Understanding Date and Time Variables in Data Science

Date and time variables are fundamental in many datasets, encompassing dates, times, or a combination of both. Like the examples we included in this reel.

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Now, correct preprocessing of these variables can significantly enrich your datasets, allowing you to extract valuable insights. For instance, by analyzing the Date of Application and Payment Date, you can visualize trends such as the number of loans dispersed over time across different risk markets.

When working with date and time variables, you can derive a plethora of features:

▶️ Day of the Week: Understand patterns by weekdays.

▶️ Month/Year: Observe monthly or yearly trends.

▶️ Time of Day: Analyze activities at different times.

By visualizing these variables, you can gain insights into borrower behaviors and risk categories in peer-to-peer lending, among other applications.

🎓 Want to learn more about extracting valuable features from date and time variables? Check out our course Feature Engineering for Machine Learning at the link in our bio for more details!

📊 How do you handle date and time variables in your projects? Share your tips and experiences in the comments below! 👇

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