{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.11.11","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"},"kaggle":{"accelerator":"none","dataSources":[{"sourceId":3004,"databundleVersionId":861823,"sourceType":"competition"}],"dockerImageVersionId":31012,"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\nfrom sklearn.model_selection import train_test_split\n\n#import sklearn\n#sklearn.set_config(transform_output=\"pandas\")\n\nimport tensorflow as tf\nimport tensorflow.keras.backend as K\nimport tensorflow.keras.layers as L\nimport tensorflow.keras.models as M\nimport tensorflow.keras.callbacks as C\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":"2025-04-17T11:48:02.348169Z","iopub.execute_input":"2025-04-17T11:48:02.348435Z","iopub.status.idle":"2025-04-17T11:48:03.460973Z","shell.execute_reply.started":"2025-04-17T11:48:02.348408Z","shell.execute_reply":"2025-04-17T11:48:03.460012Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"df_train = pd.read_csv(\"/kaggle/input/digit-recognizer/train.csv\")","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-04-17T11:50:34.438385Z","iopub.execute_input":"2025-04-17T11:50:34.439238Z","iopub.status.idle":"2025-04-17T11:50:38.06624Z","shell.execute_reply.started":"2025-04-17T11:50:34.43921Z","shell.execute_reply":"2025-04-17T11:50:38.065311Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"df_train.info()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-04-17T11:51:30.319745Z","iopub.execute_input":"2025-04-17T11:51:30.320071Z","iopub.status.idle":"2025-04-17T11:51:30.367153Z","shell.execute_reply.started":"2025-04-17T11:51:30.320047Z","shell.execute_reply":"2025-04-17T11:51:30.366327Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"LABEL = \"label\"\nFEATURES = [c for c in df_train.columns if c != LABEL]","metadata":{"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"y = df_train[LABEL]","metadata":{"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"# Transformer chaque ligne (image ) en matrice en 3 dimensions#\n\n# Utiliser la librairie numpy et les fonctions resize, reshape, concat","metadata":{"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"# Faire train_test_split","metadata":{"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"# TEster plusieurs modèles","metadata":{"trusted":true},"outputs":[],"execution_count":null}]}