semvec/pypi-packages huggingface dataset:
TOPSIS-Radhika-102003313 1.1.1
A Python package to find TOPSIS for multi-criteria decision analysis method
License: MIT
Keywords: A simple but powerful decision method

# Topsis

Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS)

MCDM for algorithm selection
Multiple-criteria decision methods (MCDM) can help decisions with more than one criterion. These methods can be applied to several business problems like supplier evaluation, project prioritizing, and raw material selection. They also can be used in personal situations like consumer goods choice. This work aims to use a multicriteria decision method to help on algorithm selection.

It is a multi-criteria decision analysis method that is based on the concept that the chosen alternative should have the shortest geometric distance to the Positive Ideal Solution (PIS) and the longest geometric solution from the Negative Ideal Solution (NIS).

## Installation

Use the package manager [pip](https://pip.pypa.io/en/stable/) to install topsis_102003313.

```bash
pip install topsis-102003313
```

-Input and output file format should be .CSV

-First column in the input file should be the object name

-Input file must have at least 2 criteria, and all criterion values should be numeric

-Weights must be numeric and comma-separated. For example, 0.25,0.25,1.0,0.25 or "0.25,0.25,1.0,0.25".

-Impacts must be comma-separated with + for criteria that are to be maximised, and - for criteria that are to be minimised.

-For example, +,-,+,- or "+, -, +, -"

## Usage

Enter csv filename followed by _.csv_ extentsion, then enter the _weights_ vector with vector values separated by commas, followed by the _impacts_ vector with comma separated signs _(+,-)_, and lastly the _outputFileName_.

## Example

Consider **sample.csv**:
|Model|Correlation|R2|RMSE|Accuracy|
|---|---|---|---|---|
|M1|0.79|0.62|1.25|60.89|
|M2|0.66|0.44|2.89|63.07|
|M3|0.56|0.31|1.57|62.87|
|M4|0.82|0.67|2.68|70.19|
|M5|0.75|0.56|1.3|80.39|

If we run the following command:

```bash
python topsis.py sample.csv "1,1,1,1" "+,-,+,+" outputFile.csv
```

we get a file named **outputFile.csv** in the directory with an additional 2 columns containing the TOPSIS score and the rank of each object:
|Model|Correlation|R2|RMSE|Accuracy|TOPSIS Score|Rank|
|---|---|---|---|---|---|---|
|M1|0.79|0.62|1.25|60.89|0.7722097345612788|2.0|
|M2|0.66|0.44|2.89|63.07|0.22559875426413367|5.0|
|M3|0.56|0.31|1.57|62.87|0.43889731728018605|4.0|
|M4|0.82|0.67|2.68|70.19|0.5238778712729114|3.0|
|M5|0.75|0.56|1.3|80.39|0.8113887082429979|1.0|

## License

MIT
Topsis-Jashan 1.1
A Python package to find TOPSIS for multi-criteria decision analysis method
License: MIT
Keywords: Easy to Use,Fascinating concept,Topsis

# TOPSIS Package in Python
Added by: Jashan


# Concept of TOPSIS
TOPSIS is an acronym that stands for Technique of Order Preference Similarity to the Ideal Solution 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.



The rankings are displayed in the form of a table using a package 'tabulate', with the 1st rank offering us the best decision, and last rank offering the worst decision making, according to TOPSIS method.

# Example
Input data 
![image](https://user-images.githubusercontent.com/72308753/213905044-a9e1eb53-f7b6-4f86-ab95-b20369625503.png)

After appling topsis 
![image](https://user-images.githubusercontent.com/72308753/213905055-f87f1a70-0e88-40ca-b5e0-9e4380d93468.png)
topsis-102003553 1.1.0
A Python package to find TOPSIS for multi-criteria decision analysis method
License: MIT
Keywords: UCS538,TIET

Project description
TOPSIS-ANALYSIS
By: Sarvagy Jain

What is TOPSIS?
Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) originated in the 1980s as a multi-criteria decision making method. TOPSIS chooses the alternative of shortest Euclidean distance from the ideal solution, and greatest distance from the negative-ideal solution.

### Installation
```bash
pip install Topsis-SARVAGY-102003553    
```


### Usage

Arguments Required:
(Assumne we have 3 attributes in dataset.)

You have to required one .csv file. (102003553-data.csv)
Pass weights to each attribute. (e.g.: [1,1,1])
Pass impacts to each attribute. (e.g.: [+,-,+])
Pass the name of the file with you want to put on .csv file. (102003553-result.csv)


Enter csv filename followed by .csv extentsion, then enter the weights string with values separated by commas, followed by the impacts string with comma separated signs (+,-) and name of file followed by -.csv- extension in which the user wants the output file

## Example
#### sample.csv
```bash
Fund Name	P1	    P2	    P3	    P4	    P5
M1	        0.72	0.52	4.4	    66.6	18.06
M2	        0.71	0.5	    4.9	    48.4	13.63
M3      	0.82	0.67	6.1	    58.2	16.45
M4	        0.67	0.45	4.3	    48.9	13.58
M5	        0.75	0.56	3.3	    60.2	16.2
M6	        0.76	0.58	6.4	    33.3	10.26
M7	        0.85	0.72	3.2	    61.9	16.67
M8	        0.73	0.53	5.8	    36.5	10.89

```

### INPUT
```python
topsis 102003553-data.csv 1,1,1,1,1 +,+,-,+,+ 102003553-result.csv
```

### OUTPUT

```bash
Fund Name	P1	    P2	    P3	    P4	    P5	    Topsis Score	Rank
M1	        0.72	0.52	4.4	    66.6	18.06	0.607089574	    2
M2	        0.71	0.5	    4.9	    48.4	13.63	0.424434575	    6
M3	        0.82	0.67	6.1	    58.2	16.45	0.811786381	    1
M4	        0.67	0.45	4.3	    48.9	13.58	0.346716421	    8
M5	        0.75	0.56	3.3	    60.2	16.2	0.486990207	    4
M6	        0.76	0.58	6.4	    33.3	10.26	0.446021381	    5
M7	        0.85	0.72	3.2	    61.9	16.67	0.568789224	    3
M8	        0.73	0.53	5.8	    36.5	10.89	0.397353478	    7

```








Change Log
==========

0.0.1 (12/11/2020)
------------------
- First Release
102017059-Aakanksha-Topsis 0.0.0
Topsis Implementation
License: MIT

# 102017059_Aakanksha_Topsis

This package is implementation of multi-criteria decision analysis using topsis. This package will accept three arguments during file execution:

dataset.csv //file which contains the models and parameters
string of weights separated by commas(,)
string of requirements (+/-) separated by commas(,) // important install pandas,sys,operator and math libraries before installing this // You can install this package using following command pip install 102017059_Aakanksha_Topsis
topsis-raunak-102017148 1.0.0
A Python package to get optimal solutiion.
License: MIT

# Description:
Topsis Module is used in Decision Making.

## Installation
```pip install topsis-raunak-102017148```

## How to use it? 
Use topsis function pass a dataframe.

## License
© 2023 Raunak Kumar

This repository is licensed under the MIT license.
See LICENSE for details.
Topsis-Shubham-102003109 1.0.2
This is a Python library for handling problems related to Multiple Criteria Decision Making(MCDM)
License: MIT

# TOPSIS

Title: **TOPSIS method for multiple-criteria decision making (MCDM)**.

Version: **1.0.1**.

Author: **Shubham Malhotra**.

Description: **Evaluation of alternatives based on multiple criteria using TOPSIS method.**

---

## How to install this package:

```
>> pip install Topsis-Shubham-102003109
```

### In Command Prompt

```
>> topsis 102003109-data.csv "1,2,1,2,1" "+,+,-,-,+" resultdata.csv
```

## Input file (data.csv)

| Model | Correlation | R^2  | RMSE | Accuracy |
| ----- | ----------- | ---- | ---- | -------- |
| P1    | 0.79        | 0.62 | 1.25 | 60.89    |
| P2    | 0.66        | 0.44 | 2.89 | 63.07    |
| P3    | 0.56        | 0.31 | 1.57 | 62.87    |
| P4    | 0.82        | 0.67 | 2.68 | 70.19    |
| P5    | 0.75        | 0.56 | 1.3  | 80.39    |

<br>

## Output file (result.csv)

| Model | Correlation | R^2  | RMSE | Accuracy | Topsis_score | Rank |
| ----- | ----------- | ---- | ---- | -------- | ------------ | ---- |
| P1    | 0.79        | 0.62 | 1.25 | 60.89    | 0.7722       | 2    |
| P2    | 0.66        | 0.44 | 2.89 | 63.07    | 0.2255       | 5    |
| P3    | 0.56        | 0.31 | 1.57 | 62.87    | 0.4388       | 4    |
| P4    | 0.82        | 0.67 | 2.68 | 70.19    | 0.5238       | 3    |
| P5    | 0.75        | 0.56 | 1.3  | 80.39    | 0.8113       | 1    |

<br>

The output file contains columns of input file along with two additional columns having Topsis_score and Rank
=======
# TOPSIS

Title: **TOPSIS method for multiple-criteria decision making (MCDM)**.

Version: **1.0.1**.

Author: **Shubham Malhotra**.

Description: **Evaluation of alternatives based on multiple criteria using TOPSIS method.**

---

## How to install this package:

```
>> pip install Topsis-Shubham-102003109
```

### In Command Prompt

```
>> topsis 102003109-data.csv "1,2,1,2,1" "+,+,-,-,+" resultdata.csv
```

## Input file (data.csv)

| Model | Correlation | R^2  | RMSE | Accuracy |
| ----- | ----------- | ---- | ---- | -------- |
| P1    | 0.79        | 0.62 | 1.25 | 60.89    |
| P2    | 0.66        | 0.44 | 2.89 | 63.07    |
| P3    | 0.56        | 0.31 | 1.57 | 62.87    |
| P4    | 0.82        | 0.67 | 2.68 | 70.19    |
| P5    | 0.75        | 0.56 | 1.3  | 80.39    |

<br>

## Output file (result.csv)

| Model | Correlation | R^2  | RMSE | Accuracy | Topsis_score | Rank |
| ----- | ----------- | ---- | ---- | -------- | ------------ | ---- |
| P1    | 0.79        | 0.62 | 1.25 | 60.89    | 0.7722       | 2    |
| P2    | 0.66        | 0.44 | 2.89 | 63.07    | 0.2255       | 5    |
| P3    | 0.56        | 0.31 | 1.57 | 62.87    | 0.4388       | 4    |
| P4    | 0.82        | 0.67 | 2.68 | 70.19    | 0.5238       | 3    |
| P5    | 0.75        | 0.56 | 1.3  | 80.39    | 0.8113       | 1    |

<br>

The output file contains columns of input file along with two additional columns having Topsis_score and Rank
topsis-gs-102003220 1.0.0
Topsis Implimentation
License: MIT

# Topsis Implimentation by Gurtaran
It takes a csv file, weigths of all columns, impacts as 1 for positive and -1 for negative and output file name as an input and give the final topsis implimented file out.

## Installation
```pip install topsis-gs-102003220```

## How to use it?
Open terminal and type file path, weights, impact as 1 or -1 and output file name.

## License

© 2023 Gurtaran Singh

This repository is licensed under the MIT license. See LICENSE for details.
topsis-102003083-Utkarsh 1.1.0
A Python package implementing TOPSIS technique.
License: MIT

# TOPSIS-Python

Submitted By: **Utkarsh Chaudhary**

  s
## What is TOPSIS

**T**echnique for **O**rder **P**reference by **S**imilarity to **I**deal
**S**olution (TOPSIS) originated in the 1980s as a multi-criteria decision
making method. TOPSIS chooses the alternative of shortest Euclidean distance
from the ideal solution, and greatest distance from the negative-ideal
solution. More details at [wikipedia](https://en.wikipedia.org/wiki/TOPSIS).

<br>

## How to use this package:

TOPSIS-102003083_Utkarsh  can be run as in the following example:



### In Command Prompt
```
>> topsis data.csv "1,1,1,1" "+,+,-,+" final.csv
```
<br>

### In Python IDLE:
```
>>> import pandas as pd
>>> from topsis_python.topsis import topsis
>>> dataset = pd.read_csv('data.csv').values
>>> d = dataset[:,1:]
>>> f = dataset[:,1:]
>>> w = [1,1,1,1]
>>> im = ["+" , "+" , "-" , "+" ]
>>> topsis(d,w,im,f)
```

<br>

## Sample dataset

The decision matrix (`a`) should be constructed with each row representing a Model alternative, and each column representing a criterion like Accuracy, R<sup>2</sup>, Root Mean Squared Error, Correlation, and many more.

Model | Correlation | R<sup>2</sup> | RMSE | Accuracy
------------ | ------------- | ------------ | ------------- | ------------
M1 |	0.79 | 0.62	| 1.25 | 60.89
M2 |  0.66 | 0.44	| 2.89 | 63.07
M3 |	0.56 | 0.31	| 1.57 | 62.87
M4 |	0.82 | 0.67	| 2.68 | 70.19
M5 |	0.75 | 0.56	| 1.3	 | 80.39

Weights (`w`) is not already normalised will be normalised later in the code.

Information of benefit positive(+) or negative(-) impact criteria should be provided in `I`.

<br>

## Output

```
Model   Score    Rank
-----  --------  ----
  1    0.77221     2
  2    0.225599    5
  3    0.438897    4
  4    0.523878    3
  5    0.811389    1
```
<br>
The rankings are displayed in the form of a table using a package 'tabulate', with the 1st rank offering us the best decision, and last rank offering the worst decision making, according to TOPSIS method.
Topsis-Arhanjit-102003116 1.0.7
This is a Python library for handling problems related to Multiple Criteria Decision Making(MCDM)
License: MIT

# TOPSIS

Title: **TOPSIS method for multiple-criteria decision making (MCDM)**.

Version: **1.0.7**.

Author: **Arhanjit Sodhi**.

Description: **Evaluation of alternatives based on multiple criteria using TOPSIS method.**

---

## How to install this package:

```
>> pip install Topsis-Arhanjit-102003116
```

### In Command Prompt

```
>> topsis inputdata.csv "1,1,1,1,1" "+,+,-,+,-" resultdata.csv
```

## Input file (data.csv)

| Model | Correlation | R^2  | RMSE | Accuracy |
| ----- | ----------- | ---- | ---- | -------- |
| P1    | 0.79        | 0.62 | 1.25 | 60.89    |
| P2    | 0.66        | 0.44 | 2.89 | 63.07    |
| P3    | 0.56        | 0.31 | 1.57 | 62.87    |
| P4    | 0.82        | 0.67 | 2.68 | 70.19    |
| P5    | 0.75        | 0.56 | 1.3  | 80.39    |

<br>

## Output file (result.csv)

| Model | Correlation | R^2  | RMSE | Accuracy | Topsis_score | Rank |
| ----- | ----------- | ---- | ---- | -------- | ------------ | ---- |
| P1    | 0.79        | 0.62 | 1.25 | 60.89    | 0.7722       | 2    |
| P2    | 0.66        | 0.44 | 2.89 | 63.07    | 0.2255       | 5    |
| P3    | 0.56        | 0.31 | 1.57 | 62.87    | 0.4388       | 4    |
| P4    | 0.82        | 0.67 | 2.68 | 70.19    | 0.5238       | 3    |
| P5    | 0.75        | 0.56 | 1.3  | 80.39    | 0.8113       | 1    |

<br>

The output file contains columns of input file along with two additional columns having Topsis_score and Rank
TOPSIS-102003105 1.0.0
A Python package to find TOPSIS for Multi-Criteria Decision Analysis Method
License: MIT
Keywords: topsis,TIET,Thapar

Topsis Value Calculator

CalcTopsis is a Python package implementing Topsis method for multi-criteria decision analysis.
Topsis stands for Technique for Order of Preference by Similarity to Ideal Solution

Just provide your input attributes and it will give you the results


## Installation

$ pip install TOPSIS-102003105

In the commandline, you can write as -
    $ python <package_name> <path to input_data_file_name> <weights as strings> <impacts as strings> <result_file_name>

E.g for input data file as data.csv, command will be like
    $ python topsis.py data.csv "1,1,1,1,1" "+,-,+,-,+" output.csv

This will give the output in output.csv file

License -> MIT

Change Log
==========

1.0.0 (22/01/2023)
------------------
- First Release
Topsis-Parush-102003404 0.2
Implements topsis on the given input data file and generates topsis score and ranks accordingly.
License: MIT

Topsis-Parush-102003404 
Topsis implementation in python. Used as a command line tool to generate topsis score and ranks of the given input data file along with weights and impacts. 
## Installation 
pip install Topsis-Parush-102003404
topsis-rupanshijain-102017010 1.1.1
TOPSIS method for multi-criteria decision making
License: MIT
Keywords: topsis,MCDM

# topsis_rupanshi_102017010
TopsisPy is a Python Package implementing [Topsis](https://en.wikipedia.org/wiki/TOPSIS) method used for multi-criteria decision analysis.
Topsis stands for 'Technique for Order of Preference by Similarity to Ideal Solution'.

## Installation
Install the Package using the command - 
```s
$ pip install topsis_rupanshi_102017010
```
## Usage
```s
python -m topsis_rupanshijain_102017010.topsis102017010 <InputDataFile> <Weights> <Impacts> <ResultFileName>
```

## Example
|   Fund Name  |   P1    |   P2    |   P3   |   P4    |   P5     |
|--------------|---------|---------|--------|---------|----------|
|   M1         |   0.94  |   0.88  |   3.8  |   40.8  |   11.61  |
|   M2         |   0.77  |   0.59  |   5.6  |   45.4  |   13.09  |
|   M3         |   0.8   |   0.64  |   7    |   57.7  |   16.54  |
|   M4         |   0.67  |   0.45  |   3.1  |   61.6  |   16.46  |
|   M5         |   0.89  |   0.79  |   5.9  |   67.7  |   18.82  |
|   M6         |   0.66  |   0.44  |   6.2  |   62.1  |   17.35  |
|   M7         |   0.73  |   0.53  |   6.6  |   34.5  |   10.59  |
|   M8         |   0.91  |   0.83  |   3.2  |   55.3  |   15.06  |
weights : [1, 1, 1, 2, 1]
impacts : [+, +, -, +, +]

**input:** 102017010.csv
```s
python3 -m topsis_rupanshijain_102017010.topsis102017010 102017010.csv "1,1,1,2,1" "+,+,-,+,+" 102017010-result.csv
```

**output:** 102017010-result.csv
|   Fund Name  |   P1    |   P2    |   P3   |   P4    |   P5     |   Performance Score    |   Rank  |
|--------------|---------|---------|--------|---------|----------|------------------------|---------|
|   M1         |   0.94  |   0.88  |   3.8  |   40.8  |   11.61  |   0.47123375880088600  |   6     |
|   M2         |   0.77  |   0.59  |   5.6  |   45.4  |   13.09  |   0.33633254123378100  |   7     |
|   M3         |   0.8   |   0.64  |   7.0  |   57.7  |   16.54  |   0.5217872008890350   |   5     |
|   M4         |   0.67  |   0.45  |   3.1  |   61.6  |   16.46  |   0.6239838869195070   |   3     |
|   M5         |   0.89  |   0.79  |   5.9  |   67.7  |   18.82  |   0.7322985618518600   |   1     |
|   M6         |   0.66  |   0.44  |   6.2  |   62.1  |   17.35  |   0.5354177643750590   |   4     |
|   M7         |   0.73  |   0.53  |   6.6  |   34.5  |   10.59  |   0.09985769514278550  |   8     |
|   M8         |   0.91  |   0.83  |   3.2  |   55.3  |   15.06  |   0.706943829440074    |   2     |

## Other notes
- The first column and first row are removed by the library before processing, in attempt to remove indices and headers. So make sure the csv follows the format as shown in sample.csv.
- Make sure the csv does not contain categorical values

## License

MIT

**Free Software, Hell Yeah!**
102003146 1.0.0
TOPSIS Implementation in Python.
License: MIT

# TOPSIS-Python

**Project 1 : UCS633**


Submitted By: **Maninder 101703325**

***

## What is TOPSIS

**T**echnique for **O**rder **P**reference by **S**imilarity to **I**deal
**S**olution (TOPSIS) originated in the 1980s as a multi-criteria decision
making method. TOPSIS chooses the alternative of shortest Euclidean distance
from the ideal solution, and greatest distance from the negative-ideal
solution. More details at [wikipedia](https://en.wikipedia.org/wiki/TOPSIS).

<br>

## How to use this package:

TOPSIS-Maninder 101703325  can be run as in the following example:



### In Command Prompt
```
>> topsis data.csv "1,1,1,1" "+,+,-,+"
```
<br>

### In Python IDLE:
```
>>> import pandas as pd
>>> from topsis_python.topsis import topsis
>>> dataset = pd.read_csv('data.csv').values
>>> d = dataset[:,1:]
>>> w = [1,1,1,1]
>>> im = ["+" , "+" , "-" , "+" ]
>>> topsis(d,w,im)
```

<br>

## Sample dataset

The decision matrix (`a`) should be constructed with each row representing a Model alternative, and each column representing a criterion like Accuracy, R<sup>2</sup>, Root Mean Squared Error, Correlation, and many more.

Model | Correlation | R<sup>2</sup> | RMSE | Accuracy
------------ | ------------- | ------------ | ------------- | ------------
M1 |	0.79 | 0.62	| 1.25 | 60.89
M2 |  0.66 | 0.44	| 2.89 | 63.07
M3 |	0.56 | 0.31	| 1.57 | 62.87
M4 |	0.82 | 0.67	| 2.68 | 70.19
M5 |	0.75 | 0.56	| 1.3	 | 80.39

Weights (`w`) is not already normalised will be normalised later in the code.

Information of benefit positive(+) or negative(-) impact criteria should be provided in `I`.

<br>

## Output

```
Model   Score    Rank
-----  --------  ----
  1    0.77221     2
  2    0.225599    5
  3    0.438897    4
  4    0.523878    3
  5    0.811389    1
```
<br>
The rankings are displayed in the form of a table using a package 'tabulate', with the 1st rank offering us the best decision, and last rank offering the worst decision making, according to TOPSIS method.
Topsis-Ravinshu-102003156 1.1.0
A convenient python package for Topsis rank and score calculation for a given dataset, weights and impacts
License: MIT
Keywords: python,TOPSIS,MCDM,MCDA,statistics,prescriptive analytics,cli

# Topsis-Ravinshu-102003156

Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) is a multi-criteria-based decision-making method.
Topsis-Ravinshu-102003156 is a python package used for dealing with Multiple Criteria Decision Making (MCDM) problems.

## Installation

Use the package manager [pip](https://pip.pypa.io/en/stable/) to install foobar.

```bash
pip install Topsis-Ravinshu-102003156
```

## Usage

Open any command line and type the following command:

```
topsis [data file] [weight] [impact] [output file]
```

Eg. topsis 102003156-data.csv "1,1,1,1,1" "+,+,-,+,+" 102003156-result.csv

## Output

The output file contains columns of input file along with two additional columns having Topsis_score and Rank
Topsis-Aditya-102053005 0.16
This is a package for implementing Topsis
License: MIT

# Topsis Package by Aditya Kalhan
Roll Number : 102053005 <br>
Subgroup : 3COE18 <br>
It takes a csv file, weights (seperated by comma) , impacts (+ or -) and outputs a result file.
# What is Topsis
TOPSIS is based on the fundamental premise that the best solution has the shortest distance from the positive-ideal solution, and the longest distance from the negative-ideal one.<br>
Selecting an appropriate Multiple Attribute Decision Making (MADM) method for a given MADM problem is always a challenging task.<br>
Within the MADM domain, the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) is highly regarded, applied and adopted MADM method due to its simplicity and underlying concept that the best solution is the one closest to the positive ideal solution and furthest from the negative ideal solution.<br>


## Installation
Use pip installer <br>
``` pip install Topsis-Aditya-102053005 == 0.16 ```

## How to use it
Open terminal and type <br>
``` 102053005 sample.csv "1,1,1,1" "+,+,-,+" result.csv ```

## Example 
| Model | Storage Space | Camera | Price | Looks |  
|-----------|:-----------:|-----------:|-----------:|-----------:|
| M1 | 16 | 12 | 250 | 5 |  
| M2 | 16 | 8 | 200 | 3 |  
| M3 | 32 | 16 | 300 | 4 |  
| M4 | 32 | 8 | 275 | 4 |  
| M5 | 16 | 16 | 225 | 2 |  

weights = [1,1,1,1] <br>
impact = ["+","+","-","+"]


## Output
| Model | Storage Space | Camera | Price | Looks | Topsis Score | Rank |
|-----------|:-----------:|-----------:|-----------:|-----------:|-----------:|-----------:|
| M1 | 16 | 12 | 250 | 5 |  0.534269 | 3 |~
| M2 | 16 | 8 | 200 | 3 |  0.308314 | 5 |
| M3 | 32 | 16 | 300 | 4 |  0.691686 | 1 |
| M4 | 32 | 8 | 275 | 4 |  0.534807 | 2 |
| M5 | 16 | 16 | 225 | 2 |  0.401222 | 4 |

Output will be saved in a CSV file whose name will be provided in the command line.
It will have all the columns along with the Topsis Score and Ranks.<br><br>