02_training_and_evaluation.ipynb

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   "metadata": {
    "id": "5b1efc36-f3ad-4c3b-8115-a9f288a541bf"
   },
   "source": [
    "# **MSA Phase 2 - Part 2**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "0QGgOnoRNo72",
   "metadata": {
    "id": "0QGgOnoRNo72"
   },
   "outputs": [],
   "source": [
    "# !pip install pmdarima"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "d5d5f0ed-4880-4958-aa39-8dcea471fc86",
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    "id": "d5d5f0ed-4880-4958-aa39-8dcea471fc86"
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   "source": [
    "# Import libraries\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import warnings\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "from sklearn.feature_selection import RFE\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.model_selection import TimeSeriesSplit\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.metrics import mean_absolute_percentage_error, mean_absolute_error, mean_squared_error, r2_score\n",
    "\n",
    "from statsmodels.tsa.arima.model import ARIMA\n",
    "from statsmodels.tsa.stattools import adfuller\n",
    "from statsmodels.graphics.tsaplots import plot_pacf, plot_acf\n",
    "\n",
    "from pmdarima.arima import auto_arima"
   ]
  },
  {
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   "execution_count": 3,
   "id": "IQDUJ8eqwfVc",
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   "outputs": [
    {
     "name": "stdout",
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     "text": [
      "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
     ]
    }
   ],
   "source": [
    "# # Only on Google Colab\n",
    "# from google.colab import drive\n",
    "# drive.mount('/content/drive')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ec06580e-27d5-4351-a602-c201b590273e",
   "metadata": {
    "id": "ec06580e-27d5-4351-a602-c201b590273e"
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   "source": [
    "### **0. Task**\n",
    "\n",
    "\n",
    "---\n",
    "\n",
    "\n",
    "In this notebook, I plan to predict the weekly sales for **selected store and department**. In specific, the idea is pick one store and one department, predict its weekly sales for the following couple weeks."
   ]
  },
  {
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   },
   "source": [
    "### **1. Load and Examine the Cleaned Dataset**\n",
    "\n",
    "\n",
    "---\n",
    "\n"
   ]
  },
  {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Store</th>\n",
       "      <th>Dept</th>\n",
       "      <th>Date</th>\n",
       "      <th>Year</th>\n",
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       "      <th>Day</th>\n",
       "      <th>IsHoliday</th>\n",
       "      <th>IsHoliday_False</th>\n",
       "      <th>IsHoliday_True</th>\n",
       "      <th>Temperature</th>\n",
       "      <th>CPI</th>\n",
       "      <th>Unemployment</th>\n",
       "      <th>Size</th>\n",
       "      <th>Weekly_Sales</th>\n",
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       "      <th>0</th>\n",
       "      <td>1</td>\n",
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       "      <td>2010-02-05</td>\n",
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       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2010-02-19</td>\n",
       "      <td>2010</td>\n",
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       "      <td>19</td>\n",
       "      <td>0</td>\n",
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       "      <td>39.93</td>\n",
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       "      <td>2010-02-26</td>\n",
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       "      <td>26</td>\n",
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       "      <td>8.106</td>\n",
       "      <td>151315</td>\n",
       "      <td>19403.54</td>\n",
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       "      <td>1</td>\n",
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       "      <td>2010-03-05</td>\n",
       "      <td>2010</td>\n",
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      "text/plain": [
       "   Store  Dept       Date  Year  Month  Day  IsHoliday  IsHoliday_False  \\\n",
       "0      1     1 2010-02-05  2010      2    5          0                1   \n",
       "1      1     1 2010-02-12  2010      2   12          1                0   \n",
       "2      1     1 2010-02-19  2010      2   19          0                1   \n",
       "3      1     1 2010-02-26  2010      2   26          0                1   \n",
       "4      1     1 2010-03-05  2010      3    5          0                1   \n",
       "\n",
       "   IsHoliday_True  Temperature         CPI  Unemployment    Size  Weekly_Sales  \n",
       "0               0        42.31  211.096358         8.106  151315      24924.50  \n",
       "1               1        38.51  211.242170         8.106  151315      46039.49  \n",
       "2               0        39.93  211.289143         8.106  151315      41595.55  \n",
       "3               0        46.63  211.319643         8.106  151315      19403.54  \n",
       "4               0        46.50  211.350143         8.106  151315      21827.90  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Path: dataw/cleaned.csv\n",
    "\n",
    "# --------- Comment Out One Line Below --------- #\n",
    "# data = pd.read_csv(\"azureml://\")  # For Azure\n",
    "# data = pd.read_csv(\"/content/drive/MyDrive/Colab Notebooks/dataw/cleaned.csv\")  # For Google Colab\n",
    "data = pd.read_csv(\"dataw/cleaned.csv\")   # For Local PC\n",
    "# --------- Comment Out One Line Above --------- #\n",
    "\n",
    "# Convert 'Date' to datetime\n",
    "data['Date'] = pd.to_datetime(data['Date'])\n",
    "\n",
    "# Drop extra column\n",
    "data = data.drop(\"Unnamed: 0\", axis=1)\n",
    "data.head()"
   ]
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   "id": "f16670c5-edbb-4d2f-b961-a5555471ce40",
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of Unique Stores: 45\n",
      "Number of Unique Dates: 143\n",
      "Number of Unique Sizes: 40\n"
     ]
    }
   ],
   "source": [
    "# Check how many stores\n",
    "unq_store = len(data[\"Store\"].unique())\n",
    "print(f\"Number of Unique Stores: {unq_store}\")\n",
    "\n",
    "# Check how many days\n",
    "unq_date = len(data[\"Date\"].unique())\n",
    "print(f\"Number of Unique Dates: {unq_date}\")\n",
    "\n",
    "# Check how many sizes\n",
    "unq_size = len(data[\"Size\"].unique())\n",
    "print(f\"Number of Unique Sizes: {unq_size}\")"
   ]
  },
  {
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     "text": [
      "For Store 1, there are 77 Departments.\n",
      "For Store 2, there are 78 Departments.\n",
      "For Store 3, there are 72 Departments.\n",
      "For Store 4, there are 78 Departments.\n",
      "For Store 5, there are 72 Departments.\n",
      "For Store 6, there are 77 Departments.\n",
      "For Store 7, there are 76 Departments.\n",
      "For Store 8, there are 76 Departments.\n",
      "For Store 9, there are 73 Departments.\n",
      "For Store 10, there are 77 Departments.\n",
      "For Store 11, there are 78 Departments.\n",
      "For Store 12, there are 71 Departments.\n",
      "For Store 13, there are 79 Departments.\n",
      "For Store 14, there are 77 Departments.\n",
      "For Store 15, there are 79 Departments.\n",
      "For Store 16, there are 76 Departments.\n",
      "For Store 17, there are 76 Departments.\n",
      "For Store 18, there are 78 Departments.\n",
      "For Store 19, there are 79 Departments.\n",
      "For Store 20, there are 78 Departments.\n",
      "For Store 21, there are 77 Departments.\n",
      "For Store 22, there are 76 Departments.\n",
      "For Store 23, there are 76 Departments.\n",
      "For Store 24, there are 77 Departments.\n",
      "For Store 25, there are 77 Departments.\n",
      "For Store 26, there are 76 Departments.\n",
      "For Store 27, there are 78 Departments.\n",
      "For Store 28, there are 72 Departments.\n",
      "For Store 29, there are 77 Departments.\n",
      "For Store 30, there are 64 Departments.\n",
      "For Store 31, there are 76 Departments.\n",
      "For Store 32, there are 77 Departments.\n",
      "For Store 33, there are 63 Departments.\n",
      "For Store 34, there are 78 Departments.\n",
      "For Store 35, there are 74 Departments.\n",
      "For Store 36, there are 63 Departments.\n",
      "For Store 37, there are 62 Departments.\n",
      "For Store 38, there are 61 Departments.\n",
      "For Store 39, there are 75 Departments.\n",
      "For Store 40, there are 77 Departments.\n",
      "For Store 41, there are 77 Departments.\n",
      "For Store 42, there are 62 Departments.\n",
      "For Store 43, there are 61 Departments.\n",
      "For Store 44, there are 62 Departments.\n",
      "For Store 45, there are 74 Departments.\n",
      "-----------------\n",
      "Min number of Departments: 61, Max number of Departments: 79\n"
     ]
    }
   ],
   "source": [
    "# Check how many departments for each store\n",
    "n_dept_ls = []\n",
    "for i in range(1, unq_store+1):\n",
    "    n_dept = len(data[data[\"Store\"] == i].Dept.unique())\n",
    "    n_dept_ls.append(n_dept)\n",
    "    print(f\"For Store {i}, there are {n_dept} Departments.\")\n",
    "print(\"-----------------\")\n",
    "print(f\"Min number of Departments: {min(n_dept_ls)}, Max number of Departments: {max(n_dept_ls)}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4e5495e6-711d-47f4-8139-fef0344233aa",
   "metadata": {
    "id": "4e5495e6-711d-47f4-8139-fef0344233aa"
   },
   "source": [
    "- The cleaned dataset contains 389387 records for different stores at different dates.\n",
    "- All features are in numerical datatype except the Date as datetime.\n",
    "- There are 14 predictors including date-related features as well as temperature, cpi, unemployment, and size.\n",
    "- There are 45 unique stores in 40 different sizes, and the records are distributed in 143 different weeks across 3 years.\n",
    "- Each store contains 61 to 79 departments.\n",
    "- The task is to predict the weekly sales with selected predictors."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b7f7aba7-60d9-48e1-b3c1-c99c68e7a66c",
   "metadata": {
    "id": "b7f7aba7-60d9-48e1-b3c1-c99c68e7a66c"
   },
   "source": [
    "### **2. Split Preprocessed data**\n",
    "\n",
    "\n",
    "---\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "379af8ee-3ed6-4afa-a6a3-7e8eae0e46b1",
   "metadata": {
    "id": "379af8ee-3ed6-4afa-a6a3-7e8eae0e46b1"
   },
   "outputs": [],
   "source": [
    "# Select which store and department here. If changed, re-run code chunk below.\n",
    "n_store = 17\n",
    "n_dept = 7"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "bd86319b-87ce-4951-921a-561bbd3be05a",
   "metadata": {
    "id": "bd86319b-87ce-4951-921a-561bbd3be05a"
   },
   "outputs": [],
   "source": [
    "# Split the train-test data in 90:10 ratio, so that there are 15 days for prediction.\n",
    "\n",
    "def split_df(n_store, n_dept, data=data, split_ratio=0.1):\n",
    "    \"\"\"Input the numbers of store and dept, extract the data from the full cleaned data and split train-test sets.\"\"\"\n",
    "\n",
    "    # Extract weekly sales of selected store and dept\n",
    "    df = data[(data['Store'] == n_store) & (data['Dept'] == n_dept)]\n",
    "\n",
    "    # Set date as index\n",
    "    df.set_index('Date', drop=True, inplace=True)\n",
    "\n",
    "    # Drop columns that will not be used\n",
    "    df = df.drop([\"Store\", \"Dept\", \"IsHoliday_False\", \"IsHoliday_True\", \"Size\"], axis=1)\n",
    "\n",
    "    # Get predictors and response\n",
    "    X = df.iloc[:, :-1]\n",
    "    y = df.iloc[:, -1]\n",
    "\n",
    "    # Create train-test split on the dataset\n",
    "    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=split_ratio, random_state=777, shuffle=False)\n",
    "\n",
    "    # Sort index\n",
    "    X_train.sort_index(inplace=True)\n",
    "    X_test.sort_index(inplace=True)\n",
    "    y_train.sort_index(inplace=True)\n",
    "    y_test.sort_index(inplace=True)\n",
    "\n",
    "    # Convert predictor Series to Dataframe\n",
    "    y_train = y_train.to_frame()\n",
    "    y_test = y_test.to_frame()\n",
    "\n",
    "    return n_store, n_dept, df, X_train, X_test, y_train, y_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "3147dfd9-07df-4dd3-8c5f-4029a92cc687",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "3147dfd9-07df-4dd3-8c5f-4029a92cc687",
    "outputId": "86a319fb-596b-427e-9a57-31608b5dccc0"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Store selected: 17\n",
      "Dept selected: 7\n",
      "df shape: (143, 8)\n",
      "X_train shape: (128, 7)\n",
      "y_train shape: (128, 1)\n",
      "X_test shape: (15, 7)\n",
      "y_test shape: (15, 1)\n"
     ]
    }
   ],
   "source": [
    "n_store, n_dept, df, X_train, X_test, y_train, y_test = split_df(n_store, n_dept)\n",
    "\n",
    "print(f\"Store selected: {n_store}\")\n",
    "print(f\"Dept selected: {n_dept}\")\n",
    "print(f\"df shape: {df.shape}\")\n",
    "print(f\"X_train shape: {X_train.shape}\")\n",
    "print(f\"y_train shape: {y_train.shape}\")\n",
    "print(f\"X_test shape: {X_test.shape}\")\n",
    "print(f\"y_test shape: {y_test.shape}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "iaII9NJ-fn8A",
   "metadata": {
    "id": "iaII9NJ-fn8A"
   },
   "source": [
    "### **3. Multiple Linear Regression**\n",
    "\n",
    "\n",
    "---\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dX19L5-gHFjX",
   "metadata": {
    "id": "dX19L5-gHFjX"
   },
   "source": [
    "#### **3.1. Feature Selection**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "o_UK6vJLz8ox",
   "metadata": {
    "id": "o_UK6vJLz8ox"
   },
   "source": [
    "Apply Recursive Feature Elimination (RFE) on multiple linear regression."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "Cn6pmB6T-xt0",
   "metadata": {
    "id": "Cn6pmB6T-xt0"
   },
   "outputs": [],
   "source": [
    "# Function of Symmetric Mean Absolute Percentage Error\n",
    "def smape(y_true, y_pred):\n",
    "    \"\"\"https://github.com/NZMSA/2024-Phase-2/blob/main/data-science/0.%20Resources/docs/evaluating-regression-models.md\"\"\"\n",
    "    # Ensure inputs are numpy arrays\n",
    "    y_true = np.array(y_true)\n",
    "    y_pred = np.array(y_pred)\n",
    "    # Calculate the numerator (absolute difference)\n",
    "    numerator = np.abs(y_true - y_pred)\n",
    "    # Calculate the denominator (average of absolute actual and predicted values)\n",
    "    denominator = (np.abs(y_true) + np.abs(y_pred)) / 2\n",
    "    # Calculate SMAPE\n",
    "    smape_value = np.mean(numerator / denominator) * 100\n",
    "    return smape_value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "w_-S-wqi2RV4",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "w_-S-wqi2RV4",
    "outputId": "fbfe73ce-b86d-4c4b-aef7-2af62d230105"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Num Features: 1\n",
      "Top Features: ['Year']\n",
      "MAE: 3485.3111\n",
      "MAPE: 23.7725%\n",
      "SMAPE: 20.7973%\n",
      "MSE: 14954343.5305\n",
      "RMSE: 3867.0846\n",
      "R-Squared: 0.0053\n",
      "-----------------------------------------\n",
      "Num Features: 2\n",
      "Top Features: ['Year', 'CPI']\n",
      "MAE: 3547.6776\n",
      "MAPE: 23.9600%\n",
      "SMAPE: 21.0222%\n",
      "MSE: 17303945.5093\n",
      "RMSE: 4159.8011\n",
      "R-Squared: 0.1783\n",
      "-----------------------------------------\n",
      "Num Features: 3\n",
      "Top Features: ['Year', 'CPI', 'Unemployment']\n",
      "MAE: 5066.9731\n",
      "MAPE: 35.3303%\n",
      "SMAPE: 28.2148%\n",
      "MSE: 36061519.2009\n",
      "RMSE: 6005.1244\n",
      "R-Squared: 0.1816\n",
      "-----------------------------------------\n",
      "Num Features: 4\n",
      "Top Features: ['Year', 'IsHoliday', 'CPI', 'Unemployment']\n",
      "MAE: 5035.0428\n",
      "MAPE: 35.2171%\n",
      "SMAPE: 28.0099%\n",
      "MSE: 36145251.5922\n",
      "RMSE: 6012.0921\n",
      "R-Squared: 0.1892\n",
      "-----------------------------------------\n",
      "Num Features: 5\n",
      "Top Features: ['Year', 'Month', 'IsHoliday', 'CPI', 'Unemployment']\n",
      "MAE: 10596.1347\n",
      "MAPE: 72.1673%\n",
      "SMAPE: 50.1387%\n",
      "MSE: 132193490.4468\n",
      "RMSE: 11497.5428\n",
      "R-Squared: 0.2444\n",
      "-----------------------------------------\n",
      "Num Features: 6\n",
      "Top Features: ['Year', 'Month', 'Day', 'IsHoliday', 'CPI', 'Unemployment']\n",
      "MAE: 11245.6158\n",
      "MAPE: 76.5513%\n",
      "SMAPE: 52.3846%\n",
      "MSE: 147443198.7611\n",
      "RMSE: 12142.6191\n",
      "R-Squared: 0.2540\n",
      "-----------------------------------------\n",
      "Num Features: 7\n",
      "Top Features: ['Year', 'Month', 'Day', 'IsHoliday', 'Temperature', 'CPI', 'Unemployment']\n",
      "MAE: 12080.4895\n",
      "MAPE: 83.4152%\n",
      "SMAPE: 54.0393%\n",
      "MSE: 187975959.3837\n",
      "RMSE: 13710.4325\n",
      "R-Squared: 0.2820\n",
      "-----------------------------------------\n"
     ]
    }
   ],
   "source": [
    "# Initialize Recursive Feature Elimination by selecting the top N_ft features\n",
    "N_ft = 1\n",
    "\n",
    "while N_ft <= len(X_train.columns):\n",
    "    rfe_mlr = RFE(estimator=LinearRegression(), n_features_to_select=N_ft)\n",
    "\n",
    "    # Fit training dataset\n",
    "    rfe_mlr_fit = rfe_mlr.fit(X_train, y_train)\n",
    "\n",
    "    print(f\"Num Features: {N_ft}\")\n",
    "    # print(f\"Num Features: {rfe_mlr_fit.n_features_}\")\n",
    "    # print(f\"Features Selector: {rfe_mlr_fit.support_}\")\n",
    "    # print(f\"Feature Ranking: {rfe_mlr_fit.ranking_}\")\n",
    "\n",
    "    # Filter out the top N_ft features\n",
    "    X_train_rfe = X_train.loc[:, rfe_mlr_fit.support_.tolist()]\n",
    "    X_test_rfe = X_test.loc[:, rfe_mlr_fit.support_.tolist()]\n",
    "    print(f\"Top Features: {X_train_rfe.columns.to_list()}\")\n",
    "\n",
    "    # Fit and predict again\n",
    "    mlr_model_rfe = LinearRegression().fit(X_train_rfe, y_train)\n",
    "    y_train_pred_rfe = mlr_model_rfe.predict(X_train_rfe)\n",
    "    y_test_pred_rfe = mlr_model_rfe.predict(X_test_rfe)\n",
    "\n",
    "    # Evaluate model after RFE\n",
    "    # --------- Mean Absolute Error --------- #\n",
    "    mlr_mae_rfe = mean_absolute_error(y_test, y_test_pred_rfe)\n",
    "    print(f\"MAE: {mlr_mae_rfe:.4f}\")\n",
    "\n",
    "    # --------- Mean Absolute Percentage Error --------- #\n",
    "    mlr_mape_rfe = mean_absolute_percentage_error(y_test, y_test_pred_rfe) * 100\n",
    "    print(f\"MAPE: {mlr_mape_rfe:.4f}%\")\n",
    "\n",
    "    # --------- Symmetric Mean Absolute Percentage Error --------- #\n",
    "    mlr_smape_rfe = smape(y_test, y_test_pred_rfe)\n",
    "    print(f\"SMAPE: {mlr_smape_rfe:.4f}%\")\n",
    "\n",
    "    # --------- Mean Squared Error --------- #\n",
    "    mlr_mse_rfe = mean_squared_error(y_test, y_test_pred_rfe)\n",
    "    print(f\"MSE: {mlr_mse_rfe:.4f}\")\n",
    "\n",
    "    # --------- Root Mean Square Error --------- #\n",
    "    mlr_rmse_rfe = np.sqrt(mlr_mse_rfe)\n",
    "    print(f\"RMSE: {mlr_rmse_rfe:.4f}\")\n",
    "\n",
    "    # --------- R-Squared --------- #\n",
    "    mlr_r2_rfe = r2_score(y_train, y_train_pred_rfe)\n",
    "    print(f\"R-Squared: {mlr_r2_rfe:.4f}\")\n",
    "\n",
    "    print(f\"-----------------------------------------\")\n",
    "\n",
    "    N_ft += 1"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "m9ib6ao097Vj",
   "metadata": {
    "id": "m9ib6ao097Vj"
   },
   "source": [
    "**Choose the number of features for best performance:** \\\n",
    "Considering the R-squared and the Errors, when the number of features are set to 3 or 4, the MAPE and SMAPE are around 35% and 28% respectively, while R-squared is below 0.2. When the number of features is 5, although the R-squared is larger than 0.2, the error rate increase significantly to >70% and >50%. Thus, I choose **4** as the number of features for multiple linear regression."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cb0a21e7-93ea-45c3-a4c1-63e7a2c8e53d",
   "metadata": {
    "id": "cb0a21e7-93ea-45c3-a4c1-63e7a2c8e53d"
   },
   "source": [
    "#### **3.2. Fit and Predict**\n",
    "\n",
    "\n",
    "---\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "yr8qwfLQJYdN",
   "metadata": {
    "id": "yr8qwfLQJYdN"
   },
   "outputs": [],
   "source": [
    "# Filter out the top features in last step\n",
    "X_train_opm = X_train[['Year', 'IsHoliday', 'CPI', 'Unemployment']]\n",
    "X_test_opm = X_test[['Year', 'IsHoliday', 'CPI', 'Unemployment']]\n",
    "\n",
    "# Fit a linear regression model on the training set\n",
    "mlr_model = LinearRegression().fit(X_train_opm, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "dq7LcDK2VlbY",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "dq7LcDK2VlbY",
    "outputId": "921d0e20-dd29-4d7b-91b7-715b70b792d6"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Year: -19476.4345\n",
      "IsHoliday: 3723.5897\n",
      "CPI: 7939.2845\n",
      "Unemployment: -4053.2594\n",
      "Interceptor: 38191051.8778\n"
     ]
    }
   ],
   "source": [
    "# Coefficients\n",
    "cols_ls = X_train_opm.columns.to_list()\n",
    "for i in range(len(cols_ls)):\n",
    "    co_nm = cols_ls[i]\n",
    "    co_ef = mlr_model.coef_[:, i][0]\n",
    "    print(f\"{co_nm}: {co_ef:.4f}\")\n",
    "\n",
    "# Interceptor\n",
    "print(f\"Interceptor: {mlr_model.intercept_[0]:.4f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "Thty5fE3MgSn",
   "metadata": {
    "id": "Thty5fE3MgSn"
   },
   "outputs": [],
   "source": [
    "# Predict on train and test set\n",
    "y_train_pred = mlr_model.predict(X_train_opm)\n",
    "y_test_pred = mlr_model.predict(X_test_opm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "fs9z9fheZj77",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 314
    },
    "id": "fs9z9fheZj77",
    "outputId": "ec2ec005-414c-4b44-c49c-a87a2cce874b"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot actual vs. predict results\n",
    "plt.figure(figsize=(15,5))\n",
    "axes = plt.axes()\n",
    "axes.plot(y_train.index, y_train, marker='o', label=\"Train Actual\")\n",
    "axes.plot(y_train.index, y_train_pred, marker='o', label=\"Train Predict\")\n",
    "axes.plot(y_test.index, y_test, marker='o', label=\"Test Actual\")\n",
    "axes.plot(y_test.index, y_test_pred, marker='o', label=\"Test Predict\")\n",
    "axes.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "8ll_WU2kNZ8b",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "8ll_WU2kNZ8b",
    "outputId": "f35ff721-4659-4669-db8f-4e44c2827889"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MAE: 5035.0428\n",
      "MAPE: 35.2171%\n",
      "SMAPE: 28.0099%\n",
      "MSE: 36145251.5922\n",
      "RMSE: 6012.0921\n",
      "R-Squared: 0.1892\n"
     ]
    }
   ],
   "source": [
    "# Evaluate model\n",
    "\n",
    "mlr_mae = mean_absolute_error(y_test, y_test_pred)\n",
    "print(f\"MAE: {mlr_mae:.4f}\")\n",
    "\n",
    "mlr_mape = mean_absolute_percentage_error(y_test, y_test_pred) * 100\n",
    "print(f\"MAPE: {mlr_mape:.4f}%\")\n",
    "\n",
    "mlr_smape = smape(y_test, y_test_pred)\n",
    "print(f\"SMAPE: {mlr_smape:.4f}%\")\n",
    "\n",
    "mlr_mse = mean_squared_error(y_test, y_test_pred)\n",
    "print(f\"MSE: {mlr_mse:.4f}\")\n",
    "\n",
    "mlr_rmse = np.sqrt(mlr_mse)\n",
    "print(f\"RMSE: {mlr_rmse:.4f}\")\n",
    "\n",
    "mlr_r2 = r2_score(y_train, y_train_pred)\n",
    "print(f\"R-Squared: {mlr_r2:.4f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "pTlKb6wsStpr",
   "metadata": {
    "id": "pTlKb6wsStpr"
   },
   "source": [
    "**Interpretation:**\n",
    "\n",
    "For the selected store 17 and department 7:\n",
    "\n",
    "- R-squared was about 0.1892, meaning only 18.92% of the variation of the response can be explained by the MLR model, suggesting poor performance of the MLR model on this dataset.\n",
    "- RMSE and MAE were quite large. For one reason, the weekly sales values are large numbers, even some relatively small amount of difference can cause large error. But since the model does not perform well, it is reasonable to see large errors.\n",
    "- MAPE and SMAPE are around 35% and 28% respectively, suggesting a high error rate.\n",
    "\n",
    "In conclusion, the linear model did not perform well. So, I will try other methods.\n",
    "\n",
    "The prediction result will be different if selecting different store and department. Change the n_store and n_dept at the beginning of section 2, then re-run all the code below to explore more.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "t8I6QAEW_aG1",
   "metadata": {
    "id": "t8I6QAEW_aG1"
   },
   "source": [
    "### **4. ARIMA**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "_kRbEoAkRV0u",
   "metadata": {
    "id": "_kRbEoAkRV0u"
   },
   "source": [
    "To perform ARIMA model, we need to extract the response features from the dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "RXms9IQOK9Si",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "RXms9IQOK9Si",
    "outputId": "2571dbb5-13ae-4add-8b52-bdfcca52f0fe"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train data shape: (128, 1)\n",
      "Test data shape: (15, 1)\n"
     ]
    }
   ],
   "source": [
    "# Extract weekly sales only\n",
    "df_ts = df[['Weekly_Sales']]\n",
    "\n",
    "# Split train-test in 90:10 ratio\n",
    "train_ts, test_ts = df_ts[0:int(len(df_ts)*0.9)], df_ts[int(len(df_ts)*0.9):]\n",
    "print(f\"Train data shape: {train_ts.shape}\")\n",
    "print(f\"Test data shape: {test_ts.shape}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "_J_CPvhFlJZs",
   "metadata": {
    "id": "_J_CPvhFlJZs"
   },
   "source": [
    "#### **4.1. Perform Augmented Dickey-Fuller test**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "I9ICMBVwWSNC",
   "metadata": {
    "id": "I9ICMBVwWSNC"
   },
   "source": [
    "ref: https://developer.ibm.com/tutorials/awb-arima-models-in-python/#step-6-train-the-arima-model7 \\\n",
    "Assuming the null hypothesis be the dataset is non-stationary."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "mPllz00rFgGZ",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "mPllz00rFgGZ",
    "outputId": "489f4549-0787-4289-b64c-87a743a62443"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "p-value: 4.5068692987772497e-07\n"
     ]
    }
   ],
   "source": [
    "#\n",
    "adf_result = adfuller(df_ts)\n",
    "print(f'p-value: {adf_result[1]}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "zdKkIojcGzhb",
   "metadata": {
    "id": "zdKkIojcGzhb"
   },
   "source": [
    "Since p-value < 0.05, the null hypothesis could be rejected. \\\n",
    "Thus, it suggests that the data is stationary. In ARIMA, d order can be set as 0.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "zj0dnH7oNyjO",
   "metadata": {
    "id": "zj0dnH7oNyjO"
   },
   "source": [
    "#### **4.2. Perform Auto ARIMA for hyperparameter tuning**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6MrcPTN-h995",
   "metadata": {
    "id": "6MrcPTN-h995"
   },
   "source": [
    "ref: https://blog.devgenius.io/finding-optimal-p-d-and-q-values-for-your-arima-model-94669a909a35"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "VauN0sR-NdJk",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "VauN0sR-NdJk",
    "outputId": "6f8386c4-6caf-48f6-baef-55f98392ad2e"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Performing stepwise search to minimize aic\n",
      " ARIMA(1,0,1)(0,0,0)[0] intercept   : AIC=2702.712, Time=0.25 sec\n",
      " ARIMA(0,0,0)(0,0,0)[0] intercept   : AIC=2744.693, Time=0.15 sec\n",
      " ARIMA(1,0,0)(0,0,0)[0] intercept   : AIC=2700.707, Time=0.23 sec\n",
      " ARIMA(0,0,1)(0,0,0)[0] intercept   : AIC=2714.767, Time=0.29 sec\n",
      " ARIMA(0,0,0)(0,0,0)[0]             : AIC=2932.181, Time=0.08 sec\n",
      " ARIMA(2,0,0)(0,0,0)[0] intercept   : AIC=2702.674, Time=0.23 sec\n",
      " ARIMA(2,0,1)(0,0,0)[0] intercept   : AIC=2704.691, Time=0.51 sec\n",
      " ARIMA(1,0,0)(0,0,0)[0]             : AIC=2726.033, Time=0.22 sec\n",
      "\n",
      "Best model:  ARIMA(1,0,0)(0,0,0)[0] intercept\n",
      "Total fit time: 1.980 seconds\n"
     ]
    }
   ],
   "source": [
    "model = auto_arima(train_ts['Weekly_Sales'], start_p=1, start_q=1,\n",
    "                      test='adf',\n",
    "                      max_p=5, max_q=5,\n",
    "                      m=1,\n",
    "                      d=0,   # data is already stationary.\n",
    "                      start_P=0,\n",
    "                      D=None,\n",
    "                      trace=True,\n",
    "                      error_action='ignore',\n",
    "                      suppress_warnings=True,\n",
    "                      stepwise=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "jBgOU8dkrkIg",
   "metadata": {
    "id": "jBgOU8dkrkIg"
   },
   "source": [
    "#### **4.3. Fit and Predict with ARIMA model**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cJIETAXniCdS",
   "metadata": {
    "id": "cJIETAXniCdS"
   },
   "source": [
    "ref: https://developer.ibm.com/tutorials/awb-arima-models-in-python/#step-6-train-the-arima-model7"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "Qn15WXdFTWOi",
   "metadata": {
    "id": "Qn15WXdFTWOi"
   },
   "outputs": [],
   "source": [
    "def build_arima(train_ts, test_ts, order=(1,0,0)):\n",
    "    # Initialize variables\n",
    "    # history sales\n",
    "    y_train_ts = train_ts[\"Weekly_Sales\"].tolist()\n",
    "    # actual sales\n",
    "    y_test_ts = test_ts[\"Weekly_Sales\"].tolist()\n",
    "    # predicted sales\n",
    "    y_pred_ts = []\n",
    "\n",
    "    for i in range(0, len(y_test_ts)):\n",
    "        # predict\n",
    "        # Try 0, 1, 2 for d order, it turns out set d as 2 could lead to the least error in result.\n",
    "        arima_model = ARIMA(y_train_ts, order=order).fit()\n",
    "        yhat = arima_model.forecast()[0]\n",
    "        # invert transformed prediction\n",
    "        y_pred_ts.append(yhat)\n",
    "        # observation\n",
    "        obs = y_test_ts[i]\n",
    "        y_train_ts.append(obs)\n",
    "\n",
    "    return y_test_ts, y_pred_ts\n",
    "\n",
    "y_test_ts, y_pred_ts = build_arima(train_ts, test_ts, order=(1,0,0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "IhdEeBdAcSyJ",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 382
    },
    "id": "IhdEeBdAcSyJ",
    "outputId": "34e88855-e39b-4f49-8128-b1bd16fb280e"
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1200x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot actual vs. predicted test data\n",
    "plt.figure(figsize=(12,5))\n",
    "axes = plt.axes()\n",
    "axes.plot(test_ts.index, y_test_ts, marker='o', color='orange', label='Actual')\n",
    "axes.plot(test_ts.index, y_pred_ts, marker='o', color='green', label='Prediction')\n",
    "axes.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "qn9pEm_FhJ5h",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 382
    },
    "id": "qn9pEm_FhJ5h",
    "outputId": "bce3129f-2f6b-4bf9-e52e-5850c5c9295c"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot training (history), test (actual), and predicted data\n",
    "plt.figure(figsize=(12,5))\n",
    "axes = plt.axes()\n",
    "axes.plot(train_ts.index, train_ts.iloc[:, 0], marker='o', label='History')\n",
    "axes.plot(test_ts.index, y_test_ts, marker='o', color='orange', label='Actual')\n",
    "axes.plot(test_ts.index, y_pred_ts, marker='o', color='green', label='Prediction')\n",
    "axes.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "_UH6wxLUcaPE",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "_UH6wxLUcaPE",
    "outputId": "3ed1104a-36b8-4d7f-ea7f-b4731dce238e"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MAE: 2893.4832\n",
      "MAPE: 18.9203%\n",
      "SMAPE: 17.4038%\n",
      "MSE: 10495505.2151\n",
      "RMSE: 3239.6767\n"
     ]
    }
   ],
   "source": [
    "# Evaluate model\n",
    "\n",
    "arima_mae = mean_absolute_error(y_test_ts, y_pred_ts)\n",
    "print(f\"MAE: {arima_mae:.4f}\")\n",
    "\n",
    "arima_mape = mean_absolute_percentage_error(y_test_ts, y_pred_ts) * 100\n",
    "print(f\"MAPE: {arima_mape:.4f}%\")\n",
    "\n",
    "arima_smape = smape(y_test_ts, y_pred_ts)\n",
    "print(f\"SMAPE: {arima_smape:.4f}%\")\n",
    "\n",
    "arima_mse = mean_squared_error(y_test_ts, y_pred_ts)\n",
    "print(f\"MSE: {arima_mse:.4f}\")\n",
    "\n",
    "arima_rmse = np.sqrt(arima_mse)\n",
    "print(f\"RMSE: {arima_rmse:.4f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "qxsY3HBygmmT",
   "metadata": {
    "id": "qxsY3HBygmmT"
   },
   "source": [
    "MAE, MSE, RMSE are large numbers because the weekly sales values are large. \\\n",
    "MAPE and SMAPE suggests the model have error rate about 18%, significantly lower than linear regression."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "Tv6dLOL2chV2",
   "metadata": {
    "id": "Tv6dLOL2chV2"
   },
   "source": [
    "#### **4.4. Another method of hyperparameter tuning: Cross Validation**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "JwIte2it1FWu",
   "metadata": {
    "id": "JwIte2it1FWu"
   },
   "source": [
    "In previous step, I use auto arima and its build-in metric AIC to choose the best order (p, q, d) for ARIMA model. In this section, I will use Cross Validation to choose (p, q, d). I would like to see if the result would be same."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "QzfdZ5ax06i_",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "QzfdZ5ax06i_",
    "outputId": "f65741e7-b286-4cf7-f646-a8996629e12d"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total number of combinations: 48\n"
     ]
    }
   ],
   "source": [
    "# Assuming p and q are within [0, 1, 2, 3] and d is within [0, 1, 2]\n",
    "# Generate all possible combinations\n",
    "ps = [0, 1, 2, 3]\n",
    "ds = [0, 1, 2]\n",
    "qs = [0, 1, 2, 3]\n",
    "\n",
    "combos = []\n",
    "\n",
    "for p in ps:\n",
    "    for d in ds:\n",
    "        for q in qs:\n",
    "            combos.append((p, d, q))\n",
    "print(f\"Total number of combinations: {len(combos)}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "HWNUBw8R27Mt",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "id": "HWNUBw8R27Mt",
    "outputId": "879604b9-81d0-4e1e-beae-d0d32a13725f"
   },
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>combo</th>\n",
       "      <th>avg_mape</th>\n",
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       "  </thead>\n",
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       "      <th>0</th>\n",
       "      <td>(0, 0, 0)</td>\n",
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       "      <th>1</th>\n",
       "      <td>(0, 0, 1)</td>\n",
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       "      <th>4</th>\n",
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       "      <td>54.261608</td>\n",
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       "      <th>6</th>\n",
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       "      <th>8</th>\n",
       "      <td>(0, 2, 0)</td>\n",
       "      <td>261.700684</td>\n",
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       "      <th>9</th>\n",
       "      <td>(0, 2, 1)</td>\n",
       "      <td>69.588529</td>\n",
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       "      <th>10</th>\n",
       "      <td>(0, 2, 2)</td>\n",
       "      <td>77.645635</td>\n",
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       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>(0, 2, 3)</td>\n",
       "      <td>79.815368</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>(1, 0, 0)</td>\n",
       "      <td>34.769050</td>\n",
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       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>(1, 0, 1)</td>\n",
       "      <td>34.910799</td>\n",
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       "      <th>14</th>\n",
       "      <td>(1, 0, 2)</td>\n",
       "      <td>34.882528</td>\n",
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       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>(1, 0, 3)</td>\n",
       "      <td>34.942585</td>\n",
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       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>(1, 1, 0)</td>\n",
       "      <td>55.700550</td>\n",
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       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>(1, 1, 1)</td>\n",
       "      <td>32.624136</td>\n",
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       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>(1, 1, 2)</td>\n",
       "      <td>53.832252</td>\n",
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       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>(1, 1, 3)</td>\n",
       "      <td>35.541810</td>\n",
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       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>(1, 2, 0)</td>\n",
       "      <td>234.536131</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>(1, 2, 1)</td>\n",
       "      <td>64.669843</td>\n",
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       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>(1, 2, 2)</td>\n",
       "      <td>69.337830</td>\n",
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       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>(1, 2, 3)</td>\n",
       "      <td>69.383444</td>\n",
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       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>(2, 0, 0)</td>\n",
       "      <td>34.890855</td>\n",
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       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>(2, 0, 1)</td>\n",
       "      <td>34.497624</td>\n",
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       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>(2, 0, 2)</td>\n",
       "      <td>38.728264</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>(2, 0, 3)</td>\n",
       "      <td>34.450839</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>(2, 1, 0)</td>\n",
       "      <td>57.824546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>(2, 1, 1)</td>\n",
       "      <td>32.773074</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>(2, 1, 2)</td>\n",
       "      <td>32.238638</td>\n",
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       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>(2, 1, 3)</td>\n",
       "      <td>51.546060</td>\n",
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       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>(2, 2, 0)</td>\n",
       "      <td>156.846278</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>(2, 2, 1)</td>\n",
       "      <td>70.747578</td>\n",
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       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>(2, 2, 2)</td>\n",
       "      <td>69.630309</td>\n",
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       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>(2, 2, 3)</td>\n",
       "      <td>57.344403</td>\n",
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       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>(3, 0, 0)</td>\n",
       "      <td>34.848317</td>\n",
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       "      <th>37</th>\n",
       "      <td>(3, 0, 1)</td>\n",
       "      <td>34.699123</td>\n",
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       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>(3, 0, 2)</td>\n",
       "      <td>30.881678</td>\n",
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       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>(3, 0, 3)</td>\n",
       "      <td>34.091631</td>\n",
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       "      <th>40</th>\n",
       "      <td>(3, 1, 0)</td>\n",
       "      <td>54.202563</td>\n",
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       "      <th>41</th>\n",
       "      <td>(3, 1, 1)</td>\n",
       "      <td>60.538142</td>\n",
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       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>(3, 1, 2)</td>\n",
       "      <td>63.935948</td>\n",
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       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>(3, 1, 3)</td>\n",
       "      <td>51.365207</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>(3, 2, 0)</td>\n",
       "      <td>362.440779</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>(3, 2, 1)</td>\n",
       "      <td>68.387950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>(3, 2, 2)</td>\n",
       "      <td>301.057628</td>\n",
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       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>(3, 2, 3)</td>\n",
       "      <td>301.130710</td>\n",
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      ],
      "text/plain": [
       "        combo    avg_mape\n",
       "0   (0, 0, 0)   36.364153\n",
       "1   (0, 0, 1)   35.836103\n",
       "2   (0, 0, 2)   35.200416\n",
       "3   (0, 0, 3)   36.088199\n",
       "4   (0, 1, 0)   58.662106\n",
       "5   (0, 1, 1)   54.261608\n",
       "6   (0, 1, 2)   51.930196\n",
       "7   (0, 1, 3)   34.594611\n",
       "8   (0, 2, 0)  261.700684\n",
       "9   (0, 2, 1)   69.588529\n",
       "10  (0, 2, 2)   77.645635\n",
       "11  (0, 2, 3)   79.815368\n",
       "12  (1, 0, 0)   34.769050\n",
       "13  (1, 0, 1)   34.910799\n",
       "14  (1, 0, 2)   34.882528\n",
       "15  (1, 0, 3)   34.942585\n",
       "16  (1, 1, 0)   55.700550\n",
       "17  (1, 1, 1)   32.624136\n",
       "18  (1, 1, 2)   53.832252\n",
       "19  (1, 1, 3)   35.541810\n",
       "20  (1, 2, 0)  234.536131\n",
       "21  (1, 2, 1)   64.669843\n",
       "22  (1, 2, 2)   69.337830\n",
       "23  (1, 2, 3)   69.383444\n",
       "24  (2, 0, 0)   34.890855\n",
       "25  (2, 0, 1)   34.497624\n",
       "26  (2, 0, 2)   38.728264\n",
       "27  (2, 0, 3)   34.450839\n",
       "28  (2, 1, 0)   57.824546\n",
       "29  (2, 1, 1)   32.773074\n",
       "30  (2, 1, 2)   32.238638\n",
       "31  (2, 1, 3)   51.546060\n",
       "32  (2, 2, 0)  156.846278\n",
       "33  (2, 2, 1)   70.747578\n",
       "34  (2, 2, 2)   69.630309\n",
       "35  (2, 2, 3)   57.344403\n",
       "36  (3, 0, 0)   34.848317\n",
       "37  (3, 0, 1)   34.699123\n",
       "38  (3, 0, 2)   30.881678\n",
       "39  (3, 0, 3)   34.091631\n",
       "40  (3, 1, 0)   54.202563\n",
       "41  (3, 1, 1)   60.538142\n",
       "42  (3, 1, 2)   63.935948\n",
       "43  (3, 1, 3)   51.365207\n",
       "44  (3, 2, 0)  362.440779\n",
       "45  (3, 2, 1)   68.387950\n",
       "46  (3, 2, 2)  301.057628\n",
       "47  (3, 2, 3)  301.130710"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Perform 5-fold cross validation for each combination\n",
    "\n",
    "warnings.simplefilter('ignore', UserWarning)\n",
    "\n",
    "average_mape_df = pd.DataFrame(columns=['combo', 'avg_mape'])\n",
    "\n",
    "for combo in combos:\n",
    "    # Set number of folds\n",
    "    n_splits = 5\n",
    "    tscv = TimeSeriesSplit(n_splits=n_splits)\n",
    "\n",
    "    # Initialize lists to store evaluation metrics\n",
    "    mape_scores = []\n",
    "\n",
    "    # Fit and predict\n",
    "    for train_index, test_index in tscv.split(df_ts):\n",
    "        train_data, test_data = df_ts.iloc[train_index], df_ts.iloc[test_index]\n",
    "        model = ARIMA(train_data, order=combo).fit()\n",
    "        predictions = model.forecast(steps=len(test_data))\n",
    "\n",
    "        # Calculate MAPE\n",
    "        mape_cur = mean_absolute_percentage_error(test_data, predictions) * 100\n",
    "        mape_scores.append(mape_cur)\n",
    "\n",
    "    # Calculate average MAPE\n",
    "    average_mape = np.mean(mape_scores)\n",
    "    average_mape_df = average_mape_df._append({'combo': combo, 'avg_mape': average_mape}, ignore_index=True)\n",
    "\n",
    "average_mape_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "6_hnMz-E9VeT",
   "metadata": {
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     "height": 80
    },
    "id": "6_hnMz-E9VeT",
    "outputId": "9c852310-a7dc-4a57-f6a1-69b9d4e3d55d"
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   "outputs": [
    {
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       "summary": "{\n  \"name\": \"average_mape_df[average_mape_df[\\\"avg_mape\\\"] == min_avg_mape]\",\n  \"rows\": 1,\n  \"fields\": [\n    {\n      \"column\": \"combo\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          [\n            3,\n            0,\n            2\n          ]\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"avg_mape\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": 30.88167795009415,\n        \"max\": 30.88167795009415,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          30.88167795009415\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>combo</th>\n",
       "      <th>avg_mape</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>(3, 0, 2)</td>\n",
       "      <td>30.881678</td>\n",
       "    </tr>\n",
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       "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
       "                                                    [key], {});\n",
       "        if (!dataTable) return;\n",
       "\n",
       "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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       "          + ' to learn more about interactive tables.';\n",
       "        element.innerHTML = '';\n",
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       "\n",
       "\n",
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      ],
      "text/plain": [
       "        combo   avg_mape\n",
       "38  (3, 0, 2)  30.881678"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "min_avg_mape = min(average_mape_df[\"avg_mape\"])\n",
    "average_mape_df[average_mape_df[\"avg_mape\"] == min_avg_mape]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "SvNhOUynEXaf",
   "metadata": {
    "id": "SvNhOUynEXaf"
   },
   "source": [
    "**With cross validation, the best order of ARIMA model should be (3, 0, 2). Let's see how it performs on test data.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "3iX8BUFaE0qU",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "3iX8BUFaE0qU",
    "outputId": "87418323-8649-467b-adb0-3918230d5942"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MAE: 2473.1485\n",
      "MAPE: 15.5256%\n",
      "SMAPE: 15.0213%\n",
      "MSE: 9400121.9991\n",
      "RMSE: 3065.9618\n"
     ]
    }
   ],
   "source": [
    "# Fit and predict\n",
    "y_test_ts, y_pred_ts = build_arima(train_ts, test_ts, order=(3,0,2))\n",
    "\n",
    "# Evaluate model\n",
    "\n",
    "arima_mae = mean_absolute_error(y_test_ts, y_pred_ts)\n",
    "print(f\"MAE: {arima_mae:.4f}\")\n",
    "\n",
    "arima_mape = mean_absolute_percentage_error(y_test_ts, y_pred_ts) * 100\n",
    "print(f\"MAPE: {arima_mape:.4f}%\")\n",
    "\n",
    "arima_smape = smape(y_test_ts, y_pred_ts)\n",
    "print(f\"SMAPE: {arima_smape:.4f}%\")\n",
    "\n",
    "arima_mse = mean_squared_error(y_test_ts, y_pred_ts)\n",
    "print(f\"MSE: {arima_mse:.4f}\")\n",
    "\n",
    "arima_rmse = np.sqrt(arima_mse)\n",
    "print(f\"RMSE: {arima_rmse:.4f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "_MF0tNWvFK_B",
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    "colab": {
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     "height": 382
    },
    "id": "_MF0tNWvFK_B",
    "outputId": "21adec43-4ae4-4225-b76c-cd600132ece4"
   },
   "outputs": [
    {
     "data": {
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ixYoEBARw7bXXsm3btrznv/nmG8LCwpg9ezaNGzcmKCiInj17sn//ftd+QwugYl1ERETOz9mrHnwpeAcU3EZD4UVEis3hcHAy+2ShPlIzU3n090dxcPa2mc7Hhv0+jNTM1EJd77+FcVH5+/uTnZ0NwNy5c9myZQtz5sxhxowZ5OTk0KNHD4KDg1m4cCGLFy/OK3qdrxk1ahTffPMNX331FYsWLeLo0aNMnTr1vPccOHAgkyZN4oMPPmDTpk18+umnBAUFERkZyc8//wzAli1b2L9/P2PGjCnwGoMHD2bFihX8+uuvJCQk4HA4uO6668jJyclrk56ezrvvvst3333HggULSExM5PHHHy/W96swNAxeREREzu98Q+CdqnSEXRO0IryISDGk56QT9GaQS67lwMGeE3sI/b/QQrVPezqNQN/Aot/H4WDu3LnMnj2bRx55hOTkZAIDA/niiy/yhr9///332O12vvjiCywWCwBff/01YWFhxMfH0717d0aPHs3TTz9NbKwxdH/cuHHMnj37nPfdunUrkydPZs6cOXTr1g2ASy65JO9553D3qlWrEhYWVuA1tm3bxq+//srixYvp2NF403nChAlERkYybdo0brnlFgBycnIYN24c9erVA+Dhhx/mlVdeKfL3qqhUrIuIiMj5HS1Ese5cEf5wAtht4GUt+VwiImKaGTNmEBQURE5ODna7nf79+/PSSy8xdOhQmjdvnm+e+po1a/j3338JDg7Od43MzEy2b99OSkoK+/fvp3379nnPeXt707Zt23P2+K9evRqr1cqVV1550V/Dpk2b8Pb2znffypUr07BhQzZt2pT3WEBAQF6hDlCjRg0OHTp00fctLBXrIiIicn7HVhvHgrZtcwptBt5BkHsCUjZAxQLmtouIyHkF+ASQ9nRaodou2L2A6yZed8F2v/X/jSvqXFGoexdF165d+eSTT/D19aVmzZp4e58uLQMD8/fQp6Wl0aZNGyZMmHDWdcLDw4t0Xyd/f/+Let3F8PHxyfe5xWIp9rSBwtCcdRERETm3nFRI+9c4D4s+dzsvK1TpYJxr3rqIyEWxWCwE+gYW6qN7ve5EhERgwVLwtbAQGRJJ93rdC3U95/D0wgoMDKR+/frUrl07X6FekNatW7Nt2zaqVq1K/fr1832EhoYSGhpKjRo1WLr09CKlubm5rFy58pzXbN68OXa7nfnz5xf4vLNn32Y79zZ2jRs3Jjc3N999jxw5wpYtW2jSpMl5v6bSoGJdREREzu3YGuMYEAEVqpy/bZVTi8xp3rqISImzelkZ09NYNO2/Bbvz89E9R2N1g2lJAwYMoEqVKvTu3ZuFCxeyc+dO4uPjefTRR9mzx1jJftiwYbz11ltMmzaNzZs389BDD521R/qZoqKiGDRoEHfffTfTpk3Lu+bkyZMBqFOnDhaLhRkzZpCcnExa2tkjFho0aEDv3r257777WLRoEWvWrOGOO+6gVq1a9O7du0S+F0WhYl1ERETOzTkE/nzz1Z2qaEV4EZHSFNs4lim3TqFWSK18j0eERDDl1iklus96UQQEBLBgwQJq165NbGwsjRs35p577iEzM5OQkBAA/ve//3HnnXcyaNAgYmJiCA4O5uabbz7vdT/55BP69u3LQw89RKNGjbjvvvs4efIkALVq1eLll1/mqaeeolq1ajz88MMFXuPrr7+mTZs2XH/99cTExOBwOPjtt9/OGvpuBoujNAbbu6nU1FRCQ0NJSUnJ+49EREREzvD33bDja2j2ArR4+fxts1NgSkXAATcfAP9qpRJRRKSsyszMZOfOndStW5cKFSpc9HVsdhsLExey/8R+agTXoHPtzm7Ro16ene9nW9g6VAvMiYiIyLnlbdsWfeG2vqEQ1gyOrzNWhY+8qSSTiYjIKVYvK12iupgdQ1xMw+BFRESkYLZsY2V3KNwweDhjKPzikskkIiJSTqhYFxERkYKlbgR7DviEQWCdwr1Gi8yJiIi4hIp1ERERKdjRM4bAF3ZLn/BTxfrRFWDLKpFYIiIi5YGKdRERESlY3nz1Qg6BBwiqB37hYM+Go6tKJpeIiEg5oGJdRERECuYs1isVoVi3WE73rmsLNxGRQinHG3R5LLvdXuxraDV4EREROZvDDsfWGOdF6VkHY976nl9OFev/c3k0ERFP4ePjg8ViITk5mfDwcCyFnXIkbsvhcJCdnU1ycjJeXl74+vpe9LVUrIuIiMjZ0nZA7gnw8oOQhkV7bXgn45i8GByOws93FxEpZ6xWKxEREezZs4ddu3aZHUdcKCAggNq1a+PldfGD2VWsi4iIyNmcQ+DDmoOXT9FeW6mN8ZrMg3ByJwRd4vp8IiIeIigoiAYNGpCTk2N2FHERq9WKt7d3sUdKqFgXERGRsx1bbRyLOgQewFoBKraBI38bW7ipWBcROS+r1YrVajU7hrgZLTAnIiIiZzt6EYvLnUmLzImIiBSLinURERE5W94w+OiLe32VU8V6sop1ERGRi6FiXURERPLLOACZBwALVGxxcddw9qynrIOcVJdFExERKS9UrIuIiEh+zvnqIQ3BO/DiruFfAwLrGlvAHV7qsmgiIiLlhYp1ERERyc85BL5idPGuo3nrIiIiF03FuoiIiOSXV6xf5OJyTpq3LiIictFUrIuIiEh+R11UrDt71o/8DXZb8a4lIiJSzmifdXdnt0HyQsjYb8z/C+8MXtqDUURESkjOCUj71zgv7jD40GbgHWQsMJe6EcKaFzueiIhIeaFi3Z0lxcHKYZC+5/RjARHQZgxExpqXS0REPNexNcbRvxZUCC/etby8oXJ7ODjXGAqvYl1ERKTQNAzeXSXFwcK++Qt1gPS9xuNJcebkEhERz+aq+epO4Z2MoxaZExERKRIV6+7IbjN61HEU8OSpx1YO1/w/ERFxPee2bZVcVKznLTK32DXXExERKSdUrLuj5IVn96jn44D0JKOdiIiIK7m6Z71Ke8ACadsh46BrrikiIlIOqFh3Rxn7XdtORESkMGzZkLLeOC/u4nJOvmEQ2tQ4P5zgmmuKiIiUAyrW3ZF/Dde2ExERKYzUjWDPAZ8wCIxy3XWdW7hp3rqIiEihqVh3R+GdjVXfsZyjgQUCIo12IiIiruKcr14xGizn+h10EaqoWBcRESkqFevuyMtqbM8GFFywO6DNaO23LiIirnXUOV892rXXda4If2QF2LJce20REREPpWLdXUXGQucpEFCrgCe9jJ51ERERV3L14nJOQfXALxzsWXB0lWuvLSIi4qFUrLuzyFi4cRdcPQ86ToSr/oLIWwA7LLkDck+anVBERDyFw+76bducLBbNWxcRESkiFevuzssK1bpAVD+o3hUuGwf+teDEVvjnCbPTiYiIp0jbCbknwMsPQhq5/vqaty4iIlIkKtbLGr9KEPONcb7tE9j3u6lxRETEQziHwIc1Ay8f11/fWawnLwGHw/XXFxER8TAq1sui6t2g4TDj/O+7IfOwuXlERKTsK6n56k6V2hhvAmQegJO7SuYeIiIiHkTFelnV8k0IbWL80bPsfvVSiIhI8eRt21ZCxbq3P1RsY5wnayi8iIjIhahYL6u8/SHme6OXYs9U2Dne7EQiIlKWlXTPOpyxyNzikruHiIiIh1CxXpZVagXNXzbOVzxqLA4kIiJSVBkHIWM/YIGw5iV3nzPnrYuIiMh5qVgv6xo/CeGXGyv4JtwJdpvZiUREpKxx9qqHXAo+QSV3nyoxxjFlHeSkltx9REREPICK9bLOywox34J3ECQvhk3vmJ1IRETKmpKer+4UUBMCo4w93Y8sK9l7iYiIlHEq1j1BUF1o84Fxvu4FOPqPuXlERKRsyZuvHl3y99JQeBERkUJRse4pLhkMETeDPQcS7oDcDLMTiYhIWVEai8s5hXcyjodVrIuIiJyPinVPYbHAZZ9BhWqQshHWPG12IhERKQtyTsCJbcZ5qRTrzhXhE4zh8CIiIlIgFeuepEIVaP+Vcb5lDOyfY24eERFxf8fXGkf/WlAhvOTvF9rMWGclJxVSNpT8/URERMooFeueptZ10GCIcf73YMg6amocERFxc0dLcb46gJc3VG5vnGveuoiIyDmpWPdErd6B4EshYx8sfwgcDrMTiYiIuyrN+epOeUPhVayLiIici4p1T+QdCDHfgcUKiT/CrolmJxIREXfl3LatUikW61oRXkRE5IJUrHuqKpdBsxeM8xVD4WSiuXlERMT92HMgZb1xXpo961U6ABZI+xcyD5XefUVERMoQFeuerOkzxrzAnBRj/rpW3RURkTOlbAR7NviEQmBU6d3XNwxCmxrnhxNK774iIiJliIp1T+blbQyHtwbAwXmw+X2zE4mIiDs5dsbichZL6d7bOW89eXHp3ldERKSMULHu6UIaQJtTRfqaZ+D4OnPziIiI+3DOVy/NIfBOVbTInIiIyPmoWC8P6t0HNa83hjouGQC2LLMTiYiIOzhWytu2nclZrB9Zod9LIiIiBShSsf7mm2/Srl07goODqVq1KjfddBNbtmzJ1yYzM5OhQ4dSuXJlgoKC6NOnDwcPHszXJjExkV69ehEQEEDVqlV54oknyM3NzdcmPj6e1q1b4+fnR/369fnmm2/OyvPxxx8TFRVFhQoVaN++PcuWLSvKl1N+WCzQ/gvwCzd61tc+Z3YiERExm8Nubs96cH3wqwL2rNNvGoiIiEieIhXr8+fPZ+jQofz999/MmTOHnJwcunfvzsmTJ/PajBgxgunTp/PTTz8xf/589u3bR2xsbN7zNpuNXr16kZ2dzZIlSxg/fjzffPMNL7zwQl6bnTt30qtXL7p27crq1asZPnw49957L7Nnz85r8+OPP/LYY4/x4osvsmrVKlq2bEmPHj04dEiryhbIvxq0/9w43zQKDsabGkdEREyWthNyUsHLD0Ibl/79LRZt4SYiInIeFofD4bjYFycnJ1O1alXmz5/PFVdcQUpKCuHh4UycOJG+ffsCsHnzZho3bkxCQgIdOnTg999/5/rrr2ffvn1Uq1YNgHHjxjFy5EiSk5Px9fVl5MiRzJw5k/Xr1+fd6/bbb+f48ePMmjULgPbt29OuXTs++ugjAOx2O5GRkTzyyCM89dRTBebNysoiK+v0ULvU1FQiIyNJSUkhJCTkYr8NZcvSe2H7lxBQG65bC76hZicSEREzJP4Mi/pCpTbQc4U5GTa+DatHQmQf6DzFnAwiIiKlLDU1ldDQ0AvWocWas56SkgJApUqVAFi5ciU5OTl069Ytr02jRo2oXbs2CQnG1iwJCQk0b948r1AH6NGjB6mpqWzYsCGvzZnXcLZxXiM7O5uVK1fma+Pl5UW3bt3y2hTkzTffJDQ0NO8jMjKyOF9+2dT6fQi6BNITYcUjZqcRERGzmDlf3anKGSvCX3zfgYiIiEe66GLdbrczfPhwOnXqRLNmzQA4cOAAvr6+hIWF5WtbrVo1Dhw4kNfmzELd+bzzufO1SU1NJSMjg8OHD2Oz2Qps47xGQZ5++mlSUlLyPpKSkor+hZd1PsHGdm4WL9j1HST+ZHYiERExQ16xbsJ8dadKbcDLBzIPwMld5uUQERFxQxddrA8dOpT169fzww8/uDJPifLz8yMkJCTfR7kU3hGaPG2cL3sA0veam0dEREqfmYvLOXn7Q8XWxrnmrYuIiORzUcX6ww8/zIwZM5g3bx4RERF5j1evXp3s7GyOHz+er/3BgwepXr16Xpv/rg7v/PxCbUJCQvD396dKlSpYrdYC2zivIRfQ/EWjRyP7GPx9l7EqsIiIlA+ZhyBjH2CBsBbmZtF+6yIiIgUqUrHucDh4+OGHmTp1Kn/99Rd169bN93ybNm3w8fFh7ty5eY9t2bKFxMREYmJiAIiJiWHdunX5Vm2fM2cOISEhNGnSJK/NmddwtnFew9fXlzZt2uRrY7fbmTt3bl4buQAvH4j5HqwV4MAc2Pqx2YlERKS0HD01BD64AfgEmZslXMW6iIhIQYpUrA8dOpTvv/+eiRMnEhwczIEDBzhw4AAZGRkAhIaGcs899/DYY48xb948Vq5cyV133UVMTAwdOnQAoHv37jRp0oQ777yTNWvWMHv2bJ577jmGDh2Kn58fAA8++CA7duzgySefZPPmzYwdO5bJkyczYsSIvCyPPfYYn3/+OePHj2fTpk0MGTKEkydPctddd7nqe+P5QhtB9DvG+eonIWWjuXlERKR0uMN8dSdnz/rxtZBzwtwsIiIibsS7KI0/+eQTALp06ZLv8a+//prBgwcD8P777+Pl5UWfPn3IysqiR48ejB07Nq+t1WplxowZDBkyhJiYGAIDAxk0aBCvvPJKXpu6desyc+ZMRowYwZgxY4iIiOCLL76gR48eeW1uu+02kpOTeeGFFzhw4ADR0dHMmjXrrEXn5AIuHQr7ZsD+2bDkTuieAFZfs1OJiEhJcs5Xr+QGxXpATQiMMhaYO7IUqne70CtERETKhWLts17WFXZ/O4+Xvg9+aw7ZR6HpM9DydbMTiYhISZreEE5shS6zoGaPC7cvaYsHwO6J0PxlaP6C2WlERERKVKnssy4eIqAmXPapcb7xLWO/WxER8Uw5aXBim3HuDj3roHnrIiIiBVCxLobafaHuQGNV+CV3at6giIinOr4GcIB/TahQ1ew0hrwV4RO0O4mIiMgpKtbltDYfQGAdOLkTVg43O42IiJQEd9hf/b/CmoN3IOSkuuVipza7jfhd8UxaN4n4XfHY7DazI4mISDmgYl1O8w2FmG8BC+z4CpKmmZ1IRERcLW8l+GhTY+Tj5Q2VjV1j3G0ofNymOKLGRNF1fFf6x/Wn6/iuRI2JIm5TnNnRRETEw6lYl/yqXgGNnzDOl90HGQfMzSMiIq511I22bTuTc956svsU63Gb4ug7uS97Uvfke3xv6l76Tu6rgl1EREqUinU5W4tXIKwlZB2GpfdA+d0wQETEs9hzIGW9ce4ui8s5Oeetu8kipza7jWGzhuHg7N+BzseGzxquIfEiIlJiVKzL2ax+0PF78PKDfb/Bv5+ZnUhERFwhZRPYs8EnxNjb3J1UOTUMPu1fyDxkbhZgYeLCs3rUz+TAQVJqEgsTF5ZiKhERKU9UrEvBwppB9JvG+arHIHWruXlERKT4zpyvbnGzPwF8wyC0qXF+OMHUKAD7T+x3aTsREZGicrPf1OJWGg6DaleBLR0S7jSGT4qISNl1zE3nqztVcZ956zWCa7i0nYiISFGpWJdzs3hBh2/AJwyOLIMNb5idSEREisMdt207U3gn4+gGK8J3rt2ZiJAILFjO2SYyJJLOtTuXYioRESlPVKzL+QVGQruxxvn6V+HwUnPziIjIxXE4zijWo81Mcm7OnvUjy8GWbWoUq5eVMT3HFLjAnNO73d/F6mUtxVQiIlKeqFiXC4vqB3VuB4fNGA6fe9LsRCIiUlQnd0JOCnj5QmgTs9MULLg++FUBexYcW2V2GmIbx9Ik/Ozvldep+f5bDm8p7UgiIlKOqFiXwmk3FvxrwYltsOpxs9OIiEhROfdXD20GXj7mZjkXi8Wt5q0npSSxMXkjAD/0+YGJsROZN2ge3970LQCvLXyNzYc3mxlRREQ8mIp1KRzfihAz3jj/dxzsnWluHhERKRrnEHh321/9v8JPFetuMG990vpJAFxR5wpua3Yb/Zr3o0tUF/o378+19a8l25bNfdPvw+6wm5xUREQ8kYp1KbzqV0PD4cb50nsgM9nUOCIiUgTOleDDok2NcUF5PeuLjXn2JpqwbgIAA5oPyPe4xWLhk16fEOgTyKLERXy+8nMz4omIiIdTsS5FE/2mMdcx8yAsu9/0P6RERKSQnMW6u/esV2prDNPPPAAnd5sWY/2h9aw9uBYfLx/6Nul71vN1wurw2lWvAfDkn0+y78S+0o4oIiIeTsW6FI21AnScYPwhtWca7Pja7EQiInIhmYcgYx9ggbAWZqc5P29/qNjaODdxKPyEtUav+rUNrqWSf6UC2zxy2SO0q9mO1KxUHvn9kdKMJyIi5YCKdSm6itHQ4lXjfOUwSNthahwREbkA53z14PrgE2xqlEI5cyi8CewOOxPXTwTOHgJ/JquXlc9v+ByrxUrcpjimbZ5WSglFRKQ8ULEuF6fR4xDeGXLTIGEg2G1mJxIRkXNxDoGv6OZD4J1MXmRuSdISElMSCfYN5oZLbzhv25bVW/JExycAGPrbUFIyU0ojooiIlAMq1uXieFkh5lvwDjZ6Pjb9n9mJRETkXI6WsWLd2bN+fC3knCj12zuHwMc2jsXfx/+C7V+48gXqV6rPvhP7eHru0yUdT0REygkV63LxgqKg7YfG+doX4egqU+OIiMg5HF9tHMtKsR5QEwLrgMMOR5aV6q2zbdlM3jgZOP8Q+DP5+/jz2fWfAfDJik9YlLioxPKJiEj5oWJdiqfuQIjsA45cWHIH5GaYnUhERM6UkwapW43zitGmRimSKp2MY3LpDoWf/e9sjmYcpXpQda6qe1WhX9e1blfujr4bgPum30dWblZJRRQRkXJCxboUj8UC7cZBheqQuglWjzQ7kYiInOn4WsAB/jXAv5rZaQrPpHnrzr3Vb296O1Yva5Fe+073d6gaWJXNhzfz5qI3SyKeiIiUIyrWpfgqVIEOp7Zw2/oh7P/D3DwiInJaWVtczsk5b/1wgjEcvhScyDrBr1t+BWBAi8INgT9TJf9KfNDzAwDeWPgGG5M3ujSfiIiULyrWxTVq9oQGQ43zv++CrKPm5hEREYNz27ayVqyHNQfvQMhJgZTSKXqnbp5KRm4Gl1a+lDY12lzUNW5teiu9GvQix57DfdPvw15KbzSIiIjnUbEurtPqbQhpCBn7YPmD4HCYnUhERPJ61qNNjVFkXt5Qub1xXkpD4Z1D4Ac0H4DFYrmoa1gsFsb2GkuQbxBLkpbw6YpPXRlRRETKERXr4jreARDzPVi8IfEn2DXB7EQiIuWbPQeOrzPOy1rPOpweCl8Ki8wdSDvAnzv+BKB/8/7Fulbt0Nq8cdUbAIz8cyR7U/cWO5+IiJQ/KtbFtSq3hWYvGOcrhsLJ3ebmEREpz1I3gz0bfEIgqK7ZaYquFBeZ+3H9j9gddtrXak/9SvWLfb2H2j1E+1rtOZF9gqG/DcWh0WYiIlJEKtbF9Zo+DZU7QE4qJAwqtYWBRETkP46eGgIf1hIsZfBXfpUY43hiG2Qml+itzhwC7wpWLyuf3/A53l7e/LLlF+I2xbnkuiIiUn6Uwd/c4va8vKHjd8bCQIfmw+b3zE4kIlI+ldWV4J18wyC0qXF+OKHEbrPtyDaW71uO1WLltma3uey6zas1Z2QnY0vTh39/mOOZx112bRER8Xwq1qVkBNeH1u8b52uehWNrzc0jIlIeOYv1SmW0WIcz5q0vLrFbOHvVr6l3DVUDq7r02s9d8RyXVr6UA2kHGDlnpEuvLSIink3FupScevdCrRuN+ZIJd4At0+xEIiLlh8NRdrdtO1MJz1t3OBwuHwJ/pgreFfjs+s8A+GzVZyzYvcDl9xAREc+kYl1KjsUC7T8Hv3BjNeI1z5mdSESk/Di5y9ij3MsHQhqbnebiOXvWjywHW7bLL79833L+Pfov/t7+3NToJpdfH+DKqCu5t9W9ANw//X4yc/XmtYiIXJiKdSlZFapC+y+N883vwcF55uYRESkvnEPgQ5uB1dfcLMUR3AD8qoA96/TX5EIT1hq96r0b9SbIN8jl13d6+5q3qR5UnS1HtvDGwjdK7D4iIuI5VKxLyYu4AerdBziM1eGzj5udSETE8x0t44vLOVksp3vXXTwUPteeyw8bfgBKZgj8mSr6V+TDaz8E4K1Fb7H+0PoSvZ+IiJR9KtaldLR+D4LqQXoSrHjY7DQiIp7PE+arOznnrSe7tlj/a+dfHDp5iMr+lelRr4dLr12QPo37cGPDG8mx53Df9Puw2W0lfk8RESm7VKxL6fAJgpjvjH1+d02A3T+anUhExLPlbdsWbWoMl8jrWV9sLJznIs6F5W5teis+Vh+XXfdcLBYLH1/3McG+wfy9528+WfFJid9TRETKLhXrUnrCY6Dps8b5sgchfa+5eUREPFVmMmTsBSxQsaXZaYqvUluweEPGfji52yWXTM9JJ25THFDyQ+DPFBESwZtXvwnA03OfJiklqdTuLSIiZYuKdSldzZ43/ujKOQ5/DwaH3exEIiKexzkEPrg++ASbGsUlvP2hUmvj3EXz1qdvmU5adhpRYVF0jOzokmsW1pB2Q4iJiCEtO42hvw3F4cLRAiIi4jlUrEvp8vKBjt+D1R8O/AlbPzI7kYiI5/GkIfBOVVw7b905BL5/s/5YLBaXXLOwvCxefH7D5/h4+TB963SmbJxSqvcXEZGyQcW6lL6QhtDqXeN89UhI2WhuHhERT3PMQ1aCP1N4J+Pogp71I+lH+P3f3wEY0KL0hsCfqWnVpjx9+dMAPPL7IxzLOGZKDhERcV8q1sUcDYZAjZ5gy4QlA8CWbXYiERHP4YnFurNn/fgayEkr1qV+2vgTufZcoqtH0yS8iQvCXZxnOj9DoyqNOHjyIE/OedK0HCIi4p5UrIs5LBbo8BX4VTbmVq57yexEIiKeIfckpG41zj2pWA+oCYF1jLVOjiwt1qWcQ+BLc2G5gvh5+/H5DZ8D8MU/XxC/K97UPCIi4l5UrIt5/GvAZZ8Z55v+Dw4tMjePiIgnOLYWcECF6uBfzew0ruWCeeu7j+9mUeIiLFjo16yfi4JdvMtrX84DbR4A4P7p95ORk2FyIhERcRcq1sVckbFQd5DRU5JwJ+Skmp1IRKRs88Qh8E55+61ffLE+cd1EALpEdaFWSC1XpCq2/+v2f9QIqsG2o9t4bcFrZscRERE3oWJdzNf2AwiMgpO7YMWjcDAedk0yjnabudlERMoaZ7FeyQOL9XBnsZ5wUVt/OhwOtxkCf6bQCqF8dJ2xO8rbS95m7cG1JicSERF3oGJdzOcTAjHfGuc7x8PcrrCkv3H8NQqS4kyNJyJSpjj3WPfEnvWwFuAdCDkpkLKpyC9fe3AtG5I34Gv1pU+TPiUQ8OLFNo7lpkY3kWvP5b7p92HTm9UiIuWeinVxD1nJBT+evhcW9lXBLiJSGPYcOL7OOPekPdadvLyhcnvj/CKGwjt71a+/9HrCKoS5MJhrfHTtR4T4hbBs7zI+Xv6x2XFERMRkKtbFfHYbrBx2jicdxmHlcA2JFxG5kNTNYM8C72AIusTsNCXjIuet2x12Jq2fBLjXEPgz1Qqpxf91+z8Anpn7DIkpiSYnEhERM6lYF/MlL4T0Pedp4ID0JKOdiIicW94Q+GiweOiveOe89eTFRXrZgt0L2JO6h1C/UK5rcF0JBHON+9vcz+W1L+dkzkmGzByCw+EwO5KIiJjEQ3+TS5mSsd+17UREyqujzpXgo02NUaKqdDCOJ7ZB5jmmUBVgwlpjCHyfxn2o4F2hJJK5hJfFi8+u/wxfqy+/bfuNHzf8aHYkERExiYp1MZ9/Dde2ExEprzx52zYn34oQ2sQ4P5xQqJdk5WYxZdMUAAa0cM8h8GdqHN6YZy5/BoBhs4ZxNOOoyYlERMQMKtbFfOGdISACsJy7TUCk0U5ERArmcJweBu+J27adqUon41jIeeu///s7xzOPUzO4JlfWubIEg7nOU5c/ReMqjTl08hCP//G42XFERMQEKtbFfF5WaDPm1CfnKNhr9jLaiYhIwU7uhpzj4OUDIU3MTlOy8uatF65Yd64C369ZP6xl5HeJn7cfn9/wOQBfr/6av3b+ZXIiEREpbSrWxT1ExkLnKRBQK//jPqHGcec3cGxtqccSESkznEPgQ5uC1dfcLCXNuSL80eVgyz5v05TMFKZvmQ647yrw59KpdiceavsQAPdPv5+MnAyTE4mISGlSsS7uIzIWbtwFV8+DjhONY2wy1LgWbJmw+DbISTM7pYiIeyoP89WdghuAX2Xjd4Pz6z6HuE1xZNmyaFylMdHVo0snnwu92e1NagXXYvux7bwy/xWz44iISClSsS7uxcsK1bpAVD/jaPWBmPHgX9PYP3jFw2YnFBFxT0fLUbFusRR6v3XnEPgBzQdgsZxnbRQ3FeIXwsfXfQzAO0veYfWB1eYGEhGRUqNiXdxfhXDoNMnYM3jneNgx3uxEIiLu5/hq41geinU4XayfZ976vhP78uZ692/evzRSlYjejXrTp3EfbA4b902/D5vdZnYkEREpBSrWpWyoegU0f9k4X/4QpGwyN4+IiDvJPAzpe4zzii3MzVJaws9YEd7hKLDJD+t/wIGDjpEdqVuxbimGc70Pr/2QUL9QVuxbwQdLPzA7joiIlAIV61J2NHkaql0NtnRYdCvkaqEdERHg9LztoPrgE2JultJSqS1YvCFjH6QnFtjkzCHwZV2N4Bq8fc3bADw37zl2Hd9lbiARESlxKtal7PCyQsfvoUI1SFkPK4eZnUhExD2Ul/3Vz+TtD5VaG+cFDIXffHgzq/avwtvLm1ub3lrK4UrGva3vpXPtzqTnpDNk5hAc5xhRICIinkHFupQt/tWNgh0LbP8cdk0yO5GIiPnyVoKPNjVGqcubt774rKcmrDV61XvU60GVgCqlmarEeFm8+PyGz/G1+jLr31lMWq/fgSIinkzFupQ91btB02eN82X3Q+o2c/OIiJitPG3bdqbwgleEdzgcTFw/EfCMIfBnalilIc9f8TwAw2YN43D6YZMTiYhISVGxLmVT8xeNRedy04z9122ZZicSETFH7klI3WKcl7divUqMcTy+BnLS8h7+e8/f7Di2g0CfQG5seKNJ4UrOk52epFnVZhxOP8z//vif2XFERKSEqFiXssnLGzpOBL/KRo/SP0+YnUhExBzH1wEOqFDdmCpUngREQEBtcNjhyLK8h50Ly93c+GYCfQPNSldifK2+fH7D51iw8O2ab5mzfY7ZkUREpAQUuVhfsGABN9xwAzVr1sRisTBt2rR8z6elpfHwww8TERGBv78/TZo0Ydy4cfnaZGZmMnToUCpXrkxQUBB9+vTh4MGD+dokJibSq1cvAgICqFq1Kk888QS5ubn52sTHx9O6dWv8/PyoX78+33zzTVG/HCnLAmpBzHfG+daPICnO3DwiImYor/PVnc7cwg3IseXw44YfAc8bAn+mDhEdGNpuKAAPznyQ9Jx0kxOJiIirFblYP3nyJC1btuTjjz8u8PnHHnuMWbNm8f3337Np0yaGDx/Oww8/zK+//prXZsSIEUyfPp2ffvqJ+fPns2/fPmJjY/Oet9ls9OrVi+zsbJYsWcL48eP55ptveOGFF/La7Ny5k169etG1a1dWr17N8OHDuffee5k9e3ZRvyQpy2peC42fNM7/vhvSdpqbR0SktB0tp/PVnfIWmTOK9Tk75nA4/TDhAeF0u6SbicFK3htXv0FESAQ7ju3gpfiXXHtxuw0OxhsLuR6MNz4Xj2Gz24jfFc+kdZOI3xWPTT9fEbdkcRRj3w+LxcLUqVO56aab8h5r1qwZt912G88//3zeY23atOHaa6/ltddeIyUlhfDwcCZOnEjfvn0B2Lx5M40bNyYhIYEOHTrw+++/c/3117Nv3z6qVasGwLhx4xg5ciTJycn4+voycuRIZs6cyfr16/Puc/vtt3P8+HFmzZpVqPypqamEhoaSkpJCSEg52ZfWE9lzYM4VcORvqHwZdFsIVl+zU4mIlI5Z7eDoCrh8MtS+xew0pe/oKpjVBnzCoO8RBky9k4nrJvJwu4f58LoPzU5X4qZvmc6NP9yI1WJl+X3LaVXDBW/aJMUZ26Om7zn9WEAEtBkDkbHnfp2UCXGb4hg2axh7Uk//fCNCIhjTcwyxjfXzFSkNha1DXT5nvWPHjvz666/s3bsXh8PBvHnz2Lp1K927dwdg5cqV5OTk0K3b6Xe7GzVqRO3atUlISAAgISGB5s2b5xXqAD169CA1NZUNGzbktTnzGs42zmsUJCsri9TU1Hwf4gG8fODyH8C3ojFncc0zZicSESkd9txTc9Ypvz3rYS3AGgA5x0lLXsm0zdMAGNDCc4fAn+mGhjdwa9NbsTls3Dv9XnLtuRd+0fkkxcHCvvkLdYD0vcbjmnJWpsVtiqPv5L75CnWAval76Tu5L3Gb9PMVcScuL9Y//PBDmjRpQkREBL6+vvTs2ZOPP/6YK664AoADBw7g6+tLWFhYvtdVq1aNAwcO5LU5s1B3Pu987nxtUlNTycjIKDDbm2++SWhoaN5HZGRksb9ecROBdaDD18b55lGwd4a5eURESkPqZrBngXcwBF1idhpzeHlDlfYA/LrmU9Jz0qlXsR7ta7U3OVjpGdNzDGEVwli1fxVj/h5z8Rey24wedQoadHnqsZXDNSS+jLLZbQybNQxHAT9f52PDZw3XkHgRN1Iixfrff//Nr7/+ysqVKxk1ahRDhw7lzz//dPWtiuzpp58mJSUl7yMpKcnsSOJKEb2h4TDjPGEQnNTPV0Q8XN7ici3BUo43eDk1b33CVmPdmv7N+2OxWMxMVKqqB1Xn3WveBeD5ec+z49iOi7tQ8sKze9TzcUB6ktFOypyFiQvP6lE/kwMHSalJLEzUz1fEXbj0N3tGRgbPPPMM7733HjfccAMtWrTg4Ycf5rbbbuPdd41fItWrVyc7O5vjx4/ne+3BgwepXr16Xpv/rg7v/PxCbUJCQvD39y8wn5+fHyEhIfk+xMNE/x9UagPZR2FJP2M+u4iIpzq22jiW1yHwTuGdSM6F2YeNQsSTV4E/l7tb3U2XqC5k5Gbw4IwHuagliTL2u7aduJX9Jwr3cytsOxEpeS4t1nNycsjJycHLK/9lrVYrdrsdMBab8/HxYe7cuXnPb9myhcTERGJiYgCIiYlh3bp1HDp0KK/NnDlzCAkJoUmTJnltzryGs43zGlJOWf2g04/gEwLJi2Hti2YnEhEpOeV92zanKh2YnAY2oE31ljSs0tDsRKXOYrHw2fWf4Wf1Y86OOXy/9vuiX8S/hmvbidtYuW8l49eML1TbGsH6+Yq4iyIX62lpaaxevZrVq1cDxhZqq1evJjExkZCQEK688kqeeOIJ4uPj2blzJ9988w3ffvstN998MwChoaHcc889PPbYY8ybN4+VK1dy1113ERMTQ4cOHQDo3r07TZo04c4772TNmjXMnj2b5557jqFDh+Ln5wfAgw8+yI4dO3jyySfZvHkzY8eOZfLkyYwYMcJF3xops4LrQfsvjPONb8I+becnIh7I4dC2bU6+FZmQboyqG1CnrclhzNOgcgNeuNLY5nbE7BEkn0wu2gXCO4N/rfM0sEBApNFO3F6OLYcf1/9Ip6860fbztszefv6/hyxYiAyJpHNt/XxF3EWRi/UVK1bQqlUrWrUy/jB47LHHaNWqVd4e6D/88APt2rVjwIABNGnShLfeeovXX3+dBx98MO8a77//Ptdffz19+vThiiuuoHr16sTFnV590mq1MmPGDKxWKzExMdxxxx0MHDiQV155Ja9N3bp1mTlzJnPmzKFly5aMGjWKL774gh49elz0N0M8SO1boMEQ4zzhTkjfZ24eERFXO7kbco4bO2KENjU7jal2HNtBwskMvIDbwyqYHcdUT3R8guZVm3Mk4wiP/fFY0V7sZT29b/25tBlttBO3dejkIV5b8BpRY6K4/efbWZK0BG8vb/o378+bV7+J5dT//suBg/e6v4dVP18Rt1GsfdbLOu2z7uFsmTC7PRxfC1W7wFV/6g8MEfEcSdNg4c3GEPhr/zE7jaleW/Aaz897nm7+MKdVZ7hmgdmRTLVs7zI6fNEBBw5mDZhFj/qF7MhI3wPTLwVbBvhWhuwj+Z8PugR6bQKrr+tDS7Gt3LeSD5Z9wA/rfyDblg1AtcBqPNj2QR5o80De8PaC9lm3YMGBg7eufouRl480Jb9IeWLaPusibsNaAS6fDN6BcCgeNrxmdiIREdfRfHUAHA4HE9ZNAGBACHB0OZwqVMqry2pdxqPtHwXgwZkPcjL7ZOFeuOZZo1APvxxuPgBXz4OOE6HTZPCpCGk7YO3zJZhciirHlsMP63+g45cdaft5W75d8y3Ztmwuq3UZ39/8PbuH7+alLi/lm4ce2ziWXcN2MW/QPCbGTmTeoHl8dsNnADw37zmW711u1pcjIv+hnnX1rHu+nRMg4Q7AAlfPhWpdzU4kIlJ882+EvdOhzRho+KjZaUyzav8q2nzWhgreFTjYwJ+Q3GPQfSlUuczsaKZKy06j6dimJKYk8liHxxjVY9T5X3BkBcxuZ5z3WAaV2+V/PmkqLIw1zq+aA9W7uT60FNrBtIN8tvIzxq0cx74TxlQ/Hy8fbm16K49c9gjtI9oX6XoOh4PbptzGTxt/ol7FevzzwD8E+wWXRHQRQT3rIqfVHQCX3A04YHF/yDx0wZeIiLi9Y1pcDmDCWqNX/YZLbyCk2uXGg4eXmJjIPQT5BvFJr08AGL10NCv2rTh3Y4cD/vmfcR51x9mFOkDkzVD/AeM8YSBkFnHxOnGJFftWMHDqQGqPrs0L8S+w78Q+qgVW46UrXyJxRCLfx35f5EIdjN0EPr3+U2qH1mb7se088vsjJZBeRIpKxbqUD20/gNAmkHkAltwJDrvZiURELl7mYWN+MUDFluZmMZHNbmPS+knAqb3VnYujqVgH4LoG13F7s9uxO+zcN/0+cmw5BTfcMw0OLTCmj7V849wXbP0ehDQy9llfeo9R5EuJy7ZlM2ndJDp+2ZF2n7fju7XfkW3Lpn2t9kyInUDiiERe7PIi1YOqF+s+Ff0r8v3N3+Nl8WL8mvFMWjfJRV+BiFwsFetSPngHGnPurP5w4A/Y+H9mJxIRuXjHVxvHoHrgU36nccXvimd/2n4qVqjItQ2uhfBTxXryYhWSp4zuMZqKFSqy+sBq3v/7/bMb2LLhnyeM80aPQ2DkuS/mHQCdfgAvX2MKxrZPSia0AMZQ91fmv0LU6Cj6x/UnYU8CPl4+3NHiDpbeu5S/7/2b/s374+vCBf861+nMc52fA4z1DnYd3+Wya4tI0alYl/IjrCm0/cg4X/s8HFpkbh4RkYul/dUB8haWu6XJLUbBUqktWLwhYx+kJ5qczj1UC6rGqO7GfPUX419k+9Ht+Rts/QjStkOF6tCkEKuAV2wJ0W8b5//8D46vd3FiWb53OXdOvZPao2vzYvyL7E/bT/Wg6rzc5WUSRyTy3c3fcVmtkluT4fkrn6djZEdSs1Lp/3N/cu25JXYvETk/FetSvlxyF0QNAIcNlvSDrCMXfo2IiLs5tto4Viq/xXpmbiY/b/oZgAEtBhgPegecfgMjWUPhnQZHD+aquleRmZvJAzMeIG9t4czDsP4V47zla+ATVLgLNnwUalxrbJG6uB/kZpRM8HIk25bNxHUTifkyhsu+uIzv135Pti2bDhEdmBg7kd3Dd/PClS8Ue6h7YXh7eTMhdgIhfiEk7Eng1fmvlvg9RaRgKtalfLFYoN0nEHypMd8zYbCGSopI2eNcXC4s2tQYZpqxdQapWalEhkRyee3LTz8R3sk4at56HufiYRW8KzB351y+XfOt8cT6VyAnBcJaQN3BRbkgdPgaKlSDlPWw+skSyV0eHEg7wMvxL1NndB0GxA3g7z1/4+Plw50t7mTZvctIuCeBfs37uXSoe2FEhUXx6fWfAvDawtdYuHthqd5fRAwq1qX88Qk29l/38oN9M2BzAXP4RETcVW46nNhinJfjnnXnEPh+zfrhZTnjz5m8eesq1s9Uv1J9XrryJQAe++MxDu1fDNvGGk+2HgVe1qJd0L8adPjGON/6EeyZ7rKs5cGyvcuMoe7v1+al+S9xIO0ANYJq8EqXV0gakcS3N39Lu1oFrMpfim5vdjuDWg7C7rAzIG4AxzKOmZpHpDxSsS7lU8WW0Ga0cb56JBxeZmocEZFCO77W2NGiQjXwr2F2GlMcyzjGb9t+A84YAu9UJcY4Hl8DOWmlnMy9PRbzGC2rteRoxlGGT7vNmBJW8/qL3zO9Zk9oOMI4X3oXpO9zXVgPlG3LZsLaCXT4ogPtv2jP92u/J8eeQ0xEDJP6TGLX8F08f+XzVAuqZnbUPB9e+yH1K9UnKTWJ+2fcf3oKhYiUChXrUn7VfwBq3wKOXFh8G2QfNzuRiMiFOeerl+PF5X7e9DPZtmyaVW1Gi2ot8j8ZEAEBtY1C9OhycwK6KR+rD1/c+AVeFi8mHdrL7+le0Prd4l00+k2oGG2sAfP3IG2NWoAzh7rfMfUOlu5diq/Vl4EtB7L8vuUsuWcJtze7vdSHuhdGsF8wE2Mn4u3lzZSNU/h69ddmRxIpV1SsS/llscBln0PQJXByl/aMFZGywTlfvWK0qTHM5BwCP6D5gIIbnLmFm+TTtnorhlWtBMCDRwNJq1CreBe0+kHHSae2Rv0TNr/ngpSeYemepdwRd8dZQ91f7foqSSOSGH/TeNrWbGt2zAtqV6sdr3V9DYBHfn+ELYe3mJxIpPxQsS7lm28odPoRvHwgKQ62fmx2IhGR8yvn27btSd3D/F3zAWO+eoGqaN76Oe0czytBh6njYyEx4wTP//V88a8Z2gjajDHO1zwDR1cW/5pllHOoe/sv2tPhyw5MWDeBHHsOHSM78kOfH9g9fDfPXfEcVQOrmh21SJ7o9ARX1b2K9Jx0+v3cj6zcLLMjiZQLKtZFKreF6HeM83/+B0dXmZtHRORc7LmQss44L6fF+qR1k3DgoHPtztQJq1Nwo7wV4RM0LPtMOWmw5lmCvGBch3sB+GDZByzf64LpAvXuhchYsOcY27mVs/UC9p/Yz0vxL1H7/drcMfUOlu1dhq/Vl0EtB7HivhUsvnsxtzW7DR+rj9lRL4qXxYtvb/qWyv6V+efAPzz313NmRxIpF1Ssi4CxZ2xEb7Bnw6LbICfV7EQiImdL3WLsbe0dBMH1zE5jigsOgQdjKzJrAOQch9TNpROsLNj0NmQegKB69LzyQwY0H4DdYefe6feSY8sp3rWdU8sCIuDENlg5zDWZ3dzSPUsZEDeAOqPr8PL8lzl48iA1g2vyWtfXSBqRxDc3fUObmm3MjukStUJq8eWNXwLwbsK7/LH9D5MTiXg+FesiYPyR0f4rY1GitH9h2QOavy4i7idvvnpLsJS/X+EbDm1gzcE1+Hj5cEvTW87d0MsbqrQ3zjUU3nAyCTadWkyu1dtg9eP9Hu9T2b8yaw+uZVTCqOLfw68SxHwPWGDHV7B7cvGv6YaycrP4bs13XPb5ZXT4sgMT100kx55Dp8hO/NDnB3YN28WzVzxb5oa6F0bvRr0Z0nYIAAOnDuTQyUMmJxLxbOXvN73IufhVgk4/gMUKu3+A7V+YnUhEJL9j5Xu+urNX/doG11LJv9L5GzvnrR9WsQ7AmmfBlgHhnSHiZgDCA8N5r4exINxL8S+x7ci24t+n2pXQ9BnjfNn9cHJ38a9ZCmx2G/G74pm0bhLxu+Kx2W1ntdl3Yh8vzHuB2qNrM3DaQJbvW46f1Y/B0YNZef9KFt29qEwPdS+sUd1H0SS8CQdPHuTuX+7Wdm4iJUjFusiZwmOg5RvG+cpH4fg6c/OIiJypHG/bZnfYmbhuInCBIfBOVbQifJ4jy2HXd8Z56/eM0WSn3NniTq655BqybFk8MOMB1xRezV+Eyu0hJwWWDDDWWnBjcZviiBoTRdfxXekf15+u47sSNSaKuE1xOBwOEpIS6P9zf+qMrsOrC17l0MlD1AquxetXvU7SiCS+7v01rWu0NvvLKDX+Pv5M6jMJP6sfM7fN5OPlWpxXpKSoWBf5r8aPQ41rjXmhi24td4vkiIibcjjK9bZtS5KWsDtlN8G+wdxw6Q0XfkGVDsbxxFbIPFyy4dyZwwGrHjPOo+40FlU9g8ViYdz14/D39mfernmu2Ufbywc6TQTvYOPNkg2vF/+aJSRuUxx9J/dlT+qefI/vTd1Ln8l9aPBhAzp+1ZFJ6yeRa8/l8tqXM7nvZHYO28kznZ8hPDDcpOTmalGtBe9cYyzO+/gfj7P24FqTE4l4JhXrIv9l8YKY8eBf01iYaMXDZicSEYH0RMg+BhZvCG1qdppSN2GtMQQ+tnEs/j7+F36BXyUIbWKcH04owWRuLikOkhcZ+6C3LLhovqTiJbzc5WXAKLwOph0s/n2DLoF2nxjn619xyxEONruNYbOG4eDs0QTOx7Yf246vly93Rd/FqvtXsfCuhdzS9BaPH+peGA9f9jC9GvQiy5ZFv5/7kZGTYXYkEY+jYl2kIBXCodMko3DfOR52jDc7kYiUd8791UObgtXP3CylLNuWzeSNxmJlhRoC71Te563bsmD1k8Z548chMPKcTUfEjKBV9VYcyzzGsFnDCjWH+4LqDoCoO4zt85YMgOzjF/d1FIPD4SA9J529qXtZf2g9C3cv5NctvzJ+9XiGzRp2Vo96QX7s+yNf9f6KVjXK3/ST87FYLHzd+2uqB1VnY/JGHv/jcbMjiXgcb7MDiLitqldA85dh7fOw/CGofBmENjY7lYiUV8756pXKX8Ew+9/ZHM04SvWg6lxV96rCv7BKR2Ox0PJarG/9CNJ2gH8NaPzkeZt6e3nzxY1fcNnnl/Hjhh+Zu2MuhzNOTx+ICIlgTM8xxDaOLVqGdh8b3/+0HbDswVNvhFsu/LozOAvuY5nHOJZx7PzHAh7LtmUXLfN/ZOSqx/hcwgPDGX/TeHp834OxK8bSvV53ejfqbXYsEY+hYl3kfJo8DQfj4eBcY/56j2XgXYjhlyIiruacrx4WbWoMMzhXgb+96e1YvayFf2H4qZ71I8vAnmPMpS4vMg/D+leN8xavg0/QBV/SukZrejXoxa9bf81XqIMxh7vv5L5MuXVK0Qp2nxDoOBHHHx05uetHjoW241jVa4pUbB/LOEaOvXj7wFstVir6V6RihYp5x2xbNvN2zbvga2sE1yjWvT1d93rd+V/M/xiVMIq7f72btTXXUiukltmxRDyCinWR8/GyQsfv4fdoSFkPq4bDZZ+anUpEyiNnsV7OetZPZJ3g1y2/AjCgRRGGwAMEXwq+lSD7qDGNoMplJZDQTa1/2ViNvWI01B1YqJfY7DZW7l9Z4HMOHFiw8Ojvj9KqeitSs1KL1sOdAbkOYPvFD5X29vLOV2znHQt67D/HIN8gLP/p0bfZbUSNiWJv6t4C561bsBAREkHn2p0vOnN58cbVbzBv1zxW7V/FwGkDmXPnHLwsmm0rUlwq1kUuxL+6UbD/1R3+/QyqdoWo281OJSLlSdYRSE8yzsNampullE3dPJWM3AwurXwpbWq0KdqLLRZjKPy+GcZQ7PJSrKdshm2nFndrNcp447kQFiYuZO+Jved83oGDvSf2cskHl1x0NG+gorc3FUPqUtG/0lkFdyX/SucsugN9As8quIvD6mVlTM8x9J3cFwuWfAW7BeM+o3uOLtpojnLK1+rLxNiJtP6sNX/t/It3Fr/DyMtHmh1LpMxTsS5SGNW7QdNnYcNrsOw+qNQGQhqYnUpEygvnfPWgS8A31NQopc05BL5/s/4XV6iFdzpdrDPcpdnc1j9PgMMGtW6E6oWf47//xP5CtbNarFQOqFz0Xm5HOoFzOmDJOQ6Nb4ZW/3eRX6DrxDaOZcqtU85abC4iJILRPUcXfY5+OdawSkM+6PkB906/l+fmPcdVda+iXa12ZscSKdNUrIsUVvMXIXkBHFoAi2+D7gnlbkVmETFJ3v7q5WsI/IG0A/y540/gIobAOznnrScvNvYcd2HPrFs68Kfx5oTFG1q9XaSXFnZu9pw759C1bteLSQcdvoSFfWDT21DjGuPNcJPFNo6ld8PeLExcyP4T+6kRXIPOtTurR/0i3N3qbmZvn81PG3+if1x/Vt2/imC/YLNjiZRZmkwiUlhe3tBxIvhVNv5w/kdblIhIKTlaPov1yRsmY3fYuazWZdSvVP/iLlKprVG4Zuw7PZXAU9ltsOp/xnmDhyCkYZFe3rl2ZyJCIvKGgP+XBQuRIZFcUeeKi88YGQv17zfOEwYaC+G5AauXlS5RXejXvB9dorqoUL9IFouFT6//lMiQSP49+i+P/P6I2ZFEyjQV6yJFEVALYr4zzrd+BElx5uYRkfLh+GrjWM6KdecQ+CLtrf5f3gGnv2/JHr6F285v4Pha8AmD5i8U+eXOOdzAWQW7S+dwt34PQhpBxn5Yeo8x4kE8RkX/ikyInYCXxYvxa8Yzad0ksyOJlFkq1kWKqua1p/er/ftuSNtpbh4R8Wy56ZC62TivGG1qlNK07cg2lu1dhtVi5bamtxXvYs6h8IcXFz+Yu8o5AWueNc6bv2CMArsIzjnc/916KyIkoujbtp2Ld6Cx37qXL+z99fRieOIxOtfpzLOdjf8eH5z5ILuO7zI3kEgZpWJd5GK0fA2qxBjb4iy+HWzZZicSEU91fB047FChKviXn/2eJ66bCEC3S7pRLaha8S5WxTlv3YN71jf+H2QehKD60GBosS4V2ziWXcN2MW/QPCbGTmTeoHnsHLbTtYutVYyG6FMLzP3zPzi+3nXXFrfwwpUvEBMRQ2pWKgPiBpBrzzU7kkiZo2LdzdnsNuJ3xTNp3STid8Vjs9vMjiQAXj5Gr4BvRTiyDNY8Y3YiEfFUZy4u5+mLo53icDhcMwTeydmzfnwN5KQV/3ru5mQibB5lnLd6G6y+xb5kqczhbvgo1OgJtkxY3A9yM1x/DzGNt5c3E2InEOIXwpKkJbw6/1WzI4mUOSrW3VjcpjiixkTRdXxX+sf1p+v4rkSNiSJuk+ZJu4XAOtDha+N88yjYO8PcPCLimZzbtpWj+eor9q1g29Ft+Hv7c1Ojm4p/wYAICKhtbGd2dHnxr+du1jxjFLxVr4CIm8xOU3gWL+jwjTFqJGU9rH7S7ETiYnUr1mVcr3EAvLbwNRbuXmhyIpGyRcW6m4rbFEffyX3z7fkJsDd1L30n9/XYgr3MjSSI6A0NhxnnCYPgpIevNCwipS+vZz3a1Bilydmr3rtRb9dt+xTuoUPhDy+DXRMAi7FwW1kbfeFfDTqMN863fqQ3vj1Qv+b9GNRyEHaHnQFxAziWcczsSCJlhop1N2Sz2xg2axgOzl4d1fnY8FnD3b+QLaIyO5Ig+m1ja6Dso7CkH2hOloi4ij3XWN0byk3Peq49lx/W/wC4aAi8k3Pe+mEPKtYdDvjnMeO87kCo1MbcPBerZk9oONw4//suY5V48SgfXvsh9SrWIyk1iQdmPIBDOwCIFIrFUY7/35KamkpoaCgpKSmEhISYHSdP/K54uo7vesF23ep2IzI0El+rb74PP6vf2Y95n/3YudoW1N5qsWIpwXfrnSMJ/vsGhXOrGJetQFtSTmyHWa0hJxWaPA3Rb5idSEQ8wfEN8FszY/XsW1KNYcMe7o/tf9Dj+x5U9q/M/v/tx8fq45oLH10Js9oaa430OewZ38vEKbDoFrD6ww3bjO1FyypbFsxub6wrUP0a6DrLM35Gkmf53uV0/KojufZcvrzxS+5udbfZkURMU9g61LsUM0kh7T9RuHeU/9z5ZwknOc2CpXhFv9e53wzw9vLm1QWvnnMkgQULw2cNp3fD3iWzwI0rBNeD9l/Aolth45tQ9Uqo2cPsVCJS1jnnq4e1LDeFi3MI/K1Nb3VdoQ4Q1gKsAZB9zNgKL7SJ665tBlsW/HNqjnfjJ8t2oQ5g9TMWbp3VBg7Mgc3vQePHzU4lLtSuVjte6/oaT819ikd+f4ROkZ1oWKWh2bFE3JqKdTdUI7hwW/M81PYhaofWJtuWTZYti2xb9lkf53w898Ltz+TAQZYtiyxbVkl8yeflwEFSahILExfSJapLqd+/0GrfAg2GGPvFJtwJ160pV9ssiUgJOHMl+HIgPSc9b+qTS4fAg7GLR+XL4FC8MW+9rBfrWz+EkzuN3zNNnjA7jWuENoY2o2HZA8aiedW6lt2h/VKgJzo9wR87/uCvnX/RP64/S+5egp+3n9mxRNyWinU31Ll2ZyJCItiburfA3mYLFiJCIvjg2g9KrKfZ4XCQa891SdF/3vb2bLYe2cqSpAvPISzsiANTtX7P+CPw+BpYMgC6zgF3HQ0gIu7PWaxXKh/F+vQt00nLTiMqLIqOkR1df4PwTkaxfngJ1L/X9dcvLZnJsP7UNlgt3zCmSXiKevfB/tmQFGds59ZzFfgEmZ1KXMTL4sW3N31Li3EtWLV/Fc/99RzvdH/H7FgibkvFuhuyelkZ03MMfSf3xYIlX8HunMM9uufoEh0SbrFY8LH64GP1IZCS/SOgsHP0y8SCetYKcPlkY/76wXmw4TVo/qLZqUSkLHI4yt22bc4h8P2b9S+ZdVI8ZZG5dS8ba6RUbGUsLOdJLBa47HM4vBRObIOVw6DDl2anEheqFVKLr278ipt+vIl3E96le73uXFPvGrNjibil8jEBrgyKbRzLlFunUCsk/xy0iJAI919srYicIwmcb0Scy33T72P036OxO+yllOwihVwK7T41zte9bBTtIiJFlZ5k7DJh8YbQpmanKXFH0o/w+7+/AzCghYuHwDtV6WAcU7dA5uGSuUdJS9kE/xr7VtN6lGeuZeBXCTp+D1hgx1ewe7LZicTFejfqzZC2QwAYOG0gySeTTU4k4p488F94zxHbOJZdw3Yxb9A8JsZOZN6geewcttOjCnU4PZIAOKtgt5z6X4tqLci0ZTJi9gi6fNOFf4/+a0bUwqs7AC65G3AYw+EzD5mdSETKGucQ+NAmxuJbHu6njT+Ra88luno0TcJLaD65XyUIaWycH/m7ZO5R0v55HBw2iOhtzOn2VNW6QNOnjfNl98PJ3abGEdd7t/u7NAlvwoG0A9z1y13azs3FbHYb8bvimbRuEvG74svGCFU5i4p1N2f1stIlqgv9mvejS1QX910NvZguNJJg9QOrGddrHEG+QSxMXEiLT1rwwdIP3LuXve2Hxh/ZGfthyZ3gzllFxP0cLV+Ly505BL5EhZ8aCp+8uGTvUxL2/wH7fjNGW0S/bXaaktf8JajcHnJSYMkdYM81O5G4UIBPAJP6TMLP6sfMbTP5ePnHZkfyGHGb4ogaE0XX8V3pH9efruO7EjUmKm8BTyk7VKyL2zjfSAKLxcIDbR9g3ZB1XFX3KjJyMxg2axhdx3dlx7EdZkcvmHcAdJps7H974A/YWA7+sBIR1zm+2jiWg2J99/HdLEpchAUL/Zr3K9mbldV563YbrPqfcX7pUGPKlafz8oFOE8E7GJIXwYY3zE4kLtaiWgveucZYYO7xPx5n3cF1Jicq++I2xdF3cl/2pO7J9/je1L30ndxXBXsZo2Jd3MqFRhJEhUUx5845jL1uLIE+gSzYvYDmnzTn42Ufu2cve1hTaPuRcb72OTi0yNw8IlJ25PWsR5saozRMWj8JgCujriQiJKJkbxbeyTgeWQb2nJK9lyvt+ApS1oNvRWj2gtlpSk/QJdBurHG+/uWyOSJCzuvhyx7mugbXkWXLot/P/cjIyTA7Uplls9sYNmtYgbtJOR8bPmu4hsSXISrWpczxsngxpN0Q1g1ZR5eoLqTnpPPw7w9z9bdXs/PYTrPjne2SuyDqDmOO4ZJ+kHXE7EQi4u6yjkB6onFeDop15xB4l++tXpDgS8G3EtgyT6+27+5yThhv+IJRqPtVMjdPaat7B0QNMKaTLRkA2SlmJxIXslgsfN37a6oFVmND8gYe/+NxsyOVWQsTF57Vo34mBw6SUpP4ZcsvpZhKikPFupRZdSvWZe7AuXx07UcE+AQQvyue5p8055Pln7hXL7vFYvQKBF8K6XsgYbCxJZOIyLk4i8igS8A31NQoJW3twbWsP7QeX6svfZv0LfkbWiynh8Inl5Gh8BvfMhYqDW4ADR4yO4052o2FwLrGQnPLH9TvUQ9TNbAq3978LQBjV4zl1y2/mpyobNp3Yl+h2vWZ3IfI9yO5+cebeW3Ba/y+7XcOndRiyO5IxbqUaV4WL4ZeNpS1D67lijpXcDLnJA/99hDXfHcNu47vMjveaT7Bxv7rXn6wbwZsft/sRCLizvL2V482M0WpmLDW6FXv1aAXYRXCSuem4WVo3vrJ3bBplHHe6h2w+pqbxyw+Icb8dYsVdv8AO781O5G4WPd63flfjLEuw92/3M3e1L0mJypbFiUu4rUFrxW6/Z7UPUzbPI3n5z3PdROvo9q71aj9fm1if4zl9QWvM/vf2RxOL6NbXHoQi6Mc75OQmppKaGgoKSkphISEmB1HisnusPPxso8Z+edIMnIzCPIN4t1r3uX+NvdjsZx/D/dSs20cLB9irOR7zWKocpnZiUqf3QbJC41V8v1rQHhn8NBdDkQu2pI7YNcEaPEqNHvO7DQlxu6wEzU6iqTUJKbcMoU+TfqUzo0Pzoe5XcC/FtyUZPS2u6vFA2D3RKjaBa7+y72zlob1rxtTArwD4drVEFzf7ETiQlm5WcR8GcM/B/7hqrpXMefOOXhZ1Ld4PtuObOOpuU/lLRxnwVLgnHXncxEhEax9cC3rDq1jxb4VrNy/khX7VrD1yNYCX1cntA5tarahbY22tKnZhjY12lA5oHKJfk3lQWHrUBXrKtY9zr9H/+WuX+5iUaKxmFu3S7rxxQ1fUCesjsnJMIbtLb4NEn+CwCi49h/wDTM7VelJioOVw4zpAE4BEdBmDETGmpdLxN3MbAopG+HKGVCrl9lpSsz8XfPpMr4LoX6hHHj8ABW8K5TOjXPT4adQcORC790QWLt07ltUh5fCHx0AC/RcAZVam53IfHYb/HU1HJoPldoab3yX19EGHmrL4S20/qw16Tnp/F+3/+PJTk+aHcktHU4/zKvzX2XsirHk2nPxsnhxT6t7aF+rPfdNvw8gX/FtwXijb8qtU4htfPbfXKlZqfyz/5+84n3l/pVsPbK1wHtHhUXRpkYb2tZsS5sabWhTsw2V/MvZWhrFpGK9EFSsey67w84HSz/gmbnPkJGbQbBvMKO6j+Le1vea38uenQKzWkPaDqNA7fgjHF7k+T3NSXGwsC+c9a7tqZ9H5ykq2EUAcjPgpyBjMa2b9kJATbMTlZj7p9/P56s+5+7ou/my95ele/NZl8HR5dBxEkTdXrr3LgyHA+ZcbgzVv2QwdPja7ETu42QS/N4Sso9Bk5EQ/ZbZicTFvlz1JfdOvxdvL2+W3L2EdrXamR3JbWTmZvLh0g95feHrpGQZiy1e1+A63u72Nk2rNgWM7duGzRqWb7G5yJBIRvccXWChfi4pmSn8c+CffD3w/x79t8C2dcPq5hXvbWu2pXWN1lT0r1iMr9SzqVgvBBXrnm/bkW3c9ctdLE4ytnrpXq87X9zwBZGhkeYGO7IC5nQ0tg3yCYOc46ef88SeZrsNfo3K36Oej8X4um/c6ZlvVIgUxeFl8Ed78AuH2IMeO+w5KzeL6qOqczzzOHMHzuWquleVboCVw2HLGLj0EWj7QeneuzASf4JFt4I1AG7YCgG1zE7kXhJ/hkV9AQtcNQeqX212InEhh8PBrVNuZcrGKdSvVJ9V968i2C/Y7Fimsjvs/Lj+R56e+zS7U3YD0LJaS97t/i7dLul2Vnub3cbCxIXsP7GfGsE16Fy781lbIl+M45nH+Wd//gJ++7HtBbatV7Fe3tB5ZwFfamuTuDkV64WgYr18sNltjFk6hmf/epbM3ExC/EJ4r/t73N3qbnN72RPuhp0F9ZS4SU+zww62LLBnnTpm/ufzM49nPPff522ZcGIbJP544XtePQ+qdSnxL03ErW371Fjtunp3uGq22WlKzLTN07j5x5upGVyTxOGJLvkjskh2TzamJVVqYwwxdye2TJjRBE7uhOYvQfMXzU7knpbeD9s/B/+acO0aqFDF7ETiQscyjtFyXEuSUpMYHD2Yr3uX39ElC3Yv4PE/Hmf5vuUA1AquxetXvc4dLe4o/X87C3As4xir9q/KN4R+x7EdBbatX6l+viH0rWu0JrSCZ+96UhAV64WgYr182XJ4C3f9chcJexIA6Fm/J5/f8DkRIRGlH8Zug1+iIOM8Pc3+1Y3i1Z57kYXyBV5T0ONnvs6RW5rfEUPHiRDVr/TvK+JOlj0I/37q8cN7b/npFqZsnML/Yv7Hu93fLf0A6XtgWqSxuvgtKcaCZe5i49uweqRRhN6w1b2yuZPckzCrLaRuhlo3whXTPHYkSnm1cPdCuozvgt1hZ1KfSdzezA2nrJSgLYe3MPLPkXn7ogf5BvFUp6cYETOCAJ8Ak9Od39GMo6zavyqveF+5byU7j+8ssG2DSg3yDaFvVaMVIX5Fq81KaiRBSVGxXggq1ssfm93G+3+/z3N/PUeWLYtQv1De7/E+g6MHl24v+8F4mNu19O7nCl5+YPU741jhP5+feayQ//PMQ5D004XvoZ51EZjdAY4shU4/QJ3bzE5TIlIyU6j2bjWybFmsun8VrWq0MifItNqQnuRe//ZkHoLpDSAnFTp8A5cMMjuRezv6j7EInz3b2Iu9wRCzE4mLvTDvBV5d8CohfiGseXANUWFRZkcqccknk3l5/suMWzEOm8OG1WLl/jb38+KVL1ItqJrZ8S7akfQj+Qr4FftW5A3p/69LK1+av4Cv3uqcUyEKmqMfERLBmJ5jijRHvzSpWC8EFevl1+bDmxk8bTBL9y4FjIU5Prv+M2qFlNKcwF2TYEn/C7fzqgA+QecvhM88Op87VwH938f++7pzFd1ePsXrrcibs76XsxeYOyUgUnPWRew2+CkYbBlw/WYIaWh2ohLx9T9fc/evd9O4SmM2PLTBvClJi243pui0eA2aPWtOhv9a/hBs+wQqtoaey0HbVl3Y5vdh1WPG76weKyCsqdmJxIVy7blc8fUVJOxJoGNkR+YPno+3l7fZsUpERk4GY5aO4Y2Fb3Ai+wQAN1x6A//X7f9oHN7Y5HQl43D6YVbuW5lvCH1iSuJZ7SxYaFilYb4h9K1qtOKP7X/Qd3Lfs7adu9Dq92ZTsV4IKtbLN5vdxqiEUbww74W8XvYxPccwsOXAkv/DsbA96+7U21NceavBQ4EFe9uxcKl6RKScS9lobNvmHQi3pHpsodbt227M3TmX17q+xrNXmFgkb/kQVj4KNa+DLjPNy+F0fAP83sJYM+TqeKh2pdmJygaHHeJ7wf5ZENYceiwzCnfxGDuP7ST602hSs1J58coXeanLS2ZHcim7w86EtRN49q9nSUpNAqB1jda8e827dK1bxkZiukDyyeS8ofMr9q9g5b6Ved+X//L28ibXXvDUTee+8juH7XS7IfEq1gtBxboAbEzeyOBpg/MW7bj+0uv59PpPqRlcgtslXbCn2UNXRy9on3WLLziyIbQpdE8An/K92quUczsnQMIdUKUjdF9sdpoSse/EPiLei8CBgx2P7qBuxbrmhTm60pjz7FsR+hw2/82RedfB/t8h4ma4Is7cLGVNxkHjjY7MQ+67wr8Uy6R1k+gf1x8vixfxg+LpXKez2ZFcYt7OeTw+53FW7V8FGFusvXH1G/RvbnytYjh08pBRvJ8xhH7vib2Feu28QfPoEtWlZAMWUWHrUP0XIOVek/AmLLlnCW9e/Sa+Vl9mbJ1B07FN+W7Nd5TYe1leVmN7NiBv9fc8pz5vM9qzCnUwVre/cZcxYqDjRON44zaoUB1SNkDCQKOHRKS8Or7aOFaMNjNFifph/Q84cNAxsqO5hTpAWAtja7TsY5C6xdws+2YbhbqXD0T/n7lZyiL/atD+1GrhWz+EvW4wUkJcql/zfgxsORC7w86AuAEcyzhmdqRi2ZS8iRsm3cBV317Fqv2rCPEL4a2r32LLw1u4o8UdKtT/o2pgVa5tcC3PX/k8026fxp7H9jD2urGFeu3+E/tLOF3J0X8FIhhDaJ66/ClW3b+KNjXacDzzOAOnDeSmH2/iQNqBkrlpZKyxPdt/984NiDB/27aS5GU1hvZH9TOOgbXhiqng5Qt7psG6l00OKGKio/8Yx4omLbhWCiasmwBA/2aFWLejpHn5QOXLjPPDS8zLYc+Ff/5nnDd4GEIamJelLKt1HTQcZpz/PRgyyu4f6FKwj679iHoV65GUmsSDMx8suU6VEnQw7SBDZgyh+SfNmbF1Bt5e3jzc7mH+feRfRl4+En8ff7MjlhmFncdfI7hGCScpOSrWRc7QtGpT/r73b16/6nV8vHz4dcuvNPm4CRPWTiiZXwgF9jTv9NxC/VyqdIDLPjXO178CiT+bm0fEDA4HHDtVrFfyzGJ98+HNrNq/CqvFyq1NbzU7jiG8o3FMNrFY3/GVMbrItxI0f968HJ4g+v8grCVkHYaEQRqt5WGC/YKZ2Gci3l7eTN4wma9Xl52919Nz0nltwWvU/7A+41Yaq7zf1Ogm1g9Zz4fXfUh4YLjZEcuczrU7ExESkbeY3H9ZsBAZEknn2mV3yoSKdZH/8Pby5pnOz7Dy/pW0rtGaY5nHuGPqHcROjuVg2kHX3/C/Pc2eNvS9sC4ZDA2HG+cJA+HYGjPTiJS+9D2QfRQs3sYaDh5o4rqJAPSo38N9/jCtcqpYP2zSGgE5qbD2VIHe/EVj/rxcPKsfdJoIVn84MMdYKV48ymW1LuPVrq8C8Mjvj7DlsMlTWC7AZrfxzepvuPTDS3l+3vOkZafRrmY7FgxewNTbptKwimfu+lEarF5WxvQ0ppX+t2B3fj6652i3W1yuKFSsi5xD82rN+fuev3m166v4ePkwbfM0mo5tasy3LIPDrsqEVu9A9WvAlg4LekNmstmJREqPs1c9tLFHrmTtcDjyhsAPaD7A5DRnqBJjHFO3QObh0r//hreMRdGCL9Ue4a4S2gRanyrS1zwNR1eZm0dc7omOT9A1qivpOen0j+tPti3b7EgF+nPHn7T5rA13/XIXe0/spU5oHSb1mcTf9/7tMQvkmS22cSxTbp1y1vbLESERbrttW1GoWBc5Dx+rD89d8Rwr7l9BdPVojmQcod/P/ej7U18OnTxkdjzP4+UNnX6AoHpwcjcsugXsOWanEikdxzx7vvrSvUvZcWwHgT6B9G7Y2+w4p/lVgpBT8x6P/F269z65Gza/Z5y3eseYQy+uUf9+Y1V9ew4s7gc5aWYnEheyeln57ubvqORfiVX7V/HsXBO3gCzA+kPruW7CdVzz3TWsObiGUL9Q3rnmHTY/vJnbm92uxeNcLLZxLLuG7WLeoHlMjJ3IvEHz2DlsZ5kv1EHFukihtKjWgmX3LuPlLi/j7eVN3KY4mo5tyuQNk82O5nn8KsEVv4B3EByaDyuHm51IpHR4eLE+Ya3Rq35To5sI9A00Oc1/mDVvffVTYM+Cal2h1g2le29PZ7FA+8/Bvxac2AqrhpudSFysVkgtvrzxSwDeTXiXOdvnmJzIWHX8/un303JcS37/93e8vbwZ1n4Y2x/dzuMdH6eCt+eNmnIXVi8rXaK60K95P7pEdSnTQ9/PpGJdpJB8rD68cOULLL9vOS2qteBw+mFum3Ibt/x0C8knNVzbpcKaQscJgAW2jYV/PzM7kUjJO7baOHpgsZ5jy+HHDT8CbjYE3ilv3nopFuvJCbD7B8ACrUYZxaW4ll9l6PgdYIHtX0LiT2YnEhe7qdFNPNjmQQAGThto2t9jJ7NP8sr8V2jwYQM+X/U5doedPo37sPGhjYzuOZrKAZVNySVlX5GL9QULFnDDDTdQs2ZNLBYL06ZNO6vNpk2buPHGGwkNDSUwMJB27dqRmJiY93xmZiZDhw6lcuXKBAUF0adPHw4ezL9wV2JiIr169SIgIICqVavyxBNPkJubm69NfHw8rVu3xs/Pj/r16/PNN98U9csRKbLo6tEsv285L1zxAt5e3kzZOIWmY5syZeMUs6N5logboYWxgAzLh8KhhebmESlJWUeNIdEAFVuam6UE/LnjT5LTkwkPCOeaeteYHedszmL9yLLSmXrjcMCqx4zzSwZ77Or/bqFaV2jylHG+9H44mXj+9lLmjOoxiibhTTiQdoC7frmrVNcVstltfLnqSxp82IAX41/kZM5J2tdqz6K7FjHl1ik0qKxtGKV4ilysnzx5kpYtW/Lxxx8X+Pz27du5/PLLadSoEfHx8axdu5bnn3+eChVOD/sYMWIE06dP56effmL+/Pns27eP2NjTcwpsNhu9evUiOzubJUuWMH78eL755hteeOGFvDY7d+6kV69edO3aldWrVzN8+HDuvfdeZs+eXdQvSaTIfK2+vNz1ZZbeu5TmVZuTnJ7MLT/dwu1TbudwugkLFHmqps9A7VvBkQsL+5wuZkQ8jbNXPbAu+IaZmaREOBeWu63pbXh7eZucpgAhlxrbptkyTv8sSlLiZGN+vDUAWrxW8vcr71q8DJUvg5zjsGQA2G1mJxIXCvAJYFKfSfhZ/Zi5bSYfLy+4RnG12f/OptWnrbh3+r3sT9tP3bC6TO47mYR7EuhUu1OpZBDPZ3EU4+0ni8XC1KlTuemmm/Ieu/322/Hx8eG7774r8DUpKSmEh4czceJE+vbtC8DmzZtp3LgxCQkJdOjQgd9//53rr7+effv2Ua1aNQDGjRvHyJEjSU5OxtfXl5EjRzJz5kzWr1+f797Hjx9n1qxZhcqfmppKaGgoKSkphISEXOR3Qcq7bFs2r85/lTcXvYnNYaNqYFU+6fWJRyxq4RZyT8Kcy40/oCtGwzWLwNvN5ruKFNemUfDP4xAZC51/NjuNS53MPkm1d6txMuckCfck0CGig9mRChZ/PeybCa1HQ6NhJXcfWybMaGS8+dj8ZWj+woVfI8V3Yjv8Hg25adD8Fe1n74E+WPoBw2YNw8/qx/L7ltO8WvMSuc/ag2t5Ys4T/LH9DwAqVqjI81c8z0PtHsLP269E7imep7B1qEvnrNvtdmbOnMmll15Kjx49qFq1Ku3bt883VH7lypXk5OTQrVu3vMcaNWpE7dq1SUhIACAhIYHmzZvnFeoAPXr0IDU1lQ0bNuS1OfMazjbOaxQkKyuL1NTUfB8ixeVr9eXVq15l6b1LaRrelEMnD9Fnch/6/9yfI+lHzI5X9nkHGgvO+YUbBfvfdxlDSEU8ibM3NyzazBQl4pctv3Ay5yT1Ktajfa32Zsc5t/BTPWElPW99yxijUPevBY0fL9l7yWnB9aDdWON8/culv5iglLhHLnuE6xpcR5Yti34/9yMjJ8Ol19+bupd7frmH6HHR/LH9D3y8fHisw2P8++i/jIgZoUJdSoRLi/VDhw6RlpbGW2+9Rc+ePfnjjz+4+eabiY2NZf78+QAcOHAAX19fwsLC8r22WrVqHDhwIK/NmYW683nnc+drk5qaSkZGwf/nfPPNNwkNDc37iIyMLPbXLOLUpmYbVt6/kmcufwYvixeT1k+i6dimTNs8zexoZV9gbaO30eJtLBC08U2zE4m4lnMleA+cu+wcAt+/eX8s7ryIWmksMpd5CNa/bpxHvwneASV3Lzlb3TshagA4bMZw+OwUsxOJC1ksFr7u/TXVAquxIXkDj//hmjfDTmSd4IV5L9DgwwZ8tforHDi4temtbH54M6N6jKKSfyWX3EekIC7vWQfo3bs3I0aMIDo6mqeeeorrr7+ecePGufJWF+Xpp58mJSUl7yMpKcnsSOJh/Lz9eP3q1/n7nr9pEt6EgycPcvOPN3NH3B0czThqdryyrWpnaPuRcb7mOdgz3dw8Iq6SmwGpm41zD1sJPvlkMrP/NdaScctV4M9UuR1YrJC+B06W0N8Ha1+E3BNQqY1RNErpa/uxsTbEyV2w/EGN1PIwVQOrMv6m8QCMXTGWX7f8etHXyrXn8tnKz2jwYQNeXfAqGbkZdIrsRMI9CfzY90cuqXiJq2KLnJNLi/UqVarg7e1NkyZN8j3euHHjvNXgq1evTnZ2NsePH8/X5uDBg1SvXj2vzX9Xh3d+fqE2ISEh+Pv7F5jPz8+PkJCQfB8iJaFdrXasvH8lT3V6Ci+LFxPWTaDp2KbF+qUhQIMHoMEQwGH0iqRsNDuRSPGlrDd6+vzCwb+m2WlcavKGydgcNtrUaEPDKg3NjnN+3gGn3ywpid714xtg+6ltKFu/BxbtnmsK31Bja1CL1dg6b2fBayxJ2dWjfg8e62DstnD3L3ez78S+Ir3e4XDw27bfaDmuJQ/MeICDJw9Sv1J9fr71ZxbetdB9190Qj+TS3xS+vr60a9eOLVu25Ht869at1KlTB4A2bdrg4+PD3Llz857fsmULiYmJxMTEABATE8O6des4dOhQXps5c+YQEhKS90ZATExMvms42zivIWK2Ct4VeLPbmyy5ewmNqjTiQNoBev/Qm4FTB3Is41heO5vdRvyueCatm0T8rnhsWqX2/NqMgapXGr1T8280trwSKcucQ+ArRnvcXtvOIfBu36vu5BwKXxLzmf95HBx2YxHBqle4/vpSeOEx0Pwl43zFUDjxr6lxxPXeuPoNWlVvxZGMIwycOhC7w16o160+sJprvruGXhN7sTF5I5X8KzGm5xg2PLSB2Max7j2VRzxSkYv1tLQ0Vq9ezerVqwFjC7XVq1fn9Zw/8cQT/Pjjj3z++ef8+++/fPTRR0yfPp2HHnoIgNDQUO655x4ee+wx5s2bx8qVK7nrrruIiYmhQwfjnaru3bvTpEkT7rzzTtasWcPs2bN57rnnGDp0KH5+xuINDz74IDt27ODJJ59k8+bNjB07lsmTJzNixAhXfF9EXKZ9RHv+eeAfnuz4JF4WL75b+x1NxzZlxtYZxG2KI2pMFF3Hd6V/XH+6ju9K1Jgo4jbFmR3bfXn5wOU/QWAdSNsOi28De67ZqUQu3lFnse5ZQ+B3HNtBwp4ELFi4vdntZscpnHDnvPXFrr3uvlmwf5bx71f0/7n22nJxmjxtvGmSmwaL+0FOBhyMh12TjKPeOC/T/Lz9mNRnEgE+AczdOZd3l7x73s6RPal7GDxtMK0/bc3cnXPxtfryRMcn2P7odh5t/yi+Vl8Tvxopz4q8dVt8fDxdu3Y96/FBgwbxzTffAPDVV1/x5ptvsmfPHho2bMjLL79M796989pmZmbyv//9j0mTJpGVlUWPHj0YO3Zs3hB3gN27dzNkyBDi4+MJDAxk0KBBvPXWW3h7n96fNT4+nhEjRrBx40YiIiJ4/vnnGTx4cKG/Fm3dJqXt7z1/M3jaYLYc2XLONhaMd22n3DpF27+dz7E18EdHsKVDw+HQ5n2zE4lcnNkd4MhS6DgJospIUVsIry14jefnPc/Vda/mz4F/mh2ncNL3wLRIY4j0LSmu2SbSnmtsGZayARo9Bq1HFf+a4honk+C3Fsb+697Bxogtp4AIYyRXpH4Pl2VfrPqC+6bfh5fFiyoBVTh08vSo3YiQCN68+k02H97MqIRRZOZmAsZimK9f9TpRYVEmpZbyoLB1aLH2WS/rVKyLGTJyMnj2r2d5/+9zF5cWLESERLBz2E6sXtZSTFfGJP4Mi/oa5x2+hksGmxpHpMjsNvgpGGwZ0GsThDYyO5FLOBwOmoxtwubDm/nqxq+4q9VdZkcqvGm1IT0Jrp4H1boU/3rbxsHyIeBbCW78F3wrFv+a4jqrnoDN7xbwxKnhzp2nqGAvwxwOB52+6kTCnnNv7ex0RZ0rePead2lXq10pJJPyzpR91kXkwvx9/Lmx4Y3nbePAQVJqEgsTF5ZSqjKqdh9o9oJxvuwBOPy3uXlEiurEVqNQtwZAcAOz07jM6gOr2Xx4M35Wv7I3QsiVW7hlp8DaU/9GNX9Jhbq7sdsg8YdzPHmqL2vlcA2JL8PsDju7U3aft423lzc/3/Iz8YPiVaiL21GxLmKC/Sf2u7Rdudb8RYi4CezZsOBmSN9rdiKRwstbXK4leNAoGufCcjc0vIHQCqEmpymicBcuMrfxTchKhuBLocGDxb+euFbyQmPqwzk5jFEWyXrjvKxamLjwgqvB59pzqRRQSYvHiVtSsS5ighrBNVzarlyzeEHMtxDaFDIPGAV7bobZqUQK55jnLS5ns9uYtH4SUIZWgT9TXs96grF6+8VK2wmbT013avWusbicuJeMQr4hXth24nbUOSJlnYp1ERN0rt2ZiJCIvMXkChLkG0SHWtrLs1B8guHKX405oUeXw7L7ofwuxyFlybHVxrFitJkpXGr+7vnsO7GPsAphXFv/WrPjFF3Flsa0hOyjkHruxUAvaPXTxoifaldBretdl09cx7+Qb4gXtp24HXWOSFmnYl3EBFYvK2N6jgE4Z8Gelp3GVd9eRVJKUmlGK7uCLjG2dLNYYdf3sFkrLoubczg8smd9wlpjCPwtTW7Bz9vP5DQXwcsHKl9mnF/svPXkBEj8EbBA6/dAw2vdU3hnY9X3c75xboGASKOdlEkX6hyxYCEyJJLOtfUzFvekYl3EJLGNY5ly6xRqhdTK93hkSCQjO40k1C+UhD0JtPq0FbP/nW1SyjKm+lXQ+tSw09Ujjb2NRdxV+h7IOmK8wRTWzOw0LpGZm8mUTVOAMjoE3qk489YdDlg1wjivd7fRUy/uyctqbM8GFFywO6Dlmx61nkR5c77OEefno3uO1s474rZUrIuYKLZxLLuG7WLeoHlMjJ3IvEHz2DlsJ291e4tVD6yiVfVWHMk4wrUTruXFeS9i04q0F3bpw1DvHmOu6eLbIXWr2YlECuYcAh/aBKwVTI3iKjO3ziQ1K9XoqapThnuqirMi/O4f4chSY4/2Fq+6Npe4XmSssT1bQK3/PHHqT+S9v2paVRl3rs6RiJAIptw6peztWCHlirfZAUTKO6uXlS5RXc56/JKKl7DkniUMnzWcT1d+yisLXmHJniVMiJ1A1cCqpR+0rLBYoO3HkLLJ+EN7wY3QfSn4lrEVqcXzOYfAh0WbGsOVnKvA92vWDy9LGe4PqHJqvZDUzcboB7/KhXtdboYxqgegyVOa61xWRMZCrd7Gqu8Z+0/93CzwVzdInAyV2kKTJ8xOKcUQ2ziW3g17szBxIftP7KdGcA061+6sHnVxe2X4N6mI56vgXYFx14/j+5u/J8AngD93/EmrT1uxKHGR2dHcm9UPOv9szEVM3QJL+mufXHE/zmK9kmfMVz+WcYyZ22YCMKBFGR4CD0ZxHtLIOD/8d+Fft2UMpCca//Y0eqxksknJ8LJCtS4Q1c84Vrvy9BD5NU/BgT/NTCcu4Owc6de8H12iuqhQlzJBxbpIGTCgxQCW37ecxlUas+/EPrp804V3Fr+DQ0Pzzs2/OlwxzRhevO83WPus2YlE8vOwxeV+3vQz2bZsmlVtRotqLcyOU3zOofDJiwvXPuMgbHjDOG/5JngHlEwuKT0NhsAlg41pVYtug7RdZicSkXJGxbpIGdEkvAnL7ltG/+b9sTlsPPnnk9z8480czzxudjT3VakNtP/KON/4f7Brorl5RJyyj8HJ3ca5hyxA5hwCX6YXljtTeCfjWNh56+tehNwTxpDpqP4ll0tKj8UC7T4xfqbZR2HhzZCbbnYqESlHVKyLlCFBvkF8f/P3fNLrE3ytvvyy5Rdaf9qalftWmh3NfUX1gyan5pAuvQeOrDA3jwicXlwuMAp8K5qZxCX2pO5h/q75gDFf3SM4e9aPLAN7zvnbHl8H2z83zlu/B2V5vr7kZ61gTKvyq2L8/3bZA1pwTkRKjX6biJQxFouFB9s+yJK7l1A3rC47j++k41cdGbdinIbFn0uL16FmL7BlGj0jGQfMTiTl3VHPGgI/ad0kHDjoXLszdcLqmB3HNUIuBd9KYMuAY2vO3c7hgFX/M4ZKR/aBqmV4FXwpWGBtuHyysc3iru9h64dmJxKRckLFukgZ1aZmG1bev5IbG95Iti2bITOHcOfUO0nLTjM7mvvxskLHCcaCUel7YGEfsGWZnUrKMw+br+5xQ+DB6B2vEmOcn28o/P5ZcGAOePlA9P+VTjYpfdW6Qqt3jPNVj8HB+ebmEZFyQcW6SBlW0b8i026bxtvd3sZqsTJh3QQu+/wyNiZvNDua+/ENhSt+AZ9Q4w/vFUM1lFHM4xwGXzHazBQuseHQBtYcXIOPlw99m/Q1O45rhTsXmTtHsW7PNXrVAS59FILrlU4uMUfD4VCnPzhssOgWOJlkdiIR8XAq1kXKOIvFwhOdnmDeoHnUCKrBpsObaPd5OyasnWB2NPcTcil0+sHoMdv+JWz9yOxEUh7lZkDqJuPcA7Ztc/aq96zfk8oBhdyPvKxwzls/fI4V4bd/bvws/SpDs+dKL5eYw2KB9p9DWEvISj41SivT7FQi4sFUrIt4iM51OrP6wdVcXfdq0nPSuWPqHTw440Eyc/WHRD41e54eqrpqBByYa24eKX9S1hs9c35VwL+W2WmKxe6wM3GdscuCRw2Bd6p8mTFPOX3P2b2o2Smw9gXjvPnL4BtW6vHEBN4BcMVUYz2Do8thuUZpiUjJUbEu4kGqBlZl9h2zef6K57Fg4dOVn9Lpq07sOLbD7GjupdH/IOqOU0MZb4U0fX+kFOUNgW9l9NSVYQlJCexO2U2QbxA3NLzB7Diu5x1wel2B/85b3/AGZB021sKof3/pZxPzBNU9PUprx1fw76dmJxIRD6ViXcTDWL2svNL1FX4f8DuV/Suzav8qWn/aml82/2J2NPdhscBln0GldsbeufNvhJwTZqeS8iJvcbloU2O4gnMIfGzjWAJ8AkxOU0KqFDBvPW0nbBltnLd611hcTsqXGtdAyzeM85WPnntdAxGRYlCxLuKhetTvwT8P/ENMRAwpWSnc9ONNPPHHE+TYLrBfcHnh7W8MZfSvASkbIGGgsfWSSEnzgG3bbHYbf27/k+/WfAfA7U1vNzlRCXIuMndmz/rqp8CeDdW7Qc3rzMkl5mv8JET2BXuOMX89fZ/ZiUTEw6hYF/FgkaGRxA+OZ0SHEQC8m/AuV317FXtT95qczE0E1ILOceDlC3umwbqXzU4kns5ug+NrjfMyWqzHbYojakwU13x/DWk5xlaR90+/n7hNcSYnKyHOnvVj/0DuSaMHNXEyYIFWo8r8VAYpBosFOnwNoU0h84CxQrwt2+xUIuJBVKyLeDhfqy/v9XiPKbdMIcQvhEWJi2j1aSv+3PGn2dHcQ5UOxpB4gPWvQOLP5uYRz3ZiG9jSwRoAwQ3MTlNkcZvi6Du5L3tS9+R7fO+JvfSd3NczC/bASGMhQIcNNrwJS+8xHq93D1RsYW42MZ9PEHSeenpb0FXDzU4kIh5ExbpIOdGnSR9W3r+SltVakpyeTPfvuvPK/Fewa+g3XDLI2D8XjOHwx9aYGkc8mHO+elgL8LKam6WIbHYbw2YNw8HZK187Hxs+azg2u620o5WspDhjbQuADa9D6mbAApU7mBpL3EhIA+g4AbDAtk9g+1dmJxIRD6FiXaQcqV+pPgn3JHBvq3tx4ODF+Be5dsK1JJ9MNjua+Vq9A9WvMXo9F/SGTNd9T2x2G/G74pm0bhLxu+I9r5iRwnMW626+v3qOLYfNhzczddNUXl/wOnfE3UGjjxqd1aN+JgcOklKTWJi4sBSTlrCkOFjYF2wZ/3nCAcvuM54XAajVy9jCD2D5EDi8zNw8IuIRvM0OICKly9/Hn89v/JzLa1/OkJlD+GP7H7T6tBWTb5lMx8iOZsczj5e3sRXP7Msgbbsx9/CqOcVe5TluUxzDZg3LV+REhEQwpucYYhvHFje1lDXH3GtxuczcTLYc3sKmw5vYmLwx77jtyDZy7Be3GOX+E/tdnNIkdhusHAYFjCTIs3I41Opd5kZJSAlp9iwcWwl7foGFsdBzJfhXMzuViJRhFofDcZ7fQp4tNTWV0NBQUlJSCAkJMTuOSKlbd3AdfX/qy9YjW/H28ubtbm8zvMNwLOV5waSUjTC7A+SegAYPQbuPL/pSzvm9/x02bMH4/k65dYoK9vLE4YC4qsbe3D2WQeV2pXbrE1kn2Hx4MxuTN+Yrynce33nOqTCBPoE0Dm9M4yqNaRLehFx7Ls/Pe/6C95o3aB5dorq4+CswwcF4mNv1wu2ungfVupR0GikrclKNN31Tt0DVK+CqP7W1n4icpbB1qIp1FetSzp3IOsF90+/jxw0/AsZ+yV/d+BWhFUJNTmaiPdONofA44LJPof79Rb6EzW4jakzUOYcNW7AQERLBzmE7sapXrnxI3wPTIsFihVvTwFrB5bc4kn7kdC958iY2HjaOSalJ53xNxQoVaRzemCZVmhjH8CY0rtKYyNBIvCynZ8s5/5vem7q3wHnrHvff9K5JsKT/hdt1nAhR/Uo+j5QdKZuNgj33BDQcBm1Gm51IRNxMYetQDYMXKeeC/YKZ1GcSnWt3ZsTsEcRtimPNgTVMuXUK0dWjzY5njogboOVrsOZZWD4UQhpD1c5FusTCxIWFnt/rEb2QcmHO/dVDGherUHc4HOxP228U42f0km86vIlDJw+d83XVg6rn9ZLnHcMbUy2wWqFG01i9rIzpOYa+k/tiwZKvYHeOFhndc7RnFOoA/jVc207Kj9BGEPMtLLwZtoyBSm2h7h1mpxKRMkjFuohgsVgYetlQ2tVqx60/3cr2Y9vp8EUHPrruI+5pdU/5HBbf5Gk4thYSf4SFfaDncgisc8GXpWWnMfvf2bz/9/uFus2+E/uKm1RMZrPbWJi4kP0n9lMjuAada3cuuGA9tto4FnK+ut1hZ/fx3QX2lKdkpZzzdXVC6xTYU17Rv+JFfHX5xTaOZcqtUwpch2F0z9GeNa0jvDMERED6Xgqet24xng8v2ht5Uk5E3gRNn4MNrxmLEYY2dfuFJUXE/WgYvIbBi+RzNOMoA6cOZOa2mQAMbDmQsdeNJdA30ORkJsg9CXMuN4qsitFwzSLwPvv7cDj9MNO3TGfq5qnM2TGHzNzMQt8iMiSSe1vfS79m/WhQueztu13eFWkBwQWxsGcqtBoFjR/LezjHlsP2Y9vP6inffHgzGbn/XYXc4GXxon6l+mf1lDes0pAg36AS+VrPVOg3KMo652rwQP6C/dQbmJ2nQKQHvUEhrmW3wfwbYP/vxpu9PVZAhSpmpxIRN6A564WgYl2kYHaHnbcXv82zfz2L3WGnaXhTptw6hUZVGpkdrfSdTIRZbSErGWrfaqwYb7Gw6/gupm2exrTN01iYuDDfIl31Ktajd8PefLf2Ow6nHy5wfm9B2tZsS/9m/bmt2W3UDK5ZUl+RuEhRFxDMnFqHrccT2Xjpc2zK9srrJd96ZOs5V173tfrSsHLDs3rKG1RqgJ+3X8l9cXJaUpyxKnz6GdNaAiKNecgq1OVCso/9f3v3Hhdlnfd//DUcFRAQRQ6CopmiqWh4TlKT1cw8YGZpp9u677Zdu8vt8Gj77abtsba2tsN259ZuubWhaYmZWYbg+ZQSJpiaB0wrwAMCoshprt8fl0xOgAwKzMC8n4/HPJi5ru9c12e4rscFn/le388XPhtszjISNhbGfGbOPiIibk3JugOUrItc2voj67n9w9vJK8kjwCeANye9ye19b3d2WM3v+EaMtBvIPl9JSsBYlhcUkJmXaddkYPhAkmKTmBo7lb6d+mKxWGzJHFDr+N53kt7BMAySs5NJPZRKlVFlWz86ZjSz+s3ilt63NMrty9K46isgCBDSNoT7Bt7HvpP72HtiD4dPH6b2uutm5fXYjrE1xpN3b98dL/1j73zWKjixEUpzzTHqoQmark0cV5gNq4dC1Tno/TgMfM7ZEYmIkylZd4CSdZH65ZXkMfPDmaw7sg6AOYPn8MK4F9yiV6/KWsW277aRsi+F5VnvcKjkhG2dh8WDhC4JtgS9a3Dt49lru006OjC6xvjeE2dPsPTrpSRnJbP52Gbbcm8Pb266+iZm9p3JpF6T8PP2a4JPKg217sg6xvzbgWm9fqK9pwe9I4fVW3ldRFqZb5fA5tvM59e9D11nODceEXEqJesOULIu4phKayXz187nz5v+DMDgyMEsuXUJMcExzg2sCZRVlpGek07KvhRW7F9B/tl82zpfD0/Gta0iKbANN09KJTRipEPbbOj43iOFR1icvZhF2YvYnb/btjzAJ4CpsVOZ2XcmP+v+M7w9NXdvczMMg+zj2Ty3+Tn+k/Wfetv/rPvPmNJrCn1K99D729cJ6zoFy6jlTR+oiLiezCdg73Pg6Qfjt0FwP2dHJCJOomTdAUrWRRpm1YFV3JVyFwWlBbRv0553kt7h5p43OzusK1ZcVsynBz4lZV8Kqw6s4kz5Gdu6IN8gbu55M0mxSYzvfgMBm6bB8XUQcBWM/wJ8Q5o0tuzj2SzKWsSi7EXkFObYlndo24EZ18xgVr9ZjIgeoV7ZJnS06ChrDq8hLSeNtMNpdl/g1GftPWvNqfm23A1H3oV+v4N+85ouWBFxXdZKWDcB8tZAQHe4cSf4aJiTiDtSsu4AJesiDfdt4bfM+GAGX3z/BQC/vu7X/OGGP7S4cbX5Jfms2L+ClH0ppOWkUV5VblsXERDB1NipJMUmMSpmFD6ePj++8fxJWD0Izn4L4Ykw+tNmKRZkGAbbv99OclYy7+95324+7ejAaGb2ncmsfrPoH9bfPafaa0SnS0+z9sha1hxew5rDazhQcMBufVuvtiR0SWD799spLiuutYCgBQtRgVHkPJxj3kXxST8oyobrV0DUpOb6KCLiaspOmUVLzx6BiAkw6mPVPxBxQ0rWHaBkXeTylFeV89jnj/HqF68CMKrrKBbdsoiIdhFOjuzSDhUcYvm+5aTsS2HLsS12SVbPDj1Jik0iKTaJwZ0HX7qn+vRX8PkIs1hQr7kQ79ic6o2l0lrJ2py1JGcn8+HXH9rdCdAntA8z+85kZt+ZXBVyVbPG1VKVVpSy+dhm0g6nsSZnDRk/ZNidG54WTwZ3Hkxit0QSuycyLGoYvl6+9RYQtFWDrzoPSwLAqIIpR8E/unk/oIi4loJMSB1hXhuu+Q3E/dHZEYlIM1Oy7gAl6yJXZsmeJdy34j5KyksI8w9j0S2LGNOt4UW3mophGOzK22VL0LOOZ9mtHxQ5yJagx3aMbViP9NEPYdOF+ZeHvQ3d/6vxAm+A0opSVh1YRXJ2Mp988wllVWW2dUM7D2Vm35nc1vc2wgPCnRKfK6qyVvFl7pek5aSx5vAaNh3dZPd7A+jdsTeJ3c3kfFTXUQS1Cap1Ww4VEDy1E1YPBt8OMO0E6M4HEcn5D2y9y3yesAyik5wbj4g0KyXrDlCyLnLlvjn1DdOXTCfreBYeFg9+P/r3PJnwpNPGUFdZq9h0dJM5B/r+5RwpPGJb52nxZFTMKJJik5jSawrRQVfYw7l7PmT/Hjx8IHE9dBx2Zdu7QkXni0jZl0JyVjJpOWm2ud89LB7c0O0GZvWdRVLvJILbBDs1zuZmGAYHCg7Yes7Tc9IpPF9o1yayXaSZnHdLZGz3sQ2a577eAoIH34Qv7jeHTdyQ2kifSkRavIy5sP9l8Aowa6AE9XZ2RCLSTJSsO0DJukjjOFdxjjmr5rBw10IAJvSYwLtJ79LBr0Oz7P985XlSD6WyfN9yVnyzgpPnTtrWtfVqy/ge40mKTeLmnjcT0rYRC8IZVtg4Hb5LgTbhZrEgv86Nt/0rkF+Sz5I9S0jOTmbbd9tsy308fZh49URm9ZvFxKsn0ta7rROjbDp5JXmk56Tbxp0fKz5mtz7QN5AxMWNsvee9OvRqurH+O+bAgf/T/MoiYs9aAek/g+PrIbAXjNsOPrXfxSMirYuSdQcoWRdpXG9lvsWcVXM4X3meLkFdWDJ9CUOjhjbJvgrPF/LJN5+wfP9yPj3wKWcrztrWtW/Tnkm9JpEUm8S4q8Y17dzkFSXw+XCzeFjIYLOH3cu1EuDDpw+zOHsx72W9x9cnvrYtb+fTjqTeSczqO4ux3cc2XpFAaxWc2AiludA2AkITmryA0pmyM2z4doOZnOesIft4tt16H08fRkSPsI07j4+Mb76iiJ+PgJNbYcR7EDOrefYpIi3D+ePwWTyc+w46T4brU0Cze4i0ekrWHaBkXaTxfZX3FdOXTudgwUG8Pbz567i/8r9D/rdRei1/OPMDH+37iOX7l5Oek06ltdK2Liowiqm9ppLUO4mELgnNOwd5yWH4bDCUF0DMnTD8HZccl2wYBlnHs0jOSmZR9iKOFh21rQv1C7VNBTc8avjlH69jyyDjYfMfz2p+URD/MkRPu8JP8KOKqgq2f7/d1nO+/fvtducDwMDwgSR2T2Rst7GM7DISfx//Rtu/w6xVsDTQLEY48Wvd5ioiNZ3aAakJYC2Dfr+Hfk85OyIRaWJK1h2gZF2kaRSdL+K+Fffx4d4PAbi1z638c/I/CfQNrH987098c+obUvamsHz/crvbucGsfF6doMdHxDt3yrK8dFg7zqz4PfB56P2Y82JxgNWwsvXYVpKzklny9RK7oQMxwTG2ivL9wvo5vtFjy8xhATWmMrtwXBI+uOyEvfqLhupx5+uPrLe7mwKge/vutp7zMd3G0NGv42Xtq1EV7YNPeoNnW7j1jKZoEpHaHXobtt8LWGDUCuh8s7MjEpEmpGTdAUrWRZqOYRi8sv0VHkt9jEprJVeHXM0Dgx7gb9v+Zlc5OyowipdvfNlWOdswDDJyM0jZm0LKvhT2ntxrt91hUcNsCXrPDj2b9TPVa/+rkPGQeQvjqE8g8kZnR+SQiqoK0nLSSM5KJmVfCiXlJbZ1fTv1ZVbfWdze93a6te9W90asVbAixr5H3Y7F7GGfnONwwvpt4be2iu1pOWl2c8sDdPTryNhuY22955eMz1mOLIYtM6HDUBi/rf72IuK+dvwSDrwO3kEwfgcEXu3siESkiShZd4CSdZGmt+27bcxYOqNGga9q1XNSzx81n5PnTrJ8/3K7ZN7Lw4sbut3A1F5TmRI7pUFVupudYcAX/wOH/nXhn60vINDFvlCox7mKc6z8ZiWLshex6sAqyqvKbeuGRw1nVr9Z3NrnVsICwswCe+Wn4fwJyP0cvnwYgCoDNpZCbhVEeEJCW/Csvulh7FoIG13rvgtKC1ibs9Y27vxgwUG79X7eflzf9Xpbxfb+Yf2dNuuAwzKfgL3PQY8HYMjrzo5GRFxZVTmkjYGTWyCoD4zbBt7tnB2ViDQBJesOULIu0jzyS/Lp+lLXGnNZ18Xf258JV09gaq+pTOw5sWVNNVZVBmk3mP9sVVf39Qpo9oJrl8VaCWWnoOwElJ3gdFEOyw6lsShnK+kncmw3t3sCYwN8mOVfQZK/QeBFH2VZCTx8Ar67aPh4lBe8HArTAoARyRAzEzDniN98bLNt3PmXuV9iXHQLvafFkyGdh9h6zodFDcPXy7fJfw2NKn0c5KXCkH9Aj/udHY2IuLrSXLPgXGkuRE+HkUtcsgaKiFwZJesOULIu0jzWHVnHmH+PqbfdhKsm8IvBvyCxe2LLnlKsNB9WDzJvCW9/rVntt7RpC67Vqqrclnhz/ic/a3tefpqa481NP1TCkjOQfAZ2XPSdi68Fbg7wYlb7dpSXnWZWfp0j1lkSDl2Hv8aaoiLW5Kxh89HNNb7A6RPax9ZzPqrrKILatOBpjAwDlnWCspPmXRYdBjs7IhFpCU5sgbTR5tRuA56FPk84OyIRaWRK1h2gZF2keSzKWsSsZfVPWZU8LZmZ/WY2Q0TNoCDDnNLNWlHLysssuFZ5rv7E++JlFcWXEbgFfEPAN9R8tAmt8fxgWTmLjnzBe9+sZn/BIbtPdak/KLWt79yus63nfGz3sa49zKGhzn0Py6PA4mkWl3OxKf1ExIUdWAA7fmHWQBn9KUSMc3ZEItKIHM1Dm2mSWRFxZxHtIhq1XYsQPAC82pnTudVgABbY+RAE94OygvqT7/PHzem/GsriCb4d60y8ayzzCYF65h/vATzV6x5+O85gV94uFmUv4u1db9tVlK+NAfhb4Gd+MDY0hsShT9Or193OreLflE5nmj8DY5Woi0jD9Pg5FOw0a6Bsvh1u3AkB3Z0dlYg0MyXrItLkErokEBUYxffF39uNSa5mwUJUYBQJXRKcEF0TObGxjkS9mgGl38PHDSxA5+ENvp0cS7x9Q8En2OyZaQIWi4WBEQMZGDGQuPA47lx2Z73vWTDwZu48lwZVR+DL/4If/gP9/wQdhzRJjE5VcCFZbz/QuXGISMtjscCgv8Pp3VCwAzZMg3FbwMvP2ZGJSDNSsi4iTc7Tw5OXb3yZ6UumY8Fil7BXV4N/6caXLjnfeotTmutYO4u3WXTO0eTbO9Aliw11btfZoXZR/R6FsDcg+09w6A3IW2M+oqZC/z9AcN+mDbQ5Fe4yfypZF5HL4dkGEj40a6AUfgXb/wdG/Mcl/waISNPQmHWNWRdpNsv2LuPhzx62m5otOjCal258yTbPequRv86cgqc+l5jKrCWpslYR83JMvXdP5Dyc8+OXMiU5kPU7OPKuOQ0cFoi5A/o9De2uatb4m8RH3eFsDtyQBuE3ODsaEWmpjm+AtLFgVMK1L0Lsr5wdkYhcIRWYc4CSdZHmV2WtYuPRjeSeySWiXQQJXRJaV496NWsVrIgxi4zVWnbNYlaFn5zjmtO4XYZle5cxfcl0gFrvnvhgxge1fylT9DXsngfHPrzwBi+46r+h72/Bz7Eee5dTXggftDef33LKLNonInK59r8CGQ+bdUhuSIUwB74MFhGX5Wge2jQDGUVE6uDp4cnomNHM7DeT0TGjW2eiDmYCHv/yhRc/vWXxwuv4l1pNog4wrfc0PpjxAZ0D7RPsqMCouhN1gKA+ZmX88TsgYrzZe3RwAXzcAzIfh/OXLlznkk7vMn/6d1WiLiJXruf/QsxdYFTBphlw9qizIxKRZqCedfWsi0hTOrbM7A05d/E869Fmot7U86w7yRXfPZG/Hnb/Bk5sNl97tYPej5q3fnq3kGv1vpfgy19B1BS4frmzoxGR1qCyFFKvM2eaCImHxI2aaUKkhdJt8A5Qsi4izcJaZVaHL801i8mFJrSqHvUmYRjww6dm0l7dS+3bAfo8CVf/0vX/Qd16D+S8Y46/7zff2dGISGtRcsQsOFd2CrrdA8PeVsE5kRZIt8GLiLgKD0+ziFzMTPOnEvX6WSzQ+Sa4MQOuex/a9TT/Oc18zLw9/sA/wFrh7CjrdlrTtolIEwiIgesWm1Ny5vwbDvyfsyMSkSakZF1ERFyXxQO6zoCJe2Dov8whBKU/wI4HYGVvyHnPvHPBlVSdN4vmgZJ1EWl84Ykw4C/m84y5cHyTU8MRkaajZF1ERFyfhxdcdS9MOmAW7mvTCUoOwdY74dMB8N1H5q3zrqBoj1kEyifErPgvItLYYh+FLreZBTk3Tb8w84iItDZK1kVEpOXw9IVeD8GkQxD3Z/AOhqJs2DAVPh8OeenOjhAKLroFXmNJRaQpWCww7F8Q3A/O58PG6VBV5uyoRKSRKVkXEZGWxzsArnkSphw2i855+sGp7ZA+FtIS4eR258VWPV49RLfAi0gT8vKHhBTzS8tT2yDjIWdHJCKNTMm6iIi0XD7tYcCfYfIhcx5iD2/IT4PPh5m97YVZzR9TdfV6jVcXkabW7iq4bhFggYNvwME3nR2RiDQiJesiItLytQ2HQa+YY9q7zzYL0333EayKgy13wpmDzROHtQoKvzKftx/QPPsUEfcWeSPE/dF8vvNBOLnNufGISKNRsi4iIq2Hf1cY9hbctAe63AoYcOQ9s3L8Fw80fRGmkoNQeRY820K7Xk27LxGRan2ehKgksJbDxlugNM/ZEYlII1CyLiIirU9QLIxcYs7THjHBrJh88B+w4ir48jE4f7Jp9ltdXC64P3h4Ns0+RER+ymKB4f+GwN7m9JabboWqcmdHJSJXSMm6iIi0XiHXwphVkLgBQkeCtQz2vQArusHup6GiuHH3V7jL/Klb4EWkuXm3g+tTwDsQTmyCzEedHZGIXKEGJ+sbNmxg0qRJREZGYrFYWL58eZ1tH3jgASwWCy+99JLd8oKCAu644w4CAwMJDg7mvvvuo6SkxK7N7t27SUhIoE2bNkRHR/Pcc8/V2P7SpUuJjY2lTZs29OvXj1WrVjX044iIiDvolGAm7KM/NQu/VZZA9u/go26w969QWdo4+7l42jYRkeYW2AuGv2s+/+bvcPjfzo1HRK5Ig5P1s2fPEhcXx2uvvXbJdikpKWzbto3IyMga6+644w727NlDamoqK1euZMOGDdx///229cXFxYwbN46uXbuSkZHB888/z9NPP80bb7xha7NlyxZmzpzJfffdR2ZmJlOnTmXq1KlkZ2c39COJiIg7sFjMQkw37oSRSyEwFsoLIPNx+LgHHFhwZbeNGsaP07YpWRcRZ4maDH3nmc+/+DkUZDg3HhG5bBbDMIzLfrPFQkpKClOnTrVb/v333zN06FBWr17NxIkTmTt3LnPnzgVg79699OnThx07djBo0CAAPvvsM2666Sa+++47IiMjef311/nNb35DXl4ePj4+APz6179m+fLl7Nu3D4DbbruNs2fPsnLlStt+hw0bxoABA1iwYIFD8RcXFxMUFERRURGBgYGX+2sQEZGWyFoJR/4DWU/D2W/NZQHdod/voOvMho85P/c9LI8yK9HfWgJebRs9ZBERhxhWWD8FflgJfl3MLynbhDo7KhG5wNE8tNHHrFutVu666y4ef/xxrrnmmhrrt27dSnBwsC1RB0hMTMTDw4Pt27fb2lx//fW2RB1g/Pjx7N+/n9OnT9vaJCYm2m17/PjxbN26tc7YysrKKC4utnuIiIib8vCC7v8FN++H+FehTRiUHIatd8GncXBsudlb7qjq+dUDY5Woi4hzWTxgxLvQ7mo4dxQ232Z+QSkiLUqjJ+t/+ctf8PLy4qGHHqp1fV5eHp06dbJb5uXlRUhICHl5ebY2YWFhdm2qX9fXpnp9bZ555hmCgoJsj+jo6IZ9OBERaX08faHXgzD5EMQ9A97BULQHNibB6qGQt8axpF23wIuIK/EJhoQU8PKH/LWw6wlnRyQiDdSoyXpGRgYvv/wyCxcuxGKxNOamG8WTTz5JUVGR7XHs2DFnhyQiIq7Cyx+u+TVMyYFrfmO+LtgB6T+D9LFwou47twAl6yLieoKvgWEXisztexGOLHJuPCLSII2arG/cuJHjx4/TpUsXvLy88PLy4ttvv+XRRx8lJiYGgPDwcI4fP273vsrKSgoKCggPD7e1yc/Pt2tT/bq+NtXra+Pr60tgYKDdQ0RExI5PMMT9ESYdgl4Pg4eP2SuVOgLWT4bTu+3bW6sgfx0c32S+Du7X3BGLiNStyy3Q59fm8+33wemvnBuPiDisUZP1u+66i927d7Nr1y7bIzIykscff5zVq1cDMHz4cAoLC8nI+LEyZXp6OlarlaFDh9rabNiwgYqKClub1NRUevXqRfv27W1t0tLS7PafmprK8OHDG/MjiYiIu2obBvEvwaQDcNV95hjQ7z+GTwfA5llQfACOLYMVMZA2BsoufBG9bba5XETEVfT/I4SPg6pS2JAE50+YXzIeWWT+tFY5O0IRqUWDq8GXlJRw8OBBAAYOHMiLL77ImDFjCAkJoUuXLjXax8TE2FWDB5gwYQL5+fksWLCAiooKZs+ezaBBg0hOTgagqKiIXr16MW7cOJ544gmys7O59957+dvf/mab4m3Lli2MGjWKZ599lokTJ7J48WL+/Oc/8+WXX9K3b1+HPouqwYuIiMOK98Pu+XD0/QsLPABrLQ0vDANL+ACipzVTcCIi9SgrgM8Gwdkc8PAFa9mP6/yiIP5lXbNEmkmTVYPfuXMnAwcOZOBAc0zeI488wsCBA5k3b57D23jvvfeIjY1l7Nix3HTTTYwcOdJuDvWgoCA+//xzcnJyiI+P59FHH2XevHl2c7GPGDGC5ORk3njjDeLi4vjggw9Yvny5w4m6iIhIgwT2gpGLYUImRNxE7Yk6wIXvwDPmqrdKRFyHbwj0fNB8fnGiDubUkxun664gERdzRfOst3TqWRcRkcuSv8689b0+Y9dC2OimjkZEpH7WKnPYzrnv6mhgMXvYJ+eAh2dzRibidpw2z7qIiEirV5rbuO1ERJraiY2XSNQBDDh3zGwnIi5BybqIiEhDtY1o3HYiIk1NXzKKtDhK1kVERBoqNMG8XbS6mFwNFvCLNtuJiLgCR788PHMQ3HeUrIhLUbIuIiLSUB6eZuVkoGbCfuF1/Esa9ykirqPeLxkvyJoHq4dAbqqSdhEnU7IuIiJyOaKnmdOz+XW2X+4XpWnbRMT11PslowWip4OXPxTshLXjIO0GOLG1mQMVkWqqBq9q8CIiciWsVWZBptJc8zbT0AT1qIuI6zq2DDIeti825xdt3g0UPQ3OH4c9z8CB/wNrubm+8yTo/0do398pIYu0No7moUrWlayLiIiIiDtx5EvGs0ch+/dweCEYVYAFut4O/X4HgVc7I2qRVkPJugOUrIuIiIiIXELxftg9H46+b762eMJV90Hfpy6MgReRhtI86yIiIiIicmUCe8HIxTAhEyInmr3sB9+AFT3gy0fh/AlnRyjSailZFxERERGRS2s/AEavhJ9tgk7Xg7UM9r0IK7qbPe8Vxc6OUKTVUbIuIiIiIiKOCb0Oxq6D0Z9B+2uhssQc2/5RN9j7V6gsdXaEIq2GknUREREREXGcxQKR4+HGnTDyAwiMhfICyHwcPu4BBxaAtcLZUYq0eErWRURERESk4SwW6HIL3JQFw94G/65Q+gPs+AWsjIWc/5iV50XksihZFxERERGRy+fhBd3/C27eD/GvQpswKDkMW++CTwfAdx+B+05AJXLZlKyLiIiIiMiV8/SFXg/C5EMQ9wx4B0NRNmyYCp8Pg7w0Z0co0qIoWRcRERERkcbj5Q/X/Bqm5MA1vwFPPzj1BaQnQloinNzu7AhFWgQl6yIiIiIi0vh8giHujzD5MPR8CDx8ID/N7GXfMBUKs5wdoYhLU7IuIiIiIiJNp20YDHoZJn0D3e8Fi4c5jn1VHGy5E84ccnaEIi5JybqIiIiIiDQ9/64w7F9w0x7ocitgwJH3zMrxXzwA5753doQiLkXJuoiIiIiINJ+gWBi5BG7MgIgJYFTCwX+Yc7RnPg7nTzo7QmlprFWQvw6OLDJ/tpIpAy2G4b7zKBQXFxMUFERRURGBgYHODkdERERExP0c3whf/T84scl87dUOej8Ksb8Cb/2PLvU4tgwyHoZz3/24zC8K4l+G6GnOi+sSHM1D1bMuIiIiIiLO0ykBEjfA6FXQfiBUnoGsp2FFd9j7IlSWOjtCcVXHlsHG6faJOphDKjZON9e3YErWRURERETEuSwWiJwAN+40b5EP7AVlpyDzUfj4ajj4BlgrnB2luBJrldmjTm03il9YljG3Rd8Sr9vgdRu8iIiIiIhrsVZCzrtmD/u5o+aygKug/++h6+1mRXmpm7UKTmyE0lxoGwGhCeDh6eyoGsYwoKIYygug/LT5s6zgx9end8HRJfVvZ+xaCBvd1NE2iKN5qFczxiQiIiIiIlI/Dy+4ajbEzDJ71ff8EUoOwZY74Otnof+foPPNZo+82HO1MdxV5T8m29WJti3p/unri9sVgtEIveKluVe+DSdRz7p61kVEREREXFtFCXzzCnz9HFQUmcs6DIMBf4awMc6NzZVUj+GucWv4hS81Ej64vITdMMxaAmU/TagdSLwrz17ZZ/JsAz4hFx7twffC84oSOLa0/ve34J51JetK1kVEREREWoby0/D187D/Zag6Zy4LTzR72jsOcW5szmatghUxNYut2VjArzOM/wLKi2q5vfyiJLy2pPyKerkt4BNsJtu1Jd7Vy31DarbxalvP5/2e2setW8w7CibnuNwQACXrDlCyLiIiIiLSApXmwZ4/mfOzVxeei0qC/n+A4GucG1tzsFZe6MU+BeWnzJ8ntsDevzTtfj18L0qw60i4a1vuHdQ0CbPtTgKwT9iv8E6CJqZk3QFK1kVEREREWrCSI5D9O8h5BwwrYIGYO6H/0xDQ/cd2rlpwzTCgssQ+6a5+lBfUXF5+yuz1rii8sv16B9fei11fT3ddvdzOVOsY/WiIf8klE3VQsu4QJesiIiIiIq1A0V7YPQ+OfWC+tnhBj/+Bvk/Bya3NU3CtqvzHZLpGgn1R8v3TZVcyJZ13EPh2AJ8O5uuCHfW/Z8waiBh7+ft0Ra76ZUwdlKw7QMm6iIiIiEgrUpABX/0Gclebrz18wFpeS8NL3CZtWM0idmWXSLxr6/muLLn8uD3bmAm3b8iFnx1+TMJ9O1zo5f7psvZm1fxqLXgMt7vR1G0iIiIiIuJeQuJhzGeQvx6++n9wcksdDS8ks1vugk5vQcVp+95uw3p5+7d4XLht/OLE+icJeG3Lvfwub38X8/A07xbYOB3zy4haxnDHv6REvQVRz7p61kVEREREWp+8tZB+w+W/38vf8V7u6uU+wWbC7kwtcAy3u1HPuoiIiIiIuK/zeY61u+q/IXJCzcTb07dp42sq0dOg85QWNYZbaqdkXUREREREWp+2EY61i7kDwkY3aSjNzsOz9X0mN+TkezRERERERESaQGiCWVCterx2DRbz9vDQhOaMSsRhStZFRERERKT1qS64BtRM2FVwTVyfknUREREREWmdoqeZ07P5dbZf7hdV+7RtIi5EY9ZFRERERKT1UsE1aaGUrIuIiIiISOumgmvSAuk2eBEREREREREXo2RdRERERERExMUoWRcRERERERFxMUrWRURERERERFyMknURERERERERF6NkXURERERERMTFKFkXERERERERcTFK1kVERERERERcjJJ1ERERERERERfj5ewAnMkwDACKi4udHImIiIiIiIi4g+r8szofrYtbJ+tnzpwBIDo62smRiIiIiIiIiDs5c+YMQUFBda63GPWl862Y1Wrlhx9+oF27dlgsFmeHIy1UcXEx0dHRHDt2jMDAQGeHI41Mx1cai86l1k/HWByh86T10zGW+hiGwZkzZ4iMjMTDo+6R6W7ds+7h4UFUVJSzw5BWIjAwUBfkVkzHVxqLzqXWT8dYHKHzpPXTMZZLuVSPejUVmBMRERERERFxMUrWRURERERERFyMknWRK+Tr68v8+fPx9fV1dijSBHR8pbHoXGr9dIzFETpPWj8dY2ksbl1gTkRERERERMQVqWddRERERERExMUoWRcRERERERFxMUrWRURERERERFyMknURERERERERF6NkXURERERERMTFKFmXFuGZZ55h8ODBtGvXjk6dOjF16lT2799v1+b8+fPMmTOHDh06EBAQwC233EJ+fr5dm4ceeoj4+Hh8fX0ZMGBAjf2sW7eOKVOmEBERgb+/PwMGDOC9995zKMbXXnuNmJgY2rRpw9ChQ/niiy/s1ufl5XHXXXcRHh6Ov78/1157LR9++GG92z169CgTJ07Ez8+PTp068fjjj1NZWWlbn5uby6xZs+jZsyceHh7MnTvXoXhdjTsf4/piPnLkCBaLpcZj27ZtDsXtblrDuXTo0CGSkpIIDQ0lMDCQGTNm1IivNrpe/Ki1HmNdLxzn6ufJhg0bmDRpEpGRkVgsFpYvX16jjWEYzJs3j4iICNq2bUtiYiIHDhyod9vucC1w5+Or64D7ULIuLcL69euZM2cO27ZtIzU1lYqKCsaNG8fZs2dtbX71q1/x8ccfs3TpUtavX88PP/zAtGnTamzr3nvv5bbbbqt1P1u2bKF///58+OGH7N69m9mzZ3P33XezcuXKS8b3/vvv88gjjzB//ny+/PJL4uLiGD9+PMePH7e1ufvuu9m/fz8rVqwgKyuLadOmMWPGDDIzM+vcblVVFRMnTqS8vJwtW7bw73//m4ULFzJv3jxbm7KyMkJDQ/ntb39LXFzcJeN0Ze56jB2JudqaNWvIzc21PeLj4+vdrjtq6efS2bNnGTduHBaLhfT0dDZv3kx5eTmTJk3CarXWuV1dL1r/MXYk5mq6Xrj+eXL27Fni4uJ47bXX6mzz3HPP8corr7BgwQK2b9+Ov78/48eP5/z583W+x12uBe56fB2JuZquA62AIdICHT9+3ACM9evXG4ZhGIWFhYa3t7exdOlSW5u9e/cagLF169Ya758/f74RFxfn0L5uuukmY/bs2ZdsM2TIEGPOnDm211VVVUZkZKTxzDPP2Jb5+/sb77zzjt37QkJCjDfffLPO7a5atcrw8PAw8vLybMtef/11IzAw0CgrK6vRftSoUcbDDz9c30dqEdzlGDsSc05OjgEYmZmZDm1H7LW0c2n16tWGh4eHUVRUZGtTWFhoWCwWIzU1tc7t6nrR+o+xIzHrelE3VztPLgYYKSkpdsusVqsRHh5uPP/887ZlhYWFhq+vr7Fo0aI6t+Wu1wJ3Ob4X03Wg9VPPurRIRUVFAISEhACQkZFBRUUFiYmJtjaxsbF06dKFrVu3XvG+qvdTm/LycjIyMuz27eHhQWJiot2+R4wYwfvvv09BQQFWq5XFixdz/vx5Ro8eXee2t27dSr9+/QgLC7MtGz9+PMXFxezZs+eKPperc5dj3BCTJ0+mU6dOjBw5khUrVjTKNt1BSzuXysrKsFgs+Pr62tq0adMGDw8PNm3aVOe2db1o/ce4IXS9qMmVzhNH5OTkkJeXZxdfUFAQQ4cOvWR87notcJfj2xC6DrR8StalxbFarcydO5frrruOvn37AuZYYR8fH4KDg+3ahoWFkZeXd9n7WrJkCTt27GD27Nl1tjl58iRVVVV2fxRr2/eSJUuoqKigQ4cO+Pr68vOf/5yUlBR69OhR57bz8vJq3W71utbKnY6xIwICAnjhhRdYunQpn3zyCSNHjmTq1Kn6w+uAlnguDRs2DH9/f5544gnOnTvH2bNneeyxx6iqqiI3N7fObet60fqPsSN0vaidq50njqiOob6/PbW9z92uBe50fB2h60DroWRdWpw5c+aQnZ3N4sWLm3Q/a9euZfbs2bz55ptcc801AGzcuJGAgADbw9FCQwBPPfUUhYWFrFmzhp07d/LII48wY8YMsrKyAJgwYYJtu9X7c1c6xvY6duzII488wtChQxk8eDDPPvssd955J88//3zDPrAbaonnUmhoKEuXLuXjjz8mICCAoKAgCgsLufbaa/HwMP9s63rxIx1je7pe1K4lnieO0LXApONrT9eB1sPL2QGINMSDDz7IypUr2bBhA1FRUbbl4eHhlJeXU1hYaPcNan5+PuHh4Q3ez/r165k0aRJ/+9vfuPvuu23LBw0axK5du2yvw8LC8PX1xdPTs0Z10Yv3fejQIf7+97+TnZ1tu9jGxcWxceNGXnvtNRYsWMA///lPSktLAfD29rZ9rp9WEK7ez+V8rpbA3Y7x5Ro6dCipqalXtI3WrqWeSwDjxo3j0KFDnDx5Ei8vL4KDgwkPD6d79+4Aul5c4G7H+HK5+/XCFc8TR1THkJ+fT0REhF181RXAdS1wv+N7udz9OtBSqWddWgTDMHjwwQdJSUkhPT2dbt262a2Pj4/H29ubtLQ027L9+/dz9OhRhg8f3qB9rVu3jokTJ/KXv/yF+++/325d27Zt6dGjh+3Rrl07fHx8iI+Pt9u31WolLS3Ntu9z584B2HpMqnl6etoq/3bu3Nm23a5duwIwfPhwsrKy7CqOp6amEhgYSJ8+fRr0uVydux7jy7Vr1y67P+7yo5Z+Ll2sY8eOBAcHk56ezvHjx5k8eTKg64W7HuPL5a7XC1c+TxzRrVs3wsPD7eIrLi5m+/bttvjc+Vrgrsf3crnrdaDFc2Z1OxFH/eIXvzCCgoKMdevWGbm5ubbHuXPnbG0eeOABo0uXLkZ6erqxc+dOY/jw4cbw4cPttnPgwAEjMzPT+PnPf2707NnTyMzMNDIzM23VUdPT0w0/Pz/jySeftNvPqVOnLhnf4sWLDV9fX2PhwoXG119/bdx///1GcHCwrRJreXm50aNHDyMhIcHYvn27cfDgQeOvf/2rYbFYjE8++aTO7VZWVhp9+/Y1xo0bZ+zatcv47LPPjNDQUOPJJ5+0a1f9OeLj441Zs2YZmZmZxp49exr0O3Y2dz3GjsS8cOFCIzk52di7d6+xd+9e409/+pPh4eFhvPXWWw3+PbuDln4uGYZhvPXWW8bWrVuNgwcPGu+++64REhJiPPLII5fcrq4Xrf8YOxKzrhc/cvXz5MyZM7ZtAcaLL75oZGZmGt9++62tzbPPPmsEBwcbH330kbF7925jypQpRrdu3YzS0tI6t+su1wJ3Pb6OxKzrQOuhZF1aBKDWx9tvv21rU1paavzyl7802rdvb/j5+RlJSUlGbm6u3XZGjRpV63ZycnIMwzCMe+65p9b1o0aNqjfGV1991ejSpYvh4+NjDBkyxNi2bZvd+m+++caYNm2a0alTJ8PPz8/o379/jWm+anPkyBFjwoQJRtu2bY2OHTsajz76qFFRUVHv76dr1671btuVuPMxri/mhQsXGr179zb8/PyMwMBAY8iQIXZT0Yi91nAuPfHEE0ZYWJjh7e1tXH311cYLL7xgWK3Werer68Xbtjat9RjreuE4Vz9P1q5dW+v77rnnHlsbq9VqPPXUU0ZYWJjh6+trjB071ti/f3+9n90drgXufHx1HXAfFsMwDERERERERETEZWjMuoiIiIiIiIiLUbIuIiIiIiIi4mKUrIuIiIiIiIi4GCXrIiIiIiIiIi5GybqIiIiIiIiIi1GyLiIiIiIiIuJilKyLiIiIiIiIuBgl6yIiIiIiIiIuRsm6iIiIiIiIiItRsi4iIiIiIiLiYpSsi4iIiIiIiLiY/w95wrnDZIpgogAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1200x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot actual vs. predicted test data\n",
    "plt.figure(figsize=(12,5))\n",
    "axes = plt.axes()\n",
    "axes.plot(test_ts.index, y_test_ts, marker='o', color='orange', label='Actual')\n",
    "axes.plot(test_ts.index, y_pred_ts, marker='o', color='green', label='Prediction')\n",
    "axes.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "zkzWb-E3FQHa",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 382
    },
    "id": "zkzWb-E3FQHa",
    "outputId": "4c1e912f-48fb-4b83-8a82-950584f44cfd"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot training (history), test (actual), and predicted data\n",
    "plt.figure(figsize=(12,5))\n",
    "axes = plt.axes()\n",
    "axes.plot(train_ts.index, train_ts.iloc[:, 0], marker='o', label='History')\n",
    "axes.plot(test_ts.index, y_test_ts, marker='o', color='orange', label='Actual')\n",
    "axes.plot(test_ts.index, y_pred_ts, marker='o', color='green', label='Prediction')\n",
    "axes.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1_2bBeR_h_ie",
   "metadata": {
    "id": "1_2bBeR_h_ie"
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   "source": [
    "### **5. Summary**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aBFuTtcVQybG",
   "metadata": {
    "id": "aBFuTtcVQybG"
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   "source": [
    "- **Steps I have taken**\n",
    "    - Loaded and examined the data from previous notebook.\n",
    "    - Splitted the dataset into train and test set.\n",
    "    - Performed feature selection for multiple linear regression. Then fit, predicted, and evaluated the model.\n",
    "    - Performed ADF test and auto arima for parameters selection of ARIMA model. Then fit, predicted, and evaluated the model.\n",
    "    - Performed cross validation on parameters selection for another ARIMA model. Then fit, predicted, and evaluated the new model.\n",
    "    - Visualization of model prediction results."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "acfbc0ce-5254-4e2b-97df-c6793d00b4b8",
   "metadata": {
    "id": "zshvymkJKuo6"
   },
   "source": [
    "- **Results**\n",
    "    - **Multiple Linear Regression**\n",
    "        - Chose the top 4 features for modeling: Year, IsHoliday, CPI, Unemployment\n",
    "        - Coefficients:\n",
    "            - Year: -19476.4345\n",
    "            - IsHoliday: 3723.5897\n",
    "            - CPI: 7939.2845\n",
    "            - Unemployment: -4053.2594\n",
    "        - Interceptor: 38191051.8778\n",
    "        - Evaluation:\n",
    "            - MAE 5035.0428\n",
    "            - MAPE: 35.2171%\n",
    "            - SMAPE: 28.0099%\n",
    "            - MSE: 36145251.5922\n",
    "            - RMSE: 6012.0921\n",
    "            - R-Squared: 0.1892\n",
    "    \n",
    "    - **ARIMA**\n",
    "        - ADF test: the data was stationary, so d was set as 0.\n",
    "        - Auto arima for tuning: (p=1, d=0, q=0) led to best AIC.\n",
    "            - Results with this setting:\n",
    "                - MAE: 2893.4832\n",
    "                - MAPE: 18.9203%\n",
    "                - SMAPE: 17.4038%\n",
    "                - MSE: 10495505.2151\n",
    "                - RMSE: 3239.6767\n",
    "        - Cross validation for tuning: (p=3, d=0, q=2) led to the least error rate.\n",
    "            - Results with this setting:\n",
    "                - MAE: 2473.1485\n",
    "                - MAPE: 15.5256%\n",
    "                - SMAPE: 15.0213%\n",
    "                - MSE: 9400121.9991\n",
    "                - RMSE: 3065.9618"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fa7404c0-ca57-48a6-8156-98b4f3984d11",
   "metadata": {},
   "source": [
    "- **Interest Findings**\n",
    "    - For MLR:\n",
    "        - R-squared is just less than 19%, bad performance for the linear model. Apparently, the dataset is not linear correlated through plotting.\n",
    "        - MAPE and SMAPE are about 1/3, not good.\n",
    "        - RMSE and MAE were quite large, maybe because the values of weekly sales are large number. A small percent of error could led to large error value.\n",
    "        - Most of the coefficients of the features are reasonable. For example, Unemployment rate is negatively related with weekly sales. Less people umemployed, the better the sales will be. What is interesting is Year is also negatively related. It may due to various reasons, but the trend is not good for a store.\n",
    "\n",
    "    - For ARIMA:\n",
    "        - ARIMA performs better than linear regression, reducing the error and capturing the trend in a better way.\n",
    "        - Auto arima and cross validation led to different tuning results. Cross validation led to better performance than auto arima. But auto arima is the suggested methods. One reason is because auto arima uses a build-in metric AIC to measure the performance, while cross validation is more customizable. AIC and MAPE might calculate in a different way.\n",
    "        - For cross validation, we need to set the scope of the target parameter. Since there might be better results out of the set scope, the result we get might be just a local best solution instead of a global one."
   ]
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   "id": "78e6f5b6-7561-4dd3-82c3-603610fea4c4",
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   "source": []
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