Computer Science Engineering graduate focused on Data Science, Machine Learning, and AI.
I enjoy building practical machine learning solutions and working with data to solve real-world problems.
- 🎓 B.Tech in Computer Science & Engineering
- 📊 Focused on Data Science and Machine Learning
- 🐍 Python and SQL
- 🤖 Scikit-learn, XGBoost, Random Forest
- 🧠 TensorFlow, Keras, CNNs
- 📈 Data preprocessing, feature engineering, model evaluation and time-series forecasting
- 🚀 Building ML applications with Streamlit
- 🔬 Contributor to peer-reviewed SmartRail AI research
- 🌱 Currently learning Generative AI, RAG and Agentic AI
Languages: Python, SQL
Data Science: Pandas, NumPy, Matplotlib, Data Analysis, Data Preprocessing, Feature Engineering
Machine Learning: Scikit-learn, XGBoost, Random Forest, Model Evaluation, Cross-Validation, Hyperparameter Tuning
Deep Learning: TensorFlow, Keras, Convolutional Neural Networks (CNN)
Tools & Deployment: Streamlit, Flask, Jupyter Notebook, GitHub
Machine learning application for stroke-risk prediction using Random Forest and Scikit-learn, with an interactive Streamlit interface.
Multimodal deep learning project combining handwriting image classification and audio-signal feature extraction using TensorFlow and Keras.
Machine-learning-based traffic prediction web application for urban traffic forecasting.
SmartRail AI: A Machine Learning-Based System for Railway Crowd and Delay Prediction
Peer-reviewed research published in the International Journal of Novel Trends and Innovation, April 2026.
Generative AI | Retrieval-Augmented Generation (RAG) | Agentic AI