I’m working on a data analytics multi-part question and need a sample draft to help me understand better.
In real estates, housing market prediction (forecasting) is crucial. There are many factors that may influence the house prices. The datasets housing.training.csv and housing.testing.csv contain 25 quantitative explanatory variables describing many aspects of residential homes in Ames, IA.
The goal of this project is to predict house prices. To this end, we will be using regression analysis.
- In Week 4 Portfolio Milestone, you’ve examined housing.training.csv dataset. Now, examine housing.testing.csv dataset and perform the same tasks as given in Week 4 Portfolio Milestone. Using R, calculate the summary statistics (minimum, maximum, mean, median, and standard deviation) and create a histogram of sale price for each dataset. Comparing with housing.training,csv dataset, describe the similarities and/or differences.
- Combine the two datasets housing.training.csv and housing.testing.csv. This can be done in R by using the function combine(). Create a histogram of sale prices for the combined dataset and compare it with the histograms from training and testing datasets. Describe the similarities and differences.
- Using only the dataset housing.training.csv, fit a linear regression model using all the explanatory variables and SalePrice as the response variable.
- What are the significant factors? How do these variables relate to the sale price? Interpret your estimated model.
- Remove all the rows with missing values (NA) from the dataset housing.testing.csv. The function complete.cases() can be used. Using only the first 20 rows from housing.testing.csv, predict the sale price. The R function predict() can perform this task. You should have 20 predicted sale prices.
- Compare the predicted sale prices to the actual sale prices from the housing.testing.csv dataset (the first 20 rows). How good is your prediction?
For each R output result, you may either type directly into a Word document or take a screenshot. If you take the screenshot, make sure that the current date is shown.
Ensure everything is clearly labeled. The report must be 10-12 pages long, including a title page and reference page (the report itself should be 8-10 pages). Cite 2-3 academic sources other than the textbook, course materials, or other information provided as part of the course materials. Follow APA format, according to CSU Global Writing Center (Links to an external site.).
