Exploratory Data Analysis (EDA)
📘 Data Science
👁 42 views
📅 Nov 14, 2025
⏱ Estimated reading time: 1 min
Introduction
Exploratory Data Analysis (EDA) is the process of examining datasets to summarize their main characteristics, identify patterns, spot anomalies, and form hypotheses.
1. Checking Dataset Structure
df.shape
df.info()
df.describe()
2. Understanding Numerical Features
df["age"].mean()
df["salary"].median()
df["age"].hist()
3. Understanding Categorical Features
df["gender"].value_counts()
df["department"].unique()
4. Detecting Outliers
import seaborn as sns
sns.boxplot(df["salary"])
5. Correlation Analysis
df.corr()
sns.heatmap(df.corr(), annot=True)
6. Pair Plot
sns.pairplot(df)
7. Handling Skewness
df["income"].skew()
Conclusion
EDA gives you a strong understanding of your dataset and guides your feature engineering and model selection decisions.
🔒 Some advanced sections are available for Registered Members
Register Now
Register Now
Share this Post
← Back to Tutorials