I’m Siddhi Chudasama, a software engineer with 2+ years of experience building production Android and backend solutions. I specialize in MVVM architecture, clean design practices, and real-time REST API pipelines using Java/Kotlin and Python, with a strong focus on secure integrations through Firebase.
I’ve also worked on AI-powered fraud detection and user behavior analysis, where I designed an anomaly-detection approach with scikit-learn, exposed it via REST APIs, and iteratively improved results to reduce false positives. Beyond development, I’ve contributed as a UX/UI designer, delivering end-to-end designs in Figma and collaborating closely with engineers to ensure pixel-accurate implementation.
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I developed an AI-powered Fraud Online Transaction Detection System that combines a robust Android client with a machine learning backend. Key Features & Technical Implementation: Backend & ML Engine: Built an anomaly detection model using Python and scikit-learn to analyze historical transaction datasets and identify risky behaviors. API Integration: Exposed the ML model via secure REST APIs to communicate seamlessly with the Android mobile application.Android Frontend: Designed and developed the user interface using Java, XML, and Material Design 3 guidelines for a smooth user experience.Security & Real-time Alerts: Integrated secure Firebase Authentication for user accounts, Firestore for data syncing, and implemented real-time push alerts for high-risk flags.Impact: Successfully reduced false-positive fraud flags through iterative model tuning and user behavioral analysis.Tech Stack Used: Java, Android SDK, Python, scikit-learn, Firebase Auth, Firestore, REST APIs, XML, Material Design 3.
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