EricPostMaster · January 11, 2020 03:05
diff --git a/iris_analysis.ipynb b/iris_analysis.ipynb
 {
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Iris_Analysis.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyNOyER6Og9OYXGOUz/siZwT",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/gist/EricPostMaster/6211f55d6dbc740485986dcae2af7eba/iris_analysis.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "33wwiRQCBRwI",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gspHDhIeBp83",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 122
        },
        "outputId": "8b3ae98f-af1a-4379-a4bd-018b64a96d78"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Go to this URL in a browser: https://accounts.google.com/o/oauth2/auth?client_id=947318989803-6bn6qk8qdgf4n4g3pfee6491hc0brc4i.apps.googleusercontent.com&redirect_uri=urn%3aietf%3awg%3aoauth%3a2.0%3aoob&response_type=code&scope=email%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdocs.test%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive.photos.readonly%20https%3a%2f%2fwww.googleapis.com%2fauth%2fpeopleapi.readonly\n",
            "\n",
            "Enter your authorization code:\n",
            "··········\n",
            "Mounted at /content/drive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "a2toKXbMBsGl",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Import supporting libraries\n",
        "\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "sns.set(color_codes = True)\n",
        "%matplotlib inline"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ma9VK8llCzjQ",
        "colab_type": "text"
      },
      "source": [
        "## Import the Dataset as the variable \"dataset\""
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4Ce8VAd1CfmP",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "821dd052-effa-4604-d705-e95622f77d73"
      },
      "source": [
        "dataset = pd.read_csv(\"/content/drive/My Drive/Iris_Data_Fisher_1936.csv\")\n",
        "\n",
        "# Use the head method to view the first 5 lines of the dataframe\n",
        "dataset.head()"
      ],
      "execution_count": 6,
      "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>sepal_length</th>\n",
              "      <th>sepal_width</th>\n",
              "      <th>petal_length</th>\n",
              "      <th>petal_width</th>\n",
              "      <th>species</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>5.1</td>\n",
              "      <td>3.5</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "      <td>setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>4.9</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "      <td>setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>4.7</td>\n",
              "      <td>3.2</td>\n",
              "      <td>1.3</td>\n",
              "      <td>0.2</td>\n",
              "      <td>setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4.6</td>\n",
              "      <td>3.1</td>\n",
              "      <td>1.5</td>\n",
              "      <td>0.2</td>\n",
              "      <td>setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5.0</td>\n",
              "      <td>3.6</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "      <td>setosa</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   sepal_length  sepal_width  petal_length  petal_width species\n",
              "0           5.1          3.5           1.4          0.2  setosa\n",
              "1           4.9          3.0           1.4          0.2  setosa\n",
              "2           4.7          3.2           1.3          0.2  setosa\n",
              "3           4.6          3.1           1.5          0.2  setosa\n",
              "4           5.0          3.6           1.4          0.2  setosa"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lQaZH--jFPh5",
        "colab_type": "text"
      },
      "source": [
        "## Summarizing the Dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MN3A64JKCvcv",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "a5f270a9-53c7-4ff9-850b-2545e2593c85"
      },
      "source": [
        "#What is the shape of the dataset?\n",
        "print(dataset.shape)"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(150, 5)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "N5yczfC4DTHd",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "2845aa67-0120-4ccb-920b-c51102de0207"
      },
      "source": [
        "#How many total items in the dataframe\n",
        "dataset.size"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "750"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BV1pfcrLEcqA",
        "colab_type": "text"
      },
      "source": [
        "There are 150 rows, 750 items, which means there are 5 columns.  We can see from the header that that is the case.  Everything checks out!"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_hSpAUq6FCF5",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Other potentially useful information"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lRFUJqFLFRpD",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 187
        },
        "outputId": "2de1ee23-161f-4e93-d3ef-8b0a243721df"
      },
      "source": [
        "dataset.info()"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 150 entries, 0 to 149\n",
            "Data columns (total 5 columns):\n",
            "sepal_length    150 non-null float64\n",
            "sepal_width     150 non-null float64\n",
            "petal_length    150 non-null float64\n",
            "petal_width     150 non-null float64\n",
            "species         150 non-null object\n",
            "dtypes: float64(4), object(1)\n",
            "memory usage: 6.0+ KB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OumBo7mmFpm9",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "outputId": "2d609487-34a7-4fc3-b2f8-85843ad1501d"
      },
      "source": [
        "dataset.describe()"
      ],
      "execution_count": 24,
      "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>sepal_length</th>\n",
              "      <th>sepal_width</th>\n",
              "      <th>petal_length</th>\n",
              "      <th>petal_width</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": [
              "       sepal_length  sepal_width  petal_length  petal_width\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": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "r-JCE31lF7j_",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 102
        },
        "outputId": "69f8c0d5-5bd7-4c2a-ea48-d5d9840ed5ce"
      },
      "source": [
        "dataset.groupby(\"species\").size()"
      ],
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "species\n",
              "setosa        50\n",
              "versicolor    50\n",
              "virginica     50\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bAf5UgAbJ9V-",
        "colab_type": "text"
      },
      "source": [
        "## Visualizations"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZqJG-zWWJDJs",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 286
        },
        "outputId": "6ddd92f3-3934-46f4-861a-68e09d8ce115"
      },
      "source": [
        "# Box and whisker plots\n",
        "dataset.plot(kind='box', sharex=False, sharey=False)"
      ],
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f8fcb9ed128>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 54
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qKKHrs-7KWD0",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 369
        },
        "outputId": "9f9f91b2-02fe-44d8-ca68-74432818ef22"
      },
      "source": [
        "# Histograms\n",
        "dataset.hist(bins = 20, figsize = (10,5), color=\"#8d608c\")\n",
        "plt.tight_layout()"
      ],
      "execution_count": 72,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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P/hHwpXnzPwW8NiLui4izasYkATAxO+sVCQ2WyhBDKzJzR8PhtKW8C/vHwKph\ni12SYLjrYKlb7IMsdSgiXg38PcXNKn8O/K1fKpIkDS8TZI2siLiB4k7t+Y7LzEu7uKnjgAsp7gC/\nhg7GZJakUdHHOljqOrtYSJIkSRVNtiCvoniyzZ0ULW+SNMqWA48Dvs+/3MTZBOteSeOkVt3bZIL8\nTIrHQkrSODmcYszXplj3ShpHbdW9TSbIdwLcd9+vmJnpbzePycnVTE9v7us222F8nTG+zhhfZxaL\nb9myCfbccw8o674G1a57B33fd5vlHW2Wd7TNlbdu3dtkgrwTYGZmtu8J8tx2B5nxdcb4OmN8nVki\nvqa7NXRU9w76vu82yzvaLO9om1fetupeHxQiSZIkVZggS5IkSRUmyJIkSVKFDwrpsXVrd2flqhVt\nrzc1tYZtW7fzwINbehCV6qp7PD2WkgbNQvXZ1NSaJdezPtM4MEHusZWrVvDJN59Ra92TLngPYCU0\nSOoeT4+lpEFjfSYtzi4WkiRJUoUJsiRJklRhgixJkiRVtNQHOSJOB14DHAAckpk/LqffTNERaa4z\n0smZ+bWuRylJkiT1Sas36V0BfAq4doF5r51LmCVJkqRh11KCnJkbACKit9FIkiRJDevGMG+XRsQE\nsAF4f2be387Kk5OruxBC+1oZ63EQDGqcgxrXnEGMrxrTIMZXZXydGfT4JEm71mmCfHhmboyIVcCZ\nwNnAUe28wfT0ZmZmZjsMoz1TU2vYtOmhvm2rE/2Ksx393H919DK+To7nXEzjvP+6YVjjW7ZsorEG\nAUlSezoaxSIzN5b/bwU+Azy7G0FJkiRJTamdIEfEHhGxrvx7AngDcH23ApMkSZKa0Oowb2cBfwD8\nBnBVREwDRwBfjIjlwHLgJ8A7exWoJEmS1A+tjmJxInDiArOe1t1wJEmSpGb5JD1JkiSpohvDvEms\nW7s7K1etaHu9bVu388CDW5ZeUJIkqU9MkNUVK1et4JNvPqPt9U664D38y5PKJUmSmmcXC0mSJKnC\nBFmSJEmqMEGWJEmSKkyQJUmSpAoTZEmSJKnCBFmSJEmqcJg3PUyr4xlPTa3pQzSSJEn9Z4Ksh+ls\nPGNJkqThZ4IsSQMoIiaBi4EnAtuAG4HjMnNTRBwGnAc8ArgZOCoz724qVkkaNfZBlqTBNAuclpmR\nmYcAvwBOjYhlwCXAuzJzPfBN4NQG45SkkWOCLEkDKDPvzcyrK5O+C+wPPB3YkpkbyunnAq/rc3iS\nNNLsYiFJA65sNX4HcCWwH3DL3LzMvCcilkXEozPz3lbfc3Jyda1Yxu0G3XErb6tGZb+MSjlaZXlb\nZ4IsSYPv08Bm4Gzg1d14w7IOD0QAAA74SURBVOnpzczMzLa1ztTUGjZteqgbmx8Ko17eTpKHUdgv\no3585xvX8i5bNlGrQcAuFpI0wCLidOAg4PWZOQPcStHVYm7+Y4CZdlqPJUm7ZoIsSQMqIj5O0ef4\nVZm5tZz8Q+AREfGc8vXxwOVNxCdJo8ouFpI0gCLiScD7gJ8B344IgJsy89URcTRwXkTsTjnMW2OB\nStIIMkGWpAGUmTcAE4vM+zZwSH8jkjrT6pNaF7Jt63YeeHBLlyOSFmeCLEmSeq7uk1ph7mmtJsjq\nH/sgS5IkSRUmyJIkSVKFCbIkSZJUYR9kaYB5U4skSf1ngiwNMG9qkSSp/+xiIUmSJFWYIEuSJEkV\nJsiSJElSxZJ9kCPidOA1wAHAIZn543L6euAiYBKYBo7JzBt7F6okSZLUe620IF8BPBe4Zd70c4Fz\nMnM9cA5wXpdjkyRJkvpuyQQ5Mzdk5sbqtIjYCzgUuKycdBlwaERMdT9ESZIkqX/qDvO2L3B7Zu4E\nyMydEXFHOX1TO280Obm6ZgidmZpa08h22zUscda1Y/uO2mXcsX0Hu61Y+BTe1Xvuar1eqsbUr+Na\ndzuDft4ZnySplxofB3l6ejMzM7N93ebU1Bo2bXqob9vqRL/inNPvL/bdVuzW0Ti/C+2fpY7v1NSa\nDscWrmcupnbOvybOn35+PuoY1viWLZtorEFAktSeuqNYbAT2iYjlAOX/e5fTJUmSpKFVK0HOzLuB\n64Ejy0lHAtdlZlvdKyRJkqRBs2SCHBFnRcRtwOOBqyLihnLW8cAJEfEz4ITytSRJkjTUluyDnJkn\nAicuMP2nwLN6EZQkSZLUFJ+kJ0mSJFWYIEuSJEkVjQ/zpsXVHSN429btPPDglh5EJEmSNPpMkAdY\n3TGCi7F6TZAlSZLqsIuFJEmSVGELsiRJXbBu7e6sXLWi1rrbt21nxcr217VLndQbJsiSJHXBylUr\nOnqMvV3qpMFhFwtJkiSpwgRZkiRJqjBBliRJkipMkCVJkqQKE2RJkiSpwgRZkiRJqjBBliRJkioc\nB1mSJLVsx/YdTE2tGYpt+iAV1WWCLEmSWrbbit06eKhJE9s0QVb77GIhSZIkVdiC3KJ1a3dn5aoV\nTYehITX/8mC/L09KkqTWmSC3aOWqFX2/pKTR0cQlSUmSVI9dLCRJkqQKW5AlaUBFxOnAa4ADgEMy\n88fl9PXARcAkMA0ck5k3NhWnJI0aW5AlaXBdATwXuGXe9HOBczJzPXAOcF6/A5OkUWaCLEkDKjM3\nZObG6rSI2As4FLisnHQZcGhETPU7PkkaVXaxkKThsi9we2buBMjMnRFxRzl9U6tvMjm5utbGx20E\nlkEvbxMP7Rg2u9o/47bvLG/rTJA1tHb1xTBulYDUrunpzczMzLa1ztTUGjZteqhHEQ2edsvbRL1T\nd4QcGJ9RchY7hp7Po22uvMuWTdRqEDBB1tBy6DSNqY3APhGxvGw9Xg7sXU6XJHWBfZAlaYhk5t3A\n9cCR5aQjgesys+XuFZKkXTNBlqQBFRFnRcRtwOOBqyLihnLW8cAJEfEz4ITytSSpS+xiIUkDKjNP\nBE5cYPpPgWf1PyJJS1m3dndWrlrR9nrbtm7ngQe39CAi1dFxghwRNwNbyn8AJ2fm1zp9X0mSpGGz\nctWKDu6PMUEeFN1qQX7t3BOeJEmSpGFmH2RJkiSpolstyJdGxASwAXh/Zt7f6op1B6uva8f2HcBo\nj5PrwPGaU/c8GPTzZy6+Hdt3sNuKetVYJ+suZdD3nyRp17rx7XB4Zm6MiFXAmcDZwFGtrlxnsPpO\nTE2tGfmxcx04XnPqDAo/6IPJV+Or+3mG4lzvRTkX2391B6tX/1VvsvLHjjSeOk6QM3Nj+f/WiPgM\ncGXHUUmS1JDObrKSNAo66oMcEXtExLry7wngDRQD2EuSJElDqdMW5McCXywfdboc+Anwzo6jkiRJ\nkhrSUYKcmf8EPK1LsUiSJEmNc5g3SZIkqcIEWZIkSarozSCgPVb3OefSOKk7HvbcWOF11P1sbtu6\nnQce9BGrkqTBMJQJct0heMBheDQ+6o6H3clnpLPhsUyQJUmDYSgTZEmSpKUsdSVtV/O8sjXeTJAl\nSdJI6vzJsibI48qb9CRJkqQKE2RJkiSpwgRZkiRJqjBBliRJkipMkCVJkqQKE2RJkiSpwgRZkiRJ\nqjBBliRJkip8UIgkaWCtW7s7K1etqLWuT0KTVJcJsiRpYK1ctcInoUnqO7tYSJIkSRW2IEt6mB3b\ndzA1tabpMCRJaowJsqSH2W3Fbh1e0pb+tU76Etfljz11wvNnvJkgS5J6rm5f4k5+dNX9secPPYHn\nz7izD7IkSZJUYYIsSZIkVZggS5IkSRX2QZYkSWpYJzcF+lCc7jNBliRJaljnIwiZIHeTCbKkxrXb\nctKNoZfqttbYUiNJo88EWVLjmhhOqbNtmiBL0ijzJj1JkiSpwhZkSVJLfLKYNJha/WzOX2b7tu2s\nWFnvCZd11627Xr+7t3WcIEfEeuAiYBKYBo7JzBs7fV9J0uKaqHt9DLk0mDrpMtbJZ7qf2+x397Zu\ndLE4FzgnM9cD5wDndeE9JUm7Zt0rST3SUQtyROwFHAq8qJx0GXB2RExl5qYlVl8OsGzZRK1tr51c\nW2u9Ttbt93rjss1hirWJbQ5TrE1ss4lYl6q3Fppfmba81kYrrHsHd5vDFGsT2xymWJvY5jDF2sQ2\n2623li2bqF33TszOzra1saqIeDrw+cx8UmXaT4CjMvNHS6z+HODa2huXpOF0OLChkzew7pWktrVV\n9zZ5k973KYK9E9jZYByS1A/LgcdR1H1Nsu6VNE5q1b2dJsgbgX0iYnlm7oyI5cDe5fSlbKXDVhRJ\nGjK/6NL7WPdKUuvarns7ukkvM+8GrgeOLCcdCVzXQh84SVJN1r2S1Fsd9UEGiIjfohhqaE/gPoqh\nhrILsUmSFmHdK0m903GCLEmSJI0SHzUtSZIkVZggS5IkSRUmyJIkSVKFCbIkSZJUYYIsSZIkVTT5\nJL2+iIgPA6cAh2Tmj+fNuxB4IXBPOenyzPxYn+K6GdhS/gM4OTO/Nm+ZRwIXAE8HdgDvzcy/G6D4\nLqS5/bc78Bfl9rcA38nMt89bZjlwFvBSYBY4NTM/N0DxnQK8E7ijnPStzHxXH2I7ALiiMulRwNrM\nfPS85RrZf23EdwoN7L9y2y8H/jMwUf7708z80rxlGjv/OhERk8DFwBOBbcCNwHHzx1husn7qpjbK\neyEN1XfdFhFXAE8AZoDNwAmZef28ZYby/F1Ii+U9hYbqk15YIvcZic9uVS9yvZFOkCPiUOAw4JZd\nLHZqZp7dp5Dme+38AznPe4EHM/PAiDgIuDYiDszMzQMSHzS3/06jSDzXZ+ZsRDx2gWXeCBwIHARM\nAtdFxFWZefOAxAfw+cx8bx/i+bWy/E+dex0RZ7JwXdDI/msjPmhg/0XEBEVCdXhm/jgingx8KyKu\nyMyZyqJNnn+dmAVOy8yrASLiE8CpwB/PW67p+qlbWi0vNPt90U3HZuYDABHxSuB84NB5ywzr+buQ\nVsoLDdQnvdBC7jMqn12gd7neyHaxiIhVwDnAO5qOpQOvB84DyMwbgR8AL2s0ogEQEauBY4APZuYs\nQGb+coFFXw98NjNnytagK4A/HKD4GhcRKym+CM9fYHYj+6+N+Jo0A6wr/34UcOe85BgGYP/VkZn3\nziWLpe8C+y+w6EjUT22Ud2TMJYuldRTn83xDef4upMXyjoQWc5+R+OxCb3O9UW5B/ghwSWbeHBG7\nWu6kiDiO4jnd78vM/92X6AqXlq1RG4D3Z+b98+bvx8N/Ed0K7Nuv4Fg6Pmhm/z0RmAY+HBHPp7hk\n9oHM3DBvuab2X6vxAbwhIl4M3AV8ODO/04f4ql4B3J6ZP1pgXtPnH+w6Pmhg/5VXBF4H/PeI+BWw\nBvi9BRYdhP3XkYhYRvHFc+UCs4e+fPMtUV5o9vuiqyLic8CLKboIvXSBRUbq+LZQXmi+Pu6GVnKf\nUTq2Pcv1RrIFOSJ+G3gG8JklFv0T4MDMPAT4EvDVst9VPxyemU8BnknxgR20y3atxNfU/lsO/CZw\nXWY+AzgZ+FJErO3DtlvRanznAk/IzCcDn6BIuCb7GypvYfBaZ6t2FV8j+y8idgPeB7wyM/cHjgD+\nurxyMGo+TfEDb9Dqp17ZVXmb/L7ousx8a2buB7yf4vMz0loo7yDUxx1pI/cZCb3O9UYyQQaeBxwM\n3FTebPZ44GvlL8Nfy8zb5y6LZubngdXlsj2XmRvL/7dSHNxnL7DYrTz8Ut9+wMbeR9dafA3uv1sp\nbiy4rNz29yg6369fYLkm9l9L8WXmXZm5vfz7f5Sx/ds+xAdAROxD8Vm5dJFFGjv/YOn4Gtx/TwX2\nzsxvldv+FvArijqnqtH916mIOJ2i/+nrF+g+AkNevvmWKm+T3xe9lJkXA89fIBkcqeM7Z7HyNl0f\nd0lLuQ+jc2x7muuNZIKcmadm5t6ZeUBmHgDcBrwkM79eXa78Ap77+yXATuD2XscXEXtExLry7wng\nDcD1Cyx6OXBcudxBFK25Xx2U+Jraf5l5D/AN4EXlttcDewE/n7fo5cDbImJZREwBrwK+MCjxzdt/\nTwUOALLX8VUcC3w5M6cXmd/I/qvYZXwN7r/bgMdHeT0vIg4GHktx6a6q6f1XW0R8nOIO91eVP5IX\n0kj91AutlLep+q7bImJ1ROxbeX0EcG/5r2poz9+qVss7APVxx1rNfRiRz26vc71R7oO8oIi4Hvi9\nzLwDuKgcXWAGeBB4RWbu6EMYjwW+WDbxLwd+QjG8zPz4PgFcGBE/pzigb8/MhwYovqb2H8DxwPkR\ncQawHTg6M++PiL8HPpSZP6AYaeBZFMM2AXwkM28aoPg+HhFPpzi228pl7upTfABvAk6sThig/ddK\nfI3sv8y8KyLeAXwhIuZaGt+SmfcO2P6rJSKeRNGF5GfAt8vfATdl5qsHpH7qqjbK22R91017AJdH\nxB4Ux+1e4Iiyb/3Qn78LaLW8TdfHPTWKn91d6cZnd2J2drbHYUqSJEnDYyS7WEiSJEl1mSBLkiRJ\nFSbIkiRJUoUJsiRJklRhgixJkiRVmCBLkiRJFSbIkiRJUsX/BaeTczhFoHuGAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 720x360 with 4 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qdJWoX3ELC_u",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 286
        },
        "outputId": "a8050b8c-ca76-4b4e-9a36-6b6c384d49c3"
      },
      "source": [
        "# Boxplot for each of the four features, grouped by species\n",
        "dataset.plot(kind=\"box\", by=\"species\")"
      ],
      "execution_count": 73,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f8fc8224eb8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 73
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BdfY_8cpM3Jl",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 750
        },
        "outputId": "a0dfc0fc-e2e4-4b88-d285-7c0e21f8b13a"
      },
      "source": [
        "# boxplot by species\n",
        "dataset.boxplot(by=\"species\", figsize=(10,10))"
      ],
      "execution_count": 88,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7f8fc6d74dd8>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f8fc6d9d438>],\n",
              "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7f8fc6d18ac8>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f8fc6d5a198>]],\n",
              "      dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 88
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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HL+TMM9e7sF6SNKGBgdXs2HF7R/usXLmK4eHtNVXUTCVuYL140eyLOX1jY7We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\nFREREdGQNLwiIiIiGpKGV0RERERD/gfKPx8VbWnm2wAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 720x720 with 4 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GpljxexSNDoo",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 302
        },
        "outputId": "2af03a12-4a4a-4470-9b74-5a4a630853c1"
      },
      "source": [
        "# Violinplot for petal length\n",
        "sns.violinplot(data=dataset, x=\"species\", y=\"petal_length\")"
      ],
      "execution_count": 89,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f8fc6acd470>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 89
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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Gvg7cFO/B29v7iMXGr4MuRCrl55cQjsboCcZoG4xRPWcera1StkrMHE6nY9wG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\nAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OKh7jUJZPh5Y",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 742
        },
        "outputId": "20b710fc-655e-4245-9f78-0ff03f6f2ab1"
      },
      "source": [
        "#Scatter matrix using Seaborn\n",
        "sns.pairplot(dataset, hue=\"species\", diag_kind=\"hist\")  #Diagonal graphs in this plot are histograms."
      ],
      "execution_count": 108,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.PairGrid at 0x7f8fbeca36a0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 108
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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hKQRnHk1s3x4ajj4RRVWZykCmb6qagqVFsZXU4Ghadib3Q1NSOSPP79sN4Jlb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DQNsWx+slwdZfP4+TW5O6wr866VjXDJSRc0lSMlr3YH5X243/VC0o0ytuFK1my4nKe+6+9Ys+Fy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\nAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 813.85x720 with 20 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fXMazOvOQ6zD",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 742
        },
        "outputId": "2b3bf83a-556c-4954-8f74-fce92028a4b9"
      },
      "source": [
        "sns.pairplot(dataset, hue=\"species\", diag_kind=\"kde\")  #Diagonal graphs in this plot are kernel density estimates."
      ],
      "execution_count": 109,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.PairGrid at 0x7f8fbe842908>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 109
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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p8rzp8uxDkY6XSJIpyiMXXdef0XX9y7qun6Pr+pcSi5GJRswwaesKUV3hGvJn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\nK7b5D9jqF3PIV9dmVdmKmZl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Sc4ikk8sOB5pfs8/Xy+kxwvzHCz9Lsdsax/BjXBPfO0A7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lhv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3dMuKzgmscQ3kaU9n0nXFYuQmaYR7pXcs+Wp+j0NHPWHguwQmNPQ4JNHfg69qH27Rdx9G0r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\nAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 813.85x720 with 20 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mIkfgbwoizTi",
        "colab_type": "text"
      },
      "source": [
        "## Predictive Models"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YfgYf3JlRdjL",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Importing metrics for evaluation\n",
        "from sklearn.metrics import confusion_matrix\n",
        "from sklearn.metrics import classification_report"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bpyQpK26bfVm",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Separating the data into dependent and independent variables\n",
        "X = dataset.iloc[:, :-1].values\n",
        "y = dataset.iloc[:, -1].values\n",
        "\n",
        "#Splitting the dataset into the Training set and the Test set\n",
        "from sklearn.model_selection import train_test_split\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.4, random_state = 0)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LuU07PZBi_Q2",
        "colab_type": "text"
      },
      "source": [
        "###Logistic Regression"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GOWPrND9cUUZ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 255
        },
        "outputId": "a8b33c68-733b-4979-f276-777a0ac6d081"
      },
      "source": [
        "#Logistic Regression\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "classifier = LogisticRegression()\n",
        "classifier.fit(X_train, y_train)\n",
        "\n",
        "y_pred = classifier.predict(X_test)\n",
        "\n",
        "#Summary of the predictions made by the classifier\n",
        "print(classification_report(y_test, y_pred))\n",
        "print(confusion_matrix(y_test, y_pred))\n",
        "#Accuracy score\n",
        "from sklearn.metrics import accuracy_score\n",
        "print(\"accuracy is\", accuracy_score(y_pred,y_test))"
      ],
      "execution_count": 125,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "      setosa       1.00      1.00      1.00        16\n",
            "  versicolor       0.85      0.96      0.90        23\n",
            "   virginica       0.94      0.81      0.87        21\n",
            "\n",
            "    accuracy                           0.92        60\n",
            "   macro avg       0.93      0.92      0.92        60\n",
            "weighted avg       0.92      0.92      0.92        60\n",
            "\n",
            "[[16  0  0]\n",
            " [ 0 22  1]\n",
            " [ 0  4 17]]\n",
            "accuracy is 0.9166666666666666\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9DurfukyjMBP",
        "colab_type": "text"
      },
      "source": [
        "### Naive Bayes"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Hn_wQnaEdlCo",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 255
        },
        "outputId": "4a007842-9f8f-4cd4-e43a-c1be060f8230"
      },
      "source": [
        "# Naive Bayes\n",
        "from sklearn.naive_bayes import GaussianNB\n",
        "classifier = GaussianNB()\n",
        "classifier.fit(X_train, y_train)\n",
        "\n",
        "y_pred = classifier.predict(X_test)\n",
        "\n",
        "#Summary of the predictions made by the classifier\n",
        "print(classification_report(y_test, y_pred))\n",
        "print(confusion_matrix(y_test, y_pred))\n",
        "\n",
        "#Accuracy Score\n",
        "from sklearn.metrics import accuracy_score\n",
        "print(\"Accuracy is:\", accuracy_score(y_pred,y_test))"
      ],
      "execution_count": 126,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "      setosa       1.00      1.00      1.00        16\n",
            "  versicolor       0.85      1.00      0.92        23\n",
            "   virginica       1.00      0.81      0.89        21\n",
            "\n",
            "    accuracy                           0.93        60\n",
            "   macro avg       0.95      0.94      0.94        60\n",
            "weighted avg       0.94      0.93      0.93        60\n",
            "\n",
            "[[16  0  0]\n",
            " [ 0 23  0]\n",
            " [ 0  4 17]]\n",
            "Accuracy is: 0.9333333333333333\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_I4RBBJojO0e",
        "colab_type": "text"
      },
      "source": [
        "### Support Vector Machine"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sIRfo876gda6",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 255
        },
        "outputId": "89bd1036-c538-4c54-dc9c-dca4c13677b1"
      },
      "source": [
        "# Support Vector Machine\n",
        "from sklearn.svm import SVC\n",
        "\n",
        "classifier = SVC()\n",
        "classifier.fit(X_train, y_train)\n",
        "\n",
        "y_pred = classifier.predict(X_test)\n",
        "\n",
        "#Summary of the predictions made by the classifier\n",
        "print(classification_report(y_test, y_pred))\n",
        "print(confusion_matrix(y_test, y_pred))\n",
        "\n",
        "#Accuracy Score\n",
        "from sklearn.metrics import accuracy_score\n",
        "print(\"Accuracy is:\", accuracy_score(y_pred,y_test))"
      ],
      "execution_count": 127,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "      setosa       1.00      1.00      1.00        16\n",
            "  versicolor       0.88      0.96      0.92        23\n",
            "   virginica       0.95      0.86      0.90        21\n",
            "\n",
            "    accuracy                           0.93        60\n",
            "   macro avg       0.94      0.94      0.94        60\n",
            "weighted avg       0.94      0.93      0.93        60\n",
            "\n",
            "[[16  0  0]\n",
            " [ 0 22  1]\n",
            " [ 0  3 18]]\n",
            "Accuracy is: 0.9333333333333333\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ymwo42SQjR9h",
        "colab_type": "text"
      },
      "source": [
        "###K-Nearest Neighbors"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kXSi6szhh1l-",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 255
        },
        "outputId": "434c17ef-4126-4b07-e9fc-aae314f89750"
      },
      "source": [
        "# K-Nearest Neighbors\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "\n",
        "classifier = KNeighborsClassifier(n_neighbors=8)     #Need to learn what this number is...\n",
        "classifier.fit(X_train, y_train)\n",
        "\n",
        "y_pred = classifier.predict(X_test)\n",
        "\n",
        "#Summary of the predictions made by the classifier\n",
        "print(classification_report(y_test, y_pred))\n",
        "print(confusion_matrix(y_test, y_pred))\n",
        "\n",
        "#Accuracy Score\n",
        "from sklearn.metrics import accuracy_score\n",
        "print(\"Accuracy is:\", accuracy_score(y_pred,y_test))"
      ],
      "execution_count": 130,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "      setosa       1.00      1.00      1.00        16\n",
            "  versicolor       0.96      0.96      0.96        23\n",
            "   virginica       0.95      0.95      0.95        21\n",
            "\n",
            "    accuracy                           0.97        60\n",
            "   macro avg       0.97      0.97      0.97        60\n",
            "weighted avg       0.97      0.97      0.97        60\n",
            "\n",
            "[[16  0  0]\n",
            " [ 0 22  1]\n",
            " [ 0  1 20]]\n",
            "Accuracy is: 0.9666666666666667\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yuEnG1APjUtp",
        "colab_type": "text"
      },
      "source": [
        "###Decision Tree"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "o_W99AEKiDZM",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 255
        },
        "outputId": "3467f429-620b-4552-d432-646efdd7cf0d"
      },
      "source": [
        "# Decision Tree\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "\n",
        "classifier = DecisionTreeClassifier()\n",
        "classifier.fit(X_train, y_train)\n",
        "\n",
        "y_pred = classifier.predict(X_test)\n",
        "\n",
        "#Summary of the predictions made by the classifier\n",
        "print(classification_report(y_test, y_pred))\n",
        "print(confusion_matrix(y_test, y_pred))\n",
        "\n",
        "#Accuracy Score\n",
        "from sklearn.metrics import accuracy_score\n",
        "print(\"Accuracy is:\", accuracy_score(y_pred,y_test))"
      ],
      "execution_count": 131,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "      setosa       1.00      1.00      1.00        16\n",
            "  versicolor       0.92      0.96      0.94        23\n",
            "   virginica       0.95      0.90      0.93        21\n",
            "\n",
            "    accuracy                           0.95        60\n",
            "   macro avg       0.96      0.95      0.95        60\n",
            "weighted avg       0.95      0.95      0.95        60\n",
            "\n",
            "[[16  0  0]\n",
            " [ 0 22  1]\n",
            " [ 0  2 19]]\n",
            "Accuracy is: 0.95\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MpjkAf8Zjas9",
        "colab_type": "text"
      },
      "source": [
        "##Discussion"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qRRwr539id94",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CgntQsYDjiub",
        "colab_type": "text"
      },
      "source": [
        "##Lessons Learned"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2pyLaF4HjrSk",
        "colab_type": "text"
      },
      "source": [
        ""
      ]
    }
  ]
 }
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