Freelancers using spaCy in Mumbai
Freelancers using spaCy in Mumbai
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Anurag Nagare
Mumbai, India
I’m an AI & Machine Learning engineer with expertise in deve
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I’m an AI & Machine Learning engineer with expertise in deve
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launched TextGenix Enterprise — an AI-powered intelligent document processing system! This platform enhances and transforms documents (PDF, DOCX, TXT, HTML, RTF) with context-aware vocabulary improvements, grammar validation, and industry-specific terminology (legal, medical, financial, technical). It comes with a sleek Gradio-based web interface featuring modern styling, interactive analytics dashboards, and real-time quality metrics like semantic preservation, grammar score, and AI confidence levels. If you’re looking to build your own AI-powered text/document platform, enhance business workflows with custom NLP models, or integrate analytics-driven AI solutions into your enterprise apps I can help.
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Neuro Lend My credit model scored 0.92 AUC. Then I changed one field on one applicant, female to male, and she went from approved to rejected. A lot of lending AI works like this: it says no in milliseconds, and nobody, not the customer and often not the bank, can say why. So I built NeuroLend. You fill in a loan application and watch the signal travel through every neuron until the output turns green or red. Then it shows why it decided, and the smallest change that would flip the answer. Rohan is rejected at 54% risk. If he cuts his other EMIs to ₹5,500 a month, or asks for ₹7.5 lakh instead of ₹9 lakh, he's approved. Here's what's happening underneath. It's a small 8 → 10 → 6 → 1 network trained on 40,000 synthetic Indian loan applications, where I deliberately gave gender zero effect on the outcome. The "why" uses Integrated Gradients against an average applicant instead of raw activations, because glowing neurons look great but explain nothing to a human. Counterfactuals come from a binary search on each input the applicant can actually change, and a fairness check re-scores every applicant with only gender flipped. That check caught the bias that accuracy missed. Retraining with gender cut out of the network dropped the effect to exactly zero, and AUC stayed at 0.92. It all runs in plain JavaScript in the browser, about 0.005 ms per decision, with no server. Code is in the comments. How does your team test lending models for bias today? Medium - https://medium.com/@anuragnagare77/neuro-lend-99cddd19faad GitHub - https://github.com/AnuragNagare/Neuro-Lend.git
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CryptoHorizon an interactive app that makes the quantum threat to encryption real instead of theoretical: a live Shor's-algorithm demo, a classical-vs-post-quantum crypto benchmark, a personal "harvest now, decrypt later" risk calculator, and a sourced Q-Day timeline, all in one dashboard. Defining feature is honesty by design: every number on screen is computed live at request time, never hardcoded RSA-2048 and ECC P-256 keys get generated, benchmarked, and compared against NIST's ML-KEM and ML-DSA in real time, and the quantum demo runs an actual Qiskit circuit against small, pre-validated semiprimes rather than a lookup table dressed up as a result. Only one thing is allowed to leak into the UI's language: a rule baked in from commit one that nothing may imply real-world RSA is broken or claim quantum advantage. Under the hood, Shor's algorithm runs for real superposition over a counting register, controlled modular exponentiation, inverse QFT, continued-fraction extraction, then a classical gcd step to pull out the factors on a FastAPI backend with no shortcuts. The risk calculator checks your required confidentiality window against five independently sourced Q-Day estimates from NIST, Google, and the Global Risk Institute, so "safe until 2035" is a real, cited comparison, not a guess. medium.com/@anuragnagare77/cryptohorizon-i-built-a-quantum-computer-that-factors-91-and-thats-the-whole-point-2c156a00d0da
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One Gate: Voice-controlled Gmail, Calendar, and Drive that can't fire the wrong thing Every "AI agent" demo shows a voice command turning into a sent email like magic, but almost nobody shows the part that actually matters: what stops it from sending the wrong thing. I built proof: an agent that runs your Gmail, Calendar, Contacts, and Drive by voice reply to a thread, schedule a meeting, archive an email, find a file hands-free end to end, but never fires anything irreversible without you saying so. The honest hard part isn't getting an LLM to sound smart, it's this: a transcript goes to LLM against a strict JSON schema and comes back as an ordered plan, every step tagged reversible or not and exactly one thing in the whole system is allowed to check that flag. Finding a thread, drafting a reply, creating a calendar event: those just run. The result covers real ground without ever feeling like it's guessing: forward or reply to email with the original quoted underneath, archive/label/trash, resolve a name to a real address through your Contacts first and your mail history as fallback, schedule an event that creates quietly and only emails the invite after a second confirmation, answer "what did John say" or "what's on my calendar Thursday" grounded in content actually fetched from your account not hallucinated. No backend, no server anywhere in the loop
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manjiri
Mumbai, India
🧬 Merging Life & Data Science for Unique Insights
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🧬 Merging Life & Data Science for Unique Insights
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NLP tool that ranks candidates against a job description using skill overlap, education match, experience and semantic similarity. Highlights Extracts skills, education and years of experience from resumes Gives each candidate a composite score using TF-IDF semantic similarity Recruiter dashboard for shortlisting
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SQL data mart for a 1,000-patient hypertension trial, tracking enrolment, adverse events, dropout and event-free survival. Highlights Normalised schema: sites, treatment arms, patients, visits, adverse events, outcomes SQL analytics for enrolment trends, adverse-event rates by arm, and dropout rates Survival-style queries for event-free outcomes
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Executive Power BI dashboard covering 5 years of hotel data across cities, booking channels, managers and loyalty members. Highlights Revenue, net profit, ADR and RevPAR in one executive view Manager benchmarking and booking-channel efficiency Guest satisfaction and loyalty member analysis
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End-to-end system that turns CRM tables into dropout predictions, with a 0–100 risk score for every student and a retention dashboard. Highlights MySQL feature layer: engagement score, academic score, enrolment tenure Random Forest model (ROC-AUC 0.74), with predictions written back to the database Power BI dashboard with risk segments, course-level risk and a priority intervention list
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