Portfolio Details

Each project in this portfolio reflects my hands-on experience in solving real-world problems using data science, machine learning, and full-stack development. From predictive models to intelligent web applications, these solutions are built with a focus on scalability, functionality, and impact.

Project Information

  • Project Name: Customer Sentiment Analyzer
  • Category: Natural Language Processing
  • Model Type: Logistic Regression (Binary & Multi-Class Classification)
  • Github Repo: Link

Technologies Used

The project is built using Python, Flask, and Scikit-learn for the backend. It employs NLP techniques such as tokenization, TF-IDF vectorization, and sentiment classification using Logistic Regression. Frontend integration is done with HTML and CSS for a simple and intuitive UI. The project also leverages Pandas and NLTK for text preprocessing and analysis.

Details of Project

Customer Sentiment Analyzer is a web-based application designed to evaluate and classify customer feedback or reviews into sentiment categories such as Positive, Negative, or Neutral. Users input raw text, and the system processes it through a trained Logistic Regression model to determine sentiment polarity.

The project emphasizes the importance of opinion mining and real-time sentiment classification in enhancing business decision-making. It showcases how customer emotions can be quantified and visualized, supporting data-backed marketing and service strategies.

Project Features

  • Accepts customer reviews or feedback through a simple text input form.
  • Classifies sentiment into Positive, Negative, or Neutral categories.
  • Uses TF-IDF vectorization and Logistic Regression for model inference.
  • Includes preprocessing steps: lowercasing, punctuation removal, and stopword filtering.
  • Lightweight, responsive Flask-based backend with real-time response capability.