Machine Learning algorithms and models
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Updated
Jun 12, 2024 - Jupyter Notebook
Machine Learning algorithms and models
Comprehensive analysis and modeling of the Wine Quality dataset, including exploratory data analysis (EDA), data preprocessing, model training, and performance evaluation using MSE and RMSE.
POLI 179 Project
This repository contains a machine learning project for email spam detection. It includes data preprocessing, model training, evaluation, and deployment using Python and scikit-learn.
This repository consist of machine learning models which can be use for predicting the future instance. More specifically this repository is a Machine Learning course for those who are interested in learning the basics of machine learning algorithms.
Compendium of free ML reading resources
This project is created using Machine Learning and Regression methods- a statistical technique to predict the outcome of event which is to verify the users’ admission eligibility level, considering the universities they have chosen. This is achieved based on the algorithms implemented, when is user feed the application with the required information
Machine Learning, EDA, Classification tasks, Regression tasks for customer churn
This website provides a platform for users to predict their likelihood of developing diabetes based on various factors.
In this Machine learning project I have selected three diseases for predict status. The disease are Kidney Disease prediction, Heart Disease prediction and Diabetes disease prediction.
Predicting Baseball Statistics: Classification and Regression Applications in Python Using scikit-learn and TensorFlow-Keras
End-to-End Machine Learning project I made as a machine learning intern @ Mentorness
Computation of training set XTX and XTY in a cross-validation setting using the fast algorithms by Engstrøm (2024).
This is a student project for a data mining course and is a simple exercise
Explore my Codsoft ML Internship tasks
Fast CPU and GPU Python implementations of Improved Kernel PLS by Dayal and MacGregor (1997) and Shortcutting Cross-Validation by Engstrøm (2024).
This toolkit is a curated collection of machine learning projects, resources, and utilities designed to assist both beginners and seasoned practitioners in their journey through the fascinating world of machine learning.
Advanced Image Enhancement and Data Recovery: Superresolution Techniques and Missing Data Handling
Here we have fully implemented a number of algorithms related to machine learning
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