Food Flow Overview

Welcome to the Food Flow project repository! Food Flow is a college project aimed at predicting the arrival of students in the mess during breakfast, lunch, snacks, and dinner based on the menu and events happening in the college. The repository contains code and datasets related to this project. Folder Structure

The main folder contains four subfolders, each corresponding to a different mealtime:

breakfast: Contains datasets and code files related to breakfast predictions.
    breakfast.csv: Dataset containing information about breakfast menus and events.
    breakfast_prediction.py: Python script for training predictive models and making predictions for breakfast.
    breakfast_prediction.ipynb: Jupyter Notebook for running code related to breakfast predictions on Google Colab.

lunch: Contains datasets and code files related to lunch predictions.
    lunch.csv: Dataset containing information about lunch menus and events.
    lunch_prediction.py: Python script for training predictive models and making predictions for lunch.
    lunch_prediction.ipynb: Jupyter Notebook for running code related to lunch predictions on Google Colab.

snacks: Contains datasets and code files related to snack predictions.
    snacks.csv: Dataset containing information about snack menus and events.
    snacks_prediction.py: Python script for training predictive models and making predictions for snacks.
    snacks_prediction.ipynb: Jupyter Notebook for running code related to snack predictions on Google Colab.

dinner: Contains datasets and code files related to dinner predictions.
    dinner.csv: Dataset containing information about dinner menus and events.
    dinner_prediction.py: Python script for training predictive models and making predictions for dinner.
    dinner_prediction.ipynb: Jupyter Notebook for running code related to dinner predictions on Google Colab.

Usage

To use the Food Flow project for predicting student arrivals during different meal times, follow these steps:

Clone the repository to your local machine.
Navigate to the specific mealtime folder (breakfast, lunch, snacks, or dinner) you want to analyze.
Use the provided dataset and code files to train predictive models or analyze patterns of student arrivals based on menu and event data.