# TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution)
## Description
### Topsis-Joyy-102117024
For: Assignment-1(UCS654) Submitted-by: **Joyy Goswami* Roll no: *102117024* Group: *3CS-1**
Topsis-Joyy-102117024 is a Python package for dealing with Multiple Criteria Decision Making(MCDM) problems by using TOPSIS
## Installation
Use the package manager *pip* to install Topsis-Joyy-102117024
pip install Topsis-Joyy-102117024
## Usage
Enter csv filename followed by .csv extension, then enter the weights vector with vector values separated by commas, followed by the impacts vector with comma-separated signs (+,-)
bash
python sample.py sample.csv "1,1,1,1,2" "+,+,-,+,+" sample-result.csv
## Example
### sample.csv
A csv file showing data for different mobile handsets having varying features
| Model | Storage space (in GB) | Camera (in MP) | Price (in $) | Looks (out of 5) |
|-------|------------------------|-----------------|---------------|------------------|
| M1 | 16 | 12 | 250 | 5 |
| M2 | 16 | 8 | 200 | 3 |
| M3 | 32 | 16 | 300 | 4 |
| M4 | 32 | 8 | 275 | 4 |
| M5 | 16 | 16 | 225 | 2 |
weights vector = [1, 1, 1, 1]
impacts vector = [+,+,-,+]
### Input:
python sample.csv "1,1,1,1" "+,+,-,+" sample-result.csv
### Output:
| Topsis-score | Rank |
|-------------|------|
| 0.534277 | 3 |
| 0.308368 | 5 |
| 0.691632 | 1 |
| 0.534737 | 2 |
| 0.401046 | 4 |
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"description": "# TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution)\r\n\r\n\r\n## Description\r\n\r\n### Topsis-Joyy-102117024\r\n\r\nFor: Assignment-1(UCS654) Submitted-by: **Joyy Goswami* Roll no: *102117024* Group: *3CS-1**\r\n\r\nTopsis-Joyy-102117024 is a Python package for dealing with Multiple Criteria Decision Making(MCDM) problems by using TOPSIS\r\n\r\n## Installation\r\n\r\nUse the package manager *pip* to install Topsis-Joyy-102117024\r\n\r\npip install Topsis-Joyy-102117024\r\n\r\n## Usage\r\n\r\nEnter csv filename followed by .csv extension, then enter the weights vector with vector values separated by commas, followed by the impacts vector with comma-separated signs (+,-)\r\n\r\nbash\r\npython sample.py sample.csv \"1,1,1,1,2\" \"+,+,-,+,+\" sample-result.csv\r\n\r\n\r\n## Example\r\n\r\n### sample.csv\r\n\r\nA csv file showing data for different mobile handsets having varying features\r\n\r\n| Model | Storage space (in GB) | Camera (in MP) | Price (in $) | Looks (out of 5) |\r\n|-------|------------------------|-----------------|---------------|------------------|\r\n| M1 | 16 | 12 | 250 | 5 |\r\n| M2 | 16 | 8 | 200 | 3 |\r\n| M3 | 32 | 16 | 300 | 4 |\r\n| M4 | 32 | 8 | 275 | 4 |\r\n| M5 | 16 | 16 | 225 | 2 |\r\n\r\nweights vector = [1, 1, 1, 1]\r\nimpacts vector = [+,+,-,+]\r\n\r\n### Input:\r\n\r\npython sample.csv \"1,1,1,1\" \"+,+,-,+\" sample-result.csv\r\n\r\n### Output:\r\n\r\n| Topsis-score | Rank |\r\n|-------------|------|\r\n| 0.534277 | 3 |\r\n| 0.308368 | 5 |\r\n| 0.691632 | 1 |\r\n| 0.534737 | 2 |\r\n| 0.401046 | 4 |\r\n",
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