👋 Hi, I'm Lomada Siva Gangi Reddy [ 🌐 Visit My Portfolio ]
- 🎓 B.Tech in Computer Science & Engineering (Data Science) — RGMCET (2021–2025) | CGPA: 8.3
- 📈 Aspiring Data Scientist / Data Analyst passionate about AI, ML, and Data Analytics
- 💼 Skilled in Python, SQL, Power BI, Excel, ML, DL, Visualization
- 📍 Based in Andhra Pradesh, open to opportunities in Hyderabad, Bengaluru, Chennai, Pune
- 🌱 Currently exploring Advanced Deep Learning & End-to-End ML Pipelines
- Programming :
Python•SQL•Java•C•R•HTML•CSS - Data Science & AI :
Data Preprocessing•Data Visualization•Data Analytics•Machine Learning•Deep Learning - Tools :
Excel•MySQL•Power BI•Jupyter Notebook•VS Code - Libraries & Frameworks :
Numpy•Pandas•Matplotlib•Seaborn•TensorFlow•Keras•Scikit-learn - Soft Skills :
Teamwork•Problem Solving•Communication•Quick Learning•Time Management
- 🚀 Building AI/ML projects with clean MLOps-style structure
- 🌐 Deploying ML models with Streamlit / FastAPI
- 🧠 Diving deeper into
**Deep Learning**,**Computer Vision**and**NLP** - 📚 Practicing DSA & SQL on LeetCode / GFG regularly
- Developed CNN + LSTM + Decision Tree + Random Forest models on bioristor IoT sensor data
- Predicted plant water stress levels to enable smart irrigation & water conservation
- Tools:
Python•TensorFlow•Scikit-learn•Pandas•Matplotlib - 📑 Published in Periodico di Mineralogia (DOI: 10.5281/zenodo.15047038)
- Built a CNN-based deep learning model to classify rice grain varieties from images
- Predicted Arborio, Basmati, Ipsala, Jasmine, Karacadag with confidence scores
- Developed an interactive Streamlit web app for real-time predictions
- Tools:
Python•TensorFlow/Keras•Streamlit•NumPy•Matplotlib
- Built a GRU-based RNN to generate text sequences from a custom corpus
- Features seed text input, beam search, and interactive Streamlit web app for real-time text generation
- Efficient lightweight architecture with fewer parameters than LSTMs for faster training
- Tools:
Python•TensorFlow/Keras•Streamlit•NumPy•Pandas•Matplotlib
- Built an interactive chatbot to retrieve real-time information from Wikipedia
- Integrated Wikipedia API for knowledge extraction
- Tools:
HTML•CSS, JavaScript•API integration - 📚 Useful for educational assistance & quick knowledge queries
- Analyzed restaurant dataset to identify patterns in ratings, food types, and pricing
- Built visualizations for customer preferences and restaurant performance evaluation
- Tools: **
Python•Pandas•NumPy•Matplotlib•Seaborn•Scikit-learn•Streamlit** - 📈 Delivered actionable insights on customer preferences, city-level trends, and restaurant performance.
| 🚀 Project | 📝 Description | 🛠 Tech Stack | 🔗 Links |
|---|---|---|---|
| 🌱 Water Stress Prediction | Predicted water stress using CNN+LSTM+ML on IoT data | Python, TensorFlow, Sklearn | Demo |
| 🌾 Rice Type Classifier | Classified rice varieties with CNN and Streamlit app | Python, Keras, Streamlit | Demo |
| 📝 Text Generation | GRU-based RNN for sequence generation with beam search | Python, TensorFlow, Streamlit | Demo |
| 🤖 Word Search Chatbot | Interactive chatbot using Wikipedia API | HTML, CSS, JS, API | Demo |
| 📊 Zomato Dashboard | Data analysis & visualizations for restaurant insights | Python, Pandas, Seaborn | Demo |
- ⭐ Enhanced CNN-Based Classification and Forecasting of Water Stress in Tomato Plant Utilizing Bioristor Data
Periodico di Mineralogia, Vol. 94, Issue 2 (2025)
- ✅ Machine Learning Internship — SkillDzire
- ✅ Java Full Stack Development — Wipro TalentNext
- ✅ SQL — Programming Hub
- ✅ Big Data & Hadoop — edX
- ✅ Data Science Guided Path — Coding Ninjas
- 🏆 Certificate of Merit — Naukri Campus Young Turks (Top 93.91 percentile)
- 🎯 HackerRank: Python, Java, SQL (5⭐ each)
- 💻 Solved 50+ coding problems on LeetCode & GeeksforGeeks
- 📚 Published research paper in an international journal (2025)
🚀 Open to collaborations in Data Science, AI & ML Projects!
Thank you for visiting my GitHub! 🌟 Check out my projects, connect with me, and let's collaborate! 🤝