SAURABH RATNAPARKHI

Eager to apply my data analysis skills in a dynamic environment. Skilled in data analytics, data visualization, SQL, Python, and ML. Aspiring to contribute to data-driven decision-making processes and eager to develop further in data analytics.

Full Stack Data Science Certification at Almabetter.

The Airbnb project involves gathering and analyzing data related to Airbnb listings, hosts, and guests in order to gain insights into the trends, patterns, and impacts of short-term rentals in the city. The project aims to provide a better understanding of the Airbnb market in New York City and its implications for the housing market, tourism industry, and local communities.  

In this challenge, participants will be required to solve a total of 30 intricate coding questions that cover a wide range of algorithms, data structures, and programming paradigms. 

• The main objective of this project is to develop a machine learning model that can accurately predict the demand for bike rentals in Seoul, South Korea, based on historical data and various relevant factors such as weather conditions, time of day, and public holidays. In this project we have used regression analysis techniques to model the bike demand data.
• We have performed lots of regression algorithms like linear regression, lasso regression, decision tree, also we tried to do hyperparameter tuning and cross validation to improve the accuracy of the model. And finally we have decided to select decision tree algorithm because it gave us high accuracy around 83% and 78% on train and test data respectively.