How can I create a correlation matrix using Seaborn in Jupyter Notebook?

How can I create a correlation matrix using Seaborn in Jupyter Notebook?

You can create a correlation matrix using the seaborn library by first calculating the correlation matrix with pandas and then using seaborn.heatmap to visualize it.

Can you show me a code example for that?

Sure! Here's a simple example:

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

# Load your dataset
df = pd.read_csv('your_data.csv')

# Calculate the correlation matrix
corr = df.corr()

# Create a heatmap
sns.heatmap(corr, annot=True, cmap='coolwarm')
plt.show()

What does the annot=True parameter do?

The annot=True parameter adds the correlation coefficient values to each cell in the heatmap, making it easier to interpret the correlations.

Can I customize the color palette of the heatmap?

Yes, you can customize the color palette by changing the cmap parameter. For example, you can use cmap='viridis' for a different color scheme.