Wine Quality Prediction

This project focuses on predicting the quality of wines based on various physicochemical properties.The dataset used in this project is the "Wine Quality" dataset, which is publicly available. The classes are ordered and not balanced (e.g. there are much more normal wines than excellent or poor ones).Your task is to predict the quality of wine using the given data.

Dataset Link

https://archive.ics.uci.edu/dataset/186/wine+quality

Run Locally

Clone the project

    git clone https://github.com/kunalshelke90/Wine-Quality-Testing.git

Go to the project directory

    cd Wine-Quality-Testing

Create a virtual environment and install dependencies:

    conda create -p myenv python=3.8 -y

   conda activate myenv

    pip install -r requirements.txt

Usage

1. Start the Flask application:

    python app.py

2. Access the application: Open your web browser and go to http://localhost:8080 to interact with the application. or http://127.0.0.1:8080

Steps included in this project:

Importing Lib
Loading Data
Understanding Data
Missing Values
Exploring Variables(Data Anylasis)
Feature Selection
Proportion of Good vs Bad Wines
Preparing Data for Modelling
Applying different models
Choosing right model
Start doing prediciton

## Workflows

1. update config.yaml

2. update schema.yaml

3. update params.yaml

4. update the entity

5. update the configuration manager in src config

6. update the components

7. update the pipeline

8. update the main.py

9. update the app.py

Learning

I learned how machine learning explainability can be used to take a deep dive into machine learning models and extract key insights that can be used in the real world. I also learned that the highest quality of wine had an alchoal content between 12% - 15%.

License

This project is licensed under the MIT License. See the LICENSE file for details

CHEERS!