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: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
For this project, I used four different classification algorithms to detect fraudulent credit card transactions.
A parallelised facial recognition program written from scratch in C with minimal dependencies
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.
The homeworks related to Machine Learning university course would be saved here.
This is a practice Repository consisting of all the notebooks I have practiced for Machine Learning from basics to Advance
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.
In This Repository you can find The Explanation and The Implementation of the Most Famous Machine Learning Algorithms
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.
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.
Utilizing machine learning techniques to model and project sales for the cannabis startup Cookies
Machine Learning Models
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.
Can we predict how long a patient will be in a hospital with a fair comparison on gender, race and health service areas?
Image processing algorithms
Project No.2 (Data Engineering) in the Data Scientist Nanodegree program. Build a machine learning pipeline to categorize emergency messages based on the need communicated by the sender.
Implements the Decision Tree (CART), AdaBoost and Random Forest algorithm from scratch using NumPy.
A simple example project on XGBoost and Adaboost.
our goal for this project is to predict the churn probability of a customer using machine learning classification techniques.
Classify default borrowers from initial loan application for Lending Club
Some decision tree algorithms implemented in C++
Employee-Absenteeism-Project-Work
HealthCare Length of Stay predictions with Booster algorithms
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.
Final Data Analytics course project repository where we have implemented Breast Cancer tumor classification into malignant and benign thereby predicting the chance of breast cancer.
Analyse the factors which lead to online shopping on a website and building predictive models for it.