elastic-net-regression
There are 0 repository under elastic-net-regression topic.
- snpnetLanguage:R 18
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 13- regularized-linear-regression-deep-dive
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 13 Oprishri / Machine-Learning-Algorithms
To know internal working of machine learning algorithms, I have implemented types of regression through scratch.
Language:Jupyter Notebook 6SarodYatawatta / smart-calibration
Deep reinforcement learning for smart calibration of radio telescopes. Automatic hyper-parameter tuning.
Language:Python 5elgabbas / Conservation-Prioritisation-Sensitivity
R code used for the analyses of the paper: Spatial conservation prioritisation in data-poor countries: a quantitative sensitivity analysis using different taxa
Language:HTML 4Labo-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 3micahwiesner67 / NY_100YR_Flood_Prediction
I created multiple models to predict the discharge volume of a 100 year flood on rivers in NY state. The discharge of 100 year flood events is dependent upon watershed drainage area, and elevation among other variables.
Language:R 2tboudart / 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.
belohith / ml-algorithms
A demonstration of the basic Machine Learning Algorithms
Language:Jupyter Notebook 1Bhevendra / ML-Regression
Regression on BOSTON dataset from sklearn
Language:Jupyter Notebook 1charumakhijani / advanced-house-price-prediction
house-price-predictionkaggle-competitionskewed-dataoutlier-detectionexploratory-data-analysisdata-visualizationmissing-value-treatmentone-hot-encodingscalingkfold-cross-validationrmse-scorelasso-regressionelastic-net-regressionkernel-ridge-regressionsupport-vector-regressiongradient-boosting-regressionxgbregressorlgbmregressorrandom-forest-regressionensemble-modelsLanguage:Jupyter Notebook 1iremustek / Predictive-Maintenance-Analysis
The project aims to enhance aircraft engine maintenance operations and planning using statistical learning and machine learning methods.
Language:Jupyter Notebook 1nalbert4 / DataModels
Data Models in R for Multiple Linear Regression and three models (Ridge, Lasso, and Elastic-Net), to predict Medicare claim costs of Type 2 diabetes patients with other diagnoses. We used Data from Entrepreneur’s Medicare Claims Synthetic Public Use Files (DE-SynPUFs) for our analysis.
Neo-Panther / ML-Project-Predicting-Emissions
ML Project implementing ANN, SVM, Random Forest, Elastic Net regression models from scratch.
Language:Jupyter Notebook 1nikhilbordekar / Yes-Bank-s-Stock-Closing-Price-Prediction-by-Regression
This project focuses on forecasting the closing prices of Yes Bank's stock. Through data analysis and predictive modeling, this project provides valuable insights for investors and traders, aiding them in making informed decisions about their investments in Yes Bank's stock.
Language:Jupyter Notebook 1PiotrTymoszuk / htGLMNET
High Throughput Light Weight Regularized Regression Modeling for Molecular Data
Language:R 1aniket15031998 / Yes-Bank-Stock-Closing-Price-Prediction-by-Regression.
This project focuses on forecasting the closing prices of Yes Bank's stock. Through data analysis and predictive modeling, this project provides valuable insights for investors and traders, aiding them in making informed decisions about their investments in Yes Bank's stock.
Language:Jupyter Notebook 0HaHaIamHarry / Commercial-Real-Estate-CRE-Loan-Credit-Risk-Model
A project aim to predict default rate of Commercial Real Estate(CRE) Loans
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liang-sarah / F1_ML
Machine Learning Predictions of 2023 Formula One Constructors' Championship Standings
Language:R 0Nandan9911 / Problems-on-car-and-house-price-prediction
Regression analysis
Language:Jupyter Notebook 0paddywardle / Health-Insurance-Regression---Python
Various Regression models including linear, polynomial, ridge, lasso and elastic net were experimented with to find which model best predicted health insurance costs. The models were evaluated using cross-validation, from which the best models were optimized using randomized search. The best model was then evaluated on the test data.
Language:Jupyter Notebook 0- HouseSalesPricePrediction
PayThePizzo / HouseSalesPricePrediction
Prediction of Sales Prices of Houses
Language:Jupyter Notebook 0 apecundo / melbourne-housing-price-prediction
Machine learning (regression) exercise on prediction of house pricing in Melbourne with post-model analysis and recommendations for maximizing home value.
Language:Jupyter Notebookdevosmitachatterjee2018 / Statistical_Learning_for_Big_Data_Report12062020
The project encompasses the statistical analysis of a high-dimensional data using different classification, feature selection, clustering and dimension reduction techniques.
Language:Pythonelectrofocus / machine-learning-practicum
Machine Learning Basics
Language:Jupyter Notebookfau-masters-collected-works-cgarbin / regression-no-libraries
Ridge, elastic net, and logistic regressions implemented without using any statistical or machine learning library. All steps are done by hand, using matrix operations as much as possible.
Language:Jupyter Notebookpcastellanoescuder / lassoloops
Lasso + Bootstrap methods for predictive modeling
Language:Rsdixit5 / Walmart-Weekly-Sales-Prediction
This project compares the different machine learning models on Walmart Weekly Sales Data and predicts the weekly sales for the test data.
Language:Jupyter NotebookShaikAyubAli / Regression-Project
Built a regression model to predict university admission using linear, polynomial, and regularized regression techniques (lasso, ridge, and elastic net) and achieved 98% accuracy.
Language:Jupyter Notebookvaibhavdangar09 / YES_BANK_STOCK_CLOSING_PRICE
Yes-Bank-Stock-Closing-Price-Prediction refers to a type of project or task in the field of data science and machine learning that involves developing predictive models to estimate the Closing Price of stock
Language:Jupyter Notebook
