Predicting_Credit_Card_Defaults_Using_Machine_Learning.ipynb

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   "source": [
    "# Predicting Credit Card Defaults: Using Machine Learning\n",
    "\n",
    "Join me as we delve into the world of classification algorithms like Support Vector Machines (SVM), Random Forest (RF), Decision Trees (DT), k-Nearest Neighbors (KNN), and Artificial Neural Networks (ANN) in Python with scikit-learn. By following this step-by-step tutorial, you'll gain the expertise to harness the predictive capabilities of these algorithms and forecast default payments for credit card clients with confidence."
   ]
  },
  {
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   "id": "17619495-476c-4a1f-b2b3-9e7ecd82ac0a",
   "metadata": {},
   "source": [
    "\n",
    "### Step 1: Import libraries and load and view the data set"
   ]
  },
  {
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   "id": "3b97f767-ed53-4696-b957-18805b0b5cd4",
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   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "from sklearn.utils import resample\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.decomposition import PCA\n",
    "import matplotlib.colors as colors\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, confusion_matrix, ConfusionMatrixDisplay\n",
    "\n",
    "df = pd.read_csv('credit_default_dataset.csv', header=1)"
   ]
  },
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   "cell_type": "markdown",
   "id": "fd6d6da9-c7f7-4f07-bc06-9ec538afbd3d",
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   "source": [
    "\n",
    "### Step 2: Explore the data set\n",
    "\n",
    "In the output provided, each row represents an individual entry. The columns represent specific features like the identification number, credit limit, sex, education, marriage status, age, payment status across several months, bill statement amounts, payment amounts, and a target variable indicating default in the following month. The numerical data in each column provides information about the respective feature or attribute. The \"default payment next month\" column represents the class label or target variable for classification tasks\n"
   ]
  },
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       "      <td>1000</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>2019</td>\n",
       "      <td>1200</td>\n",
       "      <td>1100</td>\n",
       "      <td>1069</td>\n",
       "      <td>1000</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>567</td>\n",
       "      <td>380</td>\n",
       "      <td>601</td>\n",
       "      <td>0</td>\n",
       "      <td>581</td>\n",
       "      <td>1687</td>\n",
       "      <td>1542</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
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       "      <td>0</td>\n",
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       "<p>10 rows × 25 columns</p>\n",
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       "   ID  LIMIT_BAL  SEX  EDUCATION  MARRIAGE  AGE  PAY_0  PAY_2  PAY_3  PAY_4  \\\n",
       "0   1      20000    2          2         1   24      2      2     -1     -1   \n",
       "1   2     120000    2          2         2   26     -1      2      0      0   \n",
       "2   3      90000    2          2         2   34      0      0      0      0   \n",
       "3   4      50000    2          2         1   37      0      0      0      0   \n",
       "4   5      50000    1          2         1   57     -1      0     -1      0   \n",
       "5   6      50000    1          1         2   37      0      0      0      0   \n",
       "6   7     500000    1          1         2   29      0      0      0      0   \n",
       "7   8     100000    2          2         2   23      0     -1     -1      0   \n",
       "8   9     140000    2          3         1   28      0      0      2      0   \n",
       "9  10      20000    1          3         2   35     -2     -2     -2     -2   \n",
       "\n",
       "   ...  BILL_AMT4  BILL_AMT5  BILL_AMT6  PAY_AMT1  PAY_AMT2  PAY_AMT3  \\\n",
       "0  ...          0          0          0         0       689         0   \n",
       "1  ...       3272       3455       3261         0      1000      1000   \n",
       "2  ...      14331      14948      15549      1518      1500      1000   \n",
       "3  ...      28314      28959      29547      2000      2019      1200   \n",
       "4  ...      20940      19146      19131      2000     36681     10000   \n",
       "5  ...      19394      19619      20024      2500      1815       657   \n",
       "6  ...     542653     483003     473944     55000     40000     38000   \n",
       "7  ...        221       -159        567       380       601         0   \n",
       "8  ...      12211      11793       3719      3329         0       432   \n",
       "9  ...          0      13007      13912         0         0         0   \n",
       "\n",
       "   PAY_AMT4  PAY_AMT5  PAY_AMT6  default payment next month  \n",
       "0         0         0         0                           1  \n",
       "1      1000         0      2000                           1  \n",
       "2      1000      1000      5000                           0  \n",
       "3      1100      1069      1000                           0  \n",
       "4      9000       689       679                           0  \n",
       "5      1000      1000       800                           0  \n",
       "6     20239     13750     13770                           0  \n",
       "7       581      1687      1542                           0  \n",
       "8      1000      1000      1000                           0  \n",
       "9     13007      1122         0                           0  \n",
       "\n",
       "[10 rows x 25 columns]"
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     },
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   ],
   "source": [
    "#Explore the first ten rows of the data set\n",
    "df.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
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   "source": [
    "### Step 3: Rename and Remove unnecessary Colums\n",
    "\n",
    "To clean up the data set, rename and simplify the \"default payment next month\" column to \"DEFAULT\", making it convenient for further analysis or modeling tasks. The DataFrame also no longer contains the ID column because it is considered non-informative for analysis or modeling tasks."
   ]
  },
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       "      <td>1500</td>\n",
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       "      <td>57</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>20940</td>\n",
       "      <td>19146</td>\n",
       "      <td>19131</td>\n",
       "      <td>2000</td>\n",
       "      <td>36681</td>\n",
       "      <td>10000</td>\n",
       "      <td>9000</td>\n",
       "      <td>689</td>\n",
       "      <td>679</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   LIMIT_BAL  SEX  EDUCATION  MARRIAGE  AGE  PAY_0  PAY_2  PAY_3  PAY_4  \\\n",
       "0      20000    2          2         1   24      2      2     -1     -1   \n",
       "1     120000    2          2         2   26     -1      2      0      0   \n",
       "2      90000    2          2         2   34      0      0      0      0   \n",
       "3      50000    2          2         1   37      0      0      0      0   \n",
       "4      50000    1          2         1   57     -1      0     -1      0   \n",
       "\n",
       "   PAY_5  ...  BILL_AMT4  BILL_AMT5  BILL_AMT6  PAY_AMT1  PAY_AMT2  PAY_AMT3  \\\n",
       "0     -2  ...          0          0          0         0       689         0   \n",
       "1      0  ...       3272       3455       3261         0      1000      1000   \n",
       "2      0  ...      14331      14948      15549      1518      1500      1000   \n",
       "3      0  ...      28314      28959      29547      2000      2019      1200   \n",
       "4      0  ...      20940      19146      19131      2000     36681     10000   \n",
       "\n",
       "   PAY_AMT4  PAY_AMT5  PAY_AMT6  DEFAULT  \n",
       "0         0         0         0        1  \n",
       "1      1000         0      2000        1  \n",
       "2      1000      1000      5000        0  \n",
       "3      1100      1069      1000        0  \n",
       "4      9000       689       679        0  \n",
       "\n",
       "[5 rows x 24 columns]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Rename the columns\n",
    "df.rename({'default payment next month': 'DEFAULT'}, axis='columns', inplace=True)\n",
    "\n",
    "#Remove the ID column as it is not informative\n",
    "df.drop('ID', axis=1, inplace=True)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c291768b-164d-421b-acdb-2dd89530b7b2",
   "metadata": {},
   "source": [
    "### Step 4: Analyze missing data\n",
    "\n",
    "In this step, you proceed with data preprocessing to refine the data for subsequent analysis and modeling tasks. You ensure that each column only contains acceptable values by checking for null or invalid inputs based on the data definitions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "a9190797-e5ab-4dc6-93f1-bbd39cfd7b9c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Missing or null values in SEX column: 0\n",
      "Missing or null values in MARRIAGE column: 0\n",
      "Missing or null values in EDUCATION column: 0\n",
      "Missing or null values in AGE column: 0\n",
      "Count of missing data where EDUCATION or MARRIAGE column has a value of 0: 68\n"
     ]
    }
   ],
   "source": [
    "# check dimensions for invalid values\n",
    "df['SEX'].unique()\n",
    "df['MARRIAGE'].unique()\n",
    "df['EDUCATION'].unique()\n",
    "df['AGE'].unique()\n",
    "\n",
    "# Count missing or null values\n",
    "print(\"Missing or null values in SEX column:\", len(df[pd.isnull(df.SEX)]))\n",
    "print(\"Missing or null values in MARRIAGE column:\", len(df[pd.isnull(df.MARRIAGE)]))\n",
    "print(\"Missing or null values in EDUCATION column:\", len(df[pd.isnull(df.EDUCATION)]))\n",
    "print(\"Missing or null values in AGE column:\", len(df[pd.isnull(df.AGE)]))\n",
    "\n",
    "\n",
    "# Count of missing data where EDUCATION or MARRIAGE column has a value of 0\n",
    "missing_data_count = len(df.loc[(df['EDUCATION'] == 0) | (df['MARRIAGE'] == 0)])\n",
    "print(\"Count of missing data where EDUCATION or MARRIAGE column has a value of 0:\", missing_data_count)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "35ceb6fd-bca1-4dc2-b957-f9e6a16ee55f",
   "metadata": {},
   "source": [
    "### Step 5: Explore distribution of data set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "2e08897b-7632-4493-b293-b001f969bf40",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Filter the DataFrame\n",
    "df_no_missing_data = df.loc[(df['EDUCATION'] != 0) & (df['MARRIAGE'] != 0)]\n",
    "\n",
    "# count plot on ouput variable\n",
    "ax = sns.countplot(x=df_no_missing_data['DEFAULT'], hue=df_no_missing_data['DEFAULT'], palette='rocket', legend=False)\n",
    "\n",
    "#add data labels\n",
    "ax.bar_label(ax.containers[0])\n",
    "\n",
    "# add plot title\n",
    "plt.title(\"Observations by Classification Type\")\n",
    "\n",
    "# show plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8c1cb8c5-fb26-4b63-bb79-3a5d9f2b0711",
   "metadata": {},
   "source": [
    "### Step 6: Downsample the data set\n",
    "\n",
    "In this step, you balance the data set by splitting the data set into two categories: individuals regularly paying their credit card debt (default) and individuals whose debt remains unpaid (no default). You then downsample the data set by obtaining 1,000 samples for each category, representing the default payment as Yes = 1 and No = 0. From there, you merge the two data sets to create your balanced data set.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "902eb4e7-2bf7-490b-8903-9c0231e5d21e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Length of downsampled 'no default' data: 1000\n",
      "Length of downsampled 'default' data: 1000\n",
      "Length of merged downsampled data: 2000\n"
     ]
    }
   ],
   "source": [
    "from sklearn.utils import resample\n",
    "\n",
    "# Split data into two groups based on the 'DEFAULT' column\n",
    "df_no_default = df_no_missing_data.loc[(df_no_missing_data['DEFAULT'] == 0)]\n",
    "df_default = df_no_missing_data.loc[(df_no_missing_data['DEFAULT'] == 1)]\n",
    "\n",
    "# Downsample the data sets to have equal number of samples (1000 each)\n",
    "df_no_default_downsampled = resample(df_no_default, replace=False, n_samples=1000, random_state=42)\n",
    "df_default_downsampled = resample(df_default, replace=False, n_samples=1000, random_state=42)\n",
    "\n",
    "# Check the lengths of downsampled data sets\n",
    "print(\"Length of downsampled 'no default' data:\", len(df_no_default_downsampled))\n",
    "print(\"Length of downsampled 'default' data:\", len(df_default_downsampled))\n",
    "\n",
    "# Merge the downsampled data sets\n",
    "df_downsample = pd.concat([df_no_default_downsampled, df_default_downsampled])\n",
    "print(\"Length of merged downsampled data:\", len(df_downsample))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0d7c4ed3-5c4c-4cdc-8e62-98ce2a75e6b5",
   "metadata": {},
   "source": [
    "### Step 7: Hot-encode independent variables\n",
    "\n",
    "Scikit-learn does not natively support categorical data, and as a result, you must transform this data using hot-encoding, which codes each category in a given column as a binary variable using 0 or 1."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "093e1670-f5c8-4aa5-99ff-fc99f648eb16",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>LIMIT_BAL</th>\n",
       "      <th>SEX</th>\n",
       "      <th>EDUCATION</th>\n",
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       "      <th>AGE</th>\n",
       "      <th>PAY_0</th>\n",
       "      <th>PAY_2</th>\n",
       "      <th>PAY_3</th>\n",
       "      <th>PAY_4</th>\n",
       "      <th>PAY_5</th>\n",
       "      <th>...</th>\n",
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       "      <th>PAY_6_2</th>\n",
       "      <th>PAY_6_3</th>\n",
       "      <th>PAY_6_4</th>\n",
       "      <th>PAY_6_5</th>\n",
       "      <th>PAY_6_6</th>\n",
       "      <th>PAY_6_7</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "      <th>22974</th>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6154</th>\n",
       "      <td>30000</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>34</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14072</th>\n",
       "      <td>320000</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>43</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5387</th>\n",
       "      <td>50000</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>23</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23192</th>\n",
       "      <td>150000</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>43</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8180</th>\n",
       "      <td>140000</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>28</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2000 rows × 90 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       LIMIT_BAL  SEX  EDUCATION  MARRIAGE  AGE  PAY_0  PAY_2  PAY_3  PAY_4  \\\n",
       "641       130000    2          3         1   28      0      0      0      0   \n",
       "4678      170000    1          3         1   29      0      0      0      0   \n",
       "16004     180000    2          2         1   29      0      0      0      0   \n",
       "22974     210000    2          2         2   32     -2     -2     -2     -2   \n",
       "17535     190000    2          3         1   45      0      0      0      0   \n",
       "...          ...  ...        ...       ...  ...    ...    ...    ...    ...   \n",
       "6154       30000    2          1         2   34      1      2      2      2   \n",
       "14072     320000    1          1         1   43     -1      0      0      0   \n",
       "5387       50000    1          2         2   23     -1     -1      0      0   \n",
       "23192     150000    2          3         1   43      0      0      0      0   \n",
       "8180      140000    1          2         1   28      0      0      0      0   \n",
       "\n",
       "       PAY_5  ...  PAY_5_7  PAY_6_-2  PAY_6_-1  PAY_6_0  PAY_6_2  PAY_6_3  \\\n",
       "641       -2  ...        0         1         0        0        0        0   \n",
       "4678       0  ...        0         0         0        1        0        0   \n",
       "16004      0  ...        0         0         0        1        0        0   \n",
       "22974     -2  ...        0         1         0        0        0        0   \n",
       "17535      0  ...        0         0         0        1        0        0   \n",
       "...      ...  ...      ...       ...       ...      ...      ...      ...   \n",
       "6154       2  ...        0         0         0        0        1        0   \n",
       "14072      0  ...        0         0         0        1        0        0   \n",
       "5387      -1  ...        0         0         1        0        0        0   \n",
       "23192      0  ...        0         0         0        1        0        0   \n",
       "8180       0  ...        0         0         0        1        0        0   \n",
       "\n",
       "       PAY_6_4  PAY_6_5  PAY_6_6  PAY_6_7  \n",
       "641          0        0        0        0  \n",
       "4678         0        0        0        0  \n",
       "16004        0        0        0        0  \n",
       "22974        0        0        0        0  \n",
       "17535        0        0        0        0  \n",
       "...        ...      ...      ...      ...  \n",
       "6154         0        0        0        0  \n",
       "14072        0        0        0        0  \n",
       "5387         0        0        0        0  \n",
       "23192        0        0        0        0  \n",
       "8180         0        0        0        0  \n",
       "\n",
       "[2000 rows x 90 columns]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import OneHotEncoder\n",
    "# isolate independent variables\n",
    "X = df_downsample.drop('DEFAULT', axis=1).copy()\n",
    "\n",
    "ohe = OneHotEncoder(sparse_output=False, dtype=\"int\")\n",
    "ohe.fit(X[['SEX', 'EDUCATION', 'MARRIAGE', 'PAY_0', 'PAY_2', 'PAY_3', 'PAY_4', 'PAY_5', 'PAY_6']])\n",
    "X_ohe_train = ohe.transform(X[['SEX', 'EDUCATION', 'MARRIAGE', 'PAY_0', 'PAY_2', 'PAY_3', 'PAY_4', 'PAY_5', 'PAY_6']])\n",
    "\n",
    "X_ohe_train\n",
    "\n",
    "transformed_ohe = pd.DataFrame(\n",
    "    data=X_ohe_train,\n",
    "    columns=ohe.get_feature_names_out(['SEX', 'EDUCATION', 'MARRIAGE', 'PAY_0', 'PAY_2', 'PAY_3', 'PAY_4', 'PAY_5', 'PAY_6']),\n",
    "    index=X.index,\n",
    ")\n",
    "transformed_ohe.head()\n",
    "\n",
    "# merge dataframes\n",
    "X_encoded = pd.concat([X, transformed_ohe], axis=1)\n",
    "X_encoded"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ad36b09d-9418-4788-b011-0f547160a61c",
   "metadata": {},
   "source": [
    "### Step 8: Spliting the data set for different sizes\n",
    "\n",
    "You must split the data set into two distinct sets: the training set and the test set."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "23c7aced-ab16-468e-8568-2fe5e657f7f3",
   "metadata": {},
   "source": [
    "# Split for 70:30 ratio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "00637814-311f-4eba-9621-0cdf9bc6c603",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import scale\n",
    "y = df_downsample['DEFAULT'].copy()\n",
    "X_train, X_test, y_train, y_test = train_test_split(X_encoded, y, test_size=0.3, random_state=42)\n",
    "\n",
    "#scale the data\n",
    "X_train_scaled = scale(X_train)\n",
    "X_test_scaled = scale(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1aaec4df-91e4-4d89-b655-d12a6b2db34d",
   "metadata": {},
   "source": [
    "### Support Vector Machine (SVM)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "9e5f9851-0ad1-4549-8892-7d969fafc7c6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 66.50%\n",
      "Precision: 0.73\n",
      "Recall: 0.51\n",
      "F1-score: 0.60\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.svm import SVC\n",
    "\n",
    "# Instantiate and fit the Support Vector Machine classifier\n",
    "clf_svm = SVC(kernel='linear', random_state=42)\n",
    "clf_svm.fit(X_train_scaled, y_train)\n",
    "\n",
    "# Calculate overall accuracy\n",
    "y_pred_svm = clf_svm.predict(X_test_scaled)\n",
    "accuracy_svm = accuracy_score(y_test, y_pred_svm)\n",
    "print(f'Accuracy: {accuracy_svm:.2%}')\n",
    "\n",
    "# Calculate precision, recall, and F1-score\n",
    "precision_svm = precision_score(y_test, y_pred_svm)\n",
    "recall_svm = recall_score(y_test, y_pred_svm)\n",
    "f1_svm = f1_score(y_test, y_pred_svm)\n",
    "print(f'Precision: {precision_svm:.2f}')\n",
    "print(f'Recall: {recall_svm:.2f}')\n",
    "print(f'F1-score: {f1_svm:.2f}')\n",
    "\n",
    "# Generate confusion matrix\n",
    "cm_svm = confusion_matrix(y_test, y_pred_svm)\n",
    "\n",
    "# Display confusion matrix\n",
    "disp = ConfusionMatrixDisplay(confusion_matrix=cm, display_labels=class_names)\n",
    "disp.plot(cmap=plt.cm.Blues)\n",
    "plt.title('Confusion Matrix for Support Vector Machine Classifier')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "43c94559-5735-400e-a693-12636a8e4459",
   "metadata": {},
   "source": [
    "### Decision Tree Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "f497613d-c254-470f-bcc8-cd03f60fa847",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 59.83%\n",
      "Precision: 0.60\n",
      "Recall: 0.58\n",
      "F1-score: 0.59\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "\n",
    "# Instantiate and fit the Decision Tree classifier\n",
    "clf_dt = DecisionTreeClassifier(random_state=42)\n",
    "clf_dt.fit(X_train_scaled, y_train)\n",
    "\n",
    "# Calculate predictions\n",
    "y_pred_dt = clf_dt.predict(X_test_scaled)\n",
    "\n",
    "# Calculate performance metrics\n",
    "accuracy_dt = accuracy_score(y_test, y_pred_dt)\n",
    "precision_dt = precision_score(y_test, y_pred_dt)\n",
    "recall_dt = recall_score(y_test, y_pred_dt)\n",
    "f1_dt = f1_score(y_test, y_pred_dt)\n",
    "\n",
    "# Print performance metrics\n",
    "print(f'Accuracy: {accuracy_dt:.2%}')\n",
    "print(f'Precision: {precision_dt:.2f}')\n",
    "print(f'Recall: {recall_dt:.2f}')\n",
    "print(f'F1-score: {f1_dt:.2f}')\n",
    "\n",
    "# Define class names for display\n",
    "class_names = ['Did Not Default', 'Defaulted']\n",
    "\n",
    "# Generate confusion matrix\n",
    "cm = confusion_matrix(y_test, y_pred_dt)\n",
    "\n",
    "# Display confusion matrix\n",
    "disp = ConfusionMatrixDisplay(confusion_matrix=cm, display_labels=class_names)\n",
    "disp.plot(cmap=plt.cm.Blues)\n",
    "plt.title('Confusion Matrix for Decision Tree Classifier')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "66da9723-84ad-420b-ac0b-b0676591e439",
   "metadata": {},
   "source": [
    "### Random Forest classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "642c0232-3cc5-4a9e-9115-8dff6fbbaef4",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 67.67%\n",
      "Precision: 0.73\n",
      "Recall: 0.55\n",
      "F1-score: 0.63\n"
     ]
    },
    {
     "data": {
      "image/png": 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PfiIiIpCYmIjevXtrfcZGRkZo3Lgx9u3bl2ufZ89PMiwmXVQgGo0GAPDo0SOd2l+7dg0qlQpVq1bVWu/o6AgbGxtcu3ZNa32lSpVy9VG2bFk8fPiwgBHn9sEHH6Bp06YYPHgwHBwc0KtXL/z6668vTMBy4qxRo0aubR4eHrh37x5SU1O11j//XnKGsPR5Lx06dICVlRV++eUXrFmzBo0aNcr1WebIzs7GvHnzUK1aNajVapQvXx52dnY4f/48kpKSdD5mhQoV9Jo0HxoaCltbW0RGRmLhwoWwt7fXed/nXbt2DdWqVdO6Wgt4+hnnbH+Wu7u7Xv3nJBM7d+5EaGgobGxs8PDhw1zvNy4uTk58LC0tYWdnB29vbwDI9VmamZnlSvyeP2evXbsGJyenXENBz59PhjjPrK2tYWZmJg+1Pbte13OvfPnyaN26tdZSuXJlOUZnZ+dciYuuX6O7d+8iMTERy5YtkxPgnCUn2b9z5w4AYOLEibC0tMSbb76JatWqYcSIES+dBvAi+v78ysuDBw8wevRoODg4wNzcHHZ2dvJ7fPbc+Oqrr3Dx4kW4uLjgzTffRFBQkFbS6+7ujrFjx+L7779H+fLl4evri2+++Uav79UXuXTpEgCgZcuWuT7nXbt2yZ9xDmNjY1SsWNEgx6bcOKeLCkSj0cDZ2RkXL17Ua7+ceQ4vk9/VN0KIAh8jKytL67W5uTkOHjyIffv2Ydu2bdi5cyd++eUXtGzZErt27TLYFUCFeS851Go1unXrhpUrV+Lq1asICgrKt+2MGTMwZcoUDBw4ENOnT4etrS1UKhXGjBmjc0UP0P5LXhdnz56Vf4BfuHBBq0pV1PSNNSeZAABfX1/UrFkTnTp1woIFC+RqbFZWFtq0aYMHDx5g4sSJqFmzJiwsLHDz5k34+/vn+iwNecVYQeR1fEOce4by/Nco5/Pr169fnnPagKeVPeBpIhcdHY2tW7di586dWL9+Pb799lt8/vnnmDZtmt6x1KxZE8DT87SgevbsiaNHj2LChAmoW7cuLC0tkZ2djXbt2mmdGz179kSzZs2wceNG7Nq1C7Nnz8asWbOwYcMGtG/fHgAwZ84c+Pv7Y9OmTdi1axdGjRqFkJAQHD9+vNAJUE4sq1evhqOjY67txsbaacCzlXIyPCZdVGCdOnXCsmXLcOzYMTRp0uSFbV1dXZGdnY1Lly7JfwkDwO3bt5GYmAhXV1eDxVW2bNk8b5D4/F/eAKBSqdCqVSu0atUKc+fOxYwZMzB58mTs27dP/qX8/PsAnt4z53l///03ypcvX2SXV/fp0wfLly+HSqVCr1698m23bt06+Pj44IcfftBan5iYqFX10DUB1kVqaioGDBgAT09PvP322/jqq6/w3nvvoVGjRgXqz9XVFefPn0d2drbWL4C///5b3m5IHTt2hLe3N2bMmIGPPvoIFhYWuHDhAv755x+sXLlSHlIDng7XFJSrqyv27NmDlJQUrWrX8+dTcZ5nunJ1dcXu3bvx6NEjrWqXrl8jOzs7WFlZISsrK8/vtedZWFjggw8+wAcffIAnT56gW7du+PLLLzFp0iSYmZnpdT5Xr14dNWrUwKZNm7BgwQK9L8J5+PAh9uzZg2nTpuHzzz+X1+dUlZ7n5OSE4cOHY/jw4bhz5w7q16+PL7/8Uk66AKB27dqoXbs2PvvsMxw9ehRNmzbFkiVL8MUXX+gV2/OqVKkCALC3t9fpc6aixXSWCux///sfLCwsMHjwYNy+fTvX9itXrmDBggUAng6PAcD8+fO12sydOxfA0196hlKlShUkJSXh/Pnz8rr4+PhcV0g+ePAg1745NwlNT0/Ps28nJyfUrVsXK1eu1ErsLl68iF27dsnvsyj4+Phg+vTp+Prrr/P8izWHkZFRrkrGb7/9hps3b2qty/mlre8dvPMyceJExMXFYeXKlZg7dy7c3Nzkm40WRIcOHZCQkIBffvlFXpeZmYlFixbB0tJSHuIzpIkTJ+L+/fv47rvvAPxXJXr2sxRCyOd0QXTo0AGZmZlYvHixvC4rKwuLFi3Salec55muOnTogKysLHz99dda6+fNmwdJkrQSirwYGRmhe/fuWL9+fZ4V85z7UAFPr6R9lqmpKTw9PSGEkO+lpe/5PG3aNNy/fx+DBw9GZmZmru27du3C1q1b840dyF0xfP7nW1ZWVq5hQnt7ezg7O8vfG8nJybmOX7t2bahUqgJ//zzL19cXGo0GM2bMyPO+Y89+zlT0WOmiAqtSpQrWrl0rX6r+7B3pjx49Kl/iDwBeXl7w8/PDsmXLkJiYCG9vb5w8eRIrV65E165d4ePjY7C4evXqhYkTJ+K9997DqFGj5Mujq1evrjXBNTg4GAcPHkTHjh3h6uqKO3fu4Ntvv0XFihXxzjvv5Nv/7Nmz0b59ezRp0gSDBg2SL+W3trZ+4bBfYalUKnz22WcvbdepUycEBwdjwIABePvtt3HhwgWsWbNGnouTo0qVKrCxscGSJUtgZWUFCwsLNG7cWO/5UXv37sW3336LqVOnyrewWLFiBVq0aIEpU6bgq6++0qs/ABg6dCiWLl0Kf39/nD59Gm5ubli3bh2OHDmC+fPnF2oCdH7at2+PWrVqYe7cuRgxYgRq1qyJKlWqYPz48bh58yY0Gg3Wr19fqHmFnTt3RtOmTfHJJ58gNjYWnp6e2LBhQ57zd4rrPNNV586d4ePjg8mTJyM2NhZeXl7YtWsXNm3ahDFjxsgVlheZOXMm9u3bh8aNG2PIkCHw9PTEgwcPcObMGezevVv+w6ht27ZwdHRE06ZN4eDggKioKHz99dfo2LGjfC40aNAAADB58mT06tULJiYm6Ny5c74VwQ8++EB+hM7Zs2fRu3dv+Y70O3fuxJ49e7B27do899VoNGjevDm++uorZGRkoEKFCti1axdiYmK02j169AgVK1ZEjx494OXlBUtLS+zevRt//PEH5syZA+Dp909AQADef/99VK9eHZmZmVi9erWclBaWRqPB4sWL8eGHH6J+/fro1asX7OzsEBcXh23btqFp06a5EmcqQsVyzSS9Vv755x8xZMgQ4ebmJkxNTYWVlZVo2rSpWLRokdaNTzMyMsS0adOEu7u7MDExES4uLi+8Oerznr9VQX6XTAvx9KantWrVEqampqJGjRrixx9/zHXLiD179oguXboIZ2dnYWpqKpydnUXv3r3FP//8k+sYz99WYffu3aJp06bC3NxcaDQa0blz53xvWvn8LSnyu3nh8569ZUR+8rtlxLhx44STk5MwNzcXTZs2FceOHcvzVg+bNm2Sb1b57PvMuTlqXp7tJzk5Wbi6uor69euLjIwMrXaBgYFCpVKJY8eOvfA95Pf1vn37thgwYIAoX768MDU1FbVr1871dXjROaDv8YQQIiwsTOtz+Ouvv0Tr1q2FpaWlKF++vBgyZIg4d+5crnMiv69VXjfkvX//vvjwww/lm6N++OGH+d4ctTDnWX4xvehr+yxdbo766NEjERgYKJydnYWJiYmoVq3aC2+Ompfbt2+LESNGCBcXF2FiYiIcHR1Fq1atxLJly+Q2S5cuFc2bNxflypUTarVaVKlSRUyYMEEkJSVp9TV9+nRRoUIFoVKpdL59RM7PAXt7e2FsbCzs7OxE586dxaZNm+Q2ef0cuHHjhnjvvfeEjY2NsLa2Fu+//764deuW1m010tPTxYQJE4SXl5ewsrISFhYWwsvLS3z77bdyP1evXhUDBw4UVapUEWZmZsLW1lb4+PiI3bt3a8VZ0FtG5Ni3b5/w9fUV1tbWwszMTFSpUkX4+/uLU6dOyW10+ZlDhSMJUQwzKomIiIhKGc7pIiIiIlIAky4iIiIiBTDpIiIiIlIAky4iIiIiBTDpIiIiIlIAky4iIiIiBfDmqGQQ2dnZuHXrFqysrAz6eBkiIlKGEAKPHj2Cs7NzkT1/MS0tDU+ePDFIX6ampjAzMzNIX0ph0kUGcevWLbi4uBR3GEREVEjXr18v9IO285KWlgZzq3JA5mOD9Ofo6IiYmJgSlXgx6SKDyHkUh6mnHyQj02KOhqhoxO0PLe4QiIrMo+RkVHV3KZLHbAF4WuHKfAy1px9Q2N8TWU+Q8NdKPHnyhEkXlT45Q4qSkSmTLnptaTSa4g6BqMgV+RQRY7NC/54QUsmcks6ki4iIiJQjAShsYldCpw4z6SIiIiLlSKqnS2H7KIFKZtREREREJQwrXURERKQcSTLA8GLJHF9k0kVERETK4fAiERERERUlVrqIiIhIORxeJCIiIlKCAYYXS+hAXcmMmoiIiKiEYaWLiIiIlMPhRSIiIiIF8OpFIiIiIipKrHQRERGRcji8SERERKSAUjy8yKSLiIiIlFOKK10lM1UkIiIiKmFY6SIiIiLlcHiRiIiISAGSZICki8OLRERERJQPVrqIiIhIOSrp6VLYPkogJl1ERESknFI8p6tkRk1ERERUwrDSRURERMopxffpYtJFREREyuHwIhEREREVJVa6iIiISDkcXiQiIiJSQCkeXmTSRURERMopxZWukpkqEhEREZUwTLqIiIhIOTnDi4Vd9BASEoJGjRrBysoK9vb26Nq1K6Kjo+XtDx48wMiRI1GjRg2Ym5ujUqVKGDVqFJKSkrRDl6Rcy88//6xzHBxeJCIiIuUUw/DigQMHMGLECDRq1AiZmZn49NNP0bZtW/z111+wsLDArVu3cOvWLYSGhsLT0xPXrl3DsGHDcOvWLaxbt06rrxUrVqBdu3byaxsbG53jYNJFREREr7WdO3dqvQ4LC4O9vT1Onz6N5s2bo1atWli/fr28vUqVKvjyyy/Rr18/ZGZmwtj4v3TJxsYGjo6OBYqDw4tERESkIEMMLT5NX5KTk7WW9PR0nSLIGTa0tbV9YRuNRqOVcAHAiBEjUL58ebz55ptYvnw5hBD6vHMiIiIiheQMLxZ2AeDi4gJra2t5CQkJeenhs7OzMWbMGDRt2hS1atXKs829e/cwffp0DB06VGt9cHAwfv31V0RERKB79+4YPnw4Fi1apPNb5/AiERERlUjXr1+HRqORX6vV6pfuM2LECFy8eBGHDx/Oc3tycjI6duwIT09PBAUFaW2bMmWK/O969eohNTUVs2fPxqhRo3SKl5UuIiIiUo4kGeDqxaeVLo1Go7W8LOkKCAjA1q1bsW/fPlSsWDHX9kePHqFdu3awsrLCxo0bYWJi8sL+GjdujBs3bug8rMlKFxERESmnGO5IL4TAyJEjsXHjRuzfvx/u7u652iQnJ8PX1xdqtRqbN2+GmZnZS/uNjIxE2bJldaqwAUy6iIiI6DU3YsQIrF27Fps2bYKVlRUSEhIAANbW1jA3N0dycjLatm2Lx48f48cff5Qn5gOAnZ0djIyMsGXLFty+fRtvvfUWzMzMEBERgRkzZmD8+PE6x8Gki4iIiJRTDPfpWrx4MQCgRYsWWutXrFgBf39/nDlzBidOnAAAVK1aVatNTEwM3NzcYGJigm+++QaBgYEQQqBq1aqYO3cuhgwZonMcTLqIiIhIOcU0vPgiLVq0eGmbdu3aad0UtSCYdBEREZFy+MBrIiIiIipKrHQRERGRcophePFVwaSLiIiIlMPhRSIiIiIqSqx0ERERkWIkSYJUSitdTLqIiIhIMaU56eLwIhEREZECWOkiIiIi5Uj/vxS2jxKISRcREREphsOLRERERFSkWOkiIiIixZTmSheTLiIiIlIMky4iIiIiBZTmpItzuoiIiIgUwEoXERERKYe3jCAiIiIqehxeJCIiIqIixUoXERERKUaSYIBKl2FiURqTLiIiIlKMBAMML5bQrIvDi0REREQKYKWLiIiIFFOaJ9Iz6SIiIiLllOJbRnB4kYiIiEgBrHQRERGRcgwwvCg4vEhERET0YoaY01X4qx+LB5MuIiIiUkxpTro4p4uIiIhIAax0ERERkXJK8dWLTLqIiIhIMRxeJCIiIqIixUoXERERKaY0V7qYdBEREZFiSnPSxeFFIiIiIgWw0kVERESKKc2VLiZdREREpJxSfMsIDi8SERERKYCVLiIiIlIMhxeJiIiIFFCaky4OLxIREZFicpKuwi76CAkJQaNGjWBlZQV7e3t07doV0dHRWm3S0tIwYsQIlCtXDpaWlujevTtu376t1SYuLg4dO3ZEmTJlYG9vjwkTJiAzM1PnOJh0ERER0WvtwIEDGDFiBI4fP46IiAhkZGSgbdu2SE1NldsEBgZiy5Yt+O2333DgwAHcunUL3bp1k7dnZWWhY8eOePLkCY4ePYqVK1ciLCwMn3/+uc5xSEIIYdB3RqVScnIyrK2toa49BJKRaXGHQ1QkHv7xdXGHQFRkkpOT4VDOGklJSdBoNEXSv7W1NZyHrIXKtEyh+sp+8hi3vutT4Fjv3r0Le3t7HDhwAM2bN0dSUhLs7Oywdu1a9OjRAwDw999/w8PDA8eOHcNbb72FHTt2oFOnTrh16xYcHBwAAEuWLMHEiRNx9+5dmJq+/HcfK11ERESkGEMOLyYnJ2st6enpOsWQlJQEALC1tQUAnD59GhkZGWjdurXcpmbNmqhUqRKOHTsGADh27Bhq164tJ1wA4Ovri+TkZPz55586HZdJFxEREZVILi4usLa2lpeQkJCX7pOdnY0xY8agadOmqFWrFgAgISEBpqamsLGx0Wrr4OCAhIQEuc2zCVfO9pxtuigxVy9KkoSNGzeia9eueW6PjY2Fu7s7zp49i7p16yoam1IeP36MDz/8EBEREXj06BEePnyY6wQpiKCgIISHhyMyMrLQfVHBBfq3RScfL1RzdUBaegZOnr+KoK834fK1O3m2/23Bx2j99hvoO34Zth84DwAoa22BZdP98EbVCrC1LoN7D1Ow/cB5TP92Cx6lpin5doheauaybZj13Q6tddVcHXBy3RQAQMyNu5iyYCOOR17Fk4xMtGrigVnj34d9OcMPfZFyDHn14vXr17WGF9Vq9Uv3HTFiBC5evIjDhw8XKoaCKNZKl7+/v/zhm5iYwMHBAW3atMHy5cuRnZ2t1TY+Ph7t27cv1PFatGgBSZLw888/a62fP38+3Nzc9OpLkiSEh4fr1C5nsbCwQLVq1eDv74/Tp0/rdTwAWLlyJQ4dOoSjR48iPj4e1tbWevehC39//3yTWyo6b9eviu9/O4i2A0PRLeBrmBgbYcOiAJQxyz1P4OPePshrNmZ2djZ2HDiPPuOWolH3YAyfthreb9bA3E96KfAOiPRXs7IT/t4xQ152fB8IAEj9Nx3dAr6BBAmbFo/Eju8D8SQjC73HLs31+4FKFgkGGF78/1vSazQareVlSVdAQAC2bt2Kffv2oWLFivJ6R0dHPHnyBImJiVrtb9++DUdHR7nN81cz5rzOafMyxT682K5dO8THxyM2NhY7duyAj48PRo8ejU6dOmldhuno6KhTBvsyZmZm+Oyzz5CRkVHovnS1YsUKxMfH488//8Q333yDlJQUNG7cGKtWrdKrnytXrsDDwwO1atWCo6Njib1PCeXt/VHf4qetJ/D31QRcvHQTw6f9CBcnW9T1cNFqV6t6BYzo2xIB03/M1UfSo3+xfP1hREbF4XrCQxz84x/8sO4QmtSrotTbINKLsZEKDuU18lLOxhIAcOLcVcTF38c3U/vhjaoV8EbVCvg26EOcjYrDwT/+KeaoqaQRQiAgIAAbN27E3r174e7urrW9QYMGMDExwZ49e+R10dHRiIuLQ5MmTQAATZo0wYULF3Dnzn+jDxEREdBoNPD09NQpjmJPutRqNRwdHVGhQgXUr18fn376KTZt2oQdO3YgLCxMbvd8ZenkyZOoV68ezMzM0LBhQ5w9e1an4/Xu3RuJiYn47rvvXthu8eLFqFKlCkxNTVGjRg2sXr1a3pZTFXvvvfcgSdJLq2Q2NjZwdHSEm5sb2rZti3Xr1qFv374ICAjAw4cP5XaHDx9Gs2bNYG5uDhcXF4waNUq+nLVFixaYM2cODh48CEmS0KJFCwDA6tWr0bBhQ1hZWcHR0RF9+vTROiHCwsJyDUGGh4fnm7AFBQVh5cqV2LRpk/wXxf79+1/4/qhoaCzNAAAPkx/L68zVJvhuuj8mfPUr7tx/9NI+HMtbo7NPXRw5c6nI4iQqjKvX78Kj/aeo22UqhnwWhusJDwAA6U8yIUkS1Kb/zYIxMzWGSiXh+LkrxRUuGUBx3KdrxIgR+PHHH7F27VpYWVkhISEBCQkJ+PfffwEA1tbWGDRoEMaOHYt9+/bh9OnTGDBgAJo0aYK33noLANC2bVt4enriww8/xLlz5/D777/js88+w4gRI3QuChV70pWXli1bwsvLCxs2bMhze0pKCjp16gRPT0+cPn0aQUFBGD9+vE59azQaTJ48GcHBwVr353jWxo0bMXr0aIwbNw4XL17ERx99hAEDBmDfvn0AgD/++APAfxWsnNf6CAwMxKNHjxAREQHgaRWrXbt26N69O86fP49ffvkFhw8fRkBAAABgw4YNGDJkCJo0aYL4+Hj5s8nIyMD06dNx7tw5hIeHIzY2Fv7+/nrHk2P8+PHo2bOnXIGMj4/H22+/XeD+qGAkSULI2B44HnkFUVfi5fUzxnbHyfMx2HHwwgv3//4Lf9w8NBdRO77Eo9Q0jPpibVGHTKS3Bm+44Zup/fDbwhGY88kHuHbrPjoMmYdHqWloVNsNZcxMEbRoEx6nPUHqv+mYsmAjsrKykXAvubhDp8KQDLToYfHixUhKSkKLFi3g5OQkL7/88ovcZt68eejUqRO6d++O5s2bw9HRUSsPMTIywtatW2FkZIQmTZqgX79+6N+/P4KDg3WO45WdSF+zZk2cP38+z21r165FdnY2fvjhB5iZmeGNN97AjRs38PHHH+vU9/Dhw7FgwQLMnTsXU6ZMybU9NDQU/v7+GD58OABg7NixOH78OEJDQ+Hj4wM7OzsA/1WwCvr+gKcXAABP75bbt29fjBkzBgBQrVo1LFy4EN7e3li8eDFsbW1RpkwZmJqaah1z4MCB8r8rV66MhQsXolGjRkhJSYGlpaXecVlaWsLc3Bzp6ekvfG/p6elal+YmJ/OHoCGF/q8nPKo4of2QefK69s1ro1nD6vDuN/Ol+386bz1mfbcDVV3tMWXEu/gysBvGz/q1KEMm0lubpm/I/65VrQIa1nJD7c6fI3z3GXzY5W2EzRyEcTN/wdJfDkClktC9bQN41XSBSsWpFaQfXW5JamZmhm+++QbffPNNvm1cXV2xffv2AsfxSla6gKcfUH7lw6ioKNSpUwdmZmbyupwxV12o1WoEBwcjNDQU9+7dy7P/pk2baq1r2rQpoqKidD7Gy+ScADnv8dy5cwgLC4OlpaW8+Pr6Ijs7GzExMfn2c/r0aXTu3BmVKlWClZUVvL29ATx9VEFRCgkJ0bpM18XF5eU7kU6+mvA+fJvVQuePF+LWnUR5fbOG1eFesTxi987G3WMLcPfYAgDAqlmDsWXJaK0+7tx/hEvXbmPHwQsYO+MnDOrRHA684otecdZWZVC1kj2uXr8LAGj5lgfOhgfh0q4QXImYiaXBfoi/kwi3CuWLOVIqjOIYXnxVvLKVrqioqFwT3QypX79+CA0NxRdffKH3lYuGkJPA5bzHlJQUfPTRRxg1alSutpUqVcqzj9TUVPj6+sLX1xdr1qyBnZ0d4uLi4OvriydPngAAVCpVrgzfEBcRTJo0CWPHjpVfJycnM/EygK8mvI+OLbzQedgCxN26r7Vt/spdWL3pqNa6oz9Pxqfz1mPnoYv59plTFTA1fWW/3YkAACmP0xFz8x4+KP+m1vqcyfUH/4jG3YcpaN+sdnGERwZSmh94/Ur+FN67dy8uXLiAwMDAPLd7eHhg9erVSEtLk6tdx48f1+sYKpUKISEh6NatW65hSQ8PDxw5cgR+fn7yuiNHjmhdnWBiYoKsrCy9jvms+fPnQ6PRyHe/rV+/Pv766y9UrVpV5z7+/vtv3L9/HzNnzpQTnlOnTmm1sbOzw6NHj5CamgoLCwsAeOn9uExNTV/63tRqtUGuJqX/hE7siR6+DdFn/DKkPE6DfTkrAEByShrS0jNw5/6jPCfP30h4KCdobd72hF05Dc7+dQ0pj9PhUdkJ00Z1xfHIK7ge/0DR90P0MlPmb0C7ZrXh4mSL+LtJmLlsG4xUKnT3bQAAWLP5GKq7O6J8WUucPB+DSXPXYXhvH1Rzc3hJz/Qqk6SnS2H7KImKPelKT09HQkICsrKycPv2bezcuRMhISHo1KkT+vfvn+c+ffr0weTJkzFkyBBMmjQJsbGxCA0N1fvYHTt2ROPGjbF06VKtu8xOmDABPXv2RL169dC6dWts2bIFGzZswO7du+U2bm5u2LNnD5o2bQq1Wo2yZcvme5zExEQkJCQgPT0d//zzD5YuXYrw8HCsWrVKvrJw4sSJeOuttxAQEIDBgwfDwsICf/31FyIiIvD113k/761SpUowNTXFokWLMGzYMFy8eBHTp0/XatO4cWOUKVMGn376KUaNGoUTJ05oXRWaFzc3N/z++++Ijo5GuXLlYG1tDRMTk5d8mlRYg3o0BwBsWzpGa/3waavx09YTOvXxb3oG/Lq+jRmB3WBqYoybtxOxdX8k5oVFGDpcokK7eScRgz9bgQdJj1G+rCUae1VGxIpxKF/26R8cl67dQfA3m/Ew+TEqOdti3ABfDO/TspijJiq4Yk+6du7cCScnJxgbG6Ns2bLw8vLCwoUL4efnB5Uq7ylnlpaW2LJlC4YNG4Z69erB09MTs2bNQvfu3fU+/qxZs3Jdnde1a1csWLAAoaGhGD16NNzd3bFixQr5Ng0AMGfOHIwdOxbfffcdKlSoIE+Iz8uAAQMAPJ2kV6FCBbzzzjs4efIk6tevL7epU6cODhw4gMmTJ6NZs2YQQqBKlSr44IMP8u3Xzs4OYWFh+PTTT7Fw4ULUr18foaGhePfdd+U2tra2+PHHHzFhwgR89913aNWqFYKCgjB06NB8+x0yZAj279+Phg0bIiUlBfv27dN671Q0yjYKKPQ+h09fgu+guYYKiahILZ8x8IXbg0Z2QdDILgpFQ0p5Wukq7PCigYJRmCR0mdJP9BI5T49X1x4CyejlT1onKoke/pF31ZnodZCcnAyHctZISkrSerSOIfu3trZG5VHrYKS2KFRfWempuLqwR5HFWlRe2asXiYiIiF4nxT68SERERKUHr14kIiIiUkBpvnqRw4tERERECmCli4iIiBSjUkmFfpSTKKGPgmLSRURERIrh8CIRERERFSlWuoiIiEgxvHqRiIiISAGleXiRSRcREREppjRXujini4iIiEgBrHQRERGRYkpzpYtJFxERESmmNM/p4vAiERERkQJY6SIiIiLFSDDA8CJKZqmLSRcREREphsOLRERERFSkWOkiIiIixfDqRSIiIiIFcHiRiIiIiIoUK11ERESkGA4vEhERESmgNA8vMukiIiIixZTmShfndBEREREpgJUuIiIiUo4BhhdL6A3pmXQRERGRcji8SERERERFipUuIiIiUgyvXiQiIiJSAIcXiYiIiKhIsdJFREREiuHwIhEREZECOLxIREREREWKSRcREREpJqfSVdhFHwcPHkTnzp3h7OwMSZIQHh6uU0yzZ8+W27i5ueXaPnPmTL3i4PAiERERKaY45nSlpqbCy8sLAwcORLdu3XJtj4+P13q9Y8cODBo0CN27d9daHxwcjCFDhsivrays9IqDSRcREREppjjmdLVv3x7t27fPd7ujo6PW602bNsHHxweVK1fWWm9lZZWrrT44vEhERET0/27fvo1t27Zh0KBBubbNnDkT5cqVQ7169TB79mxkZmbq1TcrXURERKQYQw4vJicna61Xq9VQq9WF6nvlypWwsrLKNQw5atQo1K9fH7a2tjh69CgmTZqE+Ph4zJ07V+e+mXQRERGRYgw5vOji4qK1furUqQgKCipU38uXL0ffvn1hZmamtX7s2LHyv+vUqQNTU1N89NFHCAkJ0TnRY9JFREREJdL169eh0Wjk14Wtch06dAjR0dH45ZdfXtq2cePGyMzMRGxsLGrUqKFT/0y6iIiISDESDDC8+P//r9FotJKuwvrhhx/QoEEDeHl5vbRtZGQkVCoV7O3tde6fSRcREREpRiVJUBUy69J3/5SUFFy+fFl+HRMTg8jISNja2qJSpUoAns4P++233zBnzpxc+x87dgwnTpyAj48PrKyscOzYMQQGBqJfv34oW7asznEw6SIiIqLX2qlTp+Dj4yO/zpmf5efnh7CwMADAzz//DCEEevfunWt/tVqNn3/+GUFBQUhPT4e7uzsCAwO15nnpgkkXERERKaY4bo7aokULCCFe2Gbo0KEYOnRontvq16+P48eP63fQPDDpIiIiIsWU5gdeM+kiIiIixaikp0th+yiJeEd6IiIiIgWw0kVERETKkQwwPFhCK11MuoiIiEgxxTGR/lXB4UUiIiIiBbDSRURERIqR/v+/wvZREjHpIiIiIsXw6kUiIiIiKlKsdBEREZFieHPUl9i8ebPOHb777rsFDoaIiIheb6X56kWdkq6uXbvq1JkkScjKyipMPERERESvJZ2Sruzs7KKOg4iIiEoBlSRBVchSVWH3Ly6FmtOVlpYGMzMzQ8VCREREr7nSPLyo99WLWVlZmD59OipUqABLS0tcvXoVADBlyhT88MMPBg+QiIiIXh85E+kLu5REeiddX375JcLCwvDVV1/B1NRUXl+rVi18//33Bg2OiIiI6HWhd9K1atUqLFu2DH379oWRkZG83svLC3///bdBgyMiIqLXS87wYmGXkkjvOV03b95E1apVc63Pzs5GRkaGQYIiIiKi11Npnkivd6XL09MThw4dyrV+3bp1qFevnkGCIiIiInrd6F3p+vzzz+Hn54ebN28iOzsbGzZsQHR0NFatWoWtW7cWRYxERET0mpD+fylsHyWR3pWuLl26YMuWLdi9ezcsLCzw+eefIyoqClu2bEGbNm2KIkYiIiJ6TZTmqxcLdJ+uZs2aISIiwtCxEBEREb22Cnxz1FOnTiEqKgrA03leDRo0MFhQRERE9HpSSU+XwvZREumddN24cQO9e/fGkSNHYGNjAwBITEzE22+/jZ9//hkVK1Y0dIxERET0mjDE8GBJHV7Ue07X4MGDkZGRgaioKDx48AAPHjxAVFQUsrOzMXjw4KKIkYiIiKjE07vSdeDAARw9ehQ1atSQ19WoUQOLFi1Cs2bNDBocERERvX5KaKGq0PROulxcXPK8CWpWVhacnZ0NEhQRERG9nji8qIfZs2dj5MiROHXqlLzu1KlTGD16NEJDQw0aHBEREb1ecibSF3YpiXSqdJUtW1Yrq0xNTUXjxo1hbPx098zMTBgbG2PgwIHo2rVrkQRKREREVJLplHTNnz+/iMMgIiKi0qA0Dy/qlHT5+fkVdRxERERUCpTmxwAV+OaoAJCWloYnT55ordNoNIUKiIiIiOh1pHfSlZqaiokTJ+LXX3/F/fv3c23PysoySGBERET0+lFJElSFHB4s7P7FRe+rF//3v/9h7969WLx4MdRqNb7//ntMmzYNzs7OWLVqVVHESERERK8JSTLMUhLpXenasmULVq1ahRYtWmDAgAFo1qwZqlatCldXV6xZswZ9+/YtijiJiIiISjS9K10PHjxA5cqVATydv/XgwQMAwDvvvIODBw8aNjoiIiJ6reRcvVjYpSTSO+mqXLkyYmJiAAA1a9bEr7/+CuBpBSznAdhEREREeSnNw4t6J10DBgzAuXPnAACffPIJvvnmG5iZmSEwMBATJkwweIBERERErwO953QFBgbK/27dujX+/vtvnD59GlWrVkWdOnUMGhwRERG9Xkrz1YuFuk8XALi6usLV1dUQsRAREdFrzhDDgyU059It6Vq4cKHOHY4aNarAwRAREdHrrTgeA3Tw4EHMnj0bp0+fRnx8PDZu3Kj1rGh/f3+sXLlSax9fX1/s3LlTfv3gwQOMHDkSW7ZsgUqlQvfu3bFgwQJYWlrqHIdOSde8efN06kySJCZdRERE9EpJTU2Fl5cXBg4ciG7duuXZpl27dlixYoX8Wq1Wa23v27cv4uPjERERgYyMDAwYMABDhw7F2rVrdY5Dp6Qr52pFopcJmjUCZhZWxR0GUZHo/+OZ4g6BqMhk/JuiyHFUKMBVfHn0oY/27dujffv2L2yjVqvh6OiY57aoqCjs3LkTf/zxBxo2bAgAWLRoETp06IDQ0FA4OzvrFEdh3zcRERGRzgx5n67k5GStJT09vcBx7d+/H/b29qhRowY+/vhjrUcdHjt2DDY2NnLCBTy9mFClUuHEiRM6H4NJFxEREZVILi4usLa2lpeQkJAC9dOuXTusWrUKe/bswaxZs3DgwAG0b99efp50QkIC7O3ttfYxNjaGra0tEhISdD5Ooa9eJCIiItKVJAEqA129eP36dWg0Gnn98/OwdNWrVy/537Vr10adOnVQpUoV7N+/H61atSpUrM9ipYuIiIgUo5IMswBPH0f47FLQpOt5lStXRvny5XH58mUAgKOjI+7cuaPVJjMzEw8ePMh3Hlie790g0RERERG9Jm7cuIH79+/DyckJANCkSRMkJibi9OnTcpu9e/ciOzsbjRs31rnfAiVdhw4dQr9+/dCkSRPcvHkTALB69WocPny4IN0RERFRKVEcD7xOSUlBZGQkIiMjATy9K0NkZCTi4uKQkpKCCRMm4Pjx44iNjcWePXvQpUsXVK1aFb6+vgAADw8PtGvXDkOGDMHJkydx5MgRBAQEoFevXjpfuQgUIOlav349fH19YW5ujrNnz8pXCiQlJWHGjBn6dkdERESliCGHF3V16tQp1KtXD/Xq1QMAjB07FvXq1cPnn38OIyMjnD9/Hu+++y6qV6+OQYMGoUGDBjh06JDWcOWaNWtQs2ZNtGrVCh06dMA777yDZcuW6RWH3hPpv/jiCyxZsgT9+/fHzz//LK9v2rQpvvjiC327IyIiIipSLVq0gBAi3+2///77S/uwtbXV60aoedE76YqOjkbz5s1zrbe2tkZiYmKhgiEiIqLXW2l+9qLew4uOjo7ybP5nHT58GJUrVzZIUERERPR6UkmSQZaSSO+ka8iQIRg9ejROnDgBSZJw69YtrFmzBuPHj8fHH39cFDESERHRa0JloKUk0nt48ZNPPkF2djZatWqFx48fo3nz5lCr1Rg/fjxGjhxZFDESERERlXh6J12SJGHy5MmYMGECLl++jJSUFHh6esLS0rIo4iMiIqLXSGme01XgxwCZmprC09PTkLEQERHRa06Fws/JUqFkZl16J10+Pj4vvCnZ3r17CxUQERER0etI76Srbt26Wq8zMjIQGRmJixcvws/Pz1BxERER0WuIw4t6mDdvXp7rg4KCkJKSUuiAiIiI6PVVkDvK59VHSWSwqy779euH5cuXG6o7IiIiotdKgSfSP+/YsWMwMzMzVHdERET0GpIkFHoifakZXuzWrZvWayEE4uPjcerUKUyZMsVggREREdHrh3O69GBtba31WqVSoUaNGggODkbbtm0NFhgRERHR60SvpCsrKwsDBgxA7dq1UbZs2aKKiYiIiF5TnEivIyMjI7Rt2xaJiYlFFA4RERG9ziQD/VcS6X31Yq1atXD16tWiiIWIiIheczmVrsIuJZHeSdcXX3yB8ePHY+vWrYiPj0dycrLWQkRERES56TynKzg4GOPGjUOHDh0AAO+++67W44CEEJAkCVlZWYaPkoiIiF4LpXlOl85J17Rp0zBs2DDs27evKOMhIiKi15gkSS98hrOufZREOiddQggAgLe3d5EFQ0RERPS60uuWESU1syQiIqJXA4cXdVS9evWXJl4PHjwoVEBERET0+uId6XU0bdq0XHekJyIiIqKX0yvp6tWrF+zt7YsqFiIiInrNqSSp0A+8Luz+xUXnpIvzuYiIiKiwSvOcLp1vjppz9SIRERER6U/nSld2dnZRxkFERESlgQEm0pfQRy/qN6eLiIiIqDBUkKAqZNZU2P2LC5MuIiIiUkxpvmWE3g+8JiIiIiL9sdJFREREiinNVy8y6SIiIiLFlOb7dHF4kYiIiEgBrHQRERGRYkrzRHomXURERKQYFQwwvFhCbxnB4UUiIiIiBbDSRURERIrh8CIRERGRAlQo/DBbSR2mK6lxExEREZUoTLqIiIhIMZIkGWTRx8GDB9G5c2c4OztDkiSEh4fL2zIyMjBx4kTUrl0bFhYWcHZ2Rv/+/XHr1i2tPtzc3HLFMHPmTL3iYNJFREREipEMtOgjNTUVXl5e+Oabb3Jte/z4Mc6cOYMpU6bgzJkz2LBhA6Kjo/Huu+/mahscHIz4+Hh5GTlypF5xcE4XERERKaY47kjfvn17tG/fPs9t1tbWiIiI0Fr39ddf480330RcXBwqVaokr7eysoKjo6P+Af8/VrqIiIioREpOTtZa0tPTDdJvUlISJEmCjY2N1vqZM2eiXLlyqFevHmbPno3MzEy9+mWli4iIiBRlqDs+uLi4aL2eOnUqgoKCCtVnWloaJk6ciN69e0Oj0cjrR40ahfr168PW1hZHjx7FpEmTEB8fj7lz5+rcN5MuIiIiUowh79N1/fp1rcRIrVYXqt+MjAz07NkTQggsXrxYa9vYsWPlf9epUwempqb46KOPEBISovNxObxIREREJZJGo9FaCpN05SRc165dQ0REhFYyl5fGjRsjMzMTsbGxOh+DlS4iIiJSTEFu+ZBXH4aUk3BdunQJ+/btQ7ly5V66T2RkJFQqFezt7XU+DpMuIiIiUkxx3JE+JSUFly9fll/HxMQgMjIStra2cHJyQo8ePXDmzBls3boVWVlZSEhIAADY2trC1NQUx44dw4kTJ+Dj4wMrKyscO3YMgYGB6NevH8qWLatzHEy6iIiI6LV26tQp+Pj4yK9z5mf5+fkhKCgImzdvBgDUrVtXa799+/ahRYsWUKvV+PnnnxEUFIT09HS4u7sjMDBQa56XLph0ERERkWKKY3ixRYsWEELku/1F2wCgfv36OH78uF7HzAuTLiIiIlJMQe4on1cfJRGvXiQiIiJSACtdREREpJhX8epFpTDpIiIiIsUUx9WLrwomXURERKSY0lzpKqnJIhEREVGJwkoXERERKaY0X73IpIuIiIgUY8gHXpc0HF4kIiIiUgArXURERKQYFSSoCjlAWNj9iwuTLiIiIlIMhxeJiIiIqEix0kVERESKkf7/v8L2URIx6SIiIiLFcHiRiIiIiIoUK11ERESkGMkAVy9yeJGIiIjoJUrz8CKTLiIiIlJMaU66OKeLiIiISAGsdBEREZFieMsIIiIiIgWopKdLYfsoiTi8SERERKQAVrqIiIhIMRxeJCIiIlIAr14kIiIioiLFShcREREpRkLhhwdLaKGLSRcREREph1cvEhEREVGRYqVLAcuWLcP06dNx8+ZNzJ07F2PGjCl0n7GxsXB3d8fZs2dRt27dQvf3rP3798PHxwcPHz6EjY2NQfumF4u5fAMH957Czeu38Sg5Ff0GvYs36lSVt+/ecRTnz0QjMfERjIyMUMHFAW07NkUlNye5zaxp3yPxQbJWv76d3kGLNm8q9j6I8lLD3hIdPB3gZmuOsmVMMX//FZy5kaTVxlljhp71nVHT3gpGKuBmUhoWHbiK+48zAAD+jV3whqMGZc1NkJaZhct3U/HL2ZuIT04vjrdEBcCrFykXf39/rFy5EgBgbGwMW1tb1KlTB71794a/vz9UKt2KhMnJyQgICMDcuXPRvXt3WFtbF0m8TJReD0+eZMCpgh0aNn4DPy7fkmt7ebuyeLdHS9iWs0ZGRiYO7z+D5YvXY/yUgbC0LCO3a93hbbzZpLb8Wq02VSR+ohdRG6sQ9/AxDl65h9HeVXJtt7c0xWe+1XHg8n1sPBePfzOyUMHGHE+yhNwm9v5jHIt5iPupT2ChNsJ7dZzwv1bVMDb8IoTI1SW9gkrz1YtMul6gXbt2WLFiBbKysnD79m3s3LkTo0ePxrp167B582YYG7/844uLi0NGRgY6duwIJyenl7an0q2GpztqeLrnu71uQw+t1x3f88ap4xeRcPMeqtaoJK9Xq01hpbEosjiJCuL8rWScv5Wc7/YedZ1x7mYSfjl7U153J+WJVpv9l+/L/76XCqyPjMeXnTxgZ2Gaqy29miQUfiJ8Cc25OKfrRdRqNRwdHVGhQgXUr18fn376KTZt2oQdO3YgLCwMAJCYmIjBgwfDzs4OGo0GLVu2xLlz5wAAYWFhqF37abWhcuXKkCQJsbGxuHLlCrp06QIHBwdYWlqiUaNG2L17t9axJUlCeHi41jobGxv5uM+KjY2Fj48PAKBs2bKQJAn+/v4AgOzsbISEhMDd3R3m5ubw8vLCunXrtPbfvn07qlevDnNzc/j4+CA2NrZwHxwpIjMzCyePXoCZuRpOFey0th3YfRLBk77Fwq9W4+CeP5CVlV1MURLpRgLgVcEaCY/SMaFlVXzdozamtquB+hXzHx0wNVKhWRVb3HmULg8/Er3KWOnSU8uWLeHl5YUNGzZg8ODBeP/992Fubo4dO3bA2toaS5cuRatWrfDPP//ggw8+gIuLC1q3bo2TJ0/CxcUFdnZ2uHjxIjp06IAvv/wSarUaq1atQufOnREdHY1KlSq9PIjnuLi4YP369ejevTuio6Oh0Whgbm4OAAgJCcGPP/6IJUuWoFq1ajh48CD69esHOzs7eHt74/r16+jWrRtGjBiBoUOH4tSpUxg3btxLj5meno709P/mUCQn5//XKxlW1MWr+HnlNmRkZMBKY4GBH3eHhaW5vP3t5vVQoaI9zMuYIS7mFnZuPYzk5FR0eq9FscVM9DIaM2OYmxih0xsOWBcZj1/O3kQdZw1GeVdGSMQlRN9Jkdu2ql4eH9SrADMTI9xKSsNXey4hK5tjiyWFChJUhRwfVJXQWheTrgKoWbMmzp8/j8OHD+PkyZO4c+cO1Go1ACA0NBTh4eFYt24dhg4dinLlygEA7Ozs4OjoCADw8vKCl5eX3N/06dOxceNGbN68GQEBAXrHY2RkBFtbWwCAvb29PKcrPT0dM2bMwO7du9GkSRMATytuhw8fxtKlS+Ht7Y3FixejSpUqmDNnDgCgRo0auHDhAmbNmvXCY4aEhGDatGl6x0qFV6WaC0b+rx8ep/6LP45ewE9hWzF8bB9YWj2d09XMp4Hc1qmCHYyMjbDxl91o1/kdnYbEiYqD9P+/hM9cT8Lvf98BAMQ9/BdV7SzQsnp5raTraMwDXIx/BBtzE7T3tMeIZpXxxe/RyGDiVSJweJH0IoSAJEk4d+4cUlJSUK5cOVhaWspLTEwMrly5ku/+KSkpGD9+PDw8PGBjYwNLS0tERUUhLi7OoHFevnwZjx8/Rps2bbTiW7VqlRxfVFQUGjdurLVfToL2IpMmTUJSUpK8XL9+3aCxU/5M1SYob1cWldyc0b2PL1QqFU4dv5hvexdXR2RnZ+PhfVYj6dX1KD0TmdkCN5PStNbfSkpDuTLaF4L8m5GN24/SEX0nBYsOxsDZWo0GlWwUjJaoYPhnbwFERUXB3d0dKSkpcHJywv79+3O1edEVhOPHj0dERARCQ0NRtWpVmJubo0ePHnjy5L9JoJIkQTx3KU5Ghn5zFlJSnv5luG3bNlSoUEFrW05lrqDUanWh+yDDEEIgMzMz3+23bt6FJElyJYzoVZSVLRBzPxVOGu2fK45WZrifmv8Eeen//9e4pN4tszQqxaUuJl162rt3Ly5cuIDAwEBUrFgRCQkJMDY2hpubm859HDlyBP7+/njvvfcAPE2Onp+8bmdnh/j4ePn1pUuX8Pjx43z7NDV9+pdgVlaWvM7T0xNqtRpxcXHw9vbOcz8PDw9s3rxZa93x48d1fi9kWOnpT3D/bqL8+uH9JNy6cQdlypihjIU59u06AY/alWGlscTj1H9x7FAkkpNSULtudQDAtZhbuH4tAVWquUCtNsG12Hhs27gfdRt6wLyMWTG9K6Kn1MYqOFj9l1TZWapRqaw5UtMzcf9xBrb/dRsj3nFH9J0U/JWQgjrOGtSraI2QiH/+v70pGruWxcX4ZDxKy0TZMqboVMsBGVnZOHeTldySgvfpojylp6cjISFB65YRISEh6NSpE/r37w+VSoUmTZqga9eu+Oqrr1C9enXcunUL27Ztw3vvvYeGDRvm2W+1atWwYcMGdO7cGZIkYcqUKcjO1r66rGXLlvj666/RpEkTZGVlYeLEiTAxMck3VldXV0iShK1bt6JDhw4wNzeHlZUVxo8fj8DAQGRnZ+Odd95BUlISjhw5Ao1GAz8/PwwbNgxz5szBhAkTMHjwYJw+fTrPKyRJGTfjbuO7r3+TX28LPwAAqP+mJ7r2bI27dx7gzPI/kZqShjIWZqhYyRFDR30AB6fyAABjYyOcP/M39uw8hszMTNjaWuOdFg3wjk/9Ynk/RM9yL1cGn7apLr/u27AiAODQlfv47tg1nL6ehLCT19HpDQf0a+iC+OQ0LDp4Ff/cTQUAZGQJ1LC3hG9Ne1iYGiEpLRPRd1IQ/Hs0HqXnX+0lelUw6XqBnTt3wsnJCcbGxihbtiy8vLywcOFC+Pn5yTdH3b59OyZPnowBAwbg7t27cHR0RPPmzeHg4JBvv3PnzsXAgQPx9ttvo3z58pg4cWKuq//mzJmDAQMGoFmzZnB2dsaCBQtw+vTpfPusUKECpk2bhk8++QQDBgxA//79ERYWhunTp8POzg4hISG4evUqbGxs5NtfAEClSpWwfv16BAYGYtGiRXjzzTcxY8YMDBw40ACfIOmrcjUXhCwYm+/2foPefeH+FVwcMHxsH0OHRWQQf99OQf8fz7ywzcEr93Hwyv08tyX+m4E5+/KfL0slhAFujlpCC12QxPMTh4gKIDk5GdbW1gjZHgkzC6viDoeoSBy9mljcIRAVmYx/UxA+3BtJSUnQaDQG7z/n98TeyDhYWhWu/5RHyWhZt5LOsR48eBCzZ8/G6dOnER8fj40bN6Jr167ydiEEpk6diu+++w6JiYlo2rQpFi9ejGrVqsltHjx4gJEjR2LLli1QqVTo3r07FixYAEtLS53j5tWLRERE9FpLTU2Fl5cXvvnmmzy3f/XVV1i4cCGWLFmCEydOwMLCAr6+vkhL++9q2r59++LPP/9EREQEtm7dioMHD2Lo0KF6xcHhRSIiIlJOMVy92L59e7Rv3z7PbUIIzJ8/H5999hm6dOkCAFi1ahUcHBwQHh6OXr16ISoqCjt37sQff/whz9detGgROnTogNDQUDg7O+sUBytdREREpBjJQP8BT4csn12efVKKrmJiYpCQkIDWrVvL66ytrdG4cWMcO3YMAHDs2DHY2NhoXSDXunVrqFQqnDhxQudjMekiIiIixUiSYRbg6WPwrK2t5SUkJETveBISEgAg1wVwDg4O8raEhATY29trbTc2Noatra3cRhccXiQiIqIS6fr161oT6V/1m3az0kVERESKkQy0AIBGo9FaCpJ05TwX+fbt21rrb9++LW9zdHTEnTt3tLZnZmbiwYMHchtdMOkiIiIi5Rgy6zIAd3d3ODo6Ys+ePfK65ORknDhxQn4WcZMmTZCYmKh1v8y9e/ciOzs71/OLX4TDi0RERPRaS0lJweXLl+XXMTExiIyMhK2tLSpVqoQxY8bgiy++QLVq1eDu7o4pU6bA2dlZvpeXh4cH2rVrhyFDhmDJkiXIyMhAQEAAevXqpfOViwCTLiIiIlJQcTx78dSpU/Dx8ZFfjx379Mkffn5+CAsLw//+9z+kpqZi6NChSExMxDvvvIOdO3fCzOy/Z9auWbMGAQEBaNWqlXxz1IULF+oVB5MuIiIiUsyzVx8Wpg99tGjRAi96AI8kSQgODkZwcHC+bWxtbbF27Vr9DvwczukiIiIiUgArXURERKSYYrgh/SuDSRcREREppxRnXRxeJCIiIlIAK11ERESkmOK4evFVwaSLiIiIFFMcVy++Kph0ERERkWJK8ZQuzukiIiIiUgIrXURERKScUlzqYtJFREREiinNE+k5vEhERESkAFa6iIiISDG8epGIiIhIAaV4SheHF4mIiIiUwEoXERERKacUl7qYdBEREZFiePUiERERERUpVrqIiIhIMbx6kYiIiEgBpXhKF5MuIiIiUlApzro4p4uIiIhIAax0ERERkWJK89WLTLqIiIhIOQaYSF9Ccy4OLxIREREpgZUuIiIiUkwpnkfPpIuIiIgUVIqzLg4vEhERESmAlS4iIiJSDK9eJCIiIlJAaX4MEIcXiYiIiBTAShcREREpphTPo2fSRURERAoqxVkXky4iIiJSTGmeSM85XUREREQKYKWLiIiIFCPBAFcvGiQS5THpIiIiIsWU4ildHF4kIiIiUgIrXURERKSY0nxzVCZdREREpKDSO8DI4UUiIiJ6rbm5uUGSpFzLiBEjAAAtWrTItW3YsGEGj4OVLiIiIlJMcQwv/vHHH8jKypJfX7x4EW3atMH7778vrxsyZAiCg4Pl12XKlClckHlg0kVERESKKY7BRTs7O63XM2fORJUqVeDt7S2vK1OmDBwdHQsZ2YtxeJGIiIhKjSdPnuDHH3/EwIEDIT1TMluzZg3Kly+PWrVqYdKkSXj8+LHBj81KFxERESnGkMOLycnJWuvVajXUavUL9w0PD0diYiL8/f3ldX369IGrqyucnZ1x/vx5TJw4EdHR0diwYUPhAn0Oky4iIiJSjCGfveji4qK1furUqQgKCnrhvj/88APat28PZ2dned3QoUPlf9euXRtOTk5o1aoVrly5gipVqhQq1mcx6SIiIiLlGHBS1/Xr16HRaOTVL6tyXbt2Dbt3735pBatx48YAgMuXLzPpIiIiItJoNFpJ18usWLEC9vb26Nix4wvbRUZGAgCcnJwKE14uTLqIiIhIMcV1a9Ts7GysWLECfn5+MDb+L/25cuUK1q5diw4dOqBcuXI4f/48AgMD0bx5c9SpU6eQkWpj0kVERESKKa7HAO3evRtxcXEYOHCg1npTU1Ps3r0b8+fPR2pqKlxcXNC9e3d89tlnhQsyD0y6iIiI6LXXtm1bCCFyrXdxccGBAwcUiYFJFxERESnGkFcvljRMuoiIiEg5pfd517wjPREREZESWOkiIiIixZTiQheTLiIiIlJOcV29+Crg8CIRERGRAljpIiIiIgUV/urFkjrAyKSLiIiIFMPhRSIiIiIqUky6iIiIiBTA4UUiIiJSTGkeXmTSRURERIopzY8B4vAiERERkQJY6SIiIiLFcHiRiIiISAGl+TFAHF4kIiIiUgArXURERKScUlzqYtJFREREiuHVi0RERERUpFjpIiIiIsXw6kUiIiIiBZTiKV1MuoiIiEhBpTjr4pwuIiIiIgWw0kVERESKKc1XLzLpIiIiIsVwIj1RIQkhAABpj1OKORKiopPxL89ven1l/JsK4L+f50UlOTn5leijODDpIoN49OgRAGBaj3eKORIiIiqMR48ewdra2uD9mpqawtHREdXcXQzSn6OjI0xNTQ3Sl1IkUdQpLZUK2dnZuHXrFqysrCCV1LpvCZKcnAwXFxdcv34dGo2muMMhMjie48oTQuDRo0dwdnaGSlU019mlpaXhyZMnBunL1NQUZmZmBulLKax0kUGoVCpUrFixuMModTQaDX8h0WuN57iyiqLC9SwzM7MSlygZEm8ZQURERKQAJl1ERERECmDSRVQCqdVqTJ06FWq1urhDISoSPMfpdcSJ9EREREQKYKWLiIiISAFMuoiIiIgUwKSLiIiISAFMuoieI0kSwsPD890eGxsLSZIQGRmpWExKe/z4Mbp37w6NRgNJkpCYmGiQfoOCglC3bl2D9EUlx7Jly+Di4gKVSoX58+cbpM+i/D7cv3+/Qc97ohxMuqhU8Pf3hyRJkCQJJiYmcHBwQJs2bbB8+XJkZ2drtY2Pj0f79u0LdbwWLVpAkiT8/PPPWuvnz58PNzc3vfp6WRL4bLucxcLCAtWqVYO/vz9Onz6t1/EAYOXKlTh06BCOHj2K+Pj4Irthor+/P7p27VokfVPh6PM98yLJyckICAjAxIkTcfPmTQwdOrRI4mWiRCUBky4qNdq1a4f4+HjExsZix44d8PHxwejRo9GpUydkZmbK7RwdHQ1ymbqZmRk+++wzZGRkFLovXa1YsQLx8fH4888/8c033yAlJQWNGzfGqlWr9OrnypUr8PDwQK1ateDo6MhHO5VSun7PvEhcXBwyMjLQsWNHODk5oUyZMkUcNdGri0kXlRpqtRqOjo6oUKEC6tevj08//RSbNm3Cjh07EBYWJrd7vrJ08uRJ1KtXD2ZmZmjYsCHOnj2r0/F69+6NxMREfPfddy9st3jxYlSpUgWmpqaoUaMGVq9eLW/LqYq99957kCTppVUyGxsbODo6ws3NDW3btsW6devQt29fBAQE4OHDh3K7w4cPo1mzZjA3N4eLiwtGjRqF1NRUAE+rdHPmzMHBgwchSRJatGgBAFi9ejUaNmwIKysrODo6ok+fPrhz547cZ1hYGGxsbLTiCQ8PzzdhCwoKwsqVK7Fp0ya5orJ///4Xvj9Sli7fM4mJiRg8eDDs7Oyg0WjQsmVLnDt3DsDTc6J27doAgMqVK0OSJMTGxuLKlSvo0qULHBwcYGlpiUaNGmH37t1ax86rwmtjY6P1vZojNjYWPj4+AICyZctCkiT4+/sDePpc2JCQELi7u8Pc3BxeXl5Yt26d1v7bt29H9erVYW5uDh8fH8TGxhbugyPKB5MuKtVatmwJLy8vbNiwIc/tKSkp6NSpEzw9PXH69GkEBQVh/PjxOvWt0WgwefJkBAcHywnN8zZu3IjRo0dj3LhxuHjxIj766CMMGDAA+/btAwD88ccfAP6rYOW81kdgYCAePXqEiIgIAE+rWO3atUP37t1x/vx5/PLLLzh8+DACAgIAABs2bMCQIUPQpEkTxMfHy59NRkYGpk+fjnPnziE8PByxsbHyL7aCGD9+PHr27ClXU+Lj4/H2228XuD9SxvPfM++//z7u3LmDHTt24PTp06hfvz5atWqFBw8e4IMPPpCTqZMnTyI+Ph4uLi5ISUlBhw4dsGfPHpw9exbt2rVD586dERcXV6CYXFxcsH79egBAdHQ04uPjsWDBAgBASEgIVq1ahSVLluDPP/9EYGAg+vXrhwMHDgAArl+/jm7duqFz586IjIzE4MGD8cknnxT2YyLKEx94TaVezZo1cf78+Ty3rV27FtnZ2fjhhx9gZmaGN954Azdu3MDHH3+sU9/Dhw/HggULMHfuXEyZMiXX9tDQUPj7+2P48OEAgLFjx+L48eMIDQ2Fj48P7OzsAPxXwSro+wMg//UeEhKCvn37YsyYMQCAatWqYeHChfD29sbixYtha2uLMmXKwNTUVOuYAwcOlP9duXJlLFy4EI0aNUJKSgosLS31jsvS0hLm5uZIT08v8Huj4pHzPXP48GGcPHkSd+7ckYfkQ0NDER4ejnXr1mHo0KEoV64cAMDOzk7+Ont5ecHLy0vub/r06di4cSM2b94sJ//6MDIygq2tLQDA3t5errimp6djxowZ2L17N5o0aQLg6bl7+PBhLF26VD7nq1Spgjlz5gAAatSogQsXLmDWrFkF+3CIXoCVLir1hBD5DoFFRUWhTp06MDMzk9fl/PDWhVqtRnBwMEJDQ3Hv3r08+2/atKnWuqZNmyIqKkrnY7xMzkMnct7juXPnEBYWBktLS3nx9fVFdnY2YmJi8u3n9OnT6Ny5MypVqgQrKyt4e3sDQIGrE1Ry5XzPnDt3DikpKShXrpzW+RQTE4MrV67ku39KSgrGjx8PDw8P2NjYwNLSElFRUQY/ly5fvozHjx+jTZs2WvGtWrVKji8qKgqNGzfW2k+f73EifbDSRaVeVFQU3N3di6z/fv36ITQ0FF988YXeVy4aQk4Cl/MeU1JS8NFHH2HUqFG52laqVCnPPlJTU+Hr6wtfX1+sWbMGdnZ2iIuLg6+vL548eQIAUKlUeP6pYkpeREDKyfmeSUlJgZOTU55z8Z6f3/es8ePHIyIiAqGhoahatSrMzc3Ro0cP+VwCnv6RUNjzKSUlBQCwbds2VKhQQWsbn+lIxYFJF5Vqe/fuxYULFxAYGJjndg8PD6xevRppaWlytev48eN6HUOlUiEkJATdunXLNSzp4eGBI0eOwM/PT1535MgReHp6yq9NTEyQlZWl1zGfNX/+fGg0GrRu3RoAUL9+ffz111+oWrWqzn38/fffuH//PmbOnAkXFxcAwKlTp7Ta2NnZ4dGjR0hNTYWFhQUAvPQeSqampoV6b6S8Z79nKlasiISEBBgbG+v1B8WRI0fg7++P9957D8DT5Oj5yet2dnaIj4+XX1+6dAmPHz/Ot09TU1MA0DqfPD09oVarERcXJ1dmn+fh4YHNmzdrrdP3e5xIVxxepFIjPT0dCQkJuHnzJs6cOYMZM2agS5cu6NSpE/r375/nPn369IEkSRgyZAj++usvbN++HaGhoXofu2PHjmjcuDGWLl2qtX7ChAkICwvD4sWLcenSJcydOxcbNmzQmqzv5uaGPXv2ICEhQesKxLwkJiYiISEB165dQ0REBHr06IG1a9di8eLFcuVh4sSJOHr0KAICAhAZGYlLly5h06ZNL5xLU6lSJZiammLRokW4evUqNm/ejOnTp2u1ady4McqUKYNPP/0UV65cwdq1a/O80uxZbm5uOH/+PKKjo3Hv3j1Wxl4xL/uead26NZo0aYKuXbti165diI2NxdGjRzF58uRcSfmzqlWrhg0bNiAyMhLnzp1Dnz59ct37q2XLlvj6669x9uxZnDp1CsOGDYOJiUm+fbq6ukKSJGzduhV3795FSkoKrKysMH78eAQGBmLlypW4cuUKzpw5g0WLFmHlypUAgGHDhuHSpUuYMGECoqOjdTpviQpMEJUCfn5+AoAAIIyNjYWdnZ1o3bq1WL58ucjKytJqC0Bs3LhRfn3s2DHh5eUlTE1NRd26dcX69esFAHH27Nl8j+ft7S1Gjx6tte7o0aMCgHB1ddVa/+2334rKlSsLExMTUb16dbFq1Sqt7Zs3bxZVq1YVxsbGufZ9Pu6cxczMTFSpUkX4+fmJ06dP52p78uRJ0aZNG2FpaSksLCxEnTp1xJdffilvHz16tPD29tbaZ+3atcLNzU2o1WrRpEkTsXnz5lyfw8aNG0XVqlWFubm56NSpk1i2bJl49sfM1KlThZeXl/z6zp07chwAxL59+/J9f6QsXb9nkpOTxciRI4Wzs7MwMTERLi4uom/fviIuLk4IIcTZs2cFABETEyPvExMTI3x8fIS5ublwcXERX3/9da7vmZs3b4q2bdsKCwsLUa1aNbF9+3ZhbW0tVqxYIffx/PkXHBwsHB0dhSRJws/PTwghRHZ2tpg/f76oUaOGMDExEXZ2dsLX11ccOHBA3m/Lli2iatWqQq1Wi2bNmonly5cLAOLhw4eG/liplJOEeG7QnIiIiIgMjsOLRERERApg0kVERESkACZdRERERApg0kVERESkACZdRERERApg0kVERESkACZdRERERApg0kVErw1/f3907dpVft2iRQuMGTNG8Tj2798PSZKQmJiYbxtJkhAeHq5zn0FBQahbt26h4oqNjYUkSS99PBMRFQ0mXURUpPz9/SFJEiRJgqmpKapWrYrg4GBkZmYW+bE3bNiQ63FF+dElUSIiKgw+8JqIily7du2wYsUKpKenY/v27RgxYgRMTEwwadKkXG2fPHkiP7y4sGxtbQ3SDxGRIbDSRURFTq1Ww9HREa6urvj444/RunVrbN68GcB/Q4JffvklnJ2dUaNGDQDA9evX0bNnT9jY2MDW1hZdunRBbGys3GdWVhbGjh0LGxsblCtXDv/73//w/FPNnh9eTE9Px8SJE+Hi4gK1Wo2qVavihx9+QGxsLHx8fAAAZcuWhSRJ8Pf3BwBkZ2cjJCQE7u7uMDc3h5eXF9atW6d1nO3bt6N69eowNzeHj4+PVpy6mjhxIqpXr44yZcqgcuXKmDJlSp4PAF+6dClcXFxQpkwZ9OzZE0lJSVrbv//+e3h4eMDMzAw1a9bEt99+q3csRFQ0mHQRkeLMzc3x5MkT+fWePXsQHR2NiIgIbN26FRkZGfD19YWVlRUOHTqEI0eOwNLSEu3atZP3mzNnDsLCwrB8+XIcPnwYDx48wMaNG1943P79++Onn37CwoULERUVhaVLl8LS0hIuLi5Yv349ACA6Ohrx8fFYsGABACAkJASrVq3CkiVL8OeffyIwMBD9+vXDgQMHADxNDrt164bOnTsjMjISgwcPxieffKL3Z2JlZYWwsDD89ddfWLBgAb777jvMmzdPq83ly5fx66+/YsuWLdi5cyfOnj2L4cOHy9vXrFmDzz//HF9++SWioqIwY8YMTJkyBStXrtQ7HiIqAsX8wG0ies35+fmJLl26CCGEyM7OFhEREUKtVovx48fL2x0cHER6erq8z+rVq0WNGjVEdna2vC49PV2Ym5uL33//XQghhJOTk/jqq6/k7RkZGaJixYrysYQQwtvbW4wePVoIIUR0dLQAICIiIvKMc9++fQKAePjwobwuLS1NlClTRhw9elSr7aBBg0Tv3r2FEEJMmjRJeHp6am2fOHFirr6eB0Bs3Lgx3+2zZ88WDRo0kF9PnTpVGBkZiRs3bsjrduzYIVQqlYiPjxdCCFGlShWxdu1arX6mT58umjRpIoQQIiYmRgAQZ8+ezfe4RFR0OKeLiIrc1q1bYWlpiYyMDGRnZ6NPnz4ICgqSt9euXVtrHte5c+dw+fJlWFlZafWTlpaGK1euICkpCfHx8WjcuLG8zdjYGA0bNsw1xJgjMjISRkZG8Pb21jnuy5cv4/Hjx2jTpo3W+idPnqBevXoAgKioKK04AKBJkyY6HyPHL7/8goULF+LKlStISUlBZmYmNBqNVptKlSqhQoUKWsfJzs5GdHQ0rKyscOXKFQwaNAhDhgyR22RmZsLa2lrveIjI8Jh0EVGR8/HxweLFi2FqagpnZ2cYG2v/6LGwsNB6nZKSggYNGmDNmjW5+rKzsytQDObm5nrvk5KSAgDYtm2bVrIDPJ2nZijHjh1D3759MW3aNPj6+sLa2ho///wz5syZo3es3333Xa4k0MjIyGCxElHBMekioiJnYWGBqlWr6ty+fv36+OWXX2Bvb5+r2pPDyckJJ06cQPPmzQE8reicPn0a9evXz7N97dq1kZ2djQMHDqB169a5tudU2rKysuR1np6eUKvViIuLy7dC5uHhIV8UkOP48eMvf5PPOHr0KFxdXTF58mR53bVr13K1i4uLw61bt+Ds7CwfR6VSoUaNGnBwcICzszOuXr2Kvn376nV8IlIGJ9IT0Sunb9++KF++PLp06YJDhw4hJiYG+/fvx6hRo3Djxg0AwOjRozFz5kyEh4fj77//xvDhw194jy03Nzf4+flh4MCBCA8Pl/v89ddfAQCurq6QJAlbt27F3bt3kZKSAisrK4wfPx6BgYFYuXIlrly5gjNnzmDRokXy5PRhw4bh0qVLmDBhAqKjo7F27VqEhYXp9X6rVauGuLg4/Pzzz7hy5QoWLlyY50UBZmZm8PPzw7lz53Do0CGMGjUKPXv2hKOjIwBg2rRpCAkJwcKFC/HPP//gwoULWLFiBebOnatXPERUNJh0EdErp0yZMjh48CAqVaqEbt26wcPDA4MGDUJaWppc+Ro3bhw+/PBD+Pn5oUmTJrCyssJ77733wn4XL16MHj16YPjw4ahZsyaGDBmC1NRUAECFChUwbdo0fPLJJ3BwcEBAQAAAYPr06ZgyZQpCQkLg4eGBdu3aYdu2bXB3dwfwdJ7V+vXrER4eDi8vLyxZsgQzZszQ6/2+++67CAwMREBAAOrWrYujR49iypQpudpVrVoV3bp1Q4cOHdC2bVvUqVNH65YQgwcPxvfff48VK1agdu3a8Pb2RlhYmBwrERUvSeQ365SIiIiIDIaVLiIiIiIFMOkiIiIiUgCTLiIiIiIFMOkiIiIiUgCTLiIiIiIFMOkiIiIiUgCTLiIiIiIFMOkiIiIiUgCTLiIiIiIFMOkiIiIiUgCTLiIiIiIFMOkiIiIiUsD/Afr8skvDxVNRAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "# Instantiate and fit the Random Forest classifier\n",
    "clf_rf = RandomForestClassifier(n_estimators=100, random_state=42)\n",
    "clf_rf.fit(X_train_scaled, y_train)\n",
    "\n",
    "# Calculate overall accuracy\n",
    "y_pred_rf = clf_rf.predict(X_test_scaled)\n",
    "accuracy_rf = accuracy_score(y_test, y_pred_rf)\n",
    "print(f'Accuracy: {accuracy_rf:.2%}')\n",
    "\n",
    "# Calculate precision, recall, and F1-score\n",
    "precision_rf = precision_score(y_test, y_pred_rf)\n",
    "recall_rf = recall_score(y_test, y_pred_rf)\n",
    "f1_rf = f1_score(y_test, y_pred_rf)\n",
    "print(f'Precision: {precision_rf:.2f}')\n",
    "print(f'Recall: {recall_rf:.2f}')\n",
    "print(f'F1-score: {f1_rf:.2f}')\n",
    "\n",
    "# Generate confusion matrix\n",
    "cm_rf = confusion_matrix(y_test, y_pred_rf)\n",
    "\n",
    "# Display confusion matrix\n",
    "disp_rf = ConfusionMatrixDisplay(confusion_matrix=cm_rf, display_labels=class_names)\n",
    "disp_rf.plot(cmap=plt.cm.Blues)\n",
    "plt.title('Confusion Matrix for Random Forest Classifier')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b8534c2e-f0e0-40e1-ac38-d29ecd0d74eb",
   "metadata": {},
   "source": [
    "### k-Nearest Neighbors (KNN) classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "e3a56599-7e2c-4f30-9ee6-66829417fad5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 64.83%\n",
      "Precision: 0.66\n",
      "Recall: 0.61\n",
      "F1-score: 0.63\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "\n",
    "# Instantiate and fit the K-Nearest Neighbors classifier\n",
    "clf_knn = KNeighborsClassifier(n_neighbors=5)\n",
    "clf_knn.fit(X_train_scaled, y_train)\n",
    "\n",
    "# Calculate overall accuracy\n",
    "y_pred_knn = clf_knn.predict(X_test_scaled)\n",
    "accuracy_knn = accuracy_score(y_test, y_pred_knn)\n",
    "print(f'Accuracy: {accuracy_knn:.2%}')\n",
    "\n",
    "# Calculate precision, recall, and F1-score\n",
    "precision_knn = precision_score(y_test, y_pred_knn)\n",
    "recall_knn = recall_score(y_test, y_pred_knn)\n",
    "f1_knn = f1_score(y_test, y_pred_knn)\n",
    "print(f'Precision: {precision_knn:.2f}')\n",
    "print(f'Recall: {recall_knn:.2f}')\n",
    "print(f'F1-score: {f1_knn:.2f}')\n",
    "\n",
    "# Generate confusion matrix\n",
    "cm_knn = confusion_matrix(y_test, y_pred_knn)\n",
    "\n",
    "# Display confusion matrix\n",
    "disp_knn = ConfusionMatrixDisplay(confusion_matrix=cm_knn, display_labels=class_names)\n",
    "disp_knn.plot(cmap=plt.cm.Blues)\n",
    "plt.title('Confusion Matrix for k-Nearest Neighbors Classifier')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "35c66c04-89ff-46f0-ac98-ff9f6b32e2d6",
   "metadata": {},
   "source": [
    "### Artificial Neural Networks (ANN) classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "2091c68f-a4df-48ce-9724-bb875d68b5b8",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 62.33%\n",
      "Precision: 0.62\n",
      "Recall: 0.60\n",
      "F1-score: 0.61\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.neural_network import MLPClassifier\n",
    "\n",
    "# Instantiate and fit the Artificial Neural Networks (ANN) classifier\n",
    "clf_ann = MLPClassifier(hidden_layer_sizes=(100,50), max_iter=300, activation='relu', solver='adam', random_state=42)\n",
    "clf_ann.fit(X_train_scaled, y_train)\n",
    "\n",
    "# Calculate overall accuracy\n",
    "y_pred_ann = clf_ann.predict(X_test_scaled)\n",
    "accuracy_ann = accuracy_score(y_test, y_pred_ann)\n",
    "print(f'Accuracy: {accuracy_ann:.2%}')\n",
    "\n",
    "# Calculate precision, recall, and F1-score\n",
    "precision_ann = precision_score(y_test, y_pred_ann)\n",
    "recall_ann = recall_score(y_test, y_pred_ann)\n",
    "f1_ann = f1_score(y_test, y_pred_ann)\n",
    "print(f'Precision: {precision_ann:.2f}')\n",
    "print(f'Recall: {recall_ann:.2f}')\n",
    "print(f'F1-score: {f1_ann:.2f}')\n",
    "\n",
    "# Generate confusion matrix\n",
    "cm_ann = confusion_matrix(y_test, y_pred_ann)\n",
    "\n",
    "# Display confusion matrix\n",
    "disp_ann = ConfusionMatrixDisplay(confusion_matrix=cm_ann, display_labels=class_names)\n",
    "disp_ann.plot(cmap=plt.cm.Blues)\n",
    "plt.title('Confusion Matrix for Artificial Neural Networks (ANN) Classifier')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ba1b1631-caf8-4c72-a7ec-3bb955c6039b",
   "metadata": {},
   "source": [
    "### Reinforcement learning (RL) : Q-learning Agent"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "ab6d9eca-13f9-4f50-b0be-13e0f4c20065",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 50.80%\n",
      "Precision: 0.52\n",
      "Recall: 0.39\n",
      "F1-score: 0.45\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.cluster import KMeans\n",
    "from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, confusion_matrix, ConfusionMatrixDisplay\n",
    "import gym\n",
    "\n",
    "# Sample data setup (replace with actual data)\n",
    "# Assuming df_no_missing_data is your preprocessed dataframe\n",
    "df_no_missing_data = pd.DataFrame({\n",
    "    'feature1': np.random.randn(2000),\n",
    "    'feature2': np.random.randn(2000),\n",
    "    'DEFAULT': np.random.randint(2, size=2000)\n",
    "})\n",
    "\n",
    "# Define your features and target\n",
    "features = df_no_missing_data.drop(columns=['DEFAULT'])\n",
    "target = df_no_missing_data['DEFAULT']\n",
    "\n",
    "# Standardize the features\n",
    "scaler = StandardScaler()\n",
    "features_scaled = scaler.fit_transform(features)\n",
    "\n",
    "# Discretize states using k-means clustering\n",
    "n_clusters = 10\n",
    "kmeans = KMeans(n_clusters=n_clusters, random_state=42)\n",
    "state_labels = kmeans.fit_predict(features_scaled)\n",
    "\n",
    "# Create a custom environment for credit default prediction\n",
    "class CreditDefaultEnv(gym.Env):\n",
    "    def __init__(self, data, labels, state_labels):\n",
    "        super(CreditDefaultEnv, self).__init__()\n",
    "        self.data = data\n",
    "        self.labels = labels\n",
    "        self.state_labels = state_labels\n",
    "        self.current_step = 0\n",
    "        self.action_space = gym.spaces.Discrete(2)  # Actions: 0 = no default, 1 = default\n",
    "        self.observation_space = gym.spaces.Discrete(n_clusters)\n",
    "        \n",
    "    def reset(self):\n",
    "        self.current_step = 0\n",
    "        return self.state_labels[self.current_step]\n",
    "\n",
    "    def step(self, action):\n",
    "        actual = self.labels[self.current_step]\n",
    "        reward = 1 if action == actual else -1  # Reward for correct prediction\n",
    "        self.current_step += 1\n",
    "        done = self.current_step >= len(self.data)\n",
    "        obs = self.state_labels[self.current_step] if not done else 0\n",
    "        return obs, reward, done, {}\n",
    "\n",
    "# Convert your dataframe to the environment\n",
    "env = CreditDefaultEnv(features_scaled, target.values, state_labels)\n",
    "\n",
    "# Simple Q-learning implementation\n",
    "n_states = n_clusters\n",
    "n_actions = env.action_space.n\n",
    "Q = np.zeros((n_states, n_actions))\n",
    "alpha = 0.1\n",
    "gamma = 0.9\n",
    "epsilon = 0.1\n",
    "\n",
    "def choose_action(state):\n",
    "    if np.random.uniform(0, 1) < epsilon:\n",
    "        return env.action_space.sample()\n",
    "    else:\n",
    "        return np.argmax(Q[state, :])\n",
    "\n",
    "# Train the Q-learning agent\n",
    "for episode in range(1000):\n",
    "    state = env.reset()\n",
    "    done = False\n",
    "    while not done:\n",
    "        action = choose_action(state)\n",
    "        next_state, reward, done, _ = env.step(action)\n",
    "        Q[state, action] = Q[state, action] + alpha * (reward + gamma * np.max(Q[next_state, :]) - Q[state, action])\n",
    "        state = next_state\n",
    "\n",
    "# Evaluate the Q-learning agent\n",
    "y_true = []\n",
    "y_pred = []\n",
    "state = env.reset()\n",
    "done = False\n",
    "while not done:\n",
    "    action = np.argmax(Q[state, :])\n",
    "    y_true.append(env.labels[env.current_step])\n",
    "    y_pred.append(action)\n",
    "    state, _, done, _ = env.step(action)\n",
    "\n",
    "# Calculate evaluation metrics\n",
    "accuracy = accuracy_score(y_true, y_pred)\n",
    "precision = precision_score(y_true, y_pred)\n",
    "recall = recall_score(y_true, y_pred)\n",
    "f1 = f1_score(y_true, y_pred)\n",
    "cm = confusion_matrix(y_true, y_pred)\n",
    "\n",
    "print(f'Accuracy: {accuracy:.2%}')\n",
    "print(f'Precision: {precision:.2f}')\n",
    "print(f'Recall: {recall:.2f}')\n",
    "print(f'F1-score: {f1:.2f}')\n",
    "\n",
    "# Display confusion matrix\n",
    "class_names = ['Did Not Default', 'Defaulted']\n",
    "disp = ConfusionMatrixDisplay(confusion_matrix=cm, display_labels=class_names)\n",
    "disp.plot(cmap='Blues')\n",
    "plt.title('Confusion Matrix for Q-learning Agent')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "31b89a9c-bac8-46e6-8092-bb8485d41131",
   "metadata": {},
   "source": [
    "# Split for 90:10 ratio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "beaa4899-c959-4053-b83c-2aa01fa88104",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import scale\n",
    "y = df_downsample['DEFAULT'].copy()\n",
    "X_train, X_test, y_train, y_test = train_test_split(X_encoded, y, test_size=0.1, random_state=42)\n",
    "\n",
    "#scale the data\n",
    "X_train_scaled = scale(X_train)\n",
    "X_test_scaled = scale(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "543b1069-fec0-4e75-ad30-320a28272885",
   "metadata": {},
   "source": [
    "### Support Vector Machine (SVM)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "e77947cc-5603-4902-b064-ab8e83b9adce",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 63.50%\n",
      "Precision: 0.67\n",
      "Recall: 0.54\n",
      "F1-score: 0.60\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.svm import SVC\n",
    "\n",
    "# Instantiate and fit the Support Vector Machine classifier\n",
    "clf_svm = SVC(kernel='linear', random_state=42)\n",
    "clf_svm.fit(X_train_scaled, y_train)\n",
    "\n",
    "# Calculate overall accuracy\n",
    "y_pred_svm = clf_svm.predict(X_test_scaled)\n",
    "accuracy_svm = accuracy_score(y_test, y_pred_svm)\n",
    "print(f'Accuracy: {accuracy_svm:.2%}')\n",
    "\n",
    "# Calculate precision, recall, and F1-score\n",
    "precision_svm = precision_score(y_test, y_pred_svm)\n",
    "recall_svm = recall_score(y_test, y_pred_svm)\n",
    "f1_svm = f1_score(y_test, y_pred_svm)\n",
    "print(f'Precision: {precision_svm:.2f}')\n",
    "print(f'Recall: {recall_svm:.2f}')\n",
    "print(f'F1-score: {f1_svm:.2f}')\n",
    "\n",
    "# Generate confusion matrix\n",
    "cm_svm = confusion_matrix(y_test, y_pred_svm)\n",
    "\n",
    "# Display confusion matrix\n",
    "disp_svm = ConfusionMatrixDisplay(confusion_matrix=cm_svm, display_labels=class_names)\n",
    "disp_svm.plot(cmap=plt.cm.Blues)\n",
    "plt.title('Confusion Matrix for Support Vector Machine Classifier')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "21493848-c1a9-4e0f-8bce-25d9abd739ed",
   "metadata": {},
   "source": [
    "### Decision Tree Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "f78bcbb7-a9a7-461c-8efd-fd66fbe45f6e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 59.00%\n",
      "Precision: 0.58\n",
      "Recall: 0.69\n",
      "F1-score: 0.63\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "\n",
    "# Instantiate and fit the Decision Tree classifier\n",
    "clf_dt = DecisionTreeClassifier(random_state=42)\n",
    "clf_dt.fit(X_train_scaled, y_train)\n",
    "\n",
    "# Calculate predictions\n",
    "y_pred_dt = clf_dt.predict(X_test_scaled)\n",
    "\n",
    "# Calculate performance metrics\n",
    "accuracy_dt = accuracy_score(y_test, y_pred_dt)\n",
    "precision_dt = precision_score(y_test, y_pred_dt)\n",
    "recall_dt = recall_score(y_test, y_pred_dt)\n",
    "f1_dt = f1_score(y_test, y_pred_dt)\n",
    "\n",
    "# Print performance metrics\n",
    "print(f'Accuracy: {accuracy_dt:.2%}')\n",
    "print(f'Precision: {precision_dt:.2f}')\n",
    "print(f'Recall: {recall_dt:.2f}')\n",
    "print(f'F1-score: {f1_dt:.2f}')\n",
    "\n",
    "# Define class names for display\n",
    "class_names = ['Did Not Default', 'Defaulted']\n",
    "\n",
    "# Generate confusion matrix\n",
    "cm = confusion_matrix(y_test, y_pred_dt)\n",
    "\n",
    "# Display confusion matrix\n",
    "disp = ConfusionMatrixDisplay(confusion_matrix=cm, display_labels=class_names)\n",
    "disp.plot(cmap=plt.cm.Blues)\n",
    "plt.title('Confusion Matrix for Decision Tree Classifier')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "510da531-1650-4389-a8a6-01f1c8c4dcf2",
   "metadata": {},
   "source": [
    "### Random Forest classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "722eb26d-30f8-420b-958b-4e9741172755",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 65.50%\n",
      "Precision: 0.66\n",
      "Recall: 0.66\n",
      "F1-score: 0.66\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "# Instantiate and fit the Random Forest classifier\n",
    "clf_rf = RandomForestClassifier(n_estimators=100, random_state=42)\n",
    "clf_rf.fit(X_train_scaled, y_train)\n",
    "\n",
    "# Calculate overall accuracy\n",
    "y_pred_rf = clf_rf.predict(X_test_scaled)\n",
    "accuracy_rf = accuracy_score(y_test, y_pred_rf)\n",
    "print(f'Accuracy: {accuracy_rf:.2%}')\n",
    "\n",
    "# Calculate precision, recall, and F1-score\n",
    "precision_rf = precision_score(y_test, y_pred_rf)\n",
    "recall_rf = recall_score(y_test, y_pred_rf)\n",
    "f1_rf = f1_score(y_test, y_pred_rf)\n",
    "print(f'Precision: {precision_rf:.2f}')\n",
    "print(f'Recall: {recall_rf:.2f}')\n",
    "print(f'F1-score: {f1_rf:.2f}')\n",
    "\n",
    "# Generate confusion matrix\n",
    "cm_rf = confusion_matrix(y_test, y_pred_rf)\n",
    "\n",
    "# Display confusion matrix\n",
    "disp_rf = ConfusionMatrixDisplay(confusion_matrix=cm_rf, display_labels=class_names)\n",
    "disp_rf.plot(cmap=plt.cm.Blues)\n",
    "plt.title('Confusion Matrix for Random Forest Classifier')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d78292d2-5aae-4cc9-9d44-801bd7f5cbdf",
   "metadata": {},
   "source": [
    "### k-Nearest Neighbors (KNN) classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "196400b1-7948-45fb-b1e7-0e643d05e817",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 68.50%\n",
      "Precision: 0.69\n",
      "Recall: 0.67\n",
      "F1-score: 0.68\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "\n",
    "# Instantiate and fit the K-Nearest Neighbors classifier\n",
    "clf_knn = KNeighborsClassifier(n_neighbors=5)\n",
    "clf_knn.fit(X_train_scaled, y_train)\n",
    "\n",
    "# Calculate overall accuracy\n",
    "y_pred_knn = clf_knn.predict(X_test_scaled)\n",
    "accuracy_knn = accuracy_score(y_test, y_pred_knn)\n",
    "print(f'Accuracy: {accuracy_knn:.2%}')\n",
    "\n",
    "# Calculate precision, recall, and F1-score\n",
    "precision_knn = precision_score(y_test, y_pred_knn)\n",
    "recall_knn = recall_score(y_test, y_pred_knn)\n",
    "f1_knn = f1_score(y_test, y_pred_knn)\n",
    "print(f'Precision: {precision_knn:.2f}')\n",
    "print(f'Recall: {recall_knn:.2f}')\n",
    "print(f'F1-score: {f1_knn:.2f}')\n",
    "\n",
    "# Generate confusion matrix\n",
    "cm_knn = confusion_matrix(y_test, y_pred_knn)\n",
    "\n",
    "# Display confusion matrix\n",
    "disp_knn = ConfusionMatrixDisplay(confusion_matrix=cm_knn, display_labels=class_names)\n",
    "disp_knn.plot(cmap=plt.cm.Blues)\n",
    "plt.title('Confusion Matrix for k-Nearest Neighbors Classifier')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2430ad5c-2d8a-4abe-9899-5594ea7b30ea",
   "metadata": {},
   "source": [
    "### Artificial Neural Networks (ANN) classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "55d26e26-4a60-4e09-bbb5-cf98ae4136e3",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\harsh\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (300) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 63.00%\n",
      "Precision: 0.62\n",
      "Recall: 0.69\n",
      "F1-score: 0.65\n"
     ]
    },
    {
     "data": {
      "image/png": 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BfF1YWJgAINauXatcl5mZKXx8fISFhYVITk5WHhcAIZfLxaNHjwq1PyFyMgevjwtbsmSJMDIyylOHn5+fACAmTZqUp478xmipO2fyGxPz5rmamwkYM2ZMnnpfz7C8mdEq7HuuaUbrt99+E0lJSaJcuXKiW7duKu1+PTPw4sULYW1tneeX/oMHD4SVlZXKek0zWure21evXuUZT/Ls2TNhZ2eX5/MGDTJaP/zwg3j16pWoWrWqSlbrzYyWQqEQVatWFb6+virvS1pamnB1dRXt2rVTrstvjFbu5y83U3Xp0iUBQHz44YfC29tbWa5bt24qma+xY8cKAOLYsWPKdS9evBCurq7CxcVFeR7kvodubm4iLS1NZd+vn4+JiYmiZs2aws3NTcTHx6u8nuq+Xwtj4MCB+X7HZWZmChsbG5Wxtf379xd169bNUzY3oyWEENOnT1fJahWU0Srsefs6ExMTMXLkyAKPq0ePHgKASu9CQdRltN58L4QQwtfXV7i5uSkfb9u27a1j6AICAoRcLi9w/PCbGa3X2/Tm3703P5dr1qwRUqlU5TwTQohFixbl6ckCIKRSqbh69Wq+bckPx2gVIDk5GQBgaWlZqPJ79uwBAIwbN05lfW4WY/fu3Srrvby8lFkWIOcXYPXq1XHr1q0it/lNuX3U27dvL/S4hcTERERHR8Pf318la1KnTh20a9dOeZyvGzFihMrjZs2a4cmTJ8rXsDD69++Pw4cP48GDBzh48CAePHigNvsC5Izfye2Tz87OxpMnT2BhYYHq1atrNNZGJpNh0KBBhSrbvn17fPrppwgJCUHPnj1hamqKxYsXF3pfb9qzZw/s7e3Rr18/5TpjY2OMGTMGKSkpOHLkiEr5Xr16wdbWtlB1P3nyBPv27VOpu1evXpBIJNi0aZPa54wcObIIR6G5LVu2QCKRYNq0aXm2FXSZuK7ec3WsrKwwduxY7NixAxcuXFBbJjIyEklJSejXrx8eP36sXMqUKQNvb28cOnSoyPtX996WKVNGmZlRKBR4+vQpXr16hUaNGml9vLlZrYsXL+Y7XUx0dDRiYmLQv39/PHnyRHm8qampaNOmDY4ePfrW75T69evDwsICR48eBZCTuapcuTI++eQTnD9/HmlpaRBC4Pjx4yrfhXv27MH//vc/NG3aVLnOwsICw4cPR3x8PK5du6ayHz8/P5iZmaltw927d9GiRQtkZWXh6NGjcHZ2Vm4zMzODiYkJDh8+jGfPnhV4LG968uQJypUrp3bb3r178eTJE5XPX79+/XDx4kVcvXo13zoDAgI0GqtVmPP2deXKlXvrVaWa/t1T5/X34vnz53j8+DFatGiBW7du4fnz5wD+/du0a9cuZGVlqa3H2toaqampiIyMLHJbCvLbb7/B09MTNWrUUPlMt27dGgDyfKZbtGgBLy8vjffDQKsAcrkcAPDixYtClf/7778hlUrh4eGhst7e3h7W1tb4+++/VdZXqVIlTx3lypXT+ANfkI8++ghNmjTB0KFDYWdnh759+2LTpk0FfkHmtrN69ep5tnl6eiq/bF/35rHkfgFpciydOnWCpaUlNm7ciHXr1qFx48Z5XstcCoUCc+fORdWqVSGTyVChQgXY2tri0qVLyg9yYVSqVEmjwZGzZ89G+fLlER0djfnz56NixYqFfu6b/v77b1StWlVlECeQ8xrnbn+dq6troeveuHEjsrKyUL9+fcTGxiI2NhZPnz6Ft7e32kG5RkZGqFy5chGOQnNxcXFwdHR8a9fnm3T1nucnICAA1tbW+c5HFRMTAwBo3bo1bG1tVZY//vhDOQC3KPJ7b1etWoU6derA1NQUNjY2sLW1xe7du3VyvAMGDICHhwdCQkLUDpDOPV4/P788x7ts2TJkZGS8tR1lypSBj48Pjh07BiAn0GrWrBmaNm2K7OxsnD59GteuXcPTp09VAq2///473++f3O2vK+iz8fHHH+PRo0c4cuQIKlWqpLJNJpNh1qxZ2Lt3L+zs7NC8eXN8//33hZ4GQd3rBuRchOLq6qocqhEbGwt3d3eULVu2wEHxmgZOwNvP2zfb+7Y5rzT9u6fOiRMn0LZtW5ibm8Pa2hq2trbKKTByz5kWLVqgV69emD59OipUqIDu3btj5cqVyMjIUNYzatQoVKtWDR07dkTlypUxePBg/P7770Vu15tiYmJw9erVPOd3tWrVACDPZ1qT7+DXMdAqgFwuh6OjI65cuaLR8wo7eVt+V/nl9+EtzD6ys7NVHpuZmeHo0aPYv38/Pv74Y1y6dAkfffQR2rVrl6esNrQ5llwymQw9e/bEqlWrsG3btnyzWUDOvFTjxo1D8+bNsXbtWuzbtw+RkZGoWbOmRlcc5fcrOD8XLlxQfvguX76s0XO1pUlbc7/MmzRpgqpVqyqX48eP49SpU3mypq9ni/6rdPWe5+dtf+Ry97FmzRpERkbmWbZv364sW9jPZy517+3atWuVcwEtX74cv//+OyIjI9G6dWudHG9uVis6Olql7bly9/HDDz+oPd7IyMhCjRlt2rQpzp49i/T0dGWgZW1tjVq1auHYsWPKIOz1QEtTBX02evbsiaSkJMybN0/t9rFjx+Kvv/5CaGgoTE1NMXXqVHh6er410LGxsVH7QzI5ORk7d+7E7du3VT57Xl5eSEtLw/r16wv8XswNnPSR1UpKSlKOoctPjRo1ABT9+y0uLg5t2rTB48eP8eOPP2L37t2IjIxEYGAggH/PK4lEgs2bN+PUqVMYPXo07t27h8GDB6Nhw4ZISUkBAFSsWBHR0dHYsWMHunXrhkOHDqFjx47w8/MrUtvepFAoULt27XzP71GjRqmU1/TvRS4Ohn+LLl26YMmSJTh16hR8fHwKLOvs7AyFQoGYmBjlLy8gZ2BnUlKSSspaW+XKlVM7Ad6bv/SAnMtf27RpgzZt2uDHH3/EzJkzMWXKFBw6dAht27ZVexwAcPPmzTzbbty4gQoVKuhtIsr+/ftjxYoVkEql6Nu3b77lNm/ejFatWmH58uUq69/8ItHljMWpqakYNGgQvLy88P777+P7779Hjx490Lhx4yLV5+zsjEuXLkGhUKgEOTdu3FBuL4rbt2/j5MmTGD16NFq0aKGyTaFQ4OOPP8b69evx1VdfvbUufcz47O7ujn379uHp06caZbUK+55rY+zYsQgLC8P06dPzXBqeO3i6YsWKaj83rytXrpzaIQDqPp/52bx5M9zc3LB161aV90Fdl2tRDRw4EN988w2mT5+uHJieK/d45XL5W4+3oPOkWbNmyMzMxK+//op79+4pA6rmzZvj2LFjsLOzQ7Vq1VQuRHF2ds73+yd3e2F9/vnn8PDwwNdffw0rKytMmjQpTxl3d3eMHz8e48ePR0xMDOrVq4c5c+Zg7dq1+dZbo0YNrFu3Ds+fP4eVlZVy/datW5Geno6FCxfmOS9v3ryJr776CidOnFDpFn1dbuAUHBxc6ICioPM2171795CZmanyt0mdrl27IjQ0FGvXri1S8Ltz505kZGRgx44dKj0d+XWtv/fee3jvvffw7bffYv369RgwYAA2bNiAoUOHAsiZkqFr167o2rUrFAoFRo0ahcWLF2Pq1Kn59ngUlru7Oy5evIg2bdrodXb7//ZP2P+AL774Aubm5hg6dCgePnyYZ3tcXJzyl1KnTp0AIM9M2j/++CMA6HQ+KHd3dzx//hyXLl1SrktMTMxzZePTp0/zPDf3aqXXU7Svc3BwQL169bBq1SqVYO7KlSv4448/lMepD61atcKMGTPw888/w97ePt9yZcqUyfOr8LfffsO9e/dU1uUGhLqYsXrixIlISEjAqlWr8OOPP8LFxQV+fn75vo5v06lTJzx48AAbN25Urnv16hV++uknWFhY5AmSCis3m/XFF1+gd+/eKkufPn3QokWLArsvXmdubq6TbqrX9erVC0IItb/YC/qlX9j3XBu5f+S2b9+e524Cvr6+kMvlmDlzptoxJblz3gE5n88bN26orLt48eJbr/Z9XW6W+PVj/vPPP3Hq1KlC11GYfeRmtd680q1hw4Zwd3fH7NmzlRmG171+bAV9zry9vWFsbIxZs2ahfPnyynkHmzVrhtOnT+PIkSN5/qB36tQJZ86cUTnW1NRULFmyBC4uLhqPk5k6dSqCgoIwefJkLFy4ULk+LS0tz1xI7u7usLS0fOvn2sfHB0KIPLOzr127Fm5ubhgxYkSez19QUBAsLCze+vkbO3YsrK2tERISUqjjK+i8zZXbzrddQe/j44MOHTpg2bJlasfvZWZmIigoKN/nqztvnz9/jpUrV6qUe/bsWZ7P85t/m548eaKyXSqVKq8gLOr37uv69OmDe/fuYenSpXm2vXz5stDzDr4NM1pv4e7ujvXr1+Ojjz6Cp6cnPvnkE9SqVQuZmZk4efKk8nJ8AKhbty78/PywZMkSJCUloUWLFjhz5gxWrVqFDz74AK1atdJZu/r27YuJEyeiR48eGDNmjPIy82rVqqkMlA0JCcHRo0fRuXNnODs749GjR/jll19QuXLlfH9RATndBR07doSPjw+GDBminN7ByspKr/dUk0qlhcq0dOnSBSEhIRg0aBDef/99XL58GevWrYObm5tKOXd3d1hbW2PRokWwtLSEubk5vL29Ne5rP3jwIH755RdMmzZNeRn1ypUr0bJlS0ydOhXff/+9RvUBwPDhw7F48WL4+/sjKioKLi4u2Lx5M06cOIGwsLAiD0Zdt24d6tWrBycnJ7Xbu3Xrhs8//7zQl4Rv3LgR48aNQ+PGjWFhYaH2ViGaaNWqFT7++GPMnz8fMTEx6NChAxQKBY4dO4ZWrVph9OjRap9X2PdcW7mX2V+8eFElcyuXy7Fw4UJ8/PHHaNCgAfr27QtbW1skJCRg9+7daNKkCX7++WcAwODBg/Hjjz/C19cXQ4YMwaNHj7Bo0SLUrFmz0BeIdOnSBVu3bkWPHj3QuXNn3L59G4sWLYKXl5fawKeoBgwYgBkzZuT5Ay2VSrFs2TJ07NgRNWvWxKBBg1CpUiXcu3cPhw4dglwux86dOwH8O3XAlClT0LdvXxgbG6Nr164wNzdH2bJl0bBhQ5w+fRpdu3ZVZg6aN2+O1NRUpKam5gm0Jk2ahF9//RUdO3bEmDFjUL58eaxatQq3b9/Gli1bitTN/cMPP+D58+f47LPPYGlpiYEDB+Kvv/5CmzZt0KdPH3h5ecHIyAjbtm3Dw4cPC8yoAzldojY2Nti/f79y8PT9+/dx6NChfCf0lMlk8PX1xW+//Yb58+fnOyGvlZUVAgICNJrANL/zNldkZCSqVKlS4NQOuVavXo327dujZ8+e6Nq1K9q0aQNzc3PExMRgw4YNSExMzPeWaO3bt1dmoT799FOkpKRg6dKlqFixIhITE5XlVq1ahV9++QU9evSAu7s7Xrx4gaVLl0Iulyt/zA8dOhRPnz5F69atUblyZfz999/46aefUK9evbdm5grj448/xqZNmzBixAgcOnQITZo0QXZ2Nm7cuIFNmzZh3759urnlncbXKZZSf/31lxg2bJhwcXERJiYmwtLSUjRp0kT89NNPKhObZWVlienTpwtXV1dhbGwsnJycCpyw9E1vXn6a3/QOQuRMRFqrVi1hYmIiqlevLtauXZvnUtsDBw6I7t27C0dHR2FiYiIcHR1Fv379xF9//ZVnH29eDr9//37RpEkTYWZmJuRyuejatWu+E5a+eRlt7qXVb5v0rzC3Oclveofx48cLBwcHYWZmJpo0aSJOnTql9rL67du3KyeYe/04X7+s+k2v15OcnCycnZ1FgwYNRFZWlkq5wMBAIZVKxalTpwo8hvze74cPH4pBgwaJChUqCBMTE1G7du0870NB58CboqKiBIACb7URHx8vAIjAwEAhRMHvQUpKiujfv7+wtrYWKGDCUiEKP72DEDlTF/zwww+iRo0awsTERNja2oqOHTuqTNaobnqHwrznRZne4U2557W61+XQoUPC19dXWFlZCVNTU+Hu7i78/f3z3D5l7dq1yomL69WrJ/bt21fghKVvUigUYubMmcLZ2VnIZDJRv359sWvXLrWvJzSc3uFNue+dus/yhQsXRM+ePYWNjY2QyWTC2dlZ9OnTRxw4cECl3IwZM0SlSpWEVCrN89mfMGGCACBmzZql8hwPDw8BQMTFxeVpU+6EpdbW1sLU1FT873//y3fCUnXvobrzMTs7W/Tr108YGRmJiIgI8fjxY/HZZ5+JGjVqCHNzc2FlZSW8vb3Fpk2b8n8hXzNmzBjh4eGhfDxnzhwBIM9r87rw8HCB1yZsze976NmzZ8LKyqrA6R3elN95m52dLRwcHMRXX31VqOMSImeKhtmzZ4vGjRsLCwsLYWJiIqpWrSo+//xzERsbm2efr9uxY4eoU6eOchLSWbNmiRUrVqicF+fPnxf9+vUTVapUETKZTFSsWFF06dJF5XO0efNm0b59e1GxYkVhYmIiqlSpIj799FORmJiY5/UoyvQOQuRMxTFr1ixRs2ZNIZPJRLly5UTDhg3F9OnTxfPnz5Xl8P8TlhaF5P8rICIiIg3cunULNWrUwN69e9GmTZvibk6+IiIi0L9/f8TFxcHBwaG4m1PqMNAiIiIqopEjRyI2NlZvcz3pgo+PD5o1a1akIQ6kPQZaRERERHrCqw6JiIiI9ISBFhEREb3TXFxcIJFI8iy5N7lOT0/HZ599BhsbG1hYWKBXr15qp3QqCnYdEhER0Tvtn3/+Ubkzw5UrV9CuXTscOnQILVu2xMiRI7F7926Eh4fDysoKo0ePhlQq1Wjuu/ww0CIiIqJSZezYsdi1axdiYmKQnJwMW1tbrF+/Hr179waQcxcCT09PnDp1Cu+9955W++KEpaQTCoUC9+/fh6WlpV5vZUBERPohhMCLFy/g6Oiot3ufpqenIzMzUyd1CTU3yZbJZJDJZAU+LzMzE2vXrsW4ceMgkUgQFRWFrKwslVtN1ahRA1WqVGGgRf8d9+/fz3cmciIiKjnu3LmDypUr67ze9PR0mFnaAK/SdFKfhYVFnrskTJs27a13L4mIiEBSUpLyri4PHjyAiYlJnvtE2tnZ4cGDB1q3k4EW6UTu7WIaT90CI1P93HCaqLgFtnYv7iYQ6U1a6gv4t21Q5Nt/vU1mZibwKg0yLz+gjIl2lWVnIuXaKty5cwdyuVy5+m3ZLABYvnw5OnbsCEdHR+3aUEgMtEgnctO3RqbmDLTonVXWQj9/gIj+S/Q+/MPIFBItAy0hyenalMvlKoHW2/z999/Yv38/tm7dqlxnb2+PzMxMJCUlqWS1Hj58CHt7e63aCXB6ByIiIjIkCQCJRMulaLteuXIlKlasiM6dOyvXNWzYEMbGxjhw4IBy3c2bN5GQkAAfHx8tD5YZLSIiIjIkiTRn0bYODSkUCqxcuRJ+fn4wMvo3/LGyssKQIUMwbtw4lC9fHnK5HJ9//jl8fHy0HggPMNAiIiKiUmD//v1ISEjA4MGD82ybO3cupFIpevXqhYyMDPj6+uKXX37RyX4ZaBEREZHh5Hb/aVuHhtq3b4/8pg41NTXFggULsGDBAu3apQYDLSIiIjKcYuo6LC4lp6VEREREJQwzWkRERGQ4xdR1WFwYaBEREZEB6aDrsAR1yJWclhIRERGVMMxoERERkeGw65CIiIhIT3jVIRERERHpAjNaREREZDjsOiQiIiLSk1LWdchAi4iIiAynlGW0Sk5ISERERFTCMKNFREREhsOuQyIiIiI9kUh0EGix65CIiIio1GNGi4iIiAxHKslZtK2jhGCgRURERIZTysZolZyWEhEREZUwzGgRERGR4ZSyebQYaBEREZHhsOuQiIiIiHSBGS0iIiIyHHYdEhEREelJKes6ZKBFREREhlPKMlolJyQkIiIiKmGY0SIiIiLDYdchERERkZ6w65CIiIiIdIEZLSIiIjIgHXQdlqA8EQMtIiIiMhx2HRIRERGRLjCjRURERIYjkejgqsOSk9FioEVERESGU8qmdyg5LSUiIiIqYZjRIiIiIsMpZYPhGWgRERGR4bDrkIiIiEhPcjNa2i4aunfvHgYOHAgbGxuYmZmhdu3aOHfunHK7v78/JBKJytKhQwetD5cZLSIiInqnPXv2DE2aNEGrVq2wd+9e2NraIiYmBuXKlVMp16FDB6xcuVL5WCaTab1vBlpERERkOMXQdThr1iw4OTmpBFGurq55yslkMtjb22vXtjew65CIiIgMpxi6Dnfs2IFGjRrhww8/RMWKFVG/fn0sXbo0T7nDhw+jYsWKqF69OkaOHIknT55ofbgMtIiIiKhESk5OVlkyMjLUlrt16xYWLlyIqlWrYt++fRg5ciTGjBmDVatWKct06NABq1evxoEDBzBr1iwcOXIEHTt2RHZ2tlZtZNchERERGUzuQHMtKwEAODk5qayeNm0agoOD8xRXKBRo1KgRZs6cCQCoX78+rly5gkWLFsHPzw8A0LdvX2X52rVro06dOnB3d8fhw4fRpk2bIjeVgRYREREZjC4DrTt37kAulytX5zd43cHBAV5eXirrPD09sWXLlnx34ebmhgoVKiA2NpaBFhEREZU+crlcJdDKT5MmTXDz5k2VdX/99RecnZ3zfc7du3fx5MkTODg4aNVGjtEiIiIiw5HoaNFAYGAgTp8+jZkzZyI2Nhbr16/HkiVL8NlnnwEAUlJSMGHCBJw+fRrx8fE4cOAAunfvDg8PD/j6+mp1uAy0iIiIyGDenBS0qIsmGjdujG3btuHXX39FrVq1MGPGDISFhWHAgAEAgDJlyuDSpUvo1q0bqlWrhiFDhqBhw4Y4duyY1nNpseuQiIiI3nldunRBly5d1G4zMzPDvn379LJfBlpERERkMLocDF8SMNAiIiIig2GgRURERKQnpS3Q4mB4IiIiIj1hRouIiIgMpwjTM6ito4RgoEVEREQGw65DIiIiItIJZrSIiIjIYCQS6CCjpZu2GAIDLSIiIjIYCXTQdViCIi12HRIRERHpCTNaREREZDClbTA8Ay0iIiIynFI2vQO7DomIiIj0hBktIiIiMhwddB0Kdh0SERER5aWLMVraX7VoOAy0iIiIyGBKW6DFMVpEREREesKMFhERERlOKbvqkIEWERERGQy7DomIiIhIJ5jRIiIiIoMpbRktBlpERERkMKUt0GLXIREREZGeMKNFREREBlPaMloMtIiIiMhwStn0Duw6JCIiItITZrSIiIjIYNh1SERERKQnDLSIiIiI9KS0BVoco0VERESkJ8xoERERkeGUsqsOGWgRERGRwbDrkIiIiIh0osRktCQSCbZt24YPPvhA7fb4+Hi4urriwoULqFevnkHbZihpaWn4+OOPERkZiRcvXuDZs2ewtrbWut7g4GBEREQgOjpa67pItz72roKPvauorLvzNA1D1p5XPva0t8QgH2fUsLdEthC49U8qJkdcRWa2wtDNJdLKtp0nsG7TQXT2/R8GDfQFACxesRuXrt7Gs2cvYGpqgmpVK+Pjj9qgkmOFYm4tFRUzWgbk7++vfMGNjY1hZ2eHdu3aYcWKFVAoVP9IJCYmomPHjlrtr2XLlpBIJNiwYYPK+rCwMLi4uGhUl0QiQURERKHK5S7m5uaoWrUq/P39ERUVpdH+AGDVqlU4duwYTp48icTERFhZWWlcR2H4+/vnG9CS4cU/ScVHy/5ULoGbLym3edpbYmb3mohKSMLnGy/i8w0Xsf1SIgREMbaYSHOxt+4j8uB5ODtVVFnv5uKAz4Z1Rdiskfjqi/6AEJjx/TpkK/hDoqSSQKLyt7FISwkapFXsXYcdOnRAYmIi4uPjsXfvXrRq1QoBAQHo0qULXr16pSxnb28PmUym9f5MTU3x1VdfISsrS+u6CmvlypVITEzE1atXsWDBAqSkpMDb2xurV6/WqJ64uDh4enqiVq1asLe3L1ERPRVdtkLgWVqWcklO//dzMaK5KyIu3sfGqLv4+2ka7ia9xNGYx8jKZqBFJcfL9EzMW7gNI4Z0hrm5mcq2dq0bwKuGMyraWsPNxQF9e7fC4yfJ+OefpOJpLJGGij3QkslksLe3R6VKldCgQQN8+eWX2L59O/bu3Yvw8HBluTczSGfOnEH9+vVhamqKRo0a4cKFC4XaX79+/ZCUlISlS5cWWG7hwoVwd3eHiYkJqlevjjVr1ii35Wa/evToAYlE8tZsmLW1Nezt7eHi4oL27dtj8+bNGDBgAEaPHo1nz54pyx0/fhzNmjWDmZkZnJycMGbMGKSmpgLIycbNmTMHR48ehUQiQcuWLQEAa9asQaNGjWBpaQl7e3v0798fjx49UtYZHh6ep3sxIiIi3yAtODgYq1atwvbt25W/HA4fPlzg8ZF+VbI2w6+DG2OVXyNMal8NthY5PziszYzhaS9HUloW5n5YBxuH/g+ze9VGTQd5MbeYSDPLVu1Fg7pVUaeWW4Hl0tMzcejoRVS0tYaNjX4y+qR/WmezdND1aEjFHmip07p1a9StWxdbt25Vuz0lJQVdunSBl5cXoqKiEBwcjKCgoELVLZfLMWXKFISEhCiDmDdt27YNAQEBGD9+PK5cuYJPP/0UgwYNwqFDhwAAZ8+eBfBvpir3sSYCAwPx4sULREZGAsjJVnXo0AG9evXCpUuXsHHjRhw/fhyjR48GAGzduhXDhg2Dj48PEhMTla9NVlYWZsyYgYsXLyIiIgLx8fHw9/fXuD25goKC0KdPH2WmMTExEe+//36R6yPt3HjwAj9E/oUvt1/F/EOxsLMyxY+9a8PMuAzsrUwB5Izj2nvlAb7cfhWxj1Iwq2ctOP7/NqL/uuOnruB2fCIG9Gmdb5nf95/DwKHfYeCwWbhwKRZfTxwAY6MyBmwl6ZRER4uG7t27h4EDB8LGxgZmZmaoXbs2zp07p9wuhMDXX38NBwcHmJmZoW3btoiJiSn6cf6//2SgBQA1atRAfHy82m3r16+HQqHA8uXLUbNmTXTp0gUTJkwodN2jRo2CqakpfvzxR7XbZ8+eDX9/f4waNQrVqlXDuHHj0LNnT8yePRsAYGtrC+DfTFXuY03UqFEDAJTHGBoaigEDBmDs2LGoWrUq3n//fcyfPx+rV69Geno6ypcvj7Jly8LExAT29vYoX748AGDw4MHo2LEj3Nzc8N5772H+/PnYu3cvUlJSNG4TAFhYWMDMzEyZabS3t4eJiUmechkZGUhOTlZZSPfO/v0Mx2Kf4PaTNEQlJOGr7VdhITNCi6oVlB/e3Vce4I/rjxD3TyoWHbuNu89eokNNu2JtN1FhPH7yHCvX/oExI3vAxCT/a7OavV8LP3wzDCFTPoGjfXn8+PMWZGa+yrc80ZuePXuGJk2awNjYGHv37sW1a9cwZ84clCtXTlnm+++/x/z587Fo0SL8+eefMDc3h6+vL9LT07Xa93/2qkMhRL6pwevXr6NOnTowNf33V7uPj0+h65bJZAgJCcHnn3+OkSNHqq1/+PDhKuuaNGmCefPmFXofbyNEzhia3GO8ePEiLl26hHXr1qmUUSgUuH37Njw9PdXWk5vRu3jxIp49e6a8iCAhIQFeXl46a++bQkNDMX36dL3VT+qlZmbjbtJLOFqbIvpuEgAg4WmaSpmEp2moaKH9eEYifbt1OxHPk1PxxdR/h3IoFALXb/6NvZFn8evKL1FGKoV5WVOYlzWFg70NqnpUhv+nP+BM1A009alVjK2noiqOqw5nzZoFJycnrFy5UrnO1dVV+W8hBMLCwvDVV1+he/fuAIDVq1fDzs4OERER6Nu3b5Hb+p8NtK5fv67yIujawIEDMXv2bHzzzTcaX3GoC9evXwfw7xudkpKCTz/9FGPGjMlTtkqVKnnWAUBqaip8fX3h6+uLdevWwdbWFgkJCfD19UVmZiYAQCqVKoO6XLq4EGDy5MkYN26c8nFycjKcnJy0rpcKZmoshYOVKQ7cyMSD5Aw8TslA5XKqg4crlzPD2fhn+dRA9N9Ru6Yrfpz5qcq6BUt3oJJjBXzQ+X2UkarpdBECAgJZWdkGaiXpmi4DrTd7U2QymdoL53bs2AFfX198+OGHOHLkCCpVqoRRo0Zh2LBhAIDbt2/jwYMHaNu2rfI5VlZW8Pb2xqlTp7QKtP6TXYcHDx7E5cuX0atXL7XbPT09cenSJZV03unTpzXah1QqRWhoKBYuXJini9LT0xMnTpxQWXfixAmVDJGxsTGys4v+QQ8LC4NcLle+qQ0aNMC1a9fg4eGRZ1HXdQcAN27cwJMnT/Ddd9+hWbNmqFGjhspAeCCnm/PFixcq49HeNl+WiYnJW49NJpNBLperLKR7w5q6oHYlOewsZfCyt0RwZ08oBHDor38AAL+dv4cP6jqimYcNHK1M4fdeFTiVM8Pv1x4Wc8uJ3s7MTIYqThVVFpnMBJYWZqjiVBEPHz3D1h3HEXc7Ef88fo4bf93BnJ82w8TEGA3qehR386mIJBLdLADg5OQEKysr5RIaGqp2n7du3cLChQtRtWpV7Nu3DyNHjsSYMWOwatUqAMCDBw8AAHZ2qsMu7OzslNuKqtgzWhkZGXjw4AGys7Px8OFD/P777wgNDUWXLl3wySefqH1O//79MWXKFAwbNgyTJ09GfHy8cvyUJjp37gxvb28sXrxY5cWdMGEC+vTpg/r166Nt27bYuXMntm7div379yvLuLi44MCBA2jSpAlkMplKP++bkpKS8ODBA2RkZOCvv/7C4sWLERERgdWrVyuvCJw4cSLee+89jB49GkOHDoW5uTmuXbuGyMhI/Pzzz2rrrVKlCkxMTPDTTz9hxIgRuHLlCmbMmKFSxtvbG2XLlsWXX36JMWPG4M8//1S5mlMdFxcX7Nu3Dzdv3oSNjQ2srKxgbGz8lleT9MHWQoYvfavD0swYz19m4er9ZARsuojnL3PGp2yLvg+TMlKMaOYGS1MjxD1OxaRtV5H4XLsxBUT/BcbGRrh+8w527zuD1NSXsLKygGf1Kvj2a39YWZkXd/PoP+DOnTsqP/TzmwZKoVCgUaNGmDlzJgCgfv36uHLlChYtWgQ/Pz+9trHYA63ff/8dDg4OMDIyQrly5VC3bl3Mnz8ffn5+kKpLGyNnwPbOnTsxYsQI1K9fH15eXpg1a1a+GbCCzJo1K89VdR988AHmzZuH2bNnIyAgAK6urli5cqVySgUAmDNnDsaNG4elS5eiUqVK+Q7cB4BBgwYByJnDq1KlSmjatCnOnDmDBg0aKMvUqVMHR44cwZQpU9CsWTMIIeDu7o6PPvoo33ptbW0RHh6OL7/8EvPnz0eDBg0we/ZsdOvWTVmmfPnyWLt2LSZMmIClS5eiTZs2CA4OzjMG7XXDhg3D4cOH0ahRI6SkpODQoUMqx06GM/P3m28tszHqLjZG3TVAa4j0L2TKvz+wy5ezxJQJ/YqxNaQPORkpbbsOc/5f2B4VBweHPOOWPT09sWXLFgA5c3UCwMOHD+Hg4KAs8/DhQ63vNiMRbw7gISqC5ORkWFlZwefb32Fkyl+a9G6a2L5qcTeBSG/SUl6gj09VPH/+XC/DQXL/TriN2YwyMu3+TmRnpOLW/N6Fbmv//v1x584dHDt2TLkuMDAQf/75J06ePAkhBBwdHREUFITx48cr21uxYkWEh4e/m4PhiYiIiHQhMDAQ77//PmbOnIk+ffrgzJkzWLJkCZYsWQIgJ8M2duxYfPPNN6hatSpcXV0xdepUODo6an1LOgZaREREZDDFMb1D48aNsW3bNkyePBkhISFwdXVFWFgYBgwYoCzzxRdfIDU1FcOHD0dSUhKaNm2K33//XWUqqaJgoEVEREQG8/pVg9rUoakuXbqgS5cuBdQpQUhICEJCQrRoWV7/yekdiIiIiN4FzGgRERGRwUilEkil2qW0hJbPNyQGWkRERGQwxdV1WFzYdUhERESkJ8xoERERkcEUx1WHxYmBFhERERlMaes6ZKBFREREBlPaMloco0VERESkJ8xoERERkcGUtowWAy0iIiIymNI2Rotdh0RERER6wowWERERGYwEOug6RMlJaTHQIiIiIoNh1yERERER6QQzWkRERGQwvOqQiIiISE/YdUhEREREOsGMFhERERkMuw6JiIiI9KS0dR0y0CIiIiKDKW0ZLY7RIiIiItITZrSIiIjIcHTQdViCJoZnoEVERESGw65DIiIiItIJZrSIiIjIYHjVIREREZGesOuQiIiIiHSCGS0iIiIyGHYdEhEREekJuw6JiIiISCeY0SIiIiKDKW0ZLQZaREREZDAco0VERESkJ6Uto8UxWkRERER6wowWERERGQy7DomIiIj0hF2HRERERO+Q4OBgZYCXu9SoUUO5vWXLlnm2jxgxQif7ZkaLiIiIDEYCHXQdFuE5NWvWxP79+5WPjYxUQ6Bhw4YhJCRE+bhs2bJFbZ4KBlpERERkMFKJBFItI62iPN/IyAj29vb5bi9btmyB24uKXYdERERUIiUnJ6ssGRkZ+ZaNiYmBo6Mj3NzcMGDAACQkJKhsX7duHSpUqIBatWph8uTJSEtL00kbmdEiIiIig9HlVYdOTk4q66dNm4bg4OA85b29vREeHo7q1asjMTER06dPR7NmzXDlyhVYWlqif//+cHZ2hqOjIy5duoSJEyfi5s2b2Lp1q3YNBQMtIiIiMiBdXnV4584dyOVy5XqZTKa2fMeOHZX/rlOnDry9veHs7IxNmzZhyJAhGD58uHJ77dq14eDggDZt2iAuLg7u7u5atZWBFhERERmMVJKzaFsHAMjlcpVAq7Csra1RrVo1xMbGqt3u7e0NAIiNjdU60OIYLSIiIipVUlJSEBcXBwcHB7Xbo6OjASDf7ZpgRouIiIgMR6KDCUc1fHpQUBC6du0KZ2dn3L9/H9OmTUOZMmXQr18/xMXFYf369ejUqRNsbGxw6dIlBAYGonnz5qhTp4527QQDLSIiIjKg4rgFz927d9GvXz88efIEtra2aNq0KU6fPg1bW1ukp6dj//79CAsLQ2pqKpycnNCrVy989dVX2jXy/zHQIiIionfahg0b8t3m5OSEI0eO6G3fDLSIiIjIYCT//5+2dZQUDLSIiIjIYHR51WFJwKsOiYiIiPSEGS0iIiIyGF1OWFoSFCrQ2rFjR6Er7NatW5EbQ0RERO+24rjqsDgVKtD64IMPClWZRCJBdna2Nu0hIiIiemcUKtBSKBT6bgcRERGVAlKJBFItU1LaPt+QtBqjlZ6eDlNTU121hYiIiN5xpa3rUOOrDrOzszFjxgxUqlQJFhYWuHXrFgBg6tSpWL58uc4bSERERO+O3MHw2i4lhcaB1rfffovw8HB8//33MDExUa6vVasWli1bptPGEREREZVkGgdaq1evxpIlSzBgwACUKVNGub5u3bq4ceOGThtHRERE75bcrkNtl5JC4zFa9+7dg4eHR571CoUCWVlZOmkUERERvZtK22B4jTNaXl5eOHbsWJ71mzdvRv369XXSKCIiIqJ3gcYZra+//hp+fn64d+8eFAoFtm7dips3b2L16tXYtWuXPtpIRERE7wjJ/y/a1lFSaJzR6t69O3bu3In9+/fD3NwcX3/9Na5fv46dO3eiXbt2+mgjERERvSNK21WHRZpHq1mzZoiMjNR1W4iIiIjeKUWesPTcuXO4fv06gJxxWw0bNtRZo4iIiOjdJJXkLNrWUVJoHGjdvXsX/fr1w4kTJ2BtbQ0ASEpKwvvvv48NGzagcuXKum4jERERvSN00fVXkroONR6jNXToUGRlZeH69et4+vQpnj59iuvXr0OhUGDo0KH6aCMRERFRiaRxRuvIkSM4efIkqlevrlxXvXp1/PTTT2jWrJlOG0dERETvnhKUkNKaxoGWk5OT2olJs7Oz4ejoqJNGERER0buJXYdv8cMPP+Dzzz/HuXPnlOvOnTuHgIAAzJ49W6eNIyIiondL7mB4bZeSolAZrXLlyqlEj6mpqfD29oaRUc7TX716BSMjIwwePBgffPCBXhpKREREVNIUKtAKCwvTczOIiIioNChtXYeFCrT8/Pz03Q4iIiIqBUrbLXiKPGEpAKSnpyMzM1NlnVwu16pBRERERO8KjQOt1NRUTJw4EZs2bcKTJ0/ybM/OztZJw4iIiOjdI5VIINWy60/b5xuSxlcdfvHFFzh48CAWLlwImUyGZcuWYfr06XB0dMTq1av10UYiIiJ6R0gkullKCo0zWjt37sTq1avRsmVLDBo0CM2aNYOHhwecnZ2xbt06DBgwQB/tJCIiIipxNM5oPX36FG5ubgByxmM9ffoUANC0aVMcPXpUt60jIiKid0ruVYfaLiWFxoGWm5sbbt++DQCoUaMGNm3aBCAn05V7k2kiIiIidUpb16HGgdagQYNw8eJFAMCkSZOwYMECmJqaIjAwEBMmTNB5A4mIiIhKKo3HaAUGBir/3bZtW9y4cQNRUVHw8PBAnTp1dNo4IiIiereUtqsOtZpHCwCcnZ3h7Oysi7YQERHRO04XXX8lKM4qXKA1f/78Qlc4ZsyYIjeGiIiI3m28BY8ac+fOLVRlEomEgRYRERHR/ytUoJV7lSHR20SM8OFtmOidVa7x6OJuApHeiOzMtxfSASmKcCWemjo0ERwcjOnTp6usq169Om7cuAEg55aC48ePx4YNG5CRkQFfX1/88ssvsLOz07Kl2h8rERERUaEV1zxaNWvWRGJionI5fvy4cltgYCB27tyJ3377DUeOHMH9+/fRs2dPnRyv1oPhiYiIiP7rjIyMYG9vn2f98+fPsXz5cqxfvx6tW7cGAKxcuRKenp44ffo03nvvPa32y4wWERERGYxEAki1XIoyFj4mJgaOjo5wc3PDgAEDkJCQAACIiopCVlYW2rZtqyxbo0YNVKlSBadOndL6eJnRIiIiIoPJDZa0rQMAkpOTVdbLZDLIZLI85b29vREeHo7q1asjMTER06dPR7NmzXDlyhU8ePAAJiYmee5uY2dnhwcPHmjXUDDQIiIiohLKyclJ5fG0adMQHBycp1zHjh2V/65Tpw68vb3h7OyMTZs2wczMTK9tLFLX4bFjxzBw4ED4+Pjg3r17AIA1a9aoDCwjIiIiepMuB8PfuXMHz58/Vy6TJ08uVBusra1RrVo1xMbGwt7eHpmZmUhKSlIp8/DhQ7VjujSlcaC1ZcsW+Pr6wszMDBcuXEBGRgaAnMFkM2fO1LpBRERE9O7SdnzW612PcrlcZVHXbahOSkoK4uLi4ODggIYNG8LY2BgHDhxQbr958yYSEhLg4+Oj/fFq+oRvvvkGixYtwtKlS2FsbKxc36RJE5w/f17rBhERERHpUlBQEI4cOYL4+HicPHkSPXr0QJkyZdCvXz9YWVlhyJAhGDduHA4dOoSoqCgMGjQIPj4+Wl9xCBRhjNbNmzfRvHnzPOutrKzypN2IiIiIXlcc9zq8e/cu+vXrhydPnsDW1hZNmzbF6dOnYWtrCyDnDjhSqRS9evVSmbBUFzQOtOzt7REbGwsXFxeV9cePH4ebm5tOGkVERETvJqlEAqmWkZamz9+wYUOB201NTbFgwQIsWLBAm2appXHX4bBhwxAQEIA///wTEokE9+/fx7p16xAUFISRI0fqvIFERET07pDqaCkpNM5oTZo0CQqFAm3atEFaWhqaN28OmUyGoKAgfP755/poIxEREVGJpHGgJZFIMGXKFEyYMAGxsbFISUmBl5cXLCws9NE+IiIieocUxxit4lTkCUtNTEzg5eWly7YQERHRO04KHYzRQsmJtDQOtFq1alXgXbMPHjyoVYOIiIiI3hUaB1r16tVTeZyVlYXo6GhcuXIFfn5+umoXERERvYPYdfgWc+fOVbs+ODgYKSkpWjeIiIiI3l26vKl0SaCzKyQHDhyIFStW6Ko6IiIiohKvyIPh33Tq1CmYmprqqjoiIiJ6B0kkmk84qq6OkkLjQKtnz54qj4UQSExMxLlz5zB16lSdNYyIiIjePRyj9RZWVlYqj6VSKapXr46QkBC0b99eZw0jIiIiKuk0CrSys7MxaNAg1K5dG+XKldNXm4iIiOgdxcHwBShTpgzat2+PpKQkPTWHiIiI3mUSHf1XUmh81WGtWrVw69YtfbSFiIiI3nG5GS1tl5JC40Drm2++QVBQEHbt2oXExEQkJyerLERERESUo9BjtEJCQjB+/Hh06tQJANCtWzeVW/EIISCRSJCdna37VhIREdE7obSN0Sp0oDV9+nSMGDEChw4d0md7iIiI6B0mkUgKvGdyYesoKQodaAkhAAAtWrTQW2OIiIiI3iUaTe9QkiJIIiIi+u9h12EBqlWr9tZg6+nTp1o1iIiIiN5dnBm+ANOnT88zMzwRERERqadRoNW3b19UrFhRX20hIiKid5xUItH6ptLaPt+QCh1ocXwWERERaau0jdEq9ISluVcdEhEREVHhFDqjpVAo9NkOIiIiKg10MBi+BN3qULMxWkRERETakEICqZaRkrbPNyQGWkRERGQwpW16B41vKk1EREREhcOMFhERERlMabvqkIEWERERGUxpm0eLXYdEREREesKMFhERERlMaRsMz0CLiIiIDEYKHXQdlqDpHdh1SERERKQnzGgRERGRwbDrkIiIiEhPpNC+O60kdceVpLYSERERlSgMtIiIiMhgJBKJTpai+u677yCRSDB27FjlupYtW+apf8SIETo4WnYdEhERkQFJ/n/Rto6iOHv2LBYvXow6derk2TZs2DCEhIQoH5ctW7aIe1HFjBYREREZTO7M8NoumkpJScGAAQOwdOlSlCtXLs/2smXLwt7eXrnI5XJdHC4DLSIiIiqZkpOTVZaMjIx8y3722Wfo3Lkz2rZtq3b7unXrUKFCBdSqVQuTJ09GWlqaTtrIrkMiIiIyKF3NzuDk5KTyeNq0aQgODs5TbsOGDTh//jzOnj2rtp7+/fvD2dkZjo6OuHTpEiZOnIibN29i69atWreRgRYREREZjC7n0bpz545KF59MJstT9s6dOwgICEBkZCRMTU3V1jd8+HDlv2vXrg0HBwe0adMGcXFxcHd316qtDLSIiIioRJLL5W8dSxUVFYVHjx6hQYMGynXZ2dk4evQofv75Z2RkZKBMmTIqz/H29gYAxMbGMtAiIiKikkPb6Rly6yisNm3a4PLlyyrrBg0ahBo1amDixIl5giwAiI6OBgA4ODho1U6AgRYREREZkKFnhre0tEStWrVU1pmbm8PGxga1atVCXFwc1q9fj06dOsHGxgaXLl1CYGAgmjdvrnYaCE0x0CIiIqJSy8TEBPv370dYWBhSU1Ph5OSEXr164auvvtJJ/Qy0iIiIyGAM3XWozuHDh5X/dnJywpEjR7SqryAMtIiIiMhginNm+OLACUuJiIiI9IQZLSIiIjKY/0LXoSEx0CIiIiKDMfRVh8WNgRYREREZTGnLaJWkoJCIiIioRGFGi4iIiAymtF11yECLiIiIDEaXN5UuCdh1SERERKQnzGgRERGRwUghgVTLzj9tn29IDLSIiIjIYNh1SEREREQ6wYwWERERGYzk///Tto6SgoEWERERGQy7DomIiIhIJ5jRIiIiIoOR6OCqQ3YdEhEREalR2roOGWgRERGRwZS2QItjtIiIiIj0hBktIiIiMhhO70BERESkJ1JJzqJtHSUFuw6JiIiI9IQZLSIiIjIYdh0SERER6QmvOiQiIiIinWBGi4iIiAxGAu27/kpQQouBFhERERkOrzokIiIiIp1goGUAS5YsgZOTE6RSKcLCwnRSZ3x8PCQSCaKjo3VS3+sOHz4MiUSCpKQknddNmlm++Ria9JuJKi2DUKVlENoPno3IE1eV28O3HkeXT8NQpWUQyjUejecv0oqxtUSaubh9Op6d/TnP8sMXfQAAMhMj/PBFH8RFzsKdI3OwatZQ2Ja3LOZWk7YkOvqvpGCglQ9/f39IJBJIJBIYGxvDzs4O7dq1w4oVK6BQKApdT3JyMkaPHo2JEyfi3r17GD58uF7ay+Do3eRY0RrTRnfHodVf4OCqCWjWqBoGBC3B9bhEAMDL9Cy08fFCoH/7Ym4pkeZa+/2A6h0mK5cPPvsJABCx/wIAYGZgL3RoVgv+k5ejy6dhsK9ghTXfDy3OJpMO5F51qO1SUnCMVgE6dOiAlStXIjs7Gw8fPsTvv/+OgIAAbN68GTt27ICR0dtfvoSEBGRlZaFz585wcHAwQKvpXdKxeW2Vx1NHdcOKLcdx7spteLo7YGT/VgCA41F/FUfziLTyJClF5fFYv1q4decfnDgfA7m5KQZ298Gwr8Jx7FzO+T06ZC3ObJ6KRrVccO5KfDG0mHRBAu0Hs5egOIsZrYLIZDLY29ujUqVKaNCgAb788kts374de/fuRXh4OAAgKSkJQ4cOha2tLeRyOVq3bo2LFy8CAMLDw1G7ds4fSjc3N0gkEsTHxyMuLg7du3eHnZ0dLCws0LhxY+zfv19l3xKJBBERESrrrK2tlft9XXx8PFq1yvmDW65cOUgkEvj7+wMAFAoFQkND4erqCjMzM9StWxebN29Wef6ePXtQrVo1mJmZoVWrVoiPj9fuhSO9yM5WYMsf55D2MhONa7sWd3OIdMrYqAz6dGyMdTtOAQDqelaBibERDp+5qSwT8/dD3El8yvOfShQGWhpq3bo16tati61btwIAPvzwQzx69Ah79+5FVFQUGjRogDZt2uDp06f46KOPlAHUmTNnkJiYCCcnJ6SkpKBTp044cOAALly4gA4dOqBr165ISEgoUpucnJywZcsWAMDNmzeRmJiIefPmAQBCQ0OxevVqLFq0CFevXkVgYCAGDhyII0eOAADu3LmDnj17omvXroiOjsbQoUMxadKkt+4zIyMDycnJKgvpx9XYe6jcfBzsmozFuNCNWPPDMNRwY3aU3i2dW9aBlYUZ1u/6EwBgZyNHRmYWklNeqpR79DQZdjby4mgi6YgUEkglWi4lKKfFrsMiqFGjBi5duoTjx4/jzJkzePToEWQyGQBg9uzZiIiIwObNmzF8+HDY2NgAAGxtbWFvbw8AqFu3LurWrausb8aMGdi2bRt27NiB0aNHa9yeMmXKoHz58gCAihUrwtraGkBOMDRz5kzs378fPj4+AHIya8ePH8fixYvRokULLFy4EO7u7pgzZw4AoHr16rh8+TJmzZpV4D5DQ0Mxffp0jdtKmqvqbIej6yYjOeUlth+4gFHBa7BrcQCDLXqnDOz2PvafuoYHj58Xd1NIz0pb1yEDrSIQQkAikeDixYtISUlRBlO5Xr58ibi4uHyfn5KSguDgYOzevRuJiYl49eoVXr58WeSMVn5iY2ORlpaGdu3aqazPzMxE/fr1AQDXr1+Ht7e3yvbcoKwgkydPxrhx45SPk5OT4eTkpINW05tMjI3g5mQLAKjnWQUXriVg0YbDCPuyXzG3jEg3nOzLoeX/quPjL5Yq1z18kgyZiTHkFmYqWa2K5eV4+IQZdCo5GGgVwfXr1+Hq6oqUlBQ4ODjg8OHDecrkZpXUCQoKQmRkJGbPng0PDw+YmZmhd+/eyMzMVJaRSCQQQqg8LysrS6N2pqTkDDTdvXs3KlWqpLItNwNXVDKZTOs6qGgUQiAz81VxN4NIZ/p39cE/z17gj9emLrl4PQGZWa/QonF17DwUDQDwcK4IJ4fyOHv5djG1lHSilKW0OEZLQwcPHsTly5fRq1cvNGjQAA8ePICRkRE8PDxUlgoVKuRbx4kTJ+Dv748ePXqgdu3asLe3zzMA3dbWFomJicrHMTExSEvLf44kExMTAEB2drZynZeXF2QyGRISEvK0Lzf75OnpiTNnzqjUdfr06UK/HqRf03/ejhPnY5Fw/wmuxt7D9J+343hUDD7s2AgA8PBxMi7fvItbdx4DAK7G3sflm3fx7HlqcTabqNAkEgkGdH0PG3b/iezsf6fOSU5Nx9rtp/BtYE80bVgVdWs4YcHXA3Hm0i1ecVjCFfc8Wt999x0kEgnGjh2rXJeeno7PPvsMNjY2sLCwQK9evfDw4UMdHC0zWgXKyMjAgwcPVKZ3CA0NRZcuXfDJJ59AKpXCx8cHH3zwAb7//ntUq1YN9+/fx+7du9GjRw80atRIbb1Vq1bF1q1b0bVrV0gkEkydOjXP3FytW7fGzz//DB8fH2RnZ2PixIkwNjbOt63Ozs6QSCTYtWsXOnXqBDMzM1haWiIoKAiBgYFQKBRo2rQpnj9/jhMnTkAul8PPzw8jRozAnDlzMGHCBAwdOhRRUVFqr2yk4vH4WQpGBq/Gw8fJkFuYoqZHJWz5aRRaeXsCAFZuPYZZS/cqy3ceHgYAWPD1QPTv+l5xNJlIIy3/Vx1ODuWxdkfeH3hfzt0ChRBYPWsoTEyMcPD0dQTN2lgMraR3xdmzZ7F48WLUqVNHZX1gYCB2796N3377DVZWVhg9ejR69uyJEydOaL1PBloF+P333+Hg4AAjIyOUK1cOdevWxfz58+Hn5wepNCcZuGfPHkyZMgWDBg3CP//8A3t7ezRv3hx2dnb51vvjjz9i8ODBeP/991GhQgVMnDgxz1V7c+bMwaBBg9CsWTM4Ojpi3rx5iIqKyrfOSpUqYfr06Zg0aRIGDRqETz75BOHh4ZgxYwZsbW0RGhqKW7duwdraWjlVBQBUqVIFW7ZsQWBgIH766Sf873//w8yZMzF48GAdvIKkrZ+mDihw+6ThnTFpeGcDtYZI9w79eQPlGqu/CCgj8xUmfL8JE77fZOBWkV7pYsLRIjw/JSUFAwYMwNKlS/HNN98o1z9//hzLly/H+vXr0bp1awDAypUr4enpidOnT+O997T70SoRbw4EIiqC5ORkWFlZ4eGT55DLeek1vZvyCwiI3gUiOxMZl5fi+XP9fI/n/p04GJ0AC0vt6k95kYzW9argzp07Km0taPywn58fypcvj7lz56Jly5aoV68ewsLCcPDgQbRp0wbPnj1TGV/t7OyMsWPHIjAwUKu2cowWERERlUhOTk6wsrJSLqGhoWrLbdiwAefPn1e7/cGDBzAxMclzEZudnR0ePHigdRvZdUhERESGo8OrDtVltN50584dBAQEIDIyEqamplruWHPMaBEREZHB6PKqQ7lcrrKoC7SioqLw6NEjNGjQAEZGRjAyMsKRI0cwf/58GBkZwc7ODpmZmUhKSlJ53sOHD5UTjWuDGS0iIiIyGIkOBsNr8vw2bdrg8uXLKusGDRqEGjVqYOLEiXBycoKxsTEOHDiAXr16Aci5nV1CQkKhJvB+GwZaRERE9M6ytLRErVq1VNaZm5vDxsZGuX7IkCEYN24cypcvD7lcjs8//xw+Pj5aX3EIMNAiIiIiA/ovTgw/d+5cSKVS9OrVCxkZGfD19cUvv/yik7oZaBEREZHh/AcirTdvnWdqaooFCxZgwYIF2lWsBgfDExEREekJM1pERERkMNreqzC3jpKCgRYREREZjKGvOixu7DokIiIi0hNmtIiIiMhg/gNj4Q2KgRYREREZTimLtNh1SERERKQnzGgRERGRwfCqQyIiIiI9KW1XHTLQIiIiIoMpZUO0OEaLiIiISF+Y0SIiIiLDKWUpLQZaREREZDClbTA8uw6JiIiI9IQZLSIiIjIYXnVIREREpCelbIgWuw6JiIiI9IUZLSIiIjKcUpbSYqBFREREBsOrDomIiIhIJ5jRIiIiIoPhVYdEREREelLKhmgx0CIiIiIDKmWRFsdoEREREekJM1pERERkMKXtqkMGWkRERGQ4OhgMX4LiLHYdEhEREekLM1pERERkMKVsLDwDLSIiIjKgUhZpseuQiIiISE+Y0SIiIiKD4VWHRERERHpS2m7Bw65DIiIiIj1hRouIiIgMppSNhWegRURERAZUyiItBlpERERkMKVtMDzHaBERERHpCQMtIiIiMhgJ/r3ysMiLhvtcuHAh6tSpA7lcDrlcDh8fH+zdu1e5vWXLlpBIJCrLiBEjdHK87DokIiIigymOIVqVK1fGd999h6pVq0IIgVWrVqF79+64cOECatasCQAYNmwYQkJClM8pW7aslq3MwUCLiIiI3mldu3ZVefztt99i4cKFOH36tDLQKlu2LOzt7XW+b3YdEhERkcFo3W342oSnycnJKktGRsZb95+dnY0NGzYgNTUVPj4+yvXr1q1DhQoVUKtWLUyePBlpaWk6OV5mtIiIiMiAdNd56OTkpLJ22rRpCA4OVvuMy5cvw8fHB+np6bCwsMC2bdvg5eUFAOjfvz+cnZ3h6OiIS5cuYeLEibh58ya2bt2qZTsZaBEREVEJdefOHcjlcuVjmUyWb9nq1asjOjoaz58/x+bNm+Hn54cjR47Ay8sLw4cPV5arXbs2HBwc0KZNG8TFxcHd3V2rNjLQIiIiIoPR5b0Oc68iLAwTExN4eHgAABo2bIizZ89i3rx5WLx4cZ6y3t7eAIDY2FgGWkRERFRy/FcmhlcoFPmO6YqOjgYAODg4aL0fBlpERET0Tps8eTI6duyIKlWq4MWLF1i/fj0OHz6Mffv2IS4uDuvXr0enTp1gY2ODS5cuITAwEM2bN0edOnW03jcDLSIiIjIYXXYdFtajR4/wySefIDExEVZWVqhTpw727duHdu3a4c6dO9i/fz/CwsKQmpoKJycn9OrVC1999ZV2jfx/DLSIiIjIYIrjXofLly/Pd5uTkxOOHDmiVXsKwkCLiIiIDOe/MkjLQDhhKREREZGeMKNFREREBlPKEloMtIiIiMhwimMwfHFi1yERERGRnjCjRURERAZTHFcdFicGWkRERGQ4pWyQFrsOiYiIiPSEGS0iIiIymFKW0GKgRURERIbDqw6JiIiISCeY0SIiIiID0v6qw5LUechAi4iIiAyGXYdEREREpBMMtIiIiIj0hF2HREREZDClreuQgRYREREZTGm7BQ+7DomIiIj0hBktIiIiMhh2HRIRERHpSWm7BQ+7DomIiIj0hBktIiIiMpxSltJioEVEREQGw6sOiYiIiEgnmNEiIiIig+FVh0RERER6UsqGaDHQIiIiIgMqZZEWx2gRERER6QkzWkRERGQwpe2qQwZaREREZDAcDE9UBEIIAMCL5ORibgmR/ojszOJuApHe5J7fud/n+pKsg78TuqjDUBhokU68ePECAODh6lTMLSEiIm28ePECVlZWOq/XxMQE9vb2qKqjvxP29vYwMTHRSV36JBH6Dl2pVFAoFLh//z4sLS0hKUk53RIqOTkZTk5OuHPnDuRyeXE3h0jneI4bnhACL168gKOjI6RS/Vwrl56ejsxM3WSGTUxMYGpqqpO69IkZLdIJqVSKypUrF3czSh25XM4/QvRO4zluWPrIZL3O1NS0RARHusTpHYiIiIj0hIEWERERkZ4w0CIqgWQyGaZNmwaZTFbcTSHSC57j9K7gYHgiIiIiPWFGi4iIiEhPGGgRERER6QkDLSIiIiI9YaBF9AaJRIKIiIh8t8fHx0MikSA6OtpgbTK0tLQ09OrVC3K5HBKJBElJSTqpNzg4GPXq1dNJXVRyLFmyBE5OTpBKpQgLC9NJnfr8HB4+fFin5z2Vbgy0qFTw9/eHRCKBRCKBsbEx7Ozs0K5dO6xYsQIKhUKlbGJiIjp27KjV/lq2bAmJRIINGzaorA8LC4OLi4tGdb0t8Hu9XO5ibm6OqlWrwt/fH1FRURrtDwBWrVqFY8eO4eTJk0hMTNTbJIb+/v744IMP9FI3aUeTz0xBkpOTMXr0aEycOBH37t3D8OHD9dJeBkf0X8VAi0qNDh06IDExEfHx8di7dy9atWqFgIAAdOnSBa9evVKWs7e318kl5aampvjqq6+QlZWldV2FtXLlSiQmJuLq1atYsGABUlJS4O3tjdWrV2tUT1xcHDw9PVGrVi3Y29vztkqlVGE/MwVJSEhAVlYWOnfuDAcHB5QtW1bPrSb6b2GgRaWGTCaDvb09KlWqhAYNGuDLL7/E9u3bsXfvXoSHhyvLvZlBOnPmDOrXrw9TU1M0atQIFy5cKNT++vXrh6SkJCxdurTAcgsXLoS7uztMTExQvXp1rFmzRrktN/vVo0cPSCSSt2bDrK2tYW9vDxcXF7Rv3x6bN2/GgAEDMHr0aDx79kxZ7vjx42jWrBnMzMzg5OSEMWPGIDU1FUBONm7OnDk4evQoJBIJWrZsCQBYs2YNGjVqBEtLS9jb26N///549OiRss7w8HBYW1urtCciIiLfIC04OBirVq3C9u3blZmTw4cPF3h8ZFiF+cwkJSVh6NChsLW1hVwuR+vWrXHx4kUAOedE7dq1AQBubm6QSCSIj49HXFwcunfvDjs7O1hYWKBx48bYv3+/yr7VZXKtra1VPqu54uPj0apVKwBAuXLlIJFI4O/vDyDnPqyhoaFwdXWFmZkZ6tati82bN6s8f8+ePahWrRrMzMzQqlUrxMfHa/fCEb2GgRaVaq1bt0bdunWxdetWtdtTUlLQpUsXeHl5ISoqCsHBwQgKCipU3XK5HFOmTEFISIgyiHnTtm3bEBAQgPHjx+PKlSv49NNPMWjQIBw6dAgAcPbsWQD/ZqpyH2siMDAQL168QGRkJICcbFWHDh3Qq1cvXLp0CRs3bsTx48cxevRoAMDWrVsxbNgw+Pj4IDExUfnaZGVlYcaMGbh48SIiIiIQHx+v/GNWFEFBQejTp48ya5KYmIj333+/yPWRYbz5mfnwww/x6NEj7N27F1FRUWjQoAHatGmDp0+f4qOPPlIGUGfOnEFiYiKcnJyQkpKCTp064cCBA7hw4QI6dOiArl27IiEhoUhtcnJywpYtWwAAN2/eRGJiIubNmwcACA0NxerVq7Fo0SJcvXoVgYGBGDhwII4cOQIAuHPnDnr27ImuXbsiOjoaQ4cOxaRJk7R9mYiUeFNpKvVq1KiBS5cuqd22fv16KBQKLF++HKampqhZsybu3r2LkSNHFqruUaNGYd68efjxxx8xderUPNtnz54Nf39/jBo1CgAwbtw4nD59GrNnz0arVq1ga2sL4N9MVVGPD4DyV3poaCgGDBiAsWPHAgCqVq2K+fPno0WLFli4cCHKly+PsmXLwsTERGWfgwcPVv7bzc0N8+fPR+PGjZGSkgILCwuN22VhYQEzMzNkZGQU+dioeOR+Zo4fP44zZ87g0aNHyu722bNnIyIiAps3b8bw4cNhY2MDALC1tVW+z3Xr1kXdunWV9c2YMQPbtm3Djh07lAG/JsqUKYPy5csDACpWrKjMrGZkZGDmzJnYv38/fHx8AOScu8ePH8fixYuV57y7uzvmzJkDAKhevTouX76MWbNmFe3FIXoDM1pU6gkh8u3eun79OurUqaNyt/ncL+zCkMlkCAkJwezZs/H48WO19Tdp0kRlXZMmTXD9+vVC7+Ntcm/+kHuMFy9eRHh4OCwsLJSLr68vFAoFbt++nW89UVFR6Nq1K6pUqQJLS0u0aNECAIqchaCSK/czc/HiRaSkpMDGxkblfLp9+zbi4uLyfX5KSgqCgoLg6ekJa2trWFhY4Pr16zo/l2JjY5GWloZ27dqptG/16tXK9l2/fh3e3t4qz9PkM070NsxoUal3/fp1uLq66q3+gQMHYvbs2fjmm280vuJQF3KDttxjTElJwaeffooxY8bkKVulShW1daSmpsLX1xe+vr5Yt24dbG1tkZCQAF9fX2RmZgIApFIp3ryjlyEvBCDDyf3MpKSkwMHBQe3YujfH670uKCgIkZGRmD17Njw8PGBmZobevXsrzyUg54eBtudTSkoKAGD37t2oVKmSyjbeQ5EMhYEWlWoHDx7E5cuXERgYqHa7p6cn1qxZg/T0dGVW6/Tp0xrtQyqVIjQ0FD179szT5ejp6YkTJ07Az89Pue7EiRPw8vJSPjY2NkZ2drZG+3xdWFgY5HI52rZtCwBo0KABrl27Bg8Pj0LXcePGDTx58gTfffcdnJycAADnzp1TKWNra4sXL14gNTUV5ubmAPDWOY5MTEy0OjYyvNc/M5UrV8aDBw9gZGSk0Y+IEydOwN/fHz169ACQExC9OQDd1tYWiYmJyscxMTFIS0vLt04TExMAUDmfvLy8IJPJkJCQoMzAvsnT0xM7duxQWafpZ5yoIOw6pFIjIyMDDx48wL1793D+/HnMnDkT3bt3R5cuXfDJJ5+ofU7//v0hkUgwbNgwXLt2DXv27MHs2bM13nfnzp3h7e2NxYsXq6yfMGECwsPDsXDhQsTExODHH3/E1q1bVQbcu7i44MCBA3jw4IHKlYPqJCUl4cGDB/j7778RGRmJ3r17Y/369Vi4cKEywzBx4kScPHkSo0ePRnR0NGJiYrB9+/YCx8ZUqVIFJiYm+Omnn3Dr1i3s2LEDM2bMUCnj7e2NsmXL4ssvv0RcXBzWr1+v9gqx17m4uODSpUu4efMmHj9+zAzYf8zbPjNt27aFj48PPvjgA/zxxx+Ij4/HyZMnMWXKlDyB+OuqVq2KrVu3Ijo6GhcvXkT//v3zzM3VunVr/Pzzz7hw4QLOnTuHESNGwNjYON86nZ2dIZFIsGvXLvzzzz9ISUmBpaUlgoKCEBgYiFWrViEuLg7nz5/HTz/9hFWrVgEARowYgZiYGEyYMAE3b94s1HlLpBFBVAr4+fkJAAKAMDIyEra2tqJt27ZixYoVIjs7W6UsALFt2zbl41OnTom6desKExMTUa9ePbFlyxYBQFy4cCHf/bVo0UIEBASorDt58qQAIJydnVXW//LLL8LNzU0YGxuLatWqidWrV6ts37Fjh/Dw8BBGRkZ5nvtmu3MXU1NT4e7uLvz8/ERUVFSesmfOnBHt2rUTFhYWwtzcXNSpU0d8++23yu0BAQGiRYsWKs9Zv369cHFxETKZTPj4+IgdO3bkeR22bdsmPDw8hJmZmejSpYtYsmSJeP1rZtq0aaJu3brKx48ePVK2A4A4dOhQvsdHhlXYz0xycrL4/PPPhaOjozA2NhZOTk5iwIABIiEhQQghxIULFwQAcfv2beVzbt++LVq1aiXMzMyEk5OT+Pnnn/N8Zu7duyfat28vzM3NRdWqVcWePXuElZWVWLlypbKON8+/kJAQYW9vLyQSifDz8xNCCKFQKERYWJioXr26MDY2Fra2tsLX11ccOXJE+bydO3cKDw8PIZPJRLNmzcSKFSsEAPHs2TNdv6xUCkmEeKMTnIiIiIh0gl2HRERERHrCQIuIiIhITxhoEREREekJAy0iIiIiPWGgRURERKQnDLSIiIiI9ISBFhEREZGeMNAioneGv78/PvjgA+Xjli1bYuzYsQZvx+HDhyGRSJCUlJRvGYlEgoiIiELXGRwcjHr16mnVrvj4eEgkkrfeGomIdIeBFhHplb+/PyQSCSQSCUxMTODh4YGQkBC8evVK7/veunVrnlsF5acwwRERkaZ4U2ki0rsOHTpg5cqVyMjIwJ49e/DZZ5/B2NgYkydPzlM2MzNTeYNgbZUvX14n9RARFRUzWkSkdzKZDPb29nB2dsbIkSPRtm1b7NixA8C/3X3ffvstHB0dUb16dQDAnTt30KdPH1hbW6N8+fLo3r074uPjlXVmZ2dj3LhxsLa2ho2NDb744gu8eUexN7sOMzIyMHHiRDg5OUEmk8HDwwPLly9HfHw8WrVqBQAoV64cJBIJ/P39AQAKhQKhoaFwdXWFmZkZ6tati82bN6vsZ8+ePahWrRrMzMzQqlUrlXYW1sSJE1GtWjWULVsWbm5umDp1qtqbbC9evBhOTk4oW7Ys+vTpg+fPn6tsX7ZsGTw9PWFqaooaNWrgl19+0bgtRKQ7DLSIyODMzMyQmZmpfHzgwAHcvHkTkZGR2LVrF7KysuDr6wtLS0scO3YMJ06cgIWFBTp06KB83pw5cxAeHo4VK1bg+PHjePr0KbZt21bgfj/55BP8+uuvmD9/Pq5fv47FixfDwsICTk5O2LJlCwDg5s2bSExMxLx58wAAoaGhWL16NRYtWoSrV68iMDAQAwcOxJEjRwDkBIQ9e/ZE165dER0djaFDh2LSpEkavyaWlpYIDw/HtWvXMG/ePCxduhRz585VKRMbG4tNmzZh586d+P3333HhwgWMGjVKuX3dunX4+uuv8e233+L69euYOXMmpk6dilWrVmncHiLSkWK+qTURveP8/PxE9+7dhRBCKBQKERkZKWQymQgKClJut7OzExkZGcrnrFmzRlSvXl0oFArluoyMDGFmZib27dsnhBDCwcFBfP/998rtWVlZonLlysp9CSFEixYtREBAgBBCiJs3bwoAIjIyUm07Dx06JACIZ8+eKdelp6eLsmXLipMnT6qUHTJkiOjXr58QQojJkycLLy8vle0TJ07MU9ebAIht27blu/2HH34QDRs2VD6eNm2aKFOmjLh7965y3d69e4VUKhWJiYlCCCHc3d3F+vXrVeqZMWOG8PHxEUIIcfv2bQFAXLhwId/9EpFucYwWEendrl27YGFhgaysLCgUCvTv3x/BwcHK7bVr11YZl3Xx4kXExsbC0tJSpZ709HTExcXh+fPnSExMhLe3t3KbkZERGjVqlKf7MFd0dDTKlCmDFi1aFLrdsbGxSEtLQ7t27VTWZ2Zmon79+gCA69evq7QDAHx8fAq9j1wbN27E/PnzERcXh5SUFLx69QpyuVylTJUqVVCpUiWV/SgUCty8eROWlpaIi4vDkCFDMGzYMGWZV69ewcrKSuP2EJFuMNAiIr1r1aoVFi5cCBMTEzg6OsLISPWrx9zcXOVxSkoKGjZsiHXr1uWpy9bWtkhtMDMz0/g5KSkpAIDdu3erBDhAzrgzXTl16hQGDBiA6dOnw9fXF1ZWVtiwYQPmzJmjcVuXLl2aJ/ArU6aMztpKRJphoEVEemdubg4PD49Cl2/QoAE2btyIihUr5snq5HJwcMCff/6J5s2bA8jJ3ERFRaFBgwZqy9euXRsKhQJHjhxB27Zt82zPzahlZ2cr13l5eUEmkyEhISHfTJinp6dyYH+u06dPv/0gX3Py5Ek4OztjypQpynV///13nnIJCQm4f/8+HB0dlfuRSqWoXr067Ozs4OjoiFu3bmHAgAEa7Z+I9IeD4YnoP2fAgAGoUKECunfvjmPHjuH27ds4fPgwxowZg7t37wIAAgIC8N133yEiIgI3btzAqFGjCpwDy8XFBX5+fhg8eDAiIiKUdW7atAkA4OzsDIlEgl27duGff/5BSkoKLC0tERQUhMDAQKxatQpxcXE4f/48fvrpJ+UA8xEjRiAmJgYTJkzAzZs3sX79eoSHh2t0vFWrVkVCQgI2bNiAuLg4zJ8/X+3AflNTU/j5+eHixYs4duwYxowZgz59+sDe3h4AMH36dISGhmL+/Pn466+/cPnyZaxcuRI//vijRu0hIt1hoEVE/zlly5bF0aNHUaVKFfTs2ROenp4YMmQI0tPTlRmu8ePH4+OPP4afnx98fHxgaWmJHj16FFjvwoUL0bt3b4waNQo1atTAsGHDkJqaCgCoVKkSpk+fjkmTJsHOzg6jR48GAMyYMQNTp05FaGgoPD090aFDB+zevRuurq4AcsZNbdmyBREREahbty4WLVqEmTNnanS83bp1Q2BgIEaPHo169erh5MmTmDp1ap5yHh4e6NmzJzp16oT27dujTp06KtM3DB06FMuWLcPKlStRu3ZttGjRAuHh4cq2EpHhSUR+I0eJiIiISCvMaBERERHpCQMtIiIiIj1hoEVERESkJwy0iIiIiPSEgRYRERGRnjDQIiIiItITBlpEREREesJAi4iIiEhPGGgRERER6QkDLSIiIiI9YaBFREREpCcMtIiIiIj05P8ATGaCxbQLzMgAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.neural_network import MLPClassifier\n",
    "\n",
    "# Instantiate and fit the Artificial Neural Networks (ANN) classifier\n",
    "clf_ann = MLPClassifier(hidden_layer_sizes=(100,50), max_iter=300, activation='relu', solver='adam', random_state=42)\n",
    "clf_ann.fit(X_train_scaled, y_train)\n",
    "\n",
    "# Calculate overall accuracy\n",
    "y_pred_ann = clf_ann.predict(X_test_scaled)\n",
    "accuracy_ann = accuracy_score(y_test, y_pred_ann)\n",
    "print(f'Accuracy: {accuracy_ann:.2%}')\n",
    "\n",
    "# Calculate precision, recall, and F1-score\n",
    "precision_ann = precision_score(y_test, y_pred_ann)\n",
    "recall_ann = recall_score(y_test, y_pred_ann)\n",
    "f1_ann = f1_score(y_test, y_pred_ann)\n",
    "print(f'Precision: {precision_ann:.2f}')\n",
    "print(f'Recall: {recall_ann:.2f}')\n",
    "print(f'F1-score: {f1_ann:.2f}')\n",
    "\n",
    "# Generate confusion matrix\n",
    "cm_ann = confusion_matrix(y_test, y_pred_ann)\n",
    "\n",
    "# Display confusion matrix\n",
    "disp_ann = ConfusionMatrixDisplay(confusion_matrix=cm_ann, display_labels=class_names)\n",
    "disp_ann.plot(cmap=plt.cm.Blues)\n",
    "plt.title('Confusion Matrix for Artificial Neural Networks (ANN) Classifier')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "12ef235c-c415-4456-b7c5-7d21912e4863",
   "metadata": {},
   "source": [
    "### Reinforcement learning (RL) : Q-learning Agent"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "dbd4378d-ebf5-4397-818b-662c4a893a2c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 50.80%\n",
      "Precision: 0.53\n",
      "Recall: 0.36\n",
      "F1-score: 0.43\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.cluster import KMeans\n",
    "from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, confusion_matrix, ConfusionMatrixDisplay\n",
    "import gym\n",
    "\n",
    "# Sample data setup (replace with actual data)\n",
    "# Assuming df_no_missing_data is your preprocessed dataframe\n",
    "df_no_missing_data = pd.DataFrame({\n",
    "    'feature1': np.random.randn(2000),\n",
    "    'feature2': np.random.randn(2000),\n",
    "    'DEFAULT': np.random.randint(2, size=2000)\n",
    "})\n",
    "\n",
    "# Define your features and target\n",
    "features = df_no_missing_data.drop(columns=['DEFAULT'])\n",
    "target = df_no_missing_data['DEFAULT']\n",
    "\n",
    "# Standardize the features\n",
    "scaler = StandardScaler()\n",
    "features_scaled = scaler.fit_transform(features)\n",
    "\n",
    "# Discretize states using k-means clustering\n",
    "n_clusters = 10\n",
    "kmeans = KMeans(n_clusters=n_clusters, random_state=42)\n",
    "state_labels = kmeans.fit_predict(features_scaled)\n",
    "\n",
    "# Create a custom environment for credit default prediction\n",
    "class CreditDefaultEnv(gym.Env):\n",
    "    def __init__(self, data, labels, state_labels):\n",
    "        super(CreditDefaultEnv, self).__init__()\n",
    "        self.data = data\n",
    "        self.labels = labels\n",
    "        self.state_labels = state_labels\n",
    "        self.current_step = 0\n",
    "        self.action_space = gym.spaces.Discrete(2)  # Actions: 0 = no default, 1 = default\n",
    "        self.observation_space = gym.spaces.Discrete(n_clusters)\n",
    "        \n",
    "    def reset(self):\n",
    "        self.current_step = 0\n",
    "        return self.state_labels[self.current_step]\n",
    "\n",
    "    def step(self, action):\n",
    "        actual = self.labels[self.current_step]\n",
    "        reward = 1 if action == actual else -1  # Reward for correct prediction\n",
    "        self.current_step += 1\n",
    "        done = self.current_step >= len(self.data)\n",
    "        obs = self.state_labels[self.current_step] if not done else 0\n",
    "        return obs, reward, done, {}\n",
    "\n",
    "# Convert your dataframe to the environment\n",
    "env = CreditDefaultEnv(features_scaled, target.values, state_labels)\n",
    "\n",
    "# Simple Q-learning implementation\n",
    "n_states = n_clusters\n",
    "n_actions = env.action_space.n\n",
    "Q = np.zeros((n_states, n_actions))\n",
    "alpha = 0.1\n",
    "gamma = 0.9\n",
    "epsilon = 0.1\n",
    "\n",
    "def choose_action(state):\n",
    "    if np.random.uniform(0, 1) < epsilon:\n",
    "        return env.action_space.sample()\n",
    "    else:\n",
    "        return np.argmax(Q[state, :])\n",
    "\n",
    "# Train the Q-learning agent\n",
    "for episode in range(1000):\n",
    "    state = env.reset()\n",
    "    done = False\n",
    "    while not done:\n",
    "        action = choose_action(state)\n",
    "        next_state, reward, done, _ = env.step(action)\n",
    "        Q[state, action] = Q[state, action] + alpha * (reward + gamma * np.max(Q[next_state, :]) - Q[state, action])\n",
    "        state = next_state\n",
    "\n",
    "# Evaluate the Q-learning agent\n",
    "y_true = []\n",
    "y_pred = []\n",
    "state = env.reset()\n",
    "done = False\n",
    "while not done:\n",
    "    action = np.argmax(Q[state, :])\n",
    "    y_true.append(env.labels[env.current_step])\n",
    "    y_pred.append(action)\n",
    "    state, _, done, _ = env.step(action)\n",
    "\n",
    "# Calculate evaluation metrics\n",
    "accuracy = accuracy_score(y_true, y_pred)\n",
    "precision = precision_score(y_true, y_pred)\n",
    "recall = recall_score(y_true, y_pred)\n",
    "f1 = f1_score(y_true, y_pred)\n",
    "cm = confusion_matrix(y_true, y_pred)\n",
    "\n",
    "print(f'Accuracy: {accuracy:.2%}')\n",
    "print(f'Precision: {precision:.2f}')\n",
    "print(f'Recall: {recall:.2f}')\n",
    "print(f'F1-score: {f1:.2f}')\n",
    "\n",
    "# Display confusion matrix\n",
    "class_names = ['Did Not Default', 'Defaulted']\n",
    "disp = ConfusionMatrixDisplay(confusion_matrix=cm, display_labels=class_names)\n",
    "disp.plot(cmap='Blues')\n",
    "plt.title('Confusion Matrix for Q-learning Agent')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7b92a96f-b6db-424b-bf5f-c3560b7cd1a4",
   "metadata": {},
   "source": [
    "# Split for 80:20 ratio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "d98aa455-3586-4391-b9ce-9ad657d6ba67",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import scale\n",
    "y = df_downsample['DEFAULT'].copy()\n",
    "X_train, X_test, y_train, y_test = train_test_split(X_encoded, y, test_size=0.2, random_state=42)\n",
    "\n",
    "#scale the data\n",
    "X_train_scaled = scale(X_train)\n",
    "X_test_scaled = scale(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0a5349cc-a74a-49d6-bf19-fe805a9acc87",
   "metadata": {},
   "source": [
    "### Support Vector Machine (SVM)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "4d2b17bc-4943-49ae-98a3-4e320cf13022",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 65.25%\n",
      "Precision: 0.71\n",
      "Recall: 0.52\n",
      "F1-score: 0.60\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.svm import SVC\n",
    "\n",
    "# Instantiate and fit the Support Vector Machine classifier\n",
    "clf_svm = SVC(kernel='linear', random_state=42)\n",
    "clf_svm.fit(X_train_scaled, y_train)\n",
    "\n",
    "# Calculate overall accuracy\n",
    "y_pred_svm = clf_svm.predict(X_test_scaled)\n",
    "accuracy_svm = accuracy_score(y_test, y_pred_svm)\n",
    "print(f'Accuracy: {accuracy_svm:.2%}')\n",
    "\n",
    "# Calculate precision, recall, and F1-score\n",
    "precision_svm = precision_score(y_test, y_pred_svm)\n",
    "recall_svm = recall_score(y_test, y_pred_svm)\n",
    "f1_svm = f1_score(y_test, y_pred_svm)\n",
    "print(f'Precision: {precision_svm:.2f}')\n",
    "print(f'Recall: {recall_svm:.2f}')\n",
    "print(f'F1-score: {f1_svm:.2f}')\n",
    "\n",
    "# Generate confusion matrix\n",
    "cm_svm = confusion_matrix(y_test, y_pred_svm)\n",
    "\n",
    "# Display confusion matrix\n",
    "disp_svm = ConfusionMatrixDisplay(confusion_matrix=cm_svm, display_labels=class_names)\n",
    "disp_svm.plot(cmap=plt.cm.Blues)\n",
    "plt.title('Confusion Matrix for Support Vector Machine Classifier')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "84f8e3bb-7d40-45ab-b7c4-11004226a796",
   "metadata": {},
   "source": [
    "### Decision Tree Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "acf2f226-7afe-4b5d-952b-e1dc556892ab",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 62.00%\n",
      "Precision: 0.62\n",
      "Recall: 0.65\n",
      "F1-score: 0.63\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "\n",
    "# Instantiate and fit the Decision Tree classifier\n",
    "clf_dt = DecisionTreeClassifier(random_state=42)\n",
    "clf_dt.fit(X_train_scaled, y_train)\n",
    "\n",
    "# Calculate predictions\n",
    "y_pred_dt = clf_dt.predict(X_test_scaled)\n",
    "\n",
    "# Calculate performance metrics\n",
    "accuracy_dt = accuracy_score(y_test, y_pred_dt)\n",
    "precision_dt = precision_score(y_test, y_pred_dt)\n",
    "recall_dt = recall_score(y_test, y_pred_dt)\n",
    "f1_dt = f1_score(y_test, y_pred_dt)\n",
    "\n",
    "# Print performance metrics\n",
    "print(f'Accuracy: {accuracy_dt:.2%}')\n",
    "print(f'Precision: {precision_dt:.2f}')\n",
    "print(f'Recall: {recall_dt:.2f}')\n",
    "print(f'F1-score: {f1_dt:.2f}')\n",
    "\n",
    "# Define class names for display\n",
    "class_names = ['Did Not Default', 'Defaulted']\n",
    "\n",
    "# Generate confusion matrix\n",
    "cm = confusion_matrix(y_test, y_pred_dt)\n",
    "\n",
    "# Display confusion matrix\n",
    "disp = ConfusionMatrixDisplay(confusion_matrix=cm, display_labels=class_names)\n",
    "disp.plot(cmap=plt.cm.Blues)\n",
    "plt.title('Confusion Matrix for Decision Tree Classifier')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "81555a3c-769a-426f-9c41-ebcbfa6aa76f",
   "metadata": {},
   "source": [
    "### Random Forest classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "99ef6b42-256c-4f7c-9a18-e1f4d6ae2689",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 67.75%\n",
      "Precision: 0.72\n",
      "Recall: 0.58\n",
      "F1-score: 0.64\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "# Instantiate and fit the Random Forest classifier\n",
    "clf_rf = RandomForestClassifier(n_estimators=100, random_state=42)\n",
    "clf_rf.fit(X_train_scaled, y_train)\n",
    "\n",
    "# Calculate overall accuracy\n",
    "y_pred_rf = clf_rf.predict(X_test_scaled)\n",
    "accuracy_rf = accuracy_score(y_test, y_pred_rf)\n",
    "print(f'Accuracy: {accuracy_rf:.2%}')\n",
    "\n",
    "# Calculate precision, recall, and F1-score\n",
    "precision_rf = precision_score(y_test, y_pred_rf)\n",
    "recall_rf = recall_score(y_test, y_pred_rf)\n",
    "f1_rf = f1_score(y_test, y_pred_rf)\n",
    "print(f'Precision: {precision_rf:.2f}')\n",
    "print(f'Recall: {recall_rf:.2f}')\n",
    "print(f'F1-score: {f1_rf:.2f}')\n",
    "\n",
    "# Generate confusion matrix\n",
    "cm_rf = confusion_matrix(y_test, y_pred_rf)\n",
    "\n",
    "# Display confusion matrix\n",
    "disp_rf = ConfusionMatrixDisplay(confusion_matrix=cm_rf, display_labels=class_names)\n",
    "disp_rf.plot(cmap=plt.cm.Blues)\n",
    "plt.title('Confusion Matrix for Random Forest Classifier')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fac72c90-83e6-4151-9c4e-d275d1f312ca",
   "metadata": {},
   "source": [
    "### k-Nearest Neighbors (KNN) classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "b4273c1a-fad9-43ee-8ebd-965e099d7763",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 65.00%\n",
      "Precision: 0.66\n",
      "Recall: 0.62\n",
      "F1-score: 0.64\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "\n",
    "# Instantiate and fit the K-Nearest Neighbors classifier\n",
    "clf_knn = KNeighborsClassifier(n_neighbors=5)\n",
    "clf_knn.fit(X_train_scaled, y_train)\n",
    "\n",
    "# Calculate overall accuracy\n",
    "y_pred_knn = clf_knn.predict(X_test_scaled)\n",
    "accuracy_knn = accuracy_score(y_test, y_pred_knn)\n",
    "print(f'Accuracy: {accuracy_knn:.2%}')\n",
    "\n",
    "# Calculate precision, recall, and F1-score\n",
    "precision_knn = precision_score(y_test, y_pred_knn)\n",
    "recall_knn = recall_score(y_test, y_pred_knn)\n",
    "f1_knn = f1_score(y_test, y_pred_knn)\n",
    "print(f'Precision: {precision_knn:.2f}')\n",
    "print(f'Recall: {recall_knn:.2f}')\n",
    "print(f'F1-score: {f1_knn:.2f}')\n",
    "\n",
    "# Generate confusion matrix\n",
    "cm_knn = confusion_matrix(y_test, y_pred_knn)\n",
    "\n",
    "# Display confusion matrix\n",
    "disp_knn = ConfusionMatrixDisplay(confusion_matrix=cm_knn, display_labels=class_names)\n",
    "disp_knn.plot(cmap=plt.cm.Blues)\n",
    "plt.title('Confusion Matrix for k-Nearest Neighbors Classifier')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "976f85e2-a732-4887-a77e-92df4cd1f98a",
   "metadata": {},
   "source": [
    "### Artificial Neural Networks (ANN) classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "99039cdf-fd13-43a7-8454-4694a0d756bc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 62.50%\n",
      "Precision: 0.62\n",
      "Recall: 0.68\n",
      "F1-score: 0.64\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.neural_network import MLPClassifier\n",
    "\n",
    "# Instantiate and fit the Artificial Neural Networks (ANN) classifier\n",
    "clf_ann = MLPClassifier(hidden_layer_sizes=(100,50), max_iter=300, activation='relu', solver='adam', random_state=42)\n",
    "clf_ann.fit(X_train_scaled, y_train)\n",
    "\n",
    "# Calculate overall accuracy\n",
    "y_pred_ann = clf_ann.predict(X_test_scaled)\n",
    "accuracy_ann = accuracy_score(y_test, y_pred_ann)\n",
    "print(f'Accuracy: {accuracy_ann:.2%}')\n",
    "\n",
    "# Calculate precision, recall, and F1-score\n",
    "precision_ann = precision_score(y_test, y_pred_ann)\n",
    "recall_ann = recall_score(y_test, y_pred_ann)\n",
    "f1_ann = f1_score(y_test, y_pred_ann)\n",
    "print(f'Precision: {precision_ann:.2f}')\n",
    "print(f'Recall: {recall_ann:.2f}')\n",
    "print(f'F1-score: {f1_ann:.2f}')\n",
    "\n",
    "# Generate confusion matrix\n",
    "cm_ann = confusion_matrix(y_test, y_pred_ann)\n",
    "\n",
    "# Display confusion matrix\n",
    "disp_ann = ConfusionMatrixDisplay(confusion_matrix=cm_ann, display_labels=class_names)\n",
    "disp_ann.plot(cmap=plt.cm.Blues)\n",
    "plt.title('Confusion Matrix for Artificial Neural Networks (ANN) Classifier')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dc63f12a-34c0-42a3-978a-4b813e9a4fa2",
   "metadata": {},
   "source": [
    "### Reinforcement learning (RL) : Q-learning Agent"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "1ffba964-9d8e-4ebc-a1db-81ccc8633ae5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 51.35%\n",
      "Precision: 0.49\n",
      "Recall: 0.18\n",
      "F1-score: 0.27\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.cluster import KMeans\n",
    "from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, confusion_matrix, ConfusionMatrixDisplay\n",
    "import gym\n",
    "\n",
    "# Sample data setup (replace with actual data)\n",
    "# Assuming df_no_missing_data is your preprocessed dataframe\n",
    "df_no_missing_data = pd.DataFrame({\n",
    "    'feature1': np.random.randn(2000),\n",
    "    'feature2': np.random.randn(2000),\n",
    "    'DEFAULT': np.random.randint(2, size=2000)\n",
    "})\n",
    "\n",
    "# Define your features and target\n",
    "features = df_no_missing_data.drop(columns=['DEFAULT'])\n",
    "target = df_no_missing_data['DEFAULT']\n",
    "\n",
    "# Standardize the features\n",
    "scaler = StandardScaler()\n",
    "features_scaled = scaler.fit_transform(features)\n",
    "\n",
    "# Discretize states using k-means clustering\n",
    "n_clusters = 10\n",
    "kmeans = KMeans(n_clusters=n_clusters, random_state=42)\n",
    "state_labels = kmeans.fit_predict(features_scaled)\n",
    "\n",
    "# Create a custom environment for credit default prediction\n",
    "class CreditDefaultEnv(gym.Env):\n",
    "    def __init__(self, data, labels, state_labels):\n",
    "        super(CreditDefaultEnv, self).__init__()\n",
    "        self.data = data\n",
    "        self.labels = labels\n",
    "        self.state_labels = state_labels\n",
    "        self.current_step = 0\n",
    "        self.action_space = gym.spaces.Discrete(2)  # Actions: 0 = no default, 1 = default\n",
    "        self.observation_space = gym.spaces.Discrete(n_clusters)\n",
    "        \n",
    "    def reset(self):\n",
    "        self.current_step = 0\n",
    "        return self.state_labels[self.current_step]\n",
    "\n",
    "    def step(self, action):\n",
    "        actual = self.labels[self.current_step]\n",
    "        reward = 1 if action == actual else -1  # Reward for correct prediction\n",
    "        self.current_step += 1\n",
    "        done = self.current_step >= len(self.data)\n",
    "        obs = self.state_labels[self.current_step] if not done else 0\n",
    "        return obs, reward, done, {}\n",
    "\n",
    "# Convert your dataframe to the environment\n",
    "env = CreditDefaultEnv(features_scaled, target.values, state_labels)\n",
    "\n",
    "# Simple Q-learning implementation\n",
    "n_states = n_clusters\n",
    "n_actions = env.action_space.n\n",
    "Q = np.zeros((n_states, n_actions))\n",
    "alpha = 0.1\n",
    "gamma = 0.9\n",
    "epsilon = 0.1\n",
    "\n",
    "def choose_action(state):\n",
    "    if np.random.uniform(0, 1) < epsilon:\n",
    "        return env.action_space.sample()\n",
    "    else:\n",
    "        return np.argmax(Q[state, :])\n",
    "\n",
    "# Train the Q-learning agent\n",
    "for episode in range(1000):\n",
    "    state = env.reset()\n",
    "    done = False\n",
    "    while not done:\n",
    "        action = choose_action(state)\n",
    "        next_state, reward, done, _ = env.step(action)\n",
    "        Q[state, action] = Q[state, action] + alpha * (reward + gamma * np.max(Q[next_state, :]) - Q[state, action])\n",
    "        state = next_state\n",
    "\n",
    "# Evaluate the Q-learning agent\n",
    "y_true = []\n",
    "y_pred = []\n",
    "state = env.reset()\n",
    "done = False\n",
    "while not done:\n",
    "    action = np.argmax(Q[state, :])\n",
    "    y_true.append(env.labels[env.current_step])\n",
    "    y_pred.append(action)\n",
    "    state, _, done, _ = env.step(action)\n",
    "\n",
    "# Calculate evaluation metrics\n",
    "accuracy = accuracy_score(y_true, y_pred)\n",
    "precision = precision_score(y_true, y_pred)\n",
    "recall = recall_score(y_true, y_pred)\n",
    "f1 = f1_score(y_true, y_pred)\n",
    "cm = confusion_matrix(y_true, y_pred)\n",
    "\n",
    "print(f'Accuracy: {accuracy:.2%}')\n",
    "print(f'Precision: {precision:.2f}')\n",
    "print(f'Recall: {recall:.2f}')\n",
    "print(f'F1-score: {f1:.2f}')\n",
    "\n",
    "# Display confusion matrix\n",
    "class_names = ['Did Not Default', 'Defaulted']\n",
    "disp = ConfusionMatrixDisplay(confusion_matrix=cm, display_labels=class_names)\n",
    "disp.plot(cmap='Blues')\n",
    "plt.title('Confusion Matrix for Q-learning Agent')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "881d10a7-b70a-4475-a270-bdcb3d4daaab",
   "metadata": {},
   "source": [
    "# Split for 60:40 ratio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "84a215d6-6036-4544-a36c-6b7818fd812b",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import scale\n",
    "y = df_downsample['DEFAULT'].copy()\n",
    "X_train, X_test, y_train, y_test = train_test_split(X_encoded, y, test_size=0.4, random_state=42)\n",
    "\n",
    "#scale the data\n",
    "X_train_scaled = scale(X_train)\n",
    "X_test_scaled = scale(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "caeaeeca-48e6-45bc-aa43-b09cdca7019c",
   "metadata": {},
   "source": [
    "### Support Vector Machine (SVM)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "9f5e87c8-bee8-4ec7-98ba-26d873a04869",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 65.75%\n",
      "Precision: 0.72\n",
      "Recall: 0.50\n",
      "F1-score: 0.59\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.svm import SVC\n",
    "\n",
    "# Instantiate and fit the Support Vector Machine classifier\n",
    "clf_svm = SVC(kernel='linear', random_state=42)\n",
    "clf_svm.fit(X_train_scaled, y_train)\n",
    "\n",
    "# Calculate overall accuracy\n",
    "y_pred_svm = clf_svm.predict(X_test_scaled)\n",
    "accuracy_svm = accuracy_score(y_test, y_pred_svm)\n",
    "print(f'Accuracy: {accuracy_svm:.2%}')\n",
    "\n",
    "# Calculate precision, recall, and F1-score\n",
    "precision_svm = precision_score(y_test, y_pred_svm)\n",
    "recall_svm = recall_score(y_test, y_pred_svm)\n",
    "f1_svm = f1_score(y_test, y_pred_svm)\n",
    "print(f'Precision: {precision_svm:.2f}')\n",
    "print(f'Recall: {recall_svm:.2f}')\n",
    "print(f'F1-score: {f1_svm:.2f}')\n",
    "\n",
    "# Generate confusion matrix\n",
    "cm_svm = confusion_matrix(y_test, y_pred_svm)\n",
    "\n",
    "# Display confusion matrix\n",
    "disp_svm = ConfusionMatrixDisplay(confusion_matrix=cm_svm, display_labels=class_names)\n",
    "disp_svm.plot(cmap=plt.cm.Blues)\n",
    "plt.title('Confusion Matrix for Support Vector Machine Classifier')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "79fcd8e1-adbf-4fbc-8194-e3ca2d97c470",
   "metadata": {},
   "source": [
    "### Decision Tree Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "0e57c563-43fc-47ad-9400-53e877e9c18d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 61.62%\n",
      "Precision: 0.63\n",
      "Recall: 0.54\n",
      "F1-score: 0.58\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "\n",
    "# Instantiate and fit the Decision Tree classifier\n",
    "clf_dt = DecisionTreeClassifier(random_state=42)\n",
    "clf_dt.fit(X_train_scaled, y_train)\n",
    "\n",
    "# Calculate predictions\n",
    "y_pred_dt = clf_dt.predict(X_test_scaled)\n",
    "\n",
    "# Calculate performance metrics\n",
    "accuracy_dt = accuracy_score(y_test, y_pred_dt)\n",
    "precision_dt = precision_score(y_test, y_pred_dt)\n",
    "recall_dt = recall_score(y_test, y_pred_dt)\n",
    "f1_dt = f1_score(y_test, y_pred_dt)\n",
    "\n",
    "# Print performance metrics\n",
    "print(f'Accuracy: {accuracy_dt:.2%}')\n",
    "print(f'Precision: {precision_dt:.2f}')\n",
    "print(f'Recall: {recall_dt:.2f}')\n",
    "print(f'F1-score: {f1_dt:.2f}')\n",
    "\n",
    "# Define class names for display\n",
    "class_names = ['Did Not Default', 'Defaulted']\n",
    "\n",
    "# Generate confusion matrix\n",
    "cm = confusion_matrix(y_test, y_pred_dt)\n",
    "\n",
    "# Display confusion matrix\n",
    "disp = ConfusionMatrixDisplay(confusion_matrix=cm, display_labels=class_names)\n",
    "disp.plot(cmap=plt.cm.Blues)\n",
    "plt.title('Confusion Matrix for Decision Tree Classifier')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1adce6b3-6dda-47d6-b40c-1630a4b7f85d",
   "metadata": {},
   "source": [
    "### Random Forest classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "7206dfad-4ea5-4740-9f4c-9a19c1c4ae5f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 68.12%\n",
      "Precision: 0.73\n",
      "Recall: 0.57\n",
      "F1-score: 0.64\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "# Instantiate and fit the Random Forest classifier\n",
    "clf_rf = RandomForestClassifier(n_estimators=100, random_state=42)\n",
    "clf_rf.fit(X_train_scaled, y_train)\n",
    "\n",
    "# Calculate overall accuracy\n",
    "y_pred_rf = clf_rf.predict(X_test_scaled)\n",
    "accuracy_rf = accuracy_score(y_test, y_pred_rf)\n",
    "print(f'Accuracy: {accuracy_rf:.2%}')\n",
    "\n",
    "# Calculate precision, recall, and F1-score\n",
    "precision_rf = precision_score(y_test, y_pred_rf)\n",
    "recall_rf = recall_score(y_test, y_pred_rf)\n",
    "f1_rf = f1_score(y_test, y_pred_rf)\n",
    "print(f'Precision: {precision_rf:.2f}')\n",
    "print(f'Recall: {recall_rf:.2f}')\n",
    "print(f'F1-score: {f1_rf:.2f}')\n",
    "\n",
    "# Generate confusion matrix\n",
    "cm_rf = confusion_matrix(y_test, y_pred_rf)\n",
    "\n",
    "# Display confusion matrix\n",
    "disp_rf = ConfusionMatrixDisplay(confusion_matrix=cm_rf, display_labels=class_names)\n",
    "disp_rf.plot(cmap=plt.cm.Blues)\n",
    "plt.title('Confusion Matrix for Random Forest Classifier')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3d32460c-54bc-40aa-a71c-bf811b578f3e",
   "metadata": {},
   "source": [
    "### k-Nearest Neighbors (KNN) classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "3963028d-d61f-4008-8e76-047de735c5bc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 64.00%\n",
      "Precision: 0.65\n",
      "Recall: 0.59\n",
      "F1-score: 0.62\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "\n",
    "# Instantiate and fit the K-Nearest Neighbors classifier\n",
    "clf_knn = KNeighborsClassifier(n_neighbors=5)\n",
    "clf_knn.fit(X_train_scaled, y_train)\n",
    "\n",
    "# Calculate overall accuracy\n",
    "y_pred_knn = clf_knn.predict(X_test_scaled)\n",
    "accuracy_knn = accuracy_score(y_test, y_pred_knn)\n",
    "print(f'Accuracy: {accuracy_knn:.2%}')\n",
    "\n",
    "# Calculate precision, recall, and F1-score\n",
    "precision_knn = precision_score(y_test, y_pred_knn)\n",
    "recall_knn = recall_score(y_test, y_pred_knn)\n",
    "f1_knn = f1_score(y_test, y_pred_knn)\n",
    "print(f'Precision: {precision_knn:.2f}')\n",
    "print(f'Recall: {recall_knn:.2f}')\n",
    "print(f'F1-score: {f1_knn:.2f}')\n",
    "\n",
    "# Generate confusion matrix\n",
    "cm_knn = confusion_matrix(y_test, y_pred_knn)\n",
    "\n",
    "# Display confusion matrix\n",
    "disp_knn = ConfusionMatrixDisplay(confusion_matrix=cm_knn, display_labels=class_names)\n",
    "disp_knn.plot(cmap=plt.cm.Blues)\n",
    "plt.title('Confusion Matrix for k-Nearest Neighbors Classifier')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0d89e91b-37c0-475d-8dd3-ac26d20a3b00",
   "metadata": {},
   "source": [
    "### Artificial Neural Networks (ANN) classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "b93eeecc-1e81-4f7a-b89b-a1fc3e7049cb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 62.38%\n",
      "Precision: 0.62\n",
      "Recall: 0.62\n",
      "F1-score: 0.62\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\harsh\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (300) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "image/png": 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HW89RiH8znK9bsmSJACA2b96sXpeeni7c3d2Fubm5SExMVJ8XAKFUKsXjx4+1Op4QrzILr48rW7NmjTAyMspRh4+PjwAgpk2blqOOvMZ45XbN5DWm5s1rNTtTMG7cuBz1vp6BeTPjpe1rrmvG6+effxbx8fGibNmyolu3bhrtfj1z8OLFC2FlZZUjExAXFycsLS011uua8crttc3MzMwxHuX58+fCxsYmx/sNOmS8vvnmG5GZmSlq1KihkfV6M+OlUqlEjRo1hLe3t8brkpKSIhwdHUX79u3V6/Ia45X9/svOZF29elUAEB9++KFo2rSpuly3bt00MmMTJkwQAMSpU6fU6168eCEcHR2Fg4OD+jrIfg2dnJxESkqKxrFfvx5jY2NFnTp1hJOTk4iOjtZ4PnP7fNXGoEGD8vyMS09PF+XLl9cYmztgwADh5uaWo2x2xksIIWbPnq2R9cov46Xtdfs6uVwuPv3003zPq2fPngKARu9DfnLLeL35WgghhLe3t3ByclI/DgkJeesYvPHjxwulUpnv+OM3M16vt+nNv3tvvi83bdokDAwMNK4zIYRYtWpVjp4uAMLAwEBcv349z7Zoi2O88pGYmAgAsLCw0Kr8gQMHAAATJ07UWJ+d5di/f7/GeldXV3UWBnj1DbFWrVr4888/C9zmN2X3ce/evVvrcQ+xsbGIiIiAr6+vRlalfv36aN++vfo8Xzdq1CiNxy1btsTTp0/Vz6E2BgwYgOPHjyMuLg5Hjx5FXFxcrtkZ4NX4n+w+/aysLDx9+hTm5uaoVauWTmN1FAoFhg4dqlVZLy8vfPLJJ5gzZw569eoFExMTrF69WutjvenAgQOwtbVF//791euMjY0xbtw4JCUl4cSJExrle/fuDWtra63qfvr0KX755ReNunv37g2ZTIaffvop130+/fTTApyF7nbu3AmZTIZZs2bl2Jbfben6es1zY2lpiQkTJmDPnj24fPlyrmXCwsIQHx+P/v3748mTJ+rF0NAQTZs2xbFjxwp8/NxeW0NDQ3XmRqVS4dmzZ8jMzMR7771X6PPNznpduXIlz+lpIiIicOfOHQwYMABPnz5Vn29ycjLatm2LkydPvvUzpWHDhjA3N8fJkycBvMpsVa1aFUOGDMGlS5eQkpICIQROnz6t8Vl44MABvP/++2jRooV6nbm5OUaOHIno6GjcuHFD4zg+Pj4wNTXNtQ1///03PDw8kJGRgZMnT8Le3l69zdTUFHK5HMePH8fz58/zPZc3PX36FGXLls1128GDB/H06VON91///v1x5coVXL9+Pc86x48fr9NYL22u29eVLVv2rXet6vp3LzevvxYJCQl48uQJPDw88OeffyIhIQHAv3+b9u3bh4yMjFzrsbKyQnJyMsLCwgrclvz8/PPPcHFxQe3atTXe023atAGAHO9pDw8PuLq6Fvq4DLzyoVQqAQAvXrzQqvxff/0FAwMDVK9eXWO9ra0trKys8Ndff2msr1atWo46ypYtq/MHQH4++ugjNG/eHMOHD4eNjQ369euHn376Kd8PzOx21qpVK8c2FxcX9Yfv6948l+wPJF3OpVOnTrCwsMD27duxZcsWNGnSJMdzmU2lUmHx4sWoUaMGFAoFKlSoAGtra1y9elX9xtZGlSpVdBpsGRQUhHLlyiEiIgLLli1DxYoVtd73TX/99Rdq1KihMSgUePUcZ29/naOjo9Z1b9++HRkZGWjYsCHu3r2Lu3fv4tmzZ2jatGmug3yNjIxQtWrVApyF7qKiolC5cuW3dpW+SV+veV7Gjx8PKyurPOfDunPnDgCgTZs2sLa21lh+/fVX9YDegsjrtd2wYQPq168PExMTlC9fHtbW1ti/f79eznfgwIGoXr065syZk+uA6+zz9fHxyXG+69atQ1pa2lvbYWhoCHd3d5w6dQrAq8CrZcuWaNGiBbKysnD+/HncuHEDz5490wi8/vrrrzw/f7K3vy6/98bgwYPx+PFjnDhxAlWqVNHYplAoMH/+fBw8eBA2NjZo1aoVFixYoPW0C7k9b8Crm1ocHR3VQzvu3r0LZ2dnlClTJt9B9roGUsDbr9s32/u2Obd0/buXmzNnzqBdu3YwMzODlZUVrK2t1VNuZF8zHh4e6N27N2bPno0KFSqge/fuWL9+PdLS0tT1jB49GjVr1kTHjh1RtWpVDBs2DIcOHSpwu950584dXL9+Pcf1XbNmTQDI8Z7W5TM4Pwy88qFUKlG5cmX88ccfOu2n7WRyed1FmNebWZtjZGVlaTw2NTXFyZMncfjwYQwePBhXr17FRx99hPbt2+coWxiFOZdsCoUCvXr1woYNGxASEpJntgt4NS/WxIkT0apVK2zevBm//PILwsLCUKdOHZ3uaMrrW3JeLl++rH4zXrt2Tad9C0uXtmZ/uDdv3hw1atRQL6dPn8a5c+dyZFVfzyb9V+nrNc/L2/7oZR9j06ZNCAsLy7Hs3r1bXVbb92e23F7bzZs3q+ci+v7773Ho0CGEhYWhTZs2ejnf7KxXRESERtuzZR/jm2++yfV8w8LCtBpz2qJFC/z+++9ITU1VB15WVlaoW7cuTp06pQ7KXg+8dJXfe6NXr16Ij4/H0qVLc90+YcIE3L59G4GBgTAxMcHMmTPh4uLy1sCnfPnyuX6xTExMxN69e3Hv3j2N956rqytSUlKwdevWfD8XswOposh6xcfHq8fg5aV27doACv75FhUVhbZt2+LJkydYtGgR9u/fj7CwMPj7+wP497qSyWTYsWMHzp07Bz8/Pzx48ADDhg1D48aNkZSUBACoWLEiIiIisGfPHnTr1g3Hjh1Dx44d4ePjU6C2vUmlUqFevXp5Xt+jR4/WKK/r34u8cHD9W3Tp0gVr1qzBuXPn4O7unm9Ze3t7qFQq3LlzR/3NDHg1UDQ+Pl4jxV1YZcuWzXVCvje/CQKvbrdt27Yt2rZti0WLFmHevHmYMWMGjh07hnbt2uV6HgBw69atHNtu3ryJChUqFNnEmAMGDMAPP/wAAwMD9OvXL89yO3bsgKenJ77//nuN9W9+sOhzRuXk5GQMHToUrq6uaNasGRYsWICePXuiSZMmBarP3t4eV69ehUql0gh6bt68qd5eEPfu3cPZs2fh5+cHDw8PjW0qlQqDBw/G1q1b8fnnn7+1rqKYkdrZ2Rm//PILnj17plPWS9vXvDAmTJiAJUuWYPbs2TluRc8ejF2xYsVc3zevK1u2bK5DBnJ7f+Zlx44dcHJywq5duzReh9y6aAtq0KBB+PLLLzF79mz1QPds2eerVCrfer75XSctW7ZEeno6fvzxRzx48EAdYLVq1QqnTp2CjY0NatasqXFji729fZ6fP9nbtTV27FhUr14dX3zxBSwtLTFt2rQcZZydnTFp0iRMmjQJd+7cQYMGDbBw4UJs3rw5z3pr166NLVu2ICEhAZaWlur1u3btQmpqKlauXJnjurx16xY+//xznDlzRqMb9XXZgVRAQIDWAUZ+1222Bw8eID09XeNvU266du2KwMBAbN68uUDB8N69e5GWloY9e/Zo9ITk1RX/wQcf4IMPPsBXX32FrVu3YuDAgdi2bRuGDx8O4NUUEF27dkXXrl2hUqkwevRorF69GjNnzsyzR0Rbzs7OuHLlCtq2bSvp7Pv/7a+4/wH/+9//YGZmhuHDh+PRo0c5tkdFRam/SXXq1AkAcsz0vWjRIgDQ63xUzs7OSEhIwNWrV9XrYmNjc9w5+ezZsxz7Zt8N9XpK93WVKlVCgwYNsGHDBo3g7o8//sCvv/6qPs+i4Onpiblz5+Lbb7+Fra1tnuUMDQ1zfGv8+eef8eDBA4112QGiPmbUnjp1KmJiYrBhwwYsWrQIDg4O8PHxyfN5fJtOnTohLi4O27dvV6/LzMzE8uXLYW5uniNo0lZ2tut///sf+vTpo7H07dsXHh4e+XZ3vM7MzEwv3Vqv6927N4QQuX6jzy8ToO1rXhjZf/R2796d49cOvL29oVQqMW/evFzHpGTPuQe8en/evHlTY92VK1feejfx67KzyK+f82+//YZz585pXYc2x8jOer15J13jxo3h7OyMoKAgdQbida+fW37vs6ZNm8LY2Bjz589HuXLl1PMetmzZEufPn8eJEydy/IHv1KkTLly4oHGuycnJWLNmDRwcHHQeZzNz5kxMnjwZn332GVauXKlen5KSkmMuJmdnZ1hYWLz1fe3u7g4hRI7Z4zdv3gwnJyeMGjUqx/tv8uTJMDc3f+v7b8KECbCyssKcOXO0Or/8rtts2e182x367u7u6NChA9atW5fr+L/09HRMnjw5z/1zu24TEhKwfv16jXLPnz/P8X5+82/T06dPNbYbGBio71As6Ofu6/r27YsHDx5g7dq1Oba9fPlS63kPdcWM11s4Oztj69at+Oijj+Di4oIhQ4agbt26SE9Px9mzZ9W3/wOAm5sbfHx8sGbNGsTHx8PDwwMXLlzAhg0b0KNHD3h6euqtXf369cPUqVPRs2dPjBs3Tn1be82aNTUG3s6ZMwcnT55E586dYW9vj8ePH+O7775D1apV8/zGBbzqXujYsSPc3d3x8ccfq6eTsLS0LNLfhDMwMNAqE9OlSxfMmTMHQ4cORbNmzXDt2jVs2bIFTk5OGuWcnZ1hZWWFVatWwcLCAmZmZmjatKnOffVHjx7Fd999h1mzZqlv216/fj1at26NmTNnYsGCBTrVBwAjR47E6tWr4evri/DwcDg4OGDHjh04c+YMlixZUuDBrVu2bEGDBg1gZ2eX6/Zu3bph7NixWt+Cvn37dkycOBFNmjSBubl5rj9togtPT08MHjwYy5Ytw507d9ChQweoVCqcOnUKnp6e8PPzy3U/bV/zwsq+rf/KlSsamV2lUomVK1di8ODBaNSoEfr16wdra2vExMRg//79aN68Ob799lsAwLBhw7Bo0SJ4e3vj448/xuPHj7Fq1SrUqVNH6xtOunTpgl27dqFnz57o3Lkz7t27h1WrVsHV1TXXQKigBg4ciLlz5+b4g21gYIB169ahY8eOqFOnDoYOHYoqVargwYMHOHbsGJRKJfbu3Qvg36kKZsyYgX79+sHY2Bhdu3aFmZkZypQpg8aNG+P8+fPo2rWrOrPQqlUrJCcnIzk5OUfgNW3aNPz444/o2LEjxo0bh3LlymHDhg24d+8edu7cWaBu8W+++QYJCQkYM2YMLCwsMGjQINy+fRtt27ZF37594erqCiMjI4SEhODRo0f5ZtyBV12o5cuXx+HDh9WDsR8+fIhjx47lOcGoQqGAt7c3fv75ZyxbtizPCYItLS0xfvx4nSZUzeu6zRYWFoZq1arlO5VEto0bN8LLywu9evVC165d0bZtW5iZmeHOnTvYtm0bYmNj8/wJNy8vL3WW6pNPPkFSUhLWrl2LihUrIjY2Vl1uw4YN+O6779CzZ084OzvjxYsXWLt2LZRKpfrL/fDhw/Hs2TO0adMGVatWxV9//YXly5ejQYMGb83caWPw4MH46aefMGrUKBw7dgzNmzdHVlYWbt68iZ9++gm//PJL0fxEX6Hviywlbt++LUaMGCEcHByEXC4XFhYWonnz5mL58uUaE61lZGSI2bNnC0dHR2FsbCzs7OzynUD1TW/e7prXdBJCvJoYtW7dukIul4tatWqJzZs357i198iRI6J79+6icuXKQi6Xi8qVK4v+/fuL27dv5zjGm7ffHz58WDRv3lyYmpoKpVIpunbtmucEqm/etpt9K/fbJiHU5mdZ8ppOYtKkSaJSpUrC1NRUNG/eXJw7dy7X2/h3796tnvDu9fN8/TbuN71eT2JiorC3txeNGjUSGRkZGuX8/f2FgYGBOHfuXL7nkNfr/ejRIzF06FBRoUIFIZfLRb169XK8DvldA28KDw8XAPL9aZDo6GgBQPj7+wsh8n8NkpKSxIABA4SVlZVAPhOoCqH9dBJCvJoq4ZtvvhG1a9cWcrlcWFtbi44dO2pMHpnbdBLavOYFmU7iTdnXdW7Py7Fjx4S3t7ewtLQUJiYmwtnZWfj6+ub4uZfNmzerJ1Ju0KCB+OWXX/KdQPVNKpVKzJs3T9jb2wuFQiEaNmwo9u3bl+vzCR2nk3hT9muX23v58uXLolevXqJ8+fJCoVAIe3t70bdvX3HkyBGNcnPnzhVVqlQRBgYGOd77U6ZMEQDE/PnzNfapXr26ACCioqJytCl7AlUrKythYmIi3n///TwnUM3tNczteszKyhL9+/cXRkZGIjQ0VDx58kSMGTNG1K5dW5iZmQlLS0vRtGlT8dNPP+X9RL5m3Lhxonr16urHCxcuFAByPDevCw4OFnhtAtm8PoeeP38uLC0t851O4k15XbdZWVmiUqVK4vPPP9fqvIR4NSVEUFCQaNKkiTA3NxdyuVzUqFFDjB07Vty9ezfHMV+3Z88eUb9+ffWkqPPnzxc//PCDxnVx6dIl0b9/f1GtWjWhUChExYoVRZcuXTTeRzt27BBeXl6iYsWKQi6Xi2rVqolPPvlExMbG5ng+CjKdhBCvpv6YP3++qFOnjlAoFKJs2bKicePGYvbs2SIhIUFdDv8/gao+yP6/QiIiItLBn3/+idq1a+PgwYNo27ZtcTcnT6GhoRgwYACioqJQqVKl4m5OqcfAi4iIqIA+/fRT3L17t8jmmtIHd3d3tGzZskBDIkj/GHgRERERSYR3NRIRERFJhIEXERERkUQYeBERERFJhIEXERERkUQ4gSrphUqlwsOHD2FhYSHpTy8QEZF+CCHw4sULVK5cuch+uzU1NRXp6el6qUsul8PExEQvdUmJgRfpxcOHD/OcKZ2IiEqO+/fvo2rVqnqvNzU1FaYW5YHMFL3UZ2tri3v37pW44IuBF+lF9s/byF19IDOUF3NriIpG9NFvirsJREXmxYtE1HSqVuCfK3ub9PR0IDMFClcfoLB/J7LSEXdjA9LT0xl4UemU3b0oM5Qz8KJ3llKpLO4mEBW5Ih8uYmRS6L8TQlZyh6gz8CIiIiLpyAAUNrgrwUOJGXgRERGRdGQGr5bC1lFCldyWExEREZUwzHgRERGRdGQyPXQ1lty+RgZeREREJB12NRIRERGRFJjxIiIiIumwq5GIiIhIKnroaizBHXYlt+VEREREJQwzXkRERCQddjUSERERSYR3NRIRERGRFJjxIiIiIumwq5GIiIhIIqW8q5GBFxEREUmnlGe8Sm7ISERERFTCMONFRERE0mFXIxEREZFEZDI9BF7saiQiIiKit2DGi4iIiKRjIHu1FLaOEoqBFxEREUmnlI/xKrktJyIiIiphmPEiIiIi6ZTyebwYeBEREZF02NVIRERERFJgxouIiIikw65GIiIiIomU8q5GBl5EREQknVKe8Sq5ISMRERFRCcOMFxEREUmHXY1EREREEmFXIxERERFJgRkvIiIikpAeuhpLcN6IgRcRERFJh12NRERERCQFZryIiIhIOjKZHu5qZMaLiIiI6O2yp5Mo7KKDwMBANGnSBBYWFqhYsSJ69OiBW7du5VpWCIGOHTtCJpMhNDRUY1tMTAw6d+6MMmXKoGLFipgyZQoyMzN1agsDLyIiInqnnThxAmPGjMH58+cRFhaGjIwMeHl5ITk5OUfZJUuWQJZLRi0rKwudO3dGeno6zp49iw0bNiA4OBhffPGFTm1hVyMRERFJpxgG1x86dEjjcXBwMCpWrIjw8HC0atVKvT4iIgILFy7ExYsXUalSJY19fv31V9y4cQOHDx+GjY0NGjRogLlz52Lq1KkICAiAXC7Xqi3MeBEREZF0iqGr8U0JCQkAgHLlyqnXpaSkYMCAAVixYgVsbW1z7HPu3DnUq1cPNjY26nXe3t5ITEzE9evXtT42M15EREQkHT1mvBITEzVWKxQKKBSKfHdVqVSYMGECmjdvjrp166rX+/v7o1mzZujevXuu+8XFxWkEXQDUj+Pi4rRuOgMvIiIiKpHs7Ow0Hs+aNQsBAQH57jNmzBj88ccfOH36tHrdnj17cPToUVy+fLkomqmBgRcRERFJR48/kn3//n0olUr16rdlu/z8/LBv3z6cPHkSVatWVa8/evQooqKiYGVlpVG+d+/eaNmyJY4fPw5bW1tcuHBBY/ujR48AINeuybww8CIiIiLp6LGrUalUagReeRFCYOzYsQgJCcHx48fh6OiosX3atGkYPny4xrp69eph8eLF6Nq1KwDA3d0dX331FR4/foyKFSsCAMLCwqBUKuHq6qp10xl4ERER0TttzJgx2Lp1K3bv3g0LCwv1mCxLS0uYmprC1tY216xVtWrV1EGal5cXXF1dMXjwYCxYsABxcXH4/PPPMWbMmLdm2l7HuxqJiIhIMjKZTC+LLlauXImEhAS0bt0alSpVUi/bt2/Xug5DQ0Ps27cPhoaGcHd3x6BBgzBkyBDMmTNHp7Yw40VERESSKUjglEslOhUXQuh8iNz2sbe3x4EDB3Su63XMeBERERFJhBkvIiIiko7s/5fC1lFCMfAiIiIiyRRHV+N/CbsaiYiIiCTCjBcRERFJprRnvBh4ERERkWQYeBERERFJpLQHXhzjRURERCQRZryIiIhIOpxOgoiIiEga7GokIiIiIkkw40VERESSkcmgh4yXftpSHBh4ERERkWRk0ENXYwmOvNjVSERERCQRZryIiIhIMqV9cD0DLyIiIpJOKZ9Ogl2NRERERBJhxouIiIiko4euRsGuRiIiIqK308cYr8LfFVl8GHgRERGRZEp74MUxXkREREQSYcaLiIiIpFPK72pk4EVERESSYVcjEREREUmCGS8iIiKSTGnPeDHwIiIiIsmU9sCLXY1EREREEmHGi4iIiCRT2jNeDLyIiIhIOqV8Ogl2NRIRERFJhBkvIiIikgy7GomIiIgkwsCLiIiISCKlPfDiGC8iIiIiiTDjRURERNIp5Xc1MvAiIiIiybCrkYiIiIgkUWIyXjKZDCEhIejRo0eu26Ojo+Ho6IjLly+jQYMGkrZNKikpKRg8eDDCwsLw4sULPH/+HFZWVoWuNyAgAKGhoYiIiCh0XVRw/r5e6OLphhr2NkhNy8CFq38i4NvduPvXY3WZvavGo0XjGhr7rd95GhO/3gYAqFujCib4tMcHDZxRztIMMbHPsH7XaazedlzKUyHK1dnLd/Ht5iOIuBmDR08SsXHBcHT2cFNvHzNnE7btv6CxT5sPXPDz0tHqxwvX/4KwM9fxx+2/YWxshHtHFkjWftIPZryKka+vr/oFMDY2ho2NDdq3b48ffvgBKpVKo2xsbCw6duxYqOO1bt0aMpkM27Zt01i/ZMkSODg46FSXTCZDaGioVuWyFzMzM9SoUQO+vr4IDw/X6XgAsGHDBpw6dQpnz55FbGwsLC0tda5DG76+vnkGuFR0mjWqjnU/n4TXsCD08vsWxkaG2LXcD2VM5BrlgkPOoFaHz9TLrOWh6m1ute3wz/MXGPnFBrj3+wqL1v+CL8Z0w4gPW0l8NkQ5pbxMQ50aVbBgSt88y7R1d8GNA1+pl7VzfTW2Z2RkonvbBhjau0URt5aKigwyjb+NBVpK8CCvYs94dejQAevXr0dWVhYePXqEQ4cOYfz48dixYwf27NkDI6NXTbS1tdXL8UxMTPD555+jd+/eMDY21kudb7N+/Xp06NABqampuH37NtasWYOmTZvihx9+wJAhQ7SuJyoqCi4uLqhbt24RtpaKy4fjvtN4PHr2ZtwN+xoNXOxw9nKUev3L1HQ8fvoi1zq27D2v8fivB0/RpJ4juni6Ye3PJ/XfaCIdtGtWB+2a1cm3jNzYCDbllXlunzayMwBg677zeZYh+i8r9jFeCoUCtra2qFKlCho1aoTp06dj9+7dOHjwIIKDg9Xl3swwXbhwAQ0bNoSJiQnee+89XL58Wavj9e/fH/Hx8Vi7dm2+5VauXAlnZ2fI5XLUqlULmzZtUm/Lzo717NkTMpnsrdkyKysr2NrawsHBAV5eXtixYwcGDhwIPz8/PH/+XF3u9OnTaNmyJUxNTWFnZ4dx48YhOTkZwKts3cKFC3Hy5EnIZDK0bt0aALBp0ya89957sLCwgK2tLQYMGIDHj//tmgoODs7RHRkaGppnmjYgIAAbNmzA7t271d8sjh8/nu/5UdFQmpsAAJ4npmis/7DDe7gb9jXObpuOL8Z0g6ki/y8QSnOTHHUQ/VeduXQXtTp8hvc/nItJ87fjWUJycTeJ9KzQ2S49dFUWp2IPvHLTpk0buLm5YdeuXbluT0pKQpcuXeDq6orw8HAEBARg8uTJWtWtVCoxY8YMzJkzRx3UvCkkJATjx4/HpEmT8Mcff+CTTz7B0KFDcezYMQDA77//DuBVJis2Nlb9WBf+/v548eIFwsLCALzKZnXo0AG9e/fG1atXsX37dpw+fRp+fn4AgF27dmHEiBFwd3dHbGys+rnJyMjA3LlzceXKFYSGhiI6Ohq+vr46tyfb5MmT0bdvX3To0AGxsbGIjY1Fs2bNClwfFYxMJkPgxD44HxGFyKhY9fodv1zEJ19sRLdRy7A4+Ff07dgEq+f65FnP+/Ud0bN9Y2wIOSNFs4kKpe0Hrvhu1mCEfDsWs/y64eylu+g74TtkZanevjOVHDI9LSVUsXc15qV27dq4evVqrtu2bt0KlUqF77//HiYmJqhTpw7+/vtvfPrpp1rVPXr0aCxduhSLFi3CzJkzc2wPCgqCr68vRo9+NaBz4sSJOH/+PIKCguDp6Qlra2sA/2ayCnp+wKubAgAgMDAQAwcOxIQJEwAANWrUwLJly+Dh4YGVK1eiXLlyKFOmDORyucYxhw0bpv6/k5MTli1bhiZNmiApKQnm5uY6t8vc3BympqZIS0vL99zS0tKQlpamfpyYmKjzsShvQf/rCxfnSug4YrHG+tcDqBtRDxH3JBF7Vo6DQ5UKiH7wRKOsi3MlbAkaiflrD+DYbzclaTdRYfTyaqz+v2v1yqhTvQoa95qN05fuwKNJrWJsGZH+/CczXgAghMgzlRgZGYn69evDxMREvc7d3V3ruhUKBebMmYOgoCA8efIkx/bIyEg0b95cY13z5s0RGRmp9THeRggB4N87M65cuYLg4GCYm5urF29vb6hUKty7dy/PesLDw9G1a1dUq1YNFhYW8PDwAADExMTora25CQwMhKWlpXqxs7Mr0uOVJgumfAjvlnXR9dNlePg4Pt+y4X9EAwCc7Kw11tdytEXoirHYEHIWC3/4pYhaSlS0HKpUQHkrc9y7/09xN4X0iF2N/1GRkZFwdHQssvoHDRoEe3t7fPnll0V2jPxkB3HZ55iUlIRPPvkEERER6uXKlSu4c+cOnJ2dc60jOTkZ3t7eUCqV2LJlC37//XeEhIQAANLT0wEABgYG6iAvW0ZGRqHb/9lnnyEhIUG93L9/v9B10qugq3NrN3T7dBliHj59a/l6NasCAB49SVCvq+1kiz0rx2Hb/t/w5cq9RdZWoqL24NFzPEtIhk2FormDm4pHaQ+8/pNdjUePHsW1a9fg7++f63YXFxds2rQJqamp6qzX+fO63eFiYGCAwMBA9OrVK0cXpYuLC86cOQMfn3/Hzpw5cwaurq7qx8bGxsjKytLpmK9bsmQJlEol2rVrBwBo1KgRbty4gerVq2tdx82bN/H06VN8/fXX6ozTxYsXNcpYW1vjxYsXSE5OhpmZGQC8db4uuVz+1nNTKBRQKBRat5XeLmhqX/Txfg8DJq9BUkoqKpa3AAAkJqUiNS0DDlUqoE+H9xB25jqeJSSjbo0q+Mq/F85cuoPrdx8CeNW9uPu7cTh6PhIrth5V15GVJfA0PqnYzo0IAJJS0nDv73+zVzEPn+La7b9RVlkGVkozfLPuILp4usGmvBL3HjzB7OW74VS1Atp8UFu9z99xz/A8MQUP4p4jS6XCtdt/AwAcq1rDvAw/k0oCmezVUtg6SqpiD7zS0tIQFxenMZ1EYGAgunTpkudUCwMGDMCMGTMwYsQIfPbZZ4iOjkZQUJDOx+7cuTOaNm2K1atXw8bGRr1+ypQp6Nu3Lxo2bIh27dph79692LVrFw4fPqwu4+DggCNHjqB58+ZQKBQoW7ZsnseJj49HXFwc0tLScPv2baxevRqhoaHYuHGj+o7DqVOn4oMPPoCfnx+GDx8OMzMz3LhxA2FhYfj2229zrbdatWqQy+VYvnw5Ro0ahT/++ANz587VKNO0aVOUKVMG06dPx7hx4/Dbb79p3C2aGwcHB/zyyy+4desWypcvD0tLS8mm3ijNPu7zaq6t/asnaKwfPXsTftz3GzIyM9H6/Vr4tJ8nypjK8eDRc+w9GoGg17oSu7VpCOtyFvio0/v4qNP76vUxD5/CrfssSc6DKC8RkTHoPnqZ+vHnS15l6Pt1fh9B//sI1+8+wLYDvyHhxUvYWlvC8/3a+OyTzlDI//38CVyzX2OS1daD5wMAdn83LsfkwkT/RcUeeB06dAiVKlWCkZERypYtCzc3Nyxbtgw+Pj4wMMi9J9Tc3Bx79+7FqFGj0LBhQ7i6umL+/Pno3bu3zsefP39+jrv2evTogaVLlyIoKAjjx4+Ho6Mj1q9fr57CAQAWLlyIiRMnYu3atahSpYp6kHxuhg4dCuDVHGJVqlRBixYtcOHCBTRq1Ehdpn79+jhx4gRmzJiBli1bQggBZ2dnfPTRR3nWa21tjeDgYEyfPh3Lli1Do0aNEBQUhG7duqnLlCtXDps3b8aUKVOwdu1atG3bFgEBARg5cmSe9Y4YMQLHjx/He++9h6SkJBw7dkzj3KlolG3il+/2B4/i0eWTpfmWmb/2AOavPaDPZhHpTYvGNfD0t+V5bt+xbMxb61jxxWCs+GKwPptFEnuV8SrszPV6akwxkIk3BwARFUBiYiIsLS2hqDcCMkP523cgKoHyCxqISrrExERUsrZCQkIClMq8J7EtTP2WlpZwGrcDhgqzQtWVlZaMP5f1KbK2FqX/7OB6IiIiondNsXc1EhERUelR2n8km4EXERERSaa039XIrkYiIiIiiTDjRURERJIxMJDBwKBwKStRyP2LEwMvIiIikgy7GomIiIhIEsx4ERERkWR4VyMRERGRREp7VyMDLyIiIpJMac94cYwXERERkUSY8SIiIiLJlPaMFwMvIiIikkxpH+PFrkYiIiJ6pwUGBqJJkyawsLBAxYoV0aNHD9y6dUu9/dmzZxg7dixq1aoFU1NTVKtWDePGjUNCQoJGPTExMejcuTPKlCmDihUrYsqUKcjMzNSpLcx4ERERkWRk0ENXI3Tb/8SJExgzZgyaNGmCzMxMTJ8+HV5eXrhx4wbMzMzw8OFDPHz4EEFBQXB1dcVff/2FUaNG4eHDh9ixYwcAICsrC507d4atrS3Onj2L2NhYDBkyBMbGxpg3b572bRdCCJ1aT5SLxMREWFpaQlFvBGSG8uJuDlGRePrb8uJuAlGRSUxMRCVrKyQkJECpVBZJ/ZaWlqj/2R4YmpgVqq6s1GRcDexW4Lb+888/qFixIk6cOIFWrVrlWubnn3/GoEGDkJycDCMjIxw8eBBdunTBw4cPYWNjAwBYtWoVpk6din/++QdyuXZ/+9jVSERERCVSYmKixpKWlqbVftldiOXKlcu3jFKphJHRq87Bc+fOoV69euqgCwC8vb2RmJiI69eva91mBl5EREQkmey7Ggu7AICdnR0sLS3VS2Bg4FuPr1KpMGHCBDRv3hx169bNtcyTJ08wd+5cjBw5Ur0uLi5OI+gCoH4cFxen9flzjBcRERFJRp93Nd6/f1+jq1GhULx13zFjxuCPP/7A6dOnc92emJiIzp07w9XVFQEBAYVraC4YeBEREVGJpFQqdRrj5efnh3379uHkyZOoWrVqju0vXrxAhw4dYGFhgZCQEBgbG6u32dra4sKFCxrlHz16pN6mLXY1EhERkWT02dWoLSEE/Pz8EBISgqNHj8LR0TFHmcTERHh5eUEul2PPnj0wMTHR2O7u7o5r167h8ePH6nVhYWFQKpVwdXXVui3MeBEREZFkimMC1TFjxmDr1q3YvXs3LCws1GOyLC0tYWpqqg66UlJSsHnzZvVgfQCwtraGoaEhvLy84OrqisGDB2PBggWIi4vD559/jjFjxmjVxZmNgRcRERFJpjh+MmjlypUAgNatW2usX79+PXx9fXHp0iX89ttvAIDq1atrlLl37x4cHBxgaGiIffv24dNPP4W7uzvMzMzg4+ODOXPm6NQWBl5ERET0TnvblKWtW7d+axkAsLe3x4EDBwrVFgZeREREJB09dDXqOHH9fwoDLyIiIpJMcXQ1/pfwrkYiIiIiiTDjRURERJIpjrsa/0sYeBEREZFk2NVIRERERJJgxouIiIgkw65GIiIiIomwq5GIiIiIJMGMFxEREUmmtGe8GHgRERGRZDjGi4iIiEgipT3jxTFeRERERBJhxouIiIgkw65GIiIiIomwq5GIiIiIJMGMFxEREUlGBj10NeqlJcWDgRcRERFJxkAmg0EhI6/C7l+c2NVIREREJBFmvIiIiEgyvKuRiIiISCKl/a5GBl5EREQkGQPZq6WwdZRUHONFREREJBFmvIiIiEg6Mj10FZbgjBcDLyIiIpJMaR9cz65GIiIiIokw40VERESSkf3/v8LWUVIx8CIiIiLJ8K5GIiIiIpIEM15EREQkGU6gqoU9e/ZoXWG3bt0K3BgiIiJ6t5X2uxq1Crx69OihVWUymQxZWVmFaQ8RERHRO0urwEulUhV1O4iIiKgUMJDJYFDIlFVh9y9OhRrjlZqaChMTE321hYiIiN5xpb2rUee7GrOysjB37lxUqVIF5ubm+PPPPwEAM2fOxPfff6/3BhIREdG7I3twfWGXkkrnwOurr75CcHAwFixYALlcrl5ft25drFu3Tq+NIyIiInqX6Bx4bdy4EWvWrMHAgQNhaGioXu/m5oabN2/qtXFERET0bsnuaizsUlLpPMbrwYMHqF69eo71KpUKGRkZemkUERERvZtK++B6nTNerq6uOHXqVI71O3bsQMOGDfXSKCIiIqJ3kc4Zry+++AI+Pj548OABVCoVdu3ahVu3bmHjxo3Yt29fUbSRiIiI3hGy/18KW0dJpXPGq3v37ti7dy8OHz4MMzMzfPHFF4iMjMTevXvRvn37omgjERERvSNK+12NBZrHq2XLlggLC9N3W4iIiIjeaQWeQPXixYuIjIwE8GrcV+PGjfXWKCIiIno3GcheLYWto6TSOfD6+++/0b9/f5w5cwZWVlYAgPj4eDRr1gzbtm1D1apV9d1GIiIiekfoo6uwJHc16jzGa/jw4cjIyEBkZCSePXuGZ8+eITIyEiqVCsOHDy+KNhIRERG9E3TOeJ04cQJnz55FrVq11Otq1aqF5cuXo2XLlnptHBEREb17SnDCqtB0Drzs7OxynSg1KysLlStX1kujiIiI6N3ErkYdffPNNxg7diwuXryoXnfx4kWMHz8eQUFBem0cERERvVuyB9cXdimptMp4lS1bViO6TE5ORtOmTWFk9Gr3zMxMGBkZYdiwYejRo0eRNJSIiIiopNMq8FqyZEkRN4OIiIhKg9Le1ahV4OXj41PU7SAiIqJSoLT/ZFCBJ1AFgNTUVKSnp2usUyqVhWoQERER0btK58ArOTkZU6dOxU8//YSnT5/m2J6VlaWXhhEREdG7x0Amg0EhuwoLu39x0vmuxv/97384evQoVq5cCYVCgXXr1mH27NmoXLkyNm7cWBRtJCIioneETKafpaTSOeO1d+9ebNy4Ea1bt8bQoUPRsmVLVK9eHfb29tiyZQsGDhxYFO0kIiIiKvF0zng9e/YMTk5OAF6N53r27BkAoEWLFjh58qR+W0dERETvlOy7Ggu7lFQ6B15OTk64d+8eAKB27dr46aefALzKhGX/aDYRERFRbkp7V6POgdfQoUNx5coVAMC0adOwYsUKmJiYwN/fH1OmTNF7A4mIiIjeFTqP8fL391f/v127drh58ybCw8NRvXp11K9fX6+NIyIiondLab+rsVDzeAGAvb097O3t9dEWIiIiesfpo6uwBMdd2gVey5Yt07rCcePGFbgxRERE9G7jTwZpYfHixVpVJpPJGHgRERHRf0pgYCB27dqFmzdvwtTUFM2aNcP8+fNRq1YtdZnU1FRMmjQJ27ZtQ1paGry9vfHdd9/BxsZGXSYmJgaffvopjh07BnNzc/j4+CAwMBBGRtp3IGpVMvsuRqK3iTkexJ+NondWWc8virsJREVGZKZJchwDFODOvlzq0MWJEycwZswYNGnSBJmZmZg+fTq8vLxw48YNmJmZAXg1hn3//v34+eefYWlpCT8/P/Tq1QtnzpwB8OqXeTp37gxbW1ucPXsWsbGxGDJkCIyNjTFv3jyt21LoMV5ERERE2iqOrsZDhw5pPA4ODkbFihURHh6OVq1aISEhAd9//z22bt2KNm3aAADWr18PFxcXnD9/Hh988AF+/fVX3LhxA4cPH4aNjQ0aNGiAuXPnYurUqQgICIBcLteqLYUNOomIiIhKlISEBABAuXLlAADh4eHIyMhAu3bt1GVq166NatWq4dy5cwCAc+fOoV69ehpdj97e3khMTMT169e1PjYzXkRERCQZmQww0NNdjYmJiRrrFQoFFApFvvuqVCpMmDABzZs3R926dQEAcXFxkMvlOSaCt7GxQVxcnLrM60FX9vbsbdpixouIiIgkYyDTzwIAdnZ2sLS0VC+BgYFvPf6YMWPwxx9/YNu2bUV8prljxouIiIhKpPv372vc0PW2bJefnx/27duHkydPomrVqur1tra2SE9PR3x8vEbW69GjR7C1tVWXuXDhgkZ9jx49Um/TVoEyXqdOncKgQYPg7u6OBw8eAAA2bdqE06dPF6Q6IiIiKiX0+SPZSqVSY8kr8BJCwM/PDyEhITh69CgcHR01tjdu3BjGxsY4cuSIet2tW7cQExMDd3d3AIC7uzuuXbuGx48fq8uEhYVBqVTC1dVV6/PXOfDauXMnvL29YWpqisuXLyMt7dXtpwkJCTrdTklERESljz67GrU1ZswYbN68GVu3boWFhQXi4uIQFxeHly9fAgAsLS3x8ccfY+LEiTh27BjCw8MxdOhQuLu744MPPgAAeHl5wdXVFYMHD8aVK1fwyy+/4PPPP8eYMWPemmnTOH/dmg58+eWXWLVqFdauXQtjY2P1+ubNm+PSpUu6VkdERERUpFauXImEhAS0bt0alSpVUi/bt29Xl1m8eDG6dOmC3r17o1WrVrC1tcWuXbvU2w0NDbFv3z4YGhrC3d0dgwYNwpAhQzBnzhyd2qLzGK9bt26hVatWOdZbWloiPj5e1+qIiIioFCmO32oUQry1jImJCVasWIEVK1bkWcbe3h4HDhzQ7eBv0DnjZWtri7t37+ZYf/r0aTg5ORWqMURERPRuM5DJ9LKUVDoHXiNGjMD48ePx22+/QSaT4eHDh9iyZQsmT56MTz/9tCjaSERERO8IAz0tJZXOXY3Tpk2DSqVC27ZtkZKSglatWkGhUGDy5MkYO3ZsUbSRiIiI6J2gc+Alk8kwY8YMTJkyBXfv3kVSUhJcXV1hbm5eFO0jIiKid0hxjPH6LynwBKpyuVyneSuIiIiIDFD4MVoGKLmRl86Bl6enZ76/Cn706NFCNYiIiIjoXaVz4NWgQQONxxkZGYiIiMAff/wBHx8ffbWLiIiI3kHsatTR4sWLc10fEBCApKSkQjeIiIiI3l0FmXk+tzpKKr3dkTlo0CD88MMP+qqOiIiI6J1T4MH1bzp37hxMTEz0VR0RERG9g2QyFHpwfanqauzVq5fGYyEEYmNjcfHiRcycOVNvDSMiIqJ3D8d46cjS0lLjsYGBAWrVqoU5c+bAy8tLbw0jIiIietfoFHhlZWVh6NChqFevHsqWLVtUbSIiIqJ3FAfX68DQ0BBeXl6Ij48vouYQERHRu0ymp38llc53NdatWxd//vlnUbSFiIiI3nHZGa/CLiWVzoHXl19+icmTJ2Pfvn2IjY1FYmKixkJEREREudN6jNecOXMwadIkdOrUCQDQrVs3jZ8OEkJAJpMhKytL/60kIiKid0JpH+OldeA1e/ZsjBo1CseOHSvK9hAREdE7TCaT5fubz9rWUVJpHXgJIQAAHh4eRdYYIiIioneZTtNJlOQIk4iIiIofuxp1ULNmzbcGX8+ePStUg4iIiOjdxZnrdTB79uwcM9cTERERkXZ0Crz69euHihUrFlVbiIiI6B1nIJMV+keyC7t/cdI68OL4LiIiIiqs0j7GS+sJVLPvaiQiIiKigtE646VSqYqyHURERFQa6GFwfQn+qUbdxngRERERFYYBZDAoZORU2P2LEwMvIiIikkxpn05C5x/JJiIiIqKCYcaLiIiIJFPa72pk4EVERESSKe3zeLGrkYiIiEgizHgRERGRZEr74HoGXkRERCQZA+ihq7EETyfBrkYiIiIiiTDjRURERJJhVyMRERGRRAxQ+O62ktxdV5LbTkRERFSiMONFREREkpHJZJAVsq+wsPsXJwZeREREJBnZ/y+FraOkYuBFREREkuHM9UREREQkCWa8iIiISFIlN19VeAy8iIiISDKlfR4vdjUSERERSYQZLyIiIpIMp5MgIiIikghnriciIiIiSTDjRURERJJhVyMRERGRREr7zPXsaiQiIiKSCDNeREREJBl2NRIRERFJpLTf1cjAi4iIiCRT2jNeJTloJCIiIipRmPEiIiIiyZT2uxoZeBEREZFk+CPZRERERCQJZryIiIhIMgaQwaCQnYWF3b84MfAiIiIiybCrkYiIiIgkwYwXERERSUb2//8KW0dJxYwXERERSSa7q7Gwiy5OnjyJrl27onLlypDJZAgNDdXYnpSUBD8/P1StWhWmpqZwdXXFqlWrNMqkpqZizJgxKF++PMzNzdG7d288evRI5/Nn4EVERETvtOTkZLi5uWHFihW5bp84cSIOHTqEzZs3IzIyEhMmTICfnx/27NmjLuPv74+9e/fi559/xokTJ/Dw4UP06tVL57awq5GIiIgkI9PDXY26djV27NgRHTt2zHP72bNn4ePjg9atWwMARo4cidWrV+PChQvo1q0bEhIS8P3332Pr1q1o06YNAGD9+vVwcXHB+fPn8cEHH2jdFma8iIiISDL67GpMTEzUWNLS0grUpmbNmmHPnj148OABhBA4duwYbt++DS8vLwBAeHg4MjIy0K5dO/U+tWvXRrVq1XDu3DmdjsXAi4iIiCSjz8DLzs4OlpaW6iUwMLBAbVq+fDlcXV1RtWpVyOVydOjQAStWrECrVq0AAHFxcZDL5bCystLYz8bGBnFxcTodi12NREREVCLdv38fSqVS/VihUBSonuXLl+P8+fPYs2cP7O3tcfLkSYwZMwaVK1fWyHLpAwMvIiIikow+p5NQKpUagVdBvHz5EtOnT0dISAg6d+4MAKhfvz4iIiIQFBSEdu3awdbWFunp6YiPj9fIej169Ai2trY6HY9djURERCQZA5l+Fn3JyMhARkYGDAw0QyJDQ0OoVCoAQOPGjWFsbIwjR46ot9+6dQsxMTFwd3fX6XjMeBEREdE7LSkpCXfv3lU/vnfvHiIiIlCuXDlUq1YNHh4emDJlCkxNTWFvb48TJ05g48aNWLRoEQDA0tISH3/8MSZOnIhy5cpBqVRi7NixcHd31+mORoCBFxEREUmoOGauv3jxIjw9PdWPJ06cCADw8fFBcHAwtm3bhs8++wwDBw7Es2fPYG9vj6+++gqjRo1S77N48WIYGBigd+/eSEtLg7e3N7777jud287Ai4iIiCRTHD+S3bp1awgh8txua2uL9evX51uHiYkJVqxYkeckrNriGC8iIiIiiTDjRURERJKRofA/cl1yfyKbgRcRERFJSB93JerzrkapsauRiIiISCIMvCSwZs0a2NnZwcDAAEuWLNFLndHR0ZDJZIiIiNBLfa87fvw4ZDIZ4uPj9V435e/Mpbvo578KLh2no2wTP+w/fkVj++iATSjbxE9j6TNWc6Dn84RkjPg8GNVaT4a95xSMnbsFSSkF+/0yIn3yH9ASR1Z+gpj9M3B71/+weW5/VLcrn2f5n78ejOfH5qBT89rqdf29G+D5sTm5LhWszKQ4DSokmZ7+lVTsasyDr68vNmzYAAAwMjJCuXLlUL9+ffTv3x++vr45JlrLS2JiIvz8/LBo0SL07t0blpaWRdLe48ePw9PTE8+fP8/xW1JUcqS8TEPdmlUwqJs7Bv9vba5l2rq7YsUXg9SPFXLNt/GImRvw6EkCdn3rh4zMLPjN2YwJ87Zi3ZdDi7TtRG/TzM0B60J/w+VbD2BkaICZw9tj1wIffDB0OVJSMzTKftrHPde70EKO/YEjF+5qrFsxrSdM5EZ4Ep9cpO0n/SiOuxr/S5jxykeHDh0QGxuL6OhoHDx4EJ6enhg/fjy6dOmCzMxMreqIiYlBRkYGOnfujEqVKqFMmTJF3Goqydo3r4PPP+2KLp5ueZZRyI1gU0GpXqyU/15Tt+7F4ci5G1j2+QC8V9cB7g2cMX/yh9j16yXE/hMvwRkQ5e3DqZvw4y8RuBn9D/6IeoTRX++Cna0VGtSsrFGurrMtxvRtBr8FoTnqSE3PxOPnSeolS6VCq4aO2HzgkkRnQYUl09NSUjHwyodCoYCtrS2qVKmCRo0aYfr06di9ezcOHjyI4OBgAEB8fDyGDx8Oa2trKJVKtGnTBleuvOoeCg4ORr169QAATk5OkMlkiI6ORlRUFLp37w4bGxuYm5ujSZMmOHz4sMaxZTIZQkNDNdZZWVmpj/u66Oho9cRwZcuWhUwmg6+vLwBApVIhMDAQjo6OMDU1hZubG3bs2KGx/4EDB1CzZk2YmprC09MT0dHRhXviqEidDr+DGl7T0KT3HEz8ehuexSept/1+7R4sLUzR0NVeva71+7VgYCBD+B9/FUdzifKkNDMBADxPfKleZ6owxtrP+2DK0v14/Dwpr13V+nk1wMu0DOw+cb3I2kmkTwy8dNSmTRu4ublh165dAIAPP/wQjx8/xsGDBxEeHo5GjRqhbdu2ePbsGT766CN1QHXhwgXExsbCzs4OSUlJ6NSpE44cOYLLly+jQ4cO6Nq1K2JiYgrUJjs7O+zcuRPAq9+Oio2NxdKlSwEAgYGB2LhxI1atWoXr16/D398fgwYNwokTJwC8+mX3Xr16oWvXroiIiMDw4cMxbdq0tx4zLS0NiYmJGgsVvbbNXLAyYDBCvxuLgLHdcfbSXXw4fiWysl79ntijp4mwLmuhsY+RkSHKKsvg0VO+RvTfIZPJEOjXEeev/YXI6Mfq9fPGdMCF6/dx8MxNreoZ1KkRdhy5htR07XohqPgZQAYDWSGXEpzz4hivAqhduzauXr2K06dP48KFC3j8+DEUCgUAICgoCKGhodixYwdGjhyJ8uVfDRy1trZW/4K5m5sb3Nz+7UqaO3cuQkJCsGfPHvj5+encHkNDQ5QrVw4AULFiRfUYr7S0NMybNw+HDx9W/4ink5MTTp8+jdWrV8PDwwMrV66Es7MzFi5cCACoVasWrl27hvnz5+d7zMDAQMyePVvntlLh9PZ6T/3/OtWroE71KmjYMwCnw+/A4/1axdgyIt0Eje8MF8eK6Dj2e/W6js1qoWVDJ3iMWKlVHU1c7VDboSJGBe4sqmZSEdBHV2HJDbsYeBWIEAIymQxXrlxBUlKSOrjK9vLlS0RFReW5f1JSEgICArB//37ExsYiMzMTL1++LHDGKy93795FSkoK2rdvr7E+PT0dDRs2BABERkaiadOmGtu1+aX1zz77TP1bV8Crmwjs7Oz00GrShUPVCihvZY4///4HHu/Xgk15Jf55/kKjTGZmFp4npsCmvLKYWkmkacG4zvB2r4VO47/Hwyf/ZmJbNnSCY+WyiN73mUb5jbP74dy1v9DVX/MnXQZ3boSrd2Jx5XasJO0m0gcGXgUQGRkJR0dHJCUloVKlSjh+/HiOMvndWTh58mSEhYUhKCgI1atXh6mpKfr06YP09HR1GZlMluOOnoyMjDeryldS0qvxEfv370eVKlU0tmVn6ApKoVAUug4qvAePnuNZQrI6qGpSzxEJL14iIjIGDVyqAQBOXrwNlUqgcV37/KoiksSCcZ3RuYULuvr/gJi4eI1tS7aewqb94Rrrzq73w/TvDuLQ2Vsa681M5OjRui7mrg0r6iaTvpXylBcDLx0dPXoU165dg7+/P6pWrYq4uDgYGRnBwcFB6zrOnDkDX19f9OzZE8CrAOnNAe3W1taIjf33W9ydO3eQkpKSZ51yuRwAkJWVpV7n6uoKhUKBmJgYeHh45Lqfi4sL9uzZo7Hu/PnzWp8L6VdSShru3f9H/fivh09x7dbfsLIsg7JKM8xfewDd2jSATXkl7v39BLOWh8LJrgLaursAAGo52qKtuyvGf7UViz7rh4zMLPzvm5/Qy6sRKllbFdNZEb0SNKEL+rSthwGf/4iklHRULGsOAEhMTtW4W/FNfz9KyBGk9WxTF0aGBtgedlWKppMe6WMeLs7j9Y5KS0tDXFwcsrKy8OjRIxw6dAiBgYHo0qULhgwZAgMDA7i7u6NHjx5YsGABatasiYcPH2L//v3o2bMn3nvvvVzrrVGjBnbt2oWuXbtCJpNh5syZUKlUGmXatGmDb7/9Fu7u7sjKysLUqVNhbGycZ1vt7e0hk8mwb98+dOrUCaamprCwsMDkyZPh7+8PlUqFFi1aICEhAWfOnIFSqYSPjw9GjRqFhQsXYsqUKRg+fDjCw8NzvXOSpBER+Re6jlqmfjxj8aubOPp3boqF0z7CjbsPsG3/b0h48RK21pZo07Q2po/qAoX832tj7VwfTPnmJ/QYvRwymQzd2jTA15M/lPxciN70cff3AQD7lwzTWD/661348ZcIneoa3LER9p26gcTkVH01j0gSDLzycejQIVSqVAlGRkYoW7Ys3NzcsGzZMvj4+KgnUD1w4ABmzJiBoUOH4p9//oGtrS1atWoFGxubPOtdtGgRhg0bhmbNmqFChQqYOnVqjrsCFy5ciKFDh6Jly5aoXLkyli5divDw8DxqBKpUqYLZs2dj2rRpGDp0KIYMGYLg4GDMnTsX1tbWCAwMxJ9//gkrKyv11BgAUK1aNezcuRP+/v5Yvnw53n//fcybNw/Dhg3L81hUdFo0ronnv3+b5/ady99+80VZSzNOlkr/SWU9v9DbPt5j1xW2OVRc9DCBaglOeEEmcpsamEhHiYmJsLS0xKOnCVAqOYib3k0FCRyISgqRmYa08wuQkFA0n+PZfyeORsTA3KJw9Se9SESbBtWKrK1FifN4EREREUmEXY1EREQkHd7VSERERCQN3tVIREREJBGZHgbXF3pwfjHiGC8iIiIiiTDjRURERJIp5UO8GHgRERGRhEp55MWuRiIiIiKJMONFREREkuFdjUREREQS4V2NRERERCQJZryIiIhIMqV8bD0DLyIiIpJQKY+82NVIREREJBFmvIiIiEgyvKuRiIiISCKl/a5GBl5EREQkmVI+xItjvIiIiIikwowXERERSaeUp7wYeBEREZFkSvvgenY1EhEREUmEGS8iIiKSDO9qJCIiIpJIKR/ixa5GIiIiIqkw40VERETSKeUpLwZeREREJBne1UhEREREkmDGi4iIiCTDuxqJiIiIJFLKh3gx8CIiIiIJlfLIi2O8iIiIiCTCjBcRERFJprTf1cjAi4iIiKSjh8H1JTjuYlcjERERkVSY8SIiIiLJlPKx9Qy8iIiISEKlPPJiVyMRERGRRJjxIiIiIsnwrkYiIiIiiZT2nwxiVyMRERGRRJjxIiIiIsmU8rH1DLyIiIhIQqU88mLgRURERJIp7YPrOcaLiIiISCLMeBEREZFkZNDDXY16aUnxYMaLiIiIJCPT06KLkydPomvXrqhcuTJkMhlCQ0NzlImMjES3bt1gaWkJMzMzNGnSBDExMertqampGDNmDMqXLw9zc3P07t0bjx490rElDLyIiIjoHZecnAw3NzesWLEi1+1RUVFo0aIFateujePHj+Pq1auYOXMmTExM1GX8/f2xd+9e/Pzzzzhx4gQePnyIXr166dwWdjUSERGRZIpjAtWOHTuiY8eOeW6fMWMGOnXqhAULFqjXOTs7q/+fkJCA77//Hlu3bkWbNm0AAOvXr4eLiwvOnz+PDz74QOu2MONFREREEtJfZ2NiYqLGkpaWpnNrVCoV9u/fj5o1a8Lb2xsVK1ZE06ZNNbojw8PDkZGRgXbt2qnX1a5dG9WqVcO5c+d0Oh4DLyIiIiqR7OzsYGlpqV4CAwN1ruPx48dISkrC119/jQ4dOuDXX39Fz5490atXL5w4cQIAEBcXB7lcDisrK419bWxsEBcXp9Px2NVIREREktFnV+P9+/ehVCrV6xUKhc51qVQqAED37t3h7+8PAGjQoAHOnj2LVatWwcPDo3CNfQMzXkRERCQZfd7VqFQqNZaCBF4VKlSAkZERXF1dNda7uLio72q0tbVFeno64uPjNco8evQItra2Oh2PgRcRERGVWnK5HE2aNMGtW7c01t++fRv29vYAgMaNG8PY2BhHjhxRb7916xZiYmLg7u6u0/HY1UhERESSKY67GpOSknD37l3143v37iEiIgLlypVDtWrVMGXKFHz00Udo1aoVPD09cejQIezduxfHjx8HAFhaWuLjjz/GxIkTUa5cOSiVSowdOxbu7u463dEIMPAiIiIiCRXHbzVevHgRnp6e6scTJ04EAPj4+CA4OBg9e/bEqlWrEBgYiHHjxqFWrVrYuXMnWrRood5n8eLFMDAwQO/evZGWlgZvb2989913urddCCF03ovoDYmJibC0tMSjpwkaAx2J3iVlPb8o7iYQFRmRmYa08wuQkFA0n+PZfydu338Ci0LW/yIxETXtKhRZW4sSx3gRERERSYRdjURERCSZgvzWYm51lFQMvIiIiEgyxTG4/r+EXY1EREREEmHGi4iIiCRTHHc1/pcw8CIiIiLplPJBXuxqJCIiIpIIM15EREQkmVKe8GLgRURERNLhXY1EREREJAlmvIiIiEhChb+rsSR3NjLwIiIiIsmwq5GIiIiIJMHAi4iIiEgi7GokIiIiyZT2rkYGXkRERCSZ0v6TQexqJCIiIpIIM15EREQkGXY1EhEREUmktP9kELsaiYiIiCTCjBcRERFJp5SnvBh4ERERkWR4VyMRERERSYIZLyIiIpIM72okIiIikkgpH+LFwIuIiIgkVMojL47xIiIiIpIIM15EREQkmdJ+VyMDLyIiIpIMB9cT6YEQAgDwIjGxmFtCVHREZlpxN4GoyGRf39mf50UlUQ9/J/RRR3Fh4EV68eLFCwBAdUe7Ym4JEREVxosXL2Bpaan3euVyOWxtbVFDT38nbG1tIZfL9VKXlGSiqENbKhVUKhUePnwICwsLyEpyDriESExMhJ2dHe7fvw+lUlnczSHSO17j0hNC4MWLF6hcuTIMDIrm3rvU1FSkp6frpS65XA4TExO91CUlZrxILwwMDFC1atXibkapo1Qq+UeJ3mm8xqVVFJmu15mYmJTIYEmfOJ0EERERkUQYeBERERFJhIEXUQmkUCgwa9YsKBSK4m4KUZHgNU7vKg6uJyIiIpIIM15EREREEmHgRURERCQRBl5EREREEmHgRfQGmUyG0NDQPLdHR0dDJpMhIiJCsjZJLSUlBb1794ZSqYRMJkN8fLxe6g0ICECDBg30UheVHGvWrIGdnR0MDAywZMkSvdRZlO/D48eP6/W6J3odAy8qFXx9fSGTySCTyWBsbAwbGxu0b98eP/zwA1QqlUbZ2NhYdOzYsVDHa926NWQyGbZt26axfsmSJXBwcNCprrcFgq+Xy17MzMxQo0YN+Pr6Ijw8XKfjAcCGDRtw6tQpnD17FrGxsUU2qaKvry969OhRJHVT4ejynslPYmIi/Pz8MHXqVDx48AAjR44skvYyWKKSgoEXlRodOnRAbGwsoqOjcfDgQXh6emL8+PHo0qULMjMz1eVsbW31cgu7iYkJPv/8c2RkZBS6Lm2tX78esbGxuH79OlasWIGkpCQ0bdoUGzdu1KmeqKgouLi4oG7durC1teXPQJVS2r5n8hMTE4OMjAx07twZlSpVQpkyZYq41UT/bQy8qNRQKBSwtbVFlSpV0KhRI0yfPh27d+/GwYMHERwcrC73ZobpwoULaNiwIUxMTPDee+/h8uXLWh2vf//+iI+Px9q1a/Mtt3LlSjg7O0Mul6NWrVrYtGmTelt2dqxnz56QyWRvzZZZWVnB1tYWDg4O8PLywo4dOzBw4ED4+fnh+fPn6nKnT59Gy5YtYWpqCjs7O4wbNw7JyckAXmXrFi5ciJMnT0Imk6F169YAgE2bNuG9996DhYUFbG1tMWDAADx+/FhdZ3BwMKysrDTaExoammfQFhAQgA0bNmD37t3qzMrx48fzPT+Sljbvmfj4eAwfPhzW1tZQKpVo06YNrly5AuDVNVGvXj0AgJOTE2QyGaKjoxEVFYXu3bvDxsYG5ubmaNKkCQ4fPqxx7NwyvVZWVhrv1WzR0dHw9PQEAJQtWxYymQy+vr4AXv2ObGBgIBwdHWFqago3Nzfs2LFDY/8DBw6gZs2aMDU1haenJ6Kjowv3xBHlg4EXlWpt2rSBm5sbdu3alev2pKQkdOnSBa6urggPD0dAQAAmT56sVd1KpRIzZszAnDlz1EHNm0JCQjB+/HhMmjQJf/zxBz755BMMHToUx44dAwD8/vvvAP7NZGU/1oW/vz9evHiBsLAwAK+yWR06dEDv3r1x9epVbN++HadPn4afnx8AYNeuXRgxYgTc3d0RGxurfm4yMjIwd+5cXLlyBaGhoYiOjlb/cSuIyZMno2/fvuqsSmxsLJo1a1bg+kgab75nPvzwQzx+/BgHDx5EeHg4GjVqhLZt2+LZs2f46KOP1AHVhQsXEBsbCzs7OyQlJaFTp044cuQILl++jA4dOqBr166IiYkpUJvs7Oywc+dOAMCtW7cQGxuLpUuXAgACAwOxceNGrFq1CtevX4e/vz8GDRqEEydOAADu37+PXr16oWvXroiIiMDw4cMxbdq0wj5NRHnij2RTqVe7dm1cvXo1121bt26FSqXC999/DxMTE9SpUwd///03Pv30U63qHj16NJYuXYpFixZh5syZObYHBQXB19cXo0ePBgBMnDgR58+fR1BQEDw9PWFtbQ3g30xWQc8PgPpbfGBgIAYOHIgJEyYAAGrUqIFly5bBw8MDK1euRLly5VCmTBnI5XKNYw4bNkz9fycnJyxbtgxNmjRBUlISzM3NdW6Xubk5TE1NkZaWVuBzo+KR/Z45ffo0Lly4gMePH6u754OCghAaGoodO3Zg5MiRKF++PADA2tpa/Tq7ubnBzc1NXd/cuXMREhKCPXv2qL8A6MLQ0BDlypUDAFSsWFGdeU1LS8O8efNw+PBhuLu7A3h17Z4+fRqrV69WX/POzs5YuHAhAKBWrVq4du0a5s+fX7Anh+gtmPGiUk8IkWd3WGRkJOrXrw8TExP1uuwPcG0oFArMmTMHQUFBePLkSa71N2/eXGNd8+bNERkZqfUx3ib7xymyz/HKlSsIDg6Gubm5evH29oZKpcK9e/fyrCc8PBxdu3ZFtWrVYGFhAQ8PDwAocJaCSq7s98yVK1eQlJSE8uXLa1xP9+7dQ1RUVJ77JyUlYfLkyXBxcYGVlRXMzc0RGRmp92vp7t27SElJQfv27TXat3HjRnX7IiMj0bRpU439dHmPE+mKGS8q9SIjI+Ho6Fhk9Q8aNAhBQUH48ssvdb6jUR+yg7jsc0xKSsInn3yCcePG5ShbrVq1XOtITk6Gt7c3vL29sWXLFlhbWyMmJgbe3t5IT08HABgYGODNXyCT8sYCkk72eyYpKQmVKlXKdWzem+P9Xjd58mSEhYUhKCgI1atXh6mpKfr06aO+loBXXxQKez0lJSUBAPbv348qVapobONvQFJxYeBFpdrRo0dx7do1+Pv757rdxcUFmzZtQmpqqjrrdf78eZ2OYWBggMDAQPTq1StHF6WLiwvOnDkDHx8f9bozZ87A1dVV/djY2BhZWVk6HfN1S5YsgVKpRLt27QAAjRo1wo0bN1C9enWt67h58yaePn2Kr7/+GnZ2dgCAixcvapSxtrbGixcvkJycDDMzMwB46xxLcrm8UOdG0nv9PVO1alXExcXByMhIpy8VZ86cga+vL3r27AngVYD05oB2a2trxMbGqh/fuXMHKSkpedYpl8sBQON6cnV1hUKhQExMjDpD+yYXFxfs2bNHY52u73EiXbCrkUqNtLQ0xMXF4cGDB7h06RLmzZuH7t27o0uXLhgyZEiu+wwYMAAymQwjRozAjRs3cODAAQQFBel87M6dO6Np06ZYvXq1xvopU6YgODgYK1euxJ07d7Bo0SLs2rVLYwC/g4MDjhw5gri4OI07E3MTHx+PuLg4/PXXXwgLC0OfPn2wdetWrFy5Up2BmDp1Ks6ePQs/Pz9ERETgzp072L17d75ja6pVqwa5XI7ly5fjzz//xJ49ezB37lyNMk2bNkWZMmUwffp0REVFYevWrbnegfY6BwcHXL16Fbdu3cKTJ0+YIfuPedt7pl27dnB3d0ePHj3w66+/Ijo6GmfPnsWMGTNyBOavq1GjBnbt2oWIiAhcuXIFAwYMyDE3WJs2bfDtt9/i8uXLuHjxIkaNGgVjY+M867S3t4dMJsO+ffvwzz//ICkpCRYWFpg8eTL8/f2xYcMGREVF4dKlS1i+fDk2bNgAABg1ahTu3LmDKVOm4NatW1pdt0SFIohKAR8fHwFAABBGRkbC2tpatGvXTvzwww8iKytLoywAERISon587tw54ebmJuRyuWjQoIHYuXOnACAuX76c5/E8PDzE+PHjNdadPXtWABD29vYa67/77jvh5OQkjI2NRc2aNcXGjRs1tu/Zs0dUr15dGBkZ5dj3zXZnLyYmJsLZ2Vn4+PiI8PDwHGUvXLgg2rdvL8zNzYWZmZmoX7+++Oqrr9Tbx48fLzw8PDT22bp1q3BwcBAKhUK4u7uLPXv25HgeQkJCRPXq1YWpqano0qWLWLNmjXj9Y2bWrFnCzc1N/fjx48fqdgAQx44dy/P8SFravmcSExPF2LFjReXKlYWxsbGws7MTAwcOFDExMUIIIS5fviwAiHv37qn3uXfvnvD09BSmpqbCzs5OfPvttzneMw8ePBBeXl7CzMxM1KhRQxw4cEBYWlqK9evXq+t48/qbM2eOsLW1FTKZTPj4+AghhFCpVGLJkiWiVq1awtjYWFhbWwtvb29x4sQJ9X579+4V1atXFwqFQrRs2VL88MMPAoB4/vy5vp9WIiET4o1OdCIiIiIqEuxqJCIiIpIIAy8iIiIiiTDwIiIiIpIIAy8iIiIiiTDwIiIiIpIIAy8iIiIiiTDwIiIiIpIIAy8iemf4+vqiR48e6setW7fGhAkTJG/H8ePHIZPJEB8fn2cZmUyG0NBQresMCAhAgwYNCtWu6OhoyGSyt/6UExEVHQZeRFSkfH19IZPJIJPJIJfLUb16dcyZMweZmZlFfuxdu3bl+GmjvGgTLBERFRZ/JJuIilyHDh2wfv16pKWl4cCBAxgzZgyMjY3x2Wef5Sibnp6u/sHjwipXrpxe6iEi0hdmvIioyCkUCtja2sLe3h6ffvop2rVrhz179gD4t3vwq6++QuXKlVGrVi0AwP3799G3b19YWVmhXLly6N69O6Kjo9V1ZmVlYeLEibCyskL58uXxv//9D2/+AtqbXY1paWmYOnUq7OzsoFAoUL16dXz//feIjo6Gp6cnAKBs2bKQyWTw9fUFAKhUKgQGBsLR0RGmpqZwc3PDjh07NI5z4MAB1KxZE6ampvD09NRop7amTp2KmjVrokyZMnBycsLMmTNz/dHw1atXw87ODmXKlEHfvn2RkJCgsX3dunVwcXGBiYkJateuje+++07nthBR0WHgRUSSMzU1RXp6uvrxkSNHcOvWLYSFhWHfvn3IyMiAt7c3LCwscOrUKZw5cwbm5ubo0KGDer+FCxciODgYP/zwA06fPo1nz54hJCQk3+MOGTIEP/74I5YtW4bIyEisXr0a5ubmsLOzw86dOwEAt27dQmxsLJYuXQoACAwMxMaNG7Fq1Spcv34d/v7+GDRoEE6cOAHgVYDYq1cvdO3aFRERERg+fDimTZum83NiYWGB4OBg3LhxA0uXLsXatWuxePFijTJ3797FTz/9hL179+LQoUO4fPkyRo8erd6+ZcsWfPHFF/jqq68QGRmJefPmYebMmdiwYYPO7SGiIlLMP9JNRO84Hx8f0b17dyGEECqVSoSFhQmFQiEmT56s3m5jYyPS0tLU+2zatEnUqlVLqFQq9bq0tDRhamoqfvnlFyGEEJUqVRILFixQb8/IyBBVq1ZVH0sIITw8PMT48eOFEELcunVLABBhYWG5tvPYsWMCgHj+/Ll6XWpqqihTpow4e/asRtmPP/5Y9O/fXwghxGeffSZcXV01tk+dOjVHXW8CIEJCQvLc/s0334jGjRurH8+aNUsYGhqKv//+W73u4MGDwsDAQMTGxgohhHB2dhZbt27VqGfu3LnC3d1dCCHEvXv3BABx+fLlPI9LREWLY7yIqMjt27cP5ubmyMjIgEqlwoABAxAQEKDeXq9ePY1xXVeuXMHdu3dhYWGhUU9qaiqioqKQkJCA2NhYNG3aVL3NyMgI7733Xo7uxmwREREwNDSEh4eH1u2+e/cuUlJS0L59e4316enpaNiwIQAgMjJSox0A4O7urvUxsm3fvh3Lli1DVFQUkpKSkJmZCaVSqVGmWrVqqFKlisZxVCoVbt26BQsLC0RFReHjjz/GiBEj1GUyMzNhaWmpc3uIqGgw8CKiIufp6YmVK1dCLpejcuXKMDLS/OgxMzPTeJyUlITGjRtjy5YtOeqytrYuUBtMTU113icpKQkAsH//fo2AB3g1bk1fzp07h4EDB2L27Nnw9vaGpaUltm3bhoULF+rc1rVr1+YIBA0NDfXWViIqHAZeRFTkzMzMUL16da3LN2rUCNu3b0fFihVzZH2yVapUCb/99htatWoF4FVmJzw8HI0aNcq1fL169aBSqXDixAm0a9cux/bsjFtWVpZ6naurKxQKBWJiYvLMlLm4uKhvFMh2/vz5t5/ka86ePQt7e3vMmDFDve6vv/7KUS4mJgYPHz5E5cqV1ccxMDBArVq1YGNjg8qVK+PPP//EwIEDdTo+EUmHg+uJ6D9n4MCBqFChArp3745Tp07h3r17OH78OMaNG4e///4bADB+/Hh8/fXXCA0Nxc2bNzF69Oh85+BycHCAj48Phg0bhtDQUHWdP/30EwDA3t4eMpkM+/btwz///IOkpCRYWFhg8uTJ8Pf3x4YNGxAVFYVLly5h+fLl6gHro0aNwp07dzBlyhTcunULW7duRXBwsE7nW6NGDcTExGDbtm2IiorCsmXLcr1RwMTEBD4+Prhy5QpOnTqFcePGoW/fvrC1tQUAzJ49G4GBgVi2bBlu376Na9euYf369Vi0aJFO7SGiosPAi4j+c8qUKYOTJ0+iWrVq6NWrF1xcXPDxxx8jNTVVnQGbNGkSBg8eDB8fH7i7u8PCwgI9e/bMt96VK1eiT58+GD16NGrXro0RI0YgOTkZAFClShXMnj0b06ZNg42NDfz8/AAAc+fOxcyZMxEYGAgXFxd06NAB+/fvh6OjI4BX46527tyJ0NBQuLm5YdWqVZg3b55O59utWzf4+/vDz88PDRo0wNmzZzFz5swc5apXr45evXqhU6dO8PLyQv369TWmixg+fDjWrVuH9evXo169evDw8EBwcLC6rURU/GQir5GoRERERKRXzHgRERERSYSBFxEREZFEGHgRERERSYSBFxEREZFEGHgRERERSYSBFxEREZFEGHgRERERSYSBFxEREZFEGHgRERERSYSBFxEREZFEGHgRERERSYSBFxEREZFE/g99xU5HGFZuNAAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.neural_network import MLPClassifier\n",
    "\n",
    "# Instantiate and fit the Artificial Neural Networks (ANN) classifier\n",
    "clf_ann = MLPClassifier(hidden_layer_sizes=(100,50), max_iter=300, activation='relu', solver='adam', random_state=42)\n",
    "clf_ann.fit(X_train_scaled, y_train)\n",
    "\n",
    "# Calculate overall accuracy\n",
    "y_pred_ann = clf_ann.predict(X_test_scaled)\n",
    "accuracy_ann = accuracy_score(y_test, y_pred_ann)\n",
    "print(f'Accuracy: {accuracy_ann:.2%}')\n",
    "\n",
    "# Calculate precision, recall, and F1-score\n",
    "precision_ann = precision_score(y_test, y_pred_ann)\n",
    "recall_ann = recall_score(y_test, y_pred_ann)\n",
    "f1_ann = f1_score(y_test, y_pred_ann)\n",
    "print(f'Precision: {precision_ann:.2f}')\n",
    "print(f'Recall: {recall_ann:.2f}')\n",
    "print(f'F1-score: {f1_ann:.2f}')\n",
    "\n",
    "# Generate confusion matrix\n",
    "cm_ann = confusion_matrix(y_test, y_pred_ann)\n",
    "\n",
    "# Display confusion matrix\n",
    "disp_ann = ConfusionMatrixDisplay(confusion_matrix=cm_ann, display_labels=class_names)\n",
    "disp_ann.plot(cmap=plt.cm.Blues)\n",
    "plt.title('Confusion Matrix for Artificial Neural Networks (ANN) Classifier')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "da26d05b-b358-4392-a931-204fcb853e34",
   "metadata": {},
   "source": [
    "### Reinforcement learning (RL) : Q-learning Agent"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "15458bf0-7364-4e18-b06d-02d0e5e9d631",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 50.80%\n",
      "Precision: 0.51\n",
      "Recall: 0.47\n",
      "F1-score: 0.49\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.cluster import KMeans\n",
    "from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, confusion_matrix, ConfusionMatrixDisplay\n",
    "import gym\n",
    "\n",
    "# Sample data setup (replace with actual data)\n",
    "# Assuming df_no_missing_data is your preprocessed dataframe\n",
    "df_no_missing_data = pd.DataFrame({\n",
    "    'feature1': np.random.randn(2000),\n",
    "    'feature2': np.random.randn(2000),\n",
    "    'DEFAULT': np.random.randint(2, size=2000)\n",
    "})\n",
    "\n",
    "# Define your features and target\n",
    "features = df_no_missing_data.drop(columns=['DEFAULT'])\n",
    "target = df_no_missing_data['DEFAULT']\n",
    "\n",
    "# Standardize the features\n",
    "scaler = StandardScaler()\n",
    "features_scaled = scaler.fit_transform(features)\n",
    "\n",
    "# Discretize states using k-means clustering\n",
    "n_clusters = 10\n",
    "kmeans = KMeans(n_clusters=n_clusters, random_state=42)\n",
    "state_labels = kmeans.fit_predict(features_scaled)\n",
    "\n",
    "# Create a custom environment for credit default prediction\n",
    "class CreditDefaultEnv(gym.Env):\n",
    "    def __init__(self, data, labels, state_labels):\n",
    "        super(CreditDefaultEnv, self).__init__()\n",
    "        self.data = data\n",
    "        self.labels = labels\n",
    "        self.state_labels = state_labels\n",
    "        self.current_step = 0\n",
    "        self.action_space = gym.spaces.Discrete(2)  # Actions: 0 = no default, 1 = default\n",
    "        self.observation_space = gym.spaces.Discrete(n_clusters)\n",
    "        \n",
    "    def reset(self):\n",
    "        self.current_step = 0\n",
    "        return self.state_labels[self.current_step]\n",
    "\n",
    "    def step(self, action):\n",
    "        actual = self.labels[self.current_step]\n",
    "        reward = 1 if action == actual else -1  # Reward for correct prediction\n",
    "        self.current_step += 1\n",
    "        done = self.current_step >= len(self.data)\n",
    "        obs = self.state_labels[self.current_step] if not done else 0\n",
    "        return obs, reward, done, {}\n",
    "\n",
    "# Convert your dataframe to the environment\n",
    "env = CreditDefaultEnv(features_scaled, target.values, state_labels)\n",
    "\n",
    "# Simple Q-learning implementation\n",
    "n_states = n_clusters\n",
    "n_actions = env.action_space.n\n",
    "Q = np.zeros((n_states, n_actions))\n",
    "alpha = 0.1\n",
    "gamma = 0.9\n",
    "epsilon = 0.1\n",
    "\n",
    "def choose_action(state):\n",
    "    if np.random.uniform(0, 1) < epsilon:\n",
    "        return env.action_space.sample()\n",
    "    else:\n",
    "        return np.argmax(Q[state, :])\n",
    "\n",
    "# Train the Q-learning agent\n",
    "for episode in range(1000):\n",
    "    state = env.reset()\n",
    "    done = False\n",
    "    while not done:\n",
    "        action = choose_action(state)\n",
    "        next_state, reward, done, _ = env.step(action)\n",
    "        Q[state, action] = Q[state, action] + alpha * (reward + gamma * np.max(Q[next_state, :]) - Q[state, action])\n",
    "        state = next_state\n",
    "\n",
    "# Evaluate the Q-learning agent\n",
    "y_true = []\n",
    "y_pred = []\n",
    "state = env.reset()\n",
    "done = False\n",
    "while not done:\n",
    "    action = np.argmax(Q[state, :])\n",
    "    y_true.append(env.labels[env.current_step])\n",
    "    y_pred.append(action)\n",
    "    state, _, done, _ = env.step(action)\n",
    "\n",
    "# Calculate evaluation metrics\n",
    "accuracy = accuracy_score(y_true, y_pred)\n",
    "precision = precision_score(y_true, y_pred)\n",
    "recall = recall_score(y_true, y_pred)\n",
    "f1 = f1_score(y_true, y_pred)\n",
    "cm = confusion_matrix(y_true, y_pred)\n",
    "\n",
    "print(f'Accuracy: {accuracy:.2%}')\n",
    "print(f'Precision: {precision:.2f}')\n",
    "print(f'Recall: {recall:.2f}')\n",
    "print(f'F1-score: {f1:.2f}')\n",
    "\n",
    "# Display confusion matrix\n",
    "class_names = ['Did Not Default', 'Defaulted']\n",
    "disp = ConfusionMatrixDisplay(confusion_matrix=cm, display_labels=class_names)\n",
    "disp.plot(cmap='Blues')\n",
    "plt.title('Confusion Matrix for Q-learning Agent')\n",
    "plt.show()\n"
   ]
  }
 ],
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