feat: Support Resid Plot

[Bhoomika2905]

Feature Request

Description

Currently, our visualization module supports only a limited subset of Seaborn Cartesian plots. To enhance analytical and visualization capabilities, we should add support for the sns.residplot() function.
A Residual Plot displays the residuals (differences between observed and predicted values) of a regression model. It’s an essential diagnostic tool for evaluating the goodness of fit — revealing nonlinearity, unequal error variance, or outliers.

Proposed Solution

Use Seaborn’s built-in sns.residplot() as the plotting layer. It should support parameters such as x, y, data, and optionally lowess=True for a locally weighted regression line to highlight residual patterns.

Additional Context

Below is a simple example demonstrating how a residual plot works:

import seaborn as sns
import matplotlib.pyplot as plt

# Load example dataset
tips = sns.load_dataset("tips")

# Create Residual Plot
sns.residplot(
    x="total_bill",
    y="tip",
    data=tips,
    lowess=True,
    color="purple"
)

plt.title("Seaborn Residual Plot Example")
plt.xlabel("Total Bill ($)")
plt.ylabel("Residuals")
plt.tight_layout()
plt.show()

Checklist

• [ x ] I have searched for similar feature requests and confirmed that this is a new request.
• [ x ] I have provided a clear and concise description of the feature.
• [ x ] I have explained the motivation behind this feature request.
• [ x ] I have outlined a proposed solution or ideas for implementing this feature.
• [ x ] I have provided any additional context or screenshots if applicable.