Real Estate Price Prediction using Machine Learning
ABSTRACT for Real Estate Price Prediction using Machine Learning:
Real Estate Price Prediction using Machine Learning : The real estate market is one of the most competitive and price-focused in existence today. With their budgets and an analysis of market tactics, people are searching to purchase a home. Creating a precise model for projecting house prices is always necessary for socioeconomic development and the welfare of citizens. such that a real estate agent or a home seller or buyer can get a gut feeling for using the model to make informed selections.
The primary drawback of the existing method is that it determines the price of a house without the essential foresight into potential future market movements, which leads to an increase in price. So, the primary goal of our study is to apply machine learning to accurately anticipate the price of a house. For the purpose of estimating home prices and attempting to provide clients with effective house pricing that respects both their goals and their budget, numerous elements must be taken into account.
In the proposed methodology, it is shown how to apply a machine learning algorithm to predict real estate/house prices using Kaggle dataset. This study addresses the application of machine learning algorithm for predicting real estate prices. To be more precise, a Random Forest Regressor model is used to predict a property’s price depending on several characteristics like location, size, number of rooms, etc..
A publicly accessible dataset with details on real estate properties in a certain area was utilised to train and test the model. The findings demonstrate that the Random Forest Regressor model can reasonably predict real estate prices. According to the study’s findings, machine learning algorithms can be used to accurately anticipate real estate prices, offering beneficial information to buyers, sellers, and real estate professionals.
PROJECT (Real Estate Price Prediction using Machine Learning) OUTPUT VIDEO:
ALGORITHM / MODEL USED for Real Estate Price Prediction using Machine Learning:
Random Forest Regressor.
- System : Pentium i3 Processor.
- Hard Disk : 500 GB.
- Monitor : 15’’ LED
- Input Devices : Keyboard, Mouse
- Ram : 4 GB.
- Operating system : Windows 10 / 11.
- Coding Language : Python 3.8.
- Web Framework : Flask.