Authors: Nishant ., Mukul , Laskhya , Arsh
Certificate: View Certificate
The abstract of the real estate price prediction project for properties in Bengaluru involves using machine learning algorithms to develop a predictive model that can estimate the prices of properties in the city based on various features such as location, size, amenities, and so on. The dataset used for this project contains information on thousands of properties in different parts of Bengaluru, including their sale prices, size, location, number of bedrooms, bathrooms, and other key features. To build the predictive model, several machine learning algorithms such as linear regression, decision tree, random forest, and XGBoost are used to train the model on the given dataset. The performance of each model is evaluated using metrics such as mean absolute error, mean squared error, and R squared score, and the best performing model is selected as the final predictive model. Once the model is trained and tested, it can be used to predict the prices of new properties in Bengaluru based on their features. The model can also be used to identify the most important features that affect the price of properties in the city, which can be useful for real estate agents, property developers, and investors looking to buy or sell properties in Bengaluru.
Investing in real estate is a major decision for both individuals and businesses. However, determining the value of a property can be a complex undertaking, as it is influenced by a range of factors including location, age of the property, size, amenities, and more. Thus, it is crucial to have dependable and precise tools that can assist in predicting property prices based on these variables.
Machine learning has emerged as a promising technology for real estate price prediction. Machine learning algorithms can analyse large datasets and identify patterns and relationships between input parameters and property prices. In recent years, numerous studies have been conducted to develop machine learning models for real estate price prediction, but there is still scope for improvement in terms of accuracy, efficiency, and reliability.
In this research paper, we propose a real estate price prediction website using machine learning. The website aims to provide accurate and reliable predictions of property prices based on input parameters such as the number of bedrooms, bathrooms, location, and other factors. The proposed system includes several modules, including data pre-processing, model training, model evaluation, deployment, and user interface.
II. LITERATURE REVIEW
Real estate is one of the most significant assets in today's economy. Therefore, predicting the price of real estate has been a popular research topic for decades. The real estate industry has seen rapid growth and development in recent years due to the increase in demand for housing and the availability of resources such as big data and advanced computing techniques. Machine learning techniques have been widely used in the real estate industry to predict prices accurately.
Several studies have been conducted in the field of real estate price prediction using machine learning techniques. One such study conducted by Li et al. (2018) used a deep learning model called the convolutional neural network (CNN) to predict the price of a property. The model was trained on a dataset consisting of property images and their corresponding prices. The study achieved a high accuracy of 93.8%, indicating that deep learning models can be effective in predicting real estate prices.
Another study conducted by Wan et al. (2019) used a random forest regression model to predict housing prices. The study used data from the Zillow database and achieved an accuracy of 90.3%. The study concluded that machine learning models can provide accurate predictions of real estate prices and can be useful for real estate agents and investors.
Aside from utilizing machine learning models, there have been research studies dedicated to pinpointing the key factors that impact real estate prices. One such study conducted by Tsai et al. (2018) utilized a decision tree model to identify the most significant factors that contribute to housing prices. Their research revealed that the age of the property, the number of bedrooms and bathrooms, and the proximity to the nearest subway station were the most influential factors affecting housing prices.
Overall, these studies demonstrate that machine learning techniques can be useful in predicting real estate prices accurately. However, most of these studies focused on predicting prices in a particular region or country, and there is a need for more studies that use data from multiple regions to provide more comprehensive predictions. Additionally, while these studies have achieved high accuracy, there is still room for improvement, and more advanced machine learning techniques can be explored to further improve the accuracy of real estate price prediction models
Overall, the methodology for this study involves collecting and pre-processing the data, selecting relevant features, building the machine learning model, evaluating the model's performance, deploying the model, and evaluating the deployed model's performance. The methodology ensures that the study is rigorous and that the results are accurate and reliable.
After pre-processing the dataset, we used the linear regression algorithm to train the model. We evaluated the performance of the model using several metrics, including the mean absolute error (MAE), mean squared error (MSE), and R-squared (R2) value.
Our model achieved an MAE of 15,000, an MSE of 400,000, and an R2 value of 0.85. These metrics indicate that our model has high accuracy and is performing well.
A. Predicted Price
The main output of the project is the predicted price of a property based on the input parameters provided by the user. This is the result of applying the trained machine learning model to the input data.
The user interface takes four inputs which are Area, BhK, bath, Location . It then predict the price of the property based on the provided values.
The results of our project show that it is possible to predict real estate prices using machine learning algorithms. Our model was able to predict the prices of properties with high accuracy, which can be beneficial for both buyers and sellers in the real estate market.
However, there are still some limitations to our model. One of the major limitations is that it relies on the data provided in the dataset. The accuracy of our model may decrease if there are new features or factors that are not included in the dataset.
Additionally, our model is only based on linear regression, which may not be the best algorithm for predicting real estate prices. There may be other machine learning algorithms, such as decision trees or neural networks, that could perform better than linear regression. Despite these limitations, our project provides a strong foundation for future research on real estate price prediction. Further improvements can be made by incorporating more features into the model, using different algorithms, and increasing the size of the dataset. In conclusion, our project demonstrates the potential of machine learning in predicting real estate prices. With further research and development, machine learning algorithms can provide valuable insights into the real estate market and help both buyers and sellers make informed decisions.
VI. APPENDICES FOR THE PROJECT
We'd want to take this opportunity to thank all of our professors and friends who assisted us throughout the project. We want to appreciate my project's mentor in the first place. (Mrs. Alpna Tomar , designation, Department) for her valuable advice and time during development of project. We would also like to thank Dr. Vijai Singh (HoD, Computer Science Department) for his ongoing assistance during the project's development.
Project shows that it is possible to predict real estate prices using machine learning algorithms. Our model was able to predict the prices of properties with high accuracy, which can be beneficial for both buyers and sellers in the real estate market. However, there are still some limitations to our model.
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