{"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":"markdown","source":"# NCA Emoji Challenge sample submission ","metadata":{}},{"cell_type":"markdown","source":"In the competition data there is a sample_submission.csv that has the format of the submission file to use.","metadata":{}},{"cell_type":"code","source":"import numpy as np # linear algebra\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\nimport os","metadata":{"execution":{"iopub.status.busy":"2022-07-31T11:02:35.772620Z","iopub.execute_input":"2022-07-31T11:02:35.773857Z","iopub.status.idle":"2022-07-31T11:02:35.780497Z","shell.execute_reply.started":"2022-07-31T11:02:35.773811Z","shell.execute_reply":"2022-07-31T11:02:35.778992Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"Read the csv file to get all the entries needed that correspond to the test.csv e_id entries. ","metadata":{}},{"cell_type":"code","source":"df_ssub =  pd.read_csv('../input/nca-emoji-challenge/sample_submission.csv')  \ndf_ssub.head()","metadata":{"execution":{"iopub.status.busy":"2022-07-31T11:02:39.922516Z","iopub.execute_input":"2022-07-31T11:02:39.923011Z","iopub.status.idle":"2022-07-31T11:02:39.958585Z","shell.execute_reply.started":"2022-07-31T11:02:39.922973Z","shell.execute_reply":"2022-07-31T11:02:39.957634Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"They all have target_emoji as the red question mark and target_nca as the model that regenerates it.\nYou will need to replace these with your predictions for each row.\nWhen you have filled in your predictions for each e_id then save your submission.csv and submit for scoring.","metadata":{}},{"cell_type":"code","source":"df_ssub.to_csv('submission.csv', index=False)","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"### Good Luck!!","metadata":{}}]}