House Price Prediction Using Machine Learning And Neural Networks

Aim:

          Housing prices keep changing day in and day out and sometimes are hyped rather than being based on valuation. Predicting housing prices with real factors is the main crux of our research project. Here we aim to make our evaluations based on every basic parameter that is considered while determining the price

 Abstract:

        Real estate is the least transparent industry in our ecosystem. Housing prices keep changing day in and day out and sometimes are hyped rather than being based on valuation. Predicting housing prices with real factors is the main crux of our research project. Here we aim to make our evaluations based on every basic parameter that is considered while determining the price. We use various regression techniques in this pathway, and our results are not sole determination of one technique rather it is the weighted mean of various techniques to give most accurate results. The results proved that this approach yields minimum error and maximum accuracy than individual algorithms applied. We also propose to use real-time neighborhood details using Google maps to get exact real-world valuations

Proposed System:

         Our dataset comprises of various essential parameters and data mining has been at the root of our system. We initially cleaned up our entire dataset and also truncated the outlier values. Further, we weighed each parameter based on its importance in determining the pricing of the system and this led us to increase the value that each parameter withholds in the system. We shortlisted 3 different machine learning algorithms and tested our system with different combinations that can guarantee best possibly reliability of our results

Advantage:

         Even after that, we followed a unique approach to increase the accuracy, our survey led to a conclusion that the actual real estate value also depends on nearby local amenities such as railway station, supermarket, school, hospital, temple, parks etc. And now we propose our unique approach that can counter this need. We carried this out with manual examples and this gave us tremendous results in terms of accuracy in prediction