Machine Learning classification problem displayed with Flask Application
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Updated
Jun 21, 2022 - Python
Machine Learning classification problem displayed with Flask Application
Building classification models to predict quality of wines. (Accuracy = 71.33%)
An ML classifier project which can predict wine quality status
Wine Predicts
Predicting wine quality using machine learning
Predicting the quality of a good bottle of wine
Example data-science web application, from "Hello, World!" to app
Wine quality prediction using ElasticNet regression with MLflow experiment tracking and DAGShub integration for model versioning and collaboration.
Using Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) on same dataset and analyzing the best one
K means clustering implementation on the wine quality dataset
Wine-quality has been predicted through supervised learning using regression and classification models.
ML algorithms for Regression, Classification, Clustering and Dimensional Reduction applied in a Wine Quality Dataset.
Predict wine quality using a machine learning model (Random Forest) served with FastAPI. Includes containerization with Docker and monitoring via Prometheus and Grafana.
Classification of wine quality using a hard_parzen and a soft_parzen with gaussian kernel models - Machine Learning course (IFT3395)
This project is designed to predict the quality of red or white wine based on various features. It utilizes Streamlit for the user interface and incorporates machine learning models from scikit-learn.
A feedforward neural network to predict wine quality based on a number of scientific factors. NOTE: This is purely an educational project. This is neither an efficient nor realistic neural network for commercial use.
App for pK spectroscopy (simple yet advanced analysis of materials of the complex acidic nature using results of the potentiometric titration).
This project implements a Wine Quality Classifier using an Artificial Neural Network (ANN) to predict the quality of wine based on its chemical properties. The model is trained with a dataset containing 11 features, such as acidity, alcohol content, and sulfur levels. Achieving 84% training accuracy and 82% accuracy on the test set.
Wine quality prediction (scores 3-8) using Random Forest, Gradient Boosting, SVM and Logistic Regression. Handles severe class imbalance with balanced weighting and Macro F1 optimisation. Bharat Intern ML 2023.
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