semvec/pypi-packages huggingface dataset:
topsis-chetanya-102303778 1.0.0
A Python package for TOPSIS decision making
License: MIT
Requires Python: >=3.6


# Topsis-Diya-102303694

## Project Description

**For:** Project-1 (UCS654 – Predictive Analytics)  
**Submitted by:** Diya
**Roll Number:** 102303694

**Topsis-Diya-102303694** is a Python package designed to solve **Multiple Criteria Decision Making (MCDM)** problems using the **Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS)** method.

The package ranks multiple alternatives based on their relative distance from the ideal best and ideal worst solutions. It is implemented as a **command-line tool**, making it easy to use for real-world decision-making applications.

---

## Installation

Install the package using `pip`:

```bash
pip install Topsis-Diya-102303694
```

---

## Usage

Run the package from the command line by providing:

- Input CSV file
- Weights vector
- Impacts vector
- Output file name

```bash
topsis data.csv "1,1,1,2" "+,-,-,+" result.csv
```

If vectors contain spaces, they must be enclosed within double quotes (`" "`).

---

## Input Format

- Input file must be in **CSV format**
- First column contains **alternatives** (e.g., items, models, options)
- Remaining columns contain **numeric criteria values**
- Minimum of **three columns** required
- No categorical values allowed in criteria columns

---

## Example

### Sample Input File (`data.csv`)

```csv
Fund Name,P1,P2,P3,P4
M1,0.67,0.45,6.5,42.6
M2,0.60,0.36,3.6,53.3
M3,0.82,0.67,3.8,63.1
M4,0.60,0.36,3.5,69.2
M5,0.76,0.58,4.8,43.0
```

### Command

```bash
topsis data.csv "1,1,1,2" "+,-,-,+" result.csv
```

---

## Output

The output CSV file contains:

- Original input data
- **TOPSIS Score** for each alternative
- **Rank** based on TOPSIS score  
  (Higher score indicates better rank)

---

## Features

- Command-line based execution
- Supports user-defined weights and impacts
- Input validation and error handling
- Ranks alternatives using TOPSIS method
- Generates results in CSV format

---

## Notes

- Number of weights must match number of criteria
- Number of impacts must match number of criteria
- Impacts must be either `+` or `-`
- Input CSV must contain only numeric values (except first column)

---

## License

[MIT License](https://opensource.org/licenses/MIT)
```
morango 0.8.7
Pure Python sqlite-based Django DB replication engine.
License: MIT
Requires Python: <3.14,>=3.6
Keywords: database, syncing, morango

# Morango

[![build](https://github.com/learningequality/morango/actions/workflows/tox.yml/badge.svg?branch=master)](https://github.com/learningequality/morango/actions)
[![image](http://codecov.io/github/learningequality/morango/coverage.svg?branch=master)](http://codecov.io/github/learningequality/morango?branch=master)
[![image](https://readthedocs.org/projects/morango/badge/?version=latest)](http://morango.readthedocs.org/en/latest/)

Morango is a pure-Python database replication engine for Django that supports peer-to-peer syncing of data. It is structured as a Django app that can be included in projects to make specific application models syncable.

Developed in support of the [Kolibri](https://github.com/learningequality/kolibri) product ecosystem, Morango includes some important features including:

-   A certificate-based authentication system to protect privacy and integrity of data
-   A change-tracking system to support calculation of differences between databases across low-bandwidth connections
-   A set of constructs to support data partitioning

## Developer documentation

See [morango.readthedocs.io](https://morango.readthedocs.io)

To build and edit the docs, run:

```bash
# install requirements
pip install -r requirements/docs.txt
pip install -e .

# build docs
make docs

# auto-build and refresh docs on edit
make docs-autobuild
```