Inventory Prediction AI

Overview

This project is a simple AI system to predict daily sales of products based on historical data. It is designed to help small businesses or schools track and forecast inventory more efficiently.

How It Works

1. A small dataset is created with columns:
   • day — day number
   • product — name of the product
   • total_sold — number of items sold
   • weather — sunny, hot, cold, etc.
   • day_type — weekday or weekend
   • special_event — promotions, holidays, etc.
2. The dataset is converted into a DataFrame using pandas for easy analysis.
3. The AI can learn patterns from the data and predict total sales for future days.

Purpose

• Understand sales trends and patterns.
• Experiment with AI and data analysis using Python.
• Learn the basics of working with datasets, pandas, and machine learning workflows.

How to Run

1. Make sure Python and pandas are installed.
2. Clone this repository.
3. Run main.py to load the dataset and start exploring.
4. Future steps include plotting trends and building predictive models.

Notes

• This project is currently a mini-experiment with a small, manually created dataset.
• More features and real data can be added later to improve predictions.