Irisclassification.ipynb

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  "metadata": {
    "colab": {
      "name": "Irisclassification.ipynb",
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "1DmNSzEzshto"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "from mpl_toolkits.mplot3d import Axes3D\n",
        "from sklearn import datasets"
      ],
      "execution_count": 67,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "O7GjitaHs1Ra",
        "outputId": "757e914e-c23a-4561-83ab-2fa2c0c80f7d"
      },
      "source": [
        "# Loading iris dataset\n",
        "iris = datasets.load_iris()\n",
        "# Printing iris dataset(Description)\n",
        "iris \n"
      ],
      "execution_count": 68,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'DESCR': '.. _iris_dataset:\\n\\nIris plants dataset\\n--------------------\\n\\n**Data Set Characteristics:**\\n\\n    :Number of Instances: 150 (50 in each of three classes)\\n    :Number of Attributes: 4 numeric, predictive attributes and the class\\n    :Attribute Information:\\n        - sepal length in cm\\n        - sepal width in cm\\n        - petal length in cm\\n        - petal width in cm\\n        - class:\\n                - Iris-Setosa\\n                - Iris-Versicolour\\n                - Iris-Virginica\\n                \\n    :Summary Statistics:\\n\\n    ============== ==== ==== ======= ===== ====================\\n                    Min  Max   Mean    SD   Class Correlation\\n    ============== ==== ==== ======= ===== ====================\\n    sepal length:   4.3  7.9   5.84   0.83    0.7826\\n    sepal width:    2.0  4.4   3.05   0.43   -0.4194\\n    petal length:   1.0  6.9   3.76   1.76    0.9490  (high!)\\n    petal width:    0.1  2.5   1.20   0.76    0.9565  (high!)\\n    ============== ==== ==== ======= ===== ====================\\n\\n    :Missing Attribute Values: None\\n    :Class Distribution: 33.3% for each of 3 classes.\\n    :Creator: R.A. Fisher\\n    :Donor: Michael Marshall (MARSHALL%PLU@io.arc.nasa.gov)\\n    :Date: July, 1988\\n\\nThe famous Iris database, first used by Sir R.A. Fisher. The dataset is taken\\nfrom Fisher\\'s paper. Note that it\\'s the same as in R, but not as in the UCI\\nMachine Learning Repository, which has two wrong data points.\\n\\nThis is perhaps the best known database to be found in the\\npattern recognition literature.  Fisher\\'s paper is a classic in the field and\\nis referenced frequently to this day.  (See Duda & Hart, for example.)  The\\ndata set contains 3 classes of 50 instances each, where each class refers to a\\ntype of iris plant.  One class is linearly separable from the other 2; the\\nlatter are NOT linearly separable from each other.\\n\\n.. topic:: References\\n\\n   - Fisher, R.A. \"The use of multiple measurements in taxonomic problems\"\\n     Annual Eugenics, 7, Part II, 179-188 (1936); also in \"Contributions to\\n     Mathematical Statistics\" (John Wiley, NY, 1950).\\n   - Duda, R.O., & Hart, P.E. (1973) Pattern Classification and Scene Analysis.\\n     (Q327.D83) John Wiley & Sons.  ISBN 0-471-22361-1.  See page 218.\\n   - Dasarathy, B.V. (1980) \"Nosing Around the Neighborhood: A New System\\n     Structure and Classification Rule for Recognition in Partially Exposed\\n     Environments\".  IEEE Transactions on Pattern Analysis and Machine\\n     Intelligence, Vol. PAMI-2, No. 1, 67-71.\\n   - Gates, G.W. (1972) \"The Reduced Nearest Neighbor Rule\".  IEEE Transactions\\n     on Information Theory, May 1972, 431-433.\\n   - See also: 1988 MLC Proceedings, 54-64.  Cheeseman et al\"s AUTOCLASS II\\n     conceptual clustering system finds 3 classes in the data.\\n   - Many, many more ...',\n",
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              "        [6.2, 3.4, 5.4, 2.3],\n",
              "        [5.9, 3. , 5.1, 1.8]]),\n",
              " 'feature_names': ['sepal length (cm)',\n",
              "  'sepal width (cm)',\n",
              "  'petal length (cm)',\n",
              "  'petal width (cm)'],\n",
              " 'filename': '/usr/local/lib/python3.7/dist-packages/sklearn/datasets/data/iris.csv',\n",
              " 'target': array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
              "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
              "        0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "        1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "        1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
              "        2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
              "        2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2]),\n",
              " 'target_names': array(['setosa', 'versicolor', 'virginica'], dtype='<U10')}"
            ]
          },
          "metadata": {},
          "execution_count": 68
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rCV8UE1ZtZEk"
      },
      "source": [
        "X = iris.data[:, :4]  # Storing features, Sepal length, Sepal width, Petal length and Petal width in the variable X\n",
        "Y = iris.target        # Storing class labels or target values in the variable Y"
      ],
      "execution_count": 69,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qrOP67Q-uCve",
        "outputId": "e5b633f8-4d55-4912-ac01-068fe9cbd4e7"
      },
      "source": [
        "X"
      ],
      "execution_count": 70,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "array([[5.1, 3.5, 1.4, 0.2],\n",
              "       [4.9, 3. , 1.4, 0.2],\n",
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              "       [6.3, 3.3, 6. , 2.5],\n",
              "       [5.8, 2.7, 5.1, 1.9],\n",
              "       [7.1, 3. , 5.9, 2.1],\n",
              "       [6.3, 2.9, 5.6, 1.8],\n",
              "       [6.5, 3. , 5.8, 2.2],\n",
              "       [7.6, 3. , 6.6, 2.1],\n",
              "       [4.9, 2.5, 4.5, 1.7],\n",
              "       [7.3, 2.9, 6.3, 1.8],\n",
              "       [6.7, 2.5, 5.8, 1.8],\n",
              "       [7.2, 3.6, 6.1, 2.5],\n",
              "       [6.5, 3.2, 5.1, 2. ],\n",
              "       [6.4, 2.7, 5.3, 1.9],\n",
              "       [6.8, 3. , 5.5, 2.1],\n",
              "       [5.7, 2.5, 5. , 2. ],\n",
              "       [5.8, 2.8, 5.1, 2.4],\n",
              "       [6.4, 3.2, 5.3, 2.3],\n",
              "       [6.5, 3. , 5.5, 1.8],\n",
              "       [7.7, 3.8, 6.7, 2.2],\n",
              "       [7.7, 2.6, 6.9, 2.3],\n",
              "       [6. , 2.2, 5. , 1.5],\n",
              "       [6.9, 3.2, 5.7, 2.3],\n",
              "       [5.6, 2.8, 4.9, 2. ],\n",
              "       [7.7, 2.8, 6.7, 2. ],\n",
              "       [6.3, 2.7, 4.9, 1.8],\n",
              "       [6.7, 3.3, 5.7, 2.1],\n",
              "       [7.2, 3.2, 6. , 1.8],\n",
              "       [6.2, 2.8, 4.8, 1.8],\n",
              "       [6.1, 3. , 4.9, 1.8],\n",
              "       [6.4, 2.8, 5.6, 2.1],\n",
              "       [7.2, 3. , 5.8, 1.6],\n",
              "       [7.4, 2.8, 6.1, 1.9],\n",
              "       [7.9, 3.8, 6.4, 2. ],\n",
              "       [6.4, 2.8, 5.6, 2.2],\n",
              "       [6.3, 2.8, 5.1, 1.5],\n",
              "       [6.1, 2.6, 5.6, 1.4],\n",
              "       [7.7, 3. , 6.1, 2.3],\n",
              "       [6.3, 3.4, 5.6, 2.4],\n",
              "       [6.4, 3.1, 5.5, 1.8],\n",
              "       [6. , 3. , 4.8, 1.8],\n",
              "       [6.9, 3.1, 5.4, 2.1],\n",
              "       [6.7, 3.1, 5.6, 2.4],\n",
              "       [6.9, 3.1, 5.1, 2.3],\n",
              "       [5.8, 2.7, 5.1, 1.9],\n",
              "       [6.8, 3.2, 5.9, 2.3],\n",
              "       [6.7, 3.3, 5.7, 2.5],\n",
              "       [6.7, 3. , 5.2, 2.3],\n",
              "       [6.3, 2.5, 5. , 1.9],\n",
              "       [6.5, 3. , 5.2, 2. ],\n",
              "       [6.2, 3.4, 5.4, 2.3],\n",
              "       [5.9, 3. , 5.1, 1.8]])"
            ]
          },
          "metadata": {},
          "execution_count": 70
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "o2rT-bvjvAOm"
      },
      "source": [
        "from sklearn.metrics import confusion_matrix\n",
        "from sklearn.metrics import classification_report\n",
        "from sklearn.model_selection import train_test_split"
      ],
      "execution_count": 71,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "p68phT0uvnjh"
      },
      "source": [
        "# Splitting dataset into train and test set\n",
        "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size = 0.2, random_state = 0)# 20% of the dataset used or testing "
      ],
      "execution_count": 72,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-xGcf8SXwIqW",
        "outputId": "c8931a4d-118b-4895-f764-dc751f6c6d47"
      },
      "source": [
        "len(X_train) # length of the training set"
      ],
      "execution_count": 73,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "120"
            ]
          },
          "metadata": {},
          "execution_count": 73
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5iwpTmlcwItw",
        "outputId": "c803d9e3-5f86-47ce-eed5-8c0326bbde22"
      },
      "source": [
        "len(Y_test) # length of the test set"
      ],
      "execution_count": 74,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "30"
            ]
          },
          "metadata": {},
          "execution_count": 74
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UI1ozXBiwsx6"
      },
      "source": [
        "from sklearn.metrics import accuracy_score"
      ],
      "execution_count": 75,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hjW9UMfhwxsZ",
        "outputId": "276ea796-b09b-4e74-c61f-5539fb40119a"
      },
      "source": [
        "# Decision Tree Classifier\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "import pandas as pd\n",
        "classifier = DecisionTreeClassifier()\n",
        "classifier.fit(X_train, Y_train)\n",
        "Y_prediction = classifier.predict(X_test)\n",
        "table=pd.DataFrame()\n",
        "results=table.assign(Actual_Label=[Y_test],Predicted_Label=[Y_prediction])# create table with actual and predicted labels\n",
        "print('.....Results of Decision Tree Classifier.....\\n') \n",
        "print(results)\n",
        "print(\"Confusion matrix:\\n\",confusion_matrix(Y_test, Y_prediction))\n",
        "# Accuracy score\n",
        "acc1 = accuracy_score(Y_test, Y_prediction) # Comparing actual labels(Y_test) with predicted labels(Y_prediction)\n",
        "print(\"Accuracy: {:.4%}\".format(acc1)) # Printing accuracy   "
      ],
      "execution_count": 76,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            ".....Results of Decision Tree Classifier.....\n",
            "\n",
            "                                        Actual_Label                                    Predicted_Label\n",
            "0  [2, 1, 0, 2, 0, 2, 0, 1, 1, 1, 2, 1, 1, 1, 1, ...  [2, 1, 0, 2, 0, 2, 0, 1, 1, 1, 2, 1, 1, 1, 1, ...\n",
            "Confusion matrix:\n",
            " [[11  0  0]\n",
            " [ 0 13  0]\n",
            " [ 0  0  6]]\n",
            "Accuracy: 100.0000%\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XwWvoMywygWV",
        "outputId": "dbb60dd2-53ee-40e3-e66a-9f656b5544c7"
      },
      "source": [
        "# Random Forest Classifier\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "classifier = RandomForestClassifier()\n",
        "classifier.fit(X_train, Y_train)\n",
        "Y_prediction = classifier.predict(X_test)\n",
        "table=pd.DataFrame()\n",
        "results=table.assign(Actual_Label=[Y_test],Predicted_Label=[Y_prediction])# create table with actual and predicted labels\n",
        "print('.....Results of Random Forest Classifier.....\\n') \n",
        "print(results)\n",
        "print(\"Confusion matrix:\\n\",confusion_matrix(Y_test, Y_prediction))\n",
        "# Accuracy score\n",
        "acc2 = accuracy_score(Y_test, Y_prediction) # Comparing actual labels(Y_test) with predicted labels(Y_prediction)\n",
        "print(\"Accuracy: {:.4%}\".format(acc2)) # Printing accuracy   "
      ],
      "execution_count": 77,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            ".....Results of Random Forest Classifier.....\n",
            "\n",
            "                                        Actual_Label                                    Predicted_Label\n",
            "0  [2, 1, 0, 2, 0, 2, 0, 1, 1, 1, 2, 1, 1, 1, 1, ...  [2, 1, 0, 2, 0, 2, 0, 1, 1, 1, 1, 1, 1, 1, 1, ...\n",
            "Confusion matrix:\n",
            " [[11  0  0]\n",
            " [ 0 13  0]\n",
            " [ 0  1  5]]\n",
            "Accuracy: 96.6667%\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ofC3jLsFIBGt",
        "outputId": "0ed9e073-22c8-4e32-d1e3-091090c4ca5c"
      },
      "source": [
        "# K-Nearest Neighbour(K-NN)\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "classifier = KNeighborsClassifier(n_neighbors=6) # K-NN with 6 neighbors\n",
        "classifier.fit(X_train, Y_train)\n",
        "Y_prediction = classifier.predict(X_test)\n",
        "table=pd.DataFrame()\n",
        "results=table.assign(Actual_Label=[Y_test],Predicted_Label=[Y_prediction])# create table with actual and predicted labels\n",
        "print('.....K-Nearest Neighbour(K-NN).....\\n') \n",
        "print(results)\n",
        "print(\"Confusion matrix:\\n\",confusion_matrix(Y_test, Y_prediction))\n",
        "# Accuracy score\n",
        "acc3 = accuracy_score(Y_test, Y_prediction) # Comparing actual labels(Y_test) with predicted labels(Y_prediction)\n",
        "print(\"Accuracy: {:.4%}\".format(acc3)) # Printing accuracy   "
      ],
      "execution_count": 78,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            ".....K-Nearest Neighbour(K-NN).....\n",
            "\n",
            "                                        Actual_Label                                    Predicted_Label\n",
            "0  [2, 1, 0, 2, 0, 2, 0, 1, 1, 1, 2, 1, 1, 1, 1, ...  [2, 1, 0, 2, 0, 2, 0, 1, 1, 1, 2, 1, 1, 1, 1, ...\n",
            "Confusion matrix:\n",
            " [[11  0  0]\n",
            " [ 0 13  0]\n",
            " [ 0  0  6]]\n",
            "Accuracy: 100.0000%\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vfdHFepqIoaG",
        "outputId": "c44b0dcb-6e63-4194-c436-f99f5d2f0bcf"
      },
      "source": [
        "# Support Vector Machine\n",
        "from sklearn.svm import SVC\n",
        "classifier = SVC()\n",
        "classifier.fit(X_train, Y_train)\n",
        "Y_prediction = classifier.predict(X_test)\n",
        "table=pd.DataFrame()\n",
        "results=table.assign(Actual_Label=[Y_test],Predicted_Label=[Y_prediction])# create table with actual and predicted labels\n",
        "print('.....Results of Support Vector Machine.....\\n') \n",
        "print(results)\n",
        "print(\"Confusion matrix:\\n\",confusion_matrix(Y_test, Y_prediction))\n",
        "# Accuracy score\n",
        "acc4 = accuracy_score(Y_test, Y_prediction) # Comparing actual labels(Y_test) with predicted labels(Y_prediction)\n",
        "print(\"Accuracy: {:.4%}\".format(acc4)) # Printing accuracy "
      ],
      "execution_count": 79,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            ".....Results of Support Vector Machine.....\n",
            "\n",
            "                                        Actual_Label                                    Predicted_Label\n",
            "0  [2, 1, 0, 2, 0, 2, 0, 1, 1, 1, 2, 1, 1, 1, 1, ...  [2, 1, 0, 2, 0, 2, 0, 1, 1, 1, 2, 1, 1, 1, 1, ...\n",
            "Confusion matrix:\n",
            " [[11  0  0]\n",
            " [ 0 13  0]\n",
            " [ 0  0  6]]\n",
            "Accuracy: 100.0000%\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "3yx9VEw9GKRQ",
        "outputId": "9fcdb4b0-4916-4502-dd25-1d559fbbf192"
      },
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "xdata = ['Decision Tree', 'Random Forest', 'K-NN',\n",
        "\t\t'SVM']\n",
        "yvalues= [acc1,acc2,acc3,acc4]\n",
        "\n",
        "fig = plt.figure(figsize = (6, 4))\n",
        "\n",
        "# creating the bar plot\n",
        "plt.bar(xdata, yvalues, color ='green',\n",
        "\t\twidth = 0.2)\n",
        "\n",
        "plt.xlabel(\"Classifiers\")\n",
        "plt.ylabel(\"Accuracy\")\n",
        "plt.title(\"Classifiers VS Accuracy\")\n",
        "plt.show()\n"
      ],
      "execution_count": 80,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}