This repository contains a machine-learning project aimed at predicting airline ratings based on customer reviews.

The project utilizes natural language processing (NLP) and various machine learning algorithms to analyze textual data and generate predictions.

Airline reviews provide valuable insights into customer satisfaction, service quality, and overall experience. This project focuses on predicting the rating of Singapore Airlines based on reviews collected from different platforms. The main steps involved in the project include:

Data Collection and Preprocessing: Importing and cleaning the dataset to handle missing values and irrelevant data.

Exploratory Data Analysis (EDA): Visualizing data distributions, understanding sentiment trends, and identifying key features.

Feature Engineering: Using NLP techniques like TF-IDF to convert textual data into meaningful numerical representations.

Model Training: Implementing various machine learning models, including Logistic Regression, Random Forest, and Gradient Boosting, to predict airline ratings.

Model Evaluation: Assessing the models using accuracy, confusion matrix, and classification report to identify the best-performing model.