A simple machine learning framework written in Swift 🤖

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)

The sample question for Interview a job in Binary options

Implemented ADMM for solving convex optimization problems such as Lasso, Ridge regression

Built house price prediction model using linear regression and k nearest neighbors and used machine learning techniques like ridge, lasso, and gradient descent for optimization in Python

Automated Essay Scoring on The Hewlett Foundation dataset on Kaggle

Double Descent Curve with Optical Random Features

Accurate estimation and robust modelling of translation dynamics at codon resolution

Evaluate various supervised learning methods to predict cohesive energies of solids (kernel ridge regression is the best)

CRAN R Package: Ridge Regression with automatic selection of the penalty parameter

Movie Recommendation System using the MovieLens dataset

Python notebooks for my graduate class on Detection, Estimation, and Learning. Intended for in-class demonstration. Notebooks illustrate a variety of concepts, from hypothesis testing to estimation to image denoising to Kalman filtering. Feel free to use or modify for your instruction or self-study.

Demo from Data Community Bydgoszcz i Toruń, 27.02.2019

Understand the relationships between various features in relation with the sale price of a house using exploratory data analysis and statistical analysis. Applied ML algorithms such as Multiple Linear Regression, Ridge Regression and Lasso Regression in combination with cross validation. Performed parameter tuning, compared the test scores and suggested a best model to predict the final sale price of a house. Seaborn is used to plot graphs and scikit learn package is used for statistical analysis.

Introduction The context is the 2016 public use NH medical claims files obtained from NH CHIS (Comprehensive Health Care Information System). The dataset contains Commercial Insurance claims, and a small fraction of Medicaid and Medicare payments for dually eligible people. The primary purpose of this assignment is to test machine learning (ML) skills in a real case analysis setting. You are expected to clean and process data and then apply various ML techniques like Linear and no linear models like regularized regression, MARS, and Partitioning methods. You are expected to use at least two of R, Python and JMP software. Data details: Medical claims file for 2016 contains ~17 millions rows and ~60 columns of data, containing ~6.5 million individual medical claims. These claims are all commercial claims that were filed by healthcare providers in 2016 in the state of NH. These claims were ~88% for residents of NH and the remaining for out of state visitors who sought care in NH. Each claim consists of one or more line items, each indicating a procedure done during the doctor’s visit. Two columns indicating Billed amount and the Paid amount for the care provided, are of primary interest. The main objective is to predict “Paid amount per procedure” by mapping a plethora of features available in the dataset. It is also an expectation that you would create new features using the existing ones or external data sources. Objectives: Step 1: Take a random sample of 1 million unique claims, such that all line items related to each claim are included in the sample. This will result in a little less than 3 million rows of data. Step 2: Clean up the data, understand the distributions, and create new features if necessary. Step 3: Run predictive models using validation method of your choice. Step 4: Write a descriptive report (less than 10 pages) describing the process and your findings.

glmnet for python

Applied Machine Learning

Harvard Project - Accuracy improvement by adding seasonality premium pricing

Scrape Bangkok condominium listing from hipflat.com and compare ML performance

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.

MDL Complexity computations and experiments from the paper "Revisiting complexity and the bias-variance tradeoff".

This repository contains only projects using regression analysis techniques. Examples include a comprehensive analysis of retail store expansion strategies using Lasso and Ridge regressions.

For quick search

Analyzes weightlifting videos for correct posture

Implementation of some Machine Learning Algorithms from scratch

Implementations of basic machine learning algorithms

Predicting miles per gallon (MPG) for a car using UCI dataset

Regression-based Zero Shot Learning