Freelancers using XGBoost in IndiaFreelancers using XGBoost in India
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Anurag Nagare's avatar
Anurag Nagare
Mumbai, India
I’m an AI & Machine Learning engineer with expertise in deve
I’m an AI & Machine Learning engineer with expertise in deve
Cover image for I’m excited to share AuditFlow AI – AI-powered continuous au...
I’m excited to share AuditFlow AI – AI-powered continuous auditing platform built specifically for Chartered Accountants and audit firms. CA's practices today are drowning in manual sampling, 40–60 hour audit cycles, talent shortages, and rising client pressure for faster delivery with lower fees. Most frauds and GST/TDS errors go undetected until the assessment stage because traditional methods check only 2–5% of transactions. AuditFlow AI changes that completely: upload any ledger/Excel/CSV and in under 10 seconds it scans 100% of transactions, flags duplicates, round-figure entries, weekend fraud, high-value anomalies, and vendor loops – with plain-English AI explanations for every red flag. Tech stack: Python, Flask, XGBoost, Isolation Forest, scikit-learn, Bootstrap 5, and trained on 5,000+ synthetic + real-world patterns
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Cover image for Everyone's building AR filters and
Everyone's building AR filters and calling it "computer vision magic." Almost nobody's asking what's actually happening underneath — that most of these effects are just clever masking, not detection. Here's proof. I built an invisibility cloak that runs entirely in the browser, no green screen, no chroma key, no model training. https://github.com/AnuragNagare/Ghost-frame
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Cover image for What your attention heatmap isn't
What your attention heatmap isn't telling you Everyone's staring at attention heatmaps and calling it "interpretability." Almost nobody's asking whether a single attention map actually tells you what the model used to make its decision. It doesn't. Not on its own. A raw attention map from layer 8 shows you what layer 8 attended to. It says nothing about how that signal got mixed, diluted, or overwritten by every layer before and after it. Attention rollout fixes this — and I built a walkthrough to show why it matters. Here's what makes it more than a "pretty heatmap" demo: Instead of visualizing one layer's attention, I traced how information actually flows through the full transformer stack. → Every layer's attention matrix is extracted, per head, per token → Multi-head attention is averaged, then combined with the residual connection (identity + attention) — this is the step most tutorials skip, and it's the one that actually matters → The combined matrices are matrix-multiplied layer by layer, rolling attention forward from input to output → The result: a single map showing genuine token-to-token influence across the entire network, not just one layer's snapshot The overlay shows you everything: → Per-layer attention vs. rolled-out attention, side by side → Token importance scores overlaid directly on the input text → A comparison view: which tokens raw attention says "matter" vs. which ones rollout says actually matter → Head-level breakdown so you can see which heads specialize vs. which are noise No black box. No "trust me, the model looked here." Just linear algebra, applied honestly across every layer instead of cherry-picking one. Built with PyTorch + HuggingFace Transformers + Matplotlib. Runs on any pretrained transformer, fully offline. ⚠️ Important: attention rollout is an approximation, not ground truth. It assumes attention is the primary information pathway, which ignores MLP layers and can still mislead for very deep models. Treat it as a debugging lens, not proof of causality.
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Cover image for Everyone's racing to add biometrics
Everyone's racing to add biometrics to logins. Almost nobody's asking what happens when you can't — or shouldn't — touch the sensor. Shared kiosks, clinical settings, accessibility needs, hygiene-sensitive environments. Fingerprint readers and face unlock assume contact or a stored faceprint. Sometimes you want authentication that touches nothing and stores no biometric image of you at all. So I built GestureAuth — a contactless authentication system where your "password" is a sequence of hand gestures performed in front of a standard webcam.
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Disha Chopra's avatar
Disha Chopra
Jaipur, India
Full-Stack Developer building AI Agents & RAG Systems
New to Contra
Full-Stack Developer building AI Agents & RAG Systems
Cover image for FinSense - AI-Powered NPA Prevention
FinSense - AI-Powered NPA Prevention Platform for Banking AI-powered, post-disbursement loan health monitoring system built for SBI-scale banking use cases. Continuously monitors borrower financial behavior after loan disbursement, scores risk on a 0–100 scale across 4 tiers using XGBoost with SHAP-based explainability, and deploys a conversational AI agent to proactively engage at-risk borrowers 60–90 days before default — instead of only scoring creditworthiness at application time like traditional systems. Built the FastAPI backend (loan, risk, agent, and dashboard routers), automated risk re-scoring via Celery + Redis, and real-time dashboard updates via WebSockets, with a React + Tailwind frontend for officer-facing risk visualization.
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Cover image for Financial Decision Intelligence Platform —
Financial Decision Intelligence Platform — Multi-Agent Risk Analysis Multi-agent platform (LangGraph) that ingests SEC 10-K filings via XBRL extraction, runs Altman Z-Score and Piotroski F-Score risk models, and generates investment committee reports. Pipeline includes RAG-based risk retrieval (FAISS + Sentence Transformers), XGBoost ML inference, and Gemini API report generation — served through a FastAPI backend with /analyze, /compare, and /report endpoints for multi-company investment ranking. Stack: Python, LangChain, LangGraph, FAISS, XGBoost, FastAPI
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Cover image for RepoSphere - GitHub-Inspired Version Control
RepoSphere - GitHub-Inspired Version Control Platform Full-stack, GitHub-inspired platform with JWT authentication, repository management, issue tracking, and real-time search. Built a RESTful API with user dashboards, and reduced code redundancy by 35% through modular architecture. Deployed on AWS Amplify.
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Cover image for SENTINEL - Edge AI Driver
SENTINEL - Edge AI Driver Assistance System Backend Lead for an edge AI ADAS (driver assistance) system built for Indian road conditions. Multi-stage ML pipeline covering sensor fusion, scene classification, risk scoring, GradCAM/XAI explainability, and federated learning. Built the backend and deployment pipeline, integrating ONNX and PyTorch runtimes to serve model inference in production.
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Neeraj Jaiswal's avatar
Neeraj Jaiswal
Chandigarh, India
A Machine Learning,research paper writing and web developer.
A Machine Learning,research paper writing and web developer.
Cover image for Titanic Survival Prediction - Streamlit App
Titanic Survival Prediction - Streamlit App
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Cover image for Supply Chain Risk and Inventory Management Dashboard
Supply Chain Risk and Inventory Management Dashboard
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Cover image for AI-Powered CRM System
AI-Powered CRM System
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Neha Madrala's avatar
Neha Madrala
Pimpri-Chinchwad, India
Power BI Data Analyst turning data into insights
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Power BI Data Analyst turning data into insights
Cover image for Medicine Supply Chain Disruption Predictor Built
Medicine Supply Chain Disruption Predictor Built a production-ready ML system that predicts drug shortage risk using real FDA data. Collected and cleaned 1703 real drug shortage records, trained an XGBoost model, and deployed it as a live REST API — accessible to anyone worldwide. The biggest challenge was identifying and removing 6 sources of data leakage that were causing fake 100% accuracy. After fixing this, the model delivers honest, generalisable predictions. The entire system is containerised with Docker, automatically rebuilt and redeployed via a Jenkins CI/CD pipeline on every code push, and visualised through an interactive Power BI dashboard. Result: A complete ML + DevOps project — from raw data to live deployed API — built independently in under 2 weeks. Live API: https://medicine-supply-predictor.onrender.com/docs GitHub: https://github.com/nehaM906/medicine-supply-predictor
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Cover image for Revolutionize Sales Data with BizPulse AI Dashboard
BizPulse — AI-Powered Business Analytics Dashboard Built for the Anything Ship & Sell Remixathon on Contra. What is BizPulse? An AI-powered business analytics tool that transforms messy, raw sales data into a clean professional dashboard instantly — no coding or spreadsheet skills needed. How it works: Upload any messy CSV or Excel file BizPulse auto-cleans your data and maps your columns Get a full analytics dashboard with KPIs, charts and AI insights Key Features: Business Health Score out of 100 KPI scorecards — Revenue, Expenses, Profit Margin, Customers 6 interactive charts with hover tooltips 5 AI-generated plain English business insights Revenue Goal Tracker with live progress bar Export as PDF and CSV No dataset? No problem! Click "Try Sample Data" on the upload screen to instantly load a realistic fictional dataset and explore the full dashboard. @Anything Try it here: https://bizpulse-879.created.app/ LinkedIn post: https://www.linkedin.com/posts/neha-madrala_anythingremixathon-buildinpublic-nocode-ugcPost-7453420153456762882-P2ve Demo Video: https://www.loom.com/share/3574386d73924ac59076493b055570c1
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Cover image for Transform Your Analytical Skills with Business Insight Mastery
The biggest shift in my learning hasn’t been technical it’s been mindset. I used to think good analysts are the ones who know more tools. Now I realize they’re the ones who understand the business better. Anyone can create charts. Not everyone can explain why it matters. That’s what I’m working on every day.
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Cover image for Data is useless. Yes, it
Data is useless. Yes, it is. Because on its own, it doesn’t say much. It’s just numbers, rows, and columns. The real value comes when that data is understood — when it answers a question, shows a pattern, or drives a decision. That’s what turns data into information. And that’s what businesses actually need.
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Madhur Goel's avatar
Madhur Goel
Gurugram, India
Power BI & ML Expert | Dashboards & Predictions
Power BI & ML Expert | Dashboards & Predictions
Cover image for PUBG Game Winner Prediction with Machine Learning
PUBG Game Winner Prediction with Machine Learning
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Cover image for Power BI Dashboard for Binge and Bash
Power BI Dashboard for Binge and Bash
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Cover image for Movie Recommendation System using Machine Learning
Movie Recommendation System using Machine Learning
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Aritra Mondal's avatar
Aritra Mondal
Kolkata, India
Building responsive apps and giving technology solutions.
Building responsive apps and giving technology solutions.
Cover image for Credit Card Fraud Detection System Development
Credit Card Fraud Detection System Development
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Cover image for GitHub - we-re-wolf/Course-Allocation-System: A modern web appl…
GitHub - we-re-wolf/Course-Allocation-System: A modern web appl…
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Cover image for DreamHome AI Property Recommendation Tool
DreamHome AI Property Recommendation Tool
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Mohammad Umar's avatar
Mohammad Umar
India
Freelance Data Scientist | Python & ML Expert
10
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Freelance Data Scientist | Python & ML Expert
Cover image for Lung Cancer Survival Prediction Model Development
Lung Cancer Survival Prediction Model Development
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Cover image for Fraud Transaction Detection System
Fraud Transaction Detection System
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Cover image for Hybrid AI Movie Recommendation System for Pre-2015 Films
Hybrid AI Movie Recommendation System for Pre-2015 Films
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