14 Machine Learning Tricks I Wish I Knew Earlier

As a data enthusiast navigating the world of machine learning, I've stumbled upon various techniques and tools that have significantly improved my workflow. These invaluable tricks, often hidden in the nooks and crannies of the Python ecosystem, have made data handling, visualization, and model training smoother and more efficient. In this article, I'm excited to share 14 machine learning tricks that I wish I had known earlier.

1. Supercharged File Saving and Loading

Saving and loading Parquet or delta files are much faster and painless compared to CSV. To put this into perspective, saving a dataframe to Parquet takes significantly less time than saving it to CSV, and the same applies to loading. The performance boost you gain from adopting Parquet or delta files can be a game-changer in your data processing pipelines.

2. Turbocharge Your Code with Parallel Execution

Joblib is a lifesaver when it comes to parallel execution. Whether you need to send thousands of HTTP requests or process large datasets, joblib can fully utilize your CPU cores, making your code lightning-fast. It's not just limited to HTTP requests; you can apply it to any picklable function, such as image resizing, web scraping, or file operations.

3. Ditch Conditionals for Dictionaries

Replacing complex conditional statements with dictionaries is a clean and efficient way to manage multiple cases in your code. It improves readability and simplifies maintenance.

4. Python's Built-In Function Caching

Since Python 3.9, you can harness the power of Python's built-in caching decorator in the "functools" module. This decorator is incredibly handy for optimizing recursive functions or those dealing with memory-heavy arguments.

from functools import cache

@cache
def factorial(n):
   return n*factorial(n-1) if n else 1
        

5. Simplify Datetime Objects

Strip away unnecessary components from datetime objects using Pandas' to_period function. Sometimes, you only need year, month, and day information, and this function lets you declutter your data effortlessly.

6. Explode Your Data with Pandas

When dealing with dataframes containing lists of values in cells, Pandas' explode function is your go-to tool. It vertically expands cells with multiple values into multiple rows, simplifying data manipulation.

7. Time Series Visualization Made Easy

Setting a datetime index in your Pandas dataframe simplifies time series visualization. You don't even need to import Matplotlib; just extract the relevant columns and call plot() to effortlessly create informative time series plots.

8. Streamline Data Processing with Pandas Pipe

Pandas pipe functionality allows you to chain multiple data preprocessing functions together in a single line of code, enhancing code readability and making debugging a breeze.

9. Master Matplotlib DPI and Figure Size

Choosing the right DPI (dots or pixels per inch) and figure size in Matplotlib is crucial to prevent loss of image quality when zooming in. Understanding these parameters ensures your plots look crisp and professional.

Image and content credit: an SO thread down below??

StackOverflow thread on the topic: https://bit.ly/3IrsLjY

10. Generate Synthetic Datasets with Faker

If you ever need synthetic data for testing or experimentation, the Faker library is your friend. It can generate random names, addresses, emails, phone numbers, and much more, making it a valuable tool for data generation.

11. Create Custom Business-Day Frequency Time Series

Learn how to generate custom business-day frequency time series using Pandas' bdate_range function with the weekmask parameter. This trick is particularly useful when you need to work with specific workweeks.

12. All-in-One Guide to Pandas Time Series Functions

Discover a treasure trove of Pandas time series functions that cover everything from missing data imputation to upsampling and downsampling. This comprehensive resource is a must-have for time series analysis.

Link to the article: https://bit.ly/3NZaIme

13. Decompose Time Series Like a Pro

Learn how to decompose time series data into its core components—seasonality, trend, and noise—using statsmodels' tsa_decompose function. This technique provides valuable insights into the underlying patterns of your time series data.

14. Dive into the Anatomy of Matplotlib

Explore the inner workings of Matplotlib to understand how to optimize figure size, DPI, and other visual elements to create high-quality, professional plots.

Source: https://bit.ly/3P6gq6H

Conclusion

These 14 machine learning tricks have been pivotal in simplifying my data science journey. I hope they prove just as valuable to you. Remember, the world of data science is a continuous learning adventure, so keep exploring, experimenting, and sharing your insights with the community.

Feel free to connect with me on LinkedIn for more discussions and insights. Let's keep the data science community thriving!