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Colourful Terraced Houses

Seattle, WA Housing Prices

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Analyzing data for the sales of houses in Seattle, WA. The data had variables that could aid in the increase of the sales price of the house. I used Python to determine what variables had a strong correlation with the price of the house.

Data​

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Analytical Techniques

  • Big data

  •  Data ethics

  •  Data mining

  •  Predictive analysis

  • Time series analysis and forecasting

  •  Using GitHub

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Performed an exploratory analysis through visualizations including scatterplots, correlation heatmaps, pair plots, and categorical plots in order to identify different relationships and develop a hypothesis. Further investigation was administered utilizing regression analysis , cluster analysis , and time - series analysis.

Utilized Tableau to continue to draw in sights using data visualization techniques. Manipulated data to create captivating composition and comparison charts, and statistical visualizations. A color-coded map was used to see what houses cost in different zip codes. In doing so we found out the closer to city center the higher the price. Bar charts were used to see what variable has the strongest correlation with the price of the home. Finally, to see if there is a seasonality to what time of month year houses sell at higher prices. Insights and visualizations were organized, highlighted, and published on Tableau.

Results

  • The variable that had the strongest correlation with the sale price of the home was square foot living space. Other variables were weak correlations.

  • The closer to city center the more the price increase for the homes.

Recommendations

  • Additional data that could be collected is the age groups that are buy the homes in the different zip codes. This way real estate agents could market the homes to the group that is most likely going to buy the homes.

  • If the city is wanting to more information, you could take a survey with the people who live closest to city center and see why they rather live there than farther from city center.

  • Depending on survey for reason you could add those items around the other zip codes. Example would be restaurants, shops, or things to do.

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