Kmeans_cluster_in_machine_learning.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Abrar171041075/Machine-Learning-project/blob/main/Kmeans_cluster_in_machine_learning.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "id": "6ddfdb79",
      "metadata": {
        "id": "6ddfdb79"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "from matplotlib import pyplot as plt\n",
        "import seaborn as sns"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import files\n",
        "files.upload()"
      ],
      "metadata": {
        "colab": {
          "resources": {
            "http://localhost:8080/nbextensions/google.colab/files.js": {
              "data": "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              "ok": true,
              "headers": [
                [
                  "content-type",
                  "application/javascript"
                ]
              ],
              "status": 200,
              "status_text": ""
            }
          },
          "base_uri": "https://localhost:8080/",
          "height": 127
        },
        "id": "0PmKDzjn0sVT",
        "outputId": "54ee67a5-c5ec-4521-b71f-fdfcc30f2331"
      },
      "id": "0PmKDzjn0sVT",
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-bad0c22d-4d5e-4a0c-ab7d-dc2bb000a04a\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-bad0c22d-4d5e-4a0c-ab7d-dc2bb000a04a\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script src=\"/nbextensions/google.colab/files.js\"></script> "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Saving mall customers.csv to mall customers (1).csv\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'mall customers.csv': b'CustomerID,Gender,Age,Annual Income (k$),Spending Score (1-100)\\r\\n1,Male,19,15,39\\r\\n2,Male,21,15,81\\r\\n3,Female,20,16,6\\r\\n4,Female,23,16,77\\r\\n5,Female,31,17,40\\r\\n6,Female,22,17,76\\r\\n7,Female,35,18,6\\r\\n8,Female,23,18,94\\r\\n9,Male,64,19,3\\r\\n10,Female,30,19,72\\r\\n11,Male,67,19,14\\r\\n12,Female,35,19,99\\r\\n13,Female,58,20,15\\r\\n14,Female,24,20,77\\r\\n15,Male,37,20,13\\r\\n16,Male,22,20,79\\r\\n17,Female,35,21,35\\r\\n18,Male,20,21,66\\r\\n19,Male,52,23,29\\r\\n20,Female,35,23,98\\r\\n21,Male,35,24,35\\r\\n22,Male,25,24,73\\r\\n23,Female,46,25,5\\r\\n24,Male,31,25,73\\r\\n25,Female,54,28,14\\r\\n26,Male,29,28,82\\r\\n27,Female,45,28,32\\r\\n28,Male,35,28,61\\r\\n29,Female,40,29,31\\r\\n30,Female,23,29,87\\r\\n31,Male,60,30,4\\r\\n32,Female,21,30,73\\r\\n33,Male,53,33,4\\r\\n34,Male,18,33,92\\r\\n35,Female,49,33,14\\r\\n36,Female,21,33,81\\r\\n37,Female,42,34,17\\r\\n38,Female,30,34,73\\r\\n39,Female,36,37,26\\r\\n40,Female,20,37,75\\r\\n41,Female,65,38,35\\r\\n42,Male,24,38,92\\r\\n43,Male,48,39,36\\r\\n44,Female,31,39,61\\r\\n45,Female,49,39,28\\r\\n46,Female,24,39,65\\r\\n47,Female,50,40,55\\r\\n48,Female,27,40,47\\r\\n49,Female,29,40,42\\r\\n50,Female,31,40,42\\r\\n51,Female,49,42,52\\r\\n52,Male,33,42,60\\r\\n53,Female,31,43,54\\r\\n54,Male,59,43,60\\r\\n55,Female,50,43,45\\r\\n56,Male,47,43,41\\r\\n57,Female,51,44,50\\r\\n58,Male,69,44,46\\r\\n59,Female,27,46,51\\r\\n60,Male,53,46,46\\r\\n61,Male,70,46,56\\r\\n62,Male,19,46,55\\r\\n63,Female,67,47,52\\r\\n64,Female,54,47,59\\r\\n65,Male,63,48,51\\r\\n66,Male,18,48,59\\r\\n67,Female,43,48,50\\r\\n68,Female,68,48,48\\r\\n69,Male,19,48,59\\r\\n70,Female,32,48,47\\r\\n71,Male,70,49,55\\r\\n72,Female,47,49,42\\r\\n73,Female,60,50,49\\r\\n74,Female,60,50,56\\r\\n75,Male,59,54,47\\r\\n76,Male,26,54,54\\r\\n77,Female,45,54,53\\r\\n78,Male,40,54,48\\r\\n79,Female,23,54,52\\r\\n80,Female,49,54,42\\r\\n81,Male,57,54,51\\r\\n82,Male,38,54,55\\r\\n83,Male,67,54,41\\r\\n84,Female,46,54,44\\r\\n85,Female,21,54,57\\r\\n86,Male,48,54,46\\r\\n87,Female,55,57,58\\r\\n88,Female,22,57,55\\r\\n89,Female,34,58,60\\r\\n90,Female,50,58,46\\r\\n91,Female,68,59,55\\r\\n92,Male,18,59,41\\r\\n93,Male,48,60,49\\r\\n94,Female,40,60,40\\r\\n95,Female,32,60,42\\r\\n96,Male,24,60,52\\r\\n97,Female,47,60,47\\r\\n98,Female,27,60,50\\r\\n99,Male,48,61,42\\r\\n100,Male,20,61,49\\r\\n101,Female,23,62,41\\r\\n102,Female,49,62,48\\r\\n103,Male,67,62,59\\r\\n104,Male,26,62,55\\r\\n105,Male,49,62,56\\r\\n106,Female,21,62,42\\r\\n107,Female,66,63,50\\r\\n108,Male,54,63,46\\r\\n109,Male,68,63,43\\r\\n110,Male,66,63,48\\r\\n111,Male,65,63,52\\r\\n112,Female,19,63,54\\r\\n113,Female,38,64,42\\r\\n114,Male,19,64,46\\r\\n115,Female,18,65,48\\r\\n116,Female,19,65,50\\r\\n117,Female,63,65,43\\r\\n118,Female,49,65,59\\r\\n119,Female,51,67,43\\r\\n120,Female,50,67,57\\r\\n121,Male,27,67,56\\r\\n122,Female,38,67,40\\r\\n123,Female,40,69,58\\r\\n124,Male,39,69,91\\r\\n125,Female,23,70,29\\r\\n126,Female,31,70,77\\r\\n127,Male,43,71,35\\r\\n128,Male,40,71,95\\r\\n129,Male,59,71,11\\r\\n130,Male,38,71,75\\r\\n131,Male,47,71,9\\r\\n132,Male,39,71,75\\r\\n133,Female,25,72,34\\r\\n134,Female,31,72,71\\r\\n135,Male,20,73,5\\r\\n136,Female,29,73,88\\r\\n137,Female,44,73,7\\r\\n138,Male,32,73,73\\r\\n139,Male,19,74,10\\r\\n140,Female,35,74,72\\r\\n141,Female,57,75,5\\r\\n142,Male,32,75,93\\r\\n143,Female,28,76,40\\r\\n144,Female,32,76,87\\r\\n145,Male,25,77,12\\r\\n146,Male,28,77,97\\r\\n147,Male,48,77,36\\r\\n148,Female,32,77,74\\r\\n149,Female,34,78,22\\r\\n150,Male,34,78,90\\r\\n151,Male,43,78,17\\r\\n152,Male,39,78,88\\r\\n153,Female,44,78,20\\r\\n154,Female,38,78,76\\r\\n155,Female,47,78,16\\r\\n156,Female,27,78,89\\r\\n157,Male,37,78,1\\r\\n158,Female,30,78,78\\r\\n159,Male,34,78,1\\r\\n160,Female,30,78,73\\r\\n161,Female,56,79,35\\r\\n162,Female,29,79,83\\r\\n163,Male,19,81,5\\r\\n164,Female,31,81,93\\r\\n165,Male,50,85,26\\r\\n166,Female,36,85,75\\r\\n167,Male,42,86,20\\r\\n168,Female,33,86,95\\r\\n169,Female,36,87,27\\r\\n170,Male,32,87,63\\r\\n171,Male,40,87,13\\r\\n172,Male,28,87,75\\r\\n173,Male,36,87,10\\r\\n174,Male,36,87,92\\r\\n175,Female,52,88,13\\r\\n176,Female,30,88,86\\r\\n177,Male,58,88,15\\r\\n178,Male,27,88,69\\r\\n179,Male,59,93,14\\r\\n180,Male,35,93,90\\r\\n181,Female,37,97,32\\r\\n182,Female,32,97,86\\r\\n183,Male,46,98,15\\r\\n184,Female,29,98,88\\r\\n185,Female,41,99,39\\r\\n186,Male,30,99,97\\r\\n187,Female,54,101,24\\r\\n188,Male,28,101,68\\r\\n189,Female,41,103,17\\r\\n190,Female,36,103,85\\r\\n191,Female,34,103,23\\r\\n192,Female,32,103,69\\r\\n193,Male,33,113,8\\r\\n194,Female,38,113,91\\r\\n195,Female,47,120,16\\r\\n196,Female,35,120,79\\r\\n197,Female,45,126,28\\r\\n198,Male,32,126,74\\r\\n199,Male,32,137,18\\r\\n200,Male,30,137,83\\r\\n'}"
            ]
          },
          "metadata": {},
          "execution_count": 2
        }
      ]
    },
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      "id": "721f3ef5",
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      },
      "outputs": [
        {
          "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"
            ],
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              "      <th>CustomerID</th>\n",
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              "      <th>Annual Income (k$)</th>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ],
      "source": [
        "df = pd.read_csv(\"mall customers.csv\")\n",
        "df.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "id": "e057ba53",
      "metadata": {
        "id": "e057ba53"
      },
      "outputs": [],
      "source": [
        "df.rename(columns={'Annual Income (k$)': 'income', 'Spending Score (1-100)':'score'}, inplace=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "id": "791c223d",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "791c223d",
        "outputId": "c5650bcc-f169-47f4-da4a-78f204f3dce9"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   CustomerID  Gender  Age  income  score\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"
            ],
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              "          const element = document.querySelector('#df-4c400ade-f4d0-4c5c-b4e3-b7569b7fb435');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ],
      "source": [
        "df.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "id": "2ea6c3eb",
      "metadata": {
        "id": "2ea6c3eb"
      },
      "outputs": [],
      "source": [
        "df.drop('CustomerID', axis=1, inplace=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "id": "9aad9991",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "9aad9991",
        "outputId": "880e9bfd-f813-4d2b-889d-af29c8af168b"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Gender  Age  income  score\n",
              "0    Male   19      15     39\n",
              "1    Male   21      15     81\n",
              "2  Female   20      16      6\n",
              "3  Female   23      16     77\n",
              "4  Female   31      17     40"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-e9dd17d0-3eb1-4313-b192-236bfa8458bd\">\n",
              "    <div 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>Gender</th>\n",
              "      <th>Age</th>\n",
              "      <th>income</th>\n",
              "      <th>score</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Male</td>\n",
              "      <td>19</td>\n",
              "      <td>15</td>\n",
              "      <td>39</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Male</td>\n",
              "      <td>21</td>\n",
              "      <td>15</td>\n",
              "      <td>81</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Female</td>\n",
              "      <td>20</td>\n",
              "      <td>16</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Female</td>\n",
              "      <td>23</td>\n",
              "      <td>16</td>\n",
              "      <td>77</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Female</td>\n",
              "      <td>31</td>\n",
              "      <td>17</td>\n",
              "      <td>40</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-e9dd17d0-3eb1-4313-b192-236bfa8458bd')\"\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 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\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",
              "    [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-e9dd17d0-3eb1-4313-b192-236bfa8458bd 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-e9dd17d0-3eb1-4313-b192-236bfa8458bd');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ],
      "source": [
        "df.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "id": "a3503e8b",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "a3503e8b",
        "outputId": "164d1561-9f6f-4cbe-bab8-a6c1e55b7779"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Gender     2\n",
              "Age       51\n",
              "income    64\n",
              "score     84\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ],
      "source": [
        "df.nunique()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "226c7d9a",
      "metadata": {
        "id": "226c7d9a"
      },
      "source": [
        "# Visuzalization"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "id": "5f1100fa",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 288
        },
        "id": "5f1100fa",
        "outputId": "e8e66f5c-6448-41a6-a75f-114efe099c3e"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "plt.scatter(df.Age , df.score)\n",
        "plt.xlabel('Age',fontsize = 20)\n",
        "plt.ylabel('Score',fontsize = 20)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "id": "21dc0928",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 288
        },
        "id": "21dc0928",
        "outputId": "665f2dc1-f948-4edf-bede-d8f3605a1cc4"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "plt.scatter(df.Age , df.income)\n",
        "plt.xlabel('Age',fontsize = 20)\n",
        "plt.ylabel('Income',fontsize = 20)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "id": "3637480d",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 288
        },
        "id": "3637480d",
        "outputId": "d4b048d3-440a-453a-c7f5-8b00d48a3715"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "plt.scatter(df.income, df.score)\n",
        "plt.xlabel('Income',fontsize = 20)\n",
        "plt.ylabel('Score',fontsize = 20)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "id": "72eb5f75",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 550
        },
        "id": "72eb5f75",
        "outputId": "292c8f9e-3c74-42ba-fd0a-f93e75eba719"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 540x540 with 12 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "#pairplot\n",
        "sns.pairplot(df[['Age','income','score']])\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "id": "1858a3ca",
      "metadata": {
        "id": "1858a3ca"
      },
      "outputs": [],
      "source": [
        "import sklearn.cluster as cluster"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "id": "b2eb01f8",
      "metadata": {
        "id": "b2eb01f8"
      },
      "outputs": [],
      "source": [
        "kmeans = cluster.KMeans() # default, n_clusters=8"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "id": "f3ad0e7c",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "f3ad0e7c",
        "outputId": "726fd444-e6d3-452a-dd5a-f63879a886b5"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([4, 3, 4, 3, 4, 3, 4, 3, 4, 3, 4, 3, 4, 3, 4, 3, 4, 3, 4, 3, 4, 3,\n",
              "       4, 3, 4, 3, 4, 3, 4, 3, 4, 3, 4, 3, 4, 3, 4, 3, 4, 3, 0, 3, 0, 0,\n",
              "       4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
              "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
              "       0, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7,\n",
              "       7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 2, 5, 2, 7, 2, 5, 2, 5, 2,\n",
              "       7, 2, 5, 2, 5, 2, 5, 2, 5, 2, 7, 2, 5, 2, 7, 2, 5, 2, 5, 2, 5, 2,\n",
              "       5, 2, 5, 2, 5, 2, 7, 2, 5, 2, 5, 2, 5, 2, 5, 2, 5, 2, 5, 2, 5, 2,\n",
              "       5, 2, 5, 2, 1, 6, 1, 6, 1, 6, 1, 6, 1, 6, 1, 6, 1, 6, 1, 6, 1, 6,\n",
              "       1, 6], dtype=int32)"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ],
      "source": [
        "predicted = kmeans.fit_predict(df[['income','score']]) # kmeans.fit_predict(df[['income','score','Age']])\n",
        "predicted"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# predicted = kmeans.fit_predict(df[['income','score']]) # kmeans.fit_predict(df[['income','score','Age']])\n",
        "# predicted"
      ],
      "metadata": {
        "id": "Nl5Hx2fT4VGZ"
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      "id": "Nl5Hx2fT4VGZ",
      "execution_count": 16,
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      "execution_count": 17,
      "id": "b9e6b5e7",
      "metadata": {
        "id": "b9e6b5e7"
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      "outputs": [],
      "source": [
        "df2 = df.copy()"
      ]
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    {
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      "execution_count": 18,
      "id": "b6335819",
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        "id": "b6335819",
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            "text/plain": [
              "     Gender  Age  income  score\n",
              "0      Male   19      15     39\n",
              "1      Male   21      15     81\n",
              "2    Female   20      16      6\n",
              "3    Female   23      16     77\n",
              "4    Female   31      17     40\n",
              "..      ...  ...     ...    ...\n",
              "195  Female   35     120     79\n",
              "196  Female   45     126     28\n",
              "197    Male   32     126     74\n",
              "198    Male   32     137     18\n",
              "199    Male   30     137     83\n",
              "\n",
              "[200 rows x 4 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-bb36aef2-faaf-4510-a608-fa1fb9425d5c\">\n",
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              "      <th></th>\n",
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              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Male</td>\n",
              "      <td>19</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Male</td>\n",
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              "      <td>81</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Female</td>\n",
              "      <td>20</td>\n",
              "      <td>16</td>\n",
              "      <td>6</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Female</td>\n",
              "      <td>23</td>\n",
              "      <td>16</td>\n",
              "      <td>77</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Female</td>\n",
              "      <td>31</td>\n",
              "      <td>17</td>\n",
              "      <td>40</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "    <tr>\n",
              "      <th>195</th>\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>Female</td>\n",
              "      <td>45</td>\n",
              "      <td>126</td>\n",
              "      <td>28</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>197</th>\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>Male</td>\n",
              "      <td>32</td>\n",
              "      <td>137</td>\n",
              "      <td>18</td>\n",
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              "    <tr>\n",
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              "      <td>137</td>\n",
              "      <td>83</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>200 rows × 4 columns</p>\n",
              "</div>\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
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              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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          "metadata": {},
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        "df2"
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    {
      "cell_type": "code",
      "execution_count": 19,
      "id": "ca89daa0",
      "metadata": {
        "id": "ca89daa0"
      },
      "outputs": [],
      "source": [
        "df2['predicted'] = predicted"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "id": "a8d81378",
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        "id": "a8d81378",
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      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Gender  Age  income  score  predicted\n",
              "0    Male   19      15     39          4\n",
              "1    Male   21      15     81          3\n",
              "2  Female   20      16      6          4\n",
              "3  Female   23      16     77          3\n",
              "4  Female   31      17     40          4"
            ],
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              "      <td>6</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
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              "                                                     [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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      "id": "45258fd4",
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      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "plt.scatter( df.score, df.income)\n",
        "plt.xlabel('score')\n",
        "plt.ylabel('income')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "id": "79b5982c",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "79b5982c",
        "outputId": "15a50e02-e9c6-4f3d-df09-125e355dfe47"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 47.72340426,  50.82978723],\n",
              "       [109.7       ,  22.        ],\n",
              "       [ 78.55172414,  82.17241379],\n",
              "       [ 25.0952381 ,  80.04761905],\n",
              "       [ 25.14285714,  19.52380952],\n",
              "       [ 79.73913043,  13.39130435],\n",
              "       [109.7       ,  82.        ],\n",
              "       [ 64.30769231,  46.8974359 ]])"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ],
      "source": [
        "centers = kmeans.cluster_centers_\n",
        "centers"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "centers[0]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "r2hJJ7hp5nJ7",
        "outputId": "372fcab7-2722-48a1-ecbf-f1b96bab4ff2"
      },
      "id": "r2hJJ7hp5nJ7",
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([47.72340426, 50.82978723])"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "markdown",
      "id": "6448b0a3",
      "metadata": {
        "id": "6448b0a3"
      },
      "source": [
        "\n",
        "\n",
        "# Cluster Visuzalization"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df2.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "sGJ2J7ix5gQ2",
        "outputId": "65704798-57d0-4046-a423-053c54d9b42f"
      },
      "id": "sGJ2J7ix5gQ2",
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Gender  Age  income  score  predicted\n",
              "0    Male   19      15     39          4\n",
              "1    Male   21      15     81          3\n",
              "2  Female   20      16      6          4\n",
              "3  Female   23      16     77          3\n",
              "4  Female   31      17     40          4"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-3a567549-734f-4ca6-bb3b-81cac527428e\">\n",
              "    <div 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>Gender</th>\n",
              "      <th>Age</th>\n",
              "      <th>income</th>\n",
              "      <th>score</th>\n",
              "      <th>predicted</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Male</td>\n",
              "      <td>19</td>\n",
              "      <td>15</td>\n",
              "      <td>39</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Male</td>\n",
              "      <td>21</td>\n",
              "      <td>15</td>\n",
              "      <td>81</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Female</td>\n",
              "      <td>20</td>\n",
              "      <td>16</td>\n",
              "      <td>6</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Female</td>\n",
              "      <td>23</td>\n",
              "      <td>16</td>\n",
              "      <td>77</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Female</td>\n",
              "      <td>31</td>\n",
              "      <td>17</td>\n",
              "      <td>40</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-3a567549-734f-4ca6-bb3b-81cac527428e')\"\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 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\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",
              "    [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-3a567549-734f-4ca6-bb3b-81cac527428e 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-3a567549-734f-4ca6-bb3b-81cac527428e');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "id": "5a657bab",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 626
        },
        "id": "5a657bab",
        "outputId": "22547cca-ad96-4154-8711-d7f3c2141038"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1152x864 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "df0 = df2[df2.predicted == 0]\n",
        "df1 = df2[df2.predicted == 1]\n",
        "df8 = df2[df2.predicted == 2]\n",
        "df3 = df2[df2.predicted == 3]\n",
        "df4 = df2[df2.predicted == 4]\n",
        "df5 = df2[df2.predicted == 5]\n",
        "df6 = df2[df2.predicted == 6]\n",
        "df7 = df2[df2.predicted == 7]\n",
        "\n",
        "plt.figure(figsize=(16,12))\n",
        "\n",
        "plt.scatter(df0['score'], df0['income'], color='blue', label='cluster 0')\n",
        "plt.scatter(df1['score'], df1['income'], color='green', label='cluster 1')\n",
        "\n",
        "plt.scatter(df8['score'], df8['income'], color='orange', label='cluster 2')\n",
        "plt.scatter(df3['score'], df3['income'], color='red', label='cluster 3')\n",
        "\n",
        "plt.scatter(df4['score'], df4['income'], color='yellow', label='cluster 4')\n",
        "plt.scatter(df5['score'], df5['income'], color='cyan', label='cluster 5')\n",
        "\n",
        "plt.scatter(df6['score'], df6['income'], color='pink', label='cluster 6')\n",
        "plt.scatter(df7['score'], df7['income'], color='#BF3416', label='cluster 7')\n",
        "\n",
        "plt.scatter(centers[:,0], centers[:,1], color='black', label = 'centroids')\n",
        "\n",
        "plt.title('Income vs Score (Cluster)',fontsize = 25)\n",
        "plt.xlabel('Income',fontsize = 20)\n",
        "plt.ylabel('Score',fontsize = 20)\n",
        "plt.show()\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "id": "c5a2fe03",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "c5a2fe03",
        "outputId": "4ac7a5b9-f34c-4cac-e2c9-f0dd20f403b5"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     Gender  Age  income  score  predicted\n",
              "124  Female   23      70     29          5\n",
              "128    Male   59      71     11          5\n",
              "130    Male   47      71      9          5\n",
              "134    Male   20      73      5          5\n",
              "136  Female   44      73      7          5"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-881fb4af-a5e0-4db6-b9e5-7a7a9812ca58\">\n",
              "    <div 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>Gender</th>\n",
              "      <th>Age</th>\n",
              "      <th>income</th>\n",
              "      <th>score</th>\n",
              "      <th>predicted</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>124</th>\n",
              "      <td>Female</td>\n",
              "      <td>23</td>\n",
              "      <td>70</td>\n",
              "      <td>29</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>128</th>\n",
              "      <td>Male</td>\n",
              "      <td>59</td>\n",
              "      <td>71</td>\n",
              "      <td>11</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>130</th>\n",
              "      <td>Male</td>\n",
              "      <td>47</td>\n",
              "      <td>71</td>\n",
              "      <td>9</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>134</th>\n",
              "      <td>Male</td>\n",
              "      <td>20</td>\n",
              "      <td>73</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>136</th>\n",
              "      <td>Female</td>\n",
              "      <td>44</td>\n",
              "      <td>73</td>\n",
              "      <td>7</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-881fb4af-a5e0-4db6-b9e5-7a7a9812ca58')\"\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 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\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",
              "    [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-881fb4af-a5e0-4db6-b9e5-7a7a9812ca58 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-881fb4af-a5e0-4db6-b9e5-7a7a9812ca58');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ],
      "source": [
        "df5.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "id": "7d3e0454",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7d3e0454",
        "outputId": "ea9d6e00-98fa-4715-8af5-812f771805a1"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(200, 5)"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ],
      "source": [
        "df2.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "id": "4849c7d1",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4849c7d1",
        "outputId": "99f9ae20-9d96-48e5-fbf9-41973a537d0d"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(21, 5)"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ],
      "source": [
        "df3.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "id": "094c0709",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 708
        },
        "id": "094c0709",
        "outputId": "3a2d575b-f655-4e57-dcd5-6052b9cf14ff"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    Gender  Age  income  score  predicted\n",
              "1     Male   21      15     81          3\n",
              "3   Female   23      16     77          3\n",
              "5   Female   22      17     76          3\n",
              "7   Female   23      18     94          3\n",
              "9   Female   30      19     72          3\n",
              "11  Female   35      19     99          3\n",
              "13  Female   24      20     77          3\n",
              "15    Male   22      20     79          3\n",
              "17    Male   20      21     66          3\n",
              "19  Female   35      23     98          3\n",
              "21    Male   25      24     73          3\n",
              "23    Male   31      25     73          3\n",
              "25    Male   29      28     82          3\n",
              "27    Male   35      28     61          3\n",
              "29  Female   23      29     87          3\n",
              "31  Female   21      30     73          3\n",
              "33    Male   18      33     92          3\n",
              "35  Female   21      33     81          3\n",
              "37  Female   30      34     73          3\n",
              "39  Female   20      37     75          3\n",
              "41    Male   24      38     92          3"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-fb458063-79c4-4482-946d-289c2d046c61\">\n",
              "    <div 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>Gender</th>\n",
              "      <th>Age</th>\n",
              "      <th>income</th>\n",
              "      <th>score</th>\n",
              "      <th>predicted</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Male</td>\n",
              "      <td>21</td>\n",
              "      <td>15</td>\n",
              "      <td>81</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Female</td>\n",
              "      <td>23</td>\n",
              "      <td>16</td>\n",
              "      <td>77</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Female</td>\n",
              "      <td>22</td>\n",
              "      <td>17</td>\n",
              "      <td>76</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>Female</td>\n",
              "      <td>23</td>\n",
              "      <td>18</td>\n",
              "      <td>94</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>Female</td>\n",
              "      <td>30</td>\n",
              "      <td>19</td>\n",
              "      <td>72</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>Female</td>\n",
              "      <td>35</td>\n",
              "      <td>19</td>\n",
              "      <td>99</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>Female</td>\n",
              "      <td>24</td>\n",
              "      <td>20</td>\n",
              "      <td>77</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>Male</td>\n",
              "      <td>22</td>\n",
              "      <td>20</td>\n",
              "      <td>79</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>Male</td>\n",
              "      <td>20</td>\n",
              "      <td>21</td>\n",
              "      <td>66</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>Female</td>\n",
              "      <td>35</td>\n",
              "      <td>23</td>\n",
              "      <td>98</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>Male</td>\n",
              "      <td>25</td>\n",
              "      <td>24</td>\n",
              "      <td>73</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>Male</td>\n",
              "      <td>31</td>\n",
              "      <td>25</td>\n",
              "      <td>73</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>Male</td>\n",
              "      <td>29</td>\n",
              "      <td>28</td>\n",
              "      <td>82</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>Male</td>\n",
              "      <td>35</td>\n",
              "      <td>28</td>\n",
              "      <td>61</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29</th>\n",
              "      <td>Female</td>\n",
              "      <td>23</td>\n",
              "      <td>29</td>\n",
              "      <td>87</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>31</th>\n",
              "      <td>Female</td>\n",
              "      <td>21</td>\n",
              "      <td>30</td>\n",
              "      <td>73</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>33</th>\n",
              "      <td>Male</td>\n",
              "      <td>18</td>\n",
              "      <td>33</td>\n",
              "      <td>92</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>35</th>\n",
              "      <td>Female</td>\n",
              "      <td>21</td>\n",
              "      <td>33</td>\n",
              "      <td>81</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>37</th>\n",
              "      <td>Female</td>\n",
              "      <td>30</td>\n",
              "      <td>34</td>\n",
              "      <td>73</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>39</th>\n",
              "      <td>Female</td>\n",
              "      <td>20</td>\n",
              "      <td>37</td>\n",
              "      <td>75</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>41</th>\n",
              "      <td>Male</td>\n",
              "      <td>24</td>\n",
              "      <td>38</td>\n",
              "      <td>92</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-fb458063-79c4-4482-946d-289c2d046c61')\"\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 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\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",
              "    [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-fb458063-79c4-4482-946d-289c2d046c61 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-fb458063-79c4-4482-946d-289c2d046c61');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ],
      "source": [
        "df3"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "67a08003",
      "metadata": {
        "id": "67a08003"
      },
      "source": [
        "# Elbow Method"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 51,
      "id": "e8f9bd12",
      "metadata": {
        "id": "e8f9bd12"
      },
      "outputs": [],
      "source": [
        "from sklearn.cluster import KMeans"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 52,
      "id": "7e8c9bad",
      "metadata": {
        "id": "7e8c9bad"
      },
      "outputs": [],
      "source": [
        "wcss = [ ] \n",
        "#  WCSS is the sum of squared distance between each point and the centroid in a cluster. When we plot the WCSS with the K value, the plot looks like an Elbow. As the number of clusters increases, the WCSS value will start to decrease."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 53,
      "id": "3cf269fb",
      "metadata": {
        "id": "3cf269fb"
      },
      "outputs": [],
      "source": [
        "cluster = range(1,15)\n",
        "for k in cluster:\n",
        "    kmeanss = KMeans(n_clusters = k)\n",
        "    kmeanss.fit(df[['income','score']])\n",
        "    wcss.append(kmeanss.inertia_) # sum of squared error"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 54,
      "id": "71222213",
      "metadata": {
        "id": "71222213",
        "outputId": "c6f3c256-013b-49c7-a4f5-437c52e92428",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[269981.28,\n",
              " 181363.59595959593,\n",
              " 106348.37306211122,\n",
              " 73679.78903948836,\n",
              " 44448.4554479337,\n",
              " 37265.86520484346,\n",
              " 30227.606513152008,\n",
              " 25005.934805966197,\n",
              " 21850.165282585636,\n",
              " 19994.409069463563,\n",
              " 17515.872164701657,\n",
              " 16247.900599424831,\n",
              " 14965.29932325739,\n",
              " 13499.210693935693]"
            ]
          },
          "metadata": {},
          "execution_count": 54
        }
      ],
      "source": [
        "wcss"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "9c2c8025",
      "metadata": {
        "id": "9c2c8025"
      },
      "source": [
        "# Elbow"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 61,
      "id": "c9cb83dc",
      "metadata": {
        "id": "c9cb83dc",
        "outputId": "8af20018-2b13-44d2-ba30-51b5aebcb68f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "plt.plot(cluster , wcss)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 35,
      "id": "1d639632",
      "metadata": {
        "id": "1d639632",
        "outputId": "d201e520-9dc4-4c4c-ebfa-0a6ae4ef8fd7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 563
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f6caf117890>]"
            ]
          },
          "metadata": {},
          "execution_count": 35
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x720 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "plt.figure(figsize=[15,10])\n",
        "plt.xlabel('Number of Clusters')\n",
        "plt.ylabel('Sum of Squared Errors')\n",
        "plt.title('Elbow Method')\n",
        "plt.plot(cluster , wcss)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 36,
      "id": "961c43fe",
      "metadata": {
        "id": "961c43fe"
      },
      "outputs": [],
      "source": [
        "#plot_knee()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 37,
      "id": "3bc652f3",
      "metadata": {
        "id": "3bc652f3",
        "outputId": "4e61fce1-0334-4c03-ca2b-5d0de2de36f4",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: kneed in /usr/local/lib/python3.7/dist-packages (0.8.1)\n",
            "Requirement already satisfied: numpy>=1.14.2 in /usr/local/lib/python3.7/dist-packages (from kneed) (1.21.6)\n",
            "Requirement already satisfied: scipy>=1.0.0 in /usr/local/lib/python3.7/dist-packages (from kneed) (1.7.3)\n"
          ]
        }
      ],
      "source": [
        "!pip install kneed"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 38,
      "id": "92b8cf1f",
      "metadata": {
        "id": "92b8cf1f"
      },
      "outputs": [],
      "source": [
        "from kneed import KneeLocator"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 39,
      "id": "858f3ecf",
      "metadata": {
        "id": "858f3ecf"
      },
      "outputs": [],
      "source": [
        "kn  = KneeLocator(cluster,wcss, curve='convex', direction='decreasing')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 40,
      "id": "e8ba2a6d",
      "metadata": {
        "id": "e8ba2a6d",
        "outputId": "cfb1ee87-9ab4-4b1d-a511-4ddc20a27d17",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<kneed.knee_locator.KneeLocator at 0x7f6cb1b2cb90>"
            ]
          },
          "metadata": {},
          "execution_count": 40
        }
      ],
      "source": [
        "kn"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 41,
      "id": "8a7ebda7",
      "metadata": {
        "id": "8a7ebda7",
        "outputId": "8db3f007-bb8b-473a-966f-30c2d7c110b7",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Optimal Number of Cluster is =  5\n"
          ]
        }
      ],
      "source": [
        "print('Optimal Number of Cluster is = ', kn.knee)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 42,
      "id": "bee84369",
      "metadata": {
        "id": "bee84369",
        "outputId": "9e05bb14-784a-4838-afd2-b4a2b9cb98db",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 390
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x432 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "kn.plot_knee()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "1c3b3fa7",
      "metadata": {
        "id": "1c3b3fa7"
      },
      "source": [
        "# After Select best K"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 43,
      "id": "d41ee168",
      "metadata": {
        "id": "d41ee168"
      },
      "outputs": [],
      "source": [
        "import sklearn.cluster as cluster\n",
        "kmeans = cluster.KMeans(n_clusters=5) # default, n_clusters=8"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 44,
      "id": "edfd4c10",
      "metadata": {
        "id": "edfd4c10",
        "outputId": "bc4c74c4-a5ff-45e2-e291-003023370d29",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([4, 0, 4, 0, 4, 0, 4, 0, 4, 0, 4, 0, 4, 0, 4, 0, 4, 0, 4, 0, 4, 0,\n",
              "       4, 0, 4, 0, 4, 0, 4, 0, 4, 0, 4, 0, 4, 0, 4, 0, 4, 0, 4, 0, 4, 1,\n",
              "       4, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 3, 2, 3, 1, 3, 2, 3, 2, 3,\n",
              "       1, 3, 2, 3, 2, 3, 2, 3, 2, 3, 1, 3, 2, 3, 2, 3, 2, 3, 2, 3, 2, 3,\n",
              "       2, 3, 2, 3, 2, 3, 2, 3, 2, 3, 2, 3, 2, 3, 2, 3, 2, 3, 2, 3, 2, 3,\n",
              "       2, 3, 2, 3, 2, 3, 2, 3, 2, 3, 2, 3, 2, 3, 2, 3, 2, 3, 2, 3, 2, 3,\n",
              "       2, 3], dtype=int32)"
            ]
          },
          "metadata": {},
          "execution_count": 44
        }
      ],
      "source": [
        "predicted = kmeans.fit_predict(df[['income','score']])\n",
        "predicted"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 45,
      "id": "991c8472",
      "metadata": {
        "id": "991c8472"
      },
      "outputs": [],
      "source": [
        "df['predicted'] = predicted"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 46,
      "id": "16b87ee2",
      "metadata": {
        "id": "16b87ee2",
        "outputId": "2a6dabef-0575-49af-c945-aed7a35fc222",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Gender  Age  income  score  predicted\n",
              "0    Male   19      15     39          4\n",
              "1    Male   21      15     81          0\n",
              "2  Female   20      16      6          4\n",
              "3  Female   23      16     77          0\n",
              "4  Female   31      17     40          4"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-1170e37c-bee8-452d-9161-57ffc375c663\">\n",
              "    <div 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>Gender</th>\n",
              "      <th>Age</th>\n",
              "      <th>income</th>\n",
              "      <th>score</th>\n",
              "      <th>predicted</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Male</td>\n",
              "      <td>19</td>\n",
              "      <td>15</td>\n",
              "      <td>39</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Male</td>\n",
              "      <td>21</td>\n",
              "      <td>15</td>\n",
              "      <td>81</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Female</td>\n",
              "      <td>20</td>\n",
              "      <td>16</td>\n",
              "      <td>6</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Female</td>\n",
              "      <td>23</td>\n",
              "      <td>16</td>\n",
              "      <td>77</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Female</td>\n",
              "      <td>31</td>\n",
              "      <td>17</td>\n",
              "      <td>40</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-1170e37c-bee8-452d-9161-57ffc375c663')\"\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 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\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",
              "    [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-1170e37c-bee8-452d-9161-57ffc375c663 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-1170e37c-bee8-452d-9161-57ffc375c663');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 46
        }
      ],
      "source": [
        "df.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 47,
      "id": "b99339cf",
      "metadata": {
        "id": "b99339cf",
        "outputId": "7652de2c-9f48-47cc-9de2-d4ddf1ae06a1",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[25.72727273, 79.36363636],\n",
              "       [55.2962963 , 49.51851852],\n",
              "       [88.2       , 17.11428571],\n",
              "       [86.53846154, 82.12820513],\n",
              "       [26.30434783, 20.91304348]])"
            ]
          },
          "metadata": {},
          "execution_count": 47
        }
      ],
      "source": [
        "center2 = kmeans.cluster_centers_\n",
        "center2"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 48,
      "id": "1981e455",
      "metadata": {
        "id": "1981e455",
        "outputId": "a14638fb-1ac4-4907-d1d0-d5aa4f373178",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 617
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7f6caa220650>"
            ]
          },
          "metadata": {},
          "execution_count": 48
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1152x864 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "df0 = df[df.predicted == 0]\n",
        "df1 = df[df.predicted == 1]\n",
        "df2 = df[df.predicted == 2]\n",
        "df3 = df[df.predicted == 3]\n",
        "df4 = df[df.predicted == 4]\n",
        "\n",
        "\n",
        "plt.figure(figsize=(16,12))\n",
        "\n",
        "plt.scatter(df0['score'], df0['income'], color='blue', label='cluster 0')\n",
        "plt.scatter(df1['score'], df1['income'], color='green', label='cluster 1')\n",
        "\n",
        "plt.scatter(df2['score'], df2['income'], color='orange', label='cluster 2')\n",
        "plt.scatter(df3['score'], df3['income'], color='red', label='cluster 3')\n",
        "\n",
        "plt.scatter(df4['score'], df4['income'], color='yellow', label='cluster 4')\n",
        "\n",
        "\n",
        "plt.scatter(center2[:,0], center2[:,1], color='black', label = 'centroids')\n",
        "\n",
        "plt.legend()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 48,
      "id": "924fd0cb",
      "metadata": {
        "id": "924fd0cb"
      },
      "outputs": [],
      "source": []
    }
  ],
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      "name": "python3"
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