# Topsis
## What is TOPSIS?
Technique for Order Preference by Similarity to Ideal Solution (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-Kriti-102017079
```
### In Command Prompt
```
>> topsis 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 | 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 Score and Rank
Raw data
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"description": "# Topsis\n\n## What is TOPSIS?\n\nTechnique for Order Preference by Similarity to Ideal Solution (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```\n>> pip install Topsis-Kriti-102017079\n```\n\n### In Command Prompt\n\n```\n>> topsis data.csv \"1,1,1,1\" \"+,+,-,+\" result.csv\n```\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 | Correlation | R<sup>2</sup> | RMSE | Accuracy |\n| ----- | ----------- | ------------- | ---- | -------- |\n| M1 | 0.79 | 0.62 | 1.25 | 60.89 |\n| M2 | 0.66 | 0.44 | 2.89 | 63.07 |\n| M3 | 0.56 | 0.31 | 1.57 | 62.87 |\n| M4 | 0.82 | 0.67 | 2.68 | 70.19 |\n| M5 | 0.75 | 0.56 | 1.3 | 80.39 |\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 | Correlation | R<sup>2</sup> | RMSE | Accuracy | Score | Rank |\n| ----- | ----------- | ------------- | ---- | -------- | ------ | ---- |\n| M1 | 0.79 | 0.62 | 1.25 | 60.89 | 0.7722 | 2 |\n| M2 | 0.66 | 0.44 | 2.89 | 63.07 | 0.2255 | 5 |\n| M3 | 0.56 | 0.31 | 1.57 | 62.87 | 0.4388 | 4 |\n| M4 | 0.82 | 0.67 | 2.68 | 70.19 | 0.5238 | 3 |\n| M5 | 0.75 | 0.56 | 1.3 | 80.39 | 0.8113 | 1 |\n\n<br>\nThe output file contains columns of input file along with two additional columns having Score and Rank\n",
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