BookRecommender/main.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Book Recommendation System Using KNN\n",
    "***"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Import Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "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": "markdown",
   "metadata": {},
   "source": [
    "## Load in Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_books = pd.read_csv(\n",
    "    'data/BX-Books.csv',\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",
    "    'data/BX-Book-Ratings.csv',\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",
    "\n",
    "df_users = pd.read_csv(\n",
    "    'data/BX-Users.csv',\n",
    "    encoding = \"ISO-8859-1\",\n",
    "    sep=\";\",\n",
    "    header=0,\n",
    "    names=['user', 'location', 'age'],\n",
    "    usecols=['user', 'location', 'age'],\n",
    "    dtype={'user': 'int32', 'location': 'str', 'age': 'float32'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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": [
    "df_books.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user</th>\n",
       "      <th>rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1.149780e+06</td>\n",
       "      <td>1.149780e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.403864e+05</td>\n",
       "      <td>2.866950e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>8.056228e+04</td>\n",
       "      <td>3.854184e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>2.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>7.034500e+04</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.410100e+05</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2.110280e+05</td>\n",
       "      <td>7.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2.788540e+05</td>\n",
       "      <td>1.000000e+01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               user        rating\n",
       "count  1.149780e+06  1.149780e+06\n",
       "mean   1.403864e+05  2.866950e+00\n",
       "std    8.056228e+04  3.854184e+00\n",
       "min    2.000000e+00  0.000000e+00\n",
       "25%    7.034500e+04  0.000000e+00\n",
       "50%    1.410100e+05  0.000000e+00\n",
       "75%    2.110280e+05  7.000000e+00\n",
       "max    2.788540e+05  1.000000e+01"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_ratings.head()\n",
    "df_ratings.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user</th>\n",
       "      <th>location</th>\n",
       "      <th>age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>nyc, new york, usa</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>stockton, california, usa</td>\n",
       "      <td>18.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>moscow, yukon territory, russia</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>porto, v.n.gaia, portugal</td>\n",
       "      <td>17.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>farnborough, hants, united kingdom</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   user                            location   age\n",
       "0     1                  nyc, new york, usa   NaN\n",
       "1     2           stockton, california, usa  18.0\n",
       "2     3     moscow, yukon territory, russia   NaN\n",
       "3     4           porto, v.n.gaia, portugal  17.0\n",
       "4     5  farnborough, hants, united kingdom   NaN"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_users.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Analyze Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_ratings.rating.value_counts(sort=False).plot(kind='bar')\n",
    "plt.title('Distribution of Ratings')\n",
    "plt.xlabel('Rating')\n",
    "plt.ylabel('Count')\n",
    "plt.savefig('images/ratings_bar.png', bbox_inches='tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZEAAAEWCAYAAACnlKo3AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy86wFpkAAAACXBIWXMAAAsTAAALEwEAmpwYAAAcV0lEQVR4nO3dfZxV1X3v8c9X8IHgA6J2gkAdU2kMSn0iitF7O9VGR00DN1Wjlwa0KL1VW9Nyk2J678s82WpbY6I32kuVgImVWB9eEDWhRBlTm6KCJiIar1PFwAQkCqJjombM7/6x18iecWYYl3POmTnzfb9e5zV7r/209po95zt77X32UURgZmaWY5daV8DMzIYuh4iZmWVziJiZWTaHiJmZZXOImJlZNoeImZllc4jYsKXCNyRtk/RwreszkCR9V9LsWtfD6p9DxGpGUkt6A9+9RlU4EfgoMCEiju0+UdLnJX2rh/KQdEg1KtiT1G4XdCtrkrSxczwiTouIxf1YV033xYY+h4jVhKRG4L8AAXy8RtU4CFgfEa/VaPsASBpZy+1XSr3ul3XlELFamQWsAhYBXbpdJO0n6TuSXpH0iKQvS3qwNP1QSSskbZX0tKSze9uIpAMlLUvztkq6MJXPAW4EjpfULukLOTsh6TxJz0p6VdJzkmaWpv2xpKfS2dZySQeVpoWkiyU9AzyTutaukbQl7fdaSYfn1Cmt/+2zFUmHSHpA0nZJL0r6dir/QZr9x6kNPpnKL0xttTW13YGl9Z6S2ny7pOvTeju3c56kf0/78RLweUm/Jel+SS+lbd8iaUxpfeslfUbS45Jek3STpIbUHfeqpO9L2je3HawKIsIvv6r+AlqBi4BjgF8BDaVpS9LrfcBkYAPwYJo2Oo2fD4wEjgJeBCb3sp0fANcDewBHAj8HTkrTzutcby/Lfh74Vg/lARyS6vIK8MFUPg44LA1PT/v4oVTP/wX8sNs6VgBjgVHAqcAaYAygtNy4XurVAlzQrawJ2NjTPMCtwF9T/NO4B3Bi930pjZ+U2vNoYHfgOuAHadr+aX8/kfbp0vS7u6DUnh3An6Xpo1I7fTSt64D0+/hqaXvrKf6ZaADGA1uAR9PvdQ/gfuDyWh+vfvX+8pmIVZ2kEym6km6LiDXAfwL/PU0bAfwhxRvHLyLiSaDct/8xii6ob0RER0Q8BtwBnNXDdiYCJwB/FRGvR8SPKM4+Zg3g7vwaOFzSqIjYFBHrUvn/AP42Ip6KiA7gb4Ajy2cjafrWiPglxZvxXsChgNJym/rY7rWSXu58AXf3Me+vKNr7wNQOD/Yx70xgYUQ8GhFvAJdRnK01AqcD6yLizrRP1wKbuy3/s4i4Lv1ufhkRrRGxIiLeiIifA18BfrfbMtdFxAsR0Qb8G/BQRDwWEa8Dd1EEig1SDhGrhdnAv0bEi2n8n9nRpXUAxX+xG0rzl4cPAo7r9gY6E3h/D9s5ENgaEa+Wyp6n+I+3PzqAXcsFkjrHfxXFtZRPUgTGJkn3SDq0VM+vleq4leIMo7ztt/crIu4H/g/wdWCLpAWS9u6jbn8eEWM6XxTh2pvPpm0/LGmdpD/uY94DKdqos17twEup3gd2q3MAG7stX/5dkbqmlkhqk/QK8C2KM5qyF0rDv+xhfM8+6ms15hCxqpI0Cjgb+F1JmyVtBv4COELSERTdTR3AhNJiE0vDG4AHym+gEbFnRPxpD5v7GTBW0l6lst8E2vpZ3Z8Cjd3KDk71awOIiOUR8VGKrqyfAP9UquefdKvnqIj4YWldXR6hHRHXRsQxFF14vw18pp/17FNEbI6ICyPiQOBPgOv7uCPrZxQBCICk0cB+FPu7idLvRZLo+nuCbvtEcQYWwJSI2Bv4I4pAszrhELFqmwG8RfFGeWR6fYiiG2NWRLwF3ElxUfZ96T/7cvfT3cBvS/qUpF3T68OSPtR9QxGxAfgh8LeS9pD0O8Aciv+G++N7wKGlbY2leFO8IyI60n/Z09Mb7RtAO0X3FsA/ApdJOgxA0j6S3tHl1intw3HpTOc14PXSut4TSWdJ6nyz30bxpt657heAD5RmvxU4X9KRKm69/huK7qX1wD3AFEkzVNx5dTE9nwGW7UXRLtsljWeAgtEGD4eIVdts4BsR8dP0H/LmiNhM0ZUzM705XQLsQ9Hf/k2KN7Y3AFLX1CnAORT/NW8GrqK4cNuTcynOJn5G0b9+eUR8vz8VjYgtwGkU/71vAZ4AXgY6z3p2Af4yrXsrRV//n6Zl70r1WpK6cZ5I6+rN3hRnMdsoupNeAv6+P/Xshw8DD0lqB5YBl0bEs2na54HFqdvt7NQ2/5viOtMm4Lco2prU/XgW8HepfpOB1aTfTS++QHGRfjtFCN05QPtkg4SKbk2zwUvSVcD7I8KfwB5EJO1CcU1kZkSsrHV9rDZ8JmKDjorPgfyOCsdSdEHdVet6GUg6VdKY1NX1OYrrG6tqXC2rIX+i1AajvSi6sA6k6LO/Glha0xpZp+Mp7qbbDXgSmJFuUbZhyt1ZZmaWzd1ZZmaWbdh1Z+2///7R2NiYtexrr73G6NGjB7ZCQ5Tboiu3R1dujx3qoS3WrFnzYkQc0NO0YRcijY2NrF69OmvZlpYWmpqaBrZCQ5Tboiu3R1dujx3qoS0kPd/bNHdnmZlZNoeImZllc4iYmVk2h4iZmWVziJiZWTaHiJmZZXOImJlZNoeImZllc4iYmVm2YfeJdRsYa9u2c978e2pdDdZfeUatq2A2rPlMxMzMsjlEzMwsW0VDRNJ6SWsl/UjS6lQ2VtIKSc+kn/umckm6VlKrpMclHV1az+w0/zOSZpfKj0nrb03LqpL7Y2ZmXVXjTOT3IuLIiJiaxucD90XEJOC+NA5wGjApveYCN0AROsDlwHHAscDlncGT5rmwtFxz5XfHzMw61aI7azqwOA0vBmaUym+OwipgjKRxwKnAiojYGhHbgBVAc5q2d0SsiuLrGW8urcvMzKqg0ndnBfCvkgL4vxGxAGiIiE1p+magIQ2PBzaUlt2Yyvoq39hD+TtImktxdkNDQwMtLS1ZO9Pe3p69bL1pGAXzpnTUuhqD5vfhY6Mrt8cO9d4WlQ6REyOiTdJvACsk/aQ8MSIiBUxFpfBaADB16tTI/YKYevhymYFy3S1LuXpt7e8QXz+zqdZVAHxsdOf22KHe26Ki3VkR0ZZ+bgHuorim8ULqiiL93JJmbwMmlhafkMr6Kp/QQ7mZmVVJxUJE0mhJe3UOA6cATwDLgM47rGYDS9PwMmBWuktrGrA9dXstB06RtG+6oH4KsDxNe0XStHRX1qzSuszMrAoq2R/RANyV7rodCfxzRHxP0iPAbZLmAM8DZ6f57wVOB1qBXwDnA0TEVklfAh5J830xIram4YuARcAo4LvpZWZmVVKxEImIZ4Ejeih/CTi5h/IALu5lXQuBhT2UrwYOf8+VNTOzLP7EupmZZXOImJlZNoeImZllc4iYmVk2h4iZmWVziJiZWTaHiJmZZXOImJlZNoeImZllc4iYmVk2h4iZmWVziJiZWTaHiJmZZXOImJlZNoeImZllc4iYmVk2h4iZmWVziJiZWTaHiJmZZXOImJlZNoeImZllc4iYmVk2h4iZmWVziJiZWTaHiJmZZXOImJlZtpG1roDZe9E4/55aVwGARc2ja10Fs5rwmYiZmWVziJiZWTaHiJmZZXOImJlZtoqHiKQRkh6TdHcaP1jSQ5JaJX1b0m6pfPc03pqmN5bWcVkqf1rSqaXy5lTWKml+pffFzMy6qsaZyKXAU6Xxq4BrIuIQYBswJ5XPAbal8mvSfEiaDJwDHAY0A9enYBoBfB04DZgMnJvmNTOzKqloiEiaAJwB3JjGBZwE3J5mWQzMSMPT0zhp+slp/unAkoh4IyKeA1qBY9OrNSKejYg3gSVpXjMzq5JKf07kq8Bngb3S+H7AyxHRkcY3AuPT8HhgA0BEdEjanuYfD6wqrbO8zIZu5cf1VAlJc4G5AA0NDbS0tGTtTHt7e/ay9aZhFMyb0rHzGYcJHxtduT12qPe2qFiISPoYsCUi1khqqtR2+iMiFgALAKZOnRpNTXnVaWlpIXfZenPdLUu5eq0/q9ppUfNoHxsl/lvZod7bopLvAicAH5d0OrAHsDfwNWCMpJHpbGQC0JbmbwMmAhsljQT2AV4qlXcqL9NbuZmZVUHFrolExGURMSEiGikujN8fETOBlcCZabbZwNI0vCyNk6bfHxGRys9Jd28dDEwCHgYeASalu712S9tYVqn9MTOzd6pFf8RfAUskfRl4DLgpld8EfFNSK7CVIhSIiHWSbgOeBDqAiyPiLQBJlwDLgRHAwohYV9U9MTMb5qoSIhHRArSk4Wcp7qzqPs/rwFm9LH8FcEUP5fcC9w5gVc3M7F3wJ9bNzCybQ8TMzLI5RMzMLJtDxMzMsjlEzMwsm0PEzMyyOUTMzCybQ8TMzLI5RMzMLJtDxMzMsjlEzMwsm0PEzMyyOUTMzCybQ8TMzLI5RMzMLJtDxMzMsjlEzMwsm0PEzMyyOUTMzCybQ8TMzLI5RMzMLJtDxMzMsjlEzMwsm0PEzMyyOUTMzCybQ8TMzLI5RMzMLJtDxMzMsjlEzMwsm0PEzMyyOUTMzCxbxUJE0h6SHpb0Y0nrJH0hlR8s6SFJrZK+LWm3VL57Gm9N0xtL67oslT8t6dRSeXMqa5U0v1L7YmZmPavkmcgbwEkRcQRwJNAsaRpwFXBNRBwCbAPmpPnnANtS+TVpPiRNBs4BDgOagesljZA0Avg6cBowGTg3zWtmZlVSsRCJQnsa3TW9AjgJuD2VLwZmpOHpaZw0/WRJSuVLIuKNiHgOaAWOTa/WiHg2It4ElqR5zcysSkb2ZyZJJ0TEv++srIflRgBrgEMozhr+E3g5IjrSLBuB8Wl4PLABICI6JG0H9kvlq0qrLS+zoVv5cb3UYy4wF6ChoYGWlpa+qt2r9vb27GXrTcMomDelY+czDhM+Nrpye+xQ723RrxABrgOO7kdZFxHxFnCkpDHAXcCh77aCAyEiFgALAKZOnRpNTU1Z62lpaSF32Xpz3S1LuXptfw+f+reoebSPjRL/rexQ723R57uApOOBjwAHSPrL0qS9gRH93UhEvCxpJXA8MEbSyHQ2MgFoS7O1AROBjZJGAvsAL5XKO5WX6a3czMyqYGfXRHYD9qQIm71Kr1eAM/taUNIB6QwESaOAjwJPAStLy84GlqbhZWmcNP3+iIhUfk66e+tgYBLwMPAIMCnd7bUbxcX3Zf3YZzMzGyB9nolExAPAA5IWRcTz73Ld44DF6brILsBtEXG3pCeBJZK+DDwG3JTmvwn4pqRWYCtFKBAR6yTdBjwJdAAXp24yJF0CLKc4K1oYEeveZR3NzOw96G+n9u6SFgCN5WUi4qTeFoiIx4Gjeih/luLOqu7lrwNn9bKuK4Areii/F7h359U3M7NK6G+I/Avwj8CNwFuVq46ZmQ0l/Q2Rjoi4oaI1MTOzIae/Hzb8jqSLJI2TNLbzVdGamZnZoNffM5HOu6Y+UyoL4AMDWx0zMxtK+hUiEXFwpStiZmZDT38fezKrp/KIuHlgq2NmZkNJf7uzPlwa3gM4GXgUcIiYmQ1j/e3O+rPyePok+pJKVMjMzIaO3CfovQb4OolZsrZtO+fNv6fW1WD9lWfUugo2zPT3msh3KO7GguIRIx8CbqtUpczMbGjo75nIP5SGO4DnI2JjBepjZmZDSL8+bJgexPgTiif47gu8WclKmZnZ0NCvEJF0NsXj188CzgYektTno+DNzKz+9bc766+BD0fEFii+KwT4Pju+K93MzIah/j47a5fOAEleehfLmplZnervmcj3JC0Hbk3jn8Tf42FmNuzt7DvWDwEaIuIzkj4BnJgm/QdwS6UrZ2Zmg9vOzkS+ClwGEBF3AncCSJqSpv1BBetmZmaD3M6uazRExNruhamssSI1MjOzIWNnITKmj2mjBrAeZmY2BO0sRFZLurB7oaQLgDWVqZKZmQ0VO7sm8mngLkkz2REaU4HdgP9WwXqZmdkQ0GeIRMQLwEck/R5weCq+JyLur3jNzMxs0Ovv94msBFZWuC5mZjbE+FPnZmaWzSFiZmbZHCJmZpbNIWJmZtkcImZmls0hYmZm2RwiZmaWrWIhImmipJWSnpS0TtKlqXyspBWSnkk/903lknStpFZJj0s6urSu2Wn+ZyTNLpUfI2ltWuZaSarU/piZ2TtV8kykA5gXEZOBacDFkiYD84H7ImIScF8aBzgNmJRec4EboAgd4HLgOOBY4PLO4EnzXFharrmC+2NmZt1ULEQiYlNEPJqGXwWeAsYD04HFabbFwIw0PB24OQqrgDGSxgGnAisiYmtEbANWAM1p2t4RsSoiAri5tC4zM6uC/n497nsiqRE4CniI4jtKNqVJm4GGNDwe2FBabGMq66t8Yw/lPW1/LsXZDQ0NDbS0tGTtR3t7e/ay9aZhFMyb0lHragwag6U9Bsvx6b+VHeq9LSoeIpL2BO4APh0Rr5QvW0RESIpK1yEiFgALAKZOnRpNTU1Z62lpaSF32Xpz3S1LuXptVf4HGRLmTekYFO2xfmZTrasA+G+lrN7boqJ3Z0nalSJAbklfrwvwQuqKIv3cksrbgImlxSeksr7KJ/RQbmZmVVLJu7ME3AQ8FRFfKU1aBnTeYTUbWFoqn5Xu0poGbE/dXsuBUyTtmy6onwIsT9NekTQtbWtWaV1mZlYFlTz/PgH4FLBW0o9S2eeAK4HbJM0BngfOTtPuBU4HWoFfAOcDRMRWSV8CHknzfTEitqbhi4BFFF/V+930MjOzKqlYiETEg0Bvn9s4uYf5A7i4l3UtBBb2UL6aHV+WNWw0zr+n1lVg3pRa18B6MhiODYBFzaNrXQWrEn9i3czMsjlEzMwsm0PEzMyyOUTMzCybQ8TMzLI5RMzMLJtDxMzMsjlEzMwsm0PEzMyyOUTMzCybQ8TMzLI5RMzMLJtDxMzMsjlEzMwsm0PEzMyyOUTMzCybQ8TMzLI5RMzMLJtDxMzMsjlEzMwsm0PEzMyyOUTMzCybQ8TMzLI5RMzMLJtDxMzMsjlEzMwsm0PEzMyyOUTMzCybQ8TMzLI5RMzMLJtDxMzMslUsRCQtlLRF0hOlsrGSVkh6Jv3cN5VL0rWSWiU9Luno0jKz0/zPSJpdKj9G0tq0zLWSVKl9MTOznlXyTGQR0NytbD5wX0RMAu5L4wCnAZPSay5wAxShA1wOHAccC1zeGTxpngtLy3XflpmZVVjFQiQifgBs7VY8HVichhcDM0rlN0dhFTBG0jjgVGBFRGyNiG3ACqA5Tds7IlZFRAA3l9ZlZmZVUu1rIg0RsSkNbwYa0vB4YENpvo2prK/yjT2Um5lZFY2s1YYjIiRFNbYlaS5FNxkNDQ20tLRkrae9vT172YE0b0pHratAw6jBUY/Bwu3R1WD5WxkM6r0tqh0iL0gaFxGbUpfUllTeBkwszTchlbUBTd3KW1L5hB7m71FELAAWAEydOjWampp6m7VPLS0t5C47kM6bf0+tq8C8KR1cvbZm/4MMOm6PrhY1jx4UfyuDwWB536iUandnLQM677CaDSwtlc9Kd2lNA7anbq/lwCmS9k0X1E8Blqdpr0ialu7KmlVal5mZVUnF/nWSdCvFWcT+kjZS3GV1JXCbpDnA88DZafZ7gdOBVuAXwPkAEbFV0peAR9J8X4yIzov1F1HcATYK+G56mZlZFVUsRCLi3F4mndzDvAFc3Mt6FgILeyhfDRz+XupoZmbvjT+xbmZm2RwiZmaWzSFiZmbZHCJmZpbNIWJmZtkcImZmls0hYmZm2fycBjMbcGvbtg+Kx/Osv/KMWleh7vlMxMzMsjlEzMwsm0PEzMyyOUTMzCybQ8TMzLI5RMzMLJtDxMzMsjlEzMwsm0PEzMyyOUTMzCybQ8TMzLI5RMzMLJtDxMzMsjlEzMwsm0PEzMyyOUTMzCybQ8TMzLI5RMzMLJtDxMzMsjlEzMws28haV8DMrFIa599T6yqwqHl0ratQUT4TMTOzbA4RMzPL5u6sd2Ft23bOGwSnx2Zmg8WQPxOR1CzpaUmtkubXuj5mZsPJkA4RSSOArwOnAZOBcyVNrm2tzMyGj6HenXUs0BoRzwJIWgJMB56saa3MzJLB0g2+/sozKrJeRURFVlwNks4EmiPigjT+KeC4iLik23xzgblp9IPA05mb3B94MXPZeuO26Mrt0ZXbY4d6aIuDIuKAniYM9TORfomIBcCC97oeSasjYuoAVGnIc1t05fboyu2xQ723xZC+JgK0ARNL4xNSmZmZVcFQD5FHgEmSDpa0G3AOsKzGdTIzGzaGdHdWRHRIugRYDowAFkbEugpu8j13idURt0VXbo+u3B471HVbDOkL62ZmVltDvTvLzMxqyCFiZmbZHCL9MNwfrSJpoqSVkp6UtE7Spal8rKQVkp5JP/etdV2rRdIISY9JujuNHyzpoXSMfDvd6DEsSBoj6XZJP5H0lKTjh/mx8Rfp7+QJSbdK2qOejw+HyE740SoAdADzImIyMA24OLXBfOC+iJgE3JfGh4tLgadK41cB10TEIcA2YE5NalUbXwO+FxGHAkdQtMuwPDYkjQf+HJgaEYdT3PBzDnV8fDhEdu7tR6tExJtA56NVho2I2BQRj6bhVyneJMZTtMPiNNtiYEZNKlhlkiYAZwA3pnEBJwG3p1mGU1vsA/xX4CaAiHgzIl5mmB4byUhglKSRwPuATdTx8eEQ2bnxwIbS+MZUNixJagSOAh4CGiJiU5q0GWioVb2q7KvAZ4Ffp/H9gJcjoiOND6dj5GDg58A3UvfejZJGM0yPjYhoA/4B+ClFeGwH1lDHx4dDxPpN0p7AHcCnI+KV8rQo7hWv+/vFJX0M2BIRa2pdl0FiJHA0cENEHAW8Rreuq+FybACkaz/TKcL1QGA00FzTSlWYQ2Tn/GgVQNKuFAFyS0TcmYpfkDQuTR8HbKlV/aroBODjktZTdG2eRHFNYEzqvoDhdYxsBDZGxENp/HaKUBmOxwbA7wPPRcTPI+JXwJ0Ux0zdHh8OkZ0b9o9WSX3+NwFPRcRXSpOWAbPT8GxgabXrVm0RcVlETIiIRopj4f6ImAmsBM5Msw2LtgCIiM3ABkkfTEUnU3wVw7A7NpKfAtMkvS/93XS2R90eH/7Eej9IOp2iH7zz0SpX1LZG1SXpRODfgLXsuA7wOYrrIrcBvwk8D5wdEVtrUskakNQE/M+I+JikD1CcmYwFHgP+KCLeqGH1qkbSkRQ3GewGPAucT/EP6rA8NiR9AfgkxV2NjwEXUFwDqcvjwyFiZmbZ3J1lZmbZHCJmZpbNIWJmZtkcImZmls0hYmZm2RwiZlUiaYakkHRoretiNlAcImbVcy7wYPppVhccImZVkJ47diLFI8DPSWW7SLo+fQ/HCkn3SjozTTtG0gOS1kha3vkIEbPBxiFiVh3TKb5z4/8BL0k6BvgE0EjxPTWfAo6Ht59Tdh1wZkQcAywEhtVTEmzoGLnzWcxsAJxL8aBGKB5/cS7F39+/RMSvgc2SVqbpHwQOB1YUj19iBMVjxc0GHYeIWYVJGkvxtN8pkoIiFAK4q7dFgHURcXyVqmiWzd1ZZpV3JvDNiDgoIhojYiLwHLAV+MN0baQBaErzPw0cIOnt7i1Jh9Wi4mY74xAxq7xzeedZxx3A+ym+j+NJ4FvAo8D29DXMZwJXSfox8CPgI1Wrrdm74Kf4mtWQpD0jol3SfsDDwAnpOzrMhgRfEzGrrbsljaH4Lo4vOUBsqPGZiJmZZfM1ETMzy+YQMTOzbA4RMzPL5hAxM7NsDhEzM8v2/wGP1WyV61eqUgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_users.age.hist(bins=[0, 10, 20, 30, 40, 50, 60, 70, 80, 90])\n",
    "plt.title('Age of Users Histogram')\n",
    "plt.xlabel('Age')\n",
    "plt.ylabel('Count')\n",
    "plt.savefig('images/age_bar.png')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>isbn</th>\n",
       "      <th>avg_rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>034545104X</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0155061224</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0446520802</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>052165615X</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0521795028</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         isbn  avg_rating\n",
       "0  034545104X         0.0\n",
       "1  0155061224         5.0\n",
       "2  0446520802         0.0\n",
       "3  052165615X         3.0\n",
       "4  0521795028         6.0"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "average_ratings = df_ratings.drop(columns='user')\n",
    "average_ratings = average_ratings.rename(columns={\"rating\":\"avg_rating\"})\n",
    "average_ratings.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "average_ratings.hist(bins=[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10])\n",
    "plt.title(\"Average Rating Histogram\")\n",
    "plt.xlabel('Average Rating')\n",
    "plt.ylabel('Count')\n",
    "plt.savefig('images/avg_ratings.png')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/thomas/Documents/Github/BookRecommender/venv/lib/python3.7/site-packages/pandas/plotting/_matplotlib/core.py:1192: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
      "  ax.set_xticklabels(xticklabels)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_ratings['isbn'].value_counts().plot()\n",
    "plt.title(\"Pre-Filter Amount of Ratings For Each Item\")\n",
    "plt.xlabel(\"ISBN\")\n",
    "plt.ylabel(\"Rating Count\")\n",
    "plt.savefig('images/pre_filter_review_count.png')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that most of the records have less then 100 ratings. These aren't really helpful to the model so we'll drop them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    340556.000000\n",
       "mean          3.376185\n",
       "std          12.436252\n",
       "min           1.000000\n",
       "25%           1.000000\n",
       "50%           1.000000\n",
       "75%           2.000000\n",
       "max        2502.000000\n",
       "Name: isbn, dtype: float64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_ratings['isbn'].value_counts().describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Gets the amount of ratings for each unique isbn. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>rating_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0971880107</th>\n",
       "      <td>2502</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0316666343</th>\n",
       "      <td>1295</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0385504209</th>\n",
       "      <td>883</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0060928336</th>\n",
       "      <td>732</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0312195516</th>\n",
       "      <td>723</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            rating_count\n",
       "0971880107          2502\n",
       "0316666343          1295\n",
       "0385504209           883\n",
       "0060928336           732\n",
       "0312195516           723"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_counts = df_ratings['isbn'].value_counts()\n",
    "df_counts = df_counts.to_frame()\n",
    "df_counts = df_counts.rename(columns={'isbn': 'rating_count'})\n",
    "df_counts.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Filters the rows to the books with more than 250 reviews."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>rating_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0971880107</th>\n",
       "      <td>2502</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0316666343</th>\n",
       "      <td>1295</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0385504209</th>\n",
       "      <td>883</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0060928336</th>\n",
       "      <td>732</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0312195516</th>\n",
       "      <td>723</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            rating_count\n",
       "0971880107          2502\n",
       "0316666343          1295\n",
       "0385504209           883\n",
       "0060928336           732\n",
       "0312195516           723"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "to_drop = df_counts[df_counts['rating_count'] <= 250].index\n",
    "df_counts.drop(to_drop, inplace=True)\n",
    "df_counts.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user</th>\n",
       "      <th>rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>48658.000000</td>\n",
       "      <td>48658.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>139446.556311</td>\n",
       "      <td>3.380492</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>80413.345508</td>\n",
       "      <td>4.046078</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>9.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>69355.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>138189.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>210485.000000</td>\n",
       "      <td>8.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>278844.000000</td>\n",
       "      <td>10.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                user        rating\n",
       "count   48658.000000  48658.000000\n",
       "mean   139446.556311      3.380492\n",
       "std     80413.345508      4.046078\n",
       "min         9.000000      0.000000\n",
       "25%     69355.000000      0.000000\n",
       "50%    138189.000000      0.000000\n",
       "75%    210485.000000      8.000000\n",
       "max    278844.000000     10.000000"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_ratings = df_ratings[df_ratings['isbn'].isin(df_counts.index)]\n",
    "df_ratings.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/thomas/Documents/Github/BookRecommender/venv/lib/python3.7/site-packages/pandas/plotting/_matplotlib/core.py:1192: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
      "  ax.set_xticklabels(xticklabels)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_ratings['isbn'].value_counts().plot()\n",
    "plt.title(\"Post-Filter Amount of Ratings For Each Item\")\n",
    "plt.xlabel(\"ISBN\")\n",
    "plt.ylabel(\"Rating Count\")\n",
    "plt.savefig('images/post_filter_review_count.png')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user</th>\n",
       "      <th>isbn</th>\n",
       "      <th>rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>276788</td>\n",
       "      <td>043935806X</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>173</th>\n",
       "      <td>276847</td>\n",
       "      <td>0446364193</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>426</th>\n",
       "      <td>276925</td>\n",
       "      <td>0316666343</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>429</th>\n",
       "      <td>276925</td>\n",
       "      <td>0385504209</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>453</th>\n",
       "      <td>276925</td>\n",
       "      <td>0804106304</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       user        isbn  rating\n",
       "84   276788  043935806X     7.0\n",
       "173  276847  0446364193     0.0\n",
       "426  276925  0316666343     0.0\n",
       "429  276925  0385504209     8.0\n",
       "453  276925  0804106304     0.0"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_ratings.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model Building"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since the dataset is so large I don't want to feed it straight into the model. So I converted into a spicy sparse matrix. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_features = df_ratings.pivot(index='isbn', columns='user', values='rating').fillna(0)\n",
    "mat_features = csr_matrix(df_features.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "knn_model = NearestNeighbors(metric = 'cosine', algorithm = 'auto')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "NearestNeighbors(metric='cosine')\n"
     ]
    }
   ],
   "source": [
    "knn_model.fit(mat_features)\n",
    "print(knn_model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'0684872153'"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "query = np.random.choice(df_features.shape[0])\n",
    "distances, indices = knn_model.kneighbors(df_features.iloc[query, :].values.reshape(-1, 18356), n_neighbors = 6)\n",
    "df_features.index[query]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Recommendations for 0684872153:\n",
      "\n",
      "1: 0440224675, with distance of 0.9545705318450928:\n",
      "2: 044023722X, with distance of 0.9556370377540588:\n",
      "3: 0316096199, with distance of 0.9577246904373169:\n",
      "4: 0060987103, with distance of 0.9578819274902344:\n",
      "5: 0385720106, with distance of 0.9581356048583984:\n"
     ]
    }
   ],
   "source": [
    "searching = []\n",
    "found_isbns = []\n",
    "isbns_distances = []\n",
    "for i in range(0, len(distances.flatten())):\n",
    "    if i == 0:\n",
    "        searching.append(df_features.index[indices.flatten()[i]])\n",
    "        print('Recommendations for {0}:\\n'.format(df_features.index[query]))\n",
    "    else:\n",
    "        found_isbns.append(df_features.index[indices.flatten()[i]])\n",
    "        isbns_distances.append(distances.flatten()[i])\n",
    "        print('{0}: {1}, with distance of {2}:'.format(i, df_features.index[indices.flatten()[i]], distances.flatten()[i]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "results = df_books[df_books['isbn'].isin(found_isbns)]\n",
    "searching_book = df_books[df_books['isbn'].isin(searching)]\n",
    "data = {'isbn': found_isbns, 'distances': isbns_distances}\n",
    "dist_dict = pd.DataFrame.from_dict(data)\n",
    "results = pd.merge(dist_dict, results, on='isbn')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>isbn</th>\n",
       "      <th>title</th>\n",
       "      <th>author</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4578</th>\n",
       "      <td>0684872153</td>\n",
       "      <td>Angela's Ashes (MMP) : A Memoir</td>\n",
       "      <td>Frank McCourt</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            isbn                            title         author\n",
       "4578  0684872153  Angela's Ashes (MMP) : A Memoir  Frank McCourt"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "searching_book.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>isbn</th>\n",
       "      <th>distances</th>\n",
       "      <th>title</th>\n",
       "      <th>author</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0440224675</td>\n",
       "      <td>0.954571</td>\n",
       "      <td>Hannibal</td>\n",
       "      <td>Thomas Harris</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>044023722X</td>\n",
       "      <td>0.955637</td>\n",
       "      <td>A Painted House</td>\n",
       "      <td>John Grisham</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0316096199</td>\n",
       "      <td>0.957725</td>\n",
       "      <td>Lucky : A Memoir</td>\n",
       "      <td>Alice Sebold</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0060987103</td>\n",
       "      <td>0.957882</td>\n",
       "      <td>Wicked: The Life and Times of the Wicked Witch...</td>\n",
       "      <td>Gregory Maguire</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0385720106</td>\n",
       "      <td>0.958136</td>\n",
       "      <td>A Map of the World</td>\n",
       "      <td>Jane Hamilton</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         isbn  distances                                              title  \\\n",
       "0  0440224675   0.954571                                           Hannibal   \n",
       "1  044023722X   0.955637                                    A Painted House   \n",
       "2  0316096199   0.957725                                   Lucky : A Memoir   \n",
       "3  0060987103   0.957882  Wicked: The Life and Times of the Wicked Witch...   \n",
       "4  0385720106   0.958136                                 A Map of the World   \n",
       "\n",
       "            author  \n",
       "0    Thomas Harris  \n",
       "1     John Grisham  \n",
       "2     Alice Sebold  \n",
       "3  Gregory Maguire  \n",
       "4    Jane Hamilton  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results.head()"
   ]
  }
 ],
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