EnsembleModels_VotingClassifier.ipynb

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      "language": "python",
      "name": "python3"
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    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
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      "file_extension": ".py",
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      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.6.8"
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    "colab": {
      "name": "EnsembleModels_VotingClassifier.ipynb",
      "provenance": [],
      "collapsed_sections": [
        "E0HIi9Bnge-1"
      ]
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QF0Td4T6wUW7"
      },
      "source": [
        "# Necessary Imports\r\n",
        "\r\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PFK76vDkgtgl"
      },
      "source": [
        "#import necessary libraries\r\n",
        "import pandas as pd\r\n",
        "import numpy as np\r\n",
        "import pandas as pd\r\n",
        "import numpy as np\r\n",
        "\r\n",
        "from sklearn.pipeline import Pipeline\r\n",
        "from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier, BaggingClassifier\r\n",
        "from sklearn.tree import DecisionTreeClassifier\r\n",
        "from sklearn.preprocessing import StandardScaler\r\n",
        "from sklearn.model_selection import GridSearchCV\r\n",
        "from sklearn.metrics import accuracy_score\r\n",
        "from sklearn.tree import export_graphviz\r\n",
        "\r\n",
        "import matplotlib.pyplot as plt\r\n",
        "import seaborn as sns\r\n",
        "\r\n",
        "from mlxtend.plotting import plot_decision_regions"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Y2OwznQxxEZU"
      },
      "source": [
        "# Dataset\r\n",
        "\r\n",
        "The data is taken from the UCI Machine Learning Repository. A csv file is available in the /raw-data directory and can also be downloaded from the source: https://archive.ics.uci.edu/ml/datasets/Heart+Disease . While there are multiple datasets available, we have use the one processed by the Cleveland Clinic Foundation. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PPnqxTaOW3Eh",
        "outputId": "c1d7c745-f112-4c47-f26a-76dd09b48da2"
      },
      "source": [
        "from google.colab import drive\r\n",
        "drive.mount(\"/content/drive\")"
      ],
      "execution_count": null,
      "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": "4lTiij6wgtjw"
      },
      "source": [
        "# step1: import raw data into a pandas dataframe\r\n",
        "rawdf = pd.read_csv(\"/content/sample_data/cleve_heart_data.csv\")\r\n",
        "data = rawdf.copy()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 195
        },
        "id": "GqnyD4mLgtmK",
        "outputId": "4acaa1ca-801a-4cb3-c450-9436cc89fa2a"
      },
      "source": [
        "#Step2: Explore its contents\r\n",
        "data.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
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              "    .dataframe tbody tr th:only-of-type {\n",
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              "    }\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>age</th>\n",
              "      <th>sex</th>\n",
              "      <th>cp</th>\n",
              "      <th>trestbps</th>\n",
              "      <th>chol</th>\n",
              "      <th>fbs</th>\n",
              "      <th>restecg</th>\n",
              "      <th>thalach</th>\n",
              "      <th>exang</th>\n",
              "      <th>oldpeak</th>\n",
              "      <th>slope</th>\n",
              "      <th>ca</th>\n",
              "      <th>thal</th>\n",
              "      <th>num</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>63.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>145.0</td>\n",
              "      <td>233.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>150.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.3</td>\n",
              "      <td>3.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>67.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>160.0</td>\n",
              "      <td>286.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>108.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.5</td>\n",
              "      <td>2.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>67.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>120.0</td>\n",
              "      <td>229.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>129.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.6</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>37.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>130.0</td>\n",
              "      <td>250.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>187.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3.5</td>\n",
              "      <td>3.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>41.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>130.0</td>\n",
              "      <td>204.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>172.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.4</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    age  sex   cp  trestbps   chol  fbs  ...  exang  oldpeak  slope   ca  thal num\n",
              "0  63.0  1.0  1.0     145.0  233.0  1.0  ...    0.0      2.3    3.0  0.0   6.0   0\n",
              "1  67.0  1.0  4.0     160.0  286.0  0.0  ...    1.0      1.5    2.0  3.0   3.0   2\n",
              "2  67.0  1.0  4.0     120.0  229.0  0.0  ...    1.0      2.6    2.0  2.0   7.0   1\n",
              "3  37.0  1.0  3.0     130.0  250.0  0.0  ...    0.0      3.5    3.0  0.0   3.0   0\n",
              "4  41.0  0.0  2.0     130.0  204.0  0.0  ...    0.0      1.4    1.0  0.0   3.0   0\n",
              "\n",
              "[5 rows x 14 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BRgVg8-8gtpA",
        "outputId": "d0513e3d-ad2e-4439-aed9-722fed3de713"
      },
      "source": [
        "data.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(303, 14)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GbrAK-7nyE6f"
      },
      "source": [
        "# 2.Data Pre-processing"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DRACw1-0yr5H",
        "outputId": "575f99c1-be12-4f9a-d989-5f4f161667bc"
      },
      "source": [
        "data.info()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 303 entries, 0 to 302\n",
            "Data columns (total 14 columns):\n",
            " #   Column    Non-Null Count  Dtype  \n",
            "---  ------    --------------  -----  \n",
            " 0   age       303 non-null    float64\n",
            " 1   sex       303 non-null    float64\n",
            " 2   cp        303 non-null    float64\n",
            " 3   trestbps  303 non-null    float64\n",
            " 4   chol      303 non-null    float64\n",
            " 5   fbs       303 non-null    float64\n",
            " 6   restecg   303 non-null    float64\n",
            " 7   thalach   303 non-null    float64\n",
            " 8   exang     303 non-null    float64\n",
            " 9   oldpeak   303 non-null    float64\n",
            " 10  slope     303 non-null    float64\n",
            " 11  ca        303 non-null    object \n",
            " 12  thal      303 non-null    object \n",
            " 13  num       303 non-null    int64  \n",
            "dtypes: float64(11), int64(1), object(2)\n",
            "memory usage: 33.3+ KB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kPHgr7JXgtsT",
        "outputId": "6390894b-6fce-401e-c1ce-96e4627f29cb"
      },
      "source": [
        "#Check object datatypes\r\n",
        "data.dtypes"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "age         float64\n",
              "sex         float64\n",
              "cp          float64\n",
              "trestbps    float64\n",
              "chol        float64\n",
              "fbs         float64\n",
              "restecg     float64\n",
              "thalach     float64\n",
              "exang       float64\n",
              "oldpeak     float64\n",
              "slope       float64\n",
              "ca           object\n",
              "thal         object\n",
              "num           int64\n",
              "dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "x5cygCoAgtv4",
        "outputId": "9839b698-77e5-4e4e-ce67-7e143d62fa05"
      },
      "source": [
        "#Step 3: Check for missing values\r\n",
        "data.isnull().values.any()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "False"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IQOZ7bP9gtzu",
        "outputId": "b8c44ff3-6796-4a4b-de6f-32f82eea7f97"
      },
      "source": [
        "#Great. No missing values to deal with. \r\n",
        "#Check values of response\r\n",
        "data[\"num\"].unique()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 2, 1, 3, 4])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LstPiZAEgt17"
      },
      "source": [
        "#Step 4: Convert response variable to bindary values i.e. make all num values >0 to 1\r\n",
        "data.loc[data.num>0,[\"num\"]] = 1"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-_ndYGaFgt5g",
        "outputId": "8b813aeb-5db3-4b5e-8194-4f9089b38eb4"
      },
      "source": [
        "data[\"num\"].unique()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 1])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nLSnjZARhdPC"
      },
      "source": [
        "#Step 5 & 6: Divide into train and test dataframes & Drop all columns except thalach and chol\r\n",
        "\r\n",
        "\r\n",
        "#Setting a random seed ensures that the splits are reproducible\r\n",
        "np.random.seed(143)\r\n",
        "\r\n",
        "msk = np.random.rand(len(data)) < 0.75\r\n",
        "train = data[msk][[\"thalach\", \"chol\", \"num\"]]\r\n",
        "test = data[~msk][[\"thalach\", \"chol\", \"num\"]]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "R80TlTr9hdRU",
        "outputId": "e0298ef5-6f31-48c2-e568-c94a11691ed0"
      },
      "source": [
        "#Check how the split looks\r\n",
        "train.shape, test.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((228, 3), (75, 3))"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "n0z-mE5NhdTe"
      },
      "source": [
        "#write train and test dataframes to csv files without index columns\r\n",
        "train.to_csv(\"train.csv\", index = False)\r\n",
        "test.to_csv(\"test.csv\", index = False)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gnhhp4Z_ge-G"
      },
      "source": [
        "#Read train and test datasets\n",
        "traindf = pd.read_csv(\"train.csv\")\n",
        "testdf = pd.read_csv(\"test.csv\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 195
        },
        "id": "5HLG_VgWge-I",
        "outputId": "72a0d222-99af-48b7-f015-34d564dfed23"
      },
      "source": [
        "#Take a look at the contents of traindf\n",
        "traindf.head()"
      ],
      "execution_count": null,
      "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",
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              "\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>thalach</th>\n",
              "      <th>chol</th>\n",
              "      <th>num</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>150.0</td>\n",
              "      <td>233.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>108.0</td>\n",
              "      <td>286.0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>129.0</td>\n",
              "      <td>229.0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>187.0</td>\n",
              "      <td>250.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>172.0</td>\n",
              "      <td>204.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   thalach   chol  num\n",
              "0    150.0  233.0    0\n",
              "1    108.0  286.0    1\n",
              "2    129.0  229.0    1\n",
              "3    187.0  250.0    0\n",
              "4    172.0  204.0    0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oY2-Z0Bige-M",
        "outputId": "df21fa53-8495-4fc7-db40-d4eb9e379f97"
      },
      "source": [
        "traindf.dtypes"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "thalach    float64\n",
              "chol       float64\n",
              "num          int64\n",
              "dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EkugWJi2ge-O"
      },
      "source": [
        "#Convert train and test dataframes into X,y numpy arrays\n",
        "X_train = traindf.values[:,:-1]\n",
        "y_train = traindf.values[:,-1]\n",
        "\n",
        "X_test = testdf.values[:,:-1]\n",
        "y_test = testdf.values[:,-1]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZQk3bNTSge-Q",
        "outputId": "383a283a-fb20-4351-9182-cfb8f1992fc7"
      },
      "source": [
        "X_train.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(228, 2)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vI8cSzkFge-Q",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 285
        },
        "outputId": "243c7dc3-3155-45f5-9357-60b7b0952291"
      },
      "source": [
        "#Visualize training data on a scatter plot\n",
        "sns.set(style='darkgrid')\n",
        "plt.scatter(X_train[y_train==0,0],X_train[y_train==0,1], c='skyblue', marker= 's', edgecolors='k', alpha =0.9, label = '0-normal')\n",
        "plt.scatter(X_train[y_train==1,0],X_train[y_train==1,1], c='darkorange', marker= '^', edgecolors='k', alpha =0.9, label = '1-diseased')\n",
        "\n",
        "plt.legend()\n",
        "plt.xlabel(\"thalach - max heart rate\")\n",
        "plt.ylabel(\"chol - serum cholesterol\")\n",
        "plt.savefig(\"/content/sample_data/eda.png\")\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 106
        },
        "id": "RXWiiWJLge-V",
        "outputId": "6328c88b-dbc0-45d0-87c1-1c2e68582f3f"
      },
      "source": [
        "#Plot correlation plot\n",
        "traindf[[\"thalach\",\"chol\"]].corr(method = \"pearson\")"
      ],
      "execution_count": null,
      "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>thalach</th>\n",
              "      <th>chol</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>thalach</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.012114</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>chol</th>\n",
              "      <td>-0.012114</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "          thalach      chol\n",
              "thalach  1.000000 -0.012114\n",
              "chol    -0.012114  1.000000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fWpwH_a1ge-W"
      },
      "source": [
        "- The Pearson coefficients indicate that the predictors are not linearly correlated."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 285
        },
        "id": "N4XYopblge-X",
        "outputId": "ae349f27-548b-4d3f-854d-0fa7bf303399"
      },
      "source": [
        "#Check whether the training set is balanced\n",
        "unique_classes,counts = np.unique(y_train,return_counts = True)\n",
        "class_names = [\"Normal\",\"Diseased\"]\n",
        "\n",
        "sns.set(style='darkgrid')\n",
        "plt.bar(class_names, counts, width=0.2, color=['steelblue','sandybrown'])\n",
        "plt.show()\n",
        "\n",
        "print(\"Proportion of diseased cases: {}\".format(round(counts[1]/len(y_train),2)))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Proportion of diseased cases: 0.46\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QI0ydQTMge-l"
      },
      "source": [
        "In this case, we define the metric for model performance as classification accuracy on the test set. Let us first build functions for measuring performance and to visualize decision boundary."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SycqlnB4ge-l"
      },
      "source": [
        "#Function to get test accuracy\n",
        "def accuracy(X, y, model):\n",
        "    return round(accuracy_score(y,model.predict(X)),2)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qu-bOGBcge-m"
      },
      "source": [
        "#function to plot decision boundary\n",
        "def plot_regions(X,y,model,ax,title):\n",
        "    plt.sca(ax)\n",
        "    plot_decision_regions(X=X, y=y.astype(np.integer), clf=model, legend =2)\n",
        "    \n",
        "    plt.title(title)\n",
        "    plt.xlabel(\"thalach\")\n",
        "    plt.ylabel(\"cols\")\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SunTnkUpge-n"
      },
      "source": [
        "## 3. Building models\n",
        "<a id=\"models\"> </a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WqT5DHRzge-p"
      },
      "source": [
        "### 3.1. Decision tree"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "os2ZX4HVge-q"
      },
      "source": [
        "#Build a pipeline\n",
        "tree_steps = [('standardizer', StandardScaler()),('tree', DecisionTreeClassifier(random_state =42))]\n",
        "tree_pipe = Pipeline(tree_steps)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Kak8z4rqge-u"
      },
      "source": [
        "#define tree params - max depth of 2 to 20\n",
        "tree_params = {\"tree__max_depth\":list(range(2,20))}"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BQhUimuage-v",
        "outputId": "86ecccaa-4bbf-433f-c8a8-0dcfa2fe5959"
      },
      "source": [
        "#Hyperparamter tuning and model fitting\n",
        "tree = GridSearchCV(tree_pipe,param_grid = tree_params, cv=10, scoring = \"accuracy\")\n",
        "tree.fit(X_train, y_train)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GridSearchCV(cv=10, error_score=nan,\n",
              "             estimator=Pipeline(memory=None,\n",
              "                                steps=[('standardizer',\n",
              "                                        StandardScaler(copy=True,\n",
              "                                                       with_mean=True,\n",
              "                                                       with_std=True)),\n",
              "                                       ('tree',\n",
              "                                        DecisionTreeClassifier(ccp_alpha=0.0,\n",
              "                                                               class_weight=None,\n",
              "                                                               criterion='gini',\n",
              "                                                               max_depth=None,\n",
              "                                                               max_features=None,\n",
              "                                                               max_leaf_nodes=None,\n",
              "                                                               min_impurity_decrease=0.0,\n",
              "                                                               min_impurity_split=None,\n",
              "                                                               min_samples_leaf=1,\n",
              "                                                               min_samples_split=2,\n",
              "                                                               min_weight_fraction_leaf=0.0,\n",
              "                                                               presort='deprecated',\n",
              "                                                               random_state=42,\n",
              "                                                               splitter='best'))],\n",
              "                                verbose=False),\n",
              "             iid='deprecated', n_jobs=None,\n",
              "             param_grid={'tree__max_depth': [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,\n",
              "                                             13, 14, 15, 16, 17, 18, 19]},\n",
              "             pre_dispatch='2*n_jobs', refit=True, return_train_score=False,\n",
              "             scoring='accuracy', verbose=0)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FzEnDy44ge-w",
        "outputId": "88a4b72e-a545-44fc-af36-5d51b4dee306"
      },
      "source": [
        "#Best hyperparameters\n",
        "tree.best_params_"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'tree__max_depth': 2}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kly1aUjJge-x",
        "outputId": "1b3926dc-1a7d-474e-b779-a0cc08dc73ec"
      },
      "source": [
        "#Performance\n",
        "DTCscore_test = tree.score(X_test,y_test)\n",
        "DTCscore_train = tree.score(X_train,y_train)\n",
        "\n",
        "print(\"Decision Tree Test Score: \",tree.score(X_test,y_test))\n",
        "print(\"Decision Tree Train Score: \",tree.score(X_train,y_train))\n",
        "\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Decision Tree Test Score:  0.7066666666666667\n",
            "Decision Tree Train Score:  0.7017543859649122\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bKfFGsFiZLGa",
        "outputId": "06c0df09-cb40-431b-da36-dc69f397efcd"
      },
      "source": [
        "yprediciton2= tree.predict(X_test)\r\n",
        "ytrue = y_test\r\n",
        "\r\n",
        "from sklearn.metrics import confusion_matrix\r\n",
        "CM = confusion_matrix(ytrue,yprediciton2)\r\n",
        "\r\n",
        "#CM visualization\r\n",
        "\r\n",
        "import seaborn as sns\r\n",
        "import matplotlib.pyplot as plt\r\n",
        "\r\n",
        "f, ax = plt.subplots(figsize=(5,5))\r\n",
        "sns.heatmap(CM,annot = True, linewidths=0.5,linecolor=\"white\",fmt=\".0f\",ax=ax, cmap=plt.cm.Blues)\r\n",
        "plt.xlabel(\"Prediction(Ypred)\")\r\n",
        "plt.ylabel(\"Ytrue\")\r\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 360x360 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "P-To1w61ge-y",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c1702673-2196-4add-a2b2-e3e5760c361e"
      },
      "source": [
        "#Visualizing classification\n",
        "fig, ax = plt.subplots(1,2, figsize = (10, 5))\n",
        "plot_regions(X_train, y_train, tree, ax[0],\"Training set\")\n",
        "plot_regions(X_test,y_test, tree,ax[1], \"Testing set\")\n",
        "\n",
        "fig.suptitle(\"Simple Decision Tree model\", fontsize=14)\n",
        "plt.savefig(\"/content/sample_data/simpletree.png\")\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:4: DeprecationWarning: Converting `np.integer` or `np.signedinteger` to a dtype is deprecated. The current result is `np.dtype(np.int_)` which is not strictly correct. Note that the result depends on the system. To ensure stable results use may want to use `np.int64` or `np.int32`.\n",
            "  after removing the cwd from sys.path.\n",
            "/usr/local/lib/python3.6/dist-packages/mlxtend/plotting/decision_regions.py:244: MatplotlibDeprecationWarning: Passing unsupported keyword arguments to axis() will raise a TypeError in 3.3.\n",
            "  ax.axis(xmin=xx.min(), xmax=xx.max(), y_min=yy.min(), y_max=yy.max())\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:4: DeprecationWarning: Converting `np.integer` or `np.signedinteger` to a dtype is deprecated. The current result is `np.dtype(np.int_)` which is not strictly correct. Note that the result depends on the system. To ensure stable results use may want to use `np.int64` or `np.int32`.\n",
            "  after removing the cwd from sys.path.\n",
            "/usr/local/lib/python3.6/dist-packages/mlxtend/plotting/decision_regions.py:244: MatplotlibDeprecationWarning: Passing unsupported keyword arguments to axis() will raise a TypeError in 3.3.\n",
            "  ax.axis(xmin=xx.min(), xmax=xx.max(), y_min=yy.min(), y_max=yy.max())\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x360 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "E0HIi9Bnge-1"
      },
      "source": [
        "### 3.2. Ensemble methods\n",
        "<a id=\"ensemble\"> </a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PBpBh9iBge-1"
      },
      "source": [
        "### 3.2.1 Bagging\n",
        "<a id=\"bagging\"> </a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nf2-Y4yBge-3"
      },
      "source": [
        "#Build a bagging pipeline\n",
        "bag_steps = [('standardizer', StandardScaler()),('bag', BaggingClassifier(random_state=42))]\n",
        "bag_pipe = Pipeline(bag_steps)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BabsY5Klge-4"
      },
      "source": [
        "- The hyperparameter in bagging is the number of trees in the ensemble. We will tune this paramter using 5-fold cross-validation."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "J2zzuG5-ge-4",
        "outputId": "1f569c0d-5f75-4c4d-8313-a599108fc307"
      },
      "source": [
        "#Hyper-parameter tuning\n",
        "#n-estimators == no. of trees\n",
        "bag_params = {\"bag__n_estimators\":list(range(2,11))}\n",
        "bag = GridSearchCV(bag_pipe,param_grid = bag_params, cv=10, scoring = \"accuracy\")\n",
        "bag.fit(X_train, y_train)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GridSearchCV(cv=10, error_score=nan,\n",
              "             estimator=Pipeline(memory=None,\n",
              "                                steps=[('standardizer',\n",
              "                                        StandardScaler(copy=True,\n",
              "                                                       with_mean=True,\n",
              "                                                       with_std=True)),\n",
              "                                       ('bag',\n",
              "                                        BaggingClassifier(base_estimator=None,\n",
              "                                                          bootstrap=True,\n",
              "                                                          bootstrap_features=False,\n",
              "                                                          max_features=1.0,\n",
              "                                                          max_samples=1.0,\n",
              "                                                          n_estimators=10,\n",
              "                                                          n_jobs=None,\n",
              "                                                          oob_score=False,\n",
              "                                                          random_state=42,\n",
              "                                                          verbose=0,\n",
              "                                                          warm_start=False))],\n",
              "                                verbose=False),\n",
              "             iid='deprecated', n_jobs=None,\n",
              "             param_grid={'bag__n_estimators': [2, 3, 4, 5, 6, 7, 8, 9, 10]},\n",
              "             pre_dispatch='2*n_jobs', refit=True, return_train_score=False,\n",
              "             scoring='accuracy', verbose=0)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 52
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sv0fZ94cge-7",
        "outputId": "7febe770-3522-4133-97f0-567078fc1900"
      },
      "source": [
        "#Best hyperparameters\n",
        "bag.best_params_"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'bag__n_estimators': 4}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 53
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eTHMEzsOge-7",
        "outputId": "d7fe857d-3a86-4292-a318-8f5d73708b39"
      },
      "source": [
        "#Performance\n",
        "print(\"Training accuracy is: {}\".format(accuracy(X_train, y_train,bag)))\n",
        "print(\"Testing accuracy is: {}\".format(accuracy(X_test, y_test,bag))) "
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Training accuracy is: 0.93\n",
            "Testing accuracy is: 0.6\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2wtGzjegZqu5",
        "outputId": "d945eb82-a6dc-4c64-f8e6-fadf0d4a9b74"
      },
      "source": [
        "yprediciton2= bag.predict(X_test)\r\n",
        "ytrue = y_test\r\n",
        "\r\n",
        "from sklearn.metrics import confusion_matrix\r\n",
        "CM = confusion_matrix(ytrue,yprediciton2)\r\n",
        "\r\n",
        "#CM visualization\r\n",
        "\r\n",
        "import seaborn as sns\r\n",
        "import matplotlib.pyplot as plt\r\n",
        "\r\n",
        "f, ax = plt.subplots(figsize=(5,5))\r\n",
        "sns.heatmap(CM,annot = True, linewidths=0.5,linecolor=\"white\",fmt=\".0f\",ax=ax, cmap=plt.cm.Blues)\r\n",
        "plt.xlabel(\"Prediction(Ypred)\")\r\n",
        "plt.ylabel(\"Ytrue\")\r\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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b3zDTDKBeLJaJNYdieXm5cnNza70CHAAupVENn3250BEAamKTtVLR/4/SBtCoNapOEQAuF6EIAAaN7ourAOBy0CkCgIHFGkVCEYC5GtXF2wBwuRg+A4CBxRpFQhGAuQIsdvG22U8KBwBLoVMEYCqGzwBgwEQLABhwSQ4AGFgsEwlFAOaiUwQAA4tlIqEIwFxWu+6PUARgKh4dBgAG1opEQhGAyZhoAQADa0UioQjAZBZrFAlFAOZiogUADLgkBwAM6BQBwMBakUgoAjCZ1TpFqw33AcBUdIoATGW1zotQBGAqqw2fCUUAprJWJBKKAExmsUaRUARgLqt97zOhCMBUZneKWVlZysvL07Fjx5STk6OuXbtKkmJjYxUcHKymTZtKkp5++mndeeedte6PUARgKpvJneLAgQP18MMP65e//GWVda+++qo3JH1FKAIwldmdYkxMzBXdH6EIwFT1OafocrnkcrmqLLfb7bLb7T7v5+mnn5bH49Ftt92mp556yqdtCUUApqpPp5idna158+ZVWZ6amqq0tDSf9rF48WI5HA6VlpbqxRdfVGZmpmbNmlXrdoQiAFPVJxRTUlKUlJRUZXldukSHwyFJCg4OVnJysh5//HGftiMUAZiqPhMtdR0m/9D58+fldrt1zTXXyOPxaO3atYqOjvZpW0IRgKkCTJ5oeeGFF7RhwwadPHlSo0ePVmhoqBYsWKC0tDS53W5VVFSoc+fOmjp1qk/7s3k8Ho+5JV9ZQxd+5u8SUE+rH+2jfQUX/F0G6qFLeLN6b7vxq1N13ib25jb1Pt7lolMEYCpu8wMAA7Mv3r7SrPaoMwAwFZ2iHwQF2PT4gA7qEWFXy6ZB+rerRG999o0+P3pG14eG6Ml7O8lh//Z+zf0nzun3nxzR0dMX/Vw1vpPz/lL9Zf1qHfp6n+4eGK9fPTtdklRWVqaXM5/R/j3/UuG/nZrx/3+v7r36+Lla/zN7ouVKIxT9IDDAppPnSvVszlc6cbZUt93QSpMHdtYT7+1S0fkyZX2wX4VnSxVgk+7r1laTBnbWE+//y99l43+1ufY6jXz4EX2+dYtKSyr/Y3XLrT2V+MAv9VLGJD9V1/BYbfhMKPpBSXmFlvzzuPf1tiNnVPifEnW+roW2HCzWuVK3d12FR3K0auqPMnEJd9w9UJK0/6t8nTzxfSg2adJEiT9/UJIUEMiZqe8w0YI6C20WpPatQnSk6PvLVd5O6aVmTQJls0lvbzvmx+qAy2OxTPRvKA4ZMkQ5OTn+LMHvAm02PXVvJ23cd1LHznzfdSRnb1fToADFdm2jE/8p9WOFwOUJsFiraHoo7t+//5LriouLzT58g2aT9KvYG1Ve4dEbHx+psr6kvELr80/oTw/30oRlX+rMxfKrXyRwmawViVchFBMSEhQREaHqbpw5ffq02Ydv0NLu7qjQZk2UuW6v3Je4schmk5oGBahNi2BCEdZksVQ0PRQjIiL09ttvKzw8vMq6u+++2+zDN1iPD+ig60ObacqaPSp1fx+IPSPscl0s16Gi82oaFKAH+0TqbEm5jp7m9riGwl1eLrfbLXdFhSoqKlRaUqLAwEAFBgWprLTU2wCUl5WptKRETYKDLfc1n1cSs88/MHjwYB07dqzaUBw0aJDZh2+QrmsZrP/Tra1KyyuU/VBP7/LXNx9SudujR396g9q0CFZpeYX2njinaev2qsxtqVvUG7Wlb/1eS958w/v6rxvW6Bf/9Zh+OeZxPfZgogr/7ZQkZTw9XpL0x3fWKNwR4ZdaGwKr/XvAAyFw1fBACOu6nAdCfPb1mTpv06dTq3of73JxSQ4Ac1msUyQUAZiKc4oAYGC1c4qEIgBTWSwTCUUAJrNYKhKKAExltXOKPMoDAAzoFAGYiokWADCwWCYSigBMZrFUJBQBmMpqEy2EIgBTcU4RAAwslomEIgCTWSwVCUUApuKcIgAYcE4RAAwslomEIgCTWSwVCUUApuKcIgAYcE4RAAwslomEIgCTWSwVCUUAprLaOUUeMgsABnSKAEzFRAsAGFgsEwlFACazWCoSigBMZbWJFkIRgKmsdk6R2WcAprLV46cusrKyFBsbq6ioKO3du9e7/ODBgxo5cqTi4uI0cuRIHTp0yKf9EYoAzGVyKg4cOFCLFy9WREREpeVTp05VcnKy8vLylJycrIyMDJ/2x/AZgKnqc07R5XLJ5XJVWW6322W32ysti4mJqfK+U6dOKT8/X4sWLZIkJSQkaPr06SoqKlJYWFiNxyYUAZiqPucUs7OzNW/evCrLU1NTlZaWVuv2TqdT4eHhCgwMlCQFBgaqbdu2cjqdhCIA/6rPPEtKSoqSkpKqLP9hl2gGQhGAqerTKVY3TK4Lh8OhgoICud1uBQYGyu12q7CwUA6Ho9ZtmWgBYDKz55+ratOmjaKjo5WbmytJys3NVXR0dK1DZ4lOEYDJzL5O8YUXXtCGDRt08uRJjR49WqGhoVqzZo2ef/55paen6/XXX5fdbldWVpZv9Xo8Ho+5JV9ZQxd+5u8SUE+rH+2jfQUX/F0G6qFLeLN6b3v8dGmdt2kfGlzv410uOkUAprLaHS2EIgBTWe3eZyZaAMCAThGAuazVKBKKAMxlsUwkFAGYi4kWADCw2kQLoQjAXNbKREIRgLkslomEIgBzcU4RAAw4pwgABlbrFLmjBQAM6BQBmMpqnSKhCMBUnFMEAAM6RQAwsFgmEooATGaxVCQUAZiKc4oAYGC1c4pcpwgABnSKAExlsUaRUARgMoulIqEIwFRWm2ixeTwej7+LAICGgokWADAgFAHAgFAEAANCEQAMCEUAMCAUAcCAUAQAA0IRAAwIRQAwIBQbiIMHD2rkyJGKi4vTyJEjdejQIX+XBB9lZWUpNjZWUVFR2rt3r7/LwWUiFBuIqVOnKjk5WXl5eUpOTlZGRoa/S4KPBg4cqMWLFysiIsLfpeAKIBQbgFOnTik/P18JCQmSpISEBOXn56uoqMjPlcEXMTExcjgc/i4DVwih2AA4nU6Fh4crMDBQkhQYGKi2bdvK6XT6uTLgx4dQBAADQrEBcDgcKigokNvtliS53W4VFhYyJAP8gFBsANq0aaPo6Gjl5uZKknJzcxUdHa2wsDA/Vwb8+PCQ2QbiwIEDSk9Pl8vlkt1uV1ZWljp16uTvsuCDF154QRs2bNDJkyfVunVrhYaGas2aNf4uC/VEKAKAAcNnADAgFAHAgFAEAANCEQAMCEUAMCAUAcCAUPwRS09P15w5cyRJ27ZtU1xcXL32k5GRofnz51+RmoqKihQfH6+LFy9ekf1djuXLl+sXv/iFJKm0tFTx8fE8pONHgFC0gNjYWHXv3l29evXSHXfcofT0dJ07d+6KHiMmJkZ5eXm1vs8YFN/JzMzUhAkTrkgdCxcu1PDhwxUSEqI5c+YoJSWl0vqDBw+qd+/e2rNnzxU5nq+Cg4M1YsQILVy48KoeF1cfoWgRCxYs0Pbt27VixQrt2rVLv/vd7yqtLy8v91NlV05paalWrFihoUOHSpImTJigkydPatmyZZIkj8ejKVOmaPTo0YqKiqrTvj0ejyoqKi6rviFDhmjFihUqLS29rP2gYSMULSY8PFx33nmn9u3bp6ioKC1evFiDBw/W4MGDJUl//etflZiYqJiYGI0aNUpfffWVd9v8/HwlJSWpV69eevLJJ1VSUuJd9+mnn+quu+7yvnY6nUpNTVX//v3Vr18/ZWZm6sCBA5o6dap27NihXr16KSYmRlLlYbgkLVu2TIMGDVLfvn01btw4FRQUeNdFRUVpyZIlGjx4sGJiYjRt2jR9d1PVF198Ibvdrnbt2kn6tjubMWOGZs+erYKCAr3zzjs6c+aMxo0bJ+nbrnXUqFHKzMzUbbfdpvj4eG3ZssV7rIceekhz5szRqFGj1KNHDx09elQHDhzQ6NGj1bdvX8XFxWnt2rXe9xcXF2vcuHHq3bu37r//fh05cqTS775du3Zq1aqVduzYUc//e7ACQtFinE6nPvroI0VHR0uSPvzwQy1btkxr165Vfn6+nn32WWVmZurTTz/VyJEjNX78eJWWlqq0tFQTJkxQYmKitm7dqvj4eG3YsKHaY7jdbj322GNq3769Nm7cqI8++kj33XefOnfurGnTpqlnz57avn27tm3bVmXbLVu2aPbs2Zo7d64+/vhjRURE6Kmnnqr0nk2bNum9997T6tWrtW7dOm3evFmStHfvXt14442V3tujRw8lJSVp8uTJmjNnjmbMmKEmTZp41+/cuVM33HCD/vGPf+iJJ55QamqqTp8+7V2/atUqTZ8+XZ9//rnCwsI0ZswYJSQk6JNPPtGcOXM0bdo07d+/X9K3pwGaNm2qjz/+WDNmzND7779f5fN16tSp0j80aHwIRYuYMGGCYmJilJycrD59+ni7pUcffVShoaEKCQnRO++8o5EjR6pHjx4KDAxUUlKSmjRpoh07duiLL75QWVmZUlJS1KRJE8XHx+vWW2+t9lg7d+5UYWGhJk+erObNm6tp06berrA2OTk5GjFihG655RYFBwfrqaee0o4dO/TNN9943zN27FjZ7Xa1b99e/fr184aMy+VSixYtquzzySef1JEjR5SYmFil5rCwMO9nuu+++3TjjTdq06ZN3vVJSUnq0qWLgoKCtHnzZkVERGjEiBEKCgpSt27dFBcXp/Xr18vtdmvDhg164okn1Lx5c3Xt2lVJSUlVamnRooVcLpdPvwtYU5C/C4Bv5s+frzvuuKPKcuMzF48fP66VK1fqz3/+s3dZWVmZCgsLZbPZFB4eLpvN5l3Xvn37ao/ldDrVvn17BQXV/Y9HYWGhbrnlFu/rFi1aKDQ0VAUFBYqMjJQkXXfddd71zZo1804a2e32aieQQkJCFBkZqS5dulRZV91nKiws9L42/n6OHTumnTt3Vgp4t9utoUOHqqioSOXl5ZXeX93v59y5c7Lb7TX/EmBphKLFGQPB4XBo3Lhxevzxx6u8b+vWrSooKJDH4/Fuc/z4cV1//fVV3utwOOR0OlVeXl4lGI3Hq07btm117Ngx7+vz58/r9OnTCg8Pr/WzREVFKTs7u9b3Gf3wMzmdTsXGxlZbr8PhUJ8+fbRo0aIq+3G73QoKCpLT6VTnzp29+/qhr7/+WmPGjKlTjbAWhs+NyAMPPKClS5fqiy++kMfj0fnz57Vp0yadPXtWPXv2VFBQkN566y2VlZVpw4YN+vLLL6vdT/fu3XXddddp9uzZOn/+vEpKSvTPf/5T0rcPxC0oKLjkDGxCQoKWL1+u3bt3q7S0VK+88oq6d+/u7RJr0r17d7lcrkoTM7UpKiryfqZ169bpwIEDuvvuu6t97z333KNDhw5p5cqVKisrU1lZmXbu3KkDBw4oMDBQgwYN0rx583ThwgXt379fK1asqLR9QUGBzpw5o549e/pcH6yHUGxEbr31Vk2fPl2ZmZnq06ePBg8erOXLl0v6dib3tdde04oVK9S3b1+tXbtWgwYNqnY/gYGBWrBggQ4fPqx7771Xd911l9atWydJ6t+/v2666SYNGDBA/fr1q7LtHXfcoYkTJyotLU0DBgzQ0aNHK81M1yQ4OFhJSUlatWqVz5+5e/fuOnz4sPr376+5c+fq1VdfVevWrat9b8uWLfXHP/5Ra9eu1Z133qkBAwZo1qxZ3oDPyMjQ+fPn9dOf/lTp6ekaPnx4pe1zcnI0bNgwBQcH+1wfrIeHzKJBKSoqUnJyslauXKmQkJAa37t8+XK9++67WrJkiel1lZaWaujQoVq8eLHatGlj+vHgP5xTRIMSFham9evX+7uMKoKDgxtkXbjyGD4DgAHDZwAwoFMEAANCEQAMCEUAMCAUAcCAUAQAg/8BU+IxHWvcBzUAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 360x360 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MKFjfsb5ge-9",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5a7d5d04-adc1-4d2e-fa97-467fa96dc7f6"
      },
      "source": [
        "#Visualizing classification\n",
        "fig, ax = plt.subplots(1,2, figsize = (10, 5))\n",
        "plot_regions(X_train,y_train, bag,ax[0], \"Training set\")\n",
        "plot_regions(X_test,y_test, bag,ax[1], \"Testing set\")\n",
        "\n",
        "fig.suptitle(\"Bagging\", fontsize=14)\n",
        "plt.savefig(\"/content/sample_data/bag.png\")\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:4: DeprecationWarning: Converting `np.integer` or `np.signedinteger` to a dtype is deprecated. The current result is `np.dtype(np.int_)` which is not strictly correct. Note that the result depends on the system. To ensure stable results use may want to use `np.int64` or `np.int32`.\n",
            "  after removing the cwd from sys.path.\n",
            "/usr/local/lib/python3.6/dist-packages/mlxtend/plotting/decision_regions.py:244: MatplotlibDeprecationWarning: Passing unsupported keyword arguments to axis() will raise a TypeError in 3.3.\n",
            "  ax.axis(xmin=xx.min(), xmax=xx.max(), y_min=yy.min(), y_max=yy.max())\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:4: DeprecationWarning: Converting `np.integer` or `np.signedinteger` to a dtype is deprecated. The current result is `np.dtype(np.int_)` which is not strictly correct. Note that the result depends on the system. To ensure stable results use may want to use `np.int64` or `np.int32`.\n",
            "  after removing the cwd from sys.path.\n",
            "/usr/local/lib/python3.6/dist-packages/mlxtend/plotting/decision_regions.py:244: MatplotlibDeprecationWarning: Passing unsupported keyword arguments to axis() will raise a TypeError in 3.3.\n",
            "  ax.axis(xmin=xx.min(), xmax=xx.max(), y_min=yy.min(), y_max=yy.max())\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x360 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0rx8O91Cge_Q"
      },
      "source": [
        "### 3.2.3. Boosting"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "d1ZNy2dlge_R"
      },
      "source": [
        "#Build an adaboost pipe\n",
        "boost_steps = [('standardizer', StandardScaler()),('boost', AdaBoostClassifier(random_state=42))]\n",
        "boost_pipe = Pipeline(boost_steps)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6QVc84Asge_S",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "47b4a0f4-1a7e-445b-8981-e2e7b0b5b3d2"
      },
      "source": [
        "#hyperparamter tuning\n",
        "#n-estimators == no. of trees and learning rate == shrinks weightage of each additional tree in the ensemble(controls for overfitting)\n",
        "boost_params = {\"boost__n_estimators\":list(range(2,20)), \"boost__learning_rate\":[0.001,0.01,0.1,1,1.4,1.8,2,3,4]}\n",
        "boost = GridSearchCV(boost_pipe,param_grid = boost_params, cv=10)\n",
        "boost.fit(X_train, y_train)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GridSearchCV(cv=10, error_score=nan,\n",
              "             estimator=Pipeline(memory=None,\n",
              "                                steps=[('standardizer',\n",
              "                                        StandardScaler(copy=True,\n",
              "                                                       with_mean=True,\n",
              "                                                       with_std=True)),\n",
              "                                       ('boost',\n",
              "                                        AdaBoostClassifier(algorithm='SAMME.R',\n",
              "                                                           base_estimator=None,\n",
              "                                                           learning_rate=1.0,\n",
              "                                                           n_estimators=50,\n",
              "                                                           random_state=42))],\n",
              "                                verbose=False),\n",
              "             iid='deprecated', n_jobs=None,\n",
              "             param_grid={'boost__learning_rate': [0.001, 0.01, 0.1, 1, 1.4, 1.8,\n",
              "                                                  2, 3, 4],\n",
              "                         'boost__n_estimators': [2, 3, 4, 5, 6, 7, 8, 9, 10, 11,\n",
              "                                                 12, 13, 14, 15, 16, 17, 18,\n",
              "                                                 19]},\n",
              "             pre_dispatch='2*n_jobs', refit=True, return_train_score=False,\n",
              "             scoring=None, verbose=0)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 58
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "so9_Z7WRge_T",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1352d3f7-b04b-4a17-f710-7f20eb8b8b17"
      },
      "source": [
        "#Best hyperparameters\n",
        "boost.best_params_"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'boost__learning_rate': 2, 'boost__n_estimators': 5}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 59
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Nf0ELQiragQ8",
        "outputId": "adbf8482-1bf9-482e-c425-559da065e851"
      },
      "source": [
        "yprediciton2= boost.predict(X_test)\r\n",
        "ytrue = y_test\r\n",
        "\r\n",
        "from sklearn.metrics import confusion_matrix\r\n",
        "CM = confusion_matrix(ytrue,yprediciton2)\r\n",
        "\r\n",
        "#CM visualization\r\n",
        "\r\n",
        "import seaborn as sns\r\n",
        "import matplotlib.pyplot as plt\r\n",
        "\r\n",
        "f, ax = plt.subplots(figsize=(5,5))\r\n",
        "sns.heatmap(CM,annot = True, linewidths=0.5,linecolor=\"white\",fmt=\".0f\",ax=ax, cmap=plt.cm.Blues)\r\n",
        "plt.xlabel(\"Prediction(Ypred)\")\r\n",
        "plt.ylabel(\"Ytrue\")\r\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 360x360 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lTia0RiJge_U",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "22712894-1672-45ec-a663-aa2e0b48abe7"
      },
      "source": [
        "Adascore=boost.score(X_test,y_test)\n",
        "\n",
        "print(\"AdaBoost Test Score: \",boost.score(X_test,y_test))\n",
        "print(\"AdaBoost Train Score: \",boost.score(X_train,y_train))\n",
        "#Performance\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "AdaBoost Test Score:  0.7066666666666667\n",
            "AdaBoost Train Score:  0.7017543859649122\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3iupe2mCge_V",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e29f89b6-3f3e-4155-c396-44f6f284a018"
      },
      "source": [
        "#Classification visualization\n",
        "fig, ax = plt.subplots(1,2, figsize = (10, 5))\n",
        "plot_regions(X_train,y_train, boost,ax[0], \"Training set\")\n",
        "plot_regions(X_test,y_test, boost,ax[1], \"Testing set\")\n",
        "\n",
        "fig.suptitle(\"Boosting\", fontsize=14)\n",
        "plt.savefig(\"/content/sample_data/boost.png\")\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:4: DeprecationWarning: Converting `np.integer` or `np.signedinteger` to a dtype is deprecated. The current result is `np.dtype(np.int_)` which is not strictly correct. Note that the result depends on the system. To ensure stable results use may want to use `np.int64` or `np.int32`.\n",
            "  after removing the cwd from sys.path.\n",
            "/usr/local/lib/python3.6/dist-packages/mlxtend/plotting/decision_regions.py:244: MatplotlibDeprecationWarning: Passing unsupported keyword arguments to axis() will raise a TypeError in 3.3.\n",
            "  ax.axis(xmin=xx.min(), xmax=xx.max(), y_min=yy.min(), y_max=yy.max())\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:4: DeprecationWarning: Converting `np.integer` or `np.signedinteger` to a dtype is deprecated. The current result is `np.dtype(np.int_)` which is not strictly correct. Note that the result depends on the system. To ensure stable results use may want to use `np.int64` or `np.int32`.\n",
            "  after removing the cwd from sys.path.\n",
            "/usr/local/lib/python3.6/dist-packages/mlxtend/plotting/decision_regions.py:244: MatplotlibDeprecationWarning: Passing unsupported keyword arguments to axis() will raise a TypeError in 3.3.\n",
            "  ax.axis(xmin=xx.min(), xmax=xx.max(), y_min=yy.min(), y_max=yy.max())\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x360 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2DlqD0xsge_V"
      },
      "source": [
        "**Results:** This is an interesting result. It is evident that AdaBoost gave us a classification region that is very similar to our simple decision tree. One possible explanation for this is that the data did not provide any further information required to reduce errors predicted by our base estimator.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SXK134COge_W"
      },
      "source": [
        "### Choosing the best model"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1wZ-9KuKge_X"
      },
      "source": [
        "Now that we have analyzed, let us choose the best performing one and see how it can be deployed in practice."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ymc0Bhpkge_Y"
      },
      "source": [
        "#Comparing performance of all models\n",
        "acc_dict = {}\n",
        "\n",
        "all_models = [tree, bag, boost]\n",
        "all_model_names = [\"tree\",\"bagging\",\"adaboost\"]\n",
        "for i in range(len(all_models)):\n",
        "    acc_dict[all_model_names[i]] = accuracy(X_test,y_test,all_models[i])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Qk9mREqmge_Y",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 338
        },
        "outputId": "14a48288-0de1-4801-f9c5-54eb45aa3fd1"
      },
      "source": [
        "#Plot performance comparison bar plot\n",
        "sns.barplot(list(acc_dict.keys()), list(acc_dict.values()))\n",
        "plt.title(\"Comparison of accuracy among baseline tree models\")\n",
        "plt.ylabel(\"Test set accuracy\")\n",
        "plt.savefig(\"/content/sample_data/perfcomparison\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  FutureWarning\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Xiw2P9439fFc"
      },
      "source": [
        "###3.3 Support Vector Machine"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "z-O8WHUp9Djq",
        "outputId": "75de8ba2-89b8-43ec-8a0e-dcfc4136ce09"
      },
      "source": [
        "from sklearn.svm import SVC\r\n",
        "svc = SVC()\r\n",
        "svc.fit(x_train, y_train)  #learning \r\n",
        "#SVM Test \r\n",
        "\r\n",
        "SVMscore_test = svc.score(x_test, y_test)\r\n",
        "SVMscore_train = svc.score(x_train, y_train)\r\n",
        "print (\"SVM Test Accuracy:\", svc.score(x_test,y_test))\r\n",
        "\r\n",
        "print (\"SVM Train Accuracy:\", svc.score(x_train, y_train))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "SVM Test Accuracy: 0.8524590163934426\n",
            "SVM Train Accuracy: 0.9132231404958677\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 339
        },
        "id": "RjO_Vwta9LLs",
        "outputId": "2d3f8b26-400a-4db5-95c8-e36ae1ce4ff3"
      },
      "source": [
        "yprediciton2= svc.predict(x_test)\r\n",
        "ytrue = y_test\r\n",
        "\r\n",
        "from sklearn.metrics import confusion_matrix\r\n",
        "CM = confusion_matrix(ytrue,yprediciton2)\r\n",
        "\r\n",
        "#CM visualization\r\n",
        "\r\n",
        "import seaborn as sns\r\n",
        "import matplotlib.pyplot as plt\r\n",
        "\r\n",
        "f, ax = plt.subplots(figsize=(5,5))\r\n",
        "sns.heatmap(CM,annot = True, linewidths=0.5,linecolor=\"white\",fmt=\".0f\",ax=ax, cmap=plt.cm.Blues)\r\n",
        "plt.xlabel(\"Prediction(Ypred)\")\r\n",
        "plt.ylabel(\"Ytrue\")\r\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 360x360 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OJv8Uo8h9mif"
      },
      "source": [
        "###3.4 Random Forest"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 355
        },
        "id": "2q8nflas9bwv",
        "outputId": "627dd4f8-01f8-478a-ffbf-60e53f7df28b"
      },
      "source": [
        "random_state = 42\r\n",
        "scores = []\r\n",
        "for each in range(1,50):\r\n",
        "    rf = RandomForestClassifier(n_estimators = each,random_state=random_state,max_depth=3)\r\n",
        "    rf.fit(x_train,y_train)\r\n",
        "    scores.append(rf.score(x_test,y_test))\r\n",
        "    \r\n",
        "plt.figure(1, figsize=(10, 5))\r\n",
        "plt.plot(range(1,50),scores,color=\"green\",linewidth=2)\r\n",
        "plt.title(\"Optimum N Estimator Value\")\r\n",
        "plt.xlabel(\"N Estimators\")\r\n",
        "plt.ylabel(\"Score(Accuracy)\")\r\n",
        "plt.grid(True)\r\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IOth0upV-J9J",
        "outputId": "693c03a8-76b9-472e-8627-dd4445fc588e"
      },
      "source": [
        "rf= RandomForestClassifier(n_estimators = 19, random_state=random_state,max_depth=3) #n_estimator = DT\r\n",
        "rf.fit(x_train,y_train) # learning\r\n",
        "RFCscore=rf.score(x_test,y_test)\r\n",
        "\r\n",
        "print(\"Random Forest Test Score: \",rf.score(x_test,y_test))\r\n",
        "print(\"Random Forest Train Score: \",rf.score(x_train,y_train))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Random Forest Test Score:  0.8852459016393442\n",
            "Random Forest Train Score:  0.8760330578512396\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 339
        },
        "id": "WHS8Ohgs-SI3",
        "outputId": "157b81e5-3ceb-4743-d4d7-52e1f514f84b"
      },
      "source": [
        "yprediciton2= rf.predict(x_test)\r\n",
        "ytrue = y_test\r\n",
        "\r\n",
        "from sklearn.metrics import confusion_matrix\r\n",
        "CM = confusion_matrix(ytrue,yprediciton2)\r\n",
        "\r\n",
        "#CM visualization\r\n",
        "\r\n",
        "import seaborn as sns\r\n",
        "import matplotlib.pyplot as plt\r\n",
        "\r\n",
        "f, ax = plt.subplots(figsize=(5,5))\r\n",
        "sns.heatmap(CM,annot = True, linewidths=0.5,linecolor=\"white\",fmt=\".0f\",ax=ax, cmap=plt.cm.Blues)\r\n",
        "plt.xlabel(\"Prediction(Ypred)\")\r\n",
        "plt.ylabel(\"Ytrue\")\r\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 360x360 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "e2NIpmVx9q4-"
      },
      "source": [
        "###3.5 Voting Classifier"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YSR2J3iM9u2h",
        "outputId": "eaa7b4f5-4022-45a2-fe6c-1ae55fd3b31e"
      },
      "source": [
        "from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier, VotingClassifier\r\n",
        "v1 = VotingClassifier(estimators = [('svc',svc),('dt',tree),('rf',rf),('ada',boost)])\r\n",
        "v1.fit(x_train,y_train) # learning\r\n",
        "vooting1=v1.score(x_test,y_test)\r\n",
        "\r\n",
        "print(\"Voting C. Test Score: \",v1.score(x_test,y_test))\r\n",
        "print(\"Voting C. Train Score: \",v1.score(x_train,y_train))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Voting C. Test Score:  0.8852459016393442\n",
            "Voting C. Train Score:  0.8884297520661157\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 339
        },
        "id": "p-s3q_zYAK0N",
        "outputId": "6d732129-7fd5-4ff4-f77e-1e2a797afe1d"
      },
      "source": [
        "yprediciton5= v1.predict(x_test)\r\n",
        "ytrue = y_test\r\n",
        "\r\n",
        "from sklearn.metrics import confusion_matrix\r\n",
        "CM = confusion_matrix(ytrue,yprediciton5)\r\n",
        "\r\n",
        "import seaborn as sns\r\n",
        "import matplotlib.pyplot as plt\r\n",
        "\r\n",
        "f, ax = plt.subplots(figsize=(5,5))\r\n",
        "sns.heatmap(CM,annot = True, linewidths=0.5,linecolor=\"white\",fmt=\".0f\",ax=ax, cmap=plt.cm.Blues)\r\n",
        "plt.xlabel(\"Prediction(Ypred)\")\r\n",
        "plt.ylabel(\"Ytrue\")\r\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 360x360 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "uC-dnOoIAPHf",
        "outputId": "9df876ab-f541-4464-b94b-4fcf2a2a0272"
      },
      "source": [
        "import plotly.graph_objs as go\r\n",
        "from plotly.offline import init_notebook_mode, iplot\r\n",
        "\r\n",
        "scores=[SVMscore_test,DTCscore_test,RFCscore,Adascore,vooting1]\r\n",
        "AlgorthmsName=[\"SVM\",\"Decision Tree\",\"Random Forest\",\"Adaboost\",'Voting Classifier -1-']\r\n",
        "\r\n",
        "#create traces\r\n",
        "\r\n",
        "trace1 = go.Scatter(\r\n",
        "    x = AlgorthmsName,\r\n",
        "    y= scores,\r\n",
        "    name='Algortms Name',\r\n",
        "    marker =dict(color='rgba(0,255,0,0.5)',\r\n",
        "               line =dict(color='rgb(0,0,0)',width=2)),\r\n",
        "                text=AlgorthmsName\r\n",
        ")\r\n",
        "data = [trace1]\r\n",
        "\r\n",
        "layout = go.Layout(barmode = \"group\",\r\n",
        "                  xaxis= dict(title= 'ML Algorithms',ticklen= 5,zeroline= False),\r\n",
        "              yaxis= dict(title= 'Prediction Scores',ticklen= 5,zeroline= False))\r\n",
        "fig = go.Figure(data = data, layout = layout)\r\n",
        "iplot(fig)"
      ],
      "execution_count": null,
      "outputs": [
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