Freelancers using Dash Plotly in Mumbai
Freelancers using Dash Plotly 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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CricVision AI - Advanced Cricket Analytics Dashboard! CricVision AI is an intelligent cricket analytics platform that leverages machine learning to provide real-time match predictions and comprehensive player insights. The dashboard predicts wicket probability, expected runs per ball, and boundary likelihood using three trained ML models with StandardScaler normalization. It features interactive match scenarios (Powerplay, Middle, and Death overs), over-by-over projections, win probability calculations, and economy rate forecasts. Users can compare players head-to-head, analyze form trends over recent innings, visualize run distribution through wagon wheels, and get AI confidence scores for all predictions - making it a complete solution for cricket enthusiasts and analysts.
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I created WealthWise Agent, a smart personal finance planner designed to craft personalized budget plans and investment strategies. This app takes into account user inputs like salary, expenses, and financial goals, and then uses a Large Language Model (Gemini) to analyze these factors based on the 50/30/20 budgeting rule. It offers a clear step-by-step reasoning log, a detailed JSON-structured financial plan, and an interactive visualization of budget allocation, empowering users to make informed choices to reach their financial goals. 💻 Tech Stack Used: Frontend/UI: Gradio (custom themed with CSS, Orbitron font) AI/Logic: Google Gemini (gemini-1.5-flash) with LangChain agents Data: yFinance API for real-time stock/ETF data, Pandas & NumPy for calculations Visualization: Plotly Express for interactive charts
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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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It all started on a Sunday at the AWS User Group Mumbai meetup. I wasn't expecting to walk away with a new obsession, but then the speaker introduced me to Temporal and everything changed. Temporal is a durable execution engine that solves one of the hardest problems in agentic AI what happens when your LLM workflow crashes mid-run? Normally you lose everything So I went home and built this: an agent that monitors your competitors around the clock tracking pricing changes, product launches, hiring signals, and strategic moves. Every 24 hours it uses Mistral (running fully on-device via Ollama) to analyze the data and synthesize a structured executive briefing delivered straight to your inbox. Sometimes the best projects start with a Sunday conversation. https://github.com/AnuragNagare/Agentic-AI-.git
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Dash Plotly
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Aniket More
Mumbai, India
Data Science/Analysis, Excel, Python, Power Bi, Scikit learn
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Data Science/Analysis, Excel, Python, Power Bi, Scikit learn
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Below is an interactive web based dashboard, using Python pandas, plotly dash. The graphs are interactive and gets updated automatically using the drop down provided.
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Hello All, Below is a sample Dashboard for a sales data prepared using excel. It uses data visualizations in excel and interactive slicers to modify the visuals.
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Here is the Capstone project I have got hands on experience on while completing professional certification course. It web Scrapes data from web for falcon rockets and builds a predictive model using machine learning algorithm using scikit learn model
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This is a practice project I worked on to exclusive on power bi to make a simple dashboard to have an interactive visual, with date sliders. With metrics like revenue per passengers, and passenger by date
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Dash Plotly
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