Iris&Cancer_Dataset.ipynb

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        "<a href=\"https://colab.research.google.com/github/Akshat111999/MLsummer19/blob/master/Iris%26Cancer%20Dataset.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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    {
      "cell_type": "code",
      "metadata": {
        "id": "LCnix-9QuHDC",
        "colab_type": "code",
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      },
      "source": [
        "# loading iris data set\n",
        "from sklearn.datasets import load_iris\n",
        "# loading cancer data set\n",
        "from sklearn.datasets import load_breast_cancer\n",
        "#for calculating accuracy score\n",
        "from sklearn.metrics import accuracy_score  "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "K2Yr3EeMy4jt",
        "colab_type": "text"
      },
      "source": [
        "## IRIS dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_8r_2fJpuk2u",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "iris_data=load_iris()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "d_JEme8Fu9je",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        },
        "outputId": "8b18a6ec-72a6-4eeb-b19b-e4be19c15cda"
      },
      "source": [
        "dir(iris_data)"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['DESCR', 'data', 'feature_names', 'filename', 'target', 'target_names']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iPJKKawHuqxQ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "iris_features=iris_data.data"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vko7WMHTu2Z9",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "iris_label=iris_data.target"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dVcNod_9vena",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# separating data into training and testing\n",
        "from sklearn.model_selection import train_test_split  "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1NfuGbV0vzD6",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#seperating data\n",
        "train_data,test_data,label_train,label_test = train_test_split(iris_features,iris_label,test_size=0.2)  # 80% training data\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "At9TMJoI2cPe",
        "colab_type": "text"
      },
      "source": [
        "KNN\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7e77sr9Uv3rS",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# importing knn classifier\n",
        "from sklearn.neighbors import KNeighborsClassifier"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TNRQ1ugnv-lt",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#calling KNN\n",
        "iris_kclf=KNeighborsClassifier()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "j5miNLivwPDw",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#training the model\n",
        "iris_ktraining=iris_kclf.fit(train_data,label_train)\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CG1wMrwfwovX",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#prediction\n",
        "iris_kpredict= iris_ktraining.predict(test_data)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lWQPSMCNw7Le",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        },
        "outputId": "391a4d4a-593c-4eae-af92-ea03dba351b6"
      },
      "source": [
        "iris_kpredict"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([2, 2, 0, 0, 0, 2, 2, 1, 1, 0, 2, 0, 2, 2, 1, 0, 1, 0, 2, 1, 1, 0,\n",
              "       0, 2, 0, 0, 2, 0, 1, 0])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        }
      ]
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      "cell_type": "code",
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        "outputId": "78f303b3-77f5-4bb0-9927-17d5f97e68bb"
      },
      "source": [
        "label_test"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([2, 2, 0, 0, 0, 2, 2, 1, 1, 0, 2, 0, 1, 2, 1, 0, 1, 0, 2, 1, 1, 0,\n",
              "       0, 2, 0, 0, 2, 0, 1, 0])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OaD9taNB20z2",
        "colab_type": "text"
      },
      "source": [
        "Accuracy"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Z_IWRhP1xEon",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#checking accuracy\n",
        "iris_kaccr=int(accuracy_score(label_test,iris_kpredict)*100)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rrBDonOKxf-8",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        },
        "outputId": "01957476-1105-44e4-8d9a-3c75dcf0ad48"
      },
      "source": [
        "iris_kaccr"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "96"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WmQlXzh32fKq",
        "colab_type": "text"
      },
      "source": [
        "Decision Tree Classifier"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lmDIOMZoxh2N",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.tree import DecisionTreeClassifier"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6H6RKjCxxv2k",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#calling decision tree classifier\n",
        "iris_dclf=DecisionTreeClassifier()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Q1-cwhN_x3NC",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# training the model\n",
        "iris_dtraining=iris_dclf.fit(train_data,label_train)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WO4b9LexyTIQ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#prediction\n",
        "iris_dpredict= iris_dtraining.predict(test_data)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uRgicfu9ybi4",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        },
        "outputId": "7fdd2456-da0a-4260-cbea-f6388c6498fe"
      },
      "source": [
        "iris_dpredict"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1, 2, 0, 0, 0, 1, 2, 1, 1, 0, 2, 0, 2, 2, 1, 0, 1, 0, 2, 1, 1, 0,\n",
              "       0, 2, 0, 0, 2, 0, 1, 0])"
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          },
          "metadata": {
            "tags": []
          },
          "execution_count": 27
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    {
      "cell_type": "code",
      "metadata": {
        "id": "RRO0lnRzydaM",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
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        "outputId": "c145e9a0-ee69-4eb1-8b5a-e6f6ee3c2536"
      },
      "source": [
        "label_test"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([2, 2, 0, 0, 0, 2, 2, 1, 1, 0, 2, 0, 1, 2, 1, 0, 1, 0, 2, 1, 1, 0,\n",
              "       0, 2, 0, 0, 2, 0, 1, 0])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cN65gHjG2kpq",
        "colab_type": "text"
      },
      "source": [
        "Accuracy"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "40SILwM6yf74",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "iris_dacc=int(accuracy_score(label_test, iris_dpredict)*100)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oyoviw-yypyp",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        },
        "outputId": "e6dc0ce9-6f65-4e80-c7c4-25776c6d53f6"
      },
      "source": [
        "iris_dacc"
      ],
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "90"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Jmcd7NaYyt3A",
        "colab_type": "text"
      },
      "source": [
        "## Cancer dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gdjZpuy0yr8Y",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "cancer_data=load_breast_cancer()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0VEFTPM6zIOZ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        },
        "outputId": "26d069b1-13b7-43d3-82d5-9d4d547f9fd5"
      },
      "source": [
        "dir(cancer_data)"
      ],
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['DESCR', 'data', 'feature_names', 'filename', 'target', 'target_names']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "T1XlSODty_yu",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "cancer_features=cancer_data.data"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MUGRiianzFA4",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "cancer_label=cancer_data.target"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8IxfkHQUzOlp",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#seperating data\n",
        "ctrain_data,ctest_data,clabel_train,clabel_test = train_test_split(cancer_features,cancer_label,test_size=0.2)  # 80% training data\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bGrKsgmV2n_R",
        "colab_type": "text"
      },
      "source": [
        "KNN"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Cq2qizFazgt1",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "cancer_kclf=KNeighborsClassifier()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UPx-cnVTztCL",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "cancer_ktraining=cancer_kclf.fit(ctrain_data,clabel_train)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KfF6e7PZz4Mz",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "cancer_kpredict=cancer_ktraining.predict(ctest_data)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lIGj2d9Mz_s2",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 129
        },
        "outputId": "96f996b3-abe6-4eeb-80ca-481b9108d152"
      },
      "source": [
        "cancer_kpredict"
      ],
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1, 1, 0, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 0, 1, 1, 0, 0, 0, 1, 1,\n",
              "       1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 1, 1, 0,\n",
              "       1, 0, 0, 1, 0, 0, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 0, 0, 1, 0, 1, 1,\n",
              "       1, 1, 1, 0, 1, 1, 1, 1, 0, 0, 1, 0, 0, 0, 1, 1, 0, 0, 1, 0, 0, 1,\n",
              "       0, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1,\n",
              "       0, 0, 1, 1])"
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          "execution_count": 39
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        "id": "t-auaQUa0Bt2",
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          "height": 129
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        "outputId": "05449d23-6d87-4c83-d419-8e500420e683"
      },
      "source": [
        "clabel_test"
      ],
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 0, 1, 1, 0, 0, 0, 1, 1,\n",
              "       1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 1, 1, 0,\n",
              "       1, 0, 0, 1, 0, 0, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 0, 0, 0, 0, 1, 1,\n",
              "       1, 0, 1, 0, 1, 1, 1, 1, 0, 0, 1, 0, 0, 0, 1, 1, 0, 0, 1, 0, 0, 1,\n",
              "       0, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1,\n",
              "       0, 0, 1, 1])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 40
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    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SZwY5LWc2rc5",
        "colab_type": "text"
      },
      "source": [
        "Accuracy"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mfRw-gNR0Dtg",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "cancer_kacc=int(accuracy_score(clabel_test,cancer_kpredict)*100)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Mahewsbu0Mq1",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        },
        "outputId": "93ef06ab-4c0d-4dfd-cb0f-15830685fd82"
      },
      "source": [
        "cancer_kacc"
      ],
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "94"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DMXPdzIg2tgv",
        "colab_type": "text"
      },
      "source": [
        "Decision Tree Classifier"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ypDdehNK0Oz0",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "cancer_dclf=DecisionTreeClassifier()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Nf0MOjh10Xb7",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#training\n",
        "cancer_dtraining=cancer_dclf.fit(ctrain_data,clabel_train)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5c9ydVJP0zaw",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#prediction\n",
        "cancer_dpredict=cancer_dtraining.predict(ctest_data)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "A9G7sRMj09X7",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 129
        },
        "outputId": "6b5355e3-b1e7-4f03-9d1f-5e917c3fa2f4"
      },
      "source": [
        "cancer_dpredict"
      ],
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1, 1, 0, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 0, 1, 1, 0, 0, 0, 0, 1,\n",
              "       1, 1, 0, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 0, 1, 0, 0, 1, 1, 0,\n",
              "       1, 0, 0, 1, 0, 0, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 0, 0, 0, 0, 1, 1,\n",
              "       1, 0, 1, 0, 1, 1, 1, 1, 0, 0, 1, 0, 0, 0, 1, 1, 0, 0, 1, 0, 0, 1,\n",
              "       0, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1,\n",
              "       0, 0, 1, 1])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3SBycfaM1ABG",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 129
        },
        "outputId": "f15949b9-199b-4da1-e2b0-a9c8d70dcb17"
      },
      "source": [
        "clabel_test"
      ],
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 0, 1, 1, 0, 0, 0, 1, 1,\n",
              "       1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 1, 1, 0,\n",
              "       1, 0, 0, 1, 0, 0, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 0, 0, 0, 0, 1, 1,\n",
              "       1, 0, 1, 0, 1, 1, 1, 1, 0, 0, 1, 0, 0, 0, 1, 1, 0, 0, 1, 0, 0, 1,\n",
              "       0, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1,\n",
              "       0, 0, 1, 1])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4QSFBXPs2xwj",
        "colab_type": "text"
      },
      "source": [
        "Accuracy"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "l5ms9hjO1B6t",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        },
        "outputId": "eaebefc8-7b6d-414f-bd85-15100d9a2456"
      },
      "source": [
        "#checking accuracy\n",
        "cancer_dacc=int(accuracy_score(clabel_test,cancer_dpredict)*100)\n",
        "cancer_dacc"
      ],
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "93"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DZOP8GDI1UOt",
        "colab_type": "text"
      },
      "source": [
        "## Plotting graph"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WN5tr8og1PJs",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import numpy as np"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0fPK4Mdn1fQ_",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "knn=[iris_kaccr,cancer_kacc]    # accuracy scores of knn classifier at values of k=3,5,7\n",
        "dtc=[iris_dacc,cancer_dacc]          # accuracy scores of decision tree classfier  \n",
        "x=np.arange(len(knn))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "D0jEaC1x110h",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 302
        },
        "outputId": "7b213e0d-276b-44ca-bb95-6f13b5374fb2"
      },
      "source": [
        "bar_width=0.15\n",
        "\n",
        "rects1=plt.bar(x, knn,width=bar_width, zorder=2 , label='KNN')\n",
        "rects2=plt.bar(x+bar_width, dtc, width=bar_width, zorder=2, label='Decision Tree Classifier' )\n",
        "\n",
        "plt.grid(axis='y')\n",
        "def autolabel(rects):\n",
        "    for rect in rects:\n",
        "        h = rect.get_height()\n",
        "        plt.text(rect.get_x()+rect.get_width()/2., 1.023*h, '%d'%int(h),ha='center', va='bottom')\n",
        "\n",
        "autolabel(rects1)\n",
        "autolabel(rects2)\n",
        "\n",
        "plt.xlabel('Datasets')\n",
        "plt.ylabel('Accuracy')\n",
        "plt.ylim(0,140)\n",
        "plt.xticks(x+bar_width*0.45, ['IRIS', 'Breast Cancer'])\n",
        "\n",
        "plt.legend()"
      ],
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7f7cf5afb860>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 56
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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RyqMnykVp/vz5PPfcc8yZM4datWrRtWtXunXrxooVKxg/fjyff/45KSkpFBQUJLpUEZFK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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ro4-NLkP2QVz",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "knn=[iris_kaccr,iris_dacc]    # accuracy scores of knn classifier at values of k=3,5,7\n",
        "dtc=[cancer_kacc,cancer_dacc]          # accuracy scores of decision tree classfier  \n",
        "x=np.arange(len(knn))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ityz34mD3w__",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 302
        },
        "outputId": "fcd3baad-b796-4ac1-bf0a-0e60bf6f7c85"
      },
      "source": [
        "bar_width=0.15\n",
        "\n",
        "rects1=plt.bar(x, knn,width=bar_width, zorder=2 , label='Iris')\n",
        "rects2=plt.bar(x+bar_width, dtc, width=bar_width, zorder=2, label='Breast Cancer' )\n",
        "\n",
        "plt.grid(axis='y')\n",
        "def autolabel(rects):\n",
        "    for rect in rects:\n",
        "        h = rect.get_height()\n",
        "        plt.text(rect.get_x()+rect.get_width()/2., 1.023*h, '%d'%int(h),ha='center', va='bottom')\n",
        "\n",
        "autolabel(rects1)\n",
        "autolabel(rects2)\n",
        "\n",
        "plt.xlabel('Datasets')\n",
        "plt.ylabel('Accuracy')\n",
        "plt.ylim(0,140)\n",
        "plt.xticks(x+bar_width*0.45, ['KNN', 'Decision Tree Classifier'])\n",
        "\n",
        "plt.legend()"
      ],
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7f7cf5afba20>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 58
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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RFX6QfKtuLena5Bv1K5K+V7uupAskvZqcjVydzLtT0onJ46slvZZsd10y7zJJ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\ncliYmVkqh4WZmaVyWJiZWSqHhZmZpfr/jY6n8UFdYfIAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uftpFC-I3-Vg",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}