Nicole Samrao’s Portfolio

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I’m a recent college graduate with a wide range of internship experience in the tech industry.

After graduating with a degree in Economics, I completed an internship at the networking company Infoblox where I worked extensively in Excel with big data to compete some really fascinating pricing projects. This experience spurred my interest in data analytics and for the last few months, I’ve been pursuing a Data Science certification in Python.

Technical Experience:

Projects:

An Exploratory Analysis of the Airbnb Listing Market in U.S. Citites

Airbnb is a pioneer in the peer to peer market and a leader in the online hospitality market. If we take a look into listing data in the U.S., what interesting trends can we find? What marketing and strategy suggestions can we make?

An investigation conducted using Pandas of Airbnb listing data to gain analytical insights to drive marketing strategy. Additionally, a linear model was created that utilizes Zillow’s residential real estate data to predict price of Airbnb listings given the housing climate of a US city.

The packages used in this project are: matplotlib, pandas, numpy, statsmodels, scikit and seaborn

An Analysis of Boston Housing Prices

An exploratory analysis of housing data in Boston neighborhoods using features such as room size, crime rate, demographics, and air quality. Also includes a statsmodels linear regression that predicts home price using these features.

The packages used in this project are: matplotlib, pandas, numpy, scipy and seaborn

Examining Racial Discrimination in the US Job Market

Racial discrimination continues to be pervasive in cultures throughout the world. This is a statistical examination using Pandas of the level of racial discrimination in the United States labor market by randomly assigning identical résumés to black-sounding or white-sounding names and observing the impact on requests for interviews from employers.

The packages used in this project are: matplotlib, pandas, numpy and math

Human Resources Analyics: Why are our best employees leaving?

Using a dataset from Kaggle containing employee satisfaction levels, promotion information, hours worked, department information, employment status etc… to analyze what kinds of employees are leaving the company and why.

The packages used in this project are: matplotlib, pandas and numpy