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Predicting demand for bike rentals in the next year

Data contains rental count depending on variables such as: weather conditions, day of the year, year, etc.. Data was collected over 2 years period. Project was focused on finding accurate predictive model for the task.

Dataset is available here: https://archive.ics.uci.edu/ml/datasets/bike+sharing+dataset

To run models:

  1. Open Bikes_rental_analysis(1).ipynb in Jupyter/Colab.
  2. Download dataset and change paths in the code.
  3. Execute.

Environment requirements:

  • Jupyter/Colab
  • Python 3.x
  • scikit-learn
  • Tensorflow 2.2.0
  • Libraries:
    • Matplotlib
    • NumPy
    • Pandas
    • Graphviz

Relevant paper: Fanaee-T, Hadi, and Gama, Joao, 'Event labeling combining ensemble detectors and background knowledge', Progress in Artificial Intelligence (2013): pp. 1-15, Springer Berlin Heidelberg, [Web Link].