{"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,"isSourceIdPinned":false}],"dockerImageVersionId":31286,"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},"outputs":[],"execution_count":null},{"cell_type":"code","source":"import pandas as pd\nfrom sklearn.ensemble import RandomForestClassifier\nimport numpy as np\n\ntrain=pd.read_csv(\"/kaggle/input/competitions/digit-recognizer/train.csv\")\ntest=pd.read_csv(\"/kaggle/input/competitions/digit-recognizer/test.csv\")\nsubmission=pd.read_csv(\"/kaggle/input/competitions/digit-recognizer/sample_submission.csv\")\n\ny=train['label']\nx=train.drop('label', axis=1)\n\nx=x/255.0\ntest=test/255.0\n\nmodel=RandomForestClassifier()\nmodel.fit(x,y)\n\npred=model.predict(test)\nprint(pred)\n\nsubmission=pd.DataFrame({\"ImageId\": range(1,len(pred)+1),\n                         \"Label\":pred})\nsubmission.to_csv(\"submission.csv\", index=False)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2026-03-07T04:53:33.218349Z","iopub.execute_input":"2026-03-07T04:53:33.218654Z","iopub.status.idle":"2026-03-07T04:54:13.081090Z","shell.execute_reply.started":"2026-03-07T04:53:33.218629Z","shell.execute_reply":"2026-03-07T04:54:13.080129Z"}},"outputs":[],"execution_count":null}]}