Topsis-AvneetKaur-102003487 1.0
Topsis

## Topsis_102003487

# TOPSIS

Submitted By: **Avneet kaur_102003487**.

Type: **Package**.

Title: **TOPSIS method for multiple-criteria decision making (MCDM)**.

Version: **1.0**.

Author: **Avneet kaur**.

Maintainer: **Avneet kaur <akaur1_be20@thapar.edu>**.

Description: **Evaluation of alternatives based on multiple criteria using TOPSIS method.**.

---

## What is TOPSIS?

 TOPSIS is based on the concept that the chosen alternative should have the shortest geometric distance from the positive ideal solution (PIS) and the longest geometric distance from the negative ideal solution.It is a method of compensatory aggregation that compares a set of alternatives, normalising scores for each criterion and calculating the geometric distance between each alternative and the ideal alternative, which is the best score in each criterion. The weights of the criteria in TOPSIS method can be calculated using Ordinal Priority Approach, Analytic hierarchy process, etc.

<br>

## How to install this package:

```
>> pip install Topsis_102003487_Avneet
```

### In Command Prompt

```
>> topsis 102003487-data.csv "1,1,1,1,1" "+,-,+,-,+" 102003487-result.csv
```

## Input file (data.csv)

| Fund Name | P1          | P2         |P3        | P4       | P5         |
| --------- | ----------- | ---------- | -------- | -------- | ---------- |
| M1        | 0.67        | 0.45       | 6.5      | 42.6     | 12.56      |
| M2        | 0.6         | 0.36       | 3.6      | 53.3     | 14.47      |
| M3        | 0.82        | 0.67       | 3.8      | 63.1     | 17.1       |
| M4        | 0.6         | 0.36       | 3.5      | 69.2     | 18.42      |
| M5        | 0.76        | 0.58       | 4.8      | 43       | 12.29      |
| M6        | 0.69        | 0.48       | 6.6      | 48.7     | 14.12      |
| M7        | 0.79        | 0.62       | 4.8      | 59.2     | 16.35      |
| M8        | 0.84        | 0.71       | 6.5      | 34.5     | 10.64      |

<br>

## Output file (result.csv)

| Fund Name | P1          | P2         |P3        | P4       | P5         | Topsis Score  | Rank  |
| --------- | ----------- | ---------- | -------- | -------- | ---------- | --------------|-------|
| M1        | 0.67        | 0.45       | 6.5      | 42.6     | 12.56      | 20.58         | 2     |
| M2        | 0.6         | 0.36       | 3.6      | 53.3     | 14.47      | 40.83         | 4     |
| M3        | 0.82        | 0.67       | 3.8      | 63.1     | 17.1       | 30.07         | 3     |
| M4        | 0.6         | 0.36       | 3.5      | 69.2     | 18.42      | 50.22         | 5     |
| M5        | 0.76        | 0.58       | 4.8      | 43       | 12.29      | 10.41         | 1     |
| M6        | 0.69        | 0.48       | 6.6      | 48.7     | 14.12      | 80.51         | 8     |
| M7        | 0.79        | 0.62       | 4.8      | 59.2     | 16.35      | 70.74         | 7     |
| M8        | 0.84        | 0.71       | 6.5      | 34.5     | 10.64      | 60.33         | 6     |

<br>

The output file contains columns of input file along with two additional columns having **Topsis_score** and **Rank**