{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.10.14","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"},"kaggle":{"accelerator":"none","dataSources":[{"sourceId":84896,"databundleVersionId":10305135,"sourceType":"competition"},{"sourceId":210710864,"sourceType":"kernelVersion"},{"sourceId":210804510,"sourceType":"kernelVersion"},{"sourceId":210833056,"sourceType":"kernelVersion"},{"sourceId":211252723,"sourceType":"kernelVersion"},{"sourceId":211282696,"sourceType":"kernelVersion"},{"sourceId":211581453,"sourceType":"kernelVersion"},{"sourceId":211629801,"sourceType":"kernelVersion"},{"sourceId":211653094,"sourceType":"kernelVersion"}],"dockerImageVersionId":30786,"isInternetEnabled":true,"language":"python","sourceType":"notebook","isGpuEnabled":false}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"markdown","source":"# Welcome to the Ensamble Notebook!\n\nIn this notebook, we thoughtfully blend four submissions to enhance our predictions and improve scores! 🚀\n\nIf this approach leads to better results, we’ll update the notebook to reflect the improvements. Stay tuned and let’s aim for the top! 🏆","metadata":{}},{"cell_type":"code","source":"import pandas as pd","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-07T08:03:43.804551Z","iopub.execute_input":"2024-12-07T08:03:43.8051Z","iopub.status.idle":"2024-12-07T08:03:43.810221Z","shell.execute_reply.started":"2024-12-07T08:03:43.805045Z","shell.execute_reply":"2024-12-07T08:03:43.809062Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"sub1 = pd.read_csv('/kaggle/input/rid-train-h2o/submission.csv')\nsub2 = pd.read_csv('/kaggle/input/insurance-competition-database/LGBR_STACK_1.03088.csv') \nsub3 = pd.read_csv('/kaggle/input/regression-with-an-insurance-ensemble/submission.csv') \n# sub4 = pd.read_csv('/kaggle/input/p04e12-blended-submission/submission.csv')\n# sub5 = pd.read_csv('/kaggle/input/insurance-competition-database/averager_1.0313127009620042.csv')","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-07T08:03:43.812114Z","iopub.execute_input":"2024-12-07T08:03:43.81245Z","iopub.status.idle":"2024-12-07T08:03:44.921107Z","shell.execute_reply.started":"2024-12-07T08:03:43.812418Z","shell.execute_reply":"2024-12-07T08:03:44.919987Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"blended = sub1.copy()\nblended['Premium Amount'] = (\n    (3/10) * sub1['Premium Amount'] +\n    (5.5/10) * sub2['Premium Amount'] +\n    (1.5/10) * sub3['Premium Amount'] #+\n    # (1/10) * sub4['Premium Amount'] +\n    # (1/10) * sub5['Premium Amount']\n)\n\nblended.to_csv('submission.csv', index=False)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-07T08:03:44.923229Z","iopub.execute_input":"2024-12-07T08:03:44.92369Z","iopub.status.idle":"2024-12-07T08:03:46.599604Z","shell.execute_reply.started":"2024-12-07T08:03:44.923639Z","shell.execute_reply":"2024-12-07T08:03:46.598432Z"}},"outputs":[],"execution_count":null}]}