Bombay_Rent_House_Analysis

Data set is taken from Kaggle: https://www.kaggle.com/jedipro/flats-for-rent-in-mumbai

Credits

1. Plotly Diagrams: https://www.kaggle.com/shreekant009/mumbai-house-price-with-plotly
2. Scatter Plot: https://www.youtube.com/watch?v=cbqZa_1vzcg&list=PLeo1K3hjS3uu7clOTtwsp94PcHbzqpAdg&index=5&t=0s

Result:

From the Bombay Rent House Analysis, There was more number of Agent who act as a middle person for renting house. The year 2020 has more number of house for rent as compared to year 2019.

Download

# Clone or download the repository
$ git clone https://github.com/Mega-Barrel/Bombay_Rent_House_Analysis.git

# Install matplotlib, plotly, seaborn, numpy, pandas
$ pip install numpy
$ pip install seaborn
$ pip install matplotlib
$ pip install seaborn
$ pip install plotly

Removing all the null values

# Displaying total null values as per column
df.isnull().sum()

# Removing all the null values
df.dropna()

Detecting the Outliers

Q1 = df2.price.quantile(0.25)
Q3 = df2.price.quantile(0.75)

IQR = Q3 - Q1

lower_limit = Q1 - 1.5*(IQR)
upper_limit = Q3 + 1.5*(IQR)

Histogram for Area Column

The Area Column follows the Bell curve or Binomial Distribution with area = 6000 highest.

User Count

In the above diagram, we can see that Agent acts as a middle-person and highest number of Flats for Rent; And builder has very very less amount of flats for Rent.

Year Count

The renter population has become more than the previous year.

Price Distribution

PairPlot

Barchart for Houses

Bar graph for Locality

In the above diagram we can see that Powai has High number of Flats for Rent with higher rent price.

Vasai, Virar West, Kalyan and many more has lower number of Flasts with less Rent