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README.md
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# π₯ Odyssey: The AI Data Science Workspace
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π CognitiveEDA: The Adaptive Intelligence Engine
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CognitiveEDA is not just another EDA tool; it's a world-class data discovery platform that intelligently adapts to your data.
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This enterprise-grade application goes beyond static profiling by automatically detecting the nature of your dataset (e.g., time-series, text-heavy) and unlocking specialized analysis modules on the fly. Powered by Google's Gemini LLM, it delivers a rich, context-aware, and deeply insightful user experience that transforms raw data into a clear narrative with actionable recommendations.
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(A GIF showcasing the adaptive UI revealing specialized tabs after data upload)
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β¨ Key Features: The "Wow" Factor
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CognitiveEDA is designed to impress data professionals by providing intelligent, context-aware analysis that feels magical.
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π§ Adaptive Analysis Modules: The UI isn't static. It intelligently detects your data's characteristics and dynamically reveals specialized tabs:
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β Time-Series Analysis: Automatically appears if date/time columns are found. Perform decomposition, check for stationarity (ADF Test), and visualize trends.
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π Text Analysis: Unlocks if long-form text columns are present. Instantly generate word clouds to visualize high-frequency terms.
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π§© Clustering (K-Means): Becomes available for datasets with strong numeric features, allowing you to discover latent groups and customer segments.
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π€ Hyper-Contextual AI Narrative: The integrated Gemini AI doesn't give a generic report. It receives context about the type of data it's analyzing, leading to far more specific and valuable insights (e.g., suggesting ARIMA for time-series or sentiment analysis for text).
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** Universal Data Ingestion:** Don't be limited to CSV. CognitiveEDA handles CSV and Excel files seamlessly.
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β‘ Performance-Aware: For massive datasets, the tool automatically samples the data for UI interactions to ensure a fast, responsive experience, while still using the full dataset for backend calculations where feasible.
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π Comprehensive Core EDA: All the essentials, done better:
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Detailed Data Profiling (Missing values, numeric stats, categorical stats).
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At-a-glance overview visuals (Data types, missing data heatmap, correlation matrix).
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Interactive deep-dive tools for exploring individual features.
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π οΈ Tech Stack
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This project leverages a modern, powerful stack for data science and web applications:
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Backend & Data Analysis: Python, Pandas, NumPy, scikit-learn, statsmodels
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Web Framework & UI: Gradio
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AI Integration: Google Generative AI (Gemini)
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Visualization: Plotly, Matplotlib, WordCloud
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π Getting Started
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You can get your own instance of CognitiveEDA running in just two steps.
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1. Prerequisites
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First, clone the repository to your local machine:
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Generated bash
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git clone https://github.com/your-repo/CognitiveEDA.git
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cd CognitiveEDA
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Use code with caution.
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Bash
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Next, install all the required dependencies using the requirements.txt file. It's highly recommended to do this within a Python virtual environment.
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Generated bash
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# Create and activate a virtual environment (optional but recommended)
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python -m venv venv
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source venv/bin/activate # On Windows, use `venv\Scripts\activate`
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@@ -67,16 +81,19 @@ python app.py
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Use code with caution.
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Bash
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The application will start and provide a local URL (e.g., http://127.0.0.1:7860) that you can open in your web browser.
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π How to Use
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Launch the application and open the URL in your browser.
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Upload your data file using the "Upload Data File" component. Supported formats are .csv, .xlsx, and .xls.
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Enter your Google Gemini API Key in the provided text field.
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Click "Build My Dashboard".
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Explore! The application will process your data and build a custom dashboard. The standard tabs (AI Narrative, Profile, Overview) will be populated, and any relevant specialized tabs (Time-Series, Text, Clustering) will automatically appear.
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Interact with the dropdowns and sliders in each tab to perform deep-dive analyses.
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π‘ Future Roadmap & Contributions
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CognitiveEDA is an evolving platform. We welcome contributions from the community!
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Potential Future Enhancements:
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Geospatial Analysis Module: Automatically detect latitude/longitude or location names and generate map-based visualizations.
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Interactive HTML Report Export: Export a single, beautiful, and fully interactive HTML file with embedded Plotly charts.
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Database Connectors: Allow users to connect directly to PostgreSQL, MySQL, or BigQuery.
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Advanced Caching: Implement more sophisticated caching to speed up re-analysis of the same data.
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How to Contribute
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Fork the repository.
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Create a new branch for your feature (git checkout -b feature/AmazingNewFeature).
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Commit your changes (git commit -m 'Add some AmazingNewFeature').
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Push to the branch (git push origin feature/AmazingNewFeature).
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Open a Pull Request.
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π License
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This project is licensed under the MIT License - see the LICENSE file for details.
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---
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# π₯ Odyssey: The AI Data Science Workspace
|
13 |
|
14 |
+
|
15 |
π CognitiveEDA: The Adaptive Intelligence Engine
|
16 |

|
17 |
|
|
|
21 |
CognitiveEDA is not just another EDA tool; it's a world-class data discovery platform that intelligently adapts to your data.
|
22 |
This enterprise-grade application goes beyond static profiling by automatically detecting the nature of your dataset (e.g., time-series, text-heavy) and unlocking specialized analysis modules on the fly. Powered by Google's Gemini LLM, it delivers a rich, context-aware, and deeply insightful user experience that transforms raw data into a clear narrative with actionable recommendations.
|
23 |
(A GIF showcasing the adaptive UI revealing specialized tabs after data upload)
|
24 |
+
|
25 |
β¨ Key Features: The "Wow" Factor
|
26 |
+
|
27 |
CognitiveEDA is designed to impress data professionals by providing intelligent, context-aware analysis that feels magical.
|
28 |
+
|
29 |
π§ Adaptive Analysis Modules: The UI isn't static. It intelligently detects your data's characteristics and dynamically reveals specialized tabs:
|
30 |
+
|
31 |
β Time-Series Analysis: Automatically appears if date/time columns are found. Perform decomposition, check for stationarity (ADF Test), and visualize trends.
|
32 |
+
|
33 |
π Text Analysis: Unlocks if long-form text columns are present. Instantly generate word clouds to visualize high-frequency terms.
|
34 |
+
|
35 |
π§© Clustering (K-Means): Becomes available for datasets with strong numeric features, allowing you to discover latent groups and customer segments.
|
36 |
+
|
37 |
π€ Hyper-Contextual AI Narrative: The integrated Gemini AI doesn't give a generic report. It receives context about the type of data it's analyzing, leading to far more specific and valuable insights (e.g., suggesting ARIMA for time-series or sentiment analysis for text).
|
38 |
** Universal Data Ingestion:** Don't be limited to CSV. CognitiveEDA handles CSV and Excel files seamlessly.
|
39 |
+
|
40 |
β‘ Performance-Aware: For massive datasets, the tool automatically samples the data for UI interactions to ensure a fast, responsive experience, while still using the full dataset for backend calculations where feasible.
|
41 |
π Comprehensive Core EDA: All the essentials, done better:
|
42 |
+
|
43 |
Detailed Data Profiling (Missing values, numeric stats, categorical stats).
|
44 |
At-a-glance overview visuals (Data types, missing data heatmap, correlation matrix).
|
45 |
Interactive deep-dive tools for exploring individual features.
|
46 |
+
|
47 |
π οΈ Tech Stack
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This project leverages a modern, powerful stack for data science and web applications:
|
49 |
Backend & Data Analysis: Python, Pandas, NumPy, scikit-learn, statsmodels
|
50 |
Web Framework & UI: Gradio
|
51 |
AI Integration: Google Generative AI (Gemini)
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Visualization: Plotly, Matplotlib, WordCloud
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53 |
+
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π Getting Started
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You can get your own instance of CognitiveEDA running in just two steps.
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56 |
1. Prerequisites
|
|
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First, clone the repository to your local machine:
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Generated bash
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git clone https://github.com/your-repo/CognitiveEDA.git
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+
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cd CognitiveEDA
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Use code with caution.
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Bash
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Next, install all the required dependencies using the requirements.txt file. It's highly recommended to do this within a Python virtual environment.
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Generated bash
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+
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# Create and activate a virtual environment (optional but recommended)
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python -m venv venv
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source venv/bin/activate # On Windows, use `venv\Scripts\activate`
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Use code with caution.
|
82 |
Bash
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83 |
The application will start and provide a local URL (e.g., http://127.0.0.1:7860) that you can open in your web browser.
|
84 |
+
|
85 |
π How to Use
|
86 |
Launch the application and open the URL in your browser.
|
87 |
Upload your data file using the "Upload Data File" component. Supported formats are .csv, .xlsx, and .xls.
|
88 |
Enter your Google Gemini API Key in the provided text field.
|
89 |
Click "Build My Dashboard".
|
90 |
+
|
91 |
Explore! The application will process your data and build a custom dashboard. The standard tabs (AI Narrative, Profile, Overview) will be populated, and any relevant specialized tabs (Time-Series, Text, Clustering) will automatically appear.
|
92 |
Interact with the dropdowns and sliders in each tab to perform deep-dive analyses.
|
93 |
π‘ Future Roadmap & Contributions
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94 |
CognitiveEDA is an evolving platform. We welcome contributions from the community!
|
95 |
Potential Future Enhancements:
|
96 |
+
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97 |
Geospatial Analysis Module: Automatically detect latitude/longitude or location names and generate map-based visualizations.
|
98 |
Interactive HTML Report Export: Export a single, beautiful, and fully interactive HTML file with embedded Plotly charts.
|
99 |
Database Connectors: Allow users to connect directly to PostgreSQL, MySQL, or BigQuery.
|
|
|
101 |
Advanced Caching: Implement more sophisticated caching to speed up re-analysis of the same data.
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102 |
How to Contribute
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Fork the repository.
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104 |
+
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Create a new branch for your feature (git checkout -b feature/AmazingNewFeature).
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Commit your changes (git commit -m 'Add some AmazingNewFeature').
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Push to the branch (git push origin feature/AmazingNewFeature).
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Open a Pull Request.
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+
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π License
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This project is licensed under the MIT License - see the LICENSE file for details.
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