🔍 Data Analytics Challenges: Handling Missing Data
In a recent project, we dove headfirst into the world of NaNs and zeros. From identifying gaps to imputing data, it was a journey of discovery.
Here’s what we learned:
Identified Missing Values: Using Pandas, we flagged those elusive gaps.
Imputed with Care: Depending on context, we filled missing values with the mean, median, or mode.
Visualised the Void: A heatmap revealed the distribution of missingness across features.
The key takeaway? Missing data matters!
Whether you’re a data enthusiast, analyst, or scientist, mastering this challenge is essential.
Let’s discuss your experiences and strategies! 👇
#DataAnalytics #MissingData #DataChallenges #DataNectar