📊 Day 16: Correlation Matrix Heatmap in Python

 

📊 Day 16: Correlation Matrix Heatmap in Python

🔹 What is a Correlation Matrix Heatmap?

A Correlation Matrix Heatmap visualizes the correlation coefficients between multiple numerical variables using colors.
It shows how strongly variables are related to each other.

---

🔹 When Should You Use It?

Use a correlation heatmap when:

• Exploring relationships between features

• Performing feature selection

• Detecting multicollinearity

• Understanding dataset structure before modeling

---

🔹 Example Scenario

Suppose you are working with:

• Housing price data

• Customer analytics data

• Financial datasets

A correlation matrix heatmap helps you quickly identify:

• Strong positive correlations

• Strong negative correlations

• Weak or no relationships

---

🔹 Key Idea Behind It

👉 Values range from -1 to +1
👉 +1 = strong positive correlation
👉 -1 = strong negative correlation
👉 0 = no correlation

---

🔹 Python Code (Correlation Matrix Heatmap)

import seaborn as sns
import matplotlib.pyplot as plt 
import pandas as pd
import numpy as np

data = np.random.rand(100, 4)
df = pd.DataFrame(data, columns=['A', 'B', 'C', 'D'])

corr = df.corr() 

sns.heatmap(corr, annot=True, cmap='coolwarm')

plt.title("Correlation Matrix Heatmap") 
plt.show()

---

🔹 Output Explanation

• Each cell shows the correlation between two variables

• Diagonal values are 1 (self-correlation)

• Dark red → strong positive correlation

• Dark blue → strong negative correlation

---

🔹 Correlation Heatmap vs Normal Heatmap

Feature	Correlation Heatmap	Normal Heatmap
Values	Correlation coefficients	Any numeric values
Range	-1 to +1	Depends on data
Use case	Feature relationships	Pattern visualization
Symmetry	Yes	Not required

---

🔹 Key Takeaways

• Correlation heatmaps reveal hidden relationships

• Essential for EDA & ML

• Helps reduce redundant features

• Easy to interpret visually