notebookpkg 3.0.0

A CLI package manager for Jupyter Notebook ML/DA templates

notebookpkg v3.0.0

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

Installation

pip install notebookpkg

CLI Commands

Install a template wired to your dataset

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

notebookpkg list

View template code without installing

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 R²
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

# 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 — [e-mail]