ML Enthusiast
Coding Everyday
Medical Background
Energetic Teammate
Impact Driven
Predicting Hotel Ratings from Reviews
By Sam Celarek
🎯 Project Overview
This project delves into the intricacies of hotel reviews to harness the power of Natural Language Processing (NLP) and machine learning. By employing advanced data preprocessing, feature engineering, and hyperparameter tuning, the goal is to predict hotel ratings solely based on review text. The analysis encompasses over 500,000+ hotel reviews and has successfully predicted positive ratings with an accuracy of 78.4%.
📊 Dataset
The dataset comprises of 500,000+ hotel reviews, capturing various attributes like review text, reviewer nationality, and the actual rating. The richness of the dataset provides a robust foundation for understanding the sentiments behind each review.
🧹 Data Wrangling
The initial phase focused on comprehending the dataset’s structure and addressing any anomalies. This involved handling missing values, outlier detection, and ensuring data consistency across various attributes.
🛠️ Feature Engineering
Feature engineering played a pivotal role in enhancing the predictive power of the models. This step involved generating metrics from the review text, such as sentiment scores and text length. Additionally, advanced NLP techniques were employed to vectorize the review text, making it amenable for machine learning.
📶 Exploratory Data Analysis (EDA)
The EDA phase was instrumental in understanding the underlying patterns within the reviews. Techniques like sentiment analysis were leveraged to glean insights from the review text. This phase revealed pivotal relationships between the review text and the given ratings, setting the stage for modeling.
🧠 Modeling
The modeling phase was marked by the application of advanced classification algorithms, with a keen focus on hyperparameter tuning. By leveraging algorithms like Random Forest and Gradient Boosting, and by fine-tuning the model parameters, an impressive accuracy of 78.4% was achieved in predicting positive ratings.
📈 Discussion of Outcomes
The project outcomes underscore the power of NLP in extracting meaningful insights from textual data. By successfully predicting hotel ratings with high accuracy, this project demonstrates the potential of machine learning in the hospitality domain, offering hotels actionable insights to enhance guest experiences.
Sam Celarek