Bineesh S P

Accomplished Data Scientist with a passion for delivering valuable data through analytical functions and data retrieval methods. Committed to helping companies advance by helping them to develop strategic plans based on predictive modeling and findings. Bringing forth a proven track record of analyzing complex data sets and serving as a strong advisor.

Google certified Professional Data Engineer

AI-Powered Travel Recommendation System

• Developed a FastAPI-based application to integrate seamlessly with web and mobile platforms, enabling scalable and low-latency recommendation services.
• Leveraged Large Language Models (LLMs) to generate personalized travel suggestions and build customized itineraries tailored to user preferences.
• Enhanced customer experience by combining traditional recommendation logic with AI-driven personalization.

Fraud Detection Engine

• Collaborated with stakeholders to analyze transaction data, identify suspicious behavior patterns, and uncover potential loopholes in existing processes.
• Designed and implemented a rule-based fraud detection system that dynamically flags anomalies and high-risk activities, strengthening fraud prevention without relying on ML models.
• Improved detection accuracy by proactively updating rules based on evolving fraud patterns and business feedback.

Planogram Image Recognition and Classification using Siamese Network(Academic project)

 Developed an image recognition engine capable of identifying and categorizing images while operating within specific constraints,  including limited product image variability, poor image quality and a smaller dataset size used to train the model. 

Reserve Forecasting in Disability Insurance

Developed a machine learning model to predict reserves at the claimant level based on the probability of recovery.

EDDGIE

Conduct an analysis to identify the top KPIs that have the most significant impact on sales, allowing retailers to focus on improving these specific areas and ultimately driving sales growth.

Plant Based Meat Analysis

Despite the fact that PBM consumption in the Netherlands has been steadily increasing, Unilever's PBM share in the Dutch market has been declining, indicating that specific factors may be hindering the company's ability to maintain its market share in the industry.

Differential Sticking

Develop a machine learning model that can prevent drill bit sticking during the drilling process, using models that monitor various parameters and provide warnings of potential risks. 

Mud Motor Efficiency

To prevent the anonymous raise of pressure during drilling, conduct feature engineering and data preparation and develop regression  and classification models to improve mud motor efficiency. 

Torque and Drag Analysis

Analyze torque and drag during different phases and sections of the drilling process to identify potential areas for improvement.

• Passed LinkedIn Machine Learning Assessment Test
• Passed Cutshot Machine learning Advanced Assessment Test
• Kaggle: Actively participating in Kaggle competitions to gain knowledge and insights into various problems and approaches,which helps me stay updated with the latest developments and advancements in the field.

Develop a personal application utilizing Large Language Models (LLM) for the purpose of classifying phishing attempts.

https://github.com/BSP-SP/Model-Deployment

Build an object detection engine utilizing a Siamese Network.

https://github.com/BSP-SP/Classification_siamese_planorama/

Implementation of RAG Technique with OpenAI API: Demonstrated practical application in utilizing the RAG technique with the OpenAI API.

https://github.com/BSP-SP/openai