fcc_book_recommendation_knn.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "id": "Y1onB6kUvo4Z"
   },
   "outputs": [],
   "source": [
    "# import libraries (you may add additional imports but you may not have to)\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from scipy.sparse import csr_matrix\n",
    "from sklearn.neighbors import NearestNeighbors\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "id": "iAQGqqO_vo4d"
   },
   "outputs": [],
   "source": [
    "# get data files\n",
    "#!wget https://cdn.freecodecamp.org/project-data/books/book-crossings.zip\n",
    "\n",
    "#!unzip book-crossings.zip\n",
    "\n",
    "books_filename = 'BX-Books.csv'\n",
    "ratings_filename = 'BX-Book-Ratings.csv'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "id": "NClILWOiEd6Q"
   },
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>isbn</th>\n",
       "      <th>title</th>\n",
       "      <th>author</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0195153448</td>\n",
       "      <td>Classical Mythology</td>\n",
       "      <td>Mark P. O. Morford</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0002005018</td>\n",
       "      <td>Clara Callan</td>\n",
       "      <td>Richard Bruce Wright</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0060973129</td>\n",
       "      <td>Decision in Normandy</td>\n",
       "      <td>Carlo D'Este</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <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",
       "    </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",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         isbn                                              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",
       "                 author  \n",
       "0    Mark P. O. Morford  \n",
       "1  Richard Bruce Wright  \n",
       "2          Carlo D'Este  \n",
       "3      Gina Bari Kolata  \n",
       "4       E. J. W. Barber  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# import csv data into dataframes\n",
    "df_books = pd.read_csv(\n",
    "    books_filename,\n",
    "    encoding = \"ISO-8859-1\",\n",
    "    sep=\";\",\n",
    "    header=0,\n",
    "    names=['isbn', 'title', 'author'],\n",
    "    usecols=['isbn', 'title', 'author'],\n",
    "    dtype={'isbn': 'str', 'title': 'str', 'author': 'str'})\n",
    "\n",
    "df_ratings = pd.read_csv(\n",
    "    ratings_filename,\n",
    "    encoding = \"ISO-8859-1\",\n",
    "    sep=\";\",\n",
    "    header=0,\n",
    "    names=['user', 'isbn', 'rating'],\n",
    "    usecols=['user', 'isbn', 'rating'],\n",
    "    dtype={'user': 'int32', 'isbn': 'str', 'rating': 'float32'})\n",
    "df_books.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user</th>\n",
       "      <th>isbn</th>\n",
       "      <th>rating</th>\n",
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       "      <td>5.0</td>\n",
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       "      <th>2</th>\n",
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       "    </tr>\n",
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       "</table>\n",
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      ],
      "text/plain": [
       "     user        isbn  rating\n",
       "0  276725  034545104X     0.0\n",
       "1  276726  0155061224     5.0\n",
       "2  276727  0446520802     0.0\n",
       "3  276729  052165615X     3.0\n",
       "4  276729  0521795028     6.0"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_ratings.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>isbn</th>\n",
       "      <th>total</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "      <td>100</td>\n",
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      "text/plain": [
       "              isbn  total\n",
       "339825  0060916508    100\n",
       "339826  0140092323    100\n",
       "339827  0312966806    100\n",
       "339828  0385424736    100\n",
       "339829  0425136981    100"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandasql as ps\n",
    "query=\"\"\"SELECT isbn, COUNT() AS total FROM df_ratings\n",
    "\n",
    "        GROUP BY isbn\n",
    "        ORDER BY total\"\"\"\n",
    "\n",
    "cleaned_df=ps.sqldf(query, locals())\n",
    "cleaned_df=cleaned_df[cleaned_df[\"total\"]>=100]\n",
    "cleaned_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user</th>\n",
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       "  </thead>\n",
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       "      <td>36554</td>\n",
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       "      <th>104380</th>\n",
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       "      <td>200</td>\n",
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       "      <td>200</td>\n",
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       "      <th>104382</th>\n",
       "      <td>225595</td>\n",
       "      <td>200</td>\n",
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      ],
      "text/plain": [
       "          user  total\n",
       "104378   26883    200\n",
       "104379   36554    200\n",
       "104380   83671    200\n",
       "104381   99955    200\n",
       "104382  225595    200"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "query=\"\"\"SELECT user, COUNT() AS total FROM df_ratings\n",
    "\n",
    "        GROUP BY user\n",
    "        ORDER BY total\"\"\"\n",
    "\n",
    "user_df=ps.sqldf(query, locals())\n",
    "user_df=user_df[user_df[\"total\"]>=200]\n",
    "user_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "df_ratings = df_ratings.merge(user_df, on = \"user\", how=\"inner\")\n",
    "df_ratings = df_ratings.merge(cleaned_df, on = \"isbn\", how=\"inner\")\n",
    "df_ratings = df_ratings.merge(df_books, on = \"isbn\", how=\"inner\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['user', 'isbn', 'rating', 'total_x', 'total_y', 'title', 'author'], dtype='object')"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_ratings.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
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       "    <tr>\n",
       "      <th>title</th>\n",
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       "      <th>A Beautiful Mind: The Life of Mathematical Genius and Nobel Laureate John Nash</th>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Without Remorse</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Year of Wonders</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>You Belong To Me</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Zen and the Art of Motorcycle Maintenance: An Inquiry into Values</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>\\O\\\" Is for Outlaw\"</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>673 rows × 888 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "user                                                254     2276    2766    \\\n",
       "title                                                                        \n",
       "1984                                                   9.0     0.0     0.0   \n",
       "1st to Die: A Novel                                    0.0     0.0     0.0   \n",
       "2nd Chance                                             0.0    10.0     0.0   \n",
       "4 Blondes                                              0.0     0.0     0.0   \n",
       "A Beautiful Mind: The Life of Mathematical Geni...     0.0     0.0     0.0   \n",
       "...                                                    ...     ...     ...   \n",
       "Without Remorse                                        0.0     0.0     0.0   \n",
       "Year of Wonders                                        0.0     0.0     0.0   \n",
       "You Belong To Me                                       0.0     0.0     0.0   \n",
       "Zen and the Art of Motorcycle Maintenance: An I...     0.0     0.0     0.0   \n",
       "\\O\\\" Is for Outlaw\"                                    0.0     0.0     0.0   \n",
       "\n",
       "user                                                2977    3363    4017    \\\n",
       "title                                                                        \n",
       "1984                                                   0.0     0.0     0.0   \n",
       "1st to Die: A Novel                                    0.0     0.0     0.0   \n",
       "2nd Chance                                             0.0     0.0     0.0   \n",
       "4 Blondes                                              0.0     0.0     0.0   \n",
       "A Beautiful Mind: The Life of Mathematical Geni...     0.0     0.0     0.0   \n",
       "...                                                    ...     ...     ...   \n",
       "Without Remorse                                        0.0     0.0     0.0   \n",
       "Year of Wonders                                        7.0     0.0     0.0   \n",
       "You Belong To Me                                       0.0     0.0     0.0   \n",
       "Zen and the Art of Motorcycle Maintenance: An I...     0.0     0.0     0.0   \n",
       "\\O\\\" Is for Outlaw\"                                    0.0     0.0     0.0   \n",
       "\n",
       "user                                                4385    6242    6251    \\\n",
       "title                                                                        \n",
       "1984                                                   0.0     0.0     0.0   \n",
       "1st to Die: A Novel                                    0.0     0.0     0.0   \n",
       "2nd Chance                                             0.0     0.0     0.0   \n",
       "4 Blondes                                              0.0     0.0     0.0   \n",
       "A Beautiful Mind: The Life of Mathematical Geni...     0.0     0.0     0.0   \n",
       "...                                                    ...     ...     ...   \n",
       "Without Remorse                                        0.0     0.0     0.0   \n",
       "Year of Wonders                                        0.0     7.0     0.0   \n",
       "You Belong To Me                                       0.0     0.0     0.0   \n",
       "Zen and the Art of Motorcycle Maintenance: An I...     0.0     0.0     0.0   \n",
       "\\O\\\" Is for Outlaw\"                                    0.0     0.0     0.0   \n",
       "\n",
       "user                                                6323    ...  274004  \\\n",
       "title                                                       ...           \n",
       "1984                                                   0.0  ...     0.0   \n",
       "1st to Die: A Novel                                    0.0  ...     0.0   \n",
       "2nd Chance                                             0.0  ...     0.0   \n",
       "4 Blondes                                              0.0  ...     0.0   \n",
       "A Beautiful Mind: The Life of Mathematical Geni...     0.0  ...     0.0   \n",
       "...                                                    ...  ...     ...   \n",
       "Without Remorse                                        0.0  ...     0.0   \n",
       "Year of Wonders                                        0.0  ...     0.0   \n",
       "You Belong To Me                                       0.0  ...     0.0   \n",
       "Zen and the Art of Motorcycle Maintenance: An I...     0.0  ...     0.0   \n",
       "\\O\\\" Is for Outlaw\"                                    0.0  ...     0.0   \n",
       "\n",
       "user                                                274061  274301  274308  \\\n",
       "title                                                                        \n",
       "1984                                                   0.0     0.0     0.0   \n",
       "1st to Die: A Novel                                    0.0     0.0     0.0   \n",
       "2nd Chance                                             0.0     0.0     0.0   \n",
       "4 Blondes                                              0.0     0.0     0.0   \n",
       "A Beautiful Mind: The Life of Mathematical Geni...     0.0     0.0     0.0   \n",
       "...                                                    ...     ...     ...   \n",
       "Without Remorse                                        0.0     0.0     0.0   \n",
       "Year of Wonders                                        0.0     0.0     0.0   \n",
       "You Belong To Me                                       0.0     0.0     0.0   \n",
       "Zen and the Art of Motorcycle Maintenance: An I...     0.0     0.0     0.0   \n",
       "\\O\\\" Is for Outlaw\"                                    0.0     8.0     0.0   \n",
       "\n",
       "user                                                274808  275970  277427  \\\n",
       "title                                                                        \n",
       "1984                                                   0.0     0.0     0.0   \n",
       "1st to Die: A Novel                                    0.0     0.0     0.0   \n",
       "2nd Chance                                             0.0     0.0     0.0   \n",
       "4 Blondes                                              0.0     0.0     0.0   \n",
       "A Beautiful Mind: The Life of Mathematical Geni...     0.0     0.0     0.0   \n",
       "...                                                    ...     ...     ...   \n",
       "Without Remorse                                        0.0     0.0     0.0   \n",
       "Year of Wonders                                        0.0     0.0     0.0   \n",
       "You Belong To Me                                       0.0     0.0     0.0   \n",
       "Zen and the Art of Motorcycle Maintenance: An I...     0.0     0.0     0.0   \n",
       "\\O\\\" Is for Outlaw\"                                    0.0     0.0     0.0   \n",
       "\n",
       "user                                                277478  277639  278418  \n",
       "title                                                                       \n",
       "1984                                                   0.0     0.0     0.0  \n",
       "1st to Die: A Novel                                    0.0     0.0     0.0  \n",
       "2nd Chance                                             0.0     0.0     0.0  \n",
       "4 Blondes                                              0.0     0.0     0.0  \n",
       "A Beautiful Mind: The Life of Mathematical Geni...     0.0     0.0     0.0  \n",
       "...                                                    ...     ...     ...  \n",
       "Without Remorse                                        0.0     0.0     0.0  \n",
       "Year of Wonders                                        0.0     0.0     0.0  \n",
       "You Belong To Me                                       0.0     0.0     0.0  \n",
       "Zen and the Art of Motorcycle Maintenance: An I...     0.0     0.0     0.0  \n",
       "\\O\\\" Is for Outlaw\"                                    0.0     0.0     0.0  \n",
       "\n",
       "[673 rows x 888 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "\n",
    "df_ratings = df_ratings.drop_duplicates(['user', 'title'])\n",
    "\n",
    "\n",
    "pivoted_df=df_ratings.pivot(index='title', columns='user', values='rating')\n",
    "pivoted_df = pivoted_df.fillna(0)\n",
    "pivoted_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "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>NearestNeighbors(algorithm=&#x27;brute&#x27;, metric=&#x27;cosine&#x27;, n_jobs=-1)</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\">NearestNeighbors</label><div class=\"sk-toggleable__content\"><pre>NearestNeighbors(algorithm=&#x27;brute&#x27;, metric=&#x27;cosine&#x27;, n_jobs=-1)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "NearestNeighbors(algorithm='brute', metric='cosine', n_jobs=-1)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "\n",
    "model = NearestNeighbors(algorithm=\"brute\",metric=\"cosine\", n_jobs=-1)\n",
    "model.fit(pivoted_df)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "id": "xAcXjkCFCh0A"
   },
   "outputs": [],
   "source": [
    "# add your code here - consider creating a new cell for each section of code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "id": "f5ZUd-L1SQz7"
   },
   "outputs": [],
   "source": [
    "# function to return recommended books - this will be tested\n",
    "def get_recommends(book = \"\"):\n",
    "    x = pivoted_df[pivoted_df.index == book]\n",
    "    dist, ind = model.kneighbors(x, n_neighbors = 6)\n",
    "    dist, ind = dist.flatten(), ind.flatten()\n",
    "    recommended_books = []\n",
    "    recommended_books.append(pivoted_df.index[ind[0]])\n",
    "    recs=[]\n",
    "    for i in reversed(range(len(dist))):\n",
    "        if i !=0:\n",
    "            recs.append([pivoted_df.index[ind[i]], dist[i]])\n",
    "        else:\n",
    "            pass\n",
    "    recommended_books.append(recs)\n",
    "\n",
    "    return recommended_books\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[\"Where the Heart Is (Oprah's Book Club (Paperback))\",\n",
       " [[\"I'll Be Seeing You\", 0.8016211],\n",
       "  ['The Weight of Water', 0.77085835],\n",
       "  ['The Surgeon', 0.7699411],\n",
       "  ['I Know This Much Is True', 0.76770747],\n",
       "  ['The Lovely Bones: A Novel', 0.7234864]]]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_recommends(\"Where the Heart Is (Oprah's Book Club (Paperback))\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "id": "jd2SLCh8oxMh"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[\"Where the Heart Is (Oprah's Book Club (Paperback))\", [[\"I'll Be Seeing You\", 0.8016211], ['The Weight of Water', 0.77085835], ['The Surgeon', 0.7699411], ['I Know This Much Is True', 0.76770747], ['The Lovely Bones: A Novel', 0.7234864]]]\n",
      "You passed the challenge! 🎉🎉🎉🎉🎉\n"
     ]
    }
   ],
   "source": [
    "books = get_recommends(\"Where the Heart Is (Oprah's Book Club (Paperback))\")\n",
    "print(books)\n",
    "\n",
    "def test_book_recommendation():\n",
    "    test_pass = True\n",
    "    recommends = get_recommends(\"Where the Heart Is (Oprah's Book Club (Paperback))\")\n",
    "    if recommends[0] != \"Where the Heart Is (Oprah's Book Club (Paperback))\":\n",
    "        test_pass = False\n",
    "    recommended_books = [\"I'll Be Seeing You\", 'The Weight of Water', 'The Surgeon', 'I Know This Much Is True']\n",
    "    recommended_books_dist = [0.8, 0.77, 0.77, 0.77]\n",
    "    for i in range(2): \n",
    "        if recommends[1][i][0] not in recommended_books:\n",
    "            test_pass = False\n",
    "        if abs(recommends[1][i][1] - recommended_books_dist[i]) >= 0.05:\n",
    "              test_pass = False\n",
    "    if test_pass:\n",
    "        print(\"You passed the challenge! 🎉🎉🎉🎉🎉\")\n",
    "    else:\n",
    "        print(\"You haven't passed yet. Keep trying!\")\n",
    "\n",
    "test_book_recommendation()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "colab": {
   "name": "fcc_book_recommendation_knn.ipynb",
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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