This is what I have so far for my python lab. How do I check for missing values in a dataset?

Lab 3: Data Preprocessing In this assignment, we will learn how to explore the raw data and preprocess it. The dataset we are going to exlore is an insurance data. It provides different features of each user as follows: . age: age of the user sex: gender of the user bmi: body mass index, providing an understanding of body children: number of children covered by health insurance / number of dependents smoker: smoker or not region: the user's residential area in the US, northeast, southeast, southwest, northwest. . Additionally, the medical cost of each user is also provided: charges: the medical cost Please follow Lecture 5_data_understanding and Lecture 6_data_preprocessing to complete following questions. Q1. Load data with Pandas and output the basic information of this dataset, such as the features and their data types. Which features are numerical features and which users are categorical features? ]: # your code Q2. Check whether there are missing values in this dataset. import pandas as pd import numpy as np column names ['age', 'sex', 'bmi','children', 'smoker', 'region', 'charges'] data pd.read_csv('insurance.csv') data.columns column_names print('Shape: ,data.shape) data.head()