Project Title: Detecting Fraudulent Transactions using Random Forest

Project Description: The objective of this project is to develop a machine learning model using Random Forest to detect fraudulent transactions. Fraudulent transactions can cause significant financial losses to organizations, and machine learning models can help identify such transactions in real-time.

As a student, you can start by collecting a dataset of transactions that includes both legitimate and fraudulent transactions. You can then preprocess the data, perform exploratory data analysis, and engineer relevant features that may help the model identify fraudulent transactions.

You can then use Random Forest, an ensemble learning method that combines multiple decision trees, to build a model that can learn the patterns of fraudulent transactions. You can train the model on the labeled dataset and evaluate its performance using metrics such as accuracy, precision, recall, and F1 score.

Once the model is trained and tested, you can deploy it in a real-time environment using web technologies such as Flask or Django. The model can be integrated into an application that can monitor transactions and flag any that are deemed suspicious.

The final deliverable can be a report that details the methodology, findings, and recommendations for the field of application.

Expected Deliverables:

1. A detailed analysis of the transaction dataset
2. A machine learning model using Random Forest to detect fraudulent transactions
3. An evaluation of the model's performance using metrics such as accuracy, precision, recall, and F1 score
4. A web application that can flag fraudulent transactions in real-time
5. A comprehensive report that details the methodology, findings, and recommendations for the field of application.

Tools and Technologies:

1. Python
2. Scikit-learn
3. Pandas
4. NumPy
5. Flask or Django

Project Timeline: As a student project, the timeline can be flexible and depend on your availability. However, you can follow this timeline:

1. Week 1: Understanding fraud detection and transaction datasets
2. Week 2-3: Data Collection and Preprocessing
3. Week 4-5: Model Development and Training
4. Week 6-7: Model Evaluation and Deployment
5. Week 8: Report Writing and Presentation.