Based on the information provided, it seems that you have a comprehensive plan for your "Fake Product Review Identification" project. I can outline some steps for you to consider as you progress with your project:

1. Data Preprocessing:

   • Separate the dataset into original and computer-generated fake reviews (OR and CG).
   • Perform data cleaning, including text normalization, handling missing values, and removing irrelevant information.

2. Feature Engineering:

   • Extract relevant features from the reviews such as sentiment, language style, and product-related keywords.
   • Ensure that the features extracted are useful for discriminating between real and fake reviews.

3. Model Selection:

   • Utilize Python with machine learning libraries such as scikit-learn, TensorFlow, or PyTorch for implementing your models.
   • Considering the research papers you've identified, you can explore transfer learning via pre-trained models like BERT, RoBERTa, or XLNet, which have shown promising results in natural language processing tasks.

4. Model Training and Hyperparameter Tuning:

   • Your teammate Wahaj Ahmad Butt (372890) can be responsible for model training and hyperparameter tuning. Consider using techniques like grid search or random search to optimize the model's hyperparameters.

5. Model Implementation & Experimentation:

   • Muhammad Ahad Sajjad (378146) can handle model implementation and experimentation to ensure that the selected models are implemented correctly and achieve the desired results.

6. Model Evaluation:

   • Muhammad Suhaib Aslam (378332) can oversee the model evaluation process, including metrics such as precision, recall, F1 score, and accuracy.

7. Validation and Performance Metrics:

   • Split the dataset into training, validation, and testing sets to evaluate model performance thoroughly.

8. Documentation and Reporting:

   • As you progress, ensure that detailed documentation of the project's progress, including experimental results, challenges faced, and insights gained, is maintained.

9. Project Review:

   • By November 26th, aim to have a basic trained model using a subset of the data for further refinement and calibration.

10. Iterative Model Improvement:

• Continuously refine the model based on the performance heuristics to improve accuracy and robustness in detecting fake reviews.

Keep in mind that this is a high-level overview, and the actual implementation might require additional steps based on the specifics of your dataset and model experimentation. If you require further assistance with any specific stage of this process, feel free to ask for more detailed guidance.

To undertake the project of creating a fake product review identification system, you will first need to install the necessary libraries. You should then preprocess your data, apply your machine learning models, and evaluate those models.

Here's a high-level guide that can help you get started:

1. Setup Environment: Install necessary packages in Python, You will need packages such as pandas, sklearn, nltk, and others. To install, you may use pip:

   pip install pandas sklearn nltk

2. Load Data: Read the data using pandas:

   import pandas as pd
   
   data = pd.read_csv('dataset.csv')

3. Data Preprocessing: Clean the data to get rid of irrelevant characters, symbols, etc:

   # Removing special characters
   data['CleanedText'] = data['Review'].str.replace("[^a-zA-Z#]", " ")
   
   # Tokenizing text
   tokenized_text = data['CleanedText'].apply(lambda x: x.split())
   
   # Remove stop-words and perform lemmatization
   from nltk.stem import WordNetLemmatizer
   from nltk.corpus import stopwords
   
   lemmatizer = WordNetLemmatizer()
   tokenized_text = tokenized_text.apply(lambda x: [lemmatizer.lemmatize(i) for i in x if i not in set(stopwords.words('english'))])
   
   # Stitch the tokens back together
   for i in range(len(tokenized_text)):
       tokenized_text[i] = ' '.join(tokenized_text[i])
   data['CleanedText'] = tokenized_text

4. Feature Extraction: Transform the cleaned reviews into a form understandable by the machine learning model, such as the Bag of Words or TF-IDF:

   from sklearn.feature_extraction.text import TfidfVectorizer
   
   tfidf_vectorizer = TfidfVectorizer(max_df=0.90, min_df=2, max_features=1000)
   tfidf = tfidf_vectorizer.fit_transform(data['CleanedText'])

5. Split Data: Allocate a portion of your data to training the model and the remainder to testing it:

   from sklearn.model_selection import train_test_split
   
   # Assuming that 'Label' is the column which identifies whether a review is fake or real (1 or 0)
   X_train, X_test, y_train, y_test = train_test_split(tfidf, data['Label'], test_size=0.2, random_state=0)

6. Model Application: Choose suitable machine learning models for training, like Logistic Regression, SVM etc. Here's an example:

   from sklearn.linear_model import LogisticRegression
   
   model = LogisticRegression()
   model.fit(X_train, y_train)
   
   predictions = model.predict(X_test)

7. Model Evaluation: Check the performance of your model:

   from sklearn import accuracy_score, confusion_matrix
   
   print("Accuracy: ", accuracy_score(y_test, predictions))
   print("Confusion Matrix: \n", confusion_matrix(y_test, predictions))

This is just a simplistic overview of what the process might look like - your actual code could vary greatly, especially while preprocessing and feature extraction based on the nature of the dataset. You'll also want to try different models and hyperparameters to see which one performs best.