Kmeans_Basic_Part1.ipynb

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      "name": "Kmeans_Basic_Part1",
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      "authorship_tag": "ABX9TyMw4b3FdJ1bp/Ihq3wzrGro",
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    "kernelspec": {
      "name": "python3",
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
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      "source": [
        "<a href=\"https://colab.research.google.com/github/ranjiGT/Cloyster/blob/master/Kmeans_Basic_Part1.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FwnF2rOjHFRI"
      },
      "source": [
        "# ***Kmeans Clustering -  Network Segmentation, Customer Segmentation***\n",
        "\n",
        "<font color='red'> ***video link***</font> https://www.youtube.com/watch?v=397Xqt_RJS8&t=19s"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "J1UCeFTX_BbA",
        "outputId": "37bdff96-ca2d-4944-cf9d-908f88d3d6e1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 402
        }
      },
      "source": [
        "import pandas as pd\n",
        "df = pd.read_csv('Mall_Customers.csv')\n",
        "df"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>CustomerID</th>\n",
              "      <th>Gender</th>\n",
              "      <th>Age</th>\n",
              "      <th>Annual Income (k$)</th>\n",
              "      <th>Spending Score (1-100)</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\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>2</td>\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>3</td>\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>4</td>\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>5</td>\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",
              "      <td>...</td>\n",
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              "    <tr>\n",
              "      <th>195</th>\n",
              "      <td>196</td>\n",
              "      <td>Female</td>\n",
              "      <td>35</td>\n",
              "      <td>120</td>\n",
              "      <td>79</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>196</th>\n",
              "      <td>197</td>\n",
              "      <td>Female</td>\n",
              "      <td>45</td>\n",
              "      <td>126</td>\n",
              "      <td>28</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>197</th>\n",
              "      <td>198</td>\n",
              "      <td>Male</td>\n",
              "      <td>32</td>\n",
              "      <td>126</td>\n",
              "      <td>74</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>198</th>\n",
              "      <td>199</td>\n",
              "      <td>Male</td>\n",
              "      <td>32</td>\n",
              "      <td>137</td>\n",
              "      <td>18</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>199</th>\n",
              "      <td>200</td>\n",
              "      <td>Male</td>\n",
              "      <td>30</td>\n",
              "      <td>137</td>\n",
              "      <td>83</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>200 rows × 5 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "     CustomerID  Gender  Age  Annual Income (k$)  Spending Score (1-100)\n",
              "0             1    Male   19                  15                      39\n",
              "1             2    Male   21                  15                      81\n",
              "2             3  Female   20                  16                       6\n",
              "3             4  Female   23                  16                      77\n",
              "4             5  Female   31                  17                      40\n",
              "..          ...     ...  ...                 ...                     ...\n",
              "195         196  Female   35                 120                      79\n",
              "196         197  Female   45                 126                      28\n",
              "197         198    Male   32                 126                      74\n",
              "198         199    Male   32                 137                      18\n",
              "199         200    Male   30                 137                      83\n",
              "\n",
              "[200 rows x 5 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LRFEKiPA_uqJ"
      },
      "source": [
        "df.rename(columns={'Annual Income (k$)' : 'Income', 'Spending Score (1-100)' : 'Expenditure'}, inplace = True)"
      ],
      "execution_count": 30,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YYxhC9hN_zWW",
        "outputId": "33464093-4a64-4a28-b6e9-2300af5ff27a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 402
        }
      },
      "source": [
        "df"
      ],
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <th>CustomerID</th>\n",
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              "      <th>Income</th>\n",
              "      <th>Expenditure</th>\n",
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              "      <td>79</td>\n",
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              "    <tr>\n",
              "      <th>196</th>\n",
              "      <td>197</td>\n",
              "      <td>Female</td>\n",
              "      <td>45</td>\n",
              "      <td>126</td>\n",
              "      <td>28</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>197</th>\n",
              "      <td>198</td>\n",
              "      <td>Male</td>\n",
              "      <td>32</td>\n",
              "      <td>126</td>\n",
              "      <td>74</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>198</th>\n",
              "      <td>199</td>\n",
              "      <td>Male</td>\n",
              "      <td>32</td>\n",
              "      <td>137</td>\n",
              "      <td>18</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>199</th>\n",
              "      <td>200</td>\n",
              "      <td>Male</td>\n",
              "      <td>30</td>\n",
              "      <td>137</td>\n",
              "      <td>83</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>200 rows × 5 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "     CustomerID  Gender  Age  Income  Expenditure\n",
              "0             1    Male   19      15           39\n",
              "1             2    Male   21      15           81\n",
              "2             3  Female   20      16            6\n",
              "3             4  Female   23      16           77\n",
              "4             5  Female   31      17           40\n",
              "..          ...     ...  ...     ...          ...\n",
              "195         196  Female   35     120           79\n",
              "196         197  Female   45     126           28\n",
              "197         198    Male   32     126           74\n",
              "198         199    Male   32     137           18\n",
              "199         200    Male   30     137           83\n",
              "\n",
              "[200 rows x 5 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eacbWXvbK5KZ",
        "outputId": "397942cb-16d8-487a-fd3b-16d7b629e62f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 373
        }
      },
      "source": [
        "df.describe(include='all')"
      ],
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>CustomerID</th>\n",
              "      <th>Gender</th>\n",
              "      <th>Age</th>\n",
              "      <th>Income</th>\n",
              "      <th>Expenditure</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>200.000000</td>\n",
              "      <td>200</td>\n",
              "      <td>200.000000</td>\n",
              "      <td>200.000000</td>\n",
              "      <td>200.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>unique</th>\n",
              "      <td>NaN</td>\n",
              "      <td>2</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>top</th>\n",
              "      <td>NaN</td>\n",
              "      <td>Female</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>freq</th>\n",
              "      <td>NaN</td>\n",
              "      <td>112</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>100.500000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>38.850000</td>\n",
              "      <td>60.560000</td>\n",
              "      <td>50.200000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>57.879185</td>\n",
              "      <td>NaN</td>\n",
              "      <td>13.969007</td>\n",
              "      <td>26.264721</td>\n",
              "      <td>25.823522</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>18.000000</td>\n",
              "      <td>15.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>50.750000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>28.750000</td>\n",
              "      <td>41.500000</td>\n",
              "      <td>34.750000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>100.500000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>36.000000</td>\n",
              "      <td>61.500000</td>\n",
              "      <td>50.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>150.250000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>49.000000</td>\n",
              "      <td>78.000000</td>\n",
              "      <td>73.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>200.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>70.000000</td>\n",
              "      <td>137.000000</td>\n",
              "      <td>99.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        CustomerID  Gender         Age      Income  Expenditure\n",
              "count   200.000000     200  200.000000  200.000000   200.000000\n",
              "unique         NaN       2         NaN         NaN          NaN\n",
              "top            NaN  Female         NaN         NaN          NaN\n",
              "freq           NaN     112         NaN         NaN          NaN\n",
              "mean    100.500000     NaN   38.850000   60.560000    50.200000\n",
              "std      57.879185     NaN   13.969007   26.264721    25.823522\n",
              "min       1.000000     NaN   18.000000   15.000000     1.000000\n",
              "25%      50.750000     NaN   28.750000   41.500000    34.750000\n",
              "50%     100.500000     NaN   36.000000   61.500000    50.000000\n",
              "75%     150.250000     NaN   49.000000   78.000000    73.000000\n",
              "max     200.000000     NaN   70.000000  137.000000    99.000000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6rIWdh8h_-RM"
      },
      "source": [
        "import seaborn as sns"
      ],
      "execution_count": 33,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wbmDB-GcAAU-",
        "outputId": "ef395aba-211b-4db1-e090-e562f3ed4fa0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 567
        }
      },
      "source": [
        "sns.pairplot(df[['Age','Income', 'Expenditure']], diag_kind='kde')"
      ],
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.PairGrid at 0x7f97529c0cf8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 34
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 540x540 with 12 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VLInWlP8AJMh"
      },
      "source": [
        "import sklearn.cluster as cluster"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eg2ksbYnANkI"
      },
      "source": [
        "kmeans = cluster.KMeans(n_clusters=5 ,init=\"k-means++\")\n",
        "kmeans = kmeans.fit(df[['Expenditure','Income']])"
      ],
      "execution_count": 35,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xsiia5iPAO2S",
        "outputId": "c1793a8d-da97-4791-b218-12660ae03e14",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 101
        }
      },
      "source": [
        "kmeans.cluster_centers_"
      ],
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[79.36363636, 25.72727273],\n",
              "       [49.51851852, 55.2962963 ],\n",
              "       [82.12820513, 86.53846154],\n",
              "       [17.11428571, 88.2       ],\n",
              "       [20.91304348, 26.30434783]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RMkSkKF3ATLw"
      },
      "source": [
        "df['Clusters'] = kmeans.labels_"
      ],
      "execution_count": 37,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qVhB40fZPdjF",
        "outputId": "fe67f734-3087-49f3-b52a-a5b239154cad",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 218
        }
      },
      "source": [
        "df['Clusters']"
      ],
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0      4\n",
              "1      0\n",
              "2      4\n",
              "3      0\n",
              "4      4\n",
              "      ..\n",
              "195    2\n",
              "196    3\n",
              "197    2\n",
              "198    3\n",
              "199    2\n",
              "Name: Clusters, Length: 200, dtype: int32"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Mlw2021sAX7p",
        "outputId": "3c9f0104-a5be-455b-9308-36a2c31bf238",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 639
        }
      },
      "source": [
        "df.head(20)"
      ],
      "execution_count": 39,
      "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>CustomerID</th>\n",
              "      <th>Gender</th>\n",
              "      <th>Age</th>\n",
              "      <th>Income</th>\n",
              "      <th>Expenditure</th>\n",
              "      <th>Clusters</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\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>2</td>\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>3</td>\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>4</td>\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>5</td>\n",
              "      <td>Female</td>\n",
              "      <td>31</td>\n",
              "      <td>17</td>\n",
              "      <td>40</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>6</td>\n",
              "      <td>Female</td>\n",
              "      <td>22</td>\n",
              "      <td>17</td>\n",
              "      <td>76</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>7</td>\n",
              "      <td>Female</td>\n",
              "      <td>35</td>\n",
              "      <td>18</td>\n",
              "      <td>6</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>8</td>\n",
              "      <td>Female</td>\n",
              "      <td>23</td>\n",
              "      <td>18</td>\n",
              "      <td>94</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>9</td>\n",
              "      <td>Male</td>\n",
              "      <td>64</td>\n",
              "      <td>19</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>10</td>\n",
              "      <td>Female</td>\n",
              "      <td>30</td>\n",
              "      <td>19</td>\n",
              "      <td>72</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>11</td>\n",
              "      <td>Male</td>\n",
              "      <td>67</td>\n",
              "      <td>19</td>\n",
              "      <td>14</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>12</td>\n",
              "      <td>Female</td>\n",
              "      <td>35</td>\n",
              "      <td>19</td>\n",
              "      <td>99</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>13</td>\n",
              "      <td>Female</td>\n",
              "      <td>58</td>\n",
              "      <td>20</td>\n",
              "      <td>15</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>14</td>\n",
              "      <td>Female</td>\n",
              "      <td>24</td>\n",
              "      <td>20</td>\n",
              "      <td>77</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>15</td>\n",
              "      <td>Male</td>\n",
              "      <td>37</td>\n",
              "      <td>20</td>\n",
              "      <td>13</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>16</td>\n",
              "      <td>Male</td>\n",
              "      <td>22</td>\n",
              "      <td>20</td>\n",
              "      <td>79</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>17</td>\n",
              "      <td>Female</td>\n",
              "      <td>35</td>\n",
              "      <td>21</td>\n",
              "      <td>35</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>18</td>\n",
              "      <td>Male</td>\n",
              "      <td>20</td>\n",
              "      <td>21</td>\n",
              "      <td>66</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>19</td>\n",
              "      <td>Male</td>\n",
              "      <td>52</td>\n",
              "      <td>23</td>\n",
              "      <td>29</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>20</td>\n",
              "      <td>Female</td>\n",
              "      <td>35</td>\n",
              "      <td>23</td>\n",
              "      <td>98</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    CustomerID  Gender  Age  Income  Expenditure  Clusters\n",
              "0            1    Male   19      15           39         4\n",
              "1            2    Male   21      15           81         0\n",
              "2            3  Female   20      16            6         4\n",
              "3            4  Female   23      16           77         0\n",
              "4            5  Female   31      17           40         4\n",
              "5            6  Female   22      17           76         0\n",
              "6            7  Female   35      18            6         4\n",
              "7            8  Female   23      18           94         0\n",
              "8            9    Male   64      19            3         4\n",
              "9           10  Female   30      19           72         0\n",
              "10          11    Male   67      19           14         4\n",
              "11          12  Female   35      19           99         0\n",
              "12          13  Female   58      20           15         4\n",
              "13          14  Female   24      20           77         0\n",
              "14          15    Male   37      20           13         4\n",
              "15          16    Male   22      20           79         0\n",
              "16          17  Female   35      21           35         4\n",
              "17          18    Male   20      21           66         0\n",
              "18          19    Male   52      23           29         4\n",
              "19          20  Female   35      23           98         0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1KQQKW4yAaxt",
        "outputId": "3fa7dad7-a453-4751-dea7-4170462eb62b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 118
        }
      },
      "source": [
        "df['Clusters'].value_counts()"
      ],
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1    81\n",
              "2    39\n",
              "3    35\n",
              "4    23\n",
              "0    22\n",
              "Name: Clusters, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mHBNDcE2AeDJ",
        "outputId": "562f7bcb-2ad4-4103-c7ec-72c80de0adf1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        }
      },
      "source": [
        "sns.scatterplot(x=\"Expenditure\", y=\"Income\",hue = 'Clusters',  data=df)"
      ],
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f975253ce80>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 41
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}