Description
This project explores the nutritional composition of everyday foods through exploratory data analysis. Using Python libraries such as pandas, NumPy, matplotlib, and seaborn, we aim to uncover insights into the nutritional landscape of common diets.
Data Source
The primary dataset is sourced from Kaggle, accessible via Nutrition Datasets. This dataset provides comprehensive information on various food items, including macronutrient profiles (carbohydrates, proteins, fats), vitamins, minerals, and other relevant attributes.
Project Goals
- Uncover Nutritional Trends and Patterns:
Explore nutrient distribution across different food groups and categories. Identify correlations between various nutrients.
- Gain Insights into Dietary Choices:
Analyze how frequently consumed foods contribute to overall nutrient intake. Identify potential areas for improvement in dietary habits.
- Develop Data Analysis and Visualization Skills:
Apply Python libraries effectively to clean, manipulate, and analyze nutritional data. Create informative visualizations to communicate findings.
Methodology
Data Acquisition and Preprocessing:
- Download the Kaggle dataset using Python libraries (e.g., kaggle API).
- Perform data cleaning tasks: handle missing values, outliers, and inconsistencies.
- Transform data into a suitable format for analysis (e.g., create categorical variables, calculate ratios).
Exploratory Data Analysis:
- Utilize pandas and NumPy for calculating descriptive statistics across food groups.
- Employ matplotlib and seaborn for creating histograms, boxplots, scatter plots, etc., - to reveal trends.
- Investigate nutrient correlations using techniques like Pearson's correlation coefficient.
Dietary Insights:
- Analyze nutrient contributions from frequently consumed foods.
- Recommend improvements in dietary habits based on analysis.
Future Directions:
- Refine analysis based on initial findings.
- Develop predictive models or hypothesis testing.
- Communicate results effectively through visualizations, reports, or presentations.
Technologies and Libraries
- Python
- Pandas
- NumPy
- Matplotlib
- Seaborn
Contribution Guidelines
Contributions to this project are welcome! To contribute:
Fork the repository, make changes, and submit pull requests. Adhere to the project's coding style and formatting guidelines. This README provides an overview of the project's objectives, methodology, and guidelines for contributions. For more details, refer to the project repository.