Classification-Iris.ipynb

{
  "nbformat": 4,
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  "metadata": {
    "colab": {
      "name": "Assignment #6.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "source": [
        "Classification problem project on dataset iris.csv"
      ],
      "cell_type": "markdown",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Wnrosut9vXjX",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "9335af84-45ef-492d-e933-ceaa4cfaba0c"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount ('/content/drive/')"
      ],
      "execution_count": 1,
      "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": "GmDkD7lKwY8y",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        },
        "outputId": "43869bd5-bb8b-495b-abcc-6866c9df8527"
      },
      "source": [
        "import os\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import sklearn\n",
        "import numpy as np\n",
        "import seaborn as sb\n",
        "from sklearn.metrics import f1_score, precision_score, recall_score\n"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/statsmodels/tools/_testing.py:19: FutureWarning: pandas.util.testing is deprecated. Use the functions in the public API at pandas.testing instead.\n",
            "  import pandas.util.testing as tm\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wuBnLS8IvvOS",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 381
        },
        "outputId": "c3e5df9a-3546-4d3c-9987-203105bc540b"
      },
      "source": [
        "%cd \"/content/drive/My Drive/Colab Notebooks/\"\n",
        "!ls \"/content/drive/My Drive/Colab Notebooks/\"\n",
        "\n",
        "directory = os.path.join('/content/drive/My Drive/Colab Notebooks/', 'iris.csv')\n",
        "if os.path.isfile(directory):\n",
        "  print(\"File ditemukan\")"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/content/drive/My Drive/Colab Notebooks\n",
            " Assignment#2.ipynb\n",
            " Assignment#3.ipynb\n",
            "'Assignment#4 (1).ipynb'\n",
            " Assignment#4.ipynb\n",
            "'Assignment 5 - Machine Learning.ipynb'\n",
            "'Assignment #6.ipynb'\n",
            "'Bonus Assignment #1.ipynb'\n",
            " Bootcamp#2.ipynb\n",
            " heart.csv\n",
            "'Intro_to_Python_Pt_2 (1).ipynb'\n",
            "'Intro_to_Python_Pt_2 (2).ipynb'\n",
            " Intro_to_Python_Pt_2.ipynb\n",
            " iris.csv\n",
            " plot_svm_regression.ipynb\n",
            " Python_Machine_Learning_Pt_1.ipynb\n",
            " Untitled0.ipynb\n",
            " Untitled2.ipynb\n",
            " Week_1_Python_1.ipynb\n",
            " Week_2_Python_Machine_Learning_2_Classification_1_Blank.ipynb\n",
            "File ditemukan\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gtrdbVd7wqux",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 537
        },
        "outputId": "d4c9795f-822a-4bef-8ee6-69b19f1599ad"
      },
      "source": [
        "dataset = pd.read_csv(directory)\n",
        "dataset.head()\n",
        "\n",
        "print(dataset.info())\n",
        "print(dataset.describe())\n",
        "print(dataset['Species'].value_counts())\n",
        "print(dataset.columns)"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 150 entries, 0 to 149\n",
            "Data columns (total 6 columns):\n",
            " #   Column         Non-Null Count  Dtype  \n",
            "---  ------         --------------  -----  \n",
            " 0   Id             150 non-null    int64  \n",
            " 1   SepalLengthCm  150 non-null    float64\n",
            " 2   SepalWidthCm   150 non-null    float64\n",
            " 3   PetalLengthCm  150 non-null    float64\n",
            " 4   PetalWidthCm   150 non-null    float64\n",
            " 5   Species        150 non-null    object \n",
            "dtypes: float64(4), int64(1), object(1)\n",
            "memory usage: 7.2+ KB\n",
            "None\n",
            "               Id  SepalLengthCm  SepalWidthCm  PetalLengthCm  PetalWidthCm\n",
            "count  150.000000     150.000000    150.000000     150.000000    150.000000\n",
            "mean    75.500000       5.843333      3.054000       3.758667      1.198667\n",
            "std     43.445368       0.828066      0.433594       1.764420      0.763161\n",
            "min      1.000000       4.300000      2.000000       1.000000      0.100000\n",
            "25%     38.250000       5.100000      2.800000       1.600000      0.300000\n",
            "50%     75.500000       5.800000      3.000000       4.350000      1.300000\n",
            "75%    112.750000       6.400000      3.300000       5.100000      1.800000\n",
            "max    150.000000       7.900000      4.400000       6.900000      2.500000\n",
            "Iris-versicolor    50\n",
            "Iris-setosa        50\n",
            "Iris-virginica     50\n",
            "Name: Species, dtype: int64\n",
            "Index(['Id', 'SepalLengthCm', 'SepalWidthCm', 'PetalLengthCm', 'PetalWidthCm',\n",
            "       'Species'],\n",
            "      dtype='object')\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eKklbJaYxz4t",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "36086bbb-9055-4153-834b-004c11c12e96"
      },
      "source": [
        "iris_setosa = dataset.loc[dataset[\"Species\"]== 'Iris-setosa']\n",
        "print(iris_setosa)\n",
        "iris_virginica = dataset.loc[dataset[\"Species\"]== 'Iris-virginica']\n",
        "print(iris_virginica)\n",
        "iris_versicolor = dataset.loc[dataset[\"Species\"]== 'Iris-versicolor']\n",
        "print(iris_versicolor)"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "    Id  SepalLengthCm  SepalWidthCm  PetalLengthCm  PetalWidthCm      Species\n",
            "0    1            5.1           3.5            1.4           0.2  Iris-setosa\n",
            "1    2            4.9           3.0            1.4           0.2  Iris-setosa\n",
            "2    3            4.7           3.2            1.3           0.2  Iris-setosa\n",
            "3    4            4.6           3.1            1.5           0.2  Iris-setosa\n",
            "4    5            5.0           3.6            1.4           0.2  Iris-setosa\n",
            "5    6            5.4           3.9            1.7           0.4  Iris-setosa\n",
            "6    7            4.6           3.4            1.4           0.3  Iris-setosa\n",
            "7    8            5.0           3.4            1.5           0.2  Iris-setosa\n",
            "8    9            4.4           2.9            1.4           0.2  Iris-setosa\n",
            "9   10            4.9           3.1            1.5           0.1  Iris-setosa\n",
            "10  11            5.4           3.7            1.5           0.2  Iris-setosa\n",
            "11  12            4.8           3.4            1.6           0.2  Iris-setosa\n",
            "12  13            4.8           3.0            1.4           0.1  Iris-setosa\n",
            "13  14            4.3           3.0            1.1           0.1  Iris-setosa\n",
            "14  15            5.8           4.0            1.2           0.2  Iris-setosa\n",
            "15  16            5.7           4.4            1.5           0.4  Iris-setosa\n",
            "16  17            5.4           3.9            1.3           0.4  Iris-setosa\n",
            "17  18            5.1           3.5            1.4           0.3  Iris-setosa\n",
            "18  19            5.7           3.8            1.7           0.3  Iris-setosa\n",
            "19  20            5.1           3.8            1.5           0.3  Iris-setosa\n",
            "20  21            5.4           3.4            1.7           0.2  Iris-setosa\n",
            "21  22            5.1           3.7            1.5           0.4  Iris-setosa\n",
            "22  23            4.6           3.6            1.0           0.2  Iris-setosa\n",
            "23  24            5.1           3.3            1.7           0.5  Iris-setosa\n",
            "24  25            4.8           3.4            1.9           0.2  Iris-setosa\n",
            "25  26            5.0           3.0            1.6           0.2  Iris-setosa\n",
            "26  27            5.0           3.4            1.6           0.4  Iris-setosa\n",
            "27  28            5.2           3.5            1.5           0.2  Iris-setosa\n",
            "28  29            5.2           3.4            1.4           0.2  Iris-setosa\n",
            "29  30            4.7           3.2            1.6           0.2  Iris-setosa\n",
            "30  31            4.8           3.1            1.6           0.2  Iris-setosa\n",
            "31  32            5.4           3.4            1.5           0.4  Iris-setosa\n",
            "32  33            5.2           4.1            1.5           0.1  Iris-setosa\n",
            "33  34            5.5           4.2            1.4           0.2  Iris-setosa\n",
            "34  35            4.9           3.1            1.5           0.1  Iris-setosa\n",
            "35  36            5.0           3.2            1.2           0.2  Iris-setosa\n",
            "36  37            5.5           3.5            1.3           0.2  Iris-setosa\n",
            "37  38            4.9           3.1            1.5           0.1  Iris-setosa\n",
            "38  39            4.4           3.0            1.3           0.2  Iris-setosa\n",
            "39  40            5.1           3.4            1.5           0.2  Iris-setosa\n",
            "40  41            5.0           3.5            1.3           0.3  Iris-setosa\n",
            "41  42            4.5           2.3            1.3           0.3  Iris-setosa\n",
            "42  43            4.4           3.2            1.3           0.2  Iris-setosa\n",
            "43  44            5.0           3.5            1.6           0.6  Iris-setosa\n",
            "44  45            5.1           3.8            1.9           0.4  Iris-setosa\n",
            "45  46            4.8           3.0            1.4           0.3  Iris-setosa\n",
            "46  47            5.1           3.8            1.6           0.2  Iris-setosa\n",
            "47  48            4.6           3.2            1.4           0.2  Iris-setosa\n",
            "48  49            5.3           3.7            1.5           0.2  Iris-setosa\n",
            "49  50            5.0           3.3            1.4           0.2  Iris-setosa\n",
            "      Id  SepalLengthCm  ...  PetalWidthCm         Species\n",
            "100  101            6.3  ...           2.5  Iris-virginica\n",
            "101  102            5.8  ...           1.9  Iris-virginica\n",
            "102  103            7.1  ...           2.1  Iris-virginica\n",
            "103  104            6.3  ...           1.8  Iris-virginica\n",
            "104  105            6.5  ...           2.2  Iris-virginica\n",
            "105  106            7.6  ...           2.1  Iris-virginica\n",
            "106  107            4.9  ...           1.7  Iris-virginica\n",
            "107  108            7.3  ...           1.8  Iris-virginica\n",
            "108  109            6.7  ...           1.8  Iris-virginica\n",
            "109  110            7.2  ...           2.5  Iris-virginica\n",
            "110  111            6.5  ...           2.0  Iris-virginica\n",
            "111  112            6.4  ...           1.9  Iris-virginica\n",
            "112  113            6.8  ...           2.1  Iris-virginica\n",
            "113  114            5.7  ...           2.0  Iris-virginica\n",
            "114  115            5.8  ...           2.4  Iris-virginica\n",
            "115  116            6.4  ...           2.3  Iris-virginica\n",
            "116  117            6.5  ...           1.8  Iris-virginica\n",
            "117  118            7.7  ...           2.2  Iris-virginica\n",
            "118  119            7.7  ...           2.3  Iris-virginica\n",
            "119  120            6.0  ...           1.5  Iris-virginica\n",
            "120  121            6.9  ...           2.3  Iris-virginica\n",
            "121  122            5.6  ...           2.0  Iris-virginica\n",
            "122  123            7.7  ...           2.0  Iris-virginica\n",
            "123  124            6.3  ...           1.8  Iris-virginica\n",
            "124  125            6.7  ...           2.1  Iris-virginica\n",
            "125  126            7.2  ...           1.8  Iris-virginica\n",
            "126  127            6.2  ...           1.8  Iris-virginica\n",
            "127  128            6.1  ...           1.8  Iris-virginica\n",
            "128  129            6.4  ...           2.1  Iris-virginica\n",
            "129  130            7.2  ...           1.6  Iris-virginica\n",
            "130  131            7.4  ...           1.9  Iris-virginica\n",
            "131  132            7.9  ...           2.0  Iris-virginica\n",
            "132  133            6.4  ...           2.2  Iris-virginica\n",
            "133  134            6.3  ...           1.5  Iris-virginica\n",
            "134  135            6.1  ...           1.4  Iris-virginica\n",
            "135  136            7.7  ...           2.3  Iris-virginica\n",
            "136  137            6.3  ...           2.4  Iris-virginica\n",
            "137  138            6.4  ...           1.8  Iris-virginica\n",
            "138  139            6.0  ...           1.8  Iris-virginica\n",
            "139  140            6.9  ...           2.1  Iris-virginica\n",
            "140  141            6.7  ...           2.4  Iris-virginica\n",
            "141  142            6.9  ...           2.3  Iris-virginica\n",
            "142  143            5.8  ...           1.9  Iris-virginica\n",
            "143  144            6.8  ...           2.3  Iris-virginica\n",
            "144  145            6.7  ...           2.5  Iris-virginica\n",
            "145  146            6.7  ...           2.3  Iris-virginica\n",
            "146  147            6.3  ...           1.9  Iris-virginica\n",
            "147  148            6.5  ...           2.0  Iris-virginica\n",
            "148  149            6.2  ...           2.3  Iris-virginica\n",
            "149  150            5.9  ...           1.8  Iris-virginica\n",
            "\n",
            "[50 rows x 6 columns]\n",
            "     Id  SepalLengthCm  ...  PetalWidthCm          Species\n",
            "50   51            7.0  ...           1.4  Iris-versicolor\n",
            "51   52            6.4  ...           1.5  Iris-versicolor\n",
            "52   53            6.9  ...           1.5  Iris-versicolor\n",
            "53   54            5.5  ...           1.3  Iris-versicolor\n",
            "54   55            6.5  ...           1.5  Iris-versicolor\n",
            "55   56            5.7  ...           1.3  Iris-versicolor\n",
            "56   57            6.3  ...           1.6  Iris-versicolor\n",
            "57   58            4.9  ...           1.0  Iris-versicolor\n",
            "58   59            6.6  ...           1.3  Iris-versicolor\n",
            "59   60            5.2  ...           1.4  Iris-versicolor\n",
            "60   61            5.0  ...           1.0  Iris-versicolor\n",
            "61   62            5.9  ...           1.5  Iris-versicolor\n",
            "62   63            6.0  ...           1.0  Iris-versicolor\n",
            "63   64            6.1  ...           1.4  Iris-versicolor\n",
            "64   65            5.6  ...           1.3  Iris-versicolor\n",
            "65   66            6.7  ...           1.4  Iris-versicolor\n",
            "66   67            5.6  ...           1.5  Iris-versicolor\n",
            "67   68            5.8  ...           1.0  Iris-versicolor\n",
            "68   69            6.2  ...           1.5  Iris-versicolor\n",
            "69   70            5.6  ...           1.1  Iris-versicolor\n",
            "70   71            5.9  ...           1.8  Iris-versicolor\n",
            "71   72            6.1  ...           1.3  Iris-versicolor\n",
            "72   73            6.3  ...           1.5  Iris-versicolor\n",
            "73   74            6.1  ...           1.2  Iris-versicolor\n",
            "74   75            6.4  ...           1.3  Iris-versicolor\n",
            "75   76            6.6  ...           1.4  Iris-versicolor\n",
            "76   77            6.8  ...           1.4  Iris-versicolor\n",
            "77   78            6.7  ...           1.7  Iris-versicolor\n",
            "78   79            6.0  ...           1.5  Iris-versicolor\n",
            "79   80            5.7  ...           1.0  Iris-versicolor\n",
            "80   81            5.5  ...           1.1  Iris-versicolor\n",
            "81   82            5.5  ...           1.0  Iris-versicolor\n",
            "82   83            5.8  ...           1.2  Iris-versicolor\n",
            "83   84            6.0  ...           1.6  Iris-versicolor\n",
            "84   85            5.4  ...           1.5  Iris-versicolor\n",
            "85   86            6.0  ...           1.6  Iris-versicolor\n",
            "86   87            6.7  ...           1.5  Iris-versicolor\n",
            "87   88            6.3  ...           1.3  Iris-versicolor\n",
            "88   89            5.6  ...           1.3  Iris-versicolor\n",
            "89   90            5.5  ...           1.3  Iris-versicolor\n",
            "90   91            5.5  ...           1.2  Iris-versicolor\n",
            "91   92            6.1  ...           1.4  Iris-versicolor\n",
            "92   93            5.8  ...           1.2  Iris-versicolor\n",
            "93   94            5.0  ...           1.0  Iris-versicolor\n",
            "94   95            5.6  ...           1.3  Iris-versicolor\n",
            "95   96            5.7  ...           1.2  Iris-versicolor\n",
            "96   97            5.7  ...           1.3  Iris-versicolor\n",
            "97   98            6.2  ...           1.3  Iris-versicolor\n",
            "98   99            5.1  ...           1.1  Iris-versicolor\n",
            "99  100            5.7  ...           1.3  Iris-versicolor\n",
            "\n",
            "[50 rows x 6 columns]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Yy1KOjk_ydNt",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "outputId": "2c763bb8-99f2-4f55-c003-910620693c07"
      },
      "source": [
        "plt.plot(iris_setosa['PetalLengthCm'], np.zeros_like(iris_setosa['PetalLengthCm']),'o')\n",
        "plt.plot(iris_virginica['PetalLengthCm'], np.zeros_like(iris_virginica['PetalLengthCm']),'o')\n",
        "plt.plot(iris_versicolor['PetalLengthCm'], np.zeros_like(iris_versicolor['PetalLengthCm']),'o')"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7fbc3dc4cac8>]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "z9qCeDtFzZ5b",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "outputId": "44995476-604a-4b8f-9788-208ab85608da"
      },
      "source": [
        "plt.plot(iris_setosa['SepalLengthCm'], np.zeros_like(iris_setosa['SepalLengthCm']),'o')\n",
        "plt.plot(iris_virginica['SepalLengthCm'], np.zeros_like(iris_virginica['SepalLengthCm']),'o')\n",
        "plt.plot(iris_versicolor['SepalLengthCm'], np.zeros_like(iris_versicolor['SepalLengthCm']),'o')"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7fbc3d722b70>]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4FGWqTonzoNZ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "ddf9b898-8596-49af-ec16-c31072a2a12b"
      },
      "source": [
        "sb.pairplot(dataset,hue=\"Species\", size=3)\n",
        "plt.show()"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/seaborn/axisgrid.py:2071: UserWarning: The `size` parameter has been renamed to `height`; please update your code.\n",
            "  warnings.warn(msg, UserWarning)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1183.25x1080 with 30 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8WlBYzD50u48",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "feature= dataset.drop(['Id','Species'],axis=1)\n",
        "label= dataset['Species']\n",
        "\n",
        "from sklearn import preprocessing\n",
        "scaler = preprocessing.StandardScaler()\n",
        "\n",
        "feature_scaled = scaler.fit_transform(feature.values)\n",
        "data_feature = pd.DataFrame(feature_scaled,columns=feature.columns)"
      ],
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0tEDXvt_Y7nX",
        "colab_type": "text"
      },
      "source": [
        "# Random Forest Classifier"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GeLVe4iO19sI",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 329
        },
        "outputId": "c245f800-f09e-4137-dde1-c991557e90d2"
      },
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "x_train, x_test, y_train, y_test = train_test_split(data_feature, label, test_size=0.15)\n",
        "\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "estimator = [50,100,200]\n",
        "for i in estimator:\n",
        "  print(\"Untuk Estimator \"+str(i))\n",
        "  rf_model = RandomForestClassifier(i, bootstrap=True, max_features = \"sqrt\")\n",
        "  rf_model.fit(x_train, y_train)\n",
        "  y_pred = rf_model.predict(x_test)\n",
        "  print('Accuracy of Random Forest on test set: {:.2f}'.format(rf_model.score(x_test, y_test)))\n",
        "  print(f1_score(y_test, y_pred, average=\"macro\"))\n",
        "  print(precision_score(y_test, y_pred, average=\"macro\"))\n",
        "  print(recall_score(y_test, y_pred, average=\"macro\"))\n",
        "  print()\n",
        "\n",
        "\n",
        "\n"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Untuk Estimator 50\n",
            "Accuracy of Random Forest on test set: 1.00\n",
            "1.0\n",
            "1.0\n",
            "1.0\n",
            "\n",
            "Untuk Estimator 100\n",
            "Accuracy of Random Forest on test set: 0.96\n",
            "0.9619047619047619\n",
            "0.9696969696969697\n",
            "0.9583333333333334\n",
            "\n",
            "Untuk Estimator 200\n",
            "Accuracy of Random Forest on test set: 0.96\n",
            "0.9619047619047619\n",
            "0.9696969696969697\n",
            "0.9583333333333334\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QUtpebBmv0w6",
        "colab_type": "text"
      },
      "source": [
        "## Kesimpulan\n",
        "Pada percobaan diatas menggunakan Algoritma RandomForestClassifier dengan nilai n_estimator yang berbeda mendapatkan hasil akurasi klasifikasi yang berbeda\n",
        "\n",
        "Sedangkan untuk nilai n_estimator yang berbeda menunjukkan pengaruh dalam hasil klasifikasi karena dalam prosesnya makin banyak tree yang terbentuk pada percabangannya sehingga mempengaruhi decision dalam menentukan kelas "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ASKT9wbmedam",
        "colab_type": "text"
      },
      "source": [
        "# SVM & LinearSVC"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7iYW5_Krea9X",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "x_train, x_test, y_train, y_test = train_test_split(data_feature, label, test_size=0.15)\n",
        "\n",
        "from sklearn.svm import SVC"
      ],
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ao82YJgOhfp7",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 225
        },
        "outputId": "452adad3-9501-4b03-b519-fce45db4b3a3"
      },
      "source": [
        "kernels = ['poly','linear']\n",
        "for i in kernels:\n",
        "  if i=='poly':\n",
        "    print(\"Dengan Kernel : \" +str(i))\n",
        "    svc_poly = SVC(kernel= 'poly' , gamma='auto', degree=3, coef0=1, C=100, random_state=None)\n",
        "    svc_poly.fit(x_train,y_train)\n",
        "    y_pred = svc_poly.predict(x_test)\n",
        "    print('Accuracy of SVR-Polynomial on test set: {:.2f}'.format(svc_poly.score(x_test, y_test)))\n",
        "    print(f1_score(y_test, y_pred, average=\"macro\"))\n",
        "    print(precision_score(y_test, y_pred, average=\"macro\"))\n",
        "    print(recall_score(y_test, y_pred, average=\"macro\"))\n",
        "    print()\n",
        "  else:\n",
        "    print(\"Dengan Kernel : \" +str(i))\n",
        "    svc_linear = SVC(kernel= 'linear' , gamma='auto', random_state=None)\n",
        "    svc_linear.fit(x_train,y_train)\n",
        "    y_pred = svc_linear.predict(x_test)\n",
        "    print('Accuracy of SVR-Linear on test set: {:.2f}'.format(svc_linear.score(x_test, y_test)))\n",
        "    print(f1_score(y_test, y_pred, average=\"macro\"))\n",
        "    print(precision_score(y_test, y_pred, average=\"macro\"))\n",
        "    print(recall_score(y_test, y_pred, average=\"macro\"))\n",
        "    print()\n"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Dengan Kernel : poly\n",
            "Accuracy of SVR-Polynomial on test set: 0.91\n",
            "0.875\n",
            "0.9259259259259259\n",
            "0.8666666666666667\n",
            "\n",
            "Dengan Kernel : linear\n",
            "Accuracy of SVR-Linear on test set: 0.96\n",
            "0.9407407407407408\n",
            "0.9583333333333334\n",
            "0.9333333333333332\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8VnLbuTNmfwm",
        "colab_type": "text"
      },
      "source": [
        "##Linear SVC"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gUmxSbLcmv86",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 86
        },
        "outputId": "407ad5fa-365b-425d-c05b-a38ecfd2d237"
      },
      "source": [
        "from sklearn.svm import LinearSVC\n",
        "linear_svc = LinearSVC( random_state=0, tol=1e-04)\n",
        "linear_svc.fit(x_train,y_train)\n",
        "y_pred = linear_svc.predict(x_test)\n",
        "print('Accuracy of LinearSVC on test set: {:.2f}'.format(linear_svc.score(x_test, y_test)))\n",
        "print(f1_score(y_test, y_pred, average=\"macro\"))\n",
        "print(precision_score(y_test, y_pred, average=\"macro\"))\n",
        "print(recall_score(y_test, y_pred, average=\"macro\"))"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Accuracy of LinearSVC on test set: 0.87\n",
            "0.8507936507936509\n",
            "0.85\n",
            "0.8554112554112554\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_vdiX15TxDka",
        "colab_type": "text"
      },
      "source": [
        "## Kesimpulan\n",
        "Pada percobaan diatas digunakan 2 algoritma yaitu SVC dengan 2 kernel (polynomial dan linear) dan algoritma LinearSVC\n",
        "\n",
        "Penggunaan SVC-Linear untuk klasifikasi pada dataset ini optimal karena dapat mengklasifikasi dengan akurat sebesar 0.96"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "is1NBkZnovsB",
        "colab_type": "text"
      },
      "source": [
        "#KNN Classifier"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "b5wPU4bGp63S",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 849
        },
        "outputId": "61fc5749-3ac8-4988-d67c-d42e49ebff6a"
      },
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "x_train, x_test, y_train, y_test = train_test_split(data_feature, label, test_size=0.15)\n",
        "\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "\n",
        "numbers = 16\n",
        "for i in range(numbers):\n",
        "  if i % 2 !=0:\n",
        "    print(\"Menggunakan nilai k : \"+str(i))\n",
        "    knn = KNeighborsClassifier(i)\n",
        "    knn.fit(x_train,y_train)\n",
        "\n",
        "    y_pred = knn.predict(x_test)\n",
        "    print('Accuracy of KNN Classifier on test set: {:.2f}'.format(knn.score(x_test, y_test)))\n",
        "    print(f1_score(y_test, y_pred, average=\"macro\"))\n",
        "    print(precision_score(y_test, y_pred, average=\"macro\"))\n",
        "    print(recall_score(y_test, y_pred, average=\"macro\"))\n",
        "    print()\n",
        "\n",
        "  else:\n",
        "    continue"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Menggunakan nilai k : 1\n",
            "Accuracy of KNN Classifier on test set: 0.96\n",
            "0.9521531100478469\n",
            "0.9444444444444445\n",
            "0.9666666666666667\n",
            "\n",
            "Menggunakan nilai k : 3\n",
            "Accuracy of KNN Classifier on test set: 0.91\n",
            "0.9074074074074074\n",
            "0.9047619047619048\n",
            "0.9333333333333332\n",
            "\n",
            "Menggunakan nilai k : 5\n",
            "Accuracy of KNN Classifier on test set: 0.91\n",
            "0.9074074074074074\n",
            "0.9047619047619048\n",
            "0.9333333333333332\n",
            "\n",
            "Menggunakan nilai k : 7\n",
            "Accuracy of KNN Classifier on test set: 0.96\n",
            "0.9521531100478469\n",
            "0.9444444444444445\n",
            "0.9666666666666667\n",
            "\n",
            "Menggunakan nilai k : 9\n",
            "Accuracy of KNN Classifier on test set: 1.00\n",
            "1.0\n",
            "1.0\n",
            "1.0\n",
            "\n",
            "Menggunakan nilai k : 11\n",
            "Accuracy of KNN Classifier on test set: 0.96\n",
            "0.9521531100478469\n",
            "0.9444444444444445\n",
            "0.9666666666666667\n",
            "\n",
            "Menggunakan nilai k : 13\n",
            "Accuracy of KNN Classifier on test set: 0.96\n",
            "0.9521531100478469\n",
            "0.9444444444444445\n",
            "0.9666666666666667\n",
            "\n",
            "Menggunakan nilai k : 15\n",
            "Accuracy of KNN Classifier on test set: 0.96\n",
            "0.9521531100478469\n",
            "0.9444444444444445\n",
            "0.9666666666666667\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iJc6_ITl4CQo",
        "colab_type": "text"
      },
      "source": [
        "## Kesimpulan\n",
        "Pada percobaan diatas menggunakan algoritma KNN Classifier dengan menggunakan variasi nilai k, didapatkan bahwa nilai k paling optimal adalah pada k=9 yang mendapat nilai akurasi 1.0"
      ]
    }
  ]
}