Myntra_EDA_Project.ipynb

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        "<a href=\"https://colab.research.google.com/github/IshanSrivastav/EDA_IN_Myntra/blob/main/Myntra_EDA_Project.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "source": [
        "import pandas as pd\n",
        "\n",
        "df = pd.read_csv('/content/Myntra.csv')"
      ]
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      "cell_type": "code",
      "source": [
        "df.head(10)"
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          "data": {
            "text/plain": [
              "                     product_name      brand_name  rating  rating_count  \\\n",
              "0   Croc Textured Two Fold Wallet     Lino Perros     0.0             0   \n",
              "1             Men Striped Sliders  Mast & Harbour     4.0            76   \n",
              "2            Printed A-line Kurta            Biba     4.3            66   \n",
              "3    Girls Floral Printed T-shirt        Anthrilo     0.0             0   \n",
              "4  Women Printed Kurta with Skirt    FASHION DWAR     0.0             0   \n",
              "5        Floral Cotton Maxi Dress         Vishudh     4.1           800   \n",
              "6                 Fitted Crop Top   Tokyo Talkies     4.4           210   \n",
              "7          Men Mesh Running Shoes         Woakers     3.4           591   \n",
              "8            Men Checked Pullover        Roadster     4.3           528   \n",
              "9         Girls Cotton Jersey Top             H&M     4.2            16   \n",
              "\n",
              "   marked_price  discounted_price                       sizes  \\\n",
              "0          1295               828                     Onesize   \n",
              "1          1299               584   UK6,UK7,UK8,UK9,UK10,UK11   \n",
              "2          1999              1599            S,M,L,XL,XXL,3XL   \n",
              "3           599               539             7-8Y,8-9Y,9-10Y   \n",
              "4          2899              2899                    S,M,L,XL   \n",
              "5          4349              1826                S,M,L,XL,XXL   \n",
              "6           499               249                    S,M,L,XL   \n",
              "7          3125               687        UK6,UK7,UK8,UK9,UK10   \n",
              "8          1999               999                    S,M,L,XL   \n",
              "9           599               599  8-10Y,10-12Y,12-14Y,14-15Y   \n",
              "\n",
              "                                        product_link  \\\n",
              "0  wallets/lino-perros/lino-perros-women-peach-co...   \n",
              "1  flip-flops/mast--harbour/mast--harbour-men-nav...   \n",
              "2  kurtas/biba/biba-women-off-white--black-printe...   \n",
              "3  tshirts/anthrilo/anthrilo-girls-white-floral-p...   \n",
              "4  kurta-sets/fashion-dwar/fashion-dwar-women-mul...   \n",
              "5  dresses/vishudh/vishudh-rust-brown-floral-prin...   \n",
              "6  tops/tokyo-talkies/tokyo-talkies-pink-fitted-c...   \n",
              "7  sports-shoes/woakers/woakers-men-white--orange...   \n",
              "8  sweaters/roadster/the-roadster-lifestyle-co-me...   \n",
              "9  tops/hm/hm-girls-pink-cotton-jersey-top/169299...   \n",
              "\n",
              "                                            img_link   product_tag  \\\n",
              "0  https://assets.myntassets.com/dpr_2,q_60,w_210...       wallets   \n",
              "1  https://assets.myntassets.com/dpr_2,q_60,w_210...    flip-flops   \n",
              "2  https://assets.myntassets.com/dpr_2,q_60,w_210...        kurtas   \n",
              "3  https://assets.myntassets.com/dpr_2,q_60,w_210...       tshirts   \n",
              "4  https://assets.myntassets.com/dpr_2,q_60,w_210...    kurta-sets   \n",
              "5  https://assets.myntassets.com/dpr_2,q_60,w_210...       dresses   \n",
              "6  https://assets.myntassets.com/dpr_2,q_60,w_210...          tops   \n",
              "7  https://assets.myntassets.com/dpr_2,q_60,w_210...  sports-shoes   \n",
              "8  https://assets.myntassets.com/dpr_2,q_60,w_210...      sweaters   \n",
              "9  https://assets.myntassets.com/dpr_2,q_60,w_210...          tops   \n",
              "\n",
              "       brand_tag  discount_amount  discount_percent  \n",
              "0    lino-perros              467                36  \n",
              "1  mast--harbour              715                55  \n",
              "2           biba              400                20  \n",
              "3       anthrilo               60                10  \n",
              "4   fashion-dwar                0                 0  \n",
              "5        vishudh             2523                58  \n",
              "6  tokyo-talkies              250                50  \n",
              "7        woakers             2438                78  \n",
              "8       roadster             1000                50  \n",
              "9             hm                0                 0  "
            ],
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>product_name</th>\n",
              "      <th>brand_name</th>\n",
              "      <th>rating</th>\n",
              "      <th>rating_count</th>\n",
              "      <th>marked_price</th>\n",
              "      <th>discounted_price</th>\n",
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              "      <th>discount_percent</th>\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Croc Textured Two Fold Wallet</td>\n",
              "      <td>Lino Perros</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1295</td>\n",
              "      <td>828</td>\n",
              "      <td>Onesize</td>\n",
              "      <td>wallets/lino-perros/lino-perros-women-peach-co...</td>\n",
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              "      <td>wallets</td>\n",
              "      <td>lino-perros</td>\n",
              "      <td>467</td>\n",
              "      <td>36</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Men Striped Sliders</td>\n",
              "      <td>Mast &amp; Harbour</td>\n",
              "      <td>4.0</td>\n",
              "      <td>76</td>\n",
              "      <td>1299</td>\n",
              "      <td>584</td>\n",
              "      <td>UK6,UK7,UK8,UK9,UK10,UK11</td>\n",
              "      <td>flip-flops/mast--harbour/mast--harbour-men-nav...</td>\n",
              "      <td>https://assets.myntassets.com/dpr_2,q_60,w_210...</td>\n",
              "      <td>flip-flops</td>\n",
              "      <td>mast--harbour</td>\n",
              "      <td>715</td>\n",
              "      <td>55</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Printed A-line Kurta</td>\n",
              "      <td>Biba</td>\n",
              "      <td>4.3</td>\n",
              "      <td>66</td>\n",
              "      <td>1999</td>\n",
              "      <td>1599</td>\n",
              "      <td>S,M,L,XL,XXL,3XL</td>\n",
              "      <td>kurtas/biba/biba-women-off-white--black-printe...</td>\n",
              "      <td>https://assets.myntassets.com/dpr_2,q_60,w_210...</td>\n",
              "      <td>kurtas</td>\n",
              "      <td>biba</td>\n",
              "      <td>400</td>\n",
              "      <td>20</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Girls Floral Printed T-shirt</td>\n",
              "      <td>Anthrilo</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>599</td>\n",
              "      <td>539</td>\n",
              "      <td>7-8Y,8-9Y,9-10Y</td>\n",
              "      <td>tshirts/anthrilo/anthrilo-girls-white-floral-p...</td>\n",
              "      <td>https://assets.myntassets.com/dpr_2,q_60,w_210...</td>\n",
              "      <td>tshirts</td>\n",
              "      <td>anthrilo</td>\n",
              "      <td>60</td>\n",
              "      <td>10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Women Printed Kurta with Skirt</td>\n",
              "      <td>FASHION DWAR</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>2899</td>\n",
              "      <td>2899</td>\n",
              "      <td>S,M,L,XL</td>\n",
              "      <td>kurta-sets/fashion-dwar/fashion-dwar-women-mul...</td>\n",
              "      <td>https://assets.myntassets.com/dpr_2,q_60,w_210...</td>\n",
              "      <td>kurta-sets</td>\n",
              "      <td>fashion-dwar</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Floral Cotton Maxi Dress</td>\n",
              "      <td>Vishudh</td>\n",
              "      <td>4.1</td>\n",
              "      <td>800</td>\n",
              "      <td>4349</td>\n",
              "      <td>1826</td>\n",
              "      <td>S,M,L,XL,XXL</td>\n",
              "      <td>dresses/vishudh/vishudh-rust-brown-floral-prin...</td>\n",
              "      <td>https://assets.myntassets.com/dpr_2,q_60,w_210...</td>\n",
              "      <td>dresses</td>\n",
              "      <td>vishudh</td>\n",
              "      <td>2523</td>\n",
              "      <td>58</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Fitted Crop Top</td>\n",
              "      <td>Tokyo Talkies</td>\n",
              "      <td>4.4</td>\n",
              "      <td>210</td>\n",
              "      <td>499</td>\n",
              "      <td>249</td>\n",
              "      <td>S,M,L,XL</td>\n",
              "      <td>tops/tokyo-talkies/tokyo-talkies-pink-fitted-c...</td>\n",
              "      <td>https://assets.myntassets.com/dpr_2,q_60,w_210...</td>\n",
              "      <td>tops</td>\n",
              "      <td>tokyo-talkies</td>\n",
              "      <td>250</td>\n",
              "      <td>50</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>Men Mesh Running Shoes</td>\n",
              "      <td>Woakers</td>\n",
              "      <td>3.4</td>\n",
              "      <td>591</td>\n",
              "      <td>3125</td>\n",
              "      <td>687</td>\n",
              "      <td>UK6,UK7,UK8,UK9,UK10</td>\n",
              "      <td>sports-shoes/woakers/woakers-men-white--orange...</td>\n",
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              "      <td>2438</td>\n",
              "      <td>78</td>\n",
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              "      <th>8</th>\n",
              "      <td>Men Checked Pullover</td>\n",
              "      <td>Roadster</td>\n",
              "      <td>4.3</td>\n",
              "      <td>528</td>\n",
              "      <td>1999</td>\n",
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              "      <td>1000</td>\n",
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              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>Girls Cotton Jersey Top</td>\n",
              "      <td>H&amp;M</td>\n",
              "      <td>4.2</td>\n",
              "      <td>16</td>\n",
              "      <td>599</td>\n",
              "      <td>599</td>\n",
              "      <td>8-10Y,10-12Y,12-14Y,14-15Y</td>\n",
              "      <td>tops/hm/hm-girls-pink-cotton-jersey-top/169299...</td>\n",
              "      <td>https://assets.myntassets.com/dpr_2,q_60,w_210...</td>\n",
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              "    60% {\n",
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              "      border-right-color: var(--fill-color);\n",
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              "    }\n",
              "    90% {\n",
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              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-2ef42e17-daf1-411a-8a4f-2b1fb443fa53 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df"
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          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.tail()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 434
        },
        "id": "6ZlMDFSHykHT",
        "outputId": "8a2e57df-2d3d-41c0-c44a-fe62ea35c01a"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                              product_name brand_name  rating  rating_count  \\\n",
              "168024          Ethnic Motifs Kaftan Dress       Soch     0.0             0   \n",
              "168025  Leather Wedge Sandals with Tassels    fitflop     0.0             0   \n",
              "168026                 Leather Wedge Pumps    fitflop     0.0             0   \n",
              "168027      V015 Professional Hair Trimmer        VGR     0.0             0   \n",
              "168028                Women Open Toe Flats     daisie     0.0             0   \n",
              "\n",
              "        marked_price  discounted_price  \\\n",
              "168024          3998              3998   \n",
              "168025          6799              6799   \n",
              "168026          5999              5999   \n",
              "168027          2695               995   \n",
              "168028          1450              1377   \n",
              "\n",
              "                                            sizes  \\\n",
              "168024                                   S/M,L/XL   \n",
              "168025         EURO36,EURO37,EURO38,EURO39,EURO40   \n",
              "168026         EURO36,EURO37,EURO38,EURO39,EURO40   \n",
              "168027                                    Onesize   \n",
              "168028  EURO36,EURO37,EURO38,EURO39,EURO40,EURO41   \n",
              "\n",
              "                                             product_link  \\\n",
              "168024  dresses/soch/soch-blue-ethnic-motifs-kaftan-dr...   \n",
              "168025  heels/fitflop/fitflop-tan-leather-wedge-sandal...   \n",
              "168026  heels/fitflop/fitflop-blue-leather-wedge-pumps...   \n",
              "168027  trimmer/vgr/vgr-men-v-015-professional-hair-tr...   \n",
              "168028  flats/daisie/daisie-women-grey-printed-open-to...   \n",
              "\n",
              "                                                 img_link product_tag  \\\n",
              "168024  https://assets.myntassets.com/dpr_2,q_60,w_210...     dresses   \n",
              "168025  https://assets.myntassets.com/dpr_2,q_60,w_210...       heels   \n",
              "168026  https://assets.myntassets.com/dpr_2,q_60,w_210...       heels   \n",
              "168027  https://assets.myntassets.com/dpr_2,q_60,w_210...     trimmer   \n",
              "168028  https://assets.myntassets.com/dpr_2,q_60,w_210...       flats   \n",
              "\n",
              "       brand_tag  discount_amount  discount_percent  \n",
              "168024      soch                0                 0  \n",
              "168025   fitflop                0                 0  \n",
              "168026   fitflop                0                 0  \n",
              "168027       vgr             1700                63  \n",
              "168028    daisie               73                 5  "
            ],
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>product_name</th>\n",
              "      <th>brand_name</th>\n",
              "      <th>rating</th>\n",
              "      <th>rating_count</th>\n",
              "      <th>marked_price</th>\n",
              "      <th>discounted_price</th>\n",
              "      <th>sizes</th>\n",
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              "      <th>168024</th>\n",
              "      <td>Ethnic Motifs Kaftan Dress</td>\n",
              "      <td>Soch</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>3998</td>\n",
              "      <td>3998</td>\n",
              "      <td>S/M,L/XL</td>\n",
              "      <td>dresses/soch/soch-blue-ethnic-motifs-kaftan-dr...</td>\n",
              "      <td>https://assets.myntassets.com/dpr_2,q_60,w_210...</td>\n",
              "      <td>dresses</td>\n",
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              "      <th>168025</th>\n",
              "      <td>Leather Wedge Sandals with Tassels</td>\n",
              "      <td>fitflop</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>6799</td>\n",
              "      <td>6799</td>\n",
              "      <td>EURO36,EURO37,EURO38,EURO39,EURO40</td>\n",
              "      <td>heels/fitflop/fitflop-tan-leather-wedge-sandal...</td>\n",
              "      <td>https://assets.myntassets.com/dpr_2,q_60,w_210...</td>\n",
              "      <td>heels</td>\n",
              "      <td>fitflop</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>168026</th>\n",
              "      <td>Leather Wedge Pumps</td>\n",
              "      <td>fitflop</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>5999</td>\n",
              "      <td>5999</td>\n",
              "      <td>EURO36,EURO37,EURO38,EURO39,EURO40</td>\n",
              "      <td>heels/fitflop/fitflop-blue-leather-wedge-pumps...</td>\n",
              "      <td>https://assets.myntassets.com/dpr_2,q_60,w_210...</td>\n",
              "      <td>heels</td>\n",
              "      <td>fitflop</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>168027</th>\n",
              "      <td>V015 Professional Hair Trimmer</td>\n",
              "      <td>VGR</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>2695</td>\n",
              "      <td>995</td>\n",
              "      <td>Onesize</td>\n",
              "      <td>trimmer/vgr/vgr-men-v-015-professional-hair-tr...</td>\n",
              "      <td>https://assets.myntassets.com/dpr_2,q_60,w_210...</td>\n",
              "      <td>trimmer</td>\n",
              "      <td>vgr</td>\n",
              "      <td>1700</td>\n",
              "      <td>63</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>168028</th>\n",
              "      <td>Women Open Toe Flats</td>\n",
              "      <td>daisie</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1450</td>\n",
              "      <td>1377</td>\n",
              "      <td>EURO36,EURO37,EURO38,EURO39,EURO40,EURO41</td>\n",
              "      <td>flats/daisie/daisie-women-grey-printed-open-to...</td>\n",
              "      <td>https://assets.myntassets.com/dpr_2,q_60,w_210...</td>\n",
              "      <td>flats</td>\n",
              "      <td>daisie</td>\n",
              "      <td>73</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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              "      background-color: #E2EBFA;\n",
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              "      buttonEl.style.display =\n",
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              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-b85d0b9b-ea9f-4e62-857e-487e0549dd82');\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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              "  }\n",
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              "    background-color: var(--disabled-bg-color);\n",
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              "  }\n",
              "\n",
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              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
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              "      border-left-color: var(--fill-color);\n",
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              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-1a61a755-4152-4c31-b22a-0100aad030f7 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"product_name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"Leather Wedge Sandals with Tassels\",\n          \"Women Open Toe Flats\",\n          \"Leather Wedge Pumps\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"brand_name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"fitflop\",\n          \"daisie\",\n          \"Soch\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"rating\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0,\n        \"min\": 0.0,\n        \"max\": 0.0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"rating_count\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"marked_price\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2228,\n        \"min\": 1450,\n        \"max\": 6799,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          6799\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"discounted_price\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2626,\n        \"min\": 995,\n        \"max\": 6799,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          6799\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sizes\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"EURO36,EURO37,EURO38,EURO39,EURO40\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"product_link\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"heels/fitflop/fitflop-tan-leather-wedge-sandals-with-tassels/18713222/buy\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"img_link\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"https://assets.myntassets.com/dpr_2,q_60,w_210,c_limit,fl_progressive/assets/images/18713222/2022/6/13/158e7fae-6aae-4b2c-b583-f9d02ad125161655099733319Heels1.jpg\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"product_tag\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"heels\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"brand_tag\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"fitflop\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"discount_amount\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 752,\n        \"min\": 0,\n        \"max\": 1700,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"discount_percent\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 27,\n        \"min\": 0,\n        \"max\": 63,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data_info = df.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ADU65ApuzCTj",
        "outputId": "3b5ac6b9-2187-46b2-b324-383b6031ff3f"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 168029 entries, 0 to 168028\n",
            "Data columns (total 13 columns):\n",
            " #   Column            Non-Null Count   Dtype  \n",
            "---  ------            --------------   -----  \n",
            " 0   product_name      168029 non-null  object \n",
            " 1   brand_name        168029 non-null  object \n",
            " 2   rating            168029 non-null  float64\n",
            " 3   rating_count      168029 non-null  int64  \n",
            " 4   marked_price      168029 non-null  int64  \n",
            " 5   discounted_price  168029 non-null  int64  \n",
            " 6   sizes             168029 non-null  object \n",
            " 7   product_link      168029 non-null  object \n",
            " 8   img_link          168029 non-null  object \n",
            " 9   product_tag       168029 non-null  object \n",
            " 10  brand_tag         168029 non-null  object \n",
            " 11  discount_amount   168029 non-null  int64  \n",
            " 12  discount_percent  168029 non-null  int64  \n",
            "dtypes: float64(1), int64(5), object(7)\n",
            "memory usage: 16.7+ MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Checking Null Values\n",
        "\n",
        "df.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 491
        },
        "id": "OPRBrx8Jzq0E",
        "outputId": "7a50a206-d461-4d73-ded2-2b7b9b13677e"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "product_name        0\n",
              "brand_name          0\n",
              "rating              0\n",
              "rating_count        0\n",
              "marked_price        0\n",
              "discounted_price    0\n",
              "sizes               0\n",
              "product_link        0\n",
              "img_link            0\n",
              "product_tag         0\n",
              "brand_tag           0\n",
              "discount_amount     0\n",
              "discount_percent    0\n",
              "dtype: int64"
            ],
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
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              "\n",
              "    .dataframe thead th {\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
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              "      <th>product_name</th>\n",
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              "    <tr>\n",
              "      <th>brand_name</th>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>rating</th>\n",
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              "    <tr>\n",
              "      <th>rating_count</th>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>marked_price</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>discounted_price</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>sizes</th>\n",
              "      <td>0</td>\n",
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              "      <th>img_link</th>\n",
              "      <td>0</td>\n",
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              "      <th>product_tag</th>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>brand_tag</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>discount_amount</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>discount_percent</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> int64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Dropping Null Values\n",
        "\n",
        "df.dropna()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 790
        },
        "id": "uMsM8R_k0H3u",
        "outputId": "307ae7e9-d1de-4bf3-9ddc-0f6b5f10b022"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                              product_name      brand_name  rating  \\\n",
              "0            Croc Textured Two Fold Wallet     Lino Perros     0.0   \n",
              "1                      Men Striped Sliders  Mast & Harbour     4.0   \n",
              "2                     Printed A-line Kurta            Biba     4.3   \n",
              "3             Girls Floral Printed T-shirt        Anthrilo     0.0   \n",
              "4           Women Printed Kurta with Skirt    FASHION DWAR     0.0   \n",
              "...                                    ...             ...     ...   \n",
              "168024          Ethnic Motifs Kaftan Dress            Soch     0.0   \n",
              "168025  Leather Wedge Sandals with Tassels         fitflop     0.0   \n",
              "168026                 Leather Wedge Pumps         fitflop     0.0   \n",
              "168027      V015 Professional Hair Trimmer             VGR     0.0   \n",
              "168028                Women Open Toe Flats          daisie     0.0   \n",
              "\n",
              "        rating_count  marked_price  discounted_price  \\\n",
              "0                  0          1295               828   \n",
              "1                 76          1299               584   \n",
              "2                 66          1999              1599   \n",
              "3                  0           599               539   \n",
              "4                  0          2899              2899   \n",
              "...              ...           ...               ...   \n",
              "168024             0          3998              3998   \n",
              "168025             0          6799              6799   \n",
              "168026             0          5999              5999   \n",
              "168027             0          2695               995   \n",
              "168028             0          1450              1377   \n",
              "\n",
              "                                            sizes  \\\n",
              "0                                         Onesize   \n",
              "1                       UK6,UK7,UK8,UK9,UK10,UK11   \n",
              "2                                S,M,L,XL,XXL,3XL   \n",
              "3                                 7-8Y,8-9Y,9-10Y   \n",
              "4                                        S,M,L,XL   \n",
              "...                                           ...   \n",
              "168024                                   S/M,L/XL   \n",
              "168025         EURO36,EURO37,EURO38,EURO39,EURO40   \n",
              "168026         EURO36,EURO37,EURO38,EURO39,EURO40   \n",
              "168027                                    Onesize   \n",
              "168028  EURO36,EURO37,EURO38,EURO39,EURO40,EURO41   \n",
              "\n",
              "                                             product_link  \\\n",
              "0       wallets/lino-perros/lino-perros-women-peach-co...   \n",
              "1       flip-flops/mast--harbour/mast--harbour-men-nav...   \n",
              "2       kurtas/biba/biba-women-off-white--black-printe...   \n",
              "3       tshirts/anthrilo/anthrilo-girls-white-floral-p...   \n",
              "4       kurta-sets/fashion-dwar/fashion-dwar-women-mul...   \n",
              "...                                                   ...   \n",
              "168024  dresses/soch/soch-blue-ethnic-motifs-kaftan-dr...   \n",
              "168025  heels/fitflop/fitflop-tan-leather-wedge-sandal...   \n",
              "168026  heels/fitflop/fitflop-blue-leather-wedge-pumps...   \n",
              "168027  trimmer/vgr/vgr-men-v-015-professional-hair-tr...   \n",
              "168028  flats/daisie/daisie-women-grey-printed-open-to...   \n",
              "\n",
              "                                                 img_link product_tag  \\\n",
              "0       https://assets.myntassets.com/dpr_2,q_60,w_210...     wallets   \n",
              "1       https://assets.myntassets.com/dpr_2,q_60,w_210...  flip-flops   \n",
              "2       https://assets.myntassets.com/dpr_2,q_60,w_210...      kurtas   \n",
              "3       https://assets.myntassets.com/dpr_2,q_60,w_210...     tshirts   \n",
              "4       https://assets.myntassets.com/dpr_2,q_60,w_210...  kurta-sets   \n",
              "...                                                   ...         ...   \n",
              "168024  https://assets.myntassets.com/dpr_2,q_60,w_210...     dresses   \n",
              "168025  https://assets.myntassets.com/dpr_2,q_60,w_210...       heels   \n",
              "168026  https://assets.myntassets.com/dpr_2,q_60,w_210...       heels   \n",
              "168027  https://assets.myntassets.com/dpr_2,q_60,w_210...     trimmer   \n",
              "168028  https://assets.myntassets.com/dpr_2,q_60,w_210...       flats   \n",
              "\n",
              "            brand_tag  discount_amount  discount_percent  \n",
              "0         lino-perros              467                36  \n",
              "1       mast--harbour              715                55  \n",
              "2                biba              400                20  \n",
              "3            anthrilo               60                10  \n",
              "4        fashion-dwar                0                 0  \n",
              "...               ...              ...               ...  \n",
              "168024           soch                0                 0  \n",
              "168025        fitflop                0                 0  \n",
              "168026        fitflop                0                 0  \n",
              "168027            vgr             1700                63  \n",
              "168028         daisie               73                 5  \n",
              "\n",
              "[168029 rows x 13 columns]"
            ],
            "text/html": [
              "\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>product_name</th>\n",
              "      <th>brand_name</th>\n",
              "      <th>rating</th>\n",
              "      <th>rating_count</th>\n",
              "      <th>marked_price</th>\n",
              "      <th>discounted_price</th>\n",
              "      <th>sizes</th>\n",
              "      <th>product_link</th>\n",
              "      <th>img_link</th>\n",
              "      <th>product_tag</th>\n",
              "      <th>brand_tag</th>\n",
              "      <th>discount_amount</th>\n",
              "      <th>discount_percent</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Croc Textured Two Fold Wallet</td>\n",
              "      <td>Lino Perros</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1295</td>\n",
              "      <td>828</td>\n",
              "      <td>Onesize</td>\n",
              "      <td>wallets/lino-perros/lino-perros-women-peach-co...</td>\n",
              "      <td>https://assets.myntassets.com/dpr_2,q_60,w_210...</td>\n",
              "      <td>wallets</td>\n",
              "      <td>lino-perros</td>\n",
              "      <td>467</td>\n",
              "      <td>36</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Men Striped Sliders</td>\n",
              "      <td>Mast &amp; Harbour</td>\n",
              "      <td>4.0</td>\n",
              "      <td>76</td>\n",
              "      <td>1299</td>\n",
              "      <td>584</td>\n",
              "      <td>UK6,UK7,UK8,UK9,UK10,UK11</td>\n",
              "      <td>flip-flops/mast--harbour/mast--harbour-men-nav...</td>\n",
              "      <td>https://assets.myntassets.com/dpr_2,q_60,w_210...</td>\n",
              "      <td>flip-flops</td>\n",
              "      <td>mast--harbour</td>\n",
              "      <td>715</td>\n",
              "      <td>55</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Printed A-line Kurta</td>\n",
              "      <td>Biba</td>\n",
              "      <td>4.3</td>\n",
              "      <td>66</td>\n",
              "      <td>1999</td>\n",
              "      <td>1599</td>\n",
              "      <td>S,M,L,XL,XXL,3XL</td>\n",
              "      <td>kurtas/biba/biba-women-off-white--black-printe...</td>\n",
              "      <td>https://assets.myntassets.com/dpr_2,q_60,w_210...</td>\n",
              "      <td>kurtas</td>\n",
              "      <td>biba</td>\n",
              "      <td>400</td>\n",
              "      <td>20</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Girls Floral Printed T-shirt</td>\n",
              "      <td>Anthrilo</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>599</td>\n",
              "      <td>539</td>\n",
              "      <td>7-8Y,8-9Y,9-10Y</td>\n",
              "      <td>tshirts/anthrilo/anthrilo-girls-white-floral-p...</td>\n",
              "      <td>https://assets.myntassets.com/dpr_2,q_60,w_210...</td>\n",
              "      <td>tshirts</td>\n",
              "      <td>anthrilo</td>\n",
              "      <td>60</td>\n",
              "      <td>10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Women Printed Kurta with Skirt</td>\n",
              "      <td>FASHION DWAR</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>2899</td>\n",
              "      <td>2899</td>\n",
              "      <td>S,M,L,XL</td>\n",
              "      <td>kurta-sets/fashion-dwar/fashion-dwar-women-mul...</td>\n",
              "      <td>https://assets.myntassets.com/dpr_2,q_60,w_210...</td>\n",
              "      <td>kurta-sets</td>\n",
              "      <td>fashion-dwar</td>\n",
              "      <td>0</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
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              "      <td>...</td>\n",
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              "      <td>...</td>\n",
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              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>168024</th>\n",
              "      <td>Ethnic Motifs Kaftan Dress</td>\n",
              "      <td>Soch</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>3998</td>\n",
              "      <td>3998</td>\n",
              "      <td>S/M,L/XL</td>\n",
              "      <td>dresses/soch/soch-blue-ethnic-motifs-kaftan-dr...</td>\n",
              "      <td>https://assets.myntassets.com/dpr_2,q_60,w_210...</td>\n",
              "      <td>dresses</td>\n",
              "      <td>soch</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>168025</th>\n",
              "      <td>Leather Wedge Sandals with Tassels</td>\n",
              "      <td>fitflop</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>6799</td>\n",
              "      <td>6799</td>\n",
              "      <td>EURO36,EURO37,EURO38,EURO39,EURO40</td>\n",
              "      <td>heels/fitflop/fitflop-tan-leather-wedge-sandal...</td>\n",
              "      <td>https://assets.myntassets.com/dpr_2,q_60,w_210...</td>\n",
              "      <td>heels</td>\n",
              "      <td>fitflop</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>168026</th>\n",
              "      <td>Leather Wedge Pumps</td>\n",
              "      <td>fitflop</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>5999</td>\n",
              "      <td>5999</td>\n",
              "      <td>EURO36,EURO37,EURO38,EURO39,EURO40</td>\n",
              "      <td>heels/fitflop/fitflop-blue-leather-wedge-pumps...</td>\n",
              "      <td>https://assets.myntassets.com/dpr_2,q_60,w_210...</td>\n",
              "      <td>heels</td>\n",
              "      <td>fitflop</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>168027</th>\n",
              "      <td>V015 Professional Hair Trimmer</td>\n",
              "      <td>VGR</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>2695</td>\n",
              "      <td>995</td>\n",
              "      <td>Onesize</td>\n",
              "      <td>trimmer/vgr/vgr-men-v-015-professional-hair-tr...</td>\n",
              "      <td>https://assets.myntassets.com/dpr_2,q_60,w_210...</td>\n",
              "      <td>trimmer</td>\n",
              "      <td>vgr</td>\n",
              "      <td>1700</td>\n",
              "      <td>63</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>168028</th>\n",
              "      <td>Women Open Toe Flats</td>\n",
              "      <td>daisie</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1450</td>\n",
              "      <td>1377</td>\n",
              "      <td>EURO36,EURO37,EURO38,EURO39,EURO40,EURO41</td>\n",
              "      <td>flats/daisie/daisie-women-grey-printed-open-to...</td>\n",
              "      <td>https://assets.myntassets.com/dpr_2,q_60,w_210...</td>\n",
              "      <td>flats</td>\n",
              "      <td>daisie</td>\n",
              "      <td>73</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>168029 rows × 13 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-b168e0f2-7942-4857-910c-8dc2d7f3aa78')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      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",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
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              "\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",
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              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-b168e0f2-7942-4857-910c-8dc2d7f3aa78 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-b168e0f2-7942-4857-910c-8dc2d7f3aa78');\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",
              "\n",
              "\n",
              "<div id=\"df-1ef5e1b3-5beb-44ad-8729-c7e4105deca9\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-1ef5e1b3-5beb-44ad-8729-c7e4105deca9')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
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              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
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              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-1ef5e1b3-5beb-44ad-8729-c7e4105deca9 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe"
            }
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "IDVx0vHX0kem",
        "outputId": "85491f05-bdb4-4e64-b22f-279a29331aa7"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "              rating   rating_count  marked_price  discounted_price  \\\n",
              "count  168029.000000  168029.000000  168029.00000     168029.000000   \n",
              "mean        2.264556     114.668658    2509.07457       1515.168757   \n",
              "std         2.101402     800.207065    2402.10918       1800.460291   \n",
              "min         0.000000       0.000000      50.00000         49.000000   \n",
              "25%         0.000000       0.000000    1295.00000        674.000000   \n",
              "50%         3.500000       5.000000    1998.00000        999.000000   \n",
              "75%         4.300000      31.000000    2999.00000       1739.000000   \n",
              "max         5.000000   55900.000000  113999.00000      45900.000000   \n",
              "\n",
              "       discount_amount  discount_percent  \n",
              "count    168029.000000     168029.000000  \n",
              "mean        993.905814         37.251909  \n",
              "std        1236.606569         24.638975  \n",
              "min           0.000000          0.000000  \n",
              "25%         202.000000         15.000000  \n",
              "50%         702.000000         40.000000  \n",
              "75%        1319.000000         59.000000  \n",
              "max       68400.000000         91.000000  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-89f90811-0076-499d-840e-a8e9ef7ffee6\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>rating</th>\n",
              "      <th>rating_count</th>\n",
              "      <th>marked_price</th>\n",
              "      <th>discounted_price</th>\n",
              "      <th>discount_amount</th>\n",
              "      <th>discount_percent</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>168029.000000</td>\n",
              "      <td>168029.000000</td>\n",
              "      <td>168029.00000</td>\n",
              "      <td>168029.000000</td>\n",
              "      <td>168029.000000</td>\n",
              "      <td>168029.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>2.264556</td>\n",
              "      <td>114.668658</td>\n",
              "      <td>2509.07457</td>\n",
              "      <td>1515.168757</td>\n",
              "      <td>993.905814</td>\n",
              "      <td>37.251909</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>2.101402</td>\n",
              "      <td>800.207065</td>\n",
              "      <td>2402.10918</td>\n",
              "      <td>1800.460291</td>\n",
              "      <td>1236.606569</td>\n",
              "      <td>24.638975</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>50.00000</td>\n",
              "      <td>49.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1295.00000</td>\n",
              "      <td>674.000000</td>\n",
              "      <td>202.000000</td>\n",
              "      <td>15.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>3.500000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>1998.00000</td>\n",
              "      <td>999.000000</td>\n",
              "      <td>702.000000</td>\n",
              "      <td>40.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>4.300000</td>\n",
              "      <td>31.000000</td>\n",
              "      <td>2999.00000</td>\n",
              "      <td>1739.000000</td>\n",
              "      <td>1319.000000</td>\n",
              "      <td>59.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>5.000000</td>\n",
              "      <td>55900.000000</td>\n",
              "      <td>113999.00000</td>\n",
              "      <td>45900.000000</td>\n",
              "      <td>68400.000000</td>\n",
              "      <td>91.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
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              "\n",
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              "      background-color: #434B5C;\n",
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              "\n",
              "    <script>\n",
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              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-89f90811-0076-499d-840e-a8e9ef7ffee6');\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",
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              "\n",
              "\n",
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              "            title=\"Suggest charts\"\n",
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              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-4823a5cf-f24f-43ef-8cca-0b5c3ba0ec52 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 8,\n  \"fields\": [\n    {\n      \"column\": \"rating\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 59406.35568406718,\n        \"min\": 0.0,\n        \"max\": 168029.0,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          168029.0,\n          2.264556118289105,\n          4.3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"rating_count\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 59806.64021194714,\n        \"min\": 0.0,\n        \"max\": 168029.0,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          168029.0,\n          114.66865838634997,\n          31.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"marked_price\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 66013.54445754885,\n        \"min\": 50.0,\n        \"max\": 168029.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          2509.0745704610513,\n          1998.0,\n          168029.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"discounted_price\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 58872.37916149949,\n        \"min\": 49.0,\n        \"max\": 168029.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          1515.1687565836849,\n          999.0,\n          168029.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"discount_amount\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 60549.917020243854,\n        \"min\": 0.0,\n        \"max\": 168029.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          993.9058138773664,\n          702.0,\n          168029.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"discount_percent\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 59393.74914409677,\n        \"min\": 0.0,\n        \"max\": 168029.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          37.25190889667855,\n          40.0,\n          168029.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Checking the unique values\n",
        "\n",
        "unique_product_tag = df['product_tag'].unique()\n",
        "unique_product_tag"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IgbwkaHe0zfG",
        "outputId": "5fac8dee-7c41-46a9-926f-e6ccdb52c80d"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['wallets', 'flip-flops', 'kurtas', 'tshirts', 'kurta-sets',\n",
              "       'dresses', 'tops', 'sports-shoes', 'sweaters', 'caps',\n",
              "       'lounge-pants', 'handbags', 'heels', 'casual-shoes', 'earrings',\n",
              "       'jeans', 'shirts', 'jackets', 'face-serum-and-gel', 'formal-shoes',\n",
              "       'trolley-bag', 'sandals', 'shorts', 'perfume-and-body-mist',\n",
              "       'sarees', 'sweatshirts', 'eyeshadow', 'clutches', 'saree-blouse',\n",
              "       'belts', 'track-pants', 'bra', 'sunglasses', 'trousers', 'boxers',\n",
              "       'body-lotion', 'flats', 'night-suits', 'thermal-set',\n",
              "       'highlighter-and-blush', 'bedsheets', 'briefs', 'toner',\n",
              "       'palazzos', 'foundation-and-primer', 'backpacks', 'skirts',\n",
              "       'wall-art', 'watches', 'ring', 'sunscreen', 'swimwear',\n",
              "       'sofa-covers', 'clocks', 'pendant', 'organisers',\n",
              "       'face-wash-and-cleanser', 'speakers', 'lipstick', 'shrug',\n",
              "       'innerwear-vests', 'dress-material', 'suits', 'leggings',\n",
              "       'clothing-set', 'bracelet', 'accessory-gift-set',\n",
              "       'body-wash-and-scrub', 'shapewear', 'deodorant', 'dinnerware',\n",
              "       'trunk', 'nightdress', 'blazers', 'hair-care-kit', 'capris',\n",
              "       'bodysuit', 'lehenga-choli', 'lingerie-set', 'messenger-bag',\n",
              "       'jewellery-set', 'bangle', 'jeggings', 'kurtis', 'lounge-tshirts',\n",
              "       'lip-care', 'tights', 'boots', 'kajal-and-eyeliner', 'duffel-bag',\n",
              "       'jumpsuit', 'headphones', 'bb-and-cc-cream', 'makeup-brushes',\n",
              "       'hair-appliance', 'tracksuits', 'sports-sandals',\n",
              "       'hand-wash-and-sanitizer', 'laptop-bag', 'necklace-and-chains',\n",
              "       'co-ords', 'face-moisturisers', 'hair-cream-and-mask',\n",
              "       'hair-colour', 'concealer', 'bedding-set', 'planters', 'rucksacks',\n",
              "       'mangalsutra', 'dupatta', 'cushion-covers', 'hair-gel-and-spray',\n",
              "       'camisoles', 'hair-serum', 'curtains-and-sheers', 'socks',\n",
              "       'carpets', 'facial-kit', 'water-bottle', 'shampoo-and-conditioner',\n",
              "       'travel-accessory', 'handkerchief', 'bath-towels',\n",
              "       'artificial-flowers-and-plants', 'badminton-racquets',\n",
              "       'mens-grooming-kit', 'shaving-brush--razor', 'yoga-mats',\n",
              "       'lip-gloss', 'cups-and-mugs', 'compact', 'mask-and-peel',\n",
              "       'bath-robe', 'tunics', 'hair-oil', 'lounge-shorts', 'ties',\n",
              "       'eyebrow-enhancer', 'booties', 'lip-liner', 'dungarees',\n",
              "       'outdoor-masks', 'body-wax-and-essentials', 'stockings',\n",
              "       'towel-set', 'nail-polish', 'table-covers', 'baby-care-products',\n",
              "       'robe', 'face-scrub-and-exfoliator', 'hat', 'toe-rings',\n",
              "       'feminine-hygiene', 'sherwani', 'blankets-quilts-and-dohars',\n",
              "       'showpieces', 'hair-accessory', 'eye-cream', 'beauty-accessory',\n",
              "       'fragrance-gift-set', 'free-gifts', 'smart-watches',\n",
              "       'mosquito-nets', 'nehru-jackets', 'gloves', 'headband',\n",
              "       'beauty-gift-set', 'pyjamas', 'mascara', 'cookware', 'waistcoat',\n",
              "       'bed-covers', 'hand-and-feet-cream', 'churidar',\n",
              "       'swimwear-accessories', 'diaper-bags', 'burqas', 'vases',\n",
              "       'head-jewellery', 'cushions', 'baby-dolls', 'umbrellas',\n",
              "       'bakeware', 'kitchen-storage', 'trimmer', 'scarves',\n",
              "       'soft-toys-and-dolls', 'coats', 'wall-decor', 'ottomans',\n",
              "       'swim-bottoms', 'nosepin', 'sleepsuit', 'skin-care-gift-set',\n",
              "       'doormats', 'dhotis', 'waist-pouch', 'anklet', 'toiletry-kit',\n",
              "       'learning-and-development-toys', 'watch-gift-set', 'diwan-set',\n",
              "       'ceiling-lamps', 'patiala', 'thermal-tops', 'rompers', 'trays',\n",
              "       'harem-pants', 'rain-jacket', 'baby-photoshoot-props',\n",
              "       'chair-cover', 'bar-and-drinkware', 'frames', 'serveware',\n",
              "       'bathroom-accessories', 'makeup-kit', 'pillows', 'home-fragrances',\n",
              "       'electric-kettle', 'body-oil', 'saree-accessories',\n",
              "       'nail-essentials', 'lubricants', 'cufflinks', 'brooch', 'candles',\n",
              "       'floor-mats--dhurries', 'mattress-protector', 'floor-lamps',\n",
              "       'duvet-cover', 'bibs', 'sports-accessories', 'pooja-essentials',\n",
              "       'runners', 'stoles', 'bath-rugs', 'wall-shelves', 'string-lights',\n",
              "       'rain-suit', 'charms', 'shawl', 'table-napkins', 'hanger',\n",
              "       'appliance-covers', 'hair-brush-and-comb', 'candle-holders',\n",
              "       'massager', 'bath-accessories', 'wall-lamps', 'shower-curtains',\n",
              "       'laundry-bag', 'salwar', 'swim-tops', 'fountains', 'table-lamps',\n",
              "       'hand-towels', 'table-placemats', 'bolster-covers',\n",
              "       'mobile-accessories', 'makeup-remover', 'cricket-bats',\n",
              "       'beard--moustache-care', 'bean-bag-cover', 'face-towels',\n",
              "       'patiala-and-dupatta', 'photo-frames', 'shaving-essentials',\n",
              "       'coasters', 'condoms', 'air-fryer', 'pillow-covers',\n",
              "       'dining-essentials', 'key-chain', 'shoe-care', 'home-gift-sets',\n",
              "       'kitchen-tools', 'action-figures-and-play-set', 'aprons',\n",
              "       'ride-on-toys', 'ties-and-cufflinks', 'fitness-bands',\n",
              "       'stationery', 'wristbands', 'activity-toys-and-games',\n",
              "       'bath-and-body-gift-set', 'mufflers', 'lingerie-accessories',\n",
              "       'salwar-and-dupatta', 'table-linen-sets', 'suspenders',\n",
              "       'kids-table', 'bar-accessories', 'throws',\n",
              "       'bath-soak-salt-and-oil', 'thermal-bottoms', 'tissues-and-wipes',\n",
              "       'baby-gear--nursery', 'hand-blender', 'bolsters',\n",
              "       'baby-apparel-gift-set', 'mirrors', 'slips', 'watch-organiser',\n",
              "       'laptop-table', 'shoe-accessories', 'cutlery', 'sindoor',\n",
              "       'helmets', 'makeup-gift-set', 'box', 'decorative-bowls',\n",
              "       'hooks-and-holders', 'baby-utensils', 'decals-and-stickers',\n",
              "       'sleeping-bag', 'toy-vehicles', 'bins', 'juicers', 'epilator',\n",
              "       'pens', 'key-holders', 'rain-trousers', 'aroma-oil-diffusers',\n",
              "       'pocket-squares', 'windchimes', 'silver-coins', 'carry-cot',\n",
              "       'festive-decor', 'egg-boiler', 'baby-bed-sets', 'watch-straps',\n",
              "       'bleach', 'construction-toys', 'potpourri', 'coffee-maker',\n",
              "       'sandwich-maker', 'footballs', 'basketballs', 'clothing-fabric',\n",
              "       'mask-chains', 'kaleeras'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Unique Brands\n",
        "\n",
        "unique_brand_tag = df['brand_tag'].unique()\n",
        "unique_brand_tag"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7dZw9hfb2Jop",
        "outputId": "28a49554-2a86-434d-edf6-49c902c0b43a"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['lino-perros', 'mast--harbour', 'biba', ..., 'myra', 'stylpro',\n",
              "       'lil-pitaara'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Stripping White Spaces and lowercase\n",
        "\n",
        "df['product_tag'] = df['product_tag'].str.strip().str.lower()\n",
        "df['brand_tag'] = df['brand_tag'].str.strip().str.lower()"
      ],
      "metadata": {
        "id": "1k-mZUGQ2dhx"
      },
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.head(20)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "ft0wI4Ax3aQl",
        "outputId": "8ff0ba88-b006-44c1-8ef6-37fa6d597b99"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                        product_name        brand_name  rating  rating_count  \\\n",
              "0      Croc Textured Two Fold Wallet       Lino Perros     0.0             0   \n",
              "1                Men Striped Sliders    Mast & Harbour     4.0            76   \n",
              "2               Printed A-line Kurta              Biba     4.3            66   \n",
              "3       Girls Floral Printed T-shirt          Anthrilo     0.0             0   \n",
              "4     Women Printed Kurta with Skirt      FASHION DWAR     0.0             0   \n",
              "5           Floral Cotton Maxi Dress           Vishudh     4.1           800   \n",
              "6                    Fitted Crop Top     Tokyo Talkies     4.4           210   \n",
              "7             Men Mesh Running Shoes           Woakers     3.4           591   \n",
              "8               Men Checked Pullover          Roadster     4.3           528   \n",
              "9            Girls Cotton Jersey Top               H&M     4.2            16   \n",
              "10                  Cotton Twill Cap               H&M     4.2            13   \n",
              "11     Women Geometric Printed Kurta           Vishudh     4.3            48   \n",
              "12  Women  Solid Cotton Lounge Pants     Mystere Paris     4.2             5   \n",
              "13        Croc Textured Handheld Bag       Accessorize     4.6            67   \n",
              "14                         Men Kurta           Jompers     4.5            36   \n",
              "15      Block Peep Toes with Buckles           FOOTONS     0.0             0   \n",
              "16                        Men Derbys                ID     4.3            67   \n",
              "17         Gold-Plated Drop Earrings     Zaveri Pearls     4.3           168   \n",
              "18             Embellished Net Dress  Toy Balloon kids     0.0             0   \n",
              "19                Men Slim Fit Jeans        Greenfibre     0.0             0   \n",
              "\n",
              "    marked_price  discounted_price  \\\n",
              "0           1295               828   \n",
              "1           1299               584   \n",
              "2           1999              1599   \n",
              "3            599               539   \n",
              "4           2899              2899   \n",
              "5           4349              1826   \n",
              "6            499               249   \n",
              "7           3125               687   \n",
              "8           1999               999   \n",
              "9            599               599   \n",
              "10           699               699   \n",
              "11          1849               647   \n",
              "12           899               899   \n",
              "13          6925              5886   \n",
              "14          2099               902   \n",
              "15          3399              1599   \n",
              "16          3985              1912   \n",
              "17          2495               499   \n",
              "18          1999               899   \n",
              "19          1999              1999   \n",
              "\n",
              "                                                sizes  \\\n",
              "0                                             Onesize   \n",
              "1                           UK6,UK7,UK8,UK9,UK10,UK11   \n",
              "2                                    S,M,L,XL,XXL,3XL   \n",
              "3                                     7-8Y,8-9Y,9-10Y   \n",
              "4                                            S,M,L,XL   \n",
              "5                                        S,M,L,XL,XXL   \n",
              "6                                            S,M,L,XL   \n",
              "7                                UK6,UK7,UK8,UK9,UK10   \n",
              "8                                            S,M,L,XL   \n",
              "9                          8-10Y,10-12Y,12-14Y,14-15Y   \n",
              "10                                            Onesize   \n",
              "11                                       S,M,L,XL,XXL   \n",
              "12                                       S,M,L,XL,XXL   \n",
              "13                                            Onesize   \n",
              "14                                       S,M,L,XL,XXL   \n",
              "15          EURO36,EURO37,EURO38,EURO39,EURO40,EURO41   \n",
              "16                          UK6,UK7,UK8,UK9,UK10,UK11   \n",
              "17                                            Onesize   \n",
              "18  1-2Y,2-3Y,3-4Y,4-5Y,5-6Y,6-7Y,7-8Y,8-9Y,9-10Y,...   \n",
              "19                               28,30,32,34,36,38,40   \n",
              "\n",
              "                                         product_link  \\\n",
              "0   wallets/lino-perros/lino-perros-women-peach-co...   \n",
              "1   flip-flops/mast--harbour/mast--harbour-men-nav...   \n",
              "2   kurtas/biba/biba-women-off-white--black-printe...   \n",
              "3   tshirts/anthrilo/anthrilo-girls-white-floral-p...   \n",
              "4   kurta-sets/fashion-dwar/fashion-dwar-women-mul...   \n",
              "5   dresses/vishudh/vishudh-rust-brown-floral-prin...   \n",
              "6   tops/tokyo-talkies/tokyo-talkies-pink-fitted-c...   \n",
              "7   sports-shoes/woakers/woakers-men-white--orange...   \n",
              "8   sweaters/roadster/the-roadster-lifestyle-co-me...   \n",
              "9   tops/hm/hm-girls-pink-cotton-jersey-top/169299...   \n",
              "10     caps/hm/hm-white-cotton-twill-cap/18212980/buy   \n",
              "11  kurtas/vishudh/vishudh-women-black-geometric-p...   \n",
              "12  lounge-pants/mystere-paris/mystere-paris-women...   \n",
              "13  handbags/accessorize/accessorize-white-croc-te...   \n",
              "14  kurtas/jompers/jompers-men-olive-green-thread-...   \n",
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              "17  earrings/zaveri-pearls/zaveri-pearls-green--of...   \n",
              "18  dresses/toy-balloon-kids/toy-balloon-kids-blue...   \n",
              "19  jeans/greenfibre/greenfibre-men-blue-slim-fit-...   \n",
              "\n",
              "                                             img_link   product_tag  \\\n",
              "0   https://assets.myntassets.com/dpr_2,q_60,w_210...       wallets   \n",
              "1   https://assets.myntassets.com/dpr_2,q_60,w_210...    flip-flops   \n",
              "2   https://assets.myntassets.com/dpr_2,q_60,w_210...        kurtas   \n",
              "3   https://assets.myntassets.com/dpr_2,q_60,w_210...       tshirts   \n",
              "4   https://assets.myntassets.com/dpr_2,q_60,w_210...    kurta-sets   \n",
              "5   https://assets.myntassets.com/dpr_2,q_60,w_210...       dresses   \n",
              "6   https://assets.myntassets.com/dpr_2,q_60,w_210...          tops   \n",
              "7   https://assets.myntassets.com/dpr_2,q_60,w_210...  sports-shoes   \n",
              "8   https://assets.myntassets.com/dpr_2,q_60,w_210...      sweaters   \n",
              "9   https://assets.myntassets.com/dpr_2,q_60,w_210...          tops   \n",
              "10  https://assets.myntassets.com/dpr_2,q_60,w_210...          caps   \n",
              "11  https://assets.myntassets.com/dpr_2,q_60,w_210...        kurtas   \n",
              "12  https://assets.myntassets.com/dpr_2,q_60,w_210...  lounge-pants   \n",
              "13  https://assets.myntassets.com/dpr_2,q_60,w_210...      handbags   \n",
              "14  https://assets.myntassets.com/dpr_2,q_60,w_210...        kurtas   \n",
              "15  https://assets.myntassets.com/dpr_2,q_60,w_210...         heels   \n",
              "16  https://assets.myntassets.com/dpr_2,q_60,w_210...  casual-shoes   \n",
              "17  https://assets.myntassets.com/dpr_2,q_60,w_210...      earrings   \n",
              "18  https://assets.myntassets.com/dpr_2,q_60,w_210...       dresses   \n",
              "19  https://assets.myntassets.com/dpr_2,q_60,w_210...         jeans   \n",
              "\n",
              "           brand_tag  discount_amount  discount_percent  \n",
              "0        lino-perros              467                36  \n",
              "1      mast--harbour              715                55  \n",
              "2               biba              400                20  \n",
              "3           anthrilo               60                10  \n",
              "4       fashion-dwar                0                 0  \n",
              "5            vishudh             2523                58  \n",
              "6      tokyo-talkies              250                50  \n",
              "7            woakers             2438                78  \n",
              "8           roadster             1000                50  \n",
              "9                 hm                0                 0  \n",
              "10                hm                0                 0  \n",
              "11           vishudh             1202                65  \n",
              "12     mystere-paris                0                 0  \n",
              "13       accessorize             1039                15  \n",
              "14           jompers             1197                57  \n",
              "15           footons             1800                52  \n",
              "16                id             2073                52  \n",
              "17     zaveri-pearls             1996                80  \n",
              "18  toy-balloon-kids             1100                55  \n",
              "19        greenfibre                0                 0  "
            ],
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              "      <th></th>\n",
              "      <th>product_name</th>\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Croc Textured Two Fold Wallet</td>\n",
              "      <td>Lino Perros</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1295</td>\n",
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              "      <td>36</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Men Striped Sliders</td>\n",
              "      <td>Mast &amp; Harbour</td>\n",
              "      <td>4.0</td>\n",
              "      <td>76</td>\n",
              "      <td>1299</td>\n",
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              "      <td>715</td>\n",
              "      <td>55</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Printed A-line Kurta</td>\n",
              "      <td>Biba</td>\n",
              "      <td>4.3</td>\n",
              "      <td>66</td>\n",
              "      <td>1999</td>\n",
              "      <td>1599</td>\n",
              "      <td>S,M,L,XL,XXL,3XL</td>\n",
              "      <td>kurtas/biba/biba-women-off-white--black-printe...</td>\n",
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              "      <td>kurtas</td>\n",
              "      <td>biba</td>\n",
              "      <td>400</td>\n",
              "      <td>20</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Girls Floral Printed T-shirt</td>\n",
              "      <td>Anthrilo</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>599</td>\n",
              "      <td>539</td>\n",
              "      <td>7-8Y,8-9Y,9-10Y</td>\n",
              "      <td>tshirts/anthrilo/anthrilo-girls-white-floral-p...</td>\n",
              "      <td>https://assets.myntassets.com/dpr_2,q_60,w_210...</td>\n",
              "      <td>tshirts</td>\n",
              "      <td>anthrilo</td>\n",
              "      <td>60</td>\n",
              "      <td>10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Women Printed Kurta with Skirt</td>\n",
              "      <td>FASHION DWAR</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>2899</td>\n",
              "      <td>2899</td>\n",
              "      <td>S,M,L,XL</td>\n",
              "      <td>kurta-sets/fashion-dwar/fashion-dwar-women-mul...</td>\n",
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              "      <td>kurta-sets</td>\n",
              "      <td>fashion-dwar</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Floral Cotton Maxi Dress</td>\n",
              "      <td>Vishudh</td>\n",
              "      <td>4.1</td>\n",
              "      <td>800</td>\n",
              "      <td>4349</td>\n",
              "      <td>1826</td>\n",
              "      <td>S,M,L,XL,XXL</td>\n",
              "      <td>dresses/vishudh/vishudh-rust-brown-floral-prin...</td>\n",
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              "      <td>vishudh</td>\n",
              "      <td>2523</td>\n",
              "      <td>58</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Fitted Crop Top</td>\n",
              "      <td>Tokyo Talkies</td>\n",
              "      <td>4.4</td>\n",
              "      <td>210</td>\n",
              "      <td>499</td>\n",
              "      <td>249</td>\n",
              "      <td>S,M,L,XL</td>\n",
              "      <td>tops/tokyo-talkies/tokyo-talkies-pink-fitted-c...</td>\n",
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              "      <td>tops</td>\n",
              "      <td>tokyo-talkies</td>\n",
              "      <td>250</td>\n",
              "      <td>50</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>Men Mesh Running Shoes</td>\n",
              "      <td>Woakers</td>\n",
              "      <td>3.4</td>\n",
              "      <td>591</td>\n",
              "      <td>3125</td>\n",
              "      <td>687</td>\n",
              "      <td>UK6,UK7,UK8,UK9,UK10</td>\n",
              "      <td>sports-shoes/woakers/woakers-men-white--orange...</td>\n",
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              "      <td>sports-shoes</td>\n",
              "      <td>woakers</td>\n",
              "      <td>2438</td>\n",
              "      <td>78</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>Men Checked Pullover</td>\n",
              "      <td>Roadster</td>\n",
              "      <td>4.3</td>\n",
              "      <td>528</td>\n",
              "      <td>1999</td>\n",
              "      <td>999</td>\n",
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              "      <td>sweaters/roadster/the-roadster-lifestyle-co-me...</td>\n",
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              "      <td>1000</td>\n",
              "      <td>50</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>Girls Cotton Jersey Top</td>\n",
              "      <td>H&amp;M</td>\n",
              "      <td>4.2</td>\n",
              "      <td>16</td>\n",
              "      <td>599</td>\n",
              "      <td>599</td>\n",
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              "      <td>tops/hm/hm-girls-pink-cotton-jersey-top/169299...</td>\n",
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              "      <td>tops</td>\n",
              "      <td>hm</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>Cotton Twill Cap</td>\n",
              "      <td>H&amp;M</td>\n",
              "      <td>4.2</td>\n",
              "      <td>13</td>\n",
              "      <td>699</td>\n",
              "      <td>699</td>\n",
              "      <td>Onesize</td>\n",
              "      <td>caps/hm/hm-white-cotton-twill-cap/18212980/buy</td>\n",
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              "      <td>caps</td>\n",
              "      <td>hm</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>Women Geometric Printed Kurta</td>\n",
              "      <td>Vishudh</td>\n",
              "      <td>4.3</td>\n",
              "      <td>48</td>\n",
              "      <td>1849</td>\n",
              "      <td>647</td>\n",
              "      <td>S,M,L,XL,XXL</td>\n",
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              "      <td>kurtas</td>\n",
              "      <td>vishudh</td>\n",
              "      <td>1202</td>\n",
              "      <td>65</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>Women  Solid Cotton Lounge Pants</td>\n",
              "      <td>Mystere Paris</td>\n",
              "      <td>4.2</td>\n",
              "      <td>5</td>\n",
              "      <td>899</td>\n",
              "      <td>899</td>\n",
              "      <td>S,M,L,XL,XXL</td>\n",
              "      <td>lounge-pants/mystere-paris/mystere-paris-women...</td>\n",
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              "      <td>lounge-pants</td>\n",
              "      <td>mystere-paris</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>Croc Textured Handheld Bag</td>\n",
              "      <td>Accessorize</td>\n",
              "      <td>4.6</td>\n",
              "      <td>67</td>\n",
              "      <td>6925</td>\n",
              "      <td>5886</td>\n",
              "      <td>Onesize</td>\n",
              "      <td>handbags/accessorize/accessorize-white-croc-te...</td>\n",
              "      <td>https://assets.myntassets.com/dpr_2,q_60,w_210...</td>\n",
              "      <td>handbags</td>\n",
              "      <td>accessorize</td>\n",
              "      <td>1039</td>\n",
              "      <td>15</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>Men Kurta</td>\n",
              "      <td>Jompers</td>\n",
              "      <td>4.5</td>\n",
              "      <td>36</td>\n",
              "      <td>2099</td>\n",
              "      <td>902</td>\n",
              "      <td>S,M,L,XL,XXL</td>\n",
              "      <td>kurtas/jompers/jompers-men-olive-green-thread-...</td>\n",
              "      <td>https://assets.myntassets.com/dpr_2,q_60,w_210...</td>\n",
              "      <td>kurtas</td>\n",
              "      <td>jompers</td>\n",
              "      <td>1197</td>\n",
              "      <td>57</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>Block Peep Toes with Buckles</td>\n",
              "      <td>FOOTONS</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>3399</td>\n",
              "      <td>1599</td>\n",
              "      <td>EURO36,EURO37,EURO38,EURO39,EURO40,EURO41</td>\n",
              "      <td>heels/footons/footons-cream-coloured-block-pee...</td>\n",
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              "      <td>heels</td>\n",
              "      <td>footons</td>\n",
              "      <td>1800</td>\n",
              "      <td>52</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>Men Derbys</td>\n",
              "      <td>ID</td>\n",
              "      <td>4.3</td>\n",
              "      <td>67</td>\n",
              "      <td>3985</td>\n",
              "      <td>1912</td>\n",
              "      <td>UK6,UK7,UK8,UK9,UK10,UK11</td>\n",
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              "      <td>id</td>\n",
              "      <td>2073</td>\n",
              "      <td>52</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>Gold-Plated Drop Earrings</td>\n",
              "      <td>Zaveri Pearls</td>\n",
              "      <td>4.3</td>\n",
              "      <td>168</td>\n",
              "      <td>2495</td>\n",
              "      <td>499</td>\n",
              "      <td>Onesize</td>\n",
              "      <td>earrings/zaveri-pearls/zaveri-pearls-green--of...</td>\n",
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              "      <td>earrings</td>\n",
              "      <td>zaveri-pearls</td>\n",
              "      <td>1996</td>\n",
              "      <td>80</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>Embellished Net Dress</td>\n",
              "      <td>Toy Balloon kids</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1999</td>\n",
              "      <td>899</td>\n",
              "      <td>1-2Y,2-3Y,3-4Y,4-5Y,5-6Y,6-7Y,7-8Y,8-9Y,9-10Y,...</td>\n",
              "      <td>dresses/toy-balloon-kids/toy-balloon-kids-blue...</td>\n",
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              "      <td>dresses</td>\n",
              "      <td>toy-balloon-kids</td>\n",
              "      <td>1100</td>\n",
              "      <td>55</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>Men Slim Fit Jeans</td>\n",
              "      <td>Greenfibre</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1999</td>\n",
              "      <td>1999</td>\n",
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              "      <td>jeans</td>\n",
              "      <td>greenfibre</td>\n",
              "      <td>0</td>\n",
              "      <td>0</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",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\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",
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              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-96d5a457-0807-4d4c-b33d-83f26145ab24 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-96d5a457-0807-4d4c-b33d-83f26145ab24');\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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              "\n",
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              "    </g>\n",
              "</svg>\n",
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              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
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              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
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              "      --fill-color: #D2E3FC;\n",
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              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
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              "      border-color: transparent;\n",
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              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-f656bc6b-a164-49ec-b34a-98bc90536382 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df"
            }
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['product_link'][1]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        },
        "id": "tCEKIBUY3biq",
        "outputId": "81c48c85-9200-415f-ef07-0d7a096fe7ed"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'flip-flops/mast--harbour/mast--harbour-men-navy-blue--grey-striped-sliders/12548958/buy'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['img_link'][1]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 53
        },
        "id": "KCI4DzL341jj",
        "outputId": "7cc0c658-d2c3-4a1b-b214-3541f2200a30"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'https://assets.myntassets.com/dpr_2,q_60,w_210,c_limit,fl_progressive/assets/images/12548958/2020/11/5/8b7ecd9b-8395-40fb-b5a4-ea3134d815b21604576826976-Mast--Harbour-Men-Flip-Flops-9011604576825368-1.jpg'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Feature Engineering - 1\n",
        "\n",
        "bins = [0, 500, 1000, 2000, 5000, 10000, df['marked_price'].max()]\n",
        "labels = ['Under 500', 'Rs 500 - Rs 1000', 'Rs 1000 - Rs 2000', 'Rs 2000 - Rs 5000', 'Rs 5000 - Rs 10000', 'Above Rs 10000']\n",
        "df['price_range'] = pd.cut(df['marked_price'], bins = bins, labels = labels, include_lowest = True)"
      ],
      "metadata": {
        "id": "Sem3NyaN5Dpl"
      },
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-QFAp-oM_z0P",
        "outputId": "93371820-80b9-4d4a-83c1-8b21955ecbc5"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(168029, 14)"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 382
        },
        "id": "UV5rTDrf7QLr",
        "outputId": "6299db33-2992-4559-c132-46f601bfcc52"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                     product_name      brand_name  rating  rating_count  \\\n",
              "0   Croc Textured Two Fold Wallet     Lino Perros     0.0             0   \n",
              "1             Men Striped Sliders  Mast & Harbour     4.0            76   \n",
              "2            Printed A-line Kurta            Biba     4.3            66   \n",
              "3    Girls Floral Printed T-shirt        Anthrilo     0.0             0   \n",
              "4  Women Printed Kurta with Skirt    FASHION DWAR     0.0             0   \n",
              "\n",
              "   marked_price  discounted_price                      sizes  \\\n",
              "0          1295               828                    Onesize   \n",
              "1          1299               584  UK6,UK7,UK8,UK9,UK10,UK11   \n",
              "2          1999              1599           S,M,L,XL,XXL,3XL   \n",
              "3           599               539            7-8Y,8-9Y,9-10Y   \n",
              "4          2899              2899                   S,M,L,XL   \n",
              "\n",
              "                                        product_link  \\\n",
              "0  wallets/lino-perros/lino-perros-women-peach-co...   \n",
              "1  flip-flops/mast--harbour/mast--harbour-men-nav...   \n",
              "2  kurtas/biba/biba-women-off-white--black-printe...   \n",
              "3  tshirts/anthrilo/anthrilo-girls-white-floral-p...   \n",
              "4  kurta-sets/fashion-dwar/fashion-dwar-women-mul...   \n",
              "\n",
              "                                            img_link product_tag  \\\n",
              "0  https://assets.myntassets.com/dpr_2,q_60,w_210...     wallets   \n",
              "1  https://assets.myntassets.com/dpr_2,q_60,w_210...  flip-flops   \n",
              "2  https://assets.myntassets.com/dpr_2,q_60,w_210...      kurtas   \n",
              "3  https://assets.myntassets.com/dpr_2,q_60,w_210...     tshirts   \n",
              "4  https://assets.myntassets.com/dpr_2,q_60,w_210...  kurta-sets   \n",
              "\n",
              "       brand_tag  discount_amount  discount_percent        price_range  \n",
              "0    lino-perros              467                36  Rs 1000 - Rs 2000  \n",
              "1  mast--harbour              715                55  Rs 1000 - Rs 2000  \n",
              "2           biba              400                20  Rs 1000 - Rs 2000  \n",
              "3       anthrilo               60                10   Rs 500 - Rs 1000  \n",
              "4   fashion-dwar                0                 0  Rs 2000 - Rs 5000  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-1e2f826d-f1e8-4cde-b32c-e617bbe55f31\" class=\"colab-df-container\">\n",
              "    <div>\n",
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              "        vertical-align: middle;\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>product_name</th>\n",
              "      <th>brand_name</th>\n",
              "      <th>rating</th>\n",
              "      <th>rating_count</th>\n",
              "      <th>marked_price</th>\n",
              "      <th>discounted_price</th>\n",
              "      <th>sizes</th>\n",
              "      <th>product_link</th>\n",
              "      <th>img_link</th>\n",
              "      <th>product_tag</th>\n",
              "      <th>brand_tag</th>\n",
              "      <th>discount_amount</th>\n",
              "      <th>discount_percent</th>\n",
              "      <th>price_range</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Croc Textured Two Fold Wallet</td>\n",
              "      <td>Lino Perros</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1295</td>\n",
              "      <td>828</td>\n",
              "      <td>Onesize</td>\n",
              "      <td>wallets/lino-perros/lino-perros-women-peach-co...</td>\n",
              "      <td>https://assets.myntassets.com/dpr_2,q_60,w_210...</td>\n",
              "      <td>wallets</td>\n",
              "      <td>lino-perros</td>\n",
              "      <td>467</td>\n",
              "      <td>36</td>\n",
              "      <td>Rs 1000 - Rs 2000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Men Striped Sliders</td>\n",
              "      <td>Mast &amp; Harbour</td>\n",
              "      <td>4.0</td>\n",
              "      <td>76</td>\n",
              "      <td>1299</td>\n",
              "      <td>584</td>\n",
              "      <td>UK6,UK7,UK8,UK9,UK10,UK11</td>\n",
              "      <td>flip-flops/mast--harbour/mast--harbour-men-nav...</td>\n",
              "      <td>https://assets.myntassets.com/dpr_2,q_60,w_210...</td>\n",
              "      <td>flip-flops</td>\n",
              "      <td>mast--harbour</td>\n",
              "      <td>715</td>\n",
              "      <td>55</td>\n",
              "      <td>Rs 1000 - Rs 2000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Printed A-line Kurta</td>\n",
              "      <td>Biba</td>\n",
              "      <td>4.3</td>\n",
              "      <td>66</td>\n",
              "      <td>1999</td>\n",
              "      <td>1599</td>\n",
              "      <td>S,M,L,XL,XXL,3XL</td>\n",
              "      <td>kurtas/biba/biba-women-off-white--black-printe...</td>\n",
              "      <td>https://assets.myntassets.com/dpr_2,q_60,w_210...</td>\n",
              "      <td>kurtas</td>\n",
              "      <td>biba</td>\n",
              "      <td>400</td>\n",
              "      <td>20</td>\n",
              "      <td>Rs 1000 - Rs 2000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Girls Floral Printed T-shirt</td>\n",
              "      <td>Anthrilo</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>599</td>\n",
              "      <td>539</td>\n",
              "      <td>7-8Y,8-9Y,9-10Y</td>\n",
              "      <td>tshirts/anthrilo/anthrilo-girls-white-floral-p...</td>\n",
              "      <td>https://assets.myntassets.com/dpr_2,q_60,w_210...</td>\n",
              "      <td>tshirts</td>\n",
              "      <td>anthrilo</td>\n",
              "      <td>60</td>\n",
              "      <td>10</td>\n",
              "      <td>Rs 500 - Rs 1000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Women Printed Kurta with Skirt</td>\n",
              "      <td>FASHION DWAR</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>2899</td>\n",
              "      <td>2899</td>\n",
              "      <td>S,M,L,XL</td>\n",
              "      <td>kurta-sets/fashion-dwar/fashion-dwar-women-mul...</td>\n",
              "      <td>https://assets.myntassets.com/dpr_2,q_60,w_210...</td>\n",
              "      <td>kurta-sets</td>\n",
              "      <td>fashion-dwar</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>Rs 2000 - Rs 5000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
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              "            title=\"Convert this dataframe to an interactive table.\"\n",
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              "\n",
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              "      display:flex;\n",
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              "\n",
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              "\n",
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              "    [theme=dark] .colab-df-convert {\n",
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              "\n",
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              "        document.querySelector('#df-1e2f826d-f1e8-4cde-b32c-e617bbe55f31 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-1e2f826d-f1e8-4cde-b32c-e617bbe55f31');\n",
              "        const dataTable =\n",
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              "                                                    [key], {});\n",
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              "\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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              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
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              "      --hover-fill-color: #174EA6;\n",
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              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-f933c465-d062-4e3d-abfa-f7c29463b5d5 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df"
            }
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Feature Engineering - 2\n",
        "\n",
        "for i in df['product_link'][:10]:\n",
        "  print(i)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2s4Nhn3F7RwF",
        "outputId": "e2eefd36-e30c-4223-e8e3-a58be3a11eb8"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "wallets/lino-perros/lino-perros-women-peach-coloured-croc-textured-two-fold-wallet/18070682/buy\n",
            "flip-flops/mast--harbour/mast--harbour-men-navy-blue--grey-striped-sliders/12548958/buy\n",
            "kurtas/biba/biba-women-off-white--black-printed-pure-cotton-a-line-kurta/13446092/buy\n",
            "tshirts/anthrilo/anthrilo-girls-white-floral-printed-t-shirt/18017006/buy\n",
            "kurta-sets/fashion-dwar/fashion-dwar-women-multicoloured-ethnic-motifs-printed-pleated-pure-cotton-kurta-with-skirt/18015232/buy\n",
            "dresses/vishudh/vishudh-rust-brown-floral-printed-cotton-anarkali-dress/7726509/buy\n",
            "tops/tokyo-talkies/tokyo-talkies-pink-fitted-crop-top/14424536/buy\n",
            "sports-shoes/woakers/woakers-men-white--orange-mesh-running-shoes/13067478/buy\n",
            "sweaters/roadster/the-roadster-lifestyle-co-men-black--teal-blue-jacquard-checked-pullover/15654428/buy\n",
            "tops/hm/hm-girls-pink-cotton-jersey-top/16929940/buy\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "'wallets/lino-perros/lino-perros-women-peach-coloured-croc-textured-two-fold-wallet/18070682/buy'.split('/')[3]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        },
        "id": "tmJ58vC99EsH",
        "outputId": "7e346a7c-9ec8-44de-ee13-187e007c2c15"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'18070682'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ids_ =[]\n",
        "for i in df['product_link']:\n",
        "  ids_.append(i.split('/')[3])\n",
        "\n",
        "print(len(ids_))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eNq84ru1-VJn",
        "outputId": "391521ff-14ad-4c6a-e7bd-0f5f888f9192"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "168029\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LZyv2G0y_uWR",
        "outputId": "528b3ce1-f122-48e6-a85d-cf5e0708e491"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(168029, 14)"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['product_id'] = ids_"
      ],
      "metadata": {
        "id": "5pj1ijF1_8u1"
      },
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 382
        },
        "id": "xN0cDzP5ARZo",
        "outputId": "23f859e8-59fb-4ef5-d934-12d45dead22c"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                     product_name      brand_name  rating  rating_count  \\\n",
              "0   Croc Textured Two Fold Wallet     Lino Perros     0.0             0   \n",
              "1             Men Striped Sliders  Mast & Harbour     4.0            76   \n",
              "2            Printed A-line Kurta            Biba     4.3            66   \n",
              "3    Girls Floral Printed T-shirt        Anthrilo     0.0             0   \n",
              "4  Women Printed Kurta with Skirt    FASHION DWAR     0.0             0   \n",
              "\n",
              "   marked_price  discounted_price                      sizes  \\\n",
              "0          1295               828                    Onesize   \n",
              "1          1299               584  UK6,UK7,UK8,UK9,UK10,UK11   \n",
              "2          1999              1599           S,M,L,XL,XXL,3XL   \n",
              "3           599               539            7-8Y,8-9Y,9-10Y   \n",
              "4          2899              2899                   S,M,L,XL   \n",
              "\n",
              "                                        product_link  \\\n",
              "0  wallets/lino-perros/lino-perros-women-peach-co...   \n",
              "1  flip-flops/mast--harbour/mast--harbour-men-nav...   \n",
              "2  kurtas/biba/biba-women-off-white--black-printe...   \n",
              "3  tshirts/anthrilo/anthrilo-girls-white-floral-p...   \n",
              "4  kurta-sets/fashion-dwar/fashion-dwar-women-mul...   \n",
              "\n",
              "                                            img_link product_tag  \\\n",
              "0  https://assets.myntassets.com/dpr_2,q_60,w_210...     wallets   \n",
              "1  https://assets.myntassets.com/dpr_2,q_60,w_210...  flip-flops   \n",
              "2  https://assets.myntassets.com/dpr_2,q_60,w_210...      kurtas   \n",
              "3  https://assets.myntassets.com/dpr_2,q_60,w_210...     tshirts   \n",
              "4  https://assets.myntassets.com/dpr_2,q_60,w_210...  kurta-sets   \n",
              "\n",
              "       brand_tag  discount_amount  discount_percent        price_range  \\\n",
              "0    lino-perros              467                36  Rs 1000 - Rs 2000   \n",
              "1  mast--harbour              715                55  Rs 1000 - Rs 2000   \n",
              "2           biba              400                20  Rs 1000 - Rs 2000   \n",
              "3       anthrilo               60                10   Rs 500 - Rs 1000   \n",
              "4   fashion-dwar                0                 0  Rs 2000 - Rs 5000   \n",
              "\n",
              "  product_id  \n",
              "0   18070682  \n",
              "1   12548958  \n",
              "2   13446092  \n",
              "3   18017006  \n",
              "4   18015232  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-c638d6e2-cb63-4408-85c7-676516c47317\" class=\"colab-df-container\">\n",
              "    <div>\n",
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              "        vertical-align: middle;\n",
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              "\n",
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              "        text-align: right;\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>product_name</th>\n",
              "      <th>brand_name</th>\n",
              "      <th>rating</th>\n",
              "      <th>rating_count</th>\n",
              "      <th>marked_price</th>\n",
              "      <th>discounted_price</th>\n",
              "      <th>sizes</th>\n",
              "      <th>product_link</th>\n",
              "      <th>img_link</th>\n",
              "      <th>product_tag</th>\n",
              "      <th>brand_tag</th>\n",
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              "      <th>discount_percent</th>\n",
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              "      <th>product_id</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Croc Textured Two Fold Wallet</td>\n",
              "      <td>Lino Perros</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1295</td>\n",
              "      <td>828</td>\n",
              "      <td>Onesize</td>\n",
              "      <td>wallets/lino-perros/lino-perros-women-peach-co...</td>\n",
              "      <td>https://assets.myntassets.com/dpr_2,q_60,w_210...</td>\n",
              "      <td>wallets</td>\n",
              "      <td>lino-perros</td>\n",
              "      <td>467</td>\n",
              "      <td>36</td>\n",
              "      <td>Rs 1000 - Rs 2000</td>\n",
              "      <td>18070682</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Men Striped Sliders</td>\n",
              "      <td>Mast &amp; Harbour</td>\n",
              "      <td>4.0</td>\n",
              "      <td>76</td>\n",
              "      <td>1299</td>\n",
              "      <td>584</td>\n",
              "      <td>UK6,UK7,UK8,UK9,UK10,UK11</td>\n",
              "      <td>flip-flops/mast--harbour/mast--harbour-men-nav...</td>\n",
              "      <td>https://assets.myntassets.com/dpr_2,q_60,w_210...</td>\n",
              "      <td>flip-flops</td>\n",
              "      <td>mast--harbour</td>\n",
              "      <td>715</td>\n",
              "      <td>55</td>\n",
              "      <td>Rs 1000 - Rs 2000</td>\n",
              "      <td>12548958</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Printed A-line Kurta</td>\n",
              "      <td>Biba</td>\n",
              "      <td>4.3</td>\n",
              "      <td>66</td>\n",
              "      <td>1999</td>\n",
              "      <td>1599</td>\n",
              "      <td>S,M,L,XL,XXL,3XL</td>\n",
              "      <td>kurtas/biba/biba-women-off-white--black-printe...</td>\n",
              "      <td>https://assets.myntassets.com/dpr_2,q_60,w_210...</td>\n",
              "      <td>kurtas</td>\n",
              "      <td>biba</td>\n",
              "      <td>400</td>\n",
              "      <td>20</td>\n",
              "      <td>Rs 1000 - Rs 2000</td>\n",
              "      <td>13446092</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Girls Floral Printed T-shirt</td>\n",
              "      <td>Anthrilo</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>599</td>\n",
              "      <td>539</td>\n",
              "      <td>7-8Y,8-9Y,9-10Y</td>\n",
              "      <td>tshirts/anthrilo/anthrilo-girls-white-floral-p...</td>\n",
              "      <td>https://assets.myntassets.com/dpr_2,q_60,w_210...</td>\n",
              "      <td>tshirts</td>\n",
              "      <td>anthrilo</td>\n",
              "      <td>60</td>\n",
              "      <td>10</td>\n",
              "      <td>Rs 500 - Rs 1000</td>\n",
              "      <td>18017006</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Women Printed Kurta with Skirt</td>\n",
              "      <td>FASHION DWAR</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>2899</td>\n",
              "      <td>2899</td>\n",
              "      <td>S,M,L,XL</td>\n",
              "      <td>kurta-sets/fashion-dwar/fashion-dwar-women-mul...</td>\n",
              "      <td>https://assets.myntassets.com/dpr_2,q_60,w_210...</td>\n",
              "      <td>kurta-sets</td>\n",
              "      <td>fashion-dwar</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>Rs 2000 - Rs 5000</td>\n",
              "      <td>18015232</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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              "\n",
              "    .colab-df-convert {\n",
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              "\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-c638d6e2-cb63-4408-85c7-676516c47317 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-c638d6e2-cb63-4408-85c7-676516c47317');\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",
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              "\n",
              "\n",
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              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "\n",
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              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
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              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
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              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
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              "    60% {\n",
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              "      border-right-color: var(--fill-color);\n",
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              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
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              "    }\n",
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              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-eb9017fc-aa70-4ad4-bec3-c1e4e7caf2ca button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df"
            }
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "des = []\n",
        "\n",
        "for i in df['product_link']:\n",
        "  des.append(i.split('/')[2])"
      ],
      "metadata": {
        "id": "Jo4LDqu6ATGp"
      },
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df['product_description'] = des"
      ],
      "metadata": {
        "id": "vAIEsOauArYA"
      },
      "execution_count": 29,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 382
        },
        "id": "SI1ySYakAv3D",
        "outputId": "d1305ada-604e-4b98-d119-a42b3ee88366"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                     product_name      brand_name  rating  rating_count  \\\n",
              "0   Croc Textured Two Fold Wallet     Lino Perros     0.0             0   \n",
              "1             Men Striped Sliders  Mast & Harbour     4.0            76   \n",
              "2            Printed A-line Kurta            Biba     4.3            66   \n",
              "3    Girls Floral Printed T-shirt        Anthrilo     0.0             0   \n",
              "4  Women Printed Kurta with Skirt    FASHION DWAR     0.0             0   \n",
              "\n",
              "   marked_price  discounted_price                      sizes  \\\n",
              "0          1295               828                    Onesize   \n",
              "1          1299               584  UK6,UK7,UK8,UK9,UK10,UK11   \n",
              "2          1999              1599           S,M,L,XL,XXL,3XL   \n",
              "3           599               539            7-8Y,8-9Y,9-10Y   \n",
              "4          2899              2899                   S,M,L,XL   \n",
              "\n",
              "                                        product_link  \\\n",
              "0  wallets/lino-perros/lino-perros-women-peach-co...   \n",
              "1  flip-flops/mast--harbour/mast--harbour-men-nav...   \n",
              "2  kurtas/biba/biba-women-off-white--black-printe...   \n",
              "3  tshirts/anthrilo/anthrilo-girls-white-floral-p...   \n",
              "4  kurta-sets/fashion-dwar/fashion-dwar-women-mul...   \n",
              "\n",
              "                                            img_link product_tag  \\\n",
              "0  https://assets.myntassets.com/dpr_2,q_60,w_210...     wallets   \n",
              "1  https://assets.myntassets.com/dpr_2,q_60,w_210...  flip-flops   \n",
              "2  https://assets.myntassets.com/dpr_2,q_60,w_210...      kurtas   \n",
              "3  https://assets.myntassets.com/dpr_2,q_60,w_210...     tshirts   \n",
              "4  https://assets.myntassets.com/dpr_2,q_60,w_210...  kurta-sets   \n",
              "\n",
              "       brand_tag  discount_amount  discount_percent        price_range  \\\n",
              "0    lino-perros              467                36  Rs 1000 - Rs 2000   \n",
              "1  mast--harbour              715                55  Rs 1000 - Rs 2000   \n",
              "2           biba              400                20  Rs 1000 - Rs 2000   \n",
              "3       anthrilo               60                10   Rs 500 - Rs 1000   \n",
              "4   fashion-dwar                0                 0  Rs 2000 - Rs 5000   \n",
              "\n",
              "  product_id                                product_description  \n",
              "0   18070682  lino-perros-women-peach-coloured-croc-textured...  \n",
              "1   12548958  mast--harbour-men-navy-blue--grey-striped-sliders  \n",
              "2   13446092  biba-women-off-white--black-printed-pure-cotto...  \n",
              "3   18017006        anthrilo-girls-white-floral-printed-t-shirt  \n",
              "4   18015232  fashion-dwar-women-multicoloured-ethnic-motifs...  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-855ce21c-762f-4f4f-b398-5c4b66fcb010\" class=\"colab-df-container\">\n",
              "    <div>\n",
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>product_name</th>\n",
              "      <th>brand_name</th>\n",
              "      <th>rating</th>\n",
              "      <th>rating_count</th>\n",
              "      <th>marked_price</th>\n",
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              "      <th>discount_amount</th>\n",
              "      <th>discount_percent</th>\n",
              "      <th>price_range</th>\n",
              "      <th>product_id</th>\n",
              "      <th>product_description</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Croc Textured Two Fold Wallet</td>\n",
              "      <td>Lino Perros</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1295</td>\n",
              "      <td>828</td>\n",
              "      <td>Onesize</td>\n",
              "      <td>wallets/lino-perros/lino-perros-women-peach-co...</td>\n",
              "      <td>https://assets.myntassets.com/dpr_2,q_60,w_210...</td>\n",
              "      <td>wallets</td>\n",
              "      <td>lino-perros</td>\n",
              "      <td>467</td>\n",
              "      <td>36</td>\n",
              "      <td>Rs 1000 - Rs 2000</td>\n",
              "      <td>18070682</td>\n",
              "      <td>lino-perros-women-peach-coloured-croc-textured...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Men Striped Sliders</td>\n",
              "      <td>Mast &amp; Harbour</td>\n",
              "      <td>4.0</td>\n",
              "      <td>76</td>\n",
              "      <td>1299</td>\n",
              "      <td>584</td>\n",
              "      <td>UK6,UK7,UK8,UK9,UK10,UK11</td>\n",
              "      <td>flip-flops/mast--harbour/mast--harbour-men-nav...</td>\n",
              "      <td>https://assets.myntassets.com/dpr_2,q_60,w_210...</td>\n",
              "      <td>flip-flops</td>\n",
              "      <td>mast--harbour</td>\n",
              "      <td>715</td>\n",
              "      <td>55</td>\n",
              "      <td>Rs 1000 - Rs 2000</td>\n",
              "      <td>12548958</td>\n",
              "      <td>mast--harbour-men-navy-blue--grey-striped-sliders</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Printed A-line Kurta</td>\n",
              "      <td>Biba</td>\n",
              "      <td>4.3</td>\n",
              "      <td>66</td>\n",
              "      <td>1999</td>\n",
              "      <td>1599</td>\n",
              "      <td>S,M,L,XL,XXL,3XL</td>\n",
              "      <td>kurtas/biba/biba-women-off-white--black-printe...</td>\n",
              "      <td>https://assets.myntassets.com/dpr_2,q_60,w_210...</td>\n",
              "      <td>kurtas</td>\n",
              "      <td>biba</td>\n",
              "      <td>400</td>\n",
              "      <td>20</td>\n",
              "      <td>Rs 1000 - Rs 2000</td>\n",
              "      <td>13446092</td>\n",
              "      <td>biba-women-off-white--black-printed-pure-cotto...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Girls Floral Printed T-shirt</td>\n",
              "      <td>Anthrilo</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>599</td>\n",
              "      <td>539</td>\n",
              "      <td>7-8Y,8-9Y,9-10Y</td>\n",
              "      <td>tshirts/anthrilo/anthrilo-girls-white-floral-p...</td>\n",
              "      <td>https://assets.myntassets.com/dpr_2,q_60,w_210...</td>\n",
              "      <td>tshirts</td>\n",
              "      <td>anthrilo</td>\n",
              "      <td>60</td>\n",
              "      <td>10</td>\n",
              "      <td>Rs 500 - Rs 1000</td>\n",
              "      <td>18017006</td>\n",
              "      <td>anthrilo-girls-white-floral-printed-t-shirt</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Women Printed Kurta with Skirt</td>\n",
              "      <td>FASHION DWAR</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>2899</td>\n",
              "      <td>2899</td>\n",
              "      <td>S,M,L,XL</td>\n",
              "      <td>kurta-sets/fashion-dwar/fashion-dwar-women-mul...</td>\n",
              "      <td>https://assets.myntassets.com/dpr_2,q_60,w_210...</td>\n",
              "      <td>kurta-sets</td>\n",
              "      <td>fashion-dwar</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>Rs 2000 - Rs 5000</td>\n",
              "      <td>18015232</td>\n",
              "      <td>fashion-dwar-women-multicoloured-ethnic-motifs...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
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              "\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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              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-a2aa1fe0-66b9-49b9-b4dc-df53bf37def0 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df"
            }
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Q1) Which Product Category dominate the dataset"
      ],
      "metadata": {
        "id": "UVH4FYSTA-8f"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "e = df['product_tag'].value_counts().head(10)\n",
        "e"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 429
        },
        "id": "RKdqH5b0AxBF",
        "outputId": "195ff3f6-1a3d-43fd-e5f8-536b6ef15316"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "product_tag\n",
              "tshirts         16978\n",
              "shirts          12044\n",
              "dresses         11836\n",
              "tops             9049\n",
              "kurta-sets       8170\n",
              "kurtas           8043\n",
              "jeans            7669\n",
              "trousers         5756\n",
              "casual-shoes     5230\n",
              "handbags         4944\n",
              "Name: count, dtype: int64"
            ],
            "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>count</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>product_tag</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>tshirts</th>\n",
              "      <td>16978</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>shirts</th>\n",
              "      <td>12044</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>dresses</th>\n",
              "      <td>11836</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>tops</th>\n",
              "      <td>9049</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>kurta-sets</th>\n",
              "      <td>8170</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>kurtas</th>\n",
              "      <td>8043</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>jeans</th>\n",
              "      <td>7669</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>trousers</th>\n",
              "      <td>5756</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>casual-shoes</th>\n",
              "      <td>5230</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>handbags</th>\n",
              "      <td>4944</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> int64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "# Plotting\n",
        "\n",
        "plt.figure(figsize = (10,6))\n",
        "common_category = df['product_tag'].value_counts().head(10)\n",
        "sns.barplot(x = common_category.values, y = common_category.index, palette = 'Blues_d')\n",
        "plt.title('Top 10 Most Common Product Categories')\n",
        "plt.xlabel('Number of Products')\n",
        "plt.ylabel('Product Category')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 671
        },
        "id": "zi0Mvp4sIPVc",
        "outputId": "1431c038-5b66-4cae-e11c-1786f88dee3b"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-32-88eaa56c6dfc>:8: FutureWarning: \n",
            "\n",
            "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `y` variable to `hue` and set `legend=False` for the same effect.\n",
            "\n",
            "  sns.barplot(x = common_category.values, y = common_category.index, palette = 'Blues_d')\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Q2) What are top 5 brands in terms of number of product offered"
      ],
      "metadata": {
        "id": "bAPZhNoDBpc5"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "t = df['brand_tag'].value_counts().head(5)\n",
        "t"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 272
        },
        "id": "1O5aSx-sB8Qd",
        "outputId": "8ea945be-ae1c-4a19-9313-5c5b42734504"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "brand_tag\n",
              "roadster         4982\n",
              "mast--harbour    2640\n",
              "dressberry       2330\n",
              "hm               2188\n",
              "max              2045\n",
              "Name: count, dtype: int64"
            ],
            "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>count</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>brand_tag</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>roadster</th>\n",
              "      <td>4982</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mast--harbour</th>\n",
              "      <td>2640</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>dressberry</th>\n",
              "      <td>2330</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>hm</th>\n",
              "      <td>2188</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>2045</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> int64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize = (10,6))\n",
        "top_brands = df['brand_tag'].value_counts().head(5)\n",
        "sns.barplot(x = top_brands.values, y = top_brands.index, palette = 'Greens_d')\n",
        "plt.title('Top 10 Brands by Number of Products')\n",
        "plt.xlabel('Number of Products')\n",
        "plt.ylabel('Brand')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 671
        },
        "id": "4kI4Y_2OJxtJ",
        "outputId": "982b7c4b-9ea6-47c8-d2f4-05b4f2b2d894"
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-34-7b458ddf4aa5>:3: FutureWarning: \n",
            "\n",
            "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `y` variable to `hue` and set `legend=False` for the same effect.\n",
            "\n",
            "  sns.barplot(x = top_brands.values, y = top_brands.index, palette = 'Greens_d')\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Q3) Which brand provides the highest average discounts"
      ],
      "metadata": {
        "id": "yOVnUQCMB8w5"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "top_10_brands = df['brand_tag'].value_counts().head(10).index\n",
        "df[df['brand_tag'].isin(top_10_brands)].groupby('brand_tag')['discount_percent'].mean().sort_values(ascending = False)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 429
        },
        "id": "L8vJjnhhCFoq",
        "outputId": "08ec80ea-85b6-497c-f54d-04e96a0827f3"
      },
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "brand_tag\n",
              "anouk                    59.037717\n",
              "dressberry               56.932618\n",
              "herenow                  55.397577\n",
              "mast--harbour            53.574621\n",
              "roadster                 53.140707\n",
              "hrx-by-hrithik-roshan    38.104058\n",
              "puma                     37.194460\n",
              "mango                    15.074670\n",
              "hm                        1.935558\n",
              "max                       0.797066\n",
              "Name: discount_percent, dtype: float64"
            ],
            "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>discount_percent</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>brand_tag</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>anouk</th>\n",
              "      <td>59.037717</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>dressberry</th>\n",
              "      <td>56.932618</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>herenow</th>\n",
              "      <td>55.397577</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mast--harbour</th>\n",
              "      <td>53.574621</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>roadster</th>\n",
              "      <td>53.140707</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>hrx-by-hrithik-roshan</th>\n",
              "      <td>38.104058</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>puma</th>\n",
              "      <td>37.194460</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mango</th>\n",
              "      <td>15.074670</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>hm</th>\n",
              "      <td>1.935558</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>0.797066</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> float64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Q4) Is there any relationship between product price and customer ratings"
      ],
      "metadata": {
        "id": "htbPteGKCGXD"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize = (10,6))\n",
        "sns.scatterplot(x = 'marked_price', y = 'rating', hue = 'discount_percent', data = df, palette = 'coolwarm', alpha = 0.6)\n",
        "plt.title('Price vs Rating with Discount Percentage')\n",
        "plt.xlabel('Marked Price')\n",
        "plt.ylabel('Rating')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "WUFYGx0kCQTZ",
        "outputId": "fe1cbe8d-9889-4ed4-f572-443e88e18f1c"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Key Insights:\n",
        "\n",
        "* No Clear Price-Rating Relationship: In many cases, you might observe that the price of a product does not necessarily correlate with its rating. For instance, expensive products might not always have higher ratings compared to cheaper ones, indicating that customer satisfaction isn't purely tied to price.\n",
        "* Price Outliers:\n",
        "You may find products with extreme prices (either very high or very low) having diverse ratings. This could suggest that price alone is not a significant factor in determining customer satisfaction or perceived product quality.\n",
        "* Discounted Products and Ratings:\n",
        "By incorporating the discount_percent as the hue in the plot, you could analyze whether discounted products tend to have higher or lower ratings. If discounted products cluster around lower ratings, it might indicate that discounts are applied to clear less desirable stock.\n",
        "* Popular Price Ranges:\n",
        "Some price ranges may have a higher density of ratings. For example, mid-priced products (₹1000-₹5000) might gather more reviews and ratings, indicating higher popularity or broader affordability among customers.\n",
        "* Price Threshold for High Ratings:\n",
        "You might identify a price threshold beyond which ratings plateau or drop. For instance, after a certain price (e.g., ₹5000), ratings may not increase, indicating diminishing returns on customer satisfaction for very high-priced items.\n",
        "* Low Ratings for Expensive Products:\n",
        "If you observe clusters of high-priced products with low ratings, it could point to customer dissatisfaction, possibly due to unmet expectations in product quality or value for money."
      ],
      "metadata": {
        "id": "9_1uxMolM8pN"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "y6inhJ2xMEKG"
      },
      "execution_count": 36,
      "outputs": []
    }
  ]
}