This project focuses on analyzing and optimizing the sales of chocolates based on various attributes. The project is divided into three key steps: analyzing a large shipment of chocolates, setting prices based on chocolate attributes, and identifying high-quality chocolates for better sales performance. This repository contains the Jupyter notebooks, datasets, and detailed analysis performed at each step.
- Introduction
- Project Workflow
- Tools and Technologies
- Results
- Challenges and Learnings
- Future Work
- How to Run the Project
- Contributing
- License
The Chocolate Sales Analysis and Optimization project aims to enhance the understanding and sales strategy of chocolates by analyzing key attributes, setting optimal prices, and identifying high-quality products. This project is structured in three steps, each addressing a different aspect of chocolate sales optimization.
Objective: Analyze a large shipment of foreign chocolates and optimize the storage structure of the data provided.
- Dataset: The chocolate specifications are stored in a file named
chocolate.csv
. This dataset contains detailed information about each chocolate, including attributes like shape, flavor, and more. - Tasks:
- Load and explore the dataset.
- Examine the dimensions and column names.
- Optimize the storage and structure of the data for further analysis.
Objective: Develop a pricing strategy for chocolates based on their attributes and research findings.
- Dataset: The optimized dataset from Step 1 is further analyzed to set prices for each chocolate.
- Tasks:
- Conduct research to determine the pricing strategy.
- Implement a pricing algorithm based on chocolate attributes like cocoa percentage, brand, and other features.
- Save the final priced dataset for further analysis.
Objective: Identify and separate high-quality chocolates from the dataset to focus on better-selling products.
- Dataset: The priced dataset from Step 2 is used to identify high-quality chocolates.
- Tasks:
- Filter out non-dark chocolates (cocoa percentage of 70% or less).
- Identify chocolates produced by companies known for high quality.
- Separate and save the high-quality chocolates for targeted sales strategies.
- Python: Programming language used for data analysis and optimization.
- Pandas: Library used for data manipulation and analysis.
- Jupyter Notebook: Environment used to write and run the code for each step of the project.
- Successfully analyzed a large dataset of chocolates and optimized the data structure.
- Developed a pricing strategy based on chocolate attributes, resulting in a comprehensive priced dataset.
- Identified high-quality chocolates, enabling targeted sales strategies to improve overall sales performance.
- Data Complexity: Managing and optimizing a large dataset required careful planning and efficient use of Pandas functions.
- Pricing Strategy: Setting prices based on attributes required a balance between research insights and data-driven decision-making.
- Quality Identification: Identifying high-quality chocolates involved filtering and analyzing the dataset based on specific criteria.
- Enhanced Pricing Model: Further refine the pricing strategy by incorporating additional market factors and consumer preferences.
- Machine Learning Integration: Explore the use of machine learning models to predict the sales performance of chocolates based on historical data.
- Global Expansion: Apply the analysis framework to datasets from other regions to optimize chocolate sales on a global scale.
- Clone the repository:
git clone https://github.com/yourusername/chocolate-sales-analysis.git cd chocolate-sales-analysis
- Install the required dependencies:
pip install -r requirements.txt
- Run the Jupyter Notebooks:
- Open and run
project1_step1.ipynb
to analyze the large shipment. - Open and run
project1_step2.ipynb
to develop the pricing strategy. - Open and run
project1_step3.ipynb
to identify high-quality chocolates.
- Open and run
Contributions are welcome! If you have suggestions for improving the analysis, enhancing the pricing model, or adding new features, feel free to open a pull request or issue.
This project is licensed under the MIT License - see the LICENSE file for details.