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Assessing your cultural and operational fit
CreditWise-Loan-Approval-System
May 29, 2026 – Present
Built an end-to-end supervised machine learning pipeline using KNN, Logistic Regression, and Naive Bayes to predict loan approval. Implemented binary classification along with exploratory data analysis (EDA), feature engineering, data preprocessing, and model evaluation using Precision, Recall, and F1-Score metrics.
View ProjectMachine_Learning
April 10, 2026 – Present
Documenting my journey of learning and building in Machine Learning.
View Projectai_recommendation_system
April 4, 2026 – Present
Real-time recommendation system with RAG and agentic AI
View Projectrepolens
April 2, 2026 – Present
AI-powered GitHub repository analyzer for architecture, risk & insights
View Projectsentinel-stream
April 2, 2026 – Present
Real-time fraud detection and decision system using ML + Reinforcement Learning
View ProjectSHE-App-AI-Women-Safety
January 22, 2026 – Present
AI-powered women safety application that proactively detects distress using voice and motion signals, triggers real-time SOS alerts, and builds trusted community support using Flutter, FastAPI, Firebase, and PyTorch.
View ProjectCultural Fit Analysis
The candidate shows a strong inclination towards AI/ML and data science projects, which might align with roles requiring innovation and problem-solving in these areas. The diversity of projects, from loan approval systems to women's safety apps, suggests a broad interest in applying technology to various problems. However, the lack of team-based projects or professional experience makes it difficult to fully assess cultural fit in a collaborative engineering environment.
Soft Skills & Operational Fit
Insufficient data to assess soft skills and operational fit. The candidate's project descriptions indicate a proactive approach to learning and building, particularly in AI/ML domains.