Mall_Customers.ipynb

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    {
      "cell_type": "markdown",
      "source": [
        "# **TASK 2**"
      ],
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        "id": "_HSVmZt5TgeC"
      }
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        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "drive.mount(\"/content/drive\")\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "data=pd.read_csv(\"/content/drive/MyDrive/Mall_Customers.csv\")"
      ],
      "metadata": {
        "id": "pd_PhmPpSKvM"
      },
      "execution_count": null,
      "outputs": []
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      "execution_count": null,
      "outputs": [
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          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     CustomerID  Gender  Age  Annual Income (k$)  Spending Score (1-100)\n",
              "0             1    Male   19                  15                      39\n",
              "1             2    Male   21                  15                      81\n",
              "2             3  Female   20                  16                       6\n",
              "3             4  Female   23                  16                      77\n",
              "4             5  Female   31                  17                      40\n",
              "..          ...     ...  ...                 ...                     ...\n",
              "195         196  Female   35                 120                      79\n",
              "196         197  Female   45                 126                      28\n",
              "197         198    Male   32                 126                      74\n",
              "198         199    Male   32                 137                      18\n",
              "199         200    Male   30                 137                      83\n",
              "\n",
              "[200 rows x 5 columns]"
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              "summary": "{\n  \"name\": \"data\",\n  \"rows\": 200,\n  \"fields\": [\n    {\n      \"column\": \"CustomerID\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 57,\n        \"min\": 1,\n        \"max\": 200,\n        \"num_unique_values\": 200,\n        \"samples\": [\n          96,\n          16,\n          31\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Gender\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Female\",\n          \"Male\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 13,\n        \"min\": 18,\n        \"max\": 70,\n        \"num_unique_values\": 51,\n        \"samples\": [\n          55,\n          26\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Annual Income (k$)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 26,\n        \"min\": 15,\n        \"max\": 137,\n        \"num_unique_values\": 64,\n        \"samples\": [\n          87,\n          101\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Spending Score (1-100)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 25,\n        \"min\": 1,\n        \"max\": 99,\n        \"num_unique_values\": 84,\n        \"samples\": [\n          83,\n          39\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
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            "text/plain": [
              "   CustomerID  Gender  Age  Annual Income (k$)  Spending Score (1-100)\n",
              "0           1    Male   19                  15                      39\n",
              "1           2    Male   21                  15                      81\n",
              "2           3  Female   20                  16                       6\n",
              "3           4  Female   23                  16                      77\n",
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              "      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-99633bdf-f2a5-47c7-9c66-2f35bd9bff53 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-99633bdf-f2a5-47c7-9c66-2f35bd9bff53');\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-32b08053-73ec-48b3-9fd6-b9a09e2a341c\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-32b08053-73ec-48b3-9fd6-b9a09e2a341c')\"\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-32b08053-73ec-48b3-9fd6-b9a09e2a341c 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": "data",
              "summary": "{\n  \"name\": \"data\",\n  \"rows\": 200,\n  \"fields\": [\n    {\n      \"column\": \"CustomerID\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 57,\n        \"min\": 1,\n        \"max\": 200,\n        \"num_unique_values\": 200,\n        \"samples\": [\n          96,\n          16,\n          31\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Gender\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Female\",\n          \"Male\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 13,\n        \"min\": 18,\n        \"max\": 70,\n        \"num_unique_values\": 51,\n        \"samples\": [\n          55,\n          26\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Annual Income (k$)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 26,\n        \"min\": 15,\n        \"max\": 137,\n        \"num_unique_values\": 64,\n        \"samples\": [\n          87,\n          101\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Spending Score (1-100)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 25,\n        \"min\": 1,\n        \"max\": 99,\n        \"num_unique_values\": 84,\n        \"samples\": [\n          83,\n          39\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data.tail()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "dsI0w3-2S0iq",
        "outputId": "fd79f26f-ba9b-4d79-db62-04752e025c9b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     CustomerID  Gender  Age  Annual Income (k$)  Spending Score (1-100)\n",
              "195         196  Female   35                 120                      79\n",
              "196         197  Female   45                 126                      28\n",
              "197         198    Male   32                 126                      74\n",
              "198         199    Male   32                 137                      18\n",
              "199         200    Male   30                 137                      83"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-6a3b4081-ffc2-44c3-99c8-9996ec63b948\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>CustomerID</th>\n",
              "      <th>Gender</th>\n",
              "      <th>Age</th>\n",
              "      <th>Annual Income (k$)</th>\n",
              "      <th>Spending Score (1-100)</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>195</th>\n",
              "      <td>196</td>\n",
              "      <td>Female</td>\n",
              "      <td>35</td>\n",
              "      <td>120</td>\n",
              "      <td>79</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>196</th>\n",
              "      <td>197</td>\n",
              "      <td>Female</td>\n",
              "      <td>45</td>\n",
              "      <td>126</td>\n",
              "      <td>28</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>197</th>\n",
              "      <td>198</td>\n",
              "      <td>Male</td>\n",
              "      <td>32</td>\n",
              "      <td>126</td>\n",
              "      <td>74</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>198</th>\n",
              "      <td>199</td>\n",
              "      <td>Male</td>\n",
              "      <td>32</td>\n",
              "      <td>137</td>\n",
              "      <td>18</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>199</th>\n",
              "      <td>200</td>\n",
              "      <td>Male</td>\n",
              "      <td>30</td>\n",
              "      <td>137</td>\n",
              "      <td>83</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-6a3b4081-ffc2-44c3-99c8-9996ec63b948')\"\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",
              "    <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-6a3b4081-ffc2-44c3-99c8-9996ec63b948 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-6a3b4081-ffc2-44c3-99c8-9996ec63b948');\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-c1ad2d1b-c3c0-4dc4-b94b-975ac823b931\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-c1ad2d1b-c3c0-4dc4-b94b-975ac823b931')\"\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",
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              "type": "dataframe",
              "summary": "{\n  \"name\": \"data\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"CustomerID\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 196,\n        \"max\": 200,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          197,\n          200,\n          198\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Gender\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Male\",\n          \"Female\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 5,\n        \"min\": 30,\n        \"max\": 45,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          45,\n          30\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Annual Income (k$)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 7,\n        \"min\": 120,\n        \"max\": 137,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          120,\n          126\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Spending Score (1-100)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 30,\n        \"min\": 18,\n        \"max\": 83,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          28,\n          83\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "49PJc45pSYhT",
        "outputId": "25b2b4e8-4a42-4537-e603-d9931535ade4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 200 entries, 0 to 199\n",
            "Data columns (total 5 columns):\n",
            " #   Column                  Non-Null Count  Dtype \n",
            "---  ------                  --------------  ----- \n",
            " 0   CustomerID              200 non-null    int64 \n",
            " 1   Gender                  200 non-null    object\n",
            " 2   Age                     200 non-null    int64 \n",
            " 3   Annual Income (k$)      200 non-null    int64 \n",
            " 4   Spending Score (1-100)  200 non-null    int64 \n",
            "dtypes: int64(4), object(1)\n",
            "memory usage: 7.9+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "-6zbWWaLSfdp",
        "outputId": "194fbdb3-6ea1-4ef7-fddb-2e13f4045ea9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       CustomerID         Age  Annual Income (k$)  Spending Score (1-100)\n",
              "count  200.000000  200.000000          200.000000              200.000000\n",
              "mean   100.500000   38.850000           60.560000               50.200000\n",
              "std     57.879185   13.969007           26.264721               25.823522\n",
              "min      1.000000   18.000000           15.000000                1.000000\n",
              "25%     50.750000   28.750000           41.500000               34.750000\n",
              "50%    100.500000   36.000000           61.500000               50.000000\n",
              "75%    150.250000   49.000000           78.000000               73.000000\n",
              "max    200.000000   70.000000          137.000000               99.000000"
            ],
            "text/html": [
              "\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>CustomerID</th>\n",
              "      <th>Age</th>\n",
              "      <th>Annual Income (k$)</th>\n",
              "      <th>Spending Score (1-100)</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>200.000000</td>\n",
              "      <td>200.000000</td>\n",
              "      <td>200.000000</td>\n",
              "      <td>200.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>100.500000</td>\n",
              "      <td>38.850000</td>\n",
              "      <td>60.560000</td>\n",
              "      <td>50.200000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>57.879185</td>\n",
              "      <td>13.969007</td>\n",
              "      <td>26.264721</td>\n",
              "      <td>25.823522</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>18.000000</td>\n",
              "      <td>15.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>50.750000</td>\n",
              "      <td>28.750000</td>\n",
              "      <td>41.500000</td>\n",
              "      <td>34.750000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>100.500000</td>\n",
              "      <td>36.000000</td>\n",
              "      <td>61.500000</td>\n",
              "      <td>50.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>150.250000</td>\n",
              "      <td>49.000000</td>\n",
              "      <td>78.000000</td>\n",
              "      <td>73.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>200.000000</td>\n",
              "      <td>70.000000</td>\n",
              "      <td>137.000000</td>\n",
              "      <td>99.000000</td>\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"data\",\n  \"rows\": 8,\n  \"fields\": [\n    {\n      \"column\": \"CustomerID\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 71.77644232399086,\n        \"min\": 1.0,\n        \"max\": 200.0,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          200.0,\n          100.5,\n          150.25\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 60.50135224289181,\n        \"min\": 13.96900733155888,\n        \"max\": 200.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          38.85,\n          36.0,\n          200.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Annual Income (k$)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 62.01038341270951,\n        \"min\": 15.0,\n        \"max\": 200.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          60.56,\n          61.5,\n          200.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Spending Score (1-100)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 61.42496609345541,\n        \"min\": 1.0,\n        \"max\": 200.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          50.2,\n          50.0,\n          200.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "from sklearn.cluster import KMeans\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "\n",
        "X = data.drop(['CustomerID', 'Gender'], axis=1)  # Exclude irrelevant columns\n",
        "scaler = StandardScaler()\n",
        "X_scaled = scaler.fit_transform(X)\n",
        "\n",
        "\n",
        "sse = []\n",
        "for k in range(1, 11):\n",
        "    kmeans = KMeans(n_clusters=k, random_state=42)\n",
        "    kmeans.fit(X_scaled)\n",
        "    sse.append(kmeans.inertia_)\n",
        "\n",
        "\n",
        "plt.plot(range(1, 11), sse, marker='o')\n",
        "plt.xlabel('Number of clusters')\n",
        "plt.ylabel('SSE (Sum of Squared Errors)')\n",
        "plt.title('Elbow Method')\n",
        "plt.show()\n",
        "\n",
        "k = 5\n",
        "kmeans = KMeans(n_clusters=k, random_state=42)\n",
        "kmeans.fit(X_scaled)\n",
        "\n",
        "\n",
        "cluster_labels = kmeans.labels_\n",
        "\n",
        "\n",
        "data['Cluster'] = cluster_labels\n",
        "\n",
        "cluster_centers = pd.DataFrame(scaler.inverse_transform(kmeans.cluster_centers_), columns=X.columns)\n",
        "print(\"Cluster Centers:\")\n",
        "print(cluster_centers)\n",
        "\n",
        "\n",
        "plt.scatter(X_scaled[:, 0], X_scaled[:, 1], c=cluster_labels, cmap='viridis')\n",
        "plt.scatter(kmeans.cluster_centers_[:, 0], kmeans.cluster_centers_[:, 1], marker='x', color='red', s=100)\n",
        "plt.xlabel('Annual Income (k$)')\n",
        "plt.ylabel('Spending Score (1-100)')\n",
        "plt.title('K-means Clustering')\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "HbZQdLUzSor4",
        "outputId": "3cf8dbc5-49e5-4ba2-a27e-276f08366d18"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Cluster Centers:\n",
            "         Age  Annual Income (k$)  Spending Score (1-100)\n",
            "0  55.638298           54.382979               48.851064\n",
            "1  32.875000           86.100000               81.525000\n",
            "2  25.185185           41.092593               62.240741\n",
            "3  46.250000           26.750000               18.350000\n",
            "4  39.871795           86.102564               19.358974\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    }
  ]
}