# TOPSIS-Python
Submitted By: **MAHESH MANI 102297002**
***
## What is TOPSIS?
**T**echnique for **O**rder **P**reference by **S**imilarity to **I**deal
**S**olution (TOPSIS) originated in the 1980s as a multi-criteria decision-making method. TOPSIS chooses the alternative of the shortest Euclidean distance from the ideal solution and greatest distance from the negative-ideal solution. More details can be found on [Wikipedia](https://en.wikipedia.org/wiki/TOPSIS).
## How to use this package:
The TOPSIS-MAHESH-MANI-102297002 package can be run as shown in the following example:
### In Command Prompt to run the code:
```bash
topsis data.csv "1,1,1,1" "+,+,-,+" out.csv
```
## Sample dataset
```csv
Name P1 P2 P3 P4 P5
M1 0.86 0.74 4.8 61.8 17.05
M2 0.83 0.69 6.6 48.5 14.16
M3 0.62 0.38 3.3 33 9.33
M4 0.83 0.69 6.4 45.5 13.36
M5 0.95 0.9 6.8 45 13.41
M6 0.61 0.37 3.6 36 10.15
M7 0.9 0.81 4.5 62.5 17.18
M8 0.89 0.79 4.7 38.9 11.32
```
## Output
```csv
Name P1 P2 P3 P4 P5 Topsis Score Rank
M1 0.86 0.74 4.8 61.8 17.05 0.7003233012352760 3
M2 0.83 0.69 6.6 48.5 14.16 0.6562533743146750 6
M3 0.62 0.38 3.3 33.0 9.33 0.0135325373752343 8
M4 0.83 0.69 6.4 45.5 13.36 0.6089902720065850 4
M5 0.95 0.9 6.8 45.0 13.41 0.7157691264417760 2
M6 0.61 0.37 3.6 36.0 10.15 0.07412480075376210 1
M7 0.9 0.81 4.5 62.5 17.18 0.711919899272116 7
M8 0.89 0.79 4.7 38.9 11.32 0.49267086270406500 5
```
## Explanation:
- **Name:** Identifies the alternatives.
- **P1, P2, P3, P4, P5:** Represents the performance values for each criterion.
- **Topsis Score:** The calculated TOPSIS score for each alternative.
- **Rank:** The ranking of each alternative based on the TOPSIS score.
Simply run the provided command in the Command Prompt, replacing the input data file name, weights, impacts, and result file name with your specific values. The output will be a new CSV file containing the results of the TOPSIS analysis.
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"description": "# TOPSIS-Python\n\nSubmitted By: **MAHESH MANI 102297002**\n\n***\n\n## What is TOPSIS?\n\n**T**echnique for **O**rder **P**reference by **S**imilarity to **I**deal\n**S**olution (TOPSIS) originated in the 1980s as a multi-criteria decision-making method. TOPSIS chooses the alternative of the shortest Euclidean distance from the ideal solution and greatest distance from the negative-ideal solution. More details can be found on [Wikipedia](https://en.wikipedia.org/wiki/TOPSIS).\n\n## How to use this package:\n\nThe TOPSIS-MAHESH-MANI-102297002 package can be run as shown in the following example:\n\n### In Command Prompt to run the code:\n```bash\ntopsis data.csv \"1,1,1,1\" \"+,+,-,+\" out.csv\n```\n\n## Sample dataset\n\n```csv\nName P1 P2 P3 P4 P5\nM1 0.86 0.74 4.8 61.8 17.05\nM2 0.83 0.69 6.6 48.5 14.16\nM3 0.62 0.38 3.3 33 9.33\nM4 0.83 0.69 6.4 45.5 13.36\nM5 0.95 0.9 6.8 45 13.41\nM6 0.61 0.37 3.6 36 10.15\nM7 0.9 0.81 4.5 62.5 17.18\nM8 0.89 0.79 4.7 38.9 11.32\n```\n\n## Output\n\n```csv\nName P1 P2 P3 P4 P5 Topsis Score Rank\nM1 0.86 0.74 4.8 61.8 17.05 0.7003233012352760 3\nM2 0.83 0.69 6.6 48.5 14.16 0.6562533743146750 6\nM3 0.62 0.38 3.3 33.0 9.33 0.0135325373752343 8\nM4 0.83 0.69 6.4 45.5 13.36 0.6089902720065850 4\nM5 0.95 0.9 6.8 45.0 13.41 0.7157691264417760 2\nM6 0.61 0.37 3.6 36.0 10.15 0.07412480075376210 1\nM7 0.9 0.81 4.5 62.5 17.18 0.711919899272116 7\nM8 0.89 0.79 4.7 38.9 11.32 0.49267086270406500 5\n```\n\n## Explanation:\n\n- **Name:** Identifies the alternatives.\n- **P1, P2, P3, P4, P5:** Represents the performance values for each criterion.\n- **Topsis Score:** The calculated TOPSIS score for each alternative.\n- **Rank:** The ranking of each alternative based on the TOPSIS score.\n\nSimply run the provided command in the Command Prompt, replacing the input data file name, weights, impacts, and result file name with your specific values. The output will be a new CSV file containing the results of the TOPSIS analysis.\n",
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