      <td>-0.677317</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>105282</th>\n",
       "      <td>-0.342755</td>\n",
       "      <td>-0.219561</td>\n",
       "      <td>-0.739929</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>105283 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        State_encoded  Country_encoded  City_encoded\n",
       "0           -0.388557        -0.219561     -0.743566\n",
       "1           -0.384741        -0.219561     -0.743306\n",
       "2           -0.380924        -0.178867     -0.743046\n",
       "3           -0.377107        -0.219561     -0.742787\n",
       "4           -0.373290        -0.138173     -0.742527\n",
       "...               ...              ...           ...\n",
       "105278      -0.380924        -0.178867     -0.696802\n",
       "105279      -0.380924        -0.178867     -0.478051\n",
       "105280      -0.300770        -0.219561     -0.716287\n",
       "105281      -0.083209         0.105991     -0.677317\n",
       "105282      -0.342755        -0.219561     -0.739929\n",
       "\n",
       "[105283 rows x 3 columns]"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scaling_encode_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "id": "731524f1",
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy import stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "id": "471c9056",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/sonhyeongjin/miniforge3/lib/python3.10/site-packages/scipy/stats/_morestats.py:1816: UserWarning: p-value may not be accurate for N > 5000.\n",
      "  warnings.warn(\"p-value may not be accurate for N > 5000.\")\n"
     ]
    }
   ],
   "source": [
    "shapiro_test = stats.shapiro(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "id": "5a39d6ba",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ShapiroResult(statistic=0.4992688298225403, pvalue=0.0)"
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "shapiro_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "id": "19b91af6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "NormaltestResult(statistic=24353.727585866523, pvalue=0.0)"
      ]
     },
     "execution_count": 173,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "from scipy.stats import normaltest\n",
    "\n",
    "    nn_test = stats.normaltest(x)\n",
    "    nn_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "id": "afe3d487",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        -0.388557\n",
       "1        -0.384741\n",
       "2        -0.380924\n",
       "3        -0.377107\n",
       "4        -0.373290\n",
       "            ...   \n",
       "105278   -0.380924\n",
       "105279   -0.380924\n",
       "105280   -0.300770\n",
       "105281   -0.083209\n",
       "105282   -0.342755\n",
       "Name: State_encoded, Length: 105283, dtype: float64"
      ]
     },
     "execution_count": 142,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scaling_encode_data.iloc[:,0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "id": "80ab24d2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "NormaltestResult(statistic=1.5506704169544296, pvalue=0.46054937506662497)"
      ]
     },
     "execution_count": 145,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 라이브러리 호출\n",
    "import numpy as np\n",
    "from scipy.stats import normaltest\n",
    "\n",
    "# 정규분포를 따르도록 랜덤하게 값을 부여\n",
    "np.random.seed(0)\n",
    "n = 10000\n",
    "x = np.random.normal(0, 500, n)\n",
    "\n",
    "nn_test = stats.normaltest(x)\n",
    "nn_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "id": "5d87e494",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_1 = unique_user['State_encoded']\n",
    "x_2 = unique_user['Country_encoded']\n",
    "x_3 = unique_user['City_encoded']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "id": "d228ff90",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 182,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x_3.hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "id": "05ac4bbb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 179,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "id": "1d9be4db",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "State_encoded\n",
       "0       9020\n",
       "2       3825\n",
       "23      3435\n",
       "22      3294\n",
       "20      3107\n",
       "        ... \n",
       "1434       1\n",
       "350        1\n",
       "1430       1\n",
       "1428       1\n",
       "2489       1\n",
       "Name: count, Length: 2490, dtype: int64"
      ]
     },
     "execution_count": 183,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_1.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "id": "66e644a5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Country_encoded\n",
       "0      59408\n",
       "1       9880\n",
       "4       6306\n",
       "7       5598\n",
       "8       3659\n",
       "       ...  \n",
       "307        1\n",
       "141        1\n",
       "309        1\n",
       "310        1\n",
       "465        1\n",
       "Name: count, Length: 466, dtype: int64"
      ]
     },
     "execution_count": 184,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_2.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "id": "460a0a72",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "City_encoded\n",
       "34       1277\n",
       "180      1004\n",
       "25        893\n",
       "14        883\n",
       "103       835\n",
       "         ... \n",
       "9193        1\n",
       "9195        1\n",
       "9196        1\n",
       "9197        1\n",
       "16718       1\n",
       "Name: count, Length: 16719, dtype: int64"
      ]
     },
     "execution_count": 185,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_3.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 226,
   "id": "3a9e60f1",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_all = (x_1+x_2+x_3)\n",
    "z = (x_all + y_all)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 227,
   "id": "7d498e41",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 227,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x_all.hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 239,
   "id": "252cf20e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 239,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "((z.sample(n=300))/300).hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 240,
   "id": "a885caf2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "105283"
      ]
     },
     "execution_count": 240,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 242,
   "id": "6b76472a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 242,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 244,
   "id": "ef04a421",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "171589"
      ]
     },
     "execution_count": 244,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "z.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 248,
   "id": "1489e6ab",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 248,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_all.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 249,
   "id": "3f20d124",
   "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",
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       "      <th></th>\n",
       "      <th>ISBN</th>\n",
       "      <th>Book-Title</th>\n",
       "      <th>Book-Author</th>\n",
       "      <th>Year-Of-Publication</th>\n",
       "      <th>Publisher</th>\n",
       "      <th>Image-URL-S</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0195153448</td>\n",
       "      <td>Classical Mythology</td>\n",
       "      <td>Mark P. O. Morford</td>\n",
       "      <td>2002</td>\n",
       "      <td>Oxford University Press</td>\n",
       "      <td>http://images.amazon.com/images/P/0195153448.0...</td>\n",
       "      <td>http://images.amazon.com/images/P/0195153448.0...</td>\n",
       "      <td>http://images.amazon.com/images/P/0195153448.0...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <th>1</th>\n",
       "      <td>0002005018</td>\n",
       "      <td>Clara Callan</td>\n",
       "      <td>Richard Bruce Wright</td>\n",
       "      <td>2001</td>\n",
       "      <td>HarperFlamingo Canada</td>\n",
       "      <td>http://images.amazon.com/images/P/0002005018.0...</td>\n",
       "      <td>http://images.amazon.com/images/P/0002005018.0...</td>\n",
       "      <td>http://images.amazon.com/images/P/0002005018.0...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0060973129</td>\n",
       "      <td>Decision in Normandy</td>\n",
       "      <td>Carlo D'Este</td>\n",
       "      <td>1991</td>\n",
       "      <td>HarperPerennial</td>\n",
       "      <td>http://images.amazon.com/images/P/0060973129.0...</td>\n",
       "      <td>http://images.amazon.com/images/P/0060973129.0...</td>\n",
       "      <td>http://images.amazon.com/images/P/0060973129.0...</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
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       "      <th>3</th>\n",
       "      <td>0374157065</td>\n",
       "      <td>Flu: The Story of the Great Influenza Pandemic...</td>\n",
       "      <td>Gina Bari Kolata</td>\n",
       "      <td>1999</td>\n",
       "      <td>Farrar Straus Giroux</td>\n",
       "      <td>http://images.amazon.com/images/P/0374157065.0...</td>\n",
       "      <td>http://images.amazon.com/images/P/0374157065.0...</td>\n",
       "      <td>http://images.amazon.com/images/P/0374157065.0...</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0393045218</td>\n",
       "      <td>The Mummies of Urumchi</td>\n",
       "      <td>E. J. W. Barber</td>\n",
       "      <td>1999</td>\n",
       "      <td>W. W. Norton &amp;amp; Company</td>\n",
       "      <td>http://images.amazon.com/images/P/0393045218.0...</td>\n",
       "      <td>http://images.amazon.com/images/P/0393045218.0...</td>\n",
       "      <td>http://images.amazon.com/images/P/0393045218.0...</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
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       "      <td>...</td>\n",
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       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>271355</th>\n",
       "      <td>0440400988</td>\n",
       "      <td>There's a Bat in Bunk Five</td>\n",
       "      <td>Paula Danziger</td>\n",
       "      <td>1988</td>\n",
       "      <td>Random House Childrens Pub (Mm)</td>\n",
       "      <td>http://images.amazon.com/images/P/0440400988.0...</td>\n",
       "      <td>http://images.amazon.com/images/P/0440400988.0...</td>\n",
       "      <td>http://images.amazon.com/images/P/0440400988.0...</td>\n",
       "      <td>19469</td>\n",
       "      <td>377</td>\n",
       "      <td>1460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>271356</th>\n",
       "      <td>0525447644</td>\n",
       "      <td>From One to One Hundred</td>\n",
       "      <td>Teri Sloat</td>\n",
       "      <td>1991</td>\n",
       "      <td>Dutton Books</td>\n",
       "      <td>http://images.amazon.com/images/P/0525447644.0...</td>\n",
       "      <td>http://images.amazon.com/images/P/0525447644.0...</td>\n",
       "      <td>http://images.amazon.com/images/P/0525447644.0...</td>\n",
       "      <td>242131</td>\n",
       "      <td>102021</td>\n",
       "      <td>140</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>271357</th>\n",
       "      <td>006008667X</td>\n",
       "      <td>Lily Dale : The True Story of the Town that Ta...</td>\n",
       "      <td>Christine Wicker</td>\n",
       "      <td>2004</td>\n",
       "      <td>HarperSanFrancisco</td>\n",
       "      <td>http://images.amazon.com/images/P/006008667X.0...</td>\n",
       "      <td>http://images.amazon.com/images/P/006008667X.0...</td>\n",
       "      <td>http://images.amazon.com/images/P/006008667X.0...</td>\n",
       "      <td>242132</td>\n",
       "      <td>17770</td>\n",
       "      <td>303</td>\n",
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       "      <th>271358</th>\n",
       "      <td>0192126040</td>\n",
       "      <td>Republic (World's Classics)</td>\n",
       "      <td>Plato</td>\n",
       "      <td>1996</td>\n",
       "      <td>Oxford University Press</td>\n",
       "      <td>http://images.amazon.com/images/P/0192126040.0...</td>\n",
       "      <td>http://images.amazon.com/images/P/0192126040.0...</td>\n",
       "      <td>http://images.amazon.com/images/P/0192126040.0...</td>\n",
       "      <td>242133</td>\n",
       "      <td>832</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>271359</th>\n",
       "      <td>0767409752</td>\n",
       "      <td>A Guided Tour of Rene Descartes' Meditations o...</td>\n",
       "      <td>Christopher  Biffle</td>\n",
       "      <td>2000</td>\n",
       "      <td>McGraw-Hill Humanities/Social Sciences/Languages</td>\n",
       "      <td>http://images.amazon.com/images/P/0767409752.0...</td>\n",
       "      <td>http://images.amazon.com/images/P/0767409752.0...</td>\n",
       "      <td>http://images.amazon.com/images/P/0767409752.0...</td>\n",
       "      <td>242134</td>\n",
       "      <td>102022</td>\n",
       "      <td>642</td>\n",
       "    </tr>\n",
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       "</table>\n",
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      ],
      "text/plain": [
       "              ISBN                                         Book-Title  \\\n",
       "0       0195153448                                Classical Mythology   \n",
       "1       0002005018                                       Clara Callan   \n",
       "2       0060973129                               Decision in Normandy   \n",
       "3       0374157065  Flu: The Story of the Great Influenza Pandemic...   \n",
       "4       0393045218                             The Mummies of Urumchi   \n",
       "...            ...                                                ...   \n",
       "271355  0440400988                         There's a Bat in Bunk Five   \n",
       "271356  0525447644                            From One to One Hundred   \n",
       "271357  006008667X  Lily Dale : The True Story of the Town that Ta...   \n",
       "271358  0192126040                        Republic (World's Classics)   \n",
       "271359  0767409752  A Guided Tour of Rene Descartes' Meditations o...   \n",
       "\n",
       "                 Book-Author Year-Of-Publication  \\\n",
       "0         Mark P. O. Morford                2002   \n",
       "1       Richard Bruce Wright                2001   \n",
       "2               Carlo D'Este                1991   \n",
       "3           Gina Bari Kolata                1999   \n",
       "4            E. J. W. Barber                1999   \n",
       "...                      ...                 ...   \n",
       "271355        Paula Danziger                1988   \n",
       "271356            Teri Sloat                1991   \n",
       "271357      Christine Wicker                2004   \n",
       "271358                 Plato                1996   \n",
       "271359   Christopher  Biffle                2000   \n",
       "\n",
       "                                               Publisher  \\\n",
       "0                                Oxford University Press   \n",
       "1                                  HarperFlamingo Canada   \n",
       "2                                        HarperPerennial   \n",
       "3                                   Farrar Straus Giroux   \n",
       "4                             W. W. Norton &amp; Company   \n",
       "...                                                  ...   \n",
       "271355                   Random House Childrens Pub (Mm)   \n",
       "271356                                      Dutton Books   \n",
       "271357                                HarperSanFrancisco   \n",
       "271358                           Oxford University Press   \n",
       "271359  McGraw-Hill Humanities/Social Sciences/Languages   \n",
       "\n",
       "                                              Image-URL-S  \\\n",
       "0       http://images.amazon.com/images/P/0195153448.0...   \n",
       "1       http://images.amazon.com/images/P/0002005018.0...   \n",
       "2       http://images.amazon.com/images/P/0060973129.0...   \n",
       "3       http://images.amazon.com/images/P/0374157065.0...   \n",
       "4       http://images.amazon.com/images/P/0393045218.0...   \n",
       "...                                                   ...   \n",
       "271355  http://images.amazon.com/images/P/0440400988.0...   \n",
       "271356  http://images.amazon.com/images/P/0525447644.0...   \n",
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       "271359  http://images.amazon.com/images/P/0767409752.0...   \n",
       "\n",
       "                                              Image-URL-M  \\\n",
       "0       http://images.amazon.com/images/P/0195153448.0...   \n",
       "1       http://images.amazon.com/images/P/0002005018.0...   \n",
       "2       http://images.amazon.com/images/P/0060973129.0...   \n",
       "3       http://images.amazon.com/images/P/0374157065.0...   \n",
       "4       http://images.amazon.com/images/P/0393045218.0...   \n",
       "...                                                   ...   \n",
       "271355  http://images.amazon.com/images/P/0440400988.0...   \n",
       "271356  http://images.amazon.com/images/P/0525447644.0...   \n",
       "271357  http://images.amazon.com/images/P/006008667X.0...   \n",
       "271358  http://images.amazon.com/images/P/0192126040.0...   \n",
       "271359  http://images.amazon.com/images/P/0767409752.0...   \n",
       "\n",
       "                                              Image-URL-L  Book-Title_encoded  \\\n",
       "0       http://images.amazon.com/images/P/0195153448.0...                   0   \n",
       "1       http://images.amazon.com/images/P/0002005018.0...                   1   \n",
       "2       http://images.amazon.com/images/P/0060973129.0...                   2   \n",
       "3       http://images.amazon.com/images/P/0374157065.0...                   3   \n",
       "4       http://images.amazon.com/images/P/0393045218.0...                   4   \n",
       "...                                                   ...                 ...   \n",
       "271355  http://images.amazon.com/images/P/0440400988.0...               19469   \n",
       "271356  http://images.amazon.com/images/P/0525447644.0...              242131   \n",
       "271357  http://images.amazon.com/images/P/006008667X.0...              242132   \n",
       "271358  http://images.amazon.com/images/P/0192126040.0...              242133   \n",
       "271359  http://images.amazon.com/images/P/0767409752.0...              242134   \n",
       "\n",
       "        Book-Author_encoded  Publisher_encoded  \n",
       "0                         0                  0  \n",
       "1                         1                  1  \n",
       "2                         2                  2  \n",
       "3                         3                  3  \n",
       "4                         4                  4  \n",
       "...                     ...                ...  \n",
       "271355                  377               1460  \n",
       "271356               102021                140  \n",
       "271357                17770                303  \n",
       "271358                  832                  0  \n",
       "271359               102022                642  \n",
       "\n",
       "[271360 rows x 11 columns]"
      ]
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   "id": "2a634103",
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       "        User-ID                            Location   Age\n",
       "0             1                  nyc, new york, usa   NaN\n",
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       "2             3     moscow, yukon territory, russia   NaN\n",
       "3             4           porto, v.n.gaia, portugal  17.0\n",
       "4             5  farnborough, hants, united kingdom   NaN\n",
       "...         ...                                 ...   ...\n",
       "278853   278854               portland, oregon, usa   NaN\n",
       "278854   278855  tacoma, washington, united kingdom  50.0\n",
       "278855   278856           brampton, ontario, canada   NaN\n",
       "278856   278857           knoxville, tennessee, usa   NaN\n",
       "278857   278858                dublin, n/a, ireland   NaN\n",
       "\n",
       "[278858 rows x 3 columns]"
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   "execution_count": 251,
   "id": "938f2695",
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       "      <td>8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1149780 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         User-ID         ISBN  Book-Rating\n",
       "0         276725   034545104X            0\n",
       "1         276726   0155061224            5\n",
       "2         276727   0446520802            0\n",
       "3         276729   052165615X            3\n",
       "4         276729   0521795028            6\n",
       "...          ...          ...          ...\n",
       "1149775   276704   1563526298            9\n",
       "1149776   276706   0679447156            0\n",
       "1149777   276709   0515107662           10\n",
       "1149778   276721   0590442449           10\n",
       "1149779   276723  05162443314            8\n",
       "\n",
       "[1149780 rows x 3 columns]"
      ]
     },
     "execution_count": 251,
     "metadata": {},
     "output_type": "execute_result"
    }
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   "source": [
    "rating_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 257,
   "id": "35dec7a1",
   "metadata": {},
   "outputs": [],
   "source": [
    "merge_data = pd.merge(rating_data,user_data,how='left',on='User-ID')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 260,
   "id": "d168e391",
   "metadata": {},
   "outputs": [],
   "source": [
    "merge_data = pd.merge(merge_data,book_data,how='left',on='ISBN')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 261,
   "id": "29db1c47",
   "metadata": {},
   "outputs": [
    {
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       "      <td>rijeka, n/a, croatia</td>\n",
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       "      <td>Help!: Level 1</td>\n",
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       "      <td>276721</td>\n",
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       "      <td>14.0</td>\n",
       "      <td>Fourth Grade Rats</td>\n",
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       "      <td>05162443314</td>\n",
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       "      <td>san antonio, texas, usa</td>\n",
       "      <td>12.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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      ],
      "text/plain": [
       "         User-ID         ISBN  Book-Rating                        Location  \\\n",
       "0         276725   034545104X            0               tyler, texas, usa   \n",
       "1         276726   0155061224            5        seattle, washington, usa   \n",
       "2         276727   0446520802            0   h, new south wales, australia   \n",
       "3         276729   052165615X            3            rijeka, n/a, croatia   \n",
       "4         276729   0521795028            6            rijeka, n/a, croatia   \n",
       "...          ...          ...          ...                             ...   \n",
       "1149775   276704   1563526298            9          cedar park, texas, usa   \n",
       "1149776   276706   0679447156            0          quebec, quebec, canada   \n",
       "1149777   276709   0515107662           10  mannington, west virginia, usa   \n",
       "1149778   276721   0590442449           10   providence, rhode island, usa   \n",
       "1149779   276723  05162443314            8         san antonio, texas, usa   \n",
       "\n",
       "          Age                                         Book-Title  \\\n",
       "0         NaN                               Flesh Tones: A Novel   \n",
       "1         NaN                                   Rites of Passage   \n",
       "2        16.0                                       The Notebook   \n",
       "3        16.0                                     Help!: Level 1   \n",
       "4        16.0  The Amsterdam Connection : Level 4 (Cambridge ...   \n",
       "...       ...                                                ...   \n",
       "1149775   NaN  Get Clark Smart : The Ultimate Guide for the S...   \n",
       "1149776  18.0  Eight Weeks to Optimum Health: A Proven Progra...   \n",
       "1149777  38.0   The Sherbrooke Bride (Bride Trilogy (Paperback))   \n",
       "1149778  14.0                                  Fourth Grade Rats   \n",
       "1149779  12.0                                                NaN   \n",
       "\n",
       "               Book-Author Year-Of-Publication                   Publisher  \\\n",
       "0               M. J. Rose                2002            Ballantine Books   \n",
       "1               Judith Rae                2001                      Heinle   \n",
       "2          Nicholas Sparks                1996                Warner Books   \n",
       "3            Philip Prowse                1999  Cambridge University Press   \n",
       "4              Sue Leather                2001  Cambridge University Press   \n",
       "...                    ...                 ...                         ...   \n",
       "1149775       Clark Howard                2000            Longstreet Press   \n",
       "1149776        Andrew Weil                1997             Alfred A. Knopf   \n",
       "1149777  Catherine Coulter                1996                  Jove Books   \n",
       "1149778     Jerry Spinelli                1996                  Scholastic   \n",
       "1149779                NaN                 NaN                         NaN   \n",
       "\n",
       "                                               Image-URL-S  \\\n",
       "0        http://images.amazon.com/images/P/034545104X.0...   \n",
       "1        http://images.amazon.com/images/P/0155061224.0...   \n",
       "2        http://images.amazon.com/images/P/0446520802.0...   \n",
       "3        http://images.amazon.com/images/P/052165615X.0...   \n",
       "4        http://images.amazon.com/images/P/0521795028.0...   \n",
       "...                                                    ...   \n",
       "1149775  http://images.amazon.com/images/P/1563526298.0...   \n",
       "1149776  http://images.amazon.com/images/P/0679447156.0...   \n",
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       "1149778  http://images.amazon.com/images/P/0590442449.0...   \n",
       "1149779                                                NaN   \n",
       "\n",
       "                                               Image-URL-M  \\\n",
       "0        http://images.amazon.com/images/P/034545104X.0...   \n",
       "1        http://images.amazon.com/images/P/0155061224.0...   \n",
       "2        http://images.amazon.com/images/P/0446520802.0...   \n",
       "3        http://images.amazon.com/images/P/052165615X.0...   \n",
       "4        http://images.amazon.com/images/P/0521795028.0...   \n",
       "...                                                    ...   \n",
       "1149775  http://images.amazon.com/images/P/1563526298.0...   \n",
       "1149776  http://images.amazon.com/images/P/0679447156.0...   \n",
       "1149777  http://images.amazon.com/images/P/0515107662.0...   \n",
       "1149778  http://images.amazon.com/images/P/0590442449.0...   \n",
       "1149779                                                NaN   \n",
       "\n",
       "                                               Image-URL-L  \\\n",
       "0        http://images.amazon.com/images/P/034545104X.0...   \n",
       "1        http://images.amazon.com/images/P/0155061224.0...   \n",
       "2        http://images.amazon.com/images/P/0446520802.0...   \n",
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       "...                                                    ...   \n",
       "1149775  http://images.amazon.com/images/P/1563526298.0...   \n",
       "1149776  http://images.amazon.com/images/P/0679447156.0...   \n",
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       "1149778  http://images.amazon.com/images/P/0590442449.0...   \n",
       "1149779                                                NaN   \n",
       "\n",
       "         Book-Title_encoded  Book-Author_encoded  Publisher_encoded  \n",
       "0                    2886.0               2027.0               27.0  \n",
       "1                   45524.0              86746.0             5187.0  \n",
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       "3                  220494.0              91670.0              372.0  \n",
       "4                  220495.0              30646.0              372.0  \n",
       "...                     ...                  ...                ...  \n",
       "1149775             63748.0              23264.0              678.0  \n",
       "1149776             48482.0              17796.0               31.0  \n",
       "1149777             15084.0                453.0              133.0  \n",
       "1149778             52321.0               3172.0               24.0  \n",
       "1149779                 NaN                  NaN                NaN  \n",
       "\n",
       "[1149780 rows x 15 columns]"
      ]
     },
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     "metadata": {},
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  {
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   "execution_count": 262,
   "id": "a13822fb",
   "metadata": {},
   "outputs": [
    {
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       "      <td>http://images.amazon.com/images/P/0155061224.0...</td>\n",
       "      <td>http://images.amazon.com/images/P/0155061224.0...</td>\n",
       "      <td>45524.0</td>\n",
       "      <td>86746.0</td>\n",
       "      <td>5187.0</td>\n",
       "      <td>seattle</td>\n",
       "      <td>washington</td>\n",
       "      <td>usa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>276727</td>\n",
       "      <td>0446520802</td>\n",
       "      <td>0</td>\n",
       "      <td>h, new south wales, australia</td>\n",
       "      <td>16.0</td>\n",
       "      <td>The Notebook</td>\n",
       "      <td>Nicholas Sparks</td>\n",
       "      <td>1996</td>\n",
       "      <td>Warner Books</td>\n",
       "      <td>http://images.amazon.com/images/P/0446520802.0...</td>\n",
       "      <td>http://images.amazon.com/images/P/0446520802.0...</td>\n",
       "      <td>http://images.amazon.com/images/P/0446520802.0...</td>\n",
       "      <td>2529.0</td>\n",
       "      <td>192.0</td>\n",
       "      <td>73.0</td>\n",
       "      <td>h</td>\n",
       "      <td>new south wales</td>\n",
       "      <td>australia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>276729</td>\n",
       "      <td>052165615X</td>\n",
       "      <td>3</td>\n",
       "      <td>rijeka, n/a, croatia</td>\n",
       "      <td>16.0</td>\n",
       "      <td>Help!: Level 1</td>\n",
       "      <td>Philip Prowse</td>\n",
       "      <td>1999</td>\n",
       "      <td>Cambridge University Press</td>\n",
       "      <td>http://images.amazon.com/images/P/052165615X.0...</td>\n",
       "      <td>http://images.amazon.com/images/P/052165615X.0...</td>\n",
       "      <td>http://images.amazon.com/images/P/052165615X.0...</td>\n",
       "      <td>220494.0</td>\n",
       "      <td>91670.0</td>\n",
       "      <td>372.0</td>\n",
       "      <td>rijeka</td>\n",
       "      <td>n/a</td>\n",
       "      <td>croatia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>276729</td>\n",
       "      <td>0521795028</td>\n",
       "      <td>6</td>\n",
       "      <td>rijeka, n/a, croatia</td>\n",
       "      <td>16.0</td>\n",
       "      <td>The Amsterdam Connection : Level 4 (Cambridge ...</td>\n",
       "      <td>Sue Leather</td>\n",
       "      <td>2001</td>\n",
       "      <td>Cambridge University Press</td>\n",
       "      <td>http://images.amazon.com/images/P/0521795028.0...</td>\n",
       "      <td>http://images.amazon.com/images/P/0521795028.0...</td>\n",
       "      <td>http://images.amazon.com/images/P/0521795028.0...</td>\n",
       "      <td>220495.0</td>\n",
       "      <td>30646.0</td>\n",
       "      <td>372.0</td>\n",
       "      <td>rijeka</td>\n",
       "      <td>n/a</td>\n",
       "      <td>croatia</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1149775</th>\n",
       "      <td>276704</td>\n",
       "      <td>1563526298</td>\n",
       "      <td>9</td>\n",
       "      <td>cedar park, texas, usa</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Get Clark Smart : The Ultimate Guide for the S...</td>\n",
       "      <td>Clark Howard</td>\n",
       "      <td>2000</td>\n",
       "      <td>Longstreet Press</td>\n",
       "      <td>http://images.amazon.com/images/P/1563526298.0...</td>\n",
       "      <td>http://images.amazon.com/images/P/1563526298.0...</td>\n",
       "      <td>http://images.amazon.com/images/P/1563526298.0...</td>\n",
       "      <td>63748.0</td>\n",
       "      <td>23264.0</td>\n",
       "      <td>678.0</td>\n",
       "      <td>cedar park</td>\n",
       "      <td>texas</td>\n",
       "      <td>usa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1149776</th>\n",
       "      <td>276706</td>\n",
       "      <td>0679447156</td>\n",
       "      <td>0</td>\n",
       "      <td>quebec, quebec, canada</td>\n",
       "      <td>18.0</td>\n",
       "      <td>Eight Weeks to Optimum Health: A Proven Progra...</td>\n",
       "      <td>Andrew Weil</td>\n",
       "      <td>1997</td>\n",
       "      <td>Alfred A. Knopf</td>\n",
       "      <td>http://images.amazon.com/images/P/0679447156.0...</td>\n",
       "      <td>http://images.amazon.com/images/P/0679447156.0...</td>\n",
       "      <td>http://images.amazon.com/images/P/0679447156.0...</td>\n",
       "      <td>48482.0</td>\n",
       "      <td>17796.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>quebec</td>\n",
       "      <td>quebec</td>\n",
       "      <td>canada</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1149777</th>\n",
       "      <td>276709</td>\n",
       "      <td>0515107662</td>\n",
       "      <td>10</td>\n",
       "      <td>mannington, west virginia, usa</td>\n",
       "      <td>38.0</td>\n",
       "      <td>The Sherbrooke Bride (Bride Trilogy (Paperback))</td>\n",
       "      <td>Catherine Coulter</td>\n",
       "      <td>1996</td>\n",
       "      <td>Jove Books</td>\n",
       "      <td>http://images.amazon.com/images/P/0515107662.0...</td>\n",
       "      <td>http://images.amazon.com/images/P/0515107662.0...</td>\n",
       "      <td>http://images.amazon.com/images/P/0515107662.0...</td>\n",
       "      <td>15084.0</td>\n",
       "      <td>453.0</td>\n",
       "      <td>133.0</td>\n",
       "      <td>mannington</td>\n",
       "      <td>west virginia</td>\n",
       "      <td>usa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1149778</th>\n",
       "      <td>276721</td>\n",
       "      <td>0590442449</td>\n",
       "      <td>10</td>\n",
       "      <td>providence, rhode island, usa</td>\n",
       "      <td>14.0</td>\n",
       "      <td>Fourth Grade Rats</td>\n",
       "      <td>Jerry Spinelli</td>\n",
       "      <td>1996</td>\n",
       "      <td>Scholastic</td>\n",
       "      <td>http://images.amazon.com/images/P/0590442449.0...</td>\n",
       "      <td>http://images.amazon.com/images/P/0590442449.0...</td>\n",
       "      <td>http://images.amazon.com/images/P/0590442449.0...</td>\n",
       "      <td>52321.0</td>\n",
       "      <td>3172.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>providence</td>\n",
       "      <td>rhode island</td>\n",
       "      <td>usa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1149779</th>\n",
       "      <td>276723</td>\n",
       "      <td>05162443314</td>\n",
       "      <td>8</td>\n",
       "      <td>san antonio, texas, usa</td>\n",
       "      <td>12.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>san antonio</td>\n",
       "      <td>texas</td>\n",
       "      <td>usa</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1149780 rows × 18 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         User-ID         ISBN  Book-Rating                        Location  \\\n",
       "0         276725   034545104X            0               tyler, texas, usa   \n",
       "1         276726   0155061224            5        seattle, washington, usa   \n",
       "2         276727   0446520802            0   h, new south wales, australia   \n",
       "3         276729   052165615X            3            rijeka, n/a, croatia   \n",
       "4         276729   0521795028            6            rijeka, n/a, croatia   \n",
       "...          ...          ...          ...                             ...   \n",
       "1149775   276704   1563526298            9          cedar park, texas, usa   \n",
       "1149776   276706   0679447156            0          quebec, quebec, canada   \n",
       "1149777   276709   0515107662           10  mannington, west virginia, usa   \n",
       "1149778   276721   0590442449           10   providence, rhode island, usa   \n",
       "1149779   276723  05162443314            8         san antonio, texas, usa   \n",
       "\n",
       "          Age                                         Book-Title  \\\n",
       "0         NaN                               Flesh Tones: A Novel   \n",
       "1         NaN                                   Rites of Passage   \n",
       "2        16.0                                       The Notebook   \n",
       "3        16.0                                     Help!: Level 1   \n",
       "4        16.0  The Amsterdam Connection : Level 4 (Cambridge ...   \n",
       "...       ...                                                ...   \n",
       "1149775   NaN  Get Clark Smart : The Ultimate Guide for the S...   \n",
       "1149776  18.0  Eight Weeks to Optimum Health: A Proven Progra...   \n",
       "1149777  38.0   The Sherbrooke Bride (Bride Trilogy (Paperback))   \n",
       "1149778  14.0                                  Fourth Grade Rats   \n",
       "1149779  12.0                                                NaN   \n",
       "\n",
       "               Book-Author Year-Of-Publication                   Publisher  \\\n",
       "0               M. J. Rose                2002            Ballantine Books   \n",
       "1               Judith Rae                2001                      Heinle   \n",
       "2          Nicholas Sparks                1996                Warner Books   \n",
       "3            Philip Prowse                1999  Cambridge University Press   \n",
       "4              Sue Leather                2001  Cambridge University Press   \n",
       "...                    ...                 ...                         ...   \n",
       "1149775       Clark Howard                2000            Longstreet Press   \n",
       "1149776        Andrew Weil                1997             Alfred A. Knopf   \n",
       "1149777  Catherine Coulter                1996                  Jove Books   \n",
       "1149778     Jerry Spinelli                1996                  Scholastic   \n",
       "1149779                NaN                 NaN                         NaN   \n",
       "\n",
       "                                               Image-URL-S  \\\n",
       "0        http://images.amazon.com/images/P/034545104X.0...   \n",
       "1        http://images.amazon.com/images/P/0155061224.0...   \n",
       "2        http://images.amazon.com/images/P/0446520802.0...   \n",
       "3        http://images.amazon.com/images/P/052165615X.0...   \n",
       "4        http://images.amazon.com/images/P/0521795028.0...   \n",
       "...                                                    ...   \n",
       "1149775  http://images.amazon.com/images/P/1563526298.0...   \n",
       "1149776  http://images.amazon.com/images/P/0679447156.0...   \n",
       "1149777  http://images.amazon.com/images/P/0515107662.0...   \n",
       "1149778  http://images.amazon.com/images/P/0590442449.0...   \n",
       "1149779                                                NaN   \n",
       "\n",
       "                                               Image-URL-M  \\\n",
       "0        http://images.amazon.com/images/P/034545104X.0...   \n",
       "1        http://images.amazon.com/images/P/0155061224.0...   \n",
       "2        http://images.amazon.com/images/P/0446520802.0...   \n",
       "3        http://images.amazon.com/images/P/052165615X.0...   \n",
       "4        http://images.amazon.com/images/P/0521795028.0...   \n",
       "...                                                    ...   \n",
       "1149775  http://images.amazon.com/images/P/1563526298.0...   \n",
       "1149776  http://images.amazon.com/images/P/0679447156.0...   \n",
       "1149777  http://images.amazon.com/images/P/0515107662.0...   \n",
       "1149778  http://images.amazon.com/images/P/0590442449.0...   \n",
       "1149779                                                NaN   \n",
       "\n",
       "                                               Image-URL-L  \\\n",
       "0        http://images.amazon.com/images/P/034545104X.0...   \n",
       "1        http://images.amazon.com/images/P/0155061224.0...   \n",
       "2        http://images.amazon.com/images/P/0446520802.0...   \n",
       "3        http://images.amazon.com/images/P/052165615X.0...   \n",
       "4        http://images.amazon.com/images/P/0521795028.0...   \n",
       "...                                                    ...   \n",
       "1149775  http://images.amazon.com/images/P/1563526298.0...   \n",
       "1149776  http://images.amazon.com/images/P/0679447156.0...   \n",
       "1149777  http://images.amazon.com/images/P/0515107662.0...   \n",
       "1149778  http://images.amazon.com/images/P/0590442449.0...   \n",
       "1149779                                                NaN   \n",
       "\n",
       "         Book-Title_encoded  Book-Author_encoded  Publisher_encoded  \\\n",
       "0                    2886.0               2027.0               27.0   \n",
       "1                   45524.0              86746.0             5187.0   \n",
       "2                    2529.0                192.0               73.0   \n",
       "3                  220494.0              91670.0              372.0   \n",
       "4                  220495.0              30646.0              372.0   \n",
       "...                     ...                  ...                ...   \n",
       "1149775             63748.0              23264.0              678.0   \n",
       "1149776             48482.0              17796.0               31.0   \n",
       "1149777             15084.0                453.0              133.0   \n",
       "1149778             52321.0               3172.0               24.0   \n",
       "1149779                 NaN                  NaN                NaN   \n",
       "\n",
       "                City            State    Country  \n",
       "0              tyler            texas        usa  \n",
       "1            seattle       washington        usa  \n",
       "2                  h  new south wales  australia  \n",
       "3             rijeka              n/a    croatia  \n",
       "4             rijeka              n/a    croatia  \n",
       "...              ...              ...        ...  \n",
       "1149775   cedar park            texas        usa  \n",
       "1149776       quebec           quebec     canada  \n",
       "1149777   mannington    west virginia        usa  \n",
       "1149778   providence     rhode island        usa  \n",
       "1149779  san antonio            texas        usa  \n",
       "\n",
       "[1149780 rows x 18 columns]"
      ]
     },
     "execution_count": 262,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "spliting = merge_data['Location'].str.split(pat=',',expand=True)\n",
    "merge_data['City'] = spliting[0].str.strip()\n",
    "merge_data['State'] = spliting[1].str.strip()\n",
    "merge_data['Country'] = spliting[2].str.strip()\n",
    "\n",
    "merge_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 264,
   "id": "e66ec3f6",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         User-ID         ISBN  Book-Rating                        Location  \\\n",
      "0         276725   034545104X            0               tyler, texas, usa   \n",
      "1         276726   0155061224            5        seattle, washington, usa   \n",
      "2         276727   0446520802            0   h, new south wales, australia   \n",
      "3         276729   052165615X            3            rijeka, n/a, croatia   \n",
      "4         276729   0521795028            6            rijeka, n/a, croatia   \n",
      "...          ...          ...          ...                             ...   \n",
      "1149775   276704   1563526298            9          cedar park, texas, usa   \n",
      "1149776   276706   0679447156            0          quebec, quebec, canada   \n",
      "1149777   276709   0515107662           10  mannington, west virginia, usa   \n",
      "1149778   276721   0590442449           10   providence, rhode island, usa   \n",
      "1149779   276723  05162443314            8         san antonio, texas, usa   \n",
      "\n",
      "          Age                                         Book-Title  \\\n",
      "0         NaN                               Flesh Tones: A Novel   \n",
      "1         NaN                                   Rites of Passage   \n",
      "2        16.0                                       The Notebook   \n",
      "3        16.0                                     Help!: Level 1   \n",
      "4        16.0  The Amsterdam Connection : Level 4 (Cambridge ...   \n",
      "...       ...                                                ...   \n",
      "1149775   NaN  Get Clark Smart : The Ultimate Guide for the S...   \n",
      "1149776  18.0  Eight Weeks to Optimum Health: A Proven Progra...   \n",
      "1149777  38.0   The Sherbrooke Bride (Bride Trilogy (Paperback))   \n",
      "1149778  14.0                                  Fourth Grade Rats   \n",
      "1149779  12.0                                                NaN   \n",
      "\n",
      "               Book-Author Year-Of-Publication                   Publisher  \\\n",
      "0               M. J. Rose                2002            Ballantine Books   \n",
      "1               Judith Rae                2001                      Heinle   \n",
      "2          Nicholas Sparks                1996                Warner Books   \n",
      "3            Philip Prowse                1999  Cambridge University Press   \n",
      "4              Sue Leather                2001  Cambridge University Press   \n",
      "...                    ...                 ...                         ...   \n",
      "1149775       Clark Howard                2000            Longstreet Press   \n",
      "1149776        Andrew Weil                1997             Alfred A. Knopf   \n",
      "1149777  Catherine Coulter                1996                  Jove Books   \n",
      "1149778     Jerry Spinelli                1996                  Scholastic   \n",
      "1149779                NaN                 NaN                         NaN   \n",
      "\n",
      "                                               Image-URL-S  ...  \\\n",
      "0        http://images.amazon.com/images/P/034545104X.0...  ...   \n",
      "1        http://images.amazon.com/images/P/0155061224.0...  ...   \n",
      "2        http://images.amazon.com/images/P/0446520802.0...  ...   \n",
      "3        http://images.amazon.com/images/P/052165615X.0...  ...   \n",
      "4        http://images.amazon.com/images/P/0521795028.0...  ...   \n",
      "...                                                    ...  ...   \n",
      "1149775  http://images.amazon.com/images/P/1563526298.0...  ...   \n",
      "1149776  http://images.amazon.com/images/P/0679447156.0...  ...   \n",
      "1149777  http://images.amazon.com/images/P/0515107662.0...  ...   \n",
      "1149778  http://images.amazon.com/images/P/0590442449.0...  ...   \n",
      "1149779                                                NaN  ...   \n",
      "\n",
      "                                               Image-URL-L Book-Title_encoded  \\\n",
      "0        http://images.amazon.com/images/P/034545104X.0...             2886.0   \n",
      "1        http://images.amazon.com/images/P/0155061224.0...            45524.0   \n",
      "2        http://images.amazon.com/images/P/0446520802.0...             2529.0   \n",
      "3        http://images.amazon.com/images/P/052165615X.0...           220494.0   \n",
      "4        http://images.amazon.com/images/P/0521795028.0...           220495.0   \n",
      "...                                                    ...                ...   \n",
      "1149775  http://images.amazon.com/images/P/1563526298.0...            63748.0   \n",
      "1149776  http://images.amazon.com/images/P/0679447156.0...            48482.0   \n",
      "1149777  http://images.amazon.com/images/P/0515107662.0...            15084.0   \n",
      "1149778  http://images.amazon.com/images/P/0590442449.0...            52321.0   \n",
      "1149779                                                NaN                NaN   \n",
      "\n",
      "         Book-Author_encoded  Publisher_encoded         City            State  \\\n",
      "0                     2027.0               27.0        tyler            texas   \n",
      "1                    86746.0             5187.0      seattle       washington   \n",
      "2                      192.0               73.0            h  new south wales   \n",
      "3                    91670.0              372.0       rijeka              n/a   \n",
      "4                    30646.0              372.0       rijeka              n/a   \n",
      "...                      ...                ...          ...              ...   \n",
      "1149775              23264.0              678.0   cedar park            texas   \n",
      "1149776              17796.0               31.0       quebec           quebec   \n",
      "1149777                453.0              133.0   mannington    west virginia   \n",
      "1149778               3172.0               24.0   providence     rhode island   \n",
      "1149779                  NaN                NaN  san antonio            texas   \n",
      "\n",
      "           Country City_encoded  State_encoded  Country_encoded  \n",
      "0              usa            0              0                0  \n",
      "1              usa            1              1                0  \n",
      "2        australia            2              2                1  \n",
      "3          croatia            3              3                2  \n",
      "4          croatia            3              3                2  \n",
      "...            ...          ...            ...              ...  \n",
      "1149775        usa         1102              0                0  \n",
      "1149776     canada         1475             82                9  \n",
      "1149777        usa        16718            109                0  \n",
      "1149778        usa          710            188                0  \n",
      "1149779        usa          408              0                0  \n",
      "\n",
      "[1149780 rows x 21 columns]\n"
     ]
    }
   ],
   "source": [
    "unique_strings = merge_data['City'].unique()\n",
    "merge_encoding = {string: idx for idx, string in enumerate(unique_strings)}\n",
    "\n",
    "# 'A' 열의 문자열을 정수로 인코딩\n",
    "merge_data['City_encoded'] = merge_data['City'].map(merge_encoding)\n",
    "\n",
    "# 문자열을 정수로 매핑할 딕셔너리 생성\n",
    "unique_strings = merge_data['State'].unique()\n",
    "merge_encoding = {string: idx for idx, string in enumerate(unique_strings)}\n",
    "\n",
    "# 'A' 열의 문자열을 정수로 인코딩\n",
    "merge_data['State_encoded'] = merge_data['State'].map(merge_encoding)\n",
    "# 문자열을 정수로 매핑할 딕셔너리 생성\n",
    "unique_strings = merge_data['Country'].unique()\n",
    "merge_encoding = {string: idx for idx, string in enumerate(unique_strings)}\n",
    "\n",
    "# 'A' 열의 문자열을 정수로 인코딩\n",
    "merge_data['Country_encoded'] =merge_data['Country'].map(merge_encoding)\n",
    "\n",
    "# 결과 출력\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 266,
   "id": "9cdd6442",
   "metadata": {},
   "outputs": [],
   "source": [
    "unique_strings = merge_data['Book-Title'].unique()\n",
    "merge_encoding = {string: idx for idx, string in enumerate(unique_strings)}\n",
    "\n",
    "# 'A' 열의 문자열을 정수로 인코딩\n",
    "merge_data['Book-Title_encoded'] =merge_data['Book-Title'].map(merge_encoding)\n",
    "\n",
    "# 문자열을 정수로 매핑할 딕셔너리 생성\n",
    "unique_strings = merge_data['Book-Author'].unique()\n",
    "merge_encoding = {string: idx for idx, string in enumerate(unique_strings)}\n",
    "\n",
    "# 'A' 열의 문자열을 정수로 인코딩\n",
    "merge_data['Book-Author_encoded'] =merge_data['Book-Author'].map(merge_encoding)\n",
    "\n",
    "# 문자열을 정수로 매핑할 딕셔너리 생성\n",
    "unique_strings = merge_data['Publisher'].unique()\n",
    "merge_encoding = {string: idx for idx, string in enumerate(unique_strings)}\n",
    "\n",
    "# 'A' 열의 문자열을 정수로 인코딩\n",
    "merge_data['Publisher_encoded'] =merge_data['Publisher'].map(merge_encoding)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 323,
   "id": "62725d54",
   "metadata": {},
   "outputs": [],
   "source": [
    "merge_encoding_data = merge_data[['Book-Title_encoded','Book-Author_encoded','Publisher_encoded','City_encoded','State_encoded','Country_encoded','User-ID']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 273,
   "id": "7ac6bb4b",
   "metadata": {},
   "outputs": [],
   "source": [
    "merge_encoding_data_sum=merge_encoding_data.sum(axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 329,
   "id": "6fb2e5a7",
   "metadata": {},
   "outputs": [],
   "source": [
    "user_unique = merge_encoding_data['User-ID'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 326,
   "id": "40bb910e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Book-Title_encoded</th>\n",
       "      <th>Book-Author_encoded</th>\n",
       "      <th>Publisher_encoded</th>\n",
       "      <th>City_encoded</th>\n",
       "      <th>State_encoded</th>\n",
       "      <th>Country_encoded</th>\n",
       "      <th>User-ID</th>\n",
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       "  <tbody>\n",
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       "      <td>1</td>\n",
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       "      <td>1</td>\n",
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       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>276727</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>276729</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>276729</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>1149775</th>\n",
       "      <td>164935</td>\n",
       "      <td>22526</td>\n",
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       "      <td>1102</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>276704</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1149776</th>\n",
       "      <td>6299</td>\n",
       "      <td>3906</td>\n",
       "      <td>359</td>\n",
       "      <td>1475</td>\n",
       "      <td>82</td>\n",
       "      <td>9</td>\n",
       "      <td>276706</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1149777</th>\n",
       "      <td>10044</td>\n",
       "      <td>602</td>\n",
       "      <td>265</td>\n",
       "      <td>16718</td>\n",
       "      <td>109</td>\n",
       "      <td>0</td>\n",
       "      <td>276709</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1149778</th>\n",
       "      <td>60289</td>\n",
       "      <td>4712</td>\n",
       "      <td>42</td>\n",
       "      <td>710</td>\n",
       "      <td>188</td>\n",
       "      <td>0</td>\n",
       "      <td>276721</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1149779</th>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>408</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>276723</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1149780 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         Book-Title_encoded  Book-Author_encoded  Publisher_encoded  \\\n",
       "0                         0                    0                  0   \n",
       "1                         1                    1                  1   \n",
       "2                         2                    2                  2   \n",
       "3                         3                    3                  3   \n",
       "4                         4                    4                  3   \n",
       "...                     ...                  ...                ...   \n",
       "1149775              164935                22526               1732   \n",
       "1149776                6299                 3906                359   \n",
       "1149777               10044                  602                265   \n",
       "1149778               60289                 4712                 42   \n",
       "1149779                   6                    6                  5   \n",
       "\n",
       "         City_encoded  State_encoded  Country_encoded  User-ID  \n",
       "0                   0              0                0   276725  \n",
       "1                   1              1                0   276726  \n",
       "2                   2              2                1   276727  \n",
       "3                   3              3                2   276729  \n",
       "4                   3              3                2   276729  \n",
       "...               ...            ...              ...      ...  \n",
       "1149775          1102              0                0   276704  \n",
       "1149776          1475             82                9   276706  \n",
       "1149777         16718            109                0   276709  \n",
       "1149778           710            188                0   276721  \n",
       "1149779           408              0                0   276723  \n",
       "\n",
       "[1149780 rows x 7 columns]"
      ]
     },
     "execution_count": 326,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merge_encoding_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 408,
   "id": "d82fb623",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "User-ID\n",
       "11676     False\n",
       "198711    False\n",
       "153662    False\n",
       "98391     False\n",
       "35859     False\n",
       "          ...  \n",
       "116180     True\n",
       "116166     True\n",
       "116154     True\n",
       "116137     True\n",
       "276723     True\n",
       "Name: count, Length: 105283, dtype: bool"
      ]
     },
     "execution_count": 408,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(merge_encoding_data['User-ID'].value_counts() <= 4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 411,
   "id": "76ebe1d5",
   "metadata": {},
   "outputs": [],
   "source": [
    "filtered_indices = merge_encoding_data['User-ID'].value_counts() <= 2\n",
    "filtered_indices = filtered_indices[filtered_indices].index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 412,
   "id": "00f3af1d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index([266154,  17910, 216004,  17907,  17877, 104863, 104885, 266129, 105480,\n",
       "       265435,\n",
       "       ...\n",
       "       116216, 116191, 116136, 116184, 116182, 116180, 116166, 116154, 116137,\n",
       "       276723],\n",
       "      dtype='int64', name='User-ID', length=71668)"
      ]
     },
     "execution_count": 412,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "filtered_indices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 414,
   "id": "9ff84f77",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "    .dataframe tbody tr th {\n",
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>Book-Title_encoded</th>\n",
       "      <th>Book-Author_encoded</th>\n",
       "      <th>Publisher_encoded</th>\n",
       "      <th>City_encoded</th>\n",
       "      <th>State_encoded</th>\n",
       "      <th>Country_encoded</th>\n",
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       "      <td>276746</td>\n",
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       "      <td>8</td>\n",
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       "      <th>...</th>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
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       "      <td>276704</td>\n",
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       "    <tr>\n",
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       "      <td>1756</td>\n",
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       "      <td>336</td>\n",
       "      <td>1102</td>\n",
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       "      <td>0</td>\n",
       "      <td>276704</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1149773</th>\n",
       "      <td>241071</td>\n",
       "      <td>6163</td>\n",
       "      <td>1073</td>\n",
       "      <td>1102</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>276704</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1149774</th>\n",
       "      <td>203774</td>\n",
       "      <td>70107</td>\n",
       "      <td>4596</td>\n",
       "      <td>1102</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>276704</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1149775</th>\n",
       "      <td>164935</td>\n",
       "      <td>22526</td>\n",
       "      <td>1732</td>\n",
       "      <td>1102</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>276704</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1065610 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         Book-Title_encoded  Book-Author_encoded  Publisher_encoded  \\\n",
       "10                        8                    8                  7   \n",
       "11                        9                    9                  0   \n",
       "12                       10                   10                  8   \n",
       "13                       11                   10                  8   \n",
       "14                       12                   11                  9   \n",
       "...                     ...                  ...                ...   \n",
       "1149771                 541                   22                 20   \n",
       "1149772                1756                 1297                336   \n",
       "1149773              241071                 6163               1073   \n",
       "1149774              203774                70107               4596   \n",
       "1149775              164935                22526               1732   \n",
       "\n",
       "         City_encoded  State_encoded  Country_encoded  User-ID  \n",
       "10                  9              7                6   276746  \n",
       "11                  9              7                6   276746  \n",
       "12                  9              7                6   276746  \n",
       "13                  9              7                6   276746  \n",
       "14                  9              7                6   276746  \n",
       "...               ...            ...              ...      ...  \n",
       "1149771          1102              0                0   276704  \n",
       "1149772          1102              0                0   276704  \n",
       "1149773          1102              0                0   276704  \n",
       "1149774          1102              0                0   276704  \n",
       "1149775          1102              0                0   276704  \n",
       "\n",
       "[1065610 rows x 7 columns]"
      ]
     },
     "execution_count": 414,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "filtered_data = merge_encoding_data.drop(merge_encoding_data.index[merge_encoding_data['User-ID'].isin(filtered_indices)])\n",
    "\n",
    "filtered_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 418,
   "id": "b80ab4e0",
   "metadata": {},
   "outputs": [],
   "source": [
    "merge_encoding_data_sum=filtered_data.sum(axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 415,
   "id": "9da7a5b9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "User-ID\n",
       "11676     13602\n",
       "198711     7550\n",
       "153662     6109\n",
       "98391      5891\n",
       "35859      5850\n",
       "          ...  \n",
       "80625         3\n",
       "80644         3\n",
       "209757        3\n",
       "80672         3\n",
       "136762        3\n",
       "Name: count, Length: 33615, dtype: int64"
      ]
     },
     "execution_count": 415,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "filtered_data['User-ID'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 423,
   "id": "54d45829",
   "metadata": {},
   "outputs": [],
   "source": [
    "filtered_data_0 = filtered_data.set_index('User-ID',inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 426,
   "id": "172d3586",
   "metadata": {},
   "outputs": [],
   "source": [
    "merge_encoding_data_sum=filtered_data.sum(axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 429,
   "id": "812c0824",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([3259])"
      ]
     },
     "execution_count": 429,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.array(merge_encoding_data_sum.sample())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 451,
   "id": "b5eb1ba0",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "merge_encoding_data_sum_0 = merge_encoding_data_sum[:-2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 453,
   "id": "beffff10",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "266402.0"
      ]
     },
     "execution_count": 453,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "1065608/4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 454,
   "id": "5d3e5fe6",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "random_state = list(range(0,1000))\n",
    "from tqdm import tqdm\n",
    "new_list_1 = []\n",
    "def normtest():\n",
    "    for i in tqdm(range(100)):\n",
    "        global new_list_1\n",
    "        new_list =[]\n",
    "        X_0, X_1= train_test_split(merge_encoding_data_sum_0, test_size = 0.5, random_state = random_state[i])\n",
    "        X_0_0, X_0_1 = train_test_split(X_0,test_size = 0.5, random_state = random_state[i])\n",
    "        X_1_0, X_1_1 = train_test_split(X_1, test_size = 0.5, random_state = random_state[i])\n",
    "        a_0 = np.array(X_0_0)\n",
    "        a_1 = np.array(X_0_1)\n",
    "        a_2 = np.array(X_1_0)\n",
    "        a_3 = np.array(X_1_1)\n",
    "        result = np.vstack((a_0, a_1,a_2,a_3))\n",
    "      \n",
    "        for j in range(4):\n",
    "            \n",
    "            storige = []\n",
    "            data = result[j]\n",
    "            for k in range(1000):\n",
    "                storige.append(sum(np.random.choice(data,size=len(data), replace = True))/len(data))\n",
    "            n_test = stats.normaltest(storige)\n",
    "            list_n_test = list(n_test)\n",
    "            list_n_test.append(j)\n",
    "            new_list.append(list_n_test)\n",
    "        new_list_1.append(new_list)\n",
    "        \n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 455,
   "id": "ebe28d1a",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|███████████████████████████████████████| 100/100 [1:35:01<00:00, 57.01s/it]\n"
     ]
    }
   ],
   "source": [
    "normtest()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 481,
   "id": "07a6c618",
   "metadata": {},
   "outputs": [],
   "source": [
    "new_list = []\n",
    "for i in range(100):\n",
    "    for j in range(3):\n",
    "        if new_list_1[i][j][1] > 0.5:\n",
    "            new_list.append(i)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 482,
   "id": "d0113c7d",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame(new_list_1)\n",
    "index_list = new_list\n",
    "import pandas as pd\n",
    "\n",
    "df = df.drop(index_list)\n",
    "\n",
    "\n",
    "\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 488,
   "id": "005db4ec",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "sum_list = []\n",
    "for i in range(len(df)):\n",
    "    a = 0\n",
    "    for j in range(3):\n",
    "        a += df.iloc[i][j][1]\n",
    "    sum_list.append(a)\n",
    "        \n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 492,
   "id": "784e9ea0",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.06073307591611569"
      ]
     },
     "execution_count": 492,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "min(sum_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 493,
   "id": "e200c1a6",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.6812530570222795,\n",
       " 1.079804045249832,\n",
       " 0.5509036094912909,\n",
       " 0.6393957441079707,\n",
       " 0.7670646277283849,\n",
       " 0.06073307591611569,\n",
       " 1.0892412612809317,\n",
       " 0.5737145149854649,\n",
       " 0.7344214857923806]"
      ]
     },
     "execution_count": 493,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 494,
   "id": "e80a9878",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>[12.849174206583179, 0.0016212025289418963, 0]</td>\n",
       "      <td>[2.106179715821261, 0.348858159936335, 1]</td>\n",
       "      <td>[2.21264167936149, 0.3307736945570026, 2]</td>\n",
       "      <td>[7.497128744286188, 0.02355153283405207, 3]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>[2.3835433958059786, 0.3036827530713745, 0]</td>\n",
       "      <td>[1.738009996322555, 0.41936861426529715, 1]</td>\n",
       "      <td>[2.0614250324789194, 0.3567526779131605, 2]</td>\n",
       "      <td>[1.9793381315466805, 0.37169967881624666, 3]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>[3.178982047518598, 0.2040294314440981, 0]</td>\n",
       "      <td>[6.3125246850048375, 0.0425846106848836, 1]</td>\n",
       "      <td>[2.379551013442231, 0.3042895673623093, 2]</td>\n",
       "      <td>[2.3363421148036063, 0.3109351039955705, 3]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>[2.7502955952048938, 0.25280222948007747, 0]</td>\n",
       "      <td>[5.410277028151129, 0.06686106200696586, 1]</td>\n",
       "      <td>[2.280541436924878, 0.3197324526209274, 2]</td>\n",
       "      <td>[4.511514005579627, 0.10479418421258108, 3]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>[4.451683139973877, 0.10797651059684384, 0]</td>\n",
       "      <td>[1.437467453359877, 0.48736900776093794, 1]</td>\n",
       "      <td>[3.5237904443205803, 0.17171910937060308, 2]</td>\n",
       "      <td>[0.011446607562276733, 0.9942930431209833, 3]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>[9.562395835430168, 0.00838594727334575, 0]</td>\n",
       "      <td>[7.461303037190769, 0.023977209149097102, 1]</td>\n",
       "      <td>[7.124851735785665, 0.02836991949367284, 2]</td>\n",
       "      <td>[2.0878937726415923, 0.35206238593402445, 3]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>[1.5531170710619615, 0.4599863170287952, 0]</td>\n",
       "      <td>[2.6065667738955285, 0.2716384331276238, 1]</td>\n",
       "      <td>[2.0565881294005215, 0.35761651112451265, 2]</td>\n",
       "      <td>[3.0515304052912993, 0.21745459635290512, 3]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>[3.332453541321766, 0.18895870673803689, 0]</td>\n",
       "      <td>[7.041078442774063, 0.029583478821793577, 1]</td>\n",
       "      <td>[2.0703043445807765, 0.3551723294256344, 2]</td>\n",
       "      <td>[0.05926607723048398, 0.9708017149337416, 3]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>[5.089527307783404, 0.07849160097306737, 0]</td>\n",
       "      <td>[2.2363136902206286, 0.3268817333846807, 1]</td>\n",
       "      <td>[2.2231023639634517, 0.3290481514346326, 2]</td>\n",
       "      <td>[2.680384404945512, 0.26179534603155846, 3]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                 0  \\\n",
       "1   [12.849174206583179, 0.0016212025289418963, 0]   \n",
       "5      [2.3835433958059786, 0.3036827530713745, 0]   \n",
       "54      [3.178982047518598, 0.2040294314440981, 0]   \n",
       "61    [2.7502955952048938, 0.25280222948007747, 0]   \n",
       "67     [4.451683139973877, 0.10797651059684384, 0]   \n",
       "79     [9.562395835430168, 0.00838594727334575, 0]   \n",
       "82     [1.5531170710619615, 0.4599863170287952, 0]   \n",
       "84     [3.332453541321766, 0.18895870673803689, 0]   \n",
       "98     [5.089527307783404, 0.07849160097306737, 0]   \n",
       "\n",
       "                                               1  \\\n",
       "1      [2.106179715821261, 0.348858159936335, 1]   \n",
       "5    [1.738009996322555, 0.41936861426529715, 1]   \n",
       "54   [6.3125246850048375, 0.0425846106848836, 1]   \n",
       "61   [5.410277028151129, 0.06686106200696586, 1]   \n",
       "67   [1.437467453359877, 0.48736900776093794, 1]   \n",
       "79  [7.461303037190769, 0.023977209149097102, 1]   \n",
       "82   [2.6065667738955285, 0.2716384331276238, 1]   \n",
       "84  [7.041078442774063, 0.029583478821793577, 1]   \n",
       "98   [2.2363136902206286, 0.3268817333846807, 1]   \n",
       "\n",
       "                                               2  \\\n",
       "1      [2.21264167936149, 0.3307736945570026, 2]   \n",
       "5    [2.0614250324789194, 0.3567526779131605, 2]   \n",
       "54    [2.379551013442231, 0.3042895673623093, 2]   \n",
       "61    [2.280541436924878, 0.3197324526209274, 2]   \n",
       "67  [3.5237904443205803, 0.17171910937060308, 2]   \n",
       "79   [7.124851735785665, 0.02836991949367284, 2]   \n",
       "82  [2.0565881294005215, 0.35761651112451265, 2]   \n",
       "84   [2.0703043445807765, 0.3551723294256344, 2]   \n",
       "98   [2.2231023639634517, 0.3290481514346326, 2]   \n",
       "\n",
       "                                                3  \n",
       "1     [7.497128744286188, 0.02355153283405207, 3]  \n",
       "5    [1.9793381315466805, 0.37169967881624666, 3]  \n",
       "54    [2.3363421148036063, 0.3109351039955705, 3]  \n",
       "61    [4.511514005579627, 0.10479418421258108, 3]  \n",
       "67  [0.011446607562276733, 0.9942930431209833, 3]  \n",
       "79   [2.0878937726415923, 0.35206238593402445, 3]  \n",
       "82   [3.0515304052912993, 0.21745459635290512, 3]  \n",
       "84   [0.05926607723048398, 0.9708017149337416, 3]  \n",
       "98    [2.680384404945512, 0.26179534603155846, 3]  "
      ]
     },
     "execution_count": 494,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 496,
   "id": "39d7a8a0",
   "metadata": {},
   "outputs": [],
   "source": [
    "seed_number = 79"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 498,
   "id": "34bfef41",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_0, X_1= train_test_split(merge_encoding_data_sum_0, test_size = 0.5, random_state = seed_number)\n",
    "X_0_0, X_0_1 = train_test_split(X_0,test_size = 0.5, random_state = seed_number)\n",
    "X_1_0, X_1_1 = train_test_split(X_1, test_size = 0.5, random_state = seed_number)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 505,
   "id": "f8e8134d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([274634,  35859, 264525, ..., 138097,  85433, 127429])"
      ]
     },
     "execution_count": 505,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.array(X_1_1.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 510,
   "id": "47c20390",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'list' object has no attribute 'value_counts'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[510], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[38;5;28;43mlist\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mX_1_1\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mindex\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mvalue_counts\u001b[49m()\n",
      "\u001b[0;31mAttributeError\u001b[0m: 'list' object has no attribute 'value_counts'"
     ]
    }
   ],
   "source": [
    "list(X_1_1.index).value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 514,
   "id": "548bce6f",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_1_1_value = pd.Series(X_1_1.index).value_counts()\n",
    "X_1_0_value = pd.Series(X_1_0.index).value_counts()\n",
    "X_0_0_value = pd.Series(X_0_0.index).value_counts()\n",
    "X_0_1_value = pd.Series(X_0_1.index).value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 515,
   "id": "1a143f79",
   "metadata": {},
   "outputs": [],
   "source": [
    "duplicate_indexes = X_1_1.index.intersection(X_1_0.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 532,
   "id": "d510a045",
   "metadata": {},
   "outputs": [],
   "source": [
    "new_list = []\n",
    "duplicate_indexes = X_0_0.index.intersection(X_0_1.index)\n",
    "new_list.append([len(duplicate_indexes.unique())/len(X_0_1.index.unique()),\"0_0\",\"0_1\"])\n",
    "duplicate_indexes = X_0_0.index.intersection(X_1_0.index)\n",
    "new_list.append([len(duplicate_indexes.unique())/len(X_1_0.index.unique()),\"0_0\",\"1_0\"])\n",
    "duplicate_indexes = X_0_0.index.intersection(X_1_1.index)\n",
    "new_list.append([len(duplicate_indexes.unique())/len(X_1_1.index.unique()),\"0_0\",\"1_1\"])\n",
    "duplicate_indexes = X_0_1.index.intersection(X_1_0.index)\n",
    "new_list.append([len(duplicate_indexes.unique())/len(X_1_0.index.unique()),\"0_1\",\"1_0\"])\n",
    "duplicate_indexes = X_0_1.index.intersection(X_1_1.index)\n",
    "new_list.append([len(duplicate_indexes.unique())/len(X_1_1.index.unique()),\"0_1\",\"1_1\"])\n",
    "duplicate_indexes = X_1_0.index.intersection(X_1_1.index)\n",
    "new_list.append([len(duplicate_indexes.unique())/len(X_1_1.index.unique()),\"1_0\",\"1_1\"])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 533,
   "id": "a0948fba",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[0.8334832188208209, '0_0', '0_1'],\n",
       " [0.8321152115211521, '0_0', '1_0'],\n",
       " [0.8351114014079396, '0_0', '1_1'],\n",
       " [0.8351755175517552, '0_1', '1_0'],\n",
       " [0.8335873430582771, '0_1', '1_1'],\n",
       " [0.834131649611728, '1_0', '1_1']]"
      ]
     },
     "execution_count": 533,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 521,
   "id": "75c6183f",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index([208019, 175271,  73331,  46398, 106950, 125031, 154186,  98391, 137589,\n",
       "       278418,\n",
       "       ...\n",
       "        11813,  86619, 122675, 145256,  79019, 232801, 163125, 216847,  84872,\n",
       "        82657],\n",
       "      dtype='int64', name='User-ID', length=27775)"
      ]
     },
     "execution_count": 521,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "X_1_0.index.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 535,
   "id": "fa3cda35",
   "metadata": {},
   "outputs": [],
   "source": [
    "storige = []\n",
    "for k in range(1000):\n",
    "    storige.append(sum(np.random.choice(merge_encoding_data_sum_0,size=len(merge_encoding_data_sum_0), replace = True))/len(merge_encoding_data_sum_0))\n",
    "n_test = stats.normaltest(storige)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 538,
   "id": "6bf80e83",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([  4.,  23.,  67., 164., 240., 249., 156.,  72.,  21.,   4.]),\n",
       " array([65995.50237423, 66044.56928064, 66093.63618704, 66142.70309345,\n",
       "        66191.76999985, 66240.83690625, 66289.90381266, 66338.97071906,\n",
       "        66388.03762547, 66437.10453187, 66486.17143828]),\n",
       " <BarContainer object of 10 artists>)"
      ]
     },
     "execution_count": 538,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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gAQAA1iNoAACA9QgaAABgPYIGAABYj6ABAADWI2gAAID1CBoAAGA9ggYAAFiv1kGzceNGDRs2TMnJyQoLC9Py5csD9htjNH36dCUlJSk6OlppaWnav39/wJjy8nKNGTNGLpdLcXFxGj9+vE6cOHFJJwIAABquWgfNyZMn1bNnT82dO/eC+2fPnq2nn35aCxYs0ObNm9W0aVOlp6fr1KlTzpgxY8Zo9+7dWrdunVavXq2NGzdq4sSJF38WAACgQYuo7Q9kZmYqMzPzgvuMMXrqqaf0yCOPaPjw4ZKkF198UYmJiVq+fLlGjhypPXv2aO3atfroo4/Ut29fSdIzzzyjoUOH6oknnlBycvIlnA4AAGiIgvoemoMHD8rr9SotLc3ZFhsbq9TUVBUVFUmSioqKFBcX58SMJKWlpSk8PFybN2++4HErKyvl9/sDbgAAADWCGjRer1eSlJiYGLA9MTHR2ef1epWQkBCwPyIiQvHx8c6Y78rPz1dsbKxzS0lJCea0AQCA5ay4yikvL08+n8+5HTp0KNRTAgAAl5GgBo3b7ZYklZWVBWwvKytz9rndbh05ciRgf1VVlcrLy50x3xUVFSWXyxVwAwAAqBHUoGnXrp3cbrcKCgqcbX6/X5s3b5bH45EkeTweVVRUaOvWrc6Y9evXq7q6WqmpqcGcDgAAaCBqfZXTiRMnVFxc7Nw/ePCgtm/frvj4eLVp00YPPPCA/vKXv6hTp05q166d/u///k/Jycm67bbbJEldu3ZVRkaGJkyYoAULFujMmTOaPHmyRo4cyRVOAADgotQ6aLZs2aJf/OIXzv3c3FxJ0rhx47R48WI99NBDOnnypCZOnKiKigoNGDBAa9euVZMmTZyfWbJkiSZPnqzBgwcrPDxc2dnZevrpp4NwOgAAoCEKM8aYUE+itvx+v2JjY+Xz+Xg/DerVNQ+/GeopAEHz+aysUE8BDUxd/v624ionAACAH0LQAAAA6xE0AADAegQNAACwHkEDAACsR9AAAADrETQAAMB6BA0AALAeQQMAAKxH0AAAAOsRNAAAwHoEDQAAsB5BAwAArEfQAAAA6xE0AADAegQNAACwHkEDAACsR9AAAADrETQAAMB6BA0AALAeQQMAAKxH0AAAAOsRNAAAwHoEDQAAsB5BAwAArEfQAAAA6xE0AADAegQNAACwHkEDAACsR9AAAADrETQAAMB6BA0AALBeRKgnAAAIjWsefjPUU6i1z2dlhXoKuEzxCg0AALAeQQMAAKxH0AAAAOsRNAAAwHoEDQAAsB5BAwAArEfQAAAA6/E5NAgZGz8DAwBweeIVGgAAYD2CBgAAWI+gAQAA1iNoAACA9QgaAABgPYIGAABYj6ABAADWI2gAAID1CBoAAGA9ggYAAFiPoAEAANYjaAAAgPUIGgAAYD2CBgAAWI+gAQAA1iNoAACA9QgaAABgPYIGAABYL+hB8+ijjyosLCzg1qVLF2f/qVOnlJOToxYtWqhZs2bKzs5WWVlZsKcBAAAakDp5hea6665TaWmpc3vvvfecfVOmTNGqVav0+uuvq7CwUIcPH9aIESPqYhoAAKCBiKiTg0ZEyO12n7fd5/Pp+eef19KlS/XLX/5SkrRo0SJ17dpVmzZtUv/+/etiOgAA4ApXJ6/Q7N+/X8nJyWrfvr3GjBmjkpISSdLWrVt15swZpaWlOWO7dOmiNm3aqKioqC6mAgAAGoCgv0KTmpqqxYsXq3PnziotLdXMmTN10003adeuXfJ6vYqMjFRcXFzAzyQmJsrr9X7vMSsrK1VZWenc9/v9wZ42AACwWNCDJjMz0/nn66+/XqmpqWrbtq1ee+01RUdHX9Qx8/PzNXPmzGBNEQAAXGHq/LLtuLg4XXvttSouLpbb7dbp06dVUVERMKasrOyC77mpkZeXJ5/P59wOHTpUx7MGAAA2qfOgOXHihA4cOKCkpCT16dNHjRs3VkFBgbN/3759Kikpkcfj+d5jREVFyeVyBdwAAABqBP1PTn/84x81bNgwtW3bVocPH9aMGTPUqFEjjRo1SrGxsRo/frxyc3MVHx8vl8ul++67Tx6PhyucAADARQt60HzxxRcaNWqUjh07platWmnAgAHatGmTWrVqJUmaM2eOwsPDlZ2drcrKSqWnp2vevHnBngYAAGhAwowxJtSTqC2/36/Y2Fj5fD7+/GSxax5+M9RTAGCZz2dlhXoKuAR1+fub73ICAADWI2gAAID1CBoAAGA9ggYAAFiPoAEAANYjaAAAgPUIGgAAYD2CBgAAWI+gAQAA1iNoAACA9QgaAABgPYIGAABYj6ABAADWI2gAAID1CBoAAGA9ggYAAFiPoAEAANYjaAAAgPUIGgAAYD2CBgAAWI+gAQAA1iNoAACA9QgaAABgPYIGAABYj6ABAADWI2gAAID1CBoAAGA9ggYAAFiPoAEAANYjaAAAgPUIGgAAYD2CBgAAWC8i1BNAcFzz8JuhngIAACHDKzQAAMB6BA0AALAef3ICAFjDxj+vfz4rK9RTaBB4hQYAAFiPoAEAANYjaAAAgPUIGgAAYD2CBgAAWI+gAQAA1iNoAACA9QgaAABgPYIGAABYj6ABAADWI2gAAID1CBoAAGA9ggYAAFiPoAEAANYjaAAAgPUIGgAAYD2CBgAAWI+gAQAA1iNoAACA9QgaAABgPYIGAABYj6ABAADWI2gAAID1IkI9gcvRNQ+/GeopAACuELb+Tvl8Vlaop1ArIX2FZu7cubrmmmvUpEkTpaam6sMPPwzldAAAgKVCFjSvvvqqcnNzNWPGDH388cfq2bOn0tPTdeTIkVBNCQAAWCpkQfPkk09qwoQJuueee9StWzctWLBAMTEx+vvf/x6qKQEAAEuF5D00p0+f1tatW5WXl+dsCw8PV1pamoqKis4bX1lZqcrKSue+z+eTJPn9/jqZX3XlN3VyXAAAbFEXv2NrjmmMCfqxQxI0X331lc6ePavExMSA7YmJidq7d+954/Pz8zVz5szztqekpNTZHAEAaMhin6q7Yx8/flyxsbFBPaYVVznl5eUpNzfXuV9dXa3y8nK1aNFCYWFhdfrYfr9fKSkpOnTokFwuV50+Fljv+sZ61x/Wun6x3vXrp663MUbHjx9XcnJy0OcQkqBp2bKlGjVqpLKysoDtZWVlcrvd542PiopSVFRUwLa4uLi6nOJ5XC4X/1LUI9a7frHe9Ye1rl+sd/36Kesd7FdmaoTkTcGRkZHq06ePCgoKnG3V1dUqKCiQx+MJxZQAAIDFQvYnp9zcXI0bN059+/bVz3/+cz311FM6efKk7rnnnlBNCQAAWCpkQXPHHXfo6NGjmj59urxer3r16qW1a9ee90bhUIuKitKMGTPO+5MX6gbrXb9Y7/rDWtcv1rt+XQ7rHWbq4topAACAesSXUwIAAOsRNAAAwHoEDQAAsB5BAwAArHdFBs2XX36psWPHqkWLFoqOjlaPHj20ZcuWgDF79uzRrbfeqtjYWDVt2lT9+vVTSUmJs//UqVPKyclRixYt1KxZM2VnZ5/3QYAlJSXKyspSTEyMEhISNHXqVFVVVQWMeeedd/Szn/1MUVFR6tixoxYvXlxn5x0qwVjvhQsXatCgQXK5XAoLC1NFRcV5j1NeXq4xY8bI5XIpLi5O48eP14kTJwLG7NixQzfddJOaNGmilJQUzZ49u07OOZQudb3Ly8t13333qXPnzoqOjlabNm10//33O9+RVoPnd3Ce27/73e/UoUMHRUdHq1WrVho+fPh5X/HCWp8TjPWuYYxRZmamwsLCtHz58oB9rPc5wVjvQYMGKSwsLOA2adKkgGPU23qbK0x5eblp27atufvuu83mzZvNZ599Zt5++21TXFzsjCkuLjbx8fFm6tSp5uOPPzbFxcVmxYoVpqyszBkzadIkk5KSYgoKCsyWLVtM//79zQ033ODsr6qqMt27dzdpaWlm27Zt5q233jItW7Y0eXl5zpjPPvvMxMTEmNzcXPPpp5+aZ555xjRq1MisXbu2fhajHgRrvefMmWPy8/NNfn6+kWS+/vrr8x4rIyPD9OzZ02zatMm8++67pmPHjmbUqFHOfp/PZxITE82YMWPMrl27zMsvv2yio6PNs88+W6drUJ+Csd47d+40I0aMMCtXrjTFxcWmoKDAdOrUyWRnZzvH4PkdvOf2s88+awoLC83BgwfN1q1bzbBhw0xKSoqpqqoyxrDWNYK13jWefPJJk5mZaSSZZcuWOdtZ73OCtd4333yzmTBhgiktLXVuPp/P2V+f633FBc20adPMgAEDfnDMHXfcYcaOHfu9+ysqKkzjxo3N66+/7mzbs2ePkWSKioqMMca89dZbJjw83Hi9XmfM/PnzjcvlMpWVlcYYYx566CFz3XXXnffY6enptT6vy1Uw1vt/bdiw4YJB8+mnnxpJ5qOPPnK2rVmzxoSFhZkvv/zSGGPMvHnzzFVXXeWsf838Onfu/BPP5vIX7PWu8dprr5nIyEhz5swZYwzPb2Pqbq0/+eQTI8n5xcFanxPM9d62bZu5+uqrTWlp6XlBw3qfE6z1vvnmm80f/vCH791fn+t9xf3JaeXKlerbt69+85vfKCEhQb1799Zzzz3n7K+urtabb76pa6+9Vunp6UpISFBqamrAS5Jbt27VmTNnlJaW5mzr0qWL2rRpo6KiIklSUVGRevToEfBBgOnp6fL7/dq9e7cz5n+PUTOm5hhXgmCs909RVFSkuLg49e3b19mWlpam8PBwbd682RkzcOBARUZGOmPS09O1b98+ff3115d2opeJulpvn88nl8uliIhzn7XJ87tu1vrkyZNatGiR2rVrp5SUFEmsdY1grfc333yj0aNHa+7cuRf8bkDW+5xgPr+XLFmili1bqnv37srLy9M333zj7KvP9b7iguazzz7T/Pnz1alTJ7399tu69957df/99+uFF16QJB05ckQnTpzQrFmzlJGRoX/961+6/fbbNWLECBUWFkqSvF6vIiMjz/sCzMTERHm9XmfMdz/VuOb+j43x+/369ttvg37uoRCM9f4pvF6vEhISArZFREQoPj6+Vv+b2K4u1vurr77Sn//8Z02cONHZxvM7uGs9b948NWvWTM2aNdOaNWu0bt06J7xZ63OCtd5TpkzRDTfcoOHDh1/wcVjvc4K13qNHj9ZLL72kDRs2KC8vT//4xz80duxYZ399rnfIvvqgrlRXV6tv3756/PHHJUm9e/fWrl27tGDBAo0bN07V1dWSpOHDh2vKlCmSpF69eumDDz7QggULdPPNN4ds7jZivetXsNfb7/crKytL3bp106OPPlqv53K5C+ZajxkzRr/61a9UWlqqJ554Qr/97W/1/vvvq0mTJvV/YpepYKz3ypUrtX79em3bti1k52GLYD2///f/CPXo0UNJSUkaPHiwDhw4oA4dOtTrOV1xr9AkJSWpW7duAdu6du3qvCu7ZcuWioiI+MExbrdbp0+fPu9Km7KyMuclTLfbfd5VTzX3f2yMy+VSdHT0JZzl5SMY6/1TuN1uHTlyJGBbVVWVysvLa/W/ie2Cud7Hjx9XRkaGmjdvrmXLlqlx48bOPp7fwV3r2NhYderUSQMHDtQ///lP7d27V8uWLZPEWtcIxnqvX79eBw4cUFxcnCIiIpw/oWZnZ2vQoEGSWO8adfXf7tTUVElScXGxpPpd7ysuaG688Ubt27cvYNt//vMftW3bVpIUGRmpfv36/eCYPn36qHHjxiooKHD279u3TyUlJfJ4PJIkj8ejnTt3BvySXbdunVwul/ME8Hg8AceoGVNzjCtBMNb7p/B4PKqoqNDWrVudbevXr1d1dbXzL5DH49HGjRt15swZZ8y6devUuXNnXXXVVbU+t8tRsNbb7/dryJAhioyM1MqVK897pYDnd909t825izFUWVkpibWuEYz1fvjhh7Vjxw5t377duUnSnDlztGjRIkmsd426en7XrHlSUpKkel7vWr2F2AIffvihiYiIMH/961/N/v37zZIlS0xMTIx56aWXnDFvvPGGady4sVm4cKHZv3+/c4nYu+++64yZNGmSadOmjVm/fr3ZsmWL8Xg8xuPxOPtrLkUbMmSI2b59u1m7dq1p1arVBS9Fmzp1qtmzZ4+ZO3fuFXfpX7DWu7S01Gzbts0899xzRpLZuHGj2bZtmzl27JgzJiMjw/Tu3dts3rzZvPfee6ZTp04Bl21XVFSYxMREc+edd5pdu3aZV155xcTExFxRl20HY719Pp9JTU01PXr0MMXFxQGXW373UuKG/PwOxlofOHDAPP7442bLli3mv//9r3n//ffNsGHDTHx8vHPpK2t9TrD+W/Jd+p7LtlnvS1/v4uJi89hjj5ktW7aYgwcPmhUrVpj27dubgQMHOseoz/W+4oLGGGNWrVplunfvbqKiokyXLl3MwoULzxvz/PPPm44dO5omTZqYnj17muXLlwfs//bbb83vf/97c9VVV5mYmBhz++23m9LS0oAxn3/+ucnMzDTR0dGmZcuW5sEHH3Que62xYcMG06tXLxMZGWnat29vFi1aFPTzDbVgrPeMGTOMpPNu/7tex44dM6NGjTLNmjUzLpfL3HPPPeb48eMBx/nkk0/MgAEDTFRUlLn66qvNrFmz6uScQ+lS17vm0vgL3Q4ePOiM4/l96Wv95ZdfmszMTJOQkGAaN25sWrdubUaPHm327t0bcAzW+pxg/Lfku74bNMaw3jUudb1LSkrMwIEDTXx8vImKijIdO3Y0U6dODfgcGmPqb73DjDGmdq/pAAAAXF6uuPfQAACAhoegAQAA1iNoAACA9QgaAABgPYIGAABYj6ABAADWI2gAAID1CBoAAGA9ggYAAFiPoAEAANYjaAAAgPUIGgAAYL3/B6cTTUr31cGiAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(storige)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 600,
   "id": "a26353c1",
   "metadata": {},
   "outputs": [],
   "source": [
    "def NB(User_id):\n",
    "    a = filtered_data.loc[int(User_id),:]['Book-Author_encoded']\n",
    "    \n",
    "    a_unique = a.unique()\n",
    "    for i in range(len(a_unique)):\n",
    "        print(f\"{User_id}가 읽은 책의 저자:{a_unique[i]}\")\n",
    "        b = filtered_data[filtered_data['Book-Author_encoded'] == a_unique[i]]\n",
    "        prob = ((pd.DataFrame(a.value_counts()).loc[a_unique[i],:][0])/len(a))/(((pd.DataFrame(a.value_counts()).loc[a_unique[i],:][0])/len(a))+(len(a)/len(b)))\n",
    "        print(f\"위 저자의 책을 읽을 NB확률{prob}\")\n",
    "        \n",
    "        \n",
    "        \n",
    "    \n",
    "     "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 601,
   "id": "c665a15e",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "276746가 읽은 책의 저자:8\n",
      "위 저자의 책을 읽을 NB확률0.991217370090266\n",
      "276746가 읽은 책의 저자:9\n",
      "위 저자의 책을 읽을 NB확률0.8493723849372385\n",
      "276746가 읽은 책의 저자:10\n",
      "위 저자의 책을 읽을 NB확률0.9858267716535433\n",
      "276746가 읽은 책의 저자:11\n",
      "위 저자의 책을 읽을 NB확률0.3571428571428571\n",
      "276746가 읽은 책의 저자:12\n",
      "위 저자의 책을 읽을 NB확률0.4929577464788732\n"
     ]
    }
   ],
   "source": [
    "NB(276746)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 572,
   "id": "5fda60d2",
   "metadata": {},
   "outputs": [],
   "source": [
    "a = filtered_data.loc[276746,:]['Book-Author_encoded']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 585,
   "id": "fdf496e2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 585,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(a.value_counts()).loc[a.unique()[1],:][0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 581,
   "id": "e3fa1981",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9"
      ]
     },
     "execution_count": 581,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a.unique()[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 565,
   "id": "b32d1e53",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9"
      ]
     },
     "execution_count": 565,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "filtered_data.loc[276746,:]['Book-Author_encoded'].unique()[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 567,
   "id": "d576ee78",
   "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>Book-Title_encoded</th>\n",
       "      <th>Book-Author_encoded</th>\n",
       "      <th>Publisher_encoded</th>\n",
       "      <th>City_encoded</th>\n",
       "      <th>State_encoded</th>\n",
       "      <th>Country_encoded</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>User-ID</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>276746</th>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>278026</th>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>380</td>\n",
       "      <td>98</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>243</th>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>646</td>\n",
       "      <td>92</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010</th>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>972</td>\n",
       "      <td>25</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3331</th>\n",
       "      <td>12740</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>1211</td>\n",
       "      <td>117</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>268315</th>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>164</td>\n",
       "      <td>54</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>271284</th>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>361</td>\n",
       "      <td>99</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>271538</th>\n",
       "      <td>12740</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>361</td>\n",
       "      <td>99</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>274692</th>\n",
       "      <td>12740</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>834</td>\n",
       "      <td>66</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>276038</th>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>151</td>\n",
       "      <td>91</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>203 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         Book-Title_encoded  Book-Author_encoded  Publisher_encoded  \\\n",
       "User-ID                                                               \n",
       "276746                    9                    9                  0   \n",
       "278026                    9                    9                  0   \n",
       "243                       9                    9                  0   \n",
       "2010                      9                    9                  0   \n",
       "3331                  12740                    9                  0   \n",
       "...                     ...                  ...                ...   \n",
       "268315                    9                    9                  0   \n",
       "271284                    9                    9                  0   \n",
       "271538                12740                    9                  0   \n",
       "274692                12740                    9                  0   \n",
       "276038                    9                    9                  0   \n",
       "\n",
       "         City_encoded  State_encoded  Country_encoded  \n",
       "User-ID                                                \n",
       "276746              9              7                6  \n",
       "278026            380             98                0  \n",
       "243               646             92                0  \n",
       "2010              972             25                0  \n",
       "3331             1211            117                0  \n",
       "...               ...            ...              ...  \n",
       "268315            164             54                0  \n",
       "271284            361             99                0  \n",
       "271538            361             99                0  \n",
       "274692            834             66                9  \n",
       "276038            151             91                0  \n",
       "\n",
       "[203 rows x 6 columns]"
      ]
     },
     "execution_count": 567,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "filtered_data[filtered_data['Book-Author_encoded'] == 9]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 441,
   "id": "0fadd93f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([ 21.,  62., 139., 228., 253., 176.,  76.,  33.,  10.,   2.]),\n",
       " array([64600.86131608, 64692.72922576, 64784.59713545, 64876.46504514,\n",
       "        64968.33295483, 65060.20086451, 65152.0687742 , 65243.93668389,\n",
       "        65335.80459357, 65427.67250326, 65519.54041295]),\n",
       " <BarContainer object of 10 artists>)"
      ]
     },
     "execution_count": 441,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(np.array(X_0_0_storige))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 609,
   "id": "776f6885",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "# 평균(mean)과 표준편차(std)를 지정하여 정규 분포를 생성\n",
    "mean = 0\n",
    "std = 1\n",
    "num_samples_1 = 1000  # 생성할 데이터 포인트의 수\n",
    "\n",
    "# 정규 분포를 따르는 데이터 생성\n",
    "normal_distribution_data = np.random.normal(mean, std, num_samples)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 627,
   "id": "9e017356",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "# 최솟값(min)과 최댓값(max)을 지정하여 균등 분포를 생성\n",
    "min_value = 0\n",
    "max_value = 100\n",
    "num_samples_2 = 1000  # 생성할 데이터 포인트의 수\n",
    "\n",
    "# 균등 분포를 따르는 데이터 생성\n",
    "uniform_distribution_data = np.random.uniform(min_value, max_value, num_samples)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 608,
   "id": "7279cbd3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([107.,  98., 107., 102., 105.,  97.,  86.,  88., 106., 104.]),\n",
       " array([0.0013537 , 0.10115597, 0.20095823, 0.3007605 , 0.40056276,\n",
       "        0.50036503, 0.60016729, 0.69996956, 0.79977182, 0.89957409,\n",
       "        0.99937635]),\n",
       " <BarContainer object of 10 artists>)"
      ]
     },
     "execution_count": 608,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(uniform_distribution_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 611,
   "id": "1dc675fd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1000"
      ]
     },
     "execution_count": 611,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "num_samples_1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 616,
   "id": "c3988c48",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'numpy.ndarray' object has no attribute 'choice'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[616], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mnormal_distribution_data\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mchoice\u001b[49m(replace\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n",
      "\u001b[0;31mAttributeError\u001b[0m: 'numpy.ndarray' object has no attribute 'choice'"
     ]
    }
   ],
   "source": [
    "normal_distribution_data.choice(replace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e8009043",
   "metadata": {},
   "outputs": [],
   "source": [
    "np.random.choice(normal_distribution)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 642,
   "id": "ece01a04",
   "metadata": {},
   "outputs": [],
   "source": [
    "sum_list = []\n",
    "sum_nu = 0\n",
    "for i in range(10000):\n",
    "    uniform_distribution_data = np.random.uniform(min_value, max_value, num_samples)\n",
    "    sum_list.append(sum(uniform_distribution_data)/1000)\n",
    "    normal_distribution_data = np.random.normal(mean, std, num_samples)\n",
    "    sum_list.append(sum(uniform_distribution_data)/1000)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 643,
   "id": "18cf1f4c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([  90.,  450., 1628., 4000., 5328., 4834., 2606.,  846.,  188.,\n",
       "          30.]),\n",
       " array([46.91221041, 47.56797398, 48.22373755, 48.87950113, 49.5352647 ,\n",
       "        50.19102827, 50.84679185, 51.50255542, 52.15831899, 52.81408257,\n",
       "        53.46984614]),\n",
       " <BarContainer object of 10 artists>)"
      ]
     },
     "execution_count": 643,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(sum_list)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}