In this case study, we will use the Ames Housing dataset to explore regression techniques and predict the sale price of houses.
The Ames Housing dataset contains the sale prices of properties in Ames, Iowa along with 80 other features. Each property has an Id associated with it.
Here are the dimensions of the training and testing sets respectively:
 "Dimensions of the training set"
 1460 81
 "Dimensions of the testing set"
 1459 81
Now, let’s combine training and testing into a single dataset and take a look at the count of missing values:
In this case study, we will explore the diamonds dataset, then build linear and non-linear regression models to predict the price of diamonds.
The diamonds dataset contains the prices in 2008 USD terms, and other attributes of almost 54,000 diamonds.
price in 2008 USD
weight of a diamond (1 carat = 0.2 gms)
quality of the cut (Fair, Good, Very Good, Premium, Ideal)
diamond color from D (best) to J (worst)
a measurement of how clear the diamond is (I1 (worst), SI2, SI1, VS2, VS1, VVS2, VVS1, IF (best))
length in mm
width in mm
depth in mm
total depth percentage = z/mean(x, y)
width of the top of diamond relative to widest point
A preliminary visual summary of the whole dataset shows all the features and their types.