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avatar Marshall-mk
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Fetal-Health

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  1. schema: '2.0'
    2. 

  2. stages:
    3. 

  3.   data_load:
    4. 

  4.     cmd: python -m stages.data_load
    5. 

  5.     deps:
    6. 

  6.     - path: stages/data_load.py
    7. 

  7.       md5: 78f03424ae8a6587684288fc88b13226
    8. 

  8.       size: 485
    9. 

  9.     outs:
    10. 

  10.     - path: data/raw/fetal_health.csv
    11. 

  11.       md5: e9a749d3003a7c2f224c92ce0ffd99df
    12. 

  12.       size: 230842
    13. 

  13.   data_split:
    14. 

  14.     cmd: python -m stages.data_split
    15. 

  15.     deps:
    16. 

  16.     - path: data/raw/fetal_health.csv
    17. 

  17.       md5: e9a749d3003a7c2f224c92ce0ffd99df
    18. 

  18.       size: 230842
    19. 

  19.     - path: stages/data_split.py
    20. 

  20.       md5: 692530f6e0ecfe33c985af181dde5189
    21. 

  21.       size: 861
    22. 

  22.     outs:
    23. 

  23.     - path: data/processed/test.csv
    24. 

  24.       md5: d7441309d322e4bc562d128e29c9d5cb
    25. 

  25.       size: 46624
    26. 

  26.     - path: data/processed/train.csv
    27. 

  27.       md5: c645c06ecbc3824984ff12a2c37a4991
    28. 

  28.       size: 184705
    29. 

  29.   featurize:
    30. 

  30.     cmd: python -m stages.featurize
    31. 

  31.     deps:
    32. 

  32.     - path: data/processed/test.csv
    33. 

  33.       md5: d7441309d322e4bc562d128e29c9d5cb
    34. 

  34.       size: 46624
    35. 

  35.     - path: data/processed/train.csv
    36. 

  36.       md5: c645c06ecbc3824984ff12a2c37a4991
    37. 

  37.       size: 184705
    38. 

  38.     - path: stages/featurize.py
    39. 

  39.       md5: e99f7c8777ea0283e0cbc5b3381cc42b
    40. 

  40.       size: 1851
    41. 

  41.     outs:
    42. 

  42.     - path: data/processed/featured_test.csv
    43. 

  43.       md5: ad692dc0dcf9c39a974bd9894f1d6a3f
    44. 

  44.       size: 111274
    45. 

  45.     - path: data/processed/featured_train.csv
    46. 

  46.       md5: ad2ab2d5e55720996bb57ceb043fa53f
    47. 

  47.       size: 1026412
    48. 

  48.   train:
    49. 

  49.     cmd: python -m stages.train
    50. 

  50.     deps:
    51. 

  51.     - path: data/processed/featured_train.csv
    52. 

  52.       md5: ad2ab2d5e55720996bb57ceb043fa53f
    53. 

  53.       size: 1026412
    54. 

  54.     - path: stages/train.py
    55. 

  55.       md5: 4c6c2fcb309caa7ec71ed937a0777460
    56. 

  56.       size: 1072
    57. 

  57.     outs:
    58. 

  58.     - path: models/model.joblib
    59. 

  59.       md5: 1cb63b2cf5601c0215e9117f82508ad7
    60. 

  60.       size: 224524
    61. 

  61.   evaluate:
    62. 

  62.     cmd: python -m stages.evaluate
    63. 

  63.     deps:
    64. 

  64.     - path: data/processed/featured_test.csv
    65. 

  65.       md5: ad692dc0dcf9c39a974bd9894f1d6a3f
    66. 

  66.       size: 111274
    67. 

  67.     - path: models/model.joblib
    68. 

  68.       md5: 1cb63b2cf5601c0215e9117f82508ad7
    69. 

  69.       size: 224524
    70. 

  70.     - path: stages/evaluate.py
    71. 

  71.       md5: b29dc9753b2cc425e6194d761793e5b3
    72. 

  72.       size: 2883
    73. 

  73.     outs:
    74. 

  74.     - path: reports/confusion_matrix.png
    75. 

  75.       md5: ebfbc727133b104921c2406e5cced283
    76. 

  76.       size: 29906
    77. 

  77.     - path: reports/confusion_matrix_data.csv
    78. 

  78.       md5: 330991b58c636ed970bc961bda9b78c7
    79. 

  79.       size: 6898
    80. 

  80.     - path: reports/metrics.json
    81. 

  81.       md5: 7c09646d920cf5c25f566a8e2774d369
    82. 

  82.       size: 32
    83.
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Integrate AWS S3
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Fetal-Health is now integrated with AWS S3!

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Integrate Azure Cloud Storage
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Set key
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Use Azure Cloud Storage!

Browsing data directories saved to Azure Cloud Storage is possible with DAGsHub. Let's configure your repository to easily display your data in the context of any commit!

Specify your Azure Storage bucket

Access key (If needed)

Congratulations!

Fetal-Health is now integrated with Azure Cloud Storage!

Delete Storage Key

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Integrate S3 compatible storage
Use S3 like remote
Select bucket
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Use any S3 compatible storage!

Browsing data directories saved to S3 compatible storage is possible with DAGsHub. Let's configure your repository to easily display your data in the context of any commit!

Specify your S3 bucket

Bucket name cannot be the same as the repository name. Please change one of them.

Access key (If needed)

Congratulations!

Fetal-Health is now integrated with your S3 compatible storage!

Delete Storage Key

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Yes