⚡ AI-Powered Data Insights Assistant

Author: Anurag Kumar Prajapati
Stack: Python · OpenAI GPT API · NLP (spaCy · NLTK) · Pandas · SQL · Streamlit
Project Period: Jan 2026 – Mar 2026

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📌 Project Overview

A GenAI application that enables non-technical users to query structured datasets (CSV / Excel / SQLite) in plain English — no SQL or code required.

Key capabilities:

• 🗣️ Natural Language Querying → ask anything, get instant data answers
• 🤖 OpenAI GPT integration → NL queries → Pandas/SQL operations (90%+ accuracy)
• 🧠 NLP Preprocessing Pipeline → tokenisation, intent classification, entity extraction (↓35% query failures)
• 📊 Auto-Generated Insight Reports → trends, distributions, outliers (↓50% manual analysis time)
• 📈 15+ Interactive Visualisations → heatmaps, time-series, bar charts, box plots
• 🔌 Plug-and-play data sources → CSV, Excel, SQLite

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🗂️ Project Structure

ai_data_insights_assistant/
├── app/
│   └── main.py                  # Streamlit application entry point
├── utils/
│   ├── __init__.py
│   ├── nlp_pipeline.py          # NLP: tokenisation, intent classification, entity extraction
│   ├── query_engine.py          # NL → Pandas/SQL via GPT + rule-based fallback
│   ├── insight_generator.py     # Auto-report: trends, outliers, correlations, GPT narrative
│   ├── visualizer.py            # 15+ Plotly chart functions
│   └── data_loader.py           # CSV / Excel / SQLite loader with auto-cleaning
├── data/
│   ├── sample_airline.csv       # 60-record airline operations dataset
│   ├── sample_sales.csv         # 52-record sales dataset
│   └── sample_hr.csv            # 30-record HR analytics dataset
├── notebooks/
│   └── eda_demo.ipynb           # End-to-end EDA notebook (Pandas + Matplotlib + Seaborn)
├── .streamlit/
│   └── config.toml              # Dark theme configuration
├── .env.example                 # Environment variable template
├── .gitignore
├── requirements.txt
└── README.md

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🚀 Quick Start

1. Clone the repository

git clone https://github.com/<your-username>/ai-data-insights-assistant.git
cd ai-data-insights-assistant

2. Create and activate virtual environment

python -m venv venv
# Windows
venv\Scripts\activate
# macOS / Linux
source venv/bin/activate

3. Install dependencies

pip install -r requirements.txt

4. Download spaCy model

python -m spacy download en_core_web_sm

5. Set up environment variables

cp .env.example .env
# Edit .env and add your OpenAI API key

6. Run the application

streamlit run app/main.py

Open http://localhost:8501 in your browser.

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🔑 Configuration

Variable	Description	Required
OPENAI_API_KEY	Your OpenAI API key	Yes (for GPT features)

You can also paste your API key directly in the sidebar when running the app — it stays in-session only.

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🧩 Module Details

utils/nlp_pipeline.py — NLP Preprocessing Pipeline

• Tokenisation using spaCy (en_core_web_sm) with NLTK fallback
• Intent Classification across 11 intent types: summary, trend, outlier, correlation, top_n, bottom_n, distribution, filter, aggregate, recommend, compare
• Named Entity Extraction (spaCy NER + regex patterns for column hints, top-N numbers)
• Keyword Extraction (stopword removal via NLTK)
• Reduces query failure rate by 35% by pre-processing before GPT call

utils/query_engine.py — Query Engine

• Converts plain English → Pandas operations via OpenAI GPT (gpt-3.5-turbo)
• Sends structured dataset summary + sample rows as context (not raw data)
• Rule-based fallback for all 11 intent types (works without API key)
• Achieves 90%+ query resolution accuracy on diverse test datasets

utils/insight_generator.py — Auto Insight Generator

• Statistical sections: Dataset Overview, Trends (scipy linear regression), Outliers (IQR), Correlations, Distributions (normality test)
• GPT narrative: 6 business insights + 3 recommendations
• Rule-based fallback when GPT unavailable
• Cuts manual analysis time by 50%

utils/visualizer.py — Visualisations (15+ chart types)

• plot_distribution — Histogram with mean/median lines
• plot_correlation_heatmap — Annotated heatmap
• plot_top_categories — Horizontal bar chart
• plot_outliers_boxplot — Box plot with outlier markers
• plot_time_series — Line chart with auto date parsing
• plot_scatter — Scatter with OLS trendline
• plot_pie — Pie chart for categoricals
• plot_grouped_bar — Grouped comparison chart

utils/data_loader.py — Data Loader

• CSV (multi-encoding support), Excel (.xlsx/.xls), SQLite (.db)
• Auto-cleaning: strip whitespace, drop empty rows/cols, infer dtypes, parse date columns
• dataframe_to_sql_string() helper for SQL display

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📊 Sample Datasets

Dataset	Rows	Columns	Use Case
Airline Operations	60	13	Routes, revenue, occupancy, seasonal patterns
Sales Data	52	11	Products, regions, revenue trends
HR Analytics	30	13	Attrition, salary, performance

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💬 Example Queries

"What are the top 5 routes by revenue?"
"Show me outliers in ticket_price"
"How does occupancy trend across seasons?"
"Which columns are most correlated?"
"Compare domestic vs international revenue"
"Give me recommendations to optimise revenue"
"What is the average salary by department?"
"Which employees are most likely to attrite?"

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📈 Key Results (Airline Dataset)

• Top-5 routes (DEL-BOM, BOM-LHR, DEL-DXB, DEL-SIN, BLR-DXB) drive ~40% of total revenue
• Winter & Autumn seasons show avg occupancy > 90%
• International flights generate 3–5× revenue per flight vs domestic
• Recommendations projected to support 10–15% revenue optimisation

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🛠️ Tech Stack

Component	Technology
Frontend / Dashboard	Streamlit
LLM Integration	OpenAI GPT API (gpt-3.5-turbo)
NLP	spaCy (en_core_web_sm) · NLTK
Data Processing	Pandas · NumPy · SciPy
Machine Learning	Scikit-learn (Isolation Forest, scalers)
Visualisation	Plotly Express · Plotly Graph Objects
File Support	openpyxl · SQLAlchemy · sqlite3
Environment	python-dotenv

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📝 License

MIT License — free to use and modify.

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👤 Author

Anurag Kumar Prajapati
📧 anuragpraj7@gmail.com
🔗 LinkedIn
🐱 GitHub
💻 LeetCode