Data preparation Checklist: What to consider before starting your AI or ML Project

In order for your AI and ML projects to work, the data needs to be prepared in a way that is useful. After all, garbage in is definitely garbage out when it comes to AI and ML projects. We’ve put together a quick checklist that helps you ensure that your data is clean, organized, and ready to be used to train your model and make accurate predictions. A little bit of extra attention and care during the data preparation stage can save a lot of time and headaches down the line. In this Data preparation Checklist, we suggest what to consider before starting your AI or ML Project.

  • Understand the problem: Understand the problem you are trying to solve before you begin preparing your data. This will help guide your data preparation process and ensure that the data you collect is relevant and useful.
  • Identify relevant data sources: Determine where you will be able to find the data you need. This could include databases, API’s, or external sources such as surveys or  interviews.
  • Gather all the data: Collect as much data as possible from the sources identified in step 2.
  • Clean and organize the data: Remove any irrelevant or duplicate data and organize the remaining data into a consistent format.
  • Check for missing data: Identify any missing data and determine if it is necessary to collect it or if it can be imputed or removed.
  • Check for outliers: Identify any outliers in the data and determine if they should be removed or if they provide valuable information. By not removing certain outlier data you can greatly skew results.
  • Check for data integrity: Ensure that the data is accurate and consistent by checking for errors and inconsistencies. 
  • Create new features: Identify any new features that can be created from the existing data to provide additional insights.
  • Normalize and scale the data: Normalize and scale the data to ensure that it is in a consistent range and format and appears similar.
  • Check for class imbalance: Identify any class imbalance which may be skewed data class proportions and determine if it needs to be addressed.
  • Handle categorical variables: Convert categorical variables such as race, gender,or hair color into numerical values to ensure that they can be used in the model.
  • Handle missing values: Determine the best way to handle missing values in the data, such as imputing or removing them.
  • Handle outliers: Determine the best way to handle outliers, such as removing or transforming them to make sure they don’t inadvertently skew your data set.
  • Consider sampling: Consider sampling the data if it is too large so that you can  process it more efficiently.
  • Consider data reduction: Consider data reduction techniques such as PCA ( Principal Component Analysis ) or LDA (linear discriminant analysis) if there are too many features.
  • Consider data augmentation: Consider augmenting techniques such as making minor changes to the dataset, flipping, cropping, or rotating data if there is not enough data.
  • Consider data balancing: Consider data balancing techniques such as oversampling or undersampling if there is a class imbalance.

This checklist serves as a guide to let you know what areas you need to focus on when it comes to prepping your data. Make sure that all the boxes have been checked and addressed before moving forward with your data preparations needs. Prepping data can be time consuming and complex. Make sure you’re setting you and your team up for success before proceeding.

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