semvec/pypi-packages huggingface dataset:
prediction-btc 0.0.1
A small example package
Requires Python: >=3.8
Keywords: prediction, bitcoin, ai

## Prediction_BTC Package untuk menganalisis sentimen Bitcoin dari teks menggunakan preprocessing, tokenization, dan model machine learning. 

## Fitur Utama 
- Membersihkan teks dari noise seperti tanda baca dan angka. 
- Tokenisasi teks dengan padding otomatis. 
- Analisis sentimen menggunakan model machine learning terlatih. 
- Mendukung klasifikasi sentimen positif dan negatif. 

## Instalasi Gunakan pip untuk menginstal package ini: pip install prediction_btc 

## Penggunaan Berikut adalah contoh sederhana penggunaan package ini: python from prediction_btc import full_prediction 

# Teks yang ingin dianalisis text = ["Bitcoin is the future!", "The market is crashing!"] 
# Prediksi sentimen result = full_prediction(text) print(result) 

## Dependencies Package ini membutuhkan library berikut: 
- Python 3.7+ 
- TensorFlow 
- Numpy 
- Pandas 
- Scikit-learn 
- Joblib  

## Lisensi Project ini dirilis di bawah lisensi MIT. 
## Kontak Jika memiliki pertanyaan atau feedback, silakan hubungi saya: Komang Nitay Prasaddas Email: nitay06@students.amikom.ac.id
102217109-topsis 1.0.1
A simple TOPSIS implementation package.
Requires Python: >=3.6


# TOPSIS Package



## Overview

The *TOPSIS (Technique for Order Preference by Similarity to Ideal Solution)* method is a multi-criteria decision analysis (MCDA) technique used to evaluate and rank alternatives based on multiple criteria. This Python package provides a simple implementation of the TOPSIS method.

## Features

- Easy to use function to perform the TOPSIS method on decision matrices.
- Accepts customizable weights for each criterion.
- Supports both beneficial and non-beneficial criteria (impacts).
- Returns preference scores and ranks for the alternatives.

    Parameters:
        data (list of lists): Decision matrix (alternatives x criteria).
        weights (list): List of weights for each criterion.
        impacts (list): '+' for beneficial, '-' for non-beneficial criteria.

    Returns:
        list: Preference scores for each alternative.
        list: Ranks of each alternative.

## Installation

You can install the *Topsis Package* using pip from PyPI.

### From PyPI:

Install the package using pip:
```bash
pip install 102217109_topsis
```