Machine learning demonstration of the Gradient Boosting algorithm and it's effectiveness on a regression dataset of house prices.

Simple GradientBoost

House Price Prediction (Kaggle)

The "House-Price-Prediction" repository contains code for a model that predicts house prices. It considers factors like bedrooms, bathrooms, and living area. With simple instructions, With the help of this model we can easily predict results as per our requirement.

This project employs machine learning to forecast housing prices in California. By scrutinizing location, housing details, and demographics, it constructs various regression models like Linear Regression, KNN, Random Forest, Gradient Boosting, and Neural Networks. These models offer invaluable insights to optimize predictive real estate investment

The objective of the project is to conduct a comprehensive analysis of a dataset of data science job postings, identifying the most important factors that influence salaries. Build predictive models that can be used to predict salaries for data science professionals, taking into account factors such as experience level, education, skills etc.

Machine Learning model for price prediction using an ensemble of four different regression methods.

Predict laptop prices using machine learning. This project leverages multiple linear regression to achieve an 82% prediction precision. Explore the influence of features like brand, specs, and more on laptop prices.

This is a hybrid recommender system that combines the paradigms of content based filtering(using gradient boosting regressor) and collaborative filtering to recommend destination spots for users/tourists based on their demography and spots liked by tourists with similar demography and likes.

This project aims to predict taxi fare amounts in New York City using a dataset of historical taxi rides. We employ machine learning techniques to create models that can estimate the total fare amount based on various features of the trips.

Predicting cement strength

Repositório do bootcamp de datascience da Alura.

In my project, I used Linear Regression and Gradient Boosting Regressor to predict house prices. I collected and preprocessed data, built the models, and enhanced accuracy with Gradient Boosting. Visualizations aided understanding, highlighting insights.

Predict Bike sharing depending on weather and week day, using data from previous months and different Machine Learning Algorithms

I code from scratch various Machine Learning algorithms.

Predicting hourly bicyclist counts on Coupure Links in Ghent, employing a Histogram Gradient Boosting regressor to forecast July values based on data from January to June, as part of the Machine Learning for Life Sciences course at Ghent University

Insurance Forecasting with EDA, feature engineering, data preprocessing, model building and hyperparameter optimization.

Predicting house prices using Linear Regression and Gradient Boosting Regressor with the factors like income, schools, hospitals and crime rates.

This repository enables an engineer to generate predictions for the mechanical bending performance of corroded beams, using a database of 725 corroded beams tested under monotonic bending. Outputs include the maximum bending moment, residual capacity percentage, yield load, yield displacement, and ultimate displacement.

Using Various Regression Algorithms to Predict House Sales