https://github.com/anuraganalog/try-every-ml-algorithm

Trying every Machine learning algorithm on a given dataset and measuring the efficiency.
https://github.com/anuraganalog/try-every-ml-algorithm

accuracy algorithms analysis classification data deep-learning efficiency learning machine metrics neural-networks regression streamlit

Last synced: 25 days ago
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Trying every Machine learning algorithm on a given dataset and measuring the efficiency.

• Host: GitHub
• URL: https://github.com/anuraganalog/try-every-ml-algorithm
• Owner: AnuragAnalog
• License: gpl-3.0
• Created: 2020-01-14T15:44:39.000Z (about 5 years ago)
• Default Branch: master
• Last Pushed: 2024-05-17T02:32:31.000Z (9 months ago)
• Last Synced: 2025-01-03T23:34:38.636Z (27 days ago)
• Topics: accuracy, algorithms, analysis, classification, data, deep-learning, efficiency, learning, machine, metrics, neural-networks, regression, streamlit
• Language: Python
• Homepage: https://share.streamlit.io/anuraganalog/try-every-ml-algorithm/app.py
• Size: 7.24 MB
• Stars: 6
• Watchers: 3
• Forks: 1
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# Try every ML algorithm

We as a Data Scientist are very lazy in trying out every different algorithm on a given dataset.

This web interface provides you a convinent way of switching between algorithms are seeing there results. With this now, you can apply machine learning models without writing a Single Piece of code.

The web application is written in streamlit.

Link to the web app is [here](https://share.streamlit.io/anuraganalog/try-every-ml-algorithm/app.py)

## How it works

* Open the App

* Upload the dataset*

* Inspect your data, if you wish

* Select the features

* Select the label

* Make a Train Test split

* Select an appropriate algorithm

* Start tweaking the hyperparameters

> This app expects a preprocessed dataset with all the NaN, Null values handled properly, One Hot encoded, and scaled

## Demo

![demo](./demo.gif)

## Required Modules

* Pandas

* Streamlit

* Scikit-learn

## Getting a copy of this repo

Clone the repository before running any commands

```python3

$ git clone https://github.com/AnuragAnalog/Try-every-ML-algorithm.git

$ cd Try-every-ML-algorithm

```

## Installation

Run the below command to install all the dependencies in your local machine to run the py script.

```python3

$ sudo pip3 install -r requirements.txt

```

## Running the app

```python3

$ streamlit run app.py

```

## Algorithms

* Regression

    * Linear Regression

    * K Nearest Neighbours

    * Decision Trees

    * Random Forest

    * Ada Boost

    * Gradient Boosting

* Classification

    * Logistic Regression

    * K Nearest Neighbours

    * Decision Trees

    * Random Forest

    * Ada Boost

    * Gradient Boosting

*Want to contribute? then fork, develop, and create a pull-request*

## Future Work

* [x] Add an option to show the code which implements the above selected algorithm with the corresponding hyperparameters.

* [x] Added code for One Hotencoding.

* [ ] Add more algorithms.

* [ ] Add some functionality for preprocessing data too.