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Python 3
Live Output and Prediction Overview

Live Output and Prediction Overview

Live Output and Prediction Overview

Live Output and Prediction Overview

Live Output: Fake Review Detection System

After executing the notebook, the Fake Review Detection System successfully classifies customer reviews as Fake or Genuine based on their textual content.

Each stage of the project contributes to the final prediction process.

Dataset Overview

Displays the structure of the Fake Reviews Dataset, including the available columns and the total number of customer reviews.

Cleaned Dataset

Shows the processed dataset after removing duplicate reviews, missing values, punctuation, stopwords, and unnecessary text.

TF-IDF Feature Matrix

Displays the numerical representation of customer reviews after applying TF-IDF Vectorization.

Logistic Regression Model

Trains the machine learning classifier using the transformed review data.

Model Evaluation

Displays evaluation metrics including Accuracy, Precision, Recall, F1-Score, Confusion Matrix, ROC Curve, and AUC Score.

Review Prediction

Predicts whether a newly entered customer review is Fake or Genuine.

Together, these outputs demonstrate how Machine Learning can automatically detect deceptive online reviews.

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Live Output and Prediction Overview

Contents

Live Output and Prediction Overview

Live Output: Fake Review Detection System

Dataset Overview

Cleaned Dataset

TF-IDF Feature Matrix

Logistic Regression Model

Model Evaluation

Review Prediction

Fake Review Detection System using Machine Learning

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Spring Boot Tutorial

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