☕️ Beverage Nutrition Analysis - Using Python and R

Overview

This project explores and analyzes beverage nutrition data to uncover trends related to calorie content, fat levels, and preparation methods. It aims to identify low-calorie beverage options and evaluates the availability of dairy-free alternatives.

Key Areas of Analysis

Exploration of Beverage Nutrition Data: Initial exploration of the dataset to understand the distribution and relationship between nutritional and preparation factors.
Calorie Content Analysis: Investigates how calorie content varies across beverage categories, sizes, and preparation methods.
Identification of Low-Calorie Options: Recommends beverages for those seeking lower-calorie options.
Preparation Method Diversity: Analyzes the diversity in beverage preparation options and its implications.
Dairy-Free Beverage Options: Assesses the availability of dairy-free alternatives.
Calorie Group Categorization: Categorizes beverages into calorie groups to facilitate nutritional choices.

Dataset

The dataset includes attributes such as beverage category, size, calorie content, fat content, and preparation method, covering a range of beverages from coffee to smoothies.

Analysis and Insights

Calorie Trends Visualizations and statistical analysis to reveal correlations between calorie content and other factors. Low-Calorie Beverage Recommendations Recommendations of specific beverages as low-calorie alternatives based on the analysis. Preparation Method Diversity Highlights which beverages offer the most preparation options and discusses potential reasons, including cost and labor implications. Dairy-Free Options Identifies beverages that offer dairy-free alternatives and discusses their importance.

Conclusion

This project provides insights into beverage nutrition, helping consumers make informed choices and encouraging coffee shops to highlight healthier and inclusive options.