Skip to main content

TOPSIS implementation using Python

Project description

TOPSIS - Python Package

By: Bhavya
Roll Number: 102303560
Course: UCS654 - Predictive Analytics using Statistics

Project Description

TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) is a multi-criteria decision-making (MCDM) method used to rank alternatives based on their distance from an ideal best and an ideal worst solution.

This Python package provides an easy-to-use command-line implementation of the TOPSIS algorithm. It allows users to input a dataset along with weights and impacts and produces a ranked output based on TOPSIS scores.

Installation

Install the package using pip:

pip install Topsis-bhavya-102303560

Usage

Run the following command:

topsis <inputFile> <weights> <impacts> <outputFile>

Example:

topsis sample.csv "1,1,1,1" "+,+,-,+" result.csv

Input File Format

  • The input file must contain at least three columns.
  • The first column should contain alternative names.
  • All other columns must contain numeric values only.
  • The number of weights and impacts must match the number of criteria.

Sample Input File

Model Storage Camera Price Looks
M1 16 12 250 5
M2 16 8 200 3
M3 32 16 300 4
M4 32 8 275 4
M5 16 16 225 2

Output

The output file contains:

  • Topsis Score
  • Rank

The alternative with Rank 1 is considered the best choice.

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_bhavya_102303560-1.0.3.tar.gz (3.1 kB view details)

Uploaded Source

Built Distribution

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

topsis_bhavya_102303560-1.0.3-py3-none-any.whl (3.6 kB view details)

Uploaded Python 3

File details

Details for the file topsis_bhavya_102303560-1.0.3.tar.gz.

File metadata

  • Download URL: topsis_bhavya_102303560-1.0.3.tar.gz
  • Upload date:
  • Size: 3.1 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.2.0 CPython/3.11.9

File hashes

Hashes for topsis_bhavya_102303560-1.0.3.tar.gz
Algorithm Hash digest
SHA256 0ab60fc7345e491253b9106f65d140d61b3dd20ff1964506590742cc72bf6cc0
MD5 fb7738a8030ae67465ccd8ee503d61eb
BLAKE2b-256 84ab4a4289b8a043a960b03d20de88efb440d20f969d0dc05128991ec1810b42

See more details on using hashes here.

File details

Details for the file topsis_bhavya_102303560-1.0.3-py3-none-any.whl.

File metadata

File hashes

Hashes for topsis_bhavya_102303560-1.0.3-py3-none-any.whl
Algorithm Hash digest
SHA256 3dd4f5fada167eb47bd5b9010fe0a0e14ba698ad1f714f2adf2701517dc77870
MD5 4cbb588a1e96a96a04f2fd6bd907f9a7
BLAKE2b-256 b7fde3f1685c7523be66878134aec5e22981d7fcdce5eca689ea574e8f51ee37

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