Skip to main content

A Python package for implementing the TOPSIS decision-making method.

Project description

# TOPSIS - Dishav_Singla-102217004

A Python implementation of the TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) method, designed for decision-making in multi-criteria decision analysis (MCDA).


Project Description

This project implements the TOPSIS technique, a popular method used in decision-making that evaluates multiple alternatives based on various criteria. The project is focused on applying TOPSIS to a given dataset with multiple alternatives and criteria. The objective is to rank and identify the best alternative by calculating the relative closeness of each alternative to the ideal solution.

Objective

  • To apply TOPSIS on a sample dataset.
  • To rank different alternatives based on defined criteria.
  • To generate a ranking list of the alternatives for decision-making purposes.

Features

  • Data Preprocessing: The dataset is cleaned and normalized.
  • TOPSIS Algorithm: Implementation of the TOPSIS method, including the calculation of:
    • Ideal and negative-ideal solutions.
    • Separation measures.
    • Relative closeness to the ideal solution.
  • Ranking: Rank the alternatives based on the relative closeness.
  • Visualizations: Plot results (optional, if applicable).

Installation

Prerequisites

Ensure you have Python 3.x installed. You can download it from the official website:

Install Dependencies

Clone this repository and install the necessary dependencies using pip:

git clone https://github.com/your-username/TOPSIS-Dishav_Singla-102217004.git
cd TOPSIS-Dishav_Singla-102217004
pip install -r requirements.txt

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

TOPSIS-DishavSingla-102217004-0.2.tar.gz (3.2 kB view details)

Uploaded Source

Built Distributions

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

Topsis_DishavSingla_102217004-0.2-py3-none-any.whl (3.8 kB view details)

Uploaded Python 3

TOPSIS_DishavSingla_102217004-0.2-py3-none-any.whl (3.8 kB view details)

Uploaded Python 3

File details

Details for the file TOPSIS-DishavSingla-102217004-0.2.tar.gz.

File metadata

File hashes

Hashes for TOPSIS-DishavSingla-102217004-0.2.tar.gz
Algorithm Hash digest
SHA256 8e79bf41c098f7532c89f46e8572974379d4986f4ea37ced9e53689e3c5dacc7
MD5 aec617df54d7484b88c38c1229f47d2a
BLAKE2b-256 49cb530fb02c01efdc566bb56b7e41b811f28c2def4e8d86952949f9fda9033b

See more details on using hashes here.

File details

Details for the file Topsis_DishavSingla_102217004-0.2-py3-none-any.whl.

File metadata

File hashes

Hashes for Topsis_DishavSingla_102217004-0.2-py3-none-any.whl
Algorithm Hash digest
SHA256 764a73a203259ae643dc9e059ba8599a84581e620ac537dfdf35d0736673061a
MD5 5909b578f3c62653fafe214b39fad936
BLAKE2b-256 9e0e54dd6903e1f3ac083523661e8064673afb745f92863a0aa12c2a09ea6c06

See more details on using hashes here.

File details

Details for the file TOPSIS_DishavSingla_102217004-0.2-py3-none-any.whl.

File metadata

File hashes

Hashes for TOPSIS_DishavSingla_102217004-0.2-py3-none-any.whl
Algorithm Hash digest
SHA256 f8c5120cc8e5859cb0a81365b03f77d7643942dd37a68a581f5865a69d547e55
MD5 a224e5c607ab5c4dfb03fba4571ca0a9
BLAKE2b-256 2d984d4267073df054d1be387f8a0b115b6a194d581e2964e503070248342886

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