├── README.md
└── TASK_1.ipynb


/README.md:
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1 | # DataScience-and-BusinessAnalytics-Intern-TheSparksFoundation
2 | 
3 | THE SPARKS FOUNDATION
4 | TASK 1 - Prediction using Supervised ML
5 | To Predict the percentage of marks of the students based on the number of hours they studied
6 | 


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/TASK_1.ipynb:
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  1 | {
  2 |   "nbformat": 4,
  3 |   "nbformat_minor": 0,
  4 |   "metadata": {
  5 |     "colab": {
  6 |       "provenance": []
  7 |     },
  8 |     "kernelspec": {
  9 |       "name": "python3",
 10 |       "display_name": "Python 3"
 11 |     },
 12 |     "language_info": {
 13 |       "name": "python"
 14 |     }
 15 |   },
 16 |   "cells": [
 17 |     {
 18 |       "cell_type": "code",
 19 |       "execution_count": 4,
 20 |       "metadata": {
 21 |         "id": "ZmTV55JL2ZCD"
 22 |       },
 23 |       "outputs": [],
 24 |       "source": [
 25 |         "import pandas as pd\n",
 26 |         "import numpy as np\n",
 27 |         "import matplotlib.pyplot as plt\n",
 28 |         "import seaborn as sns\n",
 29 |         "from sklearn.model_selection import train_test_split\n",
 30 |         "from sklearn.linear_model import LinearRegression\n",
 31 |         "from sklearn.metrics import mean_absolute_error"
 32 |       ]
 33 |     },
 34 |     {
 35 |       "cell_type": "code",
 36 |       "source": [
 37 |         "# Reading the Data\n",
 38 |         "url = \"http://bit.ly/w-data\"\n",
 39 |         "data = pd.read_csv(url)\n",
 40 |         "print(\"----Data uploaded Successfully----\")\n"
 41 |       ],
 42 |       "metadata": {
 43 |         "colab": {
 44 |           "base_uri": "https://localhost:8080/"
 45 |         },
 46 |         "id": "BZD81Mjc2iGb",
 47 |         "outputId": "a8d0ef4c-df08-45f7-d788-b45b393a593f"
 48 |       },
 49 |       "execution_count": 5,
 50 |       "outputs": [
 51 |         {
 52 |           "output_type": "stream",
 53 |           "name": "stdout",
 54 |           "text": [
 55 |             "----Data uploaded Successfully----\n"
 56 |           ]
 57 |         }
 58 |       ]
 59 |     },
 60 |     {
 61 |       "cell_type": "code",
 62 |       "source": [
 63 |         "data.head(5)"
 64 |       ],
 65 |       "metadata": {
 66 |         "colab": {
 67 |           "base_uri": "https://localhost:8080/",
 68 |           "height": 206
 69 |         },
 70 |         "id": "LdLjqZtw2uO_",
 71 |         "outputId": "7f54a40b-fd32-4c14-a336-f8f9859c3758"
 72 |       },
 73 |       "execution_count": 6,
 74 |       "outputs": [
 75 |         {
 76 |           "output_type": "execute_result",
 77 |           "data": {
 78 |             "text/plain": [
 79 |               "   Hours  Scores\n",
 80 |               "0    2.5      21\n",
 81 |               "1    5.1      47\n",
 82 |               "2    3.2      27\n",
 83 |               "3    8.5      75\n",
 84 |               "4    3.5      30"
 85 |             ],
 86 |             "text/html": [
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 89 |               "    <div>\n",
 90 |               "<style scoped>\n",
 91 |               "    .dataframe tbody tr th:only-of-type {\n",
 92 |               "        vertical-align: middle;\n",
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106 |               "      <th></th>\n",
107 |               "      <th>Hours</th>\n",
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115 |               "      <td>21</td>\n",
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118 |               "      <th>1</th>\n",
119 |               "      <td>5.1</td>\n",
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122 |               "    <tr>\n",
123 |               "      <th>2</th>\n",
124 |               "      <td>3.2</td>\n",
125 |               "      <td>27</td>\n",
126 |               "    </tr>\n",
127 |               "    <tr>\n",
128 |               "      <th>3</th>\n",
129 |               "      <td>8.5</td>\n",
130 |               "      <td>75</td>\n",
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135 |               "      <td>30</td>\n",
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177 |               "      margin-bottom: 4px;\n",
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179 |               "\n",
180 |               "    [theme=dark] .colab-df-convert {\n",
181 |               "      background-color: #3B4455;\n",
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183 |               "    }\n",
184 |               "\n",
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186 |               "      background-color: #434B5C;\n",
187 |               "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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196 |               "      buttonEl.style.display =\n",
197 |               "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
198 |               "\n",
199 |               "      async function convertToInteractive(key) {\n",
200 |               "        const element = document.querySelector('#df-f2fd974c-1480-40ff-99a7-e5005018730d');\n",
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202 |               "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
203 |               "                                                    [key], {});\n",
204 |               "        if (!dataTable) return;\n",
205 |               "\n",
206 |               "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
207 |               "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
208 |               "          + ' to learn more about interactive tables.';\n",
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210 |               "        dataTable['output_type'] = 'display_data';\n",
211 |               "        await google.colab.output.renderOutput(dataTable, element);\n",
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267 |               "    fill: var(--button-hover-fill-color);\n",
268 |               "  }\n",
269 |               "\n",
270 |               "  .colab-df-quickchart-complete:disabled,\n",
271 |               "  .colab-df-quickchart-complete:disabled:hover {\n",
272 |               "    background-color: var(--disabled-bg-color);\n",
273 |               "    fill: var(--disabled-fill-color);\n",
274 |               "    box-shadow: none;\n",
275 |               "  }\n",
276 |               "\n",
277 |               "  .colab-df-spinner {\n",
278 |               "    border: 2px solid var(--fill-color);\n",
279 |               "    border-color: transparent;\n",
280 |               "    border-bottom-color: var(--fill-color);\n",
281 |               "    animation:\n",
282 |               "      spin 1s steps(1) infinite;\n",
283 |               "  }\n",
284 |               "\n",
285 |               "  @keyframes spin {\n",
286 |               "    0% {\n",
287 |               "      border-color: transparent;\n",
288 |               "      border-bottom-color: var(--fill-color);\n",
289 |               "      border-left-color: var(--fill-color);\n",
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306 |               "    }\n",
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308 |               "      border-color: transparent;\n",
309 |               "      border-right-color: var(--fill-color);\n",
310 |               "    }\n",
311 |               "    80% {\n",
312 |               "      border-color: transparent;\n",
313 |               "      border-right-color: var(--fill-color);\n",
314 |               "      border-bottom-color: var(--fill-color);\n",
315 |               "    }\n",
316 |               "    90% {\n",
317 |               "      border-color: transparent;\n",
318 |               "      border-bottom-color: var(--fill-color);\n",
319 |               "    }\n",
320 |               "  }\n",
321 |               "</style>\n",
322 |               "\n",
323 |               "  <script>\n",
324 |               "    async function quickchart(key) {\n",
325 |               "      const quickchartButtonEl =\n",
326 |               "        document.querySelector('#' + key + ' button');\n",
327 |               "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
328 |               "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
329 |               "      try {\n",
330 |               "        const charts = await google.colab.kernel.invokeFunction(\n",
331 |               "            'suggestCharts', [key], {});\n",
332 |               "      } catch (error) {\n",
333 |               "        console.error('Error during call to suggestCharts:', error);\n",
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335 |               "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
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343 |               "    })();\n",
344 |               "  </script>\n",
345 |               "</div>\n",
346 |               "    </div>\n",
347 |               "  </div>\n"
348 |             ]
349 |           },
350 |           "metadata": {},
351 |           "execution_count": 6
352 |         }
353 |       ]
354 |     },
355 |     {
356 |       "cell_type": "code",
357 |       "source": [
358 |         "data.isnull == True"
359 |       ],
360 |       "metadata": {
361 |         "colab": {
362 |           "base_uri": "https://localhost:8080/"
363 |         },
364 |         "id": "Z0jw-bJW2xp_",
365 |         "outputId": "b2f2e132-48ea-4839-8793-7bb65fdc51eb"
366 |       },
367 |       "execution_count": 7,
368 |       "outputs": [
369 |         {
370 |           "output_type": "execute_result",
371 |           "data": {
372 |             "text/plain": [
373 |               "False"
374 |             ]
375 |           },
376 |           "metadata": {},
377 |           "execution_count": 7
378 |         }
379 |       ]
380 |     },
381 |     {
382 |       "cell_type": "code",
383 |       "source": [
384 |         "sns.set_style('darkgrid')\n",
385 |         "sns.scatterplot(y= data['Scores'], x= data['Hours'])\n",
386 |         "plt.title('Marks Vs Study Hours',size=20)\n",
387 |         "plt.ylabel('Marks Percentage', size=12)\n",
388 |         "plt.xlabel('Hours Studied', size=12)\n",
389 |         "plt.show()"
390 |       ],
391 |       "metadata": {
392 |         "colab": {
393 |           "base_uri": "https://localhost:8080/",
394 |           "height": 484
395 |         },
396 |         "id": "vHZ0BYgk20J-",
397 |         "outputId": "78de76dd-9c56-4d50-866d-5ea403504068"
398 |       },
399 |       "execution_count": 8,
400 |       "outputs": [
401 |         {
402 |           "output_type": "display_data",
403 |           "data": {
404 |             "text/plain": [
405 |               "<Figure size 640x480 with 1 Axes>"
406 |             ],
407 |             "image/png": 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\n"
408 |           },
409 |           "metadata": {}
410 |         }
411 |       ]
412 |     },
413 |     {
414 |       "cell_type": "code",
415 |       "source": [
416 |         "sns.regplot(x= data['Hours'], y= data['Scores'])\n",
417 |         "plt.title('Regression Plot',size=20)\n",
418 |         "plt.ylabel('Marks Percentage', size=12)\n",
419 |         "plt.xlabel('Hours Studied', size=12)\n",
420 |         "plt.show()\n",
421 |         "print(data.corr())"
422 |       ],
423 |       "metadata": {
424 |         "colab": {
425 |           "base_uri": "https://localhost:8080/",
426 |           "height": 536
427 |         },
428 |         "id": "b9vCWBhO219b",
429 |         "outputId": "87da3efb-b0a2-4df7-8bf4-4862dc69a46b"
430 |       },
431 |       "execution_count": 9,
432 |       "outputs": [
433 |         {
434 |           "output_type": "display_data",
435 |           "data": {
436 |             "text/plain": [
437 |               "<Figure size 640x480 with 1 Axes>"
438 |             ],
439 |             "image/png": 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\n"
440 |           },
441 |           "metadata": {}
442 |         },
443 |         {
444 |           "output_type": "stream",
445 |           "name": "stdout",
446 |           "text": [
447 |             "           Hours    Scores\n",
448 |             "Hours   1.000000  0.976191\n",
449 |             "Scores  0.976191  1.000000\n"
450 |           ]
451 |         }
452 |       ]
453 |     },
454 |     {
455 |       "cell_type": "code",
456 |       "source": [
457 |         "X = data.iloc[:, :-1].values\n",
458 |         "y = data.iloc[:, 1].values\n",
459 |         "\n",
460 |         "# Spliting the Data in two\n",
461 |         "train_X, val_X, train_y, val_y = train_test_split(X, y, random_state = 0)"
462 |       ],
463 |       "metadata": {
464 |         "id": "zHiD6ds523qV"
465 |       },
466 |       "execution_count": 10,
467 |       "outputs": []
468 |     },
469 |     {
470 |       "cell_type": "code",
471 |       "source": [
472 |         "regression = LinearRegression()\n",
473 |         "regression.fit(train_X, train_y)\n",
474 |         "print(\"---------Model Trained---------\")"
475 |       ],
476 |       "metadata": {
477 |         "colab": {
478 |           "base_uri": "https://localhost:8080/"
479 |         },
480 |         "id": "34miwAJC25jJ",
481 |         "outputId": "453dcb43-432a-4f90-c167-5cadb09f3d18"
482 |       },
483 |       "execution_count": 11,
484 |       "outputs": [
485 |         {
486 |           "output_type": "stream",
487 |           "name": "stdout",
488 |           "text": [
489 |             "---------Model Trained---------\n"
490 |           ]
491 |         }
492 |       ]
493 |     },
494 |     {
495 |       "cell_type": "code",
496 |       "source": [
497 |         "pred_y = regression.predict(val_X)\n",
498 |         "prediction = pd.DataFrame({'Hours': [i[0] for i in val_X], 'Predicted Marks': [k for k in pred_y]})\n",
499 |         "prediction"
500 |       ],
501 |       "metadata": {
502 |         "id": "ipoo2Yjf29fd"
503 |       },
504 |       "execution_count": null,
505 |       "outputs": []
506 |     },
507 |     {
508 |       "cell_type": "code",
509 |       "source": [
510 |         "compare_scores = pd.DataFrame({'Actual Marks': val_y, 'Predicted Marks': pred_y})\n",
511 |         "compare_scores"
512 |       ],
513 |       "metadata": {
514 |         "colab": {
515 |           "base_uri": "https://localhost:8080/",
516 |           "height": 269
517 |         },
518 |         "id": "R82EJHCS2_3j",
519 |         "outputId": "90e5c802-9659-4281-85a5-d231d8df4218"
520 |       },
521 |       "execution_count": 15,
522 |       "outputs": [
523 |         {
524 |           "output_type": "execute_result",
525 |           "data": {
526 |             "text/plain": [
527 |               "   Actual Marks  Predicted Marks\n",
528 |               "0            20        16.844722\n",
529 |               "1            27        33.745575\n",
530 |               "2            69        75.500624\n",
531 |               "3            30        26.786400\n",
532 |               "4            62        60.588106\n",
533 |               "5            35        39.710582\n",
534 |               "6            24        20.821393"
535 |             ],
536 |             "text/html": [
537 |               "\n",
538 |               "  <div id=\"df-0dff8b95-9e18-47c2-a4c9-e12aebe91df8\" class=\"colab-df-container\">\n",
539 |               "    <div>\n",
540 |               "<style scoped>\n",
541 |               "    .dataframe tbody tr th:only-of-type {\n",
542 |               "        vertical-align: middle;\n",
543 |               "    }\n",
544 |               "\n",
545 |               "    .dataframe tbody tr th {\n",
546 |               "        vertical-align: top;\n",
547 |               "    }\n",
548 |               "\n",
549 |               "    .dataframe thead th {\n",
550 |               "        text-align: right;\n",
551 |               "    }\n",
552 |               "</style>\n",
553 |               "<table border=\"1\" class=\"dataframe\">\n",
554 |               "  <thead>\n",
555 |               "    <tr style=\"text-align: right;\">\n",
556 |               "      <th></th>\n",
557 |               "      <th>Actual Marks</th>\n",
558 |               "      <th>Predicted Marks</th>\n",
559 |               "    </tr>\n",
560 |               "  </thead>\n",
561 |               "  <tbody>\n",
562 |               "    <tr>\n",
563 |               "      <th>0</th>\n",
564 |               "      <td>20</td>\n",
565 |               "      <td>16.844722</td>\n",
566 |               "    </tr>\n",
567 |               "    <tr>\n",
568 |               "      <th>1</th>\n",
569 |               "      <td>27</td>\n",
570 |               "      <td>33.745575</td>\n",
571 |               "    </tr>\n",
572 |               "    <tr>\n",
573 |               "      <th>2</th>\n",
574 |               "      <td>69</td>\n",
575 |               "      <td>75.500624</td>\n",
576 |               "    </tr>\n",
577 |               "    <tr>\n",
578 |               "      <th>3</th>\n",
579 |               "      <td>30</td>\n",
580 |               "      <td>26.786400</td>\n",
581 |               "    </tr>\n",
582 |               "    <tr>\n",
583 |               "      <th>4</th>\n",
584 |               "      <td>62</td>\n",
585 |               "      <td>60.588106</td>\n",
586 |               "    </tr>\n",
587 |               "    <tr>\n",
588 |               "      <th>5</th>\n",
589 |               "      <td>35</td>\n",
590 |               "      <td>39.710582</td>\n",
591 |               "    </tr>\n",
592 |               "    <tr>\n",
593 |               "      <th>6</th>\n",
594 |               "      <td>24</td>\n",
595 |               "      <td>20.821393</td>\n",
596 |               "    </tr>\n",
597 |               "  </tbody>\n",
598 |               "</table>\n",
599 |               "</div>\n",
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609 |               "  </svg>\n",
610 |               "    </button>\n",
611 |               "\n",
612 |               "  <style>\n",
613 |               "    .colab-df-container {\n",
614 |               "      display:flex;\n",
615 |               "      gap: 12px;\n",
616 |               "    }\n",
617 |               "\n",
618 |               "    .colab-df-convert {\n",
619 |               "      background-color: #E8F0FE;\n",
620 |               "      border: none;\n",
621 |               "      border-radius: 50%;\n",
622 |               "      cursor: pointer;\n",
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624 |               "      fill: #1967D2;\n",
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626 |               "      padding: 0 0 0 0;\n",
627 |               "      width: 32px;\n",
628 |               "    }\n",
629 |               "\n",
630 |               "    .colab-df-convert:hover {\n",
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632 |               "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
633 |               "      fill: #174EA6;\n",
634 |               "    }\n",
635 |               "\n",
636 |               "    .colab-df-buttons div {\n",
637 |               "      margin-bottom: 4px;\n",
638 |               "    }\n",
639 |               "\n",
640 |               "    [theme=dark] .colab-df-convert {\n",
641 |               "      background-color: #3B4455;\n",
642 |               "      fill: #D2E3FC;\n",
643 |               "    }\n",
644 |               "\n",
645 |               "    [theme=dark] .colab-df-convert:hover {\n",
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647 |               "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
648 |               "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
649 |               "      fill: #FFFFFF;\n",
650 |               "    }\n",
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652 |               "\n",
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655 |               "        document.querySelector('#df-0dff8b95-9e18-47c2-a4c9-e12aebe91df8 button.colab-df-convert');\n",
656 |               "      buttonEl.style.display =\n",
657 |               "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
658 |               "\n",
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660 |               "        const element = document.querySelector('#df-0dff8b95-9e18-47c2-a4c9-e12aebe91df8');\n",
661 |               "        const dataTable =\n",
662 |               "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
663 |               "                                                    [key], {});\n",
664 |               "        if (!dataTable) return;\n",
665 |               "\n",
666 |               "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
667 |               "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
668 |               "          + ' to learn more about interactive tables.';\n",
669 |               "        element.innerHTML = '';\n",
670 |               "        dataTable['output_type'] = 'display_data';\n",
671 |               "        await google.colab.output.renderOutput(dataTable, element);\n",
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673 |               "        docLink.innerHTML = docLinkHtml;\n",
674 |               "        element.appendChild(docLink);\n",
675 |               "      }\n",
676 |               "    </script>\n",
677 |               "  </div>\n",
678 |               "\n",
679 |               "\n",
680 |               "<div id=\"df-6766b759-2979-43f8-a3ce-47d21506e4cc\">\n",
681 |               "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-6766b759-2979-43f8-a3ce-47d21506e4cc')\"\n",
682 |               "            title=\"Suggest charts.\"\n",
683 |               "            style=\"display:none;\">\n",
684 |               "\n",
685 |               "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
686 |               "     width=\"24px\">\n",
687 |               "    <g>\n",
688 |               "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
689 |               "    </g>\n",
690 |               "</svg>\n",
691 |               "  </button>\n",
692 |               "\n",
693 |               "<style>\n",
694 |               "  .colab-df-quickchart {\n",
695 |               "      --bg-color: #E8F0FE;\n",
696 |               "      --fill-color: #1967D2;\n",
697 |               "      --hover-bg-color: #E2EBFA;\n",
698 |               "      --hover-fill-color: #174EA6;\n",
699 |               "      --disabled-fill-color: #AAA;\n",
700 |               "      --disabled-bg-color: #DDD;\n",
701 |               "  }\n",
702 |               "\n",
703 |               "  [theme=dark] .colab-df-quickchart {\n",
704 |               "      --bg-color: #3B4455;\n",
705 |               "      --fill-color: #D2E3FC;\n",
706 |               "      --hover-bg-color: #434B5C;\n",
707 |               "      --hover-fill-color: #FFFFFF;\n",
708 |               "      --disabled-bg-color: #3B4455;\n",
709 |               "      --disabled-fill-color: #666;\n",
710 |               "  }\n",
711 |               "\n",
712 |               "  .colab-df-quickchart {\n",
713 |               "    background-color: var(--bg-color);\n",
714 |               "    border: none;\n",
715 |               "    border-radius: 50%;\n",
716 |               "    cursor: pointer;\n",
717 |               "    display: none;\n",
718 |               "    fill: var(--fill-color);\n",
719 |               "    height: 32px;\n",
720 |               "    padding: 0;\n",
721 |               "    width: 32px;\n",
722 |               "  }\n",
723 |               "\n",
724 |               "  .colab-df-quickchart:hover {\n",
725 |               "    background-color: var(--hover-bg-color);\n",
726 |               "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
727 |               "    fill: var(--button-hover-fill-color);\n",
728 |               "  }\n",
729 |               "\n",
730 |               "  .colab-df-quickchart-complete:disabled,\n",
731 |               "  .colab-df-quickchart-complete:disabled:hover {\n",
732 |               "    background-color: var(--disabled-bg-color);\n",
733 |               "    fill: var(--disabled-fill-color);\n",
734 |               "    box-shadow: none;\n",
735 |               "  }\n",
736 |               "\n",
737 |               "  .colab-df-spinner {\n",
738 |               "    border: 2px solid var(--fill-color);\n",
739 |               "    border-color: transparent;\n",
740 |               "    border-bottom-color: var(--fill-color);\n",
741 |               "    animation:\n",
742 |               "      spin 1s steps(1) infinite;\n",
743 |               "  }\n",
744 |               "\n",
745 |               "  @keyframes spin {\n",
746 |               "    0% {\n",
747 |               "      border-color: transparent;\n",
748 |               "      border-bottom-color: var(--fill-color);\n",
749 |               "      border-left-color: var(--fill-color);\n",
750 |               "    }\n",
751 |               "    20% {\n",
752 |               "      border-color: transparent;\n",
753 |               "      border-left-color: var(--fill-color);\n",
754 |               "      border-top-color: var(--fill-color);\n",
755 |               "    }\n",
756 |               "    30% {\n",
757 |               "      border-color: transparent;\n",
758 |               "      border-left-color: var(--fill-color);\n",
759 |               "      border-top-color: var(--fill-color);\n",
760 |               "      border-right-color: var(--fill-color);\n",
761 |               "    }\n",
762 |               "    40% {\n",
763 |               "      border-color: transparent;\n",
764 |               "      border-right-color: var(--fill-color);\n",
765 |               "      border-top-color: var(--fill-color);\n",
766 |               "    }\n",
767 |               "    60% {\n",
768 |               "      border-color: transparent;\n",
769 |               "      border-right-color: var(--fill-color);\n",
770 |               "    }\n",
771 |               "    80% {\n",
772 |               "      border-color: transparent;\n",
773 |               "      border-right-color: var(--fill-color);\n",
774 |               "      border-bottom-color: var(--fill-color);\n",
775 |               "    }\n",
776 |               "    90% {\n",
777 |               "      border-color: transparent;\n",
778 |               "      border-bottom-color: var(--fill-color);\n",
779 |               "    }\n",
780 |               "  }\n",
781 |               "</style>\n",
782 |               "\n",
783 |               "  <script>\n",
784 |               "    async function quickchart(key) {\n",
785 |               "      const quickchartButtonEl =\n",
786 |               "        document.querySelector('#' + key + ' button');\n",
787 |               "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
788 |               "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
789 |               "      try {\n",
790 |               "        const charts = await google.colab.kernel.invokeFunction(\n",
791 |               "            'suggestCharts', [key], {});\n",
792 |               "      } catch (error) {\n",
793 |               "        console.error('Error during call to suggestCharts:', error);\n",
794 |               "      }\n",
795 |               "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
796 |               "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
797 |               "    }\n",
798 |               "    (() => {\n",
799 |               "      let quickchartButtonEl =\n",
800 |               "        document.querySelector('#df-6766b759-2979-43f8-a3ce-47d21506e4cc button');\n",
801 |               "      quickchartButtonEl.style.display =\n",
802 |               "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
803 |               "    })();\n",
804 |               "  </script>\n",
805 |               "</div>\n",
806 |               "    </div>\n",
807 |               "  </div>\n"
808 |             ]
809 |           },
810 |           "metadata": {},
811 |           "execution_count": 15
812 |         }
813 |       ]
814 |     },
815 |     {
816 |       "cell_type": "code",
817 |       "source": [
818 |         "plt.scatter(x=val_X, y=val_y, color='blue')\n",
819 |         "plt.plot(val_X, pred_y, color='Black')\n",
820 |         "plt.title('Actual vs Predicted', size=20)\n",
821 |         "plt.ylabel('Marks Percentage', size=12)\n",
822 |         "plt.xlabel('Hours Studied', size=12)\n",
823 |         "plt.show()"
824 |       ],
825 |       "metadata": {
826 |         "colab": {
827 |           "base_uri": "https://localhost:8080/",
828 |           "height": 484
829 |         },
830 |         "id": "vXmoq1uV3CD1",
831 |         "outputId": "518a0e47-a837-4897-b356-30993940e35e"
832 |       },
833 |       "execution_count": 16,
834 |       "outputs": [
835 |         {
836 |           "output_type": "display_data",
837 |           "data": {
838 |             "text/plain": [
839 |               "<Figure size 640x480 with 1 Axes>"
840 |             ],
841 |             "image/png": 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/MNiQXsidhrKzs8OcOXMKPf+bb75BbGws9u3bJwo27u7uiI2NRVhYmMp0SlGo8y/KChUq4MWLF0hOTs73HEEQ8PDhwzyPJSUlKf9SKmhn4dTUVDx48KDIdWnK2NgYvXv3xubNmxESEoJnz56hcuXKygXdrVu3VmuRasWKFeHt7Q1vb2/4+fkhICAA8fHxuHTpkmhzP13i7u6OoKAgZGRkICwsDE2aNFH7M1xcXHDw4EFkZmYiPDwcDRs2zPO8rKwsjReDN2jQQBms//77bwwePFijzylMjRo1YGlpiZSUFOXicXU5OzvD3NwcaWlpuHHjRoHnFnacDAPviqIy79GjR8o/NHv06IE+ffoU+r/u3bsDyNmMLy4uTvlZ3t7eAHLWPOzYsUPtWnLXaby+W2x+cqcfchec5uWPP/7IN/i8/pDG19dovOl///tfid9plCt3qikzMxOHDx/G5cuXldvv5zcNVRSvr4UqqQXQ2tC5c2dluP3tt980+oy2bdsq/3vv3r35nnf8+HGNR+IqV66snNI7fPiw6PdAUeXeDl/Qz7qxsbFyIXlISAgiIyPV/p5y5cqhVatWys/I3Y7hTdnZ2QX2FxkOBhsq8/bt26e8w+PNvWvyk3tedna2cqdiIOeuqdxRhZUrVypvM81LbGysSlvuQszExES8ePGiwBpyb/e9du0aLl26pHI8Pj4eixcvzvf91tbWymH63FGCN12/fh2rVq0qsA5t8vDwQK1atQDkTEHljtaYmZnl+2vz6NGjAvsZgOgWcl1ej1KnTh3lOpKDBw9i06ZNBZ7/6NEjZR/l8vDwUI7SbN++Pc+1UXK5HN98802xap04cSKAnFD88ccfIyUlJd9zC/pZz29EMdekSZNgbGyM7OxsTJs2Lc/PypX7fK03zxk+fDiAnBA1b968PJ+8vn79etHO0WS4OBVFZV5uMLGxsSnyravNmjWDnZ0d4uPjsX//fkyaNAlAzl/A33zzDSZMmIBXr15h/Pjx6NevH7p27YqqVasiIyMD9+/fR3BwME6dOqUy2pL7OIHs7GzMnz8fo0ePFq1neH0zvWHDhmHbtm3IysrCBx98gA8//BDNmzdHZmYmLl++jF9//RWZmZmoVasW/vnnH5VryH0W1pYtW3Dnzh0MHz4c48ePR82aNfHixQsEBwdj69atsLCwgL29fZ6fURL69u2L1atX48qVK4iIiACQszfMm/sK5YqOjsaYMWNQr149dO3aFY0bN1betRQbG4tDhw4pN71r0KCBRtM7pWnBggUICwvDo0ePsGzZMpw8eRL9+/dH/fr1YWpqiqSkJISHh+Ps2bMIDQ1Ft27dVPb2mT9/PkaMGIHMzEy8++67GDduHDp06ABTU1Ncv34d69atQ1JSEtzc3DSejvL29sbbb7+NXbt24cqVK+jduzdGjRqFZs2aoWLFinj27BnCwsJw6NAhuLm5qTwrytPTE48fP8apU6ewfft2NGvWTDmKU7FiReUdSq6urvjss8+wdOlS3Lt3Dz4+Phg6dCi8vLxga2uL9PR0PHnyBFevXsWRI0cQHx+PAwcOKPdeyq21c+fOOH36NE6fPo3hw4eLHqmwd+9eHDp0CI0aNSpwBJQMA4MNlWmXLl1S/ouxa9euRX6CtZGREbp164atW7ciIiICYWFhyjupvLy8sG7dOnz66ad4/vw59uzZo7I7bH68vLzQtGlTXL16FUFBQSr/Gn/9oZn169fHrFmzsHTpUjx//hxLly4VnVu5cmWsXbsWq1atyjeUTJ8+HZcvX8bt27cRFhamfHL265+xevVq/PDDD6UebARBUI4CFLSpXa579+6pPATzdXXq1MHq1at1/s6YypUrY9u2bfjkk0/w999/46+//sJff/2V7/l5PZi0SZMm+PrrrzF79mykp6dj/fr1WL9+vfJ4uXLlMH/+fFy+fLlYmy4uWrQI5ubm2LJlC+RyOVasWJHneXntpzNhwgQcPXoUGRkZmD9/vujYm0/3HjduHCwsLLBkyRKkpKQob+fPi4mJSZ67Pn/33XeYOHEiLl++jGvXrqk8cNXd3R2LFi0qcFNDMgwMNlSmvT6NVNRpqFzdu3dXbtUeGBgoukX8rbfewokTJ7Bt2zacPn0aDx48QEpKCsqXL4+aNWuiRYsWKv/KBnICk7+/P37++WecPn1aubmY8MaTk3ONGzcOdevWxa+//oobN27g1atXsLe3R8eOHfHee+8pN23Lj6WlJbZt24ZNmzbh8OHDiIqKgrGxMRwdHdGxY0eMHTtW9C/f0lCzZk14eHjg+vXrAHLu8iloF94WLVogICAAf/75J65evYrY2FgkJCQgIyMDlSpVgpubG7p164ZBgwYVaa8ZXWBnZ4ctW7bgzJkzCAoKwtWrV5GQkICsrCxYWlqiZs2a8PT0hLe3d56PTABydmh2c3PDhg0bcP78eTx79gzW1tZo1qwZxo8fjyZNmmi8IDeXsbExvvzySwwaNAg7duzAhQsXIJfLkZmZicqVK8PV1RVvvfVWnsG0QYMG2LFjB/z9/XH58mXlr1l+hg4dCm9vb2zfvh0hISHK31Ompqawt7eHq6sr2rZti+7du8Pa2lrl/RUrVkRAQAC2b9+OwMBAREZGQiaTwdnZGb1798bYsWNL5W5G0n0yIb8/cYmIiIjKGC4eJiIiIr3BYENERER6g8GGiIiI9AaDDREREekNBhsiIiLSGww2REREpDcYbIiIiEhvMNgQERGR3jDYnYcTE1NgKFsTymSAjY2lQV1zcbC/1Mc+Uw/7Sz3sL/XpY5/lXlNhDDbYCAL05he7qAzxmouD/aU+9pl62F/qYX+pzxD7jFNRREREpDcYbIiIiEhvMNgQERGR3mCwISIiIr3BYENERER6g8GGiIiI9AaDDREREekNBhsiIiLSGww2REREpDcYbIiIiEhvMNgQERGR3mCwISIiIr3BYENERERa8+zZU0m/n8GGiIiIiu3Gjeuwt7eCq2st/PLLRsnqYLAhIiKiYvnqq4Xo0qW98rWjo5NktZST7JuJiIioTEtPT0eNGnaitk2btqBXrz4SVcRgQ0RERBq4evUyunfvJGoLD38Aa2sbaQr6F6eiiIiISC1ffjlbFGq6desBuTxZ8lADcMSGiIiIiigtLQ3Ozvaiti1bdqJbt54SVaSKwYaIiIgKdfHiBfj4dBO1RUQ8RKVKlaUpKB+ciiIiIqICzZo1XRRq+vYdALk8WedCDcARGyIiIspHamoqatd2FLXt3BmITp28JaqocAw2REREpOLcuT8xYEBvUdv9+09QsaKlRBUVDaeiiIiISGTq1MmiUDNs2AjI5ck6H2oAjtgQERHRv168SEGdOtVEbYGBh9C2bft83qF7OGJDREREOHPmlEqoefAgpkyFGoDBhoiIyOC9995YDB06QPl6zJh3IZcno0KFCtIVpSFORRERERmo58+TUL++s6gtKOg4WrVqLVFFxccRGyIiIgN0/PgRlVATFRVXpkMNwGBDRERkcEaNGoqRI4cqX0+a9AHk8mSUL19ewqq0g1NRREREBuLp00S4udUWtR07dgZNmzaTqCLt44gNERGRATh48IBKqHn0KF6vQg3AYENERKT3Bg/ui/HjRypfT5s2A3J5MszMzCSsqmRwKoqIiEhPxcfHw929rqjt1KkQNGrUWKKKSh5HbIiIiPTQ9u3bRaHG1NQUT54k6nWoARhsiIiI9IogCOjVqwuGDx+ubPvss7l4/DgBJiYmElZWOjgVRUREpCfi4mLRuLGLqO2PPy7Aza2BRBWVPo7YEBER6YHt27eIQk3lypURE/PUoEINwGBDRERUpgmCgE6d2mLatA+UbV9+uRDPnj1DuXKGNzFjeFdMRESkJ548eQxPT3dR2/nzl1CvXn2JKpIeR2yIiIjKoICAX0WhxsGhKmJinqFuXcMNNYCOjdh4e3vjyZMnKu0jRozA/PnzkZ6ejmXLluHQoUPIyMhA+/btMX/+fNja2kpQLRERUekTBAFt2jTD/fuRyjY/v2WYNOlDCavSHToVbHbt2gWFQqF8HRERgfHjx6Nnz54AgCVLliA4OBgrV66EpaUlFi9ejClTpmD79u1SlUxERFRqoqL+QcuWHqK2ixevoVat2vm8w/Do1FSUtbU17OzslP87ffo0nJ2d0apVK6SkpGD37t2YPXs22rRpg0aNGmHJkiW4cuUKrl69KnXpREREJernn9eJQk3t2nUQG5vEUPMGnRqxeV1GRgb279+P8ePHQyaTISwsDJmZmWjbtq3ynLp168LJyQlXr15F06ZN1fp8mUzLBeuw3Gs1pGsuDvaX+thn6mF/qcfQ+ys7OxtNm7ojJiZa2fbddysxduy7+b5HH/usqNeis8HmxIkTSElJwcCBAwEACQk5OyZaWVmJzrOxsUF8fLzan29jY6mVOssSQ7zm4mB/qY99ph72l3oMsb8iIiLg4iLecC8qKgrOzs5Fer8h9pnOBpvdu3ejQ4cOcHBwKJHPT0xMgSCUyEfrHJks54fbkK65ONhf6mOfqYf9pR5D7a81a1Zh4cIvla8bNmyE06dDIJPJkJCQUuB7pegzhQIIDTVGXJwMDg4CvLwUMDbW3ufnXlNhdDLYPHnyBOfOncPq1auVbba2tsjMzERycrJo1CYxMRF2dnZqf4cgwKB+gwCGec3Fwf5SH/tMPewv9RhKfykUCri51cbz50nKth9++AnvvDMSgHp9UFp9FhRUDr6+ZoiO/m/prpNTNvz80uHjk1XyBbxGpxYP59qzZw9sbGzQqVMnZVujRo1gYmKC8+fPK9vu37+P6OhotdfXEBER6aI7d8Lh6FhFFGquX7+jDDW6KCioHCZMMEd0tHgRTEyMDBMmmCMoqHTHUHQu2GRnZ2PPnj0YMGCAaCtoS0tLDB48GMuWLUNoaCjCwsIwd+5ceHp6MtgQEVGZt2LFN3jrrVbK182bt0Bc3HNUreooYVUFUygAX1+zf0eFxMFGEHJe+/qa4bWdXEqczk1FnTt3DtHR0Rg8eLDKsblz58LIyAjTpk0TbdBHRERUVmVmZqJOHSekp6cr29at88egQUMkrKpoQkONRdNPbxIEGaKjZQgNNUa7dqWTbnQu2LRv3x537tzJ85iZmRnmz5/PMENERHrh5s0wdO7c9o22SI3WjkohLq5o92AX9Txt0LmpKCIiIkOwZMkiUahp374D5PLkMhNqAMDBoWgrk4t6njbo3IgNERGRPsvIyED16uJnHPr7B6Bv3/4SVaQ5Ly8FnJyyERMjU66peZ1MJsDRMefW79LCERsiIqJScvXqZZVQEx7+oEyGGgAwNgb8/HLWBslk4lGZ3Nd+fula3c+mMAw2REREpWDevLno3r2T8nW3bj0glyfD2tpGuqK0wMcnC/7+aXB0FAcbR0cB/v5ppb6PDaeiiIiISlBaWhqcne1Fbb//vgPdu/eSqCLt8/HJQq9eWSW683BRMdgQERGVkL/+uoA+fbqJ2iIiHqJSpcrSFFSCjI1Rard0F4RTUURERCXgs8+mi0JN374DIJcn62Wo0SUcsSEiItKily9folatqqK2HTv2onPnLhJVZFgYbIiIiLTk/PkQ9O8vXjsTGfkYlpZW+byDtI1TUURERFowbdoHolAzZMg7kMuTGWpKGUdsiIiIiuHFixeoU8dJ1LZ370G0a/eWRBUZNo7YEBERaSg4+LRKqHnwIIahRkIMNkRERBqYNGkchgz5b8fg0aPHQy5PRoUKFSSsijgVRUREpIbk5OeoV6+GqO3AgWNo3dpLoorodRyxISIiKqITJ46qhJqoqDiGGh3CYENERFQEY8a8gxEjhihfT5r0AeTyZJQvX17CquhNnIoiIiIqwLNnT+HqWkvUdvToaXh6NpemICoQR2yIiIjycehQkEqoefQonqFGhzHYEBER5eHtt/tj3LgRytfTps2AXJ4MMzMzCauiwnAqioiI6DUJCQlwd68jajt58k80buwhUUWkDo7YEBER/Wvfvj2iUFOuXDk8eZLIUFOGMNgQEZHBEwQBffp0w8SJ45Rts2bNQXT0U5iYmEhXGKmNU1FERGTQ4uJi0bixi6jtjz8uwM2tgUQVUXFwxIaIiAzWjh1bRaHG0tIK0dFPGWrKMAYbIiIyOIIgwNu7PaZOnaxs8/VdgMjIxyhXjpMZZRl/9YiIyKBERz9B06biEZlz5y6hXr36ElVE2sQRGyIiMhgBAb+KQo29vQNiYp4x1OgRjtgQEZHeEwQBbds2R2TkPWXb4sVL8f77H0lYFZUEBhsiItJrDx9GoUWLxqK2CxeuonbtOvm8g8oyTkUREZHe8vdfLwo1NWvWQmxsEkONHuOIDRER6Z3s7Gw0a9YQ0dFPlG1ff70C48e/J2FVVBoYbIiISK/cvx8JLy9PUdvlyzdRvXoNiSqi0sSpKCIi0htr1/4gCjUNGrgjLu45Q40B4YgNERGVeQqFAu7udfDs2TNl28qVazFixGgJqyIpMNgQEZGkFAogNNQYcXEyODgI8PJSwNi46O+/e/cO2rdvKWq7fv0OqlZ11HKlVBZwKoqIiCQTFFQOzZtXwMCBFpg8uTwGDrRA8+YVEBRUtH93f//9t6JQ06xZc8TFPWeoMWAcsSEiIkkEBZXDhAnmEARxe0yMDBMmmMPfPw0+Pll5vjczMxN161ZDWlqasu3HHzfi7beHlWTJVAZwxIaIiEqdQgH4+pr9G2pkomOCkPPa19cMCoXqe2/eDEO1ajaiUBMWdo+hhgAw2BARkQRCQ40RHW2EN0NNLkGQITraCKGh4sU2y5YtRufObZWv27V7C3J5Muzt7UuyXCpDOBVFRESlLi4u70CT33kZGRmws7MSHfP334y+fQdouzQq4zhiQ0REpc7BQSj8pH/Pu3btCszMzETtt28/YKihPDHYEBFRqfPyUsDJKRsyWd4BRyYT4OSUjaNHZ6Nr147K9i5dukEuT4aNjU1plUplDIMNERGVOmNjwM8vHQBUwo1MJkAQ0hAdbYx161Yr2wMCtmPbtt2lWieVPQw2REQkCR+fLPj7p8HRURxsbGzOA7AQtT19+hQ9e/YuxeqorGKwISIiyfj4ZOHSpVTs3fsS69a9Qq9ek5GQ0E55vE+ffoiPT0aVKlUkrJLKEt4VRUREkjI2Bjw9U1CrVlVR+/bte+Dt3VWiqqis0rkRm7i4OMycOROtW7eGh4cH+vbtixs3biiPC4KAVatWoX379vDw8MC4cePwzz//SFcwEREVy6pVy1VCTWTkY4Ya0ohOjdg8f/4cw4cPR+vWrbFx40ZUqVIFUVFRqFSpkvKcjRs3IiAgAMuWLUP16tWxatUqTJgwAYcOHVK5HZCIiHSbvb14b5q33x6GH3/cKFE1pA90Kths3LgRVatWxdKlS5VtNWrUUP63IAjYvHkzPvjgA3TtmpPkv/nmG7Rt2xYnTpxAnz59Sr1mIiJSX1xcHBo3ri9q8/VdiGnTpktUEekLnZqKOnXqFBo1aoRp06ahTZs2GDBgAHbu3Kk8/vjxY8THx6Nt2/+207a0tESTJk1w5coVKUomIiI1LVmySCXUhIXdY6ghrdCpEZtHjx5h27ZtGD9+PCZPnowbN27Az88PJiYmGDhwIOLj4wFAZWMmGxsbJCQkqPVdsqLt5q0Xcq/VkK65ONhf6mOfqceQ++vNxyIAQHx8coHvMeT+0pQ+9llRr0Wngo0gCGjUqBFmzJgBAHB3d0dERAS2b9+OgQMHavW7bGwstfp5ZYEhXnNxsL/Uxz5TjyH116NHj+Ds7CxqW7ZsGT7//PMif4Yh9Ze2GGKf6VSwsbOzQ926dUVtderUwdGjR5XHASAxMVH0JNfExES4ubmp9V2JiSkQivaokjJPJsv54Takay4O9pf62GfqMbT+mjv3M2zcuE7UdvfuP6hSxRoJCSmFvt/Q+ksb9LHPcq+pMDoVbJo1a4YHDx6I2v755x9Uq1YNAFC9enXY2dnh/PnzaNCgAQDgxYsXuHbtGoYPH67WdwkC9OYXu6gM8ZqLg/2lPvaZegyhv9686wkA5PKcqSd1r90Q+kvbDLHPdGrx8NixY3Ht2jWsW7cOUVFROHDgAHbu3IkRI0YAAGQyGcaMGYOffvoJJ0+exJ07d/DZZ5/B3t5eeZcUERFJ78GD+yqhZunSb5Whhqik6NSIjYeHB9asWYMVK1Zg7dq1qF69OubOnYt+/fopz5k4cSJevXqFefPmITk5Gc2bN8fPP//MPWyIiHTEJ598hK1bA0RtkZGPYWmpOnpDpG0yQTC0QaocCQn6M+9YGJkMsLW1NKhrLg72l/rYZ+rR5/4qaOpJU/rcXyVFH/ss95oKo/FUlEKhwMGDBzFv3jx89NFHuHPnDgAgJSUFx44dU/v2ayIiKrsiIu6qhJqVK9dy6olKnUZTUcnJyXjvvfdw/fp1WFhY4NWrVxg1ahQAwMLCAn5+fhgwYIDytm0iItJf7703Fvv37xW1PXgQgwoVKkhUERkyjUZsvvvuO0RERMDf3x8nTpzA67NZxsbG6NGjB4KDg7VWJBER6R5BEGBvb6USauTyZIYakoxGwebkyZMYPXo02rVrB1keWwHWqlULT548KXZxRESkm8LCbsDBoZKobf36Xzj1RJLTaCoqJSUF1atXz/d4VlYWFAqFxkUREZHuGjZsIE6fPilqe/hQDnNzc4kqIvqPRsHG2dkZN2/ezPd4SEiIyg7CRERUtgmCoDJKY2lphcjIxxJVRKRKo6mot99+G7t378ahQ4eU62tkMhkyMjLw/fff4+zZsxg2bJhWCyUiIulcuvSXSqjZvHk7Qw3pHI1GbMaOHYt79+5hxowZsLLKub1v5syZSEpKQlZWFoYNG4YhQ4ZotVAiIpJGnz7d8NdfF0RtT54kwsTERKKKiPKnUbCRyWTKW7qPHj2KqKgoZGdnw9nZGb169ULLli21XScREZWy7OxsVK1aWdRWrVp1XLlyS5qCiIqgWI9UaNGiBVq0aKGtWoiISEecPx+C/v17idp27NiLzp27SFQRUdHo1LOiiIhIeh06tEZ4+G1RW0zMMxgbG0tUEVHRaRRsvL2989y/JpdMJoOZmRmqVq2K1q1bY9iwYahUqVK+5xMRkfQUCgUcHauI2ho0aIjg4PMSVUSkPo3uimrVqhUsLCzw5MkTVKhQAe7u7nB3d0eFChXw5MkTWFhYoG7dukhMTMSKFSvQt29fPHr0SNu1ExGRlpw6dUIl1Ozbd5ihhsocjUZsunbtitOnT+P3339XWWNz8eJFTJ06FTNmzECnTp1w4cIFvP/++1ixYgW+//57rRRNRETa06SJG2JiokVtsbFJMDLS+DnJRJLR6Kd21apVGDVqVJ4Lh1u1aoWRI0dixYoVAKCcijp37lzxKiUiIq3KzMyEvb2VKNS0auUFuTyZoYbKLI1+cqOiopT71+SlUqVKiIqKUr6uW7cuXr16pclXERFRCTh8+CCqVbMRtR05cgpBQcckqohIOzSaiqpRowYCAwMxbNgwlWeDvHr1Cnv27BE9S0oul8Pa2rp4lRIRkVbUru2E1NQXora4uOcF3hRCVFZoFGymTJmCGTNmoFevXhgwYACcnZ0B5Izk7Nu3D3FxcVi+fDmAnFX2+/fvR7NmzbRXNRERqS0tLQ3Ozvaiti5dumHbtt0SVUSkfRoFm169eqF8+fJYvnw5fvrpJ9Gx+vXrY968eejcuTOAnIembdq0ibd7ExFJaM+e/2Hy5AmitlOnQtCoUWOJKiIqGRpv0NepUyd06tQJcrkc0dE5C8+cnJxgby/+10C5cuVQrVq14lVJREQas7dXXRPJqSfSV8Xeedje3l4lzBARkfRSU1NRu7ajqK1//0HYuPFXaQoiKgXFCjaxsbG4desWUlJSIAiCyvEBAwYU5+OJiEhDW7cG4JNPPhK1hYT8jfr1XSSqiKh0aBRs0tPT8fnnn+PYsWPIzs6GTCZTBpvXhzYZbIiISl9eU09yebIElRCVPo32sVmxYgWOHz+OTz75BAEBARAEAcuWLcMvv/yCDh06wM3NDfv27dN2rUREVICUlGSVUDNy5BiGGjIoGgWbo0ePYtCgQZg0aRLq1asHAHBwcEDbtm2xfv16WFpaYsuWLVotlIiI8vfzz+tQt251UdvFi9fw/fdrJKqISBoaBZvExER4eHgAgHKDvtd3Fu7RoweOHz+uhfKIiKgw9vZWmDv3M1GbXJ6MWrVqS1QRkXQ0Cja2trZ49uwZAKB8+fKoVKkSHjx4oDz+4sULpKena6dCIiLK07NnT1Wmnt5//0NOPZFB02jxsIeHBy5fvqx83blzZ/j7+8POzg7Z2dn49ddf0bRpU23VSEREb/jhh+/h5zdf1Hb16m04OXHfMDJsGgWb0aNH48iRI8jIyICpqSk+/vhjXLlyBZ99ljMU6uzsjC+++EKrhRIRUQ7e9USUP42CTYsWLdCiRQvla0dHRxw+fBh3796FkZER6tSpg3Llir33HxERvUYul6NRo3qitunTZ2LOnHkSVUSkezRaYxMYGIjHjx+LP8jICG5ubnBxcUFcXBwCAwO1UR8REQFYtmyxSqgJC7vHUEP0Bo2CzZw5c3DlypV8j1+7dg1z5szRuCgiIvqPvb0VVqz4VtQmlyfzcTZEedAo2OT1+ITXvXz5EsbGxhoVREREOaKjn6isp/H1XcD1NEQFKPJCmPDwcISHhytf//3331AoFCrnJScnY/v27ahdm/snEBFp6ssvZ2P9+h9FbXfu/IMqVawlqoiobChysDlx4gTWrMnZwVImk2HHjh3YsWNHnudaWVnh66+/1k6FREQGhnc9EWmuyMFm6NCh6NSpEwRBwJAhQzBt2jR06NBBdI5MJkP58uXh7OzMu6KIiNQUFfUPWrb0ELUtWfIN3ntvskQVEZU9RU4f9vb2yoVqmzdvRt26dWFjY1NihRERGZIZM6bi999/E7Xdu/cIVlaVJKqIqGzSaFilVatW2q6DiMhgceqJSHs0ni86e/Ysdu3ahUePHiE5OVnlTimZTIYTJ04Uu0AiIn0VEXEXbdu2ELV9//0ajBw5RqKKiMo+jYLNzz//jOXLl8PGxgYeHh5wdXXVdl1ERHrtnXfeUbkB48GDGFSoUEGiioj0g0bBZvPmzfDy8sKGDRtgYmKi7ZqIiPSWIAiwt1ddN8OpJyLt0GiDvuTkZPTo0YOhhohIDTdvhsHBQRxq1q3zZ6gh0iKNRmwaN26MBw8eaLsWIiK9NWLE2zhx4pio7dEjOczMzCWqiEg/aTRis2DBAhw/fhwHDhzQdj1ERHolZ+rJShRqLCwqQBAEmJsz1BBpm0YjNp988gmysrLw2WefYcGCBahatSqMjMQZSSaTYf/+/VopkoioLLpy5RJ69Ogsavv1163o08dHooqI9J9GwaZy5cqoXLkyatasqe16iIj0Qt++PXDhwnlR2+PHCTA1NZWoIiLDoFGwCQgI0HYdRER6ITs7G1WrVha1OTo64dq18LzfQERapdEam5KyevVquLq6iv7Xs2dP5fH09HQsXLgQrVu3hqenJ6ZOnYqEhAQJKyYi+k9o6DmVULN9+x6GGqJSpPHOwy9evMDWrVtx4cIFJCYmYtGiRfDw8EBSUhL27t0Lb29vjaaq6tevj02bNilfGxsbK/97yZIlCA4OxsqVK2FpaYnFixdjypQp2L59u6aXQUSkFZ06tcWtW2Gitujop3wgMFEp0+h3XGxsLEaNGoXY2FjUrFkT9+/fR2pqKoCc9Tfbt2/HkydP4Ovrq/ZnGxsbw87OTqU9JSUFu3fvxnfffYc2bdoAyAk6vXv3xtWrV9G0aVNNLoWIqFgUCgUcHauI2lxd3XD27EWJKiIybBoFm2+++QapqakIDAyEtbU12rZtKzretWtXnDlzRqOCoqKi0L59e5iZmaFp06b49NNP4eTkhLCwMGRmZoq+q27dunByctIo2MhkGpVXJuVeqyFdc3Gwv9RnqH125swpDBkyQNQWGHgI7dq1L/B9htpfmmJ/qU8f+6yo16JRsAkJCcHYsWNRr149PHv2TOV4jRo1EBMTo/bnenh4YOnSpahduzbi4+Oxdu1ajBw5EgcOHEBCQgJMTExgZSV+Cq6NjQ3i4+PV/i4bG0u131PWGeI1Fwf7S32G1Ge1atVCVFSUqE2hUKhsfVEQQ+ovbWB/qc8Q+0yjYJOWlgZra+t8j+dOS6mrY8eOyv92c3NDkyZN0LlzZxw+fFjrG1klJqbgjQeS6y2ZLOeH25CuuTjYX+ozpD7LzMyEk5ONqK1ly1Y4dOgEnj4t2p99htRf2sD+Up8+9lnuNRVGo2BTt25d/PXXX3jnnXfyPH7ixAm4u7tr8tEiVlZWqFWrFh4+fIi2bdsiMzMTycnJolGbxMTEPNfkFEYQoDe/2EVliNdcHOwv9el7nx05cghjxoj/3Dt8+CSaN2+p0XXre39pG/tLfYbYZxoFm7Fjx2L27NlwdXVFr169AORsGx4VFYU1a9bg6tWrWL16dbGLS01NxaNHj2BnZ4dGjRrBxMQE58+fR48ePQAA9+/fR3R0NBcOE1GJq1/fGc+fJ4na4uKeQ6ZPixiI9IBGwaZ///6Ijo7GqlWrsHLlSgDAe++9B0EQYGRkhOnTp6Nr165qf+7XX3+Nzp07w8nJCXK5HKtXr4aRkRF8fHxgaWmJwYMHY9myZahUqRIqVqwIPz8/eHp6MtgQUYlJT09HjRriUeHOnbtgx469ElVERAXReIOFDz74AP3798exY8cQFRWF7OxsODs7o3v37qhRo4ZGnxkbG4sZM2YgKSkJ1tbWaN68OXbu3KlczzN37lwYGRlh2rRpyMjIQPv27TF//nxNL4GIqECBgbsxadJ4UdvJk3+icWMPiSoiosLIBMHQZt9yJCToz4KqwshkgK2tpUFdc3Gwv9Snj31mb2+l0qatqSd97K+SxP5Snz72We41FUajRyrcvHkTW7Zsyff4li1bcPv2bU0+mohIUi9fvlQJNX37DoBcnsz1NERlgEbB5vvvv8f58+fzPX7hwgXl2hsiorJi+/YtqFWrqqjtzz//gr//ZokqIiJ1abTG5ubNm3j//ffzPd68eXNs2LBB46KIiEpbXlNPcnmyBJUQUXFoNGKTmpoqejilyocaGSElJUXjooiISktKSrJKqBk+fBRDDVEZpVGwqVmzJkJCQvI9fvbsWY3vjCIiKi3+/htQt251UduFC1exatWPElVERMWlUbB5++23cebMGSxduhTJyf/9qyY5ORlLlizB2bNn8fbbb2utSCIibbO3t8KcOTNFbXJ5MmrXriNRRUSkDRqtsRkzZgzCw8Px22+/ISAgAPb29gAAuVyO7Oxs9O/fH+PGjdNmnUREWvHs2VO4utYStU2cOBlfffWNNAURkVZpFGxkMhmWLl2q3KDv0aNHAIAuXbqge/fuaN26tVaLJCLShjVrVmHRoi9FbVeu3EK1atXzeQcRlTVqB5tXr15h1qxZ6N69O/r16wcvL6+SqIuISKt41xORYVB7jU358uVx7tw5pKWllUQ9RERaFR8frxJqPv74U4YaIj2l0eLh5s2b48qVK9quhYhIq77++is0bFhX1HbjRgS++ILPmCPSVxqtsZk3bx4mTJiA77//HsOHD0fVqlULfxMRUSni1BORYdIo2PTr1w8KhQIbNmzAhg0bYGxsDFNTU9E5MpkMly5d0kqRRERFFRMTjSZN3ERtc+fOwyefzMznHUSkTzQKNj169ODD4IhI58yf/wV++mm1qC08/AGsrW0kqoiISptGwWbZsmXaroOIqFg49UREgIaLh4mIdEVU1D8qoearr75mqCEyUBoHm+joaMybNw89evRAy5Yt8ddffwEAnj59Cj8/P9y6dUtrRRIR5eXTTz9Gy5YeorZ79x5h4sQPJKqIiKSm0VTUvXv3MHLkSGRnZ8PDwwMPHz5EVlYWAMDa2hqXLl3Cy5cvsWTJEq0WS0SUi1NPRJQXjUZsvv32W1haWuLo0aP49ttvIQiC6HjHjh15RxQRlYh79yJUQs3y5T8w1BARAA1HbP766y989NFHsLa2xrNnz1SOOzk5IS4urtjFERG9bvLkd7Fnzy5R24MHMahQoYJEFRGRrtEo2AiCAHNz83yPP336VGVfGyKi4uDUExEVhUZTUe7u7ggODs7zWFZWFg4ePIgmTZoUqzAiIgC4deumSqj58ceNDDVElCeNgs2kSZNw9uxZzJ8/HxEREQCAxMREnDt3Du+++y7u37+PSZMmabVQIjI8o0YNRadObURtDx/K8fbbwySqiIh0nUx4c+VvEQUGBmLJkiVISUmBIAiQyWQQBAEVK1bEggUL4OPjo+1atSohIQWaXXnZI5MBtraWBnXNxcH+Up+2+0wQBDg4VBK1lS9fHlFR+rF2jz9j6mF/qU8f+yz3mgqj0RobABgwYAC6d++OkJAQREVFITs7G87Ozmjfvj0qVqyo6ccSkYG7evUyunfvJGrbtGkL+vTpK01BRFSmqBVs4uPjsXfvXjx+/BhVqlRBjx490K1bt5KqjYgMTP/+vXD+fIio7fHjBN6MQERFVuRg8+jRIwwZMgTPnz9X7luzceNGfP311+jbl/+SIiLNZWdno2rVyqI2e3sHhIVFSFMQEZVZRV48vGbNGqSmpuKLL75AUFAQ1q5di6pVq2LZsmXIzs4uyRqJSI+Fhp5XCTXbtu1iqCEijRR5xObSpUsYNmwYRo0aBQCoV68eypUrh8mTJyMyMhL169cvsSKJSD95e7dHWNh1UVt09FOUK6fx8j8iMnBF/tMjNjYWDRs2FLW5u7tDEIQ8dx8mIsqPQqGAo2MVUVv9+i4ICflbooqISF8UeSoqKytL5V9Rua8VCoV2qyIivRUcfFol1Ozde5Chhoi0Qq3x3rCwMJiZmSlfp6amQiaT4dKlS0hJSVE5v3v37sWvkIj0RosWHnj48B9RW2xsEoyMNNorlIhIRZE36HNzc1Pvg2Uy3L59W6OiSoM+bVpUGH3cqKkksb/UV1ifZWZmolo1G1Fbs2bNceTI6VKqULfwZ0w97C/16WOfaX2Dvs2bNxerICIyTMeOHcaoUeJHIBw6dAItWrSSqCIi0mdFDjatWvEPISJSj4uLM5KSkkRtcXHPIZPJpCmIiPQeJ7aJSOvS09Nhb28lCjUdO3aGXJ7MUENEJYrBhoi0at++PahRw07UdvLkWfzvf/skqoiIDAl3wSIirXFwqKyyEzmnnoioNHHEhoiK7eXLl5DJZKJQ06dPP049EVGpY7AhomLZvn0LatasKmo7e/YiNm36XaKKiMiQcSqKiDRmb2+l0iaXJ0tQCRFRDo1GbM6fP4+ff/5Z1LZr1y506tQJbdu2xZIlS/iYBSI99uJFikqoGTt2LOLjGWqISFoaBZvVq1cjPDxc+frOnTuYP38+rK2t0apVKwQEBMDf319rRRKR7ti06WfUqVNN1HbhwhX8+uuv0hRERPQajaaiIiMjRc+B2rdvHypWrIgtW7agfPnymDdvHvbt24dJkyZprVAikl5+U09cH0xEukKjEZtXr16hYsWKytdnz55F+/btUb58eQBA48aNER0drZ0KiUhySUnPVELNhAmTuJ6GiHSORsHG0dERN27cAABERUUhIiIC7du3Vx5//vw5TE1NtVMhEUlq7dof4OJSU9R2+fJNLF36nUQVERHlT6OpqL59+2Lt2rWIi4vDvXv3UKlSJXTp0kV5/ObNm6hVq1axCtuwYQOWL1+OMWPG4IsvvgCQs037smXLcOjQIWRkZKB9+/aYP38+bG1ti/VdRJQ33vVERGWNRiM2kydPxqRJkxAbGwtHR0esXbsWVlY5fwAmJSXh4sWL8Pb21rio69evY/v27XB1dRW1L1myBKdPn8bKlSsREBAAuVyOKVOmaPw9RJS3hIQElVAzbdoMhhoi0nkajdiUK1cO06dPx/Tp01WOVa5cGSEhIRoXlJqailmzZsHPzw8//fSTsj0lJQW7d+/Gd999hzZt2gDICTq9e/fG1atX0bRpU42/k4j+8+23S/Htt0tFbTdu3IWDQ9V83kFEpDs0CjYnTpxA165dCzzn22+/xaxZs9T+7EWLFqFjx45o27atKNiEhYUhMzMTbdu2VbbVrVsXTk5OGgUbQ7qLI/daDemai8OQ+8vOTnXqqSh70xhyn2mC/aUe9pf69LHPinotGgWb6dOnY+3atejQoUOex+fNm4f//e9/agebgwcP4tatW9i1a5fKsYSEBJiYmCinvHLZ2NggPj5ere/JeZ+l2u8p6wzxmovDkPorOjoa1aqJ96bx8/NTrm8rKkPqM21gf6mH/aU+Q+wzjYLNgAEDMHXqVKxbt045LQQA2dnZmDVrFg4dOoR58+ap9ZkxMTH46quv8Msvv8DMzEyTstSSmJgCQSjxr9EJMlnOD7chXXNxGFp/LVz4JdasWSVqCw9/ABsbGyQkpBTpMwytz4qL/aUe9pf69LHPcq+pMBoFm8WLFyMjIwMffvghNm7ciBYtWiAjIwMff/wxzp49i6+//hr9+vVT6zNv3ryJxMREDBo0SNmmUCjw119/YcuWLfD390dmZiaSk5NFozaJiYmws7NT+xoEAXrzi11UhnjNxWEI/VXQXU+aXLsh9Jk2sb/Uw/5SnyH2mcYPwVy6dCkyMjIwadIk/PDDD/j5559x+fJlrFy5stD1N3nx8vLCgQMHRG1z5sxBnTp1MHHiRDg6OsLExATnz59Hjx49AAD3799HdHQ0Fw4Tqenhwyi0aNFY1LZ48VK8//5HElVERKQdGgcbIyMjfPfdd5g2bRomTpyI8uXLY/369aKpKXVUrFgRLi4uojYLCwtUrlxZ2T548GAsW7YMlSpVQsWKFeHn5wdPT08GGyI1fPbZdPz6q/hZbhERD1GpUmVpCiIi0qIiBZtNmzble6xJkyY4f/483nrrLYSHhysfjimTyTBu3DitFJlr7ty5MDIywrRp00Qb9BFR0XDDPSLSdzJBKHz2zc3NTf0Plslw+/ZtjYoqDQkJ+rOgqjAyGWBra2lQ11wc+thf9+/fg5dXM1Hbd9+twpgx47Xy+frYZyWJ/aUe9pf69LHPcq+pMEUasTl58mSxCyIiaXzwwXvYvXunqO3+/WjRg2yJiPRFkYLN6/tbZGRk4OzZs6hWrZpGIzlEVHo49UREhkbtZ0WZmJjg448/xpUrV0qiHiLSgtu3b6mEmjVr1jPUEJHeU/uuKJlMhlq1auHZs2clUQ8RFdOYMcNx5MhBUVtUVBzKly8vUUVERKVHo6d7v//++9iyZQvu37+v7XqISEOCIMDe3koUakxNTSGXJzPUEJHB0Ggfm2vXrqFy5cro27cvWrVqhWrVqsHc3FzlPF9f32IXSESFu3btCrp16yhq8/cPQN++/SWqiIhIGhoFm99//1353+fPn8/zHJlMxmBDVAoGDuyDkJCzorbHjxNgamoqUUVERNLRKNjkbsJHRNIRBAEODpVEbba2drh1K1KiioiIpKfRGhsiktbFixdUQs3Wrf9jqCEig6fxs6KISBpdu3bA9etXRW3R0U9Rrhx/OxMRafwnYXBwMH799VfcunULKSkpyOvJDLr8SAWiskahUMDRsYqorU6duggN5Z5SRES5NJqKOnr0KCZPnoyEhAT07t0b2dnZ6NOnD3r37g1zc3O4urrio48+0natRAbr7NlglVCze/cBhhoiojdoNGKzfv16eHh4YOvWrXj+/Dm2bduGwYMHo02bNnj8+DGGDRuG6tWra7tWIoPUunVTPHgg3jMqJuYZjI2NJaqIiEh3aTRiExkZid69e8PY2Fg5r5+VlQUAqF69OoYPH46NGzdqr0oiA5SVlQV7eytRqGna1BNyeTJDDRFRPjQKNubm5jAxMQEAWFlZwdTUFPHx8crjtra2ePz4sXYqJDJAx48fgZOTtagtKOg4jh0LlqgiIqKyQaOpqNq1ayMy8r/bShs0aIB9+/ahX79+UCgUCAoKgqOjo9aKJDIkDRrURmJioqgtLu45ZDKZRBUREZUdGo3YdOvWDSdPnkRGRgYAYPLkybh48SJatmwJLy8v/P3335g0aZJWCyXSdxkZGbC3txKFmrfe6gi5PJmhhoioiGRCXvdpa+Dvv//GsWPHYGxsjI4dO8LLy0sbH1tiEhJSoJ0r130yGWBra2lQ11wcUvTXgQOBmDBhjKjtxIk/4OHRtMS/W6EAQkONERcng4ODAC8vBdRdwsOfMfWwv9TD/lKfPvZZ7jUVRms7erVo0QItWrTQ1scRGQwnJ2vl4vtcpTX1FBRUDr6+ZoiO/m/w1skpG35+6fDxySrgnUREuomPVCCSyKtXr2BvbyUKNT179im1qaegoHKYMMEc0dHi74qJkWHCBHMEBXEnYyIqe4r8J9fkyZPV+mCZTIaffvpJ7YKIDMGOHVsxdar499Qff1yAm1uDUvl+hQLw9TX7d4haHGwEQQaZTICvrxl69cpSe1qKiEhKRQ42Z86cgZmZGWxtbfN8fMKbuNiRKG/29lYqbXJ5cqnWEBpqLJp+epMgyBAdLUNoqDHatVOUYmVERMVT5GDj4OCAuLg4VKlSBT4+PujTpw/s7OxKsjYivfLiRQrq1Kkmahsy5B2sXbuh1GuJiyvaPzyKeh4Rka4o8hqb4OBgbN68Ge7u7vjpp5/QqVMnjBs3Drt378aLFy9KskaiMu+3335RCTWhoZclCTUA4OBQtNskinoeEZGu0Oh278zMTAQHByMoKAhnzpxBdnY2OnToAB8fH3h7e8PU1LQkatUqfboFrjD6eNtfSdJ2f+nC1NObFAqgefMKiImRQRBUR2VkMgGOjgIuXUot0hob/oyph/2lHvaX+vSxz4p6u7dGd0WZmJiga9euWLlyJUJCQrBo0SIkJCRg+vTpfEYU0b+eP09SCTXjx78neagBAGNjwM8vHUBOiHld7ms/v3QuHCaiMqdYt3tnZGTgzz//xMmTJ3Hr1i2YmZmhWrVqhb+RSM/99NMa1K/vLGq7dCkMX3+9QqKKVPn4ZMHfPw2OjuJg4+gowN8/jfvYEFGZpPZGFdnZ2QgJCcHBgwdx4sQJpKWloU2bNli8eDG6desGCwuLkqiTqMzQxamn/Pj4ZKFXr6xi7zxMRKQrihxsLl++jKCgIBw5cgRJSUlo0qQJpk+fjl69esHa2rrwDyDScwkJCXB3ryNqmzLlE8ybt0iiiorG2Bi8pZuI9EaRg82IESNgbm6uXCScO+UUExODmJiYPN/TsGFD7VRJpOOWL/8aX3/9lajt+vU7qFqVT7knIipNak1FpaWl4dixYzh+/HiB5wmCAJlMhtu3bxerOKKyoCxNPRER6bsiB5ulS5eWZB1EZU5sbAw8PFxFbZ9//gU+/fRziSoiIqIiB5uBAweWZB1EZcqiRfOwZs1KUdutW/dha2srTUFERARAg7uiiAwdp56IiHRXsfaxITIkjx8/Ugk1CxZ8xVBDRKRDOGJDVASffz4Dmzb9LGqLiHiISpUqS1MQERHlicGGqBCceiIiKjs4FUWUj8jIeyqh5ptvvmeoISLSYRyxIcrDmDFjEBAQIGq7f/8JKlYs/MmyREQkHQYbojfY2XHqiYiorOJUFNG/wsNvq0w9rV69jqGGiKgM4YgNEYCxY0fg8OEgUVtUVCzKl+fT6omIyhIGGzJogiDAwaGSqM3Y2BhZWVlISEiBIEhUGBERaYRTUWSwrl+/qhJqfv75N8TGPpOoIiIiKi6O2JBBGjy4H86ePSNqe/QoHmZmZlKUQ0REWsJgQwYlr6kna2trhIf/I01BRESkVTo1FbV161b07dsXzZo1Q7NmzTBs2DAEBwcrj6enp2PhwoVo3bo1PD09MXXqVCQkJEhYMWlKoQBCQoyxZ085hIQYQ6Eo+e/8668LKqFmy5adDDVERHpEp0ZsqlatipkzZ6JmzZoQBAGBgYH46KOPsHfvXtSvXx9LlixBcHAwVq5cCUtLSyxevBhTpkzB9u3bpS6d1BAUVA6+vmaIjv4vVzs5ZcPPLx0+Plkl8p3du3fE1atXRG3R0U9RrpxO/RYgIqJi0qkRG29vb3Ts2BG1atVC7dq1MX36dFhYWODq1atISUnB7t27MXv2bLRp0waNGjXCkiVLcOXKFVy9elXq0qmIgoLKYcIEc0RHy0TtMTEyTJhgjqAg7QaN7Oxs2NtbiUJNzZq1IJcnM9QQEekhnQo2r1MoFDh48CBevnwJT09PhIWFITMzE23btlWeU7duXTg5OTHYlBEKBeDra/bvLdTiYCMIOa99fc20Ni31559/oGrVyqK2Xbv246+/rmvnC4iISOfo3D9Z79y5g3feeQfp6emwsLDA2rVrUa9ePdy+fRsmJiawshLvDGtjY4P4+Hi1v0cmK/wcfZF7rVJf84ULxqLppzcJggzR0TJcuGCMdu2Kl268vJohMvKeqC029hmMjY0Lfa+u9FdZwj5TD/tLPewv9eljnxX1WnQu2NSuXRuBgYFISUnB0aNH8fnnn+P333/X+vfY2BjewwylvuaXL4t6ngVsbTX7jqysLJiYmIjaPD09cfnyZbU/S+r+KovYZ+phf6mH/aU+Q+wznQs2pqamqFmzJgCgUaNGuHHjBjZv3oxevXohMzMTycnJolGbxMRE2NnZqf09iYmGs6usTJbzwy31NVtYGAMo/BEFFhYvkZCg/ojN8eNHMWLEEFFbUNAxtG7thYSElCJ/jq70V1nCPlMP+0s97C/16WOf5V5TYXQu2LwpOzsbGRkZaNSoEUxMTHD+/Hn06NEDAHD//n1ER0ejadOman+uIEBvfrGLSuprbt1aASenbMTEyJRral4nkwlwdBTQurVC7Trd3esiIUE8JRkX9xwymUzja5a6v8oi9pl62F/qYX+pzxD7TKcWDy9fvhx//fUXHj9+jDt37mD58uW4ePEi+vbtC0tLSwwePBjLli1DaGgowsLCMHfuXHh6emoUbKj0GRsDfn7pAHJCzOtyX/v5paMIy2CUMjIyYG9vJQo1bdu2h1yeDJk+TS4TEVGR6NSITWJiIj7//HPI5XJYWlrC1dUV/v7+aNeuHQBg7ty5MDIywrRp05CRkYH27dtj/vz5EldN6vDxyYK/f9q/+9j8FzwcHQW197E5cGAfJkwYLWo7fjwYTZp4aq1eIiIqW2SCYGiDVDkM6cnNMhlga2upU9esUAChocaIi5PBwUGAl5dCrZGaGjXskJ6eLmrLnXoqLl3sL13HPlMP+0s97C/16WOf5V5TYXRqxIYMh7ExNLql+9WrV6hZ00HU1qNHLwQE7NBWaUREVIbp1BobooLs3LlNJdQEB4cy1BARkRJHbKhMsLe3UmmTy5MlqISIiHQZR2xIp7148UIl1AwaNIShhoiI8sRgQzpr8+ZNqFPHSdR2/vwlrFvnL1FFRESk6zgVRTqJU09ERKQJjtiQTnn+PEkl1IwZ826ZCDUKBRASYow9e8ohJMRYa08pJyKiouOIDemM9evX4ssv54ja/v77Bpyda0pUUdEFBZX7d9PB//6t4OSUrfamg0REVDwMNqQTyvLUU1BQOUyYYK6yCVZMjAwTJpjD3z+N4YaIqJRwKooklZiYqBJqPvxwWpkJNQoF4Otr9m+oEe96nPugT19fM05LERGVEgYbksyKFd+gQYPaorZr18KxYIGfRBWpLzTU+N/pp7wf5SAIMkRHGyE0VI3nRRARkcY4FUWSeO+9sdi/f6+orayM0rwuLq5oz6Yq6nlERFQ8HLGhUpW74d7roWbmzNllMtQAgIND0Z4uV9TziIioeDhiQ6UmOPg0hgzpL2p78CAGFSpUkKii4vPyUsDJKRsxMTLlmprXyWQCHB1znl5OREQljyM2VCref3+8KNSMHj0ecnlymQ41QM5Tyv380gHkhJjX5b7280uHMZfYEBGVCo7YUIlKTn6OevVqiNqCgo6jVavWElWkfT4+WfD3T/t3H5v/Rm0cHQXuY0NEVMoYbKjEnDx5DMOHvy1qi4qKQ/ny5SWqqOT4+GShV68shIYaIy5OBgeHnOknjtQQEZUuBhsqEWPGDMeRIweVrydN+gB+fl9LWFHJMzYG2rXjWhoiIikx2JBWPXv2FK6utURtR4+ehqdnc2kKIiIig8LFw6Q1hw4FqYSaR4/iGWqIiKjUMNiQVrz9dn+MGzdC+XratBmQy5NhZmYmYVVERGRoOBVFxZKQkAB39zqitpMn/0Tjxh4SVURERIaMIzaksX379ohCjYmJCZ48SWSoISIiyTDYlHEKBRASYow9e8ohJMS4VJ4iLQgC+vTphokTxynbZs2agydPEmFiYlLyBRAREeWDU1FlWFBQuX83hfsvnzo5ZZfopnBxcbFo3NhF1PbHHxfg5tagRL6PiIhIHRyxKaOCgsphwgRz0U63ABATI8OECeYICtJ+Zt25c5so1FhaWiE6+ilDDRER6QwGmzJIoQB8fc0gCAAgDja5D2L09TXT2rSUIAjo0uUtTJnyvrLN13cBIiMfo1w5DvoREZHu4N9KZVBoqLFo+ulNgiBDdLQMoaHGxd4JNzr6CZo2FY/InDt3CfXq1S/W5xIREZUEjtiUQXFxssJPUuO8/AQE/CoKNfb2DoiJecZQQ0REOosjNmWQg4Og1fPeJAgC2rZtjsjIe8o2P79lmDTpQ40+j4iIqLQw2JRBXl4KODllIyZGplxT8zqZTICjY87TpdX18GEUWrRoLGq7ePEaatWqrXG9REREpYVTUWWQsTHg55cOICfEvC73tZ9fOoyN1ftcf/8NolBTq1ZtxMYmMdQQEVGZwWBTRvn4ZMHfPw2OjuJg4+gowN8/Ta19bLKzs9G0aQPMmTNT2fbNN9/j4sVrMDLijwgREZUdnIoqw3x8stCrVxZCQ40RFyeDg0PO9JM6IzX370fCy8tT1Hb58k1Ur15Dy9USERGVPAabMs7YGBrf0v3jj6uxYMEXytcNGrjjzJnzkMmKdzcVERGRVBhsDJBCoUDDhnXx9OlTZduqVT9i+PBRElZFRERUfAw2Bubu3Tto376lqO369TuoWtVRooqIiIi0hytDDciKFd+KQk2zZs0RF/ecoYaIiPQGR2wMQFZWFiwsLPDq1Stl208//YzBg4dKWBUREZH2MdjouZs3w9C5c1tRW1jYPdjb20tUERERUcnhVJQeW7ZssSjUtGv3FuTyZIYaIiLSWxyx0UMZGRmoXt1W1LZr1y507NgdgmaPjyIiIioTOGKjZ65fv6oSasLDH2Dw4MESVURERFR6GGz0yPz5X6Br1w7K1126dINcngwbGxsJqyIiIio9nIrSA2lpaXB2Fq+bCQjYgR49eklUERERkTQYbMq46OgnaNq0gajt7t0oVK5cRaKKiIiIpMOpKC1QKICQEGPs2VMOISHGUGj26Ca1nThxFN7e7ZSv+/TpB7k8maGGiIgMlk6N2Kxfvx7Hjh3D/fv3YW5uDk9PT8ycORN16tRRnpOeno5ly5bh0KFDyMjIQPv27TF//nzY2toW8MklJyioHHx9zRAd/V9GdHLKhp9fOnx8skrkOzMzM7FkySKsXbsKAFC3bj34+wfA3b1hiXwfERFRWaFTIzYXL17EyJEjsXPnTmzatAlZWVmYMGECXr58qTxnyZIlOH36NFauXImAgADI5XJMmTJFknqDgsphwgRzREeLn4YdEyPDhAnmCArSfm589Ogh+vXrqQw17733Ps6cOc9QQ0REBEAmCLq7s8nTp0/Rpk0b/P7772jZsiVSUlLQpk0bfPfdd+jZsycAIDIyEr1798aOHTvQtGnTIn92QkJKsfZ0USiA5s0r/BtqZCrHZTIBjo4CLl1KhbGx5t/zusOHD+Ljjz9AUlISrKwqYeXKtfDx6Vfo+2QywNbWstjXbCjYX+pjn6mH/aUe9pf69LHPcq+pMDo1FfWmlJQUAEClSpUAAGFhYcjMzETbtv/tplu3bl04OTnh6tWragUbmWoWUcuFC8ai6ac3CYIM0dEyXLhgjHbtirfoJiMjAwsXfokNG34CAHh6NsPGjb+iZs1aRXp/7rUW95oNBftLfewz9bC/1MP+Up8+9llRr0Vng012djaWLFmCZs2awcXFBQCQkJAAExMTWFlZic61sbFBfHy8Wp9vY1N46ivIa7NjhZxngeIs/7l//z6GDRuGv//+GwAwY8YMLF26FKampmp/VnGv2dCwv9THPlMP+0s97C/1GWKf6WywWbhwISIiIrB169YS+fzExOINz1lYGAOwKMJ5L5GQoNmIzf79gfjkkylISUlG5cqVsWbNevTo0QvJyekA0ov8OTJZzg93ca/ZULC/1Mc+Uw/7Sz3sL/XpY5/lXlNhdDLYLFq0CGfOnMHvv/+OqlWrKtttbW2RmZmJ5ORk0ahNYmIi7Ozs1PoOQUCxfrFbt1bAySkbMTEyCEL+a2xat1ao/T1paWmYP38uNm36GQDQokUrbNiwCdWr1yhWzcW9ZkPD/lIf+0w97C/1sL/UZ4h9plN3RQmCgEWLFuH48eP47bffUKNGDdHxRo0awcTEBOfPn1e23b9/H9HR0Wqtr9EGY2PAzy9n1EQmE//U5L7280tXe+Hw/fv30KdPN2WomTLlE+zbdxjVq9co5J1ERESkUyM2CxcuRFBQEH788UdUqFBBuW7G0tIS5ubmsLS0xODBg7Fs2TJUqlQJFStWhJ+fHzw9PUs92ACAj08W/P3T/t3H5r9RG0dHQaN9bPbu3YUZM6YhNfUFbGxssGbNenTp0l3bZRMREektnQo227ZtAwCMHj1a1L506VIMGjQIADB37lwYGRlh2rRpog36pOLjk4VevbIQGmqMuDgZHBwEeHkp1BqpefXqFXx9ZyMgYBMAwMurLdav/wWOjk4lVDUREZF+0ul9bEqSrtzbHxFxF++9Nxa3b9+ETCbD9OkzMXPmHJQrp73MqY/7GZQk9pf62GfqYX+ph/2lPn3sM73Yx0bf7dy5DZ99NgMvX6bC1tYOP/64EZ06eUtdFhERUZnFYCOB1NRUzJ07C9u2/Q4AaN++A3766Wc4OFQt5J1ERERUEAabUhYefhsTJ47FnTvhMDIywsyZszF9+iwYa+u5C0RERAaMwaaUCIKAbdt+x5w5M/Hq1SvY2ztg/fpf0K7dW1KXRkREpDcYbErBixcvMGvWJ9i9eycAoFMnb6xdu1HtTQWJiIioYAw2JSws7AYmThyLyMh7MDIywuzZvpg2bQaMjHRqb0QiIiK9wGBTQgRBwObNm+Dr+znS09Ph6OiE9et/gZdX28LfTERERBphsCkBKSnJ+PTTaQgM3AMA6Nq1O1avXg8bGxuJKyMiItJvDDZadv36Vbz33lj8888DlCtXDl98sQAffDCFU09ERESlgMFGSwRBwC+/bMD8+V8gIyMD1avXwPr1v6Bly9ZSl0ZERGQwOIygJQEBv2LOnFnIyMhAz569cfLkWYYaIiKiUsYRGy1xdHREzZq18N5772PSpA8hk8kKfxMRERFpFYONlnTr1hPduvWUugwiIiKDxqkoIiIi0hsMNkRERKQ3GGyIiIhIbzDYEBERkd5gsCEiIiK9wWBDREREeoPBhoiIiPQGgw0RERHpDQYbIiIi0hsMNkRERKQ3GGyIiIhIbzDYEBERkd5gsCEiIiK9wWBDREREeqOc1AVIRSaTuoLSk3uthnTNxcH+Uh/7TD3sL/Wwv9Snj31W1GuRCYIglGwpRERERKWDU1FERESkNxhsiIiISG8w2BAREZHeYLAhIiIivcFgQ0RERHqDwYaIiIj0BoMNERER6Q0GGyIiItIbDDZERESkNxhsiIiISG8w2Oip9evXY/DgwfD09ESbNm3w4Ycf4v79+1KXpdO2bt2Kvn37olmzZmjWrBmGDRuG4OBgqcsqMzZs2ABXV1d89dVXUpeis1avXg1XV1fR/3r27Cl1WTotLi4OM2fOROvWreHh4YG+ffvixo0bUpels7y9vVV+xlxdXbFw4UKpSys1BvsQTH138eJFjBw5Eo0bN4ZCocCKFSswYcIEHDx4EBYWFlKXp5OqVq2KmTNnombNmhAEAYGBgfjoo4+wd+9e1K9fX+rydNr169exfft2uLq6Sl2Kzqtfvz42bdqkfG1sbCxhNbrt+fPnGD58OFq3bo2NGzeiSpUqiIqKQqVKlaQuTWft2rULCoVC+ToiIgLjx483qADNYKOn/P39Ra+XLVuGNm3a4ObNm2jZsqVEVek2b29v0evp06dj27ZtuHr1KoNNAVJTUzFr1iz4+fnhp59+krocnWdsbAw7OzupyygTNm7ciKpVq2Lp0qXKtho1akhYke6ztrYWvd6wYQOcnZ3RqlUriSoqfZyKMhApKSkAwH/pFJFCocDBgwfx8uVLeHp6Sl2OTlu0aBE6duyItm3bSl1KmRAVFYX27dujS5cu+PTTTxEdHS11STrr1KlTaNSoEaZNm4Y2bdpgwIAB2Llzp9RllRkZGRnYv38/Bg8eDJlMJnU5pYYjNgYgOzsbS5YsQbNmzeDi4iJ1OTrtzp07eOedd5Ceng4LCwusXbsW9erVk7osnXXw4EHcunULu3btkrqUMsHDwwNLly5F7dq1ER8fj7Vr12LkyJE4cOAAKlasKHV5OufRo0fYtm0bxo8fj8mTJ+PGjRvw8/ODiYkJBg4cKHV5Ou/EiRNISUkxuL5isDEACxcuREREBLZu3Sp1KTqvdu3aCAwMREpKCo4ePYrPP/8cv//+O8NNHmJiYvDVV1/hl19+gZmZmdTllAkdO3ZU/rebmxuaNGmCzp074/DhwxgyZIiElekmQRDQqFEjzJgxAwDg7u6OiIgIbN++3eD+stbE7t270aFDBzg4OEhdSqniVJSeW7RoEc6cOYPffvsNVatWlbocnWdqaoqaNWuiUaNG+PTTT+Hm5obNmzdLXZZOunnzJhITEzFo0CC4u7vD3d0dFy9eREBAANzd3UULGClvVlZWqFWrFh4+fCh1KTrJzs4OdevWFbXVqVOH03dF8OTJE5w7dw5vv/221KWUOo7Y6ClBELB48WIcP34cAQEBXHCnoezsbGRkZEhdhk7y8vLCgQMHRG1z5sxBnTp1MHHiRN7tUwSpqal49OgRFxPno1mzZnjw4IGo7Z9//kG1atUkqqjs2LNnD2xsbNCpUyepSyl1DDZ6auHChQgKCsKPP/6IChUqID4+HgBgaWkJc3NziavTTcuXL0eHDh3g6OiI1NRUBAUF4eLFiyp3mFGOihUrqqzZsrCwQOXKlbmWKx9ff/01OnfuDCcnJ8jlcqxevRpGRkbw8fGRujSdNHbsWAwfPhzr1q1Dr169cP36dezcuROLFi2SujSdlp2djT179mDAgAEoV87w/po3vCs2ENu2bQMAjB49WtS+dOlSDBo0SIqSdF5iYiI+//xzyOVyWFpawtXVFf7+/mjXrp3UpZGeiI2NxYwZM5CUlARra2s0b94cO3fuVLlFl3J4eHhgzZo1WLFiBdauXYvq1atj7ty56Nevn9Sl6bRz584hOjoagwcPlroUScgEQRCkLoKIiIhIG7h4mIiIiPQGgw0RERHpDQYbIiIi0hsMNkRERKQ3GGyIiIhIbzDYEBERkd5gsCEiIiK9wWBDRKQFrq6uWL16tfL1nj174OrqisePH2vtO2bPng1vb2+tfR6RPmKwITIQuX/R3rhxI8/jo0ePLrNb+9+5cwfTpk1D586d0bhxY7z11lsYP348AgICROetW7cOJ06ckKhKIioNDDZEVKZdvnwZgwcPRnh4OIYMGYJ58+ZhyJAhMDIyUnky+/r160st2PTv3x/Xr1/nAxuJShmfFUVEknr58iUsLCw0fv+6detgaWmJXbt2wcrKSnQsMTGxuOVpzNjYmE84J5IAR2yIKF9ZWVlYu3YtunbtikaNGsHb2xsrVqxARkaG6Lw315fk8vb2xuzZs5Wvc6fDLl68iAULFqBNmzbo2LEjAODFixf46quv4O3tjUaNGqFNmzYYP348bt68WWCNDx8+RL169VRCDQDY2NiIanz58iX27t0LV1dXuLq6KmvLb+3K6tWr4erqKmrLyMjAkiVL4OXlBU9PT0yePBmxsbEq781vjU1wcDBGjBiBpk2bwtPTE5MmTUJERITK+0+cOAEfHx80btwYPj4+OH78eIH9QEQ5OGJDZGBevHiBp0+fqrRnZmaqtPn6+mLv3r3o0aMHxo8fj+vXr2P9+vWIjIzE2rVrNa5h4cKFsLa2xkcffYSXL18CAObPn4+jR49i1KhRqFu3LpKSknDp0iVERkaiYcOG+X5WtWrVcOXKFdy9excuLi75nvfNN9/A19cXHh4eGDp0KADA2dlZ7dq/+OIL7N+/Hz4+PmjWrBlCQ0MxadKkIr03MDAQs2fPRvv27TFz5ky8evUK27Ztw4gRI7B3715Ur14dAPDnn39i6tSpqFevHj799FM8e/YMc+bMQdWqVdWul8jQMNgQGZhx48ble6x+/frK/w4PD8fevXsxZMgQ+Pn5AQBGjhwJa2tr/PLLLwgNDYWXl5dGNVSqVAm//vqraKomODgYQ4cOFY3wTJw4sdDPevfddzFx4kQMGDAAHh4eaN68Odq0aYPWrVvDxMREeV7//v2xYMEC1KhRA/3799eo7vDwcOzfvx8jRozA/PnzAeT0yaeffoo7d+4U+N7U1FR89dVXGDJkCBYvXqxsHzhwIHr27In169cr27/77jvY2Nhg69atsLS0BAC0atUK7777LtfsEBWCwYbIwMybNw+1a9dWaV+2bBmys7OVr4ODgwEA48ePF5337rvv4pdffkFwcLDGwWbo0KEq60+srKxw7do1xMXFwcHBocif1a5dO2zfvh0bNmzAn3/+iStXruDnn3+GtbU1/Pz80KVLF41qzEtun4wePVrUPnbsWAQFBRX43nPnziE5ORl9+vQRjZgZGRmhSZMmuHDhAgBALpfj9u3bmDRpkjLUADnXWa9ePbx69Upbl0OklxhsiAyMh4cHGjdurNJeqVIlPHv2TPn6yZMnMDIyUpmusbOzg5WVFZ48eaJxDblTLq+bOXMmZs+ejU6dOqFhw4bo2LEjBgwYgBo1ahT6eR4eHlizZg0yMjIQHh6OEydO4Ndff8XHH3+MwMBA1KtXT+NaX5dfn9SpU6fQ9/7zzz8AckJQXipWrAgAiI6OBgDUrFlT5ZzatWvj1q1b6pRMZHAYbIioQDKZTOP3KhSKPNvNzMxU2nr37o0WLVrg+PHjCAkJgb+/PzZu3IjVq1crFxgXxtTUFB4eHvDw8ECtWrUwZ84cHDlyBFOmTCnwffldY371a0IQBAA5a33s7OxUjvMOKiLtYLAhojxVq1YN2dnZiIqKQt26dZXtCQkJSE5OFq31qFSpEpKTk0Xvz8jIQHx8vFrfaW9vj5EjR2LkyJFITEzEwIEDsW7duiIHm9c1atQIQM7UTmGsrKxU6gf+Gz3JldsnDx8+FI3S3L9/v9DvyB15srGxQdu2bfM9z8nJCQAQFRWlcuzBgweFfg+RoePt3kSUp9ww8dtvv4naN23aJDoO5Pyl/ffff4vO27lzZ5FHPBQKBVJSUkRtNjY2sLe3V7m1/E2hoaHK0ZDX5a6HeT2AWFhY5BlgnJ2dkZKSgvDwcGWbXC5XucW6Q4cOAKCyo/GbfZSXt956CxUrVsT69evzvAMtd92Nvb09GjRogL1794r6JCQkBPfu3Sv0e4gMHUdsiChPbm5uGDhwIHbs2IHk5GS0bNkSN27cwN69e9G1a1fRwuEhQ4Zg/vz5mDp1Ktq2bYvw8HD8+eefqFKlSpG+KzU1FR07dkSPHj3g5uYGCwsLnDt3Djdu3BDdJZUXPz8/vHr1Ct26dUOdOnWQmZmJy5cv4/Dhw6hWrRoGDRqkPLdhw4Y4f/48Nm3aBHt7e1SvXh1NmjRB79698d1332HKlCkYPXo00tLSsG3bNtSuXVu0j06DBg3g4+ODrVu3IiUlBZ6enggNDc1zdOVNFStWxIIFC/DZZ59h0KBB6N27N6ytrREdHY3g4GA0a9YM8+bNAwDMmDED77//PkaMGIHBgwcjKSkJv//+O+rXr6+8PZ6I8sZgQ0T58vPzQ/Xq1bF3716cOHECtra2eP/991XWrAwdOhSPHz/Grl27cPbsWTRv3hybNm0q8Nby15mbm2P48OEICQnBsWPHIAgCnJ2dMX/+fIwYMaLA93722Wc4cuQIgoODsWPHDmRmZsLJyQkjRozABx98INq4b/bs2Zg3bx5WrlyJtLQ0DBw4EE2aNEGVKlWwZs0aLFu2DN9++y2qV6+OGTNmICoqSmWDwCVLlqBKlSo4cOAATp48idatW2PDhg1Fmi7r27cv7O3tsWHDBvj7+yMjIwMODg5o0aKFKIB16NABq1atwsqVK7F8+XI4Oztj6dKlOHnyJC5evFikPiUyVDIhrzFcIiIiojKIa2yIiIhIbzDYEBERkd5gsCEiIiK9wWBDREREeoPBhoiIiPQGgw0RERHpDQYbIiIi0hsMNkRERKQ3GGyIiIhIbzDYEBERkd5gsCEiIiK9wWBDREREeoPBhoiIiPTG/wG603QuMFf6WgAAAABJRU5ErkJggg==\n"
842 |           },
843 |           "metadata": {}
844 |         }
845 |       ]
846 |     },
847 |     {
848 |       "cell_type": "code",
849 |       "source": [
850 |         "# Calculating the accuracy of the model\n",
851 |         "print('Mean absolute error: ',mean_absolute_error(val_y,pred_y))\n"
852 |       ],
853 |       "metadata": {
854 |         "colab": {
855 |           "base_uri": "https://localhost:8080/"
856 |         },
857 |         "id": "UTdyKjNz3EWC",
858 |         "outputId": "8bdf0bfc-53b8-4887-f455-1aa22c8d60aa"
859 |       },
860 |       "execution_count": 17,
861 |       "outputs": [
862 |         {
863 |           "output_type": "stream",
864 |           "name": "stdout",
865 |           "text": [
866 |             "Mean absolute error:  4.130879918502482\n"
867 |           ]
868 |         }
869 |       ]
870 |     },
871 |     {
872 |       "cell_type": "code",
873 |       "source": [
874 |         "hours = [9.25]\n",
875 |         "answer = regression.predict([hours])\n",
876 |         "print(\"Score = {}\".format(round(answer[0],3)))"
877 |       ],
878 |       "metadata": {
879 |         "colab": {
880 |           "base_uri": "https://localhost:8080/"
881 |         },
882 |         "id": "1qnrYQ-B3Gh2",
883 |         "outputId": "fbb93371-f66c-4b94-c13b-7b8910852df6"
884 |       },
885 |       "execution_count": 18,
886 |       "outputs": [
887 |         {
888 |           "output_type": "stream",
889 |           "name": "stdout",
890 |           "text": [
891 |             "Score = 93.893\n"
892 |           ]
893 |         }
894 |       ]
895 |     }
896 |   ]
897 | }


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