Comparison of model selection methods for Boston dataset

Mind Foundry OPTaaS R Client

Simple python implementation of Approximate Bayesian Computation for Model Choice

Text Classification / Sentiment Analysis with Machine Learning

Use decision trees to prepare a model on fraud data. Treating those who have taxable income <= 30000 as "Risky" and others are "Good" and A cloth manufacturing company is interested to know about the segment or attributes causes high sale.

The best model is determined using PCA evaluation for the Arrhythmia prediction

Build a predictive model to recognize fraudulent credit card transactions so that customers are not charged for items that they did not purchase.

Data Science - Support Vector Machine Work

Various variable selection methods are explored

Exercises from 'An Introduction to Statistical Learning with Applications in R' by James et al.

In this project we can see in action and in detail a big part of the ML pipeline (data wrangling,model building, model evaluation) that comprises different algorithms and approaches such as Decision Trees (RPART), Linear Discriminant Analysis (LDA), Gradient Boosting Machne (GBM), Random Forest (RF) Support Vector Machine (SVM) with or without M…

Modeling and Prediction for Movies

Model selection to get high accuracy on classification

Find the best ML algorithm & train

In the titanic shipwreck some group of people were more likely to survive than others. In this project a machine learning model was developed to predict survival

The project involves building a default risk model for a loan offering company to target high-risk customers early and minimize the cost of default events.This project involves variable selection, feature importance and model and variable selection, finally we calculate the (MAE) on test data to measure models performance