# TOPSIS Implementation
This repository contains a Python implementation of the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). TOPSIS is a powerful multi-criteria decision-making method that assists in ranking a set of alternatives based on their proximity to the ideal solution.
## Table of Contents
1. [Introduction](#introduction)
2. [Usage](#usage)
3. [Command-line Arguments](#command-line-arguments)
6. [Requirements](#requirements)
## Introduction
The Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) is a well-established method for decision-making. This Python implementation allows you to easily apply TOPSIS to your decision matrix and obtain a ranked list of alternatives.
### Key Concepts:
- Decision Matrix: Represents alternatives and criteria.
- Weights: Assign importance to criteria.
- Impacts: Indicate whether higher or lower values are favorable.
- Normalization: Ensures all criteria are on a similar scale.
- Ideal and Worst Solutions: Represent best and worst possible outcomes.
- Similarity and Dissimilarity Measures: Calculate proximity to ideal and dissimilarity to worst.
- TOPSIS Score: Combines similarity and dissimilarity measures.
- Ranking: Alternatives are ranked based on TOPSIS scores.
## Usage
1. Ensure you have Python installed on your system.
2. Clone this repository to your local machine:
```bash
git clone https://github.com/dhruvRajoria/Topsis_Dhruv
3. Navigate to the project directory:
```bash
git clone https://github.com/dhruvRajoria/Topsis_Dhruv
4. Run the TOPSIS script with the required command-line arguments:
python 102217050.py 102217050-data.csv "1,1,1,2" "+,+,-,+" result.csv
5. The TOPSIS analysis will be performed, and the result will be saved to the specified CSV file.
## Command-line Arguments
- `<InputDataFile>`: Path to the input CSV file containing the decision matrix.
- `<Weights>`: Comma-separated weights for each criterion.
- `<Impacts>`: Comma-separated impact signs for each criterion (use '+' for beneficial criteria and '-' for non-beneficial criteria).
- `<ResultFileName>`: Desired name for the output CSV result file.
## Requirements
- Python 3.x
- pandas
- numpy
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"description": "# TOPSIS Implementation\r\n\r\nThis repository contains a Python implementation of the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). TOPSIS is a powerful multi-criteria decision-making method that assists in ranking a set of alternatives based on their proximity to the ideal solution.\r\n\r\n## Table of Contents\r\n\r\n1. [Introduction](#introduction)\r\n2. [Usage](#usage)\r\n3. [Command-line Arguments](#command-line-arguments)\r\n6. [Requirements](#requirements)\r\n\r\n## Introduction\r\n\r\nThe Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) is a well-established method for decision-making. This Python implementation allows you to easily apply TOPSIS to your decision matrix and obtain a ranked list of alternatives.\r\n\r\n### Key Concepts:\r\n\r\n- Decision Matrix: Represents alternatives and criteria.\r\n- Weights: Assign importance to criteria.\r\n- Impacts: Indicate whether higher or lower values are favorable.\r\n- Normalization: Ensures all criteria are on a similar scale.\r\n- Ideal and Worst Solutions: Represent best and worst possible outcomes.\r\n- Similarity and Dissimilarity Measures: Calculate proximity to ideal and dissimilarity to worst.\r\n- TOPSIS Score: Combines similarity and dissimilarity measures.\r\n- Ranking: Alternatives are ranked based on TOPSIS scores.\r\n\r\n## Usage\r\n\r\n1. Ensure you have Python installed on your system.\r\n\r\n2. Clone this repository to your local machine:\r\n\r\n ```bash\r\n git clone https://github.com/dhruvRajoria/Topsis_Dhruv\r\n\r\n3. Navigate to the project directory:\r\n\r\n ```bash\r\n git clone https://github.com/dhruvRajoria/Topsis_Dhruv\r\n\r\n \r\n4. Run the TOPSIS script with the required command-line arguments:\r\n\r\n \r\n python 102217050.py 102217050-data.csv \"1,1,1,2\" \"+,+,-,+\" result.csv\r\n\r\n\r\n\r\n5. The TOPSIS analysis will be performed, and the result will be saved to the specified CSV file.\r\n\r\n## Command-line Arguments\r\n\r\n- `<InputDataFile>`: Path to the input CSV file containing the decision matrix.\r\n\r\n- `<Weights>`: Comma-separated weights for each criterion.\r\n\r\n- `<Impacts>`: Comma-separated impact signs for each criterion (use '+' for beneficial criteria and '-' for non-beneficial criteria).\r\n\r\n- `<ResultFileName>`: Desired name for the output CSV result file.\r\n\r\n\r\n\r\n## Requirements\r\n\r\n- Python 3.x\r\n- pandas\r\n- numpy\r\n\r\n\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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