learn how to detect online bidding fraud by bots using a Machine Learning classification Project. This is a classification problem where we will use a supervised learning algorithm to predict whether a bid is made by a human or a bot. will be using the Dataset from Kaggle, which contains information on bids made on an online auction site. The data includes various features such as the time of the bid, the id of the bidder, the bidder’s country, the auction’s id, the penny price, and the merchandise type.
Another way bots can be used for online fraud is by creating fake online accounts. These fake accounts can then be used to place bids on items or to make purchases. This type of fraud can be difficult to detect, but machine learning can be used to identify patterns in account creation and activity that are likely to be fraudulent. Machine learning is a powerful tool that can be used to detect and prevent online fraud. If you are concerned about online fraud, look for systems that use machine learning to detect and prevent it.
What will you Learn in the Project Machine Learning classification Project?
- Doing Exploring Data Analysis for a better understanding of the data
- Handling tabular data for predictive modeling
- Visualizing data for a better understanding
- Understanding corrupted data, such as missing values, and treating it
- Building tree and ensemble-based models
- Working knowledge of the scikit-learn library
- Theoretical understanding of handling missing
- Understanding of different classification models evaluation metrics
- Theoretical understanding of ensemble-based models such as Random Forest and Gradient Boosting
- google-colab [Jupyter notebook] for model building
- Matplotlib/seaborn library for visualization of the plots
- scikit-learn for model building and evaluation
Tasks to be Performed
As part of this project, we will be performing the following tasks:
Task-1: Data loading and performing the Exploratory data analysis
Task-2: Perform data pre-processing
Task-3: Model building and prediction with ensemble-based methods such as Random Forest
Task-4: Validation and Results Analysis