SDSC/open-pulse-hackathon-data-analysis huggingface dataset:
Repository: faresfawzi/kNOw-tube
Primary language: Python

# kNOw-tube

You can run in dev, with: `docker compose up --build`
- hot reloading is on

If you install new dependencies, then need to rerun: 
1. `docker compose down`
2. `docker compose up --build`

To use the extension in Chrome:
1. Go to `chrome://extensions/`
2. Enable "Developer mode" (top right)
3. Click "Load unpacked"
4. Select the `kNOwtube-chrome-plugin` folder
5. The extension should now be loaded and ready to use!
6. Use together client for the LLMs

-- 
## Example for together

``` 
response = client.chat.completions.create(
        model=model,
        messages=[
            {"role": "user", "content": prompt},
        ],
        temperature=temperature,
        # max_tokens=max_output_tokens,
    )
```

# Logitech plugin
1. Install Logitech Options+ from https://www.logitech.com/de-ch/software/logi-options-plus.html
2. open ./logitech-plugin/ExamplePlugin/Example.lplug4  with LogiPluginService
Repository: francesco-gramegna/lauehack2024team
Primary language: Jupyter Notebook

# TheCatIsForecatting


Submission for the LauzHack 2024 hackathon.

Time series forecasting for medicine demand.


## Approach

We solve the task using Gaussian processes for forecasting and SHAP for determining feature importance.


## Team members
- Luca Domeniconi 
- Francesco Gramegna
- Claudia Maiolino
- Tian Cheng Xia
Repository: hassan11196/10-Days-of-Statistics
Description: Hackerrank - 10 Days of Statistics Challenge
Primary language: Python
Topics: hackkerrank, statistics, python, maths

# 10-Days-of-Statistics
By Hackerrank

## Help Material : 

[Stat Notes](https://www3.nd.edu/~rwilliam/stats1/)
[Stat Calculator and Notes](http://onlinestatbook.com/2/index.html)

profile : https://www.hackerrank.com/hassan11196
Repository: hassan11196/AlgorithmsVisualized
Description: A Jupyter Notebook with famous graph traversal Algorithms Visualized.
Primary language: Jupyter Notebook
Topics: jupyter-notebook, python, astar-algorithm, bfs-algorithm, visualization

# AlgorithmsVisualized

[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/hassan11196/AlgorithmsVisualized.git/master)

A Jupyter Notebook with Different Graph Based Traversal and Search Algorithms Visualized.

Current list of Algorithms : 

- Breadth First Search
- A* Algorithm
- Best First Algorithm

Visualizations :

### Breadth First Search

![](breadth_first_search.gif)

### A Star

![](A_star.gif)
Repository: hassan11196/Bank-Transaction-Categorisation
Description: This repository is the code of my dissertation project for MSc Business Analytics, University College London
Primary language: Jupyter Notebook

# Bank-Transaction-Categorisation
This repository is the code of my dissertation project for MSc Business Analytics, University College London

## Dependencies
-	Scikit-learn version 0.20.3 
-	Spacy version 2.1.4 
-	Gensim version 3.7.3 
-	FastText version 0.9.1 
- xgboost version 0.9
-	Keras version 2.2.4 

## Prereqresites
Download pre-trained word-embeddings files
1. Word2Vec <a href ="https://drive.google.com/file/d/0B7XkCwpI5KDYNlNUTTlSS21pQmM/edit?usp=sharing">`GoogleNews-vectors-negative300.bin`</a> (from : https://code.google.com/archive/p/word2vec/)
2. FastText <a href ="https://dl.fbaipublicfiles.com/fasttext/vectors-english/wiki-news-300d-1M-subword.bin.zip">`wiki-news-300d-1M-subword.bin`</a> (from : https://fasttext.cc/docs/en/english-vectors.html)
3. GloVe <a href="http://nlp.stanford.edu/data/wordvecs/glove.42B.300d.zip">`glove.42B.300d.zip`</a> and <a href="http://nlp.stanford.edu/data/wordvecs/glove.840B.300d.zip">`glove.840B.300d.zip`</a> (from : https://github.com/stanfordnlp/GloVe) 
  and pre-process GloVe pre-trained models with `./utils/load_glove.py`
## Running the code
1. Edit `ROOT_PATH`, `DATA_FILES` and pre-trained word-embedding file paths in `./utils/config.py`. 
    Note that the `DATA_FILES` is in a dict object (file_reference: filename.xlsx) For example, 
    ```
     DATA_FILES = {
                    'file1': 'file1.xlsx',
                    'file2': 'file2.xlsx',
                    ...
                  }
    ```
2. Put transaction data files (.xlsx) into folder `./Transaction Data`
3. Run Jupyter Notebook files in `./notebooks/' in order

## Project file structure
```
.
├── notebooks
│   ├── 0_Split_Train_Test_Data.ipynb
│   ├── 1_Select_Text_Representation.ipynb
│   ├── 2_Add_numeric_features_and_model_selection.ipynb
│   ├── 3_Neural_Network.ipynb
│   ├── 4_Predict_Test_Data.ipynb
│   ├── grid_search.ipynb.ipynb
├── output
│   ├── embedding_benchmark_results
│   │   └── <store outputs from 1_Select_Text_Representation.ipynb>
│   ├── embedding_results
│   │   └── <store outputs from 1_Select_Text_Representation.ipynb>
│   ├── final_model
│   │   └── <store outputs from 4_Predict_Test_Data.ipynb>
│   ├── model_results
│   │   └── <store outputs from 2_Add_numeric_features_and_model_selection.ipynb and 3_Neural_Network.ipynb>
│   └── text_embeddings
│       └── <store outputs from 1_Select_Text_Representation.ipynb>
├── resources
│   ├── country_code.csv
│   ├── currency.csv
│   ├── nz_cities.csv
│   ├── nzpostcodes_v2.csv
│   └── stop_words.csv
├── Transaction Data
│   └── <Put transaction files here>
└── utils
    ├── config.py -> Edit config in this file
    ├── feature_eng_utils.py
    ├── load_glove.py
    ├── misc_utils.py
    ├── ml_models.py
    ├── neural_network_model.py
    ├── text_embedding.py
    └── txn_reader.py
 ```
Repository: hassan11196/C_Programs
Description: This repository contains basic C programs to help beginners.
Primary language: C

# C_Programs

Happy Hacktober! :octocat:  

This is a beginner friendly repository made specifically for Hacktoberfest that helps you get your first PR.
The programs are written in C and compiled using GCC. To run the programs in TurboC++ read port_to_turboC.md

## How to contribute to this repository

* Fork this repository (Click the Fork button in the top right of this page, click your Profile Image)
* Clone your fork down to your local machine

```markdown
git clone https://github.com/your-username/hacktoberfest.git
```

* Create a branch

```markdown
git checkout -b branch-name
```

* Make your changes (There are some folders for different category so upload the file accordingly)
* Commit and push

```markdown
git add .
git commit -m 'Commit message'
git push origin branch-name
```
or
* Add the file in the forked repository using upload file option then commit the changes in a new branch. 

* Create a new pull request from your forked repository (Click the `New Pull Request` button located at the top of your repo)
* Wait for your PR review and merge approval!

If you are still facing any problem follow steps given in this [website](https://thenewstack.io/getting-legit-with-git-and-github-your-first-pull-request/)
Repository: hassan11196/Chou-Fasman
Description: Implementation of Chou Fasman Alogorithm.
Primary language: C++
Topics: cpp, chou-fasman, bioinformatics, prediction

# Chou-Fasman
Implementation of Chou Fasman Alogorithm.

The Chou–Fasman method is an empirical technique for the prediction of secondary structures in proteins.


https://en.wikipedia.org/wiki/Chou%E2%80%93Fasman_method
Repository: hassan11196/Churn-NN
Description: A simple Churn Predictor using Scikit's  Multi-Layer Perceptron Classifier
Primary language: Jupyter Notebook
Topics: ml, scikit-learn, machine-learning, python, neural-network, jupyter-notebook

# Churn-NN
A simple Churn Predictor using Scikit's  Multi-Layer Perceptron Classifier
Repository: hassan11196/CompilerConstruction-A1
Primary language: Java
Topics: hacktoberfest

# Code Generator for C Functions:

## What's code generation?

In computing, code generation is the process by which a compiler's code generator converts some intermediate representation of source code into a form that can be readily executed by a machine. Sophisticated compilers typically perform multiple passes over various intermediate forms.

## About the project

In this project, tail end recursive program is generated in C from a JSON object.

Input Directory for JSON: `input_function/`

Output Directory for C code: `output_code/`

`Build and Run Class CodeGeneratorDriver.`
Repository: hassan11196/FAST-Barter
Description: FAST-Barter (AKA TabDealit). A online Platform to build Communities by exchanging items the old barter way.
Primary language: TypeScript
Topics: angular, dsc, firebase, barter, fast, nuces

## Solution Challenge 2020:

TabdealIt is a solution for Recycling, Reusing personal items. 'Tabdeal' تبدیل means exchange.
TabdealIt provides a platform for communities and individuals to exchange their items using a Barter system. TabDealIt specially aims to reduce e-waste as its one of leading contributors to Pollution.

## Tech:

TabdealIt is built on Angular with Firebase as a Backend that provides Database and Hosting Solution.

## Team:

TabDealIt was Made Possible by support, guidance and dedication from the following DSC members.

- Krinza Momin. (DSC Lead for FAST NUCES Karachi)
- Shahmeer Ahmed. (Lead Web Development)
- M. Hassan Ahmed. (Co-Lead Web Development)
- Ahsan Ali. (Team Web Development)
- Noman Anjum. (Team Web Development)
- Rida. (Team Web Development)

## Code:

Checkout the code [here](https://github.com/hassan11196/FAST-Barter).

Click here to go to [TabDealIt](https://www.tabdealit.ml) .


## For Contributors:

### References: 
If you find article that helped you, you can add them here so that it may also help others working on the project.


[https://medium.com/codingthesmartway-com-blog/firebase-cloud-storage-with-angular-394566fd529](https://medium.com/codingthesmartway-com-blog/firebase-cloud-storage-with-angular-394566fd529)

[https://codesearchonline.com/index.php/angular-firebase-cheat-sheet/](https://codesearchonline.com/index.php/angular-firebase-cheat-sheet/)

[https://alligator.io/angular/firebase-crud-operations/](https://alligator.io/angular/firebase-crud-operations/)

[https://alligator.io/angular/cloud-firestore-angularfire/](https://alligator.io/angular/cloud-firestore-angularfire/)

[https://getuikit.com/](https://getuikit.com/)


This project was generated with [Angular CLI](https://github.com/angular/angular-cli) version 8.3.19.

### Development server

Run `ng serve` for a dev server. Navigate to `http://localhost:4200/`. The app will automatically reload if you change any of the source files.

### Code scaffolding

Run `ng generate component component-name` to generate a new component. You can also use `ng generate directive|pipe|service|class|guard|interface|enum|module`.

### Build

Run `ng build` to build the project. The build artifacts will be stored in the `dist/` directory. Use the `--prod` flag for a production build.

### Running unit tests

Run `ng test` to execute the unit tests via [Karma](https://karma-runner.github.io).

## Running end-to-end tests

Run `ng e2e` to execute the end-to-end tests via [Protractor](http://www.protractortest.org/).

### Further help

To get more help on the Angular CLI use `ng help` or go check out the [Angular CLI README](https://github.com/angular/angular-cli/blob/master/README.md).
Repository: hassan11196/HacktoberFest2020
Description: Repo for HacktoberFest 2020, either its you first time using Github, first PR or a professional, aim is to help everyone to contibute and get the perk of contibution [Free Tshirt and Stickers].
Primary language: Python

# HacktoberFest2020
Repo for HacktoberFest 2020, either its you first time using Github, first PR or a professional, aim is to help everyone to contibute and get the perk of contibution [Free Tshirt and Stickers].