Projects

Airbnb: Host Analysis and Price Prediction

Analyzed 10k+ rental listings based on 96 variables by performing data cleaning, EDA, and feature scaling using Python
Predicted price of listings by fitting regression models such as Linear, Ridge, & ensemble methods, obtaining root RSME of $14

Performed Exploratory Data Analysis on Market capitalization of Bitcoin and other cryptocurrencies using Python and Seaborn
Explored how big is Bitcoin compared with the rest of the cryptocurrencies and volatility in cryptocurrencies
Developed graphs, charts, and maps using visualization libraries like Matplotlib, PyPlot, and Seaborn* Performed Exploratory Data Analysis on Market capitalization of Bitcoin and other cryptocurrencies using Python and Seaborn
Explored how big is Bitcoin compared with the rest of the cryptocurrencies and volatility in cryptocurrencies
Developed graphs, charts, and maps using visualization libraries like Matplotlib, PyPlot, and Seaborn

Performed predictive analysis on 45K+ observations to identify the customer that is most likely to subscribe to a term deposit
Forecasted customer subscription to term deposit by building classification models like decision tree, random forest, etc. in Python with an accuracy ranging from 82-84%

Created the data connection, loaded the data and joined tables
Identified dimension and measures
Used different aggregation levels to get appropriate insights
Developed dashboard to identify countries having highest prices and the price fluctuation
Created a story to showcase the variation of food prices