Exploratory Data Analysis (EDA) Overview

Exploratory Data Analysis (EDA) is a crucial step in the Data Science workflow. It helps in understanding the structure, patterns, and relationships within a dataset before applying machine learning models.

1. What is EDA?

EDA involves summarizing, visualizing, and interpreting datasets to uncover insights. The key goals are:

 

  •  Identifying missing values and outliers
  • Understanding data distributions
  • Detecting patterns, trends, and correlations
  • Preparing data for further analysis

2. Steps in EDA

Step Description
Load the Data Import data from CSV, Excel, SQL, etc.
Understand the Structure Check data types, dimensions, and basic info
Handle Missing Data Identify and deal with null values
Detect Outliers Use box plots, IQR, or Z-score
Visualize Distributions Histograms, density plots, box plots
Find Relationships Correlation matrix, scatter plots
Feature Engineering Transform or create new features

3. Performing EDA with Python

3.1. Load and Inspect the Data

import pandas as pd

# Load dataset
df = pd.read_csv("data.csv")

# Display first few rows
print(df.head())

# Get dataset shape
print(f"Rows: {df.shape[0]}, Columns: {df.shape[1]}")

# Summary of dataset
print(df.info())

# Check for missing values
print(df.isnull().sum())

3.2. Handling Missing Data

# Drop missing values
df_cleaned = df.dropna()

# Fill missing values with mean
df_filled = df.fillna(df.mean())

3.3. Detecting Outliers

import seaborn as sns
import matplotlib.pyplot as plt

# Box plot to detect outliers
plt.figure(figsize=(8, 5))
sns.boxplot(data=df)
plt.show()

3.4. Visualizing Data Distributions

# Histogram to check distribution
df.hist(figsize=(10, 8), bins=30)
plt.show()

3.5. Checking Correlations

import numpy as np

# Correlation matrix
corr_matrix = df.corr()

# Heatmap
sns.heatmap(corr_matrix, annot=True, cmap="coolwarm")
plt.show()

Summary

  • EDA helps in understanding data before applying models.
  • Use descriptive statistics to summarize data.
  • Visualizations (histograms, box plots, scatter plots, heatmaps) provide insights.
  • Handling missing values and outliers improves data quality.
  • Correlation analysis identifies relationships between variables.