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EDA Fundamentals

  • Learn the basics of Exploratory Data Analysis to understand and explore datasets.

  • What is EDA?

    EDA (Exploratory Data Analysis) is the process of:

    • Examining datasets

    • Summarizing key characteristics

    • Finding patterns

    • Detecting outliers

    • Identifying relationships

    Simple meaning:
     "Data ko samajhna before making decisions."



    EDA Process (Step-by-Step)

    Step 1: Understand the Problem

    • What is the business goal?

    • What questions need to be answered?

    Example:

    • Why are sales decreasing?

    • Which students are underperforming?

    Step 2: Collect & Load Data

import pandas as pd

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

  • Step 3: View Basic Information

df.head()      # First 5 rows
df.tail()      # Last 5 rows
df.info()      # Data types
df.describe()  # Statistical summary
  • Step 4: Check Missing Values

df.isnull().sum()

  • Step 5: Data Cleaning

    • Handle missing values

    • Remove duplicates

    • Fix data types

    • Handle outliers

    Step 6: Data Visualization

    Use charts to understand patterns:

    • Histogram → Distribution

    • Boxplot → Outliers

    • Scatterplot → Relationships

    • Heatmap → Correlation

    Example:

import seaborn as sns
sns.heatmap(df.corr(), annot=True)

  • Step 7: Feature Relationships

    • Correlation analysis

    • Grouping data

    • Comparing categories

    Understanding Data

    Understanding data means analyzing:


    1. Data Types

    • Numerical (Age, Salary)

    • Categorical (Gender, City)

    • Date/Time


    2. Distribution

    • Is data normal?

    • Is it skewed?

    • Any outliers?


    3. Patterns & Trends

    • Increasing sales trend?

    • Seasonal behavior?


    4. Correlation

    • Does salary increase with experience?

    • Are marks related to study hours?


    5. Outliers

    Extreme values that may affect analysis.

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