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Data Cleaning : Avoid These 5 Traps in Your Data

Data cleaning is a crucial step in the data preprocessing pipeline, and avoiding common traps is essential for ensuring the accuracy and reliability of your analyses. Here are five traps to avoid when cleaning your data:


Data Cleaning : Avoid These 5 Traps in Your Data

Mistake 1: Handling Missing Values

Ignoring or mishandling missing values can lead to biased or inaccurate results. Ignoring missing data without understanding its pattern can introduce significant errors in your analysis.

Failure to do so may create bias and result in erroneous, bizarre conclusions. Missing data can be handled in a variety of ways. These three are the most common:


  • Removing any rows or columns with missing data

  • Utilizing proxies such as mean, median, or mode to approximate missing values

  • utilizing random forest or other methods capable of handling these missing values


Mistake 2: Handling Outliers

Neglecting to identify and handle outliers can skew statistical measures and impact the robustness of your models.Therefore, how do you identify outliers and handle them? There are two methods:


  • Visual method: To identify the outlier, make box and scatter plots.

  • Statistical approach: To identify these outliers statistically, use z-score or IQR approaches.

Mistake 3: Handling Data Inconsistency

All of this is OK, but frequently the data isn't even readable to begin with. Data inconsistency is a prevalent problem in data cleaning. Put differently, varying date formats or case usages might provide significant challenges to data analysis. You must address this by ensuring that data formats are consistent by:


  • Standardizing the data to preserve uniformity in scales and format unit

  • Automating the work by implementing data validation checks, aka unit tests software developers use for testing features.


Mistake 4: Handling Data Type Issues

Issues can arise from more than just the format. Data kinds are also capable. Another common mistake in data cleaning is to check them when it's too late. The answer to this is really simple:


  • Inspect, cast, and change the data types you have.

  • Implement checks to verify data types at different project stages to automate the process.

Mistake 5: Handling Duplicate Data

The most frequent error in data is having duplicates. It frequently results from poor merges or joins and completely destroys your record count. To make sure there are no duplicates in the data, many data scientists neglect to perform a post-check or post-inspection. Be not one!


To dedupe your data:

  • Group it so that it’ll deduplicate automatically

  • Add checks at every stage of your analysis to prevent unwanted duplicates


A Solution to Avoiding All Five Mistakes at Once

What's the commonality among the five errors listed above? They mostly entail a manual fix after a manual assessment of the data. Now that the answer is clear, why not automate as much as you can?


As I previously mentioned, unit testing is a concept used by software developers. It guarantees that the feature they are attempting to develop meets the needs of their task. The same ought to be done with data science.



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