notebookpkg 1.2.0

A package manager for Jupyter notebook templates

notebookpkg

One command to get a ready-to-run Jupyter notebook — wired to your dataset.

Built for CDAC ML students. Stop writing boilerplate. Just install a template, open Jupyter, and run all cells.

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Install

pip install notebookpkg

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Quick Start

# See all available templates
notebookpkg list

# Install a template for your dataset
notebookpkg install linear-regression --dataset Salary_Data.csv --target Salary

# Open the generated notebook in Jupyter
jupyter notebook linear-regression_notebook.ipynb

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Available Templates

Template	What it does
eda-basic	head, shape, info, describe, nulls, dtypes, nunique
eda-visual	pairplot, heatmap, distributions
eda-full	full EDA + outliers (IQR), skewness, boxplots, value counts
linear-regression	fit, predict, visualize, coefficient, MSE, R²
polynomial-regression	PolynomialFeatures, smooth curve plot, MSE, R²
logistic-regression	StandardScaler, fit, accuracy, confusion matrix, report
knn-classifier	StandardScaler, KNN, accuracy, confusion matrix, report
naive-bayes	GaussianNB, StandardScaler, confusion matrix heatmap
lasso-ridge	LinearRegression + Lasso + Ridge, coefficient barh plots
decision-tree	criterion=entropy, max_depth=5, plot_tree, evaluation
random-forest-regressor	RFR, MSE, R², Actual vs Predicted scatter
random-forest-classifier	model1, accuracy, confusion matrix, feature importance
svm-classifier	Linear SVM → RBF SVM, AgeSalary feature engineering
kmeans-clustering	Elbow method, KMeans, silhouette score, cluster plot
multi-model-compare	LR + KNN + Naive Bayes on same dataset, comparison

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Usage Examples

# EDA
notebookpkg install eda-basic --dataset data.csv
notebookpkg install eda-visual --dataset data.csv
notebookpkg install eda-full --dataset data.csv

# Regression
notebookpkg install linear-regression --dataset Salary_Data.csv --target Salary
notebookpkg install polynomial-regression --dataset hw.csv --target Price --degree 3
notebookpkg install lasso-ridge --dataset BostonHousing.csv --target medv

# Classification
notebookpkg install logistic-regression --dataset Day5.csv --target Purchased
notebookpkg install knn-classifier --dataset Day5.csv --target Purchased
notebookpkg install naive-bayes --dataset Day5.csv --target Purchased
notebookpkg install decision-tree --dataset SNP.csv --target Purchased
notebookpkg install svm-classifier --dataset SNP.csv --target Purchased
notebookpkg install multi-model-compare --dataset Day5.csv --target Purchased

# Ensemble
notebookpkg install random-forest-regressor --dataset housing.csv --target Price
notebookpkg install random-forest-classifier --dataset iris.csv --target species

# Clustering
notebookpkg install kmeans-clustering --dataset Mall_Customers.csv

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Options

Flag	Description	Default
--dataset	Path to your CSV file	required
--target	Target column name	last column
--output	Output notebook filename	<template>_notebook.ipynb
--degree	Polynomial degree (polynomial-regression only)	2

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How It Works

1. You run notebookpkg install <template> --dataset yourdata.csv
2. The tool reads your CSV and detects column types automatically
3. It injects your dataset path, target column, and column names into the template
4. A ready-to-run .ipynb file is created in your current folder
5. Open it in Jupyter and run all cells — everything is pre-wired

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Requirements

pandas
numpy
scikit-learn
matplotlib
seaborn
nbformat
click

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Author

Priyansu Pattanaik
B.Tech (Electronics & Telecommunication) | PG Diploma in AI — CDAC Kharghar
[e-mail]

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License

MIT