Practical Exam: House sales
RealAgents is a real estate company that focuses on selling houses.

RealAgents sells a variety of types of house in one metropolitan area.

Some houses sell slowly and sometimes require lowering the price in order to find a buyer.

In order to stay competitive, RealAgents would like to optimize the listing prices of the houses it is trying to sell.

They want to do this by predicting the sale price of a house given its characteristics.

If they can predict the sale price in advance, they can decrease the time to sale.

Data
The dataset contains records of previous houses sold in the area.

Column Name Criteria
house_id Nominal. Unique identifier for houses. Missing values not possible.
city Nominal. The city in which the house is located. One of ‘Silvertown’, ‘Riverford’, ‘Teasdale’ and ‘Poppleton’. Replace missing values with “Unknown”.
sale_price Discrete. The sale price of the house in whole dollars. Values can be any positive number greater than or equal to zero. Remove missing entries.
sale_date Discrete. The date of the last sale of the house. Replace missing values with 2023-01-01.
months_listed Continuous. The number of months the house was listed on the market prior to its last sale, rounded to one decimal place. Replace missing values with mean number of months listed, to one decimal place.
bedrooms Discrete. The number of bedrooms in the house. Any positive values greater than or equal to zero. Replace missing values with the mean number of bedrooms, rounded to the nearest integer.
house_type Ordinal. One of “Terraced” (two shared walls), “Semi-detached” (one shared wall), or “Detached” (no shared walls). Replace missing values with the most common house type.
area Continuous. The area of the house in square meters, rounded to one decimal place. Replace missing values with the mean, to one decimal place.
Task 1
The team at RealAgents knows that the city that a property is located in makes a difference to the sale price.

Unfortuntately they believe that this isn’t always recorded in the data.

Calculate the number of missing values of the city.

You should use the data in the file “house_sales.csv”.

Your output should be an object missing_city, that contains the number of missing values in this column.

All required data should have been created and has the required columns and Identify and replace missing values.

import pandas as pd

Load the dataset from the CSV file

data = pd.read_csv(“house_sales.csv”)

Check for missing values in the “city” column

missing_city = data[“city”].isnull().sum()

Replace missing values in the “city” column with “Unknown”

data[“city”].fillna(“Unknown”, inplace=True) Find mistake in this code?