Case study on regularisation methods for statistics and machine learning
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Updated
Jun 3, 2019 - R
Case study on regularisation methods for statistics and machine learning
Developed student performance predicting model, showing strong understanding of predictive modeling techniques.
An interactive dashboard based on a Linear Regression model predicting demand for bike rentals in Washington, D.C.
Practicum Workshop
Linear regression, ridge, lasso
Hello! All codes belong to me. I created those codes for my Machine Learning Lab Class. Enjoy it!
I compare the accuracy of health cost prediction of four regression models: Linear, Lasso, Ridge, and Elastic Net Regression.
In this project, we use differents methods to transform our dataset (usually dimension modification) before making prediction thanks to machine learning and regressions.
This repository shows some coding in R my colleagues and I made for our Machine Learning course for the Diploma of Specialization in Data Science for Social Sciences and Public Management - PUCP
Use of different regression models to predict bike demand.
A Study of the Effect of YouTube Tech Channels on the Revenue of Newly Released Devices
A Streamlit based application for Price prediction and damage detection of secondhand cars
Diamonds Price prediction using Polars and Ski-learn
Weighted Ridge Regression Based on Nonlinear Kernels
Goal of this project was to classify whether a tweet was about a natural disaster or not.
This is the python implementation of linear regressions. The repo consists of ordinary linear regression, lasso linear regression, ridge linear regression
Regression Analysis to predict car selling price with given datasets. Used libraries: Pandas, Matplotlib, Seaborn, Scikitlearn
Archived repo - This R Package is not developed anymore (only maintenance). It was replaced by R package rchemo
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