What are the key steps in data preprocessing?

Data preprocessing is a crucial step in data science that involves transforming raw data into a clean and structured format before analysis. Proper preprocessing enhances the accuracy and efficiency of machine learning models. The key steps in data preprocessing are as follows:

Data Collection
Raw data is gathered from various sources, including databases, APIs, sensors, and files. It may contain inconsistencies, missing values, and noise that need to be addressed.

Data Cleaning
This step involves handling missing values, removing duplicates, correcting inconsistencies, and dealing with outliers. Techniques such as mean imputation, median replacement, and interpolation are used to fill missing values.

Data Transformation
Data transformation includes normalizing, scaling, and encoding categorical variables to make them suitable for machine learning algorithms. Techniques like Min-Max scaling, Standardization, and One-Hot Encoding are commonly used.

Feature Engineering
This step involves selecting, modifying, or creating new features that enhance model performance. Feature extraction and selection techniques help improve model efficiency by reducing unnecessary data.

Data Reduction
Large datasets may contain redundant or irrelevant features. Techniques like Principal Component Analysis (PCA) and feature selection methods help in dimensionality reduction while retaining essential information.

Data Splitting
The preprocessed data is divided into training, validation, and test sets to evaluate model performance effectively. A common split ratio is 70:20:10 or 80:20, depending on the dataset size.

Handling Imbalanced Data
If the dataset is imbalanced, techniques such as SMOTE (Synthetic Minority Over-sampling Technique) or undersampling can help balance class distributions.

A well-structured data preprocessing pipeline significantly improves model accuracy and performance. To gain expertise in these techniques, enrolling in a data science and machine learning course can be highly beneficial.

Visit on:- https://www.theiotacademy.co/advanced-certification-in-data-science-machine-learning-and-iot-by-eict-iitg