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

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

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.

The development of a housing price prediction model can assist a house seller or a real estate agent to make better-informed decisions based on house price valuation. In this project, I used machine learning (ML) predictive modeling and classification algorithms to predict the values of properties in California.

In my project there is an application with which you can work with a database of apartments in California, as well as view statistical reports on dataset. Also, the project implemented a prediction of the location of the house and its prices using machine learning algorithms

Usage and implementation of a machine learning algorithm to allow for house price predictions in california.

A machine learning algorithm that compared the effects of the Box-Cox and Yeo-Johnson transformations on the prediction of house prices in California.

A detailed analysis for prediction of house prices in California districts by visualizing the data, finding correlations, preparing the data for Machine learning algorithms.

This project is a California house price prediction system that utilizes machine learning techniques, specifically Linear Regression and Random Forest. The primary objective is to predict house prices in California based on various features such as location, number of bedrooms, population, and more. The project demonstrates how different regression

California House Price Prediction This project demonstrates an XGBoost Regressor model, built with Python and Scikit-learn, to predict California house prices. It achieved an R-squared score of 0.83 on the test data. The project showcases a full machine learning pipeline from data processing with Pandas to model evaluation.

This repository contains the code for the prediction of house price in California using machine learning algorithms and also employing models such Linear Regression, Elastic Net, Lasso , Random Forest Regressor and Ridge Regression.

This repository contains the implementation of Artificial Intelligence & Machine Learning – Task 2, focusing on feature engineering, model optimization, and performance comparison for a house price prediction problem. The California Housing Dataset is used to train and evaluate multiple regression models.

This project uses the California Housing dataset to predict house prices with XGBRegressor, a smart and fast machine learning model. It learns from data like income and number of rooms, then makes price predictions. It gives good results and helps us understand how data and AI can solve real-life problems in a smart way.