Ekaspreet-topsis-102017078
# Topsis in Python
Author: **Ekaspreet 102017078**
Maintainer: **Ekaspreet <ekaspreet0209@gmail.com>**.
TOPSIS: It is a for Multiple Criteria Decision Making,A Technique for Order Preference by Similarity to Ideal
More details at [wikipedia](https://en.wikipedia.org/wiki/TOPSIS).
<br>
<br>
### In Command Prompt
```
>> topsis data.csv "1,1,1,1" "+,+,-,+" result.csv
```
## Input file (data.csv)
| 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 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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