Kaggle competition: LANL Earthquake Prediction
sub.loc[:, 'healthy':] = probs
sub.to_csv('submission.csv', index=False)
 sub.head()<import_modules>
tr_data = pd.read_csv(".. /input/train.csv", iterator=True, chunksize=600_000)
 x_tr_data = pd.DataFrame()
y_tr_data = pd.Series()
for data in tr_data:
add_tr = new_column(data['acoustic_data'])
 x_tr_data = x_tr_data.append(add_tr, ignore_index=True)
 y_tr_data = y_tr_data.append(pd.Series(data['time_to_failure'].values[-1]))
del tr_data
import math, re, os
import numpy as np
import pandas as pd
from matplotlib import pyplot as plt
from kaggle_datasets import KaggleDatasets
import tensorflow as tf
import tensorflow.keras.layers as L
from sklearn import metrics
from sklearn.model_selection import train_test_split
from keras.callbacks import ModelCheckpoint<set_options>
train_pool = Pool(x_tr_data, y_tr_data)
 my_model = CatBoostRegressor(iterations=1000, loss_function='MAE', boosting_type='Ordered')
 my_model.fit(x_tr_data, y_tr_data, silent=True)
 my_model.best_score_
<define_variables><EOS> 
submission = pd.read_csv('.. /input/sample_submission.csv', index_col='seg_id')
 test_data = pd.DataFrame(columns=x_tr_data.columns, dtype=np.float64, index=submission.index)
 for seg_id in test_data.index:
seg = pd.read_csv('.. /input/test/' + seg_id + '.csv')
 add_tr = new_column(seg['acoustic_data'])
 test_data.loc[seg_id]= add_tr
submission['time_to_failure'] = my_model.predict(test_data)
 submission.to_csv('submission.csv' )