This project is a data preprocessing pipeline that includes cleaning, alignment and feature engineering techniques to improve the performance of machine learning models.
Data cleaning, alignment, and feature engineering are important pre-processing steps that are performed before building a machine learning model.
Data cleaning involves identifying and removing or correcting any errors, inconsistencies, or missing values in the data set. This step is important because errors and inconsistencies in the data can lead to poor model performance or incorrect results. Common techniques for data cleaning include identifying and removing duplicate records, imputing missing values, and handling outliers.
Data alignment involves ensuring that the data is in a format that can be easily processed by the machine learning algorithm. This step is important because different algorithms may require different data formats. Common techniques for data alignment include normalizing or standardizing the data, converting categorical variables to numerical variables, and filling missing values.
Feature engineering is the process of creating new features from the existing data set. This step is important because it can help to improve the performance of the machine learning model by providing it with additional information. Common techniques for feature engineering include creating interaction terms, principal component analysis, and feature selection.
In short, Data cleaning is to remove or correct errors, inconsistencies, or missing values in the data set. Data alignment is to ensure that the data is in a format that can be easily processed by the machine learning algorithm. Feature engineering is the process of creating new features from the existing data set that can help to improve the performance of the machine learning model.