EDA Process

🔍 EDA Process: Becoming a Data Detective

The Story of Detective Data

Imagine you’re a detective who just received a mysterious box full of clues (your data). Before solving the case, you need to explore what’s inside. That’s exactly what EDA (Exploratory Data Analysis) is!

EDA is like being a curious kid who opens a new toy box and asks:

• “What’s inside?”
• “How many pieces are there?”
• “Do any pieces go together?”

🎯 What is Exploratory Data Analysis?

EDA = Looking at your data carefully before doing anything fancy.

Think of it like this:

Before cooking a meal, you check what ingredients you have, how fresh they are, and what goes well together. EDA is “checking your ingredients” before cooking with data!

The 3 Big Questions EDA Answers:

1. What does ONE thing look like? → Univariate Analysis
2. How do TWO things relate? → Bivariate Analysis
3. How do MANY things connect? → Multivariate Analysis

🎪 Part 1: Univariate Analysis

Looking at ONE thing at a time

“Uni” means ONE. “Variate” means variable (a column of data).

Real-Life Example:

You have a bag of 100 candies. Univariate analysis asks:

• How many candies total? (count)
• What’s the most common color? (mode)
• What’s the average weight? (mean)
• Are most candies small or big? (distribution)

Python Example:

import pandas as pd
import matplotlib.pyplot as plt

# Your candy data
candies = [5, 7, 3, 8, 6, 7, 7, 4, 9, 7]

# Count, Average, Most Common
print(f"Total: {len(candies)}")
print(f"Average weight: {sum(candies)/len(candies)}")

# See the shape - histogram
plt.hist(candies, bins=5)
plt.title("Candy Weights")
plt.show()

Common Univariate Tools:

Visual Flow:

graph TD
    A["One Column of Data"] --> B{What type?}
    B -->|Numbers| C["Histogram/Box Plot"]
    B -->|Categories| D["Bar Chart/Pie Chart"]
    C --> E["Find Mean, Median, Std"]
    D --> F["Count Each Category"]

🎭 Part 2: Bivariate Analysis

How do TWO things dance together?

“Bi” means TWO. Now we’re asking: “Do these two things have a relationship?”

Real-Life Example:

• Does studying MORE hours lead to BETTER grades?
• Does eating MORE ice cream happen when it’s HOT outside?
• Do TALLER people weigh MORE?

The 3 Types of Relationships:

1. Number vs Number → Scatter plot
2. Number vs Category → Box plot by group
3. Category vs Category → Grouped bar chart

Python Example:

import seaborn as sns

# Hours studied vs Test score
hours = [1, 2, 3, 4, 5, 6, 7, 8]
scores = [50, 55, 60, 65, 70, 80, 85, 95]

# Scatter plot shows the relationship
sns.scatterplot(x=hours, y=scores)
plt.xlabel("Hours Studied")
plt.ylabel("Test Score")
plt.title("Study Time vs Grades")
plt.show()

# Calculate correlation
correlation = pd.Series(hours).corr(pd.Series(scores))
print(f"Correlation: {correlation:.2f}")

What Correlation Tells You:

Visual Flow:

graph TD
    A["Pick Two Columns"] --> B{Both Numbers?}
    B -->|Yes| C["Scatter Plot"]
    B -->|No| D{Number + Category?}
    D -->|Yes| E["Box Plot by Group"]
    D -->|No| F["Grouped Bar Chart"]
    C --> G["Calculate Correlation"]

🌈 Part 3: Multivariate Analysis

The Big Picture with MANY variables

“Multi” means MANY. Now we look at 3+ things together!

Real-Life Example:

Your health depends on:

• How much you sleep
• What you eat
• How much you exercise
• Your stress level

All these work TOGETHER! Multivariate analysis helps us see the full picture.

Common Techniques:

Python Example:

import seaborn as sns

# Load example data
tips = sns.load_dataset('tips')

# Pair plot - see EVERYTHING at once!
sns.pairplot(tips, hue='sex')
plt.show()

# Heatmap - colors show relationships
correlation_matrix = tips.corr()
sns.heatmap(correlation_matrix, annot=True)
plt.title("How Everything Connects")
plt.show()

Reading a Heatmap:

🔴 Dark Red = Strong Positive (+0.7 to +1.0)
🟠 Orange = Moderate Positive (+0.3 to +0.7)
⚪ White = No Relationship (-0.3 to +0.3)
🔵 Blue = Negative Relationship (-0.7 to -0.3)
🔵 Dark Blue = Strong Negative (-1.0 to -0.7)

Visual Flow:

graph TD
    A["Multiple Columns"] --> B["Pair Plot"]
    A --> C["Correlation Heatmap"]
    A --> D["Facet Grid"]
    B --> E["Spot Patterns Visually"]
    C --> F["Find Strong Correlations"]
    D --> G["Compare Across Groups"]
    E --> H["Deeper Investigation"]
    F --> H
    G --> H

🗺️ The Complete EDA Journey

graph TD
    A["📦 Raw Data"] --> B["🔢 Univariate"]
    B --> C["Look at each column alone"]
    C --> D["📊 Bivariate"]
    D --> E["Compare pairs of columns"]
    E --> F["🌈 Multivariate"]
    F --> G["See full picture"]
    G --> H["🎯 Ready for Analysis!"]

🎯 Quick Summary

💡 Pro Tips for Young Data Detectives

1. Always start with Univariate - Know each piece before combining
2. Look for outliers - They’re like weird puzzle pieces
3. Use colors wisely - They help spot patterns
4. Ask “Why?” - Numbers tell stories, find them!

🎉 You Did It!

Now you know how to explore data like a pro detective:

• Univariate = Look at ONE thing 🔍
• Bivariate = Compare TWO things 👀
• Multivariate = See the BIG picture 🌈

Remember: EDA is like getting to know a new friend. You learn about them one story at a time, then see how all the stories connect!

Happy Exploring! 🚀