{"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\nfrom sklearn.ensemble import RandomForestClassifier, RandomForestRegressor, AdaBoostClassifier, AdaBoostRegressor, GradientBoostingClassifier, GradientBoostingRegressor, HistGradientBoostingClassifier, HistGradientBoostingRegressor \nfrom sklearn.svm import SVC, SVR\nfrom sklearn.metrics import accuracy_score, roc_auc_score, mean_absolute_percentage_error, mean_squared_error\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 lightgbm\nfrom lightgbm.sklearn import LGBMRegressor, LGBMClassifier\nfrom catboost import CatBoostRegressor, CatBoostClassifier, Pool\nimport xgboost as xgb\n\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":"2025-04-17T11:50:38.066240Z","shell.execute_reply.started":"2025-04-17T11:50:34.439210Z","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":"X = df_train[FEATURES]\ny = df_train[LABEL]","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}]}