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.