Hello, I'm

Smriti Sunil

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About Me

Hi, I’m Smriti — a data enthusiast passionate about uncovering patterns, solving problems, and turning raw data into impactful insights. I’ve worked hands-on with Python, SQL, Power BI, and Tableau to analyze large datasets, build dashboards, and drive data-informed decisions across academic and professional settings. I love connecting data to real-world outcomes, whether it’s optimizing campaigns, improving workflows, or understanding user behavior. Alongside tech, I enjoy leading creative initiatives from organizing campus events to managing student-led marketing and community-building initiatives. I thrive where analytics meets creativity and am always eager to learn, adapt, and create meaningful impact through technology.

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Education

The University of Texas at Dallas

MS in Computer Science

Data Science Track

Sem 1 GPA: 3.667

Relavant Coursework

Database Design, Statistics for Data Science, Machine Learning, Big Data Management and Analytics

University of Mumbai

BE in Computer Engineering

Honors in Data Science

CGPA:3.87

Relavant Coursework

Python Programming, Data Structures and Algorithms, Data Warehousing and Mining, NLP, Aritficial Intelligence

Explore My

Experience

Student Assistant - Data Analyst Intern

UT Dallas - The Jonsson School

Texas, United States

September 2024 – Present

Analyzed 500+ survey responses each semester, building Excel & Power BI dashboards to guide planning.
Created reports with pivot tables and charts, highlighting enrollment, outcomes, and event insights.
Integrated Salesforce with survey data, boosting event turnout by 25% and improving conversions.
Automated data cleaning & merging with Python, cutting processing time by 40% and enabling real-time reporting.
Built optimized SQL queries & reusable scripts that cut report generation time from hours to minutes.

Project Development - Data Analyst Intern

ITECH Digital Forensics PVT LTD

Mumbai, India

June 2022 – August 2022

Analyzed web traffic using Google Analytics & SQL to optimize client targeting.
Built Tableau dashboards for 50+ products to visualize engagement and demo interest.
Assisted AWS migration, optimizing DBs and reducing page load time by 2.4s.
Enhanced UI using HTML, CSS & Bootstrap, leading to a 40% rise in brochure downloads.
Delivered weekly insights that helped increase lead response rate by 25%.

Browse My Recent

Projects

Food Delivery Platform Performance Analysis

Analyzed a large-scale dataset to evaluate delivery efficiency and optimize operations using statistical methods and visualization.

Skills Used: #Python #SQL #Pandas #NumPy #Tableau #A/BTesting #Regression

Objective: Objective: Identified patterns in delivery delays caused by distance, weather, and peak demand, and tested notification strategies to improve service reliability.
Key Achievements:
- Uncovered key delay factors using SQL and Python data analysis, enabling targeted operational improvements.
- Applied regression models and A/B testing, with simulations showing 12% fewer late deliveries and 8% higher customer ratings.
- Designed Tableau dashboards with KPI tracking, geospatial maps, and trend visualizations for real-time decision-making.

Job Market Analytics & Salary Trend Visualization

A data-driven platform analyzing over 10,000 job postings to identify skill demands, salary benchmarks, and regional trends.

Skills Used: #Python #SQL #Sckit-Learn #PowerBI

Objective: Objective: Designed a platform to analyze 10,000+ job postings to identify skill demands, salary benchmarks, and regional trends for aspiring data analysts.
Key Achievements:
- Performed data cleaning, preprocessing, and exploratory analysis using Python (Pandas, NumPy) and SQL.
- Built Scikit-learn models to forecast salary ranges with 85% accuracy.
- Created Power BI dashboards to visualize in-demand skills, salary distributions, and job density by region.
- Improved job match recommendations by 30% and helped data-driven decision-making for job seekers.

Movie Recommendation System

A personalized movie recommendation system based on user preferences using NLP techniques and machine learning.

Skills Used: #Python #SQL #NLP #ETL #Tableau #Machine Learning

Objective: Developed a personalized movie recommendation system using NLP techniques and machine learning.
Key Achievements:
- Applied TF-IDF vectorization and cosine similarity to compute relevance scores between movies based on genres, descriptions, and user interests.
- Cleaned and analyzed 50,000+ movie records using SQL and Pandas, integrating metadata and user reviews.
- Created a Tableau dashboard to visualize genre trends, user behavior, and recommendation results.
- Increased recommendation precision by 15%, driving better alignment with user preferences and improved engagement during pilot testing.

FortiShare

A secure cloud file sharing system that detects intrusions, focusing on XSS attacks and optimizing encrypted file transfers.

Skills Used: #MongoDB GridFS #Python #NodeJS #HTML #PHP

Objective:Designed a secure cloud file-sharing system for detecting XSS attacks and optimizing encrypted file transfers.
Key Achievements:
- Built a CNN-based model that classified malicious activity with 98.5% accuracy.
- Analyzed 5,000+ file access logs and improved encryption efficiency for different file types and sizes.
- Integrated Python and MongoDB GridFS to manage encrypted transfers and identify abnormal access behavior.
- Enhanced early threat detection and system resilience through advanced feature extraction and model predictions.

A/B Testing & Marketing Analytics Dashboard

Analyzed A/B test results and developed a Power BI dashboard to optimize marketing strategies and campaign performance.

Skills Used: #SQL #Python #Power BI #Excel

Objective:Optimized event strategies for the ACM student chapter using A/B testing insights and visual performance tracking to improve outreach and engagement.
Key Achievements:
- Conducted A/B testing with t-tests and chi-square tests using SQL and Python for statistically valid campaign comparisons.
- Built a Power BI dashboard showcasing KPIs, event trends, and conversion rate visualizations.
- Improved email open rates and event sign-ups by 15% through better timing and message targeting.
- Enabled data-driven decision-making for future campaign strategies and event planning.

Mushroom Classification System

Built a machine learning model to classify mushrooms as edible or poisonous using Decision Tree, Random Forest, and K-Nearest Neighbors algorithms.

Skills Used: #Python #Scikit-learn #Decision Tree #Random Forest #KNN, #Data Preprocessing

Objective:Built a classification system to determine whether a mushroom is edible or poisonous using a dataset of 20 categorical features.
Key Achievements:
- Trained and evaluated Decision Tree, Random Forest, and KNN models with cross-validation and hyperparameter tuning.
- Achieved 99.98% accuracy with KNN; selected Random Forest for deployment due to better efficiency and generalization.
- Preprocessed data by encoding categorical features, handling missing values, and splitting features/labels for model training.
- Delivered a highly accurate model to assist in food safety assessments through reliable mushroom classification.

SafeU

An SOS emergency application designed to instantly alert loved ones in times of danger, enhancing personal safety, especially for women.

Skills Used: #Android Development #Java #Maps API #Messaging API #Git/GitHub #Figma

Objective:SafeU is an emergency SOS app that instantly alerts loved ones with an SOS message and live location, ensuring quick help during emergencies.
Key Achievements:
- Implemented one-tap SOS alerts with automated messaging and live location using the Google Maps API.
- Enabled continuous real-time location sharing as the user moves during emergencies.
- Integrated automatic audio recording from the moment the SOS is triggered until it's turned off, capturing surrounding sounds for added security.
- Designed a customizable message system with multilingual support and a responsive contact management module for quick access.

Static Code Analyzer

A tool for performing static code analysis, detecting defects, bugs, and inefficiencies in Python code using machine learning classification methods.

Skills Used: #Python #MySQL #Machine Learning (KNN, Naive Bayes, Decision Trees)

Objective:The Static Code Analyzer improves Python code quality by detecting errors, inefficiencies, and bad practices through static analysis and machine learning-based classification.
Key Achievements:
- Developed a Python-based tool to classify code as “well-written” or “poorly written” using KNN, Naive Bayes, and Decision Trees.
- Integrated real-time syntax and optimization feedback to help developers improve code instantly.
- Leveraged static code metrics (e.g., readability, structure) to enhance analysis accuracy.
- Built a MySQL-backed training dataset to support continuous learning and reliable classification.