12. Heirarchical_Clustering.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Heirarchical Clustering.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyMO7lA9frDYOpjcP26zB18W",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/MainakRepositor/ML-Algorithms/blob/master/Heirarchical_Clustering.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dje6G4eYlK0w"
      },
      "source": [
        "# Heirarchical Clustering\n",
        "\n",
        "<hr>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TCzpvSDBlTJr"
      },
      "source": [
        "## 1. Importing necessary libraries"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MAL7hv7tk6ZO",
        "outputId": "2b1c2768-0a56-4058-b438-980f7f49f52b",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline\n",
        "print(\"Basic packages included successfully!\")"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Basic packages included successfully!\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8nYOiJn6lq4C"
      },
      "source": [
        "## 2. Importing datasets"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OZROVdRrloqN",
        "outputId": "fafbb6e4-35a5-4cbc-8882-dd39854c0967",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 345
        }
      },
      "source": [
        "url = 'https://raw.githubusercontent.com/MainakRepositor/Datasets-/master/Mall_Customers.csv'\n",
        "df = pd.read_csv(url,error_bad_lines=False)\n",
        "df.head(10)"
      ],
      "execution_count": 2,
      "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>Genre</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>5</th>\n",
              "      <td>6</td>\n",
              "      <td>Female</td>\n",
              "      <td>22</td>\n",
              "      <td>17</td>\n",
              "      <td>76</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",
              "    </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",
              "    </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",
              "    </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",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   CustomerID   Genre  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",
              "5           6  Female   22                  17                      76\n",
              "6           7  Female   35                  18                       6\n",
              "7           8  Female   23                  18                      94\n",
              "8           9    Male   64                  19                       3\n",
              "9          10  Female   30                  19                      72"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JXJB3f2qmETY"
      },
      "source": [
        "## 3. Splitting the data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eaggOWOXmCbk"
      },
      "source": [
        "X = df.iloc[:, [3, 4]].values"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VVyUYU3-mNFD"
      },
      "source": [
        "## 4. Using the dendrogram to find the optimal number of clusters"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6Kr45yi-mKeq",
        "outputId": "d9f843af-1f34-4d3a-fcae-d6e25f83c177",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 512
        }
      },
      "source": [
        "import scipy.cluster.hierarchy as sch\n",
        "plt.figure(figsize=(18,8))\n",
        "dendrogram = sch.dendrogram(sch.linkage(X, method = 'ward'))\n",
        "\n",
        "plt.title('Dendrogram')\n",
        "plt.xlabel('Customers')\n",
        "plt.ylabel('Euclidean distances')\n",
        "plt.show()"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1296x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zI59BaDnm0-R"
      },
      "source": [
        "## 5. Training the Hierarchical Clustering model on the dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JQ2OqShcmTgc"
      },
      "source": [
        "from sklearn.cluster import AgglomerativeClustering\n",
        "hc = AgglomerativeClustering(n_clusters = 5, affinity = 'euclidean', linkage = 'ward')\n",
        "y_hc = hc.fit_predict(X)"
      ],
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "u_q4sl1Rm65J"
      },
      "source": [
        "## 6. Visualising the clusters"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XY3klnVTm5xp",
        "outputId": "20a7a2ed-7232-4c2f-cb50-3b03acfda4f9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 458
        }
      },
      "source": [
        "plt.figure(figsize=(18,7))\n",
        "plt.scatter(X[y_hc == 0, 0], X[y_hc == 0, 1], s = 100, c = 'red', label = 'Cluster 1')\n",
        "plt.scatter(X[y_hc == 1, 0], X[y_hc == 1, 1], s = 100, c = 'blue', label = 'Cluster 2')\n",
        "plt.scatter(X[y_hc == 2, 0], X[y_hc == 2, 1], s = 100, c = 'green', label = 'Cluster 3')\n",
        "plt.scatter(X[y_hc == 3, 0], X[y_hc == 3, 1], s = 100, c = 'cyan', label = 'Cluster 4')\n",
        "plt.scatter(X[y_hc == 4, 0], X[y_hc == 4, 1], s = 100, c = 'magenta', label = 'Cluster 5')\n",
        "plt.title('Clusters of customers')\n",
        "plt.xlabel('Annual Income (k$)')\n",
        "plt.ylabel('Spending Score (1-100)')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1296x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4sPcKoy8nDoA"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}