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Mohsen M's avatar
Mohsen M
Italy
AI Chatbot Developer | Data Scientist | ML Engineer
AI Chatbot Developer | Data Scientist | ML Engineer
Cover image for Incident Response Fairness Analysis
Incident Response Fairness Analysis
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Cover image for Machine Learning for Bone Tumor Diagnostics
Machine Learning for Bone Tumor Diagnostics
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Cover image for Electricity Consumption Prediction for Malmi Office Building
Electricity Consumption Prediction for Malmi Office Building
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Dash Plotly(3)
Istiak Ahmed Khan's avatar
Istiak Ahmed Khan
Dhaka, Bangladesh
ML AI Automation Expert + Data Analyst
46
Followers
ML AI Automation Expert + Data Analyst
Cover image for Saving Lives through Early Clinical
Saving Lives through Early Clinical Risk Detection Application is live at: https://495a-35-237-151-197.ngrok-free.app The Problem: Heart failure is a leading cause of global mortality; the difference between survival and fatality often depends on how early a high-risk patient is identified. The Solution: I developed a clinical diagnostic tool that analyzes patient health markers to forecast mortality risk, assisting medical professionals in prioritizing life-saving care. Key Impacts: Early Intervention: The model identified that follow-up time is the single most critical factor in reducing fatalities, emphasizing the need for early diagnosis and consistent monitoring. Precision Diagnostics: By analyzing heart efficiency (ejection fraction) and chemical markers like serum creatinine, the tool provides a high-accuracy (84.49%) risk score for every patient. Clinical Support: The system helps doctors look past "statistical flukes" by accounting for outliers in medical data, ensuring that extreme clinical cases are caught rather than ignored. Actionable Health Insights: Demonstrated a clear link between age, heart efficiency, and chemical abundance, giving providers a data-driven framework to improve long-term patient outcomes
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Cover image for Email Marketing Analytics Dashboard –
Email Marketing Analytics Dashboard – UI/UX Design Struggling to track campaign performance across multiple channels? This dashboard is designed to give you a complete, real-time view of your marketing efforts in one clean and intuitive interface. A powerful, easy-to-use dashboard that helps you monitor email, SMS, social media, and push campaigns without the confusion of scattered data. Every key metric is presented clearly so you can make faster, smarter decisions. Key Capabilities: Track open rates, click rates, conversions, and revenue in real time, Compare performance across multiple marketing channels, Identify your top-performing campaigns instantly, Understand audience engagement with clear visual breakdowns, Spot trends and optimize campaigns quickly. Most businesses run campaigns but struggle to understand what’s actually working. This dashboard eliminates guesswork by turning your data into clear, actionable insights — helping you improve ROI and scale winning strategies. Perfect For: Digital marketers, E-commerce brands, Agencies managing multiple campaigns, Startups looking to optimize growth. If you want a high-converting, professional dashboard that not only looks great but drives real business decisions — I can help you build it.
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Cover image for CarePoint Medical Dashboard The CarePoint
CarePoint Medical Dashboard The CarePoint Medical Dashboard is a modern, user-friendly healthcare interface designed to streamline patient management and hospital operations. The UI focuses on clarity, accessibility, and real-time data visibility for medical professionals. The dashboard features a clean layout with intuitive navigation, allowing users to quickly access key modules such as patients, appointments, doctors, and reports. A soft green color palette enhances readability while aligning with healthcare aesthetics. Real-time KPI cards for patients, appointments, and revenue Patient status tracking with visual indicators (Critical, Stable, Moderate) Monthly admissions trend analysis Upcoming appointments and recent patient records Department-wise distribution insights The interface is designed for efficiency, reducing cognitive load through organized sections and clear visual hierarchy. Interactive elements and minimalistic design ensure smooth navigation and quick decision-making. This dashboard improves operational efficiency, enhances patient monitoring, and supports healthcare professionals in delivering timely and data-driven care.
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Cover image for Predictive Marketing Analytics: Optimizing Advertising
Predictive Marketing Analytics: Optimizing Advertising ROI 1. The Business Problem Companies often struggle to determine which marketing channels actually drive revenue. Without a data-driven approach, advertising budgets are often misallocated across platforms like TV, Radio, and Newspapers, leading to inefficient spending and missed sales targets. This project aimed to build a predictive model to quantify the relationship between multi-channel marketing spend and total sales. 2. Strategic Insights & Market Analysis Through a rigorous analysis of historical advertising data, I identified the specific drivers of revenue growth: Dominant Revenue Driver: TV advertising emerged as the most critical factor, showing a massive 0.9 correlation with sales. Efficiency Analysis: While Radio and Newspaper spending contributed to the marketing mix, their direct impact on sales was significantly lower (0.35 and 0.16 correlation, respectively), suggesting a need for budget reallocation. Predictive Power: My analysis revealed that 81.6% of the variance in sales can be explained by TV advertising spend alone, providing a highly reliable foundation for future budget forecasting. 3. Data-Driven Solution I developed a Linear Regression model to provide leadership with a mathematical framework for sales forecasting. Reliability: The model was validated using a 70/30 train-test split, ensuring it performs accurately on new, unseen market data. Accuracy: The system achieved a strong R-squared value of 0.79 on the test set, meaning it can accurately predict nearly 80% of sales fluctuations based on planned marketing spend. Error Management: I performed a detailed residual analysis to confirm that the model’s error terms were normally distributed, ensuring the reliability of the forecasted figures. 4. Business Impact Budget Optimization: Provided a clear mathematical equation (Sales=6.948+0.054×TV) that allows the marketing team to calculate the expected return on every dollar spent on TV advertising. Strategic Planning: Enabled the transition from "gut-feeling" marketing to precision budgeting, allowing the company to maximize ROI by prioritizing high-impact channels. Risk Mitigation: By identifying the variance that the model couldn't explain, I helped the business identify where external market factors might still influence sales, allowing for more conservative and realistic financial planning. Technical Stack Modeling: Simple Linear Regression, Statsmodels (OLS), Scikit-learn. Analytics: Python, Pandas, NumPy. Visualization: Seaborn, Matplotlib, 3D Scatter Plots
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Dash Plotly(1)
Anurag Nagare's avatar
Anurag Nagare
Mumbai, India
I’m an AI & Machine Learning engineer with expertise in deve
New to Contra
I’m an AI & Machine Learning engineer with expertise in deve
Cover image for CricVision AI - Advanced Cricket Analytics Dashboard! CricVi...
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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Cover image for I created WealthWise Agent, a smart personal finance planner...
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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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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Cover image for It all started on a
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(2)
Imaad Mahmood's avatar
Imaad Mahmood
Bahawalpur, Pakistan
Data Scientist | LLM Specialist | Machine Learning
Data Scientist | LLM Specialist | Machine Learning
Cover image for FinCast Pro Development
FinCast Pro Development
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Cover image for Built a full YouTube analytics
Built a full YouTube analytics dashboard in Google Looker Studio using real channel data from Dark Psychology Archive. The dashboard tracks 6 KPIs — Views (18,582), Watch Time (51.2 hrs), CTR (4.32%), Likes (1,302), Subscribers Gained (105), and Engagement Rate (10.31%). A monthly trend line reveals the growth curve peaking at 4,266 views in November. Content-type breakdown shows Shorts drive 71% of total views. Top performer: Hoovering at 1,232 views with 8.2% CTR. Built to help content creators make data-driven decisions on what to post, when, and in what format — without exporting CSVs manually.
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Cover image for HR teams spend hours every
HR teams spend hours every week pulling salary, performance, and turnover data from separate spreadsheets. This dashboard puts all of it in one place — live. Built this HR Analytics Dashboard in Google Looker Studio with 5 KPI cards, department salary breakdown, geo distribution map, and a ranked leaderboard sorted by total compensation. Engineering leads at $1.05M total salary. Finance close behind. Decision-makers can see that in 3 seconds instead of 3 hours.
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Cover image for Built an interactive Sales Performance
Built an interactive Sales Performance Dashboard in Google Looker Studio — fully connected to live data. Features 6 KPI cards (Revenue, Profit, Units Sold, Margin, Orders, Avg Order Value), revenue trend line chart, category donut chart, region breakdown, and salesperson leaderboard.
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Dash Plotly(1)
Mohamed MSALEK's avatar
Mohamed MSALEK
Kiel, Germany
Junior Data Scientist driving innovation
10
Followers
Junior Data Scientist driving innovation
Cover image for AI-Powered Predictive Maintenance System
AI-Powered Predictive Maintenance System
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Cover image for E-commerce Customer Behavior Analysis & Segmentation
E-commerce Customer Behavior Analysis & Segmentation
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Cover image for AI-Enhanced Personal Nutrition Assistant Mobile App
AI-Enhanced Personal Nutrition Assistant Mobile App
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IRENE MAINA's avatar
IRENE MAINA
Nairobi, Kenya
Data Analyst provided data driven decisions to stakeholders.
23
Followers
Data Analyst provided data driven decisions to stakeholders.
Cover image for House Price Analysis for Value Improvement
House Price Analysis for Value Improvement
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Cover image for Tanzanian Water Well Data Analysis Project
Tanzanian Water Well Data Analysis Project
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Cover image for https://github.com/Brene-m/AWS_Restatrt
https://github.com/Brene-m/AWS_Restatrt
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Cover image for Call Volume Prediction Model for Mtoto News
Call Volume Prediction Model for Mtoto News
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Dash Plotly(2)