MLP

A Python package for Jupyter Notebook integration and Machine Learning program execution.

Overview

MLP provides seamless access to 18 Machine Learning practical programs and a shared dataset directly inside Jupyter Notebook cells. Programs are inserted directly into the next cell for inspection and manual execution without automatic execution.

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Installation

Core Package (Lightweight)

To install MLP with only core Jupyter/IPython dependencies:

pip install mlp-jupyter

Full Machine Learning Dependencies

To install MLP along with all dependencies required to execute the machine learning algorithms (numpy, pandas, scikit-learn, matplotlib, seaborn, mlxtend, openpyxl):

pip install "mlp-jupyter[all]"

Development Installation

To install for local development and testing:

pip install -e ".[dev]"

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Python Version Compatibility

• Supported Python versions: Python 3.8+ (including 3.8, 3.9, 3.10, 3.11, 3.12, 3.13, 3.14).

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Jupyter Notebook Setup

1. Launch your Jupyter environment:

   jupyter notebook
   # or
   jupyter lab
   

2. Open a new or existing Python notebook.
3. Import the package:

   import MLP
   

(Note: The PyPI distribution name is mlp-jupyter, while the Python import name is strictly MLP).

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Public API Reference

1. MLP.list()

Displays all 18 available programs in Part A and Part B order:

import MLP

MLP.list()

Output:

AVAILABLE MLP

PART A
DV-A   Data Visualization
AP-A   Apriori Algorithm
KNN-A  K-Nearest Neighbors
LR-A   Linear Regression
NB-A   Naive Bayes
DT-A   Decision Tree
RF-A   Random Forest
MM-A   Multiple Regression

PART B
CEB-B  Data Interfaces
DP-B   Data Preprocessing
DDD-B  Data Exploration
FPG-B  Frequent Pattern Growth
SV-B   Support Vector Machine
R-B    Regression
KM-B   K-Means Clustering
AC-B   Agglomerative Clustering
DBC-B  DBSCAN
NN-B   Neural Network

2. MLP.get(program_id)

Inserts the complete, unaltered source code of the specified program into the next notebook cell:

MLP.get("LR-A")

• Accepts identifiers with or without .py extension (e.g. "LR-A" or "LR-A.py").
• Uses set_next_input(replace=False) to create a new cell below the current one.
• Preserves all indentation, comments, and structure.
• Never executes automatically; the inserted cell is ready for manual review and execution.

3. MLP.info(program_id)

Displays metadata for any program:

MLP.info("LR-A")

Output:

Program ID:          LR-A
Program filename:    LR-A.py
Program description: Linear Regression
Part:                Part A
Program number:      4

4. MLP.dataset(copy_to=None)

Retrieves the absolute path to the bundled common_dataset.csv, or optionally copies it locally:

# Get absolute path to the bundled dataset
path = MLP.dataset()
print(path)

# Copy the dataset into your current working directory
local_copy = MLP.dataset(copy_to=".")

# Or copy to a specific folder or file path
custom_copy = MLP.dataset(copy_to="data/common_dataset.csv")

The original package dataset remains untouched.

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Complete Program Catalog

Part	Program ID	Filename	Description	Program Number
Part A	DV-A	DV-A.py	Data Visualization	1
Part A	AP-A	AP-A.py	Apriori Algorithm	2
Part A	KNN-A	KNN-A.py	K-Nearest Neighbors	3
Part A	LR-A	LR-A.py	Linear Regression	4
Part A	NB-A	NB-A.py	Naive Bayes	5
Part A	DT-A	DT-A.py	Decision Tree	6
Part A	RF-A	RF-A.py	Random Forest	7
Part A	MM-A	MM-A.py	Multiple Regression	8
Part B	CEB-B	CEB-B.py	Data Interfaces	9
Part B	DP-B	DP-B.py	Data Preprocessing	10
Part B	DDD-B	DDD-B.py	Data Exploration	11
Part B	FPG-B	FPG-B.py	Frequent Pattern Growth	12
Part B	SV-B	SV-B.py	Support Vector Machine	13
Part B	R-B	R-B.py	Regression	14
Part B	KM-B	KM-B.py	K-Means Clustering	15
Part B	AC-B	AC-B.py	Agglomerative Clustering	16
Part B	DBC-B	DBC-B.py	DBSCAN	17
Part B	NN-B	NN-B.py	Neural Network	18

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Troubleshooting

Notice: MLP.get(...) requires an active IPython or Jupyter Notebook environment

• Cause: MLP.get() was called from a standard headless script or standard terminal instead of Jupyter.
• Resolution: Run MLP.get() from inside a Jupyter Notebook cell or interactive IPython shell.

ModuleNotFoundError: No module named 'pandas' (or sklearn, matplotlib)

• Cause: The specific ML program requires third-party packages that are not yet installed in your environment.
• Resolution: Install the complete dependency bundle using:

  pip install "mlp-jupyter[all]"
  

common_dataset.csv not found in DV-A

• Cause: Running DV-A from a directory without the dataset.
• Resolution: Simply enter common_dataset.csv when prompted by DV-A; it automatically resolves to the bundled dataset via MLP.dataset(). Alternatively, provide the full path or copy it locally with MLP.dataset(copy_to=".").

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License

This project is licensed under the MIT License. See LICENSE for details.