📊 AI Data Quality Analyst (Hybrid Architecture)

An intelligent data cleaning assistant that combines deterministic statistical profiling (Pandas) with probabilistic reasoning (Llama-3). This tool analyzes CSV datasets, identifies quality issues (missing values, duplicates, type mismatches), and automatically generates Python code to fix them.

🚀 Features

• Hybrid Architecture: Uses Pandas for accurate calculations (counting rows, nulls) and LLM for qualitative interpretation.
• Automated EDA: Instantly profiles datasets to find outliers and anomalies.
• Code Generation: Instead of just pointing out errors, it writes the exact pandas code to fix them.
• Scalable: Since only the metadata (statistics) is sent to the LLM, it can handle large datasets without hitting token limits.

🛠️ Tech Stack

• Analysis Engine: Python (Pandas)
• Reasoning Engine: Llama-3.3-70b (via Groq API)
• UI: Streamlit
• Architecture: Hybrid (Deterministic + Generative)

📦 Installation

1. Clone the repository:

   git clone [https://github.com/Mervecaliskann/AI-Data-Analyst.git](https://github.com/Mervecaliskann/AI-Data-Analyst.git)
   cd AI-Data-Analyst

2. Install dependencies:

   pip install -r requirements.txt

3. Set up environment variables: Create a .env file:

   GROQ_API_KEY=your_groq_api_key_here

4. Run the application:

   streamlit run app.py

📂 Usage

1. Upload a CSV file (e.g., raw sales data, customer logs).
2. View the automated statistical profile (missing values, duplicates).
3. Click "Analyze with AI" to get a detailed cleaning report and copy-pasteable Python code fixes.

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Developed by Merve Çalışkan