Techniques and methods for preparing raw data for analysis, including cleaning, normalization, and scaling.
Data Cleaning: This involves the identification and removal or correction of errors, inconsistencies, missing or duplicate data from a dataset.
Data Transformation: This includes applying statistical techniques like normalization, standardization, scaling, and discretization to make the data more suitable for the algorithm.
Feature Selection: This is the process of selecting relevant and significant features in a dataset while removing redundant and irrelevant ones to improve the performance of the algorithm.
Data Augmentation: This is the practice of artificially increasing the size of a dataset by creating new samples using various techniques such as rotation, interpolation, and color inversion.
Data Integration: This involves combining data from different sources into a single dataset, which can then be used for analysis and modeling.
Data Reduction: This includes techniques such as principal component analysis (PCA) and factor analysis to reduce the number of variables in a dataset without losing much information.
Handling Missing Data: This is the practice of dealing with missing values or removing them from a dataset using techniques such as mean/median/mode imputation, hot-deck imputation, and machine learning-based imputation methods.
Outlier Detection: This involves identifying and handling outliers (data points that are significantly different from others in the dataset) using various methods such as z-score, interquartile range (IQR), and clustering methods.
Data Normalization: This is the process of scaling data to be within a particular range so that they can be compared using various statistical methods.
Imbalanced Data: Dealing with datasets having large class type imbalances is known as an imbalanced dataset. This involves techniques such as over-sampling, under-sampling and cost-sensitive learning to make sure the dataset is balanced.
Data Encoding: The process of converting categorical data into numerical data is known as Data Encoding.
Data Resampling: This involves creating a new training dataset with an approximately equal number of occurrences for each class.
Data Combination: Combining different datasets that are useful for solving a particular problem.
Data Preprocessing Visualization: The visual representation of the data to understand which preprocessing techniques can be applied.
Data preprocessing Workflow: The steps and order of preprocessing methods applied to the dataset.
Data Cleaning: This involves fixing or removing incorrect, incomplete, or irrelevant data from a dataset.
Data Transformation: This involves transforming data into a more useful format for analysis. Examples include normalization, discretization, and feature scaling.
Feature Engineering: This involves selecting, creating, or transforming features that are relevant for analysis.
Data Integration: This involves combining data from multiple sources into a single dataset that can be used for analysis.
Data Reduction: This involves reducing the size of a dataset while preserving its essential information. Examples include feature selection and dimensionality reduction.
Data Discretization: This involves converting continuous variables into discrete variables. This is often used for numerical analysis.
Text Data Preprocessing: This involves cleaning and transforming unstructured text data for analysis. Examples include tokenization, stemming, and stop word removal.
Handling Outliers: This involves identifying and handling abnormal or unusual data points that may skew analysis results.
Imputation: This involves filling in missing values in a dataset to create a complete dataset for analysis.
Sampling: This involves selecting a representative subset of a larger dataset for analysis. Examples include random sampling and stratified sampling.
Data Normalization: This involves scaling numerical values in the dataset to have a common scale. This is often done to eliminate the effect of different units or scales.
Data Encoding: This involves converting raw data into a format that can be analyzed by machine learning models. Examples include one-hot encoding, label encoding, and target encoding.
Time Series Data Preprocessing: This involves cleaning and transforming time-series data for analysis. Examples include aggregation, interpolation, and resampling.
Feature Scaling: This involves scaling independent variables to a range that is helpful to the analysis. It can help with convergence, and can reduce the impact of irrelevant or badly scaled features.
Anomaly Detection: This involves identifying patterns, behaviors, or data points that are unexpected or unusual. It can help with fraud detection, intrusion detection, and condition monitoring.