Random Forest and GBDT Analysis

Overview This notebook provides an in-depth analysis of the Random Forest and Gradient Boosted Decision Trees (GBDT) algorithms applied to the Amazon Fine Food Reviews dataset. This dataset, which spans more than a decade of customer feedback up until October 2012, includes a comprehensive collection of approximately 500,000 reviews. Each review provides detailed insights including product information, user details, ratings, and the actual review text.

Dataset Details Content: The dataset features reviews of fine foods sold on Amazon, as well as reviews from various other Amazon categories. Scope: The data covers a significant period, providing a rich historical context for analysis. Attributes: Reviews include product identifiers, user identifiers, ratings, and textual feedback. Purpose The notebook demonstrates how to apply machine learning algorithms, specifically Random Forest and GBDT, to this dataset to derive meaningful insights and predictions. The analysis showcases:

Feature Engineering: Methods used to preprocess and engineer features from the raw review text and metadata. Model Training: Techniques for training Random Forest and GBDT models on the dataset. Evaluation: Metrics and methods used to evaluate the performance of these models. Getting the Data The dataset is available for download from Kaggle. You can access it via the following link:

Amazon Fine Food Reviews Dataset

Feel free to explore, experiment, and build upon this analysis!