From the course: Machine Learning and AI Foundations: Value Estimations
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Choose the best features for home value prediction
From the course: Machine Learning and AI Foundations: Value Estimations
Choose the best features for home value prediction
- [Instructor] Let's look at the dataset for a home value project and apply what we've learned about feature engineering to it. Let's open up view_data.py. Now let's right click and run the program and take a look at our data set again. Let's look through each feature in our data set and see if it needs any feature engineering work or if we can use it directly. First, we have year built. This feature will tell the algorithm the age of each house which seems like an important factor in the home price. This feature is numeric so we can use it without any feature engineering. The next several features are also numeric including the number of stories in the house, the number of bedrooms, the number of full bathrooms, half bathrooms, and the livable and total area and square feet. So we can skip over them. Next we have garage type. It looks like there are three possible values for garage type. There's none, which means there's no garage at all. There's attached, which means there's a…
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