# This Python 3 environment comes with many helpful analytics libraries installed
# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python
# For example, here's several helpful packages to load

import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)

# Input data files are available in the read-only "../input/" directory
# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory

import os
for dirname, _, filenames in os.walk('/kaggle/input'):
    for filename in filenames:
        print(os.path.join(dirname, filename))

# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using "Save & Run All" 
# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session
"""
This is a basic baseline using LightAutoML preset pipeline. Read the docs [here](https://lightautoml.readthedocs.io/en/latest/)
"""
#Installing LightAutoML
!pip install lightautoml
from lightautoml.automl.presets.tabular_presets import TabularAutoML
from lightautoml.tasks import Task
#Reading the data
test = pd.read_csv('../input/tabular-playground-series-sep-2021/test.csv')
train = pd.read_csv('../input/tabular-playground-series-sep-2021/train.csv')
submission = pd.read_csv('../input/tabular-playground-series-sep-2021/sample_solution.csv')
train.head()
test.head()
#Training the model using the preset pipeline.
automl = TabularAutoML(task = Task('binary'))
roles = {'target': 'claim',
        'drop':['id']}
oof_pred = automl.fit_predict(train, roles = roles)
#Getting our predictions
test_pred = automl.predict(test)
test_pred
test_pred_df = pd.DataFrame(test_pred.data)
test_pred_df
submission['claim'] = test_pred_df
submission
#Saving our predictions
submission.to_csv('submission', index = False)