semvec/pypi-packages huggingface dataset:
helpful-review-predictor 6
Predicting helpfulness of reviews
Keywords: python,webdevelopment,amazon,ecommerce,online shop,review,comment,machine learning,review analysis,helpful review predictor

# Helpful Review Predictor

The Helpful Review Predictor is a Python package that predicts the helpfulness of reviews using machine learning techniques. It takes textual reviews as input and provides a binary classification indicating whether the review is likely to be helpful or not. A prediction of 1 indicates a helpful review, while a prediction of 0 indicates a review that is not helpful.

For a comprehensive understanding of the model's training process and methodology, I have documented it in an academic research paper. To stay updated on the latest developments and access the research paper upon publication, I invite you to follow my LinkedIn profile: [Mojtaba Maleki](https://www.linkedin.com/in/mojtabamaleki02/).

## Dataset

The data used for training the model is sourced from the Amazon Electronics Reviews dataset available on Kaggle. This 5-core dataset contains product reviews from the Electronics category on Amazon from May 1996 to July 2014, totaling 1,689,188 entries.

The dataset is provided by Julian McAuley, UCSD, and is available [here](http://jmcauley.ucsd.edu/data/amazon/).

## Features

- Preprocesses textual reviews, including lowercasing, punctuation removal, contractions expansion, and lemmatization.
- Utilizes TF-IDF vectorization to convert text data into numerical feature vectors.
- Addresses class imbalance using Random Over Sampling.
- Supports training and evaluation of multiple classifiers, including Gaussian Naive Bayes, Logistic Regression, and Decision Trees.
- Performs hyperparameter tuning using Grid Search and Stratified K-Fold Cross Validation.
- Provides visualization tools for comparing different classifiers and evaluating model performance.
- Saves the best model and TF-IDF vectorizer for future use.

## Installation

You can install the Helpful Review Predictor package using pip:

```bash
pip install helpful-review-predictor
```

### Usage

```python
from helpfulReviewPredictor import PredictHelpfulness

string_input = "Your input string here"
predictor = PredictHelpfulness(string_input)
result = predictor.get_result()
print(result)  # Output: 1 for Helpful, 0 for Not Helpful
```

### Requirements

- joblib
- numpy
- scikit-learn
- scipy
- TfidfVectorizer from sklearn.feature_extraction.text


These changes provide more clarity about the purpose of the package, the dataset used, and the expected output. They also improve the formatting and readability of the document.
rlretry 0.0.11
A small example package
Requires Python: >=3.8

# TODO

Do the 429 avoidance decorator.  learn the best query interval.  use this before @rlretry.  Or maybe use it after rlretry, but just optimize for how long it takes queries to return in general?  yes.  and as an implementation, try setting the query period to the average duration taken for rlretry to return a succesful response (ie. including retries).  But weight this average towards recent results


                   Action.ABRT  Action.RETRY0  Action.RETRY_OVER_2  Action.RETRY_OVER_4  Action.RETRY_OVER_16
ClusteredFailure     -1.000002      -1.000018            21.121019            23.934760             -1.630083
RepeatableFailure    -1.000004      -1.000031            -6.006729            -3.505744             -1.631265
RandomFailure        -1.000003      28.042277            17.699553            23.064918             24.598541
TooBusyFailure       -1.000002      -1.000031            20.788450            17.161290             24.380614
                   Action.ABRT  Action.RETRY0  Action.RETRY_OVER_2  Action.RETRY_OVER_4  Action.RETRY_OVER_16
ClusteredFailure             7              9                   25                  271                     8
RepeatableFailure         3119             67                   62                   61                    67
RandomFailure               34           1632                   35                   36                    27
TooBusyFailure               9             10                  141                    4                   175
django-demian-parts 0.6.0
parts for demiansoft
Requires Python: >=3.8

django-demian_parts
==========

django-demian_parts is a Django app to use for demiansoft. 
(현재 analytics와 popup 구현)

Quick start
------------

1. Add "demian_parts" to your INSTALLED_APPS setting like this
```python
INSTALLED_APPS = [
    ...
    'demian_parts',
]
```

2. 코드를 넣고자 하는 위치에 다음을 추가 한다.
```html
# 팝업 모듈을 넣기 위해
{% load popup_tags %}
{% show_popup %}

# analytics 모듈을 넣기 위해
{% load analytics_tags %}
{% make_analytics %}
```

3. 데이터 베이스를 생성한다.
```commandline
python manage.py makemigrations demian_parts
python manage.py migrate
python manage.py createsuperuser
```

4. 프로젝트의 urls.py에 summernote를 추가하고 media 경로를 추가한다.
```python
from django.contrib import admin
from django.urls import path
from . import views
from django.conf import settings
from django.conf.urls.static import static

urlpatterns = [
    path('admin/', admin.site.urls),
    path('', views.home, name='home'),
]

if settings.DEBUG:
    urlpatterns += static(settings.MEDIA_URL, document_root=settings.MEDIA_ROOT)
```

5. settings.py에 필요한 설정을 추가한다.
```python
MEDIA_URL = '/media/'
MEDIA_ROOT = os.path.join(BASE_DIR, 'media/')
X_FRAME_OPTIONS = 'SAMEORIGIN'
```

6. http://your_url/admin/에 접속하여 popup을 생성한다.

* context example (analytics 예시)
```python
context = {
    'analytics': {
        'google_id': "UA-16872449-1",
        'naver_id': "feadf9e1b55868",
        'ads_id': "AW-1018052709",
        'ads_conversion': {
            'cta': "HdrUCNiXsd8DEOWAueUD",
            'naver': "91WCCPvk6fEBEOWAueUD",
            'call': "pThXCJXpsN8DEOWAueUD",
        },
    },
}
```

* 팝업창을 올바르게 보여주기 위해서는 bootstrap5와 bootstrap-icon이 필요하다.
* css 변수로 폰트와 색상이 설정되어 있어야한다.
- font_link에 family=Gugi 를 추가해야 한다. "family=Gugi&" 
```css
:root {
  --font-default: ...;
  --font-primary: ...;
  --font-secondary: ...;
}

:root {
  --color-default: #364d59;
  --color-primary: #feb900;
  --color-secondary: #52565e;
} 
```