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https://github.com/nafisalawalidris/hici-african-foods
HiCi African Foods: Excel dashboard & pivot table analysis of EU food rejection data to identify risks & recommend focus areas for market expansion.
https://github.com/nafisalawalidris/hici-african-foods
data-analysis data-cleaning data-visualization eu-food-rejection excel-dashboard hici-african-foods market-expansion pivot-tables
Last synced: about 1 month ago
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HiCi African Foods: Excel dashboard & pivot table analysis of EU food rejection data to identify risks & recommend focus areas for market expansion.
- Host: GitHub
- URL: https://github.com/nafisalawalidris/hici-african-foods
- Owner: nafisalawalidris
- Created: 2023-07-02T13:36:45.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2023-10-01T19:34:34.000Z (over 1 year ago)
- Last Synced: 2023-10-07T00:25:14.880Z (about 1 year ago)
- Topics: data-analysis, data-cleaning, data-visualization, eu-food-rejection, excel-dashboard, hici-african-foods, market-expansion, pivot-tables
- Homepage:
- Size: 608 KB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
HiCi African Foods - Excel Dashboard and Pivot Table
Project Overview
This project focuses on analyzing and visualizing data related to HiCi African Foods' expansion into the European Union market. The goal is to identify potential risks and provide insights for overcoming regulatory hurdles using an Excel dashboard and pivot tables.
Data Cleaning
Prior to analysis, the raw data from the EU Food Safety Standard Agency Portal was subjected to a data cleaning process to ensure accuracy and reliability. This included handling missing values, removing duplicates, and standardizing inconsistent data points.
Excel Dashboard
An Excel dashboard was created to present key findings and visualizations from the data analysis. The dashboard includes interactive charts, tables, and pivot tables to provide an intuitive and comprehensive view of the data.
Pivot Tables
Pivot tables were utilized to aggregate and summarize the data, enabling easy exploration and identification of trends. The pivot tables allow for dynamic grouping, filtering, and calculation of metrics, providing valuable insights into the food rejection patterns and associated factors.
Findings and Recommendations
Based on the analysis, the following key determinants were identified:
- The EU member country with the highest number of food rejections.
- The most rejected food products.
- The country of origin for the most rejected food products.
- The most common actions taken against food exported to the EU.
- The most reported origin (country) of food exports.
Additionally, risks associated with different food products were evaluated. Recommendations for HiCi African Foods management were provided based on the identified risks, emphasizing the need to focus on specific food products to mitigate potential regulatory hurdles.
Getting Started
To reproduce the analysis and view the Excel dashboard:
- Obtain the raw data from the EU Food Safety Standard Agency Portal.
- Perform data cleaning using the appropriate software or tools (e.g., Excel, Python, R, etc.).
- Create the Excel dashboard by importing the cleaned data and setting up interactive visualizations and pivot tables.
- Adjust the dashboard as needed to reflect the latest data or update the data source accordingly.
- Explore the dashboard to gain insights into the determinants and risks associated with food exports to the EU market.
Dependencies
The project requires the following dependencies:
- Software or tools for data cleaning (e.g., Excel, Python, R, etc.).
- Microsoft Excel for creating the dashboard and pivot tables.
Contributing
Contributions to this project are currently not accepted as it is for demonstration purposes only.
License
This project is licensed under the MIT.
