task_2.ipynb

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  "metadata": {
    "colab": {
      "name": "task 2.ipynb",
      "provenance": [],
      "mount_file_id": "1KCfhTDrcUmvnJsBp8RuwG4bzlXM8ZGdQ",
      "authorship_tag": "ABX9TyNvgDxJ+IjENd4ejJN40LTl",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/nishabalamurugan/nishabalamurugan/blob/main/task_2.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "NISHA **B**"
      ],
      "metadata": {
        "id": "3EnPehJSADdq"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "PREDICTION USING UNSUPERVISED MACHINE LEARNING"
      ],
      "metadata": {
        "id": "cAvY1OjR_8n4"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import numpy as np \n",
        "import matplotlib.pyplot as plt "
      ],
      "metadata": {
        "id": "I0EcbpsjANjF"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "READ IRIS DATASET"
      ],
      "metadata": {
        "id": "uErKhOF4AoFO"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df=pd.read_csv(\"/content/drive/MyDrive/data set/Iris.csv\") \n",
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "MuoSOhE7AsDi",
        "outputId": "808f6af9-9e7b-4930-ea50-b0ff690ddac8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "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"
            ],
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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</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>\n",
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              "        const buttonEl =\n",
              "          document.querySelector('#df-9cf789c4-e3ab-4653-96c9-c9a08c8a6c11 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-9cf789c4-e3ab-4653-96c9-c9a08c8a6c11');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
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            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "EXTRACT INDEPENDENT VARIABLES"
      ],
      "metadata": {
        "id": "TUg-z4RIF_Qw"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "x=df.iloc[:,1:5].values "
      ],
      "metadata": {
        "id": "q3WF9g9lGC9d"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "ELBOW METHOD TO FIND K"
      ],
      "metadata": {
        "id": "mJXBrggWGcmX"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.cluster import KMeans\n",
        "lis=[]\n",
        "for i in range(1,11):\n",
        "  kmeans=KMeans(n_clusters=i,random_state=0)\n",
        "  kmeans.fit(x)\n",
        "  lis.append(kmeans.inertia_)\n",
        "  \n",
        "import matplotlib.pyplot as plt\n",
        "graph=plt.plot(range(1,11),lis)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "2A5bGZUjGe-l",
        "outputId": "158b48c5-1db9-4cfc-de58-af2ed5c99f09"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "From Elbow method,the value of  is identified as 3"
      ],
      "metadata": {
        "id": "CLuM6MujG5pu"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "KMEANS CLUSTERING"
      ],
      "metadata": {
        "id": "6RMgWaI8HGQp"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "kmeans=KMeans(n_clusters=3,random_state=0)\n",
        "y_pred=kmeans.fit_predict(x)"
      ],
      "metadata": {
        "id": "W8XHO-n3HIjS"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "VISUALISING CLUSTERS"
      ],
      "metadata": {
        "id": "snh-81N3Hsbh"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "plt.scatter(x[y_pred == 0, 0], x[y_pred == 0, 1], \n",
        "            s = 100, c = 'yellow', label = 'Iris-setosa')\n",
        "plt.scatter(x[y_pred == 1, 0], x[y_pred == 1, 1], \n",
        "            s = 100, c = 'violet', label = 'Iris-versicolour')\n",
        "plt.scatter(x[y_pred == 2, 0], x[y_pred == 2, 1],\n",
        "            s = 100, c = 'red', label = 'Iris-virginica')\n",
        "plt.legend() \n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 268
        },
        "id": "vJwQkLIxHuq1",
        "outputId": "78dc86b0-8f3a-4c55-e49a-607aaab72a46"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}