⚡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.

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

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.

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.

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

Employee-Absenteeism-Project-Work

Multi-class sentiment analysis with AdaBoost on 27k Kaggle tweets: TF-IDF bigrams (10k feats), stratified split, GridSearchCV—62% acc, 0.78 AUC, robust to noise/imbalance via boosting & lemmatization.

Classify default borrowers from initial loan application for Lending Club

Machine Learning Models