Food Inflation Analysis Project¶

This project analyzes food price inflation across major economic periods, comparing trends in healthy vs. unhealthy food categories. The project uses statistical analysis, visualization, and reproducible workflows to provide insights into food price dynamics over time.

All work is performed in fully reproducible Jupyter Notebook environments, as specified in environment.yml.

You can launch the notebooks directly in your browser using Binder by clicking the badge above. Binder will automatically set up JupyterLab and install all required dependencies.

Getting Started¶

Prerequisites¶

Ensure you have Conda installed.

Environment Setup¶

Create and activate the conda environment:

conda env create -f environment.yml
conda activate food_inflation

Running notebooks¶

All notebooks are in the notebooks/ directory. Open and run them using Jupyter Notebook or Jupyter Lab:

jupyter notebook
# or
jupyter lab

When running locally, ensure the food_inflation kernel is selected by running the following:

python -m ipykernel install --user --name=food_inflation --display-name="Python (food_inflation)"

Building the Documentation¶

This project uses MyST and a Makefile to generate a static site in _build/. To build:

make html

The documentation site will be available in _build/html/.

MyST Webpage:¶

https://ucb-stat-159-f25.github.io/final-group01/

Repository Structure¶

ai-documentation.txt               # Documentation related to AI usage
contribution_statement.md          # Contribution statement
environment.yml                    # Conda environment configuration
LICENSE                            # BSD 3-Clause License
Makefile                           # Build automation
myst.yml                           # MyST configuration
main.ipynb                         # notebook that summarizes and discusses our results
notebooks/                         # Jupyter notebooks for analysis
    EDA.ipynb
    food_price_trends_analysis.ipynb
    food_inflation_across_major_economic_periods.ipynb
    healthy_vs_unhealthy_inflation.ipynb
data/                               # Raw and processed data
    preprocessed/
        CPIForecast.csv
        CPIHistoricalForecast.csv
        historicalcpi.csv
        historicalppi.csv
        PPIForecast.csv
        PPIHistoricalForecast.csv
    processed/
        processed_cpi.csv
        processed_ppi.csv
figures/                            # Generated plots and figures
outputs/                            # Generated outputs (tables, reports)
    eda_summary/
    food_price_trend_summary/
utils/                              # Utility scripts for data processing or visualization
    __init__.py
    data_loader.py
    transformers.py
    plotting.py
    analysis.py
    process_data.py
tests/
    __init__.py
    test_data_loader.py
    test_transformers.py
    test_analysis.py
    test_process_data.py
pdf_builds/                         # Jupyter notebooks in PDF
_build/                             # Built documentation/site
.github/workflows                   # for MyST GitHub Actions
logo/                               # a folder to save logo(s)
README.md                           # This file
project-description.md              # Project overview and objectives

Notebooks Overview¶

Notebook	Description
`EDA.ipynb`	Exploratory data analysis of CPI and PPI by food category
`food_price_trends_analysis.ipynb`	Analysis of food price trends across time and categories
`food_inflation_across_major_economic_periods.ipynb`	Comparing inflation patterns across major economic periods
`healthy_vs_unhealthy_inflation.ipynb`	Analysis comparing healthy vs unhealthy food inflation trends

All notebooks automatically save key outputs (tables, plots) in the outputs/ and figures/ directories for reproducibility.

Key Outputs & Figures¶

Rolling volatility of food inflation by category (5y_rolling_volatility_food_inflation_by_category.png)
Healthy vs unhealthy inflation comparison (healthy_vs_unhealthy_inflation_boxplot.png)
Annual CPI and PPI changes by category (cpi_annual_change_by_category.png, ppi_annual_change_by_category.png)
Change point detection and broken-stick regression analyses
Additional summary figures and tables saved in figures/ and outputs/

Notes¶

All notebooks are fully reproducible.

The environment is specified in environment.yml; running conda env create -f environment.yml ensures reproducibility.

Data files in data/ include both raw/preprocessed and processed summaries.

Utility functions in utils/ assist with data cleaning and processing.

License¶

This project is licensed under the BSD 3-Clause License.

References¶

Butler, J., Clark, A., Hsu, E., Li, F., & Lee, T. (2025). UCB-stat-159-f25/final-group01: Food Inflation Analysis - Final Submission. Zenodo. 10.5281/ZENODO.17972829