├── Amazon ML challenge data preprocessing intuition.docx
├── Leaderboard - Amazon ML Challenge _ HackerEarth - Google Chrome 8_2_2021 2_37_28 PM.png
├── Product_Based_Browse_Node.ipynb
├── README.md
└── submission.csv


/Amazon ML challenge data preprocessing intuition.docx:
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https://raw.githubusercontent.com/akshatprogrammer/Amazon-ML-Challenge/ba4089b58431fb63042e5abd8c28d37dae3d55f1/Amazon ML challenge data preprocessing intuition.docx


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/Leaderboard - Amazon ML Challenge _ HackerEarth - Google Chrome 8_2_2021 2_37_28 PM.png:
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https://raw.githubusercontent.com/akshatprogrammer/Amazon-ML-Challenge/ba4089b58431fb63042e5abd8c28d37dae3d55f1/Leaderboard - Amazon ML Challenge _ HackerEarth - Google Chrome 8_2_2021 2_37_28 PM.png


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/Product_Based_Browse_Node.ipynb:
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   1 | {
   2 |   "nbformat": 4,
   3 |   "nbformat_minor": 0,
   4 |   "metadata": {
   5 |     "colab": {
   6 |       "name": "engine.ipynb",
   7 |       "provenance": []
   8 |     },
   9 |     "kernelspec": {
  10 |       "name": "python3",
  11 |       "display_name": "Python 3"
  12 |     },
  13 |     "language_info": {
  14 |       "name": "python"
  15 |     }
  16 |   },
  17 |   "cells": [
  18 |     {
  19 |       "cell_type": "code",
  20 |       "metadata": {
  21 |         "colab": {
  22 |           "base_uri": "https://localhost:8080/"
  23 |         },
  24 |         "id": "EPIz_ZOBSMU-",
  25 |         "outputId": "174f0363-3c16-47ad-8136-2a90c57b6202"
  26 |       },
  27 |       "source": [
  28 |         "from google.colab import drive\n",
  29 |         "drive.mount('/content/drive')\n",
  30 |         "import pandas as pd\n",
  31 |         "import numpy as np\n",
  32 |         "import csv\n",
  33 |         "import nltk\n",
  34 |         "from nltk.corpus import stopwords\n",
  35 |         "from nltk.stem import WordNetLemmatizer\n",
  36 |         "from sklearn.feature_extraction.text import TfidfVectorizer\n",
  37 |         "from sklearn.model_selection import train_test_split\n",
  38 |         "from sklearn.feature_selection import chi2\n",
  39 |         "import re\n",
  40 |         "from sklearn.model_selection import train_test_split\n",
  41 |         "from sklearn.linear_model import LogisticRegression\n",
  42 |         "from sklearn.naive_bayes import MultinomialNB\n",
  43 |         "from sklearn.metrics import classification_report, f1_score, accuracy_score, confusion_matrix"
  44 |       ],
  45 |       "execution_count": 1,
  46 |       "outputs": [
  47 |         {
  48 |           "output_type": "stream",
  49 |           "text": [
  50 |             "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
  51 |           ],
  52 |           "name": "stdout"
  53 |         }
  54 |       ]
  55 |     },
  56 |     {
  57 |       "cell_type": "code",
  58 |       "metadata": {
  59 |         "colab": {
  60 |           "base_uri": "https://localhost:8080/",
  61 |           "height": 287
  62 |         },
  63 |         "id": "tTBsJUAVb6Xx",
  64 |         "outputId": "633d2041-56e9-4f5d-a133-6c7e7722168e"
  65 |       },
  66 |       "source": [
  67 |         "#test file\n",
  68 |         "path = \"/content/drive/MyDrive/Datasets/test.csv\"\n",
  69 |         "df_test = pd.read_csv(path, escapechar = \"\\\\\", quoting = csv.QUOTE_NONE)\n",
  70 |         "df_test.head()"
  71 |       ],
  72 |       "execution_count": 2,
  73 |       "outputs": [
  74 |         {
  75 |           "output_type": "execute_result",
  76 |           "data": {
  77 |             "text/html": [
  78 |               "<div>\n",
  79 |               "<style scoped>\n",
  80 |               "    .dataframe tbody tr th:only-of-type {\n",
  81 |               "        vertical-align: middle;\n",
  82 |               "    }\n",
  83 |               "\n",
  84 |               "    .dataframe tbody tr th {\n",
  85 |               "        vertical-align: top;\n",
  86 |               "    }\n",
  87 |               "\n",
  88 |               "    .dataframe thead th {\n",
  89 |               "        text-align: right;\n",
  90 |               "    }\n",
  91 |               "</style>\n",
  92 |               "<table border=\"1\" class=\"dataframe\">\n",
  93 |               "  <thead>\n",
  94 |               "    <tr style=\"text-align: right;\">\n",
  95 |               "      <th></th>\n",
  96 |               "      <th>PRODUCT_ID</th>\n",
  97 |               "      <th>TITLE</th>\n",
  98 |               "      <th>DESCRIPTION</th>\n",
  99 |               "      <th>BULLET_POINTS</th>\n",
 100 |               "      <th>BRAND</th>\n",
 101 |               "    </tr>\n",
 102 |               "  </thead>\n",
 103 |               "  <tbody>\n",
 104 |               "    <tr>\n",
 105 |               "      <th>0</th>\n",
 106 |               "      <td>1</td>\n",
 107 |               "      <td>Command 3M Small Kitchen Hooks, White, Decorat...</td>\n",
 108 |               "      <td>Sale Unit: PACK</td>\n",
 109 |               "      <td>[INCLUDES - 9 hooks and 12 small indoor strips...</td>\n",
 110 |               "      <td>Command</td>\n",
 111 |               "    </tr>\n",
 112 |               "    <tr>\n",
 113 |               "      <th>1</th>\n",
 114 |               "      <td>2</td>\n",
 115 |               "      <td>O'Neal Jump Hardware JAG Unisex-Adult Glove (B...</td>\n",
 116 |               "      <td>Synthetic leather palm with double-layer thumb...</td>\n",
 117 |               "      <td>[Silicone printing for a better grip. Long las...</td>\n",
 118 |               "      <td>O'Neal</td>\n",
 119 |               "    </tr>\n",
 120 |               "    <tr>\n",
 121 |               "      <th>2</th>\n",
 122 |               "      <td>3</td>\n",
 123 |               "      <td>NFL Detroit Lions Portable Party Fridge, 15.8 ...</td>\n",
 124 |               "      <td>Boelter Brands lets you celebrate your favorit...</td>\n",
 125 |               "      <td>[Runs on 12 Volt DC Power or 110 Volt AC Power...</td>\n",
 126 |               "      <td>Boelter Brands</td>\n",
 127 |               "    </tr>\n",
 128 |               "    <tr>\n",
 129 |               "      <th>3</th>\n",
 130 |               "      <td>4</td>\n",
 131 |               "      <td>Panasonic Single Line KX-TS880MX Corded Phone ...</td>\n",
 132 |               "      <td>Features: 50 Station Phonebook Corded Phone Al...</td>\n",
 133 |               "      <td>Panasonic Landline Phones doesn't come with a ...</td>\n",
 134 |               "      <td>Panasonic</td>\n",
 135 |               "    </tr>\n",
 136 |               "    <tr>\n",
 137 |               "      <th>4</th>\n",
 138 |               "      <td>5</td>\n",
 139 |               "      <td>Zero Baby Girl's 100% Cotton Innerwear Bloomer...</td>\n",
 140 |               "      <td>Zero Baby Girl Panties Set. 100% Cotton, Breat...</td>\n",
 141 |               "      <td>[Zero Baby Girl Panties, Pack of 6, 100% Cotto...</td>\n",
 142 |               "      <td>Zero</td>\n",
 143 |               "    </tr>\n",
 144 |               "  </tbody>\n",
 145 |               "</table>\n",
 146 |               "</div>"
 147 |             ],
 148 |             "text/plain": [
 149 |               "   PRODUCT_ID  ...           BRAND\n",
 150 |               "0           1  ...         Command\n",
 151 |               "1           2  ...          O'Neal\n",
 152 |               "2           3  ...  Boelter Brands\n",
 153 |               "3           4  ...       Panasonic\n",
 154 |               "4           5  ...            Zero\n",
 155 |               "\n",
 156 |               "[5 rows x 5 columns]"
 157 |             ]
 158 |           },
 159 |           "metadata": {
 160 |             "tags": []
 161 |           },
 162 |           "execution_count": 2
 163 |         }
 164 |       ]
 165 |     },
 166 |     {
 167 |       "cell_type": "code",
 168 |       "metadata": {
 169 |         "colab": {
 170 |           "base_uri": "https://localhost:8080/",
 171 |           "height": 287
 172 |         },
 173 |         "id": "q4uQ8Nfrgd9I",
 174 |         "outputId": "ff09accf-8e03-4a67-b203-9fdd9ff4f66a"
 175 |       },
 176 |       "source": [
 177 |         "#train file\n",
 178 |         "path = \"/content/drive/MyDrive/Datasets/train.csv\"\n",
 179 |         "df_train = pd.read_csv(path, escapechar = \"\\\\\", quoting = csv.QUOTE_NONE)\n",
 180 |         "df_train = df_train.dropna()\n",
 181 |         "df_train.head()"
 182 |       ],
 183 |       "execution_count": 3,
 184 |       "outputs": [
 185 |         {
 186 |           "output_type": "execute_result",
 187 |           "data": {
 188 |             "text/html": [
 189 |               "<div>\n",
 190 |               "<style scoped>\n",
 191 |               "    .dataframe tbody tr th:only-of-type {\n",
 192 |               "        vertical-align: middle;\n",
 193 |               "    }\n",
 194 |               "\n",
 195 |               "    .dataframe tbody tr th {\n",
 196 |               "        vertical-align: top;\n",
 197 |               "    }\n",
 198 |               "\n",
 199 |               "    .dataframe thead th {\n",
 200 |               "        text-align: right;\n",
 201 |               "    }\n",
 202 |               "</style>\n",
 203 |               "<table border=\"1\" class=\"dataframe\">\n",
 204 |               "  <thead>\n",
 205 |               "    <tr style=\"text-align: right;\">\n",
 206 |               "      <th></th>\n",
 207 |               "      <th>TITLE</th>\n",
 208 |               "      <th>DESCRIPTION</th>\n",
 209 |               "      <th>BULLET_POINTS</th>\n",
 210 |               "      <th>BRAND</th>\n",
 211 |               "      <th>BROWSE_NODE_ID</th>\n",
 212 |               "    </tr>\n",
 213 |               "  </thead>\n",
 214 |               "  <tbody>\n",
 215 |               "    <tr>\n",
 216 |               "      <th>0</th>\n",
 217 |               "      <td>Pete The Cat Bedtime Blues Doll, 14.5 Inch</td>\n",
 218 |               "      <td>Pete the Cat is the coolest, most popular cat ...</td>\n",
 219 |               "      <td>[Pete the Cat Bedtime Blues plush doll,Based o...</td>\n",
 220 |               "      <td>MerryMakers</td>\n",
 221 |               "      <td>0</td>\n",
 222 |               "    </tr>\n",
 223 |               "    <tr>\n",
 224 |               "      <th>1</th>\n",
 225 |               "      <td>The New Yorker NYHM014 Refrigerator Magnet, 2 ...</td>\n",
 226 |               "      <td>The New Yorker Handsome Cello Wrapped Hard Mag...</td>\n",
 227 |               "      <td>[Cat In A Tea Cup by New Yorker cover artist G...</td>\n",
 228 |               "      <td>The New Yorker</td>\n",
 229 |               "      <td>1</td>\n",
 230 |               "    </tr>\n",
 231 |               "    <tr>\n",
 232 |               "      <th>5</th>\n",
 233 |               "      <td>Men'S Full Sleeve Raglan T-Shirts Denim T-Shir...</td>\n",
 234 |               "      <td>Men'S Full Sleeve Raglan T-Shirts Denim T-Shir...</td>\n",
 235 |               "      <td>[Color: Blue,Sleeve: Full Sleeve,Material: Cot...</td>\n",
 236 |               "      <td>Bhavya Enterprise</td>\n",
 237 |               "      <td>5</td>\n",
 238 |               "    </tr>\n",
 239 |               "    <tr>\n",
 240 |               "      <th>6</th>\n",
 241 |               "      <td>Glance Women's Wallet (Black) (LW-21)</td>\n",
 242 |               "      <td>This Black wallet by Glance will be a treasure...</td>\n",
 243 |               "      <td>[The Most Comfortable Women's Wallet That You ...</td>\n",
 244 |               "      <td>Glance</td>\n",
 245 |               "      <td>6</td>\n",
 246 |               "    </tr>\n",
 247 |               "    <tr>\n",
 248 |               "      <th>7</th>\n",
 249 |               "      <td>Wild Animals Hungry Brain Educational Flash Ca...</td>\n",
 250 |               "      <td>Wild Animals are the animals that mostly stays...</td>\n",
 251 |               "      <td>[Playful learning: Flash cards develops the lo...</td>\n",
 252 |               "      <td>hungry brain</td>\n",
 253 |               "      <td>7</td>\n",
 254 |               "    </tr>\n",
 255 |               "  </tbody>\n",
 256 |               "</table>\n",
 257 |               "</div>"
 258 |             ],
 259 |             "text/plain": [
 260 |               "                                               TITLE  ... BROWSE_NODE_ID\n",
 261 |               "0         Pete The Cat Bedtime Blues Doll, 14.5 Inch  ...              0\n",
 262 |               "1  The New Yorker NYHM014 Refrigerator Magnet, 2 ...  ...              1\n",
 263 |               "5  Men'S Full Sleeve Raglan T-Shirts Denim T-Shir...  ...              5\n",
 264 |               "6              Glance Women's Wallet (Black) (LW-21)  ...              6\n",
 265 |               "7  Wild Animals Hungry Brain Educational Flash Ca...  ...              7\n",
 266 |               "\n",
 267 |               "[5 rows x 5 columns]"
 268 |             ]
 269 |           },
 270 |           "metadata": {
 271 |             "tags": []
 272 |           },
 273 |           "execution_count": 3
 274 |         }
 275 |       ]
 276 |     },
 277 |     {
 278 |       "cell_type": "code",
 279 |       "metadata": {
 280 |         "colab": {
 281 |           "base_uri": "https://localhost:8080/"
 282 |         },
 283 |         "id": "DfrjJPq8T8nZ",
 284 |         "outputId": "c6ae6051-73c4-4506-a93c-6dc5da96b52c"
 285 |       },
 286 |       "source": [
 287 |         "punctuation_signs = list(\"?:!.,;\")\n",
 288 |         "nltk.download('punkt')\n",
 289 |         "nltk.download('wordnet')\n",
 290 |         "wordnet_lemmatizer = WordNetLemmatizer()\n",
 291 |         "nltk.download('stopwords')\n",
 292 |         "stop_words = list(stopwords.words('english'))"
 293 |       ],
 294 |       "execution_count": 4,
 295 |       "outputs": [
 296 |         {
 297 |           "output_type": "stream",
 298 |           "text": [
 299 |             "[nltk_data] Downloading package punkt to /root/nltk_data...\n",
 300 |             "[nltk_data]   Package punkt is already up-to-date!\n",
 301 |             "[nltk_data] Downloading package wordnet to /root/nltk_data...\n",
 302 |             "[nltk_data]   Package wordnet is already up-to-date!\n",
 303 |             "[nltk_data] Downloading package stopwords to /root/nltk_data...\n",
 304 |             "[nltk_data]   Package stopwords is already up-to-date!\n"
 305 |           ],
 306 |           "name": "stdout"
 307 |         }
 308 |       ]
 309 |     },
 310 |     {
 311 |       "cell_type": "code",
 312 |       "metadata": {
 313 |         "id": "hw0_D27kT9AU"
 314 |       },
 315 |       "source": [
 316 |         "df_train['Title'] = df_train['TITLE'].str.replace(\"\\r\", \" \")\n",
 317 |         "df_train['Title'] = df_train['Title'].str.replace(\"\\n\", \" \")\n",
 318 |         "df_train['Title'] = df_train['Title'].str.replace(\"    \", \" \")\n",
 319 |         "df_train['Title'] = df_train['Title'].str.replace('\"', '')\n",
 320 |         "df_train['Title'] = df_train['Title'].str.lower()\n",
 321 |         "for punct_sign in punctuation_signs:\n",
 322 |         "  df_train['Title'] = df_train['Title'].str.replace(punct_sign, '')\n",
 323 |         "df_train['Title'] = df_train['Title'].str.replace(\"'s\", \"\")"
 324 |       ],
 325 |       "execution_count": 5,
 326 |       "outputs": []
 327 |     },
 328 |     {
 329 |       "cell_type": "code",
 330 |       "metadata": {
 331 |         "id": "-OU_-P-0UB_V"
 332 |       },
 333 |       "source": [
 334 |         "final_cols = [\"Title\", \"BROWSE_NODE_ID\"]\n",
 335 |         "df_train = df_train[final_cols]\n",
 336 |         "df_train = df_train.iloc[:35000, :]"
 337 |       ],
 338 |       "execution_count": 6,
 339 |       "outputs": []
 340 |     },
 341 |     {
 342 |       "cell_type": "code",
 343 |       "metadata": {
 344 |         "id": "_EJDqp4ZULE_"
 345 |       },
 346 |       "source": [
 347 |         "df_test['Title'] = df_test['TITLE'].str.replace(\"\\r\", \" \")\n",
 348 |         "df_test['Title'] = df_test['Title'].str.replace(\"\\n\", \" \")\n",
 349 |         "df_test['Title'] = df_test['Title'].str.replace(\"    \", \" \")\n",
 350 |         "df_test['Title'] = df_test['Title'].str.replace('\"', '')\n",
 351 |         "df_test['Title'] = df_test['Title'].str.lower()\n",
 352 |         "for punct_sign in punctuation_signs:\n",
 353 |         "  df_test['Title'] = df_test['Title'].str.replace(punct_sign, '')\n",
 354 |         "df_test['Title'] = df_test['Title'].str.replace(\"'s\", \"\")"
 355 |       ],
 356 |       "execution_count": 7,
 357 |       "outputs": []
 358 |     },
 359 |     {
 360 |       "cell_type": "code",
 361 |       "metadata": {
 362 |         "id": "iO2eSD_06gwR"
 363 |       },
 364 |       "source": [
 365 |         "final_cols = [\"Title\", \"PRODUCT_ID\"]\n",
 366 |         "df_test = df_test[final_cols]"
 367 |       ],
 368 |       "execution_count": 8,
 369 |       "outputs": []
 370 |     },
 371 |     {
 372 |       "cell_type": "code",
 373 |       "metadata": {
 374 |         "colab": {
 375 |           "base_uri": "https://localhost:8080/",
 376 |           "height": 203
 377 |         },
 378 |         "id": "HcXWz9VgUgoR",
 379 |         "outputId": "fe862498-dfcb-4022-b59a-29f12238f83b"
 380 |       },
 381 |       "source": [
 382 |         "df_train.head()"
 383 |       ],
 384 |       "execution_count": 9,
 385 |       "outputs": [
 386 |         {
 387 |           "output_type": "execute_result",
 388 |           "data": {
 389 |             "text/html": [
 390 |               "<div>\n",
 391 |               "<style scoped>\n",
 392 |               "    .dataframe tbody tr th:only-of-type {\n",
 393 |               "        vertical-align: middle;\n",
 394 |               "    }\n",
 395 |               "\n",
 396 |               "    .dataframe tbody tr th {\n",
 397 |               "        vertical-align: top;\n",
 398 |               "    }\n",
 399 |               "\n",
 400 |               "    .dataframe thead th {\n",
 401 |               "        text-align: right;\n",
 402 |               "    }\n",
 403 |               "</style>\n",
 404 |               "<table border=\"1\" class=\"dataframe\">\n",
 405 |               "  <thead>\n",
 406 |               "    <tr style=\"text-align: right;\">\n",
 407 |               "      <th></th>\n",
 408 |               "      <th>Title</th>\n",
 409 |               "      <th>BROWSE_NODE_ID</th>\n",
 410 |               "    </tr>\n",
 411 |               "  </thead>\n",
 412 |               "  <tbody>\n",
 413 |               "    <tr>\n",
 414 |               "      <th>0</th>\n",
 415 |               "      <td>pete the cat bedtime blues doll 145 inch</td>\n",
 416 |               "      <td>0</td>\n",
 417 |               "    </tr>\n",
 418 |               "    <tr>\n",
 419 |               "      <th>1</th>\n",
 420 |               "      <td>the new yorker nyhm014 refrigerator magnet 2 x 35</td>\n",
 421 |               "      <td>1</td>\n",
 422 |               "    </tr>\n",
 423 |               "    <tr>\n",
 424 |               "      <th>5</th>\n",
 425 |               "      <td>men full sleeve raglan t-shirts denim t-shirt ...</td>\n",
 426 |               "      <td>5</td>\n",
 427 |               "    </tr>\n",
 428 |               "    <tr>\n",
 429 |               "      <th>6</th>\n",
 430 |               "      <td>glance women wallet (black) (lw-21)</td>\n",
 431 |               "      <td>6</td>\n",
 432 |               "    </tr>\n",
 433 |               "    <tr>\n",
 434 |               "      <th>7</th>\n",
 435 |               "      <td>wild animals hungry brain educational flash ca...</td>\n",
 436 |               "      <td>7</td>\n",
 437 |               "    </tr>\n",
 438 |               "  </tbody>\n",
 439 |               "</table>\n",
 440 |               "</div>"
 441 |             ],
 442 |             "text/plain": [
 443 |               "                                               Title  BROWSE_NODE_ID\n",
 444 |               "0           pete the cat bedtime blues doll 145 inch               0\n",
 445 |               "1  the new yorker nyhm014 refrigerator magnet 2 x 35               1\n",
 446 |               "5  men full sleeve raglan t-shirts denim t-shirt ...               5\n",
 447 |               "6                glance women wallet (black) (lw-21)               6\n",
 448 |               "7  wild animals hungry brain educational flash ca...               7"
 449 |             ]
 450 |           },
 451 |           "metadata": {
 452 |             "tags": []
 453 |           },
 454 |           "execution_count": 9
 455 |         }
 456 |       ]
 457 |     },
 458 |     {
 459 |       "cell_type": "code",
 460 |       "metadata": {
 461 |         "colab": {
 462 |           "base_uri": "https://localhost:8080/",
 463 |           "height": 203
 464 |         },
 465 |         "id": "0eAngSitUlP2",
 466 |         "outputId": "173baf1b-3f82-47db-de7f-5d76a6534015"
 467 |       },
 468 |       "source": [
 469 |         "df_test.head()"
 470 |       ],
 471 |       "execution_count": 10,
 472 |       "outputs": [
 473 |         {
 474 |           "output_type": "execute_result",
 475 |           "data": {
 476 |             "text/html": [
 477 |               "<div>\n",
 478 |               "<style scoped>\n",
 479 |               "    .dataframe tbody tr th:only-of-type {\n",
 480 |               "        vertical-align: middle;\n",
 481 |               "    }\n",
 482 |               "\n",
 483 |               "    .dataframe tbody tr th {\n",
 484 |               "        vertical-align: top;\n",
 485 |               "    }\n",
 486 |               "\n",
 487 |               "    .dataframe thead th {\n",
 488 |               "        text-align: right;\n",
 489 |               "    }\n",
 490 |               "</style>\n",
 491 |               "<table border=\"1\" class=\"dataframe\">\n",
 492 |               "  <thead>\n",
 493 |               "    <tr style=\"text-align: right;\">\n",
 494 |               "      <th></th>\n",
 495 |               "      <th>Title</th>\n",
 496 |               "      <th>PRODUCT_ID</th>\n",
 497 |               "    </tr>\n",
 498 |               "  </thead>\n",
 499 |               "  <tbody>\n",
 500 |               "    <tr>\n",
 501 |               "      <th>0</th>\n",
 502 |               "      <td>command 3m small kitchen hooks white decorate ...</td>\n",
 503 |               "      <td>1</td>\n",
 504 |               "    </tr>\n",
 505 |               "    <tr>\n",
 506 |               "      <th>1</th>\n",
 507 |               "      <td>o'neal jump hardware jag unisex-adult glove (b...</td>\n",
 508 |               "      <td>2</td>\n",
 509 |               "    </tr>\n",
 510 |               "    <tr>\n",
 511 |               "      <th>2</th>\n",
 512 |               "      <td>nfl detroit lions portable party fridge 158 quart</td>\n",
 513 |               "      <td>3</td>\n",
 514 |               "    </tr>\n",
 515 |               "    <tr>\n",
 516 |               "      <th>3</th>\n",
 517 |               "      <td>panasonic single line kx-ts880mx corded phone ...</td>\n",
 518 |               "      <td>4</td>\n",
 519 |               "    </tr>\n",
 520 |               "    <tr>\n",
 521 |               "      <th>4</th>\n",
 522 |               "      <td>zero baby girl 100% cotton innerwear bloomer d...</td>\n",
 523 |               "      <td>5</td>\n",
 524 |               "    </tr>\n",
 525 |               "  </tbody>\n",
 526 |               "</table>\n",
 527 |               "</div>"
 528 |             ],
 529 |             "text/plain": [
 530 |               "                                               Title  PRODUCT_ID\n",
 531 |               "0  command 3m small kitchen hooks white decorate ...           1\n",
 532 |               "1  o'neal jump hardware jag unisex-adult glove (b...           2\n",
 533 |               "2  nfl detroit lions portable party fridge 158 quart           3\n",
 534 |               "3  panasonic single line kx-ts880mx corded phone ...           4\n",
 535 |               "4  zero baby girl 100% cotton innerwear bloomer d...           5"
 536 |             ]
 537 |           },
 538 |           "metadata": {
 539 |             "tags": []
 540 |           },
 541 |           "execution_count": 10
 542 |         }
 543 |       ]
 544 |     },
 545 |     {
 546 |       "cell_type": "code",
 547 |       "metadata": {
 548 |         "colab": {
 549 |           "base_uri": "https://localhost:8080/"
 550 |         },
 551 |         "id": "gPq6ragPUmtD",
 552 |         "outputId": "d0da8e4d-dc01-43c7-824a-2f605dbf3697"
 553 |       },
 554 |       "source": [
 555 |         "df_train.isna().sum()"
 556 |       ],
 557 |       "execution_count": 11,
 558 |       "outputs": [
 559 |         {
 560 |           "output_type": "execute_result",
 561 |           "data": {
 562 |             "text/plain": [
 563 |               "Title             0\n",
 564 |               "BROWSE_NODE_ID    0\n",
 565 |               "dtype: int64"
 566 |             ]
 567 |           },
 568 |           "metadata": {
 569 |             "tags": []
 570 |           },
 571 |           "execution_count": 11
 572 |         }
 573 |       ]
 574 |     },
 575 |     {
 576 |       "cell_type": "code",
 577 |       "metadata": {
 578 |         "id": "A0y2bReP75ic"
 579 |       },
 580 |       "source": [
 581 |         "df_test[\"Title\"].fillna(\"No Data\", inplace = True)"
 582 |       ],
 583 |       "execution_count": 12,
 584 |       "outputs": []
 585 |     },
 586 |     {
 587 |       "cell_type": "code",
 588 |       "metadata": {
 589 |         "colab": {
 590 |           "base_uri": "https://localhost:8080/"
 591 |         },
 592 |         "id": "91d96DmBaKp6",
 593 |         "outputId": "8e61dd4e-8c44-4b1e-8b7b-e7205e223dd3"
 594 |       },
 595 |       "source": [
 596 |         "df_test.isna().sum()"
 597 |       ],
 598 |       "execution_count": 13,
 599 |       "outputs": [
 600 |         {
 601 |           "output_type": "execute_result",
 602 |           "data": {
 603 |             "text/plain": [
 604 |               "Title         0\n",
 605 |               "PRODUCT_ID    0\n",
 606 |               "dtype: int64"
 607 |             ]
 608 |           },
 609 |           "metadata": {
 610 |             "tags": []
 611 |           },
 612 |           "execution_count": 13
 613 |         }
 614 |       ]
 615 |     },
 616 |     {
 617 |       "cell_type": "code",
 618 |       "metadata": {
 619 |         "id": "s4DVt4gsXQaX"
 620 |       },
 621 |       "source": [
 622 |         "X_train, X_test, y_train = df_train[\"Title\"], df_test[\"Title\"], df_train[\"BROWSE_NODE_ID\"]"
 623 |       ],
 624 |       "execution_count": 14,
 625 |       "outputs": []
 626 |     },
 627 |     {
 628 |       "cell_type": "code",
 629 |       "metadata": {
 630 |         "colab": {
 631 |           "base_uri": "https://localhost:8080/"
 632 |         },
 633 |         "id": "J5X_c72rUs1l",
 634 |         "outputId": "abd8aaef-f245-4e77-ff72-f9b2bb066067"
 635 |       },
 636 |       "source": [
 637 |         "tfidf = TfidfVectorizer(stop_words='english', ngram_range=(1,2), min_df=5)\n",
 638 |         "print('1')\n",
 639 |         "X_train_vectors_tfidf = tfidf.fit_transform(X_train)\n",
 640 |         "print(X_train_vectors_tfidf.shape)\n",
 641 |         "print('1')\n",
 642 |         "X_test_vectors_tfidf = tfidf.transform(X_test)\n",
 643 |         "print(X_test_vectors_tfidf.shape)"
 644 |       ],
 645 |       "execution_count": 15,
 646 |       "outputs": [
 647 |         {
 648 |           "output_type": "stream",
 649 |           "text": [
 650 |             "1\n",
 651 |             "(35000, 16942)\n",
 652 |             "1\n",
 653 |             "(110775, 16942)\n"
 654 |           ],
 655 |           "name": "stdout"
 656 |         }
 657 |       ]
 658 |     },
 659 |     {
 660 |       "cell_type": "code",
 661 |       "metadata": {
 662 |         "colab": {
 663 |           "base_uri": "https://localhost:8080/"
 664 |         },
 665 |         "id": "hO0q3lXFU2ZN",
 666 |         "outputId": "607a0b95-93aa-4076-b912-8dd7234b1b12"
 667 |       },
 668 |       "source": [
 669 |         "lr_tfidf=LogisticRegression(solver = 'liblinear', C=10, penalty = 'l2')\n",
 670 |         "lr_tfidf.fit(X_train_vectors_tfidf, y_train)"
 671 |       ],
 672 |       "execution_count": 16,
 673 |       "outputs": [
 674 |         {
 675 |           "output_type": "execute_result",
 676 |           "data": {
 677 |             "text/plain": [
 678 |               "LogisticRegression(C=10, class_weight=None, dual=False, fit_intercept=True,\n",
 679 |               "                   intercept_scaling=1, l1_ratio=None, max_iter=100,\n",
 680 |               "                   multi_class='auto', n_jobs=None, penalty='l2',\n",
 681 |               "                   random_state=None, solver='liblinear', tol=0.0001, verbose=0,\n",
 682 |               "                   warm_start=False)"
 683 |             ]
 684 |           },
 685 |           "metadata": {
 686 |             "tags": []
 687 |           },
 688 |           "execution_count": 16
 689 |         }
 690 |       ]
 691 |     },
 692 |     {
 693 |       "cell_type": "code",
 694 |       "metadata": {
 695 |         "id": "gpEPd5PxWude"
 696 |       },
 697 |       "source": [
 698 |         "y_predict = lr_tfidf.predict(X_test_vectors_tfidf)"
 699 |       ],
 700 |       "execution_count": 17,
 701 |       "outputs": []
 702 |     },
 703 |     {
 704 |       "cell_type": "code",
 705 |       "metadata": {
 706 |         "colab": {
 707 |           "base_uri": "https://localhost:8080/"
 708 |         },
 709 |         "id": "HSTJUhZtfLX4",
 710 |         "outputId": "69be8688-c9fc-4cfe-9f72-d5d137ee66fc"
 711 |       },
 712 |       "source": [
 713 |         "y_predict"
 714 |       ],
 715 |       "execution_count": 18,
 716 |       "outputs": [
 717 |         {
 718 |           "output_type": "execute_result",
 719 |           "data": {
 720 |             "text/plain": [
 721 |               "array([1140, 1045, 8269, ...,  800,  800,  800])"
 722 |             ]
 723 |           },
 724 |           "metadata": {
 725 |             "tags": []
 726 |           },
 727 |           "execution_count": 18
 728 |         }
 729 |       ]
 730 |     },
 731 |     {
 732 |       "cell_type": "code",
 733 |       "metadata": {
 734 |         "colab": {
 735 |           "base_uri": "https://localhost:8080/"
 736 |         },
 737 |         "id": "64QUo0xcfTJI",
 738 |         "outputId": "50e42da2-b50d-4f24-b23d-f1b19deb1f1b"
 739 |       },
 740 |       "source": [
 741 |         "len(y_predict)"
 742 |       ],
 743 |       "execution_count": 19,
 744 |       "outputs": [
 745 |         {
 746 |           "output_type": "execute_result",
 747 |           "data": {
 748 |             "text/plain": [
 749 |               "110775"
 750 |             ]
 751 |           },
 752 |           "metadata": {
 753 |             "tags": []
 754 |           },
 755 |           "execution_count": 19
 756 |         }
 757 |       ]
 758 |     },
 759 |     {
 760 |       "cell_type": "code",
 761 |       "metadata": {
 762 |         "id": "-cEEL7QfgBzn"
 763 |       },
 764 |       "source": [
 765 |         "d = {\"BROWSE_NODE_ID\" : y_predict}"
 766 |       ],
 767 |       "execution_count": 20,
 768 |       "outputs": []
 769 |     },
 770 |     {
 771 |       "cell_type": "code",
 772 |       "metadata": {
 773 |         "colab": {
 774 |           "base_uri": "https://localhost:8080/",
 775 |           "height": 417
 776 |         },
 777 |         "id": "8ymabN1ugEVa",
 778 |         "outputId": "58f65c77-f6d3-4d3c-cb60-eab928589a8f"
 779 |       },
 780 |       "source": [
 781 |         "df1 = pd.DataFrame(data=d)\n",
 782 |         "df1"
 783 |       ],
 784 |       "execution_count": 21,
 785 |       "outputs": [
 786 |         {
 787 |           "output_type": "execute_result",
 788 |           "data": {
 789 |             "text/html": [
 790 |               "<div>\n",
 791 |               "<style scoped>\n",
 792 |               "    .dataframe tbody tr th:only-of-type {\n",
 793 |               "        vertical-align: middle;\n",
 794 |               "    }\n",
 795 |               "\n",
 796 |               "    .dataframe tbody tr th {\n",
 797 |               "        vertical-align: top;\n",
 798 |               "    }\n",
 799 |               "\n",
 800 |               "    .dataframe thead th {\n",
 801 |               "        text-align: right;\n",
 802 |               "    }\n",
 803 |               "</style>\n",
 804 |               "<table border=\"1\" class=\"dataframe\">\n",
 805 |               "  <thead>\n",
 806 |               "    <tr style=\"text-align: right;\">\n",
 807 |               "      <th></th>\n",
 808 |               "      <th>BROWSE_NODE_ID</th>\n",
 809 |               "    </tr>\n",
 810 |               "  </thead>\n",
 811 |               "  <tbody>\n",
 812 |               "    <tr>\n",
 813 |               "      <th>0</th>\n",
 814 |               "      <td>1140</td>\n",
 815 |               "    </tr>\n",
 816 |               "    <tr>\n",
 817 |               "      <th>1</th>\n",
 818 |               "      <td>1045</td>\n",
 819 |               "    </tr>\n",
 820 |               "    <tr>\n",
 821 |               "      <th>2</th>\n",
 822 |               "      <td>8269</td>\n",
 823 |               "    </tr>\n",
 824 |               "    <tr>\n",
 825 |               "      <th>3</th>\n",
 826 |               "      <td>125</td>\n",
 827 |               "    </tr>\n",
 828 |               "    <tr>\n",
 829 |               "      <th>4</th>\n",
 830 |               "      <td>1922</td>\n",
 831 |               "    </tr>\n",
 832 |               "    <tr>\n",
 833 |               "      <th>...</th>\n",
 834 |               "      <td>...</td>\n",
 835 |               "    </tr>\n",
 836 |               "    <tr>\n",
 837 |               "      <th>110770</th>\n",
 838 |               "      <td>4368</td>\n",
 839 |               "    </tr>\n",
 840 |               "    <tr>\n",
 841 |               "      <th>110771</th>\n",
 842 |               "      <td>1551</td>\n",
 843 |               "    </tr>\n",
 844 |               "    <tr>\n",
 845 |               "      <th>110772</th>\n",
 846 |               "      <td>800</td>\n",
 847 |               "    </tr>\n",
 848 |               "    <tr>\n",
 849 |               "      <th>110773</th>\n",
 850 |               "      <td>800</td>\n",
 851 |               "    </tr>\n",
 852 |               "    <tr>\n",
 853 |               "      <th>110774</th>\n",
 854 |               "      <td>800</td>\n",
 855 |               "    </tr>\n",
 856 |               "  </tbody>\n",
 857 |               "</table>\n",
 858 |               "<p>110775 rows × 1 columns</p>\n",
 859 |               "</div>"
 860 |             ],
 861 |             "text/plain": [
 862 |               "        BROWSE_NODE_ID\n",
 863 |               "0                 1140\n",
 864 |               "1                 1045\n",
 865 |               "2                 8269\n",
 866 |               "3                  125\n",
 867 |               "4                 1922\n",
 868 |               "...                ...\n",
 869 |               "110770            4368\n",
 870 |               "110771            1551\n",
 871 |               "110772             800\n",
 872 |               "110773             800\n",
 873 |               "110774             800\n",
 874 |               "\n",
 875 |               "[110775 rows x 1 columns]"
 876 |             ]
 877 |           },
 878 |           "metadata": {
 879 |             "tags": []
 880 |           },
 881 |           "execution_count": 21
 882 |         }
 883 |       ]
 884 |     },
 885 |     {
 886 |       "cell_type": "code",
 887 |       "metadata": {
 888 |         "colab": {
 889 |           "base_uri": "https://localhost:8080/",
 890 |           "height": 203
 891 |         },
 892 |         "id": "ZpXMka2cgqi9",
 893 |         "outputId": "585cdf6a-1719-477b-b764-1a9f57fbdc12"
 894 |       },
 895 |       "source": [
 896 |         "df_new = pd.concat([df_test, df1], axis = 1)\n",
 897 |         "df_new.head()"
 898 |       ],
 899 |       "execution_count": 22,
 900 |       "outputs": [
 901 |         {
 902 |           "output_type": "execute_result",
 903 |           "data": {
 904 |             "text/html": [
 905 |               "<div>\n",
 906 |               "<style scoped>\n",
 907 |               "    .dataframe tbody tr th:only-of-type {\n",
 908 |               "        vertical-align: middle;\n",
 909 |               "    }\n",
 910 |               "\n",
 911 |               "    .dataframe tbody tr th {\n",
 912 |               "        vertical-align: top;\n",
 913 |               "    }\n",
 914 |               "\n",
 915 |               "    .dataframe thead th {\n",
 916 |               "        text-align: right;\n",
 917 |               "    }\n",
 918 |               "</style>\n",
 919 |               "<table border=\"1\" class=\"dataframe\">\n",
 920 |               "  <thead>\n",
 921 |               "    <tr style=\"text-align: right;\">\n",
 922 |               "      <th></th>\n",
 923 |               "      <th>Title</th>\n",
 924 |               "      <th>PRODUCT_ID</th>\n",
 925 |               "      <th>BROWSE_NODE_ID</th>\n",
 926 |               "    </tr>\n",
 927 |               "  </thead>\n",
 928 |               "  <tbody>\n",
 929 |               "    <tr>\n",
 930 |               "      <th>0</th>\n",
 931 |               "      <td>command 3m small kitchen hooks white decorate ...</td>\n",
 932 |               "      <td>1</td>\n",
 933 |               "      <td>1140</td>\n",
 934 |               "    </tr>\n",
 935 |               "    <tr>\n",
 936 |               "      <th>1</th>\n",
 937 |               "      <td>o'neal jump hardware jag unisex-adult glove (b...</td>\n",
 938 |               "      <td>2</td>\n",
 939 |               "      <td>1045</td>\n",
 940 |               "    </tr>\n",
 941 |               "    <tr>\n",
 942 |               "      <th>2</th>\n",
 943 |               "      <td>nfl detroit lions portable party fridge 158 quart</td>\n",
 944 |               "      <td>3</td>\n",
 945 |               "      <td>8269</td>\n",
 946 |               "    </tr>\n",
 947 |               "    <tr>\n",
 948 |               "      <th>3</th>\n",
 949 |               "      <td>panasonic single line kx-ts880mx corded phone ...</td>\n",
 950 |               "      <td>4</td>\n",
 951 |               "      <td>125</td>\n",
 952 |               "    </tr>\n",
 953 |               "    <tr>\n",
 954 |               "      <th>4</th>\n",
 955 |               "      <td>zero baby girl 100% cotton innerwear bloomer d...</td>\n",
 956 |               "      <td>5</td>\n",
 957 |               "      <td>1922</td>\n",
 958 |               "    </tr>\n",
 959 |               "  </tbody>\n",
 960 |               "</table>\n",
 961 |               "</div>"
 962 |             ],
 963 |             "text/plain": [
 964 |               "                                               Title  ...  BROWSE_NODE_ID\n",
 965 |               "0  command 3m small kitchen hooks white decorate ...  ...            1140\n",
 966 |               "1  o'neal jump hardware jag unisex-adult glove (b...  ...            1045\n",
 967 |               "2  nfl detroit lions portable party fridge 158 quart  ...            8269\n",
 968 |               "3  panasonic single line kx-ts880mx corded phone ...  ...             125\n",
 969 |               "4  zero baby girl 100% cotton innerwear bloomer d...  ...            1922\n",
 970 |               "\n",
 971 |               "[5 rows x 3 columns]"
 972 |             ]
 973 |           },
 974 |           "metadata": {
 975 |             "tags": []
 976 |           },
 977 |           "execution_count": 22
 978 |         }
 979 |       ]
 980 |     },
 981 |     {
 982 |       "cell_type": "code",
 983 |       "metadata": {
 984 |         "colab": {
 985 |           "base_uri": "https://localhost:8080/",
 986 |           "height": 417
 987 |         },
 988 |         "id": "0UiIR1C3g0aL",
 989 |         "outputId": "a869900d-cd40-4364-a8b6-abd3ad6dd8af"
 990 |       },
 991 |       "source": [
 992 |         "l = [\"PRODUCT_ID\", \"BROWSE_NODE_ID\"]\n",
 993 |         "df_new = df_new[l]\n",
 994 |         "df_new"
 995 |       ],
 996 |       "execution_count": 23,
 997 |       "outputs": [
 998 |         {
 999 |           "output_type": "execute_result",
1000 |           "data": {
1001 |             "text/html": [
1002 |               "<div>\n",
1003 |               "<style scoped>\n",
1004 |               "    .dataframe tbody tr th:only-of-type {\n",
1005 |               "        vertical-align: middle;\n",
1006 |               "    }\n",
1007 |               "\n",
1008 |               "    .dataframe tbody tr th {\n",
1009 |               "        vertical-align: top;\n",
1010 |               "    }\n",
1011 |               "\n",
1012 |               "    .dataframe thead th {\n",
1013 |               "        text-align: right;\n",
1014 |               "    }\n",
1015 |               "</style>\n",
1016 |               "<table border=\"1\" class=\"dataframe\">\n",
1017 |               "  <thead>\n",
1018 |               "    <tr style=\"text-align: right;\">\n",
1019 |               "      <th></th>\n",
1020 |               "      <th>PRODUCT_ID</th>\n",
1021 |               "      <th>BROWSE_NODE_ID</th>\n",
1022 |               "    </tr>\n",
1023 |               "  </thead>\n",
1024 |               "  <tbody>\n",
1025 |               "    <tr>\n",
1026 |               "      <th>0</th>\n",
1027 |               "      <td>1</td>\n",
1028 |               "      <td>1140</td>\n",
1029 |               "    </tr>\n",
1030 |               "    <tr>\n",
1031 |               "      <th>1</th>\n",
1032 |               "      <td>2</td>\n",
1033 |               "      <td>1045</td>\n",
1034 |               "    </tr>\n",
1035 |               "    <tr>\n",
1036 |               "      <th>2</th>\n",
1037 |               "      <td>3</td>\n",
1038 |               "      <td>8269</td>\n",
1039 |               "    </tr>\n",
1040 |               "    <tr>\n",
1041 |               "      <th>3</th>\n",
1042 |               "      <td>4</td>\n",
1043 |               "      <td>125</td>\n",
1044 |               "    </tr>\n",
1045 |               "    <tr>\n",
1046 |               "      <th>4</th>\n",
1047 |               "      <td>5</td>\n",
1048 |               "      <td>1922</td>\n",
1049 |               "    </tr>\n",
1050 |               "    <tr>\n",
1051 |               "      <th>...</th>\n",
1052 |               "      <td>...</td>\n",
1053 |               "      <td>...</td>\n",
1054 |               "    </tr>\n",
1055 |               "    <tr>\n",
1056 |               "      <th>110770</th>\n",
1057 |               "      <td>110771</td>\n",
1058 |               "      <td>4368</td>\n",
1059 |               "    </tr>\n",
1060 |               "    <tr>\n",
1061 |               "      <th>110771</th>\n",
1062 |               "      <td>110772</td>\n",
1063 |               "      <td>1551</td>\n",
1064 |               "    </tr>\n",
1065 |               "    <tr>\n",
1066 |               "      <th>110772</th>\n",
1067 |               "      <td>110773</td>\n",
1068 |               "      <td>800</td>\n",
1069 |               "    </tr>\n",
1070 |               "    <tr>\n",
1071 |               "      <th>110773</th>\n",
1072 |               "      <td>110774</td>\n",
1073 |               "      <td>800</td>\n",
1074 |               "    </tr>\n",
1075 |               "    <tr>\n",
1076 |               "      <th>110774</th>\n",
1077 |               "      <td>110775</td>\n",
1078 |               "      <td>800</td>\n",
1079 |               "    </tr>\n",
1080 |               "  </tbody>\n",
1081 |               "</table>\n",
1082 |               "<p>110775 rows × 2 columns</p>\n",
1083 |               "</div>"
1084 |             ],
1085 |             "text/plain": [
1086 |               "        PRODUCT_ID  BROWSE_NODE_ID\n",
1087 |               "0                1            1140\n",
1088 |               "1                2            1045\n",
1089 |               "2                3            8269\n",
1090 |               "3                4             125\n",
1091 |               "4                5            1922\n",
1092 |               "...            ...             ...\n",
1093 |               "110770      110771            4368\n",
1094 |               "110771      110772            1551\n",
1095 |               "110772      110773             800\n",
1096 |               "110773      110774             800\n",
1097 |               "110774      110775             800\n",
1098 |               "\n",
1099 |               "[110775 rows x 2 columns]"
1100 |             ]
1101 |           },
1102 |           "metadata": {
1103 |             "tags": []
1104 |           },
1105 |           "execution_count": 23
1106 |         }
1107 |       ]
1108 |     },
1109 |     {
1110 |       "cell_type": "code",
1111 |       "metadata": {
1112 |         "id": "JTyuVvuPhgSE"
1113 |       },
1114 |       "source": [
1115 |         "df_new.to_csv(\"/content/drive/MyDrive/Datasets/submission.csv\", index = False, header = True)"
1116 |       ],
1117 |       "execution_count": 24,
1118 |       "outputs": []
1119 |     },
1120 |     {
1121 |       "cell_type": "code",
1122 |       "metadata": {
1123 |         "colab": {
1124 |           "base_uri": "https://localhost:8080/",
1125 |           "height": 203
1126 |         },
1127 |         "id": "u8MNaBoTiuYD",
1128 |         "outputId": "82ac3f23-96eb-4876-eda4-e51904beb539"
1129 |       },
1130 |       "source": [
1131 |         "path = \"/content/drive/MyDrive/Datasets/submission.csv\"\n",
1132 |         "df_sub = pd.read_csv(path)\n",
1133 |         "df_sub.head()"
1134 |       ],
1135 |       "execution_count": 25,
1136 |       "outputs": [
1137 |         {
1138 |           "output_type": "execute_result",
1139 |           "data": {
1140 |             "text/html": [
1141 |               "<div>\n",
1142 |               "<style scoped>\n",
1143 |               "    .dataframe tbody tr th:only-of-type {\n",
1144 |               "        vertical-align: middle;\n",
1145 |               "    }\n",
1146 |               "\n",
1147 |               "    .dataframe tbody tr th {\n",
1148 |               "        vertical-align: top;\n",
1149 |               "    }\n",
1150 |               "\n",
1151 |               "    .dataframe thead th {\n",
1152 |               "        text-align: right;\n",
1153 |               "    }\n",
1154 |               "</style>\n",
1155 |               "<table border=\"1\" class=\"dataframe\">\n",
1156 |               "  <thead>\n",
1157 |               "    <tr style=\"text-align: right;\">\n",
1158 |               "      <th></th>\n",
1159 |               "      <th>PRODUCT_ID</th>\n",
1160 |               "      <th>BROWSE_NODE_ID</th>\n",
1161 |               "    </tr>\n",
1162 |               "  </thead>\n",
1163 |               "  <tbody>\n",
1164 |               "    <tr>\n",
1165 |               "      <th>0</th>\n",
1166 |               "      <td>1</td>\n",
1167 |               "      <td>1140</td>\n",
1168 |               "    </tr>\n",
1169 |               "    <tr>\n",
1170 |               "      <th>1</th>\n",
1171 |               "      <td>2</td>\n",
1172 |               "      <td>1045</td>\n",
1173 |               "    </tr>\n",
1174 |               "    <tr>\n",
1175 |               "      <th>2</th>\n",
1176 |               "      <td>3</td>\n",
1177 |               "      <td>8269</td>\n",
1178 |               "    </tr>\n",
1179 |               "    <tr>\n",
1180 |               "      <th>3</th>\n",
1181 |               "      <td>4</td>\n",
1182 |               "      <td>125</td>\n",
1183 |               "    </tr>\n",
1184 |               "    <tr>\n",
1185 |               "      <th>4</th>\n",
1186 |               "      <td>5</td>\n",
1187 |               "      <td>1922</td>\n",
1188 |               "    </tr>\n",
1189 |               "  </tbody>\n",
1190 |               "</table>\n",
1191 |               "</div>"
1192 |             ],
1193 |             "text/plain": [
1194 |               "   PRODUCT_ID  BROWSE_NODE_ID\n",
1195 |               "0           1            1140\n",
1196 |               "1           2            1045\n",
1197 |               "2           3            8269\n",
1198 |               "3           4             125\n",
1199 |               "4           5            1922"
1200 |             ]
1201 |           },
1202 |           "metadata": {
1203 |             "tags": []
1204 |           },
1205 |           "execution_count": 25
1206 |         }
1207 |       ]
1208 |     },
1209 |     {
1210 |       "cell_type": "code",
1211 |       "metadata": {
1212 |         "colab": {
1213 |           "base_uri": "https://localhost:8080/"
1214 |         },
1215 |         "id": "ALnvwky6rAA0",
1216 |         "outputId": "4d501fed-b5f8-44ab-9b52-c9d38c1a4911"
1217 |       },
1218 |       "source": [
1219 |         "df_sub.isna().sum()"
1220 |       ],
1221 |       "execution_count": 26,
1222 |       "outputs": [
1223 |         {
1224 |           "output_type": "execute_result",
1225 |           "data": {
1226 |             "text/plain": [
1227 |               "PRODUCT_ID        0\n",
1228 |               "BROWSE_NODE_ID    0\n",
1229 |               "dtype: int64"
1230 |             ]
1231 |           },
1232 |           "metadata": {
1233 |             "tags": []
1234 |           },
1235 |           "execution_count": 26
1236 |         }
1237 |       ]
1238 |     },
1239 |     {
1240 |       "cell_type": "code",
1241 |       "metadata": {
1242 |         "colab": {
1243 |           "base_uri": "https://localhost:8080/",
1244 |           "height": 295
1245 |         },
1246 |         "id": "m5FYWOJ3rD1x",
1247 |         "outputId": "84d5ac03-9cf2-41e7-a97b-cf1c550521c3"
1248 |       },
1249 |       "source": [
1250 |         "df_sub.describe()"
1251 |       ],
1252 |       "execution_count": 27,
1253 |       "outputs": [
1254 |         {
1255 |           "output_type": "execute_result",
1256 |           "data": {
1257 |             "text/html": [
1258 |               "<div>\n",
1259 |               "<style scoped>\n",
1260 |               "    .dataframe tbody tr th:only-of-type {\n",
1261 |               "        vertical-align: middle;\n",
1262 |               "    }\n",
1263 |               "\n",
1264 |               "    .dataframe tbody tr th {\n",
1265 |               "        vertical-align: top;\n",
1266 |               "    }\n",
1267 |               "\n",
1268 |               "    .dataframe thead th {\n",
1269 |               "        text-align: right;\n",
1270 |               "    }\n",
1271 |               "</style>\n",
1272 |               "<table border=\"1\" class=\"dataframe\">\n",
1273 |               "  <thead>\n",
1274 |               "    <tr style=\"text-align: right;\">\n",
1275 |               "      <th></th>\n",
1276 |               "      <th>PRODUCT_ID</th>\n",
1277 |               "      <th>BROWSE_NODE_ID</th>\n",
1278 |               "    </tr>\n",
1279 |               "  </thead>\n",
1280 |               "  <tbody>\n",
1281 |               "    <tr>\n",
1282 |               "      <th>count</th>\n",
1283 |               "      <td>110775.000000</td>\n",
1284 |               "      <td>110775.000000</td>\n",
1285 |               "    </tr>\n",
1286 |               "    <tr>\n",
1287 |               "      <th>mean</th>\n",
1288 |               "      <td>55388.000000</td>\n",
1289 |               "      <td>1814.854317</td>\n",
1290 |               "    </tr>\n",
1291 |               "    <tr>\n",
1292 |               "      <th>std</th>\n",
1293 |               "      <td>31978.132372</td>\n",
1294 |               "      <td>2984.057629</td>\n",
1295 |               "    </tr>\n",
1296 |               "    <tr>\n",
1297 |               "      <th>min</th>\n",
1298 |               "      <td>1.000000</td>\n",
1299 |               "      <td>0.000000</td>\n",
1300 |               "    </tr>\n",
1301 |               "    <tr>\n",
1302 |               "      <th>25%</th>\n",
1303 |               "      <td>27694.500000</td>\n",
1304 |               "      <td>507.000000</td>\n",
1305 |               "    </tr>\n",
1306 |               "    <tr>\n",
1307 |               "      <th>50%</th>\n",
1308 |               "      <td>55388.000000</td>\n",
1309 |               "      <td>1045.000000</td>\n",
1310 |               "    </tr>\n",
1311 |               "    <tr>\n",
1312 |               "      <th>75%</th>\n",
1313 |               "      <td>83081.500000</td>\n",
1314 |               "      <td>1687.000000</td>\n",
1315 |               "    </tr>\n",
1316 |               "    <tr>\n",
1317 |               "      <th>max</th>\n",
1318 |               "      <td>110775.000000</td>\n",
1319 |               "      <td>47970.000000</td>\n",
1320 |               "    </tr>\n",
1321 |               "  </tbody>\n",
1322 |               "</table>\n",
1323 |               "</div>"
1324 |             ],
1325 |             "text/plain": [
1326 |               "          PRODUCT_ID  BROWSE_NODE_ID\n",
1327 |               "count  110775.000000   110775.000000\n",
1328 |               "mean    55388.000000     1814.854317\n",
1329 |               "std     31978.132372     2984.057629\n",
1330 |               "min         1.000000        0.000000\n",
1331 |               "25%     27694.500000      507.000000\n",
1332 |               "50%     55388.000000     1045.000000\n",
1333 |               "75%     83081.500000     1687.000000\n",
1334 |               "max    110775.000000    47970.000000"
1335 |             ]
1336 |           },
1337 |           "metadata": {
1338 |             "tags": []
1339 |           },
1340 |           "execution_count": 27
1341 |         }
1342 |       ]
1343 |     },
1344 |     {
1345 |       "cell_type": "code",
1346 |       "metadata": {
1347 |         "id": "Tx07ygYxrGlu"
1348 |       },
1349 |       "source": [
1350 |         ""
1351 |       ],
1352 |       "execution_count": null,
1353 |       "outputs": []
1354 |     }
1355 |   ]
1356 | }


--------------------------------------------------------------------------------
/README.md:
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 1 | # Amazon-ML-Challenge
 2 | <b>Product Browse Node Classification</b></br>
 3 | 
 4 | ## About Amazon ML Challenge
 5 | ![](https://he-s3.s3.amazonaws.com/media/cache/0a/be/0abe8c0908dcb9e67941600739f6d651.png)</br>
 6 | Amazon ML Challenge is a two-stage competition where students from all engineering campuses across India will get a unique opportunity to work on Amazon’s dataset to bring in fresh ideas and build innovative solutions for a real-world problem statement. The top three winning teams will receive pre-placement interviews (PPIs) for ML roles at Amazon along with cash prizes and certificates.
 7 | 
 8 | ## ML Challenge Stages
 9 | ![](https://s3-ap-southeast-1.amazonaws.com/he-public-data/ML%20Challenge%20Stages_Hackerearth%201a6b4fa1.JPG)</br>
10 | 
11 | ## Datset Link🔗
12 | [Click Here for the Datset](https://he-s3.s3.ap-southeast-1.amazonaws.com/media/hackathon/amazon-ml-challenge/product-browse-node-classification-2-7ff04e5a/546b594ee0a211eb.zip?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Expires=3600&X-Amz-SignedHeaders=host&X-Amz-Signature=16c8b3b0a2fcd83db7ed5437de70012b5b4651ce5f543c1fe62512904d5f0ce7&X-Amz-Date=20210802T090306Z&X-Amz-Credential=AKIA6I2ISGOYH7WWS3G5%2F20210802%2Fap-southeast-1%2Fs3%2Faws4_request)
13 | 
14 | ## Problem
15 | Amazon catalog consists of billions of products that belong to thousands of browse nodes (each browse node represents a collection of items for sale). Browse nodes are used to help customer navigate through our website and classify products to product type groups. Hence, it is important to predict the node assignment at the time of listing of the product or when the browse node information is absent.
16 | 
17 | As part of this hackathon, you will use product metadata to classify products into browse nodes. You will have access to product title, description, bullet points etc. and labels for ~3MM products to train and test your submissions. Note that there is some noise in the data - real world data looks like this!!
18 | 
19 | ## Data Description
20 | 
21 | Full Train/Test dataset details:</br>
22 | 
23 | Key column – PRODUCT_ID</br>
24 | Input features – TITLE, DESCRIPTION, BULLET_POINTS, BRAND</br>
25 | Target column – BROWSE_NODE_ID</br>
26 | Train dataset size – 2,903,024</br>
27 | Number of classes in Train – 9,919</br>
28 | Overall Test dataset size – 110,775</br>
29 | 
30 | ## Team Members
31 | 1. [Akshat Jain](https://github.com/akshatprogrammer)
32 | 2. [Anjali Pathak](https://github.com/anjalipathak13)
33 | 3. [Aman Gupta](https://github.com/Aman-Gupta-Ji)
34 | 4. [Himank Kandpal](https://github.com/Himank0)
35 | 
36 | ## Team Ranking
37 | ![](https://github.com/akshatprogrammer/Amazon-ML-Challenge/blob/main/Leaderboard%20-%20Amazon%20ML%20Challenge%20_%20HackerEarth%20-%20Google%20Chrome%208_2_2021%202_37_28%20PM.png)
38 | </br> Out of 3290 Teams we secured 296th Position.
39 | 
40 | 
41 | ## Contributors
42 | <a href = "https://github.com/akshatprogrammer/Amazon-ML-Challenge/graphs/contributors">
43 |   <img src = "https://contrib.rocks/image?repo=akshatprogrammer/Amazon-ML-Challenge"/>
44 | </a>
45 | 
46 | # Connect With Me
47 | LinkedIn : https://www.linkedin.com/in/akshatjaingeu/<br/>
48 | Email : akshat.kodia@gmail.com<br/>
49 | Twitter : www.twitter.com/akki_aj89</br>
50 | Website : https://akshatprogrammer.github.io/portfolio/ </br>
51 | # Personal
52 | Name : Akshat Jain<br/>
53 | University : Graphic Era University, Dehradun(UK)
54 | 
55 | If any problem with this program reach me at Telegram<br/>
56 | Here is the link -> https://t.me/akki_aj89
57 | 
58 | # Gratitude
59 | Thank You, if you like it please leave a Star.
60 | 
61 | 


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