# Project Description
- for: Assignment-1(UCS654)
- Submitted by: Himanshu Bansal
- Roll no: 102103568
- Group: 3COE20
# TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution)
This Python script implements the TOPSIS method for multi-criteria decision-making. It takes a CSV file containing a decision matrix, weights, and impacts as input, and produces a ranked result based on the TOPSIS score.
## Installation
```bash
pip install Topsis-Himanshu-102103568
```
## Usage
```bash
from Topsis_Himanshu_102103568.topsis import topsis
inputFile="sample.csv"
weights="1,1,1,1"
impacts="-,+,+,+"
resultFile="result.csv"
topsis(inputFile, weights, impacts, resultFile)
```
OR
You can use this package via command line as:
```bash
python -m Topsis_Himanshu_102103568.topsis [InputDataFile as .csv] [Weights as a string] [Impacts as a string] [ResultFileName as .csv]
```
- `InputDataFile`: Path to the CSV file containing the input data.
- `Weights`: Comma-separated weights for each criterion.
- `Impacts`: Comma-separated impact direction for each criterion (`+` for maximization, `-` for minimization).
- `ResultFileName`: Name of the file to save the TOPSIS results.
## Requirements
- Python 3
- pandas
- numpy
## Input File Format
The input data should be in a CSV format with the following structure:
| Fund Name | P1 | P2 | P3 | P4 | P5 |
|-----------|------|------|------|------|-------|
| M1 | 0.84 | 0.71 | 6.7 | 42.1 | 12.59 |
| M2 | 0.91 | 0.83 | 7 | 31.7 | 10.11 |
| M3 | 0.79 | 0.62 | 4.8 | 46.7 | 13.23 |
| M4 | 0.78 | 0.61 | 6.4 | 42.4 | 12.55 |
| M5 | 0.94 | 0.88 | 3.6 | 62.2 | 16.91 |
| M6 | 0.88 | 0.77 | 6.5 | 51.5 | 14.91 |
| M7 | 0.66 | 0.44 | 5.3 | 48.9 | 13.83 |
| M8 | 0.93 | 0.86 | 3.4 | 37 | 10.55 |
## Output
The script generates a CSV file containing the TOPSIS score and rank for each object:
| Fund Name | P1 | P2 | P3 | P4 | P5 | Topsis Score | Rank |
|-----------|------|------|------|------|-------|----------------------|------|
| M1 | 0.84 | 0.71 | 6.7 | 42.1 | 12.59 | 0.41855328299643013 | 7.0 |
| M2 | 0.91 | 0.83 | 7.0 | 31.7 | 10.11 | 0.4663977143091959 | 5.0 |
| M3 | 0.79 | 0.62 | 4.8 | 46.7 | 13.23 | 0.5374784843237046 | 3.0 |
| M4 | 0.78 | 0.61 | 6.4 | 42.4 | 12.55 | 0.4295182212044884 | 6.0 |
| M5 | 0.94 | 0.88 | 3.6 | 62.2 | 16.91 | 0.5453066145383307 | 2.0 |
| M6 | 0.88 | 0.77 | 6.5 | 51.5 | 14.91 | 0.39814192807166954 | 8.0 |
| M7 | 0.66 | 0.44 | 5.3 | 48.9 | 13.83 | 0.4743648907682155 | 4.0 |
| M8 | 0.93 | 0.86 | 3.4 | 37.0 | 10.55 | 0.6392872727749049 | 1.0 |
## Error Handling
- If the input file is not found, an error message will be displayed.
- If the number of weights, impacts, or columns in the decision matrix is incorrect, a `ValueError` will be raised.
- If the columns from the 2nd to the last do not contain numeric values, a `ValueError` will be raised.
- Any unexpected errors during the execution will be displayed.
## LICENSE
(c) 2024 Himanshu Bansal
This project is licensed under the [MIT License](LICENSE).
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