Machine learning applications in volleyball (python, scikit-learn)
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Updated
Jan 2, 2022 - Jupyter Notebook
Machine learning applications in volleyball (python, scikit-learn)
In this work we attempt to fill in the gap years for the US Agricultural Census in Utah counties. Open source data from NOAA, Agricultural Census, and BLS are used leveraging Machine Learning methods and models.
Machine learning model to forecast the sales of each Rossmann store for any given date.
Practical Implementation of Linear Regression on Algerian Forest Fire Dataset.
This repository contains an ML workflow to predict house prices in Ames, Iowa. This project work is carried out under the Machine Learning module of the GeoDSc track of the Copernicus Master in Digital Earth.
Assignment submissions for the course CS771A "Introduction to Machine Learning" at IIT-K in 2022-23 II Sem.
Wine Quality Prediction using ElasticNet Regression using MLFlows
The practical works (TP) of SD-TSIA204 - Statistics: linear models course at Télécom Paris.
Practical Implementation of Linear Regression on Boston Housing Price Prediction
Penalized linear regression modeling in R and application to life expectancy data
This repository is the third project of the master's degree in AI Engineering that I am following. It aims toto optimize real estate price valuation through the use of advanced regularisation techniques in linear regression models by implementing Lasso, Ridge and Elastic Net in order to obtain accurate and stable price predictions.
We explored various approaches to deal with high-dimensional data in this study, and we compared them using simulation and soil datasets. We discovered that grouping had a significant impact on model correctness and error reduction. For the core projection step, we first looked at the properties of all the algorithms and how they function to com…
Diamond Price Predictor - Web Application: Predict diamond prices using various regression models: Linear Regression, Lasso, Ridge, ElasticNet, Decision Tree Regressor, Random Forest Regressor, and KNeighbors Regressor. The chosen Random Forest Regressor, with a remarkable accuracy of 97%, is deployed in a user-friendly Flask app
It was a competition on KAGGLE for prediction on the most sales products on bikes via their features
ML Regression application built using Flask
The repository contains some of the work done by me and 4 colleagues for a university project of the "data analysis for business" class. The project aims at identifying the best deals and strategies to take by rental agencies to maximise profits in the Brazilian House Market. On the other hand, We also analyzed good deals for mid-income households.
In this project I have implemented 15 different types of regression algorithms including Linear Regression, KNN Regressor, Decision Tree Regressor, RandomForest Regressor, XGBoost, CatBoost., LightGBM, etc. Along with it I have also performed Hyper Paramter Optimization & Cross Validation.
ElasticNet Linear Regression on Solar Power Generation
A small project addressing a regression problem explains implementation of multiple linear regression techniques, hyperparameter tuning, collinearity, model overfitting and complexity using LASSO, Ridge and Elastic net
EPS forecast with Lasso, Elastic Net and multiple linear regression
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