Task-1.ipynb

{
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  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Task-1.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "ir",
      "display_name": "R"
    },
    "language_info": {
      "name": "R"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "R7WLEF5FYYA1"
      },
      "source": [
        "### Name: DEBARTHA PAUL"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LMyWmFPNYcm1"
      },
      "source": [
        "# VIP Data Science Task-1: Iris Flowers Classification ML Project"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "C6i3JwXJYgFz"
      },
      "source": [
        "### Description:"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WgO6sas4Yiof"
      },
      "source": [
        "The iris flowers dataset contains numeric attributes. We have the attributes as:\n",
        "\n",
        "1. sepal length in cm \n",
        "\n",
        "2. sepal width in cm \n",
        "\n",
        "3. petal length in cm \n",
        "\n",
        "4. petal width in cm \n",
        "\n",
        "5. class: \n",
        "\n",
        "  (a) -- Iris Setosa \n",
        "\n",
        "  (b) -- Iris Versicolour \n",
        "\n",
        "  (c) -- Iris Virginica\n",
        "\n",
        "We have to classify them into different categories using ML."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MM9Q2S8_YtbL"
      },
      "source": [
        "### Importing libraries and visualising the data"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2HEU1DqHYw41"
      },
      "source": [
        "We first load the libraries required for our work and then we read the dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 954
        },
        "id": "Ur9zSMFyYVyA",
        "outputId": "3e74001f-ab4c-4c71-c21c-960636d49e6d"
      },
      "source": [
        "library(class)#for the KNN function\n",
        "set.seed(200)#setting the seed\n",
        "df<-read.table('iris.data',\n",
        "\t\t\t\tsep=',',header=F)#reading the file\n",
        "head(df)#a sneak peek into the dataset\n",
        "names(df)<-c('sepal.len','sepal.wd','petal.len',\n",
        "\t\t\t'petal.wd','class')#setting the names of the columns\n",
        "dim(df)#dimension of the dataset\n",
        "summary(df)#a brief summary of the dataset\n",
        "classno<-ifelse(df$class=='Iris-setosa',1,\n",
        "\t\t\t\tifelse(df$class=='Iris-virginica',2,3))#factoring on the basis of class\n",
        "\n",
        "pairs(df[,-5],col=c('#943737','#599437','#375f94')[classno],\n",
        "\t\tmain='Pairs plot of Iris',pch=16,oma=c(3,5,5,12))#plots of the data by various attributes\n",
        "par(xpd=T)\n",
        "legend(\"bottomright\",fill=c('#943737','#599437','#375f94'),\n",
        "\t\tlegend=c('Iris-setosa','Iris-virginica','Iris-versicolor'),\n",
        "\t\tcex=0.5,title='Class')"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  V1  V2  V3  V4  V5         \n",
              "1 5.1 3.5 1.4 0.2 Iris-setosa\n",
              "2 4.9 3.0 1.4 0.2 Iris-setosa\n",
              "3 4.7 3.2 1.3 0.2 Iris-setosa\n",
              "4 4.6 3.1 1.5 0.2 Iris-setosa\n",
              "5 5.0 3.6 1.4 0.2 Iris-setosa\n",
              "6 5.4 3.9 1.7 0.4 Iris-setosa"
            ],
            "text/latex": "A data.frame: 6 × 5\n\\begin{tabular}{r|lllll}\n  & V1 & V2 & V3 & V4 & V5\\\\\n  & <dbl> & <dbl> & <dbl> & <dbl> & <chr>\\\\\n\\hline\n\t1 & 5.1 & 3.5 & 1.4 & 0.2 & Iris-setosa\\\\\n\t2 & 4.9 & 3.0 & 1.4 & 0.2 & Iris-setosa\\\\\n\t3 & 4.7 & 3.2 & 1.3 & 0.2 & Iris-setosa\\\\\n\t4 & 4.6 & 3.1 & 1.5 & 0.2 & Iris-setosa\\\\\n\t5 & 5.0 & 3.6 & 1.4 & 0.2 & Iris-setosa\\\\\n\t6 & 5.4 & 3.9 & 1.7 & 0.4 & Iris-setosa\\\\\n\\end{tabular}\n",
            "text/markdown": "\nA data.frame: 6 × 5\n\n| <!--/--> | V1 &lt;dbl&gt; | V2 &lt;dbl&gt; | V3 &lt;dbl&gt; | V4 &lt;dbl&gt; | V5 &lt;chr&gt; |\n|---|---|---|---|---|---|\n| 1 | 5.1 | 3.5 | 1.4 | 0.2 | Iris-setosa |\n| 2 | 4.9 | 3.0 | 1.4 | 0.2 | Iris-setosa |\n| 3 | 4.7 | 3.2 | 1.3 | 0.2 | Iris-setosa |\n| 4 | 4.6 | 3.1 | 1.5 | 0.2 | Iris-setosa |\n| 5 | 5.0 | 3.6 | 1.4 | 0.2 | Iris-setosa |\n| 6 | 5.4 | 3.9 | 1.7 | 0.4 | Iris-setosa |\n\n",
            "text/html": [
              "<table class=\"dataframe\">\n",
              "<caption>A data.frame: 6 × 5</caption>\n",
              "<thead>\n",
              "\t<tr><th></th><th scope=col>V1</th><th scope=col>V2</th><th scope=col>V3</th><th scope=col>V4</th><th scope=col>V5</th></tr>\n",
              "\t<tr><th></th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;chr&gt;</th></tr>\n",
              "</thead>\n",
              "<tbody>\n",
              "\t<tr><th scope=row>1</th><td>5.1</td><td>3.5</td><td>1.4</td><td>0.2</td><td>Iris-setosa</td></tr>\n",
              "\t<tr><th scope=row>2</th><td>4.9</td><td>3.0</td><td>1.4</td><td>0.2</td><td>Iris-setosa</td></tr>\n",
              "\t<tr><th scope=row>3</th><td>4.7</td><td>3.2</td><td>1.3</td><td>0.2</td><td>Iris-setosa</td></tr>\n",
              "\t<tr><th scope=row>4</th><td>4.6</td><td>3.1</td><td>1.5</td><td>0.2</td><td>Iris-setosa</td></tr>\n",
              "\t<tr><th scope=row>5</th><td>5.0</td><td>3.6</td><td>1.4</td><td>0.2</td><td>Iris-setosa</td></tr>\n",
              "\t<tr><th scope=row>6</th><td>5.4</td><td>3.9</td><td>1.7</td><td>0.4</td><td>Iris-setosa</td></tr>\n",
              "</tbody>\n",
              "</table>\n"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "[1] 150   5"
            ],
            "text/latex": "\\begin{enumerate*}\n\\item 150\n\\item 5\n\\end{enumerate*}\n",
            "text/markdown": "1. 150\n2. 5\n\n\n",
            "text/html": [
              "<style>\n",
              ".list-inline {list-style: none; margin:0; padding: 0}\n",
              ".list-inline>li {display: inline-block}\n",
              ".list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n",
              "</style>\n",
              "<ol class=list-inline><li>150</li><li>5</li></ol>\n"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "   sepal.len        sepal.wd       petal.len        petal.wd    \n",
              " Min.   :4.300   Min.   :2.000   Min.   :1.000   Min.   :0.100  \n",
              " 1st Qu.:5.100   1st Qu.:2.800   1st Qu.:1.600   1st Qu.:0.300  \n",
              " Median :5.800   Median :3.000   Median :4.350   Median :1.300  \n",
              " Mean   :5.843   Mean   :3.054   Mean   :3.759   Mean   :1.199  \n",
              " 3rd Qu.:6.400   3rd Qu.:3.300   3rd Qu.:5.100   3rd Qu.:1.800  \n",
              " Max.   :7.900   Max.   :4.400   Max.   :6.900   Max.   :2.500  \n",
              "    class          \n",
              " Length:150        \n",
              " Class :character  \n",
              " Mode  :character  \n",
              "                   \n",
              "                   \n",
              "                   "
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
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PHjGxsbz5w5s379elb/cccnKS0tLS8v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\nRvwXDbUbQgxO+90AAAAASUVORK5CYII=",
            "text/plain": [
              "plot without title"
            ]
          },
          "metadata": {
            "tags": [],
            "image/png": {
              "width": 420,
              "height": 420
            }
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sPQtinXJZCrj"
      },
      "source": [
        "### Splitting the dataset into training and test set"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aTQJnzIBZJNy"
      },
      "source": [
        "We next split our dataset into training and testing sets. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BZbW4ieVY_53"
      },
      "source": [
        "t_sample<-sample(nrow(df),0.8*nrow(df))\t#contains the row numbers of the training set\n",
        "train<-df[t_sample,];test<-df[-t_sample,]"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rsGdxRz2ZQVe"
      },
      "source": [
        "We will use the K nearest neighbours to classify the dataset."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GGdpS2VpZSkL"
      },
      "source": [
        "### Value of k"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "t5dCcbeTZVf2"
      },
      "source": [
        "First, we obtain the the number of neighbours that are to be considered. We'll choose the k which has the max accuracy. We start by taking k as 1 and then simulate the accuracy incrementing k in each step."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 437
        },
        "id": "tpk_bVoOZRMu",
        "outputId": "b31357bb-b136-4412-8c4d-f836c699a72e"
      },
      "source": [
        "model_knn<-list();accuracy_knn<-numeric()\n",
        "for(i in 1:20)\n",
        "{\n",
        "\tmodel_knn[[i]]<-knn(train[,-5],test[,-5],train$class,k=i,prob=T)\n",
        "\taccuracy_knn[i]<-sum(model_knn[[i]]==test$class)/length(test$class)\n",
        "}\n",
        "plot(1:20,accuracy_knn,type='b',main='Accuracy vs. k',ylab='Accuracy',xlab='k',col='#33435c',pch=16)"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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n+h2AHQLcnlsVlTosyaXNcJCH9Ou9Xv97vKK9QOgn+h2AHQLUl2cx0WCB6bNcVs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\n",
            "text/plain": [
              "Plot with title “Accuracy vs. k”"
            ]
          },
          "metadata": {
            "tags": [],
            "image/png": {
              "width": 420,
              "height": 420
            }
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4bSbeo_CZa7U"
      },
      "source": [
        "From the plot, we find that there are many values of k which have maximum accuracy among all the values of k."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ptkka806Zcrc"
      },
      "source": [
        "We proceed to find the maximum accuracy."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "id": "Iq7HokYCZeRJ",
        "outputId": "0dc4a56c-5569-4d42-967c-b78075dcfb12"
      },
      "source": [
        "max(accuracy_knn)"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "[1] 0.9666667"
            ],
            "text/latex": "0.966666666666667",
            "text/markdown": "0.966666666666667",
            "text/html": [
              "0.966666666666667"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XPmi8qm2ZiX7"
      },
      "source": [
        "Thus, we find that the max accuracy that was obtained by this method is 96.67\\%.\n",
        "\n",
        "Further, we find the least vaue of k with maximum accuracy. This is due to the fact that a greater value of k may lead to overfitting of the data."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "id": "aNjWlDI_ZjxG",
        "outputId": "2f8d45f6-2e22-44d2-d24c-aa28afe1b85e"
      },
      "source": [
        "k<-min(which(accuracy_knn==max(accuracy_knn)))#min value of k which has the max accuracy\n",
        "k"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "[1] 10"
            ],
            "text/latex": "10",
            "text/markdown": "10",
            "text/html": [
              "10"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aS4WJFt3Zmo-"
      },
      "source": [
        "Thus, we find the value of k as 10 with an accuracy level of 96.67\\%."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "q1TKe2j3ZnxJ"
      },
      "source": [
        "### Predictions"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ll9FgvWDZr40"
      },
      "source": [
        "We finally classify the iris dataset into the respective classes."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 101
        },
        "id": "ym778xNGZnXw",
        "outputId": "f3932ca5-b7a5-4a4e-f5c8-541b78301b24"
      },
      "source": [
        "final_model <- knn(train[,-5], test[,-5], train$class, k, prob=TRUE)\n",
        "model_tab <- table(test$class, final_model)\n",
        "model_tab"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "                 final_model\n",
              "                  Iris-setosa Iris-versicolor Iris-virginica\n",
              "  Iris-setosa               8               0              0\n",
              "  Iris-versicolor           0               8              1\n",
              "  Iris-virginica            0               1             12"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "95tlkF9MZwol"
      },
      "source": [
        "We find that, while *Iris-setosa* was correctly classified, *Iris-versicolor* and *Iris-virginica* were not classified perfectly, as one of them was interchanged.\n",
        "\n",
        "The below dataframe gives the details of the test set along with the original and predicted classes:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "vIy8VEUdZ4QX",
        "outputId": "52672a78-30a6-4aa4-fe5e-b198ece0ec5c"
      },
      "source": [
        "cbind(test,final_model)"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "    sepal.len sepal.wd petal.len petal.wd class           final_model    \n",
              "12  4.8       3.4      1.6       0.2      Iris-setosa     Iris-setosa    \n",
              "14  4.3       3.0      1.1       0.1      Iris-setosa     Iris-setosa    \n",
              "21  5.4       3.4      1.7       0.2      Iris-setosa     Iris-setosa    \n",
              "28  5.2       3.5      1.5       0.2      Iris-setosa     Iris-setosa    \n",
              "32  5.4       3.4      1.5       0.4      Iris-setosa     Iris-setosa    \n",
              "33  5.2       4.1      1.5       0.1      Iris-setosa     Iris-setosa    \n",
              "43  4.4       3.2      1.3       0.2      Iris-setosa     Iris-setosa    \n",
              "47  5.1       3.8      1.6       0.2      Iris-setosa     Iris-setosa    \n",
              "60  5.2       2.7      3.9       1.4      Iris-versicolor Iris-versicolor\n",
              "65  5.6       2.9      3.6       1.3      Iris-versicolor Iris-versicolor\n",
              "68  5.8       2.7      4.1       1.0      Iris-versicolor Iris-versicolor\n",
              "69  6.2       2.2      4.5       1.5      Iris-versicolor Iris-versicolor\n",
              "73  6.3       2.5      4.9       1.5      Iris-versicolor Iris-virginica \n",
              "75  6.4       2.9      4.3       1.3      Iris-versicolor Iris-versicolor\n",
              "80  5.7       2.6      3.5       1.0      Iris-versicolor Iris-versicolor\n",
              "81  5.5       2.4      3.8       1.1      Iris-versicolor Iris-versicolor\n",
              "92  6.1       3.0      4.6       1.4      Iris-versicolor Iris-versicolor\n",
              "107 4.9       2.5      4.5       1.7      Iris-virginica  Iris-versicolor\n",
              "110 7.2       3.6      6.1       2.5      Iris-virginica  Iris-virginica \n",
              "112 6.4       2.7      5.3       1.9      Iris-virginica  Iris-virginica \n",
              "117 6.5       3.0      5.5       1.8      Iris-virginica  Iris-virginica \n",
              "125 6.7       3.3      5.7       2.1      Iris-virginica  Iris-virginica \n",
              "126 7.2       3.2      6.0       1.8      Iris-virginica  Iris-virginica \n",
              "130 7.2       3.0      5.8       1.6      Iris-virginica  Iris-virginica \n",
              "132 7.9       3.8      6.4       2.0      Iris-virginica  Iris-virginica \n",
              "133 6.4       2.8      5.6       2.2      Iris-virginica  Iris-virginica \n",
              "138 6.4       3.1      5.5       1.8      Iris-virginica  Iris-virginica \n",
              "142 6.9       3.1      5.1       2.3      Iris-virginica  Iris-virginica \n",
              "144 6.8       3.2      5.9       2.3      Iris-virginica  Iris-virginica \n",
              "149 6.2       3.4      5.4       2.3      Iris-virginica  Iris-virginica "
            ],
            "text/latex": "A data.frame: 30 × 6\n\\begin{tabular}{r|llllll}\n  & sepal.len & sepal.wd & petal.len & petal.wd & class & final\\_model\\\\\n  & <dbl> & <dbl> & <dbl> & <dbl> & <chr> & <fct>\\\\\n\\hline\n\t12 & 4.8 & 3.4 & 1.6 & 0.2 & Iris-setosa     & Iris-setosa    \\\\\n\t14 & 4.3 & 3.0 & 1.1 & 0.1 & Iris-setosa     & Iris-setosa    \\\\\n\t21 & 5.4 & 3.4 & 1.7 & 0.2 & Iris-setosa     & Iris-setosa    \\\\\n\t28 & 5.2 & 3.5 & 1.5 & 0.2 & Iris-setosa     & Iris-setosa    \\\\\n\t32 & 5.4 & 3.4 & 1.5 & 0.4 & Iris-setosa     & Iris-setosa    \\\\\n\t33 & 5.2 & 4.1 & 1.5 & 0.1 & Iris-setosa     & Iris-setosa    \\\\\n\t43 & 4.4 & 3.2 & 1.3 & 0.2 & Iris-setosa     & Iris-setosa    \\\\\n\t47 & 5.1 & 3.8 & 1.6 & 0.2 & Iris-setosa     & Iris-setosa    \\\\\n\t60 & 5.2 & 2.7 & 3.9 & 1.4 & Iris-versicolor & Iris-versicolor\\\\\n\t65 & 5.6 & 2.9 & 3.6 & 1.3 & Iris-versicolor & Iris-versicolor\\\\\n\t68 & 5.8 & 2.7 & 4.1 & 1.0 & Iris-versicolor & Iris-versicolor\\\\\n\t69 & 6.2 & 2.2 & 4.5 & 1.5 & Iris-versicolor & Iris-versicolor\\\\\n\t73 & 6.3 & 2.5 & 4.9 & 1.5 & Iris-versicolor & Iris-virginica \\\\\n\t75 & 6.4 & 2.9 & 4.3 & 1.3 & Iris-versicolor & Iris-versicolor\\\\\n\t80 & 5.7 & 2.6 & 3.5 & 1.0 & Iris-versicolor & Iris-versicolor\\\\\n\t81 & 5.5 & 2.4 & 3.8 & 1.1 & Iris-versicolor & Iris-versicolor\\\\\n\t92 & 6.1 & 3.0 & 4.6 & 1.4 & Iris-versicolor & Iris-versicolor\\\\\n\t107 & 4.9 & 2.5 & 4.5 & 1.7 & Iris-virginica  & Iris-versicolor\\\\\n\t110 & 7.2 & 3.6 & 6.1 & 2.5 & Iris-virginica  & Iris-virginica \\\\\n\t112 & 6.4 & 2.7 & 5.3 & 1.9 & Iris-virginica  & Iris-virginica \\\\\n\t117 & 6.5 & 3.0 & 5.5 & 1.8 & Iris-virginica  & Iris-virginica \\\\\n\t125 & 6.7 & 3.3 & 5.7 & 2.1 & Iris-virginica  & Iris-virginica \\\\\n\t126 & 7.2 & 3.2 & 6.0 & 1.8 & Iris-virginica  & Iris-virginica \\\\\n\t130 & 7.2 & 3.0 & 5.8 & 1.6 & Iris-virginica  & Iris-virginica \\\\\n\t132 & 7.9 & 3.8 & 6.4 & 2.0 & Iris-virginica  & Iris-virginica \\\\\n\t133 & 6.4 & 2.8 & 5.6 & 2.2 & Iris-virginica  & Iris-virginica \\\\\n\t138 & 6.4 & 3.1 & 5.5 & 1.8 & Iris-virginica  & Iris-virginica \\\\\n\t142 & 6.9 & 3.1 & 5.1 & 2.3 & Iris-virginica  & Iris-virginica \\\\\n\t144 & 6.8 & 3.2 & 5.9 & 2.3 & Iris-virginica  & Iris-virginica \\\\\n\t149 & 6.2 & 3.4 & 5.4 & 2.3 & Iris-virginica  & Iris-virginica \\\\\n\\end{tabular}\n",
            "text/markdown": "\nA data.frame: 30 × 6\n\n| <!--/--> | sepal.len &lt;dbl&gt; | sepal.wd &lt;dbl&gt; | petal.len &lt;dbl&gt; | petal.wd &lt;dbl&gt; | class &lt;chr&gt; | final_model &lt;fct&gt; |\n|---|---|---|---|---|---|---|\n| 12 | 4.8 | 3.4 | 1.6 | 0.2 | Iris-setosa     | Iris-setosa     |\n| 14 | 4.3 | 3.0 | 1.1 | 0.1 | Iris-setosa     | Iris-setosa     |\n| 21 | 5.4 | 3.4 | 1.7 | 0.2 | Iris-setosa     | Iris-setosa     |\n| 28 | 5.2 | 3.5 | 1.5 | 0.2 | Iris-setosa     | Iris-setosa     |\n| 32 | 5.4 | 3.4 | 1.5 | 0.4 | Iris-setosa     | Iris-setosa     |\n| 33 | 5.2 | 4.1 | 1.5 | 0.1 | Iris-setosa     | Iris-setosa     |\n| 43 | 4.4 | 3.2 | 1.3 | 0.2 | Iris-setosa     | Iris-setosa     |\n| 47 | 5.1 | 3.8 | 1.6 | 0.2 | Iris-setosa     | Iris-setosa     |\n| 60 | 5.2 | 2.7 | 3.9 | 1.4 | Iris-versicolor | Iris-versicolor |\n| 65 | 5.6 | 2.9 | 3.6 | 1.3 | Iris-versicolor | Iris-versicolor |\n| 68 | 5.8 | 2.7 | 4.1 | 1.0 | Iris-versicolor | Iris-versicolor |\n| 69 | 6.2 | 2.2 | 4.5 | 1.5 | Iris-versicolor | Iris-versicolor |\n| 73 | 6.3 | 2.5 | 4.9 | 1.5 | Iris-versicolor | Iris-virginica  |\n| 75 | 6.4 | 2.9 | 4.3 | 1.3 | Iris-versicolor | Iris-versicolor |\n| 80 | 5.7 | 2.6 | 3.5 | 1.0 | Iris-versicolor | Iris-versicolor |\n| 81 | 5.5 | 2.4 | 3.8 | 1.1 | Iris-versicolor | Iris-versicolor |\n| 92 | 6.1 | 3.0 | 4.6 | 1.4 | Iris-versicolor | Iris-versicolor |\n| 107 | 4.9 | 2.5 | 4.5 | 1.7 | Iris-virginica  | Iris-versicolor |\n| 110 | 7.2 | 3.6 | 6.1 | 2.5 | Iris-virginica  | Iris-virginica  |\n| 112 | 6.4 | 2.7 | 5.3 | 1.9 | Iris-virginica  | Iris-virginica  |\n| 117 | 6.5 | 3.0 | 5.5 | 1.8 | Iris-virginica  | Iris-virginica  |\n| 125 | 6.7 | 3.3 | 5.7 | 2.1 | Iris-virginica  | Iris-virginica  |\n| 126 | 7.2 | 3.2 | 6.0 | 1.8 | Iris-virginica  | Iris-virginica  |\n| 130 | 7.2 | 3.0 | 5.8 | 1.6 | Iris-virginica  | Iris-virginica  |\n| 132 | 7.9 | 3.8 | 6.4 | 2.0 | Iris-virginica  | Iris-virginica  |\n| 133 | 6.4 | 2.8 | 5.6 | 2.2 | Iris-virginica  | Iris-virginica  |\n| 138 | 6.4 | 3.1 | 5.5 | 1.8 | Iris-virginica  | Iris-virginica  |\n| 142 | 6.9 | 3.1 | 5.1 | 2.3 | Iris-virginica  | Iris-virginica  |\n| 144 | 6.8 | 3.2 | 5.9 | 2.3 | Iris-virginica  | Iris-virginica  |\n| 149 | 6.2 | 3.4 | 5.4 | 2.3 | Iris-virginica  | Iris-virginica  |\n\n",
            "text/html": [
              "<table class=\"dataframe\">\n",
              "<caption>A data.frame: 30 × 6</caption>\n",
              "<thead>\n",
              "\t<tr><th></th><th scope=col>sepal.len</th><th scope=col>sepal.wd</th><th scope=col>petal.len</th><th scope=col>petal.wd</th><th scope=col>class</th><th scope=col>final_model</th></tr>\n",
              "\t<tr><th></th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;fct&gt;</th></tr>\n",
              "</thead>\n",
              "<tbody>\n",
              "\t<tr><th scope=row>12</th><td>4.8</td><td>3.4</td><td>1.6</td><td>0.2</td><td>Iris-setosa    </td><td>Iris-setosa    </td></tr>\n",
              "\t<tr><th scope=row>14</th><td>4.3</td><td>3.0</td><td>1.1</td><td>0.1</td><td>Iris-setosa    </td><td>Iris-setosa    </td></tr>\n",
              "\t<tr><th scope=row>21</th><td>5.4</td><td>3.4</td><td>1.7</td><td>0.2</td><td>Iris-setosa    </td><td>Iris-setosa    </td></tr>\n",
              "\t<tr><th scope=row>28</th><td>5.2</td><td>3.5</td><td>1.5</td><td>0.2</td><td>Iris-setosa    </td><td>Iris-setosa    </td></tr>\n",
              "\t<tr><th scope=row>32</th><td>5.4</td><td>3.4</td><td>1.5</td><td>0.4</td><td>Iris-setosa    </td><td>Iris-setosa    </td></tr>\n",
              "\t<tr><th scope=row>33</th><td>5.2</td><td>4.1</td><td>1.5</td><td>0.1</td><td>Iris-setosa    </td><td>Iris-setosa    </td></tr>\n",
              "\t<tr><th scope=row>43</th><td>4.4</td><td>3.2</td><td>1.3</td><td>0.2</td><td>Iris-setosa    </td><td>Iris-setosa    </td></tr>\n",
              "\t<tr><th scope=row>47</th><td>5.1</td><td>3.8</td><td>1.6</td><td>0.2</td><td>Iris-setosa    </td><td>Iris-setosa    </td></tr>\n",
              "\t<tr><th scope=row>60</th><td>5.2</td><td>2.7</td><td>3.9</td><td>1.4</td><td>Iris-versicolor</td><td>Iris-versicolor</td></tr>\n",
              "\t<tr><th scope=row>65</th><td>5.6</td><td>2.9</td><td>3.6</td><td>1.3</td><td>Iris-versicolor</td><td>Iris-versicolor</td></tr>\n",
              "\t<tr><th scope=row>68</th><td>5.8</td><td>2.7</td><td>4.1</td><td>1.0</td><td>Iris-versicolor</td><td>Iris-versicolor</td></tr>\n",
              "\t<tr><th scope=row>69</th><td>6.2</td><td>2.2</td><td>4.5</td><td>1.5</td><td>Iris-versicolor</td><td>Iris-versicolor</td></tr>\n",
              "\t<tr><th scope=row>73</th><td>6.3</td><td>2.5</td><td>4.9</td><td>1.5</td><td>Iris-versicolor</td><td>Iris-virginica </td></tr>\n",
              "\t<tr><th scope=row>75</th><td>6.4</td><td>2.9</td><td>4.3</td><td>1.3</td><td>Iris-versicolor</td><td>Iris-versicolor</td></tr>\n",
              "\t<tr><th scope=row>80</th><td>5.7</td><td>2.6</td><td>3.5</td><td>1.0</td><td>Iris-versicolor</td><td>Iris-versicolor</td></tr>\n",
              "\t<tr><th scope=row>81</th><td>5.5</td><td>2.4</td><td>3.8</td><td>1.1</td><td>Iris-versicolor</td><td>Iris-versicolor</td></tr>\n",
              "\t<tr><th scope=row>92</th><td>6.1</td><td>3.0</td><td>4.6</td><td>1.4</td><td>Iris-versicolor</td><td>Iris-versicolor</td></tr>\n",
              "\t<tr><th scope=row>107</th><td>4.9</td><td>2.5</td><td>4.5</td><td>1.7</td><td>Iris-virginica </td><td>Iris-versicolor</td></tr>\n",
              "\t<tr><th scope=row>110</th><td>7.2</td><td>3.6</td><td>6.1</td><td>2.5</td><td>Iris-virginica </td><td>Iris-virginica </td></tr>\n",
              "\t<tr><th scope=row>112</th><td>6.4</td><td>2.7</td><td>5.3</td><td>1.9</td><td>Iris-virginica </td><td>Iris-virginica </td></tr>\n",
              "\t<tr><th scope=row>117</th><td>6.5</td><td>3.0</td><td>5.5</td><td>1.8</td><td>Iris-virginica </td><td>Iris-virginica </td></tr>\n",
              "\t<tr><th scope=row>125</th><td>6.7</td><td>3.3</td><td>5.7</td><td>2.1</td><td>Iris-virginica </td><td>Iris-virginica </td></tr>\n",
              "\t<tr><th scope=row>126</th><td>7.2</td><td>3.2</td><td>6.0</td><td>1.8</td><td>Iris-virginica </td><td>Iris-virginica </td></tr>\n",
              "\t<tr><th scope=row>130</th><td>7.2</td><td>3.0</td><td>5.8</td><td>1.6</td><td>Iris-virginica </td><td>Iris-virginica </td></tr>\n",
              "\t<tr><th scope=row>132</th><td>7.9</td><td>3.8</td><td>6.4</td><td>2.0</td><td>Iris-virginica </td><td>Iris-virginica </td></tr>\n",
              "\t<tr><th scope=row>133</th><td>6.4</td><td>2.8</td><td>5.6</td><td>2.2</td><td>Iris-virginica </td><td>Iris-virginica </td></tr>\n",
              "\t<tr><th scope=row>138</th><td>6.4</td><td>3.1</td><td>5.5</td><td>1.8</td><td>Iris-virginica </td><td>Iris-virginica </td></tr>\n",
              "\t<tr><th scope=row>142</th><td>6.9</td><td>3.1</td><td>5.1</td><td>2.3</td><td>Iris-virginica </td><td>Iris-virginica </td></tr>\n",
              "\t<tr><th scope=row>144</th><td>6.8</td><td>3.2</td><td>5.9</td><td>2.3</td><td>Iris-virginica </td><td>Iris-virginica </td></tr>\n",
              "\t<tr><th scope=row>149</th><td>6.2</td><td>3.4</td><td>5.4</td><td>2.3</td><td>Iris-virginica </td><td>Iris-virginica </td></tr>\n",
              "</tbody>\n",
              "</table>\n"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    }
  ]
}