loan-approval-prediction.ipynb

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      "cell_type": "code",
      "execution_count": null,
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        "id": "NIky1YG1DgTp"
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      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "from sklearn.model_selection import RandomizedSearchCV\n",
        "from scipy.stats import randint, uniform\n",
        "from sklearn.pipeline import make_pipeline\n",
        "from sklearn.pipeline import Pipeline\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.model_selection import cross_val_score\n",
        "from imblearn.over_sampling import RandomOverSampler,SMOTE\n",
        "\n",
        "\n",
        "from sklearn.metrics import confusion_matrix\n",
        "from sklearn.metrics import classification_report\n",
        "from sklearn.metrics import accuracy_score\n",
        "from sklearn.metrics import roc_auc_score\n",
        "from sklearn.metrics import roc_curve\n",
        "from sklearn.metrics import precision_recall_fscore_support\n",
        "\n",
        "\n",
        "#ML models\n",
        "from xgboost import XGBClassifier\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "#input data set\n",
        "df = pd.read_csv('/content/drive/MyDrive/loan_data_set.csv')"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import re\n",
        "#convert column headers to lower place\n",
        "df.columns = df.columns.str.lower().str.replace(' ', '_')\n",
        "#df.columns = [re.sub(r'(?<!\\s)(?=[A-Z])| ', '_', col).lower() for col in df.columns]\n",
        "\n",
        "#print columns of the dataframe\n",
        "df.columns\n",
        "\n",
        "#check structure of the data\n",
        "print(df.dtypes)\n",
        "\n",
        "#check the first five rows of the data\n",
        "print (df.head())\n",
        "print(df.columns)"
      ],
      "metadata": {
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        "id": "dvPlYKteHPzI",
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          "text": [
            "loan_id               object\n",
            "gender                object\n",
            "married               object\n",
            "dependents            object\n",
            "education             object\n",
            "self_employed         object\n",
            "applicantincome        int64\n",
            "coapplicantincome    float64\n",
            "loanamount           float64\n",
            "loan_amount_term     float64\n",
            "credit_history       float64\n",
            "property_area         object\n",
            "loan_status           object\n",
            "dtype: object\n",
            "    loan_id gender married dependents     education self_employed  \\\n",
            "0  LP001002   Male      No          0      Graduate            No   \n",
            "1  LP001003   Male     Yes          1      Graduate            No   \n",
            "2  LP001005   Male     Yes          0      Graduate           Yes   \n",
            "3  LP001006   Male     Yes          0  Not Graduate            No   \n",
            "4  LP001008   Male      No          0      Graduate            No   \n",
            "\n",
            "   applicantincome  coapplicantincome  loanamount  loan_amount_term  \\\n",
            "0             5849                0.0         NaN             360.0   \n",
            "1             4583             1508.0       128.0             360.0   \n",
            "2             3000                0.0        66.0             360.0   \n",
            "3             2583             2358.0       120.0             360.0   \n",
            "4             6000                0.0       141.0             360.0   \n",
            "\n",
            "   credit_history property_area loan_status  \n",
            "0             1.0         Urban           Y  \n",
            "1             1.0         Rural           N  \n",
            "2             1.0         Urban           Y  \n",
            "3             1.0         Urban           Y  \n",
            "4             1.0         Urban           Y  \n",
            "Index(['loan_id', 'gender', 'married', 'dependents', 'education',\n",
            "       'self_employed', 'applicantincome', 'coapplicantincome', 'loanamount',\n",
            "       'loan_amount_term', 'credit_history', 'property_area', 'loan_status'],\n",
            "      dtype='object')\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Summary Statistics\n",
        "df.describe()"
      ],
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          "height": 300
        },
        "id": "QgEld52WOFe8",
        "outputId": "a70f5453-5db5-43b2-f1ee-74507651546b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       applicantincome  coapplicantincome  loanamount  loan_amount_term  \\\n",
              "count       614.000000         614.000000  592.000000         600.00000   \n",
              "mean       5403.459283        1621.245798  146.412162         342.00000   \n",
              "std        6109.041673        2926.248369   85.587325          65.12041   \n",
              "min         150.000000           0.000000    9.000000          12.00000   \n",
              "25%        2877.500000           0.000000  100.000000         360.00000   \n",
              "50%        3812.500000        1188.500000  128.000000         360.00000   \n",
              "75%        5795.000000        2297.250000  168.000000         360.00000   \n",
              "max       81000.000000       41667.000000  700.000000         480.00000   \n",
              "\n",
              "       credit_history  \n",
              "count      564.000000  \n",
              "mean         0.842199  \n",
              "std          0.364878  \n",
              "min          0.000000  \n",
              "25%          1.000000  \n",
              "50%          1.000000  \n",
              "75%          1.000000  \n",
              "max          1.000000  "
            ],
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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",
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              "      <td>0.000000</td>\n",
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              "    <tr>\n",
              "      <th>25%</th>\n",
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              "      <td>0.000000</td>\n",
              "      <td>100.000000</td>\n",
              "      <td>360.00000</td>\n",
              "      <td>1.000000</td>\n",
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              "    <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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    {
      "cell_type": "code",
      "source": [
        "#Exploratory Data Analysis\n",
        "#distribution of the loan amounts\n",
        "n, bins, patches = plt.hist(x=df['loanamount'], bins='auto', color='blue',alpha=0.7, rwidth=0.85)\n",
        "plt.xlabel(\"Loan Amount\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 452
        },
        "id": "jQa4DpZ0JMiq",
        "outputId": "98227ca6-3cb6-4053-dadf-bab5b74a8812"
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      "execution_count": null,
      "outputs": [
        {
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          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#check the number of defaults and non-default\n",
        "df['loan_status'].value_counts()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5fct-SZlZYjG",
        "outputId": "345114e7-13dd-4042-ec9c-45f2312dde1f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Y    422\n",
              "N    192\n",
              "Name: loan_status, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Check the distribution of Loan Status across Property area\n",
        "#Semi Urban has the highest number of defaults?\n",
        "print(pd.crosstab(df['loan_status'],df['property_area'],))\n",
        "df.boxplot(column=['applicantincome'], by='property_area')\n",
        "plt.title(\"Distribution of Applicant's income by Property Area\")\n",
        "plt.suptitle('')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 549
        },
        "id": "NJVcmfxNnKxL",
        "outputId": "21ee6bd0-2d4f-40da-e010-90b854058667"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "property_area  Rural  Semiurban  Urban\n",
            "loan_status                           \n",
            "N                 69         54     69\n",
            "Y                110        179    133\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 0.98, '')"
            ]
          },
          "metadata": {},
          "execution_count": 6
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#check for null columns\n",
        "df.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NOHmjwB9OYI3",
        "outputId": "1dcf6e7b-5fde-4338-cbb4-cf78e234684b"
      },
      "execution_count": null,
      "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": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Feature Engineering\n",
        "df['total_income'] = df['applicantincome'] + df['coapplicantincome']\n",
        "print(df.head())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ztDU_PqbqOKA",
        "outputId": "63898362-8831-4d19-e879-83f0fc71d7c0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "    loan_id gender married dependents     education self_employed  \\\n",
            "0  LP001002   Male      No          0      Graduate            No   \n",
            "1  LP001003   Male     Yes          1      Graduate            No   \n",
            "2  LP001005   Male     Yes          0      Graduate           Yes   \n",
            "3  LP001006   Male     Yes          0  Not Graduate            No   \n",
            "4  LP001008   Male      No          0      Graduate            No   \n",
            "\n",
            "   applicantincome  coapplicantincome  loanamount  loan_amount_term  \\\n",
            "0             5849                0.0         NaN             360.0   \n",
            "1             4583             1508.0       128.0             360.0   \n",
            "2             3000                0.0        66.0             360.0   \n",
            "3             2583             2358.0       120.0             360.0   \n",
            "4             6000                0.0       141.0             360.0   \n",
            "\n",
            "   credit_history property_area loan_status  total_income  \n",
            "0             1.0         Urban           Y        5849.0  \n",
            "1             1.0         Rural           N        6091.0  \n",
            "2             1.0         Urban           Y        3000.0  \n",
            "3             1.0         Urban           Y        4941.0  \n",
            "4             1.0         Urban           Y        6000.0  \n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Plot a scatter plot of income against age\n",
        "plt.scatter(np.log10(df['total_income']), np.log10(df['loanamount']),color='blue', alpha=0.5)\n",
        "plt.xlabel('Total ncome')\n",
        "plt.ylabel('Loan Amount')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "id": "0mIf_hOPJ-0A",
        "outputId": "ab20a8fd-91b2-4e09-9351-bd91dd688448"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Data Visualisation\n",
        "# Barplot of Credit History by Loan Amount\n",
        "plt.bar(df['credit_history'], df['loanamount'], color='cyan', alpha=0.5)\n",
        "plt.xlabel('Credit History')\n",
        "plt.ylabel('Loan Amount')\n",
        "plt.xticks([0,1])\n",
        "plt.show()\n",
        "#those with credit history access higher loan amounts"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "id": "SZCf95zQMNg0",
        "outputId": "8a3f15db-fce6-4b94-e73c-1cbc03e56369"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Create a cross table of Loan status, and Gender and average Income\n",
        "pd.crosstab(df['gender'], df['loan_status'],  values=df['total_income'], aggfunc='mean').plot(kind='bar',figsize=(6,6),color=['blue','cyan'])\n",
        "plt.legend(['No','Yes'])\n",
        "plt.title('Bar plot of Gender by Loan Status')\n",
        "plt.ylabel('Average Income')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 600
        },
        "id": "O4JNmcmpO5xb",
        "outputId": "08463d2a-3832-4dea-ac1a-abaeac0acd37"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 600x600 with 1 Axes>"
            ],
            "image/png": 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Nnty/f3+JBmgWns1wOBzn9ZrFqVOnjgoKCrR79263MwTJyclu7Qpn5uTn55faa/9Vcaf8f/311yJ/77vvvltTpkzR119/rUWLFqlatWqKj4+/4NevVq2aQkJCivRd+vOieH5+fqpVq5Yk6dNPP1Xnzp31zjvvuLXLyspS1apVL7iWcymcsn/w4EHXur/7TH/66afq37+/pkyZ4lp36tQpt5ljZ3u+9OeZnb+2z83NdXv9QpUrV9bAgQM1cOBAHTt2TB07dtSTTz5JKLEofr7BP05eXp6+++47BQYGukbj+/v7y2azuf1Lad++fRd0RckzHThwQF988YXrcXZ2tt577z21bNlSUVFRxT6nbdu2ioiI0IwZM5STk+Nav2jRIu3YscNtRknFihUlqch/aIsTFxenwMBAvfrqq27/sn3nnXfkdDqLnalSEuPHj1d+fr7uvPPOYn/G+eu/otu0aaPLLrtML774YrHt//jjD49r6NatmyTp1VdfdVv/yiuvuD329/dXr1699NlnnxV7dud8XvuvvvzyS7cpvT/99JPWrFnjqrFQixYt1KJFC82cOVOfffaZ+vTpU+Ss0vnw9/dX165d9Z///MftJ6P09HR9+OGHuuaaa+RwOFxt//r3mT9/fqlMST7TsmXLij2bUjjO5swgWbFixWI/z8XV+tprrxU5y3G2Y+Kyyy7TihUr3Na99dZbRfZx+PBht8ehoaGqV6+e2/EIa+FMCSxv0aJF+uWXXyT9OU7gww8/1M6dO/XYY4+5/qOckJCgl156STfccIPuuOMOZWRk6PXXX1e9evVK5TfkBg0aaPDgwVq7dq0iIyP17rvvKj09/axnYgICAvT8889r4MCB6tSpk/r27euaEnzppZdq1KhRrrZt2rSR9OcAz/j4ePn7+6tPnz7F7rdatWoaN26cnnrqKd1www3q3r27kpOT9cYbb+iKK644758OOnTooGnTpmnEiBGqX7++64quubm5+vXXXzV37lwFBga6Qpifn59mzpypbt26qWnTpho4cKBq1Kih/fv3a9myZXI4HPr66689qqFly5bq27ev3njjDTmdTl199dVasmRJsdfQmDRpkpYtW6Z27drpnnvuUZMmTZSZmakNGzbo+++/V2Zm5nm9D4Xq1auna665RsOGDVNOTo5eeeUVValSRWPHji3S9u6779aYMWMkyeP3/9133y32OjkPPfSQnnnmGSUmJuqaa67R/fffrwoVKujNN99UTk6OJk+e7Gp74403asKECRo4cKCuvvpqbd26VXPnznU7k1YaRowYoRMnTqhnz56uz8bq1av18ccf69JLL3WN55H+/Ex///33eumll1S9enXFxMSoXbt2uvHGG/X+++8rLCxMTZo0UVJSkr7//ntVqVLF7bVatmwpf39/Pf/883I6nbLb7bruuusUERGhIUOGaOjQoerVq5euv/56bd68WYsXLy5yVqhJkya69tpr1aZNG1WuXFnr1q3Tp59+qgceeKBU3xeUIm9N+wHOpbgpwUFBQaZly5Zm+vTpbtNQjTHmnXfeMfXr1zd2u900atTIzJo1yzUV9UySzPDhw0tcR506dUxCQoJZvHixadGihWv/8+fPd2v31ynBhT7++GPTqlUrY7fbTeXKlU2/fv3cppoaY8zp06fNiBEjTLVq1YzNZivR9OBp06aZRo0amYCAABMZGWmGDRtmjhw54tampFOCz7Rx40Zz9913m9q1a5vAwEBTsWJF06JFCzN69Giza9euYtvfcsstpkqVKsZut5s6deqY22+/3SxZsuScdRT+jffu3etad/LkSfPggw+aKlWqmIoVK5qbbrrJpKamFjsNND093QwfPtzUqlXLBAQEmKioKNOlSxfz1ltvudoU/l3++vf6O4VTgl944QUzZcoUU6tWLWO3202HDh3M5s2bi33OwYMHjb+/v2nQoEGJXuPMvv/dkpqaaoz5c4pxfHy8CQ0NNSEhIaZz585m9erVbvs6deqUGT16tImOjjbBwcGmffv2JikpyXTq1Ml06tTpnO9FYZ/PNY150aJFZtCgQaZRo0YmNDTUBAYGmnr16pkRI0aY9PR0t7a//PKL6dixowkODjaSXFN1jxw5YgYOHGiqVq1qQkNDTXx8vPnll1+KTOc1xpi3337b1K1b1/j7+7sdW/n5+ebRRx81VatWNSEhISY+Pt7s2rWryD6eeeYZc+WVV5rw8HATHBxsGjVqZJ599tlipzDDGmzGeDjCDPAxl156qZo1a6YFCxZ4uxRY1KFDhxQdHa3x48fr8ccf93Y5wD8WY0oA4ALNnj1b+fn5uuuuu7xdCvCPxpgSADhPS5cu1fbt2/Xss8+qR48e3M0WuECEEgA4TxMmTNDq1avVvn17vfbaa94uB/jHY0wJAACwBMaUAAAAS+DnmxIoKCjQgQMHdMkll3h05U0AAHydMUZHjx5V9erV3W4iWhxCSQkcOHDAdTlnAADgudTUVNWsWfOsbQglJVB4t8/U1FTXFUQBAMC5ZWdnq1atWiW6czahpAQKf7JxOByEEgAAzkNJhj8w0BUAAFgCoQQAAFgCoQQAAFgCY0oAAPBAfn6+8vLyvF2GpQQGBp5zum9JeDWU5Ofn68knn9QHH3ygtLQ0Va9eXQMGDNC///1v14AYY4yeeOIJvf3228rKylL79u01ffp01a9f37WfzMxMjRgxQl9//bX8/PzUq1cvTZ06VaGhoa42W7Zs0fDhw7V27VpVq1ZNI0aM0NixYy96nwEA/0zGGKWlpSkrK8vbpViOn5+fYmJiFBgYeEH78Wooef755zV9+nTNmTNHTZs21bp16zRw4ECFhYXpwQcflCRNnjxZr776qubMmaOYmBg9/vjjio+P1/bt2xUUFCRJ6tevnw4ePKjExETl5eVp4MCBuvfee/Xhhx9K+nM6UteuXRUXF6cZM2Zo69atGjRokMLDw3Xvvfd6rf8AgH+OwkASERGhkJAQLqb5vwovMHrw4EHVrl37wt4X40UJCQlm0KBBbutuueUW069fP2OMMQUFBSYqKsq88MILru1ZWVnGbrebjz76yBhjzPbt240ks3btWlebRYsWGZvNZvbv32+MMeaNN94wlSpVMjk5Oa42jz76qGnYsGGJ6nQ6nUaScTqd59dRAMA/2unTp8327dvNoUOHvF2KJWVlZZnt27eb3NzcIts8+Q716kDXq6++WkuWLNGvv/4qSdq8ebNWrlypbt26SZL27t2rtLQ0xcXFuZ4TFhamdu3aKSkpSZKUlJSk8PBwtW3b1tUmLi5Ofn5+WrNmjatNx44d3U4rxcfHKzk5WUeOHClSV05OjrKzs90WAIDvKhxDEhIS4uVKrKnw+zU/P/+C9uPVn28ee+wxZWdnq1GjRvL391d+fr6effZZ9evXT9Kfp8okKTIy0u15kZGRrm1paWmKiIhw216hQgVVrlzZrU1MTEyRfRRuq1Spktu2iRMn6qmnniqlXgIAygt+sileab0vXj1T8sknn2ju3Ln68MMPtWHDBs2ZM0cvvvii5syZ482yNG7cODmdTteSmprq1XoAAPAFXj1T8sgjj+ixxx5Tnz59JEnNmzfXb7/9pokTJ6p///6KioqSJKWnpys6Otr1vPT0dLVs2VKSFBUVpYyMDLf9nj59WpmZma7nR0VFKT093a1N4ePCNmey2+2y2+2l00kAAFAiXj1TcuLEiSLzmv39/VVQUCBJiomJUVRUlJYsWeLanp2drTVr1ig2NlaSFBsbq6ysLK1fv97VZunSpSooKFC7du1cbVasWOE2rzwxMVENGzYs8tMNAACesNku7lKeeTWU3HTTTXr22Wf1zTffaN++ffriiy/00ksvqWfPnpL+/I1q5MiReuaZZ/TVV19p69atuvvuu1W9enX16NFDktS4cWPdcMMNuueee/TTTz9p1apVeuCBB9SnTx9Vr15dknTHHXcoMDBQgwcP1rZt2/Txxx9r6tSpevjhh73VdQAALooBAwbIZrNp0qRJbuu//PJL642RKYupQSWVnZ1tHnroIVO7dm0TFBRk6tata/7f//t/blN3CwoKzOOPP24iIyON3W43Xbp0McnJyW77OXz4sOnbt68JDQ01DofDDBw40Bw9etStzebNm80111xj7Ha7qVGjhpk0aVKJ62RKMAD4tpMnT5rt27ebkydPFtkmXdzFU/379zdBQUEmPDzcZGZmutZ/8cUXprRiwNneH0++Q23GGOPlXGR52dnZCgsLk9PplMPh8HY5AICL7NSpU9q7d69iYmJcF+4sdLFPNnj6rT1gwAAdPnxYu3bt0k033aTJkydL+vNMSc+ePVUYAz777DONHz9eu3btUnR0tEaMGKHRo0eX6DXO9v548h3KDfkAACjn/P399dxzz+m1117T77//XmT7+vXrdfvtt6tPnz7aunWrnnzyST3++OOaPXv2Ra2TUAIAgA/o2bOnWrZsqSeeeKLItpdeekldunTR448/rgYNGmjAgAF64IEH9MILL1zUGgklAAD4iOeff15z5szRjh073Nbv2LFD7du3d1vXvn177dy584Kv0uoJQgkAAD6iY8eOio+P17hx47xdSrG8evE0AIBnrDaDs6wwBaPsTJo0SS1btlTDhg1d6xo3bqxVq1a5tVu1apUaNGggf3//i1YboQQAAB/SvHlz9evXT6+++qpr3ejRo3XFFVfo6aefVu/evZWUlKRp06bpjTfeuKi18fMNAAAX4GJfqaQ0TJgwwXX1dElq3bq1PvnkE82bN0/NmjXT+PHjNWHCBA0YMKB0XrCEuE5JCXCdEgBWwc833nG263CA65QAAIByhlACAAAsgVACAAAsgVACAAAsgVACAAAsgVACAAAsgVACAAAsgVACAAAsgVACAAAsgXvfAABwAS72RXYtdrHbUsWZEgAAyiljjOLi4hQfH19k2xtvvKHw8HD9/vvvXqiseIQSAADKKZvNplmzZmnNmjV68803Xev37t2rsWPH6rXXXlPNmjW9WKE7QgkAAOVYrVq1NHXqVI0ZM0Z79+6VMUaDBw9W165d1apVK3Xr1k2hoaGKjIzUXXfdpUOHDrme++mnn6p58+YKDg5WlSpVFBcXp+PHj5dZrYQSAADKuf79+6tLly4aNGiQpk2bpp9//llvvvmmrrvuOrVq1Urr1q3Tt99+q/T0dN1+++2SpIMHD6pv374aNGiQduzYoeXLl+uWW26RKcNbONtMWe69nPDktssAUJZsF3tUpZdY7Zvp1KlT2rt3r2JiYhQUFOS27Z8y0DUjI0NNmzZVZmamPvvsM/3888/68ccftXjxYleb33//XbVq1VJycrKOHTumNm3aaN++fapTp85Z932298eT71DOlAAA4AMiIiJ03333qXHjxurRo4c2b96sZcuWKTQ01LU0atRIkrR7925dfvnl6tKli5o3b67bbrtNb7/9to4cOVKmNRJKAADwERUqVFCFCn9eDeTYsWO66aabtGnTJrdl586d6tixo/z9/ZWYmKhFixapSZMmeu2119SwYUPt3bu3zOojlAAA4INat26tbdu26dJLL1W9evXclooVK0r6c/ZO+/bt9dRTT2njxo0KDAzUF198UWY1EUoAAPBBw4cPV2Zmpvr27au1a9dq9+7dWrx4sQYOHKj8/HytWbNGzz33nNatW6eUlBR9/vnn+uOPP9S4ceMyq4krugIAcAEsNia3xKpXr65Vq1bp0UcfVdeuXZWTk6M6derohhtukJ+fnxwOh1asWKFXXnlF2dnZqlOnjqZMmaJu3bqVWU3MvikBZt8AsApm33jH2WaXgNk3AACgnCGUAAAASyCUAAAASyCUAAAASyCUAABQQgUFBd4uwZJKa84MU4IBADiHwMBA+fn56cCBA6pWrZoCAwNl85WpUOdgjNEff/whm82mgICAC9oXoQQAgHPw8/NTTEyMDh48qAMHDni7HMux2WyqWbOm/P39L2g/hBIAAEogMDBQtWvX1unTp5Wfn+/tciwlICDgggOJRCgBAKDECn+iuNCfKVA8BroCAABLIJQAAABLIJQAAABLIJQAAABLIJQAAABLIJQAAABLIJQAAABLIJQAAABL8GooufTSS2Wz2Yosw4cPlySdOnVKw4cPV5UqVRQaGqpevXopPT3dbR8pKSlKSEhQSEiIIiIi9Mgjj+j06dNubZYvX67WrVvLbrerXr16mj179sXqIgAAKCGvhpK1a9fq4MGDriUxMVGSdNttt0mSRo0apa+//lrz58/XDz/8oAMHDuiWW25xPT8/P18JCQnKzc3V6tWrNWfOHM2ePVvjx493tdm7d68SEhLUuXNnbdq0SSNHjtSQIUO0ePHii9tZAABwVjZTWvcbLgUjR47UggULtHPnTmVnZ6tatWr68MMPdeutt0qSfvnlFzVu3FhJSUm66qqrtGjRIt144406cOCAIiMjJUkzZszQo48+qj/++EOBgYF69NFH9c033+jnn392vU6fPn2UlZWlb7/9ttg6cnJylJOT43qcnZ2tWrVqyel0yuFwlOE7AABn5ys3prXONxMuVHZ2tsLCwkr0HWqZMSW5ubn64IMPNGjQINlsNq1fv155eXmKi4tztWnUqJFq166tpKQkSVJSUpKaN2/uCiSSFB8fr+zsbG3bts3V5sx9FLYp3EdxJk6cqLCwMNdSq1at0uwqAAAohmVCyZdffqmsrCwNGDBAkpSWlqbAwECFh4e7tYuMjFRaWpqrzZmBpHB74baztcnOztbJkyeLrWXcuHFyOp2uJTU19UK7BwAAzsEydwl+55131K1bN1WvXt3bpchut8tut3u7DAAAfIolzpT89ttv+v777zVkyBDXuqioKOXm5iorK8utbXp6uqKiolxt/jobp/Dxudo4HA4FBweXdlcAAMB5skQomTVrliIiIpSQkOBa16ZNGwUEBGjJkiWudcnJyUpJSVFsbKwkKTY2Vlu3blVGRoarTWJiohwOh5o0aeJqc+Y+CtsU7gMAAFiD13++KSgo0KxZs9S/f39VqPB/5YSFhWnw4MF6+OGHVblyZTkcDo0YMUKxsbG66qqrJEldu3ZVkyZNdNddd2ny5MlKS0vTv//9bw0fPtz188vQoUM1bdo0jR07VoMGDdLSpUv1ySef6JtvvvFKfwEA5+Yjk4zEJKO/MF62ePFiI8kkJycX2Xby5Elz//33m0qVKpmQkBDTs2dPc/DgQbc2+/btM926dTPBwcGmatWqZvTo0SYvL8+tzbJly0zLli1NYGCgqVu3rpk1a5ZHNTqdTiPJOJ1Oj/sHAKXpz8myPrAY31h8gSffoZa6TolVeTLHGgDKkq9cp8RXTiH4Qjf/kdcpAQAAvo1QAgAALIFQAgAALIFQAgAALIFQAgAALIFQAgAALIFQAgAALIFQAgAALIFQAgAALIFQAgAALIFQAgAALIFQAgAALIFQAgAALIFQAgAALIFQAgAALIFQAgAALIFQAgAALIFQAgAALIFQAgAALIFQAgAALIFQAgAALIFQAgAALIFQAgAALIFQAgAALIFQAgAALIFQAgAALIFQAgAALIFQAgAALIFQAgAALIFQAgAALIFQAgAALIFQAgAALIFQAgAALIFQAgAALIFQAgAALIFQAgAALIFQAgAALIFQAgAALIFQAgAALIFQAgAALIFQAgAALIFQAgAALIFQAgAALIFQAgAALIFQAgAALMHroWT//v268847VaVKFQUHB6t58+Zat26da7sxRuPHj1d0dLSCg4MVFxennTt3uu0jMzNT/fr1k8PhUHh4uAYPHqxjx465tdmyZYs6dOigoKAg1apVS5MnT74o/QMAACXj1VBy5MgRtW/fXgEBAVq0aJG2b9+uKVOmqFKlSq42kydP1quvvqoZM2ZozZo1qlixouLj43Xq1ClXm379+mnbtm1KTEzUggULtGLFCt17772u7dnZ2eratavq1Kmj9evX64UXXtCTTz6pt95666L2FwAAnIXxokcffdRcc801f7u9oKDAREVFmRdeeMG1Lisry9jtdvPRRx8ZY4zZvn27kWTWrl3rarNo0SJjs9nM/v37jTHGvPHGG6ZSpUomJyfH7bUbNmxYojqdTqeRZJxOp0f9A4DSJvnIYnxj8QWefId69UzJV199pbZt2+q2225TRESEWrVqpbffftu1fe/evUpLS1NcXJxrXVhYmNq1a6ekpCRJUlJSksLDw9W2bVtXm7i4OPn5+WnNmjWuNh07dlRgYKCrTXx8vJKTk3XkyJEideXk5Cg7O9ttAQAAZcuroWTPnj2aPn266tevr8WLF2vYsGF68MEHNWfOHElSWlqaJCkyMtLteZGRka5taWlpioiIcNteoUIFVa5c2a1Ncfs48zXONHHiRIWFhbmWWrVqlUJvAQDA2Xg1lBQUFKh169Z67rnn1KpVK91777265557NGPGDG+WpXHjxsnpdLqW1NRUr9YDAIAv8GooiY6OVpMmTdzWNW7cWCkpKZKkqKgoSVJ6erpbm/T0dNe2qKgoZWRkuG0/ffq0MjMz3doUt48zX+NMdrtdDofDbQEAAGXLq6Gkffv2Sk5Odlv366+/qk6dOpKkmJgYRUVFacmSJa7t2dnZWrNmjWJjYyVJsbGxysrK0vr1611tli5dqoKCArVr187VZsWKFcrLy3O1SUxMVMOGDd1m+gAAAC+6CANv/9ZPP/1kKlSoYJ599lmzc+dOM3fuXBMSEmI++OADV5tJkyaZ8PBw85///Mds2bLF3HzzzSYmJsacPHnS1eaGG24wrVq1MmvWrDErV6409evXN3379nVtz8rKMpGRkeauu+4yP//8s5k3b54JCQkxb775ZonqZPYNAKvw+qwYZt8w+8ZDnnyHev09+frrr02zZs2M3W43jRo1Mm+99Zbb9oKCAvP444+byMhIY7fbTZcuXUxycrJbm8OHD5u+ffua0NBQ43A4zMCBA83Ro0fd2mzevNlcc801xm63mxo1aphJkyaVuEZCCQCr8HpYIJQQSjzkyXeozRhjvHuuxvqys7MVFhYmp9PJ+BIAXmWzebuCi8RHvpl8oZuefId6/TLzAAAAEqEEAABYBKEEAABYAqEEAABYAqEEAABYAqEEAABYQgVvFwDr8ZUph0yGBwBrIZTAZ/lI9vKJ6yAAKB/4+QYAAFgCoQQAAFgCoQQAAFgCoQQAAFgCoQQAAFgCoQQAAFgCoQQAAFgCoQQAAFgCoQQAAFgCoQQAAFgCoQQAAFgCoQQAAFgCoQQAAFgCoQQAAFgCoQQAAFgCoQQAAFgCoQQAAFgCoQQAAFgCoQQAAFgCoQQAAFgCoQQAAFgCoQQAAFgCoQQAAFgCoQQAAFgCoQQAAFgCoQQAAFgCoQQAAFgCoQQAAFgCoQQAAFgCoQQAAFgCoQQAAFgCoQQAAFgCoQQAAFgCoQQAAFgCoQQAAFgCoQQAAFgCoQQAAFgCoQQAAFiCV0PJk08+KZvN5rY0atTItf3UqVMaPny4qlSpotDQUPXq1Uvp6elu+0hJSVFCQoJCQkIUERGhRx55RKdPn3Zrs3z5crVu3Vp2u1316tXT7NmzL0b3AACAB84rlPz444+68847FRsbq/3790uS3n//fa1cudLjfTVt2lQHDx50LWfuY9SoUfr66681f/58/fDDDzpw4IBuueUW1/b8/HwlJCQoNzdXq1ev1pw5czR79myNHz/e1Wbv3r1KSEhQ586dtWnTJo0cOVJDhgzR4sWLz6frAACgrBgPffrppyY4ONgMGTLE2O12s3v3bmOMMa+99prp1q2bR/t64oknzOWXX17stqysLBMQEGDmz5/vWrdjxw4jySQlJRljjFm4cKHx8/MzaWlprjbTp083DofD5OTkGGOMGTt2rGnatKnbvnv37m3i4+P/tq5Tp04Zp9PpWlJTU40k43Q6PerfP5XkI4vxjQXli9ePG45Pjk8POZ3OEn+Henym5JlnntGMGTP09ttvKyAgwLW+ffv22rBhg8ehaOfOnapevbrq1q2rfv36KSUlRZK0fv165eXlKS4uztW2UaNGql27tpKSkiRJSUlJat68uSIjI11t4uPjlZ2drW3btrnanLmPwjaF+yjOxIkTFRYW5lpq1arlcb8AAIBnPA4lycnJ6tixY5H1YWFhysrK8mhf7dq10+zZs/Xtt99q+vTp2rt3rzp06KCjR48qLS1NgYGBCg8Pd3tOZGSk0tLSJElpaWlugaRwe+G2s7XJzs7WyZMni61r3LhxcjqdriU1NdWjfgEAAM9V8PQJUVFR2rVrly699FK39StXrlTdunU92le3bt1c/79FixZq166d6tSpo08++UTBwcGellZq7Ha77Ha7114fAABf5PGZknvuuUcPPfSQ1qxZI5vNpgMHDmju3LkaM2aMhg0bdkHFhIeHq0GDBtq1a5eioqKUm5tb5OxLenq6oqKiJP0ZkP46G6fw8bnaOBwOrwYfAADgzuNQ8thjj+mOO+5Qly5ddOzYMXXs2FFDhgzRfffdpxEjRlxQMceOHdPu3bsVHR2tNm3aKCAgQEuWLHFtT05OVkpKimJjYyVJsbGx2rp1qzIyMlxtEhMT5XA41KRJE1ebM/dR2KZwHwAAwCLOdzRtTk6O2bZtm1mzZo05evToee1j9OjRZvny5Wbv3r1m1apVJi4uzlStWtVkZGQYY4wZOnSoqV27tlm6dKlZt26diY2NNbGxsa7nnz592jRr1sx07drVbNq0yXz77bemWrVqZty4ca42e/bsMSEhIeaRRx4xO3bsMK+//rrx9/c33377bYnr9GTkcHng9VH3jO5ndD/+ltePG45Pjk8PefId6tX3pHfv3iY6OtoEBgaaGjVqmN69e5tdu3a5tp88edLcf//9plKlSiYkJMT07NnTHDx40G0f+/btM926dTPBwcGmatWqZvTo0SYvL8+tzbJly0zLli1NYGCgqVu3rpk1a5ZHdRJKyulifGNB+eL144bjk+PTQ558h9qMMcaTMyunTp3Sa6+9pmXLlikjI0MFBQVu289nWrDVZWdnKywsTE6nUw6Hw9vllDmbzdsVXCQeffL/uXykmz6D47N88YVuevId6vHsm8GDB+u7777TrbfeqiuvvFI2nzlCAABAWfI4lCxYsEALFy5U+/bty6IeAADgozyefVOjRg1dcsklZVELAADwYR6HkilTpujRRx/Vb7/9Vhb1AAAAH+Xxzzdt27bVqVOnVLduXYWEhLjd/0aSMjMzS604AADgOzwOJX379tX+/fv13HPPKTIykoGuAACgVHgcSlavXq2kpCRdfvnlZVEPAADwUR6PKWnUqNHf3l0XAADgfHkcSiZNmqTRo0dr+fLlOnz4sLKzs90WAACA8+HxFV39/P7MMX8dS2KMkc1mU35+fulVZxFc0bWc8oVLKcpnuukzOD7LF1/oZple0XXZsmXnXRgAAMDf8TiUdOrUqSzqAAAAPs7jUCJJWVlZeuedd7Rjxw5JUtOmTTVo0CCFhYWVanEAAMB3eDzQdd26dbrsssv08ssvKzMzU5mZmXrppZd02WWXlcs7BAMAgIvD44GuHTp0UL169fT222+rQoU/T7ScPn1aQ4YM0Z49e7RixYoyKdSbGOhaTvnCCDP5TDd9Bsdn+eIL3fTkO9TjUBIcHKyNGzeqUaNGbuu3b9+utm3b6sSJE55XbHGEknLKF/5rIJ/pps/g+CxffKGbnnyHevzzjcPhUEpKSpH1qamp3D0YAACcN49DSe/evTV48GB9/PHHSk1NVWpqqubNm6chQ4aob9++ZVEjAADwAR7PvnnxxRdls9l099136/Tp05KkgIAADRs2TJMmTSr1AgEAgG/weExJoRMnTmj37t2SpMsuu0whISGlWpiVMKaknPKFH3PlM930GRyf5YsvdLNMr+jqdDqVn5+vypUrq3nz5q71mZmZqlChgk98aQMAgNLn8ZiSPn36aN68eUXWf/LJJ+rTp0+pFAUAAHyPx6FkzZo16ty5c5H11157rdasWVMqRQEAAN/jcSjJyclxDXA9U15enk6ePFkqRQEAAN/jcSi58sor9dZbbxVZP2PGDLVp06ZUigIAAL7H44GuzzzzjOLi4rR582Z16dJFkrRkyRKtXbtW3333XakXCAAAfIPHZ0rat2+vpKQk1apVS5988om+/vpr1atXT1u2bFGHDh3KokYAAOADzvs6Jb6E65SUUz7yyfeRbvoMjs/yxRe6WabXKZGkgoIC7dq1SxkZGSooKHDb1rFjx/PZJQAA8HEeh5L//ve/uuOOO/Tbb7/prydZbDab8vPzS604AADgOzwOJUOHDlXbtm31zTffKDo6WjafOZcIAADKksehZOfOnfr0009Vr169sqgHAAD4KI9n37Rr1067du0qi1oAAIAP8/hMyYgRIzR69GilpaWpefPmCggIcNveokWLUisOAAD4Do+nBPv5FT25YrPZZIwptwNdmRJcTvnCXDz5TDd9Bsdn+eIL3SzTKcF79+4978IAAAD+jsehpE6dOmVRBwAA8HElDiVfffVVidp17979vIsBAAC+q8ShpEePHudsU17HlAAAgLJX4lDy18vJAwAAlCaPr1MCAABQFgglAADAEgglAADAEgglAADAEgglAADAEs4rlGRlZWnmzJkaN26cMjMzJUkbNmzQ/v37z7uQSZMmyWazaeTIka51p06d0vDhw1WlShWFhoaqV69eSk9Pd3teSkqKEhISFBISooiICD3yyCM6ffq0W5vly5erdevWstvtqlevnmbPnn3edQIAgLLhcSjZsmWLGjRooOeff14vvviisrKyJEmff/65xo0bd15FrF27Vm+++WaRm/mNGjVKX3/9tebPn68ffvhBBw4c0C233OLanp+fr4SEBOXm5mr16tWaM2eOZs+erfHjx7va7N27VwkJCercubM2bdqkkSNHasiQIVq8ePF51QoAAMqI8VCXLl3MI488YowxJjQ01OzevdsYY8yqVatMnTp1PN2dOXr0qKlfv75JTEw0nTp1Mg899JAxxpisrCwTEBBg5s+f72q7Y8cOI8kkJSUZY4xZuHCh8fPzM2lpaa4206dPNw6Hw+Tk5BhjjBk7dqxp2rSp22v27t3bxMfHl7hGp9NpJBmn0+lx//6JJB9ZjG8sKF+8ftxwfHJ8esiT71CPz5SsXbtW9913X5H1NWrUUFpamsehaPjw4UpISFBcXJzb+vXr1ysvL89tfaNGjVS7dm0lJSVJkpKSktS8eXNFRka62sTHxys7O1vbtm1ztfnrvuPj4137KE5OTo6ys7PdFgAAULY8viGf3W4v9kv6119/VbVq1Tza17x587RhwwatXbu2yLa0tDQFBgYqPDzcbX1kZKQr/KSlpbkFksLthdvO1iY7O1snT55UcHBwkdeeOHGinnrqKY/6AgAALozHZ0q6d++uCRMmKC8vT9Kf97tJSUnRo48+ql69epV4P6mpqXrooYc0d+5cBQUFeVpGmRo3bpycTqdrSU1N9XZJAACUex6HkilTpujYsWOKiIjQyZMn1alTJ9WrV0+XXHKJnn322RLvZ/369crIyFDr1q1VoUIFVahQQT/88INeffVVVahQQZGRkcrNzXUNpC2Unp6uqKgoSVJUVFSR2TiFj8/VxuFwFHuWRPrzbJDD4XBbAABA2fL455uwsDAlJiZq5cqV2rJli44dO6bWrVsXGbdxLl26dNHWrVvd1g0cOFCNGjXSo48+qlq1aikgIEBLlixxnYFJTk5WSkqKYmNjJUmxsbF69tlnlZGRoYiICElSYmKiHA6HmjRp4mqzcOFCt9dJTEx07QMAAFiDzRhjvF1EoWuvvVYtW7bUK6+8IkkaNmyYFi5cqNmzZ8vhcGjEiBGSpNWrV0v6c0pwy5YtVb16dU2ePFlpaWm66667NGTIED333HOS/pwS3KxZMw0fPlyDBg3S0qVL9eCDD+qbb75RfHx8ierKzs5WWFiYnE6nT5w1sdm8XcFFYplPftnykW76DI7P8sUXuunJd6jHZ0peffXVYtfbbDYFBQWpXr166tixo/z9/T3ddREvv/yy/Pz81KtXL+Xk5Cg+Pl5vvPGGa7u/v78WLFigYcOGKTY2VhUrVlT//v01YcIEV5uYmBh98803GjVqlKZOnaqaNWtq5syZJQ4kAADg4vD4TElMTIz++OMPnThxQpUqVZIkHTlyRCEhIQoNDVVGRobq1q2rZcuWqVatWmVS9MXGmZJyyhf+iSKf6abP4PgsX3yhm558h3o80PW5557TFVdcoZ07d+rw4cM6fPiwfv31V7Vr105Tp05VSkqKoqKiNGrUqPPuAAAA8D0enym57LLL9Nlnn6lly5Zu6zdu3KhevXppz549Wr16tXr16qWDBw+WZq1ew5mScsoX/okin+mmz+D4LF98oZtleqbk4MGDRW54J0mnT592XbCsevXqOnr0qKe7BgAAPszjUNK5c2fdd9992rhxo2vdxo0bNWzYMF133XWSpK1btyomJqb0qgQAAOWex6HknXfeUeXKldWmTRvZ7XbZ7Xa1bdtWlStX1jvvvCNJCg0N1ZQpU0q9WAAAUH6d93VKfvnlF/3666+SpIYNG6phw4alWpiVMKaknPKFH3PlM930GRyf5YsvdLNMr1NSqFGjRmrUqNH5Ph0AAMDNeYWS33//XV999ZVSUlKUm5vrtu2ll14qlcIAAIBv8TiULFmyRN27d1fdunX1yy+/qFmzZtq3b5+MMWrdunVZ1AgAAHyAxwNdx40bpzFjxmjr1q0KCgrSZ599ptTUVHXq1Em33XZbWdQIAAB8gMehZMeOHbr77rslSRUqVNDJkycVGhqqCRMm6Pnnny/1AgEAgG/wOJRUrFjRNY4kOjpau3fvdm07dOhQ6VUGAAB8isdjSq666iqtXLlSjRs31r/+9S+NHj1aW7du1eeff66rrrqqLGoEAAA+wONQ8tJLL+nYsWOSpKeeekrHjh3Txx9/rPr16zPzBgAAnDePQkl+fr5+//13tWjRQtKfP+XMmDGjTAoDAAC+xaMxJf7+/uratauOHDlSVvUAAAAf5fFA12bNmmnPnj1lUQsAAPBhHoeSZ555RmPGjNGCBQt08OBBZWdnuy0AAADnw+Mb8vn5/V+OsZ1xZyhjjGw2m/Lz80uvOovghnzllC/cCUs+002fwfFZvvhCN8v0hnzLli0778IAAAD+jsehpFOnTmVRBwAA8HEejymRpB9//FF33nmnrr76au3fv1+S9P7772vlypWlWhwAAPAdHoeSzz77TPHx8QoODtaGDRuUk5MjSXI6nXruuedKvUAAAOAbzmv2zYwZM/T2228rICDAtb59+/basGFDqRYHAAB8h8ehJDk5WR07diyyPiwsTFlZWaVREwAA8EEeh5KoqCjt2rWryPqVK1eqbt26pVIUAADwPR6HknvuuUcPPfSQ1qxZI5vNpgMHDmju3LkaM2aMhg0bVhY1AgAAH+DxlODHHntMBQUF6tKli06cOKGOHTvKbrdrzJgxGjFiRFnUCAAAfIDHV3QtlJubq127dunYsWNq0qSJQkNDS7s2y+CKruWUL1xKUT7TTZ/B8Vm++EI3PfkO9fjnmw8++EAnTpxQYGCgmjRpoiuvvLJcBxIAAHBxeBxKRo0apYiICN1xxx1auHBhubzXDQAAuPg8DiUHDx7UvHnzZLPZdPvttys6OlrDhw/X6tWry6I+AADgI857TIkknThxQl988YU+/PBDff/996pZs6Z2795dmvVZAmNKyilf+DFXPtNNn8HxWb74QjfL9C7BZwoJCVF8fLyOHDmi3377TTt27LiQ3QEAAB92XjfkO3HihObOnat//etfqlGjhl555RX17NlT27ZtK+36AACAj/D4TEmfPn20YMEChYSE6Pbbb9fjjz+u2NjYsqgNAAD4EI9Dib+/vz755BPFx8fL39/fbdvPP/+sZs2alVpxAADAd3gcSubOnev2+OjRo/roo480c+ZMrV+/ninCAADgvJzXmBJJWrFihfr376/o6Gi9+OKLuu666/Tf//63NGsDAAA+xKMzJWlpaZo9e7beeecdZWdn6/bbb1dOTo6+/PJLNWnSpKxqBAAAPqDEZ0puuukmNWzYUFu2bNErr7yiAwcO6LXXXivL2gAAgA8p8ZmSRYsW6cEHH9SwYcNUv379sqwJAAD4oBKfKVm5cqWOHj2qNm3aqF27dpo2bZoOHTpUlrUBAAAfUuJQctVVV+ntt9/WwYMHdd9992nevHmqXr26CgoKlJiYqKNHj5ZlnQAAoJy7oHvfJCcn65133tH777+vrKwsXX/99frqq69Ksz5L4N435ZQv3HRCPtNNn8HxWb74Qjc9+Q497ynBktSwYUNNnjxZv//+uz766KML2RUAAPBxFxRKCvn7+6tHjx4enyWZPn26WrRoIYfDIYfDodjYWC1atMi1/dSpUxo+fLiqVKmi0NBQ9erVS+np6W77SElJUUJCgkJCQhQREaFHHnlEp0+fdmuzfPlytW7dWna7XfXq1dPs2bPPu68AAKBslEooOV81a9bUpEmTtH79eq1bt07XXXedbr75ZteN/UaNGqWvv/5a8+fP1w8//KADBw7olltucT0/Pz9fCQkJys3N1erVqzVnzhzNnj1b48ePd7XZu3evEhIS1LlzZ23atEkjR47UkCFDtHjx4oveXwAAcBbGYipVqmRmzpxpsrKyTEBAgJk/f75r244dO4wkk5SUZIwxZuHChcbPz8+kpaW52kyfPt04HA6Tk5NjjDFm7NixpmnTpm6v0bt3bxMfH/+3NZw6dco4nU7XkpqaaiQZp9NZml21LMlHFuMbC8oXrx83HJ8cnx5yOp0l/g716pmSM+Xn52vevHk6fvy4YmNjtX79euXl5SkuLs7VplGjRqpdu7aSkpIkSUlJSWrevLkiIyNdbeLj45Wdne0625KUlOS2j8I2hfsozsSJExUWFuZaatWqVZpdBQAAxfB6KNm6datCQ0Nlt9s1dOhQffHFF2rSpInS0tIUGBio8PBwt/aRkZFKS0uT9Odl788MJIXbC7edrU12drZOnjxZbE3jxo2T0+l0LampqaXRVQAAcBYe3yW4tDVs2FCbNm2S0+nUp59+qv79++uHH37wak12u112u92rNQAA4Gu8HkoCAwNVr149SVKbNm20du1aTZ06Vb1791Zubq6ysrLczpakp6crKipKkhQVFaWffvrJbX+Fs3PObPPXGTvp6elyOBwKDg4uq24BAAAPef3nm78qKChQTk6O2rRpo4CAAC1ZssS1LTk5WSkpKYqNjZUkxcbGauvWrcrIyHC1SUxMlMPhcN21ODY21m0fhW0K9wEAAKzBq2dKxo0bp27duql27do6evSoPvzwQy1fvlyLFy9WWFiYBg8erIcffliVK1eWw+HQiBEjFBsbq6uuukqS1LVrVzVp0kR33XWXJk+erLS0NP373//W8OHDXT+/DB06VNOmTdPYsWM1aNAgLV26VJ988om++eYbb3YdAAD81UWYDfS3Bg0aZOrUqWMCAwNNtWrVTJcuXcx3333n2n7y5Elz//33m0qVKpmQkBDTs2dPc/DgQbd97Nu3z3Tr1s0EBwebqlWrmtGjR5u8vDy3NsuWLTMtW7Y0gYGBpm7dumbWrFke1enJdKbywOtTAZlyyJRD/C2vHzccnxyfHvLkO/SC7n3jK7j3TTnlI598H+mmz+D4LF98oZsX7d43AAAApYVQAgAALIFQAgAALIFQAgAALIFQAgAALIFQAgAALIFQAgAALIFQAgAALIFQAgAALIFQAgAALIFQAgAALIFQAgAALIFQAgAALIFQAgAALIFQAgAALIFQAgAALIFQAgAALIFQAgAALIFQAgAALIFQAgAALIFQAgAALIFQAgAALIFQAgAALIFQAgAALIFQAgAALIFQAgAALIFQAgAALIFQAgAALIFQAgAALIFQAgAALIFQAgAALIFQAgAALIFQAgAALIFQAgAALIFQAgAALIFQAgAALIFQAgAALIFQAgAALIFQAgAALIFQAgAALIFQAgAALIFQAgAALIFQAgAALIFQAgAALIFQAgAALMGroWTixIm64oordMkllygiIkI9evRQcnKyW5tTp05p+PDhqlKlikJDQ9WrVy+lp6e7tUlJSVFCQoJCQkIUERGhRx55RKdPn3Zrs3z5crVu3Vp2u1316tXT7Nmzy7p7AADAA14NJT/88IOGDx+u//73v0pMTFReXp66du2q48ePu9qMGjVKX3/9tebPn68ffvhBBw4c0C233OLanp+fr4SEBOXm5mr16tWaM2eOZs+erfHjx7va7N27VwkJCercubM2bdqkkSNHasiQIVq8ePFF7S8AADgLYyEZGRlGkvnhhx+MMcZkZWWZgIAAM3/+fFebHTt2GEkmKSnJGGPMwoULjZ+fn0lLS3O1mT59unE4HCYnJ8cYY8zYsWNN06ZN3V6rd+/eJj4+vkR1OZ1OI8k4nc4L6t8/heQji/GNBeWL148bjk+OTw958h1qqTElTqdTklS5cmVJ0vr165WXl6e4uDhXm0aNGql27dpKSkqSJCUlJal58+aKjIx0tYmPj1d2dra2bdvmanPmPgrbFO7jr3JycpSdne22AACAsmWZUFJQUKCRI0eqffv2atasmSQpLS1NgYGBCg8Pd2sbGRmptLQ0V5szA0nh9sJtZ2uTnZ2tkydPFqll4sSJCgsLcy21atUqlT4CAIC/Z5lQMnz4cP3888+aN2+et0vRuHHj5HQ6XUtqaqq3SwIAoNyr4O0CJOmBBx7QggULtGLFCtWsWdO1PioqSrm5ucrKynI7W5Kenq6oqChXm59++sltf4Wzc85s89cZO+np6XI4HAoODi5Sj91ul91uL5W+AQCAkvHqmRJjjB544AF98cUXWrp0qWJiYty2t2nTRgEBAVqyZIlrXXJyslJSUhQbGytJio2N1datW5WRkeFqk5iYKIfDoSZNmrjanLmPwjaF+wAAABZQ9uNu/96wYcNMWFiYWb58uTl48KBrOXHihKvN0KFDTe3atc3SpUvNunXrTGxsrImNjXVtP336tGnWrJnp2rWr2bRpk/n2229NtWrVzLhx41xt9uzZY0JCQswjjzxiduzYYV5//XXj7+9vvv322xLVyeybcroY31hQvnj9uOH45Pj0kCffoV59TyQVu8yaNcvV5uTJk+b+++83lSpVMiEhIaZnz57m4MGDbvvZt2+f6datmwkODjZVq1Y1o0ePNnl5eW5tli1bZlq2bGkCAwNN3bp13V7jXAgl5XQxvrGgfPH6ccPxyfHpIU++Q23GGOOtszT/FNnZ2QoLC5PT6ZTD4fB2OWXOZvN2BReJj3zyfaSbPoPjs3zxhW568h1qmdk3AADAtxFKAACAJRBKAACAJRBKAACAJRBKAACAJRBKAACAJRBKAACAJRBKAACAJRBKAACAJRBKAACAJRBKAACAJRBKAACAJRBKAACAJRBKAACAJRBKAACAJRBKAACAJRBKAACAJRBKAACAJRBKAACAJRBKAACAJRBKAACAJRBKAACAJRBKAACAJRBKAACAJRBKAACAJRBKAACAJRBKAACAJRBKAACAJRBKAACAJRBKAACAJRBKAACAJRBKAACAJRBKAACAJRBKAACAJRBKAACAJRBKAACAJRBKAACAJRBKAACAJRBKAACAJRBKAACAJRBKAACAJRBKAACAJRBKAACAJRBKAACAJRBKAACAJXg1lKxYsUI33XSTqlevLpvNpi+//NJtuzFG48ePV3R0tIKDgxUXF6edO3e6tcnMzFS/fv3kcDgUHh6uwYMH69ixY25ttmzZog4dOigoKEi1atXS5MmTy7prAADAQ14NJcePH9fll1+u119/vdjtkydP1quvvqoZM2ZozZo1qlixouLj43Xq1ClXm379+mnbtm1KTEzUggULtGLFCt17772u7dnZ2eratavq1Kmj9evX64UXXtCTTz6pt956q8z7BwAAPGAsQpL54osvXI8LCgpMVFSUeeGFF1zrsrKyjN1uNx999JExxpjt27cbSWbt2rWuNosWLTI2m83s37/fGGPMG2+8YSpVqmRycnJcbR599FHTsGHDEtfmdDqNJON0Os+3e/8oko8sxjcWlC9eP244Pjk+PeTJd6hlx5Ts3btXaWlpiouLc60LCwtTu3btlJSUJElKSkpSeHi42rZt62oTFxcnPz8/rVmzxtWmY8eOCgwMdLWJj49XcnKyjhw5Uuxr5+TkKDs7220BAABly7KhJC0tTZIUGRnptj4yMtK1LS0tTREREW7bK1SooMqVK7u1KW4fZ77GX02cOFFhYWGupVatWhfeIQAAcFaWDSXeNG7cODmdTteSmprq7ZIAACj3LBtKoqKiJEnp6elu69PT013boqKilJGR4bb99OnTyszMdGtT3D7OfI2/stvtcjgcbgsAAChblg0lMTExioqK0pIlS1zrsrOztWbNGsXGxkqSYmNjlZWVpfXr17vaLF26VAUFBWrXrp2rzYoVK5SXl+dqk5iYqIYNG6pSpUoXqTcAAOBcvBpKjh07pk2bNmnTpk2S/hzcumnTJqWkpMhms2nkyJF65pln9NVXX2nr1q26++67Vb16dfXo0UOS1LhxY91www2655579NNPP2nVqlV64IEH1KdPH1WvXl2SdMcddygwMFCDBw/Wtm3b9PHHH2vq1Kl6+OGHvdRrAABQrIswG+hvLVu2zEgqsvTv398Y8+e04Mcff9xERkYau91uunTpYpKTk932cfjwYdO3b18TGhpqHA6HGThwoDl69Khbm82bN5trrrnG2O12U6NGDTNp0iSP6mRKcDldjG8sKF+8ftxwfHJ8esiT71CbMcZ4MRP9I2RnZyssLExOp9MnxpfYbN6u4CLxkU++j3TTZ3B8li++0E1PvkMtO6YEAAD4FkIJAACwBEIJAACwBEIJAACwBEIJAACwBEIJAACwBEIJAACwBEIJAACwBEIJAACwBEIJAACwBEIJAACwBEIJAACwBEIJAACwBEIJAACwBEIJAACwBEIJAACwBEIJAACwBEIJAACwBEIJAACwBEIJAACwBEIJAACwBEIJAACwBEIJAACwBEIJAACwBEIJAACwBEIJAACwBEIJAACwBEIJAACwBEIJAACwBEIJAACwBEIJAACwBEIJAACwBEIJAACwBEIJAACwBEIJAACwBEIJAACwBEIJAACwBEIJAACwBEIJAACwBEIJAACwBEIJAACwBEIJAACwBEIJAACwBEIJAACwBEIJAACwBEIJAACwBJ8KJa+//rouvfRSBQUFqV27dvrpp5+8XRIAAPhfPhNKPv74Yz388MN64okntGHDBl1++eWKj49XRkaGt0sDAACSbMYY4+0iLoZ27drpiiuu0LRp0yRJBQUFqlWrlkaMGKHHHnvMrW1OTo5ycnJcj51Op2rXrq3U1FQ5HI6LWrc3hIV5u4KLxOntAi4OH+mmz+D4LF98oZvZ2dmqVauWsrKyFHaOD3CFi1STV+Xm5mr9+vUaN26ca52fn5/i4uKUlJRUpP3EiRP11FNPFVlfq1atMq0TF5mP/MfdR7qJ8sZHPrg+0k1J0tGjRwklknTo0CHl5+crMjLSbX1kZKR++eWXIu3HjRunhx9+2PW4oKBAmZmZqlKlimw2W5nXi7JXmNx95ewX8E/C8Vm+GGN09OhRVa9e/ZxtfSKUeMput8tut7utCw8P904xKFMOh4P/6AEWxfFZfpzrDEkhnxjoWrVqVfn7+ys9Pd1tfXp6uqKiorxUFQAAOJNPhJLAwEC1adNGS5Ysca0rKCjQkiVLFBsb68XKAABAIZ/5+ebhhx9W//791bZtW1155ZV65ZVXdPz4cQ0cONDbpcEL7Ha7nnjiiSI/0wHwPo5P3+UzU4Iladq0aXrhhReUlpamli1b6tVXX1W7du28XRYAAJCPhRIAAGBdPjGmBAAAWB+hBAAAWAKhBAAAWAKhBAAAWAKhBAAAWAKhBAAAWAKhBD4lKytLM2fO1Lhx45SZmSlJ2rBhg/bv3+/lygAU2rVrlxYvXqyTJ09K+vOGbvANXKcEPmPLli2Ki4tTWFiY9u3bp+TkZNWtW1f//ve/lZKSovfee8/bJQI+7fDhw+rdu7eWLl0qm82mnTt3qm7duho0aJAqVaqkKVOmeLtElDHOlMBnPPzwwxowYIB27typoKAg1/p//etfWrFihRcrAyBJo0aNUoUKFZSSkqKQkBDX+t69e+vbb7/1YmW4WHzm3jfA2rVr9eabbxZZX6NGDaWlpXmhIgBn+u6777R48WLVrFnTbX39+vX122+/eakqXEycKYHPsNvtys7OLrL+119/VbVq1bxQEYAzHT9+3O0MSaHMzExuzucjCCXwGd27d9eECROUl5cnSbLZbEpJSdGjjz6qXr16ebk6AB06dHAb22Wz2VRQUKDJkyerc+fOXqwMFwsDXeEznE6nbr31Vq1bt05Hjx5V9erVlZaWptjYWC1cuFAVK1b0domAT/v555/VpUsXtW7dWkuXLlX37t21bds2ZWZmatWqVbrsssu8XSLKGKEEPmflypXasmWLjh07ptatWysuLs7bJQH4X06nU9OmTdPmzZtdx+jw4cMVHR3t7dJwERBKAACAJTD7BuXaq6++WuK2Dz74YBlWAqA4W7ZsKXHbFi1alGElsALOlKBci4mJKVE7m82mPXv2lHE1AP7Kz89PNpvtnFdttdlsys/Pv0hVwVsIJQAAr/Hk+iN16tQpw0pgBYQSAABgCYwpgU/5/fff9dVXXyklJUW5ublu21566SUvVQXgTNu3by/2GO3evbuXKsLFQiiBz1iyZIm6d++uunXr6pdfflGzZs20b98+GWPUunVrb5cH+Lw9e/aoZ8+e2rp1q9s4E5vNJkmMKfEBXNEVPmPcuHEaM2aMtm7dqqCgIH322WdKTU1Vp06ddNttt3m7PMDnPfTQQ4qJiVFGRoZCQkK0bds2rVixQm3bttXy5cu9XR4uAsaUwGdccskl2rRpky677DJVqlRJK1euVNOmTbV582bdfPPN2rdvn7dLBHxa1apVtXTpUrVo0UJhYWH66aef1LBhQy1dulSjR4/Wxo0bvV0iyhhnSuAzKlas6PqNOjo6Wrt373ZtO3TokLfKAvC/8vPzdckll0j6M6AcOHBA0p+zbpKTk71ZGi4SxpTAZ1x11VVauXKlGjdurH/9618aPXq0tm7dqs8//1xXXXWVt8sDfF6zZs20efNmxcTEqF27dpo8ebICAwP11ltvqW7dut4uDxcBP9/AZ+zZs0fHjh1TixYtdPz4cY0ePVqrV69W/fr19dJLL3ENBMDLFi9erOPHj+uWW27Rzp07ddNNN+nXX39VlSpVNG/ePHXp0sXbJaKMEUoAAJaVmZmpSpUquWbgoHzj5xv4pGPHjqmgoMBtncPh8FI1gG8bNGhQidq9++67ZVwJvI0zJfAZe/fu1QMPPKDly5fr1KlTrvXGGO6rAXiRn5+f6tSpo1atWp31HjhffPHFRawK3sCZEviMO++8U8YYvfvuu4qMjOR0MGARw4YN00cffaS9e/dq4MCBuvPOO1W5cmVvlwUv4EwJfEZoaKjWr1+vhg0bersUAH+Rk5Ojzz//XO+++65Wr16thIQEDR48WF27duUfED6E65TAZ1xxxRVKTU31dhkAimG329W3b18lJiZq+/btatq0qe6//35deumlOnbsmLfLw0XCzzfwGTNnztTQoUO1f/9+NWvWTAEBAW7bW7Ro4aXKAJzJz8/Pde8bxnr5FkIJfMYff/yh3bt3a+DAga51hf/hY6Ar4F1n/nyzcuVK3XjjjZo2bZpuuOEG+flxUt9XMKYEPqNJkyZq3Lixxo4dW+xAVy6eBnjH/fffr3nz5qlWrVoaNGiQ+vXrp6pVq3q7LHgBoQQ+o2LFitq8ebPq1avn7VIAnMHPz0+1a9dWq1atzjqo9fPPP7+IVcEb+PkGPuO6664jlAAWdPfddzPDBpI4UwIf8tZbb+mZZ57RoEGD1Lx58yIDXbt37+6lygAAEqEEPuRsg+UY6AoA3kcoAQAAlsA8K/ikM+99AwCwBkIJfEZ+fr6efvpp1ahRQ6GhodqzZ48k6fHHH9c777zj5eoAAIQS+Ixnn31Ws2fP1uTJkxUYGOha36xZM82cOdOLlQEAJEIJfMh7772nt956S/369ZO/v79r/eWXX65ffvnFi5UBACRCCXzI/v37i71GSUFBgfLy8rxQEQDgTIQS+IwmTZroxx9/LLL+008/VatWrbxQEQDgTFzRFT5j/Pjx6t+/v/bv36+CggJ9/vnnSk5O1nvvvacFCxZ4uzwA8HlcpwTl3p49exQTEyObzaYff/xREyZM0ObNm3Xs2DG1bt1a48ePV9euXb1dJgD4PM6UoNyrX7++Dh48qIiICHXo0EGVK1fW1q1bFRkZ6e3SAABnYEwJyr2/ngxctGiRjh8/7qVqAAB/h1ACn8MvlgBgTYQSlHs2m63IbdG5TToAWA9jSlDuGWM0YMAA2e12SX/e92bo0KGqWLGiW7vPP//cG+UBAP4XoQTlXv/+/d0e33nnnV6qBABwNkwJBgAAlsCYEgAAYAmEEgAAYAmEEgAAYAmEEgAAYAmEEgDl3oABA9SjRw9vlwHgHAglAADAEgglAHAOxhidPn3a22UA5R6hBMBFc/ToUfXr108VK1ZUdHS0Xn75ZV177bUaOXKkJCknJ0djxoxRjRo1VLFiRbVr107Lly93PX/27NkKDw/X4sWL1bhxY4WGhuqGG27QwYMHXW3y8/P18MMPKzw8XFWqVNHYsWOL3O+ooKBAEydOVExMjIKDg3X55Zfr008/dW1fvny5bDabFi1apDZt2shut2vlypVl+t4AIJQAuIgefvhhrVq1Sl999ZUSExP1448/asOGDa7tDzzwgJKSkjRv3jxt2bJFt912m2644Qbt3LnT1ebEiRN68cUX9f7772vFihVKSUnRmDFjXNunTJmi2bNn691339XKlSuVmZmpL774wq2OiRMn6r333tOMGTO0bds2jRo1Snfeead++OEHt3aPPfaYJk2apB07dqhFixZl9K4AcDEAcBFkZ2ebgIAAM3/+fNe6rKwsExISYh566CHz22+/GX9/f7N//36353Xp0sWMGzfOGGPMrFmzjCSza9cu1/bXX3/dREZGuh5HR0ebyZMnux7n5eWZmjVrmptvvtkYY8ypU6dMSEiIWb16tdvrDB482PTt29cYY8yyZcuMJPPll1+WTucBlAj3vgFwUezZs0d5eXm68sorXevCwsLUsGFDSdLWrVuVn5+vBg0auD0vJydHVapUcT0OCQnRZZdd5nocHR2tjIwMSZLT6dTBgwfVrl071/YKFSqobdu2rp9wdu3apRMnTuj66693e53c3Fy1atXKbV3btm0vpMsAPEQoAWAJx44dk7+/v9avXy9/f3+3baGhoa7/HxAQ4LbNZrMVGTNyrteRpG+++UY1atRw21Z4J+lCf72TNICyRSgBcFHUrVtXAQEBWrt2rWrXri3pzzMbv/76qzp27KhWrVopPz9fGRkZ6tChw3m9RlhYmKKjo7VmzRp17NhRknT69GmtX79erVu3liQ1adJEdrtdKSkp6tSpU+l0DkCpIJQAuCguueQS9e/fX4888ogqV66siIgIPfHEE/Lz85PNZlODBg3Ur18/3X333ZoyZYpatWqlP/74Q0uWLFGLFi2UkJBQotd56KGHNGnSJNWvX1+NGjXSSy+9pKysLLc6xowZo1GjRqmgoEDXXHONnE6nVq1aJYfDof79+5fROwDgXAglAC6al156SUOHDtWNN94oh8OhsWPHKjU1VUFBQZKkWbNm6ZlnntHo0aO1f/9+Va1aVVdddZVuvPHGEr/G6NGjdfDgQfXv319+fn4aNGiQevbsKafT6Wrz9NNPq1q1apo4caL27Nmj8PBwtW7dWv/zP/9T6n0GUHI248mPsQBQio4fP64aNWpoypQpGjx4sLfLAeBlnCkBcNFs3LhRv/zyi6688ko5nU5NmDBBknTzzTd7uTIAVkAoAXBRvfjii0pOTlZgYKDatGmjH3/8UVWrVvV2WQAsgJ9vAACAJXCZeQAAYAmEEgAAYAmEEgAAYAmEEgAAYAmEEgAAYAmEEgAAYAmEEgAAYAmEEgAAYAn/H/STSptKsy8QAAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pd.crosstab(df.property_area,df.loan_status).plot(kind='bar',figsize=(6,6),color=['blue','cyan'])\n",
        "plt.title('Property area vs Loan status')\n",
        "plt.xlabel('Property Area')\n",
        "plt.ylabel('count')\n",
        "plt.xticks(rotation=0)\n",
        "plt.show()\n",
        "#Semi urban has the highest default. This could be because they have the highest debt to income ratio."
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "A_jKCFG84S1u",
        "outputId": "e4d7dbcf-c03e-4f75-a788-b2e95bbef1b3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 600x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Create a pairplot\n",
        "# first replace 'Y' with 1 and 'N' with 0 in loan status column in order to include it in the pair plot\n",
        "#df['loan_status'] = df['loan_status'].replace({'Y': 1, 'N': 0})\n",
        "#secondly convert dependents to numbers\n",
        "#df['dependents']= df['dependents'].map(lambda x: int(x.replace('+', '')) if '+' in x else int(x))\n",
        "dep_dict = {'Y': 1, 'N': 0, '1':1, '2':2, '3+':3}\n",
        "df = df.applymap(lambda x: dep_dict.get(x) if x in dep_dict else x)\n",
        "\n",
        "#coerce dependents column to numeric data type\n",
        "df['dependents'] = pd.to_numeric(df['dependents'], errors='coerce')\n",
        "\n",
        "#create a pairplot with the numeric variables\n",
        "sns.pairplot(df[['loanamount', 'self_employed', 'applicantincome', 'coapplicantincome',\n",
        "       'loan_amount_term','credit_history','dependents', 'loan_status']])\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "sXnk7xnE-5Ex",
        "outputId": "ce8a4dd5-c58e-45b0-d390-33b79beafbd1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.PairGrid at 0x7f49fd113ca0>"
            ]
          },
          "metadata": {},
          "execution_count": 13
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1750x1750 with 56 Axes>"
            ],
            "image/png": 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dd9zRrVZP7733nk477bTAv/2VTvPnz9cTTzyhG264QY2NjbriiitUW1uradOmae3atYHflKSnnnpKS5Ys0RlnnCGz2ay5c+fqoYceCkzPz8/XP//5Ty1evFgTJ05UcXGxbr31Vl1xxRWBeb761a/q6aef1s0336ybbrpJI0eO1EsvvaQxY8bEsnnSnsPZGnF6fYfpdU0uVTW45HC2yp6TqeJcBkZE39DdXGmPvAEAAL1doso7PSmDoXvY1oAxOJytslktWjhthCYMLVCL26vsTIver6jRqo17yVUAQNpI9jUrpgquBx98UGeeeaaOPvpojR8/XpL0n//8R1lZWfrnP//Z5eV8/etfl8/n63S6yWTSnXfeqTvvvLPTefr376+nn3464u+MGzdOGzZsiDjPBRdcoAsuuCBywL2EPTsz4vR+7aZH6y+Th/jozaLlSrbVoromV8gxT9/oAACgt0tkeac79yvoGbY18KV0fr6Rn5Oph+ZN0B827dXK18oDn5eVFumheRNkzyFX+4p0Pk4BQEr+NSumCq6xY8dqz549euqpp7Rr1y5J0rx583TJJZcoJ4eHt+muOM+qGSOLtT5MU8EZI4tVnNd2YYzWX+aKb4/V8v/dnpSH+Mm4gFNIQEed5YrNatHNs09Qg9Ot92pqNLTQpoH2LOXbrFHz5q7zxujzJhfHGAAAMKxo5Z2H503oURmnOM+qmSeUaNRge8hbn7sOOgL3K+i5rt4bAr1dZW2z/u/zJuVmWeRs9SrT4tFH9fU6pr9Ng9LgJcXcrAz9YdNebSqvDvp8U3m1TJJ+/v9OSklcSK6Dtc16479HVNIvSy1ur2qaWvXO3s/19a8M0OA0OE6TxQjP74wQI5AouVkZeiKJ16yYKrhWrFihgQMH6vvf/37Q56tWrdKRI0e0bNmyuASHxMi3WXXf3HFavnpb0I3MjJHFun/uuMAJN1p/mfuqmxJ2U9teMlrD0OIG4YTLFZvVosfnT9Kjr5frphd3BOb1Hy/NLk/EvCk/0qBFT74X9B2OMQAAYCRd6Ve/p/cCy886Qbe9vCPorc9ppUW6/dze2Y18qnT13hDozeqaXGpwefTwa3uCHsb5zznheu1ItganO+RBod/G8mo1ON0aaE9yUEiquiaX9n3epDXbDgQdC2WlRRpRnCub1ZLy4zQZjPD8zggxAonkaG7VxgjXLEdzqwbas8NOj4U5li/95je/0ahRo0I+P/HEE/XYY4/1OCgk3pCCHD08b4LWLf2aXvrBV7Vu6df08LwJQW98ROuPvbY5/PR4DhYX7e3Quqae/04yfgPG5c+VtT+crkcvOVnPXjFFj7xerg0dTtSB46U58vHS4vaGfodjDAAAGEi08k5dJ/cJXVXb1KrbXt4R8jB3Y3m1bnt5h2qbGGsmnrpybwj0Zo7mzs85t7+8Q44entPigfHyUNvUGlIJK7W1iHj4tT194tpohOd3RogRSLRo9wI9vVfoKKYWXJWVlRo8eHDI5wMGDNDBgwd7HBSSI98WuXlstP7YjynK1XXfGKnf/PsTNbk8QdPiVbhKxtuhyfiNcGiubBz5Nqs+qWrUD556X4/Pn9TpWwjv7auRLavttNrZYIq2TEvQdxJ5jAEAACSCzRr5NtJmtUScHk2jy63dlfV6fP4kldiz1OD0qF92hg45nFq2epsaXe4eLR+d80mSKdVRAMnV4PJoa0WtlpxeGnL/tmrjXjV0eN6RCoyX96W++iyl0eWOeJz2hWtjqp7fdUdVg0tb9tV0up/SIUYg0XKzMjp9Lrpq417lZsVUJdWpmJY2dOhQbdq0SSNGjAj6fNOmTRoyZEhcAkPqReqPvay0SB/sr9Gpx/TXuKMKtPjp94MquXpauPIXWKobXVp12SmBBEhERVoq3oSiubLx5OdkasnppRrQL0uPXnJy0Im5yeWRzWrRQ/MmaOu+Gp0+aoAunjw8ZDDFaaVFmnnCQNmslqBjmbftAADoW4z+cM5sNqmstChsd1llpUWymHtWQ9Li9uipy6fozjU7Q7oLe+ryKWp2UXaKJ+5N0Nc1trTqoXkTQu7fykqL9NC8CWpsSf05pzjPqukji8M+3J/eh8bL68tjUDW3eiIep87W1FfEJpoRWjI2GOB8AiSa2Sw9Pn+SVr5eHpIHj8+fJEtMfQp2LqYKru9///u69tpr1draqtNPP12StG7dOt1www26/vrr4xogUqez/tjLSou0oGyErnlmqyYMK9DssYO1cNqIwAHb08GIw91g+S8E1zyzNa4VaVLy34RK9KDcSAyrxaytFTVhCyjXPLNVC6eN0B827dXWilo9e8UU3b92V9guLnzaFZQvUt962w4AgL6uN1QmZJhNWlDW9rJjx3FAFpSN6HEFV1Fulm58cXvYstRda3bq3vPH9mj5+BL3JkDbOeeX/wrf9Zsk3X1eeoz9t/i0Unl9vpDz7uLTSlMYVfL09TGoinKt+sWr/+30OL1nTnocp4lkhJaMBTlWPfCP3Z3up3vnUIZB75dlMevR18vD5oFZJt0158S4/l5MFVw//vGPVV1drR/84Adyudr6Ds3OztayZct04403xjVABEv2255DCnJ013ljVH6kQS1ur7IyzNq6vzZQ0bSpvFoLy0YEBobr6WDEnd1g+RMinhVpfpFaqsXrN9ozQpNqBDvkcKri8yZdMnm4Fk07NtByq/1xOWFoQeDYrGpwdToAsD9n/BJxjEnGfzMcAIDeqK7JpVv/skPjhxbosq8eE9Rdx21/2aGfXTDeENfrolyrVvztI00YVqiFZSOC7hOee6dCP7tgfI+W3/jFfUY4G8ur1ZgG3YX1FtybAJLL4414/+byeMNOS6aqBpd+/MJ/9MglJyvDYlZ9c9t9XqvHq8VPva8nFpza63M10hhUUlvFQW/eBq0eX8TjtNXjS3JEyZfs53excHm8EbuSTIfzCZBoLW6vNnRyvtpQXqUWd3zzIKYKLpPJpPvvv1+33HKLPvroI+Xk5GjkyJHKysqKa3AIlqq3PT9vcmnRk+91Or3F7VVxXpbWLf2aivN69hA90g1W+4qBnlaktddZS7V4/kZ7RmhSjS8dqG3Wsj//J+jE3L7lVscKK0lRuwbwn8gTdYz1hjfDAQDojaobXbro1GFhu61ZUDZC1Y3GqEzIt1l1x3ljtHz1tqD1iFfZJlp5mPJy/HBvAkiO5shjFzmcqR/bqMnVqt/PPyVs162/n39Kn+i6tdHljljB09vHoGpoibx+jVGm9wbJfn4XiyaXO2IXhc29/DgFpORfV3s0oldeXp5OOeWUeMWCCFLZdUT7JsDhBogrzrMqLytDxw7I6/FvRbvB6pedGZeKtI6GFOTo4XkTVNXgUr2zVf2yM+P+G355UQbSi/dAe4hdIO86aVL78LwJuvqZrcrNylBGu654sjIidyZ7TJEtIcdxUMx0MwMAQNpxe336w6a9nb59fvs58e2uI5ESWX6250TuYijadHSdEbp7AhItLzvyPXi0e/hkKMixanmErltX9IGuW6O13u04Zntvw/m6TTKf38WCLgqB5F9X4zykFxKlK11HJIq/CbDNatFD8yZoa0WNFj35nn7w1Pta+MS7+unaXbLGaXS4aBfsolyrjivJS8iFK9/WtuyThhUm7DektrGcykqLwk4rKy2K27ZEz0XKuw3lVcrObMsJp8ujN/57RNO+2K9b99d2uo9njCzWUQU5CTvGUnmuAAAAkXm9kbsX8niN1b1QosrPWVHKy1mUl+PGf68XTrp09wQkWnZG5HNOdpQXGJOBrlulgigvN+T38pcfOF9/KVnP72JhhC5PgURL9nU19VdpdEmyuo6oa3Lp48MN2lpRo4+PNKiuyRVoAnzL2aPDvnG6obxaN724XXVNPX9w3hcu2LXNLi0oGxGS6P6uaeqaqYBIF9Hyrq65VU9s2iuPz6fdBx269/yxmjGyWKs27g27j5PRbJ5uZgAAvVm4sqqRNEXplqa3v33eVXVRy8uUZ+LFf6/X8R4snbp7AhIt2j16bRrco0e7z0uHbhQTraRflqZ38rxo+shilfTr3cOmcL42BrqSBJJ/XU19O2t0STKaIkcbt2fS8ELd+L/bw373vX01qm1qVXWjS26vT16fT00tbuXbrCrO7bypcF2TS1UNLjmcbQOkFud2vz/dcMtI5wt7Xlam5v3ubS2cNiJkUO5rntmqvy6ZluoQ8YX+Nqsenz8pZFBQ/8On7EyLThpWqCEFOfr/vnacmlo9uuPcE9Xi8aqpxa27zxsjt9enxhZ30prN020BAKC36g1jTObnRC4H9Pa3z7sqOzNDy1dv0f1zx2n5WaPU4PSoX3aGDjmcWrZ6m/60aHKqQ+xV0r27JyDRcjIzdM0z73R6j/6/V3011SGqX3Zm2CEj/Peo/aJ0B9Ub5Nusun/uuJCywPSRxXqgj1TwDCnI0X1zx6muuTVwvs7PydRgg5SD+gKeyQBt19VkluV7/xWwl/C3bFofpuuxWFs2ta8YysvK0Hv7arRlX03QPOv3VGnZ6m26e86YTlt++LsuvGvNTl00eXhIK6/OHjx09pDi/rnjunyDZcQHHcV5Vk0aXhg02KRfvFupGa3yL518+nmTbn5pe9D4W/5BQa95ZqsmDivQsP458vl8OljnDNxYTBhWoAVlI/T02/v0/enH6uiCHI0cWJi0uPOyMzR9ZHHYbgp7SytIAEDf01vGmCzOs3Z6nZ7OdTogw2LSynkn6+HX9wTdV5SVFmnlvJOVYTFF+DZikW/jPgF9l8UsTRwW/h59emmxLObUn3NyMsx6fP4krXy9PCjOstIiPT5/knLSoBvFZDBLWnxaqW448/jAA9OGFrdSv4eSo6K6UTd2GIttWmmR7j1/rIYV5aYwMvgl4vktYDQZFiW1LE8Fl0Hk26y69/yxuvHF7WErhLp7MxKuYqj9w/v23aNs2FOl8sMNysm0hH1jyOfzadWmvZowrDBsF4bhHjxEekix7It5jyvJi7gORn3Q4W9W3p1WarEwYuVfuvispknL/ndbp4OC3nL2aI0oytW5KzcFcqV9/kht+bDy9XKdPXawTs+0aKA9O+FxH6ht1q1/2aH5Xz1GXp8vpKKZbgsAAEbVlTEmjXKNW3xaach1uqy0SItPK01hVOkly2LWIx1uiKW2spjZZNK9c8akKDIAvVGm2azFpx0nr8KfmzPToIJLJunR18s7PS/edd6JKQoseeqaXPq/z5v08GuhD0yvPn2kcqwWw5QFYnHI4Qyp3JLaxmC76cXt+vn/Oykpzx0QWb7NqrvmjNFPXtyujR0qIu+aM6ZXH6OAn9Vi0SOvfxj+miWT7jk/vmV5KrgM4kBts27/606NH1qgy756jFrcXhXkZGp4ka1LTZH9LXkaWlrV32bVTS8Gt0yRvnx4v3DaiJA3l1rcXu04UBf2jaGnLp+sTeXVWlgW+j2/jg8e4vGQIp4POpLd0inR3YAYtfIvHdQ1ubSvuinioKA//uYoXfz7t4Iqgjvmz3Uzv6JVG/fqyunHqaXVo10HHXI43bLnZKjQZo17wbP9Pn/z4+qg7jUKcjJ1XEkehV30OV6vV/v375ckDR06VGZz33izFeiNessYk1UNLi184t2w3WAtfOJd/XXJNMpokhpbPSH3Kn4b9lSpsZWxyuKNnh/Qlzk9Xi188r3w5+Yn39X//iD1XRQ63d6I50Wn25vkiJKvtqk1pHJL+vJe/N45Y3v1eaum0dXpc4qN5dWqaXT1mXv+dL5mHXI4dcdfd+qkYYVa0OF8cudfd+reb4/rM/sJfVejK0JZvrxKjXEed5gKLgNo/+D6Xx8dDpo2Y2Rx1MqKA7XNWvbnbdpSUaOH5k1QQ0vnB9mm8motmnaslpxeGtRKqzjPqr1VDWHfGPIP8twSpUDV/sFDPB5SxOtBR6paOiWyG5De9JZzslU1uFQbZeDyA3XNYQeB91f0Sm358NvvTtRR/XOS0oVA+33e5PKEVDavW/o1DbTH7efiLp0LqDCu/fv3a+EjayVJqxafqeHDh6c4IgCx6i3jGTicrWGv035GqahLNEdz5AHY66NMR/fQ8wP6uganO+K5ucGZ+nNOfZQY0iHGRGt0uSO+iNro6t3bwBFlH0eb3luk+zWrptGl13Yd0Wu7jnQ6nQou9HbR7mnifc9DBZcB+B9cdzagaHWjK6jrv/YPifOyMrTsz9u0obxKS04v1R827dUlkyM/4LNZLdpaURNUuJteWqw7zjtRj7z+ccj8WV/09ZwVpc/n9g8e/A8pOlsnexcG2I7Hg47e2tKpt7zlnAoOZ2vUYznSdH9Fr0nSkMIc3b0mtEluIroQMPI+T/cCKozNVjgg1SEAiIPeMp5Bb6moS7TcLEvE6bYo09F1vfV+COiO3KzIj8aiTU+GPAPEmGjhXjLtznSjy8+JvI+jTe8NjHDNivZsJtp0oDeIdk8T73ue3n/26wUczlbZrBY9NG+C/rBpb8iAoudPOEpS+IfELy0u00eVDq26bJIG5+do7FH5Om5Anh6fPymoQmnVxr2BwoDH+2W/0+0roPZWNep/Fk3W67sPB82/dX+tykqLAv8N90aN/8GDvwKurtmlZ78/RbYsix7813+D1mlaaZEumHh01O0S7kGHP96vHlukumaXPj7SELElSG9t6cTDk9jZszO1btfhTo/l2WMGaViRTX/74XTVN7dVJLd6vFr81PvaX9OsrAyzppcWqV92pt765HONG1qgdWHe3Il3FwJG3edGKKACAFIvWWOYJlpxnlVnjx2k808+WiX2LDU4PeqXnaFDDqdefP9Tw1TUJZot06LTRw3Q6CH5IS/CfXigTrZMKrjiparBpY8OOvT4/Ekhx+Sy1dsMez8EdEd2hlnfGjNQcycODcmD1Vv2KzvKC5DJYJJ01piB+k6YGP+8Zb/SYJSwhItWyRdtutHlZFp0xqgBOiHMtfGjA3XK6QPXRiM8w+uXndnpy/yrNu5N22czQDzlWiOfr3Kt8T1f9e6zfy9hz87Uwmkj9IdNe8P2NXz7yzv1swvGh31I7HZ79PT3p+iOv+7U1opaPTRvgm5/eUfQQIdlpUV6aN4EXfPMVk0cXqjNn3xZudVZpZp//iaXR8++U6GnLp+iB9Z+pAVfdM/WPk7/g4cml0c3dIhxWmmRLisbobc++TxQYbaxvFo/eXG7HvjO+KDWG+Fap9189gna/3mzTCaTtn1aq/FHF+j3Gz8JijdSSxAjt3qJpLe85ZwKxXlW7T7oCHsszx47SD+eNUo/eSm0y8E/LjpVv/rXf3XI4dT8shG68LebNWFYgW4750T95t+fhH2bLJ5dCBh1nxuhgAoASA+JHsM0GfJtVv34zFG6KUz3xfec37vHDukOi0m69ewT9ZOXtoe8CHf3nLEyy5fC6HqXJlernrp8iu5cszPkmHzq8ilqdhnzfgjojlafVz8+c5RufmlHSB7cNWeMWr2pH98qM0NaduYJYe9F754zVh5f7269JElms6nTF1HLSotkMffuaj6n26MbzjxBd67ZGXJtvOXsE+V09/5jwAjP8HIyzFo1/xQ9/PqekGepq+afopzM1FeYA4nW4vXolghl+RZvfM9XZJUBFOdZ9dVjw1/EpbYBRWsawz8kLu6XpTv+2naz4q8k2ximkuwPm/bqltmjdds5J2rVxr2SFLFS7Q+b9mrhtLYKgItOHab7136kE4bkK8Nk0o++ebz+enWZ/rjoVD3z/cn66QXjZbNaQiq3pLbKrPbLav95TaNLB2qbJbW1TlvyzFad8Yt/6/xH39QZP/+3rn/+A1V83qyrn9mqJU+/r4H2bJX0y9J3pwzXqstO0ZLTS2WzWgItQeqaXCHbx6itXqLxv+U8Y2Rx0OdGe8s5FfJtVt1x3hg9906Fvnpckdb+cLoenz9Jj8+fpGu/8ZWQGwqp7Xi95aUduv4bx+v4Qf3U6vFq5cUna8KwQj3w9126YsaxYX+rX3aGtlbU6OMjDWGPz+7GbcR9boQCKgAgfeTbrDquJE8nDSvUcSV5aXt968whhzOkckv68gWvQw5niiKLTV2TSx8fbohbeSbAZNLNnZS5bn5pu2Tq3Q8xk6kgxxpSuSW1beu71uxUfo6xcgyIRabJrFs6VG5JX97nZZpS/+jMarZ0ei9680vbZTX3/tY7ZpO0oGyEykqLgj4vKy3SgrIRvf7S4PWq0/P1nWt2Kg3qYRPOCM/wzGaTHnl9T9hnqY+8Xi5zbz9QAUkZMkcsy2fEuUqKFlwGkG+zyhqlSXy4liA2q0Ven7SwbIQumTxcQ/vbOh00dVN5tW6ZPVpV9c5AS5MJQwsizr/wixYuU48t0srXyjsdQHHd0q/J4fN12kqj/bI6rtP9a3fpp2Fap9msFp00rFA5mRb99nsTVWLP1p1/3akb/3d7YJ72Lc06awli1FYvXdEb3nJOlSEFObr7/LGqbXLp05pmZZjNcnu9cnt8umTycC2admxI154by6vlcLo199ebA8vxF7T72zL14L/2BP3GtNIitXq8Ov/RNyXFZ8wpI+5zIxRQAQCIl5pGl7ZW1GrJ6aVhu60x0sDjiRxDs9HlCXkpz29jebUaDTbOSseeKCJ1oZ5sjS5Ppy9SGnFbA7Fwur16P8K52elOfc1Bg8sT8frR0AdyNdNs1tNv79OEYYVaWDZCLW6vsjLM2rq/Vk+/vU+3nX1iqkNMKJ/U6fl6U3l1n2jbXJxn1fSRxWGfL05Pk2d4jS6PtkTIVa6r6Aucbm/Esny8r6tUcKU5/82QT9Kqy04Jeaju1y87eFf6uxe87S87tOGLA+rRS06O+Fv1TrdcHp+e/v5kvflxtVyeyAebzZqhV66epurGyG9q1jS5VNccuRVGS5gDOy+7rfVVx9ZpHbtOXHJ6qbZW1IR9O0Jqa4m28rXysC1Best4Dp3Jt6XPzbORHKht1rI/b9OG8qqg4639Mdaxq04ptKLZP/8ts0cHfV72RdecNY1fHpPxGnPKaPu8N1cyAwDQUUNLq1ZePEGrNgZ3AT69tEgrL56ghpb4dV+cSIkeQ7MhSjfORtlOUmIrAuMhWmv6aNOB3qCpxR1xeIYmV+rPOY3O1ogxNrb0/lytaWrRxZOHh90GC8pGqKbJpeHKTWGEidUY5doXbXpvcfXppTprzCANtGcHKo8q65pVWpKX6tAkSY0t5CqQ7LI8FVxpLNzNULiH6tNKi3SgtjmoL+Jw3QtmdWgF1nHQwxyrRZs+rpLJ1NYqqyAnWssJn/6+s1InDyuMOFe4yquOOsY2rbRIhx0tkkIrDTquW1dbmnXWEsSIrV6QOIEHNuVteRepq07/dP/x17Gi2T+fT9Lj8ycFvWF2zTNb9bMLxgfN2xfHnOrtlcwAALRXlJulX/1rT8gbjW0vpJl053nGePs80WNo5oUpUwVNzzLGbWyiKwLjIVpreVrToy8ozLXqF//6b6f3fHedNyYVYQXpn5elX64L3+2ZlB4xJlp2ZoaWr96i++eO0/KzRqnB6VG/7Awdcji1bPU2/WnR5FSHmFDRro3hnkf0NtWNLjW0uPW37QeDylLTS4u0YNoIVTem/nlKUW6WfvmvznP17j6Qq0Cyy/K9/+xnUJ3dDHV8qD6ttEg/njVKjuZW3fyt0fL6fKpudKl/rjWk0mfr/tpAJVjHVlB+00uL9YPTjtOiJ9/TwmkjOh3Ac3ppsfrnWnXGqBIV2jI1c1SJ/rXrcNj5Nn/S9v3OljWttEhb99cG/fvWc07Uxb97S5Jk75AUHSu0olWgtbi9UVuCGK3VCxKn4wObrlagTistUlaGWTarJaSFZUOLO6RZepPLE1KxK/XNMaeoZAYA9BUujzfQu0JHG8qrovagkC4cztaQl+Xal3N6Wp6xmNruI/wvHLU3vbRYFoMMX5HoisB4yM4wa1ppUdhuZKaVFik7Slf5QG/g8ngjdv2WDufm1igxtqZBjIlmsZj08LwJWvl6eUjvKg/Pm6AMo1wcYpRlMXf6XK2stEhWS+8/X7u9Pj2+MfQF5A3l1fJKuv2c1L8o1BIlV1v6QK4COZnmiGX5nEzG4OoTIt0MbSqv1k++dYLOHjdYLrdXqzZ+osvKjtXP/7lLo4bka8LQgrBNAVdt3KuH5k2QJE0YVhi2VUrbgefTs1dM0cE6p84bP0R3/vXDoAOy7c2IY3T+o2+qyeVRWWmRbj37RHnlCxqHq62iarTOe2STJAV+u/1vTh9ZrHvmjFFji1tlxxUrL9uiw44WXfy7t1TV4NKMkcUqzA3uwqxjhVa4SoL2CnIyaQmCLuvYDUu0m5kWtzfQ5eD9f9+lxacdp5/+479B85hN0g+eel/Sl60wn317X1DFrp81w6yPjzSk1dgMyUAlMwD0Tqkaeyhdxzyqj9JdR7Tp6SI/JzNi9zv2qD1BRJZpMWvJ6aWSfEEVgtNLi7Tk9FJlGuQhXrTu/dLhxaZMs0m3n3uibn95Z1Al17TSIt1+7hhlmnv3A2NA6kJXSmlwbu4t14+eyDSb9PiGT4LG4PK/XPH4hr26+ewTUh1iQn3e6NKiaSNklkKujQumjdDnTS6Fjm7fu3h9voiVRx5f6kcic0QZoiXadKBX8Ek/OO04eeULeSHhB6eVKt6DBlLBlaai3Qy53F7ZsjL04L/+q2tnfkW/fqNc18z8iuqdbjW0uFWYmxl2QMPlq7fpolOHafbYwSGtUtq/hdnU4pHVYlaL26Nzxg/WZWXHBLpXO+RwyqQvb3Q2lVfrzjU7tbBshC6ZPFxS203vG/89IpfHE2jNcs0zW7Vw2oigwUBLB+RpWFGuDtQ2a8Xfd4XtomygPTuoC7OOFVrtW6Z1NH1ksY4ryTPMYN1IPZvVEvTvAXlZEecf2t+mCcMKA92GXj/reP1h0/+pqqFtbLqOA6BuKq+WSdJdc8Zo9kMbg5ZVVlqkv+2o1MrXytNqbAYAAGKRqrGH0nnMo9wo3XFEm54ucrMyOu3C2STp5//vpB4tv8Xjldvj1eLTS3XDF91Q5WVnqLHFLbfHa5i3n+0G6P6vodWj7/7+bd0/d5yWBbZ120uHF/12s/50ee/u8guQjHFuNkKMiebyeHVRhDG40qGlXSLlZmfI4XTrrLGDdVm752r+Z3R94RgwwjhkdP0LSI2tHl39zNagLmXzsjN02OHU1c+8rycXnhrX3+v9Zz+D6srNUKvHq9FD8tXscuu6mcfrjjU7tam8Wtd9Y6SGFuZoa0VNyODVz10xVZ83toS8gdRZl4VnjBqgH878SuBhvclk0oE6p/710aGgsYf8XbUtevI9SW3jDa18rVxnjCoJVD41uTxBy54xslgPf9GqK1oXZe2ne32+oEqD9i3TNnV463DF+WOp3EKX1TW59H5FcIWpNcMc1LVn+654CmyZys4069l3KgIVuQdqm3X/3HFa9OR7mlZapB9983jN+6K7Tb+N5dVyNLuDujL0F8qveWarpPQamwEAgO5K1dhD6T7mkUmdd9tdVloko7SVaXC6O32DemN5tRqcbg20x758i1nKtJhUfqQxMIh8c6snMIi8QRpwqTgvuCeK9qJ1oZ4sjubQh4HtX2asDzMd6G3MJmnmqBKNGmIPeUl41wGH0qEhozlK163pEGOi+XzS02/vC9uC6+m392nZmaNSHWJC5WRa9PuNn3Q69Mc9549NQVTJlWvNiNhFcq419Y+5c62WiF3/5nZ4qRrojZwuj376nfE6WNcsSYGy/CGHUz/9zng5Owzt0lOpz3yEVZxnDWn54Tf9i5uhvVUNOnP0INmyLLrtLzsCF7kzRpXog/21gRZV7U/2t768Q6cc01/TSouDlrlw2oiQtzBtVovmTR6u+9fuCmlOuKBshAbnZ+nZdyoClV/tuw70/39dU6sWlI2QSQo6uftbZ4V7wOCTFO7uvn0XZve3a9HV5PLomme26ubZJ2j5WaP0aU2zrBaztu6v7fVv8CC+qhpcumvNh0EVptUNLt0zZ6zuWrNT88K8LTattEi/n3+KLv7dW4EKq8H5OXrq8skakp+tC36zOWRMLknyeH1at/Rrqmlyqa65VVv31wZagfmFG5shXbtcAtKZz+vVp59+Gvj30KFDZTYb5OkoYFCpGnso3cc8MpmlBV+M3xmufG0yyKkp0V3vZZrM8vikV7YfDNlOS04rVaZBNlS+zaq754zRTS9uD+n+7+45Y9KiDGfPseiZK6bovb2fS/ryAcRhh1PPXDFFHi/3U+j9si1mLf/WKN328s6Qe73bzx2jzDSoPMqymCN295RllJr/HvBJujhCC67Ud06XWI0tkV8uSYfWS4lmMn/xQv3r5SHHwOPzJykdbvGaWz267IvjsWOuXlY2Qs2t8X2wD6SjolyrXB5vp2X5otz4loGp4Epji08rDelftqy0SItPK5UkFdiydMtL23VZ2YhA/7vFeVblWjPCHkAPzZuga57ZquVnjdL2T+uC3h6dMLQgpMvCcJVe0pcn6GVnjtJTl0/RJb9ve7B/dGGOHp8/SS1ur4b1t+ne88foP5/Vasendbp/7jg5W72q/+KhfG5Whhqcbm2tqJE9J1PZGWbd9vJO/eujw4Hf6awrmboml5ytHt189mh5vD4daWiR2+PT+xU1uvuVj4IqCGaOKol5+6PvcThbAxWmC6eN0M2zRysn06zKumZdPHm4ngiTDxvLq2XSbi2cNkJbK2q0dX+t+mVlqH+uVd4I/T/n52TquJI8ba2oCbR8bM//VlKL26OtFTXKz8mU1WLWjS9uT8sul4B01lxXpeUvHFTBwENqqjmiVYvP1PDhw1MdFtCrpWrsoXQf88gk6dm3K4LePs/KaHsx69m3K3TDWcenNL6uSnTXex6ftPL18k7vQ+48d0yPlp8sdU0u3bnmQ500rFALOuzvu9Z8qJ9dMD7llVx5mRn6rM6pV7YfDKqEm15apGMH5OmofHrDQO/nkXTbyzvD3uvd9vIO3XVe6s85zW6vFj35XsiwD1v312rRk+9q9VVfTXWICWeSIj6juvXs0SmIKnkaWzwqzrPq/rnjVGLPUoPTo37ZGTrkcGrZ6m1qjHOLiHSUaTbr0U7KB2aZdMd5J6Yosi/VO91hh2jxv9T8P3Humg1IR15FLsvH+7pKBVeaqm1qldPl0Y++ebyWn2VWTZMrUImz8Il39crV03TrX3ZoQ3m15n0x7pXNatGqy07RrX/Zoa0VtSFjcFXWNev/+9qxOuxo0d2vfBTUSqV96yu/cJVefpvKq2UySXet2amfXzBe1gyzHli7K/imaGSx7jpvjC6bekzQjduB2mb96IX/BD2kn/bFmwxvflwdqKAK15VMuDEV/G/rrNq4N6SlDH3bojv8D2v83WmeM26wbnlph+ZNHq6sDLO2hMkrf+vIK79+nE4aWqBrntmqCUML9P9++5amlRbphSun6uMjjTKbTIF5Jw4rlMfr1cdHGtTfZpXNagk6dsN1Gbrk9FJtragJuTikS5dLQLrLKRig3KLBqQ4D6DNSNfZQuo955PNJF08Zpj9s3BvSlfiCaSOUBmOjd0miu95zuj1h72f8ZSmn2xgP8aoaXPrXR4eDXuLrOD3V5bdWn0+/W/9xUCWcf1v/bv0n+snZJ6Q0PiAZmlsjn3PSocVFx2EmpLYhJCJN7228PnXagmlTebW8BrmGxqogN0NPf3+K7vhrcGXstNIiPf39KTKZevkGUNs4bBs6OQY2lFelRS9OedkZIUO0tJebzaN49H7Jvq6SVWnos5omfVbbrKZWj7xNCvT7fM3MkcrJNOvheRPU5PIEHrYP7Z+jRy85WUMLbXI4W7WlojbseFplpUW67ewTdaC2WU0uj5av3qZVl52ieqdb/cKcYMNVerXndHl13TeO14A8q+78686Q/mU37KnSLX/ZoTvPO1EHHU41tbhVlJelm1/cEdJv9MbyavmkoHG9pOCuZDobU8F/Ye/43XTp1x7G0bFr0BZ3W+FpwbRj1erxdppXD82bIJ98uuaZrZo4rFD9c61addkpba0K13yoS6YM18In3g00m3e2ejXn0TfV5PJo+shi/eGyU7TgiXcDlVzhWk9GqnBOhy6XAABoL1VjDxlhzKM/vbVP44cVBg0Qv3V/rf70lnHGD8m3tb1B/sZ/j6ikX1bgpvWQw6nTvjKgx2USp8sTsdzlTIOHzV3hcLZqaGGOHrnkZGVYzKpvbuvNotXj1eKn3k95i0Kprbx7UYQuv6LdEwK9gRHOOXZbRsQY7bbe/3gvWhd8vb2LvmyLRT9/dbeWnTkq5Jry69f3aOk3jNEKvCeiVeSmQ0VvlsUccby8vtCdKJDs62rvvwIagH9MnYaWVhXYrLrpxe1BD7anjyzWHeeeKJ+ktz6u1k//8V/95nsTQw6Ux+dPktvr08PzJoTtSm1TebXuWvOhLis7RpJ00anDAuNrLTm9VKePGqDRQ/IDNavD+tu05PTSsC2jJKm+xa2FT7yr6SOLtaDsGL35yech823YU6UDtU5d8vu3AzGGO8n741v4xZgEQb/zxY1fpDEVOn430hhfQCSLTyuVfD5tKP+yNaFJPg0tzNE9f/uo06bwt547WlOP7a95k4frkt+/rSaX58vx6gpyZLNavphXmnhM/8Cy/cf0HeeeqB//eZuk8JVZ0R4upMMDEiBdeL1e7d+/v23crd7/IiOQlvJtVt3XbsxUv0SX0VL1u11mkpafdYLu+uvOkBZct5xzonwGOmm5PV6FNDnzSa1xeHu6f65Vv/jXfzvt1uTuNOgurCvyczL0x0Wn6uaXdoS8bf/HRafKlw5N9nyRu/y6ZXbv7vILkIxxzsnJsHSaqyZJ954/NjWBJVFuVuRHmNGmG12zx6NrZ35Fb35crYH2bLW4vWp0eXSorlk/nPkVNXtSXxGbaEY4Blweb8Tx8lyMbYk+oDDJ19XUZ34f177LvSWnl+o/Ybog27CnSrf9ZYfOGjs40BR3cH52oHLKz+31aURRro40tERssrtgWltFUPuH6M++U6GnLp+iO9eEDqrqH7urfeVVWWmR3q+oCcQnny+kBZVfXfOXD96jPaQPN93flUy0MRXyczL10g++qn7ZmSrOs6b+AQYMp6rBpYVPvKsrZhyrn3zRf7fNatHRhTa5vb5Ou0PYUF6lI44WXX3GV3T5k1+2xPLPf/Ps0YH82FBercs6VORu2FOlW2aP1rqlX1O9s1WtYfpWyMqI/JZPqrtcAtLJ/v37tfCRtWquq1be4GNTHQ7QZw0pyNHD8yaoqsGlemdr0spoqfrdrrBlWHSgrllnjR0c1ILrkMOpmsYWDck3xpiahx1OfVbbrDVhxv0dUZyrrEyLSuyxj93U4vFG7IaqJQ26IOqKTLNZyzu8vCi19V5xy0s7tCINHkj7FLnLrzSoggMSzgjnnEaXp9MYN5ZX94nxl7IyzEFjybdXVloU9Z7Z6HIsFn1a26xXwlx7jynO1dF9YFzu7IzIraOy0+AY8PrU58fLA1xJvq5SwZUC/hZbXvn03t7PddlXj9G8U4dpWH+bJGlLRW1oS6gOD8Vd7tADZXB+tu5cs1MXfzEmV2f8bwq2r0y66NRhunNN+EFVpeDu//xvmO6tagx0w7Zq496Qh/Z+7QsZ0QocHae370om2pgKhTarjivJizgPEInD2aoml0cP/muPph5bpAH9snTL2aN151936pIpx0T8bm1zqx55o1z3zx0nSUH9y8rUVqHsF64it665Vf3zrBpRnKuqBlfI9K37azstzKdLl0tAOrEVDhDNt4DUy7elpmKpJ7/rL6s7nG1d/xTnxm8dWr0+PfRa6IDLUtsDqngPuJwojS1uPRxh4OievpVphC6IuqIhygPphjR4IN3Xu/wCJGOcc8hVqbbJpQVfPHfqWMGzoGyEaptC76N7k1avTysjXHuNUoboqUito9JBuFxsP15eX8hVINnXVSq4kszfYmvLvhq99IOysG89hmsxJQU/FD/S0BKybJfbq43l1YELfmeOKsjR9JHFQZVJkcb32VherWVnjdK044qVm23R9k/rNOeRTYH4/DG7w7Q6KSst0tb9tYF/R3pIP63DvB27kjHCmAowtvaVqLYsi2oaXZowrEA3/u/2Titw/bIyzNpUXq0ffL000CWn1JYDc046Kig/wlX0ZmWadcbP/60ZI4u14ttjQ451fyvLu9YEj3c3rbRId88ZkxZvpQMAkC5iraRq37uC34yRxbpv7jgNicOb0c2tnVd4bCqvjvuAy4nSFGU9mnq4HkbogqgrHM2Re6BIhy6me8u2BnrCCHlghBgTLTvTomueeSdsy5hrntmqP185NdUhJlRvKUP0RFOrJ2LrqBfS4BjIzbJEHHsoN8uSwuiA5Ej2Nav3XwHTSF2TK6g7wrvCtJjaFKbFlF/7h+LWMIMS+iu9IlUiTS8t1rpdhzR+aIGOLswJNO2N1nXg/s+bdXRhju77+65OY/7Jt04I+a35Zcfomme2Bj5btXGvHpo3Ieh7UtuDg3vPHyuXx6uZo0rCdiWT9mMqwPDaV6IedrRoUH626pvb3iqIlFftK3LrOjzI2FRerTte3qkfn9k24Ov00uKgilz/9/3W76nS7S/v1L3nj9VNL24PHOttY+Z9pJOGFQYG/PYX5O5a86F+dsF4cgAAAMVeSdW+rN7e+j1VWr56mx6eN6HH19qmlsgPn6JNTxeNCV4PkxSx3GUK/UpasudE7oEiHbqY7i3bGugJI+SByRQlxnQIMsFyMi2aMCz8y9llpUXKyezdFQcdX4LvqDkNWgUnWlOLR00uT6cv6KdDOcqWmRFxvLx75qS+e2Ig0ZJ9XaWCK4mqGlyBG+ZILaY2lVdrYYfWIjNHDVBulkWPz5+kFrdXJf2yNb20KGisLX+lV2eVSNNHFuuGWcerusGlplaPaptcumvOGN380vYudR0YrlvE9jF7fD799eoy7f+8ObC8p9/eF3QRbnJ5dM0zW3XL2aN1+zknqrHF3a1xEdJ5TAUYn78SddnqbVq2epte+P+myu1pa3nlzyuzTEH9Pfu7Q/BX5IbLpQ3lVfqhe6SmlRbpxm+N0nce2xz0/SWnjdTruw8HPnv1o8O68VsnBB3r2ZkWrXytXK/tOhI29qoGF3kAAOjz6ppcuvUvOzR+aIEu++oxQV0G3/aXHRFfCGlfVu9o/Z6quFxr7TmRb7+iTU8X0eLs19P1MCliN1Rp8bS5C/KsFk0rLQpqfe83rbRIedY0eBjbS7Y10CMGyAOTIseYBiEmnE/Ski+6oeu4DZacNrLXd06eRyu+qOWLHpc/4qDZHbl74mZ36ivhgIRL8nU19ZnfhzjadUERrcVU++mnjxqgG846QXf9dWegQstmbavsUruH7Vv31wZuoK55ZmtQk938nExZzCbZsixqbrXIq7ZxvQ7UNOm2c0+USaZOB2r0t045cYg9YsxH6lv0fkVtoOLujFEDdMOZJ8jZoWJs4vBCnfaVAcqxWlRlMrVtly8O7K48NEjVWA7oG4YU5OiWs0fL0dwqr6+tldT0kcXasKdK1zyzVQ/Pm6Arv36c6ppbg7pDaHJ5QrrktFktWjhthCYMLZDH69NPZo9WlsWkRy8+WU2tnsCg8s5Wj37z70+C4nA0t+rYAXmBY31rRU3EuNOhixsAAFKtutGli04dFrZbmAVlI1Td2HkllSPKtTQe11qr5ctyRUfTRxaH7aUhHRXarBEHeS/sYVndJGn1lv1aWDZCy88apQanR/2yM3TI4dSft+zXtTO/0qPlJ4vJJ909Z6xufml7mC6mx8qUBk9jczMteubtfZowrDCku6dn3t6n2885MdUhAkkR6ZzzwzQ45+RkWvTsOxVhc/XZdyp069mjUx1iwtU0utTq9unq00uD9lNDS6tcrV7VNLqkAamOMnGM0NIw0bKilKOy0qAcZYQx/YBEy84w6387ua6u3rJfN5w5Kq6/Z/L5fGlQrE4fjzzyiH7605+qsrJS48eP18MPP6xTTz016vccDofy8/NVV1cnuz18RdC+qkaVH2lQi9urYf1tWruzUqs27pXU1iXhpGGFyrdlKsNilrPVo1yrRXlZGfL5fPqs1qnmVo+OLc6V0+2Vo7ltPIE8q0Uer0+NrR41uzzKzcqQx+OTxSyZzSa1uL1qdnlkz8nUhj2HVWjL0rih+fL5JJ+vbXDDnEyLappcGlKQo9te3hl0ofA/DFi+epv+uPBUHXa0qDDXqkyLWQ0trcqxWiSfSa/tPqRZJw5qiys7UzlWi1o8Hnk8bb+Rl52hTLNZjmaXCm1W+STd+pcdQS3QppcW687zTpQ1w6yGFrfqvlhWdoZZPlPbGGMNLW7lZmXIbDIpw2xSblaGGv3zdnF8hfZjMuRlZchqMau22aW87NDv++eta3bJ1u53izqZrzvjPPi/09DSqkKbVV6fT85Wr+qdbuVmWZSblaGCnMyEVuYdcjhV0+iSw+mWPSdDhTarBtqzo34v1nEtEjloe1d1JVc/Ptygc1Zu1Is/+KpyMyzymtoqkMcfna/mVq+aXG7lWi0ymdpyrMnlUU6mRYW2TB1yOJWblSGvV5KpbRubTCa9X1GjVRv3auLwQt1x7omqaXQpNyuj7fhWW6V2Q4tb/bIzlGE2q7bJpbzsDNkyLWps9cjn8+kfOw9pWmmx3F6f+mVnKCfTIrMkp8erpha3Cm1WtXp8qm9pVa617f0Fn3xfxPbltk70fujq8sPNJympx0iyj8l0yIGuSIc4u5KrkrRv3z4teXqLJGnlxRM1fPjwwGdNNYdlsRXI01Qri61A/YcMV2P1wcB8AHquq7kaTVfLJNHOT/89VK87/hraDbjUVq69/ZwTNXJgv7AxfHy4QRf+drPunztOJfasoBuxZau36bkrpuq4kryY11GStuz7XB6vV16fSblZlnYP59yymHyymM06eXj/Hv2G32c1TXI43XI0tyo/J1P9sjN0VKEtLsuWpIrqRv3kxe0dyvNFuuf8sRpWlNujZf/3kEMZZpMO17coNysj6CFmSb8sub0+fWVg7Mdbsvxnf428HreK+9nU4PKovrlV/b64h6uqb5LFkqFxQwuTEktnuVpZ0yS3x6f6Vo98kuq/uK+SpH6ZFmVYTBoUx+MGSEf/PeyQxWTSO598rhJ7dqD176E6p049tlAen09fKUn8OSfSNfW/hxzyuFuVl52tBpcn6JlQg9MpS0amIc6LPbGr0qEj9S367b8/Dr72jCzWFTOO1YB+WRo1qPdug92HHKqsdepAbXOH47RZQwpyNKggW8f38mNg35EGZZp88pjMIXlg8XnV6jNp+ICeldW6IlKu7q506JLfv91pefKpyyfr+F58nAKSVHGkQTKZtOnjqpDratlxRZLPp2FxzFVacLXz3HPPaenSpXrsscc0efJkPfjgg5o1a5Z2796tkpKSHi37QG2zbn5pR9BbjtNKi/TIxSfLJ5/+9NY+nTS0QD/75+7gbgVLi7X4tOO0bPU2/X7+Kbr5LzvCTl/45HuBrgCnlxbrtnNH696/fRTozsxmtWjV/FP0+w0fqzDXGtIf7LTSorZKtmMKddlXj5EkDc7PkUk+OVs9euHKqbpnzYe6eMpw/ewfu4IKE23NwUvlcnt12R/eVZPLo2mlRbr93BN18eNvqarBFZjv1rNP1Dv/97n+8p8DIQ8eNpRX6ea/7NDZYwdrgD070OXb4/Mn6dHXy0N+c0HZCD3z9j7Nmzw80IIm2vgK4cZk8C9r3u/e1qThhYHvR5p3xd8+0h3njel0vq7GsWVfjR65+GQ5W71a+dqekHW8+vSRGt7fpsFxGNS8o4rqRt344vaQ4+DeKA8lYh3XItGDtsdTcZ5VZccVKddiltckVTc4Nf7ofP3kpR3aWlGrRy4+WS63VytfLw/Jx0XTR6ihxaNVGz8J2Z8PzZvQ1kXnX3ZowrBCrdq4V6vmn6JHXg/d9wvKRuh7q97RxGGFunz6CPXPs2rL/32uB/+1J2g+f+55fdLPX/1v2Ka/T7+9T1fMOE7D+7c9nLghgfuhq/s53HzTRxZr8WmlWvjEu4HzWSKPkWQfk0bJAaPEGSuf16tPP/1UkjR06FCZzal/yw/o67paJunK+cnr9UXuUtvb+bt9xXlWPXvFVN328o6QWJ69YqqK83pe0V9sy5RMZt30Uuj63jNnrOSL3MtDV+2rbtRNYbbpPeeP1fAeVj75mSQtPr1UN3zxVmZetkWNLe64vEGen2FRvcerh18rD1mH288do/wo3auni/7ZmfKYrFoeZl/cPWesLGnwrumgQpv2VTfq7lc+DHu8HE3lFvqAfhaL9tU2a832gyH3UyMG5Gp4fvSXQBMt0+xWVlZ22PPJPXPGyutrSWF0yZGXadE9HSq3JAXKBSvmjElFWEmTk2HWUYU2/X7DJyEvl9x27hilQ6+3iWYzm9TkM4Uv48wZK1saFA/6ZZojlidtfWA/ARlmqSLCdXVYflZcf48WXO1MnjxZp5xyilauXClJ8nq9Gjp0qK6++motX7484ncj1d7XNbm05JmtYZvQrjh/jF7ZflAnDSvU1oqaTt82XXbmKN2/dlen0ycMKwzqhmVaaZFOavfZktNLtbWiRhOi/E775fj/fVR+tv62/aDGDyvUBxU1YfuQLyst0uyxg3Wgzhn4/rQvHq4vevK9oLiunfmVoDGIOnp8/iSt2rRXE4a1vc0YLV7/evl/d8bI4rCDgEfaD+3XfcbIYv30gvH60Qv/iTjvtv21EefrShxLTi/VkC+2b2fb9exxQ/StMYPi2nrikMOppc9/EHa7Tist0s//30mdvjXd2TbsbH178r1E6Oqb5p/VNMnr9WljeZVOGlagu1/5SJvKqwP77JUOJ2m/6aXFOmvsIN304o6Qae2Ps8fnT9LW/bVdyscV54/R37YfDCnI++c7e+xg+aSIv7m1okZnjxsiSbrxf7eHzBeP/dDV/dzVXIxnbLHGatTfi1U6xZmoFlxHyv8jl6tVVqtVqxafSUsuoId62oKrq2WSrp6ftuz7XHN/3Xk5c/VVUzWxkxZSsZaPuuPTmiYtW72t09+4b+64HlcofFbTpBsi/Mb9c8f1uCVXotcjXKVn++VHeyErXSRjf3dVZ7maTjECqbL3SEPIy8R+ZaVFuvu8MRqR4lYh+6sbQyq3/KaVFmnF+WM11ADnxZ7YXenQrAc3dDr9H9dO79UtYzgG0mcbRMrVT6sbtSxCjPedP1ZH9/L9BCT7upoGddvpweVyacuWLZo5c2bgM7PZrJkzZ2rz5tCb5JaWFjkcjqC/zkQasLrEnq2N5dWaMLQg4tumGRZzxOkThhYEfbaxw2f+5Uf7nfbf8f+7xJ6tDV/8f7hKGP+8A+3ZQd/fWF6tEntwjezG8mq5PZHrVFu+GLNrwtCCLsXbMW7/IOAdRdoP7Zexfk+Vahqjzxttvq7EMWFogQZ+cQx09lsl/bLCLqcnahpdEQe9rGkM/3tdGXw9nt+Lh+7kanvOVq8aXB6V2LPlkymwvfz7rLPtt6G8qtOHX+2Psxa3t8v56M/BzuYrsWdH/U3/sVTSL/xbEvHYD13dz13NxXjGFmusRv29WBkxV2ORUzBAtsJe3EE/kEDxztWulkm6en6KNsB6pOmxlo+6o97pjvgb9XEYl8ER5TcccfiNRK9HoyvyAO2NLmMM0J6M/d2ZruZqKmME0kVTa+fnnE3l1WpqTcw5pzvX1IYo58UGg5wXe8LRHPl8FG260XEMpG4bdCdX66PEWN8H9hOQ7OsqFVxfqKqqksfj0cCBA4M+HzhwoCorK0PmX7FihfLz8wN/Q4cO7XTZkQasbnF7g/7bmfrmyINah/t++8+6+jsdp7e4vd36bsd5GpyhB6wtK3J73KwvuhwJt7zO4u04X7hBwKMNHN5+GdFu/P3zRpsvWhxdXcd4DGoeHEOUgmEn02MdfD0Zg7Z3pju52p7D2SpHc2vb9m/u/j6LNi0rwxzz8R1uvq78ZrT5erofurqfu5OLHb8bL8k+JlOZA91hxFyNlb+rwn379mnfvn3yeuPTLRjQ28U7V7taJunq+clsMqmstCjsPGWlRbKYOu9AL9byUXc4opTp43Ge7Q2/kYx1SIZUrkdXc7W3bGugJxpbIj9oa4oyPVbdeq5ErsoWpQ++aM+ajI5jIHXbgFwFuifZ11UquGJ04403qq6uLvC3f//+Tue1Z2d2Os1fmZMVpR/5fjmdL6Oz77f/rKu/03F6Voa5W9/tOE9edvgCRqQHD1v313a6vM7i7ThfvzDbPNJ+6LgMe3bkt3/980abL1ocXV3HcMvpiWhxdzY92jbsLM5YvxcP3cnV9uzZmbLnZLZt/5zu77NI0/zHeazHd7j5ov1mV+br6X7o6n7uTi52/G68JPuYTGUOdIcRc9WvfYXVp59+KkXpgLm5rkrLX3hfS57eooWPrO327wF9VU9ztaOulkm6en7KMJu0oGxESFnTPy6lxdx5BVes5aPusEcp08fjPNsbfiMZ65AMqVyPruZqb9nWQE/Yc6Lc10eZHqtuPVciV5VjtUR8lpST2bsruDgGUrcNyFWge5J9XaWC6wvFxcWyWCw6dOhQ0OeHDh3SoEGDQubPysqS3W4P+ut02XlWzRhZHHbaYYdT07942B3pQu32eDWtC5VCftM6fOZffrTfaf8d/7/bxzg9wncPOZxB359WWqTDjuCBTqeVFun13Ye1oGyEpnfYJv4HD6s27g38dlfi7Rj3jJHFYQcBj7Qf2i9jxshiFeZGnzfafF2JY+v+Wh36Yvt29luH61viMqh5e4W51k6Pp2mlRSrMDf97kbZhZ+vbk+/FQ3dytb3iPKtsVosOO5wyyRfYXv591tlxOb20WIcczrDT/HniP867mo+HHc6QfGk/32GHM+Jv+n/ncH2LDteHH3w4Hvuhq/u5q7kYz9hijdWovxcrI+aqX/sKqx//8d9qcUXvSiynYIByiwYrJ78oUDlGSy4gsp7makddLZN09fxUlGvVc+9UaMKwQj0+f5IeveRkPT5/kiYMK9Rz71SoqJMyTndi6Yl+2RkRf6NfPCrRovxGPCrqEr0eydhOyZDK9ehqrvaWbQ30RF5WhqaXhr/GTC8tVl6U7m9j1Z1rKrna9iLkktNKw77EsuS0kVFfDDU6joHUbQNyFeieZF9Xe/fZvxusVqsmTpyodevWBT7zer1at26dpk6d2qNl59usum/uuJCb8mmlRZo0or+WnD5SHx6oC/u26fTSYi05baQWP/W+bjn7xJCT5PTSYl19+kit2rg36LPbzz1RHx6oC3y2auNeXX3aSO064Aj7O9PaVS5JX1Y27TrgUGlJnu6eM7btu9NGhByg/sLE4PycwPenlRbp9nPHaNnqbUHz3XrOifrP/lpd88xWTSst1tprp+vx+ZMCDx6ueWarTh5WEIhl1ca9WnJaaaeVYR99sd38vztjZLHunztO+bbQBxCd7Yf2FWv+7w+0Z0ecd/dBR8T5uhrHqo17NTg/R0tOHxl2u159+kid9pUBYZfTEwPt2br3/LEhx5N/0O7OxnPqbBtGWt+efC+V8m1W2SxmlR1XrOYWt+6eM0bTSou+3GdhCtbTS4u1aPoIDc7PCZurd583Rhlmk655ZquaXJ5AXobb9/5jcnppsYYU5Oj7048NmwdLThupIQU5Gpyf0+nb6h8eqAscS1//yoCE7Yeu7ufO5ps+MvR8lqhjJNnHpFFywChxdubLCqv+3fqev3Jswcq/afPmzXRbCCRRV8sk3bnG3HHeGG3bX6tFT76nHzz1vhY9+Z627a/VneeNiXgei7V81B1HF9p0Tye/cc/5Y3V0oa3Hv3FUlN84Kg6/kej1SMZ2SgYjrIcRYgQS7ehCm+4+f0zIi6fTS4t09/lj0iIPyNW269tRBTk6e+zgoJdYzh47WEcVZMfl+pbOOAaMsQ2MECOQaMm+rpp8Pl+UTnz6jueee07z58/Xb37zG5166ql68MEH9fzzz2vXrl0hY3N15HA4lJ+fr7q6uk5r8uuaXKpqcKne2ap+2ZnKsVrU0NIqn0/KtJjV4vbIbDLJ65MaWtzKy8pQntUij9enxlaPXK0eFeVlqdHlCSwjz2qR0+OR2yM1triVm5Uhs0nKspjlldTc6lGTyyN7dkZgzB+fJJMkr6/tO/380zxe1Te3LcOktpmyLGZZzSY1etzyeU2SSTL5JK/aBiS2WS3KybDIajGpodUjR3NbXDarRS0ej7yeL9clK8Msh9OlftlWtbi9amhxqzA3Q1lmixrarVO/7Aw1trhV98WycjLM8klyedq+k2vNkMVsksVsUm5W27z+3y3Os0Z9ANt+P+RmZchqMauu2aXcrNDv++eta26VzWoJ/G5Rbvj5/OvQnTgaW1pVkGOVVz45W72qd7qVm2VRrjVDBbbMhD5QPuRwqqbRJYfTLXt2hgpzrV16eBPL+vbke/HUlVxtr67JpcbmVrm9PpnMJjW4PGpsaVVRbpaktoETm1o8sue0HUvVjS3Ky85UbqZFja0eNTjbcizTYpbb61WG2dyWly0e5WVnBI5vp9urxha38rIzlGk2q7bZpbysDNm+WE6j063+eVa53F45mt2yZVmUk2mRWZLT41VTi1uFNqtaPT7Vt7TKZm3LY598ysm0qND25bZO9H7o6vLDzScpqcdIso/JdMiBrkiHOLuaq/v27dOSp7eoqeawLLYC9R8yXEfK/yOLrUCeptqg/4ab1vEzl6tVBQOPUlPNEa1afKaGDx+exLUGjKe719XOdLVM0pNrTFfPY7GWj7rj05om1TvdQeXfeN/ofVbTJEe737BnZ8T94V+i1yMZ2ykZ0mE9ouVqOsQIpFqq86Ar19RUx5gO+vo26OvrL6V+G5CrQNckKw9oF9nOhRdeqCNHjujWW29VZWWlTjrpJK1duzZq5VZX5dvC3VjnxGXZ6Lrw+yG3G/PGPl9PvxNvA+3ZMT2wiTX2dFjn7upuzCMG5CUwmvhI9H7oad4k8xhJ9jFplBwwSpyJ4G8FBiC5ulomSWTZrLux9EQyHnAcVWjTUQn+jUS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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "0R0QrxnDPdrA"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [],
      "metadata": {
        "id": "If_anhkKnvTi"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Create a box plot of Loan Amount by Loan status\n",
        "plt.clf()\n",
        "df.boxplot(column = ['loanamount'], by = 'loan_status')\n",
        "plt.title('Loan Amount by Loan Status')\n",
        "plt.suptitle('')\n",
        "plt.show()\n",
        "\n",
        "#The average applicant income seems have no significant difference.\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 479
        },
        "id": "RWYzcfuBTGP-",
        "outputId": "f9cf444e-4013-4d5a-92c2-0ef94f3c3179"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 0 Axes>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#checking the frequency of applicants whom are graduates and non-graduates\n",
        "#Most oif the applicants are graduates\n",
        "df['education'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rOag9EeOuf_N",
        "outputId": "549a8471-9aa5-490c-c705-7482d17f3ab1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Graduate        480\n",
              "Not Graduate    134\n",
              "Name: education, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#number of male and female applicants\n",
        "#there are more male than female applicants\n",
        "df['gender'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5ET1RYfCvOfl",
        "outputId": "5303a9d9-467b-4e37-f091-c7dc0884ac3a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Male      489\n",
              "Female    112\n",
              "Name: gender, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Replace missing values of the columns with the median, mode as appropriate\n",
        "df['dependents'].fillna(df['dependents'].mode()[0],inplace=True)\n",
        "df['self_employed'].fillna(df['self_employed'].mode()[0],inplace=True)\n",
        "df['loan_amount_term'].fillna(df['loan_amount_term'].mode()[0],inplace=True)\n",
        "df['credit_history'].fillna(df['credit_history'].mode()[0],inplace=True)\n",
        "df['loanamount'].fillna(df['loanamount'].median(),inplace=True)"
      ],
      "metadata": {
        "id": "XnuFeWB9wQW3"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.isnull().any()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zObL1LR6xlyP",
        "outputId": "67fb9e82-9a37-4239-9672-98abbfd61159"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "loan_id              False\n",
              "gender                True\n",
              "married               True\n",
              "dependents           False\n",
              "education            False\n",
              "self_employed        False\n",
              "applicantincome      False\n",
              "coapplicantincome    False\n",
              "loanamount           False\n",
              "loan_amount_term     False\n",
              "credit_history       False\n",
              "property_area        False\n",
              "loan_status          False\n",
              "total_income         False\n",
              "dtype: bool"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# one-hot encode the Property Area and 'Education' columns\n",
        "#To enable the machine learning algorithms read the columns\n",
        "df = pd.get_dummies(df, columns=['property_area', 'education'])\n",
        "print(df)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uAbsn7E1ZkUY",
        "outputId": "b0682325-caf3-42e2-85e7-2bdf506867f0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "      loan_id  gender married  dependents self_employed  applicantincome  \\\n",
            "0    LP001002    Male      No         0.0            No             5849   \n",
            "1    LP001003    Male     Yes         1.0            No             4583   \n",
            "2    LP001005    Male     Yes         0.0           Yes             3000   \n",
            "3    LP001006    Male     Yes         0.0            No             2583   \n",
            "4    LP001008    Male      No         0.0            No             6000   \n",
            "..        ...     ...     ...         ...           ...              ...   \n",
            "609  LP002978  Female      No         0.0            No             2900   \n",
            "610  LP002979    Male     Yes         3.0            No             4106   \n",
            "611  LP002983    Male     Yes         1.0            No             8072   \n",
            "612  LP002984    Male     Yes         2.0            No             7583   \n",
            "613  LP002990  Female      No         0.0           Yes             4583   \n",
            "\n",
            "     coapplicantincome  loanamount  loan_amount_term  credit_history  \\\n",
            "0                  0.0       128.0             360.0             1.0   \n",
            "1               1508.0       128.0             360.0             1.0   \n",
            "2                  0.0        66.0             360.0             1.0   \n",
            "3               2358.0       120.0             360.0             1.0   \n",
            "4                  0.0       141.0             360.0             1.0   \n",
            "..                 ...         ...               ...             ...   \n",
            "609                0.0        71.0             360.0             1.0   \n",
            "610                0.0        40.0             180.0             1.0   \n",
            "611              240.0       253.0             360.0             1.0   \n",
            "612                0.0       187.0             360.0             1.0   \n",
            "613                0.0       133.0             360.0             0.0   \n",
            "\n",
            "     loan_status  total_income  property_area_Rural  property_area_Semiurban  \\\n",
            "0              1        5849.0                    0                        0   \n",
            "1              0        6091.0                    1                        0   \n",
            "2              1        3000.0                    0                        0   \n",
            "3              1        4941.0                    0                        0   \n",
            "4              1        6000.0                    0                        0   \n",
            "..           ...           ...                  ...                      ...   \n",
            "609            1        2900.0                    1                        0   \n",
            "610            1        4106.0                    1                        0   \n",
            "611            1        8312.0                    0                        0   \n",
            "612            1        7583.0                    0                        0   \n",
            "613            0        4583.0                    0                        1   \n",
            "\n",
            "     property_area_Urban  education_Graduate  education_Not Graduate  \n",
            "0                      1                   1                       0  \n",
            "1                      0                   1                       0  \n",
            "2                      1                   1                       0  \n",
            "3                      1                   0                       1  \n",
            "4                      1                   1                       0  \n",
            "..                   ...                 ...                     ...  \n",
            "609                    0                   1                       0  \n",
            "610                    0                   1                       0  \n",
            "611                    1                   1                       0  \n",
            "612                    1                   1                       0  \n",
            "613                    0                   1                       0  \n",
            "\n",
            "[614 rows x 17 columns]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Correlation Map\n",
        "#Measure the  strength of the relationships amongst the variables\n",
        "#applicant income and loan amount have a fair postive correlation -0.62\n",
        "#loan status and credit history also have a fair postive correlation - 0.54\n",
        "#total income and education(graduate) have a fair positive corelation\n",
        "corr = df.corr()\n",
        "sns.heatmap(corr, cmap='coolwarm')\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 674
        },
        "id": "uoEemVB3Opmm",
        "outputId": "ed38d80f-164e-493f-8776-e24685dc61c6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-20-46aa128516fa>:6: FutureWarning: The default value of numeric_only in DataFrame.corr is deprecated. In a future version, it will default to False. Select only valid columns or specify the value of numeric_only to silence this warning.\n",
            "  corr = df.corr()\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 20
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#ML Models\n",
        "#Logistic Regression\n",
        "#predict using the model\n",
        "#df_log_reg.predict()\n",
        "#Split data into train and test\n",
        "\n",
        "X = df.drop(['loan_id', 'gender','married','self_employed','loan_status', 'applicantincome','coapplicantincome'], axis=1)\n",
        "y= df['loan_status']\n",
        "\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y,test_size=.3, random_state=42)\n",
        "\n",
        "\n",
        "# Imbalanced Data\n",
        "#the defaults are 422 far more than the non-defaults\n",
        "#undersample the defaults to be equal to the defaults\n",
        "#Undersampling technique\n",
        "#undersampling the train dataset\n",
        "\n",
        "# X_y_train = pd.concat([X_train.reset_index(drop = True),y_train.reset_index(drop = True)], axis = 1)\n",
        "\n",
        "# #Create data sets for defaults and non-defaults\n",
        "# nondefaults = X_y_train[X_y_train['loan_status'] == 0]\n",
        "# defaults = X_y_train[X_y_train['loan_status'] == 1]\n",
        "\n",
        "# # Undersample the defaults\n",
        "# defaults_under = defaults.sample(len(nondefaults))\n",
        "\n",
        "\n",
        "# # Concatenate the undersampled nondefaults with defaults\n",
        "# X_y_train_under = pd.concat([defaults_under.reset_index(drop = True),nondefaults.reset_index (drop = True)], axis = 0)\n",
        "# X_train_balanced = X_y_train_under.drop(['loan_status'], axis=1)\n",
        "# y_train_balanced= X_y_train_under['loan_status']\n",
        "\n",
        "#oversampling\n",
        "#oversample = RandomOverSampler(sampling_strategy='minority', random_state=42)\n",
        "smote = SMOTE(random_state=42)\n",
        "X_train_resampled, y_train_resampled = smote.fit_resample(X, y)\n",
        "\n",
        "\n",
        "# # fit and apply the oversampling strategy to the non-default loans in the training data\n",
        "#X_train_resampled, y_train_resampled = oversample.fit_resample(X_train, y_train)\n",
        "\n",
        "\n",
        "#scale and pipe the standard scaler and logistic model\n",
        "pipe = make_pipeline(StandardScaler(), LogisticRegression())\n",
        "# apply scaling on training data\n",
        "pipe.fit(X_train_resampled, y_train_resampled)\n",
        "Pipeline(steps=[('standardscaler', StandardScaler()),\n",
        "                ('logisticregression', LogisticRegression())])\n",
        "\n",
        "#making predictions\n",
        "y_pred = pipe.predict(X_test)\n",
        "\n",
        "\n",
        "\n",
        "#Evaluating the Logistic Regression model\n",
        "# apply scaling on testing data, without leaking training data.\n",
        "\n",
        "#check coefficients of the model\n",
        "coefficients = pipe.named_steps['logisticregression'].coef_\n",
        "#coefficients\n",
        "\n",
        "# Print the classification report\n",
        "# The recall for defaults is 0.84 meaning 84% of true defaults were predicted correctly.\n",
        "\n",
        "target_names = ['Non-Default', 'Default']\n",
        "print(classification_report(y_test,y_pred, target_names=target_names))\n",
        "\n",
        "#Create an ROC Chart of the model's performance\n",
        "# Create predictions and store them in a variable\n",
        "preds = pipe.predict_proba(X_test)\n",
        "\n",
        "# Plot the ROC curve of the probabilities of default\n",
        "prob_default = preds[:, 1]\n",
        "\n",
        "fallout, sensitivity, thresholds = roc_curve(y_test,prob_default)\n",
        "plt.plot(fallout, sensitivity, color = 'darkorange')\n",
        "plt.plot([0, 1], [0, 1], linestyle='--')\n",
        "plt.xlabel('False Positive Rate')\n",
        "plt.ylabel('True Positive Rate')\n",
        "plt.title('ROC Curve')\n",
        "plt.show()\n",
        "\n",
        "# Compute the AUC and store it in a variable\n",
        "#the auc score is 0.94\n",
        "#ROC chart shows the tradeoff between all values of our false positive rate (fallout) and true positive rate (sensitivity).\n",
        "#the roc curve shows a fair lift\n",
        "auc_log_reg = roc_auc_score(y_test ,prob_default)\n",
        "print(auc_log_reg)\n",
        "\n",
        "# Set the threshold for defaults to 0.5\n",
        "#create a dataframe\n",
        "preds_df = pd.DataFrame(prob_default, columns =['prob_default'])\n",
        "preds_df['loan_status'] = preds_df['prob_default'].apply(lambda x: 1 if x > 0.5 else 0)\n",
        "\n",
        "# Print the confusion matrix\n",
        "print(confusion_matrix(y_test,preds_df['loan_status']))\n",
        "\n",
        "\n",
        "# Store the number of loan defaults and non defaults from the prediction data\n",
        "num_defaults = preds_df['loan_status'].value_counts()[1]\n",
        "num_non_defaults = preds_df['loan_status'].value_counts()[0]\n",
        "# Store the default recall from the classification report\n",
        "#the default recall is\n",
        "default_recall = precision_recall_fscore_support(y_test,preds_df['loan_status'])[1][1]\n",
        "default_recall\n",
        "\n",
        "log_ = pipe.named_steps['logisticregression']\n",
        "#  Calculate the cross validation scores for 4 folds\n",
        "cv_scores = cross_val_score(log_,X_train_resampled, y_train_resampled,cv = 4)\n",
        "cv_scores\n",
        "#the scores array([0.7218543 , 0.73509934, 0.70198675, 0.66225166]) show that the model is consistent\n",
        "# Calculate the estimated impact of the  default recall rate\n",
        "#Estimated loss of\n",
        "#print( np.mean(df['loanamount']) * num_defaults * (1 - default_recall))\n",
        "\n",
        "#assuming a loan of $100 is given to 100,000 people\n",
        "#the expected loss would be 100,000 * 100 * (1-) = $\n",
        "#however compared to the entire loan amount of $10,000,000, it is loss of  11%\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "oTqVyJSmMEBB",
        "outputId": "31dcd3a0-6ba6-4208-ddbb-4939306a4886"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1    422\n",
            "0    422\n",
            "Name: loan_status, dtype: int64\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            " Non-Default       0.93      0.42      0.57        65\n",
            "     Default       0.76      0.98      0.86       120\n",
            "\n",
            "    accuracy                           0.78       185\n",
            "   macro avg       0.84      0.70      0.71       185\n",
            "weighted avg       0.82      0.78      0.76       185\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.753974358974359\n",
            "[[ 27  38]\n",
            " [  2 118]]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "Please also refer to the documentation for alternative solver options:\n",
            "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
            "  n_iter_i = _check_optimize_result(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "Please also refer to the documentation for alternative solver options:\n",
            "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
            "  n_iter_i = _check_optimize_result(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "Please also refer to the documentation for alternative solver options:\n",
            "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
            "  n_iter_i = _check_optimize_result(\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0.66350711, 0.77251185, 0.84834123, 0.78199052])"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# #XGBOOST\n",
        "pipe_xgb = make_pipeline(StandardScaler(), XGBClassifier())\n",
        "Pipeline(steps=[('standardscaler', StandardScaler()),\n",
        "                 ('xgbclassifier', XGBClassifier())])\n",
        "\n",
        "# #Hyper parameter tuning to find the best hyper parameters\n",
        "params = {\n",
        "    'xgbclassifier__learning_rate': uniform(0.01, 0.3), # range of learning rates\n",
        "    'xgbclassifier__max_depth': randint(1, 10), # range of max depths\n",
        "    'xgbclassifier__min_child_weight': randint(1, 10), # range of min child weights\n",
        "    'xgbclassifier__subsample': uniform(0.5, 0.5), # range of subsample values\n",
        "    'xgbclassifier__colsample_bytree': uniform(0.5, 0.5), # range of colsample_bytree values\n",
        "    'xgbclassifier__gamma': uniform(0, 1) # range of gamma values\n",
        "}\n",
        "# # Use RandomizedSearchCV to perform random search\n",
        "rs = RandomizedSearchCV(pipe_xgb, param_distributions=params, n_iter=100, cv=5, random_state=42)\n",
        "\n",
        "# # Fit the model on the training data\n",
        "rs.fit(X_train_resampled,y_train_resampled)\n",
        "\n",
        "# Print the best hyperparameters\n",
        "print('Best hyperparameters:', rs.best_params_)\n",
        "#Best hyperparameters: {'xgbclassifier__colsample_bytree': 0.8629778394351197, 'xgbclassifier__gamma': 0.8971102599525771,\n",
        "#'xgbclassifier__learning_rate': 0.27612592727953517, 'xgbclassifier__max_depth': 8, 'xgbclassifier__min_child_weight': 1,\n",
        "# 'xgbclassifier__subsample': 0.8210158230771438}\n",
        "\n",
        "\n",
        "# get the best hyperparameters\n",
        "best_params = rs.best_params_\n",
        "print(best_params)\n",
        "# create a new XGBClassifier instance with the best hyperparameters\n",
        "xgb_clf = XGBClassifier(**best_params)\n",
        "\n",
        "# create a new pipeline with StandardScaler and the updated XGBClassifier instance\n",
        "pipe_xgb_new = make_pipeline(StandardScaler(), xgb_clf)\n",
        "\n",
        "# fit the pipeline with the updated XGBClassifier to the training data\n",
        "pipe_xgb_new.fit(X_train_resampled, y_train_resampled)\n",
        "\n",
        "# try:\n",
        "#     # Use RandomizedSearchCV to perform random search\n",
        "#     rs = RandomizedSearchCV(pipe_xgb, param_distributions=params, n_iter=100, cv=5, random_state=42)\n",
        "\n",
        "#     # Fit the model on the training data\n",
        "#     rs.fit(X_train_resampled,y_train_resampled)\n",
        "# except Exception as e:\n",
        "#     print(\"Error occurred:\", e)\n",
        "\n",
        "\n",
        "\n",
        "# #making predictions\n",
        "y_xgb_predict = pipe_xgb_new.predict(X_test)\n",
        "\n",
        "\n",
        "# #classification report\n",
        "target_names = ['Non-Default', 'Default']\n",
        "print(classification_report(y_test,y_xgb_predict, target_names=target_names))\n",
        "\n",
        "\n",
        " #Evaluate the performance of the model\n",
        "\n",
        "# #checking probabilities of default\n",
        "y_xgb_pred_proba = pipe_xgb_new.predict_proba(X_test)\n",
        "y_xgb_prob_default = y_xgb_pred_proba[:,1]\n",
        "\n",
        "#auc score  higher than the logistic regressio. it has a higer lift\n",
        "auc_xgb = roc_auc_score(y_test,y_xgb_prob_default)\n",
        "auc_xgb\n",
        "\n",
        "\n",
        "# #Cross validating models\n",
        "xgb_ = pipe_xgb_new.named_steps['xgbclassifier']\n",
        "#  Calculate the cross validation scores for 4 folds\n",
        "cv_scores = cross_val_score(xgb_,X_train_resampled, y_train_resampled,cv = 4)\n",
        "\n",
        "# # Print the cross validation scores\n",
        "# #the scores are 0.84768212 0.87417219 0.86754967 0.90728477 steadily improving\n",
        "# #this shows that the model is consistent\n",
        "print(cv_scores)\n",
        "\n",
        "\n",
        "# Print the confusion matrix\n",
        "print(confusion_matrix(y_test,y_xgb_predict))\n",
        "\n",
        "\n",
        "# # Print the average accuracy and standard deviation of the scores\n",
        "print(\"Average accuracy: %0.2f (+/- %0.2f)\" % (cv_scores.mean(),\n",
        "                                               cv_scores.std() * 2))\n",
        "\n",
        "#ROC Curve\n",
        "fallout, sensitivity, thresholds = roc_curve(y_test,y_xgb_prob_default)\n",
        "plt.plot(fallout, sensitivity, color = 'darkorange')\n",
        "plt.plot([0, 1], [0, 1], linestyle='--')\n",
        "plt.xlabel('False Positive Rate')\n",
        "plt.ylabel('True Positive Rate')\n",
        "plt.title('ROC Curve')\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "v0vltQpjSPSz",
        "outputId": "e994e805-b043-4eb2-a956-554fa193f4d0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Best hyperparameters: {'xgbclassifier__colsample_bytree': 0.8057568554328403, 'xgbclassifier__gamma': 0.08159418040024036, 'xgbclassifier__learning_rate': 0.011555458832196034, 'xgbclassifier__max_depth': 4, 'xgbclassifier__min_child_weight': 4, 'xgbclassifier__subsample': 0.8249819653888826}\n",
            "{'xgbclassifier__colsample_bytree': 0.8057568554328403, 'xgbclassifier__gamma': 0.08159418040024036, 'xgbclassifier__learning_rate': 0.011555458832196034, 'xgbclassifier__max_depth': 4, 'xgbclassifier__min_child_weight': 4, 'xgbclassifier__subsample': 0.8249819653888826}\n",
            "[17:21:17] WARNING: ../src/learner.cc:767: \n",
            "Parameters: { \"xgbclassifier__colsample_bytree\", \"xgbclassifier__gamma\", \"xgbclassifier__learning_rate\", \"xgbclassifier__max_depth\", \"xgbclassifier__min_child_weight\", \"xgbclassifier__subsample\" } are not used.\n",
            "\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            " Non-Default       1.00      1.00      1.00        65\n",
            "     Default       1.00      1.00      1.00       120\n",
            "\n",
            "    accuracy                           1.00       185\n",
            "   macro avg       1.00      1.00      1.00       185\n",
            "weighted avg       1.00      1.00      1.00       185\n",
            "\n",
            "[17:21:17] WARNING: ../src/learner.cc:767: \n",
            "Parameters: { \"xgbclassifier__colsample_bytree\", \"xgbclassifier__gamma\", \"xgbclassifier__learning_rate\", \"xgbclassifier__max_depth\", \"xgbclassifier__min_child_weight\", \"xgbclassifier__subsample\" } are not used.\n",
            "\n",
            "[17:21:17] WARNING: ../src/learner.cc:767: \n",
            "Parameters: { \"xgbclassifier__colsample_bytree\", \"xgbclassifier__gamma\", \"xgbclassifier__learning_rate\", \"xgbclassifier__max_depth\", \"xgbclassifier__min_child_weight\", \"xgbclassifier__subsample\" } are not used.\n",
            "\n",
            "[17:21:17] WARNING: ../src/learner.cc:767: \n",
            "Parameters: { \"xgbclassifier__colsample_bytree\", \"xgbclassifier__gamma\", \"xgbclassifier__learning_rate\", \"xgbclassifier__max_depth\", \"xgbclassifier__min_child_weight\", \"xgbclassifier__subsample\" } are not used.\n",
            "\n",
            "[17:21:17] WARNING: ../src/learner.cc:767: \n",
            "Parameters: { \"xgbclassifier__colsample_bytree\", \"xgbclassifier__gamma\", \"xgbclassifier__learning_rate\", \"xgbclassifier__max_depth\", \"xgbclassifier__min_child_weight\", \"xgbclassifier__subsample\" } are not used.\n",
            "\n",
            "[0.69194313 0.74407583 0.90521327 0.87677725]\n",
            "[[ 65   0]\n",
            " [  0 120]]\n",
            "Average accuracy: 0.80 (+/- 0.18)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Comparing Models\n",
        "xgb_preds_df = pd.DataFrame(y_xgb_predict, columns =['loan_status'])\n",
        "# Store the number of loan defaults  from the prediction data\n",
        "num_defaults_xgb = xgb_preds_df['loan_status'].value_counts()[1]\n",
        "# Store the default recall from the classification report\n",
        "#the default recall is\n",
        "default_recall_xgb = precision_recall_fscore_support(y_test,xgb_preds_df['loan_status'])[1][1]\n",
        "default_recall\n",
        "\n",
        "\n",
        "#the scores from the classification_report() are all higher for the gradient boosted tree.\n",
        "#This means the tree model is better in all of these aspects. Let's check the ROC curve.\n",
        "# Print the logistic regression classification report\n",
        "target_names = ['Non-Default', 'Default']\n",
        "print(classification_report(y_test, preds_df['loan_status'], target_names=target_names))\n",
        "\n",
        "# Print the gradient boosted tree classification report\n",
        "print(classification_report(y_test, xgb_preds_df['loan_status'], target_names=target_names))\n",
        "\n",
        "# Print the default F-1 scores for the logistic regression\n",
        "print('The f1 score for the Logisitic Regression model is {:.2f}'.format(precision_recall_fscore_support(y_test,preds_df['loan_status'], average = 'macro')[2]))\n",
        "\n",
        "# Print the default F-1 scores for the gradient boosted tree\n",
        "print('The f1 score for the XGBoost model is {:.2f}'.format(precision_recall_fscore_support(y_test,xgb_preds_df['loan_status'], average = 'macro')[2]))\n",
        "\n",
        "\n",
        "# ROC chart components\n",
        "fallout_lr, sensitivity_lr, thresholds_lr = roc_curve(y_test,prob_default)\n",
        "fallout_gbt, sensitivity_gbt, thresholds_gbt = roc_curve(y_test,y_xgb_prob_default)\n",
        "\n",
        "# ROC Chart with both\n",
        "plt.plot(fallout_lr, sensitivity_lr, color = 'blue', label='%s' % 'Logistic Regression')\n",
        "plt.plot(fallout_gbt,sensitivity_gbt, color = 'green', label='%s' % 'GBT')\n",
        "plt.plot([0, 1], [0, 1], linestyle='--', label='%s' % 'Random Prediction')\n",
        "plt.title(\"ROC Chart for LR and GBT on the Probability of Default\")\n",
        "plt.xlabel('Fall-out')\n",
        "plt.ylabel('Sensitivity')\n",
        "plt.legend()\n",
        "plt.show()\n",
        "\n",
        "\n",
        "#gradient boosted tree  has the best overall performance.\n",
        "#checking the calibration of the two models to see how stable the default prediction performance is across probabilities.\n",
        "\n",
        "\n",
        "\n",
        "# Calculate the estimated impact of the  default recall rate\n",
        "#Estimated loss of\n",
        "print( np.mean(df['loanamount']) * num_defaults_xgb * (1 - default_recall_xgb))\n",
        "\n",
        "#assuming a loan of $100 is given to 100,000 people\n",
        "#the expected loss would be 100,000 * 100 * (1-) = $\n",
        "#however compared to the entire loan amount of $10,000,000, it is loss of\n",
        "#The Gradient boost has an improvement from the logistic regression\n",
        "\n",
        "#This shows how a slight improvement in a model can reduce losses for a business"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 836
        },
        "id": "moqfgi-MfpSI",
        "outputId": "bd5770eb-f729-45c7-a77c-ac7cef520331"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            " Non-Default       0.93      0.42      0.57        65\n",
            "     Default       0.76      0.98      0.86       120\n",
            "\n",
            "    accuracy                           0.78       185\n",
            "   macro avg       0.84      0.70      0.71       185\n",
            "weighted avg       0.82      0.78      0.76       185\n",
            "\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            " Non-Default       1.00      1.00      1.00        65\n",
            "     Default       1.00      1.00      1.00       120\n",
            "\n",
            "    accuracy                           1.00       185\n",
            "   macro avg       1.00      1.00      1.00       185\n",
            "weighted avg       1.00      1.00      1.00       185\n",
            "\n",
            "The f1 score for the Logisitic Regression model is 0.71\n",
            "The f1 score for the XGBoost model is 1.00\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Generate weight of columns\n",
        "# Get the XGBClassifier object from the pipeline\n",
        "xgb_clf = pipe_xgb_new.named_steps['xgbclassifier']\n",
        "\n",
        "# Get the feature importances\n",
        "importances = xgb_clf.feature_importances_\n",
        "#print(importances)\n",
        "# Get the feature names from the StandardScaler object in the pipeline\n",
        "feature_names = X_train_resampled.columns\n",
        "\n",
        "# Create a dictionary of feature importance scores mapped to feature names\n",
        "feature_importances = dict(zip(feature_names, importances))\n",
        "\n",
        "# Sort the feature importances by value (in descending order)\n",
        "sorted_feature_importances = dict(sorted(feature_importances.items(), key=lambda x: x[1], reverse=True))\n",
        "\n",
        "# Get the top 5 most important features\n",
        "top_features = list(sorted_feature_importances.keys())[:5]\n",
        "print('The top 5 most important features are:')\n",
        "for i in top_features:\n",
        " print(i)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1nG4B4P91K07",
        "outputId": "41b2ffda-3baa-436d-eb39-13ea56e9cdfa"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The top 5 most important features are:\n",
            "credit_history\n",
            "property_area_Rural\n",
            "education_Not Graduate\n",
            "property_area_Semiurban\n",
            "property_area_Urban\n"
          ]
        }
      ]
    }
  ]
}