Building a regression model to predict insurance charges of customers based on features using multiple regression techniques

Building models to predict house prices for Pune, India, trained on 200 data points using multiple regression techniques

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.

epigenetic clock calibration

📗 This repository provides an in-depth exploration of the predictive linear regression model tailored for Jamboree Institute students' data, with the goal of assisting their admission to international colleges. The analysis encompasses the application of Ridge, Lasso, and ElasticNet regressions to enhance predictive accuracy and robustness.

This project develops a machine learning model to predict the salaries of baseball players based on their past performance.

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

The practical works (TP) of SD-TSIA204 - Statistics: linear models course at Télécom Paris.

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

Develop a regression project incorporating Ridge, Lasso, and Elastic Net with thorough data cleaning, exploration, and visualization for improved accuracy.

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.

By leveraging pipelines, artifacts, logging, EDA, exception handling, and other components, the Diamond Price Prediction project provides a robust and scalable solution for predicting diamond prices, empowering stakeholders in the diamond industry to make data-driven decisions with confidence.

CyberSoft Machine Learning 03 - Overview

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

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.

ML | Regression Analysis| Random Forest| XGBoost| Gradient Boost| EDA| Feature Engineering| Feature selection

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.

I constructed a machine learning model to predict the quality of wine

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

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