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# **SARATH CHANDRA BANDREDDI**

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## **PROFESSIONAL SUMMARY**
> **Intern-level Python Developer** focused on AI and Django. Skilled in Python, AI development, and building robust web applications. Dedicated to continuous learning and staying updated with the latest industry trends and technologies. Eager to contribute to innovative software solutions and work collaboratively in a team environment.

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## **TECH STACK**
- **Languages**: Python, Java, JavaScript, C, R
- **Scripting**: Shell Scripting
- **Markup Languages**: HTML, CSS, Jinja Coding
- **Operating Systems**: Linux, Windows
- **IDE Tools**: VS Code, RStudio, Pycharm
- **Libraries/Frameworks**: TensorFlow, Keras, LlamaIndex, Ollama, OpenCV, Sklearn, Numpy, Pandas, Django
- **Tools / Platforms**: Kaggle, Google Colabs, Git, GitHub, AWS, Figma
- **Databases**: SQL, Oracle, MySQL

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## **EDUCATION PROFILE**
| Course                     | Institution                      | CGPA | Duration   |
|----------------------------|----------------------------------|------|------------|
| B.Tech-CSE (AI & ML)       | VVIT - Vasireddy Venkatadri      | 8.59 | 2021-2025  |
| 12th Class                 | Narayana Junior College          | 9.22 | 2020-2021  |
| 10th Class                 | Narayana High School             | 9.7  | 2018-2019  |

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## **PROJECTS**

### 1. [FACE RECOGNITION VGGFace](https://www.kaggle.com/code/sarath02003/face-recognition-using-vggface2) (Individual Project)
- **Technologies**: Tensorflow, Keras, OpenCV, Django
- **Description**: Developed a face recognition application utilizing the VGGFace architecture with TensorFlow, achieving exceptional accuracy on a 29-class dataset. The system was deployed via Hugging Face.
<!--- (Developed a face recognition application utilizing the VGGFace architecture with TensorFlow, attaining exceptional accuracy on 
        a bespoke 29-class dataset. Launched the system through Hugging face space on an online server, showcasing advanced 
        capabilities in artificial neural networks and computer vision. Check the live application ("live-exegution-link:https://huggingface.co/spaces/Sarath0x8f/Celebrity_Recognition").
        Leveraged advanced facial recognition techniques using a custom dataset created to simulate real-world conditions and developed Celebrity Recognition Application. 
        Utilized data augmentation strategies, including shear, zoom, rotation, and brightness adjustments, to enhance model robustness. 
        Implemented transfer learning with the VGGFace model to accelerate training and improve accuracy to 98, incorporating custom fully connected layers and freezing certain layers for optimal performance. 
        Explored various optimizers (Adam, Adagrad, RMSprop) to optimize the model, demonstrating the critical role of optimizer selection in deep learning tasks. Integrated advanced training callbacks, such as EarlyStopping and ReduceLROnPlateau, 
        to prevent overfitting and ensure efficient training processes. Conducted thorough evaluations of the model on training and validation datasets, achieving significant improvements in loss and accuracy metrics. 
        Developed a deep understanding of data handling, model architecture customization, and training strategies, contributing to a more effective facial recognition model. 
        Looking forward to further advancements in the field of deep learning and facial recognition.
        - Mastered creating custom datasets using OpenCV, organizing training and test sets, and achieving 97.86% accuracy.
        - Trained deep learning models for image classification, leading to the publication of two papers in peer-reviewed journals 
        focusing on advancements in facial recognition technology.
        --->
- **Key Contributions**:
    - Achieved 97.86% accuracy using custom datasets.
    - Implemented advanced data augmentation and transfer learning techniques.
    - Integrated optimization strategies for enhanced model performance.
    - [Live Application](https://huggingface.co/spaces/Sarath0x8f/Celebrity_Recognition)

### 2. [SCRAM - Secure Campus Resource and Access Management](https://github.com/21bq1a4210/E2E_Project/graphs/contributors) (Minor Project)
- **Technologies**: Gemini-pro (LLM), Google API, TensorFlow, Keras, OpenCV, Django
- **Description**: Developed a Django-based system for campus resource management, including features like user management, complaint registration, face recognition attendance, and a chatbot.
<!--- (Engineered a robust, secure Django-based system for comprehensive college resource and access management, incorporating 
        advanced features such as user management, complaint registration, and a chatbot assistant. Seamlessly integrated Gemini-Pro 
        LLM with Google API and introduced face recognition technology for enhanced attendance tracking.
        Led the development of a robust and secure Django-based system designed to streamline college resource and access management as part of my End-to-End Project. The platform integrates several key functionalities such as user management, complaint registration, attendance management system, and a chatbot assistant.
        Here are my contribution to the project:
        - User Authentication & Security: Developed a comprehensive user authentication system using Django's default authentication and implemented 2-Step Verification (2SV) for password recovery, improving overall system security and reliability. Engineered a single-page application for password recovery with two-step verification, enhancing user convenience.
        - Face Recognition Attendance System: Created a one short face recognition model using FaceNet and MTCNN to manage attendance, with a unique feature allowing students to mark their attendance only once per day and within the campus premises. This innovation ensures strict attendance integrity and security.
        - Chatbot Integration: Built and integrated the AskVVIT chatbot to assist with college-related inquiries. Initially deployed with the Gemini Pro LLM and Google API, the chatbot provided an interactive platform for students and staff. Due to response time limitations (one response per minute), the model was later replaced with LLaMA 3.2:1B and also tried with LLaMA 3:latest, significantly enhancing response efficiency.
        - Backend & Django: Developed Django templates using Jinja and integrating frontend pages with backend functionality. Created models for user registration and attendence management system.

        This project not only enhanced resource management at the college but also introduced modern technologies such as face recognition and AI-driven chatbots, setting a foundation for future advancements in academic institution management systems. 
        • Devised robust user authentication and 2-FS password authentication, enhancing system security and reliability.
        • Led the project team, developing comprehensive Django templates, seamlessly integrating custom chatbot functionalities 
        using LLM and created a one short face recognition for attendance management system .) --->
- **Key Contributions**:
    - Created a face recognition attendance system.
    - Integrated chatbot using LLaMA 3 and Google API.
    - Developed secure user authentication with 2SV.

### 3. Document and Data Query AI Agent (Individual Project)
- **Technologies**: Llama3, Ollama, LlamaIndex
- **Description**: Developed an AI agent for document-based Q&A and CSV data visualization, integrating LlamaIndex for efficient data retrieval.
<!--- (Created a sophisticated AI agent for document-based Q&A and CSV data visualization, utilizing Llama3 and Ollama for cuttingedge natural language processing. Integrated Llama Indexing for efficient data retrieval and robust document parsing within an 
        intuitive user interface.
        • Combined HuggingFace embedding generation and a resilient query retry mechanism, boosting data accuracy and system 
        reliability.
        • Demonstrated deep expertise in deep learning and real-time data processing, significantly improving system performance 
        and user experience.) --->
- **Key Contributions**:
    - Created resilient query retry mechanisms for robust document parsing.

### 4. [Personal Portfolio](https://21bq1a4210.github.io/MyPortfolio-) (Individual Project)
- **Technologies**: HTML, CSS, JavaScript
- **Description**: Designed a responsive personal portfolio showcasing my projects and skills, deployed using GitHub Pages.
<!--- (Designed and developed a responsive personal portfolio("LINK:https://21bq1a4210.github.io/MyPortfolio-/") website using HTML, CSS, and JavaScript, showcasing strong UI/UX principles. Deployed the website online using GitHub Pages, making it accessible for users worldwide. The website features multiple sections, including Home, About, Services, Portfolio, and Contact, providing a comprehensive overview of my skills and projects. Implemented interactive elements like a contact form integrated with EmailJS for seamless communication and added an AI chatbot, Tidio's Lyro, to enhance user engagement and provide additional information about me. Crafted unique visuals using AI-generated images to personalize the portfolio and demonstrate creativity. This project ignited my interest in web development, transforming initial reluctance into enthusiasm for building dynamic and interactive web experiences.) --->
- **Key Contributions**:
    - Integrated AI chatbot for enhanced engagement.
    - Implemented a contact form with EmailJS.

### 5. Object Segmentation (Individual Project)
- **Technologies**: YOLOv8, OpenCV, RoboFlow
- **Description**: Built an object segmentation model and implemented functionalities for image processing.
<!--- (Built a versatile Python script using OpenCV and scikit image for image processing tasks.like object segmentation, using YoloV8, OpenCV, sklearn. Implemented functionalities like grayscale contrast nhancement, image similarity measurement (SSIM), and object segmentation YOLOv8 Demonstrated the script s capabilities through image processing, evaluation, and visualization. Planned to explore integration of advanced deep learning models for further functionalities.
        Here is the ("notebook link: Object_Segmentation_&_ComparisonObject_Segmentation_&_Comparison.ipynb:https://colab.research.google.com/drive/1cpNEx_u70I26Ex0alZZxoFa47p6fpoDz?usp=sharing")) --->
- **Key Contributions**:
    - Developed scripts for object segmentation and image enhancement.
    - [Notebook Link](https://colab.research.google.com/drive/1cpNEx_u70I26Ex0alZZxoFa47p6fpoDz?usp=sharing)

---

## **CERTIFICATIONS**
- **Python Programming**: Kaggle, SoloLearn, HackerRank, GUVI
- **Machine Learning**: Coursera, Kaggle, Microsoft, IBM
- **Deep Learning**: Kaggle, NPTEL, OpenCV University, IBM
- **Google Skill Boost**: Introduction to Gen AI LP, Gemini for Google Cloud LP, Gen AI for Developers
- **Django**: Microsoft
- **AWS**: AWS Academy Cloud Foundations, AWS Academy Cloud Architecting
- **NPTEL (exam)**: Java (ELITE), Data Science (ELITE + SILVER)

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## **ACHIEVEMENTS**
- Runner-up in Python Intramural technical fest at RVR&JC College [LINK]

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## **PASSIONS**
- **Deep Learning Engineer**: Transforming the financial services industry.
- **Python Developer**: Building efficient, scalable products with Python.

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## **HOBBIES**
- Nurturing plants
- Exploring new knowledge
- Sketching with pencil
- Simplifying complexities through coding

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## **Contact Me or Hire Me**
- [LinkedIn](https://www.linkedin.com/in/sarath-chandra-bandreddi-07393b1aa/)
- [MyPortfolio](https://21bq1a4210.github.io/MyPortfolio-/)
- [Instagram](https://www.instagram.com/sarath_0x8f)
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