heart_attack_prediction.ipynb

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      "name": "heart attack prediction.ipynb",
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        "%matplotlib inline\n",
        "import warnings\n",
        "warnings.filterwarnings(\"ignore\")\n",
        "\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "from sklearn.metrics import confusion_matrix\n",
        "from sklearn import metrics\n",
        "from sklearn.metrics import roc_curve, auc\n",
        "from sklearn.model_selection import GridSearchCV\n",
        "from sklearn.metrics import roc_auc_score\n",
        "import math\n",
        "\n",
        "import pickle\n",
        "from tqdm import tqdm\n",
        "import os\n",
        "\n",
        "from sklearn.model_selection import train_test_split"
      ],
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      "cell_type": "code",
      "source": [
        "data=pd.read_csv('heart.csv')\n",
        "data.head()"
      ],
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      "outputs": [
        {
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              "   age  sex  cp  trtbps  chol  fbs  restecg  thalachh  exng  oldpeak  slp  \\\n",
              "0   63    1   3     145   233    1        0       150     0      2.3    0   \n",
              "1   37    1   2     130   250    0        1       187     0      3.5    0   \n",
              "2   41    0   1     130   204    0        0       172     0      1.4    2   \n",
              "3   56    1   1     120   236    0        1       178     0      0.8    2   \n",
              "4   57    0   0     120   354    0        1       163     1      0.6    2   \n",
              "\n",
              "   caa  thall  output  \n",
              "0    0      1       1  \n",
              "1    0      2       1  \n",
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              "      <td>354</td>\n",
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        "data.describe()"
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          "data": {
            "text/plain": [
              "              age         sex          cp      trtbps        chol         fbs  \\\n",
              "count  303.000000  303.000000  303.000000  303.000000  303.000000  303.000000   \n",
              "mean    54.366337    0.683168    0.966997  131.623762  246.264026    0.148515   \n",
              "std      9.082101    0.466011    1.032052   17.538143   51.830751    0.356198   \n",
              "min     29.000000    0.000000    0.000000   94.000000  126.000000    0.000000   \n",
              "25%     47.500000    0.000000    0.000000  120.000000  211.000000    0.000000   \n",
              "50%     55.000000    1.000000    1.000000  130.000000  240.000000    0.000000   \n",
              "75%     61.000000    1.000000    2.000000  140.000000  274.500000    0.000000   \n",
              "max     77.000000    1.000000    3.000000  200.000000  564.000000    1.000000   \n",
              "\n",
              "          restecg    thalachh        exng     oldpeak         slp         caa  \\\n",
              "count  303.000000  303.000000  303.000000  303.000000  303.000000  303.000000   \n",
              "mean     0.528053  149.646865    0.326733    1.039604    1.399340    0.729373   \n",
              "std      0.525860   22.905161    0.469794    1.161075    0.616226    1.022606   \n",
              "min      0.000000   71.000000    0.000000    0.000000    0.000000    0.000000   \n",
              "25%      0.000000  133.500000    0.000000    0.000000    1.000000    0.000000   \n",
              "50%      1.000000  153.000000    0.000000    0.800000    1.000000    0.000000   \n",
              "75%      1.000000  166.000000    1.000000    1.600000    2.000000    1.000000   \n",
              "max      2.000000  202.000000    1.000000    6.200000    2.000000    4.000000   \n",
              "\n",
              "            thall      output  \n",
              "count  303.000000  303.000000  \n",
              "mean     2.313531    0.544554  \n",
              "std      0.612277    0.498835  \n",
              "min      0.000000    0.000000  \n",
              "25%      2.000000    0.000000  \n",
              "50%      2.000000    1.000000  \n",
              "75%      3.000000    1.000000  \n",
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              "      <th></th>\n",
              "      <th>age</th>\n",
              "      <th>sex</th>\n",
              "      <th>cp</th>\n",
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              "      <th>chol</th>\n",
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              "      <th>exng</th>\n",
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              "      <th>count</th>\n",
              "      <td>303.000000</td>\n",
              "      <td>303.000000</td>\n",
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              "      <td>303.000000</td>\n",
              "      <td>303.000000</td>\n",
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              "      <th>mean</th>\n",
              "      <td>54.366337</td>\n",
              "      <td>0.683168</td>\n",
              "      <td>0.966997</td>\n",
              "      <td>131.623762</td>\n",
              "      <td>246.264026</td>\n",
              "      <td>0.148515</td>\n",
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              "      <td>0.326733</td>\n",
              "      <td>1.039604</td>\n",
              "      <td>1.399340</td>\n",
              "      <td>0.729373</td>\n",
              "      <td>2.313531</td>\n",
              "      <td>0.544554</td>\n",
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              "      <th>std</th>\n",
              "      <td>9.082101</td>\n",
              "      <td>0.466011</td>\n",
              "      <td>1.032052</td>\n",
              "      <td>17.538143</td>\n",
              "      <td>51.830751</td>\n",
              "      <td>0.356198</td>\n",
              "      <td>0.525860</td>\n",
              "      <td>22.905161</td>\n",
              "      <td>0.469794</td>\n",
              "      <td>1.161075</td>\n",
              "      <td>0.616226</td>\n",
              "      <td>1.022606</td>\n",
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              "      <th>min</th>\n",
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              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
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              "      <th>25%</th>\n",
              "      <td>47.500000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>120.000000</td>\n",
              "      <td>211.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>133.500000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>2.000000</td>\n",
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              "      <th>50%</th>\n",
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              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>130.000000</td>\n",
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              "      <td>1.000000</td>\n",
              "      <td>153.000000</td>\n",
              "      <td>0.000000</td>\n",
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              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>1.000000</td>\n",
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              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>61.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>140.000000</td>\n",
              "      <td>274.500000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>166.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.600000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>77.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>200.000000</td>\n",
              "      <td>564.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>202.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>6.200000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-5e7259a1-8432-4a0c-a891-423ec4d9ea8e')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-5e7259a1-8432-4a0c-a891-423ec4d9ea8e button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-5e7259a1-8432-4a0c-a891-423ec4d9ea8e');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data.drop_duplicates(keep='first',inplace=True)"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2022-03-27T06:36:43.322100Z",
          "iopub.execute_input": "2022-03-27T06:36:43.323243Z",
          "iopub.status.idle": "2022-03-27T06:36:43.336562Z",
          "shell.execute_reply.started": "2022-03-27T06:36:43.323209Z",
          "shell.execute_reply": "2022-03-27T06:36:43.335404Z"
        },
        "trusted": true,
        "id": "FlZ96HLahBaf"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y=data.output.values\n",
        "x=data.drop([\"output\"],axis=1)\n",
        "X_train, X_test, y_train, y_test = train_test_split(x, y, test_size=0.20)\n",
        "x.shape,X_train.shape,X_test.shape"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2022-03-27T06:36:57.060452Z",
          "iopub.execute_input": "2022-03-27T06:36:57.061430Z",
          "iopub.status.idle": "2022-03-27T06:36:57.071980Z",
          "shell.execute_reply.started": "2022-03-27T06:36:57.061368Z",
          "shell.execute_reply": "2022-03-27T06:36:57.071401Z"
        },
        "trusted": true,
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wEw_I3nIhBai",
        "outputId": "4079002c-4afe-45de-e7f9-8c19a67393cc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((302, 13), (241, 13), (61, 13))"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.linear_model import SGDClassifier\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "import xgboost as xgb\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "from sklearn.svm import LinearSVC\n",
        "model1=SGDClassifier()\n",
        "model2=LogisticRegression()\n",
        "model3=DecisionTreeClassifier()\n",
        "model4=KNeighborsClassifier()\n",
        "model5 = xgb.XGBClassifier()\n",
        "model6=LinearSVC()\n",
        "meta=LogisticRegression()"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2022-03-27T06:38:38.791399Z",
          "iopub.execute_input": "2022-03-27T06:38:38.791680Z",
          "iopub.status.idle": "2022-03-27T06:38:38.919145Z",
          "shell.execute_reply.started": "2022-03-27T06:38:38.791650Z",
          "shell.execute_reply": "2022-03-27T06:38:38.917868Z"
        },
        "trusted": true,
        "id": "6Jb9jq5RhBai"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "grid_values = {'loss':['log','hinge'],'penalty':['l1','l2'],'alpha':np.logspace(-50,50,101)}\n",
        "clf = GridSearchCV(model1, grid_values, cv=2, scoring='roc_auc', return_train_score=True)\n",
        "clf.fit(X_train, y_train)\n",
        "clf.best_estimator_,clf.best_score_"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2022-03-27T06:38:42.335490Z",
          "iopub.execute_input": "2022-03-27T06:38:42.336638Z",
          "iopub.status.idle": "2022-03-27T06:38:48.044294Z",
          "shell.execute_reply.started": "2022-03-27T06:38:42.336558Z",
          "shell.execute_reply": "2022-03-27T06:38:48.043715Z"
        },
        "trusted": true,
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9SsW3fNdhBak",
        "outputId": "3ff6a16e-f680-408f-9a39-db3b99d7dc8a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(SGDClassifier(alpha=0.1, penalty='l1'), 0.7700680101177613)"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "grid_values = {'penalty':['l1','l2'],'C':np.logspace(-50,50,101)}\n",
        "clf = GridSearchCV(model2, grid_values, cv=2, scoring='roc_auc', return_train_score=True)\n",
        "clf.fit(X_train, y_train)\n",
        "clf.best_estimator_,clf.best_score_"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2022-03-27T06:39:53.970994Z",
          "iopub.execute_input": "2022-03-27T06:39:53.971259Z",
          "iopub.status.idle": "2022-03-27T06:39:58.694232Z",
          "shell.execute_reply.started": "2022-03-27T06:39:53.971232Z",
          "shell.execute_reply": "2022-03-27T06:39:58.693510Z"
        },
        "trusted": true,
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CTW17aC7hBam",
        "outputId": "0da5dd01-8b6c-4940-a33d-f41025835c04"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(LogisticRegression(C=100000.0), 0.8773640216426286)"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "grid_values = {'min_samples_split':np.arange(1,1000,50),'max_depth':np.arange(2,10)}\n",
        "clf = GridSearchCV(model3, grid_values, cv=2, scoring='roc_auc', return_train_score=True)\n",
        "clf.fit(X_train, y_train)\n",
        "clf.best_estimator_,clf.best_score_\n"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2022-03-27T06:39:58.695968Z",
          "iopub.execute_input": "2022-03-27T06:39:58.696248Z",
          "iopub.status.idle": "2022-03-27T06:40:01.160032Z",
          "shell.execute_reply.started": "2022-03-27T06:39:58.696214Z",
          "shell.execute_reply": "2022-03-27T06:40:01.159138Z"
        },
        "trusted": true,
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0LFmJCeihBap",
        "outputId": "a870fc6e-66c7-4739-f251-4234e242f369"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(DecisionTreeClassifier(max_depth=8, min_samples_split=51), 0.7617572909861468)"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "grid_values = {'n_neighbors':np.arange(2,20)}\n",
        "clf = GridSearchCV(model4, grid_values, cv=2, scoring='roc_auc', return_train_score=True)\n",
        "clf.fit(X_train, y_train)\n",
        "clf.best_estimator_,clf.best_score_"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2022-03-27T06:40:01.161492Z",
          "iopub.execute_input": "2022-03-27T06:40:01.161701Z",
          "iopub.status.idle": "2022-03-27T06:40:01.563256Z",
          "shell.execute_reply.started": "2022-03-27T06:40:01.161676Z",
          "shell.execute_reply": "2022-03-27T06:40:01.562290Z"
        },
        "trusted": true,
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mIp_SYjzhBaq",
        "outputId": "6d0b98ac-b964-405d-cfd8-a04d78ad5c88"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(KNeighborsClassifier(n_neighbors=18), 0.7052647117945625)"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import warnings\n",
        "warnings.filterwarnings(\"ignore\")\n",
        "grid_values = {'learning_rate': np.logspace(-5,5,11) ,'n_estimators' : np.arange(10,1000,50)}\n",
        "clf = GridSearchCV(model5, grid_values, cv=2, scoring='roc_auc', return_train_score=True)\n",
        "clf.fit(X_train, y_train)\n",
        "clf.best_estimator_,clf.best_score_"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2022-03-27T06:42:09.858152Z",
          "iopub.execute_input": "2022-03-27T06:42:09.858579Z",
          "iopub.status.idle": "2022-03-27T06:44:32.974758Z",
          "shell.execute_reply.started": "2022-03-27T06:42:09.858530Z",
          "shell.execute_reply": "2022-03-27T06:44:32.971052Z"
        },
        "trusted": true,
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NSs9W671hBar",
        "outputId": "e31a56ae-fbfa-44f6-aee6-2ef4309bc88b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(XGBClassifier(n_estimators=310), 0.8804881317319129)"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "grid_values = {'penalty':['l1','l2'],'C':np.logspace(-50,50,101)}\n",
        "clf = GridSearchCV(model6, grid_values, cv=2, scoring='roc_auc', return_train_score=True)\n",
        "clf.fit(X_train, y_train)\n",
        "clf.best_estimator_,clf.best_score_"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2022-03-27T06:44:41.982177Z",
          "iopub.execute_input": "2022-03-27T06:44:41.982601Z",
          "iopub.status.idle": "2022-03-27T06:44:44.989823Z",
          "shell.execute_reply.started": "2022-03-27T06:44:41.982568Z",
          "shell.execute_reply": "2022-03-27T06:44:44.988419Z"
        },
        "trusted": true,
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "D_FFfVePhBas",
        "outputId": "2b2c6668-463e-468e-cd87-6bbfb88654fe"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(LinearSVC(C=1e+50), 0.8912047506574869)"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model1=SGDClassifier(alpha=0.1, loss='log', penalty='l1')\n",
        "model1.fit(X_train,y_train)\n",
        "model2=LogisticRegression(C=100000.0)\n",
        "model2.fit(X_train,y_train)\n",
        "model3=DecisionTreeClassifier(max_depth=8, min_samples_split=51)\n",
        "model3.fit(X_train,y_train)\n",
        "model4=KNeighborsClassifier(n_neighbors=18)\n",
        "model4.fit(X_train,y_train)\n",
        "model5 = xgb.XGBClassifier(learning_rate=0.01, max_depth=2, n_estimators=310)\n",
        "model5.fit(X_train,y_train)\n",
        "model6=LinearSVC(C=100.0)\n",
        "model6.fit(X_train,y_train)\n",
        "meta=LogisticRegression()"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2022-03-27T06:48:53.160380Z",
          "iopub.execute_input": "2022-03-27T06:48:53.160645Z",
          "iopub.status.idle": "2022-03-27T06:48:53.767184Z",
          "shell.execute_reply.started": "2022-03-27T06:48:53.160617Z",
          "shell.execute_reply": "2022-03-27T06:48:53.766042Z"
        },
        "trusted": true,
        "id": "i0X_63IrhBat"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import six\n",
        "import sys\n",
        "sys.modules['sklearn.externals.six'] = six\n",
        "from mlxtend.classifier import StackingClassifier\n",
        "\n",
        "sclf = StackingClassifier(classifiers=[model1, model2, model3, model4, model5, model6], meta_classifier=meta)"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2022-03-27T06:49:00.011451Z",
          "iopub.execute_input": "2022-03-27T06:49:00.011752Z",
          "iopub.status.idle": "2022-03-27T06:49:00.017511Z",
          "shell.execute_reply.started": "2022-03-27T06:49:00.011720Z",
          "shell.execute_reply": "2022-03-27T06:49:00.016617Z"
        },
        "trusted": true,
        "id": "Yc1CIA59hBau"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn import model_selection\n",
        "for clf, label in zip([model1, model2, model3, model4, model5, model6,sclf], \n",
        "                      ['SGD', \n",
        "                       'Logistic Regression', \n",
        "                       'DecisionTreeClassifier',\n",
        "                       'KNeighborsClassifier', \n",
        "                       'XGBClassifier', \n",
        "                       'DecisionTreeClassifier',\n",
        "                       'LinearSVC']):\n",
        "\n",
        "    scores = model_selection.cross_val_score(clf, X_train, y_train, \n",
        "                                              cv=3, scoring='accuracy')\n",
        "    print(\"Accuracy: %0.2f (+/- %0.2f) [%s]\" \n",
        "          % (scores.mean(), scores.std(), label))"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2022-03-27T06:49:03.714788Z",
          "iopub.execute_input": "2022-03-27T06:49:03.715084Z",
          "iopub.status.idle": "2022-03-27T06:49:07.498567Z",
          "shell.execute_reply.started": "2022-03-27T06:49:03.715051Z",
          "shell.execute_reply": "2022-03-27T06:49:07.497898Z"
        },
        "trusted": true,
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6-DOKyXghBav",
        "outputId": "1923c142-2063-4863-b1ec-bb4141533f49"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy: 0.62 (+/- 0.05) [SGD]\n",
            "Accuracy: 0.81 (+/- 0.04) [Logistic Regression]\n",
            "Accuracy: 0.72 (+/- 0.03) [DecisionTreeClassifier]\n",
            "Accuracy: 0.66 (+/- 0.04) [KNeighborsClassifier]\n",
            "Accuracy: 0.82 (+/- 0.03) [XGBClassifier]\n",
            "Accuracy: 0.73 (+/- 0.05) [DecisionTreeClassifier]\n",
            "Accuracy: 0.81 (+/- 0.03) [LinearSVC]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sclf.fit(X_train,y_train)"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2022-03-27T06:49:36.773202Z",
          "iopub.execute_input": "2022-03-27T06:49:36.773749Z",
          "iopub.status.idle": "2022-03-27T06:49:37.434900Z",
          "shell.execute_reply.started": "2022-03-27T06:49:36.773706Z",
          "shell.execute_reply": "2022-03-27T06:49:37.434221Z"
        },
        "trusted": true,
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KZQwRKeZhBaw",
        "outputId": "3b054c8a-544e-4796-cf9b-23121fb558a9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "StackingClassifier(classifiers=[SGDClassifier(alpha=0.01, loss='log',\n",
              "                                              penalty='l1'),\n",
              "                                LogisticRegression(C=100000000000000.0),\n",
              "                                DecisionTreeClassifier(max_depth=2,\n",
              "                                                       min_samples_split=51),\n",
              "                                KNeighborsClassifier(n_neighbors=19),\n",
              "                                XGBClassifier(learning_rate=0.01, max_depth=2,\n",
              "                                              n_estimators=260),\n",
              "                                LinearSVC(C=100.0)],\n",
              "                   meta_classifier=LogisticRegression())"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import accuracy_score\n",
        "print (\"accuracy with model 1 is :\"+str(accuracy_score(y_test,model1.predict(X_test))))\n",
        "print (\"accuracy with model 2 is :\"+str(accuracy_score(y_test,model2.predict(X_test))))\n",
        "print (\"accuracy with model 3 is :\"+str(accuracy_score(y_test,model3.predict(X_test))))\n",
        "print (\"accuracy with model 4 is :\"+str(accuracy_score(y_test,model4.predict(X_test))))\n",
        "print (\"accuracy with model 5 is :\"+str(accuracy_score(y_test,model5.predict(X_test))))\n",
        "print (\"accuracy with model 6 is :\"+str(accuracy_score(y_test,model6.predict(X_test))))\n",
        "print (\"accuracy with ensemble is :\"+str(accuracy_score(y_test,sclf.predict(X_test))))"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2022-03-27T06:59:38.676698Z",
          "iopub.execute_input": "2022-03-27T06:59:38.677885Z",
          "iopub.status.idle": "2022-03-27T06:59:38.731357Z",
          "shell.execute_reply.started": "2022-03-27T06:59:38.677799Z",
          "shell.execute_reply": "2022-03-27T06:59:38.730385Z"
        },
        "trusted": true,
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UK3h-IWkhBay",
        "outputId": "deefd492-7f24-42ba-917a-899da9568f0a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "accuracy with model 1 is :0.8032786885245902\n",
            "accuracy with model 2 is :0.8852459016393442\n",
            "accuracy with model 3 is :0.8032786885245902\n",
            "accuracy with model 4 is :0.7049180327868853\n",
            "accuracy with model 5 is :0.8524590163934426\n",
            "accuracy with model 6 is :0.8360655737704918\n",
            "accuracy with ensemble is :0.8524590163934426\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "as accuracy of ensemble is no better than model6 lets compare confusion and auc roc "
      ],
      "metadata": {
        "id": "iqOQM23chBaz"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import plot_confusion_matrix\n",
        "plot_confusion_matrix(sclf,X_test,y_test)"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2022-03-27T07:01:58.723315Z",
          "iopub.execute_input": "2022-03-27T07:01:58.723624Z",
          "iopub.status.idle": "2022-03-27T07:01:58.958443Z",
          "shell.execute_reply.started": "2022-03-27T07:01:58.723594Z",
          "shell.execute_reply": "2022-03-27T07:01:58.957037Z"
        },
        "trusted": true,
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "id": "MWJPNc1HhBa1",
        "outputId": "8bd7af46-895f-41fe-da66-aabc7c4f8447"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x7ffb2ca84150>"
            ]
          },
          "metadata": {},
          "execution_count": 26
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plot_confusion_matrix(model6,X_test,y_test)"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2022-03-27T07:02:28.482810Z",
          "iopub.execute_input": "2022-03-27T07:02:28.483317Z",
          "iopub.status.idle": "2022-03-27T07:02:28.707322Z",
          "shell.execute_reply.started": "2022-03-27T07:02:28.483274Z",
          "shell.execute_reply": "2022-03-27T07:02:28.706270Z"
        },
        "trusted": true,
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "bT8nF76ghBa3",
        "outputId": "1350b922-1da5-4281-a0cd-d4e435fbded4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x7ffb2c842390>"
            ]
          },
          "metadata": {},
          "execution_count": 27
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "roc_auc_score(y_test, sclf.predict(X_test))"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2022-03-27T07:02:01.012455Z",
          "iopub.execute_input": "2022-03-27T07:02:01.012741Z",
          "iopub.status.idle": "2022-03-27T07:02:01.045607Z",
          "shell.execute_reply.started": "2022-03-27T07:02:01.012712Z",
          "shell.execute_reply": "2022-03-27T07:02:01.044732Z"
        },
        "trusted": true,
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hEHuMCwQhBa3",
        "outputId": "46349050-cb18-4c1d-9b3b-e7acf9a93c33"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8526881720430108"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "roc_auc_score(y_test, model6.predict(X_test))"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2022-03-27T07:03:05.654833Z",
          "iopub.execute_input": "2022-03-27T07:03:05.655114Z",
          "iopub.status.idle": "2022-03-27T07:03:05.667988Z",
          "shell.execute_reply.started": "2022-03-27T07:03:05.655086Z",
          "shell.execute_reply": "2022-03-27T07:03:05.665827Z"
        },
        "trusted": true,
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mmolDN_MhBa4",
        "outputId": "f1b41da2-5e28-4f52-dff4-4bc349743d8b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8338709677419355"
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "thus in auc roc curve we came to know that ensemble is performing better\n",
        "but ensemble will consume much configurations and i can trade with model6 here"
      ],
      "metadata": {
        "id": "1FTu4TPphBa5"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Lets predict"
      ],
      "metadata": {
        "id": "a0XNKtW-hBa6"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "g=[28,1,1,138,170,0,1,140,2.0,1,3,2,1]\n",
        "g=np.array(g)\n",
        "model6.predict(g.reshape(1,-1))"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2022-03-27T07:40:21.510322Z",
          "iopub.execute_input": "2022-03-27T07:40:21.510619Z",
          "iopub.status.idle": "2022-03-27T07:40:21.528432Z",
          "shell.execute_reply.started": "2022-03-27T07:40:21.510587Z",
          "shell.execute_reply": "2022-03-27T07:40:21.526171Z"
        },
        "trusted": true,
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WjL3rVILhBa8",
        "outputId": "c16cb6f3-fae5-4902-c53c-ee5aa04bd315"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0])"
            ]
          },
          "metadata": {},
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "g=[58,1,1,178,210,0,1,210,4.0,1,3,2,1]\n",
        "g=np.array(g)\n",
        "model6.predict(g.reshape(1,-1))"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2022-03-27T07:40:24.051951Z",
          "iopub.execute_input": "2022-03-27T07:40:24.052239Z",
          "iopub.status.idle": "2022-03-27T07:40:24.072095Z",
          "shell.execute_reply.started": "2022-03-27T07:40:24.052208Z",
          "shell.execute_reply": "2022-03-27T07:40:24.070830Z"
        },
        "trusted": true,
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_X5-O_8WhBa8",
        "outputId": "33012781-7de6-4b34-f10d-2777312acca4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1])"
            ]
          },
          "metadata": {},
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import joblib\n",
        "joblib.dump(model6,'model6.pkl') "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xgpy2o7XjvUm",
        "outputId": "4c25c4ec-4f5f-4f29-d169-238046a8fa47"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['model6.pkl']"
            ]
          },
          "metadata": {},
          "execution_count": 38
        }
      ]
    }
  ]
}