https://github.com/floressek/artificial_intelligence_lab

Content from the AI laboratory
https://github.com/floressek/artificial_intelligence_lab

ai genetic-algorithm linear-regression nlp

Last synced: 8 days ago
JSON representation

Content from the AI laboratory

• Host: GitHub
• URL: https://github.com/floressek/artificial_intelligence_lab
• Owner: Floressek
• License: mit
• Created: 2024-05-06T20:44:10.000Z (6 months ago)
• Default Branch: main
• Last Pushed: 2024-06-19T14:06:18.000Z (5 months ago)
• Last Synced: 2024-10-10T11:25:12.436Z (28 days ago)
• Topics: ai, genetic-algorithm, linear-regression, nlp
• Language: Python
• Homepage:
• Size: 16.6 KB
• Stars: 1
• Watchers: 2
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

Awesome Lists containing this project

README

        
**Plant Health Monitoring System - Lab Codes**

This repository consists of three distinct laboratory exercises—lab_1, lab_2, and lab_3—each designed to focus on different aspects of programming: system diagnostics, regression analysis, and genetic algorithms. Below are detailed descriptions and instructions for each lab.

---

**Lab 1: Gardening System Diagnostics**

**Description:**

Lab_1 features a Python script aimed at diagnosing plant health issues based on symptoms such as yellow leaves, brown tips, wilting, spots on leaves, and stunted growth. The script uses a series of diagnostic rules to simulate a gardening advisory system, helping users to understand potential plant health problems based on visual cues.

**How to Run:**

To execute the diagnostics, navigate to the `src` directory and run the gardening system script using Python. This script does not require any external libraries, making it easy to run with a basic Python setup.

**Requirements:**

- Python 3.8 or later

---

**Lab 2: Linear Regression via Gradient Descent**

**Description:**

Lab_2 demonstrates a basic implementation of linear regression using gradient descent. It includes a script that generates random data points, applies linear regression to predict outcomes, and visually represents these predictions along with the regression line using a scatter plot. This exercise is perfect for understanding the fundamentals of machine learning in data prediction.

**How to Run:**

Navigate to the `src` directory and run the linear regression script. This script will produce a graphical output showing the data points and the fitted line, highlighting the regression model's accuracy and performance.

**Requirements:**

- Python 3.8 or later

- NumPy

- Matplotlib

---

**Lab 3: Genetic Algorithm for Optimization**

**Description:**

In lab_3, a genetic algorithm is employed to optimize a mathematical function over a series of constraints. The script iteratively enhances a population of solutions using fitness assessments, crossover, and mutation techniques. This lab is an excellent demonstration of evolutionary algorithms used for solving optimization problems in complex scenarios.

**How to Run:**

To run the genetic algorithm simulation, execute the corresponding script in the `src` directory. The output will detail each generation's best fitness and the global best solution found during the simulation.

**Requirements:**

- Python 3.8 or later

---

Each lab is designed to provide hands-on experience with practical applications in programming, data analysis, and algorithmic optimization. These labs are not only educational but also lay the groundwork for more advanced exploration in each topic.