You signed in with another tab or window. Reload to refresh your session.You signed out in another tab or window. Reload to refresh your session.You switched accounts on another tab or window. Reload to refresh your session.Dismiss alert
In this project conducted in RStudio using the R programming language, we delved into Spotify data to unravel patterns and insights contributing to music's popularity. Employing statistical techniques and leveraging the power of ggplot2 for visualizations, our analysis aimed to provide a comprehensive understanding of what drives music preferences.
EDA of Sephora sales data along with customer reviews using SQL and Excel as well as creating a Power point presentation for visuals, insights and recommendations
This repository showcases my project completed during the Accenture North America Data Analytics and Visualization Virtual Internship. This project offered an immersive experience into the exciting world of data analytics and visualization with Accenture
The company hired you because they want to know what would be the 5 properties they should invest in and why, and which 5 you would not recommend investing in at all.
EDA for Chicago PD crimes form 2001 to 2023 (almost 8 million cases) using python, pandas and streamline dashboardes. Focusing on recurring trends and insights.
Accenture empowers you to be your best—personally and professionally. Every day around the world, we work with exceptional people, the latest and greatest tech and leading companies across industries. Deep dive into the evolving world of data from an analytics and visualization perspective.
EDA for real estate data in the city of Connecticut from 2001 to 2020 using python, pandas and streamlit dashboards. Focusing on the shift in the housing market before and after 2008 financial crisis and the real estate boom in large neighboring cities.
You have been hired by Walmart to survey the revenue of their stores in the USA and point out which store would be best to expand its size. It is necessary to analyze the weekly sales of each store, calculate some important information that will be asked, and at the end of it all, indicate which store should be invested in.