Predicting House Prices with Regression.

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DESCRIPTION:

Housing price trends are not only the concern of buyers and sellers, but it also indicates the current economic situation. Many factors have an impact on house prices, such as numbers of bedrooms and bathrooms. Manual house prediction becomes difficult, hence there are many systems developed for house price prediction.

My engineering project gives you a project that aims to make a model that can give us a good house pricing prediction based on other variables. We are going to use Linear Regression for this dataset and hence it gives good accuracy. This house price prediction project has two modules namely, Admin and User. Admin can add location and view the location. Admin has the authority to add density based on per unit area.

Features of Predicting House Prices with Regression.

We are going to break everything into logical steps that allow us to ensure the cleanest, most realistic data for our model to make accurate predictions.

Load Data and Packages
Analyzing the Test Variable (Sale Price)
Multivariable Analysis
Impute Missing Data and Clean Data
Feature Transformation/Engineering
Modeling and Predictions

Advantages:

Saves time.
Easy to access the system from anywhere and anytime.

Disadvantages:

It requires an active internet connection.

Conclusion:

The above correlation heatmap shows our predicted sale prices for some of the models used. We can see that Elastic Net, Lasso, and Ridge were very similar, whilst Ensembling and Stacking were also very similar. The one standalone model with distinctly different results was the XGBoost one..

This technology used in this software:

(i) Python
(ii) Java
(iii) .net
(iv) php
(v) my sql

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