Housing Prices in India's Metropolitan cities
In the rapidly urbanizing landscape of India, metropolitan cities have emerged as the focal points of economic growth, technological advancements, and increased employment opportunities. Consequently, these cities have experienced a surge in housing demand, a phenomenon that necessitates a comprehensive study to understand the intricacies of the housing market therein. The purpose of this exploratory data analysis is to unearth the patterns, trends, and dynamics governing housing prices in India's bustling metropolitan regions, such as Mumbai, Delhi, Bangalore, Kolkata, Hyderabad, Chennai.
Coming to the dataset itself, it is taken from Ruchi Bhatia's datatset on Kaggle titled:
Housing Prices in Metropolitan Areas of India [Access Here: https://www.kaggle.com/datasets/ruchi798/housing-prices-in-metropolitan-areas-of-india]
The dataset is from the public domain and comprises data that was scraped. It includes:
- collection of prices of new and resale houses located in the metropolitan areas of India
- the amenities provided for each house
Dataset Author's note: Since for a set of houses, nothing was mentioned about certain amenities, '9' was used to mark such values, which could indicate the absence of information about the apartment but these values don't ascertain the absence of such a feature in real life.
This Analysis will be broken down into 5 parts are mandated by Jovian's Data Analytics course, Zeros to Pandas. I would recommend anyone who wishes to explore the world of programming, data and/or python to give the course a try. It is free and the structure makes it easy for someone who has little to no experience in the field to quickly get up to speed and start visualizing data (after analysing) through the help of python. You can access the course here
How to run the code
This is an executable Jupyter notebook hosted on Jovian.ml, a platform for sharing data science projects. You can run and experiment with the code in a couple of ways: using free online resources (recommended) or on your own computer.
Option 1: Running using free online resources (1-click, recommended)
The easiest way to start executing this notebook is to click the "Run" button at the top of this page, and select "Run on Binder". This will run the notebook on mybinder.org, a free online service for running Jupyter notebooks. You can also select "Run on Colab" or "Run on Kaggle".
Option 2: Running on your computer locally
Install Conda by following these instructions. Add Conda binaries to your system
PATH, so you can use the
condacommand on your terminal.
Create a Conda environment and install the required libraries by running these commands on the terminal:
conda create -n zerotopandas -y python=3.8 conda activate zerotopandas pip install jovian jupyter numpy pandas matplotlib seaborn opendatasets --upgrade
- Press the "Clone" button above to copy the command for downloading the notebook, and run it on the terminal. This will create a new directory and download the notebook. The command will look something like this:
jovian clone notebook-owner/notebook-id
- Enter the newly created directory using
cd directory-nameand start the Jupyter notebook.
You can now access Jupyter's web interface by clicking the link that shows up on the terminal or by visiting http://localhost:8888 on your browser. Click on the notebook file (it has a
.ipynb extension) to open it.
This code has been originally run on Google Colab. Download the zip file for the datasets from Kaggle following the link previously given. After doing this, upload all 6 files on the colab.
To know how to, access the following link https://saturncloud.io/blog/how-to-upload-a-folder-in-google-colab/#:~:text=Once%20your%20Google%20Drive%20is,corner%20of%20the%20file%20browser.
import os os.listdir()
['.config', 'Delhi.csv', 'Chennai.csv', 'Bangalore.csv', 'Hyderabad.csv', 'Mumbai.csv', 'Kolkata.csv', 'sample_data']