🤖 AI Agent for Predictive Analytics using LLaMA-2

📌 Project Overview

This project demonstrates how to build an AI-powered agent using a Large Language Model (LLM), specifically LLaMA-2, to assist in predictive analytics tasks. The AI agent is capable of:

• Analyzing structured datasets
• Extracting key insights and feature importance
• Performing basic statistical operations
• Recommending appropriate ML models for classification

The goal is to enhance data-driven decision-making by combining traditional analytics with the reasoning power of LLMs.

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🚀 Features

• 🧠 LLM Integration: Uses llama-cpp-python to load and interact with a quantized LLaMA-2 model.
• 📊 Dataset Analysis: Computes mean, max, min values and identifies variable correlations.
• 🔍 Feature Importance: Highlights the most relevant predictors for classification.
• 🧪 Model Suggestions: Recommends ML models like Logistic Regression, Random Forest, SVM based on dataset characteristics.
• 🧾 Human-AI Evaluation: Includes reflection on the AI agent's performance and ethical use.

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🛠️ Technologies & Libraries

• Python 3.10+
• Pandas – for data manipulation
• llama-cpp-python – to run the LLaMA-2 model locally
• Hugging Face Hub – to download the LLaMA-2 GGUF model
• Google Colab – cloud-based environment for execution

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📂 Dataset Used

A synthetic dataset with 20 entries including the following columns:

• Salary (USD/Year)
• Occupation
• Gender
• Age
• Marital Status
• Credit Lines
• Propensity to Pay (target variable)

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📈 Model Workflow

1. Data Ingestion: Load and inspect the dataset
2. LLM Setup: Configure and load LLaMA-2 using llama-cpp-python
3. Prompt Engineering: Craft user prompts to analyze the dataset
4. LLM Response: Interpret model responses for tasks such as:
   • Statistical summaries
   • Correlation detection
   • Important variable identification
   • ML model recommendations
5. Reflection & Evaluation: Assess AI output for accuracy, ethical relevance, and contextual fit

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🔐 Ethical Considerations

While LLMs can generate fast insights, not all suggested features (e.g., gender or marital status) may be ethically appropriate for predictive modeling. Human oversight is essential to:

• Validate AI outputs
• Ensure fairness and data responsibility

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📚 Learning Outcomes

• Hands-on experience integrating LLMs into analytics workflows
• Enhanced understanding of AI-assisted data exploration
• Practice with prompt design, model evaluation, and feature interpretation

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🧠 Future Enhancements

• Expand support to regression and clustering tasks
• Integrate with Streamlit for interactive dashboards
• Fine-tune LLaMA-2 for analytics-specific tasks

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👩‍💻 Author

Lasya Priya Konduru
LinkedIn | GitHub | Portfolio

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📄 License

This project is for educational purposes only and follows the licensing terms of the used models and libraries.