Portfolio - Aashi Tomar

Aashi Tomar

AI & ML Engineer

Resume

1

Machine Learning Project

1

Python Project

1

PowerBI Project

About Me

About Me

Hi, I'm Aashi Tomar 👋

I'm an aspiring AI & Machine Learning Engineer who loves turning ideas into real-world solutions using data and technology. My journey began with a simple question—how do machines learn from data?—and that curiosity turned into a genuine passion.

Since then, I've been building hands-on with Python, SQL, Machine Learning, FastAPI, MLflow, and data visualization, exploring everything from ML pipelines to MLOps. I believe the best way to learn is by building, and every project on this portfolio has taught me something new—technically and about thinking like an engineer.

I enjoy challenges, love picking up new technologies, and I'm always looking to grow—whether that's debugging a tough problem, competing in hackathons, or exploring the latest AI tools.

If you're looking for someone curious, dedicated, and excited to contribute to meaningful AI and data-driven projects, let's connect!

Thanks for visiting—I hope you enjoy exploring my work. 🚀

Key Skills

Python

Machine Learning (Supervised Learning

Classification

Regression)

SQL

Pandas & NumPy

Power BI

scikit-learn

Git & GitHub

Streamlit

FastAPI

My Projects

Credit Card Analysis Using Statistics

Domain/Function: Data Science / Statistical Analysis

SmileSense – Real-Time Face, Eye & Smile Detection

Domain/Function: Computer Vision

HR Analytics Dashboard – Employee Attrition Analysis

Domain/Function: Business Intelligence / HR Analytics

Awards & Certificate

Python: Beginner to Advanced For Data Professionals

SQL for Data Science

Math and Statistics For AI, Data Science

Master Machine Learning for Data Science & AI: Beginner to Advanced

IBM-Python for Data Science, AI & Development

Microsoft Certification: Azure Al Fundamentals

Let's Connect

Feel free to get in touch with me. I am always open to discussing new projects, creative ideas or opportunities to be part of your visions.

Download Resume

Resume

Mail Me

[email protected]

https://www.linkedin.com/in/aashi-tomar-448a51333/

Call Me

+91 8349840999

GitHub

https://github.com/Aashi1102

Machine Learning Project

Credit Card Analysis Using Statistics

Domain/Function: Data Science / Statistical Analysis

End-to-end statistical analysis of credit card customer data — correlation analysis, customer segmentation by age/gender/location, and A/B hypothesis testing on a marketing campaign using two-sample Z-tests, translating statistical findings into actionable business insights.

View Project
Engagement

View Project
on GitHUB

Other Tools

Pandas

Numpy

JupyterNotebook

Seaborn

Matplotlib

Correlation matrix showing relationships between credit score, credit limit, debt, and income

Two-sample Z-test evaluating campaign impact with critical value and rejection region

Project Details

Conducted a complete statistical analysis of credit card customer and transaction data, combining exploratory analysis with formal hypothesis testing. Built a correlation matrix to identify key relationships between credit score, credit limit, outstanding debt, and annual income, then handled outliers in credit limit data to clean the dataset for further analysis.

Segmented the customer base across age, gender, location, and occupation, uncovering that over 50% of customers fall in the 26-48 age bracket and revealing income, credit limit, and credit score patterns by segment. Analyzed transaction behavior by payment type, product category, and marital status to surface spending patterns across demographics.

Designed a two-sample Z-test to evaluate a marketing campaign's impact on transactions, calculating sample sizes across effect sizes and confirming statistical significance at 95% confidence — demonstrating the complete workflow from EDA to hypothesis-driven decision-making.

Python Project

SmileSense – Real-Time Face, Eye & Smile Detection

Domain/Function: Computer Vision

Real-time computer vision system using OpenCV and Haar Cascade classifiers to detect faces, eyes, and smiles live via webcam. A beginner-friendly project demonstrating classical ML-based facial feature detection with instant visual feedback on live video feed.

View Project
Engagement

View Project
on GitHUB

Other Tools

OpenCV

Haar Cascade Classifiers

Full view of live webcam detection with face bounding box and status labels

Core detection logic — face detection via Haar Cascade, followed by nested eye and smile detection within each face's region of interest, with real-time on-screen status labels.

Project Details

Built a real-time face, eye, and smile detection system using OpenCV and Haar Cascade classifiers. The system captures live video from a webcam, detects faces using a pre-trained cascade classifier, then searches within each detected face region for eyes and smiles independently.

Applied core computer vision preprocessing techniques including grayscale conversion and classification threshold tuning to optimize detection accuracy across varying lighting and distances. Detection results are displayed live — a green bounding box marks detected faces, with on-screen text confirming "Eyes Detected" and "Smiling" states.

The project demonstrates practical understanding of classical supervised learning concepts like feature hierarchies and cascade-based binary classification, structured as a clean, beginner-friendly codebase with clear setup instructions and modular detection logic.

PowerBI Project

HR Analytics Dashboard – Employee Attrition Analysis

Domain/Function: Business Intelligence / HR Analytics

Interactive Power BI dashboard analyzing employee attrition across age, education, salary bands, job roles, and department. Uncovers key attrition drivers from 1,473 employees (16.1% attrition rate), enabling data-driven HR decisions on retention and workforce risk.

View Project
Engagement

View Project
on GitHUB

Other Tools

DAX

Data Modeling

Data Cleaning & Transformation

Dashboard overview with attrition rate, age/salary/tenure KPIs, and department filters

Job satisfaction matrix and attrition breakdown by job role — Laboratory Technician and Research Scientist show highest counts

Project Details

Built an interactive HR Analytics Dashboard in Power BI to help organizations identify the key patterns behind employee attrition and workforce demographics. The dashboard analyzes demographics, job satisfaction, salary ranges, experience, and department to uncover the primary factors driving attrition — supporting data-driven decisions like identifying high-risk employee groups and spotting where satisfaction needs improvement.

Key insights surfaced: 1,473 total employees, 237 attritions (16.1% attrition rate), average age of 37, and average tenure of 7 years. The dashboard breaks down attrition by demographics (age, gender, education field, salary range), by job role (including satisfaction distribution), by department (HR, R&D, Sales), by tenure, and by salary band — each with dedicated filters and slicers for interactive exploration.
Built using Power BI Desktop with custom DAX measures, structured data modeling, and a full data cleaning and transformation pipeline .