Project-Predicting-Bike-Sharing

Build and train own Neural Network from scratch to predict the number of bikeshare users on a given day.

Introduction

In this project, you'll get to build a neural network from scratch to carry out a prediction problem on a real dataset! By building a neural network from the ground up, you'll have a much better understanding of gradient descent, backpropagation, and other concepts that are important to know before we move to higher-level tools such as PyTorch. You'll also get to see how to apply these networks to solve real prediction problems!

Process of project contains:

• Implement Forward Pass
• Implement Backward Pass
• Set proper Hyperparameters

The data comes from the UCI Machine Learning Database.

The result of project shown below:

Waiting for results...Done!

Results:

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                      Test Result Summary                           

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• Produces good results when running the network on full data .
• The activation function is a sigmoid .
• The backpropagation implementation is correct .
• The forward pass implementation is correct .
• The learning_rate is reasonable .
• The number of epochs is reasonable .
• The number of hidden nodes is reasonable .
• The number of output nodes is correct .
• The run method is correct .
• The update_weights implementation is correct .
• The weights are updated correctly on training .

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Congratulations! It looks like your network passed all of our tests.