Not documenting the code
In data science, while working with data, code documentation acts as a helpful guide. It helps to understand the complex patterns and instructions written in the code. If there is no documentation for the code, the new user finds it difficult to understand the preprocessing steps, ensemble techniques, and feature engineering being performed in the code. Code documentation is a collection of comments and documents that explain the working of the code. Clear documentation of our code is essential to collaborate across different teams and to share codes with developers of other organizations. Spending time to document the code will make the work easier.
Causes of Not documenting the code
Example: Feature Engineering Let’s consider the feature engineering techniques used in the code. If the code doesn’t explain how the features are chosen, future iterations of the model may miss many valuable insights behind the previous feature engineering decisions.
Key Aspects of Documentation
- Inline comments: Inline comments are like a little message the developer can include in the code. The inline comments provide extra information, context, or explanation wherever needed. Inline comments should be in plain language and they should give the descriptions in a human-friendly manner. It provides clarifications to the tricky parts of the code. We can also include reminders for future modifications or enhancements.
- Function and Module Description: Description for a function, class, or module can be placed at the beginning part code. It will describe the purpose of that module, its parameters, and their expected outcomes. Also, we can include practical examples that help the user to understand their applications.
- README files: README files act as a comprehensive guide for the entire project. It includes an overview of the project, the installation instructions, and usage details. Updates regarding the project can also be mentioned in this section. We can also place the directory structure in the README file.
Practical Tips
- Include inline comments to explain complex sections of code and provide context.
- Write comprehensive README files to serve as a project guide, including installation instructions and project updates.
6 Common Mistakes to Avoid in Data Science Code
As we know Data Science is a powerful field that extracts meaningful insights from vast data. It is our job to discover hidden secrets from the available data. Well, that is what data science is. In this world, we use computers to solve problems and bring out hidden insights. When we enter into such a big journey, there are certain things we should watch out for. Those who like playing with data know the tricky part of understanding the data and the possibility of making mistakes during the data processing.
How can I avoid mistakes in my Data Science Code?
How can I write my Data Science code more efficiently?
To answer all your questions, In this article, you get to know Six common mistakes to avoid in data science code in detail.
Table of Content
- Ignoring Data Cleaning
- Neglecting Exploratory Data Analysis
- Ignoring Feature Scaling
- Using default Hyperparameters
- Overfitting the Model
- Not documenting the code
- Conclusion
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