TASK : Analyze Correlation Between Delivery Time and Customer Ratings

**Issue:**
Understanding the relationship between delivery time and customer ratings is essential for improving our delivery services. In this task, we aim to analyze the correlation between these two variables to identify patterns and insights that can guide our decision-making.

**Goals:**
- Perform a correlation analysis between delivery time and customer ratings.
- Visualize the correlation to help interpret the results effectively.

**Instructions:**
1. Fork this repository to your GitHub account.
2. Clone your forked repository to your local machine.
3. Navigate to the project directory.
4. Locate the "delivery_data.csv" dataset in the "data" directory.
5. Use Python (e.g., pandas, Matplotlib, or Seaborn) to perform the correlation analysis.
6. Calculate the correlation coefficient between delivery time and customer ratings.
7. Create visualizations (e.g., scatter plots, correlation matrices) to visualize the relationship.
8. Interpret the results and provide insights into the correlation.

**Submission:**
Once you've completed the analysis and interpretation of the correlation, submit your work by creating a pull request to this repository. Please include your script or Notebook, the calculated correlation coefficient, visualizations, and your insights into the correlation.

If you have any questions or require clarification during the analysis, feel free to reach out. Let's gain valuable insights into our delivery service performance!

Start with data/delivery_data.csv and identify the delivery-time and customer-rating fields. Use Python with pandas, Matplotlib, or Seaborn to calculate their correlation coefficient and create a scatter plot or correlation matrix. Done means the submission includes a script or notebook, visualizations, the coefficient, and an interpretation of the results.

Written by the indexing model from the issue text.