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@ -32,7 +32,8 @@ pipeline {
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withEnv(["KAGGLE_USERNAME=${params.KAGGLE_USERNAME}",
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withEnv(["KAGGLE_USERNAME=${params.KAGGLE_USERNAME}",
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"KAGGLE_KEY=${params.KAGGLE_KEY}",
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"KAGGLE_KEY=${params.KAGGLE_KEY}",
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"CUTOFF=${params.CUTOFF}"]) {
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"CUTOFF=${params.CUTOFF}"]) {
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sh 'python3 ./download_data_and_process.py'
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sh './process_data.sh'
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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"
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archiveArtifacts artifacts: "data_test.csv, data_dev.csv, data_train.csv, column_titles.csv"
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}
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}
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}
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}
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@ -1,10 +1,10 @@
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import subprocess
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import subprocess
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import pandas as pd
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import pandas as pd
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import numpy as np
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import numpy as np
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import kaggle
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# import kaggle
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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/fake_job_postings.csv')
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data=pd.read_csv('fake_job_postings.csv/fake_job_postings.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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