Correlation Analysis

Summary

Measuring how strongly two variables move together — used both to understand a dataset and to catch multicollinearity (highly correlated features) that can destabilize some models.

How to Learn This

1Build a correlation heatmap for a sample dataset's numeric features.
2Learn why highly correlated features can cause issues specifically for linear models.
3Remember: correlation analysis reveals association, never causation on its own.

Handling Imbalanced Data

Supervised vs Unsupervised vs Reinforcement Learning

More topics in Exploratory Data Analysis & Feature Engineering

EDA Process & Techniques Handling Missing Data Outlier Detection & Treatment Feature Scaling (Normalization / Standardization)Encoding Categorical Variables Feature Creation & Selection Dimensionality Reduction (PCA Overview)Handling Imbalanced Data

Stuck on this topic? Ask an Insider

Get 1:1 guidance from people who've walked this exact path — free on the InsideEdge app.

Download