This was a unique project because it has a lot of customers coming in from various parts of United State,
like Arizona, Washington, California, Oregon etc. We discovered through thourough analaysis and visualization that they had a high churning rate.
We also noticed that, the company has a diverse customer base with varying premium and claim patterns. And the suggestions we gave helped
Through our recommendations, AutoPlus reduced churning rate by 15% and implemented strategies to enhance customer experience thereby increasing policy renewals.
This analysis was performed using Microsoft Excel
Health Quest Medical is a leading pharmaceutical company commited to delivering high quality medical solution.
The objective of this project was to conduct a thorough analysis into the company's workfore using Excel to provide
valuable insignt and also Identify key areas with shortfall for improvement. After a successful completion, we were able to advise the company about his employees
Demographics and some other descriptive statistics that are more relevant for decision makng.
Stering E-Ecommerce is a one-stop online shopping destination for a wide range of high quality products including sports and health products,
Men's & Women's fashing, computing with the mission of providing customers with the best shopping experience with the focus on quality, affordability and convenience.
After a comprehensive analysis of the company Dataset, We gained valuable insight into the store's operation
Customer Behaviour and Regional Trend. Analyzing geographical trends, we discovered that the South region generated the highest revenue and order quantity.
Covid 19 Dataset exploration in SQL Server.
Check out more of my Dashboards in Tableau.
In this project, explore the various variable that affects the gross income or revenue from the movies industry.
In this project I performed web scraping to extract real-time data on the largest companies in the United States by revenue from a Wikipedia page.
Using Python libraries such as BeautifulSoup and requests, the script fetches the webpage, parses the HTML content, and isolates the table containing the desired information.
The data is then cleaned and processed into a structured format, with headers extracted from the table columns. With the aid of pandas, I converted the table data into a DataFrame for easy manipulation.
And finally, exported the DataFrame to a CSV file, saving the data locally for further analysis or usage.