# TOPSIS
Submitted By: **Abhishek Gandhi 102003364**
***
## What is TOPSIS?
**T**echnique for **O**rder **P**reference by **S**imilarity to **I**deal **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-abhishek-102003364
```
### In Command Prompt
```
>> python <program.py> <InputDataFile> <Weights> <Impacts> <ResultFileName>
>> python 102003364.py data.csv "1,1,1,1" "+,+,-,+" 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 | Correlation | R<sup>2</sup> | RMSE | Accuracy
------|------------ | ------------ | ---- | ------------
M1 | 0.79 | 0.62 | 1.25 | 60.89
M2 | 0.66 | 0.44 | 2.89 | 63.07
M3 | 0.56 | 0.31 | 1.57 | 62.87
M4 | 0.82 | 0.67 | 2.68 | 70.19
M5 | 0.75 | 0.56 | 1.3 | 80.39
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 | Correlation | R<sup>2</sup> | RMSE | Accuracy | Topsis_score | Rank
------|------------ | --------------| ---- | -------- | ------------ | ------
M1 | 0.79 | 0.62 | 1.25 | 60.89 | 0.7722 | 2
M2 | 0.66 | 0.44 | 2.89 | 63.07 | 0.2255 | 5
M3 | 0.56 | 0.31 | 1.57 | 62.87 | 0.4388 | 4
M4 | 0.82 | 0.67 | 2.68 | 70.19 | 0.5238 | 3
M5 | 0.75 | 0.56 | 1.3 | 80.39 | 0.8113 | 1
<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\r\n\r\n\r\nSubmitted By: **Abhishek Gandhi 102003364**\r\n\r\n***\r\n\r\n## What is TOPSIS?\r\n\r\n**T**echnique for **O**rder **P**reference by **S**imilarity to **I**deal **S**olution \r\n(TOPSIS) originated in the 1980s as a multi-criteria decision making method.\r\nTOPSIS chooses the alternative of shortest Euclidean distance from the ideal solution, \r\nand greatest distance from the negative-ideal solution. \r\n\r\n<br>\r\n\r\n## How to install this package:\r\n```\r\n>> pip install topsis-abhishek-102003364\r\n```\r\n\r\n\r\n### In Command Prompt\r\n```\r\n>> python <program.py> <InputDataFile> <Weights> <Impacts> <ResultFileName>\r\n>> python 102003364.py data.csv \"1,1,1,1\" \"+,+,-,+\" result.csv\r\n```\r\n\r\n## Input file (data.csv)\r\n\r\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.\r\n\r\nModel | Correlation | R<sup>2</sup> | RMSE | Accuracy\r\n------|------------ | ------------ | ---- | ------------\r\nM1 |\t0.79 | 0.62\t | 1.25 | 60.89\r\nM2 | 0.66 | 0.44\t | 2.89 | 63.07\r\nM3 |\t0.56 | 0.31\t | 1.57 | 62.87\r\nM4 |\t0.82 | 0.67\t | 2.68 | 70.19\r\nM5 |\t0.75 | 0.56\t | 1.3 | 80.39\r\n\r\nWeights (`weights`) is not already normalised will be normalised later in the code.\r\n\r\nInformation of benefit positive(+) or negative(-) impact criteria should be provided in `impacts`.\r\n\r\n<br>\r\n\r\n## Output file (result.csv)\r\n\r\n\r\nModel | Correlation | R<sup>2</sup> | RMSE | Accuracy | Topsis_score | Rank\r\n------|------------ | --------------| ---- | -------- | ------------ | ------ \r\nM1 |\t0.79 | 0.62\t | 1.25 | 60.89 | 0.7722 | 2\r\nM2 | 0.66 | 0.44\t | 2.89 | 63.07 | 0.2255 | 5\r\nM3 |\t0.56 | 0.31\t | 1.57 | 62.87 | 0.4388 | 4\r\nM4 |\t0.82 | 0.67\t | 2.68 | 70.19 | 0.5238 | 3\r\nM5 |\t0.75 | 0.56\t | 1.3 | 80.39 | 0.8113 | 1\r\n\r\n\r\n<br>\r\nThe output file contains columns of input file along with two additional columns having **Topsis_score** and **Rank** \r\n\r\n\r\n",
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"summary": "This package can be used for implementation of Multiple criteria decision making using Topsis Algorithm. This is a python library for dealing with Multi-Criteria Decision Making (MCDM) problems by using techniques for order of preference by similarity to ideal solution (TOPSIS).",
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