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William9923/-pycaret-bliseries.ipynb

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🥕Pycaret Tutorial
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-pycaret-bliseries.ipynb
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"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "view-in-github",
"colab_type": "text"
},
"source": [
"<a href=\"https://colab.research.google.com/gist/William9923/12540b6f69e4ef685c797ed4287a650e/-pycaret-bliseries.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "Kv8h78tZR-RH"
},
"source": [
"# PyCaret🥕 with Feature Engineering🛠 \n",
"\n",
"---\n",
"Goals :\n",
"- Understanding which baseline model that we are trying to achieve using current data\n",
"- Automate modelling baseline\n",
"\n",
"![image](https://ericonanalytics.com/wp-content/uploads/2021/01/image-13.png)\n",
"\n",
"[PyCaret](https://pycaret.org/) is an open source, low-code machine learning library in Python that allows you to go from preparing your data to deploying your model within minutes in your choice of notebook environment."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "K2GMiC2cSVHn"
},
"source": [
"## ⚙️ Install PyCaret & Import Libraries\n",
"\n",
"Use this command to install pycaret (colab & kaggle not including this sacred library)\n",
"\n",
"> `pip install pycaret[full]`"
]
},
{
"cell_type": "code",
"metadata": {
"id": "-DSCsX2hRjnx",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "47805bd5-b21d-4db4-f8ef-60c9c44b7bd1"
},
"source": [
"!pip install pycaret[full]"
],
"execution_count": 2,
"outputs": [
{
"output_type": "stream",
"text": [
"Collecting pycaret[full]\n",
"\u001b[?25l Downloading https://files.pythonhosted.org/packages/bc/b6/9d620a23a038b3abdc249472ffd9be217f6b1877d2d952bfb3f653622a28/pycaret-2.3.2-py3-none-any.whl (263kB)\n",
"\u001b[K |████████████████████████████████| 266kB 4.3MB/s \n",
"\u001b[?25hCollecting mlflow\n",
"\u001b[?25l Downloading https://files.pythonhosted.org/packages/f3/c9/190a45e667b63edb76112deefa70629c2d9985603a85cb1968015fe0f327/mlflow-1.18.0-py3-none-any.whl (14.2MB)\n",
"\u001b[K |████████████████████████████████| 14.2MB 213kB/s \n",
"\u001b[?25hCollecting pandas-profiling>=2.8.0\n",
"\u001b[?25l Downloading https://files.pythonhosted.org/packages/3b/a3/34519d16e5ebe69bad30c5526deea2c3912634ced7f9b5e6e0bb9dbbd567/pandas_profiling-3.0.0-py2.py3-none-any.whl (248kB)\n",
"\u001b[K |████████████████████████████████| 256kB 37.0MB/s \n",
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" Created wheel for dash-renderer: filename=dash_renderer-1.9.1-cp37-none-any.whl size=1014873 sha256=4dc94a9fbbb3471ca468efc27986f399df613cd32e1b65848aeb38c2e10b9630\n",
" Stored in directory: /root/.cache/pip/wheels/03/a9/c5/dd5815c601b0ede164c223ffd7bafebde716ca57de06ef8aec\n",
" Building wheel for dash-core-components (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
" Created wheel for dash-core-components: filename=dash_core_components-1.16.0-cp37-none-any.whl size=3540992 sha256=5f07b012a02215ad7a6dfc89f44447ac19a979792478acebffc149f182c1706c\n",
" Stored in directory: /root/.cache/pip/wheels/86/1e/8c/e87ebba30b73c20dcd641224274febc983af88ed0fd7712a07\n",
" Building wheel for dash-html-components (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
" Created wheel for dash-html-components: filename=dash_html_components-1.1.3-cp37-none-any.whl size=319488 sha256=11622da6d80b6b78dad2e1bccf64e498de0ae3324b4f78297ff735c9cb584194\n",
" Stored in directory: /root/.cache/pip/wheels/07/f9/6c/f9b73a6ae1b7f347a92dc240293cebc267b370ba2a80added2\n",
" Building wheel for pyperclip (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
" Created wheel for pyperclip: filename=pyperclip-1.8.2-cp37-none-any.whl size=11136 sha256=902103411dfdfe8b41585bbda5082562b7b9fddacbdae42717b5889d92f93bd5\n",
" Stored in directory: /root/.cache/pip/wheels/25/af/b8/3407109267803f4015e1ee2ff23be0c8c19ce4008665931ee1\n",
"Successfully built pyod umap-learn shap alembic prometheus-flask-exporter databricks-cli phik htmlmin pynndescent gpustat dash-table dash lime SALib dash-renderer dash-core-components dash-html-components pyperclip\n",
"\u001b[31mERROR: pandas-profiling 3.0.0 has requirement requests>=2.24.0, but you'll have requests 2.23.0 which is incompatible.\u001b[0m\n",
"\u001b[31mERROR: phik 0.11.2 has requirement scipy>=1.5.2, but you'll have scipy 1.4.1 which is incompatible.\u001b[0m\n",
"\u001b[31mERROR: pyldavis 3.3.1 has requirement numpy>=1.20.0, but you'll have numpy 1.19.5 which is incompatible.\u001b[0m\n",
"\u001b[31mERROR: pyldavis 3.3.1 has requirement pandas>=1.2.0, but you'll have pandas 1.1.5 which is incompatible.\u001b[0m\n",
"\u001b[31mERROR: botocore 1.20.108 has requirement urllib3<1.27,>=1.25.4, but you'll have urllib3 1.24.3 which is incompatible.\u001b[0m\n",
"Installing collected packages: gunicorn, Mako, python-editor, alembic, pyyaml, smmap, gitdb, gitpython, websocket-client, docker, prometheus-flask-exporter, querystring-parser, databricks-cli, mlflow, tangled-up-in-unicode, multimethod, imagehash, visions, pydantic, tqdm, phik, htmlmin, pandas-profiling, threadpoolctl, scikit-learn, pyod, mlxtend, funcy, pyLDAvis, pynndescent, umap-learn, Boruta, yellowbrick, lightgbm, scikit-plot, imbalanced-learn, kmodes, pyaml, scikit-optimize, async-timeout, multidict, yarl, aiohttp, colorama, aiohttp-cors, redis, hiredis, aioredis, py-spy, opencensus-context, opencensus, blessings, gpustat, tensorboardX, ray, tune-sklearn, catboost, dash-table, brotli, flask-compress, dash-renderer, dash-core-components, dash-html-components, dash, dash-cytoscape, zope.interface, zope.event, gevent, lime, ppft, pox, pathos, SALib, slicer, shap, skope-rules, treeinterpreter, interpret-core, interpret, azure-core, cryptography, isodate, msrest, azure-storage-blob, jmespath, botocore, s3transfer, boto3, colorlog, cmaes, pyperclip, cmd2, pbr, stevedore, cliff, optuna, xgboost, pycaret\n",
" Found existing installation: PyYAML 3.13\n",
" Uninstalling PyYAML-3.13:\n",
" Successfully uninstalled PyYAML-3.13\n",
" Found existing installation: tqdm 4.41.1\n",
" Uninstalling tqdm-4.41.1:\n",
" Successfully uninstalled tqdm-4.41.1\n",
" Found existing installation: pandas-profiling 1.4.1\n",
" Uninstalling pandas-profiling-1.4.1:\n",
" Successfully uninstalled pandas-profiling-1.4.1\n",
" Found existing installation: scikit-learn 0.22.2.post1\n",
" Uninstalling scikit-learn-0.22.2.post1:\n",
" Successfully uninstalled scikit-learn-0.22.2.post1\n",
" Found existing installation: mlxtend 0.14.0\n",
" Uninstalling mlxtend-0.14.0:\n",
" Successfully uninstalled mlxtend-0.14.0\n",
" Found existing installation: yellowbrick 0.9.1\n",
" Uninstalling yellowbrick-0.9.1:\n",
" Successfully uninstalled yellowbrick-0.9.1\n",
" Found existing installation: lightgbm 2.2.3\n",
" Uninstalling lightgbm-2.2.3:\n",
" Successfully uninstalled lightgbm-2.2.3\n",
" Found existing installation: imbalanced-learn 0.4.3\n",
" Uninstalling imbalanced-learn-0.4.3:\n",
" Successfully uninstalled imbalanced-learn-0.4.3\n",
" Found existing installation: xgboost 0.90\n",
" Uninstalling xgboost-0.90:\n",
" Successfully uninstalled xgboost-0.90\n",
"Successfully installed Boruta-0.3 Mako-1.1.4 SALib-1.4.0.2 aiohttp-3.7.4.post0 aiohttp-cors-0.7.0 aioredis-1.3.1 alembic-1.4.1 async-timeout-3.0.1 azure-core-1.16.0 azure-storage-blob-12.8.1 blessings-1.7 boto3-1.17.108 botocore-1.20.108 brotli-1.0.9 catboost-0.26 cliff-3.8.0 cmaes-0.8.2 cmd2-2.1.2 colorama-0.4.4 colorlog-5.0.1 cryptography-3.4.7 dash-1.20.0 dash-core-components-1.16.0 dash-cytoscape-0.3.0 dash-html-components-1.1.3 dash-renderer-1.9.1 dash-table-4.11.3 databricks-cli-0.14.3 docker-5.0.0 flask-compress-1.10.1 funcy-1.16 gevent-21.1.2 gitdb-4.0.7 gitpython-3.1.18 gpustat-0.6.0 gunicorn-20.1.0 hiredis-2.0.0 htmlmin-0.1.12 imagehash-4.2.0 imbalanced-learn-0.7.0 interpret-0.2.5 interpret-core-0.2.5 isodate-0.6.0 jmespath-0.10.0 kmodes-0.11.0 lightgbm-3.2.1 lime-0.2.0.1 mlflow-1.18.0 mlxtend-0.18.0 msrest-0.6.21 multidict-5.1.0 multimethod-1.4 opencensus-0.7.13 opencensus-context-0.1.2 optuna-2.8.0 pandas-profiling-3.0.0 pathos-0.2.8 pbr-5.6.0 phik-0.11.2 pox-0.3.0 ppft-1.6.6.4 prometheus-flask-exporter-0.18.2 py-spy-0.3.7 pyLDAvis-3.3.1 pyaml-20.4.0 pycaret-2.3.2 pydantic-1.8.2 pynndescent-0.5.4 pyod-0.9.0 pyperclip-1.8.2 python-editor-1.0.4 pyyaml-5.4.1 querystring-parser-1.2.4 ray-1.4.1 redis-3.5.3 s3transfer-0.4.2 scikit-learn-0.23.2 scikit-optimize-0.8.1 scikit-plot-0.3.7 shap-0.39.0 skope-rules-1.0.1 slicer-0.0.7 smmap-4.0.0 stevedore-3.3.0 tangled-up-in-unicode-0.1.0 tensorboardX-2.4 threadpoolctl-2.1.0 tqdm-4.61.2 treeinterpreter-0.2.3 tune-sklearn-0.4.0 umap-learn-0.5.1 visions-0.7.1 websocket-client-1.1.0 xgboost-1.4.2 yarl-1.6.3 yellowbrick-1.3.post1 zope.event-4.5.0 zope.interface-5.4.0\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "CTjJfteJSc1v"
},
"source": [
"# Call Library\n",
"import numpy as np\n",
"import pandas as pd \n",
"import matplotlib as mpl\n",
"import matplotlib.pyplot as plt\n",
"from pycaret.regression import *"
],
"execution_count": 3,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "E9ErEgzQVa8o",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "35c44d25-f7f4-44d0-87d6-f9d8d0f8f2ba"
},
"source": [
"# Lets get the data - Because i'm poor, let's say i put it in Drive :>\n",
"\n",
"from google.colab import drive\n",
"drive.mount('/content/drive')"
],
"execution_count": 4,
"outputs": [
{
"output_type": "stream",
"text": [
"Mounted at /content/drive\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "F28UI2njZNCm",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "33cbfdd4-abd9-4fcc-f885-bfe089aba1e7"
},
"source": [
"!ls drive/MyDrive/Blibli/data/processed"
],
"execution_count": 5,
"outputs": [
{
"output_type": "stream",
"text": [
"dataset-supervised.pkl\t\t IDN_geo_cleaned.csv\n",
"dataset-supervised-processed.pkl seller_dataset_cleaned.csv\n",
"dataset-supervised-pycaret.pkl\t user_dataset_cleaned.csv\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "Gtbz0GcgTInB"
},
"source": [
"filename = \"drive/MyDrive/Blibli/data/processed/dataset-supervised-pycaret.pkl\"\n",
"data = pd.read_pickle(filename)"
],
"execution_count": 6,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "I8_CtzaoZjqR",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 224
},
"outputId": "73766121-4da3-493d-e99f-733f1b5cae99"
},
"source": [
"data.head()"
],
"execution_count": 7,
"outputs": [
{
"output_type": "execute_result",
"data": {
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" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>total_unique_item</th>\n",
" <th>product_volume_cm3_per_item</th>\n",
" <th>product_weight_g_per_item</th>\n",
" <th>order_date</th>\n",
" <th>order_day_of_week</th>\n",
" <th>order_day_of_month</th>\n",
" <th>order_quarter</th>\n",
" <th>order_is_weekend</th>\n",
" <th>order_hour</th>\n",
" <th>order_daytime</th>\n",
" <th>order_approved_date</th>\n",
" <th>order_approved_day_of_week</th>\n",
" <th>order_approved_day_of_month</th>\n",
" <th>order_approved_quarter</th>\n",
" <th>order_approved_is_weekend</th>\n",
" <th>order_approved_hour</th>\n",
" <th>order_approved_daytime</th>\n",
" <th>pickup_limit_date</th>\n",
" <th>pickup_limit_day_of_week</th>\n",
" <th>pickup_limit_day_of_month</th>\n",
" <th>pickup_limit_quarter</th>\n",
" <th>pickup_limit_is_weekend</th>\n",
" <th>estimated_date_delivery</th>\n",
" <th>actual_date_delivery</th>\n",
" <th>wd_approved_interval</th>\n",
" <th>wd_actual_delivery_interval</th>\n",
" <th>wd_estimated_delivery_interval</th>\n",
" <th>wd_pickup_limit_interval</th>\n",
" <th>is_same_area</th>\n",
" <th>log_shipping_cost</th>\n",
" <th>log_total_price</th>\n",
" <th>log_aov</th>\n",
" <th>cbrt_original_distance</th>\n",
" <th>log_pickup_distance</th>\n",
" <th>log_shipping_distance</th>\n",
" <th>log_delivering_distance</th>\n",
" <th>log_package_volume_cm3</th>\n",
" <th>log_package_weight_g</th>\n",
" <th>log_product_volume_cm3_per_item</th>\n",
" <th>log_product_weight_g_per_item</th>\n",
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" <td>2017-12-14</td>\n",
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" <td>2018-01-04</td>\n",
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" <td>9.898475</td>\n",
" <td>9.898475</td>\n",
" <td>8.589683</td>\n",
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" <td>7.901007</td>\n",
" <td>5.303305</td>\n",
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" <tr>\n",
" <th>1</th>\n",
" <td>1</td>\n",
" <td>5760.0</td>\n",
" <td>2000.0</td>\n",
" <td>2018-03-19</td>\n",
" <td>1</td>\n",
" <td>19</td>\n",
" <td>1.0</td>\n",
" <td>False</td>\n",
" <td>18</td>\n",
" <td>Evening</td>\n",
" <td>2018-03-20</td>\n",
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" <td>2018-03-29</td>\n",
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" <td>11.891019</td>\n",
" <td>8.196526</td>\n",
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" <td>2.276990</td>\n",
" <td>8.658693</td>\n",
" <td>7.601402</td>\n",
" <td>8.658693</td>\n",
" <td>7.601402</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>1</td>\n",
" <td>1827.0</td>\n",
" <td>850.0</td>\n",
" <td>2018-07-02</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>3.0</td>\n",
" <td>False</td>\n",
" <td>13</td>\n",
" <td>Afternoon</td>\n",
" <td>2018-07-02</td>\n",
" <td>1.0</td>\n",
" <td>2.0</td>\n",
" <td>3.0</td>\n",
" <td>False</td>\n",
" <td>14.0</td>\n",
" <td>Afternoon</td>\n",
" <td>2018-07-06</td>\n",
" <td>5</td>\n",
" <td>6</td>\n",
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" <td>6.746412</td>\n",
" <td>7.510431</td>\n",
" <td>6.746412</td>\n",
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" <td>20.0</td>\n",
" <td>Evening</td>\n",
" <td>2018-05-16</td>\n",
" <td>3</td>\n",
" <td>16</td>\n",
" <td>2.0</td>\n",
" <td>False</td>\n",
" <td>2018-05-22</td>\n",
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" <td>5</td>\n",
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" <td>9.079206</td>\n",
" <td>11.877569</td>\n",
" <td>11.877569</td>\n",
" <td>8.352433</td>\n",
" <td>1.552185</td>\n",
" <td>2.857277</td>\n",
" <td>2.031957</td>\n",
" <td>8.970305</td>\n",
" <td>6.150603</td>\n",
" <td>8.970305</td>\n",
" <td>6.150603</td>\n",
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" <td>1</td>\n",
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" <td>2500.0</td>\n",
" <td>2017-03-23</td>\n",
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" <td>1.0</td>\n",
" <td>False</td>\n",
" <td>12</td>\n",
" <td>Afternoon</td>\n",
" <td>2017-03-23</td>\n",
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" <td>23.0</td>\n",
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" <td>False</td>\n",
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" <td>2.462932</td>\n",
" <td>10.608242</td>\n",
" <td>7.824446</td>\n",
" <td>10.608242</td>\n",
" <td>7.824446</td>\n",
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" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" total_unique_item product_volume_cm3_per_item product_weight_g_per_item \\\n",
"0 1 2700.0 200.0 \n",
"1 1 5760.0 2000.0 \n",
"2 1 1827.0 850.0 \n",
"3 1 7866.0 468.0 \n",
"4 1 40467.0 2500.0 \n",
"\n",
" order_date order_day_of_week order_day_of_month order_quarter \\\n",
"0 2017-12-10 7 10 4.0 \n",
"1 2018-03-19 1 19 1.0 \n",
"2 2018-07-02 1 2 3.0 \n",
"3 2018-05-11 5 11 2.0 \n",
"4 2017-03-23 4 23 1.0 \n",
"\n",
" order_is_weekend order_hour order_daytime order_approved_date \\\n",
"0 True 11 Afternoon 2017-12-10 \n",
"1 False 18 Evening 2018-03-20 \n",
"2 False 13 Afternoon 2018-07-02 \n",
"3 False 20 Evening 2018-05-11 \n",
"4 False 12 Afternoon 2017-03-23 \n",
"\n",
" order_approved_day_of_week order_approved_day_of_month \\\n",
"0 7.0 10.0 \n",
"1 2.0 20.0 \n",
"2 1.0 2.0 \n",
"3 5.0 11.0 \n",
"4 4.0 23.0 \n",
"\n",
" order_approved_quarter order_approved_is_weekend order_approved_hour \\\n",
"0 4.0 True 12.0 \n",
"1 1.0 False 18.0 \n",
"2 3.0 False 14.0 \n",
"3 2.0 False 20.0 \n",
"4 1.0 False 13.0 \n",
"\n",
" order_approved_daytime pickup_limit_date pickup_limit_day_of_week \\\n",
"0 Afternoon 2017-12-14 4 \n",
"1 Evening 2018-03-26 1 \n",
"2 Afternoon 2018-07-06 5 \n",
"3 Evening 2018-05-16 3 \n",
"4 Afternoon 2017-03-29 3 \n",
"\n",
" pickup_limit_day_of_month pickup_limit_quarter pickup_limit_is_weekend \\\n",
"0 14 4.0 False \n",
"1 26 1.0 False \n",
"2 6 3.0 False \n",
"3 16 2.0 False \n",
"4 29 1.0 False \n",
"\n",
" estimated_date_delivery actual_date_delivery wd_approved_interval \\\n",
"0 2018-01-04 2017-12-18 0 \n",
"1 2018-03-29 2018-03-29 1 \n",
"2 2018-07-23 2018-07-04 0 \n",
"3 2018-05-22 2018-05-18 0 \n",
"4 2017-04-20 2017-04-07 0 \n",
"\n",
" wd_actual_delivery_interval wd_estimated_delivery_interval \\\n",
"0 8 25 \n",
"1 9 9 \n",
"2 2 21 \n",
"3 7 11 \n",
"4 15 28 \n",
"\n",
" wd_pickup_limit_interval is_same_area log_shipping_cost log_total_price \\\n",
"0 4 False 9.380168 9.898475 \n",
"1 6 False 9.363147 11.891019 \n",
"2 4 False 9.341456 10.896554 \n",
"3 5 False 9.079206 11.877569 \n",
"4 6 False 10.146865 11.607326 \n",
"\n",
" log_aov cbrt_original_distance log_pickup_distance \\\n",
"0 9.898475 8.589683 1.329263 \n",
"1 11.891019 8.196526 1.329263 \n",
"2 10.896554 10.513610 1.329263 \n",
"3 11.877569 8.352433 1.552185 \n",
"4 11.607326 7.323653 1.566181 \n",
"\n",
" log_shipping_distance log_delivering_distance log_package_volume_cm3 \\\n",
"0 2.857277 2.027167 7.901007 \n",
"1 2.857277 2.276990 8.658693 \n",
"2 2.857277 2.626134 7.510431 \n",
"3 2.857277 2.031957 8.970305 \n",
"4 2.857277 2.462932 10.608242 \n",
"\n",
" log_package_weight_g log_product_volume_cm3_per_item \\\n",
"0 5.303305 7.901007 \n",
"1 7.601402 8.658693 \n",
"2 6.746412 7.510431 \n",
"3 6.150603 8.970305 \n",
"4 7.824446 10.608242 \n",
"\n",
" log_product_weight_g_per_item \n",
"0 5.303305 \n",
"1 7.601402 \n",
"2 6.746412 \n",
"3 6.150603 \n",
"4 7.824446 "
]
},
"metadata": {
"tags": []
},
"execution_count": 7
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "VNiQgWh4Zk-c",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "77707a98-b16f-4523-c5fa-7197d271364e"
},
"source": [
"data.info()"
],
"execution_count": 8,
"outputs": [
{
"output_type": "stream",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"Int64Index: 90183 entries, 0 to 95126\n",
"Data columns (total 40 columns):\n",
" # Column Non-Null Count Dtype \n",
"--- ------ -------------- ----- \n",
" 0 total_unique_item 90183 non-null int64 \n",
" 1 product_volume_cm3_per_item 90183 non-null float64 \n",
" 2 product_weight_g_per_item 90183 non-null float64 \n",
" 3 order_date 90183 non-null datetime64[ns]\n",
" 4 order_day_of_week 90183 non-null int64 \n",
" 5 order_day_of_month 90183 non-null int64 \n",
" 6 order_quarter 90183 non-null float64 \n",
" 7 order_is_weekend 90183 non-null bool \n",
" 8 order_hour 90183 non-null int64 \n",
" 9 order_daytime 90183 non-null object \n",
" 10 order_approved_date 90183 non-null datetime64[ns]\n",
" 11 order_approved_day_of_week 90183 non-null float64 \n",
" 12 order_approved_day_of_month 90183 non-null float64 \n",
" 13 order_approved_quarter 90183 non-null float64 \n",
" 14 order_approved_is_weekend 90183 non-null bool \n",
" 15 order_approved_hour 90183 non-null float64 \n",
" 16 order_approved_daytime 90183 non-null object \n",
" 17 pickup_limit_date 90183 non-null datetime64[ns]\n",
" 18 pickup_limit_day_of_week 90183 non-null int64 \n",
" 19 pickup_limit_day_of_month 90183 non-null int64 \n",
" 20 pickup_limit_quarter 90183 non-null float64 \n",
" 21 pickup_limit_is_weekend 90183 non-null bool \n",
" 22 estimated_date_delivery 90183 non-null datetime64[ns]\n",
" 23 actual_date_delivery 90183 non-null datetime64[ns]\n",
" 24 wd_approved_interval 90183 non-null int64 \n",
" 25 wd_actual_delivery_interval 90183 non-null int64 \n",
" 26 wd_estimated_delivery_interval 90183 non-null int64 \n",
" 27 wd_pickup_limit_interval 90183 non-null int64 \n",
" 28 is_same_area 90183 non-null bool \n",
" 29 log_shipping_cost 90183 non-null float64 \n",
" 30 log_total_price 90183 non-null float64 \n",
" 31 log_aov 90183 non-null float64 \n",
" 32 cbrt_original_distance 90183 non-null float64 \n",
" 33 log_pickup_distance 90183 non-null float64 \n",
" 34 log_shipping_distance 90183 non-null float64 \n",
" 35 log_delivering_distance 90183 non-null float64 \n",
" 36 log_package_volume_cm3 90183 non-null float64 \n",
" 37 log_package_weight_g 90183 non-null float64 \n",
" 38 log_product_volume_cm3_per_item 90183 non-null float64 \n",
" 39 log_product_weight_g_per_item 90183 non-null float64 \n",
"dtypes: bool(4), datetime64[ns](5), float64(19), int64(10), object(2)\n",
"memory usage: 25.8+ MB\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "MmvagIKdZcAv"
},
"source": [
"## 🔧 Setup Pycaret\n",
"\n",
"To use pycaret's automl, you can basically enter the input data, the desired target column name, the number of folds of the cross validation, and the rest of the settings as desired.\n",
"\n",
"There are many different settings you can put in. **Normalize, remove outliers, etc**. You can add your insights.\n",
"\n",
"And here too, some missing values can be resolved, and `numeric_inmputation` was used to fill the numerical missing values with the median. (`Fare` feature)"
]
},
{
"cell_type": "code",
"metadata": {
"id": "dAloN9roZeEz",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 1000,
"referenced_widgets": [
"b485e49345604817ad7ff6a9d96e5749",
"16d56fe07070426c839192db696d5e4d",
"f15279dd33c84ce0946d937e9074856f"
]
},
"outputId": "92b8a1aa-9aba-4d80-ded7-667034520dda"
},
"source": [
"target = 'wd_actual_delivery_interval'\n",
"detector = 'wd_estimated_delivery_interval'\n",
"\n",
"cat_num_features =['order_quarter', 'order_is_weekend',\n",
" 'order_approved_quarter', 'order_approved_is_weekend',\n",
" 'pickup_limit_quarter', 'pickup_limit_is_weekend', \"is_same_area\"]\n",
"cat_str_features = [\"order_daytime\", \"order_approved_daytime\"]\n",
"\n",
"\n",
"date_feature = ['order_date', 'order_approved_date', \"pickup_limit_date\"]\n",
"hour_feature = ['order_hour', 'order_daytime', 'order_approved_hour', 'order_approved_daytime']\n",
"\n",
"ignore_features = ['order_day_of_week', 'order_day_of_month', 'order_quarter', \n",
" 'order_approved_day_of_week', 'order_approved_day_of_month', 'order_approved_quarter',\n",
" 'pickup_limit_day_of_week', 'pickup_limit_day_of_month', 'pickup_limit_quarter',\n",
" 'estimated_date_delivery', 'actual_date_delivery']\n",
"\n",
"regression = setup(\n",
" data, \n",
" target = target, session_id=123, \n",
" log_experiment=True, \n",
" experiment_name='shipping-prediction',\n",
" categorical_features = cat_num_features + cat_str_features,\n",
" numeric_features = ['wd_approved_interval', 'total_unique_item'],\n",
" date_features=date_feature,\n",
" remove_multicollinearity=True,\n",
" feature_selection=True,\n",
" ignore_low_variance=True,\n",
" ignore_features = [detector] + ignore_features,\n",
" silent=True\n",
")"
],
"execution_count": 9,
"outputs": [
{
"output_type": "display_data",
"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>Description</th>\n",
" <th>Value</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>session_id</td>\n",
" <td>123</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Target</td>\n",
" <td>wd_actual_delivery_interval</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>Original Data</td>\n",
" <td>(90183, 40)</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>Missing Values</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>Numeric Features</td>\n",
" <td>18</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>Categorical Features</td>\n",
" <td>6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>Ordinal Features</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>High Cardinality Features</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>High Cardinality Method</td>\n",
" <td>None</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>Transformed Train Set</td>\n",
" <td>(63128, 73)</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>Transformed Test Set</td>\n",
" <td>(27055, 73)</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>Shuffle Train-Test</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>Stratify Train-Test</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>Fold Generator</td>\n",
" <td>KFold</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>Fold Number</td>\n",
" <td>10</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>CPU Jobs</td>\n",
" <td>-1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>Use GPU</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>Log Experiment</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>Experiment Name</td>\n",
" <td>shipping-prediction</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>USI</td>\n",
" <td>439a</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>Imputation Type</td>\n",
" <td>simple</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21</th>\n",
" <td>Iterative Imputation Iteration</td>\n",
" <td>None</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22</th>\n",
" <td>Numeric Imputer</td>\n",
" <td>mean</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23</th>\n",
" <td>Iterative Imputation Numeric Model</td>\n",
" <td>None</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24</th>\n",
" <td>Categorical Imputer</td>\n",
" <td>constant</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25</th>\n",
" <td>Iterative Imputation Categorical Model</td>\n",
" <td>None</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26</th>\n",
" <td>Unknown Categoricals Handling</td>\n",
" <td>least_frequent</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27</th>\n",
" <td>Normalize</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>28</th>\n",
" <td>Normalize Method</td>\n",
" <td>None</td>\n",
" </tr>\n",
" <tr>\n",
" <th>29</th>\n",
" <td>Transformation</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>30</th>\n",
" <td>Transformation Method</td>\n",
" <td>None</td>\n",
" </tr>\n",
" <tr>\n",
" <th>31</th>\n",
" <td>PCA</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>32</th>\n",
" <td>PCA Method</td>\n",
" <td>None</td>\n",
" </tr>\n",
" <tr>\n",
" <th>33</th>\n",
" <td>PCA Components</td>\n",
" <td>None</td>\n",
" </tr>\n",
" <tr>\n",
" <th>34</th>\n",
" <td>Ignore Low Variance</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>35</th>\n",
" <td>Combine Rare Levels</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>36</th>\n",
" <td>Rare Level Threshold</td>\n",
" <td>None</td>\n",
" </tr>\n",
" <tr>\n",
" <th>37</th>\n",
" <td>Numeric Binning</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>38</th>\n",
" <td>Remove Outliers</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>39</th>\n",
" <td>Outliers Threshold</td>\n",
" <td>None</td>\n",
" </tr>\n",
" <tr>\n",
" <th>40</th>\n",
" <td>Remove Multicollinearity</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>41</th>\n",
" <td>Multicollinearity Threshold</td>\n",
" <td>0.9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>42</th>\n",
" <td>Remove Perfect Collinearity</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>43</th>\n",
" <td>Clustering</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>44</th>\n",
" <td>Clustering Iteration</td>\n",
" <td>None</td>\n",
" </tr>\n",
" <tr>\n",
" <th>45</th>\n",
" <td>Polynomial Features</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>46</th>\n",
" <td>Polynomial Degree</td>\n",
" <td>None</td>\n",
" </tr>\n",
" <tr>\n",
" <th>47</th>\n",
" <td>Trignometry Features</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>48</th>\n",
" <td>Polynomial Threshold</td>\n",
" <td>None</td>\n",
" </tr>\n",
" <tr>\n",
" <th>49</th>\n",
" <td>Group Features</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50</th>\n",
" <td>Feature Selection</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>51</th>\n",
" <td>Feature Selection Method</td>\n",
" <td>classic</td>\n",
" </tr>\n",
" <tr>\n",
" <th>52</th>\n",
" <td>Features Selection Threshold</td>\n",
" <td>0.8</td>\n",
" </tr>\n",
" <tr>\n",
" <th>53</th>\n",
" <td>Feature Interaction</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>54</th>\n",
" <td>Feature Ratio</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>55</th>\n",
" <td>Interaction Threshold</td>\n",
" <td>None</td>\n",
" </tr>\n",
" <tr>\n",
" <th>56</th>\n",
" <td>Transform Target</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>57</th>\n",
" <td>Transform Target Method</td>\n",
" <td>box-cox</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Description Value\n",
"0 session_id 123\n",
"1 Target wd_actual_delivery_interval\n",
"2 Original Data (90183, 40)\n",
"3 Missing Values False\n",
"4 Numeric Features 18\n",
"5 Categorical Features 6\n",
"6 Ordinal Features False\n",
"7 High Cardinality Features False\n",
"8 High Cardinality Method None\n",
"9 Transformed Train Set (63128, 73)\n",
"10 Transformed Test Set (27055, 73)\n",
"11 Shuffle Train-Test True\n",
"12 Stratify Train-Test False\n",
"13 Fold Generator KFold\n",
"14 Fold Number 10\n",
"15 CPU Jobs -1\n",
"16 Use GPU False\n",
"17 Log Experiment True\n",
"18 Experiment Name shipping-prediction\n",
"19 USI 439a\n",
"20 Imputation Type simple\n",
"21 Iterative Imputation Iteration None\n",
"22 Numeric Imputer mean\n",
"23 Iterative Imputation Numeric Model None\n",
"24 Categorical Imputer constant\n",
"25 Iterative Imputation Categorical Model None\n",
"26 Unknown Categoricals Handling least_frequent\n",
"27 Normalize False\n",
"28 Normalize Method None\n",
"29 Transformation False\n",
"30 Transformation Method None\n",
"31 PCA False\n",
"32 PCA Method None\n",
"33 PCA Components None\n",
"34 Ignore Low Variance True\n",
"35 Combine Rare Levels False\n",
"36 Rare Level Threshold None\n",
"37 Numeric Binning False\n",
"38 Remove Outliers False\n",
"39 Outliers Threshold None\n",
"40 Remove Multicollinearity True\n",
"41 Multicollinearity Threshold 0.9\n",
"42 Remove Perfect Collinearity True\n",
"43 Clustering False\n",
"44 Clustering Iteration None\n",
"45 Polynomial Features False\n",
"46 Polynomial Degree None\n",
"47 Trignometry Features False\n",
"48 Polynomial Threshold None\n",
"49 Group Features False\n",
"50 Feature Selection True\n",
"51 Feature Selection Method classic\n",
"52 Features Selection Threshold 0.8\n",
"53 Feature Interaction False\n",
"54 Feature Ratio False\n",
"55 Interaction Threshold None\n",
"56 Transform Target False\n",
"57 Transform Target Method box-cox"
]
},
"metadata": {
"tags": []
}
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "lw5X2VAQaJUC"
},
"source": [
"## ✅ Benchmark\n",
"\n",
"Models can be made individually, but they provide benchmarks by default.\n",
"\n",
"It provides a benchmark by turning all representative models used in machine learning with a small number of iters.\n",
"\n",
"Each model shows scores of **MAE, RMSE, MSE, R2 etc**."
]
},
{
"cell_type": "code",
"metadata": {
"id": "sizXB40dZSwG",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 328,
"referenced_widgets": [
"d53efe617bfb49b698b1dc577a228704",
"5d5b91b72f114f90a4acb8da915b16a5",
"ee10c668bc0047818c40a013bc206253"
]
},
"outputId": "3d52223e-f8b6-43f4-aa9f-52fa3201a0af"
},
"source": [
"best_model = compare_models(sort = 'RMSLE', \n",
" include = ['catboost', 'xgboost', 'br','lr', 'ridge', 'ransac', 'knn','dt', 'rf'],\n",
" fold=5, \n",
" n_select = 5\n",
" )"
],
"execution_count": 10,
"outputs": [
{
"output_type": "display_data",
"data": {
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" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Model</th>\n",
" <th>MAE</th>\n",
" <th>MSE</th>\n",
" <th>RMSE</th>\n",
" <th>R2</th>\n",
" <th>RMSLE</th>\n",
" <th>MAPE</th>\n",
" <th>TT (Sec)</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>catboost</th>\n",
" <td>CatBoost Regressor</td>\n",
" <td>3.6652</td>\n",
" <td>22.9532</td>\n",
" <td>4.7908</td>\n",
" <td>0.3732</td>\n",
" <td>0.4390</td>\n",
" <td>0.5064</td>\n",
" <td>16.444</td>\n",
" </tr>\n",
" <tr>\n",
" <th>xgboost</th>\n",
" <td>Extreme Gradient Boosting</td>\n",
" <td>3.7023</td>\n",
" <td>23.4585</td>\n",
" <td>4.8433</td>\n",
" <td>0.3594</td>\n",
" <td>0.4437</td>\n",
" <td>0.5089</td>\n",
" <td>20.368</td>\n",
" </tr>\n",
" <tr>\n",
" <th>rf</th>\n",
" <td>Random Forest Regressor</td>\n",
" <td>3.7948</td>\n",
" <td>24.2652</td>\n",
" <td>4.9258</td>\n",
" <td>0.3374</td>\n",
" <td>0.4553</td>\n",
" <td>0.5404</td>\n",
" <td>68.198</td>\n",
" </tr>\n",
" <tr>\n",
" <th>ridge</th>\n",
" <td>Ridge Regression</td>\n",
" <td>4.3614</td>\n",
" <td>32.3981</td>\n",
" <td>5.6819</td>\n",
" <td>0.1158</td>\n",
" <td>0.5274</td>\n",
" <td>0.6826</td>\n",
" <td>0.072</td>\n",
" </tr>\n",
" <tr>\n",
" <th>lr</th>\n",
" <td>Linear Regression</td>\n",
" <td>4.3680</td>\n",
" <td>32.4043</td>\n",
" <td>5.6828</td>\n",
" <td>0.1156</td>\n",
" <td>0.5318</td>\n",
" <td>0.6843</td>\n",
" <td>0.162</td>\n",
" </tr>\n",
" <tr>\n",
" <th>br</th>\n",
" <td>Bayesian Ridge</td>\n",
" <td>4.3625</td>\n",
" <td>32.4174</td>\n",
" <td>5.6835</td>\n",
" <td>0.1153</td>\n",
" <td>0.5276</td>\n",
" <td>0.6832</td>\n",
" <td>0.458</td>\n",
" </tr>\n",
" <tr>\n",
" <th>knn</th>\n",
" <td>K Neighbors Regressor</td>\n",
" <td>4.9280</td>\n",
" <td>38.9195</td>\n",
" <td>6.2385</td>\n",
" <td>-0.0629</td>\n",
" <td>0.5940</td>\n",
" <td>0.7857</td>\n",
" <td>1.110</td>\n",
" </tr>\n",
" <tr>\n",
" <th>dt</th>\n",
" <td>Decision Tree Regressor</td>\n",
" <td>5.2294</td>\n",
" <td>48.8952</td>\n",
" <td>6.9925</td>\n",
" <td>-0.3354</td>\n",
" <td>0.6214</td>\n",
" <td>0.6839</td>\n",
" <td>1.200</td>\n",
" </tr>\n",
" <tr>\n",
" <th>ransac</th>\n",
" <td>Random Sample Consensus</td>\n",
" <td>6.6191</td>\n",
" <td>92.4359</td>\n",
" <td>9.5263</td>\n",
" <td>-1.5214</td>\n",
" <td>0.7807</td>\n",
" <td>0.9542</td>\n",
" <td>0.580</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Model MAE MSE RMSE R2 RMSLE \\\n",
"catboost CatBoost Regressor 3.6652 22.9532 4.7908 0.3732 0.4390 \n",
"xgboost Extreme Gradient Boosting 3.7023 23.4585 4.8433 0.3594 0.4437 \n",
"rf Random Forest Regressor 3.7948 24.2652 4.9258 0.3374 0.4553 \n",
"ridge Ridge Regression 4.3614 32.3981 5.6819 0.1158 0.5274 \n",
"lr Linear Regression 4.3680 32.4043 5.6828 0.1156 0.5318 \n",
"br Bayesian Ridge 4.3625 32.4174 5.6835 0.1153 0.5276 \n",
"knn K Neighbors Regressor 4.9280 38.9195 6.2385 -0.0629 0.5940 \n",
"dt Decision Tree Regressor 5.2294 48.8952 6.9925 -0.3354 0.6214 \n",
"ransac Random Sample Consensus 6.6191 92.4359 9.5263 -1.5214 0.7807 \n",
"\n",
" MAPE TT (Sec) \n",
"catboost 0.5064 16.444 \n",
"xgboost 0.5089 20.368 \n",
"rf 0.5404 68.198 \n",
"ridge 0.6826 0.072 \n",
"lr 0.6843 0.162 \n",
"br 0.6832 0.458 \n",
"knn 0.7857 1.110 \n",
"dt 0.6839 1.200 \n",
"ransac 0.9542 0.580 "
]
},
"metadata": {
"tags": []
}
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "1zKNMAtRlugH"
},
"source": [
"## 🆕 Create Model\n",
"\n",
"From above model comparison, lets pick 5 top best model :\n",
"- "
]
},
{
"cell_type": "code",
"metadata": {
"id": "1HPELknbn24B",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 421,
"referenced_widgets": [
"750889fcfc644ca987323ce56f497f7b",
"b8a686fc6fd94b4c8672ca783720b230",
"e21f283b94e345368a27544760727911"
]
},
"outputId": "a5992d6a-6dbb-4ddd-9c5d-96e47fbc5dc4"
},
"source": [
"lr = create_model('lr')"
],
"execution_count": 11,
"outputs": [
{
"output_type": "display_data",
"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>MAE</th>\n",
" <th>MSE</th>\n",
" <th>RMSE</th>\n",
" <th>R2</th>\n",
" <th>RMSLE</th>\n",
" <th>MAPE</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>4.3367</td>\n",
" <td>29.9427</td>\n",
" <td>5.4720</td>\n",
" <td>0.1792</td>\n",
" <td>0.5224</td>\n",
" <td>0.6673</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>4.4121</td>\n",
" <td>30.6911</td>\n",
" <td>5.5400</td>\n",
" <td>0.1684</td>\n",
" <td>0.5359</td>\n",
" <td>0.7020</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>4.3435</td>\n",
" <td>30.4008</td>\n",
" <td>5.5137</td>\n",
" <td>0.1632</td>\n",
" <td>0.5266</td>\n",
" <td>0.6771</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>4.3830</td>\n",
" <td>30.5928</td>\n",
" <td>5.5311</td>\n",
" <td>0.1639</td>\n",
" <td>0.5260</td>\n",
" <td>0.6688</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>4.4384</td>\n",
" <td>47.9841</td>\n",
" <td>6.9271</td>\n",
" <td>-0.2745</td>\n",
" <td>0.5318</td>\n",
" <td>0.6909</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>4.3661</td>\n",
" <td>30.7274</td>\n",
" <td>5.5432</td>\n",
" <td>0.1550</td>\n",
" <td>0.5313</td>\n",
" <td>0.6840</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>4.3055</td>\n",
" <td>29.6480</td>\n",
" <td>5.4450</td>\n",
" <td>0.1761</td>\n",
" <td>0.5265</td>\n",
" <td>0.6779</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>4.3399</td>\n",
" <td>30.1307</td>\n",
" <td>5.4891</td>\n",
" <td>0.1715</td>\n",
" <td>0.5312</td>\n",
" <td>0.6830</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>4.3987</td>\n",
" <td>31.2868</td>\n",
" <td>5.5935</td>\n",
" <td>0.1600</td>\n",
" <td>0.5384</td>\n",
" <td>0.7004</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>4.3548</td>\n",
" <td>30.4014</td>\n",
" <td>5.5137</td>\n",
" <td>0.1611</td>\n",
" <td>0.5371</td>\n",
" <td>0.6942</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Mean</th>\n",
" <td>4.3679</td>\n",
" <td>32.1806</td>\n",
" <td>5.6568</td>\n",
" <td>0.1224</td>\n",
" <td>0.5307</td>\n",
" <td>0.6846</td>\n",
" </tr>\n",
" <tr>\n",
" <th>SD</th>\n",
" <td>0.0381</td>\n",
" <td>5.2854</td>\n",
" <td>0.4252</td>\n",
" <td>0.1325</td>\n",
" <td>0.0051</td>\n",
" <td>0.0116</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" MAE MSE RMSE R2 RMSLE MAPE\n",
"0 4.3367 29.9427 5.4720 0.1792 0.5224 0.6673\n",
"1 4.4121 30.6911 5.5400 0.1684 0.5359 0.7020\n",
"2 4.3435 30.4008 5.5137 0.1632 0.5266 0.6771\n",
"3 4.3830 30.5928 5.5311 0.1639 0.5260 0.6688\n",
"4 4.4384 47.9841 6.9271 -0.2745 0.5318 0.6909\n",
"5 4.3661 30.7274 5.5432 0.1550 0.5313 0.6840\n",
"6 4.3055 29.6480 5.4450 0.1761 0.5265 0.6779\n",
"7 4.3399 30.1307 5.4891 0.1715 0.5312 0.6830\n",
"8 4.3987 31.2868 5.5935 0.1600 0.5384 0.7004\n",
"9 4.3548 30.4014 5.5137 0.1611 0.5371 0.6942\n",
"Mean 4.3679 32.1806 5.6568 0.1224 0.5307 0.6846\n",
"SD 0.0381 5.2854 0.4252 0.1325 0.0051 0.0116"
]
},
"metadata": {
"tags": []
}
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "T6K3qIsEn26j",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 421,
"referenced_widgets": [
"17ad01dad94f4226a15a70991489452f",
"519f79e4e54c4f8ea5e30200b8e2fda7",
"6a1bc197d6e9415caf392326d28fb04d"
]
},
"outputId": "2caa50ac-ac1f-462d-be56-cefc8bf2b2f8"
},
"source": [
"ridge = create_model('ridge')"
],
"execution_count": 12,
"outputs": [
{
"output_type": "display_data",
"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>MAE</th>\n",
" <th>MSE</th>\n",
" <th>RMSE</th>\n",
" <th>R2</th>\n",
" <th>RMSLE</th>\n",
" <th>MAPE</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>4.3283</td>\n",
" <td>29.8865</td>\n",
" <td>5.4669</td>\n",
" <td>0.1807</td>\n",
" <td>0.5194</td>\n",
" <td>0.6649</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>4.4067</td>\n",
" <td>30.6597</td>\n",
" <td>5.5371</td>\n",
" <td>0.1693</td>\n",
" <td>0.5326</td>\n",
" <td>0.7000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>4.3429</td>\n",
" <td>30.3914</td>\n",
" <td>5.5128</td>\n",
" <td>0.1635</td>\n",
" <td>0.5259</td>\n",
" <td>0.6772</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>4.3793</td>\n",
" <td>30.6098</td>\n",
" <td>5.5326</td>\n",
" <td>0.1635</td>\n",
" <td>0.5226</td>\n",
" <td>0.6673</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>4.4408</td>\n",
" <td>48.4608</td>\n",
" <td>6.9614</td>\n",
" <td>-0.2872</td>\n",
" <td>0.5324</td>\n",
" <td>0.6930</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>4.3599</td>\n",
" <td>30.7071</td>\n",
" <td>5.5414</td>\n",
" <td>0.1556</td>\n",
" <td>0.5282</td>\n",
" <td>0.6826</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>4.3003</td>\n",
" <td>29.6232</td>\n",
" <td>5.4427</td>\n",
" <td>0.1768</td>\n",
" <td>0.5246</td>\n",
" <td>0.6768</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>4.3298</td>\n",
" <td>30.1071</td>\n",
" <td>5.4870</td>\n",
" <td>0.1721</td>\n",
" <td>0.5248</td>\n",
" <td>0.6798</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>4.3968</td>\n",
" <td>31.2992</td>\n",
" <td>5.5946</td>\n",
" <td>0.1597</td>\n",
" <td>0.5365</td>\n",
" <td>0.6994</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>4.3457</td>\n",
" <td>30.3534</td>\n",
" <td>5.5094</td>\n",
" <td>0.1624</td>\n",
" <td>0.5290</td>\n",
" <td>0.6924</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Mean</th>\n",
" <td>4.3630</td>\n",
" <td>32.2098</td>\n",
" <td>5.6586</td>\n",
" <td>0.1216</td>\n",
" <td>0.5276</td>\n",
" <td>0.6833</td>\n",
" </tr>\n",
" <tr>\n",
" <th>SD</th>\n",
" <td>0.0404</td>\n",
" <td>5.4351</td>\n",
" <td>0.4361</td>\n",
" <td>0.1365</td>\n",
" <td>0.0049</td>\n",
" <td>0.0118</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" MAE MSE RMSE R2 RMSLE MAPE\n",
"0 4.3283 29.8865 5.4669 0.1807 0.5194 0.6649\n",
"1 4.4067 30.6597 5.5371 0.1693 0.5326 0.7000\n",
"2 4.3429 30.3914 5.5128 0.1635 0.5259 0.6772\n",
"3 4.3793 30.6098 5.5326 0.1635 0.5226 0.6673\n",
"4 4.4408 48.4608 6.9614 -0.2872 0.5324 0.6930\n",
"5 4.3599 30.7071 5.5414 0.1556 0.5282 0.6826\n",
"6 4.3003 29.6232 5.4427 0.1768 0.5246 0.6768\n",
"7 4.3298 30.1071 5.4870 0.1721 0.5248 0.6798\n",
"8 4.3968 31.2992 5.5946 0.1597 0.5365 0.6994\n",
"9 4.3457 30.3534 5.5094 0.1624 0.5290 0.6924\n",
"Mean 4.3630 32.2098 5.6586 0.1216 0.5276 0.6833\n",
"SD 0.0404 5.4351 0.4361 0.1365 0.0049 0.0118"
]
},
"metadata": {
"tags": []
}
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "m1z2GRsaprk2",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 421,
"referenced_widgets": [
"923bdc318c6e47a19d47b8eb0f5f207a",
"c68f2032dd6847cea92620b5ea572545",
"9707ff664f694c6fb99e3ff769a4a2eb"
]
},
"outputId": "39ac3557-4d9e-4183-ab65-249418c95438"
},
"source": [
"lightgbm = create_model('lightgbm')"
],
"execution_count": 13,
"outputs": [
{
"output_type": "display_data",
"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>MAE</th>\n",
" <th>MSE</th>\n",
" <th>RMSE</th>\n",
" <th>R2</th>\n",
" <th>RMSLE</th>\n",
" <th>MAPE</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>3.7143</td>\n",
" <td>23.2160</td>\n",
" <td>4.8183</td>\n",
" <td>0.3636</td>\n",
" <td>0.4408</td>\n",
" <td>0.5114</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>3.7446</td>\n",
" <td>23.7509</td>\n",
" <td>4.8735</td>\n",
" <td>0.3565</td>\n",
" <td>0.4501</td>\n",
" <td>0.5273</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>3.7005</td>\n",
" <td>23.3439</td>\n",
" <td>4.8316</td>\n",
" <td>0.3575</td>\n",
" <td>0.4442</td>\n",
" <td>0.5221</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>3.7879</td>\n",
" <td>24.1958</td>\n",
" <td>4.9189</td>\n",
" <td>0.3388</td>\n",
" <td>0.4483</td>\n",
" <td>0.5199</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>3.7767</td>\n",
" <td>24.3205</td>\n",
" <td>4.9316</td>\n",
" <td>0.3540</td>\n",
" <td>0.4528</td>\n",
" <td>0.5347</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>3.7474</td>\n",
" <td>23.6770</td>\n",
" <td>4.8659</td>\n",
" <td>0.3489</td>\n",
" <td>0.4472</td>\n",
" <td>0.5259</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>3.6834</td>\n",
" <td>22.8147</td>\n",
" <td>4.7765</td>\n",
" <td>0.3660</td>\n",
" <td>0.4426</td>\n",
" <td>0.5196</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>3.6721</td>\n",
" <td>22.8722</td>\n",
" <td>4.7825</td>\n",
" <td>0.3711</td>\n",
" <td>0.4400</td>\n",
" <td>0.5135</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>3.7619</td>\n",
" <td>24.1493</td>\n",
" <td>4.9142</td>\n",
" <td>0.3517</td>\n",
" <td>0.4560</td>\n",
" <td>0.5359</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>3.7309</td>\n",
" <td>23.4358</td>\n",
" <td>4.8411</td>\n",
" <td>0.3533</td>\n",
" <td>0.4488</td>\n",
" <td>0.5337</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Mean</th>\n",
" <td>3.7320</td>\n",
" <td>23.5776</td>\n",
" <td>4.8554</td>\n",
" <td>0.3561</td>\n",
" <td>0.4471</td>\n",
" <td>0.5244</td>\n",
" </tr>\n",
" <tr>\n",
" <th>SD</th>\n",
" <td>0.0370</td>\n",
" <td>0.5085</td>\n",
" <td>0.0524</td>\n",
" <td>0.0087</td>\n",
" <td>0.0049</td>\n",
" <td>0.0082</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" MAE MSE RMSE R2 RMSLE MAPE\n",
"0 3.7143 23.2160 4.8183 0.3636 0.4408 0.5114\n",
"1 3.7446 23.7509 4.8735 0.3565 0.4501 0.5273\n",
"2 3.7005 23.3439 4.8316 0.3575 0.4442 0.5221\n",
"3 3.7879 24.1958 4.9189 0.3388 0.4483 0.5199\n",
"4 3.7767 24.3205 4.9316 0.3540 0.4528 0.5347\n",
"5 3.7474 23.6770 4.8659 0.3489 0.4472 0.5259\n",
"6 3.6834 22.8147 4.7765 0.3660 0.4426 0.5196\n",
"7 3.6721 22.8722 4.7825 0.3711 0.4400 0.5135\n",
"8 3.7619 24.1493 4.9142 0.3517 0.4560 0.5359\n",
"9 3.7309 23.4358 4.8411 0.3533 0.4488 0.5337\n",
"Mean 3.7320 23.5776 4.8554 0.3561 0.4471 0.5244\n",
"SD 0.0370 0.5085 0.0524 0.0087 0.0049 0.0082"
]
},
"metadata": {
"tags": []
}
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "LJ9K1xVxcXYJ",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 421,
"referenced_widgets": [
"f5d8605f0b534615be812444b29dcdb2",
"32da02e6d6d54bb8896cfad1f10d0b40",
"fed2a1b7bc26434e93a27778354607d5"
]
},
"outputId": "311de1e6-6cb2-44a4-dcd9-73a461f23ae6"
},
"source": [
"catboost = create_model('catboost')"
],
"execution_count": 14,
"outputs": [
{
"output_type": "display_data",
"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>MAE</th>\n",
" <th>MSE</th>\n",
" <th>RMSE</th>\n",
" <th>R2</th>\n",
" <th>RMSLE</th>\n",
" <th>MAPE</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>3.6444</td>\n",
" <td>22.4407</td>\n",
" <td>4.7372</td>\n",
" <td>0.3848</td>\n",
" <td>0.4326</td>\n",
" <td>0.4945</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>3.6604</td>\n",
" <td>22.9071</td>\n",
" <td>4.7861</td>\n",
" <td>0.3793</td>\n",
" <td>0.4396</td>\n",
" <td>0.5063</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>3.6086</td>\n",
" <td>22.4679</td>\n",
" <td>4.7400</td>\n",
" <td>0.3816</td>\n",
" <td>0.4336</td>\n",
" <td>0.5029</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>3.7094</td>\n",
" <td>23.3746</td>\n",
" <td>4.8347</td>\n",
" <td>0.3612</td>\n",
" <td>0.4394</td>\n",
" <td>0.5008</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>3.7230</td>\n",
" <td>23.8476</td>\n",
" <td>4.8834</td>\n",
" <td>0.3666</td>\n",
" <td>0.4464</td>\n",
" <td>0.5174</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>3.6647</td>\n",
" <td>22.8880</td>\n",
" <td>4.7841</td>\n",
" <td>0.3706</td>\n",
" <td>0.4371</td>\n",
" <td>0.5036</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>3.6312</td>\n",
" <td>22.2373</td>\n",
" <td>4.7156</td>\n",
" <td>0.3820</td>\n",
" <td>0.4359</td>\n",
" <td>0.5045</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>3.5961</td>\n",
" <td>22.1930</td>\n",
" <td>4.7109</td>\n",
" <td>0.3897</td>\n",
" <td>0.4303</td>\n",
" <td>0.4931</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>3.6697</td>\n",
" <td>23.3581</td>\n",
" <td>4.8330</td>\n",
" <td>0.3729</td>\n",
" <td>0.4449</td>\n",
" <td>0.5131</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>3.6622</td>\n",
" <td>22.7246</td>\n",
" <td>4.7670</td>\n",
" <td>0.3729</td>\n",
" <td>0.4400</td>\n",
" <td>0.5156</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Mean</th>\n",
" <td>3.6570</td>\n",
" <td>22.8439</td>\n",
" <td>4.7792</td>\n",
" <td>0.3762</td>\n",
" <td>0.4380</td>\n",
" <td>0.5052</td>\n",
" </tr>\n",
" <tr>\n",
" <th>SD</th>\n",
" <td>0.0377</td>\n",
" <td>0.5166</td>\n",
" <td>0.0539</td>\n",
" <td>0.0084</td>\n",
" <td>0.0049</td>\n",
" <td>0.0078</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" MAE MSE RMSE R2 RMSLE MAPE\n",
"0 3.6444 22.4407 4.7372 0.3848 0.4326 0.4945\n",
"1 3.6604 22.9071 4.7861 0.3793 0.4396 0.5063\n",
"2 3.6086 22.4679 4.7400 0.3816 0.4336 0.5029\n",
"3 3.7094 23.3746 4.8347 0.3612 0.4394 0.5008\n",
"4 3.7230 23.8476 4.8834 0.3666 0.4464 0.5174\n",
"5 3.6647 22.8880 4.7841 0.3706 0.4371 0.5036\n",
"6 3.6312 22.2373 4.7156 0.3820 0.4359 0.5045\n",
"7 3.5961 22.1930 4.7109 0.3897 0.4303 0.4931\n",
"8 3.6697 23.3581 4.8330 0.3729 0.4449 0.5131\n",
"9 3.6622 22.7246 4.7670 0.3729 0.4400 0.5156\n",
"Mean 3.6570 22.8439 4.7792 0.3762 0.4380 0.5052\n",
"SD 0.0377 0.5166 0.0539 0.0084 0.0049 0.0078"
]
},
"metadata": {
"tags": []
}
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "6oJGoSdEl8Mn"
},
"source": [
"## 🔄 Tune Model\n",
"\n",
"We can use `tune_model` to tune the performance of your model.\n",
"\n",
"Here I use the MSE metric to tune the model"
]
},
{
"cell_type": "code",
"metadata": {
"id": "Dgkuw9wRqkGL",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 421,
"referenced_widgets": [
"6447fbb23bba416ba06d26b2b19909c3",
"296e35f14ff84e068cfa6629154dcf1e",
"6700b89a3ead4041bab08661e1db54ea"
]
},
"outputId": "4ba4517e-2569-4d39-f02c-27024104c4c9"
},
"source": [
"tuned_catboost = tune_model(catboost, optimize = 'RMSE', n_iter=50)"
],
"execution_count": 15,
"outputs": [
{
"output_type": "display_data",
"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>MAE</th>\n",
" <th>MSE</th>\n",
" <th>RMSE</th>\n",
" <th>R2</th>\n",
" <th>RMSLE</th>\n",
" <th>MAPE</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>3.6763</td>\n",
" <td>22.7326</td>\n",
" <td>4.7679</td>\n",
" <td>0.3768</td>\n",
" <td>0.4358</td>\n",
" <td>0.4992</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>3.7062</td>\n",
" <td>23.3367</td>\n",
" <td>4.8308</td>\n",
" <td>0.3677</td>\n",
" <td>0.4456</td>\n",
" <td>0.5156</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>3.6449</td>\n",
" <td>22.8963</td>\n",
" <td>4.7850</td>\n",
" <td>0.3698</td>\n",
" <td>0.4382</td>\n",
" <td>0.5089</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>3.7475</td>\n",
" <td>23.8020</td>\n",
" <td>4.8787</td>\n",
" <td>0.3495</td>\n",
" <td>0.4436</td>\n",
" <td>0.5079</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>3.7486</td>\n",
" <td>24.0903</td>\n",
" <td>4.9082</td>\n",
" <td>0.3601</td>\n",
" <td>0.4489</td>\n",
" <td>0.5231</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>3.6910</td>\n",
" <td>23.1023</td>\n",
" <td>4.8065</td>\n",
" <td>0.3647</td>\n",
" <td>0.4396</td>\n",
" <td>0.5081</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>3.6621</td>\n",
" <td>22.5223</td>\n",
" <td>4.7458</td>\n",
" <td>0.3741</td>\n",
" <td>0.4396</td>\n",
" <td>0.5111</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>3.6462</td>\n",
" <td>22.6785</td>\n",
" <td>4.7622</td>\n",
" <td>0.3764</td>\n",
" <td>0.4363</td>\n",
" <td>0.5020</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>3.7001</td>\n",
" <td>23.6148</td>\n",
" <td>4.8595</td>\n",
" <td>0.3660</td>\n",
" <td>0.4484</td>\n",
" <td>0.5186</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>3.6937</td>\n",
" <td>23.0324</td>\n",
" <td>4.7992</td>\n",
" <td>0.3644</td>\n",
" <td>0.4425</td>\n",
" <td>0.5201</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Mean</th>\n",
" <td>3.6917</td>\n",
" <td>23.1808</td>\n",
" <td>4.8144</td>\n",
" <td>0.3670</td>\n",
" <td>0.4419</td>\n",
" <td>0.5114</td>\n",
" </tr>\n",
" <tr>\n",
" <th>SD</th>\n",
" <td>0.0346</td>\n",
" <td>0.4921</td>\n",
" <td>0.0510</td>\n",
" <td>0.0078</td>\n",
" <td>0.0045</td>\n",
" <td>0.0074</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" MAE MSE RMSE R2 RMSLE MAPE\n",
"0 3.6763 22.7326 4.7679 0.3768 0.4358 0.4992\n",
"1 3.7062 23.3367 4.8308 0.3677 0.4456 0.5156\n",
"2 3.6449 22.8963 4.7850 0.3698 0.4382 0.5089\n",
"3 3.7475 23.8020 4.8787 0.3495 0.4436 0.5079\n",
"4 3.7486 24.0903 4.9082 0.3601 0.4489 0.5231\n",
"5 3.6910 23.1023 4.8065 0.3647 0.4396 0.5081\n",
"6 3.6621 22.5223 4.7458 0.3741 0.4396 0.5111\n",
"7 3.6462 22.6785 4.7622 0.3764 0.4363 0.5020\n",
"8 3.7001 23.6148 4.8595 0.3660 0.4484 0.5186\n",
"9 3.6937 23.0324 4.7992 0.3644 0.4425 0.5201\n",
"Mean 3.6917 23.1808 4.8144 0.3670 0.4419 0.5114\n",
"SD 0.0346 0.4921 0.0510 0.0078 0.0045 0.0074"
]
},
"metadata": {
"tags": []
}
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "TZrnWcOmmGBM"
},
"source": [
"## 📊 Plotting\n",
"\n",
"### Regression Plot\n",
"\n",
"To identify the residual result from output (prediction) vs actual\n",
"\n",
"And it is good to look at the results and consider how to apply it to the ensemble."
]
},
{
"cell_type": "code",
"metadata": {
"id": "nznJlJdKl_SO",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 376
},
"outputId": "2f1781ef-84cd-4873-a779-56c2731e0afc"
},
"source": [
"plot_model(tuned_catboost)"
],
"execution_count": 17,
"outputs": [
{
"output_type": "display_data",
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 576x396 with 2 Axes>"
]
},
"metadata": {
"tags": []
}
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "sZ6vhzf1q8Da",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 378
},
"outputId": "e6aa6639-cae0-40f4-ae30-78175e5c8f42"
},
"source": [
"plot_model(tuned_catboost, \"error\")"
],
"execution_count": 18,
"outputs": [
{
"output_type": "display_data",
"data": {
"image/png": 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4LqFbuSB02h698EVHR/H5/ve//1WNkdYWhekKOIVs3rwZv/71r89qH9GW8qmoqMCiRYvw+uuv48knn0RVVRWam5vxxRdfnNWxdHR0Wg4NuH333XcoKys7q33pc/ZOhN/vx8aNGyOamX///feYMmUKVq5cqb62e/du7Ny5E1OnTm3vYeronLOEZzsMGzbsrPanK+AUU11djZtuugl1dXVYvXo1li5dir179+LZZ5+FwWBAr1698MQTT4DneaxduxZ+vx9+vx+PPvooNm3ahLKyMjz22GN47LHHACjL6bz00kvweDwYOHAgtm7dikceeQSPP/44mpubMWDAACxfvrxjT1pH5xygtalm8ejWCjje8uXPPvusukT7xo0bcffdd8fcNtqCl7E4ceIENm/ejObmZixcuBBXXXUV1q1bh40bNyInJwdPP/00PvzwQ1gsFhQWFuKXv/wlqqqqcPz4cdx444347rvvVOVLz+Hmm2/GoUOHsHr1amzduhUAcOONN+LIkSO68tXRaQfaQvkCug845YwbNw5GoxE9evRARkYGGhsbcfLkSdxxxx1YuXIldu/ejfr6eowZMwbffvstHn30UZw8eRLTpk3r6KHr6OhEgRCCr7/+OuXKF+jmFrDNZoPb7UZ6enrc7X7yk5+o1vDZEv7BcByHgoICvP766xHbvvvuu9i9ezf+/ve/49tvv8WiRYtSMgYdHZ3UwTAMli5ditLSUowdOzalmUC6BZxivv32W3WpeK/Xi5ycHADK0uYA8Prrr+OHH37Arl27sGvXLkydOhWPPPIIDh48CJZlIUlSUsdhWRaiKLbZeejonOvU1NSoaWZmsxnjxo1LeRpmt7aAO4JBgwbhzjvvxMmTJ7F27VowDIMnn3wSDz74IIxGIwoKCrB8+XJkZGTg3nvvxZ///GcwDIM1a9agZ8+eEAQBa9aswfPPPx/3OCNGjMBvf/tbFBUV4cYbb2yns9PROTegPt/hw4fj8ssvj9pMPRV0yTXheJ5Xe4DSNnS0B0R4uW4yLoiuQGc5j1jXORn27duH8ePHp3pI7Y5+Hp2LVJ9HKgNu0XSVFt0FoaOjoxOgrbIdYqErYB0dHR20v/IFdAWso6Ojg5MnT7a78gX0IJyOjo4OioqK0KtXL/Tp06fdlC+gK2AdHR0dmM1mXHPNNa1qKXk26C4IHR2dc5Ly8nK8//77Z91S8mzQFXAK2bFjB/72t7+FvLZkyRJUV1e3aD/aVpMvv/wy9u/fDwD46KOPUjNQHZ1zHBpw+/777/HDDz902Dh0BZxCpk2bhhUrVpz1fkpLS7Fz504AwM0334yxY8eipqYG77///lnvW0fnXCc822H48OEdNhbdB5xCNm/ejCNHjkAQBOzfvx8DBw6EIAgAgPr6evz85z+HIAjgOA7r1q1DcXExLrnkEsyZMwfffPMNMjMz8fLLL4e0mty/fz/mzp2LN954A6WlpXjhhRewZcsWvPvuu0hPT8e+ffvwyiuv4IUXXujgs9fR6fx0RKpZPLq1An7qqacgCAKMRmOEbN68eRgzZgwApX9DtJUoKA888EDSx6yqqkJNTQ02bdqE+vp6XHLJJQCA5557DjfccAMmT56M7du348UXX8S6detQVVWFhQsX4v7778eyZctQVlYW0mqSuh9WrVqFd955B7fffjvcbjc+/fRTXHHFFfjkk0+wYMGCllwWHZ1zks6mfAHdBZFyysrKMHr0aLAsi169eqFv374AgP379+MPf/gDVq5ciQ0bNsButwMAMjIy1K76RUVFSS37vnDhQnzwwQcAgD179mDmzJltdDY6Ot0DQgj27dvXqZQv0M0t4AceeCCpHgpjxoxRreGzhRAS0rhDG2F97rnnUFBQELI9x3ER70/EsGHD0NDQgAMHDuC8886LWmOuo6MThGEYLFmypE1aSp4NugWcYoYNG4bS0lIQQnDq1CmcOnUKADB69Gh1NYsvv/wS7733Xsx9RGs1yTBMyGvz58/H448/jiuuuKINzkJHp3tw6tSpNm8peTboCjjF9O3bF0OGDMHy5cvx3HPPqe6F22+/HZ988gmuu+46rF+/Pq7FPWLECPz3v//FX/7yF/W1QYMG4dChQ/jlL38JALjssstQV1eHSZMmte0J6eh0UcrLy/Hmm2/ivffeU5VwZ6NbuyDamyVLlsSVaxUqZffu3er/tT2Aw5ecd7vd2LZtm/r3zp07sWzZsjbrU6qj05XRBtyysrI6ldWrRVfAXZCHH34YVVVVWL9+fUcPRUenRfCihEY3j7x0M8wGLvEbWkFnzHaIha6AuyDr1q3r6CHo6LQISZbx/I7D2FZRryrgGSWFWDNtOLgUzuK6kvIFdAWso6PTDjy/4zD+XVoNlmFgNnBo5kX8u1Qp0b9rxsiUHKOjWkqeDR2igJ9++mns27cPoijilltuwaefforS0lJ1Acsbb7wRM2bM6Iih6ejopBhelLCtoh5smDJkGQbbKupx69RhKXFH9OrVC8XFxejdu3eXUL5AByjgr776CkeOHMFbb72FpqYmLF68GJMmTcLdd9+tFxTo6HRDGt08Gt18VCVr8yiy4mzrWR/HZDLhmmuuAcdxXUL5Ah2ggCdMmIALLrgAAJCVlQWv15v0Uuw6Ojpdj7x0M/LSzWjmxQhZrlWRtZby8nJ8/vnnGDt2LFiWhcHQtbyq7Z7DxHEcrFblabdp0yZMmzYNHMfhjTfewKpVq3DXXXfBZrO197B0dHTaCLOBw4ySQshhVZ4yIZhRUthq9wMNuB0/frxDW0qeDR22LP3WrVuxYcMG/PWvf8XBgweRk5OD4cOH4+WXX0ZdXR0effTRmO+lSz2HU1JSErXxjk7qEAQBFRUVHT0MnS6GJBP8o8yGffVuOP0Sskwcxhem45qhueDYlrsLKisrsX37dhBCMHLkyE5X4RZOrGXpO8Re//zzz/HSSy/hz3/+MzIzM3HRRRepslmzZuGxxx5Laj/ak/L7/QAUP5CWZHpBtBdbt27Ftm3b0NzcjKVLl2Lq1KlJv7eznIff78eoUaMirnMy7Nu3D+PHj2+DUbUv+nm0jh9NSE0ecHl5OY4cOYKioiJMnDgRmZmZuPDCC1M82tQQy1iktLsLwuVy4emnn8aGDRvUrIc77rgDVVVVAJTKsPPOO6+9h5VS/vGPf2DKlCm48sorMWfOHGzZsgUAMGfOHKxbtw6/+MUv1G5mrWHHjh2YO3cuLrnkErz88stRt+F5HkuXLsWVV16Jyy+/PKTKzul0Ys2aNZg3bx7mz5+vtrw8duwYFi5cqP6MGzcOGzdubPU4dXTCMRs4FGdbz9rt0JVSzeLR7hbwBx98gKamJqxdu1Z9bcmSJVi7di3S0tJgtVrxq1/9qr2HlVLKy8tx++2349prr8WBAwdw0003YdGiRar8j3/8I6677rpW7VuSJDz++ON45ZVXUFhYiKVLl2LWrFkYPHhwyHYmkwmvvvoq0tPTIQgCVqxYgWnTpmHMmDF48skncfHFF+P555+H3++Hz+cDoPSbePfdd9XjTJs2Te1nrKPT0RBCsH///m6jfIEOUMDLly/H8uXLI15fvHhxew8FQNuURpaVleHSSy8FAPTp00f1SxNC8Nvf/hbTpk3DyJGtSz4/cOAA+vfvr/YZvvzyy/HJJ59EKGCGYVSXhSiKEEURDMPA5XLh66+/xlNPPQVAUdTR3Alffvkl+vbti969e7dqnDo6qYZhGCxevBiHDh3C6NGju7zyBc7hSri2LI0sLy/HwIEDQQjBG2+8gbvuugsA8Prrr+PLL7+Ey+XCyZMnce2114a8b8WKFXC73RH7u//++zF69GgAytJGRUVFqqywsBAHDhyIfo6ShCVLlqCyshIrVqzA6NGjcfjwYeTm5uLBBx/EDz/8gJEjR+LnP/+5mplCef/99/WVNnQ6BadOnUJRURE4joPJZEpZ7+7OwDmrgNuqNLK2thZutxs333wz6uvrMXToUNxxxx0AlGWFVq1aFfO94Ssqa4mmmBPBcRzeffddOJ1O3HbbbSgvL4coijh06BAeeeQRjB49GuvWrcPLL78c4hLy+/349NNPcc8997T4mDo6qYT6fIcMGYIrr7yy23X/OycVcFuWRpaXl+PCCy/Ea6+9BofDgQULFmD//v0YN25cwvcmYwEXFhairq5OldXX16OwsDDufrOysjBx4kR8/vnnuPLKK1FUVKTub968eRGBvB07dmDkyJHIz89POGYdnbZCG3DLzs7uFi6HcM5JBdyWpZFlZWUYMWIEACA7OxsLFizA9u3bk1LAyVjAo0aNwokTJ1BVVYXCwkK8//77eOaZZyLPw2aDwWBAVlYWfD4fdu3ahZtuugk9e/ZEUVERjh07hkGDBuHLL79ESUlJyHvff/99XH755S05bR2dlNLdsh1i0b3s+SShpZHRONvSyLKyMgwfPlz9e9asWdi+fXur9xeOwWDAo48+iv/5n//BZZddhvnz54ek7d10002or6/H6dOnsWrVKlxxxRVYunQpJk+erPbaeOSRR/Czn/0MV1xxBQ4fPoyf/vSn6vs9Hg927dqlBhF1dNqbc0X5AueoBUxLI6kPmHK2pZEAIqzRCRMmqHnAqWL69OmYPn16VNmf/vQnAIqrItZxhw8fjs2bN0eVWa3WkFU6dHTak8rKynNG+QLnqAIGgDXTFCt1W0U9bB4eudZgFoSOjk7HUFRUhL59+6KoqKjbK1/gHFbAHMvirhkjcevUYW2+RIqOjk5ymEwmXH311V2qpeTZcE76gLWcbWmkjk57wIsSahwe8GL3a91aVlaGd999V21LazAYzgnlC5zDFrBO6yCEnDNfjs5Ae62l1lFQ5SvLMkpKSnD++ed39JDala7/CQZgWRaiGNnwWSe1SJLU7ZLhOzO0YKiZF0MKhp7fcbijh3bWaJXvxIkTW12e35XpNhawwWCA1+uFx+MJ8R8JgqC2quzKdPR5EEIgSRIkSepyqw50VdprLbWOIFz5ngsBt2h0K1MmMzMTJpMp5IPsLs3DO/o8GIaByWRCZmZmh47jXIIWDEWDFgx1RXTlG6TbmTLRrLPWNA/vjHSX89BJjrZcS62jIITgu+++05VvgG5lAevodCfaai21joS2lJw3b945r3wBXQHr6HRq1kwbjitH9kGG2QC/JCHDbMCVI/t0uYKh6upqNc3MaDRizJgxKVG+vCjhjEfosul53c4FoaPTlUi0IEBLC4baYoGBs4X2digpKcHixYtTkkWjTc87eboR/cs9XTI9T1fAOjodQEvze2nBUKr2115oG+vk5uamzOWg7edtYtmU9fNub7rOo0JHpxuR6vzezpgvfDZdzeJV/iVKz4v1ns5YSahbwDo67Uyq83s7Y75wa5VvMpZ8S/p5d9aZAaXjR6CTkK4eaNAJJdX5vZ0tX/hsWkomY8m3pJ93S2cG7W0p6xZwJ6a7BBp0Qkl1fm9b5gu3JqjXq1cv9OvXD4WFhS12OyRjyWv7eQOAX5LVVD1tel5LZgYdZSnrCrgT010CDTqhpHpBgLZYYIAqpE+O1OG0y4eCTAtmn1eUlEIyGo24+uqrwbJsi4JuLXEt3DZ1KLZX1GN3ZQOafX5k8A5M7JeP26YObdX+2mqR3kToZlQnpTWBBp2uQ6rze1O9v9/vOIwXd5ZhT2UDjjS4sKeyAS/uLMPvY0zdy8vL8a9//UvN9W1NP9+WuBbWf1EGh0/AkJ5Z6JdpxpCeWXD4BKz/oizq/mRCwItBS1m7v478rukWcCelLRcO1el4Ur0gAN3fjZPOw9EGFwbnZyLL0rrSdV6UsHH3UTR6/GAAsAwgyQSNHj827j6K28OCeuXl5Xjz7U2osbuRV9wH0yZOaNVxk7XkeVHCZ0frcMruQZPPD94v4DQvo4fFhM+O1oW4KqaVFOKlnWWwe/3wSwQmjkFOmgk/nTJU3V9Hftd0BdxJ6Qx9ADpjUn9XIdlrlyi/N1lS6cOscXpQ4/Qh3H5lANS6fKhxejAwV2nK9H1pKRY8+Axsbh+EngPxxqYfMHh7DfasnY+0VvQuSWapsEY3j4O1dti9fjAMA4ZhIMsEDW4fSuvkEIUpyzKavH64eAGSDPhYgARep3Tkd01XwGGA3toAACAASURBVJ2Utlw4NBGdPXWnI9FmpET7DEJ9p14UZKYl7Ts9G1LqwyTqP9GFAVF5eTkWPPgMGpp9kIsGgRQPBQGDH+qd+NHv/4vv71vY4vNIZmaQYTbAK0oRLg6GYeARFPcLoHxWr359DB6/BIABG9jc41deXzNtRERQr72/a7oC7sSEWAMOggyzoV0WDu2ogERnJtmMlN9vP4Q/7iqHwyfAL8o43uTGD6cdkAnBPTNbv9pDPIs61XnAxdlW9MpOQ43dE6LkCCHolW1FcbZVdTvY3IrylYuHAoFtWZbB0QYXzjR70TMjrVXnG29m0MyLSDMawIv+ECudALAaDWjmRWRZTKhxeHDc1gxRJmAYdXgQZYITtmbUODwYmKdY8h21SK+ugDsxWmvgsy+/xsyLJrS5K6AzJvV3BpLJSOFFCa/sqUCNwwuJEBAC+EQJbl7EK3sqcPvFw1t87VJdmJAMZgOH6yeUqA8SQSIwcgyyLSZcP6EEJo7FgQMHUGN3Q+g5ULF8GUbRgIziqhAlgm+qbZg7rHeLzjcZ8tLNOL8oBz+cdsDu9UOQAI5VfLvDCrJVl4FfkiHKchRLGRBkGX4p6IboqEV6dQXcBTAbOPS0GtvlhtCDf5Ek+1BqicWVLMnMRtrCh7l2+giwDINPjtSplix1pTAMg4ULFyK/dz+8sbkMEgGITBT9S5TzNnAMxvXJbfFxk8Fs4DBzcCFcvIDe2VY0OV3okaVc15mDgy4DE8fCwHIQZDnCUjaxHExcy3tupJpz26GnE0FLUoHOFZKtNAtaXKHbRLO4kkGr+LVpVOHpUW3RN5hahO9cPwP/umEW3rl+Bq4enA0SCF4ZjUbMuOhH6GE1Qw4clp62TIAeaaao7odUVZrRtLssixEMgCyLMSLtrjjbioG56TAEPhB6dQwMgwG56Z3CkNAtYJ0QOjIg0RLaM0MjWQuzNRZXPBrdPBrcPM40+9Dk9cMvyjAZWPRIM6EgwxIyG6GKJ5rFmgqOHDmCD977N0pKSrBo0SJwHAdelDCudw9sqzgNrxh8uKQZWIzr3SMkUJnqwG4y7jmzgcP1P9K4UkQZRgOLbIsR1/+opFPcy7oC1omgowISydARGRrJlr5Si+tYY7PiA4ZiFbbW4spLN8Ph9eOUwwNRDvqUPX4RJo6NORsh5OxbPmqvc82J4xCO7MWAHCvGX3ih2s+30c2jySugOMsKm5eHT5RgMXDITTPD7hPapdIskXsuniulM6ArYJ0IOiogkQwdlaGRTOlrW1hcjR4efo11SQjglwkaPaEuEe11ybQY4RWks7oudH/+hhq4D+8BiIzqtCJ8xxRgdmBmlJduhoMX0OD2gWEYmDkOhAANbh9MxuADoiMDu535XgZ0H7BOHGhAorPcsB1ZMkpLX4cVZKMkx4JhBdkRpa+AYnHdNmUoJvbLx9CCTEVJTxmKtdNHtPiYNQ4PvLygFA5ofggAn19AjcMDoPX9cWN12KP78zfUwPb9lxAlCdZ+Q5A1+AJsP3Y65D0MAaI5vRmNO7ozdGvrbPcyRbeAdboMHZWhEa7gTCyj/j/cgkupxcUAHlGOKIkgANyCrEa9WtsfN1Y+c6ObR+2patR8vQNeQYRcMAhN6X3Rw+FBgRT0PTe6eWSlmcBLspIOJhMYA+lg2WkmdbvWZGkk6+NPVBjT2dEVsE6XoaNKRluj+FORzpRhCn49w4N6WnlLrgt1LdC9On1C1LQ2jzEDnrQcIDcLTJ9hkAnQ4OZh1vie89LNyE83w2Lg0CvLCq+gFEgYWAYZZoO6XUsCu8n6+LtLq9auM1Kdc56OWqa9LVPznD4/vqluhNPnj5BV2T3g2GAJLT1rllEKD6rsigsi2euiNLGpxymHB6V1dhxz8Cits+OUw4PPjiquCkL3wXFAyYVA76EgUKrgQAi08T3a7KayqRmH6+04csaFw/V2VDY1Y1rY55Fstzb6gHD6hMD1EaI2UNc2WtcWxnTkEkytQbeAdboUHZGh0RapeX5RxLJXd2B3ZQM8fglWE4eJ/fLx9uppMBmUr+Xg/ExkW0xw8SJEWQahRQ4si0yzAYPzg0UdSTexqVOa2FBEmaDBrSjirw+U4vSJo7hw+hxkmQyoN3Bw8iJkmYBlGWSZDcg2G0It/kC2R/gPwh4Gybhmwruc0Qq88C5nnaFaM1VpkLoC1ulStFVUO9EXKtV9OZa9ugPbA0rEwDLwizK2V9Rj2as7sOXGWQCALIsJk/rnY3tFPcwGg6qACSGY1D8/pN1ksk1sPH4RvKgUjMiEgJUIDCyL5vpqfPZhGYwsg/4DB8HpFyHJBOmm4HElmcDBiyHZDTuOnUb/HhmQCYEgyTByLFiGwY5jp3H7xS3zy4Z3OeMYRO1y1paxgET3QarTIHUFfA7SHdpMtncbx1T25XD6/Nhd2QCWYQLTe0XBsQyD3ZUNcPr8qnJ9e/U0LHt1B748cRpuv4R0E4eLBhTi7dXTou47URMbQZLVLAbqWiC2GgjVB+DLPw9Tp03FyPPPB/PxSSWbAcEkBxIju8Fs4NS0QEprFsfUdjkjmjzq8C5nbRELSPY+SHUaZIco4Keffhr79u2DKIq45ZZbMGrUKNx3332QJAk9e/bEb37zG5ha0UtUJz7dqc1kS6Lk8bZr6RcqFX05jja44PFLEGUCQZJUBWzkOEgywdEGF8b1yQt5D8OwACMrv1sJ9b9qnQOMvR6GE/shMcDkSZMwY8YM1Dq9Kc9uSOY6N/MiLAYOTq8AkWhcLoySS027nLWFSyiZ8bWF66PdFfBXX32FI0eO4K233kJTUxMWL16Miy66CCtWrMD8+fPx7LPPYtOmTVixYkV7D63b0x3aTCbbbzeZh01H+RIH52eCEBm8NsWMALIogTOyIb5d6qpgGAYcy8AvRboqkqXRzYPXFHWw9noYT+wHCIG/YCCGj58IhmFCsht6Z1tVXyzLxM5uAKC6IIDWLY6Zl25WXB4AtHkfBIDVZAhR6Kl0CSU7vrZwfbS72TNhwgQ899xzAICsrCx4vV7s3r0bs2fPBgDMnDkTX375ZXsPq9vT2mT99lyiOxl+v/0Q1u8sw+7KBpSdcWF3ZQPW7yzD77cfCtkumWh6RxUIKF9gJmp+LxCcyjt9fnx1sgEeQYLTJ6CZF+H0CfAIEr462RA1cyIevCSpjXNACDjbKYAQSAUDIfQaCn+g0Y42q0J5WLNqQ6BwC/O2qUORbTHicL0D39U04XC9A9kWY9TFMaMR7TpHa2YUDnUJvX7dVPxsfBFev24q7poxslUzOe34lKZHkppRoh1fW2TDtLsC5jgOVqvylNi0aROmTZsGr9eruhzy8vJw5syZ9h5Wt6clXwJJlvG7baVY9up29ed320ohyS3r5pVqtP12nT4B3oBiqnF48cqeCvVBoY2mH6yz42CdAwfr7Dhl9+Czo3Xqdm2ZXhbv4VXj8MCgSS+jsAxg5Bi1wu1ogws2D6/0gQhsQ6BkLtg8PI42uFp0XEar9BkGwoDREPqNghjo58torE6aNpZm5NDM+5Fm5GKmje08fhp2rx8+UYLd68fO46dDHnSRi2NKURfHbHTz8Pgj3RkA4BHEqPfoyje/wG/21WLlm1+0+h7NSzcj12pCVZM75H6panKjR5opwuKXCQk5j7NxfXRYEG7r1q3YtGkT/vrXv+LSSy9VXyck3C6IzcGDB5Pabt++fS0eX2fkbM7DL8ngBB9c3sgbNM3A4mRZKWoD08c3DzdiR7VTtZZrvR68ecaGqupTuG54XsT7W8LZnEONi8exBidEzS1CCCASgmMNTnz8xW4UZ5pxxiNg38l6uPzBZtx+GajzC/D4fPjsy6/R02oEAJSYRew444zwJY7uk4WD333b4vOQZIJ/lNmwr94Nh19EtsmA8YXpuGZoLriAxq1p5uEVFEUTbgF5/CIOHvwetgwz6pp5SDG+DhIBaivKsK/enPxxXTzYZhtkazbAcgDLQc7rA0CZ8JcfKkVzVXB/VdU2OBzNcPtEmCQ/qqol7NvnVffnl2T8eusR2P2SEiwLXDu7T8Cvt36PKRk+tQPcQJOAd07a0CzIkGQCjmWQYWRx1Xm56nVu9otweXkYGIDjlEAcEwgEujw8jhz6HrWB4hPtPWpiWdQ2NMW9R/2SDAcvIdscvSud0+FEndMdcb8UWxByH0xKk/Guz4PSRi98kgwLx2JkXhompXladW93iAL+/PPP8dJLL+HPf/4zMjMzYbVa4fP5YLFYUF9fj4KCgqT2c/7558Nsjm+l7Nu3D+PHj0/FsDuUVJzHIo81auDiypF9cNGPgoGGim+3IzsrK+L9FbwB548e02q/6NmeQ8ZpB6T3KwJR8uDrDANIIBgyYiSGFmTD6fND3loJoyHyYSOxLCZPGKdmGYwZG/QVh+fPxprOxjuP320rxX4HAWuxIsMog+VY7HcQ9HWnqX7283x+GD88AdEvqXmzDJS+CkYjh+kTL0SWxYRvqhsBVMS8Hr1KhqrBOnpcQ1o68gJteMOPK3z7PUxH90DKzIcwcBygOT8GwKQLx6o9fOn+TNYMFFqj76/stANO4YcItxYDwClIyB0wBEMLsgEA21ylsFS64SMCiCTDwLGwmI3o27cPxo9X9lfj8CBjazXqXT51RRGGIeACfunzRoxCcbY14h51uVzIzFT85uH3aLKxgKz9DhTxQJPXr/qye6SZkJWdHbK/320rBWOxYkRxGrx+CWkmDgzD4CuvNWochef5uIZiu7sgXC4Xnn76aWzYsAE5OTkAgMmTJ+Ojjz4CAHz88ce4+OKL23tY5wTJVCN1hsYpsTBxLAwMC1lWpn1SYPony0ouK7Vs6Jph0XysdM0wCvUlvr16Ot5aNR1vr57eal9isq6PZl6EkWXVxjp0bDIAY6CqCwD65lgjViamMAE5PS7174uyDJfPD1GWQ/z75eXleGvTO0rhhCUjwrFKALWyLtl4QZOHD6+3CO6PKHK6v+1H69UHp6wqVwbbj9aHuISsJg4MiJKGFvhhQJBm4kJcFcneo6GxACZ2LEDTXU57Ttr9aT/fQ/UOlDe4cKjeEfH5toR2t4A/+OADNDU1Ye3ateprTz31FB5++GG89dZbKC4uxqJFi9p7WOcEySTrt1W/hVQ0TSnOtiLLYsRpzReMWpBZZqMagY5YM0yTRqVdM0xLKvKKtYUENIdWilJIkGE2gI2x6jALoua7NvMiWAZR3RAco8h7ZijHPePy4kiDCw6fAJko/uRsixFDembi6wOl2LX1Q6QZWIgFAyFqFtCkEAAFGaEKzmzgAgUWwSwIbbR/RFEOLAYOPlFZdVi7N4uBw4iiHM11aUJ9s0/ta8yLMryCUuGnzR5Q8owZhK7KHJp/rL1HZULgDzyQWYYJuUe1pdfK2nHKeeSkmfDZ0dDsC6fXjwY3r/RvZuky96G9L0I+38AoJSny820J7a6Aly9fjuXLl0e8/sorr7T3UM5Z4imbVOdYprppSm6aEY2eUN8oxyiva89Bu2YY/eIBoWuGtYZ4D5IMswEeQQIvEYiyFFI67PZLIYrVEeUBBwAOXlTzXcEoDXea/RIkjVnGMQzSTZyq8/LSzThma4bNK6jbSASweQUcPXoUn3sPggFBn2GjIDpd0dMKAFTbPeiTkxEISpnxw2lHxEoc2gdYlsWEGSUF+LCsNmx8wIySAtXNk2E2oMHDQ5BC18oTJEXJ0evS6ObhFhS3DMMw6gKfBIBHkFQFR3tQvLSzDHafAJ9fgMUjIcdixE+nDFU/F23pNQPloSRpSq+1CpMwCFbEUMJ6Xyifb6CSMCxP2e0X1fNoCXolnE4Eqey3kEz3rWRpdPNwi9Gj3B4x1AIJPwdFWcQ+h5aUoMZ6kCiVZiSk0owhgCRLgWwCRbHKRI4bXJOJco7FWcoS8MdtzYBEVMvWyDEozraiOCvogrAHlG+Iam22wV2xB/7CEZg2ZTLyhlwAfP1BzOubHghwmQ0cGAaotruVBTfV1Z0FDC/MDrk+kwcW4KPy2hCDlWGU1ymNbh6SpPV2UwgkmShtLS0mZJgN8InKtVKyC5TzZRkGXlEKUXCyLKPJ64eLV0qm/bIAQghkTRZEhtkAryCqSpwqTAZKVoVW8WebjfCnW8J6UJiRYzGq9xX9fP0B9w7V1X5ZhiiR4IOzBegKWCeCVPVbCJ8C+gRRsVTCpoDJkmE2wObhATDgWAS/z0RZOUL7BU32HFpTghprWfoMswGCrGQEUDVAoARaqM8dAPZX2+Ke5/5qG/rkZMBs4DAwLwPlZ5yQScBPTJSCh4F5Ger5HKqzB6bgCOb5AmDTsyFn5qPn4BGYMWMGTtiawWrGpoUFkGFWZhG8KKHijDOQlhbcmgGLijNO1frnRQnPff4DCFH6NlAIAZ77/AfcNWOkMkYmkPssKouTqpV/LAuLIWjJJ1sJx4sSXt17DLJMkG7kIEgSjBwHWSZ4de8xrJk2Qi00shg4OLyCJqinzCC0+8tLNyM/wwKL0YDexBrS00JbeJJhNsDIsTBxyjHp7WfiOBg4tlUWcNeqP9VpV852FQE6BWxwK7msIKHdt1oa0Gv08JBkok5NqTWDQNOW8GV6kkHb1lBbHagN0oSvTqz1OWqDUo0exdKLFvyTpOD4+uemxx0TlfOihGONLhhZBiyrfFlZFjCyDI41utTj9rCaAylg9IDKf2SGgzRwHGbNnAmGUazm8NxjCstAnT3UOD2odfGwGDmkGw1INxmQbjTAYuRQ18yjxqkE607YmmHz8AFLMNgLjWGU4NUJWzOAgCWfZUHoVgq9Mi2qJR9ZCaf8hFfC1Tg8qHV4A24KJuR3ndOr5lHnpZthNXIQZRmirARtRZlAlGWkGbmo+b20SjRa4YkS3OUCpjQ0BgCBNTDDaSm6BazTZkTtvhXIWIjlM4vrCiBQLCZRjuihYDawIdPgVJYia1cnDrfkQ1YnJqHKRTPsQItG5e+hBTlIN7LKqhZhpBtZDC1Qglc1Dg/qnD5YjAaYw5rT1Lt8qHF4MDAvEwNyM8AGonWsvR5cYxWEgWMBlgNr4DAwT0nRcvr8MUJ/ytCcPr+ShqYZLLW6WfqwoycJJctBlgGC0IcOQwgYBLMgzAYOg/IzUd7gAiHB3QuyjEH5mRGfMxMWg4twWQeeuj5RUu4rOdjVTWtRA4DNHZlLLRHldS3UNfVxWS3qXB4UZVpx6dBeIS4r7QOCAQOwQT0cXiqdLLoF3I1Idenw2e6Pdt/yi5Ka2kMI4BcliJIcYjEkU31XnG1FUaYFgiQpvlIoXyZBklCUaQkJLCZj2UaWoAZXOw4vQaVRckkmmuwGHg6vP2SFiPCm6BSZkBCLa1pJYdTtQhqZM+o/YBgm4Hek2kXViOBFCUaWVXs7sM4zYG01AJRpPv38vqm2gYQoU6jBKUIUufY6u3gBTl5Es1+Ekxfh4gUUZgSv84iiHHBs9JJqjmXULAjFkm8OTOsDljwDGDkWxxqbgzOIJCvhirOsMHJUARPIUGZWPlFS/ONZQUveyYsRqXwMACcvqpY8oNx/2yvqsf+UDRWNzdh/yobtFfURlXUMoMwMArOCdJMyM4iVLpgI3QLuBrS0y1kqe57G21eG2QADGyiUUBUTAccAHIsQCziZRkFmg3KjR7NoGEA9fnheLE2YN7BsRPMXNdrv4eGXCEwcgx5Wc0S6Go2Sk0Auq2LFhUbJGz18qBNWC1FcENSHebzRDQPLKK6ZAAaWwfFGt+pjpVP3U04v3YVqDRZrpu5HG1zwnalRG+tIBQPVCjePIKnd1cb1yYWBYxR/MoOQSjOWBcb1yQ1eZ4aJfp01bSfNBg45FiPOeCJ7UuRYgh3japwe1Di8IT57+rvW6UWN04OBuZlqFkm0NDSPPzQI5/ELiIb2dV6QIBI50ENZszcGEIkMXggaFtr+zEaWhRCl6ZF2DTxasGFglewQbZe4lqAr4G5Asl3OUtnzNJl90ahxtC+yNmqcrCvA6fOj0uFRrQ1tTL3S4VH76DYG3AVHGpxw+gTIsqJgsixGDMnPCklnYkAC0X4SiPYDbr+I4QVZIelMWSYD6lkGzkDUnZNJ5AoRJHqACwi8HrgONU4PTjQ1Qw5M1el5yITgRFOzqpDMBg6rJwzCrz8thZPXnIfZiNUTBqnjc9dXwaBRvto8X21+b8+MNJTkZ+JwvTM4sIBmGl6YpVbBOX1+VNrdkUE9Bqi0u0Ouc6z4gNnIhVwXXpQgyEppOO2oKcgyIAavizpjkqTQLANJgiix6v1yvNEV1X0DKIuVHm90YVhhDsxGZWyBsEHwswjMAqg8vD+zRJTqu/D+zHnpZuRZzTjT7FP3Q+kyzXh0UktLupy1NOAkygQuXoAoRwac6L7sgTxRu9cfsa8MswFiYMqunfIyAASZhKQBJVPZdKjODp8gKeuksYzyJWGVNo0+QcKhOjsAxRVwvNGFJo8AUUZgigo0eQQca3SFJOpXNDYDYCDLyhdKmXEyqNBMjfPSzeoKEWkGFiZW6Z8RvkJEhtkQWwGToMXPixL8oqxmNgBBX6tflEM+M61PmUR5rby8HP/45+aoypdyrLFZ/f+14wZEnZJfO26A+veheju8fgnh3hRCAK9fwqF6u3q+NS5v1POtcXpDGqhzXDCgps4goHx2oVkGSpYJCVQ4EkJgYlkYOEbd3+mAAozqGtbI86zKrIwqOXo6LBTrPc+qHJf2Z272iyFd55r9Ijx+SW16pE3Pc/oE+ALNoKrtbjAMYj6M4qEr4C5Osq30wiP51N8ZEcl38zjTzOP72ibsrjyDb08pv7+vbUJD4Fi8KOHTI3U4WNeE3ScbsP+UDbtPNuBgXRM+PVIXsi+JAGxAYbJM8P8yQdQ2f+G+WK1lkWM1qT7QkCwIKFPjHKtJPVe7T4jqm7T7BHV8NQ4PTtndSiAHwXJgUZZR43Cr0XRAybJw8iJcfgm8DLj8krpeGqWiMbI7mRZVnqSlzIsSNu6pgNOnPEgIlAeJ0ydg454K+AQRBw8eVPKKYyhfLbwoYf2OMmX1jcA1ZAPXcf2OMvW65FhMcYOJOYFc15O25pgeF5kockCxbPOsZjCBbBUpoFwZBsi3Bqsum3kRaQYONNAXjAWGZhkUZqaBZiGGj48NyLXHjeZ71h53cH6mYmVH6TrnlyS1P3Noep4m6wOMmp7XUnQF3MVJtpUejeRX2d04WNuE72ubcLC2CVV2d4gSz0s347jNBbtXUL9cMgHs3qD12OjmsevEGdi9ipJjA4EYu1fAlyfOBK1ZRslaMLJsiAVsZFk1PxQIXV23tM6u/oSvrjswNxN5aaZAL4Hgj0wI8tJMGJirfFEO1dnhj1Hp4JeIaimDQaDKLHQbiQAuXlLH1+jmcdLmjvqFP2lzq+ebYzHF7d1AFZcgxW+ZSOU1Dg+ONrqiju9oowu1Ti+uvPJKXLtkIaQYypcBMLxQaYhz3OZCg4dXLH0EHzgyARo8PI7blAdEcbY1brYE9XMeqnfEPQ8qV9LBons704yGkCCm1ciBlwikwLEkALxEkGYIpo0NyM2IOd3PSzdjQG6G+v/zi7KRbuKUHhRQrO90E4eRRaE+/nj3C0WbnpdmNMBkUH6Hp+e1BF0Bd3FozuIZtw+yTNSFDM+4fSHrdOWlm+Hw8Dhl98DJC/AKIpy8gFN2D+wePmRa7g7U2GutFZkQuHlRibhzDDyCGNXt4RZEtey3OMuKXlkWqLaKei8T9MoKBpGUlzSr62r+r50Hmw0c7pwxHGaOhUSg/pg5Vnk9cK60oisWVJ5hiuMyCMgBpTLNF0Np+iQ5WLmWIABD5e4YkX4KlTfziuUbDtNsgyhKaOYFGAwGDB42Um0RGQ7HKCW/AGD3+ONW4NkDwTRqucaCyvPTLXG308qbPLwy49K4jmRC1FQ1SpXTG/VBV+UMujrMBg4T+uWFFH8AyrlO6JcXEiQEgCavoH7OcuBvrVx9IMdAlQescScvwMWL8AoSXLzyPSJBv0qL0BVwF0dxOQA9MyzgWAYylBSgnhkWyIHgB6XR64dfkgHQdCZliZtGzTLlRxtc8EvBUlrtb7+k+MNo16xoPkIg2FWLVnIJktIDVoaSvhVeycWLkhKBhtIDVgpYtiwYbA/zY3MA0owcDCyjNk5JM3LQet+MUfq9Ior8cAILjsqTqVwDENOPTaHyEUU5cS1lmr7V5I3MLmDt9TAd3QPjsW/Q0KwoJSMXmbFAkQjUB2KfnPgPCCqP1uhdC5UPLciMux2V1zg98IkyTKziOFB6fiu+XV6SQwo7PP7o03iPX1ILO3hRApEJTIbQz9lkYEHkYCm4cl+djqrQt1ecVreT5fgzEiovzrbC7Rcj3C4yUR6arWnmpCvgLk6jm4fNw6NvTjpGFuVgZGEORhbloG9OOpq8QddCjcMTaKqiqCqqLE0GDn5RVv2dtMVhtJuWygfnZyLbYlIVGd2XkWORbTGF+My0lVw03Sm8kqvRzeP7WjuqHR64eKVww8WLqHZ4cLDWHuLHfmXPMTUTgUBR6E5exCt7jgUVdaKkTLX0NXoqE4XK8xNEt6ncL8lxK838Gis63hSfUpARamFq13AjaZkoCswgKhpccfdXEVCY2mBcNKi8MDO+ZUvlp5vjP3BUOQl4Rxgm9KlOZ1CB1+pdkdYvNJvXBwJ+jW4eX55sAC/KgWwFpbyYF2V8dbJBvV/KTtvhFqIrdLcgoey0Ytk2RnnQaaFyp0/prBcNQSYtXiIK0BVwl0cbwNKu3wWEpcYEkvotBlZJIg/8WAys2nkKUKar8QIrgkSQZTFhUv98mDgG6SYD0gKJ6SaOwaT++WpDElrJBYYN++KxqHP6VKWfYTagkS69E7COSKBstEHT46HG6cHRRqeaO0t1nSgTHG10qpaUEKNhD4XKfQlcAVTOxAlsaeUmjg1ZrUOLRKD2K/7i+Om4+6NyrY9Sq3xpwC0/oKBJrA8sAJXT7IBYMakujAAAIABJREFUULknhtKiUPng/EwYYlwaAwP1QVycbYWJY9TZGvU786IEU6CxEABYDfHVEZUn6wI7eiaBJR+Qjwj4yGNB5V+diL9UWiJ5NHQF3MXR1rFrCa9jp0n9BMHqJxrL1dbj076v0WAZRr25/75yKvrkWOEVRLj9IryCiD45Vvx95dTgGxilixbN/2QZRVkJciDViga5PDxESQ40WQ/6dmVCIEmy2kPB5fVDlBEWg1b+FmVFDgAnEmQjUHmTL74FTOXJTt3jNWMhGnllAh8rlVPLNZryBcOo8mSzL840R08Zo1C5mYufTqWVx3rghL+u/byi/Q0A3lg7C5NX2T1KJgmNURCosQpVDmBwz/guEio/k8CSp/LirLS42yWSR0NXwN2AZBZQNBs4/GTiYORZTSG+4jyrCT+ZOFhV1FV2j9oxKvyHYYI394s7y+EVpECTFhbpRgO8goQXd5arx8yzmpXgEAnkfgZ+QJTMCZqHCRJZ+wTt34H/eEQp5nZMQA4Ax5viKzgqnzusOO52VH6sIcHUPSD/rqYp7nZUfkHAxxsLKjeyDJhmW1TlS+WAsiZePKh8eM/4lh6VD8yL3yyIyr9N4Buncur+4sIe7BzDhLi/embEd/VQ+eD8TKWiL0wuQ4kJUMt7aEFOTAXHBuRA0EceCyo3JcjzTSSPhl4J180gJPbNtHbacLAAth6pRY3Di+LsNMw5L7ThCPXvunhltQJtU/FMswGD8zNDViemFWSCJMIjKK/ffvFwtdAj12JElV8z9aX5vWHtAKUYSkTS9FAYUZgT10c4olD5Qg3Iia9AqLxPTkbc7ag8lt+PQuWuBBY1ledlxreUqFyQCYg1B3JWTxBzekSeLz2uxRD/a0zlUgLnOJV/U9kQd7tvKhuUa5Okrx2MsgJG+GcsBXK+6XbJZpGYDZya2RGOIJGQ4G68+4WWfNcnsICpPFpQVEsieTR0C7gbQKvSvIKETIsRXkGKqErTIssIVBpFysL9u/RH69+tcXhw3OZWLSuqE0RCcMLmDmkH2BAjM6DBHUx9a/TwiFFZCkGG6oJIlOhO5QPy4089qTzZKXlaLEdnACqfOij+YrJUnmjlb5rf3DPDDLAshIFjoxZZUIuw1hU//5TKjbEihAGofE9VfF8mlY/pnRs3m2NMb6W3RJ7VrJQdR0GQZXUmpC18iQaVH290xQ2GHQ+4XL49ZYurgL89pVjoPiFBLCAgNyaIBSSSR0NXwF2cZCvcAOD32w9h/c4y7K1qxDFbM/ZWNWL9zjL8fvuhkH2+vXoappcUwmxQSkLNBhbTSwrx9uppAJRovhhoPK51LSglxpIa7Xf6/PDGsFS8UjBq7EpgOVD5Z0fq4m5H5ckG4T4tr427HZXXueIHr6g8y2JCujH6NDTdyKnByUSFGEfKy7Fp0yZ8f6pReYFhoxZZ0J4OOeb4qzBQec+M+NtRuRxnFqWVmw1cXAVHLdFapzduYLc2kOObrC/2VAJFTeXJ5lv/qF9+3O2ovMEbf3yJ5NHQXRBdHG2v2vD1u7S9anlRwl93V6Cyya32IPBAgsPnx193B90GAGAyGLDlxllw+vw42uDC4PzMkKVW6OrEEcUJBLBwwdWJk1GYy8YOxBl3fAVM5Y4EU3wq/742vm/y+1obxvfriTPu+IqVynslCK5QeaObhyBFt9JFKbimWVOU7mEU1l6HD98/gr45VuSa4vtsLQFdPygvviuFyvdUNsbdbk9lI4YX5cKUIAhH5d9UJXBVVDXgooGFyurJMbYhCPYNTnamwSd4gFF5oroIKk/WxTSpf8+oMQhAMT4m9e+Z4IiR6BZwFycv3QyH149TDg+cPgHeQIMQugyQdhWBo41OZY2vwHsJaFmrM+H0T0txthWxah04NuirS/aL4ubjWw5UnmUxxt2OypPNMkjgCVDlsfyNFCqXiQx/jFPm5eBab7GyrVh7HYwnvgWIjIkTJ4Lp2S/ucU80KZ+ZM0E+M5XTEt1YUPnkgfEVCZXvTJBOR+UlCVxCVJ5sFoQpQdCMyjNN8e8XKucTpN1ReZbFhDRj9A8vzci2eD04QLeAuwU2Nx8oAtC28JNDuv7HKmsFlBQubVGCXxSx7NUd+OpkAzyCCKvRgEn98/H26mkwGQxKcCPGd4WQYHCjb3Z8y5HKnXz8LwCVX9g3N+52VC7I8b+gVJ6sokk2Sp6MQlo+NgNlpyMr8ILKlyCz/1DMmDEDQtmpuPvr10MJJvbLiX+dqTzZfOY0Y3zFReXDC+Nnc1B5MhWCPTPSkraArQmyDai8ODt+QQmVJ9ubo9HNwxDDj25kmVb1A9Yt4C5OsmWeLYngXr1xOz4uq0GT1w+foDSf/risBldv3A4g0AA8xk3LS7JaqmqLM9XWyi0JEvCpvLQ2fpoXlSer+Pv1iG8RUnljgvOgcleCSigqt4e5UrTKVyoYiILhY8AwDM40J3DNBOQfHK6Jux2Vp5viKy4qTzYoVZ8giEnl5Weccbej8kQrqlE5TYWMBZUfTZA+SOXJ+oqVVThktQye/hhYBl5RjnhQ19TU4NVXX427b10BdwF4UcIZjxA9CyCQJmYxcrAaDUgzGmANdGjSrq2Vk2D6TuVOnx+fVdTDLylLhktE+e2XCD6rqIfT50dBhjlmUEUiwQbg/XskWHwyIE/gglPlpXXxezdQuS2BIqTy7RX1cbej8pIEebFUzrHxFRyVD84LnZKzTbUheb7n5WcBABwJFhml8iq7O+52VJ6sK2X3yfi+XSpvjNELmELlyWY39ElgPVJ5dlr8e5nKcy3xJ/hUnp1gOyqvsnuUXiWypnEUlHJ4Qgiq7B74/X68+eabWLx4MUaNGoXf/e53cfetK+BOjHadtAe+qIq5TlqvrDR4BQkeQQr5XZSZpk6JTDGi8xQqP1Rnh8cvRa1a8viVpufVCb5QVJ5ujv9FofJhBVlxt6Py8Qmi1VQ+NEHBAZV/XhE/SEjlXxyLn5ZF5bE6klGo/Lyw0lex/2gI/S9QU82o3B1j2R0KlV83bmDc7ag82Yq+3gkUIZUbE7gCqLxvgrxsKj/eGP9BQuVZCe4rKt+Z4EFC5TsTlBBT+eD8TJi4yMwPQmSYOA6D8zPBsiyeeOIJbN++HUajEbNnz467b90H3Imh+b0yIRAkoq46AYSukzYoNx3lZ5zBjlgEIAzBoNx0NbOBSZA1T+XBZcEjIQG5P0E+Ls2HTFaBEBLfB0flCfSbKj+RwCKk8mRLbo8kmEJTeY0j/nGpnPcLYFyNIOk5AMsBLAs5t7e6HR+4LrFKwilUziU4DypPphlPn5wMnF8U/4FI5WKCdD8qr7LHPy6VuxLcL1SerEuIziRiQeXF2fEfEFRuNnDw0oAdIeBOn4Dx2Ncwnvwe7sUPwGzgYDBwuO+++2AwGLBw4UJYLBYcPHgw5r51BdxJUVadqMXB2ia1+9dRRwOyzAZkmDh1nTRelHBEq3wDSERRDDQgZuJYcEzkgpaA0keVpo7xYnx/GC+KMYN5FJq2k0wzlPF9e2Lj1xVxt9v4dQV+16cn/Ami5FTO+xKcQ0DOy/G/8FQ+Z0gvPLPjh5jbzRnSC0DiNCQq/+vHu2Cq+Bpyei6EkvGKEtawpbQao/v0xKkECp3Kk82CSNbX+W7gIR+Ld0urMWFAIUwxmqxTqDw7QZ4ylZsTtBGl8mSDjnyMlEAKlQ/sEd/ip/Ly0w7AUQ9zxV6YKvaBbQ6m9XGnfkD5aQdGFefihhtuCB4jQYaP7oLopCirTjTA7qXWkPK6supEsOVejdODIzGmbkca3WoQLi/dHLtpt2Zdrlpn/NzYWqdPWY48hpxDsJ9tgtiaKueY+BtSeV0C1weVZ1sT+AgD8mQLDnLT4ysQKs9L0KA8L92C8vJynP52l9JS0poFRDn3/gGLy+FJkPcckH98OL7CpHJ3gjxqKj+ToGsalfMJgnVUziVQrFSelmA7Kv+6Kn6eN5VnJHhAUHky+dE8z2PVVQuRufmXsHz3MdjmRsjWbPDnz4Jr4X0QBo3Hd6fijysaugLuQP4/e28eZcdd3ft+azp15p4HtbqlllrzYMk6kmUbDzIYGwjEkFxMcIDAg5B1yYOQvDwu6yZALmRdLklWIDzy7r2QLMIDgpkSQ5g8MHjC2NKRBw2WWt0autXD6bnPPNTw/qja1adbp3672sgT8l6LxD6fcp3T51Tt2r/92/u7p/Ml3HtqrGFLrCe5t8JpyvJyyb1ZRkOVNkJmi5Vl88vqzbJsr9332v4OYXvptf0dgTugmIDV4zcPdAmPI/6rC4wcoMt1pv6TeIhJaRA/xojsEOd2+x9MP4177rkHF2Zzwhluj5x1Nv9iEfHtSfwcs9tPPOhm3U6mvIx4B6NpQTzoBnCGKVcj3hVn9IpdfpapBye+pVGqolaBes4RQdrSnoSu69DCYdhaGNXNB5G//Y+Re+tfoXzgDi99ZD+HVuRXUhAvgpWqVVzzuR9jaCaHmmlDUxwFpyc+/HpEQk405U2dwHLNE/JpowtFdMQjGF0U70SPLpawH8GESQBndHlUkxuO/Y5qMjriERwbnxOO8qGlWGuUiRxdfu0GsYYC8bVJZnPI5SulOS/5jC5vj4odCPEzM+LqC+LPCkbbyAsZ/Pg/nsSNGzthdW2AgS7fAZrkCBNhsToYcYWpUyZeZepdiW9gOuuIB1VNG55j5DJd3sJUNxAPWs/cwVx/xLtp09EyoY6fhjZ8BNrIMUhGFfk3/qnHP/Hf/wa/ffdRQG183h3MZnIjeyUCfh6sYpgYXyz6iscc+OyPcTKTRc10yllqpo2TmSwOfPbH3jHe1AnZnTrhvq7Jy6dODDA3C3FdUYRja2jDKVt2an8bWblmIVuuemPJ/Yz4MBOZEb/A6NkSnymKHzbEHz4r1nggnmRueOKzBfHSnXizTyeUlJ+Hdv5J6IqEgwcP4uprrhVOL6bfzGKWEMTXMeV+xDOMaA9xLpIjnmMaaIj3M5+PeJBgAnDGCYmMeDOTEmqOhWHbNo49dRThX30XiW9+HLH7/zdCZ9OQjCqMzg2AaXodjAMDG3ydLwDEmEi/kb0SAV9GMy0Ln3/oWfxiOINZV+3r0EAXPnTTdiiuI53OlzA45SxV68u8AGBwKovpfAkd8YinSvbgcAa6qsIwTaiKAtu2l02daGXG5RAXiYXX86fH5uF3W5kurzGtm8Rlphuf+M+GxPW4PxvKYP/6zsCNCWPzzOaVy59iUgvEgy7Jj441Lnuyo02wmrpQbV+PQ4cO4RvffFR4PvK7TN+ExyXmeya+q1vcSUj8IvP9EQ9aH90eZUY6udxgNs2IdzO6wcSD1Peapon/833/B/RF57c2mzpRGziA6sYU7EQbAOCRc9MYaG8KXEm0GnslAr6MRmVjC6UqqobplY3Vy0L+6vy00MHVjzUhVbKQIsMwLYSU5apkQPB+/KADKIOUAh1nGiKIPz0u3pQgHrRMqTMhXlISb2KW7sT7mU444kFV2KZX5jApFSLLqPXvAfq2QZIklJkyPuInMuKcMvEok/MmbjHlfsQnmEiU+LPM5yN+91PnhMcRb4uIf1/iCWblQry4QphDKucRevZhxH70eaBSRLFqQVVV3Po7d6Ky8xByb/pz5N/yX1HZc5vnfAGgWXf1hQNuOq7GXomAL5OtLBuzLGcE98qysaCCKPVGdbCN6mWLTGkR8QLTHUY8yPkMm4lUXL5yA3GlEQ8xu9/Eu+LiHPASD6aDZVriv4N4jfl7iW/tSOJHpxxnLC9koMyMoLbhakBRAUnGtk6nwWKGieSJJ5lqDuKqLP7+iGeZ35Z40FzsFKMmR5wZOuxxjalaIK4xfy/xsmkCRhXayHFow4ehjp2C5N5D2oWnUTbfCADY8ca7UMZTvuc749ZPBykLvGptm/CYlfZKBHyZ7JKyMde5rCwb62akDev5nV95CA8OZ1CznJuoZjntsXd+5SHvmKClO0Ev7otzzPJzroDuhNgREu9kcnDEf2e3WPWL+AVm1BDxuZLY0RAPqo+xe424s444CYvTDDc5N+O0GbtGfG+PeLOGeDzEpI1cHrTMq1oR/73EJ3Lia4D4M8wKh3g7cx0QX2QcOvEgqnimaeLTf/FfkPzGXyL64FegXXQ0r2u9O1C8+V2obdiHk+5KbT/jNIlP5sRVGhxvZK844MtkQcvGRheKwjIvqn7Ilqv41YUZVE3bGXppWihUDVRNG7+6MOOJmU8wpTvEg05DUJniXVWVA+eAm5gqA+K/YlpGiQed1ru9Uyx/SDxfFp+P+IVZ8ZKcuCLJlwzQtNp6veOonjnPbK4Rl5n6aOLc1g/x+4bE6SriQTU39vSIc8rEC8zSnPg5pkyOeKlRJ5BtQ54bA2wbJcOCoiiozk1CMiowOtajdPB3kXvbJ1F87R+htjEFaDr2uA/OoG36z4e94oBXYaLqhvqyMRopY9v2srIxAOhrjkKVLx2n5agqORxwFMcWy1XUTAuW5cxdsywbNdPCoiuUDgAdzCYc8aA54CD5yaARV4GJuIj/bIiZTOFyptrK4+eYllviQ7PiHCbxs0yVBvGnT5z0HaDpvK9z3Plp8ecjHmR0EQAcZ6pSiHcxm2HEtzLThIkHnTzyll19wuOIX8U4dOIzdSJAUm4W+tP3If7vn0bie38DZXbU41f97vuQ+52/QOGNf4bqjptgR5b/XVQtcZJ54BBfzeo1qL2SAw5gQaobNrUnkNQ1zJdqy8q9FDhLIiobq5k2IqqCbHW5o7Ph6JiSIlVfc9Qdu738GNg2JGvJUe/uZpbHLg8qRXhqUlwZcGpyHrduXSs8Zp2bxy4wS3ziCSY9QjzCDJ8kHnTSxSLz+Yh3N0WAcX8n190UweDgIC4++Yiv8wUAkmwI2iJ7lhGnIT7GRI7E9/S14lvH/bvm9rh6yhVGNY34xQXxyoD4NNPRRzyolsYjp84hdOpRaGfTUDNLbeyWHoOUn8MT7qSOtg2bYZ31f2/DzQfvYO4h4lEfMXYyjjeyVxxwAKPqBlmSvGm/K0VxkuEQOhJhzKwQCjFtGx2JsFc25kwAbvw+hg2vJZhqhBsZ1Q4DwFMT4qf3UxOL2NPbgQVGwIS4Xw0wWblmwWD0I4kvMhoFxINOQggq7hNmInTiQTebtrQlAPhH6VvaEjh16hSqhinscCtWSVi+BQ8Ialn397UAAJoj4tuTeFAZzBKTCiD+GJMSIh5UxvH0tPgaPT29iDegFxeYho0LczlYloUnP/vniMw41UK2oqG2bjdqA/thrN0GyAructXf9veJ1fOIB821n2SqPk5msrh5U4/wmJX2SgqCsfqhl/W2cuhlxTBhWY1TC5a11GmWLVd9l/llw/Ryu5oiCadOUE456Lyy9a2MNq/LVaYIX5UkmEw5E/EOpmWUeFwTvyfxGWZoI/E842iIzzHaCMSPMw+54xOL+K3f+i3Etuz3db4AMOvOtjs9JXY0xIPq3l7T2yI8jvhZpjGGuM54BeKaLH5wEjeZDjzirZEVKRLbgjJxBpFH7waqZbRGdMiyjJ3X34Jaz1YUb/x9ZN/+1ygd+gMYfTs9UaNptzFmlKlnJk6jnfyM+AamoYTjjewVB8zYbKHiO1JlrlhZJopzdiZ7qVYogLMzWU8U5+jFOdi2I65Dt6kE599t2+EAMDybE7b7Drv5xKATYoPWxnYkGaeZDOOUoOUWgMefZtIZxEeYv4H4DCNQTjzLqKERTzL1s8SnfDrwpNwsYBqYKpagKAoOXbNH2OF2oM9p2AhaRnVxnlniu/xcVnwc8YtMvTXxHqY+mvjD58WRMvEFJtVDnOqV5blxhA9/H4lv/zfEf/IFhAYfg3bhaY9vefN7ULz9A6htugbQLr1eT04695DCfM/EBzPi65R4G9PazPFG9qI44MHBQdx666342te+BgD46Ec/ije96U145zvfiXe+8534xS9+8WJ8rIbWFtO9tMBKa40usVy5ipqNZaNK6H81e2kczb7eVqg+u0mqImFfr5OHY8ZjeTzPXNzEn5kQO03ie9aIo6k9a1owkRU7TOLHGHUo4iNMpEI8qB5wXBcvyYlHmYoP4osNxo3LCxmEhg9DO3sECwX3750QL1GJh8NMisTlEeYBQTxoKqDgNzHUNeJBN+HWJMQPdeJBNEFs28bIL76H+D2fQeJ7n4F+/KeQCwuw4q0oX/VamF0bcV2/kzLgHmC0igu6iRlUI+Pfj40Kj+N4I3vBc8DFYhGf+tSncN111y17/c/+7M9wyy23vNAfhzVdVXBooMvL+ZJ4DgAcGujylL+KVdMbWb1SPEfCUv6vIx5BSySETN1y2qmcAFoiIXTEnZQBp7lL/Kq1zM6xy7MNnEi9Ec9XmOV7xcAFplaYuN8AQzLili1+T+JBm04cTQb/z0iaDeeZTR/i4RVlSPWlZna0GWHXER5jIn7is1lGUNzlsRCjhubyIyPiBx3x7mQYEIyP63ZXP4vMCoJ4mcndE/dt0a2WAU2HBAmSJOHen/wEyvw4rFAUtQ1XozawH2bnBm9VcXwyiz29HdjApNOIzxXEKwPidM/5GfEYkyvmeCN7wSPgUCiEL33pS+jsFCtgvZTsj2/Yiqawhmczi3hmYg7PZhbRFNbwxzds9Y7Z0d2MiCZ7Tpj+J8EZWU0auRXDxNU9zZdEuKoEXN3T7OWKd3Q3++bidHlJczeoMtQIs1NOfIgpuRqazSHOjIQh3sMMxyTelRAveYlHw8w0XJd3MjKJxFWmzpZ4sW5jcmWdr9Gz1dskDDrZI2iH2zCTKyZeqDLVJsSZhzrxfUxjAvEik2sn3lO/T2EaUC8cQ/TnX0by7r+APDfm8e1vuBOFV78Pud/7FMrX3wmza+OylM6xCWfj7QKTsiIeNBUVZxo7iNeYzWeON7IXPAJWVRVqg3Kir33ta/jyl7+MtrY2fOxjH0NrqziyAyAc9VFv6XR61Z9z2Wc7OYOjF+ZQqVqwbBuVag1HL2TwX77xU7xjx9JO69UdETwxUYBpLzlfRXJeP3PiGACn5Obo6PQlgygtG3hydBo/f+wwOtwbMKJIqDT4USOK5J3v2Jj4Jj124hRiC2Noqorzf03VPNLpNAZHxMuowZFRyMzYd7mYQzqdxhmmU+rM+BzS6TSyefHfkM0755ti6nanZrNIp9M4MSLWbjgxMol0Og3VEK8KVKOCdDoNueqUrTVyvpAkyNUy0uk0KkVx+Vul6Bw3NS0WAJ+annV+i0nxcYOTznFxW+yA43YV6XQaM/Pi32Nm3vk9vn/4gvC47x8+gav1PIoL4oi/uDCPdDqNx49loGSGoQ2nnQ7BCtXMS1CnzuGJE2ewCQuY0rtgrPdPfxwdHEM6ncbEmHgK9MTYONLpNPKz4saT/OwU0uk0HkiLz/fAkRPYYs8jUhL/vZHS7Kp9zUuiDO2OO+5Ac3Mztm/fji9+8Yv4whe+gI9//OPsf7dr1y7oujgPlU6nkUqlnvNnqxgmHnjgB8ibEiRFgeZOIc6bwAPjZfyPt+/10hAP7NmDO7/yEH51YQbFmoGopuLa9e341h/chJD70MmWq5j//tAlwYgFYK5i4/oD+5AMh5AtV1H85rNoZEUL2LxzN5LhEP5j6qjw809rSaRS+/Bw9jjwlP8F2dO7FqnULoSeWQTgH2GEEi1ojoSAUX+BlY7uTqRSKWg/HAIEGhSaHkIqlULbg+MYzvnnqNuSTUilUjDvGYRoeLkpqUilUsjd0/h7I8uZNlKpFGr3nxeer6Y5n6/zoYs4Nz3i22TR2ZpEKpWC8qNhoOzv1JWw7pzvRA4Y8/+OO9vbkEql0POzi5ic9H/o9Ljv235kBpjx/23bW1uRSqXQcXQemPYvp+toc9639Pg0RCmckhZ1zne6AIz6O+uOzg7s27cPd773IOIjQ97rZksPqgP7UduYgh1rRlFPIJVK4fopGT8d9w+qrt+xEanU1egat4DT/pUpXWvWIJVK4Vl7CF88+Zjvca/asw2p1CbknskC8L/+clrcua6S48Cj/t/fwd07kNq8vAytUqm89GfC1eeDX/3qV+Ov/uqvXrwPs8LGs0WMZ8uoGBYMy4LtOmBVljGeLWM8W8SGVuepHVJV3PPeV+PiQh4PDU/hpoFO9DbHLzmf31KlZtkYzxaRDIfw1Ngcqj4Fw1XTxlNjc7hpoBtNTHcTcY2pjSU+0CJevg+0RNDbkgTg74BpI68lGsa5RX8H3BJ1co5ZpmOOuMHkiolHdA0Q1D1H3BSJ7DtYCcv4ZL4KO5qE2dQFhCKXlJpNuuI5QTd9zjM5dOLMXpPHJ3LMGCmXn2fqbIkHnThxw8Zu/H/p5Q5YKi5CO/ckqluuxQ0buyFJEtr7N2F2Zga1jSlUB1LLBpACSyWVOrMpSryLEeYnHrRsLNXXhu8KGlRSfU7K5SlmU/mpsTkc2ry6OuCXhAP+4Ac/iI985CPo6+vD448/js2bN7/YH2nJbKc+t2ZZTlWDe9/VLAuSgWXCW1XDwJ1feQiPj8ygWDURDSk4uG55BDzF3CxTuTK2dQbfmb1hvbjYnHjQDYkzjO7BmdkSnp0SpzPuPz2OD968E/PMxh/xHLO5RrwkLtv1+NS82KETl5ikqAQLtm0jGXLUzIz+PQCkS0rNku7mS1zXAfi/d9xdrRmMCpvHg4m6oX1l/ewKI97MaEIT15nOROLkOFErQ7vwDLThNNSJ05BsG7YehaYcAgDc9I7348jG2xvOvwOW5CMVZj+DuJ/wvfd3uPwBRkb0gTOTeNXAGlbBjHiNadPneCN7wR3w8ePH8ZnPfAZjY2NQVRX33nsv3vGOd+DDH/4wIpEIotEoPv3pT7/QH8vX2mI6FAmoUehLZtuQJSwrUSP1MkmSIEsSKoblqZfd895XAwA6meiCuMVo+BF/5Jx4Ttoj56ZxoL8LTwpaaQF4fCorjs6msgVYTGQ2U3IeMs26DsDfCTe7DklnCvqJK4xDIm75o9FqAAAgAElEQVTLEG442e7nn2NK+GbGRnD33XcjJLm/mY8D0d2Ha8kQn494C5M2I24xAt/Ew6o4kicuMR1zxF+/dQ3+5Yj/Cuf1W50p0N/93g8RefAH0C4cg2Q6Tz9bVlDr2wEr3orHhjN414HNsNWo73cHAJWa88M1MZ1/xIP+vXt7xEL6xDuYTVHiWzvFLcscb2QvuAPetWsXvvrVr17y+u233/5Cf5RAlq8YaIuGMJUvw7BtLwWhyRLaYyHkK4aXsyX1MsMyl6UqSL0sGQ751hSTET/JiOeczCzitm29ODXFNEW4fHtXAt876X/c9i43jcL0s4c0GZbJHONeVq1MeqTVS4+oAPzDW4fDqdkRBRnux4ooQEHggCPu/RsSOH55IQN74mlcWLsTCxMhQPUfOV+oOZ99KiuO+ImP5ZnmGZcHHmbJbP4RjzGRLXHf5h73oiZ+7Ed3I3R+EABgdG1EbeN+1Dbsha07S/uiq2mhKOJggniaKadLj8wBrwJOMpuTDt94id7KSiMetBW+v11cH83xRvZKJxxjbTEdu3tasLYphnhIRUhVEA+pWNsUw641LZ7D9NTLLMvN9TlqaDVruXrZ4RFx9xDxA73iKhDiGUGOtZ6fZtIGxK9l+uev7WuHzESsxLOMdgPxHFN7TNxgUhDEGTkLj2eyjd+Xqh0KZRMHDx5EvHeD8HyUmwzaANLF6OMSX2By48TbdPH5iM8z5yP+4NnlI6LkxSnoT/4Y8e/+NeS5MY9vuuW3Ud73W8j+p4+j8IY/QXXbqzznCyzJZS6WxI6QOD3I/Iz48Ix4lUZ8lmldJx5UmP/5GEn0ksgBv5RNVxXcPNCFk5lFyLIMxbIgyzIs2Li5rhGjrzkK27ZhWctTd5JtQ66TmRxhRMWJP8uMOH92OotXDaxBX6s4yiQedNmWZQYtZitm4JE6E4viTR/itim+8YgrGkSBssMBaKoEX8Uj4nBXxSuc9bImi+4NOHToEHKPflf4+aieNBxSgIL/dxN2FecmmHw88c5YBID/deBwoD0pzokSLzEPROL5Sg1SOQ/t7FFoZ49AnV7aaNPOP438gT0AgObd16ICf6lJEkcKuqnnzNbz3wyj2XshZrOOeIZptCFOwZGfDc3kcGgzMMU4dI43slU74Pvuuw+33Xbbqt/oZW2S5LUVy/LSP9fnhGumDUWSUFuxc2LD2Twg9bKgItY6syFBPGi0Fw2Jz0f89JTY8Z+eygaerlszxe9JPOjf0B7XsDDv70Ta444HDjrMUreXF9ytrPONrNkKSZJQYRwXcZXJsRJvDjGdeiHHYUaZiJr4QoGJlF0+UxD/bsTnf/BPSDz+oDe+x1Z11NZf5SiOrdmCvT1OlcuNG7rw3WNjvue7cUMXgOAdZNxENeIlRpifeJRJ4RAPOr5eYwSpOd7I2BRELpdbVpP77W9/G3/0R3+E8XFx8fJvilUMEw8NZ7CuJY6d3c3Y2dWMnd3NWNcSx0N1amhxXfWVaTQs25s6HGN2oonPM8ty4klmR5j4kxfFReTEM0XGuRaLqDF6tsRDstizEleZEjni0wuMrqzLp4riFAlxe8VPIS9OLq/zdVXYWplZdMQtpgyNeAszfJK4wtyexCMaoxnhcqNR7t6yoI6dAmqVJa66nV+9O1C8+Q8cxbGb3uHKPcooun3wEeZJRzxwFyYj90h8TUIcURNnYg6Pn2Q6Dolv7xJvsnG8kbEO+O1vfzvuuusu79+/9KUv4Y477sC73/1ufPGLX4TJ3Iwvd6tXQ3P0gGVPmrJeDW0iW4JfLt+wHQ4saUL4GfH1zPBO4nlGc5f4PKMkRnwuz5Sr5UuQZfFDhLjOiMkQDzouKc9UQRAXu8sl3rzCERrrdqO2fo9X50ucWfF6nEklejxgR3CgpTEAzJSYyNbl+9e6+X3bhjwzivAT/47Etz6B2H3/E9rIMY/v+e23I/d7n3LH9+wD1OXfEz0vg8o9NjERMPHBGbEDJr6vj2mVdvnwjPh7Ib6hjakXdvnIHJM+ZHgjYx3wG97wBnzlK1+55LV/+7d/w9TUFN7ylrfgyJEjq37jl4sFVUMbZjQUiA8wO6XEgy6LLjA3KfEmplaUeDQkft9oKASFWSwSDyliB0x8kZnNRpx71BMXP0KW+Gi26klKAgAkGVZrj5daGnVFcSaZlAtxhZGwI05len5GfLEifmgSNwzx+xKvLE4vje/5j7+DfuIXkEtZmMkOJ83mLqHn7CjssH8AMOpO4hhm9IWJh3XxNUV8S6c46CDOSS4QDzE608SbIuKImvijjPwmxxsZ64A/8IEP4H3ve9+y1wYHB/HDH/4Q+XwemUwG73//+/Gxj30MpRJ36b/8jNTQLNt2dCAMy/vnejW0OWYMDvEzU+LyMuLnmG4p4hNMSRPx7czYFeLTTKPIdK6M0azYFRK/yMxmIx50qGRQ40YoEtcWJh1JyeEjDcNXituKTDqIeA8zLZp4Z5ipBXd50MaE+ar4NyP+7Nc/i/DRH0JZzMAKx1HZfhPyb/xT5H/nL1DbmELNrcedKYhXVTOrFDyvGeLvj/i2TvH+CPHJRbGfIc6NziI+zugkEw8zD1iON7JAZWgDAwPeP+/fvx9/8id/gmPHjuHaa6/Fd77zHRw5cgQbN27Ehz70oVV/gJeDkRraqalFHJ+Yx6mpS9XQgoo1PzEqrmEkPpNnoiSXB93gmGZqVIkHWUYHjUSzTKRCfJHZfeH4SmNE0xBWgNOnTyM58YxT7RBrbtgo0Jlc3TRck8kBE19gOv+IB50oUlxZXmbUoJ5/CtGf/hPk+QmPr7/utahu3IfCre9H7m2fRPna34XZ0e9F/M1uw8FCWXztEQ867fjSOTErzeFrW8QPMOKnmAoh4kHTc0dHxfXHxJuY/RuON7LnVAXRSKnsPe95D7797W+v+gO8FCzr1uluak803NT6x0dOY7Fcw7bOJtRMC5oiY7Fcwz8+ctqbCbehLSl8D+K3b12D/0tw3O1ul1GGiWyJv2ZTN3454t+M8ZpN3QCAKlM6RjyqAyLfH9WBWlnU3waIkx2XWlCHrjDHkrtkyk5Rnp3E9773PaevQzDDLeQ2gMQ0GQuCUo2Y27xSZnbniceYMefEJxbFkRlxVVac8T2Tw05Ef/5pSDXnRzSbuqBu2ggAuPXNb8Vjie2+59vqbiLFNHGVhsOBHV0JPCkY17TDbe6JMtUBxGfzjE6yy6mk08+I15gHIvF969qAx4d8j9u3zskpzzG98BxvZKt2wCKZyC984Qur/gAvpgXRbqifCVe/y00z4T5wwzboquJVOfgZ8aD5Jm5zk/hjF8QRNfGhWXEVBPFoRBcqekUjOuYFHFjaRApBpIzgcABIquIoN+l+tUEdsOg95YVJ2OefgrV5NxaT64Qz3C7OuyL1zCQJ4lHGsRI/y8hqEldlcYegSpudzzyAxCP3Qi4uOUOzrddRHNuwD5s7HMc6xAzHJN4dF6/miPcxYjfEHzknjjCJ68wSnnjQjrQEc08Sb2Zm7xHf08NMi2F4I7usjRgbN268nKd73o20G2RJgipLqDbQbpgtVDCTL2M6X8F8uYqqYSGkymgJh9CZ0DFbqKCnKYonmA63J0Zm0NscR4np9iFeYIpjiR8eEWtBEK8wToR4lVm2VSu1gAtKIKoAoqKPKPmrgGUB4vhoifvFPVJhAdr5pwDbxsGDB2FNjQpnuJEVmRQI8fPzjNqYyzNZpnXY5bbPR5Py87DDMdiS04ixmJmAXFyEFW91ZR73w2ru8o6ndNWT4+KHNfGLOWYWnctNps6beNUSf4HEK4wAFfGJBWbfw+ULPrMcyYhP5ZgGC5e3MikhjjeyK7YTLluu4vGRmYbTjh8fWa7dsFiu4eJi0ZOjLNWAQsVASJW9Koig9biPnGXEc85OY6C9CUlm6oTHAypmMfsqHq8xUx1qtoWwCojSmDSijPNtxIMW4Ae1JBr3j9nRJMzmbkS1MA4dOgTpB18Tfz7675j3I15mHnLEO5M68gv+N31nktTLQvBqNipFaBeeRmj4CJTJYZRufhea+5wRXuq+W5Ffu2/Z+J56m3c1mdclYzglKM1al3Qi1p54FEcFHXg9bt0zE/B7fIIpbSR+ghHwJz7QLk73Ec8xU6+J71nLRLYurzFzwjjeyK5YBzw0k0OxajacW1aqmhiayWFfr5P7mSmUUTWtpRvRBizTwkxd5cO2TvFFQXxtUpwhJR5EjIc+i9BcHtTJOQMP/S8kTZYhqRZ8ZBQALDlgprrM40E74YLaJdlLUkaSZBjrr0LBnUEmjkPh8aARv8Rks4lrithzEY8qNtQLzyB0Ng119Dgkt1zOVlRIhXkvrdW9bgPOwl/5q90VPbKZJyLxnWuT+IFAynHnWudafpbpmiS+viWKkwItkvXu5lpQ9bceJgdMfF2bOFVBPGj6sMBsnnK8kV2xYjyb2hOIup06tltWRoLZkZCCTW4eaTxbRM7t9a8fIw8AubLhjZs/xZSXET87J14+EY8w3WEeD+gdepPii4x4tiT2dtmShTnGcxEvMQ8H4kFTC0Gt3g3KCxloQ08sq/U1XUfTwUwRJ850BHs8xuT3iZeY/D7x81//O8R+9s9O2sQ0YazZjOINb0f29/4a1d2vQdJtFLl6rVh2kXjQoaZDU+LyMuLNjNod8W5GQJ34HbvWCY8jHrQjrYlZlRIfYzr1iAet4V+NXbERcDIcwjV9bbh/cAJmncykIkm4YcMaL2VQqZkwbAuKLDnBpDvsTQJg2BYq7s52UOWlcUYghHiUKV4n3hyVsCio92qOunk4JholHlaAvMAHcxxYfcrgchvF8PXaDsrcGMyO9R4HgGQ0hGnBQMukWzrYElWQz/l/gS1uMttiQnXikQabTfLCJLThI6htOoBIu/M59YGrYC4soLox5Y3vqbeMm4s9yzwRiW/pSOBRQcnVFnfc/CKjmkY8weQgiCeYUkni3UnxNBbik1mmDjhbQkc8ErjDcpbR0iCerxre0N2VJrncX7S0sV2xDhgAbtzYiV+en0auangOOBZScePGpYnNuqZAlWVU3PyODUByfwFdlaG7F9mmDvHTj3gPc5ERDzqSvGr6XRKo48BMQewWieeZXHG+5lw0orOt9qKKSUBBEC3HVlnfvtL5mp0bYLavW8YBYIYpeyLODTogbjApCOIL7qaYM77nqJPXnSUVMAkLXc4GmrXzEPK91/qejxxDkRELIl5gnsLEm5iHP3GTyX8RPyYoVavnQSt/TmbEGtgnMwvY3dOKx0fFG+OPj87g5s1roDKzn4hXDP+/2Hb5au2KdcAVw8TD56ZxVU8rqqaFXKWGhK4hpMh4+Nw0PmiY0FUFPckoErqGct0EXfoRErqGHnf5FHQeWIZpsCA+siB+yhMPuvETdJkfpCb3cm+aaeJnCGniQDxfY6n+2G968UrjxKuIBz2uxPzhxKVzxxB95mGoE4OQ3OvCDkVQ698Lo28nQm6d7VVrWzG86C96tXuNExEnGM0N4rYkvlaIdyXEQQLxDFM9QHxTRww/F2w+b+pwNv/ufko8jfnupy7gVQNrAqfnZpnNP+JcWSXHfx27YnPAVF42Ol/AqalFnJvN49TUIkbnC5gtlD2RnaAWtC/eZNoyiWeyTCOGy4NqowbdSOJKyVdfas7bApPSIK4zf4QuOW3ynPP1cvkBqzQURkuZuK/kp2UClulxe/QEtPHTgCSjtm43Cre8B9m3fQqlV/0ezM5+RNyk8vUbOhufzzXia1uZDjKX0+gkPyO+t1dcFUD8aqbulfjOLnGLMfE+JldMPKjMZDdTFkb8GmYIAfERprWe443sio2A68vLKAdcNsxLysvGs0VkfUarZ8tVbypyhMlzEX/kvLgW0+MBvUOVKX0hHnCaTyAL2hBxuS1Ia/PQ0BAb+dJpZpllAXGDGVlDXK7/Fm0byswFaMNHoJ09itJ1b4XcfNA5buerUEr2LRvfU2/UOnz4vLgsi7jMpAKItzCbZsR/NiQeZvmzoUm89/ptSDClksQnGX0R4h1Meo54C7O55nFGn5l4F/O+xH90clR43I9OjuK27b3i91z5EVZ19G+YzRYrMCzbu3xtONq9s3XSjRXD9MTUV1rNtL28T9Apxvv6xFED8RRzHHGmb8LjQdt9g9jlPNfltte97nWo9e8RdriRMUUQHq8ys5CIq7K8bHxP/Aefhf7sw5ArBaiTw14uMdyz6ZLxPfWmuR1uT02Ka8aJn2H0bIlvYXbpiU8xjRjEHz0v/nzEr+sXb00RD/o9B61a2MZUSxB/lBlsS5xJFbO8kV2xEfB4toiqYUGSANN1wpINKG5HHEW2sP2rYi3AC6dG58Q3AfGgXTfDzHKGeEgVd2pRYB4HIDqjWAjwpW1SbgZ2tBlQVGdcVEtPoP9OhTg3TjeHooi3HRVXMyJ/778gcfIx73UrkkRt4z5UBw7Aal0L2c1NlplNM+JBO8NCTKRHPMM4VuKSXwuea8R7W8RLfOJBR1gdnxDXFROfYrStiVOO3M+IZ5nzEd+9th2Av2bE7rXiVEYju3IjYBsoG05zhSxJUNxR8s5u5tJgN50ptSEeY5ZFxIPmHRcZeUvijFKixy93rW0Q457ul+PpLy9kEBo+Am34MC/ltsKYvRyPRxtNfqhVoA0fgZSb9Xhzz3rYqo7qpmuQv/0DyN3531C+5i2w2noBSVoatsnUWhMPWjFTZqoHiN9z3H98UD0vMfsUxMOM3jPxNFONQPzGTV3C44hv7xA3PREPOsNNZe5x4teuEwvBc7zhuVf9X/yGWFtMhyI5ikhSnVe0bRuytDQePs7kdokHVf1fZMrBiEuMpyYeNB3wYjjgwN13EG/u0W2+Utxn2QDNWAtCDSQlRVZhNv+I1ygStUyoE4PQhtPQLjwNyaiivPd21HruAABUt16L7JrUJRMkyMazztK4La7gYt7/l2uLu7PeGHUt4hajUE68Myp2mMQ3tiXwy1F/8aaNbgfZEUbGkbjCrM2JTzD1vcTvH5wQHnf/4IQzsDagalrQoaFBKpO2dwsPucSu2Ag4XzHQFg25xdi2N0pekyW0x0LIu6Hjs0xLMPGgEzEqjDAJ8VJNfFMRD6pT8FK2GLNXQrz+b2lUakattNxFTTzoQ6k6OYrw4/+GxLf+CrH7/hdCw4chGVUYHf2wmrpBAWgFiq/zBZZSC3FmjDzxCFN9QTzBrL6Id7cwjQ4uD1p215kQvy/xQwPiag7iRy+II2XifrMXyYhPLDKiPS5vJEdQb8R3MkMNON7IrlgH3BbTsbunBWubYkjoGsKaioSuYW1TDLvWtHgRcNDNtRpzHPGgmrERphGD+ItRNna5bZEJ44mTO/Kr8yV+uVMflaP3QT/5oDe+p3z165H73b9E4Y1/6uR4XQW7oFoLbUw1AvGJLKMH7PK+VrFDJ55nUh/ELzATJ4i3MBE18RAzNJR4R1J8HPHXuprZfkb8FKNVQfwkIyNAvMY4fo43sis2BaGrCm7Z1I1cxcCapghKVRORkAJZknDLpm5v1FBvk3gZQ/y6/k4AJ3yPczgQNGZdm4gB8O/4cfhvhgVWGwNJSjau86Ws+XNNt0jlArTzT0IbPoLKrltgrN8DAKhtuQ6mFkNtYL/TUbfC0RZdvyYxSnLEY0zjhMdtsTCSw4G2mDiyJR5jCqmJN4WZdJrLg27WBW1SOjSwFp99yH+T69CAM0Koixn9RLzMTOwgfvsWZkjCFseh5xl1NY43sivWAQPOqKEHhzN47Pw0CjUDMU3Fdf0dy0YN9TRFoUhAo0o0RXI4AJSZjQviQWsxY8xcHY7/ppodTcJs6QHUUKBSM9aMKtTREwgNH4E69iwkdyPPOtvsOWC1azMKXZt9T6G5EboNsYC67d5uBWYVRPyqnlZMDE35HndVj9PAcJQZBkl8dEG8sUs8ojA17S6fZxwO8WfGxcMAnhmfx82b1mCCaT4iLkmSUJOB9kfGmfMRDzMPROLzJfGjneON7IpNQQDAPzx8Co+em8JCuYpyzcJCuYpHz03hHx4+5R2TZ8oMiJ/OiHPAxINWQQwz0445/ptmXhQlyTDW7b4szld/6idI3v2XiP3iX6CNHgdsC7W121C88R0o3fB27zhx1ekSb4uLb2TiC2XxEp94nMntEj/PjC4ifs16cUca8at7xbv5xDe3iosXietMLpv4BWbIJ/GQIvsK7YRkCSG3fCVo/fEsU55HvCXCNIAwvJFdsQ64Ypj4+5+fwHypBqo6MyxgvlTD3//8hNdgYdlWw+gXcKJiy11WqszOBfFWRrKQ+NAMMz6G4b9JJi9k8PWvfx2gQn1JWr3ztW3Ic2OQ6sb2QFYg1Sow2vtQuuYtyN35SRRv+8+obToAaEsrlaDVHFGNUbAjzizdiV/dw8hMujzJtOYSb2GuPeIbOsSOlXiCWc0R746LjyMeZyJR4j1NUSR90iSJsOqtSpmMkMfPMhPIiQcd8rkau2Id8Lm5HKYL1UuWMTaA6UIV59zGiV8yXTLED64XF2ETZ4IBjy8WxVcPx39TjDbcLl68CGXeX5jGz6T8PELPPID4PZ9B4nt/g9DgUqOEvOU65N7yX1F405+juvMQ7Ojy+lLKrAYdcz9bFC/xieuMfgfxoxeZab0u729iZqS5XGIUQYhnmdQC8Q2t4vclrmrilAZxg6njrudZn5Vp/euPnvNP39TzMBM8ET8xKQ56ON7Irtgc8FS2LJSWm8qWsa0TKDBdS8SD7pBOM33xxIOK57yUjRE5Y/+G+mqHgwcPwpwS9+J7tmJ8j6vkDEuPLhs/XwvHYYX9oz365XUAomwixXdBK2amGaEn4odHxQ6E+BgzQZs46Rv7GfESE8kRP8XIQp7KLOCNu9YFlpk82C8uVyM+OLWIqs+ytGraGJxaxO6eVqxtEm9OEg/6gLiaESnieCO7YiPgZqaEhniYCVmJP3BKHJ0RLzIbMMSD1rK+lO3XqVFeWWp26NChwGmH6K++g+ijd0OdHAIUBdX+q1F4zR8i97ZPobLnNu+7C5pa6Ewy9a7Emd1+4kHHUsnMZhjxfiYXS7zANHYQzzBBAvGFonjTifhimWk+crnOjGoiHjRXfOu2tcLjiOtMSyTxoDKdq7ErNgIOKs03NC2uJSS+UGEmsLp8V3cCPx7M+B63q9tZtoXDEgpl/xs6HH45xMDPzRrV+TbsDLQtKJlz0IaPwFh/FYze7QAAaWA/jFIO1YEUauv3AKHlNwZ9dRF4Iy8bmpeCYG5Q4pvamzCa869I2NTuFOrnmRY84jf1d+GrAo3cm/qd1txWJgdMfIhpFiI+znSkEd/I5IqJB20o6UyKc9TE9zJDNInLkoyQDDSSzNZlhwNAB5OjJh4kBdHbvDpVlSvWAYcUGboiodJgKaMrSzupM4xQB/E449CJHx4RL9s8zhV1P4ei75eLydlpoaSkM77nMEJnj0LOu5KM1aLngGu9O1Dt3eF7flKX5LLoxE0msiUedDMsaJt5jKnHJT46L3aYxFPr2vDPh8/6HpdytQyiuviBQzzPTNggPlcQfz7iPYweMHGZaTkn3hbTsS4ZxlCD8ru+ZNhrtgoxkTfxfb2t0GSpYbpRkyXs6xVXmTSyK9YBOzupGqYbzINKhjVvJ/X69R34wqNnfM9z/XqnlGVLp7gNkXi+Jl62EY+GVMwKhihGGe2J59OY9oBfOz1i9O2ElWiD1dy9zPlqw4ehH/8FlLmL3mtWrBnVjftRG0h5rwVtxOAk94nriio8WndTASNM6yvxrkQExwQSkrSUzTG5WOKyzOgBuzyoalovs6lHvJOpgiCeZ4aBEr8wJy6nuzCXx+6eVq/yyM/q+YVs49+t/nVdU6DJjadva/KS4FZHPIKWiIapBj6jJaKhI776FMTLIZX4vJllNf4h61/fxCgvER9i6nKJa8wzj7jGbAxw/Pm0oJHjakzKzSwrM7Na1gC1ClBZcmpyfh7K3EXYoQiqW65D/vUfRO6tn0Bl/5sCS1DWW9CW5a3t4muAeG+c6Zp0eR/TXUl8K6PfS3wvI4NIPKjK2TVMRQ9xk9lGJd7GOCbiJxndFeJPjYkbO4ifziz4bo7XLBun3U3EnmQUzZHGD5PmiO5F3hXD9K31bYmEXpkJtxobzxax4LM5sOCOm9/QmgjctXSGccDEu5p0QKAK2NXkalAwNwvHX05GOV8rkkRtYL+rOHYE2shxVPbchsqe2wAAtU3XwGzqhNG7E1AvXe6vNiveHgEmBavjdtdvHB0TVyMQH2U6r4j3MLlO4haTqiAeXOJUHPMTn2XmFhIPWk5XZCJg4pQS8DPi61vEbfjEL7jC7CurcejfLywUcNVaJ+3i1y5d//r4YhEXFopQJFfAC3TNSRhZKGJ8sYgNbasbTX/FOuBcqSpssMi5bYVBdz63dSWB4/7HbetyoqQSswFD3GLyjhx/udjg4CC0c0chF+Yhz4wgfOQ/IFfrot66Ta2utmZMxPb6nqtb7NcuMUZy2ePTRfFDmHjQGu/TzPxA4gWmHpd40EYC02fFR0b86EXx2KyjF2fxO3s3oNxozV5nxLcxq0jie5gZc8TXM1UfxK9d39EwXWbDWfpf66YPx7NF3zbi+dLS2LGqacGwLDdHv7yqumZZbBliI7tiHXDQ0phYIzHuOiM+w0QNxE9Pi5dPxH8TytA4kxcyuOeee6COnoQ6O+K9bjZ3ozawH9WNKdjxpY2NWcZhEg9jSZinkZGfLjLPMOIdYeCiILjtcE/Y2xTDkTH/ZXRvkxOZVU1mdePyNYyeLfHNbUxLsMsLTKMD8aCtwxaTbCJeYhwT8Vlmw3u2WEFHPIJxJtc+vlhEMhxCRzyCtlio4T5PWyzk5WyDBmMhRYYqK6hZ1jLna8PJn4c4hf8GdsU64KCKT4dHxNHA4ZFZ9DbHAw8eDKniQTght1oiqGzly82kUhbauSdh6VEouVlYm3ej1rcDcoOQPF8AACAASURBVHEBtY0pVAf2w2pd23BdnQwBM4IdNirHTYQAnzmqHgeA9jCQEfxs7a5j1fUIUPTPVei6cyM3MVoAxIOKv5Qb1U/VGfFMAwdTb8QLTNBBXGMcMPGzTCRPPKjQ+hRzD03lnOaoKSbYmcqXsa3LydmKuq0qhgldVQIHYz1NUWxojeHsbN4Z5AsnBaFKEvpbY97G/WrsinXAOrOJRVxmxJqJR5mLlvhASwKDc/5P8IEWJ4fE+deXlf+tVaCNHIM2fATq+GlItgWjuRtG/14cPHgQ1cluVPfczk41VBVx/YVKM9eY74a4LInnOzscMBgRfeIRpnyLuCKJQ2/ixyfFJYvE9/SIK3CI71krLpMiPszojBAPOp3YYiJv4s1MGR/xoKvS83N5zPg0i8wUqzg/l8fWzqbAwZiuKnjPNQP4n78cxGK5hpphQVNlNIU1vOeaAXbl0MheFAc8ODiID3zgA3j3u9+Nd7zjHZiYmMBHPvIRmKaJjo4O/O3f/i1CodUrC63GgtYBa0yfOPGRRXEejvj5rPg44mFVwqJgHHpYfek3YqiZYWinfwntwjOQDOdGsCUZtd6dMFu6lzrcfvC1QOebZgTFiUdUICe45yPuVd8Sj2Ci5B/FtcRpQgTTiOHyiXlxZObxgC2Cti12XMRbmSoD4hLzdxBXuQehy5uYMjTiClNnSzzBrCCIZ5hZb8QzuZJQbiCTK2FrZ1PgumwA+PDNOyBLEn56ZhLT+RI64hG8ZnM3PnTTduE5/OwFd8DFYhGf+tSncN1113mvff7zn8ddd92F17/+9fj7v/97fOc738Fdd931vH6OnqYo+ltiDTdE+lvqlxPBVBluWN+ORy/4i6fc4JbuFJmOOY8HbGt9SZltO4Mx3bpYZeIMQsNHAABGRz9qA/tR23A17HDc+/zcxV9vSQWYF/ikpHufM/e7xw2mGZl40GknNSZlQDzORHrERxmHTlxmPDrxclWcqiC+f10Hvvi4f8PG/nXO5lXQiRhdzAOCeFtUhyotNcrUmyotTQrJMZuTxINuoAeZFr3b/WdFlvGnh3bivdduxtBMDpvaE0gyKwGRveB7OaFQCF/60pfQ2bkkvPH444/jNa95DQDglltuwWOPPeb3n18201VFuPNJy4kSI8ZDfHO3eHlHPOjNx1TusPyFNCk3A/2pexH/9/8O/cTPvderAwdQvvoNyP6nj6F0w12wFRU2zUx7DpKSBSa1QNyn9t4z4nlGy4B4e0JcXkF8eIHRcHb5+iamjMrl0ZD4+yFuMUEC8TyT6yQedJKExDh+4i3MNU98tlDxvyYkyeEAdnSJUy7E1yQjCAl0g9fQ1OmgdXxwKkU++4sTeOfXH8GH/u0JvPPrj+CzvzjBVpj42QseAauqCnVF226pVPJSDm1tbZieFktAkh0/Lqj7qrN0On3Ja3OlKuZ8bsC5YhX3P/IYWiMhHDkl3oQ7cvwM+o05DJ4VTyUYPHsWaTWLRaZWdDFbRDqdFkZ6gBMJNvq7GtnlPg5YPr5HnTrnvW6PnULlqtcinU5DT7ShvPf2ZdoOyvw4zI5+AE41QjqdRhRitbGoe1yQ+XfpdBoV5ruruN/dfEF808wXLKTTaag1cSSq1spIp9NolcWfsFWuIZ1O4/5j54TH3X9sGLe1W+jXxX9Iv24inU7j1Ig4Z3vq1CC6ihnMZMT31UxmAum0gRNDk8LjTgydQzpawonz4oqeE4NDWFudxaNM+/2jTx9HaP4izi+WfSc8W5aNo08/g7mmMOaYyROTZ0+jOhHCdLEGXfHRglCAXx4+io6ohpk5cQQ8c+Es0jlnEvPXn53FQxezkF2nPFEq4uvTcxi9OIbf3/4bMJaemx9Vb7t27YKui/NQ6XQaqVTqktfvPTUGw248f8qwAat9HVLb1mI+Noq/OeovnvOa1HaktvXhH048KPwcY7UwUqkUun42hosCUY+u5ibn8/7rSeH5ALxox4WeeQDhoz/0ZpzZagi1dbtRG9jvaDe45yr/68lLhHXM9vXeecrucdI3TgrzopLkHNf1vdOYFITBXTEFqVQKLd85iWnBPdoScs63+8GLeGLMX2xp99okUqkU7AfHAPj/ZnYoilQqhXeVInjs3/0fYu+6YTdSqR1oeXoRmPDvxmlpbUUqlcITxZPACf+01lWbNyCV2oEnK6cg6u5JdvUgldqGo5VTwJP+Tnjzxn6kUtswF7+I/338577HveX6PUht7YXdPoW//KX/iPjX7r8KqfWdQMc08Ii/WuAtqauQWteB1tkcpB+dbXgtSBKwb89V2NCWwEPDkwD8Z8fpazYgNdCNbLmK8P0jqNkGDMuCZduQJQmqLCMcUnH9gX1IhkOIZRaAn/g/FPdetQvbuppRMUwMP/UgmpKX1jUPV1Ts2rP3ko24SqUiDBRfEg44Go2iXC4jHA4jk8ksS088X7azu8lbuK3skiEOALNFcVRDfJopjSEedNrxS8YsC8rkGdh6DFZbr/NSUwcAG7W121EbSKG27qplEyTI/KYXrzSVEQ6m/cbuRBSTBf9lfrc7jHFzZxOmL/o7zM2uLkd7IgrA3wG3u+drZXJ8xEtMWoh4UH3coGI3OWZsFvECk7cintCZzTCXx5lNM+KtMWYKjMvbYrqwbIw64fJMWpB4vmIgqimoGiZ0VYVtu5efbSMWUpCvGEiGQ9A1RThjjrQgZgsVzBYq0FUFlm2jZlrQFBmyJGGu6LDVlqK9JBzw9ddfj3vvvRd33HEH7rvvPtx4443P+3vKkgxdkVFescFiAwgrsqeoFPSiHWNSC8SDqqu9qOaO7wmdTUM7m4ZcXER1Ywqlm98FADB6dyL3tk/Cjvh3OA0ODgZyvkDwFNxFRk6R+ChTd0q8hXE0xINWuBy9yAzHdHlcZ/YBXB60Amc7kxMlPsu0IhMPKs8YdMJGXGeUAl0+kS35FhlaLk+GQ4FHF7XFdOxa04xTmSzmy1XUTBuqLKElrGNbV9Jz6PGQCtln8K4sOZzO1xoNLTufpkhoCYeWnW819oI74OPHj+Mzn/kMxsbGoKoq7r33Xvzd3/0dPvrRj+Kb3/wmenp68OY3v/l5/xxtMR2STz2mJNnel/nkqPimenJ0Brh2C+K6eOudeFDxZw2i+boOv9x28eJF6M/cD234CJSFpTygmWhzxHHIFFXofAHg/PnzgZwvADCBnsdLTHaKeITRKCD+5Lg4v09cY8qyiGcYx0/89m29+MbT/tM9bt/mrDSCTtjYxbTwEu9jNBSIDzENFkMzeQy0N6HCbNYRf5YR2Xk2s4iOeATzTPBBfO/aVmG1xF63nllXFdyyqRu5ioG1iHoRKwDcsqnbSxeMuvoOjWRHFUnC6EIRHfEIdFWBLEmYLpS9ycymZWO6UMYOqenlUQe8a9cufPWrX73k9S9/+csv6OfIlqso+dTZlgwb2XLV+dKZnWjifiNSyIhP5sTRFHGuz+L56MP46le/inD6BwAAS4+htmEvagMHnE2zVVYsvPa1r0XoW08jl+zy/W8Trl8Lqq4WhnhCMS10ZcZhEl8sMeI0Lt/YksCxKX+ntNFtnqkwqQXibTFmerLLm5nUB/FTGfHQgFOZLHqb40gx046JU/rNz4gH7SALagPtCWEqYMBVf0uGQxhoT+D09KUrooEVZWFUn3vf6QkslKroTkRw29Y1y+p2N7UnoKkyqg0iAU2Vscl934phwrRsaIqMbKUGy3J6h5K6BtOyvc661dhLLOH4wtnjF8SRLfGdneLogng7U5ROfIERICAeZ34ZjgvNNKCefxrRn/0zQieXNg/f+ta3OuN7bv1D5N72SZSvuxNm54ZLHKjfU1vOzgBGFSqc+t5IZ7fQcUdcP5RgVm7EE1FxvEB8kYmkiLcyvxlxlYmoic8xQzmJ33/Gf+OqngedxDHKjKUnrjF/B/GaafsmFySXA8Hb+feubfUdbKpgKWKtmbbwOHrfimFCkiSoK0rMVFmCJEnLZCFNy8KDwxk8OTaH4dkcnhybw4PDmWVlY7qqCHPP5FRnCxWcyCzCsGxENRURTUFUU2FYNk5mFtkUTyN7SeSAXwxbw0gCEl9gir6JMwGwx8MyIBJEC7v3CNP0xfJLzLagZM4iNHwE2vmnIFWd5bCcm0V1x80AgE2bNqF0y7vZU+m4dJ4abbjZ4QS0zQcBAHPM9Ug86Oy4oJ1cGiNQTtwwxVE98bLBaDK43On88o/RqTMswsx6Ix4kp/xebAn8BS4wM+E8LgGqQKCcvHPQDjJdVdDqI4rTGgt5Di6uq75ptxqWcsXj2SIms2XEQypsADXDhKY6G2mZXNlTLwOAO7/yEB4czkCWJGiyjJrpOOQ7v/IQ7nnvq73zqbIMRbKWpSEUt2KCzhfXVRSrBiqGo4pGm3qqLKNQNdhcdyO7YiPgGJPIj3mCKExRust7m8VdN8SjYUYzwuVBal6D2re//W0kvv1JxH/8/yA0+Bikaglm61qUDtyBwq3vX8WZHFvpYuqrHaxEGwruZIWgQy+5fUfiWzuahccR70qKl9DEbZlp9XX5TkZrgfiODqZBwOVBx68zMiQej2jMzDWXR5kImHhbVG/ofAGnppY60oJuUM8WKtjYlkBrVIMquwI2MtAa1TDQlvAixzNT4lyxx23v/0CC8z0sfVW298DJlqt4fGTGq9klkyUJj4/MIEuKTa4jTegqEiEFYU1GIqQgoauOPIl7vnzFQM20UHVTSXTaqmnCMC3kmWqURnbFRsBB20vT4/51mPWckwIl3haLYKLov2Rsi61urEmjnJlUXATMGuxEOyQ43YdyYR5WrMUZVLlx//JNtV/DgpaaiSwcAkRNaZTSu8jkz4m3xpiyMZcndR2isZxJt8Z87xpxlyPxCwviTTji4zlGTtHllFv2M+IzeXFjAvFFxkEQD+IIU+s6vJysnxFvi+noiIcR0VQYloVS1UQkpECVZcR11dvwPjwijvgPj8wgta4DPU1RrGmKYHyhCEhOv53jk22saYp6pWBDMzkUq+YlqQoAKFVNDM3ksK+3DT1NUXQnwjgzk/NWqmUAimRic3vCO19cV6EpEkKyDMNeioBDsgxVkV6JgFdjGaZul3iZEVAnHmQjBAB6kuKdaI6vNM/5VsvQzjyO2E/+EYlvfgLhJ3/i8dtvvx3l138Qubd+HJXUmxo63+dSVcE53yRzPRLnJrkQTzCrFuImM7CUeNDRQEHHTd20oUN4HPF1jGMl/uot3cLjiLcxZWPENSanTPyx82JHSDxfNYS5Ypr1pqsKDg10wbJtqLKMRFiDKsuwbBuHBrq8FMSaZvG1T1xXFfzBgQEosoRCtYZizUKhWoMiS/iDA0uqZJvaE4i6ymiWbcOwbG+QQSSkeJtruqpAkiSY9lIkLcFJG0qS5J3PqStWoasyoqri5IBVBboqIxZSn1MEfMU64Cpz1xPva2NuUpeHmGoJ4tMFcfRDnHOIGoBqtQp15DgiP/8XJO/+S0Qf+VeoE4OXyDqGw2FUujcBghzqalIagBNlc5HvuhaxUDhxxq96PBESOxriB9eLd/uJB81hPsHkYolrGuPgXL6ZmTFHfGRRHFETDzM778T3rnWm+jb8bLLkbYYF3ayrMJqo9fxDN23Hb+/sRVxXUTVNxHUVv72zd1k1wtZOZvZeHbcsC8WaCcNyggzDAoo1c9k8x2Q4hAN9bchVashWDOSrBrIVA7lKDQf62rxqiWy5iplCBWFVgSJLkCVAkSWEVQUzhYqXqmiL6djZ3QRFllA0LBSrJoqGBUWWsKOr6eVRB/ySsYDV/1lmE474+qY4HoN/b/z6JsfZjDLNBMSD5IA/97nPIfbTL3mvGd2bUN2YQq1/L6CvXhx6NWZHkjDbegFZ9U07BFUb629txsy4v15Af6uT2w0qjOQ3bJWM+IV58W9B/MDaNvzgpH/lwgF3rhhTFuvxbcwEbeJrGBEg4gfWiR84xJPhEG7d0o2fnJq4pPvz1i3dnkPqYd6X+GpkHElF7AM3bMNsoYK2mH5JydaG1gT8FJoVlwNOFcTnHjqFqmFBkSTYtg1JklA1LHzuoVP48M07vXN7G7juPzfqfh2ayaFU97CobzAp15ZSFbqqQJFlGJaNWGips86wbCiy/JzqgK/YCDhoQ0RbVJxPJF5g7j7iJWa5TXzlpS0vZqAf/RG0oSc8/uY3vxlm8xqUU29E9q2fQOH1H0Rt6/XPr/O1bTTJACQJRu/Ohs63yf1qJxk5ReKc/gfxxZrYARMfY+TQiE8xurLEg07GjjJlWcSDXitBTZZkhH2u5/quTgC4eaALzRHNu/FlAM0RDTcPdHnHqEwHHvGQIsPvNlJkNBzRo6sKepqiDZ1VxTAR9hFbD4cUr7zs3GzO27izbBsWlmYkzhUrOOcGMdlyFYdHZqEpyyNbTVFweGTWi2ydVISNmmV5jtkG3H+3l9UBWzbQEQ87eWXJKX3riIdhuRM2VmtXbAQctN1yU7s4WiEu2Uw04PJkREFBICiTjDgXYAxAoZSFdvYotLNHoM44nVNmWy9qm65BDMCWLVuQf8tHhe97OU1eyEDJDKMysB9QQ76rCEqbMytZj48yrb7ESwYTAbs8wqSDiG9ua8KFRf+Jx5vbnN92mqnvJD7HbBIu8WAa0yPz4nTVyHwR+9c5S+O+pI4z85emLPqSurc0rhgmHhrOOJUDMrxGAgnAQ8MZfOimHdBVJfAGdVtMh64oDas6dEVZ9ZJ8aCbnVkhIMOry+KrsxKQUiS6Uq7Ds5REt4DTs2Baw4DrWoZkcFktVGJbtbdTZtg3LMrFoW8si26imIl+tLEXK7vmdnO9SHfBcsYK+5hjWJKMo1QxENBWqLGG+9DLWgngxrMrkr4iXRMPF6vgbd/fh+6f8FZ/euLsPQLDxLI888gis+/4XEu74HgCwNR219XtQHdgP2Dbyq6w0+HWtfsOtNj8OuJKSjYziXksSq+zQWPX+ZAxTRf+d9353Y9Ji6naJHxMMxqzn1/W34oGz/g74un4nJ9oeZ/SAXT6RFz8giHcyjol4UA0FABj3iebrX58tVPDL8zNYKNc8x2VawFyphl+en/EcSImZdkw8XzGgyTIkmJekNDRF9sRugtqm9oTrJIH6INx2nSFFogNtCaF2w4A7Gr6vOQrTspd1WpIjNk0bfe5Q09lCBSF5eUUROXddgfe9OFoQOk5NLWKhtKQF0RwJYVvnc8sBX7EpiOE5cfcQ8RMN2h3rjXjQTb2GlUqWCalS8PjU1BS0sWcBALW+XSgcejeyv/fXKN34+zCfQ5nXr2siSUmRaUxqgfgsM6mBeD8zjpx4R5JZ3bi8wqxaiAcdv85kqzwedB7hCWYmHPFzszkUfT5jsWZ5S3JNkZCrOA+BekcDALlKbUnch2lFJh7XVWiqk/tUJMeZKJKTZtAUueEDpGKYGF8sNlyu66qCmOYojVm242At20kvxDTFi0Rrpo2Erl2yjpAAJHTN65jLVwxhjwpVLcR1FfNlA7L7N3j/k4C50lKDhaMF4SgbUoRuWDam82XIEj9NupFdsRFwOyORR7xRf3i9Ef/Cw6eEx33h4VP4zzdsX9pgsG0oMyPQho9AO3cURu8Ox8ECeN3rXofadW9FqX+vM76ngYk//XO3lWMvVzpfaxUPAFmVG6th13MAnVEdwwJR7E638H9DWxwY9tez3eCOXy8xpYPEEyGmtdnjwdQqtraL29aJBxWxCdrhNpUvC2ef0ZTg4dmccPz68GwOHfEI+lvFmgz97mZYvmIgoinIlmvehpskOcv8qKYsi4BNy8LnH3oWvxjOeJtwhwa68KGbtkNxq3ZI6rE+urXhOPWwqiyLRK/v78CvRqaxUKx50WpzVMO16zqWIlHJyUOvVDwE3JI79zKeLVSc95Ok5VM+JAmW7fBkOORpQaiKhFzZgGnZUGQJibD6nLUgrlgHHLTkZbLAbCS5fCYvzv8Rl7Mz0M+mHcWx7NLyV16YBNzd3Gg0CmvbDez993yYyPnShpt4ljC8fn5HJczfeZGK2ChTbkW8xHReEQ9alhV0VlmSGalDPMZswhEP2t5+0J0j6GfENeaBSDzo5O58xUAipCDbIPhI1OnotsV0xFwt3Xp3LUFCVFueA/78Q8/i+ycuekdkyzXv3//00E4ATiRaMS0kdM2p27VtqJIEWZJQNq1lkehNA5147MI0ZFmCbdnO/7eBmwY6PSfYFtWhKhIUW7pE9FtVJK+jD5Kz6V4zrWUPKAXuRmKdoz6RWYRpAVFN8Ry/acHTgnglBxzQTk+JGydOTzkKUv3JGB6Gfx0o5SfLZbG7LJdt/NM//RMi3/2U95oVSaC2YR9qA/thtvUBkuTlhMRun+eXw+T8bMM636gE5AR/btS9YIMKhQfdNBtZYDalXN7TInasxM8vMBOqXZ5llNaJjzBpLeJ+tbhkxFuZ3HOrl3sWP8CIB506HNdVrylhZW7XtFfkpmUJuiojBNlzmJL7OlnFMPHzoQzGFouX5E5/PpTBB27YBl1V3IhaRbZccjbObKDqVho0RULLIupHzk2j6D5w6flTrBp45Nw0/m/3ffMVA22xMDK5krcRR5t8bbGwd76eZBS6KiNbsZeVq1m2DV2V0ZNc6oQr1ZzmE1uSvAMlAMXac9OCuGId8CIzV4p4d4v4iUa8Oa4in6+7UY0qtJHjsNUQjHW70BxX8apX7XfG96zfg9pACsaaLYC8PCp5safN10e3xtrtsOJtsJo6vatcQXBd3pCqAILNzpAbqfQ1x3Fm1v+R0tfspBZ6ElEA/hq+Pe4Ei0atp/VGXPbRgybzeLCiBezoEmtVEB+ZZwTe5wu4bgOfUyS+nukgIx5nUi7EaUku0/dYV25QvySfLVSQCGmoWdayHHRUk5HUNS8inC1UcHxyAQvuPeV0pQEzhQpOTC4sSy1EVAWGZcMkh+lpPC9F1NlyFU+MzCCiOWI8hmlCVZxI/AlX44Ei9F3dzW6VQhUVw4KuymhptGkmaulzLV8xEFYVLJZqMG3bqwNWJAlNYW3Vm47AFeyAt3eJUxDE40x3E/GBjiZczE5DmTzjKo49DcmowGjvg7FuFwY6mrB9+3Y0/eH/wGjZ/8Zqi60+kX85Tc3OwIwmvTIzq7lrGVfApybpmo1okkhqweEAopp4iU98jtHvJT4yx0TKLtdlxsG5XGXU1YgHrhdmxHOIB42Ug3auDTNNQJQDhptzhQFH9UuiyNFtNnA/VltMx/n5PCqGvaxqoWLYODeXX5o44aqIFarGkmOFU5MbWrFZN1eqwLLsZQI6lmUv++3rNR4k91x0dL3Gg64quHlTF57NOJuVXhOGbePmTUst0OPZIqqG5ZbgmZ5j1RQFVcPy1NDaYjqimgLDqktV2IAt2Yhoqy+7A67gKohjE+IUBPGHzoknyT50bhpnzpxB5t5vIPGtTyB+7/+L0NATrvNdj9rAAcC2EHYlBsuS+JlX4lR9nkcbHBxE6OwRhIaeAMzGy25dApoiYgficaagn3hQla5rmY4v4kGV6ZqjYsdPfJjRgiAetDOsKSK+UYnff5rRDXb5AjO3kDi3uiLek4yiJxn2NA5imopYSHWX42FvSV4xTBQqBgBn74L+B9goVAyv0iFfMZAr17zKAfoYhmUjV6551Qjji0VUaqbbwGG7DTg2Qv9/e+8aJUd5nos+de97z0zPXZeRNKMbksAg0AgEQoJsOxAnTkJsMNuA2eSs+BAlxl5eXhwndrKOl+3Y8YoT4mQTe8XZ9nZOgkOyCElshx0bJIORgAFjSwJGGt1Gt9F0z/RMX6u7LudH1Vdd1V1dX400w8y06lkLg+epqa7qnn7r/d7veZ+XY1Ex1ROA0+OhHnaPBwCArtvsTG3VartCRzebffQ6Mwjz9e0Zx1TR0CDboeloOmGdhqs2AFco0wsI3x1rsqQw21m7YyLefPNNXHjpP8CWZqHGO1F+z/uQ+80/QOFXP2l47TKs9QFlKet3Gr9QYLMTeOaZZwwJULyzoTRCUNEBlaKhJjyreQdqwotNvkwEhHfbFLKD8HuGvE1sCB+meEsQvivqXYYivF+Dp/es7Gj6xWNNHgDOZb1ryoSPUAa5Ep5ia2zxEs/ho8NDSEVER8dXKiLio8NDVuZITIgE1hkwDW2wjecYqLpm1VfJP0ZNWavNvmOs/3EBY1GJkIjh1Z1W9xuBpusYXt1plQFkRcWBE5cw0B7D1t42bO1NYmtvGwbaYzhw4pL1gOhPRiAJnDHiSTcfIjqDiqpBNDv3ACNTnilXGlaAOoCZcgXnKXMh3XDVliDilNIC4RP2wY1yEcKpn0Ecew1ashulXfchIYm46667IF63B1Mrr4faNeAq07qYM74sPLx9HhbjAyFqB239NsRWD6HQMdRUatYZ5TFbpHg8mHEyRAkMhO+hWHASPk45H+E5SmmB8AnKpgnh13VRbCFNXqSMQiK8xHNoD/PIuGzutYdrnVfXr/JWQRC+VPV+aBPer30kADy2ezNYGKN8LuZKTUf5RCUeFUWDpjHQYOpnWRaibZTPeLZoZcb1YGwz1/oTEYR4FrlKFSzD1Px2NQ1JTrAybwD43kO78aFvH8ChM2lUFAUiz2J4dSe+99Bu6xj7FGPW5moGoGGKcUdYQrogOwzZWcb4OYFcVZuOHquoOtWcyA1XbQA+SHG4Ong2jQ9ctxYrYiL4U29CHHsN/NkjYMxONm12EtA0DHTEEY1GEb/jPkxmm9coidUfbclB+Ci855/NzbTS4/VsUrPh4WF0aFOYyDR/kifDEiSBRy7bPNvrSppmLZQaJuG7KeYvhD9Jqe0S/oUT3kv3F05cwK9fO+B7GvO6FCUAm/wKSumD8JmCjGoTy0xF063AoFFsNQnvt60+LyvgWfdMmGcNvqtOds6xxiQJzuWzTIRE7FiVwv8ZvVC3KaXh1rV9ViY61BmHwLFQzFUlyX5Jx5y9ZNARlTAjpRlRVQAAIABJREFUVx2qBZFj0VFXXxV5Hs88cgdmyxX824FX8Ku7dzRsgKWiRhu2m01kR6TWop0pyLiYK1sVCPK6um6sWqxA7XMzdi5o6RKErKiYLFZdu242dXvvWG/qbsOzzz6Lr//OPYg+/y0IZ34O6Bqq/RtQvPW/I3fPHwAsi6pZK72GMjuO8H7lZX4HVV4JmOKsQ+e7Z88eFMreG135Uhks6/3qhE+FKD61Jv/KGe/pxIQPU0oVhN+7tsfzOMLvoOhsCR+lvC7hj170boEmvMAxKMju2VJeVq0l+c413v7ChO9PRiA2qbeLHFPTpjLGRpobeGIKYYLodnOyAjCGZPDZI2fxxIG3HL9327puREUepIWBAYOoyOO2dd3WMRLPISbwAGNMo+BMbS8YIFbntZAMi1iRjCAREhAROCRCAlYkI2gLi64z1xIhEZtSYVf1gd2H2I56H2KBY1CsGn7CLGteH8tYfsPk8yAdf24gHYBzRUtmwPaum9OXMhgYLTZ03XS5iPDZ6fNgKjLUnrXoioUxMDgIuZiH2rEClcGbUF13A/SIs03zjKkVpfQIWLzfacd+RxL5bYpwa4nQw3FDf8xy0Po3GgMNKSVoWQfEpqMTna/aZOi0BcL3JbxrrISPUxoiCC9QDIYJz1JmzBH+jXPeU1HeODdlaMZ9tkqPpXNNH6CayXfFwlR3LfsmV088jHMzRccGEcsAvfFwrXEiIoEzAwwAy8YRMDJd0pggKyp+fOwijlzMNkz/jYm8pds1aqyTaI9I0GHMxgvxLNojEg6cmMTv7TY6wzIFGWtSMVR1zegg03VwDIN4SMDaVMwhQ+uMSgjxXIPZjX1yRv17QJIstwBISiYvjE1gqiijI1LrwCMYzxYNvwnyfhD1nW48UkiJJBWRIAkctKpq+VMwMLJ+SeBqjR1zQEsGYPL0ZhljfEjefHoDta4bYpXHFLIQTrwO8cRr4KbOQekaQOH9n4TIsdiyeQuG9n0ZI4XmS2QicTl8yTuLI7x3b1htSeJ3nprfgO54TV0zzNkZBsrKa4yfmV/EdNH7jOmiip44ZdPHrJ93Unb7Ce9XEtgbp2iyTf78tPfmFeFLFHtLwkuU2i7hN3QnERU4FFxqgVGBwwbT57ctInqaybSZphEHxpobBRH+/u0xpKISYiIPkWOtqQ+sOTU4KtYCV15W0BGWcLZSrFVjdaP5oCMiWYE6U5Dx8qk0ZuWqQ/+bLVXx8um0Q9/7iwvTuJQrWyN6qqqKUsWYkWYPrH5GEhkdbj148qV3MFOuWg0byZCAj+3a6AiwfpIswJ8P8VBnHG1hAbmy4hg1JLAs4iHeKpHkZQWpiISJXLlBB9xpe//mgpYrQciKihfMKah2sAyDF8YmICsqZmdnMfJ//h3RH34d8e/9McKv/Su4qXPQxAjUjhWApqLH7DLKit6liomcUTS4rse7BEF4v6UFfznm3MFmJyCMHgSq5nKOYRwbbn7KXJTSpMWvoHSkEf4tiukM4YsUQ3bC+/X5/f7R5u51dt5vp5nEc9i11r2ssWttp/XFX9sRb9ouHeI5y3h892C36zEEDt5sp42KPGKiYMjGOGdZIRWVUFbcH+3lqmIFQoFjUKhWoUOHpulQdePfOnTHkjwm8WY9WwNAJGgMqpqGdEF2tA77GUkEwJKNGa5otf9GXRmBJFl5WXEkWfUlEgIvH+JESMTOgS6IPGtJ7qICD5FnsXOgywqqqaiEbX1tWFlXIlmZjGBrX1ugAwZqO5+AUeupqJrDrDlTkPGDH/wAX/7s4+AvHAM4DtWB61C44xHk7vs8yrfcC7AcZPMPdUPKOwATPktx9KLx9Zjvej+L2oYbW5wBN32hgQeA7qj3oqg7yvueuXZ+1jtwEf4MxQ+Y8H6lg34bGKpNtM4EhN+xylt/THhZUfFSE934SycnHSWFZs0Y9p93xcKeI4RIGS1TkJGUBHRGQ+BYoxbLsQw6oyG0hQTr+yArKooV1TAmZ2ruZSwDFCuqdX3j2aKZSRuvRa5A041NwnGz5TtjqQbqr5GBpuuOmq2fkUSyomL/2CUjeSL6XTOb3z9Wk435SbLmiu89tBu3D/ZA5Fmomg6RZ3H7YI9DVSHxHPYO9WJFW8SQtfW1YWtvG1a0RbB3qDeoAQMwPTtFvD0xi6mSjMrZUQgnXkcoEsP233oYqaiEX/mVX8Hm7TsxElqD6sB1rhMkxtJ53Li6G5OUmemE95vF+cW8b7jWW0p2DThoknmv7YjhfKH5ta7tiOE8Re9K3KeqlCIw4a/ta8ezHiN/ru0zVg+r2ygz5ky+N+mtqiD8zjXeGSbh/U4THr00g0ITW8hCVcPopRls6+/A+dkiqpqOkEvnVVXTrc6rTEHGe/rbMHJ22mmSBOA9/e3O2mkshJDAY4UeQVXVIHAsWMZZO7Xrdu1LbZ6p6XZvWJnCqrYIOKbmw0vAwAjYxEfX6piD1nAfEu/Mvv2UAuwty0bnneFGVt+ybJeX1aNeXuYXHGsE3Kqm4dxMCSuSYdw+2OMoZwCNNeVESGioKc8FLReAJZ5DefI8Jp//AZhjr4LPZaADKEoR4DcegMRzkGIxfOKrf417v/OTpufhiGyM9X6aEj5L2TWj8Q3nvULejtHRUd+j49+i7OS/dXEGcUoHGZm5tpLio0H4LX3e5RvCn5/1DvyEz1LMcwjv1xshnfN+XcIfS3t3Vx5Lz2Jbf4cV1UI8C4lnrcBFNnQITyRUIscaKzkYn7vIscjLVUftdM9gj7XvQQJT/RLfrtvVdRZVVYXAGROB7brdqqojKgoNrm06gKhY89vtTzSOczfdzrGmPeLQ7RKQUoAb7GY3djBwmt34lZfNBfZ9o1REQrmqNewbAf4eJHNBS5UgXn75ZezZuxev/enHwbz+QyCXgR5tA3P9f0P7vZ8GOMFankxTbCYJP0PZlCJ8G+UzoPH1mK8MmHS4+Qm+AKC7iOXreYmnGIqb/HnKSB3C/2jUuxZL+Pn2ZPBrC/nKOEUmZ/J9FHtLwvcnI+hLho1ddxglAEN3qqM3EXYEqEypgoqqgWWNTTWWNTq0MnVmUmSJHxY45OUqwgLXsMS3d5AxphSMYZiGDrJUVEJvTGqQXHEM0BuTHIHfzzh3v8jLCsI81zAj0O4vTF7Xj7zML+wlDU3XISuatZF5uSUNv1jWGXCxWMT4+DiGhoYAALFYDD9/802wUhhtW4bRft2tqHSsREdbEizjnNvUzJiagPCU4QUWLwpNtrVh49Fo8VcP8ofsV91AA1uYgqYlfAVfAIiGOEx7dPREQxx6omGMejRrkM61aYqmmPBpyqYZ4WfKlFKAyd9IqdkSPkoxxSG83xpwlTIBhPASz+HhmwbxP386WrfbL+LhmwZrJjEzhkmMyBsGMCRTFnnTJGamiLUuTSJeQ079dJABAMOy4FkWuqrZbBxZMLYlORnnzrOMY1+AZxlrnPtcVAGpqIStfW14e2IW0+WabWV7SMKmnoQjs3WUAmZ0xCT+sksBmYKMdEHGZL7cYJfZHQs5Shp+jOXngmUdgO+88050d3fjhz/8IQBg69at+M53/x5PngFKps9ALpezivX25ck2ytgVwlcpM9wIX6pQ2kFNnoV38CQfoV8dMA1K/ybcc8/N+INLL/uaZBEWJADNg2tYkHCx6L25Rvg4pRGD8LcO9eIZD0XCraZ3w/u3rMKfHXin6XHv32LM3XMT7NuRKcjoioVRoHhLED5MKVUQ3u90ZwB47PZrwDIMfnTsIibzJXTFwrhzfa8zgJjeCCGehaoZz3eOMZbBdr0q4FxCx0MiSlXVdQntp4MsU5BRqCjW8E57iaRYUayAdDydM7wRdDg2xHTd8EYgNWW/IJtcOVlBnx625GoswzRsctlLAc+//Cr23nzTZZcCUlEJsyXjYUJWJKqmI12QIXGsq7E8KfW4SVzngmVdgiiVSmBZFuWyUS5gGAbvv/su3LlptTlXSkfF1EXWL0+ePz7heW7CVykOJoSnlEWp/OXCLcSxs5OWzExiGKxfv75hx7jhd8x/q5QHjqqpTn8MFxC+l9IiS3ifpmm4nvJlJvzJjHctlvB+3dBkikMd4U9RfH7tPAkgf/fhW/D/3nU9/u7Dt+ATe7Y4sqj+RAS9MRE5uYpcRUWxqiJXUZGTq+iJig5XMrKEVjQNuXIFiqZ5LqG9OshiEo+yoiIkcIjwHMLmv0MCh5KiWrVYshnHkL5iGP8mf2rWZt0c8Lu3bkQyJGB0ctb6JxkS8Lu3bpzzueYCnfQeO36owz42cCHUF8s6A/6P//gPrFu3ruHnv3vrRuwfm8ChM2nkyhXEyxqGV3c6PkS33nY7CD9V8JaPET7nnXRR+ctF/aKcSM10KYrKhpuhmDaYfvXHfo4bSEYwcq65UmLAXK71JbzVCITvppjxEP40ZeLE6ak8tvV3INekzZeA8H7Hr7OUujjhd6zq8DzOzlcUBR/69gEcPD2JQkVFVOSwc6AL33toN0S+pp9lWLahsqXqRonA3sI7mSvhWDqHmXIVmm5kccmQgA1d8TmrAvKyghDHYrZUQVXXrb4dgdGQDNUmSVRVHRGBx0y5NuiTGKjHQrXNurngr140mjA2dSctNcdMuYq/evEdR4bptxHDD4iMrxINNZQ+iIxvodQXyzoDXrFihevP7R/iUFLCpu6k9SESxCVKW6vJU/abLP7d8G5wgz3U2I11tEQXwHIW71dVEZcojmMSS5w4m4Lw5yjqAcLPyt4POcIfpUj5CL+JMu+P8KcpGSvhI5TWZsKXKLI7O//B/7Ufz71z3pzUoGK6VMFz75zHB//XfuuY2XIFZ7IFV1XAmWwBs2XjfUlFJZyYymOqVIVqSsdU3Rg3P5bJz1kVkIpKiIg8qpoOTTP+djUNqGo6wkJN1paKSlbDkl0EAQA9sdCcX9eeYZIlPvnv+gxzro0YtPvtjIWwqj3qsK1c1R5FKhpy3G+ze7pc9cWyDsBu8LtMeO2st9E64auU/gnC0571hJ+vzbV6NBugSUBb6hB+tuQdXWdLGqbKlFUBCQyUJT7h0xRdMeHDFAtRwq+huJcRfjUlWyH85h7KmHaTH+qMI9VkMGfK1tI6W67g+eMT5twzY/2u6wwUTcfzxyeswHp0IotiRXX1ny1WVBw12+BlRUW25L4rkC25m1HRMFWqAGCc5jlgzJ/bz18BwzjHuTMMrPFD9fAaS1/fREXUCEAtwyTnmM9SgF9VxXyrL4BlXoJwQ6YgI50vYzIvY7pcgVypQipl0R4S0R2XrGVCnDIGh/B9bWHMZJpvOvWZFoN+A/BCgBZ8/bw+4TmK1ItjdXRRnvSEL1LGwxNep4jqCC9QliOEzxRkCAzgZpMrMLWZZqm4d+mD8BlKM06maGzqJUIieJ6Dm5MHz3NWvfXoxSzKVdVss63ZLjIAylUVRy9msXNNt2Pybj101Lrmjl7MNu1OVDXdOp9fGCN6dMNCUnOqICpKrVHk/GwRpapqSM9sv88BKCuqdZxxHXT1gNFEJeHtSzMNagT7DLeFKAXYy5Zk86++bAnU1Beem6dzQMtlwCnTTzRdKEPTDL2jpulIF8rIlmvi9d6k95eP8LQJQYRfqMyWBq406xl856qqSIa967bJcAjtzaaEmCB8ntLqS/iOsPfDkPDNsioCi2eaO7EptojmtxHjEqUBhPCz5YpVf7WDZYw2XpLZtkckY1kP59Kd/KzddNWKS6LRkYbGfzjG4Mn5mj02ddv57PCyajV7gBGqG0kU4lkwjO64aFlRXUf0lBXV8dS3lwzs6gF7ycAoOQCT+TJUTbfUCJP5Mlib3eNClALsZcutfe2uZcuGt0n3Thz8oOUCMGBuBNTv+jMM7ENwT6W9mwQIf3baW3JF+MUKwOFQHGrnQNPMd6770CGKNjYkcJgpeIdzwm9KeS/dCc9RZsIR/p2Jac/jCB8Tec+ARALrWDrnOQyXzHrzq5YgsizGWrYbTlkMw1iyLADoS4RrY3jqIHAM+hK1ho3BVNS1BjyYilpZnv18jO2f+vMBRib6tReO4EPf3o/HXxzHh769H1974QhUW2Hf3ihSm4umNzSKpKKS4yFCoMMIwiQQ+m10MIK5jq5oCCzLWJOZu6Ih8/eM4xayEcOoPbPU2nOpqiIeEiy53+XUnoEWDMCZgoxEWERnVDIt/4wnaWdUQtJm6ux3c23ePRnmC7r5hREYKCs2NW+yMO/DO6+t8SWK41ipUsU0ZTox4dV6WU8dCC9Qdq0Jn6RsnBL+rQlKO7XJt0VEzwBMbCEZyqdMeCK70nUifTSCAtH/Ej4vK4g06fCJCLyj42tDVxIixxi1VZBWZAYbupJWoMnLCnrjYWN+G2oBkWcZ9Jl+wAR+Nq8knsNDN64DyzIoVFXkZQWFqgqWZfDQjesc6otmnZM6amY8pNFhPFvAkYtZ65/xbMFR980UZEwVK66bYdOliqu5T1jgUFRU184/v7BfQz0WsvYMtGAATkUlpCKiqYkx3yjGWMd1RETrqTzY7j3Uh/C0MXtzH8N35WCzExDf+SkKhQIYBQ2WknYw5nfPr79wnlK3zcua7xE4iu59LsLHKd1ShOcpT03C+5WX9SciDeUCApaBpbMtUNz2CV9VdbAwDGQcpQUdYMFYsqyYxINjGh/exOyG6GxlRYUOBivbomiLiIhJPNoiIla2RaGDsb7whk1iOwbao2gLi4gIHNrCIgbao9ja1+6aiTrvtTGAsCyL9rCIuCQgLHKISwLawyJY28OyompNS3SqZvDk+kijg720kC7ImClVXFUGdhUE4F1aoDznqfBb0vAbqOeClgvA5EObNGvAnFkDniyUHUYlP6dkSTR+sUA23JhSDkeOHPE94ohWFSN8RPLO9iISg4sUUTPhb1rlPVKH8BMU20rCr6bMXCO8303CvKw0VVZEBNbKHK/pbUNU4FwDZlTgcE2vYUkak3hERdb9OJG1AmteVlBoom4oVFTrdY2MUMaqNpIRthsZYVvUaqsHjL/524d6zIkOsAZa6rqO24d6HBkrzaoVMG0hj0+YY+aNDN94xjPYf7wWqImXhRuIt4V1bz4aHeZSWrCXAqICd0WlAL+v29IytEOHDmHnzp144IEH8MADD+Dzn//8ZZ2HbAp0xZzeqF2xEDS9NsZlYsY7d6Xxi4F6tcNNN93kO7D61Sm7OUzZkZcV9FBGrxD+AiWwEj4ne5c9CH8s492IQXiRUscmvNHx5Z4+lRTdCpiJkIi9Qz1GKcAMGCxjlAL2DvVY6oa8rEDVGVdZlqYz1nsrcAw06A1fPmNaim7VcxszQrZ5RqjXigFkzJBu/pyAWLWOTxdw+GIWJ2YM+8fx6QLaw6Ij0zt8MYt0QYam6YYtpJmxEltIwBjDI3Kso+ZM/lvkWEiCbdab6Vdsr+3W+xUD/n2D57sU4Od1W16GtmPHDjzxxBNXdA571tCXiCCTnUWqLQGedZrxnKR0VdH4hQYPZ9mgPvjCnOHm191HYAEviS9JBJkmpQzrdAyDi0XvvJvw70x6N04Q/s6hHvyBx3F3DhlDNG9Z7Z1RE95vzZb48rqB+PKS4PqPD96GHX/+AxybnIVmejIMdsbxjw/eZv1OTOIhcCxEzmmew7MseK6WARszyPSGh6IGI2skM8jqbSat4+q+8MZstksYaI+ZI38UhEUePMviwIlL2HebMS/Nvjq0nNDM1eE1TK2m7NcWsj8RwdqOKE5OFVDVnKN81nZErRKOX79iwL9v8EL4AfuxmfQzY24uWDIZ8HyBaAnHswW8NZHFmXwFb00YBf/2cC1r6Ix4b0vR+IWGV/BV+jdCYYg21vs8hK9QUmDCb055e/NuTrVjW4/3lBDCyx6uanZ+nJIpE351imLIbvJ+TXGOUcpMdv6vXxo1dr4lESGOQVwyDG/++qVR65i8rCAi8nDTBUTE2uaa0ZDRfPFuH9PuJzMj2vfx6QKOTsxgNJ3H0YkZjE8XkCmUHaUFP6tDuy2kDlgj4t1sIR/euR79yTCSIQERiUcyJKA/GcbDO9dfUQOD1wihhSgF+HldoBaov/fQ7XjqwdvxvYdub/DwmAuWVAZ8/PhxfOxjH8PMzAz27duHXbt2zfkcdi0hyxi5DtESXtNTe8qv747j0PnmGdr6buNL4DPBXFAwxWxTnW+YA6Y9VvBh8++oPQxc8IhzZHzbTMW7vjtTkbGyw1vUv7LD2MCUKUZGhG82Kp2A8H6nExcoJQ3Cn52hSAxNXlZU/N2rY5gqyABjjCtXdR1TBRl/9+oY9t22GRLPGS28HqOG7IGh2UKj/ufkC/+RG9fh9bNTuGFlR8NEb7v23ZDAwdK+iwLrKC2Q1eGKZATTszm0J+INVq2pqIRrepJ44cQESrYJH2GBxWZbQwQAPLZ7M1iQxoQyumIh18YEv40OfuB3ZbCQ8DKWnwuWTABes2YN9u3bh7vuugvj4+N48MEH8dxzz0EUm++QHz58uOFnFVXD1HQWcZ5BrqoaSz1NRVzgMDWdxcuvvAqRY/H2uLcb2tvjExgZGfHVQTYyMuLjDi//OLVvA/Rou+HvYPuDGxkZwRRlF26qbBzXLrG44FGDaJdYjIyMIDPjrbXNzExj9JT3uzJ66gxGRqqYmEx7HjcxmcbIyAhOnfV2Lzs1NoYReRJnzuc8jztz4gRGypM4MO59vgOvHwYmE5BmvctM0uwERkYKOJ+TMT6VR1XTrWyQUTTwLIOzU3k89+Ih9MclVFQNpXIJHHTwHGMtyXVdR6lcwuuvv2787WWMYzjGaSHNMQAHHf924BVsShlBtqJo+H9ePIsjmRJKqoYwx2JLKowv3boSoqmPrqgaSqWyQ8tLUCqVrdetqBq4ahk58+9AZBkU8sZ7EOZZnH7nCC6Yk2DevpBGuao5lsjlqoa3L6Rx+M2fOV5jdxzYeV0CM3IUSYmDyJXxszfecBzz929lcHZyFisiHJQQa7x3k1N4/B9/jP++udHlrqJqmJFV83yND+hdUR3jSQYjEwXjcykVsL0nil3Rku/v2VLAkgnAPT09uPvuuwEAq1evRmdnJyYmJrBq1aqmv7N161ZIknO5cX6mCO1gGut7Y9B0HdMzs2hPJsAyDCqqioGNW4yn/KE0kGk+9jvV1obt27cD/99R6rUvxHHsk/uhhROAIAEMAy3ZmHVu374dPf9+HKdnm3eI9SRE43z/dQZA84DDihFs374dkRfOA9nmK4NIJInNaweAI80D9ea1A9i+fRt+rRzBv596relxv3bDRmzfvhnT8fPAgbNNj7tm80Zs39CPnxaOAhhvepyW7Mb27degZzCPT/2k+fk+uOcmrGyLIXopC/zoTNPjbrnxOmzqbkPHVA7KD07WuuvMbjpFB1gd2HrtNqM1d6aI3pcnwfFynauW0QZP/vbWlyv41EvnjdFAMFZoHGus1kSedXj0/vrf/hhvZspgGWN8kQbgzUwZXz6cwzOP3AHA+JvvPZgGJ5iG4poOga0ZipPXBYBfL0aszDGXyyEej0PTdfzalpW4eYfhNjZbriD/r2OQ+MZadl5lsX7LtjkZrcuKirGf7Ucy0WiSNCbz2Hrde6ysdS6G5ztuMs59pX7ACwlZll0TRYIlE4CfffZZTE5O4pFHHsHk5CQymQx6enrmfB77vCiWYSBy7jvHguBdPKDxC4nR0VGIJ0agmZaS4Bo/JvKnxrtkB3YQXqNocgl/+1AnXvMozdw+1Ik3ximlAJM/TnEbIzzfTIxrgvBuLbV2EJ5lvN8TwkscB4llILtsxIU4BhJnyo8iRlOPptnaDnSA0XUwnDFDDPC/2URGA+03d/LJ/dWPBpotV3DoTNp1t//QmbQ1cSIVldAZlRDiOfQlIihVFYQFHjzbuMll9zKYUFR0C1xDyeB4OodSVUWIZ6HDObOuXFXnbLQ+l02zuRqeSzyHroiwJIOvHyyZTbg77rgDr776Ku6//348+uij+OM//mPP8kMz+C34n6OMQqfxlwvanwlvznALoWYp6YY285MLUdp4CT/bxC2LgPBhwfs9DwsiBMofO+Gv6/XerCP8G5SZa4R/36Z+z+MI36zN17o+k+9PRrAmFXWdfTbQUWv1NQZjOo1xyN6AyLnPKrM3ErjVJv2MQT+ezqHYZGpHqaJarc0Sz2H3YA/OTOfx1kQWxyZzxgb0dB67PWqizfYqhzrjiIicdZ9kZh0AhEXOsUnoB343zRpbltV3bTbbYmHJZMCxWAxPPvnkvJzLz7yoRhWmEzT+chFhgVyTZJTNTiB8+g1o67eBWzUEpWOo6W4Na9oeTlEmABO+Ox7G2XzzINxtOn+NZ70fPOPZAoY6vNUIgx1G4Frf7e0FQfgSpdOM8BLPNd0UZVAza/HTikxkXus7Ezg1VYCu1lzJeJbB+s6EQ5YlcEbdUjGzZd08TrDJywD/MiUyGuhsNo8DY5ewe7AbK9uc7ysJhBWXzcyGQGgqFjRdh6rr4PRGHTBQyzABQGAZFCqNGWZ9hk5Qn6H7hd9Ns3onw/oSzuXIy5Y6lkwAnk/4mRfVHqY4elH4y0WzrMMuNRseHkYXMshMNt+l7zQdx/x65IYpbbyE91MOmK16B0zCT1J8fgkfEikGQCZ/3DTPaRaAydLYbysykWVFJR6zZQWapoNlGUQl3pJlkWVwVdEaLB9VTUdV1awJEYB/PSmZiHHoTBrFioqIqQqwT8TwGwhlRTWOgaHtZWBOPQaD/WMTlkpDVlQ8f/wizmWLDVatzx+/iEdv3WRdq314p121UD+80y/8PJj8qjlaCS0ZgAm86kMVynKGxtfDr1zNrfGKKeWs4Kt0r8WePXugvPRPnq9XNTW0fYkwTkw3D3TECWtDZxwvedRuN5jZ1FAHZaR7RwIFSoCTeNNoneKjS/gZisE74btj3raL3aYHxQpKlkT4jNnZpao6oiJv1TpVVcfRiayVcRENrlvrcMU2I80OmkzpQ98+4KjVhlFiAAAdl0lEQVQBVxQN+8cm8KFvH7A214BaIDx4etIK1DsHuh2BMFOQcfhC1jRHZ2yda2Ucuag5RurYj7NbtdqPA5zDO4+ncxjqjHtmvrKiej5w/D6Y/DgZthJaOgB74VTGu0uLxteDh7fnLnmj3XJHPRSD2rUGAKCaHW4XZyl+CybfTpk8TPgpijaW8DKliUHWdYQpul3C9yYonssmv5nS2EH4S5Tx9ZfyMla2xbA2FYfAMq5dbgLLWKPcYxKPkqKCYRhHcGUYBsVqLbBmCjJKTTTNJUWzDN79wu/mGmAErtsHe1DVNJybKWFFMozbB3scigD7fdhRfx9+j7MjERI9N9zmOqbd68FEnAxlVWtQcxAnw6AEsYxgN52uf9qyjHfINHj/8KMXBoDuCHCe2EyQaYcMYzRYAOiPGl+O1akwjl5qXoJYbepEi5RuM8L7zfgrlPNVqipSlGGbibDx3nVFvIMS4RNh74cI4Ve1RcCzQNUlFvJsze5RVlQIXJMAzDGO0kJY4DFbLpnjgYjcikEyLFqlhbxc9cy885SHWz3I5ppbuYdsrpGgZ1cFpCISylWtoWZL7kNWKo7GIB01e0vjPvwdNxfM55h2u5pjRTJi1YDdWpZbBUtGBTGf8GM6Pd9Gv1GKvIHwRfO7SiwlUTXLB6alJOFXxr13mglPG/1N+E3d3ucj/ACl3XcgFUNE8v4iEP75E8111nbe72SKqqqDayIx4xjWsns8ns5B0dzNbhRNt9QDfjvXJvIUtzYX3mv2mV1lUA/75ppf05lUVMLW3jZ0RiVwLAMNRotxZ1TClt42h92j/Ti9yXF+sZCz2ezmQ+9mh9u7jZbMgMlTWdN1VFUd2VKl4ans1+PVL0QenmMvSIwpV50bbtzUBag9a63jzAnfKKnef7yEr1BGfxO+J+Htf0z4aUrddrooYx1FBREXjdC3Y4W3VpTwfodexiQeMYlHtVR1mL1zZoZEltDkocOyztohiROOhxYDSBwLia91rhn/Yb8C/09rsiT/0bGLuJQroTtemxlGluR+N9f86mclnsPeoR7k5KojcwSAvUPOoZL240grcv1xfrEQpjjOmWvNW5tbBS2XAcuKih8fu4DDF6Zx8HQa70zLOHg6jcMXpvHjYxesp/JaSqZH4xtf1x8vzjqNddTuNY7jzNiFMMVSkfB71vd6Hkf4jT3eJjuED1HcfUIC71sFsXVFh+dxhKcZWRM+LyuISgJiEo+ExCMmmv+WeMQkwdLjVlUdEYE3ZwLW/Oo1TUdU4K1M2W6T6MwInTaJKylBxM7/+f6j+KuX3sGhM2mMpvM4dCaNv3rpHfz5fmcHpB8d8FxMZ4hpTyIkgGF0JEKC64QIx3FA0+P8YCFNcQC6qVIroOUy4ExBxk9PpTFbroJla/6t2VIVL59K157KOuXZQ+PrwFOSJJ4xOtxCZ38O2WN6cVvUUBAMpeJ47ljzJfyQuZF0cdbbt5jwKyh1W8LfsMI7UN+woh0np71fUzCbRzJF2fS4bQRn8omQiEsUudqlfBmbempLaGtqrm2Txj41NxWVcMuaThw8ncasXJOXtYUF7BzodBxHOtf6tLDDxtFec1ybiiPCMyi6SFgifG1Tz27aY5dR1Zv2AP5UBnMxnfGrMvAj0fSLhTDFsdeU4yHRMloH5l5TXg5ouQAscAyKVQVs3QaHMd9KsZZmpzLeYn0a3wD3aeQW9Fmjw81rdLx5pQCAXYM9+OuDY03Pt2vQaNPWXAxY7CD8aYq/8empPLav7sKrFMexV89N4TbKiPPN3YaUTVZUsCwAHY7JuayZkZLVSHc81DRQsyYPNC6hvZbad6zvQ75idFKRwMoyDO5Y3+c4bve6bvzPn45iply1zpcMCfi/b9ngCCCr26I4lsk3mOesbquVds7PFHFhpuSqMrg4W8L5maIVrAloKoO5+s/6demarxbe+fTHpdWU7TrlVkHLBeDxrJGdkS4gYxWjW8GOmF3L3tJTi48C8OoNI18/ikIKpWwWmhZDx7rNuBRa2bTDrTNqBBu/yoDTWe8NIsKPpr2dxAj/nj7vDPg9fe3oiHln04SXOA48w6Kqa8YSXzcaBKDr4FnW8lpY2xFHWOBQcFFghAUOazuc/rhA7QufCImuX3j7cZquoy3sfhxMy1IGNb8Dxvw5QaYgoy0awhodmCqULa+FjmgI7dFQbVVl/CIA4p9bOyfAuJaS50s/u1iYz+tbiJryUkfLBeChzjiSIRGZolzLunSAhY5kWLJ2mLn6kRN1IP43nTEOhXzzDbHOmPHH0hFicLHcvGaVXLsRH/zgLXjl+ZN4+60LTY9bbXrpns54B0zC2zMw1/OZ/LW93htdhB/o8FZLDHTE0Z+IYGNnHO+4BPWNnXFrEgLxWjiVyUPRiczLGHGzxua1ABgbY8fSuYYMs17lQb7wj+xc79kg4Oc4WVFxYGwCq9tj1oYtkT0dGJvAPjPjqpdH2X107aWK/kQEfYkQTk7lG2Rtazti1vsCzK9+dilgPq7PbqRVj/moKS9FtNwmXCIkGs5VdbFQ042lL/kSbu6lTH4w+UsewdfOd8Qb//jYmUuWzCyViGJwcBAZWndYwTj+xZPeEi7Cc5TWYcJPl703zghP+yMnGU59iYeAZRnHEv9/7BhCXzKCuChAYIG4KKAvGcH/2DHk8ABoC4tY0x5De1hAVODQHhaM/x8RHZt0RGL4wN+/iN//l1fwwN+/2Cgx9HmcfUhl/cw1+5DKenmUyDKu8iiJ5zDYGa9tHplvka7rGOyMuw6VzMuKQz97OUMlWwULMXNtqaPlAvBsuQKlSausomqYNdta++LeXVqEp1QqLL6sOAMcm52AcPJ1iMdeAZSqxa9LeWeYhL+W0h1G+B0DnZ7H1Xh/rSLHLlHG9FyawWy5gnShjBDHgmOMPyKOAUIci3ShbL3HAPDorg0ICxzylSpkDchXqggLHB7dtcE6hmyGrWqPYnNPEutScWzuSWJVexSpaMjxUPAbuMhxs6aub7ZcbTjuclQGMYlHVdObDovUdR0rk1EkQgIiPIdESMDKZBS66e5FjpvvoZLLBV76aMDfCKZWQsuVII6nc5hqYr04VapaXUbTZe/uJcLHGSDrEbvi5ncowgkAzGzKpvPVkt0Ax5s8sI5i5Uf4gU5vTwbC12cL9SD8xi7v8xH+jfMUr9/zU2BYFqWq6tqzX65qjk6uD//vF3E2W0REFKCoKniOw9lsER/+3y9angcSz+HWdd34yo8OIycrlkF5XOLx6Tu3OoZP+tmkqTedsbtq2U1nLldl0Ew9YIz8qWBVe7TBD3i6VHF4MlxttU6/JZelXvOeb7RcBuzXsGUy7y2lIrwkeS/xCV/SjSe62wBNMIzFd1CGfRL+Qs7bFpLws5TSAuFTlIyf8Nd0edeKr+lKYqgzDh2GqxjDMEbDA8OgqmrQoFt1drvngbXRBafnAcGLYxMoVBTrgaHpOgoVBS+O1UZH2UsG9bCXDIjpTLpQhqpqYKBDVTXTdCbrOMdcMy4v9UDjGHnOdRjAQutnlyLmWnKhDcdsFbRcAD6R9pZbEd5PxxcAiJTGBML/yqb+psGX8ICxGegFwouU8fCEH+ykePOavECpFRO+I055QMRDkHgOMYGHXncvOnTEBN760vg1FCeBmrErBRhjfLw9UPsNXDGJR7GqQFY0FKoKChUFBfL/K4rDdGY+p9z6rWFebbXOq7nkQkPLBWA3KZMbvyLprR4gvJ/OMAAozGabBl+g1qU8Sen6InyU4nJG+KgoeB9n8i+dnPQ8jvCMEQZdwZh8piBjTSqGtrBxbs3c8WwLC1ibilkZpl/Pg+PpHLKlKqqaBgZmRg0GVU3DjFk2AvwHrrysoKrqqGiaccWMceUVTYOi6q677POVcfnNqK+mWqfflcvViJarAVM6eC0+InkHLsLnSt7bcITv6eqB0r0WjK67d7iFjMySkohaPCmVNAPhJYHzbGKQzBv2a1Iuciw4FnBzX+RYgzdkWSGkCzJUVUNZ0RDiWbSHJcemmV/Pg1VtEVMv29jAgDopmh/hP5lgIXLmUEkYDw+R48DXTbCYb1xOR1qr1zqvRnmZX7RcAF5JyWwJT1thEr4/GcPFUnNlQJ+p7eyMhaD2mTv7LuUD8kf2q1tW4o+eaz4l9Ve3rATgf7MuFZHAsYBbQxzHwhoYeW0fRVVh8qmoZAbCxlIJA6YWKHQNp6ZqnWGyqiJfyWNTl1NuZZ+sUFEUiDzbMFmhquqICgJm5arjrdN1ICoJlneDcU90fW9eVhAReVRUDZJtqCQAREV320VaQ8RcMZeOtFbbcKvHQrQstwpaLgCvTcUhMEDVpdQqMLBaQTd3J/D8WLrpeUg7rdckHzY7gfyFVzE7ewcqFaVpdxsAgwdwLue93DqXk3EdjO4wDu4Ga5zJA8byjmM5qLra0O7LsZxlFt5H+ZITPlOUG0bvEKiajkzR2L1/dXwKql6bBMIAUHXg1fEph/+y3fPg3w684hi5TpCKSrh5TadR75Wr0DTjAZgMCRhe3enIkPzsprt5RvAunhF+zxfgyjGfLcuthJYLwLKiQuBZVF1cuwWetYLDmay3CoLwK5NRvHK2MQMmG27RzhiOHj2KaNhbPRA1Z8ydmfLucCO8rKimNWLjMcRHQeI5006RQb0YQtONn5NV/Xi2CJFlUHEJriLL1Fq0q8brMrrzpUmrrlxVcTKTw1RJBm86iJEIzACYKsk4mclhU52OORESsSkVdu1aM7wbepGvKNB0WK2+LAPcsb7XtYHBywDcr2eE3/MFuHJcTSWXuaDlHvHH0zlrzpYdvNm9RDZ0kpQaMOE5l7fIrnboWLsZw8PDyFPmmhH+xlXeHrmEP3ox6zo/DjDmyh29aIxM6k9Emk7FKFZVq/11qDOOZFh0fV+SYdHaEJMEDoLZEcYxsP5hGQYCz0ISOGTLFdibvWp+B0bZIEt5L9xANqXawgIknkVbuNEmcS676eR8UZFHVVURFd0bJ4Ld+XcXV4u8zC9aLgMmGlVdh8MARjczOhJo/HoojM86ZW31UrNCzzowDIMxincD4bf1pzw3zbb1GwE4SpkSQfjZcsVSIdRD03TMlivoihmZZ1dMMksWjJW1apqOrphkZab9iQjWtsdMPwM4/QzaDT+DVERCWOBQUrSG0TZhgcM1lC4+N/jJkC63gaGZr+zV2BARYGmh5TJgu0bVLv6v16hKlABH+G22zSs3ne+1/YZnxC1rezzPZ+c7msxKs/9c4L0/GsK/fnYKGho/SNMFEq+fNTrbZEXFmvYo2sKGEbeuG+9PW1jAmvaole1JPIeHh4fQnwgb7bSi0U7bnwjj4WHDvyERErFnqNfyQCbhjWeAPUO9c54rZodXhjSXBgZSWihVVYev7OW2IgcIsBBouQDs1Kjq5saU3qBRDXHeSyDCr0vVAjBTmm3Q+RJ+PUW1QPjzs0Uomo4Qz4GUaDkGCPEcFE3HeWKwXj8VxwbG5AHghpUdhoMXy4Bjav+wLAOeY3DDytrUielSFdf2d2DH6hS29LZhx+oUru3vQLZcdWgxH9u9GY/u2ojh1Z3Y2JXA8OpOPLprIx6zLd//6aHdeO/GfrSHRVOCJuK9G/vxTzZ1w3zDrw7Yb2nhamuIWEjQPB4CuKPlShCpqIRURMIxwCo7kO+XPavZ0u/thkb4a/trAVjtHYIebYMW77QUD4TPUPTCFm9eS4hnIfGswzPWvukmCRxEnkFF0Rs2w0SesfS9XbEwhjrjeHti1uFQpmk6NvQk0BULW+9LR0SqKQPMjSk3ZYCfcoCfiQ4LAT+76XMpLQS781eGQEVyZWi5ACzxHE5N55EtGiOJOBg14GyxilPTeetLees675IB4ftQACplQAwBDAMt0eU47hbzuJ0DXZ613Z0Dxu/1JyPoS4ZxPlsEwzCWck3XdfQlI1Zg6E9EsK4jjpNTecNjwbTTFDi2wVv2lcfuwo4//4ExDVjVwXMMNvQk8MpjdzneF5YBJvNlK7NWVB2T+TKu6Um6Bis/GlXaRIf5hp+Hw1yE/8Hu/JUhUJFcGVruETVbrmAyL0M0v0QkexR5DpN52fIVoC2VZEXF6Ogovv/svyJ28lVAaXRPiwqc9WXtioWxscfdcWyjLROVeA4P3zSIjqgElmWg6oaHbkdUwsM3DTr8AkgtNhkWEeZZJMOioxZLEBZF/OLTH8CZz92Df/+/7sCZz92DX3z6AwiLtYxUVlSomgaeZVCoqsjLCgpVFTzLQNW0Zbd09KoVX05pIdidnzsCFcmVo+Uy4OPpHEpVFSGehQ4WiqKC5zkwAMpV1bJK/MmJCc/zfO/5gyi89SpmSjLUZE9tRIYNqm7UbElTxMHffx82fOlZXMrLVnNCd0zCwd9/n+P3Hrv9GrAMY47eLqErFnYdvf3Y7s1gYYzoPnUpgzXdKc8R3V2xMN63aYUrlynIOHJxBqqmIyryVulD1XQcnZhpuR3/oLSw8AhUJFeOlgvAxACm4mJmYDeA8apXstkJ/OLFUaxpj+Ka91wPOTft2uVWUZyZ4zcPjmFjdxKDnRpyZQXxkDFh95sHxxzLscWYYBuTeJQU1RoYad0Ow6BYVRfUH2ExEJQWFh6Bx8OVo+VKEImQiB2rUiiZNoQl1bAgLFUV7FiVsgLvdU024YjUrDsqYXh4GDfdfCu0Ji3GGmDVOOzLMZEz5oiJHOe5HPO77J2PCbZ52eguq1fE6gAiAu/6JWoFBKWFhUOgIrlytFwABoBbB3sQFXlT72qUAqIij1sHaxtvVVWHWHf3TDkP4dQb4KDj2htuxJ49e8BSdnJJRrnULfeIP0JnVALHMtBgNKp0RiVs6W0LspUAl4WryVZzIdBa604YmeiLJy7h2v4OKJqGdDaHzrY4eJbFiycu4fdvMzwUYhKPZFjCdKkCxewk00Mx6H1DCPMM/tude80OOt1T3UC6rJb6cmwu/ggBAvhFUOq5MrRcBmzPRHmWRVRgwZtZrD0TzcsKIgKHqMAhKXKIiTySIQHh1ZsQG9yKgjnJQRI4iBxrddQBte46kWMtPe5yWI6RbCUREsAwOhKhRr+FAAEuB0Gp5/LQchmw30w0FZWwta8Nh4++g5mxX4AZ2gEuEkF7SMKmnoR1XH8igrUdUZycKqCqa9A1gGEBgWGxtiPq0OMu9Z33IFsJEGBpoeUCsF/zZ4nnsC0k49iZn6Gb1xFmZpDsNea27R3qdRz38M71ePKld5AtVVDRdIimt+zDO9c3nZy7lAPc1WACHiDAckDLBWCgLhOd0RGT+IZMdHR0FIlzv8DGzjimkiuhrdiAREhwzVjtetzJfBldsZCnHjcIcAECBPCDlgzANP3s6OgonnnmGei6jo/f+37cfOttmCpWgvldAQIEeFfRkgGYwE0/S4KvpmkYHh7Gnj17wDAM+pP0tyLIbAMECDCfaDkVBA3pdLoh+AYIECDAYqClM2A33HLLLejv78fAwEAQfAMECLCoWFIZ8Be/+EXce++9uO+++/Dzn/983s47NjaGmZnaYM01a9YEwTdAgACLjiUTgF955RWcPn0aTz31FL7whS/gC1/4wryc98yZM/jnf/5n/MM//ANKpdK8nDNAgAAB5gNLJgC//PLL+KVf+iUAwODgIGZmZpDP5ym/5Y3R0VHs378fmqZh48aNCIVC83GpAQIECDAvWDIBOJ1Oo7295lDW0dGBycnJyz6fXWoWbLgFCBBgKWLJbsI1GyVux+HDh11/fubMGezfvx+6rmPLli2Ix+N4/fXX5/sS33WMjIws9iVcMVrhHoDgPpYalut9LJkA3N3djXQ6bf3/S5cuoaury+M3gK1bt0KSnC5jmUwGP/rRj9Db24vh4WHE43HceOONC3LN7yZGRkawffv2xb6MK0Ir3AMQ3MdSw1K+D1mWmyaKwBIqQezatQv/+Z//CQA4cuQIuru7EYvF5nyeVCqFXbt2BWWHAAECLHksmQz4hhtuwJYtW3DfffeBYRj80R/90Zx+X1VVcJzR8bZr1y7ouh4E3wABAixpLJkADACf+tSnLuv3RkdH8cILL+Dee+9FMpkEgCD4BggQYMljyZQgLhdE7TA1NYW33nprsS8nQIAAAXxjSWXAc8XJkyfx7LPPWt4Ow8PDi31JAQIECOAbyzoDfu655wJjnQABAixbLOsAHATfAAECLGcsyxIEadK4/vrrMTw8jEql4nm8LC/uSPj5QivcRyvcAxDcx1LDUr0PEpuaNZYxup+WsyWGXC6H0dHRxb6MAAECBPCFDRs2IB6PN/x8WQZgTdNQKBQgCEJQeggQIMCSha7rqFariEajYNnGiu+yDMABAgQI0ApY1ptwAQIECLCcEQTgAAECBFgkBAE4QIAAARYJQQAOECBAgEXCstQB+8EXv/hFvPnmm2AYBp/5zGdw7bXXLvYlzRmHDh3Cxz/+caxfvx6AIWX57Gc/u8hXNTeMjo7i0UcfxUc/+lF85CMfwYULF/DpT38aqqqiq6sLf/qnfwpRFBf7Mj1Rfw+PP/44jhw5gra2NgDAI488gj179izuRfrAV77yFYyMjEBRFPzO7/wOtm3btuw+C6DxPn784x8vy88DaNEAbB/wOTY2hs985jN46qmnFvuyLgs7duzAE088sdiXcVkoFov4/Oc/j5tvvtn62RNPPIH7778fd911F/7sz/4MTz/9NO6///5FvEpvuN0DAHzyk5/E3r17F+mq5o6DBw/i2LFjeOqppzA9PY3f+I3fwM0337ysPgvA/T527ty57D4PgpYsQSzEgM8Ac4coivjmN7+J7u5u62eHDh3CnXfeCQDYu3cvXn755cW6PF9wu4fliJtuugl/8Rd/AQBIJBIolUrL7rMA3O9DVdVFvqrLR0sG4Pke8LmYOH78OD72sY/hwx/+MF566aXFvpw5gef5hknUpVLJWuamUqkl/7m43QMAfPe738WDDz6IT3ziE5iamlqEK5sbOI5DJBIBADz99NPYvXv3svssAPf74Dhu2X0eBC1ZgqjHcu01WbNmDfbt24e77roL4+PjePDBB/Hcc88tizqdHyzXz+UDH/gA2trasHnzZnzjG9/A17/+dXzuc59b7Mvyhf/6r//C008/jW9961t473vfa/18uX0W9vs4fPjwsv08WjIDvpwBn0sRPT09uPvuu8EwDFavXo3Ozk5MTEws9mVdESKRCMrlMgBgYmJiWS7tb775ZmzevBkAcMcddywbX5Kf/OQnePLJJ/HNb34T8Xh82X4W9fexXD8PoEUD8HwN+FxsPPvss/jbv/1bAMDk5CQymQx6enoW+aquDLfccov12Tz33HO47bbbFvmK5o7f+73fw/j4OACjpk1UKksZuVwOX/nKV/A3f/M3llpgOX4WbvexHD8Pgpb1gvjqV7+K1157zRrwuWnTpsW+pDkjn8/jU5/6FGZnZ1GtVrFv3z7cfvvti31ZvnH48GF8+ctfxrlz58DzPHp6evDVr34Vjz/+OGRZRn9/P770pS9BEITFvtSmcLuHj3zkI/jGN76BcDiMSCSCL33pS0ilUot9qZ546qmn8Jd/+ZdYu3at9bM/+ZM/wR/+4R8um88CcL+P3/zN38R3v/vdZfV5ELRsAA4QIECApY6WLEEECBAgwHJAEIADBAgQYJEQBOAAAQIEWCQEAThAgAABFglBAA4QIECARUIQgAMECBBgkRAE4AABAgRYJAQBOMBVjUwmg+3bt0PTNOtnv/3bv40f/vCHi3hVAa4WBAE4wFWNVCqFzs5Oyz/g+9//PhiGwS//8i8v8pUFuBpwVbihBQjghRtvvBFvvPEGVq5cia997Wv41re+tdiXFOAqQRCAA1z1uPHGG3Hw4EEcP34c99xzD1atWrXYlxTgKkHgBRHgqsf4+Dh+67d+C93d3fiXf/mXJW9IE6B1ENSAA1z16O/vR6VSwWc/+9kg+AZ4VxEE4ABXPb7zne/g7rvvxo4dOxb7UgJcZQhqwAGuWoyNjWHfvn3o7+9ftpOnAyxvBDXgAAECBFgkBCWIAAECBFgkBAE4QIAAARYJQQAOECBAgEVCEIADBAgQYJEQBOAAAQIEWCQEAThAgAABFglBAA4QIECARUIQgAMECBBgkfD/AzdJfSAOG4f0AAAAAElFTkSuQmCC\n",
"text/plain": [
"<Figure size 576x396 with 1 Axes>"
]
},
"metadata": {
"tags": []
}
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "FkvOm5Mlrh4Y"
},
"source": [
"## 🔥 End of AutoML!!"
]
}
]
}
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