In this project, I harnessed a telecom dataset to craft insightful visualizations and generate meaningful insights, leveraging the power of Python libraries such as Pandas, Seaborn, and Matplotlib to effectively analyze and communicate complex telecommunications data.
I utilized Excel to meticulously analyze store sales data, creating an exceptional dashboard with pivot tables, charts, filters, and advanced formulas. The result offers a comprehensive and visually engaging overview, facilitating informed decision-making for strategic business optimization.
I designed a Tableau dashboard featuring valuable insights derived from HR data, effectively conveying meaningful information through a variety of charts for a comprehensive understanding of human resources trends and analytics.
I employed SQL queries on Google BigQuery to extract valuable insights from the COVID-19 dataset, enabling a thorough analysis of pandemic-related data and contributing to a comprehensive understanding of the global health landscape.
I analyzed heart disease data using Python libraries (Pandas, NumPy, Seaborn and skicit-learn) and applied diverse machine learning models (SVM, KNN, NB, RF, LR) to extract valuable insights, deepening our understanding of factors influencing heart disease.
I implemented web scraping on the Wikipedia website to gather information on the list of largest companies in India. Utilizing Python libraries such as Beautiful Soup and Requests, I efficiently extracted and organized the data.