IRIS_PREDICTION_(3).ipynb

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  "metadata": {
    "colab": {
      "name": "IRIS_PREDICTION (3).ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "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/github/Deniswanja/Denis-kim/blob/master/IRIS_PREDICTION_(3).ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kOs8s2QvDwa4"
      },
      "source": [
        "import pandas \n",
        "from pandas.plotting import scatter_matrix\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn import model_selection\n",
        "from sklearn.metrics import classification_report\n",
        "from sklearn.metrics import confusion_matrix\n",
        "from sklearn.metrics import accuracy_score\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "from sklearn.discriminant_analysis import LinearDiscriminantAnalysis\n",
        "from sklearn.naive_bayes import GaussianNB\n",
        "from sklearn.svm import SVC"
      ],
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nR8b1CMjEDqG"
      },
      "source": [
        "from sklearn import datasets\n",
        "iris = datasets.load_iris()"
      ],
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7lWTdlWGED3K"
      },
      "source": [
        "import pandas as pd"
      ],
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5m_cKgNJE5Fl"
      },
      "source": [
        "iris_data= iris.data\n",
        "iris_data=pd.DataFrame(iris_data,columns=iris.feature_names)\n",
        "iris_data['class']=iris.target"
      ],
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        },
        "id": "LDd2xVlwE5UB",
        "outputId": "a3acf0f1-e152-4d5d-a2b1-954406e63ac3"
      },
      "source": [
        "iris_data\n"
      ],
      "execution_count": 11,
      "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 (cm)</th>\n",
              "      <th>sepal width (cm)</th>\n",
              "      <th>petal length (cm)</th>\n",
              "      <th>petal width (cm)</th>\n",
              "      <th>class</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>0</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>0</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>0</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>0</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>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>145</th>\n",
              "      <td>6.7</td>\n",
              "      <td>3.0</td>\n",
              "      <td>5.2</td>\n",
              "      <td>2.3</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>146</th>\n",
              "      <td>6.3</td>\n",
              "      <td>2.5</td>\n",
              "      <td>5.0</td>\n",
              "      <td>1.9</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>147</th>\n",
              "      <td>6.5</td>\n",
              "      <td>3.0</td>\n",
              "      <td>5.2</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>148</th>\n",
              "      <td>6.2</td>\n",
              "      <td>3.4</td>\n",
              "      <td>5.4</td>\n",
              "      <td>2.3</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>149</th>\n",
              "      <td>5.9</td>\n",
              "      <td>3.0</td>\n",
              "      <td>5.1</td>\n",
              "      <td>1.8</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>150 rows × 5 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "     sepal length (cm)  sepal width (cm)  ...  petal width (cm)  class\n",
              "0                  5.1               3.5  ...               0.2      0\n",
              "1                  4.9               3.0  ...               0.2      0\n",
              "2                  4.7               3.2  ...               0.2      0\n",
              "3                  4.6               3.1  ...               0.2      0\n",
              "4                  5.0               3.6  ...               0.2      0\n",
              "..                 ...               ...  ...               ...    ...\n",
              "145                6.7               3.0  ...               2.3      2\n",
              "146                6.3               2.5  ...               1.9      2\n",
              "147                6.5               3.0  ...               2.0      2\n",
              "148                6.2               3.4  ...               2.3      2\n",
              "149                5.9               3.0  ...               1.8      2\n",
              "\n",
              "[150 rows x 5 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5MywTzJVE5RI",
        "outputId": "c9be4ff4-e98e-4815-a419-40516d1b4147"
      },
      "source": [
        "print(iris_data.describe())"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "       sepal length (cm)  sepal width (cm)  ...  petal width (cm)       class\n",
            "count         150.000000        150.000000  ...        150.000000  150.000000\n",
            "mean            5.843333          3.057333  ...          1.199333    1.000000\n",
            "std             0.828066          0.435866  ...          0.762238    0.819232\n",
            "min             4.300000          2.000000  ...          0.100000    0.000000\n",
            "25%             5.100000          2.800000  ...          0.300000    0.000000\n",
            "50%             5.800000          3.000000  ...          1.300000    1.000000\n",
            "75%             6.400000          3.300000  ...          1.800000    2.000000\n",
            "max             7.900000          4.400000  ...          2.500000    2.000000\n",
            "\n",
            "[8 rows x 5 columns]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aoMG5HU_E5N0",
        "outputId": "c5391385-5ea3-4ca5-dce4-5cb18bb4fd97"
      },
      "source": [
        "print(iris_data.groupby('class').size())"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "class\n",
            "0    50\n",
            "1    50\n",
            "2    50\n",
            "dtype: int64\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 281
        },
        "id": "TGpwAIsYEENI",
        "outputId": "084e41d4-957b-4f9e-cbc7-fe1b6a463e95"
      },
      "source": [
        "iris_data.hist()\n",
        "plt.show()"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 6 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JlIuqllSF5ZM"
      },
      "source": [
        "array = iris_data.values\n",
        "X = array[:,0:4]\n",
        "Y = array[:,4]\n",
        "validation_size = 0.20\n",
        "seed = 7\n",
        "X_train, X_validation, Y_train, Y_validation = model_selection.train_test_split(X, Y, test_size=validation_size, random_state=seed)"
      ],
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xoxn5Pt-F5-E"
      },
      "source": [
        "seed = 7\n",
        "scoring = 'accuracy'"
      ],
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jyPjFj06F6P3"
      },
      "source": [
        "from sklearn.ensemble import RandomForestClassifier #ensembles"
      ],
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "g1Hofxk9F6H0",
        "outputId": "55dc0fb6-d8db-4ac9-b044-beafb5160593"
      },
      "source": [
        "models = []\n",
        "models.append(('LR', LogisticRegression(solver='liblinear', multi_class='ovr')))\n",
        "models.append(('LDA', LinearDiscriminantAnalysis()))\n",
        "models.append(('KNN', KNeighborsClassifier()))\n",
        "models.append(('CART', DecisionTreeClassifier()))\n",
        "models.append(('NB', GaussianNB()))\n",
        "models.append(('SVM', SVC(gamma='auto')))\n",
        "models.append(('RF',RandomForestClassifier()))\n",
        "# evaluate each model in turn\n",
        "results = []\n",
        "names = []\n",
        "for name, model in models:\n",
        "\tkfold = model_selection.KFold(n_splits=10, random_state=seed)\n",
        "\tcv_results = model_selection.cross_val_score(model, X_train, Y_train, cv=kfold, scoring=scoring)\n",
        "\tresults.append(cv_results)\n",
        "\tnames.append(name)\n",
        "\tmsg = \"%s: %f (%f)\" % (name, cv_results.mean(), cv_results.std())\n",
        "\tprint(msg)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_split.py:296: FutureWarning: Setting a random_state has no effect since shuffle is False. This will raise an error in 0.24. You should leave random_state to its default (None), or set shuffle=True.\n",
            "  FutureWarning\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_split.py:296: FutureWarning: Setting a random_state has no effect since shuffle is False. This will raise an error in 0.24. You should leave random_state to its default (None), or set shuffle=True.\n",
            "  FutureWarning\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_split.py:296: FutureWarning: Setting a random_state has no effect since shuffle is False. This will raise an error in 0.24. You should leave random_state to its default (None), or set shuffle=True.\n",
            "  FutureWarning\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_split.py:296: FutureWarning: Setting a random_state has no effect since shuffle is False. This will raise an error in 0.24. You should leave random_state to its default (None), or set shuffle=True.\n",
            "  FutureWarning\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_split.py:296: FutureWarning: Setting a random_state has no effect since shuffle is False. This will raise an error in 0.24. You should leave random_state to its default (None), or set shuffle=True.\n",
            "  FutureWarning\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_split.py:296: FutureWarning: Setting a random_state has no effect since shuffle is False. This will raise an error in 0.24. You should leave random_state to its default (None), or set shuffle=True.\n",
            "  FutureWarning\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_split.py:296: FutureWarning: Setting a random_state has no effect since shuffle is False. This will raise an error in 0.24. You should leave random_state to its default (None), or set shuffle=True.\n",
            "  FutureWarning\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "LR: 0.966667 (0.040825)\n",
            "LDA: 0.975000 (0.038188)\n",
            "KNN: 0.983333 (0.033333)\n",
            "CART: 0.966667 (0.040825)\n",
            "NB: 0.975000 (0.053359)\n",
            "SVM: 0.991667 (0.025000)\n",
            "RF: 0.966667 (0.040825)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vmjPLa6rHIOw",
        "outputId": "100b334e-2ecd-4eac-8519-72ac92bd202d"
      },
      "source": [
        "knn = KNeighborsClassifier()\n",
        "knn.fit(X_train, Y_train)\n",
        "predictions = knn.predict(X_validation)\n",
        "print(accuracy_score(Y_validation, predictions))\n",
        "print(confusion_matrix(Y_validation, predictions))\n",
        "print(classification_report(Y_validation, predictions))"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.9\n",
            "[[ 7  0  0]\n",
            " [ 0 11  1]\n",
            " [ 0  2  9]]\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "         0.0       1.00      1.00      1.00         7\n",
            "         1.0       0.85      0.92      0.88        12\n",
            "         2.0       0.90      0.82      0.86        11\n",
            "\n",
            "    accuracy                           0.90        30\n",
            "   macro avg       0.92      0.91      0.91        30\n",
            "weighted avg       0.90      0.90      0.90        30\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "boESXZtVHKXN",
        "outputId": "ea89709d-f3c7-474c-c079-01f5f117e947"
      },
      "source": [
        "RF=RandomForestClassifier(n_estimators=5)\n",
        "RF.fit(X_train,Y_train)\n",
        "prediction=RF.predict(X_validation)\n",
        "print(accuracy_score(Y_validation,prediction))\n",
        "print(confusion_matrix(Y_validation,predictions))\n",
        "print(classification_report(Y_validation,predictions))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.9\n",
            "[[ 7  0  0]\n",
            " [ 0 11  1]\n",
            " [ 0  2  9]]\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "         0.0       1.00      1.00      1.00         7\n",
            "         1.0       0.85      0.92      0.88        12\n",
            "         2.0       0.90      0.82      0.86        11\n",
            "\n",
            "    accuracy                           0.90        30\n",
            "   macro avg       0.92      0.91      0.91        30\n",
            "weighted avg       0.90      0.90      0.90        30\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4TrEcRR_HKmG",
        "outputId": "94e8e9bc-1717-4761-8a3f-dfa6d2fa8043"
      },
      "source": [
        "CART= DecisionTreeClassifier()\n",
        "CART.fit(X_train, Y_train)\n",
        "predictions = CART.predict(X_validation)\n",
        "print(accuracy_score(Y_validation, predictions))\n",
        "print(confusion_matrix(Y_validation, predictions))\n",
        "print(classification_report(Y_validation, predictions))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.8666666666666667\n",
            "[[ 7  0  0]\n",
            " [ 0 10  2]\n",
            " [ 0  2  9]]\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "         0.0       1.00      1.00      1.00         7\n",
            "         1.0       0.83      0.83      0.83        12\n",
            "         2.0       0.82      0.82      0.82        11\n",
            "\n",
            "    accuracy                           0.87        30\n",
            "   macro avg       0.88      0.88      0.88        30\n",
            "weighted avg       0.87      0.87      0.87        30\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "72Ny9JPRLKN9",
        "outputId": "2616f741-d490-493c-d6b3-abbcef25fd95"
      },
      "source": [
        "SVM= SVC()\n",
        "SVM.fit(X_train,Y_train)\n",
        "prediction=SVM.predict(X_validation)\n",
        "print(accuracy_score(Y_validation,prediction))\n",
        "print(confusion_matrix(Y_validation,predictions))\n",
        "print(classification_report(Y_validation,predictions))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.8666666666666667\n",
            "[[ 7  0  0]\n",
            " [ 0 10  2]\n",
            " [ 0  2  9]]\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "         0.0       1.00      1.00      1.00         7\n",
            "         1.0       0.83      0.83      0.83        12\n",
            "         2.0       0.82      0.82      0.82        11\n",
            "\n",
            "    accuracy                           0.87        30\n",
            "   macro avg       0.88      0.88      0.88        30\n",
            "weighted avg       0.87      0.87      0.87        30\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LqAO3t6fLKYM",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 521
        },
        "outputId": "64df4ded-81d2-43b6-d8a2-81ce07dee50e"
      },
      "source": [
        "NB = GaussianNB()\n",
        "NB.fit(X_train,Y_train)\n",
        "prediction=NB.predict(X_validation)\n",
        "print(accuracy_score(Y_validation,prediction))\n",
        "print(confusion_matrix(Y_validation,predictions))\n",
        "print(classification_report(Y_validation,predictions))\n",
        "from sklearn import metrics\n",
        "metrics.plot_confusion_matrix(NB, X_validation, Y_validation, cmap=plt.cm.Blues, display_labels=iris.target_names)\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.8333333333333334\n",
            "[[ 7  0  0]\n",
            " [ 0 11  1]\n",
            " [ 0  2  9]]\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "         0.0       1.00      1.00      1.00         7\n",
            "         1.0       0.85      0.92      0.88        12\n",
            "         2.0       0.90      0.82      0.86        11\n",
            "\n",
            "    accuracy                           0.90        30\n",
            "   macro avg       0.92      0.91      0.91        30\n",
            "weighted avg       0.90      0.90      0.90        30\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 521
        },
        "id": "ei6PdZB8JGP2",
        "outputId": "9f7424a7-10e4-4f71-f2e6-b34237e6025f"
      },
      "source": [
        "LD=RandomForestClassifier(n_estimators=5)\n",
        "LD.fit(X_train,Y_train)\n",
        "prediction=LD.predict(X_validation)\n",
        "print(accuracy_score(Y_validation,prediction))\n",
        "print(confusion_matrix(Y_validation,predictions))\n",
        "print(classification_report(Y_validation,predictions))\n",
        "from sklearn import metrics\n",
        "metrics.plot_confusion_matrix(LD, X_validation, Y_validation, cmap=plt.cm.Blues, display_labels=iris.target_names)\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.8666666666666667\n",
            "[[ 7  0  0]\n",
            " [ 0 11  1]\n",
            " [ 0  2  9]]\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "         0.0       1.00      1.00      1.00         7\n",
            "         1.0       0.85      0.92      0.88        12\n",
            "         2.0       0.90      0.82      0.86        11\n",
            "\n",
            "    accuracy                           0.90        30\n",
            "   macro avg       0.92      0.91      0.91        30\n",
            "weighted avg       0.90      0.90      0.90        30\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 521
        },
        "id": "CM9KpsZmkrlX",
        "outputId": "a2e12935-958a-4d03-dcdc-6c3bce3a411f"
      },
      "source": [
        "LR=RandomForestClassifier(n_estimators=5)\n",
        "LR.fit(X_train,Y_train)\n",
        "prediction=LR.predict(X_validation)\n",
        "print(accuracy_score(Y_validation,prediction))\n",
        "print(confusion_matrix(Y_validation,predictions))\n",
        "print(classification_report(Y_validation,predictions))\n",
        "from sklearn import metrics\n",
        "metrics.plot_confusion_matrix(LR, X_validation, Y_validation, cmap=plt.cm.Blues, display_labels=iris.target_names)\n",
        "plt.show()"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.8666666666666667\n",
            "[[ 7  0  0]\n",
            " [ 0 11  1]\n",
            " [ 0  2  9]]\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "         0.0       1.00      1.00      1.00         7\n",
            "         1.0       0.85      0.92      0.88        12\n",
            "         2.0       0.90      0.82      0.86        11\n",
            "\n",
            "    accuracy                           0.90        30\n",
            "   macro avg       0.92      0.91      0.91        30\n",
            "weighted avg       0.90      0.90      0.90        30\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7o_vMDXyHKxb"
      },
      "source": [
        "from mlxtend.plotting import plot_decision_regions"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 517
        },
        "id": "IYKRrgIlIyzL",
        "outputId": "40dd59ff-3f28-46b9-8164-7126c5a613c5"
      },
      "source": [
        "LDA=RandomForestClassifier(n_estimators=5)\n",
        "LDA.fit(X_train,Y_train)\n",
        "prediction=LDA.predict(X_validation)\n",
        "print(accuracy_score(Y_validation,prediction))\n",
        "print(confusion_matrix(Y_validation,predictions))\n",
        "print(classification_report(Y_validation,predictions))\n",
        "from sklearn import metrics\n",
        "metrics.plot_confusion_matrix(LDA, X_validation, Y_validation, cmap=plt.cm.Blues, display_labels=iris.target_names)\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.8666666666666667\n",
            "[[ 7  0  0]\n",
            " [ 0 11  1]\n",
            " [ 0  2  9]]\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "         0.0       1.00      1.00      1.00         7\n",
            "         1.0       0.85      0.92      0.88        12\n",
            "         2.0       0.90      0.82      0.86        11\n",
            "\n",
            "    accuracy                           0.90        30\n",
            "   macro avg       0.92      0.91      0.91        30\n",
            "weighted avg       0.90      0.90      0.90        30\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "F2a7RiGfK1Dd"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}