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.

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

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

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

Implementation of different types of machine learning algorithm and there performance comparison on a same dataset

Algoritmos de regressão na linguagem python utilizando bibliotecas como sklearn e pandas.

Seoul bike sharing demand prediction is a project that uses machine learning to predict the demand for rental bikes in Seoul, South Korea. The project aims to provide insights for the city's bike-sharing system to better manage the supply of bikes and ensure their availability to the public at the right time.

The dataset used for this project is taken from the official UCI Machine Learning Repository.

EPS forecast with Lasso, Elastic Net and multiple linear regression

Predict the cause of fire in the forest using different Regression Models

The main objective of this project is to forecast the Customer Lifetime Value (CLTV) using user and policy data.

In this project I built machine learning models using Multiple Linear Regression, Ridge regression, Lasso regression, Elasticnet regression and then created a pickle file of the regression model which gave best accuracy