This data analysis is based on the housing data of Washington DC in 2014. Through housing characteristics, location, area condition, housing conditions, locations, and other characteristics, the regression analysis of housing prices is used to predict housing prices.
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First I will display the housing dataset of the top 5. It will give us the background and detailed information about this housing data. Then, I will use some simple aggregation to describe the Washinton house datasets in the count, mean, min, and max prices rows. The highest price would be $26,590,000, and the lowest price would be $7,8000. The std shows the standard deviation in the 25%, 50%, and 75% rows showing the corresponding percentiles
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Later, I use the visualization method and correlation coefficient aggreation to find out the correlation between those categories in order to build the predicted model to help us to learn the future trend of the Washington housing market.
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Building a house price Linear Regression forecasting model and use A/B test to test and optimized testing.