topsis102203633Danishsharma 1.0.1

A Python package for TOPSIS implementation

🧠 TOPSIS Python Package by Danish Sharma 🚀

Welcome to the Topsis102203633Danishsharma package! 🐍 This Python package allows you to easily perform TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) analysis, a popular method in Multi-Criteria Decision Analysis (MCDA) to rank and evaluate alternatives based on multiple criteria.

📦 Installation

You can install the package via pip:

pip install topsis102203633Danishsharma

Alternatively, you can clone this repository and use it locally:

git clone https://github.com/Danish2op/voice-controlled-topsis-package.git
cd voice-controlled-topsis-package
python setup.py install

🎯 Features

• TOPSIS Analysis: Perform the TOPSIS algorithm on your dataset.
• Handling Missing Data: Fill missing values using various strategies like mean, median, mode, etc.
• Voice Commands: Use voice commands to run analysis, plot graphs, and more. 🎤
• Flexible: Supports different distance metrics and criteria impacts.
• Easy to Use: The interface is simple and intuitive for both beginners and experts. 😎

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📂 Package Structure

Here’s an overview of the important parts of the package:

topsis102203633Danishsharma/
├── topsis102203633Danishsharma_core/
│   ├── __init__.py
│   ├── topsis.py
│   
├── README.md
├── setup.py
├── LICENSE
├── MANIFEST.in

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🚀 Usage

Here’s a quick guide to using the Topsis class, including all available options.

🧑‍💻 Step 1: Import the package

from topsis102203633Danishsharma_core.topsis import Topsis, fill_missing_data, VoiceControl
import pandas as pd

# Load your dataset
df = pd.read_csv('path/to/your/data.csv')

# Set weights and impacts
weights = [0.3, 0.2, 0.2, 0.1, 0.2] 
impacts = ['+', '-', '+', '-', '+']
#Or you can load a file too like data set

🧑‍💻 Step 2: Create the TOPSIS Object

topsis = Topsis(df, weights, impacts, distance_metric='euclidean', missing_data_strategy='mean', show_para=True, reverse_rank=False)

• distance_metric: Choose from 'euclidean', 'manhattan', 'chebyshev', 'minkowski', or 'cosine'.
• missing_data_strategy: Choose from 'mean', 'median', 'mode', 'ffill', 'bfill', 'interpolate_linear', or 'interpolate_polynomial'.
• show_para: Set to True to display the full results including all parameters.

🧑‍💻 Step 3: Run the TOPSIS Calculation

result = topsis.calculate()
print(result)

This will give you the alternatives ranked according to the TOPSIS methodology.

🧑‍💻 Step 4: Plot the Results (Optional)

topsis.plot_graph(result)

This will plot a bar graph with the alternatives and their respective TOPSIS scores.

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🎤 Voice-Controlled Commands

You can also use voice commands to control the package.

Available Commands:

• "start": Start the TOPSIS analysis.
• "fill missing": Fill missing data in the dataset.
• "graph": Plot the graph of results.
• "exit": Exit the application.
• "help": Show the list of available commands.

Example usage:

voice_control = VoiceControl(df, weights, impacts)
voice_control.run()  # Start listening for commands

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💡 Default Parameters and Options

Here are the default parameters that you can use:

• Distance Metric: 'euclidean'
• Missing Data Strategy: 'mean'
• Show Parameters: False
• Reverse Rank: False the alternative with maximum topsis score is on top.

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🔧 Advanced Configuration

You can configure additional parameters to fit your needs:

• weights: A list of weights for each criterion.
• impacts: A list of impacts for each criterion, where '+' indicates a positive impact, and '-' indicates a negative impact.

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📑 Example with Voice Command

Here’s how you can use voice commands to perform actions like running TOPSIS analysis and plotting results.

from topsis102203633Danishsharma_core.topsis import VoiceControl
voice_control = VoiceControl(df, weights, impacts)
voice_control.run()  # Start listening for commands

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🤝 Contributing

We welcome contributions! If you'd like to contribute to this project, please feel free to submit a pull request or open an issue.

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🧑‍💻 License

This project is licensed under the MIT License.

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For more information, please visit our GitHub Repository.