jadhavaniket Goto Github PK

2016-new-coder-survey

airbnb-host-beneficiary-system

A lot of research and analysis has been conducted for the guests but not much information is available for the hosts to make decisions that would help them increase their profits. Severalfactors are at play that determine the profitability of a particular property such as the number of beds, the location, the proximity to transportation services and tourist spots etc. Through this project, we aim to helpan investor identify places in different locations that will get the highest ratings and generate profit by using success rate as a parameter. Success rate will be calculated on the basis of revenue, investment, ratings and location. The model shall help one predict the right pricing of a listing with respect to the location, services offered, guest capacity, space rented out and listing prices at similar locations. Setting the right price is prudent as it helps in increasing the number of bookings and occupancy of the space. Airbnb, over time, has become very competitive and it is important for owners to be aware of the correct market rate to minimize the chances of loss.

aniketnitinjadhav

community-empowerment-visualization

With a vision to connect people who want to develop Haiti together, this visualization project aims at bringing together organizations who want to create sustainable projects in Haiti and generate fundraising to empower various communities in Haiti

data-challenge-gdelt

Predict the effect of Political Instability on Economic Growth of a Nation

infm600

Week1 for INFM600

infm600_brilliant

Repository for Brilliant

info-seeking

Three public data sets with a document describing them.

interviewattendance_pyspark

Use of MlLib from PySpark to solve the problem of interview attendance for companies

smartcitiesresearch