Bike-sharing predictor

Help a bike-sharing company make business decisions

Returns the expected volume of business based on historical bike-rental data, so the company can predict the number of bikes they will need. First project for the Udacity Deep Learning Nanodegree.

Getting Started

Step 1: Review my code in my_answers.py and see my results in bike_sharing.ipynb.

Step 2: Do this project yourself

To make your own Bike-sharing Predictor, go here and clone the repository.

git clone https://github.com/udacity/deep-learning-v2-pytorch.git
cd deep-learning-v2-pytorch/project-tv-script-generation

Then open Predicting_bike_sharing_data.ipynb.

Prerequisites

1. Jupyter Notebooks
2. GPU
3. The bike-sharing dataset from the UCI Machine Learning Database.
4. PyTorch and Torchvision. For installation instructions see Udacity's README in the Deep Learning repository.

Tips

Watch out for size mismatch.

Almost all the challenges I ran into with this project happened as a result of size mismatch. Read the comments in my code to see where I had these problems and how I solved them.

Known Issues

To pass all unit tests, I needed to add this print statement to the test_run function under unittests in bike_sharing.ipynb. print(network.run(inputs))

The print statment should be added here:

def test_run(self):
        # Test correctness of run method
        network = NeuralNetwork(3, 2, 1, 0.5)
        network.weights_input_to_hidden = test_w_i_h.copy()
        network.weights_hidden_to_output = test_w_h_o.copy()
        print(network.run(inputs)) #Add the print statement here
        self.assertTrue(np.allclose(network.run(inputs), 0.09998924))

Authors

• @technoempathy – Layla Messner

Acknowledgments

• @udacity for the project
• @facebook for the scholarship to the Udacity Deep Learning Nanodegree