PRODUCT PROJECTS

Coming Soon

Coming Soon

- Conducted an in-depth root cause analysis of Netflix’s declining viewership, pinpointing critical factors such as platform specifics, recent updates, and recommendation system changes.
- Identified and proposed solutions to address key issues, including improving data representation, enhancing model training, and addressing covariate shift problems.
- Recommended optimization of the recommendation algorithm, focusing on improving user retention and reversing the decline in viewership.

- Develop a user-friendly platform for searching and consuming research and Analyze the product features, user personas, and areas for improvement to enhance the platform’s effectiveness and user experience.
- Conducted a comprehensive product teardown, including identifying key user personas, analyzing core features, examining the user journey, and evaluating pricing and performance metrics.
- Provided insights that led to an improved search interface, identification of potential new features, optimized user journey, and establishment of clear key metrics, contributing to increased user engagement and retention rates.

- Introduced a new "Playlist Mood Filter" feature for Spotify to address user pain points such as the inability to adapt playlists to changing moods and the inefficiency of managing diverse song collections.
- Implemented a one-click filtering system that categorizes songs by mood within existing playlists, leveraging a music mood classification model.
- This enhancement improved user retention, increased engagement, and streamlined playlist management, while also optimizing cloud resource usage by reducing unnecessary user interactions.

- Evaluated sales data to identify top-performing products and salespeople by analyzing a dataset containing detailed records of products, employees, and customer transactions.
- Used SQL queries to determine the most sold product and the salesman had the highest number of unique customers.
- This analysis enabled to focus on high-demand products and recognize top-performing employees for their contributions

- Conducted a comprehensive analysis of restaurant performance and customer behavior using a dataset that included orders, menu items, and ratings .
- Identified the most profitable restaurants and restaurants with the highest average rating.
- This analysis provided strategic insights to improve restaurant offerings and customer satisfaction.

- Analyzed flight data to identify patterns in booking trends by extracting key insights from a dataset that included flight schedules, prices, and durations.
- Utilized SQL queries to determine the month with highest number of flights.
- This analysis provided actionable insights that could help optimize airline scheduling and pricing strategies.