We analyzed common locations. In addition to the previous analysis, we can also see a few common factors that affect the price of houses. 

The price versus the living area of the house:

plt.scatter(data.price,data.sqft_living)
plt.title("Price vs Square Feet")

The output of the preceding snippet should look like the following screenshot:

The price versus the location of the area:

plt.scatter(data.price,data.long)
plt.title("Price vs Location of the area")

The output of the preceding snippet should look like the following screenshot:

The price versus the number of bedrooms:

plt.scatter(data.bedrooms,data.price)
plt.title("Bedroom and Price ")
plt.xlabel("Bedrooms")
plt.ylabel("Price")
plt.show()
sns.despine

The output of the  preceding snippet should look like the following screenshot:

The price versus the latitude:

plt.scatter(data.price,data.lat)
plt.xlabel("Price")
plt.ylabel('Latitude')
plt.title("Latitude vs Price")

The output of the preceding snippet should look like the following screenshot:

The price versus the waterfront:

plt.scatter(data.waterfront,data.price)
plt.title("Waterfront vs Price ( 0= no waterfront)")

The output of the preceding snippet should look like the following screenshot:

Many factors affect house prices in any area, including square footage, the location of the house, and the number and size of bedrooms. The preceding screenshot represents the same idea. In the next section, we are going to use a similar method to predict house prices based on other parameters.