Metadata-Version: 2.4
Name: scikit-lab-kit
Version: 0.1.5
Summary: Numbered DAML (scikit-learn) practicals: steps, code and short explanation
Requires-Python: >=3.9
Description-Content-Type: text/markdown

# scikit-lab-kit

Numbered viewer for the DAML practicals (steps, code and a short explanation), shown in **red**.
Type a number to show a practical, `m` to show the menu, `0` to exit.

## Install
```
python -m pip install scikit-lab-kit
```

## Run
```
python -m scikit_lab
```
or in IDLE / Python:
```python
import scikit_lab
scikit_lab.main()
```

## Menu
| No. | Practical |
|-----|-----------|
| 1 | Data cleaning - missing values |
| 2 | Data cleaning - noisy data (outliers) |
| 3 | Data cleaning on a dataset (loan credit) |
| 4 | Preprocessing - correlation and contingency table |
| 5 | Exploratory data analysis (EDA) |
| 6 | Decision tree |
| 7.1 | Comparing classification models |
| 7.1a | Practical 7 (uploaded) |
| 8.1 | Classification improvement (bagging, boosting) |
| 8.1a | Practical 8.1 (uploaded) |
| 9 | Clustering (K-Means, hierarchical) |
| 10 | Association - Apriori (groceries) |
| 11 | Association - Apriori (own dataset) |
| 12 | FP-Tree association |
| 13 | Classifiers explanation (PCA, SVM, Random Forest) |
| 14 | Principal Component Analysis (PCA) |
| e | V Imp (uploaded) |

This package only displays text; it does not run the practical code.
