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.

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.

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

How to train, deploy and monitor a XGBoost regression model in Amazon SageMaker and alert using AWS Lambda and Amazon SNS. SageMaker's Model Monitor will be used to monitor data quality drift using the Data Quality Monitor and regression metrics like MAE, MSE, RMSE and R2 using the Model Quality Monitor.

EDA and prediction of housing prices in California

California Housing Prices Prediction App

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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

How to train a XGBoost regression model on Amazon SageMaker, host inference on a serverless function in AWS Lambda and optionally expose as an API with Amazon API Gateway

House Price prediction machine learning project with CI/CD Pipeline

Capstone Project - California Housing Price Prediction: Used linear, DT, ensemble regression techniques (Random Forests), feature scaling and feature engineering using Principal component Analysis (PCA); achieved minimal RMSE with ensemble technique. Supervised learning, Machine Learning, Python, Jupyter Notebook.

Python file for deploying the California Housing Price Prediction Streamlit Web App using AWS services

This project focuses on predicting California housing prices using an XGBoost Regressor model trained on the California Housing dataset. The trained model is deployed via a Streamlit web application, allowing users to interactively input features and obtain price predictions.

python data science

California housing price prediction with NN, Random Forest and Linear Regression

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.

This is an educational workthrough project from the book "Hands-On ML with Scikit-Learn, Keras and TensorFlow" by Aurélien Géron. It is based on the well-known "California Housing Prices" dataset - through feature engineering I successfully improved the performance of the model used in the book.

California house price prediction is done in this notebook

How to train a XGBoost regression model on Amazon SageMaker, host inference on a Docker container running on Amazon ECS on AWS Fargate and optionally expose as an API with Amazon API Gateway.

The California Housing Price Prediction project aims to leverage advanced machine learning techniques to forecast housing prices based on various features such as location, square footage, number of bedrooms and bathrooms, and other relevant factors. By analyzing historical data and patterns, the model intends to provide valuable insights into the

ML model for predicting California housing prices using gradient descent techniques, regularization, and early stopping. Includes a Flask-based web app with interactive UI for real-time predictions.

This project demonstrates the use of Explainable AI (XAI) techniques, specifically SHAP (SHapley Additive exPlanations) and LIME (Local Interpretable Model-agnostic Explanations), to explain the predictions of a machine learning model trained on the California and Boston Housing Prices dataset.

How to train a XGBoost regression model on Amazon SageMaker and host inference as an API on a Docker container running on AWS App Runner.

Linear Regression Model for California_Housing_Price_Prediction

California housing dataset having different attributes that has been studied and analyzed to understand how it is affecting the pricing.

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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

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

Three PyTorch ML Projects: 1. MNIST Digit Classification (CNN) 2. IMDB Sentiment Analysis (Text Embedding + Feedforward) 3. California Housing Price Prediction (Regression NN). All scripts support GPU, include training, saving models.