⚡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

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

Machine Learning Cheatsheet 2024

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

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

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

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

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

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

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.

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

Classify default borrowers from initial loan application for Lending Club

Some decision tree algorithms implemented in C++

Employee-Absenteeism-Project-Work

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.

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

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

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.

Image processing algorithms