Built house price prediction model using linear regression and k nearest neighbors and used machine learning techniques like ridge, lasso, and gradient descent for optimization in Python

This repository houses machine learning models and pipelines for predicting various diseases, coupled with an integration with a Large Language Model for Diet and Food Recommendation. Each disease prediction task has its dedicated directory structure to maintain organization and modularity.

This project builds a machine learning model using Linear Regression to predict house prices in Bangalore based on features like area, number of bedrooms, and location. The model processes and cleans data to provide accurate price predictions, and is deployed via a web application built with Streamlit for easy user interaction.

This project trains a Machine Learning model to predict house prices and then exposes Jupyter notebook cells as REST Endpoints to make prediction with new information.

House Price Prediction using Supervised Machine Learning ( Linear Regression Model )

A house price prediction application using a machine learning model trained on the House Prices - Advanced Regression Techniques Dataset.

Built prediction and retrieval models for document retrieval, image retrieval, house price prediction, song recommendation, and analyzed sentiments using machine learning algorithms in Python

To predict Melbourne houses has 4694 properties available for rent and 661 properties for sale. The median unit price in Melbourne last year was 480,000 dollars. If you are looking for an investment property, consider houses in Melbourne rent out for 410 dollar PW with an annual rental yield of 3.7% and units rent for 470 dollar PW with a rental yield of 5.1%. Based on five years of sales, Melbourne has seen a compound growth rate of -4.2% for houses and -0.9% for units.

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

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

The House Price Prediction project focuses on predicting housing prices using Machine Learning. By using Scikit-learn, Scipy, Pickle, Matplotlib, Seaborn, Numpy and Pandas in Python. And Custom the Model by Tkinter.

Prediction of house prices using a machine learning model and GCP.

London House Price Prediction — Advanced Techniques This project focuses on predicting house prices in London using advanced machine learning and data preprocessing techniques. It showcases a full end-to-end pipeline including feature engineering, scaling, model tuning, and evaluation — designed for those who want to go beyond basic regression. �

House Price Prediction is a machine learning project that estimates property prices using features like location, size, and rooms. It includes data preprocessing, exploratory analysis, regression model training, and evaluation using metrics such as R², MAE, and RMSE, showcasing a complete end-to-end ML workflow.

A machine learning web app that predicts house prices across 5 major cities of Pakistan. It uses features like location, property type, area, bedrooms, and bathrooms to give an estimated price. The model achieves an impressive R² score of 99.9%, showing how accurate the predictions are.

Housing price prediction using Supervised machine learning model in order to help people living in Melbourne price their future house.

using flask web framework a machine learning model for house price prediction

This repository contains a machine learning model designed to predict house prices using the California Housing dataset. The project leverages several powerful Python libraries to process data, build the model, and evaluate its performance.

House price prediction using multiple machine learning models

The House Price Prediction project focuses on building a machine learning model that predicts the selling price of a house based on various features such as location, size, quality, and amenities. The goal is to help buyers, sellers, and real estate professionals make data-driven pricing decisions.

End to end machine learning pipeline for house price prediction using various models, the pipeline includes exploratory data analysis (EDA), data preprocessing, models training & evaluating

Primary real estate house price prediction model using Numpy & Seaborn. Takes in house features, uses machine learning to predict price. Trained on past sales data. Can be used to make informed decisions about house prices.

This project demonstrates a simple house price prediction model using Linear Regression in Python. It covers data preparation, model training, prediction, and visualization with real estate data. Perfect for beginners learning machine learning fundamentals and practical implementation with libraries like pandas, scikit-learn, and matplotlib.

A machine learning project that predicts Bangalore house prices using a Random Forest model, with end-to-end preprocessing and a Flask web app for real-time predictions.

The repository contains a Pune house price prediction system build using R programming Language. The System efficiently calculates and analyze house prices in multiple areas across Pune using machine learning models and Data science and analytical tools

This project aims to predict the rent of a house based on various features such as location, furnishing status and square footage. The machine learning model has been trained on a dataset consisting of historical rental prices for houses in various cities. This project includes a Flask web app for easy prediction of house rents.

Real Estate Price Prediction: This project uses machine learning to predict house prices based on key features like location, size, and number of rooms. By analyzing historical data, I implemented models such as linear regression and random forests to estimate prices with high accuracy, leveraging Python and data analysis tools.

PROJECT HOUSING: PRICE PREDICTION Problem Statement: Houses are one of the necessary need of each and every person around the globe and therefore housing and real estate market is one of the markets which is one of the major contributors in the world’s economy. It is a very large market and there are various companies working in the domain. Data science comes as a very important tool to solve problems in the domain to help the companies increase their overall revenue, profits, improving their marketing strategies and focusing on changing trends in house sales and purchases. Predictive modelling, Market mix modelling, recommendation systems are some of the machine learning techniques used for achieving the business goals for housing companies. Our problem is related to one such housing company. A US-based housing company named Surprise Housing has decided to enter the Australian market. The company uses data analytics to purchase houses at a price below their actual values and flip them at a higher price. For the same purpose, the company has collected a data set from the sale of houses in Australia. The data is provided in the CSV file below. The company is looking at prospective properties to buy houses to enter the market. You are required to build a model using Machine Learning in order to predict the actual value of the prospective properties and decide whether to invest in them or not. For this company wants to know: • Which variables are important to predict the price of variable? • How do these variables describe the price of the house? Business Goal: You are required to model the price of houses with the available independent variables. This model will then be used by the management to understand how exactly the prices vary with the variables. They can accordingly manipulate the strategy of the firm and concentrate on areas that will yield high returns. Further, the model will be a good way for the management to understand the pricing dynamics of a new market. Technical Requirements: • Data contains 1460 entries each having 81 variables. • Data contains Null values. You need to treat them using the domain knowledge and your own understanding. • Extensive EDA has to be performed to gain relationships of important variable and price. • Data contains numerical as well as categorical variable. You need to handle them accordingly. • You have to build Machine Learning models, apply regularization and determine the optimal values of Hyper Parameters. • You need to find important features which affect the price positively or negatively. • Two datasets are being provided to you (test.csv, train.csv). You will train on train.csv dataset and predict on test.csv file. The “Data file.csv” and “Data description.txt” are enclosed with this file.

This project uses an Appliances Energy Prediction Dataset from UCI Machine Learning Repository. The objective of the project is to predict the energy consumption of the appliances using Linear Regression and Support Vector Regression. The data is logged every 10 minutes for about 4.5 months. The house temperature and humidity conditions were monitored with a ZigBee wireless sensor network. Each wireless node transmitted the temperature and humidity conditions around 3.3 minutes. Then, the wireless data was averaged for 10 minutes periods. The energy data was logged every 10 minutes with m-bus energy meters. Weather from the nearest airport weather station (Chievres Airport, Belgium) was downloaded from a public data set from Reliable Prognosis (rp5.ru), and merged together with the experimental data sets using the date and time column. Two random variables have been included in the data set for testing the regression models and to filter out non predictive attributes (parameters).

House Rent Prediction Using Machine learning Model and React