The importance of preprocessing your data     
Lately, we've been working on a data analysis project, and we wanted to highlight why it's so important to prepare your data properly. Even if we already know it's crucial, let's take a moment to discuss the problems that can arise when we skip data preparation.

  1. Biased Analysis
  2. When you have missing values, outliers, or duplicated data, it can mess up your conclusions. For example, if you don't handle missing values correctly, your analysis might end up with biased results because incomplete data can throw off calculations and misrepresent trends. Similarly, ignoring outliers can mess up your statistical measurements, impacting the overall analysis. Plus, duplicated data can make results look bigger than they actually are and give you a false idea of patterns or trends, leading to wrong interpretations.
  3. Wrong Predictions
  4. Inconsistent data formats, like different ways of writing addresses or inconsistent capitalization, can introduce errors and mess with accurate predictions. Also, when your features are measured on different scales (like time in hours/minutes), not using data preparation techniques can lead to unreliable predictions and misleading comparisons. If you don't address these issues, your predictions might not be reliable or valid.
  5. Inefficient Analysis
  6. When your features have different scales, it can be hard to make accurate calculations and comparisons, making the analysis process slow and inefficient. Also, duplicated data can make the analysis take longer and use up unnecessary computing power, making the whole process less efficient.
All these problems can be easily avoided with some simple but thorough data preparation. In the next few weeks, we'll share some suggestions to help you with this.
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