By Nedal Mahanhwe
The goal of this project is to provide charts, maps, and interactive visualizations that help customers explore the data and determine if they want to invest in rental properties in Toronto.
Files
- A notebook of rental analysis that contains all the code have been used. rental_analysis
we calculated the number of dwelling types per year and Visualized the results using bar charts and the Pandas plot function.
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To understand rental income trends over time better,we visualized the average (mean) shelter cost for owned and rented dwellings per year and visualize it as line charts
This section will be helpfull for customer who have adoubt about if they should expect an increase or decrease in the property value over time so they can determine how long to hold the rental property
we calculated and visualized the average_house_value per year as a line chart.
we used hvplot to create an interactive visualization of the average house value with a dropdown selector for the neighbourhood.
using hvplot.line () & Groupby
this section will provide investors a tool to understand the evolution of dwelling types over the years.
we visualized the number of dwelling types per year in each neighbourhood using hvplot.bar()
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In this section, we wanted to figure out which neighbourhoods are the most expensive.
we calculated the mean house value for each neighbourhood and then sorted the values using sort_value to obtain the top 10 most expensive neighbourhoods using nlargest() on average and Plotted the results as a bar chart using hvplot.bar( )
reading in neighbourhood location data and building an interactive map with the average prices per neighbourhood, Using a scatter Mapbox object from Plotly express to create the visualization
note this part required using my Mapbox API KEY
this part is using Plotly express to create a couple of plots that investors can interactively filter and explore various factors related to the house value of Toronto's neighbourhoods.
Created a sunburst chart to conduct a cost analysis of the most expensive neighbourhoods in Toronto per year.
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provide charts, maps, and interactive visualizations that help customers explore the data and determine if they want to invest in rental properties in Toronto.
