{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.10.13","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"},"kaggle":{"accelerator":"none","dataSources":[{"sourceId":35332,"databundleVersionId":3723648,"sourceType":"competition"}],"dockerImageVersionId":30746,"isInternetEnabled":true,"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","metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","execution":{"iopub.status.busy":"2024-07-19T00:00:21.900967Z","iopub.execute_input":"2024-07-19T00:00:21.901353Z","iopub.status.idle":"2024-07-19T00:00:23.062659Z","shell.execute_reply.started":"2024-07-19T00:00:21.901322Z","shell.execute_reply":"2024-07-19T00:00:23.061258Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## Export data","metadata":{}},{"cell_type":"code","source":"sample = pd.read_csv('/kaggle/input/amex-default-prediction/sample_submission.csv')\n","metadata":{"execution":{"iopub.status.busy":"2024-07-19T00:00:23.064846Z","iopub.execute_input":"2024-07-19T00:00:23.065346Z","iopub.status.idle":"2024-07-19T00:00:25.218382Z","shell.execute_reply.started":"2024-07-19T00:00:23.065312Z","shell.execute_reply":"2024-07-19T00:00:25.21739Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"test = pd.read_csv('/kaggle/input/amex-default-prediction/test_data.csv')\n","metadata":{"execution":{"iopub.status.busy":"2024-07-19T00:00:25.219473Z","iopub.execute_input":"2024-07-19T00:00:25.219789Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"train = pd.read_csv('/kaggle/input/amex-default-prediction/train_data.csv')\n","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"train_label = pd.read_csv('/kaggle/input/amex-default-prediction/train_labels.csv')","metadata":{"trusted":true},"execution_count":null,"outputs":[]}]}