IEEE BASE PAPER TITLE:
House Price Prediction using Machine Learning Algorithm
OUR PROPOSED TITLE:
Enhanced Property Price Prediction using Machine Learning: A Data-Driven Approach
IEEE BASE PAPER ABSTRACT:
Houses are one of the fundamental needs in human society. Good and peaceful surroundings where people feel comfortable are called houses. Hence, to have a good and happy living, people need to choose a good house model. This article focuses on accurately predicting house prices by using machine learning algorithms. This model helps people in selecting the house that is suitable for their living. The main parameter people look for will be the surrounding area, house type, price, location, and some other amenities. All these factors are considered to find out the required house for living. As people are very concerned about their budgets for buying a house, the prediction of house prices should be very precise. It also helps people in choosing houses based on their budgets, which do not affect their financial state in the future. The main outcome of this model is to predict the price of a house accurately as per the user requirements. This study has attempted to implement various machine learning algorithms like Linear Regression (LR), Gradient Boosting Regressor (GBR), Histogram Gradient Boosting Regressor, and Random Forest (RF) Regressor algorithms. Finally, the algorithm that generates high accuracy is considered for predicting the house price.
PROJECT OUTPUT VIDEO:
ALGORITHM / MODEL USED:
Random Forest Regressor.
OUR PROPOSED ABSTRACT:
The “House Price Prediction using Machine Learning” project presents a comprehensive approach to predicting real estate prices by harnessing the power of advanced data analysis techniques. Developed primarily using Python programming language, the project employs the Random Forest Regressor algorithm as its core predictive model. The objective is to accurately estimate the prices of residential properties, contributing to informed decision-making in the real estate market.
In this project, a dataset containing 42,703 individual data points from the United States of America is utilized for training and evaluation. The dataset encompasses various essential features that influence property prices, including location, square footage, number of bedrooms and bathrooms, amenities, and more. By leveraging this diverse set of attributes, the Random Forest Regressor algorithm learns intricate patterns and relationships within the data, enabling it to make reliable predictions.
The project’s success is measured by the achieved performance metrics. During the training phase, the model attains a Mean Absolute Error (MAE) of 1.4606, indicating the average absolute difference between predicted and actual prices on the training set. Furthermore, on the test set, the model demonstrates its generalization capability by achieving a MAE of 3.8313. These metrics underscore the model’s ability to make accurate predictions on unseen data, enhancing its practical utility in real-world scenarios.
The Proposed House Price Prediction using Machine Learning showcases the efficacy of the Random Forest Regressor algorithm in forecasting residential property prices. The Python-based implementation leverages a dataset comprising thousands of data points from the United States, contributing to a robust and reliable predictive model.
The achieved low Mean Absolute Error values on both training and test sets emphasize the model’s accuracy and generalization potential. This project holds significant implications for individuals, investors, and real estate professionals seeking data-driven insights to navigate the dynamic real estate market.
- System : Pentium i3 Processor.
- Hard Disk : 500 GB.
- Monitor : 15’’ LED
- Input Devices : Keyboard, Mouse
- Ram : 6 GB.
- Operating system : Windows 10 / 11.
- Coding Language : Python 3.10.9
- Web Framework : Flask.
Shailendra Sharma; Deepti Arora; Gori Shankar; Priyanka Sharma; Vihaan Motwani, “House Price Prediction using Machine Learning Algorithm”, 2023 7th International Conference on Computing Methodologies and Communication (ICCMC), IEEE Conference, 2023.