Even though I have a Master’s degree in Computer Science with a focus on AI and machine learning (ML), I prefer to use DataChat’s Analyze skill over traditional tools like Python for ML tasks. With Python, I have to remember complicated syntax, know which packages to use and when, consult the package man page as packages change over time, remember how to hold out a percentage of my data for training, and more. All of this is slow, tedious, and, frankly, frustrating. With Analyze, I can get the work done so much faster because it automates all of these tedious details.

I don’t build models using Python every day. I often forget the exact syntax for the various packages that I want to use. Instead of slowly plodding through a notebook with sklearn or pandas APIs pulled up on another screen, I simply let DataChat handle all of that complexity for me. I also never have to worry about Python package management.

Not only is Analyze faster from a productivity perspective, but it also makes sure I follow data science best practices. For example, Analyze automatically:

• Cross-trains and selects the best model from multiple cutting edge ML models, so I don’t have to worry about model selection or staying on top of the latest ML literature.
• Performs k-fold cross validation to help avoid overfitting.
• Bins continuous columns to improve model performance.

Both faster and better? It’s no wonder that I prefer using DataChat for my ML needs.