Deep Learning Fraud Detection
Budget: $200 – $250 USD
I'm in need of an original, innovative solution for a fraud detection system that uses both deep learning and machine learning. Freelancer contributions should be original – no copying and pasting from Github or similar sources.
Key Requirements:
- All algorithms developed for this project must be innovative – not borrowed or modified from existing sources.
- The project will not focus on a specific type of fraud; therefore, versatility in handling various types of fraud is essential.
- I have a unique dataset in mind for training the system. This will be discussed further once the project commences.
Ideal Skills:
- Proficiency in both machine learning and deep learning
- Ability to create original algorithms
- Experience with dataset management
- Extensive knowledge of fraud detection
The main idea of my project is to combine machine learning techniques like Random Forest, Decision Trees, Support Vector Machines, Logistic Regression, K-Nearest Neighbors, XGBClassifier, CatBoostClassifier, and others. It also needs to include deep learning methods like Convolutional Neural Networks, Long Short-Term Memory, Bidirectional LSTM, DCN, ANN, RGU, GNN, and more.
Whilst the project's specific focus was not determined, a successful candidate would ideally balance conceptual development and practical implementation of the algorithms. Having innovative ideas and the ability to bring them to life is essential for this job.
Key Requirements:
- All algorithms developed for this project must be innovative – not borrowed or modified from existing sources.
- The project will not focus on a specific type of fraud; therefore, versatility in handling various types of fraud is essential.
- I have a unique dataset in mind for training the system. This will be discussed further once the project commences.
Ideal Skills:
- Proficiency in both machine learning and deep learning
- Ability to create original algorithms
- Experience with dataset management
- Extensive knowledge of fraud detection
The main idea of my project is to combine machine learning techniques like Random Forest, Decision Trees, Support Vector Machines, Logistic Regression, K-Nearest Neighbors, XGBClassifier, CatBoostClassifier, and others. It also needs to include deep learning methods like Convolutional Neural Networks, Long Short-Term Memory, Bidirectional LSTM, DCN, ANN, RGU, GNN, and more.
Whilst the project's specific focus was not determined, a successful candidate would ideally balance conceptual development and practical implementation of the algorithms. Having innovative ideas and the ability to bring them to life is essential for this job.