Why Combining EDA and Machine Learning Is Important

Exploratory Data Analysis (EDA) is an important step in the machine learning process. However, it has some challenges that can make it hard to get good results. Here are a few common problems and how to fix them:

1. Issues with Data Quality:

   • Sometimes, datasets are not complete, are messy, or have extra information that isn't helpful.
   • EDA helps find these problems, but it often takes a lot of manual work to clean things up.
   • Solution: Use automatic data cleaning tools or set up a methodical process to make sure the dataset is clean before modeling.

2. Understanding Data Patterns:

   • If we don't understand how the data is spread out, it can lead to models that don't work well.
   • Graphs may give us the wrong idea if we don’t use the right tools.
   • Solution: Try different ways to visualize the data, like using histograms or box plots. These can help us see the patterns in the data more clearly.

3. Choosing the Right Features:

   • Figuring out which features are important can be tough.
   • Picking the wrong features can lead to models that either try too hard to fit the data or don't fit enough.
   • Solution: Use EDA techniques to see which features matter most, and think about using automated tools to select the best features for better accuracy.

By tackling these challenges, combining EDA and machine learning can help us make stronger and more trustworthy decisions based on data.