🔭 I’m currently working on:
Building practical projects in Artificial Intelligence and frontend engineering, focusing on turning concepts into usable systems rather than toy demos.
👯 I’m looking to collaborate on:
Beginner-to-intermediate AI, data analysis, and frontend projects where learning, clean implementation, and real problem-solving matter more than buzzwords.
🤝 I’m looking for help with:
Improving my DSA problem-solving depth, understanding ML model behavior, and writing cleaner, more maintainable code.
🌱 I’m currently learning:
Data Analysis, AI/ML concepts, C++ & DSA for placements, and modern frontend development with a strong focus on fundamentals.
💬 Ask me about:
AI basics, frontend project structuring, GitHub project presentation, or how to learn tech without getting lost in tutorials.
⚡ Fun fact:
I care more about understanding why something works than just making it work — which slows me down early but saves me later.

🔍 Infosys Review Sense – Customer Feedback Extraction

Domain: NLP · Sentiment Analysis · Data Analysis

A system designed to analyze and extract insights from customer feedback using Natural Language Processing techniques. The project focuses on converting unstructured text reviews into meaningful sentiment and feature-level insights to support data-driven decision-making.

What it does:

Processes raw customer reviews and cleans noisy text data

Performs sentiment analysis to classify feedback (positive / negative / neutral)

Extracts key themes and patterns from large volumes of textual data

Visualizes insights to help understand customer perception at scale

Tech Stack:

Python

NLP (tokenization, text preprocessing, sentiment modeling)

Pandas, NumPy

Streamlit (for interactive analysis & visualization)

Outcome: Demonstrates practical application of NLP in real-world feedback analysis and shows the ability to move from raw text data to interpretable insights.

🔗 Repository: (https://github.com/Rit7439/Infosys_aspect_based_review_sense)