Feature Engineering: A Must for Success in Data Science

Hello data science enthusiasts! In the sixth week of our bootcamp, I will talk to you about Feature Engineering. The primary goal of feature engineering is to modify existing features or create new ones to improve the performance of a machine learning model or better represent information in the dataset. Data that has undergone a good feature engineering process makes it easier for the machine learning model to make more effective and accurate predictions when applied. It is one of the most critical steps in a data science or machine learning project.

Feature engineering can help prevent overfitting on the training data. Unnecessary or excessive features may cause the model to learn random noise in the dataset and struggle to generalize to new data. Removing overly complex features can make our model run more efficiently. As a result, feature engineering provides significant advantages such as better representation of the dataset, improved model performance, and prevention of overfitting. Therefore, performing this step diligently is crucial to achieving successful results in a data science or machine learning project.

Outliers

Outliers are values in the data that significantly deviate from the general trend. Dealing with outliers can be approached through visual inspections (such as…