Exploratory Data Analysis (EDA)

🔍 What is EDA?

EDA is the process of summarizing, visualizing, and investigating datasets to:

• Understand structure & patterns
• Detect anomalies or outliers
• Test assumptions
• Guide feature engineering & model selection

🧱 EDA Core Steps

1. Understanding the Dataset

• Know the context & columns
• Data types: numerical, categorical, datetime, text
• Identify target vs features
• Use .head(), .info(), .describe() in Python

2. Handling Missing Values

• Identify missing data (isnull(), sum())
• Strategies:
  • Remove rows/columns
  • Impute (mean, median, mode, KNN, etc.)
  • Use indicators

3. Univariate Analysis

Analysis of individual variables:

• Numerical: histograms, boxplots, KDE plots
  • Key stats: mean, median, std, skewness
• Categorical: bar plots, value counts
  • Mode, frequency

4. Bivariate/Multivariate Analysis

• Numerical vs Numerical: scatter plots, correlation matrix, pairplot
• Categorical vs Numerical: box plots, violin plots, groupby summaries
• Categorical vs Categorical: crosstabs, stacked bar charts

5. Outlier Detection

• Boxplots
• Z-score / IQR method
• Domain knowledge

6. Feature Engineering Ideas

• Transform skewed data (log, sqrt)
• Create new features (ratios, time since)
• Encode categorical variables (One-Hot, Label Encoding)
• Normalize or scale data

📊 EDA Visualization Tools (Python)

📁 Example EDA Checklist (in Python)

import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt

# Load data
df = pd.read_csv('your_data.csv')

# Overview
print(df.info())
print(df.describe())
print(df.isnull().sum())

# Univariate
sns.histplot(df['age'], kde=True)
sns.boxplot(x=df['income'])

# Bivariate
sns.scatterplot(x='age', y='income', data=df)
sns.heatmap(df.corr(), annot=True, cmap='coolwarm')

# Categorical
sns.countplot(x='gender', data=df)
sns.boxplot(x='gender', y='income', data=df)

🎯 Final Tips for Great EDA

• Tell a story: What does the data say?
• Look for relationships: Which features affect the target?
• Document assumptions & hypotheses.
• Use visuals to communicate insights.

📚 Want more?

I can help you with:

• A full Python notebook for EDA
• Practice dataset walkthroughs (e.g., Titanic, Iris, Housing)
• Industry-specific EDA (healthcare, finance, marketing)
• Tools like Pandas-Profiling, Sweetviz, Dtale

Let me know how you'd like to dive deeper — code, examples, or tutorials?

...