This project aims to find the ideal price for a specific meal (code 1028) in a dataset from a food company. Using machine learning techniques and multiple regression, the model predicts the price based on features such as ingredients, meal subcategory, number of sales, and total number of ingredients.
The project analyzes two databases: one containing information about orders and sales, and another listing the meals and their respective ingredients. After preprocessing the data, involving cleaning, formatting, and feature engineering, regression algorithms are applied to predict the ideal price for meal 1028. For more information and details, the entire process is documented in the PDF file contained in this repository.
- Programming Language: Python
- Libraries: Pandas, NumPy, Scikit-learn, XGBoost
- Tools: Spyder IDE, Jupyter Notebook, Orange