My Projects
LinkedIn Profile Analytics Dashboard
Embarked on a LinkedIn Analytics journey, I transformed raw data into actionable insights by creating a dynamic dashboard that highlights the power of consistency and visualization. It all started with a patient wait for the LinkedIn analytics data, received after 24 hours. Using Power Query, I cleaned and customized the data, setting the stage for meaningful analysis. The heart of the process was building relationships between tables and crafting challenging yet rewarding DAX formulas. Microsoft Power BI served as my canvas for bringing LinkedIn data to life through captivating visualizations, emphasizing the role of visualization in making data comprehensible. This experience underscored the importance of consistency as the glue holding the analytics together, with networking and relationship-building on LinkedIn revealed as the secret sauce for success.
FIFA Player Recommendation System
The code establishes a FIFA player recommendation system, commencing with data loading and preprocessing to ensure data quality.
Feature engineering enriches the dataset by creating new attributes based on existing player data. Data visualization techniques are employed to glean insights into player attributes and positions.
The recommendation logic, a pivotal component, sets conditions for suggesting player positions based on attributes and skills.
Ultimately, the code produces a dataset of recommended player positions, offering valuable guidance to users seeking optimal player positions in FIFA, with the potential for further refinement and improvement to enhance the system's accuracy and usability for gamers and FIFA enthusiasts.
FIFA Tableau Dashboard
The project used Tableau to analyze FIFA18 data and create visuals and a dashboard for Manchester United fans playing the career mode.
It aimed to provide insights for better team management in the game, focusing on areas like team valuations, player attributes, and player selection.
Possible enhancements for the project, if more time were available, include predictive analytics for player performance, historical data analysis, and detailed player profiles to enhance the gaming experience for fans.
Olympic Data Analytics with Azure Technologies and Power BI
The Olympic Data Analytics project, spanning, focuses on leveraging Azure Technologies and Power BI for comprehensive data analysis.
It encompasses several key components, including the implementation of Azure Data Factory to optimize data processing and reduce operational costs. Data is efficiently managed in Azure Data Lake Gen 2, ensuring data integrity and accessibility.
The project also utilizes Azure Synapse Analytics for advanced data analytics and the design of efficient data pipelines, resulting in enhanced cost-effectiveness.
Additionally, Azure Databricks is employed to expedite data processing, reducing processing times by 25%.
Beyond the technical aspects, the project provides hands-on experience in setting up Azure accounts and configuring services, enhancing proficiency with Azure Technologies.
To facilitate practical decision-making, an interactive Power BI dashboard is under development. This dashboard promises to offer comprehensive insights into Olympic data, improving data-driven decision-making for sports and analytics enthusiasts.
Car Accident Management System
Developed a Car Accident Management System, including a comprehensive relational database for analyzing nationwide car accident data.
This database aimed to facilitate insightful analytics for the Department of Transportation, with the potential to enhance accident prevention efforts.
The project also focused on query performance improvements through indexing techniques, resulting in faster data retrieval for complex analytics tasks.
Additionally, a user-friendly Python-based User Interface (UI) was created to simplify data manipulation and improve the overall user experience, enhancing database interactions.
This initiative aimed to optimize data-driven decision-making in the field of transportation safety.
Power BI Sales Analytics
Welcome to the Comprehensive Power BI Sales Analytics Project, a journey into the world of data-driven sales analysis. Starting with essential data import and preprocessing, we ensure data quality is paramount. Feature engineering enriches our sales dataset by crafting new attributes from raw data, providing deeper insights. Advanced data visualization tools, including interactive charts and maps, unveil intricate sales trends and patterns. The recommendation logic, at the project's core, sets the stage for suggesting optimal sales strategies by analyzing a variety of attributes and skills. Ultimately, our project yields a comprehensive dataset, offering valuable guidance to businesses and analysts seeking to enhance their sales strategies, with the potential for ongoing refinement and improvement, facilitating data-driven, effective decision-making.
