Indrayani Rajeshirke

Highly motivated B.Tech Computer Science student (2026) with hands-on experience in data science, machine learning, and cloud computing. Skilled in Python, Java, and C++, with knowledge of GCP, AWS, and Oracle Cloud. Interested in building simple ML and web projects while learning to apply problem-solving and coding skills in practical scenarios.

• Google Cloud Fundamentals: Core Infrastructure – Google Cloud (Aug 2025)
• Introduction to Generative AI – Google Cloud (Aug 2025)
• Oracle Certified Foundational Associate – Oracle (Aug 2025)
• Oracle Fusion AI Studio Foundation Associate – Oracle (Aug 2025)
• AWS Cloud Practitioner Essentials – AWS (Jun 2025)
• AWS Certified Machine Learning Plan: Intelligent Document Processing – AWS (Jun 2025)
• Python for Data Science – Cognitive Class (Jan 2025)
• SQL and relational databases 101 – Cognitive Class (Jan 2025)
• HackerRank Certificates – Python Basics, Java Basics, SQL Basics, CSS Basics (Jan 2025)

Flight Fare Prediction Model | Machine Learning

• Problem: Airline ticket prices are highly dynamic, making it difficult for travelers to book at the right time.
• Solution: Designed a machine learning regression model (Random Forest, XGBoost, Linear Regression) to predict airfare prices based on features like airline, source, destination, date, and duration. Conducted data cleaning, feature engineering (date-time, routes, airline codes), and applied scikit-learn models (linear regression, random forest, and xgboost)
• Impact: Achieved 85% prediction accuracy, allowing users to anticipate future fare fluctuations
• Tools: Python, Pandas, Scikit-learn, Matplotlib, Jupyter Notebook
• Delivered insights into price fluctuation trends, useful for both travelers and ticketing platforms
• Focus: predictive modeling, regression, feature engineering

Fake news detection using cloud technologies| Cloud Deployment

• Designed a cloud-native NLP pipeline to detect fake and misleading news articles.
• Deployed models on Kubernetes clusters with autoscaling, ensuring high availability and performance.
• Leveraged cloud storage + container orchestration to handle large-scale datasets.
• Focus: cloud computing, MLOps, scalable deployment

AI vs Ayurveda – Symptom-to-Remedy Recommender | ML + Healthcare

• Built a Streamlit application mapping patient symptoms to herbal remedies using supervised ML models.
• Constructed a dataset of Ayurvedic prescriptions and symptom-remedy mappings for training.
• Compared AI-generated recommendations against traditional Ayurvedic healer practices.
• Designed a user-friendly web app interface for patients to explore remedies interactively.
• Focus: Applied ML in healthcare, cultural + modern medicine integration.

CraftRaahi – Mobile Marketplace for Tribal Artisans | Web Dev + Social Impact

• Designed and developed a multi-page HTML/CSS website with navigation across Home, About, Products, Artisans, and Workshops.
• Built an interactive dashboard for artisans and buyers, including features for product listings, inventory tracking, orders, and donations.
• Applied modern UI principles with Tailwind & custom styling for clean design.
• Focused on empowering nomadic artisans by bridging them with digital buyers.

AI-Driven Wildlife Detection (Conservation Project)

• Problem: Wildlife poaching and deforestation threaten biodiversity, requiring better monitoring systems.
• Solution: Developed an AI-based object detection model (YOLOv5, TensorFlow) to identify and track animals in drone and satellite imagery.
• Approach: Implemented image preprocessing, augmentation, and CNN-based feature extraction to improve detection under varied lighting and environments.
• Impact: Enabled automated wildlife tracking with 85%+ detection accuracy, helping conservationists monitor endangered species and detect poaching hotspots more effectively.