Predict serves as a powerful starting point for creators aiming to build AI-driven platforms that optimize audience targeting, campaign performance, and engagement.

Overview

This AI-driven sentiment analysis tool processes online product reviews, classifying them as positive, negative, or neutral. It enables businesses to gain valuable insights into customer feedback, helping them improve their products and services.

My Approach

Using natural language processing (NLP) and machine learning, I built a pipeline that cleans and processes text data, extracts meaningful features, and trains multiple classification models. To enhance accuracy, I employed TF-IDF vectorization, SMOTE for class balancing, and models like Logistic Regression, Random Forest, and XGBoost.

Vision and Innovation

By transforming raw customer feedback into structured insights, this tool provides businesses with a data-driven approach to customer sentiment. The integration of AI ensures a scalable and efficient solution for sentiment classification, empowering businesses to make informed decisions based on real user experiences.

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