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 boxplots), statistical methods like calculating the Z-score, using the IQR method or handling outliers by suppression or removal from the dataset. The fewer outliers there are, the more balanced the dataset becomes. For instance, if a dataset has hundreds of outliers, suppressing them might alter the course of the dataset, potentially causing duplicate records and leading to serious problems in the future. If working with tree-based methods, it is often advisable not to touch the outliers. If there are only a few outliers, they can be removed, but this decision is entirely subjective.
Missing Values
Missing values represent values that are absent or undefined in a dataset. These values indicate the absence or undefined information in one or more observations in the dataset. Dealing with missing values is important because many machine learning models may struggle to handle missing data and this can adversely affect the model’s performance.
To handle missing values, various approaches can be used, including:
- Deletion: Simply removing observations with missing values.
- Imputation: Filling in missing values using methods such as mean, median or mode.
- Prediction-based Imputation: Using predictive models, such as KNN, to estimate and fill in missing values.
It’s crucial to choose an appropriate method based on the nature of the data and the problem at hand.
Encoding Scaling
Encoding is the process of converting categorical data into a numerical format, enabling machine learning models to work with categorical variables.
1.Label Encoding: It transforms categorical values into ordered numerical values. Each category is assigned a unique number. This method is often used with ordinal categorical variables, where the order is significant.
2. One-Hot Encoding: It transforms categorical variables into a binary (0 and 1) matrix by assigning a separate column for each category. Each column represents a category, and observations belonging to that category are marked with 1, while others are marked with 0.
3. Rare Encoding: Rare encoding is used to simplify data by combining or replacing infrequently occurring categorical values with a general category. This can help the model learn rare classes more effectively.
These encoding techniques play a crucial role in preparing data for machine learning models, allowing them to effectively handle categorical information.
Feature Extraction
Feature extraction is the process of analyzing features in a dataset and deriving new and more meaningful features from these existing features. This is typically done to reduce the number of features, enhance model performance, and eliminate unnecessary information.
In feature extraction, the goal is to transform the original features into a more compact and representative set that captures the essential information. This can involve techniques such as dimensionality reduction, where the number of features is reduced, or creating new features based on the existing ones to provide a more informative representation of the data.
The main objectives of feature extraction include improving the efficiency of machine learning models, enhancing interpretability, and reducing the risk of overfitting by focusing on the most relevant information within the dataset.
I Explained everything more detailed with codes and their explanations on my kaggle notebook;
To Be Continued…
