A regression model used to predict the price of a house in California based on the data inputted. Model is implemented using three methods: VIF, Linear, Lasso & Ridge regression. Dataset is analysed and processed beforehand.

Machine Learning model to predict the house of prices

California Houses Prices prediction app using Scikit-learn served as a Flask web app.

This repo houses a fully-functional comprehensive MLOps pipeline for California Housing Price Prediction using regression models. It includes model training, model tracking, data/model versioning, API based deployments, and performance monitoring.

Predict California housing prices using Linear Regression. Includes full EDA, data preprocessing, model training, evaluation (MSE, MAE, R²), and visualization. Make predictions for new houses based on features like location, rooms, income, and ocean proximity.

California Houses Price Prediction

An implementation of Linear Regression Algorithms to predicate prices of California houses. And Using regularization methods ('ridge' and 'lasso') to improve the results.

Predict houses prices with python by Machine learning techniques (Linear regression, Random forest )

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Using Multiple Model to predict prices using Python

Whole process of california households App on R From EDA then Model Creating to App

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Prediction Prices of Houses in different regions of California

React + python application for price prediction in california houses

this repository contains the prediction model of houses prices in California

Prediction the price of california's houses based on different features.

It is a regresstion model on the price prediction of houses in california

This is a housepricing prediction model built using the California House Price dataset to be able to predict the price of future houses in the district.

This dataset provides a concise overview of California housing data. Through an analysis of this dataset, our primary objective is to estimate the expected prices of houses in California. The fundamental goal of working with this dataset is to make predictions regarding the median house prices in the state.

The data pertains to the houses found in a given California district and some summary stats about them based on the 1990 census data. Based on the data a simple housing price prediction project

Built using Python and Flask, and leveraging Docker and Kubernetes for seamless hosting, this application facilitates user interaction with a machine learning pipeline, offering accurate price predictions for houses in California based on entered characteristics, such as neighbourhood and square footage

In this project I performed feature engineering and used Ridge and Lasso models for prediction of sales prices for houses in California also metric submissions are evaluated on Mean-Squared-Error, Root-Mean-Squared-Error, Mean Absolute Error, R2 (MSE, RMSE, MAE, R2)

This data is about California Housing Prices And you may see a lot of columns here, so these columns are about details about group of houses in a block Let's assume there are 100 blocks in a city So for each block the've calculated some statistical features out of a block median_income: Such as Median(statistics) of Income of people living a block median_house_value: Median(statistics) price of all the houses in a block. So, by using Univariate Linear Regression I have analysed the data and found the best fitting curve which would give the actual prediction price of the houses.