{"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"},"kaggle":{"accelerator":"none","dataSources":[{"sourceId":20270,"databundleVersionId":1222630,"sourceType":"competition"}],"isInternetEnabled":true,"language":"python","sourceType":"notebook","isGpuEnabled":false}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"code","source":"import numpy as np\nimport pandas as pd\n\nfrom sklearn import datasets\nfrom sklearn import model_selection","metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","trusted":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"def create_folds(data, num_splits=5):\n    data = data.copy()\n    data[\"kfold\"] = -1\n\n    kf = model_selection.StratifiedKFold(\n        n_splits=num_splits, shuffle=True, random_state=42\n    )\n\n    for fold, (train_idx, valid_idx) in enumerate(kf.split(X=data, y=data[\"target\"])):\n        data.loc[valid_idx, \"kfold\"] = fold\n\n    return data","metadata":{"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"df = pd.read_csv(\"/kaggle/input/siim-isic-melanoma-classification/train.csv\")\n\ndf_5 = create_folds(df, num_splits=5)\ndf_10 = create_folds(df, num_splits=10)\ndf_5.head()","metadata":{"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"df_5.to_csv(\"train_5folds.csv\", index=False)\ndf_10.to_csv(\"train_10folds.csv\", index=False)","metadata":{"trusted":true},"outputs":[],"execution_count":null}]}