Task_3_K_Means_Clustering_on_Iris_Data.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Task #3-K-Means Clustering on Iris Data.ipynb",
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "al3WYiVy5pnp",
        "colab_type": "text"
      },
      "source": [
        "###**Task-3 To explore Unsupervised learning**\n",
        "*From the given ‘Iris’ dataset, predict the optimum number of\n",
        "clusters and represent it visually.*"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "G23zQLiVJnKh",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Importing the libraries\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import pandas as pd\n",
        "from sklearn import datasets\n",
        "import seaborn as sns\n",
        "#Loading data set\n"
      ],
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GP57z-VuSBla",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "bef9f740-1db1-431e-be94-0b514de7a8e3"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": 26,
      "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": "code",
      "metadata": {
        "id": "3g6-yM5ySgtt",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "1f9131a1-c8bb-4d9e-aa8d-b271fba1195f"
      },
      "source": [
        "%cd \"/content/drive/My Drive/Colab Notebooks\""
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/content/drive/My Drive/Colab Notebooks\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "E8M-LUonZGv2",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "2fa3c43d-f9f3-4ce1-cca9-4db792dc5418"
      },
      "source": [
        "data_set=pd.read_csv(\"Iris.csv\")\n",
        "data_set.head()"
      ],
      "execution_count": 28,
      "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>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": 28
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9FBbRSEsZvWt",
        "colab_type": "text"
      },
      "source": [
        "### **Information about the data set**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MQEftREOaBY3",
        "colab_type": "text"
      },
      "source": [
        "*Column Id is not so relevant hence we delete it.*"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4dKePq4SaSH_",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 221
        },
        "outputId": "ece67d4a-7d60-4647-9381-7d003276d687"
      },
      "source": [
        "data_set.drop(labels='Id',axis=1,inplace=True)\n",
        "data_set.info()"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 150 entries, 0 to 149\n",
            "Data columns (total 5 columns):\n",
            " #   Column         Non-Null Count  Dtype  \n",
            "---  ------         --------------  -----  \n",
            " 0   SepalLengthCm  150 non-null    float64\n",
            " 1   SepalWidthCm   150 non-null    float64\n",
            " 2   PetalLengthCm  150 non-null    float64\n",
            " 3   PetalWidthCm   150 non-null    float64\n",
            " 4   Species        150 non-null    object \n",
            "dtypes: float64(4), object(1)\n",
            "memory usage: 6.0+ KB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9fScKX1dagR_",
        "colab_type": "text"
      },
      "source": [
        "### **Checking for Outliers to optmize the output**\n",
        "***Box Plots are best for visualizing outliers***\n",
        "*Brief function of Box Plot - Bottom black horizontal line of green box plot is  minimum value.\n",
        "First black horizontal line of rectangle shape of green box plot is First quartile or 25%.\n",
        "Second black horizontal line of rectangle shape of green box plot is Second quartile or 50% or median.\n",
        "Third black horizontal line of rectangle shape of green box plot is third quartile or 75%.\n",
        "Top black horizontal line of rectangle shape of green box plot is maximum value.\n",
        "Small diamond shape of green box plot is outlier data or erroneous data.*"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3CSf5iAmapAp",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 378
        },
        "outputId": "ad321d56-79c3-4b72-b718-c401321e1d3c"
      },
      "source": [
        "sns.boxplot(x=\"Species\",y=\"SepalLengthCm\",data=data_set)"
      ],
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f5a8dc18748>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 30
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x396 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "m0pp-eaCbusB",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 380
        },
        "outputId": "48ecb263-857d-40ac-cf4b-18962707c9d1"
      },
      "source": [
        "sns.boxplot(x=\"Species\",y=\"SepalWidthCm\",data=data_set)"
      ],
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f5a8dae0748>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 31
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x396 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "r6CzCWTzb5ae",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 378
        },
        "outputId": "5b1542b7-97dd-4d71-a1bf-9e2f5f00203e"
      },
      "source": [
        "sns.boxplot(x=\"Species\",y=\"PetalLengthCm\",data=data_set)"
      ],
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f5a8da67208>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 32
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x396 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xE2pceFVlKp-",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 378
        },
        "outputId": "d8632e29-167e-442b-d434-dc859bba48eb"
      },
      "source": [
        "sns.boxplot(x=\"Species\",y=\"PetalWidthCm\",data=data_set)"
      ],
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f5a8d975ba8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 33
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x396 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vv_j_yxohVWd",
        "colab_type": "text"
      },
      "source": [
        "##**Label Encoding Species**\n",
        "*Now since there are multiple labels in species column , Inorder to make it easy for machine to distingush between several species we perform label encoding.*"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "98po2VLnb7Te",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "8ce3bc59-4ebb-405c-bf50-6b81ade17955"
      },
      "source": [
        "from sklearn.preprocessing import LabelEncoder\n",
        "encoder=LabelEncoder()\n",
        "data_set['Encoded-Species']=encoder.fit_transform(data_set['Species'])\n",
        "data_set['Encoded-Species'].unique()"
      ],
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 1, 2])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9K65wIt-jGSk",
        "colab_type": "text"
      },
      "source": [
        "###**Feature Scaling and Removing Outliers**\n",
        "*Feature Scaling is a primitive step in pre-processing , performing feature scaling will ensure our model doesn't tend to make wrong predictions.Feature scaling will scale all the features in iris-data set(PetalLength, PetalWidth, SepalLength, SepalWidth) onto a fixed range so that no feature dominates another while prediction of a new data-point.*"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9pfgrUMbljEb",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "7b21026a-bff0-4177-cf94-223b9daf673e"
      },
      "source": [
        "data_set['Check_Outliers']=pd.cut(data_set['SepalLengthCm'],5)\n",
        "data_set[['Check_Outliers','SepalLengthCm']].groupby('Check_Outliers',as_index=False).count()"
      ],
      "execution_count": 35,
      "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>Check_Outliers</th>\n",
              "      <th>SepalLengthCm</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>(4.296, 5.02]</td>\n",
              "      <td>32</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>(5.02, 5.74]</td>\n",
              "      <td>41</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>(5.74, 6.46]</td>\n",
              "      <td>42</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>(6.46, 7.18]</td>\n",
              "      <td>24</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>(7.18, 7.9]</td>\n",
              "      <td>11</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  Check_Outliers  SepalLengthCm\n",
              "0  (4.296, 5.02]             32\n",
              "1   (5.02, 5.74]             41\n",
              "2   (5.74, 6.46]             42\n",
              "3   (6.46, 7.18]             24\n",
              "4    (7.18, 7.9]             11"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vcQ5DTVusUyM",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "dee41334-7fff-42a2-c44d-44363a594b3a"
      },
      "source": [
        "from sklearn.preprocessing import MinMaxScaler\n",
        "\n",
        "\n",
        "scaler = MinMaxScaler()\n",
        "scaler = scaler.fit(data_set[['SepalLengthCm']])\n",
        "scaled_SPL = scaler.transform(data_set[['SepalLengthCm']])\n",
        "data_set['Scaled SP Length']=scaled_SPL\n",
        "data_set.head()"
      ],
      "execution_count": 36,
      "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>Encoded-Species</th>\n",
              "      <th>Check_Outliers</th>\n",
              "      <th>Scaled SP Length</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>0</td>\n",
              "      <td>(5.02, 5.74]</td>\n",
              "      <td>0.222222</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>0</td>\n",
              "      <td>(4.296, 5.02]</td>\n",
              "      <td>0.166667</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>0</td>\n",
              "      <td>(4.296, 5.02]</td>\n",
              "      <td>0.111111</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>0</td>\n",
              "      <td>(4.296, 5.02]</td>\n",
              "      <td>0.083333</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>0</td>\n",
              "      <td>(4.296, 5.02]</td>\n",
              "      <td>0.194444</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   SepalLengthCm  SepalWidthCm  ...  Check_Outliers  Scaled SP Length\n",
              "0            5.1           3.5  ...    (5.02, 5.74]          0.222222\n",
              "1            4.9           3.0  ...   (4.296, 5.02]          0.166667\n",
              "2            4.7           3.2  ...   (4.296, 5.02]          0.111111\n",
              "3            4.6           3.1  ...   (4.296, 5.02]          0.083333\n",
              "4            5.0           3.6  ...   (4.296, 5.02]          0.194444\n",
              "\n",
              "[5 rows x 8 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "G_COt2ExSolM",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "fe096afe-4177-4503-8f08-50c94f51563a"
      },
      "source": [
        "data_set.drop(labels='Check_Outliers',axis=1,inplace=True)\n",
        "data_set['Check_Outliers']=pd.cut(data_set['Scaled SP Length'],5)\n",
        "data_set[['Check_Outliers','Scaled SP Length']].groupby('Check_Outliers',as_index=False).count()"
      ],
      "execution_count": 37,
      "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>Check_Outliers</th>\n",
              "      <th>Scaled SP Length</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>(-0.001, 0.2]</td>\n",
              "      <td>32</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>(0.2, 0.4]</td>\n",
              "      <td>41</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>(0.4, 0.6]</td>\n",
              "      <td>42</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>(0.6, 0.8]</td>\n",
              "      <td>24</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>(0.8, 1.0]</td>\n",
              "      <td>11</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  Check_Outliers  Scaled SP Length\n",
              "0  (-0.001, 0.2]                32\n",
              "1     (0.2, 0.4]                41\n",
              "2     (0.4, 0.6]                42\n",
              "3     (0.6, 0.8]                24\n",
              "4     (0.8, 1.0]                11"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "odafKSx_S085",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        },
        "outputId": "33897e70-44d3-4119-e64a-d3a76f353ba3"
      },
      "source": [
        "lower_bound=0.1\n",
        "upper_bound=0.95\n",
        "\n",
        "limit_to_bounds=data_set['SepalLengthCm'].quantile([lower_bound,upper_bound])\n",
        "limit_to_bounds"
      ],
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.10    4.800\n",
              "0.95    7.255\n",
              "Name: SepalLengthCm, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4_FHsVOYS9Q1",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        },
        "outputId": "b8546c0a-a7fd-4696-b3bd-b6ab2877489e"
      },
      "source": [
        "considerable_data=(data_set['SepalLengthCm']<limit_to_bounds.loc[upper_bound])\n",
        "considerable_data.value_counts()"
      ],
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True     142\n",
              "False      8\n",
              "Name: SepalLengthCm, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xmk-SW6nTEQF",
        "colab_type": "text"
      },
      "source": [
        "###**Final Data set without Outliers**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Y0RR9jaJTRO8",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "f29e1f39-5d36-4a45-a09b-a1ba108f306d"
      },
      "source": [
        "final_data=data_set[considerable_data].copy()\n",
        "final_data.shape"
      ],
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(142, 8)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wjjeharZzKWf",
        "colab_type": "text"
      },
      "source": [
        "###**Checking Count of the Present Species**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "n_sDcuABzJrg",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 85
        },
        "outputId": "b2e9c289-00b2-4bf8-ea3d-e431456b5251"
      },
      "source": [
        "final_data['Encoded-Species'].value_counts()"
      ],
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1    50\n",
              "0    50\n",
              "2    42\n",
              "Name: Encoded-Species, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IjmlOFOb22Jm",
        "colab_type": "text"
      },
      "source": [
        "###**Data Distribution Species Wise**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yYvZmsjX3A15",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 764
        },
        "outputId": "20773fb5-9462-4980-b297-250389752757"
      },
      "source": [
        "sns.pairplot(final_data.iloc[:,[0,1,2,3,4]],hue='Species')"
      ],
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.PairGrid at 0x7f5a8d907e80>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 42
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 815.25x720 with 20 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Um_d1vUt53hc",
        "colab_type": "text"
      },
      "source": [
        "### **Finding Value of Optimal K Using K-Elbow Visualizer**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "05BR7qbp6Hdm",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 435
        },
        "outputId": "3548045b-0c85-4c90-f5e9-fda11c7138e0"
      },
      "source": [
        "from yellowbrick.cluster import KElbowVisualizer \n",
        "from sklearn.cluster import KMeans\n",
        "model1 = KMeans() \n",
        "visualizer = KElbowVisualizer(model1, k=(1,10)) \n",
        "visualizer.fit(final_data.iloc[:,[0,1,2,3]])"
      ],
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/base.py:197: FutureWarning: From version 0.24, get_params will raise an AttributeError if a parameter cannot be retrieved as an instance attribute. Previously it would return None.\n",
            "  FutureWarning)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "KElbowVisualizer(ax=<matplotlib.axes._subplots.AxesSubplot object at 0x7f5a8d24cc18>,\n",
              "                 k=None, metric=None, model=None, timings=True)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 43
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x396 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_c31DkLx6_7j",
        "colab_type": "text"
      },
      "source": [
        "### **Visualizing Silhoutte Co-efficient using K=3**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Lyt7M_OP686_",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 435
        },
        "outputId": "260d343b-15a4-438b-d906-9a90d6cef25c"
      },
      "source": [
        "from yellowbrick.cluster import SilhouetteVisualizer\n",
        "model2 = KMeans(3)\n",
        "visualizer = SilhouetteVisualizer(model2)\n",
        "\n",
        "visualizer.fit(final_data.iloc[:,[0,1,2,3]])\n"
      ],
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/base.py:197: FutureWarning: From version 0.24, get_params will raise an AttributeError if a parameter cannot be retrieved as an instance attribute. Previously it would return None.\n",
            "  FutureWarning)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "SilhouetteVisualizer(ax=<matplotlib.axes._subplots.AxesSubplot object at 0x7f5a8d1740b8>,\n",
              "                     model=None)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 44
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x396 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "v8HHmMee7bi9",
        "colab_type": "text"
      },
      "source": [
        "### **Fixing n_clusters=3 and fitting the data to KMeans Model**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MAQPRArqTuzT",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.cluster import KMeans\n",
        "x = final_data.iloc[:, [0, 1, 2, 3]].values\n",
        "kmeans = KMeans(n_clusters = 3, init = 'k-means++')\n",
        "y_kmeans = kmeans.fit_predict(x)"
      ],
      "execution_count": 45,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YBK-o-TC7uZV",
        "colab_type": "text"
      },
      "source": [
        "###**Visualizing Clusters Based on First two Columns**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9IdvHH-lUSs8",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 366
        },
        "outputId": "7930886c-e6e0-4fe3-97f6-c8b83cb7812d"
      },
      "source": [
        "\n",
        "plt.scatter(x[y_kmeans == 0, 0], x[y_kmeans == 0, 1], \n",
        "            s = 90, c = 'brown', label = 'Iris-setosa')\n",
        "plt.scatter(x[y_kmeans == 1, 0], x[y_kmeans == 1, 1], \n",
        "            s = 90, c = 'grey', label = 'Iris-versicolour')\n",
        "plt.scatter(x[y_kmeans == 2, 0], x[y_kmeans == 2, 1],\n",
        "            s = 90, c = 'green', label = 'Iris-virginica')\n",
        "\n",
        "# Plotting the centroids of the clusters\n",
        "plt.scatter(kmeans.cluster_centers_[:, 0], kmeans.cluster_centers_[:,1], \n",
        "            s = 150, c = 'black', label = 'Centroids',marker='*')\n",
        "\n",
        "\n",
        "plt.legend()"
      ],
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7f5a8d1a8240>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 46
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x396 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DflqEzHaCj9V",
        "colab_type": "text"
      },
      "source": [
        "###**Evaluating our Model Permformance**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IH0HYUYhCim5",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 391
        },
        "outputId": "c9797ebb-09f9-4f11-8419-67034ac11d64"
      },
      "source": [
        "from sklearn.metrics import accuracy_score,mean_absolute_error,confusion_matrix,classification_report\n",
        "\n",
        "print('Confusion Matrix: \\n')\n",
        "print(confusion_matrix(y_kmeans,final_data.iloc[:,[5]]))\n",
        "print('\\nMean Absolute Error: ',mean_absolute_error(y_kmeans,final_data.iloc[:,[5]]))\n",
        "print('\\nAccuracy Score :',accuracy_score(y_kmeans,final_data.iloc[:,[5]]))\n",
        "print('\\nClassification Report:\\n')\n",
        "print(classification_report(y_kmeans,final_data.iloc[:,[5]]))"
      ],
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Confusion Matrix: \n",
            "\n",
            "[[ 0 36  1]\n",
            " [50  0  0]\n",
            " [ 0 14 41]]\n",
            "\n",
            "Mean Absolute Error:  0.7183098591549296\n",
            "\n",
            "Accuracy Score : 0.2887323943661972\n",
            "\n",
            "Classification Report:\n",
            "\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.00      0.00      0.00        37\n",
            "           1       0.00      0.00      0.00        50\n",
            "           2       0.98      0.75      0.85        55\n",
            "\n",
            "    accuracy                           0.29       142\n",
            "   macro avg       0.33      0.25      0.28       142\n",
            "weighted avg       0.38      0.29      0.33       142\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}