Regression using CNN 1D for House price prediction on 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

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.

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.

Machine Learning model to predict the house of prices

California House Price Prediction Project

A house price prediction algorithm that predict house prices in California

California House Price Prediction Using Machine Learning

California House Price Prediction Model Using Regression

California House Price Prediction using ML & Streamlit

House price prediction system of boston California housing

House Pricing Prediction based on California Census Data set.

This is a prediction project for California house prices.

House Price Prediction using California Housing Dataset & Linear Regression

House price prediction using Linear Regression and California Housing dataset

California Houses Prices prediction app using Scikit-learn served as a Flask web app.

Implementing California House Price Prediction Kaggle Competition Using MLP Based on PyTorch

django rest api for deploying the California House Price Prediction machine learning model

California Housing Price Prediction: A machine learning project using the California Housing dataset. Implements Linear Regression, Random Forest, XGBoost, and LASSO models for accurate house price predictions. Features data preprocessing, visualization.

This Zillow House Price Project is to analyze US house price data from 1966 to 2018 and create a California house pricing prediction model (Time Series model) based on time and house size rank.

MLOps_California_House_Price_Prediction is an end-to-end machine learning project designed to predict house prices in California using modern MLOps practices. This project combines data science, model development, and deployment pipelines to create a scalable and reproducible housing price prediction system.

A House Price Prediction Model using Machine Learning to estimate property prices with Random Forest and Linear Regression, trained on the California Housing Dataset for accurate predictions.

This is my personal project on Machine Learning. A house price prediction model trained on California state housing data.