california-housing-price-prediction
There are 1 repository under california-housing-price-prediction topic.
aws-samples / amazon-sagemaker-xgboost-regression-model-monitor-and-alerting
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.
Language:Jupyter Notebook 6How 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.
Language:Jupyter Notebook 4How 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
Language:Jupyter Notebook 4NisAr-PakhtoOn / California-housing-prediction
California house price prediction is done in this notebook
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.
Language:Jupyter Notebook 3ankur715 / Machine_Learning
ML, NN, NLP, ARIMA, clustering, classification, mapping
Language:Jupyter Notebook 2BALAJIHARIDASAN / Machine-Learning-project-end-to-end
This project is full scale end to end Machine learning project that used to predict the price of the california housing dataset
Language:Python 2ellieflgr / CaliforniaHousingPricesML
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.
Language:Jupyter Notebook 2mikel-brostrom / Housing_Price_Prediction
California housing price prediction with NN, Random Forest and Linear Regression
Language:Jupyter Notebook 2- Language:Jupyter Notebook 2
salim-benhamadi / predicting-house-prices
Predicting California Housing Prices using Decision Tree Regressor
Language:Jupyter Notebook 2nadavWeisler / PricePredictionSklearn
Build as part of "Building Your First scikit-learn Solution" Pluralsight course.
Language:Jupyter Notebook 1AkashHiremath856 / California_Housing
California Housing Price prediction with web-hosting using Heroku and scikit-learn for predicting.
Language:Jupyter Notebook 0dilne / CaliforniaHousing
California Housing Price Prediction - Linear Regression, Support Vector Regression, Decision Trees, and Random Forest Regression
Language:Jupyter Notebook 0kfmatovic716 / CA-HOME-PRICE-PREDICTIONS---Final-Project
Create a platform that will predict a house price based on a user-input zip code and house type
Language:Jupyter Notebook 0UpalRoy-Decisionscientist / Machine-Learning
Machine Learning Python
Language:Jupyter Notebook 0- Language:Jupyter Notebook
kbmclaren / tensorFlow-CMSC478-ML
Get started with Tensorflow/Keras API.
Language:Jupyter NotebookNikoletos-K / Ridge-regression-for-California-Housing-Dataset
🏡💲 Stochastic, full and mini-batch gradient descent for ridge regression using California Housing Dataset
Language:Jupyter NotebookPrecioux / Computational-Intelligence-Projects
Computational Intelligence Course - Spring 2023
Language:Jupyter Notebooksrikanthiremath / California-Housing-Price-Prediction
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 for each block group in California. This model should learn from the data and be able to predict the median housing price in any district, given all the other metrics. Districts or block groups are the smallest geographical units for which the US Census Bureau publishes sample data (a block group typically has a population of 600 to 3,000 people). There are 20,640 districts in the project dataset. Bonus Exercise: Predict housing prices based on median_income and plot the regression chart.
Language:Python- Language:Jupyter Notebook
