This project, conducted as part of our training at Becode.org for Accenture, involves data cleaning, modeling, forecasting, and presenting strategic recommendations for the Dragonyte Brewery. The final results are used to provide insight into the fastest growing product categories/subcategories and Dragonyte’s market position within these segments.
This project was developed by the analytics team. The team members are:
The objective of this project is to analyze the Dragonyte Brewery's business data, uncover trends, make forecasts, and provide strategic recommendations. The data analysis covers the following key aspects:
- Data cleaning and modelling.
- Establishing a database environment for connecting to Tableau.
- Projecting growth in categories/subcategories for the next 5 years.
- Identifying growing channels within these categories/subcategories.
- Assessing Dragonyte’s market position within these fast-growing categories.
- Providing predictive analytics and explanation of the model used.
- Giving strategic recommendations to the Dragonyte Board.
To run the data analysis script, follow these steps:
- Clone this GitHub repository to your local machine.
- Install the required dependencies by running
pip install -r requirements.txt. - Ensure you have Python 3.10 or higher installed.
Open the dragonyte_analysis.ipynb file. Modify the script to specify your desired input data file and parameters. Run the script using python dragonyte_analysis.ipynb. The script will clean, model, and analyze the provided data based on the project objectives and generate a report.
Remember that the Tableau dashboard is directly linked to the cleaned data. Therefore, any modifications made to the data or the cleaning process will reflect in the Tableau visualization. Ensure that changes are intentional and correctly implemented to avoid disrupting the dashboard's accuracy and effectiveness.
In this project, we faced various challenges related to data cleaning, data modeling, and data visualization. We overcame them by utilizing robust data analysis techniques and leveraging the power of Python, Tableau, and various libraries and tools. We ensured the final output was insightful, accurate, and beneficial for strategic decision-making.
For any inquiries or additional information, please feel free to contact us.