Real Estate Price Prediction
Introduction: The proposed model will use Machine Learning techniques that will be helpful for business purposes and can assist a house seller or a real estate agent for making better-predictable decisions based on housing price valuation. Housing Price Index HPI is used to e
2025-06-28 16:34:42 - Adil Khan
Real Estate Price Prediction
Project Area of Specialization Artificial IntelligenceProject SummaryIntroduction:
The proposed model will use Machine Learning techniques that will be helpful for business purposes and can assist a house seller or a real estate agent for making better-predictable decisions based on housing price valuation. Housing Price Index HPI is used to estimate the price of the house, but this indexing needs various factors to predict individual housing price. A large number of researches suggesting different traditional approaches while neglecting complex, but efficient models. The proposed model has aimed to design such a time-efficient model that uses the latest Artificial Intelligence techniques to meet the requirements.
Motivation and Need
There are several reasons behind this idea that comes under the betterment of Business Intelligence, like the Real Estate Agent needs to encourage the land exchange by promoting the vender's property, look for a property that meets the requirements of the purchaser and giving discussion to the purchaser and dealer during each progression of the cycle. In the period of data, the Real Estate Agents has not benefited from the chance of utilizing information, applications and innovation to build administrations worth and execution.
At the leading edge of technology, tech and software companies are battling to create Artificial Intelligence that will begin to not only automate parts of sales, but also allow businesses to make better decisions than people, and real estate is just one of the industries poised for disruption. So, it is worthy to have such a model that uses modern techniques to identify a particular metric to predict price and worth of a particular house, that will actually save agent’s time and provide a smarter approach to make decisions over vast databases.
Project ObjectivesObjectives
The main objective of this project is:
- To predict the prices of houses on the basis of their parameters or from the data of past years, and generate graphs to visualize the results of predictions with the help of different machine learning techniques and software libraries.
Following are the sub-objectives required to achieve the main goal:
- Exploring and collecting data.
- To test different classifiers/models to generate the best possible results or nearest prediction.
- To use different visualization approaches to find correlation between features or attributes of the housing dataset in order to clean it for more accurate results.
Methodology and Equipment/Tool
Data and tools Exploration:
A dataset will be explored and generated that contains some basic but important parameters like location, province, city, town, property type, area, rooms, baths, road-side etc. Or may have time series data of past years and will predict for a particular time target, with the help of which more accurate prices could be predicted. Tools will also be selected on the basis of programming language that will be used. Most probably, JUPITER NOTEBOOK and VISUAL STUDIO CODE will be preferred because their interfaces are quite descriptive that may help in demonstration. After the completion of model flask could be used for the tools and libraries to develop a web-server platform.
Data Cleaning and Feature Engineering:
Data cleaning and feature engineering will be required for a good model with high accuracy. All the unnecessary data and redundant features will be removed from the dataset. All those features will remain in the dataset that can be added in sensitivity list.
Modeling and Training:
An efficient model will be chosen after testing different models/classifiers like Linear Regression, Random Forest and some forecasting models etc. As predictive analysis is totally based on the accuracy of model that will be predicting the values, these given training model are being used now a days and then the most accurate one is selected after comparative analysis.
Comparing Results and Performance Analysis:
A comparison will be done on the basis of visualization and graphs after getting results from different classifiers and then the best classifier will be chosen for the prediction model.
Predicting Testing:
The model’s performance will be tested for error analysis and further betterment of the model.
Benefits of the ProjectExpected Outcome
A model that predicts houses price on the basis of parameters.
Direct Customers / Beneficiaries of the Project
- Real Estate Agents to suggest house to the buyer
- Customers who need house having particular parameters
Final deliverables contain the project report and the model code that predict prices of houses on the basis of given parameters along with visualizatiuons(graphs for the representation of results).
Report conains each and every detail related to the study alon with literature references.
Final Deliverable of the Project Software SystemCore Industry FinanceOther Industries Others Core Technology Artificial Intelligence(AI)Other Technologies OthersSustainable Development Goals Decent Work and Economic GrowthRequired Resources| Item Name | Type | No. of Units | Per Unit Cost (in Rs) | Total (in Rs) |
|---|---|---|---|---|
| Total in (Rs) | 67460 | |||
| NVIDIA GEFORCE RTX 3060 TI | Equipment | 1 | 64160 | 64160 |
| Project Report | Miscellaneous | 3 | 1100 | 3300 |