Spark_Task_2 (2).ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Spark Task-2",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-qi5JduWaBB8"
      },
      "source": [
        "## **Task 2 :- Prediction Using Unsupervised ML**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3lk_hlUAaVQL"
      },
      "source": [
        "## **Importing all the libraries such as pandas numpy and matplotlib**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "C1RyQ3p6IlmC"
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np  \n",
        "import matplotlib.pyplot as plt  "
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "35qbd2CddQno"
      },
      "source": [
        "## **Loading and Reading of the Iris dataset**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        },
        "id": "MmQtLGJOIzow",
        "outputId": "a861ffdd-2716-4c7f-da40-0d378344f5b1"
      },
      "source": [
        "data=pd.read_csv('/content/Iris.csv')\n",
        "data"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
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              "\n",
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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",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\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>146</td>\n",
              "      <td>6.7</td>\n",
              "      <td>3.0</td>\n",
              "      <td>5.2</td>\n",
              "      <td>2.3</td>\n",
              "      <td>Iris-virginica</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>146</th>\n",
              "      <td>147</td>\n",
              "      <td>6.3</td>\n",
              "      <td>2.5</td>\n",
              "      <td>5.0</td>\n",
              "      <td>1.9</td>\n",
              "      <td>Iris-virginica</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>147</th>\n",
              "      <td>148</td>\n",
              "      <td>6.5</td>\n",
              "      <td>3.0</td>\n",
              "      <td>5.2</td>\n",
              "      <td>2.0</td>\n",
              "      <td>Iris-virginica</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>148</th>\n",
              "      <td>149</td>\n",
              "      <td>6.2</td>\n",
              "      <td>3.4</td>\n",
              "      <td>5.4</td>\n",
              "      <td>2.3</td>\n",
              "      <td>Iris-virginica</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>149</th>\n",
              "      <td>150</td>\n",
              "      <td>5.9</td>\n",
              "      <td>3.0</td>\n",
              "      <td>5.1</td>\n",
              "      <td>1.8</td>\n",
              "      <td>Iris-virginica</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>150 rows × 6 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "      Id  SepalLengthCm  ...  PetalWidthCm         Species\n",
              "0      1            5.1  ...           0.2     Iris-setosa\n",
              "1      2            4.9  ...           0.2     Iris-setosa\n",
              "2      3            4.7  ...           0.2     Iris-setosa\n",
              "3      4            4.6  ...           0.2     Iris-setosa\n",
              "4      5            5.0  ...           0.2     Iris-setosa\n",
              "..   ...            ...  ...           ...             ...\n",
              "145  146            6.7  ...           2.3  Iris-virginica\n",
              "146  147            6.3  ...           1.9  Iris-virginica\n",
              "147  148            6.5  ...           2.0  Iris-virginica\n",
              "148  149            6.2  ...           2.3  Iris-virginica\n",
              "149  150            5.9  ...           1.8  Iris-virginica\n",
              "\n",
              "[150 rows x 6 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zUHoM9EfZ-Tm"
      },
      "source": [
        "## **Checking whether data has null values or not**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aOVceFluJXMX",
        "outputId": "764339d4-ef1a-4a3e-b1e8-bf2bf6fe140f"
      },
      "source": [
        "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": 19
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6YKllOICeSHS"
      },
      "source": [
        "## **This is used to find the unique values present in the data of Species**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jEXTG3FpI8Ip",
        "outputId": "7e6bae41-a40a-48ac-ddaf-06155f492961"
      },
      "source": [
        "data['Species'].unique()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['Iris-setosa', 'Iris-versicolor', 'Iris-virginica'], dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "K5b1eSdDe82y"
      },
      "source": [
        "## **Importing KMeans clustering from the sklearn package**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hWdQI21SNB8V"
      },
      "source": [
        "from sklearn.cluster import KMeans "
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "krilqWn-fKTw"
      },
      "source": [
        "## **Assigning the number of clusters required**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FN0SVvjeNuzl"
      },
      "source": [
        "clus=KMeans(n_clusters=3)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dySHIzj3fU5w"
      },
      "source": [
        "## **Reshaping of the given data**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        },
        "id": "pu54jdx_Qc8u",
        "outputId": "4a38fe69-5317-42b7-d39a-51840725ba84"
      },
      "source": [
        "x=data.iloc[:,1:-1]\n",
        "x"
      ],
      "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",
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              "\n",
              "    .dataframe tbody tr th {\n",
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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>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": 25
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-XyTZTScfmmR"
      },
      "source": [
        "## **Fitting of data into clusters**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "diW97CTrOBpy",
        "outputId": "1e7b5fe6-aa22-463c-abc6-839617fc4f36"
      },
      "source": [
        "clus.fit(x)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "KMeans(algorithm='auto', copy_x=True, init='k-means++', max_iter=300,\n",
              "       n_clusters=3, n_init=10, n_jobs=None, precompute_distances='auto',\n",
              "       random_state=None, tol=0.0001, verbose=0)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "x8DMeFKrfxag"
      },
      "source": [
        "## **Predicting and formation of cluster using different parameters**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lP484Bx3OQBq"
      },
      "source": [
        "precdclus=clus.fit_predict(x)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JFHr_tVfPd_N",
        "outputId": "1592f792-c0fe-4b3c-a8c7-4b14ef17926f"
      },
      "source": [
        "precdclus"
      ],
      "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, 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": 28
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PLrgy9qnf8Tp"
      },
      "source": [
        "### **Forming the clusters between SepalWidthCm and SepalLengthCm**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "vlQ54C49PvOg",
        "outputId": "5202dff9-feb7-41c4-dd49-33510c7ba415"
      },
      "source": [
        "c1=[[],[]]\n",
        "c2=[[],[]]\n",
        "c3=[[],[]]\n",
        "for i,j,k in zip(precdclus,data['SepalWidthCm'],data['SepalLengthCm']):\n",
        "  if i==0:\n",
        "    c1[0].append(j)\n",
        "    c1[1].append(k)\n",
        "  if i==1:\n",
        "    c2[0].append(j)\n",
        "    c2[1].append(k)\n",
        "  if i==2:\n",
        "    c3[0].append(j)\n",
        "    c3[1].append(k)\n",
        "plt.scatter(c1[0],c1[1],s=60,c='red',label = 'Iris-setosa')\n",
        "plt.scatter(c2[0],c2[1],s=70,c='blue', label = 'Iris-versicolour')\n",
        "plt.scatter(c3[0],c3[1],s=80,c='yellow',label = 'Iris-virginica')\n",
        "plt.show()\n",
        "\n",
        "\n"
      ],
      "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": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DXJHxodLgUtB"
      },
      "source": [
        "## **Forming clusters between PetalLengthCm and PetalWidthCm**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "kbIuRBShP5di",
        "outputId": "a3c00dd0-c774-42ba-c536-903de8079987"
      },
      "source": [
        "c1=[[],[]]\n",
        "c2=[[],[]]\n",
        "c3=[[],[]]\n",
        "for i,j,k in zip(precdclus,data['PetalLengthCm'],data['PetalWidthCm']):\n",
        "  if i==0:\n",
        "    c1[0].append(j)\n",
        "    c1[1].append(k)\n",
        "  if i==1:\n",
        "    c2[0].append(j)\n",
        "    c2[1].append(k)\n",
        "  if i==2:\n",
        "    c3[0].append(j)\n",
        "    c3[1].append(k)\n",
        "plt.scatter(c1[0],c1[1],c='red')\n",
        "plt.scatter(c2[0],c2[1],c='blue')\n",
        "plt.scatter(c3[0],c3[1],c='yellow')\n",
        "\n",
        "\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.PathCollection at 0x7fa315a61ed0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 30
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nx6BDuc3gk3x"
      },
      "source": [
        "## **Forming the clusters between PetalLengthCm and SepalLengthCm**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "_2R8oCZBkZ0-",
        "outputId": "f8aa4516-65f3-4d28-ae47-ba00b0c3ec25"
      },
      "source": [
        "c1=[[],[]]\n",
        "c2=[[],[]]\n",
        "c3=[[],[]]\n",
        "for i,j,k in zip(precdclus,data['PetalLengthCm'],data['SepalLengthCm']):\n",
        "  if i==0:\n",
        "    c1[0].append(j)\n",
        "    c1[1].append(k)\n",
        "  if i==1:\n",
        "    c2[0].append(j)\n",
        "    c2[1].append(k)\n",
        "  if i==2:\n",
        "    c3[0].append(j)\n",
        "    c3[1].append(k)\n",
        "plt.scatter(c1[0],c1[1],c='red')\n",
        "plt.scatter(c2[0],c2[1],c='blue')\n",
        "plt.scatter(c3[0],c3[1],c='yellow')\n",
        "\n",
        "\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.PathCollection at 0x7fa3159d2050>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 31
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3TIuZ_cIgxqK"
      },
      "source": [
        "## **Forming the clusters between petalWidthCm and SepalLengthCm**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "j49Am_Txkv_J",
        "outputId": "5ee3a2f7-0a46-449e-e95b-dbaf54410b3a"
      },
      "source": [
        "c1=[[],[]]\n",
        "c2=[[],[]]\n",
        "c3=[[],[]]\n",
        "for i,j,k in zip(precdclus,data['PetalWidthCm'],data['SepalLengthCm']):\n",
        "  if i==0:\n",
        "    c1[0].append(j)\n",
        "    c1[1].append(k)\n",
        "  if i==1:\n",
        "    c2[0].append(j)\n",
        "    c2[1].append(k)\n",
        "  if i==2:\n",
        "    c3[0].append(j)\n",
        "    c3[1].append(k)\n",
        "plt.scatter(c1[0],c1[1],c='red')\n",
        "plt.scatter(c2[0],c2[1],c='blue')\n",
        "plt.scatter(c3[0],c3[1],c='yellow')\n",
        "\n",
        "\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.PathCollection at 0x7fa315ad4ed0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xvE5L9XglPTq"
      },
      "source": [
        "### **Final prediction of the data it shows that which cluster it belongs to by giving the SepalLengthCm\tSepalWidthCm\tPetalLengthCm\tPetalWidthCm and it prints to which Species it belongs to.**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5p48z05xVfQc",
        "outputId": "ca0d073a-b879-4d28-c460-7850bcc0410e"
      },
      "source": [
        "kclus=clus.predict([[6.7,3.0,5.2,2.3]])[0]\n",
        "if kclus==0:\n",
        "  print('Iris-setosa')\n",
        "if kclus==1:\n",
        "  print('Iris-versicolor')\n",
        "if kclus==2:\n",
        "  print('Iris-virginica')\n",
        "  \n",
        " "
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Iris-virginica\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hutw-Q_vhmvh"
      },
      "source": [
        "## **Therefore it predicts by giving the SepalLengthCm SepalWidthCm PetalLengthCm PetalWidthCm i.e 6.7,3.0,5.2,2.3 and predicts and gives the specie i.e Iris-setosa**"
      ]
    }
  ]
}