books_rating_project_EDA_3.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "497959b0",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import pandas as pd\n",
    "import math"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "5269f546",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['.git',\n",
       " '.ipynb_checkpoints',\n",
       " 'books_rating_project_EDA_3.ipynb',\n",
       " 'books_rating_project_EDA_Cleaning_1.ipynb',\n",
       " 'books_rating_project_EDA_Cleaning_2.ipynb',\n",
       " 'data',\n",
       " 'README.md']"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "os.listdir()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "49fa4b78",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['cleaned_books.csv',\n",
       " 'uncleaned_books.csv',\n",
       " 'uncleaned_books.csv_bad_lines.txt',\n",
       " 'working_books.csv']"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "os.listdir('data')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "b4390985",
   "metadata": {},
   "outputs": [],
   "source": [
    "folder = 'data/'\n",
    "f_ratings = 'cleaned_books.csv'\n",
    "ratings_path = folder + f_ratings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "6ed5c9ac",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>bookID</th>\n",
       "      <th>title</th>\n",
       "      <th>authors</th>\n",
       "      <th>isbn</th>\n",
       "      <th>isbn13</th>\n",
       "      <th>language_code</th>\n",
       "      <th>num_pages</th>\n",
       "      <th>ratings_count</th>\n",
       "      <th>text_reviews_count</th>\n",
       "      <th>publication_date</th>\n",
       "      <th>publisher</th>\n",
       "      <th>average_rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Harry Potter and the Half-Blood Prince (Harry ...</td>\n",
       "      <td>J.K. Rowling/Mary GrandPré</td>\n",
       "      <td>0439785960</td>\n",
       "      <td>9780439785969</td>\n",
       "      <td>eng</td>\n",
       "      <td>652</td>\n",
       "      <td>2095690</td>\n",
       "      <td>27591</td>\n",
       "      <td>2006-09-16</td>\n",
       "      <td>Scholastic Inc.</td>\n",
       "      <td>4.57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Harry Potter and the Order of the Phoenix (Har...</td>\n",
       "      <td>J.K. Rowling/Mary GrandPré</td>\n",
       "      <td>0439358078</td>\n",
       "      <td>9780439358071</td>\n",
       "      <td>eng</td>\n",
       "      <td>870</td>\n",
       "      <td>2153167</td>\n",
       "      <td>29221</td>\n",
       "      <td>2004-09-01</td>\n",
       "      <td>Scholastic Inc.</td>\n",
       "      <td>4.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4</td>\n",
       "      <td>Harry Potter and the Chamber of Secrets (Harry...</td>\n",
       "      <td>J.K. Rowling</td>\n",
       "      <td>0439554896</td>\n",
       "      <td>9780439554893</td>\n",
       "      <td>eng</td>\n",
       "      <td>352</td>\n",
       "      <td>6333</td>\n",
       "      <td>244</td>\n",
       "      <td>2003-11-01</td>\n",
       "      <td>Scholastic</td>\n",
       "      <td>4.42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5</td>\n",
       "      <td>Harry Potter and the Prisoner of Azkaban (Harr...</td>\n",
       "      <td>J.K. Rowling/Mary GrandPré</td>\n",
       "      <td>043965548X</td>\n",
       "      <td>9780439655484</td>\n",
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       "      <td>36325</td>\n",
       "      <td>2004-05-01</td>\n",
       "      <td>Scholastic Inc.</td>\n",
       "      <td>4.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>8</td>\n",
       "      <td>Harry Potter Boxed Set  Books 1-5 (Harry Potte...</td>\n",
       "      <td>J.K. Rowling/Mary GrandPré</td>\n",
       "      <td>0439682584</td>\n",
       "      <td>9780439682589</td>\n",
       "      <td>eng</td>\n",
       "      <td>2690</td>\n",
       "      <td>41428</td>\n",
       "      <td>164</td>\n",
       "      <td>2004-09-13</td>\n",
       "      <td>Scholastic</td>\n",
       "      <td>4.78</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   bookID                                              title  \\\n",
       "0       1  Harry Potter and the Half-Blood Prince (Harry ...   \n",
       "1       2  Harry Potter and the Order of the Phoenix (Har...   \n",
       "2       4  Harry Potter and the Chamber of Secrets (Harry...   \n",
       "3       5  Harry Potter and the Prisoner of Azkaban (Harr...   \n",
       "4       8  Harry Potter Boxed Set  Books 1-5 (Harry Potte...   \n",
       "\n",
       "                      authors        isbn         isbn13 language_code  \\\n",
       "0  J.K. Rowling/Mary GrandPré  0439785960  9780439785969           eng   \n",
       "1  J.K. Rowling/Mary GrandPré  0439358078  9780439358071           eng   \n",
       "2                J.K. Rowling  0439554896  9780439554893           eng   \n",
       "3  J.K. Rowling/Mary GrandPré  043965548X  9780439655484           eng   \n",
       "4  J.K. Rowling/Mary GrandPré  0439682584  9780439682589           eng   \n",
       "\n",
       "     num_pages  ratings_count  text_reviews_count publication_date  \\\n",
       "0          652        2095690               27591       2006-09-16   \n",
       "1          870        2153167               29221       2004-09-01   \n",
       "2          352           6333                 244       2003-11-01   \n",
       "3          435        2339585               36325       2004-05-01   \n",
       "4         2690          41428                 164       2004-09-13   \n",
       "\n",
       "         publisher  average_rating  \n",
       "0  Scholastic Inc.            4.57  \n",
       "1  Scholastic Inc.            4.49  \n",
       "2       Scholastic            4.42  \n",
       "3  Scholastic Inc.            4.56  \n",
       "4       Scholastic            4.78  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_ratings = pd.read_csv(ratings_path)\n",
    "df_ratings.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "d743677b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2292"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df_ratings['publisher'].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "86fc7b1b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['bookID',\n",
       " 'title',\n",
       " 'authors',\n",
       " 'isbn',\n",
       " 'isbn13',\n",
       " 'language_code',\n",
       " '  num_pages',\n",
       " 'ratings_count',\n",
       " 'text_reviews_count',\n",
       " 'publication_date',\n",
       " 'publisher',\n",
       " 'average_rating']"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(df_ratings.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "8aa2bab1",
   "metadata": {},
   "outputs": [],
   "source": [
    "keep_cols = ['authors', 'language_code', '  num_pages', 'ratings_count', 'text_reviews_count', 'publisher', 'average_rating']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "89fc4576",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_ratings_keep = df_ratings[keep_cols]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "ebdb2907",
   "metadata": {},
   "outputs": [],
   "source": [
    "def custom_round(num: float)->float:\n",
    "    floor = float(math.floor(num))\n",
    "    f_split = floor + .33\n",
    "    s_split = floor + .66\n",
    "\n",
    "    if num >= s_split:\n",
    "        return str(floor + 1.0)\n",
    "    if num >= f_split and num < s_split:\n",
    "        return str(floor + .5)\n",
    "    \n",
    "    return str(floor)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "e4b8baa2",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\joesa\\AppData\\Local\\Temp\\ipykernel_24760\\819842606.py:1: 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",
      "  df_ratings_keep['target_buckets'] = df_ratings_keep['average_rating'].apply(custom_round)\n"
     ]
    }
   ],
   "source": [
    "df_ratings_keep['target_buckets'] = df_ratings_keep['average_rating'].apply(custom_round)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "6e32a4f0",
   "metadata": {},
   "outputs": [
    {
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       "      <td>36325</td>\n",
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       "      <td>4.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>J.K. Rowling/Mary GrandPré</td>\n",
       "      <td>eng</td>\n",
       "      <td>2690</td>\n",
       "      <td>41428</td>\n",
       "      <td>164</td>\n",
       "      <td>Scholastic</td>\n",
       "      <td>4.78</td>\n",
       "      <td>5.0</td>\n",
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      ],
      "text/plain": [
       "                      authors language_code    num_pages  ratings_count  \\\n",
       "0  J.K. Rowling/Mary GrandPré           eng          652        2095690   \n",
       "1  J.K. Rowling/Mary GrandPré           eng          870        2153167   \n",
       "2                J.K. Rowling           eng          352           6333   \n",
       "3  J.K. Rowling/Mary GrandPré           eng          435        2339585   \n",
       "4  J.K. Rowling/Mary GrandPré           eng         2690          41428   \n",
       "\n",
       "   text_reviews_count        publisher  average_rating target_buckets  \n",
       "0               27591  Scholastic Inc.            4.57            4.5  \n",
       "1               29221  Scholastic Inc.            4.49            4.5  \n",
       "2                 244       Scholastic            4.42            4.5  \n",
       "3               36325  Scholastic Inc.            4.56            4.5  \n",
       "4                 164       Scholastic            4.78            5.0  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_ratings_keep.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "3ffe69ab",
   "metadata": {},
   "outputs": [],
   "source": [
    "keep_cols = ['authors', 'language_code', '  num_pages', 'ratings_count', 'text_reviews_count', 'publisher', 'target_buckets']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "948d7ae8",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_ratings_keep = df_ratings_keep[keep_cols]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "71c63b35",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "d21aea48",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_one_hot_sparse = pd.get_dummies(df_ratings_keep, columns=['authors', 'language_code', 'publisher'], sparse=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "1cfc0bbd",
   "metadata": {},
   "outputs": [
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       "</table>\n",
       "<p>11127 rows × 8966 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         num_pages  ratings_count  text_reviews_count target_buckets  \\\n",
       "0              652        2095690               27591            4.5   \n",
       "1              870        2153167               29221            4.5   \n",
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       "3              435        2339585               36325            4.5   \n",
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       "...            ...            ...                 ...            ...   \n",
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       "11124          400             26                   4            3.5   \n",
       "11125          342             38                   4            3.5   \n",
       "11126           49              0                   0            0.0   \n",
       "\n",
       "       authors_A.B. Yehoshua/Hillel Halkin  \\\n",
       "0                                        0   \n",
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       "3                                        0   \n",
       "4                                        0   \n",
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       "11123                                    0   \n",
       "11124                                    0   \n",
       "11125                                    0   \n",
       "11126                                    0   \n",
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       "0                                                      0      \n",
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       "11122                                                  0      \n",
       "11123                                                  0      \n",
       "11124                                                  0      \n",
       "11125                                                  0      \n",
       "11126                                                  0      \n",
       "\n",
       "       authors_A.E. Cunningham/Harlan Ellison/Charles F. Miller/Terry Dowling/Paul Rhoads/Jack Vance/Tom Shippey/Gene Wolfe/David Langford/Dan Simmons/David Mathew  \\\n",
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       "4                                                      0                                                                                                              \n",
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       "11124                                                  0                                                                                                              \n",
       "11125                                                  0                                                                                                              \n",
       "11126                                                  0                                                                                                              \n",
       "\n",
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       "11124                    0                   0   \n",
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       "11126                    0                   0   \n",
       "\n",
       "       authors_A.N. Roquelaure/Anne Rice  ...  publisher_小学館  \\\n",
       "0                                      0  ...              0   \n",
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       "...                                  ...  ...            ...   \n",
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       "11124                                  0  ...              0   \n",
       "11125                                  0  ...              0   \n",
       "11126                                  0  ...              0   \n",
       "\n",
       "       publisher_小学館 [Shōgakukan]  publisher_時報出版  publisher_東立  \\\n",
       "0                                0               0             0   \n",
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       "3                                0               0             0   \n",
       "4                                0               0             0   \n",
       "...                            ...             ...           ...   \n",
       "11122                            0               0             0   \n",
       "11123                            0               0             0   \n",
       "11124                            0               0             0   \n",
       "11125                            0               0             0   \n",
       "11126                            0               0             0   \n",
       "\n",
       "       publisher_皇冠文化出版有限公司  publisher_聯經出版事業股份有限公司  publisher_英文漢聲出版股份有限公司  \\\n",
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       "...                                 ...            ...            ...  \n",
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       "\n",
       "[11127 rows x 8966 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_one_hot_sparse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "f666678d",
   "metadata": {},
   "outputs": [],
   "source": [
    "y = data_one_hot_sparse['target_buckets']\n",
    "x = data_one_hot_sparse[data_one_hot_sparse.columns.difference(['target_buckets'])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "93ac6e28",
   "metadata": {},
   "outputs": [
    {
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       "      <th>11123</th>\n",
       "      <td>236</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>61</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11124</th>\n",
       "      <td>400</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>26</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11125</th>\n",
       "      <td>342</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>38</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11126</th>\n",
       "      <td>49</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>11127 rows × 8965 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         num_pages  authors_A.B. Yehoshua/Hillel Halkin  \\\n",
       "0              652                                    0   \n",
       "1              870                                    0   \n",
       "2              352                                    0   \n",
       "3              435                                    0   \n",
       "4             2690                                    0   \n",
       "...            ...                                  ...   \n",
       "11122          272                                    0   \n",
       "11123          236                                    0   \n",
       "11124          400                                    0   \n",
       "11125          342                                    0   \n",
       "11126           49                                    0   \n",
       "\n",
       "       authors_A.D.P. Briggs/Leo Tolstoy/Fyodor Dostoyevsky  \\\n",
       "0                                                      0      \n",
       "1                                                      0      \n",
       "2                                                      0      \n",
       "3                                                      0      \n",
       "4                                                      0      \n",
       "...                                                  ...      \n",
       "11122                                                  0      \n",
       "11123                                                  0      \n",
       "11124                                                  0      \n",
       "11125                                                  0      \n",
       "11126                                                  0      \n",
       "\n",
       "       authors_A.E. Cunningham/Harlan Ellison/Charles F. Miller/Terry Dowling/Paul Rhoads/Jack Vance/Tom Shippey/Gene Wolfe/David Langford/Dan Simmons/David Mathew  \\\n",
       "0                                                      0                                                                                                              \n",
       "1                                                      0                                                                                                              \n",
       "2                                                      0                                                                                                              \n",
       "3                                                      0                                                                                                              \n",
       "4                                                      0                                                                                                              \n",
       "...                                                  ...                                                                                                              \n",
       "11122                                                  0                                                                                                              \n",
       "11123                                                  0                                                                                                              \n",
       "11124                                                  0                                                                                                              \n",
       "11125                                                  0                                                                                                              \n",
       "11126                                                  0                                                                                                              \n",
       "\n",
       "       authors_A.J. Jacobs  authors_A.M. Homes  \\\n",
       "0                        0                   0   \n",
       "1                        0                   0   \n",
       "2                        0                   0   \n",
       "3                        0                   0   \n",
       "4                        0                   0   \n",
       "...                    ...                 ...   \n",
       "11122                    0                   0   \n",
       "11123                    0                   0   \n",
       "11124                    0                   0   \n",
       "11125                    0                   0   \n",
       "11126                    0                   0   \n",
       "\n",
       "       authors_A.N. Roquelaure/Anne Rice  authors_A.S. Byatt  \\\n",
       "0                                      0                   0   \n",
       "1                                      0                   0   \n",
       "2                                      0                   0   \n",
       "3                                      0                   0   \n",
       "4                                      0                   0   \n",
       "...                                  ...                 ...   \n",
       "11122                                  0                   0   \n",
       "11123                                  0                   0   \n",
       "11124                                  0                   0   \n",
       "11125                                  0                   0   \n",
       "11126                                  0                   0   \n",
       "\n",
       "       authors_Abbie Hoffman/Anita Hoffman  \\\n",
       "0                                        0   \n",
       "1                                        0   \n",
       "2                                        0   \n",
       "3                                        0   \n",
       "4                                        0   \n",
       "...                                    ...   \n",
       "11122                                    0   \n",
       "11123                                    0   \n",
       "11124                                    0   \n",
       "11125                                    0   \n",
       "11126                                    0   \n",
       "\n",
       "       authors_Abdul Rahman Munif/Peter Theroux  ...  publisher_時報出版  \\\n",
       "0                                             0  ...               0   \n",
       "1                                             0  ...               0   \n",
       "2                                             0  ...               0   \n",
       "3                                             0  ...               0   \n",
       "4                                             0  ...               0   \n",
       "...                                         ...  ...             ...   \n",
       "11122                                         0  ...               0   \n",
       "11123                                         0  ...               0   \n",
       "11124                                         0  ...               0   \n",
       "11125                                         0  ...               0   \n",
       "11126                                         0  ...               0   \n",
       "\n",
       "       publisher_東立  publisher_皇冠文化出版有限公司  publisher_聯經出版事業股份有限公司  \\\n",
       "0                 0                     0                       0   \n",
       "1                 0                     0                       0   \n",
       "2                 0                     0                       0   \n",
       "3                 0                     0                       0   \n",
       "4                 0                     0                       0   \n",
       "...             ...                   ...                     ...   \n",
       "11122             0                     0                       0   \n",
       "11123             0                     0                       0   \n",
       "11124             0                     0                       0   \n",
       "11125             0                     0                       0   \n",
       "11126             0                     0                       0   \n",
       "\n",
       "       publisher_英文漢聲出版股份有限公司  publisher_角川書店 (Kadokawa Shoten)  \\\n",
       "0                           0                                 0   \n",
       "1                           0                                 0   \n",
       "2                           0                                 0   \n",
       "3                           0                                 0   \n",
       "4                           0                                 0   \n",
       "...                       ...                               ...   \n",
       "11122                       0                                 0   \n",
       "11123                       0                                 0   \n",
       "11124                       0                                 0   \n",
       "11125                       0                                 0   \n",
       "11126                       0                                 0   \n",
       "\n",
       "       publisher_講談社  publisher_集英社  ratings_count  text_reviews_count  \n",
       "0                  0              0        2095690               27591  \n",
       "1                  0              0        2153167               29221  \n",
       "2                  0              0           6333                 244  \n",
       "3                  0              0        2339585               36325  \n",
       "4                  0              0          41428                 164  \n",
       "...              ...            ...            ...                 ...  \n",
       "11122              0              0            113                  12  \n",
       "11123              0              0             61                   6  \n",
       "11124              0              0             26                   4  \n",
       "11125              0              0             38                   4  \n",
       "11126              0              0              0                   0  \n",
       "\n",
       "[11127 rows x 8965 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "c488a310",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "import time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "ae984289",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.3, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "212c94b2",
   "metadata": {},
   "outputs": [],
   "source": [
    "logreg = LogisticRegression(random_state=0, multi_class='ovr', solver = 'liblinear')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "af618173",
   "metadata": {},
   "outputs": [],
   "source": [
    "logreg.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d42f42af",
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions = logreg.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c281d2fe",
   "metadata": {},
   "outputs": [],
   "source": [
    "score = logreg.score(x_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "02fa1b59",
   "metadata": {},
   "outputs": [],
   "source": [
    "score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ee6b0b95",
   "metadata": {},
   "outputs": [],
   "source": [
    "duration_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6e1b03a6",
   "metadata": {},
   "outputs": [],
   "source": [
    "model.score(x, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "00ec122f",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "e845df94",
   "metadata": {},
   "outputs": [],
   "source": [
    "clf = RandomForestClassifier(n_estimators=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "a6ff3827",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Anaconda3\\envs\\env_project_learning\\lib\\site-packages\\sklearn\\utils\\validation.py:768: UserWarning: pandas.DataFrame with sparse columns found.It will be converted to a dense numpy array.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "RandomForestClassifier()"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "0dca2350",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Anaconda3\\envs\\env_project_learning\\lib\\site-packages\\sklearn\\utils\\validation.py:768: UserWarning: pandas.DataFrame with sparse columns found.It will be converted to a dense numpy array.\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "y_pred=clf.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "90336b55",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "e5afd026",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy: 0.7909553758610363\n"
     ]
    }
   ],
   "source": [
    "print('accuracy:', metrics.accuracy_score(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "d2062534",
   "metadata": {},
   "outputs": [],
   "source": [
    "keep_cols = ['  num_pages', 'ratings_count', 'text_reviews_count', 'target_buckets']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "4f2091be",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_non_cat = df_ratings_keep[keep_cols]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "b5e8b87e",
   "metadata": {},
   "outputs": [],
   "source": [
    "y = df_non_cat['target_buckets']\n",
    "x = df_non_cat[df_non_cat.columns.difference(['target_buckets'])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "00208b10",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.3, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "f3802af1",
   "metadata": {},
   "outputs": [],
   "source": [
    "clf = RandomForestClassifier(n_estimators=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "cc7002c2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "RandomForestClassifier()"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "9d1d84f0",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred=clf.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "f3b3c9b9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy: 0.7496256364180892\n"
     ]
    }
   ],
   "source": [
    "print('accuracy:', metrics.accuracy_score(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "ab34ba40",
   "metadata": {},
   "outputs": [],
   "source": [
    "keep_cols = ['authors','  num_pages', 'ratings_count', 'text_reviews_count', 'target_buckets']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "4d5680f5",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_auth = df_ratings_keep[keep_cols]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "3165e107",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_one_hot_authors = pd.get_dummies(df_auth, columns=['authors'], sparse=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "d318fbf4",
   "metadata": {},
   "outputs": [],
   "source": [
    "y = data_one_hot_authors['target_buckets']\n",
    "x = data_one_hot_authors[data_one_hot_authors.columns.difference(['target_buckets'])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "4f3b72d2",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.3, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "208f658a",
   "metadata": {},
   "outputs": [],
   "source": [
    "clf = RandomForestClassifier(n_estimators=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "98956356",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Anaconda3\\envs\\env_project_learning\\lib\\site-packages\\sklearn\\utils\\validation.py:768: UserWarning: pandas.DataFrame with sparse columns found.It will be converted to a dense numpy array.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-3 {color: black;background-color: white;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-3 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-3 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" checked><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "RandomForestClassifier()"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "fe5e5ef6",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Anaconda3\\envs\\env_project_learning\\lib\\site-packages\\sklearn\\utils\\validation.py:768: UserWarning: pandas.DataFrame with sparse columns found.It will be converted to a dense numpy array.\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "y_pred=clf.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "26778fd9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy: 0.7900569032644504\n"
     ]
    }
   ],
   "source": [
    "print('accuracy:', metrics.accuracy_score(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f5c70eb3",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0f6a9d43",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "42eb69e8",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7600b40c",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "95617a14",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "babbb237",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a36c94ac",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b53db244",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "502e7f15",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d15de6c0",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f3faffb1",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d0a4001a",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c964380e",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cb9af6f3",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "66904f7f",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_ratings.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "852a0201",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "646b5713",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_ratings.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "12388649",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_ratings.describe(include=object)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "aa06e845",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_ratings.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "06eed501",
   "metadata": {},
   "outputs": [],
   "source": [
    "?? df_ratings.corr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ad001f96",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_ratings = df_ratings.astype({'bookID': str, 'isbn13': str})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3621fc9a",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_ratings['publication_date'] = pd.to_datetime(df_ratings['publication_date'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fd96d90d",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_ratings.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "99b58743",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_ratings.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f8ed1735",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_ratings['target_rating'] = df_ratings['average_rating'].apply(custom_round)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "58aa8438",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_ratings.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1394d0bc",
   "metadata": {},
   "outputs": [],
   "source": [
    "?? pd.get_dummies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6d81a260",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_new = df_ratings['authors'].str.get_dummies(sep=\"/\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "65738939",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_new = pd.get_dummies(df_new['language_code'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0a9729e4",
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.get_dummies(df_ratings['publisher'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4cc3cca9",
   "metadata": {},
   "outputs": [],
   "source": [
    "x = 3.3\n",
    "y = 3.4\n",
    "z = 3.7"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "be223643",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "25209769",
   "metadata": {},
   "outputs": [],
   "source": [
    "custom_round(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "36ebe9b6",
   "metadata": {},
   "outputs": [],
   "source": [
    "custom_round(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "374d21c6",
   "metadata": {},
   "outputs": [],
   "source": [
    "custom_round(z)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c3843f71",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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