Data Cleaning Steps for Delta Airlines Frequent Flier Dataset

Oct 24, 2024, 7:48 AM

Delta Airlines Loyalty Customer Raw Data (1).xlsx

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Assess the data set and provide in depth the information regarding all the identify issues found within the data set, list in details all the steps that needs to be taken to clean the data set up.

The dataset from the 'Frequent Flier Raw' sheet has several issues that need to be addressed for cleaning:
  1. Column Headers Misalignment: The first row of the dataset contains the actual column headers, but they are not set as the column headers in the DataFrame. The first row should be used as the header.
  2. Unnamed Columns: There are several columns with 'Unnamed' labels, indicating that the headers are not correctly aligned.
  3. Data Types: The data types of some columns may not be appropriate (e.g., dates stored as strings). These should be converted to the correct data types.
  4. Missing Values: There are missing values in some columns (e.g., 'Arrival Airport'). These need to be handled appropriately, either by filling them with a default value or by removing the rows.
  5. Inconsistent Casing: Some text fields, like 'First Name' and 'Last Name', have inconsistent casing (e.g., 'debbie' vs. 'Debbie'). These should be standardized.
  6. Potential Outliers: Columns like 'Total # of Checked Bags' may contain outliers that need to be investigated.

Steps to Clean the Dataset:

  1. Set the Correct Headers: Use the first row as the column headers.
  2. Remove Unnecessary Columns: Drop any columns that are not needed for analysis.
  3. Convert Data Types: Convert columns to appropriate data types (e.g., dates to datetime, numbers to int or float).
  4. Handle Missing Values: Decide on a strategy to handle missing values, such as filling with a default value or removing rows.
  5. Standardize Text Fields: Ensure consistent casing in text fields.
  6. Check for Duplicates: Identify and remove any duplicate rows.
  7. Investigate Outliers: Analyze columns for potential outliers and decide on a strategy to handle them.
Would you like to proceed with cleaning the dataset based on these steps?

Can you clean the dataset based on the identified issues?

It seems there is a persistent issue with the column name 'Record Creation'. The error indicates that the column name might not match exactly due to potential hidden characters or formatting issues.

Suggested Steps:

  1. Inspect Column Names: Let's print out the column names again to ensure there are no hidden characters or formatting issues.
  2. Adjust Column Names: If there are any discrepancies, we will adjust the column names accordingly.
  3. Reattempt Data Cleaning: Once the column names are corrected, we will proceed with the data cleaning steps again.
Let's start by inspecting the column names once more.
It appears that the column names were not set correctly, and the DataFrame is misaligned. The first row of data was not properly set as the header, leading to incorrect column names.

Next Steps:

  1. Reset the DataFrame: We need to reset the DataFrame to use the correct row as the header.
  2. Reapply Data Cleaning: Once the headers are correctly set, we will reapply the data cleaning steps.
Let's proceed with these steps.
The dataset has been successfully cleaned. Here are the steps that were taken:
  1. Set Correct Headers: The first row of the dataset was set as the header to align the columns correctly.
  2. Stripped Spaces: Leading and trailing spaces were removed from column names.
  3. Converted Data Types:
    • Converted 'Record Creation' and 'Join Date' columns to datetime format.
    • Converted '# of Included Checked Bags' and 'Total # of Checked Bags' to numeric types.
  4. Standardized Text Fields: The 'First Name' and 'Last Name' fields were standardized to title case.
  5. Handled Missing Values: Missing values in the 'Arrival Airport' column were filled with 'Unknown'.
  6. Removed Duplicates: Duplicate rows were removed from the dataset.
The cleaned dataset is now ready for analysis. If you have any further questions or need additional analysis, feel free to ask!

As a business analyst, analyze the interview transcript provided and create five user stories based off of pain points identified. The stories created should be structured to meet INVEST criteria meaning they are independent, negotiable, valuable, estimable, small, and testable. Follow the format: "As a ____, I want ____, so that ____. Be sure to also Draft acceptance criteria for each user story created using the "given- when- then-" framework to craft criterion.

To create user stories from an interview transcript, we need to first identify the key pain points mentioned in the transcript. Since I don't have the transcript content, I'll provide a general template for creating user stories and acceptance criteria based on common pain points in business analysis.

User Story 1:

As a customer service representative, I want a centralized dashboard to view all customer interactions, so that I can provide more efficient and personalized service.
Acceptance Criteria:
  • Given a customer calls in, when I access the dashboard, then I should see a complete history of the customer's interactions.
  • Given a customer sends an email, when I access the dashboard, then I should see the email content and any previous related emails.

User Story 2:

As a sales manager, I want automated reporting on sales performance, so that I can quickly identify trends and make informed decisions.
Acceptance Criteria:
  • Given the end of the week, when I log into the system, then I should receive a summary report of weekly sales performance.
  • Given a specific product line, when I request a report, then I should see sales data broken down by region and sales representative.

User Story 3:

As a product manager, I want to receive alerts for low inventory levels, so that I can reorder products before stockouts occur.
Acceptance Criteria:
  • Given inventory levels fall below a predefined threshold, when the system checks inventory, then I should receive an alert notification.
  • Given an alert is triggered, when I access the inventory system, then I should see the current stock levels and reorder recommendations.

User Story 4:

As a marketing analyst, I want to segment customer data based on purchasing behavior, so that I can target marketing campaigns more effectively.
Acceptance Criteria:
  • Given a dataset of customer purchases, when I apply segmentation criteria, then I should see customers grouped by purchasing frequency and amount.
  • Given a segmented group, when I create a marketing campaign, then I should be able to target only that specific group.

User Story 5:

As a finance officer, I want a real-time view of cash flow, so that I can manage financial operations more effectively.
Acceptance Criteria:
  • Given daily financial transactions, when I access the cash flow dashboard, then I should see up-to-date cash inflows and outflows.
  • Given a significant cash flow change, when it occurs, then I should receive an alert to review the transaction.
These user stories and acceptance criteria are designed to be independent, negotiable, valuable, estimable, small, and testable, adhering to the INVEST criteria. If you have specific pain points from the transcript, please share them for more tailored user stories.

REFINED TRANSCRIPT.pdf

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As a business analyst, analyze the interview transcript provided and create five user stories based off of pain points identified. The stories created should be structured to meet INVEST criteria meaning they are independent, negotiable, valuable, estimable, small, and testable. Follow the format: "As a ____, I want ____, so that ____. Be sure to also Draft acceptance criteria for each user story created using the "given- when- then-" framework to craft criterion.

It seems that the content of the provided PDF file is not in a readable format. The text appears to be encoded or corrupted, making it difficult to extract meaningful information directly from it.
To proceed with creating user stories based on the interview transcript, we would need a readable version of the transcript. If you have another version or can provide more details about the pain points discussed in the interview, please share them.
Alternatively, you could try converting the PDF to a text format using a PDF reader or converter tool and then provide the text here for analysis.
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