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Robyn is an experimental, AI/ML-powered and open sourced Marketing Mix Modeling (MMM) package from Meta Marketing Science. Our mission is to democratise modeling knowledge, inspire the industry through innovation, reduce human bias in the modeling process & build a strong open source marketing science community.

Machine Learning ⭐ 117

Python machine learning applications in image processing, recommender system, matrix completion, netflix problem and algorithm implementations including Co-clustering, Funk SVD, SVD++, Non-negative Matrix Factorization, Koren Neighborhood Model, Koren Integrated Model, Dawid-Skene, Platt-Burges, Expectation Maximization, Factor Analysis, ISTA, FISTA, ADMM, Gaussian Mixture Model, OPTICS, DBSCAN, Random Forest, Decision Tree, Support Vector Machine, Independent Component Analysis, Latent Semantic

Machine Learning Regression ⭐ 25

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

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

Machine Learning Techniques ⭐ 20

Repository containing introduction to the main methods and models used in machine learning problems of regression, classification and clustering.

Mdl Complexity ⭐ 15

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

Predicting Baseball Statistics ⭐ 14

Predicting Baseball Statistics: Classification and Regression Applications in Python Using scikit-learn

Double Descent Curve ⭐ 11

Double Descent Curve with Optical Random Features

Self_distillation ⭐ 9

Self-Distillation with weighted ground-truth targets; ResNet and Kernel Ridge Regression

Demo from Data Community Bydgoszcz i Toruń, 27.02.2019

Detection Estimation Learning ⭐ 8

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.

Capstoneproject_house_prices_prediction ⭐ 7

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 analysi

Nba Draft Model 2018 ⭐ 6

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.

Predicting Paid Amount For Claims Data ⭐ 6

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

Machine Learning Regression Models ⭐ 5

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

Airbnb Pricing Prediction ⭐ 5

Harvard Project - Accuracy improvement by adding seasonality premium pricing

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