Topsis-Kashika-102203492 0.2

A Python package that implements the TOPSIS method for multi-criteria decision-making (MCDM).

Topsis-Kashika-102203492 Topsis-Kashika-102203492 is a Python package that implements the TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) method for multi-criteria decision-making (MCDM). The TOPSIS method is used to rank and evaluate alternatives based on multiple criteria, helping decision-makers identify the best alternative.

Features TOPSIS Algorithm: Implements the TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) method. Customizable Weights and Impacts: Allows users to define the weights (importance) and impacts (positive or negative) of criteria. CSV Input and Output: Takes an input CSV file with alternatives and their criteria, processes it, and generates an output CSV with the TOPSIS score and rank for each alternative. Command-Line Interface: Easily run the package via the command line. Installation You can install the package directly from PyPi using the following command:

bash Copy pip install Topsis-Kashika-102203492 This will install the package and its dependencies.

Usage After installing the package, you can use it via the command line.

Command Syntax: bash Copy python -m 102203492.102203492 Where:

: The path to the input CSV file (e.g., 3492-data.csv). : A comma-separated string representing the weights for each criterion (e.g., "1,1,1,2,3"). : A comma-separated string of impacts (+ for positive impact, - for negative impact) (e.g., "+,+,-,+,+"). : The path to the output CSV file where the results will be saved (e.g., 3492_result.csv). Example: bash Copy python -m 102203492.102203492 3492-data.csv "1,1,1,2,3" "+,+,-,+,+" 3492_result.csv This command will:

Read the data from 3492-data.csv. Apply the TOPSIS method with the weights 1,1,1,2,3 and impacts +,+,-,+,+. Save the resulting scores and ranks to 3492_result.csv. Example Input (3492-data.csv): The input CSV file should have the following format, where the first column contains the alternative names and the subsequent columns represent numeric values for each criterion.

Alternative Criterion 1 Criterion 2 Criterion 3 Criterion 4 Criterion 5 A1 3.5 5.0 4.0 2.0 4.5 A2 4.0 4.0 3.5 3.0 4.0 A3 2.0 3.0 5.0 4.0 5.0 Example Output (3492_result.csv): The output CSV file will include the original data along with two additional columns:

Topsis Score: The calculated score for each alternative. Rank: The rank of each alternative based on the Topsis score. Alternative Criterion 1 Criterion 2 Criterion 3 Criterion 4 Criterion 5 Topsis Score Rank A1 3.5 5.0 4.0 2.0 4.5 0.85 1 A2 4.0 4.0 3.5 3.0 4.0 0.78 2 A3 2.0 3.0 5.0 4.0 5.0 0.65 3 Dependencies This package requires the following Python libraries:

pandas: For data handling and CSV file manipulation. numpy: For numerical calculations. You can install these dependencies using:

bash Copy pip install pandas numpy License This project is licensed under the MIT License. See the LICENSE file for more details.

Author Kashika Roll Number: 102203492