Loan Approval Prediction.ipynb

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      "cell_type": "code",
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      "metadata": {
        "id": "04fb37db"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "\n",
        "from sklearn import metrics\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.model_selection import train_test_split, cross_val_score, GridSearchCV\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.preprocessing import MinMaxScaler\n",
        "from sklearn.metrics import confusion_matrix, classification_report, roc_auc_score\n",
        "from sklearn.decomposition import PCA\n",
        "from sklearn.datasets import make_classification\n",
        "from imblearn.over_sampling import SMOTE\n",
        "from sklearn.svm import SVC\n",
        "\n",
        "from sklearn.naive_bayes import GaussianNB\n",
        "from sklearn.metrics import ConfusionMatrixDisplay\n",
        "\n",
        "plt.rcParams[\"figure.figsize\"] = (6,5)\n",
        "\n",
        "import warnings\n",
        "warnings.filterwarnings(\"ignore\")"
      ],
      "id": "04fb37db"
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Data visualization"
      ],
      "metadata": {
        "id": "KtMUlgwx39T4"
      },
      "id": "KtMUlgwx39T4"
    },
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Loan_ID</th>\n",
              "      <th>Gender</th>\n",
              "      <th>Married</th>\n",
              "      <th>Dependents</th>\n",
              "      <th>Education</th>\n",
              "      <th>Self_Employed</th>\n",
              "      <th>ApplicantIncome</th>\n",
              "      <th>CoapplicantIncome</th>\n",
              "      <th>LoanAmount</th>\n",
              "      <th>Loan_Amount_Term</th>\n",
              "      <th>Credit_History</th>\n",
              "      <th>Property_Area</th>\n",
              "      <th>Loan_Status</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>LP001002</td>\n",
              "      <td>Male</td>\n",
              "      <td>No</td>\n",
              "      <td>0</td>\n",
              "      <td>Graduate</td>\n",
              "      <td>No</td>\n",
              "      <td>5849</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>360.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>Urban</td>\n",
              "      <td>Y</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>LP001003</td>\n",
              "      <td>Male</td>\n",
              "      <td>Yes</td>\n",
              "      <td>1</td>\n",
              "      <td>Graduate</td>\n",
              "      <td>No</td>\n",
              "      <td>4583</td>\n",
              "      <td>1508.0</td>\n",
              "      <td>128.0</td>\n",
              "      <td>360.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>Rural</td>\n",
              "      <td>N</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>LP001005</td>\n",
              "      <td>Male</td>\n",
              "      <td>Yes</td>\n",
              "      <td>0</td>\n",
              "      <td>Graduate</td>\n",
              "      <td>Yes</td>\n",
              "      <td>3000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>66.0</td>\n",
              "      <td>360.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>Urban</td>\n",
              "      <td>Y</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>LP001006</td>\n",
              "      <td>Male</td>\n",
              "      <td>Yes</td>\n",
              "      <td>0</td>\n",
              "      <td>Not Graduate</td>\n",
              "      <td>No</td>\n",
              "      <td>2583</td>\n",
              "      <td>2358.0</td>\n",
              "      <td>120.0</td>\n",
              "      <td>360.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>Urban</td>\n",
              "      <td>Y</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>LP001008</td>\n",
              "      <td>Male</td>\n",
              "      <td>No</td>\n",
              "      <td>0</td>\n",
              "      <td>Graduate</td>\n",
              "      <td>No</td>\n",
              "      <td>6000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>141.0</td>\n",
              "      <td>360.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>Urban</td>\n",
              "      <td>Y</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "    Loan_ID Gender Married  ... Credit_History Property_Area Loan_Status\n",
              "0  LP001002   Male      No  ...            1.0         Urban           Y\n",
              "1  LP001003   Male     Yes  ...            1.0         Rural           N\n",
              "2  LP001005   Male     Yes  ...            1.0         Urban           Y\n",
              "3  LP001006   Male     Yes  ...            1.0         Urban           Y\n",
              "4  LP001008   Male      No  ...            1.0         Urban           Y\n",
              "\n",
              "[5 rows x 13 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 2
        }
      ],
      "source": [
        "dataset = pd.read_csv('loan_dataset.csv')\n",
        "dataset.head()"
      ],
      "id": "236429bb"
    },
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      "outputs": [
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          "data": {
            "text/plain": [
              "(614, 13)"
            ]
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          "execution_count": 3
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      ],
      "source": [
        "dataset.shape"
      ],
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        "outputId": "c3475173-c63e-49e0-8416-14a01e8573d4"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<bound method DataFrame.info of       Loan_ID  Gender Married  ... Credit_History Property_Area Loan_Status\n",
              "0    LP001002    Male      No  ...            1.0         Urban           Y\n",
              "1    LP001003    Male     Yes  ...            1.0         Rural           N\n",
              "2    LP001005    Male     Yes  ...            1.0         Urban           Y\n",
              "3    LP001006    Male     Yes  ...            1.0         Urban           Y\n",
              "4    LP001008    Male      No  ...            1.0         Urban           Y\n",
              "..        ...     ...     ...  ...            ...           ...         ...\n",
              "609  LP002978  Female      No  ...            1.0         Rural           Y\n",
              "610  LP002979    Male     Yes  ...            1.0         Rural           Y\n",
              "611  LP002983    Male     Yes  ...            1.0         Urban           Y\n",
              "612  LP002984    Male     Yes  ...            1.0         Urban           Y\n",
              "613  LP002990  Female      No  ...            0.0     Semiurban           N\n",
              "\n",
              "[614 rows x 13 columns]>"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ],
      "source": [
        "dataset.info"
      ],
      "id": "b4f59746"
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              "\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>ApplicantIncome</th>\n",
              "      <th>CoapplicantIncome</th>\n",
              "      <th>LoanAmount</th>\n",
              "      <th>Loan_Amount_Term</th>\n",
              "      <th>Credit_History</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>614.000000</td>\n",
              "      <td>614.000000</td>\n",
              "      <td>592.000000</td>\n",
              "      <td>600.00000</td>\n",
              "      <td>564.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>5403.459283</td>\n",
              "      <td>1621.245798</td>\n",
              "      <td>146.412162</td>\n",
              "      <td>342.00000</td>\n",
              "      <td>0.842199</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>6109.041673</td>\n",
              "      <td>2926.248369</td>\n",
              "      <td>85.587325</td>\n",
              "      <td>65.12041</td>\n",
              "      <td>0.364878</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>150.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>9.000000</td>\n",
              "      <td>12.00000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>2877.500000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>100.000000</td>\n",
              "      <td>360.00000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>3812.500000</td>\n",
              "      <td>1188.500000</td>\n",
              "      <td>128.000000</td>\n",
              "      <td>360.00000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>5795.000000</td>\n",
              "      <td>2297.250000</td>\n",
              "      <td>168.000000</td>\n",
              "      <td>360.00000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>81000.000000</td>\n",
              "      <td>41667.000000</td>\n",
              "      <td>700.000000</td>\n",
              "      <td>480.00000</td>\n",
              "      <td>1.000000</td>\n",
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            "text/plain": [
              "       ApplicantIncome  CoapplicantIncome  ...  Loan_Amount_Term  Credit_History\n",
              "count       614.000000         614.000000  ...         600.00000      564.000000\n",
              "mean       5403.459283        1621.245798  ...         342.00000        0.842199\n",
              "std        6109.041673        2926.248369  ...          65.12041        0.364878\n",
              "min         150.000000           0.000000  ...          12.00000        0.000000\n",
              "25%        2877.500000           0.000000  ...         360.00000        1.000000\n",
              "50%        3812.500000        1188.500000  ...         360.00000        1.000000\n",
              "75%        5795.000000        2297.250000  ...         360.00000        1.000000\n",
              "max       81000.000000       41667.000000  ...         480.00000        1.000000\n",
              "\n",
              "[8 rows x 5 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ],
      "source": [
        "# gives the info for numerical columns\n",
        "dataset.describe()"
      ],
      "id": "e73471a2"
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(10,7))\n",
        "sns.heatmap(dataset[['ApplicantIncome', 'CoapplicantIncome', 'LoanAmount', 'Loan_Amount_Term', 'Credit_History']].corr(), annot=True, cmap='inferno') "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rn2rI2RAu1Yr",
        "outputId": "86f85fa0-a4be-481b-c037-84b859b6d0b5"
      },
      "id": "rn2rI2RAu1Yr",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f8c2f84a090>"
            ]
          },
          "metadata": {},
          "execution_count": 6
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x504 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "b52ac311",
        "outputId": "3da91584-4d4d-405e-eceb-9f4bc2c1d0ef"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f8c27206610>"
            ]
          },
          "metadata": {},
          "execution_count": 7
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "# check if the data is imbalanced\n",
        "sns.set_style('whitegrid')\n",
        "sns.countplot(x='Loan_Status',data=dataset)"
      ],
      "id": "b52ac311"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4bc160a1",
        "outputId": "575c56c0-de8a-4674-808d-08af27fd2836"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Y    68.729642\n",
              "N    31.270358\n",
              "Name: Loan_Status, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ],
      "source": [
        "# see the chart in details\n",
        "dataset['Loan_Status'].value_counts()/len(dataset)*100\n"
      ],
      "id": "4bc160a1"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "48824ae0",
        "outputId": "c2c4e2b7-9fef-47c2-a8a6-d1d9746bb31f"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Loan_ID               0\n",
              "Gender               13\n",
              "Married               3\n",
              "Dependents           15\n",
              "Education             0\n",
              "Self_Employed        32\n",
              "ApplicantIncome       0\n",
              "CoapplicantIncome     0\n",
              "LoanAmount           22\n",
              "Loan_Amount_Term     14\n",
              "Credit_History       50\n",
              "Property_Area         0\n",
              "Loan_Status           0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ],
      "source": [
        "# check if we have null values\n",
        "dataset.isnull().sum()"
      ],
      "id": "48824ae0"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "099b4a79",
        "outputId": "4438ec5f-fc71-43c3-d4ab-6dcb8ece8d27"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of numerical variables:  5\n",
            "['ApplicantIncome', 'CoapplicantIncome', 'LoanAmount', 'Loan_Amount_Term', 'Credit_History']\n"
          ]
        }
      ],
      "source": [
        "# divide the numerical from categorical features\n",
        "\n",
        "numerical_features = [feature for feature in dataset.columns if dataset[feature].dtypes != 'O']\n",
        "\n",
        "print('Number of numerical variables: ', len(numerical_features))\n",
        "print(numerical_features)"
      ],
      "id": "099b4a79"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9288d944",
        "outputId": "825f1395-d98f-414d-a27e-61a6c64c23b4"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Discrete Variables Count: 2\n",
            "['Loan_Amount_Term', 'Credit_History']\n"
          ]
        }
      ],
      "source": [
        "# numerical is divided in discrete and continuous\n",
        "\n",
        "df=dataset\n",
        "discrete_features=[feature for feature in numerical_features if len(df[feature].unique())<15]\n",
        "print(\"Discrete Variables Count: {}\".format(len(discrete_features)))\n",
        "print(discrete_features)"
      ],
      "id": "9288d944"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fce3857a",
        "outputId": "ce5e3760-f4d5-4ab7-d642-96b31676530c"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([360., 120., 240.,  nan, 180.,  60., 300., 480.,  36.,  84.,  12.])"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ],
      "source": [
        "df['Loan_Amount_Term'].unique()"
      ],
      "id": "fce3857a"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "75ee2478",
        "outputId": "626a0f61-db40-4a94-f81b-77509e55eada"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n"
          ]
        }
      ],
      "source": [
        "# plot the absolute frequencies for each feature\n",
        "sns.set_style('whitegrid')\n",
        "\n",
        "for feature in discrete_features:\n",
        "    sns.countplot(x=feature, data=df)\n",
        "    plt.xlabel(feature)\n",
        "    plt.ylabel('Count')\n",
        "    plt.title(feature)\n",
        "    plt.show()\n",
        "\n",
        "    print()"
      ],
      "id": "75ee2478"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "679db9b8",
        "outputId": "6ab43234-f951-4926-9572-cf834ccdfddb"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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NSK/X83gSNZErfIfqCqtGX1lcXl4Oo9F41zaCINi1MZlMEATBro2vry8UCgUAIC0tDb/++mtjSyIiokZwuEeQlJRke1xdXY0///wTL7300l23CQ8Ph8FgQElJCQRBgEajwYIFC+zanD9/Hh07dgQA7Ny5E126dGlI/URE1EQOB8HSpUv/byO5HH5+fpDL7765XC5Hbm4uMjMzYbFYkJqaipCQEOTn5yMsLAyxsbFYuXIldu7cCQ8PD7Rv3x5z5sxp/LshIqIGk1mtVqsjDadMmYL58+fXu64lcEy7efF4StNDORpnl+A2DHNVzi7BIXV91xt0i4nbVVVVcTyfiMgN1Ds09OGHH2Lp0qUwm83o3r07AMBqtUKhUGDIkCGiF0hEROKqNwjGjRuHcePGYcGCBcjOzm6JmoiIqAU5fLI4OzsbV65cwcmTJ2E2m23re/ToIUphRETUMhwOgrVr12LFihUwGo3o2rUrfvrpJ3Tr1q3ei7+IiOje5vDJ4hUrVmDdunXo1KkTVq5ciY0bN8LHx0fM2oiIqAU4HAQKhQJeXl4AgMrKSnTp0gUnTpwQrTAiImoZDg8N+fv74+rVq4iLi8Pzzz8PHx8fdOrUSczaiIioBTgcBO+//z4A4OWXX0avXr1QWlqKPn36iFYYERG1DIeHhqZMmWJ73LNnT8TGxmL69OmiFEVERC2n0VcWWywWXllMROQGeGUxEZHE8cpiIiKJqzcIzpw5Ax8fH1sI7Nu3Dzt27EDnzp0xfPhw26QyRETkmuo9RzBp0iRcv34dwM1bmGZlZaFTp07Q6/WYNWuW6AUSEZG46u0RVFRU2KaX3Lx5M1JTUzF69GhUV1cjOTlZ9AKJiEhcDZqzeN++fXjyySdvbtiq0dMdExHRPaTev+a9evVCVlYWZs+ejStXriAqKgrAzbmGPT09632B4uJiJCQkID4+Hmq1us5227dvx6OPPoqff/65AeUTEVFT1RsEr7/+Ovr164fOnTujqKjI9sf/4sWLeOWVV+66rcViQV5eHgoKCqDRaLB169Ya1yMAQFlZGVasWIEnnniikW+DiIgaq94gkMlkUKlUyMjIsJ0rAIDHHnsMTz/9tG05PT29xrY6nQ5BQUEIDAyEQqGASqWCVqut0S4/Px8vvPCC7aZ2RETUchy+11B9bp+s5haTyQR/f3/bsiAI0Ol0dm1+/fVXGI1GPPPMM1i2bJnDr6XX65tWMNlUVFTweEpMbROYU9O48neo2YJAJpM1eJvq6mrMnTsXc+bMadB2Xl5e/CA3I71ez+NJ1ESu8B2qK6xE/emPIAgwGo22ZZPJZDe8dO3aNRw9ehSjRo2CUqnE4cOH8eKLL/KEMRFRC2q2HoHVaq2xLjw8HAaDASUlJRAEARqNBgsWLLA9365dO+zfv9+2PHLkSEydOhXh4eHNVRYREdWj2YJg3rx5NXculyM3NxeZmZmwWCxITU1FSEgI8vPzERYWhtjY2OZ6eSIiaiSHg+Crr77Cu+++i0uXLsFqtcJqtUImk+HHH38EAPz973+vdbuYmBjExMTYrcvKyqq17cqVKx0th4iImonDQTB//nwsXboUXbp0EbMeIiJqYQ6fLPbz82MIEBG5IYd7BGFhYZg0aRLi4uLsbj3dr18/UQojIqKW4XAQXLt2DW3atMGePXvs1jMIiIhcm8NB0NCLvoiIyDU4HARmsxnr1q3DH3/8YXc7CQYEEZFrc/hk8ZQpU3DhwgV8++236NmzJ0wmE+677z4xayMiohbgcBCcOnUKkyZNQps2bZCSkoIPP/ywxg3kiIjI9TgcBHL5zVEkHx8fHD16FKWlpbh06ZJohRERUctw+BxBeno6rly5gqysLLz44ou4fv16nVcIExGR63A4CNLS0gAAPXv2rHVyGSIick0OB0FpaSkWLVqEgwcPArg5l/FLL72Edu3aiVYcERGJz+FzBNOnT4e3tzfy8/ORn5+P++67D9OmTROzNiIiagEO9whOnTqFRYsW2ZYnTJiA5ORkUYoiIqKW43CPoHXr1rZhIQD44Ycf0Lp1a1GKIiKiluNwj2DWrFmYOnUqysrKANz8GencuXNFK4yIiFqGw0HQtWtXbN682RYE3t7eWL58Obp27SpacUREJL4GT17v7e0Nb29vAMDy5cvrbV9cXIyEhATEx8dDrVbXeL6oqAhJSUlITk7GsGHDcOzYsYaWRERETdDgILhdbRPW385isSAvLw8FBQXQaDTYunVrjT/0SUlJ2LJlCzZt2oTMzEzexI6IqIU1KQhkMtldn9fpdAgKCkJgYCAUCgVUKlWNi9Fu9S4AoLy8vN59EhFR86r3HEFEREStf5ytVqvd7ahrYzKZ4O/vb1sWBKHWG9V9+umnKCwsxI0bN/DJJ5/UW7TZbIZer6+3HTmmoqKCx1NiQkNDnV2C23Hl71C9QXDo0CHRixgxYgRGjBiBLVu2YMmSJXjnnXfu2t7Ly4sf5Gak1+t5PImayBW+Q3WFVZOGhuojCAKMRqNt2WQyQRCEOturVCrs2LFDzJKIiOgOogZBeHg4DAYDSkpKUFlZCY1GA6VSadfGYDDYHn/99dcICgoSsyQiIrqDw9cRNGrncjlyc3ORmZkJi8WC1NRUhISEID8/H2FhYYiNjcWqVauwd+9eyOVy+Pj41DssREREzUvUIACAmJgYxMTE2K27fR6DN954Q+wSiIjoLkQdGiIionsfg4CISOIYBEREEscgICKSOAYBEZHEMQiIiCSOQUBEJHEMAiIiiWMQEBFJHIOAiEjiGARERBLHICAikjgGARGRxDEIiIgkjkFARCRxDAIiIoljEBARSZzoQVBcXIyEhATEx8dDrVbXeL6wsBCJiYlISkrCc889hzNnzohdEhER3UbUILBYLMjLy0NBQQE0Gg22bt2KY8eO2bUJDQ3F+vXrsWXLFiQkJGD+/PlilkRERHcQNQh0Oh2CgoIQGBgIhUIBlUoFrVZr1yYqKgpt2rQBAHTr1g1Go1HMkoiI6A6iTl5vMpng7+9vWxYEATqdrs7269atQ58+ferdr9lshl6vb5YaCaioqODxlJjQ0FBnl+B2XPk7JGoQNMSmTZvwyy+/YNWqVfW29fLy4ge5Gen1eh5PoiZyhe9QXWElahAIgmA31GMymSAIQo123333HZYuXYpVq1ZBoVCIWRIREd1B1HME4eHhMBgMKCkpQWVlJTQaDZRKpV2bI0eOIDc3F0uWLIGfn5+Y5RARUS1E7RHI5XLk5uYiMzMTFosFqampCAkJQX5+PsLCwhAbG4t58+bh+vXryMrKAgAEBARg6dKlYpZFRES3Ef0cQUxMDGJiYuzW3fqjDwDLly8XuwQiIroLXllMRCRxDAIiIoljEBARSRyDgIhI4hgEREQSxyAgIpI4BgERkcQxCIiIJI5BQEQkcQwCIiKJYxAQEUkcg4CISOIYBEREEscgICKSOAYBEZHEMQiIiCSOQUBEJHGiB0FxcTESEhIQHx8PtVpd4/nvv/8eKSkpeOyxx7Bt2zaxyyEiojuIGgQWiwV5eXkoKCiARqPB1q1bcezYMbs2AQEBmDNnDgYMGCBmKUREVAdR5yzW6XQICgpCYGAgAEClUkGr1SI4ONjWpnPnzgCAVq04SkVE5AyiBoHJZIK/v79tWRAE6HS6Ju/XbDZDr9c3eT90U0VFBY+nxISGhjq7BLfjyt8hUYNALF5eXvwgNyO9Xs/jSdRErvAdqiusRB2PEQQBRqPRtmwymSAIgpgvSUREDSRqEISHh8NgMKCkpASVlZXQaDRQKpViviQRETWQqEEgl8uRm5uLzMxMJCYmon///ggJCUF+fj60Wi2AmyeU+/Tpg23btmHmzJlQqVRilkRERHcQ/RxBTEwMYmJi7NZlZWXZHv/jH/9AcXGx2GUQEVEd+JtNEVXcsDi7BIe4wkkuwHWOJ5GrcclfDbmK1p4eeChH4+wy3IZhLocNicTAHgERkcQxCIiIJI5BQEQkcQwCIiKJYxAQEUkcg4CISOIYBEREEscgICKSOAYBEZHEMQiIiCSOQUBEJHEMAiIiiWMQEBFJHIOAiEjiGARERBInehAUFxcjISEB8fHxUKvVNZ6vrKzEpEmTEB8fj7S0NJw+fVrskoiI6DaiBoHFYkFeXh4KCgqg0WiwdetWHDt2zK7N2rVr4ePjg//973/IyMjAu+++K2ZJRER0B1GDQKfTISgoCIGBgVAoFFCpVLZJ62/ZuXMnUlJSAAAJCQnYu3cvrFarmGUREdFtRJ2q0mQywd/f37YsCAJ0Ol2NNgEBATeLkcvRrl07XL58GR06dKhzv2azGXq9Xpyim9mXzz3i7BLchqv8P3cV/Gw2H1f5bJrN5lrXu+Scxd26dXN2CUREbkPUoSFBEGA0Gm3LJpMJgiDUaHPu3DkAQFVVFUpLS+Hr6ytmWUREdBtRgyA8PBwGgwElJSWorKyERqOBUqm0a6NUKrFx40YAwPbt2xEVFQWZTCZmWUREdBuZVeQzs7t378bbb78Ni8WC1NRUvPjii8jPz0dYWBhiY2NhNpsxZcoU6PV6tG/fHgsXLkRgYKCYJRER0W1EDwIiIrq38cpiIiKJYxAQEUkcg4CISOIYBBL1119/4a+//nJ2GUR0D3DJC8qocc6ePYv58+dj79698PHxgdVqRVlZGaKiopCdnY3OnTs7u0QicgL+akhC0tPT8dxzzyEhIQEeHh4Abt4YcNu2bfjkk0/w2WefOblCIuDixYswmUwAbl5w+sADDzi5IvfHHoGEXL58GYmJiXbrPDw8oFKpkJ+f76SqiG7S6/WYOXMmSktLbXcgMBqN8PHxwcyZM/H44487uUL3xSCQkMcffxz/+c9/kJKSYrsZoNFoxMaNGxEaGurk6kjqcnJykJeXhyeeeMJu/eHDhzFt2jRs3rzZSZW5PwaBhLzzzjtYt24d3nvvPZw/fx7Aza533759kZaW5uTqSOrKy8trhABw8yaT5eXlTqhIOniOgIjuCbNnz8apU6cwcOBAux7r559/js6dOyM3N9fJFbovBgEBAHbt2oW+ffs6uwySuN27d0Or1dp6rB07dkRsbCxiYmKcXJl7YxAQAOC9997DxIkTnV0GETkBg0Bijh8/Xuu/uLp06eLkyojqtmbNGqSnpzu7DLfFK4slRK1W49VXXwVwc66I8PBwAMCrr74KtVrtzNKI7or/XhUXfzUkIevXr8fWrVvh6elptz4jIwMDBgzA2LFjnVQZ0d3d+Zml5sUegYTIZDLbkNDtLly4wFnh6J62aNEiZ5fg1tgjkJDp06cjIyMDQUFBCAgIAHDz/kOnTp3CjBkznFwdSV1SUlKdz128eLEFK5EeniyWmOrqauh0Ort7uYSHh9vuPUTkLE899RSWLVsGHx8fu/VWqxVDhw7Ft99+66TK3B97BBLTqlUrdOvWzdllENXwzDPP4Nq1a7Xe7qRXr15OqEg62CMgIpI4niwmIpI4BgERkcQxCMhtXLhwAa+88gri4uIwaNAgvPDCCzhx4kSj9pWTk4Nt27YBAF5//XUcO3YMALB06dJ6t42IiLBb3rBhA/Ly8gAARUVF+Pzzz+vcdv/+/fjxxx8bVTNRY/FkMbkFq9WKCRMmYODAgVi4cCEA4LfffsOlS5fw8MMPAwCqqqoglzf8I//WW2/ZHn/44YcYP358o+scNmzYXZ8/cOAA2rZti+7duzu8z8a+L6Jb+Okht7Bv3z7I5XK7P7Rdu3bF/v37MXz4cPj4+ODEiRP44osv8O677+LAgQOorKzEiBEjMHToUFitVrz55pvYs2cPAgIC7K5kHTlyJKZOnYrt27ejoqICycnJCA4OxoIFCxpc56JFi9C2bVuMGTMGK1aswOrVq+Hh4YHg4GBkZ2dj9erVaNWqFTZv3owZM2bA398f06dPx+XLl9GhQwfMmTMHnTp1Qk5ODhQKBfR6Pbp3745du3Zh9erV6NChA6qrq5GQkIA1a9agQ4cOzXJ8yb0xCMgt/PHHH3VOZXjkyBFs2bIFgYGBWLNmDdq1a4f169ejsrISQ4cORe/evaHX621BcfHiRahUKqSmptrtZ/Lkyfj000+xadOmu9ZyKyxuuXLlCpRKZY12arUaO++Jf2sAAAKhSURBVHfuhEKhwNWrV+Hj44OhQ4faggIAxo8fj5SUFKSkpGDdunWYPXs2PvjgAwCAyWSyBUm7du2wefNmZGRk4LvvvkPXrl0ZAuQwBgG5vfDwcAQGBgIA9uzZg99//x3bt28HAJSWluLkyZP4/vvvoVKp4OHhAUEQEBUV1ejXa926tV1YbNiwAb/88kuNdo8++igmT56M2NhYxMXF1bqvQ4cO2W6vkJycjPnz59ue++c//2m7EDA1NRUvvfQSMjIysH79egwaNKjR9ZP08GQxuYWQkBD8+uuvtT7Xtm1b22Or1Yo33ngDmzZtwqZNm7Bz505ER0e3VJl21Go1hg8fjiNHjmDw4MGoqqpq0PZt2rSxPQ4ICICfnx/27t0LnU6HPn36NHe55MYYBOQWoqKiUFlZiTVr1tjW/fbbbzh48KBdu+joaBQVFeHGjRsAgBMnTuD69evo0aMHvvzyS1gsFpw/fx779++v9XXkcrlt26aorq7GuXPnEBUVhcmTJ6O0tBTXr1/Hfffdh2vXrtnaRUREQKPRAAC2bNmCyMjIOveZlpaGKVOm2PUUiBzBoSFyCzKZDIsXL8bbb7+Njz76CF5eXnjwwQdrDLmkpaXhzJkzGDRoEKxWK3x9ffHBBx8gPj4e+/btQ2JiIjp16lTnbTiGDBmCZ599Fo899lijThbfYrFYMGXKFJSVlcFqtWLUqFHw8fFB3759MXHiRGi1WsyYMQMzZszAtGnTsGzZMtvJ4roolUpMmzaNw0LUYLzFBJGb+PnnnzFnzhz897//dXYp5GLYIyByA2q1GkVFRXYnk4kcxR4BUSNcvnwZGRkZNdYvX74cvr6+LV8QURMwCIiIJI6/GiIikjgGARGRxDEIiIgkjkFARCRxDAIiIon7f3cD2EkRiJ7hAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "#substitute the target values with 1 and 0\n",
        "df['Loan_Status'] = df['Loan_Status'].map({'Y': 1,'N' : 0})\n",
        "\n",
        "for feature in discrete_features:\n",
        "    data=df.copy()\n",
        "    data.groupby(feature)['Loan_Status'].mean().plot.bar()\n",
        "    plt.xlabel(feature)\n",
        "    plt.ylabel('Loan_Status')\n",
        "    plt.title(feature)\n",
        "    plt.show()"
      ],
      "id": "679db9b8"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4f602c39",
        "outputId": "c3b38b61-9dba-45d6-e681-bd30d2bee8c8"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Continuous feature Count 3\n",
            "['ApplicantIncome', 'CoapplicantIncome', 'LoanAmount']\n"
          ]
        }
      ],
      "source": [
        "# continuous features\n",
        "\n",
        "continuous_feature=[feature for feature in numerical_features if feature not in discrete_features]\n",
        "print(\"Continuous feature Count {}\".format(len(continuous_feature)))\n",
        "print(continuous_feature)"
      ],
      "id": "4f602c39"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "214bcd93",
        "outputId": "6c269344-80a2-4d9d-ba7a-559937a96e9d"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x288 with 1 Axes>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x288 with 1 Axes>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x288 with 1 Axes>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "5403.459283387622\n",
            "3812.5\n"
          ]
        }
      ],
      "source": [
        "for feature in continuous_feature:\n",
        "    data=df.copy()\n",
        "    data[feature].hist(color='green',bins=40,figsize=(8,4)) \n",
        "    plt.xlabel(feature)\n",
        "    plt.ylabel(\"Count\")\n",
        "    plt.title(feature)\n",
        "    plt.show()\n",
        "    \n",
        "print(df['ApplicantIncome'].mean())\n",
        "print(df['ApplicantIncome'].median())\n",
        "\n",
        "# normal dist- mean and median are close to each-other\n",
        "# right skewed-  MEAN > MEDIAN\n",
        "# left skewed - MEAN < MEDIAN\n",
        "\n",
        "# first graph - more skewed than normal\n",
        "# second - skewed to the right\n",
        "# third is close to a normal distribution"
      ],
      "id": "214bcd93"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2d3df536",
        "outputId": "b083b9df-0d3d-46ac-a218-a7c5bb292624"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "# boxplot every continuous feature\n",
        "#group every feature according to the target\n",
        "\n",
        "# first plot - same level \n",
        "\n",
        "for feature in continuous_feature:\n",
        "    data=df.copy()\n",
        "    data.boxplot(column=feature, by='Loan_Status')\n",
        "    plt.xlabel('Loan_Status')\n",
        "    plt.ylabel(feature)\n",
        "    plt.title(feature)\n",
        "    plt.show()"
      ],
      "id": "2d3df536"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "93b2959a",
        "outputId": "f45a556b-6927-44d1-c076-2828224add9b"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "# I chose median as the dataset has outliers\n",
        "for feature in continuous_feature:\n",
        "    data=df.copy()\n",
        "    data.groupby('Loan_Status')[feature].median().plot.bar()\n",
        "    plt.xlabel('Loan_Status')\n",
        "    plt.ylabel(feature)\n",
        "    plt.title(feature)\n",
        "    plt.show()"
      ],
      "id": "93b2959a"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "faee8a88",
        "outputId": "f1fe3388-367c-42b6-ed95-1fbf6e119903"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "6\n",
            "['Gender', 'Married', 'Dependents', 'Education', 'Self_Employed', 'Property_Area']\n"
          ]
        }
      ],
      "source": [
        "# categorical variables \n",
        "\n",
        "categorical_features=[feature for feature in df.columns if df[feature].dtypes=='O' and feature not in ['Loan_ID']]\n",
        "print(len(categorical_features))\n",
        "print(categorical_features)\n"
      ],
      "id": "faee8a88"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9ec6a0dc",
        "outputId": "828875ee-0b6f-4168-d550-0348476e4480"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Feature Gender - Number of Categories 3\n",
            "Feature Married - Number of Categories 3\n",
            "Feature Dependents - Number of Categories 5\n",
            "Feature Education - Number of Categories 2\n",
            "Feature Self_Employed - Number of Categories 3\n",
            "Feature Property_Area - Number of Categories 3\n"
          ]
        }
      ],
      "source": [
        "for feature in categorical_features:\n",
        "    print(f'Feature {feature} - Number of Categories {len(df[feature].unique())}')"
      ],
      "id": "9ec6a0dc"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8328a0dc",
        "outputId": "cce63440-1d7a-4842-a920-3d24636efdee"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "#Frequencies for each category\n",
        "for feature in categorical_features:\n",
        "    data=df.copy()      \n",
        "    sns.countplot(x=feature, data=data)\n",
        "    plt.show()"
      ],
      "id": "8328a0dc"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "c91d7126",
        "outputId": "58adf6dd-dae8-4853-af64-56d0667d02f1"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n"
          ]
        }
      ],
      "source": [
        "# frequencies for each category, grouped by loan status\n",
        "for feature in categorical_features:\n",
        "\n",
        "    data=df.copy()\n",
        "    data.groupby(feature)['Loan_Status'].mean().plot.bar()\n",
        "    plt.xlabel(feature)\n",
        "    plt.ylabel('Loan_Status')\n",
        "    plt.title(feature)\n",
        "    plt.show()   \n",
        "\n",
        "    print()"
      ],
      "id": "c91d7126"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "20032e7d"
      },
      "source": [
        "# Data pre-processing"
      ],
      "id": "20032e7d"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "2b9c850b"
      },
      "outputs": [],
      "source": [
        "# we drop ID column\n",
        "\n",
        "df.drop(columns='Loan_ID',axis=1,inplace=True)"
      ],
      "id": "2b9c850b"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "85baf803",
        "outputId": "d0efe6d7-c182-47e2-d7ac-8c786e0981f2"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Gender               2.117264\n",
              "Married              0.488599\n",
              "Dependents           2.442997\n",
              "Education            0.000000\n",
              "Self_Employed        5.211726\n",
              "ApplicantIncome      0.000000\n",
              "CoapplicantIncome    0.000000\n",
              "LoanAmount           3.583062\n",
              "Loan_Amount_Term     2.280130\n",
              "Credit_History       8.143322\n",
              "Property_Area        0.000000\n",
              "Loan_Status          0.000000\n",
              "dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ],
      "source": [
        "# check for missing values\n",
        "\n",
        "df.isnull().sum()/len(df)*100\n"
      ],
      "id": "85baf803"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "a83c3df1",
        "outputId": "0b741826-617d-4094-d81a-145706441c01"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['Loan_Amount_Term', 'Credit_History']\n"
          ]
        }
      ],
      "source": [
        "print(discrete_features)"
      ],
      "id": "a83c3df1"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ae32fcce"
      },
      "outputs": [],
      "source": [
        "# we substitute it with mode because it's a discrete feature\n",
        "df['Loan_Amount_Term'] = df['Loan_Amount_Term'].fillna(df['Loan_Amount_Term'].mode()[0])"
      ],
      "id": "ae32fcce"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "cd6e12f6"
      },
      "outputs": [],
      "source": [
        "# we substitute it with mode because it's a discrete feature\n",
        "df['Credit_History'] = df['Credit_History'].fillna(df['Credit_History'].mode()[0])"
      ],
      "id": "cd6e12f6"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7bc5ea40",
        "outputId": "0d2e4b43-df27-45db-b23b-7e2fb42e8a2d"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['ApplicantIncome', 'CoapplicantIncome', 'LoanAmount']\n"
          ]
        }
      ],
      "source": [
        "# continuous features\n",
        "print(continuous_feature)"
      ],
      "id": "7bc5ea40"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "acab3744",
        "outputId": "fc978907-43f2-49dc-8846-f4bb18a6355c"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f8c25e89cd0>"
            ]
          },
          "metadata": {},
          "execution_count": 29
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "sns.boxplot(x='LoanAmount',data=df)"
      ],
      "id": "acab3744"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "4bd0796f"
      },
      "outputs": [],
      "source": [
        "# since it has so many outliers, we use median\n",
        "df['LoanAmount'] = df['LoanAmount'].fillna(df['LoanAmount'].median())"
      ],
      "id": "4bd0796f"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "43a9b3e9",
        "outputId": "4bf0b7d4-5e59-4711-8e17-4a150777ef32"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['Gender', 'Married', 'Dependents', 'Education', 'Self_Employed', 'Property_Area']\n"
          ]
        }
      ],
      "source": [
        "# categorical features\n",
        "print(categorical_features)"
      ],
      "id": "43a9b3e9"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "e1d52939"
      },
      "outputs": [],
      "source": [
        "df['Gender'] = df['Gender'].fillna(df['Gender'].mode()[0])\n",
        "df['Married'] = df['Married'].fillna(df['Married'].mode()[0])\n",
        "df['Dependents'] = df['Dependents'].fillna(df['Dependents'].mode()[0])\n",
        "df['Self_Employed'] = df['Self_Employed'].fillna(df['Self_Employed'].mode()[0])"
      ],
      "id": "e1d52939"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "b496552f",
        "outputId": "769223cd-4909-4072-d512-e9f488964672"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Gender               0\n",
              "Married              0\n",
              "Dependents           0\n",
              "Education            0\n",
              "Self_Employed        0\n",
              "ApplicantIncome      0\n",
              "CoapplicantIncome    0\n",
              "LoanAmount           0\n",
              "Loan_Amount_Term     0\n",
              "Credit_History       0\n",
              "Property_Area        0\n",
              "Loan_Status          0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ],
      "source": [
        "df.isnull().sum()"
      ],
      "id": "b496552f"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "bd8b2bb9",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4571e624-9a81-471b-9cd0-a55b8dcba5e9"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-073d0180-9cc0-4a18-8f87-72fa1c58ffae\">\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>ApplicantIncome</th>\n",
              "      <th>CoapplicantIncome</th>\n",
              "      <th>LoanAmount</th>\n",
              "      <th>Loan_Amount_Term</th>\n",
              "      <th>Credit_History</th>\n",
              "      <th>Loan_Status</th>\n",
              "      <th>Gender_Female</th>\n",
              "      <th>Gender_Male</th>\n",
              "      <th>Married_No</th>\n",
              "      <th>Married_Yes</th>\n",
              "      <th>Dependents_0</th>\n",
              "      <th>Dependents_1</th>\n",
              "      <th>Dependents_2</th>\n",
              "      <th>Dependents_3+</th>\n",
              "      <th>Education_Graduate</th>\n",
              "      <th>Education_Not Graduate</th>\n",
              "      <th>Self_Employed_No</th>\n",
              "      <th>Self_Employed_Yes</th>\n",
              "      <th>Property_Area_Rural</th>\n",
              "      <th>Property_Area_Semiurban</th>\n",
              "      <th>Property_Area_Urban</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>5849</td>\n",
              "      <td>0.0</td>\n",
              "      <td>128.0</td>\n",
              "      <td>360.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>4583</td>\n",
              "      <td>1508.0</td>\n",
              "      <td>128.0</td>\n",
              "      <td>360.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>66.0</td>\n",
              "      <td>360.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2583</td>\n",
              "      <td>2358.0</td>\n",
              "      <td>120.0</td>\n",
              "      <td>360.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>6000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>141.0</td>\n",
              "      <td>360.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-073d0180-9cc0-4a18-8f87-72fa1c58ffae')\"\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-073d0180-9cc0-4a18-8f87-72fa1c58ffae 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-073d0180-9cc0-4a18-8f87-72fa1c58ffae');\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",
              "  "
            ],
            "text/plain": [
              "   ApplicantIncome  ...  Property_Area_Urban\n",
              "0             5849  ...                    1\n",
              "1             4583  ...                    0\n",
              "2             3000  ...                    1\n",
              "3             2583  ...                    1\n",
              "4             6000  ...                    1\n",
              "\n",
              "[5 rows x 21 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 34
        }
      ],
      "source": [
        "# Encoding categorical features\n",
        "df = pd.get_dummies(df, columns=categorical_features)\n",
        "df.head()"
      ],
      "id": "bd8b2bb9"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "K3YAOrz-TBkw"
      },
      "outputs": [],
      "source": [
        "# dropping meaningless features\n",
        "df = df.drop(['Gender_Female', 'Married_No', 'Education_Not Graduate', \n",
        "              'Self_Employed_No'], axis = 1)\n",
        "\n",
        "# keep only one-hot encoded\n",
        "new = {'Gender_Male': 'Gender', 'Married_Yes': 'Married', \n",
        "       'Education_Graduate': 'Education', 'Self_Employed_Yes': 'Self_Employed'\n",
        "      }\n",
        "\n",
        "df.rename(columns=new, inplace=True)\n",
        "\n",
        "#Square Root Transformation\n",
        "df.ApplicantIncome = np.sqrt(df.ApplicantIncome)\n",
        "df.CoapplicantIncome = np.sqrt(df.CoapplicantIncome)\n",
        "df.LoanAmount = np.sqrt(df.LoanAmount)"
      ],
      "id": "K3YAOrz-TBkw"
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "IuHfQ2rFjCLX"
      },
      "id": "IuHfQ2rFjCLX",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "d4c5253e",
        "outputId": "6a75afcf-a136-47c0-bdfe-3fca5b415a19"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-4d3e1ccc-3199-4941-918f-89df76e1a1b4\">\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>ApplicantIncome</th>\n",
              "      <th>CoapplicantIncome</th>\n",
              "      <th>LoanAmount</th>\n",
              "      <th>Loan_Amount_Term</th>\n",
              "      <th>Credit_History</th>\n",
              "      <th>Loan_Status</th>\n",
              "      <th>Gender</th>\n",
              "      <th>Married</th>\n",
              "      <th>Dependents_0</th>\n",
              "      <th>Dependents_1</th>\n",
              "      <th>Dependents_2</th>\n",
              "      <th>Dependents_3+</th>\n",
              "      <th>Education</th>\n",
              "      <th>Self_Employed</th>\n",
              "      <th>Property_Area_Rural</th>\n",
              "      <th>Property_Area_Semiurban</th>\n",
              "      <th>Property_Area_Urban</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>76.478755</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>11.313708</td>\n",
              "      <td>360.0</td>\n",
              "      <td>1.0</td>\n",
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              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>67.697858</td>\n",
              "      <td>38.832976</td>\n",
              "      <td>11.313708</td>\n",
              "      <td>360.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
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              "      <td>1</td>\n",
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              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>54.772256</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>8.124038</td>\n",
              "      <td>360.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>50.823223</td>\n",
              "      <td>48.559242</td>\n",
              "      <td>10.954451</td>\n",
              "      <td>360.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>77.459667</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>11.874342</td>\n",
              "      <td>360.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-4d3e1ccc-3199-4941-918f-89df76e1a1b4')\"\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",
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              "      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-4d3e1ccc-3199-4941-918f-89df76e1a1b4 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-4d3e1ccc-3199-4941-918f-89df76e1a1b4');\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",
              "  "
            ],
            "text/plain": [
              "   ApplicantIncome  ...  Property_Area_Urban\n",
              "0        76.478755  ...                    1\n",
              "1        67.697858  ...                    0\n",
              "2        54.772256  ...                    1\n",
              "3        50.823223  ...                    1\n",
              "4        77.459667  ...                    1\n",
              "\n",
              "[5 rows x 17 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 36
        }
      ],
      "source": [
        "df.head()"
      ],
      "id": "d4c5253e"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "f4950a3a"
      },
      "source": [
        "### Feature Scaling & Train-validation-test"
      ],
      "id": "f4950a3a"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "afaedb74"
      },
      "outputs": [],
      "source": [
        "X = df.drop('Loan_Status', axis=1)\n",
        "Y = df['Loan_Status']\n",
        "X_train,X_test, Y_train, Y_test =train_test_split(X,Y,random_state=0, test_size=0.2)\n",
        "scaler = StandardScaler().fit(X_train)\n",
        "X_train = scaler.transform(X_train)\n",
        "X_test = scaler.transform(X_test)\n",
        "X_scaled = pd.DataFrame(data=X_train, columns=X.columns)"
      ],
      "id": "afaedb74"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dbcff1ea",
        "outputId": "b4d716a1-437a-47ac-c547-5af4d1193e4f"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1    332\n",
              "0    159\n",
              "Name: Loan_Status, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 38
        }
      ],
      "source": [
        "Y_train.value_counts()"
      ],
      "id": "dbcff1ea"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7ec7c729"
      },
      "source": [
        "### PCA - eleminates unimportant/unecessary features \n",
        "They are applied with the condition that the features must be scaled."
      ],
      "id": "7ec7c729"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "c2eba048",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "27d3db5b-497f-4e42-af14-775b05069bb5"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "pca = PCA()\n",
        "\n",
        "# Determine transformed features\n",
        "\n",
        "X_pca = pca.fit(X_train)\n",
        "X_train = pca.transform(X_train)\n",
        "X_test = pca.transform(X_test)\n",
        "\n",
        "# Determine explained variance using explained_variance_ration_ attribute\n",
        "exp_var_pca = pca.explained_variance_ratio_\n",
        "\n",
        "# Cumulative sum of eigenvalues; This will be used to create step plot\n",
        "# for visualizing the variance explained by each principal component.\n",
        "cum_sum_eigenvalues = np.cumsum(exp_var_pca)\n",
        "\n",
        "# Create the visualization plot\n",
        "plt.bar(range(0,len(exp_var_pca)), exp_var_pca, alpha=0.5, align='center', label='Individual explained variance')\n",
        "\n",
        "plt.step(range(0,len(cum_sum_eigenvalues)), cum_sum_eigenvalues, where='mid',label='Cumulative explained variance')\n",
        "plt.ylabel('Explained variance ratio')\n",
        "plt.xlabel('Principal component index')\n",
        "plt.legend(loc='best')\n",
        "plt.tight_layout()\n",
        "plt.show()\n"
      ],
      "id": "c2eba048"
    },
    {
      "cell_type": "code",
      "source": [
        "dic = {}\n",
        "\n",
        "for nr_princ_comp in range(1, 15):\n",
        "    for n_n in range(1,21):\n",
        "        pca = PCA(n_components=nr_princ_comp, random_state=0)\n",
        "        X_train_pca = pca.fit_transform(X_train)\n",
        "        knn = KNeighborsClassifier(n_neighbors=n_n)\n",
        "        knn.fit(X_train_pca, Y_train)\n",
        "        \n",
        "        X_test_pca = pca.transform(X_test)\n",
        "        pred_test = knn.predict(X_test_pca)\n",
        "        auc_score = roc_auc_score(Y_test, pred_test)\n",
        "        dic[(nr_princ_comp, n_n)] = auc_score"
      ],
      "metadata": {
        "id": "hUrfPDX0duTN"
      },
      "id": "hUrfPDX0duTN",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "for (nr_princ_comp, n_n), auc_score in dic.items():\n",
        "    print(f'(Nr_Princ_Comp , N_Neighbors) : {(nr_princ_comp, n_n)} -- AUC Score : {auc_score}')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yfRkOFJ3eRvT",
        "outputId": "df0cd203-491c-475d-db52-93c9569d31c1"
      },
      "id": "yfRkOFJ3eRvT",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(Nr_Princ_Comp , N_Neighbors) : (1, 1) -- AUC Score : 0.4919191919191919\n",
            "(Nr_Princ_Comp , N_Neighbors) : (1, 2) -- AUC Score : 0.503030303030303\n",
            "(Nr_Princ_Comp , N_Neighbors) : (1, 3) -- AUC Score : 0.47525252525252526\n",
            "(Nr_Princ_Comp , N_Neighbors) : (1, 4) -- AUC Score : 0.445959595959596\n",
            "(Nr_Princ_Comp , N_Neighbors) : (1, 5) -- AUC Score : 0.46616161616161617\n",
            "(Nr_Princ_Comp , N_Neighbors) : (1, 6) -- AUC Score : 0.4767676767676768\n",
            "(Nr_Princ_Comp , N_Neighbors) : (1, 7) -- AUC Score : 0.48282828282828283\n",
            "(Nr_Princ_Comp , N_Neighbors) : (1, 8) -- AUC Score : 0.4797979797979798\n",
            "(Nr_Princ_Comp , N_Neighbors) : (1, 9) -- AUC Score : 0.46515151515151515\n",
            "(Nr_Princ_Comp , N_Neighbors) : (1, 10) -- AUC Score : 0.4787878787878788\n",
            "(Nr_Princ_Comp , N_Neighbors) : (1, 11) -- AUC Score : 0.5025252525252525\n",
            "(Nr_Princ_Comp , N_Neighbors) : (1, 12) -- AUC Score : 0.49696969696969695\n",
            "(Nr_Princ_Comp , N_Neighbors) : (1, 13) -- AUC Score : 0.49292929292929294\n",
            "(Nr_Princ_Comp , N_Neighbors) : (1, 14) -- AUC Score : 0.49292929292929294\n",
            "(Nr_Princ_Comp , N_Neighbors) : (1, 15) -- AUC Score : 0.49292929292929294\n",
            "(Nr_Princ_Comp , N_Neighbors) : (1, 16) -- AUC Score : 0.508080808080808\n",
            "(Nr_Princ_Comp , N_Neighbors) : (1, 17) -- AUC Score : 0.508080808080808\n",
            "(Nr_Princ_Comp , N_Neighbors) : (1, 18) -- AUC Score : 0.508080808080808\n",
            "(Nr_Princ_Comp , N_Neighbors) : (1, 19) -- AUC Score : 0.5191919191919191\n",
            "(Nr_Princ_Comp , N_Neighbors) : (1, 20) -- AUC Score : 0.5136363636363637\n",
            "(Nr_Princ_Comp , N_Neighbors) : (2, 1) -- AUC Score : 0.49444444444444446\n",
            "(Nr_Princ_Comp , N_Neighbors) : (2, 2) -- AUC Score : 0.444949494949495\n",
            "(Nr_Princ_Comp , N_Neighbors) : (2, 3) -- AUC Score : 0.545959595959596\n",
            "(Nr_Princ_Comp , N_Neighbors) : (2, 4) -- AUC Score : 0.5505050505050505\n",
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            "(Nr_Princ_Comp , N_Neighbors) : (12, 11) -- AUC Score : 0.6348484848484848\n",
            "(Nr_Princ_Comp , N_Neighbors) : (12, 12) -- AUC Score : 0.65\n",
            "(Nr_Princ_Comp , N_Neighbors) : (12, 13) -- AUC Score : 0.6252525252525253\n",
            "(Nr_Princ_Comp , N_Neighbors) : (12, 14) -- AUC Score : 0.6348484848484848\n",
            "(Nr_Princ_Comp , N_Neighbors) : (12, 15) -- AUC Score : 0.6196969696969696\n",
            "(Nr_Princ_Comp , N_Neighbors) : (12, 16) -- AUC Score : 0.6348484848484848\n",
            "(Nr_Princ_Comp , N_Neighbors) : (12, 17) -- AUC Score : 0.6196969696969696\n",
            "(Nr_Princ_Comp , N_Neighbors) : (12, 18) -- AUC Score : 0.6196969696969696\n",
            "(Nr_Princ_Comp , N_Neighbors) : (12, 19) -- AUC Score : 0.6252525252525253\n",
            "(Nr_Princ_Comp , N_Neighbors) : (12, 20) -- AUC Score : 0.6141414141414141\n",
            "(Nr_Princ_Comp , N_Neighbors) : (13, 1) -- AUC Score : 0.6065656565656565\n",
            "(Nr_Princ_Comp , N_Neighbors) : (13, 2) -- AUC Score : 0.5853535353535353\n",
            "(Nr_Princ_Comp , N_Neighbors) : (13, 3) -- AUC Score : 0.5944444444444444\n",
            "(Nr_Princ_Comp , N_Neighbors) : (13, 4) -- AUC Score : 0.6505050505050505\n",
            "(Nr_Princ_Comp , N_Neighbors) : (13, 5) -- AUC Score : 0.6373737373737374\n",
            "(Nr_Princ_Comp , N_Neighbors) : (13, 6) -- AUC Score : 0.6494949494949496\n",
            "(Nr_Princ_Comp , N_Neighbors) : (13, 7) -- AUC Score : 0.6484848484848484\n",
            "(Nr_Princ_Comp , N_Neighbors) : (13, 8) -- AUC Score : 0.6429292929292929\n",
            "(Nr_Princ_Comp , N_Neighbors) : (13, 9) -- AUC Score : 0.6292929292929292\n",
            "(Nr_Princ_Comp , N_Neighbors) : (13, 10) -- AUC Score : 0.6444444444444446\n",
            "(Nr_Princ_Comp , N_Neighbors) : (13, 11) -- AUC Score : 0.6252525252525253\n",
            "(Nr_Princ_Comp , N_Neighbors) : (13, 12) -- AUC Score : 0.6348484848484848\n",
            "(Nr_Princ_Comp , N_Neighbors) : (13, 13) -- AUC Score : 0.6308080808080808\n",
            "(Nr_Princ_Comp , N_Neighbors) : (13, 14) -- AUC Score : 0.6252525252525253\n",
            "(Nr_Princ_Comp , N_Neighbors) : (13, 15) -- AUC Score : 0.6308080808080808\n",
            "(Nr_Princ_Comp , N_Neighbors) : (13, 16) -- AUC Score : 0.645959595959596\n",
            "(Nr_Princ_Comp , N_Neighbors) : (13, 17) -- AUC Score : 0.6308080808080808\n",
            "(Nr_Princ_Comp , N_Neighbors) : (13, 18) -- AUC Score : 0.645959595959596\n",
            "(Nr_Princ_Comp , N_Neighbors) : (13, 19) -- AUC Score : 0.6363636363636364\n",
            "(Nr_Princ_Comp , N_Neighbors) : (13, 20) -- AUC Score : 0.6515151515151515\n",
            "(Nr_Princ_Comp , N_Neighbors) : (14, 1) -- AUC Score : 0.5873737373737373\n",
            "(Nr_Princ_Comp , N_Neighbors) : (14, 2) -- AUC Score : 0.5590909090909091\n",
            "(Nr_Princ_Comp , N_Neighbors) : (14, 3) -- AUC Score : 0.6000000000000001\n",
            "(Nr_Princ_Comp , N_Neighbors) : (14, 4) -- AUC Score : 0.6671717171717171\n",
            "(Nr_Princ_Comp , N_Neighbors) : (14, 5) -- AUC Score : 0.6318181818181818\n",
            "(Nr_Princ_Comp , N_Neighbors) : (14, 6) -- AUC Score : 0.6454545454545454\n",
            "(Nr_Princ_Comp , N_Neighbors) : (14, 7) -- AUC Score : 0.654040404040404\n",
            "(Nr_Princ_Comp , N_Neighbors) : (14, 8) -- AUC Score : 0.6636363636363636\n",
            "(Nr_Princ_Comp , N_Neighbors) : (14, 9) -- AUC Score : 0.6292929292929292\n",
            "(Nr_Princ_Comp , N_Neighbors) : (14, 10) -- AUC Score : 0.6444444444444446\n",
            "(Nr_Princ_Comp , N_Neighbors) : (14, 11) -- AUC Score : 0.6252525252525253\n",
            "(Nr_Princ_Comp , N_Neighbors) : (14, 12) -- AUC Score : 0.6404040404040403\n",
            "(Nr_Princ_Comp , N_Neighbors) : (14, 13) -- AUC Score : 0.6308080808080808\n",
            "(Nr_Princ_Comp , N_Neighbors) : (14, 14) -- AUC Score : 0.6252525252525253\n",
            "(Nr_Princ_Comp , N_Neighbors) : (14, 15) -- AUC Score : 0.6156565656565657\n",
            "(Nr_Princ_Comp , N_Neighbors) : (14, 16) -- AUC Score : 0.6308080808080808\n",
            "(Nr_Princ_Comp , N_Neighbors) : (14, 17) -- AUC Score : 0.6308080808080808\n",
            "(Nr_Princ_Comp , N_Neighbors) : (14, 18) -- AUC Score : 0.6404040404040403\n",
            "(Nr_Princ_Comp , N_Neighbors) : (14, 19) -- AUC Score : 0.6252525252525253\n",
            "(Nr_Princ_Comp , N_Neighbors) : (14, 20) -- AUC Score : 0.6404040404040403\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Best parameters (components and neighbors) for only KNN model\n",
        "# other models works best using PCA(n_components=0.90)\n",
        "n_components, n_neighbors = max(dic, key=dic.get)\n",
        "print(\"auc score\", dic[(n_components, n_neighbors)])\n",
        "print(\"components\", n_components)\n",
        "print(\"neighbors\", n_neighbors)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JqylWQbbeU64",
        "outputId": "b4dcd3fa-0570-4107-ebaa-e6bf6ad3349a"
      },
      "id": "JqylWQbbeU64",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "auc score 0.7065656565656566\n",
            "components 11\n",
            "neighbors 14\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Training only for KNN.\n",
        "# pca = PCA(random_state=0, n_components=6)\n",
        "# X_train = pca.fit_transform(X_train)\n",
        "# knn = KNeighborsClassifier(n_neighbors=12)\n",
        "# knn.fit(X_train, Y_train)\n",
        "        \n",
        "# X_test = pca.transform(X_test)\n",
        "# pred_test = knn.predict(X_test)\n",
        "# roc_auc_score(Y_test, pred_test)"
      ],
      "metadata": {
        "id": "kQlbevvNecjn"
      },
      "id": "kQlbevvNecjn",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "41db476f",
        "outputId": "353e76da-c773-4f8f-aa5a-d9b2aa83dca9"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(491, 16)"
            ]
          },
          "metadata": {},
          "execution_count": 44
        }
      ],
      "source": [
        "X_train.shape"
      ],
      "id": "41db476f"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "100d162b",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "381aa13a-e8c3-4d14-9c35-f8d524d1a874"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[0.14016912 0.10341561 0.10121114 0.09755061 0.08237967 0.07406375\n",
            " 0.07100184 0.06391005 0.05950124 0.05536783 0.05131308 0.04837278]\n",
            "0.9482567114192372\n"
          ]
        }
      ],
      "source": [
        "pca = PCA(n_components=0.90, random_state = 0)\n",
        "X_new = pca.fit(X_train)\n",
        "X_train = pca.transform(X_train)\n",
        "X_test = pca.transform(X_test)\n",
        "\n",
        "print(pca.explained_variance_ratio_)\n",
        "print(np.sum(pca.explained_variance_ratio_))"
      ],
      "id": "100d162b"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7fcbf8ea",
        "outputId": "7dbf7412-bf78-4d6b-f338-f8e5710e00db"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(491, 12)"
            ]
          },
          "metadata": {},
          "execution_count": 46
        }
      ],
      "source": [
        "X_train.shape"
      ],
      "id": "7fcbf8ea"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2Wqyc7FhYqeA",
        "outputId": "cd87ad5f-aff3-49a1-e187-affa5494f9f5"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1    332\n",
              "0    159\n",
              "Name: Loan_Status, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 47
        }
      ],
      "source": [
        "Y_train.value_counts()"
      ],
      "id": "2Wqyc7FhYqeA"
    },
    {
      "cell_type": "markdown",
      "source": [
        "## SMOTE"
      ],
      "metadata": {
        "id": "6_Yo44VB4WA4"
      },
      "id": "6_Yo44VB4WA4"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "1540efc9",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "05268a80-6984-4137-88fa-ed34cc4b0594"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0      1\n",
              "1      0\n",
              "2      0\n",
              "3      1\n",
              "4      1\n",
              "      ..\n",
              "659    0\n",
              "660    0\n",
              "661    0\n",
              "662    0\n",
              "663    0\n",
              "Name: Loan_Status, Length: 664, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 48
        }
      ],
      "source": [
        "smote = SMOTE(random_state=0)\n",
        "X_train, Y_train = smote.fit_resample(X_train, Y_train)\n",
        "Y_train"
      ],
      "id": "1540efc9"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aaaa84a4",
        "outputId": "26fbef23-a167-4e76-a89b-31b0a7cd269f"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1    332\n",
              "0    332\n",
              "Name: Loan_Status, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 49
        }
      ],
      "source": [
        "# check if it is balanced\n",
        "Y_train.value_counts()"
      ],
      "id": "aaaa84a4"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Adjbl0rwXIif",
        "outputId": "c30cc00a-c607-4478-d61a-b210cca15c71"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[-0.49365517, -0.13037993, -0.6242737 , ..., -0.03083136,\n",
              "        -0.26491953,  0.14533456],\n",
              "       [ 0.84541904,  2.07397929, -1.6286172 , ..., -0.8693729 ,\n",
              "        -1.13586537, -0.78088895],\n",
              "       [-0.1784536 , -0.4103148 ,  2.13103154, ..., -1.17321249,\n",
              "         0.30036881,  0.89056459],\n",
              "       ...,\n",
              "       [ 2.73117848,  1.1012561 , -0.11388807, ...,  0.25445983,\n",
              "         1.46140685,  0.37811982],\n",
              "       [-0.45058218, -1.0502389 ,  2.17585787, ..., -0.49337833,\n",
              "         0.42121716, -0.83224413],\n",
              "       [-1.84340964, -0.12592435,  0.23512962, ..., -0.00309164,\n",
              "        -0.81692433,  0.35275712]])"
            ]
          },
          "metadata": {},
          "execution_count": 50
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "sns.set_style('whitegrid')\n",
        "sns.countplot(x='Loan_Status',data={ \"Loan_Status\": Y_train })\n",
        "X_train"
      ],
      "id": "Adjbl0rwXIif"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "66c93345"
      },
      "source": [
        "# K-Nearest Neighbors"
      ],
      "id": "66c93345"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "066b4c48",
        "outputId": "7963688c-2e19-4744-cf24-2b42e8630069"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "KNeighborsClassifier()"
            ]
          },
          "metadata": {},
          "execution_count": 51
        }
      ],
      "source": [
        "# default number of neighbours=5\n",
        "knn = KNeighborsClassifier()\n",
        "knn.fit(X_train, Y_train)"
      ],
      "id": "066b4c48"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7c4f13d5",
        "outputId": "f76eaa74-300b-4590-dc13-0a566fae71b6"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.41      0.55      0.47        33\n",
            "           1       0.81      0.71      0.76        90\n",
            "\n",
            "    accuracy                           0.67       123\n",
            "   macro avg       0.61      0.63      0.61       123\n",
            "weighted avg       0.70      0.67      0.68       123\n",
            "\n"
          ]
        }
      ],
      "source": [
        "pred = knn.predict(X_test)\n",
        "print(classification_report(Y_test, pred))"
      ],
      "id": "7c4f13d5"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "053ebfbb",
        "outputId": "02929e8a-f162-4162-f3bf-f0e3f8f7c76a"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.6282828282828283\n"
          ]
        }
      ],
      "source": [
        "roc = roc_auc_score(Y_test, pred)\n",
        "print(roc)"
      ],
      "id": "053ebfbb"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "id": "808e56f7",
        "outputId": "54816b0d-0885-4f03-eec7-555a9dc6a1f8"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "KNN best accuracy: 73.98%\n"
          ]
        }
      ],
      "source": [
        "## Hyperparameter tuning\n",
        "\n",
        "scoreListknn = []\n",
        "for i in range(1,21):\n",
        "    KNclassifier = KNeighborsClassifier(n_neighbors = i)\n",
        "    KNclassifier.fit(X_train, Y_train)\n",
        "    scoreListknn.append(KNclassifier.score(X_test, Y_test))\n",
        "    \n",
        "plt.plot(range(1,21), scoreListknn)\n",
        "plt.xticks(np.arange(1,21,1))\n",
        "plt.xlabel(\"K value\")\n",
        "plt.ylabel(\"Score\")\n",
        "plt.show()\n",
        "KNAcc = max(scoreListknn)\n",
        "print(\"KNN best accuracy: {:.2f}%\".format(KNAcc*100))\n"
      ],
      "id": "808e56f7"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "a26df26e"
      },
      "source": [
        "In test set, which is imbalanced we must not use accuracy score, even if the accuracy would result high."
      ],
      "id": "a26df26e"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8b70d763",
        "outputId": "31f959e3-fe14-4778-cb27-39094d8b32c1"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "KNeighborsClassifier(n_neighbors=13)"
            ]
          },
          "metadata": {},
          "execution_count": 55
        }
      ],
      "source": [
        "# create a new model with no. of neighbour 11\n",
        "\n",
        "KNclassifier = KNeighborsClassifier(n_neighbors = 13)\n",
        "KNclassifier.fit(X_train, Y_train)"
      ],
      "id": "8b70d763"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "df79d8ab",
        "outputId": "9486fd26-8781-4dc4-b763-049361e5c506"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.51      0.64      0.57        33\n",
            "           1       0.85      0.78      0.81        90\n",
            "\n",
            "    accuracy                           0.74       123\n",
            "   macro avg       0.68      0.71      0.69       123\n",
            "weighted avg       0.76      0.74      0.75       123\n",
            "\n"
          ]
        }
      ],
      "source": [
        "pred = KNclassifier.predict(X_test)\n",
        "print(classification_report(Y_test, pred))"
      ],
      "id": "df79d8ab"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "1fb05269"
      },
      "outputs": [],
      "source": [
        ""
      ],
      "id": "1fb05269"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "b1f69e75"
      },
      "outputs": [],
      "source": [
        ""
      ],
      "id": "b1f69e75"
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Linear Discriminant Analysis"
      ],
      "metadata": {
        "id": "2NRPf-up2SGY"
      },
      "id": "2NRPf-up2SGY"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sVMy5KQYMhA9",
        "outputId": "53c3cc20-fb6a-43d3-c9ed-9718c2a29e11"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearDiscriminantAnalysis(tol=0.0)"
            ]
          },
          "metadata": {},
          "execution_count": 57
        }
      ],
      "source": [
        "from sklearn.discriminant_analysis import LinearDiscriminantAnalysis\n",
        "#Gaussian discriminant analysis classifier\n",
        "GDAclassifier = LinearDiscriminantAnalysis(solver='svd', tol=0.0)\n",
        "GDAclassifier.fit(X_train, Y_train)\n",
        "\n",
        "\n"
      ],
      "id": "sVMy5KQYMhA9"
    },
    {
      "cell_type": "code",
      "source": [
        "def tune_gda(params):\n",
        "  gs_GDA = GridSearchCV(estimator=GDAclassifier, \n",
        "                  param_grid=params, \n",
        "                  verbose=1,\n",
        "                  refit=True, \n",
        "                  scoring='f1') \n",
        "  gs_GDA.fit(X_train, Y_train)\n",
        "\n",
        "  print('Mean Accuracy: %.3f' % gs_GDA.best_score_)\n",
        "  print('Config: %s' % gs_GDA.best_params_)\n",
        "  print(\"\\n\")"
      ],
      "metadata": {
        "id": "f60lXUHJIBUK"
      },
      "id": "f60lXUHJIBUK",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# svd\n",
        "tune_gda({'solver': ['svd'], 'tol': np.arange(0, 1, 0.0001) })\n",
        "\n",
        "# lsqr\n",
        "tune_gda({'solver': ['lsqr'], 'shrinkage': np.arange(0, 1, 0.01) })"
      ],
      "metadata": {
        "id": "JyPT6IJz_mqt",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "12760fc9-cb9c-408b-f193-f699a0c74470"
      },
      "id": "JyPT6IJz_mqt",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Fitting 5 folds for each of 10000 candidates, totalling 50000 fits\n",
            "Mean Accuracy: 0.774\n",
            "Config: {'solver': 'svd', 'tol': 0.0}\n",
            "\n",
            "\n",
            "Fitting 5 folds for each of 100 candidates, totalling 500 fits\n",
            "Mean Accuracy: 0.774\n",
            "Config: {'shrinkage': 0.0, 'solver': 'lsqr'}\n",
            "\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Gaussian Naive Bayes"
      ],
      "metadata": {
        "id": "xnAFLkYC2Zrn"
      },
      "id": "xnAFLkYC2Zrn"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qaXRgZpcQYDw",
        "outputId": "279ab843-6379-407e-d941-bbe0ee6543ef"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GaussianNB(var_smoothing=0.9202966808760416)"
            ]
          },
          "metadata": {},
          "execution_count": 60
        }
      ],
      "source": [
        "# Gaussian classifier\n",
        "NBclassifier = GaussianNB(var_smoothing=0.9202966808760416)\n",
        "NBclassifier.fit(X_train, Y_train)"
      ],
      "id": "qaXRgZpcQYDw"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "C6i1TekHTj3N",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ebc352a2-1821-4519-e7a7-319b7d02fa26"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Fitting 5 folds for each of 500 candidates, totalling 2500 fits\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'var_smoothing': 0.9202966808760416}"
            ]
          },
          "metadata": {},
          "execution_count": 61
        }
      ],
      "source": [
        "params_NB = {'var_smoothing': np.logspace(0,-9, num=500)}\n",
        "gs_NB = GridSearchCV(estimator=NBclassifier, \n",
        "                 param_grid=params_NB, \n",
        "                 verbose=1,\n",
        "                 refit=True, \n",
        "                 scoring='f1')\n",
        "\n",
        "gs_NB.fit(X_train, Y_train)\n",
        "gs_NB.best_params_"
      ],
      "id": "C6i1TekHTj3N"
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vCCVFE93f4oW"
      },
      "source": [
        "# Logistic Regression"
      ],
      "id": "vCCVFE93f4oW"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VF7KJNythu0e",
        "outputId": "d22b12f9-a380-48bf-8a26-ef8f9a88e3bb"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LogisticRegression(random_state=42, solver='liblinear')"
            ]
          },
          "metadata": {},
          "execution_count": 62
        }
      ],
      "source": [
        "LRclassifier = LogisticRegression(C = 1.0, solver = 'liblinear', random_state = 42)\n",
        "LRclassifier.fit(X_train, Y_train)\n",
        "\n"
      ],
      "id": "VF7KJNythu0e"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ug9GctIogG7f",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "96fa1cbe-4d39-42a2-a76b-8072e663218b"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Fitting 5 folds for each of 1980 candidates, totalling 9900 fits\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'C': 1.0, 'solver': 'liblinear'}"
            ]
          },
          "metadata": {},
          "execution_count": 63
        }
      ],
      "source": [
        "from sklearn.model_selection import GridSearchCV\n",
        "paramsLR = {'solver':['liblinear', 'saga', 'newton-cg', 'saga', 'sag', 'lbfgs'], 'C': np.arange (1, 100, 0.3) }\n",
        "gsLR = GridSearchCV(estimator=LRclassifier, \n",
        "                     param_grid=paramsLR, \n",
        "                     verbose=1,\n",
        "                     refit=False, \n",
        "                     scoring='f1')\n",
        "gsLR.fit(X_train, Y_train)\n",
        "gsLR.best_params_"
      ],
      "id": "ug9GctIogG7f"
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Support Vector Machine\n"
      ],
      "metadata": {
        "id": "azGgZCd7Kpac"
      },
      "id": "azGgZCd7Kpac"
    },
    {
      "cell_type": "code",
      "source": [
        "param_grid = {'C': [0.1,1, 10, 100], 'gamma': [1,0.1,0.01,0.001],'kernel': ['rbf', 'poly', 'sigmoid', 'linear']}\n",
        "\n",
        "svc_grid_search = GridSearchCV(estimator=SVC(),param_grid=param_grid,verbose=1, refit=True, scoring='f1')\n",
        "svc_grid_search.fit(X_train,Y_train)\n",
        "\n",
        "print(svc_grid_search.best_estimator_)\n",
        "print(svc_grid_search.best_params_)\n",
        "print()\n",
        "\n",
        "svc_classifier = SVC(C=100, gamma=1, kernel='poly');\n",
        "svc_classifier.fit(X_train,Y_train);\n",
        "\n",
        "Y_pred = svc_grid_search.predict(X_test)\n",
        "print(confusion_matrix(Y_test,Y_pred))\n",
        "print(classification_report(Y_test,Y_pred))\n",
        "\n",
        "svc_classifier = SVC(C= 100, gamma= 1, kernel= 'rbf', probability=True)\n",
        "svc_classifier.fit(X_train,Y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Nim55hVfK-Ux",
        "outputId": "2a304d39-fadf-4551-9ebc-f93e33474263"
      },
      "id": "Nim55hVfK-Ux",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Fitting 5 folds for each of 64 candidates, totalling 320 fits\n",
            "SVC(C=1, gamma=1)\n",
            "{'C': 1, 'gamma': 1, 'kernel': 'rbf'}\n",
            "\n",
            "[[12 21]\n",
            " [19 71]]\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.39      0.36      0.38        33\n",
            "           1       0.77      0.79      0.78        90\n",
            "\n",
            "    accuracy                           0.67       123\n",
            "   macro avg       0.58      0.58      0.58       123\n",
            "weighted avg       0.67      0.67      0.67       123\n",
            "\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "SVC(C=100, gamma=1, probability=True)"
            ]
          },
          "metadata": {},
          "execution_count": 64
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Metrics"
      ],
      "metadata": {
        "id": "Fncl9HPaK6t5"
      },
      "id": "Fncl9HPaK6t5"
    },
    {
      "cell_type": "code",
      "source": [
        "def build_auc_plot(classifiers, X_data, y_data):\n",
        "  fig, ax = plt.subplots()\n",
        "\n",
        "  for classifier in classifiers: \n",
        "    model_name = type(classifier).__name__\n",
        "    y_pred = classifier.predict(X_data)\n",
        "    y_pred_proba = classifier.predict_proba(X_data)[::,1]\n",
        "    fpr, tpr, _ = metrics.roc_curve(y_data,  y_pred_proba)\n",
        "    \n",
        "    auc2 =  roc_auc_score(y_data, y_pred)\n",
        "    # auc = metrics.roc_auc_score(y_data, y_pred_proba)\n",
        "    plt.plot(fpr,tpr,label=f\"{model_name}, auc={int(round(auc2*100))}%\")\n",
        "\n",
        "  plt.plot([0, 1], [0, 1], color=\"navy\", lw=2, linestyle=\"--\")\n",
        "  plt.xlabel('False Positive Rate')\n",
        "  plt.ylabel('True Positive Rate')\n",
        "\n",
        "  plt.legend()\n",
        "  plt.show()\n",
        "\n",
        "def show_details(X_data, y_data, classifier, cv):\n",
        "  y_pred = classifier.predict(X_data)\n",
        "  model_name = type(classifier).__name__\n",
        "\n",
        "  scores = cross_val_score(classifier, X, Y, cv=cv)\n",
        "  class_report = classification_report(y_data, y_pred)\n",
        "  confusion = confusion_matrix(y_data, y_pred)\n",
        "  roc = roc_auc_score(y_data, y_pred)\n",
        "\n",
        "  print(f\" {model_name} \".center(60, \"=\"))\n",
        "\n",
        "  print(class_report)\n",
        "  print(confusion)\n",
        "  print(f\"{model_name} roc: {roc*100:.2f}%\")\n",
        "  print(scores)\n",
        "  print(\"%0.2f accuracy with a standard deviation of %0.2f\" % (scores.mean(), scores.std()))\n",
        "\n",
        "  disp = ConfusionMatrixDisplay(confusion_matrix=confusion, display_labels=[\"non approved\", \"approved\"])\n",
        "  disp.plot(cmap=plt.cm.Blues)\n",
        "  plt.show()\n",
        "\n",
        "  build_auc_plot([classifier], X_data, y_data)\n",
        "\n",
        "  print(\"\\n\\n\")"
      ],
      "metadata": {
        "id": "SFRyY3nlRhk4"
      },
      "id": "SFRyY3nlRhk4",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def create_report(X_data, y_data, classifiers, cv=5):\n",
        "  report = {}\n",
        "  \n",
        "  for classifier in classifiers:\n",
        "    y_pred = classifier.predict(X_data)\n",
        "    model_name = type(classifier).__name__\n",
        "\n",
        "    report[model_name] = {}\n",
        "    report[model_name]['roc'] = roc_auc_score(y_data, y_pred)\n",
        "    report[model_name]['confusion_matrix'] = confusion_matrix(y_data, y_pred)\n",
        "    report[model_name]['cross_val'] = cross_val_score(classifier, X, Y, cv=cv)\n",
        "    report[model_name]['metrics'] = classification_report(y_data, y_pred, output_dict=True)\n",
        "\n",
        "  return report"
      ],
      "metadata": {
        "id": "sqQ8teD_O8iU"
      },
      "id": "sqQ8teD_O8iU",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def build_cell(component, width=31):\n",
        "  if type(component) == str:\n",
        "    return component.center(width)\n",
        "  elif type(component) == int:\n",
        "    return f\"{component}\".center(width)\n",
        "  elif type(component) == np.float64 or type(component) == float:\n",
        "    return f\"{component*100:.2f}%\".center(width)\n",
        "  elif type(component) == list or type(component) == set:\n",
        "    return \"|\".join(map(lambda x: build_cell(x, width=width//len(component)), component))\n",
        "  else:\n",
        "    print(component)\n",
        "    print(type(component))\n",
        "\n",
        "def build_row(components, width=31):\n",
        "  center = \"|\".join([build_cell(component, width=width) for component in components])\n",
        "  row = f\"|{center}|\"\n",
        "  return row\n",
        "\n",
        "def build_table(rows, heading=None):\n",
        "  if not rows:\n",
        "    return \"\"\n",
        "  \n",
        "  rows = list(map(lambda x: build_row(x), rows))\n",
        "  row_separator = \"-\" * len(rows[0])\n",
        "  output = row_separator + \"\\n\"\n",
        "\n",
        "  if heading:\n",
        "    output += build_row([ heading ], width=len(rows[0]) - 2) + \"\\n\" + row_separator + \"\\n\"\n",
        "\n",
        "  for row in rows:\n",
        "    output += row + \"\\n\" + row_separator + \"\\n\"\n",
        "\n",
        "  return output\n",
        "\n",
        "def render_confusion_matrix(cf):\n",
        "  return f\"{cf[0][0]} {cf[0][1]} - {cf[1][0]} {cf[1][1]}\"\n",
        "\n",
        "def render_cross_val(cv):\n",
        "  return f\"{cv.mean()*100:.2f}% ({cv.std():.2f})\"\n",
        "  \n",
        "def print_report(name, report):\n",
        "  cv = len(list(report.values())[0]['cross_val'])\n",
        "\n",
        "  rows = [\n",
        "        [ '-', 'precision', 'recall', 'f1-score', 'support', 'roc', 'confusion matrix', f'cross val (cv={cv})' ],\n",
        "        [ '(class)', ['0', '1'], ['0', '1'], ['0', '1'], ['0', '1'], '-', '-', '-' ]\n",
        "  ]\n",
        "\n",
        "  for model_name, data in report.items():\n",
        "    metrics = [ [ data['metrics'][c][metric] for c in [ '0', '1' ] ] for metric in [ 'precision', 'recall', 'f1-score', 'support' ] ]\n",
        "    row = [model_name, *metrics, data['roc'], render_confusion_matrix(data['confusion_matrix']), render_cross_val(data['cross_val'])]\n",
        "    rows.append(row)\n",
        "\n",
        "  print(build_table(rows, heading=name))"
      ],
      "metadata": {
        "id": "x41BH6K4O520"
      },
      "id": "x41BH6K4O520",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "0XuKOorwNghz"
      },
      "outputs": [],
      "source": [
        "# testing parameters\n",
        "classifiers = [ NBclassifier, GDAclassifier, knn, LRclassifier, svc_classifier ]\n",
        "\n",
        "# see cv parameter\n",
        "# https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.cross_val_score.html#sklearn.model_selection.cross_val_score\n",
        "cv_generator = 6"
      ],
      "id": "0XuKOorwNghz"
    },
    {
      "cell_type": "code",
      "source": [
        "report = create_report(X_train, Y_train, classifiers, cv=cv_generator)\n",
        "print_report(\"training set\", report)"
      ],
      "metadata": {
        "id": "sd93NnMTP-WU",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f0099abe-9fc2-4b7b-878d-0f04d511c0f7"
      },
      "id": "sd93NnMTP-WU",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\n",
            "|                                                                                                                          training set                                                                                                                         |\n",
            "-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\n",
            "|               -               |           precision           |             recall            |            f1-score           |            support            |              roc              |        confusion matrix       |        cross val (cv=6)       |\n",
            "-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\n",
            "|            (class)            |       0       |       1       |       0       |       1       |       0       |       1       |       0       |       1       |               -               |               -               |               -               |\n",
            "-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\n",
            "|           GaussianNB          |     96.18%    |     64.30%    |     45.48%    |     98.19%    |     61.76%    |     77.71%    |      332      |      332      |             71.84%            |        151 181 - 6 326        |         68.73% (0.00)         |\n",
            "-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\n",
            "|   LinearDiscriminantAnalysis  |     92.51%    |     66.67%    |     52.11%    |     95.78%    |     66.67%    |     78.62%    |      332      |      332      |             73.95%            |        173 159 - 14 318       |         76.05% (0.06)         |\n",
            "-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\n",
            "|      KNeighborsClassifier     |     84.13%    |     84.55%    |     84.64%    |     84.04%    |     84.38%    |     84.29%    |      332      |      332      |             84.34%            |        281 51 - 53 279        |         64.32% (0.05)         |\n",
            "-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\n",
            "|       LogisticRegression      |     85.38%    |     66.59%    |     54.52%    |     90.66%    |     66.54%    |     76.79%    |      332      |      332      |             72.59%            |        181 151 - 31 301       |         80.79% (0.03)         |\n",
            "-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\n",
            "|              SVC              |     97.92%    |     99.39%    |     99.40%    |     97.89%    |     98.65%    |     98.63%    |      332      |      332      |             98.64%            |         330 2 - 7 325         |         69.06% (0.01)         |\n",
            "-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "sm2fX8f0NuSc",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "09f84ab9-92eb-49a2-999b-57fbdda8fe63"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\n",
            "|                                                                                                                            test set                                                                                                                           |\n",
            "-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\n",
            "|               -               |           precision           |             recall            |            f1-score           |            support            |              roc              |        confusion matrix       |        cross val (cv=6)       |\n",
            "-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\n",
            "|            (class)            |       0       |       1       |       0       |       1       |       0       |       1       |       0       |       1       |               -               |               -               |               -               |\n",
            "-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\n",
            "|           GaussianNB          |     87.50%    |     82.24%    |     42.42%    |     97.78%    |     57.14%    |     89.34%    |       33      |       90      |             70.10%            |          14 19 - 2 88         |         68.73% (0.00)         |\n",
            "-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\n",
            "|   LinearDiscriminantAnalysis  |     76.19%    |     83.33%    |     48.48%    |     94.44%    |     59.26%    |     88.54%    |       33      |       90      |             71.46%            |          16 17 - 5 85         |         76.05% (0.06)         |\n",
            "-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\n",
            "|      KNeighborsClassifier     |     40.91%    |     81.01%    |     54.55%    |     71.11%    |     46.75%    |     75.74%    |       33      |       90      |             62.83%            |         18 15 - 26 64         |         64.32% (0.05)         |\n",
            "-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\n",
            "|       LogisticRegression      |     64.29%    |     84.21%    |     54.55%    |     88.89%    |     59.02%    |     86.49%    |       33      |       90      |             71.72%            |         18 15 - 10 80         |         80.79% (0.03)         |\n",
            "-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\n",
            "|              SVC              |     39.13%    |     76.00%    |     27.27%    |     84.44%    |     32.14%    |     80.00%    |       33      |       90      |             55.86%            |          9 24 - 14 76         |         69.06% (0.01)         |\n",
            "-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\n",
            "\n"
          ]
        }
      ],
      "source": [
        "report = create_report(X_test, Y_test, classifiers, cv=cv_generator)\n",
        "print_report(\"test set\", report)"
      ],
      "id": "sm2fX8f0NuSc"
    },
    {
      "cell_type": "code",
      "source": [
        "for classifier in classifiers:\n",
        "  show_details(X_test, Y_test, classifier, cv=cv_generator)"
      ],
      "metadata": {
        "id": "Zy2ktzFEPn3B",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "35711b00-bcfc-43f5-c131-3f26ee418eea"
      },
      "id": "Zy2ktzFEPn3B",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "======================== GaussianNB ========================\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.88      0.42      0.57        33\n",
            "           1       0.82      0.98      0.89        90\n",
            "\n",
            "    accuracy                           0.83       123\n",
            "   macro avg       0.85      0.70      0.73       123\n",
            "weighted avg       0.84      0.83      0.81       123\n",
            "\n",
            "[[14 19]\n",
            " [ 2 88]]\n",
            "GaussianNB roc: 70.10%\n",
            "[0.68932039 0.68932039 0.68627451 0.68627451 0.68627451 0.68627451]\n",
            "0.69 accuracy with a standard deviation of 0.00\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "\n",
            "\n",
            "================ LinearDiscriminantAnalysis ================\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.76      0.48      0.59        33\n",
            "           1       0.83      0.94      0.89        90\n",
            "\n",
            "    accuracy                           0.82       123\n",
            "   macro avg       0.80      0.71      0.74       123\n",
            "weighted avg       0.81      0.82      0.81       123\n",
            "\n",
            "[[16 17]\n",
            " [ 5 85]]\n",
            "LinearDiscriminantAnalysis roc: 71.46%\n",
            "[0.84466019 0.69902913 0.67647059 0.76470588 0.78431373 0.79411765]\n",
            "0.76 accuracy with a standard deviation of 0.06\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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t2hUAMHDgQGRlZbV4YUREJkXmDY1b80QVWWeh9OjRo9FzhYL3QiaijkWCZNQhFJVKhc6dO0OhUMDCwgJJSUkoLS3FnDlzcPXqVfTq1Qvx8fFwdHRssg2DSdyjRw+cPn0akiShtrYW27ZtQ79+/Yz3LoiIzISx7krfYPv27UhJSdHfGzMhIQG+vr44dOgQfH19kZCQ0Hw9hnbw3nvvYefOnSgqKsIzzzyD3NxcLFu2THaBRETtRUsPoaSnpyMkJAQAEBISgiNHjjS7vsEhFKVSiXXr1v3+ioiI2oH6C3nkpXNJSQnCwsL0z9VqNdRq9T3rvfrqq5AkSf+6RqOBi4sLAMDZ2RkajabZ/RgM8Pz8fMTExCAnJweSJGHYsGGIjo6Gm5ubrDdCRNReyO1cK5VK/bBIUz7//HO4urpCo9Fg+vTp6Nu3b+N9yTijxeAQyty5czFp0iT8+9//xrFjxzBp0iRERUXJeAtERO2LMU8jdHV1BQA4OTlh/PjxOHPmDJycnFBcXAwAKC4uhlKpbLYNgwFeXV2NkJAQWFpawtLSEsHBwaipqZFVIBFRe6KQ5D0MqaqqQkVFhf7n48ePw8PDAyqVCsnJyQCA5ORkjBs3rtl2mhxCKS0tBQCMHj0aCQkJ8Pf3hyRJSEtLw7PPPiv3/RIRtQsSjDcXikajwezZswEAOp0OkydPxujRozF48GC8/fbbSExMRM+ePREfH99sO00GeFhYGCRJghACALBr1y79a5IkYe7cucZ4H0RE5sGIV1m6ublh79699yzv1q0btm/fLrudJgP8q6+++n2VERG1U6Y2F4qsKzEvXbqEn376CXfu3NEvazhXkYioI5Bghnel37RpEzIzM3H58mU8++yzOHr0KHx8fBjgRNThmFZ8yzgL5eDBg9i+fTseeeQRxMXFISUlBeXl5a1RGxGRSbFQSLIercVgD9zGxgYKhQKWlpaoqKiAk5MTrl+/3hq1ERGZjPrL5E2rD24wwAcNGoSysjJMmTIFYWFh6NSpE7y9vVujNiIik2Ji+W04wBtu3jB16lQ888wzqKiowMCBA1u6LiIikyN3LpTW0mSAnzt3rsmNzp07By8vrxYpiIjIVJlYfjcd4KtWrWpyI0mS8Omnn7ZIQR2RlaUCvZXt736jl2+0z/cFAN1GvNXWJbSIrUsC8dq0LW1dhtFtXRII7yEP1+mU0Lq3S5OjyQDfsWNHa9ZBRGTaJMBCdoCLFi2lgawLeYiIOjpTvCs9A5yISCYGOBGRmZI/Bt46QygGr8QUQiAlJQWbNm0CAFy7dg1nzpxp8cKIiExJwxCKMeYDNxZZNzXOycnB/v37AQCdO3fG8uXLW7wwIiKTIvOGxq15oorBAD9z5gzeffdd2NjYAAAcHR1RW1vb4oUREZkSCYClJMl6tBaDY+CWlpbQ6XT6sZ+SkhIoFAZzn4io3TGx08ANB3h4eDhmz54NjUaDDRs24Msvv8Tbb7/dGrUREZkUs7mUvkFQUBC8vLxw4sQJCCGwefNm9OvXrzVqIyIyGfU3dGjrKhozGODXrl2DnZ0dxo4d22hZz549W7QwIiKTYuQzTHQ6Hf7whz/A1dUVH330EfLz8xEVFYXS0lJ4eXlhzZo1sLa2brYNgwH+xhtv6H+uqalBQUEB3N3d9WelEBF1BBKMe7OGTz/9FP369UNFRQUAYO3atXjllVcQEBCAZcuWITExES+++GKzbRg8Gpmamqp/HDp0CF988QWGDRtmnHdARGRGjHUeeGFhIb755hs8//zzAOqvtzlx4gQmTpwIAAgNDUV6errBdh74SkwvLy9eyENEHZIk866YJSUlCAsL0z9Xq9VQq9X657GxsXjnnXdQWVkJALh58yYcHBxgaVkfyd27d0dRUZHB/RgM8I8//lj/c11dHc6fPw8XFxdZb4KIqL14kMmslEolkpKS7vva119/DaVSiUGDBiEzM/OhajIY4A2/IQDAwsICzz77rL6bT0TUYRjpIObp06fx1Vdf4ejRo6ipqUFFRQViYmJQVlYGrVYLS0tLFBYWwtXV1WBbzQa4TqdDZWUlFixY8PBVExGZOWPc0GHu3LmYO3cuACAzMxN///vfsW7dOkRERODgwYMICAjAnj17oFKpDLbV5EFMrVYLCwsLnD59+qELJiIydxIAC4W8x+/xzjvv4OOPP8b48eNRWlqKKVOmGNymyR74lClTsGfPHgwcOBAzZ87EpEmT0KlTJ/3rEyZM+H1VEhGZKWNfiTly5EiMHDkSAODm5obExMQH2t7gGPidO3fQrVu3ewbbGeBE1JFIrTxVrBxNBrhGo8HHH38MDw8PSJIEIf5vgnJTu7EnEVFrMLXoazLA6+rqGp2BQkTU0SlkngfeWpoMcGdnZ7z11lutWQsRkUkzmx743UMmREQdnQQJliY2CN5kgH/yySetWAYRkYlr5dulydFkgHft2rU16yAiMmn1l9KbVoI/8GRWREQdlYnlNwOciEguU7sbMAOciEgGDqEQEZkryYzOAyciosZMK74Z4EREspjlXemJiKieqc0DxQAnIpKJZ6EQEZkhCRLPQiEiMlccQiEiMlMcQiEiMkeS8XrgNTU1eOmll3Dnzh3odDpMnDgRERERyM/PR1RUFEpLS+Hl5YU1a9bA2tq6yXZM7RcKEZFJkh7gYYi1tTW2b9+OvXv3Ijk5GceOHUNOTg7Wrl2LV155BYcPH4aDg4PBe2QywImIZLKQJFkPQyRJQufOnQEAWq0WWq0WkiThxIkTmDhxIgAgNDQU6enpzbbDIRQiIpnkjqCUlJQgLCxM/1ytVkOtVjdaR6fTISwsDFeuXMGLL74INzc3ODg4wNKyPpa7d++OoqKiZvfDACcikqF+eERegiuVSiQlJTW7joWFBVJSUlBWVobZs2fj559/fuCaGOBERDK1xFmEDg4OGDlyJHJyclBWVgatVgtLS0sUFhbC1dW12W05Bk5EJIsEhcyHISUlJSgrKwMA3L59GxkZGejXrx9GjhyJgwcPAgD27NkDlUrVbDvsgRMRyWSsHnhxcTEWLlwInU4HIQQmTZqEsWPHon///pgzZw7i4+Ph6emJKVOmNNsOA5yISAZJMt4NHQYOHIjk5OR7lru5uRk8dfBuDHAiIpkUcvNbtGgZegxwIiKZ5J6F0loY4EREMjzQDR3YAydzNiRoGawtADs7O1haKvD1pwvauiT6jVlTxyI85D8BIXD+p2uY/dd/wMd7EHKSfVBWeRsA8ObyHTh76WobV2o62APvYLy9vZGdnd3WZbSJj5aEwMfnibYug+6jh7Mj3lA/i1HqGNyuqcXfY/+MsAk+AIBlHyRj71c5bVyhaZI9Bt5KGOCA/sR5oo7E0tICtjZWqNXq0MnWGoW/3mrrkkyaBOOdhWIsZplab775JgoLC1FTU4OXX34ZarUa3t7emDJlCo4fP45HHnkEGzZsgFKpRHh4OB5//HGcOnUKOp0OsbGxGDJkCDZu3IgrV64gPz8fPXv2RFRUFKKjo3Hz5k0olUrExcXB3t4eQUFBSE9Ph0KhQFVVFfz8/HDkyBFcv34dy5cvx82bN2Fra4sVK1agX79+yM/Px7x581BVVWXwJPz2TJIkzF61F7a2h/FK6FN4Jezpti6J7nL911vY+I90/L/UFbhdcwdfZ17A15kXAABL3gzE/Nf88O2pi1i+aS/u1GrbuFrTYVrxbaYBHhsbi65du+L27dt4/vnnMWHCBFRVVWHQoEGIjo7Gpk2bsGnTJixbtgxA/ZVOKSkpOHXqFKKjo7Fv3z4AwOXLl/HZZ5/B1tYWM2fORGhoKEJDQ5GYmIiVK1di8+bNGDhwIE6ePIlRo0bhm2++wdNPPw0rKyssXboUy5cvx2OPPYYffvgBy5cvx6effoqYmBhMnToVISEh2Llzp6z3U3unBpcv5bbY59UW/mvRZDh2skRlTR1mr9qLzhY1eMKzZ1uXZTRblwS2dQkPxcrKEqNGDMPX3x5Hba0WT40YioP/NQO3NIX46coNKBQSAp72wrPeb+HCpcttXa5pkHhLNaPYsWMHDh8+DAC4fv06fvnlFygUCvj7+wMAgoOD8dZbb+nXDwgIAACMGDECFRUV+ktYVSoVbG1tAQDZ2dnYuHGjfvv3338fAODv74+0tDSMGjUK+/fvx4svvojKykpkZ2cjMjJSv487d+7ct521a9cafD9W1jboN8Dz938gJqgfgMuXcvEfQzwRNrEQhWWiXb3H4S992NYlPJTgcd64VWuHiJX1Ey6p/QsxYvBj6GpVjddWpgIAnnoiF3/50zj9c3NmrF+4phXfZhjgmZmZyMjIwO7du2FnZ4fw8HDU1NTcs97dd8747V00Gp7b2dkZ3J9KpcKGDRtQWlqKc+fOYdSoUaiuroaDgwNSUlLuu42p3TevtVVW16CuTuh//urEBcx/za+Nq6K7FRSWYPhgd9jZWKG6phbPjngc2blX8OzgR/TrBIwZgtyfr7VhlSbIxP7XNrvJrMrLy+Ho6Ag7OztcvnwZOTn1R8vr6ur0k8CkpqbCx8dHv01aWhoAICsrC/b29rC3t7+nXW9vb+zfv1+//fDhwwEAnTt3xqBBgxATE4MxY8bAwsICXbp0Qe/evXHgwAEAgBACFy5cuKedvXv3tsRHYPJ+1ZTD7/UNmLpoF56b9j4mPO2F5/7zP9q6LLrL9+d+wd70bHzzjwXI2BUNhULC9j3HMcJnCI5/Ho2MXdFw6toZa7d92dalmoyGg5hyHq3F7Hrgo0ePxq5du+Dn5wd3d3cMGzYMANCpUyecOXMGW7ZsgVKpRHx8vH4bGxsbhISEQKvVIjY29r7tLl26FIsWLcK2bdv0BzEb+Pv7IzIyEjt27NAve//99/Hee+9hy5Yt0Gq18Pf3x8CBA7F48WLMmzcPW7du7bAHMR/r/Qj+/dkiXL6U266GTdqbVQlpWJWQ1mjZsYysdjFk0lJMrAMOSQjRStcMtaymzrcODw/H/PnzMXjw4DaoSp5z59tn0LXnAO824i3DK5mhrUsC22WAb10SiJf+MPGh2sjKOQvh6CZr3S63r8HTs+X/2ze7HjgRUVvhlZgtpKmrHe8e9iAi+r0k/b9MR7sJcCKilmZi+c0AJyKSRTK9U4QZ4EREMplYfjPAiYjkMlZ+X79+HfPnz4dGo4EkSfjjH/+IadOmobS0FHPmzMHVq1fRq1cvxMfHw9HRscl2zO5CHiKiNiPJfBhgYWGBhQsXIi0tDbt378Znn32Gn376CQkJCfD19cWhQ4fg6+uLhISEZtthgBMRyVCfzfL+McTFxQVeXl4AgC5duqBv374oKipCeno6QkJCAAAhISE4cuRIs+1wCIWISCa5Y+AlJSUICwvTP1er1VCr1fddt6CgALm5uRg6dCg0Gg1cXFwAAM7OztBoNM3uhwFORCST3ABXKpVISkoyuF5lZSUiIiIQHR2NLl26/GZfksGzXjiEQkQki9wBFHkpX1tbi4iICAQGBmLChAkAACcnJxQXFwMAiouLoVQqm22DAU5EJIdU3wOX8zBECIHFixejb9++mD59un65SqVCcnIyACA5ORnjxo1rth0OoRARySDzBBNZvv/+e6SkpGDAgAEIDg4GAERFRWHGjBl4++23kZiYiJ49ezaaVfV+GOBERHIZKcGHDx+Oixcv3ve17du3y26HAU5EJJPcmzXUtXAdDRjgREQymdiV9AxwIiLZTCzBGeBERDLxhg5ERGZIAmcjJCIyWyaW3wxwIiJZeEMHIiLzZWL5zQAnIpLLxPKbAU5EJJuJJTgDnIhIhoYbOpgSBjgRkUwcAyciMlMKBjgRkTky5oSyxsEAJyKSQ+bNGloTA5yISAbT638zwImIZGMPnIjITJnapfS8qTERkUySzIchixYtgq+vLyZPnqxfVlpaiunTp2PChAmYPn06bt26ZbAdBjgRkUzGuit9WFgYtm7d2mhZQkICfH19cejQIfj6+iIhIcFgOwxwIiIZGq7ElPOPISNGjICjo2OjZenp6QgJCQEAhISE4MiRIwbb4Rg4EZFcMofAS0pKEBYWpn+uVquhVqub3Uaj0cDFxQUA4OzsDI1GY3A/DHAiIpnkHsJUKpVISkr6/fuRJApS560AAAvFSURBVFkHTDmEQkQkhwQoJEnW4/dwcnJCcXExAKC4uBhKpdLgNgxwIiKZjHUQ835UKhWSk5MBAMnJyRg3bpzBbRjgREStLCoqCi+88ALy8vIwevRofPHFF5gxYwaOHz+OCRMmICMjAzNmzDDYDsfAiYhkMOZd6devX3/f5du3b3+gdhjgREQy8YYORERmysSupGeAExHJxQAnIjJL8q6ybE0McCIiGR7mFMGWwgAnIpLJxPKbAU5EJJuJJTgDnIhIpt97mXxLkYQQoq2L6OhycnJgY2PT1mUQtVs1NTUYNmzYQ7Xx448/QqvVylrX0tISHh4eD7U/ORjgRERminOhEBGZKQY4EZGZYoATEZkpBjgRkZligBMRmSkGOBGRmWKAU7tVUFCAyZMnt3UZHZ63t3dbl9BuMcCpzcm9OIJaDr8D88RL6duhgoICvP766/Dx8UF2djZcXV2xefNm2NraIjc3F++++y6qq6vRp08fxMbGwtHREeHh4RgyZAgyMzNRXl6OmJgYDB8+vFG7lZWVePPNN1FWVgatVovIyEg899xzKCgowGuvvQYvLy+cP38eHh4eWL16Nezs7KBSqTBp0iQcO3YMNjY2WLduHR599FEsXLgQ1tbWyM3NxRNPPIGQkJB76rpx4wYWLFiAxMRE/fuaNWsWUlNTcfbsWaxatQpVVVXo1q0b4uLi4OLigrNnzyI6OhoA8NRTT7X6Z99a3nzzTRQWFqKmpgYvv/wy1Go1vL29MWXKFBw/fhyPPPIINmzYAKVSifDwcDz++OM4deoUdDodYmNjMWTIEGzcuBFXrlxBfn4+evbsiaioKERHR+PmzZtQKpWIi4uDvb09goKCkJ6eDoVCgaqqKvj5+eHIkSO4fv06li9fjps3b8LW1hYrVqxAv379kJ+fj3nz5qGqqgoqlaqtP6r2TVC7k5+fLzw9PcX58+eFEEJERESI5ORkIYQQkydPFpmZmUIIIeLj48XKlSuFEEL86U9/EnFxcUIIIb755hsxbdq0e9qtra0V5eXlQgghNBqNeO6550RdXZ3Iz88XAwYMEFlZWUIIIRYuXCi2bt0qhBBi7NixYvPmzUIIIfbs2SNmzJghhBBiwYIFYsaMGUKr1TZbV1BQkLhy5YoQQoiPPvpIfPjhh+LOnTtCrVYLjUYjhBBi//79YuHChfp2Tp48KYQQYtWqVSIgIOAhP03TdPPmTSGEENXV1SIgIECUlJSIAQMGiJSUFCGEEBs3bhTLly8XQtR/t4sXLxZCCHHy5En9Z/LBBx+I0NBQUV1dLYQQ4o033hBJSUlCCCG++OILMWvWLCGEEDNnzhTfffedEKL+s46OjhZCCPHyyy+LvLw8IYQQOTk5Ijw8XN/Onj17hBBC/OMf/xDDhg1ruQ+ig+MQSjvVu3dveHp6AgC8vLxw9epVlJeXo7y8HE8++SQAIDQ0FFlZWfptxo8f32j93xJCYP369QgMDMT06dNRVFSEGzduAAB69OgBHx8fAEBQUBC+//57/XYN49ABAQHIycnRL580aRIsLCyarcvPzw8HDhwAABw4cAD+/v7Iy8vDpUuXMH36dAQHB2PLli0oKipCWVkZysvLMWLECABAcHDww36MJmvHjh0ICgrCH//4R1y/fh2//PILFAoF/P39AdS/97u/g4CAAADAiBEjUFFRgbKyMgCASqWCra0tACA7O1v/Xd29vb+/P9LS0gAA+/fvh7+/PyorK5GdnY3IyEgEBwdj2bJl+PXXX/XtNOyvPX8HpoBDKO2UtbW1/mcLCwvU1NTI3kahUECn093zempqKkpKSpCUlAQrKyuoVCp9u9JvZmn77fP7sbOzM7iOv78/IiMjMX78eEiShMceewwXL16Eh4cHdu/e3WjdhlBq7zIzM5GRkYHdu3fDzs4O4eHh9/1+7/4Omvp+5HwHKpUKGzZsQGlpKc6dO4dRo0ahuroaDg4OSElJue82cr5/enjsgXcg9vb2cHBw0PduU1JS9L1VOcrLy+Hk5AQrKyucOHGiUS/92rVryM7OBgDs27dP3xsHoO9Bp6Wl3feMhObq6tOnDxQKBTZv3gw/Pz8AgLu7O0pKSvT7q62txY8//ggHBwfY29vr20lNTZX93sxJeXk5HB0dYWdnh8uXL+v/qqmrq8PBgwcB1L/3u7+Dhh50VlYW7O3tYW9vf0+73t7e2L9/v377hmMgnTt3xqBBgxATE4MxY8bAwsICXbp0Qe/evfXfrRACFy5cuKedvXv3tsRHQP+LPfAOZvXq1fqDhW5uboiLi5O9bWBgIGbNmoXAwEAMGjQIffv21b/m7u6OnTt3Ijo6Gv3798fUqVP1r926dQuBgYGwtrbG+vXrH7guf39/rFmzBunp6QDq/1L44IMPsHLlSpSXl0On02HatGnw8PBAXFwcoqOjIUlSuz2IOXr0aOzatQt+fn5wd3fXT5PaqVMnnDlzBlu2bIFSqUR8fLx+GxsbG4SEhECr1SI2Nva+7S5duhSLFi3Ctm3b9AcxGzT8JbRjxw79svfffx/vvfcetmzZAq1WC39/fwwcOBCLFy/GvHnzsHXrVh7EbGGcTpYeWkFBAWbOnIl9+/bd85pKpUJiYiKUSmUbVNaxeHt76/8quVt4eDjmz5+PwYMHt0FV1JI4hEJEZKbYAyciMlPsgRMRmSkGOBGRmWKAExGZKQY4mTxPT08EBwdj8uTJiIiIQHV19e9ua+HChfjyyy8BAIsXL8ZPP/3U5LqZmZk4ffr0A+9DpVKhpKRE9vK7PejMfRs3bsS2bdseaBtqPxjgZPJsbW2RkpKCffv2wcrKCrt27Wr0+u+dSS8mJgb9+/dv8vWTJ0/e97Q8IlPBC3nIrAwfPhwXL15EZmYm/va3v8HBwQF5eXlIS0vD2rVrcfLkSdy5cwcvvfQSXnjhBQghsGLFChw/fhw9evSAlZWVvq27z48+evQoNmzYAJ1Oh27duiEmJga7du2CQqHA3r17sXTpUvTt2xfvvvsurl27BgCIjo6Gj48Pbt68iblz56KoqAjDhg2DnBO77jebYIPY2Nh7ZhS8cuXKfWf+ow6u7ebRIpKnYTa72tpaMXPmTLFz505x4sQJMXToUP1Mhbt27RIffvihEEKImpoaERoaKq5cuSIOHjwoXnnlFaHVakVhYaHw8fERBw4cEELUz9J35swZodFoxOjRo/VtNcz098EHH+hnVRRCiKioKHHq1CkhhBBXr14VkyZNEkIIsWLFCrFx40YhhBBff/21GDBggH6mxLuNHTtWv/x+swkKIZqcUbCpmf9+WyN1LOyBk8m7ffu2fla74cOH4/nnn0d2djYGDx4MNzc3AMDx48dx8eJF/Vwg5eXl+OWXX3Dq1CkEBATAwsICrq6uGDVq1D3t5+TkYPjw4fq2unbtet86MjIyGo2ZV1RUoLKyEqdOncKmTZsAAGPGjIGjo6PB97Rjxw4cPnwYAPSzCXbr1u2eGQXfeuutRjP/Nbhz547BfVD7xwAnk9cwBv5bnTp10v8shMCSJUvwzDPPNFrn22+/NVoddXV1+Oc//wkbG5uHakfubIJA/ax+QohmZ/6jjosHMaldePrpp/H555+jtrYWAJCXl4eqqiqMGDECBw4cgE6nQ3FxMTIzM+/ZdtiwYcjKykJ+fj4AoLS0FED9LHyVlZWN9nH3ZE65ubkA6ufYbpj58Ntvv8WtW7earbWp2QSB+88o2NzMf9SxMcCpXZgyZQr69++PsLAwTJ48GcuWLYNOp8P48ePx6KOPwt/fHwsWLNDP3Hc3pVKJv/71r/jLX/6CoKAgzJkzBwAwduxYHD58GMHBwcjKysLixYtx9uxZBAYGwt/fH59//jkAYPbs2cjKykJAQAAOHz6Mnj17Nlvr6NGjodVq4efnh3Xr1jWqqWFGwcmTJ+PEiROYPXs2gPqZ/xITExEUFISAgAAcOXLEWB8dmTHOhUJEZKbYAyciMlMMcCIiM8UAJyIyUwxwIiIzxQAnIjJTDHAiIjPFACciMlP/H9qpuJmAUHVrAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "\n",
            "\n",
            "=================== KNeighborsClassifier ===================\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.41      0.55      0.47        33\n",
            "           1       0.81      0.71      0.76        90\n",
            "\n",
            "    accuracy                           0.67       123\n",
            "   macro avg       0.61      0.63      0.61       123\n",
            "weighted avg       0.70      0.67      0.68       123\n",
            "\n",
            "[[18 15]\n",
            " [26 64]]\n",
            "KNeighborsClassifier roc: 62.83%\n",
            "[0.7184466  0.66990291 0.59803922 0.65686275 0.54901961 0.66666667]\n",
            "0.64 accuracy with a standard deviation of 0.05\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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9Nby8vBjgRNTiKCu+ZcxCOXDgALZu3YqHHnoI0dHRSExMhF6vb4raiIgUxUIjybrJYTQaERQUhDfffBNA5Tk3o0ePxogRIzBlypRqIx61qTfAbWxsoNFoYGlpiTt37sDJyQnXr1+XVSARUXNReZp8w80D//26UitXrsSrr76KgwcPwsHBAXFxcfW2UW+A9+nTB4WFhRg9ejRCQkIQHBwMT09PWQUSETUnDbUWyo0bN/Dll19i1KhRAAAhBI4dO4YXXngBQOUFdVJSUuptp94x8KqLN4wZMwZDhw7FnTt30Lt37/orJCJqZuSuhZKfn4+QkBDT/dDQUISGhpruR0VF4Z133kFRUREA4NatW3BwcIClZWUkd+zYETk5OfXup9YAP3PmTK0vOnPmDDw8POp/F0REzYjcSSharRbx8fH3fO6LL76AVqtFnz59kJqa+kD11Brgy5Ytq/VFkiTho48+eqAd029KDRX46ecic5fR8Jrr+wIwNGSuuUtoFJvn+WPAyxvMXUaD2zzPHx5/cn+gNiQ0zIk8J0+exOeff46vv/4apaWluHPnDiIjI1FYWAiDwQBLS0vcuHEDHTp0qLetWgN827ZtD1woEVGzIQEWsgNc1PrM9OnTTWeyp6am4oMPPsCqVaswefJkHDhwAH5+fti9ezd0Ol29e+G10YiIZKi6Kn1jLWb1zjvv4MMPP8SIESNQUFCA0aNH1/saWWdiEhFRw59KP2jQIAwaNAgA4OLiImvq4N0Y4EREMskfA699CKUh1TuEIoRAYmIi1q9fDwC4du0aTp061eiFEREpSWMPofwRsi5qnJGRgX379gEAWrdujcWLFzd6YUREiiLzJJ6mXO+q3gA/deoUFi5cCBsbGwCAo6MjysvLG70wIiIlkQBYSpKsW1Opdwzc0tISRqPRNPaTn58PjYaTV4io5VHYarL1B3hYWBgmTZqEvLw8rFmzBp999hmmTJnSFLURESmK3FPpm0q9AR4QEAAPDw8cO3YMQgjExsZWW0GLiKglqLygg7mrqK7eAL927Rrs7OwwfPjwao917ty5UQsjIlKUJp5hIke9AV612DgAlJaWIjs7G927dzfNSiEiagkkyL9YQ1OpN8CTkpKq3T9z5gx27NjRaAURESmVwvL7/s/E9PDw4Ik8RNQiSQq7Kma9Af7hhx+afq6oqMDZs2fh7OzcqEURESlN1ZmYSlJvgFddMQIALCws8PTTT5su+0NE1GKo7SCm0WhEUVERZs2a1VT1EBEpVkNc0KEh1RrgVVeGOHnyZFPWQ0SkSBIAC4WdhF5rgI8ePRq7d+9G7969MWHCBHh7e6NVq1am559//vkmKZCISClUdyZmWVkZ2rVrV+PimwxwImpJJDWNgefl5eHDDz+Em5sbJEmCEL8tUK60cSAioqagtOirNcArKiqqzUAhImrpNGqZB96+fXu8/fbbTVkLEZGiqaYHfveQCRFRSydBgqXCBsFrDfB//etfTVgGEZHCNfHl0uSoNcDbtm3blHUQESla5an0ykrw+17MioiopVJYfjPAiYjkUtiJmAxwIiI5OIRCRKRWkormgRMRUXXKim8GOBGRLKq8Kj0REVVS2jpQDHAiIpk4C4WISIUkSJyFQkSkVhxCISJSKQ6hEBGpkcQeOBGRKklouHngpaWlePnll1FWVgaj0YgXXngBkydPRlZWFqZNm4aCggJ4eHhgxYoVsLa2rrUdpf1FQESkWBaSJOtWH2tra2zduhV79uxBQkICDh8+jIyMDKxcuRKvvvoqDh48CAcHB8TFxdXZDgOciEgmSZJ3q78dCa1btwYAGAwGGAwGSJKEY8eO4YUXXgAABAcHIyUlpc52OIRCRCRD5RCKvEGU/Px8hISEmO6HhoYiNDS02jZGoxEhISG4cuUKXnrpJbi4uMDBwQGWlpWx3LFjR+Tk5NS5HwY4EZFMco9harVaxMfH17mNhYUFEhMTUVhYiEmTJuHHH3+873oY4EREskiNshqhg4MDBg0ahIyMDBQWFsJgMMDS0hI3btxAhw4d6nwtx8CJiGRqqDHw/Px8FBYWAgB++eUXHD16FK6urhg0aBAOHDgAANi9ezd0Ol2d7bAHTkQkgyQ13AUdcnNzMXv2bBiNRggh4O3tjeHDh6Nnz56YOnUqYmJi4O7ujtGjR9fZDgOciEgmjdz8FnU/3bt3byQkJNR43MXFpd6pg3djgBMRySR3FkpTYYATEclwXxd0qKcH3lAY4NQgcm4WIHJtHPIL7kCSJASMGIjRQ10AAHH7vsHuz45Bo9FgiNejeOsVbzNXSwDg0MYOa+e9BHfXThAC+MeS7abnJr2sw9IpIXB9bhbybxeZsUplYQ+8hfH09ER6erq5y2h0FhoNJo31waOuD6O4pBSvzdiAAT1a49aVcvz3RCY+XP0PWFtZ4lbBHXOXSr9aNn0UUr45i1dnb4GVpQXsbK0B9MHDHdpi+CB3ZF3PN3eJiiN7DLyJcBohKk9lpQfzkNYBj7o+DABoZWeDbl3a42ZBERIOpOJvwcNgbVXZV2jXto05y6RfObS2xZ89XbEt8RsAQLnBiMI7JQCAyKl/waJ1CRCiicYBVEJC5SwUObemosoe+FtvvYUbN26gtLQUr7zyCkJDQ+Hp6YnRo0fjyJEjeOihh7BmzRpotVqEhYXh0UcfxYkTJ2A0GhEVFYV+/fph3bp1uHLlCrKystC5c2dMmzYNERERuHXrFrRaLaKjo2Fvb4+AgACkpKRAo9GguLgYPj4+OHToEK5fv47Fixfj1q1bsLW1xZIlS+Dq6oqsrCzMmDEDxcXF9c7hbK6u597ChUvX8adxf0Zs3Lf4LvMnbNpxENZWlpg01gfubl3MXWKL1/VhJ9wsuIMNC/+GPm4PIyMzC3NWxaFTx/Y4dakAp7+/au4SFUlhHXB1BnhUVBTatm2LX375BaNGjcLzzz+P4uJi9OnTBxEREVi/fj3Wr1+PBQsWAKicKJ+YmIgTJ04gIiICe/fuBQBcvHgRO3bsgK2tLSZMmIDg4GAEBwcjLi4OS5cuRWxsLHr37o3jx49j8ODB+PLLL/HUU0/BysoK8+fPx+LFi9GtWzd89913WLx4MT766CNERkZizJgxCAoKwvbt2+t6G7+pKAduZzXWx9Wkin8px7xliZgcOgitrQFjeSkK837G+3P8kHkpFwvf/Ri7VrykuHWV79fmef7mLuGBtG3rAE/3rriddwPfnzuLP/ftjS8++Ac6d2qPn2+mYvM8fzi1bYWY6S+grKzc3OUqQxP3ruVQZYBv27YNBw8eBABcv34dly9fhkajga+vLwAgMDAQb7/9tml7Pz8/AMDAgQNx584d0xlQOp0Otra2AID09HSsW7fO9Pp3330XAODr64vk5GQMHjwY+/btw0svvYSioiKkp6cjPDzctI+ysrJ7trNy5cr635DGCnB0+cOfh1IYDEbMe+8jjBg+EE8/+xRwOwvt2z+Ep4cNgtS2K/7k2RWSxVcokJzQzrG1uct9IK8v/R9zl/BAnJ3scdCtF/4y/WMAwJD+ZzFrvC+6drWCp2d/AICNtTU8Pfvj2VffRW6e3pzlPrCG+oWrrPhWYYCnpqbi6NGj2LVrF+zs7BAWFobS0tIa293dw/t9b6/qvp2dXb370+l0WLNmDQoKCnDmzBkMHjwYJSUlcHBwQGJi4j1fo/be5R8hhMCyDfHo9rAz/hrwlOnxoYPccfL0j3i8bw9cuXYTBoMRbR1ambFSAoDcPD2u5txCz0ec8cPlXAwb+ChOncvCzWs/4fWlSQCA7xIXY/grKzgL5W4K+09bdQcx9Xo9HB0dYWdnh4sXLyIjIwMAUFFRYVpDICkpCV5eXqbXJCcnAwDS0tJgb28Pe3v7Gu16enpi3759ptcPGDAAANC6dWv06dMHkZGReOaZZ2BhYYE2bdqgS5cu2L9/P4DK8Dp37lyNdvbs2dMYH4Ei/e+5yzjwVQa+PX0R46atw7hp6/DNd5fhp/PCtZx8vBL+Hhat2omIyX9pkb/glGjmyk+x6Z+v4r875qBvr4ex6sMD5i5J0XgQswEMGzYMO3fuhI+PD7p3747+/Sv/3GvVqhVOnTqFjRs3QqvVIiYmxvQaGxsbBAUFwWAwICoq6p7tzp8/H3PmzMGWLVtMBzGr+Pr6Ijw8HNu2bTM99u6772LRokXYuHEjDAYDfH190bt3b8ydOxczZszA5s2bW9RBzH7u3XA4PrL6g7ezACtLLJjyonmKojqdvnAVurEran3+scCFTViNOiit6yGJZjJXqLb51mFhYZg5cyb69u1rhqrkScs43SzGwGu4ndU83xeAoSFzzV1Co9g8z980hNKcbJ7nj5f/8sIDtZGWcRpC5r/nNr9cg7u7+wPtTw7V9cCJiMyFZ2I2ktrOdrx72IOI6I+STP+nHM0mwImIGpvC8psBTkQki6S8KcIMcCIimRSW3wxwIiK5FJbfDHAiItkUluAMcCIiGSRwGiERkWpxDJyISKUY4EREqiRxCIWISJUk9sCJiFRJguImoTDAiYhkU1iCM8CJiGSSe7GGikauowoDnIhIJoV1wBngRESyKSzBGeBERDJxGiERkQpJ4DRCIiLVUlh+M8CJiGThBR2IiNRLYfnNACcikkth+c0AJyKSrYES/Pr165g5cyby8vIgSRJefPFFjB07FgUFBZg6dSquXr2Khx9+GDExMXB0dKy1HU3DlENE1LxVXdBBzv/qY2FhgdmzZyM5ORm7du3Cjh078MMPP2DTpk0YMmQI/vOf/2DIkCHYtGlTne0wwImIZJIkebf6ODs7w8PDAwDQpk0b9OjRAzk5OUhJSUFQUBAAICgoCIcOHaqzHQ6hEBHJpGmEQfDs7GxkZmbiscceQ15eHpydnQEA7du3R15eXp2vZYATEckif0HZ/Px8hISEmO6HhoYiNDS0xnZFRUWYPHkyIiIi0KZNm+p7k6R6py0ywImI5LiPCzpotVrEx8fXuU15eTkmT54Mf39/PP/88wAAJycn5ObmwtnZGbm5udBqtXW2wTFwIiIZpPu41UcIgblz56JHjx4YN26c6XGdToeEhAQAQEJCAp599tk622EPnIhIpoY6kefbb79FYmIievXqhcDAQADAtGnTMH78eEyZMgVxcXHo3LkzYmJi6myHAU5EJFNDnUo/YMAAnD9//p7Pbd26VXY7DHAiIpl4JiYRkUpxLRQiIhWqOhNTSRjgRERyKSu/GeBERHIpLL8Z4EREskiARmGD4AxwIiKZFJbfPBOTiEit2AMnIpKBV6UnIlIxTiMkIlIp9sCJiFSKAU5EpEryrnfZlBjgREQyyL3eZVNigBMRyaSw/GaAExHJprAEZ4ATEcmktFPpJSGEMHcRLV1GRgZsbGzMXQZRs1VaWor+/fs/UBvff/89DAaDrG0tLS3h5ub2QPuTgwFORKRSXAuFiEilGOBERCrFACciUikGOBGRSjHAiYhUigFORKRSDHBqtrKzszFy5Ehzl9HieXp6mruEZosBTmYn9+QIajz8DtSJp9I3Q9nZ2XjjjTfg5eWF9PR0dOjQAbGxsbC1tUVmZiYWLlyIkpISdO3aFVFRUXB0dERYWBj69euH1NRU6PV6REZGYsCAAdXaLSoqwltvvYXCwkIYDAaEh4fjueeeQ3Z2Nl5//XV4eHjg7NmzcHNzw/Lly2FnZwedTgdvb28cPnwYNjY2WLVqFR555BHMnj0b1tbWyMzMxOOPP46goKAadd28eROzZs1CXFyc6X1NnDgRSUlJOH36NJYtW4bi4mK0a9cO0dHRcHZ2xunTpxEREQEAePLJJ5v8s28qb731Fm7cuIHS0lK88sorCA0NhaenJ0aPHo0jR47goYcewpo1a6DVahEWFoZHH30UJ06cgNFoRFRUFPr164d169bhypUryMrKQufOnTFt2jRERETg1q1b0Gq1iI6Ohr29PQICApCSkgKNRoPi4mL4+Pjg0KFDuH79OhYvXoxbt27B1tYWS5YsgaurK7KysjBjxgwUFxdDp9OZ+6Nq3gQ1O1lZWcLd3V2cPXtWCCHE5MmTRUJCghBCiJEjR4rU1FQhhBAxMTFi6dKlQggh/va3v4no6GghhBBffvmlGCWgUlYAAAjKSURBVDt2bI12y8vLhV6vF0IIkZeXJ5577jlRUVEhsrKyRK9evURaWpoQQojZs2eLzZs3CyGEGD58uIiNjRVCCLF7924xfvx4IYQQs2bNEuPHjxcGg6HOugICAsSVK1eEEEK8//77YsOGDaKsrEyEhoaKvLw8IYQQ+/btE7Nnzza1c/z4cSGEEMuWLRN+fn4P+Gkq061bt4QQQpSUlAg/Pz+Rn58vevXqJRITE4UQQqxbt04sXrxYCFH53c6dO1cIIcTx48dNn8natWtFcHCwKCkpEUII8eabb4r4+HghhBCffvqpmDhxohBCiAkTJohvvvlGCFH5WUdERAghhHjllVfEpUuXhBBCZGRkiLCwMFM7u3fvFkII8fHHH4v+/fs33gfRwnEIpZnq0qUL3N3dAQAeHh64evUq9Ho99Ho9nnjiCQBAcHAw0tLSTK8ZMWJEte1/TwiB1atXw9/fH+PGjUNOTg5u3rwJAOjUqRO8vLwAAAEBAfj2229Nr6sah/bz80NGRobpcW9vb1hYWNRZl4+PD/bv3w8A2L9/P3x9fXHp0iVcuHAB48aNQ2BgIDZu3IicnBwUFhZCr9dj4MCBAIDAwMAH/RgVa9u2bQgICMCLL76I69ev4/Lly9BoNPD19QVQ+d7v/g78/PwAAAMHDsSdO3dQWFgIANDpdLC1tQUApKenm76ru1/v6+uL5ORkAMC+ffvg6+uLoqIipKenIzw8HIGBgViwYAF+/vlnUztV+2vO34EScAilmbK2tjb9bGFhgdLSUtmv0Wg0MBqNNZ5PSkpCfn4+4uPjYWVlBZ1OZ2pX+t0qbb+/fy92dnb1buPr64vw8HCMGDECkiShW7duOH/+PNzc3LBr165q21aFUnOXmpqKo0ePYteuXbCzs0NYWNg9v9+7v4Pavh8534FOp8OaNWtQUFCAM2fOYPDgwSgpKYGDgwMSExPv+Ro53z89OPbAWxB7e3s4ODiYereJiYmm3qocer0eTk5OsLKywrFjx6r10q9du4b09HQAwN69e029cQCmHnRycvI9ZyTUVVfXrl2h0WgQGxsLHx8fAED37t2Rn59v2l95eTm+//57ODg4wN7e3tROUlKS7PemJnq9Ho6OjrCzs8PFixdNf9VUVFTgwIEDACrf+93fQVUPOi0tDfb29rC3t6/RrqenJ/bt22d6fdUxkNatW6NPnz6IjIzEM888AwsLC7Rp0wZdunQxfbdCCJw7d65GO3v27GmMj4B+xR54C7N8+XLTwUIXFxdER0fLfq2/vz8mTpwIf39/9OnTBz169DA91717d2zfvh0RERHo2bMnxowZY3ru9u3b8Pf3h7W1NVavXn3fdfn6+mLFihVISUkBUPmXwtq1a7F06VLo9XoYjUaMHTsWbm5uiI6ORkREBCRJarYHMYcNG4adO3fCx8cH3bt3Ny2T2qpVK5w6dQobN26EVqtFTEyM6TU2NjYICgqCwWBAVFTUPdudP38+5syZgy1btpgOYlap+kto27ZtpsfeffddLFq0CBs3boTBYICvry969+6NuXPnYsaMGdi8eTMPYjYyLidLDyw7OxsTJkzA3r17azyn0+kQFxcHrVZrhspaFk9PT9NfJXcLCwvDzJkz0bdvXzNURY2JQyhERCrFHjgRkUqxB05EpFIMcCIilWKAExGpFAOcFM/d3R2BgYEYOXIkJk+ejJKSkj/c1uzZs/HZZ58BAObOnYsffvih1m1TU1Nx8uTJ+96HTqdDfn6+7Mfvdr8r961btw5btmy5r9dQ88EAJ8WztbVFYmIi9u7dCysrK+zcubPa8390Jb3IyEj07Nmz1uePHz9+z2l5RErBE3lIVQYMGIDz588jNTUV7733HhwcHHDp0iUkJydj5cqVOH78OMrKyvDyyy/jr3/9K4QQWLJkCY4cOYJOnTrBysrK1Nbd86O//vprrFmzBkajEe3atUNkZCR27twJjUaDPXv2YP78+ejRowcWLlyIa9euAQAiIiLg5eWFW7duYfr06cjJyUH//v0hZ2LXvVYTrBIVFVVjRcErV67cc+U/auHMt44WkTxVq9mVl5eLCRMmiO3bt4tjx46Jxx57zLRS4c6dO8WGDRuEEEKUlpaK4OBgceXKFXHgwAHx6quvCoPBIG7cuCG8vLzE/v37hRCVq/SdOnVK5OXliWHDhpnaqlrpb+3ataZVFYUQYtq0aeLEiRNCCCGuXr0qvL29hRBCLFmyRKxbt04IIcQXX3whevXqZVop8W7Dhw83PX6v1QSFELWuKFjbyn+/r5FaFvbASfF++eUX06p2AwYMwKhRo5Ceno6+ffvCxcUFAHDkyBGcP3/etBaIXq/H5cuXceLECfj5+cHCwgIdOnTA4MGDa7SfkZGBAQMGmNpq27btPes4evRotTHzO3fuoKioCCdOnMD69esBAM888wwcHR3rfU/btm3DwYMHAcC0mmC7du1qrCj49ttvV1v5r0pZWVm9+6DmjwFOilc1Bv57rVq1Mv0shMC8efMwdOjQatt89dVXDVZHRUUF/v3vf8PGxuaB2pG7miBQuaqfEKLOlf+o5eJBTGoWnnrqKXzyyScoLy8HAFy6dAnFxcUYOHAg9u/fD6PRiNzcXKSmptZ4bf/+/ZGWloasrCwAQEFBAYDKVfiKioqq7ePuxZwyMzMBVK6xXbXy4VdffYXbt2/XWWttqwkC915RsK6V/6hlY4BTszB69Gj07NkTISEhGDlyJBYsWACj0YgRI0bgkUcega+vL2bNmmVaue9uWq0W//znP/GPf/wDAQEBmDp1KgBg+PDhOHjwIAIDA5GWloa5c+fi9OnT8Pf3h6+vLz755BMAwKRJk5CWlgY/Pz8cPHgQnTt3rrPWYcOGwWAwwMfHB6tWrapWU9WKgiNHjsSxY8cwadIkAJUr/8XFxSEgIAB+fn44dOhQQ310pGJcC4WISKXYAyciUikGOBGRSjHAiYhUigFORKRSDHAiIpVigBMRqRQDnIhIpf4PBFmwU/s12XsAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "\n",
            "\n",
            "==================== LogisticRegression ====================\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.64      0.55      0.59        33\n",
            "           1       0.84      0.89      0.86        90\n",
            "\n",
            "    accuracy                           0.80       123\n",
            "   macro avg       0.74      0.72      0.73       123\n",
            "weighted avg       0.79      0.80      0.79       123\n",
            "\n",
            "[[18 15]\n",
            " [10 80]]\n",
            "LogisticRegression roc: 71.72%\n",
            "[0.82524272 0.76699029 0.7745098  0.81372549 0.83333333 0.83333333]\n",
            "0.81 accuracy with a standard deviation of 0.03\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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HDx8aPJgQwnB27bpERMRFHByKMnduOz780E3GBzJjORaCIUOGkJSUxKhRo/jiiy9Qq9WMHTvWGNmEEHlIrU7T3wUcEFCfGzceERjYkHLl7E2cTJhajoWgdevWANjb27Nq1Sog885ikb9kN2tYcnIydpEP8nRbMvNYwZKRoWPu3KPMnn2YEycGUrVqKVQqFZMmtTJ1NJFPZNtZrNVq2bp1K2FhYfz1V+ZkE7/++is9e/bkiy++MFpAkTu5nTUsL8jMYwXH77/H8847yxg1ag/37qWwceMfpo4k8qFsjwjGjRvHrVu3ePPNN5k2bRqOjo6cP3+ekSNH0rZtW2NmNGsvOj/wf2cNi4mJoXbt2oaKJ/IpjSaDadMimTnzMBkZOt54oyShoT60b1/d1NFEPpRtITh//jybN2/GwsICjUZD8+bN2b17N6VKyVUFxvRkTz+nUzGyly6eOHPmFr17byAm5i4qFQQFNWbGjDbY28v4QOLZsi0EVlZW+globGxsqFix4gsXgcjISKZPn45Op6NHjx4EBgY+tcz27dtZsGABKpWKWrVqPTXSqZD5gcWLsbGx5NKl+9SsWYZly/xo0eINU0cS+Vy2heDy5cv4+vrqH1+7di3L4y1btjz3jbVaLVOnTmXFihU4OTnRvXt3PD09qV79n0PT2NhYQkNDWbduHSVLluTevXuv0hYhzNaFC/epVUtBpVJRp05ZIiJ606xZRYoWzfF6ECGyLwTbt29/pTeOioqiUqVKVKxYEYBOnTqxd+/eLIXg559/pnfv3pQsWRKAMmXKvNI2hTA39++nMHLkLpYvP8u6dSXo2bMeAJ6eVUycTBQk2RaCVx1oLiEhAWdnZ/1jJycnoqKisiwTGxsLQM+ePdHpdAQFBeHu7v7c99VoNMTExLxUptTU1Jde11SSk5MBzKrNr8pc2rxnTxxTp57m7t1UrK0tiI6+TExMEVPHMhpz+T3/m6HabNLjRq1Wy9WrV1m1ahXx8fF88MEHbNmyhRIlsu8YtbGxeemrYAriFTRP7gEwpza/qsLe5vj4x3z8cQTh4RcAaN68ImPG1KVTp3dMnMy4Cvvv+Vlepc3PKyA5Djr3spycnIiPj9c/TkhIwMnJ6allPD09sbKyomLFilSuXFl/lCCEeNqpUzepU2ch4eEXKFbMim+/7UhkZH+qVpUb/MTLy1UhSE1N5fLlyy/0xq6ursTGxnL9+nXS0tLYtm0bnp6eWZZp27YtJ06cACAxMZHY2Fh9n4IQ4ml16pSlbNlitG9fjejooQQFvY2FhYwRJF5NjoVg3759+Pv7M2DAACDz8GLw4ME5vrGlpSUTJ05kwIABeHt707FjR1xcXAgJCWHv3r0AtGzZEgcHB7y9venXrx/BwcFyn4IQ/6LTKYSGnuLBg1QAbG2tiIz8kIiI3lSq5GDidKKwyLGPYMGCBYSHh9OnTx8g81z1jRs53+kK4OHhgYeHR5bnhg0bpv9ZpVIxZswYxowZ8yKZhTALf/55lwEDtnDo0DV+++0G333nB4CTU3ETJxOFTa6Goba3l9EJhTCW9HQtc+YcZfLk/Wg0Wpydi9Oxo4upY4lCLMdCUL16dbZs2YJWqyU2NpZVq1ZRv359Y2QTwuycOXOLgIDNnDmTeaFF//5uzJnTjlKlbE2cTBRmORaCCRMmsGTJEqytrRkxYgQtWrRg6NChxshW6OVmQDkZ8tl8XLqUyNtvLyMjQ0flyg6Ehvrg5VXN1LGEGcixEFy+fJlPP/2UTz/91Bh5zEpuBpSTweTMR7VqpenT503s7a2ZPr0NxYtbmzqSMBM5FoKZM2dy9+5d2rdvj7e3NzVq1DBGLrMhA8qZr8eP0xg7di+9etWjadPMy6bDwvxkykhhdDkWglWrVnHnzh0iIiKYOHEiarWajh07yukhIV7Bzp0XCQzcyrVrDzlw4Cpnzw5CpVJJERAmkasbysqWLUvfvn2ZMmUKtWrVYtGiRYbOJUShlJiYQr9+G+nQYQ3Xrj2kYcNyrFzZWQqAMKkcjwguXbrE9u3b2bVrFw4ODnTs2JHRo0cbI5sQhUp4+AU++mg7t2+rKVrUkilTWvHZZ02xtDTYSC9C5EqOhWDs2LF07NiRZcuWPTVWkBAidx48SCUwcAv376fi7l6J777zpUYNGXZd5A85FoKffvrJGDmEKHQURUGnUyhSxAIHh6IsWtSJ+/dTGDSokYwPJPKVbAvBsGHDCAkJyTIr2b/lNEOZEOYsNvYBgYFb8PSswujRLQD0k8YIkd9kWwjGjRsHwJIlS4wWRoiCTqvVsXDhb4wduxe1Op0LF+4wfHgTmTJS5GvZ9lI5OjoCsHbtWl5//fUs/9auXWu0gEIUFDExd3B3/55hw3agVqfTs2c9Tp8eJEVA5Hs5Xq5w5MiRp56LjIw0SBghCqKMDB3Tp0fi5raUI0euU768PZs29WTdum44OhYzdTwhcpTtrsratWtZt24d169fz9JPoFaradCggVHCCVEQWFio2LXrMmlpWgYObMDs2V44OBQ1dSwhci3bQuDr64u7uztz585lxIgR+ueLFSuGg4NMiCHMW0pKOklJaTg6FsPCQsWyZb5cv/4IT88qpo4mxAvL9tSQSqWiQoUKTJw4kWLFiun/ATx48MBoAYXIbyIjr/LWW0v44IMNKIoCgItLGSkCosDK9ohgxIgRLF26lK5du6JSqfR/8JBZJJ5MNymEuXj0SMOYMXtYtOgkAFZWRbh7N5myZaUfQBRs2RaCpUuXAplzFgth7iIi/mbQoK1cv/4IS0sLxo1ryZgxLbCxkSuCRMGX41/xqVOnqF27NnZ2dmzatIkLFy7Qr18/ypcvb4x8QpiUoigMHLiFsLAzADRqVJ7ly/1wdZXhVkThkePlo5MnT8bW1pY//viDFStW8MYbbxAcHGyMbEKYXGZfWQmKFrXk66+9OHo0QIqAKHRyLASWlpaoVCr27NlD79696d27N2q12hjZhDCJmzeTOHjwqv7x2LEtOX9+CCNGNJORQkWhlONfdbFixVi6dCmbN2+mVatW6HQ6MjIyjJFNCKNSFIWwsNPUqbOQbt1+5t69ZACsrYtQrVppE6cTwnByLATz5s3D2tqaGTNmULZsWeLj4wkICDBGNiGM5vLl+7Rtu4oBA7bw8KGGd96pQHq6ztSxhDCKHAtB2bJl8fX1JSkpiV9//RUbGxs6d+5sjGxCGJxWq2PevKO4ui5m374rvPaaHWvXdmXz5p44Oxc3dTwhjCLHQrB9+3Z69OjBjh07iIiI0P8sRGHQt+9GPvtsF8nJ6bz/visXLgylVy9XmTpSmJUcLx9dsmQJ4eHhlCmTOZtSYmIiH374IR06dDB4OCEMbeDABkRGXmXRIm98fWuaOo4QJpFjIVAURV8EABwcHLLcZSxEQfLbbzfYt+8Ko0ZlThbTqlVlLl78WG4ME2Ytx7/+Fi1aEBAQQKdOnYDMU0Xu7u4GDyZEXkpOTmfSpF+ZO/cYOp1Cs2YVadmyEoAUAWH2cvwfMGrUKHbt2sWpU6cAeO+99/Dy8jJ4MCHyyv79sQwYsJlLl+5jYaFi5MimNGwod8YL8US2hSA2NpZZs2Zx/fp1atSowahRo3BykjsqRcHx8GEqwcG7CQ09DYCrqyNhYX40bvy6iZMJkb9ke9XQ2LFjad26NfPnz6du3bp88cUXL/zmkZGRtG/fHi8vL0JDQ7NdbufOndSsWZNz58698DaEyM6ECb8SGnoaKysLpk5txcmTgVIEhHiGbI8I1Go17777LgBVq1alS5cuL/TGWq2WqVOnsmLFCpycnOjevTuenp5Ur149y3KPHz9m5cqVvPXWWy8RX4is/n0hw8SJHly58oCZM9tQt66jCVMJkb9le0Sg0Wi4cOEC0dHRREdHk5qamuVxTqKioqhUqRIVK1bE2tqaTp06PXMOg5CQEAYOHIiNjc2rtUSYNUVRWLv2HJ6eK0lL0wLw2mt2bNnSS4qAEDnI9oigbNmyfPnll/rHr732mv6xSqVi5cqVz33jhIQEnJ2d9Y+dnJyIiorKskx0dDTx8fG0atWKsLCwXAXWaDTExMTkatn/Sk1Nfel1DSE5OXMsG0Nmym9tNoT4+GSmTj3F/v23APjlFwesrYuYOJVxmcPv+b+kzXkn20KwatWqPN/Yv+l0OmbOnJml2OSGjY0NtWvXfqltxsTEvPS6hmAXmTnlpyEz5bc25yWdTuG7707x+ee7SUpKo2RJG+bMaUfTpkULbZuzU5h/z9mRNr/4utkx2AXUTk5OxMfH6x8nJCRkuepIrVbz119/0bdvXwDu3LnDkCFDWLx4Ma6uroaKJQqJixcTGThwC/v3xwLg71+TRYs6Ub68vdntJQrxqgxWCFxdXYmNjeX69es4OTmxbds25syZo3/d3t6e48eP6x/36dOH4OBgKQIiVw4evMr+/bE4OhZjwYKOdO9eR8YHEuIlGawQWFpaMnHiRAYMGIBWq6Vbt264uLgQEhJCvXr1aNOmjaE2LQqpBw9ScXAoCsCHH7px504yAQH1KVPGzsTJhCjYcjXW0ObNm7l+/TpBQUHcvHmTu3fv8uabb+b45h4eHnh4eGR5btiwYc9c1tB9Esa29vg1Np298dxlLtx6RJ1yJYyUqODSaDKYMeMg33xznJMnB+LiUgaVSkVwcHNTRxOiUMjVnMVnz55l27ZtQOaMZVOmTDF4sIJu09kbXLj16LnL1ClXAn83ucHpeY4di6NBg1CmTo3k0SMNO3deMnUkIQqdHI8IoqKi+OWXX/ST0ZQsWZL09HSDBysM6pQrwU+Dmpo6RoGkVqcxYcKvfPPNMRQFXFxKExbmpx8oTgiRd3IsBJaWlmi1Wn1HXGJiIhYWMoG3MJzjx+N4//0NXL58nyJFVIwc2YxJkzywtbUydTQhCqUcC0GfPn346KOPuHfvHvPmzWPHjh0MHz7cGNmEmXJwKMqNG4946y0nwsL8ZKRQIQwsx0Lg5+dH3bp1OXbsGIqisGjRIqpVq2aMbPmWdATnvUOHrtG8eUVUKhU1a77Gvn39aNy4PFZW5nWHsBCmkOM5nps3b2Jra0vr1q3x9PTE1taWmzdvGiNbviUdwXnn9m01PXuG07LlClat+mcIkmbNKkoREMJIcjwiGDRokP5njUZDXFwcVapU0V9FVNi8yN6+dAS/PEVRWLPmHMOG7SAxMQU7Oyv9YHFCCOPKsRBs2bIly+Po6GjWrl1rsECm9mRv/3mndWRv/9Vcu/aQwYO3EhFxEQAvr6qEhvpSubKDiZMJYZ5e+M7iunXrPjWKaGEje/uGc/x4HG3bruLx4zQcHIoyb157+vV7S4aHEMKEciwEK1as0P+s0+m4cOECjo4yvrt4OW5uzlSsWIJatV5j4UJvypWzN3UkIcxejoVArVbrfy5SpAgeHh60b9/eoKFE4ZGRoWPBghP07fsWpUvbYmNjyeHD/6NUKVtTRxNC/H/PLQRarRa1Ws2oUaOMlUcUIr//Hs///reZ06dvcfZsPN9/n3l3uhQBIfKXbAtBRkYGlpaWnD592ph5RCGQmprBtGmRzJp1mIwMHW+8UZJeveqZOpYQIhvZFoIePXrwyy+/UKtWLQYPHkyHDh2ws/tnuN927doZJaAoWI4cuU5AwGb++OMuKhUEBTVmxow22NvLnNRC5Fc59hGkpaVRqlSpLJPIgBQC8bSLFxNp2XIFOp1CzZplCAvzo3nzN0wdSwiRg2wLwb1791ixYgUuLi6oVCoURdG/Jpf6iWepXr00gYENKF3algkTPCha1GDzHgkh8lC2/1N1Ol2WK4aE+K/791MYMWIX/fu76YeHXrSok+woCFHAZFsIypYtS1BQkDGziAJkw4YYPvpoO/Hxjzl16hZnzw5CpVJJERCiAMq2EPz7VJAQT8THPyYoaDvr18cA0KLFGyxb5isFQIgCLNtC8P333xsxhsjvFEVh5crf+fTTndy/n0rx4tbMmtWWwYMbYWEhRUCIgizbQuDgIAOAiX88eJDKiBG7uH8/lQ4dqrNkSScqVZK/ESEKA7msQ2RLp1PQ6RQsLS0oVcqWpUt9SE5O54MP3pRTQUIUIjL5sHimP/64i7v7CmbOPKR/rlu3OvTpI0f5DMUAAB3+SURBVCOFClHYSCEQWaSna5kx4yBvvbWEw4evExZ2htTUDFPHEkIYkJwaEnpnztzif//bzNmz8QAEBNTnq6+85MYwIQo5+R8uSE/XMmnSfmbPPoxWq1C5sgPffedL27ZVTR1NCGEEUggElpYWHD9+A51OYdiwd5g2zZPixa1NHUsIYSRSCMxUUpKGpKQ0ype3R6VSsWyZL/Hxj2natKKpowkhjMxsCsHa49dYd+QmdpEPnrtcThPXFwY7d14kMHArVauWYt++vqhUKqpUKUWVKqVMHU0IYQJmc9XQprM3uJyYluNydcqVwN/tdSMkMr5795Lp128jHTqs4dq1hyQlabh3L8XUsYQQJmbQI4LIyEimT5+OTqejR48eBAYGZnl9xYoV/N///R9FihShdOnSzJgxg9dfN9yXcNXS1vw0qKnB3j+/UhSF9eszB4m7fVtN0aKWTJ3aik8/bYqlpdnsCwghsmGwbwGtVsvUqVNZtmwZ27ZtY+vWrVy8eDHLMrVr12b9+vVs2bKF9u3b89VXXxkqjtlSFIXevTfQo8f/cfu2Gnf3Svz++2A+/7y5FAEhBGDAQhAVFUWlSpWoWLEi1tbWdOrUib1792ZZpkmTJtjaZk5k7ubmRnx8vKHimC2VSkWdOmWxt7dm8eJO/PprP2rUKGPqWEKIfMRgp4YSEhJwdnbWP3ZyciIqKirb5cPDw3F3d8/xfTUaDTExMS+cJzk5GZ1O91LrFjRxcY+5fl1N06ZOpKam4udXhubN2+HsbMeff/5h6ngGl5qaaha/53+TNpsHQ7U5X1w1tGnTJs6fP8/q1atzXNbGxobatWu/8DbsIh+QnJz8UusWFFqtjgULTjB27D5sbS25cOEj7t27Vqjb/CwxMTHSZjMgbX7xdbNjsELg5OSU5VRPQkICTk5OTy135MgRlixZwurVq7G2lpuYXtaFC3cYMGAzR4/GAeDnV1PmCRBC5IrB+ghcXV2JjY3l+vXrpKWlsW3bNjw9PbMsc+HCBSZOnMjixYspU0bOW7+M9HQt06ZFUr/+Uo4ejaN8eXs2berJunXdeO01O1PHE0IUAAY7IrC0tGTixIkMGDAArVZLt27dcHFxISQkhHr16tGmTRtmz55NcnIyw4YNA6BcuXIsWbLEUJEKpfff30B4+AUABg5swFdfeVGyZFETpxJCFCQG7SPw8PDAw8Mjy3NPvvRBpsPMC8OGvcPZs/EsXeqDp2cVU8cRQhRAciF5AXPgQCxTpuzXP27R4g1iYj6SIiCEeGn54qohkbNHjzSMGrWbJUtOAdC6dRXc3SsByI1hQohXIoWgANi+/W8GDdpKXNwjrKwsGDeuJU2aVDB1LCFEISGFIB+7ezeZ4cN3sGbNOQDefvt1wsL8qFfP0cTJhBCFiRSCfGzq1AOsWXMOW1tLpk3zZNiwdyhSRE4DCSHylhSCfEZRFFSqzBvBpkxpRUKCmhkzPKlWrbSJkwkhCivZvcwnFEXhu+9O0azZclJTMwAoVcqWn37qLkVACGFQUgjygUuXEmnTZiWBgVs5diyOn3+ONnUkIYQZkVNDJqTV6ggJOc748ftIScmgbFk7vv22I+++W9fU0YQQZkQKgYlER9/mf//bzIkTNwDo3duVb77pIOMDCSGMTgqBiZw5E8+JEzd4/XV7li71oVOnGqaOJIQwU1IIjOjOHTVlyxYDMo8AHjxIpU+fN2WQOCGESUlnsREkJ6czcuQuKlcOISbmDpA5hWRQ0NtSBIQQJidHBAb2669XGDhwC5cu3cfCQkVk5FVq1y5r6lhCCKEnhcBAHj5MJTh4N6GhpwFwdXVk+XJ/GjUqb+JkQgiRlRQCAzh06Bo9e4Zz40YSVlYWTJjgzqhRLbC2LmLqaEII8RQpBAbg7Fyce/dSaNKkAsuW+VK3rgwSJ4TIv6QQ5AFFUdi9+zJeXlVRqVRUr16aQ4f64+bmLIPECSHyPfmWekXXrz/E13cd7duvZsWKs/rnGzYsL0VACFEgyBHBS9LpMgeJ+/zz3SQlpVGypA02NtIHIIQoeKQQvIS//77HwIFbOHDgKgCdO9di4UJvype3N3EyIYR4cVIIXtCRI9dp02YlqakZODoWY8GCjnTvXkc/h4AoGNLT04mLiyM1NdXUUfJEeno6MTExpo5hVNLmZytatCgVKlTAysoq1+8rheAFNWpUHheX0tSvX465c9tRpowMElcQxcXFYW9vT+XKlQtFEU9JScHW1tbUMYxK2vw0RVG4d+8ecXFxVKlSJdfvK72ZOdBoMpg+PZK7d5MBsLYuwuHD/+OHHzpLESjAUlNTKVOmTKEoAkI8oVKpKFOmzAsf6coRwXMcOxZHQMBmLly4Q0zMXVav7gqAvb2NiZOJvCBFQBRGL/N3LYXgGdTqNMaP30dIyHEUBWrUKMOgQQ1NHUsIIQxCTg39x969l3F1Xcw33xzHwkLF6NHN+f33wbRsWcnU0UQhU79+/Vd+j3PnzjFt2rRsX4+Li2PLli25Xh7A09MTX19ffH19+eCDD7hx48Yr58wr69atY+PGjaaO8dKGDx+Ov78//v7+eHp64u/vD8Dhw4fp2rUrvr6+dO3alaNHjwKQlpZGQEAAPj4+rFmzRv8+EyZMIDo676a0lSOCf/nrr3t4ea1CUcDNzZmwMD8aNChn6lhCZMvV1RVXV1dSUlKe+fqNGzfYunUrvr6+WZbPyQ8//EDp0qWZP38+ixcvzrF45ERRFBRFwcLi1fY9e/Xq9Urrm9o333yj/3nmzJkUL14cgFKlSrF48WKcnJz466+/CAgI4ODBgxw8eJCGDRsyePBgevXqRdeuXfnjjz/QarXUrZt3U9pKIfiXGjXKMGzYO5QtW4zPP2+GlZXcIGYO1p+K4+eT1/P0Pd9tVJFuDSu88HoxMTFMmjSJlJQU3njjDWbMmEHJkiWJiopi3LhxWFhY0KxZMw4ePMjWrVs5fvw4y5cv55tvvuHEiRNMnz4dyDxPvHr1aubMmcOlS5fw9/enS5cu1K5dm+XLl7N06VLUajXTpk3j/PnzAAQFBdG+ffssedzc3Fi1ahUAiYmJTJo0iZs3bwIwduxYGjZsSGJiIiNGjOD27du4ublx5MgR1q9fT3JyMgEBAbz11ltER0cTGhpKREQEERERpKWl4eXlxSeffEJycjLDhw8nPj4enU7H0KFD8fb25uuvv2bfvn0UKVKEFi1aMGrUKL799lvs7OwICAjgjz/+4Msvv3zqs+rTpw9vvvkmx48fJykpienTp9OoUaPnfu5Dhw4lPj4ejUZD3759ee+994DMo7YzZ84AsGPHDvbv38/MmTO5e/cukyZN4vr1zL+byZMn06BBg1z/nhVFISIigh9++AGAOnXq6F9zcXFBo9GQlpaGpaUlqampZGRkoCgKkFlMpkyZkutt5YZZF4KEhMd88skOBg9uSOvWmZdazZvXwcSphDkLDg5mwoQJvP3224SEhLBgwQLGjRvH2LFj+eKLL6hfvz5ff/31M9ddvnw5EydOpGHDhqjVamxsbBgxYoT+ix/g+PHj+uUXLVpE8eLF9aeOHj58+NR7Hjx4kLZt2wIwffp0+vXrR6NGjbh58yYBAQFERESwYMECmjRpwqBBg4iMjCQ8PFy//tWrV5k1axZubm4cOnSIq1evEh4ejqIoDBkyhN9++43ExEQcHR0JDQ0FICkpifv377N792527NiBSqXi0aNHT2WbMGECEydOfOqzAtBqtYSHh3PgwAEWLFjA999//9zPfcaMGTg4OJCamkr37t1p164dpUqVynb5adOm0bhxYxYuXIhWqyU5OfOqwvfffx+1Wv3U8qNGjaJZs2b6xydPnqRMmTJUrlz5qWV37txJnTp1sLa2pnnz5mzevJl3332XgIAA9u/fT926dXFycnpue16UWRYCRVFYvTqK4cN3kpiYwp9/3uXMmUFyFYmZ6tawwkvtvee1pKQkkpKSePvttwHo0qULw4YN49GjR6jVan2fgo+PD/v3739q/QYNGjBz5kx8fX1p164dxYoVe+72jh49yty5c/WPS5Ysqf+5X79+PHjwADs7O4YNGwbAkSNHuHjxon6Zx48fo1arOXXqFAsWLADA3d09y/uUL18eNzc3IPM8+OHDh+ncuTMAycnJxMbG0qhRI2bNmsVXX31F69atadSoERkZGdjY2DB27Fhat25Nq1atcvVZPeHl5QVA3bp1c9XHsWrVKnbv3g3ArVu3uHr16nMLwbFjx5g9ezYARYoUwd4+c1SBtWvX5rgtgK1bt+Lj4/PU83///Tdff/01y5cvB8DS0pI5c+YAmTeT9e/fnyVLlvDll19y69Yt/P39adOmTa62+TwGLQSRkZFMnz4dnU5Hjx49CAwMzPJ6WloawcHBREdH4+DgwLx586hQwbD/Ia9de8jgwVuJiMj8g27XrhpLl/pIERAFXmBgIB4eHhw4cIBevXqxbNmyl36vH374gRIlSjBy5Ei+/fZbxowZg06n4+eff8bGJveXT9vZ/XOvjaIoBAYG0rNnz6eW27BhAwcOHOCbb76hSZMmBAUFER4eztGjR9mxYwerV69m5cqVud6utbU1ABYWFmi12ucue/z4cY4cOcJPP/2Era0tffr0QaPRPLXcs577r9wcEWRkZLB79242bNiQZZn4+HiCgoKYNWsWb7zxxlPvsXbtWnx8fPj999+xt7cnODiYfv365UkhMNhVQ1qtlqlTp7Js2TK2bdvG1q1bs+xNAPzf//0fJUqUYPfu3Xz44YfZHvLmBUWnEHv4FnXrLiIi4iKlShXl++/92bGjN5UrOxhsu0Lklr29PSVKlODkyZMAbNq0icaNG1OiRAmKFSvG77//DsD27dufuf61a9eoWbMmgYGBuLq6cuXKFYoVK/bMLyaAZs2aZbkS5b+nhiwtLRk7diwbN27kwYMHtGjRQt9fAOiHOmjQoAEREREAHDp06JmnmABatGjB+vXr9XkSEhK4d+8eCQkJ2Nra4u/vT0BAABcuXECtVpOUlISHhwdjx47lzz//zNVn9TwJCQn069fvqeeTkpIoWbIktra2XLp0ibNn/xlF+LXXXuPSpUvodDr27Nmjf75p06b6vX+tVktSUhKQ+WW9adOmp/79+7TQkSNHqFq1Ks7OzvrnHj16RGBgICNGjKBhw6cvVX/48CH79+/H19eXlJQUVCoVKpUqz4ZIMdgRQVRUFJUqVaJixYoAdOrUib1791K9enX9Mvv27SMoKAiA9u3bM3XqVBRFMcjeeXpKBn/tvEba43S6davNggXeODsXz/PtCJFbKSkpuLu76x/379+fWbNm6TuLK1asyJdffglknp8fP348FhYWNG7cWH+1yb/98MMPHD9+HJVKhYuLC+7u7qhUKiwsLPDz86Nr167Url1bv/yQIUOYOnUqPj4+WFhYEBQURLt27bK8p6Ojo/7SxXHjxjF16lR8fX3RarU0atSIqVOnEhQUxGeffcbmzZtxc3OjbNmyFC9eXH/e/IkWLVpw6dIl/RGBnZ0dX331FVevXmX27NlYWFhgaWnJ5MmTUavVDB06VL8XPnr06KfaO3XqVH1n8b8/q+zcvn0bS8unv/Lc3d358ccf6dixI1WqVNGfygIYMWIEgwYNonTp0tSrV0/fpnHjxjFhwgTWr1+PhYUFkydPzvXlwNu3b6dTp05Znlu9ejXXrl1j4cKFLFy4EMjs8ylTpgwACxcuZPDgwVhYWNCyZUvWrl2Lr6/vM4+uXoZKedIVncd27NjBwYMH9VcxbNy4kaioKCZOnKhfxsfHh2XLlukrY9u2bfn5558pXbp0tu979uzZFzo0fWLPpSTOHr7Jm87FaNfO9OeDjSU1NZWiRYuaOoZR5abN6enpuLi4GCnRq0tOTtafZlm+fDl37txh1KhR+tcNtQOVG2lpafov8d9//53p06fz888/G3y7L9rmH3/8EWdn56f6GwqS3Lb577//fuagc//eEfi3AtdZbGNjk21jnqd2bYipFvNS6xZkMTHS5uyWKUgDlv36668sXboUrVZL+fLlmTlzZpb8phyALSEhgeHDh6PT6bCysmL69OlGyfKibe7fv78B0xhHbttsZWX11P+B541aarBC4OTkRHx8vP5xQkLCU5c8OTk5cevWLZydncnIyCApKem5PfVCmCtvb2+8vb1NHeOZKleuXKDv9hUG7Cx2dXUlNjaW69evk5aWxrZt2/D09MyyjKenJ7/88guQee1skyZN5OodYTQGOisqhEm9zN+1wQqBpaUlEydOZMCAAXh7e9OxY0dcXFwICQlh7969AHTv3p0HDx7g5eXFihUrGDlypKHiCJFF0aJFuXfvnhQDUag8mY/gRfsFDdpH4OHhgYeHR5bn/n3Th42NDfPnzzdkBCGeqUKFCsTFxXHnzh1TR8kT6enpLzQjVWEgbX62JzOUvYgC11ksRF6wsrJ6oRmc8ju5KMA8GKrNMgy1EEKYOSkEQghh5qQQCCGEmTPYncWG8rJ3FgshhDnTaDRZhs/4twJXCIQQQuQtOTUkhBBmTgqBEEKYOSkEQghh5qQQCCGEmZNCIIQQZk4KgRBCmLlCWQgiIyNp3749Xl5ehIaGPvV6Wloaw4cPx8vLix49ehAXF2eClHkrpzavWLECb29vfH196devHzdu3DBByryVU5uf2LlzJzVr1uTcuXNGTGcYuWnz9u3b8fb2plOnTowYMcLICfNeTm2+efMmffr0oXPnzvj6+nLgwAETpMw7Y8aMoWnTpvj4+DzzdUVRmDZtGl5eXvj6+hIdHf3qG1UKmYyMDKVNmzbKtWvXFI1Go/j6+ip///13lmVWr16tTJgwQVEURdm6dasybNgwU0TNM7lp89GjR5Xk5GRFURRlzZo1ZtFmRVGUpKQk5f3331d69OihREVFmSBp3slNm69cuaL4+/srDx48UBRFUe7evWuKqHkmN20eP368smbNGkVRFOXvv/9WWrdubYqoeebEiRPK+fPnlU6dOj3z9f379ysBAQGKTqdTzpw5o3Tv3v2Vt1nojgiioqKoVKkSFStWxNramk6dOunnP3hi3759dOnSBYD27dtz9OjRAj0ufW7a3KRJE/0Ud25ubllmjyuIctNmgJCQEAYOHFgo7kbPTZt//vlnevfuTcmSJQH0k58XVLlps0ql4vHjxwAkJSXh6Ohoiqh5pnHjxvrf37Ps3buXzp07o1KpcHNz49GjR9y+ffuVtlnoCkFCQgLOzs76x05OTiQkJDy1TLly5YDMCXTs7e25f/++UXPmpdy0+d/Cw8Nxd3c3RjSDyU2bo6OjiY+PL9CTlf9bbtocGxvLlStX6NmzJ++++y6RkZHGjpmnctPmoKAgtmzZgru7O4GBgYwfP97YMY3qv5+Js7Pzc/+/50ahKwTi+TZt2sT58+cZMGCAqaMYlE6nY+bMmYwaNcrUUYxKq9Vy9epVVq1axZw5c5gwYQKPHj0ydSyD2rZtG126dCEyMpLQ0FCCg4PR6XSmjlWgFLpC4OTklOW0R0JCAk5OTk8tc+vWLQAyMjJISkqiVKlSRs2Zl3LTZoAjR46wZMkSFi9ejLW1tTEj5rmc2qxWq/nrr7/o27cvnp6enD17liFDhhToDuPc/m17enpiZWVFxYoVqVy5MrGxsUZOmndy0+bw8HA6duwIQP369dFoNAX6CD8n//1M4uPjn/n//UUUukLg6upKbGws169fJy0tjW3btuHp6ZllGU9PT3755Rcg84qSJk2aoFKpTBE3T+SmzRcuXGDixIksXry4wJ83hpzbbG9vz/Hjx9m3bx/79u3Dzc2NxYsX4+rqasLUryY3v+e2bdty4sQJABITE4mNjaVixYqmiJsnctPmcuXKcfToUQAuXbqERqOhdOnSpohrFJ6enmzcuBFFUTh79iz29vav3C9S6KaqtLS0ZOLEiQwYMACtVku3bt1wcXEhJCSEevXq0aZNG7p3787nn3+Ol5cXJUuWZN68eaaO/Upy0+bZs2eTnJysnzO6XLlyLFmyxMTJX15u2lzY5KbNLVu25PDhw3h7e1OkSBGCg4ML9NFubto8evRoxo8fz/fff49KpWLmzJkFesfus88+48SJE9y/fx93d3c+/vhjMjIyAOjVqxceHh4cOHAALy8vbG1tmTFjxitvU4ahFkIIM1foTg0JIYR4MVIIhBDCzEkhEEIIMyeFQAghzJwUAiGEMHNSCES+VLt2bfz9/fX/njdCbP369V95e6NHj8bT0xN/f3+6dOnCmTNnXvg9xo0bx8WLFwGeujS3Z8+er5wR/vlcfHx8GDx4cI53DcfExBT40TiF4cnloyJfql+/fq6/jF9k2eyMHj2aVq1a0aFDBw4dOsSsWbPYsmXLS79fXmTK6X1HjRpF5cqVGTJkSLbLb9iwgfPnzzNx4sQ8zyIKDzkiEAWCWq2mX79+dOnSBV9fX/bs2fPUMrdv36Z37976PeaTJ08CcOjQId577z26dOnCJ598glqtfu62GjduzLVr14DMeRx8fHzw8fHh+++/ByA5OZnAwED8/Pzw8fFh+/btAPTp04dz587x9ddfk5qair+/v34+gCdHLZ9++in79+/Xb2v06NHs2LEDrVbLrFmz6NatG76+vvz44485fiZubm76wcaioqJ477336Ny5Mz179uTy5cukpaUxf/58tm/fjr+/P9u3byc5OZkxY8bQvXt3Onfu/MzPUZihVx7IWggDqFWrluLn56f4+fkpQ4cOVdLT05WkpCRFURTl3r17Stu2bRWdTqcoiqK4ubkpiqIoYWFhyqJFixRFyRzHPikpSbl3757y/vvvK2q1WlEURVm6dKny7bffPrW9UaNGKREREYqiKMr27duV7t27K+fOnVN8fHwUtVqtPH78WPH29laio6OVHTt2KOPGjdOv++jRI0VRFOWDDz7Qz3nwJNMTTx7v2rVLCQ4OVhRFUTQajeLu7q6kpKQoP/74o7Jw4UL98126dFGuXbv2VM4n75ORkaF8/PHHyoEDBxRFyZx3IT09XVEURTl8+LASFBSkKIqirF+/XpkyZYp+/Tlz5igbN25UFEVRHj58qLRr107/2QjzVeiGmBCFQ9GiRdm0aZP+cXp6OnPnzuW3337DwsKChIQE7t69S9myZfXLuLq6MnbsWDIyMmjbti21a9fm119/5eLFi/Tq1Uv/Pm5ubs/c5uzZs1m8eDGlS5dm+vTpHD16lLZt22JnZweAl5cXJ0+epGXLlsyaNYuvvvqK1q1b06hRo1y3y93dnenTp5OWlkZkZCSNGjWiaNGiHD58mD///JOdO3cCmePqX7169alxgp4caSQkJFCtWjWaN2+uX37UqFFcvXoVlUpFenr6M7d/6NAh9u3bx/LlywHQaDTcunWLatWq5boNovCRQiAKhC1btpCYmMiGDRuwsrLC09MTjUaTZZnGjRuzevVqDhw4wOjRo+nfvz8lSpSgefPmzJ07N8dtBAcH06FDB/3jJwOZ/VeVKlXYsGEDBw4c4JtvvqFJkyYEBQXlqh02Nja8/fbbHDx4kIiICLy9vYHM6QfHjx9Py5Ytn7v+kwKZkpJCQEAAa9asoW/fvoSEhPDOO++wcOFC4uLi6Nu3b7bvMX/+fKpWrZqrvMI8SB+BKBCSkpIoU6YMVlZWHDt27JlzLt+4cYPXXnuNd999lx49ehAdHY2bmxunT5/m6tWrQOb5/StXruRqm40aNWLPnj2kpKSQnJzMnj17aNSoEQkJCdja2uLv709AQAAXLlx4al1LS8ts98q9vb3ZsGGD/ugCoEWLFqxbt06/zpUrV0hOTs42m62tLePHj2fFihX6odSfDEX8ZGRdgGLFimXpE2nRogWrV6/Wz8j3rOzC/MgRgSgQfH19GTJkCL6+vtSrV++Ze7QnTpwgLCwMS0tL7OzsmDVrFqVLl+bLL7/ks88+Iy0tDYDhw4dTpUqVHLdZt25dunbtSo8ePQDo3r07derU4eDBg8yePRsLCwssLS2ZPHnyU+u+++67+Pn5UadOHebMmZPltebNmxMcHEybNm3080L06NGDGzdu0LVrVxRFoVSpUixatOi5+erUqUPNmjXZunUrAwYMYPTo0SxevBgPDw/9Mu+88w6hoaH4+/szaNAghg4dyowZM/Dz80On01GhQgWWLl2a42chCje5fFQIIcycnBoSQggzJ4VACCHMnBQCIYQwc1IIhBDCzEkhEEIIMyeFQAghzJwUAiGEMHP/DzTsNQfXBB2rAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "\n",
            "\n",
            "=========================== SVC ============================\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.39      0.27      0.32        33\n",
            "           1       0.76      0.84      0.80        90\n",
            "\n",
            "    accuracy                           0.69       123\n",
            "   macro avg       0.58      0.56      0.56       123\n",
            "weighted avg       0.66      0.69      0.67       123\n",
            "\n",
            "[[ 9 24]\n",
            " [14 76]]\n",
            "SVC roc: 55.86%\n",
            "[0.68932039 0.68932039 0.67647059 0.69607843 0.70588235 0.68627451]\n",
            "0.69 accuracy with a standard deviation of 0.01\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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gcWNnFi8OlHsCnpLBCkFKSgqurv8bu0OtVhMdHV1omREjRjBw4EBWrlxJVlYWy5Yt07tdjUZDbGzsE2XKzs5+4nXLK2mzaVhz6CoXU3N4zunhwyLn5+eTmZlZIvuq7WjFS64WZe53eODANaZNO05yciZWVireeceD99/3xMYmvcxlNRRD/W0b9WJxZGQk3bt359133+XkyZOMHTuWrVu3YmFR9OGdra0tjRo92XRxsbGxT7xueSVtNg0Vom7TtEKFIrt0mmKb/+nOnWxCQrZw+3Y2LVrUIDw8GGvrVJNu88M8zfv8qAJisEKgVqtJTk7WPU5JSUGtVhdaZt26dSxduhSA5s2bo9FoSEtLo1o1Gf9DCHOnKAqKAhYWKhwd7Zg/vxMpKRmMGtUaKysLYmNTjR3RZBisEHh6ehIfH09iYiJqtZrIyEjmzp1baBk3NzcOHz5Mjx49uHDhAhqNBicnJ0NFEuXU4w55UJLny8uKsnze3hCuXk1n+PBI2rV7ltGjXwagf/9mRk5lugxWCKysrJgyZQqDBg1Cq9XSs2dPPDw8CA0NpWnTpnTo0IHx48czadIkfvjhB1QqFbNmzUKlkos+orCSmtykPDPGUA3GoCgK339/ktGjd3Hnjobff09i+PCW2NtbGzuaSTPoNQIfHx98fHwKPTdy5Ejdz/Xq1ePHH380ZARRxhXn2/79IlDcIQ9M/Xy5qbp4MY3BgyPYt+8SAAEBHixeHChFoBTIncXCqIrzbd9cvg2bK602n/nzjzBx4j6ysvKoXr0C8+d3om/fpnKGoJRIIRBGV14HOBMlZ926WLKy8nj99aaEhnbC2VkGiStNUgjEYzOV8eqF8eTkaElP11CtWgUsLS0IDw/m779vERTUwNjRzJLcjy0eW3kfr14Y1x9/XKFFizD699+IoigANGxYXYqAEckRgXgicjpHPK7MzFw+/fQX5s37nfx8hczMXK5fz0CtrmTsaGZPCoEQwuD2749n8OAI4uJSsbBQ8cknbZg27VUqVJAeQWWBFAIzUlLn9uW8viguRVH48MPtfPvtHwB4eroQHh5My5ZyOrAskWsEZqSkzu3LeX1RXCqVisqVbbG2tmDatPYcOzZEikAZJEcEZkbO7QtDu3kzkwsXUmnVqiYAkyf70K/f8zRu7GzkZKIockQghCgRiqLw449nadRoAd26rSUtLQsAOzsrKQJlXLELQVZWliFzCCHKsaSku3Tt+iOvv76emzczadzYmczMXGPHEsWktxCcOHGCLl260LlzZwDOnz/P1KlTDZ1LCFEO5OcrhIUdp0mThURE/EXlyrZ8910Qe/b055lnpENBeaG3EHzxxReEh4dTpUoVABo2bMixY8cMHkwIUfYNHLiFoUO3cveuhuDgBsTEDGfQoBdkjKByplinhtzc3Aqv9IgZxIQQ5uPNNz1xcanIjz/2ZNOm/8hRQDmlt9eQm5sbJ06cQKVSkZuby/Lly6lbt25pZBNClDFnz15n796LjBzZGoAOHZ7j4sUPqVjx4XMpi/JB71f7qVOnsmrVKlJSUvD29iY2NpZPP/20NLIJIcoIjSaPqVP388ILSxg1aicHDyboXpMiUP7pPSK4dOnSA1NMHj9+nBdffNFgoYQQZceRI0kMHLiFc+duADBsWAs8PdV61hLlid5CMGPGDDZu3Kj3OWFcRQ0f8c/5e2VoCPE4MjJymDz5F77++ncUBTw8nFi6NBhv71rGjiZKWJGF4OTJk5w8eZLU1FSWLVume/7evXtotdpSCSeKT2b6EiVt4sR9hIYewcJCxZgxbZg6tb1MG2miiiwEubm5ZGZmotVqycjI0D1fqVIl5s+fXyrhxON52PARMn+veFITJ7bjzJnrzJ7dkRYtahg7jjCgIgvBSy+9xEsvvUT37t155hn5FimEqduy5U8WLz7G5s19sba2xNm5Inv3DjB2LFEK9F4jsLe3Z/bs2cTFxaHRaHTPL1++3KDBhBCl4/r1DD78cDtr154D4P/+7zSDBr1g5FSiNOntPvrJJ5/w3HPPkZSUxIgRI3jmmWfw9PQsjWxCCANSFIWVK6Np1GgBa9eeo0IFa0JDO/HOO17GjiZKmd4jgtu3b9O7d2+WL1+uO13Us2fP0sgmhDCQhIQ7vPfeVrZvjwOgY8fnCAsLpE6dqkZOJoxBbyGwsipYxMXFhf379+Pi4sKdO3cMHkwIYTi7dl1g+/Y4qlSxY96813j7bS8ZH8iM6S0Ew4YNIz09nXHjxvHZZ5+RkZHBhAkTSiObEKIEZWTk6O4CHjiwOVeu3GXIkBdxc3MwcjJhbHqvEbz66qs4ODhQv359VqxYwYYNG3B0dCyNbEKIEpCXl8+cOQepVetrLl5MAwqmkPz00/ZSBATwiEKg1WrZunUr4eHh/PXXXwD88ssv9O3bl88++6zUAgohntzp08m0arWUceP2cOtWFps2nTd2JFEGFXlqaOLEiVy7do3nn3+eGTNm4OLiwtmzZ/nkk0/o2LFjaWYUQjwmjSaPGTOimDXrIHl5+Tz7rCNhYYH4+9czdjRRBhVZCM6ePcuWLVuwsLBAo9HQtm1bdu/eTdWq0qtAiLLs5Mlr9Ou3gdjYm6hUMGJES2bO7ICDg62xo4kyqshTQ9bW1roJaGxtbXF3d3/sIhAVFYW/vz9+fn6EhYU9dJlt27bRpUsXAgICGD169GNtXwjxIFtbKy5cSKNBg2pERb3DN990kSIgHqnII4KLFy8SFBSke5yQkFDocURExCM3rNVqmT59OsuWLUOtVtOrVy98fX2pV+9/h6bx8fGEhYWxZs0aHB0duXXr1tO0RQizFROTRsOGCiqVisaNndm+vR8vv+yOnZ3ejoFCFF0Itm3b9lQbjo6OplatWri7uwMQEBDA3r17CxWCn376iX79+ul6IVWrVu2p9imEuUlLy+KTT3bx/fenWLOmMn37NgXA17eOkZOJ8qTIQvC0A82lpKTg6uqqe6xWq4mOji60THx8PAB9+/YlPz+fESNG4O3t/cjtajQaYmNjnyhTdnb2E69b1mVmZgI80D5TbnNRzKXNe/YkMX36CW7ezMbGxoJz5y4SG2tp7Filxlze538yVJuNetyo1Wq5fPkyK1asIDk5mTfffJOIiAgqVy56TH1bW9snHlbZlIdkvj/5zL/bZ8ptLoqptzk5+R4ffLCddetiAGjb1p2QkCYEBLQycrLSZerv88M8TZsfVUAMVgjUajXJycm6xykpKajV6geWadasGdbW1ri7u1O7dm3i4+N5/vnnDRWr3Clq5rF/k9nHzMPx41fx81tBWlo2FStaM2tWR4YPb8mff8r9AeLJ6b2zGAoORy5evPhYG/b09CQ+Pp7ExERycnKIjIzE19e30DIdO3bk6NGjAKSmphIfH6+7piAK3J95TB+Zfcw8NG7sjLNzRfz963Lu3HBGjHgJCwsZI0g8Hb1HBPv27WP27Nnk5uayb98+YmNjCQ0NZfHixY/esJUVU6ZMYdCgQWi1Wnr27ImHhwehoaE0bdqUDh060K5dOw4ePEiXLl2wtLRk7Nixcp/CQzxs5jFhHvLzFZYuPUGfPk2oUsUOe3troqLexsWlogwSJ0qM3kLw7bffsm7dOvr37w8UnIO+ckX/qQoAHx8ffHx8Cj03cuRI3c8qlYqQkBBCQkIeJ7MQZuHPP28yaFAEv/2WwB9/XOG774IBUKsrGTmZMDXFGobawUEGphKitOTmapk79zBTp+5Ho9Hi6lqJzp09jB1LmDC9haBevXpERESg1WqJj49nxYoVNG/evDSyCWF2Tp68xsCBWzh5sqCjxTvveDF37mtUrWpv5GTClOm9WDx58mTi4uKwsbFh9OjRVKpUiYkTJ5ZGNiHMyoULqbz00lJOnkymdu0q7Nr1Jt9/31WKgDA4vUcEFy9e5KOPPuKjjz4qjTxCmK26dZ3o3/95HBxs+PzzDlSqZGPsSMJM6C0Es2bN4ubNm/j7+9OlSxfq169fGrmEMHn37uUwYcJeXn+9KW3aFHSbDg8Plt5AotTpLQQrVqzgxo0bbN++nSlTppCRkUHnzp0ZPnx4aeQTwiTt3BnHkCFbSUi4w4EDlzl1aigqlUqKgDCKYt1Q5uzszIABA5g2bRoNGzZk4cKFhs4lhElKTc3irbc20anTKhIS7vDii24sX95NCoAwKr1HBBcuXGDbtm3s2rWLKlWq0LlzZ8aPH18a2YQwKevWxfD++9u4fj0DOzsrpk1rz8cft8HKqljfx4QwGL2FYMKECXTu3JmlS5c+MFaQEKJ4bt/OZsiQCNLSsvH2rsV33wVRv74Muy7KBr2FYO3ataWRQwiToygK+fkKlpYWVKlix8KFAaSlZTF0aAsZH0iUKUUWgpEjRxIaGlpoVrJ/0jdDmRDmLD7+NkOGRODrW4fx418B0E0aI0RZU2QhuH/TmL7B5YQQ/6PV5rNgwR9MmLCXjIxcYmJuMGpUa5kyUpRpRV6lcnFxAWD16tU888wzhf6tXr261AIKUV7Ext7A2/sHRo7cQUZGLn37NuXEiaFSBESZp7e7wqFDhx54LioqyiBhhCiP8vLy+fzzKLy8lnDoUCI1ajiweXNf1qzpiYtLRWPHE0KvIr+qrF69mjVr1pCYmFjoOkFGRgYvvPBCqYQTojywsFCxa9dFcnK0DB78AnPm+FGlip2xYwlRbEUWgqCgILy9vZk3bx6jR4/WPV+xYkWqVKlSKuGEKKuysnJJT8/BxaUiFhYqli4NIjHxLr6+dYwdTYjHVuSpIZVKRc2aNZkyZQoVK1bU/QO4fft2qQUUoqyJirpMs2aLefPNDSiKAoCHRzUpAqLcKvKIYPTo0SxZsoQePXqgUql0f/BQUCT27t1bKgGFKCvu3tUQErKHhQuPAWBtbcnNm5k4O8t1AFG+FVkIlixZAhTMWSyEudu+/W+GDt1KYuJdrKwsmDixHSEhr2BrKz2CRPmn96/4+PHjNGrUiAoVKrB582ZiYmJ46623qFGjRmnkE8KoFEVh8OAIwsNPAtCiRQ2+/z4YT08ZbkWYDr3dR6dOnYq9vT3nz59n2bJlPPvss4wdO7Y0sglhdAXXyipjZ2fFl1/6cfjwQCkCwuToLQRWVlaoVCr27NlDv3796NevHxkZGaWRTQijuHo1nV9/vax7PGFCO86eHcbo0S/LSKHCJOn9q65YsSJLlixhy5YttG/fnvz8fPLy8kojmxClSlEUwsNP0LjxAnr2/IlbtzIBsLGxpG5dJyOnE8Jw9BaCr776ChsbG2bOnImzszPJyckMHDiwNLIJUWouXkyjY8cVDBoUwZ07Glq1qklubr6xYwlRKvQWAmdnZ4KCgkhPT+eXX37B1taWbt26lUY2IQxOq83nq68O4+m5iH37LlG9egVWr+7Bli19cXWtZOx4QpQKvYVg27Zt9O7dmx07drB9+3bdz0KYggEDNvHxx7vIzMzljTc8iYkZzuuve8rUkcKs6O0+unjxYtatW0e1agWzKaWmpvL222/TqVMng4cTwtAGD36BqKjLLFzYhaCgBsaOI4RR6C0EiqLoigBAlSpVCt1lLER58scfV9i37xLjxhVMFtO+fW3i4j6QG8OEWdP71//KK68wcOBAAgICgIJTRd7e3gYPJkRJyszM5dNPf2HevN/Jz1d4+WV32rWrBSBFQJg9vf8Dxo0bx65duzh+/DgA//nPf/Dz8zN4MCFKyv798QwatIULF9KwsFDxySdtePFFuTNeiPuKLATx8fHMnj2bxMRE6tevz7hx41Cr5Y5KUX7cuZPN2LG7CQs7AYCnpwvh4cG0bPmMkZMJUbYU2WtowoQJvPrqq8yfP58mTZrw2WefPfbGo6Ki8Pf3x8/Pj7CwsCKX27lzJw0aNODMmTOPvQ8hijJ58i+EhZ3A2tqC6dPbc+zYECkCQjxEkUcEGRkZ9OnTB4DnnnuO7t27P9aGtVot06dPZ9myZajVanr16oWvry/16tUrtNy9e/dYvnw5zZo1e4L4QhT2z44MU6b4cOnSbWbN6kCTJi5GTCVE2VbkEYFGoyEmJoZz585x7tw5srOzCz3WJzo6mlq1auHu7o6NjQ0BAQEPncMgNDSUwYMHY2tr+3QtEWZNURRWrw5bSE8AABreSURBVD6Dr+9ycnK0AFSvXoGIiNelCAihR5FHBM7OznzxxRe6x9WrV9c9VqlULF++/JEbTklJwdXVVfdYrVYTHR1daJlz586RnJxM+/btCQ8PL1ZgjUZDbGxssZb9t+zs7Cde11gyMwvGuzGnNj+u5ORMpk8/zv791wDYuLEKNjaWRk5Vuszhff43aXPJKbIQrFixosR39k/5+fnMmjWrULEpDltbWxo1avRE+4yNjX3idY2lQlTBtKDm1Obiys9X+O6744wZs5v09BwcHW2ZO/c12rSxM9k2F8WU3+eiSJsff92iGKwDtVqtJjk5Wfc4JSWlUK+jjIwM/vrrLwYMGADAjRs3GDZsGIsWLcLT09NQsYSJiItLZfDgCPbvjwega9cGLFwYQI0aDmb3LVGIp2WwQuDp6Ul8fDyJiYmo1WoiIyOZO3eu7nUHBweOHDmie9y/f3/Gjh0rRUAUy6+/Xmb//nhcXCry7bed6dWrsYwPJMQTMlghsLKyYsqUKQwaNAitVkvPnj3x8PAgNDSUpk2b0qFDB0PtWpio27ezqVLFDoC33/bixo1MBg5sTrVqFYycTIjyrVhjDW3ZsoXExERGjBjB1atXuXnzJs8//7zejfv4+ODj41PouZEjRz50WUNfkxDll0aTx8yZv/L110c4dmwwHh7VUKlUjB3b1tjRhDAJxZqz+NSpU0RGRgIFM5ZNmzbN4MGEAPj99yReeCGM6dOjuHtXw86dF4wdSQiTo/eIIDo6mo0bN+omo3F0dCQ3N9fgwYR5y8jIYfLkX/j6699RFPDwcCI8PFg3UJwQouToLQRWVlZotVrdhbjU1FQsLGQCb2E4R44k8cYbG7h4MQ1LSxWffPIyn37qg729tbGjCWGS9BaC/v378/7773Pr1i2++uorduzYwahRo0ojmzBTVarYceXKXZo1UxMeHiwjhQphYHoLQXBwME2aNOH3339HURQWLlxI3bp1SyObyVt9JIHNp648cpmYa3dp7Fa5lBIZz2+/JdC2rTsqlYoGDaqzb99btGxZA2tr87pDWAhj0HuO5+rVq9jb2/Pqq6/i6+uLvb09V69eLY1sJm/zqSvEXLv7yGUau1Wmq5fpjph5/XoGffuuo127ZaxY8b8hSF5+2V2KgBClRO8RwdChQ3U/azQakpKSqFOnjq4XkXi4x/m2v3Zom1JKVXYoisKqVWcYOXIHqalZVKhgrRssTghRuvQWgoiIiEKPz507x+rVqw0WyFTc/7b/qNM6pv5tvygJCXd4772tbN8eB4Cf33OEhQVRu3YVIycTwjw99p3FTZo0eWAUUfFw5vpt/1GOHEmiY8cV3LuXQ5Uqdnz1lT9vvdVMhocQwoj0FoJly5bpfs7PzycmJgYXFxnfXTwZLy9X3N0r07BhdRYs6IKbm4OxIwlh9vQWgoyMDN3PlpaW+Pj44O/vb9BQwnTk5eXz7bdHGTCgGU5O9tjaWnHw4LtUrWpv7GhCiP/vkYVAq9WSkZHBuHHjSiuPMCGnTyfz7rtbOHHiGqdOJfPDDwV3p0sREKJsKbIQ5OXlYWVlxYkTJ0ozjzAB2dl5zJgRxezZB8nLy+fZZx15/fWmxo4lhChCkYWgd+/ebNy4kYYNG/Lee+/RqVMnKlT433C/r732WqkEFOXLoUOJDBy4hfPnb6JSwYgRLZk5swMODjIntRBlld5rBDk5OVStWrXQJDIghUA8KC4ulXbtlpGfr9CgQTXCw4Np2/ZZY8cSQuhRZCG4desWy5Ytw8PDA5VKhaIoutekq594mHr1nBgy5AWcnOyZPNkHOzuDzXskhChBRf5Pzc/PL9RjSIh/S0vLYvToXbzzjpdueOiFCwPki4IQ5UyRhcDZ2ZkRI0aUZhZRjmzYEMv7728jOfkex49f49SpoahUKikCQpRDRRaCf54KEuK+5OR7jBixjfXrYwF45ZVnWbo0SAqAEOVYkYXghx9+KMUYoqxTFIXly0/z0Uc7SUvLplIlG2bP7sh777XAwkKKgBDlWZGFoEoVGQBM/M/t29mMHr2LtLRsOnWqx+LFAdSqJX8jQpgC6dYhipSfr5Cfr2BlZUHVqvYsWRJIZmYub775vJwKEsKEyOTD4qHOn7+Jt/cyZs36Tfdcz56N6d9fRgoVwtRIIRCF5OZqmTnzV5o1W8zBg4mEh58kOzvP2LGEEAYkp4aEzsmT13j33S2cOpUMwMCBzfnvf/3kxjAhTJz8Dxfk5mr59NP9zJlzEK1WoXbtKnz3XRAdOz5n7GhCiFIghUBgZWXBkSNXyM9XGDmyFTNm+FKpko2xYwkhSokUAjOVnq4hPT2HGjUcUKlULF0aRHLyPdq0cTd2NCFEKZOLxWZo5844mjZdRL9+G3R3kNepU1WKgBBmSgqBGbl1K5O33tpEp06rSEi4Q3q6hlu3sowdSwhhZAY9NRQVFcXnn39Ofn4+vXv3ZsiQIYVeX7ZsGT///DOWlpY4OTkxc+ZMnnnmGUNGKhGrjySw+dSVRy4Tc+0ujd0ql1KiR1MUhfXrCwaJu349Azs7K6ZPb89HH7XBykq+Cwhh7gz2KaDVapk+fTpLly4lMjKSrVu3EhcXV2iZRo0asX79eiIiIvD39+e///2voeKUqM2nrhBz7e4jl2nsVpmuXsYvaoqi0K/fBnr3/pnr1zPw9q7F6dPvMWZMWykCQgjAgEcE0dHR1KpVC3f3gvPOAQEB7N27l3r16umWad26te5nLy8vtmzZYqg4Ja6xW2XWDm1j7Bh6qVQqGjd2xsHBhjlz/Bgy5EUZJE4IUYjBCkFKSgqurq66x2q1mujo6CKXX7duHd7e3nq3q9FoiI2NfaJM2dnZT7zuP2VmZgKUyLYMISnpHomJGbRpoyY7O5vg4Gq0bfsarq4V+PPP88aOZ3Al9T6XJ9Jm82CoNpeJ7qObN2/m7NmzrFy5Uu+ytra2NGrU6In2Exsb+8Tr/lOFqNsAJbKtkqTV5vPtt0eZMGEf9vZWxMS8z61bCWUup6GV1PtcnkibzcPTtPlRBcRghUCtVpOcnKx7nJKSglqtfmC5Q4cOsXjxYlauXImNjdzE9KRiYm4waNAWDh9OAiA4uIGcAhJCFIvBrhZ6enoSHx9PYmIiOTk5REZG4uvrW2iZmJgYpkyZwqJFi6hWrZqhopi03FwtM2ZE0bz5Eg4fTqJGDQc2b+7LmjU9qV69grHjCSHKAYMdEVhZWTFlyhQGDRqEVqulZ8+eeHh4EBoaStOmTenQoQNz5swhMzOTkSNHAuDm5sbixYsNFckkvfHGBtatiwFg8OAX+O9//XB0tDNyKiFEeWLQawQ+Pj74+PgUeu7+hz7IdJglYeTIVpw6lcySJYH4+tYxdhwhRDkkHcnLmQMH4pk2bb/u8SuvPEts7PtSBIQQT6xM9BoS+t29q2HcuN0sXnwcgFdfrYO3dy0AuTFMCPFUpBCUA9u2/c3QoVtJSrqLtbUFEye2o3XrmsaOJYQwEVIIyrCbNzMZNWoHq1adAeCll54hPDyYpk1djJxMCGFKpBCUYdOnH2DVqjPY21sxY4YvI0e2wtJSTgMJIUqWFIIyRlEUVKqCG8GmTWtPSkoGM2f6Ureuk5GTCSFMlXy9LCMUReG7747z8svfk52dB0DVqvasXdtLioAQwqCkEJQBFy6k0qHDcoYM2crvvyfx00/njB1JCGFG5NSQEWm1+YSGHmHSpH1kZeXh7FyBb77pTJ8+TYwdTQhhRqQQGMm5c9d5990tHD1aMNNZv36efP11JxkfSAhR6qQQGMnJk8kcPXqFZ55xYMmSQAIC6hs7khDCTJlNIVh9JIE1h67q5hJ4Gk86H/GNGxk4O1cECo4Abt/Opn//52WQOCGEUZnNxeLNp65wMTWnRLb1uPMRZ2bm8sknu6hdO5TY2BtAwRSSI0a8JEVACGF0ZnNEAPCck02pzzP8yy+XGDw4ggsX0rCwUBEVdZlGjZxLNYMQQjyKWRWC0nTnTjZjx+4mLOwEAJ6eLnz/fVdatKhh5GRCCFGYFAID+O23BPr2XceVK+lYW1swebI348a9go2NpbGjCSHEA6QQGICrayVu3cqideuaLF0aRJMmMkicEKLskkJQAhRFYffui/j5PYdKpaJePSd+++0dvLxcZZA4IUSZJ59STykx8Q5BQWvw91/JsmWndM+/+GINKQJCiHJBjgieUH5+wSBxY8bsJj09B0dHW2xt5RqAEKL8kULwBP7++xaDB0dw4MBlALp1a8iCBV2oUcPByMmEEOLxSSF4TIcOJdKhw3Kys/NwcanIt992plevxro5BIQQoryRQvCYWrSogYeHE82buzFv3mtUqyaDxAkhyjcpBHpoNHl8+eUhhg5tQfXqFbCxseTgwXdxcLA1djQhhCgRUgge4fffkxg4cAsxMTeIjb3JypU9AKQICCFMihSCh8jIyGHSpH2Ehh5BUaB+/WoMHfqisWMJIYRBSCH4l717LzJ4cASXLt3G0lLFmDEv8+mn7bGzk1+VEMI0yafbP/z11y38/FagKODl5Up4eDAvvOBm7FhCCGFQUgj+oX79aowc2Qpn54qMGfMy1tZyg5gQwvSZdSFISbnHhx/u4L33XuTVV+sA8NVXnYycSgghSpdZFgJFUVi5MppRo3aSmprFn3/e5OTJoXJTmBDCLBl0VLSoqCj8/f3x8/MjLCzsgddzcnIYNWoUfn5+9O7dm6SkJEPGASAh4Q4BAasZMGATqalZvPZaXTZt6itFQAhhtgxWCLRaLdOnT2fp0qVERkaydetW4uLiCi3z888/U7lyZXbv3s3bb7/Nl19+aag4KPkK8Qev0aTJQrZvj6NqVTt++KErO3b0o3btKgbbrxBClHUGKwTR0dHUqlULd3d3bGxsCAgIYO/evYWW2bdvH927dwfA39+fw4cPoyiKQfLkZuXx184E7t3LoWfPRsTEvM9bb3nJkYAQwuwZ7BpBSkoKrq6uusdqtZro6OgHlnFzK+ieaWVlhYODA2lpaTg5ORW5XY1GQ2xs7GPn8a1vh9OIxjzvWpHXXqtJWloiaWmPvZlyJzs7+4l+X+WZtNk8SJtLTrm7WGxra0ujRo0ee71GjSC2buwTrVuexcZKm82BtNk8PE2bH1VADHZqSK1Wk5ycrHuckpKCWq1+YJlr164BkJeXR3p6OlWrVjVUJCGEEA9hsELg6elJfHw8iYmJ5OTkEBkZia+vb6FlfH192bhxIwA7d+6kdevWcs5eCCFKmcFODVlZWTFlyhQGDRqEVqulZ8+eeHh4EBoaStOmTenQoQO9evVizJgx+Pn54ejoyFdffWWoOEIIIYpg0GsEPj4++Pj4FHpu5MiRup9tbW2ZP3++ISMIIYTQw6A3lAkhhCj7pBAIIYSZk0IghBBmTgqBEEKYOZViqDEdDOTUqVPY2sqcwUII8Tg0Gg1eXl4Pfa3cFQIhhBAlS04NCSGEmZNCIIQQZk4KgRBCmDkpBEIIYeakEAghhJmTQiCEEGbOJAtBVFQU/v7++Pn5ERYW9sDrOTk5jBo1Cj8/P3r37k1SUpIRUpYsfW1etmwZXbp0ISgoiLfeeosrV64YIWXJ0tfm+3bu3EmDBg04c+ZMKaYzjOK0edu2bXTp0oWAgABGjx5dyglLnr42X716lf79+9OtWzeCgoI4cOCAEVKWnJCQENq0aUNgYOBDX1cUhRkzZuDn50dQUBDnzp17+p0qJiYvL0/p0KGDkpCQoGg0GiUoKEj5+++/Cy2zcuVKZfLkyYqiKMrWrVuVkSNHGiNqiSlOmw8fPqxkZmYqiqIoq1atMos2K4qipKenK2+88YbSu3dvJTo62ghJS05x2nzp0iWla9euyu3btxVFUZSbN28aI2qJKU6bJ02apKxatUpRFEX5+++/lVdffdUYUUvM0aNHlbNnzyoBAQEPfX3//v3KwIEDlfz8fOXkyZNKr169nnqfJndEEB0dTa1atXB3d8fGxoaAgAD27t1baJl9+/bRvXt3APz9/Tl8+DBKOb6vrjhtbt26Nfb29gB4eXkVmj2uPCpOmwFCQ0MZPHiwSdyNXpw2//TTT/Tr1w9HR0cAqlWrZoyoJaY4bVapVNy7dw+A9PR0XFxcjBG1xLRs2VL3/j3M3r176datGyqVCi8vL+7evcv169efap8mVwhSUlJwdXXVPVar1aSkpDywjJubG1AwgY6DgwNp5Xgm++K0+Z/WrVuHt7d3aUQzmOK0+dy5cyQnJ9O+fftSTmcYxWlzfHw8ly5dom/fvvTp04eoqKjSjlmiitPmESNGEBERgbe3N0OGDGHSpEmlHbNU/ft34urq+sj/78VhcoVAPNrmzZs5e/YsgwYNMnYUg8rPz2fWrFmMGzfO2FFKlVar5fLly6xYsYK5c+cyefJk7t69a+xYBhUZGUn37t2JiooiLCyMsWPHkp+fb+xY5YrJFQK1Wl3otEdKSgpqtfqBZa5duwZAXl4e6enpVK1atVRzlqTitBng0KFDLF68mEWLFmFjY1OaEUucvjZnZGTw119/MWDAAHx9fTl16hTDhg0r1xeMi/u37evri7W1Ne7u7tSuXZv4+PhSTlpyitPmdevW0blzZwCaN2+ORqMp10f4+vz7d5KcnPzQ/++Pw+QKgaenJ/Hx8SQmJpKTk0NkZCS+vr6FlvH19WXjxo1AQY+S1q1bo1KpjBG3RBSnzTExMUyZMoVFixaV+/PGoL/NDg4OHDlyhH379rFv3z68vLxYtGgRnp6eRkz9dIrzPnfs2JGjR48CkJqaSnx8PO7u7saIWyKK02Y3NzcOHz4MwIULF9BoNDg5ORkjbqnw9fVl06ZNKIrCqVOncHBweOrrIgads9gYrKysmDJlCoMGDUKr1dKzZ088PDwIDQ2ladOmdOjQgV69ejFmzBj8/PxwdHTkq6++Mnbsp1KcNs+ZM4fMzEzdnNFubm4sXrzYyMmfXHHabGqK0+Z27dpx8OBBunTpgqWlJWPHji3XR7vFafP48eOZNGkSP/zwAyqVilmzZpXrL3Yff/wxR48eJS0tDW9vbz744APy8vIAeP311/Hx8eHAgQP4+flhb2/PzJkzn3qfMgy1EEKYOZM7NSSEEOLxSCEQQggzJ4VACCHMnBQCIYQwc1IIhBDCzEkhEGVSo0aN6Nq1q+7fo0aIbd68+VPvb/z48fj6+tK1a1e6d+/OyZMnH3sbEydOJC4uDuCBrrl9+/Z96ozwv99LYGAg7733nt67hmNjY8v9aJzC8KT7qCiTmjdvXuwP48dZtijjx4+nffv2dOrUid9++43Zs2cTERHxxNsriUz6tjtu3Dhq167NsGHDilx+w4YNnD17lilTppR4FmE65IhAlAsZGRm89dZbdO/enaCgIPbs2fPAMtevX6dfv366b8zHjh0D4LfffuM///kP3bt358MPPyQjI+OR+2rZsiUJCQlAwTwOgYGBBAYG8sMPPwCQmZnJkCFDCA4OJjAwkG3btgHQv39/zpw5w5dffkl2djZdu3bVzQdw/6jlo48+Yv/+/bp9jR8/nh07dqDVapk9ezY9e/YkKCiIH3/8Ue/vxMvLSzfYWHR0NP/5z3/o1q0bffv25eLFi+Tk5DB//ny2bdtG165d2bZtG5mZmYSEhNCrVy+6dev20N+jMENPPZC1EAbQsGFDJTg4WAkODlaGDx+u5ObmKunp6YqiKMqtW7eUjh07Kvn5+YqiKIqXl5eiKIoSHh6uLFy4UFGUgnHs09PTlVu3bilvvPGGkpGRoSiKoixZskT55ptvHtjfuHHjlO3btyuKoijbtm1TevXqpZw5c0YJDAxUMjIylHv37ildunRRzp07p+zYsUOZOHGibt27d+8qiqIob775pm7Og/uZ7rv/eNeuXcrYsWMVRVEUjUajeHt7K1lZWcqPP/6oLFiwQPd89+7dlYSEhAdy3t9OXl6e8sEHHygHDhxQFKVg3oXc3FxFURTl4MGDyogRIxRFUZT169cr06ZN060/d+5cZdOmTYqiKMqdO3eU1157Tfe7EebL5IaYEKbBzs6OzZs36x7n5uYyb948/vjjDywsLEhJSeHmzZs4OzvrlvH09GTChAnk5eXRsWNHGjVqxC+//EJcXByvv/66bjteXl4P3eecOXNYtGgRTk5OfP755xw+fJiOHTtSoUIFAPz8/Dh27Bjt2rVj9uzZ/Pe//+XVV1+lRYsWxW6Xt7c3n3/+OTk5OURFRdGiRQvs7Ow4ePAgf/75Jzt37gQKxtW/fPnyA+ME3T/SSElJoW7durRt21a3/Lhx47h8+TIqlYrc3NyH7v+3335j3759fP/99wBoNBquXbtG3bp1i90GYXqkEIhyISIigtTUVDZs2IC1tTW+vr5oNJpCy7Rs2ZKVK1dy4MABxo8fzzvvvEPlypVp27Yt8+bN07uPsWPH0qlTJ93j+wOZ/VudOnXYsGEDBw4c4Ouvv6Z169aMGDGiWO2wtbXlpZde4tdff2X79u106dIFKJh+cNKkSbRr1+6R698vkFlZWQwcOJBVq1YxYMAAQkNDadWqFQsWLCApKYkBAwYUuY358+fz3HPPFSuvMA9yjUCUC+np6VSrVg1ra2t+//33h865fOXKFapXr06fPn3o3bs3586dw8vLixMnTnD58mWg4Pz+pUuXirXPFi1asGfPHrKyssjMzGTPnj20aNGClJQU7O3t6dq1KwMHDiQmJuaBda2srIr8Vt6lSxc2bNigO7oAeOWVV1izZo1unUuXLpGZmVlkNnt7eyZNmsSyZct0Q6nfH4r4/si6ABUrVix0TeSVV15h5cqVuhn5HpZdmB85IhDlQlBQEMOGDSMoKIimTZs+9Bvt0aNHCQ8Px8rKigoVKjB79mycnJz44osv+Pjjj8nJyQFg1KhR1KlTR+8+mzRpQo8ePejduzcAvXr1onHjxvz666/MmTMHCwsLrKysmDp16gPr9unTh+DgYBo3bszcuXMLvda2bVvGjh1Lhw4ddPNC9O7dmytXrtCjRw8URaFq1aosXLjwkfkaN25MgwYN2Lp1K4MGDWL8+PEsWrQIHx8f3TKtWrUiLCyMrl27MnToUIYPH87MmTMJDg4mPz+fmjVrsmTJEr2/C2HapPuoEEKYOTk1JIQQZk4KgRBCmDkpBEIIYeakEAghhJmTQiCEEGZOCoEQQpg5KQRCCGHm/h9x1G9h+c9bYgAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Training roc curve\")\n",
        "build_auc_plot(classifiers, X_train, Y_train)\n",
        "\n",
        "print(\"\\n\\nTesting roc curve\")\n",
        "build_auc_plot(classifiers, X_test, Y_test)"
      ],
      "metadata": {
        "id": "xazqIvegSU9G",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 610
        },
        "outputId": "9ae2f9b3-dc98-4b67-931c-5fed21eff8ba"
      },
      "id": "xazqIvegSU9G",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training roc curve\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "\n",
            "Testing roc curve\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    }
  ],
  "metadata": {
    "colab": {
      "collapsed_sections": [
        "KtMUlgwx39T4",
        "66c93345",
        "2NRPf-up2SGY",
        "xnAFLkYC2Zrn",
        "vCCVFE93f4oW",
        "azGgZCd7Kpac",
        "Fncl9HPaK6t5"
      ],
      "name": "Final_Project_FDS.ipynb",
      "provenance": [],
      "toc_visible": true
    },
    "kernelspec": {
      "display_name": "Python 3 (ipykernel)",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.9.7"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 5
}