> ## Documentation Index
> Fetch the complete documentation index at: https://github-52.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.

# About Sumit SC: Data Science Skills and Background

> Learn about Sumit SC's background in data science, analytical skills, tools, and the methodology behind each project in this portfolio.

Sumit SC is a data science practitioner with hands-on experience applying machine learning, statistical analysis, and data visualization to real-world problems. This portfolio documents that work — each project is grounded in a concrete business or research question, worked through systematically from raw data to interpretable findings. The projects range from regression modeling on structured datasets to exploratory analysis of behavioral and financial data, reflecting a broad interest in how data can inform decisions across domains.

## Core skills

Sumit's analytical work draws on a combination of programming, statistics, and domain reasoning:

<CardGroup cols={2}>
  <Card title="Python" icon="python">
    Primary language for data manipulation, modeling, and visualization across all projects.
  </Card>

  <Card title="SQL" icon="database">
    Querying and aggregating structured data from relational sources during the analysis phase.
  </Card>

  <Card title="Machine learning" icon="brain">
    Supervised learning methods including regression, classification, and model evaluation with Scikit-learn.
  </Card>

  <Card title="Statistical analysis" icon="chart-bar">
    Hypothesis testing, correlation analysis, and distribution profiling to support data-driven conclusions.
  </Card>

  <Card title="Data visualization" icon="chart-line">
    Communicating findings through clear, well-labeled charts using Matplotlib and Seaborn.
  </Card>

  <Card title="Exploratory data analysis" icon="magnifying-glass">
    Systematic investigation of raw datasets to surface patterns, outliers, and relationships before modeling.
  </Card>
</CardGroup>

## Tools and environment

| Tool                  | Role                                                                                      |
| --------------------- | ----------------------------------------------------------------------------------------- |
| **Jupyter Notebooks** | Interactive analysis environment for iterative, reproducible work                         |
| **Pandas**            | DataFrame-based data wrangling and feature engineering                                    |
| **NumPy**             | Array operations and numerical utilities                                                  |
| **Scikit-learn**      | ML pipelines, model training, cross-validation, and metrics                               |
| **Matplotlib**        | Figure layout, axes configuration, and base plots                                         |
| **Seaborn**           | Statistical chart types including heatmaps, pairplots, and distribution plots             |
| **GitHub Pages**      | Static site hosting for the portfolio at [Sumit-SC.github.io](https://sumit-sc.github.io) |

## Types of analysis

### Exploratory data analysis

EDA projects — such as the [IMDB Movie Analysis](/projects/imdb-movie-analysis) — focus on understanding the structure and content of a dataset before drawing conclusions. This involves checking data quality, examining distributions, and identifying relationships between variables through visualization and summary statistics.

### Predictive modeling

Modeling projects — such as [Used Car Price Prediction](/projects/used-car-price-prediction) — frame a business question as a supervised learning problem, engineer relevant features, train candidate models, and evaluate performance using appropriate metrics (RMSE, MAE, R², etc.).

### Risk and case study analysis

Projects like the [Bank Loan Case Study](/projects/bank-loan-case-study) take a structured analytical approach to a domain problem, combining EDA with segmentation and risk factor identification to produce actionable insights rather than a single predictive model.

### Time-series analysis

The [ABC Call Volume Trend](/projects/call-volume-trend) project examines temporal data to identify patterns, peak periods, and trends — applying resampling, rolling statistics, and visualization techniques suited to time-indexed data.

## GitHub profile

<Card title="Sumit-SC on GitHub" icon="github" href="https://github.com/Sumit-SC">
  Browse the source notebooks, datasets, and code for every project in this portfolio.
</Card>