M SHIVA RAMA KRISHNA REDDY

Results-driven Software Developer and AI specialist skilled in Python, JavaScript, and AI/ML technologies like Machine Learning, Deep Learning, NLP, Generative AI, and RAG. Proficient in React, HTML, CSS, and backend tools like RESTful APIs and MySQL. Delivered impactful projects, including a Tax Form Auto-Filling system and Resume Parser, boosting efficiency. Experienced in scalable AI solutions at Jigya Software Services. Collaborative, innovative, and adept at problem-solving in dynamic environments. Detail-oriented, organized, and meticulous employee. Works at fast pace to meet tight deadlines. Enthusiastic team player ready to contribute to company success.

Diabetes Prediction (Machine Learning)

• Developed a Machine Learning model to predict diabetes risk using patient health data, achieving an accuracy of 87% and enabling early intervention for at-risk individuals.
• Performed extensive data preprocessing, including handling missing values, feature scaling, and correlation analysis to improve model performance.
• Evaluated multiple algorithms (e.g., Logistic Regression, Random Forest, and SVM) using cross-validation, selecting the best-performing model based on precision and recall metrics.
• Utilized Python and scikit-learn for data preprocessing, feature selection, and model training, with Pandas and NumPy for efficient data manipulation and analysis.
• Created visualizations using Matplotlib and Seaborn to interpret feature importance and model predictions, aiding in result validation.
• Technologies Used: Python, scikit-learn, Pandas, NumPy, Matplotlib, Seaborn.

Brain Tumor Prediction (Deep Learning)

• Built a Deep Learning model using convolutional neural networks (CNNs) to detect brain tumors from MRI scans, achieving a detection accuracy of 90% and supporting medical diagnostics.
• Designed and implemented a custom CNN architecture with multiple layers (convolution, pooling, and fully connected) to extract spatial features from MRI images.
• Conducted image preprocessing steps such as normalization, resizing, and augmentation (e.g., rotation, flipping) to enhance model robustness and prevent overfitting.
• Trained the model using TensorFlow and Keras, optimizing hyperparameters like learning rate and batch size, and employed early stopping to improve training efficiency.
• Validated model performance with a separate test dataset, achieving high sensitivity and specificity, and visualized results using confusion matrices and heatmaps.
• Technologies Used: Python, TensorFlow, Keras, OpenCV, Matplotlib.

E-Wallet Application

• Developed a comprehensive digital wallet and payment platform.
• Designed responsive UI using HTML, CSS, JavaScript, and built RESTful APIs with Java and Spring Boot.
• Integrated secure payment gateways and implemented robust data storage with MySQL.

Visitor Management System in Gated Communities

• Developed a Visitor Management System for gated communities, enhancing security measures by 80% and reducing visitor access processing time.
• Implemented user authentication, authorization, and reporting functionalities, reducing administrative workload by 90%