09_Hierarchical_Clustering.ipynb

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      "name": "09 Hierarchical Clustering.ipynb",
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        "# Hierarchical Clustering \n",
        "\n",
        "# Agglomerative Clustering which is a method of clustering \n",
        "# which builds a hierarchy of clusters by merging together small clusters\n",
        "\n",
        "# Silhouette Score\n",
        "# Clusters are well apart from each other as the silhouette score is closer to 1\n",
        "# Silhouette Coefficient score is a metric used to calculate the goodness of a clustering technique \n",
        "# Its value ranges from -1 to 1.\n",
        "# 1: Means clusters are well apart from each other and clearly distinguished.\n",
        "# 0: Means clusters are indifferent, or we can say that the distance between clusters is not significant.\n",
        "# -1: Means clusters are assigned in the wrong way."
      ],
      "execution_count": 1,
      "outputs": []
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      "source": [
        "# Importing Libraries\n",
        "\n",
        "from sklearn import datasets\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.cluster import AgglomerativeClustering\n",
        "from sklearn.datasets import load_iris\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "from sklearn.metrics import silhouette_score"
      ],
      "execution_count": 2,
      "outputs": []
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    {
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      "source": [
        "# Loading the Dataset\n",
        "\n",
        "iris = datasets.load_iris()\n",
        "iris_data = pd.DataFrame(iris.data)\n",
        "iris_data.columns = iris.feature_names\n",
        "iris_data['Type']=iris.target\n",
        "iris_data.head()"
      ],
      "execution_count": 3,
      "outputs": [
        {
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          "data": {
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>sepal length (cm)</th>\n",
              "      <th>sepal width (cm)</th>\n",
              "      <th>petal length (cm)</th>\n",
              "      <th>petal width (cm)</th>\n",
              "      <th>Type</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>5.1</td>\n",
              "      <td>3.5</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>4.9</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>4.7</td>\n",
              "      <td>3.2</td>\n",
              "      <td>1.3</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4.6</td>\n",
              "      <td>3.1</td>\n",
              "      <td>1.5</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5.0</td>\n",
              "      <td>3.6</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0</td>\n",
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              "  </tbody>\n",
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            "text/plain": [
              "   sepal length (cm)  sepal width (cm)  ...  petal width (cm)  Type\n",
              "0                5.1               3.5  ...               0.2     0\n",
              "1                4.9               3.0  ...               0.2     0\n",
              "2                4.7               3.2  ...               0.2     0\n",
              "3                4.6               3.1  ...               0.2     0\n",
              "4                5.0               3.6  ...               0.2     0\n",
              "\n",
              "[5 rows x 5 columns]"
            ]
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          "execution_count": 3
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      "source": [
        "# Preparing Data\n",
        "# Here we have the target variable ‘Type’. \n",
        "# We need to remove the target variable, used to work in an unsupervised learning \n",
        "# The iloc function is used to get the features we require. \n",
        "# We also use .values function to get an array of the dataset. \n",
        "# Note : We transformed the dataset to an array so that we can plot the graphs of the clusters\n",
        "\n",
        "iris_X = iris_data.iloc[:, [0, 1, 2,3]].values\n",
        "print(iris_X[:5,:]) # Printing First 5 Rows"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[5.1 3.5 1.4 0.2]\n",
            " [4.9 3.  1.4 0.2]\n",
            " [4.7 3.2 1.3 0.2]\n",
            " [4.6 3.1 1.5 0.2]\n",
            " [5.  3.6 1.4 0.2]]\n"
          ],
          "name": "stdout"
        }
      ]
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      "source": [
        "# Now we will separate the target variable from the original dataset \n",
        "# And again convert it to an array by using numpy\n",
        "iris_Y = iris_data['Type']\n",
        "iris_Y = np.array(iris_Y)\n",
        "print(iris_Y)\n",
        "# Frequency count of the Output clusters\n",
        "unique, counts = np.unique(iris_Y, return_counts=True)\n",
        "freq_1 = dict(zip(unique, counts))\n",
        "freq_1"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 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 1 1 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 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2\n",
            " 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2\n",
            " 2 2]\n"
          ],
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        },
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          "data": {
            "text/plain": [
              "{0: 50, 1: 50, 2: 50}"
            ]
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        "outputId": "6d6a1938-d925-4fc0-a686-0a8d1a3a8720"
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      "source": [
        "# Filtering Setosa\n",
        "\n",
        "Setosa = iris_data['Type'] == 0\n",
        "print(\"Filtering Setosa, True means its Setosa and False means Non Setosa\")\n",
        "print(Setosa.head())\n",
        "print(\"Top 6 Rows of Setosa\")\n",
        "Setosa_v2 = iris_data[Setosa]\n",
        "print(Setosa_v2[Setosa_v2.columns[0:2]].head())\n",
        "print(\"Last 6 Rows of Setosa\")\n",
        "print(Setosa_v2[Setosa_v2.columns[0:2]].tail())"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Filtering Setosa, True means its Setosa and False means Non Setosa\n",
            "0    True\n",
            "1    True\n",
            "2    True\n",
            "3    True\n",
            "4    True\n",
            "Name: Type, dtype: bool\n",
            "Top 6 Rows of Setosa\n",
            "   sepal length (cm)  sepal width (cm)\n",
            "0                5.1               3.5\n",
            "1                4.9               3.0\n",
            "2                4.7               3.2\n",
            "3                4.6               3.1\n",
            "4                5.0               3.6\n",
            "Last 6 Rows of Setosa\n",
            "    sepal length (cm)  sepal width (cm)\n",
            "45                4.8               3.0\n",
            "46                5.1               3.8\n",
            "47                4.6               3.2\n",
            "48                5.3               3.7\n",
            "49                5.0               3.3\n"
          ],
          "name": "stdout"
        }
      ]
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        "outputId": "083498f6-b5bc-4968-8cbb-a7c20cbeab12"
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      "source": [
        "# Filtering Setosa for 2D Plot \n",
        "\n",
        "print(\"Setosa for 2D Plot\")\n",
        "print(\"X Axis points\")\n",
        "print(iris_X[iris_Y == 0,0])\n",
        "print(\"Y Axis Points\")\n",
        "print(iris_X[iris_Y == 0,1])\n",
        "print('\\n')\n",
        "# For Setosa in Target Column i.e, iris_Y = 0 \n",
        "# In other word it should range from (0,0) to (0,1)\n",
        "plt.scatter(iris_X[iris_Y == 0, 0], iris_X[iris_Y == 0, 1], \n",
        "            s = 80, c = 'orange', label = 'Iris-setosa')\n",
        "plt.xlim([4.5,8])\n",
        "plt.ylim([2,4.5])"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Setosa for 2D Plot\n",
            "X Axis points\n",
            "[5.1 4.9 4.7 4.6 5.  5.4 4.6 5.  4.4 4.9 5.4 4.8 4.8 4.3 5.8 5.7 5.4 5.1\n",
            " 5.7 5.1 5.4 5.1 4.6 5.1 4.8 5.  5.  5.2 5.2 4.7 4.8 5.4 5.2 5.5 4.9 5.\n",
            " 5.5 4.9 4.4 5.1 5.  4.5 4.4 5.  5.1 4.8 5.1 4.6 5.3 5. ]\n",
            "Y Axis Points\n",
            "[3.5 3.  3.2 3.1 3.6 3.9 3.4 3.4 2.9 3.1 3.7 3.4 3.  3.  4.  4.4 3.9 3.5\n",
            " 3.8 3.8 3.4 3.7 3.6 3.3 3.4 3.  3.4 3.5 3.4 3.2 3.1 3.4 4.1 4.2 3.1 3.2\n",
            " 3.5 3.6 3.  3.4 3.5 2.3 3.2 3.5 3.8 3.  3.8 3.2 3.7 3.3]\n",
            "\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(2.0, 4.5)"
            ]
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          "execution_count": 7
        },
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          "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"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vN6l86eLnvni",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 395
        },
        "outputId": "e58b70b5-3623-44dd-fe31-417d81bb5275"
      },
      "source": [
        "# Filtering Versicolour\n",
        "\n",
        "Versi = iris_data['Type'] == 1\n",
        "print(\"Filtering Versicolour, True means its Versicolour and False means Non Versicolour\")\n",
        "print(Versi.head())\n",
        "print(\"Top 6 Rows of Versicolour\")\n",
        "Versi_v2 = iris_data[Versi]\n",
        "print(Versi_v2[Versi_v2.columns[0:2]].head())\n",
        "print(\"Last 6 Rows of Versicolour\")\n",
        "print(Versi_v2[Versi_v2.columns[0:2]].tail())"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Filtering Versicolour, True means its Versicolour and False means Non Versicolour\n",
            "0    False\n",
            "1    False\n",
            "2    False\n",
            "3    False\n",
            "4    False\n",
            "Name: Type, dtype: bool\n",
            "Top 6 Rows of Versicolour\n",
            "    sepal length (cm)  sepal width (cm)\n",
            "50                7.0               3.2\n",
            "51                6.4               3.2\n",
            "52                6.9               3.1\n",
            "53                5.5               2.3\n",
            "54                6.5               2.8\n",
            "Last 6 Rows of Versicolour\n",
            "    sepal length (cm)  sepal width (cm)\n",
            "95                5.7               3.0\n",
            "96                5.7               2.9\n",
            "97                6.2               2.9\n",
            "98                5.1               2.5\n",
            "99                5.7               2.8\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "M7mb70sXnTMg",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 485
        },
        "outputId": "483a8bf5-ae7f-4951-caa2-063cbbbc9cee"
      },
      "source": [
        "# Filtering Versicolour for 2D Plot \n",
        "\n",
        "print(\"Versicolour for 2D Plot\")\n",
        "print(\"X Axis points\")\n",
        "print(iris_X[iris_Y == 1,0])\n",
        "print(\"Y Axis Points\")\n",
        "print(iris_X[iris_Y == 1,1])\n",
        "print('\\n')\n",
        "plt.scatter(iris_X[iris_Y == 1, 0], iris_X[iris_Y == 1, 1], \n",
        "            s = 80, c = 'yellow', label = 'Iris-versicolour')\n",
        "\n",
        "plt.xlim([4.5,8])\n",
        "plt.ylim([2,4.5])"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Versicolour for 2D Plot\n",
            "X Axis points\n",
            "[7.  6.4 6.9 5.5 6.5 5.7 6.3 4.9 6.6 5.2 5.  5.9 6.  6.1 5.6 6.7 5.6 5.8\n",
            " 6.2 5.6 5.9 6.1 6.3 6.1 6.4 6.6 6.8 6.7 6.  5.7 5.5 5.5 5.8 6.  5.4 6.\n",
            " 6.7 6.3 5.6 5.5 5.5 6.1 5.8 5.  5.6 5.7 5.7 6.2 5.1 5.7]\n",
            "Y Axis Points\n",
            "[3.2 3.2 3.1 2.3 2.8 2.8 3.3 2.4 2.9 2.7 2.  3.  2.2 2.9 2.9 3.1 3.  2.7\n",
            " 2.2 2.5 3.2 2.8 2.5 2.8 2.9 3.  2.8 3.  2.9 2.6 2.4 2.4 2.7 2.7 3.  3.4\n",
            " 3.1 2.3 3.  2.5 2.6 3.  2.6 2.3 2.7 3.  2.9 2.9 2.5 2.8]\n",
            "\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(2.0, 4.5)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        },
        {
          "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"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qD2TL0cwocaN",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 395
        },
        "outputId": "79cbabbb-0739-45b8-fd36-9a2d71bda44b"
      },
      "source": [
        "# Filtering Virginica\n",
        "\n",
        "Virginica = iris_data['Type'] == 2\n",
        "print(\"Filtering Virginica, True means its Virginica and False means Non Virginica\")\n",
        "print(Virginica.head())\n",
        "print(\"Top 6 Rows of Virginica\")\n",
        "Virginica_v2 = iris_data[Virginica]\n",
        "print(Virginica_v2[Virginica_v2.columns[0:2]].head())\n",
        "print(\"Last 6 Rows of Virginica\")\n",
        "print(Virginica_v2[Virginica_v2.columns[0:2]].tail())"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Filtering Virginica, True means its Virginica and False means Non Virginica\n",
            "0    False\n",
            "1    False\n",
            "2    False\n",
            "3    False\n",
            "4    False\n",
            "Name: Type, dtype: bool\n",
            "Top 6 Rows of Virginica\n",
            "     sepal length (cm)  sepal width (cm)\n",
            "100                6.3               3.3\n",
            "101                5.8               2.7\n",
            "102                7.1               3.0\n",
            "103                6.3               2.9\n",
            "104                6.5               3.0\n",
            "Last 6 Rows of Virginica\n",
            "     sepal length (cm)  sepal width (cm)\n",
            "145                6.7               3.0\n",
            "146                6.3               2.5\n",
            "147                6.5               3.0\n",
            "148                6.2               3.4\n",
            "149                5.9               3.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wsnoGdx0objs",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 485
        },
        "outputId": "66e8d27b-86aa-4c81-9a11-27e28117b09d"
      },
      "source": [
        "# Filtering Virginica for 2D Plot\n",
        "\n",
        "print(\"Virginica for 2D Plot\")\n",
        "print(\"X Axis points\")\n",
        "print(iris_X[iris_Y == 2,0])\n",
        "print(\"Y Axis Points\")\n",
        "print(iris_X[iris_Y == 2,1])\n",
        "print('\\n')\n",
        "plt.scatter(iris_X[iris_Y == 2, 0], iris_X[iris_Y == 2, 1], \n",
        "            s = 80, c = 'green', label = 'Iris-virginica')\n",
        "plt.xlim([4.5,8])\n",
        "plt.ylim([2,4.5])"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Virginica for 2D Plot\n",
            "X Axis points\n",
            "[6.3 5.8 7.1 6.3 6.5 7.6 4.9 7.3 6.7 7.2 6.5 6.4 6.8 5.7 5.8 6.4 6.5 7.7\n",
            " 7.7 6.  6.9 5.6 7.7 6.3 6.7 7.2 6.2 6.1 6.4 7.2 7.4 7.9 6.4 6.3 6.1 7.7\n",
            " 6.3 6.4 6.  6.9 6.7 6.9 5.8 6.8 6.7 6.7 6.3 6.5 6.2 5.9]\n",
            "Y Axis Points\n",
            "[3.3 2.7 3.  2.9 3.  3.  2.5 2.9 2.5 3.6 3.2 2.7 3.  2.5 2.8 3.2 3.  3.8\n",
            " 2.6 2.2 3.2 2.8 2.8 2.7 3.3 3.2 2.8 3.  2.8 3.  2.8 3.8 2.8 2.8 2.6 3.\n",
            " 3.4 3.1 3.  3.1 3.1 3.1 2.7 3.2 3.3 3.  2.5 3.  3.4 3. ]\n",
            "\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(2.0, 4.5)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        },
        {
          "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"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HCShLm9HUWHn",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 286
        },
        "outputId": "d2bd50fd-12dd-48b8-aacd-c51aba490576"
      },
      "source": [
        "# Visualise Classes all at once\n",
        "\n",
        "# In this Iris dataset, we have three classes (0,1,2)\n",
        "# We visualise these classes in a 2-D graph \n",
        "# This will help us in comparing the original classes with the clusters created \n",
        "# Plot the three type of flowers on a graph\n",
        "\n",
        "plt.scatter(iris_X[iris_Y == 0, 0], iris_X[iris_Y == 0, 1], \n",
        "            s = 80, c = 'orange', label = 'Iris-setosa')\n",
        "plt.scatter(iris_X[iris_Y == 1, 0], iris_X[iris_Y == 1, 1], \n",
        "            s = 80, c = 'yellow', label = 'Iris-versicolour')\n",
        "plt.scatter(iris_X[iris_Y == 2, 0], iris_X[iris_Y == 2, 1], \n",
        "            s = 80, c = 'green', label = 'Iris-virginica')\n",
        "plt.legend()\n",
        "\n",
        "# Inference : \n",
        "# We find that we have three classes with two types of Iris flowers overlapping each other"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7ffa7d6145f8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        },
        {
          "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"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Q4QWIYUC5XG7",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "d2a9b743-a4df-4e58-9299-6ffb7acfab9e"
      },
      "source": [
        "# Plotting of Dendrogram\n",
        "\n",
        "# We make use of dendrogram to decide the number of clusters required for our dataset\n",
        "# A dendrogram is a tree diagram which illustrates the arrangement of clusters.\n",
        "import scipy.cluster.hierarchy as sch\n",
        "\n",
        "# Pick the features from the original dataset\n",
        "# We don't need Target Column in Clustering\n",
        "iris_X_1 = iris_data[['sepal length (cm)','sepal width (cm)',\n",
        "                      'petal length (cm)','petal width (cm)']]\n",
        "iris_X_1.head()"
      ],
      "execution_count": 13,
      "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>sepal length (cm)</th>\n",
              "      <th>sepal width (cm)</th>\n",
              "      <th>petal length (cm)</th>\n",
              "      <th>petal width (cm)</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>5.1</td>\n",
              "      <td>3.5</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>4.9</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>4.7</td>\n",
              "      <td>3.2</td>\n",
              "      <td>1.3</td>\n",
              "      <td>0.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4.6</td>\n",
              "      <td>3.1</td>\n",
              "      <td>1.5</td>\n",
              "      <td>0.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5.0</td>\n",
              "      <td>3.6</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   sepal length (cm)  sepal width (cm)  petal length (cm)  petal width (cm)\n",
              "0                5.1               3.5                1.4               0.2\n",
              "1                4.9               3.0                1.4               0.2\n",
              "2                4.7               3.2                1.3               0.2\n",
              "3                4.6               3.1                1.5               0.2\n",
              "4                5.0               3.6                1.4               0.2"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TSWOuFF756Fi",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 395
        },
        "outputId": "06710af3-fec8-4c5c-9c12-80a2c04c4a99"
      },
      "source": [
        "# We finally plot a Dendrogram \n",
        "# which helps us in deciding what the threshold values should be for the clustering algorithm\n",
        "# Basically, we decide the number of clusters by using this dendrogram.\n",
        "\n",
        "Z = sch.linkage(iris_X_1, method = 'median')\n",
        "plt.figure(figsize=(20,7))\n",
        "den = sch.dendrogram(Z)\n",
        "plt.title('Dendrogram for the clustering of the dataset iris)')\n",
        "plt.xlabel('Type')\n",
        "plt.ylabel('Euclidean distance in the space with other variables')"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'Euclidean distance in the space with other variables')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1440x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1zjZaTB1aKCj",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 392
        },
        "outputId": "bfc6af2f-4431-43b1-dfb7-ca24c0715e2b"
      },
      "source": [
        "# Single Linkage - Nearest Point\n",
        "\n",
        "Z = sch.linkage(iris_X_1, method = 'single')\n",
        "plt.figure(figsize=(20,7))\n",
        "den = sch.dendrogram(Z)\n",
        "plt.title('Dendrogram for the clustering of the dataset iris)')\n",
        "plt.xlabel('Type')\n",
        "plt.ylabel('Euclidean distance in the space with other variables')"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'Euclidean distance in the space with other variables')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1440x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NpQnbDUMaLkt",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 395
        },
        "outputId": "8445cacc-3dc7-43ec-9f59-7d7b5960bc80"
      },
      "source": [
        "# Complete Linkage - Farthest Point \n",
        "\n",
        "Z = sch.linkage(iris_X_1, method = 'complete')\n",
        "plt.figure(figsize=(20,7))\n",
        "den = sch.dendrogram(Z)\n",
        "plt.title('Dendrogram for the clustering of the dataset iris)')\n",
        "plt.xlabel('Type')\n",
        "plt.ylabel('Euclidean distance in the space with other variables')\n"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'Euclidean distance in the space with other variables')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1440x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zH_7oxCTaMbZ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 392
        },
        "outputId": "567b5a53-d4d9-43a6-b364-7c3c0331d5f2"
      },
      "source": [
        "# Average Linkage - Average Distance between all points\n",
        "\n",
        "Z = sch.linkage(iris_X_1, method = 'average')\n",
        "plt.figure(figsize=(20,7))\n",
        "den = sch.dendrogram(Z)\n",
        "plt.title('Dendrogram for the clustering of the dataset iris)')\n",
        "plt.xlabel('Type')\n",
        "plt.ylabel('Euclidean distance in the space with other variables')\n"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'Euclidean distance in the space with other variables')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1440x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pJnhnUlt6Hw7",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 71
        },
        "outputId": "4440bfae-dd1d-435e-d414-f7a8952b7c82"
      },
      "source": [
        "# Building an Agglomerative Clustering Model\n",
        "# Initialise Model\n",
        "# We analyse the above-created dendrogram \n",
        "# decide that we will be making 3 clusters for this dataset\n",
        "\n",
        "cluster_H = AgglomerativeClustering(n_clusters=3)\n",
        "cluster_H"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "AgglomerativeClustering(affinity='euclidean', compute_full_tree='auto',\n",
              "                        connectivity=None, distance_threshold=None,\n",
              "                        linkage='ward', memory=None, n_clusters=3)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BYHVXgB96Yxi",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 71
        },
        "outputId": "d3c38bfd-fc5d-4c4f-83a8-855e3daf9d9f"
      },
      "source": [
        "# Fitting Model\n",
        "# After building Agglomerative clustering, we will fit our iris data set\n",
        "# Note that only the independent variables from the Iris dataset \n",
        "# are taken into account for the purpose of clustering\n",
        "\n",
        "model_clt = cluster_H.fit(iris_X_1)\n",
        "model_clt"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "AgglomerativeClustering(affinity='euclidean', compute_full_tree='auto',\n",
              "                        connectivity=None, distance_threshold=None,\n",
              "                        linkage='ward', memory=None, n_clusters=3)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RCNXJPX26oCP",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "outputId": "425d233f-d1c6-4c05-9e5d-f2bf90e5b941"
      },
      "source": [
        "# Predicting Output Class\n",
        "\n",
        "print('Output Clusters are')\n",
        "pred1 = model_clt.labels_\n",
        "print(pred1)\n",
        "# Frequency count of the Output clusters\n",
        "unique, counts = np.unique(pred1, return_counts=True)\n",
        "print(dict(zip(unique, counts)))"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Output Clusters are\n",
            "[1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 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 0 0 0 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 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 0 2 2 2 2 0 2 2 2 2\n",
            " 2 2 0 0 2 2 2 2 0 2 0 2 0 2 2 0 0 2 2 2 2 2 0 0 2 2 2 0 2 2 2 0 2 2 2 0 2\n",
            " 2 0]\n",
            "{0: 64, 1: 50, 2: 36}\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oMPtUa9C7B-T",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 430
        },
        "outputId": "c142809b-4d35-4c4d-aa6c-8575e940389f"
      },
      "source": [
        "# Visualizing Output\n",
        "\n",
        "print('Original Cluster')\n",
        "print(freq_1)\n",
        "# Frequency count of the Output clusters\n",
        "unique, counts = np.unique(pred1, return_counts=True)\n",
        "print('Hierarchical Clustering Output Cluster')\n",
        "print(dict(zip(unique, counts)))\n",
        "# Silhouette Score\n",
        "print('Silhouette Score for 3 Clusters')\n",
        "print(silhouette_score(iris_X,pred1))\n",
        "print('\\n')\n",
        "\n",
        "# In the above output we got value labels: ‘0’, ‘1’  and ‘2’\n",
        "# For a better understanding, we can visualize these clusters.\n",
        "\n",
        "# We use the above-found class labels and visualise how the clusters have been formed.\n",
        "\n",
        "plt.scatter(iris_X[pred1 == 0, 0], iris_X[pred1 == 0, 1], \n",
        "            s = 80, c = 'orange', label = 'Iris-setosa')\n",
        "plt.scatter(iris_X[pred1 == 1, 0], iris_X[pred1 == 1, 1], \n",
        "            s = 80, c = 'yellow', label = 'Iris-versicolour')\n",
        "plt.scatter(iris_X[pred1 == 2, 0], iris_X[pred1 == 2, 1], \n",
        "            s = 80, c = 'green', label = 'Iris-virginica')\n",
        "plt.legend()\n",
        "\n",
        "# Inference : \n",
        "# When compared to the original classes we find that the observations of the class label \n",
        "# \"1\" has been correctly formed into a separate well-defined cluster\n",
        "# however, for the other two classes, clusters are not as correct. \n",
        "# This is mainly because, in the original dataset, these two class labels were overlapping each other \n",
        "# which makes it difficult for the clustering algorithm as it works best for clear neat separate observations. \n",
        "# Still, the clusters have been formed, more or less correctly"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Original Cluster\n",
            "{0: 50, 1: 50, 2: 50}\n",
            "Hierarchical Clustering Output Cluster\n",
            "{0: 64, 1: 50, 2: 36}\n",
            "Silhouette Score for 3 Clusters\n",
            "0.5543236611296415\n",
            "\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7ffa7a180d30>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        },
        {
          "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"
          }
        }
      ]
    }
  ]
}