Machine Learning Guide

Exploratory data analysis (EDA) sits at the critical pre-modeling stage of the data science pipeline, focusing on uncovering missing values, detecting outliers, and understanding feature distributions through both statistical summaries and visualizations, such as Pandas' info(), describe(), histograms, and box plots. Visualization tools like Matplotlib, along with processes including imputation and feature correlation analysis, allow practitioners to decide how best to prepare, clean, or transform data before it enters a machine learning model. Links Notes and resources at ocdevel.com/mlg/mla-8 Try a walking desk stay healthy & sharp while you learn & code EDA in the Data Science Pipeline Position in Pipeline: EDA is an essential pre-processing step in the business intelligence (BI) or data science pipeline, occurring after data acquisition but before model training. Purpose: The goal of EDA is to understand the data by identifying: Missing values (nulls) Outliers Feature distributions Relation