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Fake Review Detection Project Workflow Explained

Project Workflow

Workflow Diagram

Collect Dataset

↓

Load Dataset

↓

Explore Dataset

↓

Clean Text Data

↓

Preprocess Reviews

↓

Convert Text to Numerical Features

↓

Train Machine Learning Model

↓

Evaluate Performance

↓

Predict New Reviews

Project Pipeline

Step 1

Load the dataset.

Step 2

Explore the available columns.

Step 3

Clean and preprocess review text.

Step 4

Convert text into numerical vectors using TF-IDF.

Step 5

Split the dataset into training and testing sets.

Step 6

Train a Logistic Regression classifier.

Step 7

Evaluate the model using classification metrics.

Step 8

Predict whether unseen reviews are fake or real.

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Fake Review Detection System using Machine Learning

VA

Vishalini A

Fake Review Detection System using Machine Learning

MODULE 1 : Project Introduction
lessonProject Overview
lessonApplications of Fake Review Detection
lessonProject Workflow
MODULE 2 : Setting Up Google Colab
lessonImporting Required Libraries
lessonUploading the Dataset
lessonLoading the Dataset
lessonUnderstanding the Dataset
MODULE 3 : Data Cleaning and Text Preprocessing
lessonChecking for Missing Values
lessonChecking for Duplicate Reviews
lessonUnderstanding the Target Labels
lessonCleaning Review Text
lessonRemoving Stopwords
lessonLemmatization
lessonComparing Original and Cleaned Reviews
MODULE 4 : Exploratory Data Analysis (EDA)
lessonUnderstanding the Distribution of Reviews
lessonReview Length Analysis
lessonStatistical Summary of Review Length
lessonMost Common Words in Reviews
lessonVisualizing the Most Common Words
lessonCreating a Word Cloud
lessonFake vs Genuine Review Distribution
lessonCorrelation Analysis (Numerical Features)
MODULE 5 : Feature Engineering
lessonWhy Text Needs to Be Converted into Numbers
lessonConverting Reviews into TF-IDF Features
lessonPreparing the Target Variable
lessonSplitting the Dataset
lessonUnderstanding the Training and Testing Data
lessonVisualizing the Class Distribution in the Training Set
MODULE 6 : Building the Machine Learning Model
lessonIntroduction to Logistic Regression
lessonTraining the Logistic Regression Model
lessonMaking Predictions
lessonCalculating Model Accuracy
lessonVisualizing the Accuracy
lessonPredicting a Single Review
lessonTesting Multiple Reviews
lessonSaving the Trained Model
MODULE 7 : Model Evaluation
lessonIntroduction to Model Evaluation
lessonConfusion Matrix
lessonClassification Report
lessonCalculating Precision, Recall, and F1-Score
lessonComparing Evaluation Metrics
lessonROC Curve
lessonCalculating the AUC Score
lessonFeature Importance
MODULE 8 : Project Results and Final Evaluation
lessonProject Results and Final Evaluation
lessonLive Output and Prediction Overview
lessonMultiple Choice Questions (MCQs)
lessonResources

Get in Touch For Details

Fake Review Detection System using Machine Learning

MODULE 1 : Project Introduction
lessonProject Overview
lessonApplications of Fake Review Detection
lessonProject Workflow
MODULE 2 : Setting Up Google Colab
lessonImporting Required Libraries
lessonUploading the Dataset
lessonLoading the Dataset
lessonUnderstanding the Dataset
MODULE 3 : Data Cleaning and Text Preprocessing
lessonChecking for Missing Values
lessonChecking for Duplicate Reviews
lessonUnderstanding the Target Labels
lessonCleaning Review Text
lessonRemoving Stopwords
lessonLemmatization
lessonComparing Original and Cleaned Reviews
MODULE 4 : Exploratory Data Analysis (EDA)
lessonUnderstanding the Distribution of Reviews
lessonReview Length Analysis
lessonStatistical Summary of Review Length
lessonMost Common Words in Reviews
lessonVisualizing the Most Common Words
lessonCreating a Word Cloud
lessonFake vs Genuine Review Distribution
lessonCorrelation Analysis (Numerical Features)
MODULE 5 : Feature Engineering
lessonWhy Text Needs to Be Converted into Numbers
lessonConverting Reviews into TF-IDF Features
lessonPreparing the Target Variable
lessonSplitting the Dataset
lessonUnderstanding the Training and Testing Data
lessonVisualizing the Class Distribution in the Training Set
MODULE 6 : Building the Machine Learning Model
lessonIntroduction to Logistic Regression
lessonTraining the Logistic Regression Model
lessonMaking Predictions
lessonCalculating Model Accuracy
lessonVisualizing the Accuracy
lessonPredicting a Single Review
lessonTesting Multiple Reviews
lessonSaving the Trained Model
MODULE 7 : Model Evaluation
lessonIntroduction to Model Evaluation
lessonConfusion Matrix
lessonClassification Report
lessonCalculating Precision, Recall, and F1-Score
lessonComparing Evaluation Metrics
lessonROC Curve
lessonCalculating the AUC Score
lessonFeature Importance
MODULE 8 : Project Results and Final Evaluation
lessonProject Results and Final Evaluation
lessonLive Output and Prediction Overview
lessonMultiple Choice Questions (MCQs)
lessonResources
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HCL GUVI (Grab Your Vernacular Imprint) Geek Network Private Limited is a leading online learning and skills development company, incubated by IIT Madras and IIM Ahmedabad.

Established in 2014 and acquired by the HCL Group in 2022, HCL GUVI is dedicated to providing effective and high-quality learning and skilling programs that transcend language barriers in technology education. HCL GUVI today is trusted by over 4.8 million users, 20 languages and 2500+ corporate partners.

app storeplay store
Refer & Earn

HCL GUVI (Grab Your Vernacular Imprint) Geek Network Private Limited is a leading online learning and skills development company, incubated by IIT Madras and IIM Ahmedabad.

Established in 2014 and acquired by the HCL Group in 2022, HCL GUVI is dedicated to providing effective and high-quality learning and skilling programs that transcend language barriers in technology education. HCL GUVI today is trusted by over 4.8 million users, 20 languages and 2500+ corporate partners.

app storeplay storeRefer & Earn
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HCL GUVI Geek Network Pvt. Ltd.

Terms and ConditionsPrivacy Policy

HCL GUVI Geek Network Pvt. Ltd.