eda-report

Overview

Based on "Practical Statistics for Data Scientists" by Peter Bruce, Andrew Bruce, and Peter Gedeck. The core principle: before any modeling or analysis, you must understand the distribution, shape, and quality of your data. Skipping EDA leads to models trained on dirty data, misunderstood distributions, and insights that collapse under scrutiny. Structured EDA forces you to ask the right questions before committing to an approach.

Workflow

Step 1: Load and audit the dataset

Start with the basics - shape, types, and completeness.

import pandas as pd

df = pd.read_csv("data.csv")