ames-housing

Project using multiple linear regression to model prices of houses in Ames, IA.

10 very popular data analysis exercises I have practised with jupyter notebook in my spare time, including Iris flowers, Titanic, K means clustering, linnear regression and logistic regression etc.

my attempt to train a model on Ames housing dataset with scikit-learn and XGBoost .

In this repo I'll use different types of datasets to explore and implement various Exploratory Data Analysis (EDA) approaches.

Final Project for Sta 521 Predictive Modeling at Duke University in 2017 Spring

End-to-end house price prediction project using Python, scikit-learn and Streamlit, covering data preprocessing, feature engineering, model comparison, Gradient Boosting optimisation and interactive deployment.

Predicting House Prices in Ames,Iowa using advanced regression techniques (penalized regressions)

Progressive Ames house-price modeling with metric-aligned MLPs, feature engineering, CatBoost, and a TabPFN-dominant ensemble.

Competition: https://www.kaggle.com/c/house-prices-advanced-regression-techniques

Production-style scikit-learn pipelines: Ames Housing with Optuna-tuned boosting; calorie-expenditure regression with a custom RMSLE scorer

Predictive analytics dashboard for exploring housing data and estimating property prices with Python, Streamlit and machine learning.

Wyatt is an open-source house pricing AI model.

An advanced machine learning regression pipeline on the Ames Housing dataset featuring non-linear target scaling, systematic preprocessing, multi-model benchmarking, and embedded vs. filter feature selection analysis.

Using Lasso regression to predict housing prices in Ames, Iowa.

Advanced regression analysis to predict housing prices in Ames, Iowa, using Stacking Ensembles (Ridge, XGBoost, LightGBM). / Análisis de regresión avanzada para predecir los precios de la vivienda en Ames, Iowa, utilizando Ensembles de Stacking (Ridge, XGBoost, LightGBM).

Prediction of Sales Prices of Houses

Kaggle competition to predict housing prices in Ames, Iowa.

End-to-end Machine Learning regression project for predicting house prices using the Ames Housing dataset. Includes complete data preprocessing, EDA, feature engineering, model training (Linear Regression & XGBoost), hyperparameter tuning, evaluation, and a Streamlit web app for single & batch predictions.