-Designed and implemented an ELT data pipeline on Google Cloud Platform (GCP).-Utilized Google Cloud Dataflow for data ingestion.-Employed Dataform for data transformation.-Leveraged BigQuery for storing and analyzing large volumes of data.-Utilized BigQuery ML for applying machine learning techniques to the data.-Applied RFM (Recency, Frequency, Monetary) analysis and KMeans clustering for customer segmentation.Developed personalized challenges and recommendations based on customer segments.Generated reports based on customer segments and profiles using Looker Studio.Extracted necessary strategies for developing challenge algorithms from these reports.-Developed a recommendation system and product assortments using machine learning and data mining techniques.-Automated and orchestrated the data pipeline using Cloud Composer and Apache Airflow.

-Collected large volumes of accurately annotated RGB images for training YOLO models.-Captured multispectral images using UAV drones and satellites to gather detailed data on citrus plant characteristics.-Trained and finetuned YOLO models for highprecision citrus detection in RGB images, achieving quick and visually interpretable orchard monitoring.-Developed regression models using multispectral imaging data to accurately estimate citrus crop yields.-Analyzed and interpreted data from RGB and multispectral images to assess plant health and yield characteristics.-Compared the advantages and limitations of RGBbased detection and multispectral regression approaches for citrus yield estimation.-Evaluated the performance of different models in terms of accuracy, speed, and complexity.-Optimized models to improve precision in detecting small objects and enhance yield estimation accuracy.

-Developed a CRUD interface for admin.-Developed filters to enable users to quickly choose a doctor based on location and area of expertise.