{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"pygments_lexer":"ipython3","nbconvert_exporter":"python","version":"3.6.4","file_extension":".py","codemirror_mode":{"name":"ipython","version":3},"name":"python","mimetype":"text/x-python"}},"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","metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","execution":{"iopub.status.busy":"2023-09-15T06:18:29.468934Z","iopub.execute_input":"2023-09-15T06:18:29.46936Z","iopub.status.idle":"2023-09-15T06:19:05.95056Z","shell.execute_reply.started":"2023-09-15T06:18:29.469325Z","shell.execute_reply":"2023-09-15T06:19:05.949637Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Exoloring dataset\ndataset_path=\"/kaggle/input/rsna-breast-cancer-detection\"\nimport pandas as pd\n#import os\n#os.listdir(dataset_path)\n\ntrain_df_path=dataset_path+ \"/train.csv\"\ntest_df_path=dataset_path+\"/tast.csv\"\n\ntrain_df = pd.read_csv(train_df_path)","metadata":{"execution":{"iopub.status.busy":"2023-09-15T06:42:10.699963Z","iopub.execute_input":"2023-09-15T06:42:10.701136Z","iopub.status.idle":"2023-09-15T06:42:10.794793Z","shell.execute_reply.started":"2023-09-15T06:42:10.701074Z","shell.execute_reply":"2023-09-15T06:42:10.793634Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"train_df.head()","metadata":{"execution":{"iopub.status.busy":"2023-09-15T06:42:57.077872Z","iopub.execute_input":"2023-09-15T06:42:57.078296Z","iopub.status.idle":"2023-09-15T06:42:57.105539Z","shell.execute_reply.started":"2023-09-15T06:42:57.078262Z","shell.execute_reply":"2023-09-15T06:42:57.104373Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]}]}