This is the experimental assignment of my course "Machine Learning and Data Mining", which requires completing the training, testing and evaluation of the linear regression model for house price prediction based on the California Housing Prices dataset

Time Series Analysis and Modeling - Forecast future house prices with SARIMA

Using ARIMA timeseries model to forecast the price of houses in a city, using Zillow's historic house prices

This repository, jelambrar96-datatalks/house-price-predictor, appears to be a machine learning and deep learning project focused on predicting house prices. The repository likely contains code and data for training and testing models to forecast house prices based on various factors.

🌈📊📈 The Zillow Home Value Prediction project employs linear regression models on Kaggle datasets to forecast house prices. 📉💰Using Apache Spark (PySpark) within a Docker setup enables efficient data preprocessing, exploration, analysis, visualization, and model building with distributed computing for parallel computation.

This repository contains a Jupyter Notebook titled “Predicting UK Average House Prices Using ARIMA, SARIMA, and LSTM Models”, developed as part of a postgraduate dissertation. The project evaluates and compares traditional time series models with deep learning methods for forecasting average UK house prices.

🏡 House Price Prediction Using Machine Learning This project aims to build a predictive model that estimates house prices based on various features such as location, size, number of bedrooms, age of the property, and more. the model learns patterns from historical housing data to forecast future prices.

In this repository, I've implemented a Machine Learning-based Bangalore House Price Prediction model. With the aid of a few characteristics like availability, size, total square feet, bath, location, and so on, this model forecasts the price of a property in Bangalore.

Real estate transactions are quite opaque sometimes and it may be difficult for a newbie to know the fair price of any given home. Thus, multiple real estate websites have the functionality to predict the prices of houses given different features regarding it. Such forecasting models will help buyers to identify a fair price for the home and also give insights to sellers as to how to build homes that fetch them more money. It is expected to build a sale price prediction model that will aid the customers to find a fair price for their homes and also help the sellers understand what factors are fetching more money for the houses.

Predictive machine learning model for forecasting house prices

- Built loss function to evaluate various potential situations. - Removed exponential trend in the data and analyzed autocorrelation and partial autocorrelation. - Performed Augmented Dickey-Fuller test on three key variables to check for stationarity. - Tested for exogenity between variables using Granger Causality Wald test. - Created Univariate and multivariate forecasts using Vector Autoregression and Autoregressive-moving-average models. - Selected the best forecasting model as the one with the minimum loss. - Used Diebold-Mariano test to determine the statistical superiority of said forecast.

Forecasting Tehran houses' prices using machine learning (regression model)

Accurate and scalable Airbnb house price prediction, from big data cleaning to advanced ML pipelines with XGBoost.

Forecasting house prices for 19 zip codes in Pittsburgh, PA via time series modeling.

Executed a comprehensive data cleaning process and analysis to construct predictive models for forecasting Irish house prices.

A GNN model for forecasting prices of product in the auction house of the game World of Warcraft

Modeling house prices in King County, WA to enhance tax revenue forecasts for municipalities.

Predict house prices using XGBoost regression. This project preprocesses data, trains the model, and evaluates predictions to forecast house prices based on various features.

Predict-house-prices uses machine learning models to estimate property values. Includes data preprocessing, exploratory analysis, and regression-based predictions. Ideal for real estate analytics and price forecasting projects

An interactive Streamlit dashboard for visualizing, analyzing, and forecasting RBI’s House Price Index (HPI) data across major Indian cities using ARIMA, SARIMA, and Prophet models.

Crafted a regression model to forecast house prices in Dallas, pinpointing key value drivers and refining predictions through rigorous data preparation, residual checks, and model validation.

A curated collection of machine learning prediction projects developed in 2025. It includes heart disease prediction (classification), stock price forecasting (time series regression), iris dataset classification, and house price prediction. Each project covers preprocessing, modeling, evaluation, and visualization.

BESTHomeFI is an open-source web app that uses MindsDB AI & Node.js to predict or forecast house prices. The prediction is based on a trained machine-learning model developed using historical data on house prices. 🏡💻

House Price Prediction project employs advanced machine learning to forecast property values. By analyzing historical data, architectural details, and socio-economic factors, this model provides accurate estimates.

The primary purpose is to forecast the price of an art /painting in an art auction. We are attempting to develop a generic model capable of forecasting the price of any piece of art to be sold based on various factors connected with an artist, auction house, and the piece itself.

The "House Price Prediction" project employs machine learning to forecast property prices. Leveraging algorithms from scikit-learn and TensorFlow, this model provides accurate estimations, aiding buyers, sellers, and real estate professionals in making informed decisions.

# Housing Price Prediction This project focuses on predicting housing prices using machine learning techniques, specifically Random Forest and XGBoost. The goal is to develop models that accurately forecast house prices based on various features, such as square footage, number of rooms, location, and more.

The objective of the house price prediction project is to build a predictive model that accurately forecasts property values using a range of features like location, size, and other related features.

Utilizing TensorFlow, this project predicts house prices by analyzing features like square footage and location. Through data preprocessing and deep learning techniques, a regression model is trained and evaluated for accuracy. It offers a concise demonstration of TensorFlow's efficacy in real estate forecasting.

Problem Statement Real estate transactions are quite opaque sometimes and it may be difficult for a newbie to know the fair price of any given home. Thus, multiple real estate websites have the functionality to predict the prices of houses given different features regarding it. Such forecasting models will help buyers to identify a fair price for the home and also give insights to sellers as to how to build homes that fetch them more money. Chennai house sale price data is shared here and the participants are expected to build a sale price prediction model that will aid the customers to find a fair price for their homes and also help the sellers understand what factors are fetching more money for the houses?