How to Run the Backend (Flask)

1. Open a terminal and navigate to the backend folder:

cd backend

2. Install dependencies:

pip install -r requirements.txt

3. Start the Flask server:

python app.py

The API will run at http://127.0.0.1:5000.

How to Run the Frontend (React)

1. Open a terminal and navigate to the frontend folder:

cd frontend

2. Install dependencies:

npm install

3. Start the React app:

npm start

The dashboard will run at http://localhost:3000.

How to Use

• Open the React dashboard in your browser.
• Enter a product review and submit to analyze if it is fake or genuine.
• View recent analyzed reviews and dashboard statistics.

Training the Model

• Use the provided Jupyter notebook to preprocess data and train a model.
• Save the trained model (e.g., as model.pkl) and update the Flask backend to use it for predictions.

Pushing to GitHub

1. Add all files:

git add .

2. Commit:

git commit -m "Add working code and resources"

3. Push:

git push -u origin main

Notes

• For production, use a proper WSGI server for Flask and build the React app.
• Add a .gitignore to exclude unnecessary files (e.g., node_modules, .env).

Fake-Reviews-Detection

Problem Statement

Detection of fake reviews out of a massive collection of reviews having various distinct categories like Home and Office, Sports, etc. with each review having a corresponding rating, label i.e. CG(Computer Generated Review) and OR(Original Review generated by humans) and the review text.

Main task is to detect whether a given review is fraudulent or not. If it is computer generated, it is considered fake otherwise not.

Description

Description: The generated fake reviews dataset, containing 20k fake reviews and 20k real product reviews. OR = Original reviews (presumably human created and authentic); CG = Computer-generated fake reviews.

Python Libraries and Packages Used

• Numpy
• Pandas
• Matplotlib.pyplot
• Seaborn
• Warnings
• nltk
• nltk.corpus
• String
• sklearn.naive_bayes
• sklearn.feature_extraction
• sklearn.model_selection
• sklearn.ensemble
• sklearn.tree
• sklearn.linear_model
• sklearn.svc
• sklearn.neighbors

Techniques Used for Text Preprocessing

• Removing punctuation character
• Transforming text to lower case
• Eliminating stopwords
• Stemming
• Lemmatizing
• Removing digits

Transformers Used for Text Vectorization, Weighting and Normalization

• CountVectorizer Bag of Words Transformer
• TFIDF(Term Frequency-Inverse Document Frequency) Transformer

Machine Learning Algorithms Used

1. Logistic Regression
2. K Nearest Neighbors
3. Support Vector Classifier
4. Decision Tree Classifier
5. Random Forests Classifier
6. Multinomial Naive Bayes

Performance Overview of ML Models Leveraged

Support Vector Machines Classifier performed the most accurate predictions regarding the fake nature of reviews having a predictive accuracy of just over 88%, closely followed by Logistic Regression which had a prediction accuracy of a little more than 86%. Random Forests Classifier and Multinomial Naive Bayes algorithm predicted to a precision level of approximately 84%. However, the Decision Tree Classifier performed fake reviews prediction upto an accuracy of just over 73%. The worst performing algorithm was the K Nearest Neighbors algorithm which could only perform the predictions upto an accuracy level of nearly 58%.

License

CC-By Attribution 4.0 International