Business Report Optimization for Location Grading

[krisnaBukitVista]

Description

This project aims to optimize the business report by improving the grading process for properties. The grading will factor in customer reviews and the proximity of properties to nearby beaches. Additionally, the project will create visualizations to provide clear insights in the business report, enhancing decision-making and reporting accuracy.

Problem

The current location grading process for properties is not effectively utilizing customer reviews and proximity to nearby beaches. This results in inaccurate or unclear property rankings, which impacts the quality of the business report and overall decision-making. Furthermore, the report lacks visualizations that could help present the data in a more accessible and actionable format.

Solution

• Develop a refined workflow to grade properties based on a weighted system that includes customer reviews and proximity to beaches.
• Use this grading system to rank properties more effectively.
• Create clear and engaging visualizations (graphs, charts, maps) to be attached to the final business report for easy interpretation of the property rankings.

Measurement metrics

• Accuracy of grading: Measure the accuracy of the grading based on customer reviews and distance factors.
• User approval: Testify with requested user (kak Bella) to analyze whether the final outcome has matched with the initial expectation

SLA

• Accuracy grading: Fully functional without significant error
• User approval: Let the user analyze and decide whether the delivery has alligned with initial expectation

Evaluation

• The project has successfully met all expected functionalities, demonstrating robust performance and effective outcomes. Below are the key highlights of the evaluation:
• Functionality Achievement: All expected functionalities have been operational, including the generation of a Hexbin Heatmap from Airbnb listing/property data and its successful display in the CODA Document Business Plan.
• Form Submission Processing: The form submission process has been executed successfully, ensuring that all data entries are captured accurately and efficiently.
• Hexbin Visualization: The hexbin visualization has been generated and seamlessly integrated into the CODA Business Plan, providing valuable insights for analysis.
• Color-Coded Display: The hexbin heatmap effectively displays color-coded hexagons, allowing for easy interpretation of data density and distribution.
• Error-Free Performance: There were no errors encountered during the data processing and visualization generation, indicating a smooth execution of all tasks involved in the project.
• User Approval: The final delivery of the project has been approved by the user, affirming that all requirements and expectations have been met satisfactorily.

User Feedback

The user noted a limitation regarding areas that were not scrapped. Specifically, there have been inquiries for properties located outside of the known area, which suggests that there may be a need for further exploration or enhancement of the data collection methods in those regions.

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Overall Point: 5.5

Functional Complexity: 1.5

The grading system that integrates customer reviews and beach proximity is moderately complex. It involves data weighting and aggregation that affect the overall ranking process.

Technical Complexity: 1.5

Technical effort includes development of algorithms to calculate grades and possibly pulling in data from external sources, but does not indicate highly complex integrations or new algorithms.

UI/UX Complexity and Impact: 1.25

Visualizations represent a high level of UI/UX complexity as they involve the creation of graphs, charts, and maps, which significantly enhance the report's interpretability.

Testing and Quality Assurance: 1

Testing involves validating the accuracy of grading and ensuring visualizations are correctly reflecting the data, requiring integration and user-testing, but the challenge seems moderate rather than extreme.

Risk and Dependencies: 0.25

There may be minor risks associated with data accuracy and user feedback, but dependencies are not discussed as being extensive, suggesting a low risk factor.