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A project where we can try to experiment with our dataset and try it on different models. This is not exactly an AutoML. This is just for fun.

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PLAY WITH ML (An Intuitive way of implementing ML models).

Index

Demo

Link: http://playwithml.herokuapp.com/

Overview

  • This is a intuitive way of implementing Machine Learning models without writing even a single line of code.
  • This is a supervised Machine Learning based Project in which you can Upload a Dataset of your choice,
  • View the Charts and Bar graphs related to the Dataset,
  • Select the type of model (Classification or Regression),
  • Select which model to train on,
  • Click on the TRAIN button,
  • Finally get the Scores, Code and Report for your Model :)

Installation

The Code is written in Python 3.7. If you don't have Python installed you can find it here. If you are using a lower version of Python you can upgrade using the pip package, ensuring you have the latest version of pip. To install the required packages and libraries, run this command in the project directory:

git clone https://github.com/mystery2828/playwithML.git
pip install -r requirements.txt

Directory Tree

│   app.py
│   classification.py
│   LICENSE
│   Procfile
│   readme.txt
│   regressor.py
│   requirements.txt
│   setup.sh
│
├───datasets
│       abalone.csv
│       Admission_Predict.csv
│       ionosphere.csv
│       iris.csv
│       Placement_Data_Full_Class.csv
│       pulse.xlsx
│       sonar.csv
│       winequality.csv

Usage

streamlit run app.py

Deployement on Heroku

git init
git add .
git commit -m "< commit message >"
git push
git push heroku master

Bug / Feature Request

If you find a bug (the website couldn't handle the query and / or gave undesired results), kindly open an issue here by including your search query and the expected result.

Technologies Used

Team

Akash C
Akash C
Ashwin Sharma
-
Ashwin Sharma

License

Apache license

Copyright 2020 Akash C

Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at

http://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.

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A project where we can try to experiment with our dataset and try it on different models. This is not exactly an AutoML. This is just for fun.

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  • Python 87.4%
  • Jupyter Notebook 12.3%
  • Shell 0.3%