In my previous article, I just gave a basic idea on Machine Learning and Artificial Intelligence on different industry perspective. In this blog, i am focusing on a real-life use case. I will explain how machine learning model works on predicting real estate house pricing.

### Real Estate House Price Prediction: Model Building

Real estate house pricing is a well-known phenomenon in current scenarios. Based on the area where the house is, decoration, construction- the price always varies. And this feature list is pretty big what actually affects the price. In the end when a customer’s budget and expectation matches with the price of the house then a deal takes place. But it is really hard to predict the price considering all these ever-changing features. Machine learning can help to tackle this problem efficiently. Today we will try to build up a real estate price predicting model. This model is a well defined programmable agent, whose main goal is to improve its performance based on the available features. In our case the performance that we want to improve is the house price and the features are: crime rate, zones, industrial area, nitric oxides concentration, average number of rooms per dwelling, age, distance to major 5 working ground, distance to highway, TAX, pupil-teacher ratio, lower status of population. If we represent all this in a table then it looks something like this,

As we can see all the feature values are converted to numerical values. Moreover, the house price that we are going to estimate/ predict is also a numerical value like the price can be $x and $x can be any decimal value. To simplify things let’s consider all the features as y. Based on the features y the price $x will vary. Simply we want to figure out for how much of change to y , x will vary. We want to find the relation between them. And in this particular case, this relation is our model that will take the features y and map the output to $x price.

Now let’s try to formulate an equation so that it can give us the prediction in layman’s terms. we know for sure that $x is not equal to y. It’s like saying the crime rate is not the house price, It’s obviously right. But x and y are related. Crime rate can influence house price and that is practical. If we want to present this mathematically we can present like this alpha*$x = y. We know crime rate can influence house pricing but exactly how much it influences is the alpha. Now there is a big problem. We have no dependency value for alpha. Actually, this is the value that predetermines with the other known values from each row entry from the table. Like if crime rate is 0.0689% and for that, the price is $200 then in simplest form the alpha will be like 200/0.0689(say this value is m). Simple right? Now we have the value for alpha. So far what we did is well known as the training phase of the simple model. This model is well known as **linear regression** and presented in the simplest term possible. Now the model is ready to predict and the equation is like this, y/m = $x. Now we know that alpha is m. For every new entry in the future we shall need to know the crime rate and for that, the house price will be crime rate aka y multiplied by alpha which value is m. Now from this model, we can get the value $x the house price that we are looking for. Simple isn’t it? Actually, this is not that simple. For each feature like crime rate this process goes on and in the end, many feature variables arise like y1, y2, y3… And at the end, all the equations are combined to one only. In between, there is a **loss function** that determines the loss. And at the very end, we get the coefficients like alpha which maps the new values in the future.

This is just the simplified article but you may want to try it yourself. Try this.