| Name | Topsis-Deepankar-Varma-102003431 JSON |
| Version |
0.19
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| Summary | This is a topsis package of Deepankar Varma version 0.19 |
| upload_time | 2023-02-03 18:51:25 |
| maintainer | |
| docs_url | None |
| author | Deepankar Varma |
| requires_python | |
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## Topsis_Deepankar_Varma_102003431
# TOPSIS
Submitted By: _Deepankar Varma-102003431_.
Type: _Package_.
Title: **TOPSIS method for multiple-criteria decision making (MCDM)**.
Version: _0.19_.
Date: _2022-01-29_.
Author: _Deepankar Varma_.
Maintainer: **Deepankar Varma <satwikdpshrit@gmail.com>**.
Description: **Evaluation of alternatives based on multiple criteria using TOPSIS method.**.
---
## What is TOPSIS?
*Technique for **Order **Preference by **Similarity to **Ideal \*\*S*olution
(TOPSIS) originated in the 1980s as a multi-criteria decision making method.
TOPSIS chooses the alternative of shortest Euclidean distance from the ideal solution,
and greatest distance from the negative-ideal solution.
<br>
## How to install this package:
> > pip install Topsis-Deepankar-Varma-102003431==0.19
### In Command Prompt
> > topsis 102003431-data.csv "1,1,1,1,2" "-,+,+,-,+" 102003431-result.csv
## Input file (data.csv)
The decision matrix should be constructed with each row representing a Model alternative, and each column representing a criterion like Accuracy, R<sup>2</sup>, Root Mean Squared Error, Correlation, and many more.
| Model | P1 | P2 | P3 | P4 | P5 |
| ----- | --- | ---- | --- | ---- | ----- |
| M1 | 0.7 | 0.71 | 6.7 | 42.1 | 12.59 |
| M2 | 0.8 | 0.83 | 7 | 31.7 | 10.11 |
| M3 | 0.7 | 0.62 | 4.8 | 46.7 | 13.23 |
| M4 | 0.9 | 0.61 | 6.4 | 42.4 | 12.55 |
| M5 | 0.9 | 0.88 | 3.6 | 62.2 | 16.91 |
| M6 | 0.9 | 0.77 | 6.5 | 51.5 | 14.91 |
| M7 | 0.9 | 0.44 | 5.3 | 48.9 | 13.83 |
| M8 | 0.9 | 0.86 | 3.4 | 37 | 10.55 |
Weights (`weights`) is not already normalised will be normalised later in the code.
Information of benefit positive(+) or negative(-) impact criteria should be provided in `impacts`.
<br>
## Output file (result.csv)
| Model | P1 | P2 | P3 | P4 | P5 | Topsis Score | Rank |
| ----- | ---- | ---- | --- | ---- | ----- | ------------ | ---- |
| M1 | 0.93 | 0.86 | 4.4 | 52.6 | 14.7 | 0.457283053 | 6 |
| M2 | 0.67 | 0.45 | 3.7 | 47.9 | 13.18 | 0.172274243 | 8 |
| M3 | 0.61 | 0.37 | 5.8 | 65 | 17.95 | 0.560480297 | 2 |
| M4 | 0.94 | 0.88 | 6 | 40.7 | 12.13 | 0.491036776 | 3 |
| M5 | 0.69 | 0.48 | 3.8 | 55.6 | 15.14 | 0.239375223 | 7 |
| M6 | 0.93 | 0.86 | 5.3 | 47.1 | 13.55 | 0.486632047 | 4 |
| M7 | 0.93 | 0.86 | 6.9 | 69.9 | 19.65 | 0.822186901 | 1 |
| M8 | 0.95 | 0.9 | 3.1 | 61.6 | 16.64 | 0.460139442 | 5 |
<br>
The output file contains columns of input file along with two additional columns having *Topsis_score* and *Rank*
Raw data
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"description": "## Topsis_Deepankar_Varma_102003431\n\n# TOPSIS\n\nSubmitted By: _Deepankar Varma-102003431_.\n\nType: _Package_.\n\nTitle: **TOPSIS method for multiple-criteria decision making (MCDM)**.\n\nVersion: _0.19_.\n\nDate: _2022-01-29_.\n\nAuthor: _Deepankar Varma_.\n\nMaintainer: **Deepankar Varma <satwikdpshrit@gmail.com>**.\n\nDescription: **Evaluation of alternatives based on multiple criteria using TOPSIS method.**.\n\n---\n\n## What is TOPSIS?\n\n*Technique for **Order **Preference by **Similarity to **Ideal \\*\\*S*olution\n(TOPSIS) originated in the 1980s as a multi-criteria decision making method.\nTOPSIS chooses the alternative of shortest Euclidean distance from the ideal solution,\nand greatest distance from the negative-ideal solution.\n\n<br>\n\n## How to install this package:\n\n> > pip install Topsis-Deepankar-Varma-102003431==0.19\n\n### In Command Prompt\n\n> > topsis 102003431-data.csv \"1,1,1,1,2\" \"-,+,+,-,+\" 102003431-result.csv\n\n## Input file (data.csv)\n\nThe decision matrix should be constructed with each row representing a Model alternative, and each column representing a criterion like Accuracy, R<sup>2</sup>, Root Mean Squared Error, Correlation, and many more.\n\n| Model | P1 | P2 | P3 | P4 | P5 |\n| ----- | --- | ---- | --- | ---- | ----- |\n| M1 | 0.7 | 0.71 | 6.7 | 42.1 | 12.59 |\n| M2 | 0.8 | 0.83 | 7 | 31.7 | 10.11 |\n| M3 | 0.7 | 0.62 | 4.8 | 46.7 | 13.23 |\n| M4 | 0.9 | 0.61 | 6.4 | 42.4 | 12.55 |\n| M5 | 0.9 | 0.88 | 3.6 | 62.2 | 16.91 |\n| M6 | 0.9 | 0.77 | 6.5 | 51.5 | 14.91 |\n| M7 | 0.9 | 0.44 | 5.3 | 48.9 | 13.83 |\n| M8 | 0.9 | 0.86 | 3.4 | 37 | 10.55 |\n\nWeights (`weights`) is not already normalised will be normalised later in the code.\n\nInformation of benefit positive(+) or negative(-) impact criteria should be provided in `impacts`.\n\n<br>\n\n## Output file (result.csv)\n\n| Model | P1 | P2 | P3 | P4 | P5 | Topsis Score | Rank |\n| ----- | ---- | ---- | --- | ---- | ----- | ------------ | ---- |\n| M1 | 0.93 | 0.86 | 4.4 | 52.6 | 14.7 | 0.457283053 | 6 |\n| M2 | 0.67 | 0.45 | 3.7 | 47.9 | 13.18 | 0.172274243 | 8 |\n| M3 | 0.61 | 0.37 | 5.8 | 65 | 17.95 | 0.560480297 | 2 |\n| M4 | 0.94 | 0.88 | 6 | 40.7 | 12.13 | 0.491036776 | 3 |\n| M5 | 0.69 | 0.48 | 3.8 | 55.6 | 15.14 | 0.239375223 | 7 |\n| M6 | 0.93 | 0.86 | 5.3 | 47.1 | 13.55 | 0.486632047 | 4 |\n| M7 | 0.93 | 0.86 | 6.9 | 69.9 | 19.65 | 0.822186901 | 1 |\n| M8 | 0.95 | 0.9 | 3.1 | 61.6 | 16.64 | 0.460139442 | 5 |\n\n<br>\nThe output file contains columns of input file along with two additional columns having *Topsis_score* and *Rank*\n",
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