Akankhya_GRIP_Task3.ipynb

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    "colab": {
      "name": "Akankhya_GRIP_Task3.ipynb",
      "provenance": [],
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    }
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "axdjjju3KBcI"
      },
      "source": [
        "#**Unsupervised Learning- K Means clustering**\n",
        "K-mean clustering is a type of unsupervised learning, which is used when you have unlabled data. The objective of this algorithm is to identify clusters in the data, with each cluster represented by the variable.\n",
        " \n",
        "#**Problem Statement:**\n",
        "**Task**: From the given Iris Dataset, we will predict the optimum number of clusters using the 'elbow method'.\n",
        "\n",
        "#**Dataset**\n",
        "(IRIS dataset)\n",
        "https://drive.google.com/file/d/1UH-2IlbxIogzhh-hAS4e3gmiKKcSVzC1/view?usp=sharing \n",
        "\n",
        "The columns in this dataset are:\n",
        "- Id\n",
        "- SepalLengthCm\n",
        "- SepalWidthCm\n",
        "- PetalLengthCm\n",
        "- PetalWidthCm\n",
        "- Species\n",
        "\n",
        "The Iris dataset was used in R.A. Fisher's classic 1936 paper, The Use of Multiple Measurements in Taxonomic Problems, and can also be found on the UCI Machine Learning Repository.\n",
        "\n",
        "It includes three iris species with 50 samples each as well as some properties about each flower. One flower species is linearly separable from the other two, but the other two are not linearly separable from each other."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0faxuQ1oKBcL"
      },
      "source": [
        "## Importing libraries"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sWLAOGvQKBcN"
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn import datasets\n",
        "from sklearn.cluster import KMeans\n",
        "import seaborn as sb"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ifxJ3kh2zFLM"
      },
      "source": [
        "##Connecting to drive"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "co1W-nKFylon",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 67
        },
        "outputId": "84585b95-3fa0-472a-f903-d2013c3f3857"
      },
      "source": [
        "!pwd\n",
        "from google.colab import drive\n",
        "from os.path import join\n",
        "ROOT=\"/content/drive\"\n",
        "print(ROOT)\n",
        "drive.mount('/content/drive/')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/content/drive/My Drive\n",
            "/content/drive\n",
            "Drive already mounted at /content/drive/; to attempt to forcibly remount, call drive.mount(\"/content/drive/\", force_remount=True).\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Mr8KdCUQy2_o",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        },
        "outputId": "6c7d0567-7f71-4e5a-cdd0-443c2eea3087"
      },
      "source": [
        "#%ls\n",
        "#%pwd\n",
        "\n",
        "%cd 'drive/My Drive/'"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[Errno 2] No such file or directory: 'drive/My Drive/'\n",
            "/content/drive/My Drive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VmNceW9-KBcW"
      },
      "source": [
        "## Importing data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AuTmvR7lKBcX",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 118
        },
        "outputId": "b6a0e2c3-3bc5-4028-aaff-29a14f2b3236"
      },
      "source": [
        "raw_data=pd.read_csv('Iris.csv')\n",
        "# Displaying the whole dataset\n",
        "print(raw_data.head())"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "   Id  SepalLengthCm  SepalWidthCm  PetalLengthCm  PetalWidthCm      Species\n",
            "0   1            5.1           3.5            1.4           0.2  Iris-setosa\n",
            "1   2            4.9           3.0            1.4           0.2  Iris-setosa\n",
            "2   3            4.7           3.2            1.3           0.2  Iris-setosa\n",
            "3   4            4.6           3.1            1.5           0.2  Iris-setosa\n",
            "4   5            5.0           3.6            1.4           0.2  Iris-setosa\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uL8BMG-LKBcg",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 235
        },
        "outputId": "0c0d0db4-1e77-4c0a-e231-8451cf57cf47"
      },
      "source": [
        "#View the information of dataset\n",
        "raw_data.info()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 150 entries, 0 to 149\n",
            "Data columns (total 6 columns):\n",
            " #   Column         Non-Null Count  Dtype  \n",
            "---  ------         --------------  -----  \n",
            " 0   Id             150 non-null    int64  \n",
            " 1   SepalLengthCm  150 non-null    float64\n",
            " 2   SepalWidthCm   150 non-null    float64\n",
            " 3   PetalLengthCm  150 non-null    float64\n",
            " 4   PetalWidthCm   150 non-null    float64\n",
            " 5   Species        150 non-null    object \n",
            "dtypes: float64(4), int64(1), object(1)\n",
            "memory usage: 7.2+ KB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2wp6aIYZeHQt",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 134
        },
        "outputId": "348de10c-403d-414b-87b0-a29a8203545d"
      },
      "source": [
        "raw_data.isnull().sum()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Id               0\n",
              "SepalLengthCm    0\n",
              "SepalWidthCm     0\n",
              "PetalLengthCm    0\n",
              "PetalWidthCm     0\n",
              "Species          0\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BeP11j9SeTfI",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "bd167114-3822-42e8-c609-aab91ec00eae"
      },
      "source": [
        "raw_data.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(150, 6)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "x5zGJ-X4eYMC",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 94
        },
        "outputId": "f82d6dc8-3fe8-4d61-eb29-26bbfbfbf7a6"
      },
      "source": [
        "#Print the structure of the data\n",
        "raw_data.apply(lambda x: [x.unique()])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Id</th>\n",
              "      <th>SepalLengthCm</th>\n",
              "      <th>SepalWidthCm</th>\n",
              "      <th>PetalLengthCm</th>\n",
              "      <th>PetalWidthCm</th>\n",
              "      <th>Species</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14...</td>\n",
              "      <td>[5.1, 4.9, 4.7, 4.6, 5.0, 5.4, 4.4, 4.8, 4.3, ...</td>\n",
              "      <td>[3.5, 3.0, 3.2, 3.1, 3.6, 3.9, 3.4, 2.9, 3.7, ...</td>\n",
              "      <td>[1.4, 1.3, 1.5, 1.7, 1.6, 1.1, 1.2, 1.0, 1.9, ...</td>\n",
              "      <td>[0.2, 0.4, 0.3, 0.1, 0.5, 0.6, 1.4, 1.5, 1.3, ...</td>\n",
              "      <td>[Iris-setosa, Iris-versicolor, Iris-virginica]</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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            ],
            "text/plain": [
              "                                                  Id  ...                                         Species\n",
              "0  [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14...  ...  [Iris-setosa, Iris-versicolor, Iris-virginica]\n",
              "\n",
              "[1 rows x 6 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "38URrTS5RCNU",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 195
        },
        "outputId": "703753a2-c027-4ba3-9cf4-94728f37a173"
      },
      "source": [
        "#Drop Id and Species column\n",
        "raw_data_new=raw_data.copy()\n",
        "raw_data_new.drop(['Id','Species'],axis=1,inplace=True)\n",
        "raw_data_new.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>SepalLengthCm</th>\n",
              "      <th>SepalWidthCm</th>\n",
              "      <th>PetalLengthCm</th>\n",
              "      <th>PetalWidthCm</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",
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            ],
            "text/plain": [
              "   SepalLengthCm  SepalWidthCm  PetalLengthCm  PetalWidthCm\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": 48
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FgHZO02rKBcn",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 284
        },
        "outputId": "d5af6da1-b690-43b3-a4ae-26e9c1d8118e"
      },
      "source": [
        "#Statistical information of data\n",
        "raw_data_new.describe()"
      ],
      "execution_count": null,
      "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>SepalLengthCm</th>\n",
              "      <th>SepalWidthCm</th>\n",
              "      <th>PetalLengthCm</th>\n",
              "      <th>PetalWidthCm</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>150.000000</td>\n",
              "      <td>150.000000</td>\n",
              "      <td>150.000000</td>\n",
              "      <td>150.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>5.843333</td>\n",
              "      <td>3.054000</td>\n",
              "      <td>3.758667</td>\n",
              "      <td>1.198667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>0.828066</td>\n",
              "      <td>0.433594</td>\n",
              "      <td>1.764420</td>\n",
              "      <td>0.763161</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>4.300000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.100000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>5.100000</td>\n",
              "      <td>2.800000</td>\n",
              "      <td>1.600000</td>\n",
              "      <td>0.300000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>5.800000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>4.350000</td>\n",
              "      <td>1.300000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>6.400000</td>\n",
              "      <td>3.300000</td>\n",
              "      <td>5.100000</td>\n",
              "      <td>1.800000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>7.900000</td>\n",
              "      <td>4.400000</td>\n",
              "      <td>6.900000</td>\n",
              "      <td>2.500000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       SepalLengthCm  SepalWidthCm  PetalLengthCm  PetalWidthCm\n",
              "count     150.000000    150.000000     150.000000    150.000000\n",
              "mean        5.843333      3.054000       3.758667      1.198667\n",
              "std         0.828066      0.433594       1.764420      0.763161\n",
              "min         4.300000      2.000000       1.000000      0.100000\n",
              "25%         5.100000      2.800000       1.600000      0.300000\n",
              "50%         5.800000      3.000000       4.350000      1.300000\n",
              "75%         6.400000      3.300000       5.100000      1.800000\n",
              "max         7.900000      4.400000       6.900000      2.500000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lnzTbJIXKBcu",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 166
        },
        "outputId": "b99571c0-3fcd-4c29-9b2c-6cc1f9c8c1d9"
      },
      "source": [
        "#Correlation\n",
        "raw_data_new.corr()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>SepalLengthCm</th>\n",
              "      <th>SepalWidthCm</th>\n",
              "      <th>PetalLengthCm</th>\n",
              "      <th>PetalWidthCm</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>SepalLengthCm</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.109369</td>\n",
              "      <td>0.871754</td>\n",
              "      <td>0.817954</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>SepalWidthCm</th>\n",
              "      <td>-0.109369</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.420516</td>\n",
              "      <td>-0.356544</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>PetalLengthCm</th>\n",
              "      <td>0.871754</td>\n",
              "      <td>-0.420516</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.962757</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>PetalWidthCm</th>\n",
              "      <td>0.817954</td>\n",
              "      <td>-0.356544</td>\n",
              "      <td>0.962757</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "               SepalLengthCm  SepalWidthCm  PetalLengthCm  PetalWidthCm\n",
              "SepalLengthCm       1.000000     -0.109369       0.871754      0.817954\n",
              "SepalWidthCm       -0.109369      1.000000      -0.420516     -0.356544\n",
              "PetalLengthCm       0.871754     -0.420516       1.000000      0.962757\n",
              "PetalWidthCm        0.817954     -0.356544       0.962757      1.000000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 50
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4n7OibEZe0cO",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 355
        },
        "outputId": "9554c38c-2663-4d8c-cb01-c38793da1117"
      },
      "source": [
        "#heatmap\n",
        "sb.heatmap(raw_data.corr(), annot=True,linecolor='yellow',linewidths=2,square=True)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f5639a106a0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 52
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "THxi1aLZKBc0"
      },
      "source": [
        "## Finding optimum number of clusters"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mwfW7R1rKBc1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "f287f628-c4e7-4677-8d8c-c29423181773"
      },
      "source": [
        "x = df_new.iloc[:, [0,1,2,3]].values\n",
        "x"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[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",
              "       [5.4, 3.9, 1.7, 0.4],\n",
              "       [4.6, 3.4, 1.4, 0.3],\n",
              "       [5. , 3.4, 1.5, 0.2],\n",
              "       [4.4, 2.9, 1.4, 0.2],\n",
              "       [4.9, 3.1, 1.5, 0.1],\n",
              "       [5.4, 3.7, 1.5, 0.2],\n",
              "       [4.8, 3.4, 1.6, 0.2],\n",
              "       [4.8, 3. , 1.4, 0.1],\n",
              "       [4.3, 3. , 1.1, 0.1],\n",
              "       [5.8, 4. , 1.2, 0.2],\n",
              "       [5.7, 4.4, 1.5, 0.4],\n",
              "       [5.4, 3.9, 1.3, 0.4],\n",
              "       [5.1, 3.5, 1.4, 0.3],\n",
              "       [5.7, 3.8, 1.7, 0.3],\n",
              "       [5.1, 3.8, 1.5, 0.3],\n",
              "       [5.4, 3.4, 1.7, 0.2],\n",
              "       [5.1, 3.7, 1.5, 0.4],\n",
              "       [4.6, 3.6, 1. , 0.2],\n",
              "       [5.1, 3.3, 1.7, 0.5],\n",
              "       [4.8, 3.4, 1.9, 0.2],\n",
              "       [5. , 3. , 1.6, 0.2],\n",
              "       [5. , 3.4, 1.6, 0.4],\n",
              "       [5.2, 3.5, 1.5, 0.2],\n",
              "       [5.2, 3.4, 1.4, 0.2],\n",
              "       [4.7, 3.2, 1.6, 0.2],\n",
              "       [4.8, 3.1, 1.6, 0.2],\n",
              "       [5.4, 3.4, 1.5, 0.4],\n",
              "       [5.2, 4.1, 1.5, 0.1],\n",
              "       [5.5, 4.2, 1.4, 0.2],\n",
              "       [4.9, 3.1, 1.5, 0.1],\n",
              "       [5. , 3.2, 1.2, 0.2],\n",
              "       [5.5, 3.5, 1.3, 0.2],\n",
              "       [4.9, 3.1, 1.5, 0.1],\n",
              "       [4.4, 3. , 1.3, 0.2],\n",
              "       [5.1, 3.4, 1.5, 0.2],\n",
              "       [5. , 3.5, 1.3, 0.3],\n",
              "       [4.5, 2.3, 1.3, 0.3],\n",
              "       [4.4, 3.2, 1.3, 0.2],\n",
              "       [5. , 3.5, 1.6, 0.6],\n",
              "       [5.1, 3.8, 1.9, 0.4],\n",
              "       [4.8, 3. , 1.4, 0.3],\n",
              "       [5.1, 3.8, 1.6, 0.2],\n",
              "       [4.6, 3.2, 1.4, 0.2],\n",
              "       [5.3, 3.7, 1.5, 0.2],\n",
              "       [5. , 3.3, 1.4, 0.2],\n",
              "       [7. , 3.2, 4.7, 1.4],\n",
              "       [6.4, 3.2, 4.5, 1.5],\n",
              "       [6.9, 3.1, 4.9, 1.5],\n",
              "       [5.5, 2.3, 4. , 1.3],\n",
              "       [6.5, 2.8, 4.6, 1.5],\n",
              "       [5.7, 2.8, 4.5, 1.3],\n",
              "       [6.3, 3.3, 4.7, 1.6],\n",
              "       [4.9, 2.4, 3.3, 1. ],\n",
              "       [6.6, 2.9, 4.6, 1.3],\n",
              "       [5.2, 2.7, 3.9, 1.4],\n",
              "       [5. , 2. , 3.5, 1. ],\n",
              "       [5.9, 3. , 4.2, 1.5],\n",
              "       [6. , 2.2, 4. , 1. ],\n",
              "       [6.1, 2.9, 4.7, 1.4],\n",
              "       [5.6, 2.9, 3.6, 1.3],\n",
              "       [6.7, 3.1, 4.4, 1.4],\n",
              "       [5.6, 3. , 4.5, 1.5],\n",
              "       [5.8, 2.7, 4.1, 1. ],\n",
              "       [6.2, 2.2, 4.5, 1.5],\n",
              "       [5.6, 2.5, 3.9, 1.1],\n",
              "       [5.9, 3.2, 4.8, 1.8],\n",
              "       [6.1, 2.8, 4. , 1.3],\n",
              "       [6.3, 2.5, 4.9, 1.5],\n",
              "       [6.1, 2.8, 4.7, 1.2],\n",
              "       [6.4, 2.9, 4.3, 1.3],\n",
              "       [6.6, 3. , 4.4, 1.4],\n",
              "       [6.8, 2.8, 4.8, 1.4],\n",
              "       [6.7, 3. , 5. , 1.7],\n",
              "       [6. , 2.9, 4.5, 1.5],\n",
              "       [5.7, 2.6, 3.5, 1. ],\n",
              "       [5.5, 2.4, 3.8, 1.1],\n",
              "       [5.5, 2.4, 3.7, 1. ],\n",
              "       [5.8, 2.7, 3.9, 1.2],\n",
              "       [6. , 2.7, 5.1, 1.6],\n",
              "       [5.4, 3. , 4.5, 1.5],\n",
              "       [6. , 3.4, 4.5, 1.6],\n",
              "       [6.7, 3.1, 4.7, 1.5],\n",
              "       [6.3, 2.3, 4.4, 1.3],\n",
              "       [5.6, 3. , 4.1, 1.3],\n",
              "       [5.5, 2.5, 4. , 1.3],\n",
              "       [5.5, 2.6, 4.4, 1.2],\n",
              "       [6.1, 3. , 4.6, 1.4],\n",
              "       [5.8, 2.6, 4. , 1.2],\n",
              "       [5. , 2.3, 3.3, 1. ],\n",
              "       [5.6, 2.7, 4.2, 1.3],\n",
              "       [5.7, 3. , 4.2, 1.2],\n",
              "       [5.7, 2.9, 4.2, 1.3],\n",
              "       [6.2, 2.9, 4.3, 1.3],\n",
              "       [5.1, 2.5, 3. , 1.1],\n",
              "       [5.7, 2.8, 4.1, 1.3],\n",
              "       [6.3, 3.3, 6. , 2.5],\n",
              "       [5.8, 2.7, 5.1, 1.9],\n",
              "       [7.1, 3. , 5.9, 2.1],\n",
              "       [6.3, 2.9, 5.6, 1.8],\n",
              "       [6.5, 3. , 5.8, 2.2],\n",
              "       [7.6, 3. , 6.6, 2.1],\n",
              "       [4.9, 2.5, 4.5, 1.7],\n",
              "       [7.3, 2.9, 6.3, 1.8],\n",
              "       [6.7, 2.5, 5.8, 1.8],\n",
              "       [7.2, 3.6, 6.1, 2.5],\n",
              "       [6.5, 3.2, 5.1, 2. ],\n",
              "       [6.4, 2.7, 5.3, 1.9],\n",
              "       [6.8, 3. , 5.5, 2.1],\n",
              "       [5.7, 2.5, 5. , 2. ],\n",
              "       [5.8, 2.8, 5.1, 2.4],\n",
              "       [6.4, 3.2, 5.3, 2.3],\n",
              "       [6.5, 3. , 5.5, 1.8],\n",
              "       [7.7, 3.8, 6.7, 2.2],\n",
              "       [7.7, 2.6, 6.9, 2.3],\n",
              "       [6. , 2.2, 5. , 1.5],\n",
              "       [6.9, 3.2, 5.7, 2.3],\n",
              "       [5.6, 2.8, 4.9, 2. ],\n",
              "       [7.7, 2.8, 6.7, 2. ],\n",
              "       [6.3, 2.7, 4.9, 1.8],\n",
              "       [6.7, 3.3, 5.7, 2.1],\n",
              "       [7.2, 3.2, 6. , 1.8],\n",
              "       [6.2, 2.8, 4.8, 1.8],\n",
              "       [6.1, 3. , 4.9, 1.8],\n",
              "       [6.4, 2.8, 5.6, 2.1],\n",
              "       [7.2, 3. , 5.8, 1.6],\n",
              "       [7.4, 2.8, 6.1, 1.9],\n",
              "       [7.9, 3.8, 6.4, 2. ],\n",
              "       [6.4, 2.8, 5.6, 2.2],\n",
              "       [6.3, 2.8, 5.1, 1.5],\n",
              "       [6.1, 2.6, 5.6, 1.4],\n",
              "       [7.7, 3. , 6.1, 2.3],\n",
              "       [6.3, 3.4, 5.6, 2.4],\n",
              "       [6.4, 3.1, 5.5, 1.8],\n",
              "       [6. , 3. , 4.8, 1.8],\n",
              "       [6.9, 3.1, 5.4, 2.1],\n",
              "       [6.7, 3.1, 5.6, 2.4],\n",
              "       [6.9, 3.1, 5.1, 2.3],\n",
              "       [5.8, 2.7, 5.1, 1.9],\n",
              "       [6.8, 3.2, 5.9, 2.3],\n",
              "       [6.7, 3.3, 5.7, 2.5],\n",
              "       [6.7, 3. , 5.2, 2.3],\n",
              "       [6.3, 2.5, 5. , 1.9],\n",
              "       [6.5, 3. , 5.2, 2. ],\n",
              "       [6.2, 3.4, 5.4, 2.3],\n",
              "       [5.9, 3. , 5.1, 1.8]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pfdo2zbHfJIG"
      },
      "source": [
        "In cluster analysis, the elbow method is a heuristic used in determining the number of clusters in a data set. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HQnSj2NhKBdA",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "outputId": "8df144d3-fe8b-420e-e159-6516a42267a0"
      },
      "source": [
        "wcss= []\n",
        "for i in range (1,21):\n",
        "    model=KMeans(i)\n",
        "    model.fit(x)\n",
        "    wcss.append(model.inertia_)\n",
        "plt.plot(range(1,21), wcss, marker='.',markersize=20,markerfacecolor='b')\n",
        "plt.title('The Elbow method')\n",
        "plt.xlabel('Number of clusters')\n",
        "plt.ylabel('Within-Cluster-Sum-of-Squares')\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": {
        "id": "2EAo9wxTQhne",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 185
        },
        "outputId": "e250ef87-b88e-4734-8aea-11abbbc3adb0"
      },
      "source": [
        "#Finding optimum value of K with elbow method\n",
        "from sklearn.cluster import KMeans\n",
        "sse=[]\n",
        "for i in range(1,11):\n",
        "    k_mod1=KMeans(n_clusters=i)\n",
        "    k_mod1.fit(df_new.iloc[:,:-1])\n",
        "    sse.append(k_mod1.inertia_)\n",
        "sse"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[680.8244,\n",
              " 152.36870647733906,\n",
              " 78.94084142614602,\n",
              " 57.34492380952382,\n",
              " 46.53558205128205,\n",
              " 38.930963049671746,\n",
              " 34.198464618714624,\n",
              " 30.227724598930486,\n",
              " 28.207705588271384,\n",
              " 26.422543983793986]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 55
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KpCc6w4BQUml",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 367
        },
        "outputId": "b1d97162-47c7-4c63-ed55-7598a672b6b5"
      },
      "source": [
        "# Plotting the results onto a line graph to observe elbow method\n",
        "sb.set_style('darkgrid')\n",
        "plt.figure(figsize=(9,5))\n",
        "plt.plot(range(1,11),sse,marker='.',markersize=20,markerfacecolor='g')\n",
        "plt.xlabel('K-Value')\n",
        "plt.ylabel('Sum of Squared Error')\n",
        "plt.title('The Elbow Method')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'The Elbow Method')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 56
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 648x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "emEJX9VRR1Lf",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 402
        },
        "outputId": "2f26a689-377b-4d8a-f57b-29d4ea3a4683"
      },
      "source": [
        "#Above,  we could see that K=3 is a more suitable value to proceed further.\n",
        "raw_data_new"
      ],
      "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>SepalLengthCm</th>\n",
              "      <th>SepalWidthCm</th>\n",
              "      <th>PetalLengthCm</th>\n",
              "      <th>PetalWidthCm</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",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>145</th>\n",
              "      <td>6.7</td>\n",
              "      <td>3.0</td>\n",
              "      <td>5.2</td>\n",
              "      <td>2.3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>146</th>\n",
              "      <td>6.3</td>\n",
              "      <td>2.5</td>\n",
              "      <td>5.0</td>\n",
              "      <td>1.9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>147</th>\n",
              "      <td>6.5</td>\n",
              "      <td>3.0</td>\n",
              "      <td>5.2</td>\n",
              "      <td>2.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>148</th>\n",
              "      <td>6.2</td>\n",
              "      <td>3.4</td>\n",
              "      <td>5.4</td>\n",
              "      <td>2.3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>149</th>\n",
              "      <td>5.9</td>\n",
              "      <td>3.0</td>\n",
              "      <td>5.1</td>\n",
              "      <td>1.8</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>150 rows × 4 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "     SepalLengthCm  SepalWidthCm  PetalLengthCm  PetalWidthCm\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\n",
              "..             ...           ...            ...           ...\n",
              "145            6.7           3.0            5.2           2.3\n",
              "146            6.3           2.5            5.0           1.9\n",
              "147            6.5           3.0            5.2           2.0\n",
              "148            6.2           3.4            5.4           2.3\n",
              "149            5.9           3.0            5.1           1.8\n",
              "\n",
              "[150 rows x 4 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 59
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pLVHy04kR4cv",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 134
        },
        "outputId": "28663563-7f7f-46ad-d4b0-52944ebdb93d"
      },
      "source": [
        "#Model fitting \n",
        "k_mod2=KMeans(n_clusters=3,random_state=0)\n",
        "pred1=k_mod2.fit_predict(raw_data_new)\n",
        "pred1"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "       1, 1, 1, 1, 1, 1, 2, 2, 0, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
              "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 0, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
              "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 0, 2, 0, 0, 0, 0, 2, 0, 0, 0,\n",
              "       0, 0, 0, 2, 2, 0, 0, 0, 0, 2, 0, 2, 0, 2, 0, 0, 2, 2, 0, 0, 0, 0,\n",
              "       0, 2, 0, 0, 0, 0, 2, 0, 0, 0, 2, 0, 0, 0, 2, 0, 0, 2], dtype=int32)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 58
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rhpwpYKLVpMy"
      },
      "source": [
        "Every id is assigned to each cluster, i.e. id=1 to cluster 1, id=2 to cluster 2 and id=3 to cluster 3"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pr3iido-R9fJ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 402
        },
        "outputId": "b0539ba9-dc22-400b-9028-b9ced334233d"
      },
      "source": [
        "#Create new column \"Cluster\" and assign prediction 0,1,2\n",
        "raw_data_new['Cluster']= pred1\n",
        "raw_data_new"
      ],
      "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>SepalLengthCm</th>\n",
              "      <th>SepalWidthCm</th>\n",
              "      <th>PetalLengthCm</th>\n",
              "      <th>PetalWidthCm</th>\n",
              "      <th>Cluster</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>1</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",
              "      <td>1</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>1</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>1</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>1</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>145</th>\n",
              "      <td>6.7</td>\n",
              "      <td>3.0</td>\n",
              "      <td>5.2</td>\n",
              "      <td>2.3</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>146</th>\n",
              "      <td>6.3</td>\n",
              "      <td>2.5</td>\n",
              "      <td>5.0</td>\n",
              "      <td>1.9</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>147</th>\n",
              "      <td>6.5</td>\n",
              "      <td>3.0</td>\n",
              "      <td>5.2</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>148</th>\n",
              "      <td>6.2</td>\n",
              "      <td>3.4</td>\n",
              "      <td>5.4</td>\n",
              "      <td>2.3</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>149</th>\n",
              "      <td>5.9</td>\n",
              "      <td>3.0</td>\n",
              "      <td>5.1</td>\n",
              "      <td>1.8</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>150 rows × 5 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "     SepalLengthCm  SepalWidthCm  PetalLengthCm  PetalWidthCm  Cluster\n",
              "0              5.1           3.5            1.4           0.2        1\n",
              "1              4.9           3.0            1.4           0.2        1\n",
              "2              4.7           3.2            1.3           0.2        1\n",
              "3              4.6           3.1            1.5           0.2        1\n",
              "4              5.0           3.6            1.4           0.2        1\n",
              "..             ...           ...            ...           ...      ...\n",
              "145            6.7           3.0            5.2           2.3        0\n",
              "146            6.3           2.5            5.0           1.9        2\n",
              "147            6.5           3.0            5.2           2.0        0\n",
              "148            6.2           3.4            5.4           2.3        0\n",
              "149            5.9           3.0            5.1           1.8        2\n",
              "\n",
              "[150 rows x 5 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 60
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rHh9HGe6KBdG"
      },
      "source": [
        "## Visualizing the clusters"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bI882RQPV0cR"
      },
      "source": [
        "##As per species"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CJCutbw-Tp-t",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 407
        },
        "outputId": "8f985814-13c8-4516-d510-1d8cf248c871"
      },
      "source": [
        "plt.figure(figsize=(10,6))\n",
        "sb.scatterplot(x='SepalLengthCm',y='SepalWidthCm',data=df, s= 50,hue='Species',palette='cool')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f5636cc2be0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 64
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SmT6yHoZV3ZW"
      },
      "source": [
        "## As per clusters"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uVSRQ80KUCo0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 407
        },
        "outputId": "cccae761-d331-4325-e541-23bb147f8b22"
      },
      "source": [
        "plt.figure(figsize=(10,6))\n",
        "sb.scatterplot(x='SepalLengthCm',y='SepalWidthCm',data=raw_data_new, s=50, hue='Cluster',palette='coolwarm')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f5636a22828>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 70
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8SYj2CbFWB-9"
      },
      "source": [
        "##Marking the centroid of each cluster formed by k-Means "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fWM6ywDaUl8Q",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 407
        },
        "outputId": "60e0f263-9592-437e-c723-c22303cfa838"
      },
      "source": [
        "plt.figure(figsize=(10,6))\n",
        "sb.scatterplot(x='SepalLengthCm',y='SepalWidthCm',data=raw_data_new, s=50, hue='Cluster',palette='cool')\n",
        "\n",
        "#Centroid of the clusters\n",
        "plt.scatter(k_mod2.cluster_centers_[:,0],k_mod2.cluster_centers_[:,1],marker='*',s=100,label='centroid')\n",
        "plt.legend()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7f5636a005c0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 69
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZAOuZTjjKBdH",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 393
        },
        "outputId": "75865eff-7c76-4cea-da1d-1aabc01f8994"
      },
      "source": [
        "# Visualising the clusters - On the first two columns\n",
        "plt.figure(figsize=(10,6))\n",
        "plt.scatter(raw_data_new[df_new['Cluster'] == 0].iloc[:,0], raw_data_new[df_new['Cluster'] == 0].iloc[:,1], \n",
        "            s = 100, c = 'yellow', label = 'Iris-setosa')\n",
        "plt.scatter(raw_data_new[df_new['Cluster'] == 1].iloc[:,0], raw_data_new[df_new['Cluster'] == 1].iloc[:,1], \n",
        "            s = 100, c = 'red', label = 'Iris-versicolor')\n",
        "plt.scatter(raw_data_new[df_new['Cluster'] == 2].iloc[:,0], raw_data_new[df_new['Cluster'] == 2].iloc[:,1], \n",
        "            s = 100, c = 'blue', label = 'Iris-virginica')\n",
        "\n",
        "# Plotting the centroids of the clusters\n",
        "plt.scatter(k_mod2.cluster_centers_[:, 0], k_mod2.cluster_centers_[:,1],\n",
        "            s = 150, c = 'black', label = 'Centroids')\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.PathCollection at 0x7f563680e710>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 74
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    }
  ]
}