B.Tech in Artificial Intelligence | Pune, India 🇮🇳
I'm a data analyst focused on building end-to-end analytical solutions — from raw data ingestion and SQL modeling to machine learning pipelines and interactive dashboards. My work sits at the intersection of data engineering, analytics, and applied AI-ML.
- 🎓 Education: G H Raisoni Institute of Engineering & Business Management, Jalgaon
- 💼 Experience: Data Science Intern @ 3RI Technologies Pvt Ltd (Jan 2024 – Jun 2024)
- 🔍 Currently: Open to Data Analyst roles (Entry–Mid level)
- 🏗️ Latest Project: EV Charging Station Demand Analysis — 23-country ML pipeline (Python + MySQL + Prophet + Power BI + Streamlit)
- 📊 Tools I reach for first: Python, SQL, Power BI, Pandas, Scikit-learn
- 🧠 100+ LeetCode problems solved (SQL + Python)
- 📬 Reach me: sumersingpatil0193@gmail.com
From data extraction and analysis to machine learning and deployment, these are the technologies powering my projects.
 Python |
 SQL |
 HTML5 |
 CSS3 |
 NumPy |
 Pandas |
 Scikit-learn |
 TensorFlow |
 Matplotlib |
 Streamlit |
 Power BI |
 Jupyter |
 VS Code |
 Git |
Multi-country infrastructure analysis across 23 European nations — end-to-end ML + BI pipeline
- Cleaned and engineered synthetic dirty data; modeled in MySQL with analytical SQL views
- Built Random Forest demand classifier + Prophet time-series forecasting
- Deployed interactive Streamlit dashboard with Folium geospatial maps, Plotly charts, and 9 analytical tabs
- Full pipeline: Raw CSV → MySQL → Jupyter (EDA + ML) → Streamlit → Power BI
Stack: Python MySQL Streamlit Scikit-learn Prophet Folium Plotly Power BI
NLP-powered classification system on 17,880 job postings — AUC-ROC: 0.984
- Built end-to-end ML pipeline: TF-IDF vectorization → Logistic Regression → SMOTE for class imbalance
- Added SHAP explainability to surface top features driving fake job predictions
- Integrated MySQL (6 analytical views), Power BI dashboard, and deployed on Streamlit Cloud
- Chi-square hypothesis testing added to validate statistical significance of findings
Stack: Python NLP Scikit-learn MySQL Power BI Streamlit SHAP SMOTE
End-to-end data analytics project focused on inventory optimization, stock performance, supplier analysis, and supply chain insights.
- Analyzes 50,000+ retail transactions across 25 stores, 8 warehouses, and 20 suppliers
- Identifies stockout risks, slow-moving inventory, and supplier delays through end-to-end analytics
- Delivers 615 reorder recommendations and a 3-page Tableau dashboard for data-driven decisions
Stack: Python SQL Excel Pandas Tableau ETL Data Analysis Supply Chain Analytics
Content-based filtering system using cosine similarity + TMDB API integration
- Recommends movies based on genre, cast, director, and keywords
- Fetches live movie posters, trailers, and metadata via TMDB API
- Deployed as a Streamlit web app with clean UI
Stack: Python Streamlit Scikit-learn Pandas TMDB API
EDA on 1,000+ student records — identifying performance trends across demographics
- Cleaned and explored dataset using Python (Pandas, Seaborn) to uncover trends across gender, parental education, and test preparation
- Executed MySQL queries to identify key factors influencing Math, Reading, and Writing scores
- Created visualizations using Matplotlib & Seaborn to present score distributions and demographic patterns
Stack: Python MySQL Pandas Seaborn Matplotlib
"Turning Data into Intelligence, One Algorithm at a Time"
I'm passionate about leveraging AI and Machine Learning to solve real-world problems. From building intelligent recommendation systems to uncovering insights through data analysis, I believe in technology's power to create meaningful impact.
🔍 Actively seeking opportunities in AI/ML & Data Science
📚 Learning Deep Learning & Neural Networks
🚀 Building production-ready ML applications
🤝 Contributing to open-source AI projects
💼 Open to Work | 🚀 Ready to Contribute | 🎯 Focused on AI/ML