Consider that you live in a locality and you've got the following dataset that has all the transactions of different properties sold in the area along with the property details. Let's take a look at the following table:
|
Property Type |
Area (Sq feet) |
Price ($) |
|---|---|---|
|
D |
2000 |
500,000 |
|
T |
1500 |
200,000 |
|
F |
1400 |
300,000 |
|
T |
1000 |
100,000 |
|
F |
2000 |
450,000 |
|
S |
1800 |
350,000 |
|
D |
2500 |
700,000 |
|
F |
1500 |
350,000 |
Here, D means detached, S means semi detached, T means terraced, and F means flats/maisonettes.
Now, a flat is going to be available on the market of the size; 1,800 square feet. You need to predict the price at which it will be sold. This is a regression problem because you need to predict a number for the target variable. Here, the property price is the target variable and the Property Type and Area are the two features or dependent variables. A target variable in machine learning is also known as a label.
You need to come up with a model that will take the...