Amazon_ML_Challenge_Part1_cleaning.ipynb

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    "colab": {
      "name": "Amazon_ML_Challenge_Part1_cleaning.ipynb",
      "provenance": [],
      "collapsed_sections": [
        "a_3l8NHfGyU-",
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    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
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    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "r-DDf_NbGLo2",
        "outputId": "648852a1-13ca-41a7-e153-61422ba7492e"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "a_3l8NHfGyU-"
      },
      "source": [
        "#Loading Data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "m8PHP4H8kOHX"
      },
      "source": [
        "import re\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "from tqdm import tqdm\n",
        "from tqdm.notebook import tqdm_notebook\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6T85OwqVS72Q"
      },
      "source": [
        "import nltk\n",
        "from nltk.corpus import stopwords"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Gzxa26CMkOJx"
      },
      "source": [
        "train5 = pd.read_csv('/content/drive/MyDrive/Datasets/Amazon_ML_Challenge/Dataset/train.csv', escapechar = \"\\\\\", quoting = 3, nrows=500000)    #skiprows=range(1,500000),\n",
        "#train10 = pd.read_csv('/content/drive/MyDrive/Datasets/Amazon_ML_Challenge/Dataset/train.csv', escapechar = \"\\\\\", quoting = 3, skiprows=range(1,500000), nrows=500000) \n",
        "#train15 = pd.read_csv('/content/drive/MyDrive/Datasets/Amazon_ML_Challenge/Dataset/train.csv', escapechar = \"\\\\\", quoting = 3, skiprows=range(1,1000000), nrows=500000) \n",
        "#train20 = pd.read_csv('/content/drive/MyDrive/Datasets/Amazon_ML_Challenge/Dataset/train.csv', escapechar = \"\\\\\", quoting = 3, skiprows=range(1,1500000), nrows=500000) \n",
        "#train25 = pd.read_csv('/content/drive/MyDrive/Datasets/Amazon_ML_Challenge/Dataset/train.csv', escapechar = \"\\\\\", quoting = 3, skiprows=range(1,2000000), nrows=500000) \n",
        "#trainlast = pd.read_csv('/content/drive/MyDrive/Datasets/Amazon_ML_Challenge/Dataset/train.csv', escapechar = \"\\\\\", quoting = 3, skiprows=range(1,2500000), nrows=500000) \n",
        "#test= pd.read_csv('/content/drive/MyDrive/Datasets/Amazon_ML_Challenge/train.csv', escapechar = \"\\\\\",quoting = 3)\n",
        "\n",
        "#sample_submission = pd.read_csv('/content/drive/MyDrive/Datasets/Amazon_ML_Challenge/dataset/sample_submission.csv')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yaJaV92xLsVK",
        "outputId": "d666fc70-a84e-4942-e88e-fcfe18628511"
      },
      "source": [
        "train15.info()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 500000 entries, 0 to 499999\n",
            "Data columns (total 5 columns):\n",
            " #   Column          Non-Null Count   Dtype \n",
            "---  ------          --------------   ----- \n",
            " 0   TITLE           499990 non-null  object\n",
            " 1   DESCRIPTION     375114 non-null  object\n",
            " 2   BULLET_POINTS   471124 non-null  object\n",
            " 3   BRAND           490083 non-null  object\n",
            " 4   BROWSE_NODE_ID  500000 non-null  int64 \n",
            "dtypes: int64(1), object(4)\n",
            "memory usage: 19.1+ MB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yyNRVoFbGxkX"
      },
      "source": [
        "#Analyzing and cleaning"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ERYwXiePSleP"
      },
      "source": [
        "##Loading Data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 142
        },
        "id": "hkr9cOlQkOPY",
        "outputId": "9d774e92-f760-407d-90c6-8d1bc0fe40c3"
      },
      "source": [
        "train15.head(3)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>TITLE</th>\n",
              "      <th>DESCRIPTION</th>\n",
              "      <th>BULLET_POINTS</th>\n",
              "      <th>BRAND</th>\n",
              "      <th>BROWSE_NODE_ID</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Dogitos First Meow Healthy Cat Treats 40G (Chi...</td>\n",
              "      <td>Dogitos First Meow is a customized nutrition s...</td>\n",
              "      <td>[This contains essential nutrients so that the...</td>\n",
              "      <td>Dogitos</td>\n",
              "      <td>17686</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Phoenix Roasted Makhana Indian Pudina &amp; Dhania...</td>\n",
              "      <td>&lt;p&gt;Lotus Seeds or Foxnuts, popularly also know...</td>\n",
              "      <td>[Quantity: 100 Gms.,Flavor: Indian Pudina &amp; Dh...</td>\n",
              "      <td>Phoenix</td>\n",
              "      <td>32941</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Bigbanana Men's Regular Fit Polo (Singleton,Wh...</td>\n",
              "      <td>White and Blue striped T-shirt, has a polo col...</td>\n",
              "      <td>[Polo T-Shirt in Super Combed Cotton,Striped P...</td>\n",
              "      <td>Bigbanana</td>\n",
              "      <td>1213</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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              "                                               TITLE  ... BROWSE_NODE_ID\n",
              "0  Dogitos First Meow Healthy Cat Treats 40G (Chi...  ...          17686\n",
              "1  Phoenix Roasted Makhana Indian Pudina & Dhania...  ...          32941\n",
              "2  Bigbanana Men's Regular Fit Polo (Singleton,Wh...  ...           1213\n",
              "\n",
              "[3 rows x 5 columns]"
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          "metadata": {
            "tags": []
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          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 550
        },
        "id": "jY7tO7XqkORb",
        "outputId": "18e08a9d-b713-41c8-a405-ee8eb8cf8b4c"
      },
      "source": [
        "'''for name,df in zip(names,data):\n",
        "  print(name,':')\n",
        "  display(df.info())\n",
        "  print()'''"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "train :\n",
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 2903024 entries, 0 to 2903023\n",
            "Data columns (total 5 columns):\n",
            " #   Column          Dtype \n",
            "---  ------          ----- \n",
            " 0   TITLE           object\n",
            " 1   DESCRIPTION     object\n",
            " 2   BULLET_POINTS   object\n",
            " 3   BRAND           object\n",
            " 4   BROWSE_NODE_ID  int64 \n",
            "dtypes: int64(1), object(4)\n",
            "memory usage: 110.7+ MB\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "None"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "test :\n",
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 110775 entries, 0 to 110774\n",
            "Data columns (total 5 columns):\n",
            " #   Column         Non-Null Count   Dtype \n",
            "---  ------         --------------   ----- \n",
            " 0   PRODUCT_ID     110775 non-null  int64 \n",
            " 1   TITLE          110768 non-null  object\n",
            " 2   DESCRIPTION    100306 non-null  object\n",
            " 3   BULLET_POINTS  103989 non-null  object\n",
            " 4   BRAND          108345 non-null  object\n",
            "dtypes: int64(1), object(4)\n",
            "memory usage: 4.2+ MB\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "None"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "f7SL_f6NJTLa"
      },
      "source": [
        "## Handling null values"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ajpbsuu5LN18",
        "outputId": "fc36235c-d721-46e7-f81e-080333e92318"
      },
      "source": [
        "train5.isnull().sum()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "TITLE                 10\n",
              "DESCRIPTION       124886\n",
              "BULLET_POINTS      28876\n",
              "BRAND               9917\n",
              "BROWSE_NODE_ID         0\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rqjQbLPnpBeN"
      },
      "source": [
        "train5 = train5.fillna(' ')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0L8Xax3J0geh"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "M_ZqLqduJWNS"
      },
      "source": [
        "##Handling duplicates"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 538
        },
        "id": "lRPFWfBYrW6c",
        "outputId": "31f5a41c-2c90-48ef-96b0-b58445e0b3c4"
      },
      "source": [
        "train5[train5.duplicated()]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "      <th>DESCRIPTION</th>\n",
              "      <th>BULLET_POINTS</th>\n",
              "      <th>BRAND</th>\n",
              "      <th>BROWSE_NODE_ID</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>491</th>\n",
              "      <td>GENDER STREE Women Kurta Palazzo Set</td>\n",
              "      <td>Latest Style and Trendy Kurta Palazzo Set from...</td>\n",
              "      <td>[Style - Kurta Palazzo Set, Gold Print,Fabric ...</td>\n",
              "      <td>GENDER STREE</td>\n",
              "      <td>1486</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>897</th>\n",
              "      <td>Boy's &amp; Girl's Printed Cotton Blend T Shirt  (...</td>\n",
              "      <td>PRIYA Cotton Graphic Printed Half Sleeve carto...</td>\n",
              "      <td>[Cotton blend fabric The printing of this t-sh...</td>\n",
              "      <td>Generic</td>\n",
              "      <td>1003</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1931</th>\n",
              "      <td>savii Creation traditional look Cotton un Stit...</td>\n",
              "      <td>This salwar suit dress material with dupatta, ...</td>\n",
              "      <td>[ITS COME WITH 2.5 METER SUIT PIECE 2.5 METER ...</td>\n",
              "      <td>SAVII</td>\n",
              "      <td>465</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6622</th>\n",
              "      <td>Womens Trendz Handmade Silver Plated Anklet/Gh...</td>\n",
              "      <td>Self Design, Partywear, Classy And Delicate De...</td>\n",
              "      <td>[Stylish Antique Silver Plated Anklet (1 Pair)...</td>\n",
              "      <td>Womens Trendz</td>\n",
              "      <td>1149</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9708</th>\n",
              "      <td>Adidas Men's Running Shoes</td>\n",
              "      <td></td>\n",
              "      <td>[Material Type: Mesh,Lifestyle: Sports,Closure...</td>\n",
              "      <td>Adidas</td>\n",
              "      <td>701</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>499706</th>\n",
              "      <td>Soch Women's Straight Cotton Kurta</td>\n",
              "      <td></td>\n",
              "      <td>[Material: Cotton with three quarter sleeve,St...</td>\n",
              "      <td>Soch</td>\n",
              "      <td>1251</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>499719</th>\n",
              "      <td>Fadsho Designer Case for Lava Z90/Back Cover f...</td>\n",
              "      <td>Cases are designed in such a way that they per...</td>\n",
              "      <td>[Silicon Cases with Gloss Finish printing,Sili...</td>\n",
              "      <td>FADSHO</td>\n",
              "      <td>1045</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>499794</th>\n",
              "      <td>Peter England Navy Shirt</td>\n",
              "      <td></td>\n",
              "      <td>[Color : Navy,Material : 100% Cotton,Fit : Sli...</td>\n",
              "      <td>Peter England</td>\n",
              "      <td>1024</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>499864</th>\n",
              "      <td>KumkumArts Harry Potter Poster 12 x 18 Inch, H...</td>\n",
              "      <td>Kumkum Arts HD Posters Are High Quality HD Res...</td>\n",
              "      <td>[HD Poster,Beautifully designed posters inspir...</td>\n",
              "      <td>Kumkum Arts</td>\n",
              "      <td>413</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>499890</th>\n",
              "      <td>XE Looks Men's Black Patent Leather Lace-up Co...</td>\n",
              "      <td>&lt;b&gt;XE Looks present an exclusive pair of Corpo...</td>\n",
              "      <td>[Material Composition:- The raw material of th...</td>\n",
              "      <td>XE Looks</td>\n",
              "      <td>1409</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5958 rows × 5 columns</p>\n",
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            ],
            "text/plain": [
              "                                                    TITLE  ... BROWSE_NODE_ID\n",
              "491                  GENDER STREE Women Kurta Palazzo Set  ...           1486\n",
              "897     Boy's & Girl's Printed Cotton Blend T Shirt  (...  ...           1003\n",
              "1931    savii Creation traditional look Cotton un Stit...  ...            465\n",
              "6622    Womens Trendz Handmade Silver Plated Anklet/Gh...  ...           1149\n",
              "9708                           Adidas Men's Running Shoes  ...            701\n",
              "...                                                   ...  ...            ...\n",
              "499706                 Soch Women's Straight Cotton Kurta  ...           1251\n",
              "499719  Fadsho Designer Case for Lava Z90/Back Cover f...  ...           1045\n",
              "499794                           Peter England Navy Shirt  ...           1024\n",
              "499864  KumkumArts Harry Potter Poster 12 x 18 Inch, H...  ...            413\n",
              "499890  XE Looks Men's Black Patent Leather Lace-up Co...  ...           1409\n",
              "\n",
              "[5958 rows x 5 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5BBoWF2SInbN"
      },
      "source": [
        "train5.drop_duplicates(keep = 'first', inplace = True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "63WihEeCsRw0",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c61e8ece-ab8a-4e0e-9ede-e726d6c7151a"
      },
      "source": [
        "train5.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(494042, 5)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AGiHYicfVr7W"
      },
      "source": [
        "##Visualizing data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "p8dTCldmVvKv",
        "outputId": "4038ae6e-85c6-48df-ed87-48d6e613e393"
      },
      "source": [
        "#train.head(3)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>TITLE</th>\n",
              "      <th>DESCRIPTION</th>\n",
              "      <th>BULLET_POINTS</th>\n",
              "      <th>BRAND</th>\n",
              "      <th>BROWSE_NODE_ID</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Pete The Cat Bedtime Blues Doll, 14.5 Inch</td>\n",
              "      <td>Pete the Cat is the coolest, most popular cat ...</td>\n",
              "      <td>[Pete the Cat Bedtime Blues plush doll,Based o...</td>\n",
              "      <td>MerryMakers</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>The New Yorker NYHM014 Refrigerator Magnet, 2 ...</td>\n",
              "      <td>The New Yorker Handsome Cello Wrapped Hard Mag...</td>\n",
              "      <td>[Cat In A Tea Cup by New Yorker cover artist G...</td>\n",
              "      <td>The New Yorker</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>The Ultimate Self-Sufficiency Handbook: A Comp...</td>\n",
              "      <td></td>\n",
              "      <td>Skyhorse Publishing</td>\n",
              "      <td>imusti</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                               TITLE  ... BROWSE_NODE_ID\n",
              "0         Pete The Cat Bedtime Blues Doll, 14.5 Inch  ...              0\n",
              "1  The New Yorker NYHM014 Refrigerator Magnet, 2 ...  ...              1\n",
              "2  The Ultimate Self-Sufficiency Handbook: A Comp...  ...              2\n",
              "\n",
              "[3 rows x 5 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YKAZmTWZV1QC",
        "outputId": "535663c4-6e75-4d41-8ec6-d8a8d6222c63"
      },
      "source": [
        "#nodeid_distribution = (train['BROWSE_NODE_ID'].value_counts()/len(train['BROWSE_NODE_ID']))*100\n",
        "#nodeid_distribution"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1045       6.884774\n",
              "5          2.365386\n",
              "1251       1.769033\n",
              "1052       1.553830\n",
              "4          1.214444\n",
              "             ...   \n",
              "2064271    0.000036\n",
              "196697     0.000036\n",
              "1602927    0.000036\n",
              "2586763    0.000036\n",
              "2153849    0.000036\n",
              "Name: BROWSE_NODE_ID, Length: 9919, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LIN6ITXYXGC4",
        "outputId": "75482a70-bb08-409e-ce4e-593f2e5361ab"
      },
      "source": [
        "#cumsum = np.cumsum(nodeid_distribution.values)\n",
        "#cumsum"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([  6.88477368,   9.25015973,  11.01919308, ...,  99.99992861,\n",
              "        99.99996431, 100.        ])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KtRpyoQgZzZm"
      },
      "source": [
        "#cumsum_index = list(range(0,len(cumsum)))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gDaY4cYebA7h",
        "outputId": "2b5ce6f4-3343-4893-e831-c8497b334afc"
      },
      "source": [
        "#np.argwhere(cumsum>95)[0]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([2791])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "F0VhvPTrXF98",
        "outputId": "d08c5735-eb8d-4e84-a95f-c40cb64344ba"
      },
      "source": [
        "'''plt.figure(figsize = (12,6))\n",
        "plt.plot(cumsum_index,cumsum)\n",
        "plt.axhline(95, c='red')\n",
        "plt.axvline(2790, c='red')\n",
        "plt.yticks(np.arange(0,101,5))\n",
        "plt.xticks(np.arange(0,10000,500))\n",
        "plt.show()'''"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Op53-YbEfQnd"
      },
      "source": [
        "#brand_count = train['BRAND'].value_counts()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6TcLrbi9Xily",
        "outputId": "3786cfbf-01bc-4883-d439-c36b16dde1ae"
      },
      "source": [
        "#brand_distribution = (train['BRAND'].value_counts()/len(train['BRAND']))*100\n",
        "#brand_distribution"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Generic                      3.683416\n",
              "                             2.022038\n",
              "Puma                         0.489304\n",
              "United Colors of Benetton    0.409847\n",
              "Max                          0.383826\n",
              "                               ...   \n",
              "Klass Home Collection        0.000036\n",
              "I IHAYNER                    0.000036\n",
              "nhgf                         0.000036\n",
              "Orange Toys                  0.000036\n",
              "SENICC                       0.000036\n",
              "Name: BRAND, Length: 258266, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uuK10ik8Xig1",
        "outputId": "bd6dc0a8-33e9-4864-f24e-c70a7f52abcd"
      },
      "source": [
        "#brand_cumsum = np.cumsum(brand_distribution.values)\n",
        "#brand_cumsum"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([  3.68341585,   5.70545381,   6.19475786, ...,  99.99992861,\n",
              "        99.9999643 , 100.        ])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gWF7FRDcXid3",
        "outputId": "28a48ebf-001c-4a10-f5f2-c7221c346ae2"
      },
      "source": [
        "#np.argwhere(brand_cumsum>90)[0]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([68652])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vGB8iX7dfRVI",
        "outputId": "57d26186-b979-4dc9-e28e-cc0262278c50"
      },
      "source": [
        "'''plt.figure(figsize = (12,6))\n",
        "plt.plot(brand_cumsum)\n",
        "plt.axhline(90, c='red')\n",
        "plt.axvline(68652, c='red')\n",
        "plt.yticks(np.arange(0,101,5))\n",
        "plt.xticks(np.arange(0,258266,10000), rotation=90)\n",
        "plt.show()'''"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sqP8fqfFfRSs",
        "outputId": "3a13f168-5887-45d1-bcc3-9b4872686115"
      },
      "source": [
        "#brand_count[brand_count>50]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Generic                      103192\n",
              "                              56648\n",
              "Puma                          13708\n",
              "United Colors of Benetton     11482\n",
              "Max                           10753\n",
              "                              ...  \n",
              "JIVA                             51\n",
              "A-TEX INDIA                      51\n",
              "G-Shock                          51\n",
              "Start Makers                     51\n",
              "Verbier                          51\n",
              "Name: BRAND, Length: 7730, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3dt6bV_FgSdj"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "avKCValxKdtR"
      },
      "source": [
        "## Text Cleaning"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4bxOjVwWsRuJ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 142
        },
        "outputId": "2357e25b-5ad6-45ba-88a2-e245dd60e519"
      },
      "source": [
        "train5.head(3)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>TITLE</th>\n",
              "      <th>DESCRIPTION</th>\n",
              "      <th>BULLET_POINTS</th>\n",
              "      <th>BRAND</th>\n",
              "      <th>BROWSE_NODE_ID</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Dogitos First Meow Healthy Cat Treats 40G (Chi...</td>\n",
              "      <td>Dogitos First Meow is a customized nutrition s...</td>\n",
              "      <td>[This contains essential nutrients so that the...</td>\n",
              "      <td>Dogitos</td>\n",
              "      <td>17686</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Phoenix Roasted Makhana Indian Pudina &amp; Dhania...</td>\n",
              "      <td>&lt;p&gt;Lotus Seeds or Foxnuts, popularly also know...</td>\n",
              "      <td>[Quantity: 100 Gms.,Flavor: Indian Pudina &amp; Dh...</td>\n",
              "      <td>Phoenix</td>\n",
              "      <td>32941</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Bigbanana Men's Regular Fit Polo (Singleton,Wh...</td>\n",
              "      <td>White and Blue striped T-shirt, has a polo col...</td>\n",
              "      <td>[Polo T-Shirt in Super Combed Cotton,Striped P...</td>\n",
              "      <td>Bigbanana</td>\n",
              "      <td>1213</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                               TITLE  ... BROWSE_NODE_ID\n",
              "0  Dogitos First Meow Healthy Cat Treats 40G (Chi...  ...          17686\n",
              "1  Phoenix Roasted Makhana Indian Pudina & Dhania...  ...          32941\n",
              "2  Bigbanana Men's Regular Fit Polo (Singleton,Wh...  ...           1213\n",
              "\n",
              "[3 rows x 5 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GqFCSXToEYHD"
      },
      "source": [
        "train5['TEXT'] = train5['TITLE'] + ' ' + train5['DESCRIPTION'] + ' ' + train5['BULLET_POINTS'] + ' ' + train5['BRAND']\n",
        "#train['TEXT'] = train['TITLE'] + ' ' + train['DESCRIPTION'] + ' ' + train['BULLET_POINTS'] + ' ' + train['BRAND']"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QkeCBJNiGoyL"
      },
      "source": [
        "train5 = train5.drop(['TITLE', 'DESCRIPTION', 'BULLET_POINTS', 'BRAND'], axis=1)\n",
        "#train = train.drop(['TITLE', 'DESCRIPTION', 'BULLET_POINTS', 'BRAND'], axis=1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 142
        },
        "id": "NP9gwtdDMe_0",
        "outputId": "8754c000-7ca3-4702-9953-069ac72e9771"
      },
      "source": [
        "train5.head(3)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>BROWSE_NODE_ID</th>\n",
              "      <th>TEXT</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>17686</td>\n",
              "      <td>Dogitos First Meow Healthy Cat Treats 40G (Chi...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>32941</td>\n",
              "      <td>Phoenix Roasted Makhana Indian Pudina &amp; Dhania...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1213</td>\n",
              "      <td>Bigbanana Men's Regular Fit Polo (Singleton,Wh...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   BROWSE_NODE_ID                                               TEXT\n",
              "0           17686  Dogitos First Meow Healthy Cat Treats 40G (Chi...\n",
              "1           32941  Phoenix Roasted Makhana Indian Pudina & Dhania...\n",
              "2            1213  Bigbanana Men's Regular Fit Polo (Singleton,Wh..."
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5Mm-z1baGxc4"
      },
      "source": [
        "# Nltk"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "J7-vdtMFV_HP",
        "outputId": "fa717310-8b80-4a5d-a304-bde3b1ae1eeb"
      },
      "source": [
        "nltk.download('stopwords')\n",
        "stop_words = set(stopwords.words('english'))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[nltk_data] Downloading package stopwords to /root/nltk_data...\n",
            "[nltk_data]   Package stopwords is already up-to-date!\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ayjo0k2iDZaw"
      },
      "source": [
        "# import these modules\n",
        "from nltk.stem import WordNetLemmatizer\n",
        "  \n",
        "lemmatizer = WordNetLemmatizer()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yx3uCFnsEz3c",
        "outputId": "a4e028fd-b0db-4999-ac11-a00fbb6938da"
      },
      "source": [
        "from nltk import word_tokenize\n",
        "nltk.download('punkt')\n",
        "nltk.download('wordnet')\n",
        "word_tokenize('hi I am')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "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]   Package wordnet is already up-to-date!\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['hi', 'I', 'am']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LZ3MFpMYMteD"
      },
      "source": [
        "def clean(text):\n",
        "\n",
        "  cleanr = re.compile('<[^>]*>')           # remove html\n",
        "  cleantext = re.sub(cleanr, ' ', text)\n",
        "\n",
        "  cleantext = re.sub(\"[-]\", \" \" , cleantext)   # remove - sign\n",
        "\n",
        "  cleantext = re.sub(\"[^A-Za-z ]\", \" \" , cleantext)  # remove evey character except alphabet\n",
        "  cleantext = cleantext.lower()\n",
        "\n",
        "  words = nltk.tokenize.word_tokenize(cleantext)\n",
        "  words_new = [i for i in words if i not in stop_words]\n",
        "\n",
        "  w = [lemmatizer.lemmatize(word) for word in words_new if len(word)>2]\n",
        "\n",
        "  return ' '.join(w)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "GAI-8CacEeTL",
        "outputId": "2f20c9a4-a4db-4fd2-a7c3-ec68a830ff8e"
      },
      "source": [
        "clean('Pete The Cat Bedtime,virat-kohli Blues Doll, <br> sj </br> 14.5Inch 99brilliance')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'pete cat bedtime virat kohli blue doll inch brilliance'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "STNRT46qM-Tr",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "14ffc770-f7ac-4247-c6d7-969e42df55e3"
      },
      "source": [
        "train5['TEXT'] = tqdm(train5['TEXT'].apply(clean))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "100%|██████████| 494042/494042 [00:00<00:00, 1934423.64it/s]\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NEEFRb5ivJZw"
      },
      "source": [
        "#train['TEXT'] = tqdm_notebook(train['TEXT'].apply(clean))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fg7_c1CrsXlK",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 142
        },
        "outputId": "2aad7469-abed-40a0-edc5-afa29b280eb8"
      },
      "source": [
        "train5.head(3)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>BROWSE_NODE_ID</th>\n",
              "      <th>TEXT</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>17686</td>\n",
              "      <td>dogitos first meow healthy cat treat chicken s...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>32941</td>\n",
              "      <td>phoenix roasted makhana indian pudina dhania l...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1213</td>\n",
              "      <td>bigbanana men regular fit polo singleton white...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   BROWSE_NODE_ID                                               TEXT\n",
              "0           17686  dogitos first meow healthy cat treat chicken s...\n",
              "1           32941  phoenix roasted makhana indian pudina dhania l...\n",
              "2            1213  bigbanana men regular fit polo singleton white..."
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OsnsGBwwYpmw"
      },
      "source": [
        "#train.head(3)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dzU1Xn9sbDM8"
      },
      "source": [
        "# Making clean CSVs"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-_B6GSOUbH2-"
      },
      "source": [
        "train5.to_csv('/content/drive/MyDrive/Datasets/Amazon_ML_Challenge/train_clean5L.csv', index = False)\n",
        "#train.to_csv('/content/drive/MyDrive/Datasets/Amazon_ML_Challenge/dataset/train_clean.csv', index = False)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bhBOZgZvfBB6"
      },
      "source": [
        "#del(train15)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wxPKqIQBcVn_"
      },
      "source": [
        "# Loading Clean Data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_ONrRAoqZonx"
      },
      "source": [
        "'''train = pd.read_csv('/content/drive/MyDrive/Datasets/Amazon_ML_Challenge/dataset/train_clean.csv')\n",
        "train = pd.read_csv('/content/drive/MyDrive/Datasets/Amazon_ML_Challenge/dataset/train_clean.csv')\n",
        "\n",
        "data = [train, train]\n",
        "names = ['train', 'train']'''"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Marl5FJZcZp8",
        "outputId": "25a4eccf-a91e-4802-8649-c8d8d2499c06"
      },
      "source": [
        "'''for df in data:\n",
        "  print(df.isnull().sum())'''"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "TITLE                122\n",
            "DESCRIPTION       708134\n",
            "BULLET_POINTS     165024\n",
            "BRAND              63403\n",
            "BROWSE_NODE_ID         0\n",
            "dtype: int64\n",
            "PRODUCT_ID           0\n",
            "TITLE                7\n",
            "DESCRIPTION      10483\n",
            "BULLET_POINTS     6799\n",
            "BRAND             2588\n",
            "dtype: int64\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aYUEQmuodEeh"
      },
      "source": [
        "'''train = train.fillna(' ')\n",
        "train = train.fillna(' ')'''"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 142
        },
        "id": "dzk2qYUfcBzx",
        "outputId": "e48b30a8-df8f-4c75-d754-9b08cbd5bcdc"
      },
      "source": [
        "'''train.head(3)'''"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>TITLE</th>\n",
              "      <th>DESCRIPTION</th>\n",
              "      <th>BULLET_POINTS</th>\n",
              "      <th>BRAND</th>\n",
              "      <th>BROWSE_NODE_ID</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>pete cat bedtime blues doll inch</td>\n",
              "      <td>pete cat coolest popular cat town new pete the...</td>\n",
              "      <td>pete cat bedtime blues plush dollbased popular...</td>\n",
              "      <td>merrymakers</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>new yorker nyhm refrigerator magnet x</td>\n",
              "      <td>new yorker handsome cello wrapped hard magnet ...</td>\n",
              "      <td>cat tea cup new yorker cover artist gurbuz dog...</td>\n",
              "      <td>new yorker</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>ultimate selfsufficiency handbook complete gui...</td>\n",
              "      <td></td>\n",
              "      <td>skyhorse publishing</td>\n",
              "      <td>imusti</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                               TITLE  ... BROWSE_NODE_ID\n",
              "0                   pete cat bedtime blues doll inch  ...              0\n",
              "1              new yorker nyhm refrigerator magnet x  ...              1\n",
              "2  ultimate selfsufficiency handbook complete gui...  ...              2\n",
              "\n",
              "[3 rows x 5 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YovYPOnEVmSo"
      },
      "source": [
        "#END"
      ]
    }
  ]
}