Buying Product Prediction Tool

A Fuzzy Expert System for predicting the likelihood of a customer buying a product based on their age, income, and purchase history

Project Overview

This project involves the development of a Fuzzy Expert System designed to predict the likelihood of a customer buying a product based on three key input factors: age, income, and purchase history. The system leverages fuzzy logic to handle the inherent uncertainty and vagueness in customer behavior.

Input Variables

1. Age:

   • Young
   • Middle-aged
   • Old

2. Income:

   • Low
   • Medium
   • High

3. Purchase History:

   • Rarely
   • Occasionally
   • Frequently

Fuzzy Rules

The system is governed by a set of 27 fuzzy rules that define how the input variables interact to produce the output. These rules are designed to mimic the decision-making process of a human expert.

Testing the System

To validate the accuracy and reliability of the Fuzzy Expert System, a test set of 100 rules with expected outputs is generated. These test cases span a wide range of possible input combinations to ensure comprehensive coverage and robustness of the system.

Getting Started

Running the System

To run the system on your computer, you need a version of Python installed and you should install the main libraries by running the following commands in the terminal:

pip install numpy
pip install skfuzzy
pip install matplotlib
pip install streamlit

Next, navigate to the project directory in the terminal and run the following command to start the application:

streamlit run fuzzy_project_ui.py

The system interface will appear, and you can start using it fully.

Expected Results

The system will output the predicted likelihood for each test case and compare it with the expected output. The results will be logged for further analysis.

Acknowledgements

This project uses the scikit-fuzzy library for implementing fuzzy logic in Python.

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Feel free to explore, modify, and enhance this project to suit your needs. Happy coding!