Welcome back to our Data Wrangling with Python series! In the first blog of the data wrangling series, we introduced the basics of data wrangling using Python. We work on handling missing values, removing special characters, and dropping unnecessary columns to prepare our dataset for further analysis. Now, the next step is to deeply explore […]
Imagine you’re a data scientist or data analyst working for an airline. The marketing team noticed that there is a lot of feedback posted on X. The airline’s reputation is at stake as customer satisfaction is very important. They consult you to analyze the sentiment of posts to understand what’s going wrong and how to […]
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