This project focuses on analyzing a dataset of chocolates to improve their data storage, estimate prices, and ultimately identify high-quality chocolates. The project is divided into three main steps, each addressing a different aspect of chocolate data analysis.
In the first step, we explore a dataset of chocolates provided by the vendor. We clean and preprocess the data, ensuring that all necessary information is well-structured for further analysis. This step includes:
- Handling missing or inconsistent data
- Structuring the dataset for efficient analysis
In the second step, we estimate the prices of chocolates based on the given attributes. We apply statistical methods to predict the cost of each chocolate product, creating a complete dataset with estimated price values for future use.
In the final step, we focus on identifying high-quality chocolates. By analyzing various features of the chocolates, such as ingredients and ratings, we classify the chocolates produced by companies known for their premium quality. This step aims to distinguish the best products for potential business opportunities.
The dataset includes the following key attributes:
Company: The company producing the chocolateCountry: The origin country of the chocolateRating: The rating given to each chocolateIngredients: The key ingredients used in the chocolatePrice: The estimated price (added in Step 2)Cocoa Percentage: The percentage of cocoa in the chocolate
- Clone the repository:
git clone https://github.com/kiddiedrafts/Chocolate-Quality-Analysis.git
- Install the required dependencies:
pip install -r requirements.txt
- Run the Jupyter notebooks for each step:
- Step 1: Data Preprocessing: project1_step1.ipynb
- Step 2: Price Estimation: project1_step2.ipynb
- Step 3: Premium Chocolate Identification: project1_step3.ipynb
If you'd like to contribute to this project, feel free to open a pull request or report issues in the issue tracker