task_2_internship_grip_aug2021.ipynb

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    "colab": {
      "name": "task-2-internship-grip-aug2021.ipynb",
      "provenance": []
    }
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XDqpHuaaMEVw"
      },
      "source": [
        "# **TASK # 2**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6FZ-ODBXMEVw"
      },
      "source": [
        "# Using Unsupervised ML to predict optimum number of Iris clusters"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jncal-OfMEVx"
      },
      "source": [
        "# Author: Yusra Shereen"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tenBKqAbMEVy"
      },
      "source": [
        "### **Importing Libraries**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.status.busy": "2021-08-03T15:36:01.789743Z",
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          "shell.execute_reply": "2021-08-03T15:36:01.794114Z"
        },
        "trusted": true,
        "id": "h9sdiONfMEVy"
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn import metrics\n",
        "import seaborn as sns\n",
        "from sklearn.cluster import KMeans    \n",
        "from sklearn.metrics import silhouette_score  "
      ],
      "execution_count": 81,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FJ71KJqLW_Ec",
        "outputId": "7d875db3-207a-4baf-da1e-6894620e1cc5"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": 82,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "w8mCN0UmMEVz"
      },
      "source": [
        "### **Reading Data**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
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        "trusted": true,
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 203
        },
        "id": "y6ew5wG-MEVz",
        "outputId": "3e545920-6708-423f-bc24-7112cd6ff684"
      },
      "source": [
        "url = \"/content/drive/MyDrive/Data Science Internship/Iris.csv\"\n",
        "data = pd.read_csv(url)\n",
        "data.head()"
      ],
      "execution_count": 99,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "    }\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>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</td>\n",
              "      <td>5.1</td>\n",
              "      <td>3.5</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "      <td>Iris-setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>4.9</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "      <td>Iris-setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>4.7</td>\n",
              "      <td>3.2</td>\n",
              "      <td>1.3</td>\n",
              "      <td>0.2</td>\n",
              "      <td>Iris-setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>4.6</td>\n",
              "      <td>3.1</td>\n",
              "      <td>1.5</td>\n",
              "      <td>0.2</td>\n",
              "      <td>Iris-setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>5.0</td>\n",
              "      <td>3.6</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "      <td>Iris-setosa</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   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"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 99
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DP-YeN-QMEV0"
      },
      "source": [
        "### **Exploring Data**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.status.busy": "2021-08-03T15:36:05.965320Z",
          "iopub.execute_input": "2021-08-03T15:36:05.965676Z",
          "iopub.status.idle": "2021-08-03T15:36:05.986152Z",
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          "shell.execute_reply": "2021-08-03T15:36:05.985226Z"
        },
        "trusted": true,
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "NdPDUcWbMEV1",
        "outputId": "4e425323-ff6d-474f-9ae9-81c31dab79ea"
      },
      "source": [
        "data.describe()"
      ],
      "execution_count": 100,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Id</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",
              "      <td>150.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>75.500000</td>\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>43.445368</td>\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>1.000000</td>\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>38.250000</td>\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>75.500000</td>\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>112.750000</td>\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>150.000000</td>\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": [
              "               Id  SepalLengthCm  SepalWidthCm  PetalLengthCm  PetalWidthCm\n",
              "count  150.000000     150.000000    150.000000     150.000000    150.000000\n",
              "mean    75.500000       5.843333      3.054000       3.758667      1.198667\n",
              "std     43.445368       0.828066      0.433594       1.764420      0.763161\n",
              "min      1.000000       4.300000      2.000000       1.000000      0.100000\n",
              "25%     38.250000       5.100000      2.800000       1.600000      0.300000\n",
              "50%     75.500000       5.800000      3.000000       4.350000      1.300000\n",
              "75%    112.750000       6.400000      3.300000       5.100000      1.800000\n",
              "max    150.000000       7.900000      4.400000       6.900000      2.500000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 100
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "omKe-cUzMEV1"
      },
      "source": [
        "### **Data Cleaning (Checking for null values)**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.status.busy": "2021-08-03T15:36:07.396195Z",
          "iopub.execute_input": "2021-08-03T15:36:07.396526Z",
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        },
        "id": "opUiN0guMEV1",
        "outputId": "ad7f288d-2a8e-4f6b-8b27-900ff5250a2a"
      },
      "source": [
        "data.isnull().sum()\n"
      ],
      "execution_count": 101,
      "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": 101
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3fBaQnEzXvEo",
        "outputId": "c2b6c2c1-6c31-4c46-b26f-f129ad6813ad"
      },
      "source": [
        "data.info()"
      ],
      "execution_count": 102,
      "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": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BRzCg1n00D1k",
        "outputId": "ea483fca-4220-4f28-fea5-9c0c0e0b0eab"
      },
      "source": [
        "data['Species'].value_counts()"
      ],
      "execution_count": 103,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Iris-virginica     50\n",
              "Iris-setosa        50\n",
              "Iris-versicolor    50\n",
              "Name: Species, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 103
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bpI_xixlYWjZ"
      },
      "source": [
        "### **Analysing Input Data**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "Fv6ucy5BuBZp",
        "outputId": "95559b1e-90dd-4e60-a707-7a8f37d20e45"
      },
      "source": [
        "\n",
        "data['PetalLengthCm'].hist()\n",
        "plt.title('Petal Length')\n",
        "plt.show()\n",
        "data['PetalWidthCm'].hist()\n",
        "plt.title('Petal Width')\n",
        "plt.show()\n",
        "data['SepalLengthCm'].hist()\n",
        "plt.title('Sepal Length')\n",
        "plt.show()\n",
        "data['SepalWidthCm'].hist()\n",
        "plt.title('Sepal Width')\n",
        "plt.show()"
      ],
      "execution_count": 104,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 285
        },
        "id": "kqKqW_XG3cOQ",
        "outputId": "eb0f8a0f-069c-498a-de57-7a4d419633ff"
      },
      "source": [
        "colors = ['black','green','blue']\n",
        "species = ['Iris-setosa','Iris-virginica','Iris-versicolor']\n",
        "for i in range(3):\n",
        "  x = data[data['Species'] == species[i]]\n",
        "  plt.scatter(x['SepalLengthCm'],x['SepalWidthCm'],c = colors[i], label = species[i])\n",
        "\n",
        "plt.xlabel('Sepal Length')\n",
        "plt.ylabel('Sepal Width')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "execution_count": 105,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "fy0NeNRx6RvD",
        "outputId": "e80142d2-0d1c-4537-8349-b6a2d3bbd0db"
      },
      "source": [
        "colors = ['black','green','blue']\n",
        "species = ['Iris-setosa','Iris-virginica','Iris-versicolor']\n",
        "for i in range(3):\n",
        "  x = data[data['Species'] == species[i]]\n",
        "  plt.scatter(x['PetalLengthCm'],x['PetalWidthCm'],c = colors[i], label = species[i])\n",
        "\n",
        "plt.xlabel('Petal Length')\n",
        "plt.ylabel('Petal Width')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "execution_count": 106,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "vfg9JnKM6ttZ",
        "outputId": "a8d31034-94d3-4a68-ca7e-7553e56c38aa"
      },
      "source": [
        "colors = ['black','green','blue']\n",
        "species = ['Iris-setosa','Iris-virginica','Iris-versicolor']\n",
        "for i in range(3):\n",
        "  x = data[data['Species'] == species[i]]\n",
        "  plt.scatter(x['SepalLengthCm'],x['PetalLengthCm'],c = colors[i], label = species[i])\n",
        "\n",
        "plt.xlabel('Sepal Length')\n",
        "plt.ylabel('Petal Length')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "execution_count": 107,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "c6KbuSpN64Dt",
        "outputId": "265096e5-4c2c-4216-f699-0ca94ea2904c"
      },
      "source": [
        "colors = ['black','green','blue']\n",
        "species = ['Iris-setosa','Iris-virginica','Iris-versicolor']\n",
        "for i in range(3):\n",
        "  x = data[data['Species'] == species[i]]\n",
        "  plt.scatter(x['SepalWidthCm'],x['PetalWidthCm'],c = colors[i], label = species[i])\n",
        "\n",
        "plt.xlabel('Sepal Width')\n",
        "plt.ylabel('Petal Width')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "execution_count": 108,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 203
        },
        "id": "ZVbKNiZ_vacY",
        "outputId": "9c741807-cfa4-4b02-f0a5-d164c8972000"
      },
      "source": [
        "data = data.drop(columns = ['Id'])\n",
        "x = data.iloc[:,[0,1,2,3]].values\n",
        "data.head()"
      ],
      "execution_count": 109,
      "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>Species</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>Iris-setosa</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>Iris-setosa</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>Iris-setosa</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>Iris-setosa</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>Iris-setosa</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   SepalLengthCm  SepalWidthCm  PetalLengthCm  PetalWidthCm      Species\n",
              "0            5.1           3.5            1.4           0.2  Iris-setosa\n",
              "1            4.9           3.0            1.4           0.2  Iris-setosa\n",
              "2            4.7           3.2            1.3           0.2  Iris-setosa\n",
              "3            4.6           3.1            1.5           0.2  Iris-setosa\n",
              "4            5.0           3.6            1.4           0.2  Iris-setosa"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 109
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3nNAfGg480Ip"
      },
      "source": [
        "### **Finding Optimum number of Clusters**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vf_zPFAL9HU_"
      },
      "source": [
        "Using Elbow method to find optimum clusters"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "6PLL-fZfkjQM",
        "outputId": "a0157e36-cde9-4b12-cfb9-a08ce4ebabf8"
      },
      "source": [
        "ssd = []\n",
        "range_n_clusters = [2, 3, 4, 5, 6, 7, 8]\n",
        "for num_clusters in range_n_clusters:\n",
        "    kmeans = KMeans(n_clusters=num_clusters, max_iter=50)\n",
        "    kmeans.fit(x)\n",
        "    \n",
        "    ssd.append(kmeans.inertia_)\n",
        "    \n",
        "# plot the SSDs for each n_clusters\n",
        "plt.plot(ssd)"
      ],
      "execution_count": 116,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f7142d7a890>]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 116
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-REdOctM9NAk"
      },
      "source": [
        "Elbow method shows that 3 clusters for given dataset(Iris dataset) are optimum"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3zf4i9_H9Yql"
      },
      "source": [
        "### **Fitting the model on K-mean**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SkBRPwpjxJQj",
        "outputId": "5425f024-915d-4926-fe76-7f51c3aeac09"
      },
      "source": [
        "kmeans = KMeans(n_clusters=3, max_iter=50)\n",
        "y_pred = kmeans.fit_predict(x)\n",
        "kmeans.labels_"
      ],
      "execution_count": 117,
      "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, 0, 0, 2, 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, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
              "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 2, 2, 2, 2, 0, 2, 2, 2,\n",
              "       2, 2, 2, 0, 0, 2, 2, 2, 2, 0, 2, 0, 2, 0, 2, 2, 0, 0, 2, 2, 2, 2,\n",
              "       2, 0, 2, 2, 2, 2, 0, 2, 2, 2, 0, 2, 2, 2, 0, 2, 2, 0], dtype=int32)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 117
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 203
        },
        "id": "s4hiY7RbxTND",
        "outputId": "34d0ef0d-aad0-4d8e-b7bd-ea2f480372f9"
      },
      "source": [
        "data['Cluster_Id'] = kmeans.labels_\n",
        "data.head()"
      ],
      "execution_count": 118,
      "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>Species</th>\n",
              "      <th>Cluster_Id</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>Iris-setosa</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>Iris-setosa</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>Iris-setosa</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>Iris-setosa</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>Iris-setosa</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   SepalLengthCm  SepalWidthCm  ...      Species  Cluster_Id\n",
              "0            5.1           3.5  ...  Iris-setosa           1\n",
              "1            4.9           3.0  ...  Iris-setosa           1\n",
              "2            4.7           3.2  ...  Iris-setosa           1\n",
              "3            4.6           3.1  ...  Iris-setosa           1\n",
              "4            5.0           3.6  ...  Iris-setosa           1\n",
              "\n",
              "[5 rows x 6 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 118
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0KwDURLJ95Oj"
      },
      "source": [
        "### **Visualizing Clusters**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 268
        },
        "id": "rJdvvUFWyWZh",
        "outputId": "68791ed8-578a-465d-cb23-270690cfbae5"
      },
      "source": [
        "from matplotlib import style\n",
        "\n",
        "plt.figure()\n",
        "style.use('ggplot')\n",
        "\n",
        "plt.scatter(x[y_pred == 0,0],x[y_pred == 0,1],s=100, color = 'blue',label = 'Iris-Virginica',marker = '+')\n",
        "plt.scatter(x[y_pred == 1,0],x[y_pred == 1,1],s=100, color = 'green',label = 'Iris-Versicolor',marker = '*')\n",
        "plt.scatter(x[y_pred == 2,0],x[y_pred == 2,1],s=100, color = 'black',label = 'Iris-Setosa',marker = '.')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "execution_count": 122,
      "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": "8nBTKY3U9CpO"
      },
      "source": [
        ""
      ],
      "execution_count": 122,
      "outputs": []
    }
  ]
}