The Ten Commandments of Data Science Project Execution

The Ten Commandments of Data Science Project Execution

The Ten Commandments of Data Science Project Execution

Here are ten guiding principles for completing a data science project in order to truly deliver against a well-defined brief.

The Ten Commandments of Data Science Project Execution

Establishing what we, or the consumers for whom we are constructing models, want to achieve is critical when designing a data science project, but this understanding is merely a blueprint for success. Teams must follow best practices while executing projects to truly deliver on a well-defined brief. To help clarify what it can imply, I’ve compiled a list of ten points that can be applied to every data science endeavor.

1. Recognize the issue

Knowing what problem you’re solving is the most important component of fixing any challenge. Make sure you know what you’re trying to anticipate, what limits you’ll face, and what the project’s final goal will be. Early on, ask questions and seek confirmation from colleagues, domain experts, and end-users. If the answers you get match your comprehension, you’re on the correct track.

2. Understand Your Data

You’ll be able to comprehend what kind of models function well and which features to utilize if you grasp what your data signifies. The problem that the data is trying to solve will determine which model is the most successful, and the processing time will influence the project’s cost. You can emulate or improve human decision-making by leveraging and inventing important features. Understanding what each field signifies is critical to solving the problem, especially in regulated businesses where data must be anonymized and hence isn’t always clear. If you’re not sure what something means, consult a domain specialist.

The Ten Commandments of Data Science Project Execution

3. Make a Data Split

How will your model function with data that hasn’t been seen before? If it can’t generalize to new data, it doesn’t matter how well it performs on the data you’ve given it. You can validate how well your model will perform on unknown data by not letting it view part of it during training. This strategy is critical for selecting the optimal model architecture and modifying parameters to achieve the best results.

You’ll need to divide your data into two or three pieces for supervised learning issues. The training data, or data from which the model learns, is usually 75-80% of the original data, picked at random. The remaining data is the testing data, which is used to evaluate your model. You may also need a third hold-out set called the validation set, which is used to compare several supervised learning models that have been tweaked on the test data, depending on the sort of model you’re developing. In this instance, the non-training data must be divided into two data sets: testing and validation. You want to use test data to compare iterations of the same model, and validation data to compare final versions of distinct models.

The train test split function in Scikit-learn is the simplest way to split your data correctly in Python.

4. Don’t Let Test Data Float Away

It’s crucial not to include any data from the test data in your model. This can be as obvious as training on the entire data set or as subtle as applying changes before separating, such as scaling. If you normalize your data before splitting it, for example, the model learns about the test data set because the global minimum or maximum may be in the held-out data.

The Ten Commandments of Data Science Project Execution

5. Use the Correct Metrics for Evaluation

Because each situation is unique, the best approach of evaluation must be determined by the circumstances. Accuracy is the most naive – and perhaps harmful – categorization metric. Consider the challenge of cancer detection. If we want a fairly accurate model, we should always forecast “not cancer,” because we’ll be correct more than 99 percent of the time. However, since we want to detect cancer, this model isn’t particularly useful. In your classification and regression issues, think carefully about which assessment metric to utilize.

6. Maintain Simplicity

When approaching an issue, it’s critical to select the most appropriate solution for the task at hand, rather than the most intricate model. The “latest-and-greatest” may be desired by management, consumers, and even you. Occam’s Razor states that you should use the simplest model that satisfies your needs. This will not only increase visibility and reduce training time, but it will also boost performance. In summary, don’t try to kill Godzilla with a flyswatter or blast a fly with a bazooka.

7. Don’t overextend yourself (or Underfit) Your Role Model

Overfitting, also known as variance, causes the model to perform poorly on data it hasn’t seen before. The training data is simply memorized by the model. Giving the model too little information to acquire a correct representation of the problem is known as underfitting, also known as bias. Balancing these two – known as the “bias-variance trade-off” – is a crucial element of the data science process, and various challenges necessitate different compromises.

As an example, consider a simple image classifier. Its job is to determine whether or not an image contains a dog. If you overfit this model, it will only be able to recognize an image as a dog if it has seen it previously. Even if the model has seen the image previously, if you underfit it, it might not recognize it like a dog.

8. Experiment with various model architectures

Considering multiple model architectures for an issue is usually advantageous. What works best for one situation might not be the ideal solution for another. Combining basic and complex algorithms is a good idea. Try things as simple as a random forest and as complicated as a neural network if you’re working on a classification model. Extreme gradient boosting (XGBoost) outperforms neural network classifiers in many cases. A simple model is frequently the best way to solve a simple problem.

The Ten Commandments of Data Science Project Execution

9. Make sure your hyperparameters are in good shape.

Hyperparameters are numbers that are utilized to calculate the model. One hyperparameter of a decision tree, for example, is its depth, or how many questions it will ask before deciding on an answer. The default hyperparameters for a model are those that produce the best results on average. However, it is exceedingly improbable that your model would sit exactly in that sweet spot; changing parameters can significantly improve your model’s performance. Grid search, randomized search, and Bayesian-optimized search are the most frequent methods for tuning hyperparameters, however, there are a number of additional more complex strategies.

10. Make Correct Model Comparisons

Machine learning’s ultimate goal is to create a model that generalizes well. That is why it is critical to properly compare and select the best model. As previously stated, you’ll want to utilize a different holdout set for assessment than the one you used to train your hyperparameters. You should also employ proper statistical tests to evaluate the outcomes.

Try out the guiding principles for performing a data science project on your next data science project now that you have them. I’d like to know whether they were of use to you, so please let me know if they were or were not. Please leave any more commandments in the comments section below!

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