diff --git "a/PL9fwy3NUQKwYNuAEg27anya7EYamnohGd/XptbH5_PJns_raw.json" "b/PL9fwy3NUQKwYNuAEg27anya7EYamnohGd/XptbH5_PJns_raw.json"
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13126, "start": 102.82, "end": 131.26, "text": "هل الـ quantitative هل الـ numeric values الاتريبوتس احنا سابقا شفنا ان ال item موجود نفسه اشتراه و مااشتراهوش yes و no صح حرناهم و لا بتحول في بداية العملية و بتحولهم ل binary طب الآن هذا ال value كان في عندي different values ال numeric values ال quantitative كيف ممكن انا اتعامل مع ال association rules كذلك من الشغلات المهمة بالنسبة لنا في الموضوع ال measurement احنا اتعرفنا على ال support و ال confidence", "tokens": [3224, 1211, 2423, 39184, 27778, 8032, 1211, 2423, 39184, 7866, 299, 4190, 2423, 9307, 16572, 3555, 35473, 3794, 1975, 5016, 8315, 8608, 16758, 4587, 995, 13412, 5172, 8315, 16472, 2423, 3174, 3714, 29245, 23328, 8717, 36178, 3224, 1975, 8592, 2655, 23557, 3224, 4032, 19446, 33599, 2655, 23557, 3224, 2407, 8592, 2086, 4032, 572, 20328, 5016, 11331, 2288, 8315, 16095, 4032, 20193, 39894, 5016, 12610, 8978, 4724, 28259, 10632, 18863, 42213, 10632, 4032, 39894, 5016, 12610, 16095, 5296, 17434, 23032, 3555, 6024, 48506, 23758, 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تعمل تعمل تعمل تعمل تعمل تعمل تعمل تعمل تعمل تعمل تعمل تعمل تعمل تعمل تعمل تعمل تعمل تعمل تعمل تعمل تعمل تعمل تعمل تعمل تعمل تعمل تعمل تعمل تعمل تعمل تعمل تعمل تعمل تعمل تعمل تعمل تعمل تعمل", "tokens": [6027, 39184, 3925, 4419, 399, 3714, 43020, 6055, 14851, 9778, 40448, 11296, 9122, 2304, 7435, 9640, 10632, 3714, 43020, 6055, 14851, 9778, 40448, 11296, 9122, 2304, 7435, 9640, 10632, 6055, 25957, 1211, 6055, 25957, 1211, 6055, 25957, 1211, 6055, 25957, 1211, 6055, 25957, 1211, 6055, 25957, 1211, 6055, 25957, 1211, 6055, 25957, 1211, 6055, 25957, 1211, 6055, 25957, 1211, 6055, 25957, 1211, 6055, 25957, 1211, 6055, 25957, 1211, 6055, 25957, 1211, 6055, 25957, 1211, 6055, 25957, 1211, 6055, 25957, 1211, 6055, 25957, 1211, 6055, 25957, 1211, 6055, 25957, 1211, 6055, 25957, 1211, 6055, 25957, 1211, 6055, 25957, 1211, 6055, 25957, 1211, 6055, 25957, 1211, 6055, 25957, 1211, 6055, 25957, 1211, 6055, 25957, 1211, 6055, 25957, 1211, 6055, 25957, 1211, 6055, 25957, 1211, 6055, 25957, 1211, 6055, 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نكمل في الفيلم نشوف ال example اللى موجود عندنا انا الان فروح جوجل اعمل import لل pandas وعملي بدي ابني data set transaction من six rows من ست صفوف و بدي اعمل import لل", "tokens": [9566, 1829, 3555, 10874, 15042, 27884, 33962, 13546, 16490, 20292, 8315, 8717, 24793, 1211, 8978, 27188, 1829, 19528, 8717, 8592, 38688, 2423, 1365, 13672, 7578, 3714, 29245, 23328, 43242, 8315, 1975, 8315, 2423, 7649, 6156, 32887, 5016, 10874, 29245, 1211, 1975, 25957, 1211, 974, 24976, 4565, 296, 4032, 25957, 20292, 4724, 16254, 1975, 3555, 22653, 1412, 992, 14425, 9154, 2309, 13241, 9154, 8608, 2655, 20328, 5172, 38688, 4032, 4724, 16254, 1975, 25957, 1211, 974, 24976], "avg_logprob": -0.4605263177501528, "compression_ratio": 1.5421052631578946, "no_speech_prob": 0.0, "words": [{"start": 774.11, "end": 774.43, "word": "طيب", "probability": 0.6722615559895834}, {"start": 774.43, "end": 774.79, "word": " جماعة", "probability": 0.7783203125}, {"start": 774.79, "end": 775.11, "word": " الخير", "probability": 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"text": "عن طريق الـ machine learning extend بدي processing import transaction encoder وهذا ال transaction encoder انا هستخدمه من أجل انه يتحول ال data set اللي لديها ال data set هذه ل binary array وكذلك من ال نفس ال machine learning extend بدي استخدم ال frequent pattern وعملها import لل apriori هشوف في ال hand ال apriori وفي المثال في ال code اللي وراها هشوف", "tokens": [3615, 1863, 23032, 16572, 4587, 2423, 39184, 3479, 2539, 10101, 4724, 16254, 9007, 974, 14425, 2058, 19866, 37037, 15730, 2423, 14425, 2058, 19866, 1975, 8315, 8032, 14851, 9778, 40448, 3224, 9154, 5551, 7435, 1211, 16472, 3224, 7251, 2655, 5016, 12610, 2423, 1412, 992, 13672, 1829, 5296, 16254, 11296, 2423, 1412, 992, 29538, 5296, 17434, 10225, 4032, 4117, 8848, 23275, 9154, 2423, 8717, 36178, 2423, 3479, 2539, 10101, 4724, 16254, 44713, 9778, 40448, 2423, 18004, 5102, 4032, 25957, 1211, 11296, 974, 24976, 1882, 470, 7386, 8032, 8592, 38688, 8978, 2423, 1011, 2423, 1882, 470, 7386, 4032, 41185, 9673, 12984, 6027, 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"start": 850.67, "end": 874.49, "text": "عن طريق اتساوية TE.FET.TRANSFORM للـ data set اللي موجود عندها و لما احاول ان اكتب قمر طبعا عشان اشوف ال code اللي موجود عندها بعد هيك ال transaction ال array تبعت اللي هي ال binary ال race ان سميها زي ما بدك بندس data frame روح تحولت ل data frame", "tokens": [3615, 1863, 23032, 16572, 4587, 1975, 2655, 3794, 995, 2407, 10632, 19744, 13, 37, 4850, 13, 25936, 25711, 37, 2483, 44, 24976, 39184, 1412, 992, 13672, 1829, 3714, 29245, 23328, 43242, 11296, 4032, 5296, 15042, 1975, 5016, 995, 12610, 16472, 1975, 4117, 2655, 3555, 12174, 29973, 23032, 3555, 3615, 995, 6225, 8592, 7649, 1975, 8592, 38688, 2423, 3089, 13672, 1829, 3714, 29245, 23328, 43242, 11296, 39182, 39896, 4117, 2423, 14425, 2423, 10225, 6055, 3555, 34268, 13672, 1829, 39896, 2423, 17434, 2423, 4569, 16472, 8608, 2304, 1829, 11296, 30767, 1829, 19446, 47525, 4117, 4724, 41260, 3794, 1412, 3920, 12602, 2407, 5016, 6055, 5016, 12610, 2655, 5296, 1412, 3920], "avg_logprob": 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0.8226725260416666}, {"start": 857.37, "end": 857.73, "word": " عندها", "probability": 0.821044921875}, {"start": 857.73, "end": 857.87, "word": " و", "probability": 0.353515625}, {"start": 857.87, "end": 858.07, "word": " لما", "probability": 0.60491943359375}, {"start": 858.07, "end": 858.77, "word": " احاول", "probability": 0.7037353515625}, {"start": 858.77, "end": 858.95, "word": " ان", "probability": 0.499755859375}, {"start": 858.95, "end": 859.61, "word": " اكتب", "probability": 0.74188232421875}, {"start": 859.61, "end": 859.99, "word": " قمر", "probability": 0.436798095703125}, {"start": 859.99, "end": 860.57, "word": " طبعا", "probability": 0.707275390625}, {"start": 860.57, "end": 861.13, "word": " عشان", "probability": 0.77392578125}, {"start": 861.13, "end": 861.43, "word": " اشوف", "probability": 0.8658854166666666}, {"start": 861.43, "end": 861.59, "word": " ال", "probability": 0.615234375}, {"start": 861.59, "end": 861.91, "word": " code", "probability": 0.9111328125}, {"start": 861.91, "end": 862.23, "word": " اللي", "probability": 0.943603515625}, {"start": 862.23, "end": 862.61, "word": " موجود", "probability": 0.9537760416666666}, {"start": 862.61, "end": 863.07, "word": " عندها", "probability": 0.958740234375}, {"start": 863.07, "end": 864.45, "word": " بعد", "probability": 0.7587890625}, {"start": 864.45, "end": 864.77, "word": " هيك", "probability": 0.6580810546875}, {"start": 864.77, "end": 865.29, "word": " ال", "probability": 0.59033203125}, {"start": 865.29, "end": 865.91, "word": " transaction", "probability": 0.701171875}, {"start": 865.91, "end": 866.11, "word": " ال", "probability": 0.767578125}, {"start": 866.11, "end": 866.37, "word": " array", "probability": 0.86181640625}, {"start": 866.37, "end": 866.79, "word": " تبعت", "probability": 0.7289225260416666}, {"start": 866.79, "end": 866.97, "word": " اللي", "probability": 0.78564453125}, {"start": 866.97, "end": 867.09, "word": " هي", "probability": 0.373291015625}, {"start": 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"probability": 0.6503092447916666}, {"start": 873.25, "end": 873.73, "word": " تحولت", "probability": 0.814453125}, {"start": 873.73, "end": 873.85, "word": " ل", "probability": 0.79638671875}, {"start": 873.85, "end": 874.05, "word": " data", "probability": 0.958984375}, {"start": 874.05, "end": 874.49, "word": " frame", "probability": 0.912109375}], "temperature": 1.0}, {"id": 38, "seek": 89144, "start": 876.12, "end": 891.44, "text": "هنا في الأول انشأت data set عادية الري حولتها ل binary array و الأن بدي أحولها ل data frame عشان ده من خلال ال pandas أقدر أتعامل معاها و قلت له هيستخدم ال columns", "tokens": [3224, 8315, 8978, 16247, 12610, 16472, 8592, 10721, 2655, 1412, 992, 6225, 18513, 10632, 2423, 16572, 11331, 12610, 2655, 11296, 5296, 5171, 822, 10225, 4032, 16247, 1863, 4724, 16254, 5551, 5016, 12610, 11296, 5296, 1412, 3920, 6225, 8592, 7649, 11778, 3224, 9154, 16490, 1211, 6027, 2423, 4565, 296, 5551, 28543, 2288, 5551, 2655, 3615, 10943, 1211, 20449, 995, 11296, 4032, 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{"start": 880.76, "end": 883.38, "word": " binary", "probability": 0.54541015625}, {"start": 883.38, "end": 883.68, "word": " array", "probability": 0.69970703125}, {"start": 883.68, "end": 883.84, "word": " و", "probability": 0.2474365234375}, {"start": 883.84, "end": 884.28, "word": " الأن", "probability": 0.61175537109375}, {"start": 884.28, "end": 884.42, "word": " بدي", "probability": 0.3529052734375}, {"start": 884.42, "end": 884.78, "word": " أحولها", "probability": 0.797607421875}, {"start": 884.78, "end": 884.9, "word": " ل", "probability": 0.88134765625}, {"start": 884.9, "end": 885.08, "word": " data", "probability": 0.943359375}, {"start": 885.08, "end": 885.44, "word": " frame", "probability": 0.826171875}, {"start": 885.44, "end": 885.76, "word": " عشان", "probability": 0.7820638020833334}, {"start": 885.76, "end": 886.06, "word": " ده", "probability": 0.57958984375}, {"start": 886.06, "end": 886.24, "word": " من", "probability": 0.9736328125}, {"start": 886.24, "end": 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92094, "start": 893.0, "end": 920.94, "text": "هذا الـ array الموجودة عندها كcolumn لمن ال data frame اللي موجودة عندها الآن قلت له ال frequent item set تبعتي تساوي ال a priori اللي انا عملته on board فوق ال data frame work ال data frame تبعتي هي ال minimum support اللي انا بدي اشتغل عليها ستة واستخدم ال column names yes استخدمهم لان انا بتتعامل مع ال column", "tokens": [3224, 15730, 2423, 39184, 10225, 9673, 29245, 23328, 3660, 43242, 11296, 9122, 8768, 16449, 5296, 27842, 2423, 1412, 3920, 13672, 1829, 3714, 29245, 23328, 3660, 43242, 11296, 6024, 48506, 12174, 1211, 2655, 46740, 2423, 18004, 3174, 992, 6055, 3555, 3615, 31371, 6055, 3794, 995, 45865, 2423, 257, 4059, 72, 13672, 1829, 1975, 8315, 6225, 42213, 47395, 322, 3150, 6156, 30543, 2423, 1412, 3920, 589, 2423, 1412, 3920, 6055, 3555, 3615, 31371, 39896, 2423, 7285, 1406, 13672, 1829, 1975, 8315, 4724, 16254, 1975, 8592, 2655, 17082, 1211, 25894, 11296, 8608, 2655, 3660, 36764, 14851, 9778, 40448, 2423, 7738, 5288, 2086, 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