Topsis-Armandeep-Singh-Dhillon-102216076 1.0.0

A Python implementation of the TOPSIS decision-making method

README

TOPSIS Analysis in Jupyter Notebook

This README provides step-by-step instructions on how to use the TOPSIS package in a Jupyter Notebook.

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Installation

Before running the TOPSIS function, ensure the package is installed. You can either install it from PyPI or locally:

From PyPI:

!pip install Topsis_102216076_Armandeep_Singh_Dhillon

Locally:

If the package is not uploaded to PyPI, navigate to the folder containing the setup.py file and run:

!pip install .

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Usage

Step 1: Import the Package

Use the following code to import the topsis function:

from Topsis_102216076_Armandeep_Singh_Dhillon import topsis

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Step 2: Prepare Input Data

Ensure you have a CSV file (e.g., data.csv) with the following structure:

1. First column: Object/variable names (e.g., M1, M2, M3).
2. Second to last columns: Numeric data for evaluation.

Example CSV (data.csv):

Object	Criterion 1	Criterion 2	Criterion 3	Criterion 4
M1	250	16	12	5
M2	200	12	15	8
M3	300	18	10	6
M4	275	20	14	7

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Step 3: Define Inputs

Specify the following parameters:

• input_file: Path to the CSV file (e.g., data.csv).
• weights: Comma-separated weights for the criteria (e.g., 1,1,1,2).
• impacts: Comma-separated impacts (+ for beneficial, - for non-beneficial, e.g., +,+,-,+).
• output_file: Name of the output file to save the results (e.g., result.csv).

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Step 4: Run the TOPSIS Function

Use the following code to run the TOPSIS analysis:

# Import the necessary function
from Topsis_102216076_Armandeep_Singh_Dhillon import topsis

# Define inputs
input_file = "data.csv"
weights = "1,1,1,2"
impacts = "+,+,-,+"
output_file = "result.csv"

# Run TOPSIS
topsis(input_file, weights, impacts, output_file)

# Display the output file (optional)
import pandas as pd
result = pd.read_csv(output_file)
print(result)

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Step 5: Output

The output CSV file (result.csv) will include all original columns, along with two additional columns:

1. Topsis Score: The computed TOPSIS score for each object.
2. Rank: The rank of each object based on the TOPSIS score (higher score = better rank).

Example Output (result.csv):

Object	Criterion 1	Criterion 2	Criterion 3	Criterion 4	Topsis Score	Rank
M1	250	16	12	5	0.85	1
M2	200	12	15	8	0.65	3
M3	300	18	10	6	0.70	2
M4	275	20	14	7	0.60	4

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