Skip to main content

It gives the topsis analysis of your csv file with score and rankings.

Project description

TOPSIS-Package Simrat(102203201)

Overview TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) is a multi-criteria decision-making (MCDM) technique. This Python package helps you evaluate alternatives based on multiple criteria by calculating the TOPSIS score and ranking them accordingly.

With this package, you can:

  1. Input your dataset in CSV format.
  2. Specify weights for each criterion.
  3. Define whether each criterion is beneficial (+) or non-beneficial (-).
  4. Obtain a ranked CSV file as output with TOPSIS scores.

Installation

Install the package using pip install 102203201-simrat

Input Parameters

  1. Input CSV File: Path to your dataset in CSV format. The dataset must have at least three columns: The first column should contain the names of the alternatives. The subsequent columns should contain numeric values for criteria.
  2. Weights: Comma-separated numeric values representing the weight of each criterion.
  3. Impacts: Comma-separated values (+ or -) indicating whether each criterion is beneficial (+) or non-beneficial (-).
  4. Output CSV File Name: Desired file name for the output CSV containing TOPSIS scores and rankings.

License

Copyright (c) 2024 Simrat Kaur

This package is licensed under the MIT License.

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

102203201_simrat-1.0.0.tar.gz (3.9 kB view details)

Uploaded Source

Built Distribution

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

102203201_simrat-1.0.0-py3-none-any.whl (4.4 kB view details)

Uploaded Python 3

File details

Details for the file 102203201_simrat-1.0.0.tar.gz.

File metadata

  • Download URL: 102203201_simrat-1.0.0.tar.gz
  • Upload date:
  • Size: 3.9 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.0.1 CPython/3.12.4

File hashes

Hashes for 102203201_simrat-1.0.0.tar.gz
Algorithm Hash digest
SHA256 af336d9451e569366a2bca5a4c1894dfc368667db202b4412c285e12bbbeb379
MD5 76446d6b6efde416a271b8e8a95446a2
BLAKE2b-256 861880d5ca4c52f8182c38cecb6e190bff09a2a0ebfd5c0624e8db83465bc0d2

See more details on using hashes here.

File details

Details for the file 102203201_simrat-1.0.0-py3-none-any.whl.

File metadata

File hashes

Hashes for 102203201_simrat-1.0.0-py3-none-any.whl
Algorithm Hash digest
SHA256 999f39d70eabc3cbe5701f3c4f03d1d961bc697708711e13d366c0e27487c9d4
MD5 73d58a9ea75ffb712ac0e077910a1415
BLAKE2b-256 eef7bb5c126e4b6cb1810529c04f11f9250180d1487559244cff67dc88d4cbb3

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