{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.12.12","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"},"kaggle":{"accelerator":"none","dataSources":[{"sourceType":"competition","sourceId":3004,"databundleVersionId":861823}],"dockerImageVersionId":31328,"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\n\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":"2026-04-20T01:59:04.569792Z","iopub.execute_input":"2026-04-20T01:59:04.570219Z","iopub.status.idle":"2026-04-20T01:59:04.897141Z","shell.execute_reply.started":"2026-04-20T01:59:04.570186Z","shell.execute_reply":"2026-04-20T01:59:04.896326Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"import numpy as np # linear algebra\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n\nimport pandas as pd\nfrom sklearn.neighbors import KNeighborsClassifier\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.metrics import accuracy_score\n\n\ntrain = pd.read_csv('/kaggle/input/competitions/digit-recognizer/train.csv')\ntest = pd.read_csv('/kaggle/input/competitions/digit-recognizer/test.csv')\n\n\nY = train[\"label\"]\n\nX = train.drop(columns=[\"label\"])\n# X_test  = test\n\n\nknn = KNeighborsClassifier(n_neighbors=3)\n\nX_train, X_val, Y_train, Y_val = train_test_split(X, Y, test_size=0.2, random_state=1)\nX_train = X_train / 255.0\nX_val = X_val / 255.0\ntest = test / 255.0\nknn.fit(X_train, Y_train)\n\npreds = knn.predict(X_val)\n\nprint(accuracy_score(Y_val, preds))\n\nknn.fit(X, Y)\n\npreds = knn.predict(test)\n\nsubmiossion = pd.DataFrame({\"ImageId\": range(1, len(preds) + 1), \"Label\": preds})\nsubmiossion.to_csv('submission.csv', index=False)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-04-20T02:39:18.811213Z","iopub.execute_input":"2026-04-20T02:39:18.811990Z","iopub.status.idle":"2026-04-20T02:40:10.903730Z","shell.execute_reply.started":"2026-04-20T02:39:18.811952Z","shell.execute_reply":"2026-04-20T02:40:10.902597Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"","metadata":{"trusted":true},"outputs":[],"execution_count":null}]}