Skip to main content

Supplier Selection Package

Project description

Supplier Selection Model

Overview

This repository contains code for a supplier selection model. The model uses machine learning algorithms (Random Forest Regression, Linear Regression, Decision Tree, Gradient Boosting) to predict and rank suppliers based on various criteria.

Getting Started

Prerequisites

  • Python 3.x
  • Required Python packages: pandas, numpy, scikit-learn, statsmodels, matplotlib, seaborn, pyodbc, sqlalchemy, cryptography

Installation

  1. Clone the repository:

    git clone https://github.com/yourusername/supplier-selection-model.git
    

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

SupplierSelection-0.1.0.tar.gz (1.6 kB view details)

Uploaded Source

File details

Details for the file SupplierSelection-0.1.0.tar.gz.

File metadata

  • Download URL: SupplierSelection-0.1.0.tar.gz
  • Upload date:
  • Size: 1.6 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.2 CPython/3.8.6

File hashes

Hashes for SupplierSelection-0.1.0.tar.gz
Algorithm Hash digest
SHA256 647df946bb290c5a4e9ed8edd7b1cf676c4e6efcb140ac9c026eead46da332a2
MD5 7f5da52e2f4403494ddb3433d24bd920
BLAKE2b-256 1bdb796c768daa8668b68f0ba874e293a99870a120e957e319197d82e285e8c6

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page