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aufatsee/README.md
  • MSBD5001 (L1) - Foundations of Data Analytics
  • Description: the self-written Python program was prepared in Kaggle website with the Random Forest algorithm to generate the results that was determined by the Kaggle scoring automatically after the submission
  • Special Notes: Due to the design of the random forest, the data were randomly picked from the training data set and by splitting randomly, therefore, the produced prediction model may not be 100% identical at each iteration
  • Output Results: a series of results were uploaded to the Kaggle, and the best scoring was 1.00, while the rest of trials achieved only 0.93 to 0.96, respectively
  • Source: Please note that the source code "notebook-individual-randomforest.ipynb" was used to generate the submission result
  • Execution : Please upload the source code to Kaggle and under the <MSBD5001-Spring 2022> programme
  • Output: Then run all the source, and will generate the "submission.csv" file as the output
  • The source data path are using the default files stored at:
  • /kaggle/input/msbd5001-spring-2022/train.csv
  • /kaggle/input/msbd5001-spring-2022/test.csv

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