{"cells":[{"metadata":{},"cell_type":"markdown","source":"# INDOOR NEWBIE BLEND\n\n\n## Feel free to UPVOTE 👍\n\n#### Special thanks to folks with top public notebooks:\n* [Simple 👌 99% Accurate Floor Model 💯](https://www.kaggle.com/nigelhenry/simple-99-accurate-floor-model)\n* [Lightgbm(Regressor)](https://www.kaggle.com/ghaiyur/lightgbm-regressor)\n* [wifi features with lightgbm/KFold](https://www.kaggle.com/hiro5299834/wifi-features-with-lightgbm-kfold)\n* [WIFI Features Neural-Networks starter](https://www.kaggle.com/deepijongwonkim/wifi-features-neural-networks-starter)\n\n![](https://storage.googleapis.com/kaggle-competitions/kaggle/22559/logos/thumb76_76.png?t=2020-09-30-17-34-06)\n"},{"metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","trusted":true},"cell_type":"code","source":"import pandas as pd\nimport numpy as np","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"submission_file=pd.read_csv('../input/indoor-location-navigation/sample_submission.csv')","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"submission_file = pd.DataFrame(submission_file['site_path_timestamp'])\nsubmission_file['floor'] = 0\nsubmission_file['x'] = 0\nsubmission_file['y'] = 0","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"second_score = pd.read_csv('../input/indoor-best-submits/submission_2.csv')\nfirst_score = pd.read_csv('../input/indoor-best-submits/submission_1.csv')\nfourth_score = pd.read_csv('../input/indoor-best-submits/submission_3.csv')\nthird_score = pd.read_csv('../input/indoor-best-submits/submission_4.csv')","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"first_score.head(3)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"second_score.head(3)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"third_score.head(3)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"fourth_score.head(3)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"submission_file['first_floor'] = first_score.floor\nsubmission_file['first_x'] = first_score.x\nsubmission_file['first_y'] = first_score.y","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"submission_file['second_floor'] = second_score.floor\nsubmission_file['second_x'] = second_score.x\nsubmission_file['second_y'] = second_score.y","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"submission_file['third_floor'] = third_score.floor\nsubmission_file['third_x'] = third_score.x\nsubmission_file['third_y'] = third_score.y","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"submission_file['fourth_floor'] = fourth_score.floor\nsubmission_file['fourth_x'] = fourth_score.x\nsubmission_file['fourth_y'] = fourth_score.y","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"submission_file['mean_x'] = submission_file[['first_x','second_x','third_x','fourth_x']].mean(axis=1)\nsubmission_file['mean_y'] = submission_file[['first_y','second_y','third_y','fourth_y']].mean(axis=1)\nsubmission_file['frequent_floor'] = submission_file[['first_floor','second_floor','third_floor','fourth_floor']].mode(axis=1)[0]","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"submission_file.sample(10)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"final_subm = submission_file[['site_path_timestamp','frequent_floor','mean_x','mean_y']]\nfinal_subm.columns = ['site_path_timestamp', 'floor', 'x','y']\nfinal_subm.sample(6)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"final_subm.to_csv('submission.csv', index=False)","execution_count":null,"outputs":[]}],"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":4,"nbformat_minor":4}