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This is the README file for the project on model interpretation, specifically focused on building a model to predict the primary cause of car crashes using car crash data. The purpose of this document is to provide a high-level overview of the project's process, discuss the proof of concept, and outline the steps taken to achieve the results. By sharing my experience and the challenges faced during this project, I hope to provide valuable insights for future endeavors.
The main objective of this project was to develop models, iterate on their performance, and perform feature exploration to predict the primary cause of car crashes. The presentation will delve into the technical aspects of the project and highlight the process followed to reach the final results. As the capstone project is approaching, it is beneficial to encounter and address challenges now, allowing you to avoid the mistakes I made.
Splitting the process into significant stages facilitates branching, making it easier to perform further cleaning, create pipelines, or modify specific aspects for different models. Throughout the presentation, you will witness multiple perspectives on the diamond, each represented as distinct steps. This structured approach enables the creation of tailored processes for specific requirements in later stages.
- The models exhibit accurate accident prediction capabilities. However, more intriguing is the insight they provide about the underlying phenomena.
- The dataset offers an impressive level of granularity. Processing the features effectively and understanding their significance are crucial for achieving reliable model performance and obtaining desired outcomes.
- Gradually scale up the data used for model training. Starting with a smaller subset allows for efficient debugging and optimization, preventing substantial time loss during the training process.
- Refactor and recompress the project codebase. As the project grows, it is vital to manage loose ends and streamline the codebase as much as possible.
The most critical aspect to highlight is that model interpretation is an iterative process. Approximately 90% of the work lies in understanding and processing the features while explicitly identifying your goals. The devil is in the details, and there is no definitive right way, but numerous pitfalls to avoid. Engineering efforts extend beyond hyperparameter modifications. By exploring and diving deeper into the dataset, you gain insights that lead to continual improvement and enhanced results.
By sharing my experiences, I aimed to provide valuable insights into the model interpretation project. I hope that you have gained some understanding and perspective from this presentation, enabling you to approach similar projects more effectively.