README.md

CS180 Group 23 Project: Predicting Food Prices in the Philippines using Regression

An AI and ML-based model that predicts food prices in the Philippines from observed trends in historical data on food prices and the contributing factors using regression analysis

Jupyter Notebook

The documentation for the model, including the types of regression tested, the parameters tuned, and the training and testing results can be found in the notebook CS-180-Final-Project.ipynb in the root folder.

Dataset

The dataset used in training and testing was retrived from the Humanitarian Data Exchange. The .csv file for the dataset can be found as wfp_food_prices_phl.csv in the root folder. This dataset is also publicly available and can be accessed through this link.

Web App

The predictive model was incorporated into a web application to allow end users to use it as an assistive tool in budgeting and financial decisions.

Setup

First, create the python virtual environment using the virtualenv module.

pip install virtualenv
python -m venv venv

In Linux, start the virtual environment by running

source ./venv/bin/activate

Or, in Windows, by running

.\venv\Scripts\activate

Next, install the requirements by running

pip install -r requirements.txt

To run the program in debug mode, execute the following command:

python -m app.app