McDonald's Nutritional Analysis 🍔🥗

This project analyzes the nutritional information of menu items from McDonald's. Conducted as part of a data analytics internship at Oasis Infobyte, the analysis provides insights into the nutritional profile of McDonald's offerings through data exploration, visualization, and correlation analysis.

Project Overview

The goal of this project is to explore and analyze McDonald's menu data to uncover trends, correlations, and patterns within various nutritional metrics. Key analysis areas include macronutrient distribution, correlations between calorie content and fat, and insights into vitamins and minerals.

Visualizations

PROJECT ENGAGEMENT VIDEO

Key Insights and Findings

1️⃣ Menu Category Distribution

• Visualized the number of menu items across food categories to understand McDonald's diversity in offerings.

2️⃣ Nutritional Correlation

• Explored the relationship between calories and calories from fat, calculating a correlation coefficient to quantify their association.

3️⃣ Saturated Fat Analysis

• Identified the top 5 food categories with the highest percentage of saturated fat, providing insights for potential nutritional awareness.

4️⃣ Nutritional Composition by Category

• Analyzed the mean percentage of saturated fat across categories like Coffee & Tea, Smoothies & Shakes, Salads, and Chicken & Fish.

5️⃣ Vitamins & Minerals

• Assessed the mean daily values of Vitamin A, Vitamin C, Iron, and Calcium in different menu categories to evaluate nutritional content.

6️⃣ Total Fat vs. Total Carbohydrate

• Investigated the balance between total fat and total carbohydrate percentages to understand macronutrient distribution across menu items.

7️⃣ Iron and Calcium Distribution

• Visualized the distribution of iron and calcium levels across menu categories to highlight McDonald's nutritional diversity.

Skills and Tools

• Data Cleaning: Processed and cleaned data for accuracy and reliability.
• Exploratory Data Analysis (EDA): Used EDA techniques to identify trends and correlations in nutritional content.
• Data Visualization: Created visualizations using tools like Power BI and Python libraries for effective data storytelling.
• Statistical Analysis: Applied statistical methods to calculate correlation coefficients and understand nutritional relationships.

Technologies Used

• Python: Pandas, Matplotlib, Seaborn

Project Structure

• data/ - Contains the raw dataset for McDonald's menu nutritional information.
• notebooks/ - Jupyter notebooks used for data cleaning, EDA, and visualization.
• visualizations/ - Saved visualizations illustrating key findings.
• README.md - Overview and instructions for the project.

Conclusion

This project provided valuable experience in data analytics, equipping me with hands-on skills in exploring real-world datasets to derive actionable insights. I look forward to applying these skills to future projects!