258 Open source projects that are alternatives of or similar to click-through-rate-prediction

🔥🌟《Machine Learning 格物志》: ML + DL + RL basic codes and notes by sklearn, PyTorch, TensorFlow, Keras & the most important, from scratch!💪 This repository is ALL You Need!

This repo contains regression and classification projects. Examples: development of predictive models for comments on social media websites; building classifiers to predict outcomes in sports competitions; churn analysis; prediction of clicks on online ads; analysis of the opioids crisis and an analysis of retail store expansion strategies using…

Python code for common Machine Learning Algorithms

Projects I completed as a part of Great Learning's PGP - Artificial Intelligence and Machine Learning

Decision Trees, Random Forest, Dynamic Time Warping, Naive Bayes, KNN, Linear Regression, Logistic Regression, Mixture Of Gaussian, Neural Network, PCA, SVD, Gaussian Naive Bayes, Fitting Data to Gaussian, K-Means

Machine learning algorithm such as KNN,Naive Bayes,Logistic Regression,SVM,Decision Trees,Random Forest,k means and Truncated SVD on amazon fine food review

simple tutorial on Machine Learning with Scikitlearn

Text Classification Algorithms: A Survey

Go Scoring API for PMML

Simple machine learning library / 簡單易用的機器學習套件

General Assembly's 2015 Data Science course in Washington, DC

A collection of research papers on decision, classification and regression trees with implementations.

🍊 📊 💡 Orange: Interactive data analysis

Final project of IBM's course https://www.coursera.org/learn/machine-learning-with-python on coursera

텐서플로우와 머신러닝으로 시작하는 자연어처리(로지스틱회귀부터 트랜스포머 챗봇까지)

A collection of state-of-the-art algorithms for the training, serving and interpretation of Decision Forest models.

Machine Learning University: Decision Trees and Ensemble Methods

Material from "Random Forests and Gradient Boosting Machines in R" presented at Machine Learning Day '18

A tool for visualizing the structure and performance of Random Forests 🌳

Estudo e implementação dos principais algoritmos de Machine Learning em Jupyter Notebooks.

Repository for the AdaptiveRandomForest algorithm implemented in MOA 2016-04

A python library for decision tree visualization and model interpretation.

Machine Learning Lectures at the European Space Agency (ESA) in 2018

This is the implementation of Sparse Projection Oblique Randomer Forest

Predicting Amsterdam house / real estate prices using Ordinary Least Squares-, XGBoost-, KNN-, Lasso-, Ridge-, Polynomial-, Random Forest-, and Neural Network MLP Regression (via scikit-learn)

Solutions to labs and excercises from An Introduction to Statistical Learning, as Jupyter Notebooks.

Workshop (6 hours): preprocessing, cross-validation, lasso, decision trees, random forest, xgboost, superlearner ensembles

文本分类基准测试

Machine Learning with the NSL-KDD dataset for Network Intrusion Detection

Practice and tutorial-style notebooks covering wide variety of machine learning techniques

several methods for text classification

In This repository I made some simple to complex methods in machine learning. Here I try to build template style code.

Machine Learning models from scratch with a better visualisation

An Interactive Approach to Understanding Deep Learning with Keras

🏭 JavaScript Machine Learning Toolkit

Predicting the winner of 2019 cricket world cup using random forest algorithm

Misc Statistics and Machine Learning codes in R

EasyAdWords is an easy-to-use wrapper library for simple reporting and entity operations for Google AdWords.

A python library to build Model Trees with Linear Models at the leaves.

Forecast stock prices using machine learning approach. A time series analysis. Employ the Use of Predictive Modeling in Machine Learning to Forecast Stock Return. Approach Used by Hedge Funds to Select Tradeable Stocks

Formed trajectories of sets of points.Experimented on finding similarities between trajectories based on DTW (Dynamic Time Warping) and LCSS (Longest Common SubSequence) algorithms.Modeled trajectories as strings based on a Grid representation.Benchmarked KNN, Random Forest, Logistic Regression classification algorithms to classify efficiently t…

This is a Statistical Learning application which will consist of various Machine Learning algorithms and their implementation in R done by me and their in depth interpretation.Documents and reports related to the below mentioned techniques can be found on my Rpubs profile.

I will update this repository to learn Machine learning with python with statistics content and materials

A curated list of data mining papers about fraud detection.

Machine learning for C# .Net

A Lightweight Decision Tree Framework supporting regular algorithms: ID3, C4,5, CART, CHAID and Regression Trees; some advanced techniques: Gradient Boosting (GBDT, GBRT, GBM), Random Forest and Adaboost w/categorical features support for Python

Heart Disease prediction using 5 algorithms

Top 5th percentile solution to the Kaggle knowledge problem - Bike Sharing Demand

Teads SDK iOS Sample App - Check out an open-source sample of the Teads iOS SDK implementation

도서 "핸즈온 머신러닝"의 예제와 연습문제를 담은 주피터 노트북입니다.

An Interactive Approach to Understanding Supervised Learning Algorithms

Automates partnerships in the big community of discord.

Ruby Scoring API for PMML

Real time face landmarking using decision trees and NN autoencoders

It's a github repo star predictor that tries to predict the stars of any github repository having greater than 100 stars.

Abnormal Traffic Identification Classifier based on Machine Learning. My code for undergraduate graduation design.

Tools for developing binary logistic regression models

A lightweight library for designing and executing workflows in .NET Core

Jupyter Notebooks and miscellaneous

A Random Survival Forest implementation for python inspired by Ishwaran et al. - Easily understandable, adaptable and extendable.