Metadata-Version: 2.4
Name: notebookpkg
Version: 3.0.0
Summary: A CLI package manager for Jupyter Notebook ML/DA templates
Home-page: https://github.com/priyansupattanaik/notebookpkg
Author: Priyansu Pattanaik
Author-email: priyansupattanaikwork@gmail.com
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Requires-Python: >=3.8
Description-Content-Type: text/markdown
Requires-Dist: click>=8.0
Requires-Dist: pandas>=1.3
Requires-Dist: numpy>=1.21
Requires-Dist: scikit-learn>=1.0
Requires-Dist: matplotlib>=3.4
Requires-Dist: seaborn>=0.11
Requires-Dist: statsmodels>=0.13
Requires-Dist: scipy>=1.7
Requires-Dist: nbformat>=5.0
Requires-Dist: apyori>=1.1.2
Dynamic: author
Dynamic: author-email
Dynamic: classifier
Dynamic: description
Dynamic: description-content-type
Dynamic: home-page
Dynamic: requires-dist
Dynamic: requires-python
Dynamic: summary

# notebookpkg v3.0.0

A CLI tool to instantly generate ready-to-run Jupyter Notebook templates for Machine Learning and Data Analytics.

## Installation

```bash
pip install notebookpkg
```

## CLI Commands

### Install a template wired to your dataset
```bash
notebookpkg install <template> --dataset data.csv
```

### Options
| Option | Default | Description |
|---|---|---|
| `--dataset` | required | Path to your CSV file |
| `--target` | last column | Target/label column name |
| `--drop` | none | Columns to drop (comma-separated) |
| `--degree` | 2 | Polynomial degree |
| `--clusters` | 3 | Number of clusters |
| `--output` | `<template>_notebook.ipynb` | Output filename |

### List all templates
```bash
notebookpkg list
```

### View template code without installing
```bash
notebookpkg syntax linear-regression
```

## Available Templates (25)

| Template | Description |
|---|---|
| `eda-basic` | Basic EDA: shape, info, describe, nulls |
| `eda-visual` | Visual EDA: pairplot, heatmap, distributions |
| `eda-full` | Full EDA: outliers, skewness, duplicates, value counts |
| `linear-regression` | Linear Regression with MSE and RÂ² |
| `polynomial-regression` | Polynomial Regression with smooth curve |
| `logistic-regression` | Logistic Regression with confusion matrix |
| `knn-classifier` | K-Nearest Neighbors Classifier |
| `naive-bayes` | Gaussian Naive Bayes |
| `lasso-ridge` | Lasso + Ridge + ElasticNet + GridSearchCV |
| `decision-tree` | Decision Tree with tree plot |
| `random-forest-regressor` | Random Forest Regressor |
| `random-forest-classifier` | Random Forest Classifier |
| `svm-classifier` | SVM Linear + RBF with decision boundaries |
| `kmeans-clustering` | KMeans with elbow method |
| `multi-model-compare` | LR + KNN + NB comparison |
| `cross-validation` | KFold cross-validation |
| `dbscan-clustering` | DBSCAN vs KMeans with PCA view |
| `pca` | PCA dimensionality reduction + clustering |
| `association-rules` | Apriori market basket analysis |
| `arima-forecasting` | ARIMA time series forecasting |
| `text-classification` | CountVectorizer + Multinomial NB |
| `ensemble-methods` | RF + Bagging + AdaBoost + Stacking |
| `hierarchical-clustering` | Agglomerative with dendrogram |
| `moving-average` | SMA + WMA + EMA |
| `anomaly-detection` | IsolationForest anomaly detection |

## Example Usage

```bash
# Basic EDA
notebookpkg install eda-basic --dataset iris.csv

# Linear Regression
notebookpkg install linear-regression --dataset housing.csv --target price

# Classification
notebookpkg install random-forest-classifier --dataset titanic.csv --target Survived --drop "PassengerId,Name,Ticket,Cabin"

# Clustering
notebookpkg install kmeans-clustering --dataset customers.csv --clusters 5

# Polynomial Regression with degree 3
notebookpkg install polynomial-regression --dataset data.csv --target y --degree 3
```

## Author

Priyansu Pattanaik â€” priyansupattanaikwork@gmail.com
