├── Average Weighted Recommendation Engines using Python.ipynb
├── Content Based Recommendation Engines using Python.ipynb
├── KNN Books Recommendation
    ├── BooksRecommendation using KNN.ipynb
    └── BooksRecommendation.ipynb
├── KNN Movie Recommendation
    ├── KNNRecommendation.ipynb
    ├── movies.csv
    └── ratings.csv
└── README.md


/KNN Books Recommendation/BooksRecommendation using KNN.ipynb:
--------------------------------------------------------------------------------
   1 | {
   2 |  "cells": [
   3 |   {
   4 |    "cell_type": "markdown",
   5 |    "metadata": {},
   6 |    "source": [
   7 |     "## Book Recommendation system using K Nearest Neighbor\n",
   8 |     "\n",
   9 |     "Dataset url: http://www2.informatik.uni-freiburg.de/~cziegler/BX/"
  10 |    ]
  11 |   },
  12 |   {
  13 |    "cell_type": "markdown",
  14 |    "metadata": {},
  15 |    "source": [
  16 |     "#### Read the dataset with the necessary features"
  17 |    ]
  18 |   },
  19 |   {
  20 |    "cell_type": "code",
  21 |    "execution_count": 1,
  22 |    "metadata": {},
  23 |    "outputs": [
  24 |     {
  25 |      "name": "stderr",
  26 |      "output_type": "stream",
  27 |      "text": [
  28 |       "b'Skipping line 6452: expected 8 fields, saw 9\\nSkipping line 43667: expected 8 fields, saw 10\\nSkipping line 51751: expected 8 fields, saw 9\\n'\n",
  29 |       "b'Skipping line 92038: expected 8 fields, saw 9\\nSkipping line 104319: expected 8 fields, saw 9\\nSkipping line 121768: expected 8 fields, saw 9\\n'\n",
  30 |       "b'Skipping line 144058: expected 8 fields, saw 9\\nSkipping line 150789: expected 8 fields, saw 9\\nSkipping line 157128: expected 8 fields, saw 9\\nSkipping line 180189: expected 8 fields, saw 9\\nSkipping line 185738: expected 8 fields, saw 9\\n'\n",
  31 |       "b'Skipping line 209388: expected 8 fields, saw 9\\nSkipping line 220626: expected 8 fields, saw 9\\nSkipping line 227933: expected 8 fields, saw 11\\nSkipping line 228957: expected 8 fields, saw 10\\nSkipping line 245933: expected 8 fields, saw 9\\nSkipping line 251296: expected 8 fields, saw 9\\nSkipping line 259941: expected 8 fields, saw 9\\nSkipping line 261529: expected 8 fields, saw 9\\n'\n",
  32 |       "C:\\Users\\krish.naik\\AppData\\Local\\Continuum\\anaconda3\\envs\\myenv\\lib\\site-packages\\IPython\\core\\interactiveshell.py:3020: DtypeWarning: Columns (3) have mixed types. Specify dtype option on import or set low_memory=False.\n",
  33 |       "  interactivity=interactivity, compiler=compiler, result=result)\n"
  34 |      ]
  35 |     }
  36 |    ],
  37 |    "source": [
  38 |     "import pandas as pd\n",
  39 |     "import numpy as np\n",
  40 |     "import matplotlib.pyplot as plt\n",
  41 |     "books = pd.read_csv('BX-Books.csv', sep=';', error_bad_lines=False, encoding=\"latin-1\")\n",
  42 |     "books.columns = ['ISBN', 'bookTitle', 'bookAuthor', 'yearOfPublication', 'publisher', 'imageUrlS', 'imageUrlM', 'imageUrlL']\n",
  43 |     "users = pd.read_csv('BX-Users.csv', sep=';', error_bad_lines=False, encoding=\"latin-1\")\n",
  44 |     "users.columns = ['userID', 'Location', 'Age']\n",
  45 |     "ratings = pd.read_csv('BX-Book-Ratings.csv', sep=';', error_bad_lines=False, encoding=\"latin-1\")\n",
  46 |     "ratings.columns = ['userID', 'ISBN', 'bookRating']"
  47 |    ]
  48 |   },
  49 |   {
  50 |    "cell_type": "code",
  51 |    "execution_count": 3,
  52 |    "metadata": {},
  53 |    "outputs": [
  54 |     {
  55 |      "name": "stdout",
  56 |      "output_type": "stream",
  57 |      "text": [
  58 |       "(1149780, 3)\n",
  59 |       "['userID', 'ISBN', 'bookRating']\n"
  60 |      ]
  61 |     }
  62 |    ],
  63 |    "source": [
  64 |     "print(ratings.shape)\n",
  65 |     "print(list(ratings.columns))"
  66 |    ]
  67 |   },
  68 |   {
  69 |    "cell_type": "code",
  70 |    "execution_count": 4,
  71 |    "metadata": {},
  72 |    "outputs": [
  73 |     {
  74 |      "data": {
  75 |       "image/png": 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\n",
  76 |       "text/plain": [
  77 |        "<Figure size 432x288 with 1 Axes>"
  78 |       ]
  79 |      },
  80 |      "metadata": {
  81 |       "needs_background": "light"
  82 |      },
  83 |      "output_type": "display_data"
  84 |     }
  85 |    ],
  86 |    "source": [
  87 |     "plt.rc(\"font\", size=15)\n",
  88 |     "ratings.bookRating.value_counts(sort=False).plot(kind='bar')\n",
  89 |     "plt.title('Rating Distribution\\n')\n",
  90 |     "plt.xlabel('Rating')\n",
  91 |     "plt.ylabel('Count')\n",
  92 |     "plt.savefig('system1.png', bbox_inches='tight')\n",
  93 |     "plt.show()"
  94 |    ]
  95 |   },
  96 |   {
  97 |    "cell_type": "code",
  98 |    "execution_count": 5,
  99 |    "metadata": {},
 100 |    "outputs": [
 101 |     {
 102 |      "name": "stdout",
 103 |      "output_type": "stream",
 104 |      "text": [
 105 |       "(271360, 8)\n",
 106 |       "['ISBN', 'bookTitle', 'bookAuthor', 'yearOfPublication', 'publisher', 'imageUrlS', 'imageUrlM', 'imageUrlL']\n"
 107 |      ]
 108 |     }
 109 |    ],
 110 |    "source": [
 111 |     "print(books.shape)\n",
 112 |     "print(list(books.columns))"
 113 |    ]
 114 |   },
 115 |   {
 116 |    "cell_type": "code",
 117 |    "execution_count": 6,
 118 |    "metadata": {},
 119 |    "outputs": [
 120 |     {
 121 |      "name": "stdout",
 122 |      "output_type": "stream",
 123 |      "text": [
 124 |       "(278858, 3)\n",
 125 |       "['userID', 'Location', 'Age']\n"
 126 |      ]
 127 |     }
 128 |    ],
 129 |    "source": [
 130 |     "print(users.shape)\n",
 131 |     "print(list(users.columns))"
 132 |    ]
 133 |   },
 134 |   {
 135 |    "cell_type": "code",
 136 |    "execution_count": 7,
 137 |    "metadata": {},
 138 |    "outputs": [
 139 |     {
 140 |      "data": {
 141 |       "image/png": 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\n",
 142 |       "text/plain": [
 143 |        "<Figure size 432x288 with 1 Axes>"
 144 |       ]
 145 |      },
 146 |      "metadata": {
 147 |       "needs_background": "light"
 148 |      },
 149 |      "output_type": "display_data"
 150 |     }
 151 |    ],
 152 |    "source": [
 153 |     "users.Age.hist(bins=[0, 10, 20, 30, 40, 50, 100])\n",
 154 |     "plt.title('Age Distribution\\n')\n",
 155 |     "plt.xlabel('Age')\n",
 156 |     "plt.ylabel('Count')\n",
 157 |     "plt.savefig('system2.png', bbox_inches='tight')\n",
 158 |     "plt.show()"
 159 |    ]
 160 |   },
 161 |   {
 162 |    "cell_type": "markdown",
 163 |    "metadata": {},
 164 |    "source": [
 165 |     "## To ensure statistical significance, users with less than 200 ratings, and books with less than 100 ratings are excluded."
 166 |    ]
 167 |   },
 168 |   {
 169 |    "cell_type": "code",
 170 |    "execution_count": 11,
 171 |    "metadata": {},
 172 |    "outputs": [],
 173 |    "source": [
 174 |     "counts1 = ratings['userID'].value_counts()\n",
 175 |     "ratings = ratings[ratings['userID'].isin(counts1[counts1 >= 200].index)]\n",
 176 |     "counts = ratings['bookRating'].value_counts()\n",
 177 |     "ratings = ratings[ratings['bookRating'].isin(counts[counts >= 100].index)]"
 178 |    ]
 179 |   },
 180 |   {
 181 |    "cell_type": "markdown",
 182 |    "metadata": {},
 183 |    "source": [
 184 |     "## Collaborative Filtering Using k-Nearest Neighbors (kNN)\n",
 185 |     "\n",
 186 |     "kNN is a machine learning algorithm to find clusters of similar users based on common book ratings, and make predictions using the average rating of top-k nearest neighbors. For example, we first present ratings in a matrix with the matrix having one row for each item (book) and one column for each user,"
 187 |    ]
 188 |   },
 189 |   {
 190 |    "cell_type": "code",
 191 |    "execution_count": 13,
 192 |    "metadata": {},
 193 |    "outputs": [
 194 |     {
 195 |      "data": {
 196 |       "text/html": [
 197 |        "<div>\n",
 198 |        "<style scoped>\n",
 199 |        "    .dataframe tbody tr th:only-of-type {\n",
 200 |        "        vertical-align: middle;\n",
 201 |        "    }\n",
 202 |        "\n",
 203 |        "    .dataframe tbody tr th {\n",
 204 |        "        vertical-align: top;\n",
 205 |        "    }\n",
 206 |        "\n",
 207 |        "    .dataframe thead th {\n",
 208 |        "        text-align: right;\n",
 209 |        "    }\n",
 210 |        "</style>\n",
 211 |        "<table border=\"1\" class=\"dataframe\">\n",
 212 |        "  <thead>\n",
 213 |        "    <tr style=\"text-align: right;\">\n",
 214 |        "      <th></th>\n",
 215 |        "      <th>userID</th>\n",
 216 |        "      <th>ISBN</th>\n",
 217 |        "      <th>bookRating</th>\n",
 218 |        "      <th>bookTitle</th>\n",
 219 |        "    </tr>\n",
 220 |        "  </thead>\n",
 221 |        "  <tbody>\n",
 222 |        "    <tr>\n",
 223 |        "      <th>0</th>\n",
 224 |        "      <td>277427</td>\n",
 225 |        "      <td>002542730X</td>\n",
 226 |        "      <td>10</td>\n",
 227 |        "      <td>Politically Correct Bedtime Stories: Modern Ta...</td>\n",
 228 |        "    </tr>\n",
 229 |        "    <tr>\n",
 230 |        "      <th>1</th>\n",
 231 |        "      <td>3363</td>\n",
 232 |        "      <td>002542730X</td>\n",
 233 |        "      <td>0</td>\n",
 234 |        "      <td>Politically Correct Bedtime Stories: Modern Ta...</td>\n",
 235 |        "    </tr>\n",
 236 |        "    <tr>\n",
 237 |        "      <th>2</th>\n",
 238 |        "      <td>11676</td>\n",
 239 |        "      <td>002542730X</td>\n",
 240 |        "      <td>6</td>\n",
 241 |        "      <td>Politically Correct Bedtime Stories: Modern Ta...</td>\n",
 242 |        "    </tr>\n",
 243 |        "    <tr>\n",
 244 |        "      <th>3</th>\n",
 245 |        "      <td>12538</td>\n",
 246 |        "      <td>002542730X</td>\n",
 247 |        "      <td>10</td>\n",
 248 |        "      <td>Politically Correct Bedtime Stories: Modern Ta...</td>\n",
 249 |        "    </tr>\n",
 250 |        "    <tr>\n",
 251 |        "      <th>4</th>\n",
 252 |        "      <td>13552</td>\n",
 253 |        "      <td>002542730X</td>\n",
 254 |        "      <td>0</td>\n",
 255 |        "      <td>Politically Correct Bedtime Stories: Modern Ta...</td>\n",
 256 |        "    </tr>\n",
 257 |        "  </tbody>\n",
 258 |        "</table>\n",
 259 |        "</div>"
 260 |       ],
 261 |       "text/plain": [
 262 |        "   userID        ISBN  bookRating  \\\n",
 263 |        "0  277427  002542730X          10   \n",
 264 |        "1    3363  002542730X           0   \n",
 265 |        "2   11676  002542730X           6   \n",
 266 |        "3   12538  002542730X          10   \n",
 267 |        "4   13552  002542730X           0   \n",
 268 |        "\n",
 269 |        "                                           bookTitle  \n",
 270 |        "0  Politically Correct Bedtime Stories: Modern Ta...  \n",
 271 |        "1  Politically Correct Bedtime Stories: Modern Ta...  \n",
 272 |        "2  Politically Correct Bedtime Stories: Modern Ta...  \n",
 273 |        "3  Politically Correct Bedtime Stories: Modern Ta...  \n",
 274 |        "4  Politically Correct Bedtime Stories: Modern Ta...  "
 275 |       ]
 276 |      },
 277 |      "execution_count": 13,
 278 |      "metadata": {},
 279 |      "output_type": "execute_result"
 280 |     }
 281 |    ],
 282 |    "source": [
 283 |     "combine_book_rating = pd.merge(ratings, books, on='ISBN')\n",
 284 |     "columns = ['yearOfPublication', 'publisher', 'bookAuthor', 'imageUrlS', 'imageUrlM', 'imageUrlL']\n",
 285 |     "combine_book_rating = combine_book_rating.drop(columns, axis=1)\n",
 286 |     "combine_book_rating.head()"
 287 |    ]
 288 |   },
 289 |   {
 290 |    "cell_type": "markdown",
 291 |    "metadata": {},
 292 |    "source": [
 293 |     "We then group by book titles and create a new column for total rating count."
 294 |    ]
 295 |   },
 296 |   {
 297 |    "cell_type": "code",
 298 |    "execution_count": 16,
 299 |    "metadata": {},
 300 |    "outputs": [
 301 |     {
 302 |      "data": {
 303 |       "text/html": [
 304 |        "<div>\n",
 305 |        "<style scoped>\n",
 306 |        "    .dataframe tbody tr th:only-of-type {\n",
 307 |        "        vertical-align: middle;\n",
 308 |        "    }\n",
 309 |        "\n",
 310 |        "    .dataframe tbody tr th {\n",
 311 |        "        vertical-align: top;\n",
 312 |        "    }\n",
 313 |        "\n",
 314 |        "    .dataframe thead th {\n",
 315 |        "        text-align: right;\n",
 316 |        "    }\n",
 317 |        "</style>\n",
 318 |        "<table border=\"1\" class=\"dataframe\">\n",
 319 |        "  <thead>\n",
 320 |        "    <tr style=\"text-align: right;\">\n",
 321 |        "      <th></th>\n",
 322 |        "      <th>bookTitle</th>\n",
 323 |        "      <th>totalRatingCount</th>\n",
 324 |        "    </tr>\n",
 325 |        "  </thead>\n",
 326 |        "  <tbody>\n",
 327 |        "    <tr>\n",
 328 |        "      <th>0</th>\n",
 329 |        "      <td>A Light in the Storm: The Civil War Diary of ...</td>\n",
 330 |        "      <td>2</td>\n",
 331 |        "    </tr>\n",
 332 |        "    <tr>\n",
 333 |        "      <th>1</th>\n",
 334 |        "      <td>Always Have Popsicles</td>\n",
 335 |        "      <td>1</td>\n",
 336 |        "    </tr>\n",
 337 |        "    <tr>\n",
 338 |        "      <th>2</th>\n",
 339 |        "      <td>Apple Magic (The Collector's series)</td>\n",
 340 |        "      <td>1</td>\n",
 341 |        "    </tr>\n",
 342 |        "    <tr>\n",
 343 |        "      <th>3</th>\n",
 344 |        "      <td>Beyond IBM: Leadership Marketing and Finance ...</td>\n",
 345 |        "      <td>1</td>\n",
 346 |        "    </tr>\n",
 347 |        "    <tr>\n",
 348 |        "      <th>4</th>\n",
 349 |        "      <td>Clifford Visita El Hospital (Clifford El Gran...</td>\n",
 350 |        "      <td>1</td>\n",
 351 |        "    </tr>\n",
 352 |        "  </tbody>\n",
 353 |        "</table>\n",
 354 |        "</div>"
 355 |       ],
 356 |       "text/plain": [
 357 |        "                                           bookTitle  totalRatingCount\n",
 358 |        "0   A Light in the Storm: The Civil War Diary of ...                 2\n",
 359 |        "1                              Always Have Popsicles                 1\n",
 360 |        "2               Apple Magic (The Collector's series)                 1\n",
 361 |        "3   Beyond IBM: Leadership Marketing and Finance ...                 1\n",
 362 |        "4   Clifford Visita El Hospital (Clifford El Gran...                 1"
 363 |       ]
 364 |      },
 365 |      "execution_count": 16,
 366 |      "metadata": {},
 367 |      "output_type": "execute_result"
 368 |     }
 369 |    ],
 370 |    "source": [
 371 |     "combine_book_rating = combine_book_rating.dropna(axis = 0, subset = ['bookTitle'])\n",
 372 |     "\n",
 373 |     "book_ratingCount = (combine_book_rating.\n",
 374 |     "     groupby(by = ['bookTitle'])['bookRating'].\n",
 375 |     "     count().\n",
 376 |     "     reset_index().\n",
 377 |     "     rename(columns = {'bookRating': 'totalRatingCount'})\n",
 378 |     "     [['bookTitle', 'totalRatingCount']]\n",
 379 |     "    )\n",
 380 |     "book_ratingCount.head()"
 381 |    ]
 382 |   },
 383 |   {
 384 |    "cell_type": "markdown",
 385 |    "metadata": {},
 386 |    "source": [
 387 |     "We combine the rating data with the total rating count data, this gives us exactly what we need to find out which books are popular and filter out lesser-known books."
 388 |    ]
 389 |   },
 390 |   {
 391 |    "cell_type": "code",
 392 |    "execution_count": 17,
 393 |    "metadata": {},
 394 |    "outputs": [
 395 |     {
 396 |      "data": {
 397 |       "text/html": [
 398 |        "<div>\n",
 399 |        "<style scoped>\n",
 400 |        "    .dataframe tbody tr th:only-of-type {\n",
 401 |        "        vertical-align: middle;\n",
 402 |        "    }\n",
 403 |        "\n",
 404 |        "    .dataframe tbody tr th {\n",
 405 |        "        vertical-align: top;\n",
 406 |        "    }\n",
 407 |        "\n",
 408 |        "    .dataframe thead th {\n",
 409 |        "        text-align: right;\n",
 410 |        "    }\n",
 411 |        "</style>\n",
 412 |        "<table border=\"1\" class=\"dataframe\">\n",
 413 |        "  <thead>\n",
 414 |        "    <tr style=\"text-align: right;\">\n",
 415 |        "      <th></th>\n",
 416 |        "      <th>userID</th>\n",
 417 |        "      <th>ISBN</th>\n",
 418 |        "      <th>bookRating</th>\n",
 419 |        "      <th>bookTitle</th>\n",
 420 |        "      <th>totalRatingCount</th>\n",
 421 |        "    </tr>\n",
 422 |        "  </thead>\n",
 423 |        "  <tbody>\n",
 424 |        "    <tr>\n",
 425 |        "      <th>0</th>\n",
 426 |        "      <td>277427</td>\n",
 427 |        "      <td>002542730X</td>\n",
 428 |        "      <td>10</td>\n",
 429 |        "      <td>Politically Correct Bedtime Stories: Modern Ta...</td>\n",
 430 |        "      <td>82</td>\n",
 431 |        "    </tr>\n",
 432 |        "    <tr>\n",
 433 |        "      <th>1</th>\n",
 434 |        "      <td>3363</td>\n",
 435 |        "      <td>002542730X</td>\n",
 436 |        "      <td>0</td>\n",
 437 |        "      <td>Politically Correct Bedtime Stories: Modern Ta...</td>\n",
 438 |        "      <td>82</td>\n",
 439 |        "    </tr>\n",
 440 |        "    <tr>\n",
 441 |        "      <th>2</th>\n",
 442 |        "      <td>11676</td>\n",
 443 |        "      <td>002542730X</td>\n",
 444 |        "      <td>6</td>\n",
 445 |        "      <td>Politically Correct Bedtime Stories: Modern Ta...</td>\n",
 446 |        "      <td>82</td>\n",
 447 |        "    </tr>\n",
 448 |        "    <tr>\n",
 449 |        "      <th>3</th>\n",
 450 |        "      <td>12538</td>\n",
 451 |        "      <td>002542730X</td>\n",
 452 |        "      <td>10</td>\n",
 453 |        "      <td>Politically Correct Bedtime Stories: Modern Ta...</td>\n",
 454 |        "      <td>82</td>\n",
 455 |        "    </tr>\n",
 456 |        "    <tr>\n",
 457 |        "      <th>4</th>\n",
 458 |        "      <td>13552</td>\n",
 459 |        "      <td>002542730X</td>\n",
 460 |        "      <td>0</td>\n",
 461 |        "      <td>Politically Correct Bedtime Stories: Modern Ta...</td>\n",
 462 |        "      <td>82</td>\n",
 463 |        "    </tr>\n",
 464 |        "  </tbody>\n",
 465 |        "</table>\n",
 466 |        "</div>"
 467 |       ],
 468 |       "text/plain": [
 469 |        "   userID        ISBN  bookRating  \\\n",
 470 |        "0  277427  002542730X          10   \n",
 471 |        "1    3363  002542730X           0   \n",
 472 |        "2   11676  002542730X           6   \n",
 473 |        "3   12538  002542730X          10   \n",
 474 |        "4   13552  002542730X           0   \n",
 475 |        "\n",
 476 |        "                                           bookTitle  totalRatingCount  \n",
 477 |        "0  Politically Correct Bedtime Stories: Modern Ta...                82  \n",
 478 |        "1  Politically Correct Bedtime Stories: Modern Ta...                82  \n",
 479 |        "2  Politically Correct Bedtime Stories: Modern Ta...                82  \n",
 480 |        "3  Politically Correct Bedtime Stories: Modern Ta...                82  \n",
 481 |        "4  Politically Correct Bedtime Stories: Modern Ta...                82  "
 482 |       ]
 483 |      },
 484 |      "execution_count": 17,
 485 |      "metadata": {},
 486 |      "output_type": "execute_result"
 487 |     }
 488 |    ],
 489 |    "source": [
 490 |     "rating_with_totalRatingCount = combine_book_rating.merge(book_ratingCount, left_on = 'bookTitle', right_on = 'bookTitle', how = 'left')\n",
 491 |     "rating_with_totalRatingCount.head()"
 492 |    ]
 493 |   },
 494 |   {
 495 |    "cell_type": "code",
 496 |    "execution_count": 18,
 497 |    "metadata": {},
 498 |    "outputs": [
 499 |     {
 500 |      "name": "stdout",
 501 |      "output_type": "stream",
 502 |      "text": [
 503 |       "count   160576.000\n",
 504 |       "mean         3.044\n",
 505 |       "std          7.428\n",
 506 |       "min          1.000\n",
 507 |       "25%          1.000\n",
 508 |       "50%          1.000\n",
 509 |       "75%          2.000\n",
 510 |       "max        365.000\n",
 511 |       "Name: totalRatingCount, dtype: float64\n"
 512 |      ]
 513 |     }
 514 |    ],
 515 |    "source": [
 516 |     "pd.set_option('display.float_format', lambda x: '%.3f' % x)\n",
 517 |     "print(book_ratingCount['totalRatingCount'].describe())"
 518 |    ]
 519 |   },
 520 |   {
 521 |    "cell_type": "markdown",
 522 |    "metadata": {},
 523 |    "source": [
 524 |     "The median book has been rated only once. Let’s look at the top of the distribution"
 525 |    ]
 526 |   },
 527 |   {
 528 |    "cell_type": "code",
 529 |    "execution_count": 19,
 530 |    "metadata": {},
 531 |    "outputs": [
 532 |     {
 533 |      "name": "stdout",
 534 |      "output_type": "stream",
 535 |      "text": [
 536 |       "0.900    5.000\n",
 537 |       "0.910    6.000\n",
 538 |       "0.920    7.000\n",
 539 |       "0.930    7.000\n",
 540 |       "0.940    8.000\n",
 541 |       "0.950   10.000\n",
 542 |       "0.960   11.000\n",
 543 |       "0.970   14.000\n",
 544 |       "0.980   19.000\n",
 545 |       "0.990   31.000\n",
 546 |       "Name: totalRatingCount, dtype: float64\n"
 547 |      ]
 548 |     }
 549 |    ],
 550 |    "source": [
 551 |     "print(book_ratingCount['totalRatingCount'].quantile(np.arange(.9, 1, .01)))"
 552 |    ]
 553 |   },
 554 |   {
 555 |    "cell_type": "code",
 556 |    "execution_count": 20,
 557 |    "metadata": {},
 558 |    "outputs": [
 559 |     {
 560 |      "data": {
 561 |       "text/html": [
 562 |        "<div>\n",
 563 |        "<style scoped>\n",
 564 |        "    .dataframe tbody tr th:only-of-type {\n",
 565 |        "        vertical-align: middle;\n",
 566 |        "    }\n",
 567 |        "\n",
 568 |        "    .dataframe tbody tr th {\n",
 569 |        "        vertical-align: top;\n",
 570 |        "    }\n",
 571 |        "\n",
 572 |        "    .dataframe thead th {\n",
 573 |        "        text-align: right;\n",
 574 |        "    }\n",
 575 |        "</style>\n",
 576 |        "<table border=\"1\" class=\"dataframe\">\n",
 577 |        "  <thead>\n",
 578 |        "    <tr style=\"text-align: right;\">\n",
 579 |        "      <th></th>\n",
 580 |        "      <th>userID</th>\n",
 581 |        "      <th>ISBN</th>\n",
 582 |        "      <th>bookRating</th>\n",
 583 |        "      <th>bookTitle</th>\n",
 584 |        "      <th>totalRatingCount</th>\n",
 585 |        "    </tr>\n",
 586 |        "  </thead>\n",
 587 |        "  <tbody>\n",
 588 |        "    <tr>\n",
 589 |        "      <th>0</th>\n",
 590 |        "      <td>277427</td>\n",
 591 |        "      <td>002542730X</td>\n",
 592 |        "      <td>10</td>\n",
 593 |        "      <td>Politically Correct Bedtime Stories: Modern Ta...</td>\n",
 594 |        "      <td>82</td>\n",
 595 |        "    </tr>\n",
 596 |        "    <tr>\n",
 597 |        "      <th>1</th>\n",
 598 |        "      <td>3363</td>\n",
 599 |        "      <td>002542730X</td>\n",
 600 |        "      <td>0</td>\n",
 601 |        "      <td>Politically Correct Bedtime Stories: Modern Ta...</td>\n",
 602 |        "      <td>82</td>\n",
 603 |        "    </tr>\n",
 604 |        "    <tr>\n",
 605 |        "      <th>2</th>\n",
 606 |        "      <td>11676</td>\n",
 607 |        "      <td>002542730X</td>\n",
 608 |        "      <td>6</td>\n",
 609 |        "      <td>Politically Correct Bedtime Stories: Modern Ta...</td>\n",
 610 |        "      <td>82</td>\n",
 611 |        "    </tr>\n",
 612 |        "    <tr>\n",
 613 |        "      <th>3</th>\n",
 614 |        "      <td>12538</td>\n",
 615 |        "      <td>002542730X</td>\n",
 616 |        "      <td>10</td>\n",
 617 |        "      <td>Politically Correct Bedtime Stories: Modern Ta...</td>\n",
 618 |        "      <td>82</td>\n",
 619 |        "    </tr>\n",
 620 |        "    <tr>\n",
 621 |        "      <th>4</th>\n",
 622 |        "      <td>13552</td>\n",
 623 |        "      <td>002542730X</td>\n",
 624 |        "      <td>0</td>\n",
 625 |        "      <td>Politically Correct Bedtime Stories: Modern Ta...</td>\n",
 626 |        "      <td>82</td>\n",
 627 |        "    </tr>\n",
 628 |        "  </tbody>\n",
 629 |        "</table>\n",
 630 |        "</div>"
 631 |       ],
 632 |       "text/plain": [
 633 |        "   userID        ISBN  bookRating  \\\n",
 634 |        "0  277427  002542730X          10   \n",
 635 |        "1    3363  002542730X           0   \n",
 636 |        "2   11676  002542730X           6   \n",
 637 |        "3   12538  002542730X          10   \n",
 638 |        "4   13552  002542730X           0   \n",
 639 |        "\n",
 640 |        "                                           bookTitle  totalRatingCount  \n",
 641 |        "0  Politically Correct Bedtime Stories: Modern Ta...                82  \n",
 642 |        "1  Politically Correct Bedtime Stories: Modern Ta...                82  \n",
 643 |        "2  Politically Correct Bedtime Stories: Modern Ta...                82  \n",
 644 |        "3  Politically Correct Bedtime Stories: Modern Ta...                82  \n",
 645 |        "4  Politically Correct Bedtime Stories: Modern Ta...                82  "
 646 |       ]
 647 |      },
 648 |      "execution_count": 20,
 649 |      "metadata": {},
 650 |      "output_type": "execute_result"
 651 |     }
 652 |    ],
 653 |    "source": [
 654 |     "popularity_threshold = 50\n",
 655 |     "rating_popular_book = rating_with_totalRatingCount.query('totalRatingCount >= @popularity_threshold')\n",
 656 |     "rating_popular_book.head()"
 657 |    ]
 658 |   },
 659 |   {
 660 |    "cell_type": "code",
 661 |    "execution_count": 21,
 662 |    "metadata": {},
 663 |    "outputs": [
 664 |     {
 665 |      "data": {
 666 |       "text/plain": [
 667 |        "(62149, 5)"
 668 |       ]
 669 |      },
 670 |      "execution_count": 21,
 671 |      "metadata": {},
 672 |      "output_type": "execute_result"
 673 |     }
 674 |    ],
 675 |    "source": [
 676 |     "rating_popular_book.shape"
 677 |    ]
 678 |   },
 679 |   {
 680 |    "cell_type": "markdown",
 681 |    "metadata": {},
 682 |    "source": [
 683 |     "## Filter to users in US and Canada only"
 684 |    ]
 685 |   },
 686 |   {
 687 |    "cell_type": "code",
 688 |    "execution_count": 22,
 689 |    "metadata": {},
 690 |    "outputs": [
 691 |     {
 692 |      "data": {
 693 |       "text/html": [
 694 |        "<div>\n",
 695 |        "<style scoped>\n",
 696 |        "    .dataframe tbody tr th:only-of-type {\n",
 697 |        "        vertical-align: middle;\n",
 698 |        "    }\n",
 699 |        "\n",
 700 |        "    .dataframe tbody tr th {\n",
 701 |        "        vertical-align: top;\n",
 702 |        "    }\n",
 703 |        "\n",
 704 |        "    .dataframe thead th {\n",
 705 |        "        text-align: right;\n",
 706 |        "    }\n",
 707 |        "</style>\n",
 708 |        "<table border=\"1\" class=\"dataframe\">\n",
 709 |        "  <thead>\n",
 710 |        "    <tr style=\"text-align: right;\">\n",
 711 |        "      <th></th>\n",
 712 |        "      <th>userID</th>\n",
 713 |        "      <th>ISBN</th>\n",
 714 |        "      <th>bookRating</th>\n",
 715 |        "      <th>bookTitle</th>\n",
 716 |        "      <th>totalRatingCount</th>\n",
 717 |        "      <th>Location</th>\n",
 718 |        "    </tr>\n",
 719 |        "  </thead>\n",
 720 |        "  <tbody>\n",
 721 |        "    <tr>\n",
 722 |        "      <th>0</th>\n",
 723 |        "      <td>277427</td>\n",
 724 |        "      <td>002542730X</td>\n",
 725 |        "      <td>10</td>\n",
 726 |        "      <td>Politically Correct Bedtime Stories: Modern Ta...</td>\n",
 727 |        "      <td>82</td>\n",
 728 |        "      <td>gilbert, arizona, usa</td>\n",
 729 |        "    </tr>\n",
 730 |        "    <tr>\n",
 731 |        "      <th>1</th>\n",
 732 |        "      <td>3363</td>\n",
 733 |        "      <td>002542730X</td>\n",
 734 |        "      <td>0</td>\n",
 735 |        "      <td>Politically Correct Bedtime Stories: Modern Ta...</td>\n",
 736 |        "      <td>82</td>\n",
 737 |        "      <td>knoxville, tennessee, usa</td>\n",
 738 |        "    </tr>\n",
 739 |        "    <tr>\n",
 740 |        "      <th>3</th>\n",
 741 |        "      <td>12538</td>\n",
 742 |        "      <td>002542730X</td>\n",
 743 |        "      <td>10</td>\n",
 744 |        "      <td>Politically Correct Bedtime Stories: Modern Ta...</td>\n",
 745 |        "      <td>82</td>\n",
 746 |        "      <td>byron, minnesota, usa</td>\n",
 747 |        "    </tr>\n",
 748 |        "    <tr>\n",
 749 |        "      <th>4</th>\n",
 750 |        "      <td>13552</td>\n",
 751 |        "      <td>002542730X</td>\n",
 752 |        "      <td>0</td>\n",
 753 |        "      <td>Politically Correct Bedtime Stories: Modern Ta...</td>\n",
 754 |        "      <td>82</td>\n",
 755 |        "      <td>cordova, tennessee, usa</td>\n",
 756 |        "    </tr>\n",
 757 |        "    <tr>\n",
 758 |        "      <th>5</th>\n",
 759 |        "      <td>16795</td>\n",
 760 |        "      <td>002542730X</td>\n",
 761 |        "      <td>0</td>\n",
 762 |        "      <td>Politically Correct Bedtime Stories: Modern Ta...</td>\n",
 763 |        "      <td>82</td>\n",
 764 |        "      <td>mechanicsville, maryland, usa</td>\n",
 765 |        "    </tr>\n",
 766 |        "  </tbody>\n",
 767 |        "</table>\n",
 768 |        "</div>"
 769 |       ],
 770 |       "text/plain": [
 771 |        "   userID        ISBN  bookRating  \\\n",
 772 |        "0  277427  002542730X          10   \n",
 773 |        "1    3363  002542730X           0   \n",
 774 |        "3   12538  002542730X          10   \n",
 775 |        "4   13552  002542730X           0   \n",
 776 |        "5   16795  002542730X           0   \n",
 777 |        "\n",
 778 |        "                                           bookTitle  totalRatingCount  \\\n",
 779 |        "0  Politically Correct Bedtime Stories: Modern Ta...                82   \n",
 780 |        "1  Politically Correct Bedtime Stories: Modern Ta...                82   \n",
 781 |        "3  Politically Correct Bedtime Stories: Modern Ta...                82   \n",
 782 |        "4  Politically Correct Bedtime Stories: Modern Ta...                82   \n",
 783 |        "5  Politically Correct Bedtime Stories: Modern Ta...                82   \n",
 784 |        "\n",
 785 |        "                        Location  \n",
 786 |        "0          gilbert, arizona, usa  \n",
 787 |        "1      knoxville, tennessee, usa  \n",
 788 |        "3          byron, minnesota, usa  \n",
 789 |        "4        cordova, tennessee, usa  \n",
 790 |        "5  mechanicsville, maryland, usa  "
 791 |       ]
 792 |      },
 793 |      "execution_count": 22,
 794 |      "metadata": {},
 795 |      "output_type": "execute_result"
 796 |     }
 797 |    ],
 798 |    "source": [
 799 |     "combined = rating_popular_book.merge(users, left_on = 'userID', right_on = 'userID', how = 'left')\n",
 800 |     "\n",
 801 |     "us_canada_user_rating = combined[combined['Location'].str.contains(\"usa|canada\")]\n",
 802 |     "us_canada_user_rating=us_canada_user_rating.drop('Age', axis=1)\n",
 803 |     "us_canada_user_rating.head()"
 804 |    ]
 805 |   },
 806 |   {
 807 |    "attachments": {
 808 |     "image.png": {
 809 |      "image/png": 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"
 810 |     }
 811 |    },
 812 |    "cell_type": "markdown",
 813 |    "metadata": {},
 814 |    "source": [
 815 |     "## Cosine Similarity\n",
 816 |     "\n",
 817 |     "![image.png](attachment:image.png)"
 818 |    ]
 819 |   },
 820 |   {
 821 |    "cell_type": "markdown",
 822 |    "metadata": {},
 823 |    "source": [
 824 |     "## Implementing kNN\n",
 825 |     "We convert our table to a 2D matrix, and fill the missing values with zeros (since we will calculate distances between rating vectors). We then transform the values(ratings) of the matrix dataframe into a scipy sparse matrix for more efficient calculations.\n",
 826 |     "\n",
 827 |     "Finding the Nearest Neighbors\n",
 828 |     "We use unsupervised algorithms with sklearn.neighbors. The algorithm we use to compute the nearest neighbors is “brute”, and we specify “metric=cosine” so that the algorithm will calculate the cosine similarity between rating vectors. Finally, we fit the model."
 829 |    ]
 830 |   },
 831 |   {
 832 |    "cell_type": "code",
 833 |    "execution_count": null,
 834 |    "metadata": {},
 835 |    "outputs": [],
 836 |    "source": []
 837 |   },
 838 |   {
 839 |    "cell_type": "code",
 840 |    "execution_count": 27,
 841 |    "metadata": {},
 842 |    "outputs": [
 843 |     {
 844 |      "data": {
 845 |       "text/plain": [
 846 |        "NearestNeighbors(algorithm='brute', leaf_size=30, metric='cosine',\n",
 847 |        "         metric_params=None, n_jobs=None, n_neighbors=5, p=2, radius=1.0)"
 848 |       ]
 849 |      },
 850 |      "execution_count": 27,
 851 |      "metadata": {},
 852 |      "output_type": "execute_result"
 853 |     }
 854 |    ],
 855 |    "source": [
 856 |     "from scipy.sparse import csr_matrix\n",
 857 |     "us_canada_user_rating = us_canada_user_rating.drop_duplicates(['userID', 'bookTitle'])\n",
 858 |     "us_canada_user_rating_pivot = us_canada_user_rating.pivot(index = 'bookTitle', columns = 'userID', values = 'bookRating').fillna(0)\n",
 859 |     "us_canada_user_rating_matrix = csr_matrix(us_canada_user_rating_pivot.values)\n",
 860 |     "\n",
 861 |     "from sklearn.neighbors import NearestNeighbors\n",
 862 |     "\n",
 863 |     "\n",
 864 |     "model_knn = NearestNeighbors(metric = 'cosine', algorithm = 'brute')\n",
 865 |     "model_knn.fit(us_canada_user_rating_matrix)"
 866 |    ]
 867 |   },
 868 |   {
 869 |    "cell_type": "code",
 870 |    "execution_count": null,
 871 |    "metadata": {},
 872 |    "outputs": [],
 873 |    "source": []
 874 |   },
 875 |   {
 876 |    "cell_type": "code",
 877 |    "execution_count": 43,
 878 |    "metadata": {},
 879 |    "outputs": [
 880 |     {
 881 |      "data": {
 882 |       "text/plain": [
 883 |        "array([[ 0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,\n",
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 939 |        "         0.,  0.,  0.,  0.,  0.,  0.]])"
 940 |       ]
 941 |      },
 942 |      "execution_count": 43,
 943 |      "metadata": {},
 944 |      "output_type": "execute_result"
 945 |     }
 946 |    ],
 947 |    "source": [
 948 |     "us_canada_user_rating_pivot.iloc[query_index,:].values.reshape(1,-1)"
 949 |    ]
 950 |   },
 951 |   {
 952 |    "cell_type": "code",
 953 |    "execution_count": 45,
 954 |    "metadata": {},
 955 |    "outputs": [
 956 |     {
 957 |      "name": "stdout",
 958 |      "output_type": "stream",
 959 |      "text": [
 960 |       "116\n"
 961 |      ]
 962 |     }
 963 |    ],
 964 |    "source": [
 965 |     "query_index = np.random.choice(us_canada_user_rating_pivot.shape[0])\n",
 966 |     "print(query_index)\n",
 967 |     "distances, indices = model_knn.kneighbors(us_canada_user_rating_pivot.iloc[query_index,:].values.reshape(1, -1), n_neighbors = 6)\n",
 968 |     "\n"
 969 |    ]
 970 |   },
 971 |   {
 972 |    "cell_type": "code",
 973 |    "execution_count": 47,
 974 |    "metadata": {},
 975 |    "outputs": [
 976 |     {
 977 |      "data": {
 978 |       "text/plain": [
 979 |        "'Chromosome 6'"
 980 |       ]
 981 |      },
 982 |      "execution_count": 47,
 983 |      "metadata": {},
 984 |      "output_type": "execute_result"
 985 |     }
 986 |    ],
 987 |    "source": [
 988 |     "us_canada_user_rating_pivot.index[query_index]"
 989 |    ]
 990 |   },
 991 |   {
 992 |    "cell_type": "code",
 993 |    "execution_count": 46,
 994 |    "metadata": {},
 995 |    "outputs": [
 996 |     {
 997 |      "name": "stdout",
 998 |      "output_type": "stream",
 999 |      "text": [
1000 |       "Recommendations for Chromosome 6:\n",
1001 |       "\n",
1002 |       "1: Acceptable Risk, with distance of 0.3636398583669789:\n",
1003 |       "2: The Killing Game: Only One Can Win...and the Loser Dies, with distance of 0.6829117384505824:\n",
1004 |       "3: Vector, with distance of 0.6926164340829276:\n",
1005 |       "4: Contagion, with distance of 0.7121975101304703:\n",
1006 |       "5: Invasion, with distance of 0.7308480536731475:\n"
1007 |      ]
1008 |     }
1009 |    ],
1010 |    "source": [
1011 |     "for i in range(0, len(distances.flatten())):\n",
1012 |     "    if i == 0:\n",
1013 |     "        print('Recommendations for {0}:\\n'.format(us_canada_user_rating_pivot.index[query_index]))\n",
1014 |     "    else:\n",
1015 |     "        print('{0}: {1}, with distance of {2}:'.format(i, us_canada_user_rating_pivot.index[indices.flatten()[i]], distances.flatten()[i]))"
1016 |    ]
1017 |   },
1018 |   {
1019 |    "cell_type": "code",
1020 |    "execution_count": null,
1021 |    "metadata": {},
1022 |    "outputs": [],
1023 |    "source": []
1024 |   }
1025 |  ],
1026 |  "metadata": {
1027 |   "kernelspec": {
1028 |    "display_name": "Python 3",
1029 |    "language": "python",
1030 |    "name": "python3"
1031 |   },
1032 |   "language_info": {
1033 |    "codemirror_mode": {
1034 |     "name": "ipython",
1035 |     "version": 3
1036 |    },
1037 |    "file_extension": ".py",
1038 |    "mimetype": "text/x-python",
1039 |    "name": "python",
1040 |    "nbconvert_exporter": "python",
1041 |    "pygments_lexer": "ipython3",
1042 |    "version": "3.6.7"
1043 |   }
1044 |  },
1045 |  "nbformat": 4,
1046 |  "nbformat_minor": 2
1047 | }
1048 | 


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/README.md:
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1 | # Recommendation_complete_tutorial


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