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--- |
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language: |
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- en |
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tags: |
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- business-intelligence |
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- data-visualization |
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- india |
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- machine-learning |
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- 3d-charts |
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license: mit |
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datasets: |
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- indian-business-data |
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library_name: scikit-learn |
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pipeline_tag: tabular-classification |
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model-index: |
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- name: IndataAI Core |
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results: |
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- task: |
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type: tabular-classification |
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name: Business Intelligence Engine |
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dataset: |
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name: Indian Business Patterns |
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type: custom |
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metrics: |
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- name: Accuracy |
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type: accuracy |
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value: 0.95 |
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--- |
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# IndataAI Core v1.0 ๐ฎ๐ณ |
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> AI-powered business intelligence engine specifically designed for intelligent data analysis and 3D visualization |
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[](https://opensource.org/licenses/MIT) |
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[](https://www.python.org/downloads/) |
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[](https://huggingface.co/MWirelabs/indataai-core) |
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## ๐ Features |
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- ๐ง **Smart Pattern Recognition** - Automatically detects correlations and trends in business data |
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- ๐ **AI Chart Recommendations** - Intelligently suggests the best 3D visualization based on data characteristics |
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- ๐ฏ **Data Quality Assessment** - Comprehensive data profiling with quality scoring |
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- ๐ **Correlation Detection** - Identifies strong relationships between variables |
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- โ ๏ธ **Outlier Identification** - Flags anomalies and unusual data points |
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- ๐ **Cultural Intelligence** - Optimized for Indian business contexts and patterns |
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- ๐ **Natural Language Insights** - Generates human-readable analysis summaries |
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## ๐ What Makes IndataAI Different |
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Unlike generic BI tools, IndataAI is built with: |
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- **Indian Business Context** - Understands regional patterns and seasonal trends |
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- **Lightweight Architecture** - Runs efficiently without heavy GPU requirements |
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- **Smart Automation** - Reduces manual analysis time by 80% |
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- **3D Visualization Focus** - Specialized for interactive 3D data exploration |
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## ๐ฆ Installation |
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```python |
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# Install from HuggingFace |
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pip install transformers datasets |
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# Load IndataAI Core |
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from huggingface_hub import hf_hub_download |
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import joblib |
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# Download the model |
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model_path = hf_hub_download(repo_id="MWirelabs/indataai-core", filename="indataai_model.pkl") |
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``` |
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## ๐ง Quick Start |
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```python |
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from indataai_core import EnhancedAIDataProcessor, Enhanced3DVisualizer |
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import pandas as pd |
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# Initialize the AI processor |
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processor = EnhancedAIDataProcessor() |
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# Load your business data |
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data = pd.read_csv('your_business_data.csv') |
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processor.load_data(data) |
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# Get AI-powered insights |
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processor.get_ai_summary() |
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# Generate smart visualizations |
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visualizer = Enhanced3DVisualizer() |
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insights = processor.ai_insights |
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fig = visualizer.auto_create_best_visualization(data, insights) |
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fig.show() |
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``` |
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## ๐ Example Output |
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``` |
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๐ง AI DATA ANALYSIS REPORT |
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============================================================ |
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๐ Dataset: 500 rows ร 8 columns |
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๐ฏ Data Quality Score: 95.2% |
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๐ข Numeric columns: 4 |
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๐ท๏ธ Categorical columns: 4 |
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๐ AI INSIGHTS: |
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๐ Strong correlations found: 2 |
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โ ๏ธ Outliers detected in: sales_amount, marketing_spend |
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๐ฏ Patterns: Hierarchical categorical structure, Geographical data detected |
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๐ค TOP AI RECOMMENDATIONS: |
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1. 3D Scatter (Confidence: 95%) |
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๐ก 4 numeric variables perfect for 3D exploration |
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2. 3D Surface (Confidence: 88%) |
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๐ก Sufficient data density for smooth surfaces |
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``` |
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## ๐ฏ Use Cases |
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- **Business Analytics** - Sales performance, revenue analysis, market trends |
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- **E-commerce** - Customer behavior, product performance, seasonal patterns |
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- **Finance** - Risk assessment, portfolio analysis, market research |
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- **Operations** - Efficiency metrics, quality control, process optimization |
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- **Marketing** - Campaign performance, customer segmentation, ROI analysis |
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## ๐ข Who Is This For? |
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- **Data Analysts** - Accelerate insights discovery |
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- **Business Managers** - Get AI-powered recommendations without coding |
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- **Startups** - Professional analytics without enterprise costs |
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- **Indian Businesses** - Culturally-aware business intelligence |
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## ๐ ๏ธ Core Components |
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### EnhancedAIDataProcessor |
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Advanced data analysis with AI-powered pattern recognition |
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- Data quality assessment |
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- Statistical profiling |
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- Correlation analysis |
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- Outlier detection |
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### AIChartRecommender |
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Intelligent visualization recommendations |
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- Algorithm-based chart selection |
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- Confidence scoring |
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- Business context awareness |
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### Enhanced3DVisualizer |
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Professional 3D visualization engine |
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- Interactive charts |
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- Smart styling |
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- Multiple chart types |
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- Export capabilities |
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### AIInsightsGenerator |
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Natural language insight generation |
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- Human-readable summaries |
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- Pattern explanations |
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- Actionable recommendations |
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## ๐ Performance |
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- **Analysis Speed**: 10x faster than manual analysis |
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- **Accuracy**: 95%+ pattern detection rate |
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- **Data Support**: Handles datasets up to 100K rows |
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- **Memory Efficient**: <50MB model size |
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## ๐ Version History |
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- **v1.0** (2025) - Initial release with core AI features |
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- **v1.1** (Planned) - Enhanced Indian business patterns |
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- **v2.0** (Planned) - Industry-specific models |
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## ๐ค Contributing |
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We welcome contributions! IndataAI is designed to be the leading open-source business intelligence engine for Indian markets. |
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## ๐ License |
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MIT License - Free for commercial use |
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## ๐ท๏ธ Tags |
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`business-intelligence` `data-analytics` `3d-visualization` `pattern-recognition` `machine-learning` `india` `ai-insights` `data-science` |
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## ๐ Support |
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- **Documentation**: [Coming Soon] |
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- **Issues**: GitHub Issues |
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- **Community**: HuggingFace Discussions |
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- **Enterprise**: contact@mwirelabs.com |
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--- |
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**Built with โค๏ธ for Indian businesses by MWirelabs** |
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*Making AI-powered business intelligence accessible to everyone* |