nimish-verma / Wine-Quality-Prediction

Prediction of Wine Quality on the basis of various features.

Wine Quality Prediction

This repository contains a machine learning project for predicting the quality of Wine using the dataset with 11 features. A Web App is built where you can input some feature values and get the wine quality prediction.

About the Dataset

The Dataset is available on :

                   https://github.com/DivyanshKushwaha/datasets/blob/master/winequality-data.zip)

This dataset have 11 features :

• fixed acidity
• volatile acidity
• citric acid
• residual sugar
• chlorides
• free sulfur
• dioxide
• total sulfur dioxide
• density
• pH
• sulphates
• alcohol

Model Used

• ElasticNet Regression model.

Project Structure

This project is organized as follows:

• .gitignore

  This file specifies which files and directories should be ignored by Git.

• artifacts

  This folder contains the train,test and raw csv files along with the preprocessed and best model pickle file. These files are in this folder:

  • data_ingestion.py
  • data_transformation.py
  • data_validation.py
  • model_evaluation.py
  • model_trainer.py

• config

  This folder contains the artifacts files parameters like root directory, file path where the data is to be stored.

• logs

  This file helps us to record and manage application events and information, facilitating debugging and monitoring.

• research

  This directory contains Jupyter notebooks used for data exploration, visualization and model training. The coding of Data Ingestion to Model Training is done in this directory.

• src

  • This directory contains the source code for the project. The envoirement 'projectml' is created under this directory.
  • Under the directory 'projectml' , there subdirectories are given:
    • components : Source coding of artifacts files is done here. Many classes are created here like logging status , downloading , unzipping etc.
    • config : All config classes are created here.
    • constants : Constants like config_file_path, parameter_file_path are created here that we can use anywhere in the envoirement.
    • entity : All dataclasses of config.yaml are created here.
    • pipeline : This directory contains data processing or machine learning pipelines used in the project, including:
      • data_ingestion_pipeline.py : Handles the process of loading and preparing the dataset.
      • data_validation_pipeline.py : Validate the ingestion process.
      • data_transformation_pipeline.py : Performs data preprocessing and feature engineering.
      • model_trainer_pipeline.py : Defines the training pipeline for model development.
      • model_prediction_pipeline.py : Defines the pipeline for making predictions using the trained model.
    • utils : Contains utility functions used throughout the project.

• static

  All helping files to build the application functional is here.

• templates

  This folder contains the HTML files used for obtaining user input via form and flask uses these files as a rendering template

• app.py

  This is the Flask application file responsible for hosting the web application.

• main.py

  By running this file in terminal we can start the pipeline or the whole developement that initates from data_ingestion and ends with model_prediction.

• params.yaml

  It stores the parameter of the machine learning model or algorithm.

• README.md

  This file is an outcome of displaying the projects documentation.

• requirements.txt

  This file lists all the Python libraries and dependencies required to run the project.

• setup.py

  This is the setup file for the project, which may include additional configuration settings.

• template.py

  By running this file in terminal we can create a file or folder throughout the project by adding in the list.

Getting started

To get started with this project, follow these steps:

• Create an envoirement 'projectml' using the following command

   conda create -n projectml python=3.10
  

• Activate the envoirement using the following command:

   conda activate projectml
  

• Clone the repostiory to your local machine using the following command:

   git clone https://github.com/DivyanshKushwaha/Wine-Quality-Prediction.git
  

• Navigate to the project directory:

   cd Wine-Quality-Prediction
  

• We can manually create files and folders mentioned in the template.py. Also we can Create the template.py files and folders using the command:

   python template.py
  

• Install the required dependencies using pip:

   pip install -r requirements.txt
  

• Run the Flask application:

   python app.py
  

• Open your web browser and go to given address. Fill the feature values and get the wine quality prediction.

    http://127.0.0.1:8080
  

Usage

Once you have the web application running, you can use it to predict the quality of wine based on the input features. Simply provide the required information, and the application will provide you with the prediction.

Contributing

Contributions are welcome! If you'd like to contribute to this project, please follow these steps:

• Fork the repository.

• Create a new branch for your feature or bug fix:

  git checkout -b feature-name.
  

• Make your changes and commit them:

  git commit -m 'Description of your changes'.
  

• Push your changes to your fork:

  git push origin feature-name.
  

• Create a pull request on the original repository.