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Shital Patil

I am a final-year B.Tech Computer Science (AIML) student with strong skills in Python, SQL, and data analytics, and a solid understanding of software development best practices and SDLC. I have hands-on experience working on academic and internship projects involving data-driven applications, dashboards, APIs, and machine learning models. I am eager to apply my analytical thinking, programming skills, and problem-solving abilities in a Data Analyst or Technology Analyst role to support real-world, data-informed decision-making.

Projects

Biometric Attendance System

Technologies: Android Studio, Java, MySQL, Firebase


Developed a mobile-based biometric attendance system using fingerprint authentication to ensure secure and accurate user verification. Integrated real-time attendance synchronization with a centralized database and designed a user-friendly interface. The system improved reliability and transparency in attendance tracking for academic and organizational use cases.

Baldness Detector

Technologies: Python, TensorFlow, Keras, OpenCV, CNN (VGG16)


Implemented a deep learning–based image classification system to identify bald vs. non-bald individuals using convolutional neural networks. Trained and evaluated the model using VGG16 architecture, achieving reliable performance across accuracy, precision, and recall metrics. Focused on improving robustness through image preprocessing and model tuning.

PAN Card Tampering Detector

Technologies: Python, OpenCV, Image Processing, SVM


Designed an image-processing–based system to detect tampered or forged PAN cards using feature extraction and classification techniques. Applied preprocessing, edge detection, and SVM classification to achieve high detection accuracy. The project demonstrates practical application of computer vision for document verification and fraud detection.

Forest Fire Prediction Application

Technologies: Python, Machine Learning, Data Analysis, Flask, Pandas, NumPy


Developed a machine learning–based application to predict forest fire occurrences using environmental and meteorological data. Performed data preprocessing, feature analysis, and model training to identify patterns contributing to fire risk. Built a simple application interface to visualize predictions and support early warning and decision-making for forest fire prevention.

Aarogya Assist – AI-Based Medical Support System

Technologies: Python, Machine Learning, Data Analysis, Flask, Pandas, NumPy


Developed an AI-based medical assistance system designed to support basic health guidance and symptom analysis using machine learning techniques. Worked on data preprocessing, feature selection, and model development to analyze user inputs and generate predictive insights. The project focuses on leveraging data-driven approaches to assist preliminary healthcare decision-making in a user-friendly manner.

To-Do List Application

Technologies: HTML, CSS, JavaScript


Developed a simple and interactive To-Do List web application to manage daily tasks efficiently. Implemented core functionalities such as adding, deleting, and updating tasks using JavaScript DOM manipulation. Focused on clean UI design, responsive layout, and user-friendly interaction to demonstrate strong fundamentals in frontend development and client-side logic.

Psoriasis Classification Using Deep Learning

Technologies: Python, Deep Learning, CNN, TensorFlow, Keras, OpenCV


Developed a deep learning–based image classification system to identify and classify psoriasis from skin images. Implemented Convolutional Neural Networks (CNNs) using TensorFlow and Keras, along with image preprocessing techniques to improve model performance. Evaluated the model using metrics such as accuracy and loss, demonstrating the effectiveness of deep learning for medical image analysis and early disease identification.

Educational Chatbot for Kids

Technologies: Python, Natural Language Processing (NLP), Machine Learning, Flask


Developed an interactive educational chatbot designed for children to assist with basic learning concepts and queries. Implemented natural language processing techniques to understand user inputs and generate relevant responses. Focused on building a simple, engaging interface and improving response accuracy to enhance the learning experience for young users.

Achievements

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