Data cleaning and Preprocessing are essential phases in the data science pipeline that guarantee the transformation of unstructured data into usable form. These procedures entail locating and fixing mistakes, missing values, and inconsistencies in the data for accurate analysis and modeling.
Key Steps in Data Cleaning and Preprocessing:
Handling Missing Data:
Identifying Missing Values: Examine the dataset for any null or missing values. These might be the result of incorrect data storage or insufficient data collection.
Imputation: Imputation is substituting estimations for missing data, such as the mean, median, or mode, or filling them in with values that are close by (forward-fill, backward-fill, etc.).
Source: Data Cleaning and Preprocessing
Removing Missing Values: Entire rows or columns with missing values may be eliminated if the missing values are frequent or sporadic.
Dealing with Outliers:
Detection: To find anomalies, use statistical techniques (such as the Z-score and IQR) or visual aids (like box plots).
Handling: Outliers can be eliminated, limited (using maximum thresholds), or handled using strong statistical techniques, depending on the situation.
Standardization and Normalization:
Standardization: Rescaling the data to have a zero mean and one standard deviation is known as standardization. This is helpful for support vector machines and logistic regression models, among others.
Source: Data Processing Steps
Normalization: Normalization is the process of rescaling data to a certain range, often [0, 1]. This is crucial for gradient-based techniques like neural networks and distance-based algorithms like K-Nearest Neighbors (KNN).
Encoding Categorical Variables:
One-Hot Encoding: Enable algorithms to handle non-numeric input by converting category variables into binary (0/1) values.
Label Encoding: Assigning numerical values to categories (e.g., mapping “low,” “medium,” and “high” to 0, 1, 2) is known as label encoding, although it assumes that the categories have an ordinal relationship.
Removing Duplicates:
Find and eliminate duplicate entries to avoid skewing analysis and exaggerating the significance of particular data items.
Feature Scaling:
Rescaling data to guarantee that the ranges of various attributes are similar. This is important for methods like PCA and K-means clustering that depend on the size of features.
Dealing with Imbalanced Data:
Oversampling/Undersampling: Adjust the dataset so that there are an equal number of minority class examples (oversampling) and majority class examples (undersampling) to balance the classes.
Source: Art of Data Cleaning and Preparation
SMOTE (Synthetic Minority Over-sampling Technique): The Synthetic Minority Over-sampling Technique, or SMOTE for short, is a technique that balances a dataset by creating artificial cases for the minority class.
Binning:
To reduce noise and enhance the clarity of patterns, group continuous variables into intervals or bins. Putting age into age groups, for instance.
Text Preprocessing:
Tokenization, stemming, lemmatization, and stop word removal are typical preprocessing procedures for textual data that help clean and prepare text for analysis or machine learning.