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!
The data comes from the UCI Machine Learning Database. Also, you can find the data and its specifications here.
This project requires Python 3.x and the following Python libraries installed:
You will also need to have Jupyter notebook software installed to run and execute an iPython Notebook.
It's recommended to install Anaconda, a pre-packaged Python distribution that contains all of the necessary libraries and software for this project.
To complete this project, you need to follow the instructions in the notebook; they will lead you through the project. You'll ultimately be editing the my_answers.py python file, whose components are imported into the notebook at various places.
The model has reached to Training loss of 0.065 and a Validation loss of 0.152.
Finally, using the test data for prediction, it appears that the model reaches a pretty high accuracy. Also, at the first of model predictions, it shows excellent performance of predicting the actual data, or almost perfect. But after that, it seems the model doesn't catch the full correlation and can't predict much well. One potential solution is to feed the model with more data to have a better sense of the correlation and learn more thoughtfully.