Predict values
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@ -29,6 +29,11 @@ pipeline {
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copyArtifacts projectName: 'z487183-create-dataset'
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copyArtifacts projectName: 'z487183-create-dataset'
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sh './prepare-stats.sh'
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sh './prepare-stats.sh'
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archiveArtifacts 'stats.txt'
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archiveArtifacts 'stats.txt'
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sh 'python3 property_model.py'
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sh 'python3 predict_values.py'
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archiveArtifacts 'test_predictions.csv'
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archiveArtifacts 'train_predictions.csv'
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archiveArtifacts 'dev_predictions.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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@ -5,6 +5,8 @@ RUN apt-get install -y python3 python3-pip unzip
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RUN pip install pandas
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RUN pip install pandas
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RUN pip install scikit-learn
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RUN pip install scikit-learn
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RUN pip install kaggle
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RUN pip install kaggle
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RUN pip install keras
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RUN pip install tensorflow
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ARG DEBIAN_FRONTEND=noninteractive
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ARG DEBIAN_FRONTEND=noninteractive
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38
predict_values.py
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38
predict_values.py
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@ -0,0 +1,38 @@
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import pandas as pd
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from tensorflow import keras
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import csv
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model = keras.models.load_model('model.h5')
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X_test = pd.read_csv('X_test.csv').values
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X_train = pd.read_csv('X_train.csv').values
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X_val = pd.read_csv('X_val.csv').values
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with open('test_predictions.csv', "a") as file:
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writer = csv.writer(file)
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predictions = model.predict(X_test)
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writer.writerow(['PredictedPriceAboveMedian'])
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for pred in predictions:
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result = [0]
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if pred[0] > 0.5:
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result = [1]
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writer.writerow(result)
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with open('train_predictions.csv', "a") as file:
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writer = csv.writer(file)
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predictions = model.predict(X_train)
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writer.writerow(['PredictedPriceAboveMedian'])
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for pred in predictions:
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result = [0]
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if pred[0] > 0.5:
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result = [1]
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writer.writerow(result)
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with open('dev_predictions.csv', "a") as file:
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writer = csv.writer(file)
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predictions = model.predict(X_val)
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writer.writerow(['PredictedPriceAboveMedian'])
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for pred in predictions:
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result = [0]
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if pred[0] > 0.5:
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result = [1]
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writer.writerow(result)
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