# TOPSIS-Python
Submitted By: **Sanyam Goyal 102297005**
***
## What is TOPSIS
**Technique for Order Preference by Similarity to Ideal Solution (TOPSIS)**, is a decision-making method used in multi-criteria decision analysis. It is a mathematical technique that helps in ranking and selecting the best alternative from a set of options based on their proximity to an ideal solution. Check out more information here:[wikipedia](https://en.wikipedia.org/wiki/TOPSIS).
<br>
## How to use this package:
TOPSIS-SANYAM_GOYAL-102297005 can be run as in the following example:
### In Command Prompt to run the code:
```
python topsis input_data.csv "1,1,1,1" "+,+,-,+" output_data.csv
```
<br>
## Sample dataset
The decision matrix (`a`) should be constructed with each row representing a ID, and each column representing a criterion like Features
ID | Feature1 | Feature2 | Feature3 | Feature4
---|----------|----------|----------|----------
1 | 2 | 5 | 8 | 3
2 | 3 | 6 | 9 | 4
3 | 5 | 8 | 2 | 7
4 | 6 | 9 | 3 | 8
<br>
## Output
```
ID,Feature1,Feature2,Feature3,Feature4,Topsis Score,Rank
1,2,5,8,3,0.33629008441513597,3
2,3,6,9,4,0.26542732311540135,4
3,5,8,2,7,0.7345726768845987,1
4,6,9,3,8,0.6637099155848639,2
```
<br>
The rankings are displayed in the form of a table using a package 'tabulate', with the 1st rank offering us the best decision, and last rank offering the worst decision making, according to TOPSIS method.
Raw data
{
"_id": null,
"home_page": "https://www.github.com/sanyamgoyal401",
"name": "TOPSIS-SANYAM-GOYAL-102297005",
"maintainer": "",
"docs_url": null,
"requires_python": "",
"maintainer_email": "",
"keywords": "topsis,UCS654,TIET,data,science,Prashant Singh Rana",
"author": "Sanyam Goyal",
"author_email": "sanyamgoyal2859@gmail.com",
"download_url": "https://files.pythonhosted.org/packages/de/93/c54bedacab672d801bd39dcfb969225b8d3601530b0981780e9887ecc219/TOPSIS-SANYAM_GOYAL-102297005-0.0.1.tar.gz",
"platform": null,
"description": "# TOPSIS-Python\n\nSubmitted By: **Sanyam Goyal 102297005**\n\n***\n\n## What is TOPSIS\n\n**Technique for Order Preference by Similarity to Ideal Solution (TOPSIS)**, is a decision-making method used in multi-criteria decision analysis. It is a mathematical technique that helps in ranking and selecting the best alternative from a set of options based on their proximity to an ideal solution. Check out more information here:[wikipedia](https://en.wikipedia.org/wiki/TOPSIS).\n\n<br>\n\n## How to use this package:\n\nTOPSIS-SANYAM_GOYAL-102297005 can be run as in the following example:\n\n\n### In Command Prompt to run the code:\n```\npython topsis input_data.csv \"1,1,1,1\" \"+,+,-,+\" output_data.csv\n```\n<br>\n\n## Sample dataset\n\nThe decision matrix (`a`) should be constructed with each row representing a ID, and each column representing a criterion like Features\n\nID | Feature1 | Feature2 | Feature3 | Feature4\n---|----------|----------|----------|----------\n1 | 2 | 5 | 8 | 3\n2 | 3 | 6 | 9 | 4\n3 | 5 | 8 | 2 | 7\n4 | 6 | 9 | 3 | 8\n\n<br>\n\n## Output\n\n```\nID,Feature1,Feature2,Feature3,Feature4,Topsis Score,Rank\n1,2,5,8,3,0.33629008441513597,3\n2,3,6,9,4,0.26542732311540135,4\n3,5,8,2,7,0.7345726768845987,1\n4,6,9,3,8,0.6637099155848639,2\n\n```\n<br>\nThe rankings are displayed in the form of a table using a package 'tabulate', with the 1st rank offering us the best decision, and last rank offering the worst decision making, according to TOPSIS method.\n",
"bugtrack_url": null,
"license": "MIT",
"summary": "A Python package to find TOPSIS for multi-criteria decision analysis method",
"version": "0.0.1",
"project_urls": {
"Homepage": "https://www.github.com/sanyamgoyal401"
},
"split_keywords": [
"topsis",
"ucs654",
"tiet",
"data",
"science",
"prashant singh rana"
],
"urls": [
{
"comment_text": "",
"digests": {
"blake2b_256": "cb7b9de7049b29d19bc9c128479e7f90f36182390d87bdda2cdd692ac5b8eea6",
"md5": "8dc5c69fb48b34d76ca7b420da7d9b8b",
"sha256": "a5d09823432a2cc8ef37e5daa9c98b5b4fbbea794f424a6ebabba566e762491d"
},
"downloads": -1,
"filename": "TOPSIS_SANYAM_GOYAL_102297005-0.0.1-py3-none-any.whl",
"has_sig": false,
"md5_digest": "8dc5c69fb48b34d76ca7b420da7d9b8b",
"packagetype": "bdist_wheel",
"python_version": "py3",
"requires_python": null,
"size": 5297,
"upload_time": "2024-01-29T18:49:01",
"upload_time_iso_8601": "2024-01-29T18:49:01.210175Z",
"url": "https://files.pythonhosted.org/packages/cb/7b/9de7049b29d19bc9c128479e7f90f36182390d87bdda2cdd692ac5b8eea6/TOPSIS_SANYAM_GOYAL_102297005-0.0.1-py3-none-any.whl",
"yanked": false,
"yanked_reason": null
},
{
"comment_text": "",
"digests": {
"blake2b_256": "de93c54bedacab672d801bd39dcfb969225b8d3601530b0981780e9887ecc219",
"md5": "c17dc6c554e5fcde291b893a52f0e0eb",
"sha256": "0f4066f99595289373c7dfc7a47a1c797d98073005db378e52080b349561a61b"
},
"downloads": -1,
"filename": "TOPSIS-SANYAM_GOYAL-102297005-0.0.1.tar.gz",
"has_sig": false,
"md5_digest": "c17dc6c554e5fcde291b893a52f0e0eb",
"packagetype": "sdist",
"python_version": "source",
"requires_python": null,
"size": 4827,
"upload_time": "2024-01-29T18:49:03",
"upload_time_iso_8601": "2024-01-29T18:49:03.137383Z",
"url": "https://files.pythonhosted.org/packages/de/93/c54bedacab672d801bd39dcfb969225b8d3601530b0981780e9887ecc219/TOPSIS-SANYAM_GOYAL-102297005-0.0.1.tar.gz",
"yanked": false,
"yanked_reason": null
}
],
"upload_time": "2024-01-29 18:49:03",
"github": false,
"gitlab": false,
"bitbucket": false,
"codeberg": false,
"lcname": "topsis-sanyam-goyal-102297005"
}
JSON