:zap:机器学习实战（Python3）：kNN、决策树、贝叶斯、逻辑回归、SVM、线性回归、树回归

Machine Learning Cheatsheet 2024

Objective of the repository is to learn and build machine learning models using Pytorch. 30DaysofML Using Pytorch

Image classification using SVM, KNN, Bayes, Adaboost, Random Forest and CNN.Extracting features and reducting feature dimension using T-SNE, PCA, LDA.

Classification in TabularDataset

The Complete Machine Learning Repository

Customer Lifetime Value, Returns Predictions, Recommender system and sales analysis on UC Irvine online sales dataset.

This system uses the AdaBoost algorithm to efficiently identify and target potential customers for travel packages

A parallelised facial recognition program written from scratch in C with minimal dependencies

For this project, I used four different classification algorithms to detect fraudulent credit card transactions.

Automobile dataset for used Car Price Analysis to predict the price of a vehicle with their features and performance factor to provide the exact value of a vehicle for buyer seller satisfaction using exploratory data analysis and machine learning models.

An algorithmic trading strategy incursion using Adaboost machine learning classifier, to create the first volatility security suitable for long term investors.

This is a practice Repository consisting of all the notebooks I have practiced for Machine Learning from basics to Advance

The homeworks related to Machine Learning university course would be saved here.

Identify fraudulent credit card transactions so that customers are not charged for items that they did not purchase. (Python, Logistic Regression Classifier, Unbalanced dataset).

This project uses machine learning to predict the price of a used car. The model is trained on a dataset of historical car sales data, and it can then be used to predict the price of a car based on its features.

Can we predict how long a patient will be in a hospital with a fair comparison on gender, race and health service areas?

Employee-Absenteeism-Project-Work

Some decision tree algorithms implemented in C++

Implements the Decision Tree (CART), AdaBoost and Random Forest algorithm from scratch using NumPy.

Dtreehub is a lightweight decision tree framework for Python with categorical feature support. It covers regular decision tree algorithms: ID3, C4.5, CART, CHAID and regression tree, random forest and adaboost.

In This Repository you can find The Explanation and The Implementation of the Most Famous Machine Learning Algorithms

Machine Learning Models

Various machine learning algorithms implemented for practice! Welcome to this Machine Learning Algorithms repository which is a comprehensive guide for anyone looking to understand the inner workings of different ML algorithms.

Classify default borrowers from initial loan application for Lending Club

A token is created to invest in long term volatility, which is very profitable in market crisis, but also in bull markets through algorithmic trading using an Adaboost machine learning model and VIXM.

No description provided.

Utilizing machine learning techniques to model and project sales for the cannabis startup Cookies

Analyse the factors which lead to online shopping on a website and building predictive models for it.

our goal for this project is to predict the churn probability of a customer using machine learning classification techniques.