Metadata-Version: 2.4
Name: ai-practical
Version: 1.0.0
Summary: Code reference library for BSc IT Artificial Intelligence practicals
Author: AI Practical Kit Contributors
License: MIT
Keywords: artificial-intelligence,bsc-it,practicals,exam
Requires-Python: >=3.9
Description-Content-Type: text/markdown
License-File: LICENSE
Dynamic: license-file

# AI Practical Kit

A Python package that prints code snippets for the eight Artificial Intelligence practicals in the supplied BSc IT practical syllabus.

## Install locally

```bash
python -m pip install .
```

## Usage

```python
from ai_practicals import p1, p2, p5, list_practicals, list_questions, search_practicals

p1()
p1(2)
p5(3)
list_practicals()
list_questions(5)
search_practicals("Bayes")
```

Each `pN(question=1)` prints the code for that question and also returns the code as a string. Use `p1(2)` for Practical 1, Question 2.

## Available practicals

- Practical 1: Table-driven agent and 8-puzzle BFS
- Practical 2: Water jug DFS and UCS
- Practical 3: Greedy Best-First 8-puzzle
- Practical 4: A* 8-puzzle and weighted graph
- Practical 5: Bayes' theorem, Naive Bayes, Bayesian Network
- Practical 6: ML workflow, Linear Regression, MNIST binary classification
- Practical 7: K-fold, Decision Tree classification, Decision Tree regression
- Practical 8: kNN and multiclass Logistic Regression

## Optional dependencies

The code snippets are returned as text and do not require scikit-learn or pgmpy just to import the package. To execute a particular snippet, install the packages imported by that snippet. The MNIST example downloads data from OpenML and needs an internet connection on first run.
