Terrag AI: AI-Powered Data Analytics Platform

Jul 23 2025 • Muhammad Taha Nasir
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Terrag AI Inner View
Terrag AI Inner View
Terrag AI Inner View
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Project Overview

Terrag AI is a Streamlit-based AI-powered data analytics application designed to provide intelligent insights from business datasets, including customers, feedback, and sales data. It offers features like lead scoring, customer segmentation, trend forecasting, churn analysis, sentiment analysis, and natural language querying, with the ability to export results as PDF.

Situation

Businesses often struggle to derive actionable insights from diverse datasets like customer feedback, sales, and user behavior due to complex analysis requirements and lack of integrated tools for AI-driven analytics.

Task

Develop a user-friendly web application that leverages AI and machine learning to analyze business datasets, providing features like data cleaning, customer segmentation, churn prediction, and sentiment analysis, with an intuitive interface for non-technical users.

Actions

  • Built a Streamlit frontend for seamless user interaction and data visualization.
  • Integrated Pandas and NumPy for efficient data processing and cleaning.
  • Implemented KMeans clustering for customer segmentation using Scikit-learn.
  • Developed sentiment analysis using Sentence-Transformers and Transformers.
  • Added trend forecasting and churn analysis with machine learning models.
  • Enabled natural language querying for data interaction using AI models.
  • Integrated PDF export functionality for analysis reports.

Results

Terrag AI successfully empowered businesses with actionable insights, simplifying complex data analysis tasks. The project enhanced my expertise in Python, Streamlit, and machine learning, delivering a robust tool for data-driven decision-making.

Tech Stack

  • Streamlit: Web framework for building the data analytics interface.
  • python: Core language for data processing and AI model integration.
  • Pandas & NumPy: Libraries for data manipulation and analysis.
  • Scikit-learn: Machine learning library for clustering and predictive modeling.
  • Sentence-Transformers & Transformers: For sentiment analysis and NLP.
  • Matplotlib & Altair: Data visualization libraries.
  • Openpyxl: For handling Excel file uploads.
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