Heart_Disease_Prediction_using_Machine_Learning.ipynb

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      "source": [
        "# Heart Disease Prediction using Machine Learning"
      ],
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        "id": "SPmtc2TbcweA"
      }
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      "source": [
        "### Workflow of model\n",
        "\n",
        "  - Data collection\n",
        "  - Data Visualization\n",
        "  - Splitting the Features and Target\n",
        "  - Train-Test split\n",
        "  - Model Training\n",
        "  - Model Evaluation\n",
        "  - Predicting Results\n",
        "  - Saving Model\n"
      ],
      "metadata": {
        "id": "J88PvWUXTnNb"
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      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "\n",
        "from sklearn.naive_bayes import GaussianNB\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.svm import SVC\n",
        "from sklearn.naive_bayes import MultinomialNB\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.ensemble import AdaBoostClassifier\n",
        "from sklearn.ensemble import BaggingClassifier\n",
        "from sklearn.ensemble import ExtraTreesClassifier\n",
        "from sklearn.ensemble import GradientBoostingClassifier\n",
        "from xgboost import XGBClassifier\n",
        "\n",
        "\n",
        "\n",
        "# for model improvement\n",
        "from sklearn.ensemble import StackingClassifier\n",
        "from sklearn.ensemble import VotingClassifier\n",
        "\n",
        "from sklearn import metrics\n",
        "from sklearn.metrics import accuracy_score\n",
        "from sklearn.metrics import confusion_matrix\n",
        "from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score\n",
        "\n",
        "import joblib"
      ],
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      "execution_count": 49,
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      "cell_type": "markdown",
      "source": [
        "## Data Collection and Processing"
      ],
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      }
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    {
      "cell_type": "code",
      "source": [
        "# loading data into pandas data frame\n",
        "\n",
        "heart_data = pd.read_csv(\"/content/heart.csv\")\n",
        "heart_data.head(10)\n"
      ],
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          "height": 363
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        "id": "OZPSpr74du94",
        "outputId": "15b9f917-6528-480f-90b6-d7440b48f34c"
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      "execution_count": 2,
      "outputs": [
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              "   age  sex  cp  trestbps  chol  fbs  restecg  thalach  exang  oldpeak  slope  \\\n",
              "0   52    1   0       125   212    0        1      168      0      1.0      2   \n",
              "1   53    1   0       140   203    1        0      155      1      3.1      0   \n",
              "2   70    1   0       145   174    0        1      125      1      2.6      0   \n",
              "3   61    1   0       148   203    0        1      161      0      0.0      2   \n",
              "4   62    0   0       138   294    1        1      106      0      1.9      1   \n",
              "5   58    0   0       100   248    0        0      122      0      1.0      1   \n",
              "6   58    1   0       114   318    0        2      140      0      4.4      0   \n",
              "7   55    1   0       160   289    0        0      145      1      0.8      1   \n",
              "8   46    1   0       120   249    0        0      144      0      0.8      2   \n",
              "9   54    1   0       122   286    0        0      116      1      3.2      1   \n",
              "\n",
              "   ca  thal  target  \n",
              "0   2     3       0  \n",
              "1   0     3       0  \n",
              "2   0     3       0  \n",
              "3   1     3       0  \n",
              "4   3     2       0  \n",
              "5   0     2       1  \n",
              "6   3     1       0  \n",
              "7   1     3       0  \n",
              "8   0     3       0  \n",
              "9   2     2       0  "
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              "      <th>age</th>\n",
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              "      <th>chol</th>\n",
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              "      <th>0</th>\n",
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              "      <th>2</th>\n",
              "      <td>70</td>\n",
              "      <td>1</td>\n",
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              "      <td>145</td>\n",
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              "      <th>3</th>\n",
              "      <td>61</td>\n",
              "      <td>1</td>\n",
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              "      <td>148</td>\n",
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              "      <th>4</th>\n",
              "      <td>62</td>\n",
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              "      <td>0</td>\n",
              "      <td>138</td>\n",
              "      <td>294</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>106</td>\n",
              "      <td>0</td>\n",
              "      <td>1.9</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
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              "      <th>5</th>\n",
              "      <td>58</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>100</td>\n",
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              "      <th>6</th>\n",
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              "      <th>7</th>\n",
              "      <td>55</td>\n",
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    {
      "cell_type": "code",
      "source": [
        "# columns name\n",
        "\n",
        "heart_data.columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3ir8AtLB43_I",
        "outputId": "57b55170-cd89-43fa-9699-de9d52dec767"
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      "execution_count": 3,
      "outputs": [
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          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['age', 'sex', 'cp', 'trestbps', 'chol', 'fbs', 'restecg', 'thalach',\n",
              "       'exang', 'oldpeak', 'slope', 'ca', 'thal', 'target'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# shape of dataset\n",
        "\n",
        "heart_data.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rA9Eg9qtryJH",
        "outputId": "d398ac8b-4bdc-4b53-ff72-d5a0a7754ac6"
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      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1025, 14)"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
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    },
    {
      "cell_type": "code",
      "source": [
        "# describing data\n",
        "\n",
        "heart_data.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 344
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        "id": "J_EURFNcsRPf",
        "outputId": "1b2a5443-0dad-4b9a-ae3c-cd48e1d661d9"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "               age          sex           cp     trestbps        chol  \\\n",
              "count  1025.000000  1025.000000  1025.000000  1025.000000  1025.00000   \n",
              "mean     54.434146     0.695610     0.942439   131.611707   246.00000   \n",
              "std       9.072290     0.460373     1.029641    17.516718    51.59251   \n",
              "min      29.000000     0.000000     0.000000    94.000000   126.00000   \n",
              "25%      48.000000     0.000000     0.000000   120.000000   211.00000   \n",
              "50%      56.000000     1.000000     1.000000   130.000000   240.00000   \n",
              "75%      61.000000     1.000000     2.000000   140.000000   275.00000   \n",
              "max      77.000000     1.000000     3.000000   200.000000   564.00000   \n",
              "\n",
              "               fbs      restecg      thalach        exang      oldpeak  \\\n",
              "count  1025.000000  1025.000000  1025.000000  1025.000000  1025.000000   \n",
              "mean      0.149268     0.529756   149.114146     0.336585     1.071512   \n",
              "std       0.356527     0.527878    23.005724     0.472772     1.175053   \n",
              "min       0.000000     0.000000    71.000000     0.000000     0.000000   \n",
              "25%       0.000000     0.000000   132.000000     0.000000     0.000000   \n",
              "50%       0.000000     1.000000   152.000000     0.000000     0.800000   \n",
              "75%       0.000000     1.000000   166.000000     1.000000     1.800000   \n",
              "max       1.000000     2.000000   202.000000     1.000000     6.200000   \n",
              "\n",
              "             slope           ca         thal       target  \n",
              "count  1025.000000  1025.000000  1025.000000  1025.000000  \n",
              "mean      1.385366     0.754146     2.323902     0.513171  \n",
              "std       0.617755     1.030798     0.620660     0.500070  \n",
              "min       0.000000     0.000000     0.000000     0.000000  \n",
              "25%       1.000000     0.000000     2.000000     0.000000  \n",
              "50%       1.000000     0.000000     2.000000     1.000000  \n",
              "75%       2.000000     1.000000     3.000000     1.000000  \n",
              "max       2.000000     4.000000     3.000000     1.000000  "
            ],
            "text/html": [
              "\n",
              "\n",
              "  <div id=\"df-9894c536-1cce-4786-8ec8-c15169f9f0b0\">\n",
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              "  <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>target</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>1025.000000</td>\n",
              "      <td>1025.000000</td>\n",
              "      <td>1025.000000</td>\n",
              "      <td>1025.000000</td>\n",
              "      <td>1025.00000</td>\n",
              "      <td>1025.000000</td>\n",
              "      <td>1025.000000</td>\n",
              "      <td>1025.000000</td>\n",
              "      <td>1025.000000</td>\n",
              "      <td>1025.000000</td>\n",
              "      <td>1025.000000</td>\n",
              "      <td>1025.000000</td>\n",
              "      <td>1025.000000</td>\n",
              "      <td>1025.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>54.434146</td>\n",
              "      <td>0.695610</td>\n",
              "      <td>0.942439</td>\n",
              "      <td>131.611707</td>\n",
              "      <td>246.00000</td>\n",
              "      <td>0.149268</td>\n",
              "      <td>0.529756</td>\n",
              "      <td>149.114146</td>\n",
              "      <td>0.336585</td>\n",
              "      <td>1.071512</td>\n",
              "      <td>1.385366</td>\n",
              "      <td>0.754146</td>\n",
              "      <td>2.323902</td>\n",
              "      <td>0.513171</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>9.072290</td>\n",
              "      <td>0.460373</td>\n",
              "      <td>1.029641</td>\n",
              "      <td>17.516718</td>\n",
              "      <td>51.59251</td>\n",
              "      <td>0.356527</td>\n",
              "      <td>0.527878</td>\n",
              "      <td>23.005724</td>\n",
              "      <td>0.472772</td>\n",
              "      <td>1.175053</td>\n",
              "      <td>0.617755</td>\n",
              "      <td>1.030798</td>\n",
              "      <td>0.620660</td>\n",
              "      <td>0.500070</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>29.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>94.000000</td>\n",
              "      <td>126.00000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>71.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>48.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>120.000000</td>\n",
              "      <td>211.00000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>132.000000</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",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>56.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>130.000000</td>\n",
              "      <td>240.00000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>152.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.800000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <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>275.00000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>166.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.800000</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>564.00000</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",
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              "\n",
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              "\n",
              "        async function convertToInteractive(key) {\n",
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              "            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",
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              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
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              "      </script>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# dataset information\n",
        "\n",
        "heart_data.info()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Dvm_bpe1sWYw",
        "outputId": "3edf7834-9fb1-4543-d4d2-cd1f5c51597a"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 1025 entries, 0 to 1024\n",
            "Data columns (total 14 columns):\n",
            " #   Column    Non-Null Count  Dtype  \n",
            "---  ------    --------------  -----  \n",
            " 0   age       1025 non-null   int64  \n",
            " 1   sex       1025 non-null   int64  \n",
            " 2   cp        1025 non-null   int64  \n",
            " 3   trestbps  1025 non-null   int64  \n",
            " 4   chol      1025 non-null   int64  \n",
            " 5   fbs       1025 non-null   int64  \n",
            " 6   restecg   1025 non-null   int64  \n",
            " 7   thalach   1025 non-null   int64  \n",
            " 8   exang     1025 non-null   int64  \n",
            " 9   oldpeak   1025 non-null   float64\n",
            " 10  slope     1025 non-null   int64  \n",
            " 11  ca        1025 non-null   int64  \n",
            " 12  thal      1025 non-null   int64  \n",
            " 13  target    1025 non-null   int64  \n",
            "dtypes: float64(1), int64(13)\n",
            "memory usage: 112.2 KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# checking for missing values\n",
        "\n",
        "heart_data.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wosxTVRzsxcr",
        "outputId": "4c37629e-eda5-45a3-cc2b-9ad28f0c4b87"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "age         0\n",
              "sex         0\n",
              "cp          0\n",
              "trestbps    0\n",
              "chol        0\n",
              "fbs         0\n",
              "restecg     0\n",
              "thalach     0\n",
              "exang       0\n",
              "oldpeak     0\n",
              "slope       0\n",
              "ca          0\n",
              "thal        0\n",
              "target      0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# checking the distribution of target variable\n",
        "\n",
        "heart_data['target'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ruJdnTtRtFQ6",
        "outputId": "578b169f-a9e8-4ed0-8ce2-13b84df45cc1"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1    526\n",
              "0    499\n",
              "Name: target, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Data Visualization"
      ],
      "metadata": {
        "id": "c8Upz7ob5cTN"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "sns.countplot(x=heart_data[\"target\"])\n",
        "\n",
        "# distribution of target"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 466
        },
        "id": "ELfDzyHf5f9c",
        "outputId": "2aa49235-d938-418b-b07e-b59f812e7cd2"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='target', ylabel='count'>"
            ]
          },
          "metadata": {},
          "execution_count": 9
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.pairplot(heart_data, hue= 'target',vars = ['age', 'sex', 'cp', 'trestbps', 'chol' ])\n",
        "\n",
        "# pair plot in dataset of outcome with all columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "ENPkGBGi5xAk",
        "outputId": "17220ff6-5ed3-499c-f907-95266acf04aa"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.PairGrid at 0x7b2afdbe25c0>"
            ]
          },
          "metadata": {},
          "execution_count": 10
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1308.88x1250 with 30 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize= (16,9))\n",
        "sns.heatmap(heart_data.corr(), annot = True, cmap='coolwarm', linewidths = 2)"
      ],
      "metadata": {
        "id": "wr5mFm_SiPX6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 776
        },
        "outputId": "e6e12a79-8388-4d0e-c3dd-5abe8327a469"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 11
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1600x900 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "here, we have approx equal distribution of data.\n",
        "\n",
        "### Notation for Healthy and Defective Heart\n",
        "\n",
        "- 1 Represents a Defective Heart\n",
        "- 0 Represents a Healthy Heart\n"
      ],
      "metadata": {
        "id": "-S_TdfxKtx_g"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Splitting the Features and Target"
      ],
      "metadata": {
        "id": "3ZQHotcgw_Qa"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X = heart_data.drop(columns = 'target', axis = 1)\n",
        "X.head()\n",
        "\n",
        "# now X contains table without target column which will help for training the dataset"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "GR_dlVpxttSa",
        "outputId": "c8607a7e-4c72-4123-dcad-fa72b9fa5e16"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   age  sex  cp  trestbps  chol  fbs  restecg  thalach  exang  oldpeak  slope  \\\n",
              "0   52    1   0       125   212    0        1      168      0      1.0      2   \n",
              "1   53    1   0       140   203    1        0      155      1      3.1      0   \n",
              "2   70    1   0       145   174    0        1      125      1      2.6      0   \n",
              "3   61    1   0       148   203    0        1      161      0      0.0      2   \n",
              "4   62    0   0       138   294    1        1      106      0      1.9      1   \n",
              "\n",
              "   ca  thal  \n",
              "0   2     3  \n",
              "1   0     3  \n",
              "2   0     3  \n",
              "3   1     3  \n",
              "4   3     2  "
            ],
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              "    <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",
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              "  <tbody>\n",
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              "      <td>3</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>70</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>145</td>\n",
              "      <td>174</td>\n",
              "      <td>0</td>\n",
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              "      <td>125</td>\n",
              "      <td>1</td>\n",
              "      <td>2.6</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>61</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>148</td>\n",
              "      <td>203</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>161</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>62</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>138</td>\n",
              "      <td>294</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>106</td>\n",
              "      <td>0</td>\n",
              "      <td>1.9</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
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              "  </tbody>\n",
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              "    fill: #1967D2;\n",
              "    height: 32px;\n",
              "    padding: 0 0 0 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart: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-quickchart {\n",
              "    background-color: #3B4455;\n",
              "    fill: #D2E3FC;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart: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",
              "      async function quickchart(key) {\n",
              "        const containerElement = document.querySelector('#' + key);\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      }\n",
              "    </script>\n",
              "\n",
              "      <script>\n",
              "\n",
              "function displayQuickchartButton(domScope) {\n",
              "  let quickchartButtonEl =\n",
              "    domScope.querySelector('#df-41bf5e33-dca3-463d-a204-42cb72eaa3f3 button.colab-df-quickchart');\n",
              "  quickchartButtonEl.style.display =\n",
              "    google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "}\n",
              "\n",
              "        displayQuickchartButton(document);\n",
              "      </script>\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-7daad2e4-a12a-48be-b957-52816bcd6191 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-7daad2e4-a12a-48be-b957-52816bcd6191');\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": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "Y = heart_data['target']\n",
        "Y.head()\n",
        "\n",
        "# Y contains one column which includes output for validating the result after model prediction\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ucqmTLXixU-V",
        "outputId": "2506f400-40cf-4f9d-d296-8dc9ee6f2837"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    0\n",
              "1    0\n",
              "2    0\n",
              "3    0\n",
              "4    0\n",
              "Name: target, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Data Standardization"
      ],
      "metadata": {
        "id": "754jfLv2y5Xi"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "scaler = StandardScaler()"
      ],
      "metadata": {
        "id": "J49oN0zPywO7"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "scaler.fit(X)\n",
        "X_standard = scaler.transform(X)\n"
      ],
      "metadata": {
        "id": "862PZLv5zEMr"
      },
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Splitting the Data into Training data and Test data"
      ],
      "metadata": {
        "id": "Ny7l7v0Qy-a9"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size = 0.15, stratify = Y, random_state = 3 )\n",
        "\n",
        "# stratify will distribute 0 and 1 in even manner, of that prediction will be unbiased\n",
        "# test_split tells a ratio about size of test data in dataset, means 15 percent of data is test data\n",
        "# random_state tells about the randomness of data, and number tells about its extent of randomness\n",
        "\n"
      ],
      "metadata": {
        "id": "tAjOl-6-xrJD"
      },
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# checking shape of splitted data\n",
        "\n",
        "print(X.shape, X_train.shape, X_test.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "R0pJWwVRziuZ",
        "outputId": "aab9beed-4361-446d-860e-a6ed67c7d34b"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(1025, 13) (871, 13) (154, 13)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Model Training\n"
      ],
      "metadata": {
        "id": "pTcWd72X1vKJ"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 1. Logistic Regression\n",
        "\n"
      ],
      "metadata": {
        "id": "gfiqkk6u2M5l"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# instantiate the model\n",
        "lr = LogisticRegression()\n",
        "\n",
        "# training the LogisticRegression model with training data\n",
        "lr.fit(X_train, Y_train)"
      ],
      "metadata": {
        "id": "oSUZR4ME07Wt",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 213
        },
        "outputId": "2a529f62-05c5-461d-cd9b-ce1f46c10a39"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "Please also refer to the documentation for alternative solver options:\n",
            "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
            "  n_iter_i = _check_optimize_result(\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LogisticRegression()"
            ],
            "text/html": [
              "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression()</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred = lr.predict(X_test)\n",
        "\n",
        "print('Model accuracy score: {0:0.4f}'. format(accuracy_score(Y_test, y_pred)))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5kTnbyG19oEr",
        "outputId": "faa5040f-aa1e-4ab0-e1b2-f2dfb3972433"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model accuracy score: 0.8377\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 2. Naive Bayes Classifier"
      ],
      "metadata": {
        "id": "aV_3lJTv8fxs"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# instantiate the model\n",
        "gnb = GaussianNB()\n",
        "# model = gnb\n",
        "\n",
        "# fit the model\n",
        "gnb.fit(X_train, Y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "id": "URTnecuB8fB9",
        "outputId": "18d59e14-a4fb-4457-a8d5-33c226a23021"
      },
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GaussianNB()"
            ],
            "text/html": [
              "<style>#sk-container-id-8 {color: black;background-color: white;}#sk-container-id-8 pre{padding: 0;}#sk-container-id-8 div.sk-toggleable {background-color: white;}#sk-container-id-8 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-8 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-8 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-8 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-8 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-8 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-8 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-8 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-8 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-8 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-8 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-8 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-8 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-8 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-8 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-8 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-8 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-8 div.sk-item {position: relative;z-index: 1;}#sk-container-id-8 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-8 div.sk-item::before, #sk-container-id-8 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-8 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-8 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-8 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-8 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-8 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-8 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-8 div.sk-label-container {text-align: center;}#sk-container-id-8 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-8 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-8\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GaussianNB()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-12\" type=\"checkbox\" checked><label for=\"sk-estimator-id-12\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GaussianNB</label><div class=\"sk-toggleable__content\"><pre>GaussianNB()</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 60
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred = gnb.predict(X_test)\n",
        "\n",
        "y_pred\n",
        "print('Model accuracy score: {0:0.4f}'. format(accuracy_score(Y_test, y_pred)))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "I57vt5yD9cxz",
        "outputId": "2e77fe9d-0b25-4329-cfd1-73ed2c156952"
      },
      "execution_count": 61,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model accuracy score: 0.7792\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 3. K-Nearest Neighbor (KNN)"
      ],
      "metadata": {
        "id": "p5x8z5YN8e26"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# instantiate the model\n",
        "knn = KNeighborsClassifier(n_neighbors=7)\n",
        "\n",
        "\n",
        "# fit the model\n",
        "knn.fit(X_train, Y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "id": "JSx17YOC-dME",
        "outputId": "b38e9284-b63a-450b-8bbb-142a4ea58d0a"
      },
      "execution_count": 62,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "KNeighborsClassifier(n_neighbors=7)"
            ],
            "text/html": [
              "<style>#sk-container-id-9 {color: black;background-color: white;}#sk-container-id-9 pre{padding: 0;}#sk-container-id-9 div.sk-toggleable {background-color: white;}#sk-container-id-9 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-9 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-9 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-9 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-9 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-9 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-9 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-9 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-9 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-9 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-9 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-9 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-9 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-9 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-9 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-9 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-9 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-9 div.sk-item {position: relative;z-index: 1;}#sk-container-id-9 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-9 div.sk-item::before, #sk-container-id-9 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-9 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-9 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-9 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-9 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-9 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-9 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-9 div.sk-label-container {text-align: center;}#sk-container-id-9 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-9 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-9\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KNeighborsClassifier(n_neighbors=7)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-13\" type=\"checkbox\" checked><label for=\"sk-estimator-id-13\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">KNeighborsClassifier</label><div class=\"sk-toggleable__content\"><pre>KNeighborsClassifier(n_neighbors=7)</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 62
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred = knn.predict(X_test)\n",
        "\n",
        "y_pred\n",
        "print('Model accuracy score: {0:0.4f}'. format(accuracy_score(Y_test, y_pred)))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "u7kbaOju-xK4",
        "outputId": "5150f6e2-58ac-4ea0-a8a9-51acf05425fe"
      },
      "execution_count": 63,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model accuracy score: 0.7532\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 4. Decision Tree Classifier"
      ],
      "metadata": {
        "id": "b10D1rnV-07t"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Create Decision Tree classifer object\n",
        "dtc = DecisionTreeClassifier()\n",
        "\n",
        "\n",
        "# fit the model\n",
        "dtc.fit(X_train,Y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "id": "Q3wXnEgj-xHw",
        "outputId": "39370faa-d4c6-4afd-9c30-875956c6459b"
      },
      "execution_count": 64,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DecisionTreeClassifier()"
            ],
            "text/html": [
              "<style>#sk-container-id-10 {color: black;background-color: white;}#sk-container-id-10 pre{padding: 0;}#sk-container-id-10 div.sk-toggleable {background-color: white;}#sk-container-id-10 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-10 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-10 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-10 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-10 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-10 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-10 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-10 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-10 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-10 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-10 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-10 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-10 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-10 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-10 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-10 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-10 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-10 div.sk-item {position: relative;z-index: 1;}#sk-container-id-10 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-10 div.sk-item::before, #sk-container-id-10 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-10 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-10 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-10 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-10 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-10 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-10 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-10 div.sk-label-container {text-align: center;}#sk-container-id-10 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-10 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-10\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DecisionTreeClassifier()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-14\" type=\"checkbox\" checked><label for=\"sk-estimator-id-14\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DecisionTreeClassifier</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeClassifier()</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 64
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred = dtc.predict(X_test)\n",
        "\n",
        "y_pred\n",
        "print('Model accuracy score: {0:0.4f}'. format(accuracy_score(Y_test, y_pred)))\n",
        "\n",
        "# overfitted"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pwDSiGx8-xEV",
        "outputId": "eb186620-373c-4310-aa9b-50d9ac10e329"
      },
      "execution_count": 65,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model accuracy score: 1.0000\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 5. Support Vector Machine (Linear)"
      ],
      "metadata": {
        "id": "XBTfkQXu-ww0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# instantiate the model\n",
        "svm = SVC(kernel='linear')\n",
        "\n",
        "# fitting x samples and y classes\n",
        "svm.fit(X_train, Y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "id": "JXBZAgRx-wc3",
        "outputId": "3c362794-cf97-4fec-d61c-da37429826eb"
      },
      "execution_count": 66,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "SVC(kernel='linear')"
            ],
            "text/html": [
              "<style>#sk-container-id-11 {color: black;background-color: white;}#sk-container-id-11 pre{padding: 0;}#sk-container-id-11 div.sk-toggleable {background-color: white;}#sk-container-id-11 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-11 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-11 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-11 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-11 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-11 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-11 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-11 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-11 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-11 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-11 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-11 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-11 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-11 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-11 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-11 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-11 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-11 div.sk-item {position: relative;z-index: 1;}#sk-container-id-11 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-11 div.sk-item::before, #sk-container-id-11 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-11 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-11 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-11 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-11 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-11 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-11 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-11 div.sk-label-container {text-align: center;}#sk-container-id-11 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-11 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-11\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SVC(kernel=&#x27;linear&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-15\" type=\"checkbox\" checked><label for=\"sk-estimator-id-15\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SVC</label><div class=\"sk-toggleable__content\"><pre>SVC(kernel=&#x27;linear&#x27;)</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 66
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred = svm.predict(X_test)\n",
        "\n",
        "y_pred\n",
        "print('Model accuracy score: {0:0.4f}'. format(accuracy_score(Y_test, y_pred)))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VgwiidWt_kQF",
        "outputId": "92692f69-aa7d-4d71-8efe-a432454e3d94"
      },
      "execution_count": 67,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model accuracy score: 0.8312\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Multi-model training"
      ],
      "metadata": {
        "id": "ywOm7vFJtrNq"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "svc = SVC(kernel = 'sigmoid', gamma = 1.0) # A higher gamma value means that each training example will have a greater influence on the decision boundary.\n",
        "knc = KNeighborsClassifier()\n",
        "mnb = MultinomialNB()\n",
        "dtc = DecisionTreeClassifier(max_depth = 5)\n",
        "lrc = LogisticRegression(solver = 'liblinear', penalty = 'l1') # liblinear is parameter specifies the solver to use,\n",
        "# L1 penalty is a type of regularization that helps to prevent overfitting.\n",
        "\n",
        "rfc = RandomForestClassifier(n_estimators= 50, random_state = 2)  # n_estimators : the number of trees in the forest,\n",
        "# random_state : specifies the random seed that is used to initialize the random forest\n",
        "\n",
        "abc = AdaBoostClassifier(n_estimators = 50, random_state = 2)\n",
        "bc = BaggingClassifier(n_estimators = 50, random_state = 2)\n",
        "etc = ExtraTreesClassifier(n_estimators = 50, random_state = 2)\n",
        "gbdt = GradientBoostingClassifier(n_estimators = 50, random_state = 2)\n",
        "xgb = XGBClassifier(n_estimators = 50, random_state=2)"
      ],
      "metadata": {
        "id": "2syZHdfttvdL"
      },
      "execution_count": 68,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "classification = {\n",
        "    'Support Vector Classifier' : svc,\n",
        "    'K-Neighbors Classifier'  : knc,\n",
        "    'Multinomial NB'  : mnb,\n",
        "    'Decision Tree Classifier'  : dtc,\n",
        "    'Logistic Regression'  : lrc,\n",
        "    'Random Forest Classifier'  : rfc,\n",
        "    'AdaBoost Classifier': abc,\n",
        "    'Bagging Classifier' : bc,\n",
        "    'Extra Trees Classifier' : etc,\n",
        "    'Gradient Boosting Classifier' : gbdt,\n",
        "    'XGB Classifier' : xgb\n",
        "}"
      ],
      "metadata": {
        "id": "nejY7N_Ltva6"
      },
      "execution_count": 69,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def train_classifier(classification, X_train, y_train, X_test, y_test):\n",
        "  classification.fit(X_train, y_train)\n",
        "  y_pred = classification.predict(X_test)\n",
        "  accuracy = accuracy_score(y_test, y_pred)\n",
        "  precision = precision_score(y_test, y_pred)\n",
        "  matrix = confusion_matrix(y_test, y_pred)\n",
        "\n",
        "  return accuracy, precision, matrix"
      ],
      "metadata": {
        "id": "8T6ucAiKtvYo"
      },
      "execution_count": 70,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "accuracy_scores = []\n",
        "precision_scores = []\n",
        "\n",
        "for name, cls in classification.items():\n",
        "  curr_accuracy, curr_precision, matrix = train_classifier(cls, X_train, Y_train, X_test, Y_test)\n",
        "  print(\"Model name : \", name)\n",
        "  print(\"Accuracy : \", curr_accuracy)\n",
        "  print(\"Precision : \", curr_precision)\n",
        "  print(\"Confusin-Matrix : \", matrix, '\\n')\n",
        "\n",
        "  accuracy_scores.append(curr_accuracy)\n",
        "  precision_scores.append(curr_precision)\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8trskGmLt-ko",
        "outputId": "57e4a700-c8b7-4a66-b03c-b7c25e07e690"
      },
      "execution_count": 71,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model name :  Support Vector Classifier\n",
            "Accuracy :  0.512987012987013\n",
            "Precision :  0.512987012987013\n",
            "Confusin-Matrix :  [[ 0 75]\n",
            " [ 0 79]] \n",
            "\n",
            "Model name :  K-Neighbors Classifier\n",
            "Accuracy :  0.7662337662337663\n",
            "Precision :  0.8307692307692308\n",
            "Confusin-Matrix :  [[64 11]\n",
            " [25 54]] \n",
            "\n",
            "Model name :  Multinomial NB\n",
            "Accuracy :  0.7142857142857143\n",
            "Precision :  0.7215189873417721\n",
            "Confusin-Matrix :  [[53 22]\n",
            " [22 57]] \n",
            "\n",
            "Model name :  Decision Tree Classifier\n",
            "Accuracy :  0.8896103896103896\n",
            "Precision :  0.918918918918919\n",
            "Confusin-Matrix :  [[69  6]\n",
            " [11 68]] \n",
            "\n",
            "Model name :  Logistic Regression\n",
            "Accuracy :  0.8376623376623377\n",
            "Precision :  0.8375\n",
            "Confusin-Matrix :  [[62 13]\n",
            " [12 67]] \n",
            "\n",
            "Model name :  Random Forest Classifier\n",
            "Accuracy :  1.0\n",
            "Precision :  1.0\n",
            "Confusin-Matrix :  [[75  0]\n",
            " [ 0 79]] \n",
            "\n",
            "Model name :  AdaBoost Classifier\n",
            "Accuracy :  0.8961038961038961\n",
            "Precision :  0.9436619718309859\n",
            "Confusin-Matrix :  [[71  4]\n",
            " [12 67]] \n",
            "\n",
            "Model name :  Bagging Classifier\n",
            "Accuracy :  1.0\n",
            "Precision :  1.0\n",
            "Confusin-Matrix :  [[75  0]\n",
            " [ 0 79]] \n",
            "\n",
            "Model name :  Extra Trees Classifier\n",
            "Accuracy :  1.0\n",
            "Precision :  1.0\n",
            "Confusin-Matrix :  [[75  0]\n",
            " [ 0 79]] \n",
            "\n",
            "Model name :  Gradient Boosting Classifier\n",
            "Accuracy :  0.9285714285714286\n",
            "Precision :  0.9358974358974359\n",
            "Confusin-Matrix :  [[70  5]\n",
            " [ 6 73]] \n",
            "\n",
            "Model name :  XGB Classifier\n",
            "Accuracy :  1.0\n",
            "Precision :  1.0\n",
            "Confusin-Matrix :  [[75  0]\n",
            " [ 0 79]] \n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "result_dataframe = pd.DataFrame({'Algorithm': classification.keys(), 'Accuracy': accuracy_scores, 'Precision' : precision_scores}).sort_values('Precision', ascending = False)"
      ],
      "metadata": {
        "id": "u0R50EZot-iP"
      },
      "execution_count": 72,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "result_dataframe"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 394
        },
        "id": "IK5hj0xmuwH-",
        "outputId": "eaa8fa1f-cd88-4b7e-a57e-14651e34cdf8"
      },
      "execution_count": 73,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                       Algorithm  Accuracy  Precision\n",
              "5       Random Forest Classifier  1.000000   1.000000\n",
              "7             Bagging Classifier  1.000000   1.000000\n",
              "8         Extra Trees Classifier  1.000000   1.000000\n",
              "10                XGB Classifier  1.000000   1.000000\n",
              "6            AdaBoost Classifier  0.896104   0.943662\n",
              "9   Gradient Boosting Classifier  0.928571   0.935897\n",
              "3       Decision Tree Classifier  0.889610   0.918919\n",
              "4            Logistic Regression  0.837662   0.837500\n",
              "1         K-Neighbors Classifier  0.766234   0.830769\n",
              "2                 Multinomial NB  0.714286   0.721519\n",
              "0      Support Vector Classifier  0.512987   0.512987"
            ],
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              "      <th></th>\n",
              "      <th>Algorithm</th>\n",
              "      <th>Accuracy</th>\n",
              "      <th>Precision</th>\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Random Forest Classifier</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>Bagging Classifier</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>Extra Trees Classifier</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>XGB Classifier</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>AdaBoost Classifier</td>\n",
              "      <td>0.896104</td>\n",
              "      <td>0.943662</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>Gradient Boosting Classifier</td>\n",
              "      <td>0.928571</td>\n",
              "      <td>0.935897</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Decision Tree Classifier</td>\n",
              "      <td>0.889610</td>\n",
              "      <td>0.918919</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Logistic Regression</td>\n",
              "      <td>0.837662</td>\n",
              "      <td>0.837500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>K-Neighbors Classifier</td>\n",
              "      <td>0.766234</td>\n",
              "      <td>0.830769</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Multinomial NB</td>\n",
              "      <td>0.714286</td>\n",
              "      <td>0.721519</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Support Vector Classifier</td>\n",
              "      <td>0.512987</td>\n",
              "      <td>0.512987</td>\n",
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              "                                                     [key], {});\n",
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              "            '<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",
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            ]
          },
          "metadata": {},
          "execution_count": 73
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Model Improvement"
      ],
      "metadata": {
        "id": "USxNyk_wv768"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# voting classifier :  ensemble learning method that combines the predictions of several different machine learning models to produce a final prediction.\n",
        "# The models that are combined can be of different types, such as decision trees, support vector machines, or random forests.\n",
        "\n",
        "rfc = RandomForestClassifier(n_estimators= 50, random_state = 2)\n",
        "bc = BaggingClassifier(n_estimators = 50, random_state = 2)\n",
        "etc = ExtraTreesClassifier(n_estimators = 50, random_state = 2)\n",
        "xgb = XGBClassifier(n_estimators = 50, random_state=2)\n"
      ],
      "metadata": {
        "id": "rG_CXoG1v7UU"
      },
      "execution_count": 75,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "voting = VotingClassifier(estimators=[('rfc', rfc), ('bc', bc), ('et', etc), ('xgb', xgb)],voting='soft')"
      ],
      "metadata": {
        "id": "Jtdwc_aQwedT"
      },
      "execution_count": 76,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "voting.fit(X_train, Y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 109
        },
        "id": "XsSqxGduwea7",
        "outputId": "df1198e7-dea9-4b41-f27a-dd3c57d1eeac"
      },
      "execution_count": 77,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "VotingClassifier(estimators=[('rfc',\n",
              "                              RandomForestClassifier(n_estimators=50,\n",
              "                                                     random_state=2)),\n",
              "                             ('bc',\n",
              "                              BaggingClassifier(n_estimators=50,\n",
              "                                                random_state=2)),\n",
              "                             ('et',\n",
              "                              ExtraTreesClassifier(n_estimators=50,\n",
              "                                                   random_state=2)),\n",
              "                             ('xgb',\n",
              "                              XGBClassifier(base_score=None, booster=None,\n",
              "                                            callbacks=None,\n",
              "                                            colsample_bylevel=None,\n",
              "                                            colsample_bynode=None,\n",
              "                                            colsample_bytree=None,\n",
              "                                            early_sto...\n",
              "                                            gpu_id=None, grow_policy=None,\n",
              "                                            importance_type=None,\n",
              "                                            interaction_constraints=None,\n",
              "                                            learning_rate=None, max_bin=None,\n",
              "                                            max_cat_threshold=None,\n",
              "                                            max_cat_to_onehot=None,\n",
              "                                            max_delta_step=None, max_depth=None,\n",
              "                                            max_leaves=None,\n",
              "                                            min_child_weight=None, missing=nan,\n",
              "                                            monotone_constraints=None,\n",
              "                                            n_estimators=50, n_jobs=None,\n",
              "                                            num_parallel_tree=None,\n",
              "                                            predictor=None, random_state=2, ...))],\n",
              "                 voting='soft')"
            ],
            "text/html": [
              "<style>#sk-container-id-12 {color: black;background-color: white;}#sk-container-id-12 pre{padding: 0;}#sk-container-id-12 div.sk-toggleable {background-color: white;}#sk-container-id-12 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-12 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-12 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-12 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-12 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-12 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-12 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-12 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-12 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-12 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-12 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-12 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-12 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-12 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-12 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-12 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-12 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-12 div.sk-item {position: relative;z-index: 1;}#sk-container-id-12 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-12 div.sk-item::before, #sk-container-id-12 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-12 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-12 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-12 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-12 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-12 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-12 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-12 div.sk-label-container {text-align: center;}#sk-container-id-12 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-12 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-12\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>VotingClassifier(estimators=[(&#x27;rfc&#x27;,\n",
              "                              RandomForestClassifier(n_estimators=50,\n",
              "                                                     random_state=2)),\n",
              "                             (&#x27;bc&#x27;,\n",
              "                              BaggingClassifier(n_estimators=50,\n",
              "                                                random_state=2)),\n",
              "                             (&#x27;et&#x27;,\n",
              "                              ExtraTreesClassifier(n_estimators=50,\n",
              "                                                   random_state=2)),\n",
              "                             (&#x27;xgb&#x27;,\n",
              "                              XGBClassifier(base_score=None, booster=None,\n",
              "                                            callbacks=None,\n",
              "                                            colsample_bylevel=None,\n",
              "                                            colsample_bynode=None,\n",
              "                                            colsample_bytree=None,\n",
              "                                            early_sto...\n",
              "                                            gpu_id=None, grow_policy=None,\n",
              "                                            importance_type=None,\n",
              "                                            interaction_constraints=None,\n",
              "                                            learning_rate=None, max_bin=None,\n",
              "                                            max_cat_threshold=None,\n",
              "                                            max_cat_to_onehot=None,\n",
              "                                            max_delta_step=None, max_depth=None,\n",
              "                                            max_leaves=None,\n",
              "                                            min_child_weight=None, missing=nan,\n",
              "                                            monotone_constraints=None,\n",
              "                                            n_estimators=50, n_jobs=None,\n",
              "                                            num_parallel_tree=None,\n",
              "                                            predictor=None, random_state=2, ...))],\n",
              "                 voting=&#x27;soft&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-16\" type=\"checkbox\" ><label for=\"sk-estimator-id-16\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">VotingClassifier</label><div class=\"sk-toggleable__content\"><pre>VotingClassifier(estimators=[(&#x27;rfc&#x27;,\n",
              "                              RandomForestClassifier(n_estimators=50,\n",
              "                                                     random_state=2)),\n",
              "                             (&#x27;bc&#x27;,\n",
              "                              BaggingClassifier(n_estimators=50,\n",
              "                                                random_state=2)),\n",
              "                             (&#x27;et&#x27;,\n",
              "                              ExtraTreesClassifier(n_estimators=50,\n",
              "                                                   random_state=2)),\n",
              "                             (&#x27;xgb&#x27;,\n",
              "                              XGBClassifier(base_score=None, booster=None,\n",
              "                                            callbacks=None,\n",
              "                                            colsample_bylevel=None,\n",
              "                                            colsample_bynode=None,\n",
              "                                            colsample_bytree=None,\n",
              "                                            early_sto...\n",
              "                                            gpu_id=None, grow_policy=None,\n",
              "                                            importance_type=None,\n",
              "                                            interaction_constraints=None,\n",
              "                                            learning_rate=None, max_bin=None,\n",
              "                                            max_cat_threshold=None,\n",
              "                                            max_cat_to_onehot=None,\n",
              "                                            max_delta_step=None, max_depth=None,\n",
              "                                            max_leaves=None,\n",
              "                                            min_child_weight=None, missing=nan,\n",
              "                                            monotone_constraints=None,\n",
              "                                            n_estimators=50, n_jobs=None,\n",
              "                                            num_parallel_tree=None,\n",
              "                                            predictor=None, random_state=2, ...))],\n",
              "                 voting=&#x27;soft&#x27;)</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><label>rfc</label></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-17\" type=\"checkbox\" ><label for=\"sk-estimator-id-17\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier(n_estimators=50, random_state=2)</pre></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><label>bc</label></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-18\" type=\"checkbox\" ><label for=\"sk-estimator-id-18\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">BaggingClassifier</label><div class=\"sk-toggleable__content\"><pre>BaggingClassifier(n_estimators=50, random_state=2)</pre></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><label>et</label></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-19\" type=\"checkbox\" ><label for=\"sk-estimator-id-19\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">ExtraTreesClassifier</label><div class=\"sk-toggleable__content\"><pre>ExtraTreesClassifier(n_estimators=50, random_state=2)</pre></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><label>xgb</label></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-20\" type=\"checkbox\" ><label for=\"sk-estimator-id-20\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">XGBClassifier</label><div class=\"sk-toggleable__content\"><pre>XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
              "              colsample_bylevel=None, colsample_bynode=None,\n",
              "              colsample_bytree=None, early_stopping_rounds=None,\n",
              "              enable_categorical=False, eval_metric=None, feature_types=None,\n",
              "              gamma=None, gpu_id=None, grow_policy=None, importance_type=None,\n",
              "              interaction_constraints=None, learning_rate=None, max_bin=None,\n",
              "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
              "              max_delta_step=None, max_depth=None, max_leaves=None,\n",
              "              min_child_weight=None, missing=nan, monotone_constraints=None,\n",
              "              n_estimators=50, n_jobs=None, num_parallel_tree=None,\n",
              "              predictor=None, random_state=2, ...)</pre></div></div></div></div></div></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 77
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred = voting.predict(X_test)\n",
        "\n",
        "print(accuracy_score(Y_test, y_pred))\n",
        "print(confusion_matrix(Y_test, y_pred))\n",
        "print(precision_score(Y_test, y_pred))\n",
        "\n",
        "# voting model is most accurate and precise\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PFtX-soaweUE",
        "outputId": "2a79a749-be09-40ae-d87b-eef9eb67ed23"
      },
      "execution_count": 78,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1.0\n",
            "[[75  0]\n",
            " [ 0 79]]\n",
            "1.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Model Evaluation\n",
        "\n",
        "- Accuracy score\n",
        "  - 1. For training data\n",
        "  - 2. For testing data\n",
        "\n",
        "  accuracy score for both should be closer to 1\n",
        "\n",
        "- Other Metrices:\n",
        "  - 1. Accuracy\n",
        "  - 2. Precision\n",
        "  - 3. Recall\n",
        "  - 4. F1 Score\n",
        "\n",
        "- Confusion Metrix\n",
        "  "
      ],
      "metadata": {
        "id": "8oEjOLDm217m"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# accuracy of traning data\n",
        "# accuracy function measures accuracy between two values,or columns\n",
        "\n",
        "X_train_prediction = voting.predict(X_train)\n",
        "training_data_accuracy = accuracy_score(X_train_prediction, Y_train)\n",
        "\n",
        "print(\"The accuracy of training data : \", training_data_accuracy)\n"
      ],
      "metadata": {
        "id": "UzBHEoIv2ekG",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c0e0d730-c60f-4267-8847-104f41fda303"
      },
      "execution_count": 79,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The accuracy of training data :  1.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# accuracy of traning data\n",
        "# accuracy function measures accuracy between two values,or columns\n",
        "\n",
        "X_train_prediction = voting.predict(X_train)\n",
        "training_data_accuracy = accuracy_score(X_train_prediction, Y_train)\n",
        "\n",
        "print(\"The accuracy of training data : \", training_data_accuracy)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YZ2n6kLTBudI",
        "outputId": "40e7fd7f-3161-4156-be4d-885b5a5390e0"
      },
      "execution_count": 80,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The accuracy of training data :  1.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Accuracy, F1, Recall, Precision\n",
        "\n",
        "Y_pred = voting.predict(X_test)\n",
        "\n",
        "\n",
        "accuracy = accuracy_score(Y_test, Y_pred)\n",
        "print(\"Accuracy   :\", accuracy)\n",
        "precision = precision_score(Y_test, Y_pred)\n",
        "print(\"Precision  :\", precision)\n",
        "recall = recall_score(Y_test, Y_pred)\n",
        "print(\"Recall     :\", recall)\n",
        "F1_score = f1_score(Y_test, Y_pred)\n",
        "print(\"F1-score   :\", F1_score)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cp7qjm56b1m-",
        "outputId": "0265a6f6-1c3a-446c-830c-2f81a95969ba"
      },
      "execution_count": 81,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy   : 1.0\n",
            "Precision  : 1.0\n",
            "Recall     : 1.0\n",
            "F1-score   : 1.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# check results\n",
        "print(metrics.classification_report(Y_test, Y_pred))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "v6Uw2X1qo4kx",
        "outputId": "6bf1c594-e9bd-4e15-a7d6-93b2b5fe3800"
      },
      "execution_count": 82,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       1.00      1.00      1.00        75\n",
            "           1       1.00      1.00      1.00        79\n",
            "\n",
            "    accuracy                           1.00       154\n",
            "   macro avg       1.00      1.00      1.00       154\n",
            "weighted avg       1.00      1.00      1.00       154\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# confusion matrix\n",
        "\n",
        "cm = confusion_matrix(Y_test,Y_pred)\n",
        "\n",
        "#Plot the confusion matrix.\n",
        "sns.heatmap(cm,\n",
        "            annot=True,\n",
        "            fmt='g',\n",
        "            xticklabels=['malignant', 'benign'],\n",
        "            yticklabels=['malignant', 'benign'])\n",
        "plt.ylabel('Prediction',fontsize=13)\n",
        "plt.xlabel('Actual',fontsize=13)\n",
        "plt.title('Confusion Matrix',fontsize=17)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 481
        },
        "id": "4hFOfETK8bVU",
        "outputId": "37f8ab74-729d-4758-86a1-27ed8165716a"
      },
      "execution_count": 83,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Building Prediction system\n",
        "\n",
        "#### Steps :\n",
        "\n",
        "  - take input data\n",
        "  - Process the data, change into array\n",
        "  - reshape data as single element in array\n",
        "  - predict output using predict function\n",
        "  - output the value"
      ],
      "metadata": {
        "id": "cuBvSpnrJiLU"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# input feature values\n",
        "input_data = (58,0,3,150,283,1,0,162,0,1,2,0,2)\n",
        "\n",
        "# changing data to numpy array\n",
        "input_data_array = np.asarray(input_data)\n",
        "\n",
        "# reshape the array as we are predicting for one instance\n",
        "input_data_reshaped =  input_data_array.reshape(1,-1)\n",
        "\n",
        "# standarize the input data\n",
        "# std_data = scaler.transform(input_data_reshaped)\n",
        "# print(std_data[0])\n"
      ],
      "metadata": {
        "id": "LTplLPE6JMvt"
      },
      "execution_count": 84,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# predicting the result and printing it\n",
        "\n",
        "prediction = voting.predict(input_data_reshaped)\n",
        "\n",
        "print(prediction)\n",
        "\n",
        "if(prediction[0] == 0):\n",
        "    print(\"Patient has a healthy heart 💛💛💛💛\")\n",
        "\n",
        "else:\n",
        "    print(\"Patient has a heart Disease 💔💔💔💔\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ig2ETasDKh0M",
        "outputId": "2ffdda73-90c5-4283-b526-f17307a2a19a"
      },
      "execution_count": 86,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[1]\n",
            "Patient has a heart Disease 💔💔💔💔\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but RandomForestClassifier was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but BaggingClassifier was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but ExtraTreesClassifier was fitted with feature names\n",
            "  warnings.warn(\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Notations\n",
        "  - [0] : means patient has a healthy heart 💛💛💛💛\n",
        "  - [1] : means patient has a unhealthy heart 💔💔💔💔\n",
        "  "
      ],
      "metadata": {
        "id": "jMc19BLbSgfm"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Saving the model"
      ],
      "metadata": {
        "id": "JJ4FaUa7HAFj"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pickle\n",
        "# importing the library\n",
        "\n",
        "filename = \"trained_model.pkl\"\n",
        "pickle.dump(voting, open(filename, 'wb'))\n",
        "# saving file\n"
      ],
      "metadata": {
        "id": "y44nGaqO_Zcl"
      },
      "execution_count": 87,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# loading the saved model\n",
        "\n",
        "loaded_model = pickle.load(open(\"trained_model.pkl\",'rb'))"
      ],
      "metadata": {
        "id": "AXcIvdox5L6o"
      },
      "execution_count": 88,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# save the model to disk\n",
        "filename = 'heart_model.sav'\n",
        "joblib.dump(voting, filename)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xQxWj2kuprN3",
        "outputId": "dc67c7b1-38cf-44fe-ede8-7161d529d9fd"
      },
      "execution_count": 89,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['heart_model.sav']"
            ]
          },
          "metadata": {},
          "execution_count": 89
        }
      ]
    }
  ]
}