You will get an AI-integrated data analytics dashboard built with Python and ML
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Project details
You will get a custom AI-integrated Data Analytics Dashboard built with Python and Machine Learning to turn your business data into actionable insights.
This isn’t just a dashboard — it’s an intelligent system that automates reporting, predicts trends, and helps you make smarter decisions in real time.
With a deep background in AI Integration, Data Analytics, and Automation, I’ll design a fully connected solution that combines data from multiple sources, processes it with ML models, and visualizes it through clear, dynamic dashboards. Whether you need sales forecasting, performance tracking, or executive analytics, each dashboard is built to deliver measurable ROI and save hours of manual analysis.
You’ll receive a scalable, secure, and visually intuitive system — with full source code, documentation, and ongoing support options available. My goal is simple: to help you see your business clearly and act faster with AI-driven insights.
This isn’t just a dashboard — it’s an intelligent system that automates reporting, predicts trends, and helps you make smarter decisions in real time.
With a deep background in AI Integration, Data Analytics, and Automation, I’ll design a fully connected solution that combines data from multiple sources, processes it with ML models, and visualizes it through clear, dynamic dashboards. Whether you need sales forecasting, performance tracking, or executive analytics, each dashboard is built to deliver measurable ROI and save hours of manual analysis.
You’ll receive a scalable, secure, and visually intuitive system — with full source code, documentation, and ongoing support options available. My goal is simple: to help you see your business clearly and act faster with AI-driven insights.
Machine Learning Tools
Azure Machine Learning, ChatGPT, Google Sheets, Microsoft Excel, Microsoft Power BI, NumPy, pandas, Python, Python Scikit-Learn, PyTorch, scikit-learn, SQL, TensorFlowWhat's included
| Service Tiers |
Starter
$750
|
Standard
$1,500
|
Advanced
$3,000
|
|---|---|---|---|
| Delivery Time | 7 days | 14 days | 21 days |
Number of Revisions | 1 | 2 | 3 |
Number of Model Variations | 1 | 2 | 3 |
Number of Scenarios | 1 | 2 | 3 |
Number of Graphs/Charts | 2 | 4 | 6 |
Model Validation/Testing | - | ||
Model Documentation | |||
Data Source Connectivity | - | ||
Source Code |
Optional add-ons
You can add these on the next page.
Additional Revision
+$100
Additional Model Variation
+$150
Additional Scenario
+$120
Additional Graph/Chart
+$80
Model Validation/Testing
+$200
Data Source Connectivity
+$150Frequently asked questions
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About Igor
AI Developer | Machine Learning Engineer | Generative AI & RAG
100%
Job Success
Wigan, United Kingdom - 5:33 pm local time
I work end-to-end across Generative AI, LLM Engineering, AI Agent Development, AI Automation, Data Science, Data Engineering, ETL Pipelines, MLOps, and executive Dashboards in Power BI, Tableau, Looker Studio, and Google Analytics 4. The teams I work best with have data and ambition but no roadmap to ship AI in production without breaking compliance, scale, or budget.
What I build:
• GPT and Claude-powered AI Chatbot Development, AI Agent Development, and LLM Integration using OpenAI API, Anthropic Claude API, Google Gemini, LangChain, LlamaIndex, and RAG with Pinecone, Weaviate, and Chroma vector databases
• Machine Learning and Deep Learning models for fraud detection, demand forecasting, customer segmentation, recommendation engines, and NLP
• ETL Pipelines and Data Engineering on Google BigQuery, Snowflake, Databricks, AWS, Azure, and SQL
• Power BI Dashboards, Looker Studio Reports, Tableau Visualizations, and full Google Analytics 4 implementation
• Python Automation, Web Scraping, API Integration, and AI Workflow Automation for scalable business processes
Selected results in AI, Generative AI, and Machine Learning:
• GPT-powered AI Chatbot reduced support tickets 50% in one quarter
• Machine Learning microservices platform serving 2M+ users and 100M+ daily API requests
• Multilingual AI Agent handling 15,000+ daily messages, lifting NPS 20 points
• Deep Learning fraud detection model cut false positives 40%, preventing ~$2M in annual losses
• Predictive inventory AI decreased stockouts 30% and real-time customer segmentation lifted conversion 28%
• NLP-based ticket triage automation improved first-response efficiency 65%
• RAG-powered AI Chatbot embedded in web apps improved conversion 42%
• Real-time AI Call Center Assistant cut average handle time 35% and lifted first-call resolution 22%
• Predictive AI for cell tower failures reduced emergency dispatches and improved network uptime
Selected results in Data Engineering and ETL:
• Migrated Data Engineering stack to Google BigQuery: 60% faster queries, 25% lower storage costs
• Standardized ETL Pipelines for Data Mining and Data Annotation, increasing ML training throughput
• Built feature stores and reproducible MLOps training pipelines that accelerated ML iteration
• Self-correcting Data Analysis pipeline reduced reporting errors 95%
• Python competitive monitoring system mining 150+ sources daily into Google Sheets and BigQuery
• SQL clustering and replication deployed for 99.99% uptime on mission-critical apps
Selected results in Dashboards and Business Intelligence:
• Looker Studio Dashboard suite reduced delivery delays 15%
• Marketing Analytics Dashboards unifying paid media, web, and CRM data in Power BI and Looker Studio
• C-level KPI Dashboard pack (SQL + Google Analytics 4 + Excel) accelerating weekly decisions
• Server-side Google Tag Manager implementation significantly improved GA4 accuracy
• Pricing optimization engines lifted profit margins 17%
• Self-serve Analytics portal with NLQ-powered Power BI reduced ad-hoc requests 55%
• JavaScript bridge syncing legacy Excel models with cloud databases, saving 20 hours per week
Beyond implementation, I run AI Readiness Audits for executive teams that have invested in data infrastructure but not yet seen ROI, identifying high-impact AI Agent Development and Machine Learning opportunities and translating them into a 90-day rollout plan with clear success metrics and risk gates.
Tech stack: Python, SQL, JavaScript, TypeScript, AWS, Google Cloud, Azure, BigQuery, Snowflake, Databricks, dbt, Airflow, Power BI, Tableau, Looker Studio, GA4, Google Tag Manager, Excel, OpenAI API, Anthropic Claude API, Google Gemini, LangChain, LlamaIndex, Hugging Face, TensorFlow, PyTorch, scikit-learn, Machine Learning, Deep Learning, Generative AI, LLM Engineering, MLOps, NLP, ETL Pipelines, Data Mining, Data Annotation, Data Visualization, AI Integration, AI Automation, AI Agent Development, AI Chatbot Development, RAG, and Vector Databases (Pinecone, Weaviate, Chroma).
Share your data sources, target metrics, and timeline. I will reply within 24 hours with a practical rollout plan covering ETL Pipeline through to production AI Integration, plus a short list of risks I see before we start. I work as a strategic partner on production AI and Data systems, not a ticket-taker.
Steps for completing your project
After purchasing the project, send requirements so Igor can start the project.
Delivery time starts when Igor receives requirements from you.
Igor works on your project following the steps below.
Revisions may occur after the delivery date.
Data Review & Planning
I’ll analyze your data sources, clarify KPIs, and define the optimal data architecture for AI-powered analytics and visualization.
Model Development & Integration
I’ll build and train ML models in Python, connect data pipelines, and integrate the dashboard with live data sources for automation.