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README2.md

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+<!-- Custom header with green glow effect -->
+<p align="center">
+  <img src="header.svg" alt="AutoML - Automated Machine Learning Platform" width="800" />
+</p>
+
+<p>
+<p align="center">
+  <a href="https://github.com/username/Auto-ML/blob/main/LICENSE"><img src="https://img.shields.io/badge/License-MIT-blue.svg" alt="License: MIT"></a>
+  <a href="https://www.python.org/"><img src="https://img.shields.io/badge/Made%20with-Python-1f425f.svg" alt="Made with Python"></a>
+  <a href="https://streamlit.io/"><img src="https://img.shields.io/badge/Made%20with-Streamlit-FF4B4B.svg" alt="Made with Streamlit"></a>
+  <a href="https://scikit-learn.org/"><img src="https://img.shields.io/badge/Made%20with-Scikit--Learn-F7931E.svg" alt="Made with Scikit-Learn"></a>
+</p>
+
+<p align="center">
+  <a href="https://pandas.pydata.org/"><img src="https://img.shields.io/badge/Made%20with-Pandas-150458.svg" alt="Made with Pandas"></a>
+  <a href="https://numpy.org/"><img src="https://img.shields.io/badge/Made%20with-NumPy-013243.svg" alt="Made with NumPy"></a>
+  <a href="https://matplotlib.org/"><img src="https://img.shields.io/badge/Made%20with-Matplotlib-11557c.svg" alt="Made with Matplotlib"></a>
+  <a href="https://seaborn.pydata.org/"><img src="https://img.shields.io/badge/Made%20with-Seaborn-3776AB.svg" alt="Made with Seaborn"></a>
+  <a href="https://plotly.com/"><img src="https://img.shields.io/badge/Made%20with-Plotly-3F4F75.svg" alt="Made with Plotly"></a>
+  <a href="https://xgboost.readthedocs.io/"><img src="https://img.shields.io/badge/Made%20with-XGBoost-0073B7.svg" alt="Made with XGBoost"></a>
+</p>
+
+<p align="center">
+  <a href="https://python.langchain.com/"><img src="https://img.shields.io/badge/Made%20with-LangChain-00A86B.svg" alt="Made with LangChain"></a>
+  <a href="https://smith.langchain.com/"><img src="https://img.shields.io/badge/Monitored%20with-LangSmith-7742DD.svg" alt="Monitored with LangSmith"></a>
+  <a href="https://ai.google.dev/"><img src="https://img.shields.io/badge/Powered%20by-Google%20Gemini-4285F4.svg" alt="Powered by Google Gemini"></a>
+  <a href="https://groq.com/"><img src="https://img.shields.io/badge/Powered%20by-Groq-6236FF.svg" alt="Powered by Groq"></a>
+  <a href="https://www.python-dotenv.org/"><img src="https://img.shields.io/badge/Made%20with-python--dotenv-2E7D32.svg" alt="Made with python-dotenv"></a>
+  <a href="https://pickle.readthedocs.io/"><img src="https://img.shields.io/badge/Uses-pickle-8BC34A.svg" alt="Uses pickle"></a>
+</p>
+
+<p align="center">
+  <b>AutoML</b> is a powerful tool for automating the end-to-end process of applying machine learning to real-world problems. It simplifies the process of model selection, hyperparameter tuning, and downloading, making machine learning accessible to everyone.
+</p>
+
+## 🔗 Live Demo
+
+<p align="center">
+  <a href="https://automl-demo.streamlit.app" target="_blank">
+    <img src="https://img.shields.io/badge/Try%20the%20Demo-00B8D9?style=for-the-badge&logo=streamlit&logoColor=white" alt="Try the Demo" />
+  </a>
+</p>
+
+<p align="center">
+  Check out the live demo of AutoML and experience the power of automated machine learning firsthand!
+</p>
+
+## 🎬 Video Showcase
+
+<p align="center">
+  <video width="800" controls>
+    <source src="demo-video.mp4" type="video/mp4">
+    Your browser does not support the video tag.
+  </video>
+</p>
+
+<p align="center">
+  <em>See AutoML in action: This demonstration shows how to analyze data, train models, and get AI-powered insights in minutes!</em>
+</p>
+
+## ✨ Features
+
+- 📊 **Data Visualization and Analysis**: Interactive visualizations to understand your data
+  - Correlation heatmaps
+  - Distribution plots
+  - Feature importance charts
+  - Pair plots for relationship analysis
+  
+- 🧹 **Automated Data Cleaning and Preprocessing**: Handle missing values, outliers, and feature engineering
+  - Automatic detection and handling of missing values
+  - Outlier detection and treatment
+  - Feature scaling and normalization
+  - Categorical encoding (One-Hot, Label, Target encoding)
+  
+- 🤖 **Multiple ML Model Selection**: Choose from a variety of models or let AutoML select the best one
+  - Classification models: Logistic Regression, Random Forest, XGBoost, SVC, Decision Tree, KNN, Gradient Boosting, AdaBoost, Gaussian Naive Bayes, QDA, LDA
+  - Regression models: Linear Regression, Random Forest, XGBoost, SVR, Decision Tree, KNN, ElasticNet, Gradient Boosting, AdaBoost, Bayesian Ridge, Ridge, Lasso
+  
+- ⚙️ **Hyperparameter Tuning**: Optimize model performance with advanced tuning techniques
+  - Added Support for 20+ Models to easily fine tune hyperparameters
+  - Added Support for 10+ Hyperparameter Tuning Techniques
+  
+  
+- 📈 **Model Performance Evaluation**: Comprehensive metrics and visualizations
+  - Classification: Accuracy, Precision, Recall, F1-Score, ROC-AUC, Confusion Matrix
+  - Regression: MAE, MSE, RMSE, R², Residual Plots
+  
+- 🔍 **AI-powered Data Insights**: Leverage Google's Gemini for intelligent data analysis
+  - Natural language explanations of model decisions
+  - Automated feature importance interpretation
+  - Data quality assessment
+  - Trend identification and anomaly detection
+
+- 🧠 **LLM Fine-Tuning and Download**: Access and utilize pre-trained language models
+  - Download fine-tuned LLMs for specific domains
+  - Customize existing models for your specific use case
+  - Access to various model sizes (small, medium, large)
+  - Seamless integration with your data processing pipeline
+
+## 🚀 Installation
+
+### Prerequisites
+
+- Python 3.8 or higher
+- Google API key for Gemini for data insights and dataframe cleaning
+- Groq API key for LLM based test results analysis
+- langsmith API for monitoring llm calls
+
+### Setup
+
+1. Clone the repository:
+```bash
+git clone <repository-url>
+cd Auto-ML
+```
+
+2. Create a virtual environment:
+```bash
+python -m venv venv
+source venv/bin/activate  # On Windows: venv\Scripts\activate
+```
+
+3. Install dependencies:
+```bash
+pip install -r requirements.txt
+```
+
+4. Set up your environment variables:
+```bash
+# Create a .env file with your Google API key as well as other keys
+echo "GOOGLE_API_KEY=your_api_key_here" > .env
+```
+
+## 🎮 Usage
+
+Start the application:
+
+```bash
+streamlit run app.py
+```
+
+### Quick Start Guide
+
+1. **Upload Data**: Upload your CSV file
+   - Supported format: CSV
+   - Automatic data type detection
+   - Preview of first few rows
+
+2. **Explore Data**: Visualize and understand your dataset
+   - Summary statistics
+   - Correlation analysis
+   - Distribution visualization
+   - Missing value analysis
+
+3. **Preprocess**: Clean and transform your data
+   - Handle missing values (imputation strategies)
+   - Remove or transform outliers
+   - Feature scaling options
+   - Encoding categorical variables
+
+4. **Train Models**: Select models and tune hyperparameters
+   - Choose target variable and features
+   - Select machine learning algorithms
+   - Configure hyperparameter search space
+   - Set evaluation metrics
+
+5. **Evaluate**: Compare model performance
+   - Performance metrics visualization
+   - Feature importance analysis
+   - Model comparison dashboard
+   - Cross-validation results
+
+6. **Deploy**: Export your model 
+   - Download trained model as pickle file
+
+
+
+   
+## 🧩 Project Structure
+
+```
+Auto-ML/
+├── app.py                  # Main Streamlit application
+├── requirements.txt        # Project dependencies
+├── .env                    # Environment variables (API keys)
+├── README.md               # Project documentation
+├── models/                 # Saved model files
+├── logs/                   # Application logs
+└── src/                    # Source code
+    ├── __init__.py         # Package initialization
+    ├── preprocessing/      # Data preprocessing modules
+    │   ├── __init__.py
+    │   └── ...             # Data cleaning, transformation
+    ├── training/           # Model training modules
+    │   ├── __init__.py
+    │   └── ...             # Model training, evaluation
+    ├── ui/                 # User interface components
+    │   ├── __init__.py
+    │   └── ...             # Streamlit UI elements
+    └── utils/              # Utility functions
+        ├── __init__.py
+        └── ...             # Helper functions
+```
+
+
+
+# Preprocessing Pipelines
+
+1\. Data Ingestion Pipeline
+---------------------------
+
+**Purpose:** Collects raw data from multiple sources (CSV, databases, APIs).
+
+*   Reads structured/unstructured data
+*   Handles missing values and duplicates
+*   Converts raw data into a clean DataFrame
+
+2\. Data Cleaning & Preprocessing Pipeline
+------------------------------------------
+
+**Purpose:** Transforms raw data into a machine-learning-ready format.
+
+*   **Cleans Data:** Handles NaNs, outliers, and standardizes columns
+*   **Encodes Categorical Features:** One-hot encoding, label encoding
+*   **Scales Numerical Data:** MinMaxScaler, StandardScaler
+
+
+
+
+3\. Model Selection & Training Pipeline
+---------------------------------------
+
+**Purpose:** Automates the process of selecting and training.
+
+*   **Multiple Algorithms:** Trains XGBoost, RandomForest, Deep Learning models
+*   **Hyperparameter Optimization:** Finds the best config for each model
+
+
+
+6\. Model Deployment Pipeline
+-----------------------------
+
+**Purpose:** Makes the model available for real-world usage.
+
+*   Exports the Model (Pickle, ONNX, TensorFlow SavedModel)
+*   Easily Download after training
+
+
+
+# Feedback and Fallback Mechanism
+
+AutoML implements a robust feedback and fallback system to ensure reliability:
+
+1. **Data Cleaning Validation**: The system validates all cleaning operations and provides feedback on the changes made
+   - Automatic detection of cleaning effectiveness
+   - Detailed logs of transformations applied to the data
+
+2. **LLM Fallback Mechanism**: For AI-powered insights and data analysis
+   - Primary attempt uses advanced LLMs (Google Gemini/Groq)
+   - Automatic fallback to rule-based algorithms if LLM fails
+   - Graceful degradation to ensure core functionality remains available
+   - Error logging and reporting for continuous improvement
+   - LangSmith integration for monitoring and tracking all LLM calls
+
+3. **Error Feedback Loop**: Intelligent error handling during data cleaning
+   - Automatically captures errors that occur during data cleaning operations
+   - Sends error context to LLM to generate refined cleaning code
+   - Re-executes the improved cleaning process
+   - Iterative refinement ensures robust data preparation even with challenging datasets
+
+## 🤝 Contributing
+
+We welcome contributions! 
+
+### Development Setup
+
+1. Fork the repository
+2. Create a feature branch
+3. Install development dependencies:
+   ```bash
+   pip install -r requirements-dev.txt
+   ```
+4. Make your changes
+5. Run tests:
+   ```bash
+   pytest
+   ```
+6. Submit a pull request
+
+## 📄 License
+
+This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.
+
+## 🙏 Acknowledgements
+
+- [Streamlit](https://streamlit.io/) for the interactive web framework
+- [Scikit-learn](https://scikit-learn.org/) for machine learning algorithms
+- [Pandas](https://pandas.pydata.org/) for data manipulation
+- [Plotly](https://plotly.com/) for interactive visualizations
+- [Google Gemini](https://ai.google.dev/) for AI-powered insights
+- [XGBoost](https://xgboost.readthedocs.io/) for gradient boosting
+- [Seaborn](https://seaborn.pydata.org/) for statistical visualizations
+- [LangChain](https://python.langchain.com/) for large language model integration
+- [LangSmith](https://smith.langchain.com/) for LLM call tracking and monitoring
+- [Groq](https://groq.com/) for high-performance computing
+
+---
+
+<p align="center">
+  Made with ❤️ by Akash Anandani
+</p>