Data cleaning is super important for getting accurate predictions from models. It helps fix problems like missing information, weird data points, and mistakes in datasets. If the data isn’t right, it can really mess up how well the model works, leading to wrong answers. Here are some key reasons why data cleaning matters:

1. Dealing with Missing Data

• Impact: Research shows that around 20% of data in a dataset can be missing. If we don’t fix this, it might hurt how well the model predicts by up to 30%.
• Techniques: Here are some ways to handle missing data:
  • Imputation: This means filling in missing values with the average (mean), middle (median), or most common (mode) value. For example, using the average helps keep things fair.
  • Removing Records: Sometimes, it’s okay to take out data entries with missing information, especially if it’s less than 5%. This keeps the data set strong.

2. Finding and Fixing Outliers

• Impact: Outliers are data points that don’t match the rest. They can really change the results, sometimes affecting model predictions by over 50%.
• Detection Methods: We can find outliers using:
  • Statistical Tests: Like Z-scores and the Interquartile Range (IQR).
  • Visualization: Using charts like box plots and scatter plots to spot outliers easily.

3. Data Normalization

• Importance: Normalization is about making sure that different types of data are treated equally. This is especially important for certain algorithms, like k-NN and SVM, that are sensitive to how big or small values are.
• Techniques: Some common ways to normalize data include:
  • Min-Max Scaling: This changes values to range between 0 and 1.
  • Z-score Normalization: This transforms data into a standard normal spread.

Conclusion

In short, cleaning data helps make models more accurate by ensuring the information is trustworthy and useful. By taking care of missing data, outliers, and normalizing the data, data scientists can greatly improve how well their models predict things. This leads to better decisions and valuable insights.