diff --git a/Real Estate visualizer/Readme.md b/Real Estate visualizer/Readme.md new file mode 100644 index 00000000..ccdb8092 --- /dev/null +++ b/Real Estate visualizer/Readme.md @@ -0,0 +1,53 @@ +# Boston Housing Market Analysis Dashboard + +This is an interactive web dashboard built with [Dash](https://dash.plotly.com/), which visualizes data from the Boston Housing Market dataset. The application provides several graphs to analyze the housing data, including the distribution of home values, the average home value based on the number of rooms, and a scatter plot showing the relationship between home value and tax rate. + +## Features + +- **Select Highway Access Index (RAD)**: Allows users to filter data based on the RAD value, which indicates the accessibility of radial highways. +- **Graphs**: + - **Median Home Value Distribution**: A histogram showing the distribution of the median home values (MEDV). + - **Average Home Value by Room Count**: A bar chart displaying the average home value for each room count. + - **Home Value vs. Tax Rate**: A scatter plot showing the relationship between home value (MEDV) and the tax rate (TAX), with color coding based on the RAD index. + +## Prerequisites + +Make sure you have the following installed: +- Python 3.x +- Dash (`dash`, `dash_core_components`, `dash_html_components`) +- Plotly (`plotly`) +- Pandas (`pandas`) + +You can install these dependencies using pip: + +```bash +pip install dash plotly pandas +Dataset +This dashboard uses the Boston Housing dataset (data.csv). The dataset should contain the following columns: + +RM: Average number of rooms per dwelling. +RAD: Index of accessibility to radial highways. +MEDV: Median value of owner-occupied homes in $1000s. +TAX: Property tax rate. +Ensure that the data.csv file is located in the correct path for the application to load it correctly. + +How to Run +Clone this repository or download the project files to your local machine. +Make sure you have installed the necessary dependencies (dash, plotly, and pandas). +Update the dataset path in the visulaizer.py file to match the location of your data.csv. +Run the Dash application: +bash +Copy code +python visulaizer.py +Open your web browser and go to the address shown in the terminal (usually http://127.0.0.1:8050/). +App Layout +The app is composed of: + +A header (H1) with the title: "Boston Housing Market Analysis Dashboard". +A dropdown for selecting the RAD value to filter the data. +Three interactive graphs: +Median Home Value Distribution: A histogram of home values. +Average Home Value by Room Count: A bar chart showing average home values grouped by the number of rooms. +Home Value vs. Tax Rate: A scatter plot of home value vs. tax rate. +License +This project is licensed under the MIT License - see the LICENSE file for details. \ No newline at end of file diff --git a/Real Estate visualizer/data.csv b/Real Estate visualizer/data.csv new file mode 100644 index 00000000..e49db77a --- /dev/null +++ b/Real Estate visualizer/data.csv @@ -0,0 +1,512 @@ +CRIM,ZN,INDUS,CHAS,NOX,RM,AGE,DIS,RAD,TAX,PTRATIO,B,LSTAT,MEDV +0.00632,18,2.31,0,0.538,6.575,65.2,4.09,1,296,15.3,396.9,4.98,24 +0.02731,0,7.07,0,0.469,6.421,78.9,4.9671,2,242,17.8,396.9,9.14,21.6 +0.02729,0,7.07,0,0.469,7.185,61.1,4.9671,2,242,17.8,392.83,4.03,34.7 +0.03237,0,2.18,0,0.458,6.998,45.8,6.0622,3,222,18.7,394.63,2.94,33.4 +0.06905,0,2.18,0,0.458,7.147,54.2,6.0622,3,222,18.7,396.9,5.33,36.2 +0.02985,0,2.18,0,0.458,6.43,58.7,6.0622,3,222,18.7,394.12,5.21,28.7 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visualizer/visulaizer.py b/Real Estate visualizer/visulaizer.py new file mode 100644 index 00000000..55dc363f --- /dev/null +++ b/Real Estate visualizer/visulaizer.py @@ -0,0 +1,86 @@ +import dash +from dash import dcc, html +from dash.dependencies import Input, Output +import plotly.express as px +import pandas as pd + +# Load dataset +data = pd.read_csv( + r"C:\Users\Ananya\OneDrive\Documents\GitHub\master-web-development\Real Estate visualizer\data.csv" +) # Adjust path to your file location + +# Initialize the Dash app +app = dash.Dash(__name__) + +# Define the app layout +app.layout = html.Div( + children=[ + # Main title + html.H1( + "Boston Housing Market Analysis Dashboard", style={"text-align": "center"} + ), + # Highway Access Index (RAD) dropdown + html.Div( + children=[ + html.Label("Select Highway Access Index (RAD):"), + dcc.Dropdown( + id="rad_dropdown", + options=[ + {"label": str(rad), "value": str(rad)} + for rad in sorted(data["RAD"].unique()) + ], + multi=True, + placeholder="Select highway access index", + ), + ], + style={"width": "30%", "display": "inline-block"}, + ), + # Graphs for visualization + dcc.Graph(id="medv_distribution_graph"), + dcc.Graph(id="avg_value_room_graph"), + dcc.Graph(id="value_tax_scatter"), + ] +) + + +# Callback to update graphs based on RAD selection +@app.callback( + [ + Output("medv_distribution_graph", "figure"), + Output("avg_value_room_graph", "figure"), + Output("value_tax_scatter", "figure"), + ], + [Input("rad_dropdown", "value")], +) +def update_graphs(selected_rad): + # Filter data based on highway access index selection + filtered_data = data + if selected_rad: + filtered_data = filtered_data[ + filtered_data["RAD"].astype(str).isin(selected_rad) + ] + + # Plot 1: Distribution of Median Home Value (MEDV) + medv_distribution_fig = px.histogram( + filtered_data, x="MEDV", nbins=50, title="Median Home Value Distribution" + ) + + # Plot 2: Average Home Value by Room Count + avg_value_room_fig = px.bar( + filtered_data.groupby("RM")["MEDV"].mean().reset_index(), + x="RM", + y="MEDV", + title="Average Home Value by Room Count", + ) + + # Plot 3: Scatter Plot of Home Value vs Tax Rate + value_tax_scatter_fig = px.scatter( + filtered_data, x="TAX", y="MEDV", color="RAD", title="Home Value vs. Tax Rate" + ) + + return medv_distribution_fig, avg_value_room_fig, value_tax_scatter_fig + + +# Run the app +if __name__ == "__main__": + app.run_server(debug=True)