This is a regression problem to predict california housing prices.

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

The repository contains the California House Prices Prediction Project implemented with Machine Learning. The app was deployed on the Flask server, implemented End-to-End by developing a front end to consume the Machine Learning model, and deployed in Azure, Google Cloud Platform, and Heroku. Refer to README.md for demo and application link

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

A machine learning project to predict California real estate prices using regression models. Includes feature analysis, regional trends, and Power BI visualizations by Team Deciforge.

California House Prediction

This project provides a web application for predicting California housing prices using a trained machine learning model. The application allows users to input various features related to housing and get an estimated price prediction.

California house price prediction is done in this notebook

House Price Prediction of the California Dataset from Scikit Learn

A regression model used to predict the price of a house in California based on the data inputted. Model is implemented using three methods: VIF, Linear, Lasso & Ridge regression. Dataset is analysed and processed beforehand.

It is an Linear Regression model which uses fetch_california_housing dataset from sklearn.datasets to predict the house price in california

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This project uses the California Housing dataset to build a machine learning model that predicts house prices based on features such as median income, population, location, and housing age. The workflow covers data preprocessing, exploratory data analysis (EDA), feature engineering, model training, and evaluation using regression techniques.

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Chapter 2 from Aurelien Geron Book

machine learning regression problem

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House Price prediction ML project based on California dataset. Front end is a mobile app.

This repository implements House Price Prediction Model for California housing dataset using XGBoost regression algortihm

ML based WebApp for california House-Price Prediction deployed over Railways.app . Scroll down to see screenshots of the WebApp

A California house price prediction system using Random Forest regression with scikit-learn pipelines for preprocessing, featuring model comparison (Linear Regression, Decision Tree, Random Forest), stratified sampling, and automated training/inference workflows.

Implemented Spark machine learning Pipeline on AWS EMR for Collaborative Filtering to recommend users which online educational course they should take based on their viewing history. Target audience found using K-Means clustering over 2 billion data rows. • Using Kafka & Spark Structured Streaming simulated the above models as real time events with a window size of 2 minutes. • House price prediction for California residents based on Kaggle’s 2014/15 dataset using Linear Regression. Narrowed down the customers who were likely to purchase using Logistic Regression & Decision Tree Classifier along with Random Forests to choose the best performing model.

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Predicting California house prices using multivariable regression

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