The purpose of this project is to develop a dashboard using Seoul real estate data to visualize real estate market trends and assist users in making real estate investment decisions. Through this, users can easily grasp an overview of the market, price and regional trends, redevelopment predictions, and trends in the Seoul real estate market, helping them understand and navigate the flow.
• Kim SuHyun
• Song Junho
• Lim Yewon
• Han DaeHee
• Web Framework : Streamlit (ver. 1.31.0) • Please refer to requirements.txt • Our Demo pages implemented using Streamlit are as follows → Demo Pages • Main functions developed & utilized in this project are as follows.
load_data(filepath): Loads CSV data from the given file path. This function uses pandas to read the data and returns it as a DataFrame.type_scatter(df, house_type): Displays the relationship between the building area and transaction price based on the selected real estate type in a scatter plot. Visualization is done using Plotly.type_mean(df, year, month, housing_type): Visualizes the average transaction prices by district based on the specified year, month, and housing type using a bar chart.house_price_trend(df, sgg_nms, house_type): Shows the fluctuation trends of transaction prices for the selected districts and real estate types in a line chart.main(): Allows the user to select the analysis type through a user interface and visualizes the necessary data for the chosen analysis. This function constructs a dashboard using Streamlit.
• Our presentation pdf link is as follows:
Korean Version PDF
English Version PDF
• This project is licensed under the MIT Licence.