# Topsis-Samarjot-102003242

 Name Topsis-Samarjot-102003242 JSON Version 1.0 JSON download home_page Summary Python package implementing TOPSIS multi-criteria decision making method. upload_time 2023-01-25 10:37:16 maintainer docs_url None author Samarjot Singh requires_python license MIT keywords VCS bugtrack_url requirements No requirements were recorded. Travis-CI No Travis. coveralls test coverage No coveralls.
```            # TOPSIS  multi-criteria decision making - Python

**Assignment 1 : UCS654**

Submitted By: **Samarjot Singh 102003242**

***

## What is TOPSIS?

**TOPSIS**, known as Technique for Order of Preference by Similarity to Ideal Solution, is a multi-criteria decision analysis method. It compares a set of alternatives based on a pre-specified criterion. The method is used in the business across various industries, every time we need to make an analytical decision based on collected data. More details at [YouTube](https://www.youtube.com/watch?v=kfcN7MuYVeI&ab_channel=ManojMathew).

<br>

## How to run this package:

TOPSIS-Samar 102003242  can be used by running following command in CMD:

```
>> topsis 102003242-data.csv "1,1,1,2,1" "-,+,+,-,+" 102003242-result.csv
```

<br>

## Sample dataset

The decision matrix should be constructed with each row representing a Fund Name, and each column representing a criterion P1, P2, P3, P4, P5.

Fund Name | P1 | P2 | P3 | P4 | P5
------------ | ------------- | ------------ | ------------- | ------------- | ------------
M1 |	0.72 | 0.52	| 4.4 | 62.1 | 16.94
M3 |	0.72 | 0.52	| 5.7 | 48.6 | 13.91
M2 |	0.76 | 0.58	| 4.2 | 39.4 | 11.21
M4 |	0.68 | 0.46	| 6.7 | 50 | 14.46
M5 |	0.67 | 0.45	| 5.2 | 62.2 | 17.13
M6 |	0.86 | 0.74	| 5.2 | 63.8 | 17.65
M7 |	0.93 | 0.86	| 4.5 | 65.6 | 17.97
M8 |	0.78 | 0.61	| 5.4 | 69.7 | 19.12

Weights(`w`) and Impacts(`i`) will be applied later in the code.

<br>

## Output

```
Row No.   Performance Score    Rank
--------  -------------------  ------
3            0.332629          8
2            0.555383          1
1            0.548848          2
4            0.530816          3
5            0.354290          6
6            0.421567          5
7            0.435080          4
8            0.353907          7
```
<br>
The rankings are displayed in the form of a table with the 1st rank offering us the best decision and last rank offering the worst decision making, according to TOPSIS method.

```

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"description": "# TOPSIS  multi-criteria decision making - Python\r\n\r\n**Assignment 1 : UCS654**\r\n\r\nSubmitted By: **Samarjot Singh 102003242**\r\n\r\n***\r\n\r\n## What is TOPSIS?\r\n\r\n**TOPSIS**, known as Technique for Order of Preference by Similarity to Ideal Solution, is a multi-criteria decision analysis method. It compares a set of alternatives based on a pre-specified criterion. The method is used in the business across various industries, every time we need to make an analytical decision based on collected data. More details at [YouTube](https://www.youtube.com/watch?v=kfcN7MuYVeI&ab_channel=ManojMathew).\r\n\r\n<br>\r\n\r\n## How to run this package:\r\n\r\nTOPSIS-Samar 102003242  can be used by running following command in CMD:\r\n\r\n```\r\n>> topsis 102003242-data.csv \"1,1,1,2,1\" \"-,+,+,-,+\" 102003242-result.csv\r\n```\r\n\r\n<br>\r\n\r\n## Sample dataset\r\n\r\nThe decision matrix should be constructed with each row representing a Fund Name, and each column representing a criterion P1, P2, P3, P4, P5.\r\n\r\nFund Name | P1 | P2 | P3 | P4 | P5\r\n------------ | ------------- | ------------ | ------------- | ------------- | ------------\r\nM1 |\t0.72 | 0.52\t| 4.4 | 62.1 | 16.94\r\nM3 |\t0.72 | 0.52\t| 5.7 | 48.6 | 13.91\r\nM2 |\t0.76 | 0.58\t| 4.2 | 39.4 | 11.21\r\nM4 |\t0.68 | 0.46\t| 6.7 | 50 | 14.46\r\nM5 |\t0.67 | 0.45\t| 5.2 | 62.2 | 17.13\r\nM6 |\t0.86 | 0.74\t| 5.2 | 63.8 | 17.65\r\nM7 |\t0.93 | 0.86\t| 4.5 | 65.6 | 17.97\r\nM8 |\t0.78 | 0.61\t| 5.4 | 69.7 | 19.12\r\n\r\nWeights(`w`) and Impacts(`i`) will be applied later in the code.\r\n\r\n<br>\r\n\r\n## Output\r\n\r\n```\r\n Row No.   Performance Score    Rank\r\n--------  -------------------  ------\r\n  3            0.332629          8\r\n  2            0.555383          1\r\n  1            0.548848          2\r\n  4            0.530816          3\r\n  5            0.354290          6\r\n  6            0.421567          5\r\n  7            0.435080          4\r\n  8            0.353907          7\r\n```\r\n<br>\r\nThe rankings are displayed in the form of a table with the 1st rank offering us the best decision and last rank offering the worst decision making, according to TOPSIS method.\r\n",
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