Feature Engineering

Feature Engineering is a process to develop and select a feature or attribute (features) that will be used to perform data analysis or in making a machine learning model. Feature Engineering is one of the most important stages for conducting a data analysis and machine learning project, this is because the quality of the features produced can be used to produce great benefits on the performance of the model and the results of the resulting data analysis.

Example of Feature Engineering in Pandas Library. Source: kdnuggets

Feature engineering in the Pandas Library using Python Programming Language is an important part of preparing the data in the dataset before data analysis or modeling. By understanding the data, a Data Scientist and Data Analyst can take various types of appropriate steps, this can certainly improve the quality of features and improve the performance of the resulting model.

1. Perform the extraction process of an information: A Data Scientist and Data Analyst usually use feature engineering to extract relevant information from raw data on existing datasets or features. This can include several processes such as merging, calculating, and/or changing various types of features as desired and to better suit the desired goals and problems to be solved.
2. Selecting the Features used: Feature engineering can perform a feature selection process that involves selecting a subset of the most important and relevant feature types from the existing feature set. Of course, this helps a Data Scientist and Data Analyst to reduce data dimensions and avoid overfitting.
3. Transform and change features: Feature engineering can perform feature transformations that can involve and change the distribution or characteristics of certain features, so that the resulting features are better suited to the benefits and objectives created by the model to be used. For example, if a Data Scientist and Data Analyst want to normalize or log the features that are transformed into data.
4. Combination and Combining Features: A Data Scientist and Data Analyst usually in dealing with several cases can certainly use feature engineering to combine and combine available features, this is a combination of several features that can produce a new feature that is more informative than the original features created. One of them, if a Data Scientist and Data Analyst are given a dataset in the form of a height and weight data set, then the Data Scientist and Data Analyst can create a body mass index (BMI) feature to combine the two using Feature Engineering.
5. Incomplete Data Handling: In some datasets, there is missing or incomplete data. Feature engineering also includes strategies to fill in or address this missing data.
6. Identifying categorical data: When a Data Scientist and Data Analyst name categorical data, such as gender or product category. Using feature engineering can include a categorical data conversion, this can be used as a format that can be used by a model, for example such as one-hot encoding. One-hot encoding is a technique used to represent categorical variables as numerical arrays. This technique is useful for preparing categorical data so that it can be fed into machine learning algorithms that require numerical input. This technique can also avoid the problem of ordinality, which is when a categorical variable has a natural order (e.g. "small", "medium", "large").
7. Extraction of Date Data : A Data Scientist and Data Analyst can use Feature Engineering to extract single data. For example, date data often requires and requires extraction of additional information such as data on the day of the week or season of the year.
8. String Data Extraction: Feature Engineering in extracting String type data, such as product reviews or tweets. A Data Scientist and Data Analyst can use feature engineering by involving the use of various string or text processing techniques such as tokenization, removing stop words, and vectorizing words.
9. Understanding of Domain Knowledge: In its use, Feature Engineering can understand domain knowledge or Domain Knowledge that is specific can help in utilizing its features. By understanding the context of the data that can help to produce various types of features that are more informative.
10. Conducting Experiments: A data scientist and data analyst can use feature engineering which often involves an iteration and experimentation. This allows a data scientist and data analyst to try different types of transformations or various techniques and measure their impact on the results of the analysis or the resulting model.

Feature Engineering is one of the features in data analysis and machine learning. By using the right feature engineering, a Data Scientist and Data Analyst can create and produce various types of features that can improve the performance of the resulting model. So that model making is easier to interpret, and provides better understanding insights from the data analyzed.