I build data-driven and machine learning solutions with a focus on turning real-world data into practical, measurable outcomes. My work spans machine learning, data science, analytics, and privacy-preserving synthetic data generation.

I’m building toward production-minded AI/ML work: understanding the data, designing reliable pipelines, training and evaluating models, and communicating results clearly.

• Machine Learning & Predictive Modeling
• Supervised Learning
• Regression & Model Evaluation
• Data Preprocessing & Feature Engineering
• Exploratory Data Analysis (EDA)
• Statistical and Data-driven Analysis
• Model Performance Evaluation
• Synthetic Data Generation
• Privacy-Preserving Data Workflows

• Python for Data Science
• Pandas & NumPy
• Data Cleaning & Preparation
• Missing-Value Handling
• Outlier Analysis
• Categorical Encoding
• Data Visualization
• Insight Generation

Languages
Python

Data & ML
Pandas · NumPy · Scikit-learn · SDV

Visualization
Matplotlib · Seaborn

Environment
Jupyter Notebook · Kaggle

End-to-end machine learning project covering data preparation, exploratory analysis, missing-value handling, outlier analysis, categorical encoding, model training, and evaluation using regression metrics.

Focus: EDA · Preprocessing · Feature Engineering · Regression · Model Evaluation

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Exploration of synthetic data generation using SDV models, focused on creating useful data while considering privacy-preserving workflows.

Focus: Synthetic Data · SDV · Privacy-Preserving ML

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Data analytics project focused on working with lending data to explore patterns, relationships, and insights through a structured analytical workflow.

Focus: Data Analytics · EDA · Data-driven Insights

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Hands-on machine learning work developed through internship projects, providing practical experience with data preparation, analysis, and machine learning workflows.

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Real-world Problem
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Data Understanding
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Data Cleaning & Preparation
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Exploratory Data Analysis
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Feature Engineering
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Model Development
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Evaluation & Validation
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Insights & Iteration

I believe strong AI/ML work is not only about choosing an algorithm — it is about understanding the problem, preparing trustworthy data, evaluating models correctly, and translating results into useful decisions.

AI/ML Engineering · Machine Learning · Data Science · Data Analytics · Applied AI · Synthetic Data

I’m interested in opportunities where I can contribute to machine learning and AI systems, while bringing strong data science and analytical thinking to the problem.

• End-to-end machine learning projects
• Data science and analytics workflows
• Experiments with ML techniques
• Practical notebooks and implementations
• Projects focused on real-world data problems

I'm open to AI/ML, Data Science, and applied machine learning opportunities where I can build, learn, and contribute to meaningful technical projects.

⭐ If you find a project interesting, feel free to explore the repository and its implementation.