{"cells":[{"cell_type":"markdown","metadata":{},"source":"# RSNA Knee Abnormality Detection - Baseline\n\nSimple baseline: predict 0.5 for all 12 targets."},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":"import os\nimport pandas as pd\nimport numpy as np\n\nDATA_DIR = None\nfor root, dirs, files in os.walk('/kaggle/input'):\n    if 'sample_submission.csv' in files:\n        DATA_DIR = root\n        break\nprint(f'Data dir: {DATA_DIR}')\n\nsample = pd.read_csv(os.path.join(DATA_DIR, 'sample_submission.csv'))\nprint(f'Sample submission: {len(sample)} rows')\nprint(f'Columns: {sample.columns.tolist()}')"},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":"targets = ['ACL', 'MCL', 'Medial Meniscus', 'Lateral Meniscus', 'Medial OA', \n           'Lateral OA', 'PF OA', 'Effusion', 'Synovitis', \"Baker's\", 'Contusion', 'Fracture']\n\nsubmission = sample.copy()\nfor t in targets:\n    submission[t] = 0.5\n\nsubmission.to_csv('submission.csv', index=False)\nprint(f'Saved {len(submission)} predictions')\nprint(submission.head())"}],"metadata":{"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"language_info":{"name":"python","version":"3.10"},"kaggle":{"is_private":false,"datasets":[],"kernel_sources":[],"competition_sources":["rsna-knee-abnormality-detection"],"language":"python","is_internet_enabled":false,"category":"competition","dataset_sources":[],"kernel_data_sources":[]}},"nbformat":4,"nbformat_minor":4}