src/modules/modeling.ipynb

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  {
   "cell_type": "markdown",
   "metadata": {
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    "tags": []
   },
   "source": [
    "# Machine Learning\n",
    "\n",
    "In this file, instructions how to approach the challenge can be found."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We are going to work on different types of Machine Learning problems:\n",
    "\n",
    "- **Regression Problem**: The goal is to predict delay of flights.\n",
    "- **(Stretch) Multiclass Classification**: If the plane was delayed, we will predict what type of delay it is (will be).\n",
    "- **(Stretch) Binary Classification**: The goal is to predict if the flight will be cancelled."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true,
    "tags": []
   },
   "source": [
    "## Main Task: Regression Problem"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The target variable is **ARR_DELAY**. We need to be careful which columns to use and which don't. For example, DEP_DELAY is going to be the perfect predictor, but we can't use it because in real-life scenario, we want to predict the delay before the flight takes of --> We can use average delay from earlier days but not the one from the actual flight we predict.  \n",
    "\n",
    "For example, variables **CARRIER_DELAY, WEATHER_DELAY, NAS_DELAY, SECURITY_DELAY, LATE_AIRCRAFT_DELAY** shouldn't be used directly as predictors as well. However, we can create various transformations from earlier values.\n",
    "\n",
    "We will be evaluating your models by predicting the ARR_DELAY for all flights **1 week in advance**."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true,
    "tags": []
   },
   "source": [
    "## Feature Engineering"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Feature engineering will play a crucial role in this problems. We have only very little attributes so we need to create some features that will have some predictive power.\n",
    "\n",
    "- weather: we can use some weather API to look for the weather in time of the scheduled departure and scheduled arrival.\n",
    "- statistics (avg, mean, median, std, min, max...): we can take a look at previous delays and compute descriptive statistics\n",
    "- airports encoding: we need to think about what to do with the airports and other categorical variables\n",
    "- time of the day: the delay probably depends on the airport traffic which varies during the day.\n",
    "- airport traffic\n",
    "- unsupervised learning as feature engineering?\n",
    "- **what are the additional options?**: Think about what we could do more to improve the model."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Feature Selection / Dimensionality Reduction"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We need to apply different selection techniques to find out which one will be the best for our problems.\n",
    "\n",
    "- Original Features vs. PCA conponents?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Modeling"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Use different ML techniques to predict each problem.\n",
    "\n",
    "- linear / logistic / multinomial logistic regression\n",
    "- Naive Bayes\n",
    "- Random Forest\n",
    "- SVM\n",
    "- XGBoost\n",
    "- The ensemble of your own choice"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Evaluation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You have data from 2018 and 2019 to develop models. Use different evaluation metrics for each problem and compare the performance of different models.\n",
    "\n",
    "You are required to predict delays on **out of sample** data from **first 7 days (1st-7th) of January 2020** and to share the file with LighthouseLabs. Sample submission can be found in the file **_sample_submission.csv_**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "# Regression Models"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true,
    "tags": []
   },
   "source": [
    "## Feature Engineering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>op_carrier_fl_num</th>\n",
       "      <th>op_unique_carrier</th>\n",
       "      <th>origin_airport_id</th>\n",
       "      <th>dest_airport_id</th>\n",
       "      <th>distance</th>\n",
       "      <th>route</th>\n",
       "      <th>month</th>\n",
       "      <th>day</th>\n",
       "      <th>fl_date</th>\n",
       "      <th>arr_delay</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1610</td>\n",
       "      <td>UA</td>\n",
       "      <td>11618</td>\n",
       "      <td>13495</td>\n",
       "      <td>1167</td>\n",
       "      <td>11618-13495</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2019-03-01</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1519</td>\n",
       "      <td>UA</td>\n",
       "      <td>10800</td>\n",
       "      <td>11292</td>\n",
       "      <td>850</td>\n",
       "      <td>10800-11292</td>\n",
       "      <td>7</td>\n",
       "      <td>19</td>\n",
       "      <td>2019-07-19</td>\n",
       "      <td>-5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5452</td>\n",
       "      <td>OO</td>\n",
       "      <td>14487</td>\n",
       "      <td>14771</td>\n",
       "      <td>199</td>\n",
       "      <td>14487-14771</td>\n",
       "      <td>6</td>\n",
       "      <td>25</td>\n",
       "      <td>2018-06-25</td>\n",
       "      <td>119.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1054</td>\n",
       "      <td>B6</td>\n",
       "      <td>14027</td>\n",
       "      <td>12478</td>\n",
       "      <td>1028</td>\n",
       "      <td>14027-12478</td>\n",
       "      <td>11</td>\n",
       "      <td>30</td>\n",
       "      <td>2018-11-30</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>790</td>\n",
       "      <td>UA</td>\n",
       "      <td>13930</td>\n",
       "      <td>10721</td>\n",
       "      <td>867</td>\n",
       "      <td>13930-10721</td>\n",
       "      <td>3</td>\n",
       "      <td>26</td>\n",
       "      <td>2019-03-26</td>\n",
       "      <td>-8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99995</th>\n",
       "      <td>6279</td>\n",
       "      <td>YV</td>\n",
       "      <td>12266</td>\n",
       "      <td>10994</td>\n",
       "      <td>925</td>\n",
       "      <td>12266-10994</td>\n",
       "      <td>7</td>\n",
       "      <td>31</td>\n",
       "      <td>2018-07-31</td>\n",
       "      <td>226.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99996</th>\n",
       "      <td>2047</td>\n",
       "      <td>UA</td>\n",
       "      <td>12266</td>\n",
       "      <td>10721</td>\n",
       "      <td>1597</td>\n",
       "      <td>12266-10721</td>\n",
       "      <td>12</td>\n",
       "      <td>31</td>\n",
       "      <td>2018-12-31</td>\n",
       "      <td>-37.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99997</th>\n",
       "      <td>7406</td>\n",
       "      <td>OO</td>\n",
       "      <td>11433</td>\n",
       "      <td>13459</td>\n",
       "      <td>349</td>\n",
       "      <td>11433-13459</td>\n",
       "      <td>7</td>\n",
       "      <td>22</td>\n",
       "      <td>2018-07-22</td>\n",
       "      <td>-22.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99998</th>\n",
       "      <td>5151</td>\n",
       "      <td>OH</td>\n",
       "      <td>12323</td>\n",
       "      <td>11057</td>\n",
       "      <td>185</td>\n",
       "      <td>12323-11057</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>2019-03-05</td>\n",
       "      <td>109.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99999</th>\n",
       "      <td>2245</td>\n",
       "      <td>DL</td>\n",
       "      <td>10397</td>\n",
       "      <td>10423</td>\n",
       "      <td>813</td>\n",
       "      <td>10397-10423</td>\n",
       "      <td>2</td>\n",
       "      <td>17</td>\n",
       "      <td>2019-02-17</td>\n",
       "      <td>-2.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>100000 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       op_carrier_fl_num op_unique_carrier  origin_airport_id  \\\n",
       "0                   1610                UA              11618   \n",
       "1                   1519                UA              10800   \n",
       "2                   5452                OO              14487   \n",
       "3                   1054                B6              14027   \n",
       "4                    790                UA              13930   \n",
       "...                  ...               ...                ...   \n",
       "99995               6279                YV              12266   \n",
       "99996               2047                UA              12266   \n",
       "99997               7406                OO              11433   \n",
       "99998               5151                OH              12323   \n",
       "99999               2245                DL              10397   \n",
       "\n",
       "       dest_airport_id  distance        route  month  day     fl_date  \\\n",
       "0                13495      1167  11618-13495      3    1  2019-03-01   \n",
       "1                11292       850  10800-11292      7   19  2019-07-19   \n",
       "2                14771       199  14487-14771      6   25  2018-06-25   \n",
       "3                12478      1028  14027-12478     11   30  2018-11-30   \n",
       "4                10721       867  13930-10721      3   26  2019-03-26   \n",
       "...                ...       ...          ...    ...  ...         ...   \n",
       "99995            10994       925  12266-10994      7   31  2018-07-31   \n",
       "99996            10721      1597  12266-10721     12   31  2018-12-31   \n",
       "99997            13459       349  11433-13459      7   22  2018-07-22   \n",
       "99998            11057       185  12323-11057      3    5  2019-03-05   \n",
       "99999            10423       813  10397-10423      2   17  2019-02-17   \n",
       "\n",
       "       arr_delay  \n",
       "0            0.0  \n",
       "1           -5.0  \n",
       "2          119.0  \n",
       "3            3.0  \n",
       "4           -8.0  \n",
       "...          ...  \n",
       "99995      226.0  \n",
       "99996      -37.0  \n",
       "99997      -22.0  \n",
       "99998      109.0  \n",
       "99999       -2.0  \n",
       "\n",
       "[100000 rows x 10 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_data = pd.read_csv(\"../../data/raw/random_sample_flight_data.csv\")\n",
    "model_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def model_data_transformation(model_data):\n",
    "    #call functions for data merging\n",
    "    from add_location_weather import add_location\n",
    "    \n",
    "    #import datasets to merge with model data and do a little cleaning\n",
    "    model_data['fl_date'] = model_data['fl_date'].astype('datetime64')\n",
    "    airports_ids =  pd.read_csv(\"../../data/processed/Airports_lat_lon.csv\")\n",
    "    airports_ids = airports_ids.drop(['Unnamed: 0'], axis=1)\n",
    "    weather_airports = pd.read_csv(\"../../data/processed/weatherAirports_2019_2018_Jan2020_clean.csv\")\n",
    "    weather_airports['fl_date'] = weather_airports['fl_date'].astype('datetime64')\n",
    "    weather_airports = weather_airports.drop(['Unnamed: 0'], axis=1)\n",
    "    \n",
    "    #merge model data with airport ids and drop data that will be unnecesary to the model \n",
    "    df = add_location(model_data,airports_ids)\n",
    "    df = df.drop(['airport_id','iata_code','lat','lon'], axis=1)\n",
    "    \n",
    "    #merge the flight & airport data with the weather data\n",
    "    df1 = pd.merge(df,weather_airports, on = ['Key','fl_date'], how ='left')\n",
    "    df1.drop_duplicates(inplace=True)\n",
    "    \n",
    "    # transform categorical variables in numeric with LabelEncoder\n",
    "    from sklearn.preprocessing import LabelEncoder\n",
    "    le = LabelEncoder()\n",
    "\n",
    "    # fit and transform the data\n",
    "    df1['op_unique_carrier'] = le.fit_transform(df1['op_unique_carrier'])\n",
    "    df1['route'] = le.fit_transform(df1['route'])\n",
    "\n",
    "    # drop rows with missing values\n",
    "    df1 = df1.dropna().reset_index(drop=True)\n",
    "    \n",
    "    return(df1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "transformed_model_flight_df = model_data_transformation(model_data)\n",
    "transformed_model_flight_df.to_csv('../../data/processed/transformed_model_flight_data.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>op_carrier_fl_num</th>\n",
       "      <th>op_unique_carrier</th>\n",
       "      <th>origin_airport_id</th>\n",
       "      <th>dest_airport_id</th>\n",
       "      <th>distance</th>\n",
       "      <th>route</th>\n",
       "      <th>month</th>\n",
       "      <th>day</th>\n",
       "      <th>fl_date</th>\n",
       "      <th>arr_delay</th>\n",
       "      <th>Key</th>\n",
       "      <th>AvgTemp</th>\n",
       "      <th>Precipitation</th>\n",
       "      <th>Snow_mm</th>\n",
       "      <th>WindSpeed</th>\n",
       "      <th>lat</th>\n",
       "      <th>lon</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1610</td>\n",
       "      <td>22</td>\n",
       "      <td>11618</td>\n",
       "      <td>13495</td>\n",
       "      <td>1167</td>\n",
       "      <td>1962</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2019-03-01</td>\n",
       "      <td>0.0</td>\n",
       "      <td>40.692501-40.692501</td>\n",
       "      <td>0.8</td>\n",
       "      <td>1.3</td>\n",
       "      <td>30.0</td>\n",
       "      <td>15.9</td>\n",
       "      <td>40.692501</td>\n",
       "      <td>-74.168701</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4909</td>\n",
       "      <td>6</td>\n",
       "      <td>11618</td>\n",
       "      <td>14524</td>\n",
       "      <td>277</td>\n",
       "      <td>1978</td>\n",
       "      <td>12</td>\n",
       "      <td>6</td>\n",
       "      <td>2019-12-06</td>\n",
       "      <td>-17.0</td>\n",
       "      <td>40.692501-40.692501</td>\n",
       "      <td>5.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>12.2</td>\n",
       "      <td>40.692501</td>\n",
       "      <td>-74.168701</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3556</td>\n",
       "      <td>26</td>\n",
       "      <td>11618</td>\n",
       "      <td>11193</td>\n",
       "      <td>569</td>\n",
       "      <td>1928</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>2018-04-03</td>\n",
       "      <td>-9.0</td>\n",
       "      <td>40.692501-40.692501</td>\n",
       "      <td>4.9</td>\n",
       "      <td>5.8</td>\n",
       "      <td>30.0</td>\n",
       "      <td>11.1</td>\n",
       "      <td>40.692501</td>\n",
       "      <td>-74.168701</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1382</td>\n",
       "      <td>2</td>\n",
       "      <td>11618</td>\n",
       "      <td>11298</td>\n",
       "      <td>1372</td>\n",
       "      <td>1932</td>\n",
       "      <td>9</td>\n",
       "      <td>7</td>\n",
       "      <td>2019-09-07</td>\n",
       "      <td>68.0</td>\n",
       "      <td>40.692501-40.692501</td>\n",
       "      <td>20.4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.3</td>\n",
       "      <td>40.692501</td>\n",
       "      <td>-74.168701</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3517</td>\n",
       "      <td>26</td>\n",
       "      <td>11618</td>\n",
       "      <td>13495</td>\n",
       "      <td>1167</td>\n",
       "      <td>1962</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>2018-02-05</td>\n",
       "      <td>-37.0</td>\n",
       "      <td>40.692501-40.692501</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13.4</td>\n",
       "      <td>40.692501</td>\n",
       "      <td>-74.168701</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97302</th>\n",
       "      <td>5026</td>\n",
       "      <td>19</td>\n",
       "      <td>11092</td>\n",
       "      <td>11292</td>\n",
       "      <td>283</td>\n",
       "      <td>1111</td>\n",
       "      <td>9</td>\n",
       "      <td>22</td>\n",
       "      <td>2019-09-22</td>\n",
       "      <td>0.0</td>\n",
       "      <td>38.75500107-38.75500107</td>\n",
       "      <td>16.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>38.755001</td>\n",
       "      <td>-109.754997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97303</th>\n",
       "      <td>5031</td>\n",
       "      <td>19</td>\n",
       "      <td>11092</td>\n",
       "      <td>11292</td>\n",
       "      <td>283</td>\n",
       "      <td>1111</td>\n",
       "      <td>8</td>\n",
       "      <td>10</td>\n",
       "      <td>2019-08-10</td>\n",
       "      <td>-12.0</td>\n",
       "      <td>38.75500107-38.75500107</td>\n",
       "      <td>27.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>38.755001</td>\n",
       "      <td>-109.754997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97304</th>\n",
       "      <td>5079</td>\n",
       "      <td>19</td>\n",
       "      <td>11092</td>\n",
       "      <td>11292</td>\n",
       "      <td>283</td>\n",
       "      <td>1111</td>\n",
       "      <td>8</td>\n",
       "      <td>15</td>\n",
       "      <td>2018-08-15</td>\n",
       "      <td>2.0</td>\n",
       "      <td>38.75500107-38.75500107</td>\n",
       "      <td>28.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.7</td>\n",
       "      <td>38.755001</td>\n",
       "      <td>-109.754997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97305</th>\n",
       "      <td>5031</td>\n",
       "      <td>19</td>\n",
       "      <td>11092</td>\n",
       "      <td>11292</td>\n",
       "      <td>283</td>\n",
       "      <td>1111</td>\n",
       "      <td>5</td>\n",
       "      <td>26</td>\n",
       "      <td>2019-05-26</td>\n",
       "      <td>-22.0</td>\n",
       "      <td>38.75500107-38.75500107</td>\n",
       "      <td>18.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.4</td>\n",
       "      <td>38.755001</td>\n",
       "      <td>-109.754997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97306</th>\n",
       "      <td>865</td>\n",
       "      <td>12</td>\n",
       "      <td>12119</td>\n",
       "      <td>14112</td>\n",
       "      <td>861</td>\n",
       "      <td>2279</td>\n",
       "      <td>7</td>\n",
       "      <td>15</td>\n",
       "      <td>2018-07-15</td>\n",
       "      <td>105.0</td>\n",
       "      <td>39.707901-39.707901</td>\n",
       "      <td>26.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.1</td>\n",
       "      <td>39.707901</td>\n",
       "      <td>-77.729500</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>97307 rows × 17 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       op_carrier_fl_num  op_unique_carrier  origin_airport_id  \\\n",
       "0                   1610                 22              11618   \n",
       "1                   4909                  6              11618   \n",
       "2                   3556                 26              11618   \n",
       "3                   1382                  2              11618   \n",
       "4                   3517                 26              11618   \n",
       "...                  ...                ...                ...   \n",
       "97302               5026                 19              11092   \n",
       "97303               5031                 19              11092   \n",
       "97304               5079                 19              11092   \n",
       "97305               5031                 19              11092   \n",
       "97306                865                 12              12119   \n",
       "\n",
       "       dest_airport_id  distance  route  month  day     fl_date  arr_delay  \\\n",
       "0                13495      1167   1962      3    1  2019-03-01        0.0   \n",
       "1                14524       277   1978     12    6  2019-12-06      -17.0   \n",
       "2                11193       569   1928      4    3  2018-04-03       -9.0   \n",
       "3                11298      1372   1932      9    7  2019-09-07       68.0   \n",
       "4                13495      1167   1962      2    5  2018-02-05      -37.0   \n",
       "...                ...       ...    ...    ...  ...         ...        ...   \n",
       "97302            11292       283   1111      9   22  2019-09-22        0.0   \n",
       "97303            11292       283   1111      8   10  2019-08-10      -12.0   \n",
       "97304            11292       283   1111      8   15  2018-08-15        2.0   \n",
       "97305            11292       283   1111      5   26  2019-05-26      -22.0   \n",
       "97306            14112       861   2279      7   15  2018-07-15      105.0   \n",
       "\n",
       "                           Key  AvgTemp  Precipitation  Snow_mm  WindSpeed  \\\n",
       "0          40.692501-40.692501      0.8            1.3     30.0       15.9   \n",
       "1          40.692501-40.692501      5.7            0.0      0.0       12.2   \n",
       "2          40.692501-40.692501      4.9            5.8     30.0       11.1   \n",
       "3          40.692501-40.692501     20.4            0.0      0.0        6.3   \n",
       "4          40.692501-40.692501      0.3            0.0      0.0       13.4   \n",
       "...                        ...      ...            ...      ...        ...   \n",
       "97302  38.75500107-38.75500107     16.0            0.0      0.0        0.0   \n",
       "97303  38.75500107-38.75500107     27.3            0.0      0.0        0.0   \n",
       "97304  38.75500107-38.75500107     28.5            0.0      0.0       10.7   \n",
       "97305  38.75500107-38.75500107     18.3            0.0      0.0       11.4   \n",
       "97306      39.707901-39.707901     26.7            0.0      0.0        9.1   \n",
       "\n",
       "             lat         lon  \n",
       "0      40.692501  -74.168701  \n",
       "1      40.692501  -74.168701  \n",
       "2      40.692501  -74.168701  \n",
       "3      40.692501  -74.168701  \n",
       "4      40.692501  -74.168701  \n",
       "...          ...         ...  \n",
       "97302  38.755001 -109.754997  \n",
       "97303  38.755001 -109.754997  \n",
       "97304  38.755001 -109.754997  \n",
       "97305  38.755001 -109.754997  \n",
       "97306  39.707901  -77.729500  \n",
       "\n",
       "[97307 rows x 17 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('../../data/processed/transformed_model_flight_data.csv')\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['op_carrier_fl_num',\n",
       " 'op_unique_carrier',\n",
       " 'origin_airport_id',\n",
       " 'dest_airport_id',\n",
       " 'distance',\n",
       " 'route',\n",
       " 'month',\n",
       " 'day',\n",
       " 'fl_date',\n",
       " 'arr_delay',\n",
       " 'Key',\n",
       " 'AvgTemp',\n",
       " 'Precipitation',\n",
       " 'Snow_mm',\n",
       " 'WindSpeed',\n",
       " 'lat',\n",
       " 'lon']"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns.tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df.drop(['op_unique_carrier','dest_airport_id','day','fl_date','Key','lat','lon','fl_date','arr_delay'], axis=1)\n",
    "y = df['arr_delay']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   op_carrier_fl_num  origin_airport_id  distance     route  \\\n",
      "op_carrier_fl_num           1.000000          -0.021725 -0.360852 -0.018616   \n",
      "origin_airport_id          -0.021725           1.000000  0.065652  0.996187   \n",
      "distance                   -0.360852           0.065652  1.000000  0.068845   \n",
      "route                      -0.018616           0.996187  0.068845  1.000000   \n",
      "month                       0.011208          -0.004020  0.001494 -0.003994   \n",
      "AvgTemp                    -0.077079           0.011714  0.050612  0.011279   \n",
      "Precipitation               0.000282          -0.037302 -0.009797 -0.037477   \n",
      "Snow_mm                     0.010457          -0.008375 -0.015987 -0.008250   \n",
      "WindSpeed                  -0.017120           0.004950  0.049889  0.013727   \n",
      "\n",
      "                      month   AvgTemp  Precipitation   Snow_mm  WindSpeed  \n",
      "op_carrier_fl_num  0.011208 -0.077079       0.000282  0.010457  -0.017120  \n",
      "origin_airport_id -0.004020  0.011714      -0.037302 -0.008375   0.004950  \n",
      "distance           0.001494  0.050612      -0.009797 -0.015987   0.049889  \n",
      "route             -0.003994  0.011279      -0.037477 -0.008250   0.013727  \n",
      "month              1.000000  0.175803       0.019157 -0.118636  -0.120737  \n",
      "AvgTemp            0.175803  1.000000       0.022763 -0.310215  -0.105973  \n",
      "Precipitation      0.019157  0.022763       1.000000 -0.014248   0.093921  \n",
      "Snow_mm           -0.118636 -0.310215      -0.014248  1.000000   0.046682  \n",
      "WindSpeed         -0.120737 -0.105973       0.093921  0.046682   1.000000  \n"
     ]
    },
    {
     "data": {
      "image/png": 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BkSNHsHXrVqxbtw67d+9+ojiS/BTlpqKsKAs2HXzF25RV1GDp1AMZSZGN7peRFAVrV1+pbTaufshIimp0n8ryIgCAupZ+02EEAqhp6j1mClqHlbkGTIzUcTEyT7ytqlqIqOh8uHV6/Lh6uxvAzloLUTGSeWm+PY1x824Rvl7UBQe39sGmVV4YPdxCrvF/WkW5qSgtyoKNa/PKSWZylFTZAgDbR5ST+mqqRbMelFXVxduUlJShrKyG9MTLj30ceaqprkRmagzsOvlJbbfr5Iu0RNn5kZYYBbtO0nlh36kfMlOiUVPz6GuWIjDUAXS1BLibJrlO1tQCSRlC2Jo1vmiBjakAcfelf7iMuy+EransfZSVAHcnJUTebXwkTlsDcLUR4EoTYZ7Uw+/ftl4baNvRF+mNfP/pSVGw7Sgd3q6jH7JSYsTff3piFOzqh+nkh/TEqCc+LyBpV9XrtKuWjl64E3UE5SX5ENbW4nbkYdRWV8HapWfTiZcz1qW2bV/rKy3KRnLsGXTq8fITH6M9U1IWtNnrWdSseYElJSX4/fffERgYiJEjRwIA1q9fj+DgYGzcuBE9evQAAHz55ZcYNkw0n3jz5s2wsbHBvn37MGnSpCaP//3338PHxwe//fabeFvXrl3Ff7/8snSh3rhxI8zMzHDjxg24ubmJty9YsAATJkyQCqutrY0NGzZATU32r0SnTp3C9evXkZmZCXV10T8rP/74I/bv34/du3dj9uzZAETTOLdu3QpT00dPI9DX14euri6UlZVhYfHk/xSOGjUK8+aJpmwsWrQIP/30E06fPo1OnWTPwZalqqoKv//+O5ydnQEAr7zyCrZu3YqMjAzo6OigS5cuGDRoEE6dOoXJkyc/cVzp6ZUWZQEANHWNpbZr6hg3OWpWVpwNLR3pfbR0jMXHq08oFOLcoeWwcPCGkYWrzDDVVRW4+N8KuHi+BDUNneYko8UZGYrqcm5+pdT2vPxKmJtpNLmvtpYy9gX2gZqqADW1wMrf7+BSlKSDaGWhiXEjNbFjfyq27EpGF1ddLJjtgqoqIY6eypB/Yp6AuJzoyCgn+Y2Xk9Ki7IZlS7fxciKLgZkTdAytcPG/leg/4SuoqGniWkggSouymnUceSoryYOwtgZa9dKmpWuC0kLZcSotyoaWrkm98Maora1GeXEetPUbH/lVFDqaon9eSsqktxeXAQZNVGkdDaC4XLpjV1wuhI6m7PCdbAXQUAOi4hrvtHk6K6GiCohNanymy5Nq/Ps3RmlRtsx9SoqyYKfr1yB8bW01ykvyoK1nJqovMupYyYMy9STnra6qQPihFXDtLt2ujpj+E4K2LMSGz3tDSUkFKmoaGPnmL9A3sXu8TJAT1qW2bV/ru315P1TVteHo1j5nBFD70qyOXVxcHKqqquDrK/k1QlVVFT179kRsbKy4Y9enTx/x50ZGRujYsSNiY2MfefyoqChMnDixyfN//vnnOH/+PLKzs8UjdcnJyVIdOx+fhjf3duvWrdFOHQBcvnwZxcXFMDaWrpBlZWVS0z3t7e0fq1MnT+7u7uK/BQIBLCwskJmZ2cQeDWlpaYk7dQBgbm4OBwcH6OjoSG1r6rgVFRWoqKiQ2lYlrIWq4Jm/VbNF3Yk8iJC9X4rfv/DmHwAAAer9miQEIHjEL0z1PhftInufsH+/Rm76LYyZu13m57U1VTix3R9CoRB+476UGaY1DRtgho/flXRAP1n2YIp3/f8RBYKG2+opLavBmx9cgqaGMnw8DPHeTGfcTy9DZLRo1E5JANy8W4R1WxMAAHfii+Fgp4Vxo6zarGN358pBnK1TTkY+KCeyy0TT5URW2WqsnMiirKyK4dNW48yuzxC4tBcESsqwdukD2479H/sYLad+2oSPSFu9OvNwen0z8qM96eYowOjekntMt50UzXiRWU0eUU9kfd7YLl4dlHD3nhBFZY0EANDdRQnXE2pRLf8BO4n639sj283GPpNsl1V+Gmx7zPPW1FQhaKs/IBRi4CvS7er5/1ahoqwQY+f+CU1tQ8RHH8fRzQsw4b2/YGLVsYk0tJTnpy61p/a1vlsRe+DS/SWo1Jkh8Szh4w7kq1kdu4eVtH4BFT6ysj9eodbUbOSnwAdGjx4NW1tbrF+/HlZWVqitrYWbm5t4MZSHZK10+ajVL2tra2FpaYnTp083+MzAwOCxj9MSVFWlVxUTCATiTq2SkqhTVfdeP1nTXmUdo6njyhIQEICvvvpKattUgRFeU+ZKn0/DvssgmNlKOu8Pp7mVFmVDS0/yK2dZSU6DXw/r0tQxafALcVlxDjR1Gn4/Yf9+jaQbJzF67l/QMWg4mlxbU4Xj2xaiKC8VL70d2C5G60Iv5uDG7Uvi92qqorJvZKiGnDxJG2Cor9pgFK8+oRC4lya6X+5uQgnsbbXw+kQ7REaLOos5eZVITJFeBCQppRQD+7bdDf/2XQbhFbuG5aSsKBvadctJcU6DX9rr0tJ9/HLSFFMbN7yycD8qyopQW1MFTR0j7PtlEkxs3B69cwvQ1DaEQEm5QdpKi3OgqSs7baK8kP4lvaw4F0pKKtDQNmipqLaoWylC3MuWLPLzcME5HU3RKN1D2hpASRO3jBaXA7qaAtTtyuloCBqM/AGAvjbgZCHAP2cav0fbzkwAU30Bdp1tmV6d+PsvbPj915/J8JC2rmnD8lKUI/X9N1ZfHo5ONee8NTVVCNq8EIU5qRg3T7pdLchOxvXQbZj6yUEYP1h10cS6E+7HX8b1sO0YNFH62tuSnse61N7a14fSEi4hPysBQ1/76Yn2p+dPs4ZaXFxcoKamhtDQUPG2qqoqXLp0CZ07dxZvO3/+vPjvvLw83L59+7GmDbq7u+PECdnL1efk5CA2NhafffYZhgwZgs6dO4sXVZEHLy8vpKenQ0VFBS4uLlKv9vyIgoejh2lpaeJtj7r370ktXrwYBQUFUq9JSkYtcq7niZq6DvRN7MUvQ3MXaOqaIvVOuDhMTXUl0uIjYG7fvdHjmNt74l6dfQDg3u0wmNt7it8LhUKE7l+GhOhgvDQ7EHpGNg2O87BTV5CdhBdn/QkNbcMGYdpCWVkN7qWVi18JyaXIzq1AD09J/FRUBPB0M0D0zcJmHVsASUcRAK7HFsDOWnrFXFtrLaRntt3iKWoaDcuJ1hOUEzM7T6l9ACD1jnQ5aQ51TV1o6hihICsRWanRcOg6+ImO87SUVdRgZtMVybfCpLYn3wqHpYPs/LB08ETyLem8SL4ZCjNbNygry16mv72rrBY9muDhK6sAKCoVwtlSUr6VlQB7cwFSMhsfskvNEsLJUvoHWWcrAVKyGu7T3UUJJeXAndTGj+flooR72bXIkN9lW8rD7z/ltvT3mXI7HBaNfP8W9p4ywofB1Lar+Pu3cPBESv0ycisMFg6ezTrvw05dfnYSxr0jGpGrq6pS1GMW1JsBI1BSglDYkkOcDT2Pdam9tq83L+6GiXVXGFs9/q03iob32MlXszp22traeOedd/Dxxx/j6NGjuHHjBt5++22UlpZi5kzJczWWLVuGEydOIDo6GjNmzICJiQnGjRv3yOMvXrwYERERmDdvHq5du4abN2/i999/R3Z2NgwNDWFsbIx169bh7t27OHnyJPz9/Zud4MYMHToUffr0wbhx4xAUFITExESEh4fjs88+w6VLlx59gDbi4uICW1tbLF26FLdv38bhw4exYsWKFjmXuro69PT0pF5tOQ1TWVsLeh6doOchavC0HG2g59EJGraWbRYneRAIBOjmNx1Rp9YiIToYuem3cXrXYqioasCl+0vicKd2LMLF/yTftZvvNKTeCUPU6fXIz4xH1On1SL17Dt383hCHCdu/DHcjD2Lw1B+hqq4tviequkrUYamtqUbwXx8gKzUag6f8AKGwRhzm4S+Y7cmuA/cwbaId+vc2hqOdFpYs6IiKihocOyOZUvzZwo6YM91R/P71V2zh42kIK3MN2NloYvJYG7ww2BxBpyVTLHf8ew9dO+pi2kQ7WFtqYNgAM4wZYYm9h9vPyqAPy0nkyTrlZOeDcuIpKScn/1mEC3XKSTe/B+Xk1HrkZcYj6tR63LtzDt36ScpJVUUJsu/HIvu+aAp9UW4qsu/HSq3gF3ftKO7HXUBhTgoSY07g0Ia34NB1CGxdpe9Zak3dB76JmPO7EXN+N3LT43B233cozktDN1/Rc8nCDq7Asb8+EYd3852Corz7OLsvALnpcaJ9L+yB1+C3xGFqqiuRlRqLrNRY1NZUoqQgA1mpscjPShKHqawoEYcBgMLcVGSlxja6NHxrOx9bi37dlNDJVgAzA2CcrzKqqoFrCZIOw3hfZQztriS1j7OVAH5dlWCiJ3pkgpOlAOdipTsZAgDdnZUQFV+L2kb6deqqQFd7Aa7clf+9dXV5DpiBGxd248aFPcjNiEPI/gAU56WJn0sXfmgFgrcvEod36yv6/kP+DUBuRhxuXNiDGxf2oPtAyffv0W8akm+H4fKJ9cjLiMflE+uRevscPPq/8djnra2pxtHAD5CZGo3hr/2A2toalBRmoaRQ0q4amjtB38Qep3d9iYykayjITkbk6U1IuR0OJ7fWfyZiW9Wl9qKt21cAqCwvRvy1IHTu2fgtSkT1NfuhasuXL0dtbS2mTZuGoqIi+Pj4ICgoCIaGhlJhPvjgA9y5cwceHh44cOBAk/e3PeTq6opjx47h008/Rc+ePaGpqYlevXph6tSpUFJSwj///IP58+fDzc0NHTt2xOrVqzFw4MDmJkEmgUCAI0eOYMmSJXjrrbeQlZUFCwsL9O/fH+bm7fPBzIBoiuXff/+Nd955Bx4eHujRowe++eabJu9VfFboe7uhz4mt4vddfvwUAJCyZS+uzVzcVtGSC48Bs1BdVY7Q/ctQWVYAM1t3jJq1EWrqkqk7xfn3paY4Wzh4YcjUFYg49jMuHVsNPSNbDH1tJczsPMRhbpwXLVV9aO10qfMNmPgdOvpMQElBOpJunAQA7Pl5nFSYl2ZvhpVzL3kn9als25MCdTUl+L/TAbo6qrhxuxALv7iGsjLJlDBzUw2pfzg1NZTx4TsuMDNWR0VlLZJSS7FsxU2cDJVMI7p5pwiffheDOdMdMWOKPdIyyrB6/V0En2neva0tzWPgg3KybxkqHpSTF9/eKDXFS1Y5GfrqCkQE/YyIY6uhZ2yLIa+thHmdcpKVGo2DayX/iJw7tBwA4Oo9DoMmi/4uLczEuYPLH0xNMoWr91h4DXmnpZPcJFevUSgvzcPFoN9QUpgJY0tXjJmzDnpG1g/inIWiPMnsBn1jW4yZvQ4h+wNwLXQbdPTNMGDCErh4jBCHKSnIxN8/jhO/v3JqE66c2gRr5554+X1R+5OZHI29v0rqVMj+AABA5x7jMey15S2Z5McSGlMLFRXgpV7K0FAH7mUJsfV4NSrrPJZRXxsQCiXlJCVLiN1nazC4uzIGeSohrwjYdbYG97KlO2dOlgIY6AgQeafxUSU3BwEgAK4ntOzIU4fuo1Bemo+IY7+ipDALxpYd8NLbayXff1GW1D/PesY2GD1rLUL/XY7roduhrW+G/uOlv39LRy+MmLYC5//7GReOroa+sS1GTF8JC3uPxz5vcUE6EmJE7eo/K8ZJxXncvM2wcekFZWVVjH57LcIPrcChje+gqrIU+sZ2GDp1ORy6DGipLGtUW9Wl9qQt21cAuBt1GIAQzp4vtmAq6VkjEDb1ILZmOn36NAYNGoS8vDyp+9Lo2XVYtS1u6G6/bu5q3mMongf7Np5t6yi0OxNmtYdFRtoXdbVnc1rM08jMUrxl4luaiXGzf49+5ilx8YkGKipbdnRYEfmPbZ/l5NKAPo8O1EJ8zpxrs3O3FC5nSEREREREpOBatWM3cuRI6OjoyHx99913rRkVuUhOTm40PTo6OkhOTm5y/5CQkCb3JyIiIiJ6VgmUlNrs9SyS65yGgQMHoqmZnRs2bEBZmeyH3BgZKd7qilZWVk2uQGllZdXk/j4+Pi22giURERERET0/WnWyurW1dWuersU9fDTCk9LU1Hyq/YmIiIiIiIBW7tgREREREREBgICL/8jVsznBlIiIiIiI6DnCETsiIiIiImp1SsocsZMnjtgREREREREpOHbsiIiIiIiIFBynYhIRERERUavj4inyxRE7IiIiIiIiBccROyIiIiIianUCJY4xyRNzk4iIiIiISMFxxI6IiIiIiFod77GTL47YERERERERKTh27IiIiIiIiBQcp2ISEREREVGrU1LmVEx54ogdERERERGRguOIHRERERERtTouniJfHLEjIiIiIiJScOzYERERERERKThOxSQiIiIiolYnUOIYkzwxN4mIiIiIiBQcR+yIiIiIiKjVcfEU+eKIHRERERERkYLjiB0REREREbU6jtjJF0fsiIiIiIiIFBw7dkRERERERAqOUzGJiIiIiKjVcSqmfLFjR0/l5q6bbR2FdqXTxE5tHYV2Z+5JlpH6DAeznNQXv5/lpD4LM9W2jkK7U1hc29ZRaHc01Ns6Bu2PGqsOPafYsSMiIiIiolbHB5TLF3OTiIiIiIhIwbFjR0REREREpOA4FZOIiIiIiFqdkjIXT5EnjtgREREREREpOI7YERERERFRq+PjDuSLI3ZEREREREQKjiN2RERERETU6vi4A/libhIRERERESk4duyIiIiIiIgUHKdiEhERERFRq+PiKfLFETsiIiIiIiIFxxE7IiIiIiJqdRyxky+O2BERERERESk4duyIiIiIiIgUHKdiEhERERFRq+Nz7OSLuUlERERERKTg2LEjIiIiIqJWJ1AStNnrSfz2229wdHSEhoYGvL29ERIS0mT4bdu2wcPDA1paWrC0tMSbb76JnJycJzr342DHjoiIiIiIqAk7duzAggULsGTJEkRGRqJfv34YOXIkkpOTZYYPDQ3F9OnTMXPmTMTExGDXrl2IiIjArFmzWiyO7NgREREREVGrEygptdmruVauXImZM2di1qxZ6Ny5M1atWgVbW1v8/vvvMsOfP38eDg4OmD9/PhwdHeHn54c5c+bg0qVLT5ttjWLHjoiIiIiInisVFRUoLCyUelVUVMgMW1lZicuXL2P48OFS24cPH47w8HCZ+/Tt2xepqak4cuQIhEIhMjIysHv3brz44otyT8tD7NgREREREdFzJSAgAPr6+lKvgIAAmWGzs7NRU1MDc3Nzqe3m5uZIT0+XuU/fvn2xbds2TJ48GWpqarCwsICBgQF++eUXuaflIXbsiIiIiIio9QkEbfZavHgxCgoKpF6LFy9+RHSlF10RCoUNtj1048YNzJ8/H1988QUuX76Mo0ePIiEhAXPnzpVb9tXH59gREREREdFzRV1dHerq6o8V1sTEBMrKyg1G5zIzMxuM4j0UEBAAX19ffPzxxwAAd3d3aGtro1+/fvjmm29gaWn5dAmQgSN2RERERETU6hTlcQdqamrw9vZGcHCw1Pbg4GD07dtX5j6lpaVQqrdIi7KyMgDRSF9LYMeOiIiIiIioCf7+/tiwYQM2bdqE2NhYLFy4EMnJyeKplYsXL8b06dPF4UePHo29e/fi999/R3x8PMLCwjB//nz07NkTVlZWLRLHdtWxW7p0KTw9PZu1z8CBA7FgwYIWiY8sTxLHtnb69GkIBALk5+c3GiYwMBAGBgatFiciIiIiIkUxefJkrFq1CsuWLYOnpyfOnj2LI0eOwN7eHgCQlpYm9Uy7GTNmYOXKlVizZg3c3NwwceJEdOzYEXv37m2xOAqELTUW+ASKi4tRUVEBY2Pjx94nNzcXqqqq0NXVbcGYSTxJHOVt4MCB8PT0xKpVqx4rfGVlJXJzc2Fubt7oDZ6BgYFYsGBBk50/WVbsf/riIxQKcfn4Gty8sBMVZYUws3OH79gvYGTRocn94q8H4dKx1SjMSYaesR16jFgAR7dh4s8jT61FYnQw8jPjoayqAXP77ug16kMYmDoBAGprqhAR9DOSb51BUU4q1DR0YN2hL3qO9Ie2nuz50o/SaWKnJ9pPXoz8fOD04Uzoe7lBw8oMl16eh4wDJ9o0Tnknb8rlOJdObce5oI0oys+CqZULRkz5FHauPo2GT7p1Ecd2LEfW/bvQNTBD3xdmwXvgFKkw5aWFOLVvFW5eCUZZSQEMTGwwbNIidHAfIA5TmJeBE7t/RFz0WVRVVcDY3AGj3/gGlg5uT5wWw8EsJ/XF75dPOREKhbgYtAYx53eiorQQ5vbuGPDyFzB+RHty92oQLhxdjYLsZOib2KH3yAVwdh8mFeZ62HZcObURpYVZMLJwQb9xn8LKSXYZPLXzC8Sc3wm/sYvhOeCNJ0qLinLzpgrJcjVkGy6f3IiSwiwYW3TAgAmfwtq58XqTevcizu5bjpz0O9DWN4PP4Flw95sq/jwn7Q7OHVmNjNQYFOXeQ//xi+E1cIbUMS4Gr0Xc1WPIzYyHiqoGLB27w2/0RzAyd3rq9BQW1z71MQBROYk8+StuRexEZVkhTG3d0Wf05zA0b7qcJEYfw5Xjq1GYmww9Izt4DfsADl0l5ST2wt+4eeEfFOffAwAYmLnAc9A82HbsLw6zaUlnmcfu8cJH6NZvZrPToqHe/HIiFAoRcWwNblx4UE/s3NF/wqOvu3HXgnDx6GoU5CRD39gOvUYugFM36XoSHbYdkac3orQoC0bmLvAdK11PqipKcO7wCiTEnEB5ST50jazh7jcNbn2n1j8dhEIhDm+YjeRbIXhhxho4uQ19rPQ9mO32VK6FbkfkKVHdeVjfm6o79+5eRMi/y5GbfhfaembwGjwL3Xylrzl3rwbh/H+SdqbPKOl25sLRX3Ax6FepfbR0TTBzWehTp+e9UU/fnrSE+wsbfu+txeqnv9vs3C2lXYzYCYVCVFdXQ0dHp9kdJiMjo1br1AF4ZBwrKytb7NxVVVVPtN/DJVYb69S1tatnNuB6SCB8x32O8e/vgqaOKY5seAuVFcWN7pORFIkT2/3RwWsMXlnwLzp4jcHxbQuRmXxVHCYtPgJd+ryKse/uwIuzNkFYW40jG2ahqrIUAFBdWY7sezfgNXgeJnywB8Om/YKCrEQEBc5r8TS3FGVtLRReu4WYD5a1dVTkKubiEQT9EwC/UXPx9hf7YOfqg+0/z0ZBzn2Z4fOyUvH3z3Ng5+qDt7/YB79Rc3D0728RezlIHKamuhJ/rXwL+dn38MrcnzHv2//w0htfQ9dQ0qkvKylA4PKpUFZWwdQP1uOdZYcwbNIiqGvptXiaW9KzWk4A4MrJDYg6E4gBEz7HpIW7oK1rin//eAuV5Y23J2mJkQja6o+O3mMw9aN/0dF7DIK2LER6kqQ9uRN5BCH7A+AzdC4mf7gPVo4+OLhuNoryGpbB+OvHkZF8Ddp6Zi2Sxsd168oRnNkXgJ7D38FrH++HlbM39v/xNgpzZdebgpwU7F87G1bO3njt4/3oOWwuTu/9FneiJPWmqrIM+iY28Bv9IbT0TGUe597di3Dv9xqmLNyJCfP+hLCmBvt+n4mqitIWSeeTuB6yATFhgegz+jOMmbcTmjomOPrnTFRVlDS6T2ZyJE7t8Idz9zEY9/5+OHcfg1P/+CMzRVJOtPUs4DPCH2Pm7cKYebtg6dQbJ7a9h7yMO+IwU/53VurlN+FbQCCAfdfhsk7bIiJPbcDVs4HoN/5zvPLBLmjpmeLAuqbrSXpiJI795Q9X7zGY/OG/cPUeg2NbFyKjbj2JOoLQAwHwHjoXExfug6WTDw5tkK4noQeWI/lWKIZO/R5TPzkMj/5vIGT/N0iIbvjj0rWQzaIVDFvZ7Yf1fdhcTPloH6ycGq/vAFCQk4oD6+fAyskHUz7aB59hc3B237e4e1VSd9ISI3F0iz86+YzB1I//RSefMTi6WbqdAQAjiw5466sQ8evVTw60aFrp2dJiHbuKigrMnz8fZmZm0NDQgJ+fHyIiIgBIpgYGBQXBx8cH6urqCAkJaTDNsbq6GvPnz4eBgQGMjY2xaNEivPHGGxg3bpw4TP2pmA4ODvjuu+/w1ltvQVdXF3Z2dli3bt1jx3vRokVwdXWFlpYWnJyc8Pnnn0t1qOrHccaMGRg3bhwCAgJgZWUFV1dXcTy+/vprvPrqq9DR0YGVlVWD51YkJydj7Nix0NHRgZ6eHiZNmoSMjIwG59q0aROcnJygrq6ON954A2fOnMHPP/8MgUAAgUCAxMTEJtMkaypmYGAg7OzsoKWlhfHjxyMnJ+ex80iehEIhroduQffBc+HoNhxGFq4YNHk5qqvKcTfyUKP7XQ/dAhuXvug+aA4MzJzQfdAcWLv0xvXQzeIwo2ZuQEefCTCy6ABjq04YMDEAxfn3kZ0aAwBQ09TFi29vgrPHSBiYOsHc3hN9x36G7HsxKG6k8W7vsoLO4vaXq5C+P/jRgRXI+eBAdPd7Gd37T4SplTNGTPkUeoYWuHRa9q9tl8/8Az0jS4yY8ilMrZzRvf9EePpNwLmgTeIwUaF7UV5SgEnvroFtBy8YGFvDroM3LGwlo2nh/22AnpElxrwVAGsndxiY2MCxcx8Ymdm1eJpb0rNaToRCIa6e3QKfoXPh7D4cxpauGPrqclRXluP2lcbbk6tnt8DWtS98hs6BobkTfIbOgU2H3rh6VtKeRJ0JRJdeL6Nr74kwMndGv/GfQsfAAtfDpMtgcX4Gzuz9GsNe/wFKym278PSV03+ia++X4dZnIowsnDFwwhLoGFrgWpjsenMt7B/oGlpi4IQlMLJwhlufiejaawIun5LUGwt7d/QbuwgdvV6EsoqazOOMf2cjuvaaAGPLDjC17oRhrwWgKO8+MlJiWiSdzSUUChETtgUeA+fAoetwGJq7ov8ry1FTVY64q42Xk5jwLbBy7guPAbNhYOoEjwGzYeXcGzHhW8Rh7DoPgm3HAdA3cYS+iSN8hi+AipoWsup0/rR0TaVeybEnYenYC3pGti2a7oeEQiGuhWyB95C5cO4mqidDpojqyZ0mrrtXQ7bAtkNfeA+ZA0MzJ3gPmQPrDr1xNURST66eCUTnni+jSy9RPfEbK6on0eckZS4jMQqdfMbB2qUX9Ixs0LX3ZJhYdkRmarTU+bLv30TUmUAMnvSt/DPhEaJOS9f3/o3U94eiw/+BroEl+o//FEbmzujaeyK69JyAyDp1J+qMpJ0xetjOuPZG1JnNUsdSUlKGtp6p+KWpY9SiaW1rirJ4iqJosY7dJ598gj179mDz5s24cuUKXFxcMGLECOTm5kqFCQgIQGxsLNzd3Rsc4//+7/+wbds2/PnnnwgLC0NhYSH279//yHOvWLECPj4+iIyMxLx58/DOO+/g5s3Hm+ajq6uLwMBA3LhxAz///DPWr1+Pn376qcl9Tpw4gdjYWAQHB+PQIUmj+MMPP8Dd3R1XrlzB4sWLsXDhQvFqOkKhEOPGjUNubi7OnDmD4OBgxMXFYfLkyVLHvnv3Lnbu3Ik9e/YgKioKq1evRp8+ffD2228jLS0NaWlpsLVt3sXgwoULeOuttzBv3jxERUVh0KBB+Oabb5p1DHkpyk1FWVEWbDr4ircpq6jB0qkHMpIiG90vIykK1q6+UttsXP2QkRTV6D6V5UUAAHUt/abDCARQ01TsEZlnSU11JdKSYuDUVfr7du7qi9Q42WXkXlwUnBuE90NaUgxqqkU/1NyOOglrJ0/8t30ZVi70xR9fjEbo4T9QW1sj3uf21ZOwsnfD7t8/wIqFfbHuq/G4cnannFNI8lKYm4rSoizYdZRuT6ydeyAtsfH2JD0xSmofALDr5If0xCgAojKYmRoD23ptjm1HX6TXOa6wthbB2z+B16CZj5z62dJqqiuRmRID+45+UtvtO/oiLUF2XqQnRsG+Xj7Yd+qHzORo1NQ82YwRAKgsE7W9Gk20va2pKC8VZcXZsHaRLicWDj2Qmdx4OclMvgrrDtKr31l38G10n9raGsRfO4zqylKY2nnKDFNWnI2UW2fg6vNy8xPyhB7WE9t69cTKuYdUea4vIylKah8AsOsoXU+y7smoJ66+yKhzXAtHLyTEnERxQQaEQiHu3T2P/OxE2NUpq1WVZQj+60P0H/95oyPDLeVhfW/QJnT0bbQdaawNyUyJEdcdmWHq5N9D+dlJ2PRlP2z+egiObvFHQXbKU6aInict8nNiSUkJfv/9dwQGBmLkyJEAgPXr1yM4OBgbN25Ejx49AADLli3DsGHDGj3OL7/8gsWLF2P8+PEAgDVr1uDIkSOPPP+oUaMwb55oOt2iRYvw008/4fTp0+jU6dH3tXz22Wfivx0cHPDhhx9ix44d+OSTTxrdR1tbGxs2bICamvSvl76+vvjf//4HAHB1dUVYWBh++uknDBs2DMePH8e1a9eQkJAg7pht3boVXbt2RUREhDiPKisrsXXrVpiaSho2NTU1aGlpwcLC4pHpkeXnn3/GiBEjpOIWHh6Oo0ePPtHxnkZpURYAQFNXenqrpo5xk6NmZcXZ0NKR3kdLx1h8vPqEQiHOHVoOCwdvGFm4ygxTXVWBi/+tgIvnS1DT0GlOMqgFlRbnQVhbA2096e9bW88YxQXZMvcpLsyCtp5fg/C1NdUoLc6DroEZ8rJTkH/zPLr1Ho2pH6xFTmYSjm5bhtraGvQf/S4AIC8rBZdO/43ew2fA98U5uJ9wDUF/fwtlFTV49B3XIumlJ1da2Eh7omvc6BQqACgtyoamTsM2qOTB8cpKRGVQq95xtXSNUVokKYOXT66HkpIy3PtNe6p0yIM4znr142zSaDtZUpgN+04m0uH1jFFbW43y4jxo6zd/aqlQKMTZ/QGwcvKGiZXstre1lT34zjR1pNOqoWOMkvymrzv199HUMREf76Hc9Ns4tHYqaqoroKqmhSGv/QJDMxeZx7xzZT9U1bVh36Xx/4Xk7eH3L+sa+iT15OHxyh+Uufr1T0vXGCl18qjfuCU4vetzbPl6AJSUVACBAIMmfQNLR29xmLADAbBw6A5HtyFPlsin0Fh919Q1Rmmh7GtOaVEWNHWlrzlautJ1p7QoW2Yb8rCdAQBzew8Me3U5DEwdUFqUg0vBv2P36ql4ddFBaGobyimF7YtAqV3cFfbMaJGOXVxcHKqqquDrK/llQlVVFT179kRsbKy40+Lj0/hNqAUFBcjIyEDPnj3F25SVleHt7Y3a2qZvnq47+icQCGBhYYHMzMzHivvu3buxatUq3L17F8XFxaiuroaeXtOjN926dWvQqQOAPn36NHj/cMGT2NhY2NraSo22denSBQYGBlJ5ZG9vL9Wpk4fY2FhxZ7lu3B7VsauoqEBFRYXUtuoqNaioPt7DHQHgTuRBhOz9Uvz+hTf/AAAIUG9IXIhHz6uv97loF9n7hP37NXLTb2HM3O0yP6+tqcKJ7f4QCoXwG/elzDDUtup/t0Jh49/3gx2k3wuljyMU1kJbzxgvTl8GJSVlWDq4oTg/E+eCNok7dkKhEFYOXTF4gj8AwNKuC7Lu3cXl03+zY9cO3Lp8EKd3SerrS7MetCcyvvsGbUw9sspSg22yytSDbZkp0bgWshWT/Pe0s/uZ68dFKGNb3eD10yiUvf0xndq9DFn3b2PSB7Lb3tYQF3UQYf8uFb8fNv33RkIKHyOdDfOn/vetb+KAce/tRWVZERJjjiFk92KMfHuLzM7dnct74ezxUrOuo811+8pBnN4tqScvzhTVE1nX0Eel/3HqSf26JipCkm3XQrciI/kqRr35G3QMrZEWH4Gze7+Clq4pbF37IiHmJO7dvYBJC1tu5cDHIisvmsgfWdeo+vs8Kv8cOveX+szSwRNbvh2OmxH70X3gm48RaXretUjH7uFCmw0LuXQDqK2t/chjyTrGo6iqqjY4xqM6gwBw/vx5TJkyBV999RVGjBgBfX19/PPPP1ixYkWT+z1OOurGBWiYFw89SR4115MuhBoQEICvvvpKatuwyV9gxJSlj30M+y6DYGYr6XjXVIsWmyktyoZWnYUGykpyGvwyWJemjonUL+UAUFac0+DXVEDUqUu6cRKj5/4FHYOGo5y1NVU4vm0hivJS8dLbgRyta2e0dAwhUFJuMDpXWpTTYBTvIR09U5TUC19SlAMlZRVoahuIwuibQllZFUpKkuXTTCydUVyQhZrqSiirqEFX3xQmltL/jJlYOuPmlWNySBk9Lceug2BuV6c9qXnQnhRmSy1cUlac02AUoS7RKFbD9kRLV9SeaGqLymD9X+tLi3PEox734y+jtDgHm78eLP5cWFuDsAP/h6tnN+ONz08+YSqfTKNxLpKkqz5tPRPxqKckfC6UlFSg8aDeNMep3V8jPvokJs7/C7oy2t7WYtd5MExlXHfKiqWvO+XFuY+87pQVS+dPWUkONOrto6yiBj1j0fLnJjZuyLp3HTfCt8J3nPT1Mz3xEgqyEzBwysonS9hjcugyCJP9ZV9369eT+qN4dTVWTx5edzUeljmZdUl03Oqqclz4bxVeeOMXOHQZCAAwser44H66TbB17Yt7d8+jICcZGz7vKXWcoM3zYenojXHztjYzB5qnsbpTVpTTYMTtIS1d04bhi3Ok6o6WrglKmlEfAUBVXQvGlq7Iz0p6gpTQ86hFxj9dXFygpqaG0FDJ8qxVVVW4dOkSOneWvcxvffr6+jA3N8fFixfF22pqahAZ2fj876cVFhYGe3t7LFmyBD4+PujQoQOSkp68Mp0/f77B+4fTQbt06YLk5GSkpEjmTt+4cQMFBQWPzCM1NTXU1NQ0GaYpXbp0kRm3R1m8eDEKCgqkXkNeXtysc6up60DfxF78MjR3gaauKVLvhIvD1FRXIi0+Aub23Rs9jrm9J+7V2QcA7t0Og7m9p/i9UChE6P5lSIgOxkuzA6FnZNPgOA87dQXZSXhx1p/QeEanOigyZRU1WNp3RfwN6e87/kY4bJxllxFrZ8+G4WPCYGnfFcoqoh9+bF28kJuZBGGdH31yMhJFHb4Hi0LYuHRHTkaC1HFyMxKhb9wyDxal5lHT0IGBqb34ZWTuAi1dU6Tclm5P7sVFwNKh8fbEwsETKbeky0vyrTBYOHgCEJVBM5uuUscFgJTb4bB4cNyOPqIVNad8uE/80tYzQ/dBMzFmzgY5pfjxKauowcy2K5JvhUltT74VDktH2Xlh4eCJ5Hr5kHQrFGZ2blBWVpW5jyxCoRCndi/D3WvH8PK7m6Fv3DqLgjRGVV0besb24peBmQs0dUxw7650OUlPjICZXePlxMzOA/fv1rvu3Alvch8AgFDSmarr9qU9MLbqCmPLln38iZpGw+uulq4pUuvVk/txEeLyLIu5vWfDOlCvnphaN6wnqbfDYf7guLU11aitqYJAIP3vp0BJCUKhqC32GvQ2Jvv/i0kL94lfAOA75n8YPDngyTKhGRqr78m3wxttRywcPJFcP/ytMJjZdhXXHQuHhvlXt52Rpaa6ErkZcdBu5fsMWxMXT5GvFunYaWtr45133sHHH3+Mo0eP4saNG3j77bdRWlqKmTMf/xkt77//PgICAvDvv//i1q1b+OCDD5CXl9di01xcXFyQnJyMf/75B3FxcVi9ejX27dv3xMcLCwvD999/j9u3b+PXX3/Frl278MEHHwAAhg4dCnd3d7z22mu4cuUKLl68iOnTp2PAgAFNTlEFRPf+XbhwAYmJicjOzn6s0ci65s+fj6NHj4rjtmbNmse6v05dXR16enpSr6edPiIQCNDNbzqiTq1FQnQwctNv4/SuxVBR1YBL95fE4U7tWISL/0lGTt18pyH1ThiiTq9HfmY8ok6vR+rdc+jmJ3leVNj+ZbgbeRCDp/4IVXVtlBZlobQoC9VV5QBEF5jgvz5AVmo0Bk/5AUJhjTiMrIuwIlDW1oKeRyfoeYj+UdBytIGeRydo2Fq2ccyeTu9hMxAZshtRoXuQdT8Ox/4JQEFumvi5dCf2rMD+jYvE4b0HTEFBzn0c2xGArPtxiArdg8jQPegz4i1JmIFTUVacj6B/vkVOegLuXDuNsMNr4TPoNanz3ou/itDDfyA3IwnXLxzElbM7pcIoome1nAgEAnj0n45Lx9ci7lowctJu4/jfi6GipgFXL0l7Erx9EcIPSdoTj37TkHw7DJdPrEdeRjwun1iP1Nvn4NFf0p54DpiBGxd248aFPcjNiEPI/gAU56XBra+oDGpqG8LY0lXqpaSsAi1dExiaPf3z256E18A3EX1+N2LO70ZuehzO7P0ORXlpcH/wbK3QgysQ9Jfk/nF33ykozLuPM/sCkJseh5jzuxFzfg+8B0nqjWhhiVhkpsaitroSJQUZyEyNlRpROLXrK8ReOoCR01dATUMbJYVZKCnMQnVleeslvgkCgQBdfafj2pl1SIwJRl7GbYTs+RTKqhpw9pCUkzO7FuFSkGQ0rUuf6bh3NxzXzq5HflY8rp1dj/tx59C173RxmEvHfkJ64iUU5d1DbvptXDq2CukJF+Hs+ZJUHCrLi5EYHYSOPq+0fILrEQgEcO83HZdPrEX8dVE9OblDVE861LnuHv97Ec4dkdQT937TkHI7DFdOrkdeZjyunFyP1Dvn4NFPUk88BsxA7MXdiL0oqieh/wagKD8Nbr1FZU5NQwdWTj1w7tAPuHf3AgpzUnEzYi9uXfoXTg+eQ6ulZ9qgLgGAjqEV9Iwb/kDbEjwHzkDM+Tr1fZ90fQ8/tALHtkmuOW59p6Ao7z5C9gcgNyMONy7swY0Le9C9Tt3x7D8NybdE7UxunXam7nMuQ//9P9y7exEFOalIT7qKI3/OR2V5MTr1GNcq6SbF12JrMS9fvhy1tbWYNm0aioqK4OPjg6CgIBgaPv6IyKJFi5Ceno7p06dDWVkZs2fPxogRI6AsjydPyjB27FgsXLgQ7733HioqKvDiiy/i888/x9KlS5/oeB9++CEuX76Mr776Crq6ulixYgVGjBgBQNSw7t+/H++//z769+8PJSUlvPDCCw0eiSDLRx99hDfeeANdunRBWVkZEhIS4ODg8Njx6t27NzZs2IAvv/wSS5cuxdChQ/HZZ5/h66+/fqJ0Pi2PAbNQXVWO0P3LUFlWADNbd4yatRFq6pIpkcX596U69BYOXhgydQUijv2MS8dWQ8/IFkNfWwkzOw9xmBvnRcsSH1oruegCwICJ36GjzwSUFKQj6YZoetSen8dJhXlp9mZYOfeSd1JbnL63G/qckExT6fLjpwCAlC17cW1m80ZX25OuPUehrCQfZw/+iuKCLJhadcDUD9bCwNgaAFBckIXCOs+0MzS1wdQP1uLYjuW4dGo7dA3M8MLUJejsPUIcRt/IEq/5b8SxHcuxdulY6Bmao+fQaeg78m1xGCvHbpg47xec3LsSZw/+BgMTGwyfshjdeo9uvcS3gGe1nACA12BRe3JmzzJUlBXA3M4dY+dslJpiXZQn3Z5YOnphxLQVOP/fz7hwdDX0jW0xYvpKWNhL2pMO3UehvDQfEcd+FT3s27IDXnp7LfSMrFs1fc3R0WsUykvycD7oN5QWZMLY0hVj56wTx7mkMAuFeWni8PrGthg3Zx3O7AvAtZBt0NY3w8AJS9DBU1Jvigsysf2HceL3l09uwuWTm2Dt0hMT3xeVqYePU9j9i/QiMsNeDUDXXhNaKrnN0q3fLFRXVeDcgWWoLC+EqY07XnhzA1TVJbc/lBSkSY0smdt3x8DJK3Al+GdcOf4LdI1sMWjKCpjZSspJWXE2zu5ahNKiLKhp6MLQwhXDZ6yTWoETAOKvHYEQQjh5vNjyiZWh+yBRPTm7V1JPRr8tXU+K69cTBy8Mf20FLhz9GReDVkPP2BbDpq2Eed164jkKFSX5uBT8oJ5YdMBLM9dCt049Gf76Spw/shLHt3+M8tIC6BpaodfIBejaR/ph3m3JtfsolJfk42KQpL6Pnr1Wqu7UXeBN39gGY95ei5D9y3EtdDt09M3Qf/wSuHhI6o6loxdemLYC5/77Gef/e9DOvCHdzhQXZCBo64coK8mHpo4hLOw9MGnBjnbdzjytZ3XkrK0IhE96w1UbqK2tRefOnTFp0qQ264Q8LgcHByxYsEDqGXvPohX7Fab4tIpOE1t2So0iyjv5eI8aeZ4YDmY5qS9+P8tJfSrK/IenvsLi5s1QeR5oqLOc1NdCv/8rtPdGtc9ykrl4+qMDtRCzgC2PDqRg2vbpqY+QlJSEY8eOYcCAAaioqMCaNWuQkJCAV199ta2jRkRERERE1G6064dHKCkpITAwED169ICvry+uX7+O48ePP/YCLPV999130NHRkfl6+Lw9RTR37txG0zV37ty2jh4RERERUUNKSm33ega16xE7W1tbhIWFPTrgY5o7dy4mTZok8zNNTU25nQcAEhMT5Xq8pixbtgwfffSRzM8e9Qw+IiIiIiJSfO26YydvRkZGMDIyautoyJ2ZmRnMzMweHZCIiIiIqJ1oqZXun1fP5jgkERERERHRc+S5GrEjIiIiIqL2QfCM3uvWVpibRERERERECo4dOyIiIiIiIgXHqZhERERERNTqBEpcPEWeOGJHRERERESk4DhiR0RERERErY+Lp8gVc5OIiIiIiEjBsWNHRERERESk4DgVk4iIiIiIWh0XT5EvjtgREREREREpOI7YERERERFRqxMIOMYkT8xNIiIiIiIiBceOHRERERERkYLjVEwiIiIiImp9XDxFrjhiR0REREREpOA4YkdERERERK1OoMQxJnlibhIRERERESk4jtgREREREVGr4wPK5YsjdkRERERERAqOHTsiIiIiIiIFx6mYRERERETU+gQcY5In5iYREREREZGC44gdERERERG1Oi6eIl8csSMiIiIiIlJwHLGjp7Jv49m2jkK7MvfkzbaOQrtjOLhTW0eh3cljOWlAUNTWMWh/NDXaOgbtT2UVf92vr6a2rWPQ/ujrspzQ84kdOyIiIiIian1KnDwoT8xNIiIiIiIiBccROyIiIiIianUCAafNyhNH7IiIiIiIiBQcR+yIiIiIiKj18R47uWJuEhERERERKTh27IiIiIiIiBQcp2ISEREREVGrEyhx8RR54ogdERERERGRguOIHRERERERtT4Bx5jkiblJRERERESk4NixIyIiIiIiUnCciklERERERK2Pi6fIFUfsiIiIiIiIFBxH7IiIiIiIqNUJuHiKXDE3iYiIiIiIFBxH7IiIiIiIqPXxHju54ogdERERERGRgmPHjoiIiIiISMFxKiYREREREbU6gRLHmOSJuUlERERERKTgOGJHREREREStT8DFU+SJI3ZEREREREQKjh07IiIiIiIiBcepmERERERE1Pq4eIpcMTeJiIiIiIgUHEfsiIiIiIio9XHxFLniiJ2cDBw4EAsWLAAAODg4YNWqVW0aHyIiIiIien5wxK4FREREQFtb+7HCOjg4YMGCBeJOIUm8NdUeY0ZYQldHBTduF2HlH3eQkFzaaPj+fUwwfaIdrC01oaIiQOr9MvyzPwVBpzKlwpkYqeGdGU7o7W0EdXUlpNwrw/LVt3Arrrilk/TYLp3ajnNBG1GUnwVTKxeMmPIp7Fx9Gg2fdOsiju1Yjqz7d6FrYIa+L8yC98ApUmHKSwtxat8q3LwSjLKSAhiY2GDYpEXo4D5AHKYwLwMndv+IuOizqKqqgLG5A0a/8Q0sHdxaLK0tzcjPB04fzoS+lxs0rMxw6eV5yDhwoq2jJRdtUU4qyotxev9q3LpyHCVFObCw64wRU5bAyrFbi6ZVlmuh2xF5aiNKCrNgZOGCfuM+hbVz4+m/d/ciQv5djtz0u9DWM4PX4Fno5iud/rtXg3D+v9UoyE6Gvokd+oxaAGf3YZJjxEXgysmNyEqNQUlhFka9tQbO3YZKHSN4+/9wM2K/1DZzew9MWrDj6RPdTJdPb8OFYxtRXJAFU6sOGDrpU9h2aDyPkm9fxIldy5F1/w50DczQa/gseA2YKv78WvheHN68uMF+H6+5BhVV9QfHiMCFYxuRnhyN4oIsvPzOr3D1HNpgn9YiFAoRcWwNblzYiYrSQpjbuaP/hC9gZNGhyf3irgXh4tHVKMhJhr6xHXqNXACnbsOkwkSHbUfk6Y0oLcqCkbkLfMd+CisnSf6WFmXj3OEfkXI7DJVlRbB08kG/cZ/BwNRBEqYwC+GHfkDKnXBUlZfAwMwR3oNnw9njBbnmw6MIhUJcDl6D2As7UVFWCDM7d/iNe3Q+xV8PQkTQahTmJEPP2A49X1gARzdJPt2Pj8DVMxuRnRqD0qIsDJ++Bo5uDctDXkYcLhz5EWkJERDW1sLQogOGvvYTdA2t5J7WJ9UW9elZxweUyxc7di3A1NS0raOg8F572RaTx9ng21W3kHKvFG9MtsdPy9wx9Z0IlJXVyNynqKgKW3YmISm1DFXVtfDtYYzFH3RCXn4VLkbmAQB0tVXw+/fdceV6Pj5aeh15BZWwttBEUUl1ayavSTEXjyDonwCMeu0L2Lh44crZHdj+82y8s+wQ9I0bXuDyslLx989z0L3/RIyb9QNS717BkW3LoKVriM7eIwAANdWV+GvlW9DWNcYrc3+GrpE5CnPToaYh+QGirKQAgcunwqFjL0z9YD209YyQl5UCdS29Vkt7S1DW1kLhtVtI3bwX3rvWtHV05KatysmhwM+Ref8Oxs76P+jqm+H6+QP4a+WbmLvsMPQMzVst/bcjjyBkfwAGvvIFLB29EB2+AwfXzcZr/zsk8x/BgpxUHFg/B117T8Tw139AWsIVnN69DJo6hnDxEKU/LTESR7f4o/fI+XDqNgzx14NxdPNCvDx/GyzsPQAAVZVlMLHuhM69JuC/P+c3Gj+7Tv0wdOp34vfKyqpyzoFHuxFxBMd3BmDEq1/CxtkLkWf/wY5f3sbbSw9D36hhHuVnp2DnL7Ph4TcRo9/6AalxVxC0/Sto6Rqhk9cIcTh1DR3MXnZUat+6/4RWVZbCzKYj3PtOwN6177dcAh9T5KkNuHo2EIOnBMDAxAGXT/yBA+vewquf/Ac1DR2Z+6QnRuLYX/7oOUJSFo5tXYjx726D+YOycCfqCEIPBKD/hC9g4eCFG+d34NCG2Zj6sagMCoVC/Bf4LpSUVDFyxm9Q09DG1bOBOLD2LUz9+BBU1bUAAMf/XoTK8iKMevM3aGgb4k7kIRz7yx+vmNjB1LpLq+XT1dMbcC0kEAMnBcDA1AFXTvyBw+vfwuSPm8inpEgc3+aPHsPnw8FtGBKjg3H8r4UYM28bzO1E+VRdWQZjy07o6DMBwVtl15mCnGT8+/ur6NTjFfgMfx9qGrrIy4xrV52btqpPRM3BbvITKCkpwfTp06GjowNLS0usWLFC6vP6UzGXLl0KOzs7qKurw8rKCvPnixq2gQMHIikpCQsXLoRAIIDgwTzjnJwcTJ06FTY2NtDS0kK3bt3w999/S51j4MCBmD9/Pj755BMYGRnBwsICS5culQqTn5+P2bNnw9zcHBoaGnBzc8OhQ4fEn4eHh6N///7Q1NSEra0t5s+fj5KSEjnm1JObOMYaW3Ym4+y5bCQkl+Lbn25CXV0ZwweYNbpPZHQBzp7PQVJqKe6nl2PXwXuISyyGexd9cZjXXrFFZnYFAn6+hdg7RUjPrMDla/m4n17eGsl6LOeDA9Hd72V07z8RplbOGDHlU+gZWuDS6b9lhr985h/oGVlixJRPYWrljO79J8LTbwLOBW0Sh4kK3YvykgJMencNbDt4wcDYGnYdvGFh20kcJvy/DdAzssSYtwJg7eQOAxMbOHbuAyMzuxZPc0vKCjqL21+uQvr+4LaOily1RTmpqixH7JVjGPrKR7B37QEjc3sMGPs+DExscLmR87aUqNOB6NLrZXTtPRFG5s7oP/5T6BhY4HqY7HhEh/8DXQNL9B//KYzMndG190R06TkBkafqpP/MFti69oXP0DkwMneCz9A5sHHtjagzm8VhHDr3R59RC+DiPrzJ+CmrqEFbz1T80tA2kEu6m+Pi8T/h4fsyPP0mwsTSGcMmL4GeoQUiz8jOo8gHZWTY5CUwsXSGp99EePhOwIVjm6QDCgTQ0TeVetXl7DYAA8YtREevpvOoNQiFQlwL2QLvIXPh3G04jC1dMWTKclRXluNO5KFG97sasgW2HfrCe8gcGJo5wXvIHFh36I2rIZKycPVMIDr3fBldeonKoN9YURmMPifK34LsRGQkXcWAl7+EuV03GJo5of+EL1FVWYI7UYfFx0lPikI3v9dhbucOfWNb+Ax9B2qaushKvdFyGVOPUCjE9dAt8Bo8F07dhsPIwhWDJi9HdVU57kY1nk/XQ7bApkNfdB8syqfug+fAyqU3rtfJJ7tO/dHzhQVw6tZ4eYg4ugp2nQag94sfw8S6C/SMbWHfeSA0dYzlms6n0Vb1iag52LF7Ah9//DFOnTqFffv24dixYzh9+jQuX74sM+zu3bvx008/Ye3atbhz5w7279+Pbt1EU5b27t0LGxsbLFu2DGlpaUhLSwMAlJeXw9vbG4cOHUJ0dDRmz56NadOm4cKFC1LH3rx5M7S1tXHhwgV8//33WLZsGYKDRf+81tbWYuTIkQgPD8dff/2FGzduYPny5VBWVgYAXL9+HSNGjMCECRNw7do17NixA6GhoXjvvfdaKtsem5W5BkyM1MWjbABQVS1EVHQ+3Do9/uiRt7sB7Ky1EBVTIN7m29MYN+8W4etFXXBwax9sWuWF0cMt5Br/p1FTXYm0pBg4dfWV2u7c1RepcZEy97kXFwXnBuH9kJYUg5rqKgDA7aiTsHbyxH/bl2HlQl/88cVohB7+A7W1ktHP21dPwsreDbt//wArFvbFuq/G48rZnXJOIclDW5WT2tpqCGtrGvyarKKqjpQ7stvAllBTXYnM1BjYdZROj11HX6Qlyk5/emJUw/Cd/JCZEoOamqrGw3T0Q3piVLPjeO/uRWz4vC+2fjcCJ3Z8jtKinGYf42nUVFciPTkGjl38pLY7dmmijMRHwbGLb73w/ZCeFC3OIwCorCjFr4sHYc2i/ti5Zg7Sk1uvA9JchbmpKC3Kgm2d71VZRQ1Wzj2Q3khZAYCMpCipfQDpslBTXYmsezGwdZUOY+vqi4wHx62prnxwPkl9UVJShrKyGtISJPXF0tELd6OOoLw0H8LaWtyJPIya6ipYO/d8skQ/gaIH+WTjKp1Plk49kJHUeD5lJkfBpkP9PPBDRlLUY59bWFuL5NjT0DdxwOENM7H5q77Y98skJEQfb3Y6WgrrUwsSKLXd6xnEqZjNVFxcjI0bN2LLli0YNkw0h3zz5s2wsbGRGT45ORkWFhYYOnQoVFVVYWdnh549RY21kZERlJWVoaurCwsLSefC2toaH330kfj9+++/j6NHj2LXrl3o1auXeLu7uzu+/PJLAECHDh2wZs0anDhxAsOGDcPx48dx8eJFxMbGwtXVFQDg5OQk3veHH37Aq6++Kr63r0OHDli9ejUGDBiA33//HRoaGnLIrSdjZKgGAMjNr5TanpdfCXOzpuOlraWMfYF9oKYqQE0tsPL3O7gUJekgWlloYtxITezYn4otu5LRxVUXC2a7oKpKiKOnMuSfmGYqLc6DsLYG2nrSv1Jq6xmjuCBb5j7FhVnQ1vNrEL62phqlxXnQNTBDXnYK8m+eR7feozH1g7XIyUzC0W3LUFtbg/6j3wUA5GWl4NLpv9F7+Az4vjgH9xOuIejvb6GsogaPvuNaJL30ZNqqnKhr6MDG2RMhB3+DiaUTtPVMEH3hMO4lXIORmX2Lpbe+shJR+rV0pdOvqWuM0kLZ6S8tyoKmrnT6tXSNUVtbjfLiPGjrm6G0KLvBMbV0jVFSmNWs+Nl37g8XjxegZ2SFwpxUnP9vNfb9NgNTPtwDZRW1Zh3rSTVaRnRNGk1PcWE2nHRNpMPrifKorDgPOvpmMLZwwktvBMDUuiMqy4sRcXILtn4/FTM//xdG5g4tlZwnVlokSqtWvZEfLR1jFOXdb2K/7AajRZo6xuLjlT8og5oyyktKkagMGpg5QdfQCuePrMSAV76Cqpomrp4NRGlRFkrrfAfDX/8Jx/5aiE1f9IaSkgpU1DQwcsYv0DdpvdkSD9MlK83F+Y/IJ1n1sOjx60xZSQ6qKksRdWo9eoz4AL1GfYSUWyE4tvV9jJ69GVat2MFtDOsTKQp27JopLi4OlZWV6NOnj3ibkZEROnbsKDP8xIkTsWrVKjg5OeGFF17AqFGjMHr0aKioNJ71NTU1WL58OXbs2IF79+6hoqICFRUVDRZkcXd3l3pvaWmJzEzRQiFRUVGwsbERd+rqu3z5Mu7evYtt27aJtwmFQtTW1iIhIQGdO3dusM/DeNRVW1MJJeWn+0dl2AAzfPyuJJ6fLLv+IEL1AgoEDbfVU1pWgzc/uARNDWX4eBjivZnOuJ9ehsho0aidkgC4ebcI67YmAADuxBfDwU4L40ZZtYuO3UOCesv/CoUNt9XbQfq9UPo4QmEttPWM8eL0ZVBSUoalgxuK8zNxLmiTuGMnFAph5dAVgyf4AwAs7bog695dXD79Nzt27VRblJOxM7/HwcBPseqjARAoKcPSrgvcer7UNr8yy0prE+mXlV/195GVf03mqQyu3UeJ/za2dIWZrRsCvx6ChBunHzmFU/7qpRnCBtukg9cvI+JMAgBYO3nC2slT/LGNsxc2fTsel079heFTPnv66D6l21cO4vTuL8XvX5z5h+iP+t+9jG31PU5ZENTPX9GBAYjuqxzxxmqc2vkZNn3RCwIlZdh06AO7Tv2l9rl4dBUqSgsxZs6f0NA2REL0cQRtWYDx7/4FY0vZ/1s8rTtXDuLsXkk+jXxTdj492NjksernAR7VDtUjrK0FADh0HQz3/jMAACZWnZGRGIkb5/9pFx07ieerPrUKJT7uQJ7YsWsmofARPYt6bG1tcevWLQQHB+P48eOYN28efvjhB5w5cwaqqrJvpl+xYgV++uknrFq1Ct26dYO2tjYWLFiAykrpEaz6+wsEAtQ+aCA1NTWbjFdtbS3mzJkjvt+vLjs72b8SBgQE4KuvvpJOX4c3YNfxzSbP9SihF3Nw4/Yl8Xs1VdHwuJGhGnLyJGk21FdtMIpXn1AI3EsT3S93N6EE9rZaeH2iHSKjRZ3FnLxKJKZIr6yZlFKKgX3bx5x2LR1DCJSUG4y6lBblNPil8CEdPVOU1AtfUpQDJWUVaD64r0dH3xTKyqpQUlIWhzGxdEZxQRZqqiuhrKIGXX1TmFi6SB3HxNIZN68ck0PKSJ7aspwYmdnhjU/+QmVFKSrKiqFrYIY9fyyEgYnsWQstQVNblP76o3NlRTkNRtwe0tI1bRi+OAdKSiri+9+0dE1QUtgwT7Xq/ereXNr6ZtA1tEJBVtJTHac5HpYRWenR1pOdHh29hqMPJUW5UFJSgaaOgcx9BEpKsHTohrzMRHlE+6k5dBmEyf6SHz0fTocsLcqGtp7kHu2y4pwGo3h1aemaoLSoYXnR1BHlncbDMigjTN0yaGbjhsn++1FRVoTamipo6hhh98+TYGYrWmm4IDsZ18O2YcpHB8WrT5pYdUJawmVcD9uOga9IX3Plxb7LILxi1zCfymTlUyN1Cnh0Pj0ODW1DKCmpwNBc+vpjYO6M9ITWm+LdlOe1PpHieTYnmLYgFxcXqKqq4vz58+JteXl5uH37dqP7aGpqYsyYMVi9ejVOnz6Nc+fO4fp1UUdDTU0NNTXSqzyGhIRg7NixeP311+Hh4QEnJyfcuXOnWfF0d3dHampqo/Hy8vJCTEwMXFxcGrzU1GSPwC1evBgFBQVSLxuX15oVL1nKympwL61c/EpILkV2bgV6eBqKw6ioCODpZoDom4XNOrYAko4iAFyPLYCdtZZUGFtrLaRnto/FU5RV1GBp3xXxN8KltsffCIeNc3eZ+1g7ezYMHxMGS/uuUFYRdf5tXbyQm5kk/mUUAHIyEkX/yD+YGmbj0h05GQlSx8nNSJS5wiK1rbYsJw+pqWtB18AMZSUFiIsJRUfPwfJI2mNRVlGDmU1XpNyWTk/y7XBYOshOv4WDJ5Lrh78VBjPbruIVKy0cPBse81YYLBw8nyq+ZSV5KM5Pg5Ze6/2ApKyiBgu7rkiIDZPanhDbRBlx8kRCrHT6E26EwsLerdFVPYVCITJSYtvNgg9qGjrQN7EXvwzNXaCla4rUOt9rTXUl7sdFwKKRsgIA5vYNy0JKnbKgrKIGU+uGZTD1djjMZRxXXVMXmjpGyM9KRFZqNBy6iupLdVWZKEC9+30EAiVAWFv/MHLTaD7dkc6ntPgImNs3nk9mdp5S+wBA6p0wmNt7PnZclFXUYGrrhvws6etPQVZiu3nUwfNan0jxsGPXTDo6Opg5cyY+/vhjnDhxAtHR0ZgxYwaUGnkOR2BgIDZu3Ijo6GjEx8dj69at0NTUhL296H4UBwcHnD17Fvfu3UN2tuiXIBcXFwQHByM8PByxsbGYM2cO0tPTmxXPAQMGoH///nj55ZcRHByMhIQE/Pfffzh6VLSk7qJFi3Du3Dm8++67iIqKwp07d3DgwAG8/37jS1Orq6tDT09P6vW00zAbs+vAPUybaIf+vY3haKeFJQs6oqKiBsfOSJ5J99nCjpgz3VH8/vVXbOHjaQgrcw3Y2Whi8lgbvDDYHEGnJVMsd/x7D1076mLaRDtYW2pg2AAzjBlhib2HG7+HoLX1HjYDkSG7ERW6B1n343DsnwAU5KaJnzd2Ys8K7N+4SBzee8AUFOTcx7EdAci6H4eo0D2IDN2DPiPekoQZOBVlxfkI+udb5KQn4M610wg7vBY+g16TOu+9+KsIPfwHcjOScP3CQVw5u1MqjCJS1taCnkcn6HmIVnbUcrSBnkcnaNhatnHMnk5blZO46BDcjQ5BXlYq4mPCsPXHN2Bs4QgP3wmtl3gAngNnIOb8bty4sAe5GXEI2ReA4rw0uPUVpT/80Aoc2yZJv1vfKSjKu4+Q/QHIzYjDjQt7cOPCHnQfJEm/Z/9pSL4Vhssn1iM3Ix6XT6xH6u1z8BzwhjhMZUUJsu7FIuteLACgMCcVWfdixfdrVVaUIPTf/0NaYiQKc1ORevcCDm14Bxrahg2ed9fSeg59E1dDd+Nq2G5kp8Xh+M7vUJibhu79RXl0et8KHPzzE3H47gOmoDDnPo7vDEB2Whyuhu3G1bA96DVckkchB9cgPiYEeVkpyEiJxZEtnyIz5Sa695c8m6uyvAQZKbHISBHlUX52KjJSYlGQ2/rtrEAggHu/6bh8Yi3irwcjJ+02Tu5YDBU1DXTo/pI43PG/F+HcEckK1+79piHldhiunFyPvMx4XDm5Hql3zsGjn6QseAyYgdiLuxF7UVQGQ/8NQFF+Gtx6S56NePfqUdy7ewEFOSlIiD6Bg+vegqPbENh1FN3vaWDmBH0Te5zZ/SUykq+hIDsZUac3IeVOuMxnvbUUgUCAbn7TEXlyLRKig5Gbfhundy6GiqoGXDwl+XTyn0W48J8kn7r5TUPqnTBEnRLlU9Sp9bh35xy61cmnqooSZN+PRfZ9UXkoyk1F9v1YqXscPQbMRNzV/xB7YScKspMQHfYXkmJPoUufV1sh9Y+nrerTs04gUGqz17OIUzGfwA8//IDi4mKMGTMGurq6+PDDD1FQUCAzrIGBAZYvXw5/f3/U1NSgW7duOHjwIIyNRVMbli1bhjlz5sDZ2RkVFRUQCoX4/PPPkZCQgBEjRkBLSwuzZ8/GuHHjGj1HY/bs2YOPPvoIU6dORUlJCVxcXLB8+XIAohG9M2fOYMmSJejXrx+EQiGcnZ0xefLkp8scOdm2JwXqakrwf6cDdHVUceN2IRZ+cU3qGXbmphqorTMzVlNDGR++4wIzY3VUVNYiKbUUy1bcxMlQyVSIm3eK8Ol3MZgz3REzptgjLaMMq9ffRfAZ6YeYt6WuPUehrCQfZw/+Kn4I6tQP1sLA2BoAUFyQhcIcyQXR0NQGUz9Yi2M7luPSqe3QNTDDC1OXiJ9NBgD6RpZ4zX8jju1YjrVLx0LP0Bw9h05D35Fvi8NYOXbDxHm/4OTelTh78DcYmNhg+JTF6NZ7dOslvgXoe7uhz4mt4vddfvwUAJCyZS+uzWz4YFhF0VblpLysGKf2rkRhXjo0tQ3QyWsYBo1fKB71ay2u3UehvCQfF4N+RUlhFowtO2D07LXQMxKlv6QwC8V1/nHUN7bBmLfXImT/clwL3Q4dfTP0H79E/Aw7QLQ64QvTVuDcfz/j/H+roW9sixFvrBQ/ww4AMlOise9XyT+tof+K2tROPcZh2KvLoSRQRk7abdy89C8qyoqgrWcKa5eeeGH6T40+C6yldOkxCmUleQg7/BuKCzJhauWKSe+tg37dMpKbJg5vYGKLSe+vw/GdAbhyZht09M0wbPISqWduVZQV4r+/vkBJYRbUNXVhbtsFr330F6wcJdP60pKisX3ldPH7E7sCAADd+ozHSzOWt3SyG+g+aBaqq8pxdu8yVJQVwNzOHaPf3ij1fRTn3Ze6L8zSwQvDX1uBC0d/xsWg1dAztsWwaSvFz7ADgA6eo1BRko9LwQ/KoEUHvDRzLXQflEEAKC3MRNiB5Q+mNJqio89Y+Ax9R/y5srIqXpy5FuePrMCRTe+gqqIU+iZ2GDJlOew7D2jhnJHmMVCUT6H7RPlkZuuOF+vnU750Plk4eGHoqysQEfQzIo6J8mnIayvFz7ADgKzUaBxcK6kz5w6JyoCr9zgMmiz629FtGPpNWIrIk+sQ9u+3MDB1xPBpq2Hp6N3CqX58bVWfiJpDIGzuTWNEdfiNPtPWUWhX5n7S/9GBnjOGgzs9OtBzJu/kzbaOQruTX9TWMWh/dLS4qEB9BUX8l6W+mpabsamwjPRZd+qbMbCtYyBb+Y7v2+zcGpM/eXQgBfNsjkMSERERERHJ0W+//QZHR0doaGjA29sbISEhTYavqKjAkiVLYG9vD3V1dTg7O2PTpk1N7vM0OBWTiIiIiIhanwLd67Zjxw4sWLAAv/32G3x9fbF27VqMHDkSN27caHRF+UmTJiEjIwMbN26Ei4sLMjMzUV1d3WJxZMeOiIiIiIioCStXrsTMmTMxa9YsAMCqVasQFBSE33//HQEBAQ3CHz16FGfOnEF8fDyMjIwAiBZNbEmK000mIiIiIiKSg4qKChQWFkq9KioqZIatrKzE5cuXMXz4cKntw4cPR3h4uMx9Dhw4AB8fH3z//fewtraGq6srPvroI5SVlck9LQ+xY0dERERERK1PIGizV0BAAPT19aVeskbeACA7Oxs1NTUwNzeX2m5ubt7oI8ni4+MRGhqK6Oho7Nu3D6tWrcLu3bvx7rvvyj0bH+JUTCIiIiIieq4sXrwY/v7+UtvU1dWb3Kfu4z4A0UPl6297qLa2FgKBANu2bYO+vj4A0XTOV155Bb/++is0NTWfIvaysWNHREREREStT6ntJg+qq6s/siP3kImJCZSVlRuMzmVmZjYYxXvI0tIS1tbW4k4dAHTu3BlCoRCpqano0KHDk0e+EZyKSURERERE1Ag1NTV4e3sjODhYantwcDD69u0rcx9fX1/cv38fxcXF4m23b9+GkpISbGxsWiSe7NgRERERERE1wd/fHxs2bMCmTZsQGxuLhQsXIjk5GXPnzgUgmto5ffp0cfhXX30VxsbGePPNN3Hjxg2cPXsWH3/8Md56660WmYYJcComERERERG1BQV6jt3kyZORk5ODZcuWIS0tDW5ubjhy5Ajs7e0BAGlpaUhOThaH19HRQXBwMN5//334+PjA2NgYkyZNwjfffNNicWTHjoiIiIiI6BHmzZuHefPmyfwsMDCwwbZOnTo1mL7ZktixIyIiIiKi1qcke0VJejKKM/5JREREREREMnHEjoiIiIiIWp8C3WOnCJibRERERERECo4dOyIiIiIiIgXHqZhERERERNT6BFw8RZ44YkdERERERKTgOGJHREREREStT4ljTPLE3CQiIiIiIlJw7NgREREREREpOE7FJCIiIiKi1sfFU+SKI3ZEREREREQKjiN2RERERETU+gQcY5In5iYREREREZGCY8eOiIiIiIhIwXEqJhERERERtT4+x06umJtEREREREQKjiN2RERERETU+vi4A7lix46eyoRZ/ds6Cu2K4eBObR2Fdifv5M22jkK7w3LSUN5+lpP6ysrbOgbtT3mFsK2j0O5oqPMf4/pYd+h5xY4dERERERG1Pj7uQK6Ym0RERERERAqOHTsiIiIiIiIFx6mYRERERETU+rh4ilxxxI6IiIiIiEjBccSOiIiIiIhaHx9QLlfMTSIiIiIiIgXHjh0REREREZGC41RMIiIiIiJqdUIuniJXHLEjIiIiIiJScByxIyIiIiKi1ifgGJM8MTeJiIiIiIgUHEfsiIiIiIio9XHETq6Ym0RERERERAqOHTsiIiIiIiIFx6mYRERERETU6vi4A/niiB0REREREZGC44gdERERERG1Pi6eIlfMTSIiIiIiIgXHjh0REREREZGC41RMIiIiIiJqfVw8Ra44YkdERERERKTgOGJHREREREStT4ljTPLE3CQiIiIiIlJwHLEjIiIiIqJWxweUyxdH7IiIiIiIiBQcO3ZEREREREQKjlMxn3ECgQD79u3DuHHj2joqjRIKhbgcvAaxF3aioqwQZnbu8Bv3BYwsOjS5X/z1IEQErUZhTjL0jO3Q84UFcHQbJv78fnwErp7ZiOzUGJQWZWH49DVwdBsqdYzSomxcOPIjUm+HobK8CBaOPvAb+xn0TR1aIqktzsjPB04fzoS+lxs0rMxw6eV5yDhwoq2jJReXTm3HuaCNKMrPgqmVC0ZM+RR2rj6Nhk+6dRHHdixH1v270DUwQ98XZsF74BSpMOWlhTi1bxVuXglGWUkBDExsMGzSInRwHwAAqCgvxun9q3HrynGUFOXAwq4zRkxZAivHbi2a1pb2LJcToVCIi0FrEHN+JypKC2Fu744BL38B40e0J3evBuHC0dUoyE6Gvokdeo9cAGf3YVJhrodtx5VTG1FamAUjCxf0G/cprJxkl8FTO79AzPmd8Bu7GJ4D3pBb+prrasg2XD65ESWFWTC26IABEz6FtXPj9Sb17kWc3bccOel3oK1vBp/Bs+DuN1X8eU7aHZw7shoZqTEoyr2H/uMXw2vgjHrHiMDlkxuRmRKNksIsvDTzV7i4D0V7IhQKEXnyV9yK2InKskKY2rqjz+jPYWjedDlJjD6GK8dXozA3GXpGdvAa9gEcukrKSeyFv3Hzwj8ozr8HADAwc4HnoHmw7dhfHGbTks4yj93jhY/Qrd9MOaTu0YRCISKOrcGNCw/qiZ07+k949HU37loQLh5djYKcZOgb26HXyAVw6iZdT6LDtiPy9EaUFmXByNwFvmOl60lVRQnOHV6BhJgTKC/Jh66RNdz9psGtr6Scnd79BVLvnENJQSZU1bVg4dAdfV78CIZmTvLNiCa0Rd2p62LwWoQfWgnPAdMxcMISeSatfRFwjEmemJvtVGVlZVtHodVcPb0B10IC4Tvuc0yYvwtauqY4vP4tVJYXN7pPelIkjm/zh6vXGLyy8F+4eo3B8b8WIiP5qjhMdWUZjC07wXfc5zKPIRQKEbT5XRTmpmLEjN/w8gd7oWtohUPr30JVZanc09kalLW1UHjtFmI+WNbWUZGrmItHEPRPAPxGzcXbX+yDnasPtv88GwU592WGz8tKxd8/z4Gdqw/e/mIf/EbNwdG/v0Xs5SBxmJrqSvy18i3kZ9/DK3N/xrxv/8NLb3wNXUNzcZhDgZ8j/kY4xs76P8xZegBOXXzx18o3UZiX0eJpbknPajkBgCsnNyDqTCAGTPgckxbugrauKf79o+n2JC0xEkFb/dHRewymfvQvOnqPQdCWhUhPkrQndyKPIGR/AHyGzsXkD/fBytEHB9fNRlFewzIYf/04MpKvQVvPrEXS+LhuXTmCM/sC0HP4O3jt4/2wcvbG/j/eRmGu7HpTkJOC/Wtnw8rZG699vB89h83F6b3f4k6UpN5UVZZB38QGfqM/hJaeqczjVFWWwtS6Iwa98kWLpEserodsQExYIPqM/gxj5u2Epo4Jjv45E1UVJY3uk5kciVM7/OHcfQzGvb8fzt3H4NQ//shMkZQTbT0L+Izwx5h5uzBm3i5YOvXGiW3vIS/jjjjMlP+dlXr5TfgWEAhg33V4i6a5rshTG3D1bCD6jf8cr3ywC1p6pjiw7hHX3cRIHPvLH67eYzD5w3/h6j0Gx7YuREbdehJ1BKEHAuA9dC4mLtwHSycfHNogXU9CDyxH8q1QDJ36PaZ+chge/d9AyP5vkBAt+XHJ1KYrBk/6DlM/OYzRb28AhEIcXDcTtbU1LZMh9bRV3XkoPekaosN3wMSqo1zTRc8+duzaiYEDB+K9996Dv78/TExMMGzYMJw5cwY9e/aEuro6LC0t8b///Q/V1dXifRwcHLBq1Sqp43h6emLp0qXizwFg/PjxEAgE4vcAcPDgQXh7e0NDQwNOTk746quvpI7dWoRCIa6HboHX4Llw6jYcRhauGDR5OaqrynE36lCj+10P2QKbDn3RffAcGJo5ofvgObBy6Y3rIZvFYew69UfPFxbAqZvsi2VBdiIyk6+i3/gvYWbbDQZmTvAb/yWqKktwN/Kw3NPaGrKCzuL2l6uQvj+4raMiV+eDA9Hd72V07z8RplbOGDHlU+gZWuDS6b9lhr985h/oGVlixJRPYWrljO79J8LTbwLOBW0Sh4kK3YvykgJMencNbDt4wcDYGnYdvGFh2wkAUFVZjtgrxzD0lY9g79oDRub2GDD2fRiY2OByI+dVFM9qOREKhbh6dgt8hs6Fs/twGFu6Yuiry1FdWY7bVxpvT66e3QJb177wGToHhuZO8Bk6BzYdeuPqWUl7EnUmEF16vYyuvSfCyNwZ/cZ/Ch0DC1wPky4LxfkZOLP3awx7/QcoKbftpJgrp/9E194vw63PRBhZOGPghCXQMbTAtTDZ5fda2D/QNbTEwAlLYGThDLc+E9G11wRcPiWpNxb27ug3dhE6er0IZRU1mcdx7DIAfV9cCBeP1uuoNIdQKERM2BZ4DJwDh67DYWjuiv6vLEdNVTnirjZeTmLCt8DKuS88BsyGgakTPAbMhpVzb8SEbxGHses8CLYdB0DfxBH6Jo7wGb4AKmpayKrT+dPSNZV6JceehKVjL+gZ2bZouh8SCoW4FrIF3kPmwrmbqJ4MmSKqJ3cim6gnIVtg26EvvIeIrrveQ+bAukNvXK1z3b16JhCde76MLr1E9cRvrKieRJ+TlLmMxCh08hkHa5de0DOyQdfek2Fi2RGZqdHiMF17T4aVcw/oGdnA1KYrer6wAMX5aSjKvdcymVJPW9UdAKisKMHRrR9j6JRvoK6lL/e0tTdCgVKbvZ5Fz2aqFNTmzZuhoqKCsLAwfPfddxg1ahR69OiBq1ev4vfff8fGjRvxzTffPPbxIiIiAAB//vkn0tLSxO+DgoLw+uuvY/78+bhx4wbWrl2LwMBAfPvtty2SrqYU5aaitCgLNq6+4m3KKmqwdOqBjKTIRvfLTI6CTQdfqW22rn7ISIp67HPXVItGRZVV1cXblJSUoayshvTEy499HGpZNdWVSEuKgVNX6e/buasvUuNkl5F7cVFwbhDeD2lJMaiprgIA3I46CWsnT/y3fRlWLvTFH1+MRujhP8S/CNfWVkNYWwOVOuUDAFRU1ZFyh+WjPSp80J7YdZRuT6ydeyAtsfH2JD0xSmofALDr5If0xCgAojKYmRoDW9d6bU5HX6TXOa6wthbB2z+B16CZj5z62dJqqiuRmRID+45+UtvtO/oiLUF2XqQnRsG+Xj7Yd+qHzORo1NRUtVhcW1tRXirKirNh7SJdTiwceiAzuanrzlVYd+grtc26g2+j+9TW1iD+2mFUV5bC1M5TZpiy4myk3DoDV5+Xm5+QJ/SwntjWqydWzj2kynN9GUlRUvsAgF1H6XqSdU9GPXH1RUad41o4eiEh5iSKCzIgFApx7+555Gcnwq5eWX2oqqIUNyP2Qs/IBjoGFs1NbrO1dd05tWsZHLsMgF3Hvo8OTFQP77FrR1xcXPD9998DALZs2QJbW1usWbMGAoEAnTp1wv3797Fo0SJ88cUXUHqMBzqamoqG+g0MDGBhIWkMv/32W/zvf//DG2+I7vtwcnLC119/jU8++QRffvllC6SscaVFWQAATR1jqe2aOsYozpc95UG0XzY0devto2ssPt7jMDBzgo6hFS7+txL9J3wFFTVNXAsJRGlRVrOOQy2rtDgPwtoaaOtJf9/aesYoLsiWuU9xYRa09fwahK+tqUZpcR50DcyQl52C/Jvn0a33aEz9YC1yMpNwdNsy1NbWoP/od6GuoQMbZ0+EHPwNJpZO0NYzQfSFw7iXcA1GZvYtll56cqWFD9oTGW2DrCmT4v2KsmW2QSUPjldWIiqDWvWOq6VrjNIiSRm8fHI9lJSU4d5v2lOlQx7EcdarH2eTRtu3ksJs2HcykQ6vZ4za2mqUF+dBW79tp5bKS9mD70xTRzqtGjrGKGniulNWnN1gH00dE/HxHspNv41Da6eiproCqmpaGPLaLzA0c5F5zDtX9kNVXRv2XYbJ/LwlPPz+teqVeS2dJ6snD49X/qDM1a9/WrrGSKmTR/3GLcHpXZ9jy9cDoKSkAggEGDTpG1g6ekvtFx22HeGHf0R1ZSkMzJwwevamJke65KUt686tK4eRmRKDqR/tebLI03OPHbt2xMdHclNubGws+vTpA0Gd53v4+vqiuLgYqampsLOze+LzXL58GREREVIjdDU1NSgvL0dpaSm0tLRk7ldRUYGKigqpbdVVag1GNJpy58pBnN0r6TyOfPMP0R8yn2PS9LNNBPU/F0Iqvx5FWVkVw6etxpldnyFwaS8IlJRh7dJH6iZ3aj/qf7fCR33f9T8TSh9HKKyFtp4xXpy+DEpKyrB0cENxfibOBW1C/9HvAgDGzvweBwM/xaqPBkCgpAxLuy5w6/kS0pNvyC1d9ORuXT6I07sk7clLs0TtSYNyIZTRXtQjqyw12CarTD3YlpkSjWshWzHJf0+z2qGWVz8uQhnb6gavn0ah7O0KJC7qIML+XSp+P2z6742EFD5GOhvmT/3vW9/EAePe24vKsiIkxhxDyO7FGPn2FpmduzuX98LZ46VmXUeb6/aVgzi9W1JPXpwp+7orlLGtvsepJ/XrmqgISbZdC92KjOSrGPXmb9AxtEZafATO7v0KWrqmsHWVjFJ18BoNG9e+KC3MQtSZTTi2dQHGv/d3i+aVtNatO0V5aTiz51uMn7epFdPYDihw29IesWPXjmhra4v/Fsq4WAgfNBIPtyspKYm3PVRV9egh/9raWnz11VeYMGFCg880NDQa3S8gIABfffWV1Lbhk7/AiKlLH3nOh+y7DMIrdu7i9w+nQ5YVZUstNFBWnNPg1/G6RL+cSf9KWlac0+DX1EcxtXHDKwv3o6KsCLU1VdDUMcK+XybBxMatWcehlqOlYwiBknKD0bnSopwGo3gP6eiZoqRe+JKiHCgpq0BT20AURt8UysqqUFJSFocxsXRGcUEWaqoroayiBiMzO7zxyV+orChFRVkxdA3MsOePhTAwsZFvIumJOHYdBPO67UmNqD0pLWzYntQfRairsfZES1fUnmhqi8pgaWG9MlicIx71uB9/GaXFOdj89WDx58LaGoQd+D9cPbsZb3x+8glT+WQajXORJF31aeuZiEc9JeFzoaSkAo0H9UYR2XUeDFNbGded4mxo1Skn5cW5DUak6tLUMUFZsXT+lJXkQKPePsoqatAzFo3qm9i4IeveddwI3wrfcdLXz/TESyjITsDAKSufLGGPyaHLIEz2b5j+UlnX3SbS/6jrrsbDMiezLomOW11Vjgv/rcILb/wChy4DAQAmVh2Rff8mos5skurYqWvqQl1TFwamDjC398DGz3shIToYHbq/9AS58Pjaqu5kpMSgtDgH23+U/G8mrK3BvbgIXA3ZhvdXXJe6XhHJwo5dO9WlSxfs2bNHqoMXHh4OXV1dWFtbAxBNtUxLSxPvU1hYiISEBKnjqKqqoqZGehUpLy8v3Lp1Cy4usqeGNGbx4sXw9/eX2vbHseZNi1DT0IGaho74vVAohJauKVLvhMPEuguAB/dUxUeg16gPGz2OmZ0nUu+Ew73/DPG21DthMLf3bFZ8HlLX1AUAFGQlIis1Gj4j5j/RcUj+lFXUYGnfFfE3wtHJSzJdKf5GOFw9B8vcx9rZE3eunpLaFh8TBkv7rlBWUQUA2Lp4IfrCIQhrayF4MLU5JyNR1OGrN91HTV0LaupaKCspQFxMKIa+8pE8k0hPqLH2JOV2OExtJO3JvbgI9H2p8fbEwsETKbfC4Tlghnhb8q0wWDh4AhCVQTObrki5HS71CISU2+Fw7Coqgx19xsDWtY/UcQ+snYWOPmPRuef4p01qsymrqMHMtiuSb4XBxUMS5+Rb4XDqNkTmPhYOnkiIlq43SbdCYWbnBmVl1RaNb0tSVdeGqrr0D6eaOia4dzccxlaScpKeGAGfEU1ddzxw/2443HxniLfduxMOM7vuTUdAKOlM1XX70h4YW3WFsWWn5iWomRq97t4Oh2md6+79uAj0ebHx9JvbeyLldjg86lx3U+rVE1NrUT2p+wiE1NvhcHAT1ZPammrU1lRBUG/hCoGSEoTC2kekRCgzH+WtreqOnWtvvL7ooNS24O2LRQs6DXn7me3UPauLmLQVduzaqXnz5mHVqlV4//338d577+HWrVv48ssv4e/vL76/bvDgwQgMDMTo0aNhaGiIzz//HMrK0hXfwcEBJ06cgK+vL9TV1WFoaIgvvvgCL730EmxtbTFx4kQoKSnh2rVruH79epOLs6irq0Ndvf5CEsJGQj8egUCAbn7TEXlyLfRN7KFvYo/Ik2uhoqoBF0/Jr3In/1kEbX0z9Bopuuh085uGA39MQ9Sp9bDvOgRJMSdw7845jJm3TbxPVUUJCnKSxe+LclORfT8W6pr60DW0AgDEXTsKTW1D6BhYITf9NsIOfAuHrkNg6yr7Ju72TllbC9oukmm6Wo420PPohMrcApSnpDWxZ/vWe9gM7N+4CFYObrB28kTk2Z0oyE0TP5fuxJ4VKMrPxLiZ/wcA8B4wBZdObsOxHQHo3m8S7sVHITJ0DybM/lF8TO+BUxFx4i8E/fMtegx+HbmZSQg7vBY9hkjuj4qLDoEQgLG5I/Iyk3B89w8wtnCEh2/D0W5F8qyWE4FAAI/+03HpuKg9MTC1x6Xja6GipgFXL0l7Erx9EbT1zMSdPY9+07D312m4fGI9nNyGID76BFJvn8OE9yXtieeAGQjevghmtm6wcPBEzLmdKM5Lg1tfURnU1DaEprahVHyUlFWgpWvSqs/eqstr4JsI+usTmNu5wdKhO66H70BRXhrcfUVxDj24AiUFGRjxuujebnffKbgasg1n9gWgW59JSEuMRMz5PRg5fYX4mDXVlchJjwMA1FZXoqQgA5mpsVBT14KBqWiUqrKiBPlZkra3MCcVmamx0NDSh56RVWslv1ECgQBdfafj2pl10DMWXXeunl4HZVUNOHtIysmZXYugrWcOnxGiHzS79JmOIxum4drZ9bDrPATJsSdwP+4cXpz9l3ifS8d+go1rP2jrW6KqogTx144gPeEihs9YJxWHyvJiJEYHoefIT1on0XUIBAK495uOyyck190rJ0X1pO5o2PG/RdfdPg9+ZHXvNw37f5uGKyfXw9FtCBKiTyD1zjmMf1dSTzwGzMCJv0X1xNzeEzfO70RRfhrceovKnJqGDqyceuDcoR+goqoOXUNr3I+/iFuX/oXvmP8BED064G7UEdh29IWmthFKCjIQeWoDlFXVYddpQKvkUVvUHTUNHZhYuUrFQ0VdCxraBg22EzWGHbt2ytraGkeOHMHHH38MDw8PGBkZYebMmfjss8/EYRYvXoz4+Hi89NJL0NfXx9dff91gxG7FihXw9/fH+vXrYW1tjcTERIwYMQKHDh3CsmXL8P3330NVVRWdOnXCrFmzWjuZAACPgbNQXVWO0H3LUFFWADNbd7z49kapXxiL8+9LTU21cPDC0FdXICLoZ0QcWw09Y1sMeW0lzO08xGGyUqNxcK3kwcDnDi0HALh6j8OgyaK/Swszce7g8gdTRUzh6j0WXkPeaekktxh9bzf0ObFV/L7Lj58CAFK27MW1mYvbKlpPrWvPUSgrycfZg7+iuCALplYdMPWDtTAwFo1eFxdkobDOM+0MTW0w9YO1OLZjOS6d2g5dAzO8MHUJOnuPEIfRN7LEa/4bcWzHcqxdOhZ6huboOXQa+o58WxymvKwYp/auRGFeOjS1DdDJaxgGjV8oHvVTVM9qOQEAr8Gi9uTMHlF7Ym7njrFzpNuTojzp9sTS0Qsjpq3A+f9+xoWjq6FvbIsR01fCwl7SnnToPgrlpfmIOPar6IHFlh3w0ttroWdk3arpa46OXqNQXpKH80G/obQgE8aWrhg7Z504ziWFWSjMk3Tk9Y1tMW7OOpzZF4BrIdugrW+GgROWoIOnpN4UF2Ri+w/jxO8vn9yEyyc3wdqlJya+LypTGcnR2LNmujjM2f0BAIDOPcdjxGvLWzLJj61bv1morqrAuQPLUFleCFMbd7zw5gapkb2SgjSpkSVz++4YOHkFrgT/jCvHf4GukS0GTVkBM1tJOSkrzsbZXYtQWpQFNQ1dGFq4YviMdVIrcAJA/LUjEEIIJ48XWz6xMnQfJKonZ/dK6sno+tfd+vXEwQvDX1uBC0d/xsUg0XV32LSVMK9bTzxHoaIkH5eCfxU/2PulmWuhW6eeDH99Jc4fWYnj2z9GeWkBdA2t0GvkAnTtI+o0qaioIS3hMq6FbEFFWSE0dYxh5eSDCe/93eQtGvLUVnXnucR77ORKIKx/kxZRM6z8l8Wnro6vtOyUGkWUd/JmW0eh3TEczHJSX/x+lpP6VJT5D099hcWPmq73/NFQZzmpT02VeVLfOy+0dQxkK4o40mbn1u0xqs3O3VI4sZWIiIiIiEjBcSomERERERG1Pi6eIlfMTSIiIiIiIgXHETsiIiIiImp1Qi6eIlccsSMiIiIiIlJw7NgREREREREpOE7FJCIiIiKi1sfFU+SKuUlERERERKTgOGJHREREREStTgguniJPHLEjIiIiIiJScByxIyIiIiKiVifkPXZyxdwkIiIiIiJScOzYERERERERKThOxSQiIiIiotbHqZhyxdwkIiIiIiJScByxIyIiIiKiVicU8HEH8sQROyIiIiIiIgXHjh0REREREZGC41RMIiIiIiJqdXyOnXwxN4mIiIiIiBQcR+yIiIiIiKj1cfEUueKIHRERERERkYLjiB0REREREbU63mMnX8xNIiIiIiKiR/jtt9/g6OgIDQ0NeHt7IyQk5LH2CwsLg4qKCjw9PVs0fuzYERERERERNWHHjh1YsGABlixZgsjISPTr1w8jR45EcnJyk/sVFBRg+vTpGDJkSIvHkR07IiIiIiJqdUII2uzVXCtXrsTMmTMxa9YsdO7cGatWrYKtrS1+//33JvebM2cOXn31VfTp0+dJs+mxsWNHRERERETPlYqKChQWFkq9KioqZIatrKzE5cuXMXz4cKntw4cPR3h4eKPn+PPPPxEXF4cvv/xSrnFvDDt2RERERETU6oQCpTZ7BQQEQF9fX+oVEBAgM57Z2dmoqamBubm51HZzc3Okp6fL3OfOnTv43//+h23btkFFpXXWq+SqmERERERE9FxZvHgx/P39pbapq6s3uY+g3nP3hEJhg20AUFNTg1dffRVfffUVXF1dnz6yj4kdO3oq6mp8sGRd8ftvtnUU2h1BUVvHoP3JYzlpwGlcp7aOQrujdSWqraPQ7ty6r9nWUWh37saXtnUUSCFotXUE2h11dfVHduQeMjExgbKycoPRuczMzAajeABQVFSES5cuITIyEu+99x4AoLa2FkKhECoqKjh27BgGDx789Imohx07IiIiIiJqfTJGu9ojNTU1eHt7Izg4GOPHjxdvDw4OxtixYxuE19PTw/Xr16W2/fbbbzh58iR2794NR0fHFoknO3ZERERERERN8Pf3x7Rp0+Dj44M+ffpg3bp1SE5Oxty5cwGIpnbeu3cPW7ZsgZKSEtzc3KT2NzMzg4aGRoPt8sSOHRERERERtTqhAq3jOHnyZOTk5GDZsmVIS0uDm5sbjhw5Ant7ewBAWlraI59p19IEQqFQ2KYxIIX2639tHYP2pbaW1ak+BZll0arY6jbEe+wa4j12DfEeu4Z4jx09jh/faZ/32GXeuNRm5zbr4tNm524pitNNJiIiIiIiIpk4FZOIiIiIiFqdkNN65IojdkRERERERAqOI3ZERERERNTqhAKOMckTc5OIiIiIiEjBccSOiIiIiIhanRC8x06eOGJHRERERESk4NixIyIiIiIiUnCciklERERERK2Oi6fIF3OTiIiIiIhIwXHEjoiIiIiIWh0fUC5fHLEjIiIiIiJScOzYERERERERKThOxSQiIiIiolbH59jJF0fsiIiIiIiIFBxH7IiIiIiIqNXxcQfyxdwkIiIiIiJScByxIyIiIiKiVsd77OSLI3ZEREREREQKjh07IiIiIiIiBcepmERERERE1Oq4eIp8MTeJiIiIiIgUHEfsiIiIiIio1XHxFPniiN1zaOnSpfD09GzraBARERERkZxwxO4ZJxAIsG/fPowbN66to9Koa6HbcOXkRpT8P3v3HRbF8f8B/H303nuvgoqAgAUpYu9GMbYYjYktyS9NE2PUGNP1m8TEqNHYWzR2jb2LgthpVqwIKL33dvf74/Tg4EDUo+n79Tz36O3N7s0Ms3M7+5ndzU2DgZkzAofOgqWjT63pE+9eROie+chMvgNNXRN4d5+Idn6jJZ9nJN3B+UOLkJpwHXlZjxAwZCbaB42X2saje5dw5eRqpCVcQ0FuGga89xcc3Xs2VBGfm0gkwsUjS3D9/DaUFObC1NYdXYd9A0Mz5zrXuxt9BBcOL0JOejx0jWzQud9ncHTvJZXm6tnNiDi1GoW5aTAwc0LAkFmwcJBd36e2fYPr57fB/42Z8Oz6jtzK9ywxYZsReeppmxDnsa428ejuRYT+Nx+ZyXehqWMCr+4T0c5vlFSau9FHcP5QZd349peum0f3LiHi5GqkJV5HQW4a+r+3BI7tpNvEsc1f4dalPVLLTG09MOKzrS9f6BfwurcTeTLw94HD5xOg6+UGNQsTXB72IVL2nmjqbDWIkMNbcWzveuRkpcPC2hHDx0+HcxsvmWlzstKwY/0CxN+/idSkeHTrPxoj3v2yRroT+//BmaPbkZmeDC1tPbTv3BNDx3wCZRXVhi7Oc4sO3YTLJ8T9i6GZM7oOmwWrun5z7lzE6d3zkfHkN8enx0R4+Ff+5qQn3cG5g+LfnNzMR+g6dCa8uo2X2kZpcT7CD/yJuzHHUZifARPLNggaNgtmtu4NVUy56O2jjE5tFKGhKkB8ihC7QkuRkiWqNb2pvgB9OirDykgBBjoK+O9sKUJjyqXSdG+vhHYOijDWU0B5BRCXXIED58uQll37dpsT1gk1Z4zYUZO6HXEQZ3bPg0+vDzD6iz2wdPDG3uWTkJf1WGb6nIwE7F0xGZYO3hj9xR749Hwfp3f9hLvRRyRpysuKoGtoBb9Bn0NDx1jmdspKCmFs4YKuw75pkHK9rIiTqxB1eh26Bs/BiKnboaltjP/+fg+lxfm1rpMUF4kjG6fBxXswRn/xH1y8B+PIhqlIfhgtSXMn8iBC98yDT8/3MfLz3bCw98G+FZNl1vf9q8eREh8DTR2TBiljbW4/zWOv9zHqi92wcKg9jwCQk5GIvSunwMLBB6O+2A2fXlNwZrd0m0iKi8ThDdPg6jMYo6f/B1efwTi8XrpuykqLYGTpisBhc+rMn41rAN77LlTyGjxpuXwK/gJe53Yib4qaGsiNicX1T79v6qw0qMtnj2D7ul/RL3giZv+6BU6t22PJz/+HzLQkmenLykqhpaOPfsETYWXbSmaaC2cOYPemRRgwfArmLtyFsR/MxZXwo9i9aVFDFuWFxEYcRMiueejY+wOM+XIPLB29sWfZJORm1v6bs3v5ZFg6emPMl3vQsdf7CNn5E+5EVfnNKRX/5vjX8Ztz7N+v8TA2HH3H/oJxX+2Drasfdv71LvKzUxqknPLQzVMJgR5K2B1ahj93FiO3UITJg1Shqlz7OipKAmTminDwQhlyC2QPShwsFHH2WjkW7yrG8n3FUBAIMHmgKlRaQKiBdSJ/IoFCk71eRa9mqZqhoKAgfPzxx/jss8+gr68PU1NTrFixAgUFBXj33Xehra0NR0dHHDp0SLLO6dOn0bFjR6iqqsLc3BxfffUVysvLpbb5ySef4Msvv4SBgQHMzMzw7bffSj63s7MDAAwdOhQCgUDy/qmNGzfCzs4Ourq6GDVqFPLy8hqyCmSKDFmLtp2Gwc13OAzMHBEYPBtaemaICftXZvprZ7dAW88cgcGzYWDmCDff4WjTKRgRJ9dI0pjauMP/jRlo5TUAiooqMrdj16YrfAdMhZNH7wYp18sQiUSIPrMBPj3fh6N7bxiat0LPt+ajvLQYtyP217pe9JkNsG7VBT49p0Df1AE+PafAyrkzos+sl6SJOr0ObToNQ9vOw2Fg6oiAobOgpWeGq2el6zs/OwWnd/2AXm//CgXFxv1liQqRzmNgLXl86lr4kzYxdBYMTB3RtvNwtOkYjMhTlW0i6nRl3Rg8rZtWnRF1urJu7FoHwrf/Z3Byr7tNKCqpQFPHWPJS09STS7mf1+veTuQt7cgZ3J67EMl7jjV1VhrU8X0b4dd9KPx7BsPcygEj3v0S+oZmOH10u8z0RiaWGPneDHQOGgQ1DS2Zae7fjoGjiyc6BvSHkYkl2nh2QQf/voi/d6Mhi/JCIk6thVvnYWjXZTgMzRwRNGw2tPVr/82JCdsCHX1zBA2bDUMzR7TrMhxtOwfjSpXfHDNbdwQOmQEX7wFQUqr5m1NeWow70UcR8MZ0WDl1gJ6xLXz7fwxdQytEh21usLK+rAB3ZZy4UoZrDyqQnCnClpOlUFESoL1z7ft6QpoQ+8+VIepuBcorZA9iVh0oweXYCqRkiZCUIcLWUyXQ11aAlXHzPyRlnVBzxxbTiNavXw8jIyNcvHgRH3/8MT744AMMHz4cXbp0QUREBPr06YOxY8eisLAQjx49Qv/+/dGhQwdER0dj2bJlWL16NX788cca29TU1MSFCxfwyy+/4Pvvv8exY+IDk0uXLgEA1q5di6SkJMl7ALh37x727NmD/fv3Y//+/Th9+jTmz5/feJUBoKK8FKmJ12Hj6i+13MbVD0lxkTLXSYqLgo2rn9QyW9cApCZcQ0VFWYPltTHlZiaiMC8NNi6V5VRUUoGlY4da6wUAkuOipNYBABtXfyTHRQGorG/rVtJprF38kFxluyKhEMc2fwmvbhOeOaVP3iRtono5XGpvE7WVOzXhuqRNyEzjUlk3z+PR3YtYNacLNv7cBye2zkFhXsZzb0MeXud2Qi+mvKwM8fdvorWHr9Ty1h6dcT82upa1ns3JtT3i79/AgztXAQBpKYm4FhEGN++Al8qvvFWUlyIl4TpsZfzmPH5Q/98cO9cApMTX/zdHKCyHSFgBJSXpaalKymp4fD/iOUrQeAy0BdDRFCA2sUKyrEII3HtcATsz+R46qqmIb55RWNK8px2yThqGCIIme72KWvYp1hbGw8MDX3/9NQBg5syZmD9/PoyMjDBp0iQAwDfffINly5YhJiYG+/btg7W1NZYsWQKBQABXV1c8fvwYM2bMwDfffAMFBXEn4u7ujrlz5wIAnJ2dsWTJEpw4cQK9evWCsbF4Soienh7MzMyk8iIUCrFu3Tpoa2sDAMaOHYsTJ07gp59+qjX/JSUlKCkpkVpWVqYKZeUXu4aiqCALImEFNLQNpZZraBuhMDdN5jqFeenQ0Daqlt4QQmE5ivOzoKnbsqeDAZCUXb1avahrG9Y6HREQ1426VrV1tAxR8GR7tde3IQrz0iXvr5xcCQUFRbgHjH2pcryI2vKorm2Iwtx0mesU5qVBXVv6QK16mxC3m5rlLqilndXGtnUgnDz6QsfAArkZiTh/aBF2Lx2PUZ/vhKKMM/UN6XVuJ/Ri8vOyIBRWQEfXQGq5jq4hcrNl71/10cG/L/Jzs/DbnHchEgHCinIE9hmOvkPfe9ksy1VtbVtT2wgP82T3BQW56bCr5TenKD8LWvX4zVFR04K5XXtcOLIUBmYO0NA2QuyV/Uh6GA19Y9sXL1AD0tYQH/TmF0ovzy8C9LXke0A82E8Z95PEEbDmjHVCLQEHdo3I3b3yImlFRUUYGhqiXbt2kmWmpqYAgNTUVNy8eRO+vr4QCCo7Cz8/P+Tn5yMxMRE2NjY1tgkA5ubmSE1NfWZe7OzsJIO6+q43b948fPfdd1LL+r01FwPe/vaZ31e3ah2iSCRV7melF4medHx1rtN8xV7Zh5DtcyXvB078GwBq1oEIEDzjDJOsequxTMZ2ny5LTbiGmNCNGDFt5zP+Bg1M1nfXkZ/qeX3aJKquU6+6eYZW7ftL/m9o3gom1m5Y90MPPLgR8swpnC+L7YTkpcb+Iv7jvvD2Yq9dwqFdqzB64izYO7dDanICtq39BQf0VmDA8MkvmdsGUKO/eFb5ZdXX8/Uffcf+gqObZ2HlnEAIFBRhYtUGrt4DkZrQPKartndWxJtdK09OrT4gPon7tKxVyXOoMTRAGeYGCvhrT8mzEzcy1knjEPE3RK44sGtEysrSV9cKBAKpZU9/JIRCIUQyBjdPBzBVl8vaplAofKG8PGu9mTNnYtq0aVLL1oS8+B3P1DX1IVBQlIoCAEBhfgbUq50hfUpD2wiF1c6sFuVnQkFBqcmudXpZ9m27wdSmcoBeUVEKACjMTZe6IUVRfkaN6ExV4rqRrsui/AxJhFNS37k161vjSQTn8f0rKMzPwPofuks+FwkrcHbv/xB9Zj3emXPyBUtZP7XlsSgvo8ZZ9qc0tI1rps/PkGoTGtpGKKhe7ryMGtHf56WpawJtfQvkpD18qe3UB9sJvSwtbX0oKCgiJ1t6+nBeTiZ09GpvM8+yb8tSdAocAP+ewQAAS1tnlJYU4Z+/f0C/YRMlM0yaWp1tu5a+QFPHCAXVf3Pynv83R8/YBiM+/QdlJYUoKc6Hlq4JDqz9DLqGVs9djoZwI64Cv6cUS94rKYr/1dYQIK+wctiipQ7kF8lnGDPEXxlt7RSxdE8Jcmq5qUhTYp1QS8SBXTPVpk0b7Ny5U2qAFx4eDm1tbVhaWtZ7O8rKyqioqHh2wnpQVVWFqqr0QE65jjtBPYuikgpMrNoiPvas1K3W42PD4eDWQ+Y65naeuH/9lNSy+FthMLF2g6LiS2SmCamoaUFFrfKmBCKRCBraxki4HQ5jqzYAxNeGPLp3CV0Gfl7rdszsPJEQGw7PruMly+Jjz8LMzhNAZX0n3A6Xqu+E2+Gwbys+QHfxGQzrVtLX3+xdPhEuPm+gdcehL1vUZ6otj/G3w+Hg1l3mOmZ2nnhQvU3EnoWJdVtJmzCz80TC7XCpx15UrZsXVVSQhfzspFrvhCdPbCf0spSUlWHj0Bo3Y86hfafK/elmzAV4dAh64e2WlhZDUO0Oc+LBnKhK+LzpKSqpwNS6LR7GnoWTR5X+5VY4HNvV8ZtzTbp/eXgrDKY2L/abo6yqAWVVDRQX5uDhrTD4D57+3NtoCCVlQEmZ9N8qt0CEVlaKeJwuvmmbogLgaKGIA+df/nr2of7KcLNXxLK9JcjMaz5tpCrWCbVEHNg1Ux9++CEWLlyIjz/+GB999BFiY2Mxd+5cTJs27bnOftrZ2eHEiRPw8/ODqqoq9PX1GzDXz6990Ls4uulLmFi7wdyuPa6d24r8rCTJM8jO7luAgpwU9H77FwCAm98oRIdtwpnd8+DmOwJJcZG4fmEn+o5bINlmRXkpMpPvAQCEFaUoyElBWuJNKKtqQO/J9QylJQXISYuXrJObmYi0xJtQ09SFtr5FYxVfJoFAAI/Acbh8fDl0jWyhZ2yLy8eXQ0lFDa28BkrSHds8A5o6JpKDeI+Asdj111hcObESDm49cP/aCSTePofgjzdJ1vHsOh7HNs+AibUbzOw8cf3cNuRnJcGti7i+1TX1oa4p3UYUFJWgoW0EfROHRig94Bk0Hsc2VcljuHQew/cvQH5OKnqP+R8AwK3LKMSEbULonnlo6zsCyXFRuHFhJ/qM/a1ym4FjsXOJuG7s3XrgwZO6GfZJZd2UlhQgJ71Km8hIRNqjm1DTELeJ0pICXDy8BI4evaGpY4zczEc4d+APqGnq13jeXWN43duJvClqakDTyUbyXsPeCjoerijNzEFxguxHAbREPQeNxdrFs2Hr0BYOLu4IPbYTWelJCOz9JgBg96ZFyM5IxbufVN6oK+HBLQBASXER8nKykPDgFhSVlGFh7QgAaOcdiBP7/4G1veuTqZjx2LtlKdx9ukJBUbHxC1kHr27v4vDGL2Fq7QZz+/a4Gr4VeVlJcPcXt+2wvQuQn5OCvmPFvznu/qMQFboJp3fNg1uXEUh6EIlr53ei/zvSvzkZT35zKspLkZ+TgtTEm1Cp8psTdzMUEImgb2qP7LR4hP73C/RN7NG2c3Aj10D9hcaUoYeXMtJzREjPEaK7lzJKy0WIvFN5d+5R3VWQUyDCoQvigY2igvi5bQCgqAjoagpgYShASRmQkSserAQHKKO9sxLWHipBSakI2uribRWVAuXyOQ/dYFgn8icScSqmPHFg10xZWlri4MGDmD59Ojw8PGBgYIAJEyZIbr5SXwsWLMC0adOwcuVKWFpaIi4urmEy/IJaefVHcWEWLh5ZioLcVBiat8LgKSugYyCOShbmpiEvq/KgStfQGoMnr0DonnmICdsELV0TdA2eDSePPpI0BTmp+Pe3IZL3EafWIOLUGlg6dsSwjzcCAFLjr2HXX+MkaUL3zAMAtO4wFL3GNO7dQWXx6j4R5WXFOL3ze5QU5cDUxh1vTFktFbHJy3osNS3X3N4LfcYuwPlDf+LC4UXQNbRGn3G/w8zWQ5LGuX1/FBdm49LRv8QP5zV3xsBJyyX13Ry0at8fxQXZuHikMo+DJlfmsSA3DflVbg6ia2iFwZOWI3TPfMSEbYaWrgkCh0q3CXN7L/QduwDnDv2J84ee1M070nWTmnANu/+qfLh22H/iduDaYQh6vTUfCgJFZCTdxq3L/6GkKA+aOsawdOqIvuP+kPq7NKbXuZ3Im663G3xPbJS8b/PbLABAwoZdiJkws6myJXc+fn2Qn5eNAzuWIzcrHRY2Tvho1hIYGotPaOVkpSEzXXog+9P0UZL/x9+/gUthh2BgbI6fl4kfz9P/zUkQCATYu+UvZGemQktHH+7egXjjrY8ar2D15OLVH8UFWbhwZCkKcsS/OUPeXyHVv1T/zRk6ZQVO756H6NBN0NQ1QdCw2XD2rOxf8nNSsemXIZL3V06uwZWTa2Dl1BHDPxG3qZKiPJzd9zvys5OhqqkHZ4/e8Bs4tVnPNDkVVQ5lJQGCA1SgrgrEpwqxcn8JSqoEp/S1BFJBWR1NAaaNUJe8D/JUQJCnMu49qsCyveJrxrq4icv84RA1qe/bclJ8y//mjHVCzZ1AJGpG8ySoxfnr0LPTvE6EQu5O1fG66JrY69bkMMS1qbPQ7GhERDV1Fpqd2Mfqz070mrl7v/DZiei199sHGk2dBZnu3Gv4a9Rr4+zYPO9K+zKaxxXNRERERERE9MI4sCMiIiIiImrheI0dERERERE1OtFLPEOTamLEjoiIiIiIqIVjxI6IiIiIiBodI3byxYgdERERERFRC8eIHRERERERNTpG7OSLETsiIiIiIqIWjgM7IiIiIiKiFo5TMYmIiIiIqNFxKqZ8MWJHRERERETUwjFiR0REREREjU4kYsROnhixIyIiIiIiauE4sCMiIiIiImrhOBWTiIiIiIgaHW+eIl+M2BEREREREbVwjNgREREREVGjY8ROvhixIyIiIiIiauEYsSMiIiIiokbHiJ18MWJHRERERETUwnFgR0RERERE1MJxKiYRERERETU6kYhTMeWJETsiIiIiIqIWjhE7IiIiIiJqdELePEWuGLEjIiIiIiJq4TiwIyIiIiIiauE4FZOIiIiIiBodn2MnX4zYERERERERtXCM2BERERERUaPj4w7kiwM7eimpaWVNnYVmxcxEuamz0OyoqzV1DpqfouKmzkHzoxER1dRZaHYKvTybOgvNjuBAbFNnodmJCWedVNc+wLWps0DUJDiwIyIiIiKiRsdr7OSL19gRERERERG1cBzYERERERERtXCciklERERERI2ON0+RL0bsiIiIiIiIWjhG7IiIiIiIqNHx5inyxYgdERERERFRC8eBHRERERERUQvHqZhERERERNToePMU+WLEjoiIiIiIqIVjxI6IiIiIiBqdsKkz8IphxI6IiIiIiKiF48COiIiIiIioheNUTCIiIiIianS8eYp8MWJHRERERET0DEuXLoW9vT3U1NTg7e2N0NDQWtPu2rULvXr1grGxMXR0dODr64sjR440aP44sCMiIiIiokYngqDJXs9r69at+OyzzzB79mxERkYiICAA/fr1Q3x8vMz0Z86cQa9evXDw4EFcuXIF3bp1w6BBgxAZGfmy1VYrDuyIiIiIiIjq8Pvvv2PChAmYOHEiWrdujYULF8La2hrLli2TmX7hwoX48ssv0aFDBzg7O+Pnn3+Gs7Mz9u3b12B55MCOiIiIiIganUgkaLLX8ygtLcWVK1fQu3dvqeW9e/dGeHh4vbYhFAqRl5cHAwOD5/ru58GbpxARERER0WulpKQEJSUlUstUVVWhqqpaI216ejoqKipgamoqtdzU1BTJycn1+r4FCxagoKAAI0aMePFMPwMjdkRERERE9FqZN28edHV1pV7z5s2rcx2BQDrSJxKJaiyT5d9//8W3336LrVu3wsTE5KXyXRdG7IiIiIiIqNG9yE1M5GXmzJmYNm2a1DJZ0ToAMDIygqKiYo3oXGpqao0oXnVbt27FhAkTsH37dvTs2fPlMv0MjNgREREREdFrRVVVFTo6OlKv2gZ2Kioq8Pb2xrFjx6SWHzt2DF26dKn1O/7991+MHz8emzdvxoABA+Saf1kYsSMiIiIiokYnFDV1Dupv2rRpGDt2LHx8fODr64sVK1YgPj4e77//PgBxBPDRo0fYsGEDAPGgbty4cfjzzz/RuXNnSbRPXV0durq6DZJHDuyIiIiIiIjqMHLkSGRkZOD7779HUlIS3NzccPDgQdja2gIAkpKSpJ5pt3z5cpSXl+P//u//8H//93+S5e+88w7WrVvXIHnkwI6IiIiIiOgZPvzwQ3z44YcyP6s+WAsJCWn4DFXDgR0RERERETW6prx5yquIN08hIiIiIiJq4Rixq6fw8HAEBASgV69eOHz4sFy2GRISgm7dutWZZu3atRg/frxcvq85C/JQgLezAtRVgMR0EQ5cqEBaTt3rtLYRoLunIgy0gcw84ERkBW4lVF6F+1mwEvS1ap4JunirAgcuCgEA341Tlrnto1cqcPa68MUL9JKiQzfhysnVKMhNg6GZM7oGz4Klo0+t6RPvXsSZ3fORkXwHmrom8Ok+Ee7+oyWfZyTdwbmDi5CSeB15mY8QOHQmvILGS23j4rHluBd9FJmp96GkrAZz+/bwH/QFDEwdGqqYz+VKyCZcOLoa+TlpMLZwRs8Rs2DtXHudxN++iBPb5yPt8R1o65mgU++J8OpaWScx4btwYP3MGutNXxIDJWXVJ9u4hAtHVyM5/hryc9Iw7IO/0MqzYW9V/Dyaop0k3r2EKydXIzXhGgpy0zBwwl9wcm8+dVJdyOGtOLZ3PXKy0mFh7Yjh46fDuY2XzLQ5WWnYsX4B4u/fRGpSPLr1H40R735ZI92J/f/gzNHtyExPhpa2Htp37omhYz6Bsorsu6m1VAb+PnD4fAJ0vdygZmGCy8M+RMreE02drZcWdWYTLp94st+YOyMoeBasnGrfbxLuXMTp3fORkXQHWrom8Ok5ER5V9puYs9tw8+IepCfdAQCYWreF36BpMLdzl6SJDt2M6LB/kZv5CABgaOaMzn0/hH3brg1USvkYO9QM/bsZQUtTEbfuFWDJ+kQ8fFRca3o/H12MHmQGC1MVKCkJ8Ci5BDsOpeLE2SxJmnYumhg+wBTOdhow1FfGtwvvI/zKM37wm5Ge3kro5KoEdVUgPlWI/86WISWr9juAmOoL0MtHGZZGAhhoK2BfeCnCrlVIpbE3U0CghxKsjBSgoynA+iMluPGw6Y5BGpNIxIidPDFiV09r1qzBxx9/jLCwMKkLI19Gly5dkJSUJHmNGDECffv2lVo2cuRIuXxXc+bfVgG+rRVw8GIFVhwsR34RMK6XElTqOO1gZSTA8EBFxNwXYtm+csTcF2JEV0VYGlV2ECsOlOPXbWWS1/pj5QCA6w8rO+Cqn/+6rQy7z5ZDKBI1aYcaG3EQp3fPQ8feH2DM9D2wcPTGnr8nITfzscz0ORkJ2LN8MiwcvTFm+h507PU+Qnb9hDtRRyRpykqLoGtkBf9Bn0NDx1jmdh7dvQj3gDEYNXUbgj9cC1FFBXYvm4CyksIGKefzuHHpII5vm4cu/T/Ae1/vgZWTN7YunoScWuokOz0B2xZPhpWTN977eg98+72PY1t/wq2II1LpVNW08PEvYVKvp4M6ACgrLYSJlQt6j/qmQcv3IpqqnZSVFsLY0gXd3mx+dVLd5bNHsH3dr+gXPBGzf90Cp9btseTn/0NmWpLM9GVlpdDS0Ue/4Imwsm0lM82FMwewe9MiDBg+BXMX7sLYD+biSvhR7N60qCGL0iQUNTWQGxOL659+39RZkZvYKwcRsmseOvX5AG/P2ANLR2/sXlbHfpOegN1/T4alozfenrEHHXu/j1M7fsLtKvtN4t0LcPEegOGfbMDoaVugbWCOXUvfQ152iiSNlp4Z/Ad/gTHTd2LM9J2wbtUZ/638P8lgsDkaMcAEwf1MsGRDAj6eG4usnHLMn+EEdbXaDx3z8ivw795kfPr9bUyZdQtHzmTii0m28G6nLUmjpqqI+/FFWLIhoTGKIVddPZQQ0E4Je86WYvHuEuQXiTCxvypUZJ8jBgAoKwGZuUIcvliO3ELZA0AVZSApQ4g9Z0sbKOf0uuDArh4KCgqwbds2fPDBBxg4cKDk4khfX1989dVXUmnT0tKgrKyMU6dOARDfIWfAgAFQV1eHvb09Nm/eDDs7OyxcuBAqKiowMzOTvNTV1aGqqip5b2pqisWLF8PBwQHq6urw8PDAjh07JN8VEhICgUCAI0eOoH379lBXV0f37t2RmpqKQ4cOoXXr1tDR0cHo0aNRWFh5cB4UFISPPvoIH330EfT09GBoaIivv/4aIlHT3HO2c2sFhF4V4ma8CKnZwO6zFVBWAtzta2+evm0UcD9JhNBrQqTnAqHXhLifJIJv68p1CkuA/OLKVytLATJyRYhLqSxn1c/ziwFXawXEJYuQld+QJa5bRMhatO08DG6+w2Fg5oig4NnQ0jdDzNl/ZaaPObsF2vrmCAqeDQMzR7j5DkfbTsG4cmqNJI2ZrTsC3pgBF68BUFRSkbmdoR+sRttOwTA0d4axpSt6jZmHvKzHSEm43iDlfB4Xj6+Fh98wePoPh5G5I3qNnA0dfTNEnpZdJ5Gnt0DHwBy9Rs6GkbkjPP2Hw8MvGBeOrpFOKBBAS9dY6lWVo1tXdB0yFS5evRuqaC+sqdqJfZuu6DJgKpw8ml+dVHd830b4dR8K/57BMLdywIh3v4S+oRlOH90uM72RiSVGvjcDnYMGQU1DS2aa+7dj4OjiiY4B/WFkYok2nl3Qwb8v4u/daMiiNIm0I2dwe+5CJO859uzELcSVU2vh5jsM7boMh6GZI7oNmw1tfTNEh8neb6LPboGOvjm6DZsNQzNHtOsyHG6dg3HlROV+0/+dBfAMHAMTq9YwMHNEr9E/QiQSIiH2nCSNY7vucGjbFfom9tA3sYf/oKlQVtVAUlxUQxf5hQ3ta4J//0vG2cs5iEssxq/LH0JVRYDuvvq1rhNzKx9nr+Qg4XEJklJLsedoGu4nFMGtVeX+dCkmF+t2JOHs5ZYTpXvKv50STkaW43qcEClZImw9VQZlJaC9k2Kt6ySmiXDwQjmi71WgvEL2cVZsghBHL4u3+7oRiZru9SriwK4etm7dChcXF7i4uODtt9/G2rVrIRKJMGbMGPz7779SA6KtW7fC1NQUXbuKp1eMGzcOjx8/RkhICHbu3IkVK1YgNTW1Xt/79ddfY+3atVi2bBmuX7+OqVOn4u2338bp06el0n377bdYsmQJwsPDkZCQgBEjRmDhwoXYvHkzDhw4gGPHjmHx4sVS66xfvx5KSkq4cOECFi1ahD/++AOrVq16yZp6fvpagLaGAHeTKjuzCiHwMEUEa5Paw/NWxgLceyy9V957LIK1sex1FBUAdwcFRN6tvdPUVANaWQkQUUeahlZRXorUhOuwdfGXWm7r4oekB5Ey10mOi4Kti590etcApMZfQ0VF2QvnpbQoDwCgptEwz1qpr4ryUiTHX4d9G+k6sW/jh8R7suvk0f0o2Lfxq5Y+AMkPpeuktKQQf83shiUzArFtyRQkx7eMg/Pm1E6aq/KyMsTfv4nWHr5Sy1t7dMb92OgX3q6Ta3vE37+BB3euAgDSUhJxLSIMbt4BL5VfangV5aVISbgOW9dq+42rHx7Xst8kPYiCrWu1/aZ1AFLq2G/KS4tQUVEONU3ZfadQWIFbVw6gvLQQFnbtX6AkDc/MWAWGesq4ci1PsqysXISYW/lo46xZ7+14ttGCtbkqrsY24dlSOTHQFkBHQ4A7iZXTKCuEwP0kIWxNeThNzQOvsauH1atX4+233wYA9O3bF/n5+Thx4gRGjhyJqVOnIiwsDAEB4h/1zZs346233oKCggJu3bqF48eP49KlS/DxEc/fX7VqFZydnZ/5nQUFBfj9999x8uRJ+PqKD0wcHBwQFhaG5cuXSwaOAPDjjz/Cz0/8wzNhwgTMnDkT9+7dg4OD+NqoN998E6dOncKMGTMk61hbW+OPP/6AQCCAi4sLrl69ij/++AOTJk2SQ43Vn5a6eCBWUCS9PL8I0JN9wly8nhqQXyw9sMsvFkFLXXZ6V2sB1FSAqHu1D9o8HRVQUgbcfNh0p3GKCrIgElZAQ8dQarmGthEK89JkrlOQmw5bVyPp9DqGEArLUZyfBU1dk+fOh0gkwpk982Dh4A0jC9lT0hpLYb64TjSr1YmmthEKcmXXSX5uOhy0petE80mdFOVnQUvXBIZmDhj4zjwYW7qgtDgfl05uwMZfRmPCnP9gYGrXUMWRi+bSTpqz/LwsCIUV0NE1kFquo2uI3Oz0F95uB/++yM/Nwm9z3oVIBAgryhHYZzj6Dn3vZbNMDezpfqOpLWO/qaUvKchNh0b1vkRbui+pLnTvAmjpmsLGpYvU8rTHsdiyYBTKy0ugoqqBQRP/gqG500uWqmEY6InnFmblSA9es3PLYWIoO5r/lIa6Av5d5AZlJQUIhSIsXp+AiCoDxJZKW0N8vJJXVO3Yo0gk83p+oqbAgd0zxMbG4uLFi9i1axcAQElJCSNHjsSaNWuwefNm9OrVC5s2bUJAQAAePHiAc+fOYdmyZZJ1lZSU4OVVeaG+k5MT9PVrn8bw1I0bN1BcXIxevXpJLS8tLUX79tJn+NzdKy/QNjU1hYaGhmRQ93TZxYsXpdbp3LkzBILKjsjX1xcLFixARUUFFBVlTykoKSlBSUmJ1LLyMgWpa5KepZ29AIM6V25/00nxma/qQymB4Nlhclmf17aKl7MC7j4SIa+olgQA2jsp4OoDIcqbxUyI6j8SIhnLqiav9tnTyqm+vJ5O7fgeaY9vY8Snm19o/YYhXRbRi9bJk3UsHTxh6eAp+djK0QtrfhqKy6f+Qe9RX798dhtF07aTlkAgeM528wyx1y7h0K5VGD1xFuyd2yE1OQHb1v6CA3orMGD45JfMLTWKGu297jYhuw3VXA4Al46vxK0rBzDikw01fhsNTOzx9ld7UFKUiztRR3HknxkY8ck/zWJw172LPj5911ry/usF98X/eY7f2aeKioX4YPYtqKkpon1bbUx5yxJJqaWIudWyonaeTooIDqi8eG7t4SfXv1WrAEHNRfQchHzcgVxxYPcMq1evRnl5OSwtLSXLRCIRlJWVkZWVhTFjxuDTTz/F4sWLsXnzZrRt2xYeHh6SdLLU51o2oVA8ujhw4IDUdwOAqqr0j4WycmXHIxAIpN4/XfZ0ey9j3rx5+O6776SWBQ75GkFD638ThdgEER6ll0veKz6ZvaClLo7SPaWpBhTUfuMt5BcD2urS3amWmqBG5A8AdDUBBzMBtpyuqPnhEzYmAhjrCrD9TNOO6tQ19SFQUERhrnREoTAvo8ZZ46c0dWqebS7My4SCghLUNPWeOw+ndvyA+9dOYvgn/0Bbz+y515c3DS1xnRTIqBNNHdl1oqVTM5pX8KRO1LX0ZK4jUFCAuV07ZKXGySPbDao5tJPmTktbHwoKisjJzpBanpeTCR09w1rWerZ9W5aiU+AA+PcMBgBY2jqjtKQI//z9A/oNmwgFBU7Jaq6e7jey+hKNWvoSTRl9SW37zeUTq3Hx6HIM+2gtjC1da2xLUUkF+sa2AAAzm3ZIeXgVEac3oNeopr85zbmIHNy6WyB5r6wsbsf6esrIzKn8zdbTUUJ2Tt1Tt0Ui4HGqeBB0P74INhaqGDXItMUN7G48rEBCauUxgdKTc9LaGgKpqJ2mugD5RRzaUfPAX6A6lJeXY8OGDViwYAGioqIkr+joaNja2mLTpk0YMmQIiouLcfjwYWzevFkyZRMAXF1dUV5ejsjIyrn7d+/eRXZ29jO/u02bNlBVVUV8fDycnJykXtbW1s9c/1nOnz9f472zs3Ot0ToAmDlzJnJycqRe/gNn1JpeltJy8aMJnr7ScoC8QhEczSuboqICYGsqQEJq7R1lYpoIDubSZ3kcLQRISKu5TnsnBRQUA3cSa9+el5MCHqULkZJVa5JGoaikAhPrtoiPPSu1PD42HOb2sq/FMLPzRHxsuNSyh7FhMLFxg6JiHbfqqkYkEuHUju9xN+Yohv3feugavnw7kwdFJRWY2bTFg5vSdfLgZjisHGXXiaWDJx7clK6TBzfCYGZbe52IRCKkJNyscQOV5qgp20lLoaSsDBuH1rgZc05q+c2YC3Bw8Xjh7ZaWFkMgkP7pFA/mXuGr8V8RikoqMLVui/hb0vvNw9hwWNSy35jbe+Jh9f3mVhhMq+03l46vwvnDSzH0g1Uws2lXr/yIIEJFWfO4C2JRsRCPU0slr4ePipGRXQYvt8q7WSopCuDuqoUbdwrq2FJN4hPOLS8qU1oGZOSKJK+ULBFyC0Vwtqo8TlJUABzMFfAwpVlM9WmRRCJBk71eRYzY1WH//v3IysrChAkToKsrfRH0m2++idWrV+Ojjz7CG2+8gTlz5uDmzZt46623JGlcXV3Rs2dPTJ48GcuWLYOysjI+//xzqKury5zCUZW2tja++OILTJ06FUKhEP7+/sjNzUV4eDi0tLTwzjvvvFTZEhISMG3aNEyZMgURERFYvHgxFixYUOc6qqqqNaKFSsovf9OF8zeFCGingIxcETLzRAhop4iyciDmQWVHOdRPEXmFIhyPFErWebePIvzbKuBWghCu1gpwMBdg9WHpqJwAQHtHBUTdF0JYyzGXqjLQ1laAI1eaR8fsFfQujvzzJUxt3GBu1x5Xw7ciLysJ7n6jAABh+xagICcFfd7+BQDg7jcK0aGbcHr3PLTzHYGkuEhcP78T/cZV/j0rykuRkXwPACAsL0VBTgpSE29CRVUDek/OIJ/a/h1uRezH4IlLoaKmKTlLraqmDSUVtcasgho69nwX+9Z+CXNbN1g6tEdU6FbkZiahfaC4TkJ2L0BedgoGvSuuk/ZdR+FKyCYc3zYPngEj8Oh+JKLP7sQbEyvrJHTfElg6eEDfxA6lxfm4fHIDUhNuoc/ouZI0pcUFyEqrfLxJdnoiUhJuQk1TF7oGFo1Uetmaqp2UlhQgu0qd5GYkIjXxJtQ0dKHTxHVSXc9BY7F28WzYOrSFg4s7Qo/tRFZ6EgJ7vwkA2L1pEbIzUvHuJz9K1kl4cAsAUFJchLycLCQ8uAVFJWVYWDsCANp5B+LE/n9gbe/6ZCpmPPZuWQp3n65QqOPEWEukqKkBTScbyXsNeyvoeLiiNDMHxQmyHxnR3Hl3exeHNj7Zb+zb4+rZrcjLTIKHv3i/Cd27APnZKeg3TrzfePiNQtSZTQjZNQ/tuoxA0oNIXDu3E/3HV+43l46vRPiBP9HvnQXQNbSU9J3KqhpQURXfaCRs7++waxMIbX0zlJYUIPbKQSTeuYjgDxv/pmX1tftwKkYPMsXj5BI8SinBqEGmKCkV4eS5yjOg06fYIiOrFGu2idvDqEGmuP2gEI9TSqCsJEBHDx309DPAonWVjzZQU1WAhWnlsYSZsQocbNSRV1COtIzmfSOnsKvl6OaphPQcIdJzROjWXgll5UDk3cpjjxFBysgtEOHwJXGkU1EBMNEXH/MpKQigoymAuaFAMnAEABUlwFC38rjQQEecpqgYyC7gCSOqPw7s6rB69Wr07NmzxqAOAIYNG4aff/4ZERERGDNmDAYMGIDAwEDY2NhIpduwYQMmTJiAwMBAmJmZYd68ebh+/TrU1J59oPzDDz/AxMQE8+bNw/3796GnpwcvLy/MmjXrpcs2btw4FBUVoWPHjlBUVMTHH3+MyZOb5vqQsOtCKCkBAzspQk0VeJQmwsbj5SitnP0BXU3ph1gmpImw40wFurdXRDdPBWTlAdvPVOBRunQH6GAugJ6WAJF3ah+0udkJAAFw9UHzGNi5ePVHcUEWzh9ZisKcVBiat8IbU1ZAx0A8JbcgNw25WZUHVbqG1hgyZQVO756HmNBN0NQ1QVDwbDh79pGkyc9JxeZfh0jeXzm5BldOroGlU0cM/3gjAEhuk79j8Vip/PR6ax7adgpuqOLWS5sO/VFUkIWzB5YiPycVxhatMOKjFdA1FNdJfk4acjMr60TPyBojPl6B49vmIeL0JmjpmqDXyNlw9aqsk5KiXBz65xsU5KZBVV0bptZtMOaLf2BhX3nNatLDa9j8+zjJ+xPb5wEA2vkOxcDx8xu62HVqqnaSEn8NO5dU1smZPeI6ad1xKPqMado6qc7Hrw/y87JxYMdy5Galw8LGCR/NWgJDY/EANCcrDZnp0gOUn6aPkvw//v4NXAo7BANjc/y87BAAoP+bkyAQCLB3y1/IzkyFlo4+3L0D8cZbHzVewRqJrrcbfE9slLxv85v4tydhwy7ETJjZVNl6KS7e4r7k/OGlKMgV7zdDP6iy3+SkIa/qfmNkjaHvr8DpXfMQHboJmjom6PbmbLSqst9Eh/6LivIy7F/9idR3de73Ebr0/1i83bx0HN74JQpyU6Gipg1jCxcEf7iqxh03m5NtB1KhqqKAj8ZbQ1tDEbfuF2DmL3dRVFz5W2liqCx1eYmaqgI+fscKRgYqKCkVIiGpGP/7Ow6nL2RL0rSy18BvsytvIvf+GCsAwNHQDPy2Qj7PCW4op6PLoawEDPFXgboKkJAqxKqDJSitMh7V0xJIBe91NAT4bFjlMV9XD2V09VDGvccVWLFfHLG1MlbAlEGVg91BvuIb1FyOLcf20817sEvNi0DUVA8ve00lJibC2toax48fR48ePZokD0FBQfD09MTChQtfeltzN7DDqcrM5NWb0vay1Js22NcsFdVx/ejrytWyjjsbvaYKvTybOgvNTuKB2KbOQrOzc6PsRzW8ztoH1LzG8XX3v8m13Da8iR2LLnl2ogbSy6P+N/9rKRixa2AnT55Efn4+2rVrh6SkJHz55Zews7NDYGBgU2eNiIiIiIheERzYNbCysjLMmjUL9+/fh7a2Nrp06YJNmzbVuHMlEREREdHrRMTHHcgVB3YNrE+fPujTp8+zEzaikJCQps4CERERERHJEQd2RERERETU6Gq7Yzm9GD7HjoiIiIiIqIXjwI6IiIiIiKiF41RMIiIiIiJqdFWfUUwvjxE7IiIiIiKiFo4ROyIiIiIianQi3jxFrhixIyIiIiIiauE4sCMiIiIiImrhOBWTiIiIiIganRC8eYo8MWJHRERERETUwjFiR0REREREjY43T5EvRuyIiIiIiIhaOEbsiIiIiIio0fEB5fLFiB0REREREVELx4EdERERERFRC8epmERERERE1OiEvHmKXDFiR0RERERE1MIxYkdERERERI2OjzuQL0bsiIiIiIiIWjgO7IiIiIiIiFo4TsUkIiIiIqJGJwKfYydPjNgRERERERG1cIzYERERERFRo+PjDuSLETsiIiIiIqIWjhE7IiIiIiJqdHzcgXwxYkdERERERNTCMWJHL8XIkE2oqtx8YVNnodkpLeMdr6orLuEpyupiH6s3dRaaHcGB2KbOQrNjNcClqbPQ7PTfeaups9DsWJqwj6XXE4/KiYiIiIio0XEqpnxxKiYREREREVELx4gdERERERE1OqGIl2vIEyN2RERERERELRwHdkRERERERC0cp2ISEREREVGj481T5IsROyIiIiIiohaOETsiIiIiImp0jNjJFyN2RERERERELRwjdkRERERE1OiEjNjJFSN2RERERERELRwHdkRERERERC0cp2ISEREREVGjE4kETZ2FVwojdkRERERERC0cI3ZERERERNTo+LgD+WLEjoiIiIiIqIXjwI6IiIiIiKiF41RMIiIiIiJqdHyOnXwxYkdERERERNTCMWJHRERERESNjjdPkS9G7IiIiIiIiFo4DuyIiIiIiIhaOE7FJCIiIiKiRsepmPLFiB0REREREVELx4gdERERERE1Oj7uQL4YsSMiIiIiImrhOLB7AXZ2dli4cGG908fFxUEgECAqKqpB8jN+/HgMGTKkQbZNRERERNQQRKKme72KWvxUzPHjx2P9+vUAACUlJVhbWyM4OBjfffcdNDU1G+Q7L1269Fzbtra2RlJSEoyMjAAAISEh6NatG7KysqCnp1fv7cTFxcHe3h6RkZHw9PSULP/zzz8haqYt9OrZzYg4tRqFuWkwMHNCwJBZsHDwqTX9o7sXEbZ3PjKT70JTxwRe3SfCrcsoqTR3o4/gwuFFyEmPh66RDTr3+wyO7r3q/b0VFWW4cPBPxN08jdzMRKioacG6VRf4DpgGLV1TyTYKctMQvu9XJNwOR2lJAfSN7eHdczKcPPrKsYZkE4lEiDz5F2IvbUNpUS6Mrd3hO2gO9E2d61wv7tpRRBxfhNzMeOgY2MCr16ewa1tZNzcv/ItbF7YgP/sRAEDPxAme3T6EtUugJM2a2a1lbrtD3y/QLmCCHEpXPyKRCJeOLsGNC9tQUpgLUxt3BAZ/AwOzuuvgXswRXDy8CDkZ8dA1tEGnfp/BoZ10+7h2djMiQ1ajMC8NBqZO8HtDul0W5qXj3IHfkHD7LEqL8mDu4IOAIV9Dz9iuMk1uGsL3/4qEO+EoKy6Anok9vLtPhmMjtI+n2E5qig7dhMsnVqMgNw2GZs7oOmwWrBxr73MS71zE6d3zkZF8B5q6JvDpMREe/qMln6cn3cG5g4uQmnAduZmP0HXoTHh1Gy+1jdLifIQf+BN3Y46jMD8DJpZtEDRsFsxs3RuqmPUWdaZKfZg7Iyh4Fqycaq+PhKf1kXQHWrom8OkpXR8xZ7fh5sU9SE+6AwAwtW4Lv0HTYG5XWdbo0M2IDvsXuZni9mNo5ozOfT+EfduuDVTKxmPg7wOHzydA18sNahYmuDzsQ6TsPdHU2ZILkUiEy8ek+9yAofXrcy8dqexzO/aV7nMf37+EqJDVSHt0HYW5aej7zhLYu/WsdXund3yDGxe2ocvgmfAIeEdu5XteF05sRujBNcjPSYOJhRP6j5kJO5fa950Hty7i0Ob/IfXxXWjrmSCg/wR07F55/FJRXobT+1cgMuw/5GWnwMjMHr1HfI5W7gEyt3d63woc2/EHfHuPxYAxs+RePno1vRIRu759+yIpKQn379/Hjz/+iKVLl+KLL76oka6srEwu32dsbAwNDY16p1dUVISZmRmUlBpmHK2rq/tcA8TGcifyIEL3zINPz/cx8vPdsLD3wb4Vk5GX9Vhm+tyMROxbNQUW9j4Y+fluePecgjO7f8Ld6COSNElxkTiycRpcvAdj9Bf/wcV7MI5smIrkh9H1/t7y0mKkPbqBDr0/xMhpO9F//GJkp8bhwOoPpfJzfPMMZKU+wID3lmL09L1wcO+FIxumIS3xRgPUlrSroatw/ew6+A76GoM/3AZ1LSMcXjsBZSUFta6TGh+JU1unwbH9YAz5eA8c2w/GqS3TkJpQWTeaOmbw6TMNgz/cjsEfboe5Q2ec2PQRslLuSNKM+uqM1Ms/+CdAIIBt294NWubqIk+tQvSZdQgYOgdvfrodGjrG2LviPZQW59e6TnJcJI7+Mw2tvAdj5Of/oZX3YBzdOBUpVdtH1EGE7Z0H757vY/jU3TB38MH+VZXtQyQS4dC6/0NuRiL6jV+K4VN3QVvfAnuXv4eykkLJdo7/OwPZaQ/Q/92lGPnFXji064Wj/0xD2qOGbx9PsZ1Ii404iJBd89Cx9wcY8+UeWDp6Y8+yScjNlN3n5GQkYPfyybB09MaYL/egY6/3EbLzJ9yJquxzykuLoGtoBf9Bn0NDx1jmdo79+zUexoaj79hfMO6rfbB19cPOv95FfnZKg5SzvmKviOujU58P8PYMcX3srqs+0hOw+29xfbw9Yw869n4fp3b8hNtV6iPx7gW4eA/A8E82YPS0LdA2MMeupe8hr0pZtfTM4D/4C4yZvhNjpu+EdavO+G/l/0kGgy2ZoqYGcmNicf3T75s6K3IXFfKkzx0yB8M+3Q4NbWPsW/nsPvfYJnGfO2KauM899s9UpMRX9idlpUUwtHBFwJA5z8zDg2vHkRIfA00dE7mU6UVdvXAQBzfNR9CgKfjw+12wdfHGhgVTkJ0he9/JTEvEhgXvw9bFGx9+vwtdB07GgX9+xvVLRyVpju/8E5dObcPAsbPxyc/70aHbSGxe9DEeP6z5m5F4/youhWyDmbVLg5WRXk2vxMBOVVUVZmZmsLa2xltvvYUxY8Zgz549+Pbbb+Hp6Yk1a9bAwcEBqqqqEIlEyMnJweTJk2FiYgIdHR10794d0dHRUtvcu3cvfHx8oKamBiMjIwQHB0s+qz4VUyAQYNmyZejXrx/U1dVhb2+P7du3Sz6vOhUzLi4O3bp1AwDo6+tDIBBg/PjxAIDDhw/D398fenp6MDQ0xMCBA3Hv3j3Jduzt7QEA7du3h0AgQFBQEICaUzFLSkrwySefwMTEBGpqavD398elS5ckn4eEhEAgEODEiRPw8fGBhoYGunTpgtjY2Jf6O1QXdXod2nQahradh8PA1BEBQ2dBS88MV8/+KzP9tfAt0NYzR8DQWTAwdUTbzsPRumMwIkPWSNJEn9kA61Zd4NNzCvRNHeDTcwqsnDsj+sz6en+vqro23nh/DZw9+0HfxAFmdp4IDP4aaYnXpQadyXFRcA94G6a27tA1tEaHXh9ARV27wQd2IpEI189ugEfQFNi17Q1901YIfHM+KsqKcS96f63rXQ/fAAvHLvDoOhl6xg7w6DoZFo6dcT18gySNTetusHbpCl0je+ga2cOn92dQUtFAWpWDeg1tY6lX/M2TMLfvBB0D6wYtd1UikQgxoRvg3eN9OLbrDUPzVugxaj7KS4txJ7L2OogO3QBr5y7w7jEF+iYO8O4xBZbOnREdWtk+ok+vQ+uOw9Cmk7h9+L8hbh/XzonbR056HFIeRqPrsLkwtWkHfRMHBAbPRVlpAe5EHZBsJ/lhFNr5vw1TG3H78OnZOO3jKbaTmiJOrYVb52Fo12U4DM0cETRsNrT1zRATJrvPiQnbAh19cwQNmw1DM0e06zIcbTsH48rJyj7HzNYdgUNmwMV7AJSUVGpso7y0GHeijyLgjemwcuoAPWNb+Pb/GLqGVogO29xgZa2PK6fWws23sj66PamP6FrqI/qsuD66VakPt87BuHKisj76v7MAnoFjYGLVGgZmjug1+keIREIkxJ6TpHFs1x0ObbtC38Qe+ib28B80FcqqGkiKi2roIje4tCNncHvuQiTvOdbUWZGrqn2uQ7veMDRrhe716HNjwjbAyrkLvLqL+1yv7lNg6dQZMVX6XFvXQHTq+xkc2tV90ic/JwWhe35Az7d+hYJi004oO3t4PbwDg+ETNBwmFo4YMGYWdA3McPHEFpnpL53cAj1DcwwYMwsmFo7wCRoOr8BghB2q3Heiwvei66DJcPHoCgMTa3TqMRrO7fxx9tA6qW2VFBdg+9/TMeS976GmqdOQxWwWhMKme72KXomBXXXq6uqS6Nzdu3exbds27Ny5U3KN24ABA5CcnIyDBw/iypUr8PLyQo8ePZCZmQkAOHDgAIKDgzFgwABERkZKBkB1mTNnDoYNG4bo6Gi8/fbbGD16NG7evFkjnbW1NXbu3AkAiI2NRVJSEv78808AQEFBAaZNm4ZLly7hxIkTUFBQwNChQyF80vouXrwIADh+/DiSkpKwa9cumXn58ssvsXPnTqxfvx4RERFwcnJCnz59JOV7avbs2ViwYAEuX74MJSUlvPfee/Wp3nqpKC9FauJ1WLfyky6/ix+S4yJlrpP8MArWLtLpbVz8kZZwHRUV4r9nclwUbKqncfVH8pMDhhf5XgAoLc4DBAKoqld2oub2XrgTdRDFBdkQCYW4HXkAwvIyWDp1rLvwLykvKxFF+emwdKosg6KSCszsOiA1vvYypMZHw9K5i9QyS2e/WtcRCitwP+YAyksLYWzjKTNNUX46EmJPo5XPsOcvyEvIzUxEYV6aVHtQVFKBhWOHOv+OKbW0oartI+2RjPbRyg8pT7ZbUV765PtUJZ8rKChCUVEFSQ+uSJaZ23vhbtRBFBeK28edyAOoKC+DpWPDto+n2E6kVZSXIiXhOmxd/aWW27j64fED2WVLiouCjat0W7BzDUBK/DVJn/MsQmE5RMIKKFVpLwCgpKyGx/cjnqME8lVbfdjWVR8PomBbrT5sW9ddH+WlRaioKIeapq7Mz4XCCty6Im4/FnbtX6Ak1BjynvS5Vq2q9bkOHZD88Bl9bo3f28o+t75EQiFO/PslPLtOeObUz4ZWXl6Kx3HX4eQmXS4nNz/E35VdF/F3o2qkd3bzw6O466goF+875WWlUFKu3k+o4uGdK1LL9m34AS4eXeHUVrqfJqqPFn+NXXUXL17E5s2b0aNHDwBAaWkpNm7cCGNj8RSakydP4urVq0hNTYWqqngH++2337Bnzx7s2LEDkydPxk8//YRRo0bhu+++k2zXw8Ojzu8dPnw4Jk6cCAD44YcfcOzYMSxevBhLly6VSqeoqAgDAwMAgImJidQUymHDpA+KVq9eDRMTE9y4cQNubm6SMhgaGsLMzExmPgoKCrBs2TKsW7cO/fr1AwCsXLkSx44dw+rVqzF9+nRJ2p9++gldu4qvefjqq68wYMAAFBcXQ01Nrc6y1kdRQRZEwgpoaBtKLdfQNkRhXrrsvOelwUbbv0Z6obAcxQVZ0NQxQWFeOtS1pLeprmWIgty0F/7e8rIShO9fgFbtB0JFTUuyvM+4P3Bkw1SsmtMZCgpKUFJRQ793F0PXyKZ+lfCCip7kU13LSGq5mpYhCrJlTwMBxAfX1ddR1zKSbO+pzOTb2L98NCrKS6CsooEeYxZD38RJ5jbvROyBsqombNv0kvl5QynME/89Nar9rTW0DGudyiteT3b7eLq94iftQ11G+0h4Uk96Jg7Q1rfA+YO/o+ub30FZRR3RZ9ahMC8NhU/aGQD0fvsPHP1nKtZ8U6V9jG/49vEU24m02vZ9TW0jPMxLk7lOQW467LSl6+Jpn1OUnwUt3WdPB1NR04K5XXtcOLIUBmYO0NA2QuyV/Uh6GA19Y9sXL9BLelofmjXaupFUO66qIDcdGtXqQ/MZ9RG6dwG0dE1h4yJ9EJr2OBZbFoxCeXkJVFQ1MGjiXzA0l91+qOnV1ueqaxsi/1l9rnbNdQpr2edqExmyEgoKimjnP/a51msIhXnZEAoroKVbbV/QNUR+juzjiPycdGjqSteDlq4RhBXlKMzPgraeCZzb+SP88DrYufjAwMQG92+cw63IkxAKKyTrxJw/gMdx1/HBtzvkX7BmqpneIqLFeiUGdvv374eWlhbKy8tRVlaGN954QzKosrW1lQyIAODKlSvIz8+HoaH0DlhUVCSZ9hgVFYVJkyY9Vx58fX1rvH/eu2Deu3cPc+bMwfnz55Geni6J1MXHx8PNza3e2ygrK4OfX+WZI2VlZXTs2LFGBNHdvfJid3NzcwBAamoqbGxkH5iWlJSgpKREallZmQqUq52BkiIQSL8XyVgmvcIzlwtkrF9jWT2/t6KiDEc2TgNEIgS9OVfqs/OHFqKkKBdvvL8W6pr6uH/tOA6v/wzBH/0DIwv5zXu/F7UPZ//7VvK+17hltaQUPaPugBr1JxLVqBtdIzsM+WgXSovyEHf9KEJ3zES/SRtkHrTfubILjh4Da5xllLfbEfsQsqOy/gdM+Fv8n2p5F8lYVl192oegWj2Jf1jEyxQVldHnnUU4te1rrPmmEwQKirBy9oWNa6DUOhcPL0RJYS4GT1kLNU19PLh2HEc2fIah//cPDM3lf10E20k9VW8zIhFq71dQ4zORuJXJbEe16Tv2FxzdPAsr5wRCoKAIE6s2cPUeiNSExrveslY1ylF3fVQvd131cen4Sty6cgAjPtlQ429vYGKPt7/ag5KiXNyJOooj/8zAiE/+4eCumbgdsQ+nd1bpc9+T3eeiSt9Ym+r9qbgLqv/+k5Z4DTGhGzH8s53PtV6Dq7Hr1N231vxdEUk+AYABY2Zhz9pv8OdXAyAQCGBgYg2vgKGICN0NAMjOSMKBTfMwfvoqKKs0g76UWqRXYmDXrVs3LFu2DMrKyrCwsICysrLks+p3rxQKhTA3N0dISEiN7TyNnqmrq8slX8/bQQ0aNAjW1tZYuXIlLCwsIBQK4ebmhtLS0npv42lHUuPHWcaBW9V6evqZsI5Jx/PmzZOKYgJA39HfoP+Yb2ukVdfUh0BBEYW50me3CvMzapwRfEpT27hGVK0wLwMKCkpQ09QD8ORsc7U0RfkZkrPMz/O9FRVlOLJ+KnIzEjHkw3VS0bqc9HhcDduE0V/ug+GTaSFGlq54fP8Krp7djG7DpevhZdi07g5j68pB9tOpgEX56dCocgF5cX5mjWhUVepaRijKlz5LWlSQAbVq6ygqqUDHUBxJMLJyQ9qjq7gRvhF+Q6TLlBx3GTnpDxA06vcXK9hzsGvTDSOn1ayDwrx0qYvoi+poP0Dt7eNphErtafuQ2YYqt2ti5YaR0/agpCgPwooyqGsZYMefI2BiLT7BkpMej6tnN2HUF/sk04aMLFyR9EDcPoLelF/7eIrtpG517vvVolBPaeoYoaBaZKEoL1Oqz6kPPWMbjPj0H5SVFKKkOB9auiY4sPYz6BpaPXc55OVpfRRUr4+8DGjo1FEf1aJ5hbXUx+UTq3Hx6HIM+2gtjC1da2xLUUlFErE0s2mHlIdXEXF6A3qNevVuOtIS2bXpBlOb+vW51SNyVT2rz62Pxw+uoKggAxt/7i5ZJhJW4Ny+/+Fq6Hq8PetkvbclDxraelBQUER+tnS5CnIzoaUjuy60dI1qRPMKcjOgoKgEDS09AICmjgHGfLoEZaUlKMrPhra+CY5uWwB9I0sAwOO46yjIzcCyuW9KtiEUVuBh7GVcOL4Z366OhoKCohxLSq+iV+IaO01NTTg5OcHW1lZqsCKLl5cXkpOToaSkBCcnJ6nX08cRuLu748SJ57t98fnz52u8d3Wt+WMHACoq4gvwKyoqw+8ZGRm4efMmvv76a/To0QOtW7dGVlbWM9erzsnJCSoqKggLC5MsKysrw+XLl9G6tezbk9fXzJkzkZOTI/XqNWKmzLSKSiowsWqLhNvhUssTbofDrJbrLMxsPWWkPwtj67ZQVBT/Xc3sPJEQK50mPvYszOw8n+t7nw7qstMfYsgH4ohcVWWlRQAAgUB6FxEoKEAkku8Vt8qqmtAxtJW89EycoK5lhEd3K8tQUV6K5LhLMLGp/RoVExsPPL4rXe5Hd8LrXAcAIKr8Ua/q9uWdMLRoC0Nz2e1YnlTUtKBrZCt56Zs6QUPbGIm3pevg8b1LtbYfADCV1YaqtQ9jy5rtI/F2OExlbFdVXRvqWgbITotDWuI12LUVH3iUl4nbB6q3D4ECIOf28RTbSd0UlVRgat0WD2PPSi2PvxUOC3vZZTO380T8Lem6eHgrDKY2bpI+53koq2pAS9cExYU5eHgrDA7tejz3NuTlaX3E35Kuj4exddSHvScexj67Pi4dX4Xzh5di6AerYGbTrl75EUGEirL6n6SkhlXvPvf+JZjZ1t3nJt6p+bv9tM+tDxcv8R01h0/dLXlp6pjAM2gCBkxc9dxle1lKSiqwsGuLu9ely3X3ejhsnGTXhY2TZ830187C0q4tFJWk+xJlFVXoGJhCWFGO65ePwdVL3E84tvHFxz/9h//7YZfkZWnvBnffgfi/H3a9soM6PsdOvl6Jgd3z6NmzJ3x9fTFkyBAcOXIEcXFxCA8Px9dff43Lly8DAObOnYt///0Xc+fOxc2bN3H16lX88ssvdW53+/btWLNmDW7fvo25c+fi4sWL+Oijj2SmtbW1hUAgwP79+5GWlob8/Hzo6+vD0NAQK1aswN27d3Hy5ElMmzZNaj0TExOoq6vj8OHDSElJQU5OTo1ta2pq4oMPPsD06dNx+PBh3LhxA5MmTUJhYSEmTHi5Z0upqqpCR0dH6lXXNEzPruNx48IO3LiwE5kp9xC6Zx7ys5Ikz6UL378AxzbPkKR36zIKeVmPEfrfPGSm3MONCztx48JOtA+qvKmLR8BYxN8+iysnViIr5T6unFiJxNvn4BH4Tr2/V1hRjsPrPkVq4jX0HvMrhMIKFOSmoSA3TXLgqm/qAF0jW4Rsn4uUhzHISY9HZMgaJNwOh0Mdz9+RB4FAgLZ+4xBzegXirh9DVspthO6cBUVlNTh6DJSkO719Bi4fqYyStPEdh0d3wxFzZiWy0+4j5sxKPL53Dm27jJOkuXz0DyTHXUZe1iNkJt/G5aMLkfzgIhw9B0rlobQ4H3HXjsDF5000BYFAAPeAcbhyYjnuXz2GjKTbOLl1JpRU1ODcvjKvx/+dgXMHF0jeuweMRcLts4g4uRJZqfcRcXIlEu+ck3oWkkfX8bh5cQduXhS3j7D/5iEvOwlunSufN3Q3+jAe3b2AnIwEPLh2AvtWvAd7tx6wcRFfA6pnIm4fp3fMRUq8uH1EhaxBwp3wOp/PJE9sJzV5dXsX187twLVzO5CRfA8hu35GXlYS3P3Ff9uwvQtweOOXkvTu/qOQm/UYp3fNQ0byPfG653fCu3tlnyO+IdNNpCbeREV5KfJzUpCaeBPZaQ8laeJuhiLuxhnkZCTg4a2z2LF4HPRN7NG2c+XdlJuCd7d3cbVqfez8GXmZSfB4Uh+hexfg0IbK+vDwG4XczMcIqVof53bCu0dlfVw6vhLhBxai95ifoWtoKek7S6s8YiNs7+9IvHsZORmJSHsci7B9fyDxzkW07jCo8QrfQBQ1NaDj4QodD/GJDA17K+h4uELN2ryJc/Zynva5ESef9LnJsvvcE//OwPmqfa6/uM+NPCXucyNPrcSjO+fgXqXPLSspQPqjm0h/JL4cJDczEemPbkqul1bT1IehWSupl4KiEtS1jaBv4tBINSDNr+87uHJ6J66c2YnUx/dwcNM85GQkoUP3kQCAo9t+x47llccvHbqPQnb6YxzcPB+pj+/hypmduHJmF/z7Ve47Cfeicf3yUWSmJiAu9jLWL5gMkUiIgP7iYzNVdU2YWrWSeimrqkNDSw+mVq0atwKoxXolpmI+D4FAgIMHD2L27Nl47733kJaWBjMzMwQGBsLUVPxw6qCgIGzfvh0//PAD5s+fDx0dHQQGBta53e+++w5btmzBhx9+CDMzM2zatAlt2rSRmdbS0hLfffcdvvrqK7z77rsYN24c1q1bhy1btuCTTz6Bm5sbXFxcsGjRIskjDQDxA9gXLVqE77//Ht988w0CAgJkTimdP38+hEIhxo4di7y8PPj4+ODIkSPQ19evkbYhObfvj+LCbFw6+pfk4bgDJy2HjoF42kFhXprUjTB0DK0waOJyhP03H1fDNkNT1wSBQ2fDyaOPJI25vRf6jF2A84f+xIXDi6BraI0+436Hma1Hvb83PycZD66Lp3ZsWTBEKs9DPlwPK6dOUFRUxqBJyxG+fwH2r/4AZaWF0DW0Qc/R82HXpuEfstsuYCLKy0pwbu/3KC3OhbGVO/q+uwrKqpVTiwtykqQiiqa27RE0cgEijv2JiOOLoW1gjW6jFsDEurJuivLTcWb7DBTmpUFFTRv6Zq3Qe/wKqTsrAsD9mIMQQQQHjwENXtbatO82EeVlxTiz63uUFOXA1MYdgyatlpoym5/1WGqKsbmdF3qPWYALh//ExSOLoGNojV5jf4dp1fbh2R8lBdm4fOwvyUOsB05YDu0n7QMACnNTcXbv/CdTNI3h4vMGfHp+IPlcUVEZAyYsx/mDC3BwzQcoKymErpENeoyaD9vWjfcQZrYTaS5e/VFckIULR5aiICcVhuatMOT9FZJ9vyA3DXlZSZL0uobWGDplBU7vnofo0E3Q1DVB0LDZcPas7HPyc1Kx6ZchkvdXTq7BlZNrYOXUEcM/2QgAKCnKw9l9vyM/Oxmqmnpw9ugNv4FTXyjqJ08u3v1RVJCF84eXoiBXXB9DP6hSHznV6sPIGkPfX4HTu57Uh44Jur05G62q1Ed06L+oKC/D/tWfSH1X534foUv/j8XbzUvH4Y1foiA3FSpq2jC2cEHwh6tq3HGzJdL1doPviY2S921+Ez84OmHDLsRMkD2DpaXwDBL3uaG7xX2uiY07Blbvc7Ol+1wzOy/0GrMAF6v2uW//DlObyv4kNfEa9v5dOdAL3zcfAODiPQTdR81vhJI9v3ad+qMwPxun/luKvOw0mFo6Y+y0vyXTJvNy0pCdWbnvGBhbYdznf+Pg5vm4cGIztPVMMODtWWjbofIRD+VlJTi+cxGy0hKgoqqBVu6BeHPy/6D+GjzSoC7CVzRy1lQEItGrGoxsPAKBALt375Z6ltzrYvEBNp+qCotYH9WpqTaji+GbieIStpPqdLVfuwkkz9Sc7iPRXFgN4AObq7uz81ZTZ6HZsTRhH1vd8M7Ns4/961DTfff/9Xv+dZYuXYpff/0VSUlJaNu2LRYuXIiAgIBa058+fRrTpk3D9evXYWFhgS+//BLvv//+S+S6bs3zr0xERERERK80kUjUZK/ntXXrVnz22WeYPXs2IiMjERAQgH79+iE+Pl5m+gcPHqB///4ICAhAZGQkZs2ahU8++UTyPOuGwIEdERERERFRHX7//XdMmDABEydOROvWrbFw4UJYW1tj2TLZjx/6+++/YWNjg4ULF6J169aYOHEi3nvvPfz2228NlsfX7hq7hsDZrERERERELYes5zOrqqpCVbXmjQFLS0tx5coVfPXVV1LLe/fujfDw8BrpAeDcuXPo3bu31LI+ffpg9erVKCsre+ad/F8EI3ZERERERNTomvJxB/PmzYOurq7Ua968eTLzmZ6ejoqKCsmNFp8yNTVFcnKyzHWSk5Nlpi8vL0d6errMdV4WI3ZERERERPRamTlzZo1Hi8mK1lUlqHZXK5FIVGPZs9LLWi4vHNgREREREVGjEwqb7rtrm3Ypi5GRERQVFWtE51JTU2tE5Z4yMzOTmV5JSQmGhoYvluln4FRMIiIiIiKiWqioqMDb2xvHjh2TWn7s2DF06dJF5jq+vr410h89ehQ+Pj4Ncn0dwIEdERERERFRnaZNm4ZVq1ZhzZo1uHnzJqZOnYr4+HjJc+lmzpyJcePGSdK///77ePjwIaZNm4abN29izZo1WL16Nb744osGyyOnYhIRERERUaNrSTeWHzlyJDIyMvD9998jKSkJbm5uOHjwIGxtbQEASUlJUs+0s7e3x8GDBzF16lT89ddfsLCwwKJFizBs2LAGy6NAxHv100tYfIDNp6rCItZHdWqqDXOBcEtWXMJ2Up2uNieQVNdA19a3aFYDXJo6C83OnZ23mjoLzY6lCfvY6oZ3bp597MK9Tfe3+mzwq9fJMmJHRERERESNTsgxuFw1z+E7ERERERER1RsjdkRERERE1Oh4QZh8MWJHRERERETUwnFgR0RERERE1MJxKiYRERERETU6UZPePeXVuysmI3ZEREREREQtHCN2RERERETU6Pi4A/lixI6IiIiIiKiF48COiIiIiIioheNUTCIiIiIianR8jp18MWJHRERERETUwjFiR0REREREjU7Iu6fIFSN2RERERERELRwjdkRERERE1Oh4jZ18MWJHRERERETUwnFgR0RERERE1MJxKiYRERERETU6TsWUL0bsiIiIiIiIWjhG7IiIiIiIqNEJGbKTKw7s6KUoKAiaOgvNippqU+eg+akQNnUOmh81Ve431d29X9jUWWh2YsJjmzoLzU7/nbeaOgvNjvMw16bOQrOTepT7Dr2eOBWTiIiIiIiohWPEjoiIiIiIGp2Is3rkihE7IiIiIiKiFo4ROyIiIiIianQi3jxFrhixIyIiIiIiauEYsSMiIiIiokYn5DV2csWIHRERERERUQvHgR0REREREVELx6mYRERERETU6HjzFPlixI6IiIiIiKiFY8SOiIiIiIganZABO7lixI6IiIiIiKiF48COiIiIiIioheNUTCIiIiIianQizsWUK0bsiIiIiIiIWjhG7IiIiIiIqNHxaQfyxYgdERERERFRC8eIHRERERERNTohr7GTK0bsiIiIiIiIWjgO7IiIiIiIiFo4TsUkIiIiIqJGJ+LdU+SKETsiIiIiIqIWjhE7IiIiIiJqdCJhU+fg1cKIHRERERERUQvHgR0REREREVELx6mYRERERETU6IS8eYpcMWJHRERERETUwr32EbvU1FTMmTMHhw4dQkpKCvT19eHh4YFvv/0Wvr6+TZ2911JM2CZEnFyNgtw0GJg5I3DoLFg6+tSaPvHuRYTumY/M5DvQ1DWBd/eJaOc3WvJ5RtIdnD+0CKkJ15GX9QgBQ2aifdD4RihJ/YlEIlw6ugQ3LmxDSWEuTG3cERj8DQzMnOtc717MEVw8vAg5GfHQNbRBp36fwaFdL6k0185uRmTIahTmpcHA1Al+b8yChUNlfZaVFODcgQV4cP0EiguyoW1gCXf/sXDrMrr610EkEuHAqsmIjw1F3/FL4ODWUz4VUA8ikQhXji3BzQvbUFKUCxMbd/gPeXYd3b96BJeOLEJuRjx0DG3Qse9nsHerrKPH9y8h+vRqpCdeR2FeGnqPWwJ7GeXKSrmHCwd/Q9KDSxAJhdA3c0bPMX9AW99C7mWtTXNvJyE7vkHinXMoyEmFsqoGzOzaw3fAF9A3cZBvRTyn3j7K6NRGERqqAsSnCLErtBQpWbWfJTbVF6BPR2VYGSnAQEcB/50tRWhMuVSa7u2V0M5BEcZ6CiivAOKSK3DgfBnSslvG2eexQ83Qv5sRtDQVceteAZasT8TDR8W1pvfz0cXoQWawMFWBkpIAj5JLsONQKk6czZKkaeeiieEDTOFspwFDfWV8u/A+wq/kNEZxnptIJMLlY9L7UsDQ+u1Ll45U7ksd+0rvS4/vX0JUyGqkPbqOwtw09H1Hdn/y1Okd3+DGhW3oMngmPALekVv5GouBvw8cPp8AXS83qFmY4PKwD5Gy90RTZ0suIkI24cKx1cjPSYORhTN6Dp8Fa+faj0Xib1/EiR3zkf74DrT0TNC590S0D6z5OwoANy4dwN7V0+Ds0QPDPlgqWb50VnfkZj6qkd6r61voPXruyxeqGeLjDuTrtY/YDRs2DNHR0Vi/fj1u376NvXv3IigoCJmZmU2dtdfS7YiDOLN7Hnx6fYDRX+yBpYM39i6fhLysxzLT52QkYO+KybB08MboL/bAp+f7OL3rJ9yNPiJJU15WBF1DK/gN+hwaOsaNVZTnEnlqFaLPrEPA0Dl489Pt0NAxxt4V76G0OL/WdZLjInH0n2lo5T0YIz//D628B+PoxqlIeRgtSXMn6iDC9s6Dd8/3MXzqbpg7+GD/qslS9Rm2dz7iY8PQc/QvGP3lAXgEvoPQPT/iwbWaP84xoesBgUC+ha+n6JBViAldB78hcxD8yXZoaBvjwMpn1NHDSBzfNA2tvAbjzan/oZXXYBz/ZypS4ivrqLy0CIbmrvAbMqfW7eRkxOO/ZW9Bz8QBg6ZswJtT/4NXjw+gpKwq1zI+S3NvJ8ZWbdF9xM8Y/eUBDJq0ChCJsG/FBAiFFQ1TIfXQzVMJgR5K2B1ahj93FiO3UITJg1Shqlz7OipKAmTminDwQhlyC2QfdDhYKOLstXIs3lWM5fuKoSAQYPJAVai0gNOlIwaYILifCZZsSMDHc2ORlVOO+TOcoK5W+yFBXn4F/t2bjE+/v40ps27hyJlMfDHJFt7ttCVp1FQVcT++CEs2JDRGMV5KVMiTfWnIHAz7VNyf7HtWfxIXiWObxPvSiGnifelYtf6krLQIhhauCKijP3nqwbXjSImPgaaOiVzK1BQUNTWQGxOL659+39RZkaublw/i+PZ56NLvA7w7ew+snbyxbckk5GTKPhbJTk/A9iWTYe3kjXdn70GXvu/j2NafcCviSI20ORmPcGrn/2DlVHOQOH7mDnz0vzDJa9SnawEALl595VtAemW91gO77OxshIWF4X//+x+6desGW1tbdOzYETNnzsSAAQMAAAKBAKtWrcLQoUOhoaEBZ2dn7N27V2o7p0+fRseOHaGqqgpzc3N89dVXKC8Xn93dt28f9PT0IBSK7+caFRUFgUCA6dOnS9afMmUKRo+WfVanqnXr1kFPTw/79++Hi4sLNDQ08Oabb6KgoADr16+HnZ0d9PX18fHHH6OiovJAys7ODj/++CPGjRsHLS0t2Nra4r///kNaWhreeOMNaGlpoV27drh8+fJL1+nLigxZi7adhsHNdzgMzBwRGDwbWnpmiAn7V2b6a2e3QFvPHIHBs2Fg5gg33+Fo0ykYESfXSNKY2rjD/40ZaOU1AIqKKo1VlHoTiUSICd0A7x7vw7Fdbxiat0KPUfNRXlqMO5H7a10vOnQDrJ27wLvHFOibOMC7xxRYOndGdOj6yjSn16F1x2Fo02k4DEwd4f/GLGjpmeHaucr6TImLgqvPEFg6dYKOgRXadh4JI3MXpCZek/q+9Me3EHV6HbqP+En+lfAMIpEIV8M2wKv7+3Bo1xsGZq3QbeR8lJcV425U7XV0NXQDrJy7oH13cR217z4FFk6dcbVKHdm4Bj4569671u1cOrwQNq5d0XnAdBhZtoGOoTVsWwdBXctQruWsS0toJ207j4SFYwfoGFjB2KotOvb9DPnZSciTcQa6sQS4K+PElTJce1CB5EwRtpwshYqSAO2dax+BJaQJsf9cGaLuVqC8QvbAbtWBElyOrUBKlghJGSJsPVUCfW0FWBk3/5/VoX1N8O9/yTh7OQdxicX4dflDqKoI0N1Xv9Z1Ym7l4+yVHCQ8LkFSain2HE3D/YQiuLXSkqS5FJOLdTuScPZy84zSPVV1X3Jo1xuGZq3QvR77UkyYuD/xetKfeHWfAkunzuITXk/Yugai0zP6EwDIz0lB6J4f0POtX6Gg2ALOBtQi7cgZ3J67EMl7jjV1VuTq4vG18PAbBg//4TAyd0TPEbOho2+GyNOyj0Uiz2yBjoE5eo6YDSNzR3j4D4d7l2BcPLZGKp1QWIF9a76A/6CPoWdkXWM7GtoG0NI1lrzuXj0FPWMb2LTq2CDlpFdP8/8FakBaWlrQ0tLCnj17UFJSUmu67777DiNGjEBMTAz69++PMWPGSCJ6jx49Qv/+/dGhQwdER0dj2bJlWL16NX788UcAQGBgIPLy8hAZGQlAPAg0MjLC6dOnJdsPCQlB165d65XnwsJCLFq0CFu2bMHhw4cREhKC4OBgHDx4EAcPHsTGjRuxYsUK7NixQ2q9P/74A35+foiMjMSAAQMwduxYjBs3Dm+//TYiIiLg5OSEcePGNWlIvKK8FKmJ12Hj6i+13MbVD0lxkTLXSYqLgo2rn9QyW9cApCZcQ0VFWYPlVZ5yMxNRmJcGa5fKcigqqcDCsQOSayk3AKQ8jJJaBwBsXPyRHBcFQFyfaY+uw7qVdBrrVn5IqbJdM3svPLh+Evk5KRCJRHh09zyy0+Ng41L5dygrLcKxfz5H4NA5TRL1zHtSR1atpOvI3KEDUh7WXkep8VGwcq5efn+kPIyq93eLhELE3wyBrpEdDqyagPXfdcHuxSPw4Nrx5y7Hy2gJ7aSqspJC3Lq0CzoGVtDSM3ve4sqFgbYAOpoCxCZWnuiqEAL3HlfAzky+P39qKuJIdmFJ855WZGasAkM9ZVy5lidZVlYuQsytfLRx1qz3djzbaMHaXBVXY2uPcDVXtfUnFg4dkFxHf5LyMKrmflJlX6ovkVCIE/9+Cc+uE5459ZMaX0V5KZLjr8OutXTfZtfaD4/uy24fj+5Hwa61dNuwbxOA5IfSxyJnD/wFdS0DePgNr1c+rl/YC/cuwyBoopkyjUEoFDXZ61XUck8TyYGSkhLWrVuHSZMm4e+//4aXlxe6du2KUaNGwd3dXZJu/Pjxkojazz//jMWLF+PixYvo27cvli5dCmtrayxZsgQCgQCurq54/PgxZsyYgW+++Qa6urrw9PRESEgIvL29ERISgqlTp+K7775DXl4eCgoKcPv2bQQFBdUrz2VlZVi2bBkcHR0BAG+++SY2btyIlJQUaGlpoU2bNujWrRtOnTqFkSNHStbr378/pkyZAgD45ptvsGzZMnTo0AHDh4s7lxkzZsDX1xcpKSkwM2uag7CigiyIhBXQ0JaOgmhoG6EwN03mOoV56dDQNqqW3hBCYTmK87Ogqdv8p7gU5onLplEt+qOhZVjrFFTxeuk1IkbqWoaS7RU/qU/1GvVpiIS8dMn7gCGzEbJ9Djb80BUKCkqAQIBuI36Eub23JM3ZvfNgZtce9m49XqyQL+lpmWSVNz/7GXVUrfzq2pV1VB9FBRkoKy1E1KmV6NDnU3Tq/wUSYkNxdOPHGDR5PSwcG+dMaktoJ4D4Wr3wA7+hvLRQPHV18hooKjVNpFxbQ3wwlF8ovTy/CNDXku+B0mA/ZdxPEkcFmzMDPfEc1Kwc6RNf2bnlMDGs+++koa6Afxe5QVlJAUKhCIvXJyCiygCxpahtX1LXNkT+s/all+xPACAyZCUUFBTRzn/sc61HjaMwX9wnaupI/601dYxQUMuxSEFuOjR1jKqlFx+LFOVnQUvXBIl3ryDm7A68+/WeeuXjdtRxFBfloZ3v0BcqB72eXuuBHSC+xm7AgAEIDQ3FuXPncPjwYfzyyy9YtWoVxo8fDwBSgzxNTU1oa2sjNTUVAHDz5k34+vpKnU3x8/NDfn4+EhMTYWNjg6CgIISEhGDatGkIDQ3Fjz/+iJ07dyIsLAzZ2dkwNTWFq6trvfKroaEhGdQBgKmpKezs7KClpSW17Gn+nqpaBlNTUwBAu3btaixLTU2tdWBXUlJSI7JZVqYKZblfZ1TtgEskesbZKunPJFHHZnqG63bEPoTsqLwIesCEv8X/qZZfkYxl1cmql+rLBDXqR7z0qZiwjUiJj0b/d5dCS98SSfcv4cyu76ChbQzrVl3w4PpJPLp7ASOm7npm2eTlTsQ+nNlVWUf93pVdR08W1rmt6uWHSHa91Ub0ZBq1XdvucA8cDwAwsmiNlLhI3Di/pcEGdi2tnTzl7DUIVq26oDA3DVGn1+Doxs8w9KN/G+V6xPbOiniza+XgZPUBcX8lQs3BljyHX0MDlGFuoIC/9tQ+86OpdO+ij0/frZzy9fWC++L/yKiAZ9VJUbEQH8y+BTU1RbRvq40pb1kiKbUUMbead9TudsQ+nN5ZZV96r5b+pFqbl+Vl+5O0xGuICd2I4Z/tfKWjMK+CGn8fkQh1tQ/Z6QFAgJLifOxbOx193/4BGloG9fr+mPCdcGgbCG090/pnugXivVPk67Uf2AGAmpoaevXqhV69euGbb77BxIkTMXfuXMnATllZ+ip7gUAguWZOJGPQ8XRg8XR5UFAQVq9ejejoaCgoKKBNmzbo2rUrTp8+jaysrHpPw6wtL3XlT9Z6T/Mla1n19aqaN28evvvuO6ll/d6aiwFvf1vv/NdFXVMfAgVFFFaJEgBAYX4G1KtF5Z7S0Daqcba0KD8TCgpKUNPUk0u+5M2uTTeMnFY50K4oLwUgPhtc9SL6ovyMGmeUqxKXXbquivIzoK4lriu1WuqzKD9DEhUtLyvGhUML0fedxbBrEwQAMLJweXI93RpYt+qCR3fPIycjHqvmSA9gjqz/BOb23hjy4cbnrIFns23TDW/a1KyjIll1pP3idVQfapr6UFBQgr6pk9RyPVNHJD+4Uu/tPK+W1k6eUlXXhqq6NvSM7WBq64HVczrhwbVjcG4/8AVq4fnciKvA7ymVd3ZUUhT/q60hQF5h5dGDljqQXySfo4kh/spoa6eIpXtKkFPLjVaa0rmIHNy6WyB5r6wsnoKqr6eMzJzKO33q6SghO6fu6esiEfA4VdwO78cXwcZCFaMGmTb7gZ1dm24wldGfyNqXqkfkqpJHf/L4wRUUFWRg48/dJctEwgqc2/c/XA1dj7dnnaz3tqhhaGiJ+8T8HOm/dUFeRo2o3FOaOkbIz0mrll58LKKupYf0x3eRk/EIO5Z+IPlcJBIfb/3vwzaY/N1h6BvbSD7LyXiEuJvhGDplsbyKRa8JDuxkaNOmDfbs2VPvtDt37pQa4IWHh0NbWxuWlpYAKq+zW7hwIbp27QqBQICuXbti3rx5yMrKwqefftpQRZGrmTNnYtq0aVLL1oTI7yy8opIKTKzaIj72LBzdK28fHR8bDodapgCa23ni/vVTUsvib4XBxNoNiop13PauCamoaUFFrTLCKhKJoKFtjMTb4TC2bANAfODx+N4l+A74vNbtmNp6IuF2ODyeRJEAICH2LMzsPAGI69PYsi0SbodL3Y478XY47NzEBxXCinIIK8ogEEhfbyRQUJD86Hh1m4TWHd+U+nzrgsHwG/wV7Np0R0OotY7uhMOoSh0l3b+ETv1rryMTG08k3gmXRNoAIPHOWZjaetY7L4pKKjC2dkN22gOp5TlpcQ36qIOW1k5qJ5IcSDe0kjKgpEx6cJVbIEIrK0U8ThcPYhQVAEcLRRw4//LX4A71V4abvSKW7S1BZl7zG9QB4ihbUbF0/Wdkl8HLTRv3HhYBAJQUBXB31cLqrbVPQ5RFfGKx+Ued6r0v3b+EznX0J6a24v5Eal+6Xbkv1YeL12BYOUs/SunAyolo5f0GXHw45a45UFRSgZlNW8TdPAuX9pV9YtzNcDh7yD4WsXTwxN0Y6WORuJthMLMVH4sYmjlgwpx9Up+f2bsQpcUFkhuzVBUTvgsa2oZwahckn0I1Y6JX9Fq3pvJaD+wyMjIwfPhwvPfee3B3d4e2tjYuX76MX375BW+88Ua9tvHhhx9i4cKF+Pjjj/HRRx8hNjYWc+fOxbRp06CgID4Ienqd3T///IM///wTgHiwN3z4cJSVldX7+rqmpqqqClVV6YGcspzHTu2D3sXRTV/CxNoN5nbtce3cVuRnJaGd3ygAwNl9C1CQk4Leb/8CAHDzG4XosE04s3se3HxHICkuEtcv7ETfcQsk26woL0Vm8j0AgLCiFAU5KUhLvAllVQ3oGdvKtwAvQCAQwD1gHK6cWA5dI1voGtki4uRyKKmoSUU5jv87A5q6JvB9cuDhHjAWe5aORcTJlbB364EH104g8c45DP2/TZJ1PLqOx4l/Z8DE2g2mtp64cX4b8rKT4NZZXJ8qalqwcOiAc/t/hZKyKrT1LfH4/kXEXv4PfoO/AgBo6BjLvGGKlr4FdAytGrJqJAQCAdr5j0Pkyco6ijy5HErKanDyrKyjk1vEddSpn7iO2vmPxd6/xyLq1ErYtu2Bh9dP4NGdcxj8YWUdlZUUICcjXvI+LzMR6Y9vQlVdVzJw8+g6Acc3TYO5vQ8sHDshITYUD2+ewqApGxql/EDzbyc5GQm4G3UQ1i5+UNc0QEFOCiJPrYKisipsXOs/K0HeQmPK0MNLGek5IqTnCNHdSxml5SJE3qmMVo3qroKcAhEOXRAP9hQVxM+yAwBFRUBXUwALQwFKyoCMXPFBSHCAMto7K2HtoRKUlIqgrS7eVlEpUN50T3eol92HUzF6kCkeJ5fgUUoJRg0yRUmpCCfPVT6TbvoUW2RklWLNtiQAwKhBprj9oBCPU0qgrCRARw8d9PQzwKJ1lY82UFNVgIVp5W+EmbEKHGzUkVdQjrSM5nMzq6f7UsTT/sTYFhEnau5LJ57sS08He+7+Y7Fn2VhEnloJu7Y9EPekPxnyf9X6k/TK/iQ3MxHpj25CVUPcn6hp6kNNU/ruowqKSlDXNmry5z2+CEVNDWg6VUaaNOytoOPhitLMHBQnJDVhzl5Ox57vYt/aL2Fm6wZLh/aICt2K3KwktA8U94khuxcgLzsFg94VH4u0DxyFiJBNOLF9Hjz8R+DR/UhEn92JwRPExyJKyqowtmwl9R2q6joAUGO5SCjE1XO70M53SIu+Yyo1jde6xWhpaaFTp074448/cO/ePZSVlcHa2hqTJk3CrFmz6rUNS0tLHDx4ENOnT4eHhwcMDAwwYcIEfP3111LpunXrhoiICMkgTl9fH23atMHjx4/RunVreRetxWrl1R/FhVm4eGQpCnJTYWjeCoOnrICOgTj6WZibhrysyh8LXUNrDJ68AqF75iEmbBO0dE3QNXg2nDz6SNIU5KTi39+GSN5HnFqDiFNrYOnYEcM+lv80whfRvttElJcV48yu71FSlANTG3cMmrRa6ixzftZjqWm/5nZe6D1mAS4c/hMXjyyCjqE1eo39Haa2HpI0zp79UVKQjcvH/kJBbhoMzZwxcMJyaD+pTwDo/fbvOH/wdxzfPB3FhTnQ1rdAp36foa3vqMYpfD15BInrKGy3uI5MrN0xoHodZUvXkZmdF3q+tQCXjvyJS0fFddRjzO8wtamso7TEa9i3vPLBwOf2zwcAtPIegm4jxf+3d+uFgOBvEXlyBc7+9xP0jO3Re+yiGjcOaWjNuZ0oKakg6cEVxIRuQElRLtS1DGHh4IPgj/6tc7psQzsVVQ5lJQGCA1SgrgrEpwqxcn8JSqqMM/S1BFLXeehoCjBthLrkfZCnAoI8lXHvUQWW7RVfR9fFTXxW68MhalLft+Wk+DEIzdm2A6lQVVHAR+Otoa2hiFv3CzDzl7soKq6MvpoYKkvdJVlNVQEfv2MFIwMVlJQKkZBUjP/9HYfTF7IlaVrZa+C32ZV3eXx/jPjEz9HQDPy2onKw0xx4PulPQp/2JzbuGFiP/qTXmAW4WHVfelu6P0lNvIa9f1f2J+H7xH2Ii/cQdB81vxFK1rh0vd3ge6Lyd7TNb+Jjp4QNuxAzYWZTZeultfbpj6L8LJw9ID4WMbJoheEfrYCuobhPzM9JQ25m5bGInpE1hn+0Aie2z0PEafGxSK+Rs+Hq1ae2r6hV3K1w5GY+hnuXYXIrD70+BCI+8p1ewl+HmjoHzUt5OXen6iqeNVPvNaT4Wj9oRraExKKmzkKzExMe29RZaHb6D/ds6iw0O87D6nfztddJ6lHuO9W9262pcyDbxwtzm+y7F3+m02Tf3VB4eEFERERERNTCcWDXjPTr10/y0PTqr59//rmps0dEREREJDcioajJXq+i1/oau+Zm1apVKCqSPR3JwKB+zz0hIiIiIqLXDwd2zcjTxyMQERERERE9Dw7siIiIiIio0b2qUyKbCq+xIyIiIiIiauEYsSMiIiIiokbHgJ18MWJHRERERETUwjFiR0REREREjY7X2MkXI3ZEREREREQtHAd2RERERERELRynYhIRERERUaMTiTgVU54YsSMiIiIiImrhGLEjIiIiIqJGJ+TNU+SKETsiIiIiIqIWjgM7IiIiIiKiFo5TMYmIiIiIqNHx5inyxYgdERERERFRC8eIHRERERERNToRb54iV4zYERERERERtXAc2BERERERUaMTCUVN9mooWVlZGDt2LHR1daGrq4uxY8ciOzu71vRlZWWYMWMG2rVrB01NTVhYWGDcuHF4/Pjxc383B3ZERERERERy8NZbbyEqKgqHDx/G4cOHERUVhbFjx9aavrCwEBEREZgzZw4iIiKwa9cu3L59G4MHD37u7+Y1dkRERERERC/p5s2bOHz4MM6fP49OnToBAFauXAlfX1/ExsbCxcWlxjq6uro4duyY1LLFixejY8eOiI+Ph42NTb2/nwM7IiIiIiJqdMJX7HEH586dg66urmRQBwCdO3eGrq4uwsPDZQ7sZMnJyYFAIICent5zfT8HdkRERERE9FopKSlBSUmJ1DJVVVWoqqq+8DaTk5NhYmJSY7mJiQmSk5PrtY3i4mJ89dVXeOutt6Cjo/Nc389r7IiIiIiIqNE15c1T5s2bJ7nBydPXvHnzZObz22+/hUAgqPN1+fJlAIBAIKhZTpFI5vLqysrKMGrUKAiFQixduvS565MROyIiIiIieq3MnDkT06ZNk1pWW7Tuo48+wqhRo+rcnp2dHWJiYpCSklLjs7S0NJiamta5fllZGUaMGIEHDx7g5MmTzx2tAziwIyIiIiKi18zzTLs0MjKCkZHRM9P5+voiJycHFy9eRMeOHQEAFy5cQE5ODrp06VLrek8HdXfu3MGpU6dgaGhYv0JUw6mYRERERETU6EQiUZO9GkLr1q3Rt29fTJo0CefPn8f58+cxadIkDBw4UOrGKa6urti9ezcAoLy8HG+++SYuX76MTZs2oaKiAsnJyUhOTkZpaelzfT8jdvRSSkpfrbsZvSwV5abOQfOjq/3sOeWvm6Lips4BtQTtA1ybOgvNjqUJf3OqSz0a29RZaHZMetfvzoOvlTK2k8ayadMmfPLJJ+jduzcAYPDgwViyZIlUmtjYWOTk5AAAEhMTsXfvXgCAp6enVLpTp04hKCio3t/NgR0RERERETU6ofDVO1ljYGCAf/75p840VSOGdnZ2cosgciomERERERFRC8eIHRERERERNTrRKxixa0qM2BEREREREbVwHNgRERERERG1cJyKSUREREREja6hHjvwumLEjoiIiIiIqIVjxI6IiIiIiBqdSChs6iy8UhixIyIiIiIiauE4sCMiIiIiImrhOBWTiIiIiIganZDPsZMrRuyIiIiIiIhaOEbsiIiIiIio0fFxB/LFiB0REREREVELx4gdERERERE1OhGvsZMrRuyIiIiIiIhaOA7siIiIiIiIWjhOxSQiIiIiokbHqZjyxYgdERERERFRC8eIHRERERERNTqhSNjUWXilMGJHRERERETUwnFgR0RERERE1MJxKiYRERERETU63jxFvhixIyIiIiIiauEYsSMiIiIiokbHiJ18MWJHRERERETUwnFgJ0NISAgEAgGys7Nfajvjx4/HkCFD5JKnhhYXFweBQICoqKimzgoRERERvQZEIlGTvV5Fr/xUzL///hvTp09HVlYWlJTExc3Pz4e+vj46d+6M0NBQSdrQ0FAEBgYiNjYWSUlJ0NXVlWteUlNTMWfOHBw6dAgpKSnQ19eHh4cHvv32W/j6+sr1u1oSkUiEK8eW4OaFbSgpyoWJjTv8h3wDAzPnOte7f/UILh1ZhNyMeOgY2qBj389g79ZL8vnj+5cQfXo10hOvozAvDb3HLYG9W0+pbSz/0lXmtjv1nw7PoAkvX7gXFBO2GZGnVqMgNw0GZk4IGDILlo4+taZ/dPciQv+bj8zku9DUMYFX94lo5zdKKs3d6CM4f2gRctLjoWtkA9/+n8HRvbK+LhxejItH/pJaR0PbCBO+D5Nv4eTkSsgmXDi6Gvk5aTC2cEbPEbNg7Vx7HcXfvogT2+cj7fEdaOuZoFPvifDqOlryeUz4LhxYP7PGetOXxEBJWbVByvCyokM34cpJcTsxNHNG1+C620ni3Ys4s3s+MpLvQFPXBD7dJ8Ldv7IOMpLu4NzBRUhJvI68zEcIHDoTXkHja93exWPLEb7/d3h2HYeg4NnyLJpc9fZRRqc2itBQFSA+RYhdoaVIyar9R91UX4A+HZVhZaQAAx0F/He2FKEx5VJpurdXQjsHRRjrKaC8AohLrsCB82VIy24ZBws9vZXQyVUJ6qpAfKoQ/50te2ad9PJRhqWRAAbaCtgXXoqwaxVSaezNFBDoKAqrVAAAUlpJREFUoQQrIwXoaAqw/kgJbjxsfs+ounBiM0IPrkF+ThpMLJzQf8xM2LnUvt88uHURhzb/D6mP70JbzwQB/SegY/fK/rWivAyn969AZNh/yMtOgZGZPXqP+Byt3ANkbu/0vhU4tuMP+PYeiwFjZsm9fC8iImQTLhwT96dGFs7oObwe/emO+Uh/fAdaeibo3Hsi2geOlpn2xqUD2Lt6Gpw9emDYB0sly5fO6o7czEc10nt1fQu9R899+UI1EQN/Hzh8PgG6Xm5QszDB5WEfImXviabOFr0mXvmBXbdu3ZCfn4/Lly+jc+fOAMQDODMzM1y6dAmFhYXQ0NAAII7UWVhYoFWrVg2Sl2HDhqGsrAzr16+Hg4MDUlJScOLECWRmZjbI97UU0SGrEBO6DkEj5kHP2A4RJ/7GgZXvYeT0Q1BR05K5TvLDSBzfNA0den8CO7deiLt2DMf/mYrBH26CqY0HAKC8tAiG5q5w8QnGsY2fyNzO2DmhUu/jb53B6R1fw6Fdb/kW8jncjjyI0D3zEPTmNzC398K18K3Yt2Iyxny1H9r6FjXS52QkYu/KKWjbeTh6v/0rkh5EIGTH91DX0oeTRx8AQFJcJA5vmIbO/T6BQ7teuH/1GA6vn4phn2yCma2HZFsGZs4Y8sEayXsFBcWGL/ALuHHpII5vm4c+b82FlaMXIs9swdbFkzDp2wPQNahZR9npCdi2eDI8/Idj0Hu/IvFeBI5s/g4a2gZw9eojSaeqpoXJ3x+WWre5DupiIw7i9O556D58LizsvRATvgV7/p6EsTMPQEdGHeRkJGDP8slw8x2OvmN/xeMHETi5/TuoaxnA2VNcB2WlRdA1soJz+744vXtend+f/DAG18K3wsjCpUHKJy/dPJUQ6KGELSdLkZ4jRA8vZUwepIpf/i1GSZnsdVSUBMjMFSHmXhkGd1GRmcbBQhFnr5UjIVUIBQWgX0cVTB6oil+3FKO0XOYqzUZXDyUEtFPCtpBSpOeI0MNLCRP7q+LXbcUoraVOlJWAzFwhrt4XYaCvssw0KspAUoYQl2PLMa5389xvrl44iIOb5mPQuDmwaeWFS6e2YsOCKfhk3j7oGdbcbzLTErFhwfvwCXoTb77/C+JvR2Dfhh+gqW2Ath3EvxPHd/6JqPB9GPLe9zA2d8Cdq2HYvOhjTJ6zGRa2baS2l3j/Ki6FbIOZdfPZb25ePojj2+ehz+i5sHT0QlToFmxbMgkT59ben25f8qQ/ffdXPLoXgSP/ivuSqv0pAORkPMKpnf+DlVPNQeL4mTsgFFaeHEh/fAdb/nwXLl595V/IRqSoqYHcmFgkrt8F7+1Lmjo79Jp55adiuri4wMLCAiEhIZJlISEheOONN+Do6Ijw8HCp5d26dasxFXPdunXQ09PDkSNH0Lp1a2hpaaFv375ISkqSrFtRUYFp06ZBT08PhoaG+PLLL6XCvNnZ2QgLC8P//vc/dOvWDba2tujYsSNmzpyJAQMGSNIJBAIsW7YM/fr1g7q6Ouzt7bF9+3apMj169AgjR46Evr4+DA0N8cYbbyAuLk4qzdq1a9G6dWuoqanB1dUVS5culfr84sWLaN++PdTU1ODj44PIyMgXreKXIhKJcDVsA7y6vw+Hdr1hYNYK3UbOR3lZMe5G7a91vauhG2Dl3AXtu0+BvokD2nefAgunzrgaul6SxsY1EB37flbnIE1D21jq9fDGSVg4doKOobVcy/k8okLWoU2nYWjbeTgMTB0ROHQWtPTMcPXsvzLTXwvfAm09cwQOnQUDU0e07TwcbToGI/JU5QAt6vQGWLfqAp+eU2Bg6gCfnlNg1aozok6vl9qWgoIiNHWMJS91LYMGLeuLunh8LTz8hsHTfziMzB3Ra+Rs6OibIfK07DqKPL0FOgbm6DVyNozMHeHpPxwefsG4cHSNdEKBAFq6xlKv5ioiZC3adh4GN9/hMDBzRFDwbGjpmyGmlnYSc3YLtPXNERQ8GwZmjnDzHY62nYJxpUo7MbN1R8AbM+DiNQCKSrIHNABQWlKAwxuno+eoH6GqId+ZDfIW4K6ME1fKcO1BBZIzRdhyshQqSgK0d679vGZCmhD7z5Uh6m4FyitkR7FWHSjB5dgKpGSJkJQhwtZTJdDXVoCVcfP/WfVvp4STkeW4HidESpYIW0+VQVkJaO9U+4mcxDQRDl4oR/S92uskNkGIo5fF222uzh5eD+/AYPgEDYeJhSMGjJkFXQMzXDyxRWb6Sye3QM/QHAPGzIKJhSN8gobDKzAYYYeq9K/he9F10GS4eHSFgYk1OvUYDed2/jh7aJ3UtkqKC7D97+kY8t73UNPUachiPpen/anHk/6054hn9KdnxP1pzxHi/tTDfzjcuwTj4jHp/lQorMC+NV/Af9DH0DOq+ZuqoW0g1dfevXoKesY2sGnVsUHK2VjSjpzB7bkLkbznWFNnpUUQCoVN9noVNf9fIDkICgrCqVOnJO9PnTqFoKAgdO3aVbK8tLQU586dQ7du3WRuo7CwEL/99hs2btyIM2fOID4+Hl988YXk8wULFmDNmjVYvXo1wsLCkJmZid27d0s+19LSgpaWFvbs2YOSkpI68ztnzhwMGzYM0dHRePvttzF69GjcvHlTko9u3bpBS0sLZ86cQVhYmGSgWVpaCgBYuXIlZs+ejZ9++gk3b97Ezz//jDlz5mD9evFBfEFBAQYOHAgXFxdcuXIF3377rVRZGlNeZiIK89Jg1cpPskxRSQXmDh2Q8rD2wWZqfBSsnP2kllm38kfKw6gXzkthXjrib56Ga4dhL7yNl1VRXorUxOuwcZEum42LH5LiZNdHclxUzfSu/khNuI6KirLa07j4IzkuSmpZdvpDrJkbgPU/9MDhDdOQk57wkiWSv4ryUiTHX4d9G3+p5fZt/JB4T3YdPbofBfs2ftXSByD54TVJHQFAaUkh/prZDUtmBGLbkilIjr8h/wLIQUV5KVITrsPWRboObF38kPSg9nZiW60N2LoGIDVeug7q49T272HfpitsXLo8X8YbmYG2ADqaAsQmVkYFKoTAvccVsDOT78+fmooAAFBY0rynYhpoC6CjIcCdanVyP0kIW9NX+5CgvLwUj+Ouw8lNej9wcvND/F3Z+0383aga6Z3d/PAo7joqysX7TXlZaY3IvpKyKh7euSK1bN+GH+Di0RVObZvPfvO0P7VrLd2X2LX2w6P7tfendq2f3Z+ePfAX1LUM4OE3vF75uH5hL9y7DINAIHiBkhAR8BoN7M6ePYvy8nLk5eUhMjISgYGB6Nq1qySSd/78eRQVFdU6sCsrK8Pff/8NHx8feHl54aOPPsKJE5VzphcuXIiZM2di2LBhaN26Nf7++2+pa/SUlJSwbt06rF+/Hnp6evDz88OsWbMQExNT47uGDx+OiRMnolWrVvjhhx/g4+ODxYsXAwC2bNkCBQUFrFq1Cu3atUPr1q2xdu1axMfHS8ryww8/YMGCBQgODoa9vT2Cg4MxdepULF++HACwadMmVFRUYM2aNWjbti0GDhyI6dOny6Oqn1thXhoAQF3LUGq5upYhCvPS61gvHera1dbRNpRs70XcvrIHyqqasHdrummYRQVZEAkroCGrbLmy66MwL61GXWhoG0IoLEdxftaTNOk1tqmhbYiC3Mr6MrX1QK+35mPwlFXoNuIHFOamYcei0SgqyJJH0eSmMF9cR5o60uXR1DaSKk9V+bnp0NQ2kk6vI66joid1ZGjmgIHvzMObHy7DGxN/h5KyKjb+MhqZKXENUo6XIWknOtX/pka17gMFuenQqFYHGjrS7aQ+YiMOIDXhOvwGff78GW9k2hriA8T8Qunl+UWAtrp8Dx4H+ynjfpI4KticPa2TvCLpfOYXieReJ81N4f+3d+9xNd+PH8Bfp5tKF6VSkpRGolLLzFC5zHUjGeY6ZG6/YW7DfAlzyVzHvpsh5m7LbRfm2kXllqi0FBXKNaSiRJfz+6M5HCdm+3Le5/R5PR+PHo96fz7T67zX+ZzP+/O+3c9DeXkZTMyfuxaY18SD/Mqvrw/y76C6ufL7zMTcCuVlpSj6633zlnsrHNv/I+7cvIzy8nKkJ8ci9Ww47uc9fS8mndiL65f/xPu9JrzmV/W/eeH11OzF19PCgjuobvby6+nV9Hgkxe5A54FfvVKOCwmHUfzwPtxb9PgXr4K0mbxcLuyrKqryc+yAinl2hYWFiIuLw71799CgQQPY2NjAz88PAwcORGFhISIjI1G3bl04OzsjKytL5d8wNjZG/fr1FT/b2dkhJycHAJCfn48bN24oLYCip6cHHx8fpeGYPXv2RNeuXREdHY3jx49j//79+Prrr7F27VoMHjxYcd7zC6m0aNFCsVplfHw80tPTYWpqqnROcXExMjIycPv2bWRnZyMoKAiffvqp4nhpaamioXn+/Hl4enoq5hZW9jsr8+jRI5XextISg380B+nimd9wdNfTSdGdh6yq+KbSJ3Qvv8mQPX9cjv/pSV9a3E64eH2gGXOqKnsdL3ltz79uxZ/dM+WV1c2zZfUa+Sods6vXFBvndUBq3B54+Q95hdDq9txrhlylTPn05/9eFJUEALB3bgp756aKw3Xqe2PdvB44HbEZHT7+z/8e9414/vX+yzp4xffN/Xs3ELVzHnqMXqcZ75PneL2li4/8ng4hDd1bcb2q+NtQ9jo/0nu01oedpQ7+u+flozFEaOqii8DWT+fErd9fMbLj+QqQqRZVXSpvG/nLr6/PX2ueu3Z07f8l9qyfiW+mdoVMJoOljQO8W/fAmeiKUTt5d29g75YFGDx5LfQNNO99A1Ty+SB/+bWk8vMBQIZHxQ/w2/rJ6DTgKxi/4nD+pGM74dzYF6Y1ar16aCJSIYmGnYuLC+rUqYOIiAjcu3cPfn5+AABbW1s4OTkhNjYWERERaNu27Qv/DX195cniMpnsXy2VamhoiPfffx/vv/8+Zs6ciWHDhiE4OFipYVeZJxfR8vJyvP3229iyZYvKOdbW1iguLgZQMRyzefPmSsd1dSvmT/zbJV4XLFiA2bNnK5V16DMTHfvOeuV/w9GtDT6q66H4uay04ibj4f07qG5moyh/+OCuSg/Tsyp6JpSfsD58cBdGJlYv+C9e7sal08i7fQnt+y/7V//962JU3QIyHV2V3rmH919cH8am1qrnP7gLHR09GFav8dc5Vih87pyi+3dVenCepV/NGDXtGiDv9pV/8UreHGOTijqq7PU8/xT5CZNKnj4X3s+Fjo4ejExqVPrfyHR0YFfPHfdyLr+O2K/Vi/5OXvb/tLqZFYqeq4Oiv+rgyd/J37mV/SeKHtzF1sWBijJ5eRmuZcQhMXoLxiw5J3TBnZTLZVh6q1jxs95fUUyNZbhf9PS6Z2JU0UP1OgS00kfjerr4bs8j5BdqXtMo5UoZsnOeziVRqpNn6qC6key11YmmMjatAR0dXTzIU37fFBbkwsSs8uuribmVSm9eYcFd6Ojqwfiva0d1M0v0H/ctSh4/wsMHeTC1sMHBn5fAwsoeAHD98p8oLLiL74M/Uvwb5eVluJJ2GicPb8Ws0ERh75sn11OV1/iS62l1Mys8yH/x9fTO9XTk372GHd+NUhyXyyv+BheOdsPw2fthYV1XcSz/7jVcPn8MPUasfF0vi0iyJNGwA6BYFOXevXtKww79/Pxw4MABnDhxAkOG/LteCXNzc9jZ2eHEiRPw9a3o9SgtLUV8fDy8vb1f+t+6ublhz549SmUnTpzAoEGDlH728vICAHh7e+Onn36CjY0NzMxUJ1+bm5vD3t4emZmZ6N+//wt/56ZNm/Dw4UMYGRkpfsffmTZtGiZMUB5GsurgixdYqIyBoYnSSpdyuRzGpta4evEYrOwrVg8rK32MG5lxaN7lxUO9bOo2xdWLx+DhO1hRdvViLGo5Nv1HeZ5IPbUDVvaNUbN25dsfqIuungFs6jRG9oVjSlsRZF04BucmlT94sK3XFJf+jFAqy0qLhY1DY+jq6ivOyb5wDF7PLF2flRYL23pNX5ilrPQxcm9loLbz2//+Bb0BunoGsK3bGJfOx6Kh19M6unT+GBp4tqv0v7F3boqL55Tr6FJKDGwdmyjq6HlyuRy3ss/Dxv7NrJL7v9DVM4CNQ2NkpcXCxfOZv5O0Y3B2r7wObOs1xaVk5Tq4khYDm7ovroPn1W3wLgZM+U2p7NDWabCo5Qyfdp8KX0X1UQnwqES5cVJQKEeDOrq4fqdiqUpdHaB+bV3sPfHP5hVWpkcrfTRx0sX3vz5C7n3NbBQ9LgHuPl8nRXK8VUcX1+8+rRNnOx38cep/rxNNpqdngNr1GiP9z2Nw83n6vkn/8xgaeVV+fa3r0hSpCZFKZenJsbCv1xi6esrvG32DatC3rIWy0hL8efoQmrxTsbpjfbcWGDPvF6Vzd62dDis7J/h2HSb0ffPkenr5uevp5fPH8NZLrqfpScrXksvnn15Pa9o6I2iG8nXi6K/L8bi4ULEwy7OSju2CsWlNuLj7v54XRVrlSaOfXg9JzLEDKhp2MTExSEhIUPTYARUNuzVr1qC4uPiF8+texbhx4xASEoLdu3cjNTUVo0ePVtrg/O7du2jbti02b96MpKQkXLp0CWFhYfj666/RvXt3pX8rLCwM69atw4ULFxAcHIxTp07hs88+AwD0798fVlZW6N69O6Kjo3Hp0iVERUVh3LhxuHr1KgBg1qxZWLBgAb755htcuHAB586dw/r167F06VIAQL9+/aCjo4OgoCCkpKRg3759WLx48d++xmrVqsHMzEzp638djiWTyeDeahDOhv+AS8mHkHvzAiJ/ngY9fUO4NP1AcV749ik4+ccSxc/urQbi6sVYJESswb2cTCRErMG1i8fh3voTxTkljwpx5/p53LlesfDM/dyruHP9PO7fu66U4XHxA2QmHUCjd/5+grc6NPUfjD9P7EDKyZ3IvZWB6N0L8ODeDTR5r2LfpGO/L8HBLVMU5zd572Pcv3cd0XsWIPdWBlJO7kTKyZ3wajP06b/pOxBZabGIP7IGubcyEX9kDa5eOI6mfk/rK+aXhbiWfgr5d6/i5pVE7Fs/Fo+LH8C1WYDaXvureqf9ECTG7EBi7A7cuZGBwz/PR0HuDXj5VtRR5O4l+G39F4rzvfw+RsHd6zj88wLcuZGBxNgdSIzdieYdntZR9G/fIvPPaNy7nY1b2eexb+OXyMlOfeHeTKJ5+w9B8okd+PPEDuTezEDUrvm4f+8GPP7avzDmtyU4sPlpHXi0/BgF964javcC5N7MwJ8nduDPEzvx9jN/JxWL95xHztXzKC99jML8W8i5el7Ra2tgaAKr2g2UvvSqGcOweg1Y1da8BjAARCeVoJ13RQPM1lKGPm0N8LhUjrMXn+5J8HFbA3Ru/vQmXVcHqF1Thto1ZdDVBcyrV3xf0+zp8LPA1vrwbqCHLYcf49FjOUyNAFOjpz1imizmXCnaNNVD43o6qGUhQy9/fZSUAmfTny6o0ttfH52aPX32q6sD2NWUwa6mDHo6FYvS2D1XJwZ6T88BAEuziu9rVNecuXstO32C+KidiD+6EznXM7BvywLk372BZm37AAAO/rwUO354en1t1vZj5N25jn1bQ5BzPQPxR3ci/ugutOr89H2TnZGIP08fRG5ONi6nncaGJcMhl5ejdZeKvVCrGVVHrToNlL70qxnB2KQGatUR/755p/2Qv66Jz1xP773keur7MQpyr+NImPL19J33K+pET78arO0bKH1VMzKDgWF1WNs3UFpxV15ejnPHd8G9RQB0dKtGX4NudWOYebrCzLPiQbGxUx2YebrC0MFOcDKSgqrxLnoFbdq0wcOHD+Hq6opatZ6O4fbz88P9+/dRv359ODj8+yXuJ06ciBs3bmDw4MHQ0dHB0KFD0aNHD+Tn5wOoWBWzefPmWLZsGTIyMlBSUgIHBwd8+umn+PJL5Q1KZ8+eje3bt2P06NGwtbXFli1b4OZW0ZtlbGyMo0ePYsqUKQgMDMT9+/dhb2+Pdu3aKXrwhg0bBmNjYyxatAhffPEFqlevDnd3d3z++eeKLL/99htGjhwJLy8vuLm5YeHChejZU8xqkJ7+w1BaUoyY3XPw6GE+bBw80PXTUKWevQd515XG9NvW80b7fksQd+AbxB1cAbOaDmjXf6liDzsAuH01Gb/98LThcvz3EABAg7cD0KZPiKI8PWEvADnqN3267YRIDby6oLgwD6cO/Ldi42m7t/Dh8B9gZlkxrKew4DYePNM4Na9ZB90+/QHRe0KQFLMVJuY28O0xXbGHHQDYOXmj08AlOP7HNzjxxwqY13RAx0+WKu1h9yD/Fg5smoiHhXkwMrGAraMnen/+k+L3ahK3Zl3wsPAeYvd+hwf5ObCu3QC9P1sN85oVWR/k30ZB7tPtSGpYOaD3mNU4/PMCnInaAhNzG7zfZ7rSnkuPHhbgj80zUVhwG9WMTFHLwQ39J21GbScPld+vCRp6d0Fx4T2cOPAdivJzUNOuAbqPWK30d1Jw72kdmNd0QMCI1YjavQBJ0VtQ3dwG/oHTFXvYAcCD/BxsXRSg+Dk+fB3iw9fB3uUd9BqzSW2v7XWKSCiFvp4Mga0NFJtxr/n9kdIedhYmMjw7Qt2sugwTehspfvZvqgP/pvrIuFaG73+tmEf3XpOKhuDoAEOl37c9vGIbBE0WlVgKfT0goJUBjAyA7JxyrN33SGkPuxrP14mxDJ/3fPpa/Tz14eepj4zrZVj9e8WQ+jrWOhjx4dOHfR+2qLiBP51WirAozegNdG/eBUUP8hDxy3e4n3cbtezfwsAJqxTDJu/n30beM9cOS+s6GDRxFfZtDcHJI1thWsMGXQd8qdjDDgBKSx7h8M4VuHc7GwbVjNHAwxcfDV8IIw3a0uBlGvl0wcMHFdfTwoIcWNVugF5/cz3t9dlqHAl78fX0VV1OPYaC3OvweE/catSvm/nbTdDiyNPrpdviinu87I27kBQ0TVQsjVVVFzERRSb/txOu6I2QyWTYvXs3AgICREd5JUt/4Z/PswxebUSbpJgYa87Tek3xsPjvz5GajEtFf3+SxOjq8r3zPB8PzVx8RKQHDyUz+OqV2XTQnA3gNUXXkjTRESrVZeg5Yb973zp3Yb/7TeHVgIiIiIiISMtJZigmERERERFpDg7FfL3YsNMwHBlLRERERET/FBt2RERERESkduXc7uC14hw7IiIiIiIiLcceOyIiIiIiUjvOsXu92GNHRERERESk5diwIyIiIiIi0nIciklERERERGonL+fiKa8Te+yIiIiIiIi0HHvsiIiIiIhI7bh4yuvFHjsiIiIiIiItx4YdERERERGRluNQTCIiIiIiUju5nIunvE7ssSMiIiIiItJy7LEjIiIiIiK1K+fiKa8Ve+yIiIiIiIi0HHvsiIiIiIhI7bhB+evFHjsiIiIiIiItx4YdERERERGRluNQTCIiIiIiUjs5F095rdhjR0REREREpOXYY0dERERERGrHDcpfL/bYERERERERaTk27IiIiIiIiLQch2ISEREREZHacfGU14s9dkRERERERFqOPXZERERERKR28nIunvI6sceOiIiIiIhIy8nkcjkHt5JWe/ToERYsWIBp06ahWrVqouNoBNaJKtaJKtaJKtaJMtaHKtaJKtaJKtYJicCGHWm9goICmJubIz8/H2ZmZqLjaATWiSrWiSrWiSrWiTLWhyrWiSrWiSrWCYnAoZhERERERERajg07IiIiIiIiLceGHRERERERkZZjw460XrVq1RAcHMzJyc9gnahinahinahinShjfahinahinahinZAIXDyFiIiIiIhIy7HHjoiIiIiISMuxYUdERERERKTl2LAjIiIiIiLScmzYERERERERaTk27IiIiIiIiLQcG3ZEVOU9fvwYaWlpKC0tFR2FSKvI5XJw8WwiIu2gJzoA0b8hl8uxY8cOREREICcnB+Xl5UrHd+3aJSiZellYWEAmk73Subm5uW84jeYpKirCmDFjsGHDBgDAhQsX4OzsjLFjx6J27dqYOnWq4IRiFRcXw9DQUHQM0kChoaFYtmwZLl68CAB466238Pnnn2PYsGGCk4n3+PHjSj936tatKyiRWDk5OZXWh4eHh6BE6pWUlPTK50qlTkgcNuxIK40bNw6rV69GmzZtUKtWrVdu3FQ1y5cvV3x/9+5dzJ07Fx07dkSLFi0AAMePH8eBAwcwY8YMQQnFmjZtGhITExEZGYlOnTopytu3b4/g4GBJNuzKy8sxb948rFq1Crdu3VI0dmfMmIF69eohKChIdES1KywsREhICI4cOVLpDWpmZqagZGLMmDEDy5Ytw5gxY5SuJePHj8fly5cxd+5cwQnFuHjxIoYOHYpjx44plcvlcshkMpSVlQlKJkZ8fDw++eQTnD9/XtGrK5PJJFcfTZs2VXrdLyOVOiFxuEE5aSVLS0ts3rwZXbp0ER1FY/Ts2RNt2rTBZ599plT+7bff4vDhw9izZ4+YYAI5Ojrip59+wrvvvgtTU1MkJibC2dkZ6enp8Pb2RkFBgeiIajdnzhxs2LABc+bMwaeffork5GQ4Ozvj559/xrJly3D8+HHREdWub9++iIqKwsCBA2FnZ6dyczZu3DhBycSwsrLCypUr0bdvX6Xybdu2YcyYMbhz546gZGK1bNkSenp6mDp1aqV/J56enoKSieHh4QEXFxdMmTKl0gesjo6OgpKp15UrVxTfnz17FpMmTcLkyZOVHoosWbIEX3/9NQICAgSlJKlgjx1pJXNzczg7O4uOoVEOHDiAhQsXqpR37NhRkj1TAHD79m3Y2NiolBcWFkq2l3fjxo1YvXo12rVrh5EjRyrKPTw8kJqaKjCZOH/88Qf27t2Lli1bio6iEcrKyuDj46NS/vbbb0t6nmpCQgLi4+Ph6uoqOopGuHTpEnbt2gUXFxfRUYR6tgHbq1cvrFixQumhs4eHBxwcHDBjxgw27OiN4+IppJVmzZqF2bNn4+HDh6KjaIyaNWti9+7dKuV79uxBzZo1BSQSr1mzZti7d6/i5yeNuTVr1iiepkrNtWvXKr0RKy8vR0lJiYBE4llYWMDS0lJ0DI0xYMAAfP/99yrlq1evRv/+/QUk0gxubm6S7a2sTLt27ZCYmCg6hkY5d+4cnJycVMqdnJyQkpIiIBFJDXvsSCv16tUL27Ztg42NDerVqwd9fX2l42fOnBGUTJzZs2cjKCgIkZGRikbLiRMnsH//fqxdu1ZwOjEWLFiATp06ISUlBaWlpfjmm2/w559/4vjx44iKihIdT4jGjRsjOjpaZZhUWFgYvLy8BKUS66uvvsLMmTOxYcMGGBsbi46jEUJDQ3Hw4EG8++67ACquJdnZ2Rg0aBAmTJigOG/p0qWiIqrdwoUL8cUXX2D+/Plwd3dX+dwxMzMTlEyMtWvX4pNPPkFycjKaNGmiUh/dunUTlEycRo0aYe7cuQgNDVUsTPXo0SPMnTsXjRo1EpyOpIANO9JKgwcPRnx8PAYMGCDpxVOeNXjwYDRq1AgrVqzArl27IJfL4ebmhtjYWDRv3lx0PCHee+89xMbGYvHixahfvz4OHjwIb29vHD9+HO7u7qLjCREcHIyBAwfi2rVrKC8vx65du5CWloaNGzfi999/Fx1Pbby8vJSuG+np6ahVqxYfFAFITk6Gt7c3ACAjIwMAYG1tDWtrayQnJyvOk9p1t3379gAqeqqeJbXFQp44duwYYmJi8Mcff6gck2J9AMCqVavw4YcfwsHBQTHnMjExETKZTFLXVxKHi6eQVqpevToOHDiAVq1aiY5CpHUOHDiA+fPnIz4+HuXl5fD29sbMmTPRoUMH0dHUZvbs2a98bnBw8BtMQtri73r5/fz81JREM9SrVw8ffPABZsyYgVq1aomOozGKioqwefNmpKamKh6w9uvXD9WrVxcdjSSADTvSSq6urvj5558lvydMQUGBYvjP363wKLVhQgCwb98+6OrqomPHjkrlBw4cQHl5OTp37iwoGRGRdjM1NUVCQgLq168vOgoR/YWLp5BWWrJkCb744gtcvnxZdBShLCwskJOTAwCoUaMGLCwsVL6elEvR1KlTKx0OJJfLJbtSqLOzM+7evatSnpeXJ9mVZlknyoqLi7Fo0SJ06dIFPj4+8Pb2VvqSsry8PCxZsgTDhg3Dp59+imXLliE/P190LCECAwMREREhOobG2bRpE1q1aoXatWsrtkJYtmwZfvnlF8HJSAo4x4600oABA1BUVIT69evD2NhYZU5Mbm6uoGTqFR4erljNjx+wqi5evAg3NzeVcldXV6SnpwtIJN7ly5crbew+evQI165dE5BIvJfVydWrVwUkEmvo0KE4dOgQPvroI7zzzjuSm0v3IqdPn0bHjh1hZGSEd955B3K5HEuXLsW8efMU83elpEGDBpg2bRpiYmIqXUxm7NixgpKJ8/3332PmzJn4/PPPMXfuXMV1xcLCAsuXL0f37t0FJ6SqjkMxSStt2LDhpcc/+eQTNSXRPqNHj8acOXNgZWUlOsobZ2tri61bt6Jt27ZK5YcPH0a/fv0UvZ1S8OuvvwIAAgICsGHDBpibmyuOlZWV4ciRIzh06BDS0tJERVQ71knlzM3NsW/fPu7r95zWrVvDxcUFa9asgZ5exXPx0tJSDBs2DJmZmTh69KjghOpV2bL+T8hkMmRmZqoxjWZwc3PD/PnzERAQAFNTUyQmJsLZ2RnJycnw9/fndhn0xrFhRyQxZmZmSEhIkMQQs+HDh+PEiRPYvXu3Yh5Ieno6evbsiWbNmklqGwgdnYqR9zKZDM9f9vX19VGvXj0sWbIEH3zwgYh4QrBOKufm5obt27dLfg7z84yMjHD27FmVDcpTUlLg4+ODoqIiQclIUxgZGSE1NRWOjo5KDbuLFy/Cw8ODe+/SG8ehmKSVsrKyXnq8bt26akqifaT0LGfRokXo1KkTXF1dUadOHQDA1atX0bp1ayxevFhwOvUqLy8HUPGUPS4uThI9tn+HdVK5JUuWYMqUKVi1apXKfodSZmZmhqysLJWGXXZ2NkxNTQWlIk3i5OSEhIQElffNH3/8Uem0AKLXjQ070kr16tV76bwPKe6fQ6rMzc1x7NgxHDp0CImJiTAyMoKHhwd8fX1FRxPm0qVLoiNoHNaJMh8fHxQXF8PZ2VnSc5if16dPHwQFBWHx4sV47733IJPJEBMTg8mTJ6Nv376i46mdXC7Hjh07EBERgZycHMWDkid27dolKJk4kydPxv/93/+huLgYcrkcp06dwrZt27BgwQJJjRAhcdiwI6109uxZpZ9LSkpw9uxZxUR2oidkMhk6dOggqT3aXmbOnDkvPT5z5kw1JdEsR44cwZEjRyq9QV23bp2gVGL07dsX165dw/z581GrVi0unvKXxYsXQyaTYdCgQSgtLQVQMWR31KhRCAkJEZxO/caNG4fVq1ejTZs2/Dv5y5AhQ1BaWoovvvgCRUVF6NevH+zt7fHNN9/g448/Fh2PJIBz7KhK2bt3LxYtWoTIyEjRUTTWs+P+pYA37Mq8vLyUfi4pKcGlS5egp6eH+vXr48yZM4KSiTN79mzMmTMHPj4+sLOzU7lB3b17t6BkYhgbG+P48ePw9PQUHUUjFRUVISMjA3K5HC4uLjA2NhYdSQhLS0ts3rwZXbp0ER1FI925cwfl5eWwsbERHYUkhD12VKU0aNAAcXFxomOQhvi7G3Yper63G6jY3H7w4MHo0aOHgETirVq1Cj/++CMGDhwoOopGcHV15SIPL2FsbAx3d3fRMYQzNzeXzAPCf6K0tBSRkZHIyMhAv379AADXr1+HmZkZTExMBKejqo49dqSVCgoKlH6Wy+W4ceMGZs2ahdTUVCQkJIgJJlBWVhYcHBxUGi9yuRzZ2dmKBWVGjRqFr776ShILRdjZ2eHrr7/mDfsrSE5OxgcffIDLly+LjqJ2NWvWxKlTpxQrp0rdwYMHMXv2bMybN6/S/cnMzMwEJVO/wMBA/PjjjzAzM0NgYOBLz5XanLINGzZg//79WLduHYyMjETH0QhXrlxBp06dkJWVhUePHuHChQtwdnbG559/juLiYqxatUp0RKri2GNHWqlGjRqVNmAcHBywfft2QanEcnJywo0bN1SGfeTm5sLJyUmxoMz3338vIp4Qjx8/xnvvvSc6hlbIy8tDfn6+6BhCDBs2DFu3bsWMGTNER9EInTp1AgC0a9dOqVwul0Mmk0lqcSpzc3PFZ42ZmRl7/Z/Rq1cvbNu2DTY2NqhXr57KAwApDuseN24cfHx8kJiYiJo1ayrKe/TogWHDhglMRlLBhh1ppYiICKWfdXR0YG1tDRcXF8XGsVLz5KbreQ8ePIChoaGAROLxhl3VihUrlH5+0tu9adMmxQ291BQXF2P16tU4fPgwPDw8VG5Qly5dKiiZGM9fX6Vs/fr1iu9//PFHcUE00ODBgxEfH48BAwZw8ZS/xMTEIDY2FgYGBkrljo6OuHbtmqBUJCXSvAMmrefn5yc6gsaYMGECgIrVH2fMmKE0kb+srAwnT55E06ZNBaUTizfsqpYtW6b085OHIp988gmmTZsmKJVYSUlJivdIcnKy0jEp3qzy+lq5tm3bYteuXahRo4ZSeUFBAQICAhAeHi4mmCB79+7FgQMH0KpVK9FRNEZ5eXmlPdpXr17lXoekFmzYkda6cOECIiMjK13tUEpLtj9ZDEMul+PcuXNKTwoNDAzg6emJSZMmiYonFG/YVXHPNlXsoVIVHR2NH374AZmZmQgLC4O9vT02bdoEJycnyd7IR0ZG4vHjxyrlxcXFiI6OFpBILAcHB0nNt3wV77//PpYvX47Vq1cDqPicefDgAYKDg7l6KKkFF08hrbRmzRqMGjUKVlZWsLW1VbpJl8lkkhzbP3jwYKxcuZJPBemVXb16FTKZDPb29qKjaAzWCbBz504MHDgQ/fv3x6ZNm5CSkgJnZ2d89913+P3337Fv3z7REdUqKSkJANC0aVOEh4fD0tJScaysrAz79+/HDz/8ILmFh/bu3YuVK1di1apVqFevnug4GuH69eto06YNdHV1cfHiRfj4+ODixYuwsrLC0aNHufUBvXFs2JFWcnR0xOjRozFlyhTRUTRCaWkpDA0NkZCQgCZNmoiOQxqsvLwcc+fOxZIlS/DgwQMAFXsbTpw4EdOnT4eOjo7ghOrHOlHm5eWF8ePHY9CgQUr7XiYkJKBTp064efOm6IhqpaOjo3h4WNktk5GREVauXImhQ4eqO5pQFhYWKCoqQmlpKYyNjVWGuufm5gpKJtbDhw+xbds2nDlzBuXl5fD29kb//v25ciipBYdikla6d+8eevXqJTqGxtDT04Ojo6OkVqt7VXFxcQgLC0NWVpbKMCqpLU8OANOnT0doaChCQkLQsmVLyOVyxMbGYtasWSguLsa8efNER1Q71omytLQ0+Pr6qpSbmZkhLy9P/YEEu3TpEuRyOZydnXHq1ClYW1srjhkYGMDGxga6uroCE4qxfPly0RE0kpGREYYOHSq5hj5pBvbYkVYKCgpCs2bNMHLkSNFRNMb69esRFhaGzZs3Kw0VkrLt27dj0KBB6NChAw4dOoQOHTrg4sWLuHnzJnr06KG04p1U1K5dG6tWrUK3bt2Uyn/55ReMHj1akiu3sU6U1a9fHz/88APat2+v1GO3ceNGhISEICUlRXRE0iIhISEYOXKkyqIzVVVaWhpWrlyJ8+fPQyaTwdXVFZ999hlcXV1FRyMJYI8daSUXFxfMmDEDJ06cqHQD3bFjxwpKJs6KFSuQnp6O2rVrw9HREdWrV1c6LsV5h/Pnz8eyZcvwf//3fzA1NcU333wDJycnjBgxAnZ2dqLjCZGbm1vpDYarq6tkh06xTips3LgRffr0wYgRIzBu3DisW7cOMpkM169fx/HjxzFp0iRJLUz1IikpKZWOAHj+wQBVmD9/Pnr37i2Jht2OHTvQt29f+Pj4oEWLFgCguE/ZunUrRxrRG8ceO9JKTk5OLzwmk8mQmZmpxjSaYfbs2S89HhwcrKYkmqN69er4888/Ua9ePVhZWSEiIgLu7u44f/482rZtixs3boiOqHbNmzdH8+bNVfazGzNmDOLi4nDixAlBycRhnVTQ1dXFjRs3YGNjg+nTp2PZsmUoLi4GAFSrVg2TJk3CV199JTilOJmZmejRowfOnTsHmUymmG/3ZP4dh8JX7tle36rO2dkZAwYMwJw5c5TKg4ODsWnTJknem5B6sWFHRFWWg4MD9u3bB3d3d3h6emLq1Kno27cvjh8/jk6dOiE/P190RLU7evQounTpgrp166JFixaQyWQ4duwYsrOzsW/fPrRu3Vp0RLWLiopC165dVeokKysLf/zxh2TqREdHBzdv3lSs3FdUVISUlBSUl5fDzc0NJiYmghOK9eGHH0JXVxdr1qxRzLe7e/cuJk6ciMWLF0vm7+SfklLDztjYGElJSXBxcVEqv3jxIjw9PVFUVCQoGUkFh2JSlWZmZoaEhARJfKA8ER8frxjb7+bmBi8vL9GRhGndujUOHToEd3d39O7dG+PGjUN4eDgOHTqEdu3aiY6ndiUlJQgODsbBgwexd+9epKamQi6XIzAwEKNHj0bt2rVFRxTCz88PaWlp+P7773H+/HlJ18mzW8cYGxvDx8dHYBrNcvz4cYSHh8Pa2ho6OjrQ0dFBq1atsGDBAowdO1axpyhJl7+/P6Kjo1UadjExMWz4k1qwYUdVmpQ6pHNycvDxxx8jMjISNWrUgFwuR35+Ptq0aYPt27crreQmFd9++61iKNm0adOgr6+PmJgYBAYGYsaMGYLTqZ++vj6Sk5NhbW0tuZUe/07NmjXRrVs3vPvuuygvLwcAnD59GoC05k4NHjwY1apVe+k5UlxNFqgYavmk19LKygrXr19Hw4YN4ejoiLS0NMHpSBN069YNU6ZMQXx8PN59910AFXPswsLCMHv2bPz6669K5xK9bhyKSVWalIaA9OnTBxkZGdi0aRMaNWoEoGKS/yeffAIXFxds27ZNcELSBBMnToS+vj5CQkJER9EY+/fvx6BBg3D37l2Vh0EymUwyc6d0dHTQu3fvv91vS4qryQIVIwAmTpyIgIAA9OvXD/fu3cN//vMfrF69GvHx8UhOThYdUSNJ6XP4Vfe8lNJ1hdSLDTuq0qT0gWJubo7Dhw+jWbNmSuWnTp1Chw4dJLn/1LOLQTzr7t27sLGxkeQH65gxY7Bx40a4uLjAx8dHZfXUpUuXCkomjouLCzp27IiZM2eiVq1aouMI8/wcO1J24MABFBYWIjAwEJmZmfjggw+QmpqKmjVrYvv27ZIc3v0qunTpgtDQUMmuREykThyKSVRFlJeXq2z7AFQMv3sytExqXvTc6tGjRzAwMFBzGs2QnJwMb29vAMCFCxeUjj07v0pKcnJyMGHCBEk36gDp/v9/VR07dlR87+zsjJSUFOTm5sLCwkKSdde/f3/4+fnB398fDRo0eOF5+/btU2MqImljw46qNCl92LZt2xbjxo3Dtm3bFAs+XLt2DePHj5fck+Qny9bLZDKsXbtWaTW/srIyHD16VLKbxUZERIiOoHE++ugjREZGon79+qKjCMUBPC83dOhQfPPNNzA1NVWUWVpaorCwEGPGjMG6desEplM/ExMTLF26FCNHjoStrS38/PwUDT2pXV9PnjyJ3NxcdO7cWVG2ceNGBAcHo7CwEAEBAVi5cuXfzl8l+l9xKCZVaVIaipmdnY3u3bsjOTkZDg4OkMlkyMrKgru7O3755RfUqVNHdES1ebLP4ZUrV1CnTh3o6uoqjhkYGKBevXqYM2cOmjdvLioiaZCioiL06tUL1tbWcHd3V+n5Hjt2rKBk6hUVFYWWLVtCT4/PfCvzoqHdd+7cga2tLUpLSwUlE+vmzZuIjIxEZGQkoqKicOHCBdjY2Ehqn9DOnTvD398fU6ZMAQCcO3cO3t7eGDx4MBo1aoRFixZhxIgRmDVrltigVOXx6k1V2h9//AF7e3vRMdTCwcEBZ86cwaFDhxTL2Lu5uaF9+/aio6ndpUuXAABt2rTBrl27YGFhITgRabKtW7fiwIEDMDIyQmRkpFJPv0wmk0zDzs/PDwBUNmp/QiaTwdDQEC4uLvD19VV6YFKVFRQUQC6XQy6X4/79+zA0NFQcKysrw759+yQ9L9HU1BQWFhawsLBAjRo1oKenB1tbW9Gx1CohIQFfffWV4uft27ejefPmWLNmDYCKz+fg4GA27OiNY48daY0JEya88rlSXACC/l5ZWRnOnTsHR0dHNvZIwdbWFmPHjsXUqVNfeVW7qszJyQm3b99GUVERLCwsIJfLkZeXB2NjY5iYmCAnJwfOzs6IiIiAg4OD6LhvnI6OzkuH9ctkMsyePRvTp09XYyrxpkyZgqioKCQmJqJJkybw9fWFn58ffH19UaNGDdHx1MrQ0BAXL15UvB9atWqFTp064T//+Q8A4PLly3B3d8f9+/dFxiQJYI8daY1X3fxVSvPqnnfkyBEsW7ZMsUG5q6srPv/8c0n22gHA559/Dnd3dwQFBaGsrAy+vr44fvw4jI2N8fvvv8Pf3190RNIAjx8/Rp8+fdio+8v8+fOxevVqrF27VjHvMD09HSNGjMDw4cPRsmVLfPzxxxg/fjx27NghOO2bFxERAblcjrZt22Lnzp2wtLRUHDMwMICjo6PkNrIHgEWLFsHa2hrBwcHo3r27YpsdKapVqxYuXboEBwcHPH78GGfOnMHs2bMVx+/fv1/p4mZErxt77EhrJCUloXHjxpIZ/vNPffvttxg/fjw++ugjtGjRAkDFxqg7duzA0qVL8dlnnwlOqH729vb45Zdf4OPjgz179uD//u//EBERgY0bNyIiIgKxsbGiI5IGGD9+PKytrfHll1+KjqIR6tevj507d6Jp06ZK5WfPnkXPnj2RmZmJY8eOoWfPnpKaR3XlyhXUrVtX0g8Pn5WYmIioqChERkYiOjoaurq6isVT/P39JdXQGzFiBM6dO4eFCxdiz5492LBhA65fv65YfXnLli1Yvnw54uLiBCelqo4NO9Iaurq6uHnzJqytreHs7Iy4uDjUrFlTdCyNYW9vj2nTpqk04P773/9i3rx5uH79uqBk4hgaGiI9PR116tTB8OHDYWxsjOXLl+PSpUvw9PREQUGB6IikAcaOHYuNGzfC09MTHh4eKk/WpTa029jYGEePHoWPj49SeVxcHPz8/FBUVITLly+jSZMmePDggaCU6pGUlIQmTZpAR0cHSUlJLz3Xw8NDTak0U2JiIpYvX47NmzejvLxcUvuE3r59G4GBgYiNjYWJiQk2bNiAHj16KI63a9cO7777LubNmycwJUkBh2KS1qhRowYyMzNhbW2Ny5cvS3ZvthcpKChAp06dVMo7dOigWKlLamrVqoWUlBTY2dlh//79+O677wBUrILInl964ty5c/Dy8gJQsc/fs6TYO9OmTRuMGDECa9euVdTL2bNnMWrUKLRt2xZARZ09WX22KmvatKli0/amTZtCJpNVui2ETCaTVEPmibNnzypWxIyOjkZBQQGaNm2KNm3aiI6mVtbW1oiOjkZ+fj5MTExUPl/CwsKUtt0helPYsCOt0bNnT/j5+cHOzg4ymQw+Pj4vvDnPzMxUczrxunXrht27d2Py5MlK5b/88gs+/PBDQanEGjJkCHr37q34m3n//fcBVOw5JLV9lujFuLefstDQUAwcOBBvv/22oveytLQU7dq1Q2hoKICKPcyWLFkiMqZaXLp0CdbW1orv6SkLCws8ePAAnp6e8Pf3x6effgpfX1+YmZmJjiaMubl5peXPzsskepM4FJO0yv79+5Geno6xY8dizpw5ShvFPmvcuHFqTibe3LlzsXjxYrRs2VJpjl1sbCwmTpyo9GErleXbAWDHjh3Izs5Gr169FHv5bdiwATVq1ED37t0FpyPSXKmpqbhw4QLkcjlcXV3RsGFD0ZFIg/z++++Sb8gBQGBg4Cufu2vXrjeYhIgNO9JSQ4YMwYoVK17YsHvi6tWrqF27tiRWu3vVYVEymUySPZpE9GqioqIUe9qRsrS0NKxcuVJp5eExY8ZIvtF79epVyGQyyewb+6whQ4YovpfL5di9ezfMzc0Vc1Tj4+ORl5eHwMBArF+/XlRMkgg27KhKMzMzQ0JCApydnUVHITVZsWIFhg8fDkNDwxdutPyElHouiV6VgYEBbG1t0a9fPwwYMABNmjQRHUkj7NixA3379oWPj4/SqIi4uDhs3boVvXr1EpxQvcrLyzF37lwsWbJEsYiOqakpJk6ciOnTp0vigerzpkyZgtzcXKxatUoxVaSsrAyjR4+GmZkZFi1aJDghVXVs2FGVZmpqisTExCrfsCspKUHDhg3x+++/w83NTXQcoZycnHD69GnUrFnzpb2Y7LkkqtydO3ewfft2bNu2DcePH0eTJk0wYMAA9OvXTzGcWYqcnZ0xYMAAzJkzR6k8ODgYmzZtktz1ZNq0aQgNDcXs2bPRsmVLyOVyxMbGYtasWfj0008luQKktbU1YmJiVHpw09LS8N577+Hu3buCkpFUsGFHVZpUGnZAxXYHhw8fltTeQUT0Zl26dAlbt27Ftm3bkJqaCl9fX4SHh4uOJYSxsTGSkpLg4uKiVH7x4kV4enqiqKhIUDIxateujVWrVqFbt25K5b/88gtGjx6Na9euCUomjoWFBdavX4+AgACl8j179mDIkCG4d++emGAkGVwVk6iKGDNmDBYuXIi1a9dCT0+6b+0JEya80nkymUwSq/oR/S+cnJwwdepUeHp6YsaMGYiKihIdSRh/f39ER0erNOxiYmLQunVrQanEyc3NrXR1YVdXV+Tm5gpIJN6QIUMwdOhQpKen49133wVQMVw3JCREaS4e0Zsi3bs/oirm5MmTOHLkCA4ePAh3d3dUr15d6bhUVuM6e/as0s/x8fEoKytTDI25cOECdHV18fbbb4uIR6Q1YmNjsWXLFuzYsQPFxcXo1q0b5s+fLzqWMN26dcOUKVMQHx+vdNMeFhaG2bNn49dff1U6t6rz9PTEt99+qzKX+dtvv4Wnp6egVGItXrwYtra2WLZsGW7cuAEAsLOzwxdffIGJEycKTkdSwKGYVKVJafGUv3saKMXVuJYuXYrIyEhs2LABFhYWAIB79+5hyJAhaN26NT9oiSrx5ZdfYtu2bbh27Rref/999O/fHwEBATA2NkZCQgKaNm0qOqIQr7oYiFQ2K4+KikLXrl1Rt25dtGjRAjKZDMeOHUN2djb27dsnyV7MZxUUFACA5LeDIPViw46qNCnNsSNV9vb2OHjwIBo3bqxUnpycjA4dOuD69euCkhFprvfeew/9+/dHnz59YGVlhfz8fGzZsgWhoaFISEiQRKOFXs3169fx3//+F6mpqZDL5XBzc8Po0aNRu3Zt0dGIJIlDMUnrlJaWwtDQEAkJCX+7DHdKSgo/YCSsoKAAt27dUmnY5eTk4P79+4JSEWm2Y8eOAQDCw8Oxbt067Nq1C46OjujZsyfWrl0rOB1pktq1a0ty9csXuXXrFiZNmoQjR44gJycHz/ed8KEIvWls2JHW0dPTg6Oj4ytdIB0cHNSQSBxvb28cOXIEFhYW8PLygkwme+G5Z86cUWMyzdCjRw8MGTIES5YsUZoTM3nyZAQGBgpOR6R5rl69ih9//BHr16/HgwcP0Lt3b5SUlGDnzp2S3EqF+2K+XF5eHk6dOoWcnByUl5crHRs0aJCgVOIMHjwYWVlZmDFjBuzs7F76mUz0JnAoJmml9evXIywsDJs3b4alpaXoOMLMnj0bkydPhrGxMWbPnv3Sc4ODg9WUSnMUFRVh0qRJWLduHUpKSgBUPBgICgrCokWLVBaYIZKyLl26ICYmBl27dsWAAQPQqVMn6OrqQl9fH4mJiZJs2HFfzBf77bff0L9/fxQWFsLU1FSpESOTySS5MqapqSmio6MlOw+VxGPDjrSSl5cX0tPTUVJSAkdHR5UbdCn2TtGLFRYWIiMjA3K5HC4uLmzQEVVCT08PY8eOxahRo/DWW28pyqXcsKMXa9CgAbp06YL58+fD2NhYdByN4Obmhi1btsDLy0t0FJIoDsUkrfT85p9EL1O9enV4eHiIjkGk0aKjo7Fu3Tr4+PjA1dUVAwcORJ8+fUTHIg117do1jB07lo26ZyxfvhxTp07FDz/8gHr16omOQxLEHjuiKqKsrAzLli3Dzz//jKysLDx+/FjpuBSHxRDRP1dUVITt27dj3bp1OHXqFMrKyrB06VIMHToUpqamouMJ89FHH8HHxwdTp05VKl+0aBFOnTqFsLAwQcnECAwMxMcff4zevXuLjqIxLCwsUFRUhNLSUhgbG0NfX1/pOD+H6U1jw460Vl5eHnbs2IGMjAxMnjwZlpaWOHPmDGrVqgV7e3vR8dRu5syZWLt2LSZMmIAZM2Zg+vTpuHz5Mvbs2YOZM2dKcmI/Ef1v0tLSEBoaik2bNiEvLw/vv/++0kbcUmJtbY3w8HC4u7srlZ87dw7t27fHrVu3BCUTIzQ0FHPmzMGQIUPg7u6u0oiRwibtz9uwYcNLj3/yySdqSkJSxYYdaaWkpCS0b98e5ubmuHz5MtLS0uDs7IwZM2bgypUr2Lhxo+iIale/fn2sWLECXbt2hampKRISEhRlJ06cwNatW0VHJCItVVZWht9++w3r1q2TbMPOyMgICQkJaNiwoVJ5amoqvLy88PDhQ0HJxHjZhu1S2aSdSNO8+F1JpMEmTJiAwYMH4+LFizA0NFSUd+7cGUePHhWYTJybN28qniSbmJggPz8fAPDBBx9g7969IqMRkZbT1dVFQECAZBt1ANCkSRP89NNPKuXbt2+X5MIy5eXlL/ySUqOuoKBA6fuXfRG9aVw8hbRSXFwcfvjhB5Vye3t73Lx5U0Ai8erUqYMbN26gbt26cHFxwcGDB+Ht7Y24uDhUq1ZNdDwiIq02Y8YM9OzZExkZGWjbti0A4MiRI9i2bZuk5tedPHkSubm56Ny5s6Js48aNCA4ORmFhIQICArBy5UrJfO5YWFjgxo0bsLGxQY0aNSrdu04ul7MXk9SCDTvSSoaGhpU+/UpLS4O1tbWAROL16NEDR44cQfPmzTFu3Dj07dsXoaGhyMrKwvjx40XHIyLSat26dcOePXswf/587NixA0ZGRvDw8MDhw4fh5+cnOp7azJo1C/7+/oqG3blz5xAUFITBgwejUaNGWLRoEWrXro1Zs2aJDaom4eHhKCgogI2NDSIiIkTHIYnjHDvSSsOHD8ft27fx888/w9LSEklJSYqhQr6+vli+fLnoiMKdPHkSsbGxcHFxkeQkdiIiev3s7Ozw22+/wcfHBwAwffp0REVFISYmBgAQFhaG4OBgpKSkiIypVjo6OrC3t0ebNm0UX9zugERgw460UkFBAbp06YI///wT9+/fR+3atXHz5k20aNEC+/bt4wbUL9G1a1esXbsWdnZ2oqMQEWmVJ6sxZ2ZmYtKkSZJcjdnQ0BAXL16Eg4MDAKBVq1bo1KkT/vOf/wAALl++DHd3d9y/f19kTLWKjo5GVFQUIiMjcfz4cRQXF6Nu3bpo27atoqEnlb8PEosNO9Jq4eHhOHPmDMrLy+Ht7Y327duLjqTxTE1NkZiYCGdnZ9FRiIi0BldjruDo6IhNmzbB19cXjx8/Ro0aNfDbb7+hXbt2ACqGZvr5+Ul2z7aSkhIcP34ckZGRiIyMxIkTJ/Do0SO4uLggLS1NdDyq4jjHjrRa27ZtFZPYiYiI3pQnqzF//fXXShu1d+7cGf369ROYTL06deqEqVOnYuHChdizZw+MjY3RunVrxfGkpCTUr19fYEKx9PX14evri2bNmqFFixY4cOAA1qxZg/T0dNHRSALYsCOtsWLFCgwfPhyGhoZYsWLFS8/lZtxERPQ6cTXmCnPnzkVgYCD8/PxgYmKCDRs2wMDAQHF83bp16NChg8CEYhQXF+PYsWOIiIhAZGQk4uLi4OTkBD8/P3z//feSWmCHxOFQTNIaTk5OOH36NGrWrAknJ6cXnieTyZCZmanGZNqFQzGJiP65WrVqYf/+/fDy8lK6jh48eBBBQUHIzs4WHVGt8vPzYWJiAl1dXaXy3NxcmJiYKDX2qjo/Pz/ExcWhfv368PX1hZ+fH/z8/FCrVi3R0Uhi2LAjkhg27IiI/jmuxkwvoq+vDzs7OwQEBMDf3x++vr6wsrISHYskSEd0AKJ/qqSkBM7OzpJaSpmIiMRavHgxbt++DRsbGzx8+BB+fn5wcXGBqakp5s2bJzoeCZSXl4fVq1fD2NgYCxcuhL29Pdzd3fHZZ59hx44duH37tuiIJBHssSOtZG9vj8OHD6NRo0aio2idBQsWYNSoUahRo4boKEREWoerMdPfuX//PmJiYhTz7RITE/HWW28hOTlZdDSq4tiwI60UEhKC1NRUrF27Fnp6XAPoiQsXLiAyMhI5OTkoLy9XOjZz5kxBqYiItFtpaSkMDQ2RkJCAJk2aiI5DGq68vBxxcXGIiIhAREQEYmJiUFxcjLKyMtHRqIrjHTFppZMnT+LIkSM4ePAg3N3dVTYk37Vrl6Bk4qxZswajRo2ClZUVbG1tIZPJFMdkMhkbdkRE/5Kenh4cHR15Y06VKi8vx+nTpxEZGYmIiAjExsaisLAQ9vb2aNOmDf773/+iTZs2omOSBLDHjrTSkCFDXnp8/fr1akqiORwdHTF69GhMmTJFdBQioipn/fr1CAsLw+bNm2FpaSk6DmkQMzMzFBYWws7ODv7+/vD390ebNm0kvZ8ficGGHWmd0tJSbNmyBR07doStra3oOBrDzMwMCQkJXO2SiOgN8PLyQnp6OkpKSuDo6KgyUuTMmTOCkpFoP/zwA9q0aYMGDRqIjkISx6GYpHX09PQwatQonD9/XnQUjdKrVy8cPHgQI0eOFB2FiKjKCQgIgEwmA5+H0/NGjBghOgIRADbsSEs1b94cZ8+ehaOjo+goGsPFxQUzZszAiRMn4O7uDn19faXjY8eOFZSMiEh7FRUVYfLkydizZw9KSkrQrl07rFy5kvuUEZHG4VBM0kphYWGYOnUqxo8fj7fffltlSIyHh4egZOI4OTm98JhMJkNmZqYa0xARVQ2TJ0/Gd999h/79+8PIyAhbt26Fv78/wsLCREcjIlLChh1pJR0dHZWyJ0NkZDIZVy4jIqLXon79+pg3bx4+/vhjAMCpU6fQsmVLFBcXQ1dXV3A6IqKn2LAjrXTlypWXHucQTSIieh0MDAxw6dIl2NvbK8qMjIxw4cIFODg4CExGRKSMc+xIK7HhVmHChAn46quvUL16dUyYMOGl5y5dulRNqYiIqo6ysjIYGBgolenp6aG0tFRQIiKiyrFhR1otJSUFWVlZePz4sVJ5t27dBCVSr7Nnz6KkpETx/Ys8u1k5ERG9OrlcjsGDB6NatWqKsuLiYowcOVJpfveuXbtExCMiUuBQTNJKmZmZ6NGjB86dO6e0/PSTBgzn2BER0eswZMiQVzpv/fr1bzgJEdHLsWFHWunDDz+Erq4u1qxZA2dnZ5w6dQp3797FxIkTsXjxYrRu3Vp0RCIiIiIitWHDjrSSlZUVwsPD4eHhAXNzc5w6dQoNGzZEeHg4Jk6c+NJhiVVVjx49Kh1yKZPJYGhoCBcXF/Tr1w8NGzYUkI6IiIiI3iTVNeOJtEBZWRlMTEwAVDTyrl+/DqBiUZW0tDSR0YQxNzdHeHg4zpw5o2jgnT17FuHh4SgtLcVPP/0ET09PxMbGCk5KRERERK8bF08hrdSkSRMkJSXB2dkZzZs3x9dffw0DAwOsXr0azs7OouMJYWtri379+uHbb79V7PNXXl6OcePGwdTUFNu3b8fIkSMxZcoUxMTECE5LRERERK8Th2KSVjpw4AAKCwsRGBiIzMxMfPDBB0hNTUXNmjXx008/oW3btqIjqp21tTViY2PRoEEDpfILFy7gvffew507d3Du3Dm0bt0aeXl5YkISERER0RvBHjvSSh07dlR87+zsjJSUFOTm5sLCwkKyS/uXlpYiNTVVpWGXmpqqWCXU0NBQsvVDREREVJWxYUdaKT8/H2VlZbC0tFSUWVpaIjc3F3p6ejAzMxOYToyBAwciKCgIX375JZo1awaZTIZTp05h/vz5GDRoEAAgKioKjRs3FpyUiIiIiF43DsUkrdS5c2d8+OGHGD16tFL5qlWr8Ouvv2Lfvn2CkolTVlaGkJAQfPvtt7h16xYAoFatWhgzZgymTJkCXV1dZGVlQUdHB3Xq1BGcloiIiIheJzbsSCtZWloiNjYWjRo1UipPTU1Fy5YtcffuXUHJNENBQQEASLLnkoiIiEiKOBSTtNKjR49QWlqqUl5SUoKHDx8KSKRZ2KAjIiIikhY27EgrNWvWDKtXr8bKlSuVyletWoW3335bUCr18/b2xpEjR2BhYQEvL6+XLoxy5swZNSYjIiIiInViw4600rx589C+fXskJiaiXbt2AIAjR44gLi4OBw8eFJxOfbp3745q1aoBAAICAsSGISIiIiJhOMeOtFZCQgIWLVqEhIQEGBkZwcPDA9OmTcNbb70lOpralZWVISYmBh4eHrCwsBAdh4iIiIjUjA07qtJCQkIwcuRI1KhRQ3SUN87Q0BDnz5+Hk5OT6ChEREREpGY6ogMQvUnz589Hbm6u6Bhq4e7ujszMTNExiIiIiEgANuyoSpNSh/S8efMwadIk/P7777hx4wYKCgqUvoiIiIio6uJQTKrSTE1NkZiYCGdnZ9FR3jgdnafPaZ5dHVMul0Mmk6GsrExELCIiIiJSA66KSVRFREREiI5ARERERIKwYUdURfj5+SEvLw+hoaE4f/48ZDIZGjVqhKCgIJibm4uOR0RERERvEOfYEVURp0+fhouLC5YtW4bc3FzcuXMHy5YtQ/369bk5OREREVEVxzl2VKV16dIFoaGhsLOzEx3ljWvdujVcXFywZs0a6OlVdMaXlpZi2LBhyMzMxNGjRwUnJCIiIqI3hQ070lplZWXYvXu3Ytihq6srAgICFI0aqTEyMsLZs2fh6uqqVJ6SkgIfHx8UFRUJSkZEREREb5o074BJ6yUnJ6N79+64efMmGjZsCAC4cOECrK2t8euvv8Ld3V1wQvUzMzNDVlaWSsMuOzsbpqamglIRERERkTpwjh1ppWHDhqFx48a4evUqzpw5gzNnziA7OxseHh4YPny46HhC9OnTB0FBQfjpp5+QnZ2Nq1evYvv27Rg2bBj69u0rOh4RERERvUEciklaycjICKdPn0bjxo2VypOTk9GsWTM8fPhQUDJxHj9+jMmTJ2PVqlUoLS0FAOjr62PUqFEICQlBtWrVBCckIiIiojeFQzFJKzVs2BC3bt1Sadjl5OTAxcVFUCqxDAwM8M0332DBggXIyMiAXC6Hi4sLjI2NRUcjIiIiojeMPXaklfbt24cvvvgCs2bNwrvvvgsAOHHiBObMmYOQkBC0atVKca6ZmZmomEREREREasGGHWklHZ2n00NlMhkA4Mmf8rM/y2QylJWVqT8gEREREZEacSgmaaWIiAjREYiIiIiINAZ77Ehr5eXlITQ0VLGPXaNGjRAUFARzc3PR0YiIiIiI1IoNO9JKp0+fRqdOnWBoaIh33nkHcrkcp0+fxsOHD3Hw4EF4e3uLjkhEREREpDZs2JFWat26NVxcXLBmzRro6VWMKC4tLcWwYcOQmZmJo0ePCk5IRERERKQ+bNiRVjIyMsLZs2fh6uqqVJ6SkgIfHx8UFRUJSkZEREREpH46f38KkeYxMzNDVlaWSnl2djZMTU0FJCIiIiIiEocNO9JKffr0QVBQEH766SdkZ2fj6tWr2L59O4YNG4a+ffuKjkdEREREpFbc7oC00uLFiyGTyTBo0CCUlpYCAPT19TFq1CiEhIQITkdEREREpF6cY0daraioCBkZGZDL5XBxcYGxsbHoSEREREREaseGHRERERERkZbjHDsiIiIiIiItx4YdERERERGRlmPDjoiIiIiISMuxYUdERERERKTl2LAjIiIiIiLScmzYERERERERaTk27IiIiIiIiLQcG3ZERERERERa7v8BFxGFD/Z8MwQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1000x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Assuming your independent variables are stored in a pandas DataFrame called X\n",
    "corr_matrix = X.corr()\n",
    "\n",
    "# Print the correlation matrix\n",
    "print(corr_matrix)\n",
    "\n",
    "# Create a heatmap of the correlation matrix\n",
    "#Set figure size to fit all variables\n",
    "plt.figure(figsize=(10, 8))\n",
    "sns.heatmap(corr_matrix, annot=True, cmap='coolwarm')\n",
    "\n",
    "# Show the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true,
    "tags": []
   },
   "source": [
    "## Linear Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.metrics import mean_squared_error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "# Initialize the StandardScaler\n",
    "scaler = StandardScaler()\n",
    "\n",
    "# Scale the selected columns\n",
    "X_scaled = scaler.fit_transform(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# split the data into training and testing datasets\n",
    "X_train, X_test, y_train, y_test = train_test_split(X_scaled, y, test_size=0.2, random_state=42)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# train a linear regression model\n",
    "lr_model = LinearRegression()\n",
    "lr_model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Squared Error: 2754.0127494322865\n"
     ]
    }
   ],
   "source": [
    "# evaluate the model on the testing dataset\n",
    "y_pred = lr_model.predict(X_test)\n",
    "mse = mean_squared_error(y_test, y_pred)\n",
    "print('Mean Squared Error:', mse)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE: 52.47868852622259\n"
     ]
    }
   ],
   "source": [
    "import math\n",
    "\n",
    "# calculate RMSE using the square root function from the math module\n",
    "rmse = math.sqrt(mse)\n",
    "print(\"RMSE:\", rmse)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R^2 score for linear regression: 0.014288168965429504\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import r2_score\n",
    "\n",
    "# Evaluate the model using R^2 score\n",
    "r_sq = r2_score(y_test, y_pred)\n",
    "print(\"R^2 score for linear regression:\", score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.feature_selection import f_regression\n",
    "\n",
    "# Get the number of features in the model\n",
    "n_features = X_train.shape[1]\n",
    "n_features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.014174191879551601"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate the adjusted R-squared value\n",
    "n_samples = X_train.shape[0]\n",
    "adj_r_sq = 1 - (1 - r_sq) * (n_samples - 1) / (n_samples - n_features - 1)\n",
    "adj_r_sq"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Feature</th>\n",
       "      <th>F Stats</th>\n",
       "      <th>P Values</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>op_carrier_fl_num</td>\n",
       "      <td>41.769215</td>\n",
       "      <td>1.033122e-10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>origin_airport_id</td>\n",
       "      <td>7.751790</td>\n",
       "      <td>5.367229e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>distance</td>\n",
       "      <td>26.330764</td>\n",
       "      <td>2.883589e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>route</td>\n",
       "      <td>7.108965</td>\n",
       "      <td>7.671522e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>month</td>\n",
       "      <td>7.618926</td>\n",
       "      <td>5.777238e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>AvgTemp</td>\n",
       "      <td>8.695341</td>\n",
       "      <td>3.191192e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Precipitation</td>\n",
       "      <td>698.708639</td>\n",
       "      <td>2.724787e-153</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Snow_mm</td>\n",
       "      <td>45.781797</td>\n",
       "      <td>1.331174e-11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>WindSpeed</td>\n",
       "      <td>65.685365</td>\n",
       "      <td>5.365736e-16</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Feature     F Stats       P Values\n",
       "0  op_carrier_fl_num   41.769215   1.033122e-10\n",
       "1  origin_airport_id    7.751790   5.367229e-03\n",
       "2           distance   26.330764   2.883589e-07\n",
       "3              route    7.108965   7.671522e-03\n",
       "4              month    7.618926   5.777238e-03\n",
       "5            AvgTemp    8.695341   3.191192e-03\n",
       "6      Precipitation  698.708639  2.724787e-153\n",
       "7            Snow_mm   45.781797   1.331174e-11\n",
       "8          WindSpeed   65.685365   5.365736e-16"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate the F-statistic and corresponding p-values for each feature\n",
    "f_stats, p_values = f_regression(X_train, y_train)\n",
    "\n",
    "#make a list of independent variable columns names\n",
    "cols = X.columns.tolist()\n",
    "\n",
    "# zip the array and list into a list of tuples\n",
    "data = list(zip(cols, f_stats, p_values))\n",
    "\n",
    "# create a DataFrame of the results\n",
    "model_results = pd.DataFrame(data, columns=['Feature', 'F Stats', 'P Values'])\n",
    "model_results\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true,
    "tags": []
   },
   "source": [
    "## Lasso & Ridge Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import Lasso, Ridge\n",
    "from sklearn.model_selection import GridSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best parameters for Ridge regression: {'alpha': 1000}\n",
      "Best score for Ridge regression: 0.010708695385140498\n",
      "Best parameters for Lasso regression: {'alpha': 0.01}\n",
      "Best score for Lasso regression: 0.010698755446958707\n"
     ]
    }
   ],
   "source": [
    "# Define the parameter grid for the grid search\n",
    "param_grid = {'alpha': [0.001, 0.01, 0.1, 1, 10, 100, 1000]}\n",
    "\n",
    "# Create a Ridge regression model object\n",
    "ridge_model = Ridge()\n",
    "\n",
    "# Create a Lasso regression model object\n",
    "lasso_model = Lasso()\n",
    "\n",
    "# Create a grid search object for Ridge regression\n",
    "grid_search_ridge = GridSearchCV(ridge_model, param_grid, cv=5)\n",
    "\n",
    "# Create a grid search object for Lasso regression\n",
    "grid_search_lasso = GridSearchCV(lasso_model, param_grid, cv=5)\n",
    "\n",
    "# Fit the Ridge grid search to the training data\n",
    "grid_search_ridge.fit(X_train, y_train)\n",
    "\n",
    "# Fit the Lasso grid search to the training data\n",
    "grid_search_lasso.fit(X_train, y_train)\n",
    "\n",
    "# Print the best parameters and score for Ridge regression\n",
    "print(\"Best parameters for Ridge regression:\", grid_search_ridge.best_params_)\n",
    "print(\"Best score for Ridge regression:\", grid_search_ridge.best_score_)\n",
    "\n",
    "# Print the best parameters and score for Lasso regression\n",
    "print(\"Best parameters for Lasso regression:\", grid_search_lasso.best_params_)\n",
    "print(\"Best score for Lasso regression:\", grid_search_lasso.best_score_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R^2 score for Ridge regression: 0.014286026601158408\n"
     ]
    }
   ],
   "source": [
    "# train a ridge regression model\n",
    "ridge_model = Ridge(alpha=100) \n",
    "ridge_model.fit(X_train, y_train)\n",
    "\n",
    "# Make predictions on the test set\n",
    "y_pred = ridge_model.predict(X_test)\n",
    "\n",
    "# Evaluate the model using R^2 score\n",
    "score = r2_score(y_test, y_pred)\n",
    "print(\"R^2 score for Ridge regression:\", score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Squared Error: 2754.018735054383\n"
     ]
    }
   ],
   "source": [
    "# evaluate the model on the testing dataset\n",
    "mse = mean_squared_error(y_test, y_pred)\n",
    "print('Mean Squared Error:', mse)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE: 52.47874555526631\n"
     ]
    }
   ],
   "source": [
    "import math\n",
    "\n",
    "# calculate RMSE using the square root function from the math module\n",
    "rmse = math.sqrt(mse)\n",
    "print(\"RMSE:\", rmse)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R^2 score for Lasso regression: 0.014286052887941825\n"
     ]
    }
   ],
   "source": [
    "# train a lasso regression model\n",
    "lasso_model = Lasso(alpha=0.01) \n",
    "lasso_model.fit(X_train, y_train)\n",
    "\n",
    "# Make predictions on the test set\n",
    "y_pred = lasso_model.predict(X_test)\n",
    "\n",
    "# Evaluate the model using R^2 score\n",
    "score = r2_score(y_test, y_pred)\n",
    "print(\"R^2 score for Lasso regression:\", score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Squared Error: 2754.018661610873\n"
     ]
    }
   ],
   "source": [
    "# evaluate the model on the testing dataset\n",
    "mse = mean_squared_error(y_test, y_pred)\n",
    "print('Mean Squared Error:', mse)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE: 52.478744855521015\n"
     ]
    }
   ],
   "source": [
    "import math\n",
    "\n",
    "# calculate RMSE using the square root function from the math module\n",
    "rmse = math.sqrt(mse)\n",
    "print(\"RMSE:\", rmse)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true,
    "tags": []
   },
   "source": [
    "## XGBoost using XGBRegressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import xgboost as xgb\n",
    "from xgboost import XGBClassifier\n",
    "from xgboost import XGBRegressor\n",
    "from sklearn.inspection import permutation_importance\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.metrics import accuracy_score\n",
    "from hyperopt import STATUS_OK, Trials, fmin, hp, tpe\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn.metrics import r2_score \n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>op_carrier_fl_num</th>\n",
       "      <th>op_unique_carrier</th>\n",
       "      <th>origin_airport_id</th>\n",
       "      <th>dest_airport_id</th>\n",
       "      <th>distance</th>\n",
       "      <th>route</th>\n",
       "      <th>month</th>\n",
       "      <th>day</th>\n",
       "      <th>fl_date</th>\n",
       "      <th>arr_delay</th>\n",
       "      <th>Key</th>\n",
       "      <th>AvgTemp</th>\n",
       "      <th>Precipitation</th>\n",
       "      <th>Snow_mm</th>\n",
       "      <th>WindSpeed</th>\n",
       "      <th>lat</th>\n",
       "      <th>lon</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1610</td>\n",
       "      <td>22</td>\n",
       "      <td>11618</td>\n",
       "      <td>13495</td>\n",
       "      <td>1167</td>\n",
       "      <td>1962</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2019-03-01</td>\n",
       "      <td>0.0</td>\n",
       "      <td>40.692501-40.692501</td>\n",
       "      <td>0.8</td>\n",
       "      <td>1.3</td>\n",
       "      <td>30.0</td>\n",
       "      <td>15.9</td>\n",
       "      <td>40.692501</td>\n",
       "      <td>-74.168701</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4909</td>\n",
       "      <td>6</td>\n",
       "      <td>11618</td>\n",
       "      <td>14524</td>\n",
       "      <td>277</td>\n",
       "      <td>1978</td>\n",
       "      <td>12</td>\n",
       "      <td>6</td>\n",
       "      <td>2019-12-06</td>\n",
       "      <td>-17.0</td>\n",
       "      <td>40.692501-40.692501</td>\n",
       "      <td>5.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>12.2</td>\n",
       "      <td>40.692501</td>\n",
       "      <td>-74.168701</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3556</td>\n",
       "      <td>26</td>\n",
       "      <td>11618</td>\n",
       "      <td>11193</td>\n",
       "      <td>569</td>\n",
       "      <td>1928</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>2018-04-03</td>\n",
       "      <td>-9.0</td>\n",
       "      <td>40.692501-40.692501</td>\n",
       "      <td>4.9</td>\n",
       "      <td>5.8</td>\n",
       "      <td>30.0</td>\n",
       "      <td>11.1</td>\n",
       "      <td>40.692501</td>\n",
       "      <td>-74.168701</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1382</td>\n",
       "      <td>2</td>\n",
       "      <td>11618</td>\n",
       "      <td>11298</td>\n",
       "      <td>1372</td>\n",
       "      <td>1932</td>\n",
       "      <td>9</td>\n",
       "      <td>7</td>\n",
       "      <td>2019-09-07</td>\n",
       "      <td>68.0</td>\n",
       "      <td>40.692501-40.692501</td>\n",
       "      <td>20.4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.3</td>\n",
       "      <td>40.692501</td>\n",
       "      <td>-74.168701</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3517</td>\n",
       "      <td>26</td>\n",
       "      <td>11618</td>\n",
       "      <td>13495</td>\n",
       "      <td>1167</td>\n",
       "      <td>1962</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>2018-02-05</td>\n",
       "      <td>-37.0</td>\n",
       "      <td>40.692501-40.692501</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13.4</td>\n",
       "      <td>40.692501</td>\n",
       "      <td>-74.168701</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97302</th>\n",
       "      <td>5026</td>\n",
       "      <td>19</td>\n",
       "      <td>11092</td>\n",
       "      <td>11292</td>\n",
       "      <td>283</td>\n",
       "      <td>1111</td>\n",
       "      <td>9</td>\n",
       "      <td>22</td>\n",
       "      <td>2019-09-22</td>\n",
       "      <td>0.0</td>\n",
       "      <td>38.75500107-38.75500107</td>\n",
       "      <td>16.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>38.755001</td>\n",
       "      <td>-109.754997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97303</th>\n",
       "      <td>5031</td>\n",
       "      <td>19</td>\n",
       "      <td>11092</td>\n",
       "      <td>11292</td>\n",
       "      <td>283</td>\n",
       "      <td>1111</td>\n",
       "      <td>8</td>\n",
       "      <td>10</td>\n",
       "      <td>2019-08-10</td>\n",
       "      <td>-12.0</td>\n",
       "      <td>38.75500107-38.75500107</td>\n",
       "      <td>27.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>38.755001</td>\n",
       "      <td>-109.754997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97304</th>\n",
       "      <td>5079</td>\n",
       "      <td>19</td>\n",
       "      <td>11092</td>\n",
       "      <td>11292</td>\n",
       "      <td>283</td>\n",
       "      <td>1111</td>\n",
       "      <td>8</td>\n",
       "      <td>15</td>\n",
       "      <td>2018-08-15</td>\n",
       "      <td>2.0</td>\n",
       "      <td>38.75500107-38.75500107</td>\n",
       "      <td>28.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.7</td>\n",
       "      <td>38.755001</td>\n",
       "      <td>-109.754997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97305</th>\n",
       "      <td>5031</td>\n",
       "      <td>19</td>\n",
       "      <td>11092</td>\n",
       "      <td>11292</td>\n",
       "      <td>283</td>\n",
       "      <td>1111</td>\n",
       "      <td>5</td>\n",
       "      <td>26</td>\n",
       "      <td>2019-05-26</td>\n",
       "      <td>-22.0</td>\n",
       "      <td>38.75500107-38.75500107</td>\n",
       "      <td>18.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.4</td>\n",
       "      <td>38.755001</td>\n",
       "      <td>-109.754997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97306</th>\n",
       "      <td>865</td>\n",
       "      <td>12</td>\n",
       "      <td>12119</td>\n",
       "      <td>14112</td>\n",
       "      <td>861</td>\n",
       "      <td>2279</td>\n",
       "      <td>7</td>\n",
       "      <td>15</td>\n",
       "      <td>2018-07-15</td>\n",
       "      <td>105.0</td>\n",
       "      <td>39.707901-39.707901</td>\n",
       "      <td>26.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.1</td>\n",
       "      <td>39.707901</td>\n",
       "      <td>-77.729500</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>97307 rows × 17 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       op_carrier_fl_num  op_unique_carrier  origin_airport_id  \\\n",
       "0                   1610                 22              11618   \n",
       "1                   4909                  6              11618   \n",
       "2                   3556                 26              11618   \n",
       "3                   1382                  2              11618   \n",
       "4                   3517                 26              11618   \n",
       "...                  ...                ...                ...   \n",
       "97302               5026                 19              11092   \n",
       "97303               5031                 19              11092   \n",
       "97304               5079                 19              11092   \n",
       "97305               5031                 19              11092   \n",
       "97306                865                 12              12119   \n",
       "\n",
       "       dest_airport_id  distance  route  month  day     fl_date  arr_delay  \\\n",
       "0                13495      1167   1962      3    1  2019-03-01        0.0   \n",
       "1                14524       277   1978     12    6  2019-12-06      -17.0   \n",
       "2                11193       569   1928      4    3  2018-04-03       -9.0   \n",
       "3                11298      1372   1932      9    7  2019-09-07       68.0   \n",
       "4                13495      1167   1962      2    5  2018-02-05      -37.0   \n",
       "...                ...       ...    ...    ...  ...         ...        ...   \n",
       "97302            11292       283   1111      9   22  2019-09-22        0.0   \n",
       "97303            11292       283   1111      8   10  2019-08-10      -12.0   \n",
       "97304            11292       283   1111      8   15  2018-08-15        2.0   \n",
       "97305            11292       283   1111      5   26  2019-05-26      -22.0   \n",
       "97306            14112       861   2279      7   15  2018-07-15      105.0   \n",
       "\n",
       "                           Key  AvgTemp  Precipitation  Snow_mm  WindSpeed  \\\n",
       "0          40.692501-40.692501      0.8            1.3     30.0       15.9   \n",
       "1          40.692501-40.692501      5.7            0.0      0.0       12.2   \n",
       "2          40.692501-40.692501      4.9            5.8     30.0       11.1   \n",
       "3          40.692501-40.692501     20.4            0.0      0.0        6.3   \n",
       "4          40.692501-40.692501      0.3            0.0      0.0       13.4   \n",
       "...                        ...      ...            ...      ...        ...   \n",
       "97302  38.75500107-38.75500107     16.0            0.0      0.0        0.0   \n",
       "97303  38.75500107-38.75500107     27.3            0.0      0.0        0.0   \n",
       "97304  38.75500107-38.75500107     28.5            0.0      0.0       10.7   \n",
       "97305  38.75500107-38.75500107     18.3            0.0      0.0       11.4   \n",
       "97306      39.707901-39.707901     26.7            0.0      0.0        9.1   \n",
       "\n",
       "             lat         lon  \n",
       "0      40.692501  -74.168701  \n",
       "1      40.692501  -74.168701  \n",
       "2      40.692501  -74.168701  \n",
       "3      40.692501  -74.168701  \n",
       "4      40.692501  -74.168701  \n",
       "...          ...         ...  \n",
       "97302  38.755001 -109.754997  \n",
       "97303  38.755001 -109.754997  \n",
       "97304  38.755001 -109.754997  \n",
       "97305  38.755001 -109.754997  \n",
       "97306  39.707901  -77.729500  \n",
       "\n",
       "[97307 rows x 17 columns]"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_xgboost = pd.read_csv('../../data/processed/transformed_model_flight_data.csv')\n",
    "df_xgboost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df_xgboost.drop(['op_unique_carrier','dest_airport_id','day','fl_date','Key','lat','lon','fl_date','arr_delay'], axis=1)\n",
    "y = df_xgboost['arr_delay']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Split dataset into training and test\n",
    "seed = 8\n",
    "test_size = 0.2\n",
    "X_train, X_test, y_train, y_test = train_test_split(X_scaled, y, test_size=test_size, random_state=seed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "#XGBoost hyper-parameter tuning\n",
    "def hyperParameterTuning(X_train, y_train):\n",
    "    param_tuning = {\n",
    "        'learning_rate': [0.01, 0.1],\n",
    "        'max_depth': [3, 5, 7, 10],\n",
    "        'min_child_weight': [1, 3, 5],\n",
    "        'subsample': [0.5, 0.7],\n",
    "        'colsample_bytree': [0.5, 0.7],\n",
    "        'n_estimators' : [100, 200, 500],\n",
    "        'objective': ['reg:squarederror']\n",
    "    }\n",
    "\n",
    "    xgb_model = XGBRegressor()\n",
    "\n",
    "    xsearch = GridSearchCV(estimator = xgb_model,\n",
    "                           param_grid = param_tuning,                        \n",
    "                           #scoring = 'neg_mean_absolute_error', #MAE\n",
    "                           #scoring = 'neg_mean_squared_error',  #MSE\n",
    "                           cv = 5,\n",
    "                           n_jobs = -1,\n",
    "                           verbose = 1)\n",
    "\n",
    "    xsearch.fit(X_train,y_train)\n",
    "\n",
    "    return xsearch.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "#hyperParameterTuning(X_train, y_train)"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "#Best option found after tuning\n",
    "Fitting 5 folds for each of 288 candidates, totalling 1440 fits\n",
    "{'colsample_bytree': 0.5,\n",
    " 'learning_rate': 0.01,\n",
    " 'max_depth': 10,\n",
    " 'min_child_weight': 5,\n",
    " 'n_estimators': 200,\n",
    " 'objective': 'reg:squarederror',\n",
    " 'subsample': 0.7}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Initiate XGBoost object with best options after tuning\n",
    "\n",
    "xreg_model = XGBRegressor(colsample_bytree = 0.5,\n",
    "                            learning_rate = 0.01,\n",
    "                            max_depth= 10,\n",
    "                            min_child_weight= 5,\n",
    "                            n_estimators= 200,\n",
    "                            objective= 'reg:squarederror',\n",
    "                            subsample= 0.7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "XGBRegressor(base_score=None, booster=None, callbacks=None,\n",
       "             colsample_bylevel=None, colsample_bynode=None,\n",
       "             colsample_bytree=0.5, early_stopping_rounds=None,\n",
       "             enable_categorical=False, eval_metric=None, feature_types=None,\n",
       "             gamma=None, gpu_id=None, grow_policy=None, importance_type=None,\n",
       "             interaction_constraints=None, learning_rate=0.01, max_bin=None,\n",
       "             max_cat_threshold=None, max_cat_to_onehot=None,\n",
       "             max_delta_step=None, max_depth=10, max_leaves=None,\n",
       "             min_child_weight=5, missing=nan, monotone_constraints=None,\n",
       "             n_estimators=200, n_jobs=None, num_parallel_tree=None,\n",
       "             predictor=None, random_state=None, ...)"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#fit model to train data\n",
    "xreg_model.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred_train = xreg_model.predict(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Predict with test data\n",
    "preds = xreg_model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE:  2307.0628\n",
      "R2: 0.0236\n",
      "Training Score: 0.1305\n",
      "Test Score: 0.0236\n"
     ]
    }
   ],
   "source": [
    "#Test performance\n",
    "rmse = mean_squared_error(y_test, preds)\n",
    "r2 = r2_score(y_test, preds)\n",
    "train_score = xreg_model.score(X_train, y_train)\n",
    "test_score  = xreg_model.score(X_test, y_test)\n",
    "\n",
    "print(f\"RMSE:  {rmse:.4f}\")\n",
    "print(f\"R2: {r2:.4f}\" )\n",
    "print(f\"Training Score: {train_score:.4f}\" )\n",
    "print(f\"Test Score: {test_score:.4f}\" )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "pickle.dump(xreg_model, open('../../data/pickles/XBoostModel.pkl', 'wb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from xgboost import plot_importance\n",
    "\n",
    "plot_importance(xreg_model)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.12256572 0.1032055  0.10662986 0.10607631 0.09066354 0.11215416\n",
      " 0.17802964 0.08110534 0.09956986]\n"
     ]
    }
   ],
   "source": [
    "print(xreg_model.feature_importances_)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### XGBoost with k-fold cross validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create regression matrices\n",
    "data_dmatrix = xgb.DMatrix(data=X,label=y)\n",
    "#dtest_dmatrix  = xgb.DMatrix(data=X_test,label=y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "# k-fold cross validation using xgboost\n",
    "\n",
    "# Define parameters\n",
    "params = {'colsample_bytree': 0.5, 'learning_rate': 0.01, 'max_depth': 10, 'min_child_weight': 5,  'objective': 'reg:squarederror', 'subsample': 0.7}\n",
    "#params = {'objective':\"reg:squarederror\", 'learning_rate': '0.01', 'colsample_bytree':'0.3', 'max_depth': 10}\n",
    "xg_CV = xgb.cv(dtrain=data_dmatrix, params=params, nfold=5,\n",
    "                    num_boost_round=50,early_stopping_rounds=10,metrics=\"rmse\", as_pandas=True, seed=105)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>train-rmse-mean</th>\n",
       "      <th>train-rmse-std</th>\n",
       "      <th>test-rmse-mean</th>\n",
       "      <th>test-rmse-std</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>51.690366</td>\n",
       "      <td>0.857776</td>\n",
       "      <td>51.592550</td>\n",
       "      <td>3.576830</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>51.662743</td>\n",
       "      <td>0.859421</td>\n",
       "      <td>51.581213</td>\n",
       "      <td>3.576513</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>51.633555</td>\n",
       "      <td>0.859305</td>\n",
       "      <td>51.569641</td>\n",
       "      <td>3.575651</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>51.603964</td>\n",
       "      <td>0.856501</td>\n",
       "      <td>51.558410</td>\n",
       "      <td>3.578748</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>51.575365</td>\n",
       "      <td>0.858208</td>\n",
       "      <td>51.547557</td>\n",
       "      <td>3.578596</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   train-rmse-mean  train-rmse-std  test-rmse-mean  test-rmse-std\n",
       "0        51.690366        0.857776       51.592550       3.576830\n",
       "1        51.662743        0.859421       51.581213       3.576513\n",
       "2        51.633555        0.859305       51.569641       3.575651\n",
       "3        51.603964        0.856501       51.558410       3.578748\n",
       "4        51.575365        0.858208       51.547557       3.578596"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Show results for each folder\n",
    "xg_CV.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "51.19838035254391"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Show best RMSE\n",
    "best_rmse = xg_CV['test-rmse-mean'].min()\n",
    "best_rmse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "pickle.dump(xg_CV, open('../../data/pickles/XGBoostKfolders.pkl', 'wb'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true,
    "tags": []
   },
   "source": [
    "## PCA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.decomposition import PCA\n",
    "from sklearn.preprocessing import StandardScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 335,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('../../data/processed/transformed_model_flight_data.csv')\n",
    "\n",
    "X = df.drop(['op_unique_carrier','dest_airport_id','day','fl_date','Key','lat','lon','fl_date','arr_delay'], axis=1)\n",
    "X_scaled = StandardScaler().fit_transform(X)\n",
    "\n",
    "y = df['arr_delay']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 336,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape before PCA:  (97307, 9)\n",
      "Shape after PCA:  (97307, 2)\n"
     ]
    }
   ],
   "source": [
    "pca = PCA(n_components=2) \n",
    "pca_features = pca.fit_transform(X_scaled)\n",
    "\n",
    "print('Shape before PCA: ', X_scaled.shape)\n",
    "print('Shape after PCA: ', pca_features.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 337,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PC1</th>\n",
       "      <th>PC2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.810767</td>\n",
       "      <td>1.545935</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.080467</td>\n",
       "      <td>0.302233</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.050155</td>\n",
       "      <td>1.479179</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.695468</td>\n",
       "      <td>-1.407764</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.861631</td>\n",
       "      <td>1.327448</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97302</th>\n",
       "      <td>1.677175</td>\n",
       "      <td>-0.505724</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97303</th>\n",
       "      <td>1.640950</td>\n",
       "      <td>-1.067927</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97304</th>\n",
       "      <td>1.619231</td>\n",
       "      <td>-0.684343</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97305</th>\n",
       "      <td>1.648695</td>\n",
       "      <td>0.314895</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97306</th>\n",
       "      <td>0.380068</td>\n",
       "      <td>-1.269463</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>97307 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            PC1       PC2\n",
       "0      0.810767  1.545935\n",
       "1      1.080467  0.302233\n",
       "2      1.050155  1.479179\n",
       "3      0.695468 -1.407764\n",
       "4      0.861631  1.327448\n",
       "...         ...       ...\n",
       "97302  1.677175 -0.505724\n",
       "97303  1.640950 -1.067927\n",
       "97304  1.619231 -0.684343\n",
       "97305  1.648695  0.314895\n",
       "97306  0.380068 -1.269463\n",
       "\n",
       "[97307 rows x 2 columns]"
      ]
     },
     "execution_count": 337,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pca_df = pd.DataFrame(\n",
    "    data=pca_features, \n",
    "    columns=['PC1', 'PC2'])\n",
    "pca_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true,
    "tags": []
   },
   "source": [
    "### Regression with PCA Transformed Variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 349,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Split your data into training and test sets\n",
    "X_train, X_test, y_train, y_test = train_test_split(pca_df, y, test_size=0.2, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 350,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 350,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# train a linear regression model\n",
    "lr_model = LinearRegression()\n",
    "lr_model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 351,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Squared Error: 2794.544355810806\n"
     ]
    }
   ],
   "source": [
    "# evaluate the model on the testing dataset\n",
    "y_pred = lr_model.predict(X_test)\n",
    "mse = mean_squared_error(y_test, y_pred)\n",
    "print('Mean Squared Error:', mse)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 352,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE: 52.86345009371603\n"
     ]
    }
   ],
   "source": [
    "import math\n",
    "\n",
    "# calculate RMSE using the square root function from the math module\n",
    "rmse = math.sqrt(mse)\n",
    "print(\"RMSE:\", rmse)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 353,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R^2 score for linear regression: -0.00021883865331839836\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import r2_score\n",
    "\n",
    "# Evaluate the model using R^2 score\n",
    "score = r2_score(y_test, y_pred)\n",
    "print(\"R^2 score for linear regression:\", score)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true,
    "tags": []
   },
   "source": [
    "### PCA Plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 339,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(pca_df['PC1'], pca_df['PC2'])\n",
    "plt.title('Principal Component Analysis')\n",
    "plt.xlabel('PC1')\n",
    "plt.ylabel('PC2')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 340,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PC1</th>\n",
       "      <th>PC2</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>feature_names</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>op_carrier_fl_num</th>\n",
       "      <td>0.074115</td>\n",
       "      <td>0.260813</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>origin_airport_id</th>\n",
       "      <td>-0.698284</td>\n",
       "      <td>0.053459</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>distance</th>\n",
       "      <td>-0.122033</td>\n",
       "      <td>-0.235444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>route</th>\n",
       "      <td>-0.698407</td>\n",
       "      <td>0.055205</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>month</th>\n",
       "      <td>0.000183</td>\n",
       "      <td>-0.407821</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AvgTemp</th>\n",
       "      <td>-0.032208</td>\n",
       "      <td>-0.604785</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Precipitation</th>\n",
       "      <td>0.050651</td>\n",
       "      <td>-0.014837</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Snow_mm</th>\n",
       "      <td>0.022756</td>\n",
       "      <td>0.524740</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WindSpeed</th>\n",
       "      <td>-0.011039</td>\n",
       "      <td>0.250962</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        PC1       PC2\n",
       "feature_names                        \n",
       "op_carrier_fl_num  0.074115  0.260813\n",
       "origin_airport_id -0.698284  0.053459\n",
       "distance          -0.122033 -0.235444\n",
       "route             -0.698407  0.055205\n",
       "month              0.000183 -0.407821\n",
       "AvgTemp           -0.032208 -0.604785\n",
       "Precipitation      0.050651 -0.014837\n",
       "Snow_mm            0.022756  0.524740\n",
       "WindSpeed         -0.011039  0.250962"
      ]
     },
     "execution_count": 340,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Principal components correlation coefficients\n",
    "loadings = pca.components_\n",
    " \n",
    "# Number of features before PCA\n",
    "n_features = pca.n_features_\n",
    "\n",
    "# Feature names before PCA\n",
    "feature_names = X.columns.values.tolist()\n",
    "\n",
    "# PC names\n",
    "pc_list = [f'PC{i}' for i in list(range(1, n_features + 1))]\n",
    "\n",
    "# Match PC names to loadings\n",
    "pc_loadings = dict(zip(pc_list, loadings))\n",
    "\n",
    "# Matrix of corr coefs between feature names and PCs\n",
    "loadings_df = pd.DataFrame.from_dict(pc_loadings)\n",
    "loadings_df['feature_names'] = feature_names\n",
    "loadings_df = loadings_df.set_index('feature_names')\n",
    "loadings_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 341,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Get the loadings of x and y axes\n",
    "xs = loadings[0]\n",
    "ys = loadings[1]\n",
    " \n",
    "# Plot the loadings on a scatterplot\n",
    "for i, varnames in enumerate(feature_names):\n",
    "    plt.scatter(xs[i], ys[i], s=200)\n",
    "    plt.text(xs[i], ys[i], varnames)\n",
    " \n",
    "# Define the axes\n",
    "xticks = np.linspace(-0.8, 0.8, num=5)\n",
    "yticks = np.linspace(-0.8, 0.8, num=5)\n",
    "plt.xticks(xticks)\n",
    "plt.yticks(yticks)\n",
    "plt.xlabel('PC1')\n",
    "plt.ylabel('PC2')\n",
    "plt.grid()\n",
    "\n",
    "# Show plot\n",
    "plt.title('2D Loading plot')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 342,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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lUlQqFYsXL2bu3Ll8+eWXxMTEUKlSJUaOHGm5ow/g2WefZdeuXQwZMoThw4fn2XXUvn17VCoVTZo0sXT9NW/eHJVKRfPmzfO9fnee484B6wXVrVs3PvzwQzp06GDpRsvy5ptv4u3tzbp161i6dCmurq6EhITw1ltvWVpDO3bsyOLFi5k3bx5DhgzB3d2dxx57zBLea9euzeOPP87atWvZvXs3P/30k1W/W7nx8fFh6dKlfPbZZ7z77rukpqZSu3Zt5s2bl2tLoRAlSaXICo1CiFIkLi6OP//8k7Zt22YbY/TRRx/x3Xff5XunW0BAAEOHDmXYsGH3o6pCiHJIWpyEEKWKvb0906ZNo27duvTp0wcHBwcOHz7M6tWrGTx4cElXTwhRzklwEkKUKra2tqxcuZJPP/2UcePGkZqaSpUqVRg3blyuXZ1CCHE/PRBddSaTifnz57NhwwYSEhJo2rQpkyZNomrVqrmWzxqM+/fffwPmu1reeecdWVBTCCGEEEXyQExHsGDBAr7++mumTp3K+vXrUalUDBw4EL1en2v5kSNHcvPmTVasWMGKFSsICwvjjTfeuM+1FkIIIURZU+qDk16vZ/ny5QwbNoz27dsTGBjInDlzCA8PZ/v27TnKJyQkcODAAQYOHEi9evWoV68egwYN4uTJk9lWLhdCCCGEKKhSH5zOnDlDcnJytltSXVxcqFevXrZVx7PY2tri4ODApk2bSEpKIikpiR9++IFq1arluCVXCCGEEKIgSv3g8KxZY+9c6NHHx4ebN2/mKG9ra8u0adN4//33CQ4ORqVS4e3tzZo1a+7LUgdCCCGEKLtKfZLImqU4a9mHLLa2trku/qgoCmfPniUoKIi1a9eyatUqKlasyJAhQ0hKSip0PR6AMfRCCCGEuMdKfYuTnZ0dYB7rlPUzQHp6eq4z0f7888+sW7eOnTt3WmbpXbRoER07dmTjxo306dOnUPVQqVQkJKRiNBZtOQlR+mk0alxc7OV6lxNyvcsXud7li6urfbH3NpX64JTVRRcREUGVKlUs2yMiIixrKt3u0KFDVK9ePdvSBq6urlSvXp3Lly8XqS5GowmDQT5o5YVc7/JFrnf5Ite7fLgXnUWlvqsuMDAQJyenbMssJCQkcOrUKYKDg3OU9/f358qVK9m68VJTUwkNDc1z3ichhBBCCGuU+uBkY2NDr169mDVrFr///jtnzpxh5MiR+Pn50blzZ4xGI5GRkaSlpQHmFcHBvIjlmTNnLOVtbGzyXZ1bCCGEEOJuSn1wAhg+fDg9evRgwoQJvPTSS2g0GpYtW4aNjQ03b96kTZs2bNmyBTDfbbdu3ToURaFPnz707dsXnU7HV199hYuLSwm/EiGEEEI8yB6IJVdKi9jYZOkTLwe0WjXu7o5yvcsJud7li1zv8sXDwxGNpnjbiB6IFichhBBCiNJAgpMQQgghhJUkOAkhhBBCWEmCkxBCCCGElSQ4CSGEEEJYSYKTEEIIIYSVJDgJIYQQQlhJgpMQQgghhJUkOAkhhBBCWEmCkxBCCCGElSQ4CSGEEEJYSYKTEEIIIYSVJDgJIYQQQlhJgpMQQgghhJUkOAkhhBBCWEmCkxBCCCGElSQ4CSGEEEJYSYKTEEIIIYSVJDgJIYQQQlhJgpMQQgghhJUkOAkhhBBCWEmCkxBCCCGElSQ4CSGEEEJYSYKTEEIIIYSVJDgJIYQQQlhJgpMQQgghhJUkOAkhhBBCWEmCkxBCCCGElSQ4CSGEEEJYSYKTEEIIIYSVJDgJIYQQQlhJgpMQQgghhJUkOAkhhBBCWEmCkxBCCCGElSQ4CSGEEEJYSYKTEEIIIYSVJDgJIYQQQlhJgpMQQgghhJUkOAkhhBBCWEmCkxBCCCGElSQ4CSGEEEJYSYKTEEIIIYSVJDgJIYQQQlhJgpMQQgghhJUkOAkhhBBCWElb0hUQQojSTFEUVCoVABcvXmDVqmUcOXKYhIR4XF1dadSoCb169aFOncASrqkQ4n6Q4CSEELeJjo7iwoWzRETcJD4+DpPJhFqtRq/PYO3aNQQE1GXEiNF4eHgQGRnBt9+uZ/Dgfsydu5gGDR4q6eoLIe4xCU5CCAEkJMTz5587iYgIQ6VSoSiK5TmTycTff/+FTqejefNgMjJSqVmzFkFBTWnbtgM9e/Zg1aqlzJz5WQm+AiHE/SDBSQhR7p0+fZpffvnFEpZuD01ZUlJSLM9FRobz448baN26A9Wr12LYsJGkpaUBMHToICpVqkylSpX57rsNxMXFUqdOIMOGvUX9+g0sxztz5hRLlizkzJlTGAwGGjduwuDBQ6lRoybnz5+jb9+XmTZtJu3bdwTgxIljvP56f3r1epXBg4cCkJycRPfujzBmzHi6d3/Sqtfao8cTdOv2BMnJSfzyy8/o9Rm0adOOMWPG891337Bx4zekpCQTHNyct99+F1dXtyLtJ0RZI8FJCFGu/fffeXbt+v2u5apWrcq1a9f44YcfCAgIoEKFCvz55+8oCnTs+Ei2sn/88TtVq1bnzTfHoCgKn3/+KRMnjmXDhh/RaDQcPnyQt94aSuPGTXjnnYno9RmsXr2C11/vx5Ilq6hduw4+Pr4cPLjfEpwOHToAwNGjhy3nOXhwPyaTiVat2hToNa9fv5bg4OZMnjydM2dOsXjx55w9expvbx/efns8V69eYcGCuXh4eDFq1Ngi7ydEWSLBSQhRbiUkxLN7906rytarV4+UlBSOHTvG33//DYCdnR1//PEHr78+gubNW1rKGgwGZs+eh6OjEwApKclMmzaZ8+fPERhYl4UL51GxYiU++WQeGo0GgObNW/K//z3NsmWLef/9GYSEtObgwX2WYx46dICAgLqcOXOK1NRU7O3t2bt3D3Xr1sfd3aNAr9vBwZEpU2ag1Wpp1qwFW7f+RFRUFEuWrMLJyYmQkDYcOnSQEyeOFct+QpQlMh2BEKLc2rNnV67dcnkJDg6mV69ePPzwwwQEBKDT6Th//jxvvTWUb775ylKuevUaltAE4OPjC0BaWiqpqamcOXOKhx/ubAlNAM7OzrRq1ZbDhw8CEBLShmvXrhIeHkZ6ehonT56gd+++GAwG/v3XHEz27fuH1q3bFvh1161bH6321t/NHh6eVK1aFSenW3V2dXUlKSmxWPYToiyRFichRLkUHR1JRERYvmU8IqOwSUsnrHJFyzZbW1tq1apFrVq1AIiKimLnzp0sXDiXRx99LLOMXbbjZE1nYDKZSEpKRFEUPD09c57Pw9MSOoKDm2NjY8vBg/vx8fFBrVbTunU7qlWrzuHDh3B39yQiIpzWrdsV+LU7Ojrm2HZnnYtzPyHKEglOQohy6cKFcznunrudNiODmmcvoDGZCLW35etffiE4OJjAwOzzNXl5edGsWTO2bdvG9euhdz2vk5MzKpWK6OjoHM9FR0dZBlXb2dkRFNSUgwf34+vrx0MPNUKr1RIUFMzRo4dxdHTEz8+fmjVrFfzFCyEKTbrqhBDlUkTEzXy76Spcu47aZEIBqsUnolKpOHnyJAaDIUfZuLg4tFotlSpVuet57e3tCQysy44d2zEajZbtSUlJ7NnzFw0bNrZsa9WqNYcOHeDo0cM0aRIMQNOmwZw5c4pdu3YUqptOCFE0EpyEEOVSfHxcns9p9Xr8rt9EBagA3/BI2rZuTUxMDN9//z2nTp3ixo0bXL16lT179nDgwAGaNm2Ki4uLVed+7bWhhIZeY9SoYfz11y527vyN4cMHk5Ghp2/fgZZyISFtiImJ5t9/jxMUZA5OQUFNMRgMnD59qlDddEKIopGuOiFEuaMoCiaTKc/njVotKQ4OOCUnA5BuZ0uVqlV55plnOHbsGEeOHCEtLQ21Wo2XlxePPPII1atXt3qgeXBwc+bM+ZxlyxYzadJ4dDobGjcOYsKEKdSoUdNSrkKFilSrVp3w8HACA+sC4OrqRo0atbhx4zpBQU2L8C4IIQpDpRTklpJyLjY2GYMh7y9bUTZotWrc3R3lepdxa9YszTc8oSi03P0PAHvbtbrr8dRqNb16DSiu6ol7RD7f5YuHhyMaTfF2rkmLkxCiXHJ1dSM2NibP5zWZ44+s/cvS1dW9GGpVOCaTKf8QmOn2qQSEEIUjnyIhRLnk4+NPXFxsnt1rGoM5OKXb2d71WCqVCh8fv2KtX0HMmPE+W7f+dNdyGzb8iL9/hftQIyHKLglOQohyqVatAM6ePZnn8xqj+e45ve3dg5OiKNSqFVBsdSuofv0G8dxzL9y1nJeX932ojRBlmwQnIUS55OnphY+PH5GR4bm2OllanO4SnFQqFd7evnh6et2TelrD37+CtCQJcZ/IdARCiHKrVav2llm976Q1Wh+cWrVqX+x1E0KUThKchBDllouLK23bdsz1OZ1eD4De1ibfY7Ru3QEXF9dir5sQonSS4CSEKNdq1qxNt27dUKvV2VqfbNPSAcjIJTipVCrUajVt2z5M9eqy5IkQ5YmMcRJClHt169bFwcGVP//cSUREGCqVCpt0c4uTQaOxlMta287Hx4+QkHbS0iREOSTBSQghMHfbde36JNHRUVy4cBabk2cB8yziarUaV1d3fHz8qFUroEQHggshSpYEJyGEuI2npxeenl5c+nEzGcCTz/fCxltu4xdCmMkYJyGEyIUhJhoAjYNDCddECFGaSHASQohcKBkZAKjt7Uu4JkKI0kSCkxBC5EOllq9JIcQt8o0ghBBCCGElCU5CCCGEEFaS4CSEEEIIYSUJTkIIcYfcFv0VQgiQ4CSEEDko6eklXQUhRCklwUkIIe5gTE0FQOvuXsI1EUKUNhKchBDiDqbUFAC0Hp4lXBMhRGkjwUkIIe5gymxx0nl4lHBNhBCljQQnIYS4g7Q4CSHy8kAEJ5PJxNy5c2nbti2NGjWiX79+XLlyxap9N2/eTEBAAKGhofe4lkKIssKYmAiA1lOCkxAiuwciOC1YsICvv/6aqVOnsn79elQqFQMHDkSv1+e73/Xr15kyZcp9qqUQoqzIiIkBQOsmg8OFENmV+uCk1+tZvnw5w4YNo3379gQGBjJnzhzCw8PZvn17nvuZTCbGjBlD/fr172NthRBlgSEmGgCNg0MJ10QIUdqU+uB05swZkpOTadmypWWbi4sL9erV48CBA3nut2jRIjIyMnjttdfuRzWFEGVIRrS5xUktwUkIcQdtSVfgbsLCwgDw9/fPtt3Hx4ebN2/mus/x48dZvnw53377LeHh4cVWF42m1OdMUQyyrrNc7/Iht+ttjDW3ONk4OaDVyu9BWSKf7/JFpSr+Y5b64JSaeVuwjY1Ntu22trbEx8fnKJ+SksLo0aMZPXo01apVK9bg5OJiX2zHEqWfXO/y5fbrbcgc4+Th74XOxbGkqiTuIfl8i8Iq9cHJzs4OMI91yvoZID09HXv7nL/4U6dOpVq1avzvf/8r9rokJKRiNJqK/biidNFo1Li42Mv1Lidyu95ZM4cnpiuoYpNLsnqimMnnu3xxdbVHrS7e1sVSH5yyuugiIiKoUqWKZXtERASBgYE5ym/cuBEbGxuCgoIAMBqNADz++OM8+eSTvP/++4Wui9FowmCQD1p5Ide7fMntehtRg/wOlEny+S4f7sV63aU+OAUGBuLk5MS+ffsswSkhIYFTp07Rq1evHOW3bduW7fGxY8cYM2YMS5YsoWbNmvelzkIIIYQom0p9cLKxsaFXr17MmjULDw8PKlasyMyZM/Hz86Nz584YjUZiYmJwdnbGzs6OqlWrZts/a3B5hQoV8JTJ7IQQQghRBA/EbQXDhw+nR48eTJgwgZdeegmNRsOyZcuwsbHh5s2btGnThi1btpR0NYUQQghRxqkU5V70AJZNsbHJ0ideDmi1atzdHeV6lxN3Xm9FUTg/sC8AdZauLNnKiWInn+/yxcPDsdinnnggWpyEEOJ+UTIySroKQohSTIKTEELcxpSaAoDG2aWEayKEKI0kOAkhxG1MmXM4aeVmEiFELiQ4CSHEbYwp5uCkc/co4ZoIIUojCU5CCHGbrK46racEJyFEThKchBDiNsbkJAB0HtJVJ4TISYKTEELcJmuBX62HtDgJIXKS4CSEELcxxEQDoHF0KuGaCCFKIwlOQghxm4zMFie1vX0J10QIURpJcBJCiNsYJDgJIfIhwUkIIW6TkdlVp7Z3KOGaCCFKIwlOQghxG1OS+a46aXESQuRGgpMQQuRCrdOVdBWEEKWQBCchhBBCCCtJcBJCiHvIpCglXYV76vDhg7RpE8zhwwfv2zl//30bzz33OA8/3IqPP57GtGmT6dHjift2flG+aUu6AkIIUZZcCUvkr+M3ORcax42oZIwmBY1aRQUvR+pUcqNNQ3+q+jmXdDWLTUBAIIsWraB69er37ZyzZ39EpUpVePfdyXh7+/Dll8vv27mFkBYnIUS5oygKEyaMpWvXjih3tAhdTUul35l/6dSpNenp6dmeu3jxAm3aBLNp07e0aRPMli2bLc+Fx6bw4dpDTFl5gJ1Hr3MtIgmjyXxso0nhWkQSO49eZ8rKA3y49hDhseY18XJrLQkLu8mHH37As892p0OHljz++CO8/fab97VVx1q7du1k8OC+JCQk3LdzxsfH07x5S5o0CaZy5Sr37bxCgLQ4CSHKAX3KTZKij5KedJWM9EhQTNT0i+KPpEQO/72SVu2fBtwxZWRwIjkJJ42GpPR0jh49TIsWIZbjHD16BIAmTYJZtGgFFStWAmDvyTCWbzmNKTMoZf0/i6KYiL+yl7gre8lIjuKijRP/bA9i9MhhAMTHxzF06CC6du3O0qWLiIqKxN7egZ49e9OoURBxcbFs3vwDI0a8ztSpH9G+/cN5vlaDwcCaNSvZsmUzUVFRVKhQkZde6kX37k8CYDQa+eqr1fz66xauX7+OWq2iVq06DBz4Ok2bNgNg2bLFbNu2la5du7Nhw9c4OTnxxRer6N//Fdq168B//13g9OlTdO78KE5O5hnW//33OP7+FQBzwFy0aL7l/WratBlDh75peb8OHz7I8OGDGT36HVavXoFer2fixPdp3rxlvtcxaz+AFSu+YMWKL9iw4cd898nLF18s4pdftjBixGgWL57P1atX8PX1p0+ffjz22OPZzjd37iKaNAm27Dt06CAA5s9fAkCPHk/QrdsTJCcn8csvP6PXZ9CmTTvGjBnPd999w8aN35CSkkxwcHPefvtdXF3dClVnUTpIcBJClFkZ6THEXPmR9OSrmBvYTZbnGtTxAuDwwd24217BzqkqLh4dOZmcRFM3D87b2rBv355swenYscNUrFiJKlWqWbbtPRnGks2n8q1H+PGNJIQewqNmB+w9a5Aef4Po89v5YPJ16tY0h4kLF86xZMkV6td/iH/++QtfXz+++mo13bs/SVBQU9q168igQa/yxReL8g1OH3zwHn/9tYs+ffpTr14D9u//hxkz3kej0dC1a3cWLZrPd999w+uvD6NmzdpERkawfPkXTJgwlu+++xn7zGkYwsJusmvXTqZMmUZcXJzlH/uNG7/huedeoFevPtja2vH3339mO//Vq1cYPLg/VatW5d13J2E0mli1ahlvvNGflSu/wt391hqAS5YsYMyYd9Dr9TRo8FC+7yHc6hYcPLgvjz/+FI8//jSenl533S8v0dFRzJnzMX369MPX15+vvlrNtGmTqVevAVWrVivQsdavX0twcHMmT57OmTOnWLz4c86ePY23tw9vvz2eq1evsGDBXDw8vBg1amyh6yxKngQnIUSZlBxzguirP4KlK86U7Xlfb0e8PRw4dzGKTq2rkJZ0jbjo5VxIS6Fzzdq416zJ/v17s+1z7NhR2rRpz82bN3j++ScZMuIddl72IP7aQcKPf0vlVm8QefJH0hNuoLFxwq1aKxx965Jw7QCeAY/iVjWEyFM/kRRuDlopkec4rZgwKQqpSUnMnz+b33/fjkajYerUj+jXrxfffPMVQ4aMQKPRMHjwEC5dumSpz7Rpk7l58wZdu3ZnxYoviIuLJT09nZdeeoXevfsBEBzcnMuXL7Fs2WI+++wTkpOTqFChIg0bNqZOnUAAbG1teffdt/n442kcO3aEqKhITCYTrVq1oVmzW61AiqJgZ2fPH3/s4IcfvqN585Z4enpne49WrPgCW1tbPv10AY6Z6/0FBzfjhReeYt261QwZMsJS9umnn6Njx0esvqaOjk6WgOXt7WNV2MpPWloaY8dOIDi4OQCVK1elR4/H2bPnrwIHJwcHR6ZMmYFWq6VZsxZs3foTUVFRLFmyCicnJ0JC2nDo0EFOnDhWpDqLkidjnIQQZU5yzAmir3wPipE7A9Pt6gd4ce5iTOYjEyfPRWBUFBq2qEazZi25fPkSYWFhAISGXiMqKjJbd9Jfx2/c6pZTFG4eXoNzhcZUbN4Pe49qRJ3ZQtzlfwBwrtCY6/uXkRxxGu+63fBr/BIAKVH/YTAo+Pr60bhxE1q1akN6ejrjxo3C29ubvXv3YDQaAWjWrCUvvPBSttdgbqlaQL9+5q4+gB9+2EhkZAQAcXFxnD9/Do1Gy8iRbzNt2sd4e/swZMhAtm//hS1bNvPLL1sA2L37D1588WUeeaQLAF9+uZytW3+ynCsxMZGUlGS6d3+S6dNn4erqxubN32erz6FDB2jSpCm2tnYYDAYMBgMODo40bBjEgQP7spWtWbNWntfmfmnQoKHlZx8fHwDS0lILfJy6deuj1d5qi/Dw8KRq1aqWrkwAV1dXkpISi1BbURpIi5MQokzJSIs2tzRZoUGAN3/8c5W4hDTcXOw4diqCGlXccHzIRKPqtdBoNOzbt4ennnqWo0cPo9FoaNo02DIQOiwmFVfHrKMpeNZ+BNcq5tYLO/dqJIX9iybmAgDpCTdIi7tGhWZ9cfKtC0C4jSOKIR2TouDsau7CCglpzVtvjWXx4vkkJycD0K3bwzRt2pynn34uxzigpMyWqsaNmxAVFQmYxzlltVStX7+W+Ph41q1bhp+fP2fOnCI1NYXU1FQ++OA96tQJxMbGBoD//a8XL77Yk2XLFgPw6KOPsWjRfDp37kpqaiopKclUr16D/v1fA6BFi5DM8U4nLfWJj4/j99+38/vv23O8325u7tke395tV1Ls7OwsP6vV5rYEkynvsJ0XR0fHHNtsbe1yKSkedBKchBBlSszVzbd1z+Wvfh0vVCo4dzGG5o0rcPxMBG2CK4EK0qN+p169Bhw8uN8SnOrVa4Cjo5MlOKlU2Y9n517V8rNao8VeZ4t7RiIxQErkeVBpcPQJAEAxGTFlpGLj5ENGSgyRUTGWfZ999nm6dXuCQYP6EB8fj4uLC3/+uZM//9zJiy/2ZNiwkZayWS1VAE5O5mkOAgLqcuyYeWD2oUMHqFKlKjduXMfBwZ633hpKzZq1eeSRR9m792+WLv2SiRPNY27q12+AwWCwBIfWrdvz669buXTpP6KjzWv4+fj4ZXvNQUFNswUnZ2dnmjZtzksv9crxfms0GquuS2mhyrzAdwap1NRUy1gwUf5IcBJClBn6lBuZA8HzpiQZMMXqUVeyx9XZlsr+Lpy7GEPlCi5ERKXQsK6POTglXyU4qAHf/7gFRVE4duwo3bo9nv1Yd+QztebWMi0BSVeINupxyWzRSIsPRWPjgEplbtVIvHEUFBM2jt5kpMQQHxvOpUsXqV69BgBJSYmEhobSs2dv+vd/jdDQa3z44QesX7+W7t2foEYNczeXl9etMUYNGzY2vw/6dFJSzNMdJCTEExp6zXI3GsCRI4csP6elpXHp0kUAxox5M9vree+9cQBERUWSmGhewy+rdSqLi4trtseNGzfh8uVL1KpVx9J1pSgK778/kUqVKlO7dgAPiqxWpIiIcMu2hIQELl++SN269UuqWqKESXASQpQZSdHHuPPuuTtl/BmF6VIKuif80FRxoEGAFxeuxOHrFYmDvZZa1bK6k9TUq+3IirhYDh8+yM2b17MNlM6PjSmDZ8J2sQdwUGsIcXHjn/hQQEVSxFn0iWFEn9uOvWdN1LrM7hwFxo17i+TkJIKCmnLlymWcnZ15/vn/AVCpUmVGjBhF3749uXTpkiU4xcfHW85bu3YdOnZ8hF27duDr68fBg/stAWrw4KHUq9eAt99+Ez8/f5544hm0Wg2ffjqLy5fNA86HDh1J48ZB/PDDd2zevImlS78EoGLFypZWJb0++9xWyclJ2R6/+upABg/uy9tvj+SZZ57DxsaWH374jt27/2Dq1I+sev9Ki5o1a+Pj48uKFV/g6OiERqNm9eqV0tpUzsngcCFEmZGedJX8QpOSYsR02RwkDP/EoCgK9QO8uRIaz7/nomgQ4I1andX/ZqKKrwkXF1c2bdqIk5MzdevWMz9zl65AvVrHfw4VLI97VK6DS6WmgMKN/SuIu7wH9+qtqRDcm5So8wBo7d343/96kZSUxB9/7MDPz59Fi5Zna9G5evUKADVq1LRsu3Ej1NJiBPDGG8NRqVTEx8fz9tsjMZlMODk588wzPWjSJJiPP/4UOzt7Fiz4jM8/n0tERDivv26eT+r8+TMEBtaztGJdvPgfy5d/gdFotAyiDgu7me21njx5ItvjWrVq8/nnS1GpVHzwwSQmTBhLdHQUM2bMyncahdJIo9EwbdrHeHl5MWXKu8yZM5NOnTo/cK9DFC9pcRJClBkZ6ZH5Pm88lwhZN8FF6TFdTqFuLU8MRhNHTobzao/st7cb9VE0bdqM3bv/oE2bdpYxOuo7Bzflws6oB+CqvS8rqzyJn8YGQ3oiaXHX8KjZAa29GzcPr8WYngwqFSrgmWd6ULdufYYNe43//rvArl07qFMnEKPRyLFjR1i/fh1PP/2cpTsPzN1g77wzioEDX0ej0bB8+RLc3NxZvXo9Li6uREVF0rdvT9588w3+979XcHV1pXbtAM6cOcWQISN44YWXMZlM/PPP3xw8eIDvv/+WoKCm2NnZMWvWh7Ro0RI3NzcAXnttCF98sZCFC+fRtGkz/vnnb06d+hfIfndaQEAgn3wyN8/3pkmTYP76q/CzoN+577vvTi7wMQYOHEzfvoPueuy6deuzcGH+S7p8++3mHNuyJse8XWHqKUofCU5CiDJBURRQ8r8bSkkyZH+casTOVkvtau6c+S+Gh+p637GDiebNW7Jz529Wd9MBqBUjFdOjAIjXOWGvMY8LqhDcm6jTW4g+tw2TMQPnCo1wrdqC+Kv70WjMYSwwsC4rVqxl9eoVbNy4gejoKDQaNdWqVWfIkBF07/4kBoP5dZhMJnx8fHn++ZeYO3c2aWmpBAc356OPRlpaqry8vFm0aDmLFs1n1qwZ6PXpVK5clXHjJvL440+Z66tWM3PmZyxdupDVq1cQGxuDl5cPL774Mq++OsDyul55pS/29vZ8881XbNjwFQ0aNGTo0DeZNetDq9+bvGS9pvyoVKp8B5grimKZuiFv6kLdNSdEFpVy50JNIk+xsckYDPKBK+u0WjXu7o5yvR9AV49OzTc8KQYThkNxGA/GoQl2Q9vc3XLnVK5Uaqo0npDrU2u3nWPn0es5llcBqJ94kSfC/+KMYxU2+Xe4a73Djq1HSbjC1p9/yfX5oUMHcfTo4bsepyitOCWtTZvgu5Zp3LhJri05WW5fkiU/Q4cOpVevfvL5Lgc8PBzRaIp3VJK0OAkhygydrTcZaeF5Pq/SqlH72WEEMCr5hyZAZ+ed53NtGvrz++HQXJ97IvwvAHZ43T0MAPg1epFJrzbL8/m33x5vGeR9u+XLl3D27Bk++mi2VecpzbIGoufHwcEh3+cDA+ve9TgajZqaNWVhYFF4EpyEEGWGrVMVMtIiyW+AuMrW/Nfnnd12Oamxdcz7H9iqfs7UqezKhdB4bm90cjTcmnU6QeeUy553nEUFtSq5UtXPOc8yt6+NdztXVzd0Oh2BgfXuep7Srjheg4OD412Pc3uLshCFIXfVCSHKDCfPxuQXmgCwyQxOiXcLTqbM4+Wtb7e6t92FZ9Y25igA27ya3+X4Zmq1ir7d6lpV9k7vvjs514HJQoh7R4KTEKLMsHHwz2wlyvurTWVjTYuTubXJxsE/3/P5ujvQr/ttoUdRaJxgnl7guIt167D1614XX/f8u6CEEKWHBCchRJniUfXJnGuh3C4zOJGU+91XiqKASmU+jhVa1vNj0BP10GpUVE43L6wbq3XGoM57JIRaBVqNikFP1qNlPb88ywkhSh8Z4ySEKFN0th54VnmK6Cvf5VEg71CVdZOxZ5Wn0NlavwBty/p+VK/gws0JbwPwk3/bXMup1SpMJoValdzo2y1QWpqEeABJcBJClDmOHg0AheirP2YuKHdr3FPed9KpwKhg+DMWx1ENCnxObwcN8enm5UcCWzZCfT2eG1HJGE0KGrWKCl6O1KnkRpuG/vkOBBdClG4SnIQQZZKjx0PYOFYk5sqPmQv/5rWGnXm7ja0/iWv2onPwKdT54v/8AwC3hzvR89FbC9maFMWqmcaFEA8GCU5CiDJLZ+uBb51X0afcJCn6KOnJVzOnK8h83t4Xe+eqVKwRQvzFBBLi/0JbyfouuttFrv8KAI/M2bizSGgSomyR4CSEKPNsHPzxuO0OuXO8CoB/4GtotWocXByJST0EgM7Ts8DH10dGWH7WurgUrbJCiFJN7qoTQpRbym3rmplSzBMiaj0KHpwiv1oLgP+g14unYkKIUkuCkxCi3FHZ2gJgSr01y3dGbCwAugIGJ8VkIvn4MQCcmlq3xIoQ4sElwUkIUe5khSNj6q313zKiowHQuBTsjres0GRfuw4qjaaYaiiEKK0kOAkhyh2th3kAeLYWpxhzcFLb2RfoWDcWzAPA55VXi6dyQohSTYKTEKLcyRoAbkrJ2eKktrd+UkpjcjKYzFMc2FaoUIw1FEKUVhKchBDlTtYAcONtwckQEwOA2t76FqfYX7cC4PnUM8VYOyFEaSbBSQhR7mS1OBniYi3bLGOcHKwPTjFbfgLA7ZEuxVg7IURpJsFJCFHuaJzNcy0ZMsPS7VS2dlYdI/3aVcA8JkpTgFYqIcSDTYKTEKLcyeqOM8TkEpysnOk7bOVyAPxfH1J8FRNClHoSnIQQ5U7WAPCMXFqcrKEYDKRfuQyAQ916xVUtIcQDQIKTEKLcyRrHlJFLi5M1EvbtBcC5WXNUavkaFaI8kU+8EKLcyWpxMsbFFWr/8BVLAfDq8WJxVUkI8YCQ4CSEKHdUNjbZHt++Zt3d3H4nXmEWBBZCPNgkOAkhyp07B4AbU9PM27Xau+4btek7AHxe6VP8FRNClHoSnIQQ5Z4hJRkArbtHvuUURSHhr90AuLRqfc/rJYQofSQ4CSHKvawZxLV36XpLPX8OAJ2fP2qdTb5lhRBlkwQnIUS5Z0wxL/ar88i/xenm4gUA+PcfeM/rJIQonSQ4CSHKNcVgwJDV4uSRd4uTKT0dY3w8ALbVqt+XugkhSh8JTkKIckltZ15axZiWij7GfKecLp/gFPfHDsC8Lp21s4sLIcoeCU5CiHIpq3XJlJJCemQkABo31zzLR21YD4BH98fvfeWEEKWWBCchRLmUFZyMKanoo6IA0GROjHknfXj4rf0yFwgWQpRPEpwEYL7NWojyROdpHghuSk0hPdIcnLIW/71TxFdrAfAfLAv6ClHeSXASXLp0kddf71/S1SiVJFCWXTpPLwCMycm3BaecLU6KyUTKv8cBcApqcv8qKIQolSQ4PQBMRkOR9m/TJphlyxbn+fyOHdv5N/MfBmv3uZe2bNlMmzbB3Lx5o0TOn+XPP3cxdeqkAu/Xo8cTTJs2Od8y06ZNpkePJwpZM1Ecsia7zIiNJT0qKzjZ5SiXfOwoAPaBdVFpNPetfkKI0unu6wuI+y7l2B7ifttCWmg4hqQMUAAVaJ102FXyxe2Rbjg0amX18RYtWoGPj0+B6lCYfYpLSEgbFi1agWdmi0BJ+eqrNYVqcZo+fSaOjk73oEaiOGlczGOVMqKjUQzmP07Udjm76m58PhcA316971/lhBCllgSnUiTtv5OELZmPPjo1x3Njzp+libMLoSfPcemHbbTy9uS18RNY9etvHDy4n7i4OGrWrEmfPv1p06a9Zb82bYLp23cg/fu/Ztm2bNliVqz4gr/+Omj5+c6y9erVZ+3aL/npp01ERITj6+tPjx4v0KPH/+75++Du7o67u/s9P8+9UqdOYElXQVghazyTITrask2lzt4Ib0xKsvxs4+d/fyomhCjVJDiVErE/fEnkTzvMrUt52BEbTW0HRxw1GnaFR7JzxAjsbW0Y9ubbeHl58cknHzFu3CiaNm3G2bOnqVChIoAlGGWFp/j4OAC6dGmPTqejVq3aXLhwntq1A3jiiacBaNeuOWq1mj59+mNnZ8/ChXP57LNP2LDha6KiInFwcKRr1+689toQtFYsjHq7zZs3sWnTRq5cuYTJpFClSlVeeaUvnTp1BsxdddOnT2HDhh/x96/AtGmTiYgIp3LlKvz2269UqFCRJUtW0aFDS0aOHMPp06fYtWsHdnb2dOrUmddfH4at7a0ul99/38a6dau5evUy9vYOtG3bntdeG4pLZovDsmWL2bZtK127dmfDhq9xdnbG0dGBc+fMy2u0aRPM3LmLaNIk2KrX16PHEwQFNeXddycDkJCQwPz5c/jrrz8xmUw8+eQzmEymAr1novhpMoNTxm3B6U4xv2wBwOvZHvelTkKI0k+CUykQ+8OXRG7ecddyWpWacynJdPP0JlyfzqHEBDLS9Wxb/yVzV28kKKgpW7f+xLFjR5k581NSU1MZP350tmPExcXx88+bARg//j2MRiOffPIhAI6Ojvj4+HL16hUAgoKa0r//axw+fBAAW1s7wsJu8sEHH3Ls2BG++mo1FStW4umnn7P6tW7c+A2ffTaLfv0GMWTICBIS4lm7dhXvvz+B+vUfws/PL9f9jh49DKiYNm0mycnJlrD2xReLqF//Id5/fwaXL1/miy8WEhkZwbRpMwFYuXIpS5cu4plnnmfQoDe4ceM6S5cu5N9/T7BkyQpLwAoLu8muXTuZMmUaiYkJNG3aiJEjRwEKb701jurVCzdTtMlkYtSoYdy8eYM33hiGm5sH69Z9yalT/+Ll5V2oY4rikTUQPCMm7+AUmxmc3B7udF/qJIQo/SQ4lbC0C/+aW5ruwohCumLiGS8fnvDy4YPL/1Hb3oF2bh58cfkqu7//ylLWYMjAy8ub6tVr5DjOt99+TUZGBgAdOpj/Mfj33xNs2HBr/8OHDwBQpUpVDAYDRqMRgE6dOvPzzz8CKoYNe4s//9zFnj1/FSg43bhxnf/9rxevvjrAss3fvyL9+/fixImj+Pl1zf31G42MGfMOlSpVzrbdzc2djz6ajVarJSSkDRqNmrlzZ3Px4n94eXnz5ZfLeeKJpxk1aqxlnxo1ajJkyEC2bPmJZ57pYTn+kCEjaNasBVqtGnd3RxwdHVEUhQYNHrL69d1p7949nD59ko8//pRWrdoA0LRpM55/XgaGlzRLV11cXK7Pp2X+AaF2dMx17JMQonySu+pKWNgXn+fbPZclw2QuFOLiBkCy0YirVkdzF1fUwN/r1wGg0+kASEpKzPU4hw4dwNfXN9s2J6fsA5njM9fj+v77bzO7w8xz15hDE0RFmWdZ9vHxIS0t53is/AwbNpI33hhOUlISZ86cYtu2X/juu28AMBjyvnvQ1taWihUr5djeufOj2boK27d/GIDjx49w8uQJ9Ho9nTtnD2ONGgXh5+dvaUnLUrNmrQK9FmscO3YErVZLy5a3BvPb29vTsmXrYj+XKBiVjU2+z4ctXwpABZm7SQhxG2lxKkEpR/fkOhA8N0pmunLNDAmOGg3xhgw0KhVOGi2JSenYxkRgb+9ARkY8rq5uuR4nLi7WEq7y4uTkDEDXrt3p0eNFzpw5zaxZMxg9+h0CA+vi62vuTlOpVAUeq3P9eigffzydw4cPoNVqqVKlGrVqmQNLfnewubt75Lo+2J133rln3mKemJiIg4NjrmUAPDw8c4RLj3zWKSushIQEXF1dUd8x6Lik7xgU5Px9uu2xKSMDfeg1wDwNgRBCZJEWpxIU9/sWq8uqMH+px2e2ygQ4OPJfWiph6ekkGQ04azXob4aSlpaKp6enpUvL0dEx23F8fHyJjo7Ktk1zx9w0QZmT/KWlpREYWI8qVaoCkJycxJIlCy0tUgVlMpkYM2YEcXExLFmyku3bd7Nq1Vf06tW3UMcDSEjIXpeYmBjAfGeec+bSGHe+3qxteYXL4uTm5kZcXJyluzPLnfUWJe/2BX4T9/0DgHPLEFnQVwiRjQSnEpQWGn73Qpl0avOX9z8JcQA86uGJo1rDjKsXMQFa1JwMi0Kv1zNo0BBLC0erVm0BOHPmFAcP7ic+Po7k5ORsx876hyE2NoYbN65To4a5BWjPnt2sW/cl586dBWDVqmXExcVSuXKVQr3e+Pg4rl69QvfuT1G3bn1LF9vevXsACnWn2V9//Znt8R9//IZKpSIoKJj69RtgY2PD9u2/ZCtz7NhRwsPDaNiwcb7H1miK/vFo2rQZRqOR3bv/sGzLyMhg//69RT62KF7azCVYAMJXLgfA69nnS6o6QohSSrrqSpAhKcPqshpU+Oh0/BgVQYaiEODgSGtXN36JiUINbI2JxFatxtnGluDYOGJ+3YrO05OB3Z9i+/ZfOHBgHydOHKNdu45cv36d1NQU/vjjdwC2bTMHi6tXr7Bu3WpGjx4HQP36D7Fp00bCw8MAaNIkmPHjJ+VoobKWu7sH/v4V+O67b/Dx8cHZ2YV9+/6xDEwv6HgpgNOnT/L++xN59NFu/PffeZYuXcyTTz5jGQ/Vs2cfVqz4Aq1WR9u27TPvqltEtWo16NYt/wHaTk7OnDhxnEOHDlC7doBl+oKCCA5uTvPmIXz44VRiY2Px8/Njw4b1xMXFWroVRemg87g1k/id24QQIkuBglNcXBzff/89V69epU6dOjz99NPY37Eo5rVr11iwYAEzZswo1oqWNSajwapB4Vlm1grApCj8HB3JX/GxbI2Owl2n5TFPL5709EGnVrPsRihnUpKJ/yPnXXrdXN152tsXLl4ixK8C68Jv8v7Ecdip1Tzs7onW3h5nnS094hMInTMLgAAnZ6b3GcDJmzcY/dEHPNftSZxsbFEUpdDdF9Onz+Kzz2YxbdoUbGx0VKtWgw8/nM3cuZ9w7NjRAk+w+cILLxEZGcn48aNxdXWjd+++vPLKra6//v1fw9PTk2+//YafftqEi4srHTs+wsCBr2Nnl3N5jds9//yLnD59itGjh/POO5Po0iX3O/7u/ppnsnDhXJYtW0R6up5OnTrz5JPPZmuFEiUvq6su+rtvAfDtXfguZCFE2aVSrFxT4urVq7z88svExsbi5OREfHw8FSpUYN68edSvX99S7tixY/zvf//j9OnT96zSJSU2NhmDofgmLjw38NUChSdraD08McTHwR1jarL8l5pCstFIw8wB4ABGRWH0f2dp4ezK/3yLNjuy2t4erZsbWncPtJ5e6Hx8sPH2QevmhtrBAbW9Axp7e1S2tkUeO5LbrOjFIWs6guK+3qL0uTDsdUyp5pZOv1d649yuI+cHmgNTrYVfoL7LjRTiwSOf7/LFw8OxWIZd3M7qFqeZM2fi5eXF999/j7e3N3v37mXixIn06dOHZcuW0ahRo2KtWHmgddJhSLS+u+6ux3PWUePjT/ItE/r7NqZPfpfeL/akYZ0AUhMS+HHbVlJR0b1zVxyMJjJiYzDGxWJKSbHqvIqikPX1Y0xJISMlBW7kXKBXTS53MuVCbWeHJjN86Tw80Xn7oPP1Refuni183U8mk8mqMVgFnUVdlCythyf666EA6NzdST17BgCbihUlNAkhcmX1t/yRI0d477338PY2z3bcsmVL1q9fT58+fRg0aBDr1q2jZs2a96yiZZFdJV+STocW6/Hu5uFOXUhITOD7779l/aZv0Wp11K//EAsWLSMwsJ7V5zJlZGBKS8WUksrWX7cwc8nnd92nn19F2rjdfQ06U1oaprAwMsLCuNuop+gfN3Hun39ybFfZ2qF1dUXr4YHOwwOtlzc2vr7ovLzNwcvBHrW9AyobG6vC3IwZ77N16093LZe1TIx4MOg8bwUntYMjoZ/PA8Cv78CSrJYQohSzOjjp9focY0I8PDxYunQpL774IgMHDmT9+vXFXsGyzO2RbiSdXlKsx7PG00/34Omni7b2llqnM/9F7uxCh6eeIaB5i7vu4+9fIdsUAIrJhCk9HVNqqvm/lBQy4mLIiIggIzKSjJhoDDExGOLjUFJzRqjlgQ3yPJeSnkZGRBoZEeF3DV95UdnaonF2QefhweMOznTq1Rethwdad3fUdvao7WxR29qh0uks4UuWUXmwaG+bgkCl0WBMNM/tZVetWgnVqHQo7DjGoox/FOJBYXVwCggIYOPGjbRr1y7bdl9fXxYvXszLL79M//79GTp0aLFXsqxyaNQKG8/VVk+CmR8bT3scGrW6e8F7wNXVrVBzIqnUajT29tm63QrTAacYDBhTUzClpmFKTcGQmEhGRDj6iAgM0VHm8BUXhzEpMc+xX7keNz0dQ3okhqhI7K2s26U7HqtsbNE4O6F1c0fn6Wlu+fL2Rufji8bZGU1m16O1LV+ieN1+11zikcMAuD9a8JsAFEVh2LDXMtdUvEWr1eLh4Unr1u0YOPD1Qt2ZWRDTpk3myJFDfPvt5kLv89dfu9i583cmTny/QOf+6adNXLp0iWHDRgI5F+sWoqywenD4vn376N+/PzVr1qRPnz48++yz2Z7/559/eOONNzAYDBgMBhkcbqW0/05y9cOZRRskroIq48ZgV7P+3cuKXCmKgpKehjElFXVGOvYqAzGXQkkLu0lGVBSG6GgMcbEYEhJQ0tLua91UNjZoHB3RuLmh8/RC5+mNzscbnZc3Wjc3NI5OqO3tJXwVQsK+fwj7YnG2bTU/nY/mjmWI7mSITkF/IQZDeDKm+HQwKYz76WNSjekM/9/raCu5oHGxw2DI4OzZ0yxevIA6dQJYuHDZPb1G16+HkpycRJ06gYXeZ+jQQQDMn1+w1vAePZ4gKKgp7747GYDY2FiuXw+lTp0AbO6yvM39JIPDy5cSHRzeokULVq9ezZIlS4iIiMjxfEhICGvXruXdd9/lzJkzxVrJssyuZn28n+hE5I+/F/oY3k90ktBURCqVCpWdPWo7e7RaNa7ujpgqVC3wF6tiMGBKTcWYlooxJRlDdAwZkRFkREWRER2FISYaY3w8xuQUMFnX+qXo9Rj0egyxsaRfurNNyzoqnQ61gyNaVxe0Hp7ovHwyw5cXWld3NC4uaBzKX8uX5ra7S29tyzs0GRPSSdlzDWN4MqjI8QePvcaWmmlecB40vvY4tKpD48ZNSE1NZenSRZw8+W+RFo2+m9zWc7wX+1jD3d0dd/e7j2kU4kFToFuAgoKCWLhwYZ7P16tXj++//55r164VuWK3M5lMzJ8/nw0bNpCQkEDTpk2ZNGkSVatWzbV8bGwsU6dO5c8/zbNKd+3alXfeeQcHB4dirVdxcX/yFVAUIn/aUbCWJ5U5NLk/+co9q5soGJVWa+6Cc878B7lq9QIfw9z6lY4xNRVjcjLG+DjzmK+oyMwAFo0xPg5jchJKerp1x8zIMO8TH0f61asFrhOYX5va3gGNi4v5jkcfb/NUE55e5ikoMlu/HqTwpbbP/p3g/8awPMvqL8aS8vc1yGqkz+uzmrndGJFM4g9ncWhdmYAA83p34eE3WbRoHj4+vuj16ezfv49GjRozc+ZnpKens2zZIn77bRuxsTFUqVKV3r370alTl1uHVhS++24D33//LTduXMfb25snn3yGl1/ujUqlytHt1qPHE3Tp8hjp6Wls2WK+uaFVq9YMHz7K0r1++z5Dhw6ydDe2aRPM3LmLaNIkmAsXzrN8+RKOHz9CYmIi7u4edOjwMK+/PgxbWzt69HiCsLCbbN36E1u3/sSGDT9y5MihHF11Bw7sZcWKpfz333k0Gi3Nm7fk9deHWda/3LJlMx99NJUFC5Yxd+4nnD9/Fjc3d5577gV69uxj3UUV4h4r8L3Ter2ehIQEvLyyL1K6ZcsWHn74Yezs7KhcuXKxVRBgwYIFfP3118yYMQNfX19mzpzJwIED+emnn3JtAh4+fDjp6emsXLmShIQE3n33XaZMmcJHH31UrPUqTu5P9ca+QVPClsy3asyTjac9foOGSktTGWRu/bJDbWeHzt0dKhWuRUAxGDClpWFITsIYH48hNhZ9RDiGqCgyoiMxxMZiTEzClJYKVky1oBgMGBMTMCYmWO5EKzCNBrWd/a1xX94+5rm+PL3MU1C4mVu/1MUwz5dV1XHIPnLNqXFQruX0F2NJ2V3AwKkAikLK7qtcSja3wme17vz++zY6dnyEGTNmYTQaURSF8ePHcOLEMfr3H0S1ajX488+dTJo0Hr1ez2OPPQ7A4sWf89VXq3nxxZdp1qwFZ8+eYcmSBWRkZPDqqwNyrcZ3322gcuXKjB//HrGxsSxaNJ8rV66wZMnKHItPjxo1jg8+mAjAW2+No3r16kRFRTFkyADq13+I8eMno9Pp+Oefv/jmm6/w8PCkd+9+TJ8+kzFjRlCnTiB9+gzIdRHrX3/dwgcfvEenTp155ZVXiYuLY/nyJQwe3I/ly9dYZtI3mUy89944XnyxJ4MGvcFPP/3AwoXzqFWrDi1ahBTsGghxDxQoOO3evZt33nmH5557jpEjR1q2R0ZG8tZbb+Hm5sbcuXNp3rx5sVVQr9ezfPlyxowZQ/v27QGYM2cObdu2Zfv27XTv3j1b+SNHjrB//362bNlimR7h/fffZ8CAAbz11lv4+t79lv2SYlezPtU+WkjKsT3E/baFtNBw87IsCqAyz/tkV8kXt0e6ldhAcPHgUGm1aJyczF1PmX/RF5SiKCh6PcbkJAxx8eZxXjFR6CMjMUREkhEbjTEhAVNqKkqGFXOSGY2YkpMwJSeZp5s4U4ixkGo1als7NE6OaFzd0Hl5YePnj87TE42rOXyZ73y0u2v4Ut92Y4Jjgwao1DnHQhgT0s0tTdZQFIy3dcEmpifz782zrPl7DfUC61tanjQaDWPHTrCsvHDgwF727dvDlCnTLS1MLVqEkJaWyqJF8+ncuSupqal8/fUaevR4kTfeGAFAs2YtiY2N5dixI3lWSaVSMWfOApwyuyDd3NwZP340e/fuoVWrNtnKVq9eAwcH88LgWV2KJ0/+S+3aAXzwwYc4OjplnrcFhw4d4OjRw/Tu3Y86dQLR6Wxwc3PPtSvSZDKxYMFnBAc3Z8qUW6tKNGzYmF69nuerr9bwxhvDM99Chb59B/D4408D8NBDjdi1ayd79uyW4CRKBauD05kzZxgyZAi1atWiTZvsHzYPDw8WLlzI3LlzGTBgAN9//32xzel05swZkpOTadmypWWbi4sL9erV48CBAzmC08GDB/H29s52/ubNm6NSqTh06BDdull3y35uinuAWV5cmrbBpemt99hkNKDWyMSK90vWdb5f17vU09mDoz34FH6qBcVgME+OGheHMTaWjLhY9JER6MMizGO/4uMxpSRjSk+/1RWWF5MJU2oKptQUMiIjSbtwvuAVUqlQ2diivq3Fyf+ll9Bqc17zpD3X7l6nTP+GneOpZYOybVOrVDSqUJc3HxmMTqdBpVLh718BZ2dHS5nDhw+iUqlo27YdcKv1r127Dvz661auXr1EdHQUBoOBjh0fzlbPkSNH3fayzEHx9ufbtm2Hm9utu/k6dOiAVqvlxImjtGvXLsc+dz5u1aoVrVq1wmDI4MaNa1y9eoULF84TGxuLq6tbtnOpVCrLY3XmwuQajZrr168SHR3N668Py1a+atUqNGjQkKNHD6HVqi37NGrU2FJOq7XD3d2d9PS0XK9PQcnnu3y5Fw3XVv9rvGTJEgIDA1mzZk2O7jGNRkPHjh1p2bIlzz//PIsWLWLmzJnFUsGwMPMCs/7+2ZcC8fHx4ebNmznKh4eH5yhrY2ODm5tbruULwsXl/s5WLUqWXO/i5gpVC7+kj6IoGFNT0cfEoo+ORh8bS1pEJGk3bpAeHoE+NhZDYiLG1DSUu007kXUXZfqtOyQvvPsuABoHB/wf74Z3u7aoNC4YwpOtrmNNr6oMbdMbMI8d12l0eDt54GBjD+lgr1fQ6TT4+vrg7n4rOKWlJaMoCg8/3CbX46alJWIwmOtatWrFbPveztZWi0ajtjyv0aipVKlCjvLu7u7o9am4uzvm2Een02SWMT82mUzMnj2btWvXkpKSgr+/Pw0bNsTe3s5yh1rWuWxttZbHjo62ALi62hMenpBn3f39fTl16hTu7o6WfXx83LOV02o16HSaPF93YcjnWxRWgWYOf+utt/K9rdTe3p4+ffqwaNGiYqkcQGrmxId3ntfW1pb4+Phcy+dWR1tbW9KtHEibl4SEVIxGuX21rNNo1Li42Mv1Lq0c3cDRDV0V0AE574vLnykjw3yHYkQE12Z9nON5Y0oKod98S+g331q26VwqYl+hMTZuVVDr8l4c2l5nR23vark/qYLIQ6FkZJhDXWzsrUBmY2OHg4MDn3+e+xQAlSpV5vjxowBcuXIDd/dbQw7Cw8MIDb1Go0aNSU83YDSaLMc2Gk2Eh0dmO5fRaCQmJhYHB2diY5Nz7HNn/VauXMaKFSsYO3Y8HTt2winzTsR+/V4hI8OY7Vzp6QbL4+Rk8/dtfHwqKpVNZt2vZ6sLwM2bYTg7uxAbm5xtH0fH2+uc/dhFIZ/v8sXV1T7HWL6isjo4xcTE4Od393ESVatWJSoqqkiVul3WbOV3zlyenp5uGR9wZ3m9Xp9je3p6epHvqjMaTTLvRzki17uMUmlQe3gRPvezbJvrrfwSg8GEMTGR5FP/Evf7b6Rd/A+AjITrZCRcz1be3r8Rdr710cdfQx97BcWQjkprm/d5FdDfTCJr6rzbf7caNmzC2rWrMRiM1K1764aPLVs288cfOxg/fhIBAfXRarX88cdOGjS4tTbounVr+eWXn/jhh19zPfY//+whNTUdXebae7t27cRoNBAUFIzBYMqxj1qtxmg0Wh4fPXqE6tVr8NhjT1rKRUZGcOHCBerWrZdtP0VRLI9NJvNxjUYTFStWwdPTk19+2ULXro9b6nb9eignThznhRdewmAwZdvnzs/e7ccuDvL5Lh+s7GUvEKuDk4+PD6GhoTRr1izfcjdu3MDT0zPfMgWR1e0WERFBlSpVLNsjIiIIDMw5yZufnx+//fZbtm16vZ64uLhSPTBcCHH/JB48gD409wHfGmdnXFqE4NIihLjVx1GMJowpMaRFniHl+mEwGQBIvXmM1JvHLPsZkiJQae3ISLiJziX3LklTfO6t3iEhrWncuAnjxo3i1VcHULVqNU6fPsmyZUto0aIlbm5uALzwwkusX78OGxsbmjQJ5vTpU2zcuJ7XXhua5wLTkZERjB37Fs8//yLh4eEsXvw5zZuH0KRJcK7lnZyc+PffExw6dIDatQOoW7c+q1YtY/XqlTRo8BDXr1/jyy9XkJGht/QImPdz5ty5sxw5coh69bLf7atWq3nttaFMnz6FSZPeoWvXx4mPN99V5+Liyosv9sy1LkKURlYHp9atW/P111/z9NNP53mnislk4uuvv6ZRo0a5Pl8YgYGBODk5sW/fPktwSkhI4NSpU/Tq1StH+WbNmjFr1iyuXLlimedp3759ADRp0qTY6iWEeDAZk5K4uci8KLVvvwGEL1+aazlFUcBkXntN6+iJk2NrnKq1BsBk0JORcJ3Ec9swZaTc2seQRuyxr3F9qAe2brlMy2LK/c9ftVrNzJmfsXTpQlavXkFsbAxeXj68+OLL2aYZeP314bi7e7Jp07d8/fUa/P0rMGLEaJ55Ju+1Jzt16oKzswvvvTcee3t7unV7nEGD3siz/HPPvciZM6cZPXo477wziVde6Ut8fBzffvsVK1d+ga+vH48+2g21Ws3q1StISEjAxcWFl17qxdy5sxk1ahhz5izIcdxu3Z7AwcGB1atXMn78aBwdHWnRIoRBg4bkOn2BEKWV1UuuXL58mWeeeYZ27doxceLEHPM4RUdHM3XqVLZv386aNWto3LhxsVVyzpw5fP3110yfPp2KFSsyc+ZMQkND2bx5MxqNhpiYGJydnbGzs0NRFF5++WXS09OZPHkyKSkpjB8/nhYtWjBjxoy7nywfMkV/+SBLMpRdiqJwfvAAMBrxfPpZtO7uhK9YBtzqqrtd3OrjeYYdAFNGKgnnt6NS61CpNahUGlBrcKzSMvexUGoVbq80LNbXlJ87l0ER8vkub0p0yZVq1arx8ccf8/bbb9OhQwfq169PpUqVMBqN3Lhxg1OnTqHVapk6dWqxhiYwT2hpMBiYMGECaWlpNGvWjGXLlmFjY0NoaCidOnVixowZPPvss6hUKubPn8+UKVPo06cPtra2lpnDhRDlW+y2XywLPXt0f4LoH77Lt7za1RZTbN5rE6p19rjVe9Lq86td8xkDJYR4IBRocqDOnTvz448/8uWXX/LXX3+xY8cO1Go1FStWpHfv3vTs2ZOKFSsWeyU1Gg1jxoxhzJgxOZ6rVKkSZ8+ezbbN09OTuXPnFns9hBAProzoaKI2rAegxpy5qFQqMsLD891H6+uIPi6taItwZ1GZjyeEeLAVeFbF2NhYmjRpwtNPP039+rLchxCi9FNMJi6NNU8U6TdgEFpn84SQ+rsEJ5taHujPRBdTJczHu5+y1qwTQhQfq4NTQkICr732GkePHrVsa9y4MXPmzLFqmgIhhCgpEV+tAUDn44NLy1vLFWVERuS7n9bTAY2vI8aI5KK1OqlA4+OI1rN0LjQuhLCe1SOmPv30U06dOsWwYcNYvHgxY8eO5dKlS0yYMOFe1k8IIYok/dpV4nfuAKDqe+9ne86UevcFtR1aVS76ug0qlfk4QogHntUtTjt37uStt96iT58+ALRr1w5fX19Gjx5NSkpKkSeXFEKI4mbKyODKlPcAqDR6LGq7vGf9zovGxRaH1pVJ2X210PVwaF0ZjYsMDBeiLLC6xSkyMjLHmKYWLVpgNBqLvAacEELcC9fnzgHA4aFGOATWLfRxbGq449C2CqhV5kXorKEC1Coc2lbBpoZ7oc8thChdrG5xMhgMOdaAc3V1BSjyGnBCCFHckk/+S+rpUwBUHDKsyMezqeGOxsuBlD3XMIYnm4NRbuOeMrdrfBxxaCUtTUKUNQW+qy43Vs6hKYQQ94UxNZXrc2YBUHXKNFS5LEdSmO8tjYstzl1rYYhOQX8hBkN4snkZFZMCahVqV1u0vo7Y1PKQgeBClFHFEpzyWoJFCCFKwpWJ4wFw6/wotnnMLWdKy3tiy7vRejpkC0aKosj3oBDlRIGC0+TJk3FycrI8zvqLbeLEiTg63prYTaVSsWrVqmKqohBCWC9+9y4McbEAeD//Yp7lTMlJxXZOCU1ClB9WB6dmzZoBOZu3c9suXXdCiJJgiI8jfNUKAKrPnINKnff9L8ak5PtVLSFEGWJ1cFq9evW9rIcQQhSJoihcHPUmAD4v90Lnnv+dbMZibHESQpQfxbtksBBClJDoTeYFe9X29rg9/Mhdy2d15wkhREFIcBJCPPD0YWHE/Gxel636R7Os2icjIv/lVoQQIjcSnIQQDzTFaOTyhHEAVBg6Ao2D4132MLvbAr9CCJEbCU5CiAda2NLFANjVqIlT4yCr98uIkOAkhCg4CU5CiAdW6oXzJB7YD0ClMWMLtG9GpHTVCSEKToKTEOKBZEpP59qH0wCoPP491Dqbu+xxx/6pqfeiWkKIMk6CkxDigZQVmlxCWmNfo0YJ10YIUV5IcBJCPHASDx0g/dpVAHz79i/h2gghyhMJTkKIB4oxKYmbCz8HoNr0j/OdHVwIIYqbfOMIIR4YiqJwcfSbAHg+9Qw2Pj6FPo4QQhSGBCchxAMjbvs2FIMBAI/Hnyz0cUxpaeYfZHFeIUQBSXASQjwQMqKjifzmKwBqzJ6Lqgihx5RkXqdO5+NbLHUTQpQfEpyEEKWeYjJxaewoAPz6D0Tr4lKk4xmTkwGw8ZXgJIQoGAlOQohSL+KrNQDovH1wCWld5OMZk6XFSQhROBKchBClWvq1q8Tv3AFA1UnvF8sxDXGxAIUeXC6EKL8kOAkhSi1TRgZXprwHQKVRb6O2syuW42ZEmJdb0Xp5FcvxhBDlhwQnIUSpdX3uHAAcHmqIQ916xXZcfbh5gV+No1OxHVMIUT5IcBJClErJJ/8l9fQpACoOGV6sx86IkOAkhCgcCU5CiFLHmJrK9TmzAKg6ZSoqrbZYj38rODkW63GFEGWfBCchRKlz5b3xALg90gXbipWK/fhZE2CqJTgJIQpIgpMQolSJ/2s3hljzXW/eL/zvnp5L1rkTQhSUfGsIIUoNQ3w84SuXAVD949kSbIQQpY58KwkhSgVFUbg4agQA3i/1ROfhUcI1EkKInCQ4CSFKhegfvgdAbWePe6fO9+w8iqLcs2MLIco+CU5CiBKnDw8j5qcfAaj+0axCH8eaUJQ1MJwiLBIshCi/JDgJIe4bk2LKsU0xGrn87jj+ioul35l/iUiIZ9myxbRpE2z1cSMiwnn77TcJC7t59zokZa5TJwv8CiEKoXgnRxFCiNtcTQxl782DXIi7RFhyBEbFiEalwc/Rh1pu1WnpH4zuq58A0Hl7Q9h1AJ544mlatGhl9XkOHtzPnj1/MXLk23cta0xOBsBGFvgVQhSCBCchRLGLSIli7ekNXIi/hFqlztbSZFSMXE+6yc3kcM4e2cWL+81TD3g81h1OHAXAx8cXn3sUbIzJmS1OEpyEEIUgwUkIUawOhB1hzelvMGWON8q1e86kEP7Hf1z5K5SdRgNu1T0ICT9neX7ZssWsWPEFf/11EIDr10OZN282x48fIz09jVq16vDqqwMICWnNli2bmT59CgDPP/8kjz32OO++O5n09DRWrFjKH3/8Tnh4GDqdDfXq1ad363Y4ATY+PkybNpnLVy/R0sWNSc8/TVjYTapUqcbgwUMICWljqc/166EsXDiPQ4cOoCgKDRo8xBtvjKBGjZoApKens2zZIn77bRuxsTFUqVKV3r370alTl3v1NgshSogEJyFEsTkQdoSVp766a7mb2y4Q/c81HvfwxqaaGzuUJH5c822uZU0mE2PHjsTT04uJE6eg1WrZsOFrxo17i7VrvyUkpA19+vRn1aplTJs2k1q1agPwwQeTOHr0MIMHD6VixUpcu3aVpUsX8dF/F3jfxx+tlxcAl9NSiTMYeGPiZOzsHFi6dBETJozl+++34uLiQlRUFAMH9sHT05NRo8bi6OjIihVLefPNN/jyy/W4uroyfvwYTpw4Rv/+g6hWrQZ//rmTSZPGo9freeyxx4vvDRZClDgJTkKIYhGREsma09/ctZwxNYOovaE86u7F096+zH3Cm8pqFRkJ6SReiCE6NSZb+djYGC5fvkTv3v0srUB16zZgxYol6PXpVKpUmYqZy7LUqROAv38FMjIySElJYeTIMZZWn6CgpqSkJDN//qfEe3pTJXOB31STiUmVqtCh86MYDCbs7e0ZOnQQhw8foEOHTqxfv5b09DQ+/XQBnp7msFW7diCvvfYq//57HFtbG/bt28OUKdMt52rRIoS0tFQWLZpP585d0RbzWntCiJIjn2YhRLFYe/pbS/dcfvSX4lBMCo2dnFn5hAeK2jwtgFsDHxIvxLDpwhb8cbWU9/DwpFq1Gnz00VQOHNhHy5ataN48hGHD3srzHDqdjtmz5wEQFRVFaOhVrly5zJ49fwFgUBQ0mcHJWaPBx8bWsq+3tw8AqampABw7doQGDRpaQhOAl5cXGzeaB7UvXDgPlUpFSEgbDAaDpUzr1u359detXLr0H7VrB9z1fRFCPBgkOAkhiuxqQigX4i/dvaCi0HZ3LOeA/+o5E+986ytI62wOL1cSr+GgvzXHkkql4tNPP2flymX8+edOtm79Ca1WS7t2HRk9ehwuLq53ngWAffv+Ye7cT7hy5TIODo7UrFkLB4fMRX0V0GQu8Gujyj4rizpzmZesOaESEuLx96+Q50tKSIhHURS6dGmX6/NRUZESnIQoQyQ4CSGKbG/YwRx3z+Um6GwqrmoNAIf9NTjd9pwxJQMAlUpNaOKNbPt5eXkzevQ4Ro0ay4UL59i583fWrl2Fi4sLo0e/k+M816+H8s47o2nbth0ff/yppSvvu+82sG/fHgDUmcHpbpycnImLi8ux/dChA/j5+ePk5Iy9vQPz5i3Kdf+KFStbdR4hxINBJsAUQhTZhbhL+YYm10QDFSL0tDucRC17B9RaNXGnIrOVSTgbBYCimIhNi7Ns//ff4zzxRBdOnz6JSqWidu0ABg16gxo1ahIREQ7caiXKcubMafT6dHr16msJTQB795pDkwmsXkC4UaPGnDx5nNjYW2Ov4uLiGD16OH///SeNGzchNTUFRVEIDKxn+e/ixf9YvvwLjEajVecRQjwYpMVJCFFkYckReT5nn2ai908xqDOHP52r44RPlWqE/X4RtU6Ncw13Es5FW4ITQGJGsuXn2rUDsLOz44MP3qNfv0F4eHhy8OB+zp8/x/PPvwSYW4UAdu3aQUhIGwICAtFoNCxcOI+XXuqJXp/Bli0/8s8/5jFOelP+LWO3e+GFl9m69WfeemsovXv3w8bGltWrV+Dp6UXXrt1xcnKmceMmjBs3ildfHUDVqtU4ffoky5YtoUWLlri5uVl9LiFE6SfBSQhRJCbFhFHJu1XFLzrDEpoAqt9Ix/fpaqhtNET9c42ovaE4VnbBv2ttrm8+C5hbnbLY2toye/Z8Fi2ax2effUJSUiKVKlVmzJjxdOv2BABNmgQTHNycxYs/59ChA8yc+RmTJ09j+fIljB07ChcXF+rXb8C8eYsZNnQQ51KTedjK1+fr68fChctYsOAzpk9/H51OR1BQEyZOfN8yvmrmzM9YunQhq1evIDY2Bi8vH1588WVefXVAwd5MIUSpp1JkqXCrxcYmYzBY/5eqeDBptWrc3R3lehfA8J3v5BmeWh5PosW/KZiABCc1W9q4Eumhy/d4GpWGuR1nFHs9FUXh/MC+ANRZuhKAcwNeBaDeyi/lepcD8vkuXzw8HNFoindUkrQ4CSGKzM/Rh+tJuS+w6xlnvkX/dHU7dgU7kaG7+5eYv+O9WQ7FlDnFACpV/gWFECIPEpyEEEVWy606N5PDcx0gvr2lCyeiMrhawTaXPXNSq9TUdKtWzDU0M2Uu8KvzlXXqhBCFI3fVCSGKrKV/cJ531elt1FaHJjCPmWrpH1xcVcvGmBmcbGSBXyFEIUlwEkIUWRXnStRyrY66iF8patTUcq1OFedKdy9cCMbkJAB0EpyEEIUkwUkIUSx61n0edRHHDqlVKnrWfb6YapSTIS4WABvpqhNCFJIEJyFEsfBx8OKVui8U6Riv1H0BHwevuxcspIxw84SZWk/Pe3YOIUTZJoPDhRDFJtgvCAVYc/obTIqCibvf7q1GjVql4pW6LxDsF3RP66ePME/UmbXArxBCFJQEJyFEsWrmF0RVl8qsPb2BC/GX8lzDLmt7Dbeq9Ax8/p62NGXJyFyiRYKTEKKwJDgJIYqdj4MXI5u+ztXEUPbePMh/cZe5mRyOUTGiUWnwd/Slpls1WvoH37OB4LnRZ3bVaZwkOAkhCkeCkxDinqniXClbMDIpJtSqkhtaqaSnAaB2cCixOgghHmwyOFwIcd+UZGi6nUpdOuohhHjwyLeHEEIIIYSVJDgJIYQQQlhJgpMQolxQFKWkqyCEKAMkOAkhygVTaqr5BxnfJIQoAvkGEUKUCyZZ4FcIUQwkOAkhygVjZnDS+fiUcE2EEA8yCU5CiHLBmJwEgE5anIQQRSDBSQhRLhhiYwGw8ZXgJIQoPAlOQohyIWudOq2nZwnXRAjxIJPgJIQoF/QREYAs8CuEKBoJTkKIciGrxUmCkxCiKCQ4CSHKBX14ZnBykuAkhCg8CU5CiHJBSU8DQO3gUMI1EUI8yCQ4CSHKFZXMHC6EKAL5BhFCiFJC1tMTovST4CSEECUsMTGRqVMncezYEcu2oUMHMXTooBKslRAiNxKchBBlXmlvyTl//iy//PIzJpOppKsihLgLbUlXQAgh7jVTaqr5h8zxTZFhiZw5HsbN0HgaZ5b5fPofuHs54F/JlcCGfnj7OZdIXYUQpZsEJyFEmWfKXOBX4+XDprVHuXktHpVahWK61RL13fYPqFG5GRmGdC5dO4AJI61btWP8uxP57rtv2LjxG1JSkgkObs7bb7+Lq6sbRqORH374jk2bviU0NBQ3Nzc6d+5Kv36DsLW1BWDatMlERETQpUtXVq9eSXj4TapUqcbgwUMICWnD4cMHGT58MADDhw+mceMmzJ+/xFKvtWtXsXHjN8TFxVG7dh1GjhxDYGC9+/juCSFuJ8FJCFHmGTODU3iyjrDQeIBsoSnLmYt/4udVm9ZNXiEm/hp/7NrCmTOnqVjJn7ffHs/Vq1dYsGAuHh5ejBo1lpkzp7N160/07NmHoKAmnDt3lhUrvuD8+bN88sk8VCoVAGfPniI6OpIBA17D0dGJpUsXMWHCWL7/fisBAYG89dZYZs/+iLfeGktQUFNLfY4fP4per+ett95Gr89g/vw5vP32SL777me0Wvn6FqIklPpPXnp6Oh9++CG//PILaWlptG3blkmTJuFp5XpTCxcu5NNPP+Xs2bP3uKZCiNLq6qlrAKRoXchvuJNWa0ubJq+gVmvw967DxWsHiIqOZMI7s2kcXIOQkDYcOnSQEyeOcenSRX766QcGDnydPn36A9CsWUu8vLz54IP32Lt3DyEhrQFISkpi2bI1VKxYCQB7e3uGDh3E4cMH6NChE9WqVQegWrXqVK9ew1IfnU7HrFmf4eLiCkBychIffTSVy5cvUatW7WJ/n4QQd1fqB4dPnjyZv//+m3nz5rFq1SquXbvGiBEjrNr3+PHjzJ8//x7XUAhRmsXFpHB6j/kPpxQbl3zLerlVQa3WWB7b2brg6ujDvp2hxMeax0m5urqSlJTI0aOHAejS5bFsx+jUqQsajYYjRw5atrm5uVtCE4C3tw8AqVljr/JQvXpNS2gCqFChIgBJSYn57ieEuHdKdXAKDw9n06ZNTJgwgeDgYBo2bMjs2bM5cOAAR48ezXfflJQUxowZQ3Bw8P2prBCiVPpj6zns0xMASNM65ltWp7XNsU2jsUExKezckr3VOiHB3OXn4ZG99Vur1eLq6kZiYpJlm52dXbYy6sxB6ne72+/O/bK6/uTuOyFKTqkOTocOHQKgRYsWlm3Vq1fH19eXAwcO5LvvtGnTqFOnDk899dQ9raMQovSKDEvk5rV47DPMwSlDkzMYWUNR4Oa1eCLDbrX0ZLUExcREZytrMBiIj4/Dzc2tcJUWQpRqpXqMU3h4OO7u7pa7U7L4+Phw8+bNPPfbvn07u3btYvPmzezcubPY6qPRlOqcKYpJ1nWW6/3gO/dvOCq1Cgd9ZnBSFy44AajUKvPxMlt9slqzf/vtF/r2HWApt337NoxGI0FBQWi1akt5rfbW71PW75ZarUKrVWNjo7VszyqX3363lxMFI5/v8iXzY1SsSjQ4hYaG0qlTpzyfHzFiBDY2Njm229rakp6enus+4eHhTJw4kY8//hh3d/diqyuAi4t9sR5PlG5yvR984TcSUExKni1OKsVo9bEUk0L4jURsbbVoNGqaNHmIZ555hmXLlqBSmWjRogWnT59m/vz5tGjRgsce64xarbaUd3e/1U2YnGz+3XJ0tMXd3ZEKFcxjng4d2kulSr4EBgai05nHWt2+n7OzneX/t28XBSefb1FYJRqcfH192bJlS57P79q1C71en2N7eno69vY5f+kVRWHcuHE89thjtGvXrljrCpCQkIrRKGMLyjqNRo2Li71c7zIgMsw8zkirGAAwqG/9IaY1phNyZaPlsa0hBch/zFFkWCLpBgNGo4nY2GRGjx6Pj48/mzf/yNKlS/H29uH551+kX79BxMebB36np98qnyXrueTkdGJjk/HyqkCXLl1Zu3Ytf/yxi3XrNpCRYQ51t++XmJhm+f/t24X15PNdvri62lvGFBYXlVKK1yLYsmULo0eP5ujRo9lantq1a0fv3r0ZMGBAtvLXr1/n4Ycfxs7OzvJGGQwG9Ho9Dg4OvPbaawwePLjQ9YmNTcZgkA9aWafVmlsH5Ho/2BRFYdFHfwLQ6cIKAH6v1dfyvMaop+3lr9EoRhQgTevEnqrPgSr/L9nBY9tZutHEg0c+3+WLh4djsXfLluoxTk2bNsVkMnHo0CFCQkIAuHjxIuHh4bneLefr68u2bduybdu2bRuzZs1i06ZNuLq65thHCFE2qVQq1GoVplwmugQwamy47hJA5fjTgMJVtwZ3DU1qtUpCkxDlXKkOTr6+vnTv3p0JEyYwffp07O3tmTRpEs2bN6dx48YA6PV64uPjcXV1xcbGhqpVq2Y7RtZEmXduF0KUfe5eDkRH5N2ldc2tHpXjT2FU6bjhUsuq4wkhyrdSf1vBBx98QEhICEOHDqV///7UqFGDuXPnWp4/cuQIbdq04ciRIyVYSyFEaeRfyTXfu2rSdM6c8O3ACf+OmNS6fI+lUqvwrySt1kKUd6V6jFNpI33i5YOMgSg7IsMS+X7ZXjpcWosJFTtrvVqk4/V4tQnefs7FUzlRIuTzXb7cizFOpb7FSQghCsvbz5mKvuYbS1J1+S+3kh+VCvwru0poEkJIcBJClG0tmvsCkFKU4KRW0bFbQHFVSQjxAJPgJIQo0+w15jmcitLi9HD3QFzdZcJEIYQEJyFEGWeIjQWgStPaqDUqq5dgUKlArVHxyJN1qV3P5x7WUAjxIJHgJIQo0zIiwgGo0KAa/xvQDL/MO+NU6twTVNZ2v0qu/G9AMwlNQohsSvU8TkIIUVT6zOCkcXTCyd2ep3s2JjIskTPHw7gZGk9sVAomk4JarcLdywH/Sq4ENvSTgeBCiFxJcBJClGkZ4ZnBycnJss3bz9kSjLRaNW6uDsTFp8jt6UKIu5KuOiFEmaaPiADMLU55yavbTggh7iTBSQhRpinpaQCoHWS5FCFE0UlwEkKUCyq1fN0JIYpOvkmEEEIIIawkwUkIIYQQwkoSnIQQZZZikrvkhBDFS4KTEKLMMqWZB4aj0ZRsRYQQZYYEJyFEmWVKTgbAxltm/xZCFA8JTkKIMsuYnASAzte3hGsihCgrJDgJIcosY1aLk48EJyFE8ZDgJIQoswyxsQDoJDgJIYqJBCchRJmVkbnAr9bTs4RrIoQoKyQ4CSHKLH14GJB9gV8hhCgKCU5CiDIrw7LAr2MJ10QIUVZIcBJClFn6zK46jaO0OAkhiocEJyFEmaWkpwOgdnAo4ZoIIcoKCU5CiDJPpZavOiFE8ZBvEyGEEEIIK0lwEkIIIYSwkgQnIUSZpJhMJV0FIUQZJMFJCFEmmdLSzD9oNCVbESFEmSLBSQhRJmUt8Gvj7VPCNRFClCXakq6AEELcC6bMBX51vtnXqfvgg/f49dctDB48lF69Xi3Wc968eYPnn3/yruXGj59Et25PFOu5hRD3h0pRFKWkK/GgiI1NxmCQcRNlnVarxt3dUa73Aygm/BqXTu4j8voFlMuh1LySRISnLUkNquNdsRa+NRry6oB+VKxYidTUVNav34ROpym2663X6zl37qzlcXR0FO++O4Y+ffoTEtLGsr1ixUq4u7sX6VyicOTzXb54eDii0RRv55q0OAkhHniJsZEc2LaOyOv/oVKpURQTHnoDAOk6FfGRN0iICmPT999gyNAzqP9Axo4fy/79e2ndunWx1cPGxoYGDR6yPL558wZgDkq3bxdCPLgkOAkhHmhXTh9k/7Z1lrvoFMX8f1u9+f96ncqy/dTlCCp6uXLjyM/4+/myadNGS3AaPvwNYmNjWLFiXbbjv/feO1y8+B9r1nwDwNatP7F27Spu3LhO5cpVGTZsJKNGDWPs2AlWd7+ZTCbWrv2Sn37aREREOL6+/vTo8QI9evzPUmbo0EFUqVIVX18/vv/+WxITEwgKasr48ZP455+/+fLL5cTERFOv3kOMGzcBf/8Klv38/StQpUpVNmz4mrS0NJo0acrw4aOoUKFiYd9mIUQmGRwuhHhgXTl9kL1bv8RkNFgCU5as4GTQmINTTEIK4bFJBFbxxmQ0UM3Tlr///pOIzIWAH3usO+fPn+Pq1cuWY6SkpPD333/y6KPdAHNomjZtMg891IgZMz6hY8dOvPPOaIxGY4HqPWvWDJYtW0SXLo/x0Udz6NixE3PnzmblyqXZyv3++zYOHNjHuHETGTbsLQ4c2MfQoYP49tuvGTLkTUaMGM2pUyeYPfujbPvt3r2Ln3/+kREjRjNmzDucP3+OYcNeIzU1tUD1FELkJC1OQogHUmJsBPu3rcvzedsMc3AyZganU1cisNVpqFHBA4C6VXzYe+oq336zlncnTqBDh4f5+OMZ/PbbNvr1GwTAn3/uRK/X06VLVwCWLl1E69ZtGTt2AgAtWoSg0WhZvHi+1fW+evUKmzdv4rXXhlgGpzdv3hK1Ws2XX67gmWd64OrqBkBGhoHp02fh4uKSWZ8/2L//H9av30TFipUAOH/+LL/+uiXbOVJTU1i2bLWlTNWq1ejXrxdbtmzmuedesLquQoicpMVJCPFAOrDtq3wnubRNv9XiZDIpnL0aSQ1/D4xGE+l6A1qNmkrebmzatBGj0Yi9vT3t2nVgx47tlmP89tuvBAU1xdfXj9DQa4SHh9Gx4yPZztO586MFqvfhwwdQFIXWrdthMBgs/7Vp0w69Pp1jx45aylarVs0SmgA8PT1xc3O3BCIAFxdXkpKSsp3joYcaZStTp04gFSpU5PjxIwWqqxAiJ2lxEkI8cGLCrxJ5/b98y2gy7xc2alRcuhlDSnoGp69GcvpqZI6ymzau5+HOT9G1a3d+/XUL58+fw9vbhwMH9vH22+8CEBcXC4C7u0e2fT08PAtU9/j4eABeeSX3lp+oqFv1c3BwzPG8nZ3dXc/h6emVY5u7uweJiYnWVlMIkQcJTkKIB86lk/tRqdXWLauiUnH6cgTODrY80rRWjqe37jvL2jVreLjzUzRt2gxvbx927NiOj48vWq2WDh0eBsA7cyLN2NiYbPvf+fhunJycAZg7dxEODg45nvf19SvQ8XKTkBCfY1tMTAyVKlXKpbQQoiCkq04I8cCJvH7B6rXoUtL0XAmPo04lLyp5u+b4r3YlL06du8iNG9dRq9V07vwof/21i507f6NNm/Y4OjoB4OPjS8WKldi1a2e24+/ataNAdQ8KagJAXFwcgYH1LP/Fx8ezZMlCS4tUUfz773Hi4uIsj8+ePcPNm9dp2rR5kY8tRHknwUkI8cBJiA7P+0lFwS8izfLw0vkwTIpCnco5u68AAqt4oygKmzZ9B0DXrt25dOkix44doWvXbpZyKpWK/v0Hs3v3H8yaNYP9+/eybt2XfPHFIsvz1qhRoxaPPvoYH388lXXrvuTw4YNs2vQtkye/S1xcLJUrV7HqOPlJS0tj9Ojh7N79B7/+uoV33hlFzZq16Ny5a5GPLUR5J111QogHiqKYUEx53/5vn27CP0pveXz6aiQezvZ4ueYcLwTg5+GMu7M9mzf/QN++g6hRoxa1a9chMjKSZs1aZivbpUtXUlNT+Oqr1fz8849Ur16DESNG8eGHH+Ta7ZaXd96ZxJo1K9m0aSMREQvw8PCkU6cuDBr0OppiWJS4UaMgmjQJZsaMDwBo06YdQ4aMQKfTFfnYQpR3suRKAcgU/eWDLMlQ+n3z6ci8w5OiUP98EjYGBQWI8NBxw88+3+OpNRpeGvXZXa/39u2/EBAQSJUq1Szb9uz5i7fffpOVK7+iVq3aBXwlxW/oUPNUCvPnLynhmpRO8vkuX2TJFSGEAFw8fYmPvJH7kyoVMW46/KL0mFQQ7mV71+N5+Fg3o/a2bVtZsmQhAwe+jq+vL9euXWXZssUEBTUtFaFJCHHvSXASQjxwvCvWIiEqLMds4VliXHV4xmYQ6WGDUZv/X5sqtRr/ataFngkTprBo0XwWLpxLXFws7u4ePPzwI/TvP7jAr0EI8WCSrroCkKbd8kGa8ku/mPBrbF87s9iO98LQSegcvOV6lwPy+S5f7kVXndxVJ4R44Hj4Vsa7Yk1UqqJ9halUanwq1cSnYrXiqZgQosyT4CSEeCA16/IyKnURg5NaTcvHehVTjYQQ5YEEJyHEA8nZ3Zvmj/Ys0jGaP9oTZ3fvYqqREKI8kMHhQogHVtXApqAo7N+2DsVkynOw+O1UKjUqtZrmj/Y07y+EEAUgwUkI8UCrWjcYD7+qHNi2jsjr/6FSqXMNUFnbvSrWoFnnl6SlSQhRKBKchBAPPGd3bx5+cQQx4de4dHIfUdf/Iz46DMVkRKXW4Orph1fFmlSv3wIP38olXV0hxANMgpMQoszw8K2cLRgpiqnId94JIcTt5BtFCFFmSWgSQhQ3+VYRQgghhLCSBCchhBBCCCtJcBJCCCGEsJIEJyGEEEIIK0lwEkIIIYSwkgQnIYQQQggrSXASQgghhLCSBCchhBBCCCtJcBJCCCGEsJIEJyGEEEIIK0lwEkIIIYSwkgQnIYQQQggrSXASQgghhLCSBCchhBBCCCtJcBJCCCGEsJIEJyGEEEIIK0lwEkIIIYSwkgQnIYQQQggrSXASQgghhLCSBCchhBBCCCtJcBJCCCGEsJIEJyGEEEIIK0lwEkIIIYSwkgQnIYQQQggrSXASQgghhLCSBCchhBBCCCtJcBJCCCGEsJIEJyGEEEIIK5X64JSens6UKVMICQkhKCiI4cOHEx0dne8+ly9fZtCgQQQHB9OmTRsmTJhAQkLCfaqxEEIIIcqqUh+cJk+ezN9//828efNYtWoV165dY8SIEXmWz8jIYNCgQeh0OtavX8+nn37KgQMHmDBhwn2stRBCCCHKIm1JVyA/4eHhbNq0icWLFxMcHAzA7Nmz6dq1K0ePHqVx48Y59rlw4QJXrlxh3rx51KxZE4CePXsyZ86c+1l1IYQQQpRBpTo4HTp0CIAWLVpYtlWvXh1fX18OHDiQa3BydXVFpVKxYcMG3n77bZKSkvj1119zLVtQrq72KEqRDyNKOZXK/H+53uWDXO/yRa53+aJWq4r9mKU6OIWHh+Pu7o6trW227T4+Pty8eTPXfSpUqMD48eOZM2cOa9euxWQyUbt2bVavXl3k+qjVpb5nUxQjud7li1zv8kWutyisEv3NCQ0NJSAgIM//UlNTsbGxybGfra0t6enpuR5Tr9dz/vx5unTpwvr161myZAmKojBy5EhMJtO9fklCCCGEKMNKtMXJ19eXLVu25Pn8rl270Ov1Obanp6djb2+f6z4rVqxg//79bNmyBY1GA0C1atXo0qULO3bs4JFHHimeygshhBCi3CnR4KTT6SwDuHNz9uxZ4uLi0Ov12VqeIiIi8PPzy3WfQ4cOUa9ePUtoAqhatSru7u5cvny52OouhBBCiPKnVHfyNm3aFJPJZBkkDnDx4kXCw8Mtd9ndyc/Pj/Pnz6PcNuovPDycuLg4qlWrdq+rLIQQQogyTKUopfu+glGjRnH06FGmT5+Ovb09kyZNwsnJyTLYW6/XEx8fj6urKzY2Npw7d47nnnuOp556ir59+5KYmMiMGTNIS0vj22+/RafTlfArEkIIIcSDqtQHp5SUFKZPn86vv/4KQLt27ZgwYQLu7u4A7Nu3j969e/Pll19api04evQos2fP5tSpU9jb29OmTRvGjBmDh4dHib0OIYQQQjz4Sn1wEkIIIYQoLUr1GCchhBBCiNJEgpMQQgghhJUkOAkhhBBCWEmCkxBCCCGElSQ4CSGEEEJYSYKTEEIIIYSVJDjlIj09nSlTphASEkJQUBDDhw8nOjo6330uX77MoEGDCA4Opk2bNkyYMIGEhIT7VGNRFIW53rdbuHAhAQEB97CGoihMJhNz586lbdu2NGrUiH79+nHlypU8y8fGxjJq1CiaNWtGs2bNmDhxIikpKfexxqIoCnq9b7d582YCAgIIDQ29x7UUxaWg1zsyMpK33nqLFi1a0KJFC0aMGEFYWFiBzinBKReTJ0/m77//Zt68eaxatYpr164xYsSIPMtnZGQwaNAgdDod69ev59NPP+XAgQNMmDDhPtZaFFZBr/ftjh8/zvz58+9xDUVRLFiwgK+//pqpU6eyfv16VCoVAwcOzHUBcYDhw4dz7do1Vq5cydy5c/n777+ZMmXKfa61KKyCXu8s169fl+v8ACro9R45ciQ3b95kxYoVrFixgrCwMN54442CnVQR2YSFhSmBgYHKrl27LNsuXryo1KlTRzly5Eiu+5w6dUqpU6eOcubMGcu2VatWKY0bN77X1RVFVJjrnSU5OVnp0qWL0rt3b6VOnTr3uKaiMNLT05WgoCBl3bp1lm3x8fFKw4YNlZ9++ilH+cOHDyt16tRRLly4YNm2e/duJSAgQAkLC7svdRaFV9DrncVoNCovvfSS5bN87dq1+1FdUUQFvd7x8fFKnTp1lN9//92y7bffflPq1KmjxMTEWH1eaXG6Q9aCwlnLtwBUr14dX19fDhw4kOs+rq6uqFQqNmzYgF6vJyYmhl9//ZXGjRvfjyqLIijM9c4ybdo06tSpw1NPPXVP6ygK78yZMyQnJ9OyZUvLNhcXF+rVq5fr9T148CDe3t7UrFnTsq158+aoVKpsi42L0qmg1zvLokWLyMjI4LXXXrsf1RTFpKDX29bWFgcHBzZt2kRSUhJJSUn88MMPVKtWDVdXV6vPK8HpDuHh4bi7u2Nra5ttu4+PDzdv3sx1nwoVKjB+/Hg2btxIo0aNCAkJIT4+ntmzZ9+PKosiKMz1Bti+fTu7du3i/fffv9dVFEWQNXbB398/2/a8rm94eHiOsjY2Nri5ueX7+yBKh4JebzB3ty9fvpyZM2ei0WjueR1F8Sno9ba1tWXatGns37+f4OBgmjVrxtGjR/niiy9Qq62PQ+UuOIWGhhIQEJDnf6mpqdjY2OTYz9bWlvT09FyPqdfrOX/+PF26dGH9+vUsWbIERVEYOXIkJpPpXr8kkY97cb3Dw8OZOHEi06dPtyw2LUqn1NRUgBzXOK/rW5jfB1F6FPR6p6SkMHr0aEaPHk21atXuRxVFMSro9VYUhbNnzxIUFMTatWtZtWoVFStWZMiQISQlJVl9Xm3Rqv3g8fX1ZcuWLXk+v2vXrlwHlaWnp2Nvb5/rPitWrGD//v1s2bLF8hdLtWrV6NKlCzt27OCRRx4pnsqLAivu660oCuPGjeOxxx6jXbt2xVpXUfzs7OwA8x83WT9D3tfXzs4uz98HBweHe1dRUSwKer2nTp1KtWrV+N///nff6iiKT0Gv988//8y6devYuXMnTk5OgLmbtmPHjmzcuJE+ffpYdd5yF5x0Ol228Qt3Onv2LHFxcej1+mwpNiIiAj8/v1z3OXToEPXq1cvWzFu1alXc3d25fPlysdVdFFxxX+8bN26wZ88eDh8+zKZNmwAwGAwABAUF8dprrzF48ODifRGi0LKa8CMiIqhSpYple0REBIGBgTnK+/n58dtvv2XbptfriYuLw9fX995WVhRZQa/3xo0bsbGxISgoCACj0QjA448/zpNPPild8aVcQa/3oUOHqF69uiU0gXmMcvXq1Qv0b3W566q7m6ZNm2IymbINBL148SLh4eEEBwfnuo+fnx/nz59HURTLtvDwcOLi4qT5t5Qr6PX29fVl27Zt/Pjjj2zatIlNmzbx//buL6SpPo7j+GeFJDUQpD+IhjfZH52wSjeDKayLuouECtG8CcKEjBAsYYFJXnUhNoigbhKDrowu6qaim4KWBWKQIxDECtGVgYLM6er7XDXa8/jA2fNQm/P9uju/c3bO9/Bl7LOds9+5cOGCJOnhw4d8c80xe/fuldvt1uvXr1NjCwsLGh8fX7W/tbW1mpmZSZsH5udrDxw48PsLxv+Sab+fPHmiR48epd7LfX19kqTbt287npIE2ZNpv0tKSjQ1NZV2GS8ej+vz588qLy93fuD//kfA/NXZ2WmHDx+2SCRiY2Njdvz4cTt9+nRqfSKRsFgsZolEwszMPnz4YB6Px0KhkE1MTNjo6KidOnXKjh07ZsvLy9k6DTiUab//bnh4mOkIclh/f7/5fD579uyZRaNRO3PmjB05csQSiYQlk0mLxWIWj8fNzOzHjx/W1NRkjY2NNjY2Zq9evbJgMGjd3d1ZPgs4lUm//y4SiTAdwRqTSb9nZ2fN5/PZuXPnLBqNWjQatba2NgsEAjY/P+/4mASnVSwuLlooFLKamhqrqamxzs7OtDkefr65IpFIamx0dNRaW1vt4MGDFggErLu72+bm5rJRPjL0X/r9K4JTbksmk3b9+nWrq6szr9drZ8+eTX0wfvr0yXbv3m3Dw8Op7b9+/WodHR3m9XrN7/dbT0+PLS0tZat8ZCjTfv+K4LT2ZNrviYkJa2trM5/PZ3V1dXb+/PmM++0y++X6EgAAAP4V9zgBAAA4RHACAABwiOAEAADgEMEJAADAIYITAACAQwQnAAAAhwhOAAAADhGcAAAAHFp3D/kFkJ9aW1s1MjKSNlZQUKCtW7cqGAzq4sWLKioqSq2bnJzU4OCgXr58qVgspuLi4tSDmld7QKgkraysqLm5WQ0NDero6Pit5wMgNxGcAOSNyspK9fT0pJZXVlb0/v179ff3KxqN6v79+3K5XHr69Km6urpUUVGh9vZ2lZWVaWZmRkNDQzp58qRu3ryphoaGtH0vLS2pq6tL7969+8c6AOsHwQlA3nC73fJ6vWljtbW1WlxcVDgc1tjYmIqLi3Xp0iXV19drYGBAGzduTG179OhRNTc3q7u7W8+fP1dhYaEk6e3bt+rt7VUsFvuTpwMgB3GPE4C85/F4JEnT09MaGhrS8vKyrly5khaaJKmwsFCXL1/WiRMntLCwkBpvb29XaWmpHjx48EfrBpB7+MUJQN6bnJyUJO3cuVPhcFiVlZXasWPHqtv6/X75/f60sXv37mnPnj2/vU4AuY/gBCBvmJmSyWRqeX5+XiMjI7p165a8Xq88Ho9mZ2e1b9++jPZLaALwE8EJQN548+aNqqqq0sY2bNigQ4cO6dq1a3K5XHK5XPr+/XuWKgSw1hGcAOSNqqoq9fb2SpJcLpc2bdqkkpISud3u1DalpaWanp7+130kk0l9+/ZN27dv/+31Alh7uDkcQN7YsmWLqqurVV1dLY/Ho4qKirTQJEmBQEDj4+P68uXLqvt48eKF6uvr9fjx4z9RMoA1huAEYF1paWlRQUGB+vr6/nHJLh6PKxwOq6ioSMFgMEsVAshlXKoDsK6UlZXp6tWrCoVCamlpUVNTk0pKSvTx40fdvXtXU1NTunPnjjZv3pztUgHkIIITgHWnsbFR5eXlGhwc1MDAgObm5rRt2zbt379fN27c0K5du7JdIoAc5TIzy3YRAAAAawH3OAEAADhEcAIAAHCI4AQAAOAQwQkAAMAhghMAAIBDBCcAAACHCE4AAAAOEZwAAAAcIjgBAAA4RHACAABwiOAEAADg0F8Fh2DppJGhoAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Get the loadings of x and y axes\n",
    "xs = loadings[0]\n",
    "ys = loadings[1]\n",
    " \n",
    "# Plot the loadings on a scatterplot\n",
    "for i, varnames in enumerate(feature_names):\n",
    "    plt.scatter(xs[i], ys[i], s=200)\n",
    "    plt.arrow(\n",
    "        0, 0, # coordinates of arrow base\n",
    "        xs[i], # length of the arrow along x\n",
    "        ys[i], # length of the arrow along y\n",
    "        color='r', \n",
    "        head_width=0.01\n",
    "        )\n",
    "    plt.text(xs[i], ys[i], varnames)\n",
    " \n",
    "# Define the axes\n",
    "xticks = np.linspace(-0.8, 0.8, num=5)\n",
    "yticks = np.linspace(-0.8, 0.8, num=5)\n",
    "plt.xticks(xticks)\n",
    "plt.yticks(yticks)\n",
    "plt.xlabel('PC1')\n",
    "plt.ylabel('PC2')\n",
    " \n",
    "# Show plot\n",
    "plt.title('2D Loading plot with vectors')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 343,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PC1</th>\n",
       "      <th>PC2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.131278</td>\n",
       "      <td>0.082649</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.174947</td>\n",
       "      <td>0.016158</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.170039</td>\n",
       "      <td>0.079080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.112609</td>\n",
       "      <td>-0.075262</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.139514</td>\n",
       "      <td>0.070968</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97302</th>\n",
       "      <td>0.271565</td>\n",
       "      <td>-0.027037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97303</th>\n",
       "      <td>0.265700</td>\n",
       "      <td>-0.057094</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97304</th>\n",
       "      <td>0.262183</td>\n",
       "      <td>-0.036586</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97305</th>\n",
       "      <td>0.266954</td>\n",
       "      <td>0.016835</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97306</th>\n",
       "      <td>0.061540</td>\n",
       "      <td>-0.067868</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>97307 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            PC1       PC2\n",
       "0      0.131278  0.082649\n",
       "1      0.174947  0.016158\n",
       "2      0.170039  0.079080\n",
       "3      0.112609 -0.075262\n",
       "4      0.139514  0.070968\n",
       "...         ...       ...\n",
       "97302  0.271565 -0.027037\n",
       "97303  0.265700 -0.057094\n",
       "97304  0.262183 -0.036586\n",
       "97305  0.266954  0.016835\n",
       "97306  0.061540 -0.067868\n",
       "\n",
       "[97307 rows x 2 columns]"
      ]
     },
     "execution_count": 343,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pca_df_scaled = pca_df.copy()\n",
    " \n",
    "scaler_df = pca_df[['PC1', 'PC2']]\n",
    "scaler = 1 / (scaler_df.max() - scaler_df.min())\n",
    " \n",
    "for index in scaler.index:\n",
    "    pca_df_scaled[index] *= scaler[index]\n",
    "\n",
    "pca_df_scaled"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 344,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "sns.set()\n",
    " \n",
    "xs = loadings[0]\n",
    "ys = loadings[1]\n",
    "\n",
    "sns.lmplot(\n",
    "    x='PC1', \n",
    "    y='PC2',\n",
    "    data=pca_df_scaled, \n",
    "    fit_reg=False,\n",
    "    legend = False\n",
    "    )\n",
    "\n",
    "\n",
    "for i, varnames in enumerate(feature_names):\n",
    "    plt.scatter(xs[i], ys[i], s=200)\n",
    "    plt.arrow(\n",
    "        0, 0, # coordinates of arrow base\n",
    "        xs[i], # length of the arrow along x\n",
    "        ys[i], # length of the arrow along y\n",
    "        color='r', \n",
    "        head_width=0.01\n",
    "        )\n",
    "    plt.text(xs[i], ys[i], varnames)\n",
    " \n",
    "xticks = np.linspace(-0.8, 0.8, num=5)\n",
    "yticks = np.linspace(-0.8, 0.8, num=5)\n",
    "plt.xticks(xticks)\n",
    "plt.yticks(yticks)\n",
    "plt.xlabel('PC1')\n",
    "plt.ylabel('PC2')\n",
    " \n",
    "plt.title('2D Biplot')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true,
    "tags": []
   },
   "source": [
    "### Scree Plot Check"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#define PCA model to use\n",
    "pca = PCA(n_components=11)\n",
    "\n",
    "#fit PCA model to data\n",
    "pca_scree_plot = pca.fit_transform(X_scaled)\n",
    "\n",
    "PC_values = np.arange(pca.n_components_) + 1\n",
    "plt.plot(PC_values, pca.explained_variance_ratio_, 'o-', linewidth=2, color='blue')\n",
    "plt.title('Scree Plot')\n",
    "plt.xlabel('Principal Component')\n",
    "plt.ylabel('Variance Explained')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 329,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 63.33%\n",
      "Precision: 15.83%\n",
      "Recall: 25.00%\n",
      "F1 score: 19.39%\n"
     ]
    }
   ],
   "source": [
    "# Calculate accuracy, precision, recall, and F1 score\n",
    "accuracy = accuracy_score(y_test, y_pred)\n",
    "precision = precision_score(y_test, y_pred, average='macro')\n",
    "recall = recall_score(y_test, y_pred, average='macro')\n",
    "f1 = f1_score(y_test, y_pred, average='macro')\n",
    "\n",
    "print(\"Accuracy: {:.2f}%\".format(accuracy * 100))\n",
    "print(\"Precision: {:.2f}%\".format(precision * 100))\n",
    "print(\"Recall: {:.2f}%\".format(recall * 100))\n",
    "print(\"F1 score: {:.2f}%\".format(f1 * 100))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true,
    "tags": []
   },
   "source": [
    "## Linear Model Evaluation with Test Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>op_carrier_fl_num</th>\n",
       "      <th>op_unique_carrier</th>\n",
       "      <th>origin_airport_id</th>\n",
       "      <th>dest_airport_id</th>\n",
       "      <th>distance</th>\n",
       "      <th>route</th>\n",
       "      <th>day</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5390</td>\n",
       "      <td>WN</td>\n",
       "      <td>14057</td>\n",
       "      <td>11292</td>\n",
       "      <td>991.0</td>\n",
       "      <td>14057-11292</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2331</td>\n",
       "      <td>WN</td>\n",
       "      <td>15376</td>\n",
       "      <td>11292</td>\n",
       "      <td>639.0</td>\n",
       "      <td>15376-11292</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2321</td>\n",
       "      <td>NK</td>\n",
       "      <td>14843</td>\n",
       "      <td>11618</td>\n",
       "      <td>1608.0</td>\n",
       "      <td>14843-11618</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5496</td>\n",
       "      <td>OO</td>\n",
       "      <td>11109</td>\n",
       "      <td>12266</td>\n",
       "      <td>809.0</td>\n",
       "      <td>11109-12266</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2811</td>\n",
       "      <td>QX</td>\n",
       "      <td>11884</td>\n",
       "      <td>14057</td>\n",
       "      <td>279.0</td>\n",
       "      <td>11884-14057</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9995</th>\n",
       "      <td>1447</td>\n",
       "      <td>DL</td>\n",
       "      <td>12953</td>\n",
       "      <td>10397</td>\n",
       "      <td>762.0</td>\n",
       "      <td>12953-10397</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9996</th>\n",
       "      <td>167</td>\n",
       "      <td>NK</td>\n",
       "      <td>10821</td>\n",
       "      <td>14492</td>\n",
       "      <td>255.0</td>\n",
       "      <td>10821-14492</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9997</th>\n",
       "      <td>1037</td>\n",
       "      <td>UA</td>\n",
       "      <td>11618</td>\n",
       "      <td>11298</td>\n",
       "      <td>1372.0</td>\n",
       "      <td>11618-11298</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9998</th>\n",
       "      <td>6130</td>\n",
       "      <td>YX</td>\n",
       "      <td>14122</td>\n",
       "      <td>12953</td>\n",
       "      <td>335.0</td>\n",
       "      <td>14122-12953</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9999</th>\n",
       "      <td>772</td>\n",
       "      <td>AS</td>\n",
       "      <td>14679</td>\n",
       "      <td>11618</td>\n",
       "      <td>2425.0</td>\n",
       "      <td>14679-11618</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10000 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      op_carrier_fl_num op_unique_carrier  origin_airport_id  dest_airport_id  \\\n",
       "0                  5390                WN              14057            11292   \n",
       "1                  2331                WN              15376            11292   \n",
       "2                  2321                NK              14843            11618   \n",
       "3                  5496                OO              11109            12266   \n",
       "4                  2811                QX              11884            14057   \n",
       "...                 ...               ...                ...              ...   \n",
       "9995               1447                DL              12953            10397   \n",
       "9996                167                NK              10821            14492   \n",
       "9997               1037                UA              11618            11298   \n",
       "9998               6130                YX              14122            12953   \n",
       "9999                772                AS              14679            11618   \n",
       "\n",
       "      distance        route  day  \n",
       "0        991.0  14057-11292    1  \n",
       "1        639.0  15376-11292    2  \n",
       "2       1608.0  14843-11618    5  \n",
       "3        809.0  11109-12266    3  \n",
       "4        279.0  11884-14057    3  \n",
       "...        ...          ...  ...  \n",
       "9995     762.0  12953-10397    2  \n",
       "9996     255.0  10821-14492    4  \n",
       "9997    1372.0  11618-11298    7  \n",
       "9998     335.0  14122-12953    3  \n",
       "9999    2425.0  14679-11618    4  \n",
       "\n",
       "[10000 rows x 7 columns]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_week_one  = pd.read_csv(\"../../data/raw/Week_one_data_10000.csv\")\n",
    "df_week_one"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "def week_one_data_transformation(df_week_one):\n",
    "    #call functions for data merging\n",
    "    from add_location_weather import add_location\n",
    "    from add_location_weather import add_weather\n",
    "    \n",
    "    #import datasets to merge with test data\n",
    "    airports_ids =  pd.read_csv(\"../../data/processed/Airports_lat_lon.csv\")\n",
    "    airports_ids = airports_ids.drop(['Unnamed: 0'], axis=1)\n",
    "    weather_airports = pd.read_csv(\"../../data/processed/weatherAirports_2019_2018_Jan2020_clean.csv\")\n",
    "    weather_airports = weather_airports.drop(['Unnamed: 0'], axis=1)\n",
    "    \n",
    "    #merge week one test data with airport ids and drop data that will be unnecesary to the model    \n",
    "    df = add_location(df_week_one,airports_ids)\n",
    "    df = df.drop(['airport_id','iata_code','lat','lon'], axis=1)\n",
    "    \n",
    "    #merge the flight & airport data with the weather data\n",
    "    df1= add_weather(df,weather_airports)\n",
    "    \n",
    "    #get month data out of fl_date\n",
    "    import datetime\n",
    "    df1['Month'] = df1['fl_date'].dt.month\n",
    "    \n",
    "    # transform categorical variables in numeric with LabelEncoder\n",
    "    from sklearn.preprocessing import LabelEncoder\n",
    "    le = LabelEncoder()\n",
    "\n",
    "    # fit and transform the data\n",
    "    df1['op_unique_carrier'] = le.fit_transform(df1['op_unique_carrier'])\n",
    "    df1['route'] = le.fit_transform(df1['route'])\n",
    "\n",
    "    # drop rows with missing values\n",
    "    df1 = df1.dropna().reset_index(drop=True)\n",
    "    \n",
    "    return(df1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_transformed_week1_2020 = week_one_data_transformation(df_week_one)\n",
    "df_transformed_week1_2020.to_csv('../../data/processed/transformed_week1_2020.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>op_carrier_fl_num</th>\n",
       "      <th>op_unique_carrier</th>\n",
       "      <th>origin_airport_id</th>\n",
       "      <th>dest_airport_id</th>\n",
       "      <th>distance</th>\n",
       "      <th>route</th>\n",
       "      <th>day</th>\n",
       "      <th>Key</th>\n",
       "      <th>fl_date</th>\n",
       "      <th>AvgTemp</th>\n",
       "      <th>Precipitation</th>\n",
       "      <th>Snow_mm</th>\n",
       "      <th>WindSpeed</th>\n",
       "      <th>lat</th>\n",
       "      <th>lon</th>\n",
       "      <th>Month</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5390</td>\n",
       "      <td>21</td>\n",
       "      <td>14057</td>\n",
       "      <td>11292</td>\n",
       "      <td>991.0</td>\n",
       "      <td>2737</td>\n",
       "      <td>1</td>\n",
       "      <td>45.58869934-45.58869934</td>\n",
       "      <td>2020-01-01</td>\n",
       "      <td>10.8</td>\n",
       "      <td>3.8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.9</td>\n",
       "      <td>45.588699</td>\n",
       "      <td>-122.598000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5752</td>\n",
       "      <td>6</td>\n",
       "      <td>14057</td>\n",
       "      <td>14747</td>\n",
       "      <td>129.0</td>\n",
       "      <td>2764</td>\n",
       "      <td>7</td>\n",
       "      <td>45.58869934-45.58869934</td>\n",
       "      <td>2020-01-07</td>\n",
       "      <td>11.3</td>\n",
       "      <td>2.8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>24.8</td>\n",
       "      <td>45.588699</td>\n",
       "      <td>-122.598000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>789</td>\n",
       "      <td>20</td>\n",
       "      <td>14057</td>\n",
       "      <td>11292</td>\n",
       "      <td>991.0</td>\n",
       "      <td>2737</td>\n",
       "      <td>2</td>\n",
       "      <td>45.58869934-45.58869934</td>\n",
       "      <td>2020-01-02</td>\n",
       "      <td>8.2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.9</td>\n",
       "      <td>45.588699</td>\n",
       "      <td>-122.598000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1184</td>\n",
       "      <td>2</td>\n",
       "      <td>14057</td>\n",
       "      <td>14679</td>\n",
       "      <td>933.0</td>\n",
       "      <td>2763</td>\n",
       "      <td>7</td>\n",
       "      <td>45.58869934-45.58869934</td>\n",
       "      <td>2020-01-07</td>\n",
       "      <td>11.3</td>\n",
       "      <td>2.8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>24.8</td>\n",
       "      <td>45.588699</td>\n",
       "      <td>-122.598000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1552</td>\n",
       "      <td>2</td>\n",
       "      <td>14057</td>\n",
       "      <td>14262</td>\n",
       "      <td>873.0</td>\n",
       "      <td>2760</td>\n",
       "      <td>7</td>\n",
       "      <td>45.58869934-45.58869934</td>\n",
       "      <td>2020-01-07</td>\n",
       "      <td>11.3</td>\n",
       "      <td>2.8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>24.8</td>\n",
       "      <td>45.588699</td>\n",
       "      <td>-122.598000</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9911</th>\n",
       "      <td>5147</td>\n",
       "      <td>17</td>\n",
       "      <td>14006</td>\n",
       "      <td>13930</td>\n",
       "      <td>342.0</td>\n",
       "      <td>2708</td>\n",
       "      <td>6</td>\n",
       "      <td>37.06079864501953-37.06079864501953</td>\n",
       "      <td>2020-01-06</td>\n",
       "      <td>5.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.9</td>\n",
       "      <td>37.060799</td>\n",
       "      <td>-88.773804</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9912</th>\n",
       "      <td>5081</td>\n",
       "      <td>17</td>\n",
       "      <td>13121</td>\n",
       "      <td>13930</td>\n",
       "      <td>489.0</td>\n",
       "      <td>2064</td>\n",
       "      <td>2</td>\n",
       "      <td>37.8582992554-37.8582992554</td>\n",
       "      <td>2020-01-02</td>\n",
       "      <td>1.9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.8</td>\n",
       "      <td>37.858299</td>\n",
       "      <td>-80.399498</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9913</th>\n",
       "      <td>3845</td>\n",
       "      <td>14</td>\n",
       "      <td>11905</td>\n",
       "      <td>11298</td>\n",
       "      <td>140.0</td>\n",
       "      <td>1419</td>\n",
       "      <td>2</td>\n",
       "      <td>32.38399887084961-32.38399887084961</td>\n",
       "      <td>2020-01-02</td>\n",
       "      <td>12.1</td>\n",
       "      <td>4.6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.9</td>\n",
       "      <td>32.383999</td>\n",
       "      <td>-94.711502</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9914</th>\n",
       "      <td>4258</td>\n",
       "      <td>17</td>\n",
       "      <td>12003</td>\n",
       "      <td>14869</td>\n",
       "      <td>463.0</td>\n",
       "      <td>1466</td>\n",
       "      <td>6</td>\n",
       "      <td>47.48199844-47.48199844</td>\n",
       "      <td>2020-01-06</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>26.3</td>\n",
       "      <td>47.481998</td>\n",
       "      <td>-111.371002</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9915</th>\n",
       "      <td>4275</td>\n",
       "      <td>17</td>\n",
       "      <td>14150</td>\n",
       "      <td>11433</td>\n",
       "      <td>243.0</td>\n",
       "      <td>2975</td>\n",
       "      <td>3</td>\n",
       "      <td>45.57089996-45.57089996</td>\n",
       "      <td>2020-01-03</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13.5</td>\n",
       "      <td>45.570900</td>\n",
       "      <td>-84.796700</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>9916 rows × 16 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      op_carrier_fl_num  op_unique_carrier  origin_airport_id  \\\n",
       "0                  5390                 21              14057   \n",
       "1                  5752                  6              14057   \n",
       "2                   789                 20              14057   \n",
       "3                  1184                  2              14057   \n",
       "4                  1552                  2              14057   \n",
       "...                 ...                ...                ...   \n",
       "9911               5147                 17              14006   \n",
       "9912               5081                 17              13121   \n",
       "9913               3845                 14              11905   \n",
       "9914               4258                 17              12003   \n",
       "9915               4275                 17              14150   \n",
       "\n",
       "      dest_airport_id  distance  route  day  \\\n",
       "0               11292     991.0   2737    1   \n",
       "1               14747     129.0   2764    7   \n",
       "2               11292     991.0   2737    2   \n",
       "3               14679     933.0   2763    7   \n",
       "4               14262     873.0   2760    7   \n",
       "...               ...       ...    ...  ...   \n",
       "9911            13930     342.0   2708    6   \n",
       "9912            13930     489.0   2064    2   \n",
       "9913            11298     140.0   1419    2   \n",
       "9914            14869     463.0   1466    6   \n",
       "9915            11433     243.0   2975    3   \n",
       "\n",
       "                                      Key     fl_date  AvgTemp  Precipitation  \\\n",
       "0                 45.58869934-45.58869934  2020-01-01     10.8            3.8   \n",
       "1                 45.58869934-45.58869934  2020-01-07     11.3            2.8   \n",
       "2                 45.58869934-45.58869934  2020-01-02      8.2            1.0   \n",
       "3                 45.58869934-45.58869934  2020-01-07     11.3            2.8   \n",
       "4                 45.58869934-45.58869934  2020-01-07     11.3            2.8   \n",
       "...                                   ...         ...      ...            ...   \n",
       "9911  37.06079864501953-37.06079864501953  2020-01-06      5.3            0.0   \n",
       "9912          37.8582992554-37.8582992554  2020-01-02      1.9            0.0   \n",
       "9913  32.38399887084961-32.38399887084961  2020-01-02     12.1            4.6   \n",
       "9914              47.48199844-47.48199844  2020-01-06      0.1            0.0   \n",
       "9915              45.57089996-45.57089996  2020-01-03      0.5            0.0   \n",
       "\n",
       "      Snow_mm  WindSpeed        lat         lon  Month  \n",
       "0         0.0       11.9  45.588699 -122.598000      1  \n",
       "1         0.0       24.8  45.588699 -122.598000      1  \n",
       "2         0.0       11.9  45.588699 -122.598000      1  \n",
       "3         0.0       24.8  45.588699 -122.598000      1  \n",
       "4         0.0       24.8  45.588699 -122.598000      1  \n",
       "...       ...        ...        ...         ...    ...  \n",
       "9911      0.0        7.9  37.060799  -88.773804      1  \n",
       "9912      0.0        1.8  37.858299  -80.399498      1  \n",
       "9913      0.0       11.9  32.383999  -94.711502      1  \n",
       "9914      0.0       26.3  47.481998 -111.371002      1  \n",
       "9915      0.0       13.5  45.570900  -84.796700      1  \n",
       "\n",
       "[9916 rows x 16 columns]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1 = pd.read_csv('../../data/processed/transformed_week1_2020.csv')\n",
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['op_carrier_fl_num',\n",
       " 'op_unique_carrier',\n",
       " 'origin_airport_id',\n",
       " 'dest_airport_id',\n",
       " 'distance',\n",
       " 'route',\n",
       " 'day',\n",
       " 'Key',\n",
       " 'fl_date',\n",
       " 'AvgTemp',\n",
       " 'Precipitation',\n",
       " 'Snow_mm',\n",
       " 'WindSpeed',\n",
       " 'lat',\n",
       " 'lon',\n",
       " 'Month']"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.columns.tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(9916, 9)"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_2020 = df1.drop(['op_unique_carrier','dest_airport_id','day','fl_date','Key','lat','lon','fl_date'], axis=1)\n",
    "X_2020.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "# Initialize the StandardScaler\n",
    "scaler = StandardScaler()\n",
    "\n",
    "# Scale the selected columns\n",
    "X_2020_scaled = scaler.fit_transform(X_2020)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(9916,)"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_2020_pred = lr_model.predict(X_2020_scaled)\n",
    "y_2020_pred.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>op_carrier_fl_num</th>\n",
       "      <th>origin_airport_id</th>\n",
       "      <th>distance</th>\n",
       "      <th>route</th>\n",
       "      <th>AvgTemp</th>\n",
       "      <th>Precipitation</th>\n",
       "      <th>Snow_mm</th>\n",
       "      <th>WindSpeed</th>\n",
       "      <th>Month</th>\n",
       "      <th>arr_delay</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5390</td>\n",
       "      <td>14057</td>\n",
       "      <td>991.0</td>\n",
       "      <td>2737</td>\n",
       "      <td>10.8</td>\n",
       "      <td>3.8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.9</td>\n",
       "      <td>1</td>\n",
       "      <td>5.794818</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5752</td>\n",
       "      <td>14057</td>\n",
       "      <td>129.0</td>\n",
       "      <td>2764</td>\n",
       "      <td>11.3</td>\n",
       "      <td>2.8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>24.8</td>\n",
       "      <td>1</td>\n",
       "      <td>9.715648</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>789</td>\n",
       "      <td>14057</td>\n",
       "      <td>991.0</td>\n",
       "      <td>2737</td>\n",
       "      <td>8.2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.9</td>\n",
       "      <td>1</td>\n",
       "      <td>2.778864</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1184</td>\n",
       "      <td>14057</td>\n",
       "      <td>933.0</td>\n",
       "      <td>2763</td>\n",
       "      <td>11.3</td>\n",
       "      <td>2.8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>24.8</td>\n",
       "      <td>1</td>\n",
       "      <td>6.313713</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1552</td>\n",
       "      <td>14057</td>\n",
       "      <td>873.0</td>\n",
       "      <td>2760</td>\n",
       "      <td>11.3</td>\n",
       "      <td>2.8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>24.8</td>\n",
       "      <td>1</td>\n",
       "      <td>6.578645</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9911</th>\n",
       "      <td>5147</td>\n",
       "      <td>14006</td>\n",
       "      <td>342.0</td>\n",
       "      <td>2708</td>\n",
       "      <td>5.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.9</td>\n",
       "      <td>1</td>\n",
       "      <td>4.987689</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9912</th>\n",
       "      <td>5081</td>\n",
       "      <td>13121</td>\n",
       "      <td>489.0</td>\n",
       "      <td>2064</td>\n",
       "      <td>1.9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.8</td>\n",
       "      <td>1</td>\n",
       "      <td>3.747561</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9913</th>\n",
       "      <td>3845</td>\n",
       "      <td>11905</td>\n",
       "      <td>140.0</td>\n",
       "      <td>1419</td>\n",
       "      <td>12.1</td>\n",
       "      <td>4.6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.9</td>\n",
       "      <td>1</td>\n",
       "      <td>6.493686</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9914</th>\n",
       "      <td>4258</td>\n",
       "      <td>12003</td>\n",
       "      <td>463.0</td>\n",
       "      <td>1466</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>26.3</td>\n",
       "      <td>1</td>\n",
       "      <td>9.764881</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9915</th>\n",
       "      <td>4275</td>\n",
       "      <td>14150</td>\n",
       "      <td>243.0</td>\n",
       "      <td>2975</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13.5</td>\n",
       "      <td>1</td>\n",
       "      <td>6.509928</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>9916 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      op_carrier_fl_num  origin_airport_id  distance  route  AvgTemp  \\\n",
       "0                  5390              14057     991.0   2737     10.8   \n",
       "1                  5752              14057     129.0   2764     11.3   \n",
       "2                   789              14057     991.0   2737      8.2   \n",
       "3                  1184              14057     933.0   2763     11.3   \n",
       "4                  1552              14057     873.0   2760     11.3   \n",
       "...                 ...                ...       ...    ...      ...   \n",
       "9911               5147              14006     342.0   2708      5.3   \n",
       "9912               5081              13121     489.0   2064      1.9   \n",
       "9913               3845              11905     140.0   1419     12.1   \n",
       "9914               4258              12003     463.0   1466      0.1   \n",
       "9915               4275              14150     243.0   2975      0.5   \n",
       "\n",
       "      Precipitation  Snow_mm  WindSpeed  Month  arr_delay  \n",
       "0               3.8      0.0       11.9      1   5.794818  \n",
       "1               2.8      0.0       24.8      1   9.715648  \n",
       "2               1.0      0.0       11.9      1   2.778864  \n",
       "3               2.8      0.0       24.8      1   6.313713  \n",
       "4               2.8      0.0       24.8      1   6.578645  \n",
       "...             ...      ...        ...    ...        ...  \n",
       "9911            0.0      0.0        7.9      1   4.987689  \n",
       "9912            0.0      0.0        1.8      1   3.747561  \n",
       "9913            4.6      0.0       11.9      1   6.493686  \n",
       "9914            0.0      0.0       26.3      1   9.764881  \n",
       "9915            0.0      0.0       13.5      1   6.509928  \n",
       "\n",
       "[9916 rows x 10 columns]"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_2020['arr_delay'] = y_2020_pred\n",
    "X_2020"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    9916.000000\n",
       "mean        5.595790\n",
       "std         5.508357\n",
       "min        -2.699349\n",
       "25%         2.945930\n",
       "50%         4.848274\n",
       "75%         6.809710\n",
       "max        90.441257\n",
       "Name: arr_delay, dtype: float64"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_2020['arr_delay'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Export Model Evaluation to Tests folder for Mentors to Review\n",
    "X_2020.to_csv('../tests/Linear_Model_Evaluation.csv', index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true,
    "tags": []
   },
   "source": [
    "## XGBoost Regression Model Evaluation with Test Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>op_carrier_fl_num</th>\n",
       "      <th>op_unique_carrier</th>\n",
       "      <th>origin_airport_id</th>\n",
       "      <th>dest_airport_id</th>\n",
       "      <th>distance</th>\n",
       "      <th>route</th>\n",
       "      <th>day</th>\n",
       "      <th>Key</th>\n",
       "      <th>fl_date</th>\n",
       "      <th>AvgTemp</th>\n",
       "      <th>Precipitation</th>\n",
       "      <th>Snow_mm</th>\n",
       "      <th>WindSpeed</th>\n",
       "      <th>lat</th>\n",
       "      <th>lon</th>\n",
       "      <th>month</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5390</td>\n",
       "      <td>21</td>\n",
       "      <td>14057</td>\n",
       "      <td>11292</td>\n",
       "      <td>991.0</td>\n",
       "      <td>2737</td>\n",
       "      <td>1</td>\n",
       "      <td>45.58869934-45.58869934</td>\n",
       "      <td>2020-01-01</td>\n",
       "      <td>10.8</td>\n",
       "      <td>3.8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.9</td>\n",
       "      <td>45.588699</td>\n",
       "      <td>-122.598000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5752</td>\n",
       "      <td>6</td>\n",
       "      <td>14057</td>\n",
       "      <td>14747</td>\n",
       "      <td>129.0</td>\n",
       "      <td>2764</td>\n",
       "      <td>7</td>\n",
       "      <td>45.58869934-45.58869934</td>\n",
       "      <td>2020-01-07</td>\n",
       "      <td>11.3</td>\n",
       "      <td>2.8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>24.8</td>\n",
       "      <td>45.588699</td>\n",
       "      <td>-122.598000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>789</td>\n",
       "      <td>20</td>\n",
       "      <td>14057</td>\n",
       "      <td>11292</td>\n",
       "      <td>991.0</td>\n",
       "      <td>2737</td>\n",
       "      <td>2</td>\n",
       "      <td>45.58869934-45.58869934</td>\n",
       "      <td>2020-01-02</td>\n",
       "      <td>8.2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.9</td>\n",
       "      <td>45.588699</td>\n",
       "      <td>-122.598000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1184</td>\n",
       "      <td>2</td>\n",
       "      <td>14057</td>\n",
       "      <td>14679</td>\n",
       "      <td>933.0</td>\n",
       "      <td>2763</td>\n",
       "      <td>7</td>\n",
       "      <td>45.58869934-45.58869934</td>\n",
       "      <td>2020-01-07</td>\n",
       "      <td>11.3</td>\n",
       "      <td>2.8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>24.8</td>\n",
       "      <td>45.588699</td>\n",
       "      <td>-122.598000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1552</td>\n",
       "      <td>2</td>\n",
       "      <td>14057</td>\n",
       "      <td>14262</td>\n",
       "      <td>873.0</td>\n",
       "      <td>2760</td>\n",
       "      <td>7</td>\n",
       "      <td>45.58869934-45.58869934</td>\n",
       "      <td>2020-01-07</td>\n",
       "      <td>11.3</td>\n",
       "      <td>2.8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>24.8</td>\n",
       "      <td>45.588699</td>\n",
       "      <td>-122.598000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9911</th>\n",
       "      <td>5147</td>\n",
       "      <td>17</td>\n",
       "      <td>14006</td>\n",
       "      <td>13930</td>\n",
       "      <td>342.0</td>\n",
       "      <td>2708</td>\n",
       "      <td>6</td>\n",
       "      <td>37.06079864501953-37.06079864501953</td>\n",
       "      <td>2020-01-06</td>\n",
       "      <td>5.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.9</td>\n",
       "      <td>37.060799</td>\n",
       "      <td>-88.773804</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9912</th>\n",
       "      <td>5081</td>\n",
       "      <td>17</td>\n",
       "      <td>13121</td>\n",
       "      <td>13930</td>\n",
       "      <td>489.0</td>\n",
       "      <td>2064</td>\n",
       "      <td>2</td>\n",
       "      <td>37.8582992554-37.8582992554</td>\n",
       "      <td>2020-01-02</td>\n",
       "      <td>1.9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.8</td>\n",
       "      <td>37.858299</td>\n",
       "      <td>-80.399498</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9913</th>\n",
       "      <td>3845</td>\n",
       "      <td>14</td>\n",
       "      <td>11905</td>\n",
       "      <td>11298</td>\n",
       "      <td>140.0</td>\n",
       "      <td>1419</td>\n",
       "      <td>2</td>\n",
       "      <td>32.38399887084961-32.38399887084961</td>\n",
       "      <td>2020-01-02</td>\n",
       "      <td>12.1</td>\n",
       "      <td>4.6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.9</td>\n",
       "      <td>32.383999</td>\n",
       "      <td>-94.711502</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9914</th>\n",
       "      <td>4258</td>\n",
       "      <td>17</td>\n",
       "      <td>12003</td>\n",
       "      <td>14869</td>\n",
       "      <td>463.0</td>\n",
       "      <td>1466</td>\n",
       "      <td>6</td>\n",
       "      <td>47.48199844-47.48199844</td>\n",
       "      <td>2020-01-06</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>26.3</td>\n",
       "      <td>47.481998</td>\n",
       "      <td>-111.371002</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9915</th>\n",
       "      <td>4275</td>\n",
       "      <td>17</td>\n",
       "      <td>14150</td>\n",
       "      <td>11433</td>\n",
       "      <td>243.0</td>\n",
       "      <td>2975</td>\n",
       "      <td>3</td>\n",
       "      <td>45.57089996-45.57089996</td>\n",
       "      <td>2020-01-03</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13.5</td>\n",
       "      <td>45.570900</td>\n",
       "      <td>-84.796700</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>9916 rows × 16 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      op_carrier_fl_num  op_unique_carrier  origin_airport_id  \\\n",
       "0                  5390                 21              14057   \n",
       "1                  5752                  6              14057   \n",
       "2                   789                 20              14057   \n",
       "3                  1184                  2              14057   \n",
       "4                  1552                  2              14057   \n",
       "...                 ...                ...                ...   \n",
       "9911               5147                 17              14006   \n",
       "9912               5081                 17              13121   \n",
       "9913               3845                 14              11905   \n",
       "9914               4258                 17              12003   \n",
       "9915               4275                 17              14150   \n",
       "\n",
       "      dest_airport_id  distance  route  day  \\\n",
       "0               11292     991.0   2737    1   \n",
       "1               14747     129.0   2764    7   \n",
       "2               11292     991.0   2737    2   \n",
       "3               14679     933.0   2763    7   \n",
       "4               14262     873.0   2760    7   \n",
       "...               ...       ...    ...  ...   \n",
       "9911            13930     342.0   2708    6   \n",
       "9912            13930     489.0   2064    2   \n",
       "9913            11298     140.0   1419    2   \n",
       "9914            14869     463.0   1466    6   \n",
       "9915            11433     243.0   2975    3   \n",
       "\n",
       "                                      Key     fl_date  AvgTemp  Precipitation  \\\n",
       "0                 45.58869934-45.58869934  2020-01-01     10.8            3.8   \n",
       "1                 45.58869934-45.58869934  2020-01-07     11.3            2.8   \n",
       "2                 45.58869934-45.58869934  2020-01-02      8.2            1.0   \n",
       "3                 45.58869934-45.58869934  2020-01-07     11.3            2.8   \n",
       "4                 45.58869934-45.58869934  2020-01-07     11.3            2.8   \n",
       "...                                   ...         ...      ...            ...   \n",
       "9911  37.06079864501953-37.06079864501953  2020-01-06      5.3            0.0   \n",
       "9912          37.8582992554-37.8582992554  2020-01-02      1.9            0.0   \n",
       "9913  32.38399887084961-32.38399887084961  2020-01-02     12.1            4.6   \n",
       "9914              47.48199844-47.48199844  2020-01-06      0.1            0.0   \n",
       "9915              45.57089996-45.57089996  2020-01-03      0.5            0.0   \n",
       "\n",
       "      Snow_mm  WindSpeed        lat         lon  month  \n",
       "0         0.0       11.9  45.588699 -122.598000      1  \n",
       "1         0.0       24.8  45.588699 -122.598000      1  \n",
       "2         0.0       11.9  45.588699 -122.598000      1  \n",
       "3         0.0       24.8  45.588699 -122.598000      1  \n",
       "4         0.0       24.8  45.588699 -122.598000      1  \n",
       "...       ...        ...        ...         ...    ...  \n",
       "9911      0.0        7.9  37.060799  -88.773804      1  \n",
       "9912      0.0        1.8  37.858299  -80.399498      1  \n",
       "9913      0.0       11.9  32.383999  -94.711502      1  \n",
       "9914      0.0       26.3  47.481998 -111.371002      1  \n",
       "9915      0.0       13.5  45.570900  -84.796700      1  \n",
       "\n",
       "[9916 rows x 16 columns]"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Import pickle\n",
    "import pickle\n",
    "\n",
    "# Load model from file\n",
    "xgr_model = pickle.load(open('../../data/pickles/XBoostModel.pkl', \"rb\"))\n",
    "\n",
    "# Import test data\n",
    "df_txboost = pd.read_csv('../../data/processed/transformed_week1_2020.csv')\n",
    "df_txboost.rename(columns={'Month': 'month'}, inplace=True) \n",
    "df_txboost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>op_carrier_fl_num</th>\n",
       "      <th>origin_airport_id</th>\n",
       "      <th>distance</th>\n",
       "      <th>route</th>\n",
       "      <th>month</th>\n",
       "      <th>AvgTemp</th>\n",
       "      <th>Precipitation</th>\n",
       "      <th>Snow_mm</th>\n",
       "      <th>WindSpeed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5390</td>\n",
       "      <td>14057</td>\n",
       "      <td>991.0</td>\n",
       "      <td>2737</td>\n",
       "      <td>1</td>\n",
       "      <td>10.8</td>\n",
       "      <td>3.8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5752</td>\n",
       "      <td>14057</td>\n",
       "      <td>129.0</td>\n",
       "      <td>2764</td>\n",
       "      <td>1</td>\n",
       "      <td>11.3</td>\n",
       "      <td>2.8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>24.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>789</td>\n",
       "      <td>14057</td>\n",
       "      <td>991.0</td>\n",
       "      <td>2737</td>\n",
       "      <td>1</td>\n",
       "      <td>8.2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1184</td>\n",
       "      <td>14057</td>\n",
       "      <td>933.0</td>\n",
       "      <td>2763</td>\n",
       "      <td>1</td>\n",
       "      <td>11.3</td>\n",
       "      <td>2.8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>24.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1552</td>\n",
       "      <td>14057</td>\n",
       "      <td>873.0</td>\n",
       "      <td>2760</td>\n",
       "      <td>1</td>\n",
       "      <td>11.3</td>\n",
       "      <td>2.8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>24.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9911</th>\n",
       "      <td>5147</td>\n",
       "      <td>14006</td>\n",
       "      <td>342.0</td>\n",
       "      <td>2708</td>\n",
       "      <td>1</td>\n",
       "      <td>5.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9912</th>\n",
       "      <td>5081</td>\n",
       "      <td>13121</td>\n",
       "      <td>489.0</td>\n",
       "      <td>2064</td>\n",
       "      <td>1</td>\n",
       "      <td>1.9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9913</th>\n",
       "      <td>3845</td>\n",
       "      <td>11905</td>\n",
       "      <td>140.0</td>\n",
       "      <td>1419</td>\n",
       "      <td>1</td>\n",
       "      <td>12.1</td>\n",
       "      <td>4.6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9914</th>\n",
       "      <td>4258</td>\n",
       "      <td>12003</td>\n",
       "      <td>463.0</td>\n",
       "      <td>1466</td>\n",
       "      <td>1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>26.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9915</th>\n",
       "      <td>4275</td>\n",
       "      <td>14150</td>\n",
       "      <td>243.0</td>\n",
       "      <td>2975</td>\n",
       "      <td>1</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>9916 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      op_carrier_fl_num  origin_airport_id  distance  route  month  AvgTemp  \\\n",
       "0                  5390              14057     991.0   2737      1     10.8   \n",
       "1                  5752              14057     129.0   2764      1     11.3   \n",
       "2                   789              14057     991.0   2737      1      8.2   \n",
       "3                  1184              14057     933.0   2763      1     11.3   \n",
       "4                  1552              14057     873.0   2760      1     11.3   \n",
       "...                 ...                ...       ...    ...    ...      ...   \n",
       "9911               5147              14006     342.0   2708      1      5.3   \n",
       "9912               5081              13121     489.0   2064      1      1.9   \n",
       "9913               3845              11905     140.0   1419      1     12.1   \n",
       "9914               4258              12003     463.0   1466      1      0.1   \n",
       "9915               4275              14150     243.0   2975      1      0.5   \n",
       "\n",
       "      Precipitation  Snow_mm  WindSpeed  \n",
       "0               3.8      0.0       11.9  \n",
       "1               2.8      0.0       24.8  \n",
       "2               1.0      0.0       11.9  \n",
       "3               2.8      0.0       24.8  \n",
       "4               2.8      0.0       24.8  \n",
       "...             ...      ...        ...  \n",
       "9911            0.0      0.0        7.9  \n",
       "9912            0.0      0.0        1.8  \n",
       "9913            4.6      0.0       11.9  \n",
       "9914            0.0      0.0       26.3  \n",
       "9915            0.0      0.0       13.5  \n",
       "\n",
       "[9916 rows x 9 columns]"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_2020 = df_txboost.drop(['op_unique_carrier','dest_airport_id','day','fl_date','Key','lat','lon','fl_date'], axis=1)\n",
    "X_2020 = df_txboost[['op_carrier_fl_num','origin_airport_id','distance','route','month','AvgTemp','Precipitation', 'Snow_mm', 'WindSpeed']]\n",
    "X_2020"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "# make predictions for test data\n",
    "y_2020_pred = xgr_model.predict(X_2020)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>op_carrier_fl_num</th>\n",
       "      <th>origin_airport_id</th>\n",
       "      <th>distance</th>\n",
       "      <th>route</th>\n",
       "      <th>month</th>\n",
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       "      <th>2</th>\n",
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       "      <th>3</th>\n",
       "      <td>1184</td>\n",
       "      <td>14057</td>\n",
       "      <td>933.0</td>\n",
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       "      <td>24.8</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1552</td>\n",
       "      <td>14057</td>\n",
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       "      <td>2760</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9911</th>\n",
       "      <td>5147</td>\n",
       "      <td>14006</td>\n",
       "      <td>342.0</td>\n",
       "      <td>2708</td>\n",
       "      <td>1</td>\n",
       "      <td>5.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.9</td>\n",
       "      <td>87.931023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9912</th>\n",
       "      <td>5081</td>\n",
       "      <td>13121</td>\n",
       "      <td>489.0</td>\n",
       "      <td>2064</td>\n",
       "      <td>1</td>\n",
       "      <td>1.9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.8</td>\n",
       "      <td>86.662468</td>\n",
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       "    <tr>\n",
       "      <th>9913</th>\n",
       "      <td>3845</td>\n",
       "      <td>11905</td>\n",
       "      <td>140.0</td>\n",
       "      <td>1419</td>\n",
       "      <td>1</td>\n",
       "      <td>12.1</td>\n",
       "      <td>4.6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.9</td>\n",
       "      <td>92.253105</td>\n",
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       "    <tr>\n",
       "      <th>9914</th>\n",
       "      <td>4258</td>\n",
       "      <td>12003</td>\n",
       "      <td>463.0</td>\n",
       "      <td>1466</td>\n",
       "      <td>1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>26.3</td>\n",
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       "    <tr>\n",
       "      <th>9915</th>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>9916 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      op_carrier_fl_num  origin_airport_id  distance  route  month  AvgTemp  \\\n",
       "0                  5390              14057     991.0   2737      1     10.8   \n",
       "1                  5752              14057     129.0   2764      1     11.3   \n",
       "2                   789              14057     991.0   2737      1      8.2   \n",
       "3                  1184              14057     933.0   2763      1     11.3   \n",
       "4                  1552              14057     873.0   2760      1     11.3   \n",
       "...                 ...                ...       ...    ...    ...      ...   \n",
       "9911               5147              14006     342.0   2708      1      5.3   \n",
       "9912               5081              13121     489.0   2064      1      1.9   \n",
       "9913               3845              11905     140.0   1419      1     12.1   \n",
       "9914               4258              12003     463.0   1466      1      0.1   \n",
       "9915               4275              14150     243.0   2975      1      0.5   \n",
       "\n",
       "      Precipitation  Snow_mm  WindSpeed  arr_delay  \n",
       "0               3.8      0.0       11.9  88.615707  \n",
       "1               2.8      0.0       24.8  81.576683  \n",
       "2               1.0      0.0       11.9  91.907974  \n",
       "3               2.8      0.0       24.8  81.576683  \n",
       "4               2.8      0.0       24.8  81.576683  \n",
       "...             ...      ...        ...        ...  \n",
       "9911            0.0      0.0        7.9  87.931023  \n",
       "9912            0.0      0.0        1.8  86.662468  \n",
       "9913            4.6      0.0       11.9  92.253105  \n",
       "9914            0.0      0.0       26.3  64.342278  \n",
       "9915            0.0      0.0       13.5  71.601570  \n",
       "\n",
       "[9916 rows x 10 columns]"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_2020['arr_delay'] = y_2020_pred\n",
    "X_2020"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>op_carrier_fl_num</th>\n",
       "      <th>origin_airport_id</th>\n",
       "      <th>distance</th>\n",
       "      <th>route</th>\n",
       "      <th>month</th>\n",
       "      <th>AvgTemp</th>\n",
       "      <th>Precipitation</th>\n",
       "      <th>Snow_mm</th>\n",
       "      <th>WindSpeed</th>\n",
       "      <th>arr_delay</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>9916.000000</td>\n",
       "      <td>9916.000000</td>\n",
       "      <td>9916.000000</td>\n",
       "      <td>9916.000000</td>\n",
       "      <td>9916.0</td>\n",
       "      <td>9916.000000</td>\n",
       "      <td>9916.000000</td>\n",
       "      <td>9916.000000</td>\n",
       "      <td>9916.000000</td>\n",
       "      <td>9916.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2808.134429</td>\n",
       "      <td>12713.165288</td>\n",
       "      <td>798.813836</td>\n",
       "      <td>1858.248689</td>\n",
       "      <td>1.0</td>\n",
       "      <td>8.349284</td>\n",
       "      <td>1.789391</td>\n",
       "      <td>5.164381</td>\n",
       "      <td>12.604871</td>\n",
       "      <td>87.846924</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1867.691692</td>\n",
       "      <td>1510.815051</td>\n",
       "      <td>600.656478</td>\n",
       "      <td>1057.725199</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.632898</td>\n",
       "      <td>5.770032</td>\n",
       "      <td>29.983744</td>\n",
       "      <td>6.364357</td>\n",
       "      <td>8.183515</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>10135.000000</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-36.100000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>24.964878</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1179.000000</td>\n",
       "      <td>11292.000000</td>\n",
       "      <td>356.000000</td>\n",
       "      <td>950.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.600000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.800000</td>\n",
       "      <td>87.931023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2420.000000</td>\n",
       "      <td>12892.000000</td>\n",
       "      <td>633.500000</td>\n",
       "      <td>1895.500000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>7.900000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>11.500000</td>\n",
       "      <td>87.931023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>4460.500000</td>\n",
       "      <td>14057.000000</td>\n",
       "      <td>1044.000000</td>\n",
       "      <td>2760.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>12.500000</td>\n",
       "      <td>0.300000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>16.900000</td>\n",
       "      <td>87.931023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>6796.000000</td>\n",
       "      <td>16101.000000</td>\n",
       "      <td>4983.000000</td>\n",
       "      <td>3713.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>27.400000</td>\n",
       "      <td>105.400000</td>\n",
       "      <td>510.000000</td>\n",
       "      <td>41.000000</td>\n",
       "      <td>124.530708</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       op_carrier_fl_num  origin_airport_id     distance        route   month  \\\n",
       "count        9916.000000        9916.000000  9916.000000  9916.000000  9916.0   \n",
       "mean         2808.134429       12713.165288   798.813836  1858.248689     1.0   \n",
       "std          1867.691692        1510.815051   600.656478  1057.725199     0.0   \n",
       "min             1.000000       10135.000000    31.000000     0.000000     1.0   \n",
       "25%          1179.000000       11292.000000   356.000000   950.000000     1.0   \n",
       "50%          2420.000000       12892.000000   633.500000  1895.500000     1.0   \n",
       "75%          4460.500000       14057.000000  1044.000000  2760.000000     1.0   \n",
       "max          6796.000000       16101.000000  4983.000000  3713.000000     1.0   \n",
       "\n",
       "           AvgTemp  Precipitation      Snow_mm    WindSpeed    arr_delay  \n",
       "count  9916.000000    9916.000000  9916.000000  9916.000000  9916.000000  \n",
       "mean      8.349284       1.789391     5.164381    12.604871    87.846924  \n",
       "std       6.632898       5.770032    29.983744     6.364357     8.183515  \n",
       "min     -36.100000       0.000000     0.000000     0.000000    24.964878  \n",
       "25%       3.600000       0.000000     0.000000     7.800000    87.931023  \n",
       "50%       7.900000       0.000000     0.000000    11.500000    87.931023  \n",
       "75%      12.500000       0.300000     0.000000    16.900000    87.931023  \n",
       "max      27.400000     105.400000   510.000000    41.000000   124.530708  "
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_2020.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    9916.000000\n",
       "mean       87.846924\n",
       "std         8.183515\n",
       "min        24.964878\n",
       "25%        87.931023\n",
       "50%        87.931023\n",
       "75%        87.931023\n",
       "max       124.530708\n",
       "Name: arr_delay, dtype: float64"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_2020['arr_delay'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Export Model Evaluation to Tests folder for Mentors to Review\n",
    "X_2020.to_csv('../tests/XGBoost_Regression_Model_Evaluation.csv', index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "# Classifion Model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true,
    "tags": []
   },
   "source": [
    "## Feature Engineering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>op_carrier_fl_num</th>\n",
       "      <th>op_unique_carrier</th>\n",
       "      <th>origin_airport_id</th>\n",
       "      <th>dest_airport_id</th>\n",
       "      <th>distance</th>\n",
       "      <th>route</th>\n",
       "      <th>month</th>\n",
       "      <th>day</th>\n",
       "      <th>fl_date</th>\n",
       "      <th>arr_delay</th>\n",
       "      <th>Key</th>\n",
       "      <th>AvgTemp</th>\n",
       "      <th>Precipitation</th>\n",
       "      <th>Snow_mm</th>\n",
       "      <th>WindSpeed</th>\n",
       "      <th>lat</th>\n",
       "      <th>lon</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1610</td>\n",
       "      <td>22</td>\n",
       "      <td>11618</td>\n",
       "      <td>13495</td>\n",
       "      <td>1167</td>\n",
       "      <td>1962</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2019-03-01</td>\n",
       "      <td>0.0</td>\n",
       "      <td>40.692501-40.692501</td>\n",
       "      <td>0.8</td>\n",
       "      <td>1.3</td>\n",
       "      <td>30.0</td>\n",
       "      <td>15.9</td>\n",
       "      <td>40.692501</td>\n",
       "      <td>-74.168701</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4909</td>\n",
       "      <td>6</td>\n",
       "      <td>11618</td>\n",
       "      <td>14524</td>\n",
       "      <td>277</td>\n",
       "      <td>1978</td>\n",
       "      <td>12</td>\n",
       "      <td>6</td>\n",
       "      <td>2019-12-06</td>\n",
       "      <td>-17.0</td>\n",
       "      <td>40.692501-40.692501</td>\n",
       "      <td>5.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>12.2</td>\n",
       "      <td>40.692501</td>\n",
       "      <td>-74.168701</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3556</td>\n",
       "      <td>26</td>\n",
       "      <td>11618</td>\n",
       "      <td>11193</td>\n",
       "      <td>569</td>\n",
       "      <td>1928</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>2018-04-03</td>\n",
       "      <td>-9.0</td>\n",
       "      <td>40.692501-40.692501</td>\n",
       "      <td>4.9</td>\n",
       "      <td>5.8</td>\n",
       "      <td>30.0</td>\n",
       "      <td>11.1</td>\n",
       "      <td>40.692501</td>\n",
       "      <td>-74.168701</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1382</td>\n",
       "      <td>2</td>\n",
       "      <td>11618</td>\n",
       "      <td>11298</td>\n",
       "      <td>1372</td>\n",
       "      <td>1932</td>\n",
       "      <td>9</td>\n",
       "      <td>7</td>\n",
       "      <td>2019-09-07</td>\n",
       "      <td>68.0</td>\n",
       "      <td>40.692501-40.692501</td>\n",
       "      <td>20.4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.3</td>\n",
       "      <td>40.692501</td>\n",
       "      <td>-74.168701</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3517</td>\n",
       "      <td>26</td>\n",
       "      <td>11618</td>\n",
       "      <td>13495</td>\n",
       "      <td>1167</td>\n",
       "      <td>1962</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>2018-02-05</td>\n",
       "      <td>-37.0</td>\n",
       "      <td>40.692501-40.692501</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13.4</td>\n",
       "      <td>40.692501</td>\n",
       "      <td>-74.168701</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97302</th>\n",
       "      <td>5026</td>\n",
       "      <td>19</td>\n",
       "      <td>11092</td>\n",
       "      <td>11292</td>\n",
       "      <td>283</td>\n",
       "      <td>1111</td>\n",
       "      <td>9</td>\n",
       "      <td>22</td>\n",
       "      <td>2019-09-22</td>\n",
       "      <td>0.0</td>\n",
       "      <td>38.75500107-38.75500107</td>\n",
       "      <td>16.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>38.755001</td>\n",
       "      <td>-109.754997</td>\n",
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       "    <tr>\n",
       "      <th>97303</th>\n",
       "      <td>5031</td>\n",
       "      <td>19</td>\n",
       "      <td>11092</td>\n",
       "      <td>11292</td>\n",
       "      <td>283</td>\n",
       "      <td>1111</td>\n",
       "      <td>8</td>\n",
       "      <td>10</td>\n",
       "      <td>2019-08-10</td>\n",
       "      <td>-12.0</td>\n",
       "      <td>38.75500107-38.75500107</td>\n",
       "      <td>27.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>38.755001</td>\n",
       "      <td>-109.754997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97304</th>\n",
       "      <td>5079</td>\n",
       "      <td>19</td>\n",
       "      <td>11092</td>\n",
       "      <td>11292</td>\n",
       "      <td>283</td>\n",
       "      <td>1111</td>\n",
       "      <td>8</td>\n",
       "      <td>15</td>\n",
       "      <td>2018-08-15</td>\n",
       "      <td>2.0</td>\n",
       "      <td>38.75500107-38.75500107</td>\n",
       "      <td>28.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.7</td>\n",
       "      <td>38.755001</td>\n",
       "      <td>-109.754997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97305</th>\n",
       "      <td>5031</td>\n",
       "      <td>19</td>\n",
       "      <td>11092</td>\n",
       "      <td>11292</td>\n",
       "      <td>283</td>\n",
       "      <td>1111</td>\n",
       "      <td>5</td>\n",
       "      <td>26</td>\n",
       "      <td>2019-05-26</td>\n",
       "      <td>-22.0</td>\n",
       "      <td>38.75500107-38.75500107</td>\n",
       "      <td>18.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.4</td>\n",
       "      <td>38.755001</td>\n",
       "      <td>-109.754997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97306</th>\n",
       "      <td>865</td>\n",
       "      <td>12</td>\n",
       "      <td>12119</td>\n",
       "      <td>14112</td>\n",
       "      <td>861</td>\n",
       "      <td>2279</td>\n",
       "      <td>7</td>\n",
       "      <td>15</td>\n",
       "      <td>2018-07-15</td>\n",
       "      <td>105.0</td>\n",
       "      <td>39.707901-39.707901</td>\n",
       "      <td>26.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.1</td>\n",
       "      <td>39.707901</td>\n",
       "      <td>-77.729500</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>97307 rows × 17 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       op_carrier_fl_num  op_unique_carrier  origin_airport_id  \\\n",
       "0                   1610                 22              11618   \n",
       "1                   4909                  6              11618   \n",
       "2                   3556                 26              11618   \n",
       "3                   1382                  2              11618   \n",
       "4                   3517                 26              11618   \n",
       "...                  ...                ...                ...   \n",
       "97302               5026                 19              11092   \n",
       "97303               5031                 19              11092   \n",
       "97304               5079                 19              11092   \n",
       "97305               5031                 19              11092   \n",
       "97306                865                 12              12119   \n",
       "\n",
       "       dest_airport_id  distance  route  month  day     fl_date  arr_delay  \\\n",
       "0                13495      1167   1962      3    1  2019-03-01        0.0   \n",
       "1                14524       277   1978     12    6  2019-12-06      -17.0   \n",
       "2                11193       569   1928      4    3  2018-04-03       -9.0   \n",
       "3                11298      1372   1932      9    7  2019-09-07       68.0   \n",
       "4                13495      1167   1962      2    5  2018-02-05      -37.0   \n",
       "...                ...       ...    ...    ...  ...         ...        ...   \n",
       "97302            11292       283   1111      9   22  2019-09-22        0.0   \n",
       "97303            11292       283   1111      8   10  2019-08-10      -12.0   \n",
       "97304            11292       283   1111      8   15  2018-08-15        2.0   \n",
       "97305            11292       283   1111      5   26  2019-05-26      -22.0   \n",
       "97306            14112       861   2279      7   15  2018-07-15      105.0   \n",
       "\n",
       "                           Key  AvgTemp  Precipitation  Snow_mm  WindSpeed  \\\n",
       "0          40.692501-40.692501      0.8            1.3     30.0       15.9   \n",
       "1          40.692501-40.692501      5.7            0.0      0.0       12.2   \n",
       "2          40.692501-40.692501      4.9            5.8     30.0       11.1   \n",
       "3          40.692501-40.692501     20.4            0.0      0.0        6.3   \n",
       "4          40.692501-40.692501      0.3            0.0      0.0       13.4   \n",
       "...                        ...      ...            ...      ...        ...   \n",
       "97302  38.75500107-38.75500107     16.0            0.0      0.0        0.0   \n",
       "97303  38.75500107-38.75500107     27.3            0.0      0.0        0.0   \n",
       "97304  38.75500107-38.75500107     28.5            0.0      0.0       10.7   \n",
       "97305  38.75500107-38.75500107     18.3            0.0      0.0       11.4   \n",
       "97306      39.707901-39.707901     26.7            0.0      0.0        9.1   \n",
       "\n",
       "             lat         lon  \n",
       "0      40.692501  -74.168701  \n",
       "1      40.692501  -74.168701  \n",
       "2      40.692501  -74.168701  \n",
       "3      40.692501  -74.168701  \n",
       "4      40.692501  -74.168701  \n",
       "...          ...         ...  \n",
       "97302  38.755001 -109.754997  \n",
       "97303  38.755001 -109.754997  \n",
       "97304  38.755001 -109.754997  \n",
       "97305  38.755001 -109.754997  \n",
       "97306  39.707901  -77.729500  \n",
       "\n",
       "[97307 rows x 17 columns]"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2 = pd.read_csv('../../data/processed/transformed_model_flight_data.csv')\n",
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    97307.000000\n",
       "mean       769.625371\n",
       "std        587.830448\n",
       "min         16.000000\n",
       "25%        341.000000\n",
       "50%        603.000000\n",
       "75%       1005.000000\n",
       "max       5095.000000\n",
       "Name: distance, dtype: float64"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2['distance'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>op_carrier_fl_num</th>\n",
       "      <th>op_unique_carrier</th>\n",
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       "  <tbody>\n",
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       "      <th>0</th>\n",
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       "      <td>15.9</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>12.2</td>\n",
       "      <td>40.692501</td>\n",
       "      <td>-74.168701</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3556</td>\n",
       "      <td>26</td>\n",
       "      <td>11618</td>\n",
       "      <td>11193</td>\n",
       "      <td>569</td>\n",
       "      <td>1928</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>2018-04-03</td>\n",
       "      <td>-9.0</td>\n",
       "      <td>40.692501-40.692501</td>\n",
       "      <td>4.9</td>\n",
       "      <td>5.8</td>\n",
       "      <td>30.0</td>\n",
       "      <td>11.1</td>\n",
       "      <td>40.692501</td>\n",
       "      <td>-74.168701</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1382</td>\n",
       "      <td>2</td>\n",
       "      <td>11618</td>\n",
       "      <td>11298</td>\n",
       "      <td>1372</td>\n",
       "      <td>1932</td>\n",
       "      <td>9</td>\n",
       "      <td>7</td>\n",
       "      <td>2019-09-07</td>\n",
       "      <td>68.0</td>\n",
       "      <td>40.692501-40.692501</td>\n",
       "      <td>20.4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.3</td>\n",
       "      <td>40.692501</td>\n",
       "      <td>-74.168701</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3517</td>\n",
       "      <td>26</td>\n",
       "      <td>11618</td>\n",
       "      <td>13495</td>\n",
       "      <td>1167</td>\n",
       "      <td>1962</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>2018-02-05</td>\n",
       "      <td>-37.0</td>\n",
       "      <td>40.692501-40.692501</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13.4</td>\n",
       "      <td>40.692501</td>\n",
       "      <td>-74.168701</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97302</th>\n",
       "      <td>5026</td>\n",
       "      <td>19</td>\n",
       "      <td>11092</td>\n",
       "      <td>11292</td>\n",
       "      <td>283</td>\n",
       "      <td>1111</td>\n",
       "      <td>9</td>\n",
       "      <td>22</td>\n",
       "      <td>2019-09-22</td>\n",
       "      <td>0.0</td>\n",
       "      <td>38.75500107-38.75500107</td>\n",
       "      <td>16.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>38.755001</td>\n",
       "      <td>-109.754997</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97303</th>\n",
       "      <td>5031</td>\n",
       "      <td>19</td>\n",
       "      <td>11092</td>\n",
       "      <td>11292</td>\n",
       "      <td>283</td>\n",
       "      <td>1111</td>\n",
       "      <td>8</td>\n",
       "      <td>10</td>\n",
       "      <td>2019-08-10</td>\n",
       "      <td>-12.0</td>\n",
       "      <td>38.75500107-38.75500107</td>\n",
       "      <td>27.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>38.755001</td>\n",
       "      <td>-109.754997</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97304</th>\n",
       "      <td>5079</td>\n",
       "      <td>19</td>\n",
       "      <td>11092</td>\n",
       "      <td>11292</td>\n",
       "      <td>283</td>\n",
       "      <td>1111</td>\n",
       "      <td>8</td>\n",
       "      <td>15</td>\n",
       "      <td>2018-08-15</td>\n",
       "      <td>2.0</td>\n",
       "      <td>38.75500107-38.75500107</td>\n",
       "      <td>28.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.7</td>\n",
       "      <td>38.755001</td>\n",
       "      <td>-109.754997</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97305</th>\n",
       "      <td>5031</td>\n",
       "      <td>19</td>\n",
       "      <td>11092</td>\n",
       "      <td>11292</td>\n",
       "      <td>283</td>\n",
       "      <td>1111</td>\n",
       "      <td>5</td>\n",
       "      <td>26</td>\n",
       "      <td>2019-05-26</td>\n",
       "      <td>-22.0</td>\n",
       "      <td>38.75500107-38.75500107</td>\n",
       "      <td>18.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.4</td>\n",
       "      <td>38.755001</td>\n",
       "      <td>-109.754997</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97306</th>\n",
       "      <td>865</td>\n",
       "      <td>12</td>\n",
       "      <td>12119</td>\n",
       "      <td>14112</td>\n",
       "      <td>861</td>\n",
       "      <td>2279</td>\n",
       "      <td>7</td>\n",
       "      <td>15</td>\n",
       "      <td>2018-07-15</td>\n",
       "      <td>105.0</td>\n",
       "      <td>39.707901-39.707901</td>\n",
       "      <td>26.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.1</td>\n",
       "      <td>39.707901</td>\n",
       "      <td>-77.729500</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>97307 rows × 18 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       op_carrier_fl_num  op_unique_carrier  origin_airport_id  \\\n",
       "0                   1610                 22              11618   \n",
       "1                   4909                  6              11618   \n",
       "2                   3556                 26              11618   \n",
       "3                   1382                  2              11618   \n",
       "4                   3517                 26              11618   \n",
       "...                  ...                ...                ...   \n",
       "97302               5026                 19              11092   \n",
       "97303               5031                 19              11092   \n",
       "97304               5079                 19              11092   \n",
       "97305               5031                 19              11092   \n",
       "97306                865                 12              12119   \n",
       "\n",
       "       dest_airport_id  distance  route  month  day     fl_date  arr_delay  \\\n",
       "0                13495      1167   1962      3    1  2019-03-01        0.0   \n",
       "1                14524       277   1978     12    6  2019-12-06      -17.0   \n",
       "2                11193       569   1928      4    3  2018-04-03       -9.0   \n",
       "3                11298      1372   1932      9    7  2019-09-07       68.0   \n",
       "4                13495      1167   1962      2    5  2018-02-05      -37.0   \n",
       "...                ...       ...    ...    ...  ...         ...        ...   \n",
       "97302            11292       283   1111      9   22  2019-09-22        0.0   \n",
       "97303            11292       283   1111      8   10  2019-08-10      -12.0   \n",
       "97304            11292       283   1111      8   15  2018-08-15        2.0   \n",
       "97305            11292       283   1111      5   26  2019-05-26      -22.0   \n",
       "97306            14112       861   2279      7   15  2018-07-15      105.0   \n",
       "\n",
       "                           Key  AvgTemp  Precipitation  Snow_mm  WindSpeed  \\\n",
       "0          40.692501-40.692501      0.8            1.3     30.0       15.9   \n",
       "1          40.692501-40.692501      5.7            0.0      0.0       12.2   \n",
       "2          40.692501-40.692501      4.9            5.8     30.0       11.1   \n",
       "3          40.692501-40.692501     20.4            0.0      0.0        6.3   \n",
       "4          40.692501-40.692501      0.3            0.0      0.0       13.4   \n",
       "...                        ...      ...            ...      ...        ...   \n",
       "97302  38.75500107-38.75500107     16.0            0.0      0.0        0.0   \n",
       "97303  38.75500107-38.75500107     27.3            0.0      0.0        0.0   \n",
       "97304  38.75500107-38.75500107     28.5            0.0      0.0       10.7   \n",
       "97305  38.75500107-38.75500107     18.3            0.0      0.0       11.4   \n",
       "97306      39.707901-39.707901     26.7            0.0      0.0        9.1   \n",
       "\n",
       "             lat         lon  dist_cat  \n",
       "0      40.692501  -74.168701         2  \n",
       "1      40.692501  -74.168701         0  \n",
       "2      40.692501  -74.168701         1  \n",
       "3      40.692501  -74.168701         2  \n",
       "4      40.692501  -74.168701         2  \n",
       "...          ...         ...       ...  \n",
       "97302  38.755001 -109.754997         0  \n",
       "97303  38.755001 -109.754997         0  \n",
       "97304  38.755001 -109.754997         0  \n",
       "97305  38.755001 -109.754997         0  \n",
       "97306  39.707901  -77.729500         1  \n",
       "\n",
       "[97307 rows x 18 columns]"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "conditions = [\n",
    "    (df2['distance']<=350),\n",
    "    (df2['distance']>350) & (df2['distance']<1000),\n",
    "    (df2['distance']>= 1000)]\n",
    "choices = ['1.short', '2.medium', '3.long']\n",
    "df2['dist_cat'] = np.select(conditions, choices)\n",
    "\n",
    "# transform categorical variables in numeric with LabelEncoder\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "le = LabelEncoder()\n",
    "\n",
    "# fit and transform the data\n",
    "df2['dist_cat'] = le.fit_transform(df2['dist_cat'])\n",
    "df2\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    97307.000000\n",
       "mean         5.596812\n",
       "std         51.480610\n",
       "min       -475.000000\n",
       "25%        -15.000000\n",
       "50%         -6.000000\n",
       "75%          8.000000\n",
       "max       1503.000000\n",
       "Name: arr_delay, dtype: float64"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2['arr_delay'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "conditions = [\n",
    "    (df2['arr_delay']<0),\n",
    "    (df2['arr_delay']>=0) & (df2['arr_delay']<10),\n",
    "    (df2['arr_delay']>=10) & (df2['arr_delay']<100),\n",
    "    (df2['arr_delay']>=100)]\n",
    "choices = ['1.early', '2.ontime', '3.late', '4.very late']\n",
    "df2['arr_delay_cat'] = np.select(conditions, choices)\n",
    "    \n",
    "# transform categorical variables in numeric with LabelEncoder\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "le = LabelEncoder()\n",
    "\n",
    "# fit and transform the data\n",
    "df2['arr_delay_cat'] = le.fit_transform(df2['arr_delay_cat'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['op_carrier_fl_num',\n",
       " 'op_unique_carrier',\n",
       " 'origin_airport_id',\n",
       " 'dest_airport_id',\n",
       " 'distance',\n",
       " 'route',\n",
       " 'month',\n",
       " 'day',\n",
       " 'fl_date',\n",
       " 'arr_delay',\n",
       " 'Key',\n",
       " 'AvgTemp',\n",
       " 'Precipitation',\n",
       " 'Snow_mm',\n",
       " 'WindSpeed',\n",
       " 'lat',\n",
       " 'lon',\n",
       " 'dist_cat',\n",
       " 'arr_delay_cat']"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.columns.tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>op_unique_carrier</th>\n",
       "      <th>month</th>\n",
       "      <th>dist_cat</th>\n",
       "      <th>arr_delay_cat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>22</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>26</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>26</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97302</th>\n",
       "      <td>19</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97303</th>\n",
       "      <td>19</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97304</th>\n",
       "      <td>19</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97305</th>\n",
       "      <td>19</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97306</th>\n",
       "      <td>12</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>97307 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       op_unique_carrier  month  dist_cat  arr_delay_cat\n",
       "0                     22      3         2              1\n",
       "1                      6     12         0              0\n",
       "2                     26      4         1              0\n",
       "3                      2      9         2              2\n",
       "4                     26      2         2              0\n",
       "...                  ...    ...       ...            ...\n",
       "97302                 19      9         0              1\n",
       "97303                 19      8         0              0\n",
       "97304                 19      8         0              1\n",
       "97305                 19      5         0              0\n",
       "97306                 12      7         1              3\n",
       "\n",
       "[97307 rows x 4 columns]"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2 = df2.drop(['op_carrier_fl_num',\n",
    " 'origin_airport_id',\n",
    " 'dest_airport_id',\n",
    " 'distance',\n",
    " 'route',\n",
    " 'day',\n",
    " 'fl_date','arr_delay',\n",
    " 'Key',\n",
    " 'AvgTemp',\n",
    " 'Precipitation',\n",
    " 'Snow_mm',\n",
    " 'WindSpeed',\n",
    " 'lat',\n",
    " 'lon'],axis=1)\n",
    "df2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true,
    "tags": []
   },
   "source": [
    "## Random Forest Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.model_selection import GridSearchCV, train_test_split\n",
    "from sklearn.datasets import make_classification\n",
    "from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score\n",
    "\n",
    "# Initialize the random forest with optimized hyper parameters\n",
    "rfc = RandomForestClassifier(n_estimators=200, max_depth=5, \n",
    "                             min_samples_split=2, min_samples_leaf=2, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = df2['arr_delay_cat']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['op_unique_carrier', 'month', 'dist_cat', 'arr_delay_cat']"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.columns.tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df2.drop(['arr_delay_cat'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "# Initialize the StandardScaler\n",
    "scaler = StandardScaler()\n",
    "\n",
    "# Scale the selected columns\n",
    "X_scaled = scaler.fit_transform(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Split your data into training and test sets\n",
    "X_train, X_test, y_train, y_test = train_test_split(X_scaled, y, test_size=0.2, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 367,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define the hyperparameters and their possible values\n",
    "param_grid = {\n",
    "    'n_estimators': [50, 100, 200],\n",
    "    'max_depth': [None, 5, 10],\n",
    "    'min_samples_split': [2, 5, 10],\n",
    "    'min_samples_leaf': [1, 2, 4]\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(max_depth=5, min_samples_leaf=2, n_estimators=200,\n",
       "                       random_state=42)"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Fit the classifier to the training data\n",
    "rfc.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 369,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best hyperparameters:  {'max_depth': 5, 'min_samples_leaf': 2, 'min_samples_split': 2, 'n_estimators': 200}\n",
      "Best accuracy score: 62.98%\n"
     ]
    }
   ],
   "source": [
    "# Create a GridSearchCV object and fit it to the training data\n",
    "grid_search = GridSearchCV(rfc, param_grid=param_grid, cv=5)\n",
    "grid_search.fit(X_train, y_train)\n",
    "\n",
    "# Print the best hyperparameters and the corresponding accuracy score\n",
    "print(\"Best hyperparameters: \", grid_search.best_params_)\n",
    "print(\"Best accuracy score: {:.2f}%\".format(grid_search.best_score_ * 100))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Predict the labels of the test data\n",
    "y_pred = rfc.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 63.33%\n",
      "Precision: 15.83%\n",
      "Recall: 25.00%\n",
      "F1 score: 19.39%\n"
     ]
    }
   ],
   "source": [
    "# Calculate accuracy, precision, recall, and F1 score\n",
    "accuracy = accuracy_score(y_test, y_pred)\n",
    "precision = precision_score(y_test, y_pred, average='macro')\n",
    "recall = recall_score(y_test, y_pred, average='macro')\n",
    "f1 = f1_score(y_test, y_pred, average='macro')\n",
    "\n",
    "print(\"Accuracy: {:.2f}%\".format(accuracy * 100))\n",
    "print(\"Precision: {:.2f}%\".format(precision * 100))\n",
    "print(\"Recall: {:.2f}%\".format(recall * 100))\n",
    "print(\"F1 score: {:.2f}%\".format(f1 * 100))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true,
    "tags": []
   },
   "source": [
    "## Naive Bayes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.naive_bayes import GaussianNB"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "#perform Naive Bayes\n",
    "# Create a Gaussian Naive Bayes model\n",
    "gnb = GaussianNB()\n",
    "\n",
    "# Train the model using the training data\n",
    "gnb.fit(X_train, y_train)\n",
    "\n",
    "# Predict the target values for the testing data\n",
    "y_pred = gnb.predict(X_test)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 63.33%\n",
      "Precision: 15.83%\n",
      "Recall: 25.00%\n",
      "F1 score: 19.39%\n"
     ]
    }
   ],
   "source": [
    "# Calculate accuracy, precision, recall, and F1 score\n",
    "accuracy = accuracy_score(y_test, y_pred)\n",
    "precision = precision_score(y_test, y_pred, average='macro')\n",
    "recall = recall_score(y_test, y_pred, average='macro')\n",
    "f1 = f1_score(y_test, y_pred, average='macro')\n",
    "\n",
    "print(\"Accuracy: {:.2f}%\".format(accuracy * 100))\n",
    "print(\"Precision: {:.2f}%\".format(precision * 100))\n",
    "print(\"Recall: {:.2f}%\".format(recall * 100))\n",
    "print(\"F1 score: {:.2f}%\".format(f1 * 100))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true,
    "tags": []
   },
   "source": [
    "## SVM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.svm import SVC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create an SVM classifier\n",
    "svm = SVC(kernel='linear')\n",
    "\n",
    "# Train the classifier using the training data\n",
    "svm.fit(X_train, y_train)\n",
    "\n",
    "# Predict the target values for the testing data\n",
    "y_pred = svm.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 63.33%\n",
      "Precision: 15.83%\n",
      "Recall: 25.00%\n",
      "F1 score: 19.39%\n"
     ]
    }
   ],
   "source": [
    "# Calculate accuracy, precision, recall, and F1 score\n",
    "accuracy = accuracy_score(y_test, y_pred)\n",
    "precision = precision_score(y_test, y_pred, average='macro')\n",
    "recall = recall_score(y_test, y_pred, average='macro')\n",
    "f1 = f1_score(y_test, y_pred, average='macro')\n",
    "\n",
    "print(\"Accuracy: {:.2f}%\".format(accuracy * 100))\n",
    "print(\"Precision: {:.2f}%\".format(precision * 100))\n",
    "print(\"Recall: {:.2f}%\".format(recall * 100))\n",
    "print(\"F1 score: {:.2f}%\".format(f1 * 100))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true,
    "tags": []
   },
   "source": [
    "======================================================================\n",
    "## Stretch Tasks"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Multiclass Classification"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The target variables are **CARRIER_DELAY, WEATHER_DELAY, NAS_DELAY, SECURITY_DELAY, LATE_AIRCRAFT_DELAY**. We need to do additional transformations because these variables are not binary but continuos. For each flight that was delayed, we need to have one of these variables as 1 and others 0.\n",
    "\n",
    "It can happen that we have two types of delays with more than 0 minutes. In this case, take the bigger one as 1 and others as 0."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Binary Classification"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The target variable is **CANCELLED**. The main problem here is going to be huge class imbalance. We have only very little cancelled flights with comparison to all flights. It is important to do the right sampling before training and to choose correct evaluation metrics."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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