Project description
TOPSIS-ANALYSIS
By: Sarvagy Jain
What is TOPSIS?
Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) originated in the 1980s as a multi-criteria decision making method. TOPSIS chooses the alternative of shortest Euclidean distance from the ideal solution, and greatest distance from the negative-ideal solution.
### Installation
```bash
pip install Topsis-SARVAGY-102003553
```
### Usage
Arguments Required:
(Assumne we have 3 attributes in dataset.)
You have to required one .csv file. (102003553-data.csv)
Pass weights to each attribute. (e.g.: [1,1,1])
Pass impacts to each attribute. (e.g.: [+,-,+])
Pass the name of the file with you want to put on .csv file. (102003553-result.csv)
Enter csv filename followed by .csv extentsion, then enter the weights string with values separated by commas, followed by the impacts string with comma separated signs (+,-) and name of file followed by -.csv- extension in which the user wants the output file
## Example
#### sample.csv
```bash
Fund Name P1 P2 P3 P4 P5
M1 0.72 0.52 4.4 66.6 18.06
M2 0.71 0.5 4.9 48.4 13.63
M3 0.82 0.67 6.1 58.2 16.45
M4 0.67 0.45 4.3 48.9 13.58
M5 0.75 0.56 3.3 60.2 16.2
M6 0.76 0.58 6.4 33.3 10.26
M7 0.85 0.72 3.2 61.9 16.67
M8 0.73 0.53 5.8 36.5 10.89
```
### INPUT
```python
topsis 102003553-data.csv 1,1,1,1,1 +,+,-,+,+ 102003553-result.csv
```
### OUTPUT
```bash
Fund Name P1 P2 P3 P4 P5 Topsis Score Rank
M1 0.72 0.52 4.4 66.6 18.06 0.607089574 2
M2 0.71 0.5 4.9 48.4 13.63 0.424434575 6
M3 0.82 0.67 6.1 58.2 16.45 0.811786381 1
M4 0.67 0.45 4.3 48.9 13.58 0.346716421 8
M5 0.75 0.56 3.3 60.2 16.2 0.486990207 4
M6 0.76 0.58 6.4 33.3 10.26 0.446021381 5
M7 0.85 0.72 3.2 61.9 16.67 0.568789224 3
M8 0.73 0.53 5.8 36.5 10.89 0.397353478 7
```
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"description": "Project description\r\nTOPSIS-ANALYSIS\r\nBy: Sarvagy Jain\r\n\r\nWhat is TOPSIS?\r\nTechnique for Order Preference by Similarity to Ideal Solution (TOPSIS) originated in the 1980s as a multi-criteria decision making method. TOPSIS chooses the alternative of shortest Euclidean distance from the ideal solution, and greatest distance from the negative-ideal solution.\r\n\r\n### Installation\r\n```bash\r\npip install Topsis-SARVAGY-102003553 \r\n```\r\n\r\n\r\n### Usage\r\n\r\nArguments Required:\r\n(Assumne we have 3 attributes in dataset.)\r\n\r\nYou have to required one .csv file. (102003553-data.csv)\r\nPass weights to each attribute. (e.g.: [1,1,1])\r\nPass impacts to each attribute. (e.g.: [+,-,+])\r\nPass the name of the file with you want to put on .csv file. (102003553-result.csv)\r\n\r\n\r\nEnter csv filename followed by .csv extentsion, then enter the weights string with values separated by commas, followed by the impacts string with comma separated signs (+,-) and name of file followed by -.csv- extension in which the user wants the output file\r\n\r\n## Example\r\n#### sample.csv\r\n```bash\r\nFund Name\tP1\t P2\t P3\t P4\t P5\r\nM1\t 0.72\t0.52\t4.4\t 66.6\t18.06\r\nM2\t 0.71\t0.5\t 4.9\t 48.4\t13.63\r\nM3 \t0.82\t0.67\t6.1\t 58.2\t16.45\r\nM4\t 0.67\t0.45\t4.3\t 48.9\t13.58\r\nM5\t 0.75\t0.56\t3.3\t 60.2\t16.2\r\nM6\t 0.76\t0.58\t6.4\t 33.3\t10.26\r\nM7\t 0.85\t0.72\t3.2\t 61.9\t16.67\r\nM8\t 0.73\t0.53\t5.8\t 36.5\t10.89\r\n\r\n```\r\n\r\n### INPUT\r\n```python\r\ntopsis 102003553-data.csv 1,1,1,1,1 +,+,-,+,+ 102003553-result.csv\r\n```\r\n\r\n### OUTPUT\r\n\r\n```bash\r\nFund Name\tP1\t P2\t P3\t P4\t P5\t Topsis Score\tRank\r\nM1\t 0.72\t0.52\t4.4\t 66.6\t18.06\t0.607089574\t 2\r\nM2\t 0.71\t0.5\t 4.9\t 48.4\t13.63\t0.424434575\t 6\r\nM3\t 0.82\t0.67\t6.1\t 58.2\t16.45\t0.811786381\t 1\r\nM4\t 0.67\t0.45\t4.3\t 48.9\t13.58\t0.346716421\t 8\r\nM5\t 0.75\t0.56\t3.3\t 60.2\t16.2\t0.486990207\t 4\r\nM6\t 0.76\t0.58\t6.4\t 33.3\t10.26\t0.446021381\t 5\r\nM7\t 0.85\t0.72\t3.2\t 61.9\t16.67\t0.568789224\t 3\r\nM8\t 0.73\t0.53\t5.8\t 36.5\t10.89\t0.397353478\t 7\r\n\r\n```\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n",
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