This project analyzes McDonald's menu items to uncover nutritional insights and build a predictive model to classify high-calorie items. The dataset includes detailed nutritional information for various food and beverage items.

• Dataset Description
• Project Objective
• Exploratory Data Analysis (EDA)
• Feature Engineering
• Machine Learning Model
• Visualizations
• Instructions for Replication
• Future Work
• Acknowledgments

The dataset, McDonalds_menu_data.csv, contains nutritional information for menu items across categories like Breakfast, Beef & Pork, Chicken & Fish, Salads, Desserts, Beverages, etc.

• Category: The type of menu item (e.g., Breakfast, Desserts).
• Item: Name of the menu item.
• Calories: Total calories in the item.
• Total Fat, Sodium, Carbohydrates, etc.: Nutritional breakdown.

The primary goals of this project are:

1. Conduct exploratory data analysis to identify trends and patterns in McDonald's menu items.
2. Engineer new features to enhance data insights.
3. Build a machine learning model to classify menu items as high-calorie or low-calorie based on nutritional data.

Key findings from EDA include:

• High-Calorie Categories: Items in the "Smoothies & Shakes" category have the highest average calorie count.
• Sodium Levels: Many items exceed recommended daily sodium intake levels.
• Correlation Insights: Calories are strongly correlated with Total Fat and Carbohydrates.

New features created include:

• Calories_from_Fat_Percentage: Percentage of calories derived from fat.
• One-hot encoding for the categorical variable Category.

A Random Forest Classifier was used to predict whether an item is high-calorie (>500 calories).

• Accuracy: 85%
• Precision: 0.88
• Recall: 0.82

Key visualizations include:

1. Distribution of Calories across categories.
2. Correlation heatmap of numerical features.

To replicate this project locally:

1. Clone this repository:git clone https://github.com/yourusername/McDonalds_Menu_Analysis.git
2. Navigate to the project directory: cd McDonalds_Menu_Analysis
3. Install required dependencies:pip install -r requirements.txt
4. Run the Jupyter notebooks in the /notebooks folder for EDA and modeling.

Potential improvements include:

1. Adding more advanced machine learning models like XGBoost or Neural Networks.
2. Exploring additional datasets with customer preferences or sales data.

Dataset provided by McDonald's Nutrition Information (publicly available). Special thanks to contributors who inspired this analysis.