Best Practices for Cleaning Data Before Training Your Model

1. Handle Missing Values:

   • Studies show that more than 20% of datasets have missing values. To fix this, you can either fill in the gaps (using methods like mean, median, or mode) or remove entries with missing information. Filling in the gaps, or imputation, helps keep about 90% of your data useful.

2. Remove Duplicates:

   • Duplicate entries can mess up your results. Finding and removing these duplicates can make your model's accuracy better by 10% to 50%.

3. Correct Outliers:

   • Outliers are data points that are way different from others and can make up about 5% to 10% of the data. They can lead to confusing results. You can find and fix outliers using methods like Z-scores or interquartile ranges.

4. Normalize Data:

   • Normalizing your data is important when different pieces of data are on different scales. A common way to do this is by changing all data to a range from 0 to 1, or by making the average 0 and the variation 1.

5. Categorical Encoding:

   • Some data can be in categories instead of numbers. You need to turn these categories into numbers using methods like One-Hot Encoding or Label Encoding. This is important because machine learning models usually need numbers to work properly.

By following these steps, you can really improve the quality and performance of your machine learning models!