ridge-regression | Topic | Ecosyste.ms: Repos

Topic: "ridge-regression"

facebookexperimental/Robyn

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.

Language: Jupyter Notebook - Size: 392 MB - Last synced at: 12 days ago - Pushed at: 12 days ago - Stars: 1,282 - Forks: 388

JuliaStats/MultivariateStats.jl

A Julia package for multivariate statistics and data analysis (e.g. dimension reduction)

Language: Julia - Size: 1.55 MB - Last synced at: 7 days ago - Pushed at: 11 months ago - Stars: 382 - Forks: 87

YiranJing/Coronavirus-Epidemic-COVID-19

👩🏻‍⚕️Covid-19 estimation and forecast using statistical model; 新型冠状病毒肺炎统计模型预测 (Jan 2020)

Language: Jupyter Notebook - Size: 45 MB - Last synced at: over 1 year ago - Pushed at: almost 2 years ago - Stars: 243 - Forks: 69

je-suis-tm/machine-learning

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 Indexing, Principal Component Analysis, Singular Value Decomposition, K Nearest Neighbors, K Means, Naïve Bayes Mixture Model, Gaussian Discriminant Analysis, Newton Method, Coordinate Descent, Gradient Descent, Elastic Net Regression, Ridge Regression, Lasso Regression, Least Squares, Logistic Regression, Linear Regression

Language: Jupyter Notebook - Size: 7.84 MB - Last synced at: about 1 month ago - Pushed at: over 2 years ago - Stars: 234 - Forks: 51

Somnibyte/MLKit

A simple machine learning framework written in Swift 🤖

Language: Swift - Size: 3.28 MB - Last synced at: 6 days ago - Pushed at: over 6 years ago - Stars: 152 - Forks: 14

MBKraus/Predicting_real_estate_prices_using_scikit-learn

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)

Language: Python - Size: 870 KB - Last synced at: over 1 year ago - Pushed at: about 6 years ago - Stars: 142 - Forks: 51

Mayurji/MLWithPytorch

Objective of the repository is to learn and build machine learning models using Pytorch. 30DaysofML Using Pytorch

Language: Python - Size: 868 KB - Last synced at: over 1 year ago - Pushed at: over 3 years ago - Stars: 122 - Forks: 37

bhushan23/ADMM

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

Language: Jupyter Notebook - Size: 735 KB - Last synced at: over 1 year ago - Pushed at: over 3 years ago - Stars: 107 - Forks: 27

mickcrosse/mTRF-Toolbox

A MATLAB package for modelling multivariate stimulus-response data

Language: MATLAB - Size: 64.6 MB - Last synced at: 19 days ago - Pushed at: 19 days ago - Stars: 88 - Forks: 31

JuliaAI/MLJLinearModels.jl

Generalized Linear Regressions Models (penalized regressions, robust regressions, ...)

Language: Julia - Size: 583 KB - Last synced at: 7 days ago - Pushed at: about 1 year ago - Stars: 81 - Forks: 13

m-clark/models-by-example

By-hand code for models and algorithms. An update to the 'Miscellaneous-R-Code' repo.

Language: R - Size: 26.2 MB - Last synced at: 11 days ago - Pushed at: over 4 years ago - Stars: 68 - Forks: 18

englianhu/binary.com-interview-question

次元期权应征面试题范例。 #易经 #道家 #十二生肖 #姓氏堂号子嗣贞节牌坊 #天文历法 #张灯结彩 #农历 #夜观星象 #廿四节气 #算卜 #紫微斗数 #十二时辰 #生辰八字 #命运 #风水《始祖赢政之子赢家黄氏江夏堂联富•秦谏——大秦赋》万般皆下品，唯有读书高。🚩🇨🇳🏹🦔中科红旗，歼灭所有世袭制可兰经法家回教徒巫贼巫婆、洋番、峇峇娘惹。https://gitee.com/englianhu

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SravB/Computer-Vision-Weightlifting-Coach

Analyzes weightlifting videos for correct posture using pose estimation with OpenCV

Language: Jupyter Notebook - Size: 24 MB - Last synced at: 4 days ago - Pushed at: almost 6 years ago - Stars: 43 - Forks: 8

getzze/RobustModels.jl

A Julia package for robust regressions using M-estimators and quantile regressions

Language: Julia - Size: 782 KB - Last synced at: 28 days ago - Pushed at: 8 months ago - Stars: 36 - Forks: 1

shubhpawar/Automated-Essay-Scoring

Automated Essay Scoring on The Hewlett Foundation dataset on Kaggle

Language: Jupyter Notebook - Size: 8.57 MB - Last synced at: almost 2 years ago - Pushed at: about 7 years ago - Stars: 30 - Forks: 13

lightonai/double-descent-curve

Double Descent Curve with Optical Random Features

Language: Jupyter Notebook - Size: 146 KB - Last synced at: about 2 years ago - Pushed at: almost 3 years ago - Stars: 23 - Forks: 5

faizanxmulla/machine-learning-techniques

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

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schatzlab/scikit-ribo

Accurate estimation and robust modelling of translation dynamics at codon resolution

Language: Python - Size: 69.5 MB - Last synced at: 6 days ago - Pushed at: almost 8 years ago - Stars: 20 - Forks: 8

faosorios/fastmatrix

Fast computation of some matrices useful in statistics

Language: C - Size: 13.5 MB - Last synced at: 8 days ago - Pushed at: 8 days ago - Stars: 19 - Forks: 3

alexvlis/movie-recommendation-system

Movie Recommendation System using the MovieLens dataset

Language: Python - Size: 6.68 MB - Last synced at: about 1 year ago - Pushed at: about 7 years ago - Stars: 19 - Forks: 14

csinva/mdl-complexity

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

Language: Jupyter Notebook - Size: 14.4 MB - Last synced at: 4 days ago - Pushed at: almost 2 years ago - Stars: 18 - Forks: 2

SteffenMoritz/ridge

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

Language: R - Size: 797 KB - Last synced at: 18 days ago - Pushed at: about 2 years ago - Stars: 18 - Forks: 11

NikhilaThota/CapstoneProject_House_Prices_Prediction

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.

Language: Jupyter Notebook - Size: 7.91 MB - Last synced at: over 1 year ago - Pushed at: over 7 years ago - Stars: 18 - Forks: 13

Nemshan/predicting-Paid-amount-for-Claims-Data

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.

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ahaeusser/echos

Echo State Networks for Time Series Forecasting

Language: R - Size: 15.6 MB - Last synced at: 5 days ago - Pushed at: 2 months ago - Stars: 16 - Forks: 1

Kennethborup/self_distillation

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

Language: Jupyter Notebook - Size: 1.43 MB - Last synced at: almost 2 years ago - Pushed at: over 3 years ago - Stars: 15 - Forks: 0

docnok/detection-estimation-learning

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.

Language: Jupyter Notebook - Size: 3.29 MB - Last synced at: about 2 years ago - Pushed at: about 7 years ago - Stars: 15 - Forks: 8

tweichle/Predicting-Baseball-Statistics

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

Language: Jupyter Notebook - Size: 19.1 MB - Last synced at: 10 months ago - Pushed at: 10 months ago - Stars: 14 - Forks: 4

wyattowalsh/regularized-linear-regression-deep-dive

Explanations and Python implementations of Ordinary Least Squares regression, Ridge regression, Lasso regression (solved via Coordinate Descent), and Elastic Net regression (also solved via Coordinate Descent) applied to assess wine quality given numerous numerical features. Additional data analysis and visualization in Python is included.

Language: Jupyter Notebook - Size: 51 MB - Last synced at: about 1 year ago - Pushed at: over 4 years ago - Stars: 13 - Forks: 1

mdpetters/RegularizationTools.jl

A Julia package to perform Tikhonov regularization for small to moderate size problems.

Language: Julia - Size: 1.89 MB - Last synced at: 11 days ago - Pushed at: over 2 years ago - Stars: 12 - Forks: 2

mlesnoff/rchemo

R package for regression and discrimination, with special focus on chemometrics and high-dimensional data (This package is only maintained. The new current package is "Jchemo", under construction in Julia language)

Language: R - Size: 3.53 MB - Last synced at: about 2 years ago - Pushed at: about 2 years ago - Stars: 11 - Forks: 2

hanfang/glmnet_py

glmnet for python

Language: Python - Size: 3.69 MB - Last synced at: 19 days ago - Pushed at: about 8 years ago - Stars: 11 - Forks: 12

ericqu/LinearRegressionKit.jl

Linear Regression for Julia

Language: Julia - Size: 1.26 MB - Last synced at: 8 days ago - Pushed at: almost 2 years ago - Stars: 10 - Forks: 1

gsasikiran/Comparative-Evaluation-of-Pretrained-Transfer-Learning-Models-on-ASAG

Language: Python - Size: 3 MB - Last synced at: over 1 year ago - Pushed at: over 4 years ago - Stars: 10 - Forks: 11

amayumradia/AirBnB-pricing-prediction

Harvard Project - Accuracy improvement by adding seasonality premium pricing

Language: Jupyter Notebook - Size: 46.6 MB - Last synced at: over 1 year ago - Pushed at: over 8 years ago - Stars: 10 - Forks: 5

spiningup/Machine-learning-methods-for-materials-science

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

Language: Python - Size: 660 KB - Last synced at: over 1 year ago - Pushed at: almost 11 years ago - Stars: 10 - Forks: 6

SSQ/Coursera-UW-Machine-Learning-Regression

For quick search

Language: Python - Size: 56 MB - Last synced at: about 2 years ago - Pushed at: over 6 years ago - Stars: 9 - Forks: 14

sid321axn/Udacity-MLND-Capstone-Gold-Price-Prediction

Capstone Project Gold Price Prediction using Machine learning Approach for Udacity Machine Learning engineer Nanodegree Program

Language: Jupyter Notebook - Size: 5.21 MB - Last synced at: about 2 years ago - Pushed at: over 6 years ago - Stars: 8 - Forks: 14

tsitsimis/constrainedlr

Drop-in replacement of sklearn's Linear Regression with coefficients constraints

Language: Python - Size: 637 KB - Last synced at: 14 days ago - Pushed at: about 1 year ago - Stars: 7 - Forks: 0

angus924/preval

Prevalidated ridge regression is a highly-efficient drop-in replacement for logistic regression for high-dimensional data

Language: Python - Size: 26.4 KB - Last synced at: about 1 month ago - Pushed at: over 1 year ago - Stars: 7 - Forks: 0

3zhang/Python-Lasso-ElasticNet-Ridge-Regression-with-Customized-Penalties

An extension of sklearn's Lasso/ElasticNet/Ridge model to allow users to customize the penalties of different covariates. Works similar to penalty.factor parameter in R's glmnet.

Language: Jupyter Notebook - Size: 1.48 MB - Last synced at: 6 months ago - Pushed at: about 2 years ago - Stars: 7 - Forks: 0

hkiang01/Applied-Machine-Learning

Applied Machine Learning

Language: Python - Size: 58.8 MB - Last synced at: about 2 years ago - Pushed at: almost 9 years ago - Stars: 7 - Forks: 4

JayantGoel001/Machine-Learning-And-Big-Data

Language: Jupyter Notebook - Size: 12.1 MB - Last synced at: 9 days ago - Pushed at: over 3 years ago - Stars: 6 - Forks: 0

marcotav/machine-learning-regression-models

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

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viktormiok/PhD-thesis

Latex scripts to compile my PhD thesis

Language: TeX - Size: 9.91 MB - Last synced at: 2 months ago - Pushed at: 2 months ago - Stars: 5 - Forks: 2

DataSpieler12345/python-for-ds-ml

My Python learning experience 📚🖥📳📴💻🖱✏

Language: Jupyter Notebook - Size: 200 MB - Last synced at: about 1 month ago - Pushed at: 12 months ago - Stars: 5 - Forks: 0

prakHr/NeuralNetworksAndFuzzyLogic

[College Course] - Course: BITS F312 Neural Network and Fuzzy Logic

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sandipanpaul21/Logistic-regression-in-python

Logistic Regression technique in machine learning both theory and code in Python. Includes topics from Assumptions, Multi Class Classifications, Regularization (l1 and l2), Weight of Evidence and Information Value

Language: Jupyter Notebook - Size: 29.4 MB - Last synced at: about 2 years ago - Pushed at: over 3 years ago - Stars: 5 - Forks: 1

romanwerpachowski/ML

ML++ and cppyml: efficient implementations of selected ML algorithms, with Python bindings.

Language: C++ - Size: 7.59 MB - Last synced at: 29 days ago - Pushed at: over 3 years ago - Stars: 5 - Forks: 0

bissessk/Regression-Analysis-of-COVID-19

The objective of this project is to study the COVID-19 outbreak using basic statistical techniques and make short term predictions using ML regression methods.

Language: Jupyter Notebook - Size: 1.14 MB - Last synced at: about 2 years ago - Pushed at: almost 5 years ago - Stars: 5 - Forks: 2

jolars/sgdnet

Fast Sparse Linear Models for Big Data with SAGA

Language: R - Size: 2.54 MB - Last synced at: 19 days ago - Pushed at: over 5 years ago - Stars: 5 - Forks: 2

fby1997/Lasso-Regression-coordinate-gradient-descent-proximal-gradient-and-ADMM-Ridge-Regression

Use Ridge Regression and Lasso Regression in prostate cancer data

Language: Python - Size: 16.6 KB - Last synced at: about 1 year ago - Pushed at: over 5 years ago - Stars: 5 - Forks: 3

friendly/genridge

Generalized Ridge Trace Plots for Ridge Regression

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shaadclt/Diabetes-Progression-Prediction-RidgeRegression

This project involves the prediction of diabetes progression using Ridge Regression in Jupyter Notebook. The dataset contains features such as glucose level, blood pressure, body mass index, and more. Through this analysis, we aim to build a regression model that accurately predicts the progression of diabetes based on the given input features.

Language: Jupyter Notebook - Size: 8.79 KB - Last synced at: about 1 month ago - Pushed at: almost 2 years ago - Stars: 4 - Forks: 0

gvndkrishna/Kaggle-House-Price-Prediction

My solution to House-Prices Advanced Regression Techniques, A beginner-friendly project on Kaggle.

Language: Jupyter Notebook - Size: 2.56 MB - Last synced at: about 2 years ago - Pushed at: almost 3 years ago - Stars: 4 - Forks: 4

ChaitanyaC22/House-Price-Prediction-Project-for-a-US-based-housing-company

The goal of this project is to garner data insights using data analytics to purchase houses at a price below their actual value and flip them on at a higher price. This project aims at building an effective regression model using regularization (i.e. advanced linear regression: Ridge and Lasso regression) in order to predict the actual values of prospective housing properties and decide whether to invest in them or not.

Language: Jupyter Notebook - Size: 3.97 MB - Last synced at: about 2 months ago - Pushed at: almost 4 years ago - Stars: 4 - Forks: 1

nikhilkr29/AIR_QUALITY_INDEX_PREDICTION

Predicts the Quality of Air based on various factors .Different Supervised Machine Learning Algorithms were used like Linear Regression, KNN, Decision Tree, XgBoost. The best accuracy was obtained using Xgboost.

Language: Jupyter Notebook - Size: 4.41 MB - Last synced at: almost 2 years ago - Pushed at: almost 4 years ago - Stars: 4 - Forks: 0

ankitbit/Statistical_Learning_Theory

This repository corresponds to the course "Statistical Learning Theory" taught at the School of Mathematics and Statistics (FME), UPC under the MESIO-UPC-UB Joint Interuniversity Master's Program under the instructor Pedro Delicado

Language: Jupyter Notebook - Size: 40.8 MB - Last synced at: about 2 years ago - Pushed at: almost 6 years ago - Stars: 4 - Forks: 3

sdeepaknarayanan/Machine-Learning 📦

Assignments of the ML Course at IIT Gandhinagar

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VamshiTeja/ML-Algorithms

Implementation of some Machine Learning Algorithms from scratch

Language: Python - Size: 1.57 MB - Last synced at: about 1 year ago - Pushed at: over 6 years ago - Stars: 4 - Forks: 0

gosuddin/machine-learning-for-finance

ML Coursework focused on solving Computational Finance and Risk Assessment models

Language: Jupyter Notebook - Size: 1.59 MB - Last synced at: over 1 year ago - Pushed at: over 7 years ago - Stars: 4 - Forks: 9

experiencor/basic-machine-learning

Implementations of basic machine learning algorithms

Language: Jupyter Notebook - Size: 91.8 KB - Last synced at: 26 days ago - Pushed at: almost 8 years ago - Stars: 4 - Forks: 4

ostad-ai/Machine-Learning

This repository contains topics and codes related to Machine Learning and Data Science, especially in Python

Language: Jupyter Notebook - Size: 2.16 MB - Last synced at: 4 days ago - Pushed at: 4 days ago - Stars: 3 - Forks: 0

viktormiok/ragt2ridges

Ridge Estimation of Vector Auto-Regressive (VAR) Processes

Language: R - Size: 1.16 MB - Last synced at: about 2 months ago - Pushed at: about 2 months ago - Stars: 3 - Forks: 2

Nizarassad/Pan-Cancer-Analysis

The Cancer Genome Atlas Pan-Cancer analysis project

Language: Python - Size: 9.48 MB - Last synced at: 30 days ago - Pushed at: 9 months ago - Stars: 3 - Forks: 1

akash1188/Predictive-Analysis-ML

Explore ML mini-projects with Jupyter notebooks. Discover predictive analysis for commercial sales, leveraging regression models such as linear regression, decision trees, random forests, lasso, ridge, and extra-trees regressor.

Language: Jupyter Notebook - Size: 458 KB - Last synced at: about 1 year ago - Pushed at: about 1 year ago - Stars: 3 - Forks: 0

yasarigno/Predictions_on_Energy_Consumption_in_Seattle

Prediction on energy consumptions of the city of Seattle in order to reach its goal of being a carbon neutral city in 2050.

Language: Jupyter Notebook - Size: 7.06 MB - Last synced at: about 1 year ago - Pushed at: over 1 year ago - Stars: 3 - Forks: 0

madhurima-nath/regression_and_predictions 📦

Language: HTML - Size: 34.9 MB - Last synced at: over 1 year ago - Pushed at: over 1 year ago - Stars: 3 - Forks: 3

iyashk/Lasso-and-Ridge-using-ADMM

Analyzing LASSO and Ridge regressions using ADMM and Gradient Descent methods. Also the ADMM model is compared with that of Sci-Kit Learn's existing model.

Language: Jupyter Notebook - Size: 1.12 MB - Last synced at: about 2 years ago - Pushed at: over 2 years ago - Stars: 3 - Forks: 4

Labo-Lacourse/Code_chap_23_logistic_regression_regularization

Algorithmes d’apprentissage et modèles statistiques: Un exemple de régression logistique régularisée et de validation croisée pour prédire le décrochage scolaire

Language: Jupyter Notebook - Size: 7.23 MB - Last synced at: over 1 year ago - Pushed at: almost 4 years ago - Stars: 3 - Forks: 6

Aniket-Thopte/Demand-Forecasting-Public-Bike-Rental-Predictive-Modeling-

Developed multiple predictive models with 90% accuracy for forecasting the daily-hourly bike rental count using Python & Machine Learning techniques like Regression, Clustering, Ensemble, Neural Network to achieve maximum accuracy

Language: Jupyter Notebook - Size: 4.65 MB - Last synced at: about 2 years ago - Pushed at: about 4 years ago - Stars: 3 - Forks: 2

jman-9/linear-regression-practice

Practice of Linear Regression

Language: C++ - Size: 285 KB - Last synced at: 12 days ago - Pushed at: about 4 years ago - Stars: 3 - Forks: 0

sylvaincom/high-dimensional-statistics

[Python, R] My homeworks for the Statistics for high-dimensional data course of my MSc @ Mines Nancy

Language: Jupyter Notebook - Size: 1.2 MB - Last synced at: about 2 months ago - Pushed at: over 5 years ago - Stars: 3 - Forks: 0

sudeshnapal12/Machine-Learning-algorithms-Matlab

Contains ML Algorithms implemented as part of CSE 512 - Machine Learning class taken by Fransico Orabona. Implemented Linear Regression using polynomial basis functions, Perceptron, Ridge Regression, SVM Primal, Kernel Ridge Regression, Kernel SVM, Kmeans.

Language: TeX - Size: 27.9 MB - Last synced at: over 1 year ago - Pushed at: over 7 years ago - Stars: 3 - Forks: 3

rakeshjasti/Car-MPG

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

Language: Jupyter Notebook - Size: 478 KB - Last synced at: 11 months ago - Pushed at: almost 8 years ago - Stars: 3 - Forks: 1

viktormiok/PhD-thesisSM

PhD thesis supplementary materials

Language: TeX - Size: 15.8 MB - Last synced at: about 2 months ago - Pushed at: about 2 months ago - Stars: 2 - Forks: 0

LuluW8071/Laptop-Price-Prediction

A collection of machine learning models for predicting laptop prices

Language: Jupyter Notebook - Size: 1.5 MB - Last synced at: 2 months ago - Pushed at: over 1 year ago - Stars: 2 - Forks: 0

mzachow/wheat-yield-forecast-brazil

Ridge regression to forecast wheat yield variabilities for Brazil using observed and forecasted climate data.

Language: Jupyter Notebook - Size: 39.7 MB - Last synced at: over 1 year ago - Pushed at: over 1 year ago - Stars: 2 - Forks: 0

connect-midhunr/rossmann-sales-prediction

Machine learning model to forecast the sales of each Rossmann store for any given date.

Language: Jupyter Notebook - Size: 103 MB - Last synced at: about 1 year ago - Pushed at: about 2 years ago - Stars: 2 - Forks: 1

ZJW-92/Ames_House_Price_Prediction

This is a house price prediction study which utilized Exploratory Data Analysis, Dealing with Missing Values, Linear Regression with LASSO and Ridge regularization to predict house prices in the Ames Housing Data Set

Language: Jupyter Notebook - Size: 1.3 MB - Last synced at: about 2 years ago - Pushed at: over 2 years ago - Stars: 2 - Forks: 0

Masihsoniya/House-price-prediction

Predicting house price

Language: Jupyter Notebook - Size: 211 KB - Last synced at: 17 days ago - Pushed at: over 2 years ago - Stars: 2 - Forks: 0

cankobanz/understanding-supercapacitor-charge-discharge-rates-using-machine-learning

The project aims to design machine learning algorithm which is able to predict energy densities of supercapacitors by using input data that is enriched from the results came from image processing techniques.

Language: Jupyter Notebook - Size: 4.61 MB - Last synced at: over 1 year ago - Pushed at: over 2 years ago - Stars: 2 - Forks: 0

ROCCYK/Regression

Intro to Machine Learning Assignment 1

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rkp678/Project2_GroupC

Project 2 Group C - Predicting FinTech Bootcamp Graduate Salaries

Language: Jupyter Notebook - Size: 5.19 MB - Last synced at: about 2 years ago - Pushed at: about 3 years ago - Stars: 2 - Forks: 2

davidbiol/empire

R Package for Estimation of Missing Data using Penalized Iterative Regressions

Language: R - Size: 836 KB - Last synced at: about 2 years ago - Pushed at: about 3 years ago - Stars: 2 - Forks: 0

o-ikne/Data-Science-Mini-Projects

Data science Mini projects

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dteuscher1/Adjusted-Plus-Minus

Adjusted Plus Minus Models for WNBA players from the 2019 season. Adjusted Plus Minus (APM) and Regularized Adjusted Plus Minus (RAPM) models were fit providing an all in one player value metric for the WNBA.

Language: R - Size: 4.35 MB - Last synced at: 9 months ago - Pushed at: over 3 years ago - Stars: 2 - Forks: 2

ArkaB-DS/regressionProjectIITK

This is a group project for MTH416A: Regression Analysis at IIT Kanpur

Language: R - Size: 7.79 MB - Last synced at: about 2 years ago - Pushed at: over 3 years ago - Stars: 2 - Forks: 1

ahmedshahriar/Housing-Price-Prediction

Data science project on Housing Prices Dataset regression analysis

Language: Jupyter Notebook - Size: 422 KB - Last synced at: 2 months ago - Pushed at: over 3 years ago - Stars: 2 - Forks: 0

keivanipchihagh/ML-Models-from-Scratch

Implementations of Machine Learning models, Regularizers, Optimizers and different Cost functions.

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anikch/house-price-prediction-Ridge_Lasso

Housing price prediction model using Ridge and Lasso Regression.

Language: Jupyter Notebook - Size: 13 MB - Last synced at: about 2 years ago - Pushed at: over 3 years ago - Stars: 2 - Forks: 1

FarzadAziziZade/Machine-Learning-Test

Here I upload my ML test scripts written in MATLAB or Python

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UMN-LATIS/simple-smoothing-spline

Create a regression smoothing spline for a set of points.

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tboudart/Life-Expectancy-Regression-Analysis-and-Classification

I contributed to a group project using the Life Expectancy (WHO) dataset from Kaggle where I performed regression analysis to predict life expectancy and classification to classify countries as developed or developing. The project was completed in Python using the pandas, Matplotlib, NumPy, seaborn, scikit-learn, and statsmodels libraries. The regression models were fitted on the entire dataset, along with subsets for developed and developing countries. I tested ordinary least squares, lasso, ridge, and random forest regression models. Random forest regression performed the best on all three datasets and did not overfit the training set. The testing set R2 was .96 for the entire dataset and developing country subset. The developed country subset achieved an R2 of .8. I tested seven different classification algorithms to classify a country as developing or developed. The models obtained testing set balanced accuracies ranging from 86% - 99%. From best to worst, the models included gradient boosting, random forest, Adaptive Boosting (AdaBoost), decision tree, k-nearest neighbors, support-vector machines, and naive Bayes. I tuned all the models' hyperparameters. None of the models overfitted the training set.

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tboudart/Financial-Markets-Regression-Analysis

My role in this group project was to perform regression analysis on quarterly financial data to predict a company's market capitalization. I used R to develop ordinary least squares (OLS), stepwise, ridge, lasso, relaxed lasso, and elastic net regression models. I first used stepwise and OLS regression to develop a model and examine its residual plots. The plot displaying the residuals against the predicted values indicated multiplicative errors. I, therefore, took the natural log transformation of the dependent variable. The resulting model's R2 was significantly, negatively impacted. After examining scatter plots between the log transformation of market capitalization and the independent variables, I discovered the independent variables also had to be transformed to produce a linear relationship. Using the log transformation of both the dependent and independent variables, I developed models using all the regression techniques mentioned to strike a balance between R2 and producing a parsimonious model. All the models produced similar results, with an R2 of around .80. Since OLS is easiest to explain, had similar residual plots, and the highest R2 of all the models, it was the best model developed.

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