change script
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Jenkinsfile
vendored
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Jenkinsfile
vendored
@ -35,14 +35,17 @@ pipeline {
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steps {
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steps {
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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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sh 'chmod 777 ./data_processing.sh'
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sh 'chmod 777 ./data_processing.py'
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sh "./data_processing.sh ${params.CUTOFF}"
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sh "./data_processing.py ${params.CUTOFF}"
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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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stage('Artifacts') {
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stage('Artifacts') {
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steps {
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steps {
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archiveArtifacts artifacts: 'processed_data.txt'
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def artifactsList = ['hp_train.csv', 'hp_dev.csv', 'hp_test.csv'']
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artifactsList.each { artifact ->
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archiveArtifacts artifacts: artifact
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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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}
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19
data_processing.py
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19
data_processing.py
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@ -0,0 +1,19 @@
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import sklearn
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from sklearn.preprocessing import OneHotEncoder
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from sklearn.model_selection import train_test_split
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import pandas as pd
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import subprocess
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subprocess.run(["kaggle", "datasets", "download", "muhammadbinimran/housing-price-prediction-data", "--unzip"])
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housing_price_dataset = pd.read_csv('housing_price_dataset.csv')
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hp_train_test, hp_dev = sklearn.model_selection.train_test_split(housing_price_dataset, test_size=0.1)
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hp_train, hp_test = sklearn.model_selection.train_test_split(hp_train_test, test_size=1000)
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hp_train = pd.get_dummies(hp_train, columns=['Neighborhood'])
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hp_dev = pd.get_dummies(hp_dev, columns=['Neighborhood'])
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hp_test = pd.get_dummies(hp_test, columns=['Neighborhood'])
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hp_train.to_csv('hp_train.csv', index=False)
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hp_dev.to_csv('hp_dev.csv', index=False)
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hp_test.to_csv('hp_test.csv', index=False)
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