Can someone help me with this? Let me know if you need any additional materials.

Project 2.2: Recommend a City Note that this project is a continuation from Project 2.1: Data Cleanup. You must meet specifications for Project 2.1 before you can continue on with this Project 2.2 Step 1: Linear Regression Create a linear regression model off your training set and present your model. Visualizations are highly encouraged in this section. Important: Make sure you have dealt with outliers and removed one city from your training set. You should have 10 rows of data before you begin modeling the dataset. Build a linear regression model to help you predict total sales. At the minimum, answer these questions: 1. How and why did you select the predictor variables (see supplementary text) in your model? You must show that each predictor variable has a linear relationship with your target variable with a scatterplot. 2. Explain why you believe your linear model is a good model. You must justify your reasoning using the statistical results that your regression model created. . For each variable you selected, please justify how each variable is a good fit for your model by using the p-values and Rsquared values that your model produced. 3. What is the best linear regression equation based on the available data? Each coefficient should have no more than 2 digits after the decimal (ex: 1.28) Step 2: Analysis Use your model results to provide a recommendation. At the minimum, answer this question: 1. Which city would you recommend and why did you recommend this city? Project Overview This project is a continuation of Project 2.1 regarding trying to find the best city to expand for Pawdacity's newest pet store. Scenario Pawdacity is a leading pet store chain in Wyoming with 13 stores throughout the state. This year, Pawdacity would like to expand and open a 14th store. Your manager has asked you to perform an analysis to recommend the city for Pawdacity's newest store, based on predicted yearly sales. How Do I Complete this Project? This project uses skills learned throughout the "Multivariable Linear Regression\" lesson. To complete this project: Go through the course Apply the skills learned in the course to solve the business problem given in the project details section. Use our guidelines and rubric to help build your project. When you're ready, submit it to us for review using the submission template found in the supporting materials section. Skills Required In order to complete this project, you must be able to: Choose appropriate predictor variables Analyze for correlations between predictor variables Build a linear model The Business Problem Pawdacity is a leading pet store chain in Wyoming with 13 stores throughout the state. This year, Pawdacity would like to expand and open a 14th store. Your manager has asked you to perform an analysis to recommend the city for Pawdacity's newest store, based on predicted yearly sales. In the first part, you've already cleaned up the dataset and dealt with outliers. In this project, you will take this dataset that you cleaned up and use this dataset to train a linear regression model in order to predict sales Here are the criterias given to you in choosing the right city: 1 2 3 4 5 The new store should be located in a new city. That means there should be no existing stores in the new city. The total sales for the entire competition in the new city should be less than $500,000 The new city where you want to build your new store must have a population over 4,000 people (based upon the 2014 US Census estimate). The predicted yearly sales must be over $200,000. The city chosen has the highest predicted sales from the predicted set. Steps to Success Step 1: Build a Linear Regression Model Analyze the dataset you created in Project 2.1 and look at the distribution of your data. You can create histograms to look at each of your continuous and categorical data to determine the nature of the data you're working with. Important: Make sure you have dealt with outliers and removed one city from your training set. You should have 10 rows of data before you begin modeling the dataset. Build a linear regression model to help you predict total sales. Step 2: Perform the Analysis Use your regression model to calculate predicted sales for all of the cities and use the criteria given to you to make a recommendation. Data Please refer to the Supporting Materials section in Project 2.1 to access the data you need to complete this project