In my last semester I along with my team had the opportunity to work on a data mining project, we decided to make house price predictor.
Buying property can be very confusing, as there are so many factors to consider while doing so, things like in which region, how many rooms , type of property and so many other things, and because of these confusions often property buyers are misguided by brokers to pay more money, as it’s very difficult for the property buyer to estimate the price.
Here, machine learning techniques can be used to get an estimate of the property. This paper presents different machine learning techniques in order to predict the price of the house. Three different machine learning models including Linear Regression, Random Forest Algorithm, Support Vector Machine.
We will be using a dataset from kaggle, consisting details of real estate in Melbourne:
https://www.kaggle.com/anthonypino/melbourne-housing-market/version/27