Topsis-Taranpreet-102017050 0.4
Topsis implementation
License: MIT
Keywords: TOPSIS,RANKING,PERFORMANCE

# Topsis_Taranpreet_102017050
TOPSIS (technique for order performance by similarity to ideal solution) is a useful technique in dealing with multi-attribute or multi-criteria decision making (MADM/MCDM) problems in the real world.

## Installation
Install the Package using the command - 
```s
$ pip install Topsis_Taranpreet_102017050
```

## Usage
```s
python -m Topsis_Taranpreet_102017050.Topsis <InputDataFile> <Weights> <Impacts> <ResultFileName>
```

## Example

|   Fund Name  |   P1    |   P2    |   P3   |   P4    |   P5     |
|--------------|---------|---------|--------|---------|----------|
|   M1         |   0.94  |   0.88  |   6.1  |   40.1  |   12.01  |
|   M2         |   0.85  |   0.72  |   5    |   52.9  |   14.87  | 
|   M3         |   0.84  |   0.71  |   4.5  |   43.2  |   12.31  |
|   M4         |   0.73  |   0.53  |   6.7  |   43.8  |   12.94  |
|   M5         |   0.88  |   0.77  |   6.5  |   31.7  |   9.96   |
|   M6         |   0.6   |   0.36  |   4.4  |   31.2  |   9.14   |
|   M7         |   0.6   |   0.36  |   4.4  |   48    |   13.34  |
|   M8         |   0.92  |   0.85  |   5.5  |   55.2  |   15.62  |

**Input Method:** 
The Input data file is data.csv, output file is result.csv and for the Weights : [2, 2, 3, 3, 4] & Impacts : [-, +, -, +, -], run the following command:

```s
python -m Topsis_Taranpreet_102017050.Topsis data.csv "2,2,3,3,4" "-,+,-,+,-" result.csv
```

**Output generated:** 
The output file, result.csv will be as follows:

|   Fund Name  |   P1    |   P2    |   P3   |   P4    |   P5     |   Performance Score    |   Rank  |
|--------------|---------|---------|--------|---------|----------|------------------------|---------|
|   M1         |   0.94  |   0.88  |   6.1  |   40.1  |   12.01  |   0.522346027          |   3     |
|   M2         |   0.85  |   0.72  |   5    |   52.9  |   14.87  |   0.508871391          |   4     |
|   M3         |   0.84  |   0.71  |   4.5  |   43.2  |   12.31  |   0.580231695          |   1     |
|   M4         |   0.73  |   0.53  |   6.7  |   43.8  |   12.94  |   0.390517293          |   8     |
|   M5         |   0.88  |   0.77  |   6.5  |   31.7  |   9.96   |   0.503787007          |   5     |
|   M6         |   0.6   |   0.36  |   4.4  |   31.2  |   9.14   |   0.531336089          |   2     |
|   M7         |   0.6   |   0.36  |   4.4  |   48    |   13.34  |   0.493355187          |   7     |
|   M8         |   0.92  |   0.85  |   5.5  |   55.2  |   15.62  |   0.50091679           |   6     |

## Points to note:
- To remove the indices and headers, the library implicitly removes the first column and row respectively. Kindly, make sure the csv follows the format as shown in sample.csv.
- The csv should not contain categorical values.
- The csv should have atleast more than 3 columns.
- The number of Impacts an Weights should be equal to the number of feature columns.
- For maximizing a column, the impact is shown by "+" and for minimizing, "-".
- The weights should be positive and numerical.
- Separate the weights and columns by comma (,). 
- Please follow the format to run the program as given in the sample command. 

## License

MIT

**Thank You! Keep using and sharing feedbacks!**