Gurram Ram

Projects:

Medical Image Segmentation :-

• In my Medical Image Segmentation project, I used U-Net and CNNs to automate the segmentation of medical images, including CT scans and MRIs. The model accurately identified regions of interest like tumors and organs, aiding in diagnosis. I applied data augmentation to improve performance with limited data. Evaluation metrics like Dice Coefficient were used to assess model accuracy. This work enhanced diagnostic efficiency and provided reliable insights for medical professionals.

Analyzing Demand and Supply for Car Riding Services in Python :

As a Data Analyst in the ride-sharing industry, I have used Python to analyze demand and optimize supply using machine learning and statistical models. I forecast ride demand, improve driver allocation, and develop surge pricing strategies. My work has enhanced operational efficiency and revenue by balancing supply and demand. I also leverage data visualization to provide actionable insights for business decisions.

Crop (Rice Crop) Disease Detection :-

• Trained CNN models on vast datasets of annotated crop images, utilizing transfer learning and data augmentation techniques to enhance model performance and generalize across diverse crop types and disease manifestations, ensuring reliable detection across various agricultural contexts.