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b/.ipynb_checkpoints/cancer-prediction-checkpoint.ipynb |
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"/opt/conda/lib/python3.10/site-packages/scipy/__init__.py:146: UserWarning: A NumPy version >=1.16.5 and <1.23.0 is required for this version of SciPy (detected version 1.23.5\n", |
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" warnings.warn(f\"A NumPy version >={np_minversion} and <{np_maxversion}\"\n" |
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] |
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} |
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], |
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"source": [ |
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"#Importing Libraries\n", |
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"import pandas as pd\n", |
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"import numpy as np\n", |
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"import matplotlib.pyplot as plt\n", |
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"import seaborn as sns\n", |
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"\n", |
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"#For ignoring warning\n", |
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"import warnings\n", |
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"warnings.filterwarnings(\"ignore\")" |
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] |
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"text/plain": [ |
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" GENDER AGE SMOKING YELLOW_FINGERS ANXIETY PEER_PRESSURE \\\n", |
|
|
308 |
"0 M 69 1 2 2 1 \n", |
|
|
309 |
"1 M 74 2 1 1 1 \n", |
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"2 F 59 1 1 1 2 \n", |
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"3 M 63 2 2 2 1 \n", |
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"4 F 63 1 2 1 1 \n", |
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".. ... ... ... ... ... ... \n", |
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" CHRONIC DISEASE FATIGUE ALLERGY WHEEZING ALCOHOL CONSUMING \\\n", |
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321 |
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322 |
"1 2 2 2 1 1 \n", |
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"4 1 1 1 2 1 \n", |
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".. ... ... ... ... ... \n", |
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"304 2 2 1 1 2 \n", |
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"305 1 2 2 2 2 \n", |
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"306 1 1 2 2 2 \n", |
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"307 1 2 2 1 2 \n", |
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"308 1 2 2 2 2 \n", |
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"\n", |
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" COUGHING SHORTNESS OF BREATH SWALLOWING DIFFICULTY CHEST PAIN \\\n", |
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334 |
"0 2 2 2 2 \n", |
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|
335 |
"1 1 2 2 2 \n", |
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|
336 |
"2 2 2 1 2 \n", |
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337 |
"3 1 1 2 2 \n", |
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338 |
"4 2 2 1 1 \n", |
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339 |
".. ... ... ... ... \n", |
|
|
340 |
"304 2 2 2 1 \n", |
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"305 2 2 1 2 \n", |
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342 |
"306 2 1 1 2 \n", |
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|
343 |
"307 2 2 1 2 \n", |
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|
344 |
"308 1 1 2 1 \n", |
|
|
345 |
"\n", |
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|
346 |
" LUNG_CANCER \n", |
|
|
347 |
"0 YES \n", |
|
|
348 |
"1 YES \n", |
|
|
349 |
"2 NO \n", |
|
|
350 |
"3 NO \n", |
|
|
351 |
"4 NO \n", |
|
|
352 |
".. ... \n", |
|
|
353 |
"304 YES \n", |
|
|
354 |
"305 YES \n", |
|
|
355 |
"306 YES \n", |
|
|
356 |
"307 YES \n", |
|
|
357 |
"308 YES \n", |
|
|
358 |
"\n", |
|
|
359 |
"[309 rows x 16 columns]" |
|
|
360 |
] |
|
|
361 |
}, |
|
|
362 |
"execution_count": 2, |
|
|
363 |
"metadata": {}, |
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|
364 |
"output_type": "execute_result" |
|
|
365 |
} |
|
|
366 |
], |
|
|
367 |
"source": [ |
|
|
368 |
"df=pd.read_csv('/kaggle/input/lung-cancer/survey lung cancer.csv')\n", |
|
|
369 |
"df" |
|
|
370 |
] |
|
|
371 |
}, |
|
|
372 |
{ |
|
|
373 |
"cell_type": "markdown", |
|
|
374 |
"metadata": {}, |
|
|
375 |
"source": [ |
|
|
376 |
"**Note: In this dataset, YES=2 & NO=1**" |
|
|
377 |
] |
|
|
378 |
}, |
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|
379 |
{ |
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|
380 |
"cell_type": "code", |
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|
381 |
"execution_count": 3, |
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|
382 |
"metadata": { |
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383 |
"execution": { |
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384 |
"iopub.execute_input": "2023-07-17T12:59:58.250019Z", |
|
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385 |
"iopub.status.busy": "2023-07-17T12:59:58.249580Z", |
|
|
386 |
"iopub.status.idle": "2023-07-17T12:59:58.257375Z", |
|
|
387 |
"shell.execute_reply": "2023-07-17T12:59:58.256097Z", |
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388 |
"shell.execute_reply.started": "2023-07-17T12:59:58.249983Z" |
|
|
389 |
} |
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|
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}, |
|
|
391 |
"outputs": [ |
|
|
392 |
{ |
|
|
393 |
"data": { |
|
|
394 |
"text/plain": [ |
|
|
395 |
"(309, 16)" |
|
|
396 |
] |
|
|
397 |
}, |
|
|
398 |
"execution_count": 3, |
|
|
399 |
"metadata": {}, |
|
|
400 |
"output_type": "execute_result" |
|
|
401 |
} |
|
|
402 |
], |
|
|
403 |
"source": [ |
|
|
404 |
"df.shape" |
|
|
405 |
] |
|
|
406 |
}, |
|
|
407 |
{ |
|
|
408 |
"cell_type": "code", |
|
|
409 |
"execution_count": 4, |
|
|
410 |
"metadata": { |
|
|
411 |
"execution": { |
|
|
412 |
"iopub.execute_input": "2023-07-17T13:00:09.802217Z", |
|
|
413 |
"iopub.status.busy": "2023-07-17T13:00:09.801811Z", |
|
|
414 |
"iopub.status.idle": "2023-07-17T13:00:09.820917Z", |
|
|
415 |
"shell.execute_reply": "2023-07-17T13:00:09.819392Z", |
|
|
416 |
"shell.execute_reply.started": "2023-07-17T13:00:09.802186Z" |
|
|
417 |
} |
|
|
418 |
}, |
|
|
419 |
"outputs": [ |
|
|
420 |
{ |
|
|
421 |
"data": { |
|
|
422 |
"text/plain": [ |
|
|
423 |
"33" |
|
|
424 |
] |
|
|
425 |
}, |
|
|
426 |
"execution_count": 4, |
|
|
427 |
"metadata": {}, |
|
|
428 |
"output_type": "execute_result" |
|
|
429 |
} |
|
|
430 |
], |
|
|
431 |
"source": [ |
|
|
432 |
"#Checking for Duplicates\n", |
|
|
433 |
"df.duplicated().sum()" |
|
|
434 |
] |
|
|
435 |
}, |
|
|
436 |
{ |
|
|
437 |
"cell_type": "code", |
|
|
438 |
"execution_count": 5, |
|
|
439 |
"metadata": { |
|
|
440 |
"execution": { |
|
|
441 |
"iopub.execute_input": "2023-07-17T13:00:22.724314Z", |
|
|
442 |
"iopub.status.busy": "2023-07-17T13:00:22.722784Z", |
|
|
443 |
"iopub.status.idle": "2023-07-17T13:00:22.735252Z", |
|
|
444 |
"shell.execute_reply": "2023-07-17T13:00:22.733243Z", |
|
|
445 |
"shell.execute_reply.started": "2023-07-17T13:00:22.724266Z" |
|
|
446 |
} |
|
|
447 |
}, |
|
|
448 |
"outputs": [], |
|
|
449 |
"source": [ |
|
|
450 |
"#Removing Duplicates\n", |
|
|
451 |
"df=df.drop_duplicates()" |
|
|
452 |
] |
|
|
453 |
}, |
|
|
454 |
{ |
|
|
455 |
"cell_type": "code", |
|
|
456 |
"execution_count": 6, |
|
|
457 |
"metadata": { |
|
|
458 |
"execution": { |
|
|
459 |
"iopub.execute_input": "2023-07-17T13:00:32.091786Z", |
|
|
460 |
"iopub.status.busy": "2023-07-17T13:00:32.090176Z", |
|
|
461 |
"iopub.status.idle": "2023-07-17T13:00:32.106496Z", |
|
|
462 |
"shell.execute_reply": "2023-07-17T13:00:32.105099Z", |
|
|
463 |
"shell.execute_reply.started": "2023-07-17T13:00:32.091727Z" |
|
|
464 |
} |
|
|
465 |
}, |
|
|
466 |
"outputs": [ |
|
|
467 |
{ |
|
|
468 |
"data": { |
|
|
469 |
"text/plain": [ |
|
|
470 |
"0" |
|
|
471 |
] |
|
|
472 |
}, |
|
|
473 |
"execution_count": 6, |
|
|
474 |
"metadata": {}, |
|
|
475 |
"output_type": "execute_result" |
|
|
476 |
} |
|
|
477 |
], |
|
|
478 |
"source": [ |
|
|
479 |
"df.duplicated().sum()" |
|
|
480 |
] |
|
|
481 |
}, |
|
|
482 |
{ |
|
|
483 |
"cell_type": "code", |
|
|
484 |
"execution_count": 7, |
|
|
485 |
"metadata": { |
|
|
486 |
"execution": { |
|
|
487 |
"iopub.execute_input": "2023-07-17T13:00:45.082980Z", |
|
|
488 |
"iopub.status.busy": "2023-07-17T13:00:45.082554Z", |
|
|
489 |
"iopub.status.idle": "2023-07-17T13:00:45.093132Z", |
|
|
490 |
"shell.execute_reply": "2023-07-17T13:00:45.091847Z", |
|
|
491 |
"shell.execute_reply.started": "2023-07-17T13:00:45.082945Z" |
|
|
492 |
} |
|
|
493 |
}, |
|
|
494 |
"outputs": [ |
|
|
495 |
{ |
|
|
496 |
"data": { |
|
|
497 |
"text/plain": [ |
|
|
498 |
"GENDER 0\n", |
|
|
499 |
"AGE 0\n", |
|
|
500 |
"SMOKING 0\n", |
|
|
501 |
"YELLOW_FINGERS 0\n", |
|
|
502 |
"ANXIETY 0\n", |
|
|
503 |
"PEER_PRESSURE 0\n", |
|
|
504 |
"CHRONIC DISEASE 0\n", |
|
|
505 |
"FATIGUE 0\n", |
|
|
506 |
"ALLERGY 0\n", |
|
|
507 |
"WHEEZING 0\n", |
|
|
508 |
"ALCOHOL CONSUMING 0\n", |
|
|
509 |
"COUGHING 0\n", |
|
|
510 |
"SHORTNESS OF BREATH 0\n", |
|
|
511 |
"SWALLOWING DIFFICULTY 0\n", |
|
|
512 |
"CHEST PAIN 0\n", |
|
|
513 |
"LUNG_CANCER 0\n", |
|
|
514 |
"dtype: int64" |
|
|
515 |
] |
|
|
516 |
}, |
|
|
517 |
"execution_count": 7, |
|
|
518 |
"metadata": {}, |
|
|
519 |
"output_type": "execute_result" |
|
|
520 |
} |
|
|
521 |
], |
|
|
522 |
"source": [ |
|
|
523 |
"#Checking for null values\n", |
|
|
524 |
"df.isnull().sum()" |
|
|
525 |
] |
|
|
526 |
}, |
|
|
527 |
{ |
|
|
528 |
"cell_type": "code", |
|
|
529 |
"execution_count": 8, |
|
|
530 |
"metadata": { |
|
|
531 |
"execution": { |
|
|
532 |
"iopub.execute_input": "2023-07-17T13:00:58.073632Z", |
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|
533 |
"iopub.status.busy": "2023-07-17T13:00:58.073212Z", |
|
|
534 |
"iopub.status.idle": "2023-07-17T13:00:58.102068Z", |
|
|
535 |
"shell.execute_reply": "2023-07-17T13:00:58.100534Z", |
|
|
536 |
"shell.execute_reply.started": "2023-07-17T13:00:58.073593Z" |
|
|
537 |
} |
|
|
538 |
}, |
|
|
539 |
"outputs": [ |
|
|
540 |
{ |
|
|
541 |
"name": "stdout", |
|
|
542 |
"output_type": "stream", |
|
|
543 |
"text": [ |
|
|
544 |
"<class 'pandas.core.frame.DataFrame'>\n", |
|
|
545 |
"Int64Index: 276 entries, 0 to 283\n", |
|
|
546 |
"Data columns (total 16 columns):\n", |
|
|
547 |
" # Column Non-Null Count Dtype \n", |
|
|
548 |
"--- ------ -------------- ----- \n", |
|
|
549 |
" 0 GENDER 276 non-null object\n", |
|
|
550 |
" 1 AGE 276 non-null int64 \n", |
|
|
551 |
" 2 SMOKING 276 non-null int64 \n", |
|
|
552 |
" 3 YELLOW_FINGERS 276 non-null int64 \n", |
|
|
553 |
" 4 ANXIETY 276 non-null int64 \n", |
|
|
554 |
" 5 PEER_PRESSURE 276 non-null int64 \n", |
|
|
555 |
" 6 CHRONIC DISEASE 276 non-null int64 \n", |
|
|
556 |
" 7 FATIGUE 276 non-null int64 \n", |
|
|
557 |
" 8 ALLERGY 276 non-null int64 \n", |
|
|
558 |
" 9 WHEEZING 276 non-null int64 \n", |
|
|
559 |
" 10 ALCOHOL CONSUMING 276 non-null int64 \n", |
|
|
560 |
" 11 COUGHING 276 non-null int64 \n", |
|
|
561 |
" 12 SHORTNESS OF BREATH 276 non-null int64 \n", |
|
|
562 |
" 13 SWALLOWING DIFFICULTY 276 non-null int64 \n", |
|
|
563 |
" 14 CHEST PAIN 276 non-null int64 \n", |
|
|
564 |
" 15 LUNG_CANCER 276 non-null object\n", |
|
|
565 |
"dtypes: int64(14), object(2)\n", |
|
|
566 |
"memory usage: 36.7+ KB\n" |
|
|
567 |
] |
|
|
568 |
} |
|
|
569 |
], |
|
|
570 |
"source": [ |
|
|
571 |
"df.info()" |
|
|
572 |
] |
|
|
573 |
}, |
|
|
574 |
{ |
|
|
575 |
"cell_type": "code", |
|
|
576 |
"execution_count": 9, |
|
|
577 |
"metadata": { |
|
|
578 |
"execution": { |
|
|
579 |
"iopub.execute_input": "2023-07-17T13:01:08.971229Z", |
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|
580 |
"iopub.status.busy": "2023-07-17T13:01:08.970527Z", |
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|
581 |
"iopub.status.idle": "2023-07-17T13:01:09.031787Z", |
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|
582 |
"shell.execute_reply": "2023-07-17T13:01:09.030503Z", |
|
|
583 |
"shell.execute_reply.started": "2023-07-17T13:01:08.971180Z" |
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|
584 |
} |
|
|
585 |
}, |
|
|
586 |
"outputs": [ |
|
|
587 |
{ |
|
|
588 |
"data": { |
|
|
589 |
"text/html": [ |
|
|
590 |
"<div>\n", |
|
|
591 |
"<style scoped>\n", |
|
|
592 |
" .dataframe tbody tr th:only-of-type {\n", |
|
|
593 |
" vertical-align: middle;\n", |
|
|
594 |
" }\n", |
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|
595 |
"\n", |
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|
596 |
" .dataframe tbody tr th {\n", |
|
|
597 |
" vertical-align: top;\n", |
|
|
598 |
" }\n", |
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|
599 |
"\n", |
|
|
600 |
" .dataframe thead th {\n", |
|
|
601 |
" text-align: right;\n", |
|
|
602 |
" }\n", |
|
|
603 |
"</style>\n", |
|
|
604 |
"<table border=\"1\" class=\"dataframe\">\n", |
|
|
605 |
" <thead>\n", |
|
|
606 |
" <tr style=\"text-align: right;\">\n", |
|
|
607 |
" <th></th>\n", |
|
|
608 |
" <th>AGE</th>\n", |
|
|
609 |
" <th>SMOKING</th>\n", |
|
|
610 |
" <th>YELLOW_FINGERS</th>\n", |
|
|
611 |
" <th>ANXIETY</th>\n", |
|
|
612 |
" <th>PEER_PRESSURE</th>\n", |
|
|
613 |
" <th>CHRONIC DISEASE</th>\n", |
|
|
614 |
" <th>FATIGUE</th>\n", |
|
|
615 |
" <th>ALLERGY</th>\n", |
|
|
616 |
" <th>WHEEZING</th>\n", |
|
|
617 |
" <th>ALCOHOL CONSUMING</th>\n", |
|
|
618 |
" <th>COUGHING</th>\n", |
|
|
619 |
" <th>SHORTNESS OF BREATH</th>\n", |
|
|
620 |
" <th>SWALLOWING DIFFICULTY</th>\n", |
|
|
621 |
" <th>CHEST PAIN</th>\n", |
|
|
622 |
" </tr>\n", |
|
|
623 |
" </thead>\n", |
|
|
624 |
" <tbody>\n", |
|
|
625 |
" <tr>\n", |
|
|
626 |
" <th>count</th>\n", |
|
|
627 |
" <td>276.000000</td>\n", |
|
|
628 |
" <td>276.000000</td>\n", |
|
|
629 |
" <td>276.000000</td>\n", |
|
|
630 |
" <td>276.000000</td>\n", |
|
|
631 |
" <td>276.000000</td>\n", |
|
|
632 |
" <td>276.000000</td>\n", |
|
|
633 |
" <td>276.000000</td>\n", |
|
|
634 |
" <td>276.000000</td>\n", |
|
|
635 |
" <td>276.000000</td>\n", |
|
|
636 |
" <td>276.000000</td>\n", |
|
|
637 |
" <td>276.000000</td>\n", |
|
|
638 |
" <td>276.000000</td>\n", |
|
|
639 |
" <td>276.000000</td>\n", |
|
|
640 |
" <td>276.000000</td>\n", |
|
|
641 |
" </tr>\n", |
|
|
642 |
" <tr>\n", |
|
|
643 |
" <th>mean</th>\n", |
|
|
644 |
" <td>62.909420</td>\n", |
|
|
645 |
" <td>1.543478</td>\n", |
|
|
646 |
" <td>1.576087</td>\n", |
|
|
647 |
" <td>1.496377</td>\n", |
|
|
648 |
" <td>1.507246</td>\n", |
|
|
649 |
" <td>1.521739</td>\n", |
|
|
650 |
" <td>1.663043</td>\n", |
|
|
651 |
" <td>1.547101</td>\n", |
|
|
652 |
" <td>1.547101</td>\n", |
|
|
653 |
" <td>1.550725</td>\n", |
|
|
654 |
" <td>1.576087</td>\n", |
|
|
655 |
" <td>1.630435</td>\n", |
|
|
656 |
" <td>1.467391</td>\n", |
|
|
657 |
" <td>1.557971</td>\n", |
|
|
658 |
" </tr>\n", |
|
|
659 |
" <tr>\n", |
|
|
660 |
" <th>std</th>\n", |
|
|
661 |
" <td>8.379355</td>\n", |
|
|
662 |
" <td>0.499011</td>\n", |
|
|
663 |
" <td>0.495075</td>\n", |
|
|
664 |
" <td>0.500895</td>\n", |
|
|
665 |
" <td>0.500856</td>\n", |
|
|
666 |
" <td>0.500435</td>\n", |
|
|
667 |
" <td>0.473529</td>\n", |
|
|
668 |
" <td>0.498681</td>\n", |
|
|
669 |
" <td>0.498681</td>\n", |
|
|
670 |
" <td>0.498324</td>\n", |
|
|
671 |
" <td>0.495075</td>\n", |
|
|
672 |
" <td>0.483564</td>\n", |
|
|
673 |
" <td>0.499842</td>\n", |
|
|
674 |
" <td>0.497530</td>\n", |
|
|
675 |
" </tr>\n", |
|
|
676 |
" <tr>\n", |
|
|
677 |
" <th>min</th>\n", |
|
|
678 |
" <td>21.000000</td>\n", |
|
|
679 |
" <td>1.000000</td>\n", |
|
|
680 |
" <td>1.000000</td>\n", |
|
|
681 |
" <td>1.000000</td>\n", |
|
|
682 |
" <td>1.000000</td>\n", |
|
|
683 |
" <td>1.000000</td>\n", |
|
|
684 |
" <td>1.000000</td>\n", |
|
|
685 |
" <td>1.000000</td>\n", |
|
|
686 |
" <td>1.000000</td>\n", |
|
|
687 |
" <td>1.000000</td>\n", |
|
|
688 |
" <td>1.000000</td>\n", |
|
|
689 |
" <td>1.000000</td>\n", |
|
|
690 |
" <td>1.000000</td>\n", |
|
|
691 |
" <td>1.000000</td>\n", |
|
|
692 |
" </tr>\n", |
|
|
693 |
" <tr>\n", |
|
|
694 |
" <th>25%</th>\n", |
|
|
695 |
" <td>57.750000</td>\n", |
|
|
696 |
" <td>1.000000</td>\n", |
|
|
697 |
" <td>1.000000</td>\n", |
|
|
698 |
" <td>1.000000</td>\n", |
|
|
699 |
" <td>1.000000</td>\n", |
|
|
700 |
" <td>1.000000</td>\n", |
|
|
701 |
" <td>1.000000</td>\n", |
|
|
702 |
" <td>1.000000</td>\n", |
|
|
703 |
" <td>1.000000</td>\n", |
|
|
704 |
" <td>1.000000</td>\n", |
|
|
705 |
" <td>1.000000</td>\n", |
|
|
706 |
" <td>1.000000</td>\n", |
|
|
707 |
" <td>1.000000</td>\n", |
|
|
708 |
" <td>1.000000</td>\n", |
|
|
709 |
" </tr>\n", |
|
|
710 |
" <tr>\n", |
|
|
711 |
" <th>50%</th>\n", |
|
|
712 |
" <td>62.500000</td>\n", |
|
|
713 |
" <td>2.000000</td>\n", |
|
|
714 |
" <td>2.000000</td>\n", |
|
|
715 |
" <td>1.000000</td>\n", |
|
|
716 |
" <td>2.000000</td>\n", |
|
|
717 |
" <td>2.000000</td>\n", |
|
|
718 |
" <td>2.000000</td>\n", |
|
|
719 |
" <td>2.000000</td>\n", |
|
|
720 |
" <td>2.000000</td>\n", |
|
|
721 |
" <td>2.000000</td>\n", |
|
|
722 |
" <td>2.000000</td>\n", |
|
|
723 |
" <td>2.000000</td>\n", |
|
|
724 |
" <td>1.000000</td>\n", |
|
|
725 |
" <td>2.000000</td>\n", |
|
|
726 |
" </tr>\n", |
|
|
727 |
" <tr>\n", |
|
|
728 |
" <th>75%</th>\n", |
|
|
729 |
" <td>69.000000</td>\n", |
|
|
730 |
" <td>2.000000</td>\n", |
|
|
731 |
" <td>2.000000</td>\n", |
|
|
732 |
" <td>2.000000</td>\n", |
|
|
733 |
" <td>2.000000</td>\n", |
|
|
734 |
" <td>2.000000</td>\n", |
|
|
735 |
" <td>2.000000</td>\n", |
|
|
736 |
" <td>2.000000</td>\n", |
|
|
737 |
" <td>2.000000</td>\n", |
|
|
738 |
" <td>2.000000</td>\n", |
|
|
739 |
" <td>2.000000</td>\n", |
|
|
740 |
" <td>2.000000</td>\n", |
|
|
741 |
" <td>2.000000</td>\n", |
|
|
742 |
" <td>2.000000</td>\n", |
|
|
743 |
" </tr>\n", |
|
|
744 |
" <tr>\n", |
|
|
745 |
" <th>max</th>\n", |
|
|
746 |
" <td>87.000000</td>\n", |
|
|
747 |
" <td>2.000000</td>\n", |
|
|
748 |
" <td>2.000000</td>\n", |
|
|
749 |
" <td>2.000000</td>\n", |
|
|
750 |
" <td>2.000000</td>\n", |
|
|
751 |
" <td>2.000000</td>\n", |
|
|
752 |
" <td>2.000000</td>\n", |
|
|
753 |
" <td>2.000000</td>\n", |
|
|
754 |
" <td>2.000000</td>\n", |
|
|
755 |
" <td>2.000000</td>\n", |
|
|
756 |
" <td>2.000000</td>\n", |
|
|
757 |
" <td>2.000000</td>\n", |
|
|
758 |
" <td>2.000000</td>\n", |
|
|
759 |
" <td>2.000000</td>\n", |
|
|
760 |
" </tr>\n", |
|
|
761 |
" </tbody>\n", |
|
|
762 |
"</table>\n", |
|
|
763 |
"</div>" |
|
|
764 |
], |
|
|
765 |
"text/plain": [ |
|
|
766 |
" AGE SMOKING YELLOW_FINGERS ANXIETY PEER_PRESSURE \\\n", |
|
|
767 |
"count 276.000000 276.000000 276.000000 276.000000 276.000000 \n", |
|
|
768 |
"mean 62.909420 1.543478 1.576087 1.496377 1.507246 \n", |
|
|
769 |
"std 8.379355 0.499011 0.495075 0.500895 0.500856 \n", |
|
|
770 |
"min 21.000000 1.000000 1.000000 1.000000 1.000000 \n", |
|
|
771 |
"25% 57.750000 1.000000 1.000000 1.000000 1.000000 \n", |
|
|
772 |
"50% 62.500000 2.000000 2.000000 1.000000 2.000000 \n", |
|
|
773 |
"75% 69.000000 2.000000 2.000000 2.000000 2.000000 \n", |
|
|
774 |
"max 87.000000 2.000000 2.000000 2.000000 2.000000 \n", |
|
|
775 |
"\n", |
|
|
776 |
" CHRONIC DISEASE FATIGUE ALLERGY WHEEZING ALCOHOL CONSUMING \\\n", |
|
|
777 |
"count 276.000000 276.000000 276.000000 276.000000 276.000000 \n", |
|
|
778 |
"mean 1.521739 1.663043 1.547101 1.547101 1.550725 \n", |
|
|
779 |
"std 0.500435 0.473529 0.498681 0.498681 0.498324 \n", |
|
|
780 |
"min 1.000000 1.000000 1.000000 1.000000 1.000000 \n", |
|
|
781 |
"25% 1.000000 1.000000 1.000000 1.000000 1.000000 \n", |
|
|
782 |
"50% 2.000000 2.000000 2.000000 2.000000 2.000000 \n", |
|
|
783 |
"75% 2.000000 2.000000 2.000000 2.000000 2.000000 \n", |
|
|
784 |
"max 2.000000 2.000000 2.000000 2.000000 2.000000 \n", |
|
|
785 |
"\n", |
|
|
786 |
" COUGHING SHORTNESS OF BREATH SWALLOWING DIFFICULTY CHEST PAIN \n", |
|
|
787 |
"count 276.000000 276.000000 276.000000 276.000000 \n", |
|
|
788 |
"mean 1.576087 1.630435 1.467391 1.557971 \n", |
|
|
789 |
"std 0.495075 0.483564 0.499842 0.497530 \n", |
|
|
790 |
"min 1.000000 1.000000 1.000000 1.000000 \n", |
|
|
791 |
"25% 1.000000 1.000000 1.000000 1.000000 \n", |
|
|
792 |
"50% 2.000000 2.000000 1.000000 2.000000 \n", |
|
|
793 |
"75% 2.000000 2.000000 2.000000 2.000000 \n", |
|
|
794 |
"max 2.000000 2.000000 2.000000 2.000000 " |
|
|
795 |
] |
|
|
796 |
}, |
|
|
797 |
"execution_count": 9, |
|
|
798 |
"metadata": {}, |
|
|
799 |
"output_type": "execute_result" |
|
|
800 |
} |
|
|
801 |
], |
|
|
802 |
"source": [ |
|
|
803 |
"df.describe()" |
|
|
804 |
] |
|
|
805 |
}, |
|
|
806 |
{ |
|
|
807 |
"cell_type": "markdown", |
|
|
808 |
"metadata": {}, |
|
|
809 |
"source": [ |
|
|
810 |
"**In this dataset, GENDER & LUNG_CANCER attributes are in object data type. So, let's convert them to numerical values using LabelEncoder from sklearn. LabelEncoder is a utility class to help normalize labels such that they contain only values between 0 and n_classes-1. It can also be used to transform non-numerical labels (as long as they are hashable and comparable) to numerical labels. Also let's make every other attributes as YES=1 & NO=0.**" |
|
|
811 |
] |
|
|
812 |
}, |
|
|
813 |
{ |
|
|
814 |
"cell_type": "code", |
|
|
815 |
"execution_count": 10, |
|
|
816 |
"metadata": { |
|
|
817 |
"execution": { |
|
|
818 |
"iopub.execute_input": "2023-07-17T13:02:07.810197Z", |
|
|
819 |
"iopub.status.busy": "2023-07-17T13:02:07.809786Z", |
|
|
820 |
"iopub.status.idle": "2023-07-17T13:02:07.924492Z", |
|
|
821 |
"shell.execute_reply": "2023-07-17T13:02:07.923280Z", |
|
|
822 |
"shell.execute_reply.started": "2023-07-17T13:02:07.810166Z" |
|
|
823 |
} |
|
|
824 |
}, |
|
|
825 |
"outputs": [], |
|
|
826 |
"source": [ |
|
|
827 |
"from sklearn import preprocessing\n", |
|
|
828 |
"le=preprocessing.LabelEncoder()\n", |
|
|
829 |
"df['GENDER']=le.fit_transform(df['GENDER'])\n", |
|
|
830 |
"df['LUNG_CANCER']=le.fit_transform(df['LUNG_CANCER'])\n", |
|
|
831 |
"df['SMOKING']=le.fit_transform(df['SMOKING'])\n", |
|
|
832 |
"df['YELLOW_FINGERS']=le.fit_transform(df['YELLOW_FINGERS'])\n", |
|
|
833 |
"df['ANXIETY']=le.fit_transform(df['ANXIETY'])\n", |
|
|
834 |
"df['PEER_PRESSURE']=le.fit_transform(df['PEER_PRESSURE'])\n", |
|
|
835 |
"df['CHRONIC DISEASE']=le.fit_transform(df['CHRONIC DISEASE'])\n", |
|
|
836 |
"df['FATIGUE ']=le.fit_transform(df['FATIGUE '])\n", |
|
|
837 |
"df['ALLERGY ']=le.fit_transform(df['ALLERGY '])\n", |
|
|
838 |
"df['WHEEZING']=le.fit_transform(df['WHEEZING'])\n", |
|
|
839 |
"df['ALCOHOL CONSUMING']=le.fit_transform(df['ALCOHOL CONSUMING'])\n", |
|
|
840 |
"df['COUGHING']=le.fit_transform(df['COUGHING'])\n", |
|
|
841 |
"df['SHORTNESS OF BREATH']=le.fit_transform(df['SHORTNESS OF BREATH'])\n", |
|
|
842 |
"df['SWALLOWING DIFFICULTY']=le.fit_transform(df['SWALLOWING DIFFICULTY'])\n", |
|
|
843 |
"df['CHEST PAIN']=le.fit_transform(df['CHEST PAIN'])\n", |
|
|
844 |
"df['LUNG_CANCER']=le.fit_transform(df['LUNG_CANCER'])" |
|
|
845 |
] |
|
|
846 |
}, |
|
|
847 |
{ |
|
|
848 |
"cell_type": "code", |
|
|
849 |
"execution_count": 11, |
|
|
850 |
"metadata": { |
|
|
851 |
"execution": { |
|
|
852 |
"iopub.execute_input": "2023-07-17T13:02:16.199910Z", |
|
|
853 |
"iopub.status.busy": "2023-07-17T13:02:16.199507Z", |
|
|
854 |
"iopub.status.idle": "2023-07-17T13:02:16.218137Z", |
|
|
855 |
"shell.execute_reply": "2023-07-17T13:02:16.216983Z", |
|
|
856 |
"shell.execute_reply.started": "2023-07-17T13:02:16.199879Z" |
|
|
857 |
} |
|
|
858 |
}, |
|
|
859 |
"outputs": [ |
|
|
860 |
{ |
|
|
861 |
"data": { |
|
|
862 |
"text/html": [ |
|
|
863 |
"<div>\n", |
|
|
864 |
"<style scoped>\n", |
|
|
865 |
" .dataframe tbody tr th:only-of-type {\n", |
|
|
866 |
" vertical-align: middle;\n", |
|
|
867 |
" }\n", |
|
|
868 |
"\n", |
|
|
869 |
" .dataframe tbody tr th {\n", |
|
|
870 |
" vertical-align: top;\n", |
|
|
871 |
" }\n", |
|
|
872 |
"\n", |
|
|
873 |
" .dataframe thead th {\n", |
|
|
874 |
" text-align: right;\n", |
|
|
875 |
" }\n", |
|
|
876 |
"</style>\n", |
|
|
877 |
"<table border=\"1\" class=\"dataframe\">\n", |
|
|
878 |
" <thead>\n", |
|
|
879 |
" <tr style=\"text-align: right;\">\n", |
|
|
880 |
" <th></th>\n", |
|
|
881 |
" <th>GENDER</th>\n", |
|
|
882 |
" <th>AGE</th>\n", |
|
|
883 |
" <th>SMOKING</th>\n", |
|
|
884 |
" <th>YELLOW_FINGERS</th>\n", |
|
|
885 |
" <th>ANXIETY</th>\n", |
|
|
886 |
" <th>PEER_PRESSURE</th>\n", |
|
|
887 |
" <th>CHRONIC DISEASE</th>\n", |
|
|
888 |
" <th>FATIGUE</th>\n", |
|
|
889 |
" <th>ALLERGY</th>\n", |
|
|
890 |
" <th>WHEEZING</th>\n", |
|
|
891 |
" <th>ALCOHOL CONSUMING</th>\n", |
|
|
892 |
" <th>COUGHING</th>\n", |
|
|
893 |
" <th>SHORTNESS OF BREATH</th>\n", |
|
|
894 |
" <th>SWALLOWING DIFFICULTY</th>\n", |
|
|
895 |
" <th>CHEST PAIN</th>\n", |
|
|
896 |
" <th>LUNG_CANCER</th>\n", |
|
|
897 |
" </tr>\n", |
|
|
898 |
" </thead>\n", |
|
|
899 |
" <tbody>\n", |
|
|
900 |
" <tr>\n", |
|
|
901 |
" <th>0</th>\n", |
|
|
902 |
" <td>1</td>\n", |
|
|
903 |
" <td>69</td>\n", |
|
|
904 |
" <td>0</td>\n", |
|
|
905 |
" <td>1</td>\n", |
|
|
906 |
" <td>1</td>\n", |
|
|
907 |
" <td>0</td>\n", |
|
|
908 |
" <td>0</td>\n", |
|
|
909 |
" <td>1</td>\n", |
|
|
910 |
" <td>0</td>\n", |
|
|
911 |
" <td>1</td>\n", |
|
|
912 |
" <td>1</td>\n", |
|
|
913 |
" <td>1</td>\n", |
|
|
914 |
" <td>1</td>\n", |
|
|
915 |
" <td>1</td>\n", |
|
|
916 |
" <td>1</td>\n", |
|
|
917 |
" <td>1</td>\n", |
|
|
918 |
" </tr>\n", |
|
|
919 |
" <tr>\n", |
|
|
920 |
" <th>1</th>\n", |
|
|
921 |
" <td>1</td>\n", |
|
|
922 |
" <td>74</td>\n", |
|
|
923 |
" <td>1</td>\n", |
|
|
924 |
" <td>0</td>\n", |
|
|
925 |
" <td>0</td>\n", |
|
|
926 |
" <td>0</td>\n", |
|
|
927 |
" <td>1</td>\n", |
|
|
928 |
" <td>1</td>\n", |
|
|
929 |
" <td>1</td>\n", |
|
|
930 |
" <td>0</td>\n", |
|
|
931 |
" <td>0</td>\n", |
|
|
932 |
" <td>0</td>\n", |
|
|
933 |
" <td>1</td>\n", |
|
|
934 |
" <td>1</td>\n", |
|
|
935 |
" <td>1</td>\n", |
|
|
936 |
" <td>1</td>\n", |
|
|
937 |
" </tr>\n", |
|
|
938 |
" <tr>\n", |
|
|
939 |
" <th>2</th>\n", |
|
|
940 |
" <td>0</td>\n", |
|
|
941 |
" <td>59</td>\n", |
|
|
942 |
" <td>0</td>\n", |
|
|
943 |
" <td>0</td>\n", |
|
|
944 |
" <td>0</td>\n", |
|
|
945 |
" <td>1</td>\n", |
|
|
946 |
" <td>0</td>\n", |
|
|
947 |
" <td>1</td>\n", |
|
|
948 |
" <td>0</td>\n", |
|
|
949 |
" <td>1</td>\n", |
|
|
950 |
" <td>0</td>\n", |
|
|
951 |
" <td>1</td>\n", |
|
|
952 |
" <td>1</td>\n", |
|
|
953 |
" <td>0</td>\n", |
|
|
954 |
" <td>1</td>\n", |
|
|
955 |
" <td>0</td>\n", |
|
|
956 |
" </tr>\n", |
|
|
957 |
" <tr>\n", |
|
|
958 |
" <th>3</th>\n", |
|
|
959 |
" <td>1</td>\n", |
|
|
960 |
" <td>63</td>\n", |
|
|
961 |
" <td>1</td>\n", |
|
|
962 |
" <td>1</td>\n", |
|
|
963 |
" <td>1</td>\n", |
|
|
964 |
" <td>0</td>\n", |
|
|
965 |
" <td>0</td>\n", |
|
|
966 |
" <td>0</td>\n", |
|
|
967 |
" <td>0</td>\n", |
|
|
968 |
" <td>0</td>\n", |
|
|
969 |
" <td>1</td>\n", |
|
|
970 |
" <td>0</td>\n", |
|
|
971 |
" <td>0</td>\n", |
|
|
972 |
" <td>1</td>\n", |
|
|
973 |
" <td>1</td>\n", |
|
|
974 |
" <td>0</td>\n", |
|
|
975 |
" </tr>\n", |
|
|
976 |
" <tr>\n", |
|
|
977 |
" <th>4</th>\n", |
|
|
978 |
" <td>0</td>\n", |
|
|
979 |
" <td>63</td>\n", |
|
|
980 |
" <td>0</td>\n", |
|
|
981 |
" <td>1</td>\n", |
|
|
982 |
" <td>0</td>\n", |
|
|
983 |
" <td>0</td>\n", |
|
|
984 |
" <td>0</td>\n", |
|
|
985 |
" <td>0</td>\n", |
|
|
986 |
" <td>0</td>\n", |
|
|
987 |
" <td>1</td>\n", |
|
|
988 |
" <td>0</td>\n", |
|
|
989 |
" <td>1</td>\n", |
|
|
990 |
" <td>1</td>\n", |
|
|
991 |
" <td>0</td>\n", |
|
|
992 |
" <td>0</td>\n", |
|
|
993 |
" <td>0</td>\n", |
|
|
994 |
" </tr>\n", |
|
|
995 |
" </tbody>\n", |
|
|
996 |
"</table>\n", |
|
|
997 |
"</div>" |
|
|
998 |
], |
|
|
999 |
"text/plain": [ |
|
|
1000 |
" GENDER AGE SMOKING YELLOW_FINGERS ANXIETY PEER_PRESSURE \\\n", |
|
|
1001 |
"0 1 69 0 1 1 0 \n", |
|
|
1002 |
"1 1 74 1 0 0 0 \n", |
|
|
1003 |
"2 0 59 0 0 0 1 \n", |
|
|
1004 |
"3 1 63 1 1 1 0 \n", |
|
|
1005 |
"4 0 63 0 1 0 0 \n", |
|
|
1006 |
"\n", |
|
|
1007 |
" CHRONIC DISEASE FATIGUE ALLERGY WHEEZING ALCOHOL CONSUMING COUGHING \\\n", |
|
|
1008 |
"0 0 1 0 1 1 1 \n", |
|
|
1009 |
"1 1 1 1 0 0 0 \n", |
|
|
1010 |
"2 0 1 0 1 0 1 \n", |
|
|
1011 |
"3 0 0 0 0 1 0 \n", |
|
|
1012 |
"4 0 0 0 1 0 1 \n", |
|
|
1013 |
"\n", |
|
|
1014 |
" SHORTNESS OF BREATH SWALLOWING DIFFICULTY CHEST PAIN LUNG_CANCER \n", |
|
|
1015 |
"0 1 1 1 1 \n", |
|
|
1016 |
"1 1 1 1 1 \n", |
|
|
1017 |
"2 1 0 1 0 \n", |
|
|
1018 |
"3 0 1 1 0 \n", |
|
|
1019 |
"4 1 0 0 0 " |
|
|
1020 |
] |
|
|
1021 |
}, |
|
|
1022 |
"execution_count": 11, |
|
|
1023 |
"metadata": {}, |
|
|
1024 |
"output_type": "execute_result" |
|
|
1025 |
} |
|
|
1026 |
], |
|
|
1027 |
"source": [ |
|
|
1028 |
"df.head()" |
|
|
1029 |
] |
|
|
1030 |
}, |
|
|
1031 |
{ |
|
|
1032 |
"cell_type": "markdown", |
|
|
1033 |
"metadata": {}, |
|
|
1034 |
"source": [ |
|
|
1035 |
"**Note: Male=1 & Female=0. Also for other variables, YES=1 & NO=0**" |
|
|
1036 |
] |
|
|
1037 |
}, |
|
|
1038 |
{ |
|
|
1039 |
"cell_type": "code", |
|
|
1040 |
"execution_count": 12, |
|
|
1041 |
"metadata": { |
|
|
1042 |
"execution": { |
|
|
1043 |
"iopub.execute_input": "2023-07-17T13:03:09.026821Z", |
|
|
1044 |
"iopub.status.busy": "2023-07-17T13:03:09.026438Z", |
|
|
1045 |
"iopub.status.idle": "2023-07-17T13:03:09.044762Z", |
|
|
1046 |
"shell.execute_reply": "2023-07-17T13:03:09.043399Z", |
|
|
1047 |
"shell.execute_reply.started": "2023-07-17T13:03:09.026790Z" |
|
|
1048 |
} |
|
|
1049 |
}, |
|
|
1050 |
"outputs": [ |
|
|
1051 |
{ |
|
|
1052 |
"name": "stdout", |
|
|
1053 |
"output_type": "stream", |
|
|
1054 |
"text": [ |
|
|
1055 |
"<class 'pandas.core.frame.DataFrame'>\n", |
|
|
1056 |
"Int64Index: 276 entries, 0 to 283\n", |
|
|
1057 |
"Data columns (total 16 columns):\n", |
|
|
1058 |
" # Column Non-Null Count Dtype\n", |
|
|
1059 |
"--- ------ -------------- -----\n", |
|
|
1060 |
" 0 GENDER 276 non-null int64\n", |
|
|
1061 |
" 1 AGE 276 non-null int64\n", |
|
|
1062 |
" 2 SMOKING 276 non-null int64\n", |
|
|
1063 |
" 3 YELLOW_FINGERS 276 non-null int64\n", |
|
|
1064 |
" 4 ANXIETY 276 non-null int64\n", |
|
|
1065 |
" 5 PEER_PRESSURE 276 non-null int64\n", |
|
|
1066 |
" 6 CHRONIC DISEASE 276 non-null int64\n", |
|
|
1067 |
" 7 FATIGUE 276 non-null int64\n", |
|
|
1068 |
" 8 ALLERGY 276 non-null int64\n", |
|
|
1069 |
" 9 WHEEZING 276 non-null int64\n", |
|
|
1070 |
" 10 ALCOHOL CONSUMING 276 non-null int64\n", |
|
|
1071 |
" 11 COUGHING 276 non-null int64\n", |
|
|
1072 |
" 12 SHORTNESS OF BREATH 276 non-null int64\n", |
|
|
1073 |
" 13 SWALLOWING DIFFICULTY 276 non-null int64\n", |
|
|
1074 |
" 14 CHEST PAIN 276 non-null int64\n", |
|
|
1075 |
" 15 LUNG_CANCER 276 non-null int64\n", |
|
|
1076 |
"dtypes: int64(16)\n", |
|
|
1077 |
"memory usage: 36.7 KB\n" |
|
|
1078 |
] |
|
|
1079 |
} |
|
|
1080 |
], |
|
|
1081 |
"source": [ |
|
|
1082 |
"df.info()" |
|
|
1083 |
] |
|
|
1084 |
}, |
|
|
1085 |
{ |
|
|
1086 |
"cell_type": "code", |
|
|
1087 |
"execution_count": 13, |
|
|
1088 |
"metadata": { |
|
|
1089 |
"execution": { |
|
|
1090 |
"iopub.execute_input": "2023-07-17T13:03:21.075585Z", |
|
|
1091 |
"iopub.status.busy": "2023-07-17T13:03:21.075122Z", |
|
|
1092 |
"iopub.status.idle": "2023-07-17T13:03:21.366793Z", |
|
|
1093 |
"shell.execute_reply": "2023-07-17T13:03:21.365947Z", |
|
|
1094 |
"shell.execute_reply.started": "2023-07-17T13:03:21.075545Z" |
|
|
1095 |
} |
|
|
1096 |
}, |
|
|
1097 |
"outputs": [ |
|
|
1098 |
{ |
|
|
1099 |
"data": { |
|
|
1100 |
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", |
|
|
1101 |
"text/plain": [ |
|
|
1102 |
"<Figure size 640x480 with 1 Axes>" |
|
|
1103 |
] |
|
|
1104 |
}, |
|
|
1105 |
"metadata": {}, |
|
|
1106 |
"output_type": "display_data" |
|
|
1107 |
} |
|
|
1108 |
], |
|
|
1109 |
"source": [ |
|
|
1110 |
"#Let's check the distributaion of Target variable.\n", |
|
|
1111 |
"sns.countplot(x='LUNG_CANCER', data=df,)\n", |
|
|
1112 |
"plt.title('Target Distribution');" |
|
|
1113 |
] |
|
|
1114 |
}, |
|
|
1115 |
{ |
|
|
1116 |
"cell_type": "code", |
|
|
1117 |
"execution_count": 14, |
|
|
1118 |
"metadata": { |
|
|
1119 |
"execution": { |
|
|
1120 |
"iopub.execute_input": "2023-07-17T13:03:34.011760Z", |
|
|
1121 |
"iopub.status.busy": "2023-07-17T13:03:34.011341Z", |
|
|
1122 |
"iopub.status.idle": "2023-07-17T13:03:34.021280Z", |
|
|
1123 |
"shell.execute_reply": "2023-07-17T13:03:34.020070Z", |
|
|
1124 |
"shell.execute_reply.started": "2023-07-17T13:03:34.011726Z" |
|
|
1125 |
} |
|
|
1126 |
}, |
|
|
1127 |
"outputs": [ |
|
|
1128 |
{ |
|
|
1129 |
"data": { |
|
|
1130 |
"text/plain": [ |
|
|
1131 |
"1 238\n", |
|
|
1132 |
"0 38\n", |
|
|
1133 |
"Name: LUNG_CANCER, dtype: int64" |
|
|
1134 |
] |
|
|
1135 |
}, |
|
|
1136 |
"execution_count": 14, |
|
|
1137 |
"metadata": {}, |
|
|
1138 |
"output_type": "execute_result" |
|
|
1139 |
} |
|
|
1140 |
], |
|
|
1141 |
"source": [ |
|
|
1142 |
"df['LUNG_CANCER'].value_counts()" |
|
|
1143 |
] |
|
|
1144 |
}, |
|
|
1145 |
{ |
|
|
1146 |
"cell_type": "markdown", |
|
|
1147 |
"metadata": {}, |
|
|
1148 |
"source": [ |
|
|
1149 |
"**We will handle this imbalance before applyig algorithm.**" |
|
|
1150 |
] |
|
|
1151 |
}, |
|
|
1152 |
{ |
|
|
1153 |
"cell_type": "markdown", |
|
|
1154 |
"metadata": {}, |
|
|
1155 |
"source": [ |
|
|
1156 |
"**Now let's do some Data Visualizations for the better understanding of how the independent features are related to the target variable..**" |
|
|
1157 |
] |
|
|
1158 |
}, |
|
|
1159 |
{ |
|
|
1160 |
"cell_type": "code", |
|
|
1161 |
"execution_count": 17, |
|
|
1162 |
"metadata": { |
|
|
1163 |
"execution": { |
|
|
1164 |
"iopub.execute_input": "2023-07-17T13:04:40.122590Z", |
|
|
1165 |
"iopub.status.busy": "2023-07-17T13:04:40.122180Z", |
|
|
1166 |
"iopub.status.idle": "2023-07-17T13:04:40.129282Z", |
|
|
1167 |
"shell.execute_reply": "2023-07-17T13:04:40.127967Z", |
|
|
1168 |
"shell.execute_reply.started": "2023-07-17T13:04:40.122558Z" |
|
|
1169 |
} |
|
|
1170 |
}, |
|
|
1171 |
"outputs": [], |
|
|
1172 |
"source": [ |
|
|
1173 |
"# function for plotting\n", |
|
|
1174 |
"def plot(col, df=df):\n", |
|
|
1175 |
" return df.groupby(col)['LUNG_CANCER'].value_counts(normalize=True).unstack().plot(kind='bar', figsize=(8,5))" |
|
|
1176 |
] |
|
|
1177 |
}, |
|
|
1178 |
{ |
|
|
1179 |
"cell_type": "code", |
|
|
1180 |
"execution_count": 18, |
|
|
1181 |
"metadata": { |
|
|
1182 |
"execution": { |
|
|
1183 |
"iopub.execute_input": "2023-07-17T13:04:50.930141Z", |
|
|
1184 |
"iopub.status.busy": "2023-07-17T13:04:50.929732Z", |
|
|
1185 |
"iopub.status.idle": "2023-07-17T13:04:51.235604Z", |
|
|
1186 |
"shell.execute_reply": "2023-07-17T13:04:51.234532Z", |
|
|
1187 |
"shell.execute_reply.started": "2023-07-17T13:04:50.930109Z" |
|
|
1188 |
} |
|
|
1189 |
}, |
|
|
1190 |
"outputs": [ |
|
|
1191 |
{ |
|
|
1192 |
"data": { |
|
|
1193 |
"text/plain": [ |
|
|
1194 |
"<Axes: xlabel='GENDER'>" |
|
|
1195 |
] |
|
|
1196 |
}, |
|
|
1197 |
"execution_count": 18, |
|
|
1198 |
"metadata": {}, |
|
|
1199 |
"output_type": "execute_result" |
|
|
1200 |
}, |
|
|
1201 |
{ |
|
|
1202 |
"data": { |
|
|
1203 |
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", |
|
|
1204 |
"text/plain": [ |
|
|
1205 |
"<Figure size 800x500 with 1 Axes>" |
|
|
1206 |
] |
|
|
1207 |
}, |
|
|
1208 |
"metadata": {}, |
|
|
1209 |
"output_type": "display_data" |
|
|
1210 |
} |
|
|
1211 |
], |
|
|
1212 |
"source": [ |
|
|
1213 |
"plot('GENDER')" |
|
|
1214 |
] |
|
|
1215 |
}, |
|
|
1216 |
{ |
|
|
1217 |
"cell_type": "code", |
|
|
1218 |
"execution_count": 19, |
|
|
1219 |
"metadata": { |
|
|
1220 |
"execution": { |
|
|
1221 |
"iopub.execute_input": "2023-07-17T13:05:01.331531Z", |
|
|
1222 |
"iopub.status.busy": "2023-07-17T13:05:01.331115Z", |
|
|
1223 |
"iopub.status.idle": "2023-07-17T13:05:02.079833Z", |
|
|
1224 |
"shell.execute_reply": "2023-07-17T13:05:02.078587Z", |
|
|
1225 |
"shell.execute_reply.started": "2023-07-17T13:05:01.331497Z" |
|
|
1226 |
} |
|
|
1227 |
}, |
|
|
1228 |
"outputs": [ |
|
|
1229 |
{ |
|
|
1230 |
"data": { |
|
|
1231 |
"text/plain": [ |
|
|
1232 |
"<Axes: xlabel='AGE'>" |
|
|
1233 |
] |
|
|
1234 |
}, |
|
|
1235 |
"execution_count": 19, |
|
|
1236 |
"metadata": {}, |
|
|
1237 |
"output_type": "execute_result" |
|
|
1238 |
}, |
|
|
1239 |
{ |
|
|
1240 |
"data": { |
|
|
1241 |
"image/png": 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", |
|
|
1242 |
"text/plain": [ |
|
|
1243 |
"<Figure size 800x500 with 1 Axes>" |
|
|
1244 |
] |
|
|
1245 |
}, |
|
|
1246 |
"metadata": {}, |
|
|
1247 |
"output_type": "display_data" |
|
|
1248 |
} |
|
|
1249 |
], |
|
|
1250 |
"source": [ |
|
|
1251 |
"plot('AGE')" |
|
|
1252 |
] |
|
|
1253 |
}, |
|
|
1254 |
{ |
|
|
1255 |
"cell_type": "code", |
|
|
1256 |
"execution_count": 20, |
|
|
1257 |
"metadata": { |
|
|
1258 |
"execution": { |
|
|
1259 |
"iopub.execute_input": "2023-07-17T13:05:11.946306Z", |
|
|
1260 |
"iopub.status.busy": "2023-07-17T13:05:11.945895Z", |
|
|
1261 |
"iopub.status.idle": "2023-07-17T13:05:12.234421Z", |
|
|
1262 |
"shell.execute_reply": "2023-07-17T13:05:12.233346Z", |
|
|
1263 |
"shell.execute_reply.started": "2023-07-17T13:05:11.946273Z" |
|
|
1264 |
} |
|
|
1265 |
}, |
|
|
1266 |
"outputs": [ |
|
|
1267 |
{ |
|
|
1268 |
"data": { |
|
|
1269 |
"text/plain": [ |
|
|
1270 |
"<Axes: xlabel='SMOKING'>" |
|
|
1271 |
] |
|
|
1272 |
}, |
|
|
1273 |
"execution_count": 20, |
|
|
1274 |
"metadata": {}, |
|
|
1275 |
"output_type": "execute_result" |
|
|
1276 |
}, |
|
|
1277 |
{ |
|
|
1278 |
"data": { |
|
|
1279 |
"image/png": 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", |
|
|
1280 |
"text/plain": [ |
|
|
1281 |
"<Figure size 800x500 with 1 Axes>" |
|
|
1282 |
] |
|
|
1283 |
}, |
|
|
1284 |
"metadata": {}, |
|
|
1285 |
"output_type": "display_data" |
|
|
1286 |
} |
|
|
1287 |
], |
|
|
1288 |
"source": [ |
|
|
1289 |
"plot('SMOKING')" |
|
|
1290 |
] |
|
|
1291 |
}, |
|
|
1292 |
{ |
|
|
1293 |
"cell_type": "code", |
|
|
1294 |
"execution_count": 21, |
|
|
1295 |
"metadata": { |
|
|
1296 |
"execution": { |
|
|
1297 |
"iopub.execute_input": "2023-07-17T13:05:24.247071Z", |
|
|
1298 |
"iopub.status.busy": "2023-07-17T13:05:24.246678Z", |
|
|
1299 |
"iopub.status.idle": "2023-07-17T13:05:24.532184Z", |
|
|
1300 |
"shell.execute_reply": "2023-07-17T13:05:24.531012Z", |
|
|
1301 |
"shell.execute_reply.started": "2023-07-17T13:05:24.247040Z" |
|
|
1302 |
} |
|
|
1303 |
}, |
|
|
1304 |
"outputs": [ |
|
|
1305 |
{ |
|
|
1306 |
"data": { |
|
|
1307 |
"text/plain": [ |
|
|
1308 |
"<Axes: xlabel='YELLOW_FINGERS'>" |
|
|
1309 |
] |
|
|
1310 |
}, |
|
|
1311 |
"execution_count": 21, |
|
|
1312 |
"metadata": {}, |
|
|
1313 |
"output_type": "execute_result" |
|
|
1314 |
}, |
|
|
1315 |
{ |
|
|
1316 |
"data": { |
|
|
1317 |
"image/png": 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", |
|
|
1318 |
"text/plain": [ |
|
|
1319 |
"<Figure size 800x500 with 1 Axes>" |
|
|
1320 |
] |
|
|
1321 |
}, |
|
|
1322 |
"metadata": {}, |
|
|
1323 |
"output_type": "display_data" |
|
|
1324 |
} |
|
|
1325 |
], |
|
|
1326 |
"source": [ |
|
|
1327 |
"plot('YELLOW_FINGERS')" |
|
|
1328 |
] |
|
|
1329 |
}, |
|
|
1330 |
{ |
|
|
1331 |
"cell_type": "code", |
|
|
1332 |
"execution_count": 22, |
|
|
1333 |
"metadata": { |
|
|
1334 |
"execution": { |
|
|
1335 |
"iopub.execute_input": "2023-07-17T13:05:35.275485Z", |
|
|
1336 |
"iopub.status.busy": "2023-07-17T13:05:35.275098Z", |
|
|
1337 |
"iopub.status.idle": "2023-07-17T13:05:35.573687Z", |
|
|
1338 |
"shell.execute_reply": "2023-07-17T13:05:35.572152Z", |
|
|
1339 |
"shell.execute_reply.started": "2023-07-17T13:05:35.275455Z" |
|
|
1340 |
} |
|
|
1341 |
}, |
|
|
1342 |
"outputs": [ |
|
|
1343 |
{ |
|
|
1344 |
"data": { |
|
|
1345 |
"text/plain": [ |
|
|
1346 |
"<Axes: xlabel='ANXIETY'>" |
|
|
1347 |
] |
|
|
1348 |
}, |
|
|
1349 |
"execution_count": 22, |
|
|
1350 |
"metadata": {}, |
|
|
1351 |
"output_type": "execute_result" |
|
|
1352 |
}, |
|
|
1353 |
{ |
|
|
1354 |
"data": { |
|
|
1355 |
"image/png": 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", |
|
|
1356 |
"text/plain": [ |
|
|
1357 |
"<Figure size 800x500 with 1 Axes>" |
|
|
1358 |
] |
|
|
1359 |
}, |
|
|
1360 |
"metadata": {}, |
|
|
1361 |
"output_type": "display_data" |
|
|
1362 |
} |
|
|
1363 |
], |
|
|
1364 |
"source": [ |
|
|
1365 |
"plot('ANXIETY')" |
|
|
1366 |
] |
|
|
1367 |
}, |
|
|
1368 |
{ |
|
|
1369 |
"cell_type": "code", |
|
|
1370 |
"execution_count": 23, |
|
|
1371 |
"metadata": { |
|
|
1372 |
"execution": { |
|
|
1373 |
"iopub.execute_input": "2023-07-17T13:05:50.121690Z", |
|
|
1374 |
"iopub.status.busy": "2023-07-17T13:05:50.121296Z", |
|
|
1375 |
"iopub.status.idle": "2023-07-17T13:05:50.362472Z", |
|
|
1376 |
"shell.execute_reply": "2023-07-17T13:05:50.361374Z", |
|
|
1377 |
"shell.execute_reply.started": "2023-07-17T13:05:50.121651Z" |
|
|
1378 |
} |
|
|
1379 |
}, |
|
|
1380 |
"outputs": [ |
|
|
1381 |
{ |
|
|
1382 |
"data": { |
|
|
1383 |
"text/plain": [ |
|
|
1384 |
"<Axes: xlabel='PEER_PRESSURE'>" |
|
|
1385 |
] |
|
|
1386 |
}, |
|
|
1387 |
"execution_count": 23, |
|
|
1388 |
"metadata": {}, |
|
|
1389 |
"output_type": "execute_result" |
|
|
1390 |
}, |
|
|
1391 |
{ |
|
|
1392 |
"data": { |
|
|
1393 |
"image/png": 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", |
|
|
1394 |
"text/plain": [ |
|
|
1395 |
"<Figure size 800x500 with 1 Axes>" |
|
|
1396 |
] |
|
|
1397 |
}, |
|
|
1398 |
"metadata": {}, |
|
|
1399 |
"output_type": "display_data" |
|
|
1400 |
} |
|
|
1401 |
], |
|
|
1402 |
"source": [ |
|
|
1403 |
"plot('PEER_PRESSURE')" |
|
|
1404 |
] |
|
|
1405 |
}, |
|
|
1406 |
{ |
|
|
1407 |
"cell_type": "code", |
|
|
1408 |
"execution_count": 24, |
|
|
1409 |
"metadata": { |
|
|
1410 |
"execution": { |
|
|
1411 |
"iopub.execute_input": "2023-07-17T13:06:02.620442Z", |
|
|
1412 |
"iopub.status.busy": "2023-07-17T13:06:02.619758Z", |
|
|
1413 |
"iopub.status.idle": "2023-07-17T13:06:02.867478Z", |
|
|
1414 |
"shell.execute_reply": "2023-07-17T13:06:02.866444Z", |
|
|
1415 |
"shell.execute_reply.started": "2023-07-17T13:06:02.620393Z" |
|
|
1416 |
} |
|
|
1417 |
}, |
|
|
1418 |
"outputs": [ |
|
|
1419 |
{ |
|
|
1420 |
"data": { |
|
|
1421 |
"text/plain": [ |
|
|
1422 |
"<Axes: xlabel='CHRONIC DISEASE'>" |
|
|
1423 |
] |
|
|
1424 |
}, |
|
|
1425 |
"execution_count": 24, |
|
|
1426 |
"metadata": {}, |
|
|
1427 |
"output_type": "execute_result" |
|
|
1428 |
}, |
|
|
1429 |
{ |
|
|
1430 |
"data": { |
|
|
1431 |
"image/png": 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", |
|
|
1432 |
"text/plain": [ |
|
|
1433 |
"<Figure size 800x500 with 1 Axes>" |
|
|
1434 |
] |
|
|
1435 |
}, |
|
|
1436 |
"metadata": {}, |
|
|
1437 |
"output_type": "display_data" |
|
|
1438 |
} |
|
|
1439 |
], |
|
|
1440 |
"source": [ |
|
|
1441 |
"plot('CHRONIC DISEASE')" |
|
|
1442 |
] |
|
|
1443 |
}, |
|
|
1444 |
{ |
|
|
1445 |
"cell_type": "code", |
|
|
1446 |
"execution_count": 25, |
|
|
1447 |
"metadata": { |
|
|
1448 |
"execution": { |
|
|
1449 |
"iopub.execute_input": "2023-07-17T13:06:22.210287Z", |
|
|
1450 |
"iopub.status.busy": "2023-07-17T13:06:22.209908Z", |
|
|
1451 |
"iopub.status.idle": "2023-07-17T13:06:22.438476Z", |
|
|
1452 |
"shell.execute_reply": "2023-07-17T13:06:22.437350Z", |
|
|
1453 |
"shell.execute_reply.started": "2023-07-17T13:06:22.210258Z" |
|
|
1454 |
} |
|
|
1455 |
}, |
|
|
1456 |
"outputs": [ |
|
|
1457 |
{ |
|
|
1458 |
"data": { |
|
|
1459 |
"text/plain": [ |
|
|
1460 |
"<Axes: xlabel='FATIGUE '>" |
|
|
1461 |
] |
|
|
1462 |
}, |
|
|
1463 |
"execution_count": 25, |
|
|
1464 |
"metadata": {}, |
|
|
1465 |
"output_type": "execute_result" |
|
|
1466 |
}, |
|
|
1467 |
{ |
|
|
1468 |
"data": { |
|
|
1469 |
"image/png": 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", |
|
|
1470 |
"text/plain": [ |
|
|
1471 |
"<Figure size 800x500 with 1 Axes>" |
|
|
1472 |
] |
|
|
1473 |
}, |
|
|
1474 |
"metadata": {}, |
|
|
1475 |
"output_type": "display_data" |
|
|
1476 |
} |
|
|
1477 |
], |
|
|
1478 |
"source": [ |
|
|
1479 |
"plot('FATIGUE ')" |
|
|
1480 |
] |
|
|
1481 |
}, |
|
|
1482 |
{ |
|
|
1483 |
"cell_type": "code", |
|
|
1484 |
"execution_count": 26, |
|
|
1485 |
"metadata": { |
|
|
1486 |
"execution": { |
|
|
1487 |
"iopub.execute_input": "2023-07-17T13:06:33.770314Z", |
|
|
1488 |
"iopub.status.busy": "2023-07-17T13:06:33.769926Z", |
|
|
1489 |
"iopub.status.idle": "2023-07-17T13:06:34.059956Z", |
|
|
1490 |
"shell.execute_reply": "2023-07-17T13:06:34.058769Z", |
|
|
1491 |
"shell.execute_reply.started": "2023-07-17T13:06:33.770284Z" |
|
|
1492 |
} |
|
|
1493 |
}, |
|
|
1494 |
"outputs": [ |
|
|
1495 |
{ |
|
|
1496 |
"data": { |
|
|
1497 |
"text/plain": [ |
|
|
1498 |
"<Axes: xlabel='ALLERGY '>" |
|
|
1499 |
] |
|
|
1500 |
}, |
|
|
1501 |
"execution_count": 26, |
|
|
1502 |
"metadata": {}, |
|
|
1503 |
"output_type": "execute_result" |
|
|
1504 |
}, |
|
|
1505 |
{ |
|
|
1506 |
"data": { |
|
|
1507 |
"image/png": 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", |
|
|
1508 |
"text/plain": [ |
|
|
1509 |
"<Figure size 800x500 with 1 Axes>" |
|
|
1510 |
] |
|
|
1511 |
}, |
|
|
1512 |
"metadata": {}, |
|
|
1513 |
"output_type": "display_data" |
|
|
1514 |
} |
|
|
1515 |
], |
|
|
1516 |
"source": [ |
|
|
1517 |
"plot('ALLERGY ')" |
|
|
1518 |
] |
|
|
1519 |
}, |
|
|
1520 |
{ |
|
|
1521 |
"cell_type": "code", |
|
|
1522 |
"execution_count": 27, |
|
|
1523 |
"metadata": { |
|
|
1524 |
"execution": { |
|
|
1525 |
"iopub.execute_input": "2023-07-17T13:06:43.938153Z", |
|
|
1526 |
"iopub.status.busy": "2023-07-17T13:06:43.937725Z", |
|
|
1527 |
"iopub.status.idle": "2023-07-17T13:06:44.229881Z", |
|
|
1528 |
"shell.execute_reply": "2023-07-17T13:06:44.228895Z", |
|
|
1529 |
"shell.execute_reply.started": "2023-07-17T13:06:43.938119Z" |
|
|
1530 |
} |
|
|
1531 |
}, |
|
|
1532 |
"outputs": [ |
|
|
1533 |
{ |
|
|
1534 |
"data": { |
|
|
1535 |
"text/plain": [ |
|
|
1536 |
"<Axes: xlabel='WHEEZING'>" |
|
|
1537 |
] |
|
|
1538 |
}, |
|
|
1539 |
"execution_count": 27, |
|
|
1540 |
"metadata": {}, |
|
|
1541 |
"output_type": "execute_result" |
|
|
1542 |
}, |
|
|
1543 |
{ |
|
|
1544 |
"data": { |
|
|
1545 |
"image/png": 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", |
|
|
1546 |
"text/plain": [ |
|
|
1547 |
"<Figure size 800x500 with 1 Axes>" |
|
|
1548 |
] |
|
|
1549 |
}, |
|
|
1550 |
"metadata": {}, |
|
|
1551 |
"output_type": "display_data" |
|
|
1552 |
} |
|
|
1553 |
], |
|
|
1554 |
"source": [ |
|
|
1555 |
"plot('WHEEZING')" |
|
|
1556 |
] |
|
|
1557 |
}, |
|
|
1558 |
{ |
|
|
1559 |
"cell_type": "code", |
|
|
1560 |
"execution_count": 28, |
|
|
1561 |
"metadata": { |
|
|
1562 |
"execution": { |
|
|
1563 |
"iopub.execute_input": "2023-07-17T13:06:58.079765Z", |
|
|
1564 |
"iopub.status.busy": "2023-07-17T13:06:58.079352Z", |
|
|
1565 |
"iopub.status.idle": "2023-07-17T13:06:58.368718Z", |
|
|
1566 |
"shell.execute_reply": "2023-07-17T13:06:58.366479Z", |
|
|
1567 |
"shell.execute_reply.started": "2023-07-17T13:06:58.079733Z" |
|
|
1568 |
} |
|
|
1569 |
}, |
|
|
1570 |
"outputs": [ |
|
|
1571 |
{ |
|
|
1572 |
"data": { |
|
|
1573 |
"text/plain": [ |
|
|
1574 |
"<Axes: xlabel='SHORTNESS OF BREATH'>" |
|
|
1575 |
] |
|
|
1576 |
}, |
|
|
1577 |
"execution_count": 28, |
|
|
1578 |
"metadata": {}, |
|
|
1579 |
"output_type": "execute_result" |
|
|
1580 |
}, |
|
|
1581 |
{ |
|
|
1582 |
"data": { |
|
|
1583 |
"image/png": 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SAgMDJckWQOF6/Pz8bN8jAACou+pE+LRYLAoKClKLFi106dIlZ5cDBzVs2JARTwAA6ok6ET7Lubm5EWIAAABuYC5/wxEAAABcB+ETAAAAxhA+AQAAYAzhEwAAAMYQPgEAAGAM4RMAAADGED4BAABgDOETAAAAxhA+AQAAYAzhEwAAAMYQPgEAAGAM4RMAAADGED4BAABgDOETAAAAxhA+AQAAYAzhEwAAAMYQPgEAAGAM4RMAAADGED4BAABgDOETAAAAxhA+AQAAYAzhEwAAAMYQPgEAAGAM4RMAAADGED4BAABgDOETAAAAxhA+AQAAYAzhEwAAAMYQPgEAAGAM4RMAAADGED4BAABgDOETAAAAxhA+AQAAYAzhEwAAAMYQPgEAAGAM4RMAAADGED4BAABgDOETAAAAxhA+AQAAYAzhEwAAAMYQPgEAAGAM4RMAAADGED4BAABgDOETAAAAxhA+AQAAYAzhEwAAAMYQPgEAAGAM4RMAAADGED4BAABgDOETAAAAxhA+AQAAYAzhEwAAAMYQPgEAAGBMrcJnamqqQkND5eXlpYiICG3btq3a/iUlJUpOTlbr1q3l6emptm3bKj09vVYFAwAAwHW5O7pAZmamEhISlJqaqt69e+vNN99UdHS0Dhw4oFatWlW6zLBhw/TTTz8pLS1N7dq1U35+vi5fvnzNxQMAAMC1OBw+582bp/Hjxys2NlaStGDBAm3YsEGLFy9WSkpKhf7r16/XZ599pqNHj6pZs2aSpJCQkGurGgAAAC7JodPuFy9e1J49exQVFWXXHhUVpR07dlS6zJo1a9StWze9/PLLuvnmmxUWFqann35aFy5cqH3VAAAAcEkOjXyePn1apaWlCggIsGsPCAhQXl5epcscPXpU27dvl5eXlz744AOdPn1aEyZM0NmzZ6u87rOkpEQlJSW2z0VFRY6UCQAAgBtUrW44slgsdp+tVmuFtnJlZWWyWCxasWKFevTooQcffFDz5s1TRkZGlaOfKSkp8vX1tU3BwcG1KRMAAAA3GIfCp7+/v9zc3CqMcubn51cYDS0XFBSkm2++Wb6+vra2Tp06yWq16ocffqh0maSkJBUWFtqmEydOOFImAAAAblAOhU8PDw9FREQoKyvLrj0rK0uRkZGVLtO7d2+dPHlS//73v21thw4dUoMGDXTLLbdUuoynp6d8fHzsJgAAALg+h0+7JyYmaunSpUpPT9fBgwc1efJk5eTkKC4uTtIvo5ajRo2y9X/sscfUvHlzjR07VgcOHNDWrVs1depUjRs3To0aNbp+ewIAAIAbnsOPWoqJidGZM2c0Z84c5ebmKjw8XOvWrVPr1q0lSbm5ucrJybH19/b2VlZWluLj49WtWzc1b95cw4YN0/PPP3/99gIAAAAuwWK1Wq3OLuJqioqK5Ovrq8LCQk7B47c3y/fqfWBvVqGzKwBQHX7XHMfvmsNqmtd4tzsAAACMIXwCAADAGMInAAAAjCF8AgAAwBjCJwAAAIwhfAIAAMAYwicAAACMIXwCAADAGMInAAAAjCF8AgAAwBjCJwAAAIwhfAIAAMAYwicAAACMIXwCAADAGMInAAAAjCF8AgAAwBjCJwAAAIwhfAIAAMAYwicAAACMIXwCAADAGMInAAAAjCF8AgAAwBjCJwAAAIwhfAIAAMAYwicAAACMIXwCAADAGMInAAAAjCF8AgAAwBjCJwAAAIwhfAIAAMAYwicAAACMIXwCAADAGMInAAAAjCF8AgAAwBjCJwAAAIwhfAIAAMAYwicAAACMIXwCAADAGMInAAAAjCF8AgAAwBjCJwAAAIwhfAIAAMAYwicAAACMIXwCAADAGMInAAAAjCF8AgAAwBjCJwAAAIwhfAIAAMAYwicAAACMIXwCAADAGMInAAAAjCF8AgAAwBjCJwAAAIwhfAIAAMAYwicAAACMIXwCAADAGMInAAAAjCF8AgAAwBjCJwAAAIwhfAIAAMAYwicAAACMIXwCAADAGMInAAAAjCF8AgAAwBjCJwAAAIwhfAIAAMAYwicAAACMIXwCAADAGMInAAAAjCF8AgAAwBjCJwAAAIwhfAIAAMAYwicAAACMqVX4TE1NVWhoqLy8vBQREaFt27bVaLnPP/9c7u7uuv3222uzWQAAALg4h8NnZmamEhISlJycrH379qlPnz6Kjo5WTk5OtcsVFhZq1KhRuu+++2pdLAAAAFybw+Fz3rx5Gj9+vGJjY9WpUyctWLBAwcHBWrx4cbXLPfHEE3rsscfUq1evWhcLAAAA1+ZQ+Lx48aL27NmjqKgou/aoqCjt2LGjyuWWLVum7777TjNnzqzRdkpKSlRUVGQ3AQAAwPU5FD5Pnz6t0tJSBQQE2LUHBAQoLy+v0mUOHz6s6dOna8WKFXJ3d6/RdlJSUuTr62ubgoODHSkTAAAAN6ha3XBksVjsPlut1gptklRaWqrHHntMs2fPVlhYWI3Xn5SUpMLCQtt04sSJ2pQJAACAG0zNhiL/P39/f7m5uVUY5czPz68wGipJxcXF2r17t/bt26cnn3xSklRWViar1Sp3d3dt3LhR9957b4XlPD095enp6UhpAAAAcAEOjXx6eHgoIiJCWVlZdu1ZWVmKjIys0N/Hx0dfffWVsrOzbVNcXJw6dOig7Oxs9ezZ89qqBwAAgEtxaORTkhITEzVy5Eh169ZNvXr10pIlS5STk6O4uDhJv5wy//HHH7V8+XI1aNBA4eHhdsu3aNFCXl5eFdoBAABQ9zkcPmNiYnTmzBnNmTNHubm5Cg8P17p169S6dWtJUm5u7lWf+QkAAID6yWK1Wq3OLuJqioqK5Ovrq8LCQvn4+Di7HNR1s3ydXYHrmVXo7AoAVIffNcfxu+awmuY13u0OAAAAYwifAAAAMIbwCQAAAGMInwAAADCG8AkAAABjCJ8AAAAwhvAJAAAAYwifAAAAMIbwCQAAAGMInwAAADCG8AkAAABjCJ8AAAAwhvAJAAAAYwifAAAAMIbwCQAAAGMInwAAADCG8AkAAABjCJ8AAAAwhvAJAAAAYwifAAAAMIbwCQAAAGMInwAAADCG8AkAAABjCJ8AAAAwhvAJAAAAYwifAAAAMIbwCQAAAGMInwAAADCG8AkAAABjCJ8AAAAwhvAJAAAAYwifAAAAMIbwCQAAAGMInwAAADCG8AkAAABjCJ8AAAAwhvAJAAAAYwifAAAAMIbwCQAAAGMInwAAADCG8AkAAABjCJ8AAAAwhvAJAAAAYwifAAAAMIbwCQAAAGMInwAAADCG8AkAAABjCJ8AAAAwhvAJAAAAYwifAAAAMIbwCQAAAGMInwAAADCG8AkAAABjCJ8AAAAwhvAJAAAAYwifAAAAMIbwCQAAAGMInwAAADCG8AkAAABjCJ8AAAAwhvAJAAAAYwifAAAAMIbwCQAAAGMInwAAADCG8AkAAABjCJ8AAAAwhvAJAAAAYwifAAAAMIbwCQAAAGMInwAAADCG8AkAAABjCJ8AAAAwplbhMzU1VaGhofLy8lJERIS2bdtWZd/3339f/fv310033SQfHx/16tVLGzZsqHXBAAAAcF0Oh8/MzEwlJCQoOTlZ+/btU58+fRQdHa2cnJxK+2/dulX9+/fXunXrtGfPHvXr10+DBg3Svn37rrl4AAAAuBaL1Wq1OrJAz549dccdd2jx4sW2tk6dOmnIkCFKSUmp0Tq6dOmimJgYzZgxo0b9i4qK5Ovrq8LCQvn4+DhSLuC4Wb7OrsD1zCp0dgUAqsPvmuP4XXNYTfOaQyOfFy9e1J49exQVFWXXHhUVpR07dtRoHWVlZSouLlazZs2q7FNSUqKioiK7CQAAAK7PofB5+vRplZaWKiAgwK49ICBAeXl5NVrHq6++qnPnzmnYsGFV9klJSZGvr69tCg4OdqRMAAAA3KBqdcORxWKx+2y1Wiu0Veavf/2rZs2apczMTLVo0aLKfklJSSosLLRNJ06cqE2ZAAAAuMG4O9LZ399fbm5uFUY58/PzK4yG/lpmZqbGjx+vVatW6f7776+2r6enpzw9PR0pDQAAAC7AoZFPDw8PRUREKCsry649KytLkZGRVS7317/+VWPGjNHKlSv10EMP1a5SAAAAuDyHRj4lKTExUSNHjlS3bt3Uq1cvLVmyRDk5OYqLi5P0yynzH3/8UcuXL5f0S/AcNWqUXnvtNd155522UdNGjRrJ15e77wAAAOoTh8NnTEyMzpw5ozlz5ig3N1fh4eFat26dWrduLUnKzc21e+bnm2++qcuXL2vixImaOHGirX306NHKyMi49j0AAACAy3D4OZ/OwHM+YRTPw3Mcz8MDbmz8rjmO3zWH/SbP+QQAAACuBeETAAAAxhA+AQAAYAzhEwAAAMYQPgEAAGAM4RMAAADGED4BAABgjMMPmYfrCJn+sbNLcEnHvZxdAQAAdRcjnwAAADCG8AkAAABjCJ8AAAAwhvAJAAAAYwifAAAAMIbwCQAAAGMInwAAADCG8AkAAABjCJ8AAAAwhvAJAAAAYwifAAAAMIbwCQAAAGMInwAAADCG8AkAAABjCJ8AAAAwhvAJAAAAYwifAAAAMIbwCQAAAGMInwAAADDG3dkFAABQUyHTP3Z2CS7puJezKwD+DyOfAAAAMIbwCQAAAGMInwAAADCG8AkAAABjCJ8AAAAwhvAJAAAAYwifAAAAMIbwCQAAAGMInwAAADCG8AkAAABjCJ8AAAAwhvAJAAAAYwifAAAAMIbwCQAAAGMInwAAADCG8AkAAABjCJ8AAAAwhvAJAAAAYwifAAAAMIbwCQAAAGMInwAAADCG8AkAAABjCJ8AAAAwhvAJAAAAYwifAAAAMIbwCQAAAGMInwAAADCG8AkAAABjCJ8AAAAwhvAJAAAAYwifAAAAMIbwCQAAAGMInwAAADCG8AkAAABjCJ8AAAAwhvAJAAAAYwifAAAAMIbwCQAAAGMInwAAADCG8AkAAABjCJ8AAAAwhvAJAAAAYwifAAAAMIbwCQAAAGMInwAAADCmVuEzNTVVoaGh8vLyUkREhLZt21Zt/88++0wRERHy8vJSmzZt9MYbb9SqWAAAALg2h8NnZmamEhISlJycrH379qlPnz6Kjo5WTk5Opf2PHTumBx98UH369NG+ffv07LPPatKkSVq9evU1Fw8AAADX4nD4nDdvnsaPH6/Y2Fh16tRJCxYsUHBwsBYvXlxp/zfeeEOtWrXSggUL1KlTJ8XGxmrcuHH605/+dM3FAwAAwLW4O9L54sWL2rNnj6ZPn27XHhUVpR07dlS6zM6dOxUVFWXXNmDAAKWlpenSpUtq2LBhhWVKSkpUUlJi+1xYWChJKioqcqTceq+s5LyzS3BJRRars0twPfy3CUP4Xasdftdqgd81h5XnNKu1+r83h8Ln6dOnVVpaqoCAALv2gIAA5eXlVbpMXl5epf0vX76s06dPKygoqMIyKSkpmj17doX24OBgR8oFasXX2QW4ohc5asCNjP9Ca4HftVorLi6Wr2/Vx8+h8FnOYrHYfbZarRXarta/svZySUlJSkxMtH0uKyvT2bNn1bx582q3A1yroqIiBQcH68SJE/Lx8XF2OQBwzfhdgylWq1XFxcVq2bJltf0cCp/+/v5yc3OrMMqZn59fYXSzXGBgYKX93d3d1bx580qX8fT0lKenp12bn5+fI6UC18THx4cfaQB1Cr9rMKG6Ec9yDt1w5OHhoYiICGVlZdm1Z2VlKTIystJlevXqVaH/xo0b1a1bt0qv9wQAAEDd5fDd7omJiVq6dKnS09N18OBBTZ48WTk5OYqLi5P0yynzUaNG2frHxcXp+++/V2Jiog4ePKj09HSlpaXp6aefvn57AQAAAJfg8DWfMTExOnPmjObMmaPc3FyFh4dr3bp1at26tSQpNzfX7pmfoaGhWrdunSZPnqxFixapZcuWWrhwoYYOHXr99gK4Tjw9PTVz5swKl30AgKvidw03Gov1avfDAwAAANcJ73YHAACAMYRPAAAAGEP4BAAAgDGETwAAABhD+AQAAIAxtXq9JlBX/PDDD1q8eLF27NihvLw8WSwWBQQEKDIyUnFxcQoODnZ2iQAA1Ck8agn11vbt2xUdHa3g4GBFRUUpICBAVqtV+fn5ysrK0okTJ/TJJ5+od+/ezi4VAK6LEydOaObMmUpPT3d2KajHCJ+ot7p376677rpL8+fPr3T+5MmTtX37du3atctwZQDw29i/f7/uuOMOlZaWOrsU1GOET9RbjRo1UnZ2tjp06FDp/H/+85/63e9+pwsXLhiuDABqZ82aNdXOP3r0qKZMmUL4hFNxzSfqraCgIO3YsaPK8Llz504FBQUZrgoAam/IkCGyWCyqblzJYrEYrAioiPCJeuvpp59WXFyc9uzZo/79+ysgIEAWi0V5eXnKysrS0qVLtWDBAmeXCQA1FhQUpEWLFmnIkCGVzs/OzlZERITZooBfIXyi3powYYKaN2+u+fPn680337SdhnJzc1NERISWL1+uYcOGOblKAKi5iIgI7d27t8rwebVRUcAErvkEJF26dEmnT5+WJPn7+6thw4ZOrggAHLdt2zadO3dODzzwQKXzz507p927d+uee+4xXBnwfwifAAAAMIY3HAEAAMAYwicAAACMIXwCAADAGMInAAAAjCF8AnBIfn6+nnjiCbVq1Uqenp4KDAzUgAEDtHPnTlufkJCQSp+ROmvWLN1+++12bWfPnlVCQoJCQkLk4eGhoKAgjR07Vjk5OXb9xowZI4vFIovFInd3d7Vq1Ur/9V//pX/961/asmWLbV5VU0ZGhq1feHh4hTe8+Pn5KSMjw24fKlvPiy++aOuzevVq9ezZU76+vmratKm6dOmiKVOm2OaXlpYqJSVFHTt2VKNGjdSsWTPdeeedWrZsWbXHuLS0VPPnz1fXrl3l5eUlPz8/RUdH6/PPP7frl5GRUWmNS5curXLdV/YrP46JiYkqKSmpcr0BAQEaNGiQvvnmmyq/kyunyu60njt3rtzc3OyOX1XHuHzq27evrV9N/54A3Ph4zicAhwwdOlSXLl3SW2+9pTZt2uinn37Spk2bdPbsWYfXdfbsWd15553y8PBQamqqwsPDdfz4cT333HPq3r27du7cqTZt2tj6P/DAA1q2bJkuX76sAwcOaNy4cSooKNBbb72l3NxcW7+nnnpKRUVFdiHP19dXX375pSTpu+++0/LlyzV27Nhq65szZ47+8Ic/2LU1bdpUkvTpp59q+PDhmjt3rgYPHiyLxaIDBw5o06ZNtr6zZs3SkiVL9Prrr6tbt24qKirS7t279a9//avKbVqtVg0fPlyffvqpXnnlFd13330qKirSokWL1LdvX61atcruGY4+Pj769ttv7dbh6+tb7X4tW7ZMDzzwgC5duqT9+/dr7NixatKkif77v/+7wnqtVqt+/PFHPfPMM3rooYd06NAheXh42PqVfydX8vT0rHSbzzzzjNLT0zV9+nRJ0q5du2z/CNixY4eGDh2qb7/9Vj4+PpJktx0AdQfhE0CNFRQUaPv27dqyZYvtOYGtW7dWjx49arW+5ORknTx5UkeOHFFgYKAkqVWrVtqwYYPat2+viRMn6pNPPrH1Lx9plaRbbrlFMTExysjIkIeHh61dkho1aqSSkhK7tivFx8dr5syZGjFihLy8vKqsr2nTplWuY+3atbrrrrs0depUW1tYWJhdMPzoo480YcIEPfroo7a22267rZojIr377rt67733tGbNGg0aNMjWvmTJEp05c0axsbHq37+/mjRpIumXkcyqaqyKn5+fbZng4GANHjxYe/futetz5XqDgoI0efJkDR48WN9++61uvfVWW78rv5OqfPbZZ7pw4YLmzJmj5cuXa+vWrbr77rt100032fo0a9ZMktSiRQv5+fk5tD8AXAun3QHUmLe3t7y9vfXhhx/anaatjbKyMr3zzjv6/e9/XyG8NGrUSBMmTNCGDRuqHFE9evSo1q9fX6sXAiQkJOjy5ct6/fXXa1W7JAUGBuqbb77R119/XW2fv//97zp16lSN17ty5UqFhYXZBc9yU6ZM0ZkzZ5SVlVWrmitz6NAhbd68WT179qyyT0FBgVauXClJtTreaWlpGjFihBo2bKgRI0YoLS2t1vUCcH2ETwA15u7uroyMDL311lvy8/NT79699eyzz+p///d/K/SdNm2aLayWT3PnzrXNP3XqlAoKCtSpU6dKt9WpUydZrVYdOXLE1rZ27Vp5e3urUaNGatu2rQ4cOKBp06Y5vB+NGzfWzJkzlZKSosLCwir7VbYPW7ZskfTL6Gn37t116623KiQkRMOHD1d6erpdKJ83b55OnTqlwMBAde3aVXFxcXYjuZU5dOhQtcekvE+5wsJCu/pqMgo6YsQIeXt7y8vLSx06dFCXLl2UlJRk16d8vU2aNNF//Md/6J133tHgwYPVsWNHu37l38mV05Wn74uKirR69Wo9/vjjkqTHH39c7733noqKiq5a55Wu9vcEwHUQPgE4ZOjQoTp58qTWrFmjAQMGaMuWLbrjjjvsbtaRpKlTpyo7O9tuiouLq/F2yl++ZrFYbG39+vVTdna2vvzyS8XHx2vAgAGKj4+v1X6MHz9e/v7+eumll6rsU9k+lI8QNmnSRB9//LGOHDmi5557Tt7e3poyZYp69Oih8+fPS5I6d+6sr7/+Wl988YXGjh2rn376SYMGDVJsbGytai535TFp2rSpXX07duy46vLz589Xdna29u/fr7Vr1+rQoUMaOXKkXZ/y9e7Zs0dvvPGG2rZtqzfeeKPCusq/kyuniRMn2uavXLlSbdq0sV1ucPvtt6tNmzZ65513HNrna/17AnDj4JpPAA7z8vJS//791b9/f82YMUOxsbGaOXOmxowZY+vj7++vdu3a2S1Xfl2fJN10003y8/PTgQMHKt3GP//5T1ksFrVt29bW1qRJE9s6Fy5cqH79+mn27Nl2I2015e7urueff15jxozRk08+WWmfyvbh19q2bau2bdsqNjZWycnJCgsLU2Zmpu1mpgYNGqh79+7q3r27Jk+erLffflsjR45UcnKyQkNDK6wvLCysymNy8OBBSVL79u1tbQ0aNLhqjb8WGBhoW6ZDhw4qLi7WiBEj9Pzzz9var1xvx44dlZeXp5iYGG3dutVuXVd+J5VJT0/XN998I3f3//vfTVlZmdLS0vSf//mfNa75an9PAFwHI58Arlnnzp117tw5h5Zp0KCBhg0bppUrVyovL89u3oULF5SamqoBAwZUGzBmzpypP/3pTzp58mSt6n700UfVpUsXzZ49u1bL/1pISIgaN25c7bHo3LmzJFXZZ/jw4Tp8+LA++uijCvNeffVVNW/eXP37978u9ZZzc3OT9Mtxr8rkyZO1f/9+ffDBBzVe71dffaXdu3dry5YtdiOWW7du1a5du6q9XhZA3cXIJ4AaO3PmjB599FGNGzdOXbt2VdOmTbV79269/PLLevjhhx1e3wsvvKBNmzapf//+evnllxUeHq5jx47pueee06VLl7Ro0aJql+/bt6+6dOmiuXPn1vrmoRdffFEDBgyodF5xcXGFYNy4cWP5+Pho1qxZOn/+vB588EG1bt1aBQUFWrhwoS5dumQLh4888oh69+6tyMhIBQYG6tixY0pKSlJYWFiFayfLDR8+XKtWrdLo0aMrPGppzZo1WrVqle1O99oqKChQXl6eysrKdPjwYc2ZM0dhYWFVXmsq/fLopfIR7iFDhthO/ZeUlFQ4Ru7u7vL391daWpp69Oihu+++u8L6evXqpbS0NM2fP/+a9gWA62HkE0CNeXt7q2fPnpo/f77uvvtuhYeH649//KP+8Ic/1Cr8+fv764svvlC/fv30xBNPqE2bNho2bJjatGmjXbt22T3jsyqJiYn6y1/+ohMnTtRml3Tvvffq3nvv1eXLlyvMmzFjhoKCguymZ555RpJ0zz336OjRoxo1apQ6duyo6Oho5eXlaePGjerQoYMkacCAAfroo480aNAghYWFafTo0erYsaM2btxodxr6ShaLRe+++66Sk5M1f/58dezYUX369NH333+vzZs32z3KqbbGjh2roKAg3XLLLRoxYoS6dOmiTz75pMqayj311FM6ePCgVq1aZWtbv359hWN011136eLFi3r77bc1dOjQStc1dOhQvf3227p48eI17w8A12Kxll/VDwAAAPzGGPkEAACAMYRPAAAAGEP4BAAAgDGETwAAABhD+AQAAIAxhE8AAAAYQ/gEAACAMYRPAAAAGEP4BAAAgDGETwAAABhD+AQAAIAxhE8AAAAY8/8AGyRQzCH6djcAAAAASUVORK5CYII=", |
|
|
1584 |
"text/plain": [ |
|
|
1585 |
"<Figure size 800x500 with 1 Axes>" |
|
|
1586 |
] |
|
|
1587 |
}, |
|
|
1588 |
"metadata": {}, |
|
|
1589 |
"output_type": "display_data" |
|
|
1590 |
} |
|
|
1591 |
], |
|
|
1592 |
"source": [ |
|
|
1593 |
"plot('SHORTNESS OF BREATH')" |
|
|
1594 |
] |
|
|
1595 |
}, |
|
|
1596 |
{ |
|
|
1597 |
"cell_type": "code", |
|
|
1598 |
"execution_count": 29, |
|
|
1599 |
"metadata": { |
|
|
1600 |
"execution": { |
|
|
1601 |
"iopub.execute_input": "2023-07-17T13:07:09.538661Z", |
|
|
1602 |
"iopub.status.busy": "2023-07-17T13:07:09.538264Z", |
|
|
1603 |
"iopub.status.idle": "2023-07-17T13:07:09.834714Z", |
|
|
1604 |
"shell.execute_reply": "2023-07-17T13:07:09.833520Z", |
|
|
1605 |
"shell.execute_reply.started": "2023-07-17T13:07:09.538628Z" |
|
|
1606 |
} |
|
|
1607 |
}, |
|
|
1608 |
"outputs": [ |
|
|
1609 |
{ |
|
|
1610 |
"data": { |
|
|
1611 |
"text/plain": [ |
|
|
1612 |
"<Axes: xlabel='CHEST PAIN'>" |
|
|
1613 |
] |
|
|
1614 |
}, |
|
|
1615 |
"execution_count": 29, |
|
|
1616 |
"metadata": {}, |
|
|
1617 |
"output_type": "execute_result" |
|
|
1618 |
}, |
|
|
1619 |
{ |
|
|
1620 |
"data": { |
|
|
1621 |
"image/png": 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1624 |
] |
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1625 |
}, |
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1626 |
"metadata": {}, |
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1627 |
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} |
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1629 |
], |
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1630 |
"source": [ |
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1631 |
"plot('CHEST PAIN')" |
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1632 |
] |
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1633 |
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1634 |
{ |
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1635 |
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1638 |
"**From the visualizations, it is clear that in the given dataset, the features GENDER, AGE, SMOKING and SHORTNESS OF BREATH don't have that much relationship with LUNG CANCER. So let's drop those features to make this dataset more clean.**" |
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1639 |
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1640 |
}, |
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1641 |
{ |
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"execution": { |
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1651 |
} |
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1652 |
}, |
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"outputs": [ |
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{ |
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"data": { |
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" <tr style=\"text-align: right;\">\n", |
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" <th></th>\n", |
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" <th>YELLOW_FINGERS</th>\n", |
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" <th>ANXIETY</th>\n", |
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" <th>PEER_PRESSURE</th>\n", |
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|
|
1706 |
" <th>1</th>\n", |
|
|
1707 |
" <td>0</td>\n", |
|
|
1708 |
" <td>0</td>\n", |
|
|
1709 |
" <td>0</td>\n", |
|
|
1710 |
" <td>1</td>\n", |
|
|
1711 |
" <td>1</td>\n", |
|
|
1712 |
" <td>1</td>\n", |
|
|
1713 |
" <td>0</td>\n", |
|
|
1714 |
" <td>0</td>\n", |
|
|
1715 |
" <td>0</td>\n", |
|
|
1716 |
" <td>1</td>\n", |
|
|
1717 |
" <td>1</td>\n", |
|
|
1718 |
" <td>1</td>\n", |
|
|
1719 |
" </tr>\n", |
|
|
1720 |
" <tr>\n", |
|
|
1721 |
" <th>2</th>\n", |
|
|
1722 |
" <td>0</td>\n", |
|
|
1723 |
" <td>0</td>\n", |
|
|
1724 |
" <td>1</td>\n", |
|
|
1725 |
" <td>0</td>\n", |
|
|
1726 |
" <td>1</td>\n", |
|
|
1727 |
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|
|
1728 |
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|
|
1729 |
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|
|
1730 |
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|
|
1731 |
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|
|
1732 |
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|
|
1733 |
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|
|
1734 |
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|
|
1735 |
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|
|
1736 |
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|
|
1737 |
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|
|
1738 |
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|
|
1739 |
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|
|
1740 |
" <td>0</td>\n", |
|
|
1741 |
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|
|
1742 |
" <td>0</td>\n", |
|
|
1743 |
" <td>0</td>\n", |
|
|
1744 |
" <td>1</td>\n", |
|
|
1745 |
" <td>0</td>\n", |
|
|
1746 |
" <td>1</td>\n", |
|
|
1747 |
" <td>1</td>\n", |
|
|
1748 |
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|
|
1749 |
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|
|
1750 |
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|
|
1751 |
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|
|
1752 |
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|
|
1753 |
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|
|
1754 |
" <td>0</td>\n", |
|
|
1755 |
" <td>0</td>\n", |
|
|
1756 |
" <td>0</td>\n", |
|
|
1757 |
" <td>0</td>\n", |
|
|
1758 |
" <td>1</td>\n", |
|
|
1759 |
" <td>0</td>\n", |
|
|
1760 |
" <td>1</td>\n", |
|
|
1761 |
" <td>0</td>\n", |
|
|
1762 |
" <td>0</td>\n", |
|
|
1763 |
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|
|
1764 |
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|
|
1765 |
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|
|
1766 |
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|
|
1767 |
" <td>...</td>\n", |
|
|
1768 |
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|
|
1769 |
" <td>...</td>\n", |
|
|
1770 |
" <td>...</td>\n", |
|
|
1771 |
" <td>...</td>\n", |
|
|
1772 |
" <td>...</td>\n", |
|
|
1773 |
" <td>...</td>\n", |
|
|
1774 |
" <td>...</td>\n", |
|
|
1775 |
" <td>...</td>\n", |
|
|
1776 |
" <td>...</td>\n", |
|
|
1777 |
" <td>...</td>\n", |
|
|
1778 |
" <td>...</td>\n", |
|
|
1779 |
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|
|
1780 |
" <tr>\n", |
|
|
1781 |
" <th>279</th>\n", |
|
|
1782 |
" <td>1</td>\n", |
|
|
1783 |
" <td>1</td>\n", |
|
|
1784 |
" <td>1</td>\n", |
|
|
1785 |
" <td>0</td>\n", |
|
|
1786 |
" <td>0</td>\n", |
|
|
1787 |
" <td>1</td>\n", |
|
|
1788 |
" <td>1</td>\n", |
|
|
1789 |
" <td>0</td>\n", |
|
|
1790 |
" <td>1</td>\n", |
|
|
1791 |
" <td>1</td>\n", |
|
|
1792 |
" <td>0</td>\n", |
|
|
1793 |
" <td>1</td>\n", |
|
|
1794 |
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|
|
1795 |
" <tr>\n", |
|
|
1796 |
" <th>280</th>\n", |
|
|
1797 |
" <td>0</td>\n", |
|
|
1798 |
" <td>0</td>\n", |
|
|
1799 |
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|
|
1800 |
" <td>1</td>\n", |
|
|
1801 |
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|
|
1802 |
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|
|
1803 |
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|
|
1804 |
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|
|
1805 |
" <td>0</td>\n", |
|
|
1806 |
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|
|
1807 |
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|
|
1808 |
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|
|
1809 |
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|
|
1810 |
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|
|
1811 |
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|
|
1812 |
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|
|
1813 |
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|
|
1814 |
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|
|
1815 |
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|
|
1816 |
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|
|
1817 |
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|
|
1818 |
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|
|
1819 |
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|
|
1820 |
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|
|
1821 |
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|
|
1822 |
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|
|
1823 |
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|
|
1824 |
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|
|
1825 |
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|
|
1826 |
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|
|
1827 |
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|
|
1828 |
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|
|
1829 |
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|
|
1830 |
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|
|
1831 |
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|
|
1832 |
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|
|
1833 |
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|
|
1834 |
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|
|
1835 |
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|
|
1836 |
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|
|
1837 |
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|
|
1838 |
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|
|
1839 |
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|
|
1840 |
" <tr>\n", |
|
|
1841 |
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|
|
1842 |
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|
|
1843 |
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|
|
1844 |
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|
|
1845 |
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|
|
1846 |
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|
|
1847 |
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|
|
1848 |
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|
|
1849 |
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|
|
1850 |
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|
|
1851 |
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|
|
1852 |
" <td>1</td>\n", |
|
|
1853 |
" <td>1</td>\n", |
|
|
1854 |
" </tr>\n", |
|
|
1855 |
" </tbody>\n", |
|
|
1856 |
"</table>\n", |
|
|
1857 |
"<p>276 rows × 12 columns</p>\n", |
|
|
1858 |
"</div>" |
|
|
1859 |
], |
|
|
1860 |
"text/plain": [ |
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|
1861 |
" YELLOW_FINGERS ANXIETY PEER_PRESSURE CHRONIC DISEASE FATIGUE \\\n", |
|
|
1862 |
"0 1 1 0 0 1 \n", |
|
|
1863 |
"1 0 0 0 1 1 \n", |
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1864 |
"2 0 0 1 0 1 \n", |
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1865 |
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1866 |
"4 1 0 0 0 0 \n", |
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1867 |
".. ... ... ... ... ... \n", |
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1868 |
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1869 |
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1870 |
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1871 |
"282 1 1 0 0 0 \n", |
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1872 |
"283 1 1 0 0 1 \n", |
|
|
1873 |
"\n", |
|
|
1874 |
" ALLERGY WHEEZING ALCOHOL CONSUMING COUGHING SWALLOWING DIFFICULTY \\\n", |
|
|
1875 |
"0 0 1 1 1 1 \n", |
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1876 |
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1877 |
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1878 |
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1879 |
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1880 |
".. ... ... ... ... ... \n", |
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1881 |
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1882 |
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1883 |
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1884 |
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|
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1885 |
"283 0 1 1 1 1 \n", |
|
|
1886 |
"\n", |
|
|
1887 |
" CHEST PAIN LUNG_CANCER \n", |
|
|
1888 |
"0 1 1 \n", |
|
|
1889 |
"1 1 1 \n", |
|
|
1890 |
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1891 |
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1892 |
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1893 |
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1894 |
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1895 |
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1896 |
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|
|
1897 |
"282 1 0 \n", |
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|
1898 |
"283 1 1 \n", |
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|
1899 |
"\n", |
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|
1900 |
"[276 rows x 12 columns]" |
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|
1901 |
] |
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1902 |
}, |
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|
1903 |
"execution_count": 30, |
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1904 |
"metadata": {}, |
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1905 |
"output_type": "execute_result" |
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|
1906 |
} |
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1907 |
], |
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|
1908 |
"source": [ |
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|
1909 |
"df_new=df.drop(columns=['GENDER','AGE', 'SMOKING', 'SHORTNESS OF BREATH'])\n", |
|
|
1910 |
"df_new" |
|
|
1911 |
] |
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1912 |
}, |
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1913 |
{ |
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1914 |
"cell_type": "code", |
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1915 |
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1916 |
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1917 |
"execution": { |
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1918 |
"iopub.execute_input": "2023-07-17T13:07:57.771001Z", |
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1919 |
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1920 |
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1921 |
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1923 |
} |
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1924 |
}, |
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1925 |
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1926 |
{ |
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1927 |
"data": { |
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1928 |
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1929 |
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1930 |
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1933 |
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1934 |
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1936 |
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1937 |
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1938 |
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1939 |
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1940 |
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1941 |
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1942 |
"</style>\n", |
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|
1943 |
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|
1944 |
" <thead>\n", |
|
|
1945 |
" <tr style=\"text-align: right;\">\n", |
|
|
1946 |
" <th></th>\n", |
|
|
1947 |
" <th>YELLOW_FINGERS</th>\n", |
|
|
1948 |
" <th>ANXIETY</th>\n", |
|
|
1949 |
" <th>PEER_PRESSURE</th>\n", |
|
|
1950 |
" <th>CHRONIC DISEASE</th>\n", |
|
|
1951 |
" <th>FATIGUE</th>\n", |
|
|
1952 |
" <th>ALLERGY</th>\n", |
|
|
1953 |
" <th>WHEEZING</th>\n", |
|
|
1954 |
" <th>ALCOHOL CONSUMING</th>\n", |
|
|
1955 |
" <th>COUGHING</th>\n", |
|
|
1956 |
" <th>SWALLOWING DIFFICULTY</th>\n", |
|
|
1957 |
" <th>CHEST PAIN</th>\n", |
|
|
1958 |
" <th>LUNG_CANCER</th>\n", |
|
|
1959 |
" </tr>\n", |
|
|
1960 |
" </thead>\n", |
|
|
1961 |
" <tbody>\n", |
|
|
1962 |
" <tr>\n", |
|
|
1963 |
" <th>YELLOW_FINGERS</th>\n", |
|
|
1964 |
" <td>1.000000</td>\n", |
|
|
1965 |
" <td>0.558344</td>\n", |
|
|
1966 |
" <td>0.313067</td>\n", |
|
|
1967 |
" <td>0.015316</td>\n", |
|
|
1968 |
" <td>-0.099644</td>\n", |
|
|
1969 |
" <td>-0.147130</td>\n", |
|
|
1970 |
" <td>-0.058756</td>\n", |
|
|
1971 |
" <td>-0.273643</td>\n", |
|
|
1972 |
" <td>0.020803</td>\n", |
|
|
1973 |
" <td>0.333349</td>\n", |
|
|
1974 |
" <td>-0.099169</td>\n", |
|
|
1975 |
" <td>0.189192</td>\n", |
|
|
1976 |
" </tr>\n", |
|
|
1977 |
" <tr>\n", |
|
|
1978 |
" <th>ANXIETY</th>\n", |
|
|
1979 |
" <td>0.558344</td>\n", |
|
|
1980 |
" <td>1.000000</td>\n", |
|
|
1981 |
" <td>0.210278</td>\n", |
|
|
1982 |
" <td>-0.006938</td>\n", |
|
|
1983 |
" <td>-0.181474</td>\n", |
|
|
1984 |
" <td>-0.159451</td>\n", |
|
|
1985 |
" <td>-0.174009</td>\n", |
|
|
1986 |
" <td>-0.152228</td>\n", |
|
|
1987 |
" <td>-0.218843</td>\n", |
|
|
1988 |
" <td>0.478820</td>\n", |
|
|
1989 |
" <td>-0.123182</td>\n", |
|
|
1990 |
" <td>0.144322</td>\n", |
|
|
1991 |
" </tr>\n", |
|
|
1992 |
" <tr>\n", |
|
|
1993 |
" <th>PEER_PRESSURE</th>\n", |
|
|
1994 |
" <td>0.313067</td>\n", |
|
|
1995 |
" <td>0.210278</td>\n", |
|
|
1996 |
" <td>1.000000</td>\n", |
|
|
1997 |
" <td>0.042893</td>\n", |
|
|
1998 |
" <td>0.094661</td>\n", |
|
|
1999 |
" <td>-0.066887</td>\n", |
|
|
2000 |
" <td>-0.037769</td>\n", |
|
|
2001 |
" <td>-0.132603</td>\n", |
|
|
2002 |
" <td>-0.068224</td>\n", |
|
|
2003 |
" <td>0.327764</td>\n", |
|
|
2004 |
" <td>-0.074655</td>\n", |
|
|
2005 |
" <td>0.195086</td>\n", |
|
|
2006 |
" </tr>\n", |
|
|
2007 |
" <tr>\n", |
|
|
2008 |
" <th>CHRONIC DISEASE</th>\n", |
|
|
2009 |
" <td>0.015316</td>\n", |
|
|
2010 |
" <td>-0.006938</td>\n", |
|
|
2011 |
" <td>0.042893</td>\n", |
|
|
2012 |
" <td>1.000000</td>\n", |
|
|
2013 |
" <td>-0.099411</td>\n", |
|
|
2014 |
" <td>0.134309</td>\n", |
|
|
2015 |
" <td>-0.040546</td>\n", |
|
|
2016 |
" <td>0.010144</td>\n", |
|
|
2017 |
" <td>-0.160813</td>\n", |
|
|
2018 |
" <td>0.068263</td>\n", |
|
|
2019 |
" <td>-0.048895</td>\n", |
|
|
2020 |
" <td>0.143692</td>\n", |
|
|
2021 |
" </tr>\n", |
|
|
2022 |
" <tr>\n", |
|
|
2023 |
" <th>FATIGUE</th>\n", |
|
|
2024 |
" <td>-0.099644</td>\n", |
|
|
2025 |
" <td>-0.181474</td>\n", |
|
|
2026 |
" <td>0.094661</td>\n", |
|
|
2027 |
" <td>-0.099411</td>\n", |
|
|
2028 |
" <td>1.000000</td>\n", |
|
|
2029 |
" <td>-0.001841</td>\n", |
|
|
2030 |
" <td>0.152151</td>\n", |
|
|
2031 |
" <td>-0.181573</td>\n", |
|
|
2032 |
" <td>0.148538</td>\n", |
|
|
2033 |
" <td>-0.115727</td>\n", |
|
|
2034 |
" <td>0.013757</td>\n", |
|
|
2035 |
" <td>0.160078</td>\n", |
|
|
2036 |
" </tr>\n", |
|
|
2037 |
" <tr>\n", |
|
|
2038 |
" <th>ALLERGY</th>\n", |
|
|
2039 |
" <td>-0.147130</td>\n", |
|
|
2040 |
" <td>-0.159451</td>\n", |
|
|
2041 |
" <td>-0.066887</td>\n", |
|
|
2042 |
" <td>0.134309</td>\n", |
|
|
2043 |
" <td>-0.001841</td>\n", |
|
|
2044 |
" <td>1.000000</td>\n", |
|
|
2045 |
" <td>0.166517</td>\n", |
|
|
2046 |
" <td>0.378125</td>\n", |
|
|
2047 |
" <td>0.206367</td>\n", |
|
|
2048 |
" <td>-0.037581</td>\n", |
|
|
2049 |
" <td>0.245440</td>\n", |
|
|
2050 |
" <td>0.333552</td>\n", |
|
|
2051 |
" </tr>\n", |
|
|
2052 |
" <tr>\n", |
|
|
2053 |
" <th>WHEEZING</th>\n", |
|
|
2054 |
" <td>-0.058756</td>\n", |
|
|
2055 |
" <td>-0.174009</td>\n", |
|
|
2056 |
" <td>-0.037769</td>\n", |
|
|
2057 |
" <td>-0.040546</td>\n", |
|
|
2058 |
" <td>0.152151</td>\n", |
|
|
2059 |
" <td>0.166517</td>\n", |
|
|
2060 |
" <td>1.000000</td>\n", |
|
|
2061 |
" <td>0.261061</td>\n", |
|
|
2062 |
" <td>0.353657</td>\n", |
|
|
2063 |
" <td>0.108304</td>\n", |
|
|
2064 |
" <td>0.142846</td>\n", |
|
|
2065 |
" <td>0.249054</td>\n", |
|
|
2066 |
" </tr>\n", |
|
|
2067 |
" <tr>\n", |
|
|
2068 |
" <th>ALCOHOL CONSUMING</th>\n", |
|
|
2069 |
" <td>-0.273643</td>\n", |
|
|
2070 |
" <td>-0.152228</td>\n", |
|
|
2071 |
" <td>-0.132603</td>\n", |
|
|
2072 |
" <td>0.010144</td>\n", |
|
|
2073 |
" <td>-0.181573</td>\n", |
|
|
2074 |
" <td>0.378125</td>\n", |
|
|
2075 |
" <td>0.261061</td>\n", |
|
|
2076 |
" <td>1.000000</td>\n", |
|
|
2077 |
" <td>0.198023</td>\n", |
|
|
2078 |
" <td>-0.000635</td>\n", |
|
|
2079 |
" <td>0.310767</td>\n", |
|
|
2080 |
" <td>0.294422</td>\n", |
|
|
2081 |
" </tr>\n", |
|
|
2082 |
" <tr>\n", |
|
|
2083 |
" <th>COUGHING</th>\n", |
|
|
2084 |
" <td>0.020803</td>\n", |
|
|
2085 |
" <td>-0.218843</td>\n", |
|
|
2086 |
" <td>-0.068224</td>\n", |
|
|
2087 |
" <td>-0.160813</td>\n", |
|
|
2088 |
" <td>0.148538</td>\n", |
|
|
2089 |
" <td>0.206367</td>\n", |
|
|
2090 |
" <td>0.353657</td>\n", |
|
|
2091 |
" <td>0.198023</td>\n", |
|
|
2092 |
" <td>1.000000</td>\n", |
|
|
2093 |
" <td>-0.136885</td>\n", |
|
|
2094 |
" <td>0.077988</td>\n", |
|
|
2095 |
" <td>0.253027</td>\n", |
|
|
2096 |
" </tr>\n", |
|
|
2097 |
" <tr>\n", |
|
|
2098 |
" <th>SWALLOWING DIFFICULTY</th>\n", |
|
|
2099 |
" <td>0.333349</td>\n", |
|
|
2100 |
" <td>0.478820</td>\n", |
|
|
2101 |
" <td>0.327764</td>\n", |
|
|
2102 |
" <td>0.068263</td>\n", |
|
|
2103 |
" <td>-0.115727</td>\n", |
|
|
2104 |
" <td>-0.037581</td>\n", |
|
|
2105 |
" <td>0.108304</td>\n", |
|
|
2106 |
" <td>-0.000635</td>\n", |
|
|
2107 |
" <td>-0.136885</td>\n", |
|
|
2108 |
" <td>1.000000</td>\n", |
|
|
2109 |
" <td>0.102674</td>\n", |
|
|
2110 |
" <td>0.268940</td>\n", |
|
|
2111 |
" </tr>\n", |
|
|
2112 |
" <tr>\n", |
|
|
2113 |
" <th>CHEST PAIN</th>\n", |
|
|
2114 |
" <td>-0.099169</td>\n", |
|
|
2115 |
" <td>-0.123182</td>\n", |
|
|
2116 |
" <td>-0.074655</td>\n", |
|
|
2117 |
" <td>-0.048895</td>\n", |
|
|
2118 |
" <td>0.013757</td>\n", |
|
|
2119 |
" <td>0.245440</td>\n", |
|
|
2120 |
" <td>0.142846</td>\n", |
|
|
2121 |
" <td>0.310767</td>\n", |
|
|
2122 |
" <td>0.077988</td>\n", |
|
|
2123 |
" <td>0.102674</td>\n", |
|
|
2124 |
" <td>1.000000</td>\n", |
|
|
2125 |
" <td>0.194856</td>\n", |
|
|
2126 |
" </tr>\n", |
|
|
2127 |
" <tr>\n", |
|
|
2128 |
" <th>LUNG_CANCER</th>\n", |
|
|
2129 |
" <td>0.189192</td>\n", |
|
|
2130 |
" <td>0.144322</td>\n", |
|
|
2131 |
" <td>0.195086</td>\n", |
|
|
2132 |
" <td>0.143692</td>\n", |
|
|
2133 |
" <td>0.160078</td>\n", |
|
|
2134 |
" <td>0.333552</td>\n", |
|
|
2135 |
" <td>0.249054</td>\n", |
|
|
2136 |
" <td>0.294422</td>\n", |
|
|
2137 |
" <td>0.253027</td>\n", |
|
|
2138 |
" <td>0.268940</td>\n", |
|
|
2139 |
" <td>0.194856</td>\n", |
|
|
2140 |
" <td>1.000000</td>\n", |
|
|
2141 |
" </tr>\n", |
|
|
2142 |
" </tbody>\n", |
|
|
2143 |
"</table>\n", |
|
|
2144 |
"</div>" |
|
|
2145 |
], |
|
|
2146 |
"text/plain": [ |
|
|
2147 |
" YELLOW_FINGERS ANXIETY PEER_PRESSURE \\\n", |
|
|
2148 |
"YELLOW_FINGERS 1.000000 0.558344 0.313067 \n", |
|
|
2149 |
"ANXIETY 0.558344 1.000000 0.210278 \n", |
|
|
2150 |
"PEER_PRESSURE 0.313067 0.210278 1.000000 \n", |
|
|
2151 |
"CHRONIC DISEASE 0.015316 -0.006938 0.042893 \n", |
|
|
2152 |
"FATIGUE -0.099644 -0.181474 0.094661 \n", |
|
|
2153 |
"ALLERGY -0.147130 -0.159451 -0.066887 \n", |
|
|
2154 |
"WHEEZING -0.058756 -0.174009 -0.037769 \n", |
|
|
2155 |
"ALCOHOL CONSUMING -0.273643 -0.152228 -0.132603 \n", |
|
|
2156 |
"COUGHING 0.020803 -0.218843 -0.068224 \n", |
|
|
2157 |
"SWALLOWING DIFFICULTY 0.333349 0.478820 0.327764 \n", |
|
|
2158 |
"CHEST PAIN -0.099169 -0.123182 -0.074655 \n", |
|
|
2159 |
"LUNG_CANCER 0.189192 0.144322 0.195086 \n", |
|
|
2160 |
"\n", |
|
|
2161 |
" CHRONIC DISEASE FATIGUE ALLERGY WHEEZING \\\n", |
|
|
2162 |
"YELLOW_FINGERS 0.015316 -0.099644 -0.147130 -0.058756 \n", |
|
|
2163 |
"ANXIETY -0.006938 -0.181474 -0.159451 -0.174009 \n", |
|
|
2164 |
"PEER_PRESSURE 0.042893 0.094661 -0.066887 -0.037769 \n", |
|
|
2165 |
"CHRONIC DISEASE 1.000000 -0.099411 0.134309 -0.040546 \n", |
|
|
2166 |
"FATIGUE -0.099411 1.000000 -0.001841 0.152151 \n", |
|
|
2167 |
"ALLERGY 0.134309 -0.001841 1.000000 0.166517 \n", |
|
|
2168 |
"WHEEZING -0.040546 0.152151 0.166517 1.000000 \n", |
|
|
2169 |
"ALCOHOL CONSUMING 0.010144 -0.181573 0.378125 0.261061 \n", |
|
|
2170 |
"COUGHING -0.160813 0.148538 0.206367 0.353657 \n", |
|
|
2171 |
"SWALLOWING DIFFICULTY 0.068263 -0.115727 -0.037581 0.108304 \n", |
|
|
2172 |
"CHEST PAIN -0.048895 0.013757 0.245440 0.142846 \n", |
|
|
2173 |
"LUNG_CANCER 0.143692 0.160078 0.333552 0.249054 \n", |
|
|
2174 |
"\n", |
|
|
2175 |
" ALCOHOL CONSUMING COUGHING SWALLOWING DIFFICULTY \\\n", |
|
|
2176 |
"YELLOW_FINGERS -0.273643 0.020803 0.333349 \n", |
|
|
2177 |
"ANXIETY -0.152228 -0.218843 0.478820 \n", |
|
|
2178 |
"PEER_PRESSURE -0.132603 -0.068224 0.327764 \n", |
|
|
2179 |
"CHRONIC DISEASE 0.010144 -0.160813 0.068263 \n", |
|
|
2180 |
"FATIGUE -0.181573 0.148538 -0.115727 \n", |
|
|
2181 |
"ALLERGY 0.378125 0.206367 -0.037581 \n", |
|
|
2182 |
"WHEEZING 0.261061 0.353657 0.108304 \n", |
|
|
2183 |
"ALCOHOL CONSUMING 1.000000 0.198023 -0.000635 \n", |
|
|
2184 |
"COUGHING 0.198023 1.000000 -0.136885 \n", |
|
|
2185 |
"SWALLOWING DIFFICULTY -0.000635 -0.136885 1.000000 \n", |
|
|
2186 |
"CHEST PAIN 0.310767 0.077988 0.102674 \n", |
|
|
2187 |
"LUNG_CANCER 0.294422 0.253027 0.268940 \n", |
|
|
2188 |
"\n", |
|
|
2189 |
" CHEST PAIN LUNG_CANCER \n", |
|
|
2190 |
"YELLOW_FINGERS -0.099169 0.189192 \n", |
|
|
2191 |
"ANXIETY -0.123182 0.144322 \n", |
|
|
2192 |
"PEER_PRESSURE -0.074655 0.195086 \n", |
|
|
2193 |
"CHRONIC DISEASE -0.048895 0.143692 \n", |
|
|
2194 |
"FATIGUE 0.013757 0.160078 \n", |
|
|
2195 |
"ALLERGY 0.245440 0.333552 \n", |
|
|
2196 |
"WHEEZING 0.142846 0.249054 \n", |
|
|
2197 |
"ALCOHOL CONSUMING 0.310767 0.294422 \n", |
|
|
2198 |
"COUGHING 0.077988 0.253027 \n", |
|
|
2199 |
"SWALLOWING DIFFICULTY 0.102674 0.268940 \n", |
|
|
2200 |
"CHEST PAIN 1.000000 0.194856 \n", |
|
|
2201 |
"LUNG_CANCER 0.194856 1.000000 " |
|
|
2202 |
] |
|
|
2203 |
}, |
|
|
2204 |
"execution_count": 31, |
|
|
2205 |
"metadata": {}, |
|
|
2206 |
"output_type": "execute_result" |
|
|
2207 |
} |
|
|
2208 |
], |
|
|
2209 |
"source": [ |
|
|
2210 |
"#Finding Correlation\n", |
|
|
2211 |
"cn=df_new.corr()\n", |
|
|
2212 |
"cn" |
|
|
2213 |
] |
|
|
2214 |
}, |
|
|
2215 |
{ |
|
|
2216 |
"cell_type": "code", |
|
|
2217 |
"execution_count": 32, |
|
|
2218 |
"metadata": { |
|
|
2219 |
"execution": { |
|
|
2220 |
"iopub.execute_input": "2023-07-17T13:08:08.626878Z", |
|
|
2221 |
"iopub.status.busy": "2023-07-17T13:08:08.626475Z", |
|
|
2222 |
"iopub.status.idle": "2023-07-17T13:08:09.565637Z", |
|
|
2223 |
"shell.execute_reply": "2023-07-17T13:08:09.564394Z", |
|
|
2224 |
"shell.execute_reply.started": "2023-07-17T13:08:08.626831Z" |
|
|
2225 |
} |
|
|
2226 |
}, |
|
|
2227 |
"outputs": [ |
|
|
2228 |
{ |
|
|
2229 |
"data": { |
|
|
2230 |
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9sXtmYDAYtGb1fL1Vt4VKl0kaK7br8Kni42/rr63pX1PXrJ6vQoVL6c2678jNPb/erPuOCoaU0JpVyf8XipesoKLFyylfPk/ly+epxk3byMYmu04eP2RMc+L4Qb1SqaaCCxZT3ryuqlz1TeXPX0CnT2Wu6+W8ebNUu/abeqNOXXl5+ahT50/k7OyspUvSbuedOn+it99+V0FBBeXhkV9t23aQu4entm7dkiKlhRydnEw2JJk1a6befKuu6t7rzz/p1l3OLi5auGB+mukDAwNVo2ZN+d7rz19/vbbKpujPS5YsqddeqywfHx95eHioabO3VcDPT3sfSAPg2SMQn8nMmTNHgYGBCgwMVIsWLTRp0iQZDIYMK4/BYNCmTZt0+PBhk4HnrFmzlCtXLnXs2DHN4+4vYWNjY6OpU6fq119/1eLFi/XBBx/os88+U6lSpZ5J+c0pLOyKYqIjVDAk+VysrLLJP7CITp449JAj/53Qq5f0afem6vvpuxr/yyBdu3b5ifN80YXfq/PgkOSAlpVVNvkFFNHpk09W5/v3/qX83v4a//NX6t2tsYYM7KA/N6980iK/UK5du6zo6AgVKmTapgODiurE8YPpHnfyxCGFFDINMhYqXMrkmD27/5KPT4DGjPpSH3dsoP592+n335anm2d0dIT27d2mSv/LXD/NTEhI0IkTx1S8RGmT/cWLl9bhwweeynsYDAbt3bNTFy+cU0ihYk8lz+dZaOhlRUVFqEiR5Dq1ssqm4ILFdOxY+u36+LGDKlzE9HMoUrSMjt87JjExQadPHTXJV5IKFy390HzTEhhUWNv+2qjr12N09+5dbf1zgxIS4lWwYLHHygdIT0JCgo4fP6oSJcuY7C9RsowOHXo6fYskrV2zXP/7XzXZ2GR/anm+qBISEnTkyBGVLWv6q6OyZcpq3/5/9yXo0aNHtW/fPpUokbz0QbGiRXXkyBEdPJjUz1y4eFFbt25VxYqZZzZwehISEnTs2FGVLGXazkuWLKuDB/f/qzzu3r2rm7duKretrTmK+MK7FnpZ0VERKlQkxVgxuKjx+piWE8cPqVBh0/u/wkVK63g648u7d+9o29aNiou7LT//EOP+gMDC+mf3VkVEXJPBYNChg//oypULqa7XL7OEhAQdO3pUpUqb9i2lSpfVgcdo57du3pRtbtN2fuvWLTVrWk+NG72pPp921/HjR59auV9kCQkJOppOf77/Mfrz/fv2qXiJEmn+3WAwaMeO7Tp39qyKFU87DYBng6VpMpkJEyaoRYsWkpKWkLl+/bo2bNigatWqPbX3WL58uXLlMp3Z1Lt3b33xxRcmr/v27av4+HglJCTIxsZGH3/8sfHvx44dk6+vr7JmTW6iI0eOVL9+/YyvL168KDs7O5UqVUp9+vRRw4YNVbx4cfXt2/epnUtGiomOkCTltrU32W9r66Dw8KtPlLe3b5Batu4lFxcPxcREatXyGfp2SBd9MXC8cuXKvDcGyXVu+gsOW1t7RYSHPlHeYdcu649Ny1WlekPVrP22zpw+onmzxyprViuVrVD9ifJ+UURHJdWvrZ1p/drZOijsIW06OipCtnaOJvts7RwVHR1pfB167ZI2blyqmrUaq85b7+jUqSOaMW20slplU8VXaqTK888/1srGJodKpVhn/mUXExOtu3fvyN7etD7tHRwVFRn+RHnfuHFdrd6tr4SEeFlaZlGHj7qlCvi/jO63a7sU7drWzkFh19Jv11FREamOsbNzUNS9/GJjonX37t0009x/z3/r4679Ner7AWrX+i1lyZJF2bLZqFuPr+Xi6v7og4F/ISYmSnfv3pFDyr7F3kGRT9i33Hf0yCGdOXNKXT/p81Tye9FFRUXpzp07cnQ0nVHq5OSo8G0Pr/M36tRRZFSk7ty5o7Zt2qpe3XrGv9WoUUORUZFq066tDAaD7ty5o4YNG6pVy5bmOI0XSnT0vXbuYNrOHRwcFBnx7/rl+fNm6vatW/rf/6qao4gvvOjotK+pdrYOCgt71Fgx9XU45fXy/LlTGtj/IyUkxMvGJru6fDJQ7h7exr+/27KzJoz7Vl07NVGWLFlkYWGp1m17KDCo8BOe2Ysj/XbuqMiIf9efz507U7dv39JrlZPbef783vr0077y8fXTzZs3tGD+HHXu1E7jJ0yTh8fTXSr3RZPcn5vWuaOT0yP78zfrvKGoe/15mzZtVfeB/lySrl+/rjfr1FZ8fLyyZMminj17pwr44/lg8egkeEkQiM9Ejh49qu3bt2vhwqSflGXNmlVNmzbVxIkTn2ogvnLlyvrpp59M9qW8qPTs2VOtWrXStWvX9Pnnn6tKlSqqUKGCSZqUD2794IMP9NZbb+nvv/9WixYtTGby9+3bVwMHDtSnn35qErz/N+Li4hQXF2eyLz4+TtmyPdv16rZv26CZ0743vu74cdLDbCxSdMkGgyHVvsdVqHDyTB53+ci3QLD69WmpbVvXqlqNRk+U94tk+7YNmjU9eS3Ojp3v17kpgyRZPFmdGwwG5fcOUN0GH0iSPPP76fKls/rj9+UvbSD+rz/Xa8qkkcbXXbsnPbch5f9tgx7dplNVv8Fg8kEZ7hrk7ROgRk3aSJK8vP116cIZ/bZhaZqB+D82r1K5ClVllS3bY5zRyyPVZ2AwPHEbz549h34YPUm3b93S3r07NWHcaLm6uqlwkZdr1s2WP9ZpwrgRxte9Ph2a9I+U9WcwPLJK0/ocUh2TRr6PewmYO3uCbty4rs/6jlDu3HbauWOLfviuv/oN+FH58/s+XmbAQ6Ru06nHMf/VmjXL5e3tq8Cggk8lv5dF6i7CkOpzSOnXX3/RrZu3tP/AAY0ZM1qeHh6qWbOmJGnXrl2aOGmSevfqpUIhhXT+wnmNGDlS453yqE3r1uY6jRdKqrG5/t0ldOPGtZo2dYIGDByWKsiZWW3dsk6TJiSPFbv3ujdWTLOOHzVWfPR1OJ+bp74eMl43bl7Xzu2b9evPQ/XZF98bg/FrVy/UyROH9Un3QXLK66Kjh/dpyqTvZWfv9NAl4F5GadXnvxmAbNiwVlMmj9fXXw83aecFQwqpYEgh4+tChYqoXduWWrhwnj7+uPvTKvYLLe1x4cPr/Jdff9XNm7d04MB+jR0zRh4enqpxrz+XpBw5cmjqtBm6deumduzYoR9++E5u7u4qWTJztWfgeUIgPhOZMGGCEhMT5e6ePAPOYDDIyspKkZGRqdZx/69y5swpPz+/h6bJkyeP/Pz85OfnpwULFsjPz0/lypUzfiHg7++vLVu2KCEhwbhkjb29vezt7XXhwoVU+d1P87hBeEkaMmSIBgwYYLLvvVZd1fKDTx47rydRpFh5efsEGV8n3ns4WUxMpOzsk2c7xcZGpZqx/aSsrbPLzd1HoVcvPtV8n3dFipWXt+8DdZ6QTp3HRMk2xS8THpetnaPy5TOd7eGaL7/27E65duLLo1iJCvK99zBVSUpMiJeUNGvJ/oH6jYmJSjWL6UF29o6pZjTFxETK7oH/B/b2jnJz9zZJk88tv3bu3Jwqv2NH9+nK5fPq8FG/VH972dna2snSMkuqGarRUZGpZsk/LktLS7m5eUiSfAv46/y5s5o3d/pLF4gvWaqi/PwfbNdJ/UZ0VIQcHEzbtZ1d+nVqb+9onP2e1jG5be1kaWmZqu1HPyLflK5euai1axZp+LeT5OGZ9DA6L28/HTmyT+vWLFLrttz84snZ2trL0jKLIlL2LdGRsn8KAcfbt2/r903r9e57bZ44r5eFvb29smTJovBw0zqPiIhMNQEmJXe3pHsBPz8/RUSE69fx44yB+J9/+Vm1X69tnCXv5+enW7dua/CQwfrg/fdlaZl5Vza1s0u7nUdFPrqdb/ptvUZ+O1hf9BuUagmnzKx4yYrGh6lKUkJi0lgxKjpC9ibX1Mj/MFaMSvWLyqxZrYy/BvP1DdSpk0e0dvUCvd+mu+Lj4zRvznh16TZQxYonPcgyf/4COnf2hFatmJNpAvHGdp5i9ntkVKQcHtG3bNy4Tt8MH6T+Xw5OtYRTSpaWlgoKCtbFC+efuMwvuvT688iIiEf2524m/XmExo//1SQQb2lpKU9PT0lSQECgzpw5o6lTJhOIBzJQ5h1JZTKJiYmaOnWqRowYoT179hi3vXv3ysvLSzNmzMiwsjk4OKhz587q0aOHcZb722+/revXr2vs2LFmf/8+ffooOjraZHu7Rdpr05uTjU0OObu4G7d8bl6ytXPU4YO7jGkSExN0/Og+kwHr05CQEK8rV87J7gkDcS8aG5sccnZ2N2736/zIod3GNImJCTpxbJ98CjxZnRfwC9HVK6ZfIoVevSBHJ5cnyvd5lj17Drm4uBs3N3dv2dk56uAB0zZ99Mhek/U5UyrgV9DkGEk6eGCXyTF+AYV05bLpQP7qlQtySqN+N29aJW+fAOX3KvBfT+2FZWVlJT+/AP3zzw6T/Xv+2ang4ELpHPVfGZRw78uXl0n27Dnk6uph3Nw9vGVv76j9+3Ya0yQmJujwoT0KCEi/XfsHhJgcI0n79+2Q/71jsma1ko9vYKo0B/btfGi+KcXFJ/3iy8LCdMhnaZlFdzPwGTF4uVhZWcnfP1D/7DbtW3bv3qGCBZ+8b/lj8wYlJCSoStWaj06cSVhZWSkoKEh/b99usn/79u0qUrjIv87HYEhan/i+27fjZGlpOgMzy73ge0Y+V+p5YGVlpYCAQO3elaKd79qukJD0ly7ZuHGtvhn+lfp8NkBly1U0dzFfKNmz55CLq7txc3f3lp29ow7uN72mHj2813h9TIuff0Ed2G86Vjywf6f8HzK+TGJQwr3JT3cSE3XnTmIa10vLTNX2raysFBAYqJ07TfuWXTu3q9BD2vmGDWs1bOjX6tt3oMqXf3Q7NxgMOnHiuByd8jxxmV90VlZWCgwK0vbtf5vs3759uwo/Rn8ug0HxD/Tn6ad5+cbnwIuEGfGZxPLlyxUZGanWrVvLzs7O5G+NGjXShAkT1KlTJ+O+o0dTPzilYMGkQOSdO3dMnsYtSdmyZTP+PS4uTleuXDH5e9asWZUnT/oX2Y8++kjDhg3TggUL1KhRI5UvX17du3dX9+7ddfbsWTVo0ECenp66fPmyJkyYIAsLi6c2I8fa2lrW1qbL0GTLFvVU8n4SFhYWqlKtvlavnCVnF3fldXHX6hWzlC2btUqXrWJMN3nCMNnb51G9hkk/F05MTNDlS2clSXcSExQVFabz507I2jq7nF2SvjFfMPcXFS5aTo6OzoqNjdKq5TN1+9ZNlauQegmPzMTCwkKVq9bXmpWzlNfZTc4u7lqzcnaqOp8yYbjsHZxUt8GDdX5O0r06jwzT+XMnZW1jI2fnpDqvUq2Bvh3WVatXzFKJ0q/q7Omj+nPzSr39btdnfp4ZxcLCQtVrNdTyZTOSbrhcPLR82QxZZ7NRufLJa0iO+3mI7B3yqHHTtpKk6jUaaOigrlqxfJZKlKio3bv/1KGDu9Sn7yjjMTVqNdLggZ21fOkMlS77mk6dPKJNv61Qqw+6mZTh1q0b2rH9dzVr3v7ZnPRzqF79Zho54iv5+wcpKKiQVq9eqmvXrur12vUkSVP+z95dh0WV9XEA/9Jg0CWghHSJifG6dqxrd7euioGt69oK6qrrrh0oJqKiYit2ro2tgJ3EkEoNMO8fo4NDKCrDCPP97HOfZ+fOuXfOORzPPfObc8/dsAoCQTTGjMt+rseTx+EAgNSUFCQkxOPJ43CoqqmiQgXxDOudgZtha+eIcuXMIMzIwPWrl3DyxBEM9RpX5OUrakpKSmjeoiOC926BaTkLmJqaI3jvVqhraKL2/7KXfVuxzAf6+obo2n0wAKD5rx0wa8ZI7AvehqrV6uD6tQu4e+c6ps9cKjmmxW+dsGKZD2wqOsDOzgUnT+xHTEwkGjVpLUnz/n0iYmIiJXc5vH0j/kFKV1cfuroGMDOrABNTc/itXYTuvYaibBltXLt6HnfvXMO4ib5FUUU/tZS0VLwRREtev4sT4PGblyhbqjSMFezH6R/Vrn0XLPxrNuzsHeHk5IrDh4IRHRWJFr+1AwBsWL8SgpgYjJuQ3bc8fhwGAEhNSUZCQjwePw6DqqoaLC2tpc599MgB1KpdF9ra0mNYRde9W3dMnzEdzo5OcHNzw569e/Au8h06tG8PAFi2fDmio6Mwc4b4zs8dO3fC1NQUVpaWAIDQW7ewZesWdOncWXLOunX/h23bAuBg7wAXVxe8evkKq9asRt26daGiolL0hfzJdOjYDfPnzYS9vSOcnN1w6OBeREVFomUrcTv3W7cCMTHRmDhpOgBxEH7BvFkY5jUaTs6uklnGGuoaKP3xmVpCoRDPnz8V/39GBmJiohEREQYtLS2Ym5eXQynlR0lJCc2ad8T+4K0w+fiD977gLVBX10St2tnX1NUrfKCnb4TOXcVjxWbNO2DurFE4sC8AVarWwY3rF3Dv7nX8OT17rLhz+1q4e3hC38AYqSnJ+O/SSTy4fwvjJ80HAGiVKg1Hp0rYvm0V1NU1YGhogocPbuH8uWPoLodJWvLUqVM3+PrMhIODE1xcXHFgfzAiIyPRqrW4na9dswLRMdH44w9xOz9x4hh8fWZi+IjRcHZ2RezHmd3qGhqSZ8dt9F8HJ2dXWFiUR/KHDwjavQMREWEY5V3yx4oF0a1bd8ycMR1Ojs5wdXND8N49iIx8h3btOwAAVixfhujoaEz/2J/v2rkDJqamsLS0AgDcvhWKrVu3oFPnLpJzbvTfAEcnZ1hYmEMozMDFixdw6NBBTJg4qcjLR1RUVqxYgb/++gtv376Fi4sLlixZgrp1v/5cuAsXLqBevXpwdXXNFe8sbAzEKwg/Pz80btw4VxAeADp06AAfHx/cuHFDcutT165dc6V7+lQ8QHz//j0qV64s9Z6lpSWePXsGADhy5AjKlSsn9b6DgwMePnyYb/6MjIzQq1cvzJgxA+3bt4eysjIWLlyIGjVqYOXKlVi/fj2Sk5NhYmKCX375BZcuXYK2dsl/qGjT5l0gTE9HwNalSP6QBGsbR4wYMw+amqUkaWIFUVJrxyXEC+Aza6jk9fGjO3H86E7Y2btjzATxusZxcTFYv8YH798nokxZHVjbOGHCH//mOXtY0TRp3hlCYRoCty1D8ockWNk4YvhoX6k6j4vNXefzZmfX+Ylju3Di2C7Y2bvDe/xCAICltQMGD52OfXvW4/CBLTAwNEXHLkNRo6ZiPayrxW9dIUxPw2b/f/AhOQkVbZwwdsICaGll169AECU1G8nO3hVDvKZi96712LNrA4xNzDDEayoqfrbsjY2NI4aPmoVdO9YheO8mGBmVQ/eew1CrjvTzLy5fOgVABM9aDaGo6tZrhMSkBGzf5o/YWAEsrawxfeZfMDYxBQDExgkQneMho6NG9JP8f0TEI5w5HQJjY1P4+e8CAKSmpmDlikUQxERBXV0DFuUtMXbcNNRVkIfRtWrdDenpadjg9zc+fEhCRVtnTP7jrxztOlJqlqm9gytGjJqGHYF+2Bm4HiYmZhgxajps7bLvvqlVuyHeJyVid9BGb4+iLgABAABJREFUxMfFwqK8NSZMmg8jI1NJmuvXLmD1yvmS10v/mQUAaN+xDzp26gdVVVVMmDQf27etwcIFfyAtNQUmJuYYMmwyKleuKctqKRbCXr/ABL/sZ4WsPhQEAGhS2RPjOvaWV7aKpXr1GyMpKRHbtm5AbKwAVpY2mDVnIUw+9S2xAkTl6FuGD8vuW8LDH+H0qRAYm5hi46Ygyf5Xr17g3r3bmOvzd9EUpBhp2qQJEhISsG69H2JiYlDRpiKW/P23ZBweI4jBu8jsOheJsrB8xXK8efMGKioqsLCwwHAvL7Rv116Spn+//lBSUsLK1asQHR0NXV1d1P1fXQwbOjTX5yui+g0aIzExAVs2rxe3cysbzPVdBBMTcZ0LBAJERWXX+cEDe5GZmYml/y7E0n8XSvY3adoCEyZO/XhMDIb+nv0w3J07tmHnjm1wr1QZixbL/u7gn81vrboiPT0NGzcsQfKHJNhUdMKEyTmvqVFQUpYeKw4bMQ1BO/wQtHM9jE3MMGzENKm7iBMS47B6hQ/i42OhVao0ype3wfhJ8+HqVk2SZtiIadi5fS1WLZ+L9+8TYWhogo6dB6Bh4+wfwBVBw4ZNkJiYgE0b/cTt3NoG8+Yvhqnpp3Yeg6jI7Il3+/ftQWZmJv5ZshD/LMlu582atcCkyeKlIN+/f4/Fi+YhNlaA0qXLwNbOHv/8uwpOTgW/y68ka9KkKRISEuC3fh0EMTGwsamIxX8vydGfZ9d5lkiElZ/15+YWFhjmNRztPuvPU1JT8deC+YiOjoKGhgYsLS0xY+YsNGmi2JPvflo/+LwuAgIDA+Ht7Y0VK1agTp06WL16NX799Vfcv38fFSrk/1DohIQE9O7dG40aNUJkZP4PBi8sSiJFus+KqIBOnnsh7ywoHC6RUPRKafK32KJmaKCYD4eVp6SkDHlnQeEYRNyTdxYUTlY1D3lnQeEY6qrJOwsKJ/59pryzoHDeRqbKOwsKp7yZlryzoHC0tHi3T1HT0y35kyq/VWJsnLyz8FPS1i/48xE9PT1RpUoVrFy5UrLPyckJbdu2ha9v/ncCd+3aFXZ2dlBRUcHevXtlPiOea8QTERERERERERER0U8jLS0NiYmJUltaWlqudOnp6bh+/TqaNpW+46Np06a4ePFivuffsGEDHj9+jOnTpxd63vPDQDwRERERERERERER/TR8fX2ho6MjteU1uz0mJgaZmZkwMZFebtnExCTXMyw/CQ8Px6RJk7B161aoqhbdagFcl4CIiIiIiIiIiIiIfhqTJ0/GmDFjpPZpaGjkm14px1r7IpEo1z4AyMzMRPfu3TFz5kzY29sXTmYLiIF4IiIiIiIiIiIiIvppaGhofDHw/omhoSFUVFRyzX6PiorKNUseAJKSknDt2jXcvHkTw4cPBwBkZWVBJBJBVVUVx44dQ8OGDQunEDlwaRoiIiIiIiIiIiIiKnbU1dVRtWpVhISESO0PCQlB7dq1c6XX1tbGnTt3EBoaKtmGDBkCBwcHhIaGwtPTU2Z55Yx4IiIiIiIiIiIiIiqWxowZg169eqFatWqoVasW1qxZgxcvXmDIkCEAxMvcvH79Gps2bYKysjJcXV2ljjc2Noampmau/YWNgXgiIiIiIiIiIiIiKpa6dOkCgUCAWbNm4e3bt3B1dcWhQ4dgaWkJAHj79i1evHgh51wCSiKRSCTvTBD9bE6ek/8/TkWTxa6oyJXS5G+xRc3QQF3eWVA4SUkZ8s6CwjGIuCfvLCicrGoe8s6CwjHUVZN3FhRO/PtMeWdB4byNTJV3FhROeTMteWdB4Whpqcg7CwpHT1db3ln46STGxsk7Cz8lbX09eWeh0HGNeCIiIiIiIiIiIiIiGeJ0SCIiIiIiIiIiIiJ5UFKSdw6oiHBGPBERERERERERERGRDDEQT0REREREREREREQkQwzEExERERERERERERHJEAPxREREREREREREREQyxEA8EREREREREREREZEMMRBPRERERERERERERCRDDMQTEREREREREREREckQA/FERERERERERERERDLEQDwRERERERERERERkQwxEE9EREREREREREREJEOq8s4AERERERERERERkUJSUpJ3DqiIcEY8EREREREREREREZEMMRBPRERERERERERERCRDDMQTEREREREREREREckQA/FERERERERERERERDLEQDwRERERERERERERkQwxEE9EREREREREREREJEMMxBMRERERERERERERyRAD8UREREREREREREREMsRAPBERERERERERERGRDKnKOwNEREREREREREREikhJSd45oKLCGfFERERERERERERERDLEQDwRERERERERERERkQwxEE9EREREREREREREJEMMxBMRERERERERERERyRAD8UREREREREREREREMqQq7wwQERERERERERERKSQlJXnngIoIZ8QTEREREREREREREckQA/FERERERERERERERDLEQDwRERERERERERERkQwxEE9EREREREREREREJEN8WCtRHrQ0VeSdBYXzPjlD3llQOCoqfCBMUYuNT5d3FhSOob6GvLOgcLKqecg7CwpH+VqovLOgcJLrVpV3FhSOTlk1eWdB4SSncHxe1D6kZMo7CwqHz8ikn4EIbIiKgjPiiYiIiIiIiIiIiIhkiIF4IiIiIiIiIiIiIiIZYiCeiIiIiIiIiIiIiEiGGIgnIiIiIiIiIiIiIpIhBuKJiIiIiIiIiIiIiGRIVd4ZICIiIiIiIiIiIlJEIpG8c0BFhTPiiYiIiIiIiIiIiIhkiIF4IiIiIiIiIiIiIiIZYiCeiIiIiIiIiIiIiEiGGIgnIiIiIiIiIiIiIpIhPqyViIiIiIiIiIiISC74tFZFwRnxREREREREREREREQyxEA8EREREREREREREZEMMRBPRERERERERERERCRDDMQTEREREREREREREckQA/FERERERERERERERDKkKu8MEBERERERERERESkikUjeOaCiwhnxREREREREREREREQyxEA8EREREREREREREZEMMRBPRERERERERERERCRDDMQTEREREREREREREckQA/FERERERERERERERDLEQDwRERERERERERERkQwxEE9EREREREREREREJEMMxBMRERERERERERERyRAD8UREREREREREREREMqQq7wwQERERERERERERKSKRvDNARYYz4omIiIiIiIiIiIiIZIiBeCIiIiIiIiIiIiIiGWIgnoiIiIiIiIiIiIhIhhiIJyIiIiIiIiIiIiKSIQbiiYiIiIiIiIiIiIhkSFXeGSAiIiIiIiIiIiJSSCJ5Z4CKCmfEExERERERERERERHJEAPxREREREREREREREQyxEA8EREREREREREREZEMMRBPRERERERERERERCRDfFgrERERERERERERkRzwWa2KgzPiiYiIiIiIiIiIiIhkiIF4IiIiIiIiIiIiIiIZ4tI0xdyqVaswfvx4xMXFQVVV/Od8//499PT0ULNmTZw7d06S9ty5c/jll1/w6NEjNG3aFN7e3vD29pY634wZM7B3716EhoZKXs+cOTPX5zo4OODhw4cAgPr16+PMmTO50vz+++9YtWoV+vbti40bN+ZbBn9/f/Tp0yffz/50nk9CQ0NRuXJlPH36FFZWVpL9QUFBWL58OW7evIm0tDSUL18ederUwYgRI1C5cuUv1uPPQCQSYe/ujThz6iA+fEiCTUUn9O47EuYW1l887uqVs9izawOiot7A2NgMHTr1R9XqdSXv7wnyR/CeTVLHaOvo4d/lQZLXCQmx2LF9Le7duYbk5Pewd3BHzz4jYGpqUbiFLAZEIhEO7d+MC+cOISX5PSytHdGl+3CUM7PK95i3b57hQPAmvHwRjlhBJDp0HoIGjdtLpZk2uRdiBZG5jq1bvxW6dB9R2MX4aYlEIuwJ2ojTJw/gw4ckVLR1Qu9+o2Dx1XZ+BkE7NyAq8g2MTczQsfMAVPusnZ8ICcbJ4/sQHfMOAGBuboW27XujkoenJE1qagp2BKzB9evn8T4pEYZGpmjarD0aNWkjm8L+JEQiEfYGbcTpkwcldd6r38gC1PlZ7P6szjt07i9V55/bH7wNuwLXoWnz9ujRe7hk/7UrZ3HqxAE8exqG9+8TMctnDSytbAu1fMXFgf27sXvXNsTGClDB0hqDh4yEq6tHnmljBTFYt3YZIsIf4s2bV2jdpiMGD/GWShNy7CCWLPbJdeyefSehrq4hgxIUPwf278auneI6t7S0xu9DRsLVzSPPtLGCGKxdswzhEQ/x5rW4zocM9ZZKM2H8cNy5fTPXsdVr1MKs2QtlUIKS6c7TcOw8dxzhb14iNikB03sMRm3nSvLOVrG1Z88ubA/YithYAaysrDF8xGhUquSRZ9qzZ05hb/BuRISHQyhMh5W1Dfr1G4gaNWpKpUtKSsK6tatw9uxpvH+fBFPTcvDyGoWatWoXQYl+frt27cTmzZshEMTAxsYGo0ePzfe7RmhoKJYt+xfPnj1HWloqTE1N0a5de3Tv3kOS5sCB/Zg1K/f3rXPnLkBDg/05ABw6uAd7dgcgLlaAChWsMGDQSLi45t1vxMbGYIPfckREPMLbN6/QslVHDBw8UirNi+dPsW2rHx5HPEJU1DsMGDQCrdt0LoqiFBsHD0iPWwb9/oVxS2wM/D4bt7RqnXvc8rkzp4/jr/nTUbNWXfw5bZ5sClAM7d2zC9u3b4UgVgBrK2sMHz4a7vn152dPIXjvbkREfOzPrWzQN0d/fvjwAcyfNyfXsUePnWHfQiRHDMQXcw0aNMD79+9x7do11Kwp7nTPnTsHU1NTXL16FcnJyShVqhQA4PTp0zAzM4O9vf03fYaLiwuOHz8ute9T0P+TQYMGYdasWVL7Pn3uP//8g3nzcl9ge/XqhfDwcPz222/5frampib8/PwwZsyYL+Z74sSJWLRoEUaOHImZM2fCwsICL168wPnz5/HHH3/g8OHDXy2nvB06sB1HD+/CwN8nwNS0PPYFb8Ff8ybA96+N0NIqlecxEeH3sHLZLLTv2B9Vqv0PN66dx4pls/DH1H9R0dZJks7cwgrjJ2UHCZSVs2+GEYlE+PfvaVBRUcHI0bOhpVUKRw/vwl++4+AzfwM0NLVkV+if0PGjO3Dq+G707DsOxibmOHJwG5b+PQnTZq+Hpmbef4f09DQYGpmictW62L1jdZ5pxv+xFKKsLMnrN6+fYdmSSahc9ReZlONndXD/dhw5vBODfp+IcuXKI3jPZizwGY/5izbl287Dw+5h+b+zxD8yVauL69fOYfm/M/Hn9H9R0dYZAKCvb4TOXQfB2NQcAHD+7FEsWfQnZvuukQSct25ejgf3b2LIsCkwNDLF3dtXsXHDEujqGaBqtf8VTQXIwaH923Hk8C4M+n0CTMuVx749W/CXzwTMW/SFviXsHlb8OwvtO/VH1Wr/w/Vr57Hi31mYMl26bwGAJ48f4vTJAyhfwSbXedLSUmHn4IrqNethw9pFMilfcXD2zHGsXf0PhnmNhZOLO44c2ovpf47DyjVbYGxsmiu9UCiEjo4uunTrg717AvM9b6lSpbF6XYDUPgbhxc6cPo7Vq/6B1/CxcHZxx6GDezH1z3FYvfYLda6ri65d+2BPPnU+daoPhBlCyeukxAQMG9oXdes2kFk5SqLU9HTYlLNA06q1MHvbWnlnp1g7eSIEy5Yuwegx4+Hq6o79+/Zi4oTR2LgpACYmudv5rVuhqFatBgYNGoqyZcrg0OGDmDxpHFau8oO9vQMA8b+FsWNHQk9XD7Nm+8DIyBhRUZEoVap0URfvpxQScgyLFy/ChAmTUKlSJezZsxve3iMRGLgTpqa561xLSxOdOnWGra0dtLS0cOtWKHx9faClpYV27bInbZQuXRo7dwZJHctAmdi5syfgt/Zf/D50DJyc3XD08D7MmjEey1ZshpGxSa70QqEQ2tq66NS5N/YF78jznGlpqTAxLYfadepj/bqlsi5CsfNp3DLUayycnd1x+NBezJg6DitW538N1dbRReeufRD8hXELAERFvsP6dcvy/SFFUZ08GYJly5bAe/R4uLm6Y9/+vZgwcTQ2bvx6f16mbBkcPnQQf0weh5Ur/WD3sT8HxH3Lps3S/w7YtxDJF5emKeYcHBxgZmaG06dPS/adPn0abdq0QcWKFXHx4kWp/Q0afPuXRVVVVZiamkpthoaGUmlKlSqVK422tjYAQEdHJ9d7fn5+uHjxIoKDg3OdK2f5GjRogD///DPfNP/99x8WLFiAxYsXY/Hixahbty6sra1Rr149TJkyBYcOHfrmMhc1kUiEY0eC0KpND1Sr/gssyltj0O8TkZaeiv8unsj3uGNHguDiWg0tW3eHmVkFtGzdHU7OVXDsyC6pdMrKKtDV1Zds2tq6kvci373C44j76NPPGzYVHVHOrAJ69xuF1LRU/HfppKyK/FMSiUQ4dXwPmrXoBo8q/4OZuTV69RsPYXoarl3Ovy4srRzQruNgVKvRAKpqanmmKVtWF9o6+pLt7p3LMDQyg529u6yK89MRiUQ4emQXWrfpieo1xO188NBJSE9PxaWLx/M97uiRXXB1q4ZWbXrAzLwCWrXpAWeXKjh6OPsLa+WqtVGpck2UK1ce5cqVR6cuA6GpqYXH4fclaSLC7+F/dZvBydkDRkamaNCoFSpUqIinT8JkWm55Etd5EFq36YFqH+t80NCJSP9K33L0SBBc3KqhVZvuH+u8+8c6l+5bUlNTsGq5D/oPHIvSpcvmOk+duk3Rtn1vuLhWLfSyFSd7dgeiabOWaPZra1SoYIXBQ7xhaGSMQwf25JnexLQcfh/qjUaNf0XpUmXyPa+SkhL09Q2kNhL7VOfNP9b5kKHeMDIyxsEv1PmQod5o3ORXlC6dd52X1daWqusbN65CQ1MDdX9pKMuilDjVHVzQt0kr/M/FQ95ZKfZ27AhAi99aoWXLNrCyssaIkaNhZGSM4L2780w/YuRodO/eC05OzrAoXwGDBw+FhUV5XLx4XpLm0KH9SEpMxFyfBXBzqwRT03Jwd/eAra1dURXrp7Zt21a0bt0Gbdu2hbW1NcaMGQsTExMEBe3KM72DgyOaNWuOihUrwszMDL/+2gI1a9ZCaKj03TVKSkowNDSU2kgseG8gGjf5DU2btUL58lYYOHgkDA2NcfhQPv25STkM+n0UGjZqjtL5/IBkZ++Efv298Eu9xlBTU5dl9oulvXsC0aRpSzRr3hrlPx+3HMy/zn8fIh63lMrnGgoAmZmZWLhgJnr0GgBTUzNZZb9Y2rkjAC1aiPtzSytrjBgxGsZGxggOzqc/HzEa3br3gqOTMywsKmBQHv05AEBJCQYGBlIbEckXA/ElQP369XHq1CnJ61OnTqF+/fqoV6+eZH96ejouXbr0XYH4wnbgwAFMmzYN/v7+qFTp67+Ez5s3D0FBQbh69Wqe7wcEBKBMmTIYNmxYnu8rKSn9UH6LQnT0WyQkxMLVrZpkn5qaOhwdKyEi/F6+x0VE3Ierm3SAy829Wq5jIiNfw3t4J4wb3R0rls1GVNQbyXufZvd9PghVVlaBqooqwsLu/lC5ihtBzDskJsbC0Tm7TtXU1GFr744nT+5/4chvk5EhxNX/TqBWnWbFon0Wluiot0iIj4Wru3Q7d3CqhPCwL7Tz8PtS/zYAwM29OsLz+beRlZWJ/y6eRFpaKmztXCT77R3ccPPGRcTGRkMkEuH+vZt49+4V3Nyr/2DJfl4/VufSfYtrHn3Lpg3/oFJlT7i4KXag/UuEQiEiwh+hcpUaUvurVKmBBw9+rI9NSUlB397t0btnW8yYNh6PI0ruj0rfQigUIjz8EapUzVHnVWvg/v3Cu64dO3oA9eo1hqaC3TlGPwehUIiwsEeoXt1Tan/16p64e/dOgc6RlZWF5ORkaJfVluy7cP4cXFxc8ffff6Ftm1/Rt093bN7sj8zMzELNf3EkFArx8OFDeHpKL+Xj6VkTt2/fLtA5Hj16iNu3b6NyZenrZkpKClq3bomWLVtg9GhvPHr0sNDyXZwJhUI8jgiDR2Xp/tyjcnU8fKhY31OKSn7jlspVauDhD15Dt2/bAG0dXTRt1uqHzlPSCIVCPMqnP7/3jf15WW1tqf0pKSno0rktOnZshUmTxiI87FGh5ZsKmUjELa+tBGIgvgSoX78+Lly4gIyMDCQlJeHmzZv45ZdfUK9ePclM+f/++w8pKSlSgfiJEyeiTJkyUpuPT+71Zu/cuZMr3cCBA6XSrFixIleavNaFf/jwIXr06IHJkyejU6dOBSpflSpV0LlzZ0yaNCnP98PCwmBjYyO1XM7ixYul8pKQkFCgz5KXhPhYAOK12z+nraOHhITYLx6nraOf4xh9JCTESV5XtHXCoN8nYezE+eg3YCwS4mMxZ+YIvE8S10m5chVgYGiCnYHr8OFDEjIyhDiwbxsSEmKREC8orCIWC4mJ4rouqy39dyirrYvEz+r0R90OvYiUlPfwrN200M5ZHHxqyzo52rmOtp7k30Cex8XH5v1vI8cxL188waB+v6J/76bwX78Yo0bPgrmFleT9Xn1GwMzcEt7DO6N/7yZYOH8i+vTzhoOj2w+W7Of1qc5z1V8B6lwnR9+io6OPhPjsfwf/XTyJ58/C0anLoELMccmTmBiPrKxM6OpJ16eunh7iYr+/jy1f3hKjx07BtBnzMWHSDKirq2P82CF4/frlj2a52PtU53q6OepcVw9xcYVzXXv08D6ePXuC5s0ZTCD5SEiIR2ZmJvRz9C16+vqILWDfEhi4DampKWjQsJFk39u3b3DmzClkZWZh/oK/0at3P+wI3IbNm/0LM/vFUny8uM4NDKTrXF9fHwJBzBePbdmyBerUqYU+fXqjU6dOaNu2reQ9S0srTJs2HQsXLsbs2XOhoaGOgQMH4MWLF7IoRrGSmJjw8RoqPY7R1dNDXFz+4xj6fpJraM6+5Qevoffv3caxowcwYtTEH81iiZOQEI+szEzo6eeoc72C9+c7PvXnDbL78woVrDBp0p+Y6/MXpk2dDXV1dQwfPhivXrFvIZInrhFfAjRo0AAfPnzA1atXERcXB3t7exgbG6NevXro1asXPnz4gNOnT6NChQqwsclew3f8+PHo27ev1Ln+/fdfnD17Vmqfg4MD9u3bJ7WvbFnpJQh69OiBKVOmSO0zNjaWep2QkIC2bduiXr16mD179jeVcc6cOXBycsKxY8dynRfIPeu9f//+aN26NS5fvoyePXtC9IVf0tLS0pCWlia1Lz09Tabr7F68cBwb1y+WvB49zhcAoATpcojz/eUZ0znfFYlEUjvdK332y3p5wNbWGePH9sT5c8fQvEUnqKqqYsSomfBb+xe8fm8DZWVlOLtUhXulGijprl4+gYAt/0heDx0ufphNrhoXFe6dFRfPH4Gza3Xo6pbsWwMvng/BBr/sdj52Qj7tHF+v31zvi0TIuaucWXnM8V2HD8nvce3KWaxZNQ9/TF0iCcYfO7IbjyMeYPTYuTAwMsGjB7exccMS6Oga5Jr9XVxdPH8c/p/V+Zh86hwQfb1N56ry7DoXCKKwddNyjJ+8AOrqvKW7IHL37z/Wrzg6ucLRyVXy2tnZHSOH98P+4F0YMmz0d5+3JMlZv+LLY+H05UePHoCVlQ0cHJ0L5XxE3y3P6+PX2/nx48fgv2Ed5voskAq4ZWVlQVdXD+PGT4KKigocHBwRExON7QFb0bfvgMLOfTGVe7z+tTpfvXotUlJScPfuHSxbtgwWFhZo1qw5AMDNzQ1ubtmTAipVqoRevXpix45AjBs3vvCzXwwV9jWUCiCva+h31nly8gcs+msWRoyaCB0d3ULIXMmU+zuSKHcfn4cTx4/B338d5syV7s9dXFzh4pI9VnR1c8egQX2wO2gnRo4aW3gZJ6JvwkB8CWBrawsLCwucOnUKcXFxqFevHgDA1NQU1tbWuHDhAk6dOoWGDaXXMDU0NIStra3UPv0cv8ICgLq6eq50Oeno6HwxTVZWFnr06AFlZWVs2bLlmy/iFStWxKBBgzBp0iT4+flJvWdnZ4fz589DKBRC7eP63Lq6utDV1cWrV6++em5fX1/MnDlTal//gaMxcLDsLk6Vq9RGxYrZDzzMyEgHIJ69qquXHZxNSozPNXv4czq6+rlmzCclxkFHO/9jNDS1UL68DSIjs+vGytoes33WIjn5PTIyMqCtrYtZ04fBytoh3/OUBG6VasHK2lHyOuPjMj2JiXHQ+SxInpQUj7Kfrav/I2IFkXj04CYGDZ1WKOf7mVWuWkfyMFUAEH5s5/E52nliYlyuGduf09HVzzV7OzExPtfdIKqqajD5+LBWGxsHPHn8EMeOBKHfwLFIT0/DzsB1GDVmFjwq1wIAVKhQES+eR+DwwcASE4ivXLW21MNUhfn0LeL6+9Y6z/47PXsShsTEOEyf8rvk/aysLDx6eBvHj+2F36ajUFZWKZQyFXfa2rpQVlbJNYssIT4u1yz5H6GsrAx7eye8efP1615J96nOY3PWeULh1HlqairOnD6OXr0Hfj0xkYzo6OhCRUUl12zJuLi4XDNZczp5IgQL5s/FzFk+qFZNeuKFgYEhVFVVoKKS3YdbWlohNlYgNdZWRLq64joXCHLX+dee0WFuLh6f2NraQiAQYO3aNZJAfE7KyspwdnbGy5e8w0lbW+fjNVR6TJIQHwdd3fzHMfT9JOOWHH1LfEIcdHW/7xr67u1rREa+xawZ2bPhRaIsAEDr337B6rXbUM7M4vszXczp6OhCOY/+PD4uLtddTzmdPBmCBQvmYsbM3P15TsrKynB0cMKrV+xbiOSJS9OUEA0aNMDp06dx+vRp1K9fX7K/Xr16OHr0KP777z+5rg//559/4sKFCwgODpY8xPVbTZs2DWFhYdi+fbvU/m7duuH9+/dYsWLFd5138uTJSEhIkNp69x3+XecqKC2tUjAxNZdsZuZW0NHRx7271yVpMjKEePjwltQa1znZ2jpLHQMAd+9c/+IxQmE63rx+nuds7FKlykBbWxfv3r3C0ydhqFK19neUrvjQ1CwFI2NzyWZazhLa2vp4eP+GJE1GhhARYbdhY1M4sx4vXTiKsmV14eLm+fXExVzOdm5ubgUdXX3cu3NNkiYjQ4hHD27Bzv4L7dzOGXfv5Gzn12D3hXYuJpI8AyEzIwOZmRlQUpK+7CkrK3/xjpniJr86/7z+Clrn93LVeXbf4uxaBXPn+2G271rJZm3jgFp1GmG271oG4T+jpqYGWzsH3Lwp/ZyTmzevwumzGe0/SiQS4cnjcD6wFeI6t7NzwM0b0nV+48ZVODv/eJ2fO3sCQqEQDRs1++FzEX0vNTU12Ns74Nq1K1L7r127AlfX/JdcO378GHx952DqtFmoVatOrvdd3dzx+vUrZGVlSfa9evkSBgaGCh2EB8R17ujoiCtXLkvtv3LlMtzd3b/pXEKhMN/3RCIRwsLCYGjI/lxNTQ0Vbe1xK1S6Pw8NvQpHx8K7hlK2T+OW0BzjltAbV+H4nddQi/KWWLZyM/5d7i/ZPGv+D27uVfDvcn8YGpkURtaLLTU1NTjk05+7fKE/P3H8GOb5zsGfU/Puz3MSiUSIiAiHgQEfBk0kT5wRX0I0aNAAXl5eEAqFkhnxgDgQP3ToUKSmpn53ID4jIwPv3r2T2qekpAQTk+wLZnJycq40Ghoa0NPTw44dOzBv3jxs2LABZcuWzZXu0zruX2NiYoIxY8bgr7/+ktpfq1YtjB07FmPHjsXz58/Rvn17lC9fHm/fvoWfnx+UlJSgrJz/b04aGhrQ0JBehkZdPemr+SlMSkpKaNq8A/bv2woTE3OYmFrgwL6t0FDXRM3a2eu8rVnlCz09Q8mazE2atYfvHG8c3B+AylXr4Ob1C7h/7zr+mPqv5Jjt21bCo3JtGBgYIzExHvuCNyMlJRl16mavT37l8mmULasLA0NjvHr5FFs3L0OVanXg6lZyH2KZFyUlJTRo3A7HDgfA2MQMRsbmOHp4O9TUNVDNM/uOkk3rF0BH1wBt2otv0c7IEOLd2xeS/4+Pj8Grl4+hoaEJI2NzyXFZWVn47+IxeNZuIjXTTFEoKSmhWfOO2B+8FSamFjA1tcC+4C1QV9dErdqNJelWr/CBnr4ROncVt/NmzTtg7qxROLAvAFWq1sGN6xdw7+51/Dk9u53v3L4W7h6e0DcwRmpKMv67dBIP7t/C+EnzAQBapUrD0akStm9bBXV1DRgamuDhg1s4f+4YuvfM+0HPJYG4zjvgQPBWmJiaw9TUAvuDt0I9R9+yeoUv9PQNJXXetHl7+MzyxsF9n/Utd69jysc619IqBYvy1lKfpaGhiTJltKX2v3+fCEFMFOLjxGvnvnsrnoGjo6v/3bOqiqN27btg0V+zYWfnCEcnVxw5HIzoqEi0+K0dAMB//UoIBDEYO36q5JjHj8UPXk1JTUZCQjwePw6DmqoaKliK63fblvVwcHSBmbkFkpM/YH/wLjx5Eo6hw3mrMSCu84V/zYadvSOcnFxx+JB0nW9YvxKCmBiMm5C7zlNTsutcVVUNlpbSbf3okQOoVbsutLV1iq5AJUhKWireCKIlr9/FCfD4zUuULVUaxgrULxSGzp27Ye7cmXBwcIKLiysO7A9GVFQkWrcRt/M1q1cgOiYaU6ZMByAOwvvMnYkRI0fD2dlVMrNbQ0NDMhZv26Y9dgftxL//LkaHDp3x6tVLbNnijw4dOsunkD+Z7t17YPr0aXBycoKbmzv27NmNd+/eoX37DgCA5cuXISoqCjNnzgIA7Ny5A6amprC0tAIA3LoVii1bNqNz5y6Sc65duwaurm6oUKE8Pnz4gMDA7QgLe4QJEyYUefl+Rm3adsGSxXNga+sIBycXHD2yDzHRUWjeoi0AYJP/KggEMRg99k/JMU+ehAMAUlJTkJAQjydPwqGqqooKFcT9uVAoxMuXz8T/nyGEQBCNJ0/CoaWppdAzsz9p264LFi+cDVs78TX0yOFgREdHokWLj+OWDR/HLeOyr6FPPl1DP45bnny8hlawtIa6ugasrGykPqN0aXGfk3O/ourUuRt8PuvP9x8IRmRUJFq3/tifr1mBmOho/PGxPz9x/Bh8fGZixIj8+3N//3VwdnaFhYW4b9kdtAMREWHwHj1OPoWkLyo5U8PoaxiILyEaNGiAlJQUODo6SgXI69Wrh6SkJFSsWBHly5f/rnPfu3cP5cqVk9qnoaGB1NRUyeu1a9di7dq1UmmaNWuGI0eOYOXKlRCJRLnWo/9k+vTpmDFjRoHyMn78eKxcuVLqswFg4cKFqFGjBlauXIn169cjOTkZJiYm+OWXX3Dp0qXvnoVflFq07Ir09DRs8v8HH5KTULGiE8ZNXAAtrVKSNIKYKKkZvXb2rhg6fCqCdq7H7l0bYGxihqHDp0otTREbG4NVy+cgKSkBZbV1UNHWGVNnLoOhoakkTUJ8LLZvXSm+bV9XH7X/1xRt2vUqmoL/ZBo364z09DQEbl2G5OQkWFk7Yri3LzQ1s/8OsbFRUssrJcQLMG/2UMnrE8d24cSxXbC1d4f3uIWS/Y8e3EBcbBRq1lHcWZS/tRK3840bliD5QxJsKjphwuS/pNu5IApKytLtfNiIaQja4YegnethbGKGYSOmSS17k5AYh9UrfBAfHwutUqVRvrwNxk+aD1e3apI0w0ZMw87ta7Fq+Vy8f58IQ0MTdOw8AA0bty6awstJi491vmnDP5I6Hz9Zum+JFURJ/WAprvOpCNqxHkE7N3ysc+m+pSBuXr+IdasXSF6vWCp+Pkjb9r3RrmPfHytYMfJLvcZITExEwNYNiI0TwNLSBjNnL4Sxibgfjo0VIDoqUuqYkV79JP8fEf4Ip0+FwNjYFBs2BQEA3n9IwtJ/5yMuLhalS5VGxYr2mP/XCjg4cM1yAKhXvzGSkhKxbesGxMYKYGVpg1lzFsLkszqPipau8+HDsus8/FOdm5hi48c6B4BXr17g3r3bmOvzd9EUpAQKe/0CE/yyn8+y+pC4fptU9sS4jr3lla1iqWGjJkhITMCmjX4QCASwtrbB/PmLYWoqHrcLBDGIisyeALN/3x5kZmZiyd8LseTv7PFJ8+YtMPkP8ZJ5xiYmWLjoHyxftgT9+/WEoaEROnTsgu7dFXNcmFOTJk2RkJAAP791iImJQcWKFfH33/9IvivFxMQg8rM6z8rKwvLly/DmzRuoqKjAwsICXl4j0L59e0mapKQk+PrOhUAgQJkyZWBv74DVq9dKre2syOr+0ghJSYkI3O6P2FgBLC2tMW3GAhgbi/vzuDgBYnL056NH9pf8/+OIRzh7RnwNXbt+JwDx96PP0+zdvR17d2+Hq6sH5s5bWgSl+rn9Uk98Dd2+TXwNtbSywYxZ2eOWuLzGLcOlxy1nTovrfP3GINDXNWzYBIkJCdi4yQ+x+fTnkVHZfcu+/R/78yULsWRJdn/erHkLTJ4s7s/fv3+PRQvnITZWgNKly8DOzh7//rsKTk5fu6uYiGRJSVSS7sknKiSXrr6WdxYUzvvkDHlnQeFol1HsW8zlQcS5DkXOUF92D96mvCnz+XlFTvlaqLyzoHA065aMZ4sUJ1qanENW1N5Gpcg7CwpHRYWrBxe1MqUU705leStnymc85CSITZB3Fn5KBvol7w5U9vJERERERERERERERDLEQDwRERERERERERERkQzx/j4iIiIiIiIiIiIieeAKpgqDM+KJiIiIiIiIiIiIiGSIgXgiIiIiIiIiIiIiIhliIJ6IiIiIiIiIiIiISIYYiCciIiIiIiIiIiIikiEG4omIiIiIiIiIiIiIZEhV3hkgIiIiIiIiIiIiUkQieWeAigxnxBMRERERERERERERyRAD8UREREREREREREREMsRAPBERERERERERERGRDDEQT0REREREREREREQkQ3xYKxEREREREREREZE88GmtCoMz4omIiIiIiIiIiIiIZIiBeCIiIiIiIiIiIiIiGWIgnoiIiIiIiIiIiIhIhhiIJyIiIiIiIiIiIiKSIQbiiYiIiIiIiIiIiIhkSFXeGSAiIiIiIiIiIiJSRCJ5Z4CKDGfEExERERERERERERHJEAPxREREREREREREREQyxEA8EREREREREREREZEMMRBPRERERERERERERCRDfFgrERERERERERERkTyI+LhWRcEZ8UREREREREREREREMsRAPBERERERERERERGRDDEQT0REREREREREREQkQwzEExERERERERERERHJEB/WSkRERERERERERCQHfFSr4uCMeCIiIiIiIiIiIiIiGWIgnoiIiIiIiIiIiIhIhhiIJyIiIiIiIiIiIiKSIQbiiYiIiIiIiIiIiIhkiIF4IiIiIiIiIiIiIiIZYiCeiIiIiIiIiIiIiEiGGIgnIiIiIiIiIiIiIpIhBuKJiIiIiIiIiIiIiGSIgXgiIiIiIiIiIiIiIhliIJ6IiIiIiIiIiIiISIZU5Z0BIiIiIiIiIiIiIkUkEsk7B1RUGIgnykOUIE3eWVA4ZUqzOypqyakZ8s6CwtEpqybvLCicJ88/yDsLCqemh668s6BwkutWlXcWFE7quevyzoLCSfOsLO8sKJwPHzLlnQWFY1VeQ95ZUDhZDIASURHi0jRERERERERERERERDLEQDwRERERERERERERkQwxEE9EREREREREREREJEMMxBMRERERERERERERyRAD8UREREREREREREREMsRAPBERERERERERERGRDDEQT0REREREREREREQkQwzEExERERERERERERHJEAPxREREREREREREREQypCrvDBAREREREREREREpIpFI3jmgosIZ8UREREREREREREREMsRAPBERERERERERERGRDDEQT0REREREREREREQkQwzEExERERERERERERHJEAPxREREREREREREREQypCrvDBAREREREREREREpJpG8M0BFhDPiiYiIiIiIiIiIiIhkiIF4IiIiIiIiIiIiIiIZYiCeiIiIiIiIiIiIiEiGGIgnIiIiIiIiIiIiIpIhPqyViIiIiIiIiIiISA5EfFarwuCMeCIiIiIiIiIiIiIiGWIgnoiIiIiIiIiIiIhIhhiIJyIiIiIiIiIiIiKSIQbiiYiIiIiIiIiIiIhkiIF4IiIiIiIiIiIiIiIZYiCeiIiIiIiIiIiIiEiGGIgnIiIiIiIiIiIiIpIhBuKJiIiIiIiIiIiIiGSIgXgiIiIiIiIiIiIiIhliIJ6IiIiIiIiIiIiISIZU5Z0BIiIiIiIiIiIiIkUkEsk7B1RUOCOeiIiIiIiIiIiIiEiGGIgnIiIiIiIiIiIiIpIhBuKJiIiIiIiIiIiIiGSIgXgiIiIiIiIiIiIiIhniw1qJiIiIiIiIiIiI5IDPalUcnBFPRERERERERERERCRDDMQTEREREREREREREckQA/FERERERERERERERDL0U6wRf/HiRdStWxdNmjTBkSNHpN579uwZrK2tcfPmTXh4eOR5fHp6OpYsWYKtW7ciPDwcpUqVgoODAwYOHIiePXtCTU0NAPDy5UvMmDEDhw8fRkxMDMqVK4e2bdti2rRpMDAwkJyvfv368PDwwJIlS6Q+x9/fH97e3oiPj5fsS0lJwbx587B9+3Y8e/YMZcuWRf369TFz5ky4uLhI0s2YMQN79+5FaGhogetFFuU6c+YMAgIC0LVrV8n+JUuWYMmSJXj27BkAIDMzEwsWLMDGjRvx/PlzaGlpwd7eHr///jv69ev3xTrau3cv2rVrB5FIJKmzfv36wdHREQ8ePJBKu2PHDnTp0gWWlpaSz85Zx5+Ob9asmVTbiI+Ph56eHk6dOoX69etL9p86dQqLFi3C5cuXkZSUBHNzc1SrVg1eXl745ZdfClz3PwuRSISQI1tx+eJhpKS8RwVLB7Tt6AXTcpb5HnP54mFcv3oCkW+fAwDMy9uiecu+qGDpIEnjO7MP4mKjch1b638t0a6TV+EXpBgRiUQ4uG8zLpw9iOTk97CydkSXHiNgZm6V7zFvXj/DgeCNePE8HLGCSHTsMhQNm7SXSnMgeBMO7d8stU9bWw/zFu+QRTGKFZFIhAPBm3H+zMc6t3FEt55fr/P9ezfi+TNxnXfqOhSNmkrX+ZlT+3H21H4IYiIBAOXMLfFbq55wda8hy+IUC8eO7sWB4O2IjxfAwsIavfsNh6OTe77p798LxZaNK/Dq1VPo6RmiZZuuaNK0jeT9ly+fYlfgBjx58ggx0ZHo1dcLLX7rVBRFKTZEIhEOH9iMC+cOISX5PSytHdG523CUM7PK95i3b57h4L5NePlC3M7bdxqCBo2l23lmZiYO79+Eq1dOIikxDto6+vCs1RTNWnSHsrJiz7fYuWsXtmzZjBiBADbWNhgzejQqV66cZ9rQ0FAsXb4Mz589Q2paGkxNTdG+XTt079ZdKt22gAAE7Q5CZGQkdHR00KhhQ3gN84KGhkZRFOmnt2fPLmwP2IrYWAGsrKwxfMRoVKrkkWfas2dOYW/wbkSEh0MoTIeVtQ369RuIGjVqSqVLSkrCurWrcPbsabx/nwRT03Lw8hqFmrVqF0GJSoY7T8Ox89xxhL95idikBEzvMRi1nSvJO1slxr7gIOzcuQ2xAgEsrawxdNgouLl55Jn2/LnT2L9/D548Frd7S0tr9Oo9ANWq18wzfUkkEokQtMsfJ08cwIf3SbC1c0K//t6wKG/9xeOuXD6DnYHrERn5BiYmZujcdSCq16grlSbk6F4c2C8e35hbWKN3H+nxTUE/OyzsHnZsX4fHEQ+goqICSytbTJy8AOrq4r5+4YI/8PxZBBIT41C6dFm4ulVFt+6/Q0/fsJBqSf6CgnZh27bNEAgEsLa2wahRo+Hhkfc19NatUKxYsQzPnz9Daqr4Gtq2bTt07Zp9DX3y5DHWrVuDhw8f4t27txg1ajS6dOlWVMUpFnbv3oWAj3VuZW2DUSNHo1I+dX7m9Cns2ROEiIgwpKcLYW1tjf4DBsHTs5YkTUZGBjZv8sfhwwcRExON8hUqYOjQEahZs1ae5ySiovFTfENbv349RowYgfPnz+PFixffdGx6ejqaNWuGefPmYfDgwbh48SKuXLkCLy8vLF26FPfu3QMAPHnyBNWqVUNYWBgCAgIQERGBVatW4cSJE6hVqxZiY2O/Od9paWlo3Lgx1q9fj9mzZyMsLAyHDh1CZmYmPD098d9//33zOWVdLk1NTfz5558QCoX5fvaMGTOwZMkSzJ49G/fv38epU6cwaNAgxMXFfVdZSpcujaioKFy6dElq//r161GhQoWvHq+qqooTJ07g1KlTX0y3YsUKNGrUCAYGBggMDMSDBw+wefNm1K5dG6NHj/6uvMvb6RM7ce7UbrTtOAwjx/yDsmX1sHbFH0hNTc73mMcRt+FRpT5+Hz4PXqMXQ1fPCOtWTkFCfIwkzYix/2Dq7K2SbdAwHwCAu0fd/E6rMEKOBOJkSBA6dx+OiX8ug7aOPpYunvjFOk9PT4OhUTm07TAA2jr6+aYrZ2YF30WBkm3KzDWyKEKxc+xwIE4cC0LXnsMxaeoy6Ojo45+FE5Ga8vU6b9cx/zrX0zNE244DMHnackyethwOjh5YuXQ63rx+JqOSFA+XLpzEpg3L0LZDT/guWAcHJzfMmzsBMdGReaaPinyLBb6T4ODkBt8F69CmfQ9sXL8Ul/87I0mTnpYGY+Ny6NZjMHR18/83oMiOH92BU8d3o1PX4Rg3eSm0tfWwbMmkr/cthqZo3a4/tLXzrtfjRwNx/uxBdOo2HFNmrEOb9gNx4thOnD0VLKuiFAvHQkKw+O/F6NevH7Zs2gwPDw+MGu2Nd+/e5ZleS0sLnTt2wurVq7FjeyD69+uPlatWYfeePZI0h48cwfIVyzFo4EDs2B6IqVP+RMjx41i+YnlRFeundvJECJYtXYJevfti7bqNcHf3wMQJoxEZmXed37oVimrVamD+gsVYu9YflStXxeRJ4xAW9kiSRigUYuzYkXj37i1mzfbB5i2BGD9hMgyNjIqqWCVCano6bMpZwKtVZ3lnpcQ5feo4Vq38B92798HKVf5wc6uEKZPHIiqfdn/nTiiqVq2OOXMXYvmKDajkURXTpk5ARPijPNOXRPv3BeDwwZ3o228U5visgo6OPnzmjkPKF8Z9YWH38O+Smfhf3abwXbAO/6vbFP8umYGI8PuSNJcunsSmjcvQtl1P+MxbB0dHN8z3nYCYmOzxTUE+OyzsHub7TIC7ezXMnrsSs31Wo2mzdlBSUpKkcXapjJHe07Hw783wHjMLkZFvsOTv6YVcU/Jz/HgI/vlnMfr06Qd//82oVMkDY8fmfw3V1NRChw6dsGLFagQEBKJv3/5Ys2YV9u7NvoampqbBzMwcQ4d6SU0WJLETx0Pw7z+L0bt3P6zfsBmV3D0wblz+dR4aehPVa9TAXwuXwG/9RlSpUg0TJ4yVuoauWbMSwcF7MHr0OGzeEoi2bdvjj8kTpNIQUdGTeyD+w4cP2LFjB4YOHYqWLVvC39//m45fsmQJzp49ixMnTsDLywseHh6wsbFB9+7dcfnyZdjZ2QEAvLy8oK6ujmPHjqFevXqoUKECfv31Vxw/fhyvX7/GlClTvjnvS5YswaVLl3DgwAF07twZlpaWqFGjBoKCguDk5IQBAwZIZoZ/z7llUa5u3bohISEBa9euzfez9+/fj2HDhqFTp06wtrZGpUqVMGDAAIwZM+a7yqKqqoru3btj/fr1kn2vXr3C6dOn0b179y8cKVa6dGn069cPkyZNyjfNixcv4O3tDW9vb2zcuBENGzaEtbU1ateujVGjRuHatWvflXd5EolEOH9mLxo27Qq3SnVgamaFLj3HQihMQ+j10/ke1733RNSu2xJmFhVhbFIeHbuOgigrCxFhoZI0Zcrooqy2vmR7cO8yDAzLwcbWTfYF+4mJRCKcPL4HzX/rhspV68LM3Bq9+49Henoarl4+me9xVtYOaN9pMKrVaABVVbV806moKENHR1+ylS2rK4NSFC8ikQgnQvbg15biOje3sEafAeI6v/KVOu/QeTCqe+Zf5+4eteDm7gkTUwuYmFqgbYf+0NDUwtPHD/JMrygOHtiJBg1boGGjljC3sESffiNgYGiMkGN5B26Ph+yDgaEx+vQbAXMLSzRs1BL1G/6Kg/sCJWkq2jqiR++hqF2nEVTV8v83oKhEIhFOn9iDpr92g0eV/8HM3Bo9+46HMD0N167k384trRzQtuNgVK3eIN96ffrkAdw8asHVzRMGhqaoXPUXODpXxYvnYbIqTrGwLWAb2rRujbZt2sLa2hpjx4yBiYkJdgUF5ZnewcEBzZo1Q0WbijAzM0OLX39FzZo1pe5kvHPnDtzd3dG8WXOYmZmhZs2aaNq0aa47/hTVjh0BaPFbK7Rs2QZWVtYYMXI0jIyMEbx3d57pR4wcje7de8HJyRkW5Stg8OChsLAoj4sXz0vSHDq0H0mJiZjrswBubpVgaloO7u4esLW1K6pilQjVHVzQt0kr/M/FQ95ZKXGCgrajefNW+LVFa1SwtMLQYd4wMjbG/v178kw/dJg3OnfpCQdHZ5hblEf/AUNgbl4e//13oYhzLh8ikQhHDu1Cm3Y9UcPzF5SvYIOhXpORnpaKi+eP53vckUO74OZeDW3a9YC5uSXatOsBF9cqOHxolyTNoYM7Ub9hCzT4OL7p3XcEDAyMcfzj+Kagn71l4zI0+7U9WrftAYvy1ihXzgKeNetDTU1dkqbFb51gZ+8CIyNT2Du4onWb7ogIv4+MjAwZ1FrR2759G1q1ao3WrdvCysoa3t5jYGxsgj178r+GNm3aDDY2FVGunBmaN/8Vnp41cetWqCSNs7Mzhg8fiSZNmkrVJYltD9yGli1bo9XHOh/1sc735lPno7zHoEeP3nByckb58hXw+5BhsLAojwvnz0nSHD1yGL1690Wt2nVgbm6Odu06wtPTE9sDthZVsehbiLjluZVAcg/EBwYGwsHBAQ4ODujZsyc2bNjwTcHrrVu3onHjxnneaqympobSpUsjNjYWR48exbBhw6ClpSWVxtTUFD169EBgYOA3B823bduGJk2aoFIl6Vs7lZWVMXr0aNy/fx+3bt36pnN+IqtyaWtr448//sCsWbPw4cOHPD/b1NQUJ0+eRHR09HflPS8DBgxAYGAgkpPFsw38/f3RvHlzmJiYFOj4GTNm4M6dO9i1a1ee7wcFBUEoFGLChAl5vv/5DIbiIlbwDkmJcbB3rCLZp6qqDpuKbnj+9P4XjpSWnp6GzKxMaJUqm+f7GRlC3Lh2CtU9mxbLeipMgph3SEyIhZNLNck+NTV12Dm440lEwes8P1GRbzB5bBdMndQLfqvnIib67Q+fs7iLiZZtnX+SlZWJq5dPIT0tFdYVnQvtvMVNhlCIp08ewb1Sdan97u7VEfboXp7HhIfdg7u7dPpKlWrgyZNHJeYLp6wJYt4hMTEWjs5VJfvU1NRha++Op49/rJ3b2Log7GEooiJfAQBevXyMJxF34eyquEswCYVCPHz4EJ6enlL7PWt44vad2wU6x6NHj3D79m1UqZI9DvOoVAkPHz6U3JX46vVrXLx4EXXq/K/wMl9MCYVChIU9QvXq0nVevbon7t69U6BzZGVlITk5GdpltSX7Lpw/BxcXV/z9919o2+ZX9O3THZs3+yMzM7NQ80/0PYRCIcLDHqFKNen+tmrVGrh//9vafdnP2n1JFhX1FvHxsVLjCjU1dTg5eyAsLO9xCCAei7jlGIu4V6qB8I/HZGR8HN/kSONWqbrkvAX57ISEOEREPICOth6mT/XCkMHtMGvGKDx8mP+14/37RFw4fxx29i5QVf0pVv79IUKhEI8ePUSNGtL9eY0anrjzDdfQO3du57scHEkTCoUIe/QQ1XPUefUanrh7t2B1npWVheSUZGhrZ/clQmE6NNSlf/RQ19DE7dvfF6MiosIh90C8n58fevbsCQBo3rw53r9/jxMnThT4+PDwcDg6On41jUgkgpOTU57vOzk5IS4uTirwvGLFCpQpU0ZqGzJkiNRxYWFhXzznpzTfQ1blAoBhw4ZBU1MTixcvzvO4xYsXIzo6GqampnB3d8eQIUNw+PDh7yrHJx4eHqhYsSJ27doFkUgEf39/9O/fv8DHm5mZYdSoUZgyZUqegZ+wsDBoa2vD1NRUsi8oKEjq73fnTsEGxD+LpCTxUkBlyupJ7S9TVlfyXkEc3r8BOjoGsHPIeyB0784lpKa8R1XPJt+f2RIiIUG8lFNZbV2p/WW19ZCY+O3LV33O2sYRfQZMwPDR89Cj92gkJsRioe8ovH+f+EPnLe4+1at2jjrX1tZDYsKP1TkAvH71FKOGtsLwwS2wbdM/+H34dJiZ5/+MhZIuMSkBWVlZ0NGV7ld0dPWQEJ93fcfHx+aZPjMzE0lJCTLLa0mS3c6l67FsWV0kJn7fsm+fNGnWBVWr18ec6QMwauivWDB3GOo3aodqNRr80HmLs/j4eGRmZkJfX/rWdwMDfQgEgi8e+1vLlqj9vzro3bcPOnXshLZt2krea9q0KYb8/jsGDh6EmrVroV37dqhatSr69ukji2IUKwkJH+tcT3oJJT19fcTGfrnOPwkM3IbU1BQ0aNhIsu/t2zc4c+YUsjKzMH/B3+jVux92BG7D5s3+hZl9ou+SmBCPrKxM6OVs93r6iCvgsqe7dgYgNTUFv9RrKIss/nQ+jTV0dKSvh9o6eojPZxwCfByL5DhG57NjkhI/jm/ySPPpMwvy2VGRbwAAQbv80aBhS0yavADW1nbwmT0Wb9++kjouYOtq9OvdHIMHtEZMTCTGjp/79QooBvK7huoXoD9v06Yl6tWrgwED+qBDh05o3bqtDHNaciTkV+d6Xx+3fLI9YCtSU1LQsFFjyb4anjWxffs2vHz5AllZWbh65TLOnzsDgSDmC2ciIlmT60+2jx49wpUrV7B7t/iWVVVVVXTp0gXr169H48aNv3K0mEgk+uFZvJ9mjH9+nh49euRa1mX37t3w8fH57nN+a55kUS4A0NDQwKxZszB8+HAMHTo013HOzs64e/curl+/jvPnz+Ps2bNo1aoV+vbti3Xr1n13fvr3748NGzagQoUKeP/+PVq0aIFly5YV+PiJEydi9erVWL9+PTp3zr3GZc5yNmvWDKGhoXj9+jXq16+f7+yptLQ0pKWlSe0TpqdBTb1oH7x249pJ7A5cKnnd7/eZAAAl5NUOCtY2Tp/YidAbpzFk+IJ8bwG8+t9RODhVg46O4q3Vd+W/EwjYvETyeujIOQDyqHORKJ+/Q8G5uH0+W8oa1hWdMH1yH1y+eAyNmnb8oXMXJ5cvncC2TUskr728P9Z5jn+/IoiAQrhDw8TUAlNmrEJK8nvcuH4eG9f9hTETFyl0MF4sR32LRF+u7px/n0/Xl8LOVglx9fIJbN/6j+T1kOHidp6ra0F+fXzB3bh2Glcvn0CfAZNQzswKr14+RtCOldDRNYBnraY/dO7iLmebLsjYas2a1UhJTsGdu3exfPkylLewQLNmzQAA169fx/oNGzBxwgS4urji5auXWLR4MdYZGGLggAGyKkbxkrvSCzSePX78GPw3rMNcnwVSQc2srCzo6uph3PhJUFFRgYODI2JiorE9YCv69mWd088hr76mIF37qZPHsHmzH2bOnJ8rmF9SnD8XAr+1iySvJ0yaJ/6fPPuKL58r11gxr2PyOG+uv8UXPvvT+KZh41ao3+BXAICVtR3u3r2BM6cOoWv3wZLDfmvVBfUbtEBMTCSCdvlj5XJfjJ/oW2LvMBbXzZfLtnLlaqSkpODu3btYuXIZzM0t0LRps6LJYAmQq2miYNfQkJCjWL9+LXznLZTqS0aNGosF8+eiR/fOUFJSgpmZOVr81gqHDu4v7KwT0TeQayDez88PGRkZMDc3l+wTiURQU1NDXFwc9PT0vnC0mL29/VfX5rS1tYWSkhLu37+Ptm3b5nr/4cOH0NPTg6Fh9lPOdXR0YGtrK5XO2Ng412ffv5/3LeUPHz4EAMla7t9KVuX6pGfPnli4cCHmzJkDKyurXO8rKyujevXqqF69OkaPHo0tW7agV69emDJlCqytraGtrY2EhNwzIePj46Vuh/pcjx49MGHCBMyYMQO9e/f+5lv3dHV1MXnyZMycORMtW7aUes/Ozg4JCQl49+6dZFZ8mTJlYGtr+9XP8fX1xcyZM6X2dekxEt16jvqm/P0oZ9eaqGCZfRdERob4gbpJSbFSD6N8nxRfoLXFz5zchZMhgRg0zAflzK3zTBMXG4nwR6HoPeDPH8t8MeXuUQtW1rnrPDExDjq62T9MJCXFo6z21/ujb6GhoQUzc2tERb4u1PP+7Cp51IK1Te46T0jIUeeJ8blmD38PVVU1GJuIrzGW1g54/vQRTh3fgx59vH/43MWRdlkdKCsr55r9npgQn+9Db3V19ZEQlzu9iooKypTVkVleizO3Svn0LQlxUj96vk+Kz3UHzrfaG7QWTZp1RdXq4hnwZubWiBVE4tjh7QobiNfV1YWKikquWWSxsXHQ1/9ysMvcTNxf2NraIjZWgDXr1koC8atWr0KLX1tIZsnb2toiJSUVPr4+6N+vH5SV5X6jqdzo6IjrPOdsSfF4/st1fvJECBbMn4uZs3xQLccSHwYGhlBVVYGKiopkn6WlFWJjBRAKhVDjMylIjrR1dKGsrILYHLPf4+O/3u5PnzqOxYt88efUOahStfoX0xZnVavVga1d9t3bGcKP4774WOjpZV8PExPjoZPPOAQQj0Vyzpj//Jiy2nmPbxI+S6Pz8WHyX/ps3Y/7LSykJ2yYm1siJiZKap+2ti60tXVRzqw8zMwrYMSwzggPvw97e5d8y1EcfLqG5tWff+0aavbxGlqxovgaun79WgbiC0Ann3FLQer8xPEQzPOdg9lzfFG9uvQ1VE9PD77zFiItLQ2JiQkwNDTCypXLUK6cWaGXgYgKTm7fGDIyMrBp0yYsWrQIoaGhku3WrVuwtLTE1q0Fe4BE9+7dcfz4cdy8eTPPz/jw4QMMDAzQpEkTrFixAikpKVJp3r17h61bt6JLly7f/Ot1165dcfz48VzrwGdlZeHvv/+Gs7NzrvXjC0rW5VJWVoavry9WrlyJZ8+efTU/zs7iNZU/rSvv6OiY5wNQr169CgcHhzzPoa+vj9atW+PMmTPftCzN50aMGAFlZWX8888/Uvs7duwINTU1zJ8//5vPOXnyZCQkJEhtHTsP+fqBhUxTsxQMjcwkm4lpBZTV1kP4o+w2kJEhxJPHd2Bp/eU1rk+f2IUTRwMwYMhslK9gn2+6q5dDUKasDhydFXMtYU3NUjA2MZds5cwsoa2jjwf3rkvSZGQIEf7oNmxsC3ddcaEwHe/evZB8KVAUmlr51Pl92dc5IJ5ZIsxIL/TzFheqamqwtnHA7dvS/fed29dg75D3F0c7exfcyZH+9q2rsLFxKBFrocqCpmYpGBmbSzbTcpbQ1tbHowc3JGkyMoSICLv9w88sSE9Pg5Ky9HVeWVn5ux8WXxKoqanB0dERl69ckdp/5coVuLu5F/g8IpF43dZPUlPToJyjrlU+Bt8Vub4BcZ3b2zvg2jXpOr927QpcXfN/EPzx48fg6zsHU6fNQq1adXK97+rmjtevXyErK0uy79XLlzAwMGQQnuROTU0NdvYOuHFdut3fuH4Vzs75t/tTJ49h4V9zMOmPGfCsmbvdlyRaWqVgamoh2cwtrKCrqy81rsjIEOLB/dAvBrDzGovcuX0Vdh+PUVUVj29yprl7+5rkvMbG5b762UZGptDTM8SbNy+lzvP27UsYGn3h2WYfLwEZwuI/xlRTU4ODgyOu5LiGXr16BW7fcA0FgPR04dcTkfga6uCIq1dzXEOvXoGra/51HhJyFHPnzsL0GbNRu3b+z6vR0NCAkZExMjMzceb0KdStW6/Q8k6FR8T/8vyvJJLbN+gDBw4gLi4OAwYMgI6O9Iy6jh07ws/PD8OHD5fse/ToUa5zODs7w9vbGwcPHkSjRo0we/Zs/O9//0PZsmVx7do1zJ8/H35+fvDw8MCyZctQu3ZtNGvWDHPmzIG1tTXu3buH8ePHw9zcHHPnfvuabqNHj0ZwcDBatWqFRYsWwdPTE5GRkfDx8cGDBw9w/PhxqSB4SkoKQkNDpc7xadZ2TkVRrt9++w2enp5YvXq11ENTO3bsiDp16qB27dowNTXF06dPMXnyZNjb20vWrR82bBiWLVsGLy8vDB48GFpaWggJCYGfnx82b96c72f6+/tjxYoVMDD4vmVQNDU1MXPmTHh5eUntr1ChAhYtWoRRo0YhNjYWffv2hbW1NWJjY7FlyxYAkJpN9TkNDQ1oaEgvQ6OmLv9105SUlPC/em1xMiQQhoZmMDQyx8mQQKipacCjan1Juu1bFkJHxwC/tuoHQLwczdGDm9C990To65sg6ePaxOoaWtDQyH6ob1ZWFq5dDkHV6o3zrRtFo6SkhIaN2+HooQBJoPjIwQCoq2ugumf22p3+fvOhq2uIth3Et8VnZAjx9s1zAEBmhhDx8TF4+SICGhpaktnYQTtWw61STejrGyMpKR6HD2xDakoyPGsr5ozVT5SUlNCoSTscORAAY2PpOq/xWZ1vWDsfunqGaNex4HW+N8gPLm41oKdvhLTUFFy9fAphD29jxJiCLTFWUv3WshOWL/WBTUUH2Nu74MTx/YiJiUTjpq0BAAFb1yAuNgbDRvwBAGjcpDWOHdmDzf7L0bBxS4SF3cOpk4cwwnuq5JwZQiFevXom/v+MDMQJYvDsaTg0NbVgWs6iyMv4s1FSUkL9Ru1w7HAAjIzNYGRsjmOHt0NNXQPVamS3800bFkBX1wCt22W383dvX0j+PyE+Bq9ePoaGhiaMjMXt3NW9Jo4dCoCevjHKlbPEq5cROHV8N2rWVuwZaN27dcf0GdPh7OgENzc37Nm7B+8i36FD+/YAgGXLlyM6OgozZ4jviNuxcydMTU1hZSmeBRl66xa2bN2CLp8thVe37v+wbVsAHOwd4OLqglcvX2HVmtWoW7cur6MAOnfuhrlzZ8LBwQkuLq44sD8YUVGRaN2mHQBgzeoViI6JxpQp0wGIg/A+c2dixMjRcHZ2lcwE1NDQQJkyZQAAbdu0x+6gnfj338Xo0KEzXr16iS1b/NGhQ+4lCil/KWmpeCPIfmbUuzgBHr95ibKlSsNYwSYEFLYOHbpiwfxZsLd3grOzKw4eFLf7lq3aAgD81q2EICYaEyZNAyAOwi+YPxtDh3nDyclVMutYQ10DpT+2+5JMSUkJzVt0RPDeLTAtZwFTU3ME790KdQ1N1P5f9tK0K5b5QF/fULIUTPNfO2DWjJHYF7wNVavVwfVrF3D3znVMn5m9rGeL3zphxTLx+MbOzgUnT4jHN42atC7wZyspKaFlqy7YtdMflpYVYWlli7NnjuLN6xfwHi2+XkREPMDjiAdwcHRD6dJlERX5Frt2roeJiZnkh4HirmvX7pg1azqcnJzg6uqG4OA9iIx8h7ZtxdfQlSvF19Bp08R1EhS0EyYmprD8eA29desWtm3bgo4ds/tqoVCIp0+fAhCPaaKjoxEWFoZSpbRgYVG+iEv48+napTtmz54OR0dxne/7VOftxHW+auVyRMdEYepUcZ2HhBzFnNkzMMp7LFxcXCXrvmtoaEquoffu3UVMdDRs7ewREx2F9evXIkuUhe49esmnkEQEQI6BeD8/PzRu3DhXEB4AOnToAB8fH9y4cUNyK07Xrl1zpXv69CmsrKwQEhKCv//+G6tXr8a4ceNQqlQpODk5YeTIkXB1dQUgXrrk2rVrmDFjBrp06QKBQABTU1O0bdsW06dP/+otP3nR1NTEyZMn4evriz/++APPnz9H2bJl0aBBA/z333+Sz/4kLCws15PD69Wrh9OnT+c6t4aGRpGUa/78+ahdu7bUvmbNmiEgIAC+vr5ISEiAqakpGjZsiBkzZkhmP1pZWeHcuXOYMmUKmjZtitTUVNjb28Pf3x+dOnXK9/O0tLSgpaWV7/sF0adPHyxatCjXskAjRoyAk5MTFi9ejI4dOyIxMREGBgaoVasWjhw5Aje3/Gem/KzqN+oEoTAde3YtR0rye5S3dMCgoXOhqVlKkiY+LkrqB59L5w8gMzMDmzdI/wjTuHkPNP21p+R1RNhNxMdFoXpNxQ4E59SkeRekp6dj+9alSP6QBCsbR4wYM0+qzuMEUVD+rM4T4gXwnZX9vIXjR3fi+NGdsLN3x+gJ4nUx4+NisGGND96/T0SZsjqwtnHC+D/+hYHBF2bXKIimv4rrPGCLuM6tbRwxcuw8aGpl13lsbJTUrN/4eAHmzsiu85AjOxFyZCfsHNwxdqK4zhMT4rFh7XwkJsRCS6s0zC2sMWKMD5xdqhZd4X5Cteo0RNL7ROzetRHxcbEoX94aE/+YDyMj8bJe8XECxMREStIbm5TDhMnzsHnjchw7uhd6egbo038EPGtmz6aJi4vB5AmDJK8P7A/Egf2BcHKuhGkzpe9gUlSNm3WGUJiGHduWITk5CVbWjvAa5Svdt8RK9+cJ8QLMn5Pdzk+E7MKJkF2wtXfHqLELAQCdunrhYPBG7Ni2FO+T4qGjY4A6dVugecvs/l4RNW3SBAkJCVi33g8xMTGoaFMRS/7+G+XKlQMAxAhi8C4yu52LRFlYvmI53rx5AxUVFVhYWGC4lxfaf/wCDAD9+/WHkpISVq5ehejoaOjq6qLu/+piWB7P21FEDRs1QUJiAjZt9INAIIC1tQ3mz18MU1NxnQsEMYiKfCdJv3/fHmRmZmLJ3wux5O+Fkv3Nm7fA5D/EQUtjExMsXPQPli9bgv79esLQ0AgdOnZB9+4MInyLsNcvMMEvuy9efSgIANCksifGdewtr2yVCPUbNEZiYgK2blmP2FgBLK1sMMdnIUxMxO0+NlaAqKjsvubggWBkZmZi2dJFWLY0e+30Jk1bYPwExVgqslXrbkhPT8MGv7/x4UMSKto6Y/Iff0Hrs3GfQBApdQeSvYMrRoyahh2BftgZKA56jxg1HbZ22XeV1ardEO+TErE7SDy+sShvjQmTssc3Bf3sX38Tf//avGk5PrxPQgXLipj850KYmIp/AFdX18DVK+cQtNMfaWkp0NU1gLtHDYwYNS3fZ3IVN40bi6+h69f7QSCIgY1NRSxcmH0NFQhiEPnZNTQrKwsrVy7H27fia6i5uQWGDvWSBO4BICYmGn37Zo9Ntm3bgm3btqBy5SpYvnxV0RXuJ9Wosfga6r9BXOfWNhXx18K/pa6hn9d5cLD4Grp40QIsXrRAsv/XX3/DlD/FP3inp6dj7dpVePPmNbS0tFCzVm1MnToTZcuWLdrCEZEUJZGi30tLlIfgI0/knQWFU6Y0l7goagq8nLHc6JTlUgpFLSa2+N8mXtzU9NCVdxYUTnJa1tcTUaFKPXf964moUCl5Vv56IipUMQJeQ4uaVflSX09EhSqLEbEiZ2TI50zl9OpN7NcTKSALs5J35x7DMEREREREREREREREMsRAPBERERERERERERGRDDEQT0REREREREREREQkQwzEExERERERERERERHJEAPxREREREREREREREQyxEA8EREREREREREREZEMMRBPRERERERERERERCRDqvLOABEREREREREREZEiEonknQMqKpwRT0REREREREREREQkQwzEExERERERERERERHJEAPxREREREREREREREQyxEA8EREREREREREREZEMMRBPRERERERERERERCRDDMQTEREREREREREREckQA/FERERERERERERERDLEQDwRERERERERERERkQwxEE9EREREREREREREJEMMxBMRERERERERERERyZCqvDNAREREREREREREpIhEInnngIoKZ8QTEREREREREREREckQA/FERERERERERERERDLEQDwRERERERERERERkQwxEE9EREREREREREREJEMMxBMRERERERERERERyRAD8UREREREREREREREMsRAPBERERERERERERGRDDEQT0REREREREREREQkQwzEExERERERERERERHJEAPxREREREREREREREQyxEA8ERERERERERERkRyIRCJueWzfasWKFbC2toampiaqVq2Kc+fO5Zt29+7daNKkCYyMjKCtrY1atWrh6NGjP/JnLBAG4omIiIiIiIiIiIioWAoMDIS3tzemTJmCmzdvom7duvj111/x4sWLPNOfPXsWTZo0waFDh3D9+nU0aNAArVq1ws2bN2WaTyXR9/zEQFTCBR95Iu8sKJwypVXlnQWFo8yfYoucTlk1eWdB4cTEpss7CwqnpoeuvLOgcJLTsuSdBYWTeu66vLOgcJQ8K8s7CwonRsBraFGzKl9K3llQOFmMiBU5I0MdeWfhp/P8pUDeWfgpWZY3KHBaT09PVKlSBStXrpTsc3JyQtu2beHr61ugc7i4uKBLly6YNm3aN+e1oBiGISIiIiIiIiIiIqKfRlpaGhITE6W2tLS0XOnS09Nx/fp1NG3aVGp/06ZNcfHixQJ9VlZWFpKSkqCvr18oec8PA/FERERERERERERE9NPw9fWFjo6O1JbX7PaYmBhkZmbCxMREar+JiQnevXtXoM9atGgRPnz4gM6dOxdK3vPDtSCIiIiIiIiIiIiI6KcxefJkjBkzRmqfhoZGvumVlJSkXotEolz78hIQEIAZM2YgODgYxsbG35fZAmIgnoiIiIiIiIiIiIh+GhoaGl8MvH9iaGgIFRWVXLPfo6Kics2SzykwMBADBgzAzp070bhx4x/Kb0FwaRoiIiIiIiIiIiIiKnbU1dVRtWpVhISESO0PCQlB7dq18z0uICAAffv2xbZt2/Dbb7/JOpsAOCOeiIiIiIiIiIiIiIqpMWPGoFevXqhWrRpq1aqFNWvW4MWLFxgyZAgA8TI3r1+/xqZNmwCIg/C9e/fGP//8g5o1a0pm02tpaUFHR0dm+WQgnoiIiIiIiIiIiIiKpS5dukAgEGDWrFl4+/YtXF1dcejQIVhaWgIA3r59ixcvXkjSr169GhkZGfDy8oKXl5dkf58+feDv7y+zfCqJRCKRzM5OVEwFH3ki7ywonDKl+btgUVPm4mRFTqesmryzoHBiYtPlnQWFU9NDV95ZUDjJaVnyzoLCST13Xd5ZUDhKnpXlnQWFEyPgNbSoWZUvJe8sKJwsRsSKnJGh7GYbF1fPXgjknYWfklUFA3lnodAxDENEREREREREREREJEOcgkqUhwrmnIlQ1N5Fpco7CwqHs7OLXmw8Z5YVNZsKpeWdBYUT/z5T3llQOOzPi14aZ2cXOdHlm/LOgsJJNLWXdxYUzrtoficqavq66vLOAhEpEM6IJyIiIiIiIiIiIiKSIQbiiYiIiIiIiIiIiIhkiIF4IiIiIiIiIiIiIiIZYiCeiIiIiIiIiIiIiEiGGIgnIiIiIiIiIiIiIpIhBuKJiIiIiIiIiIiIiGSIgXgiIiIiIiIiIiIiIhliIJ6IiIiIiIiIiIiISIZU5Z0BIiIiIiIiIiIiIkUkEsk7B1RUOCOeiIiIiIiIiIiIiEiGGIgnIiIiIiIiIiIiIpIhBuKJiIiIiIiIiIiIiGSIgXgiIiIiIiIiIiIiIhliIJ6IiIiIiIiIiIiISIYYiCciIiIiIiIiIiIikiEG4omIiIiIiIiIiIiIZIiBeCIiIiIiIiIiIiIiGWIgnoiIiIiIiIiIiIhIhhiIJyIiIiIiIiIiIiKSIVV5Z4CIiIiIiIiIiIhIEYnknQEqMpwRT0REREREREREREQkQwzEExERERERERERERHJEAPxREREREREREREREQyxEA8EREREREREREREZEM8WGtRERERERERERERPIg4uNaFQVnxBMRERERERERERERyRAD8UREREREREREREREMsRAPBERERERERERERGRDDEQT0REREREREREREQkQwzEExERERERERERERHJkKq8M0BERERERERERESkiETyzgAVGc6IJyIiIiIiIiIiIiKSIQbiiYiIiIiIiIiIiIhkiIF4IiIiIiIiIiIiIiIZYiCeiIiIiIiIiIiIiEiG+LBWIiIiIiIiIiIiInng01oVBmfEExERERERERERERHJEAPxREREREREREREREQyxEA8EREREREREREREZEMMRBPRERERERERERERCRDfFhrCfHu3TvMnTsXBw8exOvXr2FsbAwPDw94e3ujUaNGAICLFy9izpw5uHTpElJSUmBnZ4e+ffvC29sbKioqAIBnz57B2toaN2/ehIeHh9RntG3bFrq6uvD395fsi4iIgI+PD44fP47IyEgYGhrC0dER/fv3R5cuXaCqKm5iSkpK2LNnD9q2bSt1zr59+yI+Ph579+7N9/XGjRvh6+uLSZMmSY7bu3cv2rVrB5Eo+4kWIpEI69atw/r163Hv3j1kZWXB0tISjRs3xogRI2Bra1sINS07x47swf592xEfFwuL8lbo3Xc4nJwr5Zv+/r1QbN64HK9ePoOengFatemGJs3aSN4/EbIfZ88cxauXTwEA1jYO6Np9EGztnCRpHty/hf3BAXj6JAxxcQKMnTAH1WvUlV0hiwGRSIQjB7fg0oVDSEl+jwpWjujYxQvlzKzyPebS+UO4evk43r55DgAoX8EWv7XpB0srR0makCPbcTv0AqIiX0JNTR1WNs5o1W4ATEzKy7pIPxWRSITgPRtx5tRBfPiQBJuKTujVZyTMLay/eNy1q2exZ9cGREW9gbGxGdp36o+q1aTbalxsNHYErsWd21cgTE+DiakF+g8cDytrewBAamoKdgauwc3rF/D+fSIMjUzRuEk7NGzcJq+PLNFEIhEO7d+MC2cPITn5PaysHdG5+3CYmVvle8yb189wcN8mvHgejlhBJDp0GYKGjdvnShcfF4O9Qetw/+5VpAvTYWxsjp59x6CCpb0MS/TzO3BgN3bv2obYWAEqWFpj8O8j4erqkWfa2NgYrFu7DBHhD/HmzSu0bt0Rg4d4S6UJCTmIJYt9ch27J/gk1NU1ZFCC4mdfcBB27tgKgUAAKytrDB3mDTd3jzzTnjt3Ggf27cbjx+EQCtNhaWmDXn0GoHr1mpI0z549wUb/tQgPe4jIyHcYOmwU2nfoWiRlKS527dqJzZs3QyCIgY2NDUaPHovKlSvnmTY0NBTLlv2LZ8+eIy0tFaampmjXrj26d+8hSXPgwH7MmjUz17Hnzl2AhgbbeV72BQdh585tiBUIYGlljaHDRsHNzSPPtOfPncb+/XvwRNLurdGr9wBU+6zd0/e58zQcO88dR/ibl4hNSsD0HoNR+wvjevoykUiEA/s24/yZg+Jxi40juvUY8dVxy/69G/H847ilU9ehaNQk97jlkyMHA7B393o0bNwOnbsNk0Epfl6HD+1B8J4AxMXFonwFK/QfMALOLvm313t3Q7Fh/TK8fPEM+voGaNuuO5r9mj2enjplJO7dDc11XJWqNfHntAUAgMzMDAQGbMDZMyGIj4+Fnp4BGjT8FR0794aysmLOF927Zxe2b98KQawA1lbWGD58NNwreeSZ9uzZUwjeuxsREeL+28rKBn37DUSNGtn99+HDBzB/3pxcxx49dobXUCI5UsweroR59uwZqlatipMnT2LBggW4c+cOjhw5ggYNGsDLywsAsGfPHtSrVw8WFhY4deoUHj58iFGjRmHu3Lno2rWrVEC7oK5cuYIqVargwYMHWL58Oe7evYsDBw6gf//+WLVqFe7du1co5dPU1MT8+fMRFxeXbxqRSITu3btj5MiRaNGiBY4dO4bbt2/j33//hZaWFubMyX0B+plcvHASG/2XoV37Xpj311o4Orljns9ExERH5pk+KvIt5vtMFKf7ay3atu8J/w3/4vJ/ZyRp7t8LRZ3/NcLUGUswy2cFDA2N4TN7HGIF0ZI0qakpsLSyRb8B3rIuYrFxImQHTp/cjQ6dvTBm4lJoa+th5dLJSE1NzveYiPDbqFKtAby8F8B7/N/Q1TfGyqV/ID4+RpLmccRt/K9eK3iPX4KhI32RlZWJVUv/QFpaalEU66dx6OB2HD28Cz16j8C0mSuho6OPhfMnICXlS/V7DyuXzUKtOk0wa+5a1KrTBCuXzcLjiAeSNB8+JGHu7JFQVVHBmHG+mDtvA7p2H4pSpUpL0gRsXY67t69i8NA/4DPfH02bdcTWzUtx4/oFmZb5ZxRyZAdOhuxG5+7DMWHKUmjr6GHZ35O+2M6F6WkwMDRFm/b9oa2jn2ea5A9JWDR/NFRUVDFs1FxMnbkW7Tv/Di2tMrIqSrFw9sxxrF39D7p07Y1/l22Aq4s7pk8dh6iod3mmFwqF0NHRRZeufWBtnf+PyKVKlcbmrfukNgbhxU6fOo6VK5agW/e+WLl6I1zdKuGPyWMQFZl3nd+5fRNVqtbAXJ9FWL7SH5U8qmDan+MREf5IkiYtNRXlyplhwMBh0Nc3KKqiFBshIcewePEi9OvXH5s3b4WHR2V4e4/Eu3d517mWliY6deqM1avXIDBwJ/r3H4BVq1Ziz57dUulKly6NQ4eOSG0MIOTt9KnjWLXyH3Tv3gcrV/nDza0Spkwem3+7vxOKqlWrY87chVi+YgMqeVTFtKkTpNo9fZ/U9HTYlLOAV6vO8s5KiXDscCBOHAtC1x7DMenPZdDR1sc/iyYi9Qvjx/T0NBgalUO7DgPyHbd88uzpI5w7ewjmFjaFnfWf3vlzJ7DBbyk6dOqNRX+vg5OzO+bMmoDofL6HRka+wZxZE+Dk7I5Ff69D+4694LfuH1y6eFqSZsKkOfDz3yPZlizdCGVlFdSu00CSZk/QNhw9sg8Dfx+Nf5dtRq8+Q7F3TwAOHQySdZF/SidPhmDZsiXo2asv1q3dCDd3D0yYOBqR+fTft26Folq1Gpg/fzHWrPVH5cpV8cfkcQgPk+6/S5cujaDdB6U2XkN/TiJueW4lEWfElwDDhg2DkpISrly5gtKls4NOLi4u6N+/Pz58+IBBgwahdevWWLNmjeT9gQMHwsTEBK1bt8aOHTvQpUuXAn+mSCRC3759YW9vjwsXLkj9al25cmX06NHju4L7eWncuDEiIiLg6+uLBQsW5JkmMDAQ27dvR3BwMFq3bi3Zb2Njg0aNGhVaXmTl4P4daNCwBRo2bgkA6NNvBG6FXkHIsWB06zE4V/qQY8EwMDRGn34jAADmFlZ48vgRDuzbDs+a9QAAI7ynSh0zeMh4XP7vDO7euY5f6jcHAFSuUhOVq3DW0ycikQhnT+5Fk+ZdUany/wAAPXqPw5+TuuL61VOoU/e3PI/r1W+S1OuuPbxx6+Z5hD28iRo1mwAAhgyXnrnavddY/DmxC169CEdFOzcZlObnIxKJEHIkCC3b9EC16r8AAAb+PhGjhnfAf5dOoEHDVnked+xoEFxcq6Fl6+4AgJatu+PRw1sIOboLFW3F7fzQgQDo6xtjwOCJkuMMjUylzvM4/D7q1G0GRycPAED9hi1x+tR+PHv6CFWq1ins4v60RCIRTp3Yg2YtusGjirid9+o3HpPHdsHVyydRt17LPI+ztHaApbUDACB49/o80xw7sgN6ekbo1W+cZJ+BoWmeaRXJnj2BaNq0JZo1F1+fBg/xxvUbV3Do4B707Tc0V3oTk3L4/eMM+JBjB/M9r5KSEgPC+QjaFYDmv7ZCi9/EdT7MazSuXbuM/ft3Y8DA3DMdh3mNlno9YOBQXLp4DpcunYetnbjdOzg6w8HRGQDgt26FjEtQ/GzbthWtW7eR3P04ZsxY/PffJQQF7YKX1/Bc6R0cHOHgkH3nmJmZGU6dOoXQ0Jto1y571qqSkhIMDQ1lnv+SIChoO5o3b4VfW4jb/dBh3h/b/R4MGJi7rxk6zFvqdf8BQ3Dp4jn8998FSbun71PdwQXVHVzknY0SQSQS4cTxPfj1t26oXFV8N2SfAeMxYXRnXLl8Er/Uz3vcYmXtAKuP45Y9QX75nj81NQXr1/qiZ5/ROHRga+EX4Ce3P3gHGjX+DU2aiutxwMCRCL15BUcP70XP3r/nSn/0SDAMjYwxYOBIAIBFeSs8jniI4L2BqFW7PgCgbFltqWPOnzsBDQ0N1K5TX7Lv0aN7qOFZB9Wq1QIAGJuUw/lzx/E4QjF/CNy5IwAtWrRCy5biOwtGjBiNq1f+Q3DwbgwenHvcMmKE9Lhl0OChuHDhLC5ePA87+8/6byUlGBhwrEj0M+GM+GIuNjYWR44cgZeXl1QQ/hNdXV0cO3YMAoEA48aNy/V+q1atYG9vj4CAgG/63NDQUDx48ADjxo3L99YxJSWlbzpnflRUVODj44OlS5fi1atXeaYJCAiAg4ODVBBeFnmRhQyhEE+fhMG9UnWp/e6VqiPs0d08jwkPu5c7vUd1PHn8CBkZGXkek5aehozMDJQuo53n+wQIBO+QmBgLR6eqkn2qauqwtXPDsyf3C3ye9PQ0ZGVmoHTpsvmmSUn5AAAo9YU0JU109FskJMTC1bWaZJ+amjocHCshIjz/O2geR9yHi2tVqX2ubtWkjgm9cQnW1vZY/u8MjBzWHtP/HIwzpw5IHWPn4IabNy4iLjYaIpEID+7fROS7V3B1k/63VNIJYt4hMSEWTi7Zdaqmpg5be3c8fVzwdp6XO7cuoYKVHdatmo2JYzrBd9ZQXDh76EezXKwJhUJEhD9C5So1pPZXqVIDD+7n3ccXVEpKCvr2aY/ePdtixvTxeBwR9kPnKymEQiHCwh6hajXpOq9a1RP37t0p0DmysrKQnJKMstq8ZhaEUCjEw4cP4ekp/eO+p2dN3L59u0DnePToIW7fvo3KlaX7+5SUFLRu3RItW7bA6NHeePToYaHluyQRCoUID3uEKrnafQ3cv/8N7T45OVcQjUieYiTjFunxo52DO5784LgFALZvXQpXd084OVf54XMVN0KhEI8fh6GSh/RY2MOjOh4+zHuMEvbwHjxypq9cA48jHub7PfTE8YP4X91G0NTUkuxzcnLD7ds38Ob1SwDA06cReHD/DqpUVbxJYkKhEI/CHqF6dU+p/dWre+Le3W/sv3OMW1JSUtClc1t07NgKkyaNzTVjnoiKHmfEF3MREREQiURwdHTMN01YmPiLuZOTU57vOzo6StIU1Kf0Dg7Zv7ZGRUXBxib7dr4FCxZg2LDsX2+7desmWYv+k7S0NPz2W96zjD/Xrl07eHh4YPr06fDzyz2jISwsTCovAODt7Y1169YBEP8gkV8QX94SkxKQlZUJnRy3TOro6CE+PjbPY+LjY6Gjo5cjvT4yMzORlJQAPb3cv3oHbFkNfX0juLlXzfUeiSUliOu7bFnpui1bVg+xsVEFPs+Bveuho2sAe8e8B/QikQh7g9bApqLLF9eeL2kSPrZn7ZxtV1sPMYK8b3/9dFzOW4q1dfSRkJC9XFVU9BucPLkPzZp3QsvWPfDkyUNs3bwMqmrqqPO/pgCAHr2GY4PfIowZ1QUqKipQUlJGvwFjYe+gGHckfJL4qZ1rS/8dtLV1ESsoeDvPS0z0W5w7fQANm3RAsxbd8OzpQ+zcvgKqqmrwrN3kh85dXCUmxiMrKxO6etJtWFdXD3Fxgu8+b3kLS4weOwVWVjZITv6AfXt3Yvy4IVi6fCPMzRXr2RM5JSSI61wvR53r6ekhLjbv62pOu3ZuQ2pKCurVaySLLJY48fHxyMzMhIGBdJ3r6+tDIIjJ5yixli1bIC4uDpmZmRg0aLDU84QsLa0wbdp0VKxoiw8fPiAwMAADBw7A1q0BqFChgiyKUmwl5tvu9b+h3QcgNTUFv9RrKIssEn2XT+MWbW1dqf3a2nqI/cL4sSCuXj6FF8/DMXnq8h86T3GVlCj+Hqqrm2NsrquP+Li8+424+Fh46OYe02RmZiIxMR76+tJ3MIWH3ceL50/hNXyi1P52HXogOfkDRnj1hLKyMrKystC95yDU/aVxIZSseElIiEdWZib09HP337GxBRsr7gjchtTUFDRokD1uqVDBCpMm/QkbG1skf/iAXUGBGD58MPzWb4aFBa+hRPLCQHwx92nJlYLM+M5veRaRSPTdM8Y/P87AwAChoaEAgPr16yM9PV0q7d9//43GjaUvrBMnTkRmZmaBPmv+/Plo2LAhxo4d+9W8AMCUKVMwfPhw7N69Gz4+uR9o90laWhrS0tKk9qWnpxX5Ort5/QmUkP/fJfff7GNbyCPtvr3bcOHCCUyb8Q/XD/7MtSsnsSPgH8nrwUNni/8nRyWKIMrz75OXE8d24Ma1Uxju/RfU1NTzTBMUuBxvXj/FqLGLvifbxcalC8exccNiyWvvsb4AcrddEURfbOviY3LsEImk/k6iLBGsrO3RsfNAAICllR3evHqGUyf2SQLxIUd340nEfYwaPQcGhiZ49Og2Nm/8Bzq6Brlm3JckV/47gYAt2e182AjxMzPy7EF+8O4hkUiEClb2aNO+PwDxg4vfvnmOc2cOKGwg/pNc7V70Y3drOTq5wtHJVfLa2dkdI0f0w/59uzBk6OgvHKk4cvYrIhSsiZ88eQybN/lh5qz5uYKa9DU52/nXx5irV69FSkoK7t69g2XLlsHCwgLNmomX0HNzc4ObW/aPpZUqVUKvXj2xY0cgxo0bX/jZLwFyVrcox/UyP6dOHsPmzX6YOZPtnuTr8n8nsG3TEslrr1Efxy25rqOiHxq3xMZGYcf2FRg1Zl6+Y3ZFkauf/krfnXssn895ABw/fhAVLK1hZ+8stf/CuZM4c/oYRo+ZhvIVrPD0aQTW+y2Fvr74oa2KKPe4pWBt/MTxY/D3X4c5cxdI9d8uLq5wcckeK7q6uWPQoD7YHbQTI0flHVMhItljIL6Ys7Ozg5KSEh48eCA1g+hz9vb2AIAHDx6gdu3aud5/+PAhnJ3FF0YdHR0AQEJCQq508fHxsLS0lHzup2M9PDwAiJeQsbUVP1ROVTV30zI1NZW8/0nZsmURHx//lVKK/fLLL2jWrBn++OMP9O3bV+o9Ozs7PHwofauykZERjIyMYGxs/MXz+vr6YubMmVL7Bg8ZiyHDci/lIwvaZXWgrKySa/Z7QkIcdHLMTvhEV1c/z/QqKiooU1ZHav/+4O3Yu3srpkxbBEurioWb+WLO1b0mLK2y76TIyBACAJIS46Cjk31Xwfuk+Fyz5PNyMmQnQo5ux7CR82CWz8OeggKX4+7tSxgxZhF09Yx+sAQ/N48qtWFjm30nToZQ/ONcQnwsdHWz6zcxMT7XLPnP6ejqS2bTZx8TB53PZnTr6urDzNxKKk05swq4du0sAPGPa0E7/TDCexYqeYhveS1foSJePH+MI4d2lOhAvLtHLVjZZN81lSEUt/PExDjofPZ3SEqMzzXb7Ftp6+ijXDnpGTam5Sog9Mb5HzpvcaatrQtlZRXE5ZjRlJAQB13dwgt2KSsrw97eCW/e/Jx3fxUlHR1xncfmuOMgPi4u150JOZ0+dRyLF/pg6rS5qFK1xhfTUjZdXV2oqKhAIJCu87i4uK8+x8Dc3BwAYGtrC4FAgLVr10gC8TkpKyvD2dkZL1++LJyMlyDan9p9jtnv8fFxXw2snz51HIsX+eLPqXNQpapiLddGP59KlWrBevpn45aP43Pxd6PPxi1J8dDW/vr4PD8vnoUjKTEePrOy7+DOyspCRNgdnD4ZjGWrD0FZWeULZyj+ymqLv4fGxRX8e6ierj7ic1xfE+LF30PL5vgempaWigvnTqJr9/65zrPRfwXad+iB//0insFtaVUR0dHvsHvXVoULxOvo6EJZRSXX7Pf4uDjof6X/PnkyBAsWzMWMmT6oVu3L4xZlZWU4Ojjh1SteQ39KP/djDakQcY34Yk5fXx/NmjXD8uXL8eHDh1zvx8fHo2nTptDX18eiRbln3+7btw/h4eHo1q0bAPFt20ZGRrh69apUupSUFNy7d0+y/EvlypXh6OiIhQsXIisrSwYly9u8efOwf/9+XLx4UWp/t27d8OjRIwQHB3/zOSdPnoyEhASprf/AEYWV5a9SVVODtY097ty+JrX/zu1rsHdwzfMYO3uXXOlv37oKm4oOUj+C7A8OwO6gTZj85wJUtM1/+SJFpalZCkbG5pLNtJwltLX18ejBDUmajAwhIsLvwMrG+QtnEgfhjx3ehiHD56KCpX2u90UiEXYFLsPt0Avw8l6gEA+w1NIqBRMTc8lmZm4FHR193Lt7XZImI0OIRw9vwdYu/weaVbR1ljoGAO7dvS51jK29K969lR5URr57BQMDEwBAZmYGMjMzcs3UUVZWhkhUdH2YPGhqloKxsblkK2dmCW0dfTy8n6Odh92GdcUvt/OvqWjrgsh30oHgqMhX0P/4d1BEampqsLVzwM2b0tfVmzeuwsk57z7+e4hEIjx5HA79PJYmUzRqamqwt3fAjevSdX7j+hW4uOS/FNXJk8fw14LZmPzHTHjWVJwHOBcGNTU1ODo64sqVy1L7r1y5DHd39286l/Djj4V5EYlECAsLg6Eh23lOampqsLN3wI3rV6T237h+Fc7O+bf7UyePYeFfczDpjxls9/RT0NQqBWMTc8n2adzy4L70+DH80W3Y/MC4xdGpMqbOXIMp01dJNksre9TwbIgp01eV+CA8IO43Kla0x61b0t8rb4Veg6Nj3mMUe0cX3ArNmf4qKto65pqMd+H8KQiFQtSr1zTXedLS06CU41lzysoqyCrh4/K8qKmpwcHeAdeuSfff165dgYtr/v33iePHMM93Dv6cOgu1an29/xaJRIiICIeBAR+ATiRPnBFfAqxYsQK1a9dGjRo1MGvWLLi7uyMjIwMhISFYuXIlHjx4gNWrV6Nr164YPHgwhg8fDm1tbZw4cQLjx49Hx44d0blzZ8n5xo0bBx8fH5iYmKB27dqIi4vD/Pnzoaqqip49ewIQ33a2YcMGNGnSBHXq1MHkyZPh5OQEoVCIs2fPIjo6Otd68IXBzc0NPXr0wNKlS6X2d+3aFbt370bXrl0xefJkNGvWDCYmJnj+/DkCAwO/mBcNDQ1oaEgv16Kunlzoef+S31p1xvKlc2Fj4wB7BxccDzmAmJgoNG4qfvhswNY1iBVEw2vkFABAk6ZtcOzIHmzyX4ZGjVsi7NE9nDp5CCO9p0nOuW/vNuzYvh4jvKfCyMhUMnNBU1MLmlqlAACpKcl49+615JioyLd49jQcZcpow9BI8QJnSkpK+KVhW4Qc3S4JzoccCYC6ugaqVm8gSbfFfwF0dA3Rqq14dseJYztw6MAm9O43Efr6JpK1LDU0tKDx8aFEu7Yvw/VrpzDw9xnQ0NCSpNHUKq0wywUpKSmhSfMOOLB/K0xMzWFiYoED+7dCQ10TNWtlr2e4dpUvdPUM0anLIABAk6btMW+uNw4eCECVKnVw48YF3L93HZP//FdyTNPmHeEzawQO7NuK6p718eTxQ5w+dRB9+48BAGhplYaDYyXsCFgNdXUNGBiY4NHDW7h4/hi6dh9atBUhZ0pKSmjQqB2OHgqAkbEZjE3McfTQdqira6C6Z/a6wBv9FkBXzwBt2g8AIP7S+/bNCwBAZoYQ8XExePniMTQ0NWFsLJ7R2rBxeyyc740jBwNQpfoveP70ES6cPYRuvbyLvJw/k3btumDRwtmws3OEo5MrjhwORnR0JFq0aAcA8N+wEgJBDMaOmyo55vFj8bNYUlKTkZAQj8ePw6CmqoYKltYAgG1b18PB0QVmZhZITv6A/cG78ORJOIZ68VZjAOjQsRvmz5sJe3tHODm74dDBvYiKikTLVuI691u3AjEx0Zg4aToAcRB+wbxZGOY1Gk7OrpJZaRrqGihdpgwAcYD4+fOn/2fvvqOiOto4jv9AEStNBSwoYKHaWyxJbGjsGruxxvLGjhp7NPYWW+wNe41do7FrYhITTbF3TbNLNwqCsO8f6OoKWKILIt/POXsO3J17d2b2Ybj77Ny5cT8/eKDAwNu6ePG8MmTIkOrX5ZekFi0+0uefD5WXl5cKFSqsjRs36MaNG/rww4aSpJkzZ+jWrVsaPnyEJGnt2q/k7OysvHldJUnHjh3V8uXL1KRJU+Mx58+fJ1/fQsqTx+XhGvGrdf78OfXr1y/J25cSNGzYTBPGj1DBgl7y9vbVtm2bH8Z9fUlSwILZCgq8rX4D4s4X9+/bpQnjR6pzF395eSUc9/hvIu5H6lrQbePvN0KCdOnaP8qSMZMcX+PVUKmBhYWFqlRtoB3bVsUl5x1zacf2uPPz0k+ctyxaMF529tnUoOGT5y1/SXryvOWirK0zyNEpl9JnyKhcud1MXiuddXplymwTb/vbrE69Jpo2dbTy5/eQh4ePdu3cqsDAW6r2QT1J0vKlcxUUFKieveI+h1b/oJ6+2bZ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", |
|
|
2231 |
"text/plain": [ |
|
|
2232 |
"<Figure size 1800x1800 with 2 Axes>" |
|
|
2233 |
] |
|
|
2234 |
}, |
|
|
2235 |
"metadata": {}, |
|
|
2236 |
"output_type": "display_data" |
|
|
2237 |
} |
|
|
2238 |
], |
|
|
2239 |
"source": [ |
|
|
2240 |
"#Correlation \n", |
|
|
2241 |
"cmap=sns.diverging_palette(260,-10,s=50, l=75, n=6,\n", |
|
|
2242 |
"as_cmap=True)\n", |
|
|
2243 |
"plt.subplots(figsize=(18,18))\n", |
|
|
2244 |
"sns.heatmap(cn,cmap=cmap,annot=True, square=True)\n", |
|
|
2245 |
"plt.show()" |
|
|
2246 |
] |
|
|
2247 |
}, |
|
|
2248 |
{ |
|
|
2249 |
"cell_type": "code", |
|
|
2250 |
"execution_count": 33, |
|
|
2251 |
"metadata": { |
|
|
2252 |
"execution": { |
|
|
2253 |
"iopub.execute_input": "2023-07-17T13:08:20.986460Z", |
|
|
2254 |
"iopub.status.busy": "2023-07-17T13:08:20.986042Z", |
|
|
2255 |
"iopub.status.idle": "2023-07-17T13:08:21.533075Z", |
|
|
2256 |
"shell.execute_reply": "2023-07-17T13:08:21.531701Z", |
|
|
2257 |
"shell.execute_reply.started": "2023-07-17T13:08:20.986426Z" |
|
|
2258 |
} |
|
|
2259 |
}, |
|
|
2260 |
"outputs": [ |
|
|
2261 |
{ |
|
|
2262 |
"data": { |
|
|
2263 |
"text/plain": [ |
|
|
2264 |
"<Axes: >" |
|
|
2265 |
] |
|
|
2266 |
}, |
|
|
2267 |
"execution_count": 33, |
|
|
2268 |
"metadata": {}, |
|
|
2269 |
"output_type": "execute_result" |
|
|
2270 |
}, |
|
|
2271 |
{ |
|
|
2272 |
"data": { |
|
|
2273 |
"image/png": 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3b7a2CQ4O1rx582zunTx5sho0aGD4bDKZbErt2rWtbX7++Wf16tVLFSpUkJeXlypVqqTOnTsrKSlJcXFxud5/fUlISMh1Hde38fX1VePGjbVu3TpDm8zMTJUsWVKlSpVSZmamTR83rjM4OFgmk0m7du0ytBs+fLhatWqV90MFAAAAALgcAYkC5MSJE2rUqJG++uorzZ49WwcPHtSGDRsUHh6uoUOH3lKfdevWVXJysqFs375dknTp0iU99NBDSk9P17p163T06FGtXr1a9erVU1pamiIiIgz3NW/eXIMGDTLUtWjRIs+xly1bpuTkZO3Zs0f169dX9+7dtXPnTuv1tWvXql69errrrrtsghV58fLy0rhx427pWQAAAADA7WRy4z8FAVs2CpAhQ4bIZDJp9+7dKlGihLW+bt266t+//y316enpqcDAwFyvHT58WD///LO++uorVa1aVZJUtWpVtWzZ0trG29vb+udixYqpePHiefZ3o4CAAAUGBiowMFCLFi3Se++9p/Xr16t58+aSpJiYGPXp00cWi0UxMTHq3bv3Tft85plntHDhQn322Wd6+OGHHZoHAAAAAMD1yJAoIM6cOaMNGzZo6NChhmBEjoCAgNs+ZtmyZeXh4aEPPvhAV65cue39X69o0aLy9PTU5cuXJUk//fSTdu7cqR49eqhHjx7asWOHfv7555v2ExwcrMGDBysyMlJXr1516pwBAAAAALeOgEQBcezYMVksFsP5DvaMGzdOPj4+hjJz5kybdgcPHrRpN3DgQElSxYoVNX/+fE2cOFElS5ZU69atNW3aNIcCA/mRlZWl6dOnKz09XQ8++KAkKTY2Vh06dLCeIdG+fXvFxsY61N9LL72k48ePa8WKFbd1ngAAAACQHyaT+0pBQECigLBYLJKuHQbpiDFjxigxMdFQBg8ebNMuNDTUpt2MGTOs14cOHaqUlBS9++67at68ud5//33VrVtXmzZt+ttr6tWrl3x8fFS8eHHNmTNHr732mjp06KArV67onXfeUZ8+faxt+/Tpo3feecehTI2yZctq9OjRmjhxoi5dunTT9llZWUpPTzeUrKysv7U2AAAAAIB9BCQKiJo1a8pkMunIkSMOtS9TpoxCQkIMpVSpUjbtihUrZtOufPnyhja+vr569NFHNWPGDB04cED333+/pk+f/rfXNHfuXCUmJio5OVlnzpzRqFGjJElffPGFfv/9d0VERMjT01Oenp7q2bOnfvvtN23cuNGhvkeOHKnMzExFR0fftG1UVJT8/f0NJSoq6m+tDQAAAABMbiwFAQGJAqJUqVJq166d3nrrLWVkZNhcT01Ndck8cl4Lmtsc8iswMFAhISEqV66coT4mJkY9e/a0ydzo3bu3YmJiHOrbx8dHL7/8smbMmKH09HS7bSMjI5WWlmYokZGRt7wuAAAAAMDNEZAoQKKjo3XlyhU1adJEa9eu1Y8//qgjR45o/vz51jdT5Fd2drZSUlIM5X//+58kKTExUZ07d9YHH3ygw4cP69ixY4qJiVFsbKw6d+58O5dm9eeff+qTTz5R3759Va9ePUPp27ev1q9frz///NOhvp5++mn5+/tr1apVdtuZzWb5+fkZitlsvh3LAQAAAADkgdd+FiDVqlXTvn37NGPGDI0aNUrJyckqW7asGjVqpIULF95Sn4cOHVJQUJChzmw26+LFi6pUqZKCg4M1ZcoUnThxQiaTyfp5xIgRt2NJNpYvX64SJUpYD7e8Xnh4uHx9ffWf//xHI0eOvGlfRYsW1bRp0/T44487Y6oAAAAAYF9B2TvhJiZLzmmJAAAAAFBAeDd8zt1TcIvM/QvcPQXkw4nTF902dnBpL7eN7SgyJAAAAAAAcAITKRJ2cYYEAAAAAABwOTIkAAAAAABwAhMJEnaRIQEAAAAAAFyOgAQAAAAAAHA5tmwAAAAAAOAE7NiwjwwJAAAAAADgcmRIAAAAAADgBBxqaR8ZEgAAAAAAwOUISAAAAAAAAJdjywYAAAAAAE7Bng17yJAAAAAAAAAuR4YEAAAAAABOwKGW9pEhAQAAAAAAXI6ABAAAAAAAcDm2bAAAAAAA4ATs2LCPDAkAAAAAAOByZEgAAAAAAOAEHGppHxkSAAAAAADA5ciQAAAAAADACUycImEXGRIAAAAAAMDlTBaLxeLuSQAAAAAAbs674XPunoJbZO5f4O4p3JKUtMtuGzvQv6jbxnYUWzYAAAAAAHAGdmzYxZYNAAAAAADgcmRIAAAAAADgBCRI2EeGBAAAAAAAcDkCEgAAAAAAwOXYsgEAAAAAgBOY2LNhFxkSAAAAAADA5ciQAAAAAADACUwca2kXGRIAAAAAAMDlyJAAAAAAAMAZSJCwiwwJAAAAAADgcgQkAAAAAACAy7FlAwAAAAAAJ2DHhn1kSAAAAAAAAJcjQwIAAAAAACcwkSJhFxkSAAAAAADA5QhIAAAAAAAAl2PLBgAAAAAATmDiWEu7yJAAAAAAAAAuR0DCSfr166cuXbrY1Ldq1UrDhw+3qf/oo49kuu7Ek7i4OJlMJrVv397QLjU1VSaTSQkJCYb6+Ph4dezYUWXLlpWXl5dq1KihiIgIbd261eE5WywWvf3222ratKl8fHwUEBCgxo0ba968ebpw4YKh7W+//aZixYqpdu3aufZlMpnk5eWlX375xVDfpUsX9evXz1CXkpKiYcOGqXr16jKbzapcubI6deqkzZs3W9sEBwfLZDLZlFmzZkmSTpw4Yaj39/dXs2bN9Mknnzi8fgAAAAC4nUwm95WCgIDEP5inp6c2b96s+Ph4u+2io6P14IMPqnTp0lq9erWOHDmi//znP2rRooVGjBjh8HhPPPGEhg8frs6dOys+Pl6JiYl6+eWX9fHHH2vjxo2GtnFxcerRo4cuXLigr7/+Otf+TCaTJk6caHfMEydOqFGjRvrqq680e/ZsHTx4UBs2bFB4eLiGDh1qaDt16lQlJycbyrBhwwxtvvzySyUnJ+ubb75RkyZN1LVrV33//fcOPwMAAAAAgGsQkPgHK1GihJ566im9+OKLebY5efKkhg8fruHDh+udd95R69atVa1aNbVo0UIvvPCC9u7d69BYa9as0YoVK7Rq1SqNHz9e9957r4KDg9W5c2d99dVXCg8Pt7a1WCxatmyZnnjiCT3++OOKiYnJtc9hw4bp3Xff1cGDB/Mcd8iQITKZTNq9e7e6deumWrVqqW7duho5cqR27dplaOvr66vAwEBDKVGihKFN6dKlFRgYqNq1a2vGjBm6fPnyTQM6AAAAAIBrf9ldrVo1eXl5qVGjRtq2bVuebRMSEnLNYv/hhx8cHo+AxD/c5MmTdfDgQX3wwQe5Xl+7dq0uX76ssWPH5nrd5GCuzooVKxQaGqrOnTvn2oe/v7/1c3x8vC5cuKA2bdroiSee0Jo1a3Tu3Dmb+1q0aKGOHTsqMjIy1zHPnDmjDRs2aOjQoTaBBUkKCAhwaO65uXz5spYsWSJJKlq06C33AwAAAACFwerVqzV8+HBNmDBB+/fv1/33368OHTro5MmTdu87evSoIYu9Zs2aDo9JQOIfrkKFCnrhhRc0YcIEZWdn21xPSkqSn5+fAgMDrXVr166Vj4+PtdjLUMjx448/KjQ01KE5xcTEqGfPnipSpIjq1q2rkJAQrV69Ote2UVFR2rBhQ66RtWPHjsliseR5DsWNxo0bZ1iXj4+PzVkaLVq0kI+Pj7y8vDRq1CgFBwerR48eDvUPAAAAAIXVnDlzNGDAAA0cOFB16tTRvHnzVLlyZS1cuNDufeXKlTNksRcpUsThMQlIFADjxo3Tn3/+qdjY2Fyv35gF0a5dOyUmJuq///2vMjIydOXKlZuOYbFYHMqmSE1N1bp169SnTx9rXZ8+ffKc21133aUnn3xS48aNy3XM3OaflzFjxigxMdFQmjZtamizevVq7d+/X+vXr1dISIiWLl2qUqVK2e03KytL6enphpKVleXQnAAAAAAgL+481DI/33MuXbqkb7/9Vm3btjXUt23bVjt27LC7xoYNGyooKEgPPvhgvrfLE5BwMT8/P6WlpdnUp6amys/PL9d7AgICFBkZqSlTpti87aJmzZpKS0tTSkqKtc7Hx0chISGqWrWqw/OqVauWjhw5ctN2K1eu1MWLF9W0aVN5enrK09NT48aN086dO3X48OFc75kyZYr279+vjz76yGbuJpPJoXElqUyZMgoJCTEUb29vQ5vKlSurZs2aeuSRR7R06VJFRETo1KlTdvuNioqSv7+/oURFRTk0JwAAAAD4J8rP95y//vpLV65cUfny5Q315cuXN3zXvF5QUJDefvttrV27VuvWrVNoaKgefPDBfL3pkYCEi9WuXTvXgyb37Nljd8vEsGHD5OHhoTfeeMNQ361bNxUtWlSvvPLK35rX448/rqSkJH388cc21ywWizWIEhMTo1GjRhmyFA4cOKDw8PA8syQqV66s5557TuPHjzdka5QqVUrt2rXTW2+9pYyMDJv7UlNT/9aawsLCVK9ePc2YMcNuu8jISKWlpRlKXudeAAAAAICjTG7851a+59yYvW4vkz40NFSDBg3SPffco+bNmys6OlqPPPKIXnvtNYefDwEJJ0pLS7PZYtCxY0f99NNPGjp0qA4cOKCkpCS99dZbiomJ0ZgxY/Lsy8vLS1OmTNH8+fMN9VWqVNHrr7+uN954Q3379lV8fLxOnDihffv2Wds6soenR48eioiIUK9evRQVFaW9e/fql19+0aeffqo2bdpYXwO6b98+DRw4UPXq1TOUXr16afny5bp8+XKu/UdGRuqPP/7Ql19+aaiPjo7WlStX1KRJE61du1Y//vijjhw5ovnz56t58+aGtufOnVNKSoqhpKen213XqFGjtHjxYv3+++95tjGbzfLz8zMUs9l802cGAAAAAP9U+fmeU6ZMGRUpUsQmG+LUqVM2WRP2NGvWTD/++KPD7QlIOFFCQoIaNmxoKLGxsdq2bZt++ukntW3bVvfee6/i4uIUFxen7t272+2vb9++ql69uk39sGHDtHHjRv3555/q1q2batasqYcffljHjx/Xhg0bdPfdd990riaTSStXrtScOXP04YcfKiwsTP/61780efJkde7cWe3atVNMTIzuuuuuXA+h7NKli86cOaNPPvkk1/5LlSqlcePG6eLFi4b6atWqad++fQoPD9eoUaNUr149PfTQQ9q8ebPN4SkTJ05UUFCQoeT1dpEcHTt2VHBw8E2zJAAAAACgsCpWrJgaNWqkTZs2Geo3bdqkFi1aONzP/v37FRQU5HB7kyXnZEEAAAAAwD+ad8Pn3D0Ft8jcv8DdU7gl6Revum1sP6/85R+sXr1aTzzxhBYtWqTmzZvr7bff1pIlS3To0CFVrVpVkZGR+v3337V8+XJJ0rx58xQcHKy6devq0qVLevfddzVr1iytXbtW//73vx0a0zPfqwIAAAAAAHeUiIgInT59WlOnTlVycrLq1aunzz77zPqyhOTkZJ08edLa/tKlSxo9erR+//13eXt7q27duvrvf/+rhx9+2OExyZAoJDp06KBt27blem38+PEaP368i2cEAAAAIL/IkChYzrkxQ8I3nxkS7kCGRCGxdOlSZWZm5nqtVKlSLp4NAAAAAKCwIyBRSFSsWNHdUwAAAACAwiX3N2bi//vn53AAAAAAAIA7DgEJAAAAAADgcmzZAAAAAADACUzs2bCLDAkAAAAAAOByZEgAAAAAAOAEJhIk7CJDAgAAAAAAuBwBCQAAAAAA4HJs2QAAAAAAwAnYsWEfGRIAAAAAAMDlyJAAAAAAAMAZSJGwiwwJAAAAAADgcmRIAAAAAADgBCZSJOwiQwIAAAAAALgcAQkAAAAAAOBybNkAAAAAAMAJTOzYsIsMCQAAAAAA4HImi8VicfckAFyTlZWlqKgoRUZGymw2u3s6LsO6WXdhwLpZd2HAull3YcC6C9e64VwEJIB/kPT0dPn7+ystLU1+fn7uno7LsG7WXRiwbtZdGLBu1l0YsO7CtW44F1s2AAAAAACAyxGQAAAAAAAALkdAAgAAAAAAuBwBCeAfxGw2a9KkSYXuoCDWzboLA9bNugsD1s26CwPWXbjWDefiUEsAAAAAAOByZEgAAAAAAID/196dh9WY/n8Af59TIUuWYWyDQps9g7JWdsa+FhnrmKxDg8KQLcYymOxL0VhDYcwY2SqSlC9lKYQYjBijkkpU5/eHy/NzVGTmnHPrOe/Xdbmuec59/ng31emcz3Pfn4/OsSBBRERERERERDrHggQRERERERER6RwLEkRERERERESkcyxIEH1CQkNDcfjwYSQlJYmOohU5OTmiIxAREdFH6tu3L/755x/RMYhIhgxFByDSR0uXLsXz588xd+5cAIBKpUKXLl1w9OhRAMDnn3+OEydOoG7duiJjalzz5s2xbds2WFhYiI5COrBkyRJMmDABxsbGAIBTp07B1tZWGheWmpoKd3d3rF27VmRMIo1KTU3F2wPMlEolSpYsKTCRbly9ehXZ2dnStYGBgez+humzhw8fom7duti0aRO6d+8uOo7OODo6QqFQvPc5CoUCJ06c0FEiIvnhDgkiAXbt2oU6depI1/v27cOpU6dw+vRpPHnyBE2aNJGKFXJSo0YN2NjYYNWqVaKjfHJUKhUeP34sOoZGTZ8+HampqdJ1t27d8ODBA+k6PT0dGzZsEBGNSGOio6Px1VdfSddVqlRB2bJlpX9lypRBVFSUwITacfr0aTRt2lS6trOzg42NDRo1aoRGjRqhQYMGOH78uMCE2nHr1i2MGDFCuq5evTrKlSsn/atQoQKuX78uMKF2nDlzBt9//z0GDhyIkSNHqr22y1mjRo3QsGHDPP+ZmZkhIiICISEhomMK8fDhQ4wfP150DJIBhertMj4R6UTZsmURHh4Oa2trAMDw4cORlZWFbdu2AQAiIiLQv39/3Lt3T2RMrdi3bx/GjRuHBg0aYMuWLfjiiy9ER9KJ4sWL4+7du6hQoQIAoHPnztiyZQsqV64MAHj06BGqVKmidoexsFMqlUhMTMTnn38OAChVqhRiYmJQs2ZNAPL8mkn/jBw5ErVr18b06dMBvP4537BhA6pWrQqVSgVfX1+oVCrp9V0unJ2d0bx5c0ycOBHA66/7999/R40aNaBSqeDt7Y27d+8iICBAcFLNmjRpEooXL46FCxcCeP11z549W3qd8/f3R/Xq1bF+/XqRMbXm2rVrGD58OB4+fIiJEyfC0FB9s/Wbnwc5y8rKwpo1a+Dl5YXSpUtj/vz5cHJyEh1LK2JjYxEcHAwjIyMMGDAAZcqUwZMnT+Dl5YX169fDzMwMsbGxomNSIccjG0QCvHr1Stq2DgBnz57Fd999J11XqVIFT548ERFN6/r16wd7e3uMGzcO9evXx5AhQ3K9oVm+fLmgdNrz4sULtW3cZ86cQUZGhtpzWB+mwuz777/HokWLUKRIEdFRdOrMmTMYNmyY2mN2dnZS4c3Y2BgDBgwQkEy7oqKi1P5uAcAXX3yBGjVqAACGDBmitnNELo4fP55rl1/fvn2l77epqSlGjRolIppOWFlZYeTIkXB1dcWKFSvU/n4rFArZFyR27NiB2bNnIyMjA3PmzMHo0aNzvYeRi99++w19+/bFq1evALw+hrlp0yYMGDAA9erVw969e9GtWzfBKUkOeGSDSIDatWvj1KlTAIA///wTN27cgL29vbR+//59fPbZZ6LiaV25cuVgbW2N58+f4+LFi2r/oqOjRccT5kPnVKlwWLJkiVqx6dSpU8jMzJSuU1NTMXbsWBHRtGr//v2wsbHBxYsXRUfRqXv37qF69erS9bx581C+fHnpunLlynj06JGIaFr14MEDaYcXAPj5+aFSpUrSdbly5WTZBPHu3bswMzOTrkeNGoXSpUtL16amprh//76IaFr36NEjdO/eHVOnToWPjw/u3buHhIQE6d/t27dFR9SaI0eOoFGjRhg7diyGDRuG+Ph4jB07VrbFCADw8vKCq6srnj17hmXLluH27dtwdXVFQEAAgoODWYwgjZHvbxHRJ2zMmDEYP348Tp8+jYiICDRv3lytp8TJkydhY2MjMKH2XL16FUOGDEFSUhKOHj0KR0dH0ZFIizZv3iw19MvKysLWrVulD2tyPYM8ffp0DBs2TGrm2a1bN0RHR0t3UN/0zpBbM88rV65g6tSpaN68OWbOnImZM2dCqZT/fY+iRYvi/v370s6AyZMnq63fu3cPxYsXFxFNq0qVKoWEhATp6+7Tp4/aekJCAkxMTERE0yqlUonHjx/D1NQUALBixQq19UePHsHIyEhAMu3avXs3xo8fDxsbG1y6dAnVqlUTHUknIiMj4e7ujoiICLi6uuL48eNqBUc5i4uLg5+fH0qWLImJEydi2rRpWLlyJdq0aSM6GskMCxJEAnz77bcwNDTEb7/9hjZt2sDT01Nt/a+//sLw4cMFpdOeRYsWYe7cuRg0aBB+/vlnlCpVSnQknVEoFGo7IN69lqPq1atj06ZN0nWlSpVynaN/+86yXLx79EZfjuIUL14ca9asQb9+/TBy5Ej89ttv8PDwgIGBgdrzevToISihdtjY2ODAgQNo2bJlnuuBgYGyLDDb2tril19+gYODQ57rW7duha2trW5D6UDdunVx/PhxNGvWLM/1oKAg1KtXT8eptG/kyJFYvHix3jUxtLOzg7GxMcaMGQNTU1Ps3Lkzz+fJ8ajKs2fPUKZMGQCAoaEhjI2NOSWNtIJNLYlIZypXroyNGzfq1ciwN5RKJUqXLi0VIZKTk2FiYiLdQVapVHj27BkbPMoAm3kCBw8eRN++fZGTk6P2uEKhkN3XHRAQACcnJ6xcuRJjxoyRfqezs7Oxdu1afP/999i5cyf69esnOKlmBQcHo3379nBzc8PUqVOln/fHjx9j8eLF+Pnnn3H06FG0bdtWcFLN2rRpEyZNmoQ9e/bk6pFx6NAh6Wfhm2++EZRQOwYPHoz169fr1Y0E4PURnIKM/ZTjcRWlUomTJ0+iXLlyAIAWLVpgz549uZqRN2jQQEQ8khEWJIg+QRcuXMDs2bPx22+/iY6iUfXr18fp06elirs+8fPzK9Dzhg4dquUkpG36XJDIyMiAu7s7Nm7ciOnTp2PmzJmyPmP9hru7O5YuXYpSpUqhZs2aUCgUuHXrFp4/fw43NzcsXbpUdEStWLt2LSZPnoysrCyYmJhAoVAgJSUFhoaG+Omnn2R7N93Z2Rn+/v6wsrKCpaUlFAoFrl27huvXr6Nv377Ys2eP6IgaZ2BggIcPH0qvayR/SqUSCoUiz11+bx6XY5GZdI8FCSJBjh07hqNHj8LIyAijRo1CzZo1ce3aNXh4eODQoUPo0KEDjhw5IjqmRvENjX45d+4cnj59ii5dukiP/fLLL/D09ERaWhp69eqFVatWqU2ckQOlUokFCxZIvTPc3d0xdepUtd4Zs2fPlt2buPDwcAwdOhRFixaFn58fvvzyS9GRdCoiIgK7du1CfHw8AMDc3BzOzs6ws7MTnEy77t27h3379ql93f369ZN9j4Hdu3dj9+7duHHjBoD//37Ldfzju4VWkr+7d+8W6Hlv+sgQ/VssSBAJ4Ofnh+HDh6NcuXJ4+vQpypcvj+XLl2Ps2LHo27cvvv/+e1meQeUbmvw9fPgQXl5eWL16tegoGtOlSxc4ODjA3d0dAHD58mU0btwYw4YNg7W1NZYuXYpvv/0Wc+bMERtUwwqyxRd43fRPTooUKYIJEyZg4cKFsisyEek7pVKJR48eoUKFCqKj6JS3t3eBnifHHhJEusKCBJEAjRo1gpOTEzw8PLBnzx44OTnBxsYGe/bsQa1atUTH0xqlUon4+PgPvqGRY2d2AIiNjUVwcDCMjIwwYMAAlClTBk+ePIGXlxfWr18PMzMzxMbGio6pMZUrV8ahQ4fQpEkTAMDMmTMRGhqKsLAwAMDevXvh6ekpq69Zn506dUqt+3pqaqraVl+lUintGpGTP//8s0DPk1sD1zejqz+EHfnl4d0+SPl5+vSpjhLpxtsjXvMj1x4SS5YswYQJE6SJUadOnYKtra1UcE5NTYW7u7vsJkaR7rEgQSRAqVKlcOnSJZiZmSEnJwdFixbF8ePHYW9vLzqaVr05j5gfOZ9H/O2339C3b1+8evUKAFCzZk1s2rQJAwYMQL169fD999/LbqZ3sWLFEB8fL23dbtWqFTp37owffvgBAHDnzh3Ur19ftuM/9U10dDRmzpyJ33//HcDr17n09HRpXaFQ4OzZs2jatKmoiFqR3+vam9cz4PXXnpWVpetoWvW+ka5y/7oL0uRQjl/3ypUrUbp06fc+j32Q5OPdY7YmJiZqI6zl3A+JdEv+naaIPkFpaWkoUaIEgNd/5IsVKyb787Zv7Nu3T+rYrE+8vLzg6uoKLy8vbNy4EVOmTIGrqysCAgJkewexYsWKSEhIQLVq1fDy5UtcuHABc+fOldZTU1NhZGQkMKF2zJs3L8/HS5cuDUtLS3Ts2PG9H+YKq9WrV6NVq1Zqj23btg1Vq1aFSqWCr68vvL29c41+LewuXryY5+MqlQq7d++Gt7e3LHeGJCUl5fl4eno6fv75Z3h7e0sfXORk//79+a6Fh4dj1apVsh316+TkxCOX7/jnn3+wbds2TJo0SXQUjdPXEdakeyxIEAkSFBQk3WnIycnBiRMncOXKFbXn9OjRQ0Q0rWrZsqVevqGJi4uDn58fSpYsiYkTJ2LatGlYuXKlbIsRANC5c2d4eHhg8eLFOHDgAIoXL47WrVtL65cuXZLlEaX8PrAkJyfjwYMHqFu3LoKCgmT3exAWFpbr7qidnZ30odTY2BgDBgwQEU2rGjZsmOux48ePw8PDAzdu3MC0adMwZcoUAcm069075Tk5OfD19cXcuXOhVCqxZs0aWd4t79mzZ67Hrl27hunTp+PQoUMYPHgw5s+fLyCZdn1oV8itW7fwzTff4OTJkzpKJI5KpcLRo0fh4+ODgwcPwsTERJYFCSJdYUGCSJB336h9++23atdyPbqgr549eyaNOzU0NISxsTEsLCzEhtKyBQsWoE+fPrC3t0fJkiXh5+eHIkWKSOu+vr7o2LGjwITakd8dc+B189JBgwZhxowZ2Lx5sw5Tad+9e/fU+iTMmzdPmiwCvO4p8ujRIxHRdOZ///sfPDw8cPr0aYwaNQqHDx+WXeEpL4GBgZgxYwb+/vtvTJ8+HRMmTNCLxqZ//fUXPD094efnh06dOiE6OlqWDamBD98df/78OUJDQ3WURow7d+7A19cXW7duxYMHDzB48GD8/vvvcHR0FB2NqFBjQYJIgJycHNERhKhRowYMDAxExxAmNjYWiYmJAF6/ubt+/TrS0tLUntOgQQMR0bSiQoUKOH36NFJSUlCyZMlc3/u9e/eiVKlSgtKJUblyZSxYsABDhgwRHUXjihYtivv370sj4CZPnqy2fu/ePRQvXlxENK27efMmZs6ciYCAAAwYMACxsbGyPK7wrtDQULi7u+Py5cv47rvv4O7u/sEeA3KQkpKChQsXYtWqVWjUqBFOnDihtvtLjvT1fUtmZiYCAwOxefNmhIeHo0uXLli+fDmcnZ3h4eGBOnXqiI6oVZs3b5aOnGVlZWHr1q1qI6yJNIEFCSLSmXPnzuGzzz7Ldz0rKwsXLlxAs2bNdJhKd9q1a6d2l+lNE0uFQiHrhp55fUDJycnBmTNn4OPjgwMHDug+lEBVq1bF48ePRcfQOBsbGxw4cAAtW7bMcz0wMBA2NjY6TqV9Y8eOhY+PDxwdHXH+/Hk0atRIdCSd6Nq1K06cOIHhw4fjwIEDqFSpkuhIOrFkyRIsXrwYlSpVwq5du/I8wkHyUbVqVdSpUwcuLi7Yt28fypYtCwBwdnYWnEz7qlevjk2bNknXlSpVytUDSG7Tg0gMFiSIBBg7diyWLFkiVZ23bduG3r17S9fJyckYNGgQDh8+LDKmxlWuXFmtY7O1tTWCgoKkP2j//PMPmjdvLssP5QkJCaIjfBLi4+Ph6+sLPz8/JCUloVOnTqIj6VxMTAxMTU1Fx9C4sWPHwsnJCaamphgzZozUuDM7Oxtr167FqlWrsHPnTsEpNW/9+vUoVqwYHj9+jBEjRuT7vAsXLugwlfYdOXIEhoaG8Pf3x549e/J9ntzGQHp4eMDY2Bi1a9eGn58f/Pz88nxeYGCgjpORNmRnZ0OhUEChUOjdDs87d+6IjkB6ggUJIgE2bNiAOXPmSAWIcePGoWXLltJ1ZmYmgoKCREbUinfPoN6/fz/XaDS5dnF+s41dH2VkZGDPnj3w8fFBREQEsrOzsWLFCowYMUKW0weePXuW5+MpKSmIiorC999/j1GjRuk4lfb17dsXbm5umDBhAmbMmIGaNWtCoVDg1q1beP78Odzc3NCvXz/RMTXO09NTdAQhtmzZIjqCEF9//fUHGzzKkY2NzXu/7rdH/MrJw4cPERAQAB8fH3z33Xfo0qULXFxc9PJngEhbFCq5vvsn+oQplUokJiZKOwVKlSqFmJgY2c921tevGwBOnTqV5+OlS5dG7dq1pTGwchIZGYnNmzfD398fFhYWcHFxgZOTE7744gvExMTI9uytUqnM982qQqHAt99+i5UrV8py5CkAREREYNeuXYiPjwcAmJubw9nZGXZ2doKTEdG/9fbI5veRc4Hu1q1b2LJlC/z8/PDgwQM4Oztj2LBhaNu2rSx3T5w8eRLjx49HREQETExM1NZSUlLQokULrFu3TtbTwkg3WJAgEkBfP5jr69cNQNq+nhcDAwOMGTMGP/30k6w+pBoaGmLChAlwdXWFpaWl9LiRkZGsCxL5dZo3MTGBubm5LHeFEBHpi5ycHAQFBcHHxweHDh1CqVKl8OTJE9GxNK5Hjx5wdHTM1aD4DW9vbwQHB+c76pqooHhkg4h0RqFQIDU1FcWKFZOaOD5//lza4p7fVnc5SEpKyvPx5ORkREZGYurUqahUqRJmzJih42Ta07ZtW/j4+ODx48cYMmQIOnXqpBfbXO3t7UVH+CQFBgZizpw5uHTpkugoGvWhrexvyK2HRNmyZQv0dcuth0SfPn0K9Dz2kJCPc+fO4ddff8WrV6/Qvn17dOzYEV26dEGXLl3w999/52r0KBcxMTFYvHhxvusdO3bEsmXLdJiI5IoFCSJBZs+eLY3Ae/nyJby8vKRpBHI9i6lSqWBhYaF2/XbX/TdFCjnKbxRe6dKlUaNGDRQpUgQzZsyQVUHi6NGjuHfvHrZs2YIxY8YgIyMDAwcOBADZfp/fePbsmbTF9fDhw2q9UgwMDPDVV1+JiqZVmzZtwtGjR2FkZITvvvsOtra2OHnyJL7//ntcv35dluNOe/XqJTqCECtXrhQdQQh9GGuaFzMzszxft0uXLg1LS0tMmTIFTZo0EZBMu/bv34/+/fujWLFiMDQ0xE8//YSffvoJkyZNAvB6vLWbm5vYkFry6NGj9+7aNDQ0xN9//63DRCRXPLJBJICDg0OBPpAFBwfrII3u5LeV/V36eIf5zp07qFevHp4/fy46itYcO3YMvr6+OHDgAKpVq4Z+/fqhX79+aNy4sehoGvXbb79h1qxZuHjxIoDXR5PS0tKkdYVCAX9/f9k1eFy2bBlmzJiBBg0aIC4uDgAwc+ZMLF++HBMmTMC4ceOk+fVEVLj8/PPPeT6enJyMqKgoBAUF4ejRo3B0dNRxMu1q2rQpGjZsiPXr18PQ0BALFizAypUrZXlE4121atXCsmXL0Lt37zzXAwMDMWXKFNy+fVvHyUhuWJAgIvoEhIeHw8XFRS/+sCclJWH79u3w9fXFpUuXZNczpEePHujZsydGjhwJIHevlCVLliAkJER2Y32tra0xdepUjBgxAiEhIWjbti3atm2Lffv2oUyZMqLjac22bdvw1VdfoVy5cnmup6Wl4aeffsLs2bN1nIxId+bPn4/jx48X+MZDYWFiYoLz589LuzszMzNRokQJJCYmyr7AOmHCBISEhCAqKgrFihVTW8vIyECzZs3g6OgIb29vQQlJLliQICKdKWiPiHe7Ocvd48eP4eTkhJo1a2Lz5s2i4+jUhQsXZLdDwtTUFPv27ZO2L79bkLh8+TLatWuHx48fi4ypccWLF8e1a9dQvXp1AEDRokVx6tQp2NraCk6mXUqlEmZmZjh48CDq1auXa12uzXrf/Dx/iNyKrOwhkbe4uDi0bt1adjsH3m3GDeR+TZerR48eoXHjxjAwMMD48eNhaWkJhUKBuLg4rFmzBtnZ2bhw4QIqVqwoOioVcuwhQSRAcnIydu3ahTFjxgAABg8ejIyMDGndwMAAmzZtkt1dxTJlyrz3qMqbHhJye+MO5N/4LiUlBffv34e1tTV2794tIJn2LFmyBBMmTICxsTEASB9OixYtCgBITU3F5s2bsXbtWpExNS4xMRGfffaZdB0cHIxq1apJ1yVLlkRKSoqIaFr14sULtbtoRYoUQYUKFQQm0p1atWqhefPm8PPzK/AH1sLuzp07qFGjBgYNGqT2YU3u3u0hsXPnTnTv3h2lSpUSlIi0LSgoSO37npOTgxMnTuDKlSvSYz169BARTasqVqyI8PBwjBkzBtOnT8ebe9gKhQKdOnXC2rVrWYwgjeAOCSIBli5dipiYGGzfvh3A62p7p06dpDc0Z8+ehZOTE+bMmSMwpebpcw+J/Ga4m5iYwMrKCh07dpTdHHMDAwM8fPhQ+rBiYmKC6Oho2Y95rVKlCn755Re0b98+z/WjR49i6NChePjwoY6TaZdSqcSCBQuksabu7u6YOnVqrm3NEydOFBFPa978nG/atAmenp6YOXOm2u+7XH/O9+zZgy1btiAkJARdunTBiBEj0LVr1/eOOJYjfblb/iHz58/HiRMnEBISIjqKRhXk51muN1LelpSUhJs3b0KlUsHc3Bxly5YVHYlkhAUJIgFsbW3h6emJrl27Asj9hmb//v2YN2+e1BSPqDB6d6vruz/ncv2g5uTkhPT0dPz66695rnfr1g0lSpSAv7+/jpNpl6mp6Qeb9SoUCtlt4X/75/y3336Di4sLHB0dsW3bNpQsWVK2P+dvPHjwAFu3bsXWrVuRlpaGr7/+GiNHjoS5ubnoaDqhLwWJ/PoEpKSkICoqCn/88QeCgoLQtm1bHScjosKOBQkiAcqXL4/w8HCpSVKTJk1w4MABfPHFFwBen7lt0KCB7CYu+Pr6YsSIEfmup6amYvLkyXrTR+HFixfw9/dHWloaOnToILs38PpakLh48SKaN2+O7t27Y9q0adLv+fXr17F48WL8/vvvCA8Pl13vDH317s/5tWvX0KtXLxgaGuLXX39FiRIlZPlznpfQ0FDMmTMHp06dwpMnT/TiLqq+FCTMzMzyfPzNLj83NzfZ94vRN+97v/aGQqGAj4+PDtKQnLGHBJEA6enpePnypXR9/vx5tfW0tDTk5OToOpbWTZ48GYGBgdi8eTMqVaqkthYUFIRvvvlGtm9gp06dipcvX0qj016+fAk7OzvExsaiePHimDZtGo4dO4bmzZsLTkr/lY2NDfz9/TFq1Khcje3Kli2L3bt3sxghI+/uCrGyskJkZCQGDRqEpk2bYuXKlWKC6dCLFy+wb98++Pr64ty5c+jfvz+KFy8uOhZpUEJCgugIpGNJSUn5rmVnZ+P48ePIzMxkQYL+MxYkiASoWbMmLly4kGdHduB1gSK/uxGFWUxMDIYPH466deti9erVcHZ2RmpqKiZNmoTt27dj6tSp8PT0FB1TK/744w8sXLhQut6xYwf+/PNPxMfHo3r16hgxYgQWLFiA33//XWBKzdu8ebPUUyArKwtbt26VegqkpqaKjKZVPXv2RIcOHRAUFIT4+HgAgLm5OTp27IgSJUoITqcdXbt2xa5du6Tmb15eXhg3bpzUnPeff/5B69atERsbKzCl5uW10dTExASHDh3CzJkzMWzYMN2H0pFz587Bx8cH/v7+qFWrFkaMGIGAgADZFpYB5DqKlVeDQ0CeTQ6fPXuGkiVL5uqrkJOTg+fPn+vdhCy5279/f56PHzx4EDNmzEDRokU5zpg0gkc2iASYNWsW/Pz8EBkZmWunwMOHD2Fra4uvv/4aCxYsEJRQu1auXIkffvgBDg4OuHz5MkxMTLB161Z8+eWXoqNpjYmJCS5cuIDatWsDAJydnVGqVCls3LgRABAdHY2uXbvir7/+EhlTowrSUwDgnTe50NcmpsOHD4e3t3e+Uxb27NmD9evX4+TJkzpOpl1169bF48ePMWjQIIwcORINGjQQHUkn9LXJ4f79++Hu7o7o6Ohcu1/S09NhY2ODZcuWoXv37oISkradOXMG7u7uuHjxIsaPHw8PDw9ZFx9Jd1iQIBIgNTUVtra2uH//PoYMGQILCwsoFApcu3YN27dvR9WqVREZGSnbMWIZGRkYPHgwDhw4gBIlSiAsLAwNGzYUHUurypQpg6ioKKlPhJmZGWbNmiWd0bxz5w6sra3Vxr9S4ZRf87d3yW3ahL72DNFXSqUSJUqUgKGh4XsLj0+fPtVhKtKWjh07YsCAARg1alSe676+vvD390dQUJCOk2nXqVOn0KJFCxga6u+m8qtXr8LDwwNHjhzB119/jblz50o9z4g0QX9/u4gEKlWqFM6cOYPp06dj165dSE5OBvD6Q+ugQYOwcOFC2RYjzpw5g+HDh8PIyAhHjhzB5s2b0bx5c3h5eWHy5Mmi42mNlZUVDh06BDc3N1y9ehV//vknHB0dpfW7d+9ynrdMrFix4oPPUSgUsitI6KuxY8diyZIl0tGkbdu2oXfv3tJ1cnIyBg0ahMOHD4uMqXFbtmwRHYF06MqVK1i7dm2+623atMEPP/ygw0S64ejoqLbzS5/cu3cPs2fPxvbt29GtWzdcunQJ1tbWomORDHGHBJFgKpUKf//9NwCgQoUKBdriXlh9//33WL16NcaPH4+FCxeiaNGiAAB/f3+MHz8e1tbW2Lp1qyy7lQcEBMDZ2RmtW7fG1atX0bRpUxw6dEhad3d3R0JCAvbs2SMwpWadO3cOT58+RZcuXaTHfvnlF3h6eiItLQ29evXCqlWrpJ8DKtwMDAyQmJiIChUqAHhdeL106ZLUD0euOyT09aiKvsrOzkZsbCzq168PAFi/fr1ak2oDAwOMGTOmQEc7ChNjY2NcvHgRVlZWea7HxcWhcePGstvl9+7OL31SvHhxKBQKTJgwAS1atMj3eXLsl0K6xR0SRIIpFAq9+UN38OBBHD9+HK1bt1Z7fODAgXBwcMC3336Lhg0byrLZYd++fXH48GH8/vvv6NixIyZMmKC2Xrx4cYwdO1ZQOu2YM2cOHBwcpILE5cuXMXLkSAwbNgzW1tZYunQpqlSpgjlz5ogNqmFt27ZFYGCg1MxRX6hUKgwbNkwqML148QKurq5SE8/MzEyR8bTm3fs6vM8jb/7+/tiwYQNCQ0MBvJ6gVKZMGWlL/5MnT1CsWDGMHDlSZEyNMzU1xfnz5/MtSJw/fx41atTQcSrdkPONovd58eIFAGDJkiX5PkeO/VJI97hDgkgAR0fHD/6BUygUOHHihI4S6UZaWtoHJwxs27YNQ4YM0VEi0qbKlSvj0KFDaNKkCQBg5syZCA0NRVhYGABg79698PT0lN3UBX29ozZ8+PACPU9uW/31tXeGUqnM8++YiYkJLC0tMW3aNPTp00dAMu3q0KEDRowYAWdnZwC5v9/r16+Hv78/goODRcbUuJkzZ2L79u2IjIzMdbwwMTERtra2cHFxgZeXl6CE2qFUKjF69OgPjrFdvny5jhIRyQ93SBAJ0KhRo3zXnj17hl27dsnybmJBxh3KtRjx9OlTpKenqzWCunr1KpYtWyYdXxg0aJDAhJqXlJSk9sY1NDQUnTt3lq6bNm2Ke/fuiYhGWiC3QgO9X34jAZOTkxEZGQkXFxf4+fmhf//+Ok6mXXFxcahTp06+6/b29pgxY4YOE+mGh4cHDh48CHNzc7i4uMDS0hIKhQJxcXHYsWMHqlWrBg8PD9ExteLy5csoUqRIvuv6uoMiOzsbhw4dQq9evURHoUKOBQkiAfJqepeVlYU1a9bAy8sLVatWxfz58wUk06558+Z98DkKhQKzZs3SQRrdGjduHCpXrizdRXn8+DFat26NKlWqoFatWhg2bBiys7NlVZCpWLEiEhISUK1aNbx8+RIXLlzA3LlzpfXU1FQYGRkJTKg9qampKFas2HufY2JioqM0n4a4uDh89dVXuH37tugoGjd79mzpDurLly/h5eWF0qVLA3g9ElGOevbsme/a0KFDUadOHSxbtkx2BYknT55IDUsB4Pbt2/jss8+kayMjI6SlpYmIplVvN+P29/dHUlISAKBs2bJwcXGRdTPu/fv3692Ot/e5du0afH194efnh6SkJLUeKkT/Bo9sEH0CduzYgdmzZyMjIwM//PADRo8eLcsRUzY2NvmuKRQKXL9+HS9evJDd1mbg9ZjPLVu2wMHBAQCwbNkyrF+/HteuXYOhoSGWLVuGffv2ISIiQmxQDfr2229x+fJlLF68GAcOHICfnx/++usv6U7Tjh07sHLlSkRFRQlOqln5bWV/Q6VS6eW525iYGDRu3Fh2X7eDg0OB7pDKbQv/h8THx6NZs2bSB1e5qFGjBtatW4euXbvmuX7o0CGMHz8ed+/e1XEy3VGpVHjy5AlUKpXsm3G/27RWX6WlpcHf3x8+Pj6IiIiAo6MjnJyc0KtXL5QvX150PCrk5PeJh6gQOXLkCDw8PJCQkIApU6bAzc2tQMcaCquLFy/m+Xh0dDQ8PDxw5coVfPPNNzpOpRuJiYnStAEAOHnyJHr37i0Vnnr06IFFixaJiqcVCxYsQJ8+fWBvb4+SJUvCz89Pbdurr68vOnbsKDCh9uzbtw/lypUTHYN0ICQkRHSET1JGRsYHdwkVRu3atYOXl1eeBQmVSoVFixahXbt2ApLpjkKhkKbpyJ2+37c9e/YsNm/ejD179sDc3ByDBw/GuXPn4O3t/d6jS0QfgwUJIgEiIyPh7u6OiIgIuLq64vjx43pZYU5ISMCsWbPg7++PPn364OrVqzA3NxcdSytMTEyQnJwsdSGPjIxU68KuUChk1zekQoUKOH36NFJSUlCyZEkYGBiore/du1dt67OctGzZUu/vqJF+27Rp03t3xRVWM2fOROPGjWFra4spU6bAwsICCoUC165dw7Jly3D9+nX88ssvomNqnI2NTYF2Qly4cEEHaXRny5Yt0vErfVOnTh2kp6dj0KBBOHfunFSAkGuvEBKHBQkiAezs7GBsbIwxY8bA1NQUO3fuzPN5EydO1HEy3Xjy5Anmzp2LjRs3olWrVggPD0fTpk1Fx9KqZs2awdvbG5s2bUJgYCBSU1PRtm1baf3GjRuoVq2awITak9+bOX3eQZCVlSXLY1n6qE6dOggLC5N+nkePHg0vLy/pDvLjx49hamoqu14Sbm5ueT6ekpKC8+fP49atWzh9+rSOU2lfrVq1cOzYMQwbNgwDBw6UPqSrVCpYWVnh6NGjqF27tuCUmqevjQtbtmyJK1eu4Msvv5QeO3HiBBYsWCA1pJZjE1MAuHnzJpycnODo6Ahra2vRcUjG2EOCSABTU9MCjf2UW/O3tLQ0LFu2DMuXL0ft2rWxaNEi2W7Zf1d0dDTat2+P1NRUZGVlYcaMGWqNS4cMGYISJUpg/fr1AlNqVkFH/gUGBmo5iW6ZmZnh/Pnzao3u3oiNjcXmzZuxY8cOPHr0SEA67Slbtux7X9eysrKQlpYmux4S7479NDExQXR0tNrYz8qVKyMnJ0dkTI1zdHTM83ETExNYWVlh7Nix0o4wubp48SLi4+MBAObm5rLcEaLvevfujXr16kl/rxMSElC3bl20bt0aVlZW8PX1xfz58zFp0iSxQbXgwYMH2Lp1K7Zs2YKMjAw4Oztj8ODBsLW1RXR0NI9skMbw9gyRAHfu3BEdQYhatWohNTUVEyZMgLOzMxQKBS5dupTreQ0aNBCQTrsaNWqEuLg4hIeHo1KlSrC1tVVbd3Jykt0fd33d5pqQkKB2/fz5c+zevRs+Pj6IioqCnZ2dLLe8rly5UnSET0Je93nk2PRP35p05sXGxkZvihAnT55EmzZt9G5n1/nz5zFt2jTpeseOHbCwsEBQUBCA1+9XVq1aJcuCRNWqVTFz5kzMnDkTJ0+ehK+vL1q2bImsrCxs3boVo0aNgoWFheiYJAPcIUFEOqNUKqX/VigU+b5xl9sdVNJPYWFh2Lx5MwICAmBmZobY2FiEhoaiZcuWoqMJ8erVKzx8+BDVq1cXHUWj3t0hUapUKcTExKjtkKhSpYrevK7dvXsXaWlpsLKyUnvNl4sRI0bk+Xjp0qVhaWkJFxcXWfbGeXfahJ2dHQICAlC1alXBybTL2NhY7Uhlu3bt0KJFC2nHxK1bt/Dll18iOTlZYErdSUlJwY4dO+Dr64sLFy6gXr16ed5YIvoY+lXmJPpEeHt7F+h5cush8e6d47zIbUTcG127dsWuXbukXQNeXl4YN24cypQpAwD4559/0Lp1a8TGxgpMqT0qlQr//PMPFApFnkcZ5GTJkiXw9fXF8+fP4ezsjLCwMDRs2BBGRkYoW7as6HjCxMbGynLsp0KhyLUDQo47It7l5+eHpKQktTvDo0ePho+PDwDA0tISQUFBsuuNk9/fqISEBOzYsQPz58/H6dOnpYKUXLx7A+Hq1auya8Scl3LlyuHhw4eoVq0acnJycP78eUyePFlaf/nypV5N4ihdujTGjh2LsWPHIjo6Gr6+vqIjkQxwhwSRAG+Pf8yPHHtI5OdNxd3HxwfR0dGy+8AC5L67lNc5czneRU1MTMS0adPw66+/IjU1FcDrr713795YtGgRKlasKDih5hkaGsLd3R3z5s1TmyxiZGSEmJgY2R3NKaiYmBhZFiSUSiXq1asnbWW/dOkSrKyspBG3WVlZuHr1quy+7ubNm2P06NEYPnw4gNdjrLt3746tW7fC2toa48ePR506dbB582bBSXUnIyMDX3/9NRQKBfbs2SM6jkZ9aCeQXA0aNAipqalYu3Yt9u7dC09PTyQmJkoj2gMCAjBv3jzExMQITqp5GRkZOHbsGBwdHVGqVCm1tWfPniEkJASdOnVC0aJFBSUkueAOCSIBCrJTQB+8OZMYGBiIGjVqoG/fvrJ98/pu7VcfasHPnj1DixYt8Pz5cwwfPhxWVlZQqVSIjY3Frl27EBYWhgsXLshue/O8efOwdetWbNu2Dc7OzhgyZAjq1asnOhZpiaenp9p1z549cz2nb9++uoqjMzdu3ECTJk2k64MHD6JHjx4YPHgwAGDhwoVSsUJfGBsbw93dvcANfQuTd3cC5bUzSI68vLzQoUMHmJqaQqlUwtvbWypGAMC2bdvUJmbJycaNG/Hrr7+iR48eudZMTEzg7e2Ne/fuYdy4cQLSkZywIEFEOnX//n1s3boVvr6+SEtLw4ABA/Dq1SsEBATo7Z1jufr5559hYGCAq1evSiMQ3/jhhx/QsmVLeHt7y25k2owZMzBjxgyEhobC19cXdnZ2qFWrFlQqlWyPJOmzdwsS+iIjIwMmJibSdXh4uFp/hZo1ayIxMVFENKHKlSsny34CKpUK7dq1k3YCpaeno3v37tJOoDcuXLggIp7WmJmZIS4uDrGxsahQoQKqVKmitj537lx88cUXgtJp144dOzBr1qx81ydNmoR58+axIEH/GQsSRAJUr14dFy9elM7Sr169Gl9//bXamzs56tq1K8LCwtCtWzesWrUKnTt3hoGBgaxGXeZHH8+Z//7775gxY0auYgQAfP7555g+fTo2bdoku4LEG/b29rC3t8eqVauwc+dObNmyBfb29mjWrBn69esHNzc30RE16kONza5fv66jJKQLNWrUwP/+9z/UqFEDT548wdWrV9GqVStpPTExUS8n7YSHh6NWrVqiY2hcQXYCyZWRkREaNmyY51p+j8tBfHz8e7++Bg0aSGNvif4L9pAgEuBDc+vlytDQEBMnTsSYMWNgbm4uPa4PZ+uVSiW6dOkinbU8dOgQ2rZtK239zMzMxJEjR2R1zrxcuXI4e/YsLC0t81y/du0aWrRogadPn+o4mXbdvn0bZmZmeRacLl++DB8fH+zcuROPHz8WkE57lErle6fnqFQqWU7RsbGxKVBxUW53jhctWgRvb2+MHTsWJ0+exN9//40rV65I6ytXrsRvv/2G48ePC0ypefkV3lJSUhAVFYWFCxdiwYIFcHV11XEy0oY6deogLCwM5cqVA/C6cauXl5dUaH/8+DFMTU2Rnp4uMqZWlCpVCiEhIfjyyy/zXP/f//4HBwcHqT8U0b/FHRJEnwB9qQuePn0avr6+aNKkCaysrDBkyBAMHDhQdCydGDp0qNq1i4tLrud8/fXXuoqjE8+ePZOmiOSlTJkyePbsme4C6Yi5ublaA9OBAwfC29sbFStWRP369bFy5UosXbpUcErN09feOL169ZL+W6VSYdGiRXB1dZU+wMiVu7s70tPTERgYiEqVKmHv3r1q62fOnIGzs7OgdNrTqFGjfAtvFSpUgLu7uyyLESdPnkSbNm2kIxv64tq1a8jKypKud+/eDQ8PD6kgoVKp8OLFC1HxtKpu3bo4fvx4vgWJY8eOoW7dujpORXLEHRJEAuhrt+o30tPTsXv3bvj6+iIyMhLZ2dlYvnw5RowYkauTMxVeBgYGSExMzPPIBiDfySL6/vv9PtHR0WjUqJHoGFrF77e83b17N8/HS5cu/d4CbGH37qQoOzs7BAQEoGrVqoKTadeHXs/l+ncMeN3U0s3NDbt370a3bt3U1g4dOgRnZ2csX74co0ePFpSQ5EK/ypxEn5DNmzdL0wWysrKwdetWlC9fXu05EydOFBFN64oXL44RI0ZgxIgRuH79Onx8fPDjjz/Cw8MDHTp0wK+//io6ohD79u1Dv379RMfQGJVKBQsLi3y3s7Merh/ejPXdvHkzYmJiZPnGnfRHjRo1REcQ4t3X66tXryIzM1NQGtKF0aNH49SpU+jRowesrKxgaWkJhUKBuLg43LhxAwMGDGAxgjSCBQkiAapXr45NmzZJ15UqVcK2bdvUnqNQKGRbkHibpaUllixZgkWLFuHQoUPw9fUVHUlrsrKycP36dRgZGcHCwkJ6/ODBg5g9ezauXbsmq4LEli1bREcQQh8bmOYlr7G+Pj4+omORhuTXJ+VtCoUCt27d0lEi3dq7dy927dqFGzduQKFQwNzcHIMGDZLVazjx9Xz79u3o0aMHdu7ciRs3bkClUsHS0hJz587FgAEDRMcjmeCRDSIiHYiNjUW3bt2k7b49e/bEunXrMGDAAMTExGDUqFH47rvvUK1aNcFJ6b/6UAPTNwIDA0XE06q8xvquX79e9k1r36YvRzZ+/vnnfNfu3LmDDRs2IDMzU3Y7YnJycuDs7Iy9e/fCwsICVlZWUKlUuHbtGm7evIn+/ftj165dsvvQ+u4RPBMTE8TExMDMzExwMu1SKpWoV6+e1Dvj0qVLsLKyksadZmVl4erVq7L7Of83fvzxR7i6usr66BJpBwsSRIVA/fr1cfjwYX5YLcR69OiBtLQ0TJ48GTt27IC/vz9q164NFxcXTJ48mb0zZGT48OEFep7cdpC8PdZ38ODB0lhfuU/R8fb2Vrt2d3fH1KlT9eYI3tuePn2K+fPnY926dbC1tcXixYthZ2cnOpZGLV++HF5eXvDz88t1rv7XX3/F8OHDMWvWLEyaNElMQC350AfzN+Q2TWbu3LkFet67Y1H1kb5MjCPNY0GCqBDQlztuclapUiUcPnwYjRs3RnJyMsqVK4cNGzbgm2++ER2NSCP0daxvQe4QKxQK3L59WwdpxMjIyMDy5cuxdOlSmJqaYuHChejatavoWFrRoEEDTJo0CSNGjMhz3cfHBytXrsTly5d1nEy7+MGcPoTvVenfYg8JIiIdePz4sdSNvEyZMihevDjs7e0FpyLSHH0d63vs2DHUrl1bdAwhsrOzsWnTJsydOxfFihXDqlWr4OLiIrvjCm+Lj49H+/bt811v3749xo8fr8NEusFCAxFpCwsSREQ6oFAooFQqpWulUgkjIyOBiYg0q3nz5mjevDl+/vlnaayvm5sbcnJycOzYMVSrVk2WR5MsLCxQtWpVODo6om3btnB0dNSLSQx79uzBDz/8gJSUFMyYMQNjxozJtX1fjoyNjZGcnIzq1avnuf7s2TMYGxvrOBVpi42NTYEKbHI7qkKkSzyyQVQIcBtc4adUKlG6dGnpjU1ycjJMTEzUihTA6zPYcvLs2TOULFky19eZnZ2NtLQ0mJiYCEpGuvBmrO+2bduQnJwsy7G+p0+fRmhoKEJCQnD27Fm8ePEC1atXl4oTjo6O0u4oOVEqlTA2Noazs/N7f4+XL1+uw1Ta99VXX6F69epYt25dnuuurq64d+8efv/9dx0n0y59/WD+9lEVlUqFRYsWwdXVFeXKlVN7HneQ8L0q/XssSBAVAnyRL/z8/PwK9LyhQ4dqOYnu7N+/H+7u7oiOjkbx4sXV1tLT02FjY4Nly5ahe/fughKSrmRnZ0tjfeVWkHjbq1evcPbsWYSEhCAkJAQRERHIzMxE7dq1cf36ddHxNMrBwaFAYz9Pnjypo0S6ER4eDgcHB/Tq1QtTpkyRpmzExcXhp59+wsGDBxEcHIyWLVuKjqpR7CHxGt+P5Y//b+jfYkGCqBDgizwVRh07dsSAAQMwatSoPNd9fX3h7++PoKAgHScj0q6MjAyEhYUhKCgImzZtwvPnzzkWUEb279+P0aNH59rRVrZsWWzYsAF9+/YVlIy0je/H8te1a1f4+PigcuXKoqNQIcOCBJEAN2/e/KgmaDt37kTPnj1RokQJLaYibdu7dy8OHDiAV69eoX379hg9erToSFpVpUoVnDp1Kt+f9Zs3b6JNmzb466+/dJyMSLNevHiB8PBwBAcHIyQkBFFRUTAzM4O9vT3atGkDe3t7WR7beNuTJ0+gUCjw2WefiY6iE+np6QgKCkJ8fDyA171EOnbsmGs3mBw9efIEd+7cgUKhgKmpqd58zwH9LkhcvXpVrbBqYGCAunXrCkxEcsGmlkQCvN0E7c0/U1PTfJ8/aNAg3YUjrdi4cSNcXV1hbm6OYsWKISAgAAkJCVi0aJHoaFqTlJSErKysfNdfvXqFpKQkHSYi0jx7e3tERUWhVq1aaNOmDSZMmAB7e3tUrFhRdDStS05OxsyZM+Hv7y/9LpctWxZOTk5YsGABypQpIzagFpw8eRLjx49HREQEevfurbaWkpKCunXrYv369WjdurWghNpz9epVjBkzBmfOnFF73N7eHuvWrYOlpaWgZKQNp0+fhpubG6KiogAAdnZ2SE9Px5t72QqFAkFBQe+dOkNUECxIEAkQGhoqNUEbP3683jRB02erVq3CzJkzMX/+fADA1q1bMWHCBFkXJExNTXH+/HlYWVnluX7+/Hm9mEZA8hYeHo7KlSvD0dERDg4OaNOmDcqXLy86ltY9ffoUzZs3x4MHDzB48GBYW1tLvRS2bt2KEydOIDw8HGXLlhUdVaNWrlyJb775Js9GnqVLl8a3336L5cuXy64gkZiYCHt7e1SoUAHLly+XemfExsZi06ZNaN26Na5cuYLPP/9cdFSN8vb2VrvOysrC1q1bc/2OT5w4UZexdGLt2rUYMmSI2mPBwcGoUaMGVCoVvL29sW7dOhYk6D/jkQ0iwfSpCZo+K1GiBC5fvixt88zOzoaxsTH+/PNPVKpUSXA67Zg5cya2b9+OyMjIXHeLExMTYWtrCxcXF3h5eQlKSPTfpaWl4fTp0wgJCUFwcDCio6NhYWEBe3t7ODg4SB/i5GbSpEk4ceIEjh8/nufvd8eOHdGuXTusWLFCUELtqFGjBo4cOQJra+s8169du4aOHTvizz//1HEy7XJ3d8fx48dx5swZFCtWTG0tIyMDrVq1QseOHWVXZDczM/vgcxQKBW7fvq2DNLpVu3ZtbN++HXZ2dgByH1e5ePEivvrqKx67pP+MBQmiTwSboMmbUqlEYmKi2t0juZ9FTU1NRfPmzfHnn3/CxcUFlpaWUCgUiIuLw44dO1CtWjVERESgVKlSoqMSaUxqairCwsKkfhIxMTEwNzfHlStXREfTKFNTU2zYsAGdOnXKc/3IkSNwdXXFnTt3dBtMy4oVK4YrV668tzdO/fr1kZGRoeNk2tW4cWN4eHhgwIABea7v3r0bS5Yskd3YT31mbGyMa9euSTsZAwMD0blzZ6lPyt27d2FhYYHMzEyRMUkGeGSDSJD3NUFbt24d7O3tRUckDdu8eTNKliwpXee19VNO2z5LlSqFM2fOYPr06bnOmLu4uGDhwoUsRpDslChRAuXKlUO5cuVQtmxZGBoaIi4uTnQsjXv48OF7G9rVq1cPiYmJOkykG1WrVsXly5fzLUhcunRJllMGbt++jcaNG+e73qRJE1nuEtBnpUqVQkJCglSQ6NOnj9p6QkJCnkeXiD4Wd0gQCfBuEzR7e3u9aYKmr0xNTaFQKN77HLlu+wQAlUqFJ0+eQKVSoUKFCh/8f0FUWOTk5OD8+fPSkY0zZ84gLS0tV+NiufVLqVq1Kvz9/dGqVas810+fPg0nJyc8ePBAx8m0a8KECdJNhLyOLjRr1gyOjo65eg8UdgYGBnj48GG+PSIePXqEqlWrvreRcWH0dhPTdz98p6SkoEWLFli3bh3atGkjKKH2dO/eHRUqVICvr2+e68OGDcOTJ0/w22+/6TgZyQ0LEkQCGBkZoXLlyujVq5deNUEjIpIbExMTpKWloXLlynBwcICDgwMcHR1Rq1Yt0dG0auTIkbh58yaOHTuGIkWKqK1lZmaiU6dOqFWrFnx8fAQl1I5Hjx6hcePGMDAwwPjx49WOoq1ZswbZ2dm4cOGC7G4wGBgY4MaNG/n2Q3n06BGsrKxkd9S0R48ecHR0xOTJk/Nc9/b2RnBwMPbv36/jZNoXHByM9u3bw83NDVOnTpWKUY8fP8bixYvx888/4+jRo2jbtq3gpFTYsSBBJIC+NkGjgqtfvz4OHz6MatWqiY7yr9nY2BRoJwTPHFNhtmHDBjg6OsLCwkJ0FJ26f/8+mjRpgqJFi2LcuHHSNJ3Y2FisXbsWmZmZOH/+fKF+DcvP3bt3MWbMGAQFBamNQOzUqRPWrl373jHehZVSqXzv67lKpYJCoZBdQUJfm5i+sXbtWkyePBlZWVkwMTGBQqFASkoKDA0N8dNPP2H8+PGiI5IMsCBB9AnQlyZoVHByaHg5d+7cAj3P09NTy0mISBtu376NcePG4ejRo2ofzDt06IDVq1fn22dBLpKSknDz5k2oVCqYm5vLbsTp20JDQwv0PLn1v9LXJqZvu3fvHvbt24f4+HgAgLm5Ofr16yfLYiOJwaaWRJ8AfWmCRvqFhQYieatZsyb++OMPJCUlSR9WateujXLlyglOphtly5ZF06ZNRcfQCbkVGgpKX5uYvq1atWr5Hlkh0gQWJIgE+FATtDVr1sDR0VF0TCIiojxlZ2fj6tWr0s6AZs2aSWvp6em4efMm6tWrB6VSKTAl0X/TtWtXzJ49G126dMmziamnpye6desmKJ12nTp1qkDPk2NDT9ItHtkgEkBfm6BRwcnhyIajo2OBJoucOHFCR4mISFO2bt2K1atX49y5czAwMFBby87Ohq2tLSZNmgQXFxdBCYn+O31tYgrgvcXEN3/bFQqF7CarkO5xhwSRAEuXLtXLJmikXxo1apTv2rNnz7Br1y5kZmbqLhARaYyPjw+mTJmSqxgBvJ7IMG3aNKxevZoFCSrUKlasiPDwcIwZMwbTp0/Ps4mpHIsRwOseKXlJT0/Hzz//DG9v70J904Q+HdwhQSRYcnIybt68CYVCgVq1aqFMmTKiI9EnQA47JPKSlZWFNWvWwMvLC6VLl8b8+fPh5OQkOhYRfaTPP/8ckZGR+U6USEhIQLNmzfD333/rNhiRluhTE9O85OTkwNfXF3PnzoVSqcScOXMwdOhQHsui/4w7JIgEuXPnDsaNG5drbFjnzp2xevVqWY4No/d78OABqlatCuD1KEG53XXZsWMHZs+ejYyMDMyZMwejR4+GoSH/DBEVRmlpaXj27Fm+66mpqUhPT9dhIiLt0qcmpu8KDAzEjBkz8Pfff2P69OmYMGECihYtKjoWyQTfCRIJcO/ePdjZ2cHIyAjz58+HtbU1VCoV4uLisG7dOjRv3hxRUVH44osvREclHUhMTISXlxc2b94sjQ4bNGiQ4FSac+TIEXh4eCAhIQFTpkyBm5sbSpQoIToWEf0H5ubmCA8PR4MGDfJcDwsLg7m5uY5TkbY8fPgQq1evhpeXFwCgVatWagUnAwMDHDhwQCqqkzyEhobC3d0dly9fxnfffQd3d3eULl1adCySGe6xIRLA09MTlpaWiI+Px/Tp09GrVy/07t0bM2bMwI0bN2BhYcGRiTKTnJyMwYMHo0KFCqhSpQq8vb2Rk5OD2bNno2bNmoiIiICvr6/omBoVGRkJR0dH9O7dG46Ojrh16xZmzZrFYgSRDAwaNAg//PADLl26lGstJiYGs2fPllVhVd+tXbsWycnJ0nVMTAxat26Nnj17omfPnjAwMMCKFSvEBSSN69q1Kzp27IhGjRrh1q1bWLhwIYsRpBXsIUEkQJUqVbBnzx60atUqz/VTp07ByckJf/31l46TkbaMHTsWhw4dwsCBA3HkyBHExcWhU6dOePHiBTw9PWU5412pVMLY2Bjffvvte48gTZw4UXehiEgjXr16hY4dOyIsLAzt27eHlZWVNH3g+PHjaNmyJY4dOwYjIyPRUUkDGjVqhKVLl6JDhw4Acvc5CgoKgpubG65evSoyJmmQUqmEoaEhSpQo8d6JWU+fPtVhKpIjFiSIBChatChu3bqV75GM+/fvo1atWpxAICM1atSAj48P2rdvj9u3b6N27dqYOHEiVq5cKTqa1piamhZo7Oft27d1lIiINOnVq1dYsWIFdu7cifj4eKhUKlhYWGDQoEGYNGkSihQpIjoiaUiZMmVw4cIFqQDRp08frFu3Tup1dOfOHdSpU4d9Q2TEz8+vQM8bOnSolpOQ3LEgQSSAmZkZ1q9fj06dOuW5fuTIEbi6uuLOnTu6DUZaY2RkhLt376JKlSoAgOLFiyMyMhL16tUTnIyIiOj9SpYsidOnT8PGxibP9YsXL6J169Z4/vy5jpMRUWHHHhJEAvTs2RNTp07Ncxza48eP4e7ujl69euk+GGlNTk6O2tZlAwMD9lIgIqJCwdLSEuHh4fmunz59GhYWFjpMRERywR0SRAIkJSXB1tYWiYmJcHFxgZWVFQAgNjYWO3fuRKVKlRAREYFy5coJTkqaolQq0aVLF2lM1qFDh9C2bdtcRYnAwEAR8bQmKysLK1aswK5du3Djxg0oFAqYm5tj0KBB+O6773i+nIioEFi6dCl+/PFHBAcH55qsEhMTg7Zt28LDwwNTp04VlJA0TalU5nns0sTEBJaWlpg2bRr69OkjIBnJDQsSRIIkJSVhxowZ8Pf3lzpXlylTBgMGDICXlxc+++wzsQFJo4YPH16g523ZskXLSXQnIyMDHTp0wNmzZ9G+fXtpvO21a9ekpndHjx5FsWLFREclIqL3ePXqFdq3b4/w8HB06NABlpaWUCgUuHbtGo4dO4bmzZvjxIkTLDLLyMGDB/N8PDk5GZGRkdiyZQv8/PzQv39/HScjuWFBgkgwlUolHd2oUKECFAoFHj16hA0bNmD27NmC0xH9e7Nnz4afnx8OHTqU5x21Hj16YPjw4ZgzZ46YgEREVGAvX77E8uXLsXv3bty4cQMAYG5uDmdnZ0yePFnaAUj6Yc2aNfjll19w7tw50VGokGNBgugTFBMTg8aNGyM7O1t0FNKhx48f4/PPPxcdQ2MsLCywaNEi9O3bN8/1vXv3YubMmdIbWyKSj1u3buGbb77ByZMnRUchIi2Ij49Hs2bNkJSUJDoKFXJsaklEpAPFixdXa2LauXNnPHz4ULp+9OgRKleuLCKa1vz5559o1qxZvut2dnb4888/dZiIiHTl+fPnCA0NFR2DiLQkIyODRy5JIwxFByAi0gcvXrzA2xvSzpw5g4yMDLXnyG3DmomJCR4/foxq1arluZ6YmAgTExMdpyIioo9lZmaWZ4PDtykUCty6dUtHiUi0jRs3olGjRqJjkAywIEFE9In40Ju9wsbR0RELFy5EQEBAnus//vgjHBwcdBuKiIg+2qRJk/Jdu3PnDjZs2IDMzEzdBSKtc3Nzy/PxlJQUnD9/Hrdu3cLp06d1nIrkiAUJIgHye5F/4+2t/USFlaenJ2xtbWFnZwc3Nze18bYrVqxAbGwsIiIiBKckIqIP+e6773I99vTpU8yfPx/r1q2Dra0tFi9eLCAZacvFixfzfNzExASdO3dGt27dsGrVKvj6+uo4GckNCxJEAuT3Iv+2Nm3a6CAJ6YpCoVDbAfHutRzVqVMHx44dw8iRI+Hk5CR9vSqVClZWVggKCkLdunUFpySif8PGxua9r2Hp6ek6TEO6lJGRgeXLl2Pp0qUwNTVFYGAgunbtKjoWaVhwcPB712NiYuDn58eCBP1nLEgQCfChF3mSH5VKBQsLC+kN/PPnz2FjYwOlUimty5GdnR2uXr2K6OhoaZqGhYUFz50SFXK9evUSHYF0LDs7G5s2bcLcuXNRrFgxrFq1Ci4uLrIvrhORdnHsJ9EniOPS5MfPz69Azxs6dKiWk3xaoqKi0LRpU9ExiIjoPfbs2YMffvgBKSkpmDFjBsaMGYMiRYqIjkUCcUQ9aQoLEkSfIL7Ik5w8f/4cBgYGMDY2lh6Ljo7GrFmzcPjwYf6cExVyly5dwo0bN6BQKGBubo4GDRqIjkQaplQqYWxsDGdn5/dOR1q+fLkOU5FIfK9KmsIjG0REOhAZGYkvv/wSBgYGAF4f0Xh7m2tmZiYOHjyIAQMGiIqocffv38fAgQMREREBAwMDjB8/HgsWLICrqyt27dqFnj17IiwsTHRMIvqXIiMjMXLkSMTGxkrHzhQKBerWrQsfHx/ufpKRNm3afHCsJ49uyEufPn3eu56cnKybICR73CFB9Ali1Vl+DAwM8PDhQ3z++ecAXnepjo6ORs2aNQEAjx49QpUqVWT1PXdxccHly5fxzTffICAgAKdOnUKjRo3QsGFDzJo1C2ZmZqIjEtG/FBsbC1tbW1hbW2Py5MmwtraGSqVCXFwcVqxYgevXryMiIgJ16tQRHZWI/oXhw4cX6HlbtmzRchKSOxYkiD5BLEjIj1KpRGJiolSQKFWqFGJiYtQKEpUrV0ZOTo7ImBpVtWpV7NmzBy1btkRiYiKqVKmChQsXwsPDQ3Q0IvqP+vfvj+zsbAQEBOS6M65SqdCnTx8YGRlhz549ghISEVFhwCMbRAJwXBrlRW7bXRMTE1GrVi0AQKVKlWBsbIyePXsKTkVEmhASEoI//vgjz9cthUKBGTNmcBSkjLi5uRXoeewhQUQfiwUJIgE4Lo30xZueGcDrXSLFihUTmIaINCU1NRUVK1bMd71SpUpITU3VYSLSposXL37wOXIrqhORbrAgQSSAp6en6AgkQGxsLBITEwG83tJ87do1PH/+HADw5MkTkdG0QqVSoV27djA0fP2nJiMjA927d881Ku7ChQsi4hHRf2BqaorIyEhUq1Ytz/Vz586hRo0aOk5F2hIcHCw6AhHJFAsSRAI8fvxY6iWQl6ysLFy4cAHNmjXTYSrStrZt26pdd+vWDcDru0rvTt2Qg3cLbzyuQSQfAwcOhJubGywtLVGvXj21tcuXL2PKlCkYOnSooHSkacePH0erVq24y42INI5NLYkEeHfigrW1NYKCglC9enUA8py4oO/u3r1boOfxjiIRFQYvXrxAu3btcO7cOXTo0AHW1tYAXu8EO378OJo1a4aTJ0/yA6xMKJVKFClSBM2aNYOjoyMcHR3RokWLXDveiIg+FgsSRALo48QFfZeRkYEpU6bgwIEDePXqFdq3bw9vb2+UL19edDQion/l5cuXWLFiBXbt2oUbN24AACwsLODk5ITJkyejaNGighOSpjx48AAnT55EaGgogoODkZCQgGLFiqF58+ZSgcLW1lY6okdEVFAsSBAJUJCCBHdIyMvUqVOxdu1aDB48GMWKFcOuXbvg4OCAvXv3io6mNR+aJvMGe0gQERUu9+7dQ3BwMEJCQhASEoK7d++iePHibGRKRB+NZUwiIh0IDAyEj48PnJycAAAuLi5o2bIlsrOz1SZRyMnb02RUKhUWLVoEV1dXlCtXTlwoIiL6z6pVq4aWLVsiMzMTmZmZ+Oeff3gThYj+Fe6QIBJAqVQiPj4eFSpUgEqlQrVq1RAWFgZTU1MAr3dIWFlZ8Y+7jBQpUgQJCQmoWrWq9JixsTFu3LiRb5d6uXl3JxARFV5ly5Yt0A6op0+f6iAN6cLt27cREhKC4OBgBAcHIzU1FS1atECbNm1gb2+Ppk2bwsjISHRMIipkuEOCSBALCwvpv1UqFWxsbNSu5TZxQd9lZ2fnav5laGiIrKwsQYmIiP69lStXSv+tUqkwZswYzJs3770TpKjwqlGjBp49e4ZWrVqhTZs2mDBhAr788kvZ7vAjIt1hQYJIAD8/P2miBukHlUqFYcOGqTV5e/HiBVxdXVGiRAnpscDAQBHxiIg+yrsjPSdMmIC+fftyB5RMZWZmAng9ptrAwAAGBgZQKpWCUxGRHLAgQSTAhAkTsGrVKgwZMkR0FNKRd9+8A6/7SBAREX3qEhMTce3aNenIxpIlS/DixQu0atUKDg4OsLe3x5dffskiBRF9NPaQIBJg7dq18PDwQIcOHbBx40Z89tlnoiMRaZy3t7fatbu7O6ZOnZpr1OnEiRN1GYuItIA9YvRPXFycNGkjKCgICoUCycnJomMRUSHDggSRIAkJCRg5ciRiY2OxceNG9OjRQ3QkIo0yMzP74HMUCgVu376tgzREpE0sSOiXR48eqTW4jI+PR9GiRZGRkSE6GhEVMixIEAm2evVqTJ48GdbW1jA0VD9FdeHCBUGpiIiI8ufm5qZ2vWbNGri4uKB06dJqjy9fvlyXsUhLHj9+jJCQEKkIcePGDRgZGaFZs2ZwdHSEo6MjmjdvrtYniYioINhDgkigu3fvIiAgAOXKlUPPnj1zFSSIiIg+RRcvXlS7btGiRa7dTpwWJR+VKlWCkZERmjRpgr59+8LBwQEtW7aEsbGx6GhEVMhxhwSRIJs2bcL333+P9u3bY8OGDahQoYLoSEQadfLkSYwfPx4REREwMTFRW0tJSUGLFi2wbt06tGnTRlBCIiIqiKCgILRq1UptKhQRkSawIEEkQOfOnREZGYmVK1fi66+/Fh2HSCt69OgBR0dHTJ48Oc91b29vBAcHY//+/TpORkSa9uTJEygUCjZp1gOXLl3CjRs3oFAoYG5ujgYNGoiORESFGGfzEAmQnZ2NS5cusRhBshYTE4POnTvnu96xY0f873//02EiItKk5ORkjBs3DuXLl0fFihXx+eefo3z58hg/fjynLchQZGQk6tevDxsbGwwYMAD9+/eHjY0NGjRogKioKNHxiKiQ4g4JIiLSimLFiuHKlSuoXbt2nus3b95E/fr12ZWdqBB6+vQpmjdvjgcPHmDw4MGwtraGSqVCXFwcdu7ciWrVqiE8PBxly5YVHZU0IDY2Fra2trC2tpYacb/5fq9YsQLXr19HREQE6tSpIzoqERUyLEgQEZFW1KpVC8uWLUPv3r3zXA8MDMSUKVM49pOoEJo0aRJOnDiB48ePo2LFimpriYmJ6NixI9q1a4cVK1YISkia1L9/f2RnZyMgICBXs1KVSoU+ffrAyMgIe/bsEZSQiAorFiSIiEgrJkyYgJCQEERFRaFYsWJqaxkZGdK4OG9vb0EJiejfMjU1xYYNG9CpU6c8148cOQJXV1fcuXNHt8FIKypUqIA//vgDTZo0yXM9KioKXbt2xd9//63jZERU2LEgQUREWvHo0SM0btwYBgYGGD9+PCwtLaFQKBAXF4c1a9YgOzsbFy5cyHV3lYg+fUWLFsWtW7fwxRdf5Ll+//591K5dGy9evNBxMtKGYsWKIT4+HtWqVctz/d69ezA3N+f3m4g+mqHoAEREJE8VK1ZEeHg4xowZg+nTp+NN/VuhUKBTp05Yu3YtixFEhVT58uVx586dfAsSCQkJnLghI6ampoiMjMy3IHHu3DnUqFFDx6mISA64Q4KIiLQuKSkJN2/ehEqlgrm5ORvdERVyI0eOxM2bN3Hs2DEUKVJEbS0zMxOdOnVCrVq14OPjIyghaZKnpye2bt2K33//HfXq1VNbu3z5Mrp3746hQ4di7ty5ghISUWHFggQRERERfZT79++jSZMmKFq0KMaNGwcrKysAr6cxrF27FpmZmTh//ny+d9SpcHnx4gXatWuHc+fOoUOHDrC2tgbw+vt9/PhxNGvWDCdPnszVL4iI6ENYkCAiIiKij3b79m2MGzcOR48eVTuS1aFDB6xevTrfkb9UOL18+RIrVqzArl27cOPGDQCAhYUFnJycMHnyZBQtWlRwQiIqjFiQICIiIqJ/LSkpCfHx8QCA2rVro1y5coITka7du3cPnp6e8PX1FR2FiAoZFiSIiIiI6KNkZ2fj6tWrMDc3h7Gxsdpaeno6bt68iXr16kGpVApKSLoUExODxo0bIzs7W3QUIipk+FeCiIiIiD7Ktm3bMGLEiFwNLYHXI0FHjBiBnTt3CkhGRESFCQsSRERERPRRfHx8MGXKFBgYGORaMzAwwLRp07Bx40YByYiIqDBhQYKIiIiIPsr169dhZ2eX73rTpk0RFxenw0RERFQYGYoOQERERESFS1paGp49e5bvempqKtLT03WYiLSpT58+711PTk7WTRAikh0WJIiIiIjoo5ibmyM8PBwNGjTIcz0sLAzm5uY6TkXaUrp06Q+uf/311zpKQ0RywikbRERERPRRlixZgiVLluDkyZO5ihIxMTFo164dpk2bhmnTpglKSEREhQELEkRERET0UV69eoWOHTsiLCwM7du3h5WVFRQKBeLi4nD8+HG0bNkSx44dg5GRkeiopANxcXH46quvcPv2bdFRiKiQYUGCiIiIiD7aq1evsGLFCuzcuRPx8fFQqVSwsLDAoEGDMGnSpDxHgpI8xcTEoHHjxsjOzhYdhYgKGRYkiIiIiEijsrKy8Ndff6F69eqio5AOsCBBRP8Wx34SERERkUZdvXoVZmZmomMQEdEnjgUJIiIiIiIiItI5jv0kIiIiIqJ8lS1bFgqFIt/1rKwsHaYhIjlhQYKIiIiIiPK1cuVK0RGISKZYkCAiIiKij3Lp0qX3rl+/fl1HSUgXhg4d+sHncJcEEf0bnLJBRERERB9FqVRCoVAgr7eRbx5XKBScuqAHYmNj4ePjg+3bt+PRo0ei4xBRIcMdEkRERET0URISEkRHIIGeP3+O3bt3w8fHB1FRUbCzs4OHh4foWERUCHGHBBERERFpXHR0NBo1aiQ6BmlQWFgYNm/ejICAAJiZmSE2NhahoaFo2bKl6GhEVEhx7CcRERERaURKSgrWrl2Lxo0b48svvxQdhzRkyZIlsLKygpOTEypUqICwsDBcunQJCoUCZcuWFR2PiAoxFiSIiIiI6D85efIkXFxcULlyZaxatQpdu3bF+fPnRcciDZkxYwb69u2Lu3fvYunSpWjYsKHoSEQkEyxIEBEREdFHu3//PhYsWICaNWvC2dkZZcuWxatXrxAQEIAFCxbAxsZGdETSkHnz5mHv3r0wMzODu7s7rly5IjoSEckECxJERERE9FG6du2KOnXqIDY2FqtWrcJff/2FVatWiY5FWjJjxgzcuHED27ZtQ2JiIuzs7NCwYUOoVCokJSWJjkdEhRibWhIRERHRRzE0NMTEiRMxZswYmJubS48bGRkhJiYGderUEZiOtC01NRU7duzAli1b8L///Q/NmjVDv3794ObmJjoaERUy3CFBRERERB/l9OnTSE1NRZMmTWBra4vVq1fj77//Fh2LdKRUqVJwdXXFuXPncPHiRTRr1gw//vij6FhEVAhxhwQRERER/Svp6enYvXs3fH19ERkZiezsbCxfvhwjRoxAqVKlRMcjDalatSratm0LR0dHODo6wszMLNdzXr16BSMjIwHpiKgwY0GCiIiIiP6z69evw8fHB9u2bUNycjI6dOiAX3/9VXQs0oD58+cjNDQUZ8+exYsXL1CtWjW1AsUXX3whOiIRFVIsSBARERGRxmRnZ+PQoUPw9fVlQUJmXr16hbNnzyIkJEQqUGRmZqJmzZpo27YtNmzYIDoiERUyLEgQEREREdFHS0pKwk8//YRVq1bh+fPnyM7OFh2JiAoZQ9EBiIiIiIjo0/fixQucOXMGISEhCAkJQVRUFExNTTFw4EDY29uLjkdEhRB3SBARERERUb48PT0RHByMqKgo1KxZE/b29tK/SpUqiY5HRIUYCxJERERERJQvpVKJ6tWrw8PDA/3798dnn30mOhIRyQQLEkRERERElK8jR45IxzQuXrwICwsLODg4SLskKlSoIDoiERVSLEgQEREREVGBpKam4vTp0wgNDUVwcDBiYmJQu3ZtODo6YvXq1aLjEVEhw4IEERERERF9lOzsbERGRuLXX3/F2rVrOWWDiP4VTtkgIiIiIqL3ysnJwfnz5xEcHIyQkBCcOXMGaWlp+OKLL9C7d284OjqKjkhEhRB3SBARERERUb66du2KM2fOIDU1FVWqVIGDgwMcHR3h6OiImjVrio5HRIUYd0gQEREREVG+SpcujaVLl8LR0RHm5uai4xCRjHCHBBERERERERHpnFJ0ACIiIiIi+nSdO3cOf/zxh9pjv/zyC8zMzPD5559j9OjRyMzMFJSOiAozFiSIiIiIiChfc+bMwaVLl6Try5cvY+TIkWjfvj08PDxw6NAhLFq0SGBCIiqseGSDiIiIiIjyVblyZRw6dAhNmjQBAMycOROhoaEICwsDAOzduxeenp6IjY0VGZOICiHukCAiIiIionwlJSWhYsWK0nVoaCg6d+4sXTdt2hT37t0TEY2ICjkWJIiIiIiIKF8VK1ZEQkICAODly5e4cOECmjdvLq2npqbCyMhIVDwiKsRYkCAiIiIionx17twZHh4eOH36NKZPn47ixYujdevW0vqlS5dQq1YtgQmJqLAyFB2AiIiIiIg+XQsWLECfPn1gb2+PkiVLws/PD0WKFJHWfX190bFjR4EJiaiwYlNLIiIiIiL6oJSUFJQsWRIGBgZqjz99+hQlS5ZUK1IQERUECxJEREREREREpHPsIUFEREREREREOseCBBERERERERHpHAsSRERERERERKRzLEgQERERERERkc6xIEFEREREREREOseCBBERERERERHpHAsSRERERERERKRzLEgQERERERERkc79H5bKiKF9zRzPAAAAAElFTkSuQmCC", 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2274 |
"text/plain": [ |
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2275 |
"<Figure size 1200x800 with 2 Axes>" |
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2276 |
] |
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2277 |
}, |
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|
2278 |
"metadata": {}, |
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|
2279 |
"output_type": "display_data" |
|
|
2280 |
} |
|
|
2281 |
], |
|
|
2282 |
"source": [ |
|
|
2283 |
"kot = cn[cn>=.40]\n", |
|
|
2284 |
"plt.figure(figsize=(12,8))\n", |
|
|
2285 |
"sns.heatmap(kot, cmap=\"Blues\")" |
|
|
2286 |
] |
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|
2287 |
}, |
|
|
2288 |
{ |
|
|
2289 |
"cell_type": "markdown", |
|
|
2290 |
"metadata": {}, |
|
|
2291 |
"source": [ |
|
|
2292 |
"# Feature Engineering" |
|
|
2293 |
] |
|
|
2294 |
}, |
|
|
2295 |
{ |
|
|
2296 |
"cell_type": "markdown", |
|
|
2297 |
"metadata": {}, |
|
|
2298 |
"source": [ |
|
|
2299 |
"**Feature Engineering is the process of creating new features using existing features.**" |
|
|
2300 |
] |
|
|
2301 |
}, |
|
|
2302 |
{ |
|
|
2303 |
"cell_type": "markdown", |
|
|
2304 |
"metadata": {}, |
|
|
2305 |
"source": [ |
|
|
2306 |
"The correlation matrix shows that ANXIETY and YELLOW_FINGERS are correlated more than 50%. So, lets create a new feature combining them." |
|
|
2307 |
] |
|
|
2308 |
}, |
|
|
2309 |
{ |
|
|
2310 |
"cell_type": "code", |
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|
2311 |
"execution_count": 34, |
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|
2312 |
"metadata": { |
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|
2313 |
"execution": { |
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|
2314 |
"iopub.execute_input": "2023-07-17T13:55:45.253766Z", |
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|
2315 |
"iopub.status.busy": "2023-07-17T13:55:45.253243Z", |
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2316 |
"iopub.status.idle": "2023-07-17T13:55:45.282387Z", |
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|
2317 |
"shell.execute_reply": "2023-07-17T13:55:45.280865Z", |
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|
2318 |
"shell.execute_reply.started": "2023-07-17T13:55:45.253721Z" |
|
|
2319 |
} |
|
|
2320 |
}, |
|
|
2321 |
"outputs": [ |
|
|
2322 |
{ |
|
|
2323 |
"data": { |
|
|
2324 |
"text/html": [ |
|
|
2325 |
"<div>\n", |
|
|
2326 |
"<style scoped>\n", |
|
|
2327 |
" .dataframe tbody tr th:only-of-type {\n", |
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2328 |
" vertical-align: middle;\n", |
|
|
2329 |
" }\n", |
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2330 |
"\n", |
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2331 |
" .dataframe tbody tr th {\n", |
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2332 |
" vertical-align: top;\n", |
|
|
2333 |
" }\n", |
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|
2334 |
"\n", |
|
|
2335 |
" .dataframe thead th {\n", |
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2336 |
" text-align: right;\n", |
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2337 |
" }\n", |
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|
2338 |
"</style>\n", |
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2339 |
"<table border=\"1\" class=\"dataframe\">\n", |
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|
2340 |
" <thead>\n", |
|
|
2341 |
" <tr style=\"text-align: right;\">\n", |
|
|
2342 |
" <th></th>\n", |
|
|
2343 |
" <th>YELLOW_FINGERS</th>\n", |
|
|
2344 |
" <th>ANXIETY</th>\n", |
|
|
2345 |
" <th>PEER_PRESSURE</th>\n", |
|
|
2346 |
" <th>CHRONIC DISEASE</th>\n", |
|
|
2347 |
" <th>FATIGUE</th>\n", |
|
|
2348 |
" <th>ALLERGY</th>\n", |
|
|
2349 |
" <th>WHEEZING</th>\n", |
|
|
2350 |
" <th>ALCOHOL CONSUMING</th>\n", |
|
|
2351 |
" <th>COUGHING</th>\n", |
|
|
2352 |
" <th>SWALLOWING DIFFICULTY</th>\n", |
|
|
2353 |
" <th>CHEST PAIN</th>\n", |
|
|
2354 |
" <th>LUNG_CANCER</th>\n", |
|
|
2355 |
" <th>ANXYELFIN</th>\n", |
|
|
2356 |
" </tr>\n", |
|
|
2357 |
" </thead>\n", |
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|
2358 |
" <tbody>\n", |
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2359 |
" <tr>\n", |
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2360 |
" <th>0</th>\n", |
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" <td>1</td>\n", |
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|
2394 |
" <td>0</td>\n", |
|
|
2395 |
" <td>1</td>\n", |
|
|
2396 |
" <td>0</td>\n", |
|
|
2397 |
" <td>1</td>\n", |
|
|
2398 |
" <td>0</td>\n", |
|
|
2399 |
" <td>1</td>\n", |
|
|
2400 |
" <td>0</td>\n", |
|
|
2401 |
" <td>1</td>\n", |
|
|
2402 |
" <td>0</td>\n", |
|
|
2403 |
" <td>1</td>\n", |
|
|
2404 |
" <td>0</td>\n", |
|
|
2405 |
" <td>0</td>\n", |
|
|
2406 |
" </tr>\n", |
|
|
2407 |
" <tr>\n", |
|
|
2408 |
" <th>3</th>\n", |
|
|
2409 |
" <td>1</td>\n", |
|
|
2410 |
" <td>1</td>\n", |
|
|
2411 |
" <td>0</td>\n", |
|
|
2412 |
" <td>0</td>\n", |
|
|
2413 |
" <td>0</td>\n", |
|
|
2414 |
" <td>0</td>\n", |
|
|
2415 |
" <td>0</td>\n", |
|
|
2416 |
" <td>1</td>\n", |
|
|
2417 |
" <td>0</td>\n", |
|
|
2418 |
" <td>1</td>\n", |
|
|
2419 |
" <td>1</td>\n", |
|
|
2420 |
" <td>0</td>\n", |
|
|
2421 |
" <td>1</td>\n", |
|
|
2422 |
" </tr>\n", |
|
|
2423 |
" <tr>\n", |
|
|
2424 |
" <th>4</th>\n", |
|
|
2425 |
" <td>1</td>\n", |
|
|
2426 |
" <td>0</td>\n", |
|
|
2427 |
" <td>0</td>\n", |
|
|
2428 |
" <td>0</td>\n", |
|
|
2429 |
" <td>0</td>\n", |
|
|
2430 |
" <td>0</td>\n", |
|
|
2431 |
" <td>1</td>\n", |
|
|
2432 |
" <td>0</td>\n", |
|
|
2433 |
" <td>1</td>\n", |
|
|
2434 |
" <td>0</td>\n", |
|
|
2435 |
" <td>0</td>\n", |
|
|
2436 |
" <td>0</td>\n", |
|
|
2437 |
" <td>0</td>\n", |
|
|
2438 |
" </tr>\n", |
|
|
2439 |
" <tr>\n", |
|
|
2440 |
" <th>...</th>\n", |
|
|
2441 |
" <td>...</td>\n", |
|
|
2442 |
" <td>...</td>\n", |
|
|
2443 |
" <td>...</td>\n", |
|
|
2444 |
" <td>...</td>\n", |
|
|
2445 |
" <td>...</td>\n", |
|
|
2446 |
" <td>...</td>\n", |
|
|
2447 |
" <td>...</td>\n", |
|
|
2448 |
" <td>...</td>\n", |
|
|
2449 |
" <td>...</td>\n", |
|
|
2450 |
" <td>...</td>\n", |
|
|
2451 |
" <td>...</td>\n", |
|
|
2452 |
" <td>...</td>\n", |
|
|
2453 |
" <td>...</td>\n", |
|
|
2454 |
" </tr>\n", |
|
|
2455 |
" <tr>\n", |
|
|
2456 |
" <th>279</th>\n", |
|
|
2457 |
" <td>1</td>\n", |
|
|
2458 |
" <td>1</td>\n", |
|
|
2459 |
" <td>1</td>\n", |
|
|
2460 |
" <td>0</td>\n", |
|
|
2461 |
" <td>0</td>\n", |
|
|
2462 |
" <td>1</td>\n", |
|
|
2463 |
" <td>1</td>\n", |
|
|
2464 |
" <td>0</td>\n", |
|
|
2465 |
" <td>1</td>\n", |
|
|
2466 |
" <td>1</td>\n", |
|
|
2467 |
" <td>0</td>\n", |
|
|
2468 |
" <td>1</td>\n", |
|
|
2469 |
" <td>1</td>\n", |
|
|
2470 |
" </tr>\n", |
|
|
2471 |
" <tr>\n", |
|
|
2472 |
" <th>280</th>\n", |
|
|
2473 |
" <td>0</td>\n", |
|
|
2474 |
" <td>0</td>\n", |
|
|
2475 |
" <td>0</td>\n", |
|
|
2476 |
" <td>1</td>\n", |
|
|
2477 |
" <td>1</td>\n", |
|
|
2478 |
" <td>1</td>\n", |
|
|
2479 |
" <td>0</td>\n", |
|
|
2480 |
" <td>0</td>\n", |
|
|
2481 |
" <td>0</td>\n", |
|
|
2482 |
" <td>0</td>\n", |
|
|
2483 |
" <td>0</td>\n", |
|
|
2484 |
" <td>0</td>\n", |
|
|
2485 |
" <td>0</td>\n", |
|
|
2486 |
" </tr>\n", |
|
|
2487 |
" <tr>\n", |
|
|
2488 |
" <th>281</th>\n", |
|
|
2489 |
" <td>0</td>\n", |
|
|
2490 |
" <td>0</td>\n", |
|
|
2491 |
" <td>0</td>\n", |
|
|
2492 |
" <td>0</td>\n", |
|
|
2493 |
" <td>1</td>\n", |
|
|
2494 |
" <td>1</td>\n", |
|
|
2495 |
" <td>0</td>\n", |
|
|
2496 |
" <td>0</td>\n", |
|
|
2497 |
" <td>0</td>\n", |
|
|
2498 |
" <td>0</td>\n", |
|
|
2499 |
" <td>1</td>\n", |
|
|
2500 |
" <td>0</td>\n", |
|
|
2501 |
" <td>0</td>\n", |
|
|
2502 |
" </tr>\n", |
|
|
2503 |
" <tr>\n", |
|
|
2504 |
" <th>282</th>\n", |
|
|
2505 |
" <td>1</td>\n", |
|
|
2506 |
" <td>1</td>\n", |
|
|
2507 |
" <td>0</td>\n", |
|
|
2508 |
" <td>0</td>\n", |
|
|
2509 |
" <td>0</td>\n", |
|
|
2510 |
" <td>0</td>\n", |
|
|
2511 |
" <td>0</td>\n", |
|
|
2512 |
" <td>0</td>\n", |
|
|
2513 |
" <td>0</td>\n", |
|
|
2514 |
" <td>1</td>\n", |
|
|
2515 |
" <td>1</td>\n", |
|
|
2516 |
" <td>0</td>\n", |
|
|
2517 |
" <td>1</td>\n", |
|
|
2518 |
" </tr>\n", |
|
|
2519 |
" <tr>\n", |
|
|
2520 |
" <th>283</th>\n", |
|
|
2521 |
" <td>1</td>\n", |
|
|
2522 |
" <td>1</td>\n", |
|
|
2523 |
" <td>0</td>\n", |
|
|
2524 |
" <td>0</td>\n", |
|
|
2525 |
" <td>1</td>\n", |
|
|
2526 |
" <td>0</td>\n", |
|
|
2527 |
" <td>1</td>\n", |
|
|
2528 |
" <td>1</td>\n", |
|
|
2529 |
" <td>1</td>\n", |
|
|
2530 |
" <td>1</td>\n", |
|
|
2531 |
" <td>1</td>\n", |
|
|
2532 |
" <td>1</td>\n", |
|
|
2533 |
" <td>1</td>\n", |
|
|
2534 |
" </tr>\n", |
|
|
2535 |
" </tbody>\n", |
|
|
2536 |
"</table>\n", |
|
|
2537 |
"<p>276 rows × 13 columns</p>\n", |
|
|
2538 |
"</div>" |
|
|
2539 |
], |
|
|
2540 |
"text/plain": [ |
|
|
2541 |
" YELLOW_FINGERS ANXIETY PEER_PRESSURE CHRONIC DISEASE FATIGUE \\\n", |
|
|
2542 |
"0 1 1 0 0 1 \n", |
|
|
2543 |
"1 0 0 0 1 1 \n", |
|
|
2544 |
"2 0 0 1 0 1 \n", |
|
|
2545 |
"3 1 1 0 0 0 \n", |
|
|
2546 |
"4 1 0 0 0 0 \n", |
|
|
2547 |
".. ... ... ... ... ... \n", |
|
|
2548 |
"279 1 1 1 0 0 \n", |
|
|
2549 |
"280 0 0 0 1 1 \n", |
|
|
2550 |
"281 0 0 0 0 1 \n", |
|
|
2551 |
"282 1 1 0 0 0 \n", |
|
|
2552 |
"283 1 1 0 0 1 \n", |
|
|
2553 |
"\n", |
|
|
2554 |
" ALLERGY WHEEZING ALCOHOL CONSUMING COUGHING SWALLOWING DIFFICULTY \\\n", |
|
|
2555 |
"0 0 1 1 1 1 \n", |
|
|
2556 |
"1 1 0 0 0 1 \n", |
|
|
2557 |
"2 0 1 0 1 0 \n", |
|
|
2558 |
"3 0 0 1 0 1 \n", |
|
|
2559 |
"4 0 1 0 1 0 \n", |
|
|
2560 |
".. ... ... ... ... ... \n", |
|
|
2561 |
"279 1 1 0 1 1 \n", |
|
|
2562 |
"280 1 0 0 0 0 \n", |
|
|
2563 |
"281 1 0 0 0 0 \n", |
|
|
2564 |
"282 0 0 0 0 1 \n", |
|
|
2565 |
"283 0 1 1 1 1 \n", |
|
|
2566 |
"\n", |
|
|
2567 |
" CHEST PAIN LUNG_CANCER ANXYELFIN \n", |
|
|
2568 |
"0 1 1 1 \n", |
|
|
2569 |
"1 1 1 0 \n", |
|
|
2570 |
"2 1 0 0 \n", |
|
|
2571 |
"3 1 0 1 \n", |
|
|
2572 |
"4 0 0 0 \n", |
|
|
2573 |
".. ... ... ... \n", |
|
|
2574 |
"279 0 1 1 \n", |
|
|
2575 |
"280 0 0 0 \n", |
|
|
2576 |
"281 1 0 0 \n", |
|
|
2577 |
"282 1 0 1 \n", |
|
|
2578 |
"283 1 1 1 \n", |
|
|
2579 |
"\n", |
|
|
2580 |
"[276 rows x 13 columns]" |
|
|
2581 |
] |
|
|
2582 |
}, |
|
|
2583 |
"execution_count": 34, |
|
|
2584 |
"metadata": {}, |
|
|
2585 |
"output_type": "execute_result" |
|
|
2586 |
} |
|
|
2587 |
], |
|
|
2588 |
"source": [ |
|
|
2589 |
"df_new['ANXYELFIN']=df_new['ANXIETY']*df_new['YELLOW_FINGERS']\n", |
|
|
2590 |
"df_new" |
|
|
2591 |
] |
|
|
2592 |
}, |
|
|
2593 |
{ |
|
|
2594 |
"cell_type": "code", |
|
|
2595 |
"execution_count": 35, |
|
|
2596 |
"metadata": { |
|
|
2597 |
"execution": { |
|
|
2598 |
"iopub.execute_input": "2023-07-17T13:56:10.414250Z", |
|
|
2599 |
"iopub.status.busy": "2023-07-17T13:56:10.413803Z", |
|
|
2600 |
"iopub.status.idle": "2023-07-17T13:56:10.422275Z", |
|
|
2601 |
"shell.execute_reply": "2023-07-17T13:56:10.420695Z", |
|
|
2602 |
"shell.execute_reply.started": "2023-07-17T13:56:10.414216Z" |
|
|
2603 |
} |
|
|
2604 |
}, |
|
|
2605 |
"outputs": [], |
|
|
2606 |
"source": [ |
|
|
2607 |
"#Splitting independent and dependent variables\n", |
|
|
2608 |
"X = df_new.drop('LUNG_CANCER', axis = 1)\n", |
|
|
2609 |
"y = df_new['LUNG_CANCER']" |
|
|
2610 |
] |
|
|
2611 |
}, |
|
|
2612 |
{ |
|
|
2613 |
"cell_type": "code", |
|
|
2614 |
"execution_count": 36, |
|
|
2615 |
"metadata": { |
|
|
2616 |
"execution": { |
|
|
2617 |
"iopub.execute_input": "2023-07-17T13:56:24.732024Z", |
|
|
2618 |
"iopub.status.busy": "2023-07-17T13:56:24.731634Z", |
|
|
2619 |
"iopub.status.idle": "2023-07-17T13:56:25.532044Z", |
|
|
2620 |
"shell.execute_reply": "2023-07-17T13:56:25.531037Z", |
|
|
2621 |
"shell.execute_reply.started": "2023-07-17T13:56:24.731993Z" |
|
|
2622 |
} |
|
|
2623 |
}, |
|
|
2624 |
"outputs": [], |
|
|
2625 |
"source": [ |
|
|
2626 |
"from imblearn.over_sampling import ADASYN\n", |
|
|
2627 |
"adasyn = ADASYN(random_state=42)\n", |
|
|
2628 |
"X, y = adasyn.fit_resample(X, y)" |
|
|
2629 |
] |
|
|
2630 |
}, |
|
|
2631 |
{ |
|
|
2632 |
"cell_type": "code", |
|
|
2633 |
"execution_count": 37, |
|
|
2634 |
"metadata": { |
|
|
2635 |
"execution": { |
|
|
2636 |
"iopub.execute_input": "2023-07-17T13:56:36.016337Z", |
|
|
2637 |
"iopub.status.busy": "2023-07-17T13:56:36.015942Z", |
|
|
2638 |
"iopub.status.idle": "2023-07-17T13:56:36.023812Z", |
|
|
2639 |
"shell.execute_reply": "2023-07-17T13:56:36.022689Z", |
|
|
2640 |
"shell.execute_reply.started": "2023-07-17T13:56:36.016307Z" |
|
|
2641 |
} |
|
|
2642 |
}, |
|
|
2643 |
"outputs": [ |
|
|
2644 |
{ |
|
|
2645 |
"data": { |
|
|
2646 |
"text/plain": [ |
|
|
2647 |
"477" |
|
|
2648 |
] |
|
|
2649 |
}, |
|
|
2650 |
"execution_count": 37, |
|
|
2651 |
"metadata": {}, |
|
|
2652 |
"output_type": "execute_result" |
|
|
2653 |
} |
|
|
2654 |
], |
|
|
2655 |
"source": [ |
|
|
2656 |
"len(X)" |
|
|
2657 |
] |
|
|
2658 |
}, |
|
|
2659 |
{ |
|
|
2660 |
"cell_type": "markdown", |
|
|
2661 |
"metadata": {}, |
|
|
2662 |
"source": [ |
|
|
2663 |
"# Logistic Regression" |
|
|
2664 |
] |
|
|
2665 |
}, |
|
|
2666 |
{ |
|
|
2667 |
"cell_type": "code", |
|
|
2668 |
"execution_count": 38, |
|
|
2669 |
"metadata": { |
|
|
2670 |
"execution": { |
|
|
2671 |
"iopub.execute_input": "2023-07-17T13:57:11.319537Z", |
|
|
2672 |
"iopub.status.busy": "2023-07-17T13:57:11.318292Z", |
|
|
2673 |
"iopub.status.idle": "2023-07-17T13:57:11.329030Z", |
|
|
2674 |
"shell.execute_reply": "2023-07-17T13:57:11.327463Z", |
|
|
2675 |
"shell.execute_reply.started": "2023-07-17T13:57:11.319492Z" |
|
|
2676 |
} |
|
|
2677 |
}, |
|
|
2678 |
"outputs": [], |
|
|
2679 |
"source": [ |
|
|
2680 |
"#Splitting data for training and testing\n", |
|
|
2681 |
"from sklearn.model_selection import train_test_split\n", |
|
|
2682 |
"X_train, X_test, y_train, y_test= train_test_split(X, y, test_size= 0.25, random_state=0)" |
|
|
2683 |
] |
|
|
2684 |
}, |
|
|
2685 |
{ |
|
|
2686 |
"cell_type": "code", |
|
|
2687 |
"execution_count": 39, |
|
|
2688 |
"metadata": { |
|
|
2689 |
"execution": { |
|
|
2690 |
"iopub.execute_input": "2023-07-17T13:57:21.525077Z", |
|
|
2691 |
"iopub.status.busy": "2023-07-17T13:57:21.524563Z", |
|
|
2692 |
"iopub.status.idle": "2023-07-17T13:57:21.560950Z", |
|
|
2693 |
"shell.execute_reply": "2023-07-17T13:57:21.559364Z", |
|
|
2694 |
"shell.execute_reply.started": "2023-07-17T13:57:21.525037Z" |
|
|
2695 |
} |
|
|
2696 |
}, |
|
|
2697 |
"outputs": [ |
|
|
2698 |
{ |
|
|
2699 |
"data": { |
|
|
2700 |
"text/html": [ |
|
|
2701 |
"<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression(random_state=0)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression(random_state=0)</pre></div></div></div></div></div>" |
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|
2702 |
], |
|
|
2703 |
"text/plain": [ |
|
|
2704 |
"LogisticRegression(random_state=0)" |
|
|
2705 |
] |
|
|
2706 |
}, |
|
|
2707 |
"execution_count": 39, |
|
|
2708 |
"metadata": {}, |
|
|
2709 |
"output_type": "execute_result" |
|
|
2710 |
} |
|
|
2711 |
], |
|
|
2712 |
"source": [ |
|
|
2713 |
"#Fitting training data to the model\n", |
|
|
2714 |
"from sklearn.linear_model import LogisticRegression\n", |
|
|
2715 |
"lr_model=LogisticRegression(random_state=0)\n", |
|
|
2716 |
"lr_model.fit(X_train, y_train)" |
|
|
2717 |
] |
|
|
2718 |
}, |
|
|
2719 |
{ |
|
|
2720 |
"cell_type": "code", |
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|
2721 |
"execution_count": 40, |
|
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2722 |
"metadata": { |
|
|
2723 |
"execution": { |
|
|
2724 |
"iopub.execute_input": "2023-07-17T13:57:34.116711Z", |
|
|
2725 |
"iopub.status.busy": "2023-07-17T13:57:34.116277Z", |
|
|
2726 |
"iopub.status.idle": "2023-07-17T13:57:34.129136Z", |
|
|
2727 |
"shell.execute_reply": "2023-07-17T13:57:34.127494Z", |
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2728 |
"shell.execute_reply.started": "2023-07-17T13:57:34.116677Z" |
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2729 |
} |
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|
2730 |
}, |
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|
2731 |
"outputs": [ |
|
|
2732 |
{ |
|
|
2733 |
"data": { |
|
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2734 |
"text/plain": [ |
|
|
2735 |
"array([1, 0, 0, 1, 0, 0, 1, 0, 0, 1, 1, 0, 1, 1, 0, 0, 0, 0, 1, 1, 1, 0,\n", |
|
|
2736 |
" 1, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 1, 0, 1, 1, 1, 0, 1, 0, 0, 0,\n", |
|
|
2737 |
" 1, 1, 0, 0, 1, 1, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1,\n", |
|
|
2738 |
" 0, 0, 1, 1, 1, 0, 1, 0, 0, 0, 1, 0, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1,\n", |
|
|
2739 |
" 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 0, 0, 1, 1, 1, 0, 1, 0, 1, 1, 0, 0,\n", |
|
|
2740 |
" 1, 0, 0, 0, 0, 1, 0, 1, 1, 0])" |
|
|
2741 |
] |
|
|
2742 |
}, |
|
|
2743 |
"execution_count": 40, |
|
|
2744 |
"metadata": {}, |
|
|
2745 |
"output_type": "execute_result" |
|
|
2746 |
} |
|
|
2747 |
], |
|
|
2748 |
"source": [ |
|
|
2749 |
"#Predicting result using testing data\n", |
|
|
2750 |
"y_lr_pred= lr_model.predict(X_test)\n", |
|
|
2751 |
"y_lr_pred" |
|
|
2752 |
] |
|
|
2753 |
}, |
|
|
2754 |
{ |
|
|
2755 |
"cell_type": "code", |
|
|
2756 |
"execution_count": 41, |
|
|
2757 |
"metadata": { |
|
|
2758 |
"execution": { |
|
|
2759 |
"iopub.execute_input": "2023-07-17T13:57:45.294583Z", |
|
|
2760 |
"iopub.status.busy": "2023-07-17T13:57:45.294116Z", |
|
|
2761 |
"iopub.status.idle": "2023-07-17T13:57:45.309791Z", |
|
|
2762 |
"shell.execute_reply": "2023-07-17T13:57:45.308621Z", |
|
|
2763 |
"shell.execute_reply.started": "2023-07-17T13:57:45.294545Z" |
|
|
2764 |
} |
|
|
2765 |
}, |
|
|
2766 |
"outputs": [ |
|
|
2767 |
{ |
|
|
2768 |
"name": "stdout", |
|
|
2769 |
"output_type": "stream", |
|
|
2770 |
"text": [ |
|
|
2771 |
" precision recall f1-score support\n", |
|
|
2772 |
"\n", |
|
|
2773 |
" 0 0.96 1.00 0.98 64\n", |
|
|
2774 |
" 1 1.00 0.95 0.97 56\n", |
|
|
2775 |
"\n", |
|
|
2776 |
" accuracy 0.97 120\n", |
|
|
2777 |
" macro avg 0.98 0.97 0.97 120\n", |
|
|
2778 |
"weighted avg 0.98 0.97 0.97 120\n", |
|
|
2779 |
"\n" |
|
|
2780 |
] |
|
|
2781 |
} |
|
|
2782 |
], |
|
|
2783 |
"source": [ |
|
|
2784 |
"#Model accuracy\n", |
|
|
2785 |
"from sklearn.metrics import classification_report, accuracy_score, f1_score\n", |
|
|
2786 |
"lr_cr=classification_report(y_test, y_lr_pred)\n", |
|
|
2787 |
"print(lr_cr)" |
|
|
2788 |
] |
|
|
2789 |
}, |
|
|
2790 |
{ |
|
|
2791 |
"cell_type": "markdown", |
|
|
2792 |
"metadata": {}, |
|
|
2793 |
"source": [ |
|
|
2794 |
"# Decision Tree" |
|
|
2795 |
] |
|
|
2796 |
}, |
|
|
2797 |
{ |
|
|
2798 |
"cell_type": "code", |
|
|
2799 |
"execution_count": 42, |
|
|
2800 |
"metadata": { |
|
|
2801 |
"execution": { |
|
|
2802 |
"iopub.execute_input": "2023-07-17T13:58:12.972968Z", |
|
|
2803 |
"iopub.status.busy": "2023-07-17T13:58:12.972517Z", |
|
|
2804 |
"iopub.status.idle": "2023-07-17T13:58:12.991581Z", |
|
|
2805 |
"shell.execute_reply": "2023-07-17T13:58:12.990135Z", |
|
|
2806 |
"shell.execute_reply.started": "2023-07-17T13:58:12.972934Z" |
|
|
2807 |
} |
|
|
2808 |
}, |
|
|
2809 |
"outputs": [ |
|
|
2810 |
{ |
|
|
2811 |
"data": { |
|
|
2812 |
"text/html": [ |
|
|
2813 |
"<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DecisionTreeClassifier(criterion='entropy', random_state=0)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DecisionTreeClassifier</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeClassifier(criterion='entropy', random_state=0)</pre></div></div></div></div></div>" |
|
|
2814 |
], |
|
|
2815 |
"text/plain": [ |
|
|
2816 |
"DecisionTreeClassifier(criterion='entropy', random_state=0)" |
|
|
2817 |
] |
|
|
2818 |
}, |
|
|
2819 |
"execution_count": 42, |
|
|
2820 |
"metadata": {}, |
|
|
2821 |
"output_type": "execute_result" |
|
|
2822 |
} |
|
|
2823 |
], |
|
|
2824 |
"source": [ |
|
|
2825 |
"#Fitting training data to the model\n", |
|
|
2826 |
"from sklearn.tree import DecisionTreeClassifier\n", |
|
|
2827 |
"dt_model= DecisionTreeClassifier(criterion='entropy', random_state=0) \n", |
|
|
2828 |
"dt_model.fit(X_train, y_train)" |
|
|
2829 |
] |
|
|
2830 |
}, |
|
|
2831 |
{ |
|
|
2832 |
"cell_type": "code", |
|
|
2833 |
"execution_count": 43, |
|
|
2834 |
"metadata": { |
|
|
2835 |
"execution": { |
|
|
2836 |
"iopub.execute_input": "2023-07-17T13:58:22.788292Z", |
|
|
2837 |
"iopub.status.busy": "2023-07-17T13:58:22.787895Z", |
|
|
2838 |
"iopub.status.idle": "2023-07-17T13:58:22.797530Z", |
|
|
2839 |
"shell.execute_reply": "2023-07-17T13:58:22.796613Z", |
|
|
2840 |
"shell.execute_reply.started": "2023-07-17T13:58:22.788260Z" |
|
|
2841 |
} |
|
|
2842 |
}, |
|
|
2843 |
"outputs": [ |
|
|
2844 |
{ |
|
|
2845 |
"data": { |
|
|
2846 |
"text/plain": [ |
|
|
2847 |
"array([1, 0, 0, 1, 0, 0, 0, 0, 0, 1, 1, 0, 1, 1, 0, 0, 0, 0, 1, 1, 1, 0,\n", |
|
|
2848 |
" 1, 1, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 1, 0, 1, 1, 1, 0, 1, 0, 0, 0,\n", |
|
|
2849 |
" 1, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1,\n", |
|
|
2850 |
" 1, 0, 1, 1, 1, 0, 1, 0, 0, 0, 1, 0, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1,\n", |
|
|
2851 |
" 1, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 1, 1, 1, 0, 1, 0, 1, 1, 0, 0,\n", |
|
|
2852 |
" 1, 0, 0, 0, 0, 1, 0, 1, 1, 0])" |
|
|
2853 |
] |
|
|
2854 |
}, |
|
|
2855 |
"execution_count": 43, |
|
|
2856 |
"metadata": {}, |
|
|
2857 |
"output_type": "execute_result" |
|
|
2858 |
} |
|
|
2859 |
], |
|
|
2860 |
"source": [ |
|
|
2861 |
"#Predicting result using testing data\n", |
|
|
2862 |
"y_dt_pred= dt_model.predict(X_test)\n", |
|
|
2863 |
"y_dt_pred" |
|
|
2864 |
] |
|
|
2865 |
}, |
|
|
2866 |
{ |
|
|
2867 |
"cell_type": "code", |
|
|
2868 |
"execution_count": 44, |
|
|
2869 |
"metadata": { |
|
|
2870 |
"execution": { |
|
|
2871 |
"iopub.execute_input": "2023-07-17T13:58:33.589467Z", |
|
|
2872 |
"iopub.status.busy": "2023-07-17T13:58:33.588969Z", |
|
|
2873 |
"iopub.status.idle": "2023-07-17T13:58:33.606489Z", |
|
|
2874 |
"shell.execute_reply": "2023-07-17T13:58:33.605527Z", |
|
|
2875 |
"shell.execute_reply.started": "2023-07-17T13:58:33.589428Z" |
|
|
2876 |
} |
|
|
2877 |
}, |
|
|
2878 |
"outputs": [ |
|
|
2879 |
{ |
|
|
2880 |
"name": "stdout", |
|
|
2881 |
"output_type": "stream", |
|
|
2882 |
"text": [ |
|
|
2883 |
" precision recall f1-score support\n", |
|
|
2884 |
"\n", |
|
|
2885 |
" 0 0.93 0.97 0.95 64\n", |
|
|
2886 |
" 1 0.96 0.91 0.94 56\n", |
|
|
2887 |
"\n", |
|
|
2888 |
" accuracy 0.94 120\n", |
|
|
2889 |
" macro avg 0.94 0.94 0.94 120\n", |
|
|
2890 |
"weighted avg 0.94 0.94 0.94 120\n", |
|
|
2891 |
"\n" |
|
|
2892 |
] |
|
|
2893 |
} |
|
|
2894 |
], |
|
|
2895 |
"source": [ |
|
|
2896 |
"#Model accuracy\n", |
|
|
2897 |
"dt_cr=classification_report(y_test, y_dt_pred)\n", |
|
|
2898 |
"print(dt_cr)" |
|
|
2899 |
] |
|
|
2900 |
}, |
|
|
2901 |
{ |
|
|
2902 |
"cell_type": "markdown", |
|
|
2903 |
"metadata": {}, |
|
|
2904 |
"source": [ |
|
|
2905 |
"# K Nearest Neighbor" |
|
|
2906 |
] |
|
|
2907 |
}, |
|
|
2908 |
{ |
|
|
2909 |
"cell_type": "code", |
|
|
2910 |
"execution_count": 45, |
|
|
2911 |
"metadata": { |
|
|
2912 |
"execution": { |
|
|
2913 |
"iopub.execute_input": "2023-07-17T13:59:30.385217Z", |
|
|
2914 |
"iopub.status.busy": "2023-07-17T13:59:30.384768Z", |
|
|
2915 |
"iopub.status.idle": "2023-07-17T13:59:30.399573Z", |
|
|
2916 |
"shell.execute_reply": "2023-07-17T13:59:30.397975Z", |
|
|
2917 |
"shell.execute_reply.started": "2023-07-17T13:59:30.385183Z" |
|
|
2918 |
} |
|
|
2919 |
}, |
|
|
2920 |
"outputs": [ |
|
|
2921 |
{ |
|
|
2922 |
"data": { |
|
|
2923 |
"text/html": [ |
|
|
2924 |
"<style>#sk-container-id-3 {color: black;background-color: white;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-3 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-3 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KNeighborsClassifier()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" checked><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">KNeighborsClassifier</label><div class=\"sk-toggleable__content\"><pre>KNeighborsClassifier()</pre></div></div></div></div></div>" |
|
|
2925 |
], |
|
|
2926 |
"text/plain": [ |
|
|
2927 |
"KNeighborsClassifier()" |
|
|
2928 |
] |
|
|
2929 |
}, |
|
|
2930 |
"execution_count": 45, |
|
|
2931 |
"metadata": {}, |
|
|
2932 |
"output_type": "execute_result" |
|
|
2933 |
} |
|
|
2934 |
], |
|
|
2935 |
"source": [ |
|
|
2936 |
"#Fitting K-NN classifier to the training set \n", |
|
|
2937 |
"from sklearn.neighbors import KNeighborsClassifier \n", |
|
|
2938 |
"knn_model= KNeighborsClassifier(n_neighbors=5, metric='minkowski', p=2 ) \n", |
|
|
2939 |
"knn_model.fit(X_train, y_train)" |
|
|
2940 |
] |
|
|
2941 |
}, |
|
|
2942 |
{ |
|
|
2943 |
"cell_type": "code", |
|
|
2944 |
"execution_count": 46, |
|
|
2945 |
"metadata": { |
|
|
2946 |
"execution": { |
|
|
2947 |
"iopub.execute_input": "2023-07-17T13:59:38.500801Z", |
|
|
2948 |
"iopub.status.busy": "2023-07-17T13:59:38.500362Z", |
|
|
2949 |
"iopub.status.idle": "2023-07-17T13:59:38.517684Z", |
|
|
2950 |
"shell.execute_reply": "2023-07-17T13:59:38.516316Z", |
|
|
2951 |
"shell.execute_reply.started": "2023-07-17T13:59:38.500766Z" |
|
|
2952 |
} |
|
|
2953 |
}, |
|
|
2954 |
"outputs": [ |
|
|
2955 |
{ |
|
|
2956 |
"data": { |
|
|
2957 |
"text/plain": [ |
|
|
2958 |
"array([1, 0, 0, 1, 0, 0, 1, 0, 0, 1, 1, 0, 1, 1, 0, 0, 0, 0, 1, 1, 1, 0,\n", |
|
|
2959 |
" 1, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 1, 0, 1, 1, 1, 0, 1, 0, 0, 0,\n", |
|
|
2960 |
" 1, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1,\n", |
|
|
2961 |
" 1, 0, 1, 1, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1,\n", |
|
|
2962 |
" 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 1, 1, 1, 0, 1, 0, 1, 1, 0, 0,\n", |
|
|
2963 |
" 1, 0, 0, 0, 0, 1, 0, 1, 1, 0])" |
|
|
2964 |
] |
|
|
2965 |
}, |
|
|
2966 |
"execution_count": 46, |
|
|
2967 |
"metadata": {}, |
|
|
2968 |
"output_type": "execute_result" |
|
|
2969 |
} |
|
|
2970 |
], |
|
|
2971 |
"source": [ |
|
|
2972 |
"#Predicting result using testing data\n", |
|
|
2973 |
"y_knn_pred= knn_model.predict(X_test)\n", |
|
|
2974 |
"y_knn_pred" |
|
|
2975 |
] |
|
|
2976 |
}, |
|
|
2977 |
{ |
|
|
2978 |
"cell_type": "code", |
|
|
2979 |
"execution_count": 47, |
|
|
2980 |
"metadata": { |
|
|
2981 |
"execution": { |
|
|
2982 |
"iopub.execute_input": "2023-07-17T13:59:48.508873Z", |
|
|
2983 |
"iopub.status.busy": "2023-07-17T13:59:48.508454Z", |
|
|
2984 |
"iopub.status.idle": "2023-07-17T13:59:48.528300Z", |
|
|
2985 |
"shell.execute_reply": "2023-07-17T13:59:48.526659Z", |
|
|
2986 |
"shell.execute_reply.started": "2023-07-17T13:59:48.508825Z" |
|
|
2987 |
} |
|
|
2988 |
}, |
|
|
2989 |
"outputs": [ |
|
|
2990 |
{ |
|
|
2991 |
"name": "stdout", |
|
|
2992 |
"output_type": "stream", |
|
|
2993 |
"text": [ |
|
|
2994 |
" precision recall f1-score support\n", |
|
|
2995 |
"\n", |
|
|
2996 |
" 0 0.93 1.00 0.96 64\n", |
|
|
2997 |
" 1 1.00 0.91 0.95 56\n", |
|
|
2998 |
"\n", |
|
|
2999 |
" accuracy 0.96 120\n", |
|
|
3000 |
" macro avg 0.96 0.96 0.96 120\n", |
|
|
3001 |
"weighted avg 0.96 0.96 0.96 120\n", |
|
|
3002 |
"\n" |
|
|
3003 |
] |
|
|
3004 |
} |
|
|
3005 |
], |
|
|
3006 |
"source": [ |
|
|
3007 |
"#Model accuracy\n", |
|
|
3008 |
"knn_cr=classification_report(y_test, y_knn_pred)\n", |
|
|
3009 |
"print(knn_cr)" |
|
|
3010 |
] |
|
|
3011 |
}, |
|
|
3012 |
{ |
|
|
3013 |
"cell_type": "markdown", |
|
|
3014 |
"metadata": {}, |
|
|
3015 |
"source": [ |
|
|
3016 |
"# Gaussian Naive Bayes" |
|
|
3017 |
] |
|
|
3018 |
}, |
|
|
3019 |
{ |
|
|
3020 |
"cell_type": "code", |
|
|
3021 |
"execution_count": 48, |
|
|
3022 |
"metadata": { |
|
|
3023 |
"execution": { |
|
|
3024 |
"iopub.execute_input": "2023-07-17T14:00:19.325697Z", |
|
|
3025 |
"iopub.status.busy": "2023-07-17T14:00:19.325024Z", |
|
|
3026 |
"iopub.status.idle": "2023-07-17T14:00:19.353341Z", |
|
|
3027 |
"shell.execute_reply": "2023-07-17T14:00:19.352034Z", |
|
|
3028 |
"shell.execute_reply.started": "2023-07-17T14:00:19.325642Z" |
|
|
3029 |
} |
|
|
3030 |
}, |
|
|
3031 |
"outputs": [ |
|
|
3032 |
{ |
|
|
3033 |
"data": { |
|
|
3034 |
"text/html": [ |
|
|
3035 |
"<style>#sk-container-id-4 {color: black;background-color: white;}#sk-container-id-4 pre{padding: 0;}#sk-container-id-4 div.sk-toggleable {background-color: white;}#sk-container-id-4 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-4 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-4 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-4 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-4 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-4 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-4 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-4 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-4 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-4 div.sk-item {position: relative;z-index: 1;}#sk-container-id-4 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-4 div.sk-item::before, #sk-container-id-4 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-4 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-4 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-4 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-4 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-4 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-4 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-4 div.sk-label-container {text-align: center;}#sk-container-id-4 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-4 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GaussianNB()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" checked><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GaussianNB</label><div class=\"sk-toggleable__content\"><pre>GaussianNB()</pre></div></div></div></div></div>" |
|
|
3036 |
], |
|
|
3037 |
"text/plain": [ |
|
|
3038 |
"GaussianNB()" |
|
|
3039 |
] |
|
|
3040 |
}, |
|
|
3041 |
"execution_count": 48, |
|
|
3042 |
"metadata": {}, |
|
|
3043 |
"output_type": "execute_result" |
|
|
3044 |
} |
|
|
3045 |
], |
|
|
3046 |
"source": [ |
|
|
3047 |
"#Fitting Gaussian Naive Bayes classifier to the training set \n", |
|
|
3048 |
"from sklearn.naive_bayes import GaussianNB\n", |
|
|
3049 |
"gnb_model = GaussianNB()\n", |
|
|
3050 |
"gnb_model.fit(X_train, y_train)" |
|
|
3051 |
] |
|
|
3052 |
}, |
|
|
3053 |
{ |
|
|
3054 |
"cell_type": "code", |
|
|
3055 |
"execution_count": 49, |
|
|
3056 |
"metadata": { |
|
|
3057 |
"execution": { |
|
|
3058 |
"iopub.execute_input": "2023-07-17T14:00:28.044421Z", |
|
|
3059 |
"iopub.status.busy": "2023-07-17T14:00:28.043983Z", |
|
|
3060 |
"iopub.status.idle": "2023-07-17T14:00:28.056216Z", |
|
|
3061 |
"shell.execute_reply": "2023-07-17T14:00:28.055046Z", |
|
|
3062 |
"shell.execute_reply.started": "2023-07-17T14:00:28.044387Z" |
|
|
3063 |
} |
|
|
3064 |
}, |
|
|
3065 |
"outputs": [ |
|
|
3066 |
{ |
|
|
3067 |
"data": { |
|
|
3068 |
"text/plain": [ |
|
|
3069 |
"array([1, 1, 0, 1, 0, 0, 0, 0, 0, 1, 1, 0, 1, 1, 0, 0, 0, 0, 1, 1, 1, 0,\n", |
|
|
3070 |
" 1, 1, 0, 1, 1, 0, 0, 1, 0, 0, 0, 0, 1, 0, 1, 1, 1, 0, 1, 0, 0, 0,\n", |
|
|
3071 |
" 1, 1, 0, 0, 1, 1, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 1, 0, 1, 1, 1,\n", |
|
|
3072 |
" 1, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1,\n", |
|
|
3073 |
" 0, 0, 0, 0, 1, 0, 0, 1, 0, 1, 0, 0, 1, 1, 1, 0, 1, 0, 1, 1, 0, 0,\n", |
|
|
3074 |
" 1, 1, 1, 0, 0, 1, 0, 1, 1, 0])" |
|
|
3075 |
] |
|
|
3076 |
}, |
|
|
3077 |
"execution_count": 49, |
|
|
3078 |
"metadata": {}, |
|
|
3079 |
"output_type": "execute_result" |
|
|
3080 |
} |
|
|
3081 |
], |
|
|
3082 |
"source": [ |
|
|
3083 |
"#Predicting result using testing data\n", |
|
|
3084 |
"y_gnb_pred= gnb_model.predict(X_test)\n", |
|
|
3085 |
"y_gnb_pred" |
|
|
3086 |
] |
|
|
3087 |
}, |
|
|
3088 |
{ |
|
|
3089 |
"cell_type": "code", |
|
|
3090 |
"execution_count": 50, |
|
|
3091 |
"metadata": { |
|
|
3092 |
"execution": { |
|
|
3093 |
"iopub.execute_input": "2023-07-17T14:00:37.090275Z", |
|
|
3094 |
"iopub.status.busy": "2023-07-17T14:00:37.089651Z", |
|
|
3095 |
"iopub.status.idle": "2023-07-17T14:00:37.108608Z", |
|
|
3096 |
"shell.execute_reply": "2023-07-17T14:00:37.107440Z", |
|
|
3097 |
"shell.execute_reply.started": "2023-07-17T14:00:37.090230Z" |
|
|
3098 |
} |
|
|
3099 |
}, |
|
|
3100 |
"outputs": [ |
|
|
3101 |
{ |
|
|
3102 |
"name": "stdout", |
|
|
3103 |
"output_type": "stream", |
|
|
3104 |
"text": [ |
|
|
3105 |
" precision recall f1-score support\n", |
|
|
3106 |
"\n", |
|
|
3107 |
" 0 0.95 0.89 0.92 64\n", |
|
|
3108 |
" 1 0.88 0.95 0.91 56\n", |
|
|
3109 |
"\n", |
|
|
3110 |
" accuracy 0.92 120\n", |
|
|
3111 |
" macro avg 0.92 0.92 0.92 120\n", |
|
|
3112 |
"weighted avg 0.92 0.92 0.92 120\n", |
|
|
3113 |
"\n" |
|
|
3114 |
] |
|
|
3115 |
} |
|
|
3116 |
], |
|
|
3117 |
"source": [ |
|
|
3118 |
"#Model accuracy\n", |
|
|
3119 |
"gnb_cr=classification_report(y_test, y_gnb_pred)\n", |
|
|
3120 |
"print(gnb_cr)" |
|
|
3121 |
] |
|
|
3122 |
}, |
|
|
3123 |
{ |
|
|
3124 |
"cell_type": "markdown", |
|
|
3125 |
"metadata": {}, |
|
|
3126 |
"source": [ |
|
|
3127 |
"# Multinomial Naive Bayes¶" |
|
|
3128 |
] |
|
|
3129 |
}, |
|
|
3130 |
{ |
|
|
3131 |
"cell_type": "code", |
|
|
3132 |
"execution_count": 51, |
|
|
3133 |
"metadata": { |
|
|
3134 |
"execution": { |
|
|
3135 |
"iopub.execute_input": "2023-07-17T14:01:08.764512Z", |
|
|
3136 |
"iopub.status.busy": "2023-07-17T14:01:08.764102Z", |
|
|
3137 |
"iopub.status.idle": "2023-07-17T14:01:08.783207Z", |
|
|
3138 |
"shell.execute_reply": "2023-07-17T14:01:08.781962Z", |
|
|
3139 |
"shell.execute_reply.started": "2023-07-17T14:01:08.764480Z" |
|
|
3140 |
} |
|
|
3141 |
}, |
|
|
3142 |
"outputs": [ |
|
|
3143 |
{ |
|
|
3144 |
"data": { |
|
|
3145 |
"text/html": [ |
|
|
3146 |
"<style>#sk-container-id-5 {color: black;background-color: white;}#sk-container-id-5 pre{padding: 0;}#sk-container-id-5 div.sk-toggleable {background-color: white;}#sk-container-id-5 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-5 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-5 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-5 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-5 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-5 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-5 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-5 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-5 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-5 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-5 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-5 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-5 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-5 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-5 div.sk-item {position: relative;z-index: 1;}#sk-container-id-5 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-5 div.sk-item::before, #sk-container-id-5 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-5 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-5 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-5 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-5 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-5 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-5 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-5 div.sk-label-container {text-align: center;}#sk-container-id-5 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-5 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-5\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>MultinomialNB()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" checked><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">MultinomialNB</label><div class=\"sk-toggleable__content\"><pre>MultinomialNB()</pre></div></div></div></div></div>" |
|
|
3147 |
], |
|
|
3148 |
"text/plain": [ |
|
|
3149 |
"MultinomialNB()" |
|
|
3150 |
] |
|
|
3151 |
}, |
|
|
3152 |
"execution_count": 51, |
|
|
3153 |
"metadata": {}, |
|
|
3154 |
"output_type": "execute_result" |
|
|
3155 |
} |
|
|
3156 |
], |
|
|
3157 |
"source": [ |
|
|
3158 |
"#Fitting Multinomial Naive Bayes classifier to the training set \n", |
|
|
3159 |
"from sklearn.naive_bayes import MultinomialNB\n", |
|
|
3160 |
"mnb_model = MultinomialNB()\n", |
|
|
3161 |
"mnb_model.fit(X_train, y_train)" |
|
|
3162 |
] |
|
|
3163 |
}, |
|
|
3164 |
{ |
|
|
3165 |
"cell_type": "code", |
|
|
3166 |
"execution_count": 52, |
|
|
3167 |
"metadata": { |
|
|
3168 |
"execution": { |
|
|
3169 |
"iopub.execute_input": "2023-07-17T14:01:17.961603Z", |
|
|
3170 |
"iopub.status.busy": "2023-07-17T14:01:17.961097Z", |
|
|
3171 |
"iopub.status.idle": "2023-07-17T14:01:17.974080Z", |
|
|
3172 |
"shell.execute_reply": "2023-07-17T14:01:17.972401Z", |
|
|
3173 |
"shell.execute_reply.started": "2023-07-17T14:01:17.961559Z" |
|
|
3174 |
} |
|
|
3175 |
}, |
|
|
3176 |
"outputs": [ |
|
|
3177 |
{ |
|
|
3178 |
"data": { |
|
|
3179 |
"text/plain": [ |
|
|
3180 |
"array([1, 1, 0, 0, 0, 1, 0, 0, 0, 1, 1, 0, 1, 1, 0, 0, 0, 0, 1, 1, 1, 0,\n", |
|
|
3181 |
" 1, 1, 1, 1, 1, 0, 0, 1, 1, 0, 0, 0, 1, 0, 1, 1, 1, 1, 0, 0, 1, 0,\n", |
|
|
3182 |
" 1, 1, 0, 0, 1, 1, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 1, 0, 1, 1, 1,\n", |
|
|
3183 |
" 1, 0, 1, 1, 1, 1, 1, 0, 1, 0, 0, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1,\n", |
|
|
3184 |
" 0, 0, 0, 0, 1, 1, 0, 1, 0, 1, 0, 0, 1, 1, 1, 0, 1, 0, 1, 1, 0, 0,\n", |
|
|
3185 |
" 1, 1, 1, 1, 1, 1, 0, 0, 1, 0])" |
|
|
3186 |
] |
|
|
3187 |
}, |
|
|
3188 |
"execution_count": 52, |
|
|
3189 |
"metadata": {}, |
|
|
3190 |
"output_type": "execute_result" |
|
|
3191 |
} |
|
|
3192 |
], |
|
|
3193 |
"source": [ |
|
|
3194 |
"#Predicting result using testing data\n", |
|
|
3195 |
"y_mnb_pred= mnb_model.predict(X_test)\n", |
|
|
3196 |
"y_mnb_pred" |
|
|
3197 |
] |
|
|
3198 |
}, |
|
|
3199 |
{ |
|
|
3200 |
"cell_type": "code", |
|
|
3201 |
"execution_count": 53, |
|
|
3202 |
"metadata": { |
|
|
3203 |
"execution": { |
|
|
3204 |
"iopub.execute_input": "2023-07-17T14:01:28.040907Z", |
|
|
3205 |
"iopub.status.busy": "2023-07-17T14:01:28.040418Z", |
|
|
3206 |
"iopub.status.idle": "2023-07-17T14:01:28.058761Z", |
|
|
3207 |
"shell.execute_reply": "2023-07-17T14:01:28.056659Z", |
|
|
3208 |
"shell.execute_reply.started": "2023-07-17T14:01:28.040870Z" |
|
|
3209 |
} |
|
|
3210 |
}, |
|
|
3211 |
"outputs": [ |
|
|
3212 |
{ |
|
|
3213 |
"name": "stdout", |
|
|
3214 |
"output_type": "stream", |
|
|
3215 |
"text": [ |
|
|
3216 |
" precision recall f1-score support\n", |
|
|
3217 |
"\n", |
|
|
3218 |
" 0 0.89 0.73 0.80 64\n", |
|
|
3219 |
" 1 0.75 0.89 0.81 56\n", |
|
|
3220 |
"\n", |
|
|
3221 |
" accuracy 0.81 120\n", |
|
|
3222 |
" macro avg 0.82 0.81 0.81 120\n", |
|
|
3223 |
"weighted avg 0.82 0.81 0.81 120\n", |
|
|
3224 |
"\n" |
|
|
3225 |
] |
|
|
3226 |
} |
|
|
3227 |
], |
|
|
3228 |
"source": [ |
|
|
3229 |
"#Model accuracy\n", |
|
|
3230 |
"mnb_cr=classification_report(y_test, y_mnb_pred)\n", |
|
|
3231 |
"print(mnb_cr)" |
|
|
3232 |
] |
|
|
3233 |
}, |
|
|
3234 |
{ |
|
|
3235 |
"cell_type": "markdown", |
|
|
3236 |
"metadata": {}, |
|
|
3237 |
"source": [ |
|
|
3238 |
"# Support Vector Classifier" |
|
|
3239 |
] |
|
|
3240 |
}, |
|
|
3241 |
{ |
|
|
3242 |
"cell_type": "code", |
|
|
3243 |
"execution_count": 54, |
|
|
3244 |
"metadata": { |
|
|
3245 |
"execution": { |
|
|
3246 |
"iopub.execute_input": "2023-07-17T14:01:56.796938Z", |
|
|
3247 |
"iopub.status.busy": "2023-07-17T14:01:56.796506Z", |
|
|
3248 |
"iopub.status.idle": "2023-07-17T14:01:56.814688Z", |
|
|
3249 |
"shell.execute_reply": "2023-07-17T14:01:56.812907Z", |
|
|
3250 |
"shell.execute_reply.started": "2023-07-17T14:01:56.796903Z" |
|
|
3251 |
} |
|
|
3252 |
}, |
|
|
3253 |
"outputs": [ |
|
|
3254 |
{ |
|
|
3255 |
"data": { |
|
|
3256 |
"text/html": [ |
|
|
3257 |
"<style>#sk-container-id-6 {color: black;background-color: white;}#sk-container-id-6 pre{padding: 0;}#sk-container-id-6 div.sk-toggleable {background-color: white;}#sk-container-id-6 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-6 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-6 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-6 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-6 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-6 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-6 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-6 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-6 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-6 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-6 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-6 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-6 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-6 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-6 div.sk-item {position: relative;z-index: 1;}#sk-container-id-6 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-6 div.sk-item::before, #sk-container-id-6 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-6 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-6 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-6 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-6 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-6 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-6 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-6 div.sk-label-container {text-align: center;}#sk-container-id-6 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-6 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-6\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SVC()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-6\" type=\"checkbox\" checked><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SVC</label><div class=\"sk-toggleable__content\"><pre>SVC()</pre></div></div></div></div></div>" |
|
|
3258 |
], |
|
|
3259 |
"text/plain": [ |
|
|
3260 |
"SVC()" |
|
|
3261 |
] |
|
|
3262 |
}, |
|
|
3263 |
"execution_count": 54, |
|
|
3264 |
"metadata": {}, |
|
|
3265 |
"output_type": "execute_result" |
|
|
3266 |
} |
|
|
3267 |
], |
|
|
3268 |
"source": [ |
|
|
3269 |
"#Fitting SVC to the training set \n", |
|
|
3270 |
"from sklearn.svm import SVC\n", |
|
|
3271 |
"svc_model = SVC()\n", |
|
|
3272 |
"svc_model.fit(X_train, y_train)" |
|
|
3273 |
] |
|
|
3274 |
}, |
|
|
3275 |
{ |
|
|
3276 |
"cell_type": "code", |
|
|
3277 |
"execution_count": 55, |
|
|
3278 |
"metadata": { |
|
|
3279 |
"execution": { |
|
|
3280 |
"iopub.execute_input": "2023-07-17T14:02:07.812593Z", |
|
|
3281 |
"iopub.status.busy": "2023-07-17T14:02:07.812169Z", |
|
|
3282 |
"iopub.status.idle": "2023-07-17T14:02:07.824065Z", |
|
|
3283 |
"shell.execute_reply": "2023-07-17T14:02:07.823025Z", |
|
|
3284 |
"shell.execute_reply.started": "2023-07-17T14:02:07.812527Z" |
|
|
3285 |
} |
|
|
3286 |
}, |
|
|
3287 |
"outputs": [ |
|
|
3288 |
{ |
|
|
3289 |
"data": { |
|
|
3290 |
"text/plain": [ |
|
|
3291 |
"array([1, 1, 0, 1, 0, 0, 1, 0, 0, 1, 1, 0, 1, 1, 0, 0, 0, 0, 1, 1, 1, 0,\n", |
|
|
3292 |
" 1, 1, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 1, 0, 1, 1, 1, 0, 1, 0, 0, 0,\n", |
|
|
3293 |
" 1, 1, 0, 0, 1, 1, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1,\n", |
|
|
3294 |
" 1, 0, 1, 1, 1, 1, 1, 0, 0, 0, 1, 0, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1,\n", |
|
|
3295 |
" 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 1, 1, 1, 0, 1, 0, 1, 1, 0, 0,\n", |
|
|
3296 |
" 1, 0, 0, 0, 0, 1, 0, 1, 1, 0])" |
|
|
3297 |
] |
|
|
3298 |
}, |
|
|
3299 |
"execution_count": 55, |
|
|
3300 |
"metadata": {}, |
|
|
3301 |
"output_type": "execute_result" |
|
|
3302 |
} |
|
|
3303 |
], |
|
|
3304 |
"source": [ |
|
|
3305 |
"#Predicting result using testing data\n", |
|
|
3306 |
"y_svc_pred= svc_model.predict(X_test)\n", |
|
|
3307 |
"y_svc_pred" |
|
|
3308 |
] |
|
|
3309 |
}, |
|
|
3310 |
{ |
|
|
3311 |
"cell_type": "code", |
|
|
3312 |
"execution_count": 56, |
|
|
3313 |
"metadata": { |
|
|
3314 |
"execution": { |
|
|
3315 |
"iopub.execute_input": "2023-07-17T14:02:18.077348Z", |
|
|
3316 |
"iopub.status.busy": "2023-07-17T14:02:18.076827Z", |
|
|
3317 |
"iopub.status.idle": "2023-07-17T14:02:18.094304Z", |
|
|
3318 |
"shell.execute_reply": "2023-07-17T14:02:18.092948Z", |
|
|
3319 |
"shell.execute_reply.started": "2023-07-17T14:02:18.077309Z" |
|
|
3320 |
} |
|
|
3321 |
}, |
|
|
3322 |
"outputs": [ |
|
|
3323 |
{ |
|
|
3324 |
"name": "stdout", |
|
|
3325 |
"output_type": "stream", |
|
|
3326 |
"text": [ |
|
|
3327 |
" precision recall f1-score support\n", |
|
|
3328 |
"\n", |
|
|
3329 |
" 0 0.98 0.98 0.98 64\n", |
|
|
3330 |
" 1 0.98 0.98 0.98 56\n", |
|
|
3331 |
"\n", |
|
|
3332 |
" accuracy 0.98 120\n", |
|
|
3333 |
" macro avg 0.98 0.98 0.98 120\n", |
|
|
3334 |
"weighted avg 0.98 0.98 0.98 120\n", |
|
|
3335 |
"\n" |
|
|
3336 |
] |
|
|
3337 |
} |
|
|
3338 |
], |
|
|
3339 |
"source": [ |
|
|
3340 |
"#Model accuracy\n", |
|
|
3341 |
"svc_cr=classification_report(y_test, y_svc_pred)\n", |
|
|
3342 |
"print(svc_cr)" |
|
|
3343 |
] |
|
|
3344 |
}, |
|
|
3345 |
{ |
|
|
3346 |
"cell_type": "markdown", |
|
|
3347 |
"metadata": {}, |
|
|
3348 |
"source": [ |
|
|
3349 |
"# Random Forest" |
|
|
3350 |
] |
|
|
3351 |
}, |
|
|
3352 |
{ |
|
|
3353 |
"cell_type": "code", |
|
|
3354 |
"execution_count": 57, |
|
|
3355 |
"metadata": { |
|
|
3356 |
"execution": { |
|
|
3357 |
"iopub.execute_input": "2023-07-17T14:02:46.376832Z", |
|
|
3358 |
"iopub.status.busy": "2023-07-17T14:02:46.376232Z", |
|
|
3359 |
"iopub.status.idle": "2023-07-17T14:02:46.637168Z", |
|
|
3360 |
"shell.execute_reply": "2023-07-17T14:02:46.636176Z", |
|
|
3361 |
"shell.execute_reply.started": "2023-07-17T14:02:46.376783Z" |
|
|
3362 |
} |
|
|
3363 |
}, |
|
|
3364 |
"outputs": [ |
|
|
3365 |
{ |
|
|
3366 |
"data": { |
|
|
3367 |
"text/html": [ |
|
|
3368 |
"<style>#sk-container-id-7 {color: black;background-color: white;}#sk-container-id-7 pre{padding: 0;}#sk-container-id-7 div.sk-toggleable {background-color: white;}#sk-container-id-7 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-7 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-7 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-7 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-7 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-7 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-7 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-7 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-7 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-7 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-7 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-7 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-7 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-7 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-7 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-7 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-7 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-7 div.sk-item {position: relative;z-index: 1;}#sk-container-id-7 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-7 div.sk-item::before, #sk-container-id-7 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-7 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-7 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-7 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-7 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-7 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-7 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-7 div.sk-label-container {text-align: center;}#sk-container-id-7 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-7 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-7\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-7\" type=\"checkbox\" checked><label for=\"sk-estimator-id-7\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier()</pre></div></div></div></div></div>" |
|
|
3369 |
], |
|
|
3370 |
"text/plain": [ |
|
|
3371 |
"RandomForestClassifier()" |
|
|
3372 |
] |
|
|
3373 |
}, |
|
|
3374 |
"execution_count": 57, |
|
|
3375 |
"metadata": {}, |
|
|
3376 |
"output_type": "execute_result" |
|
|
3377 |
} |
|
|
3378 |
], |
|
|
3379 |
"source": [ |
|
|
3380 |
"#Training\n", |
|
|
3381 |
"from sklearn.ensemble import RandomForestClassifier\n", |
|
|
3382 |
"rf_model = RandomForestClassifier()\n", |
|
|
3383 |
"rf_model.fit(X_train, y_train)" |
|
|
3384 |
] |
|
|
3385 |
}, |
|
|
3386 |
{ |
|
|
3387 |
"cell_type": "code", |
|
|
3388 |
"execution_count": 58, |
|
|
3389 |
"metadata": { |
|
|
3390 |
"execution": { |
|
|
3391 |
"iopub.execute_input": "2023-07-17T14:02:57.287208Z", |
|
|
3392 |
"iopub.status.busy": "2023-07-17T14:02:57.286724Z", |
|
|
3393 |
"iopub.status.idle": "2023-07-17T14:02:57.319019Z", |
|
|
3394 |
"shell.execute_reply": "2023-07-17T14:02:57.317655Z", |
|
|
3395 |
"shell.execute_reply.started": "2023-07-17T14:02:57.287163Z" |
|
|
3396 |
} |
|
|
3397 |
}, |
|
|
3398 |
"outputs": [ |
|
|
3399 |
{ |
|
|
3400 |
"data": { |
|
|
3401 |
"text/plain": [ |
|
|
3402 |
"array([1, 1, 0, 1, 0, 0, 1, 0, 0, 1, 1, 0, 1, 1, 0, 0, 0, 0, 1, 1, 1, 0,\n", |
|
|
3403 |
" 1, 1, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 1, 0, 1, 1, 1, 0, 1, 0, 0, 0,\n", |
|
|
3404 |
" 1, 1, 0, 0, 1, 1, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1,\n", |
|
|
3405 |
" 1, 0, 1, 1, 1, 1, 1, 0, 0, 0, 1, 0, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1,\n", |
|
|
3406 |
" 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 1, 1, 1, 0, 1, 0, 1, 1, 0, 0,\n", |
|
|
3407 |
" 1, 0, 0, 0, 0, 1, 0, 1, 1, 0])" |
|
|
3408 |
] |
|
|
3409 |
}, |
|
|
3410 |
"execution_count": 58, |
|
|
3411 |
"metadata": {}, |
|
|
3412 |
"output_type": "execute_result" |
|
|
3413 |
} |
|
|
3414 |
], |
|
|
3415 |
"source": [ |
|
|
3416 |
"#Predicting result using testing data\n", |
|
|
3417 |
"y_rf_pred= rf_model.predict(X_test)\n", |
|
|
3418 |
"y_rf_pred" |
|
|
3419 |
] |
|
|
3420 |
}, |
|
|
3421 |
{ |
|
|
3422 |
"cell_type": "code", |
|
|
3423 |
"execution_count": 59, |
|
|
3424 |
"metadata": { |
|
|
3425 |
"execution": { |
|
|
3426 |
"iopub.execute_input": "2023-07-17T14:03:07.454354Z", |
|
|
3427 |
"iopub.status.busy": "2023-07-17T14:03:07.453832Z", |
|
|
3428 |
"iopub.status.idle": "2023-07-17T14:03:07.471410Z", |
|
|
3429 |
"shell.execute_reply": "2023-07-17T14:03:07.469419Z", |
|
|
3430 |
"shell.execute_reply.started": "2023-07-17T14:03:07.454312Z" |
|
|
3431 |
} |
|
|
3432 |
}, |
|
|
3433 |
"outputs": [ |
|
|
3434 |
{ |
|
|
3435 |
"name": "stdout", |
|
|
3436 |
"output_type": "stream", |
|
|
3437 |
"text": [ |
|
|
3438 |
" precision recall f1-score support\n", |
|
|
3439 |
"\n", |
|
|
3440 |
" 0 0.98 0.98 0.98 64\n", |
|
|
3441 |
" 1 0.98 0.98 0.98 56\n", |
|
|
3442 |
"\n", |
|
|
3443 |
" accuracy 0.98 120\n", |
|
|
3444 |
" macro avg 0.98 0.98 0.98 120\n", |
|
|
3445 |
"weighted avg 0.98 0.98 0.98 120\n", |
|
|
3446 |
"\n" |
|
|
3447 |
] |
|
|
3448 |
} |
|
|
3449 |
], |
|
|
3450 |
"source": [ |
|
|
3451 |
"#Model accuracy\n", |
|
|
3452 |
"rf_cr=classification_report(y_test, y_rf_pred)\n", |
|
|
3453 |
"print(rf_cr)" |
|
|
3454 |
] |
|
|
3455 |
}, |
|
|
3456 |
{ |
|
|
3457 |
"cell_type": "code", |
|
|
3458 |
"execution_count": 66, |
|
|
3459 |
"metadata": { |
|
|
3460 |
"execution": { |
|
|
3461 |
"iopub.execute_input": "2023-07-17T14:09:58.278079Z", |
|
|
3462 |
"iopub.status.busy": "2023-07-17T14:09:58.277668Z", |
|
|
3463 |
"iopub.status.idle": "2023-07-17T14:09:58.366817Z", |
|
|
3464 |
"shell.execute_reply": "2023-07-17T14:09:58.365962Z", |
|
|
3465 |
"shell.execute_reply.started": "2023-07-17T14:09:58.278046Z" |
|
|
3466 |
} |
|
|
3467 |
}, |
|
|
3468 |
"outputs": [ |
|
|
3469 |
{ |
|
|
3470 |
"data": { |
|
|
3471 |
"text/plain": [ |
|
|
3472 |
"['cancer_model.pkl']" |
|
|
3473 |
] |
|
|
3474 |
}, |
|
|
3475 |
"execution_count": 66, |
|
|
3476 |
"metadata": {}, |
|
|
3477 |
"output_type": "execute_result" |
|
|
3478 |
} |
|
|
3479 |
], |
|
|
3480 |
"source": [ |
|
|
3481 |
"import joblib\n", |
|
|
3482 |
"joblib.dump(rf_model,'cancer_model.pkl')" |
|
|
3483 |
] |
|
|
3484 |
}, |
|
|
3485 |
{ |
|
|
3486 |
"cell_type": "code", |
|
|
3487 |
"execution_count": 67, |
|
|
3488 |
"metadata": { |
|
|
3489 |
"execution": { |
|
|
3490 |
"iopub.execute_input": "2023-07-17T14:11:27.399467Z", |
|
|
3491 |
"iopub.status.busy": "2023-07-17T14:11:27.399018Z", |
|
|
3492 |
"iopub.status.idle": "2023-07-17T14:11:27.479675Z", |
|
|
3493 |
"shell.execute_reply": "2023-07-17T14:11:27.478562Z", |
|
|
3494 |
"shell.execute_reply.started": "2023-07-17T14:11:27.399431Z" |
|
|
3495 |
} |
|
|
3496 |
}, |
|
|
3497 |
"outputs": [ |
|
|
3498 |
{ |
|
|
3499 |
"data": { |
|
|
3500 |
"text/plain": [ |
|
|
3501 |
"array([1, 1, 0, 1, 0, 0, 1, 0, 0, 1, 1, 0, 1, 1, 0, 0, 0, 0, 1, 1, 1, 0,\n", |
|
|
3502 |
" 1, 1, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 1, 0, 1, 1, 1, 0, 1, 0, 0, 0,\n", |
|
|
3503 |
" 1, 1, 0, 0, 1, 1, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1,\n", |
|
|
3504 |
" 1, 0, 1, 1, 1, 1, 1, 0, 0, 0, 1, 0, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1,\n", |
|
|
3505 |
" 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 1, 1, 1, 0, 1, 0, 1, 1, 0, 0,\n", |
|
|
3506 |
" 1, 0, 0, 0, 0, 1, 0, 1, 1, 0])" |
|
|
3507 |
] |
|
|
3508 |
}, |
|
|
3509 |
"execution_count": 67, |
|
|
3510 |
"metadata": {}, |
|
|
3511 |
"output_type": "execute_result" |
|
|
3512 |
} |
|
|
3513 |
], |
|
|
3514 |
"source": [ |
|
|
3515 |
"model = joblib.load('cancer_model.pkl')\n", |
|
|
3516 |
"predicted = model.predict(X_test)\n", |
|
|
3517 |
"predicted" |
|
|
3518 |
] |
|
|
3519 |
}, |
|
|
3520 |
{ |
|
|
3521 |
"cell_type": "code", |
|
|
3522 |
"execution_count": null, |
|
|
3523 |
"metadata": {}, |
|
|
3524 |
"outputs": [], |
|
|
3525 |
"source": [] |
|
|
3526 |
} |
|
|
3527 |
], |
|
|
3528 |
"metadata": { |
|
|
3529 |
"kernelspec": { |
|
|
3530 |
"display_name": "Python 3 (ipykernel)", |
|
|
3531 |
"language": "python", |
|
|
3532 |
"name": "python3" |
|
|
3533 |
}, |
|
|
3534 |
"language_info": { |
|
|
3535 |
"codemirror_mode": { |
|
|
3536 |
"name": "ipython", |
|
|
3537 |
"version": 3 |
|
|
3538 |
}, |
|
|
3539 |
"file_extension": ".py", |
|
|
3540 |
"mimetype": "text/x-python", |
|
|
3541 |
"name": "python", |
|
|
3542 |
"nbconvert_exporter": "python", |
|
|
3543 |
"pygments_lexer": "ipython3", |
|
|
3544 |
"version": "3.10.9" |
|
|
3545 |
} |
|
|
3546 |
}, |
|
|
3547 |
"nbformat": 4, |
|
|
3548 |
"nbformat_minor": 4 |
|
|
3549 |
} |