Review your data and spot any missing values. These can be blank cells or NaN values. Make a note of where these missing values are located as you will need to address them in the next steps.
Deal with Missing Data
Select a strategy to handle missing data. You can remove rows or columns with missing values, or you can fill in gaps with mean, median, or mode. Be mindful of how this does or doesn't alter the rest of your dataset.
Normalize Your Data
Prevent bias in machine learning models by normalizing. This process ensures all your data is on the same scale. Techniques include Min-Max scaling or standardization using the Z-score.
Interactive Data Visualizations
Enhance comprehension with our personalized data visualizations. Dig deeper into the data and extract value by visualizing complex patterns in a simple, intuitive manner.
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