ML_Pipeline_Classification_Clustering

This project demonstrates the machine learning pipeline for Classification using Pycaret 3.0 and Scikit Learn on Jupyter Notebook. The data used in this project is the German Credit Data dataset.

Overview 💡

This project showcases the use of Scikit learn as well as Pycaret 3.0, a low-code machine learning library, to perform Classification and Clustering analysis on the German Credit Data dataset. The project includes a Jupyter Notebook containing the code for building and evaluating the machine learning models.

Requirements 💡

To run this project, you need the following:

• Jupyter Notebook
• Scikit Learn
• Pycaret 3.0
• Python 3.x

Installation 💡

To install Pycaret 3.0, run the following command:

!pip install pycaret[full]

Dataset 💡

• The German Credit Card dataset contains information about Credit Card dataset to cluster the users based on duration, credit card history, purpose, credit amount, Guarantors, property etc.

The dataset can be found in the UCI Machine Learning Repository. Cite: Hofmann,Hans. (1994). Statlog (German Credit Data).

Usage 💡

To run the project, download the notebook files in Jupyter Notebook and execute the code cells.

Each notebook contains the following sections:

• Data Preparation: This section loads the dataset and performs data cleaning. Click here to view
• PC_Classification: This section builds and evaluates machine learning models for classification analysis using Pycaret 3.0. Click here to view
• Scikit Learn Classification: This section builds and evaluates machine learning models for classification analysis using SciKit Learn. Click here to view

Conclusion 💡

This project demonstrates the use of Pycaret 3.0 and SciKit Learn.for building and evaluating machine learning models for Classification analysis. The code and techniques used in this project can be adapted to other datasets and machine learning problems.

License 💡

This is licensed under MIT License

© Jaz 2023 😄