Kaggle competition: Natural Language Processing with Disaster Tweets
<save_to_csv><EOS> 
import pandas as pd
<SOS> metric: meanfscore Kaggle data source: natural-language-processing-with-disaster-tweets<load_from_disk>
import pandas as pd
site0 = pd.read_feather('.. /input/ucf-building-meter-reading/site0.ft')
 df_test = pd.read_feather('.. /input/ashrae-feather/test.ft' )<merge>
train_df = pd.read_csv(".. /input/nlp-getting-started/train.csv")
 test_df = pd.read_csv('/kaggle/input/nlp-getting-started/test.csv')
 gt_df = pd.read_csv(".. /input/disasters-on-social-media/socialmedia-disaster-tweets-DFE.csv" )
merged = df_test.merge(site0, left_on=['building_id', 'meter', 'timestamp'],
right_on=['building_id', 'meter', 'timestamp'], how='left' )<prepare_x_and_y>
gt_df = gt_df[['choose_one', 'text']]
gt_df['target'] =(gt_df['choose_one']=='Relevant' ).astype(int)
 gt_df['id'] = gt_df.index
gt_df
ytrue = merged[~merged['meter_reading'].isna() ]['meter_reading']
pred = submission[~merged['meter_reading'].isna() ]['meter_reading']<compute_test_metric>
merged_df = pd.merge(test_df, gt_df, on='id')
 merged_df
<load_from_disk><EOS> 
subm_df.to_csv('submission.csv', index=False )