Neural Network Solution

In the fields of Software Engineering and Data Science, it is important to have experience solving real-world scenarios. Efficiency and accuracy are essential when working with data-driven problems.

Complete the following steps (dataset selection; https://archive.ics.uci.edu/datasets )

• Formulate a deep learning problem. State the problem using quantitative language, explaining why it is important to address it and to whom.
• Sketch the approach to solving the problem and the software tools you plan to use to implement the solution.
• Provide the theoretical (algorithmic) foundations of your solution.
• Describe the dataset used and include the following (code and detailed explanations):

• Descriptive analysis of the data, including informative plots.
• Explain why the data does or does not need to be normalized or standardized, and perform the necessary transformations.
• Explain how you clean the data and handle missing values.
• Explain how you handle outliers.

Implement the deep learning model and provide the complete code, its output, and explanations.

1. Describe the theoretical foundation of the model using rigorous mathematical notation.
2. Provide a diagram detailing the architecture and analytics workflow.
3. Demonstrate how the data is processed (e.g., used to train a neural network, fitted, used to make predictions, etc.).
4. Execute your model and detail its computational results and their interpretation.
5. Define performance metrics and use them to evaluate your model (e.g., accuracy).
6. Explain what parameters are used to improve (tune) the model.
7. Deploy your application to a cloud platform.
8. Justify why you chose the cloud platform by discussing the advantages it has over the other cloud platforms available as it pertains to your project.

Summarize the overall usefulness, functionality, and performance of the model.

Write a comprehensive technical report as a Python Jupyter notebook (to include all code, code comments, all outputs, plots, and analysis). Make sure the project documentation contains:

• Problem statement
• algorithm of the solution
• analysis of the findings

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