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.

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.

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

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.

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.

This project is full scale end to end Machine learning project that used to predict the price of the california housing dataset

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

Predicting California Housing Prices using Decision Tree Regressor

ML, NN, NLP, ARIMA, clustering, classification, mapping

This repository contains a machine learning algorithm that trains a model to predict house prices based on specified features of the homes, using the California Housing Dataset.

This repository contains a machine learning algorithm that trains a Random Forest model to predict house prices based on specified features of the homes, using the California Housing Dataset. The dataset used to train and evaluate the Random Forest model to predict median housing prices.

Build as part of "Building Your First scikit-learn Solution" Pluralsight course.

Pattern Recognition assignment

Get started with Tensorflow/Keras API.

Create a platform that will predict a house price based on a user-input zip code and house type

🏡💲 Stochastic, full and mini-batch gradient descent for ridge regression using California Housing Dataset

The "Linear Regression in Machine Learning using Python and Sklearn" article's source code

Problem Statement The purpose of the project is to predict median house values in Californian districts, given many features from these districts. The project also aims at building a model of housing prices in California using the California census data. The data has metrics such as the population, median income, median housing price, and so on …