REAL STATE PRICE PRIDICTION
Our team has tackled one of the most common problems when it comes to real estate pricing: predicting home values accurately; especially in areas like Bangalore where accurate property valuations are hard to come by. Hence, we’ve come up with an innovative solution — a machine learning model utilizing regression analysis based on Bangalore house prediction dataset — that enables us to analyze critical elements like location, dwelling size and layout so that home buyers or sellers no longer need to depend solely on instinct when making significant investments. Moreover, to make this technology more easily accessible, we have integrated it into a full-blown web application via Flask.
Following data collection and cleaning from various sources, a comprehensive dataset was created. Regression analysis, a statistical technique utilized to identify relationships between variables for predicting outcomes, trained the model.
The Flask micro framework in Python subsequently converted the model into a web application with a user-friendly interface permitting input of property details for accurate price predictions. Various features have been integrated into our web application with one aim in mind- to enhance the user experience and make it more enjoyable.
Utilizing Flask, a micro web framework in Python, we created a user-friendly interface for our trained model to serve as a web application. In just seconds, users are able to input relevant property details and receive an accurate prediction of the property’s price.
Furthermore, we have included supplementary data visualization tools so that users can observe how the predicted price depends on each variable. As a result of our utilization of groundbreaking data science and web development technologies, we have created an impressive machine learning model capable of providing users not just a functional outline but also substantial perceptions into Bangalore’s real estate market. Additionally, by ensuring our web application is scalable and adaptable to changes in the industry landscape such that we can update critical datasets or retrain algorithms promptly if necessary.
In conclusion, the technological feats we achieved present robust tools for those traversing the complex terrain of Bangalore’s housing markets. By trying it out yourself, you can truly comprehend the effectiveness of machine learning in the field of real estate.
This will allow you to gain firsthand experience and a deeper understanding of how this technology operates.
Data set :
Evaluation of the model
from sklearn import metrics
MAE = metrics.mean_absolute_error(y_predict, y_test)
MSE = metrics.mean_squared_error(y_predict, y_test)
RMSE = np.sqrt(MSE)
pd.DataFrame([MAE, MSE, RMSE], index=['MAE', 'MSE', 'RMSE'], columns=['Metrics'])
