Metadata-Version: 2.4
Name: practical08
Version: 0.3.0
Summary: Searchable AIML practical program library with simple ask interface
License-Expression: MIT
Keywords: education,machine-learning,practicals,jupyter
Classifier: Programming Language :: Python :: 3
Classifier: Operating System :: OS Independent
Classifier: Topic :: Education
Requires-Python: >=3.9
Description-Content-Type: text/markdown
License-File: LICENSE
Dynamic: license-file

# Practical08

Finalized question-to-code library containing 30 AIML practical programs from Units IV, V and VI.

## Simple Jupyter use

```python
%pip install --upgrade practical08
from practical08 import ask

ask("Write a program for DBSCAN clustering")
```

`ask(question)` prints the best matching program, a brief explanation, the complete code and required libraries. It does not print an extra dictionary afterward.

## Examples

```python
ask("Implement Random Forest classifier")
ask("Calculate discounted return using gamma")
ask("Find frequent items using Apriori")
ask("Update Q value using Q-Learning")
```

## Optional exact lookup

```python
from practical08 import get_program
print(get_program("IV04")["code"])
```

## Search behavior
- Recognizes aliases such as DBSCAN / density-based clustering, XGBoost / extreme gradient boosting, and Q-learning / Q value update.
- Uses topic-focused scoring and rejects weak matches instead of silently selecting unrelated programs.
- No AI API key is required; matching uses Python's standard library and works offline after installation.
- Some returned examples need additional packages (e.g. `scikit-learn`, `numpy`, or `xgboost`) to execute.
- `ask()` prints code but never executes it automatically.
