neviya,Neviya Prakash,github

Hi there 👋
Thank you for visiting my GitHub page.

About me:

I am a Data Analytics professional with a Master’s Degree in Information Management from The University of Maryland specializing in Data Analytics & Business Intelligence.
I have worked over 2 years as a data and Digital Marketing Analyst and am passionate about data and developing insightful stories through dashboards & reports.
I am eager to learn new concepts and continue to upskill myself through online projects and courses in the field of Data Analytics!

Connect with me on LinkedIn: https://www.linkedin.com/in/neviyaprakash/

Neviya Prakash's Projects

analyze-international-debt-statistics

In this project, we are going to analyze international debt data collected by The World Bank. The dataset contains information about the amount of debt (in USD) owed by developing countries across several categories.

indeed-web-scraping

The code can enable you to web scrape job postings from Indeed.com. It has been implemented in 2 ways, one using the Selenium web driver and the other using the package Beautiful Soup.

investigating-netflix-movies

We try to discover if Netflix’s movies are getting shorter over time and which guest stars appear in the most popular episode of "The Office", using pandas and matplotlib. This will allow us to perform critical tasks such as manipulating raw data and drawing conclusions from plots created from the data.

mindbenders

Project Proposal INFM 600

neviya

Config files for my GitHub profile.

perception-of-the-covid-19-vaccine-through-twitter-sentiment-analysis

The main purpose of this project is to measure the public response to the COVID-19 vaccine with sentiment analysis. Though the vaccine has provided a new hope it has also resulted in several anti-vaccine movements In order to analyze the public opinions and emotions related to the vaccine during the pandemic, I will be utilizing recent Twitter streaming data.

predict-credit-card-approvals

Commercial banks receive a lot of applications for credit cards. Many of them get rejected for many reasons, like high loan balances, low income levels, or too many inquiries on an individual's credit report, for example. Manually analyzing these applications is mundane, error-prone, and time-consuming (and time is money!). Luckily, this task can be automated with the power of machine learning and pretty much every commercial bank does so nowadays. In this project, you will build an automatic credit card approval predictor using machine learning techniques, just like the real banks do. The dataset used in this project is the Credit Card Approval dataset from the UCI Machine Learning Repository.