README.md

metadata

title: DataMind AI
emoji: 🧠
colorFrom: blue
colorTo: indigo
sdk: docker
pinned: false

🧠 DataMind AI — Intelligent Data Analyst

An AI-powered data analysis dashboard that automatically performs EDA, generates 20+ chart types, provides AI-driven insights, forecasting, and an interactive chat analyst.

🚀 Quick Start

1. Install Dependencies

pip install -r requirements.txt

2. Set Your Groq API Key

Windows (PowerShell):

$env:GROQ_API_KEY = "your_groq_api_key_here"

Windows (CMD):

set GROQ_API_KEY=your_groq_api_key_here

Mac/Linux:

export GROQ_API_KEY="your_groq_api_key_here"

3. Run the Application

python app.py

4. Open in Browser

Navigate to: http://localhost:5000

📊 Features

• CSV Upload — Drag-and-drop any CSV file
• Simulated Datasets — Pre-built Retail Sales and E-Commerce datasets
• Automated EDA — Missing values, duplicates, outliers, correlations
• 20+ Chart Types — Line, bar, pie, heatmap, waterfall, radar, BCG, RFM, and more
• AI Chat Analyst — Ask questions about your data in natural language
• Forecasting — Time series forecasting with confidence intervals
• Key Insights — AI-generated actionable business insights
• What-If Analysis — Scenario simulation with adjustable parameters

🛠️ Tech Stack

• Backend: Python, Flask
• Frontend: HTML, CSS, JavaScript (single-page app)
• AI: Groq API (LLaMA 3.3 70B)
• Charts: Matplotlib, Seaborn
• ML: scikit-learn (KMeans), mlxtend (Apriori)
• Forecasting: statsmodels (Exponential Smoothing)

📁 Project Structure

├── app.py              # Flask backend
├── datasets.py         # Simulated dataset generators
├── eda.py              # EDA pipeline
├── charts.py           # Chart orchestrator
├── charts_core.py      # Core chart functions (12 types)
├── charts_advanced.py  # Advanced chart functions (11 types)
├── ai_analyst.py       # Groq API integration
├── templates/
│   └── index.html      # Frontend SPA
├── requirements.txt    # Dependencies
└── README.md           # This file