semvec/pypi-packages huggingface dataset:
Topsis-102216038-srestha 0.0
This is the short description
License: MIT

Here's a sample `README.md` file for your GitHub repository:

---

# TOPSIS Package

This Python package implements the **TOPSIS** (Technique for Order of Preference by Similarity to Ideal Solution) method for multi-criteria decision-making. It evaluates and ranks options based on given criteria, weights, and impacts, producing a detailed result file.

---

## Features

- Normalizes data using the vector normalization method.
- Applies weighted criteria and computes TOPSIS scores.
- Determines ideal best and worst values for each criterion.
- Ranks options based on their relative closeness to the ideal solution.

---

## Installation

Clone the repository and install the package locally:

```bash
git clone https://github.com/SresthaJain/TOPSIS/tree/main
cd Topsis-102216038
python setup.py install
```

Ensure you have the required dependencies:

```bash
pip install numpy pandas
```

---

## Input/Output

### Input File

- Must be in CSV format (e.g., `101556-data.csv`).
- Should contain **at least three columns**:
  - First column: Object/variable names (e.g., M1, M2, M3).
  - Remaining columns: Numeric values for criteria.
- Example:

| Model | Criteria1 | Criteria2 | Criteria3 |
| ----- | --------- | --------- | --------- |
| M1    | 250       | 16        | 12        |
| M2    | 200       | 20        | 10        |
| M3    | 300       | 25        | 8         |
| M4    | 400       | 30        | 6         |

### Output File

- Adds two columns:
  - **Topsis Score**: The calculated score for each object.
  - **Rank**: The rank of each object based on the TOPSIS score.
- Example:

| Model | Criteria1 | Criteria2 | Criteria3 | Score    | Rank |
| ----- | --------- | --------- | --------- | -------- | ---- |
| M4    | 400       | 30        | 6         | 0.909311 | 1    |
| M3    | 300       | 25        | 8         | 0.847279 | 2    |
| M1    | 250       | 16        | 12        | 0.285391 | 3    |
| M2    | 200       | 20        | 10        | 0.268085 | 4    |

---

## Usage

### Example Script

After installing the package, use the following script:

```python
from topsis.topsis import Topsis

# Define inputs
input_file = "101556-data.csv"
output_file = "101556-result.csv"
weights = [0.5, 0.3, 0.2]  # Criteria weights
impacts = ["+", "+", "-"]  # '+' for benefit, '-' for cost criteria

# Process TOPSIS
Topsis.process(input_file, weights, impacts, output_file)
```

### Steps

1. Ensure the input file is in the correct format.
2. Replace `weights` and `impacts` with your specific values.
3. Run the script. The results will be saved in `output_file`.

---

## License

This project is licensed under the MIT License. See the [LICENSE](LICENSE) file for details.

---

## Author

- **srestha jain**

---

Let me know if you'd like further modifications!
medAId 0.1.0
Automated Machine Learning for medical use
License: MIT
Requires Python: >=3.8
Keywords: automated machine learning, automl, machine learning, medical data

# MedAId :stethoscope:

This is a Python package designed for working with tabular data. While it is optimized for medical data use cases, it can be adapted to work with any kind of tabular dataset.

## Installation

Since this package is not available on PyPI (for now), you can install it directly from the source. Clone the repository and install the package using the following commands:

```bash
git clone https://github.com/DeptuchMateusz/autoML.git
cd medaid
pip install .
```
## More Info
For more detailed information about the package navigate to `notebook.ipynb` (polish version) or `notebook_eng.ipynb` (english version) file.