📍 Surat, Gujrat, India
I’m a data analyst who works with Python, SQL, Power BI, and Excel to turn raw data into clear insights. I focus on understanding the data, finding patterns, and presenting results in a way that supports good decision-making.
I’m currently learning more about AI and Machine Learning to strengthen my analytical approach and handle more advanced tasks. This includes understanding how models work, how to prepare data for them, and how they can support practical business questions.
Explore my Projects to see dashboards, detailed analytics, and practical experience in data-driven decision making.
| Category | Skills & Tools |
|---|---|
| Languages | Python, SQL |
| Analytics Tools | Power BI, MySQL, Excel |
| Frameworks | Pandas, NumPy, Scikit-Learn, Matplotlib, Seaborn, Streamlit |
| Platforms | Jupyter Notebook, Visual Studio Code, Google Colab Notebook |
| Soft Skills | Rapport Building, People Management, Good Communication |
| Core Focus Areas | AI, Machine Learning |
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A practical machine-learning project where I built a regression model to predict smartphone prices using key features such as brand, specifications, and performance metrics. The workflow covers web scraping, data cleaning, feature extraction, model training, and algorithm comparison to identify the most accurate predictor. The project demonstrates how structured data and ML techniques can support smarter pricing decisions and market positioning. |
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An exploratory analysis of café sales data to track how revenue changes by day, month, and product category. I analyzed 15,000+ transactions using Python to identify peak months, high-selling items, and seasonal demand patterns. I also built a dashboard that highlights top-performing categories like Juice, Coffee, Cake and visualizes sales trends, payment behavior, and location insights to support smarter menu and operational decisions. |
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A SQL-driven retail analytics project where I analyzed linked tables such as demand, inventory, pricing, and product to extract actionable insights. Using joins, subqueries, and aggregation, I identified stock inefficiencies, pricing trends, and product-level performance. The analysis turns complex relational data into clear metrics and recommendations that support smarter inventory planning and pricing strategy. |
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Ready to tackle real problems with smart analytics?
Share your business challenge, and I’ll uncover the story hidden in the data.