How can you use Seaborn to create a heatmap that visualizes the correlation matrix of a dataset, and what are the key steps involved in preprocessing the data and customizing the plot for better readability? Provide a detailed code example with explanations at an intermediate level, including handling missing values, selecting relevant columns, and adjusting the color palette and annotations.

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

# Step 1: Load a sample dataset (e.g., tips from seaborn's built-in datasets)
df = sns.load_dataset('tips')

# Step 2: Select only numeric columns for correlation analysis
numeric_df = df.select_dtypes(include=[np.number])

# Step 3: Handle missing values (if any)
numeric_df = numeric_df.dropna()

# Step 4: Compute the correlation matrix
correlation_matrix = numeric_df.corr()

# Step 5: Create a heatmap using Seaborn
plt.figure(figsize=(10, 8))
sns.heatmap(correlation_matrix, annot=True, cmap='coolwarm', center=0, linewidths=.5, fmt='.2f')
plt.title('Correlation Heatmap of Numeric Features in Tips Dataset')
plt.tight_layout()
plt.show()

Explanation:
- Step 1: We load a built-in dataset from Seaborn to work with.
- Step 2: Only numeric columns are selected because correlation is computed between numerical variables.
- Step 3: Missing values are removed to avoid errors during computation.
- Step 4: The corr() method computes pairwise correlations between columns.
- Step 5: sns.heatmap() creates a visual representation where colors represent correlation strength, annot=True adds the actual correlation coefficients, cmap='coolwarm' uses a diverging color scheme, and fmt='.2f' formats numbers to two decimal places.

#Seaborn #DataVisualization #Heatmap #Python #Pandas #CorrelationMatrix #IntermediateProgramming

By: @DataScienceQ 🚀