Freelancers using Python in Thane

Freelancers using Python in ThaneFreelancers using Python in Thane

Projects People

Abu Aasif Ansari

Bhiwandi, India

I build AI-powered data apps and dashboards

6: Followers

I build AI-powered data apps and dashboards

Cover image for Superstore Sales Dashboard — 3-Page

Superstore Sales Dashboard — 3-Page Power BI Report Built an interactive 3-page Power BI dashboard using the Superstore Sales dataset (9,994 orders across USA). Page 1 — Sales Overview: KPI cards, monthly trend, regional breakdown, category performance. Page 2 — Product Performance: Top products, category donut, profit analysis, sales vs profit scatter. Page 3 — Customer & Shipping: Segment breakdown, top 10 customers, monthly growth, ship mode distribution. Tools: Power BI, DAX, Superstore Dataset Theme: Dark professional with interactive year filter.

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Cover image for AI-Powered Data Cleaning Tool Development

AI-Powered Data Cleaning Tool Development

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Cover image for AI-Powered Data Cleaning Tool

AI-Powered Data Cleaning Tool

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Cover image for Smart Data Analyst — AI-Powered Data Analysis App

Smart Data Analyst — AI-Powered Data Analysis App

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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 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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Cover image for Everyone's talking about AI in

Everyone's talking about AI in healthcare. Nobody's building low-cost tools for the people who actually need early answers. Neurologists are overbooked. Clinical tremor assessments require in-person visits, specialist equipment, and months of waiting. The 10 million people living with Parkinson's globally and the millions more who don't yet know have no accessible way to flag early symptoms from home. So I built TremorLens a real-time hand tremor detection tool that runs entirely on a standard webcam. Here's what makes it more than just a webcam project: Instead of simple motion detection, I built a full signal processing pipeline on top of computer vision. MediaPipe tracks 21 hand landmarks per frame. The index fingertip's x/y displacement is buffered across a 3-second rolling window. scipy FFT then decomposes that signal into its frequency components and flags dominant activity in the 4–6 Hz range clinically associated with Parkinson's resting tremors. The live overlay shows you everything: → Real-time FFT power spectrum with the tremor zone highlighted → Dominant frequency readout in Hz with a 10-frame rolling average for stability → Color-coded STABLE / TREMOR DETECTED indicator → Fingertip displacement graph and movement trail → Auto-saved CSV session log timestamp, frequency, amplitude, tremor flag every session

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Cover image for Everyone's talking about quantum computing.

Everyone's talking about quantum computing. Nobody's using it to feed farmers. India loses 20–30% of its crop yield every year to diseases and pests. Not because farmers don't care — but because early detection is hard, expensive, and inaccessible to the people who need it most. The existing solutions? Either a basic image classifier trained on lab-perfect photos that fail in real field conditions, or an agronomist visit that costs time and money most small farmers don't have. So I built QuantumEdge AgriGuard — a hybrid Quantum Neural Network app where a farmer can photograph a diseased leaf on their phone and get an instant diagnosis in under 5 seconds. Here's what makes it different from just another plant disease detector: Instead of a pure classical CNN, I built a hybrid architecture — a ResNet/EfficientNet backbone extracts visual features, then passes them into a Variational Quantum Circuit (VQC) for the final classification. The quantum layer uses angle embedding + StronglyEntanglingLayers, which gives it a measurable edge on small, noisy datasets — exactly the kind of data you get from Indian field conditions. The app doesn't just tell you what disease it is. It gives you: → Confidence score → Organic + chemical remedies (India-specific) → Yield impact estimate → A live classical vs quantum accuracy comparison so you can see the difference yourself I tested the quantum advantage claim honestly — ran both models on the same downsampled PlantVillage dataset and tracked accuracy, F1-score, and inference time side by side. The results are on the dashboard. No hand-waving. Built with PennyLane + PyTorch + Plotly Dash. Designed to run on simulators today and on QpiAI-Indus 25-qubit hardware tomorrow.

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Cover image for Most AI research tools are

Most AI research tools are just a chatbot with a search button. I built something different. Every time you ask an AI to research something, you're getting one model, one pass, no quality check. It writes confidently, cites poorly, and you have no idea if what it produced is actually accurate. For anyone making real decisions from AI-generated research, that's a silent risk most people ignore. The problem gets worse at scale the longer and more complex the question, the more a single model hallucinates, misses sources, and loses structure. There's no one checking its work. So I built ResearchOS a 5-agent pipeline where each agent has one job. A Supervisor breaks down your question. A Researcher runs parallel searches across 22+ sources. An Analyst extracts data and auto-generates charts. A Writer synthesises a cited report. A Critic fact-checks it and sends it back for revision if anything is wrong. The loop runs up to 3 times before the report is approved. One question in. A full cited report with charts and PDF export in under 10 minutes. I tested it live by watching the Critic catch a missing citation mid-run and send the Writer back to fix it before approval. That's the part that makes this actually usable for real work. Built on LangGraph, Groq, Tavily, ChromaDB and runs entirely on free tiers.

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FastAPI, AI & RAG Engineer helping startups ship faster

New to Contra

FastAPI, AI & RAG Engineer helping startups ship faster

Cover image for Shadow Watch — Behavioral Intelligence

Shadow Watch — Behavioral Intelligence & Account Security Platform Description Shadow Watch is a behavioral intelligence platform that continuously analyzes user interaction patterns to build behavioral profiles, detect anomalies, and strengthen account security without introducing friction to the user experience. Instead of relying solely on passwords or one-time verification checks, Shadow Watch observes behavioral continuity over time to establish trust signals and identify potentially compromised sessions. The system combines behavioral fingerprinting, activity analysis, risk scoring, and anomaly detection to help organizations recognize suspicious account activity before it escalates into an account takeover event. Key Features Behavioral fingerprint generation User activity intelligence Trust score calculation Session anomaly detection Account takeover prevention Temporal behavior analysis Privacy-conscious architecture Tech Stack Python • FastAPI • PostgreSQL • Security Engineering • Behavioral Analytics • Risk Scoring

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Cover image for Grid CLI is a terminal-first

Grid CLI is a terminal-first developer platform designed to streamline repository management, project automation, diagnostics, and AI-assisted workflows through a unified command interface. Built around a custom shell architecture, Grid CLI enables developers to initialize projects, audit repositories, execute automated tasks, manage environments, and interact with AI-powered development tooling without leaving the terminal. The project focuses heavily on developer experience, modular command design, extensibility, and productivity automation. Key Features Custom interactive shell Repository diagnostics and auditing Project initialization workflows Automation and task execution Modular command architecture AI-assisted developer operations Cross-project workspace management Tech Stack Python • CLI Engineering • Automation • Git • Developer Tooling • Terminal UI

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Cover image for Quant Terminal is a portfolio

Quant Terminal is a portfolio analytics and asset management platform designed to help investors monitor holdings, analyse diversification, and evaluate portfolio allocation strategies. The platform provides a real-time view of portfolio performance while offering allocation analysis across multiple asset classes including equities, commodities, cryptocurrencies, and foreign exchange markets. Key Features: • Portfolio holdings dashboard • Real-time P&L monitoring • Diversification analysis • Portfolio drift detection • Strategy-based portfolio views • Asset allocation management • CSV export functionality • Interactive desktop interface Tech Stack: Python, Data Analytics, Portfolio Modelling, Financial Data Processing, Desktop Application Development Role: Sole Developer and System Designer Outcome: Built a functional portfolio management terminal capable of tracking holdings, visualising allocation drift, and supporting portfolio rebalancing decisions through quantitative analysis.

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Cover image for Anchor is an AI governance

Anchor is an AI governance platform designed to provide deterministic policy enforcement for AI systems and autonomous agents. The platform focuses on a critical problem facing enterprises adopting AI: ensuring that model-driven decisions remain explainable, auditable, and compliant with organizational policies. Key capabilities include: • Constitutional governance architecture • Policy-based execution controls • Deterministic decision validation • Replayable audit trails • Entity visibility and access governance • Runtime compliance verification • Governance observability and traceability The system is designed around the principle that governance decisions should be independently verifiable rather than dependent on opaque model behaviour. Tech Stack: Python, FastAPI, PostgreSQL, Docker, JWT Authentication, Audit Logging, Governance Engine Design Role: Founder, System Architect, Backend Engineer Project Links: Website: https://www.animuslab.dev (https://www.animuslab.dev)GitHub: https://github.com/AnimusLab/Anchor Research: https://zenodo.org/records/19734724

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Tanish Khandelwal

Data Guru. AI Solutions. ML Engineer.

Data Guru. AI Solutions. ML Engineer.

Cover image for tknishh/payclosur

tknishh/payclosur

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Cover image for tknishh/uber-mage-data-analysis

tknishh/uber-mage-data-analysis

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Cover image for tknishh/FileWise

tknishh/FileWise

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TechAPeek Services

🔍 Transforming Data into Business Insights

🔍 Transforming Data into Business Insights

Cover image for Dynamic Advertisement Overlay

Dynamic Advertisement Overlay

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Cover image for Billing Management System

Billing Management System

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Cover image for HUMOUR DETECTION USING NLP

HUMOUR DETECTION USING NLP

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Sanchet Nagarnaik

Versatile full-stack developer exploring Blockchain and AI.

5.0: Rating

Versatile full-stack developer exploring Blockchain and AI.

Cover image for Chess Delay

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Cover image for PhilTor - Phil Dunphy inspired Realtor mobile app

PhilTor - Phil Dunphy inspired Realtor mobile app

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Cover image for MongoDB Masking

MongoDB Masking

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Cover image for Social Media Integration

Social Media Integration

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Co-founder | AI Dev @ BubblebitX ⚡

Co-founder | AI Dev @ BubblebitX ⚡

Cover image for SoulResurrection: Digitizing Consciousness

SoulResurrection: Digitizing Consciousness

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Cover image for GameCre8 - AI (text to 2D games)

GameCre8 - AI (text to 2D games)

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Cover image for PantryPal App Development

PantryPal App Development

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Cover image for TrustRent Lite MVP Development

TrustRent Lite MVP Development

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Technical Virtual Assistant & Web Operations

New to Contra

Technical Virtual Assistant & Web Operations

Cover image for AI Lead Generation Workflow

AI Lead Generation Workflow

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Cover image for SEO Automation Workflow for Live Keyword Mining

SEO Automation Workflow for Live Keyword Mining

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Cover image for Developed an application designed to

Developed an application designed to capture and analyze niche, long-tail conversational keywords frequently utilized within AI interfaces. Because traditional SEO tools often overlook conversational search intent phrases, this application maps and logs specific semantic variations into structured database layouts, enabling digital creators and businesses to optimize their content strategies for next-generation search behaviors.

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Cover image for Designed and structured a comprehensive

Designed and structured a comprehensive comparative matrix detailing the unique capabilities of leading large language models (ChatGPT, Gemini, and Claude). This asset translates complex technical model parameters—such as context capacities, automation layers, and native execution engines—into highly scannable, engaging visual documentation tailored for digital platforms and tech-focused audiences.

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