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Detect the Online Bidding Fraud by Bots

In this project, we’ll learn to handle the tabular data for predictive modelling. We’ll build a classification model to identify whether the bid has been placed by robots or humans.  We will start with simple tree-based classifiers Decision Tree and move to ensemble-based methods.
Course
Curriculum

What will you Learn in the Project?

  1. Doing Exploring Data Analysis for a better understanding of the data
  2. Handling tabular data for predictive modelling 
  3. Visualizing data for a better understanding
  4. Understanding corrupted data such as missing values and treating it 
  5. Building tree and ensemble-based models

Prerequisite

  1. Working knowledge of scikit-learn library
  2. Theoretical understanding of handling missing 
  3. Understanding of different classification models evaluation metrics
  4. Theoretical understanding of ensemble-based models such as Random Forest and Gradient Boosting

Tools Used

  1. google-colab [Jupyter notebook] for model building 
  2. Matplotlib/seaborn library for visualization of the plots  
  3. 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

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Skills you will develop

Carry out the Exploratory data analysis

Handling missing values

Building tree-based ensemble models in scikit-learn

Evaluating models using different classification metrics

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