python Machine learning

Classification of grape varieties in Python using Multinomial Logistic Regression, Decision Trees and Random Forests

Implementation of a machine learning algorithm(AGB) based on the research paper.

The main repository for the project "Analysis of the load structure of the consumer".

Application of machine learning in Python | Coursera

This repository contains model files that can be used to predict stock and insurance churns using machine learning algorithms.

Files for Feature Engg, Feature Scaling, Feature Selection, Statistics and Implementation of every Machine Learning Algorithm.

census_data_model using machine learning algorithm as well as deep learning algorithm

Python Machine Learning algorithms and experiments. At the moment there is an implementation of k nearest neighbours on features projection algorithm (k-NNFP), and Voting Feature Intervals algorithm.

Code playground for commonly used machine learning models and algorithms

Welcome to my machine learning journey repository! 🚀 Here, I document my learning ML experiences, projects, and insights as I progress through my B.Sc. CSIT program at Tribhuvan University, Nepal. Explore various machine learning algorithms and models, including supervised, unsupervised, and reinforcement learning.

Artificial Intelligence, Computer Network, Machine Learning, Python, DAA, Distributed System, Internet of Things (IoT), Data Science and Analysis.

The hyperparameters of XGBoost was found using the DE algorithm. The fraud detection challenge was used for this project. The model accuracy on test data was found 89%.

Comparison of the logistic regression, decision tree, and random forest models to predict red wine quality in R.

Linear Regression is a machine learning algorithm based on supervised learning. It performs a regression task.

Final project progress will be posted here.

Splitting the advertising data (advertising.csv) into training and testing data sets, then choosing and training a classification machine learning algorithm; Getting the accuracy of the ML model; Using feature engineering skills to create new features and improve my ML model;