Exploratory Data Analysis (EDA) – Step by Step Guide 📊

Before building any machine learning model, the first step is understanding your data — and that’s where EDA comes in. EDA helps uncover hidden patterns, detect anomalies, and test assumptions to make your dataset ML-ready. 🚀

Step-by-Step EDA Workflow:

  Data Collection & Loading → Gather datasets (CSV, SQL, APIs) and load into Python/R.
  Data Cleaning → Handle missing values, duplicates, inconsistent formats.
  Univariate Analysis → Study each variable (distribution, central tendency, spread).
  Bivariate/Multivariate Analysis → Correlations, scatterplots, heatmaps to study relationships.
  Outlier Detection → Identify anomalies with boxplots, Z-scores, or IQR methods.
  Feature Engineering → Create new features, transform categorical data ,scaling/normalization.
  Data Visualization → Use matplotlib, seaborn, or plotly for interactive insights.
  Insights & Hypotheses → Summarize key findings to guide modeling.

 Tools for EDA:

Python → Pandas, NumPy, Seaborn, Matplotlib, Plotly

R → ggplot2, dplyr, tidyverse

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