ModelTraining1.ipynb

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    "language_info": {
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    {
      "cell_type": "markdown",
      "source": [
        "# Loading Dependencies\n"
      ],
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        "id": "fD7KLUQmB_pR"
      }
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      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import numpy as np"
      ],
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      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Data Loading"
      ],
      "metadata": {
        "id": "UKgHKyZCCV3P"
      }
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    {
      "cell_type": "code",
      "source": [
        "df = pd.read_csv('data.csv')\n",
        "df.head()"
      ],
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          "height": 206
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              "          Brand Category  Color Size Material  Price\n",
              "0   New Balance    Dress  White   XS    Nylon    182\n",
              "1   New Balance    Jeans  Black   XS     Silk     57\n",
              "2  Under Armour    Dress    Red    M     Wool    127\n",
              "3          Nike    Shoes  Green    M   Cotton     77\n",
              "4        Adidas  Sweater  White    M    Nylon    113"
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              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 1000,\n  \"fields\": [\n    {\n      \"column\": \"Brand\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 6,\n        \"samples\": [\n          \"New Balance\",\n          \"Under Armour\",\n          \"Puma\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Category\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 6,\n        \"samples\": [\n          \"Dress\",\n          \"Jeans\",\n          \"T-shirt\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Color\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 6,\n        \"samples\": [\n          \"White\",\n          \"Black\",\n          \"Blue\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Size\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 6,\n        \"samples\": [\n          \"XS\",\n          \"M\",\n          \"L\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Material\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 6,\n        \"samples\": [\n          \"Nylon\",\n          \"Silk\",\n          \"Denim\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Price\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 53,\n        \"min\": 10,\n        \"max\": 199,\n        \"num_unique_values\": 190,\n        \"samples\": [\n          70,\n          189,\n          117\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
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          "metadata": {},
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      "source": [
        "df.info()"
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        "outputId": "703eb6c5-1783-4983-e3f3-fa60b20abcc1"
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      "outputs": [
        {
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          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 1000 entries, 0 to 999\n",
            "Data columns (total 6 columns):\n",
            " #   Column    Non-Null Count  Dtype \n",
            "---  ------    --------------  ----- \n",
            " 0   Brand     1000 non-null   object\n",
            " 1   Category  1000 non-null   object\n",
            " 2   Color     1000 non-null   object\n",
            " 3   Size      1000 non-null   object\n",
            " 4   Material  1000 non-null   object\n",
            " 5   Price     1000 non-null   int64 \n",
            "dtypes: int64(1), object(5)\n",
            "memory usage: 47.0+ KB\n"
          ]
        }
      ]
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    {
      "cell_type": "code",
      "source": [
        "correlation_matrix = df.corr()\n",
        "print(correlation_matrix)"
      ],
      "metadata": {
        "id": "-YYpR1KYEMoe",
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          "base_uri": "https://localhost:8080/"
        },
        "outputId": "61c38afe-6d18-42e0-8c9d-c09f68c7afbe"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "       Price\n",
            "Price    1.0\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-4-becb50483c66>:1: FutureWarning: The default value of numeric_only in DataFrame.corr is deprecated. In a future version, it will default to False. Select only valid columns or specify the value of numeric_only to silence this warning.\n",
            "  correlation_matrix = df.corr()\n"
          ]
        }
      ]
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        "df.head()"
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              "          Brand Category  Color Size Material  Price\n",
              "0   New Balance    Dress  White   XS    Nylon    182\n",
              "1   New Balance    Jeans  Black   XS     Silk     57\n",
              "2  Under Armour    Dress    Red    M     Wool    127\n",
              "3          Nike    Shoes  Green    M   Cotton     77\n",
              "4        Adidas  Sweater  White    M    Nylon    113"
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              "      <td>Wool</td>\n",
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              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </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",
              "      cursor: pointer;\n",
              "      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",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-f933861b-b187-4bc6-af93-75c9d4dc51e0 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-f933861b-b187-4bc6-af93-75c9d4dc51e0');\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-7cb53eb9-e4fc-445d-8a5e-b2326ee90c22\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-7cb53eb9-e4fc-445d-8a5e-b2326ee90c22')\"\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",
              "        <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-7cb53eb9-e4fc-445d-8a5e-b2326ee90c22 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 1000,\n  \"fields\": [\n    {\n      \"column\": \"Brand\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 6,\n        \"samples\": [\n          \"New Balance\",\n          \"Under Armour\",\n          \"Puma\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Category\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 6,\n        \"samples\": [\n          \"Dress\",\n          \"Jeans\",\n          \"T-shirt\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Color\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 6,\n        \"samples\": [\n          \"White\",\n          \"Black\",\n          \"Blue\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Size\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 6,\n        \"samples\": [\n          \"XS\",\n          \"M\",\n          \"L\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Material\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 6,\n        \"samples\": [\n          \"Nylon\",\n          \"Silk\",\n          \"Denim\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Price\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 53,\n        \"min\": 10,\n        \"max\": 199,\n        \"num_unique_values\": 190,\n        \"samples\": [\n          70,\n          189,\n          117\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Data Cleaning"
      ],
      "metadata": {
        "id": "JaIS4lEeFe9-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.isnull().sum()"
      ],
      "metadata": {
        "id": "e1utyrneFmcV",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b81d78d8-6f63-490f-d548-5642090c4143"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Brand       0\n",
              "Category    0\n",
              "Color       0\n",
              "Size        0\n",
              "Material    0\n",
              "Price       0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "drop_df = df.dropna()"
      ],
      "metadata": {
        "id": "JhzoNpXnFrQo"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "drop_df.isnull().sum()"
      ],
      "metadata": {
        "id": "14zYMzfNFuXh",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "aa83c796-17dc-46fd-8386-e708b0a37dce"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Brand       0\n",
              "Category    0\n",
              "Color       0\n",
              "Size        0\n",
              "Material    0\n",
              "Price       0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "drop_df.info()"
      ],
      "metadata": {
        "id": "97W84kYhFyMp",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "800a51f0-205d-48f5-97a5-0d7f72925f79"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 1000 entries, 0 to 999\n",
            "Data columns (total 6 columns):\n",
            " #   Column    Non-Null Count  Dtype \n",
            "---  ------    --------------  ----- \n",
            " 0   Brand     1000 non-null   object\n",
            " 1   Category  1000 non-null   object\n",
            " 2   Color     1000 non-null   object\n",
            " 3   Size      1000 non-null   object\n",
            " 4   Material  1000 non-null   object\n",
            " 5   Price     1000 non-null   int64 \n",
            "dtypes: int64(1), object(5)\n",
            "memory usage: 47.0+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "no_duplicates = drop_df.drop_duplicates()"
      ],
      "metadata": {
        "id": "uEjOF11cGmA2"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "no_duplicates.info()"
      ],
      "metadata": {
        "id": "8Af0YSALGrpc",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2130cc5e-a850-4a1e-bc5a-f4f7a838b97f"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 1000 entries, 0 to 999\n",
            "Data columns (total 6 columns):\n",
            " #   Column    Non-Null Count  Dtype \n",
            "---  ------    --------------  ----- \n",
            " 0   Brand     1000 non-null   object\n",
            " 1   Category  1000 non-null   object\n",
            " 2   Color     1000 non-null   object\n",
            " 3   Size      1000 non-null   object\n",
            " 4   Material  1000 non-null   object\n",
            " 5   Price     1000 non-null   int64 \n",
            "dtypes: int64(1), object(5)\n",
            "memory usage: 54.7+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# One Hot Categorical Endcoding"
      ],
      "metadata": {
        "id": "tNKQiEj93bv7"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df['Category'].unique()"
      ],
      "metadata": {
        "id": "i88eJQpfniy_",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "fee93d64-d940-405f-d70c-a00150dd2843"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['Dress', 'Jeans', 'Shoes', 'Sweater', 'Jacket', 'T-shirt'],\n",
              "      dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.get_dummies(df, columns=['Category'], prefix='Category', dtype='int64')"
      ],
      "metadata": {
        "id": "TDHFpGlH3kLV"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df['Color'].unique()"
      ],
      "metadata": {
        "id": "J6J_fqZ3xkb7",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "35eaf041-5e56-458c-b85a-8d36af6e9bbb"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['White', 'Black', 'Red', 'Green', 'Yellow', 'Blue'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.get_dummies(df, columns=['Color'], prefix='Color', dtype='int64')"
      ],
      "metadata": {
        "id": "4KFHWIrX4bPY"
      },
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df['Size'].unique()"
      ],
      "metadata": {
        "id": "Jvn1MCYFxoBl",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f48775df-f6f6-498d-81be-e274f794f496"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['XS', 'M', 'XL', 'XXL', 'S', 'L'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.get_dummies(df, columns=['Size'], prefix='Size', dtype='int64')"
      ],
      "metadata": {
        "id": "vEDqQsGs4hKu"
      },
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df['Material'].unique()"
      ],
      "metadata": {
        "id": "d82R9O-_xtNb",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d4debfc8-9f7b-41dd-97ba-57c9dfec3c10"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['Nylon', 'Silk', 'Wool', 'Cotton', 'Polyester', 'Denim'],\n",
              "      dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.get_dummies(df, columns=['Material'], prefix='Material', dtype='int64')"
      ],
      "metadata": {
        "id": "VVunzRtJ4iG3"
      },
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df['Brand'].unique()"
      ],
      "metadata": {
        "id": "R0jQrxfKaEcN",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d5b9a140-8fce-4078-a8e2-6c337571f60f"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['New Balance', 'Under Armour', 'Nike', 'Adidas', 'Reebok', 'Puma'],\n",
              "      dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.get_dummies(df, columns=['Brand'], prefix='Brand', dtype='int64')"
      ],
      "metadata": {
        "id": "_yUn1Z5RZfVH"
      },
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df"
      ],
      "metadata": {
        "id": "0bmNFs1a4Veg",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 461
        },
        "outputId": "a15cabdf-9e47-4532-b080-1ad6e42969fe"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     Price  Category_Dress  Category_Jacket  Category_Jeans  Category_Shoes  \\\n",
              "0      182               1                0               0               0   \n",
              "1       57               0                0               1               0   \n",
              "2      127               1                0               0               0   \n",
              "3       77               0                0               0               1   \n",
              "4      113               0                0               0               0   \n",
              "..     ...             ...              ...             ...             ...   \n",
              "995    176               0                0               1               0   \n",
              "996    110               0                1               0               0   \n",
              "997    127               0                0               0               0   \n",
              "998     69               0                0               0               0   \n",
              "999    174               0                1               0               0   \n",
              "\n",
              "     Category_Sweater  Category_T-shirt  Color_Black  Color_Blue  Color_Green  \\\n",
              "0                   0                 0            0           0            0   \n",
              "1                   0                 0            1           0            0   \n",
              "2                   0                 0            0           0            0   \n",
              "3                   0                 0            0           0            1   \n",
              "4                   1                 0            0           0            0   \n",
              "..                ...               ...          ...         ...          ...   \n",
              "995                 0                 0            1           0            0   \n",
              "996                 0                 0            0           0            0   \n",
              "997                 1                 0            0           1            0   \n",
              "998                 1                 0            1           0            0   \n",
              "999                 0                 0            0           0            0   \n",
              "\n",
              "     ...  Material_Nylon  Material_Polyester  Material_Silk  Material_Wool  \\\n",
              "0    ...               1                   0              0              0   \n",
              "1    ...               0                   0              1              0   \n",
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              "999  ...               0                   0              0              1   \n",
              "\n",
              "     Brand_Adidas  Brand_New Balance  Brand_Nike  Brand_Puma  Brand_Reebok  \\\n",
              "0               0                  1           0           0             0   \n",
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              "998             0                  0           0           0             0   \n",
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              "\n",
              "     Brand_Under Armour  \n",
              "0                     0  \n",
              "1                     0  \n",
              "2                     1  \n",
              "3                     0  \n",
              "4                     0  \n",
              "..                  ...  \n",
              "995                   0  \n",
              "996                   0  \n",
              "997                   0  \n",
              "998                   1  \n",
              "999                   0  \n",
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              "      <th>999</th>\n",
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              "<p>1000 rows × 31 columns</p>\n",
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              "      border-bottom-color: var(--fill-color);\n",
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              "    20% {\n",
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              "      border-left-color: var(--fill-color);\n",
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              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-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",
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              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
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              "\n",
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              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
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              "        console.error('Error during call to suggestCharts:', error);\n",
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              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-e515116c-21a5-46e6-a833-ae0b332aefb0 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df"
            }
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Min Max Scaling"
      ],
      "metadata": {
        "id": "eI6wybEf48ZK"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.describe()"
      ],
      "metadata": {
        "id": "-N2_AudFlIvu",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 367
        },
        "outputId": "966ff56f-654e-4f80-c933-efe40430d373"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "             Price  Category_Dress  Category_Jacket  Category_Jeans  \\\n",
              "count  1000.000000     1000.000000      1000.000000     1000.000000   \n",
              "mean    106.289000        0.166000         0.191000        0.167000   \n",
              "std      53.695444        0.372267         0.393286        0.373162   \n",
              "min      10.000000        0.000000         0.000000        0.000000   \n",
              "25%      59.750000        0.000000         0.000000        0.000000   \n",
              "50%     108.000000        0.000000         0.000000        0.000000   \n",
              "75%     150.000000        0.000000         0.000000        0.000000   \n",
              "max     199.000000        1.000000         1.000000        1.000000   \n",
              "\n",
              "       Category_Shoes  Category_Sweater  Category_T-shirt  Color_Black  \\\n",
              "count     1000.000000       1000.000000       1000.000000   1000.00000   \n",
              "mean         0.172000          0.160000          0.144000      0.16300   \n",
              "std          0.377569          0.366789          0.351265      0.36955   \n",
              "min          0.000000          0.000000          0.000000      0.00000   \n",
              "25%          0.000000          0.000000          0.000000      0.00000   \n",
              "50%          0.000000          0.000000          0.000000      0.00000   \n",
              "75%          0.000000          0.000000          0.000000      0.00000   \n",
              "max          1.000000          1.000000          1.000000      1.00000   \n",
              "\n",
              "       Color_Blue  Color_Green  ...  Material_Nylon  Material_Polyester  \\\n",
              "count  1000.00000  1000.000000  ...     1000.000000         1000.000000   \n",
              "mean      0.16300     0.162000  ...        0.155000            0.175000   \n",
              "std       0.36955     0.368635  ...        0.362086            0.380157   \n",
              "min       0.00000     0.000000  ...        0.000000            0.000000   \n",
              "25%       0.00000     0.000000  ...        0.000000            0.000000   \n",
              "50%       0.00000     0.000000  ...        0.000000            0.000000   \n",
              "75%       0.00000     0.000000  ...        0.000000            0.000000   \n",
              "max       1.00000     1.000000  ...        1.000000            1.000000   \n",
              "\n",
              "       Material_Silk  Material_Wool  Brand_Adidas  Brand_New Balance  \\\n",
              "count    1000.000000    1000.000000   1000.000000        1000.000000   \n",
              "mean        0.173000       0.172000      0.166000           0.164000   \n",
              "std         0.378437       0.377569      0.372267           0.370461   \n",
              "min         0.000000       0.000000      0.000000           0.000000   \n",
              "25%         0.000000       0.000000      0.000000           0.000000   \n",
              "50%         0.000000       0.000000      0.000000           0.000000   \n",
              "75%         0.000000       0.000000      0.000000           0.000000   \n",
              "max         1.000000       1.000000      1.000000           1.000000   \n",
              "\n",
              "        Brand_Nike   Brand_Puma  Brand_Reebok  Brand_Under Armour  \n",
              "count  1000.000000  1000.000000   1000.000000         1000.000000  \n",
              "mean      0.165000     0.168000      0.158000            0.179000  \n",
              "std       0.371366     0.374053      0.364924            0.383544  \n",
              "min       0.000000     0.000000      0.000000            0.000000  \n",
              "25%       0.000000     0.000000      0.000000            0.000000  \n",
              "50%       0.000000     0.000000      0.000000            0.000000  \n",
              "75%       0.000000     0.000000      0.000000            0.000000  \n",
              "max       1.000000     1.000000      1.000000            1.000000  \n",
              "\n",
              "[8 rows x 31 columns]"
            ],
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              "\n",
              "  <div id=\"df-9ae31b32-e4cb-4064-89ab-081ec3bd6c4d\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Price</th>\n",
              "      <th>Category_Dress</th>\n",
              "      <th>Category_Jacket</th>\n",
              "      <th>Category_Jeans</th>\n",
              "      <th>Category_Shoes</th>\n",
              "      <th>Category_Sweater</th>\n",
              "      <th>Category_T-shirt</th>\n",
              "      <th>Color_Black</th>\n",
              "      <th>Color_Blue</th>\n",
              "      <th>Color_Green</th>\n",
              "      <th>...</th>\n",
              "      <th>Material_Nylon</th>\n",
              "      <th>Material_Polyester</th>\n",
              "      <th>Material_Silk</th>\n",
              "      <th>Material_Wool</th>\n",
              "      <th>Brand_Adidas</th>\n",
              "      <th>Brand_New Balance</th>\n",
              "      <th>Brand_Nike</th>\n",
              "      <th>Brand_Puma</th>\n",
              "      <th>Brand_Reebok</th>\n",
              "      <th>Brand_Under Armour</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>1000.000000</td>\n",
              "      <td>1000.000000</td>\n",
              "      <td>1000.000000</td>\n",
              "      <td>1000.000000</td>\n",
              "      <td>1000.000000</td>\n",
              "      <td>1000.000000</td>\n",
              "      <td>1000.000000</td>\n",
              "      <td>1000.00000</td>\n",
              "      <td>1000.00000</td>\n",
              "      <td>1000.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>1000.000000</td>\n",
              "      <td>1000.000000</td>\n",
              "      <td>1000.000000</td>\n",
              "      <td>1000.000000</td>\n",
              "      <td>1000.000000</td>\n",
              "      <td>1000.000000</td>\n",
              "      <td>1000.000000</td>\n",
              "      <td>1000.000000</td>\n",
              "      <td>1000.000000</td>\n",
              "      <td>1000.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>106.289000</td>\n",
              "      <td>0.166000</td>\n",
              "      <td>0.191000</td>\n",
              "      <td>0.167000</td>\n",
              "      <td>0.172000</td>\n",
              "      <td>0.160000</td>\n",
              "      <td>0.144000</td>\n",
              "      <td>0.16300</td>\n",
              "      <td>0.16300</td>\n",
              "      <td>0.162000</td>\n",
              "      <td>...</td>\n",
              "      <td>0.155000</td>\n",
              "      <td>0.175000</td>\n",
              "      <td>0.173000</td>\n",
              "      <td>0.172000</td>\n",
              "      <td>0.166000</td>\n",
              "      <td>0.164000</td>\n",
              "      <td>0.165000</td>\n",
              "      <td>0.168000</td>\n",
              "      <td>0.158000</td>\n",
              "      <td>0.179000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>53.695444</td>\n",
              "      <td>0.372267</td>\n",
              "      <td>0.393286</td>\n",
              "      <td>0.373162</td>\n",
              "      <td>0.377569</td>\n",
              "      <td>0.366789</td>\n",
              "      <td>0.351265</td>\n",
              "      <td>0.36955</td>\n",
              "      <td>0.36955</td>\n",
              "      <td>0.368635</td>\n",
              "      <td>...</td>\n",
              "      <td>0.362086</td>\n",
              "      <td>0.380157</td>\n",
              "      <td>0.378437</td>\n",
              "      <td>0.377569</td>\n",
              "      <td>0.372267</td>\n",
              "      <td>0.370461</td>\n",
              "      <td>0.371366</td>\n",
              "      <td>0.374053</td>\n",
              "      <td>0.364924</td>\n",
              "      <td>0.383544</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>10.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>59.750000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
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              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>108.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>150.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
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              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>199.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.00000</td>\n",
              "      <td>1.00000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>8 rows × 31 columns</p>\n",
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              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
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              "      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-cdb14c89-ee3d-4eca-a15f-91d0c54854d3 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe"
            }
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import MinMaxScaler"
      ],
      "metadata": {
        "id": "B_E3qA2lA0mZ"
      },
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "scaler = MinMaxScaler()"
      ],
      "metadata": {
        "id": "sNHFaBRjDoPH"
      },
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "standardized_data = scaler.fit_transform(df)"
      ],
      "metadata": {
        "id": "2jhnPboSMFaf"
      },
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "standardized_df = pd.DataFrame(standardized_data, columns=df.columns)"
      ],
      "metadata": {
        "id": "b49ChDspUhl6"
      },
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.info()"
      ],
      "metadata": {
        "id": "js1IRjAFDtcn",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "cb46417a-821e-4e34-ebb9-3b5d8cb10829"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 1000 entries, 0 to 999\n",
            "Data columns (total 31 columns):\n",
            " #   Column              Non-Null Count  Dtype\n",
            "---  ------              --------------  -----\n",
            " 0   Price               1000 non-null   int64\n",
            " 1   Category_Dress      1000 non-null   int64\n",
            " 2   Category_Jacket     1000 non-null   int64\n",
            " 3   Category_Jeans      1000 non-null   int64\n",
            " 4   Category_Shoes      1000 non-null   int64\n",
            " 5   Category_Sweater    1000 non-null   int64\n",
            " 6   Category_T-shirt    1000 non-null   int64\n",
            " 7   Color_Black         1000 non-null   int64\n",
            " 8   Color_Blue          1000 non-null   int64\n",
            " 9   Color_Green         1000 non-null   int64\n",
            " 10  Color_Red           1000 non-null   int64\n",
            " 11  Color_White         1000 non-null   int64\n",
            " 12  Color_Yellow        1000 non-null   int64\n",
            " 13  Size_L              1000 non-null   int64\n",
            " 14  Size_M              1000 non-null   int64\n",
            " 15  Size_S              1000 non-null   int64\n",
            " 16  Size_XL             1000 non-null   int64\n",
            " 17  Size_XS             1000 non-null   int64\n",
            " 18  Size_XXL            1000 non-null   int64\n",
            " 19  Material_Cotton     1000 non-null   int64\n",
            " 20  Material_Denim      1000 non-null   int64\n",
            " 21  Material_Nylon      1000 non-null   int64\n",
            " 22  Material_Polyester  1000 non-null   int64\n",
            " 23  Material_Silk       1000 non-null   int64\n",
            " 24  Material_Wool       1000 non-null   int64\n",
            " 25  Brand_Adidas        1000 non-null   int64\n",
            " 26  Brand_New Balance   1000 non-null   int64\n",
            " 27  Brand_Nike          1000 non-null   int64\n",
            " 28  Brand_Puma          1000 non-null   int64\n",
            " 29  Brand_Reebok        1000 non-null   int64\n",
            " 30  Brand_Under Armour  1000 non-null   int64\n",
            "dtypes: int64(31)\n",
            "memory usage: 242.3 KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 273
        },
        "id": "TNf8zzKZTruc",
        "outputId": "26132589-1361-4a02-b3fe-cbae291368ea"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Price  Category_Dress  Category_Jacket  Category_Jeans  Category_Shoes  \\\n",
              "0    182               1                0               0               0   \n",
              "1     57               0                0               1               0   \n",
              "2    127               1                0               0               0   \n",
              "3     77               0                0               0               1   \n",
              "4    113               0                0               0               0   \n",
              "\n",
              "   Category_Sweater  Category_T-shirt  Color_Black  Color_Blue  Color_Green  \\\n",
              "0                 0                 0            0           0            0   \n",
              "1                 0                 0            1           0            0   \n",
              "2                 0                 0            0           0            0   \n",
              "3                 0                 0            0           0            1   \n",
              "4                 1                 0            0           0            0   \n",
              "\n",
              "   ...  Material_Nylon  Material_Polyester  Material_Silk  Material_Wool  \\\n",
              "0  ...               1                   0              0              0   \n",
              "1  ...               0                   0              1              0   \n",
              "2  ...               0                   0              0              1   \n",
              "3  ...               0                   0              0              0   \n",
              "4  ...               1                   0              0              0   \n",
              "\n",
              "   Brand_Adidas  Brand_New Balance  Brand_Nike  Brand_Puma  Brand_Reebok  \\\n",
              "0             0                  1           0           0             0   \n",
              "1             0                  1           0           0             0   \n",
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              "3             0                  0           1           0             0   \n",
              "4             1                  0           0           0             0   \n",
              "\n",
              "   Brand_Under Armour  \n",
              "0                   0  \n",
              "1                   0  \n",
              "2                   1  \n",
              "3                   0  \n",
              "4                   0  \n",
              "\n",
              "[5 rows x 31 columns]"
            ],
            "text/html": [
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              "      <th>Price</th>\n",
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              "      <th>Brand_Adidas</th>\n",
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              "      <th>Brand_Nike</th>\n",
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              "<p>5 rows × 31 columns</p>\n",
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              "    <div class=\"colab-df-buttons\">\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
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              "      buttonEl.style.display =\n",
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              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-180d613d-59c6-45c7-943d-a2aa7567c38d');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
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              "      --hover-bg-color: #E2EBFA;\n",
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              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
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              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
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              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
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              "  }\n",
              "\n",
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              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
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              "      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-fab245d4-acb0-4be0-a9b6-78b10549f786 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df"
            }
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "standardized_df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 273
        },
        "id": "lqDQ3etnVwOG",
        "outputId": "4a24635c-160f-4ec3-bd93-a78064bde903"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      Price  Category_Dress  Category_Jacket  Category_Jeans  Category_Shoes  \\\n",
              "0  0.910053             1.0              0.0             0.0             0.0   \n",
              "1  0.248677             0.0              0.0             1.0             0.0   \n",
              "2  0.619048             1.0              0.0             0.0             0.0   \n",
              "3  0.354497             0.0              0.0             0.0             1.0   \n",
              "4  0.544974             0.0              0.0             0.0             0.0   \n",
              "\n",
              "   Category_Sweater  Category_T-shirt  Color_Black  Color_Blue  Color_Green  \\\n",
              "0               0.0               0.0          0.0         0.0          0.0   \n",
              "1               0.0               0.0          1.0         0.0          0.0   \n",
              "2               0.0               0.0          0.0         0.0          0.0   \n",
              "3               0.0               0.0          0.0         0.0          1.0   \n",
              "4               1.0               0.0          0.0         0.0          0.0   \n",
              "\n",
              "   ...  Material_Nylon  Material_Polyester  Material_Silk  Material_Wool  \\\n",
              "0  ...             1.0                 0.0            0.0            0.0   \n",
              "1  ...             0.0                 0.0            1.0            0.0   \n",
              "2  ...             0.0                 0.0            0.0            1.0   \n",
              "3  ...             0.0                 0.0            0.0            0.0   \n",
              "4  ...             1.0                 0.0            0.0            0.0   \n",
              "\n",
              "   Brand_Adidas  Brand_New Balance  Brand_Nike  Brand_Puma  Brand_Reebok  \\\n",
              "0           0.0                1.0         0.0         0.0           0.0   \n",
              "1           0.0                1.0         0.0         0.0           0.0   \n",
              "2           0.0                0.0         0.0         0.0           0.0   \n",
              "3           0.0                0.0         1.0         0.0           0.0   \n",
              "4           1.0                0.0         0.0         0.0           0.0   \n",
              "\n",
              "   Brand_Under Armour  \n",
              "0                 0.0  \n",
              "1                 0.0  \n",
              "2                 1.0  \n",
              "3                 0.0  \n",
              "4                 0.0  \n",
              "\n",
              "[5 rows x 31 columns]"
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              "\n",
              "  <div id=\"df-4c586a4b-4385-4430-8116-5317bda76963\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Price</th>\n",
              "      <th>Category_Dress</th>\n",
              "      <th>Category_Jacket</th>\n",
              "      <th>Category_Jeans</th>\n",
              "      <th>Category_Shoes</th>\n",
              "      <th>Category_Sweater</th>\n",
              "      <th>Category_T-shirt</th>\n",
              "      <th>Color_Black</th>\n",
              "      <th>Color_Blue</th>\n",
              "      <th>Color_Green</th>\n",
              "      <th>...</th>\n",
              "      <th>Material_Nylon</th>\n",
              "      <th>Material_Polyester</th>\n",
              "      <th>Material_Silk</th>\n",
              "      <th>Material_Wool</th>\n",
              "      <th>Brand_Adidas</th>\n",
              "      <th>Brand_New Balance</th>\n",
              "      <th>Brand_Nike</th>\n",
              "      <th>Brand_Puma</th>\n",
              "      <th>Brand_Reebok</th>\n",
              "      <th>Brand_Under Armour</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.910053</td>\n",
              "      <td>1.0</td>\n",
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              "      <td>0.0</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
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              "      <td>1.0</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.248677</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.619048</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.354497</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.544974</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
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              "      <td>0.0</td>\n",
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              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 31 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-4c586a4b-4385-4430-8116-5317bda76963')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
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              "  </svg>\n",
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              "\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",
              "      cursor: pointer;\n",
              "      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",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-4c586a4b-4385-4430-8116-5317bda76963 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-4c586a4b-4385-4430-8116-5317bda76963');\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-bb82de9a-6969-4272-b6c0-2f4353fd1b85\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-bb82de9a-6969-4272-b6c0-2f4353fd1b85')\"\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",
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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",
              "      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-bb82de9a-6969-4272-b6c0-2f4353fd1b85 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "standardized_df"
            }
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "standardized_df.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 367
        },
        "id": "7Juio3gbV94Q",
        "outputId": "24914139-3722-44a3-ec60-2ca7637ade4b"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "             Price  Category_Dress  Category_Jacket  Category_Jeans  \\\n",
              "count  1000.000000     1000.000000      1000.000000     1000.000000   \n",
              "mean      0.509466        0.166000         0.191000        0.167000   \n",
              "std       0.284103        0.372267         0.393286        0.373162   \n",
              "min       0.000000        0.000000         0.000000        0.000000   \n",
              "25%       0.263228        0.000000         0.000000        0.000000   \n",
              "50%       0.518519        0.000000         0.000000        0.000000   \n",
              "75%       0.740741        0.000000         0.000000        0.000000   \n",
              "max       1.000000        1.000000         1.000000        1.000000   \n",
              "\n",
              "       Category_Shoes  Category_Sweater  Category_T-shirt  Color_Black  \\\n",
              "count     1000.000000       1000.000000       1000.000000   1000.00000   \n",
              "mean         0.172000          0.160000          0.144000      0.16300   \n",
              "std          0.377569          0.366789          0.351265      0.36955   \n",
              "min          0.000000          0.000000          0.000000      0.00000   \n",
              "25%          0.000000          0.000000          0.000000      0.00000   \n",
              "50%          0.000000          0.000000          0.000000      0.00000   \n",
              "75%          0.000000          0.000000          0.000000      0.00000   \n",
              "max          1.000000          1.000000          1.000000      1.00000   \n",
              "\n",
              "       Color_Blue  Color_Green  ...  Material_Nylon  Material_Polyester  \\\n",
              "count  1000.00000  1000.000000  ...     1000.000000         1000.000000   \n",
              "mean      0.16300     0.162000  ...        0.155000            0.175000   \n",
              "std       0.36955     0.368635  ...        0.362086            0.380157   \n",
              "min       0.00000     0.000000  ...        0.000000            0.000000   \n",
              "25%       0.00000     0.000000  ...        0.000000            0.000000   \n",
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              "75%       0.00000     0.000000  ...        0.000000            0.000000   \n",
              "max       1.00000     1.000000  ...        1.000000            1.000000   \n",
              "\n",
              "       Material_Silk  Material_Wool  Brand_Adidas  Brand_New Balance  \\\n",
              "count    1000.000000    1000.000000   1000.000000        1000.000000   \n",
              "mean        0.173000       0.172000      0.166000           0.164000   \n",
              "std         0.378437       0.377569      0.372267           0.370461   \n",
              "min         0.000000       0.000000      0.000000           0.000000   \n",
              "25%         0.000000       0.000000      0.000000           0.000000   \n",
              "50%         0.000000       0.000000      0.000000           0.000000   \n",
              "75%         0.000000       0.000000      0.000000           0.000000   \n",
              "max         1.000000       1.000000      1.000000           1.000000   \n",
              "\n",
              "        Brand_Nike   Brand_Puma  Brand_Reebok  Brand_Under Armour  \n",
              "count  1000.000000  1000.000000   1000.000000         1000.000000  \n",
              "mean      0.165000     0.168000      0.158000            0.179000  \n",
              "std       0.371366     0.374053      0.364924            0.383544  \n",
              "min       0.000000     0.000000      0.000000            0.000000  \n",
              "25%       0.000000     0.000000      0.000000            0.000000  \n",
              "50%       0.000000     0.000000      0.000000            0.000000  \n",
              "75%       0.000000     0.000000      0.000000            0.000000  \n",
              "max       1.000000     1.000000      1.000000            1.000000  \n",
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              "[8 rows x 31 columns]"
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              "        vertical-align: middle;\n",
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              "      <th>Category_Jacket</th>\n",
              "      <th>Category_Jeans</th>\n",
              "      <th>Category_Shoes</th>\n",
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              "      <th>Category_T-shirt</th>\n",
              "      <th>Color_Black</th>\n",
              "      <th>Color_Blue</th>\n",
              "      <th>Color_Green</th>\n",
              "      <th>...</th>\n",
              "      <th>Material_Nylon</th>\n",
              "      <th>Material_Polyester</th>\n",
              "      <th>Material_Silk</th>\n",
              "      <th>Material_Wool</th>\n",
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              "      <th>Brand_Nike</th>\n",
              "      <th>Brand_Puma</th>\n",
              "      <th>Brand_Reebok</th>\n",
              "      <th>Brand_Under Armour</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
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              "      <th>count</th>\n",
              "      <td>1000.000000</td>\n",
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              "      <td>1000.00000</td>\n",
              "      <td>1000.000000</td>\n",
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              "      <td>1000.000000</td>\n",
              "      <td>1000.000000</td>\n",
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              "      <th>mean</th>\n",
              "      <td>0.509466</td>\n",
              "      <td>0.166000</td>\n",
              "      <td>0.191000</td>\n",
              "      <td>0.167000</td>\n",
              "      <td>0.172000</td>\n",
              "      <td>0.160000</td>\n",
              "      <td>0.144000</td>\n",
              "      <td>0.16300</td>\n",
              "      <td>0.16300</td>\n",
              "      <td>0.162000</td>\n",
              "      <td>...</td>\n",
              "      <td>0.155000</td>\n",
              "      <td>0.175000</td>\n",
              "      <td>0.173000</td>\n",
              "      <td>0.172000</td>\n",
              "      <td>0.166000</td>\n",
              "      <td>0.164000</td>\n",
              "      <td>0.165000</td>\n",
              "      <td>0.168000</td>\n",
              "      <td>0.158000</td>\n",
              "      <td>0.179000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>0.284103</td>\n",
              "      <td>0.372267</td>\n",
              "      <td>0.393286</td>\n",
              "      <td>0.373162</td>\n",
              "      <td>0.377569</td>\n",
              "      <td>0.366789</td>\n",
              "      <td>0.351265</td>\n",
              "      <td>0.36955</td>\n",
              "      <td>0.36955</td>\n",
              "      <td>0.368635</td>\n",
              "      <td>...</td>\n",
              "      <td>0.362086</td>\n",
              "      <td>0.380157</td>\n",
              "      <td>0.378437</td>\n",
              "      <td>0.377569</td>\n",
              "      <td>0.372267</td>\n",
              "      <td>0.370461</td>\n",
              "      <td>0.371366</td>\n",
              "      <td>0.374053</td>\n",
              "      <td>0.364924</td>\n",
              "      <td>0.383544</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
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              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>0.263228</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>0.518519</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
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              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>0.740741</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.00000</td>\n",
              "      <td>1.00000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>8 rows × 31 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
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              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-0be3fe98-ffc6-4100-92cc-d50031208520');\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",
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              "\n",
              "  .colab-df-quickchart:hover {\n",
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              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
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              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
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              "\n",
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              "\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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              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
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              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-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",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
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              "      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",
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              "      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",
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              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "  </div>\n"
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe"
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          },
          "metadata": {},
          "execution_count": 31
        }
      ]
    }
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}