Case Study: You are asked to help a real estate agent predict prices for home in the USA. It would be great if you could somehow create a model that allows them to put in a few features of a house and return an estimate of what the house would sell for. You are given information about US homes in the dataset: assignment1_usa_housing.csv (note that this is fabricated dataset). The data contains the following columns: 'Avg. Area Income': Avg. Income of residents of the city house is located in. 'Avg. Area House Age': Avg Age of Houses in same city 'Avg. Area Number of Rooms': Avg Number of Rooms for Houses in same city 'Avg. Area Number of Bedrooms': Avg Number of Bedrooms for Houses in same city 'Area Population': Population of city house is located in Price': Price that the house sold at "Address': Address for the house Assignment Tasks: The goal is to build a predictive model using housing dataset and evaluate your models. NEED TO USE PANDAS AND SCIKIT-LEARN LIBRARIES!! 1. Using Python, Perform Exploratory Data Analysis to understand the dataset and capture your observations in the assignment report - 20 points 2. Using various python libraries develop machine learning model(s) to predict Price. You can use any ML algorithm of your choice from sklearn. - 30 points please use assignment1_usa_housing.csv as a input directory no real data is currenlty needed just need the python code