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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.

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California House Price Prediction Using Machine Learning

building a House price prediction system using Machine Learning with Python. California house price prediction. This is an important Machine Learning project & use case.

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.

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.

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 machine learning-based house price prediction system using the California housing dataset. Trained with a Random Forest Regressor and powered by a full preprocessing pipeline.

🏡 California Housing Price Prediction Model 📌 Overview This project predicts median house prices in California using Machine Learning techniques. It demonstrates a complete end-to-end ML workflow — from data preprocessing and feature engineering to model training, saving, and prediction using new input data.

This is a Machine Learning Project to demonstrate house price prediction using california housing dataset. It has a frontend website built using html, css , javascript where it takes input predicts and gives output

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

(California) house price prediction using machine learning

California House Price Prediction using Machine Learning

California House Price Prediction using Machine Learning

House price prediction using california_housing _data using Machine Learning Concepts

House price prediction based on california housing dataset using machine learning

California House Price Prediction using Machine Learning (RandomForest) and Scikit-learn.

House price prediction app based on california housing dataset using machine learning

Machine learning project for California house price prediction using regression, feature engineering, and model tuning.

This is a machine learning project related to regression model. The project includes the house price prediction using California House Price dataset

California Housing Price Prediction is an end-to-end Machine Learning project that predicts median house values across California using the California Housing Dataset.

The House Price Prediction project is a machine learning regression model designed to predict median house prices using the California Housing dataset from Scikit-learn.

This notebook builds a house price prediction model using California housing data, focusing on machine learning techniques to forecast median house values.

🏠 House Price Prediction (California Housing Dataset) This project predicts California house prices using the California Housing Dataset and XGBoost Regressor. It demonstrates data analysis, visualization, feature correlations, model training, and evaluation using modern machine learning techniques.

In this task on House Price Prediction using machine learning, our task is to use data from the California census to create a machine learning model to predict house prices in the State.

This project is House Price Prediction using Machine Learning with Python building and learning from data "from sklearn.datasets import fetch_california_housing

Machine Learning project implementing Linear Regression for house price prediction using the California Housing dataset. Includes EDA, Visualization, model evaluation and report.

House Price Prediction using Regression — A complete end-to-end machine learning pipeline to predict California house prices using the California Housing Dataset. Includes EDA, feature engineering, model training (Linear, Tree, RF), and evaluation with visual insights.