Skip to main content

TOPSIS implementation

Project description

TOPSIS – Technique for Order Preference by Similarity to Ideal Solution

This Python package implements the TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) method. It allows users to rank alternatives based on multiple criteria using a command-line interface.


📌 What is TOPSIS?

TOPSIS is a Multi-Criteria Decision-Making (MCDM) technique that identifies solutions from a finite set of alternatives based on:

  • Closest distance to the ideal best solution
  • Farthest distance from the ideal worst solution

🔧 Requirements

The package requires the following Python libraries:

  • Python ≥ 3.7
  • pandas
  • numpy

These dependencies are installed automatically.


📥 Installation

Install the package from PyPI using:

pip install Topsis-RishabhSharma-102303286

Project details


Release history Release notifications | RSS feed

This version

1.0

Download files

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

Source Distribution

topsis_rishabhsharma_102303286-1.0.tar.gz (2.7 kB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

topsis_rishabhsharma_102303286-1.0-py3-none-any.whl (3.2 kB view details)

Uploaded Python 3

File details

Details for the file topsis_rishabhsharma_102303286-1.0.tar.gz.

File metadata

File hashes

Hashes for topsis_rishabhsharma_102303286-1.0.tar.gz
Algorithm Hash digest
SHA256 8a48808144e9391506fa40e11d7fd845ff776fdcadc6efdfb14125b913fdb85f
MD5 7536df0f3be0c9152836d84cf1013ff8
BLAKE2b-256 16ff30b846f841a519eebe80cae021a2fddc720f3f5b6980b317e44d118b4356

See more details on using hashes here.

File details

Details for the file topsis_rishabhsharma_102303286-1.0-py3-none-any.whl.

File metadata

File hashes

Hashes for topsis_rishabhsharma_102303286-1.0-py3-none-any.whl
Algorithm Hash digest
SHA256 fbe1865bf89e6d5f8f61d400da425f0f676d2ac9548086f4b5b15ffa5e97350d
MD5 e5cf10fc4d11f1e3e592780b58c8aa83
BLAKE2b-256 52d811cf7c39e9fc08be8585c0ab0d8dccfe83bad62ad212c12029dabe41d570

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