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Feature engineering involves creating new variables from existing data from "summary" of Data Science for Business by Foster Provost,Tom Fawcett
Feature engineering is a critical step in the data preparation process. It involves creating new variables from existing data, which can help improve the performance of machine learning models. By transforming the raw data into new features, data scientists can capture more information and relationships that may not be apparent in the original dataset. This process requires a deep understanding of the domain and the data, as well as creativity and experimentation. There are many techniques that data scientists can use for feature engineering. One common approach is to create interaction terms by multiplying two or more variables together. This can help capture non-linear relationships between the variables and improve the model's predictive power. Another technique is to encode categorical variables using techniques such as one-hot encoding or label encoding. This can help the model better understand the categorical variables and their relationships with the target variable. Feature engineering also involves scaling and normalizing the data to ensure that all variables are on the same scale. This can help prevent certain variables from dominating the model and skewing the results. In addition, data scientists can use dimensionality reduction techniques such as principal component analysis (PCA) to reduce the number of features in the dataset while preserving as much information as possible.- Feature engineering is a crucial step in the data science workflow. It requires a combination of domain knowledge, creativity, and technical skills to extract meaningful insights from the data and build accurate machine learning models. By creating new variables from existing data, data scientists can uncover hidden patterns and relationships that can lead to better decision-making and improved business outcomes.
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