Topsis-Vikas-102303451 1.0.0

A Python package for TOPSIS implementation

TOPSIS-Vikas-102303451

Project Description

TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) is a multi-criteria decision analysis method. It evaluates alternatives based on their geometric distance to the ideal best value and the ideal worst value.

🔄 System Flowchart

graph LR
    A[Data Input<br>(CSV/Excel)] --> B[Data Validation<br>(Check Numeric/Weights)]
    B --> C[Normalization &<br>Weight Application]
    C --> D[Ideal Solution<br>(Best & Worst)]
    D --> E[Ranking &<br>Result Generation]
    style A fill:#e1f5fe,stroke:#01579b,stroke-width:2px
    style B fill:#fff9c4,stroke:#fbc02d,stroke-width:2px
    style C fill:#e8f5e9,stroke:#2e7d32,stroke-width:2px
    style D fill:#f3e5f5,stroke:#7b1fa2,stroke-width:2px
    style E fill:#ffebee,stroke:#c62828,stroke-width:2px

This project implements TOPSIS in Python and provides three interfaces:

1. Command Line Interface (CLI): For quick local usage.
2. Python Package: Reusable library for your scripts.
3. Web Service: A user-friendly web app to upload data and get results via email.

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🚀 Live Web Service

The web application is deployed and accessible at:

https://topsis-vikas-102303451.vercel.app

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🛠️ System Architecture

Workflow Diagram

graph TD
    A[User] -->|Uploads File .csv/.xlsx| B(Web Interface)
    B -->|Inputs Weights & Impacts| C{Flask Backend}
    C -->|Validates Data| D[Topsis Logic Module]
    D -->|Calculates Scores| E[Result Generator]
    E -->|Generates Result File| F[Email Service]
    F -->|Sends Attachment| A
    E -->|Direct Download| A

How TOPSIS Works (Mathematics)

1. Normalization: Normalize the decision matrix so that each criterion is comparable.
2. Weighting: Multiply the normalized matrix by the weights of each criterion.
3. Ideal Best & Worst: Identify the ideal best ($V^+$) and ideal worst ($V^-$) values for each column.
   • For Benefit (+): Max value is best, Min is worst.
   • For Cost (-): Min value is best, Max is worst.
4. Separation Measures: Calculate Euclidean distance of each alternative from $V^+$ and $V^-$.
5. Score Calculation: $P_i = \frac{S_i^-}{S_i^+ + S_i^-}$.
6. Ranking: Sort alternatives by score in descending order.

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📦 Installation & Usage

1. Python Package

Install from PyPi:

pip install Topsis-Vikas-102303451

Use in your code:

from topsis_vikas import topsis

# topsis(input_file, weights, impacts, output_file)
topsis("data.csv", "1,1,1,1", "+,+,+,-", "output.csv")

2. Command Line Interface

topsis data.csv "1,1,1,1" "+,+,+,-" result.csv

• Weights: Comma-separated (e.g., 1,1,1,1)
• Impacts: Comma-separated + or - (e.g., +,+,+,-)

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💻 Web Application

Features

• Modern UI: Clean, responsive interface using CSS Gradients and Glassmorphism.
• Auto-Analysis: Automatically detects the number of criteria in your file.
• Email Delivery: Results delivered directly to your inbox.

Tech Stack

• Frontend: HTML5, CSS3, JavaScript (Fetch API)
• Backend: Python, Flask
• Data Processing: Pandas, NumPy
• Deployment: Vercel

Screenshots

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👨‍💻 Author

Vikas Verma

• Roll Number: 102303451
• Email: [e-mail]
• GitHub: vikasverma

Constructed with ❤️ for the Thapar Institute of Engineering & Technology.