This portfolio brings together data science and machine learning work spanning predictive modeling, exploratory data analysis, time-series analysis, and data visualization. Whether you are exploring a specific project or evaluating the breadth of techniques applied, you will find detailed write-ups, notebooks, and findings for each case study. The projects draw from real-world domains — automotive pricing, financial risk, entertainment analytics, and environmental detection — giving each analysis practical relevance beyond academic exercises.Documentation Index
Fetch the complete documentation index at: https://github-52.mintlify.app/llms.txt
Use this file to discover all available pages before exploring further.
Project categories
The portfolio is organized around four core types of analytical work:- Predictive modeling — building and evaluating regression and classification models to generate actionable predictions
- Exploratory data analysis (EDA) — uncovering patterns, distributions, and relationships within raw datasets
- Time-series analysis — examining trends, seasonality, and volume patterns over time
- Data visualization — communicating findings clearly through charts, plots, and summary statistics
Featured projects
Used Car Price Prediction
ML regression model to estimate used car market prices based on vehicle attributes and historical data.
IMDB Movie Analysis
Exploratory analysis of movie ratings, genres, and revenue trends across decades of IMDB data.
Bank Loan Case Study
Risk analysis identifying key factors behind loan defaults to support data-driven credit decisions.
ABC Call Volume Trend
Time-series analysis of inbound call patterns to identify peak periods and operational trends.
Additional projects — including Stock Analysis and Oil Spill Detection — are also available in the portfolio.
Tools and technologies
Every project in this portfolio is built on a standard Python data science stack:| Tool | Purpose |
|---|---|
| Python | Core programming language for all data work |
| Pandas | Data loading, cleaning, and transformation |
| NumPy | Numerical computing and array operations |
| Scikit-learn | Machine learning model training and evaluation |
| Matplotlib | Base plotting and figure generation |
| Seaborn | Statistical visualization built on Matplotlib |
| Jupyter Notebooks | Interactive development and reproducible analysis |
About this site
This portfolio is hosted on GitHub Pages via theSumit-SC repository. Pages are built from Jupyter notebooks and markdown write-ups committed to the source repository. Cloudflare handles CDN delivery, and reader comments are powered by Giscus. See the deployment guide for the full hosting setup.