Skip to main content

A small package that showcases topsis approach

Project description

This is a simple package to use topsis (Technique for Order of preference by Similarity to Ideal Solution) approach to select best among many things based on different attributes, such as selecting the best Machine Learning algorithms based on correlation,R-square,root mean square error and accuracy. You can use this simple package to get ranks on basis of topsis approach, ypu simply need to pass NumPy array after pre-processing your data along with weights between 0 and 1 and all weights should sum to 1 to get correct results and you need to pass impacts as well in form of '+' or '-' where '+' indicates that you need to maximise value of that particular column and '-' indicates that you need to minimise value of that particular column. After passing all these parameters you will get your rank row.

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

toparu_arushigupta-0.0.2.tar.gz (2.9 kB view details)

Uploaded Source

Built Distribution

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

toparu_arushigupta-0.0.2-py3-none-any.whl (3.6 kB view details)

Uploaded Python 3

File details

Details for the file toparu_arushigupta-0.0.2.tar.gz.

File metadata

  • Download URL: toparu_arushigupta-0.0.2.tar.gz
  • Upload date:
  • Size: 2.9 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.1.1 pkginfo/1.4.2 requests/2.22.0 setuptools/45.1.0 requests-toolbelt/0.9.1 tqdm/4.41.1 CPython/3.6.5

File hashes

Hashes for toparu_arushigupta-0.0.2.tar.gz
Algorithm Hash digest
SHA256 dd8394558bde6ca87fe87cacb6dcf120f7b90f3b3df8ffa0747031b533b5fa11
MD5 f78e11f138d21dd9260328017630efe4
BLAKE2b-256 ae0d718c87f36144541a8e2f53c217ee508d5c8af0fe4793e9450bb924031963

See more details on using hashes here.

File details

Details for the file toparu_arushigupta-0.0.2-py3-none-any.whl.

File metadata

  • Download URL: toparu_arushigupta-0.0.2-py3-none-any.whl
  • Upload date:
  • Size: 3.6 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.1.1 pkginfo/1.4.2 requests/2.22.0 setuptools/45.1.0 requests-toolbelt/0.9.1 tqdm/4.41.1 CPython/3.6.5

File hashes

Hashes for toparu_arushigupta-0.0.2-py3-none-any.whl
Algorithm Hash digest
SHA256 d31f163de23219112fad33c62d0bc1a3b0cb319ffd3427206679a2b9ae8ba6b1
MD5 21a5c059c3f24cf5362d662dfe2fc221
BLAKE2b-256 6b976adcfe4af674aa20ebf9d15289d9ad26424ff32568897a5c1ba168d6559b

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