{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Hollywood_clustering.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Q-Zlfgg_2c7L",
        "outputId": "da909070-acbd-475c-a5ae-27834f0c0c1c"
      },
      "source": [
        "!which python3"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/bin/python3\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1GBbUvNl2tGM"
      },
      "source": [
        "import numpy as np\r\n",
        "from sklearn.cluster import KMeans \r\n",
        "import pandas as pd\r\n",
        "import matplotlib.pyplot as plt\r\n",
        "%matplotlib inline \r\n",
        "import seaborn as sns"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hN-RNN4u2zEV"
      },
      "source": [
        "df=pd.read_csv('/content/ActorsDataset.csv', index_col=0)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 223
        },
        "id": "gW1gyyRb3D4m",
        "outputId": "0b776dfe-5a10-4bfd-a78a-e2199317e15c"
      },
      "source": [
        "df.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Gender</th>\n",
              "      <th>Age</th>\n",
              "      <th>Net Worth, Millions $</th>\n",
              "      <th>Number of children</th>\n",
              "      <th>Name</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ID</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Male</td>\n",
              "      <td>78</td>\n",
              "      <td>300</td>\n",
              "      <td>5</td>\n",
              "      <td>Harrison Ford</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Male</td>\n",
              "      <td>47</td>\n",
              "      <td>50</td>\n",
              "      <td>2</td>\n",
              "      <td>Neil Patrick Harris</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Male</td>\n",
              "      <td>72</td>\n",
              "      <td>16</td>\n",
              "      <td>2</td>\n",
              "      <td>Jeremy Irons</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Male</td>\n",
              "      <td>41</td>\n",
              "      <td>10</td>\n",
              "      <td>2</td>\n",
              "      <td>Oscar Isaac</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Male</td>\n",
              "      <td>47</td>\n",
              "      <td>7</td>\n",
              "      <td>2</td>\n",
              "      <td>Patrick Wilson</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Gender  Age  Net Worth, Millions $  Number of children                 Name\n",
              "ID                                                                            \n",
              "1    Male   78                    300                   5        Harrison Ford\n",
              "2    Male   47                     50                   2  Neil Patrick Harris\n",
              "3    Male   72                     16                   2         Jeremy Irons\n",
              "4    Male   41                     10                   2          Oscar Isaac\n",
              "5    Male   47                      7                   2       Patrick Wilson"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 428
        },
        "id": "ukKILJ9kj97Z",
        "outputId": "30fcf02b-21ee-44d0-84e6-659f6ad32ee8"
      },
      "source": [
        "df"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Gender</th>\n",
              "      <th>Age</th>\n",
              "      <th>Net Worth, Millions $</th>\n",
              "      <th>Number of children</th>\n",
              "      <th>Name</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ID</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Male</td>\n",
              "      <td>78</td>\n",
              "      <td>300</td>\n",
              "      <td>5</td>\n",
              "      <td>Harrison Ford</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Male</td>\n",
              "      <td>47</td>\n",
              "      <td>50</td>\n",
              "      <td>2</td>\n",
              "      <td>Neil Patrick Harris</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Male</td>\n",
              "      <td>72</td>\n",
              "      <td>16</td>\n",
              "      <td>2</td>\n",
              "      <td>Jeremy Irons</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Male</td>\n",
              "      <td>41</td>\n",
              "      <td>10</td>\n",
              "      <td>2</td>\n",
              "      <td>Oscar Isaac</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Male</td>\n",
              "      <td>47</td>\n",
              "      <td>7</td>\n",
              "      <td>2</td>\n",
              "      <td>Patrick Wilson</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>252</th>\n",
              "      <td>Female</td>\n",
              "      <td>85</td>\n",
              "      <td>30</td>\n",
              "      <td>5</td>\n",
              "      <td>Julie Andrews</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>253</th>\n",
              "      <td>Female</td>\n",
              "      <td>24</td>\n",
              "      <td>10</td>\n",
              "      <td>0</td>\n",
              "      <td>Hailee Steinfeld</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>254</th>\n",
              "      <td>Female</td>\n",
              "      <td>50</td>\n",
              "      <td>16</td>\n",
              "      <td>1</td>\n",
              "      <td>Minnie Driver</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>255</th>\n",
              "      <td>Female</td>\n",
              "      <td>74</td>\n",
              "      <td>50</td>\n",
              "      <td>0</td>\n",
              "      <td>Liza Minnelli</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>256</th>\n",
              "      <td>Female</td>\n",
              "      <td>60</td>\n",
              "      <td>50</td>\n",
              "      <td>2</td>\n",
              "      <td>Julianne Moore</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>256 rows × 5 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "     Gender  Age  ...  Number of children                 Name\n",
              "ID                ...                                         \n",
              "1      Male   78  ...                   5        Harrison Ford\n",
              "2      Male   47  ...                   2  Neil Patrick Harris\n",
              "3      Male   72  ...                   2         Jeremy Irons\n",
              "4      Male   41  ...                   2          Oscar Isaac\n",
              "5      Male   47  ...                   2       Patrick Wilson\n",
              "..      ...  ...  ...                 ...                  ...\n",
              "252  Female   85  ...                   5        Julie Andrews\n",
              "253  Female   24  ...                   0     Hailee Steinfeld\n",
              "254  Female   50  ...                   1       Minnie Driver \n",
              "255  Female   74  ...                   0        Liza Minnelli\n",
              "256  Female   60  ...                   2       Julianne Moore\n",
              "\n",
              "[256 rows x 5 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vFRu1zta3Ikb",
        "outputId": "8620270b-f0c8-40b7-a12e-2b6401941f39"
      },
      "source": [
        "df.info()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 256 entries, 1 to 256\n",
            "Data columns (total 5 columns):\n",
            " #   Column                 Non-Null Count  Dtype \n",
            "---  ------                 --------------  ----- \n",
            " 0   Gender                 256 non-null    object\n",
            " 1   Age                    256 non-null    int64 \n",
            " 2   Net Worth, Millions $  256 non-null    int64 \n",
            " 3   Number of children     256 non-null    int64 \n",
            " 4   Name                   256 non-null    object\n",
            "dtypes: int64(3), object(2)\n",
            "memory usage: 12.0+ KB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "veuZ12ss3LbL",
        "outputId": "cb8ad357-cb58-426e-b0a9-da012a84b593"
      },
      "source": [
        "df.describe()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Age</th>\n",
              "      <th>Net Worth, Millions $</th>\n",
              "      <th>Number of children</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>256.000000</td>\n",
              "      <td>256.000000</td>\n",
              "      <td>256.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>51.433594</td>\n",
              "      <td>79.718750</td>\n",
              "      <td>1.792969</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>15.213625</td>\n",
              "      <td>97.725942</td>\n",
              "      <td>1.531406</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>20.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>40.000000</td>\n",
              "      <td>16.000000</td>\n",
              "      <td>0.750000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>50.000000</td>\n",
              "      <td>40.000000</td>\n",
              "      <td>2.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>60.000000</td>\n",
              "      <td>100.000000</td>\n",
              "      <td>3.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>90.000000</td>\n",
              "      <td>500.000000</td>\n",
              "      <td>9.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "              Age  Net Worth, Millions $  Number of children\n",
              "count  256.000000             256.000000          256.000000\n",
              "mean    51.433594              79.718750            1.792969\n",
              "std     15.213625              97.725942            1.531406\n",
              "min     20.000000               3.000000            0.000000\n",
              "25%     40.000000              16.000000            0.750000\n",
              "50%     50.000000              40.000000            2.000000\n",
              "75%     60.000000             100.000000            3.000000\n",
              "max     90.000000             500.000000            9.000000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 528
        },
        "id": "Qysbd6_I3i2f",
        "outputId": "4d413eb5-dfab-48c9-9c6b-47bdc8402ead"
      },
      "source": [
        "sns.set_style('whitegrid')\r\n",
        "sns.lmplot('Age','Net Worth, Millions $',data=df, hue='Gender',\r\n",
        "           palette='coolwarm',height=6,aspect=1,fit_reg=False)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  FutureWarning\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.FacetGrid at 0x7efe3378aa20>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 504.125x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rHq8YTqMdkRh"
      },
      "source": [
        "df1 = df[['Age','Net Worth, Millions $','Gender','Number of children']]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 380
        },
        "id": "3aSETACKd0n0",
        "outputId": "766b2bec-38ab-4720-dcd8-176a5b4fa701"
      },
      "source": [
        "cor = df1.corr() \r\n",
        "sns.heatmap(cor, square = True)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7efe33692710>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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Ypk2bhtLSUty+fbvauvz8/PDdd9+hrKwMFRUVaNasGdzd3XHx4kW5JZGVlQWtVgtHR0e0adMGvXv3lscH/Pz84O7urlh/9+7d4ebmBgcHB/j4+KCwsNCar4jQ0FAYDAbs378fwcHBVl3zMFlZWQgLCwMAPP/887h+/TpKS0sBAAEBAVCr1WjdujVat26Na9euwdvbG9988w3ef/99HD9+HC4uLlXqnDt3LvLy8rBp0yZER0fLrRMiatgei+6m30ycOBHbt2/HnTt35GMmkwlbt27Frl27sGvXLhw+fBjNmjWrtp6mTZvCw8MD27dvR9euXQEAPXr0wKFDh3Dt2jX8/ve/r/Z6Z2fnas+r1Wr5s6OjIyRJsvTVAACDBw/G7t270aFDBzRv3tyqa2rqwdgqKirg6emJHTt2wNvbGx9//LHcyrqfRqPBihUrMH78eIwZMwYzZsyol/iIyLYks8rqTRSrk0SrVq0QEhKCpKQk+djAgQOxceNGef+7774DADRr1qzaFoW/vz82bNiAHj16ALiXJBISEtCjRw+oVCr06tULe/fuhSRJKC4uxvHjx9G9e/cq9Vi6z/1WrFiBffv2KZ5v2rQp5syZg+jo2q182atXL+zevRvAve4vV1fXapOO0WhE06ZNERYWhsmTJz/0xeTff/89AMDBwQHdunWrlKiJqOF6LGY33W/SpEmVpqcuWLAAp06dgk6nQ2hoqDzTaPDgwdi3b99DB66Be+MSBQUF8nTSbt26oaioSN4PDg6Gt7c3wsLCMHHiRMydOxdt27atUo+rqyt69uyJ4cOHywPXSs6ePYs2bdpUW0ar1aJbt27V/xEsmDFjBvLy8qDT6bBixYpK04eV4oqMjERYWBji4uIwbdq0KmX27dsHvV6P7du3Y/LkyViwYEGtYiQi+9AQupsazfskJk+ejLVr14oOo1ZiY2Mxc+ZMq8ryfRLV4/skLOP7JCyr7fskDrtFWl12UFGS5UL1oNE8cd3QEwQA9OnTR3QIRFSHzLD/2U2NJkk8Dvr27Ss6BCKqQxUNYAoskwQRkSBsSRARkaIG8PZSJgkiIlHYkiAiIkUNoSVh/2/hJiJ6TJlqsFkjIyMDQ4cORXBwsLwe3INSUlIQGhoKrVaL2bNnW6yTLQkiIkEkVd11N0mShIULF2LdunXQaDSIjIxEYGAgOnfuLJfJz89HfHw8Nm/ejJYtW+LatWsW62VLgohIEBNUVm+W5ObmwsPDA+7u7lCr1dBqtfIrEH6zdetWjB8/Hi1btgQAPPnkkxbrZUuCiEiQmix3kZiYiMTERHlfr9dDr9fL+0ajEW5ubvK+RqNBbm5upTry8/MBAGPHjoXJZMKMGTPwwgsvVHtfJgkiIkFqMnD9YFJ4FJIk4eLFi9i4cSOKiorw0ksvITk5udo3b7K7iYhIEJNKZfVmiUajQVFRkbxvNBqh0WiqlAkMDESTJk3g7u6OTp06ya0LJUwSRESCmGuwWeLn54f8/HwUFBSgvLwcBoMBgYGBlcoMGTIER48eBXDv9dP5+fnVvsQNYHcTEZEwFXX4LJ2TkxNiYmIQFRUFSZIQEREBLy8vrFy5Er6+vggKCsKgQYPw9ddfIzQ0FI6Ojpg3bx5cXV2rrbfRLBXe2HCp8OpxqXDLuFS4ZbVdKnxTh5esLjv+yue1utejYkuCiEiQhvALnUmCiEgQk/0v3cQkQUQkSkNYu4lJgohIEIktCSIiUsKWBBERKWKSICIiRQ3gFddMEkREorAlQUREipgkiIhIEWc3ERGRIrYkiIhIEZMEEREp4tpNRESkiGs3ERGRInY3kTBdXJ8SHYJd47sSLEs9sVp0CI89qQF0ODFJEBEJwpYEEREpsv92BJMEEZEwbEkQEZEizm4iIiJFHLgmIiJFDaG7yUF0AEREjZUJZqs3a2RkZGDo0KEIDg5GfHx8lfM7duxAv379EBYWhrCwMGzbts1inWxJEBEJUpedTZIkYeHChVi3bh00Gg0iIyMRGBiIzp07VyoXGhqKmJgYq+tlS4KISBBTDTZLcnNz4eHhAXd3d6jVami1WqSnp9c6RrYkiIgEsbYbCQASExORmJgo7+v1euj1ennfaDTCzc1N3tdoNMjNza1ST1paGo4dOwZPT0/Mnz8f7du3r/a+TBJERIJINSj7YFJ4FIMHD8bw4cOhVquxZcsWvPnmm0hISKj2GnY3EREJYq7Bf5ZoNBoUFRXJ+0ajERqNplIZV1dXqNVqAMDo0aORl5dnsV4mCSIiQepyTMLPzw/5+fkoKChAeXk5DAYDAgMDK5W5evWq/PnAgQN45plnLNbL7iYiIkFqMiZhiZOTE2JiYhAVFQVJkhAREQEvLy+sXLkSvr6+CAoKwsaNG3HgwAE4OjqiZcuWWLp0qcV6VWaz2f4f+aMa66bpKzoEu+bWpKXoEOwelwq3rEmb39fq+mmdxlhd9tP8rbW616NiS4KISJC6bEnUFyYJIiJBuHYTEREpaghrNzFJEBEJYs3UVtGYJIiIBGFLgoiIFJkawORSJgkiIkE4cE1ERIo4JkFERIo4JkFERIr4MB0RESlidxMRESlidxMRESmSzPafJpgkiIgEsf8UwSRBRCQMxySIiEgRZzcREZGihvDOt1q/47pLly5YtmyZvL927VrExsbWtloAwN/+9jekpqbWSV3V2bt3L4YNG4YJEyZYVX7ChAk4efJkleMnT57E4sWLAQA7duzAwoULH3q9v7//owdLRI+NunzHdX2pdUtCrVYjLS0NU6dORevWresipjpRUVEBJyfrvl5SUhIWLVqEXr161eqefn5+8PPze6RraxIvET0epAYwdF3rloSTkxP0ej02bNhQ5dyDLYHffkFnZmbipZdewrRp0xAUFIQPPvgAu3fvRmRkJHQ6HS5duiRf88033yA8PBxDhw7FwYMHAQCSJGH58uWIiIiATqfDli1b5HpffPFFREdHQ6vVVolnz5490Ol0GD58ON5//30AQFxcHLKzs7FgwQIsX768yjXx8fHQ6XQYMWIEPvjgA/l4amoqIiMjMXToUBw/fly+/6uvvlqljoKCAuj1euh0Onz00Ufy8Qfjre57TZgwAbNmzUJISAhmz57dIJqpRFQ9s9ls9SZKnfx0HT9+PEaMGIGoqCirrzlz5gxSUlLQqlUrBAUFYfTo0UhKSsKGDRuwceNGLFiwAABQWFiIpKQkXLp0CS+//DL69++PL774Ai4uLti+fTvKy8sxduxYDBgwAABw+vRpJCcnw93dvdL9jEYjPvjgA+zYsQMtWrTApEmTsH//fsyYMQOZmZmYN29elVbAoUOHcODAAWzduhVNmzbF9evX5XOSJCEpKQmHDh1CXFwc1q9fr/hdlyxZgnHjxmHkyJHYtGlTpXP3x5uYmFjt9zIYDGjXrh3GjRuHrKysWrd8iEishjBwXeuWBAA0b94cYWFhSEhIsPoaPz8/tGvXDmq1Gk8//bT8j6G3tzcKCwvlcsOGDYODgwM6deoEd3d3/PDDD/j666+xa9cuhIWFYfTo0bh+/TouXrwo1/tgggDujRf06dMHrVu3hpOTE3Q6HY4dO1ZtjEeOHEF4eDiaNm0KAGjVqpV8Ljg4GADQrVu3SvE+TE5OjtyyCQsLq/J3+C3e6r5X9+7d4ebmBgcHB/j4+Fi8JxHZP3MN/hOlzjrBJ06ciPDwcISHh8vHHB0dYTLd63MzmUy4e/eufE6tVsufHRwc5H0HBwdIkiSfU6lUle6jUqlgNpvx9ttvY9CgQZXOZWZmwtnZua6+UrWU4lXy4Pf4zf3xVve97v97OTo6WnVPIrJvdf3SoYyMDCxZsgQmkwmjR4/G1KlTH1ruyy+/xKxZs5CUlGRxHLVOWhLAvV/ZISEhSEpKko917NgReXl5AIADBw5UShLWSk1NhclkwqVLl1BQUABPT08MHDgQmzdvluu7cOECysrKqq2ne/fuOHbsGIqLiyFJEgwGA3r37l3tNf3798eOHTtw584dAKjU3VQT/v7+MBgMAIDdu3crlnuU70VEDZcEs9WbxbokCQsXLsSaNWtgMBiwZ88enDt3rkq50tJSJCQk4Nlnn7UqxjqdTjNp0qRKfe5jxozB9OnTMWLECAwaNOiRfuW3b98ekZGRuH37Nv7+97/jd7/7HUaPHo3CwkKEh4fDbDbD1dUVq1atqraedu3aYfbs2Zg4cSLMZjMCAgIwZMiQaq954YUXcObMGURERKBJkyYICAjAG2+8UePvsGDBAsyZMwdr1qxBYGCgYrlH+V5E1HDV5ZhEbm4uPDw85O5rrVaL9PR0dO7cuVK5lStXYsqUKVi7dq1V9arMnCbzWOqm6Ss6BLvm1qSl6BDsXuqJ1aJDsHtN2vy+Vtf36/BHq8v+9aNpSExMlPf1ej30er28n5qaisOHD2PJkiUAgC+++AK5ubmIiYmRy+Tl5WH16tWIjY3FhAkTHjph50GcmE9EJEhNWhIPJoUa38tkwrJly7B06dIaXVdnYxJERFQzdTm7SaPRoKioSN43Go3QaDTy/u3bt3H27Fm8/PLLCAwMxIkTJzBt2rSHrh5xP7YkiIgEqcvefj8/P+Tn56OgoAAajQYGgwErVqyQz7u4uCAzM1PeZ3cTEZGdq8uXDjk5OSEmJgZRUVGQJAkRERHw8vLCypUr4evri6CgoEeqlwPXjykOXFePA9eWceDastoOXD/r1t/qsv+v6Jta3etRsSVBRCQIXzpERESK6vqJ6/rAJEFEJAhbEkREpKguB67rC5MEEZEg7G4iIiJF7G4iIiJFbEkQEZEitiSIiEiRmQPXRESkhLObiIhIUV2+dKi+MEkQEQnSEJbOY5IgIhKEs5uIiEgRZzcREZEidjcREZEizm4iIiJFHJMgIiJF7G4iIiJFfE6CiIgUsSVBRESKOHBNRESKGsLAtYPoAIiIGiuz2Wz1Zo2MjAwMHToUwcHBiI+Pr3J+8+bN0Ol0CAsLw7hx43Du3DmLdTJJEBEJYq7Bf5ZIkoSFCxdizZo1MBgM2LNnT5UkoNPpkJycjF27diEqKgpLly61WC+TBBGRIHXZksjNzYWHhwfc3d2hVquh1WqRnp5eqUzz5s3lz3fu3IFKpbJYL8ckiIgEqcnspsTERCQmJsr7er0eer1e3jcajXBzc5P3NRoNcnNzq9SzadMmrFu3Dnfv3sWGDRss3pdJ4jGVZ8wUHQIRWXC3vLBG5e9PCo9q/PjxGD9+PJKTk/Hpp59i+fLl1ZZndxMR0WNAo9GgqKhI3jcajdBoNIrltVot9u/fb7FeJgkioseAn58f8vPzUVBQgPLychgMBgQGBlYqk5+fL3/+6quv4OHhYbFedjcRET0GnJycEBMTg6ioKEiShIiICHh5eWHlypXw9fVFUFAQPv/8cxw5cgROTk5o0aKFxa4mAFCZG8Jz4UREJAS7m4iISBGTBBERKWKSICIiRUwSRESkiLObiMhuZWdno7CwEJIkycdGjhwpMKLGhy0Jqhc///wz3nrrLURFRQEAzp07h23btgmOyn6kpKSgoKAAAHDmzBkEBwdj4MCB+PLLLwVHZj/mzp2L//mf/0FWVhZOnjyJkydP4tSpU6LDanQ4BZbqRVRUFMLDw7F69Wrs3r0bFRUVGDVqFJKTk0WHZhfCw8OxZcsWqNVqTJs2DXq9Hl27dsXkyZP5N/o/w4YNQ0pKilWL0FH9YUuC6kVJSQlCQ0Ph4HDvf2JOTk7y58YuLi4OV69eRXx8PD755BNkZ2fj5MmT2Lp1K0pLSxEXF4djx46JDlM4Ly8v/PTTT6LDaPQ4JkH1wtnZGSUlJfKvwBMnTsDFxUVwVPZhxowZyMzMxFNPPYWSkhIMHDgQM2fOBAD85z//wYwZMwRHaB9KSkqg1WrRvXt3NGnSRD6+evVqgVE1PuxuonqRl5eHRYsW4fvvv4eXlxdKSkqwcuVK+Pj4iA7NLpw/fx6xsbFQq9WYM2cO2rVrh3PnzmHfvn2YNm2a6PDswtGjRx96vE+fPjaOpHFjkqB6U1FRgQsXLsBsNsPT07PSr0EiaxQWFuLixYvo378/7ty5A0mSKr04h+ofkwTVi7S0tCrHXFxc4O3tjSeffFJARNTQbN26Fd8/tMgAAA5KSURBVImJibhx4wb279+P/Px8vPPOO1a9KIfqDsckqF4kJSXhxIkT6Nu3L4B7XQfdunXD5cuXMX36dM51J4s2bdqEbdu2YcyYMQCATp06obi4WHBUjQ+TBNULSZKQkpKCNm3aALj33MSbb76JrVu34qWXXmKSIIvUajXUarW8X1FRITCaxotzEqle/Pjjj3KCAIAnn3wSP/74I1q1agUnJ/42AYC9e/eitLQUALBq1SrMmDEDeXl5gqOyH71798bq1avxyy+/4Ouvv8Zrr71W5SU6VP+YJKhe9OnTB6+++ip27tyJnTt3Ytq0aejTpw/Kyso4Ffb/rFq1Cs2bN8fx48dx5MgRREZG4t133xUdlt2YO3cuWrduDW9vbyQmJiIgIACvv/666LAaHf6ko3rxzjvvIC0tDVlZWQAAX19fXLt2Dc7Ozti4caPg6OyDo6MjAODQoUMYM2YM/vjHP+Ljjz8WHJV9kCQJWq0Wqamp8pgEicGWBNULlUoFd3d3ODo6Yv/+/cjMzMQzzzwjOiy7otFoEBMTg5SUFAQEBKC8vBwmk0l0WHbB0dERnp6euHLliuhQGj1OgaU6deHCBRgMBuzZsweurq4IDQ3FZ599hoMHD4oOze7cuXMHhw8fhre3Nzp16oSrV6/i7NmzGDhwoOjQ7ML48eNx+vRpdO/eHU2bNpWP84lr22KSoDrl4+ODXr16YcmSJfDw8AAABAUFIT09XXBk9kmSJPz888+VlsLu0KGDwIjsB5+4tg8ck6A6FRcXB4PBgJdffhmDBg2CVqsFf4c83MaNGxEXF4c2bdpUWvyQq8Dew2RgH9iSoHpRVlaG9PR0GAwGfPvttwgLC5PfmUD3BAcHY+vWrXB1dRUdil3x9/evdnnw7OxsG0ZDbElQvXB2doZOp4NOp8ONGzeQmpqKf/3rX0wS93Fzc+N04IfIyckBAHz88cdo27YtwsLCAAC7d+/m0uECsCVBJMhbb72FCxcu4I9//GOlJ4tfeeUVgVHZjxEjRmD37t0Wj1H94hRYIkE6dOiAAQMG4O7du7h9+7a80T3Ozs7YvXs3JEmCyWTC7t274ezsLDqsRoctCSLBfksMzZo1ExyJfbl8+TKWLFmC7OxsqFQq9OzZE2+99Raeeuop0aE1KkwSRIKcPXsW8+bNw40bNwAArq6uWL58Oby8vARHRvRfTBJEgowdOxavv/46+vXrBwDIzMzERx99hC1btgiOTKxFixZVO7vp7bfftmE0xNlNRIKUlZXJCQIA+vbti7KyMoER2QdfX1/RIdB9mCSIBHF3d8c//vGPSlM83d3dBUcl3qhRo0SHQPdhdxORIDdu3EBsbKy8Uu5zzz2HmTNnomXLloIjE2vJkiVYsGABoqOjH3qeazfZFpMEEdmVU6dOwdfXl2s32QkmCSIb4y9lakg4JkFkY7+NQUyaNElwJPYtKysLcXFxuHLlCioqKmA2m6FSqbiisI2xJUFEdikkJATz58+Hr69vpVVyuSCibbElQWRjOp2u2vNcKvweFxcXBAQEiA6j0WNLgsjGCgsLqz3fsWNHG0Vin/Ly8gAAe/fuhSRJ+NOf/lRpAcRu3bqJCq1RYpIgIrsyYcIExXMqlQoJCQk2jIaYJIhsTOmlOr8NzPKlOmRPmCSIyC59+OGHiIqKQosWLQDce/jws88+w1//+lfBkTUufJ8EkY2VlpYCAK5fv/7Qje7JyMiQEwQAtGzZEhkZGQIjapw4u4nIxmbPno1//vOfCA8Ph0qlwv2NeT4H8F+SJKG8vFwetP7ll19QXl4uOKrGh91NRGSX4uPjcfDgQYSHhwMAduzYgcDAQEyZMkVwZI0LkwSRjf02xVMJp3j+V0ZGBo4cOQIA6N+/PwYNGiQ4osaHSYLIxnx8fODl5SU/OfxgdxOneJI9YZIgsrH169fjyy+/hIuLC0JDQxEcHMz3W5PdYpIgEqSgoAAGgwHp6eno0KEDoqOj8Yc//EF0WESVcAoskSDu7u4ICgrCgAEDcPLkSVy4cEF0SHZh4sSJAID3339fcCQEcAoskc3d34Jo3749QkNDER0djSeeeEJ0aHbhp59+QnZ2Ng4cOACtVosHOzs4sG9b7G4isjEfHx906dIFQUFBaN68eZUlOl555RVBkdmH1NRUJCUlISsrC76+vpXOcWDf9pgkiGwsNjb2oWs3/WbGjBk2jMZ+/eMf/8Bf/vIX0WE0ekwSRGS30tPTcfz4cQD33m09ePBgwRE1PkwSRGSXVqxYgdzcXPklTQaDAX5+fnjjjTcER9a4MEkQkV3S6XTYtWuX/OpSSZIwcuRIvrnPxjgFlojs1s2bN+XPt27dEhhJ48UpsER2Yv/+/Wjbti2effZZ0aHYhVdffRWjRo1C3759YTabcezYMcyZM0d0WI0Ou5uI7MSHH36Is2fPoqKiAmvWrBEdjl24evUqTp48CQDo3r072rZtKziixodJgoiIFLG7iUig7OxsFBYWQpIk+djIkSMFRkRUGZMEkSBz585FQUEBfHx84OjoCODeE8VMEmRPmCSIBDl16hRSUlKqffq6sZIkCVqtFqmpqaJDafQ4BZZIEC8vL/z000+iw7BLjo6O8PT0xJUrV0SH0uixJUFkY9HR0QCA27dvQ6vVonv37mjSpIl8fvXq1aJCsys3b96U/z5NmzaVj/PvY1tMEkQ2NmnSJNEhNAivvfaa6BAInAJLJMz777+PuXPnWjzWmBUWFuLixYvo378/7ty5A0mS0Lx5c9FhNSockyAS5JtvvqlyLCMjQ0Ak9mnr1q2YNWsWYmJiAABGo5FLhwvA7iYiG/v3v/+NzZs349KlS/IKp8C9MYqePXsKjMy+bNq0Cdu2bcOYMWMAAJ06dUJxcbHgqBofJgkiG9PpdHjhhRfw4YcfYvbs2fLxZs2aoVWrVgIjsy9qtRpqtVrer6ioEBhN48UkQWRjLi4ucHZ2xunTp9GxY0fR4dit3r17Y/Xq1fjll1/w9ddf49///jcCAwNFh9XocEyCSAA+B2DZnDlz0Lp1a3h7eyMxMREBAQF4/fXXRYfV6HB2E5Eg48ePx+nTp/kcQDXKy8vxww8/QKVSwdPTs1L3E9kGkwSRIEePHn3o8T59+tg4Evv01Vdf4Z133sHTTz8Ns9mMy5cv4+9//zsCAgJEh9aoMEkQCfTzzz9Xel/Ck08+KTgi+xESEoJ//vOf8PDwAABcunQJU6dO5XpONsYxCSJBUlJSMHr0aKSmpmLv3r3yZ7qnWbNmcoIAAHd3dzRr1kxgRI0TZzcRCbJ69WokJSXJrYfi4mL8+c9/RkhIiODIxEpLSwMA+Pr6YsqUKRg2bBhUKhVSU1Ph5+cnOLrGh0mCSBCz2Vype6lVq1Zg7y9w8OBB+XObNm1w7NgxAEDr1q3x66+/igqr0eKYBJEgy5cvx9mzZ6HVagHc637q0qUL124iu8IkQWRjN27cQMuWLQHc61rJysoCAPTq1QvBwcEiQ7MrBQUF+Pzzz1FYWFjpaWtOEbYtJgkiG3v++efh6uqKnj17wt/fHz179oSnp6fosOzOiBEjEBkZCW9vbzg4/HeODacI2xaTBJEAFy5cQE5OjrwVFxfj2WefRc+ePTFlyhTR4dmF0aNHY9u2baLDaPSYJIgEu3TpEg4dOoSEhAQYjUbk5uaKDskuJCcn4+LFixgwYEClJ627desmMKrGh0mCyMays7ORk5ODEydO4Mcff4S7uzueffZZ9OjRA127duXSE/9nxYoV2LVrF55++mmoVCoAgEqlQkJCguDIGhcmCSIb8/HxQdeuXfHnP/8ZwcHBldZtov8KDg6GwWBg0hSMz0kQ2djhw4flsYjExERIkoSuXbuiR48e8Pf3h7u7u+gQ7YKXlxdu3brFpUoEY0uCSLA7d+5g+/bt2LBhAy5fvozvvvtOdEh2YcKECfjf//1f+Pn5oUmTJvJxToG1LbYkiGzs1q1blWY2fffdd/Dw8MDgwYP5+tL7zJw5U3QIBLYkiGyuX79+ctdSz5494efnhyeeeEJ0WEQPxSRBRHbJ399fntV09+5dVFRUoGnTpsjOzhYcWePC7iYisks5OTnyZ7PZjPT0dJw4cUJgRI0T3ydBRHZPpVJhyJAh+M9//iM6lEaHLQkiQbKysvDcc89ZPNZY/fZeCQAwmUw4deoUfve73wmMqHHimASRIKNGjcLOnTstHmus5s+fL392dHREx44dMWbMGD43YWNsSRDZ2P2L+q1bt04+XlpaCkmSBEZmX5YuXSo6BAKTBJHN3b17F2VlZZAkCbdv35aPN2/eHJ988onAyOxDXFyc4jmVSoW//OUvNoyG2N1EJEhhYSE6duyIO3fucP2m+3z22WdVjpWVlWH79u24fv16pVlPVP+YJIgEycnJwYIFC1BWVoavvvoKZ86cwZYtW/Duu++KDs1ulJaWIiEhAUlJSRg2bBgmTZrEMQkb4xRYIkHee+89rF27Fq1atQJwb3XY48ePC47KPly/fh0fffQRRowYAUmSsHPnTsydO5cJQgCOSRAJ1L59+0r797+ms7Favnw59u3bhzFjxiA5ORnNmjUTHVKjxiRBJEj79u2RnZ0NlUqFu3fvIiEhAc8884zosIRbt24d1Go1Pv3000orvprNZqhUKi7LYWMckyASpLi4GEuWLMGRI0dgNpsxYMAALFiwAK6urqJDI5IxSRARkSJ2NxHZGJ8DoIaESYLIxpydnascu/85ACYJsifsbiISiM8BkL1jS4JIgOvXr2PdunVITk6WF/Vr2bKl6LCIqmCSILIxPgdADQm7m4hszMfHB2q1Go6OjvLrOQE+B0D2iUmCiIgUcQ0AIiJSxCRBRESKmCSIiEgRkwQRESlikiAiIkX/H06lQJuOoMBLAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sMqyuIg64NjJ"
      },
      "source": [
        "X= df.iloc[:, [1,2]].values"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hzBlXtU94h9H"
      },
      "source": [
        "wcss = []\r\n",
        "K = range(1,10)\r\n",
        "for k in K:\r\n",
        "    kmeanModel = KMeans(n_clusters=k)\r\n",
        "    kmeanModel.fit(X)\r\n",
        "    wcss.append(kmeanModel.inertia_)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 458
        },
        "id": "FHYNPx325BUD",
        "outputId": "a4c611f6-cae0-4fa9-9c30-61d0d8e639e8"
      },
      "source": [
        "plt.figure(figsize=(14,7))\r\n",
        "plt.plot(K, wcss, 'bx-')\r\n",
        "plt.xlabel('Number of Clusters')\r\n",
        "plt.ylabel('WCSS')\r\n",
        "plt.title('The Elbow Method')\r\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1008x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UqOJwUnq7c4L"
      },
      "source": [
        "kmeans = KMeans(n_clusters= 3, init='k-means++', random_state=0)\r\n",
        "pred_y= kmeans.fit_predict(X)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "h65vNmP58jhY",
        "outputId": "ffb564fe-9cdb-4f12-ac6e-ed78be14ac32"
      },
      "source": [
        "plt.scatter(X[pred_y == 0, 0], X[pred_y == 0, 1], s = 100, c = 'navy', label = 'Cluster #1')\r\n",
        "plt.scatter(X[pred_y == 1, 0], X[pred_y == 1, 1], s = 100, c = 'teal', label = 'Cluster #2')\r\n",
        "plt.scatter(X[pred_y == 2, 0], X[pred_y == 2, 1], s = 100, c = 'gold', label = 'Cluster #3')\r\n",
        "plt.scatter(kmeans.cluster_centers_[:, 0], kmeans.cluster_centers_[:, 1], s = 300, c = 'coral', label = 'Cluster Centers')\r\n",
        "plt.title('Clusters of Hollywood actors/actress')\r\n",
        "plt.xlabel('Age')\r\n",
        "plt.ylabel('Net Worth, Millions $')\r\n",
        "plt.legend()\r\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "c_NVgt_kOl81",
        "outputId": "9d47d465-8033-410f-c04e-581cc2dd94d8"
      },
      "source": [
        "print(kmeans.labels_)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[1 0 0 0 0 1 2 0 0 0 2 0 0 0 2 0 0 0 2 2 2 2 1 0 0 0 1 0 0 2 0 0 0 0 0 0 2\n",
            " 0 0 0 0 0 0 0 0 2 2 0 1 0 0 0 0 0 0 0 0 1 0 0 2 0 2 1 0 0 0 0 1 2 0 0 0 0\n",
            " 2 0 2 1 2 0 0 0 2 0 0 0 2 1 0 0 0 0 0 0 0 0 2 2 2 1 2 2 2 1 0 0 0 2 1 1 1\n",
            " 0 1 0 0 0 2 2 1 0 0 0 0 0 1 2 0 0 0 0 2 0 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
            " 0 2 1 1 2 2 2 0 1 0 2 0 2 2 0 2 0 0 2 0 2 0 0 0 0 0 0 0 0 1 2 0 0 0 0 0 0\n",
            " 0 0 0 0 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 0 1 0 2 0 0 0 0 0 0 0 0 0 2 0 0 0\n",
            " 0 0 0 0 0 2 0 0 2 0 0 0 0 0 0 0 0 0 0 0 0 0 2 0 2 0 0 2 0 0 0 0 0 0]\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}