Metadata-Version: 2.1
Name: Topsis-Mansimar-101803292
Version: 0.0.1
Summary: This package can be used to calculate the topsis score of multiple component data and rank them accordingly
Home-page: UNKNOWN
Author: Mansimar Anand
Author-email: anandmansimar@gmail.com
License: MIT
Description: # TOPSIS Package in Python
        
        UCS538  Data Science Fundamentals
        Assignment06 - TOPSIS
        
        Submitted by: Mansimar Anand
        
        Roll no: 101803292
        
        * * *
        
        ## Brief About TOPSIS
        
        TOPSIS stands for â€˜Technique of Order Preference Similarity to the Ideal Solutionâ€™ and is a pretty straightforward MCDA method. As the name implies, the method is based on finding an ideal and an anti-ideal solution and comparing the distance of each one of the alternatives to those.
        
        * * *
        
        ## Installation
        #### Before running, make sure you have pandas installed on your system
        ```
        >> pip install TOPSIS-Mansimar-101803292
        ```
        
        ## Usage
        
        ```
        >> python
        >>> from topsis_analysis.topsispackage import topsis
        >>> topsis("data.csv","1,1,1,2","+,+,-,+","output.csv")
        ```
        ## Sample Input
        
        This input was used to test the module
        
        <table><thead><tr><th>Model</th><th>Corr</th><th>Rseq</th><th>RMSE</th><th>Accuracy</th></tr></thead><tbody><tr><td>M1</td><td>0.79</td><td>0.62</td><td>1.25</td><td>60.89</td></tr><tr><td>M2</td><td>0.66</td><td>0.44</td><td>2.89</td><td>63.07</td></tr><tr><td>M3</td><td>0.56</td><td>0.31</td><td>1.57</td><td>62.87</td></tr><tr><td>M4</td><td>0.82</td><td>0.67</td><td>2.68</td><td>70.19</td></tr><tr><td>M5</td><td>0.75</td><td>0.56</td><td>1.3</td><td>80.39</td></tr></tbody></table>
        
        ## Output 
        
        
        <table><thead><tr><th>Model</th><th align="right">Corr</th><th align="center">Rseq</th><th>RMSE</th><th>Accuracy</th><th>Topsis Score</th><th>Rank</th></tr></thead><tbody><tr><td>M1</td><td align="right">0.79</td><td align="center">0.62</td><td>1.25</td><td>60.89</td><td>0.639133</td><td>2.0</td></tr><tr><td>M2</td><td align="right">0.66</td><td align="center">0.44</td><td>2.89</td><td>63.07</td><td>0.212592</td><td>5.0</td></tr><tr><td>M3</td><td align="right">0.56</td><td align="center">0.31</td><td>1.57</td><td>62.87</td><td>0.407846</td><td>4.0</td></tr><tr><td>M4</td><td align="right">0.82</td><td align="center">0.67</td><td>2.68</td><td>70.19</td><td>0.519153</td><td>3.0</td></tr><tr><td>M5</td><td align="right">0.75</td><td align="center">0.56</td><td>1.3</td><td>80.39</td><td>0.828267</td><td>1.0</td></tr></tbody></table>
        
        
        ## License
        
        Â© 2020 Mansimar Anand
        
        This repository is licensed under the MIT license. See LICENSE for details.
        
        
        
        
Platform: UNKNOWN
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Description-Content-Type: text/markdown
