Feature Engineering: A Complete Guide to Transforming Raw Data

Introduction

The act of choosing, modifying, and converting raw data into features using domain knowledge that may be applied to features is known as feature engineering. To enable machine learning to do new tasks, new features must be designed and trained. Any measurable input that may be applied to predict a value is called a "feature object. Here feature can be numerical, categorical or text based, Feature engineering is the process of applying statistical or machine learning techniques to transform raw data? into desired features.

Feature Engineering involves various Process:

1.Feature Creation : Creating Some useful features using domain knowledge? or? observing the patterns or merging the current features to create new features ,which improves the model performance of a machine learning model.

2.Feature Transformation : The process of changing the features into a representation that the machine learning model can use better is called feature transformation.? In order to convert the values of a given column (feature) and prevent computational errors by ensuring that all features fall within the model's permitted range or scale using statistical method.

3.Feature Extraction:? Feature Extraction is a extracting a new features using existing features without changing the important information or the original relationships. Feature extraction will reduce the data? dimensionality? without losing a? relevant information using dimensionality reduction like PCA.

4.Feature Selection : Feature selection is the process of? selecting or identifying a features that are relevant to improve the model’s performance and interpretability? using statistical techniques which helps in finding the relation between features and target vector is correlation, univariate feature analysis.

The steps involved in Feature Engineering :

1.Imputation

One of the most frequent issues encountered while preparing your data for machine learning is missing values. One technique for handling missing values is imputation.

Two categories of imputations exist:

• Numerical imputation: When specific data points are unavailable, numerical imputation is used to fill the missing values with statistical measures like mean, median
• Categorical Imputation:. When specific data points are unavailable, Categorical imputation is used to fill the missing values with highest value or mode

2.Handling Outliers

Outlier (extreme high value or low value) treatment is the process of removing outliers from the data set. More accurate data representations can be obtained by applying this strategy at different levels. This stage needs to be completed before starting the model training process. One might utilize the Z-score and standard deviation to find outliers.

Outliers can be dealt with in a few different ways:

• Removal: Items containing outliers are eliminated in order to tidy up the distribution.
• Replacing Values: Using the proper imputation, outliers can be replaced with similar missing data.
• Capping : It is the process of substituting a random value or one from a flexible range for the highest and lowest values.?

3. One-Hot Encoding

One method for converting categorical data into numerical values that machine learning models can use is called one-hot encoding. Using this method, every category is converted into a binary value that indicates whether it exists or not. Take the categorical variable "Colour" for instance, which has three categories: Green, White and Black. This variable would be converted into three binary variables Color_Green , Colour_white, and Colour_Black—by one-hot encoding. The values of each variable would be 1 in the case that the matching category is present and 0 in the absence of it.

4.Scaling

To improve model performance, standardize or normalize numerical features to make sure they are on a similar scale.

There are two common approaches to scaling:

• Normalization:It involves scaling all values inside a specific range between 0 and 1.
• Z-scores : this is another name for standardization. It is the act of measuring something while keeping the standard deviation in mind.

5. Transformers

The most popular method among data scientists is log transform. Its main application is to change a skewed distribution from skewed to less skewed or normal. In this transform, the values in a column that we take the log of are used as the column. Data that is difficult to understand is handled by it, and it becomes closer to what is needed for typical applications.

6.Binning

One way to transform continuously fluctuating variables into categorical variables is through binning. During this process, the range of values of the continuous variable is separated into many bins, each of which is assigned a category value.

Suppose that we wish to group or bin the ages of the individuals in a dataset that contains their ages. Grouping Ages We wish to make age divisions: 0–20 years , 21–30 years,31–40 years,41–50 years,51–60 and 61–70 years old

7. Text Data Processing

When working with text data, carry out operations such stop word removal, stemming, and tokenization.

Conclusion

A critical stage in the data preprocessing pipeline is feature engineering, which turns unprocessed data into useful features that enhance machine learning model performance. We can completely utilize our data by carefully choosing, developing, and polishing features, which will result in more reliable and accurate models. Every technique is essential to getting the data ready for analysis, from handling missing values and encoding categorical categories to scaling and normalizing the data.