{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.10.17","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"},"kaggle":{"accelerator":"tpu1vmV38","dataSources":[{"sourceId":96164,"databundleVersionId":11418275,"sourceType":"competition"}],"dockerImageVersionId":31041,"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","trusted":true,"execution":{"iopub.status.busy":"2025-05-23T15:56:22.420268Z","iopub.execute_input":"2025-05-23T15:56:22.421553Z","iopub.status.idle":"2025-05-23T15:56:26.118429Z","shell.execute_reply.started":"2025-05-23T15:56:22.421529Z","shell.execute_reply":"2025-05-23T15:56:26.113478Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"import pandas as pd\nimport numpy as np\nfrom sklearn.preprocessing import StandardScaler\nfrom sklearn.model_selection import train_test_split\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-05-23T15:56:26.119401Z","iopub.execute_input":"2025-05-23T15:56:26.119731Z","iopub.status.idle":"2025-05-23T15:56:29.374903Z","shell.execute_reply.started":"2025-05-23T15:56:26.119704Z","shell.execute_reply":"2025-05-23T15:56:29.370183Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"pd.read_csv('/kaggle/input/drw-crypto-market-prediction/sample_submission.csv')","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-05-23T15:56:29.376551Z","iopub.execute_input":"2025-05-23T15:56:29.376847Z","iopub.status.idle":"2025-05-23T15:56:29.735622Z","shell.execute_reply.started":"2025-05-23T15:56:29.376826Z","shell.execute_reply":"2025-05-23T15:56:29.731372Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"train=pd.read_parquet('/kaggle/input/drw-crypto-market-prediction/train.parquet')","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-05-23T15:56:29.737000Z","iopub.execute_input":"2025-05-23T15:56:29.737224Z","execution_failed":"2025-05-23T16:13:39.463Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"train","metadata":{"trusted":true,"execution":{"execution_failed":"2025-05-23T16:13:39.464Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"train.shape","metadata":{"trusted":true,"execution":{"execution_failed":"2025-05-23T16:13:39.464Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"train.info()","metadata":{"trusted":true,"execution":{"execution_failed":"2025-05-23T16:13:39.464Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"X=train.drop('label',axis=1)\ny=train.label\nX_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=4)\nX_train = pd.DataFrame(X_train).replace([np.inf, -np.inf], np.nan).ffill().bfill()\nX_test = pd.DataFrame(X_test).replace([np.inf, -np.inf], np.nan).ffill().bfill()\n\nscaler=StandardScaler()\nX_train_prepared=scaler.fit_transform(X_train)\nX_test_prepared=scaler.transform(X_test)","metadata":{"trusted":true,"execution":{"execution_failed":"2025-05-23T16:13:39.464Z"}},"outputs":[],"execution_count":null}]}