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Widada Widada's avatar
Widada Widada
Yogyakarta, Indonesia
Data automation for real estate, e-com & SaaS
$25k+
Earned
2x
Hired
5.0
Rating
39
Followers
Data automation for real estate, e-com & SaaS
Cover image for Fullstack Developer & Data Engineer
Fullstack Developer & Data Engineer
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Cover image for Nexus CRM. a CRM dashboard
Nexus CRM. a CRM dashboard template built with Next.js 16, TypeScript, Tailwind CSS v4, and shadcn/ui. 15 pages including a Kanban lead board, deal and contact detail views, and analytics charts. Dark and light theme, responsive across mobile to desktop, fully typed with strict mode. Mock data is structured to mirror a real API response, so swapping in a backend doesn't mean rewriting the UI. Check live version here: https://nexuscrm1.netlify.app/dashboard
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40
Cover image for Fullstack Development: Build Scrapers and AI Integration
Fullstack Development: Build Scrapers and AI Integration
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16
Cover image for n8n Zillow Scraper for Property Managers
n8n Zillow Scraper for Property Managers
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17
scikit-learn(1)
Hyacinth Ampadu's avatar
Hyacinth Ampadu
Ghana
Senior Agentic AI Architect | Multi-Agentic + Voice
Senior Agentic AI Architect | Multi-Agentic + Voice
Cover image for Transforming Leads into Customers with Predictive Modeling
Transforming Leads into Customers with Predictive Modeling
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11
Cover image for KidFriendlySocial - Making social media a safe place for Kids
KidFriendlySocial - Making social media a safe place for Kids
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32
Cover image for Forecasting NFT Trade Performance
Forecasting NFT Trade Performance
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9
Cover image for Optimizing Rail Safety
Optimizing Rail Safety
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27
scikit-learn(4)
kaze nesia's avatar
kaze nesia
Surabaya, Indonesia
Full-Stack Data Specialist | Automation & Predictive
Full-Stack Data Specialist | Automation & Predictive
Cover image for AI Student Success Intelligence Platform A
AI Student Success Intelligence Platform A twelve‑module analytics platform analyzed 50,000 learners across six countries to predict dropout, model engagement, and simulate interventions, finding that a composite Student Engagement Index (SEI)—built from Time Commitment, Academic Quality, Platform Activity, and Social Learning—is the strongest predictor of dropout (behavior beats demographics), an ensemble of XGBoost/LightGBM/CatBoost achieved 99.72% AUC and F1 = 0.9522, risk tiers were highly precise (Low Risk = 0.0% dropout; Critical Risk = 99.7%), multi‑dimensional “Full Interventions” produced the largest simulated risk reductions, and correcting a data‑leakage issue (attendance proxy) was essential to preserve model integrity.
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234
Cover image for Advanced Crypto Market Signal Engine with High Precision SELL
Crypto Market Intelligence & Alpha Signal Engine An end-to-end Colab pipeline ingests market feeds, engineers 43 signals, detects regimes (KMeans/HMM/GMM), models 24‑hour alpha with a Random Forest (ROC‑AUC 0.7714), flags anomalies (Isolation Forest/Autoencoders), and backtests strategies; key findings: the SELL signal is highly precise (only 5.14% of SELLs rose next day), anomalies are often bullish (36.87% up vs 25.49% normal), price‑level context and regime probabilities drive predictions, and the model favors low‑volatility, defensive assets during downturns.
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183
Cover image for Global Retail Intelligence System: Product
Global Retail Intelligence System: Product Success Prediction and Strategic Market Analysis A multi-stage ML pipeline analyzed 44,888 Adidas SKUs using XGBoost and Random Forest to predict product success, demand trajectories, and stockout risk, finding that subcategory is the dominant success driver (~6× more explanatory than price, discount, or geography), the Success Classifier reached 94.3% accuracy and the Stockout Risk model 0.99 ROC‑AUC, 42.5% of products carry markdowns with deep discounts (≥30%) often eroding margins, 323 high-performing SKUs are under‑distributed and present near‑term expansion opportunities, the Budget tier outperforms Premium/Luxury in conversion to high performers, and 653 SKUs were flagged as high demand with elevated stockout risk requiring urgent replenishment.
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132
Cover image for AI-Driven Global Smartphone Sales Strategy
AI-Driven Global Smartphone Sales Strategy Optimizer An end-to-end ML project used four years of global sales data and 132,000+ simulations to optimize pricing across 52 countries, identifying the exact product, channel, and price to maximize profit. Key findings: the “Discount Myth”—discounting has almost no effect on volume but erodes margins; switching from blanket 20% discounts to AI‑optimized pricing yields a 15.1% revenue gain (about $73,993 preserved per simulation). The B2B channel is optimal in 90% of markets. The production XGBoost model achieves 99.73% accuracy, and ultra‑premium products (notably the Samsung Neo QLED 8K) consistently generate the highest revenue.
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131
scikit-learn(5)
Gilliard Léda's avatar
Gilliard Léda
Brazil
Senior Data Scientist | Machine Learning | Predictive
New to Contra
Senior Data Scientist | Machine Learning | Predictive
Cover image for Introducing GeoAlerta AI — an
Introducing GeoAlerta AI — an AI-powered climate intelligence platform built to help emerging cities predict and respond to floods, heavy rainfall, and environmental risks in real time. GeoAlerta AI combines machine learning, geospatial analytics, terrain intelligence, and weather forecasting APIs to transform environmental data into actionable public insights. Key capabilities include: • Flood risk prediction • Real-time rainfall monitoring • Geospatial vulnerability mapping • AI-driven environmental scoring • Interactive smart-city dashboards • Decision support for public managers and civil defense teams Our mission is to democratize climate intelligence and bring advanced AI capabilities to vulnerable regions that are often underserved by traditional monitoring systems. Built with: Python • FastAPI • Machine Learning • GeoPandas • OpenWeather API • Interactive AI Dashboards GeoAlerta AI represents the intersection of Artificial Intelligence, ClimateTech, and Public Impact. #AI #ClimateTech #MachineLearning #SmartCities #GeoAI #DataScience #Python #Innovation
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Cover image for App - GeoAlerta
App - GeoAlerta
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Cover image for At Aggill Intelligence⁠�, we are
At Aggill Intelligence⁠�, we are developing AI-driven solutions focused on public impact, territorial intelligence and climate resilience. One of our flagship projects is: 🌎 GeoAlerta AI A climate intelligence platform designed for emerging cities, combining: • Geospatial analytics • Flood-risk prediction • Rainfall monitoring • Machine learning • Territorial dashboards • Real-time decision support Our mission is simple: Transform data into decisions that protect lives. Currently exploring partnerships and global opportunities involving: Climate AI Smart Cities Public Sector Innovation Geospatial Intelligence Predictive Analytics #AI #DataScience #ClimateTech #GeoAI #MachineLearning #SmartCities #Python #TerritorialIntelligence #PublicImpact #IntelPartner #IBMPartner
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64
Cover image for GeoAlerta AI is an intelligent
GeoAlerta AI is an intelligent geospatial analytics platform designed to predict and mitigate climate-related risks, especially urban flooding in emerging cities. By integrating satellite data, terrain elevation models, rainfall patterns, and machine learning algorithms, the system generates real-time risk scores and interactive maps to support decision-making.
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77
scikit-learn(5)
Gary Ernstzen's avatar
Gary Ernstzen
Cape Town, South Africa
AI Workflow Automation for SMEs, Boost Productivity & Profit
AI Workflow Automation for SMEs, Boost Productivity & Profit
Cover image for Predictive Maintenance & AI Insights We created a bespoke AI...
Predictive Maintenance & AI Insights We created a bespoke AI-driven platform to revolutionize customer support for a prominent e-commerce enterprise. Confronted with a surge of inquiries and operational challenges, the client sought a scalable solution. Our team deployed a system leveraging machine learning models that automates ticket management, classifies inquiries, and delivers real-time predictive insights. This implementation has greatly diminished manual workload, enhanced response times, and boosted customer satisfaction through a more efficient and tailored support experience.
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Cover image for Self-Hosted AI Assistant Platform We created an innovative s...
Self-Hosted AI Assistant Platform We created an innovative solution by combining n8n and Ollama, facilitating seamless, local interactions with Large Language Models (LLMs). This self-hosted platform empowers businesses to operate AI models securely and privately on their own infrastructure, thereby removing the necessity for costly cloud services. With real-time data processing, economical model utilization, and improved AI response times, this platform is perfect for enterprises aiming to harness AI for experimentation, learning, and prototyping without depending on external cloud services. The solution not only ensures security and privacy but is also scalable to accommodate increasing user demands and expanding AI capabilities.
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Cover image for Smart Booking & Scheduling System We developed a customized ...
Smart Booking & Scheduling System We developed a customized platform that connects tutors and alumni with students through the integration of Wix Bookings. This platform simplifies the booking and scheduling process, offering both flexibility and user-friendliness. Tutors and alumni are able to manage their availability, create profiles, and communicate efficiently with students, while students can effortlessly book sessions and share feedback. The outcome is a scalable solution that caters to the needs of individual tutors as well as larger educational institutions.
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Cover image for AI-Driven Customer Support Efficiency We created a tailored ...
AI-Driven Customer Support Efficiency We created a tailored AI-driven platform designed to revolutionize customer support for a prominent e-commerce company. Confronted with a surge in inquiries and operational hurdles, the client sought a scalable solution. Our team deployed a system utilizing machine learning models to automate ticket management, classify inquiries, and deliver real-time predictive insights. This implementation has notably diminished manual workload, enhanced response times, and boosted customer satisfaction by offering a more efficient and personalized support experience.
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scikit-learn(4)