Google – Smart Search Data Insights
Scenario: Built a Python based data science solution to analyze massive search query logs, detect trends, user intent patterns, and improve ranking accuracy.
Live Work:
- Process large scale search datasets
- Build trend prediction models
- Create interactive analytics dashboard
Outcome: Improved search relevance accuracy rate
Meta – Social Behavior Analytics
Scenario: Developed a Python analytics model to study user engagement, content interactions, sentiment trends, and optimize feed ranking strategy effectively.
Live Work:
- Analyze user interaction datasets
- Perform sentiment classification
- Train engagement prediction model
Outcome: Boosted user engagement significantly
Netflix – Streaming Pattern Predictor
Scenario: Designed a Python machine learning pipeline to evaluate viewing patterns, predict churn probability, and enhance personalized content suggestions.
Live Work:
- Clean and transform watch history
- Build churn prediction model
- Deploy recommendation algorithm
Outcome: Reduced subscriber churn rate
Spotify – Music Taste Intelligence
Scenario: Implemented Python based data science workflow to analyze listening behavior, cluster user preferences, and refine song recommendation engine.
Live Work:
- Perform audio feature analysis
- Cluster users by taste patterns
- Optimize recommendation accuracy
Outcome: Enhanced music recommendation quality