Enhancing_Recommendation_System.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# Exploratory Data Analysis\n",
        "\n",
        "The e-commerce data set has 15 attributes out of these labels, and we need the product name and description for this project.\n",
        "\n",
        "Let's import all the libraries required."
      ],
      "metadata": {
        "id": "GSKhNMWaiApP"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "9mC2jQ-WBP1t"
      },
      "outputs": [],
      "source": [
        "# Data Manipulation\n",
        "\n",
        "import pandas as pd\n",
        "\n",
        "import numpy as np\n",
        "\n",
        "# Visualization\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "import seaborn as sns\n",
        "\n",
        "# NLP for text pre-processing\n",
        "\n",
        "import nltk\n",
        "\n",
        "import scipy\n",
        "\n",
        "import re\n",
        "\n",
        "from scipy import spatial\n",
        "\n",
        "from nltk.tokenize.toktok import ToktokTokenizer\n",
        "\n",
        "from nltk.corpus import stopwords\n",
        "\n",
        "from nltk.tokenize import sent_tokenize, word_tokenize\n",
        "\n",
        "from nltk.stem import PorterStemmer\n",
        "\n",
        "tokenizer = ToktokTokenizer()\n",
        "\n",
        "# other libraries\n",
        "\n",
        "import gensim\n",
        "\n",
        "from gensim.models import Word2Vec\n",
        "\n",
        "import itertools\n",
        "\n",
        "from sklearn.decomposition import PCA\n",
        "\n",
        "from sklearn.feature_extraction. text import TfidfVectorizer\n",
        "\n",
        "from sklearn.decomposition import PCA\n",
        "\n",
        "# Import linear_kernel\n",
        "\n",
        "from sklearn.metrics.pairwise import linear_kernel\n",
        "\n",
        "# remove warnings\n",
        "\n",
        "import warnings\n",
        "\n",
        "warnings.filterwarnings (action = 'ignore')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
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        "id": "CzNnD9eNBpyB",
        "outputId": "7e7c497c-78af-4623-dd38-480f9741a00c"
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      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
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        "colab": {
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          "height": 531
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        "id": "m9efjAhOBl7f",
        "outputId": "a763f71d-33f2-44ec-ec46-d814ee3eb035"
      },
      "outputs": [
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          "data": {
            "text/plain": [
              "                            uniq_id            crawl_timestamp  \\\n",
              "0  c2d766ca982eca8304150849735ffef9  2016-03-25 22:59:23 +0000   \n",
              "1  7f7036a6d550aaa89d34c77bd39a5e48  2016-03-25 22:59:23 +0000   \n",
              "2  f449ec65dcbc041b6ae5e6a32717d01b  2016-03-25 22:59:23 +0000   \n",
              "3  0973b37acd0c664e3de26e97e5571454  2016-03-25 22:59:23 +0000   \n",
              "4  bc940ea42ee6bef5ac7cea3fb5cfbee7  2016-03-25 22:59:23 +0000   \n",
              "\n",
              "                                         product_url  \\\n",
              "0  http://www.flipkart.com/alisha-solid-women-s-c...   \n",
              "1  http://www.flipkart.com/fabhomedecor-fabric-do...   \n",
              "2  http://www.flipkart.com/aw-bellies/p/itmeh4grg...   \n",
              "3  http://www.flipkart.com/alisha-solid-women-s-c...   \n",
              "4  http://www.flipkart.com/sicons-all-purpose-arn...   \n",
              "\n",
              "                            product_name  \\\n",
              "0    Alisha Solid Women's Cycling Shorts   \n",
              "1    FabHomeDecor Fabric Double Sofa Bed   \n",
              "2                             AW Bellies   \n",
              "3    Alisha Solid Women's Cycling Shorts   \n",
              "4  Sicons All Purpose Arnica Dog Shampoo   \n",
              "\n",
              "                               product_category_tree               pid  \\\n",
              "0  [\"Clothing >> Women's Clothing >> Lingerie, Sl...  SRTEH2FF9KEDEFGF   \n",
              "1  [\"Furniture >> Living Room Furniture >> Sofa B...  SBEEH3QGU7MFYJFY   \n",
              "2  [\"Footwear >> Women's Footwear >> Ballerinas >...  SHOEH4GRSUBJGZXE   \n",
              "3  [\"Clothing >> Women's Clothing >> Lingerie, Sl...  SRTEH2F6HUZMQ6SJ   \n",
              "4  [\"Pet Supplies >> Grooming >> Skin & Coat Care...  PSOEH3ZYDMSYARJ5   \n",
              "\n",
              "   retail_price  discounted_price  \\\n",
              "0         999.0             379.0   \n",
              "1       32157.0           22646.0   \n",
              "2         999.0             499.0   \n",
              "3         699.0             267.0   \n",
              "4         220.0             210.0   \n",
              "\n",
              "                                               image  is_FK_Advantage_product  \\\n",
              "0  [\"http://img5a.flixcart.com/image/short/u/4/a/...                    False   \n",
              "1  [\"http://img6a.flixcart.com/image/sofa-bed/j/f...                    False   \n",
              "2  [\"http://img5a.flixcart.com/image/shoe/7/z/z/r...                    False   \n",
              "3  [\"http://img5a.flixcart.com/image/short/6/2/h/...                    False   \n",
              "4  [\"http://img5a.flixcart.com/image/pet-shampoo/...                    False   \n",
              "\n",
              "                                         description       product_rating  \\\n",
              "0  Key Features of Alisha Solid Women's Cycling S...  No rating available   \n",
              "1  FabHomeDecor Fabric Double Sofa Bed (Finish Co...  No rating available   \n",
              "2  Key Features of AW Bellies Sandals Wedges Heel...  No rating available   \n",
              "3  Key Features of Alisha Solid Women's Cycling S...  No rating available   \n",
              "4  Specifications of Sicons All Purpose Arnica Do...  No rating available   \n",
              "\n",
              "        overall_rating         brand  \\\n",
              "0  No rating available        Alisha   \n",
              "1  No rating available  FabHomeDecor   \n",
              "2  No rating available            AW   \n",
              "3  No rating available        Alisha   \n",
              "4  No rating available        Sicons   \n",
              "\n",
              "                              product_specifications  \n",
              "0  {\"product_specification\"=>[{\"key\"=>\"Number of ...  \n",
              "1  {\"product_specification\"=>[{\"key\"=>\"Installati...  \n",
              "2  {\"product_specification\"=>[{\"key\"=>\"Ideal For\"...  \n",
              "3  {\"product_specification\"=>[{\"key\"=>\"Number of ...  \n",
              "4  {\"product_specification\"=>[{\"key\"=>\"Pet Type\",...  "
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              "      <th></th>\n",
              "      <th>uniq_id</th>\n",
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              "      <th>is_FK_Advantage_product</th>\n",
              "      <th>description</th>\n",
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              "      <th>brand</th>\n",
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              "      <th>0</th>\n",
              "      <td>c2d766ca982eca8304150849735ffef9</td>\n",
              "      <td>2016-03-25 22:59:23 +0000</td>\n",
              "      <td>http://www.flipkart.com/alisha-solid-women-s-c...</td>\n",
              "      <td>Alisha Solid Women's Cycling Shorts</td>\n",
              "      <td>[\"Clothing &gt;&gt; Women's Clothing &gt;&gt; Lingerie, Sl...</td>\n",
              "      <td>SRTEH2FF9KEDEFGF</td>\n",
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              "      <td>379.0</td>\n",
              "      <td>[\"http://img5a.flixcart.com/image/short/u/4/a/...</td>\n",
              "      <td>False</td>\n",
              "      <td>Key Features of Alisha Solid Women's Cycling S...</td>\n",
              "      <td>No rating available</td>\n",
              "      <td>No rating available</td>\n",
              "      <td>Alisha</td>\n",
              "      <td>{\"product_specification\"=&gt;[{\"key\"=&gt;\"Number of ...</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>7f7036a6d550aaa89d34c77bd39a5e48</td>\n",
              "      <td>2016-03-25 22:59:23 +0000</td>\n",
              "      <td>http://www.flipkart.com/fabhomedecor-fabric-do...</td>\n",
              "      <td>FabHomeDecor Fabric Double Sofa Bed</td>\n",
              "      <td>[\"Furniture &gt;&gt; Living Room Furniture &gt;&gt; Sofa B...</td>\n",
              "      <td>SBEEH3QGU7MFYJFY</td>\n",
              "      <td>32157.0</td>\n",
              "      <td>22646.0</td>\n",
              "      <td>[\"http://img6a.flixcart.com/image/sofa-bed/j/f...</td>\n",
              "      <td>False</td>\n",
              "      <td>FabHomeDecor Fabric Double Sofa Bed (Finish Co...</td>\n",
              "      <td>No rating available</td>\n",
              "      <td>No rating available</td>\n",
              "      <td>FabHomeDecor</td>\n",
              "      <td>{\"product_specification\"=&gt;[{\"key\"=&gt;\"Installati...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>f449ec65dcbc041b6ae5e6a32717d01b</td>\n",
              "      <td>2016-03-25 22:59:23 +0000</td>\n",
              "      <td>http://www.flipkart.com/aw-bellies/p/itmeh4grg...</td>\n",
              "      <td>AW Bellies</td>\n",
              "      <td>[\"Footwear &gt;&gt; Women's Footwear &gt;&gt; Ballerinas &gt;...</td>\n",
              "      <td>SHOEH4GRSUBJGZXE</td>\n",
              "      <td>999.0</td>\n",
              "      <td>499.0</td>\n",
              "      <td>[\"http://img5a.flixcart.com/image/shoe/7/z/z/r...</td>\n",
              "      <td>False</td>\n",
              "      <td>Key Features of AW Bellies Sandals Wedges Heel...</td>\n",
              "      <td>No rating available</td>\n",
              "      <td>No rating available</td>\n",
              "      <td>AW</td>\n",
              "      <td>{\"product_specification\"=&gt;[{\"key\"=&gt;\"Ideal For\"...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0973b37acd0c664e3de26e97e5571454</td>\n",
              "      <td>2016-03-25 22:59:23 +0000</td>\n",
              "      <td>http://www.flipkart.com/alisha-solid-women-s-c...</td>\n",
              "      <td>Alisha Solid Women's Cycling Shorts</td>\n",
              "      <td>[\"Clothing &gt;&gt; Women's Clothing &gt;&gt; Lingerie, Sl...</td>\n",
              "      <td>SRTEH2F6HUZMQ6SJ</td>\n",
              "      <td>699.0</td>\n",
              "      <td>267.0</td>\n",
              "      <td>[\"http://img5a.flixcart.com/image/short/6/2/h/...</td>\n",
              "      <td>False</td>\n",
              "      <td>Key Features of Alisha Solid Women's Cycling S...</td>\n",
              "      <td>No rating available</td>\n",
              "      <td>No rating available</td>\n",
              "      <td>Alisha</td>\n",
              "      <td>{\"product_specification\"=&gt;[{\"key\"=&gt;\"Number of ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>bc940ea42ee6bef5ac7cea3fb5cfbee7</td>\n",
              "      <td>2016-03-25 22:59:23 +0000</td>\n",
              "      <td>http://www.flipkart.com/sicons-all-purpose-arn...</td>\n",
              "      <td>Sicons All Purpose Arnica Dog Shampoo</td>\n",
              "      <td>[\"Pet Supplies &gt;&gt; Grooming &gt;&gt; Skin &amp; Coat Care...</td>\n",
              "      <td>PSOEH3ZYDMSYARJ5</td>\n",
              "      <td>220.0</td>\n",
              "      <td>210.0</td>\n",
              "      <td>[\"http://img5a.flixcart.com/image/pet-shampoo/...</td>\n",
              "      <td>False</td>\n",
              "      <td>Specifications of Sicons All Purpose Arnica Do...</td>\n",
              "      <td>No rating available</td>\n",
              "      <td>No rating available</td>\n",
              "      <td>Sicons</td>\n",
              "      <td>{\"product_specification\"=&gt;[{\"key\"=&gt;\"Pet Type\",...</td>\n",
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          "metadata": {},
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      ],
      "source": [
        "data=pd.read_csv(\"/content/drive/MyDrive/flipkart_com-ecommerce_sample.csv\")\n",
        "data.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
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        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(20000, 15)"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ],
      "source": [
        "data.shape"
      ]
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    {
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          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 20000 entries, 0 to 19999\n",
            "Data columns (total 15 columns):\n",
            " #   Column                   Non-Null Count  Dtype  \n",
            "---  ------                   --------------  -----  \n",
            " 0   uniq_id                  20000 non-null  object \n",
            " 1   crawl_timestamp          20000 non-null  object \n",
            " 2   product_url              20000 non-null  object \n",
            " 3   product_name             20000 non-null  object \n",
            " 4   product_category_tree    20000 non-null  object \n",
            " 5   pid                      20000 non-null  object \n",
            " 6   retail_price             19922 non-null  float64\n",
            " 7   discounted_price         19922 non-null  float64\n",
            " 8   image                    19997 non-null  object \n",
            " 9   is_FK_Advantage_product  20000 non-null  bool   \n",
            " 10  description              19998 non-null  object \n",
            " 11  product_rating           20000 non-null  object \n",
            " 12  overall_rating           20000 non-null  object \n",
            " 13  brand                    14136 non-null  object \n",
            " 14  product_specifications   19986 non-null  object \n",
            "dtypes: bool(1), float64(2), object(12)\n",
            "memory usage: 2.2+ MB\n"
          ]
        }
      ],
      "source": [
        "data.info()"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Here are the observations.\n",
        "\n",
        "* The data set has a total of 15 columns and 20,000 observations.\n",
        "\n",
        "* is_FK_Advantage_product is a boolean, the retail_price and discounted price columns are numerical, and the remaining are categorical.\n"
      ],
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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Let's add a new length column to give the total length of the 'description' input variable.\n",
        "\n",
        "data['length'] = data['description'].str.len()\n",
        "\n",
        "# Add a new column for the number of words in the description before text preprocessing.\n",
        "\n",
        "data['no_of_words'] = data['description'].apply(lambda x: len(x.split()) if isinstance(x, str) else np.nan)\n",
        "\n",
        "#The following is the word count distribution for 'description'.\n",
        "\n",
        "bins = [0, 100, 300, 500, 800, np.inf]\n",
        "\n",
        "labels = ['0-100', '100-200', '200-500', '500-800', '>800']\n",
        "\n",
        "data['bins'] = pd.cut(data.no_of_words, bins=bins, labels=labels)\n",
        "\n",
        "words_distribution = data.groupby('bins').size().reset_index(name='word_counts')\n",
        "\n",
        "sns.barplot(x='bins', y='word_counts', data=words_distribution)\n",
        "\n",
        "plt.title(\"Word distribution per bin\")\n",
        "\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Data Preprocessing\n",
        "\n",
        "Data preprocessing includes data cleaning, preparation, transformation, and dimensionality reduction, which convert the raw data into a form that is suitable for further processing."
      ],
      "metadata": {
        "id": "4BP-dKDbjPaJ"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 614
        },
        "id": "oa3SIa_3CE5C",
        "outputId": "63d0c96b-dec5-4073-cc52-44db1d8c74c7"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                         missing  percent\n",
              "brand                       5864  0.29320\n",
              "retail_price                  78  0.00390\n",
              "discounted_price              78  0.00390\n",
              "product_specifications        14  0.00070\n",
              "image                          3  0.00015\n",
              "bins                           2  0.00010\n",
              "no_of_words                    2  0.00010\n",
              "length                         2  0.00010\n",
              "description                    2  0.00010\n",
              "product_rating                 0  0.00000\n",
              "overall_rating                 0  0.00000\n",
              "uniq_id                        0  0.00000\n",
              "crawl_timestamp                0  0.00000\n",
              "pid                            0  0.00000\n",
              "product_category_tree          0  0.00000\n",
              "product_name                   0  0.00000\n",
              "product_url                    0  0.00000\n",
              "is_FK_Advantage_product        0  0.00000"
            ],
            "text/html": [
              "\n",
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              "      <th>percent</th>\n",
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              "      <th>brand</th>\n",
              "      <td>5864</td>\n",
              "      <td>0.29320</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>retail_price</th>\n",
              "      <td>78</td>\n",
              "      <td>0.00390</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>discounted_price</th>\n",
              "      <td>78</td>\n",
              "      <td>0.00390</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>product_specifications</th>\n",
              "      <td>14</td>\n",
              "      <td>0.00070</td>\n",
              "    </tr>\n",
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              "      <th>image</th>\n",
              "      <td>3</td>\n",
              "      <td>0.00015</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>bins</th>\n",
              "      <td>2</td>\n",
              "      <td>0.00010</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>no_of_words</th>\n",
              "      <td>2</td>\n",
              "      <td>0.00010</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>length</th>\n",
              "      <td>2</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>description</th>\n",
              "      <td>2</td>\n",
              "      <td>0.00010</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>product_rating</th>\n",
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              "      <td>0.00000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>overall_rating</th>\n",
              "      <td>0</td>\n",
              "      <td>0.00000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>uniq_id</th>\n",
              "      <td>0</td>\n",
              "      <td>0.00000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>crawl_timestamp</th>\n",
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              "      <td>0.00000</td>\n",
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              "      <td>0.00000</td>\n",
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              "      <th>product_category_tree</th>\n",
              "      <td>0</td>\n",
              "      <td>0.00000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>product_name</th>\n",
              "      <td>0</td>\n",
              "      <td>0.00000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>product_url</th>\n",
              "      <td>0</td>\n",
              "      <td>0.00000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>is_FK_Advantage_product</th>\n",
              "      <td>0</td>\n",
              "      <td>0.00000</td>\n",
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              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-e368a51c-f1ab-4723-8d53-f731bd893482 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-e368a51c-f1ab-4723-8d53-f731bd893482');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ],
      "source": [
        "missing = pd.DataFrame(data.isnull().sum()).rename (columns = {0: 'missing' })\n",
        "\n",
        "missing['percent'] = missing['missing'] /len(data)\n",
        "\n",
        "missing.sort_values ('percent', ascending = False)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Above output shows that nearly 30% of the brand variables have missing values. Other variables have a negligible number of missing values."
      ],
      "metadata": {
        "id": "0MLj847ajZDa"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Again, let's move into text preprocessing using multiple methods."
      ],
      "metadata": {
        "id": "PrTc9uxAjveS"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "## Text Preprocessing\n",
        "\n",
        "There is a lot of unwanted information present in the text data. Let's clean it up. Text preprocessing tasks include\n",
        "\n",
        "* Converting the text data to lowercase\n",
        "\n",
        "* Removing/replacing the punctuations\n",
        "\n",
        "* Removing/replacing the numbers\n",
        "\n",
        "* Removing extra whitespaces\n",
        "\n",
        "* Removing stop words\n",
        "\n",
        "* Stemming and lemmatization"
      ],
      "metadata": {
        "id": "LQoKl_DQjsKl"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MYXd6DNzCE62",
        "outputId": "ce43f4d3-d2df-4090-922e-af17f9b23706"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    key features of alisha solid women s cycling s...\n",
              "1    fabhomedecor fabric double sofa bed finish col...\n",
              "2    key features of aw bellies sandals wedges heel...\n",
              "3    key features of alisha solid women s cycling s...\n",
              "4    specifications of sicons all purpose arnica do...\n",
              "Name: description, dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ],
      "source": [
        "# Remove punctuation\n",
        "\n",
        "data['description'] = data['description'].str.replace(r'[^\\w\\d\\s]',' ')\n",
        "\n",
        "# Replace whitespace between terms with a single space\n",
        "\n",
        "data['description'] = data['description'].str.replace(r'\\s+',' ')\n",
        "\n",
        "# Remove leading and trailing whitespace\n",
        "\n",
        "data['description'] = data['description'].str.replace(r'^\\s+|\\s+?$','')\n",
        "\n",
        "# converting to lower case\n",
        "\n",
        "data['description'] = data['description'].str.lower()\n",
        "\n",
        "data['description'].head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_HUMucDVCE-V",
        "outputId": "b9eec375-7deb-429b-cccd-5fde9d8dcaf3"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[nltk_data] Downloading package stopwords to /root/nltk_data...\n",
            "[nltk_data]   Unzipping corpora/stopwords.zip.\n"
          ]
        }
      ],
      "source": [
        "import nltk\n",
        "nltk.download('stopwords')\n",
        "#Removing stop words\n",
        "stop=stopwords.words('english')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "id": "ChvpuhmeCOrn",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4f72da01-3c4a-4c50-8bf0-4c6ff6352d4e"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    features alisha solid women cycling shorts cot...\n",
              "1    fabhomedecor fabric double sofa bed finish col...\n",
              "2    features aw bellies sandals wedges heel casual...\n",
              "3    features alisha solid women cycling shorts cot...\n",
              "4    specifications sicons purpose arnica dog shamp...\n",
              "Name: description, dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ],
      "source": [
        "import re\n",
        "\n",
        "pattern = r'\\b(?:{})\\b'.format('|'.join(stop))\n",
        "\n",
        "data['description'] = data['description'].str.replace(pattern, '')\n",
        "\n",
        "# Removing single characters\n",
        "\n",
        "data['description'] = data['description'].str.replace(r'\\s+', ' ')\n",
        "\n",
        "data['description'] = data['description'].apply(lambda x: \" \".join([word for word in str(x).split() if len(word) > 1]))\n",
        "\n",
        "# Removing domain related stop words from description\n",
        "\n",
        "specific_stop_words = [\"rs\", \"flipkart\", \"buy\", \"com\", \"free\", \"day\", \"cash\", \"replacement\", \"guarantee\", \"genuine\", \"key\", \"feature\", \"delivery\", \"products\", \"product\", \"shipping\", \"online\", \"india\", \"shop\"]\n",
        "\n",
        "data['description'] = data['description'].apply(lambda x: \" \".join(word for word in str(x).split() if word not in specific_stop_words))\n",
        "\n",
        "data['description'].head()"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Let's also see what are the most occurred words in the corpus and understand the data better."
      ],
      "metadata": {
        "id": "PPEguaEXkhCM"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 675
        },
        "id": "Z1l5t_VhCOzE",
        "outputId": "632281b0-9b52-46ee-c075-f233733075ef"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[nltk_data] Downloading package punkt to /root/nltk_data...\n",
            "[nltk_data]   Unzipping tokenizers/punkt.zip.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[('women', 10574), ('features', 7199), ('price', 6705), ('shirt', 5794), ('color', 5743), ('type', 5377), ('details', 5300), ('specifications', 5155), ('men', 5032), ('material', 4974)]\n"
          ]
        }
      ],
      "source": [
        "import nltk\n",
        "nltk.download('punkt')\n",
        "\n",
        "# Top frequent words after removing domain related stop words\n",
        "a = data['description'].str.cat(sep=' ')\n",
        "words = nltk.tokenize.word_tokenize(a)\n",
        "\n",
        "# Filter out non-alphabetic words and stopwords\n",
        "words = [word for word in words if re.match(r'^[a-zA-Z]+$', word) and word.lower() not in stopwords.words('english')]\n",
        "\n",
        "word_dist = nltk.FreqDist(words)\n",
        "\n",
        "# Plotting the top 10 most frequent words\n",
        "plt.figure(figsize=(10, 6))\n",
        "word_dist.plot(10, cumulative=False)\n",
        "\n",
        "# Displaying the most common words\n",
        "print(word_dist.most_common(10))"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Model Building\n",
        "\n",
        "So far, we have tried to understand data to build better solutions. Now we need to solve problems using algorithms.\n",
        "\n",
        "There are two models we want to build.\n",
        "\n",
        "* A content-based recommendation system\n",
        "\n",
        "* A product search engine\n",
        "\n",
        "Let's use different NLP techniques, such as TF-IDF and word embeddings. TF-IDF and word embeddings can be used with both models. From the implementation point of view, both models are almost the same. But the problem each solves is different.\n",
        "\n",
        "So, let's use the TF-IDF approach to solve with a content-based recommendation system and word embeddings for the search engine. But note that reserve can also be done.\n",
        "\n",
        "Let's start with the recommendation system."
      ],
      "metadata": {
        "id": "QgAMw9HHk1ck"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Content-based Recommendation System\n",
        "\n",
        "Now that you know about content-based recommender systems, let's start implementing one.\n",
        "\n",
        "For content-based systems, let's use the TF-IDF approach."
      ],
      "metadata": {
        "id": "fmO5oiD4k_GN"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0ZjQHY56CXIO",
        "outputId": "5520b3b0-e04e-4af8-fab4-f56854f9bfcf"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(20000, 26163)"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ],
      "source": [
        "#text cleaning\n",
        "\n",
        "data['description'] = data['description'].fillna('')\n",
        "\n",
        "#define the vectorizer\n",
        "\n",
        "T_vec = TfidfVectorizer (stop_words='english')\n",
        "\n",
        "# get the vectors\n",
        "\n",
        "T_vec_matrix = T_vec.fit_transform(data['description'])\n",
        "\n",
        "#shape\n",
        "\n",
        "T_vec_matrix.shape"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "There are 26,000 unique words in the description.\n",
        "\n",
        "Next, let's calculate similarity scores for each combination and generate matrix.\n",
        "\n",
        "A cosine similarity is used in this project. We need to write a function that takes product descriptions as input and lists N most similar items/products.\n",
        "\n",
        "We also need to do reverse mapping of product names to their indices."
      ],
      "metadata": {
        "id": "kAeMNkwtlM5-"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "biI9gCStCXRI",
        "outputId": "0e879c63-5a04-46eb-93ec-830948bab9b6"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "product_name\n",
              "Alisha Solid Women's Cycling Shorts            0\n",
              "FabHomeDecor Fabric Double Sofa Bed            1\n",
              "AW Bellies                                     2\n",
              "Alisha Solid Women's Cycling Shorts            3\n",
              "Sicons All Purpose Arnica Dog Shampoo          4\n",
              "                                           ...  \n",
              "WallDesign Small Vinyl Sticker             19995\n",
              "Wallmantra Large Vinyl Stickers Sticker    19996\n",
              "Elite Collection Medium Acrylic Sticker    19997\n",
              "Elite Collection Medium Acrylic Sticker    19998\n",
              "Elite Collection Medium Acrylic Sticker    19999\n",
              "Length: 20000, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ],
      "source": [
        "# Reversing the map of indices and product\n",
        "\n",
        "product_index = pd. Series (data.index, index=data['product_name']).drop_duplicates()\n",
        "\n",
        "product_index"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "In the following steps, everything is wrapped under a single function to make testing easier.\n",
        "\n",
        "1. Obtain the index given the product.\n",
        "\n",
        "2. Obtain cosine similarity scores.\n",
        "\n",
        "3. Sort the scores.\n",
        "\n",
        "4. Get the top N results from the list.\n",
        "\n",
        "5. Output the product names.\n",
        "\n"
      ],
      "metadata": {
        "id": "s-kSWXvpldii"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "id": "T3MG_OqvCly4",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "bb8481ae-aea4-41b5-d379-9b6601d8cab1"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Enter a product name: Engage Urge and Urge Combo Set\n",
            "Similar products\n",
            "\n",
            "\n",
            "Engage Rush and Urge Combo Set\n",
            "Engage Urge-Mate Combo Set\n",
            "Engage Jump and Urge Combo Set\n",
            "Engage Fuzz and Urge Combo Set\n",
            "Engage Mate+Urge Combo Set\n",
            "Engage Urge+Tease Combo Set\n",
            "Engage Combo Set\n",
            "Engage Combo Set\n",
            "Engage Combo Set\n"
          ]
        }
      ],
      "source": [
        "# Function that takes in product title as input and outputs the most similar product\n",
        "\n",
        "def predict_products (text):\n",
        "\n",
        "    # getting index\n",
        "    index = product_index[text]\n",
        "\n",
        "    # Obtaining the pairwsie similarity scores\n",
        "    score_matrix = linear_kernel (T_vec_matrix[index], T_vec_matrix)\n",
        "\n",
        "    matching_sc= list(enumerate (score_matrix[0]))\n",
        "\n",
        "    # Sort the product based on the similarity scores\n",
        "    matching_sc= sorted(matching_sc, key=lambda x: x[1], reverse=True)\n",
        "\n",
        "    # Getting the scores of the 10 most similar product\n",
        "    matching_sc= matching_sc[1:10]\n",
        "\n",
        "    # Getting the product indices\n",
        "    product_indices = [i[0] for i in matching_sc]\n",
        "\n",
        "    # Show the similar products\n",
        "    return data['product_name'].iloc[product_indices]\n",
        "\n",
        "recommended_product = predict_products(input(\"Enter a product name: \"))\n",
        "\n",
        "if recommended_product is not None:\n",
        "\n",
        "    print (\"Similar products\")\n",
        "    print(\"\\n\")\n",
        "    for product_name in recommended_product:\n",
        "        print(product_name)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Let's look one more example"
      ],
      "metadata": {
        "id": "J2T7kXnplzgR"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "id": "0XSkkERkCl5J",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "81dd92ad-a7e3-4217-8c3d-af0256366450"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Enter a product name: Lee Parke Running Shoes\n",
            "Similar products\n",
            "\n",
            "\n",
            "Lee Parke Walking Shoes\n",
            "N Five Running Shoes\n",
            "Knight Ace Kraasa Sports Running Shoes, Cycling Shoes, Walking Shoes\n",
            "WorldWearFootwear Running Shoes, Walking Shoes\n",
            "reenak Running Shoes\n",
            "Chazer Running Shoes\n",
            "Glacier Running Shoes\n",
            "Sonaxo Men Running Shoes\n",
            "ETHICS Running Shoes\n"
          ]
        }
      ],
      "source": [
        "recommended_product = predict_products(input(\"Enter a product name: \"))\n",
        "\n",
        "if recommended_product is not None:\n",
        "\n",
        "    print (\"Similar products\")\n",
        "    print(\"\\n\")\n",
        "    for product_name in recommended_product:\n",
        "        print(product_name)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Observe the results. If a customer clicks Lee Parke Running Shoes, they get recommendations based on any other brand running shoes or Lee Parke's any other products.\n",
        "\n",
        "* Lee Parke Walking Shoes is there because of the Lee Parke brand.\n",
        "\n",
        "* The rest of the recommendations are running shoes by a different brand.\n",
        "\n",
        "You can also add price as a feature and get only products in the price range of the customer's selected product.\n",
        "\n",
        "This is one version of the recommendation system using NLP. To get better results, you can do the following things.\n",
        "\n",
        "* A better approach to the content-based recommender system can be applied by creating the user profile (currently not in the scope of the data set).\n",
        "\n",
        "* Use word embeddings as features.\n",
        "\n",
        "* Try different distance measures.\n",
        "\n",
        "That's it. We explored how to build a recommendation system using natural language processing.\n",
        "\n",
        "Now let's move on to another interesting use case which is a product search engine."
      ],
      "metadata": {
        "id": "1_2xNfjBl_Df"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Product Search Engine\n",
        "\n",
        "The next problem statement is optimizing the search engine to get better search results.\n",
        "\n",
        "The biggest challenge is that most search engines are string matching and might not perform well in all circumstances.\n",
        "\n",
        "For example, if the user searches \"guy shirt\", the search results should have all the results that have men, a boy, and so on. The search should not work based on string match, but the other similar words should also consider.\n",
        "\n",
        "The best way to solve this problem is word embeddings.\n",
        "\n",
        "Word embeddings are N-dimensional vectors for each word that captures the meaning of the word along with context.\n",
        "\n",
        "word2vec is one of the methods to construct such an embedding. It uses a shallow neural network to build the embeddings. There are two ways the embeddings can be built: skip-gram and CBOW (common bag-of-words).\n",
        "\n",
        "The CBOW method takes each word's context as the input and predicts the word corresponding to the context. The input to the network context and passed to the hidden layer with N neurons. Then at the end, the output layer predicts the word using the softmax layer. The hidden layer neuron's weight is considered the vector that captured the meaning and context.\n",
        "\n",
        "The skip-gram model is the reverse of CBOW. The word is the input, and the network predicts context.\n",
        "\n",
        "That's the brief theory about word embeddings and how it works. We can build the embeddings or use existing trained ones. It takes a lot of data and resources to build one, and for domains like healthcare, we need to build our own because generalized embeddings won't perform well."
      ],
      "metadata": {
        "id": "SaHSmCupmPlB"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Implementation\n",
        "\n",
        "Let's use the pretrained word2vec model on the news data set by Google. The trained model can be imported, and vectors can be obtained for each word. Then, any of the similarity measures can be leveraged to rank the results."
      ],
      "metadata": {
        "id": "X9G97Wgzm7jz"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 16,
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          "base_uri": "https://localhost:8080/",
          "height": 458
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        "outputId": "921f5c94-428a-4848-c517-4674f3277e43"
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      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "What would you like to searchbag\n",
            "Ranked Documents :\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                        product_name  \\\n",
              "0                Alisha Solid Women's Cycling Shorts   \n",
              "1                FabHomeDecor Fabric Double Sofa Bed   \n",
              "2                                         AW Bellies   \n",
              "3                Alisha Solid Women's Cycling Shorts   \n",
              "4              Sicons All Purpose Arnica Dog Shampoo   \n",
              "...                                              ...   \n",
              "19995        Wallmantra Large Vinyl Stickers Sticker   \n",
              "19996  Wallmantra Extra Large Vinyl Stickers Sticker   \n",
              "19997  Wallmantra Extra Large Vinyl Stickers Sticker   \n",
              "19998  Wallmantra Extra Large Vinyl Stickers Sticker   \n",
              "19999             Uberlyfe Extra Large Vinyl Sticker   \n",
              "\n",
              "                                             description     score  \n",
              "0      features alisha solid women cycling shorts cot...  0.107118  \n",
              "1      fabhomedecor fabric double sofa bed finish col...  0.107118  \n",
              "2      features aw bellies sandals wedges heel casual...  0.107118  \n",
              "3      features alisha solid women cycling shorts cot...  0.107118  \n",
              "4      specifications sicons purpose arnica dog shamp...  1.000000  \n",
              "...                                                  ...       ...  \n",
              "19995  wallmantra large vinyl stickers sticker 1369 w...  0.113536  \n",
              "19996  wallmantra extra large vinyl stickers sticker ...  0.113536  \n",
              "19997  wallmantra extra large vinyl stickers sticker ...  0.113536  \n",
              "19998  wallmantra extra large vinyl stickers sticker ...  0.113536  \n",
              "19999  uberlyfe extra large vinyl sticker pack price ...  0.098476  \n",
              "\n",
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          "metadata": {},
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      ],
      "source": [
        "# Creating list containing description of each product as sublist\n",
        "\n",
        "fin=[]\n",
        "\n",
        "for i in range(len(data['description'])):\n",
        "    temp=[]\n",
        "    temp.append(data['description'][i])\n",
        "    fin=fin+temp\n",
        "\n",
        "data1 = data[['product_name', 'description']]\n",
        "\n",
        "#import the word2vec\n",
        "\n",
        "from gensim.models import KeyedVectors\n",
        "\n",
        "filename = (\"/content/drive/MyDrive/GoogleNews-vectors-negative300.bin\")\n",
        "\n",
        "model = KeyedVectors.load_word2vec_format(filename, binary=True,limit=50000)\n",
        "\n",
        "#Preprocessing\n",
        "\n",
        "def remove_stopwords (text, is_lower_case=False):\n",
        "    pattern = r'[^a-zA-Z0-9\\s]'\n",
        "    text = re.sub (pattern, '', text[0])\n",
        "    tokens = nltk.word_tokenize(text)\n",
        "    tokens = [token.strip() for token in tokens]\n",
        "    if is_lower_case:\n",
        "        filtered_tokens = [token for token in tokens if token not in stop]\n",
        "    else:\n",
        "        filtered_tokens = [token for token in tokens if token.lower() not in stop]\n",
        "    filtered_text=' '.join(filtered_tokens)\n",
        "    return filtered_text\n",
        "\n",
        "def get_embedding(word):\n",
        "    if word in model.key_to_index:\n",
        "        return model[word]\n",
        "    else:\n",
        "        return np.zeros(model.vector_size)\n",
        "\n",
        "data['word_embeddings'] = data['description'].apply(lambda x: np.mean([get_embedding(word) for word in nltk.word_tokenize(remove_stopwords(x))], axis=0))\n",
        "\n",
        "# Obtaining the average vector for all the documents\n",
        "\n",
        "out_dict = {}\n",
        "\n",
        "for sen in fin:\n",
        "    average_vector = (np.mean(np.array([get_embedding(x) for x in nltk.word_tokenize(remove_stopwords (sen))]), axis=0))\n",
        "    dict = {sen : (average_vector) }\n",
        "    out_dict.update(dict)\n",
        "\n",
        "# Get the similarity between the query and documents\n",
        "\n",
        "def get_sim(query_embedding, average_vector_doc):\n",
        "    sim = 1 - scipy.spatial.distance.cosine(query_embedding.flatten(), average_vector_doc.flatten())\n",
        "    return sim\n",
        "\n",
        "# Rank all the documents based on the similarity\n",
        "\n",
        "def Ranked_documents (query):\n",
        "    global rank\n",
        "    query_words = (np.mean (np.array([get_embedding(x) for x in nltk.word_tokenize(query.lower())], dtype=float), axis=0))\n",
        "    rank  = []\n",
        "    for k,v in out_dict.items():\n",
        "        rank.append((k, get_sim(query_words, v)))\n",
        "    rank = sorted (rank, key=lambda t: t[1], reverse=True)\n",
        "    dd = pd.DataFrame (rank, columns=['Desc', 'score'])\n",
        "    rankfin = pd.merge(data1, dd, left_on='description', right_on='Desc')\n",
        "    rankfin = rankfin[['product_name', 'description', 'score']]\n",
        "    print('Ranked Documents :')\n",
        "    return rankfin\n",
        "\n",
        "# Call the IR function with a query\n",
        "\n",
        "query=input(\"What would you like to search\")\n",
        "Ranked_documents(query)\n",
        "#output"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Advanced Search Engine Using PyTerrier and Sentence-BERT\n",
        "\n",
        "Let's few advanced deep learning-based solutions to solve this problem.Below Figure shows the entire framework for this approach.\n",
        "\n",
        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)"
      ],
      "metadata": {
        "id": "-74obKJOovhU"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Indexing is an important part of information retrieval (IR) systems. For indexing, we use DFIndexer. Indexing simplifies the retrieval process.\n",
        "\n",
        "BatchRetrieve is one of the most widely used PyTerrier objects. It uses a pre-existing Terrier index data structure.\n",
        "\n",
        "**NeuSpell** is an open source package for correcting spellings based on the context. This package has ten spell-checkers based on various neural models. To implement this model, import the BERTChecker package from NeuSpell.\n",
        "\n",
        "BERTChecker works for multiple languages, including English, Arabic, Hindi, and Japanese.\n",
        "\n",
        "Let's use the PyTerrier and Sentence-BERT libraries and proceed with implementation.\n",
        "\n",
        "The following installs the required packages and libraries."
      ],
      "metadata": {
        "id": "1_wyOs2Rpn0c"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "id": "RJ0vNlS5Cs3c",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5b36c4ee-00d7-4ef6-e515-170459156584"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[nltk_data] Downloading package punkt to /root/nltk_data...\n",
            "[nltk_data]   Package punkt is already up-to-date!\n",
            "[nltk_data] Downloading package wordnet to /root/nltk_data...\n",
            "[nltk_data] Downloading package stopwords to /root/nltk_data...\n",
            "[nltk_data]   Package stopwords is already up-to-date!\n"
          ]
        }
      ],
      "source": [
        "import pandas as pd\n",
        "\n",
        "import numpy as np\n",
        "\n",
        "import seaborn as sns\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "import string\n",
        "\n",
        "import re\n",
        "\n",
        "%matplotlib inline\n",
        "\n",
        "import nltk\n",
        "\n",
        "nltk.download('punkt')\n",
        "\n",
        "nltk.download('wordnet')\n",
        "\n",
        "nltk.download('stopwords')\n",
        "\n",
        "from nltk.corpus import stopwords\n",
        "\n",
        "from nltk.tokenize import word_tokenize\n",
        "\n",
        "from nltk.stem.wordnet import WordNetLemmatizer\n",
        "\n",
        "lem = WordNetLemmatizer()\n",
        "\n",
        "stop_words = set (stopwords.words('english'))\n",
        "\n",
        "exclude = set(string.punctuation)\n",
        "\n",
        "import string"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "id": "wtKVLwcbCycQ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "21e8a2c6-5d0d-4da7-a5b7-aecb920bedab"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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            "  Downloading trec_car_tools-2.6-py3-none-any.whl (8.4 kB)\n",
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            "\u001b[?25hCollecting warc3-wet>=0.2.3 (from ir_datasets>=0.3.2->python-terrier)\n",
            "  Downloading warc3_wet-0.2.3-py3-none-any.whl (13 kB)\n",
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            "Collecting zlib-state>=0.1.3 (from ir_datasets>=0.3.2->python-terrier)\n",
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            "\u001b[?25hCollecting pyautocorpus>=0.1.1 (from ir_datasets>=0.3.2->python-terrier)\n",
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            "  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "Collecting unlzw3>=0.2.1 (from ir_datasets>=0.3.2->python-terrier)\n",
            "  Downloading unlzw3-0.2.2-py3-none-any.whl (6.1 kB)\n",
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            "  Downloading cwl-eval-1.0.12.tar.gz (31 kB)\n",
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            "Collecting heapdict (from chest->python-terrier)\n",
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            "  Downloading multiset-2.1.1-py2.py3-none-any.whl (8.8 kB)\n",
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            "Collecting cbor>=1.0.0 (from trec-car-tools>=2.5.4->ir_datasets>=0.3.2->python-terrier)\n",
            "  Downloading cbor-1.0.0.tar.gz (20 kB)\n",
            "  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "Building wheels for collected packages: python-terrier, ir_measures, chest, wget, cwl-eval, pyautocorpus, warc3-wet-clueweb09, zlib-state, cbor\n",
            "  Building wheel for python-terrier (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for python-terrier: filename=python_terrier-0.9.2-py3-none-any.whl size=112258 sha256=7c769cb1f8f87e1912f99f3fb317588122a7bfae80b1f934a828a22dd35466fb\n",
            "  Stored in directory: /root/.cache/pip/wheels/be/a8/0b/581cbefb648c84263ebf7bbfca2484e81d59f7732fd34ca386\n",
            "  Building wheel for ir_measures (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for ir_measures: filename=ir_measures-0.3.3-py3-none-any.whl size=61188 sha256=02b15566edd48bd79366b8666e072452f2bbd669ff96cb5ce418ac627d38e066\n",
            "  Stored in directory: /root/.cache/pip/wheels/9f/0e/22/718279f23fef1673a4c5e433881c25080a6afaa147e007183e\n",
            "  Building wheel for chest (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for chest: filename=chest-0.2.3-py3-none-any.whl size=7612 sha256=318301c2df5ea402f44cdf06657b8be4d4ee56834b570ec1bf9a28c06520b6c5\n",
            "  Stored in directory: /root/.cache/pip/wheels/88/cf/99/4773b31f855f9ecedc32a0ae400f7a4a3001b37c439b6d1a73\n",
            "  Building wheel for wget (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for wget: filename=wget-3.2-py3-none-any.whl size=9657 sha256=aa38062a6c3051aae7462025f821cec92831bc16b28b53c816664bbafafee4ce\n",
            "  Stored in directory: /root/.cache/pip/wheels/8b/f1/7f/5c94f0a7a505ca1c81cd1d9208ae2064675d97582078e6c769\n",
            "  Building wheel for cwl-eval (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for cwl-eval: filename=cwl_eval-1.0.12-py3-none-any.whl size=38068 sha256=0cb650c7f454fa2ad5889c602af9d348b44137d6199d82d38bb9ed82e337ae0d\n",
            "  Stored in directory: /root/.cache/pip/wheels/3d/c1/94/94a3e5379b1aa8fb7c7f1ad1956305d5edc98ef745b6067d87\n",
            "  Building wheel for pyautocorpus (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for pyautocorpus: filename=pyautocorpus-0.1.9-cp310-cp310-linux_x86_64.whl size=189746 sha256=4f75aa07fb785bc113e02868ecc7a4b3ee5abfcead496a42153700107c5f9138\n",
            "  Stored in directory: /root/.cache/pip/wheels/e2/be/43/ec1e16468c393f6e474e3be6ec6f17fad2216c088328348630\n",
            "  Building wheel for warc3-wet-clueweb09 (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for warc3-wet-clueweb09: filename=warc3_wet_clueweb09-0.2.5-py3-none-any.whl size=18918 sha256=96fa5453d94229aae8af10aaae13d8f3fae843e0ea9469ef655598c0311f4af1\n",
            "  Stored in directory: /root/.cache/pip/wheels/1a/d7/91/7ffb991df87e62355d945745035470ba2616aa3d83a250b5f9\n",
            "  Building wheel for zlib-state (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for zlib-state: filename=zlib_state-0.1.5-cp310-cp310-linux_x86_64.whl size=26617 sha256=bf7e52389e6049bbc84a268c093225a10ad5dbf98ded197f2805fd296c6d751e\n",
            "  Stored in directory: /root/.cache/pip/wheels/f8/7a/4e/8fa633b918bea99c052211d41caf8529707d7eae1099d5e5dd\n",
            "  Building wheel for cbor (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for cbor: filename=cbor-1.0.0-cp310-cp310-linux_x86_64.whl size=57642 sha256=71b90ba53781ad045474eaf59198c871c3fdf5fb2502375246013bf2763c2a9d\n",
            "  Stored in directory: /root/.cache/pip/wheels/85/df/c9/b39e40eccaf76dbd218556639a6dc81562226f4c6a64902c85\n",
            "Successfully built python-terrier ir_measures chest wget cwl-eval pyautocorpus warc3-wet-clueweb09 zlib-state cbor\n",
            "Installing collected packages: wget, warc3-wet-clueweb09, warc3-wet, typish, pyjnius, multiset, ijson, heapdict, cbor, zlib-state, unlzw3, trec-car-tools, pytrec_eval_terrier, pyautocorpus, nptyping, matchpy, lz4, dill, deprecated, cwl-eval, chest, ir_measures, inscriptis, ir_datasets, python-terrier\n",
            "Successfully installed cbor-1.0.0 chest-0.2.3 cwl-eval-1.0.12 deprecated-1.2.14 dill-0.3.6 heapdict-1.0.1 ijson-3.2.2 inscriptis-2.3.2 ir_datasets-0.5.5 ir_measures-0.3.3 lz4-4.3.2 matchpy-0.5.5 multiset-2.1.1 nptyping-1.4.4 pyautocorpus-0.1.9 pyjnius-1.5.0 python-terrier-0.9.2 pytrec_eval_terrier-0.5.5 trec-car-tools-2.6 typish-1.9.3 unlzw3-0.2.2 warc3-wet-0.2.3 warc3-wet-clueweb09-0.2.5 wget-3.2 zlib-state-0.1.5\n",
            "terrier-assemblies 5.7 jar-with-dependencies not found, downloading to /root/.pyterrier...\n",
            "Done\n",
            "terrier-python-helper 0.0.7 jar not found, downloading to /root/.pyterrier...\n",
            "Done\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "PyTerrier 0.9.2 has loaded Terrier 5.7 (built by craigm on 2022-11-10 18:30) and terrier-helper 0.0.7\n",
            "\n"
          ]
        }
      ],
      "source": [
        "!pip install python-terrier\n",
        "import pyterrier as pt\n",
        "if not pt.started():\n",
        "    pt.init()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "id": "wgw7400rCyo_",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c3d1bc35-e84d-4608-eaf5-cc1a31b15258"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting sentence-transformers\n",
            "  Downloading sentence-transformers-2.2.2.tar.gz (85 kB)\n",
            "\u001b[?25l     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/86.0 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m86.0/86.0 kB\u001b[0m \u001b[31m7.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "Collecting transformers<5.0.0,>=4.6.0 (from sentence-transformers)\n",
            "  Downloading transformers-4.30.2-py3-none-any.whl (7.2 MB)\n",
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            "\u001b[?25hRequirement already satisfied: tqdm in /usr/local/lib/python3.10/dist-packages (from sentence-transformers) (4.65.0)\n",
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            "Collecting sentencepiece (from sentence-transformers)\n",
            "  Downloading sentencepiece-0.1.99-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (1.3 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.3/1.3 MB\u001b[0m \u001b[31m50.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting huggingface-hub>=0.4.0 (from sentence-transformers)\n",
            "  Downloading huggingface_hub-0.15.1-py3-none-any.whl (236 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m236.8/236.8 kB\u001b[0m \u001b[31m23.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from huggingface-hub>=0.4.0->sentence-transformers) (3.12.2)\n",
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            "Collecting tokenizers!=0.11.3,<0.14,>=0.11.1 (from transformers<5.0.0,>=4.6.0->sentence-transformers)\n",
            "  Downloading tokenizers-0.13.3-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (7.8 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m7.8/7.8 MB\u001b[0m \u001b[31m70.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting safetensors>=0.3.1 (from transformers<5.0.0,>=4.6.0->sentence-transformers)\n",
            "  Downloading safetensors-0.3.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (1.3 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.3/1.3 MB\u001b[0m \u001b[31m44.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: click in /usr/local/lib/python3.10/dist-packages (from nltk->sentence-transformers) (8.1.3)\n",
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            "Building wheels for collected packages: sentence-transformers\n",
            "  Building wheel for sentence-transformers (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for sentence-transformers: filename=sentence_transformers-2.2.2-py3-none-any.whl size=125926 sha256=54ffb98d498332c5aca0775c20735747580c95d776ffe6c35b1b0042fcc3e475\n",
            "  Stored in directory: /root/.cache/pip/wheels/62/f2/10/1e606fd5f02395388f74e7462910fe851042f97238cbbd902f\n",
            "Successfully built sentence-transformers\n",
            "Installing collected packages: tokenizers, sentencepiece, safetensors, huggingface-hub, transformers, sentence-transformers\n",
            "Successfully installed huggingface-hub-0.15.1 safetensors-0.3.1 sentence-transformers-2.2.2 sentencepiece-0.1.99 tokenizers-0.13.3 transformers-4.30.2\n",
            "Collecting neuspell\n",
            "  Downloading neuspell-1.0.0-py3-none-any.whl (184 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m184.3/184.3 kB\u001b[0m \u001b[31m5.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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            "  Downloading jsonlines-3.1.0-py3-none-any.whl (8.6 kB)\n",
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            "Collecting pytorch-pretrained-bert (from neuspell)\n",
            "  Downloading pytorch_pretrained_bert-0.6.2-py3-none-any.whl (123 kB)\n",
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            "Collecting boto3 (from pytorch-pretrained-bert->neuspell)\n",
            "  Downloading boto3-1.26.164-py3-none-any.whl (135 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m135.9/135.9 kB\u001b[0m \u001b[31m12.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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            "Collecting botocore<1.30.0,>=1.29.164 (from boto3->pytorch-pretrained-bert->neuspell)\n",
            "  Downloading botocore-1.29.164-py3-none-any.whl (11.0 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m11.0/11.0 MB\u001b[0m \u001b[31m53.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting jmespath<2.0.0,>=0.7.1 (from boto3->pytorch-pretrained-bert->neuspell)\n",
            "  Downloading jmespath-1.0.1-py3-none-any.whl (20 kB)\n",
            "Collecting s3transfer<0.7.0,>=0.6.0 (from boto3->pytorch-pretrained-bert->neuspell)\n",
            "  Downloading s3transfer-0.6.1-py3-none-any.whl (79 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m79.8/79.8 kB\u001b[0m \u001b[31m10.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2->torch>=1.6.0->neuspell) (2.1.3)\n",
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            "Requirement already satisfied: python-dateutil<3.0.0,>=2.1 in /usr/local/lib/python3.10/dist-packages (from botocore<1.30.0,>=1.29.164->boto3->pytorch-pretrained-bert->neuspell) (2.8.2)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil<3.0.0,>=2.1->botocore<1.30.0,>=1.29.164->boto3->pytorch-pretrained-bert->neuspell) (1.16.0)\n",
            "Installing collected packages: jsonlines, jmespath, botocore, s3transfer, boto3, pytorch-pretrained-bert, neuspell\n",
            "Successfully installed boto3-1.26.164 botocore-1.29.164 jmespath-1.0.1 jsonlines-3.1.0 neuspell-1.0.0 pytorch-pretrained-bert-0.6.2 s3transfer-0.6.1\n",
            "\u001b[31mERROR: neuspell/ is not a valid editable requirement. It should either be a path to a local project or a VCS URL (beginning with bzr+http, bzr+https, bzr+ssh, bzr+sftp, bzr+ftp, bzr+lp, bzr+file, git+http, git+https, git+ssh, git+git, git+file, hg+file, hg+http, hg+https, hg+ssh, hg+static-http, svn+ssh, svn+http, svn+https, svn+svn, svn+file).\u001b[0m\u001b[31m\n",
            "\u001b[0mCloning into 'neuspell'...\n",
            "remote: Enumerating objects: 689, done.\u001b[K\n",
            "remote: Counting objects: 100% (689/689), done.\u001b[K\n",
            "remote: Compressing objects: 100% (310/310), done.\u001b[K\n",
            "remote: Total 689 (delta 350), reused 682 (delta 348), pack-reused 0\u001b[K\n",
            "Receiving objects: 100% (689/689), 74.39 MiB | 23.60 MiB/s, done.\n",
            "Resolving deltas: 100% (350/350), done.\n",
            "Obtaining file:///content/neuspell (from -r /content/neuspell/extras-requirements.txt (line 1))\n",
            "  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "Requirement already satisfied: transformers in /usr/local/lib/python3.10/dist-packages (from neuspell==1.0.0->-r /content/neuspell/extras-requirements.txt (line 1)) (4.30.2)\n",
            "Requirement already satisfied: tqdm in /usr/local/lib/python3.10/dist-packages (from neuspell==1.0.0->-r /content/neuspell/extras-requirements.txt (line 1)) (4.65.0)\n",
            "Requirement already satisfied: torch>=1.6.0 in /usr/local/lib/python3.10/dist-packages (from neuspell==1.0.0->-r /content/neuspell/extras-requirements.txt (line 1)) (2.0.1+cu118)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (from neuspell==1.0.0->-r /content/neuspell/extras-requirements.txt (line 1)) (1.22.4)\n",
            "Requirement already satisfied: jsonlines in /usr/local/lib/python3.10/dist-packages (from neuspell==1.0.0->-r /content/neuspell/extras-requirements.txt (line 1)) (3.1.0)\n",
            "Requirement already satisfied: sentencepiece in /usr/local/lib/python3.10/dist-packages (from neuspell==1.0.0->-r /content/neuspell/extras-requirements.txt (line 1)) (0.1.99)\n",
            "Requirement already satisfied: pytorch_pretrained_bert in /usr/local/lib/python3.10/dist-packages (from neuspell==1.0.0->-r /content/neuspell/extras-requirements.txt (line 1)) (0.6.2)\n",
            "Requirement already satisfied: spacy in /usr/local/lib/python3.10/dist-packages (from neuspell==1.0.0->-r /content/neuspell/extras-requirements.txt (line 1)) (3.5.3)\n",
            "Collecting allennlp==1.5.0 (from neuspell==1.0.0->-r /content/neuspell/extras-requirements.txt (line 1))\n",
            "  Downloading allennlp-1.5.0-py3-none-any.whl (517 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m517.1/517.1 kB\u001b[0m \u001b[31m24.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hINFO: pip is looking at multiple versions of allennlp to determine which version is compatible with other requirements. This could take a while.\n",
            "\u001b[31mERROR: Cannot install neuspell, neuspell[elmo]==1.0.0 and neuspell[spacy]==1.0.0 because these package versions have conflicting dependencies.\u001b[0m\u001b[31m\n",
            "\u001b[0m\n",
            "The conflict is caused by:\n",
            "    neuspell[spacy] 1.0.0 depends on torch>=1.6.0\n",
            "    neuspell[elmo] 1.0.0 depends on torch>=1.6.0\n",
            "    allennlp 1.5.0 depends on torch<1.8.0 and >=1.6.0\n",
            "\n",
            "To fix this you could try to:\n",
            "1. loosen the range of package versions you've specified\n",
            "2. remove package versions to allow pip attempt to solve the dependency conflict\n",
            "\n",
            "\u001b[31mERROR: ResolutionImpossible: for help visit https://pip.pypa.io/en/latest/topics/dependency-resolution/#dealing-with-dependency-conflicts\u001b[0m\u001b[31m\n",
            "\u001b[0m2023-06-30 03:46:57.925413: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n",
            "To enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
            "2023-06-30 03:46:58.964191: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\n",
            "Collecting en-core-web-sm==3.5.0\n",
            "  Downloading https://github.com/explosion/spacy-models/releases/download/en_core_web_sm-3.5.0/en_core_web_sm-3.5.0-py3-none-any.whl (12.8 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m12.8/12.8 MB\u001b[0m \u001b[31m78.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: spacy<3.6.0,>=3.5.0 in /usr/local/lib/python3.10/dist-packages (from en-core-web-sm==3.5.0) (3.5.3)\n",
            "Requirement already satisfied: spacy-legacy<3.1.0,>=3.0.11 in /usr/local/lib/python3.10/dist-packages (from spacy<3.6.0,>=3.5.0->en-core-web-sm==3.5.0) (3.0.12)\n",
            "Requirement already satisfied: spacy-loggers<2.0.0,>=1.0.0 in /usr/local/lib/python3.10/dist-packages (from spacy<3.6.0,>=3.5.0->en-core-web-sm==3.5.0) (1.0.4)\n",
            "Requirement already satisfied: murmurhash<1.1.0,>=0.28.0 in /usr/local/lib/python3.10/dist-packages (from spacy<3.6.0,>=3.5.0->en-core-web-sm==3.5.0) (1.0.9)\n",
            "Requirement already satisfied: cymem<2.1.0,>=2.0.2 in /usr/local/lib/python3.10/dist-packages (from spacy<3.6.0,>=3.5.0->en-core-web-sm==3.5.0) (2.0.7)\n",
            "Requirement already satisfied: preshed<3.1.0,>=3.0.2 in /usr/local/lib/python3.10/dist-packages (from spacy<3.6.0,>=3.5.0->en-core-web-sm==3.5.0) (3.0.8)\n",
            "Requirement already satisfied: thinc<8.2.0,>=8.1.8 in /usr/local/lib/python3.10/dist-packages (from spacy<3.6.0,>=3.5.0->en-core-web-sm==3.5.0) (8.1.10)\n",
            "Requirement already satisfied: wasabi<1.2.0,>=0.9.1 in /usr/local/lib/python3.10/dist-packages (from spacy<3.6.0,>=3.5.0->en-core-web-sm==3.5.0) (1.1.2)\n",
            "Requirement already satisfied: srsly<3.0.0,>=2.4.3 in /usr/local/lib/python3.10/dist-packages (from spacy<3.6.0,>=3.5.0->en-core-web-sm==3.5.0) (2.4.6)\n",
            "Requirement already satisfied: catalogue<2.1.0,>=2.0.6 in /usr/local/lib/python3.10/dist-packages (from spacy<3.6.0,>=3.5.0->en-core-web-sm==3.5.0) (2.0.8)\n",
            "Requirement already satisfied: typer<0.8.0,>=0.3.0 in /usr/local/lib/python3.10/dist-packages (from spacy<3.6.0,>=3.5.0->en-core-web-sm==3.5.0) (0.7.0)\n",
            "Requirement already satisfied: pathy>=0.10.0 in /usr/local/lib/python3.10/dist-packages (from spacy<3.6.0,>=3.5.0->en-core-web-sm==3.5.0) (0.10.2)\n",
            "Requirement already satisfied: smart-open<7.0.0,>=5.2.1 in /usr/local/lib/python3.10/dist-packages (from spacy<3.6.0,>=3.5.0->en-core-web-sm==3.5.0) (6.3.0)\n",
            "Requirement already satisfied: tqdm<5.0.0,>=4.38.0 in /usr/local/lib/python3.10/dist-packages (from spacy<3.6.0,>=3.5.0->en-core-web-sm==3.5.0) (4.65.0)\n",
            "Requirement already satisfied: numpy>=1.15.0 in /usr/local/lib/python3.10/dist-packages (from spacy<3.6.0,>=3.5.0->en-core-web-sm==3.5.0) (1.22.4)\n",
            "Requirement already satisfied: requests<3.0.0,>=2.13.0 in /usr/local/lib/python3.10/dist-packages (from spacy<3.6.0,>=3.5.0->en-core-web-sm==3.5.0) (2.27.1)\n",
            "Requirement already satisfied: pydantic!=1.8,!=1.8.1,<1.11.0,>=1.7.4 in /usr/local/lib/python3.10/dist-packages (from spacy<3.6.0,>=3.5.0->en-core-web-sm==3.5.0) (1.10.9)\n",
            "Requirement already satisfied: jinja2 in /usr/local/lib/python3.10/dist-packages (from spacy<3.6.0,>=3.5.0->en-core-web-sm==3.5.0) (3.1.2)\n",
            "Requirement already satisfied: setuptools in /usr/local/lib/python3.10/dist-packages (from spacy<3.6.0,>=3.5.0->en-core-web-sm==3.5.0) (67.7.2)\n",
            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from spacy<3.6.0,>=3.5.0->en-core-web-sm==3.5.0) (23.1)\n",
            "Requirement already satisfied: langcodes<4.0.0,>=3.2.0 in /usr/local/lib/python3.10/dist-packages (from spacy<3.6.0,>=3.5.0->en-core-web-sm==3.5.0) (3.3.0)\n",
            "Requirement already satisfied: typing-extensions>=4.2.0 in /usr/local/lib/python3.10/dist-packages (from pydantic!=1.8,!=1.8.1,<1.11.0,>=1.7.4->spacy<3.6.0,>=3.5.0->en-core-web-sm==3.5.0) (4.6.3)\n",
            "Requirement already satisfied: urllib3<1.27,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests<3.0.0,>=2.13.0->spacy<3.6.0,>=3.5.0->en-core-web-sm==3.5.0) (1.26.16)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests<3.0.0,>=2.13.0->spacy<3.6.0,>=3.5.0->en-core-web-sm==3.5.0) (2023.5.7)\n",
            "Requirement already satisfied: charset-normalizer~=2.0.0 in /usr/local/lib/python3.10/dist-packages (from requests<3.0.0,>=2.13.0->spacy<3.6.0,>=3.5.0->en-core-web-sm==3.5.0) (2.0.12)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests<3.0.0,>=2.13.0->spacy<3.6.0,>=3.5.0->en-core-web-sm==3.5.0) (3.4)\n",
            "Requirement already satisfied: blis<0.8.0,>=0.7.8 in /usr/local/lib/python3.10/dist-packages (from thinc<8.2.0,>=8.1.8->spacy<3.6.0,>=3.5.0->en-core-web-sm==3.5.0) (0.7.9)\n",
            "Requirement already satisfied: confection<1.0.0,>=0.0.1 in /usr/local/lib/python3.10/dist-packages (from thinc<8.2.0,>=8.1.8->spacy<3.6.0,>=3.5.0->en-core-web-sm==3.5.0) (0.0.4)\n",
            "Requirement already satisfied: click<9.0.0,>=7.1.1 in /usr/local/lib/python3.10/dist-packages (from typer<0.8.0,>=0.3.0->spacy<3.6.0,>=3.5.0->en-core-web-sm==3.5.0) (8.1.3)\n",
            "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2->spacy<3.6.0,>=3.5.0->en-core-web-sm==3.5.0) (2.1.3)\n",
            "\u001b[38;5;2m✔ Download and installation successful\u001b[0m\n",
            "You can now load the package via spacy.load('en_core_web_sm')\n",
            "--2023-06-30 03:47:11--  https://storage.googleapis.com/bert_models/2018_11_23/multi_cased_L-12_H-768_A-12.zip\n",
            "Resolving storage.googleapis.com (storage.googleapis.com)... 172.253.122.128, 172.253.63.128, 142.250.31.128, ...\n",
            "Connecting to storage.googleapis.com (storage.googleapis.com)|172.253.122.128|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 662903077 (632M) [application/zip]\n",
            "Saving to: ‘multi_cased_L-12_H-768_A-12.zip’\n",
            "\n",
            "multi_cased_L-12_H- 100%[===================>] 632.19M   249MB/s    in 2.5s    \n",
            "\n",
            "2023-06-30 03:47:13 (249 MB/s) - ‘multi_cased_L-12_H-768_A-12.zip’ saved [662903077/662903077]\n",
            "\n",
            "Archive:  multi_cased_L-12_H-768_A-12.zip\n",
            "   creating: multi_cased_L-12_H-768_A-12/\n",
            "  inflating: multi_cased_L-12_H-768_A-12/bert_model.ckpt.meta  \n",
            "  inflating: multi_cased_L-12_H-768_A-12/bert_model.ckpt.data-00000-of-00001  \n",
            "  inflating: multi_cased_L-12_H-768_A-12/vocab.txt  \n",
            "  inflating: multi_cased_L-12_H-768_A-12/bert_model.ckpt.index  \n",
            "  inflating: multi_cased_L-12_H-768_A-12/bert_config.json  \n"
          ]
        }
      ],
      "source": [
        "!pip install -U sentence-transformers\n",
        "\n",
        "!pip install neuspell\n",
        "\n",
        "!pip install -e neuspell/\n",
        "\n",
        "!git clone https://github.com/neuspell/neuspell; cd neuspell\n",
        "\n",
        "import os\n",
        "\n",
        "os.chdir(\"/content/neuspell\")\n",
        "\n",
        "!pip install -r /content/neuspell/extras-requirements.txt\n",
        "\n",
        "!python -m spacy download en_core_web_sm\n",
        "\n",
        "#Unzipping the multi-linguistic packages\n",
        "\n",
        "!wget https://storage.googleapis.com/bert_models/2018_11_23/multi_cased_L-12_H-768_A-12.zip\n",
        "\n",
        "!unzip *.zip"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#importing neuspell\n",
        "\n",
        "from neuspell import BertChecker\n",
        "\n",
        "from sklearn.metrics. pairwise import cosine_similarity\n",
        "\n",
        "from sentence_transformers import SentenceTransformer\n",
        "\n",
        "model = SentenceTransformer('sentence-transformers/bert-base-nli-mean-tokens')\n",
        "\n",
        "# Load the data set.\n",
        "\n",
        "df=pd.read_csv(\"/content/drive/MyDrive/flipkart_com-ecommerce_sample.csv\")"
      ],
      "metadata": {
        "id": "gJpcbqrHCqY9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 450,
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        },
        "outputId": "66f4673a-be87-464d-dc01-e9407b24cd1b"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "data folder is set to `/content/neuspell/neuspell/../data` script\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)821d1/.gitattributes:   0%|          | 0.00/391 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "9770357b50b54294a1855bd176c37108"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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          "data": {
            "text/plain": [
              "                            uniq_id            crawl_timestamp  \\\n",
              "0  c2d766ca982eca8304150849735ffef9  2016-03-25 22:59:23 +0000   \n",
              "1  7f7036a6d550aaa89d34c77bd39a5e48  2016-03-25 22:59:23 +0000   \n",
              "2  f449ec65dcbc041b6ae5e6a32717d01b  2016-03-25 22:59:23 +0000   \n",
              "3  0973b37acd0c664e3de26e97e5571454  2016-03-25 22:59:23 +0000   \n",
              "4  bc940ea42ee6bef5ac7cea3fb5cfbee7  2016-03-25 22:59:23 +0000   \n",
              "\n",
              "                                         product_url  \\\n",
              "0  http://www.flipkart.com/alisha-solid-women-s-c...   \n",
              "1  http://www.flipkart.com/fabhomedecor-fabric-do...   \n",
              "2  http://www.flipkart.com/aw-bellies/p/itmeh4grg...   \n",
              "3  http://www.flipkart.com/alisha-solid-women-s-c...   \n",
              "4  http://www.flipkart.com/sicons-all-purpose-arn...   \n",
              "\n",
              "                            product_name  \\\n",
              "0    Alisha Solid Women's Cycling Shorts   \n",
              "1    FabHomeDecor Fabric Double Sofa Bed   \n",
              "2                             AW Bellies   \n",
              "3    Alisha Solid Women's Cycling Shorts   \n",
              "4  Sicons All Purpose Arnica Dog Shampoo   \n",
              "\n",
              "                               product_category_tree               pid  \\\n",
              "0  [\"Clothing >> Women's Clothing >> Lingerie, Sl...  SRTEH2FF9KEDEFGF   \n",
              "1  [\"Furniture >> Living Room Furniture >> Sofa B...  SBEEH3QGU7MFYJFY   \n",
              "2  [\"Footwear >> Women's Footwear >> Ballerinas >...  SHOEH4GRSUBJGZXE   \n",
              "3  [\"Clothing >> Women's Clothing >> Lingerie, Sl...  SRTEH2F6HUZMQ6SJ   \n",
              "4  [\"Pet Supplies >> Grooming >> Skin & Coat Care...  PSOEH3ZYDMSYARJ5   \n",
              "\n",
              "   retail_price  discounted_price  \\\n",
              "0         999.0             379.0   \n",
              "1       32157.0           22646.0   \n",
              "2         999.0             499.0   \n",
              "3         699.0             267.0   \n",
              "4         220.0             210.0   \n",
              "\n",
              "                                               image  is_FK_Advantage_product  \\\n",
              "0  [\"http://img5a.flixcart.com/image/short/u/4/a/...                    False   \n",
              "1  [\"http://img6a.flixcart.com/image/sofa-bed/j/f...                    False   \n",
              "2  [\"http://img5a.flixcart.com/image/shoe/7/z/z/r...                    False   \n",
              "3  [\"http://img5a.flixcart.com/image/short/6/2/h/...                    False   \n",
              "4  [\"http://img5a.flixcart.com/image/pet-shampoo/...                    False   \n",
              "\n",
              "                                         description       product_rating  \\\n",
              "0  Key Features of Alisha Solid Women's Cycling S...  No rating available   \n",
              "1  FabHomeDecor Fabric Double Sofa Bed (Finish Co...  No rating available   \n",
              "2  Key Features of AW Bellies Sandals Wedges Heel...  No rating available   \n",
              "3  Key Features of Alisha Solid Women's Cycling S...  No rating available   \n",
              "4  Specifications of Sicons All Purpose Arnica Do...  No rating available   \n",
              "\n",
              "        overall_rating         brand  \\\n",
              "0  No rating available        Alisha   \n",
              "1  No rating available  FabHomeDecor   \n",
              "2  No rating available            AW   \n",
              "3  No rating available        Alisha   \n",
              "4  No rating available        Sicons   \n",
              "\n",
              "                              product_specifications  \n",
              "0  {\"product_specification\"=>[{\"key\"=>\"Number of ...  \n",
              "1  {\"product_specification\"=>[{\"key\"=>\"Installati...  \n",
              "2  {\"product_specification\"=>[{\"key\"=>\"Ideal For\"...  \n",
              "3  {\"product_specification\"=>[{\"key\"=>\"Number of ...  \n",
              "4  {\"product_specification\"=>[{\"key\"=>\"Pet Type\",...  "
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              "      <th></th>\n",
              "      <th>uniq_id</th>\n",
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              "      <td>c2d766ca982eca8304150849735ffef9</td>\n",
              "      <td>2016-03-25 22:59:23 +0000</td>\n",
              "      <td>http://www.flipkart.com/alisha-solid-women-s-c...</td>\n",
              "      <td>Alisha Solid Women's Cycling Shorts</td>\n",
              "      <td>[\"Clothing &gt;&gt; Women's Clothing &gt;&gt; Lingerie, Sl...</td>\n",
              "      <td>SRTEH2FF9KEDEFGF</td>\n",
              "      <td>999.0</td>\n",
              "      <td>379.0</td>\n",
              "      <td>[\"http://img5a.flixcart.com/image/short/u/4/a/...</td>\n",
              "      <td>False</td>\n",
              "      <td>Key Features of Alisha Solid Women's Cycling S...</td>\n",
              "      <td>No rating available</td>\n",
              "      <td>No rating available</td>\n",
              "      <td>Alisha</td>\n",
              "      <td>{\"product_specification\"=&gt;[{\"key\"=&gt;\"Number of ...</td>\n",
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              "      <th>1</th>\n",
              "      <td>7f7036a6d550aaa89d34c77bd39a5e48</td>\n",
              "      <td>2016-03-25 22:59:23 +0000</td>\n",
              "      <td>http://www.flipkart.com/fabhomedecor-fabric-do...</td>\n",
              "      <td>FabHomeDecor Fabric Double Sofa Bed</td>\n",
              "      <td>[\"Furniture &gt;&gt; Living Room Furniture &gt;&gt; Sofa B...</td>\n",
              "      <td>SBEEH3QGU7MFYJFY</td>\n",
              "      <td>32157.0</td>\n",
              "      <td>22646.0</td>\n",
              "      <td>[\"http://img6a.flixcart.com/image/sofa-bed/j/f...</td>\n",
              "      <td>False</td>\n",
              "      <td>FabHomeDecor Fabric Double Sofa Bed (Finish Co...</td>\n",
              "      <td>No rating available</td>\n",
              "      <td>No rating available</td>\n",
              "      <td>FabHomeDecor</td>\n",
              "      <td>{\"product_specification\"=&gt;[{\"key\"=&gt;\"Installati...</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>f449ec65dcbc041b6ae5e6a32717d01b</td>\n",
              "      <td>2016-03-25 22:59:23 +0000</td>\n",
              "      <td>http://www.flipkart.com/aw-bellies/p/itmeh4grg...</td>\n",
              "      <td>AW Bellies</td>\n",
              "      <td>[\"Footwear &gt;&gt; Women's Footwear &gt;&gt; Ballerinas &gt;...</td>\n",
              "      <td>SHOEH4GRSUBJGZXE</td>\n",
              "      <td>999.0</td>\n",
              "      <td>499.0</td>\n",
              "      <td>[\"http://img5a.flixcart.com/image/shoe/7/z/z/r...</td>\n",
              "      <td>False</td>\n",
              "      <td>Key Features of AW Bellies Sandals Wedges Heel...</td>\n",
              "      <td>No rating available</td>\n",
              "      <td>No rating available</td>\n",
              "      <td>AW</td>\n",
              "      <td>{\"product_specification\"=&gt;[{\"key\"=&gt;\"Ideal For\"...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0973b37acd0c664e3de26e97e5571454</td>\n",
              "      <td>2016-03-25 22:59:23 +0000</td>\n",
              "      <td>http://www.flipkart.com/alisha-solid-women-s-c...</td>\n",
              "      <td>Alisha Solid Women's Cycling Shorts</td>\n",
              "      <td>[\"Clothing &gt;&gt; Women's Clothing &gt;&gt; Lingerie, Sl...</td>\n",
              "      <td>SRTEH2F6HUZMQ6SJ</td>\n",
              "      <td>699.0</td>\n",
              "      <td>267.0</td>\n",
              "      <td>[\"http://img5a.flixcart.com/image/short/6/2/h/...</td>\n",
              "      <td>False</td>\n",
              "      <td>Key Features of Alisha Solid Women's Cycling S...</td>\n",
              "      <td>No rating available</td>\n",
              "      <td>No rating available</td>\n",
              "      <td>Alisha</td>\n",
              "      <td>{\"product_specification\"=&gt;[{\"key\"=&gt;\"Number of ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>bc940ea42ee6bef5ac7cea3fb5cfbee7</td>\n",
              "      <td>2016-03-25 22:59:23 +0000</td>\n",
              "      <td>http://www.flipkart.com/sicons-all-purpose-arn...</td>\n",
              "      <td>Sicons All Purpose Arnica Dog Shampoo</td>\n",
              "      <td>[\"Pet Supplies &gt;&gt; Grooming &gt;&gt; Skin &amp; Coat Care...</td>\n",
              "      <td>PSOEH3ZYDMSYARJ5</td>\n",
              "      <td>220.0</td>\n",
              "      <td>210.0</td>\n",
              "      <td>[\"http://img5a.flixcart.com/image/pet-shampoo/...</td>\n",
              "      <td>False</td>\n",
              "      <td>Specifications of Sicons All Purpose Arnica Do...</td>\n",
              "      <td>No rating available</td>\n",
              "      <td>No rating available</td>\n",
              "      <td>Sicons</td>\n",
              "      <td>{\"product_specification\"=&gt;[{\"key\"=&gt;\"Pet Type\",...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "\n",
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              "\n",
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              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-a74e04b9-d82d-4c71-b2ea-f4d09554e8b3 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
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              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-a74e04b9-d82d-4c71-b2ea-f4d09554e8b3');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "id": "Fiysvsk9xwZW",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "01da39e2-3a1d-4b05-cf84-de42f220deaf"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Index(['uniq_id', 'crawl_timestamp', 'product_url', 'product_name',\n",
            "       'product_category_tree', 'pid', 'retail_price', 'discounted_price',\n",
            "       'image', 'is_FK_Advantage_product', 'description', 'product_rating',\n",
            "       'overall_rating', 'brand', 'product_specifications'],\n",
            "      dtype='object')\n"
          ]
        }
      ],
      "source": [
        "print(df.columns)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(df.product_category_tree)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-flAEFQZD-9u",
        "outputId": "821dad9a-b15e-4748-9179-30bcfb366222"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0        [\"Clothing >> Women's Clothing >> Lingerie, Sl...\n",
            "1        [\"Furniture >> Living Room Furniture >> Sofa B...\n",
            "2        [\"Footwear >> Women's Footwear >> Ballerinas >...\n",
            "3        [\"Clothing >> Women's Clothing >> Lingerie, Sl...\n",
            "4        [\"Pet Supplies >> Grooming >> Skin & Coat Care...\n",
            "                               ...                        \n",
            "19995    [\"Baby Care >> Baby & Kids Gifts >> Stickers >...\n",
            "19996    [\"Baby Care >> Baby & Kids Gifts >> Stickers >...\n",
            "19997    [\"Baby Care >> Baby & Kids Gifts >> Stickers >...\n",
            "19998    [\"Baby Care >> Baby & Kids Gifts >> Stickers >...\n",
            "19999    [\"Baby Care >> Baby & Kids Gifts >> Stickers >...\n",
            "Name: product_category_tree, Length: 20000, dtype: object\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Data Preprocessing\n",
        "Let's do some more text preprocessing"
      ],
      "metadata": {
        "id": "SMVTnQ1pp_o6"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df['product_category_tree'] = df['product_category_tree'].str.replace('>>', ',')\n",
        "df['product_category_tree'] = df['product_category_tree'].str.replace('\"', '')"
      ],
      "metadata": {
        "id": "1a5-DFAOCcQH"
      },
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(df.product_category_tree)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5Tz0DFzLB99o",
        "outputId": "bd111031-185d-4fa4-bfde-955be8de8f26"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0        [Clothing , Women's Clothing , Lingerie, Sleep...\n",
            "1        [Furniture , Living Room Furniture , Sofa Beds...\n",
            "2        [Footwear , Women's Footwear , Ballerinas , AW...\n",
            "3        [Clothing , Women's Clothing , Lingerie, Sleep...\n",
            "4        [Pet Supplies , Grooming , Skin & Coat Care , ...\n",
            "                               ...                        \n",
            "19995    [Baby Care , Baby & Kids Gifts , Stickers , Wa...\n",
            "19996    [Baby Care , Baby & Kids Gifts , Stickers , Wa...\n",
            "19997    [Baby Care , Baby & Kids Gifts , Stickers , El...\n",
            "19998    [Baby Care , Baby & Kids Gifts , Stickers , El...\n",
            "19999    [Baby Care , Baby & Kids Gifts , Stickers , El...\n",
            "Name: product_category_tree, Length: 20000, dtype: object\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {
        "id": "8e4rSxf9h8No",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "374c2549-b0ea-4c56-bdbd-140e466a08bd"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: neuspell in /usr/local/lib/python3.10/dist-packages (1.0.0)\n"
          ]
        }
      ],
      "source": [
        "!pip install neuspell\n",
        "!pip install neuspell neuspell[elmo] neuspell[spacy] --no-deps"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "metadata": {
        "id": "jaJ8pxd2ZsRe"
      },
      "outputs": [],
      "source": [
        "# drop unwamted columns\n",
        "df.drop(['crawl_timestamp', 'product_url', 'image',\"retail_price\",\"discounted_price\",\"is_FK_Advantage_product\", \"product_rating\", \"overall_rating\", \"product_specifications\"],axis=1, inplace=True)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 337
        },
        "id": "o0z6TmOwEkvW",
        "outputId": "c105874f-15dd-4e48-cc79-aeb93972d1d9"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                            uniq_id                           product_name  \\\n",
              "0  c2d766ca982eca8304150849735ffef9    Alisha Solid Women's Cycling Shorts   \n",
              "1  7f7036a6d550aaa89d34c77bd39a5e48    FabHomeDecor Fabric Double Sofa Bed   \n",
              "2  f449ec65dcbc041b6ae5e6a32717d01b                             AW Bellies   \n",
              "3  0973b37acd0c664e3de26e97e5571454    Alisha Solid Women's Cycling Shorts   \n",
              "4  bc940ea42ee6bef5ac7cea3fb5cfbee7  Sicons All Purpose Arnica Dog Shampoo   \n",
              "\n",
              "                               product_category_tree               pid  \\\n",
              "0  [Clothing , Women's Clothing , Lingerie, Sleep...  SRTEH2FF9KEDEFGF   \n",
              "1  [Furniture , Living Room Furniture , Sofa Beds...  SBEEH3QGU7MFYJFY   \n",
              "2  [Footwear , Women's Footwear , Ballerinas , AW...  SHOEH4GRSUBJGZXE   \n",
              "3  [Clothing , Women's Clothing , Lingerie, Sleep...  SRTEH2F6HUZMQ6SJ   \n",
              "4  [Pet Supplies , Grooming , Skin & Coat Care , ...  PSOEH3ZYDMSYARJ5   \n",
              "\n",
              "                                         description         brand  \n",
              "0  Key Features of Alisha Solid Women's Cycling S...        Alisha  \n",
              "1  FabHomeDecor Fabric Double Sofa Bed (Finish Co...  FabHomeDecor  \n",
              "2  Key Features of AW Bellies Sandals Wedges Heel...            AW  \n",
              "3  Key Features of Alisha Solid Women's Cycling S...        Alisha  \n",
              "4  Specifications of Sicons All Purpose Arnica Do...        Sicons  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-074fb06f-1f9f-4aac-81f1-806a99b432aa\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>uniq_id</th>\n",
              "      <th>product_name</th>\n",
              "      <th>product_category_tree</th>\n",
              "      <th>pid</th>\n",
              "      <th>description</th>\n",
              "      <th>brand</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>c2d766ca982eca8304150849735ffef9</td>\n",
              "      <td>Alisha Solid Women's Cycling Shorts</td>\n",
              "      <td>[Clothing , Women's Clothing , Lingerie, Sleep...</td>\n",
              "      <td>SRTEH2FF9KEDEFGF</td>\n",
              "      <td>Key Features of Alisha Solid Women's Cycling S...</td>\n",
              "      <td>Alisha</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>7f7036a6d550aaa89d34c77bd39a5e48</td>\n",
              "      <td>FabHomeDecor Fabric Double Sofa Bed</td>\n",
              "      <td>[Furniture , Living Room Furniture , Sofa Beds...</td>\n",
              "      <td>SBEEH3QGU7MFYJFY</td>\n",
              "      <td>FabHomeDecor Fabric Double Sofa Bed (Finish Co...</td>\n",
              "      <td>FabHomeDecor</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>f449ec65dcbc041b6ae5e6a32717d01b</td>\n",
              "      <td>AW Bellies</td>\n",
              "      <td>[Footwear , Women's Footwear , Ballerinas , AW...</td>\n",
              "      <td>SHOEH4GRSUBJGZXE</td>\n",
              "      <td>Key Features of AW Bellies Sandals Wedges Heel...</td>\n",
              "      <td>AW</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0973b37acd0c664e3de26e97e5571454</td>\n",
              "      <td>Alisha Solid Women's Cycling Shorts</td>\n",
              "      <td>[Clothing , Women's Clothing , Lingerie, Sleep...</td>\n",
              "      <td>SRTEH2F6HUZMQ6SJ</td>\n",
              "      <td>Key Features of Alisha Solid Women's Cycling S...</td>\n",
              "      <td>Alisha</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>bc940ea42ee6bef5ac7cea3fb5cfbee7</td>\n",
              "      <td>Sicons All Purpose Arnica Dog Shampoo</td>\n",
              "      <td>[Pet Supplies , Grooming , Skin &amp; Coat Care , ...</td>\n",
              "      <td>PSOEH3ZYDMSYARJ5</td>\n",
              "      <td>Specifications of Sicons All Purpose Arnica Do...</td>\n",
              "      <td>Sicons</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-074fb06f-1f9f-4aac-81f1-806a99b432aa')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
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              "  </svg>\n",
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              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
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              "\n",
              "    .colab-df-convert {\n",
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              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
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              "      fill: #174EA6;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
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              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
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              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "        const buttonEl =\n",
              "          document.querySelector('#df-074fb06f-1f9f-4aac-81f1-806a99b432aa button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-074fb06f-1f9f-4aac-81f1-806a99b432aa');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 30,
      "metadata": {
        "id": "xNGInsbbZ8IF"
      },
      "outputs": [],
      "source": [
        "#drop duplicate products\n",
        "\n",
        "uniq_prod=df.copy()\n",
        "\n",
        "uniq_prod.drop_duplicates (subset =\"product_name\", keep = \"first\", inplace = True)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import nltk\n",
        "from nltk.corpus import stopwords\n",
        "\n",
        "# Download the stopwords corpus\n",
        "nltk.download('stopwords')\n",
        "\n",
        "# Remove stop words and modify certain words in the description column\n",
        "stop_words = set(stopwords.words('english'))\n",
        "\n",
        "df['description'] = df['description'].astype(str).str.lower().str.split()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pNp6gPziIS46",
        "outputId": "29e01ac3-1a8d-4bf6-a965-6c7a2ad64351"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[nltk_data] Downloading package stopwords to /root/nltk_data...\n",
            "[nltk_data]   Package stopwords is already up-to-date!\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(df.description)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0q_MjKE5IW_0",
        "outputId": "e0174d89-af39-4e50-8c44-c9f0c61a7a37"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0        [key, features, of, alisha, solid, women's, cy...\n",
            "1        [fabhomedecor, fabric, double, sofa, bed, (fin...\n",
            "2        [key, features, of, aw, bellies, sandals, wedg...\n",
            "3        [key, features, of, alisha, solid, women's, cy...\n",
            "4        [specifications, of, sicons, all, purpose, arn...\n",
            "                               ...                        \n",
            "19995    [buy, walldesign, small, vinyl, sticker, for, ...\n",
            "19996    [buy, wallmantra, large, vinyl, stickers, stic...\n",
            "19997    [buy, elite, collection, medium, acrylic, stic...\n",
            "19998    [buy, elite, collection, medium, acrylic, stic...\n",
            "19999    [buy, elite, collection, medium, acrylic, stic...\n",
            "Name: description, Length: 20000, dtype: object\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 33,
      "metadata": {
        "id": "RSr2fABYZ-lz"
      },
      "outputs": [],
      "source": [
        "# Remove stop words and punctuations and then perform tokenization and lemmatization.\n",
        "\n",
        "def filter_keywords (doc):\n",
        "  doc=doc.lower()\n",
        "  stop_free = \" \".join([i for i in doc.split() if i not in stop_words])\n",
        "  punc_free = \" \".join(ch for ch in stop_free if ch not in exclude)\n",
        "  word_tokens = word_tokenize(punc_free)\n",
        "  filtered_sentence = [(lem.lemmatize (w, \"v\")) for w in word_tokens]\n",
        "  return filtered_sentence"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 34,
      "metadata": {
        "id": "dOmAj73RaCiZ"
      },
      "outputs": [],
      "source": [
        "# Apply the filter_keywords function to selected columns to obtain the keywords for each column.\n",
        "\n",
        "uniq_prod['product'] = uniq_prod['product_name'].apply(filter_keywords)\n",
        "\n",
        "uniq_prod['brand'] = uniq_prod['brand'].astype(\"str\").apply(filter_keywords)\n",
        "\n",
        "uniq_prod['description']=df.description"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#combine all keywords for each product\n",
        "\n",
        "uniq_prod[\"keywords\"] = uniq_prod['product'].apply(lambda x: [x]) + uniq_prod['brand'].apply(lambda x: [x]) + df['product_category_tree'].apply(lambda x: [x])\n",
        "\n",
        "uniq_prod[\"keywords\"] = uniq_prod[\"keywords\"].apply(lambda x: ' '.join(str(item) for item in x))\n",
        "\n",
        "#Creating a 'docno' column, which gives recommendations.\n",
        "\n",
        "uniq_prod['docno'] = uniq_prod['product_name'].astype(str)\n",
        "\n",
        "keywords=uniq_prod[\"keywords\"]"
      ],
      "metadata": {
        "id": "iAoDZmR18WHi"
      },
      "execution_count": 35,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(keywords)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_3I_arMxMH90",
        "outputId": "ac83d93c-cf80-41de-b293-1b01b427fd6f"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0        ['a', 'l', 'i', 's', 'h', 'a', 's', 'o', 'l', ...\n",
            "1        ['f', 'a', 'b', 'h', 'o', 'm', 'e', 'd', 'e', ...\n",
            "2        ['a', 'w', 'b', 'e', 'l', 'l', 'i', 'e', 's'] ...\n",
            "4        ['s', 'i', 'c', 'o', 'n', 's', 'p', 'u', 'r', ...\n",
            "5        ['e', 't', 'e', 'r', 'n', 'a', 'l', 'g', 'a', ...\n",
            "                               ...                        \n",
            "19936    ['p', 'u', 'r', 'p', 'l', 'e', 'w', 'o', 'm', ...\n",
            "19948    ['u', 'b', 'e', 'r', 'l', 'y', 'f', 'e', 'l', ...\n",
            "19958    ['w', 'i', 't', 'c', 'h', 'e', 's', 'c', 'o', ...\n",
            "19962    ['s', 't', 'y', 'l', 'i', 's', 't', 'r', 'y', ...\n",
            "19976    ['u', 'b', 'e', 'r', 'l', 'y', 'f', 'e', 'e', ...\n",
            "Name: keywords, Length: 12676, dtype: object\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "uniq_prod[\"keywords\"] = uniq_prod[\"keywords\"].apply(lambda x: ' '.join([word for word in x.split() if word.isalpha()]))"
      ],
      "metadata": {
        "id": "Ok0gyJASMScL"
      },
      "execution_count": 37,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(uniq_prod[\"keywords\"])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iDJviXpSMV6L",
        "outputId": "b6b9c80b-42df-4429-b231-d770954e014d"
      },
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0        Clothing Sleep Swimwear Shorts Alisha Shorts A...\n",
            "1        Living Room Furniture Sofa Beds Futons FabHome...\n",
            "2                                   Footwear Ballerinas AW\n",
            "4        Supplies Grooming Skin Coat Care Shampoo Sicon...\n",
            "5                        Gandhi Super Series Crystal Paper\n",
            "                               ...                        \n",
            "19936                                             Footwear\n",
            "19948               Care Baby Kids Gifts Stickers Uberlyfe\n",
            "19958                                             Footwear\n",
            "19962                                             Footwear\n",
            "19976               Care Baby Kids Gifts Stickers Uberlyfe\n",
            "Name: keywords, Length: 12676, dtype: object\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#drop unwanted columns\n",
        "\n",
        "uniq_prod.drop(['product', 'brand','pid', 'product_name'], axis=1, inplace=True)\n",
        "\n",
        "uniq_prod.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 337
        },
        "id": "NwnwRSHL0tAN",
        "outputId": "50326699-3a29-4216-f996-dc3eb57eea33"
      },
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "execute_result",
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              "                            uniq_id  \\\n",
              "0  c2d766ca982eca8304150849735ffef9   \n",
              "1  7f7036a6d550aaa89d34c77bd39a5e48   \n",
              "2  f449ec65dcbc041b6ae5e6a32717d01b   \n",
              "4  bc940ea42ee6bef5ac7cea3fb5cfbee7   \n",
              "5  c2a17313954882c1dba461863e98adf2   \n",
              "\n",
              "                               product_category_tree  \\\n",
              "0  [Clothing , Women's Clothing , Lingerie, Sleep...   \n",
              "1  [Furniture , Living Room Furniture , Sofa Beds...   \n",
              "2  [Footwear , Women's Footwear , Ballerinas , AW...   \n",
              "4  [Pet Supplies , Grooming , Skin & Coat Care , ...   \n",
              "5  [Eternal Gandhi Super Series Crystal Paper Wei...   \n",
              "\n",
              "                                         description  \\\n",
              "0  [key, features, of, alisha, solid, women's, cy...   \n",
              "1  [fabhomedecor, fabric, double, sofa, bed, (fin...   \n",
              "2  [key, features, of, aw, bellies, sandals, wedg...   \n",
              "4  [specifications, of, sicons, all, purpose, arn...   \n",
              "5  [key, features, of, eternal, gandhi, super, se...   \n",
              "\n",
              "                                            keywords  \\\n",
              "0  Clothing Sleep Swimwear Shorts Alisha Shorts A...   \n",
              "1  Living Room Furniture Sofa Beds Futons FabHome...   \n",
              "2                             Footwear Ballerinas AW   \n",
              "4  Supplies Grooming Skin Coat Care Shampoo Sicon...   \n",
              "5                  Gandhi Super Series Crystal Paper   \n",
              "\n",
              "                                               docno  \n",
              "0                Alisha Solid Women's Cycling Shorts  \n",
              "1                FabHomeDecor Fabric Double Sofa Bed  \n",
              "2                                         AW Bellies  \n",
              "4              Sicons All Purpose Arnica Dog Shampoo  \n",
              "5  Eternal Gandhi Super Series Crystal Paper Weig...  "
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              "      <td>[Footwear , Women's Footwear , Ballerinas , AW...</td>\n",
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              "      <td>Supplies Grooming Skin Coat Care Shampoo Sicon...</td>\n",
              "      <td>Sicons All Purpose Arnica Dog Shampoo</td>\n",
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              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "  "
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          "metadata": {},
          "execution_count": 39
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      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Building Search Engine"
      ],
      "metadata": {
        "id": "vNCIyrBRq5RV"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!rm -rf /content/iter_index_porter\n",
        "\n",
        "pd_indexer = pt.DFIndexer(\"/content/haw_pd_index\")\n",
        "\n",
        "indexref = pd_indexer.index (uniq_prod[ \"keywords\"], uniq_prod[\"docno\"])\n",
        "\n",
        "# Let's implementing the NeuSpell spell checker on the user query and save it to an object.\n",
        "\n",
        "spellcheck = BertChecker()\n",
        "\n",
        "# folder where the model is saved\n",
        "\n",
        "spellcheck.from_pretrained(ckpt_path=f\"/content/bert_multicased_L-12_H-768_A-12\" )\n",
        "\n",
        "X=input(\"Search Engine:\")\n",
        "\n",
        "query=spellcheck.correct(X)\n",
        "\n",
        "print(query)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ONN0Mo8TXk87",
        "outputId": "1eac024c-640b-468a-ebb7-263f120aea11"
      },
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Search Engine: womns clothing\n",
            "women clothing\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "\n",
        "# Perform ranking and retrieval using Pyterrier and Sentence-BERT\n",
        "\n",
        "prod_ret = pt.BatchRetrieve(indexref, wmodel='TF_IDF', properties={'termpipelines': 'Stopwords'})\n",
        "\n",
        "pr = prod_ret.compile()\n",
        "\n",
        "output = pr.search(query)\n",
        "\n",
        "docno = list(output['docno'])\n",
        "\n",
        "transform = model.encode(docno)\n",
        "\n",
        "# Create embeddings and re-ranking using PyTerrier and cosine similarity\n",
        "\n",
        "embedding = {}\n",
        "\n",
        "for i, product in enumerate(docno):\n",
        "    embedding[product] = transform[i]\n",
        "\n",
        "q_embedding = model.encode(query).reshape(1, -1)\n",
        "\n",
        "l = []\n",
        "\n",
        "for product in embedding.keys():\n",
        "    score = cosine_similarity(q_embedding, embedding[product].reshape(1, -1))[0][0]\n",
        "    l.append([product, score])\n",
        "\n",
        "output2 = pd.DataFrame(l, columns=['product_name', 'score'])\n",
        "\n",
        "# Let's look at the results.\n",
        "\n",
        "output2.sort_values(by='score', ascending=False).head(10)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 380
        },
        "id": "8mtqGJDRhEsN",
        "outputId": "06ff3ded-99bd-4ddf-e24d-e84ad5b6d103"
      },
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Applying 8 rules\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "             product_name     score\n",
              "56        Caprese Satchel  0.837144\n",
              "9    Amatra Hand-held Bag  0.817025\n",
              "18    ESBEDA Shoulder Bag  0.815713\n",
              "59     Basta Shoulder Bag  0.813524\n",
              "7     DLOOP Hand-held Bag  0.806032\n",
              "8     Prajo Hand-held Bag  0.805503\n",
              "19    Maayas Shoulder Bag  0.803320\n",
              "4     Viari Hand-held Bag  0.803078\n",
              "12  kamyaart Shoulder Bag  0.802783\n",
              "57   Murcia Hand-held Bag  0.798739"
            ],
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              "\n",
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              "      <th>56</th>\n",
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              "      <th>59</th>\n",
              "      <td>Basta Shoulder Bag</td>\n",
              "      <td>0.813524</td>\n",
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              "      <th>7</th>\n",
              "      <td>DLOOP Hand-held Bag</td>\n",
              "      <td>0.806032</td>\n",
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              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>Prajo Hand-held Bag</td>\n",
              "      <td>0.805503</td>\n",
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              "      <th>19</th>\n",
              "      <td>Maayas Shoulder Bag</td>\n",
              "      <td>0.803320</td>\n",
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              "      <th>4</th>\n",
              "      <td>Viari Hand-held Bag</td>\n",
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              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>kamyaart Shoulder Bag</td>\n",
              "      <td>0.802783</td>\n",
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              "    <tr>\n",
              "      <th>57</th>\n",
              "      <td>Murcia Hand-held Bag</td>\n",
              "      <td>0.798739</td>\n",
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              "</div>\n",
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              "\n",
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              "        const buttonEl =\n",
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              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-f1eaddb0-43e7-4704-bb6e-e645a109df81');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 55
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "In the output, there is a list of products that are a part of women's clothing. The corresponding scores represent relevance. Note that the results are very relevant to the search query"
      ],
      "metadata": {
        "id": "B0mWsc67rYob"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Enhancing E-commerce Using an Advanced Engine and Recommendation System ends here."
      ],
      "metadata": {
        "id": "ns1aCWgQrfr9"
      }
    }
  ],
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