HeartPredctionFailureFinal.ipynb

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      "source": [
        "import sys\n",
        "\n",
        "sys.path.append(\"../\")\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "import xgboost as xgb\n",
        "\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.feature_extraction import DictVectorizer\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.model_selection import train_test_split, KFold\n",
        "from sklearn.metrics import auc, roc_auc_score, roc_curve, confusion_matrix, classification_report\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from sklearn.metrics import accuracy_score, confusion_matrix, classification_report,plot_confusion_matrix\n",
        "from utils import plot_roc_curve\n",
        "from sklearn.svm import SVC\n",
        "from sklearn.neighbors import KNeighborsClassifier"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Import the Dataset"
      ],
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      "source": [
        "df = pd.read_csv('heart.csv')\n",
        "df.head(10)"
      ],
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              "   Age Sex ChestPainType  RestingBP  Cholesterol  FastingBS RestingECG  MaxHR  \\\n",
              "0   40   M           ATA        140          289          0     Normal    172   \n",
              "1   49   F           NAP        160          180          0     Normal    156   \n",
              "2   37   M           ATA        130          283          0         ST     98   \n",
              "3   48   F           ASY        138          214          0     Normal    108   \n",
              "4   54   M           NAP        150          195          0     Normal    122   \n",
              "5   39   M           NAP        120          339          0     Normal    170   \n",
              "6   45   F           ATA        130          237          0     Normal    170   \n",
              "7   54   M           ATA        110          208          0     Normal    142   \n",
              "8   37   M           ASY        140          207          0     Normal    130   \n",
              "9   48   F           ATA        120          284          0     Normal    120   \n",
              "\n",
              "  ExerciseAngina  Oldpeak ST_Slope  HeartDisease  \n",
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              "   Age Sex ChestPainType  RestingBP  Cholesterol  FastingBS RestingECG  MaxHR  \\\n",
              "0   40   M           ATA        140          289          0     Normal    172   \n",
              "1   49   F           NAP        160          180          0     Normal    156   \n",
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          },
          "metadata": {},
          "execution_count": 152
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      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Changing the names of the columns of the dataset for a better format and lower case."
      ],
      "metadata": {
        "id": "Zpn50zLJZTVf"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "new_columns_names = [\"age\",\n",
        "                     \"sex\",\n",
        "                     \"chest_pain_type\",\n",
        "                     \"resting_bp\",\n",
        "                     \"cholesterol\",\n",
        "                     \"fasting_bs\",\n",
        "                     \"resting_ecg\",\n",
        "                     \"max_hr\",\n",
        "                     \"exercise_angina\",\n",
        "                     \"oldpeak\",\n",
        "                     \"st_slope\",\n",
        "                     \"heart_disease\"]"
      ],
      "metadata": {
        "id": "vt2HBFHVZPFW"
      },
      "execution_count": 153,
      "outputs": []
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    {
      "cell_type": "code",
      "source": [
        "df.columns = new_columns_names"
      ],
      "metadata": {
        "id": "EPws6UnNZZW-"
      },
      "execution_count": 154,
      "outputs": []
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      "execution_count": 155,
      "outputs": [
        {
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          "data": {
            "text/plain": [
              "   age sex chest_pain_type  resting_bp  cholesterol  fasting_bs resting_ecg  \\\n",
              "0   40   M             ATA         140          289           0      Normal   \n",
              "1   49   F             NAP         160          180           0      Normal   \n",
              "2   37   M             ATA         130          283           0          ST   \n",
              "3   48   F             ASY         138          214           0      Normal   \n",
              "4   54   M             NAP         150          195           0      Normal   \n",
              "\n",
              "   max_hr exercise_angina  oldpeak st_slope  heart_disease  \n",
              "0     172               N      0.0       Up              0  \n",
              "1     156               N      1.0     Flat              1  \n",
              "2      98               N      0.0       Up              0  \n",
              "3     108               Y      1.5     Flat              1  \n",
              "4     122               N      0.0       Up              0  "
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              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
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      "cell_type": "code",
      "source": [
        "df.isna().sum()"
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        "id": "S2um8CcLZeQl",
        "outputId": "0b2c30d9-3cb1-4352-af9d-9b90bb79c4fc"
      },
      "execution_count": 156,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "age                0\n",
              "sex                0\n",
              "chest_pain_type    0\n",
              "resting_bp         0\n",
              "cholesterol        0\n",
              "fasting_bs         0\n",
              "resting_ecg        0\n",
              "max_hr             0\n",
              "exercise_angina    0\n",
              "oldpeak            0\n",
              "st_slope           0\n",
              "heart_disease      0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 156
        }
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              "0"
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          "metadata": {},
          "execution_count": 157
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      },
      "execution_count": 158,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 918 entries, 0 to 917\n",
            "Data columns (total 12 columns):\n",
            " #   Column           Non-Null Count  Dtype  \n",
            "---  ------           --------------  -----  \n",
            " 0   age              918 non-null    int64  \n",
            " 1   sex              918 non-null    object \n",
            " 2   chest_pain_type  918 non-null    object \n",
            " 3   resting_bp       918 non-null    int64  \n",
            " 4   cholesterol      918 non-null    int64  \n",
            " 5   fasting_bs       918 non-null    int64  \n",
            " 6   resting_ecg      918 non-null    object \n",
            " 7   max_hr           918 non-null    int64  \n",
            " 8   exercise_angina  918 non-null    object \n",
            " 9   oldpeak          918 non-null    float64\n",
            " 10  st_slope         918 non-null    object \n",
            " 11  heart_disease    918 non-null    int64  \n",
            "dtypes: float64(1), int64(6), object(5)\n",
            "memory usage: 86.2+ KB\n"
          ]
        }
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    },
    {
      "cell_type": "markdown",
      "source": [
        "Using the describe() method to see statistics of numerical columns in the dataset"
      ],
      "metadata": {
        "id": "AuhOJ5kSZ7EX"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.describe().round(2)"
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          "height": 300
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        "id": "BO0nWSlZZ4C9",
        "outputId": "65771128-aa3e-423a-95d6-569fbc674dcd"
      },
      "execution_count": 159,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "          age  resting_bp  cholesterol  fasting_bs  max_hr  oldpeak  \\\n",
              "count  918.00      918.00       918.00      918.00  918.00   918.00   \n",
              "mean    53.51      132.40       198.80        0.23  136.81     0.89   \n",
              "std      9.43       18.51       109.38        0.42   25.46     1.07   \n",
              "min     28.00        0.00         0.00        0.00   60.00    -2.60   \n",
              "25%     47.00      120.00       173.25        0.00  120.00     0.00   \n",
              "50%     54.00      130.00       223.00        0.00  138.00     0.60   \n",
              "75%     60.00      140.00       267.00        0.00  156.00     1.50   \n",
              "max     77.00      200.00       603.00        1.00  202.00     6.20   \n",
              "\n",
              "       heart_disease  \n",
              "count         918.00  \n",
              "mean            0.55  \n",
              "std             0.50  \n",
              "min             0.00  \n",
              "25%             0.00  \n",
              "50%             1.00  \n",
              "75%             1.00  \n",
              "max             1.00  "
            ],
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              "      <th>count</th>\n",
              "      <td>918.00</td>\n",
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              "      <td>918.00</td>\n",
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              "      <th>mean</th>\n",
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              "      <td>25.46</td>\n",
              "      <td>1.07</td>\n",
              "      <td>0.50</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>28.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>60.00</td>\n",
              "      <td>-2.60</td>\n",
              "      <td>0.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>47.00</td>\n",
              "      <td>120.00</td>\n",
              "      <td>173.25</td>\n",
              "      <td>0.00</td>\n",
              "      <td>120.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>54.00</td>\n",
              "      <td>130.00</td>\n",
              "      <td>223.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>138.00</td>\n",
              "      <td>0.60</td>\n",
              "      <td>1.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>60.00</td>\n",
              "      <td>140.00</td>\n",
              "      <td>267.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>156.00</td>\n",
              "      <td>1.50</td>\n",
              "      <td>1.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>77.00</td>\n",
              "      <td>200.00</td>\n",
              "      <td>603.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>202.00</td>\n",
              "      <td>6.20</td>\n",
              "      <td>1.00</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 159
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Defining a map for **sex** column"
      ],
      "metadata": {
        "id": "Ub7eErwDaEtn"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.sex.value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FR60e70MaA49",
        "outputId": "60dc6927-cfcd-45f2-b558-7208ef1639ab"
      },
      "execution_count": 160,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "M    725\n",
              "F    193\n",
              "Name: sex, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 160
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sex_map = {\"F\": 0,\n",
        "           \"M\": 1}\n",
        "\n",
        "df.sex = df.sex.map(sex_map)\n",
        "df.sex = df.sex.astype(int)"
      ],
      "metadata": {
        "id": "jPrNTzX3aRqO"
      },
      "execution_count": 161,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Defining a map for **chest_pain_type** column"
      ],
      "metadata": {
        "id": "YzY9f-QKaWtR"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.chest_pain_type.value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tf23DGDiaVEU",
        "outputId": "fd6c9c2c-af08-4b20-a6e2-54fd4c9d0df6"
      },
      "execution_count": 162,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "ASY    496\n",
              "NAP    203\n",
              "ATA    173\n",
              "TA      46\n",
              "Name: chest_pain_type, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 162
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "chest_pain_type_map = {\"ASY\": 0,\n",
        "                       \"NAP\": 1,\n",
        "                       \"ATA\": 2,\n",
        "                       \"TA\": 3}\n",
        "\n",
        "df.chest_pain_type = df.chest_pain_type.map(chest_pain_type_map)\n",
        "df.chest_pain_type = df.chest_pain_type.astype(int)"
      ],
      "metadata": {
        "id": "n9Z1y_W5ahYT"
      },
      "execution_count": 163,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Defining the map for resting_ecg column"
      ],
      "metadata": {
        "id": "o71WOPUdaju-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.resting_ecg.value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bXqOdunnakVN",
        "outputId": "948e1b6d-2a86-4525-8005-38e3cc8476db"
      },
      "execution_count": 164,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Normal    552\n",
              "LVH       188\n",
              "ST        178\n",
              "Name: resting_ecg, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 164
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "resting_ecg_map = {\"Normal\": 0,\n",
        "                   \"LVH\": 1,\n",
        "                   \"ST\": 2}\n",
        "\n",
        "df.resting_ecg = df.resting_ecg.map(resting_ecg_map)\n",
        "df.resting_ecg = df.resting_ecg.astype(int)"
      ],
      "metadata": {
        "id": "F29Mtk0fanli"
      },
      "execution_count": 63,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Defining the map for exercise_angina column"
      ],
      "metadata": {
        "id": "sfQBG9PdapD_"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.exercise_angina.value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tAUj9iZxapdO",
        "outputId": "63592c98-760a-413d-e4dc-b6eb66255c61"
      },
      "execution_count": 64,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "N    547\n",
              "Y    371\n",
              "Name: exercise_angina, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 64
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "exercise_angina_map = {\"N\": 0,\n",
        "                       \"Y\": 1}\n",
        "\n",
        "df.exercise_angina = df.exercise_angina.map(exercise_angina_map)\n",
        "df.exercise_angina = df.exercise_angina.astype(int)"
      ],
      "metadata": {
        "id": "Nh6vX0F2asLv"
      },
      "execution_count": 65,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Defining the map for st_slop column."
      ],
      "metadata": {
        "id": "2xp1MCnOatom"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.st_slope.value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SJ2n_zUGauI_",
        "outputId": "52b7547a-95a5-4da2-81cb-184a71c609af"
      },
      "execution_count": 66,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Flat    460\n",
              "Up      395\n",
              "Down     63\n",
              "Name: st_slope, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 66
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "st_slop_map = {\"Flat\": 0,\n",
        "               \"Up\": 1,\n",
        "               \"Down\": 2}\n",
        "\n",
        "df.st_slope = df.st_slope.map(st_slop_map)\n",
        "df.st_slope = df.st_slope.astype(int)"
      ],
      "metadata": {
        "id": "RfofKuN1aws_"
      },
      "execution_count": 67,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6o9qtyfDax-O",
        "outputId": "315f674d-0029-4871-9ccc-0199a719a466"
      },
      "execution_count": 68,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 918 entries, 0 to 917\n",
            "Data columns (total 12 columns):\n",
            " #   Column           Non-Null Count  Dtype  \n",
            "---  ------           --------------  -----  \n",
            " 0   age              918 non-null    int64  \n",
            " 1   sex              918 non-null    int64  \n",
            " 2   chest_pain_type  918 non-null    int64  \n",
            " 3   resting_bp       918 non-null    int64  \n",
            " 4   cholesterol      918 non-null    int64  \n",
            " 5   fasting_bs       918 non-null    int64  \n",
            " 6   resting_ecg      918 non-null    int64  \n",
            " 7   max_hr           918 non-null    int64  \n",
            " 8   exercise_angina  918 non-null    int64  \n",
            " 9   oldpeak          918 non-null    float64\n",
            " 10  st_slope         918 non-null    int64  \n",
            " 11  heart_disease    918 non-null    int64  \n",
            "dtypes: float64(1), int64(11)\n",
            "memory usage: 86.2 KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Picking a color pallete for the charts and setting the default seaborn style"
      ],
      "metadata": {
        "id": "_RdidEWda0H2"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "sns.set()\n",
        "\n",
        "colors = sns.color_palette(n_colors=len(df.columns))"
      ],
      "metadata": {
        "id": "4VpoZUeua0mm"
      },
      "execution_count": 69,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Defining a list with the categorical columns of the dataset"
      ],
      "metadata": {
        "id": "YXWeVHGCa4Y_"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "categorical_columns = [\"sex\", \"chest_pain_type\", \"resting_ecg\", \"exercise_angina\", \"st_slope\", \"fasting_bs\", \"heart_disease\"]"
      ],
      "metadata": {
        "id": "IBxEVIzXa5HI"
      },
      "execution_count": 70,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Plotting histograms and countplots of the data in the dataset"
      ],
      "metadata": {
        "id": "7wE4_hYTa69n"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "fig, ax = plt.subplots(nrows=len(df.columns))\n",
        "fig.set_size_inches((10,75))\n",
        "\n",
        "for idx, column in enumerate(df.columns):\n",
        "    \n",
        "    if column in categorical_columns:\n",
        "        ax[idx].bar(x=df[column].value_counts().index, height=df[column].value_counts().values, color=colors[idx])\n",
        "        ax[idx].set_title(f\"{column} bar plot\")\n",
        "        ax[idx].set_xlabel(column)\n",
        "        ax[idx].set_ylabel(\"Count\")\n",
        "        ax[idx].set_xticks(range(len(df[column].value_counts().index)))\n",
        "    else:\n",
        "        ax[idx].hist(df[column], color=colors[idx])\n",
        "        ax[idx].set_title(f\"{column} distribution\")\n",
        "        ax[idx].set_xlabel(column)\n",
        "        ax[idx].set_ylabel(\"Frequency\")\n",
        "        \n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "9it2GGlma8nu",
        "outputId": "d21673d9-7130-4c7e-ac11-dd50317880dc"
      },
      "execution_count": 71,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x5400 with 12 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Plotting some boxplots of numerical columns to check if there are some outliers"
      ],
      "metadata": {
        "id": "lpqw11Q8bS0j"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "for column in df.columns:\n",
        "    \n",
        "    if column not in categorical_columns:\n",
        "        sns.boxplot(data=df[column])\n",
        "        plt.title(f\"{column} boxplot\")    \n",
        "    plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "DFstwbt6bRfG",
        "outputId": "8f3a2056-57a5-405b-f61b-8ce781e1c3aa"
      },
      "execution_count": 72,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Removing the rows where the **cholesterol** and the **resting_bp** is equal to zero."
      ],
      "metadata": {
        "id": "DIPSTmoSbXl7"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df = df[~(df.cholesterol == 0)].copy()\n",
        "df = df[~(df.resting_bp == 0)].copy()"
      ],
      "metadata": {
        "id": "g8yZgBRGbaCG"
      },
      "execution_count": 73,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Plotting the correlation matrix of the columns"
      ],
      "metadata": {
        "id": "mpHTk2uMbcIm"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "fig, ax = plt.subplots()\n",
        "fig.set_size_inches((10,10))\n",
        "\n",
        "ax = sns.heatmap(data=df.corr(), annot=True)\n",
        "ax.set_title(\"Correlation Matrix\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 690
        },
        "id": "S01jL1oqbdPe",
        "outputId": "50ef48d6-87dc-4555-dc26-11669290f2e4"
      },
      "execution_count": 74,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x720 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "A bar plot of the correlation of the columns with the target column (heart_disease)"
      ],
      "metadata": {
        "id": "1AYUBTmObfEf"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "fig, ax = plt.subplots()\n",
        "fig.set_size_inches((5,5))\n",
        "\n",
        "ax = df.drop(\"heart_disease\", axis=1).corrwith(df.heart_disease).plot(kind=\"bar\")\n",
        "ax.set_title(\"Correlation with heart_disease bar plot\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 418
        },
        "id": "3IaNU5EIbffd",
        "outputId": "2324b04a-d679-4277-86d7-9afdf6746981"
      },
      "execution_count": 75,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ],
            "image/png": 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06NED69atUx3kS0RU0Ax64iYjIwN//fUXtm3bhrS0NAwdOhSVKlVC9+7d37qNuLgkZGa+fbDa2FgiJub1A2plMEQNQ9UpLjUMVae41DBUncL4WoyNjVC+fOnXPi4lJO3t7REZGYmMjAyYmJggIyMDUVFRua7vrFSpEjp37gwzMzOYmZmhXbt2uHXrVr5CMjNT5Csktc/RN0PUMFSd4lLDUHWKSw1D1Slqr0XK7raNjQ0cHByUWT6OHDkCBwcHZdS8lpubGy5dugQhBDQaDa5evZqvWWSIiAxN2mDy2bNnY+fOnejUqRN27tyJOXPmAMiaLkl7fWu3bt1gY2ODrl27onv37vjwww/zvFSJiKiwKHKXJcbEJOZrM9rW1grR0QlvXlEHhqhhqDrFpYah6hSXGoaqUxhfi7GxEWxsLF//uKxOEREVRwxJIiIVDEkiIhUMSSIiFQxJIiIVDEkiIhWcT5LoH8SqTElYmL/+z97W1irXspep6Uh4kaLPbhVqDEmifxAL8xJwn3QwX885vNwT+h/ZWHhxd5uISAVDkohIBUOSiEgFQ5KISAVDkohIBUOSiEgFQ5KISAVDkohIBUOSiEgFQ5KISAVDkohIBUOSiEgFJ7ggIunUZhsqajMNMSSJSLr8zjZUmGca4u42EZEKhiQRkQqGJBGRCoYkEZEKhiQRkQqGJBGRCoYkEZEKhiQRkQqGJBGRCoYkEZEKhiQRkQqGJBGRCoYkEZEKhiQRkQqGJBGRCoYkEZEKhiQRkQqGJBGRCmkhGRISAm9vb3Tq1Ane3t4IDQ197boPHjxA/fr1sXjxYlnliYj0QlpIzpo1C/3798fJkyfRv39/zJw5M8/1MjIyMGvWLLRv315WaSIivZESkjExMQgODoabmxsAwM3NDcHBwYiNjc217qZNm9CmTRtUr15dRmkiIr2ScrfEiIgI2NnZwcTEBABgYmKCChUqICIiAtbW1sp6d+7cwaVLl7Bjxw6sW7funWrZ2Fjm+zl53cJSNkPUMFSd4lLDUHWKS42Cri+7hqz2DHZLWY1GgxkzZuC7775TwvRdxMQkIjNTvPX6trZWiI7W780qDVHDUHWKSw1D1SlqNd41OPJb/13qyHwf8/OeGRsbqW58SQlJe3t7REZGIiMjAyYmJsjIyEBUVBTs7e2VdaKjo/Ho0SMMHz4cAPDixQsIIZCYmIh58+bJ6AYRkXRSQtLGxgYODg44cuQIPD09ceTIETg4OOTY1a5UqRKuXbum/LxmzRokJydj6tSpMrpARKQX0s5uz549Gzt37kSnTp2wc+dOzJkzBwAwbNgwBAUFySpDRGRQ0o5J1qxZE76+vrmWb968Oc/1x4wZI6s0EZHe8IobIiIVDEkiIhUMSSIiFQxJIiIVDEkiIhUMSSIiFQxJIiIVDEkiIhUMSSIiFQxJIiIVDEkiIhUMSSIiFQxJIiIVDEkiIhUMSSIiFQxJIiIVDEkiIhUMSSIiFQxJIiIVBrvvtr5ZlSkJC/O8X05e9wB+mZqOhBcp+u4WERVxxSYkLcxLwH3Swbde//ByT+T3Vuj5DWKAYUxU1BWbkDSE/AYx8G5hTESFB49JEhGpYEgSEalgSBIRqWBIEhGpYEgSEalgSBIRqWBIEhGpYEgSEalgSBIRqWBIEhGpYEgSEalgSBIRqWBIEhGpYEgSEalgSBIRqWBIEhGpYEgSEamQNjN5SEgIfHx8EB8fj3LlymHx4sWoXr16jnXWrl2LY8eOwdjYGKamppgwYQJatmwpqwtERNJJC8lZs2ahf//+8PT0xMGDBzFz5kzs2LEjxzr16tXDkCFDULJkSdy5cwefffYZLl26BAsLC1ndICKSSsrudkxMDIKDg+Hm5gYAcHNzQ3BwMGJjY3Os17JlS5QsWRIAULt2bQghEB8fL6MLRER6ISUkIyIiYGdnBxMTEwCAiYkJKlSogIiIiNc+58CBA3j//fdRsWJFGV0gItKLArlb4m+//YZVq1Zh69at+X6ujY2ltH687jawssmsY4g+F5cahqpTXGoUdH3ZNWS1JyUk7e3tERkZiYyMDJiYmCAjIwNRUVGwt7fPte5///tfTJkyBevWrUONGjXyXSsmJhGZmSLX8nd5Q6Kj83ez13d90/NbR62+rLaKew1D1SlqNQz1GTbE3+Ob6r9te8bGRqobX1J2t21sbODg4IAjR44AAI4cOQIHBwdYW1vnWO/WrVuYMGECVq9ejbp168ooTUSkV9LGSc6ePRs7d+5Ep06dsHPnTsyZMwcAMGzYMAQFBQEA5syZg5cvX2LmzJnw9PSEp6cn/vrrL1ldICKSTtoxyZo1a8LX1zfX8s2bNyv/379/v6xyREQGwStuiIhUFMjZbSp4VmVKwsI8719/XgfdX6amI+FFir67RVToMCT/oSzMS8B90sG3Xv/wck/o/3w0UeHD3W0iIhXckqQiTe2wAcBDB6Q7hiQVafk9bADw0AHlD3e3iYhUMCSJiFQwJImIVDAkiYhUMCSJiFTw7DbRW+AVSv9cDEmit8ArlP65uLtNRKSCIUlEpIIhSUSkgiFJRKSCIUlEpIJnt0lv8jtsBuDQGSp8GJKkN5yhh4oD7m4TEalgSBIRqWBIEhGpYEgSEalgSBIRqWBIEhGpYEgSEalgSBIRqWBIEhGpYEgSEalgSBIRqWBIEhGpYEgSEalgSBIRqWBIEhGpYEgSEalgSBIRqWBIEhGpYEgSEalgSBIRqZAWkiEhIfD29kanTp3g7e2N0NDQXOtkZGRgzpw5aN++PTp06ABfX19Z5YmI9EJaSM6aNQv9+/fHyZMn0b9/f8ycOTPXOocPH8ajR49w6tQp7NmzB2vWrMHjx49ldYGISDopIRkTE4Pg4GC4ubkBANzc3BAcHIzY2Ngc6x07dgy9e/eGsbExrK2t0b59e5w4cUJGF4iI9ENIEBQUJLp27ZpjWZcuXcQff/yRY5mbm5u4efOm8vOmTZvEvHnzZHRBpKal63V9Qz2H/WK/9Pkc9iv/zylR0CGdXzExicjMFG+9vq2tFaKj9Xu7e5k1bG2t4D7pYL6ec3i5p9T6Ren9Kug6/N0XXA1Z75exsRFsbCxf+xwpu9v29vaIjIxERkYGgKwTNFFRUbC3t8+1Xnh4uPJzREQEKlasKKMLRER6ISUkbWxs4ODggCNHjgAAjhw5AgcHB1hbW+dYr3PnzvD19UVmZiZiY2Nx+vRpdOrUSUYXiIj0QtrZ7dmzZ2Pnzp3o1KkTdu7ciTlz5gAAhg0bhqCgIACAp6cnqlSpgo4dO6JPnz4YNWoUqlatKqsLRETSSTsmWbNmzTzHPW7evFn5v4mJiRKeRERFAa+4ISJSwZAkIlLBkCQiUsGQJCJSwZAkIlLBkCQiUsGQJCJSwZAkIlLBkCQiUsGQJCJSwZAkIlLBkCQiUsGQJCJSwZAkIlLBkCQiUsGQJCJSwZAkIlLBkCQiUsGQJCJSwZAkIlLBkCQiUsGQJCJSwZAkIlLBkCQiUsGQJCJSwZAkIlLBkCQiUsGQJCJSwZAkIlLBkCQiUsGQJCJSwZAkIlLBkCQiUsGQJCJSwZAkIlLBkCQiUsGQJCJSwZAkIlLBkCQiUsGQJCJSUULXBlJSUjBt2jTcvn0bJiYmmDp1Ktq2bZtrvdOnT2PdunVIS0uDEAJeXl4YMmSIruWJiPRK55DcsmULLC0t8csvvyA0NBQDBgzAqVOnULp06Rzr2draYv369bCzs0NCQgJ69uyJevXqwdnZWdcuEBHpjc6728ePH4e3tzcAoHr16vjkk09w4cKFXOvVr18fdnZ2AAArKyvUrFkTT5480bU8EZFe6bwlGR4ejsqVKys/29vb4+nTp6rPuX//Pn7//XfMmTMn3/VsbCzz/RxbW6t8P6cw1jBU/eL0fhWn12KI+ny/cntjSPbo0QPh4eF5PnblypV8FQOAqKgojBw5ErNmzVK2LPMjJiYRmZnirde3tbVCdHRCvuvkh8wa7/oBklm/KL1fBV2Hv/uCqyHr/TI2NlLd+HpjSPr7+6s+XqlSJTx58gTW1tYAgIiICLi4uOS5bkxMDAYPHoyhQ4eiS5cubypNRFTgdD4m2blzZ+zZswcAEBoaiqCgILRs2TLXenFxcRg8eDAGDBiA3r1761qWiMggdA7JL7/8Ei9evECHDh0wYsQIzJ07F5aWWZuuq1atws8//wwA2LRpE0JDQ7Fnzx54enrC09MT+/fv17U8EZFe6XziplSpUli9enWej40bN075/9SpUzF16lRdyxERGRSvuCEiUsGQJCJSwZAkIlLBkCQiUsGQJCJSwZAkIlLBkCQiUsGQJCJSwZAkIlLBkCQiUsGQJCJSwZAkIlLBkCQiUsGQJCJSwZAkIlLBkCQiUsGQJCJSwZAkIlLBkCQiUsGQJCJSwZAkIlLBkCQiUsGQJCJSwZAkIlLBkCQiUsGQJCJSwZAkIlLBkCQiUsGQJCJSwZAkIlLBkCQiUsGQJCJSwZAkIlLBkCQiUsGQJCJSwZAkIlLBkCQiUlGioDtARFlepqbj8HLPfD+H9IshSVRIJLxIQUIey21trRAdndcjZAg6726npKRg/Pjx6NChAzp37oxz586prp+amopu3bqhZ8+eupYmItI7nUNyy5YtsLS0xC+//IINGzbg22+/RVJS0mvXX7lyJerXr69rWSIig9A5JI8fPw5vb28AQPXq1fHJJ5/gwoULea4bGBiI0NBQeHrm77gLEVFB0Tkkw8PDUblyZeVne3t7PH36NNd6ycnJWLhwIebMmaNrSSIig3njiZsePXogPDw8z8euXLny1oWWLFmC/v37w87ODqGhoW/9vFfZ2Fjm+zm2tlbvXK8w1TBU/eL0fhWX18L3q+DqvzEk/f39VR+vVKkSnjx5AmtrawBAREQEXFxccq33f//3f7hw4QLWrVuH1NRUPH/+HO7u7jh8+HC+OhwTk4jMTPHW6xvizKDMGu/6AZJZvyi9XwVdp7jUMFSdwvi3YmxspLrxpfMQoM6dO2PPnj1wdHREaGgogoKCsHz58lzrZQ/Da9euYfHixfDz89O1PBGRXul8TPLLL7/Eixcv0KFDB4wYMQJz586FpWVWKq9atQo///yzzp0kIiooOm9JlipVCqtXr87zsXHjxuW53MXFhVuRRFQk8NptIiIVDEkiIhUMSSIiFQxJIiIVDEkiIhWcKo2IiiRDzb/JkCSiIul1828Ccq/s4e42EZEKhiQRkQqGJBGRCh6TLGR4MyiiwoUhWcgY6mA0Eb0d7m4TEalgSBIRqWBIEhGpYEgSEalgSBIRqWBIEhGpYEgSEalgSBIRqWBIEhGpYEgSEakocpclGhsbGeQ5hbGGoeoUlxqGqlNcahiqTmF7LW9az0gIIWR0iIioOOLuNhGRCoYkEZEKhiQRkQqGJBGRCoYkEZEKhiQRkQqGJBGRCoYkEZEKhiQRkQqGJBGRCobkO4qMjMy17M6dO3qrFxsbq7e2te2fO3cO586d03ut4iAlJSXXP9kSEorH7YMDAwNzLTtw4EAB9OTdFMuQDA0NRb9+/eDq6goAuH37NtasWSO1Rr9+/XDhwgXl5927d2PkyJFSawDAzZs30bZtW/To0QMAEBQUhBkzZkitcerUKXTp0gU7d+7Ev//9b3Tr1g2nT5+W0nZeYaKvYLl+/TqSkpIAAL6+vpg5cybCwsKk1tBycnJCw4YNc/xzdHTEgAED8ODBA53bF0LA29tbQk/V3blzB97e3qhfvz4cHByUfzLNnTs3x3ty7Ngx/PTTT1JraP3666/YuXMnAODZs2cICQnRvVFRDH3++eciICBAeHh4CCGEyMjIEF27dpVa4++//xZubm5i0aJFYsKECeLzzz8XUVFRUmsIIYS3t7f4+++/haenp7JM9mvp3LmzePDggfJzSEiI6Ny5s5S2a9euLerUqSNq166d61+dOnWk1NByc3MTmZmZ4u7du6JLly5i/fr1YuDAgVJraG3YsEFs27ZNJCQkiBcvXojt27eL77//Xuzfv1989tlnUmoMGTJExMfHS2nrdby9vcWVK1eEu7u7iIyMFCtWrBAbN26UWuPOnTvC3d1dREdHizNnzohu3bqJ6OhoqTWEEGLjxlW8ANAAACAASURBVI2if//+okOHDkIIISIiIkTfvn11brfITZX2NhISEtCqVSusWLECAGBsbAxTU1OpNT788EPMmjULX3zxBcqXL4+DBw/C2tpaag0A0Gg0+PDDD3Msk/1azM3N8cEHHyg/V69eHRYWFlLa1uchiFeVKFECRkZGuHDhAvr164eBAwfixIkTeql18uRJ+Pn5KT8PGjQIPXv2hJ+fH7Zt2yalhqWlJXr06IFWrVqhVKlSyvJvvvlGSvsAkJaWhmbNmkEIgQoVKmDChAnw8vLC8OHDpdWoXbs2pk2bhsGDByMzMxNbt27Fe++9J619rSNHjmD//v3o3bs3AKBixYpITEzUud1iGZImJibQaDQwMsqaJy4yMhLGxnKPLOzYsQM7duzAli1bEBwcDG9vbyxatAiNGjWSWsfMzAxJSUnKa7l37x7Mzc2l1mjXrh3Wr1+PXr16QQgBPz8/tGvXDi9fvoQQAiVLlpRSJy4uDjdv3gQANGjQAOXKlZPSrlZ6ejpu3ryJX375BfPmzQMAZGRkSK2hlZKSgrCwMFStWhUAEBYWhuTkZABZnz8ZPvroI3z00UdS2nodbV/Lli2LO3fuwM7ODnFxcVLaXrJkSY6fjYyMULNmTWzfvh2A3LAHAAsLi1wbENq/G10Uy5Ds378/Ro8ejbi4OKxZswYHDhzAhAkTpNYICAjA3r17YW1tDRcXFzRq1AiTJ0/GqVOnpNb56quv8OWXXyIqKgo+Pj64ePEili5dKrXG2rVrAQCrVq3KsfyHH36AkZER/vzzT51rXLx4EVOmTFGOd02fPh1Lly5F8+bNdW5ba9y4cZg5cyaaNm2Kjz76CCEhIahWrZq09rMbP348evXqhU8++QRA1nHvOXPmICkpCZ07d5ZSY/To0VLaUdO1a1fExcVh+PDh6NevHzIzMzF27FgpbWff+gWAjh07Smn3dSpWrIjAwEAYGRkhMzMTGzZskPIlU2wn3Q0MDMS5c+cghICrqyucnZ2lti+EyPUtlZCQACsrK6l1gKytlIsXL0IIgRYtWujtD1+fevbsiaVLl6JmzZoAgPv372PKlCk5dlmLmpiYGGXLuH79+rCxsZHafmpqKg4dOoSwsDCkp6cry2VvgWlpNBqkpqbC0tJSL+3rW3R0NKZOnYrffvsNRkZGcHZ2xrJly3T+vRTLLUkAcHZ2lh6M2T18+BDTpk1DZGQkzp49i9u3b+Ps2bMYM2aM9FoVK1ZUXkvlypWltw9kDQHKvitcvnx5qe2np6crAQkANWvWzPGHL0NiYiLWrVuHq1evAgCaNm2KkSNH6uWP/u7du6hSpYoygiIpKQl///231N3jcePGQaPRoF69ejAzM5PW7qsePXqER48e5Tg00bp1a6k1Ll26hD///BOpqanKMtlbyra2tti6dStSUlKQmZmJ0qVLS2m3WIakl5dXrq08KysrNGjQAEOHDpXy5s2ePRtff/01li9fDgBwcHDAN998Iz0kAwMDMWnSJOVESmpqKlasWIGGDRtKq3Hq1CnMmDEDdevWBZC1Kzxv3jy0b99eWg1ra2v4+fmhZ8+eAAB/f3/pJ7qmT58OS0tLfPvttwAAPz8/TJ8+HatXr5ZaBwB8fHywZ88e5WdTU1NMnTpV6pbxw4cPcfz4cWnt5WX58uXw9fVFzZo1leP2RkZGUkNy2bJlCAoKwr1799CuXTucOXMGzZo1k9Z+dgEBATm+JKW8Dp3PjxdCS5cuFaNHjxanT58Wp0+fFmPGjBHz5s0T06ZNE5MnT5ZSo2fPnkIIkWNoTvb/y+Lu7i6uXbum/Hz9+nXh7u4utYY+hwBpPXz4UPTu3Vs4OjoKR0dH0adPH/Ho0SOpNfLqs+zXoaUdXvamZboYNmyYSEhIkNrmq9q3b6/3Gm5ubkKj0Sif26dPn4qhQ4dKr7NixQrh5uYm1q9fL9avXy88PDzEypUrdW63WG5JXr9+Pce3fNu2bdG3b1/s2bMHXbt2lVLDEGfQtZo0aaL8Xx+HEPQ5BAjIOsP866+/Yu/evcpgb1m7QtlVqFABsbGxyhZqXFwc7OzspNcBsoYbZT+7/ejRI2lntbVnha2srODl5YWWLVvm2N2WeUzS1tZW78cgzczMlOFZGo0GdnZ2ePr0qfQ6J06cgL+/v3LCaNCgQejRowfGjx+vU7vFMiTj4uKQmpqqDJVJS0vD8+fPYWRkJO2P/9Uz6P7+/pg4caKUtrNr3rw5Dh06BA8PDwDA4cOH0aJFCylta694ed0QIFlMTEywZ88eeHt76yUctaFSvnx5eHp6om3btgCA8+fP6+249OjRo9GvXz9ldy4gIADz58+X0rb2j/yDDz7I8eWlDw0aNMDEiRPRuXPnHEPLZO5uly5dGikpKXBycoKPjw9sbW2lfglrlSlTJsdwNXNzc5QpU0bndovl2e2VK1ciICAAXbp0AZD1DdOyZUuMGDECU6dOxQ8//CCljvYMOpC1taqPP8imTZsiPj5e2ZJIS0tTxhcaGRnh119/fee269SpAyMjI+T1EZA19Edr8eLFqF+/vrThMdm96fepr6E0ISEhuHLlCgAUyKiDdevW6Xwp7MCBA3MtMzIywo4dO3RqN7tnz56hTJkyyMjIwLZt25CQkICBAweiUqVK0moAwMKFC3H//n3lEt5Dhw6hRo0ayvHPdw3+YhmSAHDu3Dlcu3YNRkZGaNKkibJ1IcuECRMwd+5cmJqawtPTE3FxcRgxYgS+/PJLqXWePHmi+ri+znZnl30X9l1pw97CwgIlS5ZUhlDpEvL5JSNUsktMTMTDhw+VE16G1qNHD/j7+xdI7Xch43OkJq/A19Il+Ivl7nZCQgJu3LiB+/fv4+XLl7h9+za2bdsm9dsxJCQEVlZWOHHiBJo2bQofHx/06dNHekj+/vvv6NSpE0qUKLhf1ZdffqnzH+P+/fsl9ebd/fLLL9JCMiAgADNnzoSJiQnOnj2LoKAgrF27Fhs2bJDS/tvQZftGezz13r17eT7+6qWwurh58ybGjx+PzMxMBAQEICgoCHv37lWuipLl3//+t9T2tIrlLEDTp0+HiYkJQkND4e3tDRMTE9SrV09qDe0Yv+vXr6NVq1YoWbKkXk7cHDlyBK6urli1alWe07MZgoydjcqVK6Ns2bKIj49H5cqVlX+GJHOnafXq1di3b59yzMvR0RGPHj2S1v7b0OWSO+3x0+HDh+f6N2LECFldBAB899132Lx5szL21tHRETdu3JBaQ+vixYtYvHgxFi9ejMuXL0tps1iG5MOHDzF+/HhYWFjAzc0NGzduzHNOO13UrFkTQ4cOxblz59CsWTO8fPlSavta69evx3/+8x9oNBp4eXlh7NixyjgwQ5Fx/WtAQAC6deumjCMNCgrCV199pXO7+SHjdWRna2ub42d9DviWbePGjQCAs2fP5vp35swZqbUMMUkLAPz4449YvHgxypQpgzJlymDRokXYsmWLzu0Wy91t7YfV1NQU8fHxKFu2rPSJZBcvXoxLly6hdu3aKFWqFCIjIzFp0iSpNbSqVKmCyZMnw9XVFRMnTsTFixdRpUoVzJo1S69XFcmk3fIaNmwYgILZ8pKpdOnSePbsmRK8165d08slqWpkbBnntbttZWUldeiUISZpAYCDBw9i9+7dypCmgQMHol+/fjofAiuWIVm9enXEx8fD3d0d3t7esLKykn5w3cLCIscVKXZ2dnoZk5eWloZjx47h559/RkZGBsaPH4+uXbvi1q1b+Oabb3D27FnpNV8laze1oLe8ZO5uT548GcOGDcPjx48xcOBAhIaGYv369dLaB7KmmatTp06OZRcvXkTLli0BAFu3btW5xvDhwxEREaEEfEJCAmxsbGBmZoYVK1agQYMGOtcwxCQtWtnHfMoa/1ksQ3LZsmUAgMGDB8PR0REJCQnKB6uocXV1hYuLC3x8fODk5KQsd3Z21tulXa/q0KGDzm0YYssrr5nOs4+bkxEqWvXq1cOOHTuUY2tOTk45xuTJOJM7atQoDBs2DH379kVmZiZWrlyJc+fOKZ9lGWeK27VrBxcXF+UL//Tp07h69So6dOiABQsWwNfXV+carVu3Ro0aNZRJWr7++mu9DJf65JNPMG3aNGU+yX379imzNOmi2A4BKi6ioqJQoUKFHMsSExOlXiXx6rx/wP+udZcVxDdv3sTs2bPx+PFj1KlTR9nykvEh1tKO+8yuRIkSqFevHubNm4caNWpIq/UmMobnPHv2DFOmTEGZMmXw7NkzvP/++5g5c6a0+T0BwNPTEwcPHsxzmbu7Ow4fPiyljkajUW6lUKNGDb2M1khOTsbatWuVYWWffvopRo4cmWvKtvwqlluSxcmIESNy/bENHDhQ6vi4mJgYBAYGKlsTZ86cgaOjI44fP44uXbrg66+/1rlG/fr1Vbe8ZJgwYQLMzc2VK4f8/f0RFxeHqlWrYtasWXobIpIXGdse7733HgYMGIApU6bAysoK3333ndSABIDMzEzcuHFDmTDlv//9LzIzMwFA2mgNQ0zSAmRdqTRlyhSpbQIonhNcFAcajUYkJycLDw8PkZKSIpKTk0VycrKIjIwUnTp1klrriy++yHEvlefPn4tBgwaJhIQE0aVLFyk15s+f/1bLdNGjR4/XLnNzc5Na6026d++ucxsLFy4UHh4e4v79++LkyZOibdu24ujRoxJ69z/Xr18XrVq1Eh07dhQdO3YUrVq1EtevXxeJiYnCz89PSg19T9Kyc+dO1X+64pZkIbVhwwZlZvDsB88tLS0xePBgqbUiIyNRtmxZ5ecyZcogOjoalpaW0k6u5DUE6/r161La1jLELRUMKSkpCXv37oW5uTlq1KgBBwcHTJw4UdokLUDWse1ffvlF2RX+4IMPlN+59vI+GfQ5Scsff/whtb1XMSQLqdGjR2P06NGYO3cuZs6c+dr1ZJwg+PDDDzFjxowccz3WrFkTaWlpOu9yHT9+HMePH8eTJ08wbtw4ZXliYqL0SQ6y31JBCIHg4GDpt1R4W0LC7varE2ZUrVoV//nPf3Ru91UZGRkwMzNDRkaGMixL5hU3+pykBcgarK5PPHFTxMk4QZCYmIi1a9fi2rVrALK+9UeNGoWSJUvixYsXOoXwnTt3EBwcjDVr1uS4d4qlpSWaNWsmfZquZ8+e4datWwD0c0uFt7V27VqMGjVK53b0PaP3rl27sGzZMpQrV0456WVkZCR1QLk+J2nJ7tixY2jVqhUsLS2xatUq3Lp1CxMnTtR5+B9Dsojr3r07Dhw4UNDdeKP4+Hjpd0d8nbS0tBy3IpB9sgMwzIiA183orR3iJkO7du2wY8cOvV4iaqhJWrRn42/duoV58+Zh0KBB2LVrF3bv3q1Tu9zdLuJkXGpniBtObdiwQdk6HTRokLIr7OnpKa3GqVOnMH/+fERHRwP4383aZE75pmWIEQEBAQHw9/dHz549MXfuXIwaNUq5NYUstra2er+G3lDX6GuHFV2+fBm9e/eGu7u7lLGxDEkyyA2nrly5Ah8fH5w/fx52dnZYuXIlhg8fLjUkly5diu+//x4NGjTQ2yzxWlFRUfDz81NOeI0aNQpjxozBf/7zH/Tp00dKSBpiRu9PP/0US5YsQbdu3XJcKijjmGTTpk1Vv8RlT5NnZGSEY8eO4dixY1i3bh2ArPGZumJIFnEyjpYY4oZTWtevX0eHDh1gZ2cnfcKJsmXLSh979zqGGBFgiBm9tYdqTpw4oSyTdUxSOz3evn37EB8fD29vbwghsG/fvhzvnSwzZszA5s2b0atXL1StWhWhoaFwcXHRvWGdBxFRgfrhhx90bsMQN5z64osvxMyZM0Xbtm1FfHy80Gg00scubtiwQezatUvExcUp40qTk5Ol1tAaM2aM+Pbbb8WNGzfEjRs3xIwZM8To0aNFampqnuM130V0dLRITU0VycnJYu3atWLRokXiyZMnUto2pLzeD+2N9Axp1qxZ7/Q8nrgp5AxxvHDSpEn4448/9HrDqdjYWBw6dAgNGjRAgwYN8PjxY/z222/KsCMZsk8Gob0thb6OSepzREBBiImJyXEGXeatFTp27Ijdu3cr70lsbCz69euHkydPSqvxNt51JAh3tws5QxwvNMQNp6ytrdGrVy88fPgQQNb0b1WqVJFa486dO1LbU2NpaYmpU6fm+ZisgHzw4AE2bNiAR48e5fiC3Ldvn5T2gazjgj4+PoiJiYGxsTE0Gg3KlSsn9Xjh559/ju7du6NNmzYAsk5IyZ7YV58YkoWcIY4X6utGWdkVhtsdyGSILfxx48bB09MTPXr00NsVQ0uXLsVPP/2ECRMmwN/fH/v27cPjx4+l1hgwYAAaNWqkXGE1YMAA1K5dW2oNfWJIFnJVq1aVPuuPlna4yq5du/J8fMCAAdJq6XPS3c8//xzbt2/PdTZV6PFmY4bYwi9RogSGDh2ql7az++CDD5Ceng4jIyP07t0bPXv2xIQJE6TWqFOnTq65MYsKhmQhp88b1P/999/o0qWL3q991dLXpLvaCVwNebMxQ2zht2zZEgEBAVLvgf0q7dhCOzs7nD17FpUrV8bz58+ltO3l5ZXnCAbtl5fMwwZv412HhTEkCzl9Hi/UXib4r3/9K9eWamJiotRa+px0Vzvf5rFjx5QtVa3NmzfnWiaDPrfwtZo1a4aRI0fC2NgYZmZmetkyHjRoEJ4/f45x48Zh0qRJSEhIwPTp06W0/eoxW+1kI7rO7/g648aNw6pVq1677F2/RHl2m/I86yf7ns63bt3CrFmz9DrpriFeh5YhRgR06NABkyZNQt26dXNsBRnyLpP79u1Dr169dGrj0aNHmDx5sjLKoG7duli6dKkyW5Msef2uZUwczC3JQsoQxwvT09Oh0WiQmZmJly9fKgPTExIS8rwVgi7edLsDXVy+fBmXLl1CVFRUjmuqExMTpd7XJjtDjAgoW7aswWcvetWuXbt0DslZs2ahT58+8PLyAgD4+flh5syZ2LZtm4wuYu/evdizZw9CQ0Nz9DUhIUHK74ghWUgZ4nihIeesBLKOr+rj+JqpqSlKly4NIyOjHLtyFSpUwPDhw6XXAwwzIqB9+/b4+eef0aVLlxyXDOpjwo7XkfElExsbmyO8vLy8sGPHDp3b1WrevDmqVauGefPmKVvyqampsLKygqOjo87tc3eb3jhnpS5ed/2uPo6v3b17F7Vq1ZLWXl4MOSLAkIPjX0fG4YoePXpg+fLlyj2GQkJCMGnSJPj5+cnoomL8+PGYN28eTE1N4enpibi4OIwYMYK3lP0nePDgAe7cuYO0tDRlWffu3aW1P3HiRGRmZsLY2Bh3797F33//jQ4dOkg5+2zIM86XL1+Gvb09rKysMGXKFAQFBeHbb7+VOsGrIUcEGHJwvD5NmDABAwYMgIODA4Cs15XXVHO6Cg0NhZWVFU6cOAEXFxdMmzYNffr0YUgWdzt27MCePXsQHR0NR0dHBAYGonHjxlJDctCgQdi5cyeSkpLw5ZdfolatWrh48SIWLVqkc9vZTzKkp6fnuE2A7Dvm+fn5YfDgwbh69SpiY2OxcOFCzJ8/X2pIGnJEQGEgY0ezVatWOHr0KG7evAkgazJkfVy2qR3Uf/36dbRu3RolS5aUMhuUfueTIp3t3bsXvr6+sLe3x5YtW+Dr64vSpUtLrSGEQKlSpXD+/Hn06dMHW7Zswe3bt6XWCAoKQocOHTB69GiMGjUKHTt2lF5De1XKtWvX4O7ujoYNG+rtxM3AgQPfallRkJiY+NrfhYwvSiDrUs22bduibdu2eruuvWbNmhg6dCjOnTuHZs2a4eXLl1LaZUgWcmZmZihVqhQyMzMhhECtWrUQGhoqtUZqairS0tJw+fJlZVZt2fMxLliwAAsXLsTJkydx6tQpLFiwAPPmzZNaw8LCAps2bcLRo0fRvHlzCCGkzCeYXXp6OlJSUpQRASkpKUhJSUFUVJT0EQGGEBAQgG7dumHMmDEAsr7MvvrqK+XxonSVzOLFi9G3b19s374dpUqVwvPnzzFp0iSd22VIFnIlS5aERqNBnTp1sHTpUvz73/9W7ossS9euXdG8eXM8fvwYDRs2RHR0dI6zqTKkpKTkuK1Bs2bNpIfKd999h+joaEyePBm2trYICwuDu7u71BobNmyAk5MT7t69iwYNGsDJyQlOTk7o2rWr9FqGoL1cVDscS+blooZmYWGB9u3bK+Mv7ezs0KpVK90bfvfZ2cgQ/vrrL5GUlCSePXsmpk+fLsaMGSOCg4Ol14mPjxcZGRlCCCESExPF06dPpbbv7e0trl69qvx87do14e3tLbWGIc2ZM6eguyCFdl5HT09PZVn2/xPvu13oaYe0lCpVCgsWLNBLDSEETp06hdDQUEyZMgVxcXGIioqCnZ2dtBrTp0/HuHHjlDPmGo0Gq1evltY+kDV4ePPmzbnuLihzTJ6WPkcEGJI+LxctLjhOspCLiorC/PnzlcldmzVrhunTpyvXK8uwcOFCxMTE4Pbt2zhx4gTi4uIwbNgw6RMQaDSaHGe3TU1NpbY/ZswY1KxZE0ePHsW4ceOwf/9+1K1bF5MnT5ZaBwB69uypjAjo2bMnatWqBVtbW2knOgzFEJeLFnU8JlnIffPNN6hVqxYOHTqEQ4cOoVatWlKvDwayth6WLVum3D+lfPnyObbEZElPT4epqSmMjY3x8OFD3Lt3T2r7Dx8+xPjx42FhYQE3Nzds3LgRgYGBUmtoCQOMCDAE7eWiy5Ytw9ChQ3H06FEG5Cu4u13IRUdH57gEbuTIkTh69KjUGubm5jmuipF9YgjIugZ42bJlKFeunFJL1g2ntLS7uqampoiPj0fZsmURGxsrrf3sso8I+OyzzwDIHxFgCImJiShdujRat26Nu3fv4uLFi0XysIE+MSQLuffffx8PHz5EtWrVAGTNqFK9enWpNbRbqkIIPH78GJs2bUKjRo2k1ti6dSuOHDmilxlsQkNDUb16dVSvXh3x8fFwd3eHt7c3rKysULduXen1gP+NCKhWrZreRgQYgj4vJCgueEyykBsyZAhu3LihhNaNGzfQsGFD5WqPV+fPexeJiYlYtGgRzp49CwBwdXXF9OnTpc7717dvX+zevVtae9n17NkTfn5+ygzlABAYGIiEhAS0bNlS+pU9Ws+fP4eVlRWMjY2RlJSExMREqSe7DEF7bbavry+ePn2KMWPGSJlerDjhlmQh5+7unmP8nZubm9T2MzIysHXrVsyfP19qu1ra446ffvoplixZgm7duuXY4vrwww91rvHy5UucPHkS4eHhCAgIUJYbGxvj8uXLepl5yBAjAgyhuBw20CduSRZxs2fPxuzZs3Vqo1evXnqbSt/V1fW1j8k6Jnn69Gns2bMHgYGBuU46GBkZ6WUIkKFGBOjbDz/8gO3bt6NatWrYu3cvYmJiMGrUKOzdu7egu1Z4FNQATZKje/fuOrexZs0a8eOPP4pnz56J5ORk5Z9Mly5dEs+fP1d+fv78ubhy5YrUGgsXLpTanhoPDw+RmZmZY+C1m5ubwerLpO8LCYo67m4TfvjhBwBZN9TS17yFS5cuzTEvoaWlJZYsWSL11grTpk2T1tabGGJEgD6FhYWhatWqyuGQ6OjoHI8XtcMG+sSQJIPMW6gNXi1jY2NkZGTova6+GGJEgD7Nnz8fGzduzHPmdtlDs4o6hiQZROnSpXHz5k3Ur18fAHDz5k293TXPEHx8fLBo0SJER0ejT58+cHV1hY+PT0F3661t3LgRAJQRDfR6DMkirqiciZwyZQpGjRqlnM2+d++esptf1Oh7RIAhXblyBY6Ojsr12i9evMDt27dzzNj0j1fAx0TpDcaOHftWy4qC+Ph4cf78eXH+/HkRHx9f0N3RiZeXV0F3QQpPT0+RmZmp/JyRkSHlZGBxwi3JQi6vuf0ePHhQAD3RXdmyZfUyZrEgtGnTBlu2bEH37t1zHDYw5J0MZRDF7FixPjAkCyl930uYdGOIEQGGUNyOFesDB5MXUk+ePMHjx48xb968HLd7tbS0RO3atZX7uRDp4r///S/GjBmT61hx9vuw/9MxJIuQtLQ0PH/+HLa2tgXdFSoGMjMzcffuXdjb2+P3338HADRo0ABly5Yt4J4VLkXj1Og/2IQJE5CQkICXL1/C3d0d3bp1w5YtWwq6W1QMGBsbY8qUKcqx4tatWzMg88CQLORCQkJgZWWF8+fPw8XFBQEBAThw4EBBd4uKiWrVquHx48cF3Y1CjSduCjl93XCdCACSkpLg4eGBRo0a5ThhI2MKvuKCIVnIaW+4/uDBA0yaNEnaDdeJAMDDwwMeHh4F3Y1CjSduCrmXL1/i0qVLqF27NqpWrYrIyEj89ddfcu4nTERvxP22Qs7CwgI1a9bEX3/9BSBrXFu9evUKuFdUXISGhqJfv37KvJ+3b9/GmjVrCrhXhQtDspDz9/fH119/je+++w5A1i1mx48fX8C9ouJi9uzZ+Prrr5Vrtx0cHHDixIkC7lXhwpAs5LZv3479+/crH+IaNWrg2bNnBdwrKi4SEhLQqlUr5dJEY2Nj6fdDL+oYkoWcqakpSpcunWMZr7YhWUxMTKDRaJSQjIyM5OiJV/DdKOTKlSuHkJAQ5UN88OBBVKxYsYB7RcVF//79MXr0aMTFxWHNmjXo378/hgwZUtDdKlR4druQCwkJwaRJk/DgwQNYW1vDwsICGzZswPvvv1/QXaNiIjAwEOfOnYMQAq6urnB2di7oLhUqDMkiICMjA6GhoRBC4IMPPuDuNklz4cKFXMPJ9uzZA29v7wLqUeHD3e0iIC0tTTlOFBISoty8iUhXS5cuxfLly5GZmYnk5GRMnDgRR48eLehuFSrckizkdu3ahWXLlqFcuXLKcUneqIlkSUlJwbx58/DgwQO8ePECXbt2xciRI3nyJhtelljIbd26FUeOHEHln1PhVgAAAXhJREFUypULuitUDJUsWRIff/wxLl68CGNjY3z66acMyFcwJAs5W1tbBiTpzZgxY5CcnIyDBw8iLCwMU6ZMQa9evfK81ew/FUOykNIed/z000+xZMkSdOvWDebm5srj2pmkiXTx8ccfY8CAAQgLC0P9+vXh6+uLb7/9tqC7VagwJAupV7/Js18qxmOSJMvHH38Md3d3mJiY4OzZs3j06BE0Gk1Bd6tQYUgWUtqbxl++fBmOjo4oU6YMgP/dF5lIhtWrV2Pfvn0YNmwYAMDR0RFhYWEF3KvChUdoC7mlS5cq120DWTcCW7JkSQH2iIqbV++ZxGu3c2JIFnK8LzLpU+nSpfHs2TPlM3bt2rUcX8rE3e1Cj/dFJn2aPHkyhg0bhsePH2PgwIEIDQ3F+vXrC7pbhQoHkxdyvC8y6VtCQgJu3LgBAHByclKOf1MWhmQR8Pz5c94XmaiAMCSJiFTwxA0RkQqGJBGRCoYkEZEKhiQRkQqGJBGRiv8HfDgKU2p6o3AAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Calculating the feature importance using AUCARACY score of the columns"
      ],
      "metadata": {
        "id": "50cBHuZqbirI"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data = df.copy()\n",
        "\n",
        "COLUMNS = [col for col in data.columns if col != \"heart_disease\"]\n",
        "TARGET = \"heart_disease\"\n",
        "\n",
        "train_size = 0.6\n",
        "test_size = 0.2\n",
        "val_size = 0.2\n",
        "\n",
        "X_full_train, X_test, y_full_train, y_test = train_test_split(data[COLUMNS],\n",
        "                                                         data[TARGET],\n",
        "                                                         test_size=test_size,\n",
        "                                                         random_state=42)\n",
        "\n",
        "X_train, X_val, y_train, y_val = train_test_split(X_full_train,\n",
        "                                                  y_full_train,\n",
        "                                                  test_size=val_size/(train_size+test_size),\n",
        "                                                  random_state=42)\n",
        "\n",
        "features_auc_scores = []\n",
        "\n",
        "for column in COLUMNS:\n",
        "    \n",
        "    auc_score = roc_auc_score(y_true=y_train, y_score=X_train[column])\n",
        "    features_auc_scores.append([column, auc_score])\n",
        "    \n",
        "feature_importance_df = pd.DataFrame(data=features_auc_scores, columns=[\"feature\", \"auc_score\"])\n",
        "feature_importance_df[\"correlation\"] = data.drop(\"heart_disease\", axis=1).corrwith(data.heart_disease).values"
      ],
      "metadata": {
        "id": "jxHbbO4Nbmwx"
      },
      "execution_count": 76,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "A table with **auc_score** and correlation with **heart_disease** of each column in the dataset"
      ],
      "metadata": {
        "id": "6QEoGnztbpCZ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "feature_importance_df.sort_values(\"auc_score\", ascending=False)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 394
        },
        "id": "Sz6ZVJwAbraS",
        "outputId": "6955fbb8-610e-4be3-fa36-9f64b0689f06"
      },
      "execution_count": 77,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "            feature  auc_score  correlation\n",
              "8   exercise_angina   0.784142     0.551834\n",
              "9           oldpeak   0.768320     0.495696\n",
              "0               age   0.654454     0.298617\n",
              "3        resting_bp   0.617774     0.173242\n",
              "1               sex   0.613242     0.292779\n",
              "6       resting_ecg   0.566400     0.132872\n",
              "4       cholesterol   0.553046     0.103866\n",
              "5        fasting_bs   0.549722     0.160594\n",
              "7            max_hr   0.272872    -0.377212\n",
              "10         st_slope   0.243454    -0.441972\n",
              "2   chest_pain_type   0.221841    -0.463177"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-a0f80fa5-5886-43ff-bfb8-9b2c2dd20c78\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>feature</th>\n",
              "      <th>auc_score</th>\n",
              "      <th>correlation</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>exercise_angina</td>\n",
              "      <td>0.784142</td>\n",
              "      <td>0.551834</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>oldpeak</td>\n",
              "      <td>0.768320</td>\n",
              "      <td>0.495696</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>age</td>\n",
              "      <td>0.654454</td>\n",
              "      <td>0.298617</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>resting_bp</td>\n",
              "      <td>0.617774</td>\n",
              "      <td>0.173242</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>sex</td>\n",
              "      <td>0.613242</td>\n",
              "      <td>0.292779</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>resting_ecg</td>\n",
              "      <td>0.566400</td>\n",
              "      <td>0.132872</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>cholesterol</td>\n",
              "      <td>0.553046</td>\n",
              "      <td>0.103866</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>fasting_bs</td>\n",
              "      <td>0.549722</td>\n",
              "      <td>0.160594</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>max_hr</td>\n",
              "      <td>0.272872</td>\n",
              "      <td>-0.377212</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>st_slope</td>\n",
              "      <td>0.243454</td>\n",
              "      <td>-0.441972</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>chest_pain_type</td>\n",
              "      <td>0.221841</td>\n",
              "      <td>-0.463177</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-a0f80fa5-5886-43ff-bfb8-9b2c2dd20c78')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-a0f80fa5-5886-43ff-bfb8-9b2c2dd20c78 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-a0f80fa5-5886-43ff-bfb8-9b2c2dd20c78');\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": 77
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Choosing the features that will be added in the model"
      ],
      "metadata": {
        "id": "9fgZiUgjbszv"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "logistic_reg = LogisticRegression(solver=\"liblinear\", C=1.0, max_iter=100, random_state=42)"
      ],
      "metadata": {
        "id": "IqyElYuNbtre"
      },
      "execution_count": 78,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "FEATURES = [\"exercise_angina\", \"oldpeak\", \"age\", \"sex\", \"chest_pain_type\", \"st_slope\", \"fasting_bs\"]\n",
        "\n",
        "train_size = 0.6\n",
        "test_size = 0.2\n",
        "val_size = 0.2\n",
        "\n",
        "X_full_train, X_test, y_full_train, y_test = train_test_split(data[FEATURES],\n",
        "                                                            data[TARGET],\n",
        "                                                            test_size=test_size,\n",
        "                                                            random_state=42)\n",
        "\n",
        "X_train, X_val, y_train, y_val = train_test_split(X_full_train,\n",
        "                                                    y_full_train,\n",
        "                                                    test_size=val_size/(train_size+test_size),\n",
        "                                                    random_state=42)\n",
        "\n",
        "dv = DictVectorizer(sparse=False)\n",
        "\n",
        "full_train_dict = X_full_train.to_dict(orient=\"records\")\n",
        "train_dict = X_train.to_dict(orient=\"records\")\n",
        "test_dict = X_test.to_dict(orient=\"records\")\n",
        "val_dict = X_val.to_dict(orient=\"records\")\n",
        "\n",
        "X_full_train = dv.fit_transform(full_train_dict)\n",
        "X_train = dv.fit_transform(train_dict)\n",
        "X_test = dv.fit_transform(test_dict)\n",
        "X_val = dv.fit_transform(val_dict)\n",
        "\n",
        "logistic_reg.fit(X=X_train, y=y_train)\n",
        "\n",
        "y_pred = logistic_reg.predict(X=X_val)\n",
        "auc_score = roc_auc_score(y_true=y_val.values, y_score=y_pred)\n",
        "print(auc_score*100)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "p_hbXCJBbvts",
        "outputId": "3504bef4-a0d6-43f1-e62f-9ae430a32b73"
      },
      "execution_count": 79,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "86.35789093535571\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(18,18))\n",
        "plt.subplot(3,2,1)\n",
        "plt.style.use('seaborn')\n",
        "plt.tight_layout()\n",
        "sns.set_context('talk')\n",
        "sns.histplot(data=df, x='age', hue=\"HeartDisease\",multiple=\"stack\",palette='magma')\n",
        "plt.title('Age vs HeartDisease')\n",
        "\n",
        "plt.subplot(3,2,2)\n",
        "plt.style.use('seaborn')\n",
        "plt.tight_layout()\n",
        "sns.set_context('talk')\n",
        "sns.histplot(data=df, x='RestingBP', hue=\"HeartDisease\",multiple=\"stack\",palette='magma')\n",
        "plt.title('RestingBP vs HeartDisease')\n",
        "\n",
        "plt.subplot(3,2,3)\n",
        "plt.style.use('seaborn')\n",
        "plt.tight_layout()\n",
        "sns.set_context('talk')\n",
        "sns.histplot(data=df, x='Cholesterol', hue=\"HeartDisease\",multiple=\"stack\",palette='magma')\n",
        "plt.title('Cholesterol vs HeartDisease')\n",
        "\n",
        "plt.subplot(3,2,4)\n",
        "plt.style.use('seaborn')\n",
        "plt.tight_layout()\n",
        "sns.set_context('talk')\n",
        "sns.histplot(data=df, x='FastingBS', hue=\"HeartDisease\",multiple=\"stack\",palette='magma')\n",
        "plt.title('FastingBS vs HeartDisease')\n",
        "\n",
        "plt.subplot(3,2,5)\n",
        "plt.style.use('seaborn')\n",
        "plt.tight_layout()\n",
        "sns.set_context('talk')\n",
        "sns.histplot(data=df, x='MaxHR', hue=\"HeartDisease\",multiple=\"stack\",palette='magma')\n",
        "plt.title('MaxHR vs HeartDisease')\n",
        "\n",
        "plt.subplot(3,2,6)\n",
        "plt.style.use('seaborn')\n",
        "plt.tight_layout()\n",
        "sns.set_context('talk')\n",
        "sns.histplot(data=df, x='Oldpeak', hue=\"HeartDisease\",multiple=\"stack\",palette='magma')\n",
        "plt.title('Oldpeak vs HeartDisease')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 814
        },
        "id": "DXYDtSpvd5A5",
        "outputId": "bd81951f-6f49-4aa7-c5b5-23ea1999206c"
      },
      "execution_count": 145,
      "outputs": [
        {
          "output_type": "error",
          "ename": "ValueError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-145-241b8fe5bf8b>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtight_layout\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0msns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_context\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'talk'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m \u001b[0msns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhistplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'age'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhue\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"HeartDisease\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mmultiple\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"stack\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mpalette\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'magma'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      7\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtitle\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Age vs HeartDisease'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      8\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/seaborn/distributions.py\u001b[0m in \u001b[0;36mhistplot\u001b[0;34m(data, x, y, hue, weights, stat, bins, binwidth, binrange, discrete, cumulative, common_bins, common_norm, multiple, element, fill, shrink, kde, kde_kws, line_kws, thresh, pthresh, pmax, cbar, cbar_ax, cbar_kws, palette, hue_order, hue_norm, color, log_scale, legend, ax, **kwargs)\u001b[0m\n\u001b[1;32m   1428\u001b[0m ):\n\u001b[1;32m   1429\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1430\u001b[0;31m     p = _DistributionPlotter(\n\u001b[0m\u001b[1;32m   1431\u001b[0m         \u001b[0mdata\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1432\u001b[0m         \u001b[0mvariables\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0m_DistributionPlotter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_semantics\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlocals\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/seaborn/distributions.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, data, variables)\u001b[0m\n\u001b[1;32m    109\u001b[0m     ):\n\u001b[1;32m    110\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 111\u001b[0;31m         \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__init__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvariables\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mvariables\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    112\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    113\u001b[0m     \u001b[0;34m@\u001b[0m\u001b[0mproperty\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/seaborn/_core.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, data, variables)\u001b[0m\n\u001b[1;32m    603\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__init__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvariables\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m{\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    604\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 605\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0massign_variables\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvariables\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    606\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    607\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mvar\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcls\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_semantic_mappings\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/seaborn/_core.py\u001b[0m in \u001b[0;36massign_variables\u001b[0;34m(self, data, variables)\u001b[0m\n\u001b[1;32m    666\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    667\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minput_format\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"long\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 668\u001b[0;31m             plot_data, variables = self._assign_variables_longform(\n\u001b[0m\u001b[1;32m    669\u001b[0m                 \u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mvariables\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    670\u001b[0m             )\n",
            "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/seaborn/_core.py\u001b[0m in \u001b[0;36m_assign_variables_longform\u001b[0;34m(self, data, **kwargs)\u001b[0m\n\u001b[1;32m    901\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    902\u001b[0m                 \u001b[0merr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34mf\"Could not interpret value `{val}` for parameter `{key}`\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 903\u001b[0;31m                 \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0merr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    904\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    905\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mValueError\u001b[0m: Could not interpret value `HeartDisease` for parameter `hue`"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1296x1296 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 1. Logistic Regression"
      ],
      "metadata": {
        "id": "WJH83qEScEYP"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**1.1 Train and Validation Data**"
      ],
      "metadata": {
        "id": "DNFjaPk0cIkj"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Hyperparameter tunning"
      ],
      "metadata": {
        "id": "xixAuqTecOxm"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "c_values = [0.01, 0.1, 0.5, 1.0, 1.5, 10, 100]\n",
        "\n",
        "for C in c_values:\n",
        "\n",
        "    logistic_reg = LogisticRegression(solver=\"liblinear\", C=C, max_iter=100, random_state=42)\n",
        "    logistic_reg.fit(X=X_train, y=y_train)\n",
        "    y_pred = logistic_reg.predict(X=X_val)\n",
        "    auc_score = roc_auc_score(y_true=y_val.values, y_score=y_pred)\n",
        "    print(f\"C: {C} | score: {auc_score*100}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Cwy48BkVcSMF",
        "outputId": "4bb57a29-7ac7-4541-8791-6e66cafbc28e"
      },
      "execution_count": 80,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "C: 0.01 | score: 81.74431202600215\n",
            "C: 0.1 | score: 83.73058866016613\n",
            "C: 0.5 | score: 86.35789093535571\n",
            "C: 1.0 | score: 86.35789093535571\n",
            "C: 1.5 | score: 86.35789093535571\n",
            "C: 10 | score: 84.94944023113035\n",
            "C: 100 | score: 84.94944023113035\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "logistic_reg = LogisticRegression(solver=\"liblinear\", C=0.5, max_iter=100, random_state=42)\n",
        "logistic_reg.fit(X=X_train, y=y_train)\n",
        "y_pred = logistic_reg.predict(X=X_val)\n",
        "auc_score = roc_auc_score(y_true=y_val.values, y_score=y_pred)\n",
        "print(auc_score*100)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Rdrq1e-ycYAl",
        "outputId": "2108cc23-26ce-45ca-a371-372fd8723368"
      },
      "execution_count": 81,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "86.35789093535571\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Confusion Matrix**"
      ],
      "metadata": {
        "id": "HGsZSIXkcbZO"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "sns.heatmap(confusion_matrix(y_true=y_val, y_pred=y_pred), annot=True)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 268
        },
        "id": "X92YL6Tacdz1",
        "outputId": "539fd2cd-e6c7-4f21-d45b-147f9d62addd"
      },
      "execution_count": 82,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Classification Report**"
      ],
      "metadata": {
        "id": "MrHZnbHkcgY_"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "print(classification_report(y_true=y_val, y_pred=y_pred))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NKEI74FrcjJn",
        "outputId": "86be6a2f-7d56-40f7-df40-63e7ee6db584"
      },
      "execution_count": 83,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.85      0.91      0.88        78\n",
            "           1       0.89      0.82      0.85        71\n",
            "\n",
            "    accuracy                           0.87       149\n",
            "   macro avg       0.87      0.86      0.86       149\n",
            "weighted avg       0.87      0.87      0.87       149\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "ROC Curve"
      ],
      "metadata": {
        "id": "GzuU8fz6clBI"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plot_roc_curve(model=logistic_reg, \n",
        "               data1=(X_val, y_val), \n",
        "               data1_label=\"Validation\",\n",
        "               data2=(X_train, y_train),\n",
        "               data2_label=\"Train\",\n",
        "               title=\"LogisticRegression ROC Curve\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 301
        },
        "id": "Ki79X5-Vcm0i",
        "outputId": "8945c263-92a5-4531-ec38-0992311c698e"
      },
      "execution_count": 84,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#2.  Decision Tree Classifier"
      ],
      "metadata": {
        "id": "PY-UconP37rM"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**2.1. Train and Validation Data**"
      ],
      "metadata": {
        "id": "WQteokCa4BEs"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "dt = DecisionTreeClassifier(random_state=42)"
      ],
      "metadata": {
        "id": "YuWfbuEC4QNX"
      },
      "execution_count": 85,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dt.fit(X=X_train, y=y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XbbS8pHr4S6-",
        "outputId": "a2940494-93e7-4eac-969c-309c9fbc8fa1"
      },
      "execution_count": 86,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DecisionTreeClassifier(random_state=42)"
            ]
          },
          "metadata": {},
          "execution_count": 86
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred = dt.predict(X=X_val)\n",
        "auc_score = roc_auc_score(y_true=y_val.values, y_score=y_pred)\n",
        "print(auc_score*100)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AQ4Pu18u4WEl",
        "outputId": "ff0859ab-2d81-4de5-b908-695626898b81"
      },
      "execution_count": 87,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "81.04008667388949\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Hyperparemeters tunning"
      ],
      "metadata": {
        "id": "lFWjXW6x4Xve"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "criterion_list = [\"gini\", \"entropy\"]\n",
        "\n",
        "for criterion in criterion_list:\n",
        "    \n",
        "    dt = DecisionTreeClassifier(criterion=criterion, random_state=42)\n",
        "    dt.fit(X=X_train, y=y_train)\n",
        "    y_pred = dt.predict(X=X_val)\n",
        "    auc_score = roc_auc_score(y_true=y_val.values, y_score=y_pred)\n",
        "    print(f\"criterion: {criterion} | score: {auc_score}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wkTHhNiq4aX9",
        "outputId": "28f5e254-1257-4eef-c9ea-1cb819446db9"
      },
      "execution_count": 88,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "criterion: gini | score: 0.8104008667388949\n",
            "criterion: entropy | score: 0.755958829902492\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "max_depth_list = [1,2,3,4,5,6,7,8,9,10,15,20,25,50,100,200]\n",
        "\n",
        "for max_depth in max_depth_list:\n",
        "    \n",
        "    dt = DecisionTreeClassifier(criterion=\"gini\", max_depth=max_depth, random_state=42)\n",
        "    dt.fit(X=X_train, y=y_train)\n",
        "    y_pred = dt.predict(X=X_val)\n",
        "    auc_score = roc_auc_score(y_true=y_val.values, y_score=y_pred)\n",
        "    print(f\"max_depth: {max_depth} | score: {auc_score}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-YfXwftp-BO6",
        "outputId": "6c8bca96-a166-4a5f-f0f4-59bd20f9d686"
      },
      "execution_count": 89,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "max_depth: 1 | score: 0.7463885879378839\n",
            "max_depth: 2 | score: 0.8077825929938606\n",
            "max_depth: 3 | score: 0.8584326471650415\n",
            "max_depth: 4 | score: 0.8366738894907908\n",
            "max_depth: 5 | score: 0.8097688696280245\n",
            "max_depth: 6 | score: 0.8232213795594077\n",
            "max_depth: 7 | score: 0.8085048754062838\n",
            "max_depth: 8 | score: 0.8232213795594077\n",
            "max_depth: 9 | score: 0.8161791260382809\n",
            "max_depth: 10 | score: 0.7892741061755146\n",
            "max_depth: 15 | score: 0.8104008667388949\n",
            "max_depth: 20 | score: 0.8104008667388949\n",
            "max_depth: 25 | score: 0.8104008667388949\n",
            "max_depth: 50 | score: 0.8104008667388949\n",
            "max_depth: 100 | score: 0.8104008667388949\n",
            "max_depth: 200 | score: 0.8104008667388949\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "min_samples_leaf_list = [1,2,3,4,5,6,7,8,9,10,15,20,25,50,100,200]\n",
        "\n",
        "for min_samples_leaf in min_samples_leaf_list:\n",
        "    \n",
        "    dt = DecisionTreeClassifier(criterion=\"gini\", max_depth=3, min_samples_leaf=min_samples_leaf, random_state=42)\n",
        "    dt.fit(X=X_train, y=y_train)\n",
        "    y_pred = dt.predict(X=X_val)\n",
        "    auc_score = roc_auc_score(y_true=y_val.values, y_score=y_pred)\n",
        "    print(f\"min_samples_leaf: {min_samples_leaf} | score: {auc_score}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pt1N9cwu-Dpl",
        "outputId": "92424eb2-054c-403e-a03e-567cb21aad6a"
      },
      "execution_count": 90,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "min_samples_leaf: 1 | score: 0.8584326471650415\n",
            "min_samples_leaf: 2 | score: 0.8584326471650415\n",
            "min_samples_leaf: 3 | score: 0.8584326471650415\n",
            "min_samples_leaf: 4 | score: 0.8584326471650415\n",
            "min_samples_leaf: 5 | score: 0.8584326471650415\n",
            "min_samples_leaf: 6 | score: 0.8584326471650415\n",
            "min_samples_leaf: 7 | score: 0.8315276273022751\n",
            "min_samples_leaf: 8 | score: 0.8315276273022751\n",
            "min_samples_leaf: 9 | score: 0.8315276273022751\n",
            "min_samples_leaf: 10 | score: 0.8315276273022751\n",
            "min_samples_leaf: 15 | score: 0.8379378837125316\n",
            "min_samples_leaf: 20 | score: 0.8263813651137595\n",
            "min_samples_leaf: 25 | score: 0.8263813651137595\n",
            "min_samples_leaf: 50 | score: 0.8077825929938606\n",
            "min_samples_leaf: 100 | score: 0.7463885879378839\n",
            "min_samples_leaf: 200 | score: 0.8033586132177681\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dt = DecisionTreeClassifier(criterion=\"gini\", max_depth=3, min_samples_leaf=1, random_state=42)\n",
        "dt.fit(X=X_train, y=y_train)\n",
        "y_pred = dt.predict(X=X_val)\n",
        "auc_score = roc_auc_score(y_true=y_val.values, y_score=y_pred)\n",
        "print(auc_score*100)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9ODJdrq3-FLu",
        "outputId": "30b39844-17e1-4868-d8b4-603a5afbc3bb"
      },
      "execution_count": 91,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "85.84326471650415\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Confusion matrix**"
      ],
      "metadata": {
        "id": "90r7z9wC-GtV"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "sns.heatmap(confusion_matrix(y_true=y_val, y_pred=y_pred), annot=True)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 268
        },
        "id": "Dc-psT9D-JHj",
        "outputId": "31a1e2cb-64fe-439b-973f-0b5e565d7be8"
      },
      "execution_count": 92,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Classification report**"
      ],
      "metadata": {
        "id": "N8vyYnn7-Kkx"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "print(classification_report(y_true=y_val, y_pred=y_pred))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pLpXyq2g-L1U",
        "outputId": "7c79eaba-9ce7-4195-ef04-f70a98c664dc"
      },
      "execution_count": 93,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.86      0.87      0.87        78\n",
            "           1       0.86      0.85      0.85        71\n",
            "\n",
            "    accuracy                           0.86       149\n",
            "   macro avg       0.86      0.86      0.86       149\n",
            "weighted avg       0.86      0.86      0.86       149\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**ROC curve**"
      ],
      "metadata": {
        "id": "8QfXRP14-NEE"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plot_roc_curve(model=dt, \n",
        "               data1=(X_val, y_val), \n",
        "               data1_label=\"Validation\",\n",
        "               data2=(X_train, y_train),\n",
        "               data2_label=\"Train\",\n",
        "               title=\"DecisionTreeClassifier ROC Curve\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 301
        },
        "id": "m0VOeQZF-N9D",
        "outputId": "dfe5978e-6b5d-4ff6-b5c7-654a4fadc1ed"
      },
      "execution_count": 94,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**2.2. Full Train and Test Data**"
      ],
      "metadata": {
        "id": "mJeTCQTi-SkO"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "dt = DecisionTreeClassifier(criterion=\"gini\", max_depth=3, min_samples_leaf=1, random_state=42)\n",
        "dt.fit(X=X_full_train, y=y_full_train)\n",
        "y_pred = dt.predict(X=X_test)\n",
        "auc_score = roc_auc_score(y_true=y_test.values, y_score=y_pred)\n",
        "print(auc_score*100)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mkyZdFc0-V4b",
        "outputId": "6dfa10ff-2d68-4d1a-f670-5185a8a78a08"
      },
      "execution_count": 95,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "90.00713139597076\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Confusion matrix**"
      ],
      "metadata": {
        "id": "wxPNcgSM-ZJ1"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "sns.heatmap(confusion_matrix(y_true=y_test, y_pred=y_pred), annot=True)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 268
        },
        "id": "VdD8G1TT-Xxv",
        "outputId": "e0359ec9-9313-4979-c8ce-19af62ce4aa7"
      },
      "execution_count": 96,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(classification_report(y_true=y_test, y_pred=y_pred))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0DYZeiTJ-cx_",
        "outputId": "5112948b-f56a-4399-bcc5-d3f8dbb6df9a"
      },
      "execution_count": 97,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.89      0.90      0.90        71\n",
            "           1       0.91      0.90      0.90        79\n",
            "\n",
            "    accuracy                           0.90       150\n",
            "   macro avg       0.90      0.90      0.90       150\n",
            "weighted avg       0.90      0.90      0.90       150\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plot_roc_curve(model=dt, \n",
        "               data1=(X_test, y_test), \n",
        "               data1_label=\"Test\",\n",
        "               data2=(X_full_train, y_full_train),\n",
        "               data2_label=\"Full Train\",\n",
        "               title=\"DecisionTreeClassifier ROC Curve\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 301
        },
        "id": "52tN6ckX-eDr",
        "outputId": "b9f7fed6-f200-43b4-cd3f-0e8bfb74f15c"
      },
      "execution_count": 98,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#3. Random Forest Classifier"
      ],
      "metadata": {
        "id": "4RPbIDw2-g-q"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**3.1. Train and Validation Data**"
      ],
      "metadata": {
        "id": "Dp8ln5_s-l1J"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "rf = RandomForestClassifier(n_estimators=100, random_state=42, n_jobs=-1)"
      ],
      "metadata": {
        "id": "8MM5Rj6n-lCh"
      },
      "execution_count": 99,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "rf.fit(X=X_train, y=y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "boExBOb_-tue",
        "outputId": "e59ef424-89c1-42de-f98b-22f2c1b4b609"
      },
      "execution_count": 100,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomForestClassifier(n_jobs=-1, random_state=42)"
            ]
          },
          "metadata": {},
          "execution_count": 100
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred = rf.predict(X=X_val)\n",
        "auc_score = roc_auc_score(y_true=y_val.values, y_score=y_pred)\n",
        "print(auc_score*100)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "w242jDvJ-vVW",
        "outputId": "c7c500f3-6de2-4bdc-9ed6-5a1eff6f6230"
      },
      "execution_count": 101,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "83.02636330805345\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Hyperparameters tunning**"
      ],
      "metadata": {
        "id": "u5p8FW_j-zt6"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "criterion_list = [\"gini\", \"entropy\"]\n",
        "\n",
        "for criterion in criterion_list:\n",
        "    \n",
        "    rf = RandomForestClassifier(n_estimators=100, criterion=criterion, random_state=42, n_jobs=-1)\n",
        "    rf.fit(X=X_train, y=y_train)\n",
        "    y_pred = rf.predict(X=X_val)\n",
        "    auc_score = roc_auc_score(y_true=y_val.values, y_score=y_pred)\n",
        "    print(f\"criterion: {criterion} | score: {auc_score*100}\")\n",
        "    "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "E-DWnTs2-w-d",
        "outputId": "a359d0c0-f8e4-4126-af47-c79cb1568e46"
      },
      "execution_count": 102,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "criterion: gini | score: 83.02636330805345\n",
            "criterion: entropy | score: 84.30841459010472\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "n_estimators_list = [1, 5, 10, 25, 50, 75, 100, 250, 500, 1000, 2000]\n",
        "\n",
        "for n_estimator in n_estimators_list:\n",
        "    \n",
        "    rf = RandomForestClassifier(n_estimators=n_estimator, max_depth=5, criterion=\"entropy\", random_state=42, n_jobs=-1)\n",
        "    rf.fit(X=X_train, y=y_train)\n",
        "    y_pred = rf.predict(X=X_val)\n",
        "    auc_score = roc_auc_score(y_true=y_val.values, y_score=y_pred)\n",
        "    print(f\"n_estimator: {n_estimator} | score: {auc_score}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ofWu5MP5-3ve",
        "outputId": "e6134993-4221-42f0-bb92-1adcc9738075"
      },
      "execution_count": 103,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "n_estimator: 1 | score: 0.7963163596966414\n",
            "n_estimator: 5 | score: 0.8373058866016613\n",
            "n_estimator: 10 | score: 0.8565366558324304\n",
            "n_estimator: 25 | score: 0.8366738894907908\n",
            "n_estimator: 50 | score: 0.8507583965330444\n",
            "n_estimator: 75 | score: 0.8507583965330444\n",
            "n_estimator: 100 | score: 0.8507583965330444\n",
            "n_estimator: 250 | score: 0.8507583965330444\n",
            "n_estimator: 500 | score: 0.8507583965330444\n",
            "n_estimator: 1000 | score: 0.8507583965330444\n",
            "n_estimator: 2000 | score: 0.8507583965330444\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "max_depth_list = [1,2,3,4,5,10,15,20,25,50,75,100,200]\n",
        "\n",
        "for max_depth in max_depth_list:\n",
        "    \n",
        "    rf = RandomForestClassifier(n_estimators=10, criterion=\"entropy\", max_depth=max_depth, random_state=42, n_jobs=-1)\n",
        "    rf.fit(X=X_train, y=y_train)\n",
        "    y_pred = rf.predict(X=X_val)\n",
        "    auc_score = roc_auc_score(y_true=y_val.values, y_score=y_pred)\n",
        "    print(f\"max_depth: {max_depth} | score: {auc_score}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8SmZbwlR-6DO",
        "outputId": "1cf0b89c-2451-4eb8-eefd-db4934072723"
      },
      "execution_count": 104,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "max_depth: 1 | score: 0.8424521487901768\n",
            "max_depth: 2 | score: 0.8180751173708921\n",
            "max_depth: 3 | score: 0.8373058866016613\n",
            "max_depth: 4 | score: 0.844348140122788\n",
            "max_depth: 5 | score: 0.8565366558324304\n",
            "max_depth: 10 | score: 0.8308956301914049\n",
            "max_depth: 15 | score: 0.8161791260382809\n",
            "max_depth: 20 | score: 0.8232213795594077\n",
            "max_depth: 25 | score: 0.8232213795594077\n",
            "max_depth: 50 | score: 0.8232213795594077\n",
            "max_depth: 75 | score: 0.8232213795594077\n",
            "max_depth: 100 | score: 0.8232213795594077\n",
            "max_depth: 200 | score: 0.8232213795594077\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "rf = RandomForestClassifier(n_estimators=10, criterion=\"entropy\", max_depth=5, max_features=\"sqrt\", random_state=42, n_jobs=-1)\n",
        "rf.fit(X=X_train, y=y_train)\n",
        "y_pred = rf.predict(X=X_val)\n",
        "auc_score = roc_auc_score(y_true=y_val.values, y_score=y_pred)\n",
        "print(auc_score)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cNp2TBCE-7q9",
        "outputId": "2f17005f-d2ae-406c-b643-89d4b460b506"
      },
      "execution_count": 105,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.8565366558324304\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Confusion matrix**"
      ],
      "metadata": {
        "id": "uUiJQA18--zz"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "sns.heatmap(confusion_matrix(y_true=y_val, y_pred=y_pred), annot=True)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 268
        },
        "id": "c69_fEba-9f-",
        "outputId": "7703b212-8c0b-4223-ec62-34eabd613800"
      },
      "execution_count": 106,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Classification Report**"
      ],
      "metadata": {
        "id": "SmYs4ZCh_Bn2"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(classification_report(y_true=y_val, y_pred=y_pred))\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dM2xOYCe_EU4",
        "outputId": "b2db97fd-8fdd-4d8c-d33f-6d09ec5d029c"
      },
      "execution_count": 107,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.84      0.91      0.87        78\n",
            "           1       0.89      0.80      0.84        71\n",
            "\n",
            "    accuracy                           0.86       149\n",
            "   macro avg       0.86      0.86      0.86       149\n",
            "weighted avg       0.86      0.86      0.86       149\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**ROC Curve**"
      ],
      "metadata": {
        "id": "z9I45eq-_FpA"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "plot_roc_curve(model=rf, \n",
        "               data1=(X_val, y_val), \n",
        "               data1_label=\"Validation\",\n",
        "               data2=(X_full_train, y_full_train),\n",
        "               data2_label=\"Train\",\n",
        "               title=\"RandomForestClassifer ROC Curve\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 301
        },
        "id": "8mvhhXid_HiA",
        "outputId": "258171c0-b9af-4f70-e871-e94672bcc17e"
      },
      "execution_count": 108,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**3.2. Full Train and Test Data**"
      ],
      "metadata": {
        "id": "Mi_oMF8S_K5R"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "rf = RandomForestClassifier(n_estimators=10, criterion=\"gini\", max_depth=5, max_features=\"sqrt\", random_state=42, n_jobs=-1)\n",
        "rf.fit(X=X_full_train, y=y_full_train)\n",
        "y_pred = rf.predict(X=X_test)\n",
        "auc_score_rfc = roc_auc_score(y_true=y_test.values, y_score=y_pred)\n",
        "print(auc_score_rfc*100)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jRtyROxC_OlN",
        "outputId": "e6fb6cb4-d186-43d1-8dce-946823748c23"
      },
      "execution_count": 109,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "86.35229096095561\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Confusion Matrix**"
      ],
      "metadata": {
        "id": "bTx_il1__Q1o"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "sns.heatmap(confusion_matrix(y_true=y_test, y_pred=y_pred), annot=True)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 268
        },
        "id": "kOSi7wh4_O5t",
        "outputId": "bcdc9113-575a-4155-d734-a057d6f1cb11"
      },
      "execution_count": 110,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Classification Report**"
      ],
      "metadata": {
        "id": "F_MTCI2f_T7a"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(classification_report(y_true=y_test, y_pred=y_pred))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IsfPfXll_XaF",
        "outputId": "ee218f91-b026-4b4e-cd42-fb1e6dfeeb1e"
      },
      "execution_count": 111,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.80      0.93      0.86        71\n",
            "           1       0.93      0.80      0.86        79\n",
            "\n",
            "    accuracy                           0.86       150\n",
            "   macro avg       0.87      0.86      0.86       150\n",
            "weighted avg       0.87      0.86      0.86       150\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**ROC Curve**"
      ],
      "metadata": {
        "id": "Cfe93Kk0_Yp4"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plot_roc_curve(model=rf, \n",
        "               data1=(X_test, y_test), \n",
        "               data1_label=\"Test\",\n",
        "               data2=(X_full_train, y_full_train),\n",
        "               data2_label=\"Full Train\",\n",
        "               title=\"RandomForestClassifer ROC Curve\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 301
        },
        "id": "uYoLOIaD_Xru",
        "outputId": "e756311c-96b4-4308-af3a-5ecf9232f451"
      },
      "execution_count": 112,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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MmMCbb76ZLADat2/P0qVL2bVrF82bN6dJkyYsX7481WskSpUqxY4dO7hz5w6dOnWibt26jBw5kurVq2Nvb0+hQoWYOXMm06ZN46WXXsLOzu6Z4aWUtGrVips3b+Ls7EyVKlXMz7dt25ZBgwYxduxY6tSpQ6dOnTh27NgLvU95nUpR5KZDQgghUiY9CSGEEKmSkBBCCJEqCQkhhBCpkpAQQgiRKgkJIYQQqZKQEEIIkao8d8V1ZGQMJlPGz+p1cipIeHh0NlSUc0mb8wdpc/6Q2Tar1SqKFEl94so8FxImk5KpkHiybn4jbc4fpM35Q3a0WYabhBBCpEpCQgghRKokJIQQQqTKIiHh5+dHq1atqFy5crLppJ9mNBqZPXs2bdq0oW3btua7hwkhhLAei4RE69at2b59e5o39di7dy+3bt3C39+fL774gpUrV3L79m1LlCeEECIVFjm7Ka3bQj5x4MABunbtilqtpmjRorRp04bvvvuOQYMGWaBCYS2KomDJc1BMJgVTPpv42FJtVhTT8xeyEKPRiNFktHYZeUKOOQU2KCgo2e0v3d3dzTeEEXlTTHwSk9adIibeYO1S8i0VJmxVBmxVidioDNiqkh79zKPnbFVJj59PfPxa0uPnkpI9tlUloVXlnJBI+UareY/JpLDjXCAPEowMnLuJomWqPH+lDMoxIZFVnJwKPn+hVLi4FMrCSnIHa7Y5MTSamHgDTWq6U8a9sNXqyCiVyUDxuz+iMcY/f2ErUykKalMiGmM8GmPCo/9MCU/9/Py7vAEY1TqMahuMGhtMGhuMGnuMGluMahtiNDY80NhgUuuyuTXiabeDQ1j06Wdc+v1PGtaszpRy5XF0yvp/zzkmJNzd3bl79675Ll//7VmkV3h4dKYuKHFxKURo6MMMr5ebZVWbH8Qk8nfwQ/4JiSYxKf1d/Oi4JACqly5Co2rPv9tYVnjSZkVRUB6GQipDEgoKSnw0ysMwTA/DUKLDMD0MxxQdjnL/3x6uyibzX0osQqVCY2OHSWODytYOdAVR6WxR6e1Ab/foZ50d6B/9X6X/72M70NmiUmus3ZIMycv/nk0mE2vWrGThQj/0ehuWLl1Fz559cHRyyFSb1WpVml+uc0xItG/fnp07d/LKK68QFRXFoUOH2L59u7XLEk9RFIXIhwn8fe8hfwc/5O97D7kVEk3kw4RMb1OnVePsaJeFVSanGBJJ+usUJD361n+/oC2J0fEk/X4CU8Q/z1n7Xyo7B1SFnNE4lQTXcqi0emzqv4nKNoeHBHn7D2Z+pFKpOH78CC+/3JqFC5dQrJh7tu7PIiExb948/P39CQsLo3///jg6OrJ//34GDx7MqFGjqFGjBj4+Ply+fJlXXnkFgOHDh5vvNyyy33/vYqsAoVFx5kC4de8hfwdHm7/9q1RQrGgBKpdypLRbIUq7FaKUW0EK2Gb/kINiNGC6H4wp8s6j/6LuoiTEpris6WEYyoNg8+P/xpltyyGgSvkkP5W+ACoHZ9QFnVBpbbKqfCEyLCEhgRUrltCjRy9KlizF5s3bsbOzQ6VSZfu+89w9rmW4Kf2etDk6LonJH6V+AFmjVlHc2Z5SxR6FQelihSjpUhAbffYOQTwKg3uPw+Duv/+/HwzK42EilQpVIVdUdimPxarUGnQ12qF1rwyAk3MhwsMef85aG1SaHNOZzjb5+Xc7Lzh37md8fUfw+++/MWfOfIYOHZHicpltc64ZbhLW8zA2kZh4A3Uru1DC5d9fFseCesoUc8DD2R6dNvsuqVGMSY/D4G7yQLgfDE9Oq1SpUDm4oilSHG2ZOqiLFkft6IHa0R2VVp/ufWls7VHZ5JyzcIRITUxMDAsWzGP9+jV4eBTn88+/onXrVyxeh4RELpeQaOTHC7dJyMAB4yfs7W2IiUkwDyHVq+xKw6puL1yToigoD4LTdd58/PerMEUFwZMO7dNhULYe6iIeqIsUR124WIbCQIjcbunSRXz00Wr69x/EtGmzKFTIwSp1SEjkcr//E8nOI9dfeDtajRpnR9sMrWOKvY8p4p9Hf+RN/wZC0q+HMd1P/zUuKvsi2DTsJmEg8r3796MIDw+jXLkKjBrlS5s2r9CoUROr1iQhkcs9+ds8o189yhTL2DeN1MYwFUUxD/MkXv4fxru//menBkxR91Di7qe5fdtWQ59fhFqNtkSNR6daCpGP/e9/+5k40Rc3t2J8//1RHBwKWz0gQEJCAEpiHMZ7v4NJwRh+i6Q/Tjy6huAparcK5p9VqNCUrI6maCnUzqVQO3qg0v7nrCatHpVafr2EeJ6QkBCmTp3Inj1fU61aDRYvXm6Rs5bSS/4V53PG4L+I3TPvqWdUaIp7oqnU7NF5roC2TF00RVOfnFEIkTm//HKNLl1eJSYmhilTZjB8+Gh0upx15bqERA5iUhQOX7hjPpCcHvciUr4+IMXtR0cQ97/FKIlxoFIRp1FjeBAOGh0q+6LYtR6GqkBh1PZFMlO+ECKdjEYjGo2GSpUq07GjN8OGjaRSpcrWLitFEhI5SHBELNu/T/l+G2mxs9FQOO4uiVd+SnM5wz/XMEXeQV2kBGqX0tja6khUFUBfuxMqfYHMli2ESCeTycSWLZvYtOkjDhw4ROHCjixdusraZaVJQiIHeXIN4FCfatSv4pqudZL+OoXh9xMYvw945mriZ6jU6L06oa//BiqVKk9dcCRETnf9+p/4+o7k9OmTtGjRkri4OAoXdrR2Wc8lIZFDpefAlSnuAQlHN4Fag9qtArryDdFVapb6CmqNnF4qhIUZjUZWr17BokXzsbW1Y8WKtXTv3jNHHZxOi4RELpZw5kswGdG4V6bAqxOtXY4QIgVqtZqTJ4/Tpk07Fiz4EDc3y8x4nFUkJHIzw6N7Adi1HWnlQoQQT0tISGDZsg/p2bNPsgn5ciOL3ONaZD3TgxAMN35GVbiYXIgmRA7y889naNWqKYsX+3HgwF6AXBsQID2JXMdw91cSL+7FeCcAALVD+g5wCyGyV3R0NPPnz2bTpvUUL16CHTu+plWrNtYu64VJSFjRpT/DWLP7KsYnpzU9/p/6qQNaismI8U4AijERQ+AFDH8+Os1V41YRtWs5bBp1t3TZQogULF26iE2b1jNw4BCmTJlBwYJ543bIEhJWdC8iFoNRoX3DUmg1j0b+9Fo11coWNS9j/OcKcQeXP3qgUqGr2hpN8aroyta1RslCiKdERUUSHh5G+fIVGT16LO3adaRBg4bWLitLSUjkAN5NymBno8UUex8l/iHE3MN4PxFj8F8YAs8BYNt2JBqXMqgLOlm5WiEEwN69e5g0aRzFirlz6NAxHBwK57mAAAmJHENJSiBm+5h/76vwmKqQC7qqrdCWriUT5gmRAwQHBzN58nj27dtDjRq1WLZsda655iEz5K9ODmD8eQcxd6+BoqCt0BhtmTqgVqNxlp6DEDnJtWtX6dLlVeLi4pg2bRbDho3McRPyZTUJCSuyiw+lpu4Wyq9HALB9eTC6Sk2tW5QQ4hkGgwGtVkvlylXw9u7MsGEjqVChorXLsgi5TsKKqtz8nIGFjgCgb9BNAkKIHMZkMrFx4zqaNavP/ftR6HQ6Fi9ekW8CAqQnka3OBAQTEhWX6ut1ExO4mlgCrzcHoXctZcHKhBDP8+eff+DrO4Kffz5Ny5atiY+Pp3Bha1dleRIS2cRgNLH+219Q0limZmETBm0B9C6lUKmlUydETmA0Glm5cikffriAAgUKsHLlOrp1eytPH5xOi4RENlKAzs3L0rFR6RRfj9+xH9firuZrJIQQ1qdWqzl9+iTt2nXkgw8+xNU1f89qICGRzdQq1TMhYHoQijH8bzAmWqkqIcTT4uLiWLZsEb169aVUqdK5ekK+rCYhYQXxRzdiDPodAJWNvZWrESJ/O336FL6+w7l+/S+cnV0YPHiYBMRTJCSsQDEkonGriE2zt1EXcbd2OULkS9HRD5k3bxYff7yBUqVKs3PnHlq0aGntsnIcGQy3Fr0dGqeSchW1EFaydOmHbN68kXfeeZejR09LQKRC/kJZWOKvRzCFBqIpWdPapQiR70REhBMREUGFCo8m5OvQ4VXq1Wtg7bJyNOlJWJApJpKE41sA0JXPexOBCZFTKYrC3r27adasAe+8MwBFUXBwKCwBkQ4SEhaUcOpzALSla8vV1UJYSHDwPfr3783AgW9TvHgJli9fk2+vecgMiw03BQYGMmnSJKKionB0dMTPz48yZcokWyY8PJzJkycTFBSEwWCgYcOGTJs2Da0294+KKcYkjKE3QKvHtvVQa5cjRL5w7dpVXn/9VRIS4pk+fQ7Dho3IE39PLMliPYmZM2fSs2dPDh48SM+ePZkxY8Yzy6xbt47y5cuzd+9evv32W3755Rf8/f0tVWK2Sjj1OcrDMNROpVBpbaxdjhB5WlJSEgCVK1fBx6cLhw//xMiRYyQgMsEiIREeHk5AQACdOnUCoFOnTgQEBBAREZFsOZVKRUxMDCaTicTERJKSknBzc7NEidlOSYgFwK7lECtXIkTeZTQaWb9+DZ6enkRFRaLT6fjww2WUL59/JuTLahaJ1aCgINzc3NBoNABoNBpcXV0JCgqiaNF/b9X57rvvMnLkSJo1a0ZcXBy9evWibt2M3abTyalgput0ccm6e9ImGUwAONiYsAu/RmJCFKqi7riVL59l+8gKWdnm3ELanDcFBAQwcOBATp8+TceOHSlUSJ8v2v207Ghvjup7fffdd1SuXJlPPvmEmJgYBg8ezHfffUf79u3TvY3w8GhMprSm1UuZi0shQkMfZni91BiMj0LC6e8fCTl/AgC1a7ks3ceLyuo25wbS5rzHaDSybNmHLF26iIIFC7JmzQaGDh1IWFh0nm73f2X2c1arVWl+ubZISLi7uxMcHIzRaESj0WA0GgkJCcHdPfnVxtu2bWP+/Pmo1WoKFSpEq1atOHPmTIZCwlJCImM593soipJyID0JKrViAK0NBTpPl7vMCZEN1Go158+f5dVXvZk3byEuLi5y9lIWskhIODk54enpyb59+/Dx8WHfvn14enomG2oCKFGiBMeOHaNmzZokJiZy6tQp2rZta4kSM+zg2X84fOFOmsuoADsbLajUaIqWsExhQuQDcXFxLFmykN69+1K6dBk+/ngbtra21i4rT7LYcNOsWbOYNGkSa9aswcHBAT8/PwAGDx7MqFGjqFGjBlOmTGHmzJl4e3tjNBpp2LAh3bp1s1SJGWIyKTjY61k0rHGKryuGRExBv5P0/VbQ6C1cnRB518mTJ/D1HUFg4A3c3NwYNGioBEQ2slhIlC9fnp07dz7z/IYNG8w/lypVis2bN1uqpBemAnRaTbLnFGMSCT99StJvx8zP6au3sXBlQuQ9Dx8+YM6cmXzyySZKly7Drl17ad68hbXLyvNy1IHr3M4U94DYXTNQYqMA0NftjMa5NNrSta1cmRC537Jli/n0080MHTqC996bir29TLNvCRISWcgY9Ls5IOzfWoS6kIuVKxIidwsPDyc8PIxKlSozZsw4OnbsRN269a1dVr4iczdlEcVoIOn344AEhBAvSlEUvvnmK5o1q8ewYYNQFIVChRwkIKxAQiKLGG9fw/jPFfT1XpeAEOIFBAXdpW/ft3jnnQGULl2GVas+klNarUiGm7KA6X4wcQeXAaAtWcvK1QiRe129eoXOnTtiMCQxe/Z8hgwZZp6pQViHhMQLUGEi/vQOTJGPrpfQlm+I2qmUlasSIvdJSkpCp9NRpYonb7zRlWHDRlK2bDlrlyWQ4aYXUkT1kKQr32EKCURdpDg2DbuhUstbKkR6GY1G1q5dRZMmdc0T8i1cuFQCIgeRnkQWsGnSE13FJtYuQ4hc5ddfA/D1Hc6FC+d55ZX2JCYmWbskkQIJCSGERRmNRpYsWciyZR/i4ODARx99TOfOb8jB6RxKxkaEEBalVqu5dOkC3t6dOX78LK+//qYERA4mPYnMUhTaas5auwohcoXY2FgWLfqAvn0HUKZMWT7+eBs2NnKHxtxAehKZVCTxHtU0NwFQFy1p3WKEyMFOnDhGixaNWL16OT/88D2ABEQuIiGRCYohkZbBnwBg23IIGicJCSH+68GD+4wbN4ouXRrR6d4AACAASURBVDqhVqvZvfsAAwfK7XtzGwmJDFIUhfjD6wH401QCbbkGVq5IiJxp+fIlbN++leHDR3P48EmaNGlm7ZJEJsgxiQxTMASeA8DfUJ86GnkLhXgiLCyMiIhw84R83t4+eHnVsXZZ4gVITyKTAgo3I1iR25EKAY962Lt2ffnMhHwSELmfhIQQ4oXcuXOb3r27MWzYIMqWLcfq1evllNY8RMZKhBCZdvXqZXx8OmIyGZk79wMGDRoqE/LlMRISQogMS0xMRK/XU6VKVbp168HQoSMoU6astcsS2UCGm4QQ6WYwGFi1ajlNmtQlMjICnU7HggWLJSDyMOlJCCHS5ZdfruHrO5xLly7SoUMnDAajtUsSFiAhIYRIk9FoZNGiD1ixYgmOjkXYuPETvL07y8HpfEKGm4QQaVKr1Vy7doUuXbpy4sTPvPba6xIQ+Yj0JIQQz4iJiWHhwvn07z/IPCGfXq+3dlnCCqQnIYRI5ujRw7Ro0Zi1a1fy44+HACQg8jEJCSEEAPfvRzFmzHC6dvVBq9Xw7bffMWDAYGuXJaxMQkIIAcCKFUv54ovPGDVqLIcPn6RRI7klr5BjEkLkayEhIUREhFOliidjxozDx+d1atb0snZZIgeRnoQQ+ZCiKHzxxWc0a1aP4cOHmCfkk4AQ/yUhIUQ+c/v2P7z11huMHDmUihUrs27dJjmlVaTKYsNNgYGBTJo0iaioKBwdHfHz86NMmTLPLHfgwAHWrl2LoiioVCo2b96Ms7OzpcoUIk+7cuUSPj4dURSF+fMXMmDAENRq+a4oUmexkJg5cyY9e/bEx8eHPXv2MGPGDLZu3ZpsmatXr7Jq1So++eQTXFxcePjwoZx6J0QWSEhIwMbGhqpVq9OzZ2/eeWc4pUqVtnZZIhewyFeI8PBwAgIC6NSpEwCdOnUiICCAiIiIZMtt2bKFAQMG4OLiAkChQoXkhulCvACDwcCCBQvME/JptVref3+hBIRIN4uERFBQEG5ubuZ55jUaDa6urgQFBSVb7vr16/zzzz/06tWL119/nTVr1qAoiiVKTD+jwdoVCJEuV69eoX37VkyePJmaNb0wGk3WLknkQjnqFFij0cjvv//O5s2bSUxMZNCgQXh4eNC5c+d0b8PJqWCm9+/iUui5y9xaMxwAjU6HWq1K1zo5WW6vPzPyepuNRiMzZ87Ez88PJycnvvrqK9544w1rl2Vxef1zTkl2tNkiIeHu7k5wcDBGoxGNRoPRaCQkJAR3d/dky3l4eNC+fXv0ej16vZ7WrVtz5cqVDIVEeHg0JlPGex8uLoUIDX343OUMUSGobAryp00NTKbYdK2TU6W3zXlJfmizoiicO3eBN97oxpw586lUqXSeb/N/5YfP+b8y22a1WpXml2uLDDc5OTnh6enJvn37ANi3bx+enp4ULVo02XKdOnXixIkTKIpCUlISp0+fpkqVKpYoMf3UWrSVm5OoKWDtSoQwi46OZvr0SQQG3kClUvHxx9tYuXIdRYoUff7KQqTBYue+zZo1i23bttGuXTu2bdvG7NmzARg8eDBXr14F4NVXX8XJyYmOHTvSuXNnKlSowJtvvmmpEoXIlX788RAvvdSQ9evXcvToYQB0Op2VqxJ5hcWOSZQvX56dO3c+8/yGDRvMP6vVaiZPnszkyZMtVVaGmKIjwCR34xI5Q2RkBDNmTOGLLz6jYsVKfPvtQRo2bGTtskQeI1fRZEDcweWg0aItU8fapQjBqlXL+eqrL/D1Hc8PP5yQgBDZIked3ZTTKTER6Co2QVusIvCbtcsR+VBwcDAREeF4elbF13c8nTu/QY0aNa1dlsjDpCfxHEp8NIZ7fxLz5RSU+IegkrdMWJ6iKOzYsZ3mzeszYsQ7KIpCwYKFJCBEtpOexHPE/bgO4+1r5se6qi9brxiRL9269Tfjx4/myJEfadiwMUuXrpIJ+YTFvFBI/PHHH1SqVCmrasmRlMQ41M6lsWnQFY1rOVR6OfVVWM6VK5d47bUOqFQqFixYTL9+A2VCPmFRzw2J6Ohobt68iYeHh/m6ht9++41Vq1Zx7Ngxrly5ku1FWpvKthAbz8MvgefMzyUkGbG3k9MMRfaIj4/H1taWqlWr06dPX4YMeZeSJUtZuyyRD6UZEkeOHMHX15e4uDh0Oh2LFi3i3Llz7Nmzh65du/L9999bqk6r++vOfRzs9VQt/e/FSeU8HKxYkciLkpKSWLVqGVu3buaHH45TtKgTc+cusHZZIh9LMySWLVvGpEmT8PHxYdeuXUyaNIlWrVpx6NAhChcubKkac4zyHoXp9UreHl4T1nPlyiVGjx7OL79cxcenCzltbkuRP6U5uHn79m26d++Ora0tPXr0wGAw8P777+fLgBAiuxiNRubOnUm7di0JCwtly5bP2LBhC05OTtYuTYi0Q8Jk+ndqYY1GQ4ECBbCzs8v2ooTIT9RqNX/++Ts9evTixImf6dixk7VLEsIszeGm+Ph4evXqZX4cExOT7DHA9u3bs6eyHMB0/x6mkOtoSlS3dikij4mOfsgHH8xl4MB3KFeuPJs2fSrzLYkcKc2QeP/995M9zm+T7cWf/AwAlZ0coBZZ54cf/Bk/fgx3796hUqUqlCtXXgJC5FhphsTrr78OQFRUFI6OjhYpKKcwxUZhigpCVcgF2xYD4dfT1i5J5HIREeFMnz6ZnTt3ULlyFfbt86d+/YbWLkuINKV5TOLSpUs0b96cxo0b06pVK3799VdL1WV1CT/vRImNxKbxW6jUGmuXI/KA1atX8M03XzF27EQOHTouASFyhTRDws/Pj9dee429e/fSrl07/Pz8LFWX9SXEonZ0RyczvooXcO9eEAEBvwDg6zue778/xqRJ07CxsbFyZUKkT5rDTdevX2fbtm1oNBp8fX1p2bKlpeqyqGs3wtm8+ieSDP+ezTVEfwcjGjYtPw5ATFwSyHQ5Ip0UReGzzz5l5syplC5dhkOHjlGwYCGqVZOTIETukmZIGAwGNJpHQy16vZ6kpCSLFGVp/4RGE/kwgZdqeaDRPEoCt1sx3CtQgfrOrublmtVwT20TQpjdvBnIuHGjOX78CE2aNGPJkpUyIZ/ItdIMicTERCZOnGh+HBsbm+wxwMKFC7OnMit4q3VFbPQalKQEojfHUMmzAjVqV7Z2WSIXeTQhX3vUag2LFi2jT59+MiGfyNXSDImhQ4em+TivMj0MBUBdyMXKlYjc4ukJ+fr2HciQIcMoXryEtcsS4oWlGRJlypShU6f8d/Wn8uBxSDi4PmdJkd8lJiaycuVSPv10Cz/+eIKiRZ2YPfv9568oRC6RZj94xowZlqojRzE9DAEkJETaLl48T9u2LfDze1/uLy3yrDR7Eko+nYbS9CAUdHZgY2/tUkQOZDQamTdvFmvXrsTV1Y2tW3fQvn1Ha5clRLZIMyRMJhOnT59OMywaN26c5UVZk5KUgOHvi6hsC8oZKSJFarWawMAb9Or1NjNnzsXBQWZFFnnXc89umjp1aqohoVKp+OGHH7KlMGtJOP8NSnQ4aie5C5j414MH95k/fw5DhgyjXLkKbNz4CVqt3CJe5H1p/pbb2dnluRB4rqR4AOza+1q5EJFTfP/9d4wfP4bg4HtUrVqdcuUqSECIfENO4E6Bys4BtX0Ra5chrCwsLIyhQwfSq1c3HB0dOXDgEG+/3d/aZQlhUWmGRH49cC0EwNq1K9m7dzcTJkzm+++PUadOPWuXJITFpdlnvnjxoqXqECJHCAq6S0REBNWqVcfXdwJvvtkdT8+q1i5LCKuR4SYheNRr/vTTLTRr1oBRo4ahKAoFCxaUgBD5noSEyPcCA2/wxhvejBs3ilq1vNi48RM5/VmIx+QUDZGvXb58kddea49Wq2Px4hX07t1XAkKIp1isJxEYGEj37t1p164d3bt35+bNm6kue+PGDWrVqpW/bnIkLCouLg6AatVqMGDAEE6c+Jk+ffpJQAjxHxYLiZkzZ9KzZ08OHjxIz549U50Xymg0MnPmTNq0aWOp0kQ+kpCQwMKF82nUqDbh4eFotVpmzpyLu7uHtUsTIkeySEiEh4cTEBBgnlG2U6dOBAQEEBER8cyy69ev5+WXX6ZMmTKWKE3kI+fPn6Vu3bp8+OECmjZtjlotvQYhnscixySCgoJwc3Mz3+VOo9Hg6upKUFAQRYsWNS/322+/ceLECbZu3cqaNWsytS8np4IZXqeg/aP7DTs7F+ShrZ5YtRoXl0KZ2n9ukx/aaTAYmDhxIsuWLaN48eLs27ePV1991dplWVR++Jz/S9qcNXLMgeukpCSmT5/OBx98YA6TzAgPj8ZkythFgNExCQCEhUWjxCdiMpkIDX2Y6RpyCxeXQvminYqi8Mcf1+nbdwDLly8hIUGVL9r9RH75nJ8mbU4/tVqV5pdri4SEu7s7wcHBGI1GNBoNRqORkJAQ3N3/vWd0aGgot27dYsiQIQA8ePAARVGIjo5m7ty5ligTACUxHrR6i+1PZI/796N4//3ZDB063Dwhn0ajwcEh//3xEOJFWCQknJyc8PT0ZN++ffj4+LBv3z48PT2TDTV5eHhw5swZ8+OVK1cSGxvLe++9Z4kSzUwRt1AXkdtO5mbffXeAiRN9CQkJpmZNL8qVq/BCvVMh8jOLnd00a9Ystm3bRrt27di2bRuzZ88GYPDgwVy9etVSZaRJMSRgigpC4yzThOdGoaGhDBnSj7ff7kGRIkX57rsf6d27r7XLEiJXs9gxifLly7Nz585nnt+wYUOKy48cOTK7S3qGEnkHFAW1U2mL71u8uHXrVnHgwD4mTZrGiBFj0Otl2FCIF5VjDlznBEr4LQDpSeQid+7cJiIigho1ajJ27ES6dXuLypWrWLssIfIMmbvpKcrDENBoURV0tnYp4jlMJhObN2+kefOG+PqOQFEU7O3tJSCEyGISEskooNLI1Aw53I0bf/H666/y3ntjqVOnHps2bZXPTIhsIsNNIle5dOkCr73WHr3ehmXLVvPWW70lIITIRhISIleIjY2lQIEC1KhRiyFD3mXQoHcoVsz9+SsKIV6IDDeJHC0hIYEFC+aaJ+TTaDRMmzZLAkIIC5GehMixzp49g6/vCP7443e6dXtLJuQTwgokJESOYzAYmDVrKhs2rKN48RLs2LGLVq3aWrssIfIlGW4SOY5Wq+Xu3bv07z+IY8dOS0AIYUXSk3haYhwqnY21q8iXoqIimTt3FsOGjaBChYps2LBF5lsSIgeQnsRTlAfBqAsXs3YZ+c7+/Xtp1qwBn322ldOnTwJIQAiRQ0hIPOVRSLhZu4x8Izg4mIED36Z//164urpx8OBhmZBPiBxGhpsesyER4u6jkp6ExaxfvwZ///8xdepM3n13FDqdztolCSH+Q0LiMRfNoxvRyHBT9rp9+x8iIyOoUaMWY8dOpEePXlSsWMnaZQkhUiHDTY+5ah4AEhLZxWQysWnT+scT8o00T8gnASFEziYh8Ziz+nFPwsHFypXkPX/99Sc+Ph2YPHk89es34OOPP5X5loTIJWS46TGNygSASu5vnaUuXjzPa6+1x87OjhUr1tK9e08JCCFyEQkJkS1iYmKwt7enZk0vhg0bycCB7+DmJmeOCZHbyHCTyFLx8fHMnz+HRo1qExYWhkajYcqUGRIQQuRS0pMQWebMmdP4+g7nr7/+pEePXmi1ckGcELmdhIR4YQaDgenTJ/HxxxsoUaIkX3zxDS1btrZ2WUKILCDDTeKFabVaQkNDGTToHY4ePS0BIUQeIj0JkSmRkRHMnfvoSukKFSqyfv1m1Gr5ziFEXiP/qkWG7d27h2bNGrBjx3bOnj0DIAEhRB4lPQmRbsHB95g0aTz7939LzZpe7NjxNTVq1LR2WUKIbCQhIdJt/fq1HDp0kGnTZvPuuyPRauXXR4i8Tv6VP6bBBCo5ZfO/bt36m/v3o8wT8vXs2Zvy5StauywhhIXIQPJj9qoEsC1o7TJyDKPRyIYNa3nppUaMHTvKPCGfBIQQ+Yv0JB6zVyegspGQAPjjj9/x9R3B2bNnaNWqDR9+uFzmWxIWYTQaiIwMxWBIfKHthISoMZlMWVRV7vC8Nmu1eooUcUGjydiffQmJx+xV8WBbxNplWN3Fi+fx9m6Hvb09q1ev5803u0tACIuJjAzF1rYA9vbFXuj3TqtVYzDkr5BIq82KohAT84DIyFCcnd0ztt2sKC49AgMDmTRpElFRUTg6OuLn50eZMmWSLbN69WoOHDiAWq1Gp9Ph6+tL8+bNLVJfQXUC5OOeRHT0QwoWLETNml6MGDGGgQPfwcVFpk0XlmUwJL5wQIhnqVQq7O0diI6OyvC6FjsmMXPmTHr27MnBgwfp2bMnM2bMeGaZmjVr8tVXX7F3717mz5+Pr68v8fHxFqnPXpWAyraQRfaVk8TFxTF37kwaNqxNaGgoGo2GSZOmSUAIq5GAyB6ZfV8tEhLh4eEEBATQqVMnADp16kRAQAARERHJlmvevDl2dnYAVK5cGUVRiIrKePJlmGJ6dOA6n/Ukjh07RsuWTVi5cimvvNIevV7uMS2ESM4iw01BQUG4ubmh0Tw6xVSj0eDq6kpQUBBFixZNcZ3du3dTqlQpihXL/tuJao3xqFUKqnxydpPBYGDq1Ils3ryRUqXK8NVX3/LSSy9buywhcpTBg/uSlJSEwZDEP//comzZ8gBUqlSZKVNmpmsbFy6cw2Aw0KBBo+wsNVvlyAPXP//8M8uXL+fjjz/O8LpOThn/Q19YbwCgkLMzRV3yx5BTTMwDxowZw7x587C3t7d2ORblkk8+46flljaHhKjRarNmgONFt7N586cA3L17l/79e7Nt244Mb+Py5QvExcXRpEmTF6olvZ7XZrVaneHfBYuEhLu7O8HBwRiNRjQaDUajkZCQENzdnz3KfvHiRSZMmMCaNWsoV65chvcVHh6NyaRkaJ3Eh4+GtKKTdBhDH2Z4n7lBREQ4s2dPZ8SIMVSsWIkVK9bj5laY0NCHxMbmzTanxMWlEKF59DNOTW5qs8lkypKzkrLy7Caj0QQoGAwmTp06wdatH5OQkIhOp2PkyLFUr16DW7du8v77s4mPj8dkMtKhgzcNGzbmm292YTKZ+PnnM7Ru/Qp9+vTLkppSkp42m0ymZ34X1GpVml+uLRISTk5OeHp6sm/fPnx8fNi3bx+enp7PDDVduXIFX19fVqxYQbVq1SxRGgA6Q+yjH/LggWtFUfj222+YPHk8UVFRNG7clIoVK8mEfCLH++lqECeuBGVqXZUKlDS+Kzar6U7TGhk7FfTOndts2bKJJUtWYm9fkBs3rjN+/Ci+/no/X3/9Fc2avUSfPv0BePDgAQ4ODvj4dCEuLo4RI8Zkqh05gcWGm2bNmsWkSZNYs2YNDg4O+Pn5ATB48GBGjRpFjRo1mD37URI/febTwoULqVy5crbWpjPEAOS5i+nu3Qti4sSxfPfdfry8arNz57dUq1bd2mUJkSudOXOKO3duM3z4EPNzRqORiIhwvLxqs2bNCuLj46lTpx516tSzYqVZy2IhUb58eXbu3PnM8xs2bDD/vGvXLkuVk4zW+Pg0W30Bq+w/u2zc+BFHjvzAzJnzeOedd2VCPpGrNK2R8W/7T2THxXSKotCwYWOmT5/zzGsvv9ya6tVr8vPPp9m2bQv793/LjBlzs3T/1iJjDgA87peqcv/bcfNmIJcvXwRg7NiJHDlyiuHDR0lACPGCGjRoxJkzp7hx47r5uV9//QWA27f/oWhRJzp29KZ//8EEBDx63t7enpiYaKvUm1XkL0ceYTQa2bhxHR98MJeKFSvj73+EAgUKUK5ceWuXJkSeULJkKWbMmMuCBXNJSEjAYEiiRo1aeHpW48cfv8ff/zt0Oi0qlYrRo8cB8NJLLZkyZQL9+vXM9gPX2UWlKGkd3sl9MnN205W92ykb9D26Xmuwtc99Q06//fYrvr7DOX/+HG3btmPRomV4eBR/7nq56ayXrCJtztnu3fubYsVKv/B2ZO6mlKX0/uaIs5tE9rlw4Rze3u1wcHBg3bpNvP76mzKtgRAiy0hI5FJPJuSrVas2o0ePY8CAITg7O1u7LCFEHpP7j9TmM7GxscyaNY0GDbzME/JNnDhFAkIIkS2kJ5GL/PTTcXx9R3DzZiB9+vTDxkZv7ZKEEHmchEQuYDAYeO+9cXz66WZKly7Drl17ad68hbXLEkLkAzLclAtotVqiox8wdOgIjh49LQEhhLAY6UnkUGFhYcyaNZXRo8dRsWIl1q7dJPMtCWFhb77pjV6vR6+3AaBOnbqMGjUu1eUPHNjLyZPHmTdvIRcunGP16uVs2vRpsmU++WQThw//AMCdO//g6FjUPBOzn98S3Nyef3uEsLBQZs+exsqVH2W2aekmIZHDKIrCN998xdSpE3nw4AHNm7eQCfmEsKJ58/woV65Clm2vb9+B9O07EIARI4bw1lt9aNo0+W2aTSYTKpUq1dPZnZ1dLBIQICGRo9y9e4eJE33x9/+OOnXqsnTpajw9q1q7LCHEU57uLaT0OLM2bfqIwMAbxMREExx8j3XrNrN168dcunSBpKQkHB0dmTx5BsWKuRMUdJdBg/qwf/+jHkmzZvUYOnQ4R44c5v79+wwfPoqXX279wm0FCYkcZfPmjRw/fpQ5c+YzePAw8538hMiPkv74iaTfj2VqXZVKRVqTSegqv4SuUtN0bWvatPfMw03Dho3MVD3pFRBwjY8/3o6joyMAvXv3M08zvnfvbtauXcHs2R+kuK69vT0bN27lypVLzJgxWUIir7hx4zoPHtzHy6sOvr4T6NmzD2XLZvxmS0KI7PHf4aYDB/Zm274aN25qDgiA06d/4uuvdxIXF4vRaExz3bZt2wFQrVoNwsJCSUhIwMbG5oVrkpCwEqPRyEcfrcHPbx6VKlUxT8gnASHEI7pKTdP9bf+/snPuJo1Gk2x+uMTEhCzbtp3dv3PH3bsXxMqVS9iwYSseHsW5evUys2dPS3XdJ72dJyMQzwuV9JKjoVbw668BdOzYmlmzpvLSSy+zdevnMt+SELlE8eIluX79TxITE0lKSuLw4R+zZT8xMTFotTqcnJwwmUzs3m2l++1YZa/52JMJ+QoXLsz69Zvx8ekiASFELlK9eg3q1WtAnz7dcHZ2oUKFioSHh2X5fsqXr0DLlm3o3bsbhQs70rhxU/O9YixJpgrHMlOFP3hwHweHwphMJpYsWUj//oNxcnLKln2lV26aQjqrSJtzNpkqPPOya6pwGW7KZjExMUyfPplGjWoTEhKCWq1m/PhJVg8IIYRIDxluykbHjh1h7NhR3Lp1k379BmJnZ2vtkoQQIkMkJLKBwWBg4kRftm37hHLlyrN79wGaNGlm7bKEECLDZLgpG2i1WmJjYxgxYgyHD5+UgBBC5FoSElkkNDSUd98dzB9//A7A2rWbmDFjDnZ2dlauTAghMk9C4gUpisJXX31B8+b1+fbbb7h06QKAnNYqhMgTJCRewJ07t+nVqyvvvjuYsmXL88MPJ+jW7S1rlyWEEFlGDly/gM2bN3Ly5AnmzVvAwIHvyIR8QuQxT+4nodPpMRiS6NGjN97enbN0+wsXLs3SqcizmoREBl2//if379+nTp16jB07kT59+lG6dBlrlyWEyCZPJvi7ceMvBgzoTePGTXF2drF2WRYjIZFOBoOBtWtXsWjRfCpX9jRPyCcBIUT+UK5cBQoVciA0NIQLF86zc+fnGAxJAAwfPoZ69RoAj3oH7du/ytmzZwgPD+Ott3rzxhvdAbh8+SKLFy8AwMurTrLpzH/99ReWLfuQ+Pg4bG3tGDNmPJ6e1cz3jvD2fp0zZ06SkJDAjBnz2LNnFwEB19DrbViwYDFubq7Z0m4JiXS4du0qY8YM58qVS3Ts6I2f32I5MC2EBXTu3PGZ51577XUGDBhMbGwsPXu++czrPXr0onfvPoSHhzNwYJ9nXu/XbyCdO7+R4VquXLlE4cKOVKhQCQ+P4rRt2w6VSsWtWzcZPfpdvvnmgHnZ+Ph4PvpoM0FBd3n77e506OCNVqtl5swpzJgxlzp16vHDD9/z9dc7AUhKSmLq1IlMmTKTevUacPbsGaZOncgXX+wG4P79+9Ss6cXQoSP47LOtjBkzjJUrP+K996bx4YcL2LXrS959d0SG25QeEhLPcf78Wby92+HoWIRNm7bSqZOPBIQQ+ci0ae+hKAp37txm7twF6HQ67ty5zaxZUwkNDUWr1RIREU54eBhOTs4AtGnzCgDu7h7m3kdSUhK2trbUqVMPgNat27Jo0fsA3Lr1NzqdztwbqV+/ITqdjlu3/qZAgQLY2RUwX29VqVIVXFxcqVixMgBVqlTh7Nkz2dZ+CYlU3L8fReHCjtSuXZcJEybTr99AihQpau2yhMhXdu8+kOprBQoUSPN1JyenNF9PryfHJH788RDz58+mRo1azJo1lREjfHnppZcxmUy0adOMxMRE8zp6vd78s1qtxmg0pLL19H3h1Ot1ybb35N4Rjx5rsuzeESmx2CmwgYGBdO/enXbt2tG9e3du3rz5zDJGo5HZs2fTpk0b2rZty86dOy1Vnll0dDTTpr2XbEI+X98JEhBC5HOtWrWhfv1GfPrpFqKjo3F39wBg//5vkwVEakqVKk1CQoJ5uu/Dhw8RHf3Q/FpSUhIXLpwDHo1gGAwGSpV68RlxX5TFehIzZ86kZ8+e+Pj4sGfPHmbMmMHWrVuTLbN3715u3bqFv78/UVFRdO7cmcaNG1OiRAmL1Hjs+FGmTJvIrVt/M2DAYAoUkKulhRD/Gjp0BAMH9sbXdyJTpoynUKFCNGzYhMKFCz93Xb1ez6xZ77N48QJUKhW1atXGza0YADqdjvffX5jswPW8eX7odLrnbDX7WeR+EuHh4bRr144zZ86g0TzqGjVs2BB/f3+KFv33G/qQIUPo0qUL7du3B2DOKcxxygAACPFJREFUnDl4eHgwaNCgDOwr4/eTuLh7KxvXLWLnhb8pX74CS5euolGjJhnaRm6Um+4zkFWkzTmb3E8i87LrfhIW6UkEBQXh5uZmvthMo9Hg6upKUFBQspAICgrCw8PD/Njd3Z179+5laF9pNTY1jsU8iDXAhAkTmDNnDra2+WdKbxeXQtYuweKkzTlXSIgarTZrRsGzaju5yfParFarM/y7kOcOXGemJ1GqQUt2HL9KXAI8fJjEw4dJ2VRdzpKbvmFmFWlzzmYymbKkByA9iZSZTKZnfhdyxJ3p3N3dCQ4ONh+BNxqNhISE4O7u/sxyd+/eNT8OCgqiWLFi2V6fRq2hoEPu+KYlhBCWZJGQcHJywtPTk3379gGwb98+PD09kw01AbRv356dO3diMpmIiIjg0KFDtGvXzhIlCiFyCAscJs2XMvu+WmzQbtasWWzbto127dqxbds2Zs+eDcDgwYO5evUqAD4+PpQoUYJXXnmFbt26MXz4cEqWLGmpEoUQVqbV6omJeSBBkcUURSEm5gFarf75C/+HRc5usqTMHJOA3DVum1WkzflDbmqz0WggMjIUg+H51x2kRa1WYzLlr2MSz2uzVqunSBEXNJrkh6JzxNlNQgiRHhqNFmdn9+cv+By5KRizSna1Of+dIyaEECLdJCSEEEKkKs8NN6nVmZ+h9UXWza2kzfmDtDl/yEybn7dOnjtwLYQQIuvIcJMQQohUSUgIIYRIlYSEEEKIVElICCGESJWEhBBCiFRJSAghhEiVhIQQQohUSUgIIYRIlYSEEEKIVOWrkAgMDKR79+60a9eO7t27c/PmzWeWMRqNzJ49mzZt2tC2bVt27txp+UKzUHravHr1al599VW8vb3p0qULx48ft3yhWSg9bX7ixo0b1KpVCz8/P8sVmA3S2+YDBw7g7e1Np06d8Pb2JiwszLKFZqH0tDk8PJwhQ4bg7e1Nhw4dmDVrFgaDwfLFZgE/Pz9atWpF5cqV+eOPP1JcJlv+fin5SJ8+fZTdu3cryv/bu7uQpvo4DuDfqRiGXmQvthEuemGubiqlF2lRW9RcQzCW9GI0JHZRVxV1ERiFId14kbCIQCrqIvJCBSsvwosxSDMRduELOcIMV5oSQRpb2++5CEZip+c8z7Zzkn0/sIvDjvD9ctj/584Z54hIR0eHnD59etE+7e3tUl9fL/F4XGZmZsRms8nExITWUdNGTedAICBzc3MiIjI8PCzl5eUyPz+vac50UtNZROTHjx9SV1cnFy9elFu3bmkZMe3UdA6FQlJVVSVTU1MiIvL161f5/v27pjnTSU3nmzdvJo9tNBoVj8cjz5490zRnuvT398vk5KQcOHBARkdHf7tPJtavrPkmMTMzg6GhIbjdbgCA2+3G0NAQZmdnF+z3/PlzHDt2DDk5OSguLsbBgwfR3d2tR+SUqe1ss9lQUFAAALBYLBARfPnyRfO86aC2MwDcu3cP+/fvx/r16zVOmV5qOz948AD19fVYvXo1AKCoqAjLli3TPG86qO1sMBjw7ds3JBIJRKNRxGIxlJSU6BE5ZRUVFTAa//ysjUysX1kzJCKRCEpKSpCbmwsAyM3NxZo1axCJRBbtZzKZkttGoxEfP37UNGu6qO38q46ODpSWlmLt2rVaxUwrtZ1HRkYQDAbh9Xp1SJleajuHw2FMTEzg1KlTqKmpwZ07d5bsY0LVdj537hzevXuHvXv3Jl/l5eV6RNZEJtavrBkS9O9ev36N27dvo7m5We8oGRWLxdDQ0IAbN24kF5lsEI/HMTo6ivv37+PRo0cIBALo7OzUO1ZGdXd3w2KxIBgMIhAI4M2bN0v2zIBesmZIGI1GfPr0CfF4HMDPD8zU1NSir29GoxGTk5PJ7UgksmT/q1bbGQAGBwdx+fJl+P1+bNiwQeuoaaOm8/T0NN6/fw+fzwe73Y6HDx/i6dOnaGho0Ct2StQeZ5PJBKfTifz8fBQWFsLhcCAUCukROWVqOz9+/BjV1dXIyclBUVER7HY7+vr69IisiUysX1kzJFauXAmr1Yquri4AQFdXF6xWK4qLixfs53Q60dbWhkQigdnZWbx8+RKHDx/WI3LK1HYOhUK4cOECWlpasHXrVj2ipo2aziaTCX19fejp6UFPTw/OnDmD2tpaNDY26hU7JWqPs9vtRjAYhIggFouht7cXZWVlekROmdrO69atQyAQAABEo1G8evUKmzdv1jyvVjKyfqV02XuJGRsbE4/HI4cOHRKPxyPhcFhERM6ePSuhUEhEfv7i5dq1a+JwOMThcMiTJ0/0jJwyNZ2PHj0qu3btkurq6uRrZGREz9gpUdP5Vy0tLUv+101qOsfjcWlqahKn0ykul0uampokHo/rGTslajqPj4+L1+sVt9stVVVVcv36dYnFYnrG/t8aGxvFZrOJ1WqVyspKcblcIpL59YtPpiMiIkVZc7qJiIj+Ow4JIiJSxCFBRESKOCSIiEgRhwQRESnikCAiIkV5egcgWursdjs+f/684BYfra2tOHHiBJYvXw4AWLFiBY4fPw6fzwfg540UCwoKYDAYUFhYCJfLhStXrmTVbUJoaeCQIEqDu3fvorKyMrn94cMHAEB/fz/y8vIwODgIr9eLsrIy7Nu3DwDQ2dkJs9mM8fFx1NXVYePGjaitrdUlP5ESnm4i0sD27duxadMmvH37dtF7ZrMZO3bswPDwsA7JiP6MQ4Iow0QEAwMDGBsbw5YtWxa9Hw6HMTAwALPZrEM6oj/j6SaiNDh//nzyesLOnTtx9epVAMDu3bthMBiwatUqXLp0CXv27En+TU1NDRKJBObn53HkyBGcPHlSl+xEf8IhQZQGfr//t9ckent7kZf3+49Ze3s7SktL8eLFCzQ3N2Nubg75+fma5CVSi6ebiHRkMBjgcrmwbds2+P1+veMQLcIhQfQX8Pl8aGtrw/T0tN5RiBbgkCD6C1gsFlRUVKC1tVXvKEQL8HkSRESkiN8kiIhIEYcEEREp4pAgIiJFHBJERKSIQ4KIiBRxSBARkSIOCSIiUsQhQUREijgkiIhI0T++Tc5FuHc0sQAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#4. XGB Classifier"
      ],
      "metadata": {
        "id": "XJhOTpHp_gJy"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**4.1. Train and Validation Data**"
      ],
      "metadata": {
        "id": "KYlDYEuY_jdx"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "xgb_cl = xgb.XGBClassifier()"
      ],
      "metadata": {
        "id": "NTv9NvdG_psQ"
      },
      "execution_count": 113,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "xgb_cl.fit(X_train, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "D_P-HRoD_reT",
        "outputId": "88fa199b-cd19-4169-e441-dc81fb423ffe"
      },
      "execution_count": 114,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "XGBClassifier()"
            ]
          },
          "metadata": {},
          "execution_count": 114
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred = xgb_cl.predict(X_val)\n",
        "auc_score = roc_auc_score(y_true=y_val.values, y_score=y_pred)\n",
        "print(auc_score*100)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FlxHk0WJ_t_Z",
        "outputId": "ce177c16-13b5-4d24-dd50-40cbce79665f"
      },
      "execution_count": 115,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "82.96316359696641\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Hyperparameters Tunning**"
      ],
      "metadata": {
        "id": "DOxGd1VM_zYg"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "n_estimators_list = [1, 5, 10, 15, 20, 25, 50, 100, 200, 500, 1000, 2000]\n",
        "\n",
        "for n_estimator in n_estimators_list:\n",
        "    \n",
        "    xgb_cl = xgb.XGBClassifier(objective=\"binary:logistic\", \n",
        "                           n_estimators = n_estimator\n",
        "                           )\n",
        "    xgb_cl.fit(X_train, y_train)\n",
        "    y_pred = xgb_cl.predict(X_val)\n",
        "    auc_score = roc_auc_score(y_true=y_val.values, y_score=y_pred)\n",
        "    print(f\"n_estimator: {n_estimator} | score: {auc_score}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Y6x3Xlcz_3Y_",
        "outputId": "a95f73c4-f17d-4552-b1d5-8010c530fa93"
      },
      "execution_count": 116,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "n_estimator: 1 | score: 0.8584326471650415\n",
            "n_estimator: 5 | score: 0.8584326471650415\n",
            "n_estimator: 10 | score: 0.8584326471650415\n",
            "n_estimator: 15 | score: 0.844348140122788\n",
            "n_estimator: 20 | score: 0.844348140122788\n",
            "n_estimator: 25 | score: 0.8437161430119177\n",
            "n_estimator: 50 | score: 0.8437161430119177\n",
            "n_estimator: 100 | score: 0.829631635969664\n",
            "n_estimator: 200 | score: 0.8488624052004333\n",
            "n_estimator: 500 | score: 0.8219573853376669\n",
            "n_estimator: 1000 | score: 0.8085048754062838\n",
            "n_estimator: 2000 | score: 0.8155471289274105\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "max_depth_list = [1,2,3,4,5,6,7,8,9,10,20,50]\n",
        "\n",
        "for max_depth in max_depth_list:\n",
        "    \n",
        "    xgb_cl = xgb.XGBClassifier(objective=\"binary:logistic\", \n",
        "                           n_estimators = 5,\n",
        "                           max_depth=max_depth\n",
        "                           )\n",
        "    xgb_cl.fit(X_train, y_train)\n",
        "    y_pred = xgb_cl.predict(X_val)\n",
        "    auc_score = roc_auc_score(y_true=y_val.values, y_score=y_pred)\n",
        "    print(f\"max_depth: {max_depth} | score: {auc_score}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "S9iZkACo_496",
        "outputId": "24af9186-7ae7-46f6-bb93-368c67ea3085"
      },
      "execution_count": 117,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "max_depth: 1 | score: 0.7463885879378839\n",
            "max_depth: 2 | score: 0.8077825929938606\n",
            "max_depth: 3 | score: 0.8584326471650415\n",
            "max_depth: 4 | score: 0.8308956301914049\n",
            "max_depth: 5 | score: 0.7892741061755146\n",
            "max_depth: 6 | score: 0.7892741061755146\n",
            "max_depth: 7 | score: 0.7892741061755146\n",
            "max_depth: 8 | score: 0.7892741061755146\n",
            "max_depth: 9 | score: 0.7892741061755146\n",
            "max_depth: 10 | score: 0.7892741061755146\n",
            "max_depth: 20 | score: 0.7892741061755146\n",
            "max_depth: 50 | score: 0.7892741061755146\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "learning_rate_list = [0.3, 0.29, 0.28, 0.27, 0.26, 0.25, 0.2, 0.15, 0.1, 0.01]\n",
        "\n",
        "for lr in learning_rate_list:\n",
        "    \n",
        "    xgb_cl = xgb.XGBClassifier(objective=\"binary:logistic\", \n",
        "                           learning_rate=lr,\n",
        "                           n_estimators=5,\n",
        "                           max_depth=6\n",
        "                           )\n",
        "    xgb_cl.fit(X_train, y_train)\n",
        "    y_pred = xgb_cl.predict(X_val)\n",
        "    auc_score = roc_auc_score(y_true=y_val.values, y_score=y_pred)\n",
        "    print(f\"learning rate: {lr} | score: {auc_score}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "acIY6ahi_6YJ",
        "outputId": "155d05da-d733-48ed-fb56-40db90822854"
      },
      "execution_count": 118,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "learning rate: 0.3 | score: 0.8437161430119177\n",
            "learning rate: 0.29 | score: 0.8232213795594077\n",
            "learning rate: 0.28 | score: 0.8513903936439148\n",
            "learning rate: 0.27 | score: 0.8513903936439148\n",
            "learning rate: 0.26 | score: 0.8373058866016613\n",
            "learning rate: 0.25 | score: 0.8373058866016613\n",
            "learning rate: 0.2 | score: 0.8373058866016613\n",
            "learning rate: 0.15 | score: 0.8033586132177681\n",
            "learning rate: 0.1 | score: 0.7892741061755146\n",
            "learning rate: 0.01 | score: 0.7764535933550019\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "xgb_cl = xgb.XGBClassifier(objective=\"binary:logistic\", \n",
        "                           learning_rate=0.27,\n",
        "                           n_estimators=5,\n",
        "                           max_depth=6\n",
        "                           )"
      ],
      "metadata": {
        "id": "fBYIQNxb_7zW"
      },
      "execution_count": 119,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "xgb_cl.fit(X_train, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZfJQdSd__9Vw",
        "outputId": "35136f7d-875b-47b9-a4de-20a5c8eab96e"
      },
      "execution_count": 120,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "XGBClassifier(learning_rate=0.27, max_depth=6, n_estimators=5)"
            ]
          },
          "metadata": {},
          "execution_count": 120
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred = xgb_cl.predict(X_val)\n",
        "auc_score = roc_auc_score(y_true=y_val.values, y_score=y_pred)\n",
        "print(auc_score*100)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qq3-7Lb5_-vF",
        "outputId": "b09f8ae3-1aec-4770-aa6a-83285684c70e"
      },
      "execution_count": 121,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "85.13903936439148\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Confusion Matrix**"
      ],
      "metadata": {
        "id": "epVFusjqAA3k"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "sns.heatmap(confusion_matrix(y_true=y_val, y_pred=y_pred), annot=True)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 268
        },
        "id": "1Ww75-SFAEIp",
        "outputId": "683a0c85-c223-4e7e-9d7e-062f81637f6b"
      },
      "execution_count": 122,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Classification Report**"
      ],
      "metadata": {
        "id": "IC--jrOMAFN_"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(classification_report(y_true=y_val, y_pred=y_pred))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nou9CreaAHXK",
        "outputId": "41f547c4-55c6-4deb-a093-b400a754a500"
      },
      "execution_count": 123,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.85      0.87      0.86        78\n",
            "           1       0.86      0.83      0.84        71\n",
            "\n",
            "    accuracy                           0.85       149\n",
            "   macro avg       0.85      0.85      0.85       149\n",
            "weighted avg       0.85      0.85      0.85       149\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**ROC Curve**"
      ],
      "metadata": {
        "id": "WzK4y8U-AIeT"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plot_roc_curve(model=xgb_cl, \n",
        "               data1=(X_val, y_val), \n",
        "               data1_label=\"Validation\",\n",
        "               data2=(X_train, y_train),\n",
        "               data2_label=\"Train\",\n",
        "               title=\"XGBClassifier ROC Curve\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 301
        },
        "id": "x88qpseLAKbx",
        "outputId": "7c7b91b7-1ff4-4f6d-e1db-4439fd53f127"
      },
      "execution_count": 124,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**4.2. Full Train and Test Data**"
      ],
      "metadata": {
        "id": "_6vYkcjYASDk"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "xgb_cl = xgb.XGBClassifier(objective=\"binary:logistic\", \n",
        "                           learning_rate=0.27,\n",
        "                           n_estimators=5,\n",
        "                           max_depth=6\n",
        "                           )\n",
        "xgb_cl.fit(X_full_train, y_full_train)\n",
        "y_pred = xgb_cl.predict(X_test)\n",
        "auc_score = roc_auc_score(y_true=y_test.values, y_score=y_pred)\n",
        "print(auc_score*100)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FH8WGYlgAVAT",
        "outputId": "4d232ceb-feed-487c-862a-f14716a4a066"
      },
      "execution_count": 125,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "86.91388839365305\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Confusion matrix**"
      ],
      "metadata": {
        "id": "ZkTkJVb8AYJM"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "sns.heatmap(confusion_matrix(y_true=y_test, y_pred=y_pred), annot=True)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 268
        },
        "id": "UR4v-KzZAW7V",
        "outputId": "ef279916-d4fa-4c09-e3d5-5f24a71a1fab"
      },
      "execution_count": 126,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAWAAAAD7CAYAAABUt054AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAWiklEQVR4nO3de3RU5b3G8SczaYIgw+RC0iS6ysK2dFxYrQ5Sqyw8iRDUcIlayYmXFFSqNjl42lByUBPkIgZSFRtYwLHF0CKouBQJHEKt1lrgoBRpG4JCEVqEIUiSYZRLIDNz/rAnirnNJCZvZvP9uPZazp693/lFXA9vfnvPu6OCwWBQAIAeZzNdAACcrwhgADCEAAYAQwhgADCEAAYAQwhgADAkuic/7OyxD3vy4xABLkgdYboE9FJNZw516fxw8uZriYO79Fmd1aMBDAA9JuA3XUGHCGAA1hQMmK6gQwQwAGsKEMAAYESQGTAAGOJvMl1BhwhgANbERTgAMIQWBAAYwkU4ADCDi3AAYAozYAAwxH/WdAUdIoABWBMtCAAwhBYEABjCDBgADOmmGXBjY6Mef/xxbd26VbGxsbriiis0e/Zs7d+/X0VFRfJ6vXI6nSotLdWgQYPaHYsABmBJwUD3XIRbsGCBYmNjVVVVpaioKB07dkySVFJSotzcXI0fP15r165VcXGxVqxY0e5YBDAAawpjBuzz+eTz+Vrsdzgccjgcza9PnDihV199VW+99ZaioqIkSYmJiaqrq1NNTY2WL18uScrKytLs2bNVX1+v+Pj4Nj+XAAZgTWH0gCsqKlReXt5if35+vgoKCppfHzx4UE6nU+Xl5dq2bZv69eunqVOnqk+fPkpOTpbdbpck2e12JSUlyePxEMAAzkNhLMaTl5en7OzsFvu/OPuVJL/fr4MHD+rSSy/V9OnT9Ze//EX333+/Fi5c2KkSCWAA1hTGDPjLrYa2pKSkKDo6WllZWZKkyy+/XHFxcerTp49qa2vl9/tlt9vl9/t19OhRpaSktDseT0UGYE2BQOhbiOLj4zV8+HBt3rxZkrR//37V1dVp0KBBcrlcqqyslCRVVlbK5XK1236QpKhgMBjs/E8YHp6KjC/jqchoS1efinx688qQj+1z7R0hH3vw4EHNmDFDXq9X0dHReuihhzRy5Ejt27dPRUVF8vl8cjgcKi0t1eDB7T9tmQCGUQQw2tLlAH77NyEf22fEXV36rM6iBwzAkoJBnogBAGawFgQAGMJaEABgCDNgADCEx9IDgCG0IADAEFoQAGAIAQwAhtCCAABDuAgHAIbQggAAQ2hBAIAhzIABwBACGAAM6bmVdjuNAAZgTU3cBQEAZnARDgAMoQcMAIbQAwYAQ5gBA4AhBDAAmBH081BOADCDGTAAGMJtaABgSKD33wVhM13A+WDD63/Q2NwpGpYxQWN+OEl/3lmtQ55aDb32Rg27Ibt5W7L8edOlwqDbbx+nv/31DzresFcf7N6s66692nRJkS0QCH0zhBlwN9vyzg49tXi5ymYV6bJLh+jjunpJUlPTZxcItm5co+hou8kS0QvckDFC8+Y+rNw7HtA7776nlJRk0yVFPi7CYdGvfqv7J+Xq8qEuSVLywERJ0iFPrcmy0MuUFBdqztyntO2dHZKkw4ePGK7IArppZpuenq6YmBjFxsZKkgoLCzVixAjt3LlTxcXFamxsVFpamhYsWKCEhIR2xwqpBdHQ0KDdu3dr9+7damho6PpPcJ7w+/3a9f5eNXiP68bbJytjwp2a+4vFOt3Y2HzM6FvzlDHhTj0y90k1eI8brBam2Gw2XXXVdzVwYILer/mTDny4XQufnqM+ffqYLi2yBYKhb2F65plntHbtWq1du1YjRoxQIBDQtGnTVFxcrKqqKrndbpWVlXU4TrsB/M9//lN5eXkaPXq0CgsLVVhYqNGjRysvL08HDhwIu+jzTV29V01NTdr05p+0YnGZ1jy3SLv37tPS51YpboBDq59dqE0vV+iFX/9SJ06e1PTH5psuGQYkJw9UTEyMbrnlZl2ffouuGjZK37tiqB6eMdV0aZEtGAh966Lq6mrFxsbK7XZLknJycrRx48YOz2s3gH/+85/r1ltv1bZt27R+/XqtX79e27Zt0y233KLp06d3uWiri42NkSTdcdtYDUyMV5xzgPImZuvtre+qb98LNNT1bUVH25UYH6eHf/qgtryzQydOnDRcNXraqVOnJUmLFi/XkSNHVVfXoKcWLtOYMemGK4twYcyAfT6fPvrooxabz+drdejCwkKNHTtWM2fOlM/nk8fjUWpqavP78fHxCgQC8nq97ZbYbgB7vV6NGzdONtvnh9lsNo0fP17Hj/PrckcGOPorOSlRior6fOcX//2L/rU/EAELiOCr5fUe18GDhxX8wp99kP8PuiwYCIS8VVRUKCMjo8VWUVHRYtyVK1fqtdde08svv6xgMKhZs2Z1usZ2L8I5nU5VVlbq5ptvVtS/AiIYDGrdunVyOByd/tDzSfZNo/T8mtd03ffdirbb9ZsXXtHIHwzXX3e9r/4XXqhvXJwq3yefat5TSzTse99V/wv7mS4ZBlSseEE/eXCSqqre1NmzTXroP+7Thg2vmy4rsoVxF0ReXp6ys7Nb7G8t51JSUiRJMTExys3N1QMPPKC7775bhw8fbj6mvr5eNptNTqez3c9tN4CfeOIJlZSUaNasWUpO/uy2mNraWn3nO9/RE0880fFPBf14Uq4ajvuUlXOvYmJilJk+QlPycvT7P27RwqXPqb7Bq379+uqaYVdqwWO0dc5Xc+Y+rYSEeO3e9bZOn27US2vW6fF5z5guK7KFcXHN4XCENKk8efKk/H6/+vfvr2AwqA0bNsjlcmno0KE6ffq0tm/fLrfbrdWrV2vMmDEdjhcVDOF3nfr6enk8HkmfpX98fHwIP1JLZ4992KnzYF0XpI4wXQJ6qaYzh7p0/omZ/x7ysf1mrgrpuIMHD6qgoEB+v1+BQECXXHKJHnnkESUlJWnHjh0qKSk55za0xMTEdscLKYC/KgQwvowARlu6HMDFOSEf22/W6i59VmfxRQwA1sRiPABgSAQsxkMAA7CkYBNrQQCAGcyAAcAQesAAYAgzYAAwI0gAA4AhXIQDAEOYAQOAIQQwAJgRCUt6EsAArIkZMAAYQgADgBnBJr6IAQBm9P78JYABWBNfxAAAUwhgADCEFgQAmEELAgAMCTYRwABgBi0IADAjAtZjJ4ABWBQBDABmMAMGAEOCTaYr6BgBDMCSmAEDgCEEMACYEowyXUGHbKYLAIDuEAyEvnVGeXm5hgwZoj179kiSdu7cqXHjxikzM1OTJ09WXV1dh2MQwAAsKRiICnkL165du7Rz506lpaVJkgKBgKZNm6bi4mJVVVXJ7XarrKysw3EIYACWFPBHhbyF48yZM5o1a5ZmzpzZvK+6ulqxsbFyu92SpJycHG3cuLHDsegBA7CkcFoLPp9PPp+vxX6HwyGHw3HOvoULF2rcuHG66KKLmvd5PB6lpqY2v46Pj1cgEJDX65XT6WzzcwlgAJYUTmuhoqJC5eXlLfbn5+eroKCg+fV7772n6upqFRYWfiU1EsAALCmcp9Ln5eUpOzu7xf4vz37fffdd7du3TxkZGZKkI0eO6J577tFdd92lw4cPNx9XX18vm83W7uxXIoABWFQ4M+DWWg2tmTJliqZMmdL8Oj09XUuWLNE3v/lNvfjii9q+fbvcbrdWr16tMWPGdDgeAQzAksK9uNYVNptN8+fPV0lJiRobG5WWlqYFCxZ0eF5UMBjORL1rzh77sKc+ChHigtQRpktAL9V05lCXzv/wstEhHzv4b5u69FmdxQwYgCUFI+CbcAQwAEtiLQgAMCTADBgAzKAFAQCG9ORdEJ1FAAOwpM4sstPTCGAAlkQPGAAMoQcMAIb03FfMOo8ABmBJtCAAwJAAF+HOdc1leT35cYgApw6/bboEWBQzYAAwhItwAGAIM2AAMCQCboIggAFYkz/Q+x/6TgADsKQIWI2SAAZgTUHRAwYAIwIR0AQmgAFYUoAZMACYQQsCAAzxE8AAYAZ3QQCAIQQwABhCDxgADImA1SgJYADWxG1oAGCI33QBISCAAVhSIKr7ZsAPPvigPvroI9lsNvXt21ePPvqoXC6X9u/fr6KiInm9XjmdTpWWlmrQoEFtjkMAA7Ck7vwmcmlpqfr37y9Jev311zVjxgy98sorKikpUW5ursaPH6+1a9equLhYK1asaHOc3r9eGwB0QiCMLVz/H76S9OmnnyoqKkp1dXWqqalRVlaWJCkrK0s1NTWqr69vcxxmwAAsKZy7IHw+n3w+X4v9DodDDoej1XMefvhhbd68WcFgUM8++6w8Ho+Sk5Nlt9slSXa7XUlJSfJ4PIqPj291DAIYgCWF81XkiooKlZeXt9ifn5+vgoKCVs+ZO3euJOnVV1/V/PnzNXXq1LBrJIABWFI4M+C8vDxlZ2e32N/W7PeLJkyYoOLiYn39619XbW2t/H6/7Ha7/H6/jh49qpSUlDbPJYABWFI4vd32Wg1fduLECfl8vuZgfeONNzRgwAAlJCTI5XKpsrJS48ePV2VlpVwuV5vtB4kABmBR3XUXxKlTpzR16lSdOnVKNptNAwYM0JIlSxQVFaWZM2eqqKhIixcvlsPhUGlpabtjEcAALKm7voqcmJioF198sdX3LrnkEr300kshj0UAA7AkVkMDAEP8vX8pCAIYgDUxAwYAQwhgADAkAp5KTwADsCYWZAcAQ2hBAIAhLMgOAIbQggAAQ2hBAIAh3AUBAIYEIiCCCWAAlsRFOAAwhB4wABjCXRAAYAg9YAAwpPfHLwEMwKLoAQOAIf4ImAMTwAAsiRkwABjCRTgAMKT3xy8BDMCiaEEAgCGRcBHOZroAK7t90i1asfG/teXA71Xy9IxWj7n3P3+k7Z63dfWIq3q4Opi24fU/aGzuFA3LmKAxP5ykP++s1iFPrYZee6OG3ZDdvC1Z/rzpUiNSQMGQN1OYAXejj2uP6VdPr9A111+t2D6xLd5P+0aqbhh7vT4+csxAdTBpyzs79NTi5SqbVaTLLh2ij+vqJUlNTZ8tIbN14xpFR9tNlhjxev/8lxlwt3pzwx/11sa3dbzheKvvT5/3U/1yzhKdPXu2hyuDaYt+9VvdPylXlw91yWazKXlgopIHJpouy1KYAaNNGVnX62zjGW1+439Nl4Ie5vf7tev9vfq3676vG2+frDNnzih9xA/0s/x7mo8ZfWueoqKka4ZdqZ/95B7FOQcYrDgyRcJFOGbABvTtd4F+8l9TVPboM6ZLgQF19V41NTVp05t/0orFZVrz3CLt3rtPS59bpbgBDq1+dqE2vVyhF379S504eVLTH5tvuuSIFAzjn1A1NDTovvvuU2ZmpsaOHav8/HzV13/WPtq5c6fGjRunzMxMTZ48WXV1dR2O1+kAHjt2bGdPPe9NKZysDWs2yfPREdOlwIDY2BhJ0h23jdXAxHjFOQcob2K23t76rvr2vUBDXd9WdLRdifFxevinD2rLOzt04sRJw1VHHr+CIW+hioqK0r333quqqiqtW7dOF198scrKyhQIBDRt2jQVFxerqqpKbrdbZWVlHY7Xbgvi73//e5vvNTQ0hFw0zjXsuquUnDpQt/1ogiQpLsGpeUtnacWilapYxBVvqxvg6K/kpEQp6gsL1ka1sXjtv/YHgpFwSal36Y4WhNPp1PDhw5tfX3HFFVq1apWqq6sVGxsrt9stScrJyVFGRobmzZvX7njtBnBWVpbS0tIUbOUP3+v1dqb+84rdbpc92i6b3S673aaY2Bj5m/x68PaHFB39+X/6iv9ZpqdmlmvLG9sMVouelH3TKD2/5jVd9323ou12/eaFVzTyB8P1113vq/+FF+obF6fK98mnmvfUEg373nfV/8J+pkuOOOH8peXz+eTz+VrsdzgccjgcrY8fCGjVqlVKT0+Xx+NRampq83vx8fEKBALyer1yOp1tfm67AZyWlqbnn39eycnJLd4bOXJke6dC0j0P3a0phZObX990W6aWlf1ay36x/JzjAoGAPjn+iU6dPNXTJcKQH0/KVcNxn7Jy7lVMTIwy00doSl6Ofv/HLVq49DnVN3jVr19fXTPsSi14bLrpciNSOL8zVFRUqLy8vMX+/Px8FRQUtHrO7Nmz1bdvX91555363e9+16ka2w3g0aNH69ChQ60G8KhRozr1geeTZb9Y3iJsWzPu6tt7oBr0Jl+Ljtajhfl6tDD/nP03jbpeN4263kxRFhPO7WV5eXnKzs5usb+t2W9paan+8Y9/aMmSJbLZbEpJSdHhw4eb36+vr5fNZmt39it1EMDTp7f9N+8jjzzS7sAAYFI4dze012r4sieffFLV1dVatmyZYmI+u6A6dOhQnT59Wtu3b5fb7dbq1as1ZsyYDsfiPmAAltTUDV+w2Lt3r5YuXapBgwYpJydHknTRRRdp0aJFmj9/vkpKStTY2Ki0tDQtWLCgw/EIYACWFM4MOFTf+ta39MEHH7T63pVXXql169aFNR4BDMCSIuGbcAQwAEtq7fbZ3oYABmBJPJIIAAyJhAXZCWAAlsQMGAAMoQcMAIZwFwQAGNId9wF/1QhgAJZEDxgADPEHe38TggAGYEm0IADAkEh4iggBDMCSen/8EsAALIqLcABgCAEMAIZwFwQAGMJdEABgCGtBAIAh9IABwBBmwABgiD8C1kMjgAFYEt+EAwBDuAsCAAxhBgwAhjADBgBDmAEDgCF8FRkADImEFoTNdAEA0B2CwUDIWzhKS0uVnp6uIUOGaM+ePc379+/fr4kTJyozM1MTJ07UgQMHOhyLAAZgSQEFQ97CkZGRoZUrVyotLe2c/SUlJcrNzVVVVZVyc3NVXFzc4VgEMABLCgaDIW/hcLvdSklJOWdfXV2dampqlJWVJUnKyspSTU2N6uvr2x2LHjAASwpnZuvz+eTz+VrsdzgccjgcHZ7v8XiUnJwsu90uSbLb7UpKSpLH41F8fHyb5xHAACzJHwi9t1tRUaHy8vIW+/Pz81VQUPBVlnUOAhiAJYVzF0ReXp6ys7Nb7A9l9itJKSkpqq2tld/vl91ul9/v19GjR1u0Kr6MAAZgSeH0dkNtNbQlISFBLpdLlZWVGj9+vCorK+VyudptP0hSVLAHF810p4zoqY9ChNj6twrTJaCX+lri4C6dP3DAkJCP/fj4ByEfO2fOHG3atEnHjh1TXFycnE6n1q9fr3379qmoqEg+n08Oh0OlpaUaPLj9n4EAhlEEMNrS1QBOdHw75GOP+fZ0fFA3oAUBwJLCuQhnCgEMwJJ4JhwAGMIz4QDAEJajBABDImE1NAIYgCUxAwYAQwIsyA4AZnARDgAMiYQA7tFvwgEAPseC7ABgCAEMAIYQwABgCAEMAIYQwABgCAEMAIYQwABgCAEMAIYQwABgCAHcw/bv36+JEycqMzNTEydO1IEDB0yXBMNKS0uVnp6uIUOGaM8eM88mgxkEcA8rKSlRbm6uqqqqlJubq+LiYtMlwbCMjAytXLlSaWlppktBDyOAe1BdXZ1qamqUlZUlScrKylJNTY3q6+sNVwaT3G63UlJSTJcBAwjgHuTxeJScnCy73S5JstvtSkpKksfjMVwZABMIYAAwhADuQSkpKaqtrZXf75ck+f1+HT16lF8/gfMUAdyDEhIS5HK5VFlZKUmqrKyUy+VSfHy84coAmMCC7D1s3759Kioqks/nk8PhUGlpqQYPHmy6LBg0Z84cbdq0SceOHVNcXJycTqfWr19vuiz0AAIYAAyhBQEAhhDAAGAIAQwAhhDAAGAIAQwAhhDAAGAIAQwAhhDAAGDI/wHwKajFowP00AAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Classification Report**"
      ],
      "metadata": {
        "id": "8BHGdyjlAa14"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(classification_report(y_true=y_test, y_pred=y_pred))"
      ],
      "metadata": {
        "id": "WwjyIgw5AdbI",
        "outputId": "cdf404de-72db-46c2-cdea-9f94198d0f4c",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 127,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.82      0.92      0.87        71\n",
            "           1       0.92      0.82      0.87        79\n",
            "\n",
            "    accuracy                           0.87       150\n",
            "   macro avg       0.87      0.87      0.87       150\n",
            "weighted avg       0.87      0.87      0.87       150\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**ROC Curve**"
      ],
      "metadata": {
        "id": "3zp52rlMAeeT"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plot_roc_curve(model=xgb_cl, \n",
        "               data1=(X_test, y_test), \n",
        "               data1_label=\"Test\",\n",
        "               data2=(X_full_train, y_full_train),\n",
        "               data2_label=\"Full Train\",\n",
        "               title=\"XGBClassifier ROC Curve\")"
      ],
      "metadata": {
        "id": "LHnRte0lAg1F",
        "outputId": "83c3551e-7a34-4761-cea6-e71095e7c3a9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 301
        }
      },
      "execution_count": 128,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#5. K Value Estimation"
      ],
      "metadata": {
        "id": "7_2Em26bGq5o"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from matplotlib import pyplot\n",
        "error_rate= []\n",
        "for i in range(1,40):\n",
        "    knn = KNeighborsClassifier(n_neighbors = i)\n",
        "    knn.fit(X_train,y_train)\n",
        "    pred_i = knn.predict(X_test)\n",
        "    error_rate.append(np.mean(pred_i != y_test))\n",
        "    print(i,np.mean(pred_i != y_test))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "f85ScHtQ7D_o",
        "outputId": "20fe8389-3674-4577-bc55-8f4c14bb9bb4"
      },
      "execution_count": 129,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1 0.22\n",
            "2 0.24666666666666667\n",
            "3 0.18\n",
            "4 0.21333333333333335\n",
            "5 0.15333333333333332\n",
            "6 0.16666666666666666\n",
            "7 0.17333333333333334\n",
            "8 0.18666666666666668\n",
            "9 0.17333333333333334\n",
            "10 0.22\n",
            "11 0.18\n",
            "12 0.20666666666666667\n",
            "13 0.18666666666666668\n",
            "14 0.19333333333333333\n",
            "15 0.2\n",
            "16 0.22666666666666666\n",
            "17 0.22\n",
            "18 0.22\n",
            "19 0.21333333333333335\n",
            "20 0.22\n",
            "21 0.22\n",
            "22 0.25333333333333335\n",
            "23 0.24\n",
            "24 0.26\n",
            "25 0.24666666666666667\n",
            "26 0.24666666666666667\n",
            "27 0.24\n",
            "28 0.24666666666666667\n",
            "29 0.23333333333333334\n",
            "30 0.26\n",
            "31 0.24\n",
            "32 0.24666666666666667\n",
            "33 0.24666666666666667\n",
            "34 0.24\n",
            "35 0.24\n",
            "36 0.24\n",
            "37 0.22666666666666666\n",
            "38 0.24\n",
            "39 0.24\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize = (10,6))\n",
        "plt.plot(range(1,40),error_rate,color = 'black',linestyle = '--',marker = 'o',markerfacecolor='red',markersize = 8)\n",
        "plt.title('Error Rate vs K')\n",
        "plt.xlabel('K')\n",
        "plt.ylabel('Error Rate')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 427
        },
        "id": "aHo5qdO7GkcC",
        "outputId": "954dd880-c7a5-4f35-e8ac-3c20ff7d963c"
      },
      "execution_count": 130,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'Error Rate')"
            ]
          },
          "metadata": {},
          "execution_count": 130
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#6. KNN Model"
      ],
      "metadata": {
        "id": "0idKmoOqG2P1"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "classifier = KNeighborsClassifier(n_neighbors=36)\n",
        "classifier.fit(X_train, y_train)\n",
        "y_pred = classifier.predict(X_test)\n",
        "print(classification_report(y_test, y_pred))\n",
        "print('\\n')\n",
        "print('------------------------')\n",
        "print('Confusion Matrix')\n",
        "print('------------------------')\n",
        "print('')\n",
        "print(confusion_matrix(y_test, y_pred))\n",
        "plot_confusion_matrix(classifier, X_test, y_test,cmap=\"binary\") \n",
        "plt.grid(False)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 639
        },
        "id": "CoUoWk_oG5SV",
        "outputId": "f80d1b6d-40bd-4ef5-ca97-45181e369e87"
      },
      "execution_count": 131,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.71      0.85      0.77        71\n",
            "           1       0.83      0.68      0.75        79\n",
            "\n",
            "    accuracy                           0.76       150\n",
            "   macro avg       0.77      0.76      0.76       150\n",
            "weighted avg       0.77      0.76      0.76       150\n",
            "\n",
            "\n",
            "\n",
            "------------------------\n",
            "Confusion Matrix\n",
            "------------------------\n",
            "\n",
            "[[60 11]\n",
            " [25 54]]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.8/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function plot_confusion_matrix is deprecated; Function `plot_confusion_matrix` is deprecated in 1.0 and will be removed in 1.2. Use one of the class methods: ConfusionMatrixDisplay.from_predictions or ConfusionMatrixDisplay.from_estimator.\n",
            "  warnings.warn(msg, category=FutureWarning)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAATwAAAEQCAYAAAAta8hLAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAeeElEQVR4nO3df1RUdf7H8ecAAqISooKgJkdMYzN/NYaVZmmGxyXd7Icumr9qTRNPtqWRFij+KNS1H/5Y2uNmtbKZltqCmVRmP9xS2VVTyVRCUxzhK4gEIugw3z86zok0GNyBmeG+HufMOTP33vnc98zBl5/P/dx7x2Sz2WyIiBiAl6sLEBFpKAo8ETEMBZ6IGIYCT0QMQ4EnIoahwBMRw/BxdQEiIjWpqKhg4cKFfP311/j5+dGzZ0/mzZtHbm4uCQkJFBcXExQUREpKChERETW2ZdJ5eCLizubPn4+XlxfPPfccJpOJM2fO0Lp1a8aOHcsDDzzA8OHD+eCDD3j//fd5++23a2xLgScibqusrIwBAwbw+eef06xZM/vywsJCYmJi2LlzJ97e3litVqKjo8nMzCQ4OPg329OQVkQaXElJCSUlJVcsDwwMJDAw0P76xIkTBAUFsXz5cnbu3EmzZs148skn8ff3JzQ0FG9vbwC8vb0JCQnBYrEo8ESk/hUVFdUYNr/k6+vLiBEjOHfuXLXl8fHxTJs2zf7aarVy4sQJfve73/Hss8+yb98+Jk+ezKuvvnpNNXpM4PXr14+TJ0+6ugxx0LFjx1xdgjSw4OBg+vfvX+u/0/bt2/Pll1/ywQcfYLVaq637Ze8OICwsDB8fH2JjYwHo0aMHLVu2xN/fn/z8fKxWq31IW1BQQFhYWI379pjAO3nyJMePH3d1GSJSg7y8PH788ccatzGZTAC1hhP8HKLR0dHs2LGDfv36kZubS2FhIREREURFRZGRkcHw4cPJyMggKiqq1h6mx0xaREREKPA8iIf8WYmTRUZG1vrvtGPHjuTk5Djc5okTJ5g1axbFxcX4+Pgwffp0BgwYQE5ODgkJCZSUlBAYGEhKSgqdOnWqsS0FntQLD/mzEifr3LmzQ4F39OjRBqqoOo8Z0oqI+/Py8sLLq+YLuGpbX58UeCLiNCaTyX6MrqZtXEWBJyJO5cpAq40CT0ScRj08ETEMBZ6IGIYCT0QMQ7O0ImIY6uGJiGEo8ETEUHRaiogYgnp4ImIYmrQQEcNQD09EDEOBJyKGocATEcNQ4ImIoei0FBExBM3SiohhaEgrIoahwBMRw1DgiYhhKPBExDAUeCJiGCaTqdZZWAWeiDQK6uGJiGEo8ETEMBR4ImIYCjwRMQxdWiYihqKbB4iIIWhIKyKGocATEcNQ4ImIYSjwRMQwNEsrIoahHp6IGIqzA23gwIH4+vri5+cHwDPPPEP//v3Zu3cviYmJVFRU0K5dOxYvXkyrVq1qbEuBJyJOU189vNdee40uXbrYX1dVVTFjxgxefPFFzGYzK1euZMmSJbz44os1tuO6wbSINDqXA6+2B4DFYuHkyZPVHiUlJQ7t58CBA/j5+WE2mwEYNWoUH330Ua3vUw9PRJymLj280aNHk5eXV21dfHw806ZNu+I9zzzzDDabjVtuuYU///nPWCwWwsPD7euDg4OpqqqiuLiYoKCg39y3Ak9EnKYus7RpaWlYrdZq6wIDA6/YPi0tjbCwMCorK1mwYAHJyckMHjz4mupT4ImI09SlhxcWFuZQm5e38/X1JS4ujilTpjB27FhOnTpl36aoqAgvL68ae3egY3gi4kR1OYbniPPnz/PTTz8BYLPZ+PDDD4mKiqJbt25cuHCBrKwsANauXcuQIUNqbU89PBFxKmeellJYWMi0adOwWq1UVVURGRlJUlISXl5eLFq0iKSkpGqnpdRGPbwGMnLkSLKzsyktLeXo0aP069cP+Pkco++++46ysjK2bdvG9ddf7+JKZfny5ZjNZvz8/Bg/frx9eWVlJQ8++CARERGYTCa2b9/ushrdlbN7eB06dGDTpk2kp6ezefNmXnvtNUJCQgDo3bs36enpZGZmsnr1alq3bl1rewq8BnDPPfeQkpLChAkTaNGiBXfeeSc//PADrVq1YsOGDbzwwgsEBweTlZXFu+++6+pyDS88PJznn3+eiRMnXrGuX79+rFmzhrZt27qgMvd3edKitoerNNiQNjc3l4SEBPu0cUpKChEREQ21e5eaO3cuycnJ7Ny5E8B+sPVPf/oTBw8e5L333gNgzpw5nDlzhq5du/L999+7rF6jGzFiBABZWVmcPHnSvtzX15fp06cD4O3t7ZLa3J27X1rWYFGblJREXFwcW7duJS4ujsTExIbatUt5eXlhNptp06YNR44c4cSJEyxbtgx/f39uuukm9u3bZ9/2/Pnz5OTkcNNNN7mwYpFr5+whrbM1SOAVFhaSnZ1NbGwsALGxsWRnZ1NUVNQQu3ep0NBQfH19efDBB+nfvz89e/akV69ePP/88zRv3pxz585V2/7cuXO0aNHCRdWK/G8UePx8CUloaKh9GODt7U1ISAgWi6Uhdu9S5eXlACxbtozTp09TWFjI0qVLGTp0KKWlpVecaBkYGGifhhfxNAo8gysuLubEiRPYbDb7ssvPDx48SI8ePezLAwICiIyM5ODBgw1ep4izuGvYQQMFXlhYGPn5+fbLSKxWKwUFBQ6fae3pVq9ezbRp02jTpg1BQUE89dRTZGRksHHjRrp168aIESPw8/MjMTGRb7/9VhMWLnbp0iUuXLiA1WrFarVy4cIFLl26BEBFRQUXLlwAfj5N5cKFC9X+MzM6d5+lbZA9t2rViqioKDIyMgDIyMggKiqK4ODghti9y82bN4/du3dz+PBhvvvuO/bs2cOCBQs4c+YMDzzwAAsWLODs2bNER0czatQoV5drePPnz6dp06a89NJLrFmzhqZNmzJ//nwAunbtStOmTcnLyyMmJoamTZty/PhxF1fsPtx9SGuyNdB/Tzk5OSQkJFBSUkJgYCApKSl06tTJ4fdHREToD8uDqNdjTGPGjCE/P7/GbUJDQ1mzZk0DVVRdg52HFxkZyfr16xtqdyLiAu5+Hp6upRURp1HgiYhhKPBExDBMJlOts7AKPBFpFNTDExHDUOCJiGEo8ETEMBR4ImIYdfnVMldQ4ImIU7n6BgE1UeCJiNNoSCsihqHAExHDUOCJiGEo8ETEMDRLKyKGoR6eiBiKTksREUNQD09EDEOBJyKGocATEcPQLK2IGIbH9vBmzJjhUGGLFi1yakEi4rk8NvA6duzYkHWISCPhkaelxMfHN2QdItIIeGwP79d27NjB5s2bKSoqIjU1lf3791NaWsptt91Wn/WJiAdx90kLh/b8j3/8gzlz5hAREcHu3bsB8Pf359VXX63X4kTEs1zu4dX2uBbLly+na9euHD58GIC9e/cybNgwYmJimDhxIoWFhbW24VDgvfXWW6xevZpJkybZ07lTp07k5uZeU+Ei0jjVV+AdPHiQvXv30q5dOwCqqqqYMWMGiYmJbN26FbPZzJIlS2ptx6HAKysrIywszP6BAC5dukSTJk3qXLiINF71EXiVlZUkJyczZ84c+7IDBw7g5+eH2WwGYNSoUXz00Ue1tuVQ4PXp04e//e1v1Za9/fbbREdH16FsEWns6hJ4FouFkydPVnuUlJRc0earr77KsGHDaN++vX2ZxWIhPDzc/jo4OJiqqiqKi4trrM+hSYvnn3+eyZMns379esrKyoiJiaFZs2a8/vrrDn0JImIcjvbgRo8eTV5eXrVl8fHxTJs2zf56z549HDhwgGeeecYptTkUeCEhIbz//vvs37+fvLw8wsLC6N69u0tnW0TE/dRlljYtLQ2r1VptXWBgYLXXu3fvJicnh0GDBgFw+vRpHn30UR555BFOnTpl366oqAgvLy+CgoJq3LfDp6VUVVVx8eJFAKxWKzabzdG3iohB1OU8vMvzAjWZNGkSkyZNsr8eOHAgqampdO7cmXXr1pGVlYXZbGbt2rUMGTKk1vYcCrxDhw4xdepUKisrCQ0N5fTp0/j5+bFixQpuvPFGR5oQEQNoqBOPvby8WLRoEUlJSVRUVNCuXTsWL15c6/scCrxZs2YxevRoJkyYgMlkwmaz8eabbzJr1iw2bNjwPxcvIo1DfQfetm3b7M979+5Nenp6nd7v0EG4Y8eOMW7cOHuhJpOJsWPHcuzYsTrtTEQat/o88dgZHAq8AQMGVEtWgM8++4y77rqrPmoSEQ9lMpnsExe/9XDLa2l/eXsoq9XKU089Rbdu3Wjbti2nT5/mwIED9pkTERHw4JsH/Pr2UF26dLE/79y5M/369au/qkTEI3ls4On2UCJSVx4beL9WWVlJbm4uZ8+erXYOnm4PJSKXNYrAy8rKYvr06VRWVlJaWkrz5s0pKyujbdu2fPrpp/Vdo4h4iEYReC+++CKPPfYY48ePp0+fPuzatYvly5fTtGnT+q5PRDxIo7gB6LFjxxg7dmy1ZZMmTeLNN9+sj5pExIO56zl44GDgtWjRgtLSUgDatGnD0aNHKSkp4fz58/VanIh4Fnc/8dihIe3gwYP5/PPPue+++3jggQcYO3YsPj4+xMTE1Hd9IuJBGsUxvNmzZ9ufP/roo/To0YOysjL69+9fb4WJiOdpFIH3a5dvqywi8kseG3hxcXEOFZaWlubUgkTEc7n7LO1vBt5DDz3UkHXU6s0336S8vNzVZYiDIiIiXF2COKh9+/Z89dVXTmnLY3t4999/f0PWISKNhKtPPanJNR3DExG5Go/t4YmI1JUCT0QMw2MnLURE6srde3gORW1lZSUvv/wygwYN4pZbbgHgq6++Ys2aNfVanIh4Fne/tMyhwFu4cCGHDx9myZIl9mJvuOEG3nnnnXotTkQ8i7sHnkND2k8++YTMzEwCAgLs4+/Q0FDy8/PrtTgR8Twef1pKkyZNsFqt1ZYVFRURFBRUL0WJiGdqFMfwhgwZwrPPPsuJEycAKCgoIDk5md///vf1WpyIeJbafqLRkVnceq3PkY2eeuop2rdvz7BhwygpKSEmJoaQkBCmTp1a3/WJiAdpFMfwfH19mTVrFrNmzaKoqIiWLVu69ThdRFzD3Ye0DgXe5aHsZWVlZfbnHTp0cG5FIuKxGkXgDR48GJPJVO3nGS8X/d1339VPZSLicRpF4B06dKja6//7v/9j+fLluhGoiFzBnQ93XdN0SZs2bZg9ezZLly51dj0i4sHcfZb2mq+l/eGHH3RDThGpplEMaX99u/fy8nKOHj2q01JEpJpGEXi/vt1706ZNufHGG3UbbxGpxuMDz2q18s033zBv3jx8fX0boiYR8VAeH3je3t7s2LHDrWdeRMQ9mEymWicl6polTzzxBCdPnsTLy4uAgABeeOEFoqKiyM3NJSEhgeLiYoKCgkhJSal11OnQdMm4ceNYtmwZFy9erFOhImIs9XFpWUpKCv/617/YtGkTEydOZNasWQAkJSURFxfH1q1biYuLIzExsda2auzhZWRkEBsby5o1azhz5gyrV68mODi4WsHbt2+vU/Ei0njVx5C2RYsW9uelpaWYTCYKCwvJzs5m9erVAMTGxjJv3jyKiooIDg7+zbZqDLzExERiY2NZvHhxnQoUEWOqS+BZLJYrbjsXGBhIYGDgFe+ZPXs2O3bswGazsWrVKiwWC6GhoXh7ewM/H3oLCQnBYrFce+BdvpTs1ltvrfEDiIhA3QJv9OjR5OXlVVsXHx/PtGnTrnjPggULANi0aROLFi3iySefvKb6agy8qqoqvvnmm2rX0P7abbfddk07FpHGpy6Bl5aWdtUeXk3+8Ic/kJiYSNu2bcnPz8dqteLt7Y3VaqWgoICwsLAa319j4FVWVjJ79uzfDDyTycSnn35a4w5ExDjq8jONtYUT/HxnppKSEvu227Zt47rrrqNVq1ZERUWRkZHB8OHDycjIICoqqsbhLNQSeE2bNlWgiUidOPMUtvLycp588knKy8vx8vLiuuuuIzU1FZPJxJw5c0hISGDlypUEBgaSkpJSa3v6XVoRcRpnz9K2bt2adevWXXVdZGQk69evr1N9Dk1aiIg4wqOvtNizZ09D1SEijYBHB56ISF0o8ETEMOoyS+sKCjwRcRr18ETEUNz5zkoKPBFxGvXwRMQwFHgiYhiatBARw1APT0QMQ4EnIoahwBMRQ9FpKSJiCOrhiYhhaJZWRAxDPTwRMQwFnogYhgJPRAxDgScihmEymWqdlFDgNWIXL15kxYoV7Nmzh9LSUsLCwhg3bhx9+vQhPz+fCRMm4O/vb9/+wQcfJC4uzoUVC8DatWvp1asXly5dAuD06dMMGjSo2jaLFi3i4YcfZsCAARw/ftwVZbod9fCAlJQUtm7dSl5eHunp6XTp0qUhdusWrFYrrVu3ZtGiRbRp04bdu3fz0ksvsXLlSvs269evx9vb24VVytUkJiby7rvvXnWd2WymY8eODVyR+3P3wGuQE2IGDRpEWloa7dq1a4jduRV/f3/GjBlDaGgoXl5eREdHExoaypEjR1xdmlwjb29v5s6dS1JSkqtLcTuXA6+2h6s0SOCZzWaHfmXcCM6ePUteXl613sH48eN55JFHWLp0KefOnXNhdfJLM2fO5L///S/vvfceffv2tS9/9NFH2bVrF4cOHXJhde7J3QNPx/Aa0KVLl1i8eDH33HMPHTp0oLy8nFdeeYXIyEhKSkpYuXIlixcvZv78+a4u1fBeeukljhw5wsWLF7nvvvtYtWoVQ4cO5eLFi8TFxXHfffe5ukS35O5DWgVeA6mqqmLJkiX4+PgwZcoUAJo2bWo/ntmyZUumTJnCmDFjOH/+PAEBAa4s1/D27t1rf/7+++8zbNgw7r77bvr27ctrr73GTz/95MLq3Je7z9K67qI2A7HZbLzyyisUFxcze/ZsfHyu/v/M5T8Em83WkOWJA2w2GyaTiTvuuIPnnnuO3bt3s3v3bgA2bNjAsGHDXFyhe9CQVli+fDknTpxg4cKF+Pn52ZcfOnSI5s2bEx4eTmlpKampqXTv3p1mzZq5sFoJDAykZ8+e7Ny5k0uXLhEbG8utt97K3LlzSU9Pr9aD2b17N4899hjZ2dkurNh9aEgLzJ8/n8zMTM6cOcOECRMICgpi8+bNDbFrl8vPz2fLli00adKE0aNH25dPmzYNk8nEW2+9RXFxMQEBAfTq1YuZM2e6sFoB8PHx4emnnyYyMpKqqipycnKYNGkSubm5V92+qKiIioqKBq7SPbl74JlsHjJ+2r59O+Xl5a4uQxx0+TiluL/27dvz1VdfOaWtrKysWsPfz88Ps9nslP3VlYa0IuI07t7DU+CJiNPoBqAiYij6TQsRMQQNaUXEMBR4ImIYCjwRMQxnT1qcPXuWmTNn8uOPP+Lr60vHjh1JTk4mODiYvXv3kpiYSEVFBe3atWPx4sW0atWq5n07vGcRkVo4+9Iyk8nEY489xtatW0lPT6dDhw4sWbKEqqoqZsyYQWJiIlu3bsVsNrNkyZJa21PgiYjT1CXwLBYLJ0+erPYoKSmp1l5QUBDR0dH21z179uTUqVMcOHCg2gnMo0aN4qOPPqq1Pg1pRcSpHO3BjR49mry8vGrL4uPjmTZt2lW3r6qq4p133mHgwIFYLBbCw8Pt64KDg6mqqqK4uJigoKDf3KcCT0Scpi6TFmlpaVit1mrrAgMDf/N98+bNIyAggDFjxvDxxx9fU30KPBFxmroEXl3ugp6SksLx48dJTU3Fy8uLsLAwTp06ZV9fVFSEl5dXjb070DE8EXGiy7O0tT3qYunSpRw4cIAVK1bg6+sLQLdu3bhw4QJZWVnAz78yN2TIkFrbUg9PRJzG2efhHTlyhNdff52IiAhGjRoF/Hx3lxUrVrBo0SKSkpKqnZZSGwWeiDiNswPvhhtu4Pvvv7/qut69e5Oenl6n+hR4IuI0utJCRAxFd0sREUNQD09EDEM3ABURw1APT0QMQ4EnIoahwBMRw1DgiYhhmEymWiclFHgi0iiohycihqHAExHDUOCJiGEo8ETEMBR4ImIYmqUVEcNQD09EDEOBJyKGocATEcNQ4ImIYSjwRMQwHLm5p24AKiKNhn7TQkQMwZGw05BWRBoFBZ6IGIYCT0QMw8vLC5vNVuM2CjwRaRTUwxMRw1DgiYih6LQUETEEdw47UOCJiBMp8ETEMFx52ZgjFHgi4jTq4TmJv7+/q0uQOmjfvr2rSxAHtW3b1mltuXvgmWy1nSUoItJIuPeAW0TEiRR4ImIYCjwRMQwFnogYhgJPRAxDgScihqHAExHDUOCJiGEo8ETEMBR4LpCbm8vIkSOJiYlh5MiRHDt2zNUlSQ1SUlIYOHAgXbt25fDhw64uR/4HCjwXSEpKIi4ujq1btxIXF0diYqKrS5IaDBo0iLS0NNq1a+fqUuR/pMBrYIWFhWRnZxMbGwtAbGws2dnZFBUVubgy+S1ms5mwsDBXlyFOoMBrYBaLhdDQULy9vQHw9vYmJCQEi8Xi4spEGj8FnogYhgKvgYWFhZGfn4/VagXAarVSUFCgIZNIA1DgNbBWrVoRFRVFRkYGABkZGURFRREcHOziykQaP90A1AVycnJISEigpKSEwMBAUlJS6NSpk6vLkt8wf/58MjMzOXPmDC1btiQoKIjNmze7uiy5Bgo8ETEMDWlFxDAUeCJiGAo8ETEMBZ6IGIYCT0QMQ4EnJCQk8PLLLwOQlZVFTExMg+y3a9euHD9+/KrrHnnkEdavX+9QOwMHDuTf//73NdXwv7xXPI8Cz0MMHDiQ7t2706tXL26//XYSEhIoKytz+n7MZjNbt26tdbsNGzbwxz/+0en7F6lPCjwPkpqayp49e9i4cSMHDhzgr3/96xXbXLp0yQWViXgGBZ4HCg0NpX///hw5cgT4eWiYlpbGvffey7333gvAZ599xvDhwzGbzYwaNYpDhw7Z35+dnc39999Pr169mD59OhUVFfZ1O3fu5M4777S/tlgsxMfH07dvX6Kjo0lOTiYnJ4ekpCT27t1Lr169MJvNAFRWVpKSksJdd93F7bffTmJiIhcuXLC3tWrVKvr160e/fv147733HP68P/74I2PHjiU6Opro6GiefvppSkpKqm2zf/9+hg4dSp8+fXjuueeqfaaavgsxFgWeB7JYLHzxxRdERUXZl33yySesW7eODz/8kOzsbGbNmkVycjI7d+5k5MiRPPHEE1RWVlJZWcnUqVMZPnw4u3btYsiQIWRmZl51P1arlccff5zw8HC2bdvGF198wdChQ4mMjGTu3Ln07NmTPXv2kJWVBcCSJUvIzc1l06ZNZGZmUlBQwIoVKwD44osveOONN3jjjTfIzMzk66+/dvjz2mw2Hn/8cb788ku2bNnC6dOnWbZsWbVt0tPT+fvf/87HH39Mbm4uK1euBKjxuxDjUeB5kKlTp2I2m4mLi6NPnz5MnjzZvm7SpEkEBQXh7+/Pu+++y8iRI+nRowfe3t7cf//9NGnShL1797Jv3z4uXrzIuHHjaNKkCUOGDOHmm2++6v6+/fZbCgoKmDlzJgEBAfj5+dl7c79ms9lYt24ds2bNIigoiObNm/P444/brzndsmULI0aMoEuXLgQEBBAfH+/w5+7YsSN33HEHvr6+BAcHM2HCBHbv3l1tm9GjRxMWFkZQUBBTpkyx77em70KMx8fVBYjjVqxYwe23337Vdb+8vdSpU6fYtGkTa9assS+7ePEiBQUFmEwmQkNDMZlM9nXh4eFXbdNisRAeHo6PT+1/JkVFRZSXlzNixAj7MpvNRlVVFQAFBQV069bNvq4ut0s/c+YMCxYsICsri7KyMmw2G4GBgdW2+eXnDw8Pp6CgAKj5uxDjUeA1Er8MsLCwMCZPnsyUKVOu2G7Xrl3k5+djs9ns7zl16hQdOnS4YtuwsDAsFguXLl26IvR+uT+Ali1b4u/vz+bNmwkNDb2irV/f1fnUqVMOf7alS5diMplIT08nKCiITz75hOTk5Grb/LrtkJAQ+2f4re9CjEdD2kbooYceYu3atezbtw+bzcb58+fZvn07paWl9OzZEx8fH95++20uXrxIZmYm+/fvv2o73bt3p02bNvzlL3/h/PnzVFRU8J///Af4+b5++fn59mNhXl5ePPTQQyxcuJDCwkIA8vPz+fLLLwEYMmQIGzdu5OjRo5SXl7N8+XKHP09ZWRkBAQG0aNGC/Px8Vq1adcU2//znPzl9+jTFxcWkpqYydOjQWr8LMR4FXiN08803M2/ePJKTk+nTpw/33nsvGzZsAMDX15dly5axceNGbr31Vj788EMGDx581Xa8vb1JTU3l+PHj3H333dx5551s2bIFgL59+9K5c2f69etHdHQ0ADNmzKBjx448/PDD9O7dm/Hjx5ObmwvAgAEDGDduHOPGjWPw4MH07dvX4c8THx9PdnY2ZrOZSZMm2Weifyk2NpaJEydyzz33cP3119t7dDV9F2I8uh+eiBiGengiYhgKPBExDAWeiBiGAk9EDEOBJyKGocATEcNQ4ImIYSjwRMQwFHgiYhj/D57DTZYdnpR9AAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#7. Support Vector Classifier"
      ],
      "metadata": {
        "id": "r0kXXWcvHSGd"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "classifier = SVC(kernel='rbf', random_state=100)\n",
        "classifier.fit(X_train, y_train)\n",
        "y_pred = classifier.predict(X_test)\n",
        "print(classification_report(y_test, y_pred))\n",
        "print('\\n')\n",
        "print('------------------------')\n",
        "print('Confusion Matrix')\n",
        "print('------------------------')\n",
        "print('')\n",
        "print(confusion_matrix(y_test, y_pred))\n",
        "plot_confusion_matrix(classifier, X_test, y_test,cmap=\"binary\") \n",
        "plt.grid(False)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 635
        },
        "id": "4cP2O-bhHX3X",
        "outputId": "8dbeb0e1-76da-4bc9-c522-26a268a11685"
      },
      "execution_count": 143,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.65      0.80      0.72        71\n",
            "           1       0.77      0.61      0.68        79\n",
            "\n",
            "    accuracy                           0.70       150\n",
            "   macro avg       0.71      0.71      0.70       150\n",
            "weighted avg       0.71      0.70      0.70       150\n",
            "\n",
            "\n",
            "\n",
            "------------------------\n",
            "Confusion Matrix\n",
            "------------------------\n",
            "\n",
            "[[57 14]\n",
            " [31 48]]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.8/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function plot_confusion_matrix is deprecated; Function `plot_confusion_matrix` is deprecated in 1.0 and will be removed in 1.2. Use one of the class methods: ConfusionMatrixDisplay.from_predictions or ConfusionMatrixDisplay.from_estimator.\n",
            "  warnings.warn(msg, category=FutureWarning)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 8. Gradient Boosting Classifier"
      ],
      "metadata": {
        "id": "yFeDJJP2Hf6V"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.ensemble import GradientBoostingClassifier\n",
        "clff = GradientBoostingClassifier(n_estimators=100, learning_rate=0.2, max_depth=1, random_state=23)\n",
        "clff.fit(X_train, y_train)\n",
        "y_pred=clff.predict(X_test)\n",
        "print(classification_report(y_test, y_pred))\n",
        "print('')\n",
        "print('------------------------')\n",
        "print('Confusion Matrix')\n",
        "print('------------------------')\n",
        "print('')\n",
        "print(confusion_matrix(y_test, y_pred))\n",
        "plot_confusion_matrix(clff, X_test, y_test,cmap=\"binary\") \n",
        "plt.grid(False)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 621
        },
        "id": "M9hPCtFxHj0T",
        "outputId": "d59082ba-232e-4715-ffa1-0d559eab7cce"
      },
      "execution_count": 133,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.88      0.92      0.90        71\n",
            "           1       0.92      0.89      0.90        79\n",
            "\n",
            "    accuracy                           0.90       150\n",
            "   macro avg       0.90      0.90      0.90       150\n",
            "weighted avg       0.90      0.90      0.90       150\n",
            "\n",
            "\n",
            "------------------------\n",
            "Confusion Matrix\n",
            "------------------------\n",
            "\n",
            "[[65  6]\n",
            " [ 9 70]]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.8/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function plot_confusion_matrix is deprecated; Function `plot_confusion_matrix` is deprecated in 1.0 and will be removed in 1.2. Use one of the class methods: ConfusionMatrixDisplay.from_predictions or ConfusionMatrixDisplay.from_estimator.\n",
            "  warnings.warn(msg, category=FutureWarning)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 9. Naive Bayes Algorithm"
      ],
      "metadata": {
        "id": "mFA6U3QkrU6A"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.naive_bayes import GaussianNB\n",
        "\n",
        "nb = GaussianNB()\n",
        "\n",
        "nb.fit(X_train,y_train)\n",
        "\n",
        "Y_pred_nb = nb.predict(X_test)"
      ],
      "metadata": {
        "id": "MOhLXU7FqjaG"
      },
      "execution_count": 134,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "Y_pred_nb.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CKVNPJRcrBCl",
        "outputId": "fac1132b-222f-48ce-affe-f6507c754d1b"
      },
      "execution_count": 135,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(150,)"
            ]
          },
          "metadata": {},
          "execution_count": 135
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "score_nb = round(accuracy_score(Y_pred_nb,y_test)*100,2)\n",
        "\n",
        "print(\"The accuracy score achieved using Naive Bayes is: \"+str(score_nb)+\" %\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6KhQMLBjrEY6",
        "outputId": "2cb6603c-c326-47e3-d53c-b3b47df52782"
      },
      "execution_count": 136,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The accuracy score achieved using Naive Bayes is: 82.67 %\n"
          ]
        }
      ]
    }
  ]
}