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@ -37,7 +37,7 @@ pipeline {
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// sh './process_data.sh'
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// sh './process_data.sh'
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sh 'ls'
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sh 'ls'
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sh 'python3 ./download_data_and_process.py'
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sh 'python3 ./download_data_and_process.py'
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archiveArtifacts artifacts: "data_test.csv, data_dev.csv, data_train.csv, column_titles.csv, fake_job_postings.csv"
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archiveArtifacts artifacts: "data_test.csv, data_dev.csv, data_train.csv, column_titles.csv, data.csv"
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}
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}
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}
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}
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}
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}
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@ -5,7 +5,13 @@ import numpy as np
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# kaggle.api.authenticate()
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# kaggle.api.authenticate()
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# kaggle.api.dataset_download_files('shivamb/real-or-fake-fake-jobposting-prediction', path='fake_job_postings.csv', unzip=True)
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# kaggle.api.dataset_download_files('shivamb/real-or-fake-fake-jobposting-prediction', path='fake_job_postings.csv', unzip=True)
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data=pd.read_csv('fake_job_postings.csv')
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data=pd.read_csv('data.csv')
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data = data.replace(np.nan, '', regex=True)
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data = data.replace(np.nan, '', regex=True)
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print("="*20)
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print("="*20)
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@ -4,7 +4,6 @@ echo $KAGGLE_USERNAME
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kaggle datasets download -d shivamb/real-or-fake-fake-jobposting-prediction
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kaggle datasets download -d shivamb/real-or-fake-fake-jobposting-prediction
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unzip -o real-or-fake-fake-jobposting-prediction.zip
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unzip -o real-or-fake-fake-jobposting-prediction.zip
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ls
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ls
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cat fake_job_postings.csv > data.csv
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echo "Save column titles"
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echo "Save column titles"
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head -n 1 fake_job_postings.csv > column_titles.csv
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head -n 1 fake_job_postings.csv > column_titles.csv
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tail -n +2 fake_job_postings.csv > data_not_shuf.csv
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tail -n +2 fake_job_postings.csv > data_not_shuf.csv
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