Data Science Graduate | AI Enthusiast | Cloud & DevOps Explorer

🎓 Data Science Graduate passionate about AI, Machine Learning, and Data Analytics

🔭 Building intelligent solutions using Python, Machine Learning, Deep Learning, and Cloud Technologies

🌱 Currently learning Generative AI, LLMs, MLOps, and Advanced Machine Learning

☁️ Exploring AWS Cloud, Docker, Kubernetes, and scalable AI deployments

💡 Interested in AI Engineering, Data Science, Machine Learning, and Cloud-Based Solutions

📫 Email: adithiraskonda@gmail.com

⚡ Fun Fact: I enjoy transforming complex data into meaningful insights and real-world solutions.

"Turning data into decisions and ideas into intelligent solutions."

Specializing in Machine Learning, NLP, Generative AI, Predictive Analytics, and Cloud-Native Solutions.

• Machine Learning
• Deep Learning
• XGBoost
• Natural Language Processing (NLP)
• Pandas
• NumPy
• Scikit-Learn
• Data Visualization
• Power BI
• Tableau
• Feature Engineering
• Exploratory Data Analysis (EDA)
• Generative AI
• Large Language Models (LLMs)

📌 Built a machine learning model using XGBoost to predict customer churn through data preprocessing, feature engineering, and exploratory data analysis.

Tech Stack: Python, Pandas, XGBoost, Scikit-Learn

📌 Developed an NLP-based system to identify fake product reviews using text preprocessing, feature extraction, and classification techniques.

Tech Stack: Python, NLP, Pandas, Scikit-Learn

📌 Designed an intelligent network intrusion detection system leveraging machine learning techniques for threat detection and cybersecurity analytics.

Tech Stack: Python, Machine Learning, Streamlit, Data Analytics

📌 Built a predictive analytics solution to estimate equipment failures and remaining useful life using deep learning and time-series analysis.

Tech Stack: Python, TensorFlow, LSTM, Pandas

📌 Implemented an intelligent route-planning system using the A* algorithm to calculate optimal paths for geospatial navigation.

Tech Stack: Python, A* Algorithm, Data Structures

📌 Architected a scalable cloud infrastructure using AWS services including VPC, EC2, RDS, Load Balancer, Auto Scaling, and Monitoring.

Tech Stack: AWS, EC2, RDS, CloudWatch, DynamoDB

📌 Implemented CI/CD and GitOps workflows with containerization, Kubernetes orchestration, automated deployments, and monitoring.

Tech Stack: Docker, Kubernetes, GitHub Actions, Jenkins, AWS

• Data Analytics with Python — NPTEL (2024)
• Microsoft Azure Artificial Intelligence Fundamentals (AI-900) — Microsoft (2023)
• Data Science, Artificial Intelligence & Machine Learning — Edyst (2023)
• Python: Introduction to Data Science & Machine Learning A-Z — Udemy (2023)

• Python Programming — OpenEDG Python Institute (2024)
• SQL (Advanced) — HackerRank (2025)

• AWS Cloud Practitioner Essentials — AWS (2026)
• CCNA Enterprise Networking, Security and Automation — Cisco (2024)

• Business Intelligence & Analytics using Power BI (2023)
• Deloitte Data Analytics Virtual Experience Program (2025)

• 🎓 Data Science Graduate
• 🏆 10+ Certifications
• ☁️ AWS Cloud Enthusiast
• 🤖 AI & Machine Learning Practitioner
• 🐍 Python Developer
• 🚀 Open to Opportunities

• 🤖 Generative AI & LLM Applications
• 📊 Machine Learning & Predictive Analytics
• ☁️ Cloud-Based AI Solutions
• 🐍 Advanced Python Development
• 🚀 Building End-to-End Data Science Projects