{"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)\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":"2022-10-25T16:44:13.922434Z","iopub.execute_input":"2022-10-25T16:44:13.923430Z","iopub.status.idle":"2022-10-25T16:44:13.952904Z","shell.execute_reply.started":"2022-10-25T16:44:13.923315Z","shell.execute_reply":"2022-10-25T16:44:13.951479Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"train_1 = pd.read_csv('/kaggle/input/tabular-playground-series-oct-2022/train_1.csv')","metadata":{"execution":{"iopub.status.busy":"2022-10-25T16:44:35.882844Z","iopub.execute_input":"2022-10-25T16:44:35.883258Z","iopub.status.idle":"2022-10-25T16:45:25.125279Z","shell.execute_reply.started":"2022-10-25T16:44:35.883224Z","shell.execute_reply":"2022-10-25T16:45:25.123938Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"train_1.head()","metadata":{"execution":{"iopub.status.busy":"2022-10-25T16:45:26.497685Z","iopub.execute_input":"2022-10-25T16:45:26.498055Z","iopub.status.idle":"2022-10-25T16:45:26.527844Z","shell.execute_reply.started":"2022-10-25T16:45:26.498023Z","shell.execute_reply":"2022-10-25T16:45:26.526255Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"train_1.info()","metadata":{"execution":{"iopub.status.busy":"2022-10-25T16:46:41.543158Z","iopub.execute_input":"2022-10-25T16:46:41.543673Z","iopub.status.idle":"2022-10-25T16:46:41.570379Z","shell.execute_reply.started":"2022-10-25T16:46:41.543633Z","shell.execute_reply":"2022-10-25T16:46:41.568924Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"To check any null values exists in the data set","metadata":{}},{"cell_type":"code","source":"temp = train_1.isnull().sum()","metadata":{"execution":{"iopub.status.busy":"2022-10-25T16:55:21.810979Z","iopub.execute_input":"2022-10-25T16:55:21.811467Z","iopub.status.idle":"2022-10-25T16:55:22.216301Z","shell.execute_reply.started":"2022-10-25T16:55:21.811420Z","shell.execute_reply":"2022-10-25T16:55:22.214787Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"pd.DataFrame(temp)","metadata":{"execution":{"iopub.status.busy":"2022-10-25T16:55:48.000683Z","iopub.execute_input":"2022-10-25T16:55:48.001248Z","iopub.status.idle":"2022-10-25T16:55:48.018375Z","shell.execute_reply.started":"2022-10-25T16:55:48.001201Z","shell.execute_reply":"2022-10-25T16:55:48.016929Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"train_1['team_scoring_next'].unique()","metadata":{"execution":{"iopub.status.busy":"2022-10-25T16:53:34.388177Z","iopub.execute_input":"2022-10-25T16:53:34.388700Z","iopub.status.idle":"2022-10-25T16:53:34.476155Z","shell.execute_reply.started":"2022-10-25T16:53:34.388655Z","shell.execute_reply":"2022-10-25T16:53:34.474939Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"train_1['team_scoring_next']","metadata":{},"execution_count":null,"outputs":[]}]}