Wine Quality Prediction

Project Overview

Project Overview

Key Features

Business Impact

Next Steps

This project aims to predict the quality of wines using various machine learning algorithms. It utilizes the MLflow platform to manage the end-to-end machine learning lifecycle, including data preprocessing, model training, hyperparameter tuning, and deployment.

• Data preprocessing pipeline for cleaning and transforming the dataset.

• Support for multiple machine learning algorithms for wine quality prediction.

• Hyperparameter tuning using grid search or random search.

• Tracking and logging experiments with MLflow for easy comparison.

• REST API endpoint for making predictions using the trained model.

• Dockerized environment for seamless deployment.

Sales Target Dashboard, a powerful tool designed to facilitate efficient tracking, analysis, and management of sales targets. This dynamic dashboard offers a comprehensive view of sales performance metrics, enabling businesses to monitor progress, make informed decisions, and drive their teams towards achieving set targets.

Web Application with Streamlit and Gradio , hosted on AWS and Streamlit cloud.

Source :