{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"pygments_lexer":"ipython3","nbconvert_exporter":"python","version":"3.6.4","file_extension":".py","codemirror_mode":{"name":"ipython","version":3},"name":"python","mimetype":"text/x-python"}},"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)\nimport seaborn as sns\nimport matplotlib.pyplot as plt\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","execution":{"iopub.status.busy":"2022-11-08T18:25:56.352692Z","iopub.execute_input":"2022-11-08T18:25:56.353623Z","iopub.status.idle":"2022-11-08T18:25:57.546593Z","shell.execute_reply.started":"2022-11-08T18:25:56.353501Z","shell.execute_reply":"2022-11-08T18:25:57.545073Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"trainDf=pd.read_csv(\"/kaggle/input/digit-recognizer/train.csv\")\ntestDf=pd.read_csv(\"/kaggle/input/digit-recognizer/test.csv\")","metadata":{"execution":{"iopub.status.busy":"2022-11-08T18:51:36.661278Z","iopub.execute_input":"2022-11-08T18:51:36.661804Z","iopub.status.idle":"2022-11-08T18:51:40.856095Z","shell.execute_reply.started":"2022-11-08T18:51:36.661764Z","shell.execute_reply":"2022-11-08T18:51:40.854344Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"trainDf.head()","metadata":{"execution":{"iopub.status.busy":"2022-11-08T18:51:47.387310Z","iopub.execute_input":"2022-11-08T18:51:47.387777Z","iopub.status.idle":"2022-11-08T18:51:47.412061Z","shell.execute_reply.started":"2022-11-08T18:51:47.387743Z","shell.execute_reply":"2022-11-08T18:51:47.410600Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"trainDf.shape","metadata":{"execution":{"iopub.status.busy":"2022-11-08T18:54:42.149862Z","iopub.execute_input":"2022-11-08T18:54:42.151347Z","iopub.status.idle":"2022-11-08T18:54:42.160845Z","shell.execute_reply.started":"2022-11-08T18:54:42.151291Z","shell.execute_reply":"2022-11-08T18:54:42.159528Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"testDf.head()","metadata":{"execution":{"iopub.status.busy":"2022-11-08T18:51:53.206666Z","iopub.execute_input":"2022-11-08T18:51:53.207098Z","iopub.status.idle":"2022-11-08T18:51:53.233439Z","shell.execute_reply.started":"2022-11-08T18:51:53.207058Z","shell.execute_reply":"2022-11-08T18:51:53.232118Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"testDf.shape","metadata":{"execution":{"iopub.status.busy":"2022-11-08T18:54:54.000812Z","iopub.execute_input":"2022-11-08T18:54:54.001389Z","iopub.status.idle":"2022-11-08T18:54:54.010699Z","shell.execute_reply.started":"2022-11-08T18:54:54.001343Z","shell.execute_reply":"2022-11-08T18:54:54.008967Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"trainY = trainDf[\"label\"]\n\ntrainX = trainDf.drop(labels = [\"label\"],axis = 1) ","metadata":{"execution":{"iopub.status.busy":"2022-11-08T18:57:03.881106Z","iopub.execute_input":"2022-11-08T18:57:03.881526Z","iopub.status.idle":"2022-11-08T18:57:03.971419Z","shell.execute_reply.started":"2022-11-08T18:57:03.881496Z","shell.execute_reply":"2022-11-08T18:57:03.969730Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"plt.figure(figsize=(12,5))\ni = sns.countplot(trainY, palette=\"icefire\")\nplt.title(\"Numbers\")\ntrainY.value_counts()","metadata":{"execution":{"iopub.status.busy":"2022-11-08T19:03:19.164544Z","iopub.execute_input":"2022-11-08T19:03:19.165029Z","iopub.status.idle":"2022-11-08T19:03:19.430039Z","shell.execute_reply.started":"2022-11-08T19:03:19.164978Z","shell.execute_reply":"2022-11-08T19:03:19.429095Z"},"trusted":true},"execution_count":null,"outputs":[]}]}