A Machine Learning Project implemented from scratch which involves web scraping, data engineering, exploratory data analysis and machine learning to predict housing prices in New York Tri-State Area.

The "House Price Prediction" project focuses on predicting housing prices using machine learning techniques. By leveraging popular Python libraries such as NumPy, Pandas, Scikit-learn (sklearn), Matplotlib, Seaborn, and XGBoost, this project provides an end-to-end solution for accurate price estimation.

No description available

Unlocking Melbourne's housing market secrets! This project demystifies property prices by training a Random Forest model on key features. From a simple linear model to a fine-tuned powerhouse, it achieves an impressive 85% accuracy, turning data into predictive power.

Building Toronto Housing dataset from scratch to predict real estate prices

Housing price prediction using Regularised linear regression

基于postgresql+机器学习库MadLib的上海地区二手房价格预测及推荐）

No description available

This notebook explores the housing dataset from Kaggle to predict Sales Prices of housing using advanced regression techniques such as feature engineering and gradient boosting.

"Buying a house is a stressful thing." We built a model to predict the prices of residential homes in Ames, Iowa, using advanced regression techniques. This model will provide buyers with a rough estimate of what the houses are actually worth. We first analyzed the data to find trends. Then dimensionality reduction was performed on the dataset using PCA algorithm and feature selection module in sklearn package for python 3.5. The final house prices are predicted using linear regression models like Ridge and Lasso. We also utilised advanced regression techniques like gradient boosting using XGBoost library in python 3.5.

MLOps Paris Housing Price Prediction.

A Linear regression project to predict the selling price of a house. Data set contained features of house using which we predicted the price. Dataset was taken from kaggle.

A Machine Learning Project to predict Bangalore House Prices.

No description available

This project aims to develop a linear regression model to predict housing prices in California using the California Housing dataset. The project explores the impact of feature selection and scaling on the model's performance, with a focus on improving the accuracy of price predictions.

Investigate housing data set and build an ML Pipeline that automates feature engineering, feature selection, and training and testing models for estimation and prediction of housing prices.

This project is a linear regression modeling of kings county housing price prediction. The data set was provided by Flatiron School for Data Science Immersive course.

Ames Iowa housing price prediction.

This is a end to end Machine learning project that predict the prices of the House in a area.

No description available

Code for paper Housing Price Prediction using Graph Convolutional Transformer Network

Build a model of housing prices to predict median house values in California using the provided dataset. Train the model to learn from the data to predict the median housing price in any district, given all the other metrics. Predict housing prices based on median_income and plot the regression chart for it.

A machine learning project to predict the housing price based on Kaggle Housing Prices Competition

No description available

Predicting housing prices for homes in Northern Virginia area

[Vercel-app] MLOps Paris Housing Price Prediction.

Regression using CNN 1D for House price prediction on California Housing Dataset

Housing Prices Prediction using ML models

Predicting California housing prices using multi-model regression. Includes EDA, preprocessing, feature engineering, and model comparison (Linear Regression, Decision Tree, Random Forest, KNN) to identify the best predictor based on RMSE and R².

Training and Deployment of model which predicts house prices around Boston using Neural Networks (keras)