Feauture Engineering. What it is and how it is applied in Data Analytics

Features Engineering. Introduction.

Feature engineering is the process of creating new variables (features) from existing data variables or transforming raw data into a format that improves the performance of the machine learning model. It involves extracting valuable information, selecting relevant features and representing data in a more informative way to improve the accuracy and effectiveness of predictive models.

Key aspects of feature engineering include:

• Creation of New Featuress: Generate new variables by combining, transforming or extracting information from existing features in the dataset. For example, creating new features from date/time data, extracting keywords from text, or converting categorical variables into numerical representations (e.g., one-hot coding).
• Scaling and NormalizationTo scale characteristics to a similar range or normalize them to ensure uniformity, especially in cases where variables have different scales or units.
• Coding Categorical VariablesConvert categorical variables to a numerical format that machine learning models can interpret, such as one-hot coding or label coding.
• Clustering or Discretization: Grouping continuous variables into bins or categories to simplify complex data and capture nonlinear relationships.
• Feature SelectionIdentify and select the most relevant features using techniques such as correlation analysis, statistical tests or using algorithms that automatically rank or eliminate less important features.
• Outlier handling: Transform or treat outliers in a way that mitigates their impact on model performance.

Effective feature engineering is essential as it directly influences the performance and efficiency of machine learning models. Well-designed features can help models better capture patterns and relationships within the data, leading to more accurate predictions or classifications.

Therefore, Python is a fundamental tool to become a data analyst, but it is only a part of a whole. It is a necessary but not sufficient condition. At Ubiqum, with our project-based methodology (learning by doing), students practice the entire analysis process described above in a comprehensive manner and learn both Python and R. At Ubiqum, a student completes several complete projects during the course, starting from a simple project and concluding with a highly complex one.