Feature Engineering is a crucial step in Data Collection to enhance the accuracy of Machine Learning models. Today, we invite a Data Scientist from SCB TechX, Khun. Jan, Kuliga Kitsachoke to share insights on Feature Engineering techniques. If you’re interested in applying them, click to read our 1-minute tips now! Here are the 5 essential Feature Engineering tips:
- Imputation: Replace missing data (Null) with Mode, Mean, Median, or 0.
- Log Transform: Reduce data skewness by taking the Log of the variable, commonly used for numerical data with a wide range like sales figures.
- One-Hot Encoding: Transform categorical data into column-wise representation, assigning 1 to corresponding data and 0 to others.
4. Handling Outliers: Trim data points higher or lower than the majority, considering Quartiles or Percentiles.
5. Binning: Group numerical data into bins, such as Income categorized into High, Medium, or Low.
Implementing these techniques can help eliminate data errors and enhance model accuracy, suitable for various applications and businesses.
Lastly, SCB TechX presents a comprehensive array of Data Management services via TechX Data & AI Solutions, meticulously designed by industry experts.
If you are interested, please feel free to contact us at ▶️ contact@scbtechx.io
Or visit us for more details at ▶️ https://bit.ly/3QjtHgl
