Real Estate and Venues Analysis of Delhi

By:

Abhishek Sharma

Introduction

• Delhi is the Capital of India having a population of around 30 million people.

• It is necessary for anyone to do the analysis of Delhi in terms of economic wellness and locality status.

Facts About Delhi

• Crowded.
• Culturally diverse.
• A business-friendly environment with tons of business opportunities.
• Highly competitive market.

Data Description

• I found Housing Prices for different areas in Delhi on the makaan.com website.
• I used Geocoding API of MapMyIndia to get the latitude and longitude of areas.
• I used Foursquare’s Places API to get venues near a particular location.

Methodology

Through web scraping, I collected housing prices data from makaan.com

Many null values and irrelevant stuff were present in the data.

So, after cleaning the dataset I converted it into this form.

Through Geocoding API of mapmyindia I fetched latitudes, longitudes, PIN codes and district for each area in my data set

I plotted fetched coordinates on the map of Delhi using folium geolocation and maps visualization library.

Using Foursquare API, for every location I fetched a maximum of 100 venues like ATMs, Restaurants, etc. that lies within a 1000 m radius from the longitude and latitude of center of the location.

I used the Process of One Hot Encoding to convert Categorical values into Binary values and then I calculated their frequencies.

Then, I found the Top 5 most common venues occurring in an area.

Now, It is time to cluster this data using the K-Means Clustering Algorithm. The results obtained from clustering is as follows:

Then, I normalized the housing prices data frame using Min-Max to create a rating scale from (0-100) for housing prices.

After this, I created bins and generated a histogram.

Results

The Results of this clustering are as follows:

• Cluster-0 (tomato) has a higher no. of ATMs, Indian Restaurants, Markets, Shops. These are the locations where people go shopping or hang out.

• Cluster-1 (mediumpurple) has a higher no. of Hotels, Asian Restaurants, Gyms and Neighborhoods. So these are residential areas.

• Cluster-2 (mediumtorquoise) has a higher no. of Gardens and parks. These areas are greener than other areas.

• Cluster-3 (aquamarine) has airports and also some high-end facility providers. These areas are downtown and posh areas.

• Cluster-4 (burlywood) has a higher no. of metro stations, pizza places and other businesses. These areas are well-connected areas by metros.

For economic rating, I assigned categories to the areas based on their price ratings.

Categories:

• Rating(0-20)=> Category I

• Rating(21-40)=> Category II

• Rating(41-60)=> Category III

• Rating(61-80)=> Category IV

• Rating(81-100)=> Category V

Higher the prices of houses higher are the chance that people with better economic conditions live in that area.

People living in the area of Category V has the highest economic strength and people living in the area of Category I have lowest economic strength.

Discussion

Every day people are turning to big cities to start a business or find jobs and all these people require a place in Delhi. For this reason, people can achieve better outcomes through their access to the platforms where such information is provided.

Not only for investors but also city managers can manage the city more regularly by using similar data analysis types or platforms.

Conclusion

The maps generated through this project are useful for the vast majority of people. Many countries and companies generate these kinds of maps, which help them in deeply analyzing the present conditions of the state. There are so many companies that are democratizing data like this, which gives insights for businesses. Geographical and Economic insights are a boon for everyone, whether it is a big company or an individual.