Introduction
We’ll start with an overview of how machine learning models work and how they are used.
Your cousin has made millions of dollars speculating on real estate. He’s offered to become business partners with you because of your interest in data science. He’ll supply the money, and you’ll supply models that predict how much various houses are worth.
You ask your cousin how he’s predicted real estate values in the past. and he says it is just intuition. But more questioning reveals that he’s identified price patterns from houses he has seen in the past, and he uses those patterns to make predictions for new houses he is considering.
Machine learning works the same way. We’ll start with a model called the Decision Tree.
decision trees are easy to understand, and they are the basic building block for some of the best models in data science.
It divides houses into only two categories. You predict the price of a new house by finding out which category it’s in, and the prediction is the historical average price from that category.
This captures the relationship between house size and price. We use data to decide how to break the houses into two groups, and then again to determine the predicted price in each group. This step of capturing patterns from data is called fitting or training the model. The data used to fit the model is called the training data.
The details of how the model is fit (e.g. how to split up the data) is complex enough that we will save it for later. After the model has been fit, you can apply it to new data to predict prices of additional homes.
you can check more on https://www.kaggle.com/
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