I have built a Model using Random Forest Regressor of California Housing Prices Dataset to predict the price of the Houses in California.

Build a Web App called Menara to Predict, Forecast House Prices and search GreatSchools in California - Bay Area

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

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 house prices using the California housing dataset.

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

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

California house price prediction is done in this notebook

A Streamlit app that predicts California median house prices using XGBoost and the California Housing dataset.

A Streamlit app that predicts California median house prices using XGBoost and the California Housing dataset.

train California house pricing dataset with tensorflow linear regressor

House Price Prediction of the California Dataset from Scikit Learn

A complete Machine Learning Regression project for predicting the prices of houses in California based on various attributes.

This project focuses on predicting house prices in California using Deep Neural Networks (DNN). The goal is to develop a robust and accurate model that can predict housing prices based on various features, providing valuable insights for real estate stakeholders and potential buyers.

This repository contains a machine learning model designed to predict house prices using the California Housing dataset. The project leverages several powerful Python libraries to process data, build the model, and evaluate its performance.

Streamlit app + machine learning model to predict California house prices

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

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

A machine learning model that predicts the prices of houses in California given a certain dataset

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.

this repository contains California housing price machine learning model . various regression models are used to predict median house value .

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.

A machine learning project that predicts California house prices using a full preprocessing pipeline and a Random Forest model. Includes automated data cleaning, feature engineering, model training, and inference using Python, Pandas, NumPy, Scikit-Learn, and Joblib

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.

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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This is a simple linear regression model and it is designed to predict house prices in California.

This project is a based on gurugon dataset which helps to predict gurugon house prices