Data Preprocessing in Machine Learning: A Complete Guide
Data preprocessing is a crucial step in machine learning that ensures your data is clean, structured, and ready for model training. Without proper preprocessing, your model might struggle with accuracy, efficiency, and generalization. From handling missing values to feature scaling, preprocessing enhances data quality and improves model performance. If you want to master data preprocessing and advance your AI skills, consider enrolling in the Data Scientist Course in Thane to gain hands-on expertise. 1. Understanding Data Preprocessing Why is Data Preprocessing Important? Improves data quality and consistency Reduces noise and irrelevant features Enhances model accuracy and efficiency Helps in better generalization to unseen data Steps in Data Preprocessing Data collection and integration Data cleaning and handling missing values Data transformation and feature scaling Data reduction and dimensionality reduction 2. Handling Missing Data Techniques to Deal with Missing Values Removal M...