A day to day plan for this challenge. Covers both theoritical and practical aspects

Scraping statistics, predicting NBA player performance with neural networks and boosting algorithms, and optimising lineups for Draft Kings with genetic algorithm. Capstone Project for Machine Learning Engineer Nanodegree by Udacity.

A repository for implementation of artificial intelligence algorithm which includes machine learning and deep learning algorithm as well as classical AI search algorithm

A memory efficient GBDT on adaptive distributions. Much faster than LightGBM with higher accuracy. Implicit merge operation.

Implementation of a Partial Least Squares Regressor

This repository is for every beginner who just finished with the theoretical part of Machine Learning and wants to have a hands on experience of various kinds of algorithm and make predictions on a real time dataset.

The main aim of this project is to tell that the certain project will be successful or it will fail by applying machine learning algorithm. In this , LOGISTIC REGRESSION is used to determine the success of the project by splitting the data into training and testing models and predicting a successful one.

Machine learning Regression problem with easy understandable solutions

My implementation of the regression algorithms

In this repository you can find many different, small, projects which demonstrate regression techniques using python programming language

Sparse Spectrum Gaussian Process Regression

Estimation and inference from generalized linear models using explicit and implicit methods for bias reduction

Notebooks and Python about data science

A Linear Regression model to predict the car prices for the U.S market to help a new entrant understand important pricing variables in the U.S automobile industry. A highly comprehensive analysis with detailed explanation of all steps; data cleaning, exploration, visualization, feature selection, model building, evaluation & MLR assumptions validity.

R package for automatic hyper parameter tuning and ensembles with deep learning, gradient boosting machines, and random forests. Powered by h2o.

Jupyter notebook that outlines the process of creating a machine learning predictive model. Predicts the peak "Wins Shared" by the current draft prospects based on numerous features such as college stats, projected draft pick, physical profile and age. I try out multiple models and pick the best performing one for the data from my judgement.

Regression with Python & R.

Movie Revenue & Ratings Prediction Using 5000 IMDB Movies [Python, Machine Learning, GitHub]

Git repository for IBM Professional Certification on Data Science

This is repository about the MachineLaering Basics including all the Machine learning Algorithms

We have used four regression algorithms to predict crime in the United States and have also developed prediction systems.

Deep Robust Regression Methods

Moody's Bond Rating Classifier and USPHCI Economic Activity Forecast Modeling

It is the repo I listed my kernels in Kaggle. You can access it in detail from my Kaggle address https://www.kaggle.com/bulentsiyah.

Linear, ridge, lasso and elastic net regression in Julia using a videogames sales dataset

Black-box spike and slab variational inference, example with linear models

This time I am doing it using R language. let's see the results. The solutions includes eda(exploratory data analysis), data visualizations, modelling with Machine learning Models such as XgBoost and AdaBooost etc and check the performance using rmse metrics etc to compare the results.

Using machine learning algorithms to predict first innings score in limited overs cricket matches

Predicting the age of abalone using multiple regression in R