ARCHI KODWANI
Data ScienceAI is analyzing your overall score…
Identifying your key strengths…
Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
AI is analyzing your overall score…
Identifying your key strengths…
Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
Data Science with less than a year in Machine Learning, Python, and Data Systems.
MSc Big Data Analytics candidate at St. Xavier's College with hands-on experience building end-to-end ML pipelines and data systems for real-world risk and prediction problems. Engineered a 36-feature AQI forecasting model achieving R² = 0.8056 (Gradient Boosting) and deployed a real-time face recognition system using Django REST API and MySQL. Proficient in Python, SQL, Power BI, and statistical modeling. Seeking an Analytics & Risk Management internship at American Express to apply predictive modeling, EDA, and data-driven decision-making to credit and fraud risk challenges.
St. Xavier's College (Autonomous)
Master of Science · Big Data Analytics
August 1, 2026 – Present
Indus University
Bachelor of Computer Applications (BCA)
N/A – June 30, 2025
Bengaluru AQI Prediction - Risk Modeling and Statistical Analysis
January 1, 2025 – January 1, 2026
Engineered a 36-dimensional feature matrix from 3,120 daily CPCB AQI records (2015-2023), incorporating 14-day lag variables, rolling statistics (3/7/14/30-day windows), and cyclical calendar features methodology directly analogous to credit risk feature construction. Trained and benchmarked 6 ML regression models on chronological 80/20 train-test split; Gradient Boosting delivered best predictive performance: R2 = 0.8056, MAE = 8.11, RMSE = 10.86. Fitted SARIMA(1,1,1)×(1,1,0,7) time series model as a forecasting benchmark; generated 30-day ahead forecast with 95% confidence intervals for risk scenario planning. Conducted residual diagnostics, feature importance analysis, and correlation analysis - confirming lag features as dominant predictors; translated findings into actionable insights for stakeholders.
View ProjectAutomated Attendance System - Real-Time Data Pipeline
January 1, 2024 – January 1, 2025
Designed and deployed an end-to-end automated attendance system eliminating manual processes and proxy-attendance fraud, reducing per-class overhead to near zero. Integrated OpenCV for real-time image capture; built Dlib/face_recognition pipeline for facial embedding extraction and identity matching at scale. Developed Django REST API backend for real-time attendance logging; architected MySQL schema for embedding and record storage with sub-second query response. Built responsive admin dashboard (HTML, CSS, Bootstrap, AJAX) enabling real-time monitoring and reporting for administrators.
Journey Junction - Full-Stack Data-Driven Web Application
January 1, 2022 – January 1, 2023
Built full-stack travel booking portal with tour package discovery, reservation flow, e-payment processing, and cancellation management. Designed normalized MySQL database schema for bookings and payments; implemented role-based access control for Admin and Customer modules. Validated system with structured test plans covering functional, security, and cross-browser testing; implemented UML design artifacts.
Power BI Fundamentals
Unknown
June 1, 2026 – Present
SQL for Data Analysis
Unknown
June 1, 2026 – Present
Kaggle - Intro to Machine Learning
Kaggle Learn
March 1, 2026 – Present
Kaggle - Data Cleaning
Kaggle Learn
February 1, 2026 – Present
Kaggle - Feature Engineering
Kaggle Learn
February 1, 2026 – Present
Cultural Fit Analysis
The candidate's projects show a blend of academic rigor (AQI prediction) and practical application (Automated Attendance System, Journey Junction). The pursuit of a Master's in Big Data Analytics and multiple Kaggle certifications indicates a strong drive for continuous learning and self-improvement, which aligns well with a growth-oriented culture. The diverse technical stack used across projects suggests an openness to new technologies and a broad interest in data-driven solutions. However, the lack of professional experience makes it challenging to fully assess cultural fit in a corporate environment.
Soft Skills & Operational Fit
The candidate demonstrates strong problem-solving skills through complex project implementations like the AQI prediction and automated attendance system. The ability to translate technical findings into actionable insights for stakeholders (AQI project) indicates good communication potential. The project diversity suggests adaptability and a proactive learning attitude. However, without direct work experience, operational fit regarding team collaboration, stress handling, and project management in a corporate setting is difficult to assess.