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The exercise will use the housing sales prices versus metropolitan populations dataset. The dataset contains data of cities, states, population, and median house sales. The objective is to predict the 2019 median housing price using a single feature 2018 population estimate. This is a classic case of univariate linear regression. The dataset was collected and refined by Purdue Global researchers from https://www.census.gov/. To use this dataset, please add the following references at the end of the document: References United States Census Bureau. (2020). Population and housing unit estimates tables. Retrieved from https://www.census.gov/programs-surveys/popest/data/tables.html Linear regression (LR) is an algorithm used to find the linear relation between predictor, also known as independent or feature, variables and target (dependent) variables. The LR model attempts to fit a line that specifies the relation between the predictor(s) and target. Linear regression with one predictor variable is known as univariate linear regression. The application will produce two linear regression models to predict sale prices. The objective of this exercise is to predict the median housing prices in a certain year based on the estimated population of the year before. You are required to write a 1-page analysis answering the following two questions. The analysis does not have to follow APA style. 1. Discuss the validity of the model in terms of root mean squared error in the evaluation part. 2. Can you think of ways to change the model to perform better? For example, using a different algorithm or changing some parameters in the current one? Follow the steps below to guide you through your deliverables for Spark: 1. Download the dataset Assignment2_housing_data.csv, and save it in your assignment folder. 2. Launch PyCharm by running Cloudera QuickStart virtual machine. 3. In PyCharm, create a new project and add a Python file. 4. Import proper libraries. If the library does not exist, it must be installed.
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