{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.12.13","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"},"kaggle":{"accelerator":"none","dataSources":[],"dockerImageVersionId":28755,"isInternetEnabled":false,"language":"python","sourceType":"notebook","isGpuEnabled":false}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"code","source":"# This Python 3 environment comes with many helpful analytics libraries installed\n# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python\n# For example, here's several helpful packages to load\n\nimport numpy as np # linear algebra\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n\n# Input data files are available in the read-only \"../input/\" directory\n# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n\nimport os\nfor dirname, _, filenames in os.walk('/kaggle/input'):\n    for filename in filenames:\n        print(os.path.join(dirname, filename))\n\n# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session\n\n# Use the kagglehub client library to attach Kaggle resources like competitions, datasets, and models to your session\n# Learn more about kagglehub: https://github.com/Kaggle/kagglehub/blob/main/README.md\n\nimport kagglehub\n# kagglehub.dataset_download('<owner>/<dataset-slug>')","metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","trusted":true,"execution":{"iopub.status.busy":"2026-08-09T14:51:58.190872Z","iopub.execute_input":"2026-08-09T14:51:58.191698Z","execution_failed":"2026-08-09T14:54:45.8Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"import pandas as pd\nfrom pathlib import Path\n\n# Load the files\ntrain_df = pd.read_csv('/kaggle/input/competitions/rsna-knee-abnormality-detection/train.csv')\ntest_df = pd.read_csv('/kaggle/input/competitions/rsna-knee-abnormality-detection/test.csv')\n\nprint(\"✅ Data loaded!\")\nprint(f\"Train: {train_df.shape}\")\nprint(f\"Test: {test_df.shape}\")\nprint(\"\\n\" + str(train_df.head()))","metadata":{"trusted":true,"execution":{"execution_failed":"2026-08-09T14:54:45.8Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"# ===== CREATE BASELINE SUBMISSION =====\nconditions = ['ACL', 'MCL', 'Medial Meniscus', 'Lateral Meniscus', \n              'Medial OA', 'Lateral OA', 'PF OA', 'Effusion', \n              'Synovitis', 'Baker\\'s', 'Contusion', 'Fracture']\n\n# Create submission with 0.5 predictions (neutral baseline)\nsubmission = test_df[['StudyInstanceUID']].copy()\n\nfor col in conditions:\n    submission[col] = 0.5\n\nprint(\"✅ Submission created!\")\nprint(submission)\n\n# Save it\nsubmission.to_csv('/kaggle/working/submission.csv', index=False)\nprint(\"\\n✅ Saved to /kaggle/working/submission.csv\")","metadata":{"trusted":true,"execution":{"execution_failed":"2026-08-09T14:54:45.8Z"}},"outputs":[],"execution_count":null}]}