Task_2_Prediction_using_Unsupervised_ML.ipynb

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      "name": "Task 2_Prediction_using_Unsupervised_ML.ipynb",
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "geOIjoDNvFaQ"
      },
      "source": [
        "#**The Sparks Foundation - GRIP June 2021**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TNZc6U0BvPoW"
      },
      "source": [
        "##**Task 2**: Prediction using Unsupervised ML\n",
        "\n",
        "####**Level**: Beginner"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UbWdirAuvjHW"
      },
      "source": [
        "##**Author**: [**Aishwarya Muttineni**](https://www.linkedin.com/in/aishwarya-muttineni-047b5a200/)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "T51SXngEvn9F"
      },
      "source": [
        "###**Problem statement**\n",
        "\n",
        "• From the given ‘Iris’ dataset, predict the optimum number of clusters and represent it visually."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RKNnY3emv5vd"
      },
      "source": [
        "###**Work Flow**\n",
        "\n",
        "1) Data collection\n",
        "\n",
        "2) Data Pre-processing\n",
        "\n",
        "3) Data visualisation\n",
        "\n",
        "4) Data preperation\n",
        "\n",
        "5) Choosing optimum number of clusters\n",
        "\n",
        "6) KMeans model training\n",
        "\n",
        "7) Visualising the clusters"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9idJOWRHwFdT"
      },
      "source": [
        "**Importing libraries**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OCrMQ6XKu9em"
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eh-vPViRwKwL"
      },
      "source": [
        "**Data collection and Pre-processing**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5qaB9nAOwIxm",
        "outputId": "010335c2-d87e-476a-fe55-72e5e5e38ba4"
      },
      "source": [
        "# loading dataset into pandas dataframe\n",
        "iris_df = pd.read_csv(\"Iris.csv\")\n",
        "print(\"Data set collected successfully\")"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Data set collected successfully\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 357
        },
        "id": "jZu9hqT-wQIz",
        "outputId": "91da424b-c4e9-42c6-a892-2a5c4769944c"
      },
      "source": [
        "# check first 10 attributes\n",
        "iris_df.head(10)"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
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              "</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>5</th>\n",
              "      <td>6</td>\n",
              "      <td>5.4</td>\n",
              "      <td>3.9</td>\n",
              "      <td>1.7</td>\n",
              "      <td>0.4</td>\n",
              "      <td>Iris-setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>7</td>\n",
              "      <td>4.6</td>\n",
              "      <td>3.4</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.3</td>\n",
              "      <td>Iris-setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>8</td>\n",
              "      <td>5.0</td>\n",
              "      <td>3.4</td>\n",
              "      <td>1.5</td>\n",
              "      <td>0.2</td>\n",
              "      <td>Iris-setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>9</td>\n",
              "      <td>4.4</td>\n",
              "      <td>2.9</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "      <td>Iris-setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>10</td>\n",
              "      <td>4.9</td>\n",
              "      <td>3.1</td>\n",
              "      <td>1.5</td>\n",
              "      <td>0.1</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\n",
              "5   6            5.4           3.9            1.7           0.4  Iris-setosa\n",
              "6   7            4.6           3.4            1.4           0.3  Iris-setosa\n",
              "7   8            5.0           3.4            1.5           0.2  Iris-setosa\n",
              "8   9            4.4           2.9            1.4           0.2  Iris-setosa\n",
              "9  10            4.9           3.1            1.5           0.1  Iris-setosa"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EugZByDCwSry"
      },
      "source": [
        "**Exploratory data analysis**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VqKO3pknwT3y",
        "outputId": "ae702d3b-1ad6-427f-c137-48dc94f727ad"
      },
      "source": [
        "# getting the shape of data set\n",
        "iris_df.shape"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(150, 6)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-60Yt_Y5wV5j",
        "outputId": "536bc4bc-6a24-48ef-ed8b-883f5cb7a83e"
      },
      "source": [
        "# getting the attributes\n",
        "iris_df.columns"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['Id', 'SepalLengthCm', 'SepalWidthCm', 'PetalLengthCm', 'PetalWidthCm',\n",
              "       'Species'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VTCWVp9TwYHK",
        "outputId": "723ff997-71bc-482b-b635-3ff0a6b8cd68"
      },
      "source": [
        "# getting the datatypes \n",
        "iris_df.dtypes"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Id                 int64\n",
              "SepalLengthCm    float64\n",
              "SepalWidthCm     float64\n",
              "PetalLengthCm    float64\n",
              "PetalWidthCm     float64\n",
              "Species           object\n",
              "dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "J2d4P5PywZ0D",
        "outputId": "e2744c47-47cb-4042-a9e1-8c00158bebd2"
      },
      "source": [
        "# getting the information about dataset\n",
        "iris_df.info()"
      ],
      "execution_count": 8,
      "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": "6OxsTrIHwboy"
      },
      "source": [
        "# removing the id\n",
        "iris_df = iris_df.drop(['Id'], axis = 1)"
      ],
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "IBjesxCOwdta",
        "outputId": "7d78d893-1617-46c0-c624-7301c4b32f18"
      },
      "source": [
        "# getting the statistical values \n",
        "iris_df.describe()"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "    }\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>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": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "R9Q0snu-wfTk",
        "outputId": "e0d92277-283a-42ea-deb4-56f6dfa80aa5"
      },
      "source": [
        "# checking the null values if any\n",
        "iris_df.isnull().sum()"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "SepalLengthCm    0\n",
              "SepalWidthCm     0\n",
              "PetalLengthCm    0\n",
              "PetalWidthCm     0\n",
              "Species          0\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MPy5V6LRwhjq",
        "outputId": "026e46e7-9e30-4c4c-fe67-3cce2056da97"
      },
      "source": [
        "# checkin the duplicate values if any\n",
        "iris_df.duplicated().sum()"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "3"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SXsS0lMBwjey"
      },
      "source": [
        "# removing duplicated values\n",
        "iris_df.drop_duplicates(inplace = True)"
      ],
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7rjLAAvYwlzZ",
        "outputId": "3dda01ff-f6f6-4a89-cd00-b141a5276431"
      },
      "source": [
        "iris_df.shape"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(147, 5)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rjB6V5Q3wpHq"
      },
      "source": [
        "**Data visualisation**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 743
        },
        "id": "EdyhqZ6swncS",
        "outputId": "f3290b86-3827-45ff-cc81-d9fc0d45eda0"
      },
      "source": [
        "# pair plot\n",
        "sns.pairplot(iris_df, plot_kws = {'color' : 'red'})"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.PairGrid at 0x7f910dc0f310>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x720 with 20 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 504
        },
        "id": "KkqExygSwr4h",
        "outputId": "4b7c0a08-7592-4fc9-aa69-ab3f000f75ce"
      },
      "source": [
        "# heatmap\n",
        "plt.figure(figsize = (8,8))\n",
        "correlation = iris_df.corr()\n",
        "sns.heatmap(correlation, annot = True)"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f910418a410>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x576 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CYHkbGpIwwER"
      },
      "source": [
        "**Data preperation**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vxVAMiLUwtzy"
      },
      "source": [
        "x = iris_df.iloc[:,0:4].values"
      ],
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "a2Gnu9M8wy5K",
        "outputId": "07237ff6-82b2-4725-aee5-e21fda0e8680"
      },
      "source": [
        "x"
      ],
      "execution_count": 18,
      "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",
              "       [5. , 3.2, 1.2, 0.2],\n",
              "       [5.5, 3.5, 1.3, 0.2],\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",
              "       [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": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gnCqhog-w1rz"
      },
      "source": [
        "target = iris_df['Species']"
      ],
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "k0hVzzF1w3_K",
        "outputId": "8baa72bf-103a-4447-a8ad-20055a7dbc96"
      },
      "source": [
        "target.unique()"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['Iris-setosa', 'Iris-versicolor', 'Iris-virginica'], dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IzRiMp1gw7Lb"
      },
      "source": [
        "**Choosing the optimum number of clusters**\n",
        "\n",
        "We need to find the optimum number of clusters.For choosing the number of clusters, we are using the parameter WCSS(Within Clusters Sum of Squares).In this we find the distance of the datapoints from the centeroid.It should be minimum."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Tn2v9iVsw5jB"
      },
      "source": [
        "# finding WCSS for different number of clusters\n",
        "# for 10 clusters\n",
        "from sklearn.cluster import KMeans\n",
        "WCSS = []\n",
        "for i in range(1,11):\n",
        "    kmeans = KMeans(n_clusters = i, init = 'k-means++', random_state = 3)\n",
        "    kmeans.fit(x)\n",
        "    WCSS.append(kmeans.inertia_)"
      ],
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dwrUF1IGxBJV",
        "outputId": "e46d5ebe-2653-44ec-bbad-5c2a12a89fcc"
      },
      "source": [
        "WCSS"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[663.895238095238,\n",
              " 151.77145833333336,\n",
              " 77.91989035087718,\n",
              " 56.64237065018315,\n",
              " 45.816421929824564,\n",
              " 38.400902116402115,\n",
              " 34.053056433353916,\n",
              " 29.844580051212407,\n",
              " 27.891253827090782,\n",
              " 26.03090165852666]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 410
        },
        "id": "cz5zpMBuxCoO",
        "outputId": "11028f32-1b1a-4cdf-c021-fc128d609403"
      },
      "source": [
        "# plot the elbow graph to see which number of clusters has the minimum WCSS values\n",
        "sns.set()  # gives basic parameters of the graph\n",
        "plt.figure(figsize = (6,6))\n",
        "plt.plot(range(1,11), WCSS, marker = 'o')\n",
        "plt.title('WCSS Values')\n",
        "plt.xlabel('Number of Clusters')\n",
        "plt.ylabel('WCSS')\n",
        "plt.grid(color = 'black')\n",
        "plt.show()"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "H_JeqaWQxGq-"
      },
      "source": [
        "Optimum number of clusters are 3."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "e7k77jMIxKdt"
      },
      "source": [
        "**Model training**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "liqpTWx7xEk1"
      },
      "source": [
        "# training the K-Means clustering model\n",
        "kmeans = KMeans(n_clusters = 3, init = 'k-means++', random_state = 13)"
      ],
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SSdvajTxxNdV"
      },
      "source": [
        "# return a label for each data points based on their clusters\n",
        "y_pred = kmeans.fit_predict(x)"
      ],
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gCADCnmPxPNX",
        "outputId": "711b9fd4-f891-4c56-fda1-47e14c3cc787"
      },
      "source": [
        "y_pred"
      ],
      "execution_count": 26,
      "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, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
              "       0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
              "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 2, 2, 2, 2, 0, 2, 2, 2, 2, 2,\n",
              "       2, 0, 0, 2, 2, 2, 2, 0, 2, 0, 2, 0, 2, 2, 0, 0, 2, 2, 2, 2, 2, 0,\n",
              "       2, 2, 2, 2, 0, 2, 2, 2, 2, 2, 2, 0, 2, 2, 0], dtype=int32)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HPvVHihZxTCa"
      },
      "source": [
        "**Visualising the clusters**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 501
        },
        "id": "ft7Yz_zjxUKK",
        "outputId": "e1e92841-7011-43d8-f0c9-b15bdacd0bea"
      },
      "source": [
        "# plotting the datapoints and centroids\n",
        "plt.figure(figsize=(8,8))\n",
        "plt.scatter(x = x[y_pred==0,0], y = x[y_pred==0,1], s = 50, c = 'yellow', label = 'Iris-setosa')\n",
        "plt.scatter(x = x[y_pred==1,0], y = x[y_pred==1,1], s = 50, c = 'green', label = 'Iris-versicolor')\n",
        "plt.scatter(x = x[y_pred==2,0], y = x[y_pred==2,1], s = 50, c = 'red', label = 'Iris-virginica')\n",
        "plt.scatter(kmeans.cluster_centers_[:,0], kmeans.cluster_centers_[:,1], s = 100, c ='black', label = 'Centroid')\n",
        "plt.title('Iris data clusters')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JoEylIBwxXzr"
      },
      "source": [
        "**Conclusion**\n",
        "\n",
        "The Optimum number of clusters are 3\n",
        "\n",
        "##**Task 2 Completed !**"
      ]
    }
  ]
}