Data
Science
Masters Student passionate about leveraging machine learning, statistical analysis, and data visualization to solve complex real-world problems.
"In data we trust. In analysis we find insights that drive impact."
Featured
Projects
Predictive Analytics Model
Built an ensemble machine learning model using Python (scikit-learn, XGBoost) to predict customer churn with 92% accuracy. Performed EDA, feature engineering, and hyperparameter tuning.
Python • scikit-learn • pandas • TensorFlow
COVID-19 Data Analysis
Comprehensive analysis of pandemic trends across regions using statistical methods and geospatial visualization. Created interactive dashboards for public health insights.
R • Tableau • SQL • ggplot2
NLP Text Classification
Developed a deep learning model for sentiment analysis using LSTM and BERT. Achieved state-of-the-art results on benchmark datasets.
Python • PyTorch • NLTK • Transformers
Time Series Forecasting
Implemented ARIMA and Prophet models to forecast stock prices and market trends. Evaluated performance using various metrics and validated on test sets.
Python • statsmodels • Prophet • Matplotlib
"The best insights come from asking the right questions of your data."
Technical
Skills
Programming Languages
Python • R • SQL • Java • JavaScript
Machine Learning & AI
scikit-learn • TensorFlow • PyTorch • XGBoost • Neural Networks
Data Analysis & Visualization
pandas • NumPy • Tableau • Matplotlib • Seaborn • Plotly
Big Data & Databases
Apache Spark • Hadoop • PostgreSQL • MongoDB • Google BigQuery
Tools & Platforms
Jupyter • Git • Docker • AWS • Google Cloud • Kaggle
Statistical Methods
Hypothesis Testing • Regression • Classification • Clustering • Time Series
"Every dataset tells a story waiting to be discovered."
Research
Interests
Deep Learning & Computer Vision
Exploring convolutional neural networks, image segmentation, and object detection for real-world applications.
Natural Language Processing
Investigating transformer models, language representation, and applications in text generation and understanding.
Explainable AI (XAI)
Focused on interpretability and transparency in machine learning models for ethical and trustworthy AI systems.
Statistical Inference
Applying Bayesian methods and causal inference to extract meaningful insights from complex datasets.